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When it is not found, a full rebuild will be done. -config: cd1c560e11615d523ded5407dca515bb +config: 7db6c5f1d358ea27b67bbc582be4e17e tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/_build/html/README.html b/_build/html/README.html new file mode 100644 index 0000000..1f668c3 --- /dev/null +++ b/_build/html/README.html @@ -0,0 +1,491 @@ + + + + + + + + + + + cider-documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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cider-documentation#

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This repository contains the source files for the documentation for the Cider software package. The docs are exposed here. The Cider repository is here.

+

To update this documentation:

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  • Make changes to source

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  • Build the changes using jupyter-book

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b/_build/html/_sources/README.md new file mode 100644 index 0000000..f288bfd --- /dev/null +++ b/_build/html/_sources/README.md @@ -0,0 +1,9 @@ +# cider-documentation + +This repository contains the source files for the documentation for the Cider software package. The docs are exposed [here](https://global-policy-lab.github.io/cider-documentation/intro.html). The Cider repository is [here](https://github.com/Global-Policy-Lab/cider). + +To update this documentation: +* Make changes to source +* Build the changes using jupyter-book +* Copy the contents of _build/html into docs. You can check you did this right by confirming that docs/index.html exists +* Propagate the changes to the remote gh-pages branch on Github diff --git a/docs/_sources/_sources/quickstart/documentation_fairness.ipynb b/_build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_fairness.ipynb similarity index 99% rename from docs/_sources/_sources/quickstart/documentation_fairness.ipynb rename to _build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_fairness.ipynb index fc79b6a..409a3c4 100644 --- a/docs/_sources/_sources/quickstart/documentation_fairness.ipynb +++ b/_build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_fairness.ipynb @@ -59,7 +59,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_fairness_5_0.png" + } + }, "output_type": "display_data" } ], @@ -314,7 +318,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_fairness_11_0.png" + } + }, "output_type": "display_data" } ], @@ -344,4 +352,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/_sources/_sources/quickstart/documentation_featurizer.ipynb b/_build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_featurizer.ipynb similarity index 99% rename from docs/_sources/_sources/quickstart/documentation_featurizer.ipynb rename to _build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_featurizer.ipynb index bbad8d4..adcb03f 100644 --- a/docs/_sources/_sources/quickstart/documentation_featurizer.ipynb +++ b/_build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_featurizer.ipynb @@ -203,7 +203,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_10_1.png" + } + }, "output_type": "display_data" }, { @@ -213,7 +217,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_10_2.png" + } + }, "output_type": "display_data" }, { @@ -223,7 +231,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_10_3.png" + } + }, "output_type": "display_data" }, { @@ -233,7 +245,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_10_4.png" + } + }, "output_type": "display_data" }, { @@ -243,7 +259,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_10_5.png" + } + }, "output_type": "display_data" }, { @@ -253,7 +273,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_10_6.png" + } + }, "output_type": "display_data" }, { @@ -263,7 +287,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_10_7.png" + } + }, "output_type": "display_data" }, { @@ -273,7 +301,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_10_8.png" + } + }, "output_type": "display_data" } ], @@ -13050,7 +13082,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_15_0.png" + } + }, "output_type": "display_data" }, { @@ -13060,7 +13096,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_15_1.png" + } + }, "output_type": "display_data" }, { @@ -13070,7 +13110,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_15_2.png" + } + }, "output_type": "display_data" }, { @@ -13087,7 +13131,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_15_4.png" + } + }, "output_type": "display_data" }, { @@ -13097,7 +13145,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_15_5.png" + } + }, "output_type": "display_data" }, { @@ -13107,7 +13159,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_15_6.png" + } + }, "output_type": "display_data" }, { @@ -13117,7 +13173,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_15_7.png" + } + }, "output_type": "display_data" }, { @@ -13127,7 +13187,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_15_8.png" + } + }, "output_type": "display_data" }, { @@ -13137,7 +13201,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_15_9.png" + } + }, "output_type": "display_data" } ], @@ -13167,4 +13235,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/_sources/_sources/quickstart/documentation_home_location.ipynb b/_build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_home_location.ipynb similarity index 99% rename from docs/_sources/_sources/quickstart/documentation_home_location.ipynb rename to _build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_home_location.ipynb index a9d264d..6f4cc8a 100644 --- a/docs/_sources/_sources/quickstart/documentation_home_location.ipynb +++ b/_build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_home_location.ipynb @@ -244,7 +244,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_home_location_10_0.png" + } + }, "output_type": "display_data" } ], @@ -283,7 +287,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_home_location_12_1.png" + } + }, "output_type": "display_data" } ], @@ -404,7 +412,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_home_location_15_0.png" + } + }, "output_type": "display_data" } ], @@ -434,4 +446,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/_sources/_sources/quickstart/documentation_ml.ipynb b/_build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_ml.ipynb similarity index 99% rename from docs/_sources/_sources/quickstart/documentation_ml.ipynb rename to _build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_ml.ipynb index e2b814b..86a0772 100644 --- a/docs/_sources/_sources/quickstart/documentation_ml.ipynb +++ b/_build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_ml.ipynb @@ -250,7 +250,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_ml_9_0.png" + } + }, "output_type": "display_data" }, { @@ -260,7 +264,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_ml_9_1.png" + } + }, "output_type": "display_data" }, { @@ -445,4 +453,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/_sources/_sources/quickstart/documentation_satellite.ipynb b/_build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_satellite.ipynb similarity index 99% rename from docs/_sources/_sources/quickstart/documentation_satellite.ipynb rename to _build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_satellite.ipynb index 87189b7..22cf5fd 100644 --- a/docs/_sources/_sources/quickstart/documentation_satellite.ipynb +++ b/_build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_satellite.ipynb @@ -69,7 +69,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_satellite_5_0.png" + } + }, "output_type": "display_data" } ], @@ -106,7 +110,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_satellite_7_1.png" + } + }, "output_type": "display_data" } ], @@ -243,4 +251,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/_sources/_sources/quickstart/documentation_targeting.ipynb b/_build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_targeting.ipynb similarity index 99% rename from docs/_sources/_sources/quickstart/documentation_targeting.ipynb rename to _build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_targeting.ipynb index 3a53f25..43b73de 100644 --- a/docs/_sources/_sources/quickstart/documentation_targeting.ipynb +++ b/_build/html/_sources/docs/_build/jupyter_execute/quickstart/documentation_targeting.ipynb @@ -176,7 +176,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_targeting_7_0.png" + } + }, "output_type": "display_data" }, { @@ -186,7 +190,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_targeting_7_1.png" + } + }, "output_type": "display_data" } ], @@ -216,7 +224,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_targeting_9_0.png" + } + }, "output_type": "display_data" }, { @@ -226,7 +238,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_targeting_9_1.png" + } + }, "output_type": "display_data" } ], @@ -256,7 +272,11 @@ "
" ] }, - "metadata": {}, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_targeting_11_0.png" + } + }, "output_type": "display_data" }, { @@ -365,4 +385,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/_build/html/_sources/quickstart/config.md b/_build/html/_sources/quickstart/config.md index c6dee20..c158908 100644 --- a/_build/html/_sources/quickstart/config.md +++ b/_build/html/_sources/quickstart/config.md @@ -1,27 +1,46 @@ # Configuration file -The configuration file - example at `configs/config.yml` - is used to store all relevant configurations, like paths to the datasets and spark parameters. It should be appropriately edited before executing the code.
+The configuration file is used to store all relevant configurations, like paths to the datasets and spark parameters. It should be appropriately edited before executing the code. It is written in [YAML](https://yaml.org/). -The first parameters to set are those related to spark: +## Spark parameters +Spark parameters are set under the `spark` heading. Syntax for specifying Spark parameters derives from [Spark\'s own property names](https://spark.apache.org/docs/latest/configuration.html#available-properties). For example, to conigure the parameter `spark.app.name` in Cider config, we'd use +``` +spark: + app: + name: "my_first_cider_app" +``` + +Here is a more complete example config. It's not meant as an endorsement of these specific config values; optimal choices vary greatly based on your environment and use case. ``` -spark: - app_name: "mm" - files: - max_partition_bytes: 67108864 +spark: + app: + name: "my_first_cider_app" + master: "local[*]" + sql: + shuffle: + partitions: 144 driver: - memory: "8g" // driver memory - max_result_size: "2g" // maximum result size when collecting to driver - loglevel: "ERROR" + memory: "8G" + maxResultSize: "2G" + supervise: true + executor: + memory: "8G" + rpc: + askTimeout: "600s" + loglevel: "WARN" + logConf: true ``` -Next we specify folder and file locations. File subpaths are given relative to a "parent" directory: Either the `input_data` directory or the `working` directory (file subpaths should not have leading slashes). The locations of the parent directories must be specified with absolute paths (with leading slashes). Cider will not modify files under the `input_data` directory. It will use the `working` directory for program outputs, some of which may act as inputs for later steps. For example, the featurizer writes features to the `working` directory, and then the ml module reads features back in from that directory. At present, file names/sub-paths written programmatically under the `working` directory are hard-coded and can't be specified in config. +## File and folder locations + +Under the `path` heading, we specify folder and file locations. File subpaths are given relative to a "parent" directory: Either the `input_data` directory or the `working` directory (if you'd rather specify an absolute path, use a leading slash). The locations of the parent directories must be specified with absolute paths (with leading slashes). Cider will not modify files under the `input_data` directory. It will use the `working` directory for program outputs, some of which may act as inputs for later steps. For example, the featurizer writes features to the `working` directory, and then the ml module reads features back in (from that same directory, unless a different one is specified as input). At present, file names/sub-paths written programmatically under the `working` directory are hard-coded and can't be specified in config. ``` path: input_data: - directory_path: "/Users/example/Documents/GD/cider/synthetic_data/" + directory_path: "/example/path/to/input/data/" file_paths: antennas: "antennas.csv" cdr: "cdr.csv" @@ -32,17 +51,19 @@ path: population: "population_tgo_2019-07-01.tif" poverty_scores: null recharges: "recharges.csv" - rwi: "TGO_relative_wealth_index.csv" + rwi: "relative_wealth_index.csv" shapefiles: regions: "regions.geojson" cantons: "cantons.geojson" prefectures: "prefectures.geojson" user_consent: null working: - directory_path: "/Users/example/Documents/GD/cider/working_directory/" + directory_path: "/example/path/to/working/directory" ``` -The featurizer module expects certain column and column names, and we can define them in the following section of the config file: +## Column names + +Cider expects certain columns to be present, and we can specify their names under the `col_names` heading (this is not a complete list): ``` col_names: @@ -81,10 +102,11 @@ col_names: geo: "tower_id" ``` -We also have to set a few parameters that will affect the behaviour of some modules: +## Miscellaneous parameters + +Under the `params` heading we are able to specify certain miscellaneous parameters that affect Cider behavior: ``` -params: cdr: weekend: [1, 7] // definition of weekend (Sun = 1 to Sat = 7) start_of_day: 7 // hour when day starts (used to define day/night) @@ -104,7 +126,9 @@ params: opt_in_default: false // if true opt-in is set as default, i.e. all users give their consent unless they opt-out ``` -Finally, we can set the hyper-parameters that will be tested during a grid-search performed by the ML module: +## ML tuning parameters + +Under the `hyperparams` heading, we set the hyper-parameters that will be tested during a grid-search performed by the ML module: ``` hyperparams: diff --git a/_build/html/_sources/quickstart/documentation_fairness.ipynb b/_build/html/_sources/quickstart/documentation_fairness.ipynb index fc79b6a..db7a4df 100644 --- a/_build/html/_sources/quickstart/documentation_fairness.ipynb +++ b/_build/html/_sources/quickstart/documentation_fairness.ipynb @@ -15,8 +15,8 @@ "metadata": {}, "outputs": [], "source": [ - "from datastore import DataStore\n", - "from fairness import Fairness" + "from cider.datastore import DataStore\n", + "from cider.fairness import Fairness" ] }, { @@ -34,7 +34,11 @@ "metadata": {}, "outputs": [], "source": [ - "datastore = DataStore(cfg_dir='./configs/config.yml')\n", + "# This path should point to your cider installation, where configs and data for this demo are located.\n", + "from pathlib import Path\n", + "cider_installation_directory = Path('../../cider')\n", + "\n", + "datastore = DataStore(config_file_path_string= cider_installation_directory / 'configs' / 'config_quickstart.yml')\n", "fairness = Fairness(datastore=datastore)" ] }, @@ -325,9 +329,9 @@ ], "metadata": { "kernelspec": { - "display_name": "cider", + "display_name": "conda_cider", "language": "python", - "name": "cider" + "name": "conda_cider" }, "language_info": { "codemirror_mode": { @@ -339,7 +343,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.8.16" } }, "nbformat": 4, diff --git a/_build/html/_sources/quickstart/documentation_featurizer.ipynb b/_build/html/_sources/quickstart/documentation_featurizer.ipynb index bbad8d4..60dbbe1 100644 --- a/_build/html/_sources/quickstart/documentation_featurizer.ipynb +++ b/_build/html/_sources/quickstart/documentation_featurizer.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 7, - "id": "e03ae9c8", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "sys.path.insert(0, '../../cider/cider')" - ] - }, { "cell_type": "markdown", "id": "96d8501c", @@ -22,12 +11,28 @@ { "cell_type": "code", "execution_count": 8, + "id": "2113663e-c731-42e1-82e3-80ee40abbbe7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "id": "ec77078c", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "from datastore import DataStore\n", - "from featurizer import Featurizer" + "from cider.datastore import DataStore\n", + "from cider.featurizer import Featurizer" ] }, { @@ -40,21 +45,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "id": "44d61e7d", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "WARNING: An illegal reflective access operation has occurred\n", - "WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/Users/luciomelito/Library/Caches/pypoetry/virtualenvs/cider-n28xxEz6-py3.8/lib/python3.8/site-packages/pyspark/jars/spark-unsafe_2.12-3.1.2.jar) to constructor java.nio.DirectByteBuffer(long,int)\n", - "WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform\n", - "WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations\n", - "WARNING: All illegal access operations will be denied in a future release\n", - "21/10/26 10:50:27 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n", - "Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties\n", "Setting default log level to \"WARN\".\n", "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" ] @@ -63,6 +63,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "23/03/31 13:21:02 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n", "Loading CDR...\n" ] }, @@ -87,7 +88,11 @@ } ], "source": [ - "datastore = DataStore(cfg_dir='../../cider/configs/config.yml')\n", + "# This path should point to your cider installation, where configs and data for this demo are located.\n", + "from pathlib import Path\n", + "cider_installation_directory = Path('../../cider')\n", + "\n", + "datastore = DataStore(config_file_path_string= cider_installation_directory / 'configs' / 'config_quickstart.yml')\n", "featurizer = Featurizer(datastore=datastore, clean_folders=True)" ] }, @@ -101,9 +106,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "7060dbd4", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stderr", @@ -116,7 +123,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of spammers identified: 20\n" + "Number of spammers identified: 19\n" ] } ], @@ -133,9 +140,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "0702d156", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -143,6 +152,14 @@ "text": [ "Outliers removed: 2020-01-24, 2020-02-06\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/leo/Documents/gpl/cider/cider/datastore.py:435: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n", + " timeseries = timeseries.groupby('day', as_index=False).agg('sum')\n" + ] } ], "source": [ @@ -160,9 +177,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "62e500d0", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stderr", @@ -175,7 +194,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'CDR': {'Days': 60, 'Transactions': 92719, 'Subscribers': 980, 'Recipients': 980}, 'Recharges': {'Days': 60, 'Transactions': 9452, 'Subscribers': 782}, 'Mobile Data': {'Days': 60, 'Transactions': 9548, 'Subscribers': 591}, 'Mobile Money': {'Days': 60, 'Transactions': 5469, 'Subscribers': 682, 'Recipients': 682}}\n" + "{'CDR': {'Days': 59, 'Transactions': 91299, 'Subscribers': 981, 'Recipients': 981}, 'Recharges': {'Days': 59, 'Transactions': 9315, 'Subscribers': 783}, 'Mobile Data': {'Days': 59, 'Transactions': 9388, 'Subscribers': 592}, 'Mobile Money': {'Days': 59, 'Transactions': 5371, 'Subscribers': 682, 'Recipients': 682}}\n" ] } ], @@ -185,9 +204,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "ed688b3c", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stderr", @@ -198,9 +219,9 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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", 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\n", 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\n", 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", 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\n", 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", 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\n", 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", 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\n", + "image/png": 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", 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\n", 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -291,12440 +312,38 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "8593e4f9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating CDR features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.78% of the records are missing a location. \n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:58:28 to 2020-02-29 19:01:57\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 08:02:22 to 2020-02-29 23:54:24\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 13:39:38 to 2020-02-29 14:37:41\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 09:28:21 to 2020-02-29 20:23:01\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 07:00:19 to 2020-02-29 09:40:21\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 03:21:11 to 2020-02-29 18:02:09\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 09:56:21 to 2020-02-29 23:57:46\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 01:03:12 to 2020-02-29 02:43:26\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:52:24 to 2020-02-29 11:42:57\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 04:44:01 to 2020-02-29 23:06:35\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.49% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-02 03:40:27 to 2020-02-29 00:12:48\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.57% of the records are missing a location. (0 + 1) / 1]\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 03:48:29 to 2020-02-29 05:50:46\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:28:44 to 2020-02-29 17:36:12\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 01:37:47 to 2020-02-29 17:18:33\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 01:46:52 to 2020-02-29 06:19:17\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 03:39:34 to 2020-02-29 00:30:27\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 04:22:25 to 2020-02-28 20:07:26\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 06:09:35 to 2020-02-29 22:25:05\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 12:25:22 to 2020-02-29 14:30:24\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 02:11:34 to 2020-02-29 23:04:17\n", - "\u001b[32m[x]\u001b[0m 202 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 116 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 172 records from 2020-01-01 17:52:09 to 2020-02-28 13:14:49\n", - "\u001b[32m[x]\u001b[0m 157 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 07:20:17 to 2020-02-29 14:03:57\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-02 03:27:13 to 2020-02-29 20:49:05\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 09:47:41 to 2020-02-29 22:38:38\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 13:29:21 to 2020-02-29 18:03:55\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 02:15:58 to 2020-02-29 10:10:34\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 00:52:24 to 2020-02-29 00:14:59\n", - "\u001b[32m[x]\u001b[0m 162 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 01:14:48 to 2020-02-29 13:26:22\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 06:34:07 to 2020-02-29 18:05:49\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.49% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 04:01:14 to 2020-02-29 12:07:56\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 01:00:20 to 2020-02-29 19:04:41\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.48% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 121 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 01:27:12 to 2020-02-29 16:13:13\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 10:05:32 to 2020-02-29 21:16:11\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 11:21:07 to 2020-02-29 23:39:12\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.28% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 07:00:54 to 2020-02-29 21:49:02\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 01:27:12 to 2020-02-29 16:31:12\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 02:34:52 to 2020-02-29 16:38:45\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 03:24:27 to 2020-02-29 21:54:37\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 11:00:45 to 2020-02-29 15:52:57\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 15.77% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 03:09:53 to 2020-02-29 12:21:43\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:58:28 to 2020-02-28 18:44:36\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 231 records from 2020-01-01 03:52:41 to 2020-02-29 22:46:51\n", - "\u001b[32m[x]\u001b[0m 208 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 03:36:59 to 2020-02-29 15:03:57\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 04:27:28 to 2020-02-29 23:46:11\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 12:30:33 to 2020-02-29 23:12:55\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 17:11:28 to 2020-02-29 13:29:33\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.80% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 01:35:55 to 2020-02-29 13:57:38\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:48:08 to 2020-02-29 19:37:22\n", - "\u001b[32m[x]\u001b[0m 162 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 05:31:30 to 2020-02-29 11:42:47\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 17:18:35 to 2020-02-29 22:52:28\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:49:51 to 2020-02-29 23:43:54\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 12:14:48 to 2020-02-29 05:14:09\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 00:02:23 to 2020-02-29 04:30:38\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:38:12 to 2020-02-29 22:40:44\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 11:40:35 to 2020-02-29 14:50:49\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 08:05:52 to 2020-02-29 04:34:58\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.12% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 11:49:11 to 2020-02-29 09:49:15\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.77% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 07:16:45 to 2020-02-29 14:59:55\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 02:31:00 to 2020-02-29 17:04:31\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 02:53:30 to 2020-02-28 21:25:47\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 04:27:06 to 2020-02-29 12:14:27\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 20.28% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:28:33 to 2020-02-29 09:47:41\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 11:32:39 to 2020-02-29 21:26:59\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 03:08:35 to 2020-02-29 10:00:54\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.19% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:55:46 to 2020-02-29 18:51:39\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.13% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:03:05 to 2020-02-29 21:34:29\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 02:49:27 to 2020-02-29 20:21:36\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 09:38:33 to 2020-02-29 19:32:37\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 12:48:26 to 2020-02-29 16:17:36\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 12:45:52 to 2020-02-29 23:23:05\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 07:28:59 to 2020-02-29 05:46:34\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 05:53:19 to 2020-02-29 21:26:59\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:57:13 to 2020-02-29 14:26:48\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:08:25 to 2020-02-29 22:09:30\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 04:07:51 to 2020-02-29 13:52:46\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 05:52:03 to 2020-02-28 18:58:31\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 12:57:16 to 2020-02-29 21:20:09\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 07:13:05 to 2020-02-28 16:09:11\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.34% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:06:09 to 2020-02-29 07:10:32\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 00:31:13 to 2020-02-29 17:31:43\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:41:44 to 2020-02-29 16:58:15\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.28% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:15:52 to 2020-02-29 14:25:57\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.01% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:15:58 to 2020-02-29 17:03:05\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 19.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 04:27:26 to 2020-02-28 01:05:10\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 08:35:50 to 2020-02-29 08:56:49\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 00:51:01 to 2020-02-29 20:49:01\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 00:59:21 to 2020-02-29 15:45:38\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:41:38 to 2020-02-29 19:43:50\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:16:05 to 2020-02-29 21:00:11\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 05:49:59 to 2020-02-29 23:09:21\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 06:06:09 to 2020-02-29 20:06:41\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 152 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 00:07:47 to 2020-02-29 15:59:29\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 07:59:44 to 2020-02-29 20:10:13\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 10:20:32 to 2020-02-29 11:48:07\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.42% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 10:51:08 to 2020-02-29 22:29:32\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 04:41:31 to 2020-02-29 13:01:07\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:51:18 to 2020-02-29 23:28:57\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:10:56 to 2020-02-29 17:14:13\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 06:45:54 to 2020-02-29 23:29:34\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 06:35:29 to 2020-02-28 20:10:44\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:21:35 to 2020-02-29 20:04:42\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 00:53:05 to 2020-02-29 18:09:25\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:00:20 to 2020-02-29 19:50:28\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:57:13 to 2020-02-29 16:14:39\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 20.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 03:22:54 to 2020-02-29 17:47:16\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 01:46:43 to 2020-02-29 14:15:00\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 18:18:39 to 2020-02-28 23:48:47\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.87% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-02 20:01:48 to 2020-02-29 22:22:26\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.88% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:24:24 to 2020-02-29 23:01:18\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 08:07:10 to 2020-02-29 15:07:03\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 01:57:59 to 2020-02-29 19:05:21\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 12:44:09 to 2020-02-29 16:35:23\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.05% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 02:31:00 to 2020-02-29 22:55:09\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 09:23:16 to 2020-02-29 18:26:24\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 11:55:57 to 2020-02-29 16:58:22\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:45:45 to 2020-02-29 23:28:32\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.88% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:02:04 to 2020-02-29 21:57:07\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-03 00:38:24 to 2020-02-29 21:29:26\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 15:06:09 to 2020-02-29 23:04:36\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 03:12:37 to 2020-02-29 19:05:06\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 04:21:10 to 2020-02-28 22:13:13\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 01:21:42 to 2020-02-29 23:25:01\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.51% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 12:53:27 to 2020-02-29 13:48:37\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 14:48:27 to 2020-02-29 15:25:22\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 18:26:42 to 2020-02-29 21:32:05\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 01:18:39 to 2020-02-29 20:23:01\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 06:39:48 to 2020-02-29 13:09:53\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 00:13:51 to 2020-02-29 23:53:09\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 00:24:08 to 2020-02-29 20:19:22\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 01:21:42 to 2020-02-29 20:57:52\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.72% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 153 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 03:29:54 to 2020-02-29 17:26:58\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.25% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 06:36:20 to 2020-02-29 09:36:01\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 10:13:06 to 2020-02-28 22:45:21\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 05:02:14 to 2020-02-29 19:40:07\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 10:44:03 to 2020-02-28 23:39:29\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 17:49:47 to 2020-02-29 21:34:29\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 169 records from 2020-01-01 15:46:08 to 2020-02-29 21:44:00\n", - "\u001b[32m[x]\u001b[0m 159 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:43:25 to 2020-02-29 22:47:34\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 00:13:51 to 2020-02-29 22:06:16\n", - "\u001b[32m[x]\u001b[0m 202 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 03:52:41 to 2020-02-29 19:06:30\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 06:11:54 to 2020-02-29 21:59:04\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 14:12:11 to 2020-02-28 21:31:59\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.70% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 04:52:06 to 2020-02-29 16:18:56\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 00:51:11 to 2020-02-29 09:44:58\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 03:24:30 to 2020-02-29 19:50:13\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 03:13:37 to 2020-02-29 01:17:34\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:26:40 to 2020-02-29 16:59:51\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 11:33:43 to 2020-02-29 07:24:10\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.42% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 11:42:31 to 2020-02-29 12:10:25\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 03:12:37 to 2020-02-29 13:24:21\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:28:41 to 2020-02-29 22:35:18\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 03:35:24 to 2020-02-29 23:56:35\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 20.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 03:04:43 to 2020-02-29 23:10:29\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 07:31:08 to 2020-02-29 18:32:05\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:12:42 to 2020-02-29 18:26:47\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 156 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 03:47:54 to 2020-02-29 13:29:41\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 04:57:30 to 2020-02-29 14:59:14\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 00:37:14 to 2020-02-29 13:01:07\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 02:14:43 to 2020-02-29 22:22:49\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 05:02:37 to 2020-02-29 16:17:49\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:17:53 to 2020-02-29 23:00:24\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 227 records from 2020-01-01 02:52:39 to 2020-02-29 20:29:29\n", - "\u001b[32m[x]\u001b[0m 205 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.72% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 03:09:08 to 2020-02-29 21:49:26\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 07:16:35 to 2020-02-29 19:15:07\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 18:18:39 to 2020-02-29 11:54:35\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:30:00 to 2020-02-29 21:06:10\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 07:27:08 to 2020-02-29 22:51:25\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 01:46:18 to 2020-02-29 17:06:31\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 11:18:53 to 2020-02-29 23:02:01\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.05% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 05:21:34 to 2020-02-29 20:11:21\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 01:59:49 to 2020-02-29 07:33:19\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 02:12:58 to 2020-02-29 17:33:19\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.77% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 03:36:59 to 2020-02-29 17:57:28\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:20:52 to 2020-02-29 14:55:36\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 00:20:30 to 2020-02-29 19:30:39\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.13% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:43:54 to 2020-02-29 18:21:43\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:51:11 to 2020-02-29 06:50:05\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 00:31:13 to 2020-02-29 05:14:29\n", - "\u001b[32m[x]\u001b[0m 206 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 05:55:09 to 2020-02-29 21:07:33\n", - "\u001b[32m[x]\u001b[0m 160 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 08:31:41 to 2020-02-29 20:50:31\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 06:12:59 to 2020-02-29 11:02:17\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.26% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 00:49:14 to 2020-02-29 15:22:57\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 04:29:28 to 2020-02-29 14:23:41\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 15:25:43 to 2020-02-29 19:04:03\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 00:02:04 to 2020-02-29 23:04:36\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 00:20:52 to 2020-02-29 22:22:26\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 04:02:43 to 2020-02-29 21:42:46\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:21:10 to 2020-02-29 19:43:47\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 09:38:10 to 2020-02-29 23:23:38\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 01:46:43 to 2020-02-29 23:19:35\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 05:59:43 to 2020-02-29 12:36:13\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.51% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 07:44:45 to 2020-02-29 21:59:22\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 153 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 230 records from 2020-01-01 00:41:38 to 2020-02-29 23:52:22\n", - "\u001b[32m[x]\u001b[0m 209 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 01:42:54 to 2020-02-29 16:27:03\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 02:12:00 to 2020-02-29 20:31:44\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 13:56:11 to 2020-02-29 22:15:35\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:42:44 to 2020-02-29 13:01:05\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 03:27:45 to 2020-02-29 12:12:52\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 05:34:51 to 2020-02-29 07:53:20\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 06:13:03 to 2020-02-29 23:54:48\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:02:19 to 2020-02-29 16:18:46\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.70% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 09:28:04 to 2020-02-29 22:47:56\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 01:56:40 to 2020-02-29 11:13:46\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 02:14:38 to 2020-02-29 12:38:32\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.99% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 04:42:54 to 2020-02-29 17:13:08\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 11:25:09 to 2020-02-29 19:49:35\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-02 14:23:06 to 2020-02-29 18:15:07\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 228 records from 2020-01-01 01:56:55 to 2020-02-29 14:42:45\n", - "\u001b[32m[x]\u001b[0m 206 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 03:43:48 to 2020-02-29 19:42:36\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 168 records from 2020-01-01 08:04:07 to 2020-02-29 17:16:20\n", - "\u001b[32m[x]\u001b[0m 154 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 09:28:04 to 2020-02-29 15:36:00\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 14:34:53 to 2020-02-29 22:30:03\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 01:07:55 to 2020-02-29 18:43:30\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 03:52:41 to 2020-02-29 15:51:29\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 04:01:48 to 2020-02-29 09:50:09\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.91% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 17:41:50 to 2020-02-29 10:31:31\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 14:43:48 to 2020-02-28 21:08:36\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 15:12:23 to 2020-02-29 15:58:23\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 02:37:02 to 2020-02-29 15:45:51\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 01:56:53 to 2020-02-29 13:32:03\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 19:48:52 to 2020-02-29 19:04:41\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 20.10% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 04:54:05 to 2020-02-29 21:38:34\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.25% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 12:12:42 to 2020-02-29 14:49:18\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.75% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 21:14:06 to 2020-02-29 23:34:14\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 01:26:02 to 2020-02-29 03:42:11\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 13:41:47 to 2020-02-29 16:21:06\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 01:22:53 to 2020-02-29 20:00:32\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 02:25:29 to 2020-02-29 19:50:28\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 04:20:55 to 2020-02-29 12:26:36\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 02:32:52 to 2020-02-29 18:30:02\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.75% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 02:36:50 to 2020-02-29 22:45:42\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 12:54:30 to 2020-02-29 21:20:20\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 13:45:25 to 2020-02-29 11:04:17\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 03:01:27 to 2020-02-28 22:30:35\n", - "\u001b[32m[x]\u001b[0m 207 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 05:49:46 to 2020-02-29 20:45:44\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 08:42:10 to 2020-02-29 19:35:12\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 03:16:50 to 2020-02-28 22:17:05\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 00:19:47 to 2020-02-29 23:01:17\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-02 03:03:59 to 2020-02-29 11:41:10\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 20.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 13:35:55 to 2020-02-29 19:51:37\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 02:02:25 to 2020-02-29 23:48:33\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:49:28 to 2020-02-29 23:05:57\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 08:18:35 to 2020-02-29 23:01:06\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.60% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 04:15:45 to 2020-02-29 10:45:06\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 03:17:17 to 2020-02-29 21:02:26\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 11:58:03 to 2020-02-29 19:14:39\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 226 records from 2020-01-01 01:26:28 to 2020-02-29 22:50:13\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 01:59:49 to 2020-02-29 19:36:19\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 227 records from 2020-01-01 06:09:09 to 2020-02-29 11:02:17\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 00:40:16 to 2020-02-29 23:25:01\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 150 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 226 records from 2020-01-01 07:45:25 to 2020-02-29 22:42:21\n", - "\u001b[32m[x]\u001b[0m 206 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 12:49:39 to 2020-02-29 23:01:06\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 14:15:48 to 2020-02-28 21:09:03\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 00:33:45 to 2020-02-29 11:30:34\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.99% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 09:05:13 to 2020-02-29 18:30:36\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 02:24:32 to 2020-02-29 22:15:35\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:37:55 to 2020-02-29 23:33:35\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 06:42:35 to 2020-02-29 22:10:09\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:54:21 to 2020-02-29 17:54:40\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 03:41:03 to 2020-02-29 20:36:35\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 04:52:45 to 2020-02-29 12:13:03\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 05:36:32 to 2020-02-29 16:30:01\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.05% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 09:46:32 to 2020-02-29 04:15:45\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:35:29 to 2020-02-29 21:19:51\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 10:19:48 to 2020-02-29 16:52:33\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 08:04:07 to 2020-02-29 12:49:56\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:34:48 to 2020-02-29 18:09:17\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:15:52 to 2020-02-29 19:30:39\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 03:10:31 to 2020-02-29 19:44:47\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 04:25:08 to 2020-02-29 07:57:40\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.75% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 04:54:43 to 2020-02-29 17:45:46\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 05:03:52 to 2020-02-29 19:04:03\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 05:25:08 to 2020-02-29 19:32:19\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 03:09:51 to 2020-02-29 18:04:17\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 10:11:12 to 2020-02-29 20:06:56\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 01:14:59 to 2020-02-29 14:43:28\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 05:01:12 to 2020-02-29 17:05:28\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 03:04:21 to 2020-02-29 11:11:20\n", - "\u001b[32m[x]\u001b[0m 203 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 07:10:08 to 2020-02-29 14:04:50\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 7.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 155 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 07:45:28 to 2020-02-29 08:32:41\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 01:14:51 to 2020-02-29 18:05:49\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:14:28 to 2020-02-29 21:08:53\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 04:48:09 to 2020-02-29 20:50:18\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 07:29:29 to 2020-02-29 21:59:04\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 09:46:52 to 2020-02-29 21:08:53\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.51% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 21:47:59 to 2020-02-29 19:23:16\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 04:42:54 to 2020-02-29 14:54:39\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.42% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:52:38 to 2020-02-29 06:41:38\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 00:40:20 to 2020-02-29 23:56:26\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 02:15:22 to 2020-02-29 22:22:49\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 04:17:04 to 2020-02-28 13:27:45\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 11:27:06 to 2020-02-29 22:55:42\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 07:10:08 to 2020-02-29 10:04:48\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 228 records from 2020-01-01 02:42:07 to 2020-02-29 23:41:52\n", - "\u001b[32m[x]\u001b[0m 208 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 03:24:51 to 2020-02-28 17:32:17\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 05:02:29 to 2020-02-29 15:43:55\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 00:40:20 to 2020-02-29 15:28:37\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 02:44:18 to 2020-02-29 22:25:05\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:23:45 to 2020-02-29 18:15:33\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 07:26:11 to 2020-02-29 12:41:38\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.41% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 09:55:38 to 2020-02-29 06:04:11\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 10:36:44 to 2020-02-29 23:40:39\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 00:53:36 to 2020-02-29 20:49:05\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 00:57:30 to 2020-02-29 08:28:26\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.13% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 03:50:23 to 2020-02-29 20:45:50\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.03% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 05:03:02 to 2020-02-29 18:25:09\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 09:05:42 to 2020-02-29 13:29:00\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.48% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 12:48:36 to 2020-02-29 23:11:35\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-02 07:06:24 to 2020-02-29 19:24:08\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:56:29 to 2020-02-29 14:34:24\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 09:46:40 to 2020-02-28 14:29:50\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:10:38 to 2020-02-29 19:49:36\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 14:37:54 to 2020-02-29 20:57:06\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.70% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 06:18:32 to 2020-02-29 23:54:48\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-02 01:32:34 to 2020-02-29 12:14:27\n", - "\u001b[32m[x]\u001b[0m 204 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-02 05:43:59 to 2020-02-29 19:40:07\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 04:36:18 to 2020-02-29 20:44:35\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 16:55:19 to 2020-02-29 04:23:12\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 152 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 00:34:42 to 2020-02-29 19:53:25\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 00:39:49 to 2020-02-29 21:00:48\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 6.77% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 02:16:18 to 2020-02-28 23:20:55\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 09:20:18 to 2020-02-29 20:57:06\n", - "\u001b[32m[x]\u001b[0m 202 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.80% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 22:25:36 to 2020-02-29 22:32:23\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:02:29 to 2020-02-29 19:45:26\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 7.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 16:13:46 to 2020-02-29 15:31:10\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 6.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 01:27:17 to 2020-02-29 13:24:23\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 04:16:42 to 2020-02-29 21:44:21\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 05:09:02 to 2020-02-29 22:18:23\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 02:19:17 to 2020-02-29 23:15:26\n", - "\u001b[32m[x]\u001b[0m 159 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 11:04:47 to 2020-02-29 19:24:08\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:39:57 to 2020-02-29 20:44:35\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 10:16:59 to 2020-02-29 20:32:54\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.88% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 07:23:27 to 2020-02-29 16:44:37\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 12:20:05 to 2020-02-29 15:50:56\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:43:54 to 2020-02-29 19:09:12\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 08:37:11 to 2020-02-29 14:26:32\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:17:50 to 2020-02-29 14:25:57\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 09:46:04 to 2020-02-29 14:00:38\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-02 03:01:33 to 2020-02-29 20:29:29\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 04:46:36 to 2020-02-29 17:36:42\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 04:54:05 to 2020-02-29 23:43:32\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 06:52:20 to 2020-02-29 23:13:39\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.31% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 04:55:46 to 2020-02-29 18:55:25\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 01:56:40 to 2020-02-29 19:32:00\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.28% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:34:01 to 2020-02-29 15:00:03\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 13:21:57 to 2020-02-29 23:11:35\n", - "\u001b[32m[x]\u001b[0m 162 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 06:49:38 to 2020-02-29 22:46:23\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:06:36 to 2020-02-29 11:51:02\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 07:25:46 to 2020-02-29 13:35:25\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 09:41:55 to 2020-02-29 23:14:42\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 10:08:09 to 2020-02-29 17:30:19\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.25% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 06:26:40 to 2020-02-29 20:43:27\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 04:27:03 to 2020-02-29 13:13:13\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 117 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 00:31:38 to 2020-02-29 23:03:09\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 01:24:38 to 2020-02-29 23:27:18\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 07:40:13 to 2020-02-29 20:49:01\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 09:38:39 to 2020-02-29 20:47:56\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 11:11:12 to 2020-02-29 22:08:42\n", - "\u001b[32m[x]\u001b[0m 206 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 02:49:38 to 2020-02-29 19:06:30\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.54% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 03:47:43 to 2020-02-29 21:54:56\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 00:10:34 to 2020-02-29 13:48:25\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 22:57:11 to 2020-02-29 21:01:52\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:08:57 to 2020-02-29 03:55:46\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 05:25:33 to 2020-02-29 23:01:17\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:24:38 to 2020-02-29 22:16:13\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 08:39:19 to 2020-02-29 05:28:25\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.72% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 08:45:53 to 2020-02-29 23:06:38\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.31% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 168 records from 2020-01-01 10:24:00 to 2020-02-29 21:20:09\n", - "\u001b[32m[x]\u001b[0m 149 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 08:05:13 to 2020-02-29 18:32:05\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 02:51:12 to 2020-02-29 16:11:12\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 03:12:20 to 2020-02-29 19:14:39\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 00:21:35 to 2020-02-29 08:26:36\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 00:52:27 to 2020-02-29 23:20:17\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 18:26:54 to 2020-02-29 22:30:56\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:01:32 to 2020-02-29 07:31:49\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 04:35:42 to 2020-02-29 16:52:49\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 09:47:54 to 2020-02-29 00:08:21\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:32:08 to 2020-02-29 08:37:32\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.28% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-02 20:21:35 to 2020-02-29 19:36:08\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 05:51:44 to 2020-02-29 20:57:52\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 00:21:12 to 2020-02-29 21:49:47\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 00:49:14 to 2020-02-29 18:25:09\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 02:44:18 to 2020-02-29 14:37:41\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.19% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 04:29:26 to 2020-02-29 21:20:52\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 03:18:24 to 2020-02-29 15:03:36\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 21:13:20 to 2020-02-29 16:14:22\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:50:07 to 2020-02-29 07:22:58\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 06:28:53 to 2020-02-28 21:11:22\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 04:27:28 to 2020-02-29 20:45:44\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 00:17:50 to 2020-02-29 18:05:58\n", - "\u001b[32m[x]\u001b[0m 157 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 151 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 06:43:37 to 2020-02-29 03:16:14\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 02:29:29 to 2020-02-29 19:45:43\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-02 06:03:36 to 2020-02-29 23:08:55\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 00:18:39 to 2020-02-29 19:03:46\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 229 records from 2020-01-01 07:17:58 to 2020-02-29 19:15:10\n", - "\u001b[32m[x]\u001b[0m 207 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 12:31:51 to 2020-02-29 19:57:09\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-02 12:01:17 to 2020-02-29 14:53:58\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.72% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 02:00:03 to 2020-02-29 20:24:53\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 02:00:13 to 2020-02-29 21:45:47\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 10:12:25 to 2020-02-29 19:51:55\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 02:30:00 to 2020-02-29 18:42:58\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 12:38:32 to 2020-02-29 17:54:48\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 15:35:32 to 2020-02-29 23:41:52\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.19% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 19:02:13 to 2020-02-29 22:44:21\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.05% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 156 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 00:04:26 to 2020-02-28 10:33:16\n", - "\u001b[32m[x]\u001b[0m 209 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:20:28 to 2020-02-29 23:24:06\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 02:51:40 to 2020-02-29 23:13:31\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 229 records from 2020-01-01 01:05:50 to 2020-02-29 10:16:35\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 14:01:49 to 2020-02-29 17:51:26\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 03:06:24 to 2020-02-29 20:19:04\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 02:33:51 to 2020-02-29 21:35:43\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 00:40:02 to 2020-02-29 21:48:29\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 04:44:08 to 2020-02-29 15:03:36\n", - "\u001b[32m[x]\u001b[0m 160 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 18:59:46 to 2020-02-29 22:22:27\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 10:30:02 to 2020-02-29 23:47:12\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 00:33:20 to 2020-02-29 14:30:58\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:00:07 to 2020-02-29 19:15:13\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 174 records from 2020-01-01 03:59:43 to 2020-02-29 16:40:15\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 121 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 04:41:31 to 2020-02-29 15:08:20\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:02:25 to 2020-02-29 22:05:17\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 02:14:07 to 2020-02-29 16:38:34\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.60% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 19:21:48 to 2020-02-28 23:49:23\n", - "\u001b[32m[x]\u001b[0m 162 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 05:49:59 to 2020-02-29 21:20:52\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 08:53:18 to 2020-02-29 23:33:19\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 01:57:59 to 2020-02-29 16:08:32\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 05:51:44 to 2020-02-29 13:00:28\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 06:01:17 to 2020-02-29 23:08:59\n", - "\u001b[32m[x]\u001b[0m 159 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 06:08:39 to 2020-02-29 20:00:39\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 10:47:14 to 2020-02-29 19:02:28\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 12:48:26 to 2020-02-29 15:33:28\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 05:16:39 to 2020-02-29 20:41:23\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:45:54 to 2020-02-29 05:42:10\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.31% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 09:31:02 to 2020-02-29 20:21:01\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.52% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 112 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 165 records from 2020-01-01 00:45:00 to 2020-02-29 23:32:09\n", - "\u001b[32m[x]\u001b[0m 156 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 09:30:34 to 2020-02-29 23:10:39\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 06:33:13 to 2020-02-29 11:48:12\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 19:34:30 to 2020-02-29 11:41:10\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:23:42 to 2020-02-29 23:33:35\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 09:54:49 to 2020-02-29 19:32:19\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 11:12:29 to 2020-02-29 18:08:00\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 05:53:34 to 2020-02-29 21:31:12\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 08:52:46 to 2020-02-29 17:52:22\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 150 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 231 records from 2020-01-01 01:26:02 to 2020-02-29 17:51:26\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 11:27:19 to 2020-02-29 10:10:39\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 15:14:24 to 2020-02-29 22:10:09\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.52% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 16:15:21 to 2020-02-29 17:25:59\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.54% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:43:36 to 2020-02-29 23:36:33\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 08:52:31 to 2020-02-29 23:58:06\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 18:59:46 to 2020-02-29 20:25:52\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 113 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 160 records from 2020-01-01 01:45:45 to 2020-02-29 13:53:41\n", - "\u001b[32m[x]\u001b[0m 150 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:45:49 to 2020-02-29 21:53:05\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 09:05:05 to 2020-02-29 20:19:04\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 226 records from 2020-01-01 09:46:32 to 2020-02-29 23:14:42\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 03:12:43 to 2020-02-29 00:59:07\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.48% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 06:04:12 to 2020-02-29 23:36:16\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 09:43:25 to 2020-02-29 15:30:34\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 08:34:57 to 2020-02-29 07:11:37\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 01:42:54 to 2020-02-29 04:34:58\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 23:46:31 to 2020-02-29 10:01:50\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-02 17:12:47 to 2020-02-29 20:16:07\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.75% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 01:34:53 to 2020-02-29 20:37:41\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 05:01:08 to 2020-02-29 05:49:03\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 231 records from 2020-01-01 00:06:20 to 2020-02-29 21:35:43\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 01:20:16 to 2020-02-29 20:27:31\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 01:03:22 to 2020-02-29 21:09:59\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:27:10 to 2020-02-29 04:40:27\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 23:41:34 to 2020-02-29 19:45:26\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-02 07:08:13 to 2020-02-29 10:54:39\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 10:57:27 to 2020-02-29 17:24:42\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 03:16:07 to 2020-02-29 23:38:58\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 00:28:12 to 2020-02-29 09:15:28\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 09:41:18 to 2020-02-29 17:36:12\n", - "\u001b[32m[x]\u001b[0m 203 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 18:05:39 to 2020-02-29 23:06:56\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 01:24:33 to 2020-02-29 18:30:53\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 02:15:22 to 2020-02-29 21:39:17\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 10:22:05 to 2020-02-29 12:29:06\n", - "\u001b[32m[x]\u001b[0m 204 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 10:22:51 to 2020-02-29 21:49:47\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 07:24:58 to 2020-02-29 23:35:04\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 18:37:00 to 2020-02-29 14:27:07\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 05:39:57 to 2020-02-29 20:58:24\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 17:47:09 to 2020-02-29 13:52:57\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-02 10:24:46 to 2020-02-28 07:19:30\n", - "\u001b[32m[x]\u001b[0m 157 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:08:16 to 2020-02-29 20:59:44\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:14:05 to 2020-02-29 21:39:17\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 16:50:11 to 2020-02-29 20:50:18\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-02 06:06:57 to 2020-02-29 20:19:22\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 01:41:44 to 2020-02-29 10:17:32\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.70% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 04:48:09 to 2020-02-29 01:53:03\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:27:31 to 2020-02-29 16:04:33\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 04:54:43 to 2020-02-29 20:37:41\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 02:24:33 to 2020-02-29 21:07:00\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 13:24:25 to 2020-02-29 18:50:30\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 06:31:59 to 2020-02-29 23:57:46\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 02:41:52 to 2020-02-29 20:24:53\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.70% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 03:16:07 to 2020-02-29 18:34:22\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:56:24 to 2020-02-29 17:47:07\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 04:38:47 to 2020-02-29 10:40:19\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 00:00:42 to 2020-02-29 14:02:32\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.87% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 07:00:54 to 2020-02-29 18:21:47\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 02:17:18 to 2020-02-29 17:40:22\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:05:11 to 2020-02-28 09:23:33\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 01:20:32 to 2020-02-29 10:05:07\n", - "\u001b[32m[x]\u001b[0m 202 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.03% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 02:51:32 to 2020-02-29 18:06:58\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 12:12:31 to 2020-02-29 22:09:30\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 22:56:40 to 2020-02-29 12:12:16\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-02 07:33:58 to 2020-02-29 20:27:25\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 19:56:25 to 2020-02-29 17:44:09\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.51% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 17:11:14 to 2020-02-29 22:01:58\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 170 records from 2020-01-01 09:59:41 to 2020-02-29 03:00:13\n", - "\u001b[32m[x]\u001b[0m 156 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 230 records from 2020-01-01 06:05:22 to 2020-02-29 23:55:45\n", - "\u001b[32m[x]\u001b[0m 203 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 06:54:21 to 2020-02-29 19:17:36\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 01:27:17 to 2020-02-29 23:01:42\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 16:13:38 to 2020-02-29 11:28:03\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 17:33:30 to 2020-02-29 23:52:24\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:16:27 to 2020-02-29 15:49:36\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 17.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:14:51 to 2020-02-28 21:58:10\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 05:35:27 to 2020-02-29 20:21:01\n", - "\u001b[32m[x]\u001b[0m 160 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 116 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 170 records from 2020-01-01 05:41:29 to 2020-02-29 15:17:29\n", - "\u001b[32m[x]\u001b[0m 157 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.41% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 22:26:40 to 2020-02-29 23:16:32\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 06:07:02 to 2020-02-29 05:42:15\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 230 records from 2020-01-01 08:26:20 to 2020-02-29 20:00:32\n", - "\u001b[32m[x]\u001b[0m 207 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:50:00 to 2020-02-29 23:29:34\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 174 records from 2020-01-01 04:59:13 to 2020-02-29 06:16:38\n", - "\u001b[32m[x]\u001b[0m 156 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.49% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 03:47:30 to 2020-02-29 10:31:02\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.25% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 10:36:17 to 2020-02-29 23:56:35\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 20:18:51 to 2020-02-29 14:59:14\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 08:21:41 to 2020-02-29 21:42:46\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 03:13:37 to 2020-02-29 15:56:56\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 04:15:45 to 2020-02-29 15:46:34\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 13:31:48 to 2020-02-29 21:09:59\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 12:56:57 to 2020-02-29 22:57:37\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 06:34:07 to 2020-02-29 22:05:17\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 08:53:09 to 2020-02-29 16:58:22\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 06:52:20 to 2020-02-29 10:45:57\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.01% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 05:15:10 to 2020-02-29 23:55:45\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 01:08:34 to 2020-02-29 22:46:23\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 01:50:19 to 2020-02-29 03:27:44\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 03:07:43 to 2020-02-29 16:06:30\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 04:58:55 to 2020-02-29 20:10:42\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 02:51:12 to 2020-02-29 20:59:55\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.77% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 00:18:39 to 2020-02-29 22:47:34\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 05:29:52 to 2020-02-29 23:23:38\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 115 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 09:09:00 to 2020-02-29 09:47:43\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:56:29 to 2020-02-29 22:21:41\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.31% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:23:42 to 2020-02-29 21:01:09\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:45:00 to 2020-02-29 22:57:56\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.52% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 00:06:20 to 2020-02-29 14:39:58\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 16:53:19 to 2020-02-29 06:58:45\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 14:37:54 to 2020-02-29 17:07:43\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 161 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 233 records from 2020-01-02 07:43:31 to 2020-02-29 23:36:05\n", - "\u001b[32m[x]\u001b[0m 212 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 00:40:16 to 2020-02-29 14:56:21\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:03:05 to 2020-02-28 19:35:04\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 20:13:02 to 2020-02-29 22:00:49\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 12:20:59 to 2020-02-29 04:27:44\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 00:50:07 to 2020-02-29 23:03:55\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 04:21:23 to 2020-02-29 22:48:07\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.26% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:33:02 to 2020-02-29 20:32:45\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 02:00:13 to 2020-02-29 04:33:03\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-02 07:04:16 to 2020-02-29 21:24:36\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 00:18:19 to 2020-02-29 17:44:09\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:24:08 to 2020-02-29 14:24:40\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 04:33:16 to 2020-02-29 18:38:00\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.54% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 05:40:48 to 2020-02-29 21:07:00\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 06:22:39 to 2020-02-29 23:52:56\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 14:57:36 to 2020-02-29 22:19:20\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.99% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 03:38:04 to 2020-02-29 21:03:12\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:05:50 to 2020-02-29 15:03:55\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 02:30:00 to 2020-02-29 18:33:05\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 01:16:05 to 2020-02-29 23:26:11\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 09:26:54 to 2020-02-29 21:29:25\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 8.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 11:45:38 to 2020-02-29 12:27:46\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:44:47 to 2020-02-29 23:06:35\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 01:09:21 to 2020-02-29 15:30:34\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 05:37:57 to 2020-02-29 23:53:09\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.10% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 06:35:58 to 2020-02-29 16:57:20\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 01:07:55 to 2020-02-29 23:56:26\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.41% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 09:49:30 to 2020-02-29 22:30:03\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 06:46:08 to 2020-02-29 23:42:20\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 05:45:26 to 2020-02-29 07:52:16\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 16:41:39 to 2020-02-28 12:59:40\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.91% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 08:37:16 to 2020-02-29 13:24:21\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 10:51:03 to 2020-02-29 20:59:55\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:14:05 to 2020-02-29 23:39:12\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:36:16 to 2020-02-29 12:19:19\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.31% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 01:37:03 to 2020-02-29 23:33:19\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 13:33:02 to 2020-02-29 18:49:11\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 02:16:59 to 2020-02-29 04:31:28\n", - "\u001b[32m[x]\u001b[0m 159 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 15.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 02:14:38 to 2020-02-29 12:30:51\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 08:19:42 to 2020-02-29 23:27:18\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 10:07:19 to 2020-02-29 18:30:19\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.48% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 01:20:16 to 2020-02-29 14:42:45\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:04:26 to 2020-02-29 22:18:23\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 04:23:42 to 2020-02-29 17:32:13\n", - "\u001b[32m[x]\u001b[0m 157 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 18:36:37 to 2020-02-29 12:58:01\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 03:39:34 to 2020-02-29 21:03:48\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:18:33 to 2020-02-29 14:34:24\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 02:41:52 to 2020-02-29 17:02:43\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 07:37:28 to 2020-02-29 18:50:30\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 11:35:07 to 2020-02-29 19:15:08\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-02 13:12:02 to 2020-02-28 15:35:26\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:55:46 to 2020-02-29 22:32:38\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 01:33:02 to 2020-02-28 21:01:34\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 05:51:58 to 2020-02-29 15:14:26\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 02:47:26 to 2020-02-29 20:21:55\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 151 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 11:39:31 to 2020-02-29 14:45:06\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 02:14:43 to 2020-02-29 16:50:28\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-02 07:00:11 to 2020-02-29 21:45:47\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 151 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 227 records from 2020-01-01 12:35:29 to 2020-02-29 23:11:27\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 16.75% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 04:54:00 to 2020-02-29 13:56:16\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 02:36:50 to 2020-02-29 11:55:12\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 05:34:27 to 2020-02-29 23:56:05\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 07:00:54 to 2020-02-29 22:32:41\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 14:31:21 to 2020-02-29 20:32:54\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 01:24:55 to 2020-02-29 08:45:02\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 21:04:29 to 2020-02-29 23:42:31\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 172 records from 2020-01-01 09:29:13 to 2020-02-29 15:38:31\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 14:07:39 to 2020-02-29 18:11:07\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 09:47:54 to 2020-02-28 21:28:57\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.01% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 05:29:31 to 2020-02-29 12:31:33\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 09:18:08 to 2020-02-29 20:03:25\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.12% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 07:48:32 to 2020-02-29 14:54:04\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 07:18:19 to 2020-02-29 21:32:57\n", - "\u001b[32m[x]\u001b[0m 162 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 00:50:00 to 2020-02-29 19:54:43\n", - "\u001b[32m[x]\u001b[0m 207 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 02:26:06 to 2020-02-29 19:15:07\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 07:11:13 to 2020-02-29 23:52:24\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:38:12 to 2020-02-29 23:19:19\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 20:13:02 to 2020-02-29 18:32:03\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 06:06:14 to 2020-02-29 10:14:06\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 00:06:38 to 2020-02-29 06:05:47\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:12:11 to 2020-02-29 21:54:56\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 03:20:52 to 2020-02-29 18:20:39\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 121 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 00:57:30 to 2020-02-29 23:33:15\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 19:40:10 to 2020-02-29 23:47:12\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 22:18:48 to 2020-02-29 11:39:04\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 00:28:41 to 2020-02-29 14:41:12\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:37:14 to 2020-02-29 16:48:02\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 02:31:43 to 2020-02-29 21:00:11\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 152 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 07:04:32 to 2020-02-29 17:13:08\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 112 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 04:21:23 to 2020-02-29 18:03:55\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 233 records from 2020-01-01 05:36:32 to 2020-02-29 23:54:24\n", - "\u001b[32m[x]\u001b[0m 207 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 17:38:47 to 2020-02-29 16:18:38\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 08:20:52 to 2020-02-29 22:57:24\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 01:14:59 to 2020-02-29 22:01:58\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 05:52:03 to 2020-02-29 22:31:40\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:09:02 to 2020-02-29 19:36:08\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 09:45:25 to 2020-02-29 21:46:54\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 13:56:46 to 2020-02-28 23:10:24\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.34% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:16:44 to 2020-02-29 23:04:17\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 05:34:15 to 2020-02-29 23:10:39\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 06:13:45 to 2020-02-29 23:27:54\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 04:26:57 to 2020-02-29 07:22:35\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.97% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 06:35:29 to 2020-02-29 20:47:56\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 03:44:48 to 2020-02-29 23:24:58\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 06:19:01 to 2020-02-29 16:50:28\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 01:34:53 to 2020-02-29 23:19:19\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 08:02:05 to 2020-02-29 19:34:49\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 16:23:59 to 2020-02-29 14:52:10\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 01:22:35 to 2020-02-29 15:34:40\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-02 04:59:51 to 2020-02-29 23:58:06\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 00:21:10 to 2020-02-29 23:53:08\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.70% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 00:21:12 to 2020-02-29 15:21:54\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 04:23:42 to 2020-02-29 17:31:43\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 05:41:29 to 2020-02-29 20:58:25\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 08:53:13 to 2020-02-29 14:54:56\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 16.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 18:05:39 to 2020-02-28 21:08:02\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 00:02:12 to 2020-02-29 05:44:32\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 04:19:43 to 2020-02-29 13:40:53\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 07:13:05 to 2020-02-29 23:16:32\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 08:00:59 to 2020-02-29 20:01:55\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 05:31:28 to 2020-02-29 17:32:38\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 06:42:05 to 2020-02-29 16:02:11\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 10:07:02 to 2020-02-29 20:06:41\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:24:55 to 2020-02-29 17:41:33\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 03:53:19 to 2020-02-29 23:09:21\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 07:27:08 to 2020-02-29 08:01:45\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 01:22:53 to 2020-02-29 23:10:31\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 21:36:32 to 2020-02-29 17:42:16\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:55:52 to 2020-02-28 20:47:54\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:06:38 to 2020-02-29 23:06:56\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 03:02:40 to 2020-02-29 20:20:16\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 01:10:28 to 2020-02-29 17:24:42\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:08:16 to 2020-02-29 20:06:25\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 07:12:28 to 2020-02-29 21:22:08\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:18:19 to 2020-02-29 17:27:34\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 11:15:34 to 2020-02-29 18:25:27\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 06:18:28 to 2020-02-29 22:16:13\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 15:46:54 to 2020-02-29 21:34:41\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-02 00:02:03 to 2020-02-29 22:22:27\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 04:44:01 to 2020-02-29 17:46:38\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 05:13:42 to 2020-02-29 23:56:05\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 05:21:34 to 2020-02-29 13:22:36\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 05:36:25 to 2020-02-28 19:53:35\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 06:44:29 to 2020-02-29 11:06:23\n", - "\u001b[32m[x]\u001b[0m 159 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 7.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 01:22:36 to 2020-02-29 16:14:55\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 08:31:59 to 2020-02-29 07:11:45\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 18:41:14 to 2020-02-29 21:51:24\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 08:42:04 to 2020-02-29 23:01:18\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 01:54:54 to 2020-02-29 18:05:58\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 05:31:30 to 2020-02-29 23:36:16\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 08:10:12 to 2020-02-29 20:36:59\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 08:12:12 to 2020-02-29 23:43:32\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 07:08:19 to 2020-02-29 16:14:39\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 01:37:47 to 2020-02-29 21:20:20\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.80% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 09:45:45 to 2020-02-29 16:52:33\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 12:45:52 to 2020-02-29 12:54:21\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 12:38:32 to 2020-02-29 20:45:50\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 117 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 01:09:21 to 2020-02-29 07:02:25\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 00:27:31 to 2020-02-29 21:23:03\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:17:07 to 2020-02-29 20:06:56\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:00:12 to 2020-02-29 13:57:49\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 09:02:29 to 2020-02-29 17:26:12\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.51% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 02:43:25 to 2020-02-29 20:58:24\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 05:16:28 to 2020-02-29 16:01:06\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.19% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 07:04:50 to 2020-02-29 16:19:51\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 00:54:21 to 2020-02-29 18:00:54\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 17:52:32 to 2020-02-29 16:41:54\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:34:41 to 2020-02-28 14:03:29\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:44:34 to 2020-02-29 20:20:14\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 07:48:33 to 2020-02-29 23:08:55\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 08:45:29 to 2020-02-29 12:23:51\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:46:52 to 2020-02-29 00:51:05\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 02:51:53 to 2020-02-29 13:57:49\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 08:34:31 to 2020-02-29 23:26:11\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 21:17:50 to 2020-02-29 16:29:36\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 00:20:30 to 2020-02-29 13:15:15\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 113 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 01:03:20 to 2020-02-29 20:58:25\n", - "\u001b[32m[x]\u001b[0m 162 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 01:03:22 to 2020-02-29 10:44:06\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:59:21 to 2020-02-29 15:24:35\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 116 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 159 records from 2020-01-01 04:29:41 to 2020-02-29 02:14:42\n", - "\u001b[32m[x]\u001b[0m 152 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 01:14:29 to 2020-02-29 10:17:05\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-01 06:54:58 to 2020-02-29 23:22:42\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 20:24:41 to 2020-02-29 20:21:55\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 05:35:27 to 2020-02-29 14:00:44\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 17:36:44 to 2020-02-29 04:35:26\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 10:05:32 to 2020-02-29 17:01:27\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 08:06:30 to 2020-02-29 15:35:15\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.12% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 04:07:51 to 2020-02-29 20:53:31\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:55:03 to 2020-02-29 20:10:42\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.31% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:40:02 to 2020-02-29 23:59:45\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 01:26:14 to 2020-02-29 14:30:58\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 02:15:49 to 2020-02-29 22:09:05\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 04:12:20 to 2020-02-29 14:06:49\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 227 records from 2020-01-01 05:39:57 to 2020-02-29 16:41:08\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 02:03:01 to 2020-02-29 23:40:39\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 14:45:51 to 2020-02-29 21:07:33\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 10:14:44 to 2020-02-29 14:01:37\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 00:30:40 to 2020-02-29 13:01:05\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 15:33:36 to 2020-02-29 13:29:03\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 06:42:05 to 2020-02-29 23:29:58\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 10:38:23 to 2020-02-29 22:29:46\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 06:29:23 to 2020-02-29 23:29:58\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 07:02:59 to 2020-02-29 15:52:57\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 12:06:02 to 2020-02-29 20:05:18\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 23:28:27 to 2020-02-29 19:57:00\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 04:16:42 to 2020-02-29 21:53:05\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.10% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 08:32:12 to 2020-02-29 23:52:56\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 05:03:35 to 2020-02-29 20:37:30\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 15:26:57 to 2020-02-29 23:00:24\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.54% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:52:27 to 2020-02-29 20:37:30\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.80% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 03:47:43 to 2020-02-29 06:41:38\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 05:58:42 to 2020-02-29 22:32:23\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.54% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 10:20:34 to 2020-02-29 18:48:06\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 116 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 13:35:55 to 2020-02-29 09:24:18\n", - "\u001b[32m[x]\u001b[0m 160 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 19.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 06:07:43 to 2020-02-29 21:03:12\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.12% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-02 06:42:32 to 2020-02-29 02:17:22\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:52:38 to 2020-02-29 22:46:51\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:18:39 to 2020-02-29 22:40:44\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 02:33:51 to 2020-02-29 20:59:56\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 03:27:23 to 2020-02-29 16:51:39\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 07:00:19 to 2020-02-29 16:14:22\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 04:22:21 to 2020-02-29 21:57:07\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 03:13:38 to 2020-02-29 09:21:04\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 03:02:40 to 2020-02-29 21:50:19\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 02:52:39 to 2020-02-29 20:03:25\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 07:23:16 to 2020-02-29 14:50:40\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 01:19:28 to 2020-02-29 21:40:42\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 13:07:52 to 2020-02-29 18:20:39\n", - "\u001b[32m[x]\u001b[0m 159 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-02 06:01:47 to 2020-02-29 17:08:49\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 15:49:17 to 2020-02-29 20:56:26\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.12% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 115 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 170 records from 2020-01-01 01:45:49 to 2020-02-29 23:48:33\n", - "\u001b[32m[x]\u001b[0m 156 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 07:03:05 to 2020-02-28 23:59:41\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 01:19:28 to 2020-02-29 21:46:54\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 04:30:26 to 2020-02-29 23:10:31\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.60% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 04:31:39 to 2020-02-29 16:50:20\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.10% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 117 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 168 records from 2020-01-01 06:59:20 to 2020-02-29 12:03:03\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 7.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 07:55:40 to 2020-02-29 17:32:38\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 00:36:16 to 2020-02-29 11:57:21\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 05:34:15 to 2020-02-29 23:02:01\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 07:55:27 to 2020-02-29 18:57:37\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.42% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 08:06:00 to 2020-02-29 13:00:28\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-02 03:45:50 to 2020-02-29 23:36:05\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 00:17:53 to 2020-02-29 21:49:02\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:10:35 to 2020-02-29 16:09:28\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 01:08:16 to 2020-02-28 17:14:45\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.13% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 02:36:24 to 2020-02-29 23:08:59\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 13:09:18 to 2020-02-29 23:46:11\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.91% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 165 records from 2020-01-01 00:10:38 to 2020-02-29 20:03:58\n", - "\u001b[32m[x]\u001b[0m 150 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 7.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:53:43 to 2020-02-29 23:01:42\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 01:22:26 to 2020-02-29 14:56:34\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 10:28:44 to 2020-02-29 21:51:46\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 07:04:50 to 2020-02-29 20:03:58\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 15:41:26 to 2020-02-29 22:55:19\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.77% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 01:24:33 to 2020-02-29 18:10:45\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 04:07:26 to 2020-02-29 20:27:44\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.99% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 02:50:53 to 2020-02-29 20:37:29\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 01:36:16 to 2020-02-29 11:52:36\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 19:09:23 to 2020-02-29 02:31:03\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 21:27:32 to 2020-02-29 12:46:53\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 00:12:42 to 2020-02-29 15:42:06\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 02:33:21 to 2020-02-29 12:11:47\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:55:00 to 2020-02-29 17:17:37\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:01:32 to 2020-02-29 14:45:06\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 16:41:24 to 2020-02-29 19:50:13\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 15.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 06:19:51 to 2020-02-29 21:59:22\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.26% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 18:46:22 to 2020-02-29 18:47:48\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.34% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 19:29:56 to 2020-02-29 07:11:32\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 114 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-01 00:51:01 to 2020-02-29 20:34:18\n", - "\u001b[32m[x]\u001b[0m 153 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 07:19:32 to 2020-02-29 23:20:17\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 03:54:29 to 2020-02-29 22:45:42\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 02:29:54 to 2020-02-28 14:54:50\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.01% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 00:09:47 to 2020-02-29 23:28:32\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.80% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 10:55:58 to 2020-02-29 16:41:08\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 03:12:20 to 2020-02-29 20:21:36\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.49% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 19:06:01 to 2020-02-29 22:39:56\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 00:32:11 to 2020-02-29 19:33:39\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 06:42:06 to 2020-02-29 23:19:35\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 06:55:52 to 2020-02-29 17:32:11\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 04:12:20 to 2020-02-28 08:32:00\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:20:52 to 2020-02-29 18:48:06\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 05:03:23 to 2020-02-29 20:53:31\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.05% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 00:16:27 to 2020-02-29 11:18:44\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 04:48:23 to 2020-02-29 06:06:19\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 01:50:19 to 2020-02-29 10:19:08\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 10:05:43 to 2020-02-29 18:19:47\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 20:36:42 to 2020-02-29 14:04:50\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 152 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 12:26:49 to 2020-02-29 18:42:32\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:02:19 to 2020-02-29 23:59:45\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:21:11 to 2020-02-29 20:26:19\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:51:18 to 2020-02-29 10:15:29\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:18:33 to 2020-02-29 19:43:50\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 114 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-01 01:30:42 to 2020-02-29 19:32:00\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 00:19:47 to 2020-02-29 14:36:51\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 08:58:27 to 2020-02-29 20:36:13\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 01:20:32 to 2020-02-29 13:27:52\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 01:08:16 to 2020-02-29 17:51:50\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 12:56:35 to 2020-02-29 20:14:36\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 01:08:34 to 2020-02-29 09:49:15\n", - "\u001b[32m[x]\u001b[0m 156 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 12:17:24 to 2020-02-29 16:41:54\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 00:32:08 to 2020-02-29 23:19:01\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.42% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 03:41:08 to 2020-02-29 17:42:16\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 7.91% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 05:47:05 to 2020-02-29 03:54:28\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 10:46:16 to 2020-02-29 23:42:20\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 19:43:59 to 2020-02-29 20:39:23\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 10:36:44 to 2020-02-29 23:03:55\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:02:23 to 2020-02-29 18:40:40\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.13% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 117 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-01 00:31:38 to 2020-02-29 18:40:40\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 06:49:46 to 2020-02-29 16:38:34\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 04:58:32 to 2020-02-29 21:44:00\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 16:24:15 to 2020-02-29 11:13:37\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 02:49:38 to 2020-02-29 19:13:03\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 12:44:17 to 2020-02-29 23:15:26\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:53:51 to 2020-02-29 20:34:18\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 03:06:24 to 2020-02-29 21:49:11\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.88% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 03:37:27 to 2020-02-29 20:20:01\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 05:13:02 to 2020-02-29 11:51:00\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 05:56:47 to 2020-02-29 11:53:32\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.41% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 03:27:23 to 2020-02-29 22:35:43\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 02:12:00 to 2020-02-29 20:04:42\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 232 records from 2020-01-01 10:45:23 to 2020-02-29 20:28:04\n", - "\u001b[32m[x]\u001b[0m 203 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 11:58:03 to 2020-02-29 20:20:14\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 150 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 13:54:39 to 2020-02-29 23:32:09\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 19:37:02 to 2020-02-29 22:42:36\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-02 10:55:47 to 2020-02-28 23:55:44\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 02:24:32 to 2020-02-29 23:13:39\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.42% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 07:43:38 to 2020-02-29 19:13:03\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 02:29:48 to 2020-02-29 19:06:26\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 08:40:38 to 2020-02-29 19:07:04\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:19:13 to 2020-02-29 15:42:13\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:33:20 to 2020-02-29 21:01:24\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 228 records from 2020-01-01 04:14:56 to 2020-02-29 20:29:21\n", - "\u001b[32m[x]\u001b[0m 207 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 00:43:36 to 2020-02-29 03:44:43\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 01:01:37 to 2020-02-29 22:07:35\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 02:16:15 to 2020-02-29 22:57:56\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 05:14:01 to 2020-02-29 08:37:33\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 09:38:51 to 2020-02-29 15:49:00\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 01:46:18 to 2020-02-29 20:25:28\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 02:38:57 to 2020-02-29 22:52:28\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 03:04:43 to 2020-02-29 18:30:53\n", - "\u001b[32m[x]\u001b[0m 204 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.60% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 05:49:46 to 2020-02-29 06:23:10\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 03:41:08 to 2020-02-29 17:41:33\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 01:26:14 to 2020-02-29 21:51:24\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 06:11:54 to 2020-02-28 11:05:18\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 03:12:25 to 2020-02-29 23:36:33\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 03:49:50 to 2020-02-29 19:51:37\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 05:03:23 to 2020-02-29 23:05:57\n", - "\u001b[32m[x]\u001b[0m 202 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 04:25:08 to 2020-02-29 07:25:51\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 04:29:41 to 2020-02-29 22:31:40\n", - "\u001b[32m[x]\u001b[0m 202 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 02:29:48 to 2020-02-29 15:38:31\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 01:36:16 to 2020-02-29 15:37:50\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 01:56:53 to 2020-02-28 19:55:47\n", - "\u001b[32m[x]\u001b[0m 160 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 06:23:16 to 2020-02-29 20:32:45\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.75% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 02:16:15 to 2020-02-29 22:55:19\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 238 records from 2020-01-01 09:41:15 to 2020-02-29 13:56:16\n", - "\u001b[32m[x]\u001b[0m 210 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 04:36:18 to 2020-02-29 21:29:25\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-02 04:30:15 to 2020-02-29 23:23:05\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 00:53:43 to 2020-02-29 19:23:16\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 01:37:27 to 2020-02-29 20:39:23\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:00:07 to 2020-02-29 04:28:07\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.99% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 03:35:24 to 2020-02-29 23:42:31\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 11:07:04 to 2020-02-29 17:06:58\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 03:24:51 to 2020-02-29 21:01:24\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 6.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 00:08:25 to 2020-02-29 13:38:58\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:09:47 to 2020-02-29 17:06:58\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 00:53:43 to 2020-02-29 04:22:55\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 01:42:47 to 2020-02-29 22:00:49\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.10% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:19:13 to 2020-02-29 21:26:49\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 01:56:55 to 2020-02-29 16:52:49\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 03:24:27 to 2020-02-29 11:15:15\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 04:27:26 to 2020-02-29 21:06:10\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 06:00:53 to 2020-02-29 10:48:48\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 01:28:18 to 2020-02-29 22:48:07\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 01:55:03 to 2020-02-29 20:43:27\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 6.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 04:08:22 to 2020-02-29 06:56:48\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 04:52:06 to 2020-02-29 11:08:37\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.97% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 05:29:08 to 2020-02-29 23:11:27\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 05:34:51 to 2020-02-29 11:46:16\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 16:37:51 to 2020-02-29 17:33:19\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 15:23:09 to 2020-02-29 18:03:01\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 01:35:55 to 2020-02-29 11:38:15\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 15.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 17:37:55 to 2020-02-29 17:16:20\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.05% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 02:32:52 to 2020-02-29 21:09:16\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 151 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 04:19:47 to 2020-02-29 16:54:59\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 03:16:50 to 2020-02-29 18:59:25\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 18:12:20 to 2020-02-29 16:54:52\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 09:43:25 to 2020-02-29 19:03:46\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 06:46:08 to 2020-02-29 20:25:28\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 112 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 159 records from 2020-01-01 07:15:23 to 2020-02-29 18:26:47\n", - "\u001b[32m[x]\u001b[0m 148 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 16:10:46 to 2020-02-29 18:06:58\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 07:55:27 to 2020-02-29 23:34:24\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 17:45:32 to 2020-02-29 13:38:02\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.26% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 16:09:46 to 2020-02-29 22:35:18\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 10:26:44 to 2020-02-29 05:02:58\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 19:22:56 to 2020-02-29 10:44:32\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 114 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 164 records from 2020-01-01 03:51:53 to 2020-02-29 05:24:49\n", - "\u001b[32m[x]\u001b[0m 151 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-02 09:37:16 to 2020-02-29 22:55:09\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:02:12 to 2020-02-29 17:02:43\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 02:19:58 to 2020-02-29 14:45:30\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 05:30:58 to 2020-02-29 19:08:48\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 06:18:28 to 2020-02-29 22:13:58\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 11:39:19 to 2020-02-29 21:03:48\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 00:30:00 to 2020-02-29 15:10:29\n", - "\u001b[32m[x]\u001b[0m 203 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:00:12 to 2020-02-29 17:32:11\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 05:01:12 to 2020-02-29 10:44:32\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.10% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 12:56:16 to 2020-02-29 18:09:25\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.60% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 00:55:00 to 2020-02-28 18:41:15\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 04:01:48 to 2020-02-29 07:38:55\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 00:53:36 to 2020-02-29 17:27:34\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 08:21:41 to 2020-02-29 21:49:26\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 04:37:45 to 2020-02-29 23:53:08\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 06:22:55 to 2020-02-29 21:32:05\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 06:34:41 to 2020-02-29 23:43:54\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 04:01:14 to 2020-02-29 18:02:48\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 03:59:43 to 2020-02-29 10:27:56\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 05:13:02 to 2020-02-29 17:08:49\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:07:47 to 2020-02-29 17:16:05\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 03:46:24 to 2020-02-29 23:06:38\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 02:11:34 to 2020-02-29 23:39:27\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 02:14:07 to 2020-02-29 13:32:26\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:32:11 to 2020-02-29 14:46:03\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:28:12 to 2020-02-29 23:39:27\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 6.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 09:35:26 to 2020-02-29 18:51:11\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 03:04:43 to 2020-02-29 15:02:45\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 09:05:42 to 2020-02-29 13:27:52\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.91% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-02 00:29:41 to 2020-02-29 22:55:42\n", - "\u001b[32m[x]\u001b[0m 160 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 00:10:35 to 2020-02-29 23:33:15\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 06:33:55 to 2020-02-29 20:31:44\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 10:27:25 to 2020-02-29 15:28:39\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 15:00:23 to 2020-02-29 19:32:20\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.41% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 08:10:12 to 2020-02-29 15:34:52\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:53:43 to 2020-02-29 21:32:57\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 02:42:07 to 2020-02-29 19:19:37\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 07:53:52 to 2020-02-29 10:47:46\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 05:25:33 to 2020-02-29 19:49:35\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 06:53:47 to 2020-02-29 07:25:51\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 01:37:27 to 2020-02-29 15:30:39\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 229 records from 2020-01-01 21:45:43 to 2020-02-29 22:47:56\n", - "\u001b[32m[x]\u001b[0m 212 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 16:41:24 to 2020-02-29 16:33:04\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.13% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:42:47 to 2020-02-29 12:51:23\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 05:31:28 to 2020-02-29 10:27:08\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.99% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 05:34:47 to 2020-02-29 07:23:10\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-02 01:37:40 to 2020-02-29 16:54:52\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.49% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-02 09:34:56 to 2020-02-29 22:32:41\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 00:37:55 to 2020-02-29 11:07:26\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 115 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 00:00:42 to 2020-02-29 22:08:42\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 00:10:34 to 2020-02-29 15:22:18\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 02:47:26 to 2020-02-29 21:19:51\n", - "\u001b[32m[x]\u001b[0m 206 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 06:57:41 to 2020-02-29 19:33:39\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 08:37:16 to 2020-02-29 16:49:58\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 11:01:00 to 2020-02-29 16:10:43\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 10:38:25 to 2020-02-29 15:30:39\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Successfully exported 1000 object(s) to ./outputs/featurizer//datasets/bandicoot_features/0to1000/0.csv\n", - "\r", - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating international features...\n", - "Calculating spatial features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/luciomelito/Library/Caches/pypoetry/virtualenvs/cider-n28xxEz6-py3.8/lib/python3.8/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n", - " in_crs_string = _prepare_from_proj_string(in_crs_string)\n", - "../../cider/cider/featurizer.py:325: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.\n", - "Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.\n", - "\n", - "Left CRS: +init=epsg:4326 +type=crs\n", - "Right CRS: EPSG:4326\n", - "\n", - " antennas = gpd.sjoin(antennas, shapefile, op='within', how='left').drop('index_right', axis=1)\n", - "../../cider/cider/featurizer.py:325: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.\n", - "Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.\n", - "\n", - "Left CRS: +init=epsg:4326 +type=crs\n", - "Right CRS: EPSG:4326\n", - "\n", - " antennas = gpd.sjoin(antennas, shapefile, op='within', how='left').drop('index_right', axis=1)\n", - "../../cider/cider/featurizer.py:325: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.\n", - "Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.\n", - "\n", - "Left CRS: +init=epsg:4326 +type=crs\n", - "Right CRS: EPSG:4326\n", - "\n", - " antennas = gpd.sjoin(antennas, shapefile, op='within', how='left').drop('index_right', axis=1)\n", - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating recharges features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating mobile data features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating mobile money features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], + "execution_count": 26, + "id": "acc99715-7c14-41a2-a269-a7fa57e1fa46", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "featurizer.cdr_features()\n", + "featurizer.cdr_features_spark()\n", "featurizer.international_features()\n", "featurizer.location_features()\n", "featurizer.recharges_features()\n", "featurizer.mobiledata_features()\n", "featurizer.mobilemoney_features()\n", + "\n", "featurizer.all_features()" ] }, + { + "cell_type": "markdown", + "id": "dcefc331-6b93-4d7e-8445-8cf58c75430b", + "metadata": {}, + "source": [ + "Now we read the features back with pandas in to see what the table looks like. This works fine because our synthetic dataset is small, but such files can be too large to fit in memory if the number of subscribers is large; Cider uses [pyspark](https://spark.apache.org/docs/latest/api/python/) to manage large datasets. Another option for working with large datasets is [dask](https://www.dask.org/)." + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 32, "id": "2d077b0c", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -12748,15 +367,15 @@ " \n", " \n", " name\n", - " cdr_reporting__number_of_records\n", - " cdr_active_days__allweek__allday__callandtext\n", - " cdr_active_days__allweek__day__callandtext\n", - " cdr_active_days__allweek__night__callandtext\n", - " cdr_active_days__weekday__allday__callandtext\n", - " cdr_active_days__weekday__day__callandtext\n", - " cdr_active_days__weekday__night__callandtext\n", - " cdr_active_days__weekend__allday__callandtext\n", - " cdr_active_days__weekend__day__callandtext\n", + " active_days_allweek_allday\n", + " active_days_allweek_day\n", + " active_days_allweek_night\n", + " active_days_weekday_allday\n", + " active_days_weekday_day\n", + " active_days_weekday_night\n", + " active_days_weekend_allday\n", + " active_days_weekend_day\n", + " active_days_weekend_night\n", " ...\n", " mobilemoney_outgoing_p2p_amount_min\n", " mobilemoney_outgoing_p2p_amount_max\n", @@ -12773,260 +392,233 @@ " \n", " \n", " 0\n", - " dsBHAdXrrk\n", - " 184\n", - " 58\n", + " AASxnSdfla\n", + " 56\n", " 46\n", " 50\n", - " 41\n", - " 32\n", + " 40\n", + " 31\n", " 35\n", - " 17\n", - " 14\n", + " 16\n", + " 15\n", + " 15\n", " ...\n", - " 26.361252\n", - " 66.175190\n", - " 198.212410\n", - " 144.32855\n", - " 270.12857\n", - " 148.945410\n", - " 78.153360\n", - " 218.63590\n", - " 8.0\n", - " 8.0\n", + " 31.799332\n", + " 58.921886\n", + " 187.554639\n", + " 138.324660\n", + " 228.47707\n", + " 138.780946\n", + " 106.525330\n", + " 180.20863\n", + " 6.0\n", + " 6.0\n", " \n", " \n", " 1\n", - " JGPCbfDGes\n", - " 209\n", - " 59\n", - " 49\n", - " 50\n", - " 42\n", - " 35\n", - " 34\n", - " 17\n", + " ANnpaBqoKb\n", + " 55\n", + " 45\n", + " 47\n", + " 40\n", + " 36\n", + " 33\n", + " 15\n", + " 9\n", " 14\n", " ...\n", - " 33.154373\n", - " 60.508873\n", - " 166.658227\n", - " 83.04488\n", - " 209.16385\n", - " 115.873907\n", - " 22.536007\n", - " 173.27129\n", - " 6.0\n", - " 6.0\n", + " 37.976850\n", + " 57.337646\n", + " 182.190155\n", + " 158.963560\n", + " 223.29039\n", + " 137.548449\n", + " 106.978870\n", + " 185.31354\n", + " 3.0\n", + " 3.0\n", " \n", " \n", " 2\n", - " dYwshzRseD\n", - " 205\n", - " 59\n", - " 50\n", - " 52\n", - " 42\n", - " 36\n", - " 37\n", - " 17\n", - " 14\n", + " AWrbWzWkYp\n", + " 55\n", + " 45\n", + " 46\n", + " 39\n", + " 32\n", + " 33\n", + " 16\n", + " 13\n", + " 13\n", " ...\n", - " 28.580658\n", - " 63.042980\n", - " 192.916470\n", - " 136.88690\n", - " 234.86200\n", - " 152.284147\n", - " 104.606220\n", - " 200.48862\n", + " 53.090126\n", + " 58.511192\n", + " 202.108686\n", + " 108.117546\n", + " 272.34055\n", + " 146.345222\n", + " 52.118343\n", + " 219.25041\n", " 4.0\n", " 4.0\n", " \n", " \n", " 3\n", - " ygMEXUQDbn\n", - " 218\n", + " AaJProAtlR\n", " 56\n", - " 51\n", - " 46\n", + " 48\n", + " 48\n", " 41\n", - " 37\n", + " 36\n", " 34\n", " 15\n", + " 12\n", " 14\n", " ...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 32.858997\n", + " 68.323395\n", + " 235.638050\n", + " 194.502760\n", + " 287.86615\n", + " 188.334709\n", + " 143.178710\n", + " 242.73692\n", + " 4.0\n", + " 4.0\n", " \n", " \n", " 4\n", - " YtvkGlMWwe\n", - " 212\n", - " 57\n", - " 52\n", - " 45\n", - " 41\n", - " 38\n", - " 31\n", + " ApDvhzOUJr\n", + " 56\n", + " 39\n", + " 51\n", + " 40\n", + " 28\n", + " 36\n", " 16\n", - " 14\n", + " 11\n", + " 15\n", " ...\n", - " 35.715935\n", - " 66.577260\n", - " 182.742442\n", - " 151.35474\n", - " 245.24483\n", - " 131.224984\n", - " 91.455580\n", - " 190.52692\n", - " 6.0\n", - " 6.0\n", + " 44.307076\n", + " 52.375270\n", + " 192.653709\n", + " 185.676930\n", + " 199.63050\n", + " 144.312531\n", + " 133.301650\n", + " 155.32341\n", + " 2.0\n", + " 2.0\n", " \n", " \n", "\n", - "

5 rows × 1203 columns

\n", + "

5 rows × 1122 columns

\n", "" ], "text/plain": [ - " name cdr_reporting__number_of_records \\\n", - "0 dsBHAdXrrk 184 \n", - "1 JGPCbfDGes 209 \n", - "2 dYwshzRseD 205 \n", - "3 ygMEXUQDbn 218 \n", - "4 YtvkGlMWwe 212 \n", + " name active_days_allweek_allday active_days_allweek_day \\\n", + "0 AASxnSdfla 56 46 \n", + "1 ANnpaBqoKb 55 45 \n", + "2 AWrbWzWkYp 55 45 \n", + "3 AaJProAtlR 56 48 \n", + "4 ApDvhzOUJr 56 39 \n", "\n", - " cdr_active_days__allweek__allday__callandtext \\\n", - "0 58 \n", - "1 59 \n", - "2 59 \n", - "3 56 \n", - "4 57 \n", + " active_days_allweek_night active_days_weekday_allday \\\n", + "0 50 40 \n", + "1 47 40 \n", + "2 46 39 \n", + "3 48 41 \n", + "4 51 40 \n", "\n", - " cdr_active_days__allweek__day__callandtext \\\n", - "0 46 \n", - "1 49 \n", - "2 50 \n", - "3 51 \n", - "4 52 \n", + " active_days_weekday_day active_days_weekday_night \\\n", + "0 31 35 \n", + "1 36 33 \n", + "2 32 33 \n", + "3 36 34 \n", + "4 28 36 \n", "\n", - " cdr_active_days__allweek__night__callandtext \\\n", - "0 50 \n", - "1 50 \n", - "2 52 \n", - "3 46 \n", - "4 45 \n", + " active_days_weekend_allday active_days_weekend_day \\\n", + "0 16 15 \n", + "1 15 9 \n", + "2 16 13 \n", + "3 15 12 \n", + "4 16 11 \n", "\n", - " cdr_active_days__weekday__allday__callandtext \\\n", - "0 41 \n", - "1 42 \n", - "2 42 \n", - "3 41 \n", - "4 41 \n", + " active_days_weekend_night ... mobilemoney_outgoing_p2p_amount_min \\\n", + "0 15 ... 31.799332 \n", + "1 14 ... 37.976850 \n", + "2 13 ... 53.090126 \n", + "3 14 ... 32.858997 \n", + "4 15 ... 44.307076 \n", "\n", - " cdr_active_days__weekday__day__callandtext \\\n", - "0 32 \n", - "1 35 \n", - "2 36 \n", - "3 37 \n", - "4 38 \n", - "\n", - " cdr_active_days__weekday__night__callandtext \\\n", - "0 35 \n", - "1 34 \n", - "2 37 \n", - "3 34 \n", - "4 31 \n", - "\n", - " cdr_active_days__weekend__allday__callandtext \\\n", - "0 17 \n", - "1 17 \n", - "2 17 \n", - "3 15 \n", - "4 16 \n", - "\n", - " cdr_active_days__weekend__day__callandtext ... \\\n", - "0 14 ... \n", - "1 14 ... \n", - "2 14 ... \n", - "3 14 ... \n", - "4 14 ... \n", - "\n", - " mobilemoney_outgoing_p2p_amount_min mobilemoney_outgoing_p2p_amount_max \\\n", - "0 26.361252 66.175190 \n", - "1 33.154373 60.508873 \n", - "2 28.580658 63.042980 \n", - "3 NaN NaN \n", - "4 35.715935 66.577260 \n", + " mobilemoney_outgoing_p2p_amount_max \\\n", + "0 58.921886 \n", + "1 57.337646 \n", + "2 58.511192 \n", + "3 68.323395 \n", + "4 52.375270 \n", "\n", " mobilemoney_outgoing_p2p_balance_before_mean \\\n", - "0 198.212410 \n", - "1 166.658227 \n", - "2 192.916470 \n", - "3 NaN \n", - "4 182.742442 \n", + "0 187.554639 \n", + "1 182.190155 \n", + "2 202.108686 \n", + "3 235.638050 \n", + "4 192.653709 \n", "\n", " mobilemoney_outgoing_p2p_balance_before_min \\\n", - "0 144.32855 \n", - "1 83.04488 \n", - "2 136.88690 \n", - "3 NaN \n", - "4 151.35474 \n", + "0 138.324660 \n", + "1 158.963560 \n", + "2 108.117546 \n", + "3 194.502760 \n", + "4 185.676930 \n", "\n", " mobilemoney_outgoing_p2p_balance_before_max \\\n", - "0 270.12857 \n", - "1 209.16385 \n", - "2 234.86200 \n", - "3 NaN \n", - "4 245.24483 \n", + "0 228.47707 \n", + "1 223.29039 \n", + "2 272.34055 \n", + "3 287.86615 \n", + "4 199.63050 \n", "\n", " mobilemoney_outgoing_p2p_balance_after_mean \\\n", - "0 148.945410 \n", - "1 115.873907 \n", - "2 152.284147 \n", - "3 NaN \n", - "4 131.224984 \n", + "0 138.780946 \n", + "1 137.548449 \n", + "2 146.345222 \n", + "3 188.334709 \n", + "4 144.312531 \n", "\n", " mobilemoney_outgoing_p2p_balance_after_min \\\n", - "0 78.153360 \n", - "1 22.536007 \n", - "2 104.606220 \n", - "3 NaN \n", - "4 91.455580 \n", + "0 106.525330 \n", + "1 106.978870 \n", + "2 52.118343 \n", + "3 143.178710 \n", + "4 133.301650 \n", "\n", " mobilemoney_outgoing_p2p_balance_after_max mobilemoney_outgoing_p2p_txns \\\n", - "0 218.63590 8.0 \n", - "1 173.27129 6.0 \n", - "2 200.48862 4.0 \n", - "3 NaN NaN \n", - "4 190.52692 6.0 \n", + "0 180.20863 6.0 \n", + "1 185.31354 3.0 \n", + "2 219.25041 4.0 \n", + "3 242.73692 4.0 \n", + "4 155.32341 2.0 \n", "\n", " mobilemoney_outgoing_p2p_contacts \n", - "0 8.0 \n", - "1 6.0 \n", + "0 6.0 \n", + "1 3.0 \n", "2 4.0 \n", - "3 NaN \n", - "4 6.0 \n", + "3 4.0 \n", + "4 2.0 \n", "\n", - "[5 rows x 1203 columns]" + "[5 rows x 1122 columns]" ] }, - "execution_count": 5, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", - "pd.read_csv('./outputs/featurizer/datasets/features.csv').head()" + "path_to_all_features = datastore.cfg.path.working.directory_path / 'featurizer' / 'datasets' / 'features.csv'\n", + "pd.read_csv(path_to_all_features).head()" ] }, { @@ -13039,15 +631,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 51, "id": "b117b92f", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { - "image/png": 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FhfDee+8JGo1GCAkJEa5cuWLa19TPOocPHxZCQkKEkJAQ4euvvxYqKysFQRAEg8EgbNy40fQ5q/pnoerXuOWWW2q0/Tk5OcKYMWMEjUYjPPHEE436nRKRdTH3PmV8746MjKzxficI5t+76nuvEwRBSEpKMvveXr0NnD9/vun9tri4WJgwYYKg0WiE0NBQYfjw4cLp06dNr/v2228FjUYjhIWFCfn5+abty5cvN53v7rvvFpKTk037tmzZYnoP3bRpk2m7Xq83Xevpp5+u0Q6fP39euPfeewWNRiO8/fbbNbI31GbUJT8/Xxg4cKCg0WiEBQsW1Mh//PhxYdiwYYJGoxFefPHFGq/76aefBI1GIzzyyCP1nv9mc+fOFTQajbBw4ULTtoqKCqF///6CRqMR/vOf/5h9nfH+NBqN8K9//cv0uaaiokJ47rnnBI1GI/Tv39/0PVcQ6m9/33rrLdPnoOp/lnl5eab29YEHHhCqqqpM+6r/eU6ePFm4cOGCad+2bduEkJCQWt95m9smNqeNnz9/vunvRvXfw+nTp02fxzZu3Gj290tNwxG9RK3AOJo3PDzc9PQQuP70TyKRID8/v9bCLd999x10Oh3uvfdeLFiwwLTauZ2dHRYtWgSNRoOioiKzpQwawziq11zpAePUkeqjef/zn/9Aq9ViwoQJ+Mc//lGjntFdd92FF154AQDabFSvSqUy/bdxFPKpU6dQWloKDw8PLFy4sEamrl274tlnnwVw/Sm0cWqInZ2d6anrzVNkgP+toGq892vXriE3NxddunQxjcY2mjBhAiZPnoz77rsPWq22tW6ViMiiGMs3bN++vdY+Y9kG4zHmNKf9aM77e3VOTk747LPP0LVrV9O2Rx99FL6+vgDQ6NGYximMLVnx+uDBg7C1tcU999xTq/xDaGioqYZjSkpKs6/RWoxtbUFBQaudc9SoUaaRwnZ2dlCr1aioqMDx48chkUjw5ptvwsXFxXS8TCbDiy++CJlMBkEQWjTS+csvv4QgCHj44Yfx1FNPwdbWFsD1+v8TJkww/d1bvny52Vk+c+fOxZgxY0z/7+7ujieffBIAcPz48WbnIiLr8vLLL8PV1RX5+flYtmxZm1+vS5cueOedd6BWqwFcH7VqXDTMYDBgyZIl6Nmzp+n4Rx99FDKZDFVVVTh//nyt80kkEnz++ecIDAw0bRs9ejTmzp0LAPj2229N27dv347ExEQEBATg008/rdEOBwUF4dNPP4VUKsUvv/yCa9eu1bqWuTajPqtXr0ZBQQE0Gg0+/vjjGu11eHg4vvzyS0gkEkRFRdVbLrEx8vPzsXfvXgCo0TbIZLJGr78zdOhQPPfcc6bPNTKZzFSeori4uFGfBXJycvDrr7/Czs4On3/+eY0/S2dnZ3z00Ufw9vbG6dOnsXPnzlqvt7W1xfLly02fiQDg7rvvxqBBgwDU3b41tk1sbht/7tw5AMDEiRNNfR8A0LNnT8yfPx+jR49u1Khrahg7eolaqKqqyjS11NjBaNStWzfccsstAGo3Crt37wYAPPjgg7XOKZFI8OWXX2LPnj21ztlYY8aMgZ2dHWJiYmo0slqtFnv27IGNjU2NL6TGRqKu6xk7rc+cOYOrV682K1N9qk/TkEgkAK7X54uLi8O2bdvMTqm1t7cHcP0DTUVFhWm7sRN3+/btNbafO3cOSUlJcHd3x+DBgwFcbywdHBxQWFiIV199tdaHn6VLl+Kjjz4yleEgIupsQkND4efnh6ysLCQmJpq2FxcX4+DBg7jlllvg6elZ5+ub03409/3daODAgVAoFLW2BwQEAECjH84Zz2GuE7CxHnvsMZw4cQLvvfee2f3Ge7GE2nPGqbTGdrY1GD/nVCeXy7F3714cO3asRseBUUVFBbp06QIAKC8vb9Z1S0pKTCUopk2bZvaYhx9+GDKZDFeuXMHp06dr7R82bFitbU39O0RE5OTkZCpxsGnTJlNnYVuJiIio1UFqLIFga2tr6tAzsrW1NXWQmqsJ3L9//1r1YYH/fU89ffq06ftkdHQ0AGDkyJFmO+WMJQkqKyvN1to312bUZ8+ePQCAyZMnmx7mVdezZ0/0798fgiCYvl83119//YXKyko4Ozvj9ttvr7HP+L05OTnZbKkrI3Ptiqenp+mzQGPalr1796KyshI9e/Y024bK5XKMHDnSdOzNQkJCzJbbaqh9a2yb2Nw23tjxvGTJEsTGxtYoITF9+nQsX7681sArap7a/1KIqEn279+Pq1evQiqVmn1jGjt2LBISEnDo0CFkZWWhW7duqKiowJUrVwDAVJvnZtVHBjeHs7Mzhg0bhh07dmDLli2YPn06gOsrq1dUVOD222+Hu7s7gOtv3JcvXwYAfPLJJ/jqq6/MntPGxgZ6vR5paWlwc3NrUb6bVW88HBwcauxTKBQ4ffo0EhMTkZGRgYyMDJw7dw5paWmmY6qP+AoPD0dgYCBSUlKwe/du00g042jesWPHQiq9/pzL1tYWCxYswLJly7BhwwZs2LABXl5euP322zFs2DAMHTrU1DATEXVWo0aNwsqVK7Ft2zbTyJHo6GhUVlaaFhc1p6XtR1Pf343qqhds7Lita2G5m3Xt2hVarRb5+fmNOr4uxo7TQ4cOITk52XQviYmJpva+sZnaUvUahq2lvs8DCoUCFy9eREJCAi5cuICLFy/i/PnzSEpKMj3gbe7vJTMzE3q9HnZ2dggODjZ7jL29PXr06IGzZ88iPT0d4eHhNfab+3vUGp3/RGR9xo4di6ioKOzevRtLlixBVFRUjRmLrcnce5dxhKRara4xS+bm/YIg1NoXFhZW53UcHBxQXFyMtLQ0uLq6mkakbtmyBXFxcWZfl52dDQA12nKjpn6HTE9PrzcjcL2z9+jRo6Zjm8tYY3nUqFG1OpUHDRoEV1dXXLt2DevWratRT7e6uj6fyOVylJWVNarNM/6OL1y4YBqpfTPjg3Nzv2Pjd/ybNfQZqaltYlPbeONCu8eOHcOMGTPg4OCAQYMG4Y477sCIESNqjA6nlmFHL1ELGcs2GAwG3HHHHXUeZzAYsH79evzjH/+oMWVSqVS2Wbbx48djx44d2Lx5s6mj11jOwFjaAaj5ZLf6aK66mFvls6WM0zp8fHxqfDg5cuQI3n333RojcSQSCfz8/DBu3Lg6V4MdP348/vWvf2HTpk0YPXo0BEEwjbyuXrICuD6dyc/PD99//z1iY2Nx+fJlrF+/HuvXr4dKpcITTzyBZ555prVvmYjIYhg7erdv347/+7//AwBs3boVEomk3o7elrQfzX1/B1Bjyp855r7ImhMQEID09HSzU1nN0el0yMjIQFBQUI3tv/76K7788kvk5OTUyBgWFoaQkBDs27evUedva8a2tkePHq12zrqmWV66dAnLli1DdHR0jT8PNzc3jB49Gvv27TO7YGxjGf/u2dvbmx7emmP8nGVuFFtDf4+IiJpiyZIlGDNmDLKysvDJJ5/g9ddfb5PrtPb3x/rOp1QqUVxcbBqZaRyck5mZWediYEbmvjM2dWq+8b27vhIP9b3PN1ZqaipOnDgBAFizZg3WrFlT57FbtmzBa6+9ZjZTa3w+Mf6OCwoK6h09XP3Y6sx19DcmQ1PaxOa08X379sXGjRvx1VdfYefOnSguLsb27duxfft207+dxYsXt+rDaGvFjl6iFtBqtabpKy4uLnW+OWq1WpSUlGDDhg2YP39+jemmpaWlbfZmduedd6JLly6Ij49HdnY27OzscOjQIdjb25vqDAGoMWL10KFDNerstJeEhAQANafznDt3Do899hh0Oh1uvfVWjB8/HiEhIQgMDIRarUZaWlqdHQH3338/Pv30U+zZswdarRZnz57F5cuXERISYrYMw7BhwzBs2DAUFxcjJiYGBw8exK5du3Dp0iV89tlnUKlUmDlzZpvcOxGR2MLDw+Ht7Y3k5GSkpqbC3d0d+/fvb7BsQ3Pbj5a8v7emYcOGYdeuXYiJiYHBYKi3wxAAYmNj8fjjj8PNzQ1//vknXFxcsHbtWixevBjA9QeJw4YNQ3BwMPz9/SGTybBmzRqL6eg119Y2pDklJ8rKyjBr1ixcuHABPj4+mDp1Knr37o3AwEDTaK4hQ4Y0+bzVGUfKGUdI1fVnZ/wS3JYP1omIAMDLywvPP/883n77baxevRrjxo1r8DXmOt2aW9KmuUpLS+vcZ+w8dXR0BPC/dv+zzz6r90Fwa1EqlSgqKqq35EFrvM8bB2/JZDI4OzvXeVxOTg5KS0uxefNmUw3+1mb8HU+fPh3//Oc/2+QaLdGSNj4wMBAfffQRdDodjh07hkOHDmHPnj04ffo0oqKiUFZWhi+++KI9b6dTYkcvUQts2bIF5eXlkMlk2LJli6kWzc127NiBZ599FtnZ2di3bx+GDRsGZ2dn5OfnIyUlxew0iTVr1uCvv/7CPffcU+eUjYbIZDLcc889WLNmDaKjoyGRSFBVVYWRI0fWmErk6OgIFxcX5OXlITU11ewX9aqqKsTExKBbt27w8fExW1OxuS5duoRDhw4BQI3yFz/99BN0Oh0GDx6Mb7/9ttY1jdOCzPHy8sLAgQNx+PBh7N+/3/SE9ubRvDqdDunp6aiqqkJYWBgcHBwwcuRIjBw5Eq+//jpeffVVbNy4EX/++Sc7eomoU7v77rvxww8/YMeOHfD29oZOp2vwS1xz24+WvL+3phEjRmDZsmXIz8/HH3/8gQceeKDe49euXQvg+rRI470aF6mZP38+5s2bV+s17XUvDYmLizNNa62+2Irxd2+s33uz5tTl37FjBy5cuAAnJyesX7++1pfmioqKFi8I1717d9jY2KCyshLnzp0z+xC3tLTUNK3Vz8+vRdcjImqM6dOnY9OmTTh27Bhef/11s+UbqpcFMPfeayz5017qKnmQmZkJrVYLqVRqmgni5+eHs2fP1ruQZkJCAlQqFXx9fc3W02+KgIAAHD9+HImJibj11lvNHmOcGdTc93lBEBAVFQXg+kzPl156qc5jJ0yYgDNnzmDdunVt1tHr7+8PAPX+jlNSUlBaWgpfX986+yDaSnPaeIPBgKysLFy+fBkDBw6ETCbDwIEDMXDgQPzjH//A+vXr8dprr2HHjh0oKSlps7In1oKLsRG1gPHJ3/Dhw+t9g73zzjtNT7fWrVsH4H9PuYy1gKoTBAEbN27E4cOHTYvQVB+p0tgpqcD1xgi4vliOcUX1mzs7gf8VX//111/NnicqKgqzZ8/GhAkT6n3q2xxvv/02DAYDgoKCMHz4cNP2rKwsANcLypvrWK6+wJ25ukHV733Pnj2QSqW1VkTfvn07xo0bhxdeeKHW71UqlZoWM7CE2opERG3JWM98586d2LZtGyQSiWlbfZrTfrT0/b21eHh4mBab+fjjj+ut77djxw5s3boVAPDUU0+ZthvvxVz9wPLyclPJJDFrvlZWVuKdd94BcP3zR/VOUePnl9LSUrOd0uZW9G6I8Xfi7e1tdmTUH3/8YarfV/330pTPOiqVCgMGDAAA/PLLL2aPWbt2LSorK+Hk5IRevXo17SaIiJpBKpXirbfegp2dHc6dO4djx47VOsY4OhYwX2O1Oe+7LRETE2Oqt1+d8b311ltvNZUpuPPOOwFc/w5rbrHUzMxMPPLIIxg3bpxpFklLGD9jrFu3rsbiXUanTp0y/Y5vXkCtsWJjY03tlvH7Y10eeughAMDJkyeRlJTUrOsZ1TUTZejQoZBKpYiNjTXb2avX6/HMM89g0qRJ+O6771qUoTma08ZfvXoVd999N2bOnFmjzJXRbbfdZvpvfu9uOXb0EjVTVlYWjhw5AgANjgCytbU1HbN7927k5ubiiSeegJ2dHf7880+sWrXK9CZYWVmJjz/+GAkJCXBycjI1NtWnoly6dKnROSMiIuDr64uYmBgcOXIEbm5uNd5IjZ544gnI5XJERUXhk08+qdFw79u3D0uXLgVwvXFrrVIT6enpWLBgAaKjo2FnZ4e33nqrxhd+49PMv/76CxcuXDBtLywsxDvvvGOquQvA7AeNUaNGQalUYvv27UhOTsbgwYNrjZ6+8847oVKpkJKSgnfeeafGFNWsrCzTSK366i8TEXUGERERcHNzw/Hjx7F371707dsXXl5eDb6uOe1HS9/fW9NLL72EHj164OrVq5g6dSrWr19fY4RVaWkpVq5cieeeew7A9QV3qneAG+/lv//9b42ppRkZGXj66adN99fW91GXxMREPPbYYzh16hQcHR1rTQPt0aOHaTX29957z5SzoqICH3/8sdlV0xti/J0kJSXV6LCorKzEunXrTJ3OxusYNfWzzjPPPAOpVIo1a9bgm2++MXUCCIKA33//HR9//DEAYMGCBazHS0TtRqPRYM6cOQDMP7RSKpUICQkBAPzrX/8y1bLV6/X48ccfsXHjxvYLi+ujiufNm1ejA27dunX4/vvvIZFI8Oyzz5q2jx07Fv7+/rhw4QLmz59fY9ZHeno6nnnmGej1eoSFhWHw4MEtzjZ16lS4uLjg3LlzePHFF2uMFD1x4gTmz58PQRBwzz33NPuBnnHgVa9evepc3NNo3LhxplHKxgFczWVs87RabY16tr6+vhg3bhyqqqrw9NNP11jLoLCwEC+99BLS09OhVCqbPfO3JZrTxnt4eGDgwIEwGAx48cUXa/xd02q1pva6X79+rNHbCli6gaiZ/vjjDwiCAFdXVwwdOrTB4ydNmoSVK1eisrISGzduxJw5c7B06VK8/vrr+PDDD7Fq1Sp069YNmZmZKCwshEKhwMcff2z68uXk5ARPT09kZ2fjkUceQffu3fHee+812BgB1+vVrlixAgBw3333mR09FRQUhPfffx8LFy7E119/jZ9++gkBAQHIz883PbW77bbb8OKLLzbht3TdP/7xjxpF4SsqKnDlyhXTBwO1Wo0PP/yw1uqls2fPRlRUFK5cuYIxY8YgICAAEokE6enp0Ol0CA0NRXZ2NgoKCnDlypVaq7iqVCqMHDnSVOex+gJ01Y/54IMPMG/ePPz444/47bff4OvrC51OhwsXLkCv16NXr16mD2tERJ2VRCLB3XffjZ9//hllZWWNrr3XnPajpe/vrUmtVuP777/HggULcOzYMbz22mt4++23TaUBUlJSTB2/EydONHVcGy1YsAALFizAwYMHMXToUAQEBKCkpAQXLlyAIAgYNGgQDh8+jNzcXAiCAIlE0ib3cfOXvbKyMmRnZyM/Px/A9QVSVqxYUWtqq42NDebNm4e3334bf//9Nw4dOoRu3bohIyMDxcXFmD9/Pj7//PMmZRk5ciR69eqF06dPY+7cuejevTscHR2RmZmJoqIiODk5wdfXF0lJSTWmKDf1s05kZCRee+01LFu2DB9//DG+/fZb+Pr64vLly6bPGDNnzjQtSEtE1F7mzp2LrVu3IiUlxez+BQsWYP78+Th69CiGDRsGf39/XL58GXl5eZg5cyY2bNjQJgtgmzN06FAcOXIEd911F4KDg5Gfn28a4btw4ULTDEfgemnAL774Ao8//jj27NmDO++8E0FBQaisrDSVw/P09MSXX37ZKtlcXFywYsUKzJ07F3///Teio6MRHByMkpIS0yycyMhIvPXWW806f3l5uWm2TkODt4Dro7FHjRqFP//8E1FRUVi4cGGDi5/Vxc/PDwqFAuXl5RgzZgw8PDzw/fffmx7KXrp0CUeOHMHEiRPh7+8PpVKJtLQ0lJWVwc7ODsuXLzdbArKtNbeNX7ZsGSZNmoTY2Fjcdddd8PX1hZ2dHTIyMlBaWgonJ6dm/zlSTRzRS9RMxrINY8eOrVFnqS5+fn4YOHAggP9NSZ04cSLWrVuH++67D7a2tkhKSoJMJsO4ceOwYcOGWkXMly9fjj59+qC8vByZmZnIyMhoVNbqpRrMdXYa3Xvvvfj9998xadIkODk5ISkpCfn5+ejTpw9effVVfPPNN81qyE6dOoX4+HjTT1JSEvR6Pfr374//+7//w/bt2zFixIhar+vevbupZqKXlxfS09Nx+fJlhIaG4pVXXsG6detMU3R27dpV770rlUqMGjXK7DEjR47Ef//7X4waNQoqlQrnz59HTk4OevbsiUWLFuHXX3+td6VXIqLOovr7ZGPKNhg1tf1ojff31uTh4YFffvkFn376KUaNGgVnZ2ekp6cjJSUFbm5uuP/++7F69Wq8++67tUaGjho1Cj///DOGDBkClUqFpKQkFBcX4/bbb8fnn3+OH374AW5ubiguLjbNBGoL1dvZ+Ph4JCcnw9bWFoMGDcIrr7yCLVu21LkI24wZM7BixQrceuutptr1YWFh+Oabb/Doo482OYutrS3++9//4tlnn0VwcDByc3ORmpoKNzc3PPbYY4iKisK0adMA1J6i3NTPOo888gjWrFmD++67D3Z2djhz5gykUilGjx6N77//Hq+++mqT8xMRtZRMJsNbb71V58O9kSNH4ocffjBN009NTYWPjw8++OCDdn/f6tWrF3755RdERkYiPT0dRUVFuO222/Ddd9/h8ccfr3V8UFAQ/vjjD8ydOxc9evRAeno6MjIy4Ovri8ceewwbN26Et7d3q+Xr378/oqKi8Oijj8LLywvnz59HQUEBBgwYgHfeecfUOdocxpqwdnZ2uO+++xr1msmTJwMACgoKTJ3EzaFWq/Hpp58iODgYhYWFuHz5sqnNU6vV+O6777B06VL0798f165dw7lz5+Do6Ihx48Zh/fr1jRps1haa28Z3794dv/32G6ZMmQJvb29kZmYiPT0dHh4emDVrFjZt2tSoQWzUMInQlGKfREQdzIYNG/DKK69g/Pjx+OCDD8SOQ0RERERERETUJjiil4g6tQ0bNgCAabEdIiIiIiIiIqLOiDV6iajTOX36NLp06YJ169bhyJEj0Gg0iIyMFDsWEREREREREVGbYekGIup0brvtNly7dg0AIJVK8f3337Ojl4iIiIiIiIg6NZZuIKJO59Zbb4VMJkNAQAA+++wzdvISERERERERUafHEb1tQK/XIzs7G56enrC1ZXUMIiKi1sD2lYiIqHWxbSUi6lw4orcNZGdn46677kJ2drbYUYiIiDoNtq9ERESti20rEVHnwo5eIiIiIiIiIiIiog6OHb1EREREREREREREHRw7eomIiIiIiIiIiIg6OHb0EhEREREREREREXVw7OglIiIiIiIiIiIi6uDY0UtERERERERERETUwbGjl4iIiIiIiIiIiKiDY0cvERERERERERERUQfHjl4iIiIiIiIiIiKiDo4dvUREREREREREREQdnK3YAYhIfEJVFXR5edAXFQESCWxVKsi6doXExkbsaERERNRIBp0OumvXUFVaCrsuXWDn4gKJlOM6iIiIzDFUVKA8JwcwGGDr6Ag7Z2dIJBKxYxG1CDt6iayUYDCgMD4eubt2QXv+PKQyGWzVagiCAENZGfRaLRReXnDo2RNdIiKgDgnhl0UiIiILIxgMKIiLQ250NEqSk2GjVsNGoYC+uBiCXg/nyEi4jR4N+27dxI5KREQkOkNFBa7t24e8AwdQmp5+/aGojQ30xcWwUSrhOmQI3O+9FzYKhdhRiZqFHb1EVkh77hwurFwJia0tuvTrB9fhw2GrUtU4xlBZiYrsbJSmpyPj++9RpdXCZcgQdB0xAgoPD5GSExERkZH23DlkfPstIJHA6dZb4TZ6dI0vppWFhSg+eRLn3noLrkOHwvuhhyCVyURMTEREJA7BYMDV6Ghk//47FN7e6BIRAa8HHzTNYhUEARXZ2SiIjUXurl3we+opOPbqJXJqoqZjRy+RFREEAZc3bkTujh3oOnIkHMLC6jxWamcH++7dYd+9O1yHDoXu2jUUHT+OpMWLoQ4JgdeECVAGBLRjeiIiIgKuf1m9tH49ru3eja4jR0IdGmp2qqldly5wGTIEjv364eq2bTi3bBkCn3sOdk5O7R+aiIhIJGWZmUj/978BiQTeDz0EuZmBSxKJBAovL3iOH4+StDSkrViBbtOmoevQoSIkJmo+dvQSWQnBYMCFVatQmpKC7rNnw1atbtLrZa6u6DpiBFyGDEHR8eNI/ugjqAID0W3aNCg8PdsoNREREVVXVV6OtC++QGVBAbo/9litGTnm2KpU8JwwAfkHDiDpzTeh+ec/IXN2boe0RERE4hEEAVe3bcPljRvhOmwYHPv2bVQNXlVAALpNnYpLa9ZAamsLl8GD2yEtUetgwU0iKyAIAjJ//BFlmZnoNm1akzt5q5PKZHAaMAB+Tz0FW2dnJC1ejIu//gqDTteKiYmIiOhmVeXlSP7wQ8BgQLeHH25UJ6+RRCKBy5AhcOjdG+ffew96rbYNkxIREYmrqqwMqZ99htxdu+Dz6KPocsstTVpoTe7mBu+HHkLmDz+gJDm5DZMStS529BJZgSt//43iU6fgNXFiq9Xmk9rZwWXQIPg+8QTK0tOR+PLL0J471yrnJiIiopoMOh2SP/wQNvb2cL/vPlNNwaZyHjwY9j4+SFuxAoLB0MopiYiIxFd++TLO/vOfgCCg2/TpzZ7FInd3h/u99yL1s89QVVrayimJ2gY7eok6Oe3588iOioLXQw+1ycqhtmo1PMePh+sddyDlk09w+Y8/+MWRiIioFQkGA9K/+gpSOzu4jxnTpBFJN5NIJOg6YgT0Wi2y//ijFVMSERGJr+jkSSQtXYou/frB/Z57ILVtWcVStUYDZY8eyPzvf1spIVHbYkcvUSdWVVaG9C+/hPvo0bBzdGzTa6lDQtB91izkx8QgbcUKGCoq2vR6RERE1uLS+vWouHLl+kjeFnTyGkmkUnjefz+ubt/O6ahERNRpXI2ORtqXX8Jz/Hh06dev1c7rOnw4ik+eRNHJk612TqK2wo5eok4s69dfofDxgTokpF2uZ+foiG5Tp6KqvBznli2DvqSkXa5LRETUWRUmJODa3r3wfOCBFo9Kqs7WwQFdR4zAhZUrIej1rXZeIiKi9iYYDLj466/IjoqCzyOPQOnn16rnt5HL0XXkSGT+978Qqqpa9dxErY0dvUSdVGl6OvJjY9F1+PB2va7U1hYeY8dC5u6Oc2+/jcqiona9PhERUWdRkZuL9G++gef48U1aeK2x1D17wkalQvbmza1+biIiovYgVFUh/d//RtHx4+g+YwZkLi5tch1VcDCkdnbI3bu3Tc5P1FrY0UvUCQmCgIzvv4fr0KGwsbdv9+sb6//Z+/oi+YMPUFVW1u4ZiIiIOjJjXV6nAQNg7+PTJteQSCRwGzUKV/76C7r8/Da5BhERUVsx6HRI+fRT6K5cQbepU2GjVLbZtSQSCboOH47Lv/2GqvLyNrsOUUuxo5eoEyqMi0NVaSkcb7lFtAwSiQSuw4ZB5uKClE8+4bRQIiKiJriydSsM5eVwjoxs0+vYdekCx759cfm339r0OkRERK3JoNcj9bPPYKiogNeDD0JqZ9fm11R4e0Ph5YXcPXva/FpEzcWOXqJORjAYcGn9ergOHdoqC7a0hEQigdvo0RD0elz8+WdRsxAREXUU5Tk5yP7jj+uLr0nb/uO686BBKDh6FGVZWW1+LSIiopYSqqqQ9vnnMOh08Lz/fkhsbNrt2s6DBuHK5s0wcCATWSh29BJ1MgVHjgASCZSBgWJHAXB9ZW+PceNQEB+Pa/v3ix2HyKodPHgQjz76KCIjIxEREYEZM2ZgbxPrjKWlpeH555/HsGHD0LdvX4wbNw4//fQTDAZDo17/z3/+EyEhIdiwYUNzboGo0xMEARnffgvnQYMgc3Zul2va2NvDaeBAjuolIiKLJwgCMn/4AZUFBfAcP75dO3mB66N67ZydkX/oULtel6ix2NFL1IkIgoDLGzbA5fbbRR/NW52NQgGvBx7Axf/+F+U5OWLHIbJKGzZswOzZs5GQkIDw8HD069cPCQkJmDNnDtasWdOoc5w9exaTJk3C5s2b4e3tjaFDhyI7Oxtvv/02Fi5c2ODr9+zZ0+hrEVmr/IMHUZmfD6cBA9r1uk4RESg+fZrtNBERWbScv/5CcWIiPB94oN07eY2cIyORHRUFQRBEuT5RfdjRS9SJFJ86BaGqCsoePcSOUovc3R3Ogwcj/csvIVRViR2HyKrk5ORg8eLFcHBwwG+//YaVK1fi22+/xc8//wy1Wo1ly5Yhp4HOHUEQsHDhQmi1WnzwwQf45ZdfsGLFCmzduhUhISGIiorC1q1b63x9QUEBXnvttda+NaJOpaqsDBd/+QXuo0e3S8mG6qRyObpERCAnKqpdr0tERNRYxWfOIGfTJnhNmgQbuVy0HPb+/hCqqqA9e1a0DER1YUcvUSeS89df6HLrrRY1mrc6pwEDAEFAzl9/iR2FyKqsXr0aOp0Os2bNgkajMW0PDw/HnDlzUFFR0eBI2wMHDiApKQkDBw7E+PHjTdtdXFywZMkSAMBPP/1U5+vffPNNFBQUoG/fvi27GaJO7PIff0Dp5wdFt26iXN/p1ltREBuLyoICUa5PRERUl8rCQqR98QU8xo6FXZcuomaRSCTocsstuFLPIAcisbCjl6iTKMvKQml6Ohx69RI7Sp0kEgnc77kHOZs3o+LKFbHjEFmNffv2AQBGjhxZa59xW0O1eus7R0REBFxdXREXFwetVltr/6ZNm/DXX39h3rx5CA4ObnJ+ImtQkZODa7t2wfXOO0XLYKNUQh0Whqs7d4qWgYiI6GaCIODCqlVw6NULKguZverQpw+0iYnQ5eWJHYWoBnb0EnUSV7dtQ5dbboHU1lbsKPWyc3aG04AByPz+e9Y0ImoHgiAgOTkZUqkUPcx8MPb394dUKkVycnK9/yaTk5MBoMaI4OoCAgJgMBiQkpJSY3tOTg6WLl2Kvn37Ys6cOS24E6LOLWvNGjgNGABbtVrUHF0iIpC7axcEriZOREQWIv/wYZRfugSXIUPEjmJiI5dD3asXcnftEjsKUQ3s6CXqBAwVFcg/fBiOt9widpRGcY6MRHl2NoqOHxc7ClGnV1hYCJ1OBycnJ8hkslr7bW1t4ezsjLKyMpSUlNR5nis3RuG7ubmZ3W/cnpubW2P7a6+9hoqKCrz33nuwacKCGUVFRbh48WKNn+zs7Ea/nqgjKUlNhTYpqd0XYDNH7u4Ouy5dUJCQIHYUIiIi6LVaXPzpJ3jce6/FDWpyDA/Htb17IRgMYkchMrGsfyVE1Cz5R45A0a0b7BwdxY7SKBIbG7jeeScurl4Nxz59RFstlcgalJWVAQDs7e3rPEahUAAASkpKoK5jNKHxPMZj6zpHaWmpadvPP/+Mffv24ZVXXjE7mrg+P/zwA1asWNGk1xB1RIIg4OLPP8Pl9tshNfMwRgxdbrkFV7dvh7MFdDwTEZF1u7xhA1QajWj16+sj9/CAVCaD9swZiy6hSNaFI3qJOoHcnTvhGB4udowmUQUFwcbeHrm7d4sdhahTk0ob39TXV7rBOBq3ocUeDTdGNFy4cAEffvghBgwYgJkzZzY6g9HMmTMRHR1d42f16tVNPg+RpSs+dQqV167B0YIWKlSFhKDswgVU3DRCn4iIqD2VZWUh7+BBiyrZUJ1EIoFD797I3bNH7ChEJuzoJergyi9fRvnly1AFBYkdpUkkEglchw/H5Y0bYdDpxI5D1GkplUoAQEVFRZ3HlJeX1zjWHOOIYOOxdZ1DpVKhqqoKixYtAgC8++67DXYOm+Po6AgfH58aP56enk0+D5ElEwQBl9auhcuQIZA04aFMW5Pa2kIdFoa8AwfEjkJERFYs65df4DxoEGxVKrGj1MmhVy8UJiSg6sbsNyKxsXQDUQeXd+AAHHr27JDlDxSenlB4eeFqdDQ87r1X7DhEnZJarYZSqUR+fj70ej1sb6ptptfrkZ+fD7lcDsd6yr+4u7vjzJkzyM3NRWBgYK39V69eBXC9Vu/27duRkJCA7t2747PPPqtx3PEbtbnXrl2LgwcPYtSoURg1alRLb5OoQzJ+MVSHhYkdpRaHXr1wdetWeN5/f7Me1hAREbVESWoqStPS4DZ6tNhR6mWrUkHp54eCI0fgescdYsch4oheoo5MEATkHTwIh549xY7SbC63346cTZtgqGe0IRE1n0QiQVBQEKqqqpCenl5rf1paGgwGAzQaTb3nCQ4OBgAkJyfX2icIAlJTU2FjY4PAwEBTnd7MzExERUXV+MnIyAAAJCQkICoqCklJSS28Q6KOSRAEXFq//vpoXgvsSFV06waDXo/StDSxoxARkRW6/NtvcB40yOIWYDNHHRaGa/v3ix2DCAA7eok6tNLUVMBggNzLS+wozSb38ICiWzdc3blT7ChEndbQoUMBADt27Ki1z7ht2LBhjTpHdHR0rX3x8fHIy8tD//79oVarMXHiRCQlJZn9mTRpEoDrJR2SkpIwf/78Ft0bUUdVGB8PQa+HqoGHLGKRSCRw6NkTefziSkRE7awkNRWlFy7A8ZZbxI7SKKrgYJSmp6OyoEDsKETs6CXqyPIOHIC6Z0+LHAnUFM6Rkbjy998Q9HqxoxB1ShMnToRcLsfKlStx6tQp0/aTJ09i1apVUCgUmDZtmml7RkYGUlJSUFxcbNo2cOBABAcH48CBA1i7dq1pe15eHt58800AwOzZs9vhbog6PkEQcOm33+By220W3YarQ0NRcOQIhBuLLBIREbWHnE2b4DRgQIcYzQsAUjs7qIODkXf4sNhRiNjRS9RRCQYD8mNiOnTZBiOFtzfsnJzYMBK1ER8fHyxatAharRZTpkzB448/jscffxxTp05FSUkJli5dCldXV9Pxs2bNwpgxY7B9+3bTNqlUinfeeQdKpRJvvPEGJk+ejHnz5uGee+5BUlISJk+ejBEjRohxe0QdTtGxYxAqKy12NK+R3M0NEju76zOIiIiI2kHF1asoPn0aXfr2FTtKk6g5C4YsBDt6iTqokvPnYaNUQlatc6Yjc4qMRE5UFARBEDsKUac0ffp0fP311+jbty/i4+Nx6tQpRERE4LvvvsP48eMbdY7w8HCsW7cOo0ePxoULF3DgwAF4e3vjzTffxJIlS9r2Bog6CUEQcHnDBjgPHmzRo3mN1BoN8o8cETsGERFZiStbt8IxPBxSuVzsKE2i9PeHLjcXFVeuiB2FrFzHGAdPRLXkx8RY/EigplAGBODarl0oPnUKjn36iB2HqFMaPnw4hg8f3uBxO+upmR0UFITly5c3O8OyZcuwbNmyZr+eqKMrPnUK+pISqENDxY7SKOrQUFz+/Xd0mzKlQ3RMExFRx1VVVoa8ffvQvQOWA5NIpVCHhCA/NhaeY8eKHYesGEf0EnVAgsGAgiNHoA4JETtKq5FIJOgSEYErW7eKHYWIiKjNXN64scOM5gUAmbs7IAgoy8gQOwoREXVyeYcOwd7XF3ZduogdpVnUoaHIP3RI7Bhk5djRS9QBlaamQmJnB1nXrmJHaVUOvXujJDkZFTk5YkchIiJqddqkJOiuXetQ9fUlEglUgYEoPHZM7ChERNSJCYKAqzt2wPGWW8SO0mz2vr7Q5eWhnN9nSUTs6CXqgPKPHoUqOLjDjAZqLKmdHRzDw3Gl2gJQREREncXl33+Hc2QkJNKO9RFcGRiIwvh4sWMQEVEnVpqWhiqtFsqAALGjNJtEKr0+qpeLjJOIOtanTCICABTGxUHdierzVtelXz/k7d8Pg04ndhQiIqJWU5qejrKMDDh0wDr09r6+KL90CfriYrGjEBFRJ3V1xw449u3b4QczqUNCUBATI3YMsmLs6CXqYMqzs1FVUgK5l5fYUdqEnZMT5F5eXOGbiIg6lcu//w6nAQMgte14ayFLbW2h9PND0cmTYkchIqJOqKq8HAVHj8IxPFzsKC1m3707dPn5qLhyRewoZKXY0UvUwRTGx3fKsg3VdenbF7nR0WLHICIiahXlly9De/YsuvTrJ3aUZlMGBLB8A1EdDh48iEcffRSRkZGIiIjAjBkzsHfv3iadIy0tDc8//zyGDRuGvn37Yty4cfjpp59gMBga9fp//vOfCAkJwYYNG5pzC0SiKoyLg72PD2zVarGjtJhEKoVao0F+bKzYUchKsaOXqIMpiIuDKihI7BhtShUUhIrsbJRlZYkdhYiIqMWyo6LQJSICUplM7CjNpgwMRNGpUxAa2elEZC02bNiA2bNnIyEhAeHh4ejXrx8SEhIwZ84crFmzplHnOHv2LCZNmoTNmzfD29sbQ4cORXZ2Nt5++20sXLiwwdfv2bOn0dciskS5e/d2qIVKG6IOCUE+yzeQSNjRS9SB6LValGVkwN7PT+wobUpiYwOHPn2Qu3u32FGIiIhaRJeXh8KjR+HUv7/YUVrErksX2CgUKMvMFDsKkcXIycnB4sWL4eDggN9++w0rV67Et99+i59//hlqtRrLli1DTk5OvecQBAELFy6EVqvFBx98gF9++QUrVqzA1q1bERISgqioKGzdurXO1xcUFOC1115r7Vsjaje6vDyUpqVB1YnWoLH384Pu6lVU5OaKHYWsEDt6iTqQohMnYO/nB6mdndhR2pxjnz7IP3AAgl4vdhQiIqJmy/nrLziEh8NGqRQ7SovZ+/uj+PRpsWMQWYzVq1dDp9Nh1qxZ0FTrpAoPD8ecOXNQUVHR4EjbAwcOICkpCQMHDsT48eNN211cXLBkyRIAwE8//VTn6998800UFBSgb9++LbsZIpHkHTwIdWhop/qOK5FKoQoORgHXnSERsKOXqAMpTEiAKiBA7BjtQubqCjsnJy78QkREHZZeq0Xevn1wGjBA7Citwp4LshHVsG/fPgDAyJEja+0zbmuoVm9954iIiICrqyvi4uKg1Wpr7d+0aRP++usvzJs3D8HBwU3OT2QJ8g4c6FRlG4zUGg3LN5Ao2NFL1EEIBgOKTp6EspPX563OoXdv5O7ZI3YMIiKiZrm6fTtUwcGwc3QUO0qrUPr5oeT8eRg424YIgiAgOTkZUqkUPXr0qLXf398fUqkUycnJEAShzvMkJycDQI0RwdUFBATAYDAgJSWlxvacnBwsXboUffv2xZw5c1pwJ0TiKb90CfqiIth37y52lFZn7++P8kuXUFlQIHYUsjLs6CXqIEpTU2Hr4NBpviw2hjosDMWnT0NfXCx2FCIioiYxVFTgyvbtcI6MFDtKq7Gxt4edqytKb+pwIrJGhYWF0Ol0cHJygszMQou2trZwdnZGWVkZSkpK6jzPlStXAABubm5m9xu3595U6/O1115DRUUF3nvvPdjY2DT3NohElXfwINRhYZBIO1/XlNTWFqqgIBQcPSp2FLIyFvuv6eDBg3j00UcRGRmJiIgIzJgxo8FpL/VJTU1FeHg4vvzyyzqPKSoqwocffojRo0cjPDwcI0aMwHvvvWd2mgxReytISIDSzGiBzsxGoYAqMBD5rG1EREQdTO7evbDv1g2yrl3FjtKqlH5+KGKdXiKUlZUBAOzt7es8RqFQAEC9Hb3G8xiPrescpaWlpm0///wz9u3bh+eee87saOK6FBUV4eLFizV+srOzG/16otYkCALyDh2CQ1iY2FHaDMs3kBgssqN3w4YNmD17NhISEhAeHo5+/fohISEBc+bMabCYvTl5eXmYN28eKioq6jxGq9XikUcewapVqyCRSHDnnXdCIpHgu+++w8MPP4xijigkkRUlJEBpJfV5q1OHhSHvRu0yIiKijkCoqkLO5s1wGjhQ7Citzr57d2jPnBE7BpHopE0YgVhf6QbjaFyJRFLvOQwGAwDgwoUL+PDDDzFgwADMnDmz0RkA4IcffsBdd91V42f69OlNOgdRaym7cAGCXg+5l5fYUdqMskcPlKano7KoSOwoZEUsrqM3JycHixcvhoODA3777TesXLkS3377LX7++Weo1WosW7YMOTk5jT7fuXPnMHXq1Fo1jW726aefIikpCZMnT8Zff/2F5cuXY+vWrRg/fjySk5Px6aeftvDOiJqvsrAQFbm5sPfxETtKu1MFBqL80iVU3DRdjYiIyFLlHzkCW7W6U7bbCh8flKalQWCdXrJySqUSAOodTFReXl7jWHOMI4KNx9Z1DpVKhaqqKixatAgA8O677zbYOXyzmTNnIjo6usbP6tWrm3QOotaSHxMDdWhok/8edyRSOzuoevRAYXy82FHIilhcR+/q1auh0+kwa9asGgXpw8PDMWfOHFRUVDRqVG9ZWRlWrFiByZMnIz09HT71fNAuKirCunXroFarsWjRItPTWVtbWyxevBhdunTB+vXra0yXIWpPRadOQenvD4kV1t+S2NhAHRqKvIMHxY5CRETUIEEQkBMVBacBA8SO0iZsFArYOTujND1d7ChEolKr1VAqlcjPz4fezIMPvV6P/Px8yOVyONazxoa7uzuA2jV4ja5evQrgeq3e7du3IyEhAa6urvjss8/w4osvmn5iY2MBAGvXrsWLL76Ibdu21TqXo6MjfHx8avx4eno2+d6JWkoQhOsdvSEhYkdpcyqNBvmHD4sdg6yIxXX07rsxRXvkyJG19hm3NaZW799//43PP/8carUaK1aswIQJE+o89siRIygvL8egQYOgVqtr7FOpVBg8eDDKy8txhHVCSSRFx45B6e8vdgzROPTsifwDB8SOQURE1CDt2bOoKi2FKjhY7ChtRuHjA21SktgxiEQlkUgQFBSEqqoqpJt58JGWlgaDwVBj8JI5wTfeK5KTk2vtEwQBqampsLGxQWBgoGngUWZmJqKiomr8ZGRkAAASEhIQFRWFJP4bJQtWlpEBoaoKcit40KAKCkJJcjL09dTqJmpNFtXRKwgCkpOTIZVKzRaV9/f3h1QqRXJycr11jgDAyckJ8+fPx9atW3H33XfXe6yxUQ2u4wO5MQsbSxKDYDBcH9FrZQuxVafo3h16rRZlWVliRyEiIqpXzqZNcLr11k49FdXexwfFrNNLhKFDhwIAduzYUWufcduwYcMadY7o6Oha++Lj45GXl4f+/ftDrVZj4sSJSEpKMvszadIkANdLOiQlJWH+/PktujeitmQczduZ20ojqUwGpb8/CuPixI5CVsKiOnoLCwuh0+ng5OQEmUxWa7+trS2cnZ1RVlZW78qlADBixAjMmzcPKpWqwetWnw5jjnH7tWvXau3jyqXU1krT02Fjbw+7Ll3EjiIaiUQCdWgop7wQEZFFK790CSWpqXDo00fsKG3Kvnt3lJw/D+HG4lBE1mrixImQy+VYuXIlTp06Zdp+8uRJrFq1CgqFAtOmTTNtz8jIQEpKSo2FvgcOHIjg4GAcOHAAa9euNW3Py8vDm2++CQCYPXt2O9wNUfsQBAEFsbFWUbbBSBUSgvyYGLFjkJWwFTtAdWVlZQD+V5DeHIVCAQAoKSmpVWahuYxTYOq6rvGa5mr0/vDDD1ixYkWr5CAyp+jkSSgDAsSOITp1aCiubtsGr4kTreLJLxERdTxXtmxBl1tugdTOTuwobcrWwQFShQLlly51ygXniBrLx8cHixYtwtKlSzFlyhRERkYCAGJiYqDX6/H+++/D1dXVdPysWbOQlZWFd999FxMnTgQASKVSvPPOO5g5cybeeOMNrF+/Hu7u7oiNjUVhYSEmT56MESNGiHJ/RG2h/NIlVJWXQ+7lJXaUdqMKCsLVrVtRVVoKm3oWZyRqDRbV0WtcBK0xGird0BQ2Nxa4aqjzyGBm1MLMmTPxwAMP1NiWnZ2N6dOnt1o+sm5Fx46hy623ih1DdIpu3VBVXo7yixdh37272HGIiIhq0Gu1yDt8GH5z5ogdpV3Y+/igJDmZHb1k9aZPnw5vb2+sWrUK8fHxkMlkiIiIwNy5czF48OBGnSM8PBzr1q3D8uXLERMTg/Pnz8PPzw/PP/88HnrooTa+A6L2VXDkCNQajVUN3rFRKGDv74+ChAS43n672HGok7Oojl7ljScbFRUVdR5TXl5e49jWYBzJazx3U67p6OhY7yqqRC1RVVqKssxMeN70MMEaSSQSOISGIu/wYXRjRy8REVmY3N27oQoOhm0rzTizdHJPT2jPn0fXO+8UOwqR6IYPH47hw4c3eNzOnTvr3BcUFITly5c3O8OyZcuwbNmyZr+eqL3kx8bC9Y47xI7R7tQaDfIPH2ZHL7U5i6rRq1aroVQqkZ+fD71eX2u/Xq9Hfn4+5HJ5q3auuru7AwByc3PN7m+ohi9RWylOTIS9j0+nnwLaWKqQEBTExoodg4iIqAahqgpXt22DU//+YkdpN4pu3VB6Y0FjIiKixqi4ehWVeXlWOUNTFRwM7ZkzqLpRspSorVhUR69EIkFQUBCqqqqQnp5ea39aWhoMBgM0Gk2rXjc4OBgAkFzHh9WUlBQAQIgVFQsny1B4/Djs/f3FjmExFN7e10c5Z2WJHYWIiMikMCEBtmo1FFZUb1Du7g5dbi6qzKxhQUREZE7B0aNQBQdD0oSynZ2FjUIBez8/FCYkiB2FOjmL+9c1dOhQAMCOHTtq7TNuGzZsWKtec8CAAVAoFDh06FCtBddKSkpw6NAhKJVK9LeiURokPkEQuBDbTSQSCdQhISg4ckTsKERERCZXtm2DY79+YsdoVxIbG8i9vFCSmip2FCIi6iCM9XmtlTokBHmHDokdgzo5i+vonThxIuRyOVauXIlTp06Ztp88eRKrVq2CQqHAtGnTTNszMjKQkpKC4uLiZl9TqVRiwoQJKCwsxJtvvmkqG6HX67F06VIUFRXh4YcfhtpKaq6RZajIzoZQWQkZS4bUoNJokB8TI3YMIiIiAED55csoy8yEOjRU7CjtTuHlhRKWbyAiokbQFxejLDPTqmesqoKDoT17luUbqE1Z1GJsAODj44NFixZh6dKlmDJlCiIjIwEAMTEx0Ov1eP/99+Hq6mo6ftasWcjKysK7776LiRMnNvu6zz33HGJiYvD7778jLi4OPXv2RGJiIjIzM9GrVy/Mnz+/xfdG1BTG0bzWtBppY9h3747KggJU5ORA7uEhdhwiIrJyV3fsQJfwcEhtLe5jdZuTe3tDe+6c2DGIiKgDKExIgLJHD6tsL41sFAoo/fxQEBcH1yFDxI5DnZTFjegFgOnTp+Prr79G3759ER8fj1OnTiEiIgLfffcdxo8f3ybXdHJywq+//ooZM2ZAr9dj165dkEqleOKJJ/DDDz9ApVK1yXWJ6lJ47BjLNpghkUqhDg5Gflyc2FGIiMjKGXQ65B04AMdbbhE7iigU3t4oTU2FIAhiRyEiIguXf+QIVEFBYscQnSokBHkHD4odgzoxi32UMnz4cAwfPrzB43bu3Nmo882fP7/BUblOTk54/fXX8frrrzfqnERtxVBZiZJz5+B2111iR7FIquBgFMTGwnPMGLGjEBGRFSs4ehRyT0/YOTmJHUUUtg4OkEil0F27BnnXrmLHISIiC2WoqID27Fl0bUQfT2enCg7G1W3boC8pgS0HFFIbsMgRvUTWruTcOcjc3GCjVIodxSLZ+/ujPCsLlYWFYkchIiIrdjU6Go7h4WLHEI1EIoH8xqheIiKiuhSdPAmFtzds7O3FjiI6G7kcSn9/FHKGKrURdvQSWaDCEyegtOIi9Q2R2tpCFRiIwvh4saMQEZGVKs/ORnlWllWvHg4Acnd3lLCjl4iI6lEQFwdVYKDYMSyGOjSU5RuozbCjl8gCFZ04AWWPHmLHsGiqoCDkx8aKHYOIiKzUtT174Ni7NyQ2NmJHEZXC2xulKSlixyAiIgslGAwoPHYMKit/MFqdKigIJSkpqCwqEjsKdULs6CWyMLr8fOiuXYPC21vsKBZNGRiIkvPnUVVWJnYUIiKyMoLBgLz9++HQp4/YUUQn9/RE6YULEAwGsaMQEZEFKjl/HrZqNey6dBE7isWQymRQBQai4MgRsaNQJ8SOXiILU3zyJFQ9ekAi5T/P+tgoFFD4+KDo1CmxoxARkZUpTkyE1N4ecnd3saOIzlalglQuR8WVK2JHISIiC1QQFwdVUJDYMSwOyzdQW2FPEpGFKTx2DPasz9sofApKRERiuLZ3Lxx69RI7hsVQeHmhNC1N7BhERGRhBEG43tEbHCx2FIujDAxEWWYmdHl5YkehToYdvUQWRKiqQvHp01CxPm+jqDQaFJ04AaGqSuwoRERkJarKy1GYkMCO3mrkHh4oYZ1eIiK6ScXlyzCUl0Pu6Sl2FIsjtbWFWqNBfkyM2FGok2FHL5EFKUlJgW2XLrB1cBA7Sodg5+gI2y5doD13TuwoRERkJQrj4mDfvTtsVSqxo1gMuacnR/QSEVEtBfHxUAUHQyKRiB3FIqnDwli+gVodO3qJLEjh8eNQBgSIHaNDUQUGoiAuTuwYRERkJa4dOAB1WJjYMSyK3MMDZZmZEARB7ChERGRBCo4eZdmGeij9/aG7epV17qlVsaOXyIIUJiSwbEMTqYKDURgfzy+XRI1w8OBBPProo4iMjERERARmzJiBvXv3NukcaWlpeP755zFs2DD07dsX48aNw08//QSDwWD2+ISEBDz11FMYOHAgwsPDcd999+GLL75AeXl5a9wSUbvSFxej5Px5qDUasaNYFFuVClKZDLqrV8WOQkREFqKysBDlWVmw9/UVO4rFkkil1xdlO3xY7CjUibCjl8hC6PLzocvNhcLHR+woHYrcwwOGigpUXL4sdhQii7ZhwwbMnj0bCQkJCA8PR79+/ZCQkIA5c+ZgzZo1jTrH2bNnMWnSJGzevBne3t4YOnQosrOz8fbbb2PhwoW1jt+/fz+mT5+O3bt3w9/fH0OGDEFRURGWL1+OKVOmQKvVtvZtErWp/CNHoAoMhFQmEzuKxZF7eqL0wgWxYxARkYUoOn4cyh49ILW1FTuKRXMIC0M+yzdQK+K/OCILUXT8OFQ9ekAi5fOXppBIJFAFB6MgPh6e3t5ixyGySDk5OVi8eDEcHBzw888/Q3NjNOKJEycwe/ZsLFu2DHfeeSc8PDzqPIcgCFi4cCG0Wi0++OADjB8/HgCQl5eHWbNmISoqCnfffTdGjx4NAKioqMALL7wAQRCwYsUK3H333QCA8vJy/N///R927dqFVatW4f/+7//a9uaJWlHegQNwDA8XO4ZFkru5oTQtDc4DBogdhYiILED+kSNQBQWJHcPiKbp3h764GGVZWbDv1k3sONQJsEeJyEIUxsdDybINzaIKDEQh6/QS1Wn16tXQ6XSYNWuWqZMXAMLDwzFnzhxUVFQ0OKr3wIEDSEpKwsCBA02dvADg4uKCJUuWAAB++ukn0/Y9e/agoKAAw4cPN3XyAoBCocAzzzwDANi3b19r3B5Ru6gsKEBZZibb6jpwQTYiIjIy6HTQnj0LVWCg2FEsnkQigTosDPmHDokdhToJdvQSWQBDZSWKz5zhl8dmsvf3R9nFi9AXF4sdhcgiGTtUR44cWWufcVtDtXrrO0dERARcXV0RFxdnKscwatQo7N69G2+88Uat40tKSgAAtpzKRx1IwdGjUAUFcQpqHeQeHijLyBA7BhERWYCiU6eg8PKCjb292FE6BIewMOQdOsR1Z6hVsKOXyAJoz56FzM0NtiqV2FE6JKmtLZR+fig6cULsKEQWRxAEJCcnQyqVooeZh0n+/v6QSqVITk6u98NlcnIyANQYEVxdQEAADAYDUlJSTNu8vLzg5eVV47irV6/io48+AgDcf//9Tb4fIrHkHTrERdjqYevoCEGvR2VBgdhRiIhIZAVHj0LJ0byNJvfygqDX84EptQp29BJZgIK4OE5raSFlYCAKWL6BqJbCwkLodDo4OTlBZmYBKVtbWzg7O6OsrMw00tacK1euAADc3NzM7jduz83NNbv/u+++w4wZMzB8+HAkJSXh6aefxvTp05t6O0SiqCwsRFlGBmfe1EMikUDu5YXS9HSxoxARkYgEgwFFx45BFRwsdpQOQyKRQB0ayvIN1CrY0UskMkEQUBgfz4awhVRBQSg6dQqCXi92FCKLUlZWBgCwr2fqnEKhAIB6O3qN5zEeW9c5SktLze7fsmULYmNjUVlZCYlEguzsbOTl5dV5vaKiIly8eLHGT3Z2dp3HE7UlU9kGOzuxo1g0uZsbSjkaiYjIqpWkpMDG3h4yZ2exo3Qo6tBQ5B0+zPIN1GIsMkYksrKMDEAqhaxrV7GjdGi2ajVkLi7QnjsHh549xY5DZDGk0sY/063vg6WNjQ2A6yMO6mMwGMxuX758OZycnJCamoqPP/4Yv//+OxITE7Fx40aztXp/+OEHrFixotHZidpSfkwM1KGhYseweDJ3d5RxRC8RkVUriIuDkoOYmkzu4QEAKE1Lg4oziKgFOKKXSGSF8fFQBQU12HlCDVP26IGChASxYxBZFKVSCQCoqKio85jy8vIax5pjHBFsPLauc6jqqDXu4eEBuVyOsLAwfPXVVwgJCcG5c+ewZcsWs8fPnDkT0dHRNX5Wr15dZz6itqIvKUFpaipLLDWC3N2d9QWJiKxc4dGjULOjt8lM5RsOHxY7CnVw7OglEhnr87YeVXAwCuPjxY5BZFHUajWUSiXy8/OhN1PaRK/XIz8/H3K5HI6OjnWex93dHUDdNXivXr0KoO4avtXZ2dnh3nvvBQAkJiaaPcbR0RE+Pj41fjw9PRs8N1FrK0xIgNLfH1IzNa6pJpmrK3R5eaiq44EQERF1buWXL6OqrAzymxbjpcZxCA1FfmwsyzdQizSro3f27Nn4448/TPX6iKh5dLm50F29CntfX7GjdApyDw8YystRfvmy2FGImqyt2laJRIKgoCBUVVUh3cyU6rS0NBgMBmg0mnrPE3xjZEZycnKtfYIgIDU1FTY2Ngi88eBq69atWLRoEQ7VsaiEcWE4c53PRJakIDaWdfQbSWJjA5mbG8ovXhQ7CpEJv7sStZ+CuDiogoM5W7WZZDcGVpSmpYmchDqyZnX0Hjp0CC+//DJuu+02LFy4EPv37+cTB6JmMDWETaihSXWTSCRQBQaikOUbqANqy7Z16NChAIAdO3bU2mfcNmzYsEadIzo6uta++Ph45OXloX///lCr1QCudyD//vvv+PXXX82eb9++fQCAnqypTRbMUFGB4sREdvQ2Acs3kKXhd1ei9lNw5AhUQUFix+iwJBIJHEJCkB8bK3YU6sCa1bv03nvvYfDgwdDpdPjzzz8xZ84c3HHHHXj//fdx9uzZ1s5I1Gnlx8RA1cAoOmoaZWAgyzdQh9SWbevEiRMhl8uxcuVKnDp1yrT95MmTWLVqFRQKBaZNm2banpGRgZSUFBQXF5u2DRw4EMHBwThw4ADWrl1r2p6Xl4c333wTwPVRU0bjx4+HQqHAli1bEBUVZdpeVVWF5cuX49ChQ+jWrZuphAORJSo6eRIKb2/Y3KhRTQ2Tde2KUi7IRhaE312J2kdlQQHKL1+G0t9f7Cgdmio0FAUxMXwgRc0mEVrwtyc3NxebNm3Cn3/+aaqxJ5FIEBwcjPHjx2Ps2LHwuLFyoDW5ePEi7rrrLkRHR8PHx0fsOGShKgsLcfrFFxGwYAGkZlacp+YxVFYibfly9P7sM9jWsSgUkSVrq7Z19erVWLp0Kezs7BAZGQkAiImJgV6vx/vvv4/x48ebjh0xYgSysrLw7rvvYuLEiabtJ06cwMyZM1FaWoq+ffvC3d0dsbGxKCwsxOTJk/HWW2/VuObvv/+OV199FVVVVejVqxc8PT1x9uxZZGVlwcXFBd9++22TRvSyfaX2lv7vf8PG3h5OAwaIHaXDKL1wAfmHDiH0xgMgIkvB767msW2l1pK7cyfyjxyB5/33ix2lQxMEARf+/W8EPvccO82pWVrU0Vtdamoq/vjjD/z111/IzMwEANjY2GDgwIEYP348Ro0aVe9q3p0JG0tqjNxdu5AfEwPPap0r1DourVsHt1Gj4DJokNhRiFqktdvWXbt2YdWqVUhMTIRMJkNISAjmzp2LwYMH1ziuro5e4HqN3uXLlyMmJgY6nQ5+fn6YMmUKHnroIdjY2NS6ZkJCAr755hvEx8ejpKQE7u7uGD58OJ588skmf6Fm+0rtSTAYcOLZZ9F95kzYdekidpwOo6qsDOlffom+33zD0lRksfjd9X/YtlJrOf/++1AFBcGBZblaLHfnTsg9POA9aZLYUagDarWO3upSUlLw999/49tvv0X5jVV3FQoF7r33XjzyyCOdvh4fG0tqjHPvvAN1aCgcwsLEjtLpFMTFQV9UhIBnnhE7ClGrsfa2FWD7Su1Lm5SEC6tWwfexx8SO0uGkffEFQt54A3IrHB1JHY+1t69sW6k1VJWV4eT8+fCfNw82crnYcTq8sosXkbtjB3q+/77YUagDatXH7EVFRVi/fj0+/PBDrFq1CmVlZRAEAe43Vg7csGEDHnzwQbzxxhtcZZusWmVREUrT0liovo2ogoJQdOIEBINB7ChELca2lUgcBXFxbKebSe7hgVIuyEYWju0rUespPH4c9r6+7ORtJYpu3aAvLkZ5To7YUagDanFh0PLyckRHR2PTpk3Yv38/9Ho9BEGAUqnEhAkTMH78eAwaNAjl5eX466+/8OGHH2L9+vVQKBR47bXXWuMeiDqcgqNHoQoMhNTOTuwonZJdly6wdXBASXIy1Fzsjjogtq1E4iuIi4PH2LFix+iQZF27oiwjA86sbUwWhu0rUdsoiI3lw9FWJJFIoAoORsGRI/DkZxFqomZ19Or1euzfvx9RUVHYuXMnysvLIQgCpFIpBg8ebKprZF9thWJ7e3s8+OCDcHNzw5NPPomoqCg2lmS18g8ehEPv3mLH6NSUPXqgMCGBHb3UYbBtJbIc5Zcvw1BeztIDzSR3d0fZhQtixyACwPaVqK0ZdDoUnTwJv6eeEjtKp6LSaFAQG8uOXmqyZnX03n777SgqKoKxvG9wcDDuv/9+3H///Q0urBIYGAgAqKqqas6liTq8ysJClF64AA+uRtqmVMHByN2+Hd0efljsKESNwraVyHIUxMdDFRwMiUQidpQOSe7ujrz9+8WOQQSA7StRWys6dQpyT0/YqlRiR+lUlH5+yP7jD1QWFnJRWGqSZnX0FhYWwsXFBffddx8mTJiAXr16Nfq1VVVVeOKJJ5r0GqLOJO/wYaiCg1m2oY0pvLxQWViIiqtXIXdzEzsOUYPYthJZjsKjR9Glf3+xY3RYdi4uqCwqQlVZGWyqjZIkEgPbV6K2lR8bC3VwsNgxOh2JjQ1UPXqg8NgxdB02TOw41IE0q6P3q6++wh133AEbG5smv9bX1xcvvvhicy5L1Cnk7d8P58hIsWN0ehKpFKqgIBQeOwb3u+8WOw5Rg9i2ElkGfXExyi5ehOfEiWJH6bAkUinkbm4oy8xkCSUSHdtXorYj6PUoSkhA98ceEztKp6QMDETB0aPs6KUmkTbnRdu2bcO3337bqGPffvttzJgxozmXIep0ynNyoMvNhTIgQOwoVsHYMBJ1BGxbiSxD4fHjUPr7Q2rb4jWLrZrsRkcvkdjYvhK1neLERNi5uMDO0VHsKJ2SKjAQ2jNnYNDpxI5CHUizOno3btyIPXv2NOrYmJgYnDhxojmXIep08g4cgENYGCTSZv3ToyZSBQSgNCUFVWVlYkchahDbViLLUHD0KJQ36nJS88nd3LggG1kEtq9EbSfv8GGoQ0LEjtFp2djbQ+7lheLERLGjUAfS4FCF9PR0rF27ttb2rKwsfPDBB3W+ThAEXLp0CefPn0fXrl1blpKoExAEAXkHDsD93nvFjmI1pHI5FD4+KDp1Cs4DBogdh8iEbSuRZTLo9ShOTITL7beLHaXDk7m7I//QIbFjkJVh+0rUfgS9HoXx8eg+a5bYUTo11Y1Zql1uuUXsKNRBNNjR6+fnh8OHD+PMmTOmbRKJBDk5Ofjuu+/qfa1xZdNx48a1MCZRx1eakgJUVUHh7S12FKui6tEDhXFx7Ogli8K2lcgyac+ehczVFbZqtdhROjy5uzvKs7IgGAycyUTthu0rUfspPnMGds7OsOvSRewonZoqMBCX1q6FIAiQSCRix6EOoMGOXolEgqVLl2L16tWmbRs3bkTXrl0xdOjQel+nVCoREhKCiVzMggjX9u6FQ+/efHNuZ6rgYGT++CO/aJJFYdtKZJkK4+OhYtmGVmFjbw+pQgFdbi7k7u5ixyErwfaVqP3kHTrEsg3twM7VFZBKUX7xIuy7dxc7DnUAjVplonfv3nj33XdN/79x40b4+fnV2EZEdTNUViI/NpbTWkRg5+QEW7UaJcnJXPmbLArbViLLIggCCuLj4fXAA2JH6TTkHh4oy8hgRy+1K7avRG3PwLIN7UYikVyfpXrsGDt6qVGatZxwdHQ05HJ5a2ch6rQKjx2D3N2d01pEouzRA4Xx8ezoJYvGtpVIXOVZWRCqqiBzcxM7Sqch69oVpRkZcLr1VrGjkBVj+0rU+opPnYLM1ZXfb9uJMjAQhfHx8GRpGWqEZs1j7tatG4vUEzXBtd274dCrl9gxrJY6OBgF8fFixyCqF9tWInEVxMdDHRTEEkutSO7ujrILF8SOQVaO7StR68s7cADq0FCxY1gNe19flGVkQF9SInYU6gAaHNE7ePBgSCQSrFmzBt1vDBMfPHhwky4ikUhw8ODB5iUk6uAqCwqgPX8ebqNGiR3Fasm9vaEvLkZFTg7kHh5ixyFi20pkgQrj4uA0cKDYMToVubs78vbvFzsGWRG2r0Rtz1BRgcJjx+D35JNiR7EaUjs72Pv6ovjUKThHRoodhyxcgx29+fn5kEgk0Ov1NbY1BUdGkDW7tn8/1CEhkMpkYkexWhKJBKqgIBTEx8Pj3nvFjkPEtpXIwlQWFqL80iXY+/qKHaVTsXNxQWVREarKy2GjUIgdh6wA21eitld47BgU3brBVq0WO4pVsffzQ+Hx4+zopQY12NH7448/AgC8vb1rbSOi+gmCgGu7d8Nt9Gixo1g9VVAQCo4eZUcvWQS2rUSWpej4cSh79IDUtlnLV1AdJFIp5F27oiwzE+rgYLHjkBVg+0rU9q7t3w+HsDCxY1gdVWAgLq1ZA0EQ+ECK6tXgp9mBZqawmdtGRLWVnD8PoaoKim7dxI5i9ZQBAciJioJeq+XTZxId21Yiy1Jw9ChUgYFix+iUZO7uKMvIYEcvtQu2r0RtS19cDO3Zs3C76y6xo1gdOxcXALg+A4n9C1SPZi3G1pCUlBScO3cOBoOhLU5P1GHk7t4Nhz59+MTNAkjt7KD090fh8eNiRyFqFratRG3DoNOh+MwZdvS2EbmbGxdkI4vG9pWo8fIPH4YqKAhSuVzsKFZHIpFA2aMHik6cEDsKWbhmd/QWFxfjq6++wq+//mralpOTg4kTJ2Ls2LEYP348Ro0ahSNHjrRKUKKOpqqsDAVHj8KxTx+xo9ANyqAgFPA9iSwY21ai9lecmAi5hwdslEqxo3RKMnd3lLKjl0TG9pWodVzbtw8OvXqJHcNqKf39UXjsmNgxyMI1q6O3sLAQkyZNwvLly7Fv3z7T9n/+859ITEyEIAiwtbXFxYsXMWfOHGRmZrZaYKKOIj8mBkpfX5YJsCCqoCAUnz4NQ2Wl2FGIamHbSiSOgrg4juZtQ3J3d5RnZUHgaEkSCdtXotZRfvkyKq5ehTIgQOwoVsve3x+lKSkw6HRiRyEL1qyO3u+//x4XLlyAr68vHnjgAQBAVlYW9uzZA6lUih9++AEJCQmYPXs2ysvLsWrVqlYNTdQR5O7aBQeO5rUotioV5B4eKE5MFDsKUS1sW4nan2AwoDAhASrWj20zNvb2sLG3R8XVq2JHISvF9pWodVzbsweOvXtDIm2TCqDUCDYKBeTu7tCePy92FLJgzfoXumvXLtja2uI///kPRo4cCQCIjo4GAERERCAyMhK2trZ4/vnn4ejoiIMHD7ZeYqIOoCwrC7qrVzlCyAKpWL6BLBTbVqL2V5qeDqmdHWSurmJH6dRkHh4oy8gQOwZZKbavRC0nVFVdL9vAgUyis/fzQ9HJk2LHIAvWrI7ezMxMBAQEoFu1lf727dsHiUSCO+64w7TNzs4OPj4+uHLlSsuTEnUg1/bsub4Im42N2FHoJqrgYBTGx3MKKVkctq1E7a8gLo6jeduBvGtXlKanix2DrBTbV6KWKzp5ErZdukDu5iZ2FKun9PdHMRdko3o0q6NXp9PBzs6uxv8fPXoUADBo0KAax5aUlEDKof1kRQS9Hnn793MRNgslc3GBjVKJkpQUsaMQ1cC2laj9FR49ClVQkNgxOj2ZuzvKuCAbiYTtK1HL5e7eDcfevcWOQQAU3bqh4soV6IuLxY5CFqpZrZi3tzcyMjJQXl4OADh06BDKysrg7OyM8PBw03Hp6em4ePEifHx8WictUQdQeOwY7FxcOA3UgqmCglBw4wM+kaVg20rUviquXEFlUREU1Ub5UduQs3QDiYjtK1HLVBYWojgxEeqwMLGjEACJjQ3s/fy47gzVqVkdvXfeeSe0Wi1effVVREdH47333oNEIsGoUaNMx5w4cQLz58+HwWCoMSWGqLPL3bWLo3ktnEqjQcHRoxAEQewoRCZsW4naV0FcHFRBQVxUph3YOTujqrQUeq1W7Chkhdi+ErVM7p49UIeEwEahEDsK3WDv64tClm+gOjTrk+1TTz0FLy8v/PXXX5g3bx7S0tLg5OSEuXPnArj+lPThhx/G+fPn4evriyeeeKJVQxNZqsqCAmjPn4c6NFTsKFQPuacnDDodyrOyxI5CZMK2lah9FRw9yvq87UQikVwf1cvyDSQCtq9EzScYDLi2axe63HKL2FGoGqW/P7Qc0Ut1sG3Oi1xcXPDbb79h5cqVpgZx9uzZ8PDwAAAEBARAqVTinnvuwcKFC9GlS5dWDU1kqa7t3w91SAikMpnYUageEokEao0GBUeOwJ7T88hCsG0laj/64mKUZWTAc/x4saNYDbm7O0ovXIBDr15iRyErw/aVqPmKT5+GxM4Oci8vsaNQNTI3N1SVl6Pi6lUukEe1NKujF7jeYC5atMjsPk9PTxw6dAgydnaRFREEAdd274ZbtWlgZLnUGg1yd++G1wMPiB2FyIRtK1H7KExIgDIgANJqCzRR25J5eKA0LU3sGGSl2L4SNc/V7dvh2LcvJBKJ2FGoGolEAqW/P4oTEyEfNkzsOGRh2qwoGRtKsjalKSkQ9HooOEK0Q1D4+KCyoAAVV66IHYWo0di2ErWO/NhYlm1oZ3IPD5SydANZKLavRLXpcnNRfPYsHHv3FjsKmWHv64vikyfFjkEWqNkjegHg5MmTOH78OLRaLaqqqupd2GjevHktuRSRxcvdswcOvXvzaWcHIZFKodZokH/kCDzvu0/sOEQmbFuJ2lZVeTm0Z8+i64gRYkexKnI3N+hyc2HQ6VjiikTB9pWoaa7u3AnH3r35nm2hlH5+uLh/PwRBYB8E1dCsjl6dTofnnnsOO3fubPBY4186NpbUmRl0OhTExqL77NliR6EmUGk0KIiJYUcvWQS2rUTto+jkSSi6dYONvb3YUayKxMYGsq5dUZaZCVVgoNhxyIqwfSVqOkNlJa7t3o1uU6eKHYXqYOfsDImNDcqzsrjuDNXQrI7e7777DtHR0QAAX19fBAQEQC6Xt2owoo6kMCEBcg8P2HHxhg5F6eeHnD//hO7aNchcXcWOQ1aObStR+yg4coRlG0RiLN/Ajl5qT2xfiZouPzYWMjc3yLp2FTsK1cPezw/FZ86wo5dqaFZH759//gmJRILXXnsNjzzySGtnIupwjGUbqGOR2NhAFRyM/CNH4HHPPWLHISvHtpWo7Rn0ehQdPw7fxx8XO4pVkru5oTQ1FWDZDGpHbF+Jmu7Kli1wGjBA7BjUAHs/PxSfPAn3u+8WOwpZkGYtxpaZmQkvLy82lEQAKgsLUXL+PNQhIWJHoWZQh4Qg//BhsWMQsW0lagfaxETIXF1h6+AgdhSrJPfyQmlamtgxyMqwfSVqmpLkZOgLCzn7ogNQ+vlBm5QEwWAQOwpZkGZ19KpUKqjV6tbOQtQh5R0+DFVwMIvUd1D2/v4ov3QJurw8saOQlWPbStT28mNjWbZBRHIPD5RfvgxDZaXYUciKsH0laporW7eiS0QEJNJmdRdRO7JVq2GjUqEsI0PsKGRBmvUv99Zbb0VaWhry2DFChLx9++DQq5fYMaiZpLa2UAcHo+DIEbGjkJVj20rUtgSDAYXx8ZyBIyKpnR1krq4ov3hR7ChkRdi+EjWeLj8fhcePw7FvX7GjUCPZ+/mhODFR7BhkQZrV0fvss88CAF577TXodLpWDUTUkZRfvgzdtWtQ+vuLHYVaQB0airxDh8SOQVaObStR29KeOwcbtRp2zs5iR7Fqcg8PlLB8A7Ujtq9EjZcbHQ2Hnj1ho1CIHYUayd7XF0WnTokdgyxIsxZjy8jIwKRJk/DLL7/gjjvuwMCBA+Hh4QE7Ozuzx0skErz00kstCkpkifIOHIBDz56c1tLBKQMCkLNpE3R5eZC5uIgdh6xUe7WtBw8exNdff42kpCRUVlaiV69emDNnDu64445GnyMtLQ2ff/454uLiUFBQAF9fX0yePBnTp0+H1Mz74bFjx7By5UokJCSgqKgITk5OiIyMxDPPPINA1n+jdlLAsg0WQe7hwQXZqF3xuytR4xgqK5G7cye6TZ0qdhRqAqWvL6789RcEvR4S22Z18VEnIxEEQWjqi0JDQyGRSFD9pRKJxOyxgiBAIpHgzJkzzU/ZwVy8eBF33XUXoqOj4ePjI3YcaiOCIOD088/DY+xYKLy9xY5DLZSzeTMcevWCx733ih2FrFR7tK0bNmzAK6+8AplMhkGDBsFgMCAmJgaVlZVYunQpHn744QbPcfbsWUyfPh1arRYRERFwdXVFTEwMioqKMG7cOHz00Uc1jv/zzz/x8ssvo6qqCj179oS3tzeSk5ORnp4Oe3t7rFy5EgMauaoz21dqLsFgwKl//APekydD1rWr2HGsWnlWFq7u3Ime77wjdhSyEvzuWj+2rWR0bf9+XN2xA90a8XmQLEvGd9/B/8knoQoKEjsKWYBmdfdPmDChzsaRyFqUpqZCEATIvbzEjkKtQB0aivxDh9jRS6Jp67Y1JycHixcvhoODA37++WdoNBoAwIkTJzB79mwsW7YMd955Jzw8POo8hyAIWLhwIbRaLT744AOMHz8eAJCXl4dZs2YhKioKd999N0aPHm3avnjxYgiCgC+++AIjR440neebb77Bv/71LyxcuBDbtm2rc2QVUWsoTU2FRCZjJ68FkLm7oyI7GwadjgvZUrvgd1eihgmCgCt//w2nyEixo1Az2HfvjqLTp9nRSwCa2dH73nvvtXYOog4n7+BBOISF8YNjJ6H090fOpk2ouHIFcnd3seOQFWrrtnX16tXQ6XR46qmnTJ28ABAeHo45c+bgk08+wZo1a7BgwYI6z3HgwAEkJSVh4MCBpk5eAHBxccGSJUswdepU/PTTT6aO3m3btqG0tBQTJkwwdfIC10dSPfXUU/j7779x5swZJCQkYODAgW1w10TX5cfEQF3t7z2JR2pnB1nXrijLzISKpVuoHfC7K1HDSpKToddq2VHYQdn7+qL49Gl4Vft8TtaLhUWJmkEwGJB/+DAcevYUOwq1EomNDdQhIcg7fFjsKERtYt++fQBQo8PVyLht7969zT6HsYxDXFwctFotAMBgMKBnz54YNGiQ2fP531jI8sqVK427CaJmEAQB+bGxUIeGih2FblB4eqIkNVXsGEREdMOVrVvRJSKCg5g6KHtfX5SmpsJQWSl2FLIALe7oTUpKwsqVK/Hqq6+aRgGVlJTgt99+46qm1Glpz56FjVLJKaCdjDosDPkHD4odg6jV21ZBEJCcnAypVIoePXrU2u/v7w+pVIrk5GTUV7o/OTkZAGqMCK4uICAABoMBKSkpAIBp06Zh48aNeOCBB2odazAYcPr0aQCAp6dnk++JqLFK09IgkUggc3MTOwrdIPf2Rsm5c2LHICvE765EtVUWFKDoxAk4hoeLHYWayUahgKxrV5Tc+AxO1q3ZS/IVFhbitddeQ3R0NID/Fa4HgMzMTLz22mtYvnw5vvnmG4SEhLROWiILkXfoEEcGdUL2vr7QFxejLCsL9t26iR2HrFBbta2FhYXQ6XRwcXGBzExNTFtbWzg7O+PatWsoKSmBWq02ex7jyFu3OjrMjNtzc3MbzLRhwwZkZGTA3d0dt9xyS639RUVFKCoqqrEtOzu7wfMS3Sw/JgaqkBCOUrIgCm9vFMTGih2DrAi/uxLVLXfXLjiEhcFGoRA7CrWAva8vihMT4cB+CqvXrBG9Op0Ojz32GHbs2AF7e3uMGDEC7tVqWgqCAEdHR+Tk5GDGjBm4dOlSqwUmEptQVYWCo0fhEBYmdhRqZRKJBA5hYcjjqF4SQVu2rWVlZQAAe3v7Oo9R3PhwX1JS0uB5FHV8ETBuLy0trTfPqVOnsGzZMgDACy+8AFvb2s+df/jhB9x11101fqZPn17veYluJggC8mNi2GZbGFnXrtAXF0NfXCx2FLIC/O5KVDehqgq5O3eiS79+YkehFjLW6SVqVkfvf//7X5w+fRoDBgzAjh078MUXX8DHx8e0PywsDDt37sSAAQNQXFyMlStXtlpgIrEVJybCrksX2Dk7ix2F2oC6Z0/kHzxY7/R1orbQlm2rVNr45r6+v/s2NjYA0ODISIPBUOe+EydO4PHHH0dpaSmmTJmCCRMmmD1u5syZiI6OrvGzevXqhm+AqJrStDQAgIyLbFoUiUQChbc3p5hSu+B3V6K6FSYkwNbREXIPD7GjUAvZ+/igLD0dBpahsXrN6ujdtGkTbG1t8dFHH8HFxcXsMWq1Gh999BFsbW1Ni7cQdQYs29C5yT09AYkEJTdqkRK1l7ZsW5VKJQCgoqKizmPKy8trHGuOcUSw8di6zqFSqczu37NnD2bOnImCggJMnDgRixcvrvNajo6O8PHxqfHDWr7UVPkxMVCzbINFknt5sa2ldsHvrkR1u7J9OxzNlNCijkcql0Pu6Qnt+fNiRyGRNaujNy0tDUFBQfBo4KmPh4cHevTogZycnGaFI7I0gl6Pwvh4dvR2YhKJBA49eyJv/36xo5CVacu2Va1WQ6lUIj8/H3q9vtZ+vV6P/Px8yOVyODo61nke41TXumrwXr16FYD5Gr6//vor5s6di9LSUsyePRvvvPNOk0YaEzWVsWwD22zLpPD2hpYLslE74HdXIvMqrlxB2YULbCc7EUX37izfQM3r6JVIJKY6fQ0xGAxmF34h6oiKEhNh5+ICuy5dxI5CbcihZ0/kx8ZCMNMhRtRW2rJtlUgkCAoKQlVVFdLT02vtT0tLg8FggEajqfc8wcHBAIBkM6PwBEFAamoqbGxsEBgYWGPfihUrsHjxYgiCgFdeeQUvv/wyR1hSmzOWbeB0VMuk8PZGaXo6hHpKvRC1hvb47nrw4EE8+uijiIyMREREBGbMmIG9e/c26RxpaWl4/vnnMWzYMPTt2xfjxo3DTz/9VGc5pGPHjuHZZ5/Fbbfdht69e2PIkCF44YUXkMKSKNRIubt3w6FXL0jNrJVAHZPSz48dvdS8jt6AgABcvHgRFy9erPe4jIwMJCcno0ePHs0KR2Rp8g8fhrqBjhDq+OycnSFzcUHhiRNiRyEr0tZt69ChQwEAO3bsqLXPuG3YsGGNOodx1fLq4uPjkZeXh/79+0OtVpu2//jjj/j8889hZ2eHjz/+GLNmzWpSbqLmyj98GOrQUD5UsFC2KhVsVSqUZWaKHYU6ubZuXzds2IDZs2cjISEB4eHh6NevHxISEjBnzhysWbOmUec4e/YsJk2ahM2bN8Pb2xtDhw5FdnY23n77bSxcuLDW8X/++SemTZuGHTt2wMPDA8OGDYNKpcKmTZvw4IMP4siRI026B7I+QlUVru3dC8e+fcWOQq1I0a0byi9eRFUjH25R59Ssjt5x48ahqqoKixYtQkFBgdljCgoK8OKLLwIA7r333mYHJLIULNtgXRx69kQea7RRO2rrtnXixImQy+VYuXIlTp06Zdp+8uRJrFq1CgqFAtOmTTNtz8jIQEpKCoqLi03bBg4ciODgYBw4cABr1641bc/Ly8Obb74JAJg9e7Zpe1JSEj744AMAwPvvv48xY8Y0KTNRcwmCgPzDh+EQFiZ2FKqHwscH2qQksWNQJ9eW7WtOTg4WL14MBwcH/Pbbb1i5ciW+/fZb/Pzzz1Cr1Vi2bFmDpSAEQcDChQuh1WrxwQcf4JdffsGKFSuwdetWhISEICoqClu3bjUdn5eXZ5ol88UXX2Djxo344osvsGXLFjz//PMoKyvDwoULUVlZ2ej7IOtTePw47BwdITdTbos6LqmdHRTdurFttXLNGqM/bdo0bN68GXFxcRgzZgwGDx5sekL63XffITU1Fdu2bUNhYSGCg4Mxffr0Jl/j4MGD+Prrr5GUlITKykr06tULc+bMwR133NHoc6SlpeHzzz9HXFwcCgoK4Ovri8mTJ2P69Om16gLq9Xr069cPujpWKPTw8Gjy9BvqXIoTE2Hn7MyyDVZCHRaG9K++gr6kBLZ1LCxF1Jraum318fHBokWLsHTpUkyZMgWRkZEAgJiYGOj1erz//vtwdXU1HT9r1ixkZWXh3XffxcSJEwEAUqkU77zzDmbOnIk33ngD69evh7u7O2JjY1FYWIjJkydjxIgRpnN8+eWXqKyshKOjI3bt2oVdu3aZzTZlyhTceuutTbofovqUpqRAYmsLGb/AWjR7Hx9oz5yB+6hRYkehTqwt29fVq1dDp9PhqaeeqlH+KDw8HHPmzMEnn3yCNWvWYMGCBXWe48CBA0hKSsLAgQMxfvx403YXFxcsWbIEU6dOxU8//YTRo0cDALZt24bS0lJMmDABI0eONB0vkUjw1FNP4e+//8aZM2eQkJCAgQMHNvpeyLrk7toFhz59xI5BbUDRvTuKTp9GFy6yZ7Wa1dErk8mwatUqLFq0CLt378bmzZtN+z744AMIggAAGDBgAD7++GPI5fImnX/Dhg145ZVXIJPJMGjQIBgMBsTExGDOnDlYunQpHn744QbPcfbsWUyfPh1arRYRERHo06cPYmJi8Pbbb+P48eP46KOPahyfnJwMnU4HX19f9DUzfcHJyalJ90CdT96NlbvJOtjY20MZEICC2Fh0HT5c7DhkBdq6bQWA6dOnw9vbG6tWrUJ8fDxkMhkiIiIwd+5cDB48uFHnCA8Px7p167B8+XLExMTg/Pnz8PPzw/PPP4+HHnqoxrHGlcuLiooQFRVV5zlvu+02dvRSq8o7fBjqkBCWbbBw9t2749revRAEgX9W1Gbasn01tnPVO1yNRo4ciU8++QR79+6tt6O3vnNERETA1dUVcXFx0Gq1UKvVMBgM6NmzJwYNGmT2fP7+/jhz5gyuXLnS6Psg61JZWAjt2bNwu+susaNQG1D6+yN3506xY5CIml11u0uXLvj6669x6tQpREdHIyUlBVqtFvb29vDz88Odd97ZrCeI1ae//Pzzz6YnoydOnMDs2bOxbNky3HnnnfWumnrz9Bfjk9G8vDzMmjULUVFRuPvuu01PRQHgzJkzAK5PbZ07d26Tc1PnJlRVoTAuDt1ZW9KqOPTqhdw9e9jRS+2mrdrW6oYPH47hjfg7vbOeD4hBQUFYvnx5g+eIj49vUjai1iAYDMiPiYH3TQ8eyPLY3hhIUXHlChRcNI/aUFu0r4IgIDk5GVKp1GxdX39/f0ilUiQnJ9f7MMO4wGldC6IGBATg2rVrSElJQd++fTFt2rQapZaqMxgMOH1jISZPT88m3Q9Zj7yDB6HSaCBtxqABsnwKLy/ocnOhLy6GrYOD2HFIBC1aXrGqqgre3t6YMWMGHBwcYGdn1+JAYkx/AYDExEQAQK9evVp8D9T5FJ89CzsnJ5ZtsDKqwEBc2bIF5Tk5/AJK7aYt2lYia1Jy/jxsFArWHewAJBIJ7H19UZKUxHaW2lxrt6+FhYXQ6XRwcXGBTCartd/W1hbOzs64du0aSkpKaixUWp1x5K1bHe9Zxu25ubkNZtqwYQMyMjLg7u6OWzhtm+pwbe9euDahJCZ1LBIbG9h3747ixEQ43yjVRtalSR29VVVViIqKwq5duxAXF4dr167V2O/j44NbbrkFY8aMwZ133tmsKVhiTH8B/jeit3fv3k3OTJ1fQUwMVHU8ZafOS2JjA4eePXFt715048gwaiPt0bYSWZO8gwdZaqkDUXTrhuLERHY6UKtr6/a17Maq9vb29nUeo1AoAKDejl7jeYzH1nWO0tLSevOcOnUKy5YtAwC88MILsLWt/VW/qKgIRUVFNbZlZ2fXe17qXEovXIBeq4W9n5/YUagN2fv6oujUKXb0WqlGd/TGxMTgtddeQ1ZWlqmO0c0yMzNx8eJFbNq0CUFBQXj77bfN1ruti1jTXwRBwJkzZ+Dm5obo6GisWbMGKSkpkMvluO222zBv3jyzecg6CAYDCo4eRbdHHhE7ConAsU8fXN6wAd4PPgjJTYs4ErVUe7StRNZEqKpCwZEjbLM7EKW/Py6tXcs6vdSq2qN9vXlx7/rUlQEAbGxsAKDBv/8Gg6HOfSdOnMCcOXNQWlqKKVOmYMKECWaP++GHH7BixYqGA1Onlbd/Pxx79eL7bSen9PdH9p9/ih2DRNKojt6tW7fixRdfRGVlJeRyOYYPH45+/fqhW7duUKlU0Ol0KCkpQWpqKhISEnDo0CGcP38eM2bMwOeff45hw4Y1KoxY018yMzOh1Wqh1Wrxz3/+ExEREYiMjMSZM2ewefNm7N69GytXrkT//v0bdR/UuWiTkmCjVkPm7Cx2FBKB3MMDUoUCxYmJcOSIf2pF7dW2ElmT4sRE2Do6ss3uQOxcXCAYDKjIzobCy0vsONQJtFf7qlQqAQAVFRV1HlNeXl7jWHOMI4KNx9Z1DpVKZXb/nj178H//938oLS3FxIkTsXjx4jqvNXPmTDzwwAM1tmVnZ2P69Ol1voY6D8FgQN6hQ/CePFnsKNTGZO7uqCotRUVuLuRdu4odh9pZgx292dnZePXVV1FZWYlRo0ZhyZIlcHFxqfc1ly9fxltvvYWdO3fipZdewtatW+HciA/cYk1/Mdbn9fDwwL///W+EhYUBAPR6PT7++GP85z//wXPPPYft27fXWoWV0186v/yYGKhZtsGqOfTqhWt79rCjl1pNe7atRNYk7+BBqG98jqOOQSKRQOnvj+LTp9nRSy3Wnu2rWq2GUqlEfn4+9Hp9rVIJer0e+fn5kMvlcHR0rPM87u7uOHPmDHJzcxEYGFhr/9WrVwGYH8T066+/YunSpaiqqsLs2bOxaNGiekdqOjo61puFOjftmTOwUSpZw94KSCQSKAMCUHz6NOQcHGJ1Gpxv8ssvv6CkpAT33Xcfli9f3mBDCQBeXl748ssvMWrUKBQXF2PdunWNCyPS9JfRo0dj9+7dWLdunamTF7g+gvill15Cr169kJOTgx07dtQ6xw8//IC77rqrxg+fiHYegsGAgiNHoA4NFTsKicixd28UHjsGfUmJ2FGok2jPtpXIWhh0OhTExcGBHb0djr2fH4pOnBA7BnUC7dm+SiQSBAUFoaqqCunp6bX2p6WlwWAw1FlO0Cg4OBjA/8oPVicIAlJTU2FjY1OrE3jFihVYvHgxBEHAK6+8gpdffpnT8ale1/bvh0PPnmLHoHZi7+uLopMnxY5BImiwZ3Xfvn2QSqVYuHBhk0/+/PPPQxAE7N27t1HHizX9RSKRwMvLCx5mVvuVSqWm6TunTp2qtX/mzJmIjo6u8bN69eo6s1HHUpKcDBt7e8hcXcWOQiKyUSqh6tED+YcOiR2FOon2bFuJrEXh8eNQeHrC1sFB7CjUREp/fxSfPQuhnhqkRI3R3u3r0KFDAcDsgCDjtoZKQRjPER0dXWtffHw88vLy0L9//xqzWX/88Ud8/vnnsLOzw8cff4xZs2Y1OjNZJ0NlJQrj49nRa0WUAQHQJiaybbVCDXb0ZmVl1dkJ2hB/f3+4ubkhNTW1UcffPP3lZk2Z/gL8rwbvzeqb/mJO1xs1Tcx1HDs6OsLHx6fGj6enZ6POS5YvPzYWKpZtIAAOffogd/dusWNQJ9GebSuRtcg7cIBlGzooW7Uado6OKOX7GrVQe7evEydOhFwux8qVK2sMCjp58iRWrVoFhUKBadOmmbZnZGQgJSUFxcXFpm0DBw5EcHAwDhw4gLVr15q25+Xl4c033wQAzJ4927Q9KSkJH3zwAQDg/fffx5gxY5p8r2R9ik6cgMzdnQ9DrYhdly6QyGQou3hR7CjUzhqs0VtaWgo/P79mX8DNzQ3nzp1r1LHG6S8nTpxAeno6goKCauxvyvSXPXv2IDk5GZGRkTX2mZv+snr1ahw5cgSTJ0/GbbfdVut8F2/8w2AHrnURBAEFsbHwevBBsaOQBVAGBODKli0ozciA0tdX7DjUwbVn20pkDfQlJSg+fRqud9whdhRqJmWPHig8dgyqmz7/EzVFe7evPj4+WLRoEZYuXYopU6aYvnvGxMRAr9fj/fffh2u1mYGzZs1CVlYW3n33XUycOBHA9Rmk77zzDmbOnIk33ngD69evh7u7O2JjY1FYWIjJkydjxIgRpnN8+eWXqKyshKOjI3bt2oVdu3aZzTZlyhTceuutzfk1UCeUd+gQyxFaIaW/P4pPneL3VyvT4IjeyspK2NnZNfsCCoUCVVVVjT5ejOkvmZmZ+Pvvv7Fx48Zax1dUVGDr1q0AgCFDhjT6PqjjK01JgcTGBjIWqycAEqkUjn364BpH9VIraO+2laizKzhyBMqAANjUs6AvWTZVYCAK4uLEjkEdnBjt6/Tp0/H111+jb9++iI+Px6lTpxAREYHvvvsO48ePb9Q5wsPDsW7dOowePRoXLlzAgQMH4O3tjTfffBNLliypcey+ffsAXF8UPCoqqs6fjIyMJt0HdV5V5eUoOn4c6pAQsaNQO1OyBr5VanBEb3ubOHEiVq1ahZUrV2LIkCHofWOV+/qmv1RWVsLd3R0ON6Yh3Dz9ZfLkyQDqnv4yadIk/Pjjj4iKisLIkSMxevRoANc/KLz11lvIysrCHXfcgV69erXL74AsQ35sLNQhIVzUgEwcw8OR+cMP6DZlCqQymdhxiIjohrz9++Fw4zMjdUwKHx9U5uVBl5cHWSMW0CKyJMOHD8fw4cMbPG7nzp117gsKCsLy5csbPEd8fHyTshEVHT8ORbdusL2xRhFZD3t/f+Rs3gyDTsfvr1akwRG97c04/UWr1WLKlCl4/PHH8fjjj2Pq1KkoKSnB0qVLa01/GTNmDLZv327aZpz+olQq8cYbb2Dy5MmYN28e7rnnHiQlJdWa/hIUFISXX34ZALBgwQJMmjQJCxYswMiRI7Fu3Tr06NED7733Xvv9Ekh0giAgPyaG01uoBjsnJ8g9PTniiIjIguiuXUNZZiaUN61ITx2LRCqFKigIhQkJYkchIupUWLbBetkoFJC7u0PLkm9WpVEjenU6HS5dutSsC+h0uia/Zvr06fD29saqVasQHx8PmUyGiIgIzJ07F4MHD27UOYzTX5YvX46YmBicP38efn5+eP755/HQQw/VOv7RRx9FcHAwVq1ahRMnTiApKQne3t54+umn8eSTT0LFp19WpTQtDRKJBLIbC/sRGTmGhyM3OhoujXwvIqpLe7etRJ1V3sGDUIeEQGprcRPVqImUPXqgIC4ObnfdJXYU6sDYvhL9T1V5OWvYWzl7f38UnTwJR858shqN+kR86tQp3NXOH7jac/qL0eDBgxvdkUydG8s2UF1UwcG4un07ynNyoGjGis5ERmK0rUSdjSAIuLZvH9xGjhQ7CrUCZY8euLJlC6rKylhvmZqN7SvR/xQeOwZ7Hx++p1oxZUAAcqOjgalTxY5C7aRRpRsEQWjRD1FHIggC8g8fhorTW8gMqa0tHHv3Rm4dKxwTNRbbVqKWK01Lg6GiAgofH7GjUCuwUShg7+eHAtYgpRZg+0r0P/mHD0PFRdismsLbG7q8PFQWFIgdhdpJgyN6o6Oj2yMHkcUoS0+HBICcozWpDo7h4cj65Rd0mzQJEk4VpmZg20rUOq7t2QOH3r05A6cTUYeEIO/AAbjefrvYUagDYvtK9D8s20DA9Rr4yhvlG1yHDhU7DrWDBnsounXr1h45iCxG3o3RvPzSSHWRde0KOxcXFCQkwHnAALHjUAfEtpWo5QyVlciPiUH3mTPFjkKtSB0cjKvbtkGv1cJWrRY7DnUwbF+J/qfw2DEoWLaBcL18Q+GxY+zotRKNKt1AZC0EQUB+TMz/t3fvcVGVCR/AfzPDMDAMw/2moqIyIAgIKqKmZpqubdaub1muldrl7bJtbftulm1tqdVml22z2qw0Ky9529Is08QLKiIiouIN5eKN+/0Ow8yc9w9jVgIUFHjm8vt+Pny2Pedh/J2Hw3kennPO88B18GDRUcjCNS/KRkREYlQePQpHHx8o3d1FR6EuJFepoA4KQsXhw6KjEBFZtfKDB6HR6UTHIAugHjAA1SdOQDKZREehHsCBXqKr1OXkQCaTwdHHR3QUsnCa0FDU5eSgsbhYdBQiIrtUsmsXtBERomNQN3AdPBil+/aJjkFEZLWap23gQC8BgFKrhYOrK+qys0VHoR7AgV6iq5QlJUETEsJpG+i65EolXLkoGxGREPqyMtRmZUHDhVNtkktwMBry8tBQUCA6ChGRVapKT4dT795QqNWio5CFUA8YgMqjR0XHoB7AgV6iX0gm05XXW8LCREchK6GNikJpQgIkg0F0FCIiu1KakADXsDDIlUrRUagbyBQKaIcMQemePaKjEBFZpfKkJD7NSy00z9NLto8DvUS/qD13DgonJ6g4bQN1kOqXuSHZYBIR9RzJZEJJQgK0kZGio1A30kZGonTfPkhGo+goRERWxaTXoyo9HS4c6KWrOAcGorGwEE2VlaKjUDfjQC/RL8oOHIAmJER0DLIy2qgoFHNRNiKiHlN94gTkjo5Q+fuLjkLdyNHb+8rN1LQ00VGIiKxK5bFjcAoIgIOLi+goZEFkCgXUQUGoOn5cdBTqZhzoJQIgGY2oSEnhtA3UaZrQUNRlZ6OxpER0FCIiu1AcHw+3oUM5n74dcIuORuHWraJjEBFZlfKDB+HCB5ioDS4DB6IiNVV0DOpmHOglAlB18iQc3Nzg6OEhOgpZGblSCdfwcM4jSETUA/RlZag5cwauvDFrFzShoWgsKkLd+fOioxARWQWTXo+q48f5piq1ST1gAKpPnYKJa8zYNA70EgEo278froMHi45BVkobFYWSPXs4jyARUTcr2b0bmrAwyFUq0VGoB8gUCrjFxPCpXiKiDqo6fpzTNlC7HDQaOHp5oTYjQ3QU6kYc6CW7Z2xoQGVaGjQc6KUbpPL1hVKrReWxY6KjEBHZLJPBgJJdu+AWHS06CvUgt6FDUXXsGPScIomI6LrKkpK4CBtdk3rAAFQcOSI6BnUjDvSS3atMS4NTnz5w0GhERyErpo2KQnF8vOgYREQ2qyIlBY5eXlD5+oqOQj1I4ewMbVQUCrZsER2FiMiimRobr0zbEBoqOgpZME1wMCqPHIEkSaKjUDfhQC/ZvdJ9+zhtA900zeDBqMvKgr60VHQUIiKbVPTTT3AbNkx0DBLAPTYW5UlJaKqoEB2FiMhiVR49CqfevTltA12To68vJIMBDbm5oqNQN+FAL9m1pspK1J47x8nq6aY1L8pWwkXZiIi6XG1mJpoqKuAyaJDoKCSAg4sLXCMi+FQvEdE1lCUl8e9aui6ZTAaX4GBUpKaKjkLdhAO9ZNfKDhyARqeD3NFRdBSyAdqoKJTu2QPJZBIdhYjIphT++CPchg2DTM6uq73yiItD2f79fHOGiKgNxvp6VJ88yYFe6hCXQYNQcfiw6BjUTdhbJrtWuncvXIcMER2DbITK1xcKjQZVx4+LjkJEZDMai4pQfeoU3KKiREchgRw0GrgNHYq8//xHdBQiIotTceQInAMDoXB2Fh2FrIBz375oLCyEvrxcdBTqBhzoJbtVd/EiDNXVcO7XT3QUsiHayEgU79wpOgYRkc0o3LoV2qFDIVepREchwdzj4lB55Aga8vJERyEisihl+/dDExYmOgZZCZlCAZdBg1DJ6RtsEgd6yW6VJiRAO2QIZDKZ6ChkQ1zDwlCbkQF9WZnoKEREVq+pqgrlSUlw5yJsBEDh5ASPkSNx+ZtvREchIrIYTZWVqM3MhCY4WHQUsiIuwcEoP3RIdAzqBhzoJbtkMhhQduAAXCMiREchGyN3dIRm8GCUJiSIjkJEZPWKfvoJmsGD4eDqKjoKWQi34cNRf/48qk+fFh2FiMgilCcnwyU4mOvOUKe4DBiAupwcGKqrRUehLsaBXrJLlUeOwNHbG46enqKjkA3SRkWhJCGBi7IREd0EQ20tSnbtgsfIkaKjkAWROzjAa/x4XF69mu0sERGuTNvgymkbqJPkjo5QBwWhMi1NdBTqYhzoJbtUsns3tHyal7qJU0AA5I6OqD55UnQUIiKrVbR9O1wGDYLS3V10FLIwmrAwSAYDypOSREchIhKqIS8P+pISqIOCREchK+QSHIzy5GTRMaiLcaCX7I6+pAS1WVnQhIaKjkI2TBsZiZJdu0THICKySobaWhT//DM8Ro8WHYUskEwmg/dttyF33TqY9HrRcYiIhCnZuxeu4eGQyTm0Q53nMmgQajIyYKyrEx2FuhCvBmR3SvbsgWt4OORKpegoZMNcw8NRdeIEmiorRUchMjtw4AAeeughjBw5EjExMXjwwQexd+/eTn1GTk4O/vKXv2D8+PGIiorCtGnTsHLlSpg68Ap1WVkZ4uLi8Le//e1GD4HsROHWrXAJDuYUS9Qu58BAqAICULh1q+goRERCSEYjyvbt47ozdMMUTk5w7t8fFUeOiI5CXYgDvWRXJKMRJQkJcBs6VHQUsnEKJydodDqU7t8vOgoRAODbb7/F3LlzkZaWhsjISERHRyMtLQ2PPfYY1q1b16HPOHPmDO655x78+OOP6NWrF8aOHYuCggK8/vrrmDdv3jW/t76+Hs888wzKy8u74nDIhhmqq1ESHw9PPs1L1+E1fjyKtm3jTVUisktV6elw0Gqh8vERHYWsmCYkBGUHDoiOQV2IA71kVyqPHoVSq4XK11d0FLID2qgolO7eDUmSREchO1dYWIhXX30Vrq6u+M9//oPPP/8cy5cvx5o1a6DRaPDGG2+gsLDwmp8hSRLmzZuHmpoavP322/jmm2/w0UcfYfv27QgJCcGWLVuwffv2Nr83Pz8fs2fPRkpKSnccHtmY/M2boRk8mHPz0nU5enjAdcgQ5G3cKDoKEVGPK9m9G65DhoiOQVbOJTgYtefOwVBbKzoKdREO9JJdKY6PhzYqSnQMshNOffpAMplQk5EhOgrZudWrV0Ov12POnDnQ6XTm7ZGRkXjsscfQ2Nh43ad6ExMTkZGRgdjYWNx9993m7Z6ennjttdcAACtXrmzxPQaDAatWrcLvfvc7HDt2DIGBgV13UGST9CUlKNu3D55jxoiOQlbCc/RoVKSkoD43V3QUIqIeoy8vR/Xp03ANDxcdhaycQqWCun9/VKamio5CXYQDvWQ3GgoKUJeTA83gwaKjkJ2QyWTQRkVxUTYSbt++fQCASZMmtdrXvO16c/Ve6zNiYmLg5eWF1NRU1NTUmLenpqZi0aJFMBgMWLBgAZ566qkbPgayD3n/+Q/coqPhoNGIjkJWQuHsDI+4OOR+843oKEREPaZ0zx64Dh4MhUolOgrZAE1oKEoTE0XHoC7CgV6yG8U7dkAbFQW5g4PoKGRHtEOGoDItja/CkDCSJCEzMxNyuRwDBgxotb9///6Qy+XIzMy85jQjmZmZANDiieCrBQUFwWQyISsry7zN2dkZc+bMwY4dO3D//fff5JGQrau7eBGVR4/CfeRI0VHIyrgNG4b6Cxf4Bg0R2QXJaETJrl1wi44WHYVshEtwMOpyctBUUSE6CnUBDvSSXTA2NKBs/342htTjFGo1XAYORBnvkJIglZWV0Ov1cHd3h6OjY6v9Dg4O8PDwQH19PWqvcUOiqKgIAODTzoIfzdtLSkrM2yIjIzF//nx4enrezCGQnchdswaeo0ZB4eQkOgpZGbmDAzzHjsXlNWs4Lz4R2bzKtDQoXF2h8vMTHYVshFyphCY4GGUHD4qOQl2AA71kF8r274dz375QurmJjkJ2SBsZiZJdu/jHJwlRX18P4MrTte1x+mVg7VoDvc2f49TOIFzz9rq6uhvK+WtVVVW4fPlyi6+CgoIu+WyyPFXp6WgsKIBbTIzoKGSlXIcMgbGujnMMEpHNK9y6Fe7DhomOQTZGEx6Osv37RcegLsB32MnmSSYTCn/6Cb6TJ4uOQnbKuX9/GLdvR112NlwGDhQdh+yMXN7xe7rXuhmhUCgAXJl7+lpMJlOH/71r+eqrr/DRRx91yWeRZZNMJlxevRpe48dD9st5RtRZMpkMXuPGIXf9erjFxEDWiWsfEZG1qM3ORmNxMQJCQ0VHIRuj7tcPRT/8gIb8fDgFBIiOQzeBPSCyeVXHjkHm4AAnrvZOgshkMmgjI1HMRdlIALVaDQBobGxst0xDQ0OLsm1pfiK4uWx7n+Hi4nJDOX9t9uzZ2LlzZ4uv1atXd8lnk2Up2bMHMgcHuISEiI5CVk49cCDkDg4oO3BAdBQiom7R/DQvb2ZRV5PJ5XANC0PpLwswk/Xi1YFsXuHWrXAfPvy6T6ERdSdtZCQqUlJg/OX1d6KeotFooFarUV5eDoPB0Gq/wWBAeXk5VCoVtFptu5/j6+sLoOUcvFcrLi4G0P4cvp2l1WrRp0+fFl/+/v5d8tlkOYz19cj/z3/gfdttbKfppslkMniNH4+8jRshtXG9IyKyZo1FRahOT4c2Kkp0FLJRrhERKN23D1IXvaFHYnCgl2xa3fnzaMjLg+vgwaKjkJ1z0Gig7t8fZUlJoqOQnZHJZBg0aBCMRiPOnz/fan9OTg5MJhN0Ot01Pyc4OBgAkJmZ2WqfJEnIzs6GQqHAQE5PQp1Q8P33UPfvz1cEqcs49+0Lpbs7SvbuFR2FiKhL5W/eDLeYGC5aSt1G5esLB7Ua1SdOiI5CN4EDvWTTCr7/Hu4jRnDOP7II2qgolOzcKToG2aGxY8cCAOLj41vta942fvz4Dn3GzjbO4SNHjqCsrAzDhg2DRqO52bhkJxpLSlCyaxe8rnPuEXWW1y23oOC772DS60VHISLqEo0lJag8fBjuI0aIjkI2znXIEJTs2SM6Bt0EDvSSzWooLET1qVNwGzpUdBQiAIA6KAiG6mrUZmeLjkJ2Zvr06VCpVPj8889x4qo79Onp6Vi2bBmcnJzwhz/8wbz94sWLyMrKQnV1tXlbbGwsgoODkZiYiPXr15u3l5WVYcGCBQCAuXPn9sDRkK3IXbsWbsOGwcHVVXQUsjFOvXvD0dcXJbt3i45CRNQlCjZvhnboUCh+WTOBqLu4hoejKj0dhqv+DiDrwoFeslmFW7bALToacpVKdBQiAFctysaneqmH9enTBy+88AJqampw//3345FHHsEjjzyCmTNnora2FgsXLoSXl5e5/Jw5c3DHHXdgx44d5m1yuRxvvvkm1Go1XnnlFcyYMQNPP/00fvOb3yAjIwMzZszAbbfdJuLwyArVZmai5vRpeIwcKToK2SjPW25BwZYtfKqXiKxeQ34+KlJS2GZSj1A4O8MlOJiLslkxDvSSTdKXlqIiJQVuw4aJjkLUgjYq6sqibHV1oqOQnZk1axaWLl2KqKgoHDlyBCdOnEBMTAxWrFiBu+++u0OfERkZiQ0bNmDKlCm4cOECEhMT0atXLyxYsACvvfZa9x4A2QxJknBp1Sp4jh0LuaOj6Dhko5z8/eHk74/iXbtERyEiuim5a9fCfeRIPs1LPcZt6FAU79wJSZJER6Eb4CA6AFF3KNiyBdrISDi4uIiOQtSCg0YDdVAQyhIT4XP77aLjkJ2ZMGECJkyYcN1yu64xMDJo0CAsWbLkhv796dOnY/r06Tf0vWQ7Kg4dgrG2FtqICNFRyMZ53nIL8jZsgM+ECXzDi4isUk1GBmqzstDvf/9XdBSyI059+gAAak6fhmtYmOA01Fl8opdsTlNFBcoPHIB7bKzoKERtcouKQnF8PO+QEpHdMRkMyF23Dt4TJkAmZzeUupfKzw9OvXtzyiQiskqS0YiLK1bA+9ZbIVcqRcchOyKTyeA2dCiKfv5ZdBS6Aexhk80p+P57uEZEwIErv5OFcu7fHya9HjUZGaKjEBH1qOL4eCjd3KAOChIdheyE55gxKPzhBxgbGkRHISLqlKL4eMgdHaHhE5UkgGtEBGpOnYK+pER0FOokDvSSTdGXlKAsMREecXGioxC1SyaTwS06GsW8Q0pEdsRQW4uC77+H1623io5CdkTl6wvnwEAUx8eLjkJE1GH60lIUfPcdfG6/HTKZTHQcskMKlQquEREoumpxZrIOHOglm5K/aRO0Q4fyaV6yeK4REag6cQJNFRWioxAR9YjCH36Ay4ABUPn6io5CdsZjzBgUbt0KY3296ChERNclSRIuLFsGt2HD4OjtLToO2TH3YcNQmpDAt2KsDAd6yWY05OejIiUFHiNHio5CdF0KJye4hoVx3kAisgv6sjKU7NoFz7FjRUchO6Ty8YG6Xz8UbdsmOgoR0XWVJiRAX1oKz1GjREchO6f08IBzYCBK9+0THYU6gQO9ZDNy162D+8iRUDg7i45C1CFuMTEo2bkTpqYm0VGIiLpV3n/+A21UFJRaregoZKc8x4xB0fbtMNTWio5CRNSuhvx85K5dC7/f/hYyhUJ0HCK4jxyJwh9+gGQwiI5CHcSBXrIJtZmZqM3MhPvw4aKjEHWYyscHjr6+KE9OFh2FiKjbNOTloTI1lfPnk1COXl5wCQ5G4Y8/io5CRNQmU1MTcj78EJ633MJpjshiOPfuDaWbG8oOHhQdhTqIA71k9SRJwqVVq+B5yy2QK5Wi4xB1ivuwYSjcuhWSJImOQkTULXLXr+cbN2QRPMeMQcnOnZwfn4gs0uWVK6FwcYFbTIzoKEQteMTFoWDTJkgmk+go1AEc6CWrV56cDGNtLbQREaKjEHWaeuBAmOrrUXP6tOgoRERdrjY7G7XnzsF92DDRUYigdHODa0QE8r/7TnQUIqIWSvbsQVV6Onx/+1vIZDLRcYhacO7fH3JHR5QnJYmOQh3AgV6yaqbGRuSuWQOfSZMgk/N0Jusjk8ngPmIECrZsER2FiKjL5X7zDTzHjOEbN2QxPOLiUH7wIBoKC0VHISICANScPYvctWsRMH06FCqV6DhErchkMniOG4e8jRs5V68V4MgYWbWCH36AKiAAzn37io5CdMNcIyJQf+EC6i9dEh2FiKjLVJ04AX1JCbRRUaKjEJk5uLjAPTYWuWvWiI5CRITGwkJkf/AB/KZNg6O3t+g4RO1S9+sHBzc3lCQkiI5C18GBXrJaDYWFKP75Z3jfdpvoKEQ3Re7gALeYGBT88IPoKEREXUKSpCtP844dyzduyOK4jxiB2qws1GRkiI5CRHasqbIS595+Gx6jR8NlwADRcYiuy2vcOOR/+y2M9fWio9A1sOdNVkmSJFz68kt4jBoFpVYrOg7RTXMbNgxVR4+isahIdBQioptWcegQTAYDNKGhoqMQtSJXKuE1fjwurVzJhWWISAhjfT0y33kHGp0O7lx8jayEU0AA1EFByN+0SXQUugYO9JJVKj94EPriYrgPHy46ClGXUDg5wS06GgXffy86ChHRTZEMBuSuXw+v8eO5oAxZLNfwcECSULJnj+goRGRnTI2NyHz3XTh6e8Nz7FjRcYg6xWv8eJTu2YOG/HzRUagdHOglq2OorsbllSvhO3UqZAqF6DhEXcZ9xAhUHDoEfUmJ6ChERDesJCEBDhoNXIKCREchapdMJoPP7bcjb+NGGGpqRMchIjth0uuR9c9/QuHkBJ/Jk3lDlKyOg0YDj9GjcWH5cr4VY6E40EtW59LXX0MTFganXr1ERyHqUgq1GtroaOR/953oKEREN8TY0ID8776D1/jxoqMQXZfKzw+uoaG4zIXZiKgHNA/yQiaD7x13cJCXrJb78OEw1tSgZPdu0VGoDRzoJatSfugQas6dgxdfcSEb5TFyJCpSU/kqDBFZpcKtW+Hcpw+cAgJERyHqEM9x41B1/DiqT54UHYWIbJixoQGZ774LyGTwmzaNC5WSVZPJ5fC94w7kbdiAxuJi0XHoV3h1IavRVFGBS19+Cf8774Tc0VF0HKJuoXB2hvuIEcjbsEF0FCKiTmmqqEDx9u18mpesikKlgs/kybiwfDmMDQ2i4xCRDTLW1SFz8WLIHR3hd+edHOQlm6Dy8YFHXBxyPvoIksEgOg5dhVcYsgqSyYScf/8b2qFD4dS7t+g4RN3Kffhw1GRkoDYzU3QUIqIOy9u4EdrISCjd3UVHIeoUTXAwVP7+yOUUDkTUxZoqK3H29dfh4O5+ZboGDvKSDXGPjYVMLkfexo2io9BVeJUhq1CwZQuMdXXwHDNGdBSibid3dITX+PG49NVXnOCeiKxC3YULqEhNhceoUaKjEN0Qn9tvR2VaGiqPHhUdhYhsRGNRETIWLoRz//7wuf12zslLNkcmk8H3t79FaWIiyg8dEh2HfsGBXrJ41adOoWjbNs5lRHbFdcgQmAwGlCUmio5CRHRNkiTh0tdfw+uWW6BwdhYdh+iGKJyc4Hfnnbjw+efQl5WJjkNEVq7u/HlkLFwIt6FD4TV2LAd5yWY5uLgg4Pe/x8UvvkDdhQui4xA40EsWTl9SgpyPP4b/tGlQarWi4xD1GJlMBp+JE5G7di0MNTWi4xARtavi0CEYqqqgHTpUdBSim+Lcty/coqOR8+GHnG+QiG5Y5bFjOPfWW/CeOBHuw4eLjkPU7ZwCAuBz++3IevddNJaUiI5j9zjQSxbL2NCArH/+E+4jRkAdFCQ6DlGPc+rdG5qQEFxevVp0FCKiNhnr63Fp1aorr6TyrRuyAR6jRwMALnO+XiK6AcU7d+L80qUI+J//gWtoqOg4RD3GNSwMbiNGIHPxYjRVVYmOY9fYIyeLJBmNyPnwQyi9vOAeGys6DpEwXuPGoerECVSlp4uOQkTUSt6GDVD37w/nwEDRUYi6hEwmg9+0aahMS0Pxrl2i4xCRlZBMJlxatQqFP/yAPg88AOc+fURHIupxHiNGwGXQIJz7xz9gqK4WHcducaCXLI4kSbj4xRcw1NTAd8oUzmdEdk2uUsF36lRc+OwzNpZEZFFqMzNRlpQE71tvFR2FqEspnJ0R8D//g7wNG3ijlYiuy1hfj6x//hM1GRno8+CDcPT0FB2JSBjPsWPhHBjIwV6BONBLFkWSJFxevRq12dkImD4dMoVCdCQi4VyCgqAJCcGFZcsgSZLoOEREMOn1OP/pp/CZNAkKtVp0HKIu5+jlhYDf/Q45//436s6fFx2HiCxUY3ExMhYsgEwuR+/77uOipGT3ZDIZvG69Fc6Bgch4/XU0VVSIjmR3ONBLFkOSJFxetQpVx4+j1733Qu7oKDoSkcXwHD8eDfn5KNq2TXQUIiLkbdgApacnXMPCREch6jbOffvCd/JkZL7zDhry8kTHISILU33mDDJeew2uYWHw+c1v+JAS0S+aB3s1Oh0yFixAY2Gh6Eh2hQO9ZBEkoxEXly9H9alT6H3//bwTSvQrcgcHBPz+9yj4/ntUnzolOg4R2bHK48dRlpQEn9tvFx2FqNtpQkPhNW4czv3jH/xDlYjMinfuRPYHH8D3jjvgPmIEpxskaoPn6NFwGz4cGYsW8e2YHsSBXhLOWF+PrPfeQ/3ly3zdhegalO7u8L/rLuR89BHqc3NFxyEiO6QvL8eFTz+F3513wsHFRXQcoh6hjYyER1wczr7xBho42Etk10wGAy4sX47CH39EnwcegMuAAaIjEVk095gY+EyciHOLF6Pq+HHRcewCB3pJqIb8fJx59VXIlEr0uuceyFUq0ZGILJq6f3943XorMt9+G/qyMtFxiMiOmPR6ZP3zn3CLjoa6Xz/RcYh6lFtMDDzi4nDu9dc5jQORnWqqqMC5N95AY34+Ah96iIuuEXWQJjQUAb//PXI++QQle/aIjmPzONBLQkiShNL9+5GxcCHcoqPhyzmNiDpMGxEBt+honH39dQ72ElGPkEwmnP/0UzhoNPAYPVp0HCIh3KKj4XnLLTj7xht8BZXIztRmZeHMK69AFRAA/+nT+YASUSc5Bwaiz6xZyP/uO+SuX89FxrsRB3qpxzVVVCD7gw9QsGkTet93H9yGDhUdicjqeIwcCW1EBM6+/joai4pExyEiGyZJEi59/TUaCwrgO3Uq5yEku6aNjITPpEk4t3gxqk+eFB2HiHpAyZ49yHznHXhPnAivsWPZDhLdIEcvL/R58EFUpqUh5+OPYdLrRUeySRzopR5jMhhQ+NNPOPXii1A4OaHP7NlQ+fmJjkVktTzi4uAWE4OMBQtQm50tOg4R2SBJkpC7di2qT59GwD33QK5Uio5EJJwmNBT+d9+N7A8/RFlSkug4RNRNTE1NuPjFFyjYvBl9Zs2CJiREdCQiq+fg4oLeM2fCWFODs2++iabKStGRbA4HeqnbSUYjSvfvx6l581Bx6BB6/+EP8Bo/HnIHB9HRiKye+7Bh8Jk8GZlvv42SvXtFxyEiGyIZjbjw+eeoOnYMvWfMgMLJSXQkIouh7tcPvWfOxOXVq5H//fd8BZXIxuhLS3F20SI05Oejz+zZcPT2Fh2JyGbIlUr43XUXnHr1wpm//x11Fy6IjmRTONJG3cZYX4/SvXtRtG0bFC4u8Ln9dqj79xcdi8jmaHQ6KD08UPDdd6g5fRqBDz0EhbOz6FhEZMWaKiuR89FHkAwG9J45E3JHR9GRiCyOytcXgQ89hLyNG9GQl4d+jzzCp96JbEDViRM4/8kncBs2DB5xcZyqgagbyGQyeI0dC0cvL5z7xz/Qd+5ceIwcKTqWTeBAL3UpSZJQe+4cSvfuRfmhQ1D37w/fqVPhHBgoOhqRTVP5+CBwzhyU7NyJ0/Pno+8jj0AbESE6FhFZoaqTJ3Hh00/hOngwPMeNg0zOF8CI2uPg6oo+s2ah8IcfcPaNNzDg2Wfh6OEhOhYR3QDJZEL+t9+iZNcu+E2bxoeUiHqAa1gYlJ6euLx6NWozM9H7/vshUyhEx7JqHOilmyZJEupyclB+6BAqkpMBmQyuYWHo9+ijcHB1FR2PyG7IHR3hO3UqarOycOHzz+ESHIzeM2dCxVfNiKgDDNXVyF2/HpVpafD9zW/gMnCg6EhEVkHu6Aj/3/8e5QcO4Mwrr2DAn/7EuTyJrIy+tBQ5//43pKYmBM6dCweNRnQkIrvh5O+PwNmzUbBlCzJefx0Dnn4ajl5eomNZLQ700g0x6fWoPn0alampqEhLg9zBAS46HfzuugsqPz++3kIkkMvAgXB+9FGUHzyIM3/7G7zGjYPfnXdC6eYmOhoRWSCTXo/i+HgUbNkC19BQ9H3kEc7HS9RJMpkMnmPGQOXvj6x//Qu+kyfD/+67+UQ8kYWTJAnlSUm4tHIl3IcPvzJVA39viXqcQq1GrxkzUH7wIE6//DICH3oInqNGiY5llTjQSx3WVFGByrQ0VKSmoubMGaj8/KAeOBC9Z8zg5PREFkauVMJr7Fi4DR2K8oMHcWrePHiOHg3fqVOh8vUVHY+ILICxrg7Fu3ahaNs2OAUEXHkDwMdHdCwiq+YycCD6zp2Lgi1bUHX8OPo/+STbXSILpS8vx6Uvv0T95cvoNWMGnPz9RUcismsymQyeo0ZB3b8/8jZsQPnBgwicPRuOnp6io1kVix3oPXDgAJYuXYqMjAw0NTUhPDwcjz32GMaNG9fhz8jJycGHH36I1NRUVFRUoG/fvpgxYwZmzZoFeRt36QoLC/Hxxx8jMTERxcXFCAgIwF133YXHHnsMjna6CElDXh7KDx9GRUoKGgsL4TJwINQDBsB7wgQo1GrR8YjoOhxcXeFz++3wiItDxeHDOPPKK3AZNAg+kyZBGxEBmYPFNgPUTdi+Uv2lSyiOj0dZUhJcBg5Er3vugcrPT3QsIpvh4OqK3jNnoiIlBWf+/nf43XUX/KZM4ZyDNoxtq3WRjEYUxcejYNMmuA0disA5cyBnn5jIYjgFBCBw7lyUHziA0y+9BL/f/ha+U6ZwceAOkkmSJIkO8Wvffvst5s+fD0dHR8TFxcFkMiE5ORlNTU1YuHAh7rvvvut+xpkzZzBr1izU1NQgJiYGXl5eSE5ORlVVFaZNm4Z33323RfmCggLcd999KCgoQFhYGAIDA3HkyBEUFxcjNjYWX3zxBZQdXEX38uXLmDhxInbu3Ik+ffrcUB2IIkkSGi5fRnlyMsqTk2Gsq4OLTgdNcDCc+/ZlB5XIypmamlB98iSqT5xAU3k53GNj4REXB41Ox9fU7ADbV/tlqKlB+cGDKElIQFNZGbSRkXCLjuZc+kTdTF9WhuKff4apoQF9HniAC6XaILat1kOSJFSmpiJ3/XoonJ3hPXEi32QhsnD60lKUJiSgsaAAvnfeCZ9bb4VcpRIdy6JZ3EBvYWEhJk2aBJVKhTVr1kCn0wEAjh8/jrlz56KpqQk7duyA3zWePJEkCXfffTcyMjLw9ttv4+677wYAlJWVYc6cOcjIyMCSJUswZcoU8/c88cQT2L17N5599lk89dRTAIC6ujr88Y9/xIEDB/DCCy/g4Ycf7tAxWGNj2ZCXh7KkJJQfPAhjfT1cQ0PhEhoKp169ON8ukY3Sl5Wh5tQp1Jw9C0N19ZWBn6gouIaHQ+nuLjoedTG2r/bHUFuLyrQ0lB04gNpz5+AyaBBcw8OhDgrijR2iHiRJEmrPnkXpnj1QenoiYPp0uIaHs49tA9i2WgfJaER5SgoKvv8eUlMTvMaNg3rgQP4OElmRhrw8lB88iPpLl+A5ejQ8b7nlSp+Wv8etWFwvf/Xq1dDr9ZgzZ465oQSAyMhIPPbYY2hsbMS6deuu+RmJiYnIyMhAbGysuaEEAE9PT7z22msAgJUrV5q3Z2dnY8+ePejbty+eeOIJ83a1Wo033ngDCoUCq1at6qIjtBz1ubnI37QJp158EWffeAMNeXnwmTwZ/Z96Ct4TJ8K5d2/+0hDZMEdPT3jecgv6PvwwAmfPhoObG0r27MHJefNw4v/+D+c/+wwlu3ej7sIFSAaD6Lh0k9i+2oeGwkIU/fwzzr75Jk48+yxK9+yBOigIQX/8I/zvugsuAwdykJeoh8lkMmhCQtD3scegCQ3FxRUrcOqFF1C0fTuaKitFx6ObwLbVstXn5iJ3wwakP/ssCn/4AR5xcQicOxcugwbx71wiK+PUqxcCpk9H4Jw5MNbXI3vJEpz4859xYdkylCUmoqGwEJLJJDqmRbC4iWj27dsHAJg0aVKrfZMmTcL777+PvXv34plnnrmhz2h+FSY1NRU1NTXQaDTYv38/JEnChAkTWs1/1KtXL4SFhSE9PR2ZmZkYNGjQzRyeUJLBgJrMTFSmpaEyNRXG+npoQkLgPWECnAID2dgR2TGluzvcY2LgHhMDyWSCvrgY9ZcvoyI1FQVbtqCpogJOAQFwDgyEc9++cOrdG04BAXD09uagkZVg+2p7JJMJjYWFqD13DtVnzqD61ClIej3UAwZAM3gw/O64g6+2EVkQmVwObUQEXIcMQf2FC6g8dgx5GzfCuU8faKOj4RoaCnVQEOQdfOWexGPbalmMDQ2oPXcOVSdOoDItDcbaWmgGD0ave+/loohENkLp5gavcePgOXYs9CUlqMvJQUlCAhq++Qam+nqo/P2h8vWFo68vHD09oXR3h4ObG5RubnDQaqFQq21+7MuiBnolSUJmZibkcjkGDBjQan///v0hl8uRmZkJSZLa/eFkZmYCQIu7qlcLCgpCaWkpsrKyEBUVZS4fHBzcZvkBAwYgPT0dZ8+etarG0tjQgPoLF1Bz7hyqT51CbWYmHD084BwUBN+pU6EKCLD5E5yIOk8ml0Pl59dicSaTXo/G4mLoi4pQm5WF8kOHoC8pgbGuDo5eXlD5+pq/x9HHB45eXnD09raLhtQasH21XiaDAYbqahgqKqAvLUVjcTEa8/NRf+kS6nNzoVCprtx4+eUpB0dvb/7OEVk4mUwGdf/+UPfvD1NTE+ovXEBdTg7K9u+HvqQEKl9f8++16pc2VenhAaW7O+TOzvwdtxBsW8UxGQzQl5SgsaAADXl5qLt4EfU5OWgsKTE/mOA7eTJUnIaQyGbJZDKofHxazLNtbGiAvrQUTRUVMFRWorqoCMa6OhhramCorYWxpgaS0QgHV9crg7/u7nD09ISjtzeUnp5X/ob18oKjh4dVL1puUckrKyuh1+vh6enZ5kqhDg4O8PDwQGlpKWpra6HRaNr8nKKiIgCATzsTqzdvLykpaVHet527fL8uf7WqqipUVVW12JabmwvgyiT53UkyGmGsrYWhpgZNVVUwlJdfafCKitBYWAh9RQVUXl5XBl8CAqCKiIDC2Rl6AGUA8MtxExF1iIMD0KsX0KsXZABU+GUQqrISVZWVMJSWwnDhAgzV1TBWV6OpuhoAoNRq4aDVXmlMm//bxQUKFxco1GrI1Wo4ODtDrlJB5ugovEPu7+8PBytu2NvC9rVrSJIEqbERxsZGmBobYaqvh7G+HsaGhiv/3dAAY309TL/8r6TXw9TUdOXLYACMxiuvlEnSla//fjAgSVc+32SCZDDAZDDA1NgIGI2QOzvDQaOBwsUFSldXOGi1UA4cCGVsLBycnWECUAegzmRi205kjVxdgchIOERGQm4woKmsDOXl5WjKzITx2DHzH6eG2torf6BqNFCo1VC4uMBBozFfHxx+3a46O0Ph5GQR7SvbVrat1yJJEkwNDeb201hXd+V/a2rQVFMDY1UVmiorzV/Gmho4uLpC6e4OpZvblQGaYcPg/MubZo0AGgG2iUT2yMEB8Pa+8vULOYDmq7TJYICprg76ujrU19bCUFUFY0GBeWzNWFMDQ00NFGo1HNzd4fjLgLCDuzuUrq5QaDRwcHW90j//pa0V9YZre22rRbW29fX1AABnZ+d2yzg5OQHANRvL5s9pLtveZ9TV1d1Q+at99dVX+Oijj9r8vlmzZrW5nYiILJctLkbC9pWIiERi28q2lYiIulZ7batFDfT+eo6ha5GufhLmVxQKBQBc96616ZeJmjtb/mqzZ8/G73//+xbb9Ho9Ll26hP79+5s/2xoVFBRg1qxZWL16Nfz9/UXHsVisp45hPXUM66ljurOebLHe2b5aB/7+9yzWd89iffcsS6tvS8jQ1Wy5bbW084c6hj8368OfmXWylJ9be/+2RQ30qtVqAEBjY2O7ZRoaGlqUbUvzXdXmsu19houLS6fKt/VvarVaaLXaVtvbmqfJWvn7+9vcHfjuwHrqGNZTx7CeOob11DFsX60Lz+uexfruWazvnsX67j720Lby/LFO/LlZH/7MrJOl/twsaql0jUYDtVqN8vJyGAyGVvsNBgPKy8uhUqnabKCaNc9X1Na8RABQXFwM4L/zF3W0fHvzIBEREVkytq9ERERdi20rERFZIosa6JXJZBg0aBCMRiPOnz/fan9OTg5MJlO7K5I2a16BtHlF0qtJkoTs7GwoFAoMHDjwuuUBICsrC0D7K6ESERFZMravREREXYttKxERWSKLGugFgLFjxwIA4uPjW+1r3jZ+/PgOfcbOnTtb7Tty5AjKysowbNgw84T4zeV3797dai6jvLw8nD59Gr1798agQYM6eTRERESWge0rERFR12LbSkRElsbiBnqnT58OlUqFzz//HCdOnDBvT09Px7Jly+Dk5IQ//OEP5u0XL15EVlYWqqurzdtiY2MRHByMxMRErF+/3ry9rKwMCxYsAADMnTvXvD0wMBBjx45FdnY2PvjgA/P2uro6vPzyyzAajS3K2wutVounn376mq8aEeupo1hPHcN66hjWU+exfbV8PK97Fuu7Z7G+exbru2fYatvK88c68edmffgzs06W/nOTSddaAlSQ1atXY+HChVAqlRg5ciQAIDk5GQaDAYsXL8bdd99tLnvbbbchNzcX//jHPzB9+nTz9uPHj2P27Nmoq6tDVFQUfH19cejQIVRWVmLGjBlYtGhRi3/z0qVLmDlzJoqLi6HT6RAUFIQjR46guLgY48aNwyeffAIHB4tau46IiKhT2L4SERF1LbatRERkSSxyoBe48irKsmXLcOrUKTg6OiIkJARPPvkkRo0a1aJce40lcGXeoiVLliA5ORl6vR79+vXD/fffj3vvvRcKhaLVv5mfn48lS5Zg7969qK6uRmBgIO6++27Mnj0bKpWqW4+XiIioJ7B9JSIi6lpsW4mIyFJY7EAvEREREREREREREXWMxc3RS2JUVFRg7NixCAkJaXN/Tk4O/vKXv2D8+PGIiorCtGnTsHLlylYLANi6a9VTfn4+QkJC2v2aOXOmgMQ9Z9OmTdc8/vfff79F+fT0dDzxxBMYM2YMoqOjce+992LLli2C0vecztTT4cOHr1n2r3/9q8Aj6X65ubl46aWXMG7cOAwZMgRjx47FK6+8guLi4lZleY0iS/Xtt98iJCQEhw8fbnN/QkICHnnkEcTGxmLIkCGYMGEC/v73v6OgoKDN8oWFhfj73/+OiRMnIjIyElOmTMHHH38MvV7fnYdhNa5X37/26KOPIiQkBMnJyW3ur6qqwjvvvIMpU6YgMjISt912G9566y3U1NR0ZWyrdb36rq2txZIlSzB16lREREQgNjYWTzzxBNLT09ssz/q+tuvV99GjR/H444+bryeTJk3Cm2++icrKyjbLs77ty/XOn872pdge9Qz2I6wP+yLWyZb6NJy4hwAACxYsQFFRUZv7zpw5g1mzZqGmpgYxMTGIiIhAcnIyXn/9dRw7dgzvvvtuD6cV51r1dOrUKQBASEgIdDpdq/1BQUHdmk2006dPAwDGjBkDT0/PVvsHDx5s/u/ExEQ8/vjjMJlMGDFiBJydnZGUlIS//vWvyMzMxHPPPddjuXtaZ+qp+ZyKjo5Gnz59WpWNiYnpppTipaenY+7cuaiuroZOp0NERAROnDiB9evX4+DBg9i4cSPc3NwA8BpFlistLa3VvIpX++yzz/Dee+9BLpcjMjISXl5eOH36NNatW4cdO3Zg1apVGDhwoLl8QUEB7rvvPhQUFCAsLAzh4eE4cuQIlixZgoMHD+KLL76AUqnsiUOzSNer719bs2YN9u3b1+7+mpoaPPDAA8jIyEBQUBBuvfVWnDx5EitWrMC+ffuwdu1auLq6dkV0q3S9+q6oqMBDDz2EjIwM+Pn5Yfz48bh48SJ2796NxMRErF69GpGRkebyrO9ru159x8fH49lnn4XBYEBUVBS8vb2Rnp6Or776Cnv27MHatWtb9DtY3/bleudPZ/tSbI96BvsR1od9Eetkc30aiezeli1bJJ1OZ/66mslkkqZNmybpdDpp06ZN5u2lpaXm7du2bevpyEJcq54kSZI+/PBDSafTSZs3bxaQTrwHHnhA0ul0UkFBwTXL1dfXS6NGjZLCw8OlpKQk8/YLFy5I48aNk3Q6nZSent7dcYXpaD1JkiS9+OKLkk6nkw4fPtwDySxHY2OjNHnyZEmn00lff/21eXtDQ4P0pz/9SdLpdNKiRYskSeI1iizXtm3bpOjoaHObkZKS0mL/uXPnpNDQUGno0KHSkSNHzNv1er302muvSTqdTpoxY0aL73n88cclnU4nffzxx+ZttbW10pw5cySdTictX768ew/Kgl2vvn/t/Pnz0tChQ83lDx482KrMokWLJJ1OJ7388suS0WiUJEmSmpqapOeff17S6XTSwoULu+VYrEFH6nvevHmSTqeTnnvuOamxsdG8fdmyZZJOp5OmTZvWojzru33Xq++mpiZp9OjRUmhoqLR9+3bz9oaGBvN149f1x/q2H9c7f26kL8X2qPuxH2F92BexTrbYp+HUDXausLAQCxcuRHR0dJuT/CcmJiIjIwOxsbEtVoz19PTEa6+9BgBYuXJlT8UV5nr1BPz36cvw8PCejGYxzpw5A29vb/j5+V2z3ObNm1FaWopp06YhLi7OvL1v377mqQhs+ZzqaD0BV84puVze4ilfe7B161acP38e06ZNw4MPPmjerlKpMH/+fHh7eyMnJwcAr1FkeQoKCjBv3jw888wzMJlM8Pb2brPc5s2bYTKZMHfuXERHR5u3K5VKvPTSS/D09MTRo0eRm5sLAMjOzsaePXvQt29fPPHEE+byarUab7zxBhQKBVatWtW9B2eBOlrfVzMajXjhhRegVCrbfAMHuPK63YYNG6DRaPDCCy9ALr/SZXZwcMCrr74KNzc3bNy4EXV1dV16PJauo/Wdl5eHzZs3IzAwEG+99RYcHR3N+x555BGEh4ejvr4eZWVlAFjf7elofWdkZKCkpAShoaGYPHmyebtKpcJTTz0FAEhJSTFvZ33bh46eP53tS7E96l7sR1gf9kWsky33aTjQa+f+9re/Qa/XY/HixW3ub36NYNKkSa32xcTEwMvLC6mpqTY/P8z16gm48kq+Wq22+Ska2nLp0iVUVVV1aJC7+ZyaOHFiq30TJkyAQqHA3r17uzyjJehMPen1emRlZWHAgAFQq9U9kM5y/PzzzwCAuXPnttoXEBCAxMRELF++HACvUWR5/vWvf2Hz5s0YMmQI1q1bhwEDBrRZTqlUIiQkBCNGjGhzX/N0Lc3TBe3fvx+SJGHChAnmDmOzXr16ISwsDLm5ucjMzOziI7JsHa3vqy1btgxpaWl45ZVX4OXl1WaZlJQUNDQ0IC4uDhqNpsU+FxcXjBo1Cg0NDS0Gz+xBR+v7559/hiRJmDVrVos/iJp9++232LFjh3kqAdZ32zpa383XhNLSUhgMhhb7ysvLAcA83RHA+rYXHT1/OtuXYnvUvdiPsD7si1gnW+7TcKDXjjXPB/PXv/4V/fr1a7NM84W+vbtMQUFBMJlMyMrK6raconWknioqKpCXl4egoCCsWLECd911F6KionDLLbfglVdeQWFhYQ+n7lnN8856eXlh0aJFuP322xEREWGe3L+xsdFc9ty5cwDaPqc0Gg18fX1RVlaGkpKSngnfgzpbT01NTejduzfef/99TJ061TyJ++LFi1FVVSXqMLrdqVOnoFQqERoaivz8fHz22Wd4+eWX8e677+L48eMtyvIaRZZmwIABWLx4MTZs2NDuAqcA8Mwzz+D777/HqFGjWu2rq6szn9v+/v4A/nuuBwcHt/vvAsDZs2dvKr+16Wh9Nztz5gw+/PBDTJkyBdOmTWu3XEfrOyMj4wZSW6+O1nfzW04RERGora3Fhg0b8Oqrr2LRokWIj4+HJEktyrO+29bR+h40aBACAgJQWFiIefPm4eLFi6ivr0dSUhIWLFgAuVze4uYp69s+dPT86Wxfiu1R92I/wvqwL2KdbLlPw8XY7NSFCxfwzjvvYNSoUZg1a1a75ZrvAPr4+LS5v3m7LQ7KAR2vp+YBvJMnT+Ls2bMYMWIE/P39kZ6ejvXr12P37t34+uuvO3R3zxo1X/y+/fZbuLu7IyYmBn5+fjhx4gSWLFmCffv24csvv4STkxOKi4sBXPucys/PR0lJSYdee7Emnamn5rIJCQlISUlpcU598cUX2LVrF7755ps2F3SzZnq9Hvn5+fD398e2bdvwt7/9DfX19eb9n3/+OR555BHMmzcPAK9RZHn+93//96Y/4/PPP0ddXR0iIiIQEBAA4L/nuq+vb5vfY6/nemfqW6/X4/nnn4dWqzW/jtyejrRVwJUnKO1JR+v74sWLAK7cCJ82bZr51WEAWLVqFUaNGoWPPvrI/KQL67ttHa1vpVKJJUuW4Omnn8aPP/6IH3/80bzP19cXy5Ytw5gxY8zbWN/2oaPnT2f7UmyPuhf7EdaHfRHrZMt9Gj7Ra4ea54ORy+V48803IZPJ2i3bPMDi5OTU5v7m7bY4L0xn6ql5UE6n0+Gnn37CihUr8Nlnn2Hnzp248847UVxcbJ5/1hY1D3RPnToVu3fvxieffIJVq1bhhx9+QGhoKNLS0vCvf/0LgH2fU52pp+aysbGx2LlzJz777DOsWLECP//8M0aNGoXz58/j1VdfFXUo3ab5tcDKykq88MILmDRpErZt24aUlBS8//77cHd3x/Lly7Fu3ToA9n0+kW1KSEjAp59+Crlcjueff968nef6zfvggw9w9uxZLFy48Lo3yZrr0dnZuc39rO9rq66uBgDMnz8f7u7uWLt2LVJTU7FmzRqEhIQgKSmpRRvG+r55ffv2xbRp06BQKBAZGYkJEybAx8cHRUVFWLZsGSoqKsxlWd90tc62L2yPLBv7EZaNfRHrY419Gg702qHm+WBefPFF9OrV65plmxceu9YgJwCYTKYuy2cpOlNPc+bMQXx8PL766isEBgaat6vVarz++uvw8/PDyZMncfTo0W5OLcaSJUvw448/4u23324xn2yfPn3w1ltvQSaTYd26dWhqaoJCoYBMJrPLc6oz9TR//nxs27YNn3zySYtOgKenJxYvXgy1Wo0dO3aY787bCr1eD+BKZ3TkyJF49913ERQUBK1WizvuuMM8T/bHH38MSZLs+hpFtmfPnj3405/+BKPRiOeeew4jR4407+O5fnNSU1PxxRdf4K677mpzHspfY33fnOZruVKpxJdffono6GhoNBoMGzYMy5cvh4uLC3788Ufzwpqs75tTXl6OmTNn4ptvvsGKFSuwYcMGLF26FLt27cK9996LAwcO4OmnnzaXZ33T1Tp7PvD8sVzsR1g29kWskzX2aTjQa2ea54O59dZbce+99163fPNdiIaGhjb3N293cXHpupAWoLP1pFAoEBgY2OZdOWdnZ8TFxQG4MrWDLVKpVBg0aFCbk5MPHjwY/v7+qKurw/nz5+Hs7AxJklrMR3s1Wz2ngM7Vk1KpRFBQUKsJ3AHAz88PYWFhkCTJ/DS5rbj6KYOZM2e22n/rrbfCz88PhYWF5vMJsL9rFNmejRs34o9//CMaGxvx9NNPt3qdrKPnur0t3tgRdXV1ePHFF+Hj44NXXnmlQ9/D+r45zdfyO++8E1qttsU+Hx8f3HbbbZAkCYcOHQLA+r5Zy5cvR3Z2Np566qkWAzuOjo549dVXERQUhJSUFBw+fBgA65ta6mxfiuePZWI/wrKxL2K9rLFPwzl67cz777+PpqYmNDU1tZpKoPmOQvP2l156Cb6+vjh9+jRKSkowcODAVp93vflHrFVn6+l6r100zzV79Vyj9sTb2xv5+fmor6+Hr68vqqqqUFxcbF4N9mq2ek51xNX11JGygO2dU66urlAqlWhqamrz/ACurAxcWFiI8vJyu71GkW15//33sXTpUshkMsyfPx9z5sxpVaZ5Tr325s5rPtfbm3vPnn3zzTe4ePEiQkJCsHDhwhb7mhfMWLp0KTZs2ID7778fw4cP73B989rStuZ+Ue/evdvc37y9vLwcQMfPb9Z325r/uLx6Ht5mSqUSo0ePRk5ODk6dOsXzm1rpbF+K7ZHlYT/C8rEvYr2ssU/DgV470zwPSGJiYrtltmzZAgD485//jODgYCQkJCAzM7PFEwIAIEkSsrOzoVAo2uwUWLPO1tOaNWtw9uxZ/PGPf2xzxcbLly8D+O+qp7akpqYGixcvRmVlJf75z3/CwaH1ZeXq4w8ODkZmZiaysrJaDeTV1NSgqKgInp6eNrcQW2fradGiRSgoKMDChQvh5eV1zbK2pPl6cubMGRQWFiI0NLRVmeZG08vLy26vUWQbJEnCyy+/jI0bN8LR0RGLFy/GHXfc0WbZ5pV7m/8Y+LXm1dDbWzXdnjW36RkZGe2ucHzgwAEAwOjRozF8+PAO13dHVte2RzqdDgcPHmx3eqHmP3Ka2zfW982pqqoC8N/XRX+teXtTUxMA1je11Nm+FNsjy8F+hPVgX8R6WWOfhlM32JmVK1eaLy6//mruBDb//z59+mDs2LEAgJ07d7b6rCNHjqCsrAzDhg1r8/Vya9bZesrIyMD27dvx008/tfqs0tJSJCYmQqlUtuo82QIXFxfs2LED27dvR0pKSqv9CQkJKC8vh06ng6+vr/mcio+Pb1V2165dMBqNGD9+fLfn7mmdrae0tDTEx8dj165drcqePXsWp0+fhru7O8LDw3sifo8aN24cAGDbtm2t9mVnZyM3Nxe+vr4IDAy022sU2Ya33noLGzduhEajwfLly9v94wyA+VzfvXt3qzm98vLycPr0afTu3RuDBg3q1szW6E9/+lO7bfqoUaMAAF9//TUyMjIwffp0AMCIESPg5OSEpKSkVotl1NbWIikpCWq1GsOGDevx47EGzdfx+Ph4GAyGFvv0ej2Sk5MBwFx/rO+bM2DAAABX+hK/ZjQacfDgQQAw3zxlfdPVOtuXYntkOdiPsB7si1gva+zTcKCXrik2NhbBwcFITEzE+vXrzdvLysqwYMECAMDcuXNFxbMY9913HwBgxYoVSE1NNW+vra3FSy+9hJqaGtxzzz02+VqFTCbDjBkzAACLFi1CYWGhed/FixfNr6Y8+eSTAIApU6bAy8sL3333XYs/SC5duoT33nsPMpmszdeNrF1n66n5nHr//ffNd/2AK7978+fPh9FoxKOPPtrmfL/W7v7774darcamTZvMT84DQGVlJV5++WWYTCbMmjULcrmc1yiyWnv37sWXX34JBwcHfPrpp4iNjb1m+eYbG9nZ2fjggw/M2+vq6vDyyy/DaDTyXO9CarUav/vd71BZWYkFCxaYO/YGgwELFy5EVVUV7rvvPt5Easfo0aMRGhqK8+fP480334TRaARwZfqrt99+G5cvX8aYMWPMA5Ss75vT3GdYunRpi36owWDA22+/jbNnzyI4ONi8ZgTrm67W2b4U2yPLwH6E7eO12jJYY59GJkmS1G2fTlYlLCwMRqOx1asEx48fx+zZs1FXV4eoqCj4+vri0KFDqKysxIwZM7Bo0SJBicVor57eeustrFixAnK5HDExMfDw8MDhw4dRXl6O4cOHY9myZeaJuW1NQ0MDHn74YaSmpra4O5WcnAy9Xo+HH34YL7zwgrn8zp078cwzz8BoNGLEiBFwcXHBwYMHUV9fj+eeew5PPPGEqEPpVp2pJ5PJhD//+c/Yvn07lEolhg8fDmdnZyQnJ6O2thZTp07Fe++91+5rmtZu69ateP7552EwGBAeHg5fX18cPXoU5eXliIuLw7Jly6BUKgHwGkWW7cEHH8ShQ4ewevVqDB8+3Lz9nnvuQXp6Ovz8/K75x9mTTz5pfl320qVLmDlzJoqLi6HT6RAUFIQjR46guLgY48aNwyeffNLmtDD2pL36bs+cOXOQlJSEr7/+utVbNxUVFbj//vuRk5ODwMBAhIWF4dSpU7h06RLCw8OxcuVKu1/o8Vr1nZWVhdmzZ6O4uBi9e/fG4MGDcfbsWVy8eBEBAQFYtWpViymcWN/Xd636fu+99/DZZ59BJpNh6NCh8PT0xOnTp5GXlwdvb2989dVXLZ7UY33bn2udP53tS7E96jnsR1gf9kWsky31afhbTNcVGRmJDRs2YMmSJUhOTsa5c+fQr18//OUvf8G9994rOp7FePHFFxEVFYVVq1bh1KlTMJlM6Nu3Lx599FHMnj3bPChli5ycnPDll1/iyy+/xJYtW5CcnAxHR0cMHToUDz74ICZPntyi/MSJE7Fy5Up8/PHHOHbsGCRJQkhICObMmYOpU6cKOoru15l6ksvl+OCDD7Bu3Tps2LABaWlpkMvlGDRoEGbMmIF77rkHMplM4NF0rzvuuANBQUH45JNPkJKSgszMTAQGBuLhhx/G3LlzW/w+8RpF1qaiogLp6ekAgMLCwhZPrv/avffea/4DLTAw0Hyu7927FxcuXEBgYCAeeughzJ49m3+cdTF3d3esXbsWH330EeLj47F7924EBATg0UcfxRNPPME/rK5j4MCB2LRpE5YuXYpdu3YhISEBPj4+mDVrFp588slWbzmxvm/O//3f/yEmJgYrV65Eeno6Tpw4AV9fXzzwwAN4/PHHWy2wxPqmq3W2L8X2SCz2I+wHr9WWwdr6NHyil4iIiIiIiIiIiMjKcY5eIiIiIiIiIiIiIivHgV4iIiIiIiIiIiIiK8eBXiIiIiIiIiIiIiIrx4FeIiIiIiIiIiIiIivHgV4iIiIiIiIiIiIiK8eBXiIiIiIiIiIiIiIrx4FeIiIiIiIiIiIiIivHgV4iIiIiIiIiIiIiK8eBXiIiIiIiIiIiIiIrx4FeIiIiIiIiIiIiIiv3/91hf4yxoQhiAAAAAElFTkSuQmCC\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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", "text/plain": [ - "
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" ] }, "metadata": {}, "output_type": "display_data" }, + { + "ename": "AnalysisException", + "evalue": "Column 'cdr_active_days__allweek__day__callandtext' does not exist. Did you mean one of the following? [active_days_allweek_allday, active_days_weekday_allday, active_days_allweek_day, active_days_allweek_night, active_days_weekday_night, active_days_weekend_allday, active_days_weekday_day, active_days_weekend_night, call_duration_allweek_day_call_max, call_duration_allweek_day_call_mean, call_duration_allweek_day_call_std, active_days_weekend_day, call_duration_allweek_day_call_median, call_duration_allweek_day_call_min, interevent_time_allweek_day_call_max, interevent_time_allweek_day_call_mean, interevent_time_allweek_day_call_std, balance_of_contacts_allweek_day_call_max, balance_of_contacts_allweek_day_call_mean, balance_of_contacts_allweek_day_call_std, call_duration_allweek_allday_call_max, call_duration_allweek_allday_call_mean, call_duration_allweek_allday_call_std, call_duration_allweek_day_call_kurtosis, call_duration_allweek_day_call_skewness, interevent_time_allweek_day_call_median, interevent_time_allweek_day_call_min, balance_of_contacts_allweek_day_call_median, balance_of_contacts_allweek_day_call_min, call_duration_allweek_allday_call_median, call_duration_allweek_allday_call_min, call_duration_allweek_night_call_max, call_duration_allweek_night_call_mean, call_duration_allweek_night_call_std, interevent_time_allweek_allday_call_max, interevent_time_allweek_allday_call_mean, interevent_time_allweek_allday_call_std, interevent_time_allweek_day_call_kurtosis, interevent_time_allweek_day_call_skewness, number_of_contacts_allweek_day_call, number_of_contacts_allweek_day_text, balance_of_contacts_allweek_allday_call_max, balance_of_contacts_allweek_allday_call_mean, balance_of_contacts_allweek_allday_call_std, balance_of_contacts_allweek_day_call_kurtosis, balance_of_contacts_allweek_day_call_skewness, call_duration_allweek_allday_call_kurtosis, call_duration_allweek_allday_call_skewness, call_duration_allweek_night_call_median, call_duration_allweek_night_call_min, call_duration_weekday_day_call_max, call_duration_weekday_day_call_mean, call_duration_weekday_day_call_std, call_duration_weekend_day_call_max, call_duration_weekend_day_call_mean, call_duration_weekend_day_call_std, entropy_of_contacts_allweek_day_call, entropy_of_contacts_allweek_day_text, entropy_of_contacts_weekday_allday_text, interactions_per_contact_allweek_day_call_max, interactions_per_contact_allweek_day_call_mean, interactions_per_contact_allweek_day_call_std, interevent_time_allweek_allday_call_median, interevent_time_allweek_allday_call_min, interevent_time_allweek_night_call_max, interevent_time_allweek_night_call_mean, interevent_time_allweek_night_call_std, number_of_contacts_allweek_allday_text, number_of_contacts_weekday_allday_text, percent_at_home_allweek_allday, percent_at_home_weekday_allday, percent_nocturnal_weekday_call, percent_nocturnal_weekday_text, response_delay_text_allweek_day_max, response_delay_text_allweek_day_mean, response_delay_text_allweek_day_median, response_delay_text_allweek_day_skewness, response_delay_text_weekday_allday_max, response_delay_text_weekday_allday_mean, response_rate_text_weekday_allday, balance_of_contacts_allweek_allday_call_median, balance_of_contacts_allweek_allday_call_min, balance_of_contacts_allweek_night_call_max, balance_of_contacts_allweek_night_call_mean, balance_of_contacts_allweek_night_call_std, call_duration_allweek_night_call_kurtosis, call_duration_allweek_night_call_skewness, call_duration_weekday_day_call_median, call_duration_weekday_day_call_min, call_duration_weekend_day_call_median, call_duration_weekend_day_call_min, entropy_of_contacts_allweek_allday_text, frequent_antennas_weekday_allday, interactions_per_contact_allweek_day_call_median, interactions_per_contact_allweek_day_call_min, interevent_time_allweek_allday_call_kurtosis, interevent_time_allweek_allday_call_skewness, interevent_time_allweek_day_text_max, interevent_time_allweek_day_text_mean, interevent_time_allweek_day_text_median, interevent_time_allweek_day_text_std, interevent_time_allweek_night_call_median, interevent_time_allweek_night_call_min, number_of_contacts_allweek_allday_call, percent_at_home_allweek_day, response_delay_text_allweek_day_min, response_delay_text_allweek_day_std, response_delay_text_weekday_allday_min, response_delay_text_weekday_allday_std, balance_of_contacts_allweek_allday_call_kurtosis, balance_of_contacts_allweek_allday_call_skewness, balance_of_contacts_allweek_day_text_max, balance_of_contacts_allweek_day_text_mean, balance_of_contacts_allweek_day_text_median, balance_of_contacts_allweek_day_text_std, balance_of_contacts_allweek_night_call_median, balance_of_contacts_allweek_night_call_min, balance_of_contacts_weekday_day_call_max, balance_of_contacts_weekday_day_call_mean, balance_of_contacts_weekday_day_call_std, balance_of_contacts_weekend_day_call_max, balance_of_contacts_weekend_day_call_mean, balance_of_contacts_weekend_day_call_std, call_duration_weekday_allday_call_max, call_duration_weekday_allday_call_mean, call_duration_weekday_allday_call_std, call_duration_weekday_day_call_kurtosis, call_duration_weekday_day_call_skewness, call_duration_weekend_day_call_kurtosis, call_duration_weekend_day_call_skewness, entropy_of_antennas_weekday_allday, entropy_of_contacts_allweek_allday_call, entropy_of_contacts_weekday_day_text, entropy_of_contacts_weekend_allday_text, frequent_antennas_allweek_allday, interactions_per_contact_allweek_allday_call_max, interactions_per_contact_allweek_allday_call_mean, interactions_per_contact_allweek_allday_call_std, interactions_per_contact_allweek_day_call_kurtosis, interactions_per_contact_allweek_day_call_skewness, interevent_time_allweek_day_text_kurtosis, interevent_time_allweek_day_text_min, interevent_time_allweek_day_text_skewness, interevent_time_allweek_night_call_kurtosis, interevent_time_allweek_night_call_skewness, interevent_time_weekday_day_call_max, interevent_time_weekday_day_call_mean, interevent_time_weekday_day_call_std, interevent_time_weekend_day_call_max, interevent_time_weekend_day_call_mean, interevent_time_weekend_day_call_std, number_of_antennas_allweek_allday, number_of_antennas_weekday_allday, number_of_contacts_allweek_night_call, number_of_contacts_allweek_night_text, number_of_contacts_weekday_day_text, number_of_contacts_weekend_allday_text, number_of_interactions_in_allweek_day_call, number_of_interactions_in_allweek_day_text, number_of_interactions_in_weekday_allday_text, number_of_interactions_out_weekday_allday_text, percent_at_home_allweek_night, percent_at_home_weekday_night, percent_at_home_weekend_allday, percent_nocturnal_allweek_call, percent_nocturnal_allweek_text, percent_nocturnal_weekend_call, percent_nocturnal_weekend_text, radius_of_gyration_weekday_allday, response_delay_text_allweek_allday_max, response_delay_text_allweek_allday_mean, response_delay_text_allweek_allday_median, response_delay_text_allweek_allday_skewness, response_delay_text_allweek_day_kurtosis, response_delay_text_weekday_allday_median, response_delay_text_weekday_day_max, response_delay_text_weekday_day_mean, response_delay_text_weekday_day_median, response_delay_text_weekday_day_std, response_delay_text_weekend_allday_max, response_delay_text_weekend_allday_mean, response_rate_text_allweek_allday, response_rate_text_weekday_night, response_rate_text_weekend_allday, balance_of_contacts_allweek_day_text_kurtosis, balance_of_contacts_allweek_day_text_min, balance_of_contacts_allweek_day_text_skewness, balance_of_contacts_allweek_night_call_kurtosis, balance_of_contacts_allweek_night_call_skewness, balance_of_contacts_weekday_allday_text_max, balance_of_contacts_weekday_allday_text_min, balance_of_contacts_weekday_allday_text_std, balance_of_contacts_weekday_day_call_median, balance_of_contacts_weekday_day_call_min, balance_of_contacts_weekend_day_call_median, balance_of_contacts_weekend_day_call_min, call_duration_weekday_allday_call_median, call_duration_weekday_allday_call_min, call_duration_weekday_night_call_max, call_duration_weekday_night_call_mean, call_duration_weekday_night_call_std, call_duration_weekend_allday_call_max, call_duration_weekend_allday_call_mean, call_duration_weekend_allday_call_std, entropy_of_antennas_allweek_allday, entropy_of_contacts_allweek_night_call, entropy_of_contacts_allweek_night_text, entropy_of_contacts_weekday_night_text, frequent_antennas_allweek_day, frequent_antennas_weekday_night, frequent_antennas_weekend_allday, interactions_per_contact_allweek_allday_call_median, interactions_per_contact_allweek_allday_call_min, interactions_per_contact_allweek_night_call_max, interactions_per_contact_allweek_night_call_mean, interactions_per_contact_allweek_night_call_std, interactions_per_contact_weekday_day_call_max, interactions_per_contact_weekday_day_call_mean, interactions_per_contact_weekday_day_call_std, interactions_per_contact_weekend_day_call_max, interactions_per_contact_weekend_day_call_mean, interactions_per_contact_weekend_day_call_std, interevent_time_allweek_allday_text_max, interevent_time_allweek_allday_text_mean, interevent_time_allweek_allday_text_median, interevent_time_allweek_allday_text_min, interevent_time_allweek_allday_text_std, interevent_time_weekday_allday_text_max, interevent_time_weekday_allday_text_min, interevent_time_weekday_allday_text_std, interevent_time_weekday_day_call_median, interevent_time_weekday_day_call_min, interevent_time_weekend_day_call_median, interevent_time_weekend_day_call_min, number_of_antennas_allweek_day, number_of_contacts_weekday_night_text, number_of_interactions_alldir_weekday_allday_text, number_of_interactions_in_allweek_allday_text, number_of_interactions_out_allweek_day_call, number_of_interactions_out_allweek_day_text, percent_at_home_weekday_day, percent_pareto_interactions_weekday_allday_text, radius_of_gyration_allweek_allday, response_delay_text_allweek_allday_min, response_delay_text_allweek_allday_std, response_delay_text_allweek_night_max, response_delay_text_allweek_night_mean, response_delay_text_allweek_night_median, response_delay_text_allweek_night_skewness, response_delay_text_weekday_day_kurtosis, response_delay_text_weekday_day_min, response_delay_text_weekday_day_skewness, response_delay_text_weekday_night_max, response_delay_text_weekday_night_mean, response_delay_text_weekday_night_std, response_delay_text_weekend_allday_min, response_delay_text_weekend_allday_std, response_delay_text_weekend_day_max, response_delay_text_weekend_day_mean, response_delay_text_weekend_day_median, response_delay_text_weekend_day_skewness, response_rate_text_allweek_day, response_rate_text_weekday_day, balance_of_contacts_allweek_allday_text_max, balance_of_contacts_allweek_allday_text_mean, balance_of_contacts_allweek_allday_text_median, balance_of_contacts_allweek_allday_text_min, balance_of_contacts_allweek_allday_text_std, balance_of_contacts_weekday_allday_text_mean, balance_of_contacts_weekday_day_call_kurtosis, balance_of_contacts_weekday_day_call_skewness, balance_of_contacts_weekday_day_text_std, balance_of_contacts_weekend_day_call_kurtosis, balance_of_contacts_weekend_day_call_skewness, call_duration_weekday_allday_call_kurtosis, call_duration_weekday_allday_call_skewness, call_duration_weekday_night_call_median, call_duration_weekday_night_call_min, call_duration_weekend_allday_call_median, call_duration_weekend_allday_call_min, call_duration_weekend_night_call_max, call_duration_weekend_night_call_mean, call_duration_weekend_night_call_std, entropy_of_antennas_allweek_day, entropy_of_antennas_weekday_night, entropy_of_antennas_weekend_allday, entropy_of_contacts_weekday_allday_call, entropy_of_contacts_weekday_day_call, entropy_of_contacts_weekend_day_call, entropy_of_contacts_weekend_day_text, frequent_antennas_allweek_night, frequent_antennas_weekday_day, interactions_per_contact_allweek_allday_call_kurtosis, interactions_per_contact_allweek_allday_call_skewness, interactions_per_contact_allweek_day_text_max, interactions_per_contact_allweek_day_text_mean, interactions_per_contact_allweek_day_text_median, interactions_per_contact_allweek_day_text_std, interactions_per_contact_allweek_night_call_median, interactions_per_contact_allweek_night_call_min, interactions_per_contact_weekday_allday_text_max, interactions_per_contact_weekday_allday_text_min, interactions_per_contact_weekday_allday_text_std, interactions_per_contact_weekday_day_call_median, interactions_per_contact_weekday_day_call_min, interactions_per_contact_weekend_day_call_median, interactions_per_contact_weekend_day_call_min, interevent_time_allweek_allday_text_kurtosis, interevent_time_allweek_allday_text_skewness, interevent_time_allweek_night_text_max, interevent_time_allweek_night_text_mean, interevent_time_allweek_night_text_std, interevent_time_weekday_allday_call_max, interevent_time_weekday_allday_call_mean, interevent_time_weekday_allday_call_std, interevent_time_weekday_allday_text_mean, interevent_time_weekday_day_call_kurtosis, interevent_time_weekday_day_call_skewness, interevent_time_weekday_day_text_std, interevent_time_weekend_day_call_kurtosis, interevent_time_weekend_day_call_skewness, number_of_antennas_allweek_night, number_of_antennas_weekday_night, number_of_antennas_weekend_allday, number_of_contacts_weekday_allday_call, number_of_contacts_weekday_day_call, number_of_contacts_weekend_day_call, number_of_contacts_weekend_day_text, number_of_interactions_in_allweek_allday_call, number_of_interactions_in_weekday_day_text, number_of_interactions_in_weekend_allday_text, number_of_interactions_out_allweek_allday_text, number_of_interactions_out_weekday_day_text, number_of_interactions_out_weekend_allday_text, percent_at_home_weekend_night, percent_pareto_durations_weekday_allday, percent_pareto_interactions_allweek_day_call, percent_pareto_interactions_allweek_day_text, radius_of_gyration_allweek_day, radius_of_gyration_weekday_night, radius_of_gyration_weekend_allday, response_delay_text_allweek_allday_kurtosis, response_delay_text_allweek_night_min, response_delay_text_allweek_night_std, response_delay_text_weekday_allday_kurtosis, response_delay_text_weekday_allday_skewness, response_delay_text_weekday_night_median, response_delay_text_weekday_night_min, response_delay_text_weekend_allday_median, response_delay_text_weekend_day_min, response_delay_text_weekend_day_std, response_rate_text_allweek_night, response_rate_text_weekend_night, balance_of_contacts_allweek_allday_text_kurtosis, balance_of_contacts_allweek_allday_text_skewness, balance_of_contacts_allweek_night_text_max, balance_of_contacts_allweek_night_text_mean, balance_of_contacts_allweek_night_text_std, balance_of_contacts_weekday_allday_call_max, balance_of_contacts_weekday_allday_call_mean, balance_of_contacts_weekday_allday_call_std, balance_of_contacts_weekday_day_text_max, balance_of_contacts_weekday_day_text_mean, balance_of_contacts_weekday_day_text_min, balance_of_contacts_weekday_night_call_max, balance_of_contacts_weekday_night_call_mean, balance_of_contacts_weekday_night_call_std, balance_of_contacts_weekend_allday_call_max, balance_of_contacts_weekend_allday_call_mean, balance_of_contacts_weekend_allday_call_std, balance_of_contacts_weekend_allday_text_max, balance_of_contacts_weekend_allday_text_min, balance_of_contacts_weekend_allday_text_std, call_duration_weekday_night_call_kurtosis, call_duration_weekday_night_call_skewness, call_duration_weekend_allday_call_kurtosis, call_duration_weekend_allday_call_skewness, call_duration_weekend_night_call_median, call_duration_weekend_night_call_min, entropy_of_antennas_allweek_night, entropy_of_antennas_weekday_day, entropy_of_contacts_weekend_night_text, frequent_antennas_weekend_night, interactions_per_contact_allweek_day_text_kurtosis, interactions_per_contact_allweek_day_text_min, interactions_per_contact_allweek_day_text_skewness, interactions_per_contact_allweek_night_call_kurtosis, interactions_per_contact_allweek_night_call_skewness, interactions_per_contact_weekday_allday_text_mean, interactions_per_contact_weekday_day_call_kurtosis, interactions_per_contact_weekday_day_call_skewness, interactions_per_contact_weekday_day_text_std, interactions_per_contact_weekend_day_call_kurtosis, interactions_per_contact_weekend_day_call_skewness, interevent_time_allweek_night_text_median, interevent_time_allweek_night_text_min, interevent_time_weekday_allday_call_median, interevent_time_weekday_allday_call_min, interevent_time_weekday_day_text_max, interevent_time_weekday_day_text_mean, interevent_time_weekday_day_text_min, interevent_time_weekday_night_call_max, interevent_time_weekday_night_call_mean, interevent_time_weekday_night_call_std, interevent_time_weekend_allday_call_max, interevent_time_weekend_allday_call_mean, interevent_time_weekend_allday_call_std, interevent_time_weekend_allday_text_max, interevent_time_weekend_allday_text_min, interevent_time_weekend_allday_text_std, number_of_antennas_weekday_day, number_of_contacts_weekend_night_text, number_of_interactions_alldir_allweek_day_call, number_of_interactions_alldir_allweek_day_text, number_of_interactions_alldir_weekday_day_text, number_of_interactions_alldir_weekend_allday_text, number_of_interactions_in_allweek_night_call, number_of_interactions_in_allweek_night_text, number_of_interactions_in_weekday_night_text, number_of_interactions_out_allweek_allday_call, number_of_interactions_out_weekday_night_text, percent_at_home_weekend_day, percent_pareto_durations_allweek_allday, percent_pareto_interactions_allweek_allday_text, percent_pareto_interactions_weekday_day_text, percent_pareto_interactions_weekend_allday_text, radius_of_gyration_allweek_night, radius_of_gyration_weekday_day, response_delay_text_allweek_night_kurtosis, response_delay_text_weekday_night_kurtosis, response_delay_text_weekday_night_skewness, response_delay_text_weekend_day_kurtosis, response_delay_text_weekend_night_max, response_delay_text_weekend_night_mean, response_delay_text_weekend_night_std, response_rate_text_weekend_day, balance_of_contacts_allweek_night_text_median, balance_of_contacts_allweek_night_text_min, balance_of_contacts_weekday_allday_call_median, balance_of_contacts_weekday_allday_call_min, balance_of_contacts_weekday_allday_text_median, balance_of_contacts_weekday_day_text_median, balance_of_contacts_weekday_night_call_median, balance_of_contacts_weekday_night_call_min, balance_of_contacts_weekday_night_text_max, balance_of_contacts_weekday_night_text_min, balance_of_contacts_weekday_night_text_std, balance_of_contacts_weekend_allday_call_median, balance_of_contacts_weekend_allday_call_min, balance_of_contacts_weekend_allday_text_mean, balance_of_contacts_weekend_day_text_max, balance_of_contacts_weekend_day_text_mean, balance_of_contacts_weekend_day_text_median, balance_of_contacts_weekend_day_text_std, balance_of_contacts_weekend_night_call_max, balance_of_contacts_weekend_night_call_mean, balance_of_contacts_weekend_night_call_std, call_duration_weekend_night_call_kurtosis, call_duration_weekend_night_call_skewness, entropy_of_antennas_weekend_night, entropy_of_contacts_weekend_allday_call, frequent_antennas_weekend_day, interactions_per_contact_allweek_allday_text_max, interactions_per_contact_allweek_allday_text_mean, interactions_per_contact_allweek_allday_text_median, interactions_per_contact_allweek_allday_text_min, interactions_per_contact_allweek_allday_text_std, interactions_per_contact_weekday_allday_call_max, interactions_per_contact_weekday_allday_call_mean, interactions_per_contact_weekday_allday_call_std, interactions_per_contact_weekday_day_text_max, interactions_per_contact_weekday_day_text_mean, interactions_per_contact_weekday_day_text_min, interactions_per_contact_weekday_night_call_max, interactions_per_contact_weekday_night_call_mean, interactions_per_contact_weekday_night_call_std, interactions_per_contact_weekend_allday_call_max, interactions_per_contact_weekend_allday_call_mean, interactions_per_contact_weekend_allday_call_std, interactions_per_contact_weekend_allday_text_max, interactions_per_contact_weekend_allday_text_min, interactions_per_contact_weekend_allday_text_std, interevent_time_allweek_night_text_kurtosis, interevent_time_allweek_night_text_skewness, interevent_time_weekday_allday_call_kurtosis, interevent_time_weekday_allday_call_skewness, interevent_time_weekday_allday_text_median, interevent_time_weekday_day_text_median, interevent_time_weekday_night_call_median, interevent_time_weekday_night_call_min, interevent_time_weekday_night_text_max, interevent_time_weekday_night_text_min, interevent_time_weekday_night_text_std, interevent_time_weekend_allday_call_median, interevent_time_weekend_allday_call_min, interevent_time_weekend_allday_text_mean, interevent_time_weekend_day_text_max, interevent_time_weekend_day_text_mean, interevent_time_weekend_day_text_median, interevent_time_weekend_day_text_std, interevent_time_weekend_night_call_max, interevent_time_weekend_night_call_mean, interevent_time_weekend_night_call_std, number_of_antennas_weekend_night, number_of_contacts_weekend_allday_call, number_of_interactions_alldir_allweek_allday_text, number_of_interactions_alldir_weekday_night_text, number_of_interactions_in_weekday_allday_call, number_of_interactions_in_weekday_day_call, number_of_interactions_in_weekend_day_call, number_of_interactions_in_weekend_day_text, number_of_interactions_out_allweek_night_call, number_of_interactions_out_allweek_night_text, number_of_interactions_out_weekday_allday_call, number_of_interactions_out_weekday_day_call, number_of_interactions_out_weekend_day_call, number_of_interactions_out_weekend_day_text, percent_pareto_durations_allweek_day, percent_pareto_durations_weekday_night, percent_pareto_durations_weekend_allday, percent_pareto_interactions_allweek_allday_call, percent_pareto_interactions_weekday_night_text, radius_of_gyration_weekend_night, response_delay_text_weekend_allday_kurtosis, response_delay_text_weekend_allday_skewness, response_delay_text_weekend_night_median, response_delay_text_weekend_night_min, response_delay_text_weekend_night_skewness, balance_of_contacts_allweek_night_text_kurtosis, balance_of_contacts_allweek_night_text_skewness, balance_of_contacts_weekday_allday_call_kurtosis, balance_of_contacts_weekday_allday_call_skewness, balance_of_contacts_weekday_day_text_kurtosis, balance_of_contacts_weekday_day_text_skewness, balance_of_contacts_weekday_night_call_kurtosis, balance_of_contacts_weekday_night_call_skewness, balance_of_contacts_weekday_night_text_mean, balance_of_contacts_weekend_allday_call_kurtosis, balance_of_contacts_weekend_allday_call_skewness, balance_of_contacts_weekend_day_text_kurtosis, balance_of_contacts_weekend_day_text_min, balance_of_contacts_weekend_day_text_skewness, balance_of_contacts_weekend_night_call_median, balance_of_contacts_weekend_night_call_min, entropy_of_antennas_weekend_day, entropy_of_contacts_weekday_night_call, interactions_per_contact_allweek_allday_text_kurtosis, interactions_per_contact_allweek_allday_text_skewness, interactions_per_contact_allweek_night_text_max, interactions_per_contact_allweek_night_text_mean, interactions_per_contact_allweek_night_text_std, interactions_per_contact_weekday_allday_call_median, interactions_per_contact_weekday_allday_call_min, interactions_per_contact_weekday_allday_text_median, interactions_per_contact_weekday_day_text_median, interactions_per_contact_weekday_night_call_median, interactions_per_contact_weekday_night_call_min, interactions_per_contact_weekday_night_text_max, interactions_per_contact_weekday_night_text_min, interactions_per_contact_weekday_night_text_std, interactions_per_contact_weekend_allday_call_median, interactions_per_contact_weekend_allday_call_min, interactions_per_contact_weekend_allday_text_mean, interactions_per_contact_weekend_day_text_max, interactions_per_contact_weekend_day_text_mean, interactions_per_contact_weekend_day_text_median, interactions_per_contact_weekend_day_text_std, interactions_per_contact_weekend_night_call_max, interactions_per_contact_weekend_night_call_mean, interactions_per_contact_weekend_night_call_std, interevent_time_weekday_day_text_kurtosis, interevent_time_weekday_day_text_skewness, interevent_time_weekday_night_call_kurtosis, interevent_time_weekday_night_call_skewness, interevent_time_weekday_night_text_mean, interevent_time_weekend_allday_call_kurtosis, interevent_time_weekend_allday_call_skewness, interevent_time_weekend_day_text_kurtosis, interevent_time_weekend_day_text_min, interevent_time_weekend_day_text_skewness, interevent_time_weekend_night_call_median, interevent_time_weekend_night_call_min, number_of_antennas_weekend_day, number_of_contacts_weekday_night_call, number_of_interactions_alldir_allweek_allday_call, number_of_interactions_alldir_weekday_allday_call, number_of_interactions_alldir_weekday_day_call, number_of_interactions_alldir_weekend_day_call, number_of_interactions_alldir_weekend_day_text, number_of_interactions_in_weekend_night_text, number_of_interactions_out_weekend_night_text, percent_pareto_durations_allweek_night, percent_pareto_durations_weekday_day, percent_pareto_interactions_allweek_night_call, percent_pareto_interactions_allweek_night_text, percent_pareto_interactions_weekday_allday_call, percent_pareto_interactions_weekday_day_call, percent_pareto_interactions_weekend_day_call, percent_pareto_interactions_weekend_day_text, radius_of_gyration_weekend_day, response_delay_text_weekend_night_kurtosis, balance_of_contacts_weekday_allday_text_kurtosis, balance_of_contacts_weekday_allday_text_skewness, balance_of_contacts_weekend_allday_text_median, balance_of_contacts_weekend_night_call_kurtosis, balance_of_contacts_weekend_night_call_skewness, balance_of_contacts_weekend_night_text_max, balance_of_contacts_weekend_night_text_min, balance_of_contacts_weekend_night_text_std, entropy_of_contacts_weekend_night_call, interactions_per_contact_allweek_night_text_median, interactions_per_contact_allweek_night_text_min, interactions_per_contact_weekday_allday_call_kurtosis, interactions_per_contact_weekday_allday_call_skewness, interactions_per_contact_weekday_day_text_kurtosis, interactions_per_contact_weekday_day_text_skewness, interactions_per_contact_weekday_night_call_kurtosis, interactions_per_contact_weekday_night_call_skewness, interactions_per_contact_weekday_night_text_mean, interactions_per_contact_weekend_allday_call_kurtosis, interactions_per_contact_weekend_allday_call_skewness, interactions_per_contact_weekend_day_text_kurtosis, interactions_per_contact_weekend_day_text_min, interactions_per_contact_weekend_day_text_skewness, interactions_per_contact_weekend_night_call_median, interactions_per_contact_weekend_night_call_min, interevent_time_weekday_allday_text_kurtosis, interevent_time_weekday_allday_text_skewness, interevent_time_weekend_allday_text_median, interevent_time_weekend_night_call_kurtosis, interevent_time_weekend_night_call_skewness, interevent_time_weekend_night_text_max, interevent_time_weekend_night_text_min, interevent_time_weekend_night_text_std, number_of_contacts_weekend_night_call, number_of_interactions_alldir_allweek_night_call, number_of_interactions_alldir_allweek_night_text, number_of_interactions_alldir_weekend_night_text, number_of_interactions_in_weekend_allday_call, number_of_interactions_out_weekend_allday_call, percent_initiated_conversations_allweek_day, percent_initiated_conversations_weekend_day, percent_initiated_interactions_allweek_day, percent_initiated_interactions_weekday_allday, percent_initiated_interactions_weekend_day, percent_pareto_durations_weekend_night, percent_pareto_interactions_weekend_night_text, balance_of_contacts_weekday_night_text_median, balance_of_contacts_weekend_night_text_mean, interactions_per_contact_allweek_night_text_kurtosis, interactions_per_contact_allweek_night_text_skewness, interactions_per_contact_weekday_allday_text_kurtosis, interactions_per_contact_weekday_allday_text_skewness, interactions_per_contact_weekend_allday_text_median, interactions_per_contact_weekend_night_call_kurtosis, interactions_per_contact_weekend_night_call_skewness, interactions_per_contact_weekend_night_text_max, interactions_per_contact_weekend_night_text_min, interactions_per_contact_weekend_night_text_std, interevent_time_weekday_night_text_median, interevent_time_weekend_night_text_mean, number_of_interactions_alldir_weekend_allday_call, number_of_interactions_in_weekday_night_call, number_of_interactions_out_weekday_night_call, percent_initiated_conversations_allweek_night, percent_initiated_conversations_weekday_allday, percent_initiated_conversations_weekend_night, percent_initiated_interactions_allweek_night, percent_initiated_interactions_weekend_night, percent_pareto_durations_weekend_day, percent_pareto_interactions_weekend_allday_call, balance_of_contacts_weekend_allday_text_kurtosis, balance_of_contacts_weekend_allday_text_skewness, interactions_per_contact_weekday_night_text_median, interactions_per_contact_weekend_night_text_mean, interevent_time_weekend_allday_text_kurtosis, interevent_time_weekend_allday_text_skewness, number_of_interactions_alldir_weekday_night_call, number_of_interactions_in_weekend_night_call, number_of_interactions_out_weekend_night_call, percent_initiated_conversations_weekday_day, percent_initiated_conversations_weekday_night, percent_initiated_interactions_allweek_allday, percent_initiated_interactions_weekday_day, percent_initiated_interactions_weekday_night, percent_initiated_interactions_weekend_allday, percent_pareto_interactions_weekday_night_call, balance_of_contacts_weekday_night_text_kurtosis, balance_of_contacts_weekday_night_text_skewness, balance_of_contacts_weekend_night_text_median, interactions_per_contact_weekend_allday_text_kurtosis, interactions_per_contact_weekend_allday_text_skewness, interevent_time_weekday_night_text_kurtosis, interevent_time_weekday_night_text_skewness, interevent_time_weekend_night_text_median, number_of_interactions_alldir_weekend_night_call, percent_initiated_conversations_allweek_allday, percent_initiated_conversations_weekend_allday, percent_pareto_interactions_weekend_night_call, balance_of_contacts_weekend_night_text_kurtosis, balance_of_contacts_weekend_night_text_skewness, interactions_per_contact_weekday_night_text_kurtosis, interactions_per_contact_weekday_night_text_skewness, interactions_per_contact_weekend_night_text_median, interevent_time_weekend_night_text_kurtosis, interevent_time_weekend_night_text_skewness, interactions_per_contact_weekend_night_text_kurtosis, interactions_per_contact_weekend_night_text_skewness, name];\n'Project [name#510300, 'cdr_active_days__allweek__day__callandtext]\n+- Project [name#510300, active_days_allweek_allday#510301, active_days_allweek_day#510302, active_days_allweek_night#510303, active_days_weekday_allday#510304, active_days_weekday_day#510305, active_days_weekday_night#510306, active_days_weekend_allday#510307, active_days_weekend_day#510308, active_days_weekend_night#510309, number_of_contacts_allweek_allday_call#510310, number_of_contacts_allweek_allday_text#510311, number_of_contacts_allweek_day_call#510312, number_of_contacts_allweek_day_text#510313, number_of_contacts_allweek_night_call#510314, number_of_contacts_allweek_night_text#510315, number_of_contacts_weekday_allday_call#510316, number_of_contacts_weekday_allday_text#510317, number_of_contacts_weekday_day_call#510318, number_of_contacts_weekday_day_text#510319, number_of_contacts_weekday_night_call#510320, number_of_contacts_weekday_night_text#510321, number_of_contacts_weekend_allday_call#510322, number_of_contacts_weekend_allday_text#510323, ... 685 more fields]\n +- Join Inner, (name#510300 = name#566678)\n :- Relation [name#510300,active_days_allweek_allday#510301,active_days_allweek_day#510302,active_days_allweek_night#510303,active_days_weekday_allday#510304,active_days_weekday_day#510305,active_days_weekday_night#510306,active_days_weekend_allday#510307,active_days_weekend_day#510308,active_days_weekend_night#510309,number_of_contacts_allweek_allday_call#510310,number_of_contacts_allweek_allday_text#510311,number_of_contacts_allweek_day_call#510312,number_of_contacts_allweek_day_text#510313,number_of_contacts_allweek_night_call#510314,number_of_contacts_allweek_night_text#510315,number_of_contacts_weekday_allday_call#510316,number_of_contacts_weekday_allday_text#510317,number_of_contacts_weekday_day_call#510318,number_of_contacts_weekday_day_text#510319,number_of_contacts_weekday_night_call#510320,number_of_contacts_weekday_night_text#510321,number_of_contacts_weekend_allday_call#510322,number_of_contacts_weekend_allday_text#510323,... 685 more fields] csv\n +- Project [name#566678]\n +- Relation [name#566678,active_days_allweek_allday#566679,active_days_allweek_day#566680,active_days_allweek_night#566681,active_days_weekday_allday#566682,active_days_weekday_day#566683,active_days_weekday_night#566684,active_days_weekend_allday#566685,active_days_weekend_day#566686,active_days_weekend_night#566687,number_of_contacts_allweek_allday_call#566688,number_of_contacts_allweek_allday_text#566689,number_of_contacts_allweek_day_call#566690,number_of_contacts_allweek_day_text#566691,number_of_contacts_allweek_night_call#566692,number_of_contacts_allweek_night_text#566693,number_of_contacts_weekday_allday_call#566694,number_of_contacts_weekday_allday_text#566695,number_of_contacts_weekday_day_call#566696,number_of_contacts_weekday_day_text#566697,number_of_contacts_weekday_night_call#566698,number_of_contacts_weekday_night_text#566699,number_of_contacts_weekend_allday_call#566700,number_of_contacts_weekend_allday_text#566701,... 685 more fields] csv\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAnalysisException\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfeaturizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeature_plots\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/gpl/cider/cider/featurizer.py:739\u001b[0m, in \u001b[0;36mFeaturizer.feature_plots\u001b[0;34m(self, read_from_disk)\u001b[0m\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m subscribers \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 738\u001b[0m users \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeatures[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcdr\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mjoin(subscribers, how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minner\u001b[39m\u001b[38;5;124m'\u001b[39m, on\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 739\u001b[0m \u001b[38;5;28mslice\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43musers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mname\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[43m[\u001b[49m\u001b[43ma\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtoPandas()\n\u001b[1;32m 740\u001b[0m \u001b[38;5;28mslice\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mslice_name\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m slice_name\n\u001b[1;32m 741\u001b[0m boxplot\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mslice\u001b[39m)\n", + "File \u001b[0;32m~/miniconda3/envs/cider/lib/python3.8/site-packages/pyspark/sql/dataframe.py:2023\u001b[0m, in \u001b[0;36mDataFrame.select\u001b[0;34m(self, *cols)\u001b[0m\n\u001b[1;32m 2002\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mselect\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39mcols: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumnOrName\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataFrame\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 2003\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Projects a set of expressions and returns a new :class:`DataFrame`.\u001b[39;00m\n\u001b[1;32m 2004\u001b[0m \n\u001b[1;32m 2005\u001b[0m \u001b[38;5;124;03m .. versionadded:: 1.3.0\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2021\u001b[0m \u001b[38;5;124;03m [Row(name='Alice', age=12), Row(name='Bob', age=15)]\u001b[39;00m\n\u001b[1;32m 2022\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2023\u001b[0m jdf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_jdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_jcols\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcols\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2024\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DataFrame(jdf, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msparkSession)\n", + "File \u001b[0;32m~/miniconda3/envs/cider/lib/python3.8/site-packages/py4j/java_gateway.py:1321\u001b[0m, in \u001b[0;36mJavaMember.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1315\u001b[0m command \u001b[38;5;241m=\u001b[39m proto\u001b[38;5;241m.\u001b[39mCALL_COMMAND_NAME \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1316\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcommand_header \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1317\u001b[0m args_command \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1318\u001b[0m proto\u001b[38;5;241m.\u001b[39mEND_COMMAND_PART\n\u001b[1;32m 1320\u001b[0m answer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client\u001b[38;5;241m.\u001b[39msend_command(command)\n\u001b[0;32m-> 1321\u001b[0m return_value \u001b[38;5;241m=\u001b[39m \u001b[43mget_return_value\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1322\u001b[0m \u001b[43m \u001b[49m\u001b[43manswer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgateway_client\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1324\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m temp_arg \u001b[38;5;129;01min\u001b[39;00m temp_args:\n\u001b[1;32m 1325\u001b[0m temp_arg\u001b[38;5;241m.\u001b[39m_detach()\n", + "File \u001b[0;32m~/miniconda3/envs/cider/lib/python3.8/site-packages/pyspark/sql/utils.py:196\u001b[0m, in \u001b[0;36mcapture_sql_exception..deco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 192\u001b[0m converted \u001b[38;5;241m=\u001b[39m convert_exception(e\u001b[38;5;241m.\u001b[39mjava_exception)\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(converted, UnknownException):\n\u001b[1;32m 194\u001b[0m \u001b[38;5;66;03m# Hide where the exception came from that shows a non-Pythonic\u001b[39;00m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;66;03m# JVM exception message.\u001b[39;00m\n\u001b[0;32m--> 196\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m converted \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "\u001b[0;31mAnalysisException\u001b[0m: Column 'cdr_active_days__allweek__day__callandtext' does not exist. Did you mean one of the following? [active_days_allweek_allday, active_days_weekday_allday, active_days_allweek_day, active_days_allweek_night, active_days_weekday_night, active_days_weekend_allday, active_days_weekday_day, active_days_weekend_night, call_duration_allweek_day_call_max, call_duration_allweek_day_call_mean, call_duration_allweek_day_call_std, active_days_weekend_day, call_duration_allweek_day_call_median, call_duration_allweek_day_call_min, interevent_time_allweek_day_call_max, interevent_time_allweek_day_call_mean, interevent_time_allweek_day_call_std, balance_of_contacts_allweek_day_call_max, balance_of_contacts_allweek_day_call_mean, balance_of_contacts_allweek_day_call_std, call_duration_allweek_allday_call_max, call_duration_allweek_allday_call_mean, call_duration_allweek_allday_call_std, call_duration_allweek_day_call_kurtosis, call_duration_allweek_day_call_skewness, interevent_time_allweek_day_call_median, interevent_time_allweek_day_call_min, balance_of_contacts_allweek_day_call_median, balance_of_contacts_allweek_day_call_min, call_duration_allweek_allday_call_median, call_duration_allweek_allday_call_min, call_duration_allweek_night_call_max, call_duration_allweek_night_call_mean, call_duration_allweek_night_call_std, interevent_time_allweek_allday_call_max, interevent_time_allweek_allday_call_mean, interevent_time_allweek_allday_call_std, interevent_time_allweek_day_call_kurtosis, interevent_time_allweek_day_call_skewness, number_of_contacts_allweek_day_call, number_of_contacts_allweek_day_text, balance_of_contacts_allweek_allday_call_max, balance_of_contacts_allweek_allday_call_mean, balance_of_contacts_allweek_allday_call_std, balance_of_contacts_allweek_day_call_kurtosis, balance_of_contacts_allweek_day_call_skewness, call_duration_allweek_allday_call_kurtosis, call_duration_allweek_allday_call_skewness, call_duration_allweek_night_call_median, call_duration_allweek_night_call_min, call_duration_weekday_day_call_max, call_duration_weekday_day_call_mean, call_duration_weekday_day_call_std, call_duration_weekend_day_call_max, call_duration_weekend_day_call_mean, call_duration_weekend_day_call_std, entropy_of_contacts_allweek_day_call, entropy_of_contacts_allweek_day_text, entropy_of_contacts_weekday_allday_text, interactions_per_contact_allweek_day_call_max, interactions_per_contact_allweek_day_call_mean, interactions_per_contact_allweek_day_call_std, interevent_time_allweek_allday_call_median, interevent_time_allweek_allday_call_min, interevent_time_allweek_night_call_max, interevent_time_allweek_night_call_mean, interevent_time_allweek_night_call_std, number_of_contacts_allweek_allday_text, number_of_contacts_weekday_allday_text, percent_at_home_allweek_allday, percent_at_home_weekday_allday, percent_nocturnal_weekday_call, percent_nocturnal_weekday_text, response_delay_text_allweek_day_max, response_delay_text_allweek_day_mean, response_delay_text_allweek_day_median, response_delay_text_allweek_day_skewness, response_delay_text_weekday_allday_max, response_delay_text_weekday_allday_mean, response_rate_text_weekday_allday, balance_of_contacts_allweek_allday_call_median, balance_of_contacts_allweek_allday_call_min, balance_of_contacts_allweek_night_call_max, balance_of_contacts_allweek_night_call_mean, balance_of_contacts_allweek_night_call_std, call_duration_allweek_night_call_kurtosis, call_duration_allweek_night_call_skewness, call_duration_weekday_day_call_median, call_duration_weekday_day_call_min, call_duration_weekend_day_call_median, call_duration_weekend_day_call_min, entropy_of_contacts_allweek_allday_text, frequent_antennas_weekday_allday, interactions_per_contact_allweek_day_call_median, interactions_per_contact_allweek_day_call_min, interevent_time_allweek_allday_call_kurtosis, interevent_time_allweek_allday_call_skewness, interevent_time_allweek_day_text_max, interevent_time_allweek_day_text_mean, interevent_time_allweek_day_text_median, interevent_time_allweek_day_text_std, interevent_time_allweek_night_call_median, interevent_time_allweek_night_call_min, number_of_contacts_allweek_allday_call, percent_at_home_allweek_day, response_delay_text_allweek_day_min, response_delay_text_allweek_day_std, response_delay_text_weekday_allday_min, response_delay_text_weekday_allday_std, balance_of_contacts_allweek_allday_call_kurtosis, balance_of_contacts_allweek_allday_call_skewness, balance_of_contacts_allweek_day_text_max, balance_of_contacts_allweek_day_text_mean, balance_of_contacts_allweek_day_text_median, balance_of_contacts_allweek_day_text_std, balance_of_contacts_allweek_night_call_median, balance_of_contacts_allweek_night_call_min, balance_of_contacts_weekday_day_call_max, balance_of_contacts_weekday_day_call_mean, balance_of_contacts_weekday_day_call_std, balance_of_contacts_weekend_day_call_max, balance_of_contacts_weekend_day_call_mean, balance_of_contacts_weekend_day_call_std, call_duration_weekday_allday_call_max, call_duration_weekday_allday_call_mean, call_duration_weekday_allday_call_std, call_duration_weekday_day_call_kurtosis, call_duration_weekday_day_call_skewness, call_duration_weekend_day_call_kurtosis, call_duration_weekend_day_call_skewness, entropy_of_antennas_weekday_allday, entropy_of_contacts_allweek_allday_call, entropy_of_contacts_weekday_day_text, entropy_of_contacts_weekend_allday_text, frequent_antennas_allweek_allday, interactions_per_contact_allweek_allday_call_max, interactions_per_contact_allweek_allday_call_mean, interactions_per_contact_allweek_allday_call_std, interactions_per_contact_allweek_day_call_kurtosis, interactions_per_contact_allweek_day_call_skewness, interevent_time_allweek_day_text_kurtosis, interevent_time_allweek_day_text_min, interevent_time_allweek_day_text_skewness, interevent_time_allweek_night_call_kurtosis, interevent_time_allweek_night_call_skewness, interevent_time_weekday_day_call_max, interevent_time_weekday_day_call_mean, interevent_time_weekday_day_call_std, interevent_time_weekend_day_call_max, interevent_time_weekend_day_call_mean, interevent_time_weekend_day_call_std, number_of_antennas_allweek_allday, number_of_antennas_weekday_allday, number_of_contacts_allweek_night_call, number_of_contacts_allweek_night_text, number_of_contacts_weekday_day_text, number_of_contacts_weekend_allday_text, number_of_interactions_in_allweek_day_call, number_of_interactions_in_allweek_day_text, number_of_interactions_in_weekday_allday_text, number_of_interactions_out_weekday_allday_text, percent_at_home_allweek_night, percent_at_home_weekday_night, percent_at_home_weekend_allday, percent_nocturnal_allweek_call, percent_nocturnal_allweek_text, percent_nocturnal_weekend_call, percent_nocturnal_weekend_text, radius_of_gyration_weekday_allday, response_delay_text_allweek_allday_max, response_delay_text_allweek_allday_mean, response_delay_text_allweek_allday_median, response_delay_text_allweek_allday_skewness, response_delay_text_allweek_day_kurtosis, response_delay_text_weekday_allday_median, response_delay_text_weekday_day_max, response_delay_text_weekday_day_mean, response_delay_text_weekday_day_median, response_delay_text_weekday_day_std, response_delay_text_weekend_allday_max, response_delay_text_weekend_allday_mean, response_rate_text_allweek_allday, response_rate_text_weekday_night, response_rate_text_weekend_allday, balance_of_contacts_allweek_day_text_kurtosis, balance_of_contacts_allweek_day_text_min, balance_of_contacts_allweek_day_text_skewness, balance_of_contacts_allweek_night_call_kurtosis, balance_of_contacts_allweek_night_call_skewness, balance_of_contacts_weekday_allday_text_max, balance_of_contacts_weekday_allday_text_min, balance_of_contacts_weekday_allday_text_std, balance_of_contacts_weekday_day_call_median, balance_of_contacts_weekday_day_call_min, balance_of_contacts_weekend_day_call_median, balance_of_contacts_weekend_day_call_min, call_duration_weekday_allday_call_median, call_duration_weekday_allday_call_min, call_duration_weekday_night_call_max, call_duration_weekday_night_call_mean, call_duration_weekday_night_call_std, call_duration_weekend_allday_call_max, call_duration_weekend_allday_call_mean, call_duration_weekend_allday_call_std, entropy_of_antennas_allweek_allday, entropy_of_contacts_allweek_night_call, entropy_of_contacts_allweek_night_text, entropy_of_contacts_weekday_night_text, frequent_antennas_allweek_day, frequent_antennas_weekday_night, frequent_antennas_weekend_allday, interactions_per_contact_allweek_allday_call_median, interactions_per_contact_allweek_allday_call_min, interactions_per_contact_allweek_night_call_max, interactions_per_contact_allweek_night_call_mean, interactions_per_contact_allweek_night_call_std, interactions_per_contact_weekday_day_call_max, interactions_per_contact_weekday_day_call_mean, interactions_per_contact_weekday_day_call_std, interactions_per_contact_weekend_day_call_max, interactions_per_contact_weekend_day_call_mean, interactions_per_contact_weekend_day_call_std, interevent_time_allweek_allday_text_max, interevent_time_allweek_allday_text_mean, interevent_time_allweek_allday_text_median, interevent_time_allweek_allday_text_min, interevent_time_allweek_allday_text_std, interevent_time_weekday_allday_text_max, interevent_time_weekday_allday_text_min, interevent_time_weekday_allday_text_std, interevent_time_weekday_day_call_median, interevent_time_weekday_day_call_min, interevent_time_weekend_day_call_median, interevent_time_weekend_day_call_min, number_of_antennas_allweek_day, number_of_contacts_weekday_night_text, number_of_interactions_alldir_weekday_allday_text, number_of_interactions_in_allweek_allday_text, number_of_interactions_out_allweek_day_call, number_of_interactions_out_allweek_day_text, percent_at_home_weekday_day, percent_pareto_interactions_weekday_allday_text, radius_of_gyration_allweek_allday, response_delay_text_allweek_allday_min, response_delay_text_allweek_allday_std, response_delay_text_allweek_night_max, response_delay_text_allweek_night_mean, response_delay_text_allweek_night_median, response_delay_text_allweek_night_skewness, response_delay_text_weekday_day_kurtosis, response_delay_text_weekday_day_min, response_delay_text_weekday_day_skewness, response_delay_text_weekday_night_max, response_delay_text_weekday_night_mean, response_delay_text_weekday_night_std, response_delay_text_weekend_allday_min, response_delay_text_weekend_allday_std, response_delay_text_weekend_day_max, response_delay_text_weekend_day_mean, response_delay_text_weekend_day_median, response_delay_text_weekend_day_skewness, response_rate_text_allweek_day, response_rate_text_weekday_day, balance_of_contacts_allweek_allday_text_max, balance_of_contacts_allweek_allday_text_mean, balance_of_contacts_allweek_allday_text_median, balance_of_contacts_allweek_allday_text_min, balance_of_contacts_allweek_allday_text_std, balance_of_contacts_weekday_allday_text_mean, balance_of_contacts_weekday_day_call_kurtosis, balance_of_contacts_weekday_day_call_skewness, balance_of_contacts_weekday_day_text_std, balance_of_contacts_weekend_day_call_kurtosis, balance_of_contacts_weekend_day_call_skewness, call_duration_weekday_allday_call_kurtosis, call_duration_weekday_allday_call_skewness, call_duration_weekday_night_call_median, call_duration_weekday_night_call_min, call_duration_weekend_allday_call_median, call_duration_weekend_allday_call_min, call_duration_weekend_night_call_max, call_duration_weekend_night_call_mean, call_duration_weekend_night_call_std, entropy_of_antennas_allweek_day, entropy_of_antennas_weekday_night, entropy_of_antennas_weekend_allday, entropy_of_contacts_weekday_allday_call, entropy_of_contacts_weekday_day_call, entropy_of_contacts_weekend_day_call, entropy_of_contacts_weekend_day_text, frequent_antennas_allweek_night, frequent_antennas_weekday_day, interactions_per_contact_allweek_allday_call_kurtosis, interactions_per_contact_allweek_allday_call_skewness, interactions_per_contact_allweek_day_text_max, interactions_per_contact_allweek_day_text_mean, interactions_per_contact_allweek_day_text_median, interactions_per_contact_allweek_day_text_std, interactions_per_contact_allweek_night_call_median, interactions_per_contact_allweek_night_call_min, interactions_per_contact_weekday_allday_text_max, interactions_per_contact_weekday_allday_text_min, interactions_per_contact_weekday_allday_text_std, interactions_per_contact_weekday_day_call_median, interactions_per_contact_weekday_day_call_min, interactions_per_contact_weekend_day_call_median, interactions_per_contact_weekend_day_call_min, interevent_time_allweek_allday_text_kurtosis, interevent_time_allweek_allday_text_skewness, interevent_time_allweek_night_text_max, interevent_time_allweek_night_text_mean, interevent_time_allweek_night_text_std, interevent_time_weekday_allday_call_max, interevent_time_weekday_allday_call_mean, interevent_time_weekday_allday_call_std, interevent_time_weekday_allday_text_mean, interevent_time_weekday_day_call_kurtosis, interevent_time_weekday_day_call_skewness, interevent_time_weekday_day_text_std, interevent_time_weekend_day_call_kurtosis, interevent_time_weekend_day_call_skewness, number_of_antennas_allweek_night, number_of_antennas_weekday_night, number_of_antennas_weekend_allday, number_of_contacts_weekday_allday_call, number_of_contacts_weekday_day_call, number_of_contacts_weekend_day_call, number_of_contacts_weekend_day_text, number_of_interactions_in_allweek_allday_call, number_of_interactions_in_weekday_day_text, number_of_interactions_in_weekend_allday_text, number_of_interactions_out_allweek_allday_text, number_of_interactions_out_weekday_day_text, number_of_interactions_out_weekend_allday_text, percent_at_home_weekend_night, percent_pareto_durations_weekday_allday, percent_pareto_interactions_allweek_day_call, percent_pareto_interactions_allweek_day_text, radius_of_gyration_allweek_day, radius_of_gyration_weekday_night, radius_of_gyration_weekend_allday, response_delay_text_allweek_allday_kurtosis, response_delay_text_allweek_night_min, response_delay_text_allweek_night_std, response_delay_text_weekday_allday_kurtosis, response_delay_text_weekday_allday_skewness, response_delay_text_weekday_night_median, response_delay_text_weekday_night_min, response_delay_text_weekend_allday_median, response_delay_text_weekend_day_min, response_delay_text_weekend_day_std, response_rate_text_allweek_night, response_rate_text_weekend_night, balance_of_contacts_allweek_allday_text_kurtosis, balance_of_contacts_allweek_allday_text_skewness, balance_of_contacts_allweek_night_text_max, balance_of_contacts_allweek_night_text_mean, balance_of_contacts_allweek_night_text_std, balance_of_contacts_weekday_allday_call_max, balance_of_contacts_weekday_allday_call_mean, balance_of_contacts_weekday_allday_call_std, balance_of_contacts_weekday_day_text_max, balance_of_contacts_weekday_day_text_mean, balance_of_contacts_weekday_day_text_min, balance_of_contacts_weekday_night_call_max, balance_of_contacts_weekday_night_call_mean, balance_of_contacts_weekday_night_call_std, balance_of_contacts_weekend_allday_call_max, balance_of_contacts_weekend_allday_call_mean, balance_of_contacts_weekend_allday_call_std, balance_of_contacts_weekend_allday_text_max, balance_of_contacts_weekend_allday_text_min, balance_of_contacts_weekend_allday_text_std, call_duration_weekday_night_call_kurtosis, call_duration_weekday_night_call_skewness, call_duration_weekend_allday_call_kurtosis, call_duration_weekend_allday_call_skewness, call_duration_weekend_night_call_median, call_duration_weekend_night_call_min, entropy_of_antennas_allweek_night, entropy_of_antennas_weekday_day, entropy_of_contacts_weekend_night_text, frequent_antennas_weekend_night, interactions_per_contact_allweek_day_text_kurtosis, interactions_per_contact_allweek_day_text_min, interactions_per_contact_allweek_day_text_skewness, interactions_per_contact_allweek_night_call_kurtosis, interactions_per_contact_allweek_night_call_skewness, interactions_per_contact_weekday_allday_text_mean, interactions_per_contact_weekday_day_call_kurtosis, interactions_per_contact_weekday_day_call_skewness, interactions_per_contact_weekday_day_text_std, interactions_per_contact_weekend_day_call_kurtosis, interactions_per_contact_weekend_day_call_skewness, interevent_time_allweek_night_text_median, interevent_time_allweek_night_text_min, interevent_time_weekday_allday_call_median, interevent_time_weekday_allday_call_min, interevent_time_weekday_day_text_max, interevent_time_weekday_day_text_mean, interevent_time_weekday_day_text_min, interevent_time_weekday_night_call_max, interevent_time_weekday_night_call_mean, interevent_time_weekday_night_call_std, interevent_time_weekend_allday_call_max, interevent_time_weekend_allday_call_mean, interevent_time_weekend_allday_call_std, interevent_time_weekend_allday_text_max, interevent_time_weekend_allday_text_min, interevent_time_weekend_allday_text_std, number_of_antennas_weekday_day, number_of_contacts_weekend_night_text, number_of_interactions_alldir_allweek_day_call, number_of_interactions_alldir_allweek_day_text, number_of_interactions_alldir_weekday_day_text, number_of_interactions_alldir_weekend_allday_text, number_of_interactions_in_allweek_night_call, number_of_interactions_in_allweek_night_text, number_of_interactions_in_weekday_night_text, number_of_interactions_out_allweek_allday_call, number_of_interactions_out_weekday_night_text, percent_at_home_weekend_day, percent_pareto_durations_allweek_allday, percent_pareto_interactions_allweek_allday_text, percent_pareto_interactions_weekday_day_text, percent_pareto_interactions_weekend_allday_text, radius_of_gyration_allweek_night, radius_of_gyration_weekday_day, response_delay_text_allweek_night_kurtosis, response_delay_text_weekday_night_kurtosis, response_delay_text_weekday_night_skewness, response_delay_text_weekend_day_kurtosis, response_delay_text_weekend_night_max, response_delay_text_weekend_night_mean, response_delay_text_weekend_night_std, response_rate_text_weekend_day, balance_of_contacts_allweek_night_text_median, balance_of_contacts_allweek_night_text_min, balance_of_contacts_weekday_allday_call_median, balance_of_contacts_weekday_allday_call_min, balance_of_contacts_weekday_allday_text_median, balance_of_contacts_weekday_day_text_median, balance_of_contacts_weekday_night_call_median, balance_of_contacts_weekday_night_call_min, balance_of_contacts_weekday_night_text_max, balance_of_contacts_weekday_night_text_min, balance_of_contacts_weekday_night_text_std, balance_of_contacts_weekend_allday_call_median, balance_of_contacts_weekend_allday_call_min, balance_of_contacts_weekend_allday_text_mean, balance_of_contacts_weekend_day_text_max, balance_of_contacts_weekend_day_text_mean, balance_of_contacts_weekend_day_text_median, balance_of_contacts_weekend_day_text_std, balance_of_contacts_weekend_night_call_max, balance_of_contacts_weekend_night_call_mean, balance_of_contacts_weekend_night_call_std, call_duration_weekend_night_call_kurtosis, call_duration_weekend_night_call_skewness, entropy_of_antennas_weekend_night, entropy_of_contacts_weekend_allday_call, frequent_antennas_weekend_day, interactions_per_contact_allweek_allday_text_max, interactions_per_contact_allweek_allday_text_mean, interactions_per_contact_allweek_allday_text_median, interactions_per_contact_allweek_allday_text_min, interactions_per_contact_allweek_allday_text_std, interactions_per_contact_weekday_allday_call_max, interactions_per_contact_weekday_allday_call_mean, interactions_per_contact_weekday_allday_call_std, interactions_per_contact_weekday_day_text_max, interactions_per_contact_weekday_day_text_mean, interactions_per_contact_weekday_day_text_min, interactions_per_contact_weekday_night_call_max, interactions_per_contact_weekday_night_call_mean, interactions_per_contact_weekday_night_call_std, interactions_per_contact_weekend_allday_call_max, interactions_per_contact_weekend_allday_call_mean, interactions_per_contact_weekend_allday_call_std, interactions_per_contact_weekend_allday_text_max, interactions_per_contact_weekend_allday_text_min, interactions_per_contact_weekend_allday_text_std, interevent_time_allweek_night_text_kurtosis, interevent_time_allweek_night_text_skewness, interevent_time_weekday_allday_call_kurtosis, interevent_time_weekday_allday_call_skewness, interevent_time_weekday_allday_text_median, interevent_time_weekday_day_text_median, interevent_time_weekday_night_call_median, interevent_time_weekday_night_call_min, interevent_time_weekday_night_text_max, interevent_time_weekday_night_text_min, interevent_time_weekday_night_text_std, interevent_time_weekend_allday_call_median, interevent_time_weekend_allday_call_min, interevent_time_weekend_allday_text_mean, interevent_time_weekend_day_text_max, interevent_time_weekend_day_text_mean, interevent_time_weekend_day_text_median, interevent_time_weekend_day_text_std, interevent_time_weekend_night_call_max, interevent_time_weekend_night_call_mean, interevent_time_weekend_night_call_std, number_of_antennas_weekend_night, number_of_contacts_weekend_allday_call, number_of_interactions_alldir_allweek_allday_text, number_of_interactions_alldir_weekday_night_text, number_of_interactions_in_weekday_allday_call, number_of_interactions_in_weekday_day_call, number_of_interactions_in_weekend_day_call, number_of_interactions_in_weekend_day_text, number_of_interactions_out_allweek_night_call, number_of_interactions_out_allweek_night_text, number_of_interactions_out_weekday_allday_call, number_of_interactions_out_weekday_day_call, number_of_interactions_out_weekend_day_call, number_of_interactions_out_weekend_day_text, percent_pareto_durations_allweek_day, percent_pareto_durations_weekday_night, percent_pareto_durations_weekend_allday, percent_pareto_interactions_allweek_allday_call, percent_pareto_interactions_weekday_night_text, radius_of_gyration_weekend_night, response_delay_text_weekend_allday_kurtosis, response_delay_text_weekend_allday_skewness, response_delay_text_weekend_night_median, response_delay_text_weekend_night_min, response_delay_text_weekend_night_skewness, balance_of_contacts_allweek_night_text_kurtosis, balance_of_contacts_allweek_night_text_skewness, balance_of_contacts_weekday_allday_call_kurtosis, balance_of_contacts_weekday_allday_call_skewness, balance_of_contacts_weekday_day_text_kurtosis, balance_of_contacts_weekday_day_text_skewness, balance_of_contacts_weekday_night_call_kurtosis, balance_of_contacts_weekday_night_call_skewness, balance_of_contacts_weekday_night_text_mean, balance_of_contacts_weekend_allday_call_kurtosis, balance_of_contacts_weekend_allday_call_skewness, balance_of_contacts_weekend_day_text_kurtosis, balance_of_contacts_weekend_day_text_min, balance_of_contacts_weekend_day_text_skewness, balance_of_contacts_weekend_night_call_median, balance_of_contacts_weekend_night_call_min, entropy_of_antennas_weekend_day, entropy_of_contacts_weekday_night_call, interactions_per_contact_allweek_allday_text_kurtosis, interactions_per_contact_allweek_allday_text_skewness, interactions_per_contact_allweek_night_text_max, interactions_per_contact_allweek_night_text_mean, interactions_per_contact_allweek_night_text_std, interactions_per_contact_weekday_allday_call_median, interactions_per_contact_weekday_allday_call_min, interactions_per_contact_weekday_allday_text_median, interactions_per_contact_weekday_day_text_median, interactions_per_contact_weekday_night_call_median, interactions_per_contact_weekday_night_call_min, interactions_per_contact_weekday_night_text_max, interactions_per_contact_weekday_night_text_min, interactions_per_contact_weekday_night_text_std, interactions_per_contact_weekend_allday_call_median, interactions_per_contact_weekend_allday_call_min, interactions_per_contact_weekend_allday_text_mean, interactions_per_contact_weekend_day_text_max, interactions_per_contact_weekend_day_text_mean, interactions_per_contact_weekend_day_text_median, interactions_per_contact_weekend_day_text_std, interactions_per_contact_weekend_night_call_max, interactions_per_contact_weekend_night_call_mean, interactions_per_contact_weekend_night_call_std, interevent_time_weekday_day_text_kurtosis, interevent_time_weekday_day_text_skewness, interevent_time_weekday_night_call_kurtosis, interevent_time_weekday_night_call_skewness, interevent_time_weekday_night_text_mean, interevent_time_weekend_allday_call_kurtosis, interevent_time_weekend_allday_call_skewness, interevent_time_weekend_day_text_kurtosis, interevent_time_weekend_day_text_min, interevent_time_weekend_day_text_skewness, interevent_time_weekend_night_call_median, interevent_time_weekend_night_call_min, number_of_antennas_weekend_day, number_of_contacts_weekday_night_call, number_of_interactions_alldir_allweek_allday_call, number_of_interactions_alldir_weekday_allday_call, number_of_interactions_alldir_weekday_day_call, number_of_interactions_alldir_weekend_day_call, number_of_interactions_alldir_weekend_day_text, number_of_interactions_in_weekend_night_text, number_of_interactions_out_weekend_night_text, percent_pareto_durations_allweek_night, percent_pareto_durations_weekday_day, percent_pareto_interactions_allweek_night_call, percent_pareto_interactions_allweek_night_text, percent_pareto_interactions_weekday_allday_call, percent_pareto_interactions_weekday_day_call, percent_pareto_interactions_weekend_day_call, percent_pareto_interactions_weekend_day_text, radius_of_gyration_weekend_day, response_delay_text_weekend_night_kurtosis, balance_of_contacts_weekday_allday_text_kurtosis, balance_of_contacts_weekday_allday_text_skewness, balance_of_contacts_weekend_allday_text_median, balance_of_contacts_weekend_night_call_kurtosis, balance_of_contacts_weekend_night_call_skewness, balance_of_contacts_weekend_night_text_max, balance_of_contacts_weekend_night_text_min, balance_of_contacts_weekend_night_text_std, entropy_of_contacts_weekend_night_call, interactions_per_contact_allweek_night_text_median, interactions_per_contact_allweek_night_text_min, interactions_per_contact_weekday_allday_call_kurtosis, interactions_per_contact_weekday_allday_call_skewness, interactions_per_contact_weekday_day_text_kurtosis, interactions_per_contact_weekday_day_text_skewness, interactions_per_contact_weekday_night_call_kurtosis, interactions_per_contact_weekday_night_call_skewness, interactions_per_contact_weekday_night_text_mean, interactions_per_contact_weekend_allday_call_kurtosis, interactions_per_contact_weekend_allday_call_skewness, interactions_per_contact_weekend_day_text_kurtosis, interactions_per_contact_weekend_day_text_min, interactions_per_contact_weekend_day_text_skewness, interactions_per_contact_weekend_night_call_median, interactions_per_contact_weekend_night_call_min, interevent_time_weekday_allday_text_kurtosis, interevent_time_weekday_allday_text_skewness, interevent_time_weekend_allday_text_median, interevent_time_weekend_night_call_kurtosis, interevent_time_weekend_night_call_skewness, interevent_time_weekend_night_text_max, interevent_time_weekend_night_text_min, interevent_time_weekend_night_text_std, number_of_contacts_weekend_night_call, number_of_interactions_alldir_allweek_night_call, number_of_interactions_alldir_allweek_night_text, number_of_interactions_alldir_weekend_night_text, number_of_interactions_in_weekend_allday_call, number_of_interactions_out_weekend_allday_call, percent_initiated_conversations_allweek_day, percent_initiated_conversations_weekend_day, percent_initiated_interactions_allweek_day, percent_initiated_interactions_weekday_allday, percent_initiated_interactions_weekend_day, percent_pareto_durations_weekend_night, percent_pareto_interactions_weekend_night_text, balance_of_contacts_weekday_night_text_median, balance_of_contacts_weekend_night_text_mean, interactions_per_contact_allweek_night_text_kurtosis, interactions_per_contact_allweek_night_text_skewness, interactions_per_contact_weekday_allday_text_kurtosis, interactions_per_contact_weekday_allday_text_skewness, interactions_per_contact_weekend_allday_text_median, interactions_per_contact_weekend_night_call_kurtosis, interactions_per_contact_weekend_night_call_skewness, interactions_per_contact_weekend_night_text_max, interactions_per_contact_weekend_night_text_min, interactions_per_contact_weekend_night_text_std, interevent_time_weekday_night_text_median, interevent_time_weekend_night_text_mean, number_of_interactions_alldir_weekend_allday_call, number_of_interactions_in_weekday_night_call, number_of_interactions_out_weekday_night_call, percent_initiated_conversations_allweek_night, percent_initiated_conversations_weekday_allday, percent_initiated_conversations_weekend_night, percent_initiated_interactions_allweek_night, percent_initiated_interactions_weekend_night, percent_pareto_durations_weekend_day, percent_pareto_interactions_weekend_allday_call, balance_of_contacts_weekend_allday_text_kurtosis, balance_of_contacts_weekend_allday_text_skewness, interactions_per_contact_weekday_night_text_median, interactions_per_contact_weekend_night_text_mean, interevent_time_weekend_allday_text_kurtosis, interevent_time_weekend_allday_text_skewness, number_of_interactions_alldir_weekday_night_call, number_of_interactions_in_weekend_night_call, number_of_interactions_out_weekend_night_call, percent_initiated_conversations_weekday_day, percent_initiated_conversations_weekday_night, percent_initiated_interactions_allweek_allday, percent_initiated_interactions_weekday_day, percent_initiated_interactions_weekday_night, percent_initiated_interactions_weekend_allday, percent_pareto_interactions_weekday_night_call, balance_of_contacts_weekday_night_text_kurtosis, balance_of_contacts_weekday_night_text_skewness, balance_of_contacts_weekend_night_text_median, interactions_per_contact_weekend_allday_text_kurtosis, interactions_per_contact_weekend_allday_text_skewness, interevent_time_weekday_night_text_kurtosis, interevent_time_weekday_night_text_skewness, interevent_time_weekend_night_text_median, number_of_interactions_alldir_weekend_night_call, percent_initiated_conversations_allweek_allday, percent_initiated_conversations_weekend_allday, percent_pareto_interactions_weekend_night_call, balance_of_contacts_weekend_night_text_kurtosis, balance_of_contacts_weekend_night_text_skewness, interactions_per_contact_weekday_night_text_kurtosis, interactions_per_contact_weekday_night_text_skewness, interactions_per_contact_weekend_night_text_median, interevent_time_weekend_night_text_kurtosis, interevent_time_weekend_night_text_skewness, interactions_per_contact_weekend_night_text_kurtosis, interactions_per_contact_weekend_night_text_skewness, name];\n'Project [name#510300, 'cdr_active_days__allweek__day__callandtext]\n+- Project [name#510300, active_days_allweek_allday#510301, active_days_allweek_day#510302, active_days_allweek_night#510303, active_days_weekday_allday#510304, active_days_weekday_day#510305, active_days_weekday_night#510306, active_days_weekend_allday#510307, active_days_weekend_day#510308, active_days_weekend_night#510309, number_of_contacts_allweek_allday_call#510310, number_of_contacts_allweek_allday_text#510311, number_of_contacts_allweek_day_call#510312, number_of_contacts_allweek_day_text#510313, number_of_contacts_allweek_night_call#510314, number_of_contacts_allweek_night_text#510315, number_of_contacts_weekday_allday_call#510316, number_of_contacts_weekday_allday_text#510317, number_of_contacts_weekday_day_call#510318, number_of_contacts_weekday_day_text#510319, number_of_contacts_weekday_night_call#510320, number_of_contacts_weekday_night_text#510321, number_of_contacts_weekend_allday_call#510322, number_of_contacts_weekend_allday_text#510323, ... 685 more fields]\n +- Join Inner, (name#510300 = name#566678)\n :- Relation [name#510300,active_days_allweek_allday#510301,active_days_allweek_day#510302,active_days_allweek_night#510303,active_days_weekday_allday#510304,active_days_weekday_day#510305,active_days_weekday_night#510306,active_days_weekend_allday#510307,active_days_weekend_day#510308,active_days_weekend_night#510309,number_of_contacts_allweek_allday_call#510310,number_of_contacts_allweek_allday_text#510311,number_of_contacts_allweek_day_call#510312,number_of_contacts_allweek_day_text#510313,number_of_contacts_allweek_night_call#510314,number_of_contacts_allweek_night_text#510315,number_of_contacts_weekday_allday_call#510316,number_of_contacts_weekday_allday_text#510317,number_of_contacts_weekday_day_call#510318,number_of_contacts_weekday_day_text#510319,number_of_contacts_weekday_night_call#510320,number_of_contacts_weekday_night_text#510321,number_of_contacts_weekend_allday_call#510322,number_of_contacts_weekend_allday_text#510323,... 685 more fields] csv\n +- Project [name#566678]\n +- Relation [name#566678,active_days_allweek_allday#566679,active_days_allweek_day#566680,active_days_allweek_night#566681,active_days_weekday_allday#566682,active_days_weekday_day#566683,active_days_weekday_night#566684,active_days_weekend_allday#566685,active_days_weekend_day#566686,active_days_weekend_night#566687,number_of_contacts_allweek_allday_call#566688,number_of_contacts_allweek_allday_text#566689,number_of_contacts_allweek_day_call#566690,number_of_contacts_allweek_day_text#566691,number_of_contacts_allweek_night_call#566692,number_of_contacts_allweek_night_text#566693,number_of_contacts_weekday_allday_call#566694,number_of_contacts_weekday_allday_text#566695,number_of_contacts_weekday_day_call#566696,number_of_contacts_weekday_day_text#566697,number_of_contacts_weekday_night_call#566698,number_of_contacts_weekday_night_text#566699,number_of_contacts_weekend_allday_call#566700,number_of_contacts_weekend_allday_text#566701,... 685 more fields] csv\n" + ] + }, { "data": { - "image/png": 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"text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -13144,13 +753,21 @@ "source": [ "featurizer.feature_plots()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d45de12-459e-4df9-8451-c0308466f431", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "conda_cider", "language": "python", - "name": "python3" + "name": "conda_cider" }, "language_info": { "codemirror_mode": { @@ -13162,7 +779,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.1" + "version": "3.8.16" } }, "nbformat": 4, diff --git a/_build/html/_sources/quickstart/documentation_home_location.ipynb b/_build/html/_sources/quickstart/documentation_home_location.ipynb index a9d264d..871e188 100644 --- a/_build/html/_sources/quickstart/documentation_home_location.ipynb +++ b/_build/html/_sources/quickstart/documentation_home_location.ipynb @@ -15,8 +15,8 @@ "metadata": {}, "outputs": [], "source": [ - "from datastore import DataStore\n", - "from home_location import HomeLocator" + "from cider.datastore import DataStore\n", + "from cider.home_location import HomeLocator" ] }, { @@ -44,7 +44,11 @@ } ], "source": [ - "datastore = DataStore(cfg_dir='./configs/config.yml')\n", + "# This path should point to your cider installation, where configs and data for this demo are located.\n", + "from pathlib import Path\n", + "cider_installation_directory = Path('../../cider')\n", + "\n", + "datastore = DataStore(config_file_path_string= cider_installation_directory / 'configs' / 'config_quickstart.yml')\n", "home_locator = HomeLocator(datastore=datastore, clean_folders=True)" ] }, @@ -429,7 +433,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.1" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/_build/html/_sources/quickstart/documentation_ml.ipynb b/_build/html/_sources/quickstart/documentation_ml.ipynb index e2b814b..ebaf493 100644 --- a/_build/html/_sources/quickstart/documentation_ml.ipynb +++ b/_build/html/_sources/quickstart/documentation_ml.ipynb @@ -12,11 +12,39 @@ "cell_type": "code", "execution_count": 1, "id": "ec77078c", - "metadata": {}, - "outputs": [], + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/leo/miniconda3/envs/cider/lib/python3.8/site-packages/autogluon/core/scheduler/seq_scheduler.py:119: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n", + " if searcher is 'auto':\n" + ] + }, + { + "ename": "OSError", + "evalue": "dlopen(/Users/leo/miniconda3/envs/cider/lib/python3.8/site-packages/lightgbm/lib_lightgbm.so, 0x0006): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\n Referenced from: /Users/leo/miniconda3/envs/cider/lib/python3.8/site-packages/lightgbm/lib_lightgbm.so\n Reason: tried: '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/lib/libomp.dylib' (no such file), '/usr/lib/libomp.dylib' (no such file, not in dyld cache)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcider\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatastore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DataStore\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcider\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mml\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Learner\n", + "File \u001b[0;32m~/Documents/gpl/cider/cider/ml.py:42\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mhelpers\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m make_dir\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjoblib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dump, load \u001b[38;5;66;03m# type: ignore[import]\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlightgbm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LGBMRegressor \u001b[38;5;66;03m# type: ignore[import]\u001b[39;00m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DataFrame \u001b[38;5;28;01mas\u001b[39;00m PandasDataFrame\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstats\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m spearmanr \u001b[38;5;66;03m# type: ignore[import]\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/cider/lib/python3.8/site-packages/lightgbm/__init__.py:8\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03m\"\"\"LightGBM, Light Gradient Boosting Machine.\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \n\u001b[1;32m 4\u001b[0m \u001b[38;5;124;03mContributors: https://github.com/microsoft/LightGBM/graphs/contributors.\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpathlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbasic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Booster, Dataset, Sequence, register_logger\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcallback\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m early_stopping, log_evaluation, print_evaluation, record_evaluation, reset_parameter\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mengine\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CVBooster, cv, train\n", + "File \u001b[0;32m~/miniconda3/envs/cider/lib/python3.8/site-packages/lightgbm/basic.py:110\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LightGBMError(lib\u001b[38;5;241m.\u001b[39mLGBM_GetLastError()\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m 107\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lib\n\u001b[0;32m--> 110\u001b[0m _LIB \u001b[38;5;241m=\u001b[39m \u001b[43m_load_lib\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 113\u001b[0m NUMERIC_TYPES \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mint\u001b[39m, \u001b[38;5;28mfloat\u001b[39m, \u001b[38;5;28mbool\u001b[39m)\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_safe_call\u001b[39m(ret: \u001b[38;5;28mint\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/miniconda3/envs/cider/lib/python3.8/site-packages/lightgbm/basic.py:101\u001b[0m, in \u001b[0;36m_load_lib\u001b[0;34m()\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(lib_path) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 101\u001b[0m lib \u001b[38;5;241m=\u001b[39m \u001b[43mctypes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcdll\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLoadLibrary\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlib_path\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 102\u001b[0m lib\u001b[38;5;241m.\u001b[39mLGBM_GetLastError\u001b[38;5;241m.\u001b[39mrestype \u001b[38;5;241m=\u001b[39m ctypes\u001b[38;5;241m.\u001b[39mc_char_p\n\u001b[1;32m 103\u001b[0m callback \u001b[38;5;241m=\u001b[39m ctypes\u001b[38;5;241m.\u001b[39mCFUNCTYPE(\u001b[38;5;28;01mNone\u001b[39;00m, ctypes\u001b[38;5;241m.\u001b[39mc_char_p)\n", + "File \u001b[0;32m~/miniconda3/envs/cider/lib/python3.8/ctypes/__init__.py:451\u001b[0m, in \u001b[0;36mLibraryLoader.LoadLibrary\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 450\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mLoadLibrary\u001b[39m(\u001b[38;5;28mself\u001b[39m, name):\n\u001b[0;32m--> 451\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dlltype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/cider/lib/python3.8/ctypes/__init__.py:373\u001b[0m, in \u001b[0;36mCDLL.__init__\u001b[0;34m(self, name, mode, handle, use_errno, use_last_error, winmode)\u001b[0m\n\u001b[1;32m 370\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_FuncPtr \u001b[38;5;241m=\u001b[39m _FuncPtr\n\u001b[1;32m 372\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m handle \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 373\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle \u001b[38;5;241m=\u001b[39m \u001b[43m_dlopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 374\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 375\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_handle \u001b[38;5;241m=\u001b[39m handle\n", + "\u001b[0;31mOSError\u001b[0m: dlopen(/Users/leo/miniconda3/envs/cider/lib/python3.8/site-packages/lightgbm/lib_lightgbm.so, 0x0006): Library not loaded: /usr/local/opt/libomp/lib/libomp.dylib\n Referenced from: /Users/leo/miniconda3/envs/cider/lib/python3.8/site-packages/lightgbm/lib_lightgbm.so\n Reason: tried: '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/opt/libomp/lib/libomp.dylib' (no such file), '/usr/local/lib/libomp.dylib' (no such file), '/usr/lib/libomp.dylib' (no such file, not in dyld cache)" + ] + } + ], "source": [ - "from datastore import DataStore\n", - "from ml import Learner" + "from cider.datastore import DataStore\n", + "from cider.ml import Learner" ] }, { @@ -46,15 +74,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "\r", - "[Stage 42:===================> (1 + 2) / 3]\r", - "\r", " \r" ] } ], "source": [ - "datastore = DataStore(cfg_dir='./configs/config.yml')\n", + "# This path should point to your cider installation, where configs and data for this demo are located.\n", + "from pathlib import Path\n", + "cider_installation_directory = Path('../../cider')\n", + "\n", + "datastore = DataStore(config_file_path_string= cider_installation_directory / 'configs' / 'config_quickstart.yml')\n", "learner = Learner(datastore=datastore, clean_folders=True)" ] }, @@ -426,9 +455,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "conda_cider", "language": "python", - "name": "python3" + "name": "conda_cider" }, "language_info": { "codemirror_mode": { @@ -440,7 +469,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.1" + "version": "3.8.16" } }, "nbformat": 4, diff --git a/_build/html/_sources/quickstart/documentation_satellite.ipynb b/_build/html/_sources/quickstart/documentation_satellite.ipynb index 87189b7..cb0023b 100644 --- a/_build/html/_sources/quickstart/documentation_satellite.ipynb +++ b/_build/html/_sources/quickstart/documentation_satellite.ipynb @@ -15,8 +15,8 @@ "metadata": {}, "outputs": [], "source": [ - "from datastore import DataStore\n", - "from satellite import Satellite" + "from cider.datastore import DataStore\n", + "from cider.satellite import Satellite" ] }, { @@ -43,7 +43,11 @@ } ], "source": [ - "datastore = DataStore(cfg_dir='../../cider/configs/config.yml')\n", + "# This path should point to your cider installation, where configs and data for this demo are located.\n", + "from pathlib import Path\n", + "cider_installation_directory = Path('../../cider')\n", + "\n", + "datastore = DataStore(config_file_path_string= cider_installation_directory / 'configs' / 'config_quickstart.yml')\n", "satellite = Satellite(datastore=datastore)" ] }, @@ -238,7 +242,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.1" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/_build/html/_sources/quickstart/documentation_targeting.ipynb b/_build/html/_sources/quickstart/documentation_targeting.ipynb index 3a53f25..704e4c4 100644 --- a/_build/html/_sources/quickstart/documentation_targeting.ipynb +++ b/_build/html/_sources/quickstart/documentation_targeting.ipynb @@ -15,8 +15,8 @@ "metadata": {}, "outputs": [], "source": [ - "from datastore import DataStore\n", - "from targeting import Targeting" + "from cider.datastore import DataStore\n", + "from cider.targeting import Targeting" ] }, { @@ -34,7 +34,11 @@ "metadata": {}, "outputs": [], "source": [ - "datastore = DataStore(cfg_dir='./configs/config.yml')\n", + "# This path should point to your cider installation, where configs and data for this demo are located.\n", + "from pathlib import Path\n", + "cider_installation_directory = Path('../../cider')\n", + "\n", + "datastore = DataStore(config_file_path_string= cider_installation_directory / 'configs' / 'config_quickstart.yml')\n", "targeting = Targeting(datastore=datastore)" ] }, @@ -360,7 +364,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.1" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/_build/html/_sources/quickstart/installation.md b/_build/html/_sources/quickstart/installation.md index 5835daa..dfccc57 100644 --- a/_build/html/_sources/quickstart/installation.md +++ b/_build/html/_sources/quickstart/installation.md @@ -1,9 +1,36 @@ ## Installation +### Using Poetry To install, and manage dependencies and virtual environments this project uses Poetry. Follow the [instructions](https://python-poetry.org/docs/) to install Poetry. -From the root directory `poetry update` followed by `poetry install` - this will establish a venv with all the needed dependencies. +From the root directory `poetry update` followed by `poetry install` - this will establish a virtual environment with all the needed dependencies. -Once your venv is made you can use `poetry run [command]` to run a single CLI command inside the venv. +Once your virtual environment is made you can use `poetry run [command]` to run a single CLI command inside the virtual environment. -You can use `poetry shell` to enter into the venv. \ No newline at end of file +You can use `poetry shell` to enter into the virtual environment. + +### Using Pip +Another option is to install dependencies using Pip: + +1. Run `pip install .` in the top-level directory to automatically install Cider's dependencies. +2. Add Cider's top-level directory to your PYTHONPATH: + +```export PYTHONPATH=$PYTHONPATH:/path/to/cider``` + +You'll likely want to add this line to your .bashrc, .zshrc, or similar. + +### On Apple M1 +The above may not work on Apple M1 processors due to certain dependencies being unavailable or behaving badly. One option to sidestep this issue is to use [conda](https://docs.conda.io/en/latest/) to create a virtual environment which uses x86 architecture (rather than the ARM64 architecture which Apple's M1 chips use). + +[This guide](https://towardsdatascience.com/how-to-manage-conda-environments-on-an-apple-silicon-m1-mac-1e29cb3bad12) contains details on setting up Conda and creating such an environment. Summarized, the steps are as follows: + +1. Install conda, likely [Miniconda](https://docs.conda.io/projects/continuumio-conda/en/latest/user-guide/install/) +2. Run the following commands: +``` +CONDA_SUBDIR=osx-64 conda create -n my_env_name python=3.8 +conda activate my_env_name +conda config --env --set subdir osx-64 +``` +3. Run `conda install swig` + +And then follow either of the installation procedures above from within the environment you just created. \ No newline at end of file diff --git a/_build/html/_sources/quickstart/poverty_survey.md b/_build/html/_sources/quickstart/poverty_survey.md index 7307292..7a1df41 100644 --- a/_build/html/_sources/quickstart/poverty_survey.md +++ b/_build/html/_sources/quickstart/poverty_survey.md @@ -1,8 +1,8 @@ # Poverty Outcome Generation from Survey Data -Set up the configuration file and load some survey data (see {doc}`standardized data formats <../data_formats/survey_data>`) for file schema). +Set up the configuration file and load some survey data (see {doc}`standardized data formats <../data_formats/survey_data>` for file schema). ``` -datastore = DataStore('/path/to/config/file') +datastore = DataStore('/path/to/config_file.yml') outcomes_generator = SurveyOutcomesGenerator(datastore=datastore, clean_folders=True) ``` diff --git 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rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem} diff --git a/_build/html/_static/copybutton.css b/_build/html/_static/copybutton.css index 40eafe5..f1916ec 100644 --- a/_build/html/_static/copybutton.css +++ b/_build/html/_static/copybutton.css @@ -35,7 +35,8 @@ div.highlight { position: relative; } -.highlight:hover button.copybtn { +/* Show the copybutton */ +.highlight:hover button.copybtn, button.copybtn.success { opacity: 1; } diff --git a/_build/html/_static/copybutton.js b/_build/html/_static/copybutton.js index 40ac331..02c5c82 100644 --- a/_build/html/_static/copybutton.js +++ b/_build/html/_static/copybutton.js @@ -102,18 +102,25 @@ const clearSelection = () => { } } -// Changes tooltip text for two seconds, then changes it back +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + const temporarilyChangeTooltip = (el, oldText, newText) => { el.setAttribute('data-tooltip', newText) el.classList.add('success') - setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000) - setTimeout(() => el.classList.remove('success'), 2000) + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) } // Changes the copy button icon for two seconds, then changes it back const temporarilyChangeIcon = (el) => { el.innerHTML = iconCheck; - setTimeout(() => {el.innerHTML = iconCopy}, 2000) + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) } const addCopyButtonToCodeCells = () => { @@ -125,7 +132,8 @@ const addCopyButtonToCodeCells = () => { } // Add copybuttons to all of our code cells - const codeCells = document.querySelectorAll('div.highlight pre') + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) codeCells.forEach((codeCell, index) => { const id = codeCellId(index) codeCell.setAttribute('id', id) @@ -141,10 +149,25 @@ function escapeRegExp(string) { return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string } +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + // Callback when a copy button is clicked. Will be passed the node that was clicked // should then grab the text and replace pieces of text that shouldn't be used in output function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; var match; @@ -199,7 +222,12 @@ function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onl var copyTargetText = (trigger) => { var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - return formatCopyText(target.innerText, '', false, true, true, true, '', '') + + // get filtered text + let exclude = '.linenos, .gp'; + + let text = filterText(target, exclude); + return formatCopyText(text, '', false, true, true, true, '', '') } // Initialize with a callback so we can modify the text before copy diff --git a/_build/html/_static/copybutton_funcs.js b/_build/html/_static/copybutton_funcs.js index b9168c5..dbe1aaa 100644 --- a/_build/html/_static/copybutton_funcs.js +++ b/_build/html/_static/copybutton_funcs.js @@ -2,10 +2,25 @@ function escapeRegExp(string) { return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string } +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + // Callback when a copy button is clicked. Will be passed the node that was clicked // should then grab the text and replace pieces of text that shouldn't be used in output export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; var match; diff --git a/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css new file mode 100644 index 0000000..3225661 --- /dev/null +++ b/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css @@ -0,0 +1 @@ +.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) 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rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem} diff --git a/_build/html/_static/locales/ar/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/ar/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..15541a6 Binary files /dev/null and b/_build/html/_static/locales/ar/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/ar/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/ar/LC_MESSAGES/booktheme.po index b5c145f..eff38df 100644 --- a/_build/html/_static/locales/ar/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/ar/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: ar\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "بواسطة" + msgid "repository" msgstr "مخزن" -msgid "open issue" -msgstr "قضية مفتوحة" - -msgid "Contents" -msgstr "محتويات" - msgid "Fullscreen mode" msgstr "وضع ملء الشاشة" -msgid "Download this page" -msgstr "قم بتنزيل هذه الصفحة" +msgid "Contents" +msgstr "محتويات" msgid "Download source file" msgstr "تنزيل ملف المصدر" -msgid "Launch" -msgstr "إطلاق" - msgid "Edit this page" msgstr "قم بتحرير هذه الصفحة" -msgid "Toggle navigation" -msgstr "تبديل التنقل" +msgid "Last updated on" +msgstr "آخر تحديث في" -msgid "Theme by the" -msgstr "موضوع بواسطة" +msgid "Print to PDF" +msgstr "طباعة إلى PDF" -msgid "Source repository" -msgstr "مستودع المصدر" +msgid "suggest edit" +msgstr "أقترح تحرير" -msgid "Last updated on" -msgstr "آخر تحديث في" +msgid "Download this page" +msgstr "قم بتنزيل هذه الصفحة" + +msgid "Toggle navigation" +msgstr "تبديل التنقل" msgid "By the" msgstr "بواسطة" @@ -50,32 +47,29 @@ msgstr "بواسطة" msgid "Sphinx Book Theme" msgstr "موضوع كتاب أبو الهول" -msgid "Open an issue" -msgstr "افتح قضية" - -msgid "next page" -msgstr "الصفحة التالية" +msgid "previous page" +msgstr "الصفحة السابقة" -msgid "Copyright" -msgstr "حقوق النشر" +msgid "Source repository" +msgstr "مستودع المصدر" -msgid "Search this book..." -msgstr "بحث في هذا الكتاب ..." +msgid "open issue" +msgstr "قضية مفتوحة" -msgid "Print to PDF" -msgstr "طباعة إلى PDF" +msgid "Download notebook file" +msgstr "تنزيل ملف دفتر الملاحظات" -msgid "By" -msgstr "بواسطة" +msgid "Copyright" +msgstr "حقوق النشر" -msgid "previous page" -msgstr "الصفحة السابقة" +msgid "Theme by the" +msgstr "موضوع بواسطة" -msgid "Search the docs ..." -msgstr "ابحث في المستندات ..." +msgid "Open an issue" +msgstr "افتح قضية" -msgid "Download notebook file" -msgstr "تنزيل ملف دفتر الملاحظات" +msgid "next page" +msgstr "الصفحة التالية" -msgid "suggest edit" -msgstr "أقترح تحرير" +msgid "Launch" +msgstr "إطلاق" diff --git a/_build/html/_static/locales/bg/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/bg/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..da95120 Binary files /dev/null and b/_build/html/_static/locales/bg/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/bg/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/bg/LC_MESSAGES/booktheme.po index 4e1bc06..088f04f 100644 --- a/_build/html/_static/locales/bg/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/bg/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: bg\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "От" + msgid "repository" msgstr "хранилище" -msgid "open issue" -msgstr "отворен брой" - -msgid "Contents" -msgstr "Съдържание" - msgid "Fullscreen mode" msgstr "Режим на цял екран" -msgid "Download this page" -msgstr "Изтеглете тази страница" +msgid "Contents" +msgstr "Съдържание" msgid "Download source file" msgstr "Изтеглете изходния файл" -msgid "Launch" -msgstr "Стартиране" - msgid "Edit this page" msgstr "Редактирайте тази страница" -msgid "Toggle navigation" -msgstr "Превключване на навигацията" +msgid "Last updated on" +msgstr "Последна актуализация на" -msgid "Theme by the" -msgstr "Тема от" +msgid "Print to PDF" +msgstr "Печат в PDF" -msgid "Source repository" -msgstr "Хранилище на източника" +msgid "suggest edit" +msgstr "предложи редактиране" -msgid "Last updated on" -msgstr "Последна актуализация на" +msgid "Download this page" +msgstr "Изтеглете тази страница" + +msgid "Toggle navigation" +msgstr "Превключване на навигацията" msgid "By the" msgstr "По" @@ -50,32 +47,29 @@ msgstr "По" msgid "Sphinx Book Theme" msgstr "Тема на книгата Sphinx" -msgid "Open an issue" -msgstr "Отворете проблем" - -msgid "next page" -msgstr "Следваща страница" +msgid "previous page" +msgstr "предишна страница" -msgid "Copyright" -msgstr "Авторско право" +msgid "Source repository" +msgstr "Хранилище на източника" -msgid "Search this book..." -msgstr "Търсене в тази книга ..." +msgid "open issue" +msgstr "отворен брой" -msgid "Print to PDF" -msgstr "Печат в PDF" +msgid "Download notebook file" +msgstr "Изтеглете файла на бележника" -msgid "By" -msgstr "От" +msgid "Copyright" +msgstr "Авторско право" -msgid "previous page" -msgstr "предишна страница" +msgid "Theme by the" +msgstr "Тема от" -msgid "Search the docs ..." -msgstr "Търсене в документите ..." +msgid "Open an issue" +msgstr "Отворете проблем" -msgid "Download notebook file" -msgstr "Изтеглете файла на бележника" +msgid "next page" +msgstr "Следваща страница" -msgid "suggest edit" -msgstr "предложи редактиране" +msgid "Launch" +msgstr "Стартиране" diff --git a/_build/html/_static/locales/bn/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/bn/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..6b96639 Binary files /dev/null and b/_build/html/_static/locales/bn/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/bn/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/bn/LC_MESSAGES/booktheme.po index 6722be2..80b5808 100644 --- a/_build/html/_static/locales/bn/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/bn/LC_MESSAGES/booktheme.po @@ -8,32 +8,26 @@ msgstr "" "Language: bn\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "খোলা সমস্যা" - -msgid "Download this page" -msgstr "এই পৃষ্ঠাটি ডাউনলোড করুন" +msgid "By" +msgstr "দ্বারা" msgid "Download source file" msgstr "উত্স ফাইল ডাউনলোড করুন" -msgid "Launch" -msgstr "শুরু করা" - msgid "Edit this page" msgstr "এই পৃষ্ঠাটি সম্পাদনা করুন" -msgid "Toggle navigation" -msgstr "নেভিগেশন টগল করুন" +msgid "Last updated on" +msgstr "সর্বশেষ আপডেট" -msgid "Theme by the" -msgstr "থিম দ্বারা" +msgid "Print to PDF" +msgstr "পিডিএফ প্রিন্ট করুন" -msgid "Source repository" -msgstr "উত্স সংগ্রহস্থল" +msgid "Download this page" +msgstr "এই পৃষ্ঠাটি ডাউনলোড করুন" -msgid "Last updated on" -msgstr "সর্বশেষ আপডেট" +msgid "Toggle navigation" +msgstr "নেভিগেশন টগল করুন" msgid "By the" msgstr "দ্বারা" @@ -41,26 +35,29 @@ msgstr "দ্বারা" msgid "Sphinx Book Theme" msgstr "স্পিনিক্স বুক থিম" -msgid "Open an issue" -msgstr "একটি সমস্যা খুলুন" +msgid "previous page" +msgstr "আগের পৃষ্ঠা" -msgid "next page" -msgstr "পরবর্তী পৃষ্ঠা" +msgid "Source repository" +msgstr "উত্স সংগ্রহস্থল" + +msgid "open issue" +msgstr "খোলা সমস্যা" + +msgid "Download notebook file" +msgstr "নোটবুক ফাইল ডাউনলোড করুন" msgid "Copyright" msgstr "কপিরাইট" -msgid "Search this book..." -msgstr "এই বইটি অনুসন্ধান করুন ..." - -msgid "Print to PDF" -msgstr "পিডিএফ প্রিন্ট করুন" +msgid "Theme by the" +msgstr "থিম দ্বারা" -msgid "By" -msgstr "দ্বারা" +msgid "Open an issue" +msgstr "একটি সমস্যা খুলুন" -msgid "previous page" -msgstr "আগের পৃষ্ঠা" +msgid "next page" +msgstr "পরবর্তী পৃষ্ঠা" -msgid "Download notebook file" -msgstr "নোটবুক ফাইল ডাউনলোড করুন" +msgid "Launch" +msgstr "শুরু করা" diff --git a/_build/html/_static/locales/ca/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/ca/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..a4dd30e Binary files /dev/null and b/_build/html/_static/locales/ca/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/ca/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/ca/LC_MESSAGES/booktheme.po index 546e395..0f4bdf5 100644 --- a/_build/html/_static/locales/ca/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/ca/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: ca\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "número obert" - -msgid "Download this page" -msgstr "Descarregueu aquesta pàgina" +msgid "By" +msgstr "Per" msgid "Download source file" msgstr "Baixeu el fitxer font" -msgid "Launch" -msgstr "Llançament" - msgid "Edit this page" msgstr "Editeu aquesta pàgina" -msgid "Toggle navigation" -msgstr "Commuta la navegació" +msgid "Last updated on" +msgstr "Darrera actualització el" -msgid "Theme by the" -msgstr "Tema del" +msgid "Print to PDF" +msgstr "Imprimeix a PDF" -msgid "Source repository" -msgstr "Dipòsit de fonts" +msgid "suggest edit" +msgstr "suggerir edició" -msgid "Last updated on" -msgstr "Darrera actualització el" +msgid "Download this page" +msgstr "Descarregueu aquesta pàgina" + +msgid "Toggle navigation" +msgstr "Commuta la navegació" msgid "By the" msgstr "Per la" @@ -41,29 +38,29 @@ msgstr "Per la" msgid "Sphinx Book Theme" msgstr "Tema del llibre Esfinx" -msgid "Open an issue" -msgstr "Obriu un número" +msgid "previous page" +msgstr "Pàgina anterior" -msgid "next page" -msgstr "pàgina següent" +msgid "Source repository" +msgstr "Dipòsit de fonts" -msgid "Copyright" -msgstr "Copyright" +msgid "open issue" +msgstr "número obert" -msgid "Search this book..." -msgstr "Cerca en aquest llibre ..." +msgid "Download notebook file" +msgstr "Descarregar fitxer de quadern" -msgid "Print to PDF" -msgstr "Imprimeix a PDF" +msgid "Copyright" +msgstr "Copyright" -msgid "By" -msgstr "Per" +msgid "Theme by the" +msgstr "Tema del" -msgid "previous page" -msgstr "Pàgina anterior" +msgid "Open an issue" +msgstr "Obriu un número" -msgid "Download notebook file" -msgstr "Descarregar fitxer de quadern" +msgid "next page" +msgstr "pàgina següent" -msgid "suggest edit" -msgstr "suggerir edició" +msgid "Launch" +msgstr "Llançament" diff --git a/_build/html/_static/locales/cs/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/cs/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..c39e01a Binary files /dev/null and b/_build/html/_static/locales/cs/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/cs/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/cs/LC_MESSAGES/booktheme.po index 4889556..12632b5 100644 --- a/_build/html/_static/locales/cs/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/cs/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: cs\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Podle" + msgid "repository" msgstr "úložiště" -msgid "open issue" -msgstr "otevřené číslo" - -msgid "Contents" -msgstr "Obsah" - msgid "Fullscreen mode" msgstr "Režim celé obrazovky" -msgid "Download this page" -msgstr "Stáhněte si tuto stránku" +msgid "Contents" +msgstr "Obsah" msgid "Download source file" msgstr "Stáhněte si zdrojový soubor" -msgid "Launch" -msgstr "Zahájení" - msgid "Edit this page" msgstr "Upravit tuto stránku" -msgid "Toggle navigation" -msgstr "Přepnout navigaci" +msgid "Last updated on" +msgstr "Naposledy aktualizováno" -msgid "Theme by the" -msgstr "Téma od" +msgid "Print to PDF" +msgstr "Tisk do PDF" -msgid "Source repository" -msgstr "Zdrojové úložiště" +msgid "suggest edit" +msgstr "navrhnout úpravy" -msgid "Last updated on" -msgstr "Naposledy aktualizováno" +msgid "Download this page" +msgstr "Stáhněte si tuto stránku" + +msgid "Toggle navigation" +msgstr "Přepnout navigaci" msgid "By the" msgstr "Podle" @@ -50,32 +47,29 @@ msgstr "Podle" msgid "Sphinx Book Theme" msgstr "Téma knihy Sfinga" -msgid "Open an issue" -msgstr "Otevřete problém" - -msgid "next page" -msgstr "další strana" +msgid "previous page" +msgstr "předchozí stránka" -msgid "Copyright" -msgstr "autorská práva" +msgid "Source repository" +msgstr "Zdrojové úložiště" -msgid "Search this book..." -msgstr "Hledat v této knize ..." +msgid "open issue" +msgstr "otevřené číslo" -msgid "Print to PDF" -msgstr "Tisk do PDF" +msgid "Download notebook file" +msgstr "Stáhnout soubor poznámkového bloku" -msgid "By" -msgstr "Podle" +msgid "Copyright" +msgstr "autorská práva" -msgid "previous page" -msgstr "předchozí stránka" +msgid "Theme by the" +msgstr "Téma od" -msgid "Search the docs ..." -msgstr "Hledat v dokumentech ..." +msgid "Open an issue" +msgstr "Otevřete problém" -msgid "Download notebook file" -msgstr "Stáhnout soubor poznámkového bloku" +msgid "next page" +msgstr "další strana" -msgid "suggest edit" -msgstr "navrhnout úpravy" +msgid "Launch" +msgstr "Zahájení" diff --git a/_build/html/_static/locales/da/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/da/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..f43157d Binary files /dev/null and b/_build/html/_static/locales/da/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/da/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/da/LC_MESSAGES/booktheme.po index 675a05f..145e211 100644 --- a/_build/html/_static/locales/da/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/da/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: da\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Ved" + msgid "repository" msgstr "lager" -msgid "open issue" -msgstr "åbent nummer" - -msgid "Contents" -msgstr "Indhold" - msgid "Fullscreen mode" msgstr "Fuldskærmstilstand" -msgid "Download this page" -msgstr "Download denne side" +msgid "Contents" +msgstr "Indhold" msgid "Download source file" msgstr "Download kildefil" -msgid "Launch" -msgstr "Start" - msgid "Edit this page" msgstr "Rediger denne side" -msgid "Toggle navigation" -msgstr "Skift navigation" +msgid "Last updated on" +msgstr "Sidst opdateret den" -msgid "Theme by the" -msgstr "Tema af" +msgid "Print to PDF" +msgstr "Udskriv til PDF" -msgid "Source repository" -msgstr "Kildelager" +msgid "suggest edit" +msgstr "foreslå redigering" -msgid "Last updated on" -msgstr "Sidst opdateret den" +msgid "Download this page" +msgstr "Download denne side" + +msgid "Toggle navigation" +msgstr "Skift navigation" msgid "By the" msgstr "Ved" @@ -50,32 +47,29 @@ msgstr "Ved" msgid "Sphinx Book Theme" msgstr "Sphinx bogtema" -msgid "Open an issue" -msgstr "Åbn et problem" - -msgid "next page" -msgstr "Næste side" +msgid "previous page" +msgstr "forrige side" -msgid "Copyright" -msgstr "ophavsret" +msgid "Source repository" +msgstr "Kildelager" -msgid "Search this book..." -msgstr "Søg i denne bog ..." +msgid "open issue" +msgstr "åbent nummer" -msgid "Print to PDF" -msgstr "Udskriv til PDF" +msgid "Download notebook file" +msgstr "Download notesbog-fil" -msgid "By" -msgstr "Ved" +msgid "Copyright" +msgstr "ophavsret" -msgid "previous page" -msgstr "forrige side" +msgid "Theme by the" +msgstr "Tema af" -msgid "Search the docs ..." -msgstr "Søg i dokumenterne ..." +msgid "Open an issue" +msgstr "Åbn et problem" -msgid "Download notebook file" -msgstr "Download notesbog-fil" +msgid "next page" +msgstr "Næste side" -msgid "suggest edit" -msgstr "foreslå redigering" +msgid "Launch" +msgstr "Start" diff --git a/_build/html/_static/locales/de/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/de/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..648b565 Binary files /dev/null and b/_build/html/_static/locales/de/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/de/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/de/LC_MESSAGES/booktheme.po index 70eaab5..eb2f605 100644 --- a/_build/html/_static/locales/de/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/de/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: de\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Durch" + msgid "repository" msgstr "Repository" -msgid "open issue" -msgstr "offenes Thema" - -msgid "Contents" -msgstr "Inhalt" - msgid "Fullscreen mode" msgstr "Vollbildmodus" -msgid "Download this page" -msgstr "Laden Sie diese Seite herunter" +msgid "Contents" +msgstr "Inhalt" msgid "Download source file" msgstr "Quelldatei herunterladen" -msgid "Launch" -msgstr "Starten" - msgid "Edit this page" msgstr "Bearbeite diese Seite" -msgid "Toggle navigation" -msgstr "Navigation umschalten" +msgid "Last updated on" +msgstr "Zuletzt aktualisiert am" -msgid "Theme by the" -msgstr "Thema von der" +msgid "Print to PDF" +msgstr "In PDF drucken" -msgid "Source repository" -msgstr "Quell-Repository" +msgid "suggest edit" +msgstr "vorschlagen zu bearbeiten" -msgid "Last updated on" -msgstr "Zuletzt aktualisiert am" +msgid "Download this page" +msgstr "Laden Sie diese Seite herunter" + +msgid "Toggle navigation" +msgstr "Navigation umschalten" msgid "By the" msgstr "Bis zum" @@ -50,32 +47,29 @@ msgstr "Bis zum" msgid "Sphinx Book Theme" msgstr "Sphinx-Buch-Thema" -msgid "Open an issue" -msgstr "Öffnen Sie ein Problem" - -msgid "next page" -msgstr "Nächste Seite" +msgid "previous page" +msgstr "vorherige Seite" -msgid "Copyright" -msgstr "Urheberrechte ©" +msgid "Source repository" +msgstr "Quell-Repository" -msgid "Search this book..." -msgstr "Dieses Buch durchsuchen ..." +msgid "open issue" +msgstr "offenes Thema" -msgid "Print to PDF" -msgstr "In PDF drucken" +msgid "Download notebook file" +msgstr "Notebook-Datei herunterladen" -msgid "By" -msgstr "Durch" +msgid "Copyright" +msgstr "Urheberrechte ©" -msgid "previous page" -msgstr "vorherige Seite" +msgid "Theme by the" +msgstr "Thema von der" -msgid "Search the docs ..." -msgstr "Durchsuchen Sie die Dokumente ..." +msgid "Open an issue" +msgstr "Öffnen Sie ein Problem" -msgid "Download notebook file" -msgstr "Notebook-Datei herunterladen" +msgid "next page" +msgstr "Nächste Seite" -msgid "suggest edit" -msgstr "vorschlagen zu bearbeiten" +msgid "Launch" +msgstr "Starten" diff --git a/_build/html/_static/locales/el/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/el/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..fca6e93 Binary files /dev/null and b/_build/html/_static/locales/el/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/el/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/el/LC_MESSAGES/booktheme.po index 222cfa3..9e19fb2 100644 --- a/_build/html/_static/locales/el/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/el/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: el\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Με" + msgid "repository" msgstr "αποθήκη" -msgid "open issue" -msgstr "ανοιχτό ζήτημα" - -msgid "Contents" -msgstr "Περιεχόμενα" - msgid "Fullscreen mode" msgstr "ΛΕΙΤΟΥΡΓΙΑ ΠΛΗΡΟΥΣ ΟΘΟΝΗΣ" -msgid "Download this page" -msgstr "Λήψη αυτής της σελίδας" +msgid "Contents" +msgstr "Περιεχόμενα" msgid "Download source file" msgstr "Λήψη αρχείου προέλευσης" -msgid "Launch" -msgstr "Εκτόξευση" - msgid "Edit this page" msgstr "Επεξεργαστείτε αυτήν τη σελίδα" -msgid "Toggle navigation" -msgstr "Εναλλαγή πλοήγησης" +msgid "Last updated on" +msgstr "Τελευταία ενημέρωση στις" -msgid "Theme by the" -msgstr "Θέμα από το" +msgid "Print to PDF" +msgstr "Εκτύπωση σε PDF" -msgid "Source repository" -msgstr "Αποθήκη πηγής" +msgid "suggest edit" +msgstr "προτείνω επεξεργασία" -msgid "Last updated on" -msgstr "Τελευταία ενημέρωση στις" +msgid "Download this page" +msgstr "Λήψη αυτής της σελίδας" + +msgid "Toggle navigation" +msgstr "Εναλλαγή πλοήγησης" msgid "By the" msgstr "Από το" @@ -50,32 +47,29 @@ msgstr "Από το" msgid "Sphinx Book Theme" msgstr "Θέμα βιβλίου Sphinx" -msgid "Open an issue" -msgstr "Ανοίξτε ένα ζήτημα" - -msgid "next page" -msgstr "επόμενη σελίδα" +msgid "previous page" +msgstr "προηγούμενη σελίδα" -msgid "Copyright" -msgstr "Πνευματική ιδιοκτησία" +msgid "Source repository" +msgstr "Αποθήκη πηγής" -msgid "Search this book..." -msgstr "Αναζήτηση σε αυτό το βιβλίο ..." +msgid "open issue" +msgstr "ανοιχτό ζήτημα" -msgid "Print to PDF" -msgstr "Εκτύπωση σε PDF" +msgid "Download notebook file" +msgstr "Λήψη αρχείου σημειωματάριου" -msgid "By" -msgstr "Με" +msgid "Copyright" +msgstr "Πνευματική ιδιοκτησία" -msgid "previous page" -msgstr "προηγούμενη σελίδα" +msgid "Theme by the" +msgstr "Θέμα από το" -msgid "Search the docs ..." -msgstr "Αναζήτηση στα έγγραφα ..." +msgid "Open an issue" +msgstr "Ανοίξτε ένα ζήτημα" -msgid "Download notebook file" -msgstr "Λήψη αρχείου σημειωματάριου" +msgid "next page" +msgstr "επόμενη σελίδα" -msgid "suggest edit" -msgstr "προτείνω επεξεργασία" +msgid "Launch" +msgstr "Εκτόξευση" diff --git a/_build/html/_static/locales/eo/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/eo/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..d1072bb Binary files /dev/null and b/_build/html/_static/locales/eo/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/eo/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/eo/LC_MESSAGES/booktheme.po index bdf994b..11d0adc 100644 --- a/_build/html/_static/locales/eo/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/eo/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: eo\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "De" + msgid "repository" msgstr "deponejo" -msgid "open issue" -msgstr "malferma numero" - -msgid "Contents" -msgstr "Enhavo" - msgid "Fullscreen mode" msgstr "Plenekrana reĝimo" -msgid "Download this page" -msgstr "Elŝutu ĉi tiun paĝon" +msgid "Contents" +msgstr "Enhavo" msgid "Download source file" msgstr "Elŝutu fontodosieron" -msgid "Launch" -msgstr "Lanĉo" - msgid "Edit this page" msgstr "Redaktu ĉi tiun paĝon" -msgid "Toggle navigation" -msgstr "Ŝalti navigadon" +msgid "Last updated on" +msgstr "Laste ĝisdatigita la" -msgid "Theme by the" -msgstr "Temo de la" +msgid "Print to PDF" +msgstr "Presi al PDF" -msgid "Source repository" -msgstr "Fonto-deponejo" +msgid "suggest edit" +msgstr "sugesti redaktadon" -msgid "Last updated on" -msgstr "Laste ĝisdatigita la" +msgid "Download this page" +msgstr "Elŝutu ĉi tiun paĝon" + +msgid "Toggle navigation" +msgstr "Ŝalti navigadon" msgid "By the" msgstr "Per la" @@ -50,32 +47,29 @@ msgstr "Per la" msgid "Sphinx Book Theme" msgstr "Sfinksa Libro-Temo" -msgid "Open an issue" -msgstr "Malfermu numeron" - -msgid "next page" -msgstr "sekva paĝo" +msgid "previous page" +msgstr "antaŭa paĝo" -msgid "Copyright" -msgstr "Kopirajto" +msgid "Source repository" +msgstr "Fonto-deponejo" -msgid "Search this book..." -msgstr "Serĉu ĉi tiun libron ..." +msgid "open issue" +msgstr "malferma numero" -msgid "Print to PDF" -msgstr "Presi al PDF" +msgid "Download notebook file" +msgstr "Elŝutu kajeran dosieron" -msgid "By" -msgstr "De" +msgid "Copyright" +msgstr "Kopirajto" -msgid "previous page" -msgstr "antaŭa paĝo" +msgid "Theme by the" +msgstr "Temo de la" -msgid "Search the docs ..." -msgstr "Serĉu la dokumentojn ..." +msgid "Open an issue" +msgstr "Malfermu numeron" -msgid "Download notebook file" -msgstr "Elŝutu kajeran dosieron" +msgid "next page" +msgstr "sekva paĝo" -msgid "suggest edit" -msgstr "sugesti redaktadon" +msgid "Launch" +msgstr "Lanĉo" diff --git a/_build/html/_static/locales/es/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/es/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..ba2ee4d Binary files /dev/null and b/_build/html/_static/locales/es/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/es/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/es/LC_MESSAGES/booktheme.po index b23d3ec..94f936a 100644 --- a/_build/html/_static/locales/es/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/es/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: es\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Por" + msgid "repository" msgstr "repositorio" -msgid "open issue" -msgstr "Tema abierto" - -msgid "Contents" -msgstr "Contenido" - msgid "Fullscreen mode" msgstr "Modo de pantalla completa" -msgid "Download this page" -msgstr "Descarga esta pagina" +msgid "Contents" +msgstr "Contenido" msgid "Download source file" msgstr "Descargar archivo fuente" -msgid "Launch" -msgstr "Lanzamiento" - msgid "Edit this page" msgstr "Edita esta página" -msgid "Toggle navigation" -msgstr "Navegación de palanca" +msgid "Last updated on" +msgstr "Ultima actualización en" -msgid "Theme by the" -msgstr "Tema por el" +msgid "Print to PDF" +msgstr "Imprimir en PDF" -msgid "Source repository" -msgstr "Repositorio de origen" +msgid "suggest edit" +msgstr "sugerir editar" -msgid "Last updated on" -msgstr "Ultima actualización en" +msgid "Download this page" +msgstr "Descarga esta pagina" + +msgid "Toggle navigation" +msgstr "Navegación de palanca" msgid "By the" msgstr "Por el" @@ -50,32 +47,29 @@ msgstr "Por el" msgid "Sphinx Book Theme" msgstr "Tema del libro de la esfinge" -msgid "Open an issue" -msgstr "Abrir un problema" - -msgid "next page" -msgstr "siguiente página" +msgid "previous page" +msgstr "pagina anterior" -msgid "Copyright" -msgstr "Derechos de autor" +msgid "Source repository" +msgstr "Repositorio de origen" -msgid "Search this book..." -msgstr "Buscar este libro ..." +msgid "open issue" +msgstr "Tema abierto" -msgid "Print to PDF" -msgstr "Imprimir en PDF" +msgid "Download notebook file" +msgstr "Descargar archivo de cuaderno" -msgid "By" -msgstr "Por" +msgid "Copyright" +msgstr "Derechos de autor" -msgid "previous page" -msgstr "pagina anterior" +msgid "Theme by the" +msgstr "Tema por el" -msgid "Search the docs ..." -msgstr "Buscar los documentos ..." +msgid "Open an issue" +msgstr "Abrir un problema" -msgid "Download notebook file" -msgstr "Descargar archivo de cuaderno" +msgid "next page" +msgstr "siguiente página" -msgid "suggest edit" -msgstr "sugerir editar" +msgid "Launch" +msgstr "Lanzamiento" diff --git a/_build/html/_static/locales/et/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/et/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..983b823 Binary files /dev/null and b/_build/html/_static/locales/et/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/et/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/et/LC_MESSAGES/booktheme.po index 664c246..e371f3b 100644 --- a/_build/html/_static/locales/et/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/et/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: et\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Kõrval" + msgid "repository" msgstr "hoidla" -msgid "open issue" -msgstr "avatud küsimus" - -msgid "Contents" -msgstr "Sisu" - msgid "Fullscreen mode" msgstr "Täisekraanirežiim" -msgid "Download this page" -msgstr "Laadige see leht alla" +msgid "Contents" +msgstr "Sisu" msgid "Download source file" msgstr "Laadige alla lähtefail" -msgid "Launch" -msgstr "Käivitage" - msgid "Edit this page" msgstr "Muutke seda lehte" -msgid "Toggle navigation" -msgstr "Lülita navigeerimine sisse" +msgid "Last updated on" +msgstr "Viimati uuendatud" -msgid "Theme by the" -msgstr "Teema" +msgid "Print to PDF" +msgstr "Prindi PDF-i" -msgid "Source repository" -msgstr "Allikahoidla" +msgid "suggest edit" +msgstr "soovita muuta" -msgid "Last updated on" -msgstr "Viimati uuendatud" +msgid "Download this page" +msgstr "Laadige see leht alla" + +msgid "Toggle navigation" +msgstr "Lülita navigeerimine sisse" msgid "By the" msgstr "Autor" @@ -50,32 +47,29 @@ msgstr "Autor" msgid "Sphinx Book Theme" msgstr "Sfinksiraamatu teema" -msgid "Open an issue" -msgstr "Avage probleem" - -msgid "next page" -msgstr "järgmine leht" +msgid "previous page" +msgstr "eelmine leht" -msgid "Copyright" -msgstr "Autoriõigus" +msgid "Source repository" +msgstr "Allikahoidla" -msgid "Search this book..." -msgstr "Otsige sellest raamatust ..." +msgid "open issue" +msgstr "avatud küsimus" -msgid "Print to PDF" -msgstr "Prindi PDF-i" +msgid "Download notebook file" +msgstr "Laadige sülearvuti fail alla" -msgid "By" -msgstr "Kõrval" +msgid "Copyright" +msgstr "Autoriõigus" -msgid "previous page" -msgstr "eelmine leht" +msgid "Theme by the" +msgstr "Teema" -msgid "Search the docs ..." -msgstr "Dokumentidest otsimine ..." +msgid "Open an issue" +msgstr "Avage probleem" -msgid "Download notebook file" -msgstr "Laadige sülearvuti fail alla" +msgid "next page" +msgstr "järgmine leht" -msgid "suggest edit" -msgstr "soovita muuta" +msgid "Launch" +msgstr "Käivitage" diff --git a/_build/html/_static/locales/fi/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/fi/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..d8ac054 Binary files /dev/null and b/_build/html/_static/locales/fi/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/fi/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/fi/LC_MESSAGES/booktheme.po index c4b0d30..2ba0faa 100644 --- a/_build/html/_static/locales/fi/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/fi/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: fi\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Tekijä" + msgid "repository" msgstr "arkisto" -msgid "open issue" -msgstr "avoin ongelma" - -msgid "Contents" -msgstr "Sisällys" - msgid "Fullscreen mode" msgstr "Koko näytön tila" -msgid "Download this page" -msgstr "Lataa tämä sivu" +msgid "Contents" +msgstr "Sisällys" msgid "Download source file" msgstr "Lataa lähdetiedosto" -msgid "Launch" -msgstr "Tuoda markkinoille" - msgid "Edit this page" msgstr "Muokkaa tätä sivua" -msgid "Toggle navigation" -msgstr "Vaihda navigointia" +msgid "Last updated on" +msgstr "Viimeksi päivitetty" -msgid "Theme by the" -msgstr "Teeman tekijä" +msgid "Print to PDF" +msgstr "Tulosta PDF-tiedostoon" -msgid "Source repository" -msgstr "Lähteen arkisto" +msgid "suggest edit" +msgstr "ehdottaa muokkausta" -msgid "Last updated on" -msgstr "Viimeksi päivitetty" +msgid "Download this page" +msgstr "Lataa tämä sivu" + +msgid "Toggle navigation" +msgstr "Vaihda navigointia" msgid "By the" msgstr "Mukaan" @@ -50,32 +47,29 @@ msgstr "Mukaan" msgid "Sphinx Book Theme" msgstr "Sphinx-kirjan teema" -msgid "Open an issue" -msgstr "Avaa ongelma" - -msgid "next page" -msgstr "seuraava sivu" +msgid "previous page" +msgstr "Edellinen sivu" -msgid "Copyright" -msgstr "Tekijänoikeus" +msgid "Source repository" +msgstr "Lähteen arkisto" -msgid "Search this book..." -msgstr "Hae tästä kirjasta ..." +msgid "open issue" +msgstr "avoin ongelma" -msgid "Print to PDF" -msgstr "Tulosta PDF-tiedostoon" +msgid "Download notebook file" +msgstr "Lataa muistikirjatiedosto" -msgid "By" -msgstr "Tekijä" +msgid "Copyright" +msgstr "Tekijänoikeus" -msgid "previous page" -msgstr "Edellinen sivu" +msgid "Theme by the" +msgstr "Teeman tekijä" -msgid "Search the docs ..." -msgstr "Hae dokumenteista ..." +msgid "Open an issue" +msgstr "Avaa ongelma" -msgid "Download notebook file" -msgstr "Lataa muistikirjatiedosto" +msgid "next page" +msgstr "seuraava sivu" -msgid "suggest edit" -msgstr "ehdottaa muokkausta" +msgid "Launch" +msgstr "Tuoda markkinoille" diff --git a/_build/html/_static/locales/fr/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/fr/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..f663d39 Binary files /dev/null and b/_build/html/_static/locales/fr/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/fr/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/fr/LC_MESSAGES/booktheme.po index 93762ce..e18714c 100644 --- a/_build/html/_static/locales/fr/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/fr/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: fr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Par" + msgid "repository" msgstr "dépôt" -msgid "open issue" -msgstr "signaler un problème" - -msgid "Contents" -msgstr "Contenu" - msgid "Fullscreen mode" msgstr "Mode plein écran" -msgid "Download this page" -msgstr "Téléchargez cette page" +msgid "Contents" +msgstr "Contenu" msgid "Download source file" msgstr "Télécharger le fichier source" -msgid "Launch" -msgstr "lancement" - msgid "Edit this page" msgstr "Modifier cette page" -msgid "Toggle navigation" -msgstr "Basculer la navigation" +msgid "Last updated on" +msgstr "Dernière mise à jour le" -msgid "Theme by the" -msgstr "Thème par le" +msgid "Print to PDF" +msgstr "Imprimer au format PDF" -msgid "Source repository" -msgstr "Dépôt source" +msgid "suggest edit" +msgstr "suggestion de modification" -msgid "Last updated on" -msgstr "Dernière mise à jour le" +msgid "Download this page" +msgstr "Téléchargez cette page" + +msgid "Toggle navigation" +msgstr "Basculer la navigation" msgid "By the" msgstr "Par le" @@ -50,32 +47,29 @@ msgstr "Par le" msgid "Sphinx Book Theme" msgstr "Thème du livre Sphinx" -msgid "Open an issue" -msgstr "Ouvrez un problème" - -msgid "next page" -msgstr "page suivante" +msgid "previous page" +msgstr "page précédente" -msgid "Copyright" -msgstr "droits d'auteur" +msgid "Source repository" +msgstr "Dépôt source" -msgid "Search this book..." -msgstr "Rechercher dans ce livre ..." +msgid "open issue" +msgstr "signaler un problème" -msgid "Print to PDF" -msgstr "Imprimer au format PDF" +msgid "Download notebook file" +msgstr "Télécharger le fichier notebook" -msgid "By" -msgstr "Par" +msgid "Copyright" +msgstr "droits d'auteur" -msgid "previous page" -msgstr "page précédente" +msgid "Theme by the" +msgstr "Thème par le" -msgid "Search the docs ..." -msgstr "Rechercher dans les documents ..." +msgid "Open an issue" +msgstr "Ouvrez un problème" -msgid "Download notebook file" -msgstr "Télécharger le fichier notebook" +msgid "next page" +msgstr "page suivante" -msgid "suggest edit" -msgstr "suggestion de modification" +msgid "Launch" +msgstr "lancement" diff --git a/_build/html/_static/locales/hr/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/hr/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..eca4a1a Binary files /dev/null and b/_build/html/_static/locales/hr/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/hr/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/hr/LC_MESSAGES/booktheme.po index 7e94599..c1207c3 100644 --- a/_build/html/_static/locales/hr/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/hr/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: hr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Po" + msgid "repository" msgstr "spremište" -msgid "open issue" -msgstr "otvoreno izdanje" - -msgid "Contents" -msgstr "Sadržaj" - msgid "Fullscreen mode" msgstr "Način preko cijelog zaslona" -msgid "Download this page" -msgstr "Preuzmite ovu stranicu" +msgid "Contents" +msgstr "Sadržaj" msgid "Download source file" msgstr "Preuzmi izvornu datoteku" -msgid "Launch" -msgstr "Pokrenite" - msgid "Edit this page" msgstr "Uredite ovu stranicu" -msgid "Toggle navigation" -msgstr "Uključi / isključi navigaciju" +msgid "Last updated on" +msgstr "Posljednje ažuriranje:" -msgid "Theme by the" -msgstr "Tema autora" +msgid "Print to PDF" +msgstr "Ispis u PDF" -msgid "Source repository" -msgstr "Izvorno spremište" +msgid "suggest edit" +msgstr "predloži uređivanje" -msgid "Last updated on" -msgstr "Posljednje ažuriranje:" +msgid "Download this page" +msgstr "Preuzmite ovu stranicu" + +msgid "Toggle navigation" +msgstr "Uključi / isključi navigaciju" msgid "By the" msgstr "Od strane" @@ -50,32 +47,29 @@ msgstr "Od strane" msgid "Sphinx Book Theme" msgstr "Tema knjige Sphinx" -msgid "Open an issue" -msgstr "Otvorite izdanje" - -msgid "next page" -msgstr "sljedeća stranica" +msgid "previous page" +msgstr "Prethodna stranica" -msgid "Copyright" -msgstr "Autorska prava" +msgid "Source repository" +msgstr "Izvorno spremište" -msgid "Search this book..." -msgstr "Pretražite ovu knjigu ..." +msgid "open issue" +msgstr "otvoreno izdanje" -msgid "Print to PDF" -msgstr "Ispis u PDF" +msgid "Download notebook file" +msgstr "Preuzmi datoteku bilježnice" -msgid "By" -msgstr "Po" +msgid "Copyright" +msgstr "Autorska prava" -msgid "previous page" -msgstr "Prethodna stranica" +msgid "Theme by the" +msgstr "Tema autora" -msgid "Search the docs ..." -msgstr "Pretražite dokumente ..." +msgid "Open an issue" +msgstr "Otvorite izdanje" -msgid "Download notebook file" -msgstr "Preuzmi datoteku bilježnice" +msgid "next page" +msgstr "sljedeća stranica" -msgid "suggest edit" -msgstr "predloži uređivanje" +msgid "Launch" +msgstr "Pokrenite" diff --git a/_build/html/_static/locales/id/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/id/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..d07a06a Binary files /dev/null and b/_build/html/_static/locales/id/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/id/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/id/LC_MESSAGES/booktheme.po index fbeca80..ae1b27a 100644 --- a/_build/html/_static/locales/id/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/id/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: id\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Oleh" + msgid "repository" msgstr "gudang" -msgid "open issue" -msgstr "masalah terbuka" - -msgid "Contents" -msgstr "Isi" - msgid "Fullscreen mode" msgstr "Mode layar penuh" -msgid "Download this page" -msgstr "Unduh halaman ini" +msgid "Contents" +msgstr "Isi" msgid "Download source file" msgstr "Unduh file sumber" -msgid "Launch" -msgstr "Meluncurkan" - msgid "Edit this page" msgstr "Edit halaman ini" -msgid "Toggle navigation" -msgstr "Alihkan navigasi" +msgid "Last updated on" +msgstr "Terakhir diperbarui saat" -msgid "Theme by the" -msgstr "Tema oleh" +msgid "Print to PDF" +msgstr "Cetak ke PDF" -msgid "Source repository" -msgstr "Repositori sumber" +msgid "suggest edit" +msgstr "menyarankan edit" -msgid "Last updated on" -msgstr "Terakhir diperbarui saat" +msgid "Download this page" +msgstr "Unduh halaman ini" + +msgid "Toggle navigation" +msgstr "Alihkan navigasi" msgid "By the" msgstr "Oleh" @@ -50,32 +47,29 @@ msgstr "Oleh" msgid "Sphinx Book Theme" msgstr "Tema Buku Sphinx" -msgid "Open an issue" -msgstr "Buka masalah" - -msgid "next page" -msgstr "halaman selanjutnya" +msgid "previous page" +msgstr "halaman sebelumnya" -msgid "Copyright" -msgstr "hak cipta" +msgid "Source repository" +msgstr "Repositori sumber" -msgid "Search this book..." -msgstr "Telusuri buku ini ..." +msgid "open issue" +msgstr "masalah terbuka" -msgid "Print to PDF" -msgstr "Cetak ke PDF" +msgid "Download notebook file" +msgstr "Unduh file notebook" -msgid "By" -msgstr "Oleh" +msgid "Copyright" +msgstr "hak cipta" -msgid "previous page" -msgstr "halaman sebelumnya" +msgid "Theme by the" +msgstr "Tema oleh" -msgid "Search the docs ..." -msgstr "Telusuri dokumen ..." +msgid "Open an issue" +msgstr "Buka masalah" -msgid "Download notebook file" -msgstr "Unduh file notebook" +msgid "next page" +msgstr "halaman selanjutnya" -msgid "suggest edit" -msgstr "menyarankan edit" +msgid "Launch" +msgstr "Meluncurkan" diff --git a/_build/html/_static/locales/it/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/it/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..53ba476 Binary files /dev/null and b/_build/html/_static/locales/it/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/it/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/it/LC_MESSAGES/booktheme.po index f64a72b..9803243 100644 --- a/_build/html/_static/locales/it/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/it/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: it\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Di" + msgid "repository" msgstr "repository" -msgid "open issue" -msgstr "questione aperta" - -msgid "Contents" -msgstr "Contenuti" - msgid "Fullscreen mode" msgstr "Modalità schermo intero" -msgid "Download this page" -msgstr "Scarica questa pagina" +msgid "Contents" +msgstr "Contenuti" msgid "Download source file" msgstr "Scarica il file sorgente" -msgid "Launch" -msgstr "Lanciare" - msgid "Edit this page" msgstr "Modifica questa pagina" -msgid "Toggle navigation" -msgstr "Attiva / disattiva la navigazione" +msgid "Last updated on" +msgstr "Ultimo aggiornamento il" -msgid "Theme by the" -msgstr "Tema di" +msgid "Print to PDF" +msgstr "Stampa in PDF" -msgid "Source repository" -msgstr "Repository di origine" +msgid "suggest edit" +msgstr "suggerisci modifica" -msgid "Last updated on" -msgstr "Ultimo aggiornamento il" +msgid "Download this page" +msgstr "Scarica questa pagina" + +msgid "Toggle navigation" +msgstr "Attiva / disattiva la navigazione" msgid "By the" msgstr "Dal" @@ -50,32 +47,29 @@ msgstr "Dal" msgid "Sphinx Book Theme" msgstr "Tema del libro della Sfinge" -msgid "Open an issue" -msgstr "Apri un problema" - -msgid "next page" -msgstr "pagina successiva" +msgid "previous page" +msgstr "pagina precedente" -msgid "Copyright" -msgstr "Diritto d'autore" +msgid "Source repository" +msgstr "Repository di origine" -msgid "Search this book..." -msgstr "Cerca in questo libro ..." +msgid "open issue" +msgstr "questione aperta" -msgid "Print to PDF" -msgstr "Stampa in PDF" +msgid "Download notebook file" +msgstr "Scarica il file del taccuino" -msgid "By" -msgstr "Di" +msgid "Copyright" +msgstr "Diritto d'autore" -msgid "previous page" -msgstr "pagina precedente" +msgid "Theme by the" +msgstr "Tema di" -msgid "Search the docs ..." -msgstr "Cerca nei documenti ..." +msgid "Open an issue" +msgstr "Apri un problema" -msgid "Download notebook file" -msgstr "Scarica il file del taccuino" +msgid "next page" +msgstr "pagina successiva" -msgid "suggest edit" -msgstr "suggerisci modifica" +msgid "Launch" +msgstr "Lanciare" diff --git a/_build/html/_static/locales/iw/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/iw/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..a45c657 Binary files /dev/null and b/_build/html/_static/locales/iw/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/iw/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/iw/LC_MESSAGES/booktheme.po index a2a09f0..cb646cf 100644 --- a/_build/html/_static/locales/iw/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/iw/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: iw\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "על ידי" + msgid "repository" msgstr "מאגר" -msgid "open issue" -msgstr "בעיה פתוחה" - -msgid "Contents" -msgstr "תוכן" - msgid "Fullscreen mode" msgstr "מצב מסך מלא" -msgid "Download this page" -msgstr "הורד דף זה" +msgid "Contents" +msgstr "תוכן" msgid "Download source file" msgstr "הורד את קובץ המקור" -msgid "Launch" -msgstr "לְהַשִׁיק" - msgid "Edit this page" msgstr "ערוך דף זה" -msgid "Toggle navigation" -msgstr "החלף ניווט" +msgid "Last updated on" +msgstr "עודכן לאחרונה ב" -msgid "Theme by the" -msgstr "נושא מאת" +msgid "Print to PDF" +msgstr "הדפס לקובץ PDF" -msgid "Source repository" -msgstr "מאגר המקורות" +msgid "suggest edit" +msgstr "מציע לערוך" -msgid "Last updated on" -msgstr "עודכן לאחרונה ב" +msgid "Download this page" +msgstr "הורד דף זה" + +msgid "Toggle navigation" +msgstr "החלף ניווט" msgid "By the" msgstr "דרך" @@ -50,32 +47,29 @@ msgstr "דרך" msgid "Sphinx Book Theme" msgstr "נושא ספר ספינקס" -msgid "Open an issue" -msgstr "פתח גיליון" - -msgid "next page" -msgstr "עמוד הבא" +msgid "previous page" +msgstr "עמוד קודם" -msgid "Copyright" -msgstr "זכויות יוצרים" +msgid "Source repository" +msgstr "מאגר המקורות" -msgid "Search this book..." -msgstr "חפש בספר זה ..." +msgid "open issue" +msgstr "בעיה פתוחה" -msgid "Print to PDF" -msgstr "הדפס לקובץ PDF" +msgid "Download notebook file" +msgstr "הורד קובץ מחברת" -msgid "By" -msgstr "על ידי" +msgid "Copyright" +msgstr "זכויות יוצרים" -msgid "previous page" -msgstr "עמוד קודם" +msgid "Theme by the" +msgstr "נושא מאת" -msgid "Search the docs ..." -msgstr "חפש במסמכים ..." +msgid "Open an issue" +msgstr "פתח גיליון" -msgid "Download notebook file" -msgstr "הורד קובץ מחברת" +msgid "next page" +msgstr "עמוד הבא" -msgid "suggest edit" -msgstr "מציע לערוך" +msgid "Launch" +msgstr "לְהַשִׁיק" diff --git a/_build/html/_static/locales/ja/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/ja/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..1cefd29 Binary files /dev/null and b/_build/html/_static/locales/ja/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/ja/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/ja/LC_MESSAGES/booktheme.po index 216d540..4ec4083 100644 --- a/_build/html/_static/locales/ja/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/ja/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: ja\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "著者" + msgid "repository" msgstr "リポジトリ" -msgid "open issue" -msgstr "未解決の問題" - -msgid "Contents" -msgstr "目次" - msgid "Fullscreen mode" msgstr "全画面モード" -msgid "Download this page" -msgstr "このページをダウンロード" +msgid "Contents" +msgstr "目次" msgid "Download source file" msgstr "ソースファイルをダウンロード" -msgid "Launch" -msgstr "起動" - msgid "Edit this page" msgstr "このページを編集" -msgid "Toggle navigation" -msgstr "ナビゲーションを切り替え" +msgid "Last updated on" +msgstr "最終更新日" -msgid "Theme by the" -msgstr "のテーマ" +msgid "Print to PDF" +msgstr "PDFに印刷" -msgid "Source repository" -msgstr "ソースリポジトリ" +msgid "suggest edit" +msgstr "編集を提案する" -msgid "Last updated on" -msgstr "最終更新日" +msgid "Download this page" +msgstr "このページをダウンロード" + +msgid "Toggle navigation" +msgstr "ナビゲーションを切り替え" msgid "By the" msgstr "によって" @@ -50,32 +47,29 @@ msgstr "によって" msgid "Sphinx Book Theme" msgstr "スフィンクスの本のテーマ" -msgid "Open an issue" -msgstr "問題を報告" - -msgid "next page" -msgstr "次のページ" +msgid "previous page" +msgstr "前のページ" -msgid "Copyright" -msgstr "Copyright" +msgid "Source repository" +msgstr "ソースリポジトリ" -msgid "Search this book..." -msgstr "この本を検索..." +msgid "open issue" +msgstr "未解決の問題" -msgid "Print to PDF" -msgstr "PDFに印刷" +msgid "Download notebook file" +msgstr "ノートブックファイルをダウンロード" -msgid "By" -msgstr "著者" +msgid "Copyright" +msgstr "Copyright" -msgid "previous page" -msgstr "前のページ" +msgid "Theme by the" +msgstr "のテーマ" -msgid "Search the docs ..." -msgstr "ドキュメントを検索..." +msgid "Open an issue" +msgstr "問題を報告" -msgid "Download notebook file" -msgstr "ノートブックファイルをダウンロード" +msgid "next page" +msgstr "次のページ" -msgid "suggest edit" -msgstr "編集を提案する" +msgid "Launch" +msgstr "起動" diff --git a/_build/html/_static/locales/ko/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/ko/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..06c7ec9 Binary files /dev/null and b/_build/html/_static/locales/ko/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/ko/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/ko/LC_MESSAGES/booktheme.po index 8c19ab6..d1d1882 100644 --- a/_build/html/_static/locales/ko/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/ko/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: ko\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "으로" + msgid "repository" msgstr "저장소" -msgid "open issue" -msgstr "열린 문제" - -msgid "Contents" -msgstr "내용" - msgid "Fullscreen mode" msgstr "전체 화면으로보기" -msgid "Download this page" -msgstr "이 페이지 다운로드" +msgid "Contents" +msgstr "내용" msgid "Download source file" msgstr "소스 파일 다운로드" -msgid "Launch" -msgstr "시작하다" - msgid "Edit this page" msgstr "이 페이지 편집" -msgid "Toggle navigation" -msgstr "탐색 전환" +msgid "Last updated on" +msgstr "마지막 업데이트" -msgid "Theme by the" -msgstr "테마별" +msgid "Print to PDF" +msgstr "PDF로 인쇄" -msgid "Source repository" -msgstr "소스 저장소" +msgid "suggest edit" +msgstr "편집 제안" -msgid "Last updated on" -msgstr "마지막 업데이트" +msgid "Download this page" +msgstr "이 페이지 다운로드" + +msgid "Toggle navigation" +msgstr "탐색 전환" msgid "By the" msgstr "에 의해" @@ -50,32 +47,29 @@ msgstr "에 의해" msgid "Sphinx Book Theme" msgstr "스핑크스 도서 테마" -msgid "Open an issue" -msgstr "이슈 열기" - -msgid "next page" -msgstr "다음 페이지" +msgid "previous page" +msgstr "이전 페이지" -msgid "Copyright" -msgstr "저작권" +msgid "Source repository" +msgstr "소스 저장소" -msgid "Search this book..." -msgstr "이 책 검색 ..." +msgid "open issue" +msgstr "열린 문제" -msgid "Print to PDF" -msgstr "PDF로 인쇄" +msgid "Download notebook file" +msgstr "노트북 파일 다운로드" -msgid "By" -msgstr "으로" +msgid "Copyright" +msgstr "저작권" -msgid "previous page" -msgstr "이전 페이지" +msgid "Theme by the" +msgstr "테마별" -msgid "Search the docs ..." -msgstr "문서 검색 ..." +msgid "Open an issue" +msgstr "이슈 열기" -msgid "Download notebook file" -msgstr "노트북 파일 다운로드" +msgid "next page" +msgstr "다음 페이지" -msgid "suggest edit" -msgstr "편집 제안" +msgid "Launch" +msgstr "시작하다" diff --git a/_build/html/_static/locales/lt/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/lt/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..4468ba0 Binary files /dev/null and b/_build/html/_static/locales/lt/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/lt/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/lt/LC_MESSAGES/booktheme.po index 368967f..b8efd38 100644 --- a/_build/html/_static/locales/lt/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/lt/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: lt\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Iki" + msgid "repository" msgstr "saugykla" -msgid "open issue" -msgstr "atviras klausimas" - -msgid "Contents" -msgstr "Turinys" - msgid "Fullscreen mode" msgstr "Pilno ekrano režimas" -msgid "Download this page" -msgstr "Atsisiųskite šį puslapį" +msgid "Contents" +msgstr "Turinys" msgid "Download source file" msgstr "Atsisiųsti šaltinio failą" -msgid "Launch" -msgstr "Paleiskite" - msgid "Edit this page" msgstr "Redaguoti šį puslapį" -msgid "Toggle navigation" -msgstr "Perjungti naršymą" +msgid "Last updated on" +msgstr "Paskutinį kartą atnaujinta" -msgid "Theme by the" -msgstr "Tema" +msgid "Print to PDF" +msgstr "Spausdinti į PDF" -msgid "Source repository" -msgstr "Šaltinio saugykla" +msgid "suggest edit" +msgstr "pasiūlyti redaguoti" -msgid "Last updated on" -msgstr "Paskutinį kartą atnaujinta" +msgid "Download this page" +msgstr "Atsisiųskite šį puslapį" + +msgid "Toggle navigation" +msgstr "Perjungti naršymą" msgid "By the" msgstr "Prie" @@ -50,32 +47,29 @@ msgstr "Prie" msgid "Sphinx Book Theme" msgstr "Sfinkso knygos tema" -msgid "Open an issue" -msgstr "Atidarykite problemą" - -msgid "next page" -msgstr "Kitas puslapis" +msgid "previous page" +msgstr "Ankstesnis puslapis" -msgid "Copyright" -msgstr "Autorių teisės" +msgid "Source repository" +msgstr "Šaltinio saugykla" -msgid "Search this book..." -msgstr "Ieškoti šioje knygoje ..." +msgid "open issue" +msgstr "atviras klausimas" -msgid "Print to PDF" -msgstr "Spausdinti į PDF" +msgid "Download notebook file" +msgstr "Atsisiųsti nešiojamojo kompiuterio failą" -msgid "By" -msgstr "Iki" +msgid "Copyright" +msgstr "Autorių teisės" -msgid "previous page" -msgstr "Ankstesnis puslapis" +msgid "Theme by the" +msgstr "Tema" -msgid "Search the docs ..." -msgstr "Ieškoti dokumentuose ..." +msgid "Open an issue" +msgstr "Atidarykite problemą" -msgid "Download notebook file" -msgstr "Atsisiųsti nešiojamojo kompiuterio failą" +msgid "next page" +msgstr "Kitas puslapis" -msgid "suggest edit" -msgstr "pasiūlyti redaguoti" +msgid "Launch" +msgstr "Paleiskite" diff --git a/_build/html/_static/locales/lv/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/lv/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..74aa4d8 Binary files /dev/null and b/_build/html/_static/locales/lv/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/lv/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/lv/LC_MESSAGES/booktheme.po index 066fa4e..c66fe62 100644 --- a/_build/html/_static/locales/lv/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/lv/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: lv\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Autors" + msgid "repository" msgstr "krātuve" -msgid "open issue" -msgstr "atklāts jautājums" - -msgid "Contents" -msgstr "Saturs" - msgid "Fullscreen mode" msgstr "Pilnekrāna režīms" -msgid "Download this page" -msgstr "Lejupielādējiet šo lapu" +msgid "Contents" +msgstr "Saturs" msgid "Download source file" msgstr "Lejupielādēt avota failu" -msgid "Launch" -msgstr "Uzsākt" - msgid "Edit this page" msgstr "Rediģēt šo lapu" -msgid "Toggle navigation" -msgstr "Pārslēgt navigāciju" +msgid "Last updated on" +msgstr "Pēdējoreiz atjaunināts" -msgid "Theme by the" -msgstr "Autora tēma" +msgid "Print to PDF" +msgstr "Drukāt PDF formātā" -msgid "Source repository" -msgstr "Avota krātuve" +msgid "suggest edit" +msgstr "ieteikt rediģēt" -msgid "Last updated on" -msgstr "Pēdējoreiz atjaunināts" +msgid "Download this page" +msgstr "Lejupielādējiet šo lapu" + +msgid "Toggle navigation" +msgstr "Pārslēgt navigāciju" msgid "By the" msgstr "Ar" @@ -50,32 +47,29 @@ msgstr "Ar" msgid "Sphinx Book Theme" msgstr "Sfinksa grāmatas tēma" -msgid "Open an issue" -msgstr "Atveriet problēmu" - -msgid "next page" -msgstr "nākamā lapaspuse" +msgid "previous page" +msgstr "iepriekšējā lapa" -msgid "Copyright" -msgstr "Autortiesības" +msgid "Source repository" +msgstr "Avota krātuve" -msgid "Search this book..." -msgstr "Meklēt šajā grāmatā ..." +msgid "open issue" +msgstr "atklāts jautājums" -msgid "Print to PDF" -msgstr "Drukāt PDF formātā" +msgid "Download notebook file" +msgstr "Lejupielādēt piezīmju grāmatiņu" -msgid "By" -msgstr "Autors" +msgid "Copyright" +msgstr "Autortiesības" -msgid "previous page" -msgstr "iepriekšējā lapa" +msgid "Theme by the" +msgstr "Autora tēma" -msgid "Search the docs ..." -msgstr "Meklēt dokumentos ..." +msgid "Open an issue" +msgstr "Atveriet problēmu" -msgid "Download notebook file" -msgstr "Lejupielādēt piezīmju grāmatiņu" +msgid "next page" +msgstr "nākamā lapaspuse" -msgid "suggest edit" -msgstr "ieteikt rediģēt" +msgid "Launch" +msgstr "Uzsākt" diff --git a/_build/html/_static/locales/ml/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/ml/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..2736e8f Binary files /dev/null and b/_build/html/_static/locales/ml/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/ml/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/ml/LC_MESSAGES/booktheme.po index 2b0fd76..aaa2cfd 100644 --- a/_build/html/_static/locales/ml/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/ml/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: ml\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "തുറന്ന പ്രശ്നം" - -msgid "Download this page" -msgstr "ഈ പേജ് ഡൗൺലോഡുചെയ്യുക" +msgid "By" +msgstr "എഴുതിയത്" msgid "Download source file" msgstr "ഉറവിട ഫയൽ ഡൗൺലോഡുചെയ്യുക" -msgid "Launch" -msgstr "സമാരംഭിക്കുക" - msgid "Edit this page" msgstr "ഈ പേജ് എഡിറ്റുചെയ്യുക" -msgid "Toggle navigation" -msgstr "നാവിഗേഷൻ ടോഗിൾ ചെയ്യുക" +msgid "Last updated on" +msgstr "അവസാനം അപ്‌ഡേറ്റുചെയ്‌തത്" -msgid "Theme by the" -msgstr "പ്രമേയം" +msgid "Print to PDF" +msgstr "PDF- ലേക്ക് പ്രിന്റുചെയ്യുക" -msgid "Source repository" -msgstr "ഉറവിട ശേഖരം" +msgid "suggest edit" +msgstr "എഡിറ്റുചെയ്യാൻ നിർദ്ദേശിക്കുക" -msgid "Last updated on" -msgstr "അവസാനം അപ്‌ഡേറ്റുചെയ്‌തത്" +msgid "Download this page" +msgstr "ഈ പേജ് ഡൗൺലോഡുചെയ്യുക" + +msgid "Toggle navigation" +msgstr "നാവിഗേഷൻ ടോഗിൾ ചെയ്യുക" msgid "By the" msgstr "എഴുതിയത്" @@ -41,29 +38,29 @@ msgstr "എഴുതിയത്" msgid "Sphinx Book Theme" msgstr "സ്ഫിങ്ക്സ് പുസ്തക തീം" -msgid "Open an issue" -msgstr "ഒരു പ്രശ്നം തുറക്കുക" +msgid "previous page" +msgstr "മുൻപത്തെ താൾ" -msgid "next page" -msgstr "അടുത്ത പേജ്" +msgid "Source repository" +msgstr "ഉറവിട ശേഖരം" -msgid "Copyright" -msgstr "പകർപ്പവകാശം" +msgid "open issue" +msgstr "തുറന്ന പ്രശ്നം" -msgid "Search this book..." -msgstr "ഈ പുസ്തകം തിരയുക ..." +msgid "Download notebook file" +msgstr "നോട്ട്ബുക്ക് ഫയൽ ഡൺലോഡ് ചെയ്യുക" -msgid "Print to PDF" -msgstr "PDF- ലേക്ക് പ്രിന്റുചെയ്യുക" +msgid "Copyright" +msgstr "പകർപ്പവകാശം" -msgid "By" -msgstr "എഴുതിയത്" +msgid "Theme by the" +msgstr "പ്രമേയം" -msgid "previous page" -msgstr "മുൻപത്തെ താൾ" +msgid "Open an issue" +msgstr "ഒരു പ്രശ്നം തുറക്കുക" -msgid "Download notebook file" -msgstr "നോട്ട്ബുക്ക് ഫയൽ ഡൺലോഡ് ചെയ്യുക" +msgid "next page" +msgstr "അടുത്ത പേജ്" -msgid "suggest edit" -msgstr "എഡിറ്റുചെയ്യാൻ നിർദ്ദേശിക്കുക" +msgid "Launch" +msgstr "സമാരംഭിക്കുക" diff --git a/_build/html/_static/locales/mr/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/mr/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..fe53010 Binary files /dev/null and b/_build/html/_static/locales/mr/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/mr/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/mr/LC_MESSAGES/booktheme.po index 51d011f..09e4128 100644 --- a/_build/html/_static/locales/mr/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/mr/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: mr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "खुला मुद्दा" - -msgid "Download this page" -msgstr "हे पृष्ठ डाउनलोड करा" +msgid "By" +msgstr "द्वारा" msgid "Download source file" msgstr "स्त्रोत फाइल डाउनलोड करा" -msgid "Launch" -msgstr "लाँच करा" - msgid "Edit this page" msgstr "हे पृष्ठ संपादित करा" -msgid "Toggle navigation" -msgstr "नेव्हिगेशन टॉगल करा" +msgid "Last updated on" +msgstr "अखेरचे अद्यतनित" -msgid "Theme by the" -msgstr "द्वारा थीम" +msgid "Print to PDF" +msgstr "पीडीएफवर मुद्रित करा" -msgid "Source repository" -msgstr "स्त्रोत भांडार" +msgid "suggest edit" +msgstr "संपादन सुचवा" -msgid "Last updated on" -msgstr "अखेरचे अद्यतनित" +msgid "Download this page" +msgstr "हे पृष्ठ डाउनलोड करा" + +msgid "Toggle navigation" +msgstr "नेव्हिगेशन टॉगल करा" msgid "By the" msgstr "द्वारा" @@ -41,29 +38,29 @@ msgstr "द्वारा" msgid "Sphinx Book Theme" msgstr "स्फिंक्स बुक थीम" -msgid "Open an issue" -msgstr "एक मुद्दा उघडा" +msgid "previous page" +msgstr "मागील पान" -msgid "next page" -msgstr "पुढील पृष्ठ" +msgid "Source repository" +msgstr "स्त्रोत भांडार" -msgid "Copyright" -msgstr "कॉपीराइट" +msgid "open issue" +msgstr "खुला मुद्दा" -msgid "Search this book..." -msgstr "हे पुस्तक शोधा ..." +msgid "Download notebook file" +msgstr "नोटबुक फाईल डाउनलोड करा" -msgid "Print to PDF" -msgstr "पीडीएफवर मुद्रित करा" +msgid "Copyright" +msgstr "कॉपीराइट" -msgid "By" -msgstr "द्वारा" +msgid "Theme by the" +msgstr "द्वारा थीम" -msgid "previous page" -msgstr "मागील पान" +msgid "Open an issue" +msgstr "एक मुद्दा उघडा" -msgid "Download notebook file" -msgstr "नोटबुक फाईल डाउनलोड करा" +msgid "next page" +msgstr "पुढील पृष्ठ" -msgid "suggest edit" -msgstr "संपादन सुचवा" +msgid "Launch" +msgstr "लाँच करा" diff --git a/_build/html/_static/locales/ms/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/ms/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..f02603f Binary files /dev/null and b/_build/html/_static/locales/ms/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/ms/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/ms/LC_MESSAGES/booktheme.po index 6f8a250..8b8e61d 100644 --- a/_build/html/_static/locales/ms/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/ms/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: ms\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "isu terbuka" - -msgid "Download this page" -msgstr "Muat turun halaman ini" +msgid "By" +msgstr "Oleh" msgid "Download source file" msgstr "Muat turun fail sumber" -msgid "Launch" -msgstr "Lancarkan" - msgid "Edit this page" msgstr "Edit halaman ini" -msgid "Toggle navigation" -msgstr "Togol navigasi" +msgid "Last updated on" +msgstr "Terakhir dikemas kini pada" -msgid "Theme by the" -msgstr "Tema oleh" +msgid "Print to PDF" +msgstr "Cetak ke PDF" -msgid "Source repository" -msgstr "Repositori sumber" +msgid "suggest edit" +msgstr "cadangkan edit" -msgid "Last updated on" -msgstr "Terakhir dikemas kini pada" +msgid "Download this page" +msgstr "Muat turun halaman ini" + +msgid "Toggle navigation" +msgstr "Togol navigasi" msgid "By the" msgstr "Oleh" @@ -41,29 +38,29 @@ msgstr "Oleh" msgid "Sphinx Book Theme" msgstr "Tema Buku Sphinx" -msgid "Open an issue" -msgstr "Buka masalah" +msgid "previous page" +msgstr "halaman sebelumnya" -msgid "next page" -msgstr "muka surat seterusnya" +msgid "Source repository" +msgstr "Repositori sumber" -msgid "Copyright" -msgstr "hak cipta" +msgid "open issue" +msgstr "isu terbuka" -msgid "Search this book..." -msgstr "Cari buku ini ..." +msgid "Download notebook file" +msgstr "Muat turun fail buku nota" -msgid "Print to PDF" -msgstr "Cetak ke PDF" +msgid "Copyright" +msgstr "hak cipta" -msgid "By" -msgstr "Oleh" +msgid "Theme by the" +msgstr "Tema oleh" -msgid "previous page" -msgstr "halaman sebelumnya" +msgid "Open an issue" +msgstr "Buka masalah" -msgid "Download notebook file" -msgstr "Muat turun fail buku nota" +msgid "next page" +msgstr "muka surat seterusnya" -msgid "suggest edit" -msgstr "cadangkan edit" +msgid "Launch" +msgstr "Lancarkan" diff --git a/_build/html/_static/locales/nl/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/nl/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..e59e7ec Binary files /dev/null and b/_build/html/_static/locales/nl/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/nl/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/nl/LC_MESSAGES/booktheme.po index 4065b58..97b685f 100644 --- a/_build/html/_static/locales/nl/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/nl/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: nl\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Door" + msgid "repository" msgstr "repository" -msgid "open issue" -msgstr "open probleem" - -msgid "Contents" -msgstr "Inhoud" - msgid "Fullscreen mode" msgstr "Volledig scherm" -msgid "Download this page" -msgstr "Download deze pagina" +msgid "Contents" +msgstr "Inhoud" msgid "Download source file" msgstr "Download het bronbestand" -msgid "Launch" -msgstr "Lancering" - msgid "Edit this page" msgstr "bewerk deze pagina" -msgid "Toggle navigation" -msgstr "Schakel navigatie" +msgid "Last updated on" +msgstr "Laatst geupdate op" -msgid "Theme by the" -msgstr "Thema door de" +msgid "Print to PDF" +msgstr "Afdrukken naar pdf" -msgid "Source repository" -msgstr "Bronopslagplaats" +msgid "suggest edit" +msgstr "suggereren bewerken" -msgid "Last updated on" -msgstr "Laatst geupdate op" +msgid "Download this page" +msgstr "Download deze pagina" + +msgid "Toggle navigation" +msgstr "Schakel navigatie" msgid "By the" msgstr "Door de" @@ -50,32 +47,29 @@ msgstr "Door de" msgid "Sphinx Book Theme" msgstr "Sphinx-boekthema" -msgid "Open an issue" -msgstr "Open een probleem" - -msgid "next page" -msgstr "volgende bladzijde" +msgid "previous page" +msgstr "vorige pagina" -msgid "Copyright" -msgstr "auteursrechten" +msgid "Source repository" +msgstr "Bronopslagplaats" -msgid "Search this book..." -msgstr "Zoek in dit boek ..." +msgid "open issue" +msgstr "open probleem" -msgid "Print to PDF" -msgstr "Afdrukken naar pdf" +msgid "Download notebook file" +msgstr "Download notebookbestand" -msgid "By" -msgstr "Door" +msgid "Copyright" +msgstr "auteursrechten" -msgid "previous page" -msgstr "vorige pagina" +msgid "Theme by the" +msgstr "Thema door de" -msgid "Search the docs ..." -msgstr "Doorzoek de documenten ..." +msgid "Open an issue" +msgstr "Open een probleem" -msgid "Download notebook file" -msgstr "Download notebookbestand" +msgid "next page" +msgstr "volgende bladzijde" -msgid "suggest edit" -msgstr "suggereren bewerken" +msgid "Launch" +msgstr "Lancering" diff --git a/_build/html/_static/locales/no/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/no/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..6cd15c8 Binary files /dev/null and b/_build/html/_static/locales/no/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/no/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/no/LC_MESSAGES/booktheme.po index c4391bb..d5f0a84 100644 --- a/_build/html/_static/locales/no/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/no/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: no\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Av" + msgid "repository" msgstr "oppbevaringssted" -msgid "open issue" -msgstr "åpent nummer" - -msgid "Contents" -msgstr "Innhold" - msgid "Fullscreen mode" msgstr "Fullskjerm-modus" -msgid "Download this page" -msgstr "Last ned denne siden" +msgid "Contents" +msgstr "Innhold" msgid "Download source file" msgstr "Last ned kildefilen" -msgid "Launch" -msgstr "Start" - msgid "Edit this page" msgstr "Rediger denne siden" -msgid "Toggle navigation" -msgstr "Bytt navigasjon" +msgid "Last updated on" +msgstr "Sist oppdatert den" -msgid "Theme by the" -msgstr "Tema av" +msgid "Print to PDF" +msgstr "Skriv ut til PDF" -msgid "Source repository" -msgstr "Kildedepot" +msgid "suggest edit" +msgstr "foreslå redigering" -msgid "Last updated on" -msgstr "Sist oppdatert den" +msgid "Download this page" +msgstr "Last ned denne siden" + +msgid "Toggle navigation" +msgstr "Bytt navigasjon" msgid "By the" msgstr "Ved" @@ -50,32 +47,29 @@ msgstr "Ved" msgid "Sphinx Book Theme" msgstr "Sphinx boktema" -msgid "Open an issue" -msgstr "Åpne et problem" - -msgid "next page" -msgstr "neste side" +msgid "previous page" +msgstr "forrige side" -msgid "Copyright" -msgstr "opphavsrett" +msgid "Source repository" +msgstr "Kildedepot" -msgid "Search this book..." -msgstr "Søk i denne boken ..." +msgid "open issue" +msgstr "åpent nummer" -msgid "Print to PDF" -msgstr "Skriv ut til PDF" +msgid "Download notebook file" +msgstr "Last ned notatbokfilen" -msgid "By" -msgstr "Av" +msgid "Copyright" +msgstr "opphavsrett" -msgid "previous page" -msgstr "forrige side" +msgid "Theme by the" +msgstr "Tema av" -msgid "Search the docs ..." -msgstr "Søk i dokumentene ..." +msgid "Open an issue" +msgstr "Åpne et problem" -msgid "Download notebook file" -msgstr "Last ned notatbokfilen" +msgid "next page" +msgstr "neste side" -msgid "suggest edit" -msgstr "foreslå redigering" +msgid "Launch" +msgstr "Start" diff --git a/_build/html/_static/locales/pl/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/pl/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..9ebb584 Binary files /dev/null and b/_build/html/_static/locales/pl/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/pl/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/pl/LC_MESSAGES/booktheme.po index bd03ba4..c674c55 100644 --- a/_build/html/_static/locales/pl/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/pl/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: pl\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Przez" + msgid "repository" msgstr "magazyn" -msgid "open issue" -msgstr "otwarty problem" - -msgid "Contents" -msgstr "Zawartość" - msgid "Fullscreen mode" msgstr "Pełny ekran" -msgid "Download this page" -msgstr "Pobierz tę stronę" +msgid "Contents" +msgstr "Zawartość" msgid "Download source file" msgstr "Pobierz plik źródłowy" -msgid "Launch" -msgstr "Uruchomić" - msgid "Edit this page" msgstr "Edytuj tę strone" -msgid "Toggle navigation" -msgstr "Przełącz nawigację" +msgid "Last updated on" +msgstr "Ostatnia aktualizacja" -msgid "Theme by the" -msgstr "Motyw autorstwa" +msgid "Print to PDF" +msgstr "Drukuj do PDF" -msgid "Source repository" -msgstr "Repozytorium źródłowe" +msgid "suggest edit" +msgstr "zaproponuj edycję" -msgid "Last updated on" -msgstr "Ostatnia aktualizacja" +msgid "Download this page" +msgstr "Pobierz tę stronę" + +msgid "Toggle navigation" +msgstr "Przełącz nawigację" msgid "By the" msgstr "Przez" @@ -50,32 +47,29 @@ msgstr "Przez" msgid "Sphinx Book Theme" msgstr "Motyw książki Sphinx" -msgid "Open an issue" -msgstr "Otwórz problem" - -msgid "next page" -msgstr "Następna strona" +msgid "previous page" +msgstr "Poprzednia strona" -msgid "Copyright" -msgstr "prawa autorskie" +msgid "Source repository" +msgstr "Repozytorium źródłowe" -msgid "Search this book..." -msgstr "Przeszukaj tę książkę ..." +msgid "open issue" +msgstr "otwarty problem" -msgid "Print to PDF" -msgstr "Drukuj do PDF" +msgid "Download notebook file" +msgstr "Pobierz plik notatnika" -msgid "By" -msgstr "Przez" +msgid "Copyright" +msgstr "prawa autorskie" -msgid "previous page" -msgstr "Poprzednia strona" +msgid "Theme by the" +msgstr "Motyw autorstwa" -msgid "Search the docs ..." -msgstr "Przeszukaj dokumenty ..." +msgid "Open an issue" +msgstr "Otwórz problem" -msgid "Download notebook file" -msgstr "Pobierz plik notatnika" +msgid "next page" +msgstr "Następna strona" -msgid "suggest edit" -msgstr "zaproponuj edycję" +msgid "Launch" +msgstr "Uruchomić" diff --git a/_build/html/_static/locales/pt/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/pt/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..d0ddb87 Binary files /dev/null and b/_build/html/_static/locales/pt/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/pt/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/pt/LC_MESSAGES/booktheme.po index 8ac25a1..7ca36d9 100644 --- a/_build/html/_static/locales/pt/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/pt/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: pt\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "De" + msgid "repository" msgstr "repositório" -msgid "open issue" -msgstr "questão aberta" - -msgid "Contents" -msgstr "Conteúdo" - msgid "Fullscreen mode" msgstr "Modo tela cheia" -msgid "Download this page" -msgstr "Baixe esta página" +msgid "Contents" +msgstr "Conteúdo" msgid "Download source file" msgstr "Baixar arquivo fonte" -msgid "Launch" -msgstr "Lançamento" - msgid "Edit this page" msgstr "Edite essa página" -msgid "Toggle navigation" -msgstr "Alternar de navegação" +msgid "Last updated on" +msgstr "Última atualização em" -msgid "Theme by the" -msgstr "Tema por" +msgid "Print to PDF" +msgstr "Imprimir em PDF" -msgid "Source repository" -msgstr "Repositório fonte" +msgid "suggest edit" +msgstr "sugerir edição" -msgid "Last updated on" -msgstr "Última atualização em" +msgid "Download this page" +msgstr "Baixe esta página" + +msgid "Toggle navigation" +msgstr "Alternar de navegação" msgid "By the" msgstr "Pelo" @@ -50,32 +47,29 @@ msgstr "Pelo" msgid "Sphinx Book Theme" msgstr "Tema do livro Sphinx" -msgid "Open an issue" -msgstr "Abra um problema" - -msgid "next page" -msgstr "próxima página" +msgid "previous page" +msgstr "página anterior" -msgid "Copyright" -msgstr "direito autoral" +msgid "Source repository" +msgstr "Repositório fonte" -msgid "Search this book..." -msgstr "Pesquise este livro ..." +msgid "open issue" +msgstr "questão aberta" -msgid "Print to PDF" -msgstr "Imprimir em PDF" +msgid "Download notebook file" +msgstr "Baixar arquivo de notebook" -msgid "By" -msgstr "De" +msgid "Copyright" +msgstr "direito autoral" -msgid "previous page" -msgstr "página anterior" +msgid "Theme by the" +msgstr "Tema por" -msgid "Search the docs ..." -msgstr "Pesquise os documentos ..." +msgid "Open an issue" +msgstr "Abra um problema" -msgid "Download notebook file" -msgstr "Baixar arquivo de notebook" +msgid "next page" +msgstr "próxima página" -msgid "suggest edit" -msgstr "sugerir edição" +msgid "Launch" +msgstr "Lançamento" diff --git a/_build/html/_static/locales/ro/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/ro/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..3c36ab1 Binary files /dev/null and b/_build/html/_static/locales/ro/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/ro/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/ro/LC_MESSAGES/booktheme.po index d0f7d2f..1a86498 100644 --- a/_build/html/_static/locales/ro/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/ro/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: ro\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "De" + msgid "repository" msgstr "repertoriu" -msgid "open issue" -msgstr "problema deschisă" - -msgid "Contents" -msgstr "Cuprins" - msgid "Fullscreen mode" msgstr "Modul ecran întreg" -msgid "Download this page" -msgstr "Descarcă această pagină" +msgid "Contents" +msgstr "Cuprins" msgid "Download source file" msgstr "Descărcați fișierul sursă" -msgid "Launch" -msgstr "Lansa" - msgid "Edit this page" msgstr "Editați această pagină" -msgid "Toggle navigation" -msgstr "Comutare navigare" +msgid "Last updated on" +msgstr "Ultima actualizare la" -msgid "Theme by the" -msgstr "Tema de" +msgid "Print to PDF" +msgstr "Imprimați în PDF" -msgid "Source repository" -msgstr "Depozit sursă" +msgid "suggest edit" +msgstr "sugerează editare" -msgid "Last updated on" -msgstr "Ultima actualizare la" +msgid "Download this page" +msgstr "Descarcă această pagină" + +msgid "Toggle navigation" +msgstr "Comutare navigare" msgid "By the" msgstr "Langa" @@ -50,32 +47,29 @@ msgstr "Langa" msgid "Sphinx Book Theme" msgstr "Tema Sphinx Book" -msgid "Open an issue" -msgstr "Deschideți o problemă" - -msgid "next page" -msgstr "pagina următoare" +msgid "previous page" +msgstr "pagina anterioară" -msgid "Copyright" -msgstr "Drepturi de autor" +msgid "Source repository" +msgstr "Depozit sursă" -msgid "Search this book..." -msgstr "Căutați în această carte ..." +msgid "open issue" +msgstr "problema deschisă" -msgid "Print to PDF" -msgstr "Imprimați în PDF" +msgid "Download notebook file" +msgstr "Descărcați fișierul notebook" -msgid "By" -msgstr "De" +msgid "Copyright" +msgstr "Drepturi de autor" -msgid "previous page" -msgstr "pagina anterioară" +msgid "Theme by the" +msgstr "Tema de" -msgid "Search the docs ..." -msgstr "Căutați documente ..." +msgid "Open an issue" +msgstr "Deschideți o problemă" -msgid "Download notebook file" -msgstr "Descărcați fișierul notebook" +msgid "next page" +msgstr "pagina următoare" -msgid "suggest edit" -msgstr "sugerează editare" +msgid "Launch" +msgstr "Lansa" diff --git a/_build/html/_static/locales/ru/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/ru/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..6b8ca41 Binary files /dev/null and b/_build/html/_static/locales/ru/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/ru/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/ru/LC_MESSAGES/booktheme.po index 9b55bfe..e64ee9f 100644 --- a/_build/html/_static/locales/ru/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/ru/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: ru\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "По" + msgid "repository" msgstr "хранилище" -msgid "open issue" -msgstr "открытый вопрос" - -msgid "Contents" -msgstr "Содержание" - msgid "Fullscreen mode" msgstr "Полноэкранный режим" -msgid "Download this page" -msgstr "Загрузите эту страницу" +msgid "Contents" +msgstr "Содержание" msgid "Download source file" msgstr "Скачать исходный файл" -msgid "Launch" -msgstr "Запуск" - msgid "Edit this page" msgstr "Редактировать эту страницу" -msgid "Toggle navigation" -msgstr "Переключить навигацию" +msgid "Last updated on" +msgstr "Последнее обновление" -msgid "Theme by the" -msgstr "Тема от" +msgid "Print to PDF" +msgstr "Распечатать в PDF" -msgid "Source repository" -msgstr "Исходный репозиторий" +msgid "suggest edit" +msgstr "предложить редактировать" -msgid "Last updated on" -msgstr "Последнее обновление" +msgid "Download this page" +msgstr "Загрузите эту страницу" + +msgid "Toggle navigation" +msgstr "Переключить навигацию" msgid "By the" msgstr "Посредством" @@ -50,32 +47,29 @@ msgstr "Посредством" msgid "Sphinx Book Theme" msgstr "Тема книги Сфинкс" -msgid "Open an issue" -msgstr "Открыть вопрос" - -msgid "next page" -msgstr "Следующая страница" +msgid "previous page" +msgstr "Предыдущая страница" -msgid "Copyright" -msgstr "авторское право" +msgid "Source repository" +msgstr "Исходный репозиторий" -msgid "Search this book..." -msgstr "Искать в этой книге ..." +msgid "open issue" +msgstr "открытый вопрос" -msgid "Print to PDF" -msgstr "Распечатать в PDF" +msgid "Download notebook file" +msgstr "Скачать файл записной книжки" -msgid "By" -msgstr "По" +msgid "Copyright" +msgstr "авторское право" -msgid "previous page" -msgstr "Предыдущая страница" +msgid "Theme by the" +msgstr "Тема от" -msgid "Search the docs ..." -msgstr "Искать в документах ..." +msgid "Open an issue" +msgstr "Открыть вопрос" -msgid "Download notebook file" -msgstr "Скачать файл записной книжки" +msgid "next page" +msgstr "Следующая страница" -msgid "suggest edit" -msgstr "предложить редактировать" +msgid "Launch" +msgstr "Запуск" diff --git a/_build/html/_static/locales/sk/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/sk/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..59bd0dd Binary files /dev/null and b/_build/html/_static/locales/sk/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/sk/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/sk/LC_MESSAGES/booktheme.po index 3e6fd9f..42a959f 100644 --- a/_build/html/_static/locales/sk/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/sk/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: sk\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Autor:" + msgid "repository" msgstr "Úložisko" -msgid "open issue" -msgstr "otvorené vydanie" - -msgid "Contents" -msgstr "Obsah" - msgid "Fullscreen mode" msgstr "Režim celej obrazovky" -msgid "Download this page" -msgstr "Stiahnite si túto stránku" +msgid "Contents" +msgstr "Obsah" msgid "Download source file" msgstr "Stiahnite si zdrojový súbor" -msgid "Launch" -msgstr "Spustiť" - msgid "Edit this page" msgstr "Upraviť túto stránku" -msgid "Toggle navigation" -msgstr "Prepnúť navigáciu" +msgid "Last updated on" +msgstr "Posledná aktualizácia dňa" -msgid "Theme by the" -msgstr "Téma od" +msgid "Print to PDF" +msgstr "Tlač do PDF" -msgid "Source repository" -msgstr "Zdrojové úložisko" +msgid "suggest edit" +msgstr "navrhnúť úpravu" -msgid "Last updated on" -msgstr "Posledná aktualizácia dňa" +msgid "Download this page" +msgstr "Stiahnite si túto stránku" + +msgid "Toggle navigation" +msgstr "Prepnúť navigáciu" msgid "By the" msgstr "Podľa" @@ -50,32 +47,29 @@ msgstr "Podľa" msgid "Sphinx Book Theme" msgstr "Téma knihy Sfinga" -msgid "Open an issue" -msgstr "Otvorte problém" - -msgid "next page" -msgstr "ďalšia strana" +msgid "previous page" +msgstr "predchádzajúca strana" -msgid "Copyright" -msgstr "Autorské práva" +msgid "Source repository" +msgstr "Zdrojové úložisko" -msgid "Search this book..." -msgstr "Hľadať v tejto knihe ..." +msgid "open issue" +msgstr "otvorené vydanie" -msgid "Print to PDF" -msgstr "Tlač do PDF" +msgid "Download notebook file" +msgstr "Stiahnite si zošit" -msgid "By" -msgstr "Autor:" +msgid "Copyright" +msgstr "Autorské práva" -msgid "previous page" -msgstr "predchádzajúca strana" +msgid "Theme by the" +msgstr "Téma od" -msgid "Search the docs ..." -msgstr "Hľadať v dokumentoch ..." +msgid "Open an issue" +msgstr "Otvorte problém" -msgid "Download notebook file" -msgstr "Stiahnite si zošit" +msgid "next page" +msgstr "ďalšia strana" -msgid "suggest edit" -msgstr "navrhnúť úpravu" +msgid "Launch" +msgstr "Spustiť" diff --git a/_build/html/_static/locales/sl/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/sl/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..87bf26d Binary files /dev/null and b/_build/html/_static/locales/sl/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/sl/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/sl/LC_MESSAGES/booktheme.po index dd99bbe..39c6a42 100644 --- a/_build/html/_static/locales/sl/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/sl/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: sl\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Avtor" + msgid "repository" msgstr "odlagališče" -msgid "open issue" -msgstr "odprto vprašanje" - -msgid "Contents" -msgstr "Vsebina" - msgid "Fullscreen mode" msgstr "Celozaslonski način" -msgid "Download this page" -msgstr "Prenesite to stran" +msgid "Contents" +msgstr "Vsebina" msgid "Download source file" msgstr "Prenesite izvorno datoteko" -msgid "Launch" -msgstr "Kosilo" - msgid "Edit this page" msgstr "Uredite to stran" -msgid "Toggle navigation" -msgstr "Preklopi navigacijo" +msgid "Last updated on" +msgstr "Nazadnje posodobljeno dne" -msgid "Theme by the" -msgstr "Tema avtorja" +msgid "Print to PDF" +msgstr "Natisni v PDF" -msgid "Source repository" -msgstr "Izvorno skladišče" +msgid "suggest edit" +msgstr "predlagajte urejanje" -msgid "Last updated on" -msgstr "Nazadnje posodobljeno dne" +msgid "Download this page" +msgstr "Prenesite to stran" + +msgid "Toggle navigation" +msgstr "Preklopi navigacijo" msgid "By the" msgstr "Avtor" @@ -50,32 +47,29 @@ msgstr "Avtor" msgid "Sphinx Book Theme" msgstr "Tema knjige Sphinx" -msgid "Open an issue" -msgstr "Odprite številko" - -msgid "next page" -msgstr "Naslednja stran" +msgid "previous page" +msgstr "Prejšnja stran" -msgid "Copyright" -msgstr "avtorske pravice" +msgid "Source repository" +msgstr "Izvorno skladišče" -msgid "Search this book..." -msgstr "Poiščite to knjigo ..." +msgid "open issue" +msgstr "odprto vprašanje" -msgid "Print to PDF" -msgstr "Natisni v PDF" +msgid "Download notebook file" +msgstr "Prenesite datoteko zvezka" -msgid "By" -msgstr "Avtor" +msgid "Copyright" +msgstr "avtorske pravice" -msgid "previous page" -msgstr "Prejšnja stran" +msgid "Theme by the" +msgstr "Tema avtorja" -msgid "Search the docs ..." -msgstr "Poiščite dokumente ..." +msgid "Open an issue" +msgstr "Odprite številko" -msgid "Download notebook file" -msgstr "Prenesite datoteko zvezka" +msgid "next page" +msgstr "Naslednja stran" -msgid "suggest edit" -msgstr "predlagajte urejanje" +msgid "Launch" +msgstr "Kosilo" diff --git a/_build/html/_static/locales/sr/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/sr/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..ec740f4 Binary files /dev/null and b/_build/html/_static/locales/sr/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/sr/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/sr/LC_MESSAGES/booktheme.po index 12284e0..30a6cc2 100644 --- a/_build/html/_static/locales/sr/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/sr/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: sr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Од стране" + msgid "repository" msgstr "спремиште" -msgid "open issue" -msgstr "отворено издање" - -msgid "Contents" -msgstr "Садржај" - msgid "Fullscreen mode" msgstr "Режим целог екрана" -msgid "Download this page" -msgstr "Преузмите ову страницу" +msgid "Contents" +msgstr "Садржај" msgid "Download source file" msgstr "Преузми изворну датотеку" -msgid "Launch" -msgstr "Лансирање" - msgid "Edit this page" msgstr "Уредите ову страницу" -msgid "Toggle navigation" -msgstr "Укључи / искључи навигацију" +msgid "Last updated on" +msgstr "Последње ажурирање" -msgid "Theme by the" -msgstr "Тхеме би" +msgid "Print to PDF" +msgstr "Испис у ПДФ" -msgid "Source repository" -msgstr "Изворно спремиште" +msgid "suggest edit" +msgstr "предложи уређивање" -msgid "Last updated on" -msgstr "Последње ажурирање" +msgid "Download this page" +msgstr "Преузмите ову страницу" + +msgid "Toggle navigation" +msgstr "Укључи / искључи навигацију" msgid "By the" msgstr "Од" @@ -50,32 +47,29 @@ msgstr "Од" msgid "Sphinx Book Theme" msgstr "Тема књиге Спхинк" -msgid "Open an issue" -msgstr "Отворите издање" - -msgid "next page" -msgstr "Следећа страна" +msgid "previous page" +msgstr "Претходна страница" -msgid "Copyright" -msgstr "Ауторско право" +msgid "Source repository" +msgstr "Изворно спремиште" -msgid "Search this book..." -msgstr "Претражите ову књигу ..." +msgid "open issue" +msgstr "отворено издање" -msgid "Print to PDF" -msgstr "Испис у ПДФ" +msgid "Download notebook file" +msgstr "Преузмите датотеку бележнице" -msgid "By" -msgstr "Од стране" +msgid "Copyright" +msgstr "Ауторско право" -msgid "previous page" -msgstr "Претходна страница" +msgid "Theme by the" +msgstr "Тхеме би" -msgid "Search the docs ..." -msgstr "Претражите документе ..." +msgid "Open an issue" +msgstr "Отворите издање" -msgid "Download notebook file" -msgstr "Преузмите датотеку бележнице" +msgid "next page" +msgstr "Следећа страна" -msgid "suggest edit" -msgstr "предложи уређивање" +msgid "Launch" +msgstr "Лансирање" diff --git a/_build/html/_static/locales/sv/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/sv/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..be951be Binary files /dev/null and b/_build/html/_static/locales/sv/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/sv/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/sv/LC_MESSAGES/booktheme.po index 203c917..8953d7f 100644 --- a/_build/html/_static/locales/sv/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/sv/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: sv\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Förbi" + msgid "repository" msgstr "förvar" -msgid "open issue" -msgstr "öppet problem" - -msgid "Contents" -msgstr "Innehåll" - msgid "Fullscreen mode" msgstr "Fullskärmsläge" -msgid "Download this page" -msgstr "Ladda ner den här sidan" +msgid "Contents" +msgstr "Innehåll" msgid "Download source file" msgstr "Ladda ner källfil" -msgid "Launch" -msgstr "Lansera" - msgid "Edit this page" msgstr "Redigera den här sidan" -msgid "Toggle navigation" -msgstr "Växla navigering" +msgid "Last updated on" +msgstr "Senast uppdaterad den" -msgid "Theme by the" -msgstr "Tema av" +msgid "Print to PDF" +msgstr "Skriv ut till PDF" -msgid "Source repository" -msgstr "Källförvar" +msgid "suggest edit" +msgstr "föreslå redigering" -msgid "Last updated on" -msgstr "Senast uppdaterad den" +msgid "Download this page" +msgstr "Ladda ner den här sidan" + +msgid "Toggle navigation" +msgstr "Växla navigering" msgid "By the" msgstr "Vid" @@ -50,32 +47,29 @@ msgstr "Vid" msgid "Sphinx Book Theme" msgstr "Sphinx boktema" -msgid "Open an issue" -msgstr "Öppna ett problem" - -msgid "next page" -msgstr "nästa sida" +msgid "previous page" +msgstr "föregående sida" -msgid "Copyright" -msgstr "upphovsrätt" +msgid "Source repository" +msgstr "Källförvar" -msgid "Search this book..." -msgstr "Sök i den här boken ..." +msgid "open issue" +msgstr "öppet problem" -msgid "Print to PDF" -msgstr "Skriv ut till PDF" +msgid "Download notebook file" +msgstr "Ladda ner anteckningsbokfilen" -msgid "By" -msgstr "Förbi" +msgid "Copyright" +msgstr "upphovsrätt" -msgid "previous page" -msgstr "föregående sida" +msgid "Theme by the" +msgstr "Tema av" -msgid "Search the docs ..." -msgstr "Sök i dokumenten ..." +msgid "Open an issue" +msgstr "Öppna ett problem" -msgid "Download notebook file" -msgstr "Ladda ner anteckningsbokfilen" +msgid "next page" +msgstr "nästa sida" -msgid "suggest edit" -msgstr "föreslå redigering" +msgid "Launch" +msgstr "Lansera" diff --git a/_build/html/_static/locales/ta/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/ta/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..29f52e1 Binary files /dev/null and b/_build/html/_static/locales/ta/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/ta/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/ta/LC_MESSAGES/booktheme.po index eefce8a..2100227 100644 --- a/_build/html/_static/locales/ta/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/ta/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: ta\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "திறந்த பிரச்சினை" - -msgid "Download this page" -msgstr "இந்தப் பக்கத்தைப் பதிவிறக்கவும்" +msgid "By" +msgstr "வழங்கியவர்" msgid "Download source file" msgstr "மூல கோப்பைப் பதிவிறக்குக" -msgid "Launch" -msgstr "தொடங்க" - msgid "Edit this page" msgstr "இந்தப் பக்கத்தைத் திருத்தவும்" -msgid "Toggle navigation" -msgstr "வழிசெலுத்தலை நிலைமாற்று" +msgid "Last updated on" +msgstr "கடைசியாக புதுப்பிக்கப்பட்டது" -msgid "Theme by the" -msgstr "வழங்கிய தீம்" +msgid "Print to PDF" +msgstr "PDF இல் அச்சிடுக" -msgid "Source repository" -msgstr "மூல களஞ்சியம்" +msgid "suggest edit" +msgstr "திருத்த பரிந்துரைக்கவும்" -msgid "Last updated on" -msgstr "கடைசியாக புதுப்பிக்கப்பட்டது" +msgid "Download this page" +msgstr "இந்தப் பக்கத்தைப் பதிவிறக்கவும்" + +msgid "Toggle navigation" +msgstr "வழிசெலுத்தலை நிலைமாற்று" msgid "By the" msgstr "மூலம்" @@ -41,29 +38,29 @@ msgstr "மூலம்" msgid "Sphinx Book Theme" msgstr "ஸ்பிங்க்ஸ் புத்தக தீம்" -msgid "Open an issue" -msgstr "சிக்கலைத் திறக்கவும்" +msgid "previous page" +msgstr "முந்தைய பக்கம்" -msgid "next page" -msgstr "அடுத்த பக்கம்" +msgid "Source repository" +msgstr "மூல களஞ்சியம்" -msgid "Copyright" -msgstr "பதிப்புரிமை" +msgid "open issue" +msgstr "திறந்த பிரச்சினை" -msgid "Search this book..." -msgstr "இந்த புத்தகத்தைத் தேடுங்கள் ..." +msgid "Download notebook file" +msgstr "நோட்புக் கோப்பைப் பதிவிறக்கவும்" -msgid "Print to PDF" -msgstr "PDF இல் அச்சிடுக" +msgid "Copyright" +msgstr "பதிப்புரிமை" -msgid "By" -msgstr "வழங்கியவர்" +msgid "Theme by the" +msgstr "வழங்கிய தீம்" -msgid "previous page" -msgstr "முந்தைய பக்கம்" +msgid "Open an issue" +msgstr "சிக்கலைத் திறக்கவும்" -msgid "Download notebook file" -msgstr "நோட்புக் கோப்பைப் பதிவிறக்கவும்" +msgid "next page" +msgstr "அடுத்த பக்கம்" -msgid "suggest edit" -msgstr "திருத்த பரிந்துரைக்கவும்" +msgid "Launch" +msgstr "தொடங்க" diff --git a/_build/html/_static/locales/te/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/te/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..0a5f4b4 Binary files /dev/null and b/_build/html/_static/locales/te/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/te/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/te/LC_MESSAGES/booktheme.po index 0373b70..ef56fae 100644 --- a/_build/html/_static/locales/te/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/te/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: te\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "ఓపెన్ ఇష్యూ" - -msgid "Download this page" -msgstr "ఈ పేజీని డౌన్‌లోడ్ చేయండి" +msgid "By" +msgstr "ద్వారా" msgid "Download source file" msgstr "మూల ఫైల్‌ను డౌన్‌లోడ్ చేయండి" -msgid "Launch" -msgstr "ప్రారంభించండి" - msgid "Edit this page" msgstr "ఈ పేజీని సవరించండి" -msgid "Toggle navigation" -msgstr "నావిగేషన్‌ను టోగుల్ చేయండి" +msgid "Last updated on" +msgstr "చివరిగా నవీకరించబడింది" -msgid "Theme by the" -msgstr "ద్వారా థీమ్" +msgid "Print to PDF" +msgstr "PDF కి ముద్రించండి" -msgid "Source repository" -msgstr "మూల రిపోజిటరీ" +msgid "suggest edit" +msgstr "సవరించమని సూచించండి" -msgid "Last updated on" -msgstr "చివరిగా నవీకరించబడింది" +msgid "Download this page" +msgstr "ఈ పేజీని డౌన్‌లోడ్ చేయండి" + +msgid "Toggle navigation" +msgstr "నావిగేషన్‌ను టోగుల్ చేయండి" msgid "By the" msgstr "ద్వారా" @@ -41,29 +38,29 @@ msgstr "ద్వారా" msgid "Sphinx Book Theme" msgstr "సింహిక పుస్తక థీమ్" -msgid "Open an issue" -msgstr "సమస్యను తెరవండి" +msgid "previous page" +msgstr "ముందు పేజి" -msgid "next page" -msgstr "తరువాతి పేజీ" +msgid "Source repository" +msgstr "మూల రిపోజిటరీ" -msgid "Copyright" -msgstr "కాపీరైట్" +msgid "open issue" +msgstr "ఓపెన్ ఇష్యూ" -msgid "Search this book..." -msgstr "ఈ పుస్తకాన్ని శోధించండి ..." +msgid "Download notebook file" +msgstr "నోట్బుక్ ఫైల్ను డౌన్లోడ్ చేయండి" -msgid "Print to PDF" -msgstr "PDF కి ముద్రించండి" +msgid "Copyright" +msgstr "కాపీరైట్" -msgid "By" -msgstr "ద్వారా" +msgid "Theme by the" +msgstr "ద్వారా థీమ్" -msgid "previous page" -msgstr "ముందు పేజి" +msgid "Open an issue" +msgstr "సమస్యను తెరవండి" -msgid "Download notebook file" -msgstr "నోట్బుక్ ఫైల్ను డౌన్లోడ్ చేయండి" +msgid "next page" +msgstr "తరువాతి పేజీ" -msgid "suggest edit" -msgstr "సవరించమని సూచించండి" +msgid "Launch" +msgstr "ప్రారంభించండి" diff --git a/_build/html/_static/locales/tg/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/tg/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..b21c6c6 Binary files /dev/null and b/_build/html/_static/locales/tg/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/tg/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/tg/LC_MESSAGES/booktheme.po index 1807ae2..c4aae01 100644 --- a/_build/html/_static/locales/tg/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/tg/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: tg\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Бо" + msgid "repository" msgstr "анбор" -msgid "open issue" -msgstr "барориши кушод" - -msgid "Contents" -msgstr "Мундариҷа" - msgid "Fullscreen mode" msgstr "Ҳолати экрани пурра" -msgid "Download this page" -msgstr "Ин саҳифаро зеркашӣ кунед" +msgid "Contents" +msgstr "Мундариҷа" msgid "Download source file" msgstr "Файли манбаъро зеркашӣ кунед" -msgid "Launch" -msgstr "Оғоз" - msgid "Edit this page" msgstr "Ин саҳифаро таҳрир кунед" -msgid "Toggle navigation" -msgstr "Гузаришро иваз кунед" +msgid "Last updated on" +msgstr "Last навсозӣ дар" -msgid "Theme by the" -msgstr "Мавзӯъи аз" +msgid "Print to PDF" +msgstr "Чоп ба PDF" -msgid "Source repository" -msgstr "Анбори манбаъ" +msgid "suggest edit" +msgstr "пешниҳод вироиш" -msgid "Last updated on" -msgstr "Last навсозӣ дар" +msgid "Download this page" +msgstr "Ин саҳифаро зеркашӣ кунед" + +msgid "Toggle navigation" +msgstr "Гузаришро иваз кунед" msgid "By the" msgstr "Бо" @@ -50,32 +47,29 @@ msgstr "Бо" msgid "Sphinx Book Theme" msgstr "Сфинкс Мавзӯи китоб" -msgid "Open an issue" -msgstr "Масъаларо кушоед" - -msgid "next page" -msgstr "саҳифаи оянда" +msgid "previous page" +msgstr "саҳифаи қаблӣ" -msgid "Copyright" -msgstr "Ҳуқуқи муаллиф" +msgid "Source repository" +msgstr "Анбори манбаъ" -msgid "Search this book..." -msgstr "Ин китобро ҷустуҷӯ кунед ..." +msgid "open issue" +msgstr "барориши кушод" -msgid "Print to PDF" -msgstr "Чоп ба PDF" +msgid "Download notebook file" +msgstr "Файли дафтарро зеркашӣ кунед" -msgid "By" -msgstr "Бо" +msgid "Copyright" +msgstr "Ҳуқуқи муаллиф" -msgid "previous page" -msgstr "саҳифаи қаблӣ" +msgid "Theme by the" +msgstr "Мавзӯъи аз" -msgid "Search the docs ..." -msgstr "Ҷустуҷӯи ҳуҷҷатҳо ..." +msgid "Open an issue" +msgstr "Масъаларо кушоед" -msgid "Download notebook file" -msgstr "Файли дафтарро зеркашӣ кунед" +msgid "next page" +msgstr "саҳифаи оянда" -msgid "suggest edit" -msgstr "пешниҳод вироиш" +msgid "Launch" +msgstr "Оғоз" diff --git a/_build/html/_static/locales/th/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/th/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..abede98 Binary files /dev/null and b/_build/html/_static/locales/th/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/th/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/th/LC_MESSAGES/booktheme.po index a286feb..4c64505 100644 --- a/_build/html/_static/locales/th/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/th/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: th\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "โดย" + msgid "repository" msgstr "ที่เก็บ" -msgid "open issue" -msgstr "เปิดปัญหา" - -msgid "Contents" -msgstr "สารบัญ" - msgid "Fullscreen mode" msgstr "โหมดเต็มหน้าจอ" -msgid "Download this page" -msgstr "ดาวน์โหลดหน้านี้" +msgid "Contents" +msgstr "สารบัญ" msgid "Download source file" msgstr "ดาวน์โหลดไฟล์ต้นฉบับ" -msgid "Launch" -msgstr "เปิด" - msgid "Edit this page" msgstr "แก้ไขหน้านี้" -msgid "Toggle navigation" -msgstr "ไม่ต้องสลับช่องทาง" +msgid "Last updated on" +msgstr "ปรับปรุงล่าสุดเมื่อ" -msgid "Theme by the" -msgstr "ธีมโดย" +msgid "Print to PDF" +msgstr "พิมพ์เป็น PDF" -msgid "Source repository" -msgstr "ที่เก็บซอร์ส" +msgid "suggest edit" +msgstr "แนะนำแก้ไข" -msgid "Last updated on" -msgstr "ปรับปรุงล่าสุดเมื่อ" +msgid "Download this page" +msgstr "ดาวน์โหลดหน้านี้" + +msgid "Toggle navigation" +msgstr "ไม่ต้องสลับช่องทาง" msgid "By the" msgstr "โดย" @@ -50,32 +47,29 @@ msgstr "โดย" msgid "Sphinx Book Theme" msgstr "ธีมหนังสือสฟิงซ์" -msgid "Open an issue" +msgid "previous page" +msgstr "หน้าที่แล้ว" + +msgid "Source repository" +msgstr "ที่เก็บซอร์ส" + +msgid "open issue" msgstr "เปิดปัญหา" -msgid "next page" -msgstr "หน้าต่อไป" +msgid "Download notebook file" +msgstr "ดาวน์โหลดไฟล์สมุดบันทึก" msgid "Copyright" msgstr "ลิขสิทธิ์" -msgid "Search this book..." -msgstr "ค้นหาหนังสือเล่มนี้ ..." - -msgid "Print to PDF" -msgstr "พิมพ์เป็น PDF" - -msgid "By" -msgstr "โดย" - -msgid "previous page" -msgstr "หน้าที่แล้ว" +msgid "Theme by the" +msgstr "ธีมโดย" -msgid "Search the docs ..." -msgstr "ค้นหาเอกสาร ..." +msgid "Open an issue" +msgstr "เปิดปัญหา" -msgid "Download notebook file" -msgstr "ดาวน์โหลดไฟล์สมุดบันทึก" +msgid "next page" +msgstr "หน้าต่อไป" -msgid "suggest edit" -msgstr "แนะนำแก้ไข" +msgid "Launch" +msgstr "เปิด" diff --git a/_build/html/_static/locales/tl/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/tl/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..8df1b73 Binary files /dev/null and b/_build/html/_static/locales/tl/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/tl/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/tl/LC_MESSAGES/booktheme.po index 2e09439..2deaaab 100644 --- a/_build/html/_static/locales/tl/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/tl/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: tl\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "bukas na isyu" - -msgid "Download this page" -msgstr "I-download ang pahinang ito" +msgid "By" +msgstr "Ni" msgid "Download source file" msgstr "Mag-download ng file ng pinagmulan" -msgid "Launch" -msgstr "Ilunsad" - msgid "Edit this page" msgstr "I-edit ang pahinang ito" -msgid "Toggle navigation" -msgstr "I-toggle ang pag-navigate" +msgid "Last updated on" +msgstr "Huling na-update noong" -msgid "Theme by the" -msgstr "Tema ng" +msgid "Print to PDF" +msgstr "I-print sa PDF" -msgid "Source repository" -msgstr "Pinagmulan ng imbakan" +msgid "suggest edit" +msgstr "iminumungkahi i-edit" -msgid "Last updated on" -msgstr "Huling na-update noong" +msgid "Download this page" +msgstr "I-download ang pahinang ito" + +msgid "Toggle navigation" +msgstr "I-toggle ang pag-navigate" msgid "By the" msgstr "Sa pamamagitan ng" @@ -41,29 +38,29 @@ msgstr "Sa pamamagitan ng" msgid "Sphinx Book Theme" msgstr "Tema ng Sphinx Book" -msgid "Open an issue" -msgstr "Magbukas ng isyu" +msgid "previous page" +msgstr "Nakaraang pahina" -msgid "next page" -msgstr "Susunod na pahina" +msgid "Source repository" +msgstr "Pinagmulan ng imbakan" -msgid "Copyright" -msgstr "Copyright" +msgid "open issue" +msgstr "bukas na isyu" -msgid "Search this book..." -msgstr "Maghanap sa librong ito ..." +msgid "Download notebook file" +msgstr "Mag-download ng file ng notebook" -msgid "Print to PDF" -msgstr "I-print sa PDF" +msgid "Copyright" +msgstr "Copyright" -msgid "By" -msgstr "Ni" +msgid "Theme by the" +msgstr "Tema ng" -msgid "previous page" -msgstr "Nakaraang pahina" +msgid "Open an issue" +msgstr "Magbukas ng isyu" -msgid "Download notebook file" -msgstr "Mag-download ng file ng notebook" +msgid "next page" +msgstr "Susunod na pahina" -msgid "suggest edit" -msgstr "iminumungkahi i-edit" +msgid "Launch" +msgstr "Ilunsad" diff --git a/_build/html/_static/locales/tr/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/tr/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..029ae18 Binary files /dev/null and b/_build/html/_static/locales/tr/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/tr/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/tr/LC_MESSAGES/booktheme.po index fe21499..f80c643 100644 --- a/_build/html/_static/locales/tr/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/tr/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: tr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Tarafından" + msgid "repository" msgstr "depo" -msgid "open issue" -msgstr "Açık konu" - -msgid "Contents" -msgstr "İçindekiler" - msgid "Fullscreen mode" msgstr "Tam ekran modu" -msgid "Download this page" -msgstr "Bu sayfayı indirin" +msgid "Contents" +msgstr "İçindekiler" msgid "Download source file" msgstr "Kaynak dosyayı indirin" -msgid "Launch" -msgstr "Başlatmak" - msgid "Edit this page" msgstr "Bu sayfayı düzenle" -msgid "Toggle navigation" -msgstr "Gezinmeyi değiştir" +msgid "Last updated on" +msgstr "Son güncelleme tarihi" -msgid "Theme by the" -msgstr "Tarafından tema" +msgid "Print to PDF" +msgstr "PDF olarak yazdır" -msgid "Source repository" -msgstr "Kaynak kod deposu" +msgid "suggest edit" +msgstr "düzenleme öner" -msgid "Last updated on" -msgstr "Son güncelleme tarihi" +msgid "Download this page" +msgstr "Bu sayfayı indirin" + +msgid "Toggle navigation" +msgstr "Gezinmeyi değiştir" msgid "By the" msgstr "Tarafından" @@ -50,32 +47,29 @@ msgstr "Tarafından" msgid "Sphinx Book Theme" msgstr "Sfenks Kitap Teması" -msgid "Open an issue" -msgstr "Bir sorunu açın" - -msgid "next page" -msgstr "sonraki Sayfa" +msgid "previous page" +msgstr "önceki sayfa" -msgid "Copyright" -msgstr "Telif hakkı" +msgid "Source repository" +msgstr "Kaynak kod deposu" -msgid "Search this book..." -msgstr "Bu kitabı ara ..." +msgid "open issue" +msgstr "Açık konu" -msgid "Print to PDF" -msgstr "PDF olarak yazdır" +msgid "Download notebook file" +msgstr "Defter dosyasını indirin" -msgid "By" -msgstr "Tarafından" +msgid "Copyright" +msgstr "Telif hakkı" -msgid "previous page" -msgstr "önceki sayfa" +msgid "Theme by the" +msgstr "Tarafından tema" -msgid "Search the docs ..." -msgstr "Belgelerde ara ..." +msgid "Open an issue" +msgstr "Bir sorunu açın" -msgid "Download notebook file" -msgstr "Defter dosyasını indirin" +msgid "next page" +msgstr "sonraki Sayfa" -msgid "suggest edit" -msgstr "düzenleme öner" +msgid "Launch" +msgstr "Başlatmak" diff --git a/_build/html/_static/locales/uk/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/uk/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..16ab789 Binary files /dev/null and b/_build/html/_static/locales/uk/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/uk/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/uk/LC_MESSAGES/booktheme.po index ae85786..13fc4e3 100644 --- a/_build/html/_static/locales/uk/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/uk/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: uk\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Автор" + msgid "repository" msgstr "сховище" -msgid "open issue" -msgstr "відкритий випуск" - -msgid "Contents" -msgstr "Зміст" - msgid "Fullscreen mode" msgstr "Повноекранний режим" -msgid "Download this page" -msgstr "Завантажте цю сторінку" +msgid "Contents" +msgstr "Зміст" msgid "Download source file" msgstr "Завантажити вихідний файл" -msgid "Launch" -msgstr "Запуск" - msgid "Edit this page" msgstr "Редагувати цю сторінку" -msgid "Toggle navigation" -msgstr "Переключити навігацію" +msgid "Last updated on" +msgstr "Останнє оновлення:" -msgid "Theme by the" -msgstr "Тема від" +msgid "Print to PDF" +msgstr "Друк у форматі PDF" -msgid "Source repository" -msgstr "Джерело сховища" +msgid "suggest edit" +msgstr "запропонувати редагувати" -msgid "Last updated on" -msgstr "Останнє оновлення:" +msgid "Download this page" +msgstr "Завантажте цю сторінку" + +msgid "Toggle navigation" +msgstr "Переключити навігацію" msgid "By the" msgstr "По" @@ -50,32 +47,29 @@ msgstr "По" msgid "Sphinx Book Theme" msgstr "Тема книги \"Сфінкс\"" -msgid "Open an issue" -msgstr "Відкрийте випуск" - -msgid "next page" -msgstr "Наступна сторінка" +msgid "previous page" +msgstr "Попередня сторінка" -msgid "Copyright" -msgstr "Авторське право" +msgid "Source repository" +msgstr "Джерело сховища" -msgid "Search this book..." -msgstr "Шукати в цій книзі ..." +msgid "open issue" +msgstr "відкритий випуск" -msgid "Print to PDF" -msgstr "Друк у форматі PDF" +msgid "Download notebook file" +msgstr "Завантажте файл блокнота" -msgid "By" -msgstr "Автор" +msgid "Copyright" +msgstr "Авторське право" -msgid "previous page" -msgstr "Попередня сторінка" +msgid "Theme by the" +msgstr "Тема від" -msgid "Search the docs ..." -msgstr "Шукати в документах ..." +msgid "Open an issue" +msgstr "Відкрийте випуск" -msgid "Download notebook file" -msgstr "Завантажте файл блокнота" +msgid "next page" +msgstr "Наступна сторінка" -msgid "suggest edit" -msgstr "запропонувати редагувати" +msgid "Launch" +msgstr "Запуск" diff --git a/_build/html/_static/locales/ur/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/ur/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..de8c84b Binary files /dev/null and b/_build/html/_static/locales/ur/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/ur/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/ur/LC_MESSAGES/booktheme.po index 4c4c637..a39526d 100644 --- a/_build/html/_static/locales/ur/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/ur/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: ur\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "کھلا مسئلہ" - -msgid "Download this page" -msgstr "اس صفحے کو ڈاؤن لوڈ کریں" +msgid "By" +msgstr "بذریعہ" msgid "Download source file" msgstr "سورس فائل ڈاؤن لوڈ کریں" -msgid "Launch" -msgstr "لانچ کریں" - msgid "Edit this page" msgstr "اس صفحے میں ترمیم کریں" -msgid "Toggle navigation" -msgstr "نیویگیشن ٹوگل کریں" +msgid "Last updated on" +msgstr "آخری بار تازہ کاری ہوئی" -msgid "Theme by the" -msgstr "کے ذریعہ تھیم" +msgid "Print to PDF" +msgstr "پی ڈی ایف پرنٹ کریں" -msgid "Source repository" -msgstr "ماخذ ذخیرہ" +msgid "suggest edit" +msgstr "ترمیم کی تجویز کریں" -msgid "Last updated on" -msgstr "آخری بار تازہ کاری ہوئی" +msgid "Download this page" +msgstr "اس صفحے کو ڈاؤن لوڈ کریں" + +msgid "Toggle navigation" +msgstr "نیویگیشن ٹوگل کریں" msgid "By the" msgstr "کی طرف" @@ -41,29 +38,29 @@ msgstr "کی طرف" msgid "Sphinx Book Theme" msgstr "سپنکس بک تھیم" -msgid "Open an issue" -msgstr "ایک مسئلہ کھولیں" +msgid "previous page" +msgstr "سابقہ ​​صفحہ" -msgid "next page" -msgstr "اگلا صفحہ" +msgid "Source repository" +msgstr "ماخذ ذخیرہ" -msgid "Copyright" -msgstr "کاپی رائٹ" +msgid "open issue" +msgstr "کھلا مسئلہ" -msgid "Search this book..." -msgstr "اس کتاب کو تلاش کریں…" +msgid "Download notebook file" +msgstr "نوٹ بک فائل ڈاؤن لوڈ کریں" -msgid "Print to PDF" -msgstr "پی ڈی ایف پرنٹ کریں" +msgid "Copyright" +msgstr "کاپی رائٹ" -msgid "By" -msgstr "بذریعہ" +msgid "Theme by the" +msgstr "کے ذریعہ تھیم" -msgid "previous page" -msgstr "سابقہ ​​صفحہ" +msgid "Open an issue" +msgstr "ایک مسئلہ کھولیں" -msgid "Download notebook file" -msgstr "نوٹ بک فائل ڈاؤن لوڈ کریں" +msgid "next page" +msgstr "اگلا صفحہ" -msgid "suggest edit" -msgstr "ترمیم کی تجویز کریں" +msgid "Launch" +msgstr "لانچ کریں" diff --git a/_build/html/_static/locales/vi/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/vi/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..2bb3255 Binary files /dev/null and b/_build/html/_static/locales/vi/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/vi/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/vi/LC_MESSAGES/booktheme.po index 2c42502..72a7219 100644 --- a/_build/html/_static/locales/vi/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/vi/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: vi\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Bởi" + msgid "repository" msgstr "kho" -msgid "open issue" -msgstr "vấn đề mở" - -msgid "Contents" -msgstr "Nội dung" - msgid "Fullscreen mode" msgstr "Chế độ toàn màn hình" -msgid "Download this page" -msgstr "Tải xuống trang này" +msgid "Contents" +msgstr "Nội dung" msgid "Download source file" msgstr "Tải xuống tệp nguồn" -msgid "Launch" -msgstr "Phóng" - msgid "Edit this page" msgstr "chỉnh sửa trang này" -msgid "Toggle navigation" -msgstr "Chuyển đổi điều hướng thành" +msgid "Last updated on" +msgstr "Cập nhật lần cuối vào" -msgid "Theme by the" -msgstr "Chủ đề của" +msgid "Print to PDF" +msgstr "In sang PDF" -msgid "Source repository" -msgstr "Kho nguồn" +msgid "suggest edit" +msgstr "đề nghị chỉnh sửa" -msgid "Last updated on" -msgstr "Cập nhật lần cuối vào" +msgid "Download this page" +msgstr "Tải xuống trang này" + +msgid "Toggle navigation" +msgstr "Chuyển đổi điều hướng thành" msgid "By the" msgstr "Bằng" @@ -50,32 +47,29 @@ msgstr "Bằng" msgid "Sphinx Book Theme" msgstr "Chủ đề sách nhân sư" -msgid "Open an issue" -msgstr "Mở một vấn đề" - -msgid "next page" -msgstr "Trang tiếp theo" +msgid "previous page" +msgstr "trang trước" -msgid "Copyright" -msgstr "Bản quyền" +msgid "Source repository" +msgstr "Kho nguồn" -msgid "Search this book..." -msgstr "Tìm kiếm cuốn sách này ..." +msgid "open issue" +msgstr "vấn đề mở" -msgid "Print to PDF" -msgstr "In sang PDF" +msgid "Download notebook file" +msgstr "Tải xuống tệp sổ tay" -msgid "By" -msgstr "Bởi" +msgid "Copyright" +msgstr "Bản quyền" -msgid "previous page" -msgstr "trang trước" +msgid "Theme by the" +msgstr "Chủ đề của" -msgid "Search the docs ..." -msgstr "Tìm kiếm tài liệu ..." +msgid "Open an issue" +msgstr "Mở một vấn đề" -msgid "Download notebook file" -msgstr "Tải xuống tệp sổ tay" +msgid "next page" +msgstr "Trang tiếp theo" -msgid "suggest edit" -msgstr "đề nghị chỉnh sửa" +msgid "Launch" +msgstr "Phóng" diff --git a/_build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..0e3235d Binary files /dev/null and b/_build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.po index 4098aed..e62bd5a 100644 --- a/_build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/zh_CN/LC_MESSAGES/booktheme.po @@ -8,71 +8,68 @@ msgstr "" "Language: zh_CN\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "repository" -msgstr "资料库" - -msgid "open issue" -msgstr "公开的问题" +msgid "By" +msgstr "作者：" -msgid "Contents" -msgstr "内容" +msgid "repository" +msgstr "仓库" msgid "Fullscreen mode" msgstr "全屏模式" -msgid "Download this page" -msgstr "下载此页面" +msgid "Contents" +msgstr "目录" msgid "Download source file" msgstr "下载源文件" -msgid "Launch" -msgstr "发射" - msgid "Edit this page" -msgstr "编辑这个页面" +msgstr "编辑此页面" -msgid "Toggle navigation" -msgstr "切换导航" +msgid "Last updated on" +msgstr "上次更新时间：" -msgid "Theme by the" -msgstr "主题由" +msgid "Print to PDF" +msgstr "列印成 PDF" -msgid "Source repository" -msgstr "源库" +msgid "suggest edit" +msgstr "提出修改建议" -msgid "Last updated on" -msgstr "上次更新时间：" +msgid "Download this page" +msgstr "下载此页面" + +msgid "Toggle navigation" +msgstr "显示或隐藏导航栏" msgid "By the" -msgstr "由" +msgstr "作者：" msgid "Sphinx Book Theme" -msgstr "狮身人面像书主题" +msgstr "Sphinx Book 主题" -msgid "Open an issue" -msgstr "打开一个问题" +msgid "previous page" +msgstr "上一页" -msgid "next page" -msgstr "下一页" +msgid "Source repository" +msgstr "源码库" -msgid "Copyright" -msgstr "版权" +msgid "open issue" +msgstr "创建议题" -msgid "Search this book..." -msgstr "搜索这本书..." +msgid "Download notebook file" +msgstr "下载笔记本文件" -msgid "Print to PDF" -msgstr "列印成PDF" +msgid "Copyright" +msgstr "版权" -msgid "By" -msgstr "通过" +msgid "Theme by the" +msgstr "主题作者：" -msgid "previous page" -msgstr "上一页" +msgid "Open an issue" +msgstr "创建议题" -msgid "Search the docs ..." -msgstr "搜索文档..." +msgid "next page" +msgstr "下一页" -msgid "Download notebook file" -msgstr "下载笔记本文件" +msgid "Launch" +msgstr "启动" diff --git a/_build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo b/_build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..9116fa9 Binary files /dev/null and b/_build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo differ diff --git a/_build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.po b/_build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.po index 52cff5e..4a9ced7 100644 --- a/_build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.po +++ b/_build/html/_static/locales/zh_TW/LC_MESSAGES/booktheme.po @@ -8,74 +8,68 @@ msgstr "" "Language: zh_TW\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "repository" -msgstr "資料庫" - -msgid "open issue" -msgstr "公開的問題" +msgid "By" +msgstr "作者：" -msgid "Contents" -msgstr "內容" +msgid "repository" +msgstr "儲存庫" msgid "Fullscreen mode" -msgstr "全屏模式" +msgstr "全螢幕模式" -msgid "Download this page" -msgstr "下載此頁面" +msgid "Contents" +msgstr "目錄" msgid "Download source file" -msgstr "下載源文件" - -msgid "Launch" -msgstr "發射" +msgstr "下載原始檔" msgid "Edit this page" -msgstr "編輯這個頁面" +msgstr "編輯此頁面" -msgid "Toggle navigation" -msgstr "切換導航" +msgid "Last updated on" +msgstr "最後更新時間：" -msgid "Theme by the" -msgstr "主題由" +msgid "Print to PDF" +msgstr "列印成 PDF" -msgid "Source repository" -msgstr "源庫" +msgid "suggest edit" +msgstr "提出修改建議" -msgid "Last updated on" -msgstr "上次更新時間：" +msgid "Download this page" +msgstr "下載此頁面" + +msgid "Toggle navigation" +msgstr "顯示或隱藏導覽列" msgid "By the" -msgstr "由" +msgstr "作者：" msgid "Sphinx Book Theme" -msgstr "獅身人面像書主題" +msgstr "Sphinx Book 佈景主題" -msgid "Open an issue" -msgstr "打開一個問題" - -msgid "next page" -msgstr "下一頁" +msgid "previous page" +msgstr "上一頁" -msgid "Copyright" -msgstr "版權" +msgid "Source repository" +msgstr "來源儲存庫" -msgid "Search this book..." -msgstr "搜索這本書..." +msgid "open issue" +msgstr "公開的問題" -msgid "Print to PDF" -msgstr "列印成PDF" +msgid "Download notebook file" +msgstr "下載 Notebook 檔案" -msgid "By" -msgstr "通過" +msgid "Copyright" +msgstr "Copyright" -msgid "previous page" -msgstr "上一頁" +msgid "Theme by the" +msgstr "佈景主題作者：" -msgid "Search the docs ..." -msgstr "搜索文檔..." +msgid "Open an issue" +msgstr "開啟議題" -msgid "Download notebook file" -msgstr "下載筆記本文件" +msgid "next page" +msgstr "下一頁" -msgid "suggest edit" -msgstr "建議編輯" +msgid "Launch" +msgstr "啟動" diff --git a/_build/html/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css b/_build/html/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css new file mode 100644 index 0000000..3356631 --- /dev/null +++ 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+} + +div.highlight .-Color[class*=-C71] { + color: #5FAF5F +} + +div.highlight .-Color[class*=-BGC71] { + background-color: #5FAF5F +} + +div.highlight .-Color[class*=-C72] { + color: #5FAF87 +} + +div.highlight .-Color[class*=-BGC72] { + background-color: #5FAF87 +} + +div.highlight .-Color[class*=-C73] { + color: #5FAFAF +} + +div.highlight .-Color[class*=-BGC73] { + background-color: #5FAFAF +} + +div.highlight .-Color[class*=-C74] { + color: #5FAFD7 +} + +div.highlight .-Color[class*=-BGC74] { + background-color: #5FAFD7 +} + +div.highlight .-Color[class*=-C75] { + color: #5FAFFF +} + +div.highlight .-Color[class*=-BGC75] { + background-color: #5FAFFF +} + +div.highlight .-Color[class*=-C76] { + color: #5FD700 +} + +div.highlight .-Color[class*=-BGC76] { + background-color: #5FD700 +} + +div.highlight .-Color[class*=-C77] { + color: #5FD75F +} + +div.highlight .-Color[class*=-BGC77] { + background-color: #5FD75F +} + +div.highlight .-Color[class*=-C78] { + color: #5FD787 +} + +div.highlight .-Color[class*=-BGC78] { + background-color: #5FD787 +} + +div.highlight .-Color[class*=-C79] { + color: #5FD7AF +} + +div.highlight .-Color[class*=-BGC79] { + background-color: #5FD7AF +} + +div.highlight .-Color[class*=-C80] { + color: #5FD7D7 +} + +div.highlight .-Color[class*=-BGC80] { + background-color: #5FD7D7 +} + +div.highlight .-Color[class*=-C81] { + color: #5FD7FF +} + +div.highlight .-Color[class*=-BGC81] { + background-color: #5FD7FF +} + +div.highlight .-Color[class*=-C82] { + color: #5FFF00 +} + +div.highlight .-Color[class*=-BGC82] { + background-color: #5FFF00 +} + +div.highlight .-Color[class*=-C83] { + color: #5FFF5F +} + +div.highlight .-Color[class*=-BGC83] { + background-color: #5FFF5F +} + +div.highlight .-Color[class*=-C84] { + color: #5FFF87 +} + +div.highlight .-Color[class*=-BGC84] { + background-color: #5FFF87 +} + +div.highlight .-Color[class*=-C85] { + color: #5FFFAF +} + +div.highlight .-Color[class*=-BGC85] { + background-color: #5FFFAF +} + +div.highlight .-Color[class*=-C86] { + color: #5FFFD7 +} + +div.highlight .-Color[class*=-BGC86] { + background-color: #5FFFD7 +} + +div.highlight .-Color[class*=-C87] { + color: #5FFFFF +} + +div.highlight .-Color[class*=-BGC87] { + background-color: #5FFFFF +} + +div.highlight .-Color[class*=-C88] { + color: #870000 +} + +div.highlight .-Color[class*=-BGC88] { + background-color: #870000 +} + +div.highlight .-Color[class*=-C89] { + color: #87005F +} + +div.highlight .-Color[class*=-BGC89] { + background-color: #87005F +} + +div.highlight .-Color[class*=-C90] { + color: #870087 +} + +div.highlight .-Color[class*=-BGC90] { + background-color: #870087 +} + +div.highlight .-Color[class*=-C91] { + color: #8700AF +} + +div.highlight .-Color[class*=-BGC91] { + background-color: #8700AF +} + +div.highlight .-Color[class*=-C92] { + color: #8700D7 +} + +div.highlight .-Color[class*=-BGC92] { + background-color: #8700D7 +} + +div.highlight .-Color[class*=-C93] { + color: #8700FF +} + +div.highlight .-Color[class*=-BGC93] { + background-color: #8700FF +} + +div.highlight .-Color[class*=-C94] { + color: #875F00 +} + +div.highlight .-Color[class*=-BGC94] { + background-color: #875F00 +} + +div.highlight .-Color[class*=-C95] { + color: #875F5F +} + +div.highlight .-Color[class*=-BGC95] { + background-color: #875F5F +} + +div.highlight .-Color[class*=-C96] { + color: #875F87 +} + +div.highlight .-Color[class*=-BGC96] { + background-color: #875F87 +} + +div.highlight .-Color[class*=-C97] { + color: #875FAF +} + +div.highlight .-Color[class*=-BGC97] { + background-color: #875FAF +} + +div.highlight .-Color[class*=-C98] { + color: #875FD7 +} + +div.highlight .-Color[class*=-BGC98] { + background-color: #875FD7 +} + +div.highlight .-Color[class*=-C99] { + color: #875FFF +} + +div.highlight .-Color[class*=-BGC99] { + background-color: #875FFF +} + +div.highlight .-Color[class*=-C100] { + color: #878700 +} + +div.highlight .-Color[class*=-BGC100] { + background-color: #878700 +} + +div.highlight .-Color[class*=-C101] { + color: #87875F +} + +div.highlight .-Color[class*=-BGC101] { + background-color: #87875F +} + +div.highlight .-Color[class*=-C102] { + color: #878787 +} + +div.highlight .-Color[class*=-BGC102] { + background-color: #878787 +} + +div.highlight .-Color[class*=-C103] { + color: #8787AF +} + +div.highlight .-Color[class*=-BGC103] { + background-color: #8787AF +} + +div.highlight .-Color[class*=-C104] { + color: #8787D7 +} + +div.highlight .-Color[class*=-BGC104] { + background-color: #8787D7 +} + +div.highlight .-Color[class*=-C105] { + color: #8787FF +} + +div.highlight .-Color[class*=-BGC105] { + background-color: #8787FF +} + +div.highlight .-Color[class*=-C106] { + color: #87AF00 +} + +div.highlight .-Color[class*=-BGC106] { + background-color: #87AF00 +} + +div.highlight .-Color[class*=-C107] { + color: #87AF5F +} + +div.highlight .-Color[class*=-BGC107] { + background-color: #87AF5F +} + +div.highlight .-Color[class*=-C108] { + color: #87AF87 +} + +div.highlight .-Color[class*=-BGC108] { + background-color: #87AF87 +} + +div.highlight .-Color[class*=-C109] { + color: #87AFAF +} + +div.highlight .-Color[class*=-BGC109] { + background-color: #87AFAF +} + +div.highlight .-Color[class*=-C110] { + color: #87AFD7 +} + +div.highlight .-Color[class*=-BGC110] { + background-color: #87AFD7 +} + +div.highlight .-Color[class*=-C111] { + color: #87AFFF +} + +div.highlight .-Color[class*=-BGC111] { + background-color: #87AFFF +} + +div.highlight .-Color[class*=-C112] { + color: #87D700 +} + +div.highlight .-Color[class*=-BGC112] { + background-color: #87D700 +} + +div.highlight .-Color[class*=-C113] { + color: #87D75F +} + +div.highlight .-Color[class*=-BGC113] { + background-color: #87D75F +} + +div.highlight .-Color[class*=-C114] { + color: #87D787 +} + +div.highlight .-Color[class*=-BGC114] { + background-color: #87D787 +} + +div.highlight .-Color[class*=-C115] { + color: #87D7AF +} + +div.highlight .-Color[class*=-BGC115] { + background-color: #87D7AF +} + +div.highlight .-Color[class*=-C116] { + color: #87D7D7 +} + +div.highlight .-Color[class*=-BGC116] { + background-color: #87D7D7 +} + +div.highlight .-Color[class*=-C117] { + color: #87D7FF +} + +div.highlight .-Color[class*=-BGC117] { + background-color: #87D7FF +} + +div.highlight .-Color[class*=-C118] { + color: #87FF00 +} + +div.highlight .-Color[class*=-BGC118] { + background-color: #87FF00 +} + +div.highlight .-Color[class*=-C119] { + color: #87FF5F +} + +div.highlight .-Color[class*=-BGC119] { + background-color: #87FF5F +} + +div.highlight .-Color[class*=-C120] { + color: #87FF87 +} + +div.highlight .-Color[class*=-BGC120] { + background-color: #87FF87 +} + +div.highlight .-Color[class*=-C121] { + color: #87FFAF +} + +div.highlight .-Color[class*=-BGC121] { + background-color: #87FFAF +} + +div.highlight .-Color[class*=-C122] { + color: #87FFD7 +} + +div.highlight .-Color[class*=-BGC122] { + background-color: #87FFD7 +} + +div.highlight .-Color[class*=-C123] { + color: #87FFFF +} + +div.highlight .-Color[class*=-BGC123] { + background-color: #87FFFF +} + +div.highlight .-Color[class*=-C124] { + color: #AF0000 +} + +div.highlight .-Color[class*=-BGC124] { + background-color: #AF0000 +} + +div.highlight .-Color[class*=-C125] { + color: #AF005F +} + +div.highlight .-Color[class*=-BGC125] { + background-color: #AF005F +} + +div.highlight .-Color[class*=-C126] { + color: #AF0087 +} + +div.highlight .-Color[class*=-BGC126] { + background-color: #AF0087 +} + +div.highlight .-Color[class*=-C127] { + color: #AF00AF +} + +div.highlight .-Color[class*=-BGC127] { + background-color: #AF00AF +} + +div.highlight .-Color[class*=-C128] { + color: #AF00D7 +} + +div.highlight .-Color[class*=-BGC128] { + background-color: #AF00D7 +} + +div.highlight .-Color[class*=-C129] { + color: #AF00FF +} + +div.highlight .-Color[class*=-BGC129] { + background-color: #AF00FF +} + +div.highlight .-Color[class*=-C130] { + color: #AF5F00 +} + +div.highlight .-Color[class*=-BGC130] { + background-color: #AF5F00 +} + +div.highlight .-Color[class*=-C131] { + color: #AF5F5F +} + +div.highlight .-Color[class*=-BGC131] { + background-color: #AF5F5F +} + +div.highlight .-Color[class*=-C132] { + color: #AF5F87 +} + +div.highlight .-Color[class*=-BGC132] { + background-color: #AF5F87 +} + +div.highlight .-Color[class*=-C133] { + color: #AF5FAF +} + +div.highlight .-Color[class*=-BGC133] { + background-color: #AF5FAF +} + +div.highlight .-Color[class*=-C134] { + color: #AF5FD7 +} + +div.highlight .-Color[class*=-BGC134] { + background-color: #AF5FD7 +} + +div.highlight .-Color[class*=-C135] { + color: #AF5FFF +} + +div.highlight .-Color[class*=-BGC135] { + background-color: #AF5FFF +} + +div.highlight .-Color[class*=-C136] { + color: #AF8700 +} + +div.highlight .-Color[class*=-BGC136] { + background-color: #AF8700 +} + +div.highlight .-Color[class*=-C137] { + color: #AF875F +} + +div.highlight .-Color[class*=-BGC137] { + background-color: #AF875F +} + +div.highlight .-Color[class*=-C138] { + color: #AF8787 +} + +div.highlight .-Color[class*=-BGC138] { + background-color: #AF8787 +} + +div.highlight .-Color[class*=-C139] { + color: #AF87AF +} + +div.highlight .-Color[class*=-BGC139] { + background-color: #AF87AF +} + +div.highlight .-Color[class*=-C140] { + color: #AF87D7 +} + +div.highlight .-Color[class*=-BGC140] { + background-color: #AF87D7 +} + +div.highlight .-Color[class*=-C141] { + color: #AF87FF +} + +div.highlight .-Color[class*=-BGC141] { + background-color: #AF87FF +} + +div.highlight .-Color[class*=-C142] { + color: #AFAF00 +} + +div.highlight .-Color[class*=-BGC142] { + background-color: #AFAF00 +} + +div.highlight .-Color[class*=-C143] { + color: #AFAF5F +} + +div.highlight .-Color[class*=-BGC143] { + background-color: #AFAF5F +} + +div.highlight .-Color[class*=-C144] { + color: #AFAF87 +} + +div.highlight .-Color[class*=-BGC144] { + background-color: #AFAF87 +} + +div.highlight .-Color[class*=-C145] { + color: #AFAFAF +} + +div.highlight .-Color[class*=-BGC145] { + background-color: #AFAFAF +} + +div.highlight .-Color[class*=-C146] { + color: #AFAFD7 +} + +div.highlight .-Color[class*=-BGC146] { + background-color: #AFAFD7 +} + +div.highlight .-Color[class*=-C147] { + color: #AFAFFF +} + +div.highlight .-Color[class*=-BGC147] { + background-color: #AFAFFF +} + +div.highlight .-Color[class*=-C148] { + color: #AFD700 +} + +div.highlight .-Color[class*=-BGC148] { + background-color: #AFD700 +} + +div.highlight .-Color[class*=-C149] { + color: #AFD75F +} + +div.highlight .-Color[class*=-BGC149] { + background-color: #AFD75F +} + +div.highlight .-Color[class*=-C150] { + color: #AFD787 +} + +div.highlight .-Color[class*=-BGC150] { + background-color: #AFD787 +} + +div.highlight .-Color[class*=-C151] { + color: #AFD7AF +} + +div.highlight .-Color[class*=-BGC151] { + background-color: #AFD7AF +} + +div.highlight .-Color[class*=-C152] { + color: #AFD7D7 +} + +div.highlight .-Color[class*=-BGC152] { + background-color: #AFD7D7 +} + +div.highlight .-Color[class*=-C153] { + color: #AFD7FF +} + +div.highlight .-Color[class*=-BGC153] { + background-color: #AFD7FF +} + +div.highlight .-Color[class*=-C154] { + color: #AFFF00 +} + +div.highlight .-Color[class*=-BGC154] { + background-color: #AFFF00 +} + +div.highlight .-Color[class*=-C155] { + color: #AFFF5F +} + +div.highlight .-Color[class*=-BGC155] { + background-color: #AFFF5F +} + +div.highlight .-Color[class*=-C156] { + color: #AFFF87 +} + +div.highlight .-Color[class*=-BGC156] { + background-color: #AFFF87 +} + +div.highlight .-Color[class*=-C157] { + color: #AFFFAF +} + +div.highlight .-Color[class*=-BGC157] { + background-color: #AFFFAF +} + +div.highlight .-Color[class*=-C158] { + color: #AFFFD7 +} + +div.highlight .-Color[class*=-BGC158] { + background-color: #AFFFD7 +} + +div.highlight .-Color[class*=-C159] { + color: #AFFFFF +} + +div.highlight .-Color[class*=-BGC159] { + background-color: #AFFFFF +} + +div.highlight .-Color[class*=-C160] { + color: #D70000 +} + +div.highlight .-Color[class*=-BGC160] { + background-color: #D70000 +} + +div.highlight .-Color[class*=-C161] { + color: #D7005F +} + +div.highlight .-Color[class*=-BGC161] { + background-color: #D7005F +} + +div.highlight .-Color[class*=-C162] { + color: #D70087 +} + +div.highlight .-Color[class*=-BGC162] { + background-color: #D70087 +} + +div.highlight .-Color[class*=-C163] { + color: #D700AF +} + +div.highlight .-Color[class*=-BGC163] { + background-color: #D700AF +} + +div.highlight .-Color[class*=-C164] { + color: #D700D7 +} + +div.highlight .-Color[class*=-BGC164] { + background-color: #D700D7 +} + +div.highlight .-Color[class*=-C165] { + color: #D700FF +} + +div.highlight .-Color[class*=-BGC165] { + background-color: #D700FF +} + +div.highlight .-Color[class*=-C166] { + color: #D75F00 +} + +div.highlight .-Color[class*=-BGC166] { + background-color: #D75F00 +} + +div.highlight .-Color[class*=-C167] { + color: #D75F5F +} + +div.highlight .-Color[class*=-BGC167] { + background-color: #D75F5F +} + +div.highlight .-Color[class*=-C168] { + color: #D75F87 +} + +div.highlight .-Color[class*=-BGC168] { + background-color: #D75F87 +} + +div.highlight .-Color[class*=-C169] { + color: #D75FAF +} + +div.highlight .-Color[class*=-BGC169] { + background-color: #D75FAF +} + +div.highlight .-Color[class*=-C170] { + color: #D75FD7 +} + +div.highlight .-Color[class*=-BGC170] { + background-color: #D75FD7 +} + +div.highlight .-Color[class*=-C171] { + color: #D75FFF +} + +div.highlight .-Color[class*=-BGC171] { + background-color: #D75FFF +} + +div.highlight .-Color[class*=-C172] { + color: #D78700 +} + +div.highlight .-Color[class*=-BGC172] { + background-color: #D78700 +} + +div.highlight .-Color[class*=-C173] { + color: #D7875F +} + +div.highlight .-Color[class*=-BGC173] { + background-color: #D7875F +} + +div.highlight .-Color[class*=-C174] { + color: #D78787 +} + +div.highlight .-Color[class*=-BGC174] { + background-color: #D78787 +} + +div.highlight .-Color[class*=-C175] { + color: #D787AF +} + +div.highlight .-Color[class*=-BGC175] { + background-color: #D787AF +} + +div.highlight .-Color[class*=-C176] { + color: #D787D7 +} + +div.highlight .-Color[class*=-BGC176] { + background-color: #D787D7 +} + +div.highlight .-Color[class*=-C177] { + color: #D787FF +} + +div.highlight .-Color[class*=-BGC177] { + background-color: #D787FF +} + +div.highlight .-Color[class*=-C178] { + color: #D7AF00 +} + +div.highlight .-Color[class*=-BGC178] { + background-color: #D7AF00 +} + +div.highlight .-Color[class*=-C179] { + color: #D7AF5F +} + +div.highlight .-Color[class*=-BGC179] { + background-color: #D7AF5F +} + +div.highlight .-Color[class*=-C180] { + color: #D7AF87 +} + +div.highlight .-Color[class*=-BGC180] { + background-color: #D7AF87 +} + +div.highlight .-Color[class*=-C181] { + color: #D7AFAF +} + +div.highlight .-Color[class*=-BGC181] { + background-color: #D7AFAF +} + +div.highlight .-Color[class*=-C182] { + color: #D7AFD7 +} + +div.highlight .-Color[class*=-BGC182] { + background-color: #D7AFD7 +} + +div.highlight .-Color[class*=-C183] { + color: #D7AFFF +} + +div.highlight .-Color[class*=-BGC183] { + background-color: #D7AFFF +} + +div.highlight .-Color[class*=-C184] { + color: #D7D700 +} + +div.highlight .-Color[class*=-BGC184] { + background-color: #D7D700 +} + +div.highlight .-Color[class*=-C185] { + color: #D7D75F +} + +div.highlight .-Color[class*=-BGC185] { + background-color: #D7D75F +} + +div.highlight .-Color[class*=-C186] { + color: #D7D787 +} + +div.highlight .-Color[class*=-BGC186] { + background-color: #D7D787 +} + +div.highlight .-Color[class*=-C187] { + color: #D7D7AF +} + +div.highlight .-Color[class*=-BGC187] { + background-color: #D7D7AF +} + +div.highlight .-Color[class*=-C188] { + color: #D7D7D7 +} + +div.highlight .-Color[class*=-BGC188] { + background-color: #D7D7D7 +} + +div.highlight .-Color[class*=-C189] { + color: #D7D7FF +} + +div.highlight .-Color[class*=-BGC189] { + background-color: #D7D7FF +} + +div.highlight .-Color[class*=-C190] { + color: #D7FF00 +} + +div.highlight .-Color[class*=-BGC190] { + background-color: #D7FF00 +} + +div.highlight .-Color[class*=-C191] { + color: #D7FF5F +} + +div.highlight .-Color[class*=-BGC191] { + background-color: #D7FF5F +} + +div.highlight .-Color[class*=-C192] { + color: #D7FF87 +} + +div.highlight .-Color[class*=-BGC192] { + background-color: #D7FF87 +} + +div.highlight .-Color[class*=-C193] { + color: #D7FFAF +} + +div.highlight .-Color[class*=-BGC193] { + background-color: #D7FFAF +} + +div.highlight .-Color[class*=-C194] { + color: #D7FFD7 +} + +div.highlight .-Color[class*=-BGC194] { + background-color: #D7FFD7 +} + +div.highlight .-Color[class*=-C195] { + color: #D7FFFF +} + +div.highlight .-Color[class*=-BGC195] { + background-color: #D7FFFF +} + +div.highlight .-Color[class*=-C196] { + color: #FF0000 +} + +div.highlight .-Color[class*=-BGC196] { + background-color: #FF0000 +} + +div.highlight .-Color[class*=-C197] { + color: #FF005F +} + +div.highlight .-Color[class*=-BGC197] { + background-color: #FF005F +} + +div.highlight .-Color[class*=-C198] { + color: #FF0087 +} + +div.highlight .-Color[class*=-BGC198] { + background-color: #FF0087 +} + +div.highlight .-Color[class*=-C199] { + color: #FF00AF +} + +div.highlight .-Color[class*=-BGC199] { + background-color: #FF00AF +} + +div.highlight .-Color[class*=-C200] { + color: #FF00D7 +} + +div.highlight .-Color[class*=-BGC200] { + background-color: #FF00D7 +} + +div.highlight .-Color[class*=-C201] { + color: #FF00FF +} + +div.highlight .-Color[class*=-BGC201] { + background-color: #FF00FF +} + +div.highlight .-Color[class*=-C202] { + color: #FF5F00 +} + +div.highlight .-Color[class*=-BGC202] { + background-color: #FF5F00 +} + +div.highlight .-Color[class*=-C203] { + color: #FF5F5F +} + +div.highlight .-Color[class*=-BGC203] { + 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i=t.nextElementSibling;for(;i;){if(i.matches(e))return[i];i=i.nextElementSibling}return[]},focusableChildren(t){const e=["a","button","input","textarea","select","details","[tabindex]",'[contenteditable="true"]'].map(t=>t+':not([tabindex^="-"])').join(",");return this.find(e,t).filter(t=>!zt(t)&&Bt(t))}},Oe={endCallback:null,leftCallback:null,rightCallback:null},xe={endCallback:"(function|null)",leftCallback:"(function|null)",rightCallback:"(function|null)"};class ke extends ye{constructor(t,e){super(),this._element=t,t&&ke.isSupported()&&(this._config=this._getConfig(e),this._deltaX=0,this._supportPointerEvents=Boolean(window.PointerEvent),this._initEvents())}static get Default(){return Oe}static get DefaultType(){return xe}static get NAME(){return"swipe"}dispose(){fe.off(this._element,".bs.swipe")}_start(t){this._supportPointerEvents?this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX):this._deltaX=t.touches[0].clientX}_end(t){this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX-this._deltaX),this._handleSwipe(),Yt(this._config.endCallback)}_move(t){this._deltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this._deltaX}_handleSwipe(){const t=Math.abs(this._deltaX);if(t<=40)return;const e=t/this._deltaX;this._deltaX=0,e&&Yt(e>0?this._config.rightCallback:this._config.leftCallback)}_initEvents(){this._supportPointerEvents?(fe.on(this._element,"pointerdown.bs.swipe",t=>this._start(t)),fe.on(this._element,"pointerup.bs.swipe",t=>this._end(t)),this._element.classList.add("pointer-event")):(fe.on(this._element,"touchstart.bs.swipe",t=>this._start(t)),fe.on(this._element,"touchmove.bs.swipe",t=>this._move(t)),fe.on(this._element,"touchend.bs.swipe",t=>this._end(t)))}_eventIsPointerPenTouch(t){return this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static isSupported(){return"ontouchstart"in document.documentElement||navigator.maxTouchPoints>0}}const Le={ArrowLeft:"right",ArrowRight:"left"},De={interval:5e3,keyboard:!0,pause:"hover",ride:!1,touch:!0,wrap:!0},Se={interval:"(number|boolean)",keyboard:"boolean",pause:"(string|boolean)",ride:"(boolean|string)",touch:"boolean",wrap:"boolean"};class Ie extends we{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=Te.findOne(".carousel-indicators",this._element),this._addEventListeners(),"carousel"===this._config.ride&&this.cycle()}static get Default(){return De}static get DefaultType(){return Se}static get NAME(){return"carousel"}next(){this._slide("next")}nextWhenVisible(){!document.hidden&&Bt(this._element)&&this.next()}prev(){this._slide("prev")}pause(){this._isSliding&&Ht(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval(()=>this.nextWhenVisible(),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?fe.one(this._element,"slid.bs.carousel",()=>this.cycle()):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void fe.one(this._element,"slid.bs.carousel",()=>this.to(t));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?"next":"prev";this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&fe.on(this._element,"keydown.bs.carousel",t=>this._keydown(t)),"hover"===this._config.pause&&(fe.on(this._element,"mouseenter.bs.carousel",()=>this.pause()),fe.on(this._element,"mouseleave.bs.carousel",()=>this._maybeEnableCycle())),this._config.touch&&ke.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of Te.find(".carousel-item img",this._element))fe.on(t,"dragstart.bs.carousel",t=>t.preventDefault());const t={leftCallback:()=>this._slide(this._directionToOrder("left")),rightCallback:()=>this._slide(this._directionToOrder("right")),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout(()=>this._maybeEnableCycle(),500+this._config.interval))}};this._swipeHelper=new ke(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=Le[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=Te.findOne(".active",this._indicatorsElement);e.classList.remove("active"),e.removeAttribute("aria-current");const i=Te.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add("active"),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n="next"===t,s=e||Gt(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>fe.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r("slide.bs.carousel").defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),Vt(s),i.classList.add(l),s.classList.add(l);this._queueCallback(()=>{s.classList.remove(l,c),s.classList.add("active"),i.classList.remove("active",c,l),this._isSliding=!1,r("slid.bs.carousel")},i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return Te.findOne(".active.carousel-item",this._element)}_getItems(){return Te.find(".carousel-item",this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Qt()?"left"===t?"prev":"next":"left"===t?"next":"prev"}_orderToDirection(t){return Qt()?"prev"===t?"left":"right":"prev"===t?"right":"left"}static jQueryInterface(t){return this.each((function(){const e=Ie.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}fe.on(document,"click.bs.carousel.data-api","[data-bs-slide], [data-bs-slide-to]",(function(t){const e=Mt(this);if(!e||!e.classList.contains("carousel"))return;t.preventDefault();const i=Ie.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===ve.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),fe.on(window,"load.bs.carousel.data-api",()=>{const t=Te.find('[data-bs-ride="carousel"]');for(const e of t)Ie.getOrCreateInstance(e)}),Xt(Ie);const Ne={parent:null,toggle:!0},Pe={parent:"(null|element)",toggle:"boolean"};class je extends we{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=Te.find('[data-bs-toggle="collapse"]');for(const t of i){const e=jt(t),i=Te.find(e).filter(t=>t===this._element);null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return Ne}static get DefaultType(){return Pe}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter(t=>t!==this._element).map(t=>je.getOrCreateInstance(t,{toggle:!1}))),t.length&&t[0]._isTransitioning)return;if(fe.trigger(this._element,"show.bs.collapse").defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove("collapse"),this._element.classList.add("collapsing"),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i="scroll"+(e[0].toUpperCase()+e.slice(1));this._queueCallback(()=>{this._isTransitioning=!1,this._element.classList.remove("collapsing"),this._element.classList.add("collapse","show"),this._element.style[e]="",fe.trigger(this._element,"shown.bs.collapse")},this._element,!0),this._element.style[e]=this._element[i]+"px"}hide(){if(this._isTransitioning||!this._isShown())return;if(fe.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=this._element.getBoundingClientRect()[t]+"px",Vt(this._element),this._element.classList.add("collapsing"),this._element.classList.remove("collapse","show");for(const t of this._triggerArray){const e=Mt(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0;this._element.style[t]="",this._queueCallback(()=>{this._isTransitioning=!1,this._element.classList.remove("collapsing"),this._element.classList.add("collapse"),fe.trigger(this._element,"hidden.bs.collapse")},this._element,!0)}_isShown(t=this._element){return t.classList.contains("show")}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ft(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren('[data-bs-toggle="collapse"]');for(const e of t){const t=Mt(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=Te.find(":scope .collapse .collapse",this._config.parent);return Te.find(t,this._config.parent).filter(t=>!e.includes(t))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=je.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}fe.on(document,"click.bs.collapse.data-api",'[data-bs-toggle="collapse"]',(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=jt(this),i=Te.find(e);for(const t of i)je.getOrCreateInstance(t,{toggle:!1}).toggle()})),Xt(je);const Me="ArrowUp",He="ArrowDown",We='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Fe=Qt()?"top-end":"top-start",Be=Qt()?"top-start":"top-end",ze=Qt()?"bottom-end":"bottom-start",qe=Qt()?"bottom-start":"bottom-end",Re=Qt()?"left-start":"right-start",Ve=Qt()?"right-start":"left-start",$e={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ke={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Qe extends we{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=Te.next(this._element,".dropdown-menu")[0]||Te.prev(this._element,".dropdown-menu")[0]||Te.findOne(".dropdown-menu",this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return $e}static get DefaultType(){return Ke}static get NAME(){return"dropdown"}toggle(){return this._isShown()?this.hide():this.show()}show(){if(zt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!fe.trigger(this._element,"show.bs.dropdown",t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add("show"),this._element.classList.add("show"),fe.trigger(this._element,"shown.bs.dropdown",t)}}hide(){if(zt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!fe.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._popper&&this._popper.destroy(),this._menu.classList.remove("show"),this._element.classList.remove("show"),this._element.setAttribute("aria-expanded","false"),ve.removeDataAttribute(this._menu,"popper"),fe.trigger(this._element,"hidden.bs.dropdown",t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Wt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError("dropdown".toUpperCase()+': Option "reference" provided type "object" without a required "getBoundingClientRect" method.');return t}_createPopper(){if(void 0===n)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Wt(this._config.reference)?t=Ft(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=It(t,this._menu,e)}_isShown(){return this._menu.classList.contains("show")}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Re;if(t.classList.contains("dropstart"))return Ve;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Be:Fe:e?qe:ze}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(ve.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=Te.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(t=>Bt(t));i.length&&Gt(i,e,t===He,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Qe.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=Te.find('[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled).show');for(const i of e){const e=Qe.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Me,He].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(We)?this:Te.prev(this,We)[0]||Te.next(this,We)[0]||Te.findOne(We,t.delegateTarget.parentNode),o=Qe.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}fe.on(document,"keydown.bs.dropdown.data-api",We,Qe.dataApiKeydownHandler),fe.on(document,"keydown.bs.dropdown.data-api",".dropdown-menu",Qe.dataApiKeydownHandler),fe.on(document,"click.bs.dropdown.data-api",Qe.clearMenus),fe.on(document,"keyup.bs.dropdown.data-api",Qe.clearMenus),fe.on(document,"click.bs.dropdown.data-api",We,(function(t){t.preventDefault(),Qe.getOrCreateInstance(this).toggle()})),Xt(Qe);class Xe{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"padding-right",e=>e+t),this._setElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","padding-right",e=>e+t),this._setElementAttributes(".sticky-top","margin-right",e=>e-t)}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"padding-right"),this._resetElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","padding-right"),this._resetElementAttributes(".sticky-top","margin-right")}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,i(Number.parseFloat(s))+"px")})}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&ve.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,t=>{const i=ve.getDataAttribute(t,e);null!==i?(ve.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)})}_applyManipulationCallback(t,e){if(Wt(t))e(t);else for(const i of Te.find(t,this._element))e(i)}}const Ye={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Ue={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ge extends ye{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Ye}static get DefaultType(){return Ue}static get NAME(){return"backdrop"}show(t){if(!this._config.isVisible)return void Yt(t);this._append();const e=this._getElement();this._config.isAnimated&&Vt(e),e.classList.add("show"),this._emulateAnimation(()=>{Yt(t)})}hide(t){this._config.isVisible?(this._getElement().classList.remove("show"),this._emulateAnimation(()=>{this.dispose(),Yt(t)})):Yt(t)}dispose(){this._isAppended&&(fe.off(this._element,"mousedown.bs.backdrop"),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ft(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),fe.on(t,"mousedown.bs.backdrop",()=>{Yt(this._config.clickCallback)}),this._isAppended=!0}_emulateAnimation(t){Ut(t,this._getElement(),this._config.isAnimated)}}const Je={autofocus:!0,trapElement:null},Ze={autofocus:"boolean",trapElement:"element"};class ti extends ye{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return Je}static get DefaultType(){return Ze}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),fe.off(document,".bs.focustrap"),fe.on(document,"focusin.bs.focustrap",t=>this._handleFocusin(t)),fe.on(document,"keydown.tab.bs.focustrap",t=>this._handleKeydown(t)),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,fe.off(document,".bs.focustrap"))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=Te.focusableChildren(e);0===i.length?e.focus():"backward"===this._lastTabNavDirection?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?"backward":"forward")}}const ei={backdrop:!0,focus:!0,keyboard:!0},ii={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class ni extends we{constructor(t,e){super(t,e),this._dialog=Te.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Xe,this._addEventListeners()}static get Default(){return ei}static get DefaultType(){return ii}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){if(this._isShown||this._isTransitioning)return;fe.trigger(this._element,"show.bs.modal",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add("modal-open"),this._adjustDialog(),this._backdrop.show(()=>this._showElement(t)))}hide(){if(!this._isShown||this._isTransitioning)return;fe.trigger(this._element,"hide.bs.modal").defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove("show"),this._queueCallback(()=>this._hideModal(),this._element,this._isAnimated()))}dispose(){for(const t of[window,this._dialog])fe.off(t,".bs.modal");this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ge({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new ti({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=Te.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),Vt(this._element),this._element.classList.add("show");this._queueCallback(()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,fe.trigger(this._element,"shown.bs.modal",{relatedTarget:t})},this._dialog,this._isAnimated())}_addEventListeners(){fe.on(this._element,"keydown.dismiss.bs.modal",t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()}),fe.on(window,"resize.bs.modal",()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()}),fe.on(this._element,"mousedown.dismiss.bs.modal",t=>{fe.one(this._element,"click.dismiss.bs.modal",e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())})})}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide(()=>{document.body.classList.remove("modal-open"),this._resetAdjustments(),this._scrollBar.reset(),fe.trigger(this._element,"hidden.bs.modal")})}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(fe.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains("modal-static")||(t||(this._element.style.overflowY="hidden"),this._element.classList.add("modal-static"),this._queueCallback(()=>{this._element.classList.remove("modal-static"),this._queueCallback(()=>{this._element.style.overflowY=e},this._dialog)},this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Qt()?"paddingLeft":"paddingRight";this._element.style[t]=e+"px"}if(!i&&t){const t=Qt()?"paddingRight":"paddingLeft";this._element.style[t]=e+"px"}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=ni.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}fe.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=Mt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),fe.one(e,"show.bs.modal",t=>{t.defaultPrevented||fe.one(e,"hidden.bs.modal",()=>{Bt(this)&&this.focus()})});const i=Te.findOne(".modal.show");i&&ni.getInstance(i).hide();ni.getOrCreateInstance(e).toggle(this)})),Ae(ni),Xt(ni);const si=".bs.offcanvas",oi=`load${si}.data-api`,ri=`click${si}.data-api`,ai={backdrop:!0,keyboard:!0,scroll:!1},li={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class ci extends we{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return ai}static get DefaultType(){return li}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){if(this._isShown)return;if(fe.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented)return;this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Xe).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add("showing");this._queueCallback(()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add("show"),this._element.classList.remove("showing"),fe.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})},this._element,!0)}hide(){if(!this._isShown)return;if(fe.trigger(this._element,"hide.bs.offcanvas").defaultPrevented)return;this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add("hiding"),this._backdrop.hide();this._queueCallback(()=>{this._element.classList.remove("show","hiding"),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Xe).reset(),fe.trigger(this._element,"hidden.bs.offcanvas")},this._element,!0)}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ge({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():fe.trigger(this._element,"hidePrevented.bs.offcanvas")}:null})}_initializeFocusTrap(){return new ti({trapElement:this._element})}_addEventListeners(){fe.on(this._element,"keydown.dismiss.bs.offcanvas",t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():fe.trigger(this._element,"hidePrevented.bs.offcanvas"))})}static jQueryInterface(t){return this.each((function(){const e=ci.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}fe.on(document,ri,'[data-bs-toggle="offcanvas"]',(function(t){const e=Mt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),zt(this))return;fe.one(e,"hidden.bs.offcanvas",()=>{Bt(this)&&this.focus()});const i=Te.findOne(".offcanvas.show");i&&i!==e&&ci.getInstance(i).hide();ci.getOrCreateInstance(e).toggle(this)})),fe.on(window,oi,()=>{for(const t of Te.find(".offcanvas.show"))ci.getOrCreateInstance(t).show()}),fe.on(window,"resize.bs.offcanvas",()=>{for(const t of Te.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&ci.getOrCreateInstance(t).hide()}),Ae(ci),Xt(ci);const ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),hi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,di=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,fi=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!ui.has(i)||Boolean(hi.test(t.nodeValue)||di.test(t.nodeValue)):e.filter(t=>t instanceof RegExp).some(t=>t.test(i))},pi={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]};const gi={allowList:pi,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},mi={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},_i={entry:"(string|element|function|null)",selector:"(string|element)"};class bi extends ye{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return gi}static get DefaultType(){return mi}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map(t=>this._resolvePossibleFunction(t)).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},_i)}_setContent(t,e,i){const n=Te.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Wt(e)?this._putElementInTemplate(Ft(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)fi(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const vi=new Set(["sanitize","allowList","sanitizeFn"]),yi={AUTO:"auto",TOP:"top",RIGHT:Qt()?"left":"right",BOTTOM:"bottom",LEFT:Qt()?"right":"left"},wi={allowList:pi,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},Ai={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class Ei extends we{constructor(t,e){if(void 0===n)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return wi}static get DefaultType(){return Ai}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),fe.off(this._element.closest(".modal"),"hide.bs.modal",this._hideModalHandler),this.tip&&this.tip.remove(),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=fe.trigger(this._element,this.constructor.eventName("show")),e=(qt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this.tip&&(this.tip.remove(),this.tip=null);const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),fe.trigger(this._element,this.constructor.eventName("inserted"))),this._popper?this._popper.update():this._popper=this._createPopper(i),i.classList.add("show"),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._queueCallback(()=>{fe.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1},this.tip,this._isAnimated())}hide(){if(!this._isShown())return;if(fe.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented)return;const t=this._getTipElement();if(t.classList.remove("show"),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1,this._isHovered=null;this._queueCallback(()=>{this._isWithActiveTrigger()||(this._isHovered||t.remove(),this._element.removeAttribute("aria-describedby"),fe.trigger(this._element,this.constructor.eventName("hidden")),this._disposePopper())},this.tip,this._isAnimated())}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove("fade","show"),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add("fade"),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new bi({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains("fade")}_isShown(){return this.tip&&this.tip.classList.contains("show")}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=yi[e.toUpperCase()];return It(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)fe.on(this._element,this.constructor.eventName("click"),this._config.selector,t=>{this._initializeOnDelegatedTarget(t).toggle()});else if("manual"!==e){const t="hover"===e?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i="hover"===e?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");fe.on(this._element,t,this._config.selector,t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?"focus":"hover"]=!0,e._enter()}),fe.on(this._element,i,this._config.selector,t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?"focus":"hover"]=e._element.contains(t.relatedTarget),e._leave()})}this._hideModalHandler=()=>{this._element&&this.hide()},fe.on(this._element.closest(".modal"),"hide.bs.modal",this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout(()=>{this._isHovered&&this.show()},this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout(()=>{this._isHovered||this.hide()},this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=ve.getDataAttributes(this._element);for(const t of Object.keys(e))vi.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ft(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=Ei.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Xt(Ei);const Ci={...Ei.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},Ti={...Ei.DefaultType,content:"(null|string|element|function)"};class Oi extends Ei{static get Default(){return Ci}static get DefaultType(){return Ti}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=Oi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Xt(Oi);const xi={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},ki={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Li extends we{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return xi}static get DefaultType(){return ki}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ft(t.target)||document.body,t.rootMargin=t.offset?t.offset+"px 0px -30%":t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map(t=>Number.parseFloat(t))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(fe.off(this._config.target,"click.bs.scrollspy"),fe.on(this._config.target,"click.bs.scrollspy","[href]",t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}}))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver(t=>this._observerCallback(t),t)}_observerCallback(t){const e=t=>this._targetLinks.get("#"+t.target.id),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=Te.find("[href]",this._config.target);for(const e of t){if(!e.hash||zt(e))continue;const t=Te.findOne(e.hash,this._element);Bt(t)&&(this._targetLinks.set(e.hash,e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add("active"),this._activateParents(t),fe.trigger(this._element,"activate.bs.scrollspy",{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))Te.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add("active");else for(const e of Te.parents(t,".nav, .list-group"))for(const t of Te.prev(e,".nav-link, .nav-item > .nav-link, .list-group-item"))t.classList.add("active")}_clearActiveClass(t){t.classList.remove("active");const e=Te.find("[href].active",t);for(const t of e)t.classList.remove("active")}static jQueryInterface(t){return this.each((function(){const e=Li.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(window,"load.bs.scrollspy.data-api",()=>{for(const t of Te.find('[data-bs-spy="scroll"]'))Li.getOrCreateInstance(t)}),Xt(Li);const Di="ArrowLeft",Si="ArrowRight",Ii="ArrowUp",Ni="ArrowDown",Pi='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',ji='.nav-link:not(.dropdown-toggle), .list-group-item:not(.dropdown-toggle), [role="tab"]:not(.dropdown-toggle), '+Pi;class Mi extends we{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),fe.on(this._element,"keydown.bs.tab",t=>this._keydown(t)))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const 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--pst-color-paragraph: var(--pst-color-text-base); - --pst-color-link: 0, 91, 129; - --pst-color-link-hover: 227, 46, 0; - --pst-color-headerlink: 198, 15, 15; - --pst-color-headerlink-hover: 255, 255, 255; - --pst-color-preformatted-text: 34, 34, 34; - --pst-color-preformatted-background: 250, 250, 250; - --pst-color-inline-code: 232, 62, 140; - - --pst-color-active-navigation: 19, 6, 84; - --pst-color-navbar-link: 77, 77, 77; - --pst-color-navbar-link-hover: var(--pst-color-active-navigation); - --pst-color-navbar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-link: 77, 77, 77; - --pst-color-sidebar-link-hover: var(--pst-color-active-navigation); - --pst-color-sidebar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-expander-background-hover: 244, 244, 244; - --pst-color-sidebar-caption: 77, 77, 77; - --pst-color-toc-link: 119, 117, 122; - --pst-color-toc-link-hover: var(--pst-color-active-navigation); - --pst-color-toc-link-active: var(--pst-color-active-navigation); - - /***************************************************************************** - * Icon - **/ - - /* font awesome icons*/ - --pst-icon-check-circle: "\f058"; - --pst-icon-info-circle: "\f05a"; - --pst-icon-exclamation-triangle: "\f071"; - --pst-icon-exclamation-circle: "\f06a"; - --pst-icon-times-circle: "\f057"; - --pst-icon-lightbulb: "\f0eb"; - - /***************************************************************************** - * Admonitions - **/ - - --pst-color-admonition-default: var(--pst-color-info); - --pst-color-admonition-note: var(--pst-color-info); - --pst-color-admonition-attention: var(--pst-color-warning); - --pst-color-admonition-caution: var(--pst-color-warning); - --pst-color-admonition-warning: var(--pst-color-warning); - --pst-color-admonition-danger: var(--pst-color-danger); - --pst-color-admonition-error: var(--pst-color-danger); - --pst-color-admonition-hint: var(--pst-color-success); - --pst-color-admonition-tip: var(--pst-color-success); - --pst-color-admonition-important: var(--pst-color-success); - - --pst-icon-admonition-default: var(--pst-icon-info-circle); - --pst-icon-admonition-note: var(--pst-icon-info-circle); - --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle); - --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-error: var(--pst-icon-times-circle); - --pst-icon-admonition-hint: var(--pst-icon-lightbulb); - --pst-icon-admonition-tip: var(--pst-icon-lightbulb); - --pst-icon-admonition-important: var(--pst-icon-exclamation-circle); - - /***************************************************************************** - * versionmodified - **/ - - --pst-color-versionmodified-default: var(--pst-color-info); - --pst-color-versionmodified-added: var(--pst-color-success); - --pst-color-versionmodified-changed: var(--pst-color-warning); - --pst-color-versionmodified-deprecated: var(--pst-color-danger); - - --pst-icon-versionmodified-default: var(--pst-icon-exclamation-circle); - --pst-icon-versionmodified-added: var(--pst-icon-exclamation-circle); - --pst-icon-versionmodified-changed: var(--pst-icon-exclamation-circle); - --pst-icon-versionmodified-deprecated: var(--pst-icon-exclamation-circle); -} diff --git a/_build/html/_static/togglebutton.css b/_build/html/_static/togglebutton.css index b751c3d..54a6787 100644 --- a/_build/html/_static/togglebutton.css +++ b/_build/html/_static/togglebutton.css @@ -8,21 +8,39 @@ } /* Toggle buttons inside admonitions so we see the title */ -.toggle.admonition { +.admonition.toggle { position: relative; } /* Titles should cut off earlier to avoid overlapping w/ button */ -.toggle.admonition .admonition-title { +.admonition.toggle .admonition-title { padding-right: 25%; cursor: pointer; } +/* Hovering will cause a slight shift in color to make it feel interactive */ +.admonition.toggle .admonition-title:hover { + box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 1%); +} + +/* Hovering will cause a slight shift in color to make it feel interactive */ +.admonition.toggle .admonition-title:active { + box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 3%); +} + +/* Remove extra whitespace below the admonition title when hidden */ +.admonition.toggle-hidden { + padding-bottom: 0; +} + +.admonition.toggle-hidden .admonition-title { + margin-bottom: 0; +} + /* hides all the content of a page until de-toggled */ -.toggle-hidden.admonition .admonition-title ~ * { +.admonition.toggle-hidden .admonition-title ~ * { height: 0; margin: 0; - float: left; /* so they overlap when hidden */ opacity: 0; visibility: hidden; } @@ -37,14 +55,12 @@ button.toggle-button { border: none; outline: none; - /* Positioning just inside the amonition title */ - margin-right: 0.5em; + /* Positioning just inside the admonition title */ position: absolute; - right: 0em; - top: .5em; - float: right; + right: 0.5em; padding: 0px; - display: flex; + border: none; + outline: none; } /* Display the toggle hint on wide screens */ @@ -64,15 +80,15 @@ button.toggle-button { stroke: currentColor; /* So that we inherit the color of other text */ } -/* Rotate icon with admonitions so that it points down */ -.admonition.toggle .tb-icon { - transform: rotate(-90deg); +/* The icon should point right when closed, down when open. */ +/* Open */ +.admonition.toggle button .tb-icon { + transform: rotate(90deg); } -/* When the admonition is hidden, icon should flip upwards but retain rotation */ -/* We scaleX, in order to flip along Y, because it is rotated */ -.admonition.toggle .toggle-button-hidden .tb-icon { - transform: rotate(-90deg) scaleX(-1); +/* Closed */ +.admonition.toggle button.toggle-button-hidden .tb-icon { + transform: rotate(0deg); } /* With details toggles, we don't rotate the icon so it points right */ @@ -93,29 +109,29 @@ details.toggle-details { margin: 1em 0; } + details.toggle-details summary { display: flex; align-items: center; - width: fit-content; cursor: pointer; list-style: none; - border-radius: .4em; - border: 1px solid #ccc; - background: #f8f8f8; + border-radius: .2em; + border-left: 3px solid #1976d2; + background-color: rgb(204 204 204 / 10%); padding: 0.2em 0.7em 0.3em 0.5em; /* Less padding on left because the SVG has left margin */ - font-size: 0.8em; -} - -.toggle-details__summary-text { - margin-left: 0.2em; + font-size: 0.9em; } details.toggle-details summary:hover { - background: #f6f6f6; + background-color: rgb(204 204 204 / 20%); } details.toggle-details summary:active { - background: #eee; + background: rgb(204 204 204 / 28%); +} + +.toggle-details__summary-text { + margin-left: 0.2em; } details.toggle-details[open] summary { diff --git a/_build/html/_static/togglebutton.js b/_build/html/_static/togglebutton.js index 116a836..215a7ee 100644 --- a/_build/html/_static/togglebutton.js +++ b/_build/html/_static/togglebutton.js @@ -25,20 +25,24 @@ var initToggleItems = () => { } // This is the button that will be added to each item to trigger the toggle var collapseButton = ` - `; - item.insertAdjacentHTML("afterbegin", collapseButton); + title = item.querySelector(".admonition-title") + title.insertAdjacentHTML("beforeend", collapseButton); thisButton = document.getElementById(buttonID); // Add click handlers for the button + admonition title (if admonition) - thisButton.addEventListener('click', toggleClickHandler); admonitionTitle = document.querySelector(`#${toggleID} > .admonition-title`) if (admonitionTitle) { + // If an admonition, then make the whole title block clickable admonitionTitle.addEventListener('click', toggleClickHandler); admonitionTitle.dataset.target = toggleID admonitionTitle.dataset.button = buttonID + } else { + // If not an admonition then we'll listen for the button click + thisButton.addEventListener('click', toggleClickHandler); } // Now hide the item for this toggle button unless explicitly noted to show @@ -60,6 +64,7 @@ var initToggleItems = () => { // Now move the toggle-able content inside of the details block details = item.previousElementSibling details.appendChild(item) + item.classList.add("toggle-details__container") // Set up a click trigger to change the text as needed details.addEventListener('click', (click) => { @@ -73,9 +78,9 @@ var initToggleItems = () => { } // Update the inner text for the proper hint if (details.open) { - summary.querySelector("span").innerText = toggleHintShow; + summary.querySelector("span.toggle-details__summary-text").innerText = toggleHintShow; } else { - summary.querySelector("span").innerText = toggleHintHide; + summary.querySelector("span.toggle-details__summary-text").innerText = toggleHintHide; } }); @@ -102,12 +107,22 @@ var toggleHidden = (button) => { } var toggleClickHandler = (click) => { + // Be cause the admonition title is clickable and extends to the whole admonition + // We only look for a click event on this title to trigger the toggle. + if (click.target.classList.contains("admonition-title")) { - // If it's an admonition title, the button will be just before - button = click.target.previousElementSibling; + button = click.target.querySelector(".toggle-button"); + } else if (click.target.classList.contains("tb-icon")) { + // We've clicked the icon and need to search up one parent for the button + button = click.target.parentElement; + } else if (click.target.tagName == "polyline") { + // We've clicked the SVG elements inside the button, need to up 2 layers + button = click.target.parentElement.parentElement; + } else if (click.target.classList.contains("toggle-button")) { + // We've clicked the button itself and so don't need to do anything + button = click.target; } else { - // If not, we've clicked the button itself or its content, so search upwards - button = click.currentTarget; + console.log(`[togglebutton]: Couldn't find button for ${click.target}`) } target = document.getElementById(button.dataset['button']); toggleHidden(target); diff --git a/_build/html/_static/vendor/fontawesome/6.1.2/LICENSE.txt b/_build/html/_static/vendor/fontawesome/6.1.2/LICENSE.txt new file mode 100644 index 0000000..cc557ec --- /dev/null +++ b/_build/html/_static/vendor/fontawesome/6.1.2/LICENSE.txt @@ -0,0 +1,165 @@ +Fonticons, Inc. (https://fontawesome.com) + +-------------------------------------------------------------------------------- + +Font Awesome Free License + +Font Awesome Free is free, open source, and GPL friendly. You can use it for +commercial projects, open source projects, or really almost whatever you want. +Full Font Awesome Free license: https://fontawesome.com/license/free. + +-------------------------------------------------------------------------------- + +# Icons: CC BY 4.0 License (https://creativecommons.org/licenses/by/4.0/) + +The Font Awesome Free download is licensed under a Creative Commons +Attribution 4.0 International License and applies to all icons packaged +as SVG and JS file types. + +-------------------------------------------------------------------------------- + +# Fonts: SIL OFL 1.1 License + +In the Font Awesome Free download, the SIL OFL license applies to all icons +packaged as web and desktop font files. + +Copyright (c) 2022 Fonticons, Inc. (https://fontawesome.com) +with Reserved Font Name: "Font Awesome". + +This Font Software is licensed under the SIL Open Font License, Version 1.1. +This license is copied below, and is also available with a FAQ at: +http://scripts.sil.org/OFL + +SIL OPEN FONT LICENSE +Version 1.1 - 26 February 2007 + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide +development of collaborative font projects, to support the font creation +efforts of academic and linguistic communities, and to provide a free and +open framework in which fonts may be shared and improved in partnership +with others. + +The OFL allows the licensed fonts to be used, studied, modified and +redistributed freely as long as they are not sold by themselves. The +fonts, including any derivative works, can be bundled, embedded, +redistributed and/or sold with any software provided that any reserved +names are not used by derivative works. The fonts and derivatives, +however, cannot be released under any other type of license. The +requirement for fonts to remain under this license does not apply +to any document created using the fonts or their derivatives. + +DEFINITIONS +"Font Software" refers to the set of files released by the Copyright +Holder(s) under this license and clearly marked as such. This may +include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the +copyright statement(s). + +"Original Version" refers to the collection of Font Software components as +distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, +or substituting — in part or in whole — any of the components of the +Original Version, by changing formats or by porting the Font Software to a +new environment. + +"Author" refers to any designer, engineer, programmer, technical +writer or other person who contributed to the Font Software. + +PERMISSION & CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining +a copy of the Font Software, to use, study, copy, merge, embed, modify, +redistribute, and sell modified and unmodified copies of the Font +Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, +in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, +redistributed and/or sold with any software, provided that each copy +contains the above copyright notice and this license. These can be +included either as stand-alone text files, human-readable headers or +in the appropriate machine-readable metadata fields within text or +binary files as long as those fields can be easily viewed by the user. + +3) No Modified Version of the Font Software may use the Reserved Font +Name(s) unless explicit written permission is granted by the corresponding +Copyright Holder. This restriction only applies to the primary font name as +presented to the users. + +4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font +Software shall not be used to promote, endorse or advertise any +Modified Version, except to acknowledge the contribution(s) of the +Copyright Holder(s) and the Author(s) or with their explicit written +permission. + +5) The Font Software, modified or unmodified, in part or in whole, +must be distributed entirely under this license, and must not be +distributed under any other license. The requirement for fonts to +remain under this license does not apply to any document created +using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are +not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT +OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE +COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL +DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING +FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM +OTHER DEALINGS IN THE FONT SOFTWARE. + +-------------------------------------------------------------------------------- + +# Code: MIT License (https://opensource.org/licenses/MIT) + +In the Font Awesome Free download, the MIT license applies to all non-font and +non-icon files. + +Copyright 2022 Fonticons, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in the +Software without restriction, including without limitation the rights to use, copy, +modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, +and to permit persons to whom the Software is furnished to do so, subject to the +following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, +INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A +PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT +HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION +OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +-------------------------------------------------------------------------------- + +# Attribution + +Attribution is required by MIT, SIL OFL, and CC BY licenses. Downloaded Font +Awesome Free files already contain embedded comments with sufficient +attribution, so you shouldn't need to do anything additional when using these +files normally. + +We've kept attribution comments terse, so we ask that you do not actively work +to remove them from files, especially code. They're a great way for folks to +learn about Font Awesome. + +-------------------------------------------------------------------------------- + +# Brand Icons + +All brand icons are trademarks of their respective owners. The use of these +trademarks does not indicate endorsement of the trademark holder by Font +Awesome, nor vice versa. **Please do not use brand logos for any purpose except +to represent the company, product, or service to which they refer.** diff --git a/_build/html/_static/vendor/fontawesome/6.1.2/css/all.min.css b/_build/html/_static/vendor/fontawesome/6.1.2/css/all.min.css new file mode 100644 index 0000000..b9a01fb --- /dev/null +++ b/_build/html/_static/vendor/fontawesome/6.1.2/css/all.min.css @@ -0,0 +1,6 @@ +/*! + * Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2022 Fonticons, Inc. + */ +.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-duotone,.fa-light,.fa-regular,.fa-solid,.fa-thin,.fab,.fad,.fal,.far,.fas,.fat{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-1x{font-size:1em}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-6x{font-size:6em}.fa-7x{font-size:7em}.fa-8x{font-size:8em}.fa-9x{font-size:9em}.fa-10x{font-size:10em}.fa-2xs{font-size:.625em;line-height:.1em;vertical-align:.225em}.fa-xs{font-size:.75em;line-height:.08333em;vertical-align:.125em}.fa-sm{font-size:.875em;line-height:.07143em;vertical-align:.05357em}.fa-lg{font-size:1.25em;line-height:.05em;vertical-align:-.075em}.fa-xl{font-size:1.5em;line-height:.04167em;vertical-align:-.125em}.fa-2xl{font-size:2em;line-height:.03125em;vertical-align:-.1875em}.fa-fw{text-align:center;width:1.25em}.fa-ul{list-style-type:none;margin-left:var(--fa-li-margin,2.5em);padding-left:0}.fa-ul>li{position:relative}.fa-li{left:calc(var(--fa-li-width, 2em)*-1);position:absolute;text-align:center;width:var(--fa-li-width,2em);line-height:inherit}.fa-border{border-radius:var(--fa-border-radius,.1em);border:var(--fa-border-width,.08em) var(--fa-border-style,solid) var(--fa-border-color,#eee);padding:var(--fa-border-padding,.2em .25em .15em)}.fa-pull-left{float:left;margin-right:var(--fa-pull-margin,.3em)}.fa-pull-right{float:right;margin-left:var(--fa-pull-margin,.3em)}.fa-beat{-webkit-animation-name:fa-beat;animation-name:fa-beat;-webkit-animation-delay:var(--fa-animation-delay,0);animation-delay:var(--fa-animation-delay,0);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-bounce{-webkit-animation-name:fa-bounce;animation-name:fa-bounce;-webkit-animation-delay:var(--fa-animation-delay,0);animation-delay:var(--fa-animation-delay,0);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1))}.fa-fade{-webkit-animation-name:fa-fade;animation-name:fa-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-beat-fade,.fa-fade{-webkit-animation-delay:var(--fa-animation-delay,0);animation-delay:var(--fa-animation-delay,0);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s)}.fa-beat-fade{-webkit-animation-name:fa-beat-fade;animation-name:fa-beat-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-flip{-webkit-animation-name:fa-flip;animation-name:fa-flip;-webkit-animation-delay:var(--fa-animation-delay,0);animation-delay:var(--fa-animation-delay,0);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-shake{-webkit-animation-name:fa-shake;animation-name:fa-shake;-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-shake,.fa-spin{-webkit-animation-delay:var(--fa-animation-delay,0);animation-delay:var(--fa-animation-delay,0);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal)}.fa-spin{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-duration:var(--fa-animation-duration,2s);animation-duration:var(--fa-animation-duration,2s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-spin-reverse{--fa-animation-direction:reverse}.fa-pulse,.fa-spin-pulse{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-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\ No newline at end of file diff --git a/_build/html/api/targeting.html b/_build/html/api/targeting.html index 413b899..5c8cc86 100644 --- a/_build/html/api/targeting.html +++ b/_build/html/api/targeting.html @@ -1,35 +1,44 @@ + - + + + - - + Targeting API Reference - + + + + + - - + + + - - - - + + + + + - + - + - - + + - + + @@ -37,12 +46,12 @@ - + - + + - - - - - - + + - - - - - - - - -
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\ No newline at end of file diff --git a/_build/html/data_formats/antennas.html b/_build/html/data_formats/antennas.html index dd72235..8092a83 100644 --- a/_build/html/data_formats/antennas.html +++ b/_build/html/data_formats/antennas.html @@ -1,35 +1,44 @@ + - + + + - - + Antennas - + + + + + - - + + + - - - - + + + + + - + - + - - + + - + + @@ -37,12 +46,12 @@ - + - + + - - - - - - + + - - - - - - - - -
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\ No newline at end of file diff --git a/_build/html/data_formats/mobile_data.html b/_build/html/data_formats/mobile_data.html index dbb9cd5..17468ac 100644 --- a/_build/html/data_formats/mobile_data.html +++ b/_build/html/data_formats/mobile_data.html @@ -1,35 +1,44 @@ + - + + + - - + Mobile Data Usage - + + + + + - - + + + - - - - + + + + + - + - + - - + + - + + @@ -37,12 +46,12 @@ - + - + + - - - - - - + + - - - - - - - - -
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+ + \ No newline at end of file diff --git a/docs/_sources/quickstart/documentation_featurizer.html b/_build/html/docs/_build/jupyter_execute/quickstart/documentation_featurizer.html similarity index 97% rename from docs/_sources/quickstart/documentation_featurizer.html rename to _build/html/docs/_build/jupyter_execute/quickstart/documentation_featurizer.html index 3cef7e8..aec85c2 100644 --- a/docs/_sources/quickstart/documentation_featurizer.html +++ b/_build/html/docs/_build/jupyter_execute/quickstart/documentation_featurizer.html @@ -1,552 +1,398 @@ + - + + + - - + Featurizer - + + + + + - - + + + - - - - + + + + + - - - - - - - - - - - - - - - - - - - - - + + + + + + + + - + + - - - - - - - + + + + + + + - - - - - - - - - - - - - - - - - - + + + - - - - - - - - - + + + + + + - - - - - - - - -
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\ No newline at end of file diff --git a/_build/html/docs/_build/jupyter_execute/quickstart/documentation_home_location.html b/_build/html/docs/_build/jupyter_execute/quickstart/documentation_home_location.html new file mode 100644 index 0000000..45499b1 --- /dev/null +++ b/_build/html/docs/_build/jupyter_execute/quickstart/documentation_home_location.html @@ -0,0 +1,738 @@ + + + + + + + + + + + Home Location Inference + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+ +
+
+ + + +
+

Home Location Inference

+ +
+
+ +
+
+
+ + + + +
+ +
+

Home Location Inference#

+
+
+
from datastore import DataStore
+from home_location import HomeLocator
+
+
+
+
+

Load some CDR data, antennas data, and (optionally) spatial files, whose paths have been defined in config.yml. Define the geographic granularity for inference (options are ‘antenna_id’, ‘tower_id’ if a tower_id is provided in the antennas files, or any of the spatial files). Optionally load wealth prediction data to construct a poverty map and ground truth location data to evaluate accuracy.

+
+
+
datastore = DataStore(cfg_dir='./configs/config.yml')
+home_locator = HomeLocator(datastore=datastore, clean_folders=True)
+
+
+
+
+
Loading CDR...
+Loading antennas...
+Warning: 10 antennas missing location
+
+
+
+
+

Filter to a specific date range.

+
+
+
home_locator.ds.filter_dates('2020-01-05', '2020-02-01')
+
+
+
+
+

Try running the three different home location algorithms, specifying towers as the requested geographic level of aggregation.

+
+

Note

+

The three algorithms currently implemented are relatively simple; more sophisticate methods to infer users’ home locations do exist.

+
+
+
+
home_locator.get_home_locations(algo='count_transactions', geo='tower_id')
+home_locator.get_home_locations(algo='count_days', geo='tower_id')
+home_locator.get_home_locations(algo='count_modal_days', geo='tower_id')
+
+
+
+
+
                                                                                
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
subscriber_idtower_idcount_modal_days
0JGPCbfDGest354
1YtvkGlMWwet394
2dYwshzRseDt264
3dsBHAdXrrkt352
4ygMEXUQDbnt274
............
995amzyXHglBxt73
996dJSvXqUVSYt643
997ptudOnjdRUt355
998uXZrufHOmEt794
999zZkqaZFAtzt804
+

1000 rows × 3 columns

+
+
+

Generate a population map based on inferred home locations using the unique days algorithm. Use points at each of the tower locations.

+
+
+
home_locator.map(algo='count_days', geo='tower_id', kind='population', voronoi=False)
+
+
+
+
+../../../../_images/02106cd4eee328acfe7274cba260d23775534215a1a4a711469b1681d2cd52dd.png +
+
+

Evaluate the accuracy of the home location inference using the ground truth data. Generate maps and a table of precision and recall for each tower.

+
+
+
home_locator.accuracy(algo='count_days', geo='tower_id')
+home_locator.map(algo = 'count_days', geo='tower_id', kind='recall', voronoi=False)
+
+
+
+
+
Observations with inferred home location: 1000 (1000 unique)
+Observations with ground truth home location: 1000 (1000 unique)
+Observations with both: 1000 (1000 unique)
+Overall accuracy: 0.02
+
+
+../../../../_images/5e655a02438d82bffdd4eb22ddd78506d0337a4f72f28433f9786253679f741e.png +
+
+
+
+
home_locator.accuracy_tables[('tower_id', 'count_days')].head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
tower_idrecallprecisionoverall_accuracy
67t70.0769230.1666670.017
15t220.2500000.1428570.017
33t390.2142860.0909090.017
66t690.1000000.0555560.017
37t420.1538460.0555560.017
+
+
+

Use population density maps to identify which areas are over/under-represented in your sample.

+
+
+
home_locator.pop_comparison(algo='count_days', geo='tower_id')
+
+
+
+
+../../../../_images/59b6b042ec7270bd8a0d43d16bd5e8c586f70747ba4717fcfd4c00cdc9de1ee5.png +
+
+
+ + + + +
+ + + + +
+ +
+
+
+ +
+ + + + +
+
+
+
+ +
+ +

+By UC Berkeley Global Policy Lab +

+ +
+ +
+ +

+ + © Copyright 2022. +
+ +

+ +
+ +
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+ +
+ +
+
+ + +
+
+
+ + + + + +
+
+ + \ No newline at end of file diff --git a/_build/html/docs/_build/jupyter_execute/quickstart/documentation_ml.html b/_build/html/docs/_build/jupyter_execute/quickstart/documentation_ml.html new file mode 100644 index 0000000..4afcf99 --- /dev/null +++ b/_build/html/docs/_build/jupyter_execute/quickstart/documentation_ml.html @@ -0,0 +1,765 @@ + + + + + + + + + + + Machine Learning + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
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+ + + Ctrl+K +
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+ +
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+ +
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+ +
+ +
+
+ + + +
+

Machine Learning

+ +
+
+ +
+
+
+ + + + +
+ +
+

Machine Learning#

+
+
+
from datastore import DataStore
+from ml import Learner
+
+
+
+
+

Initialize data store object, then learner, automatically loading feature file produced by featurizer, along with file of data labels, and merging features to labels.

+
+
+
datastore = DataStore(cfg_dir='./configs/config.yml')
+learner = Learner(datastore=datastore, clean_folders=True)
+
+
+
+
+
Number of observations with features: 1000 (1000 unique)
+Number of observations with labels: 50 (50 unique)
+Number of matched observations: 50 (50 unique)
+
+
+
[Stage 42:===================>                                      (1 + 2) / 3]
+
+                                                                                
+
+
+
+
+

Experiment quickly with untuned models to get a sense of accuracy. Lasso, Ridge, random forest, and gradient boosting models are implemented natively, other models can be implemented by hand.

+
+
+
lasso_scores = learner.untuned_model('lasso')
+randomforest_scores = learner.untuned_model('randomforest')
+print('LASSO', lasso_scores)
+print('Random Forest', randomforest_scores)
+
+
+
+
+
LASSO {'train_r2': '1.00 (0.00)', 'test_r2': '-0.19 (0.48)', 'train_rmse': '6.65 (-3.02)', 'test_rmse': '15665.32 (-1859.46)'}
+Random Forest {'train_r2': '0.84 (0.01)', 'test_r2': '-0.02 (0.12)', 'train_rmse': '6200.40 (-319.50)', 'test_rmse': '14937.78 (-1482.62)'}
+
+
+
+
+

Fine-tune a gradient boosting model, tuning hyperparameters over cross validation, and produce predictions for all labeled observations out-of-sample over cross-validation. Also generate predictions for all subscribers in the feature dataset.

+
+
+
gradientboosting_scores = learner.tuned_model('gradientboosting')
+print('Gradient Boosting (Tuned)', gradientboosting_scores)
+learner.oos_predictions('gradientboosting', kind='tuned')
+learner.population_predictions('gradientboosting', kind='tuned')
+
+
+
+
+
[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
+Gradient Boosting (Tuned) {'train_r2': '0.80 (0.03)', 'test_r2': '0.07 (0.12)', 'train_rmse': '6808.46 (-409.84)', 'test_rmse': '14259.12 (-1920.92)'}
+[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
+[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
+[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
+[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
namepredicted
0dsBHAdXrrk67249.768675
1JGPCbfDGes69289.940765
2dYwshzRseD85109.109600
3ygMEXUQDbn82018.137664
4YtvkGlMWwe76983.875546
.........
5amzyXHglBx83987.844414
6zZkqaZFAtz82984.849245
7uXZrufHOmE87731.514440
8dJSvXqUVSY76191.160752
9YosNCLWrFL85338.034626
+

1000 rows × 2 columns

+
+
+

Evaluate the model’s accuracy. Produce a scatterplot of true vs. predicted values with a LOESS fit and a bar plot of the most important features. Generate a table showing the targeting accuracy, precision, and recall of the predictions for nine hypothetical targeting scenarios (targeting between 10% and 90% of the population).

+
+
+
learner.scatter_plot('gradientboosting', kind='tuned')
+learner.feature_importances_plot('gradientboosting', kind='tuned')
+learner.targeting_table('gradientboosting', kind='tuned')
+
+
+
+
+../../../../_images/df2223b2c50f4cabb153cab22e195534134e74fac0d7536d96e50306912f623d.png +../../../../_images/55fc51d64fda5244690d51680fa82c57f361d15698ccf63013a77215f73f0e49.png +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Proportion of Population TargetedPearsonSpearmanAUCAccuracyPrecisionRecall
010%0.560.50.7393%65%65%
120%0.560.50.7381%53%53%
230%0.560.50.7371%52%52%
340%0.560.50.7367%59%59%
450%0.560.50.7365%65%65%
560%0.560.50.7369%74%74%
670%0.560.50.7379%85%85%
780%0.560.50.7375%84%84%
890%0.560.50.7384%91%91%
+
+
+
+ + + + +
+ + + + +
+ +
+
+
+ +
+ + + + +
+
+
+
+ +
+ +

+By UC Berkeley Global Policy Lab +

+ +
+ +
+ +

+ + © Copyright 2022. +
+ +

+ +
+ +
+ +
+ +
+ +
+ +
+
+ + +
+
+
+ + + + + +
+
+ + \ No newline at end of file diff --git a/_build/html/docs/_build/jupyter_execute/quickstart/documentation_satellite.html b/_build/html/docs/_build/jupyter_execute/quickstart/documentation_satellite.html new file mode 100644 index 0000000..55a717b --- /dev/null +++ b/_build/html/docs/_build/jupyter_execute/quickstart/documentation_satellite.html @@ -0,0 +1,606 @@ + + + + + + + + + + + Satellite + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+ + + Ctrl+K +
+
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+ + +
+ + + + + + + + + + + +
+ +
+ +
+
+ + + +
+

Satellite

+ +
+
+ +
+
+
+ + + + +
+ +
+

Satellite#

+
+
+
from datastore import DataStore
+from satellite import Satellite
+
+
+
+
+

We first load some datasets - whose paths have been specified in the config file - using the datastore. The satellite module will by default try to load antenna/tower coordinates, shapefiles defining (administrative) boundaries, the Relative Wealth Index for the country of interest, and population density raster data.

+
+
+
datastore = DataStore(cfg_dir='../../cider/configs/config.yml')
+satellite = Satellite(datastore=datastore)
+
+
+
+
+
Loading antennas...
+Warning: 10 antennas missing location
+
+
+
+
+

The module can aggregate the RWI - which provides an asset index measure at a resolution of 2.4km x 2.4km - at one of the geographic levels provided by the antennas/towers, in which case their voronoi tessellation will be used - or one of the loaded shapefiles.

+In this case, we have loaded (synthetic) data for the country of Togo, and for aggregations at the tower level we will run:

+
+
+
satellite.aggregate_scores(dataset='rwi', geo='tower_id')
+
+
+
+
+../../../../_images/e8a15b5988fd76c6a079240aac56b6430a8e700954e40040ed2d2dde2fbc6fc2.png +
+
+

while for cantons, admin level 3 in Togo, we will run:

+
+
+
satellite.aggregate_scores(dataset='rwi', geo='cantons')
+
+
+
+
+
../../cider/satellite.py:152: RuntimeWarning: invalid value encountered in double_scalars
+  score = (score * pop).sum() / pop.sum()
+
+
+../../../../_images/79535c38022ede8c2a8cb72c550f861dd5d4ad8e97ffe96e96ac4027e197d67d.png +
+
+

The table is also saved as a geojson, storing the identifier of each region, its score - in this case its Relative Wealth Index - its population, and its geometry.

+
+
+
import geopandas as gpd
+gpd.read_file('../../cider/outputs/satellite/maps/cantons_rwi.geojson').head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
regionscorepopgeometry
01-0.28359716017.569922MULTIPOLYGON (((0.75228 6.83786, 0.75137 6.840...
12-0.4135654367.875125MULTIPOLYGON (((0.69026 6.80602, 0.69627 6.806...
23-0.3944289757.760028MULTIPOLYGON (((0.63102 6.74430, 0.63295 6.747...
34-0.4209522884.407900MULTIPOLYGON (((0.67259 6.85123, 0.67714 6.849...
45-0.1121902907.861156MULTIPOLYGON (((0.75269 6.84116, 0.75137 6.840...
+
+
+
+ + + + +
+ + + + +
+ +
+
+
+ +
+ + + + +
+
+
+
+ +
+ +

+By UC Berkeley Global Policy Lab +

+ +
+ +
+ +

+ + © Copyright 2022. +
+ +

+ +
+ +
+ +
+ +
+ +
+ +
+
+ + +
+
+
+ + + + + +
+
+ + \ No newline at end of file diff --git a/_build/html/docs/_build/jupyter_execute/quickstart/documentation_targeting.html b/_build/html/docs/_build/jupyter_execute/quickstart/documentation_targeting.html new file mode 100644 index 0000000..1436ab6 --- /dev/null +++ b/_build/html/docs/_build/jupyter_execute/quickstart/documentation_targeting.html @@ -0,0 +1,678 @@ + + + + + + + + + + + Targeting Evaluations + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + +
+ + +
+ + + + + + +
+ + +
+ + + + + + + + + + + +
+ +
+ +
+
+ + + +
+

Targeting Evaluations

+ +
+
+ +
+
+
+ + + + +
+ +
+

Targeting Evaluations#

+
+
+
from datastore import DataStore
+from targeting import Targeting
+
+
+
+
+

Set up the configuration file and load some targeting simulation data, using the datastore object.

+
+
+
datastore = DataStore(cfg_dir='./configs/config.yml')
+targeting = Targeting(datastore=datastore)
+
+
+
+
+

Create a table that compares targeting methods to a ground truth poverty measure. Three threshold-agnostic targeting metrics will be calculated: Pearson correlation (between ground truth and proxies), Spearman correlation, and a threshold-agnostic Area Under the Reciever Operating Characteristic Curve Score (AUC). Additionally, provide percentile targeting thresholds for ground truth and proxy to obtain four threshold-specific targeting metrics: accuracy, precision, recall, and threshold-specific AUC. As with all calculations in this module, the table can be calculated with or without sample weights.

+
+
+
targeting.targeting_table('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], 50, 20)
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Targeting MethodPearsonSpearmanAUC (Threshold-Agnostic)AccuracyPrecisionRecallAUC (Threshold-Specific)
0proxy10.8533700.8568800.8823190.7001.0000.4000.925204
1proxy20.8163230.8182750.8695010.6980.9950.3980.921336
2proxy30.7752430.7726710.8460710.6880.9700.3880.886284
3proxy40.7311960.7249840.8177990.6800.9500.3800.866024
+
+
+

Draw threshold-agnostic receiver operatoring characteristic (ROC) curves and precision-recall curves. Threshold-agnostic ROC curves are drawn by taking a grid matched targeting thresholds (that is, targeting K% of the population according to a proxy and the same K% of the population according to the ground-truth, for a krid of K between 0 and 100), and calculating the false positive rate and true positive rate of the targeting method for each point in the grid. Threshold-agnostic precision-reccall curves are calculated in the same way, so precision and recall are balanced by construction.

+
+
+
targeting.roc_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=None, weighted=False)
+targeting.precision_recall_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=None, weighted=False)
+
+
+
+
+../../../../_images/87598737ac4454d1a0f20533c22da6eb040b970020023848313229a5fb240db0.png +../../../../_images/ba1056d7bcb7c86625496fdf93c88cca61a3092f413a1691eb14db109a062932.png +
+
+

Draw threshold-specific ROC curves and precision-recall curves. Threshold-specific ROC curves are calculated by holding the share of the population targeted according to the ground-truth measure constant, and varying the share of the population targeted on the proxy measure to obtain false positive and true positive rate trade-offs.

+
+
+
targeting.roc_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=50, weighted=False)
+targeting.precision_recall_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=50, weighted=False)
+
+
+
+
+../../../../_images/228f12239cc16183e099afd9d3109989bd4a5ca97041dee75016d4ec69809166.png +../../../../_images/145650c79c5477ac5672a6a53833dc4778ed80d51b3459ce4a27ec779ce92657.png +
+
+

Provide a budget (provided as the level of benefits assigned to each individual UBI program, in the same units as the ground truth poverty measure) to draw social welfare curves for a set of targeting methods. Individual welfare is assumed to be a convex function of pre-program poverty and benefits assigned. Generate a table with the share of population targeted and corresponding transfer size that optimizes social welfare for each proxy poverty measure that could be used for targeting.

+
+
+
targeting.utility_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], .01, weighted=False)
+targeting.utility_table('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], .01, weighted=False)
+
+
+
+
+../../../../_images/df7204157563862d4ee46fe97536b4da8950340f54e6f7c5a2d0f5118e4bbd5d.png +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ProxyOptimal Share of Population TargetedMaximum UtilityOptimal Transfer Size
0proxy126.255102-1225.9544390.038168
1proxy29.081633-817.0806670.111111
2proxy338.377551-1606.0653290.026110
3proxy435.346939-1582.8120990.028329
+
+
+
+ + + + +
+ + + + +
+ +
+
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+ +
+ + + + +
+
+
+
+ +
+ +

+By UC Berkeley Global Policy Lab +

+ +
+ +
+ +

+ + © Copyright 2022. +
+ +

+ +
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+ +
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+ + \ No newline at end of file diff --git a/_build/html/genindex.html b/_build/html/genindex.html index c00e33b..5e565ad 100644 --- a/_build/html/genindex.html +++ b/_build/html/genindex.html @@ -1,34 +1,44 @@ + - + + + Index - + + + + + - - + + + - - - - + + + + + - + - + - - + + - + + @@ -36,12 +46,12 @@ - + - + + - - - - - - + + - - - - - - - - -
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+By UC Berkeley Global Policy Lab

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\ No newline at end of file diff --git a/_build/html/quickstart/config.html b/_build/html/quickstart/config.html index 2f98004..f009f49 100644 --- a/_build/html/quickstart/config.html +++ b/_build/html/quickstart/config.html @@ -1,35 +1,44 @@ + - + + + - - + Configuration file - + + + + + - - + + + - - - - + + + + + - + - + - - + + - + + @@ -37,12 +46,12 @@ - + - + + - - - - - - + + - - - - - - - - -
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\ No newline at end of file diff --git a/_build/html/quickstart/documentation_fairness.html b/_build/html/quickstart/documentation_fairness.html index adee5f6..ba53c0c 100644 --- a/_build/html/quickstart/documentation_fairness.html +++ b/_build/html/quickstart/documentation_fairness.html @@ -1,35 +1,44 @@ + - + + + - - + Fairness Evaluations - + + + + + - - + + + - - - - + + + + + - + - + - - + + - + + @@ -37,12 +46,12 @@ - + - + + - - - - - - + + - - - - - - - - -
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+By UC Berkeley Global Policy Lab

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\ No newline at end of file diff --git a/docs/_autosummary/cider.html b/docs/_autosummary/cider.html index 04cdae6..0854319 100644 --- a/docs/_autosummary/cider.html +++ b/docs/_autosummary/cider.html @@ -1,48 +1,44 @@ + - + + + - - + cider - + + + + + - - + + + - - - - + + + + + - + - + - - - - - - - - - - - - - - - + + - + + @@ -50,27 +46,12 @@ - + - - - - - - - - - - - - - - - - + + - - - - - - + + - - - - - - - - -
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cider-documentation + +This repository contains the source files for the documentation for the Cider software package. The docs are exposed [here](https://global-policy-lab.github.io/cider-documentation/intro.html). The Cider repository is [here](https://github.com/Global-Policy-Lab/cider). + +To update this documentation: +* Make changes to source +* Build the changes using jupyter-book +* Copy the contents of _build/html into docs. You can check you did this right by confirming that docs/index.html exists +* Propagate the changes to the remote gh-pages branch on Github diff --git a/docs/_sources/_autosummary/cider.datastore.html b/docs/_sources/_autosummary/cider.datastore.html deleted file mode 100644 index 760ec33..0000000 --- a/docs/_sources/_autosummary/cider.datastore.html +++ /dev/null @@ -1,578 +0,0 @@ - - - - - - - - - cider.datastore - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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cider.datastore#

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- - By UC Berkeley Global Policy Lab
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cider

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cider#

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Modules

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- - By UC Berkeley Global Policy Lab
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/_sources/_autosummary/cider.datastore.rst b/docs/_sources/_sources/_autosummary/cider.datastore.rst deleted file mode 100644 index eb2983d..0000000 --- a/docs/_sources/_sources/_autosummary/cider.datastore.rst +++ /dev/null @@ -1,32 +0,0 @@ -cider.datastore -=============== - -.. automodule:: cider.datastore - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - DataStore - DataType - InitializerInterface - OptDataStore - - - - - - - - - diff --git a/docs/_sources/_sources/_autosummary/cider.rst b/docs/_sources/_sources/_autosummary/cider.rst deleted file mode 100644 index ac73282..0000000 --- a/docs/_sources/_sources/_autosummary/cider.rst +++ /dev/null @@ -1,38 +0,0 @@ -cider -===== - -.. automodule:: cider - - - - - - - - - - - - - - - - - - - -.. rubric:: Modules - -.. autosummary:: - :toctree: - :recursive: - - cider.datastore - cider.fairness - cider.featurizer - cider.home_location - cider.ml - cider.satellite - cider.survey - cider.targeting - diff --git a/docs/_sources/_sources/api/datastore.rst b/docs/_sources/_sources/api/datastore.rst deleted file mode 100644 index 3943300..0000000 --- a/docs/_sources/_sources/api/datastore.rst +++ /dev/null @@ -1,16 +0,0 @@ -Datastore API Reference -============= - -DataStore Class --------- - -.. autoclass:: cider.datastore.DataStore - :members: - :private-members: - -OptDataStore Class --------- - -.. autoclass:: cider.datastore.OptDataStore - :members: - :private-members: \ No newline at end of file diff --git a/docs/_sources/_sources/api/fairness.rst b/docs/_sources/_sources/api/fairness.rst deleted file mode 100644 index f805b31..0000000 --- a/docs/_sources/_sources/api/fairness.rst +++ /dev/null @@ -1,6 +0,0 @@ -Fairness API Reference -====================== - -.. autoclass:: cider.fairness.Fairness - :members: - :private-members: \ No newline at end of file diff --git a/docs/_sources/_sources/api/featurizer.rst b/docs/_sources/_sources/api/featurizer.rst deleted file mode 100644 index 1e1a1d4..0000000 --- a/docs/_sources/_sources/api/featurizer.rst +++ /dev/null @@ -1,6 +0,0 @@ -Featurizer API Reference -============= - -.. autoclass:: cider.featurizer.Featurizer - :members: - :private-members: \ No newline at end of file diff --git a/docs/_sources/_sources/api/home_location.rst b/docs/_sources/_sources/api/home_location.rst deleted file mode 100644 index 498c7c9..0000000 --- a/docs/_sources/_sources/api/home_location.rst +++ /dev/null @@ -1,6 +0,0 @@ -Home Location API Reference -============= - -.. autoclass:: cider.home_location.HomeLocator - :members: - :private-members: \ No newline at end of file diff --git a/docs/_sources/_sources/api/ml.rst b/docs/_sources/_sources/api/ml.rst deleted file mode 100644 index 3fdcde2..0000000 --- a/docs/_sources/_sources/api/ml.rst +++ /dev/null @@ -1,6 +0,0 @@ -ML API Reference -============= - -.. autoclass:: cider.ml.Learner - :members: - :private-members: \ No newline at end of file diff --git a/docs/_sources/_sources/api/poverty_survey.rst b/docs/_sources/_sources/api/poverty_survey.rst deleted file mode 100644 index 975ef18..0000000 --- a/docs/_sources/_sources/api/poverty_survey.rst +++ /dev/null @@ -1,6 +0,0 @@ -Survey API Reference -============= - -.. autoclass:: cider.survey.SurveyOutcomeGenerator - :members: - :private-members: \ No newline at end of file diff --git a/docs/_sources/_sources/api/satellite.rst b/docs/_sources/_sources/api/satellite.rst deleted file mode 100644 index f3fee1b..0000000 --- a/docs/_sources/_sources/api/satellite.rst +++ /dev/null @@ -1,6 +0,0 @@ -Satellite API Reference -============= - -.. autoclass:: cider.satellite.Satellite - :members: - :private-members: \ No newline at end of file diff --git a/docs/_sources/_sources/api/targeting.rst b/docs/_sources/_sources/api/targeting.rst deleted file mode 100644 index 6f746c6..0000000 --- a/docs/_sources/_sources/api/targeting.rst +++ /dev/null @@ -1,6 +0,0 @@ -Targeting API Reference -============= - -.. autoclass:: cider.targeting.Targeting - :members: - :private-members: \ No newline at end of file diff --git a/docs/_sources/_sources/data_formats/antennas.md b/docs/_sources/_sources/data_formats/antennas.md deleted file mode 100644 index f89c6f7..0000000 --- a/docs/_sources/_sources/data_formats/antennas.md +++ /dev/null @@ -1,32 +0,0 @@ -# Antennas -Example file: `synthetic_data/antennas.csv`
-| antenna_id | tower_id | latitude | longitude | -|------------|----------|----------|-----------| -| KB2 | KB | 5.1632 | 11.5091 | -| KB8 | KB | 5.1632 | 11.5091 | -| AJK1 | AJ | 6.9012 | 12.3911 | -| OI12 | OI | | | - - -## Fields
-***antenna_id**: _string_
-Unique ID for the antenna, matches to antenna_id in CDR datasets - - -**tower_id**: _string_
-Unique ID for the cell tower. Many antennas can (and typically will) have a single tower ID. Tower ID is optional, it -need not be included if not provided by the mobile network operator (or tower IDs could be inferred using spatial -clustering of antennas). - - -**latitude**: _float_
-Latitude of the antenna - - -**longitude**: _float_
-Longitude of the antenna - - -```{note} -Columns without a preceding asterisk '*' are optional -``` diff --git a/docs/_sources/_sources/data_formats/cdr.md b/docs/_sources/_sources/data_formats/cdr.md deleted file mode 100644 index 0866726..0000000 --- a/docs/_sources/_sources/data_formats/cdr.md +++ /dev/null @@ -1,31 +0,0 @@ -# CDR (call and text) - -Example file: `synthetic_data/cdr.csv`
- -| txn_type | caller_id | recipient_id | timestamp | duration | caller_antenna | recipient_antenna | international | -|----------|-----------|--------------|---------------------|----------|----------------|-------------------|---------------| -| call | jb9f3j | ad8b3b | 2020-01-01 00:00:42 | 253.0 | AJK3 | KB3 | domestic | -| text | ad8b3b | bncz83 | 2020-02-12 10:12:08 | | KB9 | KB8 | domestic | -| text | oewf9ras | bncz83 | 2020-03-30 14:50:01 | 15.0 | KB8 | OI14 | domestic | -| call | bncz83 | jb9f3j | 2020-06-15 22:38:31 | | OI12 | AJK1 | international | - - -## Fields
-***txn_type**: _string_
-Chosen from {call, text} - -***caller_id**: _string_
-Unique ID for the caller or sender - -***recipient_id**: _string_
-Unique ID for the recipient - -***timestamp**: _string_
-String in format ‘YYYY-MM-DD hh:mm:ss’ representing the start time of a call or the time an text is sent - -**duration**: _float_
-Call duration in seconds. NaN or empty string for SMS messages or calls missing duration - -```{note} -Columns without a preceding asterisk '*' are optional -``` diff --git a/docs/_sources/_sources/data_formats/features.md b/docs/_sources/_sources/data_formats/features.md deleted file mode 100644 index dc33d35..0000000 --- a/docs/_sources/_sources/data_formats/features.md +++ /dev/null @@ -1,21 +0,0 @@ -# Features -Example file: `synthetic_outputs/featurizer/datasets/features.csv`
-| name | feature1 | feature2 | ... | feature185 | -|----------|----------|----------|-----|------------| -| jb9f3j | 590 | 86 | ... | 5 | -| ad8b3b | 389 | 55 | ... | 12 | -| oewf9ras | | | ... | 3 | -| bncz83 | 198 | 51 | ... | | - - -## Fields
-***name**: _string_
-Unique ID for the subscriber - -**features**: _float_
-The file can have any number of features, named uniquely. This format is automatically produced by the featurizer class, -but custom features can be added to feature files. - -```{note} -Columns without a preceding asterisk '*' are optional -``` diff --git a/docs/_sources/_sources/data_formats/home_locations.md b/docs/_sources/_sources/data_formats/home_locations.md deleted file mode 100644 index 1846c14..0000000 --- a/docs/_sources/_sources/data_formats/home_locations.md +++ /dev/null @@ -1,29 +0,0 @@ -# Home Location Labels - -Example file: `synthetic_data/home_locations.csv`
- -| subscriber_id | antenna_id | tower_id | polygon1 | ... | polygon12 | -|---------------|------------|----------|----------|-----|-----------| -| jb9f3j | a135 | t23 | A | ... | J | -| ad8b3b | a121 | t1 | B | ... | K | -| oewf9ras | a12 | | D | ... | N | -| bncz83 | a52 | t98 | F | ... | P | - - -## Fields
-***subscriber_id**: _string_
-Unique ID for each subscriber - -**antenna_id**: _string_
-Ground truth for home antenna (if available) - -**tower_id**: _string_
-Ground truth for home tower (if available) - -**polygon1**...: _strings_
-Ground truth for polygons, must match to shapefile names - - -```{note} -Columns without a preceding asterisk '*' are optional -``` diff --git a/docs/_sources/_sources/data_formats/mobile_data.md b/docs/_sources/_sources/data_formats/mobile_data.md deleted file mode 100644 index a93833f..0000000 --- a/docs/_sources/_sources/data_formats/mobile_data.md +++ /dev/null @@ -1,24 +0,0 @@ -# Mobile Data Usage -Example file: `synthetic_data/mobiledata.csv`
-| caller_id | volume | timestamp | -|-----------|--------|---------------------| -| ioa032 | 50.12 | 2020-02-09 17:18:56 | -| j510da | 59.32 | 2020-02-18 21:45:42 | -| g831d | 12.56 | 2020-03-16 22:56:01 | -| g913kj9a | 108.91 | 2020-03-30 08:31:33 | - - -## Fields
-***caller_id**: _string_
-Unique ID for the subscriber - -**volume**: _float_
-Volume of data consumed (upload or download), in any denomination - -***timestamp**: _string_
-String in format ‘YYYY-MM-DD hh:mm:ss’ representing the time of the recharge - - -```{note} -Columns without a preceding asterisk '*' are optional -``` diff --git a/docs/_sources/_sources/data_formats/mobile_money.md b/docs/_sources/_sources/data_formats/mobile_money.md deleted file mode 100644 index 4819563..0000000 --- a/docs/_sources/_sources/data_formats/mobile_money.md +++ /dev/null @@ -1,46 +0,0 @@ -# Mobile Money -Example file: `synthetic_data/mobilemoney.csv`
-| txn_type | caller_id | recipient_id | timestamp | amount | sender_balance_before | sender_balance_after | recipient_balance_before | recipient_balance_after | -|----------|-----------|--------------|---------------------|--------|-----------------------|----------------------|--------------------------|-------------------------| -| cashin | jb9f3j | | 2020-01-01 00:00:42 | 234.0 | 500.0 | 734.0 | | | -| cashout | ad8b3b | | 2020-02-12 10:12:08 | 195.0 | 200.5 | 5.5 | | | -| p2p | oewf9ras | bncz83 | 2020-03-30 14:50:01 | 2.4 | 100.0 | 97.6 | 78.1 | 80.5 | -| billpay | bncz83 | jb9f3j | 2020-06-15 22:38:31 | 76.1 | 150.0 | 73.9 | 12.9 | 89.0 | -| other | bncz83 | ad8b3b | 2020-08-16 23:10:10 | 502.0 | 872.0 | 370.0 | 0.00 | 502.0 | - - -## Fields
-***txn_type**: _string_ -Any string describing the type of the transaction; the dataset can define any number of categories for transactions, -but keep in mind that features will be calculated separately for each transaction type so compute time scales with the -number of categories. Common categories include {cashin, cashout, billpay, p2p, other}. - -***caller_id**: _string_
-Unique ID for the caller or sender - -***recipient_id**: _string_
-Unique ID for the recipient - -***timestamp**: _string_
-String in format ‘YYYY-MM-DD hh:mm:ss’ representing the start time of a call or the time an text is sent - -***amount**: _float_
-Amount of money transferred (in any currency) - -**sender_balance_before**: _float_
-Balance of sender before transaction (in any currency). Only provided by some mobile network operators. - -**sender_balance_after**: _float_
-Balance of sender after transaction (in any currency). Only provided by some mobile network operators. - -**recipient_balance_after**: _float_
-Balance of the recipient after transaction, where applicable (in any currency). Only provided by some mobile network -operators. - -**recipient_balance_before**: _float_
-Balance of the recipient after transaction, where applicable (in any currency). Only provided by some mobile network -operators. - -```{note} -Columns without a preceding asterisk '*' are optional -``` diff --git a/docs/_sources/_sources/data_formats/poverty_labels.md b/docs/_sources/_sources/data_formats/poverty_labels.md deleted file mode 100644 index 9add301..0000000 --- a/docs/_sources/_sources/data_formats/poverty_labels.md +++ /dev/null @@ -1,25 +0,0 @@ -# Poverty Labels - -Example file: `synthetic_data/labels.csv`
- -| name | label | weight | -|----------|--------|--------| -| jb9f3j | 100.12 | 12.80 | -| ad8b3b | 136.91 | 21.59 | -| oewf9ras | 78.52 | 7.55 | -| bncz83 | 121.00 | 22.12 | - - -## Fields
-***name**: _string_
-Unique ID for each subscriber - -***label**: _float_ or _bool_
-Wealth label for the observation; can be numeric for regression, or categorical for classification - -**weight**: _float_
-Weight to assign the observation (i.e. from a survey sampling weight or response weight) - -```{note} -Columns without a preceding asterisk '*' are optional -``` diff --git a/docs/_sources/_sources/data_formats/recharges.md b/docs/_sources/_sources/data_formats/recharges.md deleted file mode 100644 index ff25f70..0000000 --- a/docs/_sources/_sources/data_formats/recharges.md +++ /dev/null @@ -1,24 +0,0 @@ -# Recharges -Example file: `synthetic_data/recharges.csv`
-| caller_id | amount | timestamp | -|-----------|--------|---------------------| -| ioa032 | 96.05 | 2020-02-21 09:05:11 | -| j510da | 102.32 | 2020-05-01 11:19:22 | -| g831d | 51.71 | 2020-05-20 15:20:09 | -| g913kj9a | 12.52 | 2020-06-10 01:58:12 | - - -## Fields
-***caller_id**: _string_
-Unique ID for the subscriber - -**amount**: _float_
-Amount of the recharge (in any currency) - -***timestamp**: _string_
-String in format ‘YYYY-MM-DD hh:mm:ss’ representing the time of the recharge - - -```{note} -Columns without a preceding asterisk '*' are optional -``` diff --git a/docs/_sources/_sources/data_formats/spatial.md b/docs/_sources/_sources/data_formats/spatial.md deleted file mode 100644 index 89fdba6..0000000 --- a/docs/_sources/_sources/data_formats/spatial.md +++ /dev/null @@ -1,28 +0,0 @@ -# Spatial Files -Example file: `synthetic_data/prefectures.geojson`
-```{note} -Spatial data is loaded with geopandas, and can be stored as a shapefile or geojson. -``` -| region | geometry | | -|----------|-------------------|---| -| Region A | Multipolygon(...) | | -| Region B | Multipolygon(...) | | -| Region C | Multipolygon(...) | | -| Region D | Multipolygon(...) | | - - -## Fields
-***region**: _string_
-Unique ID for each polygon in the shapefile. Must be unique. - - -**amount**: _float_
-Amount of the recharge (in any currency) - -***geometry**: _polygon_
-Shapely polygon, as loaded from a shapefile or geojson - usually in WKT format - - -```{note} -Columns without a preceding asterisk '*' are optional -``` diff --git a/docs/_sources/_sources/data_formats/survey_data.md b/docs/_sources/_sources/data_formats/survey_data.md deleted file mode 100644 index 4a6f6f0..0000000 --- a/docs/_sources/_sources/data_formats/survey_data.md +++ /dev/null @@ -1,25 +0,0 @@ -# Survey Data - -Example file: `synthetic_data/survey.csv`
- -| unique_id | weight | outcome | col1 | ... | col35 | -|-----------|--------|---------|------|-----|-----------| -| jb9f3j | 12.6 | 1.5 | 5.2 | ... | category3 | -| ad8b3b | 1.6 | 2.7 | 6.1 | ... | category6 | -| oewf9ras | 3.2 | 4.1 | 1.5 | ... | | -| bncz83 | 6.0 | 0.9 | 2.6 | ... | category2 | - - -## Fields
-***unique_id**: _string_
-Unique ID for each survey observation - -**weight**: _float_
-Weight for each survey observation (if sample weights are used) - -***outcome** and **col1**...**col35**: _mixed types_
-Survey data. Can have any column names and be of any type (continuous, categorical, or binary) - -```{note} -Columns without a preceding asterisk '*' are optional -``` diff --git a/docs/_sources/_sources/data_formats/targeting_data.md b/docs/_sources/_sources/data_formats/targeting_data.md deleted file mode 100644 index 3b2e702..0000000 --- a/docs/_sources/_sources/data_formats/targeting_data.md +++ /dev/null @@ -1,35 +0,0 @@ -# Targeting and Fairness Data - -Example file: `synthetic_data/targeting.csv`
- -| unique_id | weight | outcome | col1 | ... | col35 | -|-----------|--------|---------|------|-----|-----------| -| jb9f3j | 12.6 | 1.5 | 5.2 | ... | category3 | -| ad8b3b | 1.6 | 2.7 | 6.1 | ... | category6 | -| oewf9ras | 3.2 | 4.1 | 1.5 | ... | | -| bncz83 | 6.0 | 0.9 | 2.6 | ... | category2 | - - -## Fields
-***unique_id**: _string_
-Unique ID for each survey observation - -**weight**: _float_
-Weight for each survey observation (if sample weights are used) - -**outcome1**...**outcome20**: _float_
-"Ground truth" poverty outcomes. Can have any column names. - -**proxy1**...**proxy20**: _float_
-Poverty proxies used for targeting. Can have any column names. - -**characteristic1**...**characteristic20**: _object_
-Characteristics for fairness analysis. Must be of categorical type. - -```{warning} -All proxy measures should go in the same direction! -``` - -```{note} -Columns without a preceding asterisk '*' are optional -``` diff --git a/docs/_sources/_sources/intro.md b/docs/_sources/_sources/intro.md deleted file mode 100644 index d611067..0000000 --- a/docs/_sources/_sources/intro.md +++ /dev/null @@ -1,7 +0,0 @@ -# Introduction - -Cider is a set of software tools for targeting poverty with mobile phone data. Download cider from [github](https://github.com/emilylaiken/cider). - -Cider is based on the poverty targeting methods described in [Aiken et al. (2022)](https://www.nature.com/articles/s41586-022-04484-9). A goal of cider is to make these methods easier to use and replicable across contexts. A second goal is making it straightforward to compare targeting methods, including the phone-based approach built by cider: while the empirical results on the accuracy of the phone-based approach in [Aiken et al. (2022)](https://www.nature.com/articles/s41586-022-04484-9) are promising, it is still not known how the phone-based approach compares to alternatives across contexts. - -Because cider works on digital data, it has two significant strengths: it can identify and reach people rapidly (speed) and do so on the order of millions of people (scale). Therefore, emergencies such as large-scale shocks like famines and flooding that require speed and scale may be the most natural use cases for cider. It may also be possible to combine the phone-based approach in cider with alternative and more traditional approaches to poverty targeting; an article [by GiveDirectly](https://medium.com/center-for-effective-global-action/how-precision-aid-and-machine-learning-based-targeting-can-complement-existing-social-protection-de3bc3211fd2) discusses the beginnings of such a complementary approach. diff --git a/docs/_sources/_sources/introduction/contexts.md b/docs/_sources/_sources/introduction/contexts.md deleted file mode 100644 index 81c96aa..0000000 --- a/docs/_sources/_sources/introduction/contexts.md +++ /dev/null @@ -1,4 +0,0 @@ -# Recommended contexts for cider - - - diff --git a/docs/_sources/_sources/introduction/features.md b/docs/_sources/_sources/introduction/features.md deleted file mode 100644 index e26dcb8..0000000 --- a/docs/_sources/_sources/introduction/features.md +++ /dev/null @@ -1,33 +0,0 @@ -# Overview of features - -Cider provides tooling for predicting poverty with machine learning and remote data such as phone metadata and satellite imagery. It also provides tooling to evaluate the performance of these estimations. Cider is divided into seven modules, which include the creation of poverty indices from underlying survey data as a ground-truth with which to train machine learning models, feature engineering from mobile phone metadata and supervised learning, and comparisons to counterfactual targeting methods and bias audits for the finished predictions. Each of the six modules is described below; for more information, visit Cider's documentation or [Github repo](https://github.com/emilylaiken/cider/). - - -## Poverty index construction -Creating poverty indices or a proxy-means test is a standard practice in international development. While consumption surveys are considered the most comprehensive approach to understanding a household’s wealth and are used in economics research, they often take hours to complete. Their length often makes them infeasible to use in emergency response or poverty alleviation programs. Therefore, economists and aid practitioners create poverty indices that select a few components from a longer survey (such as a consumption survey) that are most useful for summarizing overall poverty. The current statistical process for creating indices is manual and time-consuming. This package quickly generates an index that can be used in a survey to determine poverty more rapidly. The results of this survey can be used to collect training data for the machine learning poverty prediction rapidly. They can also be used as a standalone tool to create poverty short indices to assess a household’s poverty (without any use of machine learning). - -Suppose the survey's components haven't been picked yet. In that case, this module can also select the N components of a survey - an asset index, proxy-means test, etc. - that are most predictive of poverty, building the set of questions that should be asked when collecting ground truth data. A dataset of survey responses linked to unique IDs is then uploaded, constructing each observation’s poverty description based on underlying components. - -## Feature engineering from mobile phone data -Mobile phone data typically contain records of each transaction (call, text, balance recharge, mobile money transaction, etc.) completed on a cellular network. This module automates the calculation of subscriber-level features from the mobile phone metadata, using the uploaded mobile phone metadata datasets and features of interest specification. - -## Machine learning for poverty prediction -This module trains a machine learning model to predict poverty from mobile phone features and uses that model to produce poverty predictions for all subscribers that have consented to the use of their data. - -First, upload a ground-truth poverty measure for a set of subscribers (like the poverty measure generated by the poverty index module or a hand-crafted poverty score). Next, upload features (like those produced by the feature engineering module) for all subscribers that have consented. - -The module will match ground-truth poverty data to features, train the machine learning model to predict poverty from the phone features, and report the model’s accuracy metrics on a test set. Multiple machine learning models will be trained during the training stage, and the best model, based on a pre-specified metric, will be selected and used to predict poverty for the entire user base. - -## Targeting comparisons -It is critical to compare how accurate mobile phone data targeting is compared to other targeting methods (at the very least, in comparison to geographic targeting methods). Upload a set of ground-truth poverty observations (which can be any set, including the data produced by the poverty index module and used in the machine learning module) along with a set of targeting options. The targeting module will compare the targeting options based on inclusion and exclusion errors, helping the program manager make a more informed choice of targeting mechanism - - -## Bias audits -When considering an algorithmic targeting method, it is essential to check whether the algorithm is biased for or against particular subgroups. Upload a set of ground-truth poverty observations (which can be any set, including the data produced by the poverty index module and used in the machine learning module) along with observations on demographics, such as gender, age, ethnicity, religion, or other characteristics sensitive to the project's context. Also, upload the poverty proxy that will be used to target (such as the phone-based wealth predictions produced by the machine learning module or any other individual-level proxy poverty measure). This module will check for bias in the targeting method against each subgroup identified in the ground truth data. - -## Home location inference -Poverty is frequently concentrated in geographic clusters so that aid may be targeted only to subscribers living in certain areas. Mobile phone data can be used to estimate where subscribers live, based on the location of the cell towers through which they place transactions. Upload a dataset of mobile phone transactions. This module will infer the most likely location of each subscriber at the cell tower level (or an alternatively administrative level, if a shapefile is provided). If ground truth location data is available for a subset of subscribers, this module will also evaluate the accuracy of the mobile phone-based location inference. - -## Opt-in and opt-out workflows -In order to make the preservation of people’s privacy and the respect of their consent as easy as possible, this module implements opt-in and opt-out features that can be integrated in all processes that use sensitive data (raw CDR data, processed features etc.). The user of the toolkit can set the required capability at start time in the configuration file, with opt-in as default resulting in no mobile phone subscribers being initially included in the analysis, and vice versa for opt-out. Afterwards, they can flexibly provide lists of users that have opted-in or out, respectively, to include or exclude them from all the subsequent analyses. - diff --git a/docs/_sources/_sources/introduction/goals.md b/docs/_sources/_sources/introduction/goals.md deleted file mode 100644 index 9c00326..0000000 --- a/docs/_sources/_sources/introduction/goals.md +++ /dev/null @@ -1,11 +0,0 @@ -# Goals for cider - -Cider is a set of software tools for creating poverty predictions based on non-traditional data sources (such as call detail records and satellite imagery) and for evaluating the accuracy, fairness, representativity, and reliability of those predictions. Our goals for cider are to enable: - -- Policymakers and aid workers, working in conjunction with telecom operators, to remotely and rapidly estimate the poverty of individuals; - -- Policymakers to compare and evaluate targeting options when designing poverty alleviation programs, allowing them to choose the optimal option or combination of options; - -- Policymakers to test for bias and fairness in poverty predictions; - -- Researchers and developers to collaborate and add to the code to create toolkit for poverty targeting diff --git a/docs/_sources/_sources/introduction/opensource.md b/docs/_sources/_sources/introduction/opensource.md deleted file mode 100644 index f5f35c6..0000000 --- a/docs/_sources/_sources/introduction/opensource.md +++ /dev/null @@ -1,46 +0,0 @@ -# Open source management - -## How to install and run Cider - -For more information visit CIDER’s documentation below or [github repo](https://github.com/emilylaiken/cider). - -## Roadmap for additional features - -- **Encryption/Decryption upon consent:** We are adding functionality for personally identifying data to be encrypted prior to consent and only decrypted for analysis after a subscriber has consented. We are also building functionality for the encryption keys to be held by a third party for maximum security and auditing transparency. - -- **Individual explanations of predictions:** We plan to implement model-agnostic prediction explanations for mobile phone-based poverty estimates with [LIME](https://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf). - -## Ground rules for maintainers and contributors - -### Review committee - -Bug reports, documentation, and code contribution will be reviewed by [Emily Aiken](https://emilylaiken.github.io/) (UC Berkeley) and [Lucio Melito](https://github.com/LucioMelito) (GiveDirectly). - -### How a contribution is reviewed and accepted - -- **To report a bug,** create an issue template on GitHub. Please include a full working example for the bug, including synthetic data if necessary. - -- **To add additional documentation,** create a new branch of Cider’s [documentation repo](https://github.com/LucioMelito/cider-book), with `git checkout -b name-of-new-branch`. Commit your changes and push your new branch to the github repo, and create a pull request with the outline of your changes. Cider can always use additional documentation, so we appreciate your help! - -- **To write code,** please create a new branch of [Cider](https://github.com/emilylaiken/cider/) with `git checkout -b name-of-new-branch`. Commit your changes and push your new branch to the Github repo. Run the tests as indicated in the readme of the repo. Then, create a pull request. In your pull request, please include a description of all changes. For each change, include the files and line numbers that have been changed. - -### The types of contributions we'll accept - -We will accept contributions to any of the modules in the code; we are particularly interested in engineering additional features and improving machine learning pipelines. - -### When it's an appropriate time to follow up - -You can expect a response to an issue or suggestion within three months. We spend about one hour per month on this project. - -## Methodology used to build this open-source guide - -We modeled this guide on nine related open source guides: -- [Flowminder Flowkit open source code](https://github.com/Flowminder/FlowKit) -- [Thinking Machines Data Science Poverty Mapping open source code](https://github.com/thinkingmachines/ph-poverty-mapping) -- [Apache Fineract: A Platform for Microfinance](https://github.com/apache/fineract/blob/develop/README.md) [[Link 2]](https://cwiki.apache.org/confluence/display/FINERACT/FAQ) -- [GSMA Innovative Data for Urban Planning guidelines](https://www.gsma.com/mobilefordevelopment/wp-content/uploads/2021/07/Innovative-Data-for-Urban-Planning-Opportunities-and-Challenges-Associated-with-Public-Private-Data-Partnerships-SPREADS.pdf) -- [CommCare: supports frontline workers in low-resource settings](https://github.com/dimagi/commcare-hq/blob/master/README.md) [[Link 2]](https://commcare-hq.readthedocs.io/) -- [X-Road: exchange information securely over the Internet](https://github.com/nordic-institute/X-Road-development/blob/master/README.md) -- [SORMAS: Surveillance, Outbreak Response Management and Analysis System](https://github.com/hzi-braunschweig) [[Link 2]](https://github.com/hzi-braunschweig/SORMAS-Project/blob/development/README.md) -- [OCHA-Bucky: A COVID-19 model to inform humanitarian operations](https://ocha-bucky.readthedocs.io/en/latest/) -- [Bandicoot: An open-source python package to analyze mobile phone metadata](https://cpg.doc.ic.ac.uk/bandicoot/) diff --git a/docs/_sources/_sources/introduction/privacy.md b/docs/_sources/_sources/introduction/privacy.md deleted file mode 100644 index dfa174c..0000000 --- a/docs/_sources/_sources/introduction/privacy.md +++ /dev/null @@ -1,11 +0,0 @@ -# Adherance to data privacy standards - -Cider works with individual mobile phone data, which is personally identifiable information. The use of cider must be in compliance with local data protection laws. We additionally recommend referring to the following guidelines to design a data flow that adheres to international standards: - -- [Data Protection Manual from the IOM (International Organisation for Migration)](https://www.google.com/url?q=https://publications.iom.int/books/iom-data-protection-manual&sa=D&source=editors&ust=1633755101445000&usg=AOvVaw14AneDHPkaqvfhGa12IaPj) - -- [PPP guidelines from the GSMA (Global System for Mobile Communications Association)](https://www.gsma.com/mobilefordevelopment/wp-content/uploads/2021/07/Innovative-Data-for-Urban-Planning-Opportunities-and-Challenges-Associated-with-Public-Private-Data-Partnerships-SPREADS.pdf) - -- [Data Protection Handbook for Humanitarian Action from the ICRC (International Committee of the Red Cross)](https://www.icrc.org/en/data-protection-humanitarian-action-handbook) - -We also strongly recommend that parties deploy the opt-in and opt-out functionality that determines which subscribers have their data analyzed. Cider's roadmap plan also has extra layers of encryption functionality and workflows, where PII is encrypted before consent and only decrypted for analysis after a subscriber has consented. diff --git a/docs/_sources/_sources/introduction/research.md b/docs/_sources/_sources/introduction/research.md deleted file mode 100644 index e7e31d5..0000000 --- a/docs/_sources/_sources/introduction/research.md +++ /dev/null @@ -1,27 +0,0 @@ -# Research and resources - -## Poverty prediction and targeting with digital data - -Cider was developed from a line of research on estimating poverty from machine learning and mobile phone data that dates back to 2015. The following papers provide context on this research and discussions of the accuracy, fairness, and practicality of phone-based targeting in several contexts. - -- [Aiken, E. et al. (2021). Machine learning and mobile phone data can improve the targeting of humanitarian assistance. *Nature* 603 pages 864-870.](https://www.nature.com/articles/s41586-022-04484-9) - -- [Aiken, E. et al. (2020). Targeting development aid with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan. *ACM SIGCAS Conference on Computing and Sustainable Societies*.](https://dl.acm.org/doi/10.1145/3378393.3402274) - -- [Blumenstock, J. (2018). Estimating economic characteristics with phone data. *American Economic Review: Papers and Proceedings*.](https://www.aeaweb.org/articles?id=10.1257/pandp.20181033) - -- [Blumenstock, J., Cadamuro, G., and On, R. (2015). Predicting poverty and wealth from mobile phone metadata. *Science* 350.](https://www.unhcr.org/innovation/wp-content/uploads/2016/11/blumenstock-science-2015.pdf) - -## AI ethics for the humanitarian and development fields - -- [DSEG - Data Science and Ethics Group (2020). A Framework For the Ethical Use of Advanced Data Science Methods in the Humanitarian Sector.](https://www.humanitarianresponse.info/sites/www.humanitarianresponse.info/files/documents/files/dseg_ethical_framework_april_2020.pdf) - -- [Leslie, D. (2019). Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector. The Alan Turing Institute.](https://www.turing.ac.uk/research/publications/understanding-artificial-intelligence-ethics-and-safety) - -- [High-level Expert Group on Artificial Intelligence set up by the European Commision (2019). Ethics Guidelines for Trustworthy AI.](https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai) - -- [The International Development Innovation Alliance (IDIA) Working Group on Artificial Intelligence & Development. Artificial Intelligence in International Development: A Discussion Paper.](https://static1.squarespace.com/static/5b156e3bf2e6b10bb0788609/t/5e1f0a37e723f0468c1a77c8/1579092542334/AI+and+international+Development_FNL.pdf) - -- [fAIr LAC: Latin America and the Caribbean Alliance for Ethical and Responsible Use of AI.](https://oecd.ai/wonk/idbs-initiative-for-responsible-ethical-ai-in-latin-america-caribbean-fairlac) - - diff --git a/docs/_sources/_sources/introduction/trainingdata.md b/docs/_sources/_sources/introduction/trainingdata.md deleted file mode 100644 index e4f1806..0000000 --- a/docs/_sources/_sources/introduction/trainingdata.md +++ /dev/null @@ -1,26 +0,0 @@ -# Recommendations: Training Data - -Collecting robust and representative data is critical to any successful deployment of Cider. Here are several recommendations: - -- **Number of observations that should be collected in a ground truth poverty survey:** Returns to additional training data for poverty prediction have been documented in past research (Blumenstock et al., 2015). In general, accuracy increases with additional training data, but with diminishing marginal returns to data collection. - - -- **Survey collection - phone vs. in-person:** While it is generally accepted that field surveys collect higher quality data than phone surveys, the specific structural and economic constraints of each targeting project will dictate whether a phone or in-person survey is more appropriate. It is worth noting that more complex poverty outcomes like income and consumption can typically only be collected in a high quality field survey; phone surveys typically focus on asset indices or other proxies for consumption or income, such as a proxy-means test. - - -- **Poverty outcomes collection:** When possible, multiple poverty measures should be collected for validation purposes. Past studies using wealth from mobile phone metadata have used a variety of poverty outcomes to train machine learning models, including asset indices, consumption, proxy-means tests (PMTs) and poverty probability indices (PPIs). Consumption is accepted as the gold-standard of poverty in development economics, so consumption should be collected where possible (however, eliciting consumption typically requires a multi-hour consumption module in a field survey). Recent work has shown that well-calibrated PMTs and PPIs are preferable to asset indices, if there is an up-to-date PMT or PPI for the country in question, or if it is possible to custom-calibrate a PMT based on recently-collected consumption data. - - -- **Number of components in a PMT or asset index:** Past work on wealth prediction and targeting has used indices or PMTs with between 8 and 30 components. Index accuracy typically increases with the number of underlying components, but there are diminishing marginal returns to increasing the number of components after a point. Moreover, the more components collected in a survey the more fatigue or boredom on the part of the respondent, which may decrease survey quality. -Some contexts have standardized and tested asset indices or PMTs. If there is no such standardized index available, but past survey data from the country in question is available, one option is to use forward selection of features for a PMT (implemented in CIDER’s survey module) and look for an “elbow” in accuracy curves where returns to additional features start to decrease. In choosing the number of features in the PMT, the total survey duration (including, for example, demographic information for bias checks or other poverty measures for validation) should also be considered to prevent respondent fatigue. - - -- **Timeline and frequency for survey and phone metadata collection (wealth prediction recalibration):** Recent research from [Aiken et al. (2021)](https://www.nber.org/papers/w29070) suggests that the quality of phone-based poverty prediction degrades when phone data or survey data are out-of-date. In that application of phone-based targeting in rural Togo, the authors find that targeting accuracy decreases by 4-5% when phone data or survey data are out-of-date, and precision decreases by 10-14%. -Note that there are three particular concerns when it comes to data collection and model recalibration: (1) Turnover of SIM cards on the mobile phone network, (2) the transitory nature of poverty, and (3) applying a machine learning model calibrated on “old” survey data to contemporary CDR (risking “model drift”). - -1. One concern with using “old” CDR for a contemporary aid program is the turnover of SIMs on the mobile phone network. Suppose the CDR data used for targeting is even six months out of date. In that case, a sizable number of subscribers will be prevented from registering because they are not associated with underlying CDR from which to produce a wealth prediction. For example, in Togo, Aiken et al. (2021) estimate that around 3% of mobile network subscribers leave the network in a given month, so around 18% of subscribers would have switched SIM cards after six months. CDR should be taken from as close to the program start date as possible to maximize program coverage. -2. People move in and out of poverty with some frequency, though the timescale for this movement is typically years. To capture the state of poverty that is contemporary with the program time period, survey data should be gathered as close to the program start date as possible. Initial results suggest that it is not essential that survey data overlap with the time period of CDR, but both should be taken from as close to the program as possible. -3. The two above concerns suggest that it is necessary to “refresh” CDR data frequently and to “refresh” survey data somewhat less frequently. It is worth noting that there is a specific concern with training a model on “old” CDR and survey data and using the model to project onto CDR gathered contemporarily, called “model drift.” Specifically, the relationship between phone behavior and poverty shifts over time: for example, the relationship over the holidays may not be the same as the relationship in other months. To combat this effect, even if survey data is not “refreshed,” the model should be consistently refreshed (that is, retrained). - - -- **Representativity checks of training data:** Bias audits should be grounded in the cultural context in which the program is conducted, either with qualitative interviews with stakeholders (especially potential beneficiaries) or based on existing legal frameworks. If possible, qualitative or legal work should be conducted to assess which subgroups are vulnerable and should be audited for in fairness checks. Qualitative work should consist of interviews or focus groups with program stakeholders, including local government, NGOs, and most importantly potential program beneficiaries. Alternatively, fairness classes could be based on the legal context in question, checking fairness for subgroups that are protected by the law. For example, in the US, race, religion, nationality, sex, age, disability status, and veteran status are all protected classes. diff --git a/docs/_sources/_sources/introduction/verification.md b/docs/_sources/_sources/introduction/verification.md deleted file mode 100644 index 5e9973d..0000000 --- a/docs/_sources/_sources/introduction/verification.md +++ /dev/null @@ -1,32 +0,0 @@ -# Recommendations: Output verification - -Cider users should implement governance structures that achieve standards for data ethics, making sure humanitarian programs are targeting and delivering aid to those in need without infringing human rights. - -Output verifications increase accountability and transparency with recipients and non-recipients, partners, funders, and humanitarian and development communities as a whole. It also creates the necessary governance to minimize bias and discrimination (which is also present in in-person targeting) and maximize fairness on the large-scale use of sensitive data, exponentiating the positive impact of AI. The verification process should combine internal and external audits, such as a research institution or contracted technical auditor. Other resources for setting up output verifications can be found here: - -- [AI for humanitarian action: Human rights and ethics](https://international-review.icrc.org/articles/ai-humanitarian-action-human-rights-ethics-913) from the ICRC (International Committee of the Red Cross)] -- [Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector](https://www.turing.ac.uk/sites/default/files/2019-06/understanding_artificial_intelligence_ethics_and_safety.pdf) from the Alan Turing Institute - -## Internal audits - -- **Targeting comparisons:** It is critical to compare phone-based targeting to alternative targeting mechanisms in the context of interest to ensure that phone-based targeting is the most appropriate method for the circumstances (see the targeting module). Ideally, CDR-based targeting should be compared to any other method that could be feasibly implemented at scale, and the best targeting method should be selected for the program. However, which counterfactual targeting approaches can be assessed depends on which survey data is collected. Geographic targeting methods are valuable counterfactual targeting methods. Typically, some location data will be available (whether self-reported in the survey, recorded by field enumerators with geographic coordinates, or available in administrative data). If this data is available, compare the CDR-based targeting method to geographic targeting at every feasible geography, at minimum at the admin-2 level but ideally at the admin-3 or admin-4 level. If the ground-truth poverty measure in the survey is consumption or income, collect data to construct a proxy-means test and asset index and compare targeting on these proxy poverty measures. Always also compare to random targeting as a naive baseline. - -- **Bias audits:** It is also vital to audit the algorithm for systematic bias for or against specific subgroups. If possible, qualitative or legal work should be conducted to assess which subgroups are vulnerable and should be audited for fairness checks. Qualitative work should consist of interviews or focus groups with program stakeholders, including local government, NGOs, and, most importantly, potential program beneficiaries. Alternatively, fairness classes could be based on the legal context in question, checking fairness for subgroups protected by the law. For example, in the US, race, religion, nationality, sex, age, disability status, and veteran status are all protected classes. The bias audit feature in Cider will present audits for two types of systematic biases: a systematic over- or under-prediction of a subgroup’s poverty, or a systematic over- or under-targeting of a subgroup (known as demographic parity). - -## External audits - -External audits provide the transparency needed to stakeholders by allowing third-party verification of an implementer’s results. Auditors often assess if outputs are fair and impartial and if data protection protocols are in place. In addition to transparency, the third-party audits address concerns about the conflict of interest that might exist in internal audits. AI tools applied to the development and humanitarian sectors should aim to achieve the highest accountability standards, and for that, outputs should be independently audited. Cider's open source license and internal audit mechanisms aim to provide sufficient information to be auditable by independent parties. It is encouraged that these independent audits be done by organizations with both the technical capacity and experience in the field. - -More resources on third party audits can be found here: - -- [AI for humanitarian action: Human rights and ethics](https://international-review.icrc.org/articles/ai-humanitarian-action-human-rights-ethics-913) from the ICRC (International Committee of the Red Cross) -- [Brundage et. al. (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://arxiv.org/pdf/2004.07213.pdf) - -Although the Cider team does not endorse any specific auditing company, here is a brief list of organizations focused on AI fairness auditing: - -- [Orcaa | O'Neil Risk Consulting & Algorithmic Auditing](https://orcaarisk.com/) -- [Parity AI](https://www.getparity.ai/) -- [Saidot AI](https://www.saidot.ai/) -- [Cognitive Scale](https://www.cognitivescale.com/) -- [Accenture Applied Intelligence](https://www.accenture.com/us-en/services/ai-artificial-intelligence-index) -- [Deloitte | Model Guardian](https://www2.deloitte.com/de/de/pages/risk/solutions/ai-fairness-with-model-guardian.html) diff --git a/docs/_sources/_sources/quickstart/config.md b/docs/_sources/_sources/quickstart/config.md deleted file mode 100644 index 0e98f9e..0000000 --- a/docs/_sources/_sources/quickstart/config.md +++ /dev/null @@ -1,140 +0,0 @@ -# Configuration file - -The configuration file - example at `configs/config.yml` - is used to store all relevant configurations, like paths to \ -the datasets and spark parameters. It should be appropriately edited before executing the code.
- -The first parameters to set are those related to spark: - -``` -spark: - app_name: "mm" - files: - max_partition_bytes: 67108864 - driver: - memory: "8g" // driver memory - max_result_size: "2g" // maximum result size when collecting to driver - loglevel: "ERROR" -``` - -Then we set the paths to the folders that store our files, and also specify the file names: - -``` -path: - data: "/Users/example/Documents/GD/cider/synthetic_data/" - features: '/Users/example/Documents/GD/cider/outputs/featurizer/datasets/features.csv' - file_names: - antennas: 'antennas.csv' - cdr: 'cdr.csv' - home_ground_truth: 'home_locations.csv' - labels: 'labels.csv' - mobiledata: 'mobiledata.csv' - mobilemoney: 'mobilemoney.csv' - population: 'population_tgo_2019-07-01.tif' - poverty_scores: null - recharges: 'recharges.csv' - rwi: '/TGO_relative_wealth_index.csv' - shapefiles: - regions: 'regions.geojson' - cantons: 'cantons.geojson' - prefectures: 'prefectures.geojson' - user_consent: null - outputs: "/Users/example/Documents/GD/cider/outputs/" // output folder - wd: "/Users/example/Documents/GD/cider/" // working directory -``` - -The featurizer module expects certain column and column names, and we can define them in the following section of the -config file: - -``` -col_names: - cdr: - txn_type: "txn_type" - caller_id: "caller_id" - recipient_id: "recipient_id" - timestamp: "timestamp" - duration: "duration" - caller_antenna: "caller_antenna" - recipient_antenna: "recipient_antenna" - international: "international" - antennas: - antenna_id: "antenna_id" - tower_id: "tower_id" - latitude: "latitude" - longitude: "longitude" - recharges: - caller_id: "caller_id" - amount: "amount" - timestamp: "timestamp" - mobiledata: - caller_id: "caller_id" - volume: "volume" - timestamp: "timestamp" - mobilemoney: - txn_type: "txn_type" - caller_id: "caller_id" - recipient_id: "recipient_id" - timestamp: "timestamp" - amount: "amount" - sender_balance_before: "sender_balance_before" - sender_balance_after: "sender_balance_after" - recipient_balance_before: "recipient_balance_before" - recipient_balance_after: "recipient_balance_after" - geo: 'tower_id' -``` - -We also have to set a few parameters that will affect the behaviour of some modules: - -``` -params: - cdr: - weekend: [1, 7] // definition of weekend (Sun = 1 to Sat = 7) - start_of_day: 7 // hour when day starts (used to define day/night) - end_of_day: 19 // hour when night starts (used to define day/night) - home_location: - filter_hours: null // hours to filter out when inferring home locations - automl: // params used by the autoML libraries - autosklearn: - time_left: 3600 - n_jobs: 1 - memory_limit: 3072 - autogluon: - time_limit: 3600 - eval_metric: 'r2' - label: 'label' - sample_weight: 'weight' - opt_in_default: false // if true opt-in is set as default, i.e. all users give their consent unless they opt-out -``` - -Finally, we can set the hyper-parameters that will be tested during a grid-search performed by the ML module" - -``` -hyperparams: - 'linear': - 'dropmissing__threshold': [0.9, 1] - 'droplowvariance__threshold': [ 0, 0.01 ] - 'winsorizer__limits': [!!python/tuple [0., 1.], !!python/tuple [0.005, .995]] - 'lasso': - 'dropmissing__threshold': [ 0.9, 1 ] - 'droplowvariance__threshold': [ 0, 0.01 ] - 'winsorizer__limits': [!!python/tuple [0., 1.], !!python/tuple [0.005, .995]] - 'model__alpha': [ .001, .01, .05, .03, .1 ] - 'ridge': - 'dropmissing__threshold': [ 0.9, 1 ] - 'droplowvariance__threshold': [ 0, 0.01 ] - 'winsorizer__limits': [!!python/tuple [0., 1.], !!python/tuple [0.005, .995]] - 'model__alpha': [ .001, .01, .05, .03, .1 ] - 'randomforest': - 'dropmissing__threshold': [ 0.9, 1 ] - 'droplowvariance__threshold': [ 0, 0.01 ] - 'winsorizer__limits': [!!python/tuple [0., 1.], !!python/tuple [0.005, .995]] - 'model__max_depth': [ 2, 4, 6, 8, 10 ] - 'model__n_estimators': [ 50, 100, 200 ] - 'gradientboosting': - 'dropmissing__threshold': [ 0.99 ] - 'droplowvariance__threshold': [ 0.01 ] - 'winsorizer__limits': [!!python/tuple [0., 1.], !!python/tuple [0.005, .995]] - 'model__min_data_in_leaf': [ 10, 20, 50 ] - 'model__num_leaves': [ 5, 10, 20 ] - 'model__learning_rate': [ 0.05, 0.075, 0.1 ] - 'model__n_estimators': [ 50, 100, 200 ] -``` \ No newline at end of file diff --git a/docs/_sources/_sources/quickstart/installation.md b/docs/_sources/_sources/quickstart/installation.md deleted file mode 100644 index 5835daa..0000000 --- a/docs/_sources/_sources/quickstart/installation.md +++ /dev/null @@ -1,9 +0,0 @@ -## Installation - -To install, and manage dependencies and virtual environments this project uses Poetry. Follow the [instructions](https://python-poetry.org/docs/) to install Poetry. - -From the root directory `poetry update` followed by `poetry install` - this will establish a venv with all the needed dependencies. - -Once your venv is made you can use `poetry run [command]` to run a single CLI command inside the venv. - -You can use `poetry shell` to enter into the venv. \ No newline at end of file diff --git a/docs/_sources/_sources/quickstart/poverty_survey.md b/docs/_sources/_sources/quickstart/poverty_survey.md deleted file mode 100644 index 7307292..0000000 --- a/docs/_sources/_sources/quickstart/poverty_survey.md +++ /dev/null @@ -1,37 +0,0 @@ -# Poverty Outcome Generation from Survey Data -Set up the configuration file and load some survey data (see {doc}`standardized data formats <../data_formats/survey_data>`) for file schema). - -``` -datastore = DataStore('/path/to/config/file') -outcomes_generator = SurveyOutcomesGenerator(datastore=datastore, clean_folders=True) -``` - -Calculate PCA asset index and proxy-means test (PMT). Only use binary and continuous columns in the asset index. - -``` -asset_index = outcomes_generator.asset_index(cols=['con1', 'con2', 'bin1', 'bin2']) -``` - -Select five components to be used in the proxy-means test using forward selection of predictors with a linear regression. Calculate a proxy-means test with these components and obtain out-of-sample PMT predictions for the training survey. - -``` -selected_cols, scores = outcomes_generator\ - .select_features('consumption', - ['con1', 'con2', 'cat1', 'cat2', 'bin1', 'bin2'], - 5, - method=LinearRegession()) -pmt = outcomes_generator.fit_pmt('consumption', - selected_cols, - model_name='linear', - winsorize=False, - scale=True) -``` - -`[OUT] r2 score: 0.56` - - -Use the trained proxy-means test on another survey dataset. - -``` -predictions = outcomes_generator.pretrained_pmt('/path/to/other/data.csv', selected_cols, 'linear') -``` \ No newline at end of file diff --git a/docs/_sources/api/datastore.html b/docs/_sources/api/datastore.html deleted file mode 100644 index eff9469..0000000 --- a/docs/_sources/api/datastore.html +++ /dev/null @@ -1,914 +0,0 @@ - - - - - - - - - Datastore API Reference - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Datastore API Reference#

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DataStore Class#

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-class DataStore(config_file_path_string, spark=True)#
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  • config_file_path_string (str) –

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-_load_antennas(dataframe=None)#
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Load antennas data: use file path specified in config as default, or spark/pandas df

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dataframe (Optional[Union[pyspark.sql.dataframe.DataFrame, pandas.core.frame.DataFrame]]) – spark/pandas df to assign if available

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Load cdr data: use file path specified in config as default, or spark/pandas df

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-_load_fairness()#
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Load fairness data.

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-_load_features()#
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Load phone usage features to be used for training ML model and subsequent poverty prediction

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-_load_home_ground_truth()#
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Load ground truth data for home locations

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-_load_labels()#
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Load labels to train ML model on

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-_load_mobiledata(dataframe=None)#
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Load mobile data: use file path specified in config as default, or spark/pandas df

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dataframe (Optional[Union[pyspark.sql.dataframe.DataFrame, pandas.core.frame.DataFrame]]) – spark/pandas df to assign if available

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-_load_mobilemoney(dataframe=None)#
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Load mobile money data: use file path specified in config as default, or spark/pandas df

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dataframe (Optional[Union[pyspark.sql.dataframe.DataFrame, pandas.core.frame.DataFrame]]) – spark/pandas df to assign if available

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-_load_poverty_scores()#
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Load poverty scores (e.g. those produced by the ML module)

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Return type
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-_load_recharges(dataframe=None)#
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Load recharges data: use file path specified in config as default, or spark/pandas df

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dataframe (Optional[Union[pyspark.sql.dataframe.DataFrame, pandas.core.frame.DataFrame]]) – spark/pandas df to assign if available

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-_load_shapefiles()#
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Iterate through shapefiles specified in config and load them in self.shapefiles dictionary

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-_load_targeting()#
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Load targeting data.

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-deduplicate()#
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Remove duplicate rows from alla available datasets

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-filter_dates(start_date, end_date)#
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Filter data outside [start_date, end_date] (inclusive) in all available datasets

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  • start_date (str) – e.g. ‘2020-01-01’

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-load_data(data_type_map)#
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Load all datasets defined by data_type_map; raise an error if any of them failed to load

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data_type_map (Mapping[cider.datastore.DataType, Optional[Union[pyspark.sql.dataframe.DataFrame, pandas.core.frame.DataFrame]]]) – mapping between DataType(s) and dataframes, if provided. If None look at config file

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-merge()#
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Merge features and labels, split into x and y dataframes

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Return type
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None

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-remove_survey_outliers(cols, num_sds=2.0, dry_run=False)#
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Removes observations with outliers in the columns listed in ‘cols’ from the survey data.

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  • cols (List[str]) – Columns used to identify outliers.

    • -

  • num_sds (float) – Number of standard deviations used to identify outliers.

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  • dry_run (bool) – If True, only prints the number of outliers without removing them.

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Set[str]

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OptDataStore Class#

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config_file_path_string (str) –

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Create table of all user ids present in the datasets, and whether they should be included in the analysis or not -This is defined by the opt_in_default parameter specified in the config file

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read_from_file (bool) –

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-opt_in(user_ids)#
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Update the user consent table based on list of user ids that have opted in

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user_ids (List[str]) – list of user ids to flag as opted in, i.e. include = True

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-opt_out(user_ids)#
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Update the user consent table based on list of user ids that have opted out

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user_ids (List[str]) – list of user ids to flag as opted out, i.e. include = False

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- - By UC Berkeley Global Policy Lab
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/api/fairness.html b/docs/_sources/api/fairness.html deleted file mode 100644 index c2d6be2..0000000 --- a/docs/_sources/api/fairness.html +++ /dev/null @@ -1,622 +0,0 @@ - - - - - - - - - Fairness API Reference - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Fairness API Reference#

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-class Fairness(datastore, dataframe=None, clean_folders=False)#
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datastore (cider.datastore.DataStore) –

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-demographic_parity(var1, var2, characteristic, p, weighted=False)#
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var1 = groundtruth -var2 = proxies. The thing that you’re evaluating the fairness of. -characteristic = how the group is divided -p = target the top p percent

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Performs the chi-squared independence test between the proxy variable and the sensitive characterisrtic

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var2 – the proxy variable -characteristic – sensitive characteristic

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var1 = groundtruth -var2 = proxies. The thing that you’re evaluating the fairness of. -characteristic = how the group is divided -p = target the top p percent

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var1 = groundtruth -var2 = proxies. The thing that you’re evaluating the fairness of. -characteristic = how the group is divided -p = target the top p percent

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Featurizer API Reference#

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Join all feature datasets together, save to disk, and assign to attribute

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Compute CDR features using bandicoot library and save to disk

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Compute CDR features using spark and save to disk

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Compute time series of transactions, save to disk, and plot if requested

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Compute summary statistics of datasets

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Plot the distribution of a select number of features

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Load features from disk if already computed

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Use ground truth data on users’ homes to compute accuracy metrics for the inferred home locations

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Infer home locations of users based on their CDR transactions

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pandas.core.frame.DataFrame

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Plot distribution of homes, accuracy of home locations, or average poverty score (predicted using the ML model -or otherwise) on a map

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Use a population density raster - e.g. FB D4G’s high resolution density maps - to compare the distribution of -inferred home locations to the population

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  • geo (str) – compare pop distribution at geographic level specified by argument ‘geo’

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  • algo (str) – algorithm responsible for the home inference

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None

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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
-

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- -
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ML API Reference#

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-class Learner(datastore, clean_folders=False, kfold_tune=3, kfold_predict=5)#
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Parameters
-
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Return type
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None

-
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-automl(model_name)#
-

Trains ML models using AutoML - the libraries’ parameters are specified in the config file. All models, -including the ensembles, are saved to disk.

-
-
Parameters
-

model_name (str) – The name of the AutoML library to use - currently it only supports ‘autogluon’ (AutoGluon).

-
-
Return type
-

None

-
-
-
- -
-
-feature_importances(model_name, kind='tuned')#
-

Computes the relative of features for a trained model.

-
-
Parameters
-
    -

  • model_name (str) – The name of the model.

    • -

  • kind (str) – The type of model, i.e. untuned, tuned, or automl.

    • -
-
-
Return type
-

pandas.core.frame.DataFrame

-
-
-

Returns: The pandas df with all features and their importance.

-
- -
-
-feature_importances_plot(model_name, kind='tuned', n_features=20)#
-

Produces horizontal bar plots of already calculated feature importances.

-
-
Parameters
-
    -

  • model_name (str) – The name of the model.

    • -

  • kind (str) – The type of model, i.e. untuned, tuned, or automl.

    • -

  • n_features (int) – The number of top features to use in the plot.

    • -
-
-
Return type
-

None

-
-
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- -
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-oos_predictions(model_name, kind='tuned')#
-

Computes out-of-sample predictions for all training + test samples.

-
-
Parameters
-
    -

  • model_name (str) – The name of the model.

    • -

  • kind (str) – The type of model, i.e. untuned, tuned, or automl.

    • -
-
-
Return type
-

pandas.core.frame.DataFrame

-
-
-

Returns: The pandas df with true and predicted values for all training + test samples.

-
- -
-
-population_predictions(model_name, kind='tuned', chunksize=100)#
-

Computes predictions for all population samples, i.e. those samples that do not have ground-truth data.

-
-
Parameters
-
    -

  • model_name (str) – The name of the model.

    • -

  • kind (str) – The type of model, i.e. untuned, tuned, or automl.

    • -

  • n_chunks – The number of chunks to divide the full population dataset in.

    • -

  • chunksize (int) –

    • -
-
-
Return type
-

pandas.core.frame.DataFrame

-
-
-

Returns: The pandas df with predicted values.

-
- -
-
-scatter_plot(model_name, kind='tuned')#
-

Charts the out-of-sample predictions and the true values as a scatter plot, computes the correlation coefficient -between the two series, and superimposed the fitted LOESS curve.

-
-
Parameters
-
    -

  • model_name (str) – The name of the model.

    • -

  • kind (str) – The type of model, i.e. untuned, tuned, or automl.

    • -
-
-
Return type
-

None

-
-
-
- -
-
-targeting_table(model_name, kind='tuned')#
-

Computes classification metrics using OOS predictions and when targeting 10 different subsets of the population, -the bottom p percentile where p = [0, 10, …, 90].

-
-
Parameters
-
    -

  • model_name (str) – The name of the model.

    • -

  • kind (str) – The type of model, i.e. untuned, tuned, or automl.

    • -
-
-
Return type
-

pandas.core.frame.DataFrame

-
-
-

Returns: The pandas df with correlation, accuracy, precision, recall, AUC, when targeting the bottom p -percentile of the population, where p = [0, 10, …, 90].

-
- -
-
-tuned_model(model_name)#
-

Trains the ML model specified by ‘model_name’ and returns the R2 and the RMSE it obtained on the train and test -sets by all models. During training, it will try the grid of hyper-parameters specified in the config file for -the corresponding model. It also saves the trained models and computes its their feature importance.

-
-
Parameters
-

model_name (str) – The name of the model to use - one of [‘linear’, ‘lasso’, ‘ridge’, ‘randomforest’,

-
-
Return type
-

Dict[str, str]

-
-
-

‘gradientboosting’]

-

Returns: A dict of R2 and RMSE scores obtained on training and test sets by the best model.

-
- -
-
-untuned_model(model_name)#
-

Trains the ML model specified by ‘model_name’ and returns the R2 and the RMSE it obtained on the train and test -sets. It also saves the trained model and computes its feature importance.

-
-
Parameters
-

model_name (str) – The name of the model to use - one of [‘linear’, ‘lasso’, ‘ridge’, ‘randomforest’,

-
-
Return type
-

Dict[str, str]

-
-
-

‘gradientboosting’]

-

Returns: A dict of R2 and RMSE scores obtained on training and test sets.

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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
-

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- -
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/api/poverty_survey.html b/docs/_sources/api/poverty_survey.html deleted file mode 100644 index b5b8734..0000000 --- a/docs/_sources/api/poverty_survey.html +++ /dev/null @@ -1,690 +0,0 @@ - - - - - - - - - Survey API Reference - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Survey API Reference

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Survey API Reference#

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-class SurveyOutcomeGenerator(datastore, dataframe=None, clean_folders=False)#
-
-
Parameters
-
    -

  • datastore (cider.datastore.DataStore) –

    • -

  • dataframe (Optional[pandas.core.frame.DataFrame]) –

    • -

  • clean_folders (bool) –

    • -
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Return type
-

None

-
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-asset_index(cols, use_weights=True)#
-

Derives an asset index - first component of (weighted) PCA - from specified set of columns.

-
-
Parameters
-
    -

  • cols (List[str]) – Columns (i.e. survey questions) to use to derive the index.

    • -

  • use_weights (bool) – If true, uses a weighted PCA.

    • -
-
-
Return type
-

pandas.core.frame.DataFrame

-
-
-

Returns: Pandas df containing asset index of surveyed users.

-
- -
-
-fit_pmt(outcome, cols, model_name='linear', kfold=5, use_weights=True, log_transform=False, scale=False, winsorize=False)#
-

Trains a model to predict an outcome variable using a set of survey questions - this will be the PMT. -Also provides metrics on modelling performance.

-
-
Parameters
-
    -

  • outcome (str) – Target variable.

    • -

  • cols (List[str]) – Independent variables.

    • -

  • model_name (str) – ML model to use - can be one of [‘linear’, ‘lasso’, ‘ridge’, ‘randomforest’, ‘gradientboosting’]

    • -

  • kfold (int) – Number of cross-validation folds to use.

    • -

  • use_weights (bool) – If true, uses survey weights.

    • -

  • log_transform (bool) – If true, log transforms outcome variable.

    • -

  • scale (bool) – If true, standard scales inputs.

    • -

  • winsorize (bool) – If ture, winsorizes (.005, .995) inputs.

    • -
-
-
Return type
-

pandas.core.frame.DataFrame

-
-
-

Returns: Pandas df of outcome variable, in-sample predictions, and out-of-sample predictions.

-
- -
-
-pretrained_pmt(other_data_in, cols, model_name, dataset_name='other_data')#
-

Use a pre-trained PMT model to predict the outcome variable in another dataset - check that the latter has the -same columns and data types of the original survey that was used to train the PMT

-
-
Parameters
-
    -

  • other_data_in (Union[str, pandas.core.frame.DataFrame]) – new dataset to get predictions for, either as path or as pandas df

    • -

  • cols (List[str]) – input columns

    • -

  • model_name (str) – PMT model to use

    • -

  • dataset_name (str) – name of new dataset

    • -
-
-
Return type
-

pandas.core.frame.DataFrame

-
-
-

Returns: pandas df of user id and predicted outcome variable

-
- -
-
-select_features(outcome, cols, n_features, method='correlation', model_name='', kfold=5, use_weights=True, log_transform=False, plot=True)#
-

Selects the top-N features that are most useful to predict the outcome variable.

-
-
Parameters
-
    -

  • outcome (str) – Target variable.

    • -

  • cols (List[str]) – Independent variables.

    • -

  • n_features (int) – Number of features to select.

    • -

  • method (str) –

    Method to use to select the top-N features - it can be: -‘correlation’: return top N features most correlated with the outcome -‘lasso’: use LASSO regressions for a grid of penalties to select features, use the one which has the

    -
    -

    closest to n_features features

    -
    -

    OTHER STRING: forward selection with a machine learning model

    -

    • -

  • model_name (str) – Name to use when saving outputs.

    • -

  • kfold (int) – Number of cross-validation folds to use.

    • -

  • use_weights (bool) – If true, uses survey weights.

    • -

  • log_transform (bool) – If true, log transforms outcome variable.

    • -

  • plot (bool) – If true, plots performance metrics.

    • -
-
-
Return type
-

Tuple[List[str], pandas.core.frame.DataFrame]

-
-
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Returns: Top-N features selected, pandas df of scores.

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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
-

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Satellite API Reference

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Satellite API Reference#

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-class Satellite(datastore, dataframes=None, clean_folders=False)#
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Parameters
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  • datastore (cider.datastore.DataStore) –

    • -

  • dataframes (Optional[Dict[str, Optional[Union[pandas.core.frame.DataFrame, pyspark.sql.dataframe.DataFrame]]]]) –

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-aggregate_scores(geo, dataset='rwi')#
-

Aggregates wealth index contained in a raster dataset, like the Relative Wealth Index, to a certain geographic -level, taking into account population density levels.

-
-
Parameters
-
    -

  • geo (str) – Geographic level of aggregation: the corresponding antennas or admin boundaries shapefiles have to have -been loaded.

    • -

  • dataset (str) – Which wealth/income map to use - only ‘rwi’ for now.

    • -
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Return type
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None

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- - By UC Berkeley Global Policy Lab
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Targeting API Reference

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Targeting API Reference#

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-class Targeting(datastore, clean_folders=False)#
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-
Parameters
-
-
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Return type
-

None

-
-
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-
-auc_overall(var1, var2, weighted=False, n_grid=99)#
-

Computed the ROC-AUC scores at ‘n_grid’ equally spaced percentile thresholds.

-
-
Parameters
-
    -

  • var1 (str) – The first variable to use in the analysis; it has to be a column in the targeting dataset.

    • -

  • var2 (str) – The second variable to use in the analysis; it has to be a column in the targeting dataset.

    • -

  • weighted (bool) – If True the weighted version of the dataset will be used.

    • -

  • n_grid (int) – The number of percentiles to compute the AUC scores for.

    • -
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-
Return type
-

Dict[str, List[float]]

-
-
-

Returns: A dict with false/true positive rates and AUC scores for ‘n_grid’ percentiles between 0 and 100.

-
- -
-
-auc_threshold(var1, var2, p, t=None, weighted=False)#
-

Computes the ROC-AUC score when the numerical variables are turned into binary using a certain threshold.

-
-
Parameters
-
    -

  • var1 (str) – The first variable to use in the analysis; it has to be a column in the targeting dataset.

    • -

  • var2 (str) – The second variable to use in the analysis; it has to be a column in the targeting dataset.

    • -

  • p (Optional[Union[float, int]]) – The percentile to use to convert numerical values into binary.

    • -

  • t (Optional[Union[float, int]]) – The threshold to use to convert numerical values into binary.

    • -

  • weighted (bool) – If True the weighted version of the dataset will be used.

    • -
-
-
Return type
-

Dict[str, List[float]]

-
-
-

Returns: A dict with AUC score and false/true positive rate.

-
- -
-
-binary_metrics(var1, var2, p1, p2, t1=None, t2=None, weighted=False)#
-

Computes several classification metrics for two numerical variables, usually a ground-truth one and its -prediction; these are first converted into binary variables using the percentiles or thresholds provided.

-
-
Parameters
-
    -

  • var1 (str) – The first variable to use in the analysis; it has to be a column in the targeting dataset.

    • -

  • var2 (str) – The second variable to use in the analysis; it has to be a column in the targeting dataset.

    • -

  • p1 (Union[int, float]) – Percentile for the first variable.

    • -

  • p2 (Optional[Union[float, int]]) – Percentile for the second variable.

    • -

  • t1 (Optional[Union[float, int]]) – Threshold for the first variable.

    • -

  • t2 (Optional[Union[float, int]]) – Threshold for the second variable.

    • -

  • weighted (bool) – If True the weighted version of the dataset will be used.

    • -
-
-
Return type
-

Dict[str, float]

-
-
-

Returns: A dictionary with results on accuracy, precision, recall, true and false positive rates.

-
- -
-
-precision_recall_curves(groundtruth, proxies, p=None, t=None, weighted=False, n_grid=99, colors=None)#
-

Plots the ROC curves for all targeting methods specified by ‘proxies’, using the percentile/threshold -provided OR a set of ‘n_grid’ equally spaced percentiles.

-
-
Parameters
-
    -

  • groundtruth (str) – The name of the groundtruth column in the targeting dataset.

    • -

  • proxies (List[str]) – The list of targeting methods to be compared against each other; each name should have a -corresponding column in the targeting dataset.

    • -

  • p (Optional[Union[float, int]]) – The percentile to use to convert numerical values into binary.

    • -

  • t (Optional[Union[float, int]]) – The threshold to use to convert numerical values into binary.

    • -

  • weighted (bool) – If True the weighted version of the dataset will be used.

    • -

  • n_grid (int) – The number of equally-spaced percentiles to compute the metrics for.

    • -

  • colors (Optional[Dict[int, str]]) – Color palette to use.

    • -
-
-
Return type
-

None

-
-
-
- -
-
-roc_curves(groundtruth, proxies, p=None, t=None, weighted=False, colors=None)#
-

Plots the ROC curves for all targeting methods specified by ‘proxies’, using the percentile or threshold -provided.

-
-
Parameters
-
    -

  • groundtruth (str) – The name of the groundtruth column in the targeting dataset.

    • -

  • proxies (List[str]) – The list of targeting methods to be compared against each other; each name should have a -corresponding column in the targeting dataset.

    • -

  • p (Optional[Union[float, int]]) – The percentile to use to convert numerical values into binary.

    • -

  • t (Optional[Union[float, int]]) – The threshold to use to convert numerical values into binary.

    • -

  • weighted (bool) – If True the weighted version of the dataset will be used.

    • -

  • colors (Optional[Dict[int, str]]) – Dict mapping proxy names to the color to use (in the default color palette)

    • -
-
-
Return type
-

None

-
-
-
- -
-
-targeting_table(groundtruth, proxies, p1, p2, t1=None, weighted=False)#
-

Produces a table with the following metrics for all targeting methods specified by ‘proxies’: Pearson and -Spearman correlation, threshold-specific and threshold-agnostic AUC, accuracy, precision, and recall.

-
-
Parameters
-
    -

  • groundtruth (str) – The name of the groundtruth column in the targeting dataset.

    • -

  • proxies (List[str]) – The list of targeting methods to be compared against each other; each name should have a -corresponding column in the targeting dataset.

    • -

  • p1 (Optional[Union[float, int]]) – The percentile threshold to use for the groundtruth variable.

    • -

  • p2 (Optional[Union[float, int]]) – The percentile threshold to use for the proxy variables.

    • -

  • t1 (Optional[Union[float, int]]) – The threshold to use for the groundtruth variable.

    • -

  • weighted (bool) – If True the weighted version of the dataset will be used.

    • -
-
-
Return type
-

pandas.core.frame.DataFrame

-
-
-

Returns: The pandas dataframe with all the performance metrics as columns and the proxies as rows.

-
- -
-
-static threshold_to_percentile(p, t, data, var, test_percentiles=True)#
-

If a percentile is provided, checks if it’s between 0 and 100 and if so returns it; if a threshold is provided, -returns the fraction of records in the dataset and for column ‘var’ that are below the threshold.

-
-
Parameters
-
    -

  • p (Optional[Union[float, int]]) – The percentile.

    • -

  • t (Optional[Union[float, int]]) – The threshold.

    • -

  • data (pandas.core.frame.DataFrame) – The pandas dataframe containing the data.

    • -

  • var (str) – The column with the records to use to compute the percentile.

    • -

  • test_percentiles (bool) – If true checks that the percentile provided is correct.

    • -
-
-
Return type
-

Union[float, int]

-
-
-

Returns: The percentile corresponding to the input (threshold or percentile) provided.

-
- -
-
-utility(var1, var2, transfer_size, p, t=None, weighted=False, rho=3)#
-

Computes the constant relative risk-aversion (CRRA) [Hanna & Olken (2018)] utility when P% of the population is -targeted and they receive transfers of size ‘transfer_size’.

-
-
Parameters
-
    -

  • var1 (str) – The first variable to use in the analysis; it has to be a column in the targeting dataset.

    • -

  • var2 (str) – The second variable to use in the analysis; it has to be a column in the targeting dataset.

    • -

  • transfer_size (Union[float, int]) – The size of the cash transfer.

    • -

  • p (Optional[Union[float, int]]) – The percentile to use to convert numerical values into binary.

    • -

  • t (Optional[Union[float, int]]) – The threshold to use to convert numerical values into binary.

    • -

  • weighted (bool) – If True the weighted version of the dataset will be used.

    • -

  • rho (Union[int, float]) – The rho parameter in the constant relative risk-aversion (CRRA) utility function.

    • -
-
-
Return type
-

float

-
-
-

Returns: The utility obtained by targeting that fraction of the population with that transfer size.

-
- -
-
-utility_curves(groundtruth, proxies, ubi_transfer_size, weighted=False, n_grid=99, colors=None)#
-

Plots the utility curves of all targeting methods specified by ‘proxies’, when targeting ‘n_grid’ equally -spaced fractions of the population.

-
-
Parameters
-
    -

  • groundtruth (str) – The name of the groundtruth column in the targeting dataset.

    • -

  • proxies (List[str]) – The list of targeting methods to be compared against each other; each name should have a -corresponding column in the targeting dataset.

    • -

  • ubi_transfer_size (Union[int, float]) – The size of the cash transfer if the entire population was targeted.

    • -

  • weighted (bool) – If True the weighted version of the dataset will be used.

    • -

  • n_grid (int) – The number of equally-spaced percentiles to compute the metrics for.

    • -

  • colors (Optional[Dict[int, str]]) – Color palette to use.

    • -
-
-
Return type
-

None

-
-
-
- -
-
-utility_grid(groundtruth, proxies, ubi_transfer_size, weighted=False, n_grid=99)#
-

Computes the utility achieved by all targeting methods specified by ‘proxies’, when targeting ‘n_grid’ equally -spaced fractions of the population.

-
-
Parameters
-
    -

  • groundtruth (str) – The name of the groundtruth column in the targeting dataset.

    • -

  • proxies (List[str]) – The list of targeting methods to be compared against each other; each name should have a -corresponding column in the targeting dataset.

    • -

  • ubi_transfer_size (Union[int, float]) – The size of the cash transfer if the entire population was targeted.

    • -

  • weighted (bool) – If True the weighted version of the dataset will be used.

    • -

  • n_grid (int) – The number of equally-spaced percentiles to compute the metrics for.

    • -
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Return type
-

Tuple[numpy.ndarray, List[float], Dict[str, List[float]]]

-
-
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- -
-
-utility_table(groundtruth, proxies, ubi_transfer_size, weighted=False, n_grid=99)#
-

Computes the optimal share of the population targeted, maximum utility, and optimal transfer size of all -targeting methods specified by ‘proxies’, out of ‘n_grid’ equally spaced fractions of the population.

-
-
Parameters
-
    -

  • groundtruth (str) – The name of the groundtruth column in the targeting dataset.

    • -

  • proxies (List[str]) – The list of targeting methods to be compared against each other; each name should have a -corresponding column in the targeting dataset.

    • -

  • ubi_transfer_size (Union[int, float]) –

    • -

  • weighted (bool) – If True the weighted version of the dataset will be used.

    • -

  • n_grid (int) – The number of equally-spaced percentiles to compute the metrics for.

    • -
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Return type
-

pandas.core.frame.DataFrame

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Returns: The pandas dataframe with optimal utility metrics for each targeting method.

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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/data_formats/antennas.html b/docs/_sources/data_formats/antennas.html deleted file mode 100644 index 59443ca..0000000 --- a/docs/_sources/data_formats/antennas.html +++ /dev/null @@ -1,665 +0,0 @@ - - - - - - - - - Antennas - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Antennas

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Contents

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Antennas#

-

Example file: synthetic_data/antennas.csv

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

antenna_id

tower_id

latitude

longitude

KB2

KB

5.1632

11.5091

KB8

KB

5.1632

11.5091

AJK1

AJ

6.9012

12.3911

OI12

OI

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Fields
#

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*antenna_id: string
-Unique ID for the antenna, matches to antenna_id in CDR datasets

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tower_id: string
-Unique ID for the cell tower. Many antennas can (and typically will) have a single tower ID. Tower ID is optional, it -need not be included if not provided by the mobile network operator (or tower IDs could be inferred using spatial -clustering of antennas).

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latitude: float
-Latitude of the antenna

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longitude: float
-Longitude of the antenna

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Note

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Columns without a preceding asterisk ‘*’ are optional

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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
-

-
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/data_formats/cdr.html b/docs/_sources/data_formats/cdr.html deleted file mode 100644 index 108bf82..0000000 --- a/docs/_sources/data_formats/cdr.html +++ /dev/null @@ -1,685 +0,0 @@ - - - - - - - - - CDR (call and text) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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CDR (call and text)

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Contents

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CDR (call and text)#

-

Example file: synthetic_data/cdr.csv

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

txn_type

caller_id

recipient_id

timestamp

duration

caller_antenna

recipient_antenna

international

call

jb9f3j

ad8b3b

2020-01-01 00:00:42

253.0

AJK3

KB3

domestic

text

ad8b3b

bncz83

2020-02-12 10:12:08

KB9

KB8

domestic

text

oewf9ras

bncz83

2020-03-30 14:50:01

15.0

KB8

OI14

domestic

call

bncz83

jb9f3j

2020-06-15 22:38:31

OI12

AJK1

international

-
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Fields
#

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*txn_type: string
-Chosen from {call, text}

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*caller_id: string
-Unique ID for the caller or sender

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*recipient_id: string
-Unique ID for the recipient

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*timestamp: string
-String in format ‘YYYY-MM-DD hh:mm:ss’ representing the start time of a call or the time an text is sent

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duration: float
-Call duration in seconds. NaN or empty string for SMS messages or calls missing duration

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Note

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Columns without a preceding asterisk ‘*’ are optional

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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
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Features

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Contents

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Features#

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Example file: synthetic_outputs/featurizer/datasets/features.csv

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

name

feature1

feature2

…

feature185

jb9f3j

590

86

…

5

ad8b3b

389

55

…

12

oewf9ras

…

3

bncz83

198

51

…

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Fields
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*name: string
-Unique ID for the subscriber

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features: float
-The file can have any number of features, named uniquely. This format is automatically produced by the featurizer class, -but custom features can be added to feature files.

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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
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Home Location Labels

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Home Location Labels#

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Example file: synthetic_data/home_locations.csv

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subscriber_id

antenna_id

tower_id

polygon1

…

polygon12

jb9f3j

a135

t23

A

…

J

ad8b3b

a121

t1

B

…

K

oewf9ras

a12

D

…

N

bncz83

a52

t98

F

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Fields
#

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*subscriber_id: string
-Unique ID for each subscriber

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antenna_id: string
-Ground truth for home antenna (if available)

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tower_id: string
-Ground truth for home tower (if available)

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polygon1…: strings
-Ground truth for polygons, must match to shapefile names

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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/data_formats/mobile_data.html b/docs/_sources/data_formats/mobile_data.html deleted file mode 100644 index 6235dfd..0000000 --- a/docs/_sources/data_formats/mobile_data.html +++ /dev/null @@ -1,656 +0,0 @@ - - - - - - - - - Mobile Data Usage - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Mobile Data Usage

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Contents

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Mobile Data Usage#

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Example file: synthetic_data/mobiledata.csv

- - - - - - - - - - - - - - - - - - - - - - - - - -

caller_id

volume

timestamp

ioa032

50.12

2020-02-09 17:18:56

j510da

59.32

2020-02-18 21:45:42

g831d

12.56

2020-03-16 22:56:01

g913kj9a

108.91

2020-03-30 08:31:33

-
-

Fields
#

-

*caller_id: string
-Unique ID for the subscriber

-

volume: float
-Volume of data consumed (upload or download), in any denomination

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*timestamp: string
-String in format ‘YYYY-MM-DD hh:mm:ss’ representing the time of the recharge

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Note

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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
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Mobile Money

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Contents

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Mobile Money#

-

Example file: synthetic_data/mobilemoney.csv

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

txn_type

caller_id

recipient_id

timestamp

amount

sender_balance_before

sender_balance_after

recipient_balance_before

recipient_balance_after

cashin

jb9f3j

2020-01-01 00:00:42

234.0

500.0

734.0

cashout

ad8b3b

2020-02-12 10:12:08

195.0

200.5

5.5

p2p

oewf9ras

bncz83

2020-03-30 14:50:01

2.4

100.0

97.6

78.1

80.5

billpay

bncz83

jb9f3j

2020-06-15 22:38:31

76.1

150.0

73.9

12.9

89.0

other

bncz83

ad8b3b

2020-08-16 23:10:10

502.0

872.0

370.0

0.00

502.0

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Fields
#

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*txn_type: string -Any string describing the type of the transaction; the dataset can define any number of categories for transactions, -but keep in mind that features will be calculated separately for each transaction type so compute time scales with the -number of categories. Common categories include {cashin, cashout, billpay, p2p, other}.

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*caller_id: string
-Unique ID for the caller or sender

-

*recipient_id: string
-Unique ID for the recipient

-

*timestamp: string
-String in format ‘YYYY-MM-DD hh:mm:ss’ representing the start time of a call or the time an text is sent

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*amount: float
-Amount of money transferred (in any currency)

-

sender_balance_before: float
-Balance of sender before transaction (in any currency). Only provided by some mobile network operators.

-

sender_balance_after: float
-Balance of sender after transaction (in any currency). Only provided by some mobile network operators.

-

recipient_balance_after: float
-Balance of the recipient after transaction, where applicable (in any currency). Only provided by some mobile network -operators.

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recipient_balance_before: float
-Balance of the recipient after transaction, where applicable (in any currency). Only provided by some mobile network -operators.

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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/data_formats/poverty_labels.html b/docs/_sources/data_formats/poverty_labels.html deleted file mode 100644 index 4ad5cb5..0000000 --- a/docs/_sources/data_formats/poverty_labels.html +++ /dev/null @@ -1,656 +0,0 @@ - - - - - - - - - Poverty Labels - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Poverty Labels

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Poverty Labels#

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Example file: synthetic_data/labels.csv

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name

label

weight

jb9f3j

100.12

12.80

ad8b3b

136.91

21.59

oewf9ras

78.52

7.55

bncz83

121.00

22.12

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Fields
#

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*name: string
-Unique ID for each subscriber

-

*label: float or bool
-Wealth label for the observation; can be numeric for regression, or categorical for classification

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weight: float
-Weight to assign the observation (i.e. from a survey sampling weight or response weight)

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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/data_formats/recharges.html b/docs/_sources/data_formats/recharges.html deleted file mode 100644 index 2cc2d5b..0000000 --- a/docs/_sources/data_formats/recharges.html +++ /dev/null @@ -1,656 +0,0 @@ - - - - - - - - - Recharges - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Recharges

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Contents

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Recharges#

-

Example file: synthetic_data/recharges.csv

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caller_id

amount

timestamp

ioa032

96.05

2020-02-21 09:05:11

j510da

102.32

2020-05-01 11:19:22

g831d

51.71

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12.52

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#

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*caller_id: string
-Unique ID for the subscriber

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amount: float
-Amount of the recharge (in any currency)

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-String in format ‘YYYY-MM-DD hh:mm:ss’ representing the time of the recharge

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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
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Spatial Files

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Contents

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Spatial Files#

-

Example file: synthetic_data/prefectures.geojson

-
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Note

-

Spatial data is loaded with geopandas, and can be stored as a shapefile or geojson.

-
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region

geometry

Region A

Multipolygon(…)

Region B

Multipolygon(…)

Region C

Multipolygon(…)

Region D

Multipolygon(…)

-
-

Fields
#

-

*region: string
-Unique ID for each polygon in the shapefile. Must be unique.

-

amount: float
-Amount of the recharge (in any currency)

-

*geometry: polygon
-Shapely polygon, as loaded from a shapefile or geojson - usually in WKT format

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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
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Survey Data

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Contents

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unique_id

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outcome

col1

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col35

jb9f3j

12.6

1.5

5.2

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category3

ad8b3b

1.6

2.7

6.1

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category6

oewf9ras

3.2

4.1

1.5

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bncz83

6.0

0.9

2.6

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category2

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*unique_id: string
-Unique ID for each survey observation

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weight: float
-Weight for each survey observation (if sample weights are used)

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-Survey data. Can have any column names and be of any type (continuous, categorical, or binary)

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Targeting and Fairness Data

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Contents

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Targeting and Fairness Data#

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Example file: synthetic_data/targeting.csv

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unique_id

weight

outcome

col1

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col35

jb9f3j

12.6

1.5

5.2

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category3

ad8b3b

1.6

2.7

6.1

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oewf9ras

3.2

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bncz83

6.0

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*unique_id: string
-Unique ID for each survey observation

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weight: float
-Weight for each survey observation (if sample weights are used)

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outcome1…outcome20: float
-“Ground truth” poverty outcomes. Can have any column names.

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proxy1…proxy20: float
-Poverty proxies used for targeting. Can have any column names.

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characteristic1…characteristic20: object
-Characteristics for fairness analysis. Must be of categorical type.

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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/docs/_build/jupyter_execute/quickstart/documentation_fairness.ipynb b/docs/_sources/docs/_build/jupyter_execute/quickstart/documentation_fairness.ipynb new file mode 100644 index 0000000..409a3c4 --- /dev/null +++ b/docs/_sources/docs/_build/jupyter_execute/quickstart/documentation_fairness.ipynb @@ -0,0 +1,355 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6b6116b0", + "metadata": {}, + "source": [ + "# Fairness Evaluations" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4fef10db", + "metadata": {}, + "outputs": [], + "source": [ + "from datastore import DataStore\n", + "from fairness import Fairness" + ] + }, + { + "cell_type": "markdown", + "id": "c6a8962b", + "metadata": {}, + "source": [ + "Set up the configuration file and load some fairness simulation data, using the datastore." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a8ec56fc", + "metadata": {}, + "outputs": [], + "source": [ + "datastore = DataStore(cfg_dir='./configs/config.yml')\n", + "fairness = Fairness(datastore=datastore)" + ] + }, + { + "cell_type": "markdown", + "id": "69543452", + "metadata": {}, + "source": [ + "Create a \"rank residuals boxplot\". Provide (1) a ground-truth poverty measure, (2) a set of proxy poverty measures that could be used for targeting, and (3) a characteristic of interest to evaluate fairness across (the characteristic must take on at least two distinct values that appear in the dataset). The \"rank residual\" for each observation will be calculated: the difference between the ranking of that observation according to the proxy and according to the ground truth. We then visualize the distribution of rank residuals for each group; if proxy measures are fair targeting methods the mean of all distributions should be close to 0. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "43e2301f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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0group0-0.21 (0.36)-0.02 (0.38)-0.03 (0.39)-0.01 (0.37)
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4Anova F-Stat61.11950.5983121.363950.806921
5Anova p-value2.83854e-360.6161930.2523760.4901
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" + ], + "text/plain": [ + " characteristic1 proxy1 proxy2 proxy3 proxy4\n", + "0 group0 -0.21 (0.36) -0.02 (0.38) -0.03 (0.39) -0.01 (0.37)\n", + "1 group1 -0.07 (0.37) 0.02 (0.38) -0.02 (0.37) -0.01 (0.40)\n", + "2 group2 0.11 (0.37) -0.00 (0.38) 0.02 (0.37) 0.03 (0.37)\n", + "3 group3 0.19 (0.35) -0.00 (0.36) 0.03 (0.39) -0.01 (0.40)\n", + "4 Anova F-Stat 61.1195 0.598312 1.36395 0.806921\n", + "5 Anova p-value 2.83854e-36 0.616193 0.252376 0.4901" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fairness.rank_residuals_table('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], 'characteristic1')" + ] + }, + { + "cell_type": "markdown", + "id": "06d03955", + "metadata": {}, + "source": [ + "Provide a targeting threshold (in percentile) to calculate demographic parity: the difference between the share of a group that is targeted according to each proxy measure and the share of the group that is targeted according to the ground-truth proxy measure. Differences are provided as percentage points. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ea075fcd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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characteristic1Group's share of populationShare of Group in Target Populationproxy1proxy2proxy3proxy4
0group024.90%45.38%25.7028114.4176717.6305225.622490
1group125.70%46.69%12.062257-1.9455255.4474714.669261
2group225.90%54.44%-15.830116-1.544402-4.633205-8.108108
3group323.50%53.62%-22.978723-0.851064-8.936170-2.127660
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" + ], + "text/plain": [ + " characteristic1 Group's share of population \\\n", + "0 group0 24.90% \n", + "1 group1 25.70% \n", + "2 group2 25.90% \n", + "3 group3 23.50% \n", + "\n", + " Share of Group in Target Population proxy1 proxy2 proxy3 proxy4 \n", + "0 45.38% 25.702811 4.417671 7.630522 5.622490 \n", + "1 46.69% 12.062257 -1.945525 5.447471 4.669261 \n", + "2 54.44% -15.830116 -1.544402 -4.633205 -8.108108 \n", + "3 53.62% -22.978723 -0.851064 -8.936170 -2.127660 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fairness.demographic_parity_table('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], 'characteristic1', 50)" + ] + }, + { + "cell_type": "markdown", + "id": "6aa137d1", + "metadata": {}, + "source": [ + "Visualize the above table as a heatmap with bubbles. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e2c02587", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_fairness_11_0.png" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fairness.demographic_parity_plot('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], 'characteristic1', 50)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cider", + "language": "python", + "name": "cider" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/docs/_sources/docs/_build/jupyter_execute/quickstart/documentation_featurizer.ipynb b/docs/_sources/docs/_build/jupyter_execute/quickstart/documentation_featurizer.ipynb new file mode 100644 index 0000000..adcb03f --- /dev/null +++ b/docs/_sources/docs/_build/jupyter_execute/quickstart/documentation_featurizer.ipynb @@ -0,0 +1,13238 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "id": "e03ae9c8", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '../../cider/cider')" + ] + }, + { + "cell_type": "markdown", + "id": "96d8501c", + "metadata": {}, + "source": [ + "# Featurizer" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ec77078c", + "metadata": {}, + "outputs": [], + "source": [ + "from datastore import DataStore\n", + "from featurizer import Featurizer" + ] + }, + { + "cell_type": "markdown", + "id": "7f3fd778", + "metadata": {}, + "source": [ + "Load some mobile phone metadata. See {doc}`standardized data formats <../data_formats/cdr>` for file schemas. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "44d61e7d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: An illegal reflective access operation has occurred\n", + "WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/Users/luciomelito/Library/Caches/pypoetry/virtualenvs/cider-n28xxEz6-py3.8/lib/python3.8/site-packages/pyspark/jars/spark-unsafe_2.12-3.1.2.jar) to constructor java.nio.DirectByteBuffer(long,int)\n", + "WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform\n", + "WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations\n", + "WARNING: All illegal access operations will be denied in a future release\n", + "21/10/26 10:50:27 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n", + "Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties\n", + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading CDR...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading recharges...\n", + "SUCCESS!\n", + "Loading mobile data...\n", + "Loading mobile data...\n", + "Loading antennas...\n", + "Warning: 10 antennas missing location\n" + ] + } + ], + "source": [ + "datastore = DataStore(cfg_dir='../../cider/configs/config.yml')\n", + "featurizer = Featurizer(datastore=datastore, clean_folders=True)" + ] + }, + { + "cell_type": "markdown", + "id": "be520940", + "metadata": {}, + "source": [ + "Remove duplicate records, filter to just a specific date range, remove outlier days and spammers based on call and text volumes, and remove duplicate records in CDR, recharges, mobile data records, and mobile money records." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7060dbd4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of spammers identified: 20\n" + ] + } + ], + "source": [ + "# Deduplication\n", + "featurizer.ds.deduplicate()\n", + "\n", + "# Filter to just January 1 - February 28 (inclusive)\n", + "featurizer.ds.filter_dates('2020-01-01', '2020-02-28')\n", + "\n", + "# Remove transactions involving spammers who place 1.8+ calls/texts per active day\n", + "spammers = featurizer.ds.remove_spammers(spammer_threshold=1.8)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0702d156", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Outliers removed: 2020-01-24, 2020-02-06\n" + ] + } + ], + "source": [ + "# Remove all records from days more than 2 standard deviations from the mean transaction volume\n", + "outlier_days = featurizer.ds.filter_outlier_days(num_sds=2)" + ] + }, + { + "cell_type": "markdown", + "id": "b9e04d2e", + "metadata": {}, + "source": [ + "Produce summary statistics and plots." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "62e500d0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'CDR': {'Days': 60, 'Transactions': 92719, 'Subscribers': 980, 'Recipients': 980}, 'Recharges': {'Days': 60, 'Transactions': 9452, 'Subscribers': 782}, 'Mobile Data': {'Days': 60, 'Transactions': 9548, 'Subscribers': 591}, 'Mobile Money': {'Days': 60, 'Transactions': 5469, 'Subscribers': 682, 'Recipients': 682}}\n" + ] + } + ], + "source": [ + "print(featurizer.diagnostic_statistics())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ed688b3c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_featurizer_10_8.png" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "featurizer.diagnostic_plots()" + ] + }, + { + "cell_type": "markdown", + "id": "c2adfb27", + "metadata": {}, + "source": [ + "Featurize the data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8593e4f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating CDR features...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 9.78% of the records are missing a location. \n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:58:28 to 2020-02-29 19:01:57\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.96% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 08:02:22 to 2020-02-29 23:54:24\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.20% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 13:39:38 to 2020-02-29 14:37:41\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.93% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 09:28:21 to 2020-02-29 20:23:01\n", + "\u001b[32m[x]\u001b[0m 195 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 07:00:19 to 2020-02-29 09:40:21\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.20% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 03:21:11 to 2020-02-29 18:02:09\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 09:56:21 to 2020-02-29 23:57:46\n", + "\u001b[32m[x]\u001b[0m 164 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.78% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 01:03:12 to 2020-02-29 02:43:26\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.55% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:52:24 to 2020-02-29 11:42:57\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 04:44:01 to 2020-02-29 23:06:35\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.49% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-02 03:40:27 to 2020-02-29 00:12:48\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.57% of the records are missing a location. (0 + 1) / 1]\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 03:48:29 to 2020-02-29 05:50:46\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:28:44 to 2020-02-29 17:36:12\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.47% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 01:37:47 to 2020-02-29 17:18:33\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 01:46:52 to 2020-02-29 06:19:17\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.90% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 03:39:34 to 2020-02-29 00:30:27\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.35% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 04:22:25 to 2020-02-28 20:07:26\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.02% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 06:09:35 to 2020-02-29 22:25:05\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 14.08% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 12:25:22 to 2020-02-29 14:30:24\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.16% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 02:11:34 to 2020-02-29 23:04:17\n", + "\u001b[32m[x]\u001b[0m 202 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.14% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 116 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 172 records from 2020-01-01 17:52:09 to 2020-02-28 13:14:49\n", + "\u001b[32m[x]\u001b[0m 157 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 07:20:17 to 2020-02-29 14:03:57\n", + "\u001b[32m[x]\u001b[0m 195 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.86% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-02 03:27:13 to 2020-02-29 20:49:05\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.29% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 09:47:41 to 2020-02-29 22:38:38\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 13:29:21 to 2020-02-29 18:03:55\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.95% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 02:15:58 to 2020-02-29 10:10:34\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 00:52:24 to 2020-02-29 00:14:59\n", + "\u001b[32m[x]\u001b[0m 162 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.71% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 01:14:48 to 2020-02-29 13:26:22\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.71% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 06:34:07 to 2020-02-29 18:05:49\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.49% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 04:01:14 to 2020-02-29 12:07:56\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.02% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 01:00:20 to 2020-02-29 19:04:41\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.48% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 121 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 01:27:12 to 2020-02-29 16:13:13\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 18.81% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 10:05:32 to 2020-02-29 21:16:11\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 11:21:07 to 2020-02-29 23:39:12\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.28% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 07:00:54 to 2020-02-29 21:49:02\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.23% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 01:27:12 to 2020-02-29 16:31:12\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.67% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 02:34:52 to 2020-02-29 16:38:45\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 03:24:27 to 2020-02-29 21:54:37\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.35% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 11:00:45 to 2020-02-29 15:52:57\n", + "\u001b[32m[x]\u001b[0m 195 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 15.77% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 03:09:53 to 2020-02-29 12:21:43\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.95% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:58:28 to 2020-02-28 18:44:36\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.69% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 231 records from 2020-01-01 03:52:41 to 2020-02-29 22:46:51\n", + "\u001b[32m[x]\u001b[0m 208 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.83% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 03:36:59 to 2020-02-29 15:03:57\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 04:27:28 to 2020-02-29 23:46:11\n", + "\u001b[32m[x]\u001b[0m 165 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 12:30:33 to 2020-02-29 23:12:55\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.95% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 17:11:28 to 2020-02-29 13:29:33\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.80% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 01:35:55 to 2020-02-29 13:57:38\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.16% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:48:08 to 2020-02-29 19:37:22\n", + "\u001b[32m[x]\u001b[0m 162 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 05:31:30 to 2020-02-29 11:42:47\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 17:18:35 to 2020-02-29 22:52:28\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.39% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:49:51 to 2020-02-29 23:43:54\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.65% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 12:14:48 to 2020-02-29 05:14:09\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.20% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 00:02:23 to 2020-02-29 04:30:38\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.45% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:38:12 to 2020-02-29 22:40:44\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.23% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 11:40:35 to 2020-02-29 14:50:49\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.08% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 08:05:52 to 2020-02-29 04:34:58\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 12.12% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 11:49:11 to 2020-02-29 09:49:15\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.77% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 07:16:45 to 2020-02-29 14:59:55\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.73% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 02:31:00 to 2020-02-29 17:04:31\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.84% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 02:53:30 to 2020-02-28 21:25:47\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.02% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 04:27:06 to 2020-02-29 12:14:27\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 20.28% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:28:33 to 2020-02-29 09:47:41\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 11:32:39 to 2020-02-29 21:26:59\n", + "\u001b[32m[x]\u001b[0m 200 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 03:08:35 to 2020-02-29 10:00:54\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.19% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:55:46 to 2020-02-29 18:51:39\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.13% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:03:05 to 2020-02-29 21:34:29\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 02:49:27 to 2020-02-29 20:21:36\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.98% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 09:38:33 to 2020-02-29 19:32:37\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 12:48:26 to 2020-02-29 16:17:36\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 12:45:52 to 2020-02-29 23:23:05\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.23% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 07:28:59 to 2020-02-29 05:46:34\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 05:53:19 to 2020-02-29 21:26:59\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:57:13 to 2020-02-29 14:26:48\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:08:25 to 2020-02-29 22:09:30\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 04:07:51 to 2020-02-29 13:52:46\n", + "\u001b[32m[x]\u001b[0m 163 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 05:52:03 to 2020-02-28 18:58:31\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 12:57:16 to 2020-02-29 21:20:09\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 07:13:05 to 2020-02-28 16:09:11\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 10.34% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:06:09 to 2020-02-29 07:10:32\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 00:31:13 to 2020-02-29 17:31:43\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:41:44 to 2020-02-29 16:58:15\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.28% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:15:52 to 2020-02-29 14:25:57\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.01% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:15:58 to 2020-02-29 17:03:05\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 19.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 04:27:26 to 2020-02-28 01:05:10\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.94% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 08:35:50 to 2020-02-29 08:56:49\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.16% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 00:51:01 to 2020-02-29 20:49:01\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 00:59:21 to 2020-02-29 15:45:38\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:41:38 to 2020-02-29 19:43:50\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.90% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:16:05 to 2020-02-29 21:00:11\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.35% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 05:49:59 to 2020-02-29 23:09:21\n", + "\u001b[32m[x]\u001b[0m 163 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.32% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 06:06:09 to 2020-02-29 20:06:41\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.39% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 152 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 00:07:47 to 2020-02-29 15:59:29\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 07:59:44 to 2020-02-29 20:10:13\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 10:20:32 to 2020-02-29 11:48:07\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.42% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 10:51:08 to 2020-02-29 22:29:32\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 10.81% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 04:41:31 to 2020-02-29 13:01:07\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:51:18 to 2020-02-29 23:28:57\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.69% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:10:56 to 2020-02-29 17:14:13\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.85% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 06:45:54 to 2020-02-29 23:29:34\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.21% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 06:35:29 to 2020-02-28 20:10:44\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.92% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:21:35 to 2020-02-29 20:04:42\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 00:53:05 to 2020-02-29 18:09:25\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.85% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:00:20 to 2020-02-29 19:50:28\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.36% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:57:13 to 2020-02-29 16:14:39\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 20.93% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 03:22:54 to 2020-02-29 17:47:16\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.32% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 01:46:43 to 2020-02-29 14:15:00\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 18:18:39 to 2020-02-28 23:48:47\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.87% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-02 20:01:48 to 2020-02-29 22:22:26\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.88% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:24:24 to 2020-02-29 23:01:18\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.23% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 08:07:10 to 2020-02-29 15:07:03\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.02% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 01:57:59 to 2020-02-29 19:05:21\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.92% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 12:44:09 to 2020-02-29 16:35:23\n", + "\u001b[32m[x]\u001b[0m 167 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.05% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 02:31:00 to 2020-02-29 22:55:09\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 09:23:16 to 2020-02-29 18:26:24\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 11:55:57 to 2020-02-29 16:58:22\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.86% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:45:45 to 2020-02-29 23:28:32\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 10.88% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:02:04 to 2020-02-29 21:57:07\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-03 00:38:24 to 2020-02-29 21:29:26\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 15:06:09 to 2020-02-29 23:04:36\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 18.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 03:12:37 to 2020-02-29 19:05:06\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.90% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 04:21:10 to 2020-02-28 22:13:13\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 01:21:42 to 2020-02-29 23:25:01\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.51% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 12:53:27 to 2020-02-29 13:48:37\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 14:48:27 to 2020-02-29 15:25:22\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.23% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 18:26:42 to 2020-02-29 21:32:05\n", + "\u001b[32m[x]\u001b[0m 167 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.21% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 01:18:39 to 2020-02-29 20:23:01\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.23% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 06:39:48 to 2020-02-29 13:09:53\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.30% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 00:13:51 to 2020-02-29 23:53:09\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 00:24:08 to 2020-02-29 20:19:22\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 01:21:42 to 2020-02-29 20:57:52\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.72% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 153 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 03:29:54 to 2020-02-29 17:26:58\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.25% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 06:36:20 to 2020-02-29 09:36:01\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.69% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 10:13:06 to 2020-02-28 22:45:21\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.32% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 05:02:14 to 2020-02-29 19:40:07\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 10:44:03 to 2020-02-28 23:39:29\n", + "\u001b[32m[x]\u001b[0m 167 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 17:49:47 to 2020-02-29 21:34:29\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 14.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 169 records from 2020-01-01 15:46:08 to 2020-02-29 21:44:00\n", + "\u001b[32m[x]\u001b[0m 159 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.18% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:43:25 to 2020-02-29 22:47:34\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 00:13:51 to 2020-02-29 22:06:16\n", + "\u001b[32m[x]\u001b[0m 202 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 03:52:41 to 2020-02-29 19:06:30\n", + "\u001b[32m[x]\u001b[0m 163 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.20% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 06:11:54 to 2020-02-29 21:59:04\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.29% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 14:12:11 to 2020-02-28 21:31:59\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.70% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 04:52:06 to 2020-02-29 16:18:56\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.38% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 00:51:11 to 2020-02-29 09:44:58\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 03:24:30 to 2020-02-29 19:50:13\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 03:13:37 to 2020-02-29 01:17:34\n", + "\u001b[32m[x]\u001b[0m 164 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.53% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:26:40 to 2020-02-29 16:59:51\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 11:33:43 to 2020-02-29 07:24:10\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 18.42% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 11:42:31 to 2020-02-29 12:10:25\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.16% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 03:12:37 to 2020-02-29 13:24:21\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.07% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:28:41 to 2020-02-29 22:35:18\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 03:35:24 to 2020-02-29 23:56:35\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 20.20% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 03:04:43 to 2020-02-29 23:10:29\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.62% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 07:31:08 to 2020-02-29 18:32:05\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 12.98% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:12:42 to 2020-02-29 18:26:47\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 156 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 03:47:54 to 2020-02-29 13:29:41\n", + "\u001b[32m[x]\u001b[0m 200 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.47% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 04:57:30 to 2020-02-29 14:59:14\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 00:37:14 to 2020-02-29 13:01:07\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.96% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 02:14:43 to 2020-02-29 22:22:49\n", + "\u001b[32m[x]\u001b[0m 200 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.38% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 05:02:37 to 2020-02-29 16:17:49\n", + "\u001b[32m[x]\u001b[0m 166 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.73% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:17:53 to 2020-02-29 23:00:24\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.45% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 227 records from 2020-01-01 02:52:39 to 2020-02-29 20:29:29\n", + "\u001b[32m[x]\u001b[0m 205 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.72% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 03:09:08 to 2020-02-29 21:49:26\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 07:16:35 to 2020-02-29 19:15:07\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.14% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 18:18:39 to 2020-02-29 11:54:35\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.96% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:30:00 to 2020-02-29 21:06:10\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 07:27:08 to 2020-02-29 22:51:25\n", + "\u001b[32m[x]\u001b[0m 198 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.61% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 01:46:18 to 2020-02-29 17:06:31\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.61% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 11:18:53 to 2020-02-29 23:02:01\n", + "\u001b[32m[x]\u001b[0m 200 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.05% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 05:21:34 to 2020-02-29 20:11:21\n", + "\u001b[32m[x]\u001b[0m 164 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 01:59:49 to 2020-02-29 07:33:19\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 11.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 02:12:58 to 2020-02-29 17:33:19\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.77% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 03:36:59 to 2020-02-29 17:57:28\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:20:52 to 2020-02-29 14:55:36\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 00:20:30 to 2020-02-29 19:30:39\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.13% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:43:54 to 2020-02-29 18:21:43\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.78% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:51:11 to 2020-02-29 06:50:05\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 00:31:13 to 2020-02-29 05:14:29\n", + "\u001b[32m[x]\u001b[0m 206 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 05:55:09 to 2020-02-29 21:07:33\n", + "\u001b[32m[x]\u001b[0m 160 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.86% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 08:31:41 to 2020-02-29 20:50:31\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.32% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 06:12:59 to 2020-02-29 11:02:17\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.26% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 00:49:14 to 2020-02-29 15:22:57\n", + "\u001b[32m[x]\u001b[0m 167 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 04:29:28 to 2020-02-29 14:23:41\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 15:25:43 to 2020-02-29 19:04:03\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 00:02:04 to 2020-02-29 23:04:36\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.78% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 00:20:52 to 2020-02-29 22:22:26\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 04:02:43 to 2020-02-29 21:42:46\n", + "\u001b[32m[x]\u001b[0m 166 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.30% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:21:10 to 2020-02-29 19:43:47\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.71% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 09:38:10 to 2020-02-29 23:23:38\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 01:46:43 to 2020-02-29 23:19:35\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 05:59:43 to 2020-02-29 12:36:13\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.51% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 07:44:45 to 2020-02-29 21:59:22\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 153 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 230 records from 2020-01-01 00:41:38 to 2020-02-29 23:52:22\n", + "\u001b[32m[x]\u001b[0m 209 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 9.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 01:42:54 to 2020-02-29 16:27:03\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 02:12:00 to 2020-02-29 20:31:44\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 13:56:11 to 2020-02-29 22:15:35\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.39% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:42:44 to 2020-02-29 13:01:05\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.82% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 03:27:45 to 2020-02-29 12:12:52\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 05:34:51 to 2020-02-29 07:53:20\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 06:13:03 to 2020-02-29 23:54:48\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:02:19 to 2020-02-29 16:18:46\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.70% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 09:28:04 to 2020-02-29 22:47:56\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.24% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 01:56:40 to 2020-02-29 11:13:46\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 02:14:38 to 2020-02-29 12:38:32\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.99% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 04:42:54 to 2020-02-29 17:13:08\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.85% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 11:25:09 to 2020-02-29 19:49:35\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.08% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-02 14:23:06 to 2020-02-29 18:15:07\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.47% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 228 records from 2020-01-01 01:56:55 to 2020-02-29 14:42:45\n", + "\u001b[32m[x]\u001b[0m 206 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.39% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 03:43:48 to 2020-02-29 19:42:36\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.71% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 168 records from 2020-01-01 08:04:07 to 2020-02-29 17:16:20\n", + "\u001b[32m[x]\u001b[0m 154 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 09:28:04 to 2020-02-29 15:36:00\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 10.73% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 14:34:53 to 2020-02-29 22:30:03\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 01:07:55 to 2020-02-29 18:43:30\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.24% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 03:52:41 to 2020-02-29 15:51:29\n", + "\u001b[32m[x]\u001b[0m 163 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 04:01:48 to 2020-02-29 09:50:09\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.91% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 17:41:50 to 2020-02-29 10:31:31\n", + "\u001b[32m[x]\u001b[0m 199 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.08% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 14:43:48 to 2020-02-28 21:08:36\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.20% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 15:12:23 to 2020-02-29 15:58:23\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.29% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 02:37:02 to 2020-02-29 15:45:51\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.08% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 01:56:53 to 2020-02-29 13:32:03\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.47% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 19:48:52 to 2020-02-29 19:04:41\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 20.10% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 04:54:05 to 2020-02-29 21:38:34\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.25% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 12:12:42 to 2020-02-29 14:49:18\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.75% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 21:14:06 to 2020-02-29 23:34:14\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.23% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 01:26:02 to 2020-02-29 03:42:11\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.92% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 13:41:47 to 2020-02-29 16:21:06\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 01:22:53 to 2020-02-29 20:00:32\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 02:25:29 to 2020-02-29 19:50:28\n", + "\u001b[32m[x]\u001b[0m 167 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 04:20:55 to 2020-02-29 12:26:36\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.38% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 02:32:52 to 2020-02-29 18:30:02\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.75% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 02:36:50 to 2020-02-29 22:45:42\n", + "\u001b[32m[x]\u001b[0m 198 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 12:54:30 to 2020-02-29 21:20:20\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 13:45:25 to 2020-02-29 11:04:17\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 03:01:27 to 2020-02-28 22:30:35\n", + "\u001b[32m[x]\u001b[0m 207 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 05:49:46 to 2020-02-29 20:45:44\n", + "\u001b[32m[x]\u001b[0m 201 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.90% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 08:42:10 to 2020-02-29 19:35:12\n", + "\u001b[32m[x]\u001b[0m 198 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.30% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 03:16:50 to 2020-02-28 22:17:05\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 00:19:47 to 2020-02-29 23:01:17\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-02 03:03:59 to 2020-02-29 11:41:10\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 20.30% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 13:35:55 to 2020-02-29 19:51:37\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 02:02:25 to 2020-02-29 23:48:33\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:49:28 to 2020-02-29 23:05:57\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.63% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 08:18:35 to 2020-02-29 23:01:06\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.60% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 04:15:45 to 2020-02-29 10:45:06\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 03:17:17 to 2020-02-29 21:02:26\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 11:58:03 to 2020-02-29 19:14:39\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.95% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 226 records from 2020-01-01 01:26:28 to 2020-02-29 22:50:13\n", + "\u001b[32m[x]\u001b[0m 199 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 12.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 01:59:49 to 2020-02-29 19:36:19\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 18.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 227 records from 2020-01-01 06:09:09 to 2020-02-29 11:02:17\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 00:40:16 to 2020-02-29 23:25:01\n", + "\u001b[32m[x]\u001b[0m 165 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 150 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 226 records from 2020-01-01 07:45:25 to 2020-02-29 22:42:21\n", + "\u001b[32m[x]\u001b[0m 206 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 12:49:39 to 2020-02-29 23:01:06\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.92% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 14:15:48 to 2020-02-28 21:09:03\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.29% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 00:33:45 to 2020-02-29 11:30:34\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.99% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 09:05:13 to 2020-02-29 18:30:36\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.02% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 02:24:32 to 2020-02-29 22:15:35\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:37:55 to 2020-02-29 23:33:35\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.67% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 06:42:35 to 2020-02-29 22:10:09\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:54:21 to 2020-02-29 17:54:40\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 03:41:03 to 2020-02-29 20:36:35\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 04:52:45 to 2020-02-29 12:13:03\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.65% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 05:36:32 to 2020-02-29 16:30:01\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.05% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 09:46:32 to 2020-02-29 04:15:45\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:35:29 to 2020-02-29 21:19:51\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.18% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 10:19:48 to 2020-02-29 16:52:33\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 08:04:07 to 2020-02-29 12:49:56\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:34:48 to 2020-02-29 18:09:17\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.78% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:15:52 to 2020-02-29 19:30:39\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.62% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 03:10:31 to 2020-02-29 19:44:47\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 11.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 04:25:08 to 2020-02-29 07:57:40\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.75% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 04:54:43 to 2020-02-29 17:45:46\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.36% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 05:03:52 to 2020-02-29 19:04:03\n", + "\u001b[32m[x]\u001b[0m 195 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 05:25:08 to 2020-02-29 19:32:19\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 03:09:51 to 2020-02-29 18:04:17\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.45% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 10:11:12 to 2020-02-29 20:06:56\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.83% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 01:14:59 to 2020-02-29 14:43:28\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.96% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 05:01:12 to 2020-02-29 17:05:28\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.45% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 03:04:21 to 2020-02-29 11:11:20\n", + "\u001b[32m[x]\u001b[0m 203 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.86% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 07:10:08 to 2020-02-29 14:04:50\n", + "\u001b[32m[x]\u001b[0m 163 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 7.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 155 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 07:45:28 to 2020-02-29 08:32:41\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 01:14:51 to 2020-02-29 18:05:49\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 18.18% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:14:28 to 2020-02-29 21:08:53\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.32% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 04:48:09 to 2020-02-29 20:50:18\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 07:29:29 to 2020-02-29 21:59:04\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 09:46:52 to 2020-02-29 21:08:53\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.51% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 21:47:59 to 2020-02-29 19:23:16\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 04:42:54 to 2020-02-29 14:54:39\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 14.42% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:52:38 to 2020-02-29 06:41:38\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.24% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 00:40:20 to 2020-02-29 23:56:26\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 02:15:22 to 2020-02-29 22:22:49\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 04:17:04 to 2020-02-28 13:27:45\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 11:27:06 to 2020-02-29 22:55:42\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 07:10:08 to 2020-02-29 10:04:48\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.67% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 228 records from 2020-01-01 02:42:07 to 2020-02-29 23:41:52\n", + "\u001b[32m[x]\u001b[0m 208 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.73% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 03:24:51 to 2020-02-28 17:32:17\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 05:02:29 to 2020-02-29 15:43:55\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 18.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 00:40:20 to 2020-02-29 15:28:37\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 02:44:18 to 2020-02-29 22:25:05\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:23:45 to 2020-02-29 18:15:33\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.62% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 07:26:11 to 2020-02-29 12:41:38\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.41% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 09:55:38 to 2020-02-29 06:04:11\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 10:36:44 to 2020-02-29 23:40:39\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 00:53:36 to 2020-02-29 20:49:05\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.29% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 00:57:30 to 2020-02-29 08:28:26\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 9.13% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 03:50:23 to 2020-02-29 20:45:50\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.03% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 05:03:02 to 2020-02-29 18:25:09\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 09:05:42 to 2020-02-29 13:29:00\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.48% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 12:48:36 to 2020-02-29 23:11:35\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-02 07:06:24 to 2020-02-29 19:24:08\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:56:29 to 2020-02-29 14:34:24\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.96% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 09:46:40 to 2020-02-28 14:29:50\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:10:38 to 2020-02-29 19:49:36\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.32% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 14:37:54 to 2020-02-29 20:57:06\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.70% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 06:18:32 to 2020-02-29 23:54:48\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.21% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-02 01:32:34 to 2020-02-29 12:14:27\n", + "\u001b[32m[x]\u001b[0m 204 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.96% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-02 05:43:59 to 2020-02-29 19:40:07\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 04:36:18 to 2020-02-29 20:44:35\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 16:55:19 to 2020-02-29 04:23:12\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 152 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 00:34:42 to 2020-02-29 19:53:25\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.38% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 00:39:49 to 2020-02-29 21:00:48\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 6.77% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 02:16:18 to 2020-02-28 23:20:55\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 13.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 09:20:18 to 2020-02-29 20:57:06\n", + "\u001b[32m[x]\u001b[0m 202 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.80% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 22:25:36 to 2020-02-29 22:32:23\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.21% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:02:29 to 2020-02-29 19:45:26\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 7.32% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 16:13:46 to 2020-02-29 15:31:10\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 6.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 01:27:17 to 2020-02-29 13:24:23\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 04:16:42 to 2020-02-29 21:44:21\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 05:09:02 to 2020-02-29 22:18:23\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.86% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 02:19:17 to 2020-02-29 23:15:26\n", + "\u001b[32m[x]\u001b[0m 159 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 11:04:47 to 2020-02-29 19:24:08\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:39:57 to 2020-02-29 20:44:35\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.29% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 10:16:59 to 2020-02-29 20:32:54\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.88% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 07:23:27 to 2020-02-29 16:44:37\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.85% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 12:20:05 to 2020-02-29 15:50:56\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.78% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:43:54 to 2020-02-29 19:09:12\n", + "\u001b[32m[x]\u001b[0m 166 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.21% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 08:37:11 to 2020-02-29 14:26:32\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:17:50 to 2020-02-29 14:25:57\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 09:46:04 to 2020-02-29 14:00:38\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-02 03:01:33 to 2020-02-29 20:29:29\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.29% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 04:46:36 to 2020-02-29 17:36:42\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 04:54:05 to 2020-02-29 23:43:32\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 9.55% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 06:52:20 to 2020-02-29 23:13:39\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.31% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 04:55:46 to 2020-02-29 18:55:25\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 01:56:40 to 2020-02-29 19:32:00\n", + "\u001b[32m[x]\u001b[0m 163 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.28% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:34:01 to 2020-02-29 15:00:03\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.84% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 13:21:57 to 2020-02-29 23:11:35\n", + "\u001b[32m[x]\u001b[0m 162 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.90% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 06:49:38 to 2020-02-29 22:46:23\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:06:36 to 2020-02-29 11:51:02\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.30% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 07:25:46 to 2020-02-29 13:35:25\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 09:41:55 to 2020-02-29 23:14:42\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 10:08:09 to 2020-02-29 17:30:19\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.25% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 06:26:40 to 2020-02-29 20:43:27\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 04:27:03 to 2020-02-29 13:13:13\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.29% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 117 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 00:31:38 to 2020-02-29 23:03:09\n", + "\u001b[32m[x]\u001b[0m 164 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.18% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 01:24:38 to 2020-02-29 23:27:18\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 07:40:13 to 2020-02-29 20:49:01\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 09:38:39 to 2020-02-29 20:47:56\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.67% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 11:11:12 to 2020-02-29 22:08:42\n", + "\u001b[32m[x]\u001b[0m 206 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.83% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 02:49:38 to 2020-02-29 19:06:30\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.54% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 03:47:43 to 2020-02-29 21:54:56\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.94% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 00:10:34 to 2020-02-29 13:48:25\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.45% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 22:57:11 to 2020-02-29 21:01:52\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 13.93% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:08:57 to 2020-02-29 03:55:46\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.24% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 05:25:33 to 2020-02-29 23:01:17\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.69% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:24:38 to 2020-02-29 22:16:13\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.63% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 08:39:19 to 2020-02-29 05:28:25\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 18.72% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 08:45:53 to 2020-02-29 23:06:38\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.31% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 168 records from 2020-01-01 10:24:00 to 2020-02-29 21:20:09\n", + "\u001b[32m[x]\u001b[0m 149 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 08:05:13 to 2020-02-29 18:32:05\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 02:51:12 to 2020-02-29 16:11:12\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.14% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 03:12:20 to 2020-02-29 19:14:39\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.82% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 00:21:35 to 2020-02-29 08:26:36\n", + "\u001b[32m[x]\u001b[0m 198 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 00:52:27 to 2020-02-29 23:20:17\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.21% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 18:26:54 to 2020-02-29 22:30:56\n", + "\u001b[32m[x]\u001b[0m 166 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.39% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:01:32 to 2020-02-29 07:31:49\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 04:35:42 to 2020-02-29 16:52:49\n", + "\u001b[32m[x]\u001b[0m 166 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 09:47:54 to 2020-02-29 00:08:21\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.29% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:32:08 to 2020-02-29 08:37:32\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.28% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-02 20:21:35 to 2020-02-29 19:36:08\n", + "\u001b[32m[x]\u001b[0m 201 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.14% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 05:51:44 to 2020-02-29 20:57:52\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.73% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 00:21:12 to 2020-02-29 21:49:47\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.45% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 00:49:14 to 2020-02-29 18:25:09\n", + "\u001b[32m[x]\u001b[0m 199 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.98% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 02:44:18 to 2020-02-29 14:37:41\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 10.19% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 04:29:26 to 2020-02-29 21:20:52\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 03:18:24 to 2020-02-29 15:03:36\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.98% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 21:13:20 to 2020-02-29 16:14:22\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:50:07 to 2020-02-29 07:22:58\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 06:28:53 to 2020-02-28 21:11:22\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 04:27:28 to 2020-02-29 20:45:44\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 00:17:50 to 2020-02-29 18:05:58\n", + "\u001b[32m[x]\u001b[0m 157 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.40% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 151 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 06:43:37 to 2020-02-29 03:16:14\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.86% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 02:29:29 to 2020-02-29 19:45:43\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-02 06:03:36 to 2020-02-29 23:08:55\n", + "\u001b[32m[x]\u001b[0m 163 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 00:18:39 to 2020-02-29 19:03:46\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 229 records from 2020-01-01 07:17:58 to 2020-02-29 19:15:10\n", + "\u001b[32m[x]\u001b[0m 207 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.18% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 12:31:51 to 2020-02-29 19:57:09\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.36% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-02 12:01:17 to 2020-02-29 14:53:58\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.72% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 02:00:03 to 2020-02-29 20:24:53\n", + "\u001b[32m[x]\u001b[0m 201 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.14% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 02:00:13 to 2020-02-29 21:45:47\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 10:12:25 to 2020-02-29 19:51:55\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.93% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 02:30:00 to 2020-02-29 18:42:58\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.14% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 12:38:32 to 2020-02-29 17:54:48\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 15:35:32 to 2020-02-29 23:41:52\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.19% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 19:02:13 to 2020-02-29 22:44:21\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.05% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 156 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 00:04:26 to 2020-02-28 10:33:16\n", + "\u001b[32m[x]\u001b[0m 209 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 13.40% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:20:28 to 2020-02-29 23:24:06\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 02:51:40 to 2020-02-29 23:13:31\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 229 records from 2020-01-01 01:05:50 to 2020-02-29 10:16:35\n", + "\u001b[32m[x]\u001b[0m 199 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 14:01:49 to 2020-02-29 17:51:26\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.35% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 03:06:24 to 2020-02-29 20:19:04\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.14% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 02:33:51 to 2020-02-29 21:35:43\n", + "\u001b[32m[x]\u001b[0m 201 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.21% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 00:40:02 to 2020-02-29 21:48:29\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.40% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 04:44:08 to 2020-02-29 15:03:36\n", + "\u001b[32m[x]\u001b[0m 160 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 18:59:46 to 2020-02-29 22:22:27\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.35% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 10:30:02 to 2020-02-29 23:47:12\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.47% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 00:33:20 to 2020-02-29 14:30:58\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.94% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:00:07 to 2020-02-29 19:15:13\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.07% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 174 records from 2020-01-01 03:59:43 to 2020-02-29 16:40:15\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 121 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 04:41:31 to 2020-02-29 15:08:20\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:02:25 to 2020-02-29 22:05:17\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 02:14:07 to 2020-02-29 16:38:34\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.60% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 19:21:48 to 2020-02-28 23:49:23\n", + "\u001b[32m[x]\u001b[0m 162 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 05:49:59 to 2020-02-29 21:20:52\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 13.61% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 08:53:18 to 2020-02-29 23:33:19\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.61% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 01:57:59 to 2020-02-29 16:08:32\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 05:51:44 to 2020-02-29 13:00:28\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.53% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 06:01:17 to 2020-02-29 23:08:59\n", + "\u001b[32m[x]\u001b[0m 159 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 06:08:39 to 2020-02-29 20:00:39\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 10:47:14 to 2020-02-29 19:02:28\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.92% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 12:48:26 to 2020-02-29 15:33:28\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.98% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 05:16:39 to 2020-02-29 20:41:23\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:45:54 to 2020-02-29 05:42:10\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.31% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 09:31:02 to 2020-02-29 20:21:01\n", + "\u001b[32m[x]\u001b[0m 199 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.52% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 112 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 165 records from 2020-01-01 00:45:00 to 2020-02-29 23:32:09\n", + "\u001b[32m[x]\u001b[0m 156 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 09:30:34 to 2020-02-29 23:10:39\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 06:33:13 to 2020-02-29 11:48:12\n", + "\u001b[32m[x]\u001b[0m 199 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 19:34:30 to 2020-02-29 11:41:10\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:23:42 to 2020-02-29 23:33:35\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 09:54:49 to 2020-02-29 19:32:19\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 11:12:29 to 2020-02-29 18:08:00\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 05:53:34 to 2020-02-29 21:31:12\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.39% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 08:52:46 to 2020-02-29 17:52:22\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 150 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 231 records from 2020-01-01 01:26:02 to 2020-02-29 17:51:26\n", + "\u001b[32m[x]\u001b[0m 200 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 11.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 11:27:19 to 2020-02-29 10:10:39\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 15:14:24 to 2020-02-29 22:10:09\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.52% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 16:15:21 to 2020-02-29 17:25:59\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.54% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:43:36 to 2020-02-29 23:36:33\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.07% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 08:52:31 to 2020-02-29 23:58:06\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 18:59:46 to 2020-02-29 20:25:52\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.62% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 113 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 160 records from 2020-01-01 01:45:45 to 2020-02-29 13:53:41\n", + "\u001b[32m[x]\u001b[0m 150 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 18.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:45:49 to 2020-02-29 21:53:05\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.02% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 09:05:05 to 2020-02-29 20:19:04\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.93% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 226 records from 2020-01-01 09:46:32 to 2020-02-29 23:14:42\n", + "\u001b[32m[x]\u001b[0m 200 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.62% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 03:12:43 to 2020-02-29 00:59:07\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.48% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 06:04:12 to 2020-02-29 23:36:16\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.24% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 09:43:25 to 2020-02-29 15:30:34\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 08:34:57 to 2020-02-29 07:11:37\n", + "\u001b[32m[x]\u001b[0m 199 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 01:42:54 to 2020-02-29 04:34:58\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 23:46:31 to 2020-02-29 10:01:50\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.69% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-02 17:12:47 to 2020-02-29 20:16:07\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.75% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 01:34:53 to 2020-02-29 20:37:41\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.85% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 05:01:08 to 2020-02-29 05:49:03\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.69% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 231 records from 2020-01-01 00:06:20 to 2020-02-29 21:35:43\n", + "\u001b[32m[x]\u001b[0m 200 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 01:20:16 to 2020-02-29 20:27:31\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 01:03:22 to 2020-02-29 21:09:59\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:27:10 to 2020-02-29 04:40:27\n", + "\u001b[32m[x]\u001b[0m 198 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.85% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 23:41:34 to 2020-02-29 19:45:26\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-02 07:08:13 to 2020-02-29 10:54:39\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 10:57:27 to 2020-02-29 17:24:42\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.94% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 03:16:07 to 2020-02-29 23:38:58\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.83% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 00:28:12 to 2020-02-29 09:15:28\n", + "\u001b[32m[x]\u001b[0m 166 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 09:41:18 to 2020-02-29 17:36:12\n", + "\u001b[32m[x]\u001b[0m 203 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 18:05:39 to 2020-02-29 23:06:56\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 01:24:33 to 2020-02-29 18:30:53\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.84% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 02:15:22 to 2020-02-29 21:39:17\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.14% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 10:22:05 to 2020-02-29 12:29:06\n", + "\u001b[32m[x]\u001b[0m 204 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 10:22:51 to 2020-02-29 21:49:47\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.14% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 07:24:58 to 2020-02-29 23:35:04\n", + "\u001b[32m[x]\u001b[0m 195 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 13.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 18:37:00 to 2020-02-29 14:27:07\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.85% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 05:39:57 to 2020-02-29 20:58:24\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 17:47:09 to 2020-02-29 13:52:57\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-02 10:24:46 to 2020-02-28 07:19:30\n", + "\u001b[32m[x]\u001b[0m 157 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:08:16 to 2020-02-29 20:59:44\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:14:05 to 2020-02-29 21:39:17\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.84% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 16:50:11 to 2020-02-29 20:50:18\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-02 06:06:57 to 2020-02-29 20:19:22\n", + "\u001b[32m[x]\u001b[0m 166 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 01:41:44 to 2020-02-29 10:17:32\n", + "\u001b[32m[x]\u001b[0m 165 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.70% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 04:48:09 to 2020-02-29 01:53:03\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.90% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:27:31 to 2020-02-29 16:04:33\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.95% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 04:54:43 to 2020-02-29 20:37:41\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.08% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 02:24:33 to 2020-02-29 21:07:00\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 13:24:25 to 2020-02-29 18:50:30\n", + "\u001b[32m[x]\u001b[0m 200 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 06:31:59 to 2020-02-29 23:57:46\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.40% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 02:41:52 to 2020-02-29 20:24:53\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.70% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 03:16:07 to 2020-02-29 18:34:22\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.16% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:56:24 to 2020-02-29 17:47:07\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.90% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 04:38:47 to 2020-02-29 10:40:19\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 00:00:42 to 2020-02-29 14:02:32\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 14.87% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 07:00:54 to 2020-02-29 18:21:47\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.18% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 02:17:18 to 2020-02-29 17:40:22\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:05:11 to 2020-02-28 09:23:33\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 01:20:32 to 2020-02-29 10:05:07\n", + "\u001b[32m[x]\u001b[0m 202 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.03% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 02:51:32 to 2020-02-29 18:06:58\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 12:12:31 to 2020-02-29 22:09:30\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.29% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 22:56:40 to 2020-02-29 12:12:16\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.63% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-02 07:33:58 to 2020-02-29 20:27:25\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 19:56:25 to 2020-02-29 17:44:09\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.51% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 17:11:14 to 2020-02-29 22:01:58\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.71% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 170 records from 2020-01-01 09:59:41 to 2020-02-29 03:00:13\n", + "\u001b[32m[x]\u001b[0m 156 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.65% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 230 records from 2020-01-01 06:05:22 to 2020-02-29 23:55:45\n", + "\u001b[32m[x]\u001b[0m 203 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 06:54:21 to 2020-02-29 19:17:36\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.94% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 01:27:17 to 2020-02-29 23:01:42\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.67% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 16:13:38 to 2020-02-29 11:28:03\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 17:33:30 to 2020-02-29 23:52:24\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.84% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:16:27 to 2020-02-29 15:49:36\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 17.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:14:51 to 2020-02-28 21:58:10\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 05:35:27 to 2020-02-29 20:21:01\n", + "\u001b[32m[x]\u001b[0m 160 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.53% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 116 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 170 records from 2020-01-01 05:41:29 to 2020-02-29 15:17:29\n", + "\u001b[32m[x]\u001b[0m 157 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.41% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 22:26:40 to 2020-02-29 23:16:32\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.90% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 06:07:02 to 2020-02-29 05:42:15\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.61% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 230 records from 2020-01-01 08:26:20 to 2020-02-29 20:00:32\n", + "\u001b[32m[x]\u001b[0m 207 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:50:00 to 2020-02-29 23:29:34\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.07% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 174 records from 2020-01-01 04:59:13 to 2020-02-29 06:16:38\n", + "\u001b[32m[x]\u001b[0m 156 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.49% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 03:47:30 to 2020-02-29 10:31:02\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.25% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 10:36:17 to 2020-02-29 23:56:35\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.36% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 20:18:51 to 2020-02-29 14:59:14\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 08:21:41 to 2020-02-29 21:42:46\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.02% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 03:13:37 to 2020-02-29 15:56:56\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 04:15:45 to 2020-02-29 15:46:34\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 13:31:48 to 2020-02-29 21:09:59\n", + "\u001b[32m[x]\u001b[0m 195 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 12:56:57 to 2020-02-29 22:57:37\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 06:34:07 to 2020-02-29 22:05:17\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 08:53:09 to 2020-02-29 16:58:22\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 06:52:20 to 2020-02-29 10:45:57\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 14.01% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 05:15:10 to 2020-02-29 23:55:45\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.95% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 01:08:34 to 2020-02-29 22:46:23\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 01:50:19 to 2020-02-29 03:27:44\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.35% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 03:07:43 to 2020-02-29 16:06:30\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 04:58:55 to 2020-02-29 20:10:42\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.90% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 02:51:12 to 2020-02-29 20:59:55\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.77% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 00:18:39 to 2020-02-29 22:47:34\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.47% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 05:29:52 to 2020-02-29 23:23:38\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.40% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 115 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 09:09:00 to 2020-02-29 09:47:43\n", + "\u001b[32m[x]\u001b[0m 158 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.08% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:56:29 to 2020-02-29 22:21:41\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.31% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:23:42 to 2020-02-29 21:01:09\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:45:00 to 2020-02-29 22:57:56\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.52% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 00:06:20 to 2020-02-29 14:39:58\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.02% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 16:53:19 to 2020-02-29 06:58:45\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 14:37:54 to 2020-02-29 17:07:43\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 161 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 233 records from 2020-01-02 07:43:31 to 2020-02-29 23:36:05\n", + "\u001b[32m[x]\u001b[0m 212 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 00:40:16 to 2020-02-29 14:56:21\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.55% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:03:05 to 2020-02-28 19:35:04\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 20:13:02 to 2020-02-29 22:00:49\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.95% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 12:20:59 to 2020-02-29 04:27:44\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.93% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 00:50:07 to 2020-02-29 23:03:55\n", + "\u001b[32m[x]\u001b[0m 198 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.62% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 04:21:23 to 2020-02-29 22:48:07\n", + "\u001b[32m[x]\u001b[0m 195 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.26% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:33:02 to 2020-02-29 20:32:45\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.95% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 02:00:13 to 2020-02-29 04:33:03\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.65% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-02 07:04:16 to 2020-02-29 21:24:36\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 00:18:19 to 2020-02-29 17:44:09\n", + "\u001b[32m[x]\u001b[0m 200 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.36% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:24:08 to 2020-02-29 14:24:40\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.92% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 04:33:16 to 2020-02-29 18:38:00\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.54% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 05:40:48 to 2020-02-29 21:07:00\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 06:22:39 to 2020-02-29 23:52:56\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 14:57:36 to 2020-02-29 22:19:20\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.99% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 03:38:04 to 2020-02-29 21:03:12\n", + "\u001b[32m[x]\u001b[0m 166 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:05:50 to 2020-02-29 15:03:55\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 02:30:00 to 2020-02-29 18:33:05\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.23% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 01:16:05 to 2020-02-29 23:26:11\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 09:26:54 to 2020-02-29 21:29:25\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 8.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 11:45:38 to 2020-02-29 12:27:46\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:44:47 to 2020-02-29 23:06:35\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 01:09:21 to 2020-02-29 15:30:34\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 05:37:57 to 2020-02-29 23:53:09\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.10% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 06:35:58 to 2020-02-29 16:57:20\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 01:07:55 to 2020-02-29 23:56:26\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.41% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 09:49:30 to 2020-02-29 22:30:03\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 06:46:08 to 2020-02-29 23:42:20\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.29% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 05:45:26 to 2020-02-29 07:52:16\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 16:41:39 to 2020-02-28 12:59:40\n", + "\u001b[32m[x]\u001b[0m 165 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 18.91% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 08:37:16 to 2020-02-29 13:24:21\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 10:51:03 to 2020-02-29 20:59:55\n", + "\u001b[32m[x]\u001b[0m 166 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.82% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:14:05 to 2020-02-29 23:39:12\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:36:16 to 2020-02-29 12:19:19\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.31% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 01:37:03 to 2020-02-29 23:33:19\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 13:33:02 to 2020-02-29 18:49:11\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.85% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 02:16:59 to 2020-02-29 04:31:28\n", + "\u001b[32m[x]\u001b[0m 159 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 15.24% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 02:14:38 to 2020-02-29 12:30:51\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 18.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 08:19:42 to 2020-02-29 23:27:18\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.81% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 10:07:19 to 2020-02-29 18:30:19\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.48% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 01:20:16 to 2020-02-29 14:42:45\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.14% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:04:26 to 2020-02-29 22:18:23\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 04:23:42 to 2020-02-29 17:32:13\n", + "\u001b[32m[x]\u001b[0m 157 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 18:36:37 to 2020-02-29 12:58:01\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.24% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 03:39:34 to 2020-02-29 21:03:48\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.18% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:18:33 to 2020-02-29 14:34:24\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 02:41:52 to 2020-02-29 17:02:43\n", + "\u001b[32m[x]\u001b[0m 198 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.21% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 07:37:28 to 2020-02-29 18:50:30\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 11:35:07 to 2020-02-29 19:15:08\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.08% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-02 13:12:02 to 2020-02-28 15:35:26\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.92% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:55:46 to 2020-02-29 22:32:38\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.47% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 01:33:02 to 2020-02-28 21:01:34\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.83% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 05:51:58 to 2020-02-29 15:14:26\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 02:47:26 to 2020-02-29 20:21:55\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 151 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 11:39:31 to 2020-02-29 14:45:06\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.78% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 02:14:43 to 2020-02-29 16:50:28\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-02 07:00:11 to 2020-02-29 21:45:47\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 18.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 151 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 227 records from 2020-01-01 12:35:29 to 2020-02-29 23:11:27\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 16.75% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 04:54:00 to 2020-02-29 13:56:16\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.02% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 02:36:50 to 2020-02-29 11:55:12\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.47% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 05:34:27 to 2020-02-29 23:56:05\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 07:00:54 to 2020-02-29 22:32:41\n", + "\u001b[32m[x]\u001b[0m 201 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 14:31:21 to 2020-02-29 20:32:54\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 01:24:55 to 2020-02-29 08:45:02\n", + "\u001b[32m[x]\u001b[0m 165 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.63% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 21:04:29 to 2020-02-29 23:42:31\n", + "\u001b[32m[x]\u001b[0m 198 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 172 records from 2020-01-01 09:29:13 to 2020-02-29 15:38:31\n", + "\u001b[32m[x]\u001b[0m 158 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 14:07:39 to 2020-02-29 18:11:07\n", + "\u001b[32m[x]\u001b[0m 167 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 09:47:54 to 2020-02-28 21:28:57\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.01% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 05:29:31 to 2020-02-29 12:31:33\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.73% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 09:18:08 to 2020-02-29 20:03:25\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.12% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 07:48:32 to 2020-02-29 14:54:04\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 07:18:19 to 2020-02-29 21:32:57\n", + "\u001b[32m[x]\u001b[0m 162 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.16% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 00:50:00 to 2020-02-29 19:54:43\n", + "\u001b[32m[x]\u001b[0m 207 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 02:26:06 to 2020-02-29 19:15:07\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 07:11:13 to 2020-02-29 23:52:24\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:38:12 to 2020-02-29 23:19:19\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 12.94% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 20:13:02 to 2020-02-29 18:32:03\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.39% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 06:06:14 to 2020-02-29 10:14:06\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 00:06:38 to 2020-02-29 06:05:47\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.93% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:12:11 to 2020-02-29 21:54:56\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 03:20:52 to 2020-02-29 18:20:39\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.18% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 121 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 00:57:30 to 2020-02-29 23:33:15\n", + "\u001b[32m[x]\u001b[0m 166 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.47% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 19:40:10 to 2020-02-29 23:47:12\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 22:18:48 to 2020-02-29 11:39:04\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 00:28:41 to 2020-02-29 14:41:12\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:37:14 to 2020-02-29 16:48:02\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.71% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 02:31:43 to 2020-02-29 21:00:11\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 152 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 07:04:32 to 2020-02-29 17:13:08\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 112 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 04:21:23 to 2020-02-29 18:03:55\n", + "\u001b[32m[x]\u001b[0m 167 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 233 records from 2020-01-01 05:36:32 to 2020-02-29 23:54:24\n", + "\u001b[32m[x]\u001b[0m 207 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 17:38:47 to 2020-02-29 16:18:38\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.63% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 08:20:52 to 2020-02-29 22:57:24\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.82% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 01:14:59 to 2020-02-29 22:01:58\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.81% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 05:52:03 to 2020-02-29 22:31:40\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:09:02 to 2020-02-29 19:36:08\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 9.71% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 09:45:25 to 2020-02-29 21:46:54\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 13:56:46 to 2020-02-28 23:10:24\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.34% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:16:44 to 2020-02-29 23:04:17\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 05:34:15 to 2020-02-29 23:10:39\n", + "\u001b[32m[x]\u001b[0m 165 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 06:13:45 to 2020-02-29 23:27:54\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 04:26:57 to 2020-02-29 07:22:35\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.97% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 06:35:29 to 2020-02-29 20:47:56\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.08% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 03:44:48 to 2020-02-29 23:24:58\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.38% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 06:19:01 to 2020-02-29 16:50:28\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 01:34:53 to 2020-02-29 23:19:19\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.32% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 08:02:05 to 2020-02-29 19:34:49\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.20% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 16:23:59 to 2020-02-29 14:52:10\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.02% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 01:22:35 to 2020-02-29 15:34:40\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-02 04:59:51 to 2020-02-29 23:58:06\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.83% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 00:21:10 to 2020-02-29 23:53:08\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.70% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 00:21:12 to 2020-02-29 15:21:54\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.08% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 04:23:42 to 2020-02-29 17:31:43\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.73% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 05:41:29 to 2020-02-29 20:58:25\n", + "\u001b[32m[x]\u001b[0m 163 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 08:53:13 to 2020-02-29 14:54:56\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 16.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 18:05:39 to 2020-02-28 21:08:02\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.78% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 00:02:12 to 2020-02-29 05:44:32\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 04:19:43 to 2020-02-29 13:40:53\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 07:13:05 to 2020-02-29 23:16:32\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.81% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 08:00:59 to 2020-02-29 20:01:55\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 05:31:28 to 2020-02-29 17:32:38\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 06:42:05 to 2020-02-29 16:02:11\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 10:07:02 to 2020-02-29 20:06:41\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:24:55 to 2020-02-29 17:41:33\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 03:53:19 to 2020-02-29 23:09:21\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.82% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 07:27:08 to 2020-02-29 08:01:45\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.20% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 01:22:53 to 2020-02-29 23:10:31\n", + "\u001b[32m[x]\u001b[0m 167 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.84% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 21:36:32 to 2020-02-29 17:42:16\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:55:52 to 2020-02-28 20:47:54\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:06:38 to 2020-02-29 23:06:56\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.69% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 03:02:40 to 2020-02-29 20:20:16\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.16% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 01:10:28 to 2020-02-29 17:24:42\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:08:16 to 2020-02-29 20:06:25\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 13.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 07:12:28 to 2020-02-29 21:22:08\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.63% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:18:19 to 2020-02-29 17:27:34\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.84% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 11:15:34 to 2020-02-29 18:25:27\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.82% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 06:18:28 to 2020-02-29 22:16:13\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 15:46:54 to 2020-02-29 21:34:41\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.55% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-02 00:02:03 to 2020-02-29 22:22:27\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 04:44:01 to 2020-02-29 17:46:38\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.38% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 05:13:42 to 2020-02-29 23:56:05\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 05:21:34 to 2020-02-29 13:22:36\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.96% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 05:36:25 to 2020-02-28 19:53:35\n", + "\u001b[32m[x]\u001b[0m 195 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.73% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 06:44:29 to 2020-02-29 11:06:23\n", + "\u001b[32m[x]\u001b[0m 159 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 7.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 01:22:36 to 2020-02-29 16:14:55\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 08:31:59 to 2020-02-29 07:11:45\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.69% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 18:41:14 to 2020-02-29 21:51:24\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 08:42:04 to 2020-02-29 23:01:18\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 01:54:54 to 2020-02-29 18:05:58\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 05:31:30 to 2020-02-29 23:36:16\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 08:10:12 to 2020-02-29 20:36:59\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.81% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 08:12:12 to 2020-02-29 23:43:32\n", + "\u001b[32m[x]\u001b[0m 165 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 13.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 07:08:19 to 2020-02-29 16:14:39\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.73% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 01:37:47 to 2020-02-29 21:20:20\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.80% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 09:45:45 to 2020-02-29 16:52:33\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.36% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 12:45:52 to 2020-02-29 12:54:21\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 12:38:32 to 2020-02-29 20:45:50\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.53% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 117 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 01:09:21 to 2020-02-29 07:02:25\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.86% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 00:27:31 to 2020-02-29 21:23:03\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:17:07 to 2020-02-29 20:06:56\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.95% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:00:12 to 2020-02-29 13:57:49\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 09:02:29 to 2020-02-29 17:26:12\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.51% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 02:43:25 to 2020-02-29 20:58:24\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 05:16:28 to 2020-02-29 16:01:06\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.19% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 07:04:50 to 2020-02-29 16:19:51\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.39% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 00:54:21 to 2020-02-29 18:00:54\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 17:52:32 to 2020-02-29 16:41:54\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:34:41 to 2020-02-28 14:03:29\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.98% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:44:34 to 2020-02-29 20:20:14\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 11.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 07:48:33 to 2020-02-29 23:08:55\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.85% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 08:45:29 to 2020-02-29 12:23:51\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.62% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:46:52 to 2020-02-29 00:51:05\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 02:51:53 to 2020-02-29 13:57:49\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 08:34:31 to 2020-02-29 23:26:11\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.20% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 21:17:50 to 2020-02-29 16:29:36\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.36% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 00:20:30 to 2020-02-29 13:15:15\n", + "\u001b[32m[x]\u001b[0m 165 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.36% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 113 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 01:03:20 to 2020-02-29 20:58:25\n", + "\u001b[32m[x]\u001b[0m 162 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.86% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 01:03:22 to 2020-02-29 10:44:06\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.35% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:59:21 to 2020-02-29 15:24:35\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.69% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 116 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 159 records from 2020-01-01 04:29:41 to 2020-02-29 02:14:42\n", + "\u001b[32m[x]\u001b[0m 152 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 01:14:29 to 2020-02-29 10:17:05\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-01 06:54:58 to 2020-02-29 23:22:42\n", + "\u001b[32m[x]\u001b[0m 158 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 20:24:41 to 2020-02-29 20:21:55\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.62% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 05:35:27 to 2020-02-29 14:00:44\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 17:36:44 to 2020-02-29 04:35:26\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.47% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 10:05:32 to 2020-02-29 17:01:27\n", + "\u001b[32m[x]\u001b[0m 195 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 12.63% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 08:06:30 to 2020-02-29 15:35:15\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.12% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 04:07:51 to 2020-02-29 20:53:31\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.62% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:55:03 to 2020-02-29 20:10:42\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.31% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:40:02 to 2020-02-29 23:59:45\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 01:26:14 to 2020-02-29 14:30:58\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.85% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 02:15:49 to 2020-02-29 22:09:05\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.55% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 04:12:20 to 2020-02-29 14:06:49\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.45% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 227 records from 2020-01-01 05:39:57 to 2020-02-29 16:41:08\n", + "\u001b[32m[x]\u001b[0m 201 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.82% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 02:03:01 to 2020-02-29 23:40:39\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 14:45:51 to 2020-02-29 21:07:33\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 10:14:44 to 2020-02-29 14:01:37\n", + "\u001b[32m[x]\u001b[0m 165 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 00:30:40 to 2020-02-29 13:01:05\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 15:33:36 to 2020-02-29 13:29:03\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 06:42:05 to 2020-02-29 23:29:58\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 10:38:23 to 2020-02-29 22:29:46\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 06:29:23 to 2020-02-29 23:29:58\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.16% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 07:02:59 to 2020-02-29 15:52:57\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 12:06:02 to 2020-02-29 20:05:18\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 9.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 23:28:27 to 2020-02-29 19:57:00\n", + "\u001b[32m[x]\u001b[0m 165 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 04:16:42 to 2020-02-29 21:53:05\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.10% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 08:32:12 to 2020-02-29 23:52:56\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.16% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 05:03:35 to 2020-02-29 20:37:30\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.93% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 15:26:57 to 2020-02-29 23:00:24\n", + "\u001b[32m[x]\u001b[0m 200 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.54% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:52:27 to 2020-02-29 20:37:30\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.80% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 03:47:43 to 2020-02-29 06:41:38\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 05:58:42 to 2020-02-29 22:32:23\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.54% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 10:20:34 to 2020-02-29 18:48:06\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 116 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 13:35:55 to 2020-02-29 09:24:18\n", + "\u001b[32m[x]\u001b[0m 160 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 19.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 06:07:43 to 2020-02-29 21:03:12\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.12% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-02 06:42:32 to 2020-02-29 02:17:22\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:52:38 to 2020-02-29 22:46:51\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.93% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:18:39 to 2020-02-29 22:40:44\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 02:33:51 to 2020-02-29 20:59:56\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.63% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 03:27:23 to 2020-02-29 16:51:39\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.67% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 07:00:19 to 2020-02-29 16:14:22\n", + "\u001b[32m[x]\u001b[0m 164 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 10.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 04:22:21 to 2020-02-29 21:57:07\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 03:13:38 to 2020-02-29 09:21:04\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.94% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 03:02:40 to 2020-02-29 21:50:19\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 02:52:39 to 2020-02-29 20:03:25\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 07:23:16 to 2020-02-29 14:50:40\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 01:19:28 to 2020-02-29 21:40:42\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.61% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 13:07:52 to 2020-02-29 18:20:39\n", + "\u001b[32m[x]\u001b[0m 159 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-02 06:01:47 to 2020-02-29 17:08:49\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.53% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 15:49:17 to 2020-02-29 20:56:26\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.12% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 115 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 170 records from 2020-01-01 01:45:49 to 2020-02-29 23:48:33\n", + "\u001b[32m[x]\u001b[0m 156 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.98% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 07:03:05 to 2020-02-28 23:59:41\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 01:19:28 to 2020-02-29 21:46:54\n", + "\u001b[32m[x]\u001b[0m 164 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 04:30:26 to 2020-02-29 23:10:31\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.60% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 04:31:39 to 2020-02-29 16:50:20\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.10% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 117 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 168 records from 2020-01-01 06:59:20 to 2020-02-29 12:03:03\n", + "\u001b[32m[x]\u001b[0m 158 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 7.96% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 07:55:40 to 2020-02-29 17:32:38\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 00:36:16 to 2020-02-29 11:57:21\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.08% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 05:34:15 to 2020-02-29 23:02:01\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.63% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 07:55:27 to 2020-02-29 18:57:37\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.42% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 08:06:00 to 2020-02-29 13:00:28\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.66% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-02 03:45:50 to 2020-02-29 23:36:05\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.90% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 00:17:53 to 2020-02-29 21:49:02\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 12.20% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:10:35 to 2020-02-29 16:09:28\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.30% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 01:08:16 to 2020-02-28 17:14:45\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.13% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 02:36:24 to 2020-02-29 23:08:59\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 13:09:18 to 2020-02-29 23:46:11\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.91% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 165 records from 2020-01-01 00:10:38 to 2020-02-29 20:03:58\n", + "\u001b[32m[x]\u001b[0m 150 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 7.32% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:53:43 to 2020-02-29 23:01:42\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.53% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 01:22:26 to 2020-02-29 14:56:34\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.90% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 10:28:44 to 2020-02-29 21:51:46\n", + "\u001b[32m[x]\u001b[0m 166 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 07:04:50 to 2020-02-29 20:03:58\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 15:41:26 to 2020-02-29 22:55:19\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.77% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 01:24:33 to 2020-02-29 18:10:45\n", + "\u001b[32m[x]\u001b[0m 158 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 04:07:26 to 2020-02-29 20:27:44\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.99% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 02:50:53 to 2020-02-29 20:37:29\n", + "\u001b[32m[x]\u001b[0m 163 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.18% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 01:36:16 to 2020-02-29 11:52:36\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 19:09:23 to 2020-02-29 02:31:03\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 21:27:32 to 2020-02-29 12:46:53\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.98% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 00:12:42 to 2020-02-29 15:42:06\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.78% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 02:33:21 to 2020-02-29 12:11:47\n", + "\u001b[32m[x]\u001b[0m 166 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.81% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:55:00 to 2020-02-29 17:17:37\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.85% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:01:32 to 2020-02-29 14:45:06\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 16:41:24 to 2020-02-29 19:50:13\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 15.23% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 06:19:51 to 2020-02-29 21:59:22\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.26% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 18:46:22 to 2020-02-29 18:47:48\n", + "\u001b[32m[x]\u001b[0m 195 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.34% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 19:29:56 to 2020-02-29 07:11:32\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 114 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-01 00:51:01 to 2020-02-29 20:34:18\n", + "\u001b[32m[x]\u001b[0m 153 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 07:19:32 to 2020-02-29 23:20:17\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 03:54:29 to 2020-02-29 22:45:42\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 02:29:54 to 2020-02-28 14:54:50\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.01% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 00:09:47 to 2020-02-29 23:28:32\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.80% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 10:55:58 to 2020-02-29 16:41:08\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.69% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 03:12:20 to 2020-02-29 20:21:36\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.49% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 19:06:01 to 2020-02-29 22:39:56\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.20% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 00:32:11 to 2020-02-29 19:33:39\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 06:42:06 to 2020-02-29 23:19:35\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.40% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 06:55:52 to 2020-02-29 17:32:11\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.07% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 04:12:20 to 2020-02-28 08:32:00\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.30% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:20:52 to 2020-02-29 18:48:06\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.38% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 05:03:23 to 2020-02-29 20:53:31\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.05% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 00:16:27 to 2020-02-29 11:18:44\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 14.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 04:48:23 to 2020-02-29 06:06:19\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.14% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 01:50:19 to 2020-02-29 10:19:08\n", + "\u001b[32m[x]\u001b[0m 193 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 10:05:43 to 2020-02-29 18:19:47\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.93% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 20:36:42 to 2020-02-29 14:04:50\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 152 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 12:26:49 to 2020-02-29 18:42:32\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.86% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:02:19 to 2020-02-29 23:59:45\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.23% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:21:11 to 2020-02-29 20:26:19\n", + "\u001b[32m[x]\u001b[0m 167 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:51:18 to 2020-02-29 10:15:29\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.95% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:18:33 to 2020-02-29 19:43:50\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.62% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 114 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-01 01:30:42 to 2020-02-29 19:32:00\n", + "\u001b[32m[x]\u001b[0m 158 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.55% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 00:19:47 to 2020-02-29 14:36:51\n", + "\u001b[32m[x]\u001b[0m 199 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.35% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 08:58:27 to 2020-02-29 20:36:13\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 01:20:32 to 2020-02-29 13:27:52\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 01:08:16 to 2020-02-29 17:51:50\n", + "\u001b[32m[x]\u001b[0m 199 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 12:56:35 to 2020-02-29 20:14:36\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.14% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 01:08:34 to 2020-02-29 09:49:15\n", + "\u001b[32m[x]\u001b[0m 156 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.81% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 12:17:24 to 2020-02-29 16:41:54\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 00:32:08 to 2020-02-29 23:19:01\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.42% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 03:41:08 to 2020-02-29 17:42:16\n", + "\u001b[32m[x]\u001b[0m 195 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 7.91% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 05:47:05 to 2020-02-29 03:54:28\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 10:46:16 to 2020-02-29 23:42:20\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 19:43:59 to 2020-02-29 20:39:23\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.38% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 10:36:44 to 2020-02-29 23:03:55\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.92% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:02:23 to 2020-02-29 18:40:40\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 18.13% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 117 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-01 00:31:38 to 2020-02-29 18:40:40\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 06:49:46 to 2020-02-29 16:38:34\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 04:58:32 to 2020-02-29 21:44:00\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 16:24:15 to 2020-02-29 11:13:37\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 02:49:38 to 2020-02-29 19:13:03\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.47% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 12:44:17 to 2020-02-29 23:15:26\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:53:51 to 2020-02-29 20:34:18\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 03:06:24 to 2020-02-29 21:49:11\n", + "\u001b[32m[x]\u001b[0m 201 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.88% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 03:37:27 to 2020-02-29 20:20:01\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.63% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 05:13:02 to 2020-02-29 11:51:00\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 05:56:47 to 2020-02-29 11:53:32\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.41% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 03:27:23 to 2020-02-29 22:35:43\n", + "\u001b[32m[x]\u001b[0m 164 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.20% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 02:12:00 to 2020-02-29 20:04:42\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 232 records from 2020-01-01 10:45:23 to 2020-02-29 20:28:04\n", + "\u001b[32m[x]\u001b[0m 203 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.71% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 11:58:03 to 2020-02-29 20:20:14\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 10.84% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 150 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 13:54:39 to 2020-02-29 23:32:09\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 19:37:02 to 2020-02-29 22:42:36\n", + "\u001b[32m[x]\u001b[0m 175 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-02 10:55:47 to 2020-02-28 23:55:44\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.35% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 02:24:32 to 2020-02-29 23:13:39\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.42% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 07:43:38 to 2020-02-29 19:13:03\n", + "\u001b[32m[x]\u001b[0m 200 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.15% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 02:29:48 to 2020-02-29 19:06:26\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 08:40:38 to 2020-02-29 19:07:04\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:19:13 to 2020-02-29 15:42:13\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:33:20 to 2020-02-29 21:01:24\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 228 records from 2020-01-01 04:14:56 to 2020-02-29 20:29:21\n", + "\u001b[32m[x]\u001b[0m 207 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 00:43:36 to 2020-02-29 03:44:43\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 01:01:37 to 2020-02-29 22:07:35\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.89% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 02:16:15 to 2020-02-29 22:57:56\n", + "\u001b[32m[x]\u001b[0m 163 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.65% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 05:14:01 to 2020-02-29 08:37:33\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 09:38:51 to 2020-02-29 15:49:00\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 01:46:18 to 2020-02-29 20:25:28\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 02:38:57 to 2020-02-29 22:52:28\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.27% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 03:04:43 to 2020-02-29 18:30:53\n", + "\u001b[32m[x]\u001b[0m 204 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.60% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 05:49:46 to 2020-02-29 06:23:10\n", + "\u001b[32m[x]\u001b[0m 197 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 03:41:08 to 2020-02-29 17:41:33\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.55% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 01:26:14 to 2020-02-29 21:51:24\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 12.04% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 06:11:54 to 2020-02-28 11:05:18\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 03:12:25 to 2020-02-29 23:36:33\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.07% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 03:49:50 to 2020-02-29 19:51:37\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 05:03:23 to 2020-02-29 23:05:57\n", + "\u001b[32m[x]\u001b[0m 202 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.78% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 04:25:08 to 2020-02-29 07:25:51\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 04:29:41 to 2020-02-29 22:31:40\n", + "\u001b[32m[x]\u001b[0m 202 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.65% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 02:29:48 to 2020-02-29 15:38:31\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.69% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 01:36:16 to 2020-02-29 15:37:50\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.24% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 01:56:53 to 2020-02-28 19:55:47\n", + "\u001b[32m[x]\u001b[0m 160 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 06:23:16 to 2020-02-29 20:32:45\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.75% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 02:16:15 to 2020-02-29 22:55:19\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.61% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 238 records from 2020-01-01 09:41:15 to 2020-02-29 13:56:16\n", + "\u001b[32m[x]\u001b[0m 210 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.23% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 04:36:18 to 2020-02-29 21:29:25\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-02 04:30:15 to 2020-02-29 23:23:05\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.40% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 00:53:43 to 2020-02-29 19:23:16\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 01:37:27 to 2020-02-29 20:39:23\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.94% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:00:07 to 2020-02-29 04:28:07\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 10.99% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 03:35:24 to 2020-02-29 23:42:31\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 11:07:04 to 2020-02-29 17:06:58\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.92% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 03:24:51 to 2020-02-29 21:01:24\n", + "\u001b[32m[x]\u001b[0m 187 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 6.84% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 00:08:25 to 2020-02-29 13:38:58\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:09:47 to 2020-02-29 17:06:58\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.92% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 00:53:43 to 2020-02-29 04:22:55\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 01:42:47 to 2020-02-29 22:00:49\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.10% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:19:13 to 2020-02-29 21:26:49\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.71% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 01:56:55 to 2020-02-29 16:52:49\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.79% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 03:24:27 to 2020-02-29 11:15:15\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.35% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 04:27:26 to 2020-02-29 21:06:10\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.07% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 06:00:53 to 2020-02-29 10:48:48\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 01:28:18 to 2020-02-29 22:48:07\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 01:55:03 to 2020-02-29 20:43:27\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 6.67% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 04:08:22 to 2020-02-29 06:56:48\n", + "\u001b[32m[x]\u001b[0m 180 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 04:52:06 to 2020-02-29 11:08:37\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.97% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 05:29:08 to 2020-02-29 23:11:27\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.98% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 05:34:51 to 2020-02-29 11:46:16\n", + "\u001b[32m[x]\u001b[0m 158 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 16:37:51 to 2020-02-29 17:33:19\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.40% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 15:23:09 to 2020-02-29 18:03:01\n", + "\u001b[32m[x]\u001b[0m 192 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.02% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 01:35:55 to 2020-02-29 11:38:15\n", + "\u001b[32m[x]\u001b[0m 178 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 15.90% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 17:37:55 to 2020-02-29 17:16:20\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.05% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 02:32:52 to 2020-02-29 21:09:16\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 151 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 04:19:47 to 2020-02-29 16:54:59\n", + "\u001b[32m[x]\u001b[0m 198 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.11% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 03:16:50 to 2020-02-29 18:59:25\n", + "\u001b[32m[x]\u001b[0m 165 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.67% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 18:12:20 to 2020-02-29 16:54:52\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.95% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 09:43:25 to 2020-02-29 19:03:46\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.62% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 06:46:08 to 2020-02-29 20:25:28\n", + "\u001b[32m[x]\u001b[0m 191 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.84% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 112 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 159 records from 2020-01-01 07:15:23 to 2020-02-29 18:26:47\n", + "\u001b[32m[x]\u001b[0m 148 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.24% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 16:10:46 to 2020-02-29 18:06:58\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 07:55:27 to 2020-02-29 23:34:24\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 17:45:32 to 2020-02-29 13:38:02\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.26% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 16:09:46 to 2020-02-29 22:35:18\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.37% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 10:26:44 to 2020-02-29 05:02:58\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.95% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 19:22:56 to 2020-02-29 10:44:32\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.46% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 114 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 164 records from 2020-01-01 03:51:53 to 2020-02-29 05:24:49\n", + "\u001b[32m[x]\u001b[0m 151 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-02 09:37:16 to 2020-02-29 22:55:09\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.29% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:02:12 to 2020-02-29 17:02:43\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 02:19:58 to 2020-02-29 14:45:30\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 9.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 05:30:58 to 2020-02-29 19:08:48\n", + "\u001b[32m[x]\u001b[0m 184 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.33% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 06:18:28 to 2020-02-29 22:13:58\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 11:39:19 to 2020-02-29 21:03:48\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.39% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 00:30:00 to 2020-02-29 15:10:29\n", + "\u001b[32m[x]\u001b[0m 203 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.44% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:00:12 to 2020-02-29 17:32:11\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.30% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 05:01:12 to 2020-02-29 10:44:32\n", + "\u001b[32m[x]\u001b[0m 167 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.10% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 12:56:16 to 2020-02-29 18:09:25\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.60% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 00:55:00 to 2020-02-28 18:41:15\n", + "\u001b[32m[x]\u001b[0m 198 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.56% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 04:01:48 to 2020-02-29 07:38:55\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.45% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 00:53:36 to 2020-02-29 17:27:34\n", + "\u001b[32m[x]\u001b[0m 196 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.58% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 08:21:41 to 2020-02-29 21:49:26\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 04:37:45 to 2020-02-29 23:53:08\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.74% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 06:22:55 to 2020-02-29 21:32:05\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.35% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 06:34:41 to 2020-02-29 23:43:54\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.65% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 04:01:14 to 2020-02-29 18:02:48\n", + "\u001b[32m[x]\u001b[0m 194 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.09% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 03:59:43 to 2020-02-29 10:27:56\n", + "\u001b[32m[x]\u001b[0m 173 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 05:13:02 to 2020-02-29 17:08:49\n", + "\u001b[32m[x]\u001b[0m 167 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.92% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:07:47 to 2020-02-29 17:16:05\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33mWarning: 14.29% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 03:46:24 to 2020-02-29 23:06:38\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 02:11:34 to 2020-02-29 23:39:27\n", + "\u001b[32m[x]\u001b[0m 164 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.40% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 02:14:07 to 2020-02-29 13:32:26\n", + "\u001b[32m[x]\u001b[0m 186 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.17% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:32:11 to 2020-02-29 14:46:03\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 10.68% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:28:12 to 2020-02-29 23:39:27\n", + "\u001b[32m[x]\u001b[0m 185 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 6.84% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 09:35:26 to 2020-02-29 18:51:11\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.95% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 03:04:43 to 2020-02-29 15:02:45\n", + "\u001b[32m[x]\u001b[0m 176 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.85% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 09:05:42 to 2020-02-29 13:27:52\n", + "\u001b[32m[x]\u001b[0m 171 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.91% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-02 00:29:41 to 2020-02-29 22:55:42\n", + "\u001b[32m[x]\u001b[0m 160 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.92% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 00:10:35 to 2020-02-29 23:33:15\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 06:33:55 to 2020-02-29 20:31:44\n", + "\u001b[32m[x]\u001b[0m 174 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 8.53% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 10:27:25 to 2020-02-29 15:28:39\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.21% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 15:00:23 to 2020-02-29 19:32:20\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 17.41% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 08:10:12 to 2020-02-29 15:34:52\n", + "\u001b[32m[x]\u001b[0m 182 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.06% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:53:43 to 2020-02-29 21:32:57\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.64% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 02:42:07 to 2020-02-29 19:19:37\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.71% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 07:53:52 to 2020-02-29 10:47:46\n", + "\u001b[32m[x]\u001b[0m 189 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.24% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 05:25:33 to 2020-02-29 19:49:35\n", + "\u001b[32m[x]\u001b[0m 172 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.76% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 06:53:47 to 2020-02-29 07:25:51\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.22% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 01:37:27 to 2020-02-29 15:30:39\n", + "\u001b[32m[x]\u001b[0m 183 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.59% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 229 records from 2020-01-01 21:45:43 to 2020-02-29 22:47:56\n", + "\u001b[32m[x]\u001b[0m 212 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 16:41:24 to 2020-02-29 16:33:04\n", + "\u001b[32m[x]\u001b[0m 181 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.13% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:42:47 to 2020-02-29 12:51:23\n", + "\u001b[32m[x]\u001b[0m 166 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.83% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 05:31:28 to 2020-02-29 10:27:08\n", + "\u001b[32m[x]\u001b[0m 168 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 16.99% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 05:34:47 to 2020-02-29 07:23:10\n", + "\u001b[32m[x]\u001b[0m 190 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 14.61% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-02 01:37:40 to 2020-02-29 16:54:52\n", + "\u001b[32m[x]\u001b[0m 170 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.49% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-02 09:34:56 to 2020-02-29 22:32:41\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 9.78% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 00:37:55 to 2020-02-29 11:07:26\n", + "\u001b[32m[x]\u001b[0m 201 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.43% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 115 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 00:00:42 to 2020-02-29 22:08:42\n", + "\u001b[32m[x]\u001b[0m 161 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 12.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 00:10:34 to 2020-02-29 15:22:18\n", + "\u001b[32m[x]\u001b[0m 169 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.18% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 02:47:26 to 2020-02-29 21:19:51\n", + "\u001b[32m[x]\u001b[0m 206 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 11.57% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 06:57:41 to 2020-02-29 19:33:39\n", + "\u001b[32m[x]\u001b[0m 195 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.24% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 08:37:16 to 2020-02-29 16:49:58\n", + "\u001b[32m[x]\u001b[0m 179 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 13.50% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 11:01:00 to 2020-02-29 16:10:43\n", + "\u001b[32m[x]\u001b[0m 188 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n", + "\u001b[33mWarning: 15.00% of the records are missing a location.\n", + " No antennas file was given and records are using antennas for position.\n", + "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", + "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 10:38:25 to 2020-02-29 15:30:39\n", + "\u001b[32m[x]\u001b[0m 177 contacts\n", + "\u001b[32m[ ]\u001b[0m No attribute stored\n", + "\u001b[32m[ ]\u001b[0m No antenna stored\n", + "\u001b[32m[ ]\u001b[0m No recharges\n", + "\u001b[32m[x]\u001b[0m Has home\n", + "\u001b[32m[x]\u001b[0m Has texts\n", + "\u001b[32m[x]\u001b[0m Has calls\n", + "\u001b[32m[ ]\u001b[0m No network\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Successfully exported 1000 object(s) to ./outputs/featurizer//datasets/bandicoot_features/0to1000/0.csv\n", + "\r", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating international features...\n", + "Calculating spatial features...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/luciomelito/Library/Caches/pypoetry/virtualenvs/cider-n28xxEz6-py3.8/lib/python3.8/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n", + " in_crs_string = _prepare_from_proj_string(in_crs_string)\n", + "../../cider/cider/featurizer.py:325: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.\n", + "Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.\n", + "\n", + "Left CRS: +init=epsg:4326 +type=crs\n", + "Right CRS: EPSG:4326\n", + "\n", + " antennas = gpd.sjoin(antennas, shapefile, op='within', how='left').drop('index_right', axis=1)\n", + "../../cider/cider/featurizer.py:325: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.\n", + "Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.\n", + "\n", + "Left CRS: +init=epsg:4326 +type=crs\n", + "Right CRS: EPSG:4326\n", + "\n", + " antennas = gpd.sjoin(antennas, shapefile, op='within', how='left').drop('index_right', axis=1)\n", + "../../cider/cider/featurizer.py:325: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.\n", + "Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.\n", + "\n", + "Left CRS: +init=epsg:4326 +type=crs\n", + "Right CRS: EPSG:4326\n", + "\n", + " antennas = gpd.sjoin(antennas, shapefile, op='within', how='left').drop('index_right', axis=1)\n", + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating recharges features...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating mobile data features...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating mobile money features...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "featurizer.cdr_features()\n", + "featurizer.international_features()\n", + "featurizer.location_features()\n", + "featurizer.recharges_features()\n", + "featurizer.mobiledata_features()\n", + "featurizer.mobilemoney_features()\n", + "featurizer.all_features()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2d077b0c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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namecdr_reporting__number_of_recordscdr_active_days__allweek__allday__callandtextcdr_active_days__allweek__day__callandtextcdr_active_days__allweek__night__callandtextcdr_active_days__weekday__allday__callandtextcdr_active_days__weekday__day__callandtextcdr_active_days__weekday__night__callandtextcdr_active_days__weekend__allday__callandtextcdr_active_days__weekend__day__callandtext...mobilemoney_outgoing_p2p_amount_minmobilemoney_outgoing_p2p_amount_maxmobilemoney_outgoing_p2p_balance_before_meanmobilemoney_outgoing_p2p_balance_before_minmobilemoney_outgoing_p2p_balance_before_maxmobilemoney_outgoing_p2p_balance_after_meanmobilemoney_outgoing_p2p_balance_after_minmobilemoney_outgoing_p2p_balance_after_maxmobilemoney_outgoing_p2p_txnsmobilemoney_outgoing_p2p_contacts
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" + ], + "text/plain": [ + " name cdr_reporting__number_of_records \\\n", + "0 dsBHAdXrrk 184 \n", + "1 JGPCbfDGes 209 \n", + "2 dYwshzRseD 205 \n", + "3 ygMEXUQDbn 218 \n", + "4 YtvkGlMWwe 212 \n", + "\n", + " cdr_active_days__allweek__allday__callandtext \\\n", + "0 58 \n", + "1 59 \n", + "2 59 \n", + "3 56 \n", + "4 57 \n", + "\n", + " cdr_active_days__allweek__day__callandtext \\\n", + "0 46 \n", + "1 49 \n", + "2 50 \n", + "3 51 \n", + "4 52 \n", + "\n", + " cdr_active_days__allweek__night__callandtext \\\n", + "0 50 \n", + "1 50 \n", + "2 52 \n", + "3 46 \n", + "4 45 \n", + "\n", + " cdr_active_days__weekday__allday__callandtext \\\n", + "0 41 \n", + "1 42 \n", + "2 42 \n", + "3 41 \n", + "4 41 \n", + "\n", + " cdr_active_days__weekday__day__callandtext \\\n", + "0 32 \n", + "1 35 \n", + "2 36 \n", + "3 37 \n", + "4 38 \n", + "\n", + " cdr_active_days__weekday__night__callandtext \\\n", + "0 35 \n", + "1 34 \n", + "2 37 \n", + "3 34 \n", + "4 31 \n", + "\n", + " cdr_active_days__weekend__allday__callandtext \\\n", + "0 17 \n", + "1 17 \n", + "2 17 \n", + "3 15 \n", + "4 16 \n", + "\n", + " cdr_active_days__weekend__day__callandtext ... \\\n", + "0 14 ... \n", + "1 14 ... \n", + "2 14 ... \n", + "3 14 ... \n", + "4 14 ... \n", + "\n", + " mobilemoney_outgoing_p2p_amount_min mobilemoney_outgoing_p2p_amount_max \\\n", + "0 26.361252 66.175190 \n", + "1 33.154373 60.508873 \n", + "2 28.580658 63.042980 \n", + "3 NaN NaN \n", + "4 35.715935 66.577260 \n", + "\n", + " mobilemoney_outgoing_p2p_balance_before_mean \\\n", + "0 198.212410 \n", + "1 166.658227 \n", + "2 192.916470 \n", + "3 NaN \n", + "4 182.742442 \n", + "\n", + " mobilemoney_outgoing_p2p_balance_before_min \\\n", + "0 144.32855 \n", + "1 83.04488 \n", + "2 136.88690 \n", + "3 NaN \n", + "4 151.35474 \n", + "\n", + " mobilemoney_outgoing_p2p_balance_before_max \\\n", + "0 270.12857 \n", + "1 209.16385 \n", + "2 234.86200 \n", + "3 NaN \n", + "4 245.24483 \n", + "\n", + " mobilemoney_outgoing_p2p_balance_after_mean \\\n", + "0 148.945410 \n", + "1 115.873907 \n", + "2 152.284147 \n", + "3 NaN \n", + "4 131.224984 \n", + "\n", + " mobilemoney_outgoing_p2p_balance_after_min \\\n", + "0 78.153360 \n", + "1 22.536007 \n", + "2 104.606220 \n", + "3 NaN \n", + "4 91.455580 \n", + "\n", + " mobilemoney_outgoing_p2p_balance_after_max mobilemoney_outgoing_p2p_txns \\\n", + "0 218.63590 8.0 \n", + "1 173.27129 6.0 \n", + "2 200.48862 4.0 \n", + "3 NaN NaN \n", + "4 190.52692 6.0 \n", + "\n", + " mobilemoney_outgoing_p2p_contacts \n", + "0 8.0 \n", + "1 6.0 \n", + "2 4.0 \n", + "3 NaN \n", + "4 6.0 \n", + "\n", + "[5 rows x 1203 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "pd.read_csv('./outputs/featurizer/datasets/features.csv').head()" + ] + }, + { + "cell_type": "markdown", + "id": "a93c1f6e", + "metadata": {}, + "source": [ + "Plot the distributions of some of the features." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b117b92f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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" + ], + "text/plain": [ + " subscriber_id tower_id count_modal_days\n", + "0 JGPCbfDGes t35 4\n", + "1 YtvkGlMWwe t39 4\n", + "2 dYwshzRseD t26 4\n", + "3 dsBHAdXrrk t35 2\n", + "4 ygMEXUQDbn t27 4\n", + ".. ... ... ...\n", + "995 amzyXHglBx t7 3\n", + "996 dJSvXqUVSY t64 3\n", + "997 ptudOnjdRU t35 5\n", + "998 uXZrufHOmE t79 4\n", + "999 zZkqaZFAtz t80 4\n", + "\n", + "[1000 rows x 3 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "home_locator.get_home_locations(algo='count_transactions', geo='tower_id')\n", + "home_locator.get_home_locations(algo='count_days', geo='tower_id')\n", + "home_locator.get_home_locations(algo='count_modal_days', geo='tower_id')" + ] + }, + { + "cell_type": "markdown", + "id": "b9e04d2e", + "metadata": {}, + "source": [ + "Generate a population map based on inferred home locations using the unique days algorithm. Use points at each of the tower locations." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "62e500d0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_home_location_10_0.png" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "home_locator.map(algo='count_days', geo='tower_id', kind='population', voronoi=False)" + ] + }, + { + "cell_type": "markdown", + "id": "ab7eea79", + "metadata": {}, + "source": [ + "Evaluate the accuracy of the home location inference using the ground truth data. Generate maps and a table of precision and recall for each tower." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ed688b3c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Observations with inferred home location: 1000 (1000 unique)\n", + "Observations with ground truth home location: 1000 (1000 unique)\n", + "Observations with both: 1000 (1000 unique)\n", + "Overall accuracy: 0.02\n" + ] + }, + { + "data": { + "image/png": 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\n", 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tower_idrecallprecisionoverall_accuracy
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\n", + "
" + ], + "text/plain": [ + " tower_id recall precision overall_accuracy\n", + "67 t7 0.076923 0.166667 0.017\n", + "15 t22 0.250000 0.142857 0.017\n", + "33 t39 0.214286 0.090909 0.017\n", + "66 t69 0.100000 0.055556 0.017\n", + "37 t42 0.153846 0.055556 0.017" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "home_locator.accuracy_tables[('tower_id', 'count_days')].head()" + ] + }, + { + "cell_type": "markdown", + "id": "02307268", + "metadata": {}, + "source": [ + "Use population density maps to identify which areas are over/under-represented in your sample." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8f2fb066", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1000 rows × 2 columns

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" + ], + "text/plain": [ + " name predicted\n", + "0 dsBHAdXrrk 67249.768675\n", + "1 JGPCbfDGes 69289.940765\n", + "2 dYwshzRseD 85109.109600\n", + "3 ygMEXUQDbn 82018.137664\n", + "4 YtvkGlMWwe 76983.875546\n", + ".. ... ...\n", + "5 amzyXHglBx 83987.844414\n", + "6 zZkqaZFAtz 82984.849245\n", + "7 uXZrufHOmE 87731.514440\n", + "8 dJSvXqUVSY 76191.160752\n", + "9 YosNCLWrFL 85338.034626\n", + "\n", + "[1000 rows x 2 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gradientboosting_scores = learner.tuned_model('gradientboosting')\n", + "print('Gradient Boosting (Tuned)', gradientboosting_scores)\n", + "learner.oos_predictions('gradientboosting', kind='tuned')\n", + "learner.population_predictions('gradientboosting', kind='tuned')" + ] + }, + { + "cell_type": "markdown", + "id": "b9e04d2e", + "metadata": {}, + "source": [ + "Evaluate the model’s accuracy. Produce a scatterplot of true vs. predicted values with a LOESS fit and a bar plot of the most important features. Generate a table showing the targeting accuracy, precision, and recall of the predictions for nine hypothetical targeting scenarios (targeting between 10% and 90% of the population). " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "62e500d0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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890%0.560.50.7384%91%91%
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" + ], + "text/plain": [ + " Proportion of Population Targeted Pearson Spearman AUC Accuracy \\\n", + "0 10% 0.56 0.5 0.73 93% \n", + "1 20% 0.56 0.5 0.73 81% \n", + "2 30% 0.56 0.5 0.73 71% \n", + "3 40% 0.56 0.5 0.73 67% \n", + "4 50% 0.56 0.5 0.73 65% \n", + "5 60% 0.56 0.5 0.73 69% \n", + "6 70% 0.56 0.5 0.73 79% \n", + "7 80% 0.56 0.5 0.73 75% \n", + "8 90% 0.56 0.5 0.73 84% \n", + "\n", + " Precision Recall \n", + "0 65% 65% \n", + "1 53% 53% \n", + "2 52% 52% \n", + "3 59% 59% \n", + "4 65% 65% \n", + "5 74% 74% \n", + "6 85% 85% \n", + "7 84% 84% \n", + "8 91% 91% " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "learner.scatter_plot('gradientboosting', kind='tuned')\n", + "learner.feature_importances_plot('gradientboosting', kind='tuned')\n", + "learner.targeting_table('gradientboosting', kind='tuned')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/docs/_sources/docs/_build/jupyter_execute/quickstart/documentation_satellite.ipynb b/docs/_sources/docs/_build/jupyter_execute/quickstart/documentation_satellite.ipynb new file mode 100644 index 0000000..22cf5fd --- /dev/null +++ b/docs/_sources/docs/_build/jupyter_execute/quickstart/documentation_satellite.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0b21a558", + "metadata": {}, + "source": [ + "# Satellite" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ebb85fa1", + "metadata": {}, + "outputs": [], + "source": [ + "from datastore import DataStore\n", + "from satellite import Satellite" + ] + }, + { + "cell_type": "markdown", + "id": "25a38ad6", + "metadata": {}, + "source": [ + "We first load some datasets - whose paths have been specified in the config file - using the datastore. The satellite module will by default try to load antenna/tower coordinates, shapefiles defining (administrative) boundaries, the Relative Wealth Index for the country of interest, and population density raster data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "51b4ff10", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading antennas...\n", + "Warning: 10 antennas missing location\n" + ] + } + ], + "source": [ + "datastore = DataStore(cfg_dir='../../cider/configs/config.yml')\n", + "satellite = Satellite(datastore=datastore)" + ] + }, + { + "cell_type": "markdown", + "id": "6a67fb5a", + "metadata": {}, + "source": [ + "The module can aggregate the RWI - which provides an asset index measure at a resolution of 2.4km x 2.4km - at one of the geographic levels provided by the antennas/towers, in which case their voronoi tessellation will be used - or one of the loaded shapefiles.

\n", + "In this case, we have loaded (synthetic) data for the country of Togo, and for aggregations at the tower level we will run:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0e4501ff", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_satellite_5_0.png" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "satellite.aggregate_scores(dataset='rwi', geo='tower_id')" + ] + }, + { + "cell_type": "markdown", + "id": "2a27bc8d", + "metadata": {}, + "source": [ + "while for cantons, admin level 3 in Togo, we will run:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ebb03cf9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "../../cider/satellite.py:152: RuntimeWarning: invalid value encountered in double_scalars\n", + " score = (score * pop).sum() / pop.sum()\n" + ] + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " Targeting Method Pearson Spearman AUC (Threshold-Agnostic) Accuracy \\\n", + "0 proxy1 0.853370 0.856880 0.882319 0.700 \n", + "1 proxy2 0.816323 0.818275 0.869501 0.698 \n", + "2 proxy3 0.775243 0.772671 0.846071 0.688 \n", + "3 proxy4 0.731196 0.724984 0.817799 0.680 \n", + "\n", + " Precision Recall AUC (Threshold-Specific) \n", + "0 1.000 0.400 0.925204 \n", + "1 0.995 0.398 0.921336 \n", + "2 0.970 0.388 0.886284 \n", + "3 0.950 0.380 0.866024 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "targeting.targeting_table('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], 50, 20)" + ] + }, + { + "cell_type": "markdown", + "id": "efd10b81", + "metadata": {}, + "source": [ + "Draw threshold-agnostic receiver operatoring characteristic (ROC) curves and precision-recall curves. Threshold-agnostic ROC curves are drawn by taking a grid matched targeting thresholds (that is, targeting K% of the population according to a proxy and the same K% of the population according to the ground-truth, for a krid of K between 0 and 100), and calculating the false positive rate and true positive rate of the targeting method for each point in the grid. Threshold-agnostic precision-reccall curves are calculated in the same way, so precision and recall are balanced by construction." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3c6be1fd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_targeting_7_1.png" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "targeting.roc_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=None, weighted=False)\n", + "targeting.precision_recall_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=None, weighted=False)" + ] + }, + { + "cell_type": "markdown", + "id": "e958431d", + "metadata": {}, + "source": [ + "Draw threshold-specific ROC curves and precision-recall curves. Threshold-specific ROC curves are calculated by holding the share of the population targeted according to the ground-truth measure constant, and varying the share of the population targeted on the proxy measure to obtain false positive and true positive rate trade-offs. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0b9f4d08", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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HqErnNCEEVVVVbNq0iTvuuIOysjLc3Ny4/fbbW5TV6/X85S9/sbfCNU1I+vjjj6mpqWHGjBn85S9/adaiMXHiRB599FEA3nnnHfv2ffv2sWvXLpydnXnvvfcICQmx7xsyZIi9BWnFihVt1r20tJSSkhI8PDxadM3PmDGD2bNnM23atJPmCAT48MMPaWxsZPTo0Tz77LM4OTnZ940ZM4aXX34ZgE8++YTy8vIWx19zzTXcdNNN9q54V1dXezddWlraKR+/NcXFxVx//fUtfmbMmMHQoUO57777qKmpYdSoUTz33HPNjv3mm2/Iz89nyJAhPP/88/bhHGBrvXjhhRcAW2tTUwtNYWEhy5cvR1EU3nnnnWYtL/Hx8fYWpKa/R15eHrm5uTg6OvLcc881ewxXV1cee+wxAKqrqyksLOzw8z+ZpjFtJ453+7O19VlomiDSlKHiRE0tSCe2pnbms9M0rnDMmDEEBgbat7u6uvLUU08xatSoFl253t7exMTEEB4e3vkn3UFarZZ33nmHyMhI+7ZJkyYxevRooOUkmib333+/fVhA0+u6Zs0ajh07RlRUFG+++Sa+vr728rGxsbz55ptoNBq+/PJLe5dyRkYGZrOZqKgoBg8e3Kxe99xzD5dccglTpkyhoqKiRR0iIyN55ZVX7Pl3NRoNDz/8MO7u7ggh7F3gYJv8I4Tg2muv5e6770ans3WkKorCjBkz7H/D+fPnY7Va23y9Ghsb7d3yL7/8crO/4fDhw0+aX7kzmrr9LRYL48aNY/ny5Rw+fJj169dz7733oigKH3zwAV988YX9mIaGBgAcHR3bPG9TXtSmsmDL1LJw4UIGDx5MTk4OWq2W22+/3d5jdujQIdauXcvcuXObZWQpLCyksrKy65601CrZ9S+dU9pKxtzEy8uL+fPnt9r1bzQam40ra9I05m3atGmtnnPatGn885//JCEhgeLiYvz8/Ni4cSNgm7zVWv7Fa6+9lrFjx570i9XLyws3NzcqKyt5+umnue2225pd3P/5z3+e9Lk22bRpEwA33HBDq/snTZpEcHAweXl57Nixo0VQPHbs2BbHREdH2/9dU1PTLJBrD5PJ1OYXube3NzNnzmTq1Kn2L/0TrVu3DoDLLrus1bF0Y8aMwcPDg9LSUo4ePUq/fv3YuHEjQgj69evX6ni1iRMnsmLFCnvQERwczM6dO2loaGj1S+vEbSd+YXWFphsJi8XSpeftiLY+C5dddhkvv/wyO3futOccBdt7YOPGjWi12maTUDrz2WkabrNkyRKMRiOTJk2yf7mHhobyv//9r0ufa2d169at1S7i2NhYNm3a1GqACNCvX78W25re05MmTWo1QbzRaMRoNJKYmMiOHTuYNm0aISEhaLVaEhMTefXVV7n22mubTdA5Mfj/o/Hjx9sDziYGg4GwsDCOHj1qr3ttba19gl5b149rr72W1157jaKiIo4ePdpspvyJdu/eTUNDA4GBga0m858+fTr//Oc/u+zzFBoayo033oinpycPPvigfXtwcDAPPfQQ7u7u/Otf/+Ktt95i5syZODo6nnI8OICqqgAtrj29evVi0aJFrR7zxhtv4Onpydy5cwHb+OCnn36azMxMwNZ48fLLL590yIHUeTJQlc4pf0z43zQ+KSAggP79+zN16tRWB8mDLbfnH9XU1JCfnw/YLjbvv/9+q8dqtVosFot9gkTT+KW2BvG7urqeciaqTqfjwQcf5MUXX2Tp0qUsXbqUoKAgRo4cydixYxk9enSz1tHW1NTU2Gek9ujRo81y3bt3Jy8vj4yMjBb7WhtPdeKX6claUdryx0kvNTU1LF++nNdee43Kykrc3d3bnKjVlAFg4cKFfP/9962WaWpJTU9Pp1+/fqf8e+j1+lYTgjs6OpKWlsahQ4fIzMwkOzublJQUUlNT7WWavri6SlNrWlVVFaqqNptU9mdp7bMAtpunsWPHsnbtWn7++WduvPFGwDamtbGxkZEjR9onk3T2szNx4kT69u3LwYMHefbZZ/nb3/5G7969GTVqFOPHj6d3795n4Bl3XFsrpjVdX0wmU6v7Wrupa3pP//zzz+zdu7fV8zYt+pCeng7Y/kY333wzn376KR9++CEffvghkZGRjBo1irFjxzJ8+PAWY2RPVfemm5OmMdLZ2dlYLBb0en2bnx0nJyeio6NJTEwkIyOjzUC1KSj740S4Jg4ODkRGRpKYmNhs+wcffGC/8T+VHj168Ne//hWAQYMGMWjQoDbL3nTTTbzzzjtUVlayd+9eRo4cab+eNj3/1jRdW07W6nqiHTt2sG3bNv7v//4PV1dXampqmDdvHqqq8tRTT1FTU8MHH3zA7bffzsqVK08rPaHUOhmoSueUthL+t0drLRknZgc4duzYKc9RXV0NYG+RaCsobq85c+YQERHBp59+yq5du8jPz+e7777ju+++w8XFhTvuuIN58+a1efyJ9W+thaxJUz1PLN+krS+7JuK3GfnfffcdS5YsabXMyZL7gy1wv+mmm4iOjuaOO+7gk08+wWKxtJjUAdiHGrQnZdXp/D2SkpJ46aWX2LFjR7PtISEhXH311Xz77bftPldHNLXqWq1Wjh8/3q7UOhkZGfj6+na4ZbstrX0Wmlx55ZWsXbuWH3/80R6oNnX7Nw0NgM5/dgwGA59//jkff/wxy5cvJzMzk4MHD3Lw4EHeffdd4uLieO6555olXD8bOhNQtHVM03s6Ozub7Ozsk56j6XUCeOqpp+jZsyeLFy/mwIEDZGRkkJGRwaJFi/Dy8uKRRx5h9uzZna5709/QycnppDdMJ7t+/LHeJ7u5bm0p4IyMjDZ7X/7oj63EJ2MwGIiJieHQoUPk5eUBvw+3OVl3fNPwqPYuqvLGG2/g5+dnn9T27bffUllZyfPPP2//29TW1vLxxx+zZs2aNnsfpM6Tgap0QTvxorp9+/Z2J9Jvutuur68/7TqMHTuWsWPHUl1dzc6dO9m2bRvr168nLy+Pt956CxcXF2655ZZWjz0xMKupqbGnUfmjpi/K0wms8/Pz2/2F0pYRI0Zw11138f7777Nw4UIGDBjQIgWXk5MT1dXVfPfdd+1uXevo36O4uJg5c+ZQUVFBt27dmDVrFt27dycmJgYvLy9MJtMZC1R79uyJn58fxcXFbN++vV2B6pNPPsnhw4e59957W2SfEG0sAtDZLtZx48bh4eHBvn37KCgoQK/Xs337dpycnLjkkkvs5Tr72QHb32vevHnMmzeP9PR0tm/fztatW9m8eTMpKSnccccd/PzzzxfM7Omm1+qtt95qM7VaW6644gquuOIKSktL2bFjB1u3bmX9+vWUlZXx17/+FW9vbyZNmtSpejXd3NbX15+0db8914+mILSurq7NMq29J1955ZVOrxRmtVpRVbXNm+2mz0bT/qYhTbm5uW2esymobW2Z3T9at24dBw4c4G9/+5v9GnT06FGg+RCQpoVoDh8+LAPVM0BOppIuaO7u7vYv2OPHj7daxmq1sm3bNjIzM+3d4E2tYid2EZ+otLSUWbNm8cgjj7Q5FtFkMpGcnExCQgJgy9U3adIk/va3v7Fu3Tp7SqK2ur+bjmnqxm2rVUsIYd/XnotvWx544AGSkpJa/emI++67zz7Z6bnnnqOsrKzZ/qY6tvX3AFveyrS0NHv3a9N63m39PSwWC9dddx0PPvggJSUlLFmyhIqKCmJiYvj666+5+eabGTRokL0VpasnUJ1IURR7wLdo0aJWU/acKCkpif3792OxWJoN72gab9daF3RVVVWnA1WDwcCUKVMQQrBu3Tp++eUXrFYrkyZNatZq39nPTnl5OXv37rX/3aOiorjhhht49913WbNmDX5+ftTV1dlTYV0I2vOe3r9/P8nJyfa/W319PceOHbP3LPj4+DBt2jReeuklNm7caM9terLrw6mEhYWh1Woxm832SW5/VFdXZx+OcLLrR9NnMCkpqdWbJ1VVWx161FnXXXcdPXv25OOPP251v8lksr92TQFqUxq8xMTEVrv/S0tLycnJQaPRnPImWVVV3nzzTUJDQ5u1ajdNhjuxVbupJ6QzE1OlU5OBqnTBa5pM1NZ68ytXrmTu3LnMmDHD3lrQNAlow4YNrXYjrVmzhsOHD5OWltZmd9WaNWu4/PLLefTRR1tc2DUaDcOGDQNOPUayKSl1W93va9eupbCwEL1e3+okhz+bXq/nn//8J4qiUFFRwb///e9m+8eNGwfYZv+39oW3e/du5syZw7Rp0+ytHyNHjkRRFHv36B9t376d/fv3s3PnTry8vOwtKtHR0a2ORfvuu+/s/+7MGN1Tuf3223F0dCQjI4PXX3+9zXKNjY387W9/A2wTbk5M7N/UgtUURJzodJPiz5gxw36eNWvWAM1n+zfpzGfnscce44Ybbmj2GjcJCAiwBxVn4nU/W5re08uXL281QMrOzuamm27i8ssvZ//+/YBtjPZVV13FSy+91KK8wWCwD404nTHULi4u9owCbV0/vvnmG8xmM56eni3ykp5o0KBB9kmOTZPHTrR27dpmqzidLqPRiBCCH374odWbva+//tqeK7spQA0NDaVHjx6YTKZWA/ymXpSxY8e2yCP8Rz/88APJycncf//9zVp0m4YXFBUV2bc1/bu9wwmkjpGBqnTBu+OOO3BwcGDlypW88cYbzb5INm/ebJ99f80119i71ocPH07fvn2prq7mgQcesE9oAti1a5c9SXfTLNDWjBs3DhcXF9LS0njppZeadVvn5ubaZz+fuNRoa5qCns2bN/PCCy80O8+mTZt45plnANt42BPT4pxN/fv3Z+bMmQAsW7aM3bt32/fdcMMNeHl52WfOnjhm7/DhwzzyyCOAbSZ/U8t2ZGQkU6ZMQVVVHnjgAfvEDrC1nvz9738H4Oabb0ar1dqP27p1K4cOHbKXra+vZ8GCBXz44Yf2bSebeNFZoaGhPP3004AtxdNDDz3UYkzukSNHmDNnDgcOHMDJyYl//etfzWYiN3UnLlq0qNmx27Zts6ck66wBAwYQHh7Ozp072b17N35+fq2uTtSZz87ll18OwPvvv8+WLVuanW/VqlXs3bsXjUbTbNWlsrIy0tLSmiVhP59Mnz6dyMhIMjMzW1wvMjIymDdvHhaLhe7du9sXgZg6dSparZYtW7bw0UcfNeuZSU5O5uuvvwZOfX04lXnz5qHRaPj6669ZsGCB/XGEEPYJkAAPPvjgScez6/V6+4pMzz77bLNhQgcOHOAf//jHadXzj2655Rb0ej3Jycn89a9/tY+fbap30w3wY4891mxIw9133w3Yhhzs3LnTvn3dunW8++67KIrCHXfccdLHtlgsvP3228TExLS4gWtavOCrr75CCIHJZGLp0qUAJ538JXWeHKMqXfBiY2P517/+xeOPP84HH3zAwoULiYqKory83N7yNmLECHtuTbB1377xxhvceuut7Ny5k/HjxxMXF0d1dbV9ssSsWbPsLVOtcXFx4d///jf3338/n3/+OUuWLCE8PNy+4orFYqFnz57ceeedJ61/TEwM//nPf3jsscdYuHAhS5YsISYmhrKyMnv9p06dysMPP3yar1TXevTRR1m7di0VFRX84x//YPny5ej1enx8fHj77beZN28eS5cu5ccffyQ2Npaamhp7ABofH98iGHvuuefIzs7myJEjTJkyhdjYWMxms31p29GjR9tXmbrmmmv44osvyM3NZfbs2URGRuLo6EhmZiZ1dXWEhISg0WjIzs5u1jLSla699lqsVisvv/wyq1atYtWqVQQEBODv709xcbF9Frifnx9vvvlmi6wOt956KytXrqSkpIQrrrjC/hrl5OTQv39/XFxcWgSCHXHFFVfYUyBNmzat1dQ+nfnsXHnllfz666/88ssv3H777QQGBuLr60tRUZH9tX7kkUeapUj74osvzsoSql3FYDDw7rvvcvvtt7Nx40bGjRtnf39mZGRgtVoJDAzkvffesx8TFhbGU089xQsvvMB//vMfFixYQGhoKLW1tWRmZiKEYNy4cVx99dWnVbehQ4fyzDPP8OKLL/Laa6/xv//9j/DwcPLz8+0B9S233GKfWHcyc+bM4fDhw/z4449cf/319vHXqampRERE4Onp2a5Jku0RExPDCy+8wDPPPMOyZcv4+eefiYqKoqSkxP4+evjhh1uMCZ4yZQozZ85kyZIlzJkzh9jYWCwWi70n5uGHHz5lQPndd9+RlZXFW2+91WJc71VXXcUnn3zCjz/+yIEDBzCZTBQXFzNkyJBW0/FJp0+2qEoXhalTp7J8+XJmzZqFp6cnSUlJlJeX07t3b55++mkWLFjQYiZtSEgIS5cu5b777iMiIoK0tDRKS0sZMGAAr776Ki+++OIpH3fSpEksWrSIyZMn4+LiQkpKCoWFhfTo0YMnnniCr776ql0zvSdPntys/k1jsEaPHs3bb7/Nm2++ecrZ/X82b29ve+toampqs7FmgwcPZuXKlcyZM4egoCBSU1MpKCjAaDTy4IMP8uWXX7aYQezh4cHixYt54okn6N69O9nZ2eTl5dG9e3f+9re/8d///tf+N3R3d+e7777j5ptvJjIyktzcXDIzM4mIiOD+++9nxYoV9nyz69evP2OvwQ033MDPP//MXXfdRe/evamrq+PYsWPU1dXRv39//u///o9Vq1a1+sUZGhrKd999x1VXXYWXl5d9mMkDDzzA559/ftKZ/e1xYkvRibP9/6ijnx1FUXjttdd45pln6NevHzU1NSQmJiKE4JJLLuHTTz+1t3pdSGJjY1mxYgX33nsv0dHRZGRkkJWVRXh4OLfddhvLli0jODi42TE333wz7733HmPGjEGr1dpf20GDBvHCCy/w/vvvd2gmfFtuuukmvv76a6ZNm4ZerychIQGNRsOll17Kp59+am/9PxWtVstrr73Giy++SK9evcjLy6O0tJQZM2awePHiLsta0WTGjBl8++23TJ8+HVdXV1JSUrBYLEycOJGFCxdyzz33tHrciy++yEsvvUSfPn3Izc2loKCA/v378/rrr7d5TJPGxkbee+89evbsyaWXXtpiv6urK59//jkTJkygrKwMs9nMtddey/vvv39WUtFdDBTR1pRSSZIkSZIkSTqLZPgvSZIkSZIknZNkoCpJkiRJkiSdk2SgKkmSJEmSJJ2TZKAqSZIkSZIknZNkoHoOsVgs5OTktLnSkSRJkiRJ0sVEBqrnkIKCAiZOnGjPryhJkiRJknQxOy8C1aVLlxIfH8+ePXta3Z+ens4jjzzC2LFj6du3L5dffjkLFy5sc+m5wsJC/va3vzFx4kT69OnDpZdeyrvvvtvqmtq1tbU89dRTDB48mGHDhvHQQw+1uU74G2+8Qf/+/SkpKen8k5UkSZIkSZKA8yBQ3b9/P88//3yb+xMTE5k1axY//vgjwcHBjB49moKCAl544QUef/zxFuULCgqYPXs2X3/9Ne7u7owbN47a2lrmz5/P7bff3mJN4RdeeIGlS5cSHh5Or169+OWXX7jjjjtadM8XFRXx2Wefccstt5wzy1hKkiRJkiSdz87pQLVpCb66urpW9wshePzxx6mpqeHf//43X375Je+88w6//PIL8fHxrFy5kl9++aXZMf/4xz8oKCjgL3/5C8uWLWP+/PmsXr2aESNGsGvXLhYuXGgvW1lZyYoVK7jkkktYsmQJH330EU8//TTJycktli585513cHBwOOUawpIkSZIkSVL7nJOBakFBAY8//jgPPvggqqq22UK5detWkpKSGDJkSLPlAL29vfnHP/4B0CzwPH78OBs2bCA8PLzZMmrOzs68+OKLaLVaFi1aZN+emZmJ1Wpl+PDh9m1N/05PT2923iVLlnD33Xd3+RJykiRJkiRJF6tzMlB98803WbFiBb169eLrr78mOjq61XKbN28GbOup/9GAAQPw8fFh79691NTUALBlyxaEEIwfP77FmrzBwcH06NGD3NxcUlNTAexrjTc0NNjL1dbWAuDi4mLf9sYbb+Dn58eNN97Y2acsSZIkSZIk/cE5GahGR0fzr3/9i2+//Zb4+Pg2yzUFlEajsdX9UVFRqKpKWlpas/JxcXFtPi5AcnIyAKGhoXh7e/PNN9+Qnp5OeXk5//3vf1EUhT59+gBw8OBBVq9ezf3334+Dg0Mnnq0kSZIkSZLUGt3ZrkBr7rrrrnaVKyoqAsDPz6/V/U3bm2bhN5X39/dvV3mdTseTTz7Jk08+yZQpU+zlbr31Vrp16wbAq6++SkxMDFdddVW76tykqqqKqqqqZttaS0tVWVxG6ur9CKvo0PkvJhadSpWrFZRTlzVorfg716JtR9m21HgHUuMdBErnTuKk0zPQLxytck7eJ0qSdIEraajhaFk+AN6OzvT2DmnXdkNdNSFleYSFdkMJjqWkoYaMtP24leThojcQEtm7ze1qYDSlDbVkpR/ArSSP8kYPHD3CUDz88A4wUJh3uH3b3X3xqaqjujQHx5pKCt11OHrZyvtU1FBdliu3d2C7a1AwseFeZ+eN2E7nZKDaXvX19QA4Ojq2ur9pe9NkrI6WB7jyyiuJiopi9erVNDY2MmLECMaPHw/Ahg0b2LVrF2+//TZarRawJe2vq6uzDxtoy2effcY777xzyueYvv0IIXmypfZUyrUKh1x15Bo0pwwgXdQa+tcfplfjMRxEy5Rk7ZHr5MpWv2B2+QRSp9N3+PjDZbnMjR+ORgarkiSdIY1WC4fKcjlQko1Oo2VuvG2ORW5tBYvTdgPQ2zvYHpCebHvO9mVcl5kEgBg0BSU4ltzaCjKObuGqHFtvpbBYWmzf5DycNRlFVFoqGRnjhDnhOIEVdRxydyXboR7I4hJfPeTkEFhd0WK7NqsA3xoNBz0t5BjqgWxmV5jwMimAJ9kelWQX2spfW96Il1lu78j2kLJCGaieSU3BoXKKwKQpn2pHyzfp06ePvav/xDKvvfYaffv2ZfLkyQgheP3111m4cCH19fWEhoby17/+lXHjxrX6GLfcckuLVtiCgoIW41z7XTGGkrwCLI2dC6guCgUNeB0sZ2ylGXwcoJ8nBDm1GrCWVpo4nOrAlpLh7PQYQfdIV3pGu+HipG3XQymqFW3GUQKPbWd2VjLX5B7HGt0XS9xA0OlBtdp+rLbfamAUwqP5ZMAtBWmsyDyERtFwi3GoDFYlSTojKhrr+ChxK256Rwb7hdu39/AK4j9Dbd8/WkXbfHvviehS9qLNzUAt/RbNmGvo4RVE9IhZmIIOIaL64uQTaC8fNe1eas2NaBUtzk4uVFQ1EJSjIdR/JmW6OhRPB3yEhjgvF8L25ENlDPVahfGTInD08UAxOFK7+BBqXTj1mvCW282hWP31TBgdaN+u1DditjaAuZHxOi2ODi627SYzZlOd3N6B7VpHpz/3TdkJ53Wg6uRke4FPnOx0oqbtTROf2lve2dn5lI+9YsUKkpOT+eyzzwBYvnw5CxYs4Oabb2bAgAEsWLCABx98kFWrVhESEtLieHd391O2ujbxDQ5sV7mLVhSIoYL6I4XUbMtGXVeAIcwD9ylx6Dybt54HBkLPeCgsrWXPkQIOp5Vz5HgNw/oEMbRP0ClvYgDwDoYBlyCKsxGHN6Ek7ECXsrf1sjoDyuhZKP3Go/wWkF4W3guB4PvMw2gVhZvihqLp5DACSZKkEx0pyyPW3Q9HnZ4AZ3ee6ncp4a5ezW6I9RotOr0jFGchUvajVhWjmXqnbbuqom5eAg7OiLBu9vL6oGgIaj6x2ZpSDnlVWErrMQ8MYkNWPsmZZVxep+JabULr4cDIkbHoA2zZcCzh3mic9Wicm/dCudw+sNXn0tZ2nPRAKxl2nPRAK9/fcvvJt5/jzutA1d/fn4SEBEpKSoiJiWmxv7i4GPh97GnT2NS2Vo5qKt/WGNYmJpOJ+fPnM2rUKIYNGwbAokWLMBqNPPvsswCEh4czc+ZMvvzySx577LFOPDupIxSNgnOfQJx6+FN3sICaLZlUrU3De1bPVssH+LgwbWwMI6sb2bovl20H8iitbGDyiEj0uva1cCp+YSgTbkSMvgaKMm0bNVrQ6my/he2CL9YvRqQdQHPpXBQ3bwCmhffGKgQ/Zh1BURRujB0ig1VJkjpNFSorMg/xc/Yxpob1ZEZkXwAi3XzsZYSqojRlvKmrQl30vG1sf/gJ10nPADR3vw7O7s1u3NUGC3X78nA0+qLztQU7NUcKsWRVovN2wlJnJj23goE9AvAP98LNywlF37ynquk4SeqI8zpQjYuLY+PGjaSmpjJ06NBm+4QQHD9+HK1Waw9im2b7N83+/6Om7ABtZRFosmjRIvLz85uNMT1+/Dhjx45tVjdonm9VOvMUnQaXgcFgVanemIEppxJDqEeb5T3dHLhsTBR+3k5s2ZdLVU0jV4yPxcWp/eNOFb0BQlrPJKG56i+IwxsRG75G/fzvKBNvQtPN9l69PLw3qhCsyj6KVtFwfcyg9rXoSpIk/cGx8gJ+zj7GqMAYpoX2QAhhv56oqz9F5KdBRRGa2/+F4uqJ4uKB5sr7ISgGxdnNfh5FowGX5tfM+iOFVK09jjBbEToNh7LLSc2qoK6ugTseGIpeZwtI7zL6YtC3bxiVJLXXeT04bvTo0QCsW7euxb59+/ZRVlbGwIED7Un4m8qvX7++xTjUvLw8EhISCAkJITY2ts3HrK6u5r///S9Tp06lZ8/f70KFEM2WX22auNU0Llb6czn3D0LjYqB6cyZCnDxjgqIoDOkdxOXjYiguq2fxjwkUl7e+GlpHKYqCps84NDf/A7wDET8tQP3xA0RdNYqicGVEHyaHdmdjfgpfpe1BPUVdJUmSwPadU9FYR7XJNmStl3cwf/ON5MaMRDQfPQ4lOb8XtpjA0x9lwCVg+f17Sonp1yxIbXZ+VXAsrZQNu7PZnFaCNtITn1v64zo4hJTMchwdtAwdGMqJlywZpEpnwnndojpkyBDi4uLYunUr33zzDbNnzwagrKyM5557DoC5c+fay4eFhTF69Gg2b97MW2+9xcMPPwzYZvk/++yzWK3WZuVbs2DBAmpqanjooYeabY+Li7MvLuDq6sqmTZsAThr0SmeOotfiOjyMqrVpmDIqcIg69azGuAgv3F0NLF+Xylc/JTJtbDTRoZ5dUx+vADTXPonYvQqx/XtEZgLK2NkoPUZwdWQ/AFbnJNBgNTPHOEymrpIkqU1rchL4NW0vXrVVDI7sy/geIwEIOn4IkX4YJao3J+br01zWvpSPAMKqUrsnj/rDhRwJcKKgsgFvD0cYFYneyzbP46bLe6DTymuU9OdQxKmam84BN998M7t27eKLL75g0KBBzfYdOnSIW265hbq6Ovr27Yu/vz+7du2isrKS2bNn8/zzzzcrn52dzfXXX09xcTFGo5GoqCj27dtHcXExY8aM4f3330enaz1+LywsZPLkyVx55ZX885//bLZvxYoVPP7444SEhGA0Gtm6dSsODg788ssv+Pj4tHq+P8rJyWHixImsW7eO0NDQDrxCUmuEVaXkf3tRnPT43NS33d3q1bUmVvyaSlFZHcP6BjGsTzAaTdd1yYuSXNS1n0NeKoR1QzNpDnj6syr7KCsyD9HXO4Q7u49Cr5GtE5J0MRCZxxCp+wBQwrujxA20b69N3EFWTTlVgZEMGzkTgIJ1C/E7uAGAmgGX4DHuOlv52kpwcEbpRMo8gPziGtRVKWgLa3GI9cZhXBROno5ySJJ0Vp3XLapgSx317bffMn/+fHbu3ElKSgoRERE88sgjXHPNNS3Kh4WF2ctv2rSJzMxMwsLCmDNnDrfcckubQSrA22+/DcB9993XYt+VV15JbW0tH330EVu2bKFXr148/fTT7Q5Spa6naDW4jAinalUKjSmlOBp9T30Q4OZi4Nop8azbmcWOg/nkFdUwdXR0h8atnrReviForn0CcWgTYvN3trGrwy9n6sBLcdIZ+CptD28f2cC8HmNw7OQXjiRJ5x5RX41I2YtI2Y/iF4pmjO07SpQXIJJt+UtxcrUHqiX5qeiTdhHy2/YmAd2GITz9UbyCcPf/Pe2U4tL2ePyTMZmtbNmXy4HEInrotYy9shtO7bxeStKZdl60qF4sZItq1xOqoOSTfSgaBZ9b+qN0oGVUCMHR1FLW7czE0aDjsjFRhAW2L6VYux+jphx1/ZeQshccXSEsnkxPPxbWV2DwDeWB3hNw1csFHyTpfCXqa1B+CzJFxhHUpW+Ahz9Kn7FoBk9pUb7RasFBa2swUYVgXW4iw/yjcDO0vlBNZ6mq7avfkldFzYF8ljU0EhXmxcj+ITgYZG+OdO6Qgeo5RAaqZ0ZDUgkV3yficZkRp54nTz3WmuKyOn7YmEZFdSMj+gUzpHc78612gEg/jEjahchOhOoyAKr0BjK8A4mZNg93z9aXCZYk6dwjTA2I3T8hUvdDQy2au15FUTQIqwXKC8AnpNVrSFZNGR8c28y08F6MDGyZcrEr5BRUc+x4KcezK7jM1w3DgQK0Hg64X9MLB4+uDYYlqSuc913/knQqDkYfdP4u1GzNwrGbL8pJJgGoJivWygaslQ0oOg2GCE/8vJ25cXoP1mzPZOv+PA6nlBAV4kFUqAdhgW721CynQ4nqjRLV25ahoLIEkZ2IkrafbumHKfnuPzje/A8MDjIHoSSdF3R6xOHN4B2E0nusbaU6nQZFqwPf1hshkioKefvoBlx1DgR3oAu/vsFMWZVt5r+rkwEPN1sPTFllAzkF1ZRV1hMW6E5MuCcAaTkVJGeUERXigb7WgkO0Fx6XGdE4yHBAOjfJFtVziGxRPXMa0sqoWHoM98mxOMR6Yy2vx1LegLWiHmt5A5bfglNRb2l2nNvYSFyG2P4WQgiSMspJSi8jM78Ki0VFq1UIC3QjJsyTnrG+XT4TNm3vL0Ru/IbjQVFEX/sUWjnBSpLOSaIsH6xWFL/frhcWc4cmNTVaLXx3fB+XR/TBvQPd/Bt2Z7PvWCEAA3sEMHZwGAB7jhSwaW8OOp2Gob0DGdonGID6mkb0Tnp0Wg1CFaCcellxSTqbZKB6DpGB6pkjhKBs8SHMedXNdyig9XBE6+lo++3hYPvt7kjdnlwakkrwmGbEqUfzIQMWq0puYTXpOZWk51ZSXtWIt4cjE4dFEBbYel7Czkpc8ylxhzeTHD+Y7tPu6dJzS5J0+kTKPtRf/gfewWiuf/qUgV+j1UJ+XSX5dZVEufkQ6Ny5SVBgG2uanluJTqvg5uJgSyUF1DWYsVhU3FwM9vo0pJVRuSoZryu7Ywjr/GNK0p9JtvVLFwVFUXCfHEv9kUK0Ho7oPB3RejmhdXdocyiA/jIj1joTlatS0LgYcIjwtO/TaTVEBHsQEezBOCA9p5Jfd2by7S9J9IjxYeygUJwcu2bGvnHiHFIqizEm7SbZOxDj8Bldcl5Jkk6fqK9B/eVj8A5Cc/m9zYLUeouJjOoyCuorcdU5MNg/EoB9JVl8mrwDgCg3H57oO7lDrZqZeZU4GHQE+rqg0SjEhHm2KOP8h+tP3eFCqn5OQefvgsZNTtCUzh+dalHNzs5m9+7d5ObmUlZWRlVVFQ4ODvj6+hIZGUn//v2Jioo6E/W9oMkW1XOP2mCh7MtDWKsa8b6+N3p/1zbLmi1Wdh7KZ8+RQgwGDWMGhtEz1qdLutWsZhNZX/yD4PIiCqfcTnj34ad9TkmSOkfUVUFjPYpXgO3/BRmoPsFk1FdSbWqgn6+t+/1IWR5vH90AQE+vIB7sNR6A8sY6MqtLCXT2wM/RFa2m/UOGUjLL+WnTcUICXJk1Ob7dx1mrGqndnYPbmEgUuYKUdB5pd6CakZHB119/zS+//EJ+fr59+4mHn/iFHBwczPTp07nhhhsICAjowipfuGSgem6yVjdS+sVBUMHnxj5oTzEztqS8nnU7MsktqiE61INpY6O7ZMJVXW0FVQv/gUtjAw2Tb8FXq0NUlUJVGaK6FMyNaMbMRjkhr6IkSV1HFGSgbvzKtlhHdF+0Vz4AQGF9Fa8dWkelqR4fBxdeHHwFiqJQZzGRXVNOoLM77vrTT5xvMlv539LDeLo5cNXEOBxPMQHKXFSL1s2ApotyQEvS2XDKQDUzM5M333yT1atXY7VaAfD09CQuLo7w8HBcXV1xcnKiurqaiooKCgsLOXr0qH2te71ez4wZM7jjjjuIiIg488/oPCYD1XOXuaSWssWH0LgY8Lmhzykv/EII9icWsWFXNiH+rsyYGIuD4fRH2pQVZaL96hVcLabf66bTY3b1xLGhFsXBGe1Nf0eRGQIkqUucOClKlBegrnwfYvuTHhBGTPQAFEXBKlQ+T95JD69AenuH4KwznLH6lJTX4+5qwHCSVlEhBA3HiqlcnYqj0QfPae1veZWkc02bgarZbOadd97hk08+wWQy0a9fP6ZPn87IkSNP2a1vtVpJSEhgy5YtLFu2jMzMTPR6Pffeey933303Wq3sdmiNDFTPbabsSsq+PYLezwWva3qhcTx14JmUXsaqLen4eDoyc5IR5y5o2SgpziEr/QBZCqQIlQxTHRahEl1dwcOJ+0jyDWbbwEkEuXoS7OyBo1ZPWWMtpQ21lDbWUtZYS6WpgW6eAUwIjifExfO06yRJFxqhqoiD6xG7fkRz3dMoHraVmkoaavgidTfHyvO5u/toBvzWzX+mpGVVkJBeyrQx0SdtkRVC2PdbKxsoXrAHfag7npd3Q+t65gJnSTrT2gxUL7/8ctLS0pg2bRr33HMPMTGdTz68d+9ePvvsM1avXk337t1ZtmxZp891IZOB6rmvIbWUihWJ6ANc8bqmZ7tyD6bnVrJyfRpuLnpmXmLE3bVrJzJYVZXC+ipyaitwOvArPY5s5ceYPvzk7ceJH26NouDt4IKPgwtOOj1Hy/Mxq1aMHv6MDzbS1ycUrdK16bUk6Xyl/vIx4uhWiOiB5pJbUNxtgerS9ANsyE9mRkRfxgXHoTlDnxkhBD9tSicpowwfTydmXhKHq3PLgLPxeBkNyaU0ppfjO3eA/Qa6IakEh1jvk+aNlqTzQZuB6u23387jjz9OfHzXdRkcPnyY119/nU8++aTLznkhkYHq+aEhpZSK7xPRB7riNat9wWpuUTXL16ai12uYNdmIt4fTGambECrq8vmQlYB19hMUuHlislrwcXTFw+DY7Eu1xtzI1sI0NualUNpYi5fBmTFBsYwMjMHDcGbqJ0nnImFuhLICaKxFCe9h25Z/HFFRhCl2AKnVJfTytuUhbbRaqLU04u3gcsbrtfNQHoqiMLBngH3CldpoAUVB89syp6WLDmAprcchxhu30RGnHEMvSecbmUf1HCID1fNHQ3IJFSuTbMHqNT3RtGP8aXFZHUvWJKMKmDEhluCTZBA4HaK+GnXhc6DVo7npbygOJw86VaFyuCyP9XnJJFQUoEGht08IowNj6OkVdMZajCTpbBBCQE05ipu37f8VRagfP2Xb6eqF9q5X7eU+SNjMkbI8LELlpcFX4uN4ZoPT4rI6sgurGdC95QRkU14VNVuzMGVV4n5JDM59AgGwVDagdTGg6OTnVLowyUD1HCID1fNLQ1IJFSsT0Qe721pWDacee11e1cCytSlU15q4dGQk3aJ9zkjdRG4K6jf/RokbiDLtbvvYNSFUqKmAqlLwD0fRNx+GUFRfzZaCNLYVHqfa3ICXgzMjA6KJdPPB0+CMl4MTLjoHuZKNdN4RVgtiy1JE6n6orUBz75soegeEakXs+gnFO4hCJxc8gmJx+m3y1MKUnThodfT3CSPG3feM3bSpqmDr/lz2Hi3E0UHL3Kt6tZh8Wbs7l9o9uTh198Oxlz963zPfoitJ5wIZqJ5DZKB6/rEHq4GuOBp90Xo7ofNyQuvp2ObYsPoGC99vSCW3sIbhfYMZ1jfojAR+6q6fEFuWoPQYAVaLbYnH8kJoyhhgcETpNhSl91iUgOYZOSyqlUNluWwuSCOhPL/ZWFedosHTwYkAJ3cG+oYzwDcMpzM4y1mSOkuY6kFnQPlt6WHr538HF0+UuP4o3Yfbb9RMVgsrsw6zNieRSaHdmBnV/8+tpxAsX5eKs5OOMQPDcHLUIYTAnF+DIdi20p1QBcJibVfvjSRdSNoMVOfMmXP6J1cUPvvss9M+z8VCBqrnp/rEYqrXHUetM/++8belWZ0HBOMyMLjFMRarytrtmRxLK6VbtDeTR0Si6+JJD0KoqD98ACn7wMMHvIJQvAPBKxDFxR2Ruh+RtBusZvCPQOk9xha4/mGoQJWpnuKGGioa66kw1VFhqqeisZ706hKKG2rQKRr6+IQwxC+SXt7B6DUyq4d0dom6asT+NYgD61Em3Iim+zDbdlVFaSW5/r6SLP6bsIWRATHMjOqHi/7Mr9xUV2+mtLLBvuSyVVXt41CtVQ1UrU2jMa0c7+t6y+VOpYtam4Fqt27d2j7I3o3Y8tAT9ymKQkJCQlfU86IgA9Xzm1pvxlLegLW8Hkt5PabsSsw5VXhe0Q3HeN8W5YUQ7D5SwJZ9uQT7uXDFhNgWyx52BWG1oGhbb4URDbWIhB2Iw5ugJAec3dFc9VCLFtZWjxWCjOpSdhZnsKc4i2pzA846Pf18whjoG043zwB0MmiVzgJ1x0rEthUQ2x/N8CtQ/FqmkKo1N+KsM6AoCkIIcmorCHP1OuN1E0JwNLWUjXuy0Wk13D6zd4ub1IrvE2k8XobryHCcB4agaORQG+ni1Wag+sUXX7R6wE8//cTevXuJiIjgiiuuID4+Hnd3dxoaGjh+/DgrVqwgISGBCRMmcP311zN69Ogz+gQuJDJQvbAIi0rZ14cxF9Xic0Mf9AGtT55KzrDlWvVwdeDaqd1wakcWga4mhID8NNQf/wsNtWgun4cS2avdx1uFSmJFAbuKMjlQmkOD1YyzzkA/n1AG+obT3TOwQ8tESlJHiNI8RHYimn4TbP9vqIW6KhTvoFbLH68q4b8Jm5kR2ZfhAdF/ZlUxW6x8tvwobi4GJg2PwMez5WRHYVGx1pjQecoZ/JLUoTGqmzdv5s4772TatGm88sor6PUtW3+EEPz973/n22+/5bXXXuOyyy7r0gpfyGSgeuGx1pgoXXQAUPC5uS9al9bHcmYXVLN0TTL+Ps7MmmzskiVXO0PUlKMufRPK8lEuuQVNz5EdPodZtXKsPJ99JVkcKM2lwWrG0+DEX3pNINhFdmFKXUdYLbabq9R9oHdAc+d/UE4xMz+hvIC3j27Ay8GJe7qP+VNaUVVV0Giy4PRbj0l1rQlXZ32zselqowVhsqJ1O/PDDiTpfNKhQPWGG24gKSmJLVu24OTUdsobk8nEqFGjCA0NZenSpV1S0YuBDFQvTObCGkoXH0Lv74L3tb3bTCOTklnOyg1pRIV6cMX4mLPWAika61BXvgdZCSgjr0YZclmnJ3uZVStHy/P5ImUXAA/3niiDValLqT99CJ5+KP0noji5nbJ8o9XC8owDTA/v/aeMRa1vtLBq83Hq6i1cf1k3tK2MRRcWlfLvjmKtbsR37gCZakqSTtChT0NCQgLR0dEnDVIBDAYDERERHD9+/LQqJ0kXAn2AKx5T4zDnVVO1Nq3Vsd0AcRFeTBwWTnpOJWu3ZbZZ7kxTHJxt41S7DUNsXYr4dRFCVTt1Lr1GSz+fUB7tMxGANw6vI7+usiurK11kRMYR1IQd9v9rLrsTzYgZJw1SSxpqKPjtfeeg1XFtzKA/JUi1WlW++imRrPxq+sT7oWljrGnlqmRM2ZW4jgyXQaok/UGHBsO5uLiQm5uLqqpoTtLa09jYSGZmJl5eZ75LRZLOB07d/LCU1FG7PRudnzMuA0NaLdc33p+6egvbD+bh7KRn9MCz07KuaHUw9XZw9UTs+RlRWYLmsrtRHJ07db5AZw8e7TOR1w6t4/VD63ikz0SCnGXLqtR+QgjE7p8QW5ZBQAQifkiLGfyqUKkw1dtXjcqvq+STpO3k1VUS4uLJk30n/6k5gLVaDUP7BOHp7kCwX9sLfDj1CkAf4o5TD/8/rW6SdL7o0K3bgAEDKC8v56233jppueeff57q6mpGjBhxWpWTpAuJ68hwHGK9qV6fTu3u3DZbTIf1DaKP0Y/dRwrYd6zwT67l7xRFg2bMNSiTboasBNTFL9hysXZSU7AK8PqhdfYWLklqF4sJkbATJX4ImtmPtwhS06tKeHH/z7x2aJ19m7POgLPOwOjAGG6PH9GlQaoQgrLKBsyW33sbikrryCuqITmz3L6tR4xPsyC1dk8uFSsTKfl0P5aKBgAcorxwGdAyjZ0kSR0co5qYmMjs2bMxm80MHTqU6dOnExsbi5OTE3V1dSQkJLBkyRKOHTuGm5sbS5YsISysZVoQqXVyjOqFTzVZqfwpmcaUUhyMPnhMiUPTyix/VRX8sDGN1KwKhvQOZHjf4FbHtv1ZRE6ybdyq1YLmsjtRovt2+lz5dZW8/lswMcc4lHiPAAxtpM+SpKZUhwCivgYcXVoEnCarhad3f49WUZge3pvRQbFntE5b9+eSmF5GZXUjc6/qhZe7bXb+x0sPU1HdCMBN03vg79OyB6Ls68NYKxvQejvjPj4KXStlJEn6XYdXplqzZg1PPfUUNTU1rd6dCiEICAjgjTfeYMCAAV1W0YuBDFQvDkII6nbnUr0pA62XE55Xdmt1OUSLVeXXnVkcSSnB39uZy8ZE4e1x8vHhZ5KoKkX9/h0oykYZdRXK4M5Pssqvq+SNw79SaapHp2iI8/Cnp1cQPbyCCHb2kEu0SgCI/OOom7+zpUtzat51LoTArFrtNznpVSUEOXvgqOv6XMQl5fXo9Ro8XG3jWn/cmEaj2UpMmCc9on3Q621ZOnIKq7FYVJyd9Ph72wJQtdFC/dEinPvbVqATqpB5USWpAzq1hGpZWRmLFy9my5YtZGZmUl1djYeHB1FRUUycOJFrrrkGV9e2x+NIrZOB6sXFlF1JxfeJCLMV98mxbY5PS8ksZ832TCwWlbGDQukT73fWAjlhbkSs/gSRtBslfjDKJbeiGDqX69FktZBcWcSx8nyOleeTX18FgKfBiW6eAXTzDKSbZyBeDrLF6WIkshNRl8+3LUIx6zEUj98XzShrrOWr1D2YhcqDPcedsc+D2aKy81Aee44UMqxvEMP62rrnT2zlPelzEIKK5Qk0ppXhc3O/NnMpS5LUtk4FqtKZIQPVi4+1xkTFykTMOVXog9zQOOlQHHQoDlo0Djo0znqcegVQp6r8sjWDzLwqokI9GD0gFCdHHXqdBr1O86cGrrZJLasQW5eCdxCaK+5D8Qo87fOWNdb+FrQWkFRRSI3F1oUa4ORGN89ABvqGY/Twl62tFwk1YQdi109oZj6MckKu01pzI8/s/h6LULkiog+TQrqhOUPvidSscr5fn0aPGB/GDgq150Ftr/qjRVT+lIzb+ChcBrU+gVKSpJPrkkC1trYWF5eTJ1mWTk0GqhcnYVWp2ZGNObvKlvS70Ypqsv1GFWjcDHhMicMQ4cmBxCI27c3Bam3+sTXoNeh1WvtvvV6D4bffHq4OxIR7EuTbcmzfadU78yjqjwtAtaC59HaUuK4b6qMKQW5tBYkVBSRWFJJSVUSj1UKEqzeTQ7vT3zcMrSLT+Fzo2lr+d3N+Kt29AvF17PoWyvoGC44OWvvSqsVl9a2ONW0PoQoaEotx7H72ekEk6XzXqUC1oKCATz/9lE2bNpGZacv3eOzYMQoLC3n00Ue57bbbmDBhwpmo7wVNBqrSiYQQmPNrqFyVjLWsHqd+gbiNjaKqwUxecS1msxWTxYrJrGIy235bLFZMFhWz2YrZomIyq1TWNKKqAldnPbHhXsRFeBLi79ZmTscO1bGqFHXl+1CYjjJoCsqoq1E0Xb+qlslqYUdROmtyEymqr8bX0YVJId0ZGRAtJ2JdQNTDm8Fqti+FCrYE/SszD+Gg1XF5RJ8z9thZ+VUcSi4mPaeS8UPC6RXne+qDJEk64zp8hd+8eTOPPPIINTU19vQ6TXeKOTk57Nmzh71793Lfffdx//33d21tJekioigKhmA3fOf0o3pLJnV78jClV+BxWRw9YnzafZ4Gk4Xj2ZWkZpVzJKWEA4lFODnqmDIqiqiQ08tlqrj7oLn2CcSGL235Vgsz0IydDX5hKF3Y4mnQ6hgTFMeowBgOlOayOucYX6XtYUXGQWI9/Ihxt/1EunrLwPU8JFQVsW0ZYtdPENkb0XcciqKhrKGWVw+tpbSxlnFBce0eG9oejSYLtfVm+wTFwtJacgqq6RblTZBf53sI1XozFSuTcB0RhiFU5gqWpNPVoRbVrKwsrrzySurr65k6dSrTpk3jvffeIyEhgYSEBEpLS3n11VdZtmwZiqLw/vvvM27cuDNY/QuLbFGVTsaUU0nlTylYKxtw6uWPYzc/DOEeKB1IW2U2W0nPq2LnwTzKqxu5ZrKRoJMkIu8I9egWxNpFYDWDowuEdUMJ744S3h08A7o8h2VaVTHbCtNJrSqm8LeJWBpFIdzVm9GBsYwMiJbdrecJYbWgfvMvFN8wlPHXo/w2c18VgoUpOxkZEE2sR9ckwxdCkJxRzvpdWQT4uHDVpDjANnFKq1FOq6eh/mgRlauSAfCa1ROHSLnojSSdrg4Fqs888wxLlizhoYce4p577gHghhtuYP/+/SQkJNjLffTRR7z66quMGTOGBQsWdH2tL1AyUJVORTVZqdmcQf3hIoTZiuKowzHWGwejLw4Rnu1efrGu3syXqxJpNFm5bmo3vD06N3P/j0RtJSLzKGQlILISoOa3xOcOTqAzgKIBjRY0v/32CkAJiUMJMYJ/eLPxiMJqgZJcREE6FGaAgxNKUAwExzSbXANQY24graqEtKoSjpXnk11bzujAWK6LGYjuDAxFkLrGiWNQhbkRoTOwpSANT4MTfXzOzOSjyppGPll2BD8vJyYMDe/0jZpQBXUH8lGrG3EbGwWApayO+iNFOHbzRe8vZ/hLUlfoUKA6btw46uvr2bZtG1qt7eLfWqBqtVoZOXIkADt27Gj1XFJLMlCV2ktYVBozymlIKqExrQzRaEUxaHEZGorLoJB2BawVVQ18tSoRnVbDdZd1w9XZ0LV1FAIqimwBa0kOqNbfflRQVYTVbNteUWQ7QGeAoGgUzwBESTYUZYHVYtvn6ALmxt//7+aNEhwDod1Qeo60t8CBbRnNFRmH+DnnGLHuftzdfTTunUyhJZ056pHNiN2r0Fz3FIqTG5WmehYkbCG1qpghfpHc3q3rVjZUVYFVVdHrbN9beUU1BPq6dLr1VJitlK9IxJRejj7UHe/resvWe0k6Qzo0mKu0tJT4+Hh7kNoWrVZLaGgoiYmJp1U5SZJap+g0OMb64Bjrg7CqmDIrqDtYQM3mTOoPF+I2IRrHGO9WjxWqwFrZgIeHI1dNiuObn5NYujaF2VPicTR03fhORVFsLaZeASctJ2oqIC8FkZOCyE1GJO8C31CUfhMgMAolIAo8fG1BblEWIj8N8lIRuamQtBuxdzWaSTfbhhgAGkXDVVH9CHHx5POUnbx84Gfm9RhLmKvshj1XqJu+Rez5GcJ7/L5NCOotZm4xDmO4f1SXPVZBSS1rtmcQ7OfKxGERAASfbmunToPGWY/75Fic+55+ajZJktrWoW8ld3d38vLy2lW2sLAQT0/PztRJkqQOULQaHKK9cYj2pjG9nKpfj1Ox9BgOMV64jY9G5+WEWmemMb3c/iMaLBiivPCbHs8V42NZti6FFb+mMvMSI7o/ealWxdUTjINRjINPXlCrs7W4BkXDgEuA31JkrV2E+t2rKN2Ho4y9FsXZDYAh/pH4O7nx/rFN/Pvgam41DmegX/gZfjZSeyhh3Wy/R81E0djeb14Ozjw7YGqX5kQtKqvjy58ScHbUEx7kftrns1Y3onVzQFEUPC8zdkENJUk6lQ59I/Xr14/y8nJ+/vnnk5b76aefKC4upm/fzq8HLklSxzlEeeF7a3/cxkZiyqqi5JN9lHy2n6J3d1L5UzKmzAocYrxxGR6GKbOC0oUHCDHYMgDkFtbww4Y0TGbr2X4a7aZE9EQz5zmUodMRSbtQP3ka9fBmhFABiHTz4en+Uwh18WJB4hZe2v8zG/NTqLeYznLNL25KVG80Y64hu66C949twvTbkI6uClKbRrT5eTkxfkg4t87oSVxE51vUhVWlam0aJf/bh6W8vkvqKElS+3RojOru3bu5+eabcXNz45lnnmHq1KnMnTvXPkbVYrGwfPlyXnzxRRoaGvjf//7HiBFdN87oQifHqEpdyVrTSM3mTCzlDThEeuIQ7YUuwNU+ls6UU0XFigSEWcVjmpEkq5Vfd2Xh5e7IFeNj7Gl7zheiNA917eeQmwIuHuAfgeIfjuIfjsU3lM215WwtOk5ObQV6jZaBvmGMDIghTq529acQKXsRxTkow69AURRSK4t55+gGnHR6Hu0zqUuS99fUmfh1ZxYxYZ70jO26PKgV3yfSkFSC86AQ3MZGonRBDmJJktqnwwn/33vvPebPn4+iKGg0tqUbrVYrsbGx5OTk0NDQgBCCuXPn8sQTT5ypel+QZKAq/dms1Y2UL0/AUlCD68hwyiI9+HFzOhaLyqUjIzFGtj7O9VwlhIpI3AXphxHFWVCWD02XOGd3lPE3kBUczdaCNHYVZ9JgNePr6MIgvwiG+EUS4uJ5Vut/oRLZiahL3wD/CDTX/B+KTs+63EQ25KfwcK8JeDue/sqGxWV1LFmTTKPZythBYfTr1vl0VmqDBWtlA/oAW/Bsyq1CmK0y3ZQknQWdWplq9erVzJ8/n9TU1Bb7QkJCmDdvHjNnzuySCl5MZKAqnQ3CbKVydSoNx4pxiPNBOyaCH3dkkl9cy4AeAYweGIJWc34uVyrMjbYUV8VZiCNboCAdZcAlKKNnYQb2lWSzsziDxPICVATBzh4M9otgsF8Efk5uZ7v6F4zG79/FXJTFsiFTuLH3WDS/LQbRaLXg0EULNJjMVn7eks7I/iH4eHauN0A1WajdnkPd/nw0Lnp87xgoW9sl6SzrVKDaJDs7m9TUVGpqanByciIyMpLY2NiurN9FRQaq0tkihKBubx7VG9LRujviNt3I9pwK9icUEezvyuBegfh7O+PqrD9vv7iF1YLY9A1i/zoIjkUz7W4UN1uLcZWpgX0lWewuziS1qhiAgb7hXBnZhwCn05+EczETQvDsjmU01leic/Hi0T6T8HPqmhyjJrOVhkYL7q4OXXI+td5Mycf7MER44jIkFL3/6bf0SpJ0ek4rUJW6lgxUpbPNlFNFxcpE1Hoz7hOiyXI3sGZ7JmaLbXKSk4MOf29n/HycCfB2JsDHGY/fZkGfL9SkXYjVn4JOj+ayu1AiejbbX9ZYy+b8VNblJmEWVkYHxjItvBcehvNrzO7ZVFdbgbJ1GY59J6AERHCgNAdPgxMRrt5d9l4pr2rg+/WpgMLNl/fodE5Ua3UjCIHW3ZZrV220oHGQy/BK0rmi04FqdXU1FRUVNDY2nrScbGFtPxmoSucCtc5MxY9JmDIqcOzuh9P4KEpqGykqrae4vI6i0jpKK+qxqrZLh6ODlgAfFwJ9XAjwdSYi2AN9O1fIOltEWT7qyvegNB9l2OUow6aj/GEFqypTPT9mHWFTQSo6RcPEkG5MConHVS8XD2iLEIKkI5sI2PA1rmYT2vHXo+k/scsfx2pV+WjJYayqYNqYaCKCO97q3bSyVM3mTAxhHnhd3ePUB0mS9KfrcKC6evVq3nrrLY4fP37qkysKx44d63TlLjYyUJXOFUIIanfkULM1E62nE56Xx9snloAtUCitaKCgtJaCkloKS2opqahHCPB0d+DSkZGE+J/bYzyFuRGxdiEiYTsExaCZeieKp1+LckX11XyfeYjdxZkAuOkd8HdyJ8DJ7bcfd4weAbjou3Zlr/NRYX0V72z5ljsyk9BPuIng6D5den4hhL1FNiWznAAf5053+5tyqyj78hCGcE/cL4lB5yVbzCXpXNShQHX79u3cdtttdCS2latTtZ8MVKVzTWNWBZU/JKHWW3AdHobL0FCUNhYEMFusZOdX8+uuLKpqTAzoEcDI/sH2ZSvPVWrCDsSvi0BVUSbcgNJjZKvd09k15RyryKeovprCumoK66uoMjcAtvyf3TwC6O8bRl+f0ItqmIAoK0Ds/QVl5FUozu4kVxQS4+GHVum6VnWzRWXnoTxq6sxMGdV1q1ZZyurQejmdV0NXJOli06FA9fbbb2fr1q0MHTqURx99lNjYWJydnc9k/S4qMlCVzkVqvZmqdcdpSChGF+CKx2Vx6H3bnmRiMlvZvDeHg0nF50/ralUJ6qr/QW4yxA1EM2kOSjsm/NRbzOTWVnCwLIcDJdkUNdSgANHufowJimVYFy4Fei5StyxF3fUTqkaD/or7UKK7fpGXRpOFL35MoKKqkR4xPkweEdnp8ahqvZmG1DKce598WV9Jks4dHQpUhw4disViYePGjbi6ds2sTel3MlCVzmUNSSVUrklFmKy4jozAZXDISROfZ+VXsXpbBlU1JnrH+RIa6IaPpxPeHo5/+jKt7SFUFbHnZ8S25eDkhjLqKtuyrJr2tQgLIcirq2R/STZ7S7LIq6vk6qh+XBp64Yx9FEJAQw3Kb6m7Mg/+yrGEbRwM78ZDQ67EUac/I4+7ZV8O4UHup7UMqrXGRPm3R7CU1+N720B0nnKssSSdDzoUqPbv35+oqCiWLl16Jut00ZKBqnSus9aaqFqbRmNyKVpvJwwh7uh8ndH5uaDzdUbr0nycZlPr6uGUEtTfJl8pCni4OeD7W9Dq7eGEj6cj3u6O6PVnf5iAKMxEXfMZFGWCZ4BtJaX4IfY16dvDKlQ+TtzGnpKsCyJYFUKFtAOou34CswnNnH+gKBreP7aJ0oZaHuw1HndD1wZ+tfVmXJy6LvCt/CWFhoRiPGf0wCHSs8vOK0nSmdWhQHXWrFnk5eWxZcsWNOdpAvBzmQxUpfOBEIKGxBLqDxVgKalDrTPb92mc9bbA1ee3n9/+LRy1VFQ1UlJRT2lFPaUVDZSU11NR3cCJVyA3FwP+3s4YI72IDfc8a+NbhRCQth912wooyQHvIJThV6IYB6K0c+xls2A1sh+Xhp2/waqor0H96HHb6l6Dp6D0Go2i0WKyWrAIFWdd104kO5RczIbd2Vwz2UiQX9f03qkmK9by+maTAiVJOvd1KFncddddx7PPPsvChQu55ZZbzlSdJEk6hymKglN3P5y622bIW2tNWIrrsJTUYimuxVJaT/3RIoTJaj9G6+2E96ye+PxhSVarVaW8qpGyynrKKhsoq2wgt6iGtOwK9DoNxkgvesT4EBrg9qdOeFEUBWIHoInpByl7Ubd/j/jxA8QWX5SY/igx/SA4FuUkqyppFQ23dRuBkqSwNOMAwHkVrIqGWtBoUQyOKE6uVM94kH2o7C/P46qacqLdfTFodXR1roPDycWs3Z5JVIgHvl6dnwNhrTVRvSEd12Fh6Hyc0Ri0aGSQKknnnTavsq0tj9q3b18GDBjAv//9b1JTU5k4cSL+/v4YDG1fqmQeVUm6sGldDGhdDM26U4UQqNUmLKW2ALZmezZl3xzB+4Y+zYYHaLUafL2c8D0hNZAQgpzCGo6llZKSWcbR1FLcXAz07+ZPv+7+f+r4VkXRgHEwmtiBiOTdiITtiIPrEfvWgIMzSlRviOmPEjeg1bGsWkXD3PjhAOdVsCrMjajL3kJoNGhnP4GiKKxsqGRLQRrBzh7UW01n7LHDg9zpG+/HuMFhaDv5t25ILaVyVQrCbMUh0gudj5z0K0nnqza7/rt37376J5d5VDtEdv1LFypTbhXl3x5B6+mE93W90Ti2rzPHbLGSllXB4ZQSsguqcXc1MHpgKMYIr7OWUkiYGiDzGOL4AcTxg1BfA+Hd0Uy7p81MAVah8knSdnYXZ3JlRF8uC+/ZarlzQWFdBeZlbxNQmMHOwZcyavRswJZPViDOyJKyZrMVqxA4GrpmRShzUS3V64/jPilGBqmSdJ5rM1Dt1q1blzyAzKPafjJQlS5kjenllC89hj7IDe9reqJ0cOJUZl4lG/fkUFJeT5CfC2MHhRHsf3a7coWqIo5uQfz6Bbh5o7nyARSf4FbLWoXKZ8k72FmUwZSwHsyI6HvO5O88MZF+QnkBB35diL+zOx6DpjDIL+KMPnZ5VQMrN6Th5mJgxoTYTr8mjenlGELdO/y+kiTp3NbpJVSlricDVelC15BUQsXKRAyRXnhd1b3NxQPaoqqCY2mlbN2fS229mZgwTwJ9nXFzccDNxYC7qwFXZz3aP3myp8hLRf3+XbCY0Fx2V5v5RFUhWJy6m80FqUwINnJN9EA0ZzlYPVaez/eZh3iw13icdQasQsVkteDUxROkWlPXYOaTZUdQFLhsdDSRIR4dPodQBTVbMqndmYPryHBcR4SfgZpKknS2yED1HCIDVeliUHeogKpfUnGM98VjevxJc7G2xWS2sudoAYeTS6itN7fY7+ftRGyYLXOA75+08pCoLkNd8Q4UZaGMnokyaEqrjyuE4Nv0fazLTWJkQAw3xQ1G04WrOHXEmpwElqTvJ8TFk4fMAhc3bzQ9hv+pdTiQWERUiAcebp1bCrUhtZSKZQk49QnEfWI0ik5mpJGkC0mXBapFRUWsXbsWVVUZNWoUkZGRXXHai4oMVKWLRe2uHKo3ZqDzdcaxhz+O3XzReXQuD6fZolJTa6Kq1kRVbSNVNSayC6rJK6oBwMPVQEy4F9GhHni6OeDipO/0JJ1TEeZGxC+fIJJ3Q0Qv2ySriB4oHn7NywnByszD/Jh9hMF+Ecw1Dv/TW4EB9hZncaA0hzlWgeaXj1GMg1Cm3XNGA3tVFeSX1HTZamVCCMw5VRjCOt4aK0nSua/DgeqhQ4d44403MBqNPPXUUwDs27ePO+64g/r6egA0Gg0PPfQQd955Z9fX+AImA1XpYlJ/pJC6gwWY86oB0Ae54djNF8d4X7SdbF07UW29mePZFaRmV5CVV4VV/f1S5+Sow9VJj6uzgSA/F/oY/XDuouTyQgjE7lWI/WuhttK20cMfJaI7SngPiOlnT2v1S/YxlmYcoJtnANPCexPn7nfGW3/LGmvxMjjbH0eoKuriF8DgiObqh1HO0OpSYHtt1mzP5GhqCTdf3rNZtod2n8OqUrsrB42rg1wKVZIuAh0KVI8fP87MmTNpaGhg5MiRfPTRRwBcfvnlpKSk4OvrS0xMDHv37sVqtfLZZ58xZMiQM1b5C40MVKWLkaWygYbEEhoSi7EU1QKgD3bD0eiLo9EHbSdbWk9kMlvJLaqhptZETb2Z2jozNXUmqmtNFJfXo9Uo9IjxYWDPALw9Oh48tUYIAWX5iMxjiKxjkJMEpgYIjEYz/R4Udx8ANuWnsixjP3UWMyHOnowNjmOofySO2q4PGJMri3j36AaujurP2KC43+vaUAcKKA5ndob83qMFbNyTw9DeQYwcENLh4y3l9VQsS8BSWodTnwA8Lo079UGSJJ3XOhSo/vWvf+Xbb79l0qRJPPXUU4SEhHDkyBFmzZqFo6Mjq1evxt/fn1WrVvHwww8zefJk5s+ffybrf0GRgap0sbOU1dmC1uRSLMW2oFUX4Iqj0Qen3gEtlmjtCqUV9ew7VsixtFKsqiAq1IMB3f3x9XLGyUGHphNjaFsjrBZE8h7EuoWg0aKZeqctDytgslrYVZzJhrxksmvLcdTqGOYfzdigWIJdPLvk8dOrS3jt0Dp8HFz4S+/xeNXVgJMbiuOfl76prsHMkZQSBvcK7FTLsTBbKV96DOdBITjGeJ/6AEmSznsdClQvueQSysrK2Lx5M87Otovb22+/zbvvvssll1zC22+/bS87ZswYhBBs3ry562t9gZKBqiT9zlJeT0NyKY0pJZjza9C4OeA9uxc6765p8fyjunozB5KKOJhYTH2jxb7d0UGLs6MeJwcdYUFuDOsTfFrBqygvQF35PpTkoAydblua9bfxqUIIjleXsDE/hb3FWViESoy7H2MCYxngG4bhJCthnYpZtfLd8f1MD++Fa2M96lcvgXcg2pmPdvqc7XVi+quOsFY30pBSiiHYDX1g14xplSTp/NKhq15RURExMTH2IBVgy5YtKIrCyJEjm5X19/eXOVQlSeo0nZcTrkNDcR0airmghvIlRyn78hBe1/REfwbypzo76RnRL4TBvYLIzKukps5MXYOZ+gYLdQ0WaupM7DiYT25hDdPHRuPk2LmuecUrEM31zyB+/QKx8wdEXqotpZWLB4qiEOPuR4y7H9dEDWB7UTqbC1L5JHk7Xx/fwzD/KMYExRHk3P6JQ41WCw5aHXqNlutjByGsFtRlb0JjHZrRszr1HDoiM6+KHQfzuGJ8TIdes6bsEABOvfzxmCoDVUm6GHUoUNVoNJhMvy+dV1FRweHDhwEYPrx5SpOSkhKcnM5My4ckSRcXfaAr3tf3puybI5R9dRivmT0xhHT9CkkAep2G2HCvVvcdTS1h7fZMvvgxgSvHx+Ln3bluc0VvQLl0LmpIHGLdItSPnoDAKJSQWJTgOAiOwc3Rhcmh3bkkpBvJlUVsLkhlY34q6/OSGeofxZURffB2dDnp4/yam8Ta3ET+r+8leP02/lTR6lAGXYri6oXif2aT+R9OKWbDrmw83BxOmYbMlFNFQ3IJ7hOiATCEeeA6JgLHWB+5upQkXcQ6FKhGR0eTlJREUVER/v7+rFmzBlVViYiIICLi9wve1q1bKSgooF+/fl1dX0mSLlI6b2d8ru9D2TdHKP/2CJ4zeuAQ6fmn1qFnrC/eHo6s3JDGlz8lcunISOKjOj9WUtNrFCIwCnFkMyIvFbHnF4T6E6CAbwhK7zEofcYS7xlAvGcA1aYGVucm8GtuEnuKMxkfHM/UsB646FtmSVibm8i3x/fRzycU1z/s1/QY0ek6t8ViVcnKr0KjKPbE/QoKYYFuTBoe0ebyqEII6vbnU70+HZ23k32YgK1FPazL6ylJ0vmlQ4Hq9OnTOXr0KLfeeiujR49myZIlKIrCVVddBUBZWRlLlizhgw8+QFEUpk+ffkYqLUnSxUnr4Yj39X0o//YI5UuP4jk9Hkej759ahyA/V26c3oOV61P5cdNxisrqGNk/pNPjVhXfEJRx1wG2PKwUpCNyUxAZRxDrFyP2r0UzaibEDcTN4MjMqP6MDzLyfdZh1uYmsLUwlSmhPRkZGI2r/vcMCUHO7gz3j+Jm41C0igaRl4YozkLpM67LU2BlF1SxfF0qZotKeJCbPVDtFedLr7hT/31MmRU4RHnicVn8ObOsrCRJ54YOTaYym83cd999bNq0yb6tb9++LFy4EIPBwL59+7jhhhsAuPTSS3n99dfRauW6y+0lJ1NJUvuo9WbKlxzDnF+N1tsJhygvHCI90Yd6oDH8Odccq1Vl/a5sDiUX4+vlxIQh4YR24YQfIQSkH0bd/C2U5kFQDJox16CE/J6SKbe2gqXpBzhSngeAr6MLka4+RLrZfsJdvXHQ6hCmetSFz4FQ0cz5J4qh8ym/zBYrCWlllFTUM2GobbnS+gYzW/bnEhvmRViQG7p2LKggVGEfDiDMVtBpZJAqSVILnVqZauPGjSQlJREeHs7EiRPR620D5CsqKnjssceYPn06M2bM6Oq6XvBkoCpJ7aearNQfKqAxvRxTThVYVNAqGELcceodgGP3M588HyAls5wNu7OprjURH+XNmIGhuHVhGi2hWhFHtyG2LbMtIBDTH82oq1F8gu1lMqpLSaos5GhZHlk15dRbbcvK6jVaRgfGcnnucRz2/oJm9hPNAt2OMltUFn5/lIrqRjzdHLjp8h4Y9B2/MWhILaV6fTpe1/RC53n6eXIlSbpwddkSqtLpk4GqJHWOsKiYcqowZZTTkFaGtaweh2gv3CfHdskqV6ditljZfaSA3YcL0GgUhvYJYkCPgHa1LLaXMDci9q5G7PkZzI0o3YfbUlt52LrW8+sq+c/BNTjpDDzYcxxFDdXsLc5iZ1EGeqFypcZAn4FT8HM6vVbf3UfyCfBxISzQrVM3AtWbMqjdmYPOzwXPGd1loCpJ0kmd0UA1NzeXkJCOrz5ysZKBqiSdPqEK6vbnUb0pE0Wr4DY+CqdeAX9K62pFdSMbd2eTll2Bm4vBNkYz1rdrW1jrqxG7fkIc+BWEQOkzFmXodBblJXKoNJfH+17SLBgtaahhdU4CWwvSsArBYL8Ipob1JNjl1CmuhBAcTS2lrsHMkN5BXVJ/U24VpswKXIaEoui6LpCXJOnC1OFAtbKyku+++47U1FQaGhpQVbXZfqvVSmNjI0VFRaSmpnL06NEurfCFTAaqktR1LOX1VP6cgjmnCkOkJx6XxqJ1/3Na7zJyK9lztICs/GoUBSKDPeht9CUq1AOtpmuCM1FdhtjxA+LIZtDqYMg0ynqPwu+3layEUBHrFqH0Go0SGEWlqZ41OQlsyk/FpFro7xPGZeG9CHNtPRWXyWxl5YY0MvOqCA9yY+Ylxk4F++aSWmp35eIxOVYGppIkdViHAtWSkhJmzZpFYWEhTYcpisKJp2i6kAkh0Ol0HDlypIurfOGSgaokda2m1Ec1mzIQqkDjpEfRa1D0WhSd7bfOzxmnXgHo/U6ek7QzKqsbOZJSwpHUEmrrzbg46RnUM4C+3fy7ZFjA4bJcfOrrCNi7GlL3QUAEmsm3gasnYv2XiMQdKJPmoOkz1n5MjbmBtblJrM9LpsFqpo93CJeF9yTKrfnsfItVZeWGNKJDPOgT37nxvtWbM6jdlYti0OI9uxf6gK5fqEGSpAtbhwLVV155hU8//RRnZ2cuu+wynJycWLhwIYMGDWLgwIEUFBSwYcMGKisrGTZsGO+9916zVaykk5OBqiSdGZaKBuoP5KM2WBBmK8KiIkxWhNmKubAWVIE+yBWn3oE4dvNF49D5pUpbo6qC9NxK9icUkpVfjbuLgZEDQugW5d2pAFAVgh+zDvND1hH6+YRyb48xiOQ9qOsWQWMd9J8ER7eg9JtgG8faymPUWUysz0tibW4SdRYTvbyCuNU4HLcTMgJ0ZulTYVHtLae1+/KwltbjOjIcjXPnVvKSJOni1qFA9bLLLiM9Pd0enIJtRarY2FgWLlwIQGlpKbfffjtJSUl8/vnnDB48+MzU/AIkA1VJ+vOpdWbqjxVRf7gQS0kdil6DY7wvLsPC0Hl1/ep6mXlVbN6bQ1FZHf7ezoweGEpEcMdW2SptqOWf+36kv08YN8QOxqDVIcoLEaoFdvyASNoFPiFoJtwIzr+NVxUCECAAV08UJ1vrZoPFzIb8FH7IOoyP4kZ8ZSTTRsXg0cFJaKa8amq2ZqJoFLxm9uzQsZIkSW3pUKDav39/PD09Wb9+vX3b7bffzt69e9m3bx+a38ZeJSQkcNVVVzF58mTmz5/f9bVuxYoVK/jiiy9ITk5GVVWioqK4+uqruemmm1rkck1PT+ftt99m7969VFRUEB4ezuzZs7nxxhvtz6FJcXExzz33HFu3bsXFxYUJEybw2GOP4e7e8ovl0UcfZc+ePaxevRoHh47PNJaBqiSdPUIIzAU11B8qpCGhCKEKXAaF4DIsrMtzswohSEwvY+u+XKpqTUSHejBldFSbqze1pqShBh8HFxACsWcVYvv3KPFD0Ey5HZGyD3XdQqiravsELh621a98Q8EnhAThxE+HTCiqlsljw+kTHtjuulirGij+cC8aRx3Og0JwGRIic6JKktQlOtS/ZbVa8fVtPo4pMjKSbdu2kZWVRWRkJADdu3cnNDSUgwcPdllFT+bf//43//vf/zAYDAwePBitVsuePXt46aWX2LVrF++88479opmYmMiNN95ITU0NAwYMoHfv3uzcuZMXXniBgwcP8uqrrzY796OPPsrOnTsZMmQIAF9//TX5+fl8+OGHzcolJCTw448/8vzzz3cqSJUk6exSFAVDkBuGIDdcR4ZTvTGd2p051B8twm1cFI7dfLss+FIUhe7RPsRFeLE/oYit+3P56qdErpoY12ZLZnp1CTsK07k2ZhAaRcHX0dYiKvJSEVuWQtxAlFEzbeePG4Am1IjIOmZrSVUUQPmt/gJRVQYlOYjSXMTBDWAx4aoLxtN1AmMa12Nea6I0rDtewbEokb1QTpEhQOvuiOf0eAyRnl0+bEKSpItbh64o3t7elJaWNtsWFmZbizklJcUeqAJ4eHiQnJx8+jU8hcTERD7++GO8vb1ZvHgxUVFRABQWFnL99dezdu1aVq9ezaWXXooQgscff5yamhr+/e9/c+WVVwK2pV9vvfVWVq5cySWXXMKll14KQFJSEjt37uTWW2/lqaeeAmzjdD/55BNSUlKIi/s9cfarr75qb8WVJOn8pnU14DktHlO/IKrWHafyhyTq9ufjPjG6SycE6bQaBvcKJNDXme/Xp7H4pwSuHB9LsH/zx0iqKOTdYxtx0zsyzdyAu+H3IQlKSBya65+BwKhmgbTi5IoSP6TVxz0x3BaqCpXFhJfkcktJHtaSQMryUnA9shlxeBPC0RXNlNtQovs2O4elrA5TdhXOfW0tr47xf+5StpIkXRw6NO20d+/e5Ofns3v3bvu2mJgYhBDs2rXLvs1kMpGTk9Nq93hX2759O0IIrrjiCnuQChAQEGBfzrWpvlu3biUpKYkhQ4bYg1SwBeD/+Mc/AOxjbQGOHz8O2MbhNmn6d3p6erM6bNmyhYceekguGStJFxBDiDs+N/XF/dJYLGV1lH5+gMpVyVhrGrv0ccIC3bn+su4Y9Fq+/SWJpPSyZvv3l2bj7eDC//WZhLvBCfXYNtR9a+37laDoTrX2ZuVXse1gPnj6o8QNwDB8Ok6Xz8N97su8MeZq/tVjCLWOzqjL56Nu/BphtQDQkFJK6cKD1GzJRG20nN6TlyRJOokOBapXX301Qgjuvvtu3njjDSwWC4MGDcLDw4Mvv/ySFStWkJyczN/+9jcqKyuJjo4+U/W2a7o4FxYWtthXXl4OgKenJwCbN28GYNKkSS3KDhgwAB8fH/bu3UtNTQ2APdBuaGiwl6utrQXAxcWWykYIwauvvkqfPn3sLbGSJF04FI2Cc59A/O4YhPPgEOoTiin5aC81W7NQTdYuexxvD0duuKwbgb4u/LjpODsP5dlT/82OHsDjfS/BQ2dA/fULxM//Qxw/gBDqKc7atrSsCpatTSE1qwKzpfl53AyOPNTvElxC4ngqpgcZ0X0Re1ejfvUKoqKY+sOFaL2d8Lm5r+zqlyTpjOpQoDp+/HhmzpxJXV0dH3/8MVqtFicnJ2699VYsFgtPPvkkV155JStWrEBRFO64444zVW+70aNHoygKP//8MwsWLKCsrIyqqiq+++47Pv/8czw8PJg50zZuKzU1FQCj0djquaKiolBVlbS0NADi4+MxGAx89tlnFBYWUlBQwGeffYajoyPdunUDYNWqVRw5coRHHnnkjD9XSZLOHo2jDvdxUfjeNhCHaG9qtmVR8r+91B0upKsW+HNy1DNzspHu0d5s3Z/HZz8cISO3EgUFZ51tdStRlIUycDKaqx9GUTqfizXheCl+3s7MnhKPQd+yJ8hRq+f+nuMYEhjLt7oQ9kZdjqgoRF30HK5R1fhc3+dPW0BBkqSLV4dvhV988UUmTpzI9u3b7a2Z99xzDw0NDXz++efU19fj7u7OvHnzGDt27CnOdvpiYmJ4/vnnefHFF3nttdd47bXX7Pv69+/Pyy+/TFCQbem/oqIiAPz8/Fo9V9P2kpISAHx9fbn//vt5/fXXGTNmDGBrwX3yySfx8fHBbDbz5ptvMnLkyGbDA9qjqqqKqqrmM3ILCgo6dA5Jkv58Ok9HPK/ohimniuoN6VT9nIIpoxyPqcYuWXlJp9Wgi2qkoKoQpcqfpWtTCPJ1ZljfECJD3NHMegxFd/o5SaeMjkJVRatBahNRbWLaLicsBQHkOZv4bNAlzEk7jGb9AkTRURh/PYqh61N4SZIkNelUn82ECROYMGGC/f+KovDwww/zwAMPUFZWho+Pz586VnPAgAEMHz6cHTt20KdPHzQaDQcPHuTw4cMsXryYp59+GkVRqK+vB8DRsfVWgKbtdXV19m133303vXv3ZuPGjWg0GiZOnGjPIfvNN9+QlZXF66+/bi9vMplobGzEzc2Nk/nss8945513Tut5S5J09hhC3fG+sQ+1u3Kp2ZSBWmfGc0b30+4KrzE38nP2MYxR/twS25fEjdvZnVvLsnV1BPg4M6xvMNGhHh0ek2q1qmzem0Nvox8+nk62lbFOcZnWuhpQ9BrcJ8eS7lfLnuO7yI/ryyOhcTjsXY3ISUFz2Z0oQWd+mJckSRenLh1cpNPp8Pf378pTntKBAwe47bbbCAkJ4YcffiAkJASwjVm9//77+fzzz3F1deUvf/mLPXg+1QVeVZuP1xoxYgQjRoxotq22tpb33nuPKVOm0KtXL0wmE3//+99ZuXIlZrMZo9HICy+8QN++zWfKNrnlllu46qqrmm0rKCjgxhtv7NDzlyTp7FEUBdehoWjdDFSuSqFs8SG8ZvVE28Fk+Sdy1TvwZL/JeDu6oPy6mN6H19Mjuj+JxqvYlVDKil9T8fVyYkivQIyR3mg0pw5Yq2tN/LAxjfziWtxcDPh4nrwVVG20oHHQoWg1+FzXB4DhgIeTM/9N2MzfDTqmjLySkfvXo/vqZTTDr0AZMg1Fc/otypIkSSdqd6BaV1fH7t272bNnD4WFhVRUVKAoCm5ubkRHR9OvXz+GDRvWImH+mfbSSy9RW1vLiy++aA9SwTbr//XXX2fKlCl8+umn3HXXXTg52S7OJ06OOlHT9qaJUifzySefUFFRwUMPPQTAggULWL58OfPmzSM8PJy33nqLefPm8csvv+Dq2jKdjbu7+5+SFUGSpDPPqYc/GmcDFSsSKP3iEF6zeqD3PfV1pIkqVLYUpDEyMAatosHPydYjI6L6gIsH+qHT6KNo6NktiKT0MnYdLuCnzels3Z/H4F6B9Ij1sbWQtqHBZKGyupHpY6MxRnq3WU4IQc3WLBqOFeNzU98Wy5728Ari//pewpLj+1laWcRKY19uyEpm4LbllCfvRjP1Drz8wtv9vCVJkk7llIFqfX09CxYsYNGiRfbZ8IB98sCJrZNeXl7MnTuXW265BYPBcAaq21xDQwOHDh3C3d2dPn36tNgfFhZGVFQUKSkpZGZm4u/vT0JCAiUlJcTExLQoX1xcDLQ9hrVJWVkZH3/8MTNnzrTnjl20aBFjx47lgQceAGzDCB588EFWrlzJ9ddff5rPVJKkc51DpCfe1/WmfMlRyhYfwvPK7jhEeJ7yOKuq8nHSNvaUZOGqd6S/VxCK1nZpVqL7oET/fm3TajT0iPGle7QPqVkV7Dqcz9odmWw/mMfwfsH0jvt9UQIhBKoQaDUa/LycuX1mb/S6k/f112zKoHZXLk69A1AcW/96CHXx4i+9J2CyWkiuLOJoeC+yEncyJeUAukXPkxjbl7BxN+Li5tXOV06SJKltJw1Ui4qKuOeee0hISEAIgU6nIzY2luDgYFxcXDCbzdTU1HD8+HHy8vIoKyvj9ddfZ/Xq1fz3v//F27vtO/euUF1djRDipK24Td39ZrOZuLg4Nm7cSGpqKkOHDm1WTgjB8ePH0Wq1rQaxJ3r33XexWq3cd999gC0NVnl5ebPjYmNjgeb5ViVJurDpA1zxvrEv5d8dpfybIzjEeOM6Ihx9YNuLBBwsy2VPSRZXR/ajv1cQ6op3UHxD0Iy5ps1jFEUhLsKL2HBPsguq2X4gj7XbMzmSUsKkYRE4OGhZtyMTZ0c9l46MRFGUUwapAPpQD5wFuI2NPOUQKYNWRy/vYHp5B0PMQEqLs8hbv5iYlP00HD9CYq8RxIyejd4gMwNIktR5bQaqVquVe++9l2PHjuHh4cEDDzzAjBkzWu3GBltQu2TJEv73v/9x5MgR5s2bx1dffXXGKg7g4+ODp6cnFRUVHDp0qEWramFhIWlpaej1eqKjoxk9ejQfffQR69atazEWdN++fZSVlTFkyJA2nyNAdnY2X3/9NbfeeisBAQHA763LZrPZXq5pGIFcAECSLi46D0d8bu5L3d48anfnUrrwAA6xvwWsAa6YrBb2l2YzxM8WDPb1CeG+HmPp7R2E+Pl/kHEYYvu367EURSE8yJ2wQDcSjpexaU82i344hkYBjVbDqP4hpzyHuaQWnbczikbBMcYbx5jONTD4+IXjM/tJCrKOUrf+S+IObqQ8cReVAy4hKn4IipsPiv7M97RJknRhaTNQXbFiBUePHiU0NJTPP/+c4ODgk57I39+fe++9lyuuuIKbbrqJgwcP8ssvv5zRJPgajYZZs2bx0Ucf8cwzz/DRRx/Zg8eysjIee+wxzGYz1113HS4uLgwZMoS4uDi2bt3KN998w+zZs+1ln3vuOQDmzp170sd84403cHJy4s4777Rv8/b2xsfHh23btmE2m9Hr9WzatAn4vWVVkqSLh8agw3V4OM4Dgm0B655cSj8/QFWogZ0OJeQZ6vHr70BUUBBajYY+PiEIqwVhNqGMmIGmT8dS+ymKQo8YH6LDPNi6L5eDScU4aDU4tdF9D6CarNRsy6JuTy5uE6JxGXDya3x7BYb3hFteIPPoFnRblxG5/XvE9u8RAE5u4O4Dbt4ogZEo3YejuJ3ZnjdJks5vimgjU/Udd9zB1q1b+eSTTxg2bFiHTvrrr78yb948Jk+ezPz587ukom1pbGzkjjvuYNeuXTg4ODB48GAUReHgwYNUVVXRr18/PvnkE5ydnQE4dOgQt9xyC3V1dfTt2xd/f3927dpFZWUls2fP5vnnn2/zsY4ePcrMmTN55JFHuOuuu5rte++993jrrbeIiYkhKCiIrVu3EhISwk8//YSDQ/tmAOfk5DBx4kTWrVtHaGho518USZLOKWqDhdq9uVTszkZvPmGHVkHr4YjGRQ9WgbCoCKsKFoFQVTTOBnSejmg9HdF62H7rfJ3RuvzeMmmxqmzZl4uXuwN9421ZVwpKalm3I5PC0joCfV0YOyiUkIDmKfNq9+RSvT4dpz4BuI2NQnOSoLazrKqVXQfXkZBxmAgUhjt54FBbBVUlUF4AigIRPdH0GgXR/bokP6wkSReWNgPVsWPH0tDQwM6dOzt8UqvVyqBBg/Dx8WHt2rWnPuA0mc1mFi9ezIoVKzh+/DiqqhIZGcn06dO59dZbW0zsSk1NZf78+ezcuROTyURERATXXXcd11xzzUm76ufOnUtKSgpr165tkYtVVVXee+89vvzyS6qqqhgyZAjPPvssUVFR7X4eMlCVpAtHcX0Nq3OOYdDquCZ6AGAL3Ki1YCmvx1regKWsDmtmNqrGBcXREUWngFaDotWARkGtNWGtaMBa1Qjqb5dqBRyNvjgPDsHs6ciKX1MpKKllUK9Axgz8/bqhqoKE46Vs2ZdLbb2Z2HBPRvUOwvu3bATCqmIurMEQfOazjxwpy+N/SVtRULiz2yi6ewUiKooQR7cijm6FmnJwdEHpNgwlsicEx6E4Op/xekmSdO5rM1Dt378/kZGRLFu2rFMnvvzyy8nJyWH//v2nVcGLiQxUJenCcKw8n7ePbECjKIwJiuXamEEtyojSPNSfP4LCTJReo9FMvrXN8wlVoFY3YqlowJReTt2hAkSjFV2wGwd0ED4kDGNU613oZouVvUcKKNmVS+8qE/m9/ek2IgLP08j12hlF9dW8f2wT+XVVzIzqx6SQbiiKglBVyDqGOLoFkbofrBZAAd8QlJA4CIlDCe+B4nzyRVQkSbowtdnX09DQYM872hkuLi5t5iuVJEm6kHX3DOS2+OHEefjj6dBGy6BWBw21KNPuQTG2DGRPpGhsQwS0Ho4Ywj3QDghCpJRStzePXpWNaKuO0zBO4Bjn0+JYvU5LzxoLtZUmajwcOJxbwbalVXi4GggPcrdNxgpyw9nxzHa7+zu58UTfyXyavIPv0vezvzSH7p4BxLr7ExUWj2NkL4S5EfLTEXkpiNxkxLFtcHA9QquzjWcdNAXFO/CM1lOSpHNLm4GqEKLDS/Sd6M9O/C9JknS21ZobcdE7oCgKg/0jW+wX6YchMBLFyQ3F0x/N3Jc7tJqTyWxlzbYMisrquHF6D3z7B9OYUkrN9mwqlifg3D8It3FRKDoNQhUov61a5dw3EK2nIwE9/fGrMZGeW0lmXhXJGeUcTikBwM/LibDfMgiEBrjiYOj6MauOOj13dx/FurwkdhSm82PWEQSgQSHM1Ys4D3/GBMYSEN7N9nqpVijKsrW2HtmKOLIFYvqiGTwVJVhOVJWki0HXX4kkSZIuQkfK8vgwcQv39xxHnEfLpaTVo1sRv3yMMuQylFEzAToUpAoh+G51MoWltYzsH4Jep0FRFBzjfXGI9aZ6UwZ1e/JozK7EIdKThuRSfG7qi9bFgNbNAedetowoHm4O9OvmT79u/qiqoLC0lqz8arLyqziYWMS+Y4UoCvh7OxMW5E73KG/8vLtuvKiiKEwK6cakkG7UW0ykVZWQWlVMWlUxG/KSWZebxGC/cKaG9SLYxQMCo1ACoxDDr0Ts/xVxYB1q2gEIjkUzepZteIAkSRcsGahKkiSdpvSqEv6bsJlAZ3dCXVquyCTKCxGrP4Xw7ijDrujUYyiKwuBegRj0GiKCPZrv02pwHx+NIcyDihWJ1JXUofNzQZiscJKVXDUahSA/V4L8XBnaJwiLVSW/uIbsgmqy86vZd6yQPUcKiI/yZmS/YDzduzZ5v5PO8PuiAUCVqYE1uQlszEthd3EmA3zDmRbeixAXTxRnd5SRMxBDpiKObEHsXoX69SsoPUagjJ6F4uJxikeTJOl81OZkqm7duuHm5kb37t07deJjx45RW/v/7N13eFRV+sDx753JTHrvPSEhgQChdwSkKoggYlsVC666tvW36qqr7i7rWtbF3nZBRRYVCwKCqEjv0ntCgFQC6T2ZTKbd3x8xozEFAglJyPt5Hh7h3HPvvDcmmXfOPec9VSQnJ19UgF2JLKYSonMqqK7gs9S93BU3DA9943P7bSl7UKJ7ozRxvNFzbCqnskroHund5FQs1WzFWmnCwbv2usaTRVRuz8JSUIVzn0Dcx0WjucDH+EaThX1H89iXnIfNqtInzo9hfUNwdW7b+ayVZiPrzqSw8WwKRquFgX4RzIzuh5/TL5uxqOYa1F3fou5dAzo9yojrUPqORdHIJitCXE6aTVQv+uKKIolqC0iiKsTlRbWYL7g2aHWNhe+2pJF5tpxZk+KICG5YRspSaqR0RTKqyYrfnAG1Za2oLT1VuT2Lql3ZaD0d8biqO44RXhd8H5UGE7sO53DkRCEarcLAhECGJgbjoG3btQhV5hrWnTnOujPHsakqE8N6clV4Ak7aX76manEuto2fQmYS+IejGXerTAcQ4jLSZKL6zjvvtMoLPPTQQ61yna5AElUhOg+Lzcr3p5OYGNajXuJUR60oxvblv1BGzkTTY2iLr7/7SA47Dp5l3NAI+nT3azCiqlptFHywD7XGgtc18Th2a1ieypRdRtn3J7GWGnEZEIz76CgU3YWPOJaUG9lx4CwpGcUE+7sybWwMbi5tvy1qSY2BZekH2V2Qgafemeui+jI0IBrNz18TVVXh5D5smz6HyhKUHkNrpwPIrldCdHpNJqri0pNEVYjOY8mpPWzKOckDCaPp61v/51WtrsT2xctQWYrmpidR/MPPeb0ak5XTueXERtTOca02WiitMBLs79b0ORklaD2d7I/9G2MzWanckoHhQA5abyc8r45DH3pxRf5PZJawZls6ep2Wa8Z2IzTg0tQ4TSsv5Iu0fWRUFBHl5sO0yER6eQfbk3jVXIO6e3XtdABFgzJ0KsrAybLjlRCdmCSqHYgkqkJ0Dmnlhfzr0I9MDO3JrG79GxxXq8qwffNO7ar08Phmr6WqKjsOnOXg8XxMFiv3XJ+Iu2vjo5S2GgvVh/NwGRTS4vKBNVmllH1/EltFDS59g3EdHo7W7cJHQwtLqlm58RTlVSauHBJOYpz/RZU0PF82VWV3fgYrMg5RYjIQ6uLFxLAeDPaPxOHn+alqaQG2LV/Cqf3g6Ydm9E0Q2/+SxCeEaF1NJqpvvvkm995770UV/f+tyspKFixYwP/93/+12jUvJ5KoCtF5HCk+Q4J3MFrl53mhVWW1Bfx9a1ewt6QW9bJ1J9A5aBncO4ggv8aX6atmK8VfHsWcW4nvbX3RBTY90toUm8lC5ZZMDAdzUBw0uAwKxXVwKBrHC19s9f2WdNLPlNG7ux/jhka0+bzVOhablT0FmfyYncxZQxleemcmhPZgVFAszj+PoKpZSdg2LoGisxDTD83EO1Bc2n7LWCFE62kyUR00aBBOTk784Q9/4IYbbkCvv/BP3pWVlXz++ecsWLAAVVXZvXv3BV/rciaJqhAdW2PJp6raasslbfkKvALR/O6Z80pQK6pM9pFTm01Fo2n+nKrd2VRszsDr2h44xftd+E0AlpJqKrdmYkwpRHF2wG1YOC79glEcWp5kqqrKjoNn2XU4h0BfF6aNjcHD7dJtz6qqKsdKcliTncSJsnw89c78IeEKot1rv0aqzYq6fy3q9uWgd0YzYTZK9wGXLD4hxMVpMlE9e/YszzzzDDt37sTb25vp06czdepU+vTpc14XNpvN7N27l2XLlrFu3TqMRiNjxoxh7ty5BAYGtupNXC4kURWi47KqNt49tpmBfhGMDIqxt6sVxdgWPgOBUWgmzkbxCW72OharjY27sziRUcLt0xLOO6lTbSrmM+Xow1uvXqg5t4KKLZmYMkvRuOrRR3iiC3FHH+KOg7+rvYrA+TiVVcIP2zLQaODqUd2IDrv0dU3Tygv5MGU7pTXV3B43lGEB0fZjauEZbD98APlZtbVXr7wFpantbYUQHcY556h+//33vP7662RlZaEoCl5eXiQmJhIbG0t4eDju7u44OTlRUVFBaWkpeXl5HDp0iGPHjmEymVBVle7du/Pggw9y1VVXXar76pQkURWiY7KpKktO7WFL7inuihvOsMDoesfVwmzwDUFRzp3YrdqUysnMEob0CWJEv9BzjqTaDGY0Lm27GKgmowTDoVzMZyuwVZpqGx006ALdcO4VgHNi4HmNEpeUG1m1KZXCkmqGJgYzvG/IOe+vtVWajfw3eRsnyvKZFNaT66L6oqmbnmG1oP70Leru1eDmhWby3SgRF1YrXAhxaZzXYiqbzcbq1atZsmQJ+/fvrz2xmV9adY/HBg8ezB133MH48eNbL+LLmCSqQnRMRouZfx9eSy/vEGZG96tNeI5tR+lzxXklp7+WW1hFRZWJ7pENd7D6rao9Z6j86TS+t/VtdmV/a1FVFVtFDaazFZjPVmDKKsNSUIUuzAPPyd1x8Dl3DGaLlfU/ZZGUWkRksAdXj47GxenSrrq32mx8kbaPzTkn6e0dwj09RuDs8Mv0NTUnrXZ0tSQfZeR1KEOubvH/RyHEpdHiVf9FRUXs2rWL3bt3c+bMGYqLi6moqMDR0REfHx+io6Pp27cvI0aMkEf8LSSJqhAdl9FqxlHjAKioP3yEmrwTzfWPoUQmnPPcgmIDPl5OaDXnnwxVJ+VTtvoEjnG+eE3rgXKJRyahNnGtPpJHxaZ0VIsNt5GRuA4OPWcsqqpy5GQhG3dl4ajX4u/tgl6vxVGvxVFX+18fTyeiQj3RX0Rd13PZnHOSz1P3EuDkzrWRiSR4B/+y0Mpcg7p2EerxXRDTH81Vc1Ac2/7DgBCiZaQ8VQciiaoQHYfJamF5xkGuieiDq+6XeaS2pB2oP3yIMmIGmmHTznmdjDNlrNyYSv+eAVwx8Px/rs05FVTtOYPnlLgLWuTUmqyVNZSvS6PmZBEOAa54XtX9vKoO5BVV8dOhHAxGMzUma+0fsxWLxQaAVqsQFeJJXJQ33cI8cbzArV6bc6I0jwXHt1NuNqJVNMR5BpDoE0qibyi+jq6oB9ahbv4SvPzRXPuQvWqDEKJjkES1A5FEVYiOodpi5p1jm0gtL+C+nlfQ3++Xgv2qzYZ6ch9K3KBzztssKDHw6bfJ+Ho5cf2EOFycO3fheWNKIeXrUrEZzOjCPHCK98Mp3g9tE3Vfm2K12jhbUMXJzBJOZpZQVW1Gq1EID3Yn2N8Nf29n/H1c8HDVt0rtU6tqI7W8kMNFZzhcfIa86nIAwl29uSI4lmEmMw7fLwBzTe281bhBF/2aQojWIYlqByKJqhAdw6GibOYnb+POuGEMDogCQDVWoTg1XuO0Kaqqsj8pj17d/XA6j9FC1WLDcCAHlwHBLVpxfynZjBYM+89iTCnEUmgABfThnrVJaw9/NE4tGxVVVZWcn5PWtOxSSspr7Mf0Oi3+3s5EhHgwoGdAq4245lWXc6T4LD/lpXO6qgQnrQNj3f256tBW9PmZ0K0vmsSxENUbpQXTNYQQrU8S1Q5EElUhOo5iYxU+PyemtkObULcvQ3PTU+d8NKyqKiXlNfh4OrX4Nct+PEX1oVy8b+yNY6TXhYR9SZkLqzAeL8R4vBBrSTWKkwPuo6POu0pAY0xmK4Wl1RQWV1NQYiC/2EBOQRVOjlqG9AmmX4+AVttUQFVVMiqK2JRzkr0FmahWC78rLmBwbgYO1ZXg7oPSZzRK7ytQ3Lxa5TWFEC0jiWoHIomqEO0r11BO0G92LrIdWI+68TOITkRzzR9QdE0/5jabrfy4I5PU7FJmX9sLL/fzL3xfk15CydJjuA4Nw3101IXeQrtQVRVLbiXlm9IxZ5ejC3bHY2LMBe2e1Zi8oiq27T9D5tly3Fx0jOgXSkKMb6uWvqo0G9mRl87GsymUVldyE1pG5GejPX0cNFqU7gNQrrgBxcO31V5TCHFukqh2IJKoCtF+DhVl837SFu7teQUDfj0nNT8L9dBGlHG3omibf/S8alMqp7JKGNk/lMG9g1o0qqiqKsbjhTjF+7XLCv/WoKoqxqQCKjalY6s249IvGLdRkS2eDtCUrJxytu0/Q25hFd4eTvSK9SU6zBM/L+dWmcsKtYvoVmUdYV32cdx0jtwREE3P0yfh0EZQlNpyVv3GoWjarlqBEOIXkqh2IJKoCtE+8gzlvHhwDQHO7jyROAGdRgOq2uJkpKi0mooqE1Ghl35Xpo7EZrRQuS0Tw8EcNM46XAaG4NI3CE0rLCZTVZVTWaXsPpJDXpEBAHdXPdGhnnQL8yQ82B2dw8UnkVmVxfzvxC5OV5XQzzeMW/yjcd/2NaQfgYBINBPvQAmMvOjXEUI0TxLVDkQSVSHaR5W5hs9T93JdVD+89U6oaxeBzYpy1ZxmC8HbbCqHUvLpHeuH7gLqgdpMVkq/ScZlQAhOMT4XcwsdkjmvkorNGZgyS1F0Gpx7B+IyMKTVNi+oNJhIP1NGenYZmWfLMVts6HUaenbzpW98AH4X+TpW1ca6M8dZlXkEgAG+YUw2GAja/T1Ul6P0n4AyZArKb6aLCCFajySqHYgkqkK0L9VqwfbdfDi5D2X4tSjDrm3ykXJeURU/7sigoLiaicMj6RPn38LXslGyLAlTZile03vi1P3ynftoLqjCsPcM1UkFYFNx7O6LS98g9BGerVbdwGK1cSavgqTUYk5kFGO1qYQGuJEY70/3SO+LWoBVUF3B2jPH2Z2fQbXVTJiDI7flZRORdri2g18YSniP2u1Yw+Jl4wAhWlGTiermzZtb5QXGjBnTKtfpCiRRFeLS2pGXRg/PQPvqflVVUdcvBu8gNAMnNXtuUmoRW/dlc+XQCLpHeLV4jqS13EjRkiO4DQ/HJTHogu+hM7FWmjAcyMFwMAfVaEHRa9FHeeEU44M+2rvF9VibUm20cCy1kMMpBZRW1ODs6EBCrC+J3f3xvoBqDHVMVgsHik6zPTeNlLI8Qg2VXG2x0ddQjvZsGljNoGggJBbNFdejhMS2yv0I0ZU1maj26NHjoienK4pCUlLSRV2jK5FEVYhLZ2deGh+f+Ikxwd25JeaX4v2qqjb5u+9MXgUhAW4oioKqqj8/ar7w+ZA2kxWNvustylHNVmoyS6lJLaEmrRhbpQkAXYg77qOj0Ie3zhxfVVXJyinnUEoBaafLsKkqYYFu9IlrjVHWSjbnnGDD2RPoNVpmhPfiClVBOX0c9dh2qCxBSRiBcsUsFNeuPWdZiIvRZKI6bty4VnmBDRs2tMp1ugJJVIW4NCpMRv6y5xu6efjxULeBaFbPR3PFLJTgbo32V1WVDbuyOJRSwLSxMXSP9L7g1zaeLMIx2rvdt0XtKFRVxZJfRU1qMdVH87CW1eAyKBT3KyJb9WtUVW3m2KlCjpwspKyiBidHLb27+zO0TzCOF/FhIddQxpLUvRwvzSPCzZvfxQwmyskNdde3qPt+BJ0eZfgMlH5XSqUAIS6AzFHtQCRRFeLSOVVWQJgKum/egtICNNc+iBLdp9G+KRnFrN6cRv+eAYweFIb2AncrMhzNo/z7k7iNisRtePi5T+hibCYrFZvSqT6Ui4OvC55T4tAFtU4t1jqqqnI6t4LDJwo4kVGCm4uOcUMjiI248A8fqqqytzCLpWn7KTNVMyooluuj++NUXoRt46eQmQR+YWjG/Q4lLL4V70aIy58kqh2IJKpCtK0qcw2uul+K8KvFOdiWv4lm0l0o4U0nEBarjSMnCunXw/+Cp0RZigwULtyPPsIL75kJMqLajJr0Esp+OInNYMZtWDiuw8LaZEvZswWVrNuZSWFJNTHhXowbGoH7RcyTNVrMrMo6wvozKfg6uXBX3AhiPPzg1H5smz6HimKUuMEoo2ehePi14p0IcfmSRLUDkURViLZTXFPFKwfXMjq4O1eHxtuL96tWyzkL+bcWw5E8nOL9uuS81JayGS2Ur0vFmFyAg58LbiMicIzzbbXC/nWsNhv7k/LYeTAHRYGR/UPp1yPgona9Si0v4KOUHRQZDVwdnsA1EX3QWM2oe9eg7vm+tkbv4KtRBl+Fojv/3cuE6IqaTFSHDx9+8RdXFHbs2HHR1+kqJFEVom0YLCZeOfgjFcZK/lZejqvJiGb6Q83WSLVabfywLZ1BvYMI9HW9oNe1FBuwFBhwipfRswtlPFFIxZZMrCXVOPi54Do8HKe41t+9q6yihvW7Msk4U46LkwM9u/mSEOOLv4/LBV2v2mLmy7R97MhLI9LNh7vjRxDk4oFaXoS69SvUlD3g7oMy+kY08YNb9V6EuJw0u+r/oi+uKCQnJ1/0dboKSVSFaBtW1cYHRzZx28HNOBVmowyZijJiBkozc03X7czk8IkCrhnTjbiolhfjN54souy7Eyh6Lf73DES5iOoAXZ1qUzGmFFK5MwtrUTVaX2fchoXj1N23Vb+uqqqSfqaMIycKST9Ths2m4u/tTEKMLz26+eJ6ATtr7S/M4pOTuzHZrMyK7s+Y4O61VSOyT2DbtATys1D6j0cZc3Oz349CdFVNJqq7d+9ulRcYMmRIq1ynK5BEVYjWo6oqZaZqvBxd7P9Wd36DEhCJEtu/2XMrqkws+uYYfeP9uWJgy38WVZtK0eKDoFHwnt4DrceF1+4Uv1BVlZoTRVTuyMJSaACtgi7EHccIL/QRXuiC3VptLmu10UxKRglJqUXkFlah1SgM7hPE4N7B6Fo4v7i0xsCiEz+RVJpLT68g7ogbhrejC6rNVju6uu9HiOmHZsq9MhVAiN+QOaodiCSqQrQOk9XCp6f2cKwkh78OuBoPfct3CiqrrMHdRX/ecxXNBVXYDGYcI70AsFbWoHHSyaKpNqCqKqaMUmoySzFllWLJqwJA0WnQR3rh3CsAxxifVktai0qr+elwDinpxXi46Rk7OJyY8JZt8qCqKltyT7E0bT9aRcPNMYMYGhCFoijYDm5A3fgZBESgmf4IiptXq8QtxOWgTRNVg8GAi8uFze/piiRRFaJ1vHV0I8dKcrgmvBdTDm9DiR9yXvMAK6pMuLnoWpSAWMtrKF93iprUEhwCXPGd3a/VF/yI5tmqzZhOl2HKLMN4sghblQnF2QHnnv449w5EF9g6Ja5O55azYVcWRaVGokI9uHJwRIt3uiqormDhiZ9ILS+gn28Yt8YOwUPvhJp2CNvq/4KTK5rr/ojiJ+8BQsAFJKoWi4V169Zx6tQpjEYjNput3nGr1UpNTQ35+fns3bu31aYQdAWSqArROk6U5lFjs9Ar6SfU3d+hTJiNJrH57ZzLKmv4/LvjxEV5c+WQiPN+LZvJQtH/DuKcEIBL/2A0FzCPUbQe1aZiyiih+mg+xlNFYFVx8HdBF+SO1sMRjYcjWndHtB61f1o66mq12Th0vIAdB89isdoY2T+EQb2CWvThxKbaWHvmOCszDqPXaunrE0Yfn1B6mc3oVr0H5ho0U+9rsq6vEF1JixLVyspKbrvtNlJSUs7Zt24bQllMdf4kURXiwtlUGwrKL1uhmmuwfTIXJSwezcQ7mj3XWGNhyXfJGIwWbrqqB37ezU8VsFaZwKqi9aidT9jctqui/diqzRiPF1J9vABrSTW2KnO944pOg2N3X5wTAtBHerWokkBVtZkNu7I4mVlCfJQ3k0ZGoXNo2cKus1WlfH/6GEdLcjBYTGgUhf56N248uh230kI0w65BGXatLLISXVqLEtW3336bd999F41Gw9ChQ3F1dWXdunX07NmTbt26kZeXx8GDB7FarQwaNIh//OMfREdHt2X8lxVJVIW4cF+k7sVgMXFH3HA0dcmq0QAOOhSH5kc5bTaVLfuy6R7hRWige7N9TTkVlH6TjNbDCZ9b+kiC2omoFhvWihqsFTXYymswnSnHmFKIWmNF46LDqac/zgn+OAS6ndf/V1VV2XM0l237z+Dv48y1V8bi6dbyxVBW1UZ6eSGHi89ypPgMBRXF3JyVwvDCHAwhsbhOewCNq+eF3LIQnV6LEtUZM2aQkpLCG2+8weTJk7FarQwdOpT+/fuzYMECAFJTU/n9739Pfn4+S5cubZUyV12FJKpCXJif8tJZeGIn40PjuSE0ATSaNlk9bas2U/DfvWhcHPCa3rPV5j6K9qNabNSkFVOdVEBNWnHtVIEgN9yGhOHY3fe8RlnTs8v4bksaGo3CNWO7ER7kcVExFVRXsi33FMbDG5mRlkSNTk/W6Bvo0WsU+ku0OYUQHUWLniecPn0aX19fJk+eDIBWqyUhIYEDBw7Y+8TExPDSSy9hsVhYuHBh60YrhBCNSK8oIs4zgOvDe2Nb/R9sX/4b1WZt9hxVVdm4O4uz+ZXn/ToaZx2e18The3s/SVIvE4qDBqc4P7xn9CTggaF4TIhBNVooXXmcwo/2YTiUi2qxNXuN6DBPfje1J85ODiz98QR7juZQVlnDha5V9nd247roflx/zcMkX3UXZq2WHus+4Yflr7M959QFX1eIzqhFH81qamro1q1bvbbo6Gj27NnD6dOnCQ8PB2Do0KEEBASwd+/e1otUCCGacEvsIEwWE6x4G7KSUSbegaJpfr7ggeR8DiTn4+zoQEhA00mntdyI8VQxrgNCAHCK9W3V2EXHoXFywKV/MM59g6g5WUTlrtOU/3iKyu1ZuPQLQh/uiUOgW6Nb4Hp7OnHLlJ78sC2drfvOsHXfGZydHAjyc7X/CQ1wQ9+CDQr0WgcG9ByBLbofFd/9l6kZR0ktfo+Pe49kSr+JBLpc3MitEJ1BixJVLy8vysvL67XVJadpaWn2vwMEBARw4sSJVghRCCEasqo28qsrCHapnbund9BjixsECSPQJDS/BbTZbGXP0Vxiwr0YmhjcZL+azFJKVx0Hq4pTnB9aN32r3oPomBSNglO8H45xvpgyy6janU3l9qyfD4KDnwu6YA90IW44xfraKz046rVce2UM+cUGcguqyC2s/ZOeXQaAVqMQGeJBbIQ3MeGeODudX4UIjZMLHtc9ii1pBxEblxC563vWZSWjHXoNEyL74HCOD2VCdGYtSlQTEhLYunUrJ0+epHv37gB069YNVVU5cOAAY8bUln+x2Wzk5OTg7NzyIttCCHEuqqqy5NRefspP5+/9JuPn6gVwzhJUdXQ6LbdM6YGj3qHZRTOGgzloXfV4Te8pSWoXpCgKjlFeOEZ5YTOYMeVUYP75jzGlgOrDuVQ4puM2LByXASEoDhoURSHQ15VAX1f6/nydGpP154S1lJNZpaRll6EoEBboTvdIb+KjfHB2av7tWFEUtL1GoolOpGbjZ1yVspu87z/kk/iB9Eq8kig3H/yczm8RmBCdSYsWU33zzTc8+eST+Pn58cc//pHrr7+eyspKxowZg6IozJ8/n549e/Lf//6X+fPn07dvX7744ou2jP+yIouphDg/a04nsSzjINf5hDNx9w8oQ65G02vUOc+zWG1oFOW8d5uy1VhAURp91Cu6NlVVseRXUbktk5q0ErSejriNjsIp3q/RZLGuhJmqquQXGziVWcrJrBKKy4xoNQoxEV70jvUjItjjvL4/1cxjGNcsRF9Zwm6fIFaHRlPh6km4qzfhbt5EuvswwDdcFl+JTq9FiarNZuPee+9l27ZtODg4cOjQIbRaLS+99BKLFi1q8MP58ssvM3369FYP+nIliaoQ5yevupyDaYeYsGMlmKrRXPcoSkhss+eoqsqPOzKoqjYzY1z3RpMB1aZSvi4V14EhOPjKrnri/NRklFKxKQ1LgQFdsDuug0OxGS1YSqqxllRjLTFiKTOiD/PAY2IsDl6/7GZVUGzg6KlCktOKMNZYcXfVkxDjS89uvvicY9cr1VyD9adVqPvXodgspIfHsy60G0k2CyabFQ+dE5PDExgdFCsJq+i0LmhnqiVLlrBr1y7eeecdAEwmE08//TTfffcdqqqi0Wi49dZbeeaZZ9ok6MuVJKpCnD+1qgzb2kVoRkxHCYg8Z/9Dx/NZvyuLoYnBjOwf2vB6qkr5mlNUH8nDY1IsLn2D2iJscZlSbSrVx/Ko3JqFrcpU26hVcPByRuvthMZNjzGpAFQVt1GRtVMFfvVhyWK1kXa6lCMnC8k8W7sWxMvDkZgwL6LDPAkNdEPbROF/taoMdc/3qIc2gqpCr5FkJAxnVXE2yaW5krCKTq3FiWpz8vLyOHv2LJGRkfj4+LTWZbsMSVSFaNrx0lw255zkrrjhF/Rmu/PgWXKLqpgxLrbRR7OW4mqK/ncAl0GhuI86d+IrRGNsJiuWvMra7VrdHeslo9byGsrXnqImrQRdsDueV3XHwa/hyH1FlYnU06WkZZdyOqcCq03FUa8lItgDNxcdzo4OODs64ORU+19/HxecHR1QK0pQd3+HemQzKArKwMmk9RjCqpwUjpfm4aFz4prIPowOavxnQIiO6IIT1cOHD5OYmFivbe3atWg0Gq688ko0suVbi0miKkTjsiqLefXwOnwcXfmzsw9OkQkoF7BTj82mNjv/z1JqROvpKG/ios2oqooxuYDyDWmoJiuuA0PQ+jijOGhrF2M5aFB0GrTezmhd9ZjMVjLPlpOWXUp2XiXVRjMmc/26rs6ODkwaEUVMhFfta5QXoW5fjpq8E9x90Iy9mVP+YazMOsKJsnwSvIK4I24YXo4yvUV0fC1OVE+cOMEjjzxCVlYW27ZtqzdyOmfOHHbs2EF0dDSvv/468fHxrR7w5UwSVSEatzrrKNtyT/G0bxSuK99F6XMFmgmzz3leWUUNJeVGokKbTmrN+ZU4+LtKciouKZvBTPn6VIzHC5vsowt2w7GbD47dvOtt62q12jCarFQbLVQaTGzdn01BcTWJcf6MGRyGzqF28Z+afQLbhk+hMBsie6NceTPbair5Km0/Dhott8YOZpC/PD0QHVuLEtX09HRmzZpFVVUVXl5efP7550RFRdmPv/322yxfvpyzZ8/i7e3NihUrCAwMbIu4L0uSqArRtOrqSvSLngMnVzS/ewZF33z5O0O1mc+/P47JbGXOzD7oGim0XpNRQsnXSbiPicJ1UMN5q0K0NZvRgmqyoFpsqGZb7X9NVsy5FdSklmDOqQBA46LDMcYHl0Eh6Pxc613DYrWxff8Z9iXl4ePpxJTR3QjwqR0tVW1W1IMbUXesAKsZpd94CnuP5KPTR0mvKGKIfxS3xA7CxUHKr4mOqUWJ6pNPPsk333zD1KlTeemll9DrG35jm0wmnn32WVauXMktt9zC3/72t1YN+HImiaoQ9RVUV+Lv/MuuUWrGUfDwQ/E590KnlRtPkXGmnFmT4hrdecpSUk3R/w6i9XTE55ZENI6yyER0PDaDmZr0EmrSiqlJLUE1W3Hq4Yfr8PAGCWvm2XJ+2JZOdY2FUQNCGdAz0D7VRa0qQ926tHY6gMYB+oxiU0g0Swuy8HR05o7uw+jpLQsIRcfTokR19OjRGI1GtmzZgpNT02UzDAYDo0aNwsvLiw0bNrRKoF2BJKpC/GJbbiqfndrDY4njifHwb/H5lQYTxWVGIoIb32ZStdio2JqJ64BgtOcoAyRER2CrNlO19wyGfTlNJqzVRjM/7sgk9XQpgb4uXDkkot4HNbUkr3bBVfJOACpjB7DQ04tkbIwJ7s7M6H44ac9vxywhLoUWJap9+vQhPj6epUuXnrPvzJkzOXHiBEePHr2oALsSSVSFqJVeUci8Q+vo7hnAw25+KHmZKIOvRjnHan9VVbGpapNlfIS4HDRIWOP9cB0Wji6gNmFVVZWU9GK27Mum0mAmIcaXUQNCcXP55SmoWl6EuvcH1CNbUW0WMiJ78h8vXxzdfbgzbhjdPQPa6/aEqKdFz7r8/PzIzc09r75FRUV4eDQ+kiGEEM1x1znR2yeE2yP7wpIXUB30KIOuOud5e4/lkXa6lOsmdEffyJxUqN0WVRfkhi7IvbXDFuKS0DjrcL+idl511d4zGPbnYEwpxDHWB7fh4eiC3OnRzZdu4V7sPpLDvmN5nMoqYVhiCP17BqDValA8fFHG3Yo69BrU3auJPrSJl844sDYsljcN5YwJT2B6ZKLUXRXtrkXDDn379qWoqIgvv/yy2X4rV64kLy+Pfv36XUxsQoguys/JjT8kjMYl+ScoL0Iz8Q4Uh+YfR2aeLWPb/mxcnXXoHBr/1VaTXkL52lSq9ue0RdhCXFJ1Cav/fYNxGxGB6XQZRYsPUbz0GKbsMnQahVEDwpg9vRehge5s2ZfNxyuOcuh4PmZLbYkrxdUTzZW/QzN7LpqweCanH+OfyfsoOLqVf+7/ntTygna+S9HVtejR/759+7jtttvQarXcfffdzJw5s96q/9OnT7NixQoWLFiA2Wxm0aJFDBkypC3ivizJo3/R1W3LPUV/3whcdbWPKFWbDbKPo0QknPPcA8l5HDlZyM1X92h0NFW1qRR+sA9Fr8Hnd33R6BsfcRWis7LVWDAczKFqzxnUagtoFLReTjj4uuDg40yxonI4r4KMihp0LjoGJATSN94fR/0vo6ZqxlFsm7+AorNkePjwfWA4AQkjmBHVT0ZXRbtocR3V+fPn89prr9nruel0OpydnamursZsNgO182MeffRR7r///taP+DImiaroyn7KT2dhyk6ujUxkakTvC7qG1WpDq236QZGlpBoUpd5e60JcbmwmKzWnirAUGrAUV2MpMmAtNYLtl7d7k4OGEkWlSqfFLdQd/zg/AuL80TpoaktaHd6Mbfd3KJUl5Di5sDs8jj4jZtLdV8q4iUvrgnam+umnn5g/fz67d+/GYrHY2zUaDf369eO+++5jzJgxrRpoVyCJquiqykzVPLNnJdHuvjzaexzKsW0obt4o0X2aPU9VVfKLDQT6ujbZx1JSjdZNj9LEnFUhugLVasNaaqxNXIursRYbqM6vwlJkwMFamwaYFah016MEu+MV54dftCeatP0Yd32LY3EOZQ56smL70uPKW3Fyljne4tK44C1UAaqrq8nJyaG0tBRnZ2fCw8Nxc2tYr1CcH0lURVd2sCibbu5+uBvKsS36K0T1Rjv9oWbP2Z+cx6bdp7lhcjzhjSyOMp4opOz7kzj3CsBjQkxbhS5Ep1ZRWEVBcj7VGaU4FlXj9vMWrTUaBTXSk6AhYSiWLAp3LCMo/zSljs6UX3ED0YkyICXa3kVNOHF2dqZbt26tFYsQogvr51v74cz240LQOqAZd2uz/bPzKti85zQx4V6EBTb8gGzKqaD0m+Pogt1wHSof/IRoirufK+5XRMMVtf8uL6gkPymfyuRC/NJLKU0vRXXR4d7rDs7G5aPbt4SIdf/jRNJ2Qqbci5uHX/vegLisXfCI6oYNG9iyZQvp6elUVlby9ddfU15ezscff8zNN99MQIDUYGspGVEVXc2KjEO46RwZHxJvn/euGiqg4DRKZPMLqGpMFjbtOc3YwRE4NrIwSlVVjEkFOMX7oTRRBUAI0TRVVTl+ooCMnacJKKsh2GRDAziEuVOiP0hk1laqtTryh04hZvBUNFK/WLSBFieq2dnZ/PGPfyQpKQmo/UZWFIXk5GSOHj3KrFmzcHV15e2332bEiBFtEvTlShJV0ZXsLchkwfHtjAnuzu9iB5/3eRarDYcmFkxZq0xYCg04Rnq1UpRCCIvVxsHkfPYfOktYuYk+Ris6iwpheszl3xFSdZpMvxC8rroHn4DI9g5XXGZa9PGnsrKSu+66i2PHjhEcHMztt99OeHi4/bizszORkZFUVVVx3333ceLEiVYPWAhxediVn0GMhx83dhuAWlWGddV7qJUlzZ6zPymPT79NotpoaXDMZrJQ8nUSpSuSsTVyXAhxYRy0Ggb1DuL2WYl4DA3j+0BnjrhosZwx4VAxgdPeswgoKsPps39yfN3/sFrN7R2yuIy0KFH98MMPOX36NJMmTeKHH37gL3/5C/7+v+zBHRMTw3fffcfVV1+N2Wzmww8/bPWAhRCXhzvihvFgwhi0qort2/ch/QgYKprsv+twDpv2nMbbwwmdruGvror1aVjyK/G8Jh6Nk9R7FKK1OTs6MGpAGHfd2JfAibFsjfLgpJMWfaE75bYbOaObQLdDO8n74EnOpB5o73DFZaJFieqPP/6IXq/n+eefR6/XN9pHq9Uyd+5cnJ2d2bVrV6sEKYTo/IqNVbx9dCPFNVUAuOkccdU5wplTcDYVZeIdKAERTZ7v6e5Ir1hfrhkT0+ijf7dRkXhd2wOnGJ82uwchBOgctCTG+XPz9X2InJnAoQRfsvQa3A2h5Kq3kldzBeqqpSQvewNjMx8+hTgfLRp2yM7OJjY2Fk9Pz2b7eXh4EB0dLY/+hRAApJTmMT95GxbVRn51BT6Ov9Q9VSJ6oLnznyjegc1eo0e0Dz2i6yehqqqCTUXRatC6O6J1d2yT+IUQDSmKQmSIJ5EhnhiqzWQczYX9OQSX+1OtuZrsHAuFi/9H6Ih+dOszsr3DFZ1Ui0ZU9Xo9ZWVl59W3qqoKFxeXCwpKCHF5URQFL0dn/tJvMj28ggBQTdW/HG8iST16spD9SXlNXrfqp2yKvzyKzWRt3YCFEC3i4qwjYXA4CfcNwfWGXjj4ONPH4EBQVT+StxnZ+sVCjNUyuiparkWJalxcHGfPnuX48ePN9ktKSiIzM5O4uLiLCk4IcXmI8wzgmf5XE+jiAYBaY8C2+O/Y9q5p8pzUrFLW7swg/UwZNlvD4iQVmzOo3JaJ1sMRpZE5q0KI9uEe5U3YnQPwuj4BFxcdAysdcc+JZ93/NnFs/872Dk90Mi367X799dejqipPPPEEGRkZjfbJyMjg//7v/1AUhenTp7dGjEKITqjcZGTN6SRsP1fA0/xcJxVAXf8plBejhMQ2eq6qqhw4nkeAjwvTxsag0SgN+mhcHHDpH4znVd3tNViFEB2Doig4dfMh5PeDcL0yGj+bjYHl3hRvV1n5yUpKS4rbO0TRSbSojqqqqtx///1s3rwZvV5PQkICGRkZlJWVceutt5KWlsbu3buxWCwMHjyYjz/+GK1W9tc+X1JHVVwurDYbbxzdQHpFEc/2v5qgn0dSAVSLGdvKd1GCu6EZfm2T1zBbrFgsKs4/r+C3VtRgPFmE64CQNo9fCNG6bNVmijaewnKsELOikOSioouuZsykcThopUqHaFqLC/6bTCZeeeUVPv/8cyyWhrUKNRoN06ZN469//Suurq6NXKFtnDlzhnfffZdt27ZRXFyMt7c3Y8eO5ZFHHqlXQgsgPT2dt99+m3379lFaWkpERAQ33ngjt956a4OdNQoKCpg7dy7bt2/H1dWVcePG8fjjj+Ph4cFvPfbYY+zdu5cff/wRR8eWL+qQRFVcLjbnnOSzU3u4K344wwKiGxxXVRVUFeU3P28VVSYUBdxc6lcVMRzOpWJjOqpNxf+egbJoSohOylJk4Ow3h9AXWSnRKpxwqyJ2eCC9+vRt79BEB3XBW6jm5+ezZcsWUlNTqaystBf7Hz16dL1NAC6FI0eOcNddd1FRUUFcXBwREREcPXqU3NxcIiIiWLp0qb1SwfHjx7n11luprKxkwIAB+Pr6smvXLsrLy5k2bRrz5s2rd+3Zs2eza9cuhgwZAsDu3bsZPXo0CxYsqNcvOTmZ6667jueff54bbrjhgu5DElVxubCpNo4Un6Wv7y/fx6rNBtUVKK6NVw0xW2x88X3t/Pdbr+n5y5aqqkrpN8dRayx4TO6Og5dT29+AEKLNqKqKIaWAoh+OozdryHDUkO9dxMgpw/D19T/3BUSXcsGJ6vmwWq1t/ujfZDIxbdo0MjIyePbZZ7n99tsBqKmp4YknnmDNmjXcfvvtPPvss6iqyvTp00lJSeGVV16xz6EtLi7mzjvvJCUlhbfeeovJkycDkJKSwrXXXsudd97J008/DcDLL7/MwoUL+fbbb+nevbs9jjlz5nD27Fm+/fbbC75nSVRFZ1e3pXJjbLu/Q937A5rb/obi4dvg+A/b0klKLWLGuFi6hXvVv67t5xHYJrZOFUJ0PqrZSuHmk5gPFGBF4bgLqGHlXDllAjpd47XaRdfTZr/19+3bx3XXXddWl7f77rvvyMjIYNq0afYkFcDR0ZGnn34aPz8/0tPTAdi+fTspKSkMGTKk3kIvHx8f/v73vwOwePFie3taWhoAw4cPt7fV/b3umgA7d+5k27ZtPProozInV3RZleYa/nXoR06V5Tc4puZlou5YgRLRE9wbL8gfF+XN6IFh9iTVlF2OpdgAgKJRJEkV4jKj6LT4T+hBwO8HQ6COPgaITvXkhw+3svenHe0dnuggzjmDOT09nWXLlpGamoqqqvTp04ebbroJX9+GIyIAZWVl/Pvf/2bZsmW04WCt3Y8//gjAXXfd1eBYcHAw27dvt/9769atAEyYMKFB37ppAPv27aOyshI3Nzf7PFSj0WjvV1VVu6tO3fxbVVWZN28eiYmJ9pFYIboam2rjg+PbOV1ZglbTSEJpNUNQNMr425scce0W5kW3nx8kmPOrKFl2DAc/V3xu6SOr+oW4jDl4ORE+eyjVqUXUfJfE4ApHcn6Cb5K+o9+4HkRGdWvvEEU7ajZR/eijj3j11Vex2Wz2pHPTpk3873//4/3336d///71+i9btox///vflJaWoqrqOXewag1JSUnodDp69OhBTk4Oq1atIisrCy8vLyZNmkRiYqK976lTpwCarO8aHR1NUVERqamp9O3bl/j4ePR6PYsWLaJ///6oqsqiRYtwcnKiR48eAHz//fccPXqUjz/+uM3vVYiOqtBYRXZVKbfEDiba3a/BcSUkFs1NTzVIOCuqTJzIKGZAQuAvc1JtKqUrk1F0WryuiZMkVYguwjnGl4gHRlK6OxP/HacJLPIgdVUOx3yOMXLqKDy9vNs7RNEOmkxUd+zYwSuvvAKAl5cX/fr1Q1EUdu3aRWlpKQ8//DBr1qzB1dWVkpISnnrqKbZs2WJPaGfMmMGf//znNg3eZDKRk5NDUFAQP/zwA8888wzV1b/sdrNgwQLmzJljjyM/v/aR5G+rANSpay8sLATAz8+Phx56iNdee43Ro0cDtbXhnnrqKXx9fTGbzbzxxhuMHDmy3vSA81FeXk55eXm9ttzc3BZdQ4iOIsDZnbkDr8H1N/PK1OwTENwNRevQIOG0WG2s2pRKUWk13SO98XCrXcmvaBQ8p8aj6DRoPWThlBBdiaLV4D08Gs++YeSuOUbsqQpsZ71J/t9RasIMjJg2Dp1O195hikuoyUT1k08+AWDs2LH8+9//xt3dHYDS0lIefPBB9u/fz4oVKxg7dix33303WVlZqKpKXFwcf/3rXxk0aFCbB19ZWQnUTjd48sknueqqq3jwwQfx9fVl27ZtzJ07lw8//JDIyEhuuukmexLr5NT4m19du8FgsLfdd9999OnTh82bN6PRaBg/frz93r788kuysrJ47bXX7P1NJhM1NTX2r1dTFi1axDvvvHPhNy9EB1BSY8BD74RW0TRMUs+cxLZ0Hsrgq1BGzmxw7sHj+eQWVjFtbAwebo6oFhtoFRRFQR/c/M+PEOLypnHREXJdPyxlRrK+P0j4aTOaVFeOvrcDEhzoP3Fke4coLpEmE9Xk5GQcHBx48cUX6yVdXl5evPTSS0yaNIktW7awYsUKMjMzcXR05MEHH2TOnDmXbEGRyWQCoLq6mlGjRtUrLTVlyhRcXFy47777ePfdd7nxxhvtcZ3rUaLNZqv37xEjRjBixIh6bVVVVbz33ntcddVV9O7dG5PJxN/+9jdWrVqF2WwmLi6Of/7zn/Tt23htuDvuuKPBYrPc3FxuvfXW87t5IdpZldnEq4fX0c3Dj7vj6/98qNUV2L59Hzz8UAZe1ej5/XsG4OPpRLcwL1SrjZJlSTh4O+MxMeZShC+E6AQcPJ3odvMwzOXVnFixC798LbqDKkeSNuIyyo+YgX3aO0TRxppMVIuKioiOjsbHp+EK3YiICEJDQ9m6dSs2m43u3bvz+uuvExvb+HaIbeXXI6O33HJLg+Njx44lMDCQvLw8MjIycHZ2Buovjvq1uvbz2ahg4cKFlJaW8uijjwIwf/58VqxYwQMPPEBERARvvvkmDzzwAGvWrMHNza3B+R4eHo1uGiBEZ2BTVT5K2UFRTRV3BjUy7cXJDWXARJToRBQnl3qH6kpYaTWa2iRVVSn7/iSmzFKcEqSGohCiIZ2HM71mj8VQWkHy1z8RUOKMdkMZB35aR/CUOIKiI9o7RNFGmqz3YjKZmk2kfHx8sNls9O7dm88///ySJ6kA7u7u9rkqTdUdDQmp3W6xpKSEgIAA4Jc5qL9VUFAAND2HtU5xcTEfffQR119/PVFRUUDtVIkxY8bw8MMPM336dJ588kkKCwtZtWpVi+9LiM4gyt2Hm7sNItaz4c+LoihoBl+N4hdar72q2sxnq5PJKaj8pdGmggJuo6Nw6R3Y1mELIToxFy93Bs6ZiP7GaHJdjQQanDB/ncW+D9ZSWVJ+7guITueCCxPqdDr7wqJLuVXqr2m1WmJiah8T5uXlNdqnLin19fW1F+ivW/3/a6qqkpaWVu+aTXn33XexWq08+OCDQG0SXFJSUu+8usT91/VWhbhcaBSFaZGJjAnpXq/dlrQDNe1Qo+dYrTa+3ZRKUZkRh1/VRFW0GjynxOE6JLTR84QQ4rcCI0IZ8MAEjBO8KdGbCC1xpuCjQ+z7dD2mmpr2Dk+0oouuoP3bElWXWt1q/B9++KHBsbS0NM6cOUNAQADh4eFcccUVAKxfv75B3/3791NcXMzAgQMbfVRf5/Tp03zxxRfcfvvtBAbWjv7UVTowm832fnXTCGQDAHE5sakqpytLGj2mntiL+uPH2A5saLSGckl5DUVlRiaNiMLfx4XqY/mY82pHVhVFkTJUQogW69a/F4mPjKOwn5YajY3Qs45kvruLA8s3Y7FY2js80QouOlFt70Ts5ptvxsXFhRUrVtR7zF5WVsazzz6LzWbj1ltvRaPRMGTIELp378727dv58ssv7X2Li4uZO3cu0PjGAb/2+uuv4+zszO9//3t7m4+PD76+vuzYscOerG7ZsgWgXaZECNFWduSl8s8D33OqrKBeu2q1YNuxAoKi0Uy9r9Gk08/bmbtn9qZ7gBulq45T9t0JKn86fYkiF0JcznpPHE7swyM5282CAgSf0nLy7W0c27C7vUMTF+mcO1N1dKGhobzwwgs88cQTPP744yxcuJCAgAAOHjxISUkJw4YNY86cOQBoNBpefPFF7rjjDp577jmWLl1KQEAAu3fvpqysjBtvvJFx48Y1+VrHjh3ju+++409/+lODzQxuu+023nzzTaZPn27fESssLIxrrrmmTe9fiEulzFTN1+kHiPMMIMajflF/ReuA5vrHwMkFRedY/7yKGjzc9CiKgpPeAavJhCmrDLeREbgOaXxuuRBCtJSDgwMDrh9LjaGaI8u245friNM+E0eObMRpmC/dhyae+yKiw2k2Ua2oqGDPnj1NHgPYu3dvs1ulDh48+CLCOz9TpkwhOjqa999/nz179nDq1CnCw8O5++67ueuuu+oVB05MTOSrr77irbfeYteuXZw8eZLIyEj+9Kc/ccMNNzT7OvPmzcPPz4/Zs2c3OHb//fdjs9lYsmQJp0+fZuTIkTz77LM4Ojo2ciUhOh9VVYn3DGJGVF8URUFVVdRj21DiBqHonVHcG+4aczq3gm82nKRfoAfDh4SjdXdE66bH/95BKDqZFiOEaH2OLs4Mum0CFSWlpHy9h8BSZxy2lHNw93q8x4UT2avx3SlFx6SoTWSZPXr0uOg5Y4qikJSUdFHX6Eqys7MZP34869evb7KKgRAdgaqqqFu+Qt23BmX0DWgGNayVWlRazSffJtHDptC3oBqnHn54TY1vh2iFEF1ZwekcTq8+SkCFMwqQ52ok5GopadVZNDtHVVXVi/rz28L5QojOx2gxU26qrt+YlVybpPYdhzJwUqPn+Xg6MapHAH0Lq9GFuOM+NvoSRCuEEPX5hwcz4P6JKNNDyXcxElTlhGVpFvv+u5bCs7J1eUfX5IiquPRkRFV0RJ+n7mV3fibPD5pWb5tU9ewpCI5p8OSlqtqMq/Mv022MJ4vQh3qgcZH9uYUQ7S/9UDLlW84SYHTEokC+ZzUx0/vjFeB37pPFJXfRq/6FEJevo8Vn2XT2BIP9I3HV6VF/VZpKCYltkKTuT87jo2VHKCg22NucuvtKkiqE6DCi+/ak78PjqRztUVuDtdSZsv8dZ++Ha6koKW3v8MRvSKIqhGiSs4OOXt4hXB/dDzX1ILYPn0JNP9Jo35OZJWzafZqIYA/0h3Kp2nvmEkcrhBDnr/vQRBIfGUfJMGcqdGbCip0p+vAIexfKLlcdiSSqQogmxXj483DvsejyMrB9+x/wC4fQ7o329fZwIj7ah/EB7lTvPYu1zHiJoxVCiJbrecVAev/xSgr766hysBJW6Ezhh4fY+/FaqsokYW1vkqgKIepRVZWdeWlYf70Y0tMfJaYvmuv+iKJ3avQ8P29nplwRjelYProwD1k8JYToVHpPGEqvR8dS2E+LwcFKWIEz+R8cYu+idZKwtqNOX/BfCNG61p45ztfpB1BQGBZYm2wqrp4o1/yhQV9VVTmQnE+fOH90DhoURcH7+l6oZiuKVj4HCyE6n94Th8NEOLJmB9pkE2H5TuR/cIgiXyM9pg/FzdujvUPsUuSdRAhhd7qyhGXpBxngF84QNFiXvYFaY2iy/0+Hcti05zSnMouxVdduH6w4aNA4y+IpIUTn1mfyCOIeGkVBooZqbe0Ia+GHh9j7kSy6upQkURVC2IW5enFL7CDudAtAXfpvKDoD1ZWN9s0vNrDz0FkSYnwJPVNJ4f8O2pNVIYS4HDg4ONBn8ggSfp4SUOVgJazImeIPj7D3I1l0dSlIoiqEsFMUhTHB3dGV5YOLB5qbnkLxCmi0r7+3M1NGd2OUrxuGXdk4RnnJSKoQ4rLVe+Jwej06lqJBeip0FsKKnCn46BB7P1lHjaH63BcQF0QK/ncgUvBftAdVVfk8dR+9fYLp4xP6S7vFjOJw7sTTmFqMYf9ZvK9LQHGQz75CiK7hyI87cThmwtuipVKjUh5ups/MK9Cdx+9Ncf6aXEw1e/bsi764oigsWrTooq8jhGg7G8+eYFPOCbrnZWHz9EfT+wqAJpPUnw6dxcNNT0JM7S4uTjE+OHbzblD8XwghLmd9Jg3HMs7CkW+345qmEJKpJ+2tnRgirfSePkoS1lbSZKK6e/fuJk+qe0NqbDD218fkjUuIjs1ss7LhbArjHJzpv+dH1MBI1F4jUZTGR0ZP55az4+BZ+sT6Em1WcYr3Q1EU+VkXQnRJDg4O9J8xBlONiSPLt+F1Rkdwmo7Mt3ZSFmSm1/RhOLm6tneYnVqTiepzzz3XaPt3333Hvn37iIyM5NprryU+Ph4PDw+MRiNpaWl88803JCcnM27cOG655ZY2C1wIcfF0Gi1P9p2E07fvg5Mrmmv+0GSSCrDrcA5eHo4MMUPZqhQUnRanGJ9LGLEQQnQ8ekc9A28eh6mmhqMrd+B2WkvoGUfO/mc/xb5G4qcPxt3bq73D7JRaNEd169at/P73v2fq1Km8/PLL6HQNh7VVVeVvf/sbX331Fa+++ipTpkxp1YAvZzJHVVwqZpsVqE1UAVRTNVSUoPiGNH+e2UpZcj6WNak4JwbhMSlGRlOFEOI3LBYLx77/CV2qBR+zAzUKFHpUE3ZVAoERoee+gLBrUaL6u9/9jpSUFLZt24azs3OT/UwmE6NGjSIsLIxly5a1SqBdgSSq4lJZfHIXOYYy/pQwBgedY4vOVa02DIdycekXjKKRJFUIIZpzbMNubEcq8TfpsQAFrka8R4URldijvUPrFFq0RDc5OZlu3bo1m6QC6PV6IiMjSUtLu6jghBCt70DhabblpjKmohzls3+iVhQ327+ssoYvvj9OUUlt4X9Fq8F1QIgkqUIIcR56jRtCnz+Oo2qcFwUuRgKqnNCvKeTwWxtI2rynvcPr8FqUqLq6unLmzBlsv94DvBE1NTVkZmbi7e19UcEJIVpfqamawYoDgw5sAL0zOLs32dditfH91nQseRVYvjyGOb/x4v9CCCGaFzOwN/0fnIB2Zjg5HtV4mfT47K4h+Y1NHFi+GbNFNkxpTIsS1QEDBlBSUsKbb77ZbL/nn3+eiooKRowYcVHBCSFa35UhcdwV2A1c3NFM+0OztVILS6ox5FVyZZkFjQIaFym3IoQQFyMoJpKB903E844Esv2rcbRqCT6lJfOtnexdvI6qMtnt6tdaNEf1+PHj3HjjjZjNZoYOHco111xDbGwszs7OGAwGkpOT+frrr0lKSsLd3Z2vv/6a8PDwtoz/siJzVEVbMlkt6LW/FPpQrRYUbZOFP+zK0ooxbUzH+7oEHHyan/YjhBCiZeoqBbhka/CyaGsXXrlXEzohjqCYyPYOr921eGeqtWvX8vTTT1NZWdnoal9VVQkMDOT1119nwIABrRZoVyCJqmgrZpuVFw78wBDPQKbEDmq2r6HazPH0Yvr3DPilLrJNlTmpQgjRhiwWC8kb9kByNX4mPTagwKkGpwHe9BjZdfOpcw+n/MbEiRMZOHAgn332Gdu2bSMzM5OKigo8PT2Jjo5m/Pjx3HDDDbi5ubVFvEKIC/DD6WPkVZYw5vB2bKdPormy8RrHFVUmlv54Am2pkfCscvwmx9YW9JckVQgh2pSDgwN9Jg2HSZBx+DjF27Pxr3RCt8NA0p5NGCOg99QR6B317R3qJdXiRBXAx8eHhx56iIceeqi14xFCtDJVVckxlHNXRTlOJXkoV9zQZN/dR3JwKjUytsyEairFVmVG69a1fikKIUR7i0rsQVRiD4pz80n77hA+JU74pCqcfmcXJT5Goicn4hsS2N5hXhItfvQv2o48+hdtRVVVrOv+h2KsQjvtgSb7Waw2in84iZJTic+NvdF6tKzGqhBCiNZnqqnh2Lc7cDyt4GN2sNdj9RgaSMzAPu0dXpu6oET1yJEjHDp0iMrKSqxWK81dQkZdz58kqqK1mW1W++5T0HABlaqq7D6SQ7dwL/y9XWrbbCpqjQWNs6zwF0KIjub49v0YD5TgX+2IFijSWTBFKvSaMvyynBbQokTVZDLxf//3f2zYsOGcfVVVRVEUkpOTLyrArkQSVdGa8qrLee3weu6MGURPv4bVN4wmCz9sTSctu4xx/m706h+KLrjpmqpCCCE6jtyMbLLXJeNT5oSLTcGgUSn2MhI5qTf+4cHtHV6radEc1YULF7J+/XoAIiIiiI6OxtFRHg0K0dFYbTY+Or4DvdFA7NdvYBt7M5r4wfX6VBnMnMmvZGqAB55H86k0q3hP79lOEQshhGiJoKgwgu4JqzctIKzYmZrPUznonIRjoic9Rzdf5aUzaFGiunLlShRF4ZlnnuG2225rq5iEEBepxGSA6goeP30KjbESxb/hCL2vlzN3Te1Jxf8Ooo/xwWtqfDtEKoQQ4mLoHR3pf/2VAJz46TBV+/LxMzih22Xk+L7NVAVZ6DFlMK6eHu0c6YVp0aP/xMRE/Pz8zuvRv2g5efQvWpPZWIVm6asogyaj6TEUgGqjmQPH8xmaGIxWU7sxnaW4Gq2nI4q2RRvVCSGE6KBK8gtI/f4gnkWOuFs1mBQodKvGa1go3foltHd4LdKiEVVXV1epjypEB2a0mNCaa9A5u6NzckX93bMoPyekNpvK6i1pnMmtIKbGRsDgMBSNIrtNCSHEZcY7wJ9Bd0ys3URg/W7UFCNBFc5o1hZzbNMmasJUek0ZhqNLx//936IhlEGDBpGenk5xcXFbxSOEuECqxUzGstcp+t9fsdZUA9iTVIDj6UVk5VQwJdQbdUsmxhOF7RWqEEKIS8DBwYE+k0eQ+Mg4NNdHkO1djbNVS0i6jpz39rLvv2spL+rYOV2LEtUHH3wQgGeeeQaTydQmAQkhWk4111Dx6T/onn2C0vAeaHQNFzn27ObLdaOi8UguQBfijlO8XztEKoQQoj0EdYtg0D0TCXtoKLnxNip0FoLLnTm59kB7h9asFj36z8rKYtasWSxZsoTRo0czZMgQAgMD0ekar7eoKApPPPFEqwQqhGiaonMkq/dITpbmcu2Vs+uNpNaVilMUhahoHwxDwtB380ZRZFtUIYToavSOevpdOxqAipJSAtw79pTOFi2m6tGjB4qi1Cvw39SbndRRbTlZTCUulk1V0fzqZ9JYY+HrtScY0T+U6FDPdoxMCCGEaLkWjajOmDFDRmGE6EBUi5nyw5vw6DceRaOpl6Sqqsqa7RkUlFTjCNRkleIY4dVusQohhBAt1aJE9eWXX26rOIQQF6Bs29e471/LQa2W/n3H1TtWVW0mv9jAFQNCcTtRTMneM/jNGYCDj0s7RSuEEEK0TIsSVSFEx2EqyMblwDoO+IfSI2Fkg+NuLnpmX9sLpchA8fencE4MkiRVCCFEp3JRiWpNTQ02m61em9Vqpaamhvz8fDZs2GCvFCCEaF1Wdx8Odx+A18BJuP1qlb/1559JrUaDo16L1cMR58Qg3EdHtVOkQgghxIVp0WIqgO+++4733nuP9PT0BklqY2Qx1fmTxVSiNWzbn03m2XJuvCoenYO2vcMRQgghLliLRlR37drFY489xvnktl5eXowc2fBxpBDi4lQW52IqzcOnW98Gx7LzKth9JJdesX5Y00pQ/F1x8O74O48IIYQQjWlRwf/FixejqiojRozg008/5euvv0ZRFKZPn84PP/zAxx9/zNSpUwEIDg6WxVdCtLIaYxWF37yJ88p3MVaUNDheUmbEx9OJUYHulK5KoXJrZjtEKYQQQrSOFo2oHjx4EL1ez7x58/Dx8QEgOjqaw4cPExUVRVRUFMOGDcPX15fFixezZMkSZs+e3SaBC9EVpX7zFt1L8skYMZ3u7t4NjveJ86dnpBfFH+zHwdcFj8mx7RClEEII0TpaNKJaWlpKeHi4PUkFiIuLIzMzk6qqKnvbQw89hE6nY/Xq1a0XqRACz7G3kHnlLXQfdm29dkO12f53B0cdXjN64j2rFxpHKewhhBCi82pRouro6IijY/09xCMiIlBVldTUVHubh4cHUVFRpKent06UQnRhFcU5WKsrAQgNjCK2/4R6x8sra1i44igHDp21zx/Xh3qgddNf8liFEEKI1tSiRDUkJITs7GzM5l9Gb8LDwwFISUmp11dVVaqrq1shRCG6rqLcdGqWvMDZr19t9LjNpvLDtnQ0Fhshe3Oo3CJzUoUQQlw+WpSoDhs2jIqKCl577TX7yE2vXr1QVZWVK1fa27KyskhPTyc4OLj1IxaiiygtyUPz1b/RWy1oR1zXaB+L1YajTstURYtaakQf6XVpgxRCCCHaUIsS1TvuuANHR0c+/vhjxo0bh8lkIiEhgYSEBPbu3cs999zDv/71L26//XasVisDBw5sq7iFuOy5e/qT2ncM1dc9Ski3xEb76HVapo3uhrunE+5jo3GM8rq0QQohhBBtqMUF/7dt28bTTz+N0Whkz549AOzZs4c5c+ZgMplQFAVVVfH09OSbb74hKCioTQK/HEnBf3G+zBYr1UYLHm61c8brfowVRWnPsIQQQohW1eIlwaNGjWL9+vUcO3bM3jZ48GC+/vprFi9ezJkzZ4iKiuLOO++UJFWIFjJZLXyz6xtG6ZwJGnR1k4nnlr3ZnD5ZyKzxsbiFeEqCKoQQ4rJ0QbVr9Ho9/fv3r9fWvXt3/vGPf7RKUEJ0RVbVxqJjm5l6YCNeWh30HQd6pwb90k6Xciw5n+kGG9XfnsB1zkAUbYtm8QghhBCdgry7CdFBlNQYiDy+m4Caapym3o/SSJIK4O6qZ6xNwbHajMek7pKkCiGEuGxJNXAhOgg/JzdGXvMQ2rMnUcLjm+zn7+OC17SemHMrZPGUEEKIy5oMxQjRzpJKcrDabAC4urijxA5o0MdQbWbLvmys1tp+ugBXXBJlDrgQQojLmySqQrSjvQWZvH1kA1mr3kEtON1oH5PZyvL1J8k9kEPhyuOoPyerQgghxOVOElUh2snZqjI+StnJ7QU5RKQeQs1tfMvhY6eKsOZWckW5CW1FjSSqQgghugxJVIVoJ8EuHtwa3J0hZ9NQ+oxB02d0o/369fBnUrg3Du6OeF/fC41eppYLIYToGuQdT4hLzKba0CgaFEVhZMxA1FtDwMOvXh+rzUZRqZEAHxcURSFwUiyq0YLGWddOUQshhBCXXosT1ezsbHbt2sWePXs4c+YMxcXFlJeX4+joiJ+fH1FRUfTv35+RI0fK7kpC/Ea5ycgbRzYwK6IXPf0iUBQFxSe4Xh+L1ca3m1LJPVvOLaOi8Yz2qe0nSaoQQogu5rwSVYvFwvLly/n8889JSkoCftmy8deys7M5ePAg33zzDQD9+vXjzjvvZPLkya0YshCd1xepe8k3lBGx+StUd1+YdGeDXaXW7cwkLbuM63Q6jCtTcL9/MBpHefghhBCi6znnu9+KFSt48803yc3NRVVV/P396d+/P927dyciIgI3NzdcXFwoLy+ntLSUvLw8Dh06xKFDhzhw4AAHDx4kKiqK++67jxkzZlyCWxKiY6oy15BeUcRDVZU4ZyXDuFsb3fp0SJ9gultUnPecxXVUhCSpQgghuixFbWxoFMjKyuKpp55i//79BAYGMmPGDKZOnUpcXNx5XVhVVQ4ePMiyZcv4/vvvqaqqYvjw4Tz//POEhoa26k1cLrKzsxk/fjzr16+XaROXqRqLGe2GT9HYrCiT77YnqgajGb1Oi8PPu0xZigxU7TuLx4QYFE3DZFYIIYToCppMVPv374+npycPPvgg1113HQ4OFz6qU1NTw9KlS3n//fepqqriwIEDF3yty5kkqpcvVVXtSamqqqDaUDRaAKprLHz5/XH8fFyYOrpbe4YphBBCdChNlqf6wx/+wI8//sgNN9xwUUkqgKOjI7feeitr167l3nvvvahrCdHZpFcU8trebynOTgGoXRj1c5JqsdpYsf4kpRU19OnuhzG1GFuNpT3DFUIIITqMJhPVe++9F71e36ov5uzszB/+8IdWvaYQHZnVZmNxyi4mJP2Ex/I3Uasr6h3XahQiQzyYMrobwYpC6YpkKrdltlO0QgghRMciqzSEaEMZlUX0Sj9C76JclNE3oji71zuuKAoj+oWiWmwUfnwAjasOt5GR7RStEEII0bFIoipEG4rx8MfviptQTu5HGTjJ3r7j4BmC/d2IDvUEQHHQ4DYyAo2rDo2T/FgKIYQQ0Eyi2rNnz4u+uKIo9rqrQnRVnkHREBRt//e+Y7n8dCiHfj0C7IkqgHNP//YITwghhOiwmpyjqqrqef9pqr/NZrtkNyJER3IwP5NDq97BaKg/J9VstnLgeD7dI70ZOzgcw8EcqvafbacohRBCiI6tyRHVVatWNWiz2Ww8//zz7Nu3j+nTpzNr1izi4+Nxd3enpqaG1NRUli1bxpIlSxgzZgzz5s1r0+CF6IjMNitpP33D9FOHoMcw6D7Ifkyn03Lz1T1wcnTAdLKI8rWpOHbzxqV/cKPF/4UQQoiurMlEtXv37g3aFi9ezL59+3jyySe588476x1zdHQkISGBhIQE4uPj+etf/8rChQt58MEHWz1oITqyQzmnGJeRhCEwCrfYgQAUlVbj4eaIzkGDm4seVVUpP5CDLsQdr2t7SJIqhBBCNKLJR/+NWbJkCX5+fg2S1N+64YYbCAoKYvny5RcT2wUrLS3liiuuID4+vtHj6enp/OlPf2LMmDH07duXadOmsXjx4kanKhQUFPDQQw/Rv39/Ro0axV//+lfKy8sbve5jjz3GmDFjqKmpadX7EZ3LwJA4asbejNukO1EUheKyar74IYX1P/1SdkpRFLxmJuB9fS8UnbYdoxVCCCE6rhYlqmfOnCEoKOi8+vr4+FBYWHhBQV2suXPnkp+f3+ix48ePM2vWLFavXk1ISAhXXHEFubm5/POf/+TPf/5zg/6PPfYYa9eupXfv3kRHR/PFF1/w2GOPNeiXnJzM6tWreeihh3B0dGz1exKdh6IoBCaORfEPp8Zk4eu1J9EoMCwxGEtJNdYqEwAavVZW+AshhBDNaFGi6u/vz6lTp5ocUayTl5dHSkoKwcHBFxXchfj222/57rvvGj2mqip//vOfqays5JVXXmHJkiW88847rFmzhvj4eFatWsWaNWvs/VNSUti1axd33nknixcvZvHixdx1111s2bKFkydP1rv2vHnziI6OZubMmW16f6Ljyqgo4tD+H7HaftlZylHvwMCEQGZOiMPVaKX4s8OULk+miZ2LhRBCCPErLUpUx40bR3V1NY899hiVlZWN9ikoKODhhx/GarUyderUVgnyfOXl5fGPf/yD/v37o9U2fJy6fft2UlJSGDJkCNOnT7e3+/j48Pe//x2onYdbJy0tDYDhw4fb2+r+np6ebm/buXMn27Zt49FHH230dcXlz6aq7Nq9it6bvsBydHu9YwMSAvFzd6Tkq6OgUfC8urvMSRVCCCHOQ4ueO9577738+OOPbNu2jfHjxzNmzBhiY2NxdnbGYDCQnJzM5s2bqa6uJi4u7pxzWVvbM888g8lk4l//+hdXX311g+Nbt24FYMKECQ2ODRgwAF9fX/bt20dlZSVubm54eHgAYDQa7f2qqqoAcHV1BWpHaefNm0diYiKTJ09u9XsSncP+3DRGpezD6O6DS6+R7E/Kw8XJgR7dfIHax/wek2Nx8HfFwdOpnaMVQgghOocWJap+fn58/PHHPPXUUxw8eJCVK1fWGxmqe5x55ZVX8vzzz+Pm5ta60Tbjs88+Y+vWrTz33HNERja+BeWpU6cAiIuLa/R4dHQ0RUVFpKam0rdvX+Lj49Hr9SxatIj+/fujqiqLFi3CycmJHj16APD9999z9OhRPv744za5L9E5JPoEczamH07dB5NXUsOWvdl0j/Qm1ssFBx9nAJxifds5SiGEEKJzafFKjqioKD7//HP27t3L1q1bycjIoLKyEg8PD6Kjoxk/fjy9evVqi1iblJmZyb///W+GDx/Orbfe2mS/ugVW/v6N7wBU1163CMzPz4+HHnqI1157jdGjRwO1C2WeeuopfH19MZvNvPHGG4wcObLe9IDzUV5e3mCub25ubouuIToOvaMLUZPnALB97QlcXXRc4etK4cL9eE6Oxbl3YDtHKIQQQnQ+F7zkeNCgQQwaNOjcHduY1WrlySefRKPR8OKLLzY796+6uhoAJ6fGH73WtRsMBnvbfffdR58+fdi8eTMajYbx48fb7/vLL78kKyuL1157zd7fZDJRU1ODu7t7s3EvWrSId9555/xuUnRYpyuKUYvOEBHVx952zZgYynMqMCxLQhfsjmN3GUkVQgghLkSnr43zwQcfcODAAf75z38SEhLSbN+6hU7nWsjy23qqI0aMYMSIEfXaqqqqeO+997jqqqvo3bs3JpOJv/3tb6xatQqz2UxcXBz//Oc/6du3b6Ovcccdd3DdddfVa8vNzW12RFh0LFbVxv4tXzAlZS/mWY+ji+gJgKNei2+AKxW9AnAbHo7GsdP/mAkhhBDtosl30FmzZqEoCm+99Za9zNSsWbNadHFFUfjqq68uLsJmHD9+nLfffpuxY8dyww03nLO/s3PtXMFfL476tbr2uoVSzVm4cCGlpaU8+uijAMyfP58VK1bwwAMPEBERwZtvvskDDzzAmjVrGp2r6+HhYV+sJTqnnSm7mHDyAFWBUTj4dmPl2hNcOSQCH08nNM46PCfFtneIQgghRKfWZKJ69OhRFEWpl9QdPXq0RRdv6xI8r7/+OmazGbPZzOOPP17vWN2oaF37X/7yFwICAkhOTqawsJCYmJgG1ysoKACansNap7i4mI8++ojrr7+eqKgoAD755BPGjBnDww8/DNROI3jkkUdYtWoVt9xyy0Xdp+iYEvwiKAuOJnDyPXyzM5PsvApsBhMWm4qDt3N7hyeEEEJ0ek0mqi+99BJQP2mra+so6uaSbt++vck+q1atAuDRRx+le/fubN68mVOnTjF06NB6/VRVJS0tDa1W22gS+2vvvvsuVquVBx98EICSkhJKSkrqnRcbWzua9ut6q+Ly4uMXCjc9TUGxgdM5WYwZHI7DvhyK0orxv2+wPPIXQgghLlKT76S/nT/ZVFt7+nVx/t9KSEjAarWSkpJib7viiiv44IMPWL9+fYO5oPv376e4uJghQ4Y0W1br9OnTfPHFF9x5550EBtau5K4ry2U2m+396kaiZQOAy09G+mF8vIPw8AoAwN/HhTum98KpwEBpcgGuI2ReqhBCCNEaWrQzVWc3ZMgQunfvzvbt2/nyyy/t7cXFxcydOxeAu+66q9lrvP766zg7O/P73//e3ubj44Ovry87duywJ6tbtmwBfhlZFZeHyqpSXL5bQOXSefUW3Xl5OKF10+PUww+3oeHtGKEQQghx+Why2Gfz5s2t8gJjxoxpleu0hroSVnfccQfPPfccS5cuJSAggN27d1NWVsaNN97IuHHjmjz/2LFjfPfdd/zpT3/C09Oz3rHbbruNN998k+nTpxMcHMz27dsJCwvjmmuuaevbEpdQ9ur/ElNjoGTyXWzdfwaT2cqEYZEoioIu0A2vaT3aO0QhhBDistFkonrfffdd9GIoRVFISkq6qGu0tsTERL766iveeustdu3axcmTJ4mMjORPf/rTOSsHzJs3Dz8/P2bPnt3g2P3334/NZmPJkiWcPn2akSNH8uyzz+Lo6NhWtyLagVPCMNL8wnB2jWXf9hMkxvljKTCg9XSUx/1CCCFEK1PUugmWv9HcyGJLbNiwoVWu0xVkZ2czfvx41q9fT1hYWHuHI5qxcXcWGWfKuWVkFGVfHkUf7on39J7tHZYQQghxWWlyCEgSTCF+cTplFyGxA9BqdQCMHRxOdYKJ8k8PoygK7qOj2jdAIYQQ4jLUpRZTCXEhcjKOELh6ASfXLsJirV1ApSgKLm6OuI2MwHtWL6mbKoQQQrSBC55Ut3XrVjZt2kRGRgZVVVW4uLgQERHBqFGjGD9+fJsX+xfiUrBazdh+/BiDTo9rn2l8+PURJg2PJDrcCwCX3oHtG6AQQghxGWtxolpUVMSjjz7K3r17gV9qiALs3LmTL774gv79+/P666/b64wK0WnZVCqielPhFcqO3flgs+G26wyGEiMuiUHtHZ0QQghxWWtRolpTU8Pdd99NSkoKzs7OTJo0iYSEBFxcXKisrOTo0aNs2LCB/fv3c//99/PFF1+g1+vbKnYh2pxWp6fHpLsoKTeiPXOSq/R6rKklEOvT3qEJIYQQl70WJaqffPIJKSkp9OjRg//+97+Njpjm5uZy7733cvz4cb788ktuu+22VgtWiEvFZrORl3WM4Kg+AHh7OHHrVT0o/eoojsPDcekX3M4RCiGEEJe/Fi2mWr16NRqNhjfffLPJx/pBQUG8+eabAKxateriIxSiHaTu+ZaAZW9wYPd2zBYrAA7OOnxvScRtZEQ7RyeEEEJ0DS1KVNPT04mNjSUyMrLZftHR0cTGxpKenn5RwQnRHirLiwjY9T0pHpFsP+XMnu9PYDPUbo2r6LSyUFAIIYS4RFqUqKqqilarPa++Wq3Wvu+9EJ2JzWYjxzeMHZ5TCTbZ6HaimIotGe0dlhBCCNHltChRjYiI4OTJk+Tn5zfbLy8vj1OnTsnuSqJT8vDyJ/53f2Foj1BGlJpw8HXB/cro9g5LCCGE6HJalKhOnDgRi8XCE088QWVlZaN9KisreeKJJ7BarUycOLFVghTiUjAZDZQV5wC1Bf379AnCe2o83rN6oXG84JLDQgghhLhAivrrQqjnUFFRwbXXXktubi6+vr5MmzaNhIQEXF1dqaysJDk5mVWrVlFYWEhISAjffPMN7u7ubRn/ZSU7O5vx48ezfv16GY1uB8dXvYuaVYxh9G0M7CMjqEIIIUR7a9Ewkbu7OwsXLuT+++8nIyODjz/+uEEfVVWJjo7m3XfflSRVdBo5GUfxTU1lifcNJBwsobxCi/uwcBSNLJwSQggh2kuLn2dGRUXx7bffsnr1ajZv3lxvC9Xo6GjGjBnDlClT0Ol0bRGvEG3CZjGxwfMKXDU6ehfXYNGXwfDw9g5LCCGE6NJa9OhftC159N++TGYrxetTIbkQvzv74+Dt3N4hCSGEEF1aq60QKSkpwcPD47zLVwnREVSWF+Ho6IbO0RG9TkvgpO5Y+gZLkiqEEEJ0AOe16n/Lli28++67VFdX12s3m83MmzePIUOGMGLECPr27cucOXM4dOhQmwQrRGuy2WwcX76IxUu2UFpe+72taBR0wTK3WgghhOgImh1RLSoq4r777uPYsWMAXHPNNfZdqVRV5d577+Wnn36ibvaAxWJh+/bt7N69m9dee03KU4kOLWXX9xyyJlKjd4XDedSEeuIY7d3eYQkhhBDiZ02OqNpsNubMmcPRo0fR6XQMHToUvV5vP/7555+zc+dOAAYOHMiKFSvYunUrf/nLX1BVlWeeeYaCgoK2vwMhLlBxhY1KjRvT+kRi3JlNTXpJe4ckhBBCiF9pckR15cqVHD9+nKioKN59911iYmLqHV+wYAFQW7Lqvffew9PTE4DZs2djMpmYN28eX3zxBQ899FAbhi/EhRs5aRr9qk0YVxzH6uGI26jI9g5JCCGEEL/S5Ijqjz/+iKIozJ07t0GSevToUc6ePYuiKEyfPt2epNa55ZZbcHBwYMOGDW0TtRAXobL8l5FTV2c9Xtf2wHt6DzR6WQgohBBCdCRNjqgmJSXh6enJ0KFDGxzbsWOH/e9jx45tcNzV1ZXo6GjOnDnTOlEK0UpqjFWs+morOjcHZt00BQCtqx6tq/4cZwohhBDiUmtyRLW4uJjQ0NBGj+3atQsABwcHBg0a1GgfJyenBlUChGhvG1b9QI5DCL6B3lRsycBaZmzvkIQQQgjRhCZHVJ2cnGhsLwCLxcL+/ftRFIV+/frh6OjY6PkFBQUNpgQI0Z5sNhuVNQ4EaAsY4tWPirWpaL2dcOkT1N6hCSFEq6qpqaG4uJiKigqsVmt7hyM6Aa1Wi7u7Oz4+Pk3mdu2hyUQ1ODiYzMxMrFZrvSL+u3btorq6GkVRGDVqVKPnpqWlkZubS79+/Vo9YCEulEaj4YbbpmOsNFC28DD6CE+cewe2d1hCCNGqampqyMrKwtvbm6ioKHQ6HYqitHdYogNTVRWz2Ux5eTlZWVlERER0mGS1yUf/o0aNwmAwsGzZsnrtn332mf3vkydPbvTc9957D0VRGDFiRCuFKcTFqa422P/u5OaC98wEPCbFyi9vIcRlp7i4GG9vb/z8/NDr9fJ7TpyToijo9Xr8/Pzw9vamuLi4vUOya3JE9bbbbuOzzz7j73//O2lpafTp04fNmzezfv16FEVhwoQJREVF1TvHYDDw9ttv8+233+Lo6Mh1113X1vELcU5ZWRmsXp9FYrSNkT8v/tOHerRvUEII0UYqKioavD8Lcb48PDzIyMggODi4vUMBzvHo/4UXXuDJJ5/k448/rncsIiKCuXPn1mt79NFH2bZtG1VVVQD88Y9/JDw8vPUjFqIFrFYrWzcew6z4EB4eSOWu07gODEVxOK/dg4UQotOxWq3odLr2DkN0UjqdrkPNa252C9UpU6YQExPDxx9/TFJSEg4ODgwfPpx77rkHLy+ven1PnjxJZWUlHh4ePProo/zud79ry7iFOC8lxUUYbY7EeRXjdsCDyvSzOEZ6oQtyb+/QhBCizcjjfnGhOtr3TrOJKkB8fDwvvfTSOS/0wAMPoNPpGDlyJK6urq0SnBAXy88/gNtuGYnpaCFVGzPxmBQrSaoQQgjRSZwzUT1fU6dOba1LCdEqzGYzOp0ORydn9APDcAz2lLmpQgghRCfS5ES9nTt3tskLbt++vU2uK8Sv7d21k8//t5mM3ceAn1c0SpIqhBBCdCpNJqp33XUX999/PykpKa3yQocPH+bOO+/knnvuaZXrCdEUQ1Ulx49UM6LUEee9lagWW3uHJIQQQlxSb7/9NvHx8eTm5rZ3KBelyUT1nXfe4ciRI1x33XXcf//9bNiwocWrwKqqqvjqq6+45ZZbuOmmm0hLS+P999+/6KCFaM6J48kMLtfjrCh4z+gpK/yFEEJ0KevWreM///lPe4fRKpqcozphwgQGDx7MG2+8wVdffcXmzZtxc3Nj2LBh9OvXj9jYWMLDw3F3d8fZ2ZmKigpKS0vJy8vj4MGDHDhwgEOHDlFTU4ODgwO33HILf/rTn3Bzc7uU9ye6oH4DB1Psn4O7kwe6AFnYJ4QQouv49NNPeemll7BYLO0dSqtodjGVp6cnf/vb37j77ruZP38+3377LWvXrmXdunXnvLCqqjg7OzN79mzuvvtuAgNlq0rRtn693a9PRMcoVCyEEEJcCqmpqfzrX/9i8+bNeHt7YzKZ7LXtO7PzeiYaHh7O888/z7Zt23j99de58cYbiY6ORq/Xo6qq/Y9WqyU6OpoZM2bw6quvsmPHDp5++mlJUsUlsXn5Wg6+vZ7KotL2DkUIIUQHVTd3c926daxatYpp06aRmJjI+PHjeeWVVygrK7P3XbZsGfHx8SxevJhXXnmFgQMHMnDgQP72t7/Z+5SUlPDKK68wefJkevfuzZAhQ5gzZw5bt26t97o//vgj8fHx9O/fn7Nnz9Y7tnfvXnr27MnAgQPJzs7m+eefJz4+nnfeeafRe1i0aFGD43//+9/ZvHkzI0eOZNmyZQ3q3XdWLSpP5erqytVXX83VV19tbzMYDFRUVKDX6/H09ESjkfmA4tI7nZlB4Gk3PKwqeq2+vcMRQgjRwS1dupSNGzcSExPD2LFjOXDgAB9++CFbt25l8eLF9RK9Tz75hOzsbEaNGkVhYSHR0dEAZGVlcdttt5GXl0dQUBDjxo2jpKSEnTt3sm3bNh5++GEeeughACZNmsTUqVNZvXo1//jHP+xzSA0GA08//TQ2m41nn32WsLAwZs6cySeffMKqVavs5//aihUrUBSFGTNm2Nt69+7NXXfdxbhx49rui9YOLrqOqouLCy4uLq0RixAX7PT2E3SzuGAe5oPeS74fhRBCNG/jxo3cfffd/PnPf0ZRFIxGI4888gibN2/mrbfe4q9//au9b0ZGBu+99x7jx48HwGazoaoqjz76KHl5edx66608/fTT9q1rDx8+zO9//3vefvttEhMTGT16NADPPfccu3btYuPGjaxdu5aJEycyb948srKymDx5Mtdddx0AvXr1okePHhw/fpxDhw7Rt29feywnTpwgKSmJIUOGEBYWZm9/8skn2/xr1h5areC/EO1pxO8mceZwKuGJMe0dihBCdGg789LYkZfW3mG0yIjAbgwP7Naq14yLi+OJJ56wbxnq5OTESy+9xJgxY1i+fDlPPfWUvW9oaKg9SQXQaDTs3r2bY8eOERMTwzPPPGNfIwGQmJjIU089xVNPPcWHH35oT1S9vb2ZO3cuDz74IC+88AIODg589tln+Pv7M3fu3HrxzZw5kxdffJGVK1fWS1SXL19uP94VyHN6cdkIlSRVCCHEebr66qsbTFf09fWlf//+GAwGjhw5Ym/v0aNHg/P37NkD1D7S/3WSWueqq65Cq9Wyf//+euU9J0yYwDXXXENOTg4PPvggAC+99BLe3t71zr/22mvR6XSsXr0as9kM1C4aXrVqFS4uLkyePPkC77xzkRFV0akdeG8dFmeVwXdNbO9QhBCiUxjeBqOTnVFkZGSj7UFBQQDk5+fb2zw9PRv0qzseGhra6HWcnZ3x8fGhoKCAsrIyfHx87Meee+45Nm/eTEVFBVOmTOGKK65ocL63tzfjxo1jzZo1bN26lXHjxrF9+3YKCgqYOXNml5l2KSOqotM6smYnwVVOaGvaOxIhhBCdTVOLv1VVBag3StpY37p+zbHZandG1OvrL/I9dOgQFRUVQO3W8r9Oin/t+uuvB2DVqlUArFy5EsA+l7UrkERVdFpqSjVVGpW4m4a2dyhCCCE6mby8vEbb60pH1Y2sNiUgIACA7OzsRo9XVlZSXFyMk5NTvc2OysvLefbZZ1EUhauuuoqysrJ6C7d+bdSoUQQEBLBp0yYqKyvZtGkT4eHhDB48+Jz3d7mQRFV0WvH3DMdhUgBu3h7tHYoQQohOZsuWLQ3aCgoKOHz4MN7e3vTq1avZ8+uSxbVr1za6xfyaNWtQVZUhQ4bUa3/hhRfIz8/npptuYt68ecTGxrJx40b7Iqlf02q1zJgxA4PBwBtvvEFFRQUzZsywLwDrCiRRFZ2Wo4szkX3i2zsMIYQQndD27dv58ssv7f82GAw89dRTmM1mbrvttkYXSP3akCFDSEhIIDU1lRdeeMG+4Ang6NGjvPLKKwDceuut9vaNGzeyYsUKAgICePzxx9HpdMydOxdFUXjxxRcbHeWtW93/6aefNqid2hVc8GKqqqoqKisr7bXEmhISEnKhLyFEA6YaE8nzt6KJc6bP5BHtHY4QQohOKigoiOeee44vv/ySkJAQ9u3bR2FhIcOHD+fee+895/mKovDaa69xxx138Omnn7JhwwYSExMpKSlh3759WK1WHnjgAcaOHQtAWVkZzz33HADPPvss7u7uAAwaNIgbb7yRL774gueee4758+fXe53o6GgGDBjA/v37G9RO7QpanKiuW7eON954g9TU1HP2VRSFpKSkCwpMiMYc/nwzYUZnzpQY2zsUIYQQndhNN92Ej48PCxcutM/9vPvuu5k9e7a9cP+5REdHs3z5chYsWMD69evZsGEDHh4ejB49mjvuuIPhw4fb+77wwgsUFBQwbty4BqWlHn/8cdavX8/mzZtZunQps2bNqne8LlHtKrVTf01Rz2fZ2s927NjBnDlzzmulW53jx49fUGBdUXZ2NuPHj2f9+vVd7hPT+agxVHPm/b0YHCz0ePAKHBykupoQQvxWcnIyPXv2bO8wOqy3336bd955hz/+8Y888MAD7R3OOamqyuTJkykqKmLbtm04Ozu3+Wt2pO+hFr3Tf/DBB6iqyuDBg3n00UeJjo7GycmprWIToh5HF2dcZkTgaLVKkiqEEOKyZjQa0ev1vP/++2RmZnL77bdfkiS1o2nRu/3hw4dxdXXlP//5D66urm0VkxBNCoppvECzEEIIcTmZOHEipaWlmEwmPD09z2ve7OWoRav+rVYrUVFRkqSKS6o4N5+D76znzInOtTe1EEIIcaH69OkDQK9evViwYIG9bmtX06IR1ejo6CYL2wrRVjK/PkxgtRPl+SUQ197RCCGE6MwefvhhHn744fYO45zee++99g6hQ2jRiOott9xCWVkZn332WVvFI0Q96YeSCTI4keteTY9RA9s7HCGEEEJcQi0aUb3hhhs4fPgwL774IqdOnWL06NEEBgY2W8YhNjb2ooMUXVd0354cLdhF94G92zsUIYQQQlxiLUpUBwwYANTOVV2yZAlLlixptr/UURWtofeEoe0dghBCCCHaQYsSVYPB0KKLt6TeqhC/dnL3YcoO5dH7dyNwksV7QgghRJfUokRViveLS8FsMWPZXoK/1ZmyolJJVIUQQoguqkWLqYS4FI4s24q3RUthWA2BEaHtHY4QQggh2skFb+9TVlbGTz/9REZGBlVVVbi4uBAREcGwYcPw8fFpzRhFF9N7+iiOrtpO3xlj2jsUIYQQQrSjFieqqqry9ttvs3DhQoxGY8MLOjhw55138uijj6LValslSNG16B31DJh1ZXuHIYQQQoh21uJE9YknnmD16tWoqkpISAg9e/bExcWFiooKkpOTycvL44MPPuDs2bO8+uqrbRGzuEwdXLkVgH7XXtHOkQghhBCiI2hRovrDDz/w7bff4uHhwYsvvsiECRMa9Fm7di3PPvss3333HVdffXWjfYT4reLcfDxPgklRMdWY0Dvq2zskIYQQQrSzFi2m+vLLL1EUhddee63JBHTixIn8+9//RlVVvvrqq1YJUlz+MpYfxtGmYBvkIkmqEEII0UJGo5H33nuPadOm0bdvX/r168fMmTNZvHgxNputvcO7YC0aUT127BghISGMGjWq2X6jR48mJCSEY8eOXVRwousIndqT7J9SGDi6+e8tIYQQQtRXVVXF7NmzOXr0KF5eXgwePBiz2cyhQ4f45z//yc6dO3n77bc75dqhFhf8j4iIOK++vr6+pKSkXFBQousJjAiVUlRCCCHEBfjPf/7D0aNHGT58OG+99RYeHh4AZGdnM2fOHNavX89XX33FzTff3M6RtlyLHv0HBASQlpZGTU1Ns/2MRiOpqan4+fldVHDi8rfvyw1kHjvR3mEIIYQQndby5csBeP755+1JKkBYWBhPPPEEAKtXr26X2C5WixLVkSNHYjAYzrma/9VXX6W6upqRI0deVHDi8pZ24BhBmXpKNp5u71CEEEJ0EW+//Tbx8fGsW7eOVatWMW3aNBITExk/fjyvvPIKZWVl9r7Lli0jPj6exYsX88orrzBw4EAGDhzI3/72N3ufkpISXnnlFSZPnkzv3r0ZMmQIc+bMYevWrfVe98cffyQ+Pp7+/ftz9uzZesf27t1Lz549GThwINnZ2Tz//PPEx8fzzjvvNHoPixYtsh+vqqoiKiqKxMREwsPDG/SNjo4GID8//4K/Zu2pRYnqnDlzcHR0ZPHixdx3331s2rSJvLw8KisrycvLY+PGjdx777188sknODo6MmfOnLaKW1wGDFvysSkQNLV7e4cihBCii1m6dCmPP/44VquVsWPHYjKZ+PDDD7ntttsoLS2t1/eTTz5h0aJFDBo0iKioKHvyl5WVxfTp0/nwww8xGo2MGzeO+Ph4du7cyT333FMv0Zw0aRJTp07FYDDwj3/8w95uMBh4+umnsdlsPPvss4SFhTFz5kwAVq1a1WjsK1asQFEUZsyYgaurK5988kmTC9iPHDkCQFBQ0AV/rdpTi+aoRkZGMm/ePB577DE2b97Mli1bGvRRVRUnJyf+/e9/ExUV1VpxisuQpq8rBfmVDIg+v3nPQgghRGvZuHEjd999N3/+859RFAWj0cgjjzzC5s2beeutt/jrX/9q75uRkcF7773H+PHjAbDZbKiqyqOPPkpeXh633norTz/9NDqdDoDDhw/z+9//nrfffpvExERGjx4NwHPPPceuXbvYuHEja9euZeLEicybN4+srCwmT57MddddB0CvXr3o0aMHx48f59ChQ/Tt29cey4kTJ0hKSmLIkCGEhYU1e48mk4n3338fqE2UO6MWF/yfMGEC33zzDfPnz2fLli0UFhbaj/n5+TFmzBjmzJlDt27dWjXQ5litVpYsWcLy5ctJS0vDarUSHh7OlClTuOeee3B0dKzX/8iRI7z77rscOXIEg8FAbGwss2fPZtq0aQ2unZ6ezty5c9m/fz9+fn5MnTqVhx56qME1AW655RbMZjNLly5ts3u9nCSMHdLeIQghRJdjS9qBenRbe4fRIkrvUWgSRrTqNePi4njiiSdQFAUAJycnXnrpJcaMGcPy5ct56qmn7H1DQ0PtSSqARqNh9+7dHDt2jJiYGJ555pl6K+oTExN56qmneOqpp/jwww/tiaq3tzdz587lwQcf5IUXXsDBwYHPPvsMf39/5s6dWy++mTNn8uKLL7Jy5cp6iWrdfNS6UdemqKrK008/TUZGBjExMdxwww0X+JVqXy169F8nKiqKF198kW3btrF37142b97M3r172bZtGy+88MIlT1IfeOABnn/+edLS0ujbty9DhgwhPz+ft956i9tvv53q6mp7/+3bt3PLLbewZcsWYmNjGTp0KCdOnODxxx/n9ddfr3dti8XC/fffz65duxgyZAje3t7Mnz+fF198sUEc69atY//+/Tz22GNtfs+dXdLmPVSVlbd3GEIIIbqwq6++Go2mfhrk6+tL//79MRgM9kfmAD169Ghw/p49e4DakcrGyj5dddVVaLVa9u/fj9VqtbdPmDCBa665hpycHB588EEAXnrpJby9veudf+2116LT6Vi9ejVmsxmozXlWrVqFi4sLkydPbvLerFYrf/nLX/j222/x9PTk7bffRq/vnDXKWzyi+ltubm64ubm1RiwX5KuvvmLTpk3Ex8ezYMECAgMDASguLuaBBx7gwIEDvPfeezz22GMYjUb76rePPvqIYcOGAbVzTG6//Xb+85//MHHiRHr37g3Ali1byMjI4JlnnmH27NkAPPLII3z55Zc88cQT9vu2Wq28/vrrjBgxguHDh1/qL0Gnkpd1Brc9NZw8sod+D40/9wlCCCFalSZhBLTy6GRnFBkZ2Wh73VzOXy8+8vT0bNCv7nhoaOOlFZ2dnfHx8aGgoICysjJ8fHzsx5577jk2b95MRUUFU6ZM4YorGm4d7u3tzbhx41izZg1bt25l3LhxbN++nYKCAmbOnImLi0ujr2swGPjTn/7Exo0b8fLy4sMPPyQmJqaJr0LH12Si+sorr6AoCvfcc489y3/llVdadHFFUeyJYVupGwL/y1/+Yk9SAXx8fPj73//O9OnTWb16NY899hjffPMNRUVFzJw5056kAkRERPD444/z+OOPs3jxYv71r38BkJaWBlAv+Rw6dChr1qwhMzOTXr16AbWrAlNTU3n55Zfb9F4vB2dXJROoOuE+PKC9QxFCCNGF/XY0tY6qqgD1Rkkb61vXrzl1O0L9djTz0KFDVFRUALVPevPz8wkIaPi+eP3117NmzRpWrVrFuHHjWLlyJYB9LutvFRYWcu+993Ls2DGCgoL44IMP6N69cy9YbjJR/eijj1AUhVmzZtkT1bq286Gq6iVJVL29venWrRuJiYkNjtUt5qr71FNXKuLX80zqXHnllWi12noLxOpqkRmNRntbVVUVAK6urgDU1NTwzjvvMHnyZPr06dMKd3R5s3rCWX01gwbKDlRCCCHaT15eXqPtdaWjgoKCOHXqVJPn1yWW2dnZjR6vrKykuLgYJyenek+ey8vLefbZZ1EUhcmTJ/PDDz/w17/+lf/85z8NrjFq1CgCAgLYtGkTlZWVbNq0ifDwcAYPHtyg75kzZ5g9ezbZ2dnExcWxYMGCTrvS/9eaTFRnzJiBoii4u7s3aOtIGvsfW+e3JRlOnjwJ1E6g/i03NzcCAgLIycmhsLAQPz8/e+I5f/58/vGPf1BSUsLSpUvx8/Ozr7RbtGgRhYWFPProo615W5etQbdNaO8QhBBCCLZs2cKdd95Zr62goIDDhw/j7e1Nr169mk1U65LFtWvX8sgjjzSYp7pmzRpUVWXIkPoLh1944QXy8/O5+eabefbZZzl16hQbN25k+fLlDUZKtVotM2bMYP78+bzxxhtUVFRw5513NsjFSktLueuuu8jOzmbw4MG8//779fK3zqzJRLWxx9id6dG2qqq8+eabwC8lGQoKCgDw9/dv9Bx/f/96iWrPnj256aab+OKLL/jxxx8B0Ol0zJs3DwcHB8rKyliwYAEzZ86011Q7X+Xl5ZSX119QlJub26JrdCZ5WWdki1QhhBAdxvbt2/nyyy+58cYbgdq5nU899RRms5nbbrut0QVSvzZkyBASEhJISkrihRdeqFee6ujRo/bpkrfeeqv9nI0bN7JixQoCAgJ4/PHH0el0zJ07l9tuu40XX3yRESNG1JvGCLWr++fPn8+nn35qr536W3PnziUzM5OEhAQWLFiAs7PzxXxpOpSLXkzVUb322mvs2bMHPz8/7rnnHgD76n8nJ6dGz6lrNxgM9rZ//OMfjBw5kt27d+Pm5sbVV19tX/33n//8B5PJxEMPPWTvbzQasVgs51xgtmjRoiZ3nLjclOYXYvgqjf0uyQz4g4yoCiGEaH9BQUE899xzfPnll4SEhLBv3z4KCwsZPnw499577znPVxSF1157jTvuuINPP/2UDRs2kJiYSElJCfv27bNXJRo7diwAZWVlPPfccwA8++yz9hHPQYMGceONN/LFF1/w3HPPMX/+/HqvEx0dzYABA9i/f3+jtVNPnjzJ999/D9ROS6x7jd/y8fHhL3/5S4u+Rh3BBSWqZ8+epaKigvj4eHvb4sWLWblyJVarlTFjxvD73/++yRVpbe3NN99k/vz56PV63njjDftKO61Wa58725y6yc91Jk+e3KAMRE5ODp9++im33347gYGBlJeX8+STT7Jp0yZsNhsDBgzgxRdfbHKk9Y477mgwxJ+bm1vvk9flIm3pQYJtTjj1bLhqUgghhGgPN910Ez4+PixcuNA+9/Puu+9m9uzZ9pHRc4mOjmb58uUsWLCA9evXs2HDBjw8PBg9ejR33HFHvcXYL7zwAgUFBYwbN65BTvH444+zfv16Nm/ezNKlS5k1a1a943WJamO1U7dt22Zf2FVXMqsxoaGhnTJRRW2hl19+WU1ISFCfeOIJe9t7772n9ujRQ42Pj1fj4+PVHj16qLNmzVJNJlNLL39RzGaz+txzz6lxcXFqnz591E2bNtU7PmjQIDUuLk41Go2Nnj9r1iw1Li5OTUpKOudrPfXUU+qgQYPU0tJS+78TExPVhQsXql988YU6ZMgQ9ZprrlEtFst5x3/69Gk1Li5OPX369Hmf09GZzWZ1/7tr1X3vrW3vUIQQoks4n/ewruytt95S4+Li1Hfffbe9QzkvNptNnThxojpgwADVYDBcktfsSN9DLSr4/91337Fw4UJsNpt9tNRoNPLBBx8AteUSXn75ZWJjYzl69Ciffvpp62fWTaiqquL+++/niy++wMPDgw8//JAxY8bU61O3Qq9urupvnWsOa52TJ0+yYsUK7rnnHjw9PamoqOCbb77hhhtu4M477+TGG2/kvvvu48SJE2zfvr0V7q7zcnBwoP8DE0j8/dj2DkUIIYToNIxGIzabjffee4/MzEyuu+66y2ru6flq0aP/ZcuWoSgKL7/8MtOnTwdqSz5VVVUREhLCSy+9BNSuhJs8eTLff/99gxV1baGsrIy77rqLY8eOERwczPz58xtd2d+9e3dOnTpFampqgzkelZWV5Ofn4+Pjg5+fX7Ov9+qrr+Lr62vfBCAzMxOr1VpvR67Y2FigdgvWuq3TuprS/EK8Amq/lg4Ol+10aCGEEKLVTZw4kdLSUkwmE56enuc1b/Zy1KIR1aSkJIKCguxJKsDmzZtRFIVx48bZ20JDQ4mMjLQXzG9LJpPJXtw2NjaWzz//vNEkFbDv/LBu3boGxzZs2GCfX9ucvXv3snHjRh588EH7J5u6Oa11W5zBL7VXz7Vq8HKVduAY5f87zv5lm9o7FCGEEKLTqSuR2atXLxYsWNDohgBdQYsS1fLy8gajjXWPtn+7daher7evsm9Lb731FgcPHiQ4OJjFixc3W9x28uTJ+Pr6snz5cjZv3mxvP336NK+++iqKopxzBHjevHlERkZyww032Nu6deuGg4MDW7ZssU9orts4oG5ktSsxW8wYN9dOowjp1+0cvYUQQohL5+GHHyYlJYUHHnigvUNp1nvvvceRI0dYtmwZffv2be9w2k2Lnsd6eHhQWFho//epU6fIycnBwcGBoUOH2tuNRiNZWVn4+vq2XqSNKCkpYfHixUBt2YUXX3yxyb7z5s3Dzc2N559/nkceeYT77ruPwYMH4+rqyk8//UR1dTX/93//Zy891Zi1a9dy4MABXnvttXqPst3c3JgxY4Z9pZ6Liwu7d++mf//+9bZq7SpKcwrQqAoFwUYGdoto73CEEEII0Um1KFHt168fGzdu5P/bu++oqI63gePfpYNYQFQUG5bFioIRYy8Y0URjRKMmNjRRE41GQ4jdJQAAThdJREFUjVGxJfZobLElsST2xMSCEvNTY6+xd8WCFQuiIFVYYO/7B+/euLIgKAjo8znHc3Tu3Luzd1h5du7MM1u3bsXHx4dffvkFgDp16hjlDZ05cyaxsbHUr18/a1v7jKNHj6qP2M+fP8/58+fTrDt9+nQgZfvUFStWMH/+fE6fPo2iKLi5ueHn50erVq3SPD85OZmZM2dSpUoV3n333VTHx44di42NDYGBgSQmJuLj45NmLrPXXZFSJSjYzwmNee7axUwIIYQQeYtGMTyrzoDDhw/Ts2dPFEWhQIEC6s5KCxcupGHDhgQFBTF06FCCg4PRaDSsWLGCWrVqZVvjXzchISF4e3uzY8eOVIu98oonMTHYPmezAyGEENnn4sWLVK5cOaebIfKw3PQzlKk5qnXq1GHq1Kk4OjoSGRmJvb09I0eOVBcpmZubc/XqVezs7Jg7d64EqW+YUxv3cu/nk1w7mfbIthBCCCFERmU6Z1CbNm1o3bo1jx49wsHBwWhVe5kyZZg5cyZNmjTJsV2pRM6IDAunwFUNyRqFUtqyOd0cIYQQQrwGXii5pUajMZlr1MrKyuT8TfH6u3M+GDsFnnjaYJMvX043RwghhBCvgTQD1atXrwJQtmxZdYW7oSwz3sT0TG+iKk1qE1MjirIOBXK6KUIIIYR4TaQZqLZu3RozMzM2b96Mq6srkPLYPzM0Gg0XLlx4uRaKXE2XoMPM3AwLCwvsJUgVQgghRBZK99G/Ycclg0wkCHih+iLvObNiDzaxFrj61SJfQQlUhRBCCJF10gxUg4KCMlQm3lzXTp6neIQtj6x1EqQKIYQQIsu90GKqtOj1eszMMpXxSuRh4efuUURjQ9H3yud0U4QQQog3Wnx8PIsXLyYwMJA7d+5gb2+Pp6cnn376KZ6enjndvBf2QlHlX3/9Re/evUlKSjIq//rrr2nXrh2bNm3KksaJ3O2tbs2x/7gizuXL5HRThBBCiDdWUlISffr0Ye7cuURFRdGgQQPKly/Pzp076dKlCxs2bMjpJr6wTI2oKorCyJEjCQgIAODmzZuUL//faNrNmze5ePEiw4cP59ixY4wfPz5LGytyh6SkJDUTROESxXK4NUIIIcSb7ddff+Xw4cPUrl2bn376Sd3W/t9//+XTTz9l/PjxeHt7U6BA3puml6kR1d9//50NGzZga2vL119/TfHixY2OL1y4kG+//Zb8+fPz559/8vfff2dpY0XucO7HPRxb9E9ON0MIIYQQoD7JHjVqlBqkArz99tvUq1ePuLg4Tpw4kVPNeymZClTXrVuHRqNh4cKF9OrVK9XuU46OjnTu3Jl58+ahKAqrV6/O0saKnHdq416c463R5HRDhBBCiBcwd+5c3Nzc2L59O4GBgbRp0wZ3d3e8vb2ZNm0akZGRat3169fj5ubGihUrmDZtGrVq1aJWrVp88803ap2IiAimTZuGj48P1apVw8vLi08++YR9+/YZve62bdtwc3PDw8ODu3fvGh07duwYlStXplatWoSEhDBhwgTc3NyYN2+eyfewbNkyo+N//PEHAQEBVK5cOVXd2NhYAKOdRPOSTAWqwcHBuLq68tZbb6Vbz8vLi1KlSkkO1deQ5lYS0eZ6qn5cL6ebIoQQQrywtWvXMnToUJKTk2nSpAk6nY4lS5bQtWtXHj9+bFR35cqVLFu2jLfeeouyZcuq+eVv3bpF27ZtWbJkCfHx8TRr1gw3NzcOHTrEp59+ahRotmjRgvfee4+4uDijqZFxcXH4+/uj1+sZPXo0JUuWxNfXF4DAwECTbQ8ICECj0fDBBx8AYGtrmypIVRSFP//8kxMnTuDs7EydOnVe9pbliEzNUTUzM8PS0jJDdfPnz09YWNgLNUrkXlU+b8ijm3dlm1QhhBB52q5du+jVqxfDhg1Do9EQHx/PwIED2bNnD3PmzGHs2LFq3Rs3brBgwQK8vb2BlCxHiqIwaNAgQkND6dKlC/7+/mqMdObMGXr37s3cuXNxd3enUaNGAIwZM4bDhw+za9cu/vnnH9555x2mT5/OrVu38PHxoV27dgBUrVqVSpUqERQUxOnTp6lRo4balsuXL3PhwgW8vLwoWbJkqvd19+5dJk2aRFBQECEhIVSsWJFZs2ZhZWWVbfcyO2UqUC1TpgyXL1/m/v37ODs7p1nv4cOHXL58Wf3GIV4flhaWsspfCCHysAvBDzl35WFONyNTqlV0okp5pyy9plar5euvv0ajSZnMZmNjw5QpU2jcuDEbNmxgxIgRal0XFxc1SIWUgbsjR45w/vx5ypcvz6hRo4werbu7uzNixAhGjBjBkiVL1EDVwcGBcePG0b9/fyZNmoSFhQWrV6+mSJEijBs3zqh9vr6+TJ48mU2bNhkFqoYV/IZR12cFBwezfft29d96vZ4rV65QsWLFF71VOSpTj/5btmxJUlISgwcPJiIiwmSd6OhovvrqK5KTk/Hx8cmSRoqclRD3hHM/7OJ04P6cbooQQgiRJVq1apUq93vhwoXx8PAgLi6Os2fPquWVKlVKdf7Ro0eBlEf6puZ/tmzZEnNzc06cOEFycrJa3rx5c1q3bs29e/fo378/AFOmTMHBwcHo/Pfffx9LS0s2b95MYmIiAMnJyQQGBmJnZ5dmjFWjRg2OHz/OwYMHmTBhAqGhoQwePJjNmzdn5LbkOpkaUe3SpQsBAQGcPHmS5s2b06RJE7RaLXZ2djx58oSrV6+ye/duoqKiKFOmDH5+ftnUbPEqnf1tPyV1ttxLTHp+ZSGEELlalfJZPzqZF5UpY/rpoOGJ8YMHD9SyggULpqpnOO7i4mLyOra2tjg6OhIWFkZkZCSOjo7qsTFjxrBnzx6io6N59913adiwYarzHRwcaNasGVu3bmXfvn00a9aMAwcOEBYWhq+vb6oF7QaGFFT29vZ07NgRe3t7Bg8ezJw5c3jvvfdMnpObZSpQzZcvH4sXL2b48OEcPXqUzZs3G6WgUhQFSInmZ82aZZQiQeRN8bGxOD624YG1Dg/fZjndHCGEECJLpLWTpiGWeXqU1FRdQ7306PV6gFTzQ0+fPk10dDQABw4c4MGDBxQtWjTV+e3bt2fr1q0EBgbSrFkzNQ2VYS5rRvj4+GBtbc2NGzeIi4tLM8DNrTK9hWqJEiVYsWIFJ0+eZPfu3dy+fZvHjx9jY2ND2bJladiwIXXr1s2OtoocYJMvHwU+qoidTkZThRBCvD5CQ0NNlhtSRzk7O3P16tU0zzcEliEhISaPx8TEEB4ejo2NjdHAXVRUFKNHj0aj0eDj48OWLVsYO3YsP/30U6prNGjQgKJFi7J7925iYmLYvXs3pUqVonbt2kbXmzdvHvHx8SY3WjIzM8PCwoKEhASjKQh5RaYDVQMPDw88PDyysi0il3IqkfbCOSGEECIv2rt3b6opimFhYZw5cwYHBweqVq2abqBqCBb/+ecfBg4cmGqe6tatW1EUBS8vL6PySZMm8eDBAzp37szo0aO5evUqu3btYsOGDalGSs3Nzfnggw9YuHAhs2fPJjo6Gj8/P3UBGKQsAvvzzz+Ji4uje/fuVKhQwegax48fJzY2llKlSpE/f/4M35/cIlOLqZ716NEjjh07xq5du4CUYfCYmJgsaZjIWfdvhHBmzk5unr2U000RQgghstyBAwf4448/1H/HxcUxYsQIEhMT6dq163MT5Ht5eVGlShWCg4OZNGmSuuAJ4Ny5c0ybNg1IWd9jsGvXLgICAihatChDhw7F0tKScePGodFomDx5sslRXsPq/lWrVhnlTjWwsrJSA9xRo0YZ5YANDg5m5MiRAPTq1SsDdyX3eaER1R07djB//nwuXrwIgEaj4cKFC9y+fZsPPviATp068dVXX6n7wYu8JzTwMk4JVsTrdDndFCGEECLLOTs7M2bMGP744w9KlCjB8ePHefjwIXXr1qVPnz7PPV+j0TBz5kx69OjBqlWr2LlzJ+7u7kRERHD8+HGSk5Pp168fTZo0ASAyMpIxY8YAMHr0aHV086233qJjx46sWbOGMWPGsHDhQqPXcXV1xdPTkxMnTqSZO3XIkCGcOnWKU6dO0aJFCzw9PYmOjubMmTPodDo6duzIxx9//JJ3LGdkekR13rx5fPHFF+quU+bm5uqE4jt37hAXF8fSpUvp27dvnpwLIeDayfMUibfiXqEnlK9VPaebI4QQQmS5Tp06MW7cOKKjo9m9ezeFChVi2LBhLFq0KMPJ8V1dXdmwYQM9e/bE0tKSnTt3EhwcTKNGjVi6dClffvmlWnfSpEmEhYXRrFmzVKmlhg4dipOTE3v27GHt2rWpXsfT0xNIO3eqvb09q1evZuDAgTg5ObF//36CgoKoWbMms2fPZsKECRm9LbmORsnIsrX/d+jQIXr27Im9vT1Dhw7l3Xff5bPPPuPkyZNcvHgRvV7Ppk2bmDBhAnFxcYwaNYquXbtmZ/tfKyEhIXh7e7Njxw6T35hepUuHTlG6egVsJXODEELkKRcvXjS557tIMXfuXObNm8eXX35Jv379cro5z6UoCj4+Pjx69Ij9+/dja2ub7a+Zm36GMjWiumzZMjQaDd9//z2dO3dWc3WpFzMz44MPPmD27NkoiqKmURB5j1vdmhKkCiGEEDkkPj4evV7PggULuHnzJu3atXslQWpuk6lJpKdOncLZ2ZmmTZumW69hw4aUKFEi3dVyIve5uPcYTy5EoO1UB3uHAs8/QQghhBDZ4p133uHx48fodDoKFiyYoXmzr6NMjajGxsam2uIrLY6OjiQlSe7NvCIh7gnmx+JwiLUhMTEhp5sjhBBCvNGqV09ZI1K1alUWLVpkckOAN0GmRlSLFi3K9evXSUpKSndFv06n4/r16xQpUuSlGyhejXObDuGSbMXd8km4FpV+E0II8XoaMGAAAwYMyOlmPNeCBQtyugm5QqZGVOvXr098fLzJ3ROeNn/+fGJjY6lXr95LNU68OjU6NCK0Knj6NsnppgghhBBCAJkcUe3Tpw+BgYHMnz+fO3fu8N577xEfHw+k5AcLDg7m999/JzAwEGtr6zybXPZNZGFhQY13G+R0M4QQQgghVJkKVEuWLMmcOXMYPHgwGzZsICAgQD329ttvAylpFKytrZk6dSqurq5Z2liR9U6s3QUKuLdrKBs0CCGEECJXyXTC/4YNG7Jx40Y+/vhjnJ2dURRF/ePg4EC7du1Yv349LVu2zI72iix0/0YIhW9YYhtihkKG0+kKIYQQQrwSmRpCu337NqVKlcLFxYWxY8cyduxYYmNjiYmJwc7OTt0OTOQNd/8Oophig2UDRywtLHO6OUIIIYQQRjI1otqvXz+aN2/O48eP1bJ8+fJRrFgxCVLzoHIdahLmpqdCbdkmVQghhBC5T6YC1Vu3bmFtbU2hQoWyqTniVSpU1Ima7zfK6WYIIYQQQpiUqUC1QIEC6ip/kXcdW7Gdq0fP5nQzhBBCCCHSlalA9fPPP+fOnTtMnTqVJ0+eZFebRDa6uPcYJe7bEH00NKebIoQQQgiRrkwtpkpISKBmzZosXbqUVatWUbFiRYoUKYK1tbXJ+hqNhtmzZ2dFO0UWSEpKQnM8lgQzM8r4uud0c4QQQggh0pWpQHXq1KloNBoURUGn03H+/Pl062s0mpdqnMhaFhYWmNdzIDoiGlfnN3PPYCGEEOJNcOvWLdq2bYuDgwM7d+7M6ea8sEwFqv3795fgM4+rWEdGUoUQQojXWXJyMsOGDSMuLg4HB4ecbs5LyVSgOmDAgOxqh8hGSUlJnFq9m4qtPClYxDGnmyOEEEKIbLR48WJOnjyZ083IEpnemUrkPafX7qFkqA1Xt53I6aYIIYQQIhsFBQUxd+5cateundNNyRLPDVR3795Nnz59qFu3Lu7u7vj4+DB58mRCQ2XVeF4Qfv8BTiHWRFgk496pcU43RwghhMhRc+fOxc3Nje3btxMYGEibNm1wd3fH29ubadOmERkZqdZdv349bm5urFixgmnTplGrVi1q1arFN998o9aJiIhg2rRp+Pj4UK1aNby8vPjkk0/Yt2+f0etu27YNNzc3PDw8uHv3rtGxY8eOUblyZWrVqkVISAgTJkzAzc2NefPmmXwPy5YtM3lcp9Px9ddfU6hQIUaPHv2ytypXSDdQnTBhAp9//jn79u0jIiICnU7HzZs3WbFiBe+//z7Hjh17Ve0UL6iAkyMPS+mwqO8g26QKIYQQ/2/t2rUMHTqU5ORkmjRpgk6nY8mSJXTt2tVoB06AlStXsmzZMt566y3Kli2Lq6sr8N+CpSVLlhAfH0+zZs1wc3Pj0KFDfPrpp0aBZIsWLXjvvfeIi4tj/PjxanlcXBz+/v7o9XpGjx5NyZIl8fX1BSAwMNBk2wMCAtBoNHzwwQdG5T/88AOXL19mwoQJr83mTGnOUd2xYwerVq0CoGbNmjRr1gx7e3tu3brFpk2bCA8P58svv2THjh3Y2Ni8sgaLzLGwsKBWp2Y53QwhhBAiV9m1axe9evVi2LBhaDQa4uPjGThwIHv27GHOnDmMHTtWrXvjxg0WLFiAt7c3AHq9HkVRGDRoEKGhoXTp0gV/f38sLVMGhM6cOUPv3r2ZO3cu7u7uNGqUsgvkmDFjOHz4MLt27eKff/7hnXfeYfr06dy6dQsfHx/atWsHQNWqValUqRJBQUGcPn2aGjVqqG25fPkyFy5cwMvLi5IlS6rlx48f55dffsHX15emTZty//79bL+Hr0Kageq6devQaDT07duXQYMGGR37/PPP+fTTTzl37hz/+9//1Bsrco/oiMdc2X4S93YNsbDI1Jo5IYQQr7En50KJO5e3pu/ZVSuGbbViWXpNrVbL119/rWYzsrGxYcqUKTRu3JgNGzYwYsQIta6Li4sapAKYmZlx5MgRzp8/T/ny5Rk1ahTm5ubqcXd3d0aMGMGIESNYsmSJGqg6ODgwbtw4+vfvz6RJk7CwsGD16tUUKVKEcePGGbXP19eXyZMns2nTJqNAdcOGDepxg7i4OEaMGEGxYsUYNWpUFt6lnJfmo//z589ToEABkyv9CxYsyIABA1AU5bVZVfa6ubLmKCVuWHL18JmcbooQQgiR67Rq1QozM+MwqHDhwnh4eBAXF8fZs/9tNV6pUqVU5x89ehRIeaT/dJBq0LJlS8zNzTlx4gTJyclqefPmzWndujX37t2jf//+AEyZMiVVGqn3338fS0tLNm/eTGJiIpCSdiowMBA7Ozt8fHzUut999x23b99m8uTJ2NvbZ/ZW5GppDrU9fvyYChUqmLz5AB4eHgCyqCoXCjpwAudoW+7ZxeNRv0FON0cIIUQuYpsNo5N5UZkyZUyWOzs7A/DgwQO1rGDBgqnqGY67uLiYvI6trS2Ojo6EhYURGRmJo+N/6SHHjBnDnj17iI6O5t1336Vhw4apzndwcKBZs2Zs3bqVffv20axZMw4cOEBYWBi+vr7Y2dkBsGfPHtasWcPHH39MvXr1Mvju8440A1WdTpfm1qgA+fPnB1KGm0XuUqJSWa6cO07ZNpLcXwghhDDl2dFUA0VRAIwG6kzVNdRLj16vB8DKysqo/PTp00RHRwNw4MABHjx4QNGiqXeMbN++PVu3biUwMJBmzZqxadMmAKMpl99//z2QEjgPHTpULY+PjwdSshIYyqdPn/7cNuc2aQaqiqJkaBcqQyeI3KNAYUdq9X0np5shhBBC5FppPRE2pI5ydnbm6tWraZ5vCCxDQkJMHo+JiSE8PBwbGxujx/FRUVGMHj0ajUaDj48PW7ZsYezYsfz000+prtGgQQOKFi3K7t27iYmJYffu3ZQqVcooR6phwHD79u0m2xEXF6dmD8iLgaok/H+N3Dx/mUuHTuV0M4QQQohcb+/evanKwsLCOHPmDA4ODlStWjXd8w3B4j///GM0B9Vg69atKIqCl5eXUfmkSZN48OABnTp1Yvr06VSoUIFdu3api6SeZm5uzgcffEBcXByzZ88mOjqaDz74wGggcefOnVy6dCnVnz179gApUxMMZXmRBKqviaSkJGK238H2QAxRj8JzujlCCCFErnbgwAH++OMP9d+GlfOJiYl07do1zTU6Bl5eXlSpUoXg4GAmTZqkLngCOHfuHNOmTQOgS5cuavmuXbsICAigaNGiDB06FEtLS8aNG4dGo0lzMyXD6v5Vq1aZzJ36uks3b5FOp0u1e0Jm65QoUeLFWiYy5fS6vbjorAgp+oTShR2ff4IQQgjxBnN2dmbMmDH88ccflChRguPHj/Pw4UPq1q1Lnz59nnu+RqNh5syZ9OjRg1WrVrFz507c3d2JiIjg+PHjJCcn069fP5o0aQJAZGQkY8aMAWD06NHqWp+33nqLjh07smbNGsaMGcPChQuNXsfV1RVPT09OnDiRKnfqmyDdQPXcuXNGecOepdFo0q2j0Wi4cOHCy7VQZEihisW4G34P986yTaoQQgjxPJ06dcLR0ZFff/1VnfvZq1cvunfvribufx5XV1c2bNjAokWL2LFjBzt37qRAgQI0atSIHj16ULduXbXupEmTCAsLo1mzZkappQCGDh3Kjh072LNnD2vXrqVDhw5Gxw2B6tO5U98UGiWNZWumcoa9iKCgoCy5zpsgJCQEb29vduzY8cZ9YxJCCJE1Ll68SOXKlXO6GbnW3LlzmTdvHl9++SX9+vXL6eY8l6Io+Pj48OjRI/bv34+trW22v2Zu+hlKdwtVkftd/vcMTmWdcXROndZCCCGEEHlTfHw8VlZW/Pjjj9y8eZNu3bq9kiA1t0kzUE0rga3IPWIiojA/EMn9f8NxHCSBqhBCCPG6eOedd3j8+DE6nY6CBQtmaN7s60hW/edhl9YcJp9eQ3Jlq+dXFkIIIUSeUb16dQCqVq3KokWLTG4I8CZIdzGVyN00RS25o3lCLR9J7i+EEEJkxIABAxgwYEBON+O5FixYkNNNyBUkUM3DPH2b5HQThBBCCCGyjTz6z4OuHD5DYlLi8ysKIYQQQuRhEqjmMTfPX8Z2bxRnluzO6aYIIYQQQmQrCVTzkKSkJKK330HRQNFGrjndHCGEELlUGinShXiu3PazI4FqHqJPTibBJonQovGUqlwhp5sjhBAiFzI3Nzfad16IzEhMTMTc3Dynm6GSxVR5iJW1NbX6ygp/IYQQacufPz9RUVE4OTnldFNEHhQVFUX+/PlzuhkqGVHNI26ev5zTTRBCCJEHODo6EhERwcOHD9HpdLnuUa7IfRRFQafT8fDhQyIiInB0dMzpJqlkRDUPOLPlAE5nFU5dvU/Nto1yujlCCCFyMWtra0qXLk14eDg3btwgOTk5p5sk8gBzc3Py589P6dKlsba2zunmqCRQzeViI6PId15PnBlUbFIzp5sjhBAiD7C2tqZ48eIUL148p5sixEuRQDWXiw5/TLx5EsmVrMhXsEBON0cIIYQQ4pWRQDWXc3YtjdMXJbCwkK4SQgghxJvljV1MdfDgQbp3706dOnXw9PSkW7du7N27N1W906dP8+GHH+Lu7o6Pjw9LlixBr9enqqfT6fD29mbgwIFZ3lYJUoUQQgjxJnojA9X169fTs2dPTp48ibu7Ox4eHpw8eZLevXuzZs0atV5UVBS9e/cmKCiIevXqYWFhwbRp01i8eHGqa/7222/cu3ePQYMGvcJ3IoQQQgjx+nrjhupCQ0P55ptvyJ8/P6tXr0ar1QJw5swZevbsyaRJk2jSpAnFihVj48aNREZGsmDBAry9vUlOTqZTp0788ssv9OnTR71mTEwMP/74I76+vpQrVy6n3poQQgghxGvljRtRXbVqFTqdDj8/PzVIBXB3d6d3794kJCSoo6rXrl0DoG7dukBK6gYvLy8iIiJ4/Pixeu7ixYuJi4tjwIABr+6NCCGEEEK85t64QHXfvn0ANG/ePNUxQ5lhrmqBAimr7OPj49U6sbGxmJmZYWtrC0BYWBjLli2ja9euFCtWLFvbLoQQQgjxJnmjAlVFUbh69SpmZmYmH9GXLVsWMzMzrl69iqIoVK9eHYD58+cTExPD2bNn2bJlC5UrV1aT4c6bNw9zc3OjqQBCCCGEEOLlvVFzVCMjI9HpdDg6OmJlZZXquIWFBQ4ODjx69IjY2FiaN29OgwYNWLlyJStXrgTAzs6O0aNHA3Djxg3Wrl3LgAEDKFSoUKbaEhUVRVRUlFHZnTt3ALh///4LvDshhBBCiFfP2dk52zIUvVGB6pMnTwDUx/am2NjYACmP+O3t7Vm4cCEbN27kzJkzODk50bZtW0qVKgXAzJkzcXBwoEePHur5sbGxmJubq9dJy7Jly5g3b57JY126dMnU+xJCCCGEyCk7duygZMmS2XLtNypQNTPL+EwHRVGAlAVUvr6++Pr6Gh0/c+YMW7du5ZtvvsHW1pa7d+8ybNgwjh49ipmZGQ0bNmTy5Mk4OTmZvH6PHj1o166dUdmtW7fo2bMny5cvx8XFJZPvTuSE+/fv06VLF1atWoWzs3NON0dkkPRb3iN9ljdJv+U9L9Jn2dm3b1SgamdnB0BCQkKadQwLpwx10zJ9+nRKly7Nhx9+CMDw4cO5evUqEydOJCYmhtmzZ+Pv78+iRYtMnl+gQAF1sdazXFxcsu2bicgezs7O0md5kPRb3iN9ljdJv+U9uaXP3qhA1d7eHjs7OyIiIkhKSko1nyIpKYmIiAisra3TDCIB9uzZw+HDh5kxYwaWlpZcuXKFI0eOMGLECDVwDQkJYeXKlQQHB1O+fPlsfV9CCCGEEK+jN2rVv0ajoUKFCiQnJ3Pjxo1Ux69fv45erzfKr/osvV7PzJkzqVy5Mu+99x7wX77VpzMJVKhQQb2mEEIIIYTIvDcqUAVo2LAhANu3b091zFDWuHHjNM/ftGkTQUFBDBkyBI1GA6QErwCJiYlqPcMUAnNz86xpuBBCCCHEG+aNC1R9fX2xtrZm0aJFnDt3Ti0/e/YsixcvxsbGho8//tjkuTqdjjlz5uDl5UWjRo3UcsMIrGGjAEVR1I0FDCOrGVGgQAG++OKLdKcdiNxF+ixvkn7Le6TP8ibpt7wnt/WZRjEsb3+DrFq1ivHjx2NpaUmdOnUAOHz4MElJSUydOpW2bduaPG/p0qVMmTKFNWvWULNmTaNjfn5+HDp0CC8vL2JjYzl//jytW7dmxowZ2f12hBBCCCFeS29koAqwa9cuFi9ezIULF7CyssLNzY3PP/+cunXrmqwfExODt7c3tWrVYsGCBamOR0ZGMmHCBHbs2IG5uTktW7bE39+ffPnyZfdbEUIIIYR4Lb2xgaoQQgghhMjd3rg5qq/awYMH6d69O3Xq1MHT05Nu3bqpc1mfdvr0aT788EPc3d3x8fFhyZIl6iKtp+l0Ory9vRk4cOCraP5rKzk5mZUrV9K+fXs8PDxwd3fnvffeY/78+Sbz7J49e5bPPvuM+vXr4+HhwYcffkhgYKDJa1+/fh0/Pz/c3d1p1qwZM2bMSDN370cffUSHDh2y9L29KR4/fkzDhg1xc3Mzefz69esMGTKExo0bU6NGDdq0acOKFStMfq7CwsL44osv8PDwoEGDBowdOzbVFscGX331FY0bN043H7MwdufOHUaOHEmjRo2oVq0aDRs2ZMyYMYSFhaWqK/2We2zcuJGOHTtSs2ZN3N3dadu2LcuWLSM5OTlVXem3nLN+/Xrc3Nw4duyYyeOZ6RuA0NBQxo4di7e3txqTzJ8/H51Ol6pubGws/v7+1K5dm7fffptBgwYRGhpq8rqzZs3Cw8ODhw8fZur9yYhqNlq/fj3+/v5YWVnx9ttvo9frOXz4MImJiYwfP55OnToBEBUVRfPmzXny5An169fn9u3bXL16la+++oo+ffoYXXPZsmVMnTqVv/76yygdlsi45ORk+vXrx+7du7Gzs6NGjRpYWFhw+vRpoqKiqFGjBsuWLVO32j1w4AB9+/ZFr9dTu3ZtbG1tOXToEPHx8Xz22WcMHjxYvXZSUhLvvfcet27don79+kRERHDu3Dk6d+7MuHHjjNqxfft2+vfvz9KlS9OcciLSNnjwYP7++28ALl26ZHQsKCiILl26EBMTg6enJ4ULF+bw4cNERUXRpk0bpk+fblS/e/fuHD58GC8vLwCOHDlCo0aNUm3YcfHiRdq1a8eECRPUnMkifWfPnqVnz55ER0ej1WopXbo0586d4/79+5QuXZq1a9dSsGBBQPotN5k2bRpLlizBysqK2rVrY25uzrFjx4iLi6N58+bMmzdPzXwj/ZZzTp48Sa9evYiLi2PVqlW89dZbRscz2zf379+nU6dO3L9/nypVqlCqVClOnDhBWFgYXl5e/PLLL1haWqr1/f39Wb9+PdWqVcPBwYEDBw5QoUIFNmzYYJSr/sGDB7Ro0QI/Pz8GDRqUuTepiGxx//59pVq1akqtWrWUS5cuqeWnT59WPD09lerVqyv3799XFEVRli9frmi1WmX79u2KoihKUlKS0r59e6VOnTpG14yOjlbq1KmjjBo16tW9kdfQb7/9pmi1WqVNmzZqHyiKojx69Ejp1KmTotVqlenTpyuKoihPnjxR6tatq1StWlU5dOiQWvfmzZtKo0aNFK1Wq5w9e1Yt37Fjh6LVapVly5apZQMGDFAqVaqkREdHq2VJSUnKu+++q/j5+WXnW31tBQYGKlqtVv3zNL1er7Rp00bRarVKQECAWv7o0SO1fMuWLWp5UFCQotVqlcmTJ6tlU6ZMUbRarXL58mWja/fq1Utp2bKlkpSUlE3v7PWSkJCgtGjRQtFqtcry5cvV8vj4eGXAgAGKVqtVJkyYoCiK9FtucvHiRcXNzU15++23lWvXrqnl9+/fV5o2bWrUF9JvOWfLli2Kh4eH+v/g0aNHjY5ntm8URVH69u2raLVaZf78+WpZbGys4ufnp2i1WmXJkiVq+ePHj5XKlSsr/fv3V8sM8cyuXbuMrjtmzBjFy8vL6PdgRsmj/2yyatUqdDodfn5+RhsIuLu707t3bxISElizZg3w34YBhlE1c3NzvLy8iIiI4PHjx+q5ixcvJi4ujgEDBry6N/Ia2rBhAwAjR46kWLFiarmjoyPffvstAJs3bwZSHn09evSINm3a8Pbbb6t1S5cuzdChQwFYsWKFWv5sXwLUqVMHvV7PzZs31bL169cTHBzMkCFDsvjdvf5CQ0MZP348Hh4eJvMUHzhwgEuXLuHl5WWUwePp/n1enxn+/vSGHYcOHWL//v0MGjRI8iNn0N9//82NGzdo06YN3bp1U8utra3x9/fHyclJvcfSb7nHoUOHUBSF999/H1dXV7W8WLFiavrGo0ePAtJvOeH+/fsMGzaMgQMHotfrcXJyMlnvRfpm9+7dlC5dms8++0wtt7OzY9KkSZibm7Ny5Uq1/ObNmyQnJz+3L69du8a6devo27cv9vb2mX6/EqhmE0Me1ebNm6c6ZigzzFU15CozbBIAKfM+zMzM1MfPYWFhLFu2jK5duxoFVyLzHBwcKFeuHO7u7qmOlS1bFkh5TAH/9aO3t3equk2bNsXc3NxoznFafQmoGSASEhKYN28ePj4+VK9ePQve0Ztl1KhR6HQ6pk6davJ4ep89w6Ov48ePExMTA2SszxRFYfr06ep8LZEx27ZtA6Bnz56pjhUvXpwDBw6wZMkSQPotNzE80jc11zAiIgKAQoUKAdJvOWH27Nls3LiRatWqsWbNmjSnAWa2b/bv34+iKDRt2hQzM+PwsESJElSpUoU7d+5w9epVIGN9CSlzU4sUKUKXLl1e6P1KoJoNFEXh6tWrmJmZmfwBKlu2LGZmZly9ehVFUdRgZf78+cTExHD27Fm2bNlC5cqVsba2BmDevHmYm5unmrMqMu+nn37if//7H3Z2dqmOnT17FgBnZ2cArly5AmByW117e3uKFi1KeHi4Ojnc0JcLFy4kIiKCa9eusXbtWpycnChZsiSQMs/44cOHmZ+nI1i9ejX79u1j6NChlClTxmQdw3+iaW2F7Orqil6vJzg4GAA3NzesrKxYtmwZoaGh3L9/n2XLlmFjY0OlSpUA+N///se5c+dkBDyTLly4gKWlJZUqVeLevXssXLiQ0aNHM336dM6cOWNUV/ot92jYsCEajYYtW7awcOFCwsPDiYqKYu3atSxfvpyCBQvSvn17QPotJ5QrV46pU6fy559/prmYFDLfN4b6FStWTPN1AS5fvgxAyZIlcXR05I8//uD69etERETw888/o9Fo1IGg06dPs23bNr744gs1nsksi+dXEZkVGRmJTqfD0dERKyurVMctLCxwcHDg0aNHxMbG0rx5cxo0aMDKlSvVYXU7OztGjx4NwI0bN1i7di0DBgxQv8WKrKcoCj/88AMALVq0AFBXJRcpUsTkOUWKFOHevXs8fPgQJycnKleuTKdOnVizZo06mmRpacn06dOxsLAgMjKSRYsW4evra/RITTzfzZs3+f7776lbt26638wNo+Hp9RmgfrlwcnLiiy++YObMmeqOcxqNhhEjRlC4cGESExOZPXs29evXl0VvmaDT6bh37x7Ozs5s2bKFUaNG8eTJE/X4okWL+OSTTxg2bBgg/ZablC9fngkTJjBp0iRmzJhhtHGNh4cHU6ZMoXjx4oD0W07I6IBVZvvGUL9o0aIZqm9hYcGIESMYMWIELVu2VOv5+fmpXzqmT59O+fLladeuXYbabIoEqtnA8J+x4bG9KTY2NkDKMLm9vT0LFy5k48aNnDlzBicnJ9q2bUupUqUAmDlzJg4ODvTo0UM9PzY2FnNzc/U64uXNnDmTo0eP4uTkxKeffgr815dp3WdDeVxcnFo2fvx46tevz5EjR7C3t6dVq1bqh/ann35Cp9PxxRdfqPXj4+NJSkp6obk7b4rk5GSGDx+OmZkZkydPVh9NmvIifda3b1+qV6/Onj17MDMzw9vbW109+8cff3Dr1i1mzpyp1tfpdCQkJJA/f/6Xfm+vK8PjxMjISIYPH07Lli3p378/hQsXZv/+/YwbN44lS5ZQpkwZOnXqJP2Wy3h6elK3bl3+/fdf3N3dMTMz4/Tp05w9e5bVq1czcuRINBqN9Fsultm+eZG+bNu2La6urmzbto2EhATq1atH06ZNAdi9ezdHjhxh7ty56jzjpKQk4uLiMrU9qwSq2eDZuR3pUf4/O5i5uTm+vr74+voaHT9z5gxbt27lm2++wdbWlrt37zJs2DCOHj2KmZkZDRs2ZPLkyWlOphYZ88MPP7Bw4UKsrKyYPXs2jo6OQEq/KIqSbmAEpMpH5+Pjk2pu1b1791i1ahXdunWjWLFiREVFMXz4cHbv3o1er8fT05PJkyfLSKsJixcv5uTJk0ycOJESJUqkW9fwH2Jm+6xevXrUq1fPqCw2NpYFCxbQsmVLqlWrhk6n45tvviEwMJDExES0Wi0TJ06kRo0aL/CuXm+GnItPnjyhQYMGRmlw3n33Xezs7Ojbty/z58+nY8eO0m+5yKlTp+jVqxcuLi789ddfuLi4AClzVr/44guWL1+Ovb09X375pfRbLpbZvnnRvnR3d0+15kOv1zNjxgxq1KhBixYtUBSFmTNnsmLFCp48eULJkiUZM2YMTZo0ee77kDmq2cAw9zG9BMWGycem5kk+bfr06ZQuXVrNITd8+HCuXr3KxIkTGTZsGIcPH8bf3z+LWv7mSUpKYuzYsSxYsABra2vmzZtH7dq11eO2trYoipJmXxr6MSNb5c6ZMwdra2v1sc2UKVM4ePAgw4cPZ8KECVy7do2BAweaTKb9JgsKCmLu3Lk0adIkQ7kUDU8ynp7g/7TM9Nmvv/7K48eP1fnECxcuJCAggL59+zJt2jRiY2Pp16+fOnoo/vP0qMxHH32U6niTJk0oVqwYoaGh3LhxQ/otF5k8eTKxsbFMmjRJDVIhZdX/zJkzsbCwYOnSpTx58kT6LRfLbN9ktP7z4hZIyZhz+fJldZ5xQEAACxcupEOHDsyaNYv8+fMzcOBA7ty589xryYhqNrC3t8fOzo6IiAiSkpKMkt5CSnAUERGBtbV1usPfe/bs4fDhw8yYMQNLS0uuXLnCkSNHGDFihPoLOyQkhJUrVxIcHEz58uWz9X29bmJjY/nyyy/Zt28fBQoUYMGCBUZBKqTM1YmKiiIsLExdDPW0581hNbhy5QoBAQEMGjSIggULEh0dzcaNG/n444/x8/MDUh6VTp06lQMHDqhzt0TKitHExEQSExPVlGAGhm/2hvKRI0dStGhRLl68yMOHD01+JjLaZ+Hh4fzyyy+0b99ezQaxcuVKGjdurKaIs7GxYeDAgQQGBpoMxt5k+fPnx9LSksTERJOfHUhZSRwaGkpERIT0Wy4RHx/PmTNnKFCggMnMKKVKlcLV1ZUrV65w8+ZN6bdcLLN9Y5ibmtbOUYb6ac1hNdDpdMyZM4cGDRqoaR1XrlyJVqtV196ULl2a9u3b89tvv6X6f/1ZMqKaDTQaDRUqVCA5OZkbN26kOn79+nX0en2aK/Eg5RfwzJkzqVy5Mu+99x7wX/65pzMJVKhQQb2myLjIyEi6devGvn37KF68OKtWrUoVpMJ/qx8NqyKfFhMTw4MHD3B0dHzu1IsZM2ZQuHBhunfvDvyXf0768vkM86EOHDhAYGCg0R/D1BnDv+Pi4tQ+M6xgfZqiKFy7dg1zc/PnfrGbP38+ycnJ9O/fH0hJyxMREWF0nvRZ2p6+x2ltqWj4hVi4cGHpt1wiOjoaRVHSncJmeEScmJgo/ZaLZbZv0qsP//0eTC92gZSg9N69e0ZZG65du2bUl4bXykhfSqCaTRo2bAikbJP5LENZ48aN0zx/06ZNBAUFMWTIEHW+iGH0KDExUa1nGIqXhMgZp9Pp6NOnD+fPn6dChQr8/vvvaX7w0uvHnTt3kpycnG4/Ahw7doxdu3bRv39/9dGK9GXGrVixgkuXLpn8Y7hXhn+XLFlS7bMdO3akutaJEycIDw+nVq1a6S5eu337NmvWrFHnE8N/88mlzzLO8GRgy5YtqY5du3aNO3fuULRoUUqVKiX9lksULlyYQoUK8fjx41QpxCDlS0dwcDCWlpaUK1dO+i0Xy2zfGOrv2rUr1TzUu3fvcvHiRVxcXNQvDKZER0fz888/06pVK6pWraqWK4pi1JeGhVsZ6UsJVLOJr68v1tbWLFq0iHPnzqnlZ8+eZfHixdjY2Kg7fDzLMGzu5eVl9AjYEEwZEswriqIm9E3vB0cYmzNnDqdOnaJ48eKsWLFCzZlqio+PD4ULF2bDhg3s2bNHLb99+zYzZsxAo9Goj+7TMn36dMqUKWM0v7JcuXJYWFiwd+9e9T9kQ79KX74cLy8vKlasyIEDB/jjjz/U8vDwcMaNGweYTkD/tFmzZmFra0vv3r3VMkdHRwoXLszBgwfV/3Clz9LXuXNn7OzsCAgIIDAwUC2PjIxk9OjR6PV6unTpgpmZmfRbLmFmZkaHDh2AlM01nh4NDw8PZ+jQoSQmJtK+fXvy5csn/ZaLZbZvDF8Yr127pqZqhJSnWqNHjyY5Ofm5fblw4UJiYmJS5QmvWLGi0eYCmelLjWL4LSmy3KpVqxg/fjyWlpbUqVMHgMOHD5OUlMTUqVONtjR72tKlS5kyZQpr1qyhZs2aRsf8/Pw4dOgQXl5exMbGcv78eVq3bm2U506kLSIigiZNmhAfH0/VqlXT3NEDUFcp79ixQ13kVLt2bfLly8e///7LkydPGDx4sNFWc8/6559/1JyBhikcBqNGjWLt2rVUq1YNOzs7jhw5goeHB7///nvWvNk3QJUqVUhOTubSpUtG5WfOnKFHjx7ExcVRo0YNihYtypEjR4iMjKRjx45MmDAhzWueP3+e9u3bM2TIkFT5ChcsWMAPP/xA+fLl1Z2VXFxc+Pvvv184mfXr7u+//+brr78mKSmJqlWrUrRoUU6dOkVERARvv/02ixcvxtLSEpB+yy0SEhL49NNPOXLkCNbW1tSuXRuNRsPp06eJioqiZs2a/Prrr+qiGum3nNWtWzeOHDnCqlWr1DRfBpntm9u3b/PRRx8RFhaGVqvF1dWVEydOEBYWRqNGjfjxxx9TrbsxCA0NpUWLFrRt25bx48cbHdu4cSPDhg3DxcUFrVbLgQMHsLa2ZuvWrRQuXDjd9yeBajbbtWsXixcv5sKFC1hZWeHm5sbnn3+eZiLjmJgYvL29qVWrFgsWLEh1PDIykgkTJrBjxw7Mzc1p2bIl/v7+GVpRKVK2dDRMzH+ep4OfEydOMH/+fE6fPo2iKFSoUAE/Pz9atWqV5vnJycm0bt0aGxsb1q9fnyrlR0JCAtOmTVNTrzRs2JAxY8Y8d9GB+E9agSqkzLOaM2cOhw8fRqfTUaZMGTp37syHH36Y7uOmnj17cuXKFbZv354qn6Ber2fBggX89ttvREVF4eXlxejRoyWl2HNcvHiRH3/8kaNHjxIbG0upUqVo27YtPXv2VINUA+m33CExMZHVq1ezceNGrl27hl6vp2zZsrRu3Ro/P79Um9lIv+Wc9AJVyHzf3Lt3jzlz5rB3716io6PVz2uPHj3S/YIwevRoAgMD2bZtm8mt3levXs3ixYt58OAB1apVY+TIkSYX7D1LAlUhhBBCCJEryRxVIYQQQgiRK0mgKoQQQgghciUJVIUQQgghRK4kgaoQQgghhMiVJFAVQgghhBC5kgSqQgghhBAiV5JAVQghhBBC5EqmtxcQQuSIPXv2sHHjRk6dOsXDhw+xsrKiaNGi1KlTh/bt21OtWrVU56xfvx5/f3+qVq3K+vXrc6DVuU9ycjILFixgw4YNPHjwgIIFC/L111/zwQcfpHmOIWm2KZaWltjZ2VG6dGmaNWtG9+7d0927PLcJCQnB29sbSNm8Iis2CDH8fBYoUEAtGzFiBBs2bKBXr14MHz78pV8jK8ydO5d58+Zl+rwdO3ZQsmTJbGjRq2Oqj7LDypUrmTBhAl5eXqxYsSJbX0u8eSRQFSIXSEpKYujQofzvf/8DwNnZGTc3N6KioggJCSE4OJjffvuNnj175poAIDf76aef1OCkQoUKmJmZUbx48QydW7x48VR1ExMTiYiI4OzZs5w9e5aAgABWrFhhcveVN8HSpUuZO3cuv/32W7YHQS+rePHieHp6pio/d+4cOp2OsmXL4ujomOp4Xt+iMy/1kRDpkUBViFxg9uzZ/O9//8PV1ZWZM2dSpUoV9Vh8fDzLly9n1qxZ/PLLL5QoUYJu3brlYGtzvy1btgDw2WefMXjw4Eyd2759+zS32T1x4gR9+/bl5s2bTJw4kblz5750W/OiKVOmmCwfMmQIvXv3xsHB4RW3KG0dOnSgQ4cOqcqbNWvGnTt36Nu3L76+vjnQsuyVVh8JkdfIHFUhclhcXByrVq0CUgLWp4NUABsbG/r06cPnn38OwM8//4xer3/l7cxLwsPDAUzue/0yPD09+eqrrwDYvn07Dx48yNLr53VFixalfPnyJkcohRDiRUigKkQOu3HjBnFxcVhZWVGpUqU063344YcAhIWFce/evVfVvDwpKSkJACsrqyy/drNmzQDQ6/WcP38+y68vhBDiPxKoCpHDLCxSZuDodDoOHTqUZr3ixYsTEBDAzp07cXZ2NlknPDyciRMn0rRpU6pVq0aTJk349ttvefTokcn6t2/fZtKkSbRp0wZPT0+qVatGgwYN6N+/P//++2+q+t26dcPNzY0TJ04wbtw4PD098fT0xM/Pz2iU9+jRo/Tv35969epRrVo1GjduzMiRI7l582Zmbo1q27ZtfPLJJ3h5eVGtWjWaNm3KyJEjuX79usn2PX78GIDu3bvj5ubGiBEjXuh1TcmfP7/699jYWKNjUVFRzJ07lzZt2lCjRg08PDzw9fVl8eLFxMfHp7qWob2nTp1i165ddOzYkZo1a9KgQQMGDBjAuXPnUp0zd+5c3NzcGDhwoMn2TZ06NVPvOSEhgVWrVtGjRw/q1q1LtWrVeOutt/jwww/55Zdf0Ol0qV7boE2bNri5uXH48GEgZTGVm5sbU6dOTfU6N27cYOzYsTRr1oxq1arh5eVFz5491XnZz3Jzc8PDwwNFUfjzzz/x9fWlZs2a1KpVi549e3LgwIEMvb+XsWvXLgYMGEDjxo2pXr06Hh4etGrViilTphAWFmZUNyQkBDc3N1q3bs3Vq1fp1KkT1atXp0GDBqxcuVKtFxoaysSJE3nnnXeoXr06zZs3Z/bs2cTHx9OsWTPc3NwICQlJ1ZaMfqae10eZvd7TLl68yMCBA2nQoAE1a9bko48+Yu/evZm6p0JklgSqQuSwcuXKqYty+vfvzw8//MC1a9dM1q1cuTIuLi6Ym5unOhYeHk779u1ZsWIF1tbWlCxZktDQUH777Tc6dOhAVFSUUf39+/fTunVrli9fzr179yhdujSlSpXi8ePHbN++HT8/P/766y+T7Zg6dSqrV6/GxcWFfPnyUaRIEczMUv47WbBgAV27dmX79u3o9Xq0Wi1xcXGsW7eOtm3bsmfPngzfG71ez9ChQxkwYAD79+/Hzs4ONzc3IiMj1ett27ZNra/VavH09FSDf8O/y5Ytm+HXfJ6nf5E//YXhxo0btG3blnnz5hEcHEzZsmUpWbIkFy5c4Pvvv6djx46pghuDjRs38vnnn3Pp0iXKlStHUlIS27Zto3PnzmkGclkhOjqajz76iPHjx3P06FEKFiyIVqvF3NycM2fOMHXqVPr166fWf3ZhUpUqVfD09DQK3k3Zvn07bdu2Zc2aNURERODm5oa9vT0HDx5k0KBBfPXVVyQnJ5s8d8yYMYwePZqQkBDKlSuHXq/n4MGDfPLJJ0Z9n9VGjRrFZ599xrZt2zAzM0Or1VKgQAGuXbvG0qVLad++PREREanOi46O5pNPPuHy5ctUqFCB6OhoypcvD8CVK1fUz+iDBw+oWLEi8fHx/Pjjj3Tv3p2EhASTbcnMZyojffQin9F//vmHjh07snXrVpKSkihfvjxBQUH07t2bv//++4XvsxDPpQghcty2bdsUNzc3RavVqn+aNGmiDBs2TFm3bp0SGhqa5rnr1q1Tz2natKly+vRp9dipU6eUGjVqKFqtVvn555/V8oSEBKVBgwaKVqtVJk+erCQkJKjHwsLCFD8/P0Wr1SqtWrUyeq2uXbuqr7Vt2zZFURQlOTlZiYiIUBRFUbZu3apotVrF09NT2bx5s3qeTqdT5s+frx67c+dOhu7L3LlzFa1Wq9SqVUvZuXOnWv7kyRNl8uTJilarVapXr65cunTJ6DwvLy9Fq9Uq//77b4Ze5+n3NmfOnHTrjRw5UtFqtUqdOnXU+6bT6RQfHx9Fq9UqXbt2Ve7du6fWv3btmtKmTRtFq9UqH3/8scnX1Gq1Sq9evZTw8HD1eob3V7NmTaP+nzNnjqLVapUBAwaYbN93332naLVaZfjw4WrZ7du31deJiYlRy6dMmaL2c0hIiFqelJSkLF26VD3n6Z8pRVHU8mfv+/DhwxWtVqt89913Ru+/evXqilarVSZMmKDExcWpx/bs2aO89dZbilarVWbNmmXyNSpXrqysWLFCSU5OVhRFUaKjo9X79uzPZ2Y0bdpU0Wq1yrp161Id27Fjh3rvDx06ZHTs8OHDSs2aNRWtVqssXLhQLX/6Hrdo0UJ5+PChoiiKEhERoej1eiU5OVl5//33Fa1Wq/Tp00f9zOj1emXlypVKpUqV1PNv376tXvdFP1Np9dGLXO/hw4eKp6enotVqle+//15JTExUFEVRYmJilMGDB6uv1bVr1wzdeyEyQ0ZUhcgF3nnnHRYvXoyLi4tadvfuXQICAvD396dx48b07NnzuXMip02bhru7u/rvGjVqqLlDT548qZafO3eOuLg4ihUrxrBhw4zmcjo5OdG/f38Arl+/bnLhloeHB++88w4AZmZmFCpUCIA5c+YAMHLkSN599121vqWlJf369aNVq1bExMSwdOnS596TuLg4fvnlFwDGjx9P06ZN1WM2Njb4+/vj7e1NQkICCxYseO71XoZOp+Py5ctMmjSJdevWAdCvXz/1vm3evJnr16/j5OTEggULjEZaXV1dWbhwIXZ2dhw7dszkaFXx4sWZO3euulre0tISf39/vLy8iIuLM3p0nJWOHDmCRqPB39/f6GfP3NycHj16ULp0aQCuXr36wq+xaNEiEhISaNiwIaNHj8bW1lY91qhRI3V1+q+//mpyhPLDDz+ka9eu6oi9vb09X375JQDBwcHExMS8cNvScvDgQSwtLenatStvv/220TEvLy/1Zzs4ONjk+b169aJw4cIAFCpUCI1Gw44dOwgKCsLZ2ZlZs2apnxmNRkOXLl3w8/Mzea2s/Ey96PV+++03YmJi8PLyYujQoeoTi3z58vHdd99RpkyZDL22EC9CAlUhcokGDRqwbds2Fi1aROfOndUgAVAfd3bo0IHff//d5PmFChUyucq9QoUKAOq8TUhZvX78+HG2bdtmchqBIZjQ6/UmH0fWrFkzVdmtW7e4cuUKZmZmRr8An9a6dWuADM1rO3bsGLGxsTg6OtKyZUuTdQxpuvbu3Zvmo+PMmjdvHm5ubkZ/qlevTps2bVi+fDmQEog8nSLMEHy2adPG5GNwZ2dnNbDfvXt3quPt27fHzs4uVbkhrdKuXbte+n2Zsn79es6cOUODBg1SHdPpdGr+zSdPnrzwaxj6+uOPPzZ5vHnz5pQoUYL4+HiT86IbN26cqqxcuXLq37MjUB09ejSnT59OM02Z4fOR1n0x9fnYuXMnkPIzYqqvTd2frP5Mvej19u/fD8D777+fqr6VlZXJciGyiuRRFSIXsbCwoFGjRjRq1AiAe/fucfDgQbZs2cLevXvR6/WMGzcODw8PowUTkJIayBTDL0VTAaeNjQ3nz5/nwoUL3Lp1i1u3bnH58mWjRUqmRlSLFCmSqsww6mZmZkavXr1MtsWwoOjmzZsoioJGozFZD1LmfELKohrDaNqzqlatCqQsanr48GGWJOB/NuG/RqPB2tqaggULUqlSJXx8fHB1dTXZ1sqVK6d53SpVqrBx40a17tNM7TgGKXNsISXAyC5WVlaEhYVx/Phxrl+/rm4wERQUpAZiiqK80LVjYmLUebnPpl17WuXKlbl7967Je2OqT59Oxp9VX1CeZW5uTkJCAv/++y/BwcHcvn2bmzdvcv78eXXkN637YurzYZh3/uzn1qBUqVLY29sbBd5Z/Zl60esZ+sUw1/ZZ6WUrEeJlSaAqRC5WvHhx2rdvT/v27Tl06BD9+vUjLi6OtWvXMmrUKKO6lpaWmbr20aNHmTJlitF0Ao1GQ5kyZWjTpg2bNm1K81xTu/YYfsEmJSVx4sSJdF9br9cTGxub7jakhhX16W33+fTIVExMTJYEqukl/E9LRtpqOPZspgAgzZ2DDOfEx8eTmJiY6T5+nsjISL777jsCAwNJTExUywsVKkT9+vUJCgoyuQI9o55+rxnpR1P35nnv+UWD6PTo9Xp+/vlnli5davQkwtraGnd3d/R6PcePH0/zfFOfD8N1np768Kx8+fIZBapZ/Zl60esZzjM1Egw8dzGdEC9DAlUhcthXX33FqVOnGDp0KK1atUqzXt26denQoQPLly9/4TRPBpcvX6ZXr17odDreeust2rZti5ubG+XLl8fe3p7r16+nG6iaYvglVrFixTSzBbzI9dJ7tBsdHa3+PSv2r39RmWmrqV/2plJXPX09e3v7VAFbWgFaRh/TK4rCZ599xokTJ3B0dKRr1664u7tToUIFdUS5c+fOLxWoPvtFIq2A5nmB0Kv2ww8/8NNPP2FhYUHXrl3x8vKiYsWKlC5dGgsLC2bOnJluoGqKIUA1FYwbPHssuz5Tmb1egQIFePToUZptT+vnV4isIHNUhchhsbGxhISEZGiOmZOTE4C6EONFrVixAp1OR926dVm+fDkdO3akRo0a6mjM/fv3M31Nw4KKkJAQo9ybT3v48CHHjh0jNDT0udczPF6/dOlSmjtxGUaDbW1t05z68CoY2nrx4sU06xja+vTcY4O0FisFBQUB/80zBtQ5xWnd47RSYD3r5MmTnDhxAgsLC37//Xf69+9Pw4YNjaY9vMjPwdPy58+vPga/cOGCyTqKoqjHcsOinMTERHUu8sSJExkzZgw+Pj6UK1dOXUT0IvfF8Nj88uXLJo/fu3cv1RedrP5Mvej1DD/fhp/HZ6WVTk+IrCCBqhA5zDCK+tdff3HmzJk06yUnJ/PPP/8AUL9+/Zd6zTt37gAp8+VMLaZau3at0etmRIUKFXBxceHJkyds3LjRZJ0ZM2bQpUsXBg8e/Nzr1apVC3t7eyIiItiyZYvJOoatZ99+++0057G+CoYFP3/99ZfRKK/B/fv32bFjB4DJhUsbN25MNUKqKIraD82bN1fLCxYsCJBqswNIGbU9evRohtps+Bmwt7c3GSAeOHBA3QHNsNOXgWEeZEYeuxvmW//2228mj2/fvp3Q0FAsLS2pU6dOhtqencLDw4mLiwNMzzl+9OiRuiDu2fuSHm9vbwD+/vtvkyOQT3/mDF7mM2Wqj170eoa2r1u3LtWXRr1en+a1hMgKEqgKkcPee+89PDw80Ol09OrVixUrVqQKdoKDg+nXrx9nz56lSpUqaa7YzShDAvy///7baBpBZGQkkydPNnosmFYS8mdpNBo1OfzkyZPZvHmzeiwpKYnFixezfv16gDQXcjwtX7589OzZE4CxY8carZZPSEhgypQp7Ny5E0tLyzR3aXpV3n33XVxdXXn48CH9+vUzGnG7fv06ffr04cmTJ3h4eKi/9J92/vx5vv32W/Ve63Q6Jk6cyPHjxylSpAgfffSRWtfDwwNIWezydAqhhw8fMmjQICIjIzPUZsPPwOPHj1m9erVartfr+eeffxgyZIha9uzom+ER8t27d5/7Op988gk2Njbs27ePiRMnGk1N2Lt3rzrXunv37uoTg5xUuHBhdc7wsztzXbx4kU8++US9xxn9bAC0aNGCihUrcvfuXb766iujDTg2bdrEzz//nOqcl/lMmeqjF71ex44dKVasGOfPn2fMmDFqoJ2QkMC3336b7pMEIV6WzFEVIodZWFjw008/MXjwYA4ePMjEiROZOnUqJUuWxN7enocPH6ojW9WrV2f+/PkvvaimZ8+eBAYG8uDBAzXIMqzu1el0VKpUifv37/P48WMePHhgchWzKR06dODKlSssXbqUIUOGMGXKFIoVK0ZISIi6mKR///5GI4Tp+fzzz7l27RqbN2+mb9++lChRgsKFC3Pt2jViY2OxtbVl4sSJ6a4ofxWsrKyYP38+n376KUeOHMHb25sKFSqg1+u5cuUKiqLg5ubGrFmzTI5gV6xYkd9//52///6bMmXKcOvWLSIjIylYsCCzZ882WiBTpUoVWrRowbZt25gyZQrLli2jYMGCXL16FQsLCz755BOWLFny3DZXr14db29vduzYwbhx4/j5559xcnLi7t27hIeHY2trS40aNTh9+jQPHjwwOtewje6QIUMoV64cgwcPNjlSDCmPvL///nuGDh3KihUrWLduHeXLlyc8PFwd1W3VqlWGRtlfBQsLC/r378+UKVPYuHEju3fvpmTJkkRGRqrzdevUqcPhw4dT3ZfnXXfmzJl069aN7du3c+DAAcqXL8+jR4+4d+8e1apVU7fMNUwxgBf/TKXVRy9yPXt7e2bNmkXfvn1Zu3Yt27Zto0yZMty8eZOoqCiaNm2abSnUhJARVSFygUKFCvHrr7/y888/4+vrS8mSJQkPDycoKAi9Xk/jxo2ZOnUqa9asyZKV7aVKlWLjxo20a9eO4sWLc+PGDe7du0elSpXw9/fnzz//VKcXZPYXkL+/P0uWLKFZs2bo9Xp1XluDBg1YsGBBpkY/zc3NmTFjBrNmzaJevXrExsZy6dIlChcuzMcff8yGDRvUvI85rXz58gQEBPD555/j6uqq3tPq1aszcuRI/vzzT6P5n0/r3LkzM2bMwMXFhcuXL5MvXz46derE+vXrTebGnTlzJsOGDUOr1fLw4UNCQ0Np3rw569evN9o+83l++OEH/P39qVy5MtHR0Vy5coX8+fPTqVMnAgIC1OwHe/bsMXrkO3nyZOrUqYOiKNy4ceO5i/tatGhBQEAAHTp0oFChQgQFBambAMydO5fZs2dneUaDl+Hn58dPP/1E7dq1MTc35/Lly+h0Opo3b86yZcv48ccfsbS05MqVK9y+fTvD19VqtQQEBNC+fXvy58/PpUuX1CcCs2fPVus9mzXgRT5T6fXRi1yvVq1arF+/Hl9fX+zs7Lh8+TIuLi5Mnz49zRy5QmQFjZIduT2EEEI8V7du3Thy5Ahjxoyha9euOd0ckYOCg4N59913sbS05OzZs+nmQxXiTSIjqkIIIUQ2+/3332nVqhXz5883eXzfvn1ASvJ8CVKF+I8EqkIIIUQ2q1q1KteuXWPx4sWptords2cPP/zwA5AyDUQI8R9ZTCWEEEJks+rVq9OhQwfWrl1Ljx49cHFxwcHBgQcPHqiLsj788EM6dOiQwy0VIneRQFUIIYR4BSZOnIi3tzerV68mODiYS5cuUahQIRo1akTHjh155513crqJQuQ6sphKCCGEEELkSjJHVQghhBBC5EoSqAohhBBCiFxJAlUhhBBCCJErSaAqhBBCCCFyJQlUhRBCCCFEriSBqhBCCCGEyJX+DwVptJnS3SH7AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/selker/code/cider-documentation/docs/_build/jupyter_execute/quickstart/documentation_targeting_9_1.png" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "targeting.roc_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=50, weighted=False)\n", + "targeting.precision_recall_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=50, weighted=False)" + ] + }, + { + "cell_type": "markdown", + "id": "74161fcc", + "metadata": {}, + "source": [ + "Provide a budget (provided as the level of benefits assigned to each individual UBI program, in the same units as the ground truth poverty measure) to draw social welfare curves for a set of targeting methods. Individual welfare is assumed to be a convex function of pre-program poverty and benefits assigned. Generate a table with the share of population targeted and corresponding transfer size that optimizes social welfare for each proxy poverty measure that could be used for targeting." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f5fc41e1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Introduction#

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Cider is a set of software tools for targeting poverty with mobile phone data. Download cider from github.

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Cider is based on the poverty targeting methods described in Aiken et al. (2022). A goal of cider is to make these methods easier to use and replicable across contexts. A second goal is making it straightforward to compare targeting methods, including the phone-based approach built by cider: while the empirical results on the accuracy of the phone-based approach in Aiken et al. (2022) are promising, it is still not known how the phone-based approach compares to alternatives across contexts.

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Because cider works on digital data, it has two significant strengths: it can identify and reach people rapidly (speed) and do so on the order of millions of people (scale). Therefore, emergencies such as large-scale shocks like famines and flooding that require speed and scale may be the most natural use cases for cider. It may also be possible to combine the phone-based approach in cider with alternative and more traditional approaches to poverty targeting; an article by GiveDirectly discusses the beginnings of such a complementary approach.

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Cider provides tooling for predicting poverty with machine learning and remote data such as phone metadata and satellite imagery. It also provides tooling to evaluate the performance of these estimations. Cider is divided into seven modules, which include the creation of poverty indices from underlying survey data as a ground-truth with which to train machine learning models, feature engineering from mobile phone metadata and supervised learning, and comparisons to counterfactual targeting methods and bias audits for the finished predictions. Each of the six modules is described below; for more information, visit Cider’s documentation or Github repo.

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Creating poverty indices or a proxy-means test is a standard practice in international development. While consumption surveys are considered the most comprehensive approach to understanding a household’s wealth and are used in economics research, they often take hours to complete. Their length often makes them infeasible to use in emergency response or poverty alleviation programs. Therefore, economists and aid practitioners create poverty indices that select a few components from a longer survey (such as a consumption survey) that are most useful for summarizing overall poverty. The current statistical process for creating indices is manual and time-consuming. This package quickly generates an index that can be used in a survey to determine poverty more rapidly. The results of this survey can be used to collect training data for the machine learning poverty prediction rapidly. They can also be used as a standalone tool to create poverty short indices to assess a household’s poverty (without any use of machine learning).

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Suppose the survey’s components haven’t been picked yet. In that case, this module can also select the N components of a survey - an asset index, proxy-means test, etc. - that are most predictive of poverty, building the set of questions that should be asked when collecting ground truth data. A dataset of survey responses linked to unique IDs is then uploaded, constructing each observation’s poverty description based on underlying components.

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Mobile phone data typically contain records of each transaction (call, text, balance recharge, mobile money transaction, etc.) completed on a cellular network. This module automates the calculation of subscriber-level features from the mobile phone metadata, using the uploaded mobile phone metadata datasets and features of interest specification.

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Machine learning for poverty prediction#

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This module trains a machine learning model to predict poverty from mobile phone features and uses that model to produce poverty predictions for all subscribers that have consented to the use of their data.

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First, upload a ground-truth poverty measure for a set of subscribers (like the poverty measure generated by the poverty index module or a hand-crafted poverty score). Next, upload features (like those produced by the feature engineering module) for all subscribers that have consented.

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The module will match ground-truth poverty data to features, train the machine learning model to predict poverty from the phone features, and report the model’s accuracy metrics on a test set. Multiple machine learning models will be trained during the training stage, and the best model, based on a pre-specified metric, will be selected and used to predict poverty for the entire user base.

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Targeting comparisons#

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It is critical to compare how accurate mobile phone data targeting is compared to other targeting methods (at the very least, in comparison to geographic targeting methods). Upload a set of ground-truth poverty observations (which can be any set, including the data produced by the poverty index module and used in the machine learning module) along with a set of targeting options. The targeting module will compare the targeting options based on inclusion and exclusion errors, helping the program manager make a more informed choice of targeting mechanism

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Bias audits#

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When considering an algorithmic targeting method, it is essential to check whether the algorithm is biased for or against particular subgroups. Upload a set of ground-truth poverty observations (which can be any set, including the data produced by the poverty index module and used in the machine learning module) along with observations on demographics, such as gender, age, ethnicity, religion, or other characteristics sensitive to the project’s context. Also, upload the poverty proxy that will be used to target (such as the phone-based wealth predictions produced by the machine learning module or any other individual-level proxy poverty measure). This module will check for bias in the targeting method against each subgroup identified in the ground truth data.

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Poverty is frequently concentrated in geographic clusters so that aid may be targeted only to subscribers living in certain areas. Mobile phone data can be used to estimate where subscribers live, based on the location of the cell towers through which they place transactions. Upload a dataset of mobile phone transactions. This module will infer the most likely location of each subscriber at the cell tower level (or an alternatively administrative level, if a shapefile is provided). If ground truth location data is available for a subset of subscribers, this module will also evaluate the accuracy of the mobile phone-based location inference.

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Opt-in and opt-out workflows#

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In order to make the preservation of people’s privacy and the respect of their consent as easy as possible, this module implements opt-in and opt-out features that can be integrated in all processes that use sensitive data (raw CDR data, processed features etc.). The user of the toolkit can set the required capability at start time in the configuration file, with opt-in as default resulting in no mobile phone subscribers being initially included in the analysis, and vice versa for opt-out. Afterwards, they can flexibly provide lists of users that have opted-in or out, respectively, to include or exclude them from all the subsequent analyses.

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Goals for cider#

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Cider is a set of software tools for creating poverty predictions based on non-traditional data sources (such as call detail records and satellite imagery) and for evaluating the accuracy, fairness, representativity, and reliability of those predictions. Our goals for cider are to enable:

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For more information visit CIDER’s documentation below or github repo.

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Bug reports, documentation, and code contribution will be reviewed by Emily Aiken (UC Berkeley) and Lucio Melito (GiveDirectly).

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  • To report a bug, create an issue template on GitHub. Please include a full working example for the bug, including synthetic data if necessary.

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  • To write code, please create a new branch of Cider with git checkout -b name-of-new-branch. Commit your changes and push your new branch to the Github repo. Run the tests as indicated in the readme of the repo. Then, create a pull request. In your pull request, please include a description of all changes. For each change, include the files and line numbers that have been changed.

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We will accept contributions to any of the modules in the code; we are particularly interested in engineering additional features and improving machine learning pipelines.

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You can expect a response to an issue or suggestion within three months. We spend about one hour per month on this project.

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We modeled this guide on nine related open source guides:

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Adherance to data privacy standards#

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Cider works with individual mobile phone data, which is personally identifiable information. The use of cider must be in compliance with local data protection laws. We additionally recommend referring to the following guidelines to design a data flow that adheres to international standards:

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Poverty prediction and targeting with digital data#

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Cider was developed from a line of research on estimating poverty from machine learning and mobile phone data that dates back to 2015. The following papers provide context on this research and discussions of the accuracy, fairness, and practicality of phone-based targeting in several contexts.

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Collecting robust and representative data is critical to any successful deployment of Cider. Here are several recommendations:

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  • Number of observations that should be collected in a ground truth poverty survey: Returns to additional training data for poverty prediction have been documented in past research (Blumenstock et al., 2015). In general, accuracy increases with additional training data, but with diminishing marginal returns to data collection.

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  • Poverty outcomes collection: When possible, multiple poverty measures should be collected for validation purposes. Past studies using wealth from mobile phone metadata have used a variety of poverty outcomes to train machine learning models, including asset indices, consumption, proxy-means tests (PMTs) and poverty probability indices (PPIs). Consumption is accepted as the gold-standard of poverty in development economics, so consumption should be collected where possible (however, eliciting consumption typically requires a multi-hour consumption module in a field survey). Recent work has shown that well-calibrated PMTs and PPIs are preferable to asset indices, if there is an up-to-date PMT or PPI for the country in question, or if it is possible to custom-calibrate a PMT based on recently-collected consumption data.

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  • Number of components in a PMT or asset index: Past work on wealth prediction and targeting has used indices or PMTs with between 8 and 30 components. Index accuracy typically increases with the number of underlying components, but there are diminishing marginal returns to increasing the number of components after a point. Moreover, the more components collected in a survey the more fatigue or boredom on the part of the respondent, which may decrease survey quality. -Some contexts have standardized and tested asset indices or PMTs. If there is no such standardized index available, but past survey data from the country in question is available, one option is to use forward selection of features for a PMT (implemented in CIDER’s survey module) and look for an “elbow” in accuracy curves where returns to additional features start to decrease. In choosing the number of features in the PMT, the total survey duration (including, for example, demographic information for bias checks or other poverty measures for validation) should also be considered to prevent respondent fatigue.

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  • Timeline and frequency for survey and phone metadata collection (wealth prediction recalibration): Recent research from Aiken et al. (2021) suggests that the quality of phone-based poverty prediction degrades when phone data or survey data are out-of-date. In that application of phone-based targeting in rural Togo, the authors find that targeting accuracy decreases by 4-5% when phone data or survey data are out-of-date, and precision decreases by 10-14%. -Note that there are three particular concerns when it comes to data collection and model recalibration: (1) Turnover of SIM cards on the mobile phone network, (2) the transitory nature of poverty, and (3) applying a machine learning model calibrated on “old” survey data to contemporary CDR (risking “model drift”).

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  1. One concern with using “old” CDR for a contemporary aid program is the turnover of SIMs on the mobile phone network. Suppose the CDR data used for targeting is even six months out of date. In that case, a sizable number of subscribers will be prevented from registering because they are not associated with underlying CDR from which to produce a wealth prediction. For example, in Togo, Aiken et al. (2021) estimate that around 3% of mobile network subscribers leave the network in a given month, so around 18% of subscribers would have switched SIM cards after six months. CDR should be taken from as close to the program start date as possible to maximize program coverage.

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  2. People move in and out of poverty with some frequency, though the timescale for this movement is typically years. To capture the state of poverty that is contemporary with the program time period, survey data should be gathered as close to the program start date as possible. Initial results suggest that it is not essential that survey data overlap with the time period of CDR, but both should be taken from as close to the program as possible.

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  3. The two above concerns suggest that it is necessary to “refresh” CDR data frequently and to “refresh” survey data somewhat less frequently. It is worth noting that there is a specific concern with training a model on “old” CDR and survey data and using the model to project onto CDR gathered contemporarily, called “model drift.” Specifically, the relationship between phone behavior and poverty shifts over time: for example, the relationship over the holidays may not be the same as the relationship in other months. To combat this effect, even if survey data is not “refreshed,” the model should be consistently refreshed (that is, retrained).

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  • Representativity checks of training data: Bias audits should be grounded in the cultural context in which the program is conducted, either with qualitative interviews with stakeholders (especially potential beneficiaries) or based on existing legal frameworks. If possible, qualitative or legal work should be conducted to assess which subgroups are vulnerable and should be audited for in fairness checks. Qualitative work should consist of interviews or focus groups with program stakeholders, including local government, NGOs, and most importantly potential program beneficiaries. Alternatively, fairness classes could be based on the legal context in question, checking fairness for subgroups that are protected by the law. For example, in the US, race, religion, nationality, sex, age, disability status, and veteran status are all protected classes.

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Cider users should implement governance structures that achieve standards for data ethics, making sure humanitarian programs are targeting and delivering aid to those in need without infringing human rights.

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Output verifications increase accountability and transparency with recipients and non-recipients, partners, funders, and humanitarian and development communities as a whole. It also creates the necessary governance to minimize bias and discrimination (which is also present in in-person targeting) and maximize fairness on the large-scale use of sensitive data, exponentiating the positive impact of AI. The verification process should combine internal and external audits, such as a research institution or contracted technical auditor. Other resources for setting up output verifications can be found here:

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  • Targeting comparisons: It is critical to compare phone-based targeting to alternative targeting mechanisms in the context of interest to ensure that phone-based targeting is the most appropriate method for the circumstances (see the targeting module). Ideally, CDR-based targeting should be compared to any other method that could be feasibly implemented at scale, and the best targeting method should be selected for the program. However, which counterfactual targeting approaches can be assessed depends on which survey data is collected. Geographic targeting methods are valuable counterfactual targeting methods. Typically, some location data will be available (whether self-reported in the survey, recorded by field enumerators with geographic coordinates, or available in administrative data). If this data is available, compare the CDR-based targeting method to geographic targeting at every feasible geography, at minimum at the admin-2 level but ideally at the admin-3 or admin-4 level. If the ground-truth poverty measure in the survey is consumption or income, collect data to construct a proxy-means test and asset index and compare targeting on these proxy poverty measures. Always also compare to random targeting as a naive baseline.

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  • Bias audits: It is also vital to audit the algorithm for systematic bias for or against specific subgroups. If possible, qualitative or legal work should be conducted to assess which subgroups are vulnerable and should be audited for fairness checks. Qualitative work should consist of interviews or focus groups with program stakeholders, including local government, NGOs, and, most importantly, potential program beneficiaries. Alternatively, fairness classes could be based on the legal context in question, checking fairness for subgroups protected by the law. For example, in the US, race, religion, nationality, sex, age, disability status, and veteran status are all protected classes. The bias audit feature in Cider will present audits for two types of systematic biases: a systematic over- or under-prediction of a subgroup’s poverty, or a systematic over- or under-targeting of a subgroup (known as demographic parity).

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External audits provide the transparency needed to stakeholders by allowing third-party verification of an implementer’s results. Auditors often assess if outputs are fair and impartial and if data protection protocols are in place. In addition to transparency, the third-party audits address concerns about the conflict of interest that might exist in internal audits. AI tools applied to the development and humanitarian sectors should aim to achieve the highest accountability standards, and for that, outputs should be independently audited. Cider’s open source license and internal audit mechanisms aim to provide sufficient information to be auditable by independent parties. It is encouraged that these independent audits be done by organizations with both the technical capacity and experience in the field.

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The configuration file - example at configs/config.yml - is used to store all relevant configurations, like paths to
-the datasets and spark parameters. It should be appropriately edited before executing the code.

-

The first parameters to set are those related to spark:

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spark: 
-  app_name: "mm" 
-  files:
-    max_partition_bytes: 67108864
-  driver:
-    memory: "8g" // driver memory
-    max_result_size: "2g" // maximum result size when collecting to driver
-  loglevel: "ERROR"
-
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-

Then we set the paths to the folders that store our files, and also specify the file names:

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path:
-  data: "/Users/example/Documents/GD/cider/synthetic_data/"
-  features: '/Users/example/Documents/GD/cider/outputs/featurizer/datasets/features.csv'
-  file_names:
-    antennas: 'antennas.csv'
-    cdr: 'cdr.csv'
-    home_ground_truth: 'home_locations.csv'
-    labels: 'labels.csv'
-    mobiledata: 'mobiledata.csv'
-    mobilemoney: 'mobilemoney.csv'
-    population: 'population_tgo_2019-07-01.tif'
-    poverty_scores: null
-    recharges: 'recharges.csv'
-    rwi: '/TGO_relative_wealth_index.csv'
-    shapefiles:
-      regions: 'regions.geojson'
-      cantons: 'cantons.geojson'
-      prefectures: 'prefectures.geojson'
-    user_consent: null
-  outputs: "/Users/example/Documents/GD/cider/outputs/"  // output folder
-  wd: "/Users/example/Documents/GD/cider/"  // working directory
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The featurizer module expects certain column and column names, and we can define them in the following section of the -config file:

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col_names:
-  cdr:
-    txn_type: "txn_type"
-    caller_id: "caller_id"
-    recipient_id: "recipient_id"
-    timestamp: "timestamp"
-    duration: "duration"
-    caller_antenna: "caller_antenna"
-    recipient_antenna: "recipient_antenna"
-    international: "international"
-  antennas:
-    antenna_id: "antenna_id"
-    tower_id: "tower_id"
-    latitude: "latitude"
-    longitude: "longitude"
-  recharges:
-    caller_id: "caller_id"
-    amount: "amount"
-    timestamp: "timestamp"
-  mobiledata:
-    caller_id: "caller_id"
-    volume: "volume"
-    timestamp: "timestamp"
-  mobilemoney:
-    txn_type: "txn_type"
-    caller_id: "caller_id"
-    recipient_id: "recipient_id"
-    timestamp: "timestamp"
-    amount: "amount"
-    sender_balance_before: "sender_balance_before"
-    sender_balance_after: "sender_balance_after"
-    recipient_balance_before: "recipient_balance_before"
-    recipient_balance_after: "recipient_balance_after"
-  geo: 'tower_id'
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We also have to set a few parameters that will affect the behaviour of some modules:

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params:
-  cdr:
-    weekend: [1, 7] // definition of weekend (Sun = 1 to Sat = 7)
-    start_of_day: 7 // hour when day starts (used to define day/night)
-    end_of_day: 19 // hour when night starts (used to define day/night)
-  home_location:
-    filter_hours: null // hours to filter out when inferring home locations
-  automl: // params used by the autoML libraries
-    autosklearn:
-      time_left: 3600
-      n_jobs: 1
-      memory_limit: 3072
-    autogluon:
-      time_limit: 3600
-      eval_metric: 'r2'
-      label: 'label'
-      sample_weight: 'weight'
-  opt_in_default: false // if true opt-in is set as default, i.e. all users give their consent unless they opt-out
-
-
-

Finally, we can set the hyper-parameters that will be tested during a grid-search performed by the ML module”

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hyperparams:
-  'linear':
-    'dropmissing__threshold': [0.9, 1]
-    'droplowvariance__threshold': [ 0, 0.01 ]
-    'winsorizer__limits': [!!python/tuple [0., 1.], !!python/tuple [0.005, .995]]
-  'lasso':
-    'dropmissing__threshold': [ 0.9, 1 ]
-    'droplowvariance__threshold': [ 0, 0.01 ]
-    'winsorizer__limits': [!!python/tuple [0., 1.], !!python/tuple [0.005, .995]]
-    'model__alpha': [ .001, .01, .05, .03, .1 ]
-  'ridge':
-    'dropmissing__threshold': [ 0.9, 1 ]
-    'droplowvariance__threshold': [ 0, 0.01 ]
-    'winsorizer__limits': [!!python/tuple [0., 1.], !!python/tuple [0.005, .995]]
-    'model__alpha': [ .001, .01, .05, .03, .1 ]
-  'randomforest':
-    'dropmissing__threshold': [ 0.9, 1 ]
-    'droplowvariance__threshold': [ 0, 0.01 ]
-    'winsorizer__limits': [!!python/tuple [0., 1.], !!python/tuple [0.005, .995]]
-    'model__max_depth': [ 2, 4, 6, 8, 10 ]
-    'model__n_estimators': [ 50, 100, 200 ]
-  'gradientboosting':
-    'dropmissing__threshold': [ 0.99 ]
-    'droplowvariance__threshold': [ 0.01 ]
-    'winsorizer__limits': [!!python/tuple [0., 1.], !!python/tuple [0.005, .995]]
-    'model__min_data_in_leaf': [ 10, 20, 50 ]
-    'model__num_leaves': [ 5, 10, 20 ]
-    'model__learning_rate': [ 0.05, 0.075, 0.1 ]
-    'model__n_estimators': [ 50, 100, 200 ]
-
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-

- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
-

-
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- -
- - -
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/quickstart/config.md b/docs/_sources/quickstart/config.md index c6dee20..c158908 100644 --- a/docs/_sources/quickstart/config.md +++ b/docs/_sources/quickstart/config.md @@ -1,27 +1,46 @@ # Configuration file -The configuration file - example at `configs/config.yml` - is used to store all relevant configurations, like paths to the datasets and spark parameters. It should be appropriately edited before executing the code.
+The configuration file is used to store all relevant configurations, like paths to the datasets and spark parameters. It should be appropriately edited before executing the code. It is written in [YAML](https://yaml.org/). -The first parameters to set are those related to spark: +## Spark parameters +Spark parameters are set under the `spark` heading. Syntax for specifying Spark parameters derives from [Spark\'s own property names](https://spark.apache.org/docs/latest/configuration.html#available-properties). For example, to conigure the parameter `spark.app.name` in Cider config, we'd use +``` +spark: + app: + name: "my_first_cider_app" +``` + +Here is a more complete example config. It's not meant as an endorsement of these specific config values; optimal choices vary greatly based on your environment and use case. ``` -spark: - app_name: "mm" - files: - max_partition_bytes: 67108864 +spark: + app: + name: "my_first_cider_app" + master: "local[*]" + sql: + shuffle: + partitions: 144 driver: - memory: "8g" // driver memory - max_result_size: "2g" // maximum result size when collecting to driver - loglevel: "ERROR" + memory: "8G" + maxResultSize: "2G" + supervise: true + executor: + memory: "8G" + rpc: + askTimeout: "600s" + loglevel: "WARN" + logConf: true ``` -Next we specify folder and file locations. File subpaths are given relative to a "parent" directory: Either the `input_data` directory or the `working` directory (file subpaths should not have leading slashes). The locations of the parent directories must be specified with absolute paths (with leading slashes). Cider will not modify files under the `input_data` directory. It will use the `working` directory for program outputs, some of which may act as inputs for later steps. For example, the featurizer writes features to the `working` directory, and then the ml module reads features back in from that directory. At present, file names/sub-paths written programmatically under the `working` directory are hard-coded and can't be specified in config. +## File and folder locations + +Under the `path` heading, we specify folder and file locations. File subpaths are given relative to a "parent" directory: Either the `input_data` directory or the `working` directory (if you'd rather specify an absolute path, use a leading slash). The locations of the parent directories must be specified with absolute paths (with leading slashes). Cider will not modify files under the `input_data` directory. It will use the `working` directory for program outputs, some of which may act as inputs for later steps. For example, the featurizer writes features to the `working` directory, and then the ml module reads features back in (from that same directory, unless a different one is specified as input). At present, file names/sub-paths written programmatically under the `working` directory are hard-coded and can't be specified in config. ``` path: input_data: - directory_path: "/Users/example/Documents/GD/cider/synthetic_data/" + directory_path: "/example/path/to/input/data/" file_paths: antennas: "antennas.csv" cdr: "cdr.csv" @@ -32,17 +51,19 @@ path: population: "population_tgo_2019-07-01.tif" poverty_scores: null recharges: "recharges.csv" - rwi: "TGO_relative_wealth_index.csv" + rwi: "relative_wealth_index.csv" shapefiles: regions: "regions.geojson" cantons: "cantons.geojson" prefectures: "prefectures.geojson" user_consent: null working: - directory_path: "/Users/example/Documents/GD/cider/working_directory/" + directory_path: "/example/path/to/working/directory" ``` -The featurizer module expects certain column and column names, and we can define them in the following section of the config file: +## Column names + +Cider expects certain columns to be present, and we can specify their names under the `col_names` heading (this is not a complete list): ``` col_names: @@ -81,10 +102,11 @@ col_names: geo: "tower_id" ``` -We also have to set a few parameters that will affect the behaviour of some modules: +## Miscellaneous parameters + +Under the `params` heading we are able to specify certain miscellaneous parameters that affect Cider behavior: ``` -params: cdr: weekend: [1, 7] // definition of weekend (Sun = 1 to Sat = 7) start_of_day: 7 // hour when day starts (used to define day/night) @@ -104,7 +126,9 @@ params: opt_in_default: false // if true opt-in is set as default, i.e. all users give their consent unless they opt-out ``` -Finally, we can set the hyper-parameters that will be tested during a grid-search performed by the ML module: +## ML tuning parameters + +Under the `hyperparams` heading, we set the hyper-parameters that will be tested during a grid-search performed by the ML module: ``` hyperparams: diff --git a/docs/_sources/quickstart/documentation_fairness.html b/docs/_sources/quickstart/documentation_fairness.html deleted file mode 100644 index dde3a79..0000000 --- a/docs/_sources/quickstart/documentation_fairness.html +++ /dev/null @@ -1,811 +0,0 @@ - - - - - - - - - Fairness Evaluations - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Fairness Evaluations

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Fairness Evaluations#

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from datastore import DataStore
-from fairness import Fairness
-
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Set up the configuration file and load some fairness simulation data, using the datastore.

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datastore = DataStore(cfg_dir='./configs/config.yml')
-fairness = Fairness(datastore=datastore)
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Create a “rank residuals boxplot”. Provide (1) a ground-truth poverty measure, (2) a set of proxy poverty measures that could be used for targeting, and (3) a characteristic of interest to evaluate fairness across (the characteristic must take on at least two distinct values that appear in the dataset). The “rank residual” for each observation will be calculated: the difference between the ranking of that observation according to the proxy and according to the ground truth. We then visualize the distribution of rank residuals for each group; if proxy measures are fair targeting methods the mean of all distributions should be close to 0.

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fairness.rank_residuals_plot('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], 'characteristic1')
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-../../_images/documentation_fairness_5_0.png -
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To test the statistical significance of the difference in means for each proxy, use an ANOVA test. Results are presented in a table that records the mean and standard deviation of rank residuals by proxy and group, and the ANOVA test for the difference in means between groups. Look for large F statistics and p-values less than 0.05.

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fairness.rank_residuals_table('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], 'characteristic1')
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characteristic1proxy1proxy2proxy3proxy4
0group0-0.21 (0.36)-0.02 (0.38)-0.03 (0.39)-0.01 (0.37)
1group1-0.07 (0.37)0.02 (0.38)-0.02 (0.37)-0.01 (0.40)
2group20.11 (0.37)-0.00 (0.38)0.02 (0.37)0.03 (0.37)
3group30.19 (0.35)-0.00 (0.36)0.03 (0.39)-0.01 (0.40)
4Anova F-Stat61.11950.5983121.363950.806921
5Anova p-value2.83854e-360.6161930.2523760.4901
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Provide a targeting threshold (in percentile) to calculate demographic parity: the difference between the share of a group that is targeted according to each proxy measure and the share of the group that is targeted according to the ground-truth proxy measure. Differences are provided as percentage points.

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fairness.demographic_parity_table('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], 'characteristic1', 50)
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characteristic1Group's share of populationShare of Group in Target Populationproxy1proxy2proxy3proxy4
0group024.90%45.38%25.7028114.4176717.6305225.622490
1group125.70%46.69%12.062257-1.9455255.4474714.669261
2group225.90%54.44%-15.830116-1.544402-4.633205-8.108108
3group323.50%53.62%-22.978723-0.851064-8.936170-2.127660
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Visualize the above table as a heatmap with bubbles.

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fairness.demographic_parity_plot('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], 'characteristic1', 50)
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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
-

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- -
- - -
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/quickstart/documentation_fairness.ipynb b/docs/_sources/quickstart/documentation_fairness.ipynb index fc79b6a..db7a4df 100644 --- a/docs/_sources/quickstart/documentation_fairness.ipynb +++ b/docs/_sources/quickstart/documentation_fairness.ipynb @@ -15,8 +15,8 @@ "metadata": {}, "outputs": [], "source": [ - "from datastore import DataStore\n", - "from fairness import Fairness" + "from cider.datastore import DataStore\n", + "from cider.fairness import Fairness" ] }, { @@ -34,7 +34,11 @@ "metadata": {}, "outputs": [], "source": [ - "datastore = DataStore(cfg_dir='./configs/config.yml')\n", + "# This path should point to your cider installation, where configs and data for this demo are located.\n", + "from pathlib import Path\n", + "cider_installation_directory = Path('../../cider')\n", + "\n", + "datastore = DataStore(config_file_path_string= cider_installation_directory / 'configs' / 'config_quickstart.yml')\n", "fairness = Fairness(datastore=datastore)" ] }, @@ -325,9 +329,9 @@ ], "metadata": { "kernelspec": { - "display_name": "cider", + "display_name": "conda_cider", "language": "python", - "name": "cider" + "name": "conda_cider" }, "language_info": { "codemirror_mode": { @@ -339,7 +343,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.8.16" } }, "nbformat": 4, diff --git a/docs/_sources/quickstart/documentation_featurizer.ipynb b/docs/_sources/quickstart/documentation_featurizer.ipynb index bbad8d4..cb7adbc 100644 --- a/docs/_sources/quickstart/documentation_featurizer.ipynb +++ b/docs/_sources/quickstart/documentation_featurizer.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 7, - "id": "e03ae9c8", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "sys.path.insert(0, '../../cider/cider')" - ] - }, { "cell_type": "markdown", "id": "96d8501c", @@ -21,13 +10,15 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "ec77078c", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "from datastore import DataStore\n", - "from featurizer import Featurizer" + "from cider.datastore import DataStore\n", + "from cider.featurizer import Featurizer" ] }, { @@ -40,21 +31,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "id": "44d61e7d", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "WARNING: An illegal reflective access operation has occurred\n", - "WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/Users/luciomelito/Library/Caches/pypoetry/virtualenvs/cider-n28xxEz6-py3.8/lib/python3.8/site-packages/pyspark/jars/spark-unsafe_2.12-3.1.2.jar) to constructor java.nio.DirectByteBuffer(long,int)\n", - "WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform\n", - "WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations\n", - "WARNING: All illegal access operations will be denied in a future release\n", - "21/10/26 10:50:27 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n", - "Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties\n", "Setting default log level to \"WARN\".\n", "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" ] @@ -63,6 +49,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "23/03/31 13:21:02 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n", "Loading CDR...\n" ] }, @@ -87,7 +74,11 @@ } ], "source": [ - "datastore = DataStore(cfg_dir='../../cider/configs/config.yml')\n", + "# This path should point to your cider installation, where configs and data for this demo are located.\n", + "from pathlib import Path\n", + "cider_installation_directory = Path('../../cider')\n", + "\n", + "datastore = DataStore(config_file_path_string= cider_installation_directory / 'configs' / 'config_quickstart.yml')\n", "featurizer = Featurizer(datastore=datastore, clean_folders=True)" ] }, @@ -101,9 +92,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "7060dbd4", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stderr", @@ -116,7 +109,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of spammers identified: 20\n" + "Number of spammers identified: 19\n" ] } ], @@ -133,9 +126,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "0702d156", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -143,6 +138,14 @@ "text": [ "Outliers removed: 2020-01-24, 2020-02-06\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/leo/Documents/gpl/cider/cider/datastore.py:435: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n", + " timeseries = timeseries.groupby('day', as_index=False).agg('sum')\n" + ] } ], "source": [ @@ -160,9 +163,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "62e500d0", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stderr", @@ -175,7 +180,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'CDR': {'Days': 60, 'Transactions': 92719, 'Subscribers': 980, 'Recipients': 980}, 'Recharges': {'Days': 60, 'Transactions': 9452, 'Subscribers': 782}, 'Mobile Data': {'Days': 60, 'Transactions': 9548, 'Subscribers': 591}, 'Mobile Money': {'Days': 60, 'Transactions': 5469, 'Subscribers': 682, 'Recipients': 682}}\n" + "{'CDR': {'Days': 59, 'Transactions': 91299, 'Subscribers': 981, 'Recipients': 981}, 'Recharges': {'Days': 59, 'Transactions': 9315, 'Subscribers': 783}, 'Mobile Data': {'Days': 59, 'Transactions': 9388, 'Subscribers': 592}, 'Mobile Money': {'Days': 59, 'Transactions': 5371, 'Subscribers': 682, 'Recipients': 682}}\n" ] } ], @@ -185,9 +190,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "ed688b3c", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stderr", @@ -198,9 +205,9 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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uhdb+rEREROT4pUKviIiIDAgmk8n/dVcGlgWKr+uuK8PdIiMjmTVrFo888gjvvvtulws78+bN45NPPuGvf/0r3/3ud1vM6a2vr+f555/nwgsvpKioqEvnO5aYmBhOP/30Fh+LjIxk7ty5gDcD1TeI7uhb5u+5554WB9195zvf4a677mLhwoVNtvu6l6dNm8aQIUNaPG9ycjL/+c9/2Lx5MzfeeGOzx8eNG4fZ3Pkb4ubOnUtiYmKLj82cOdMfo7Bu3Tr/9v/973+A98MLX55rS8aOHet//oYNG1rc51jr9/3evvXWW7z++utNPjgBb4bsAw88wPe///1O/z3szPWDN9IAvHEfO3fubPLYjh07/APRfEPSupvvexuafn+PGDGCZcuWsXXrVq644opWn390hvDRx4JvrrW2tpZVq1Y1ecxms/mHGyq2QURERFqi6AYREREZEI7uMuxMXEKgVVdXA7TYgelz6623+mMJwFvc2bBhA3//+9+pra0lJiaGH/3oR5x44okBW5fZbGbu3Ln+Qur+/ftZt24dX3zxBWvXrqWhoQHwdif+6Ec/4u233+5Ssbo148aNa1Kc/7YxY8awYsUKampqyM/PJyUlhXHjxnHSSSexfv169uzZw5VXXkliYiKzZ8/2/9dSF6XH4yEnJ8d/3GPJyMho9bFBgwa18+padqy4A4PBQEZGBjabjX379vm37969G/D+nT7W2o7mi7f4tmOtf/Hixdx1113Y7Xbuv/9+Hn30UU444QRmz57N3LlzmxTZO6sz1w9w/vnn8+ijj+J0Ovnggw+aHMfXzRsbG9vloYft5fvehta/v32d4WVlZRw6dIhDhw6xZ88etm/f3mQYne9DDJ/Zs2cTExNDeXk5H3zwQZNM7hUrVuByubBYLJx//vmBvCQREREZINTRKyIiIgNCWlqa/+ujb4fuDZWVlf6u4qPzPL8tOTmZsWPH+v+bMWMGN998M6+//jqRkZEcOnSI66+/3j98qTuMGDGCRYsW+Ydx3XLLLf4CbGZmpj86ItBiYmLa/XhlZaX/6z//+c9NcliLi4v597//zU9/+lNOPvlkfvSjH/HFF180OVZ5eTkulwvoWqxHV7OLfVmsrfFd89EfVHTmQ4uqqqoWtx9r/RdddBH33HOPP/qgsbGRL774gkcffZRzzz2XCy64gL/97W/+DwI6ozPX73ve7NmzAfjwww/xeDyAt0j6wQcfAN5icFe6rTvi6AiRlr6/d+zYwc9//nNOOukkZs6cyRVXXMFdd93FCy+8wKZNm/x/F1tiNpv92dOff/55k85qX2zDrFmz2vz+ERERkeOTOnpFRERkQJg4caL/602bNnVoUNGWLVv45JNPOOWUU5gxY0aTDM7O+Prrr/1fjx07tsPPz8jI4PHHH+fGG2/E6XTys5/9jLfeeosRI0Z0+FgNDQ2UlJRQVlbG5MmTj7lveHg4t956KwkJCf7M13Xr1jXJFG2Pb3cptsTX8dieYxyd6RoTE8MzzzzD3r17Wb58OStXrmTPnj2AN7Jj1apVrFq1yt+hChyzsNaT2nvNR1+v0+kEvJ3IvmF6bbFYLJ1a39VXX82FF17IRx99xCeffML69ev9hd2srCyeeOIJ3nnnHV555ZU2i7Yt6cz1+yxYsICVK1eSl5fH1q1bmTZtGhs3bvTHi/RklMGxvr9feeUVHnroIX8xGrzDIUeOHMm4ceOYMWMGBQUF/u+vlixYsIBXXnmF+vp6Pv30U+bPn09OTg5fffUVoNgGERERaZ06ekVERGRAGD16tL+77tsZn21ZsWIFL7zwAt///vf59NNPu7yWozNSp02b1qljzJs3j8svvxzwRjrcfffdnSpYPvroo5x55plcfvnlTToRj+Wyyy7zD5PrTHd0bW1tm/sc3aXbkrKyMv/XLXXhjh49mp/97Ge89957fPHFF/zud7/jrLPO8ncjv/DCC2zduhXwZv6297zdqa3uXN81H329vkiShoaGJt3fx/ovPT2902uMjIzksssu469//SsbN27kH//4B9ddd50/Pzc7O5snn3yyU8fuzPX7nH766f6O5I8//rjJ/9PS0pgyZUqn1tQZR39/T58+3f/1rl27eOSRR/B4PCQmJvLYY4+xbt06Vq1axfPPP8/tt9/O3Llz/cX71kyePJlhw4YB31zjJ598AngH6rWWbS0iIiKiQq+IiIgMCEaj0d/pdvDgwXYXe+vq6njvvfcACAkJ4bTTTuvSOlwuF++88w7gva37pJNO6vSx7rzzTn/x+quvvuK1117r8DGGDh3q//rbkQatMZlMBAUFATQbnuW7Pf5Yt/AXFha2eY7s7OxjPu4buhUfH+//PXC73Rw+fLjZELaEhAQuuuginn76aZ544gn/dt8AtqCgIP8ANl/mbWu+//3vM3/+/HZ3z3bEsa7Z5XL513Z0l+jIkSMBOHz48DGL1G63m2eeeYalS5eyffv2Dq+trKyMTZs2NcmftVqtzJw5k7vvvpv333/fP7zPNyCuozpz/T7BwcH+zvKVK1cC+CNNOtK931XV1dX+4uukSZOaDKZbunSp/8OYJ598kosvvrjFzueCgoI2z+P7t2zNmjXY7Xb/YLazzjqry3cciIiIyMClQq+IiIgMGNdee63/tu8HHniAmpqaNp/zxBNP+DsNL7/88i7nsL744ov+QueiRYu6NMgsIiKCu+++2//rJUuWNOl0bY958+b5v/7b3/7WJPOzNV999ZW/qDhjxoxmawLIy8tr9flr165t8xx5eXmtFiSLi4v9RelTTjnFv/3uu+/mrLPO4vrrr2+10OzLcgVvzqyPr+C+ZcsW/+3+31ZVVcWXX37J3r17mzw3UFatWkV9fX2Lj3366af+v4dHX7NvwJjb7Wbp0qWtHvu///0vS5Ys4Z577uHdd9/t0Lq+/PJLZs6cyaJFi/ydo98WExPjH4LW2d+bzlz/0RYsWAB4P8j57LPP/AP2fNt7wh//+Ef/372rr766yWOHDx/2fz1u3LgWn2+325vkXrfW3XvhhRcC3u74lStX+j/c6MlrFRERkf5HhV4REREZMAYPHsytt94KwP79+7nhhhvIz89vcV+3282f//xnXn31Vf9zb7755i6d/8MPP+TPf/4zACkpKVx33XVdOh7ABRdcwAknnAB4C5F/+MMfOvT84cOH+zses7KyuPHGG4/ZUZiXl+fPth0yZAjnnHNOk8dHjx4NeLsSfZ2VR3vzzTfZtm1bu9b261//ulkx3uFw8Ktf/Qq73Y7BYGhSTJszZw7g7SZ++umnWzzmihUr/F+PHz/e//WVV14JeAtrv/nNb/zD8o72yCOP+LdffPHF7bqGjigvL+eBBx5okt8KUFRUxKOPPgp4C6pHd6ieffbZ/q7qZ555psXf2+LiYh555BHAm4N7xRVXdGhdU6ZM8cdb/OUvf2nxA5KSkhI2bdoEtF7EbEtnrv9oJ510kr+r2Lf/hAkTGD58eKfW01Evv/wyr7/+OuD9Pft2Vu7RkRNr1qxp9nyn08kDDzzAwYMH/dta+nsIkJqaytSpUwHvh1EOh4P4+HhmzpzZxasQERGRgUzD2ERERGRA+cEPfsDevXtZtmwZX331Feeeey4LFixg9uzZJCUl0djYyNdff82///1v9u7dC3i7VJ966qkWs0F9KioqyMzMbLLN4XBQVVVFVlYWn376qT+7MyIigmeeeSZgt1jfc889XHLJJbhcLv7973+zcOFCJk2a1O7nP/jggxw8eJCdO3eyfv16zj//fM4++2xmz57N4MGDMZlMFBYWsnbtWt577z0aGhoIDg7mT3/6kz+qweeCCy7g7bffBuCOO+7gxhtvZNq0aVRXV7N8+XLee+89UlNT/d2WrQkODmbv3r1ceuml/PCHPyQ9PZ28vDyef/55f2zDVVdd1eQ6zz77bEaOHEl2djZ/+9vfyM7OZsGCBSQlJVFRUcEXX3zBv/71L8AbWXH0ELlJkyZxxRVX8K9//YtVq1axcOFCrr32WoYNG0Z+fj5vvvmmvxP5kksu6dDvb3sFBwfz9ttvU1RUxPe+9z3i4+PZuXMnzz33HCUlJQD89re/bfL3xmq18vDDD/PDH/6Quro6rrrqKhYtWsTcuXOxWCzs3LmTv//97/7nX3vttf5ifEfWtXjxYv70pz9x8OBBLrzwQhYvXsyoUaMA2Lt3Ly+88AIVFRUYDAa+//3v99j1H81oNPKd73yHv//97/7u2UANJispKWn2/d3Y2EhlZSV79uxhxYoV7Nq1C/B+KPTnP/+52XC5s88+m/fffx/wdp/v37/fX6zdt28f//rXv/z/5vgcK896wYIFbN261X+t559/vj+DWkRERKQlBs+3P1IXERER6efcbjf/93//x1/+8hfsdvsx9x0zZgy///3vWyyO5ebmcsYZZ3To3BkZGTzxxBOtFtvefvttfxzDo48+yiWXXNKu495///3+bsKJEyeydOnSZoWmY6msrOTxxx/n3//+d7OOym8bPnw4jz76qL9I9W2/+c1veOONN1p8bNiwYfzxj3/0X9dLL73UJKf46quvZsOGDZx33nkYDAY++OCDFo9zySWX8NBDDzUrbO3fv5/rr7/+mDnAKSkpPP/88/6BVj4Oh4N7773Xn6HckjPPPJMnn3zSHwEC3j9TgIULF/LAAw+0+Lyj/1zfeOONJsPBfM//2c9+xn/+8x/279/f7Plms5l777231W7cDz74gF/96letRh8AXHrppTz44IPNfs/as36n08ldd93lL1S2xGKxcPfdd7No0aJW92lJIK7fZ8+ePf74ApPJxOeff+7PcO6o9evXc80113ToOSeccAJPPPGEv7P4aB6PhzvuuOOYv4cRERHceeed3HvvvQDcfvvt/PCHP2xx3/LycmbPnu3v+l26dGm3fAAhIiIiA4c6ekVERGTAMRqN3HLLLXz3u9/l3XffZe3atezfv5+KigqMRiNxcXFMmjSJ888/nzPPPLPTXXImk4nQ0FCSkpIYN24cZ511FvPmzeuWrruf/vSnrFixgoqKCnbs2MFbb73FZZdd1u7nR0VF8fDDD3PttdfyySefsHbtWgoKCigvL8ftdpOQkEBGRgbnnnsuZ599tn8YW0seeOAB5syZw+uvv87OnTtpaGggJSWFc889l8WLF7crw9VgMPDHP/6RE044gTfffJMDBw4QHBzMhAkTWLRoEaeffnqLzxsxYgTvv/8+r732GitXriQ7O5u6ujoiIiIYOXIkZ511FldccQXBwcHNnmuxWHjssceYP38+b7zxBlu3bqW8vJzQ0FAmTJjAd7/7Xc4///x2/552VFRUFEuXLuXZZ5/lww8/pLi4mOjoaE4++WR+8IMfHLMT9/zzz2fGjBm8/PLL/O9//yMnJ4fGxkZiYmKYOnUqCxcu9Of5dobZbOYPf/gD5513Hu+88w47d+6ktLQUi8XC4MGDOfXUU7nyyiv9w+E6oyvX75ORkcGwYcM4ePAgJ598cqeLvO1hNpsJDw9nyJAhTJgwgfPOO++Y0QkGg4EnnniCmTNn8vbbb7Nnzx7q6+sJDw8nLS2NWbNm8b3vfY/ExEReeeUV9uzZw4cffthqoTcmJoYTTzyRNWvWMGzYMBV5RUREpE3q6BURERERkX6hoqKCWbNm4XA4+N3vfsdFF13U20vqNi6Xizlz5mCz2bj11lu55ZZbentJIiIi0sdpGJuIiIiIiPQLK1aswOFwEBoayllnndXby+lWa9aswWazYTAYuPDCC3t7OSIiItIPqNArIiIiIiJ9XklJCX//+98B7xC2sLCwXl5R96mtrWXJkiUAnHrqqaSmpvbyikRERKQ/UEaviIiIiIj0SV9++SWvvfYaYWFh/O9//6OkpASLxcLixYt7e2kBl52dzR/+8AdiYmLYuHEjhw4dAuBHP/pRL69MRERE+gsVekVEREREpE+yWq189NFHTbb95Cc/YdiwYb2zoG4UHh7Op59+2mTbFVdcwYknnthLKxIREZH+ZsAVep1OJ4WFhQwePBizecBdnoiIiIjIcSMtLY3hw4eTl5dHcnIy1113HVdeeWVvL6tbJCQkMHbsWLKzs4mPj+eyyy7jxhtv7O1liYiISD9i8Hg8nt5eRCDl5uZyxhln8Omnn5KSktLbyxERERERERERERHpdhrGJiIiIiIiIiIiItLPqdArIiIiIiIiIiIi0s+p0CsiIiIiIiIiIiLSz6nQKyIiIiIiIiIiItLPqdArIiIiIiIiIiIi0s+p0CsiIiIiIiIiIiLSz6nQKyIiIiIiIiIiItLPqdArIiIiIiIiIiIi0s+p0CsiIiIiIiIiIiLSz6nQKyIiIiIiIiIiItLPmXt7ASIiIiIiIiI9oXrnaspXvoqzqhRzZBwx8xYRMWFOby9LRKRLGhsbKSsro7q6GpfL1dvLkXYymUxEREQQGxtLUFBQQI6pQq+IiIiIiIgMeNU7V2Nb/hweZyMAziobtuXPAajYKyL9VmNjI4cPHyYmJoZhw4ZhsVgwGAy9vSxpg8fjweFwUFVVxeHDh0lLSwtIsVfRDSIiIiIiIjLgla981V/k9fE4Gylf+WovrUhEpOvKysqIiYkhPj4eq9WqIm8/YTAYsFqtxMfHExMTQ1lZWUCOq0KviIiIiIiIDHjOqtIObRcR6Q+qq6uJjIzs7WVIF0RGRlJdXR2QY6nQKyIiIiIiIgOeOTKuQ9tFRPoDl8uFxWLp7WVIF1gsloBlK6vQKyIiIiIiIgNezLxFYLI22WYwB3m3i4j0Y4pr6N8C+eenQq+IiIiIiIgMeBET5hB7+jdFXVNoFPEX3KhBbCIiMmCYe3sBIiIiIiIiIj3BGp/i/zru7MWEj5/Vi6sREREJLHX0ioiIiIiIyHHBUZrv/9pZZevFlYiIiASeCr0iIiIiIiJyXHCUFWCwBmMMClWhV0REOu2pp54iIyOD//u///Nv++Uvf0lGRgb/+c9/em1dim4QERERERGR44KjLB9LbDK4nTgrVegVEZGBRR29IiIiIiIiclxwlBVgiU3CFBGvjl4RERlwVOgVERERERGRAc/jdOCsLMESm4Q5SoVeEREZeBTdICIiIiIiIgOeo6IIPG4ssckYzFbc9dW4HY0YLUG9vTQREelmK1as4PXXX2fPnj24XC6GDx/OokWLWLBgAUajtw+2oqKCf/zjH6xatYrDhw9jt9uJjY3lpJNO4qabbmLEiBG9fBVtU6FXREREREREBjxHaT6AN6P3CGeVDWvckN5akoiI9IDf/OY3vPHGG1itVk488UQsFgvr16/nrrvuYufOndxzzz3YbDYWLlxIbm4uQ4cO5ZRTTqG+vp6dO3fy3nvvsXLlSpYtW0ZSUlJvX84xdajQm5eXxzPPPMMXX3xBWVkZMTExnHbaafzkJz8hISGhyb4HDhzgqaeeYvPmzVRUVJCWlsbll1/OokWL/JXyoxUVFfHMM8+wZs0aSkpKSEpKYsGCBfzgBz/AarV27SpFRERERETkuOYoLwDAEpuEx2kHVOgVERnoPv74Y9544w1SUlL4xz/+QWpqKgCFhYUsXLiQl19+mfnz5/Puu++Sm5vLddddxy9/+UsMBgMANTU1/OAHP2DLli28++673HTTTb15OW1qd6F3x44dXH/99VRXVzN69GgmTpzIzp07efPNN/nyyy956623iIqKAmD37t0sWrSImpoapk2bxsSJE1m/fj0PPfQQ27Zt44knnmhybN9vbmFhIePGjWP8+PFs2bKFJUuW8OWXX/LCCy9gsVgCe+UiIiIiIiJy3HCU5mMMjcQUEo45Kh4AZ6VyekVkYPts02E+2XC4t5fRIWedmMbpM9ICcqzXX38dgHvuucdf5AUYPHgwP/7xj3n55Zc5cOAAMTExzJ49m1tvvdVf5AUIDw/nO9/5Dlu2bKGgoCAga+pO7Sr02u127rjjDqqrq7nnnnu4+uqrAWhsbOQXv/gFH330EU899RT33HMPHo+HO++8k5qaGh5//HEuvPBCAMrKyrjuuutYtmwZZ511Fuecc47/+Pfffz+FhYXcdttt3HzzzQDU1dXx4x//mLVr1/Lyyy+zePHiQF+7iIiIiIiIHCccZQVYYr233JojYgGDBrKJiAxgHo+HjRs3YrFYmDVrVrPHL7/8ci6//PJWn19WVsaePXvYtGkTAA6Ho9vWGijtKvR+8MEHHDx4kPnz5/uLvABBQUHcfffdbN68mQMHDgCwZs0a9uzZw4knnugv8gLExsZy//33c+WVV/Lyyy/7C7379+9n1apVpKWlceONN/r3Dw0N5eGHH+bMM8/klVdeUaFXREREREREOs1Rlk/IiCkAGEwWTOHRuFToFZEB7vQZgeuO7W/Ky8txOBwkJSW1Kyng8OHDvPLKK2zZsoUDBw5QU1MD4O/w9Xg83breQGhXoffjjz8G4Prrr2/2WFJSEmvWrPH/+n//+x8AZ555ZrN9p02bRlxcHJs3b6ampobw8HC++OILPB4P8+bNa5bdm5yczLhx49ixYwdZWVmkp6e3/8pEREREREREAHdjPa6a8iaD2MyR8eroFREZwFwuV7v3XbZsGXfddRcul4thw4YxZ84cRo4cycSJEyksLOQ3v/lNN640cNpV6N21axcWi4UxY8ZQUFDAsmXLOHz4MNHR0Zx99tlMmjTJv29WVhYAo0ePbvFYw4cPp7S0lOzsbCZPnuzff9SoUS3uP2LECHbs2MHevXtV6BUREREREZEOO3oQm485Mh57yaHeWpKIiHSzqKgoLBYLNpsNp9OJ2dy0DFpeXs5HH31Eeno69913H0ajkWeffZa5c+c22e/ll1/uyWV3SZuFXrvdTkFBAYMHD+bDDz/k17/+NfX19f7H//a3v3HDDTdw5513AlBcXAxAQkJCi8fzbbfZbE32T0xMbNf+IiIiIiIiIh3hKGuh0BsVT13WZjweT5PBOyIiMjBYrVYmTJjA1q1bWbduHbNnz27y+KpVq7jvvvuYPXs2tbW1TJkypVmRF/AnGbjd7h5Zd1cY29rBl0dRWVnJXXfdxZlnnsmHH37Ixo0befLJJ4mOjub555/njTfeAPAXgYODg1s8nm97XV1dp/Y/WlVVFbm5uU3+KywsbOuSRERERERE5DjiKM0Hmnf0epx23PU1vbUsERHpZosWLQLg4YcfpqioyL+9sLCQJUuWYDAYuOmmmwDYu3cvOTk5/n1cLhfPPvssK1euBKCxsbEHV9457eroBW9BdtasWTzxxBP+x84//3xCQ0P50Y9+xDPPPMPll1+OyWQCaPMTUV8VvKP7H+2f//wnTz/9dFuXICIiIiIiIscxR1k+psh4jJYg/zZzZDwAzqoSTKERvbU0ERHpRvPnz2ft2rW8/fbbnHvuuZx44om4XC42b95MXV0dN954I9OnT+f888/ngw8+YP78+Zx44omYzWa2b99OSUkJ6enpZGVl9Yu0gTYLvUd32l555ZXNHj/ttNMYNGgQRUVFHDx4kJCQEAAaGhpaPJ5ve1hYGEC79w8NDW322LXXXsvFF1/cZFthYaG/Wi8iIiIiIiLiKCvAelQ3L4DJX+gtJWjwiN5YloiI9IBHHnmEE088kTfeeIMNGzbgcrkYPXo0V111FRdddJF/nxEjRrB8+XLWrVuHxWJhxIgR3HTTTVx22WXMmjWLr776irKyMmJjY3v3go6hzUJvREQEFosFh8NBSkpKi/skJydTVFREeXk5iYmJZGZmYrPZGDlyZLN9S0pKgG+yd33ZvK1VxX37t5ThGxkZSWRkZFuXICIiIiIiIscpj8eDoyyfsHGnNtnu7+itLOmNZYmISA8xGAxcfPHFzZpFjxYSEsKtt97Krbfe2uLjGzZsaPLrlvZ97LHHeOyxx7q+4C5oM6PXZDL5C7ZHZ1kczVekjYuLY9SoUQBkZWU128/j8bB///4mxzzW/gDZ2dkAjB49uq2lioiIiIiIiDThrq/G3VCLJTa5yXZTWCSYzDirS3tpZSIiIoHVZqEXYM6cOQB8+OGHzR7bv38/eXl5JCYmkpqa6p9g9+mnnzbbd8uWLZSVlTF9+nTCw8MB/PuvXLmyWQ5vfn4+mZmZDBkyhPT09A5cloiIiIiIiIg3nxfA+q1Cr8FgxBwZr45eEREZMNpV6L3iiisIDQ3l3XffZdmyZf7tlZWV3HPPPbjdbhYtWoTRaOTEE09k1KhRrFmzhjfffNO/b1lZGb/97W8BuP766/3bfcXh/fv38+c//9m/va6ujnvuuQeXy9VkfxEREREREZH2cpR6C72WuKRmj5kj43BWqaNXREQGhjYzegGGDBnCww8/zC9+8QvuuOMOXnzxRRITE/nqq68oLy/n5JNP5oYbbgDAaDTyyCOPcO2113Lvvffy1ltvkZiYyIYNG6isrOTyyy/n9NNPb3L8++67jyuvvJLnnnuOzz77jOHDh7NlyxZKSkqYM2dOi0PgRERERERERNriKCsAgxFzVPO5L+bIBOoP7eyFVYmIiAReuwq9AOeffz7Dhw/n2WefZePGjWRlZZGamsrixYu5/vrrsVgs/n0nTZrE0qVLWbJkCevXr2ffvn0MHTqUn//851x22WXNjp2amurff/Xq1Rw6dIjU1FSuueYarr32Wszmdi9TRERERERExM9RVoAlZhAGU/P3lebIOFzVZXjcLgxGUy+sTkREJHA6VEEdO3YsS5Ysade+6enp7d4XICkpiUcffbQjyxERERERERE5JkdZfrNBbD7myHjwuHHVlHu/FhER6cfaldErIiIiIiIi0t94PG5vR29s83xewF/cdVbaenJZIiIi3UKFXhERERERERmQXNXleJz21jt6o44UeqtU6BURkf5PhV4REREREREZkBxl+QBtd/Sq0CsiIgOACr0iIiIiIiIyIDlKjxR641ru6DUGhWIMClWhV0REBgQVekVERERERGRAcpTlYzBbMUXEtrqPKTJeGb0iIjIgqNArIiIiIiIiA5JvEJvB0PpbX3NkvDp6RURkQFChV0RERERERAYkX6H3WMxRKvSKiMjAoEKviIiIiIiIDDgelxNHRRGW2JbzeX3MkfG466txOxp7aGUiItLTPB7PgD6fjwq9IiIiIiIiMuA4K4vB7Wq7ozcy3ru/unpFRAak5cuXc8cdd/TY+Xbs2MHll1/eY+c7mgq9IiIiIiIiMuA4SgsAsMS13dELKvSKiAxEW7Zs4ec//znFxcU9ds4rr7yS7du399j5jqZCr4iIiIiIiAw4jvIjhd62ohuijhR6K1XoFREZaNxu93FxTh8VekVERERERGTAcZTmYwwOwxgSccz9zBGxgAFXVWnPLExERKSbqNArIiIiIiIiA46jLB9LbDIGg+GY+xlMFkzh0TirSnpoZSIi0hN++ctfsmjRIgA2bNhARkYGv/zlL/2Pr169msWLF3PCCScwadIk5s+fz/PPP4/dbvfvU11dzdy5c8nIyOCFF15ocny73c78+fPJyMjgxRdf5O233yYjIwOXywVARkYGp59+eg9c6TdU6BUREREREZEBx15W0OYgNh9zZDxOdfSKiAwoU6dOZdasWQDExcUxf/58pk6dCsAzzzzDD37wAzZs2MCoUaOYM2cONpuNxx9/nO9///v+Ym9ERAQPPvggAE899RT5+fn+4y9ZsoS9e/dy8sknc91115GWlsb8+fP9HzDOnz+fM888sycvGXOPnk1ERERERESkm7kdjbiqbG3m8/qYI+Oxlxzq5lWJiEhPWrhwISNHjuSLL75g5MiRPPHEEwCsXbuWJUuWkJyczF//+ldGjRoFQF1dHbfffjufffYZTz/9ND//+c8BmDNnDt/97nd56623ePDBB3n22WfZtm0bL7zwApGRkTz22GMYDAZmzJjBjBkz+OCDD3C5XP7z9SQVekVERERERGRAcZYXAnSgozeOuqzNeDyeNqMeRET6k+rtq6je9llvL6NDIiafTsSk07rt+M8//zwA99xzj7/ICxAaGsrDDz/MvHnzePXVV7nllluwWq0A3H333axZs4bPPvuMDz74gKeeegqXy8V9991HUlL7ftb0BEU3iIiIiIiIyIBiL/PeWtvujt6oBDxOO+76mu5cloiI9DKXy8WmTZsAOOmkk5o9Hhsby7hx46ipqWHXrl3+7eHh4Tz00EMA3HHHHezfv5/vfOc7fOc73+mZhbeTOnpFRERERERkQHGUFgDt7+g1RcYB4KwqwRQa0W3rEhHpaRGTTuvW7tj+pqKigoaGBgCmT59+zH0LCgqYMmWK/9ezZs1iwYIFvPfeewQHB/Ob3/ymO5faKSr0ioiIiIiIyIDiKMvHFB6DMSikXfubIxMAcFaVEjR4RHcuTUREepHL5QIgJCSkzUFpCQkJTX5dV1fHli1bAGhoaOCDDz7gyiuv7J6FdpIKvSIiIiIiIjKgOMoK2h3bAN6MXgBnZUl3LUlERPqA6OhoLBYLTqeT3/3ud5hMpnY/9/e//z25ubmcfvrprFmzhscff5zZs2eTkpLSjSvuGGX0ioiIiIiIyIDiKMtvd2wDgCksCkxmnNWl3bgqERHpad8esGm1Wpk8eTIOh4N169Y1299ut3PJJZfwve99j9zcXP/2devW8frrr5OQkMDvfvc7br75Zurq6vj1r3+Nx+M55jl7kgq9IiIiIiIiMmC46mtw11V1qNBrMBgxR8aro1dEZIAJCgoCoLq62r/t2muvBeC+++5j7969/u1Op5MHH3yQr7/+mrq6On+nbm1trb+g++tf/5rIyEhuuOEGRo0axZdffslrr73W5JxWq7XZOXuKCr0iIiIiIiIyYDjKfIPY2h/dAN74BmeVOnpFRAaSlJQUzGYzmZmZLF68mGeffZazzz6ba6+9ltzcXC655BKuuOIKbrnlFs4880zefPNNYmNj+eMf/+g/xuOPP05eXh7z5s3jvPPOA8BisfDb3/4Wg8HAE088QU5Ojn//YcOGAXDVVVdx++239+j1qtArIiIiIiIiA4ajLB8AS1xHC70JOKts3bEkERHpJdHR0Tz44IMMGTKEDRs2sHbtWgB+9atf8cwzz3DCCSeQnZ3N6tWrCQ4O5uqrr+bdd99lxAjvYM5169bxxhtvEBoayn333dfk2NOnT+fyyy+nrq6OX/3qV/4Ih9/+9reMHTuW7Oxs1q5dS0VFRY9dr8Hz7SCJfi43N5czzjiDTz/9tE+FIYuIiIiIiEj3K/v8X1Ss+TfD73wNg9nS/ueteo2Kte8w/Jf/wmBs/3AeEZHelJmZydixY3t7GdJFgfpzVEeviIiIiIiIDBiOsnzMUfEdKvICmCPjwePGVVPeTSsTERHpXir0ioiIiIiIyIDhKCvocD4vHCn0guIbRESk31KhV0RERERERAYEj8eDoyy/c4XeqCOF3koVekVEpH9SoVdEREREREQGBFdtBR57A5bYpA4/Vx29IiLS36nQKyIiIiIiIgOCoywfoFOFXmNQKMagUBV6RUSk3zL39gJERERERER6y6rNOby0IhNbeT3xMSFcc95YTpue2tvLkk5ylBYAYInreHQDgCkyXtENIiLSb6nQKyIiIiIix6VVm3N4euk2Gh0uAErK63l66TYAFXv7KUdZPpjM/hiGjjJHxqujV0RE+i1FN4iIiIiIyHHppRWZ/iKvT6PDxUsrMntpRdJVjrJ8LDGDMRhNnXq+Cr0i0h95PJ7eXoJ0QSD//NTRKyIiIiIixyVbeX2Htkvf5ygr6FQ+r485Kh53fTVuRyNGS1AAVybtUb1zNeUrX8VZVYo5Mo6YeYuImDCnt5cl0qeZTCYcDgdWq7W3lyKd5HA4MJk69wHlt6mjV0REREREjkvxMSEd2i59m8ftwlFeiCW2c/m8AObIOAB19faC6p2rsS1/7sjvvQdnlQ3b8ueo3rm6t5cm0qdFRERQVVXV28uQLqiqqiIiIiIgx1KhV0REREREjkvXnDcWq7npW6Igi4lrzhvbSyuSrnBW2cDl7GKhN+GbY0mPKl/5Kh5nY5NtHmcj5Stf7aUVifQPsbGxlJeXY7PZsNvtinHoJzweD3a7HZvNRnl5ObGxsQE5rqIbRERERETkuHTa9FQKbLW89vEeAMJCzNx48SQNYuunHKX5AFjiuhDd4OvorVSht6c5q0o7tF1EvIKCgkhLS6OsrIyDBw/icrnafpL0CSaTiYiICNLS0ggKCkxckAq9IiIiIiJy3EpKCAfAZDQwfcwgFXn7MUdZAUDXOnoj4gADLhUXe5w5Mq7FTmpf8V1EWhcUFERSUhJJSZ3/oEsGBkU3iIiIiIjIcaugpAaDASaPTiA7t6K3lyNd4CgrwGANxhQW3eljGMwWTOHROKtKArcwaZfo077XbJvBHETMvEW9sBoRkf5JhV4RERERETlu5dtqSYgOYdywWPJKaqmtd/T2kqSTHGX5WGKTMRgMXTqOOTJecQG9ICghrcmvzZHxxF9wIxET5vTSikRE+h8VekVERERE5LiVb6shOT6c9NRoAPbnVfbugqTTvIXert+27C30qqO3p9Xt2wgYCBt3KgZrMKm3PKsir4hIB6nQKyIiIiIix638klqSEsJIT4kGYF9ORa+uRzrH43TgrLR1KZ/Xx5sVW6rJ9T2sbt8mgoaMJmToBDz2Bg3EExHpBBV6RURERETkuFRVa6em3kFyfDhR4UEkxIQop7efclQUgccdmI7eqAQ8jkbc9TUBWJm0h7O6jMaCbEJHzcCa6I1wsJcc7uVViYj0P+beXoCIDDyrNufw0opMbOX1xMeEcM15YzXBWkRERPqcfJu3kJccHwZAeko0+1To7ZccpfkAAenoNUXGAeCssmEKjejy8aRtdfs2ARA2agbmI7//jpLDMGpGby5LRKTfUUeviATUqs05PL10GyXl9XiAkvJ6nl66jVWbc3p7aSIiIiJN5JfUApB0VKG3wFZLjQay9TuOMl+hNxAZvQmAt9ArPaNu3ybM0YlYElIxBodhiozHXqL3DyIiHaVCr4gE1EsrMml0uJpsa3S4eGlFZi+tSERERKRl+bYajAYYHHek0HtkIJviG/ofR1kBxtBITCHhXT6Wr6PUWamBbD3B7Wik/uAOQkfNwGAwAGBNSMVerOgGEZGOUqFXRALKVl7foe0iIiIivaWgpJaEmFAsZu/bIt9AtiwNZOt3HGUFAYltADCFRYHJjLO6NCDHk2OrP7Adj9NO6FExDdaENOyluXjcrmM8U0REvk2FXhEJqPiYkA5tFxEREekt+bYafz4vQGSYlcTYULLU0dvvOMryAxLbAGAwGDFHxKmjt4fU7duEISiUkLRx/m3WhDRwOXGUFfTiykRE+h8VekUkoBadM6bZtiCLiWvOG9sLqxERERFpmcfjId9WS3JC01v901OiVOjtZ9yN9bhqygPW0QtgjorHWaWO3u7m8bip27eJ0BFTMJgs/u3WhDQA7Dbl9IqIdIS5vTu+++673HXXXa0+fuONN/Kzn/3M/+vPP/+c559/np07d+JwOEhJSeGCCy7gBz/4AUFBQc2eX1RUxDPPPMOaNWsoKSkhKSmJBQsW8IMf/ACr1drByxKR3hIV7v3+NpsMOF0e4qKCue6CcZw2PbWXVyYiIiLyjapaO3UNziYdveCNb1i7vYCaOjvhoXof0h/4uj4tcYHp6AUwR8ZTf+jrgB1PWtaYn42rtqJJbAOAJX4IYMBRnANjZvbO4kRE+qF2F3ozM72DlE499VRiY2ObPT527Dfdeq+++ioPPPAARqORGTNmEBYWxtatW3nqqadYvXo1L7/8cpNib2FhIQsXLqSwsJBx48Yxfvx4tmzZwpIlS/jyyy954YUXsFgszc4pIn3P51tzCQ+x8LMrp/HgC+v5+femMSk9obeXJSIiItJEfkktQLOO3lFHBrJl5VYwZXRiTy9LOsFRlg+ANZAdvZHxuKrL8LhdGIymgB1XmqrbtwkMRkLTpzXZbrQEYY4ZhL1EA9lERDqi3YXeXbt2AfDoo48yaNCgVvcrKyvjd7/7HcHBwbz44otMm+b9B7uqqorFixezbds2Xn31VRYvXux/zv33309hYSG33XYbN998MwB1dXX8+Mc/Zu3atbz88stN9heRvqnB7uTLHQXMnZZCxtAYALJyKlXoFRERkT4n31YD0Kyjd6RvIFtupQq9/YSvo9ccMzhgxzRHxoPHjaum3Pu1dIu6fRsJTh2DKSSi2WPWhDQVekVEOqjdGb27d+8mPj7+mEVegE2bNtHY2Mjs2bP9RV6AyMhIbrjhBgA2btzo375//35WrVpFWloaN954o397aGgoDz/8MCaTiVdeeaXdFyQivWfD14U02F3MnZpCVHgQCTEhZCvjTkRERPqgfFstRqOBxNjQJtsjQq0MjgslK6eidxYmHeYoy8cUGY/R0jwisLN8xV1nlS1gx5SmHJXF2IsPNYtt8LEmpOEoK8DttPfwykRE+q92dfTm5ORQVVXF3Llz29zXaPTWjouKipo9Vl5eDkB0dLR/2xdffIHH42HevHn+5/okJyczbtw4duzYQVZWFunp6e1Zroj0ks+35BEXFcz4EXEAjBwSRXZeRe8uSkREBrRVm3N4aUUmtvJ64mNCuOa8scqFl3bJL6lhUGwoZlPz3peRKdHsU6G333CUFWCNDVw+LxxV6K20QUpADy1H1O3bDEDoqBNafNyamAYeN47SfIIGDevBlYmI9F/t6uj15fPGxcXx4IMPctZZZzFx4kTOOeccnnnmGRobG/37Tp8+ndDQULZv387DDz9MUVERtbW1fPLJJ/z5z38mKCiI733ve/79s7KyABg1alSL5x4xYgQAe/fu7dwVikiPqKq1s3l3EXOmpmA0GgDvMJO8klrqGhy9vDoRERmIVm3O4eml2ygpr8cDlJTX8/TSbazarCnt0rZ8W22z2AafUSnRFJfVUVWrTsK+zuPx4CjLxxLAfF4Ac5Q6ertb3b6NWGKTsca1/GdnTfB+aKf4BhGR9mtXodeXz/v222/z/vvvk56ezuTJkykqKmLJkiVce+21NDQ0ABATE8Mf//hHoqKieOmll5gzZw7Tpk3jlltuIS4ujldeeYWJEyf6j11cXAxAYmLL+VcJCd5sT5ut+Q/YqqoqcnNzm/xXWFjYgcsXkUBZuz0fl9vD3KlD/Nt8GXfZeZW9tCoRERnIXlqRSaPD1WRbo8PFSysye2lF0l94PB4KbDUktVLoTffn9Fb03KKkU9z11bgbarG0UizsLGNQKMagUBV6u4m7sZ76Q18TOrrl2AYAS2wSGM04VOgVEWm3dkU3+Dp6zzvvPB555BFCQ705Vrm5ufz4xz9m69at/OlPf+KXv/wlAGPHjuWMM85g2bJlTJo0ibCwMLZv387+/fv55z//yaOPPorVagWgvr4egODg4BbP7dteV1fX7LF//vOfPP300x25XhHpJqu25JKSGM6IIVH+bSNTvF9n51YwcaSGWIiISGCVlNe3uN3WynYRn4rqRuobXSTHh7f4+NGvYaZlaCBbX+YoywfAEhPY6AYAU2S8Cr3dpO7AV+BytprPC2AwWbDEJWMvVqFXRKS92lXoXbJkCTk5OaSlpfkLtAApKSk89thjXHzxxbzxxhvcfvvt5Ofns2jRIoxGI2+99RZjxowBvN23v/jFL3j//fcxmUw8/vjjAJhMJgAMBsMx1+B2u5ttu/baa7n44oubbCssLGTRokXtuSwRCZCS8nq+3l/KonPHNPlejokIJi4qmOxcdfSKiEjgOJxu3lmV1erj8TEhPbga6Y/ybbUAJCe03NEbHmolKT5MOb39gKP0SKE3LvCFXnNkvDejVwKubu8mjMHhBKeMOeZ+1oRUGvP29dCqRET6v3ZFNwQFBZGent6kyOszduxYBg8eTF1dHQcPHuRPf/oTJSUl3H333f4iL0BkZCSPP/440dHRLFu2jLy8PABCQrwvxH3RD9/m2+7rIj5aZGQkKSkpTf4bPHhwey5JRALof1/lAjB3avNJFekp0brtUUREAmb3wTJ+9uQqXl6RyejUaKzmpi9ngywmrjlvbC+tTvqL/JIagFY7esH7GiZbr2H6PEdZARhNmKMC33ltjozHWV0a8OMe7zxuF3XZWwhNn4bBaDrmvtaENJyVxbjtulNDRKQ92lXobUt8vPeW7Pr6ejZs2ADAqaee2my/qKgopkyZgtvtZvfu3cA32bwtZfAClJSUNNlPRPqez7fkkZEW02LO3ciUaPJKajSQTUREuqS23sH//Xsbdz79P2obnNy7+CT+8NO53Hr5FBKOdPCaTQZuuWwyp01P7eXVSl+Xb6vFZDSQeIzu7/SUaIrL66msaWx1H+l9jrJ8LNGDMJjadbNqh5ij4nHXVeF26O9AIDXm7cNdV3XM2AYfa0IaAPaS3O5elojIgNBmobempoZ7772Xn/zkJzidzhb3yc31/qM7ePBgqqqqgG8iGb7Nt93h8BZ9Ro0aBUBWVsu332VnZwMwevTotpYqIr3gcGEV+/MrmTNtSIuPp6dE4fHAgfyqHl6ZiIgMBB6PhzXb87n58U/5aN1B5s8ewTO/mMeJ4713cZ02PZUX7jmbS+elAwZOnRzYgUwyMOXbahgcF4rJ1PrbofRUb06v7kzq2xxlBd6hXd3AHBkHoJzeAKvdtxGMJkJHTGlzX2uC94M7uwayiYi0S5uF3rCwMD755BM++ugjNm7c2Ozxzz//nPLyckaPHk1iYiIjRozwb/+22tpatm3bBuCPdZg9ezYAK1eubJbDm5+fT2ZmJkOGDCE9Pb2DlyYiPeHzrXkYDTB7csuF3pFHplbr1kcREemokvJ6Hn5xA4/9cyPR4cE8cdscfnDhREKDLc32zRgag9PlJjtPufDStgJbLUnHiG0AGDkkGlChty/zeNzdXOhNAFToDbS6fZsIGToeY3DLGdlHM0cnYjBbcajQKyLSLm0Weg0GA5dffjkADz74IEVFRf7HDh8+zAMPPADATTfdBMDChQsB+N3vfufvxgVvrMM999yDzWZj9uzZDBs2DIDU1FRmz57N/v37+fOf/+zfv66ujnvuuQeXy8X111/fxcsUke7g8Xj4fEsuk0clEBMZ3OI+sZHBxEYG6U2SiIi0m8vt4b3V2fz495/y1b4SFs8fzx9/OodRqTGtPmd0mvexPYfKe2qZ0k95PB4KbLWtDmLzCQuxkBwfRpYGsvVZruoyPE47ltju6eT3d/RqIFvAOMoKcNhy2xXbAGAwmrDEp2IvyenmlYmIDAztCjK6+eab2bRpE5s3b+bcc89l+vTpAKxfvx673c7ixYs5//zzAbjyyivZvHkz77//PgsWLGD69OmEhoayY8cObDYbw4YN49FHH21y/Pvuu48rr7yS5557js8++4zhw4ezZcsWSkpKmDNnDldeeWWAL1tEAmHP4XKKyuq48uyMY+43MiWarFx1WImISNv251Xy9NKv2JdTwfQxidx06WQGxTYfyvttcVEhJMSEqNArbSqraqDB7jrmIDaf9NRodh0o64FVSWc4SvMBsMR1U6E3Ig4w4KrSQLZAqd23CaDdhV4Aa2Iq9fu3ddeSREQGlHYVeoODg/nHP/7BP/7xD5YtW8b69euxWq1MmTKFq6++mrPPPtu/r8Fg4A9/+AOzZ8/mzTffZOfOndjtdoYMGcJ3v/tdvv/97xMREdHk+KmpqSxdupQlS5awevVqDh06RGpqKtdccw3XXnstZnPgg/VFpOs+35yLxWxk5sRj3y6XnhLN5swiGhqdBAfp+1lERJpraHTy+sd7eHd1NpGhVu68agazpiRjMBjafYyMtBj2HFJRTo4t31YLQHILQ2S/bVRqNKu35lFR3Uh0RFB3L006yFFWANBtHb0GswVTWBTOqpJuOf7xqG7fJiwJaViiB7X7OdaENGq2r8JVV40pNKLtJ4iIHMfaXXGxWq388Ic/5Ic//GG79r/ooou46KKL2r2QpKSkZp2+ItJ3uVxu/rctjxPHDW4xK/FoI4dE4fbA/vxKxg2P66EViohIf7F5dxH/9+/tFJfVcc7JQ7nugnGEh1o7fJyMobF8sS2fsqoGYluJFBLJLzlS6E1ou6PXN2sgK7eCGWPbX5iSnuEoy8dgtmKKaD3WpavMUQk41dEbEK76GhoO7yJ65kUdep41IQ0Au+0wIWnju2FlIiIDh1rrRKRTtu2zUVljZ+60lDb3TU+NBiA7V4VeEZHj3arNOby0IhNbeT2xUcEkRIew+1A5qYPCeezHsxg/ovM/J8YM/Sant627TeT4VWCrwWwyEh8d0ua+I4dEYTCo0NtX+QaxGQxtjp7pNHNkHHYNAguI+v1bwePuUGwDHFXoLc5RoVdEpA3d9xNRRAa0z7fmEhZsZsbYxDb3jY0MJjpCA9lERI53qzbn8PTSbZSU1+MBSisb2H2onFMmJvHnn5/WpSIvwIghUZhNBsU3yDHl22oZHBeKydh2LEhosIXk+HANZOujHGX53Rbb4GOOjMdZVYrH4+nW8xwPavdtwhQWRVByeoeeZ4qIxRgUikMFdxGRNqnQKyId1mB3sm5HPqdMSsZiNrW5v8FgID0lmmwVekVEjmsvrcik0eFqtn1fbkW7fp60xWoxMWJIFHsOayCbtC6/pKZdg9h8RqVG68PqPsjjcuIoL8IS273d+6bIeDyORtz1Nd16noHO43JSn7WFkJHTMRg79u+9wWDAkpCmzmoRkXZQoVdEOmzjriLqG13tim3wGTkkipyiahrszm5cWftV71zN4ad+xP6Hv8vhp35E9c7Vvb0kEZEBz1Ze36HtnZExNJZ9ORW4XO6AHVMGDrfbQ4GtluSEtgex+YxMiaa0soHyqoZuXJl0lLOyGDzubi/0mqPiveersnXreQa6hpxM3I11hI3uWGyDjzUhDbstR53VIiJtUKFXRDrs8y25xEYGM2FkfLufMzIlGrcHDhZUdePK2qd652psy5878oLdg7PKhm35cyr2ioh0s/iYljNRW9veGRlpMTTaXRwqrA7YMWXgKK1swO50kxzf/kLvqCOzBtTV27c4SgsAsMR1c3RDhAq9gVC3bxMGk4WQ4ZM79XxrQiru+hpcNRWBXZiIyACjQq+IdEhNnZ3Nu4uYM3VIu7LtfNKPTK3O7gMZd+UrX8XjbGyyzeNspHzlq720IhGR48PV545pti3IYuKa88YG7BwZ/oFsyumV5vJt3tvvOxLdMMI3kK0PvIaRb9jL8gG6P6NXHb1d5vF4qN23ieBhEzBagzt1DGtCKoDiG0RE2qBCr4h0yJrt+ThdHuZObX9sA0B8dDBR4Vayciu7aWXt56wq7dB2EREJjKFJUQBEhFowAAkxIdxy2WROm54asHMMig0lOjyI3YeU0yvN5dtqAUjqQHRDSJCZlMTwPvEaRr7hKMvHGByOMSSiW89jCosCk1mF3i5wlObhLC8kbNQJnT6GNSENUKFXRKQt5t5egIj0L59vyWNIQhgjU6I69DyDwcDIIX1jmIk5Mq7FF+vmyK5NexcRkWPblFkEwDN3nk5MROe6utpiMBjIGBrDHhV6pQUFtlqsZiPxUR2LC0lPiWbbvpJuWpV0hrOsAEtsEgZD++8w6wyDwYg5ouXXjtI+dfs2ARA6qnP5vOAtuJvConCo0Csickzq6BWRdrNV1LNzv42501I79aJ6ZEoUh4uqsbcwcb0nxcxbBCZLk20Gc5B3u4iIdJtNmUWkp0Z3W5HXJ2NoDHklNVTX2bv1PNL/5JfUMDg+DGMH4qfAW+gtq2qktDJwgwOla+xHCr09wRwVj7NShd7Oqt27Eeug4V1uqrAkpGEvyQnQqkREBiYVekWk3f73VR4eD8ydOqRTz09Picbt9vT6QLaICXMInzD7mw1GI3Hn/4iICXN6b1EiIgNcdZ2dPYfKmDFmULefy5fTu/ewunqlqXxbbYcGsfmkHxnIlq34hj7B7WjEVWXr9nxeH3NkvDp6O8lVV0Vj3l5CR3c+tsHHmpCKvSQHj8cdgJWJiAxMKvSKSLut2pLLqNRokhPaP8DkaL6BbH0hvsFgMGIMCSf+/BvB7SboSO6XiIh0j617inF7YMbYxG4/V3pKNEYDim+QJtxuD4WltR0axOYzIjkKowH2aSBbn+AoKwDAEtdzhV5XdRked+/eldYf1WVtAY+bsC7ENvhYE9LwOBpwVipGpS2rNuew+KGPWXD7f1j80Mes2qxOaJHjhQq9ItIuOUXV7M+rZO60jg1hO1pCTAgRoZY+MbW6MT+LoKSRhGWcBAYjtZlre3tJIiID2ubdxUSEWklPjen2c4UGW0gbHKlCrzRhq6jH4XST3IFBbD7BQWZSBkX0iQ+r5ahCb09FN0TGg8eNq0b/pnRU3b6NmMJjsQ4e0eVj+QeyFSun91hWbc7h6aXbKCmvxwOUlNfz9NJtKvaKHCdU6BWRdvl8ay5GA8ye0rnYBjgykC0lmuy83r3t0e1oxF5ymKCkdEyhkYQMn0hN5lo8Hk+vrktEZKByuz1s3l3E9DGJmDqYjdpZGUNj2HO4HLdb/7aLV76tBoCkTkQ3gLdTPCu3Qq8X+gB/oTemBwu9oPiGDvI4HdTt/4rQUTMCMjTPmpAKoJzeNry0IpPGb81EaXS4eGlFZi+tSER6kgq9ItImj8fD6i15TEpPIDayawN00lOiOVxYhcPZe7e+2YsOgsdNUNJIAMLGnIKzvBB70YFeW5OIyECWlVtBZY2d6WO7P5/XZ8zQGGrrHeSV1PTYOaVvy7fVAnQqugG8r2Eqqhspq2oI5LKkExxl+ZjCYzAGhfTI+fyF3n48kK1652oOP/Uj9j/8XQ4/9SOqd67u9nPWH/4aj70hILENAMagUMyR8ThK1NF7LLbylodGtrZdRAYWFXpFpE17D5dTUFrL3Gmd7+b1SU+Jxunq3YFsjQXZAAQlpQMcFd+wrtfWJCIykG3OLMJggGkZ3Z/P65MxNBZQTq98I7+kFqvF1OkPrUcdGcimnN7e5yjL77FBbADmqP7d0Vu9czW25c8dWb8HZ5UN2/Lnur3YW7d3IwazleBhEwJ2TEtCmjp62xAf0/IHIK1tF5GBRYVeEWnT51vzsJiNzJzY9RfUI1OiAMjqxanVjQXZmMKiMUV4iwCm0AjFN4iIdKNNu4vISIshMszaY+cckhBOWLCZPYdV6BWvfFsNyfFhGDsZHzIsORKjoW8MlT3eOcoKeiyfF7ydpIag0H5b6C1f+SoeZ2OTbR5nI+UrX+22c3o8Hur2bSJkxGSMlqCAHdeamIa9NFeD8Y7hmvPGYvxWVIbBAFedM6aXViQiPUmFXhE5JpfLzf++yuOEcYMIC7F0+XiDYkMJD7GQ3YtvkhoLvIPYjs4KCxur+AYRke5QWdPIvpwKZvRgbAOA0WhgdFoMew6V9eh5pe/KL6ntdD4vQLDVTNrgyD4xVPZ45qqvwV1X1aOFXvDGN/TXQq+zqrRD2wPBXnwIZ5WN0ADFNvhY41PB5fTnNEtzM8YOwmiEYKsJAxARZsHjgdoGZ28vTUR6gAq9InJM27NsVFQ3MndqSkCO5x3IFtVrhV53Yz0OWx5ByelNtoeN9sU3rO2VdYmIDFRb9hTj8cD0MT1b6AVvfMOhgirqG/Xm9njncrkpKqsluQuFXvDemaSBbL3LP4itB6MbAMyRcf02o9cZHN2h7YFQt28TYCA0PcCF3oQ0QAPZjmXVllycLg+P/ngW7/3hQl797XlMy0jk5RW7KFFOr8iAp0KviBzTqi25hAabA9qJlZ4SzcGCahxOd8CO2V6NhfsBj38Qm483vmESNZnr9OZNRCSANmUWER0RxIghUT1+7oyhMbg9qANTKKmox+nykJzQuUFsPqNSoqmssWOr0EC23uIoywfAEtfThd4EnNXd1wHbnZbVT8Xu8b71r3R5M6o9Hthc3/X5G62p27eJoOR0zOHRAT2uJX4IYMCugWwt8ng8fPTlIdJTokhPiQa8jTY3XToJtweefXub3uuIDHAq9IpIqxodLtbtKOCUiclYLaaAHXdkSjROl5tDhT0/kO3bg9iOFjZ2pje+oVDxDSIigeBye9iyu5jpYxI7nYvaFaPTYgDYrfiG416+rRagyx296UcGsmXlKvu5tzjK8sFgxBLds3cJmCPjcNdV4XY0tr1zH/N5eQprGkYD8ErNLIqc4ZS4I5nOLuoP7gj4+Zw15TTm7wt4bAOA0RKEJXYwDhV6W7T3cDkHC6o4++RhTbYPjgtj0Tlj2LiriDXb83tncSLSI1ToFZFWbdpVRH2jk9OmBSa2wcc3kC27FwayNRZkYY6MxxTWvLMsbPRJYDRRu1vxDSIigbD3UDk19Y4ez+f1iQyzMiQhjD2HVJQ73hWU1AB0uaN3WHIURqOhV4fKHu8cZQWYoxIwmLs+O6IjzFHxQPfm2naX+JgQHFhweQwccCXySNUlPFl1HmVEUbj0d0fueAucun2bAQgbfUJAj+tjSUhTR28rPvryEMFWE3OnNu/WXjB7BCNTovjLOzuoqbP3wupEpCeo0Csirfp8ay4xEUFMSI8P6HGT4sIICzb3Sk5vY0E21m/FNviYQiMIGTaRml1rdUuTiEgAbNpdhNFoYMroxF5bQ8bQWPYcLte/68e5fFstIUEmYiKCunScIIuJoYMjFAfSixxlBT0+iA28w9gAnFUlPX7urrrmvLEMtZRS4IrGgRkAlzkUx7zbMAaHUfivh3GUFwbsfHX7NmGOSsByJE830KwJqTjKCnE7Vaw8Wl2Dg9Vf5TFnagqhwc0/CDGZjNxy2RSqahr5x/JdvbBCEekJKvSKSItq6uxs3FXE7KlDMAX4dlvvQLZosnq40Ouqr8FZXkhwcvPYBp+wsafgrChSfIOISABsyixi7LBYwkN6tvPuaBlDY6iobqRYA2iOa/m2WpLiwjEYuv6aJv3Iaxh9eNDzPB4PjrL8Hh/EBt8Uel39sKN37rQU0oMrOOyMA8AA/Ojiicw+dSJJV96Lx+2k4PUHcdZUdPlcbkcj9Qe2ETpqRkC+31piTUgDjxuHLa9bjt9ffb4ll0a7i3NOHtrqPukp0SyYM5KPvjzEzuz+OVxQRI5NhV4RadHaHQU4XW7mTg1sbIPPyJRoDhZU4XT13EA2Xz5vax29AGEZJ4LRRE3mmp5alojIgFRW1cD+vEqmj+m9bl6AjCM5vXuU03tcyy+pISmha/m8Pump0VTV2jW9vhe4airw2Bt6p6M3Ig4w4Kzsf8UxZ0URJmcdOa54Ljh1OB4gPNQKgDU+hcELf42ruozCNx7G3di1v9f1B3fgcdoJHdU9sQ1wpNALim/4lo/WH2J4ciSjjmSJt2bROWNIjA3l6aXbcDhdPbM4aWLV5hwWP/QxC27/D4sf+phVm3N6e0kygKjQKyIt+nxLLsnxYW2+UOiskUOicDjd5BRVd8vxW+IfxDa49UKvKSSCkGGTqM1cp04dEZEu2LK7CKDX8nl9hiVFYrWYlNN7HHO53BSV1XV5EJuPb5L9vl6IoDreOcq8Q6QscT3f0WswWzCFRfXL6Abfa+DDzjjOOXkoEaFWvtj2TTds8JDRDLr0DuxFByl663d4nI5On6tu3yYM1hBCho7r8rpbY4lNAqNZhd6jZOVUkJ1byTknDW2zkzo4yMyPL51MXkkNb/53Xw+tUHxWbc7h6aXbKCmvxwOUlNfz9NJtKvZKwKjQKyLNlFbWsyPbxtxpKd12y5V/anUPZtw1FmRhjhmMKeTYg1jCxs48Et8Q2MEUIiLHk02ZxcRFBTMsKbJX12EyGRmVGq1C73GsqLwOl9sTsELvsKRITEZDr8waON45ygoAeqWjF8AcldAvh7E1FmThNpgodEczJCGcUyYlsXFXIY2Ob7o5Q9Onk/CdH1N/cAfFy57C4+n4XXcej5u6fZsIHTEFg6n7InsMJjPW+GQcJSqM+Xy0/hBWi4m501Pbtf+0MYmcNi2Ftz7by+HCqm5enRztpRWZTb73ABodLl5akdlLK5KBRoVeEWnmf1/l4fHAnBamtQZKUlwYIUHmHs3pbSzIJugYsQ0+38Q3rO2BVYmIDDxOl5ute4uZMXZQt31g2BFjhsaQnVepW1R7QV+4PTW/pBaApPhjf9DbXlaLiaFJkezTQLYe5yjLB5PZn5fb08yRcf22o7fCkkhMdDhWi4lTJyVT3+jy33nhEzHpNGJPv5raXWso/fjFDt/dZi/Yj6umnNDRMwK5/BZZ4lOxq9ALQH2jk8+35DBrcnKHMvFvWDCBkCAzTy/dhtutOxl7SmuxP7bjIA6oL7wmOB6o0CsizXy+JZf0lChSEiO67RxGo4GRKVFk51Z22zmO5qypwFVlI+gYg9h8volvWKv4BhGRTsg8WEZdg5PpY3o3tsEnY2gMTpeb7Lye+ZkjXn3l9tR8Ww0AyQHK6AVvfEO2BrL1OEdZPpaYwRiMpl45vzkyHmdVab/6c/d43DQW7CfXHe/vap+UHn8kviG/2f5RJ19I1Enzqdr0ARVr3+nQuWr3bQKDkdCR0wOy9mOxJqThrCzucqbwQPC/r/Kob3Rx7snDOvS86IggblgwgcyDZXz05cFuWZt8w+F08cxb21p9PD4mpAdX0/P6ymuC44EKvSLSRG5xNVm5lcyd1r7bfrpi5JBoDuRX4uqBgWx2Xz5vOzp6AcLHnYKzoljxDSIinbA5swizycDkUb3Tdfdto/0D2RTf0JP6yu2pBSW1hASZiQ4PCtgx01Ojqa5zUFRWF7BjStscZQVYYns+n9fHFBmPx9GIu76m19bQUY7SfDz2evbWRZN8pKvdZDJyyqQkNnxd2Ox71GAwEHvGNYRPmEP5qlep+uq/7T5X3b5NBKdkYArtvmYRH/9ANpuKRB99eZDUQRGMGRbT4eeePiOVyaPi+cfyXZRWqmjeXWwV9dz9zBo+XHeQE8cNwmpuWooLspi45ryxvbS6ntFXXhMcD1ToFZEmVm/Nw2CA2VO6/0V0ekoUdqebnOLuf7HsHUJhIGjQiHbtHzr6BMU3iIh00qbMIsaPiCM0uPsyGjsiLiqEhJgQFXp7WGu3ofb07an5tlqSE8ICGiOSnhIF0GN3Jgl43C4c5YW9MojNxxzl/fDKWWXrtTV0VGNBFgBZ9dFNutpnTU6mwd48vgHAYDCS8J2bCRkxBdsHf6F278Y2z+OssmEvOkDoqO6PbQCwJh4p9B7nA9kO5Fey93AF557cdAhb9c7VHH7qR+x/+LscfupHVO9c3eLzDQYDN393Mk6nm7+8s6Onln1c2ZFl42dPfs7hoiruvvYE7r3hZG69fAqhwWYA4qKDueWyyZzWznzl/qqvvCY4HqjQKyJ+Ho+HVVtymTgynrio7r91ZOSRqdU9MZCtsSALS/wQjEHtuy7FN4iIdE5JeT2HCqv7TGyDT0ZaDHsOlfX2Mo4rrd2G2tO3p+bbavydjIEyLCkSs8nAvhx9eNBTnFU2cDmxxPTOIDYAc0R/LPRm4zFZKXJFNfk+mDgynsgwK1981Ty+AcBgsjDo0jsIGjyC4nf+SEPO7mOep27fJoAeK/SaoxMxmK3HfU7vR18ewmI2Mm/GN0XC6p2rsS1/7sjfUw/OKhu25c+1WuxNjg/nirMzWLejgHU7Cnpo5QOfx+Ph3c+zuecvawkLsfCH2+ZyyiTvB1WnTU/l9kXeiJO7rjphwBd5oe+8JjgeqNArIn77cioosNUyd1pKj5xvSEI4IUGmbp9a7fF4jgxiazuf92j++IYjsQ8iItK2zUe6w2aM7WOF3qGxFJfXU1bV0NtLOW5cc95YjN9qou3p21MdTjfFZXX+bNJAsZi9A9l6cqjs8c5R6i1IWuJ6sdDbLzt6s2mMSMGNkaSjvg9MJiMzJyaxYVfz+AYfozWEwQt/hTkynsI3Hzlm92zt3k1YYpOwxHXfMOejGQxGrAmpOI7jjt4Gu5NVm3M4dVIyEaFW//byla/icTY22dfjbKR85autHuvi09IZlhTJX97ZTl2Do9vWfLxoaHTy+1c28/x7Ozlp/GD++NM5pA5qGmkyLCkSgIMFx8edIWeekNZs2/EQWdEbVOgVEb/Pt+ZiNhn9nzR2N6PRwPDkqG5/k+SqLsNVW9HufF4ff3zD7nXdtDIRkYFnU2YRibGhpCQGtoOyq8YMVU5vT5s1ZQgmkxHTkWpvVLi1x29PLS6vw+0J7CA2n/SUaLJyK3XnTw9xlHk7DXs1ozcsCozmflPo9bhd2AsPUGYdjNEAg+Oafh/44hs2ZzaPb/AxhUUx+Mp7MZitFLz+IM7Kkmb7uO311B/aQeioGQGNSGmLJSENe/HxW+hdsy2f2gYnZ588tMl2Z1Vpi/u3th3AbDJy6+VTKKtq4KUPlJnaFfklNdyxZDVrtuVxzfljufvaE1qMskqIDiEsxMKBgqpeWGXPO1xYTZDFRJDVO0wz/jiJrOgNKvSKCAAut4f/bc3jhHGDCA/puUzF9JRo9udX4XJ335ukxnxvNllQcsc6ek0hEYQMn0TtLsU3iIi0h8PpYtu+EmaMSezRN/vtMWJIFGaTQfENPSgrtwKH082tl0/BbDJw5glpPf6GLr/EOwcg0NENAKNSo6mtd1BYqoFsPcFRlo/BGoIpLLrX1mAwGDFHxvWbQq+9JAeP006uK46EmFAs3xoA5YtvWLOt5fgGH0t0IoOvuAePvYGC1x/EVVfd5PH6/dvB5eyx2AYfa0IartoKXHXHR6Hs2z768hBDEsKYMCKuyXZzZFyL+7e23Wd0WgzzZ43gg7UH2H1QPys7Y8OuQn7+p88pq2rg/h/M5LIzRrf6eshgMDAsKZKD+QP/72+BrZZ1O/JZMGeEv4P3yZ+epiJvN1GhV0QA2JFVQnl1I3On9kxsg8/IlGjsDhe5xdVt79xJjQVZYDRhTRza9s7fEj72FJyVim8QEWmPr/eX0mB39bnYBgCrxcSIIVHsOayO3p6yI8tbDJsxdhCj02LYntXzxbF8Wy1Ak1vWA8U/a0DxDT3CUVaAJTap1z9EMkfG46zsH4Ve3yC2PbVRLcaXtCe+wSdo0DAGXf5LnBXFFL7xMG77NzE4tfs2YgwOJzhlTGAvoA3WBG+R6HjM6T1UWEXmwTLOPmlYs++JmHmLMJiDmmwzmIOImbeozeMuOncMcVEhPLX0KxxOd0DXPJC53R5e+2g3Dz6/nsHxYTz5s9OYmpHY5vOGJ0VysKAKdzc2PfUF763Oxmg08J1ZIxhy5I6vvJLuH8h+vFKhV0QA+HxLHiFBZmaM69k3599Mra7otnM0FmZjTUjDaAlqe+dvCR19IhjN1GSu7YaViYgMLJsyi7GYjUxMj+/tpbQoY2gs+3IqcLn05rUnbM+yMXRwBFHhQUxMjyc7t4La+p7NfswvqSEsxEJkmLXtnTto6OBIzCZjjwyVFW9HryW29/J5fcxR8bj6SUdvY0E2hqBQMsssJCe03NU+e/KQNuMbfELSxpN48c9oLMim6O0/4HE58bhd1GVtJnTkVAwmc6Av4ZisCd7Mz+Ox0Pvx+kOYTQbOOKF5R2TEhDnEX3AjHCkAG4PDib/gRiImzGnzuKHBFm66dBKHC6t5e+W+gK97IKqps/PgC+t5/eM9nHFCKr+7ZTaDYkPb9dxhyVHUNzopLh+4d4ZU1dr5ZONhTpuWSmxkMEOO/FuUW6xCb3dRoVfkOLdqcw7XP/gR/914GLfHw7rtx751K9CGJEYQZDWRlds9IfTfDGLrWD6vjykknJDhE6nNXKf4BhGRNmzeXcTEkfEEW3v2zX57ZaTF0Gh3cfA4ycPrTQ6nm8yDZf6i/6T0eNwe+PpA6xmR3SHfVktSfFi3dIFazEaGJWsgW0/wOB04K0p6NZ/XxxwRh7O6DI/72B2wfYG9IBtTwnBqG1ytDiScMDKOyDArX7QR3+ATlnES8ef+gPrsLeS/ej+H//wD3HVV1O3/iuqdqwO5/DaZImIxBocddwPZ7A4XKzflcPKEJKLCW25kCRt1Ahx57xI57ex2FXl9Thw3mFmTk/nXJ3u79a5L8L4XXfzQxyy4/T8sfuhjVm3uWtE+0Mdry4H8Sn72p8/5am8xN186idsWTiXIYmr384cnRx45zsB9XbJi3QEa7S4uOs37ftwXI5Ovjt5uo0KvyHFs1eYcnl66DVuF99arRruLp5du6/YfiEczGQ2MSI7qto5eZ0UR7vqaThd64Zv4hkbFN4iItKqwtJbc4hqmj237VsXekuEbyKb4hm6393A5jXYXk44UescMjcViNvrjHHpKvq221QJXIIxKiSY7t2LA33bb2xzlhYAHS1wfKPRGJYDHjaumoreXckwep4PGokM0RHhj2Vrr6DUdGcS8cVchDXZnu44dOe1sQsecTGNOJq46b7OGu74a2/LnerTYazAYsMSnYj/OCr1rdxRQXefg3JOHtbqPs7LY/7Wjou1u7W/74UUTCbKaeOatbd3275vvvWhJeT0eoKS8vkvvRQN9vDbPtyWXO5b8D7vDzaM3z+K8U4Z3+EPFtMERGAxwML97mp56m93h4v0vDjB9TCJDB3uL2iajgaT4MEU3dKO+2W4hIj3ipRWZzfK4Gh0uXlqR2aPB6CNTovjvhsO43B7/ZO5A8RVng5I6NojtaN74hr9Qm7mW4A4OdBMROV74bvvti/m8PoNiQ4kOD2LPoXLOP2V4by9nQNuRbcNggAkjvYVeq8XEmKGxPZrT63C6sJXXkdyNr2lGpkSzYt1BCktrWy2kSdc5yrzdpn2io/fIQCtnVUmbw616k734ELidlFoGA55j5lTPmpTMh+sOsnl3MadOat/vcWNe89v6Pc5Gyle+2qHu0a6yJqRRm+kdnNzb+c095aMvDzI4LvSYMUmOCm+h1xgcjrO844XemMhgrv/OeJ5e+hX/3XiYs0/q+KyTY/F4PDy/7OsW34v+6Y2t/HtlVoePmVNcjcvVtCjd6HDxt//sZMSQKJLiw5sNJOwMp8vNi8u+5r3/7Wf8iDjuunoGMZHBnTpWsNVMcnwYBwbonUartuRSUd3Ixac1fQ89JCGcw4Xd2y1+PFOhV+Q4Ziuv79D27pKeEs37Xxwgv6SG1EERAT12Y0EWmMxYEzv/Js8b3zCJ2sy1xJ5+9XHzIlJEpCM27S4mOT6M5Pi+W+wyGAxkDI1hzyFNE+9uO7JsDE+KIiL0m2zcienxvP7xbqrr7E22d5fC0jrcHkhO6MaO3tRowDuQTYXe7uMoKwDoGxm9kQkA3oFsPTvDuEN8g9gOO+MwGkuPmRk6YWQcUeFW1mzLb3eh11Xd8r+jzqqejWexJqRSvbUGV0055ojYHj13b8grqWFndinXnD8W4zEaZHwdvcFDx9NweFenznXWiWms3JzDC8u+5oRxg4iJ6Fwx08flcvP1gVK+3FnIlzsLqKhubGU/D4Pj2pdxe7TWYpmqau38+PcrMRoNJMWFkpIYQUpiOCmJEaQO8v4/LMTS6nFXbc7hpRWZ2MrriY0KJthqJq+khgVzRnD9d8ZjNnWteDwsKYr9Xezord65mvKVr+KsKsUcGUfMvEU9+oFLS9xuD+9+nsWIIVH+u3t8hiSEs+HrQlwuN6Yu/v5Jcyr0ihzH4mNCKGmhqBsfE9Kj6zh6anXgC73ZBA0ajsHU+g/v9ggfO5OS97fQWJCtrl4RkW9pdLjYvq+Ec2cO6+2ltCljaAzrvy6kqtbeLQO6xHurZubBsmZd05PS43ntI9iZXcrMid1fsPPl/3VndEPa4AgsZiP7ciqYM7UPV/36OUdZAcbQSEzB3fdn2V7mKG/BwtnHB7I1FmRjDIngQJWFQTGhxyxGmUxGZk5MZuXmHBrsznblrJsj41r8PejpLmdrom8g2+HjotD70ZeHMBkNnHlC2jH3c1YUY7AEEZw8iro963E31mEM6ljx1Gg0cMtlk7n1iVX87d2d3Hn1jA6vt9HhYuueYr7cWcCGr4uorrNjNRuZmpFIQ6OT6rrmAzoTYkL49fUndfhcix/6uMX3ttERQSyeP57c4hpyi6vJKaph8+4inEd1/8ZEBJE6KIIhieGkHlUI3pldwjNvbfd3HpdWeiMPzz9lGD+4cGKH19iS4cmRrN2RT32jk5Cgjpfoqneuxrb8OTxOb+HcWWXDtvw5gF4t9m7eXUROUQ23L5rerFFqSEI4LreHorI6fUjaDVToFTmOXXPeWJ7819YmuUtBFhPXnDe2R9eRmhiO1WIiO7eSeQG8vdLjcdNYsJ+IiXO7fKxv4hvWqNArIvItO7Js2J1upvfh2AYfX07v3sPlfTpmoj/bc6gch9PdrINndFo0VouJHdm2nin02mqB1rNJA8FsMjI8OZLsbhoqK16Osvw+EdsAYAwKxRAU2i8KvUFJ6RTk15HUjq72WZM7Ft8QM29Rk+ISgMEcRMy8RV1ad0dZ473vHewlhwkdMaVHz93THE4Xn248zInjB7cZFeCoKMYcnYg5ZpD/10GDhnX4nCmJESw8azSvfribedNTOGHc4DafU1NnZ8OuIr7cWcCWPcU02l2EhVg4YdwgTp6QxLSMREKCzP5M3aPjG7ryXvSa88a2eLwb5o9vFkvocrkpLKsjt6ia3OIacoq9/1+9JZfahrazqjdmFnFTp1bZ3LCkSDweOFRYxZihHf+wonzlq02+D6F3YlS+7Z1V2cRHhzBrcvN/T4Yc+bmcW1KjQm83UKFX5Dg2d1oKf3lnB3anC4fDTXxMCNecN7ZH83nB20UwvBumVjtK8/HY67s0iM3nm/iGdcSefo3iG0REjrI5s4ggq4kJI/puXqVPeko0RoO3GKlCb/fYnmXDaIDx3/r7YDGbGDcstscGsuXbaokItXR7TER6SjQrN+fidnuOeSu1dNw3tyPbMFiCqN65utdvRwYwR8b36UKv29GIvSSH6FEzKNhRw7gRbRePJozwxjd88VVeuwq9vj+H3r5d3BQWhSksGntxzw2T7i1f7vTejXKsIWw+zsoSLFGJWKK9P+ec5UWdKvQCXDpvFKu35vHk61sJshoprWho9r6xtLLeG8mwo4Ad2TZcbg+xkUGcPiOVmROSmJge36yr3PdcXyxCV9+LduR4JpORIQnhDEkI5+jeYY/HQ0V1o7/79//+vb3FcwUy6nB4chQAB/I7V+htLS6lp2NUjrYvp5wd2TZuWNBytMWQRG9xN18D2bqFCr0ix7HsvEpq6h3ctnAqZ5547Nt/ult6SjSfbcoJ6JskXzZZVwaxHS183CmULHuaxvwsgoeMCsgxRUT6O4/Hw6bdRUxKj8dqMfX2ctoUGmwhbXCkcnq70Y5sGyNSolvMPJyYHs/LKzKprGkkKjyoW9eRX1LTI5nR6SnRfLD2IAWltf4uJem6b9+O7HE09onbkeFIbEFl3y302osOgMeNIzqN+sbSdn0fmExGTpmYzGcdiG+ImDCn1/8swJvT6yg53NvL6HYff3mIxJgQpoxOaHNfZ0URwaljMEf7Ono7PpDNx2I2cuqkJP71yV6q67zbSsrrWfLmV6z/upDi8jr2Hq4AYEhCGBfNHcnMiUmMSo1p833dadNTA9pk1NXjGQwGYiKDiYkMZmJ6PEs/29ftUYcJMSGEBps52Mmc3tZiVIyhgY1E7Ih3VmUTGmxudYhfZJiViFArucUq9HYHpR6LHMc2fF2IwQAnjOv9jqaRQ6Kob3SSbwvcP/aNBdkYLEFY4ocE5Hje+AYztbvXBuR4IiIDQb6tlsLSun7VHZsxNIa9h8ubRBdJYDTYnew5VMakkS1Pg/fFOezM7v5Oo4LS2nbdst5V6UcGsu3Lqej2cx1PjnU7cm8zRybgrO69brm2NBZkA2AzeyNS2juQ8NTJyTTaXWzOLO62tXUHS0IadlsOHo+7t5fSbQpstXy1r4SzTxraZvHU1VCLu7EOc1QippBwjMFhOLtQ6AX4dFPzjmmH080X2/LxeODq88byf3eeznO/PJPrvjOejKGxA+IOh2vOG0vQtz7EDnTUocFgYFhSJAfyWx4m15aYeYug2TwaA+66airWvoPH07OvdYrK6lizLY9zTx5GaHDrc3KGJISRp47ebqFCr8hxbMOuQsYMje32jpr28L1JCmTGXWNBNkGDR2AwBqbDzBQcRuiIydRmruvxH5giIn3Vpkzvm8fpY/pPoXfM0BhqG5x6g9ENdh8sw+nyMDG95UJvemo0wVYT27NKunUddocLW0V9j3T0pg2KwGo2kqVCb0D1xduRfcyRcbjrqnA7GtveuRc05mdhCo8hv9bbldve74MJI+KIDg/ii2153bm8gLMmpOFxNOKs6F8F6o74ZMMhjAbadRemr6hriU4EwBw9CEd51wq9x4oq+ONP53L5maMDPlS7Lzhteiq3XDaZhJgQDHi7b2+5bHLAow6HJ0dxsKCqU+8xIybMIWz0Cf5fmyPjib/gRsLGzqRs5SsUv/0E7sbARU205b3V2RgMBubPHnHM/YYkhiu6oZsoukHkOGWrqCc7t5Jrzu/ZwWutSR3knVqdlVvB3Gldn1rtcbuwFx4gYtrZAVjdN8LGzqQua7PiG0REjtiUWUTqoAgGxXZsmndvyjiSgbfnUNmAfGPam7Zn2TAaDYwb3nLOoNlkZNyIOHZkd+9t7wWltXg8kBzf/R29JpOR4UOiAj5r4HjX2u3I5sjezwI3R3k/yHBWlWKN6xtD4o7mG8SWb6vFZDSQ2M7bzE0mIzMnJnUovqEvsCZ6i5/2khwsMW0PC+tvnC43n2w4zAnjBhMX1fafpbPC+0GaOcpb6LVEJ2Iv7lq0RXxMSIsRBgkBjDDoqwIdL9GSYUmR1Dc6KS6v79TrKWeVjaCkkQxZ/Lh/W8TkM6hMHkXZZy9jL7mLQd+9E2t8199nH0tNnZ2P1x9iztQhxEcf++/GkIRwPt2YQ12D45idv9Jx6ugVOU5t3FUIwEnj+8aLoUBPrbaX5OBx2gkOUD6vjz++IVPxDSIi9Y1OdmaX9qvYBvC+uQgLNrP7UHlvL2XA2ZFlY1Rq9DHftE0aGU9OUQ3lVQ3dto78kloAknqg0AvenN79eRWKAwkg7+3ITQfpGcxB3u29zBzpK/R2b2d6Z7gb63CU5hOUNJJ8Ww2D40IxtTAMqTWzpvS/+AZf8cpeMjAHsm3cVUhFdSNnn9xy3um3OSq93bvmozt6K4u7FG3RExEGx7PhyZEAHOhETq+rtpLGvH2EjjqhyXaDwUD0yQtI+t5vcNVXk/fiL6ndvT4g623NinUHabC7uPi0tt+D+zLtdXdV4LX7X/x3332XjIyMVv978sknm+xfVlbGww8/zBlnnMGECRM45ZRT+PnPf87BgwdbPH5RURG/+c1vOOOMM5g0aRLnnHMOzzzzDHa7vUsXKCIt27CriMFxoX2qk2nkkGiyA/QmyTeIzZo0ssvHOto38Q1rFd8gIse97ftKcLrcTB+T2NtL6RCj0cDotBj2qNAbUPWNTvblVPhzeFvji3Xozq7egiOZ/8k9NBwtPSWa+kaX3rAGUMSEOYRPnO3/te925L4w/MtX6HX1gRiJb2ss3A94vIXeklqSOhhfMn5EPNHhQfyvH8U3GINCMUfGD9iBbB9+eYj4qGCmZ7TvZ62zogRDUCjGYO8HXZboQeBy4qru/M+8noowOF4NHRyJwQAHCzqe01uXtRnwEDpqRouPhwybSMoNv8can0LRvx+nbOUreNyuLq64OYfTxftf7GfK6ASGJ0e1uf+QRF+htzbgaznetftejMzMTABOPfVUYmOb34o1duw3n+Tk5ORw9dVXU1BQwNChQznttNPYt28fy5cvZ82aNfz73/8mJeWblvHCwkIWLlxIYWEh48aNY/z48WzZsoUlS5bw5Zdf8sILL2CxqJVbJFAaGp1s21fCeTOHYTD0nZD8kSnRrFh3kMKy2i5n6jUWZGMMCsUSG/iO5bCxpxyJb9hH8JDRAT9+d1q1OYeXVmRiK68nPiaEa84bqxdoItJpm3cXExJkYtzw3r+VuqMyhsby5n/36JbBANp1oBSX28PEVgax+YwcEkVosJntWTbmTO3YbaTVO1dTvvJVnFWlmCPjiJm3qMXCX76tlsgwK+EhPfNnO+rIrIGs3Io+9SF6f+eqsmGOHkTqzc/0qdes5gjvv3nOyu6NIOkM3yA26+ARFJR+waRRx/5+/DaT0cDMSUl8timHhkYnwUH9I77BkpCGfQAWeovL6ti6p5iFZ2a0uzPbWVGEJSrR/z1jjvHedeOoKOxS9ElPRBgcr4KDzCTFhXWqo7d270ZMkfFYBw1rdR9zZDzJVz+I7ePnqVj7Do0F+0m86GeYQgP38+rzLXmUVTVy2xXtu6M2OT4MgwHyivUBaaC1+1/tXbt2AfDoo48yaNCxb8/71a9+RUFBATfccAN33HEHRqMRt9vNI488wssvv8zDDz/Ms88+69///vvvp7CwkNtuu42bb74ZgLq6On784x+zdu1aXn75ZRYvXtyZ6xORFmzdW4LD6ebEPhLb4JOe4v3kLzunssuFXntBNtakkRgMgU+oCR19wpH4hnX9qtC7anMOTy/dRqPD+wluSXk9Ty/dBqAXbSLSYR6Ph027i5gyOhGLuf+lgWUMjcHt8RbmJqUn9PZyBoQdWTbMJgNjW8nn9TGZjIwfEceOrI4Vyap3rsa2/Dk8Tu8ALGeVDdvy5wCaFXvzS2p7JJ/XJyUxHKvFRFZuBfP0MzUgXHXV1B/YQfTMC/tUkRfAYLZgCotuMUO4tzXmZ2GOSqDSFUSj3UVyXMe/D2ZNTmbF2oNs2l3ErMlDumGVgWdNTKPy4HY8LicGU/8oTrfHxxsOAXDWSW0PYfNxVJZgifmmZmOJ9n7tLC+CtPGBXaAEzLDkSA7md6yj1+20U39gGxGT5rX576TBbCHh/BsJSkrH9tHfyHvhFwy69E6Cko49NK09PB4P73yexbCkSKaObt9rKovZRGJMqO6E6QbtflW+e/du4uPj2yzybtq0iQ0bNjB9+nTuvPNOjEbvKYxGI3fccQdDhgwhLy8Pl8tbaNi/fz+rVq0iLS2NG2+80X+c0NBQHn74YUwmE6+88kpnrk1EWrFxVyFhwWbGj+hbHVhpgyMxm4xdHmbicTpoLDpEUIBjG3z6a3zDSysy/UVen0aHi5dWZPbSikSkPztcVE1JeT3Tx/SvfF6f0WkxAIpvCKDtWTZGp8W0a4DTpPR48m21lFa2fxJ4+cpX/UVeH4+zkfKVrzbbN99W02OxDeAtXo8cEkVWTkWPnXOgq92zHjxuwsac0ttLaZE5Mr5vFnp9g9h8OdWd+D7wxTd8sS0/0MvrNtaEVHA5cZQX9vZSAsblcvPfDYeZlpFIYkz7BnR5PB6cFcWYo7/52WyOigeDEUdFUXctVQJgWFIUBaW1NDQ62/2choM78DgaW41taEnk1DNJvvohPG43+S/9murtKzuz3Ca27CnmcGE1F582skMfzA1JDFehtxu0q9Cbk5NDVVUV48e3/enPRx99BMB1113X7LHg4GA+++wz3nvvPUwmb5D3F198gcfjYd68ef6isE9ycjLjxo0jLy+PrKys9ixVRNrgdnvYuKuIaWMGYe7AYIaeYDEbGZYU0eVCr734ELidBAV4ENvRwsaegrPKRmP+vm47R6C1NCkXwNbKdhGRY9mc6X3DOGNs/8rn9YkMszIkIUyF3gCprXeQnVvhz99tiy/eYXsHunqdreShfnt7g91JaWVDj3b0AoxMiWJ/XiUuDWQLiNrdazHHDMY6eHhvL6VF5qi+V+h11VXjrCjyD2IDOvV94Itv2LirqENFp95kTfB2vA6k+IbNu4sprWzgnJOHtfs57vpqPI4GLFHfdFUaTBbMkXE4K/rPgL3j0fDkSDweOFTY/q7e2n2bMFiCCR7asU7t4CGjSLnh9wQNGU3JsqexrfgrHpejo0v2e2dVFrGRwcye0rE4ppSEcPJLavpV81R/0K4qjy+fNy4ujgcffJCzzjqLiRMn+gemNTZ+88m6L+Jh0qRJlJWV8dJLL3Hvvffy2GOPsW7dumbH9hVwR40a1eK5R4zwtpHv3bu3A5clIq3Zm1NORU1jn4tt8BmZEk12XmWX/rH3DWLrro5eOBLfYPLGN/R1pZX1PPKPDa0+Hh8T0oOrEZGBYlNmMcOTI4mL6r//hmQMjWXPoXK9wQiArw+U4vbQ5iA2n+HJUYSHWDoU39BatuS3txfYvJ2MXY2B6qhRqdE02F3kFVf36HkHIlddFfUHdhA+dmafi23wMR3p6O1L/340FnrzeYOSRlJgq8VsMpLQzk7Qb5s9eQh2h4tNu/tHF6glbggYjNiLB06h96MvDxETEcQJ49p/54zjSDH36I5e768T1dHbxw1LigTaP5DN4/FQt28TISMmYzRbO3w+U1gUSd/7DVEnX0jVlo/If/m+Vj9QPZbs3Aq27bOxYPaIDkd5JSeE02B3UVrZ0OHzSuva9afgK96+/fbbvP/++6SnpzN58mSKiopYsmQJ1157LQ0N3j+YQ4e8GTL79+/nvPPO4+GHH+bNN9/kxRdf5LrrruOOO+7A6fzmU8HiYu8/RImJLXeDJCR4P4my2Zq/CKyqqiI3N7fJf4WFA+dWDZHusOHrQoxGAzP66IT09JRoausdFJXVdfoYjQXZGEMjMUd1X+aiKTiM0OF9O77B5fbw/hf7uel3n7E5s4jZU5KxfuuHr9Vs5JrzxrZyBBGRltU1ONh1oLTfxjb4jBkaQ0VNY5d+5ojXjiwbFrORMUOPnc/rYzQamDAyrkMdvTHzFoHB1HSjyerdfpR8m++W9Z7u6I0G6PKdSQK1ezZ4YxvG9s3YBvBGN3gcjbjr+85tx/5BbEkjybfVMjguFJOxc4XycSPiiI4I4ouv+kd8g9EShCVm8IDp6LVV1LMps5AzT0zr0F2Yzsojhd5vvQ+yRA/yZvRKnzUoNpSQIDMH2pnTay88gKu6jLAOxDZ8m8FoIu6Ma0i85HbsxYfIe+FO6g9/3aFjvPt5NiFBJs6ZOazD5085Ei2j+IbAaldKua+j97zzzuORRx4hNNT7qWBubi4//vGP2bp1K3/605/45S9/SU1NDQaDgZ/85CdMnjyZX/ziF6SkpLBlyxbuv/9+li1bRnJyMj//+c8BqK/33jIcHBzc4rl92+vqmr8A/+c//8nTTz/dwUsWOb5t3FXE+OFxhId2/FO/npB+1JukwZ0YHgFHsskGdywfqDPCxp1CXdZmGvP39bmhbAfyK3lm6Tb2HC5nyugEbr50MknxYazanMNLKzL9MQ5nnpCmQWwi0mFf7S3B5fYwY2z/LvRmHClK7jlU3umfOeK1PcvGmKGxWC2mtnc+YmJ6PF/uLKS4rI7E2La7DsMyTsJmfg48JjxOOwCR089uYRBb529Z74qUxAiCrCaycis5vfPvuwWozTwS2zCob8Y2wJHcU7xDAQM5ub4rGvOzsMQmYQoOI7+kpktd7SajgVMmJvHfjTk0NDoJDur7A84sCak4SnJ6exkB8cmGw7g9cPZJQzv0PF88gyW6aVOPOXoQrtoK3I5GjJaggK1TAsdgMDAsKbLdHb11+zYBBkLTp3f53OFjT8Ean0LRW7+n4NXfEnfmtRiCw6lY9RrOqlLMkXHEzFvU7OdtSXk9q7/KY8HsEYSHWDp83iGJ3xR6J4/SYNxAaddHQ0uWLGH58uU8/vjj/iIvQEpKCo899hgGg4E33ngDh8OB3W7H4/EwePBg/vKXvzBmzBjCw8OZM2cO//d//4fJZOKf//wnNTXeF2C+rN62CjJut7vZtmuvvZZPP/20yX+vvtp8GIOIeBWV1XGwoIoTx/fdN+ZDkyIwmwydHmbidjRiL8np1tgGn7BRvviGtd1+rvZqsDv5x/tf87MnP6egtJbbvzeNB344k6Qjb3ZPm57KC/eczX9+v4AhCeFk51X28opFpD/alFlEWLCZMUNjenspXTJ0sLcwt+ewcnq7orrOzoH8ynbn8/pMSve+qWtvV2/Nzv/hcTQy+Mp7Gf7LNzCGhOOubf5zrMBWS3REEKHBHX/T2RUmo0ED2QLAVVdF/cEdhI89pc/GNgCYI74p9PYVvkFsbreHAlstyV3sap91JL5hY2b/6AS1JqThKC/EfeSDoP7K5fbwyYZDTBmd0OEPIZ0VxRhDwjEGNf3wzBIz6Mjj/ePP8ng1LDmSg/ntizGs3beJoJTRmMKiAnJua0IaQ65/jNCR0yj9+AVsy54+8u+bB2eVDdvy56jeubrJc977n/cugvmzR3TqnLGRwQRZTeQVq6M3kNpV6A0KCiI9PR2rtXkH4NixYxk8eDB1dXUcPHjQ34F72WWXYTY3/dRvzJgxTJ48mYaGBr766isAQkK8uW6+6Idv820/usDsExkZSUpKSpP/Bg/um7mjIn3Bhq+90SYnjuu73ycWs4m0wZFk53auAGkvOgAeN0HJ3TeIzccYHEboiCnUZK7rE/ENW/cUc+sTK/n3yizmTU/l2bvO4LTpqS2+STIaDXxn1nD2HC5nrwocItIBHo+HzbuLmJqRiKmPDfXsKJPJyKjUaPYcKuvtpfRrO7NL8XQgn9cnbVAEkWFWdmS3XSjzeDxUbVqBNXEYwaljMZjMhGWcTO2+jbgdjU32zbfV9ng3r096SjT78ytxuZo3qUj71O5ZfyS2YWZvL+WYju7o7QucNeW4qkuxJo2ktLIBu9Pd5e8DX3zDmm39I77BmpgGHjcOW15vL6VLtu4ppqS8nnNO7lg3L3gzes1RzSP6fJm9DsU39GnDk6OobXC2OkTbx1lVir0wu0uxDS0xBocx6LI7vR8UeJr+HPM4Gylf+U1jZW29g4++PMTsyUNI7GQWuNFoYEh8uKIbAiwgr87j470/5Orr64mN9d4CN2TIkBb3TU5OBqC83FtY8GXztpTBC1BSUtJkPxHpvA27CklJDCc5oWeHk3RUeko02XkVnSqeNuZ3/yC2o4WNnYmrykZj/r4eOV9LKmsa+cNrm/nNX9dhMhp45KZTue2KqUSGHTue4/QZqYQEmVi+5kAPrVREBoID+VWUVTX2+9gGn4y0GPbnVWJ3uHp7Kf3WjmwbVouJ0WnRHXqe0Whg4sh4tme1PdCqMXc39uKDRM44z/8BZtjYmXjsDdTv/6rJvgW2rt2y3hXpqdE02l3kqjup02oz1/X52AbwDjLCaO4zhV5fPm9wcjr5Nl98Sde+D3zxDRszi2hodLb9hF5mjffGkfX3nN6P1x8iKtzKSeOTOvxcZ2Vxs9gG8Gb0gjp6+7rh7RzIVpe1GYDQUScEfA0GgxF3Y8uF5qOHtX305SHqG51cdFrX3ncPSVShN9DaLPTW1NRw77338pOf/KTJELWj5ebmwv+zd97hTZ7n/v+82sOWJe+9sAGzDGHPQDYheza7TVfapOOcnI5f29OV9DSn53SlSZvmpGmTZpSQHRKSQAIh7A0GbLwwNt6yLVuSZc3394eQwXhJtpZBn+vqdU6kd9zC0vs8z/3c9/cLpKenM3myV6fSZ7J2Pr6EblKS1x23uLgYgOrq6iGPr6nxDli+68aIEWNs9PY5OVpjjOpqXh9F2QmYe520jbKTORT25hqkcYnI4v0zgxkvkZRvEEWRTXtO8Y3//oRthxq588rJPPnoKr9bZzUqOZfNy+XzQ410W+yjnxAjRowYeGUbAC6JUlPPQJmSl4jLLVIbk7IZM2XVRqblJyKX+a/P62NmUTJGk42WjpEN8br3foBEFUfcjOX9r6nzZiBRx2M5Zwy22V109tj7JYvCTVHMkG1cTBTZBvAmQ2S6pOhK9AoSFGn5QTUkXDZ74sg3yBMzQCKb0Inezp4+dh9r4Yr5uchlgdXliaKIq7t9yIpeiUaHIFfhjCV6o5q8M4nek00jz0l6q/Yh06chT84OSRwyXdKQrwsyOX2NlThdHt79vIZZRcn9495YyUzR0tbZi9MV+Ia7+ehW6v/0dWp/dRv1f/r6IGmJi5VRnxxarZaNGzfy0UcfsXfv3kHvf/bZZ3R1dTF58mRSU1NZscIrzvzhhx8OOrarq4ujR4+iUCiYOXMmAMuXeydrmzdvHqTD29TURHl5OVlZWRQVhb4NO0aMC5kDJ9pwuUUWTI/+RO94XKu92mThqeaF8+Ubwtem2dhu4SfP7OCPaw+RnRrPH/99JfdeUxKQCQ7AmqUFOF0ePt59KkSRxogR40JjX3krRdkJGOKHNtKdaEw5ozNccSomYzMWui126pp7Atbn9eGTexhJp9fV04H1xG7iZ182wETIK9+wkN6qff3yDc1nElzj1SYdK5kpcaiV0phO7xg5K9uwJNKh+IVMl4yrO0oSvU3VyJOzkCjUNLVbUMgkJCeox33daQVe+YZth6NfDkGQylAkZ05oQ7ZP9tbj8YgBm7ABuK0mRJcD2RAVvYIgIDek9pu1xYhO1EoZGUlaTo5Q0etx9GE7eQRN8byQbYgZVt2DIDvPtE+QgiDQ9I//R+X//Ygkay03Xzr+dXd2Shwe8ez47S/mo1sxvv/MqDrCFyOjJnoFQeCOO+4A4LHHHqO19ewOUH19Pb/85S8B+MY3vgHAddddR2pqKrt37+bZZ5/tP9Zut/Ozn/0Mi8XCTTfdRHy815k0JyeH5cuXU1tbyx//+Mf+43t7e/nJT36C2+3mS1/6UhA+aowYFzd7jrUQr1FMCOOc/AwdUolATYCJXo+9F2dHU1gTvQDakiVn5BuG7kwIJk6Xh39tPMG3/nczNadNPHxbKU88vIzcdN2YrpeTFs/s4hQ+2FEX0xOMESPGqFh6HZw41cncC0S2AbxGIKkGdUynd4wcrfG2cQaqz+sjOzUOfbySshESvT0HN4LHg+6Sqwe9py1Z4pVvqDkEELSW9bEilQgUZumpilX0jglr+Y4zsg35kQ7FL2QJybijoKJXFEUcLV4jNvAmTNKTtUgk408CSSUCS2dlsq+8DdsEkG+Qp+RO2Ipej0fko12nmFWUPCapPV8SVz5ERS94dXpjFb3Rj8+QbThsJ48gup1oJwdftsFH/IwVJK95CJkuGRCQ6ZJJueER8r7zNxIvuw93ZyMP6zaRvut3WCt2javgKSvV+10PVL6hc9OLiK6BXann6whfrMhGPwS++c1vsm/fPvbv388111zD3LlzAdi9ezcOh4MHH3yQa6+9FoC4uDh+97vf8bWvfY3f/va3vPXWWxQUFHD06FFaW1uZPHky3/ve9wZc/2c/+xl33XUXzzzzDJ9++ikFBQUcOHCA9vZ2VqxYwV133RXkjx0jxsWF2+1hX3kr80rSwmKcYz66la7NL+Pq6UCmS8Kw6h7iZ6zw+3yFXEpuenzAhmz2llpADIsR27loi+fRLpVhPb4dVVbwZGa27G/gxQ3lGLtsJBvUXDY3hx1lTTS0WlhWmslXb5pJom78FXVrlhXwq7/vYfexFpbMygxC5DFixIgE5z8z7l9dwsq5OUG9x8ET7XhELhh9Xh9T8hKpmGCJ3vGOtcGirMaISiGlKEc/pvMFwavTW1bTjiiKg6qTRLcT88GNaIouQW4Y3JWkzj8j31CxA+3UhTS1n2lZj5B0A3jlGzbsOInb7ZnwhoXhxG3txlZ3FP3im6JetsGHLD4Jl7kT0eNGkAQuXRIs3D1G3Nbu/mKHJqOFrCB6ciwtzeT97SfZd7yV5XOG9uKJFhQpuViPbcNj7/UaSk0Qtuxv4Ll3j9JtcdBrd7Jlf0PAY7ir25voHaqiF7w6vbbaw0M+a2NEDwUZOnYdbabP4UKlGJyy663ah0SpQZVTEtI44mesGHJeUZe0hJ93evh/Sx1ktHxO6xv/gzwpC/3im4ibsRxBKg/oPr5n1Wja9t4NrVqsJ/ZgrdyN2zp0J9a5OsIXK37NPFQqFf/4xz949NFHyc7OZvfu3Rw8eJDZs2fzpz/9iR/84AcDjp8/fz7vvvsuN998M2azmc8//xylUsnXv/51Xn31VXS6gZVnOTk5rFu3jltuuYXOzk62bNlCQkICjz76KE899RQymV/56BgxYgxDxakuzL3OsMg2BKuFoihbT/XpwAzZwm3E5qNfvmGcu5nnsmV/A0+tO0x7lw0RaO+ysXZTJSazg59+eSE/uH9+UJK8APOnpZNqUMdM2WLEmMAM9cx4at1htuwPbvvqvopW4jUKinOivzskEKbkGWjvstHRHbg2fCSIpnbFI9VGphUkIRtHQnNmUTKdPfYhq3ms5btwW03o5q0e8lxBIvXKN1R65RuajBYSdUrUysitH4py9DhcHupbzRGLYSIy0WQbAGQJKSB6cFtMEY3DZ8SmzCjC7RFpNvaSEcSq9mkFSRjilWw7Ev3yDYqUXAAcxtMRjsR/fGN4t8UBgNnqHNMY7jxT0StLSBnyfZk+DdHlwG01jSveGKElP1OHKEJ9y+AxRBQ99FbvRz1pDoI0MuPcW1tq0MVruOSG28l56ElSb/o3BKmc9vVPU//nR+je+36/nJI/aFRyDPHK/o3acxE9bmx1ZRg/+hv1Tz1E4/Pfx7TjTaQaHRLl0Bu6w+kLX0z4/c1QKBR87Wtf42tf+5pfx+fk5PDEE0/4HUhGRga//vWv/T4+RowY/rP7WAsyqcAlU0JvnNO1+eVhWygCqTSalK1n45562k02Ug3+7cbbm2uQJaQi1YxNxmA8aEuW0Fu1D3tjFarsKeO+3osbyrEP4QCvVEiYH2RDPalE4NolBfzj/eOcaukhb4wyEDFixIgcQz0z7E43L24oD1pVr8cjsr+ilblTU5EGoR04mvDp9FbWd7F45vg1LUNNsMbaccdh7qOh1cxl88b3HfPJPpRVG8lOjR/wXve+DcgTM1EXlg57vrZkCeZDm7DVHKKp3RHUBNdYKMpOAKDmtImCzISIxjKRsFbsRJ6YMWFkG+BsQsHV0x7R5IK9uQYkUhRpebSbbLjcHjKDWNUulQgsmZXJxt2nsNldEd1IGQ1Fivd55GirD2qnXSgJ1hjuMrUh1SYgUQxdDCI3pJ05rhVZ3IW1YXsh4Rs3Tjb1MDl34N/J3lSN22ryGoJHgJNN3Rw40cb915b0G7DGTV+GdtpSbDUHMO14i46Pn6dr2+skzF+Dbu41SNWjj8lZqXH9m70epx1b7WGslXu8Gvw2M4JMgbqgFO2KO9EUz0Oq0fVvep87HxJkSgyr7gnNh59ARO8TOkaMGEFjz7EWZkxKRqMKrI1iLAznPBxoC8WkcxZJgSR6w13N60NbPI92iZTmV3+J6LCPu43W2DV0VVmHqW88YQ7LlQvzeOWjCt7fdpJv3jb8YjpGjIlAtLS0h5PhnhntXbZxtWie+2/ZqCyk27L0gtLn9TEpKwGZVMKJU10snhn9EjbDjanhblc8Wj0+fV4fmclaEnUqjlQbWb2koP91e3MN9sYTJF31IIIwfMVwv3xD+XaajdOYPy2y39HM5DjUShlVDSauWBC4odLFyESUbQCQ6byVk5FuFbY3V6NIyUUiU9BsNAHBNyRcNkHkG2T6VAS5ckLp9A43hg/3+nC4utuQDaPPC96KXgBnVyuq7KkBXTtG+Eg1aFArpdQ1D5Yx7K3cC4IE9aQ5EYgM3v6sBpVCyjWL8we8LggCmqK5aIrm0tdQTtf2N+n67FVMO99Cd8nVJCy4Hlm8Ydg5el6ilJ7yvbS8vhNb7SFEp93bNVs0F+2UhagLZw/awPDN7S+2Ob8/xBK9MWJc4DS2W2hst7BmacHoBwcBiVKDx9476PVAqxwKMhOQSARqTnf7teh295pxmVrRzbkyoPsEC2v1fhBFRIc3EetrowXGNNgkG9S0DzG5SzaEptJMp1WwYk42n+5v4P4104hTh35TIEaMUHD+7v54f4sTheGeGQD/9ofPuP2yySyamRFQJe75/5ZHTFoERIo91UB2MMKOGuQyKZOyEqg4NbTeW7Qh0yUNubEa7orCIzVG1EoZk7LGV7UqCAKzipI5VDlQp7d73wYEuYr4mStHPv+MfIPl2OdYLXljMjEKJhKJwKTshIC9Bi5mJqJsA5xT0dvdHrEYRFHE3lzT/2/XdMa5PtiGhCVn5Bs+P9wY1YleQZCgSM7BOYESvYkJKjq6BxdzBDrvd5raRix68Uk6+EzbYkQnEolAXrqOk009g97rrd6HKqfEryrZYNPRbWPrwdOsXlJAvEYx7HGqnBIyvvBj7K11mHa8Sffu9+jZ+wHKnCn0NVSC2ytR4uox0v7e05h2vMmVxkYEhYe+0wbiZ61CM3kB6rzpo8pTDKcjfLETcweIEeMCZ8+xFoCw6PNaq/Z5k7znVd2MpYVCKZeSmxZPtZ+u1faWM9pkYTZi89G1+WU4T593PK6f968uQSkfaOqhlEu5f3XoRPfXLCvA7nDz6d6JMzGOEeN8Rmppv5C5f3UJsvOSuAqZhCsX5NLb5+KJF/fy8G8+YePuUzhd/mmJn/9vWe7IIk9qxL3z1aDGHi1MyTNQ1WDC7Q6O1nooMay6B843fpJIw96uWFbdzvTCpKAYjs0sSsZksffr2rp7e7Ae20b8rJVIVKNXJmqnLUF02imRNwa1ZX2sFGXrqW3qxjUBvk/RgLV8x4STbQCvT4Og1ES0otdlasXTZz1rxNZuRSGXBs3LwYdUIrB0Vib7y1ux2V1BvXawkafk4mgPrkZ9KCkewswy0Hm/6HHj6jYOq88LIJErkcYl4jS1jiXMGGGkIDOBuuaeAX41TlMbjrZ6NJPnRSSm9z6vxeMRuWF5oV/HK9PySbv538l56EniZq2kr+5of5K3H48LZ0cTtuIr+F33avpufILka76KprA0YhrEFwKxRG+MGBc4e463kJ+hIy0xtK6zzs4m2t/5I4q0ApLXfAOZ7mwbZ8LSW8a00+arhvHHkM1nxKZI92/gCTbBbqNdOTeHh26Z2f/fKQY1j9xeGjStzaEoytZTkp/I+u0n8Xj8N8GLESOaiJaW9nCzcm4OGSna/ordFIOab90xm2/fOYe//OByvn/fPJRyGU++doiv/ddG3tlaQ98oC/Vz/81EETQSB9MUpy/Yf8speQYcTjd1zYMraKKN+BkrkMUngkQGCAgyOYJUhmbSJWGLoaPbRmO7ddyyDT7O1ekFMB/6BNHtRDf3Gr/OV+fNwK3QMltxKuIVveAdU50uz5BmOjEG4rZ2Yzt1DO3UxRNKtsGHt8I+chW9Z82IvcUOTUYLmclaJCHQUl9amonD5WHv8ZagXzuYKFJycFtNuHuj/3lutTkpqzZSnKMnxaBGYGzzfrfFBB4Xcv3I0jVyQxquWKI36snP1GG1OWk3ne3W6q3aB3glA8NNb5+TD3fWsWRWJulJgW2myhMzSLn2oeEP8HhIXHkPp9wpnG4f3BkcI3BiKfIYMS5gzL0Ojp/s5NZVoa1y9ThstKz7b5BISbvt+8j1qehKL8PTZ6X+qYdwtNSO6bqTsvR8sreBju4+kvUjty7Zm2uQJ2Yi9aPqJxSEoo22MEsPwPfuncuKOeFpk16ztID/fXk/ByvbmDv1wtPhDCZb9jfw4oZyjF02kg1q7l9dEtJEfLQQ7Z9bGp+I2zw4EXmhO/B2mftobLNw+xWTufeagRVAUonA8tlZLCvN5MCJNtZ9UsVz7xxl7cZKrl9eyHXLBrfgiaKIoFAhOrwLjF5RwVfiNmPyaC7Yf8speYkAnKjvYlK2PrLBjIK9tQ5XdztJV32ZhPnXYm+to/G5R+ne9Q6JYarq9SVkZwYp0ZuWqCHFoOZItZE1S/Lo2f8hqvyZ/cZKoyFIpHQkTGOG/SBpusgvcXwVetWnTRSOU9riQqdftmHa0kiHMiZkumRc3UN7VIQDe3MNglTe/1tpareSmx4/ylljo6QgiUSdkm2Hm8I2Nx0L/YZs7Q2o86ZHOJqRWb+9Fmufi4dvKx3X2OOr0h2pohe8Or22urIx3ydGeCjI8I4bdc09/X41vVX7kCdlIk8Mv5fAxj31WPtc3Lxy7HkFmS552PVyaqIGqUSgyWgZT5gxzhCr6I0R4wJmf3krHo/IwhDKNoiiSPt7T+PsaCL15n9Drj9rACBRadHNv5beE7txtAUuB1B0ZrJT44d8QySN2MDbRivIlANflMrH1UbrqyrLy9CNJ7SAWDIrE0O8kvXbTobtnhORLfsbeGrdYa/RFV7Dq6fWHWbL/onTJjgWJsLnVqTmDnrtYnDg3VnWjEeE5aXD6yYKgsDcqWk88fAyfvPIcqbmG3jlowq+/PjH/O3do3R0e5O6oijSuekf3iTvGSmed3rn0ug2kCS1oA5j1Wg4STWo0ccrOTEBdHotZVtAIiNu+nLA2x6pnb6M7j3rcZnDE/+RaiNatbzfHXy8CILAzEnJHK0xYqnch6vHSMLc1QFdo1pajFJw4Wk4EpSYxkN6khatSkZ1gynSoUQ9/bINqRPTuE6mS8Y1xAZjuLA3V6NIy0eQynC7PbR2WkMmXyKVCCyZGf3yDYoU71wg2g3ZbHYX73xWy/xpaePeYHR1e3V3ZaNV9OrTcJs78bgcIx4XI7LkZXg3a+rO6PR67L3YTh1DUzw/7LG43R7e2VrD9MIkJucaxnydodbLvjm6TCohPUnL6bZYojcYxBK9MWJcwOw53oo+XklxztgfyKPRvesdrBU7SVx1D5qC0kHvJyy4DkGhomv76wFfuyBLh0SA6lHMTFyWLtzmDhQRTPTGz1hB8pqHzkpWCBKkKi3ayQvGfM1TzT3IpAJZYWxBlcskXLM4n/0VrTSfMfOIMZgXN5Rjd7oHvGZ3unlxQ3mEIgoP0f65LUc/x1ZzEHXh7AHyMYlXPXjBGzVsP9xEdmqc31VcJQWJ/PTLi/jTf6xiwbQM3t1aw1d+tYmnXjtA/Zt/onvPenTz15B8/SNI4pMpc+ay2zMDRXIO5oMf073vwxB/ovAjCAJTcg2cONUZ6VBGRHS7sBzdiqZ4LlLN2b934oovIHrcmMYw3o6FshojMwqTAjL4G41ZRcmYe52073gPmS45YB3CI5YkbIIaS/mOoMU0VryGbHq/vQYuVvplG0qWTEjZBvAmej29PXic9tEPDjKix429pbbfo6LdZMPlFkMqX7JsdlbUyzdI4xORqLRRn+jdsOMk5l4Hd14xedzX8hmsyRJG7rKQGVIBMaIGgjFGR6OSk56k4WSTdx3cW3sIPC40EZBt2H6kifYuG7eMo5oXzl8vC8h0ySSveah/jp6dGkdjeyzRGwwi39cUI0aMkOB0eThQ0cqSWZkh0egC6K09TOfml9GWLCZh0Y1DHiNVx6Obew3dO9/BseJOFEn+u/SqFDKy/TBkszd7jdhUARixhaL9/FzXT1tdGc0v/4KOT14gZfXXx3S9upYectLikQXB5CYQrl6Ux2ubKvlgx0m+fMOMsN57omDssgX0+oVCexR/bntrHe3v/xlV7jTS7/h/CFIZtvrjNP/zP5EqgmtIE210mfs4WmPkjiumBJwoyc/Q8R/3zuXe1VN569MTpB57Gbe8jnLdUkqm30J9i5m/dd5Er+jgiFjEojnXMfXUa3R89H+Ijl70S24J0aeKDFPyDOw+1kKP1YFOO7yjdCTprTmI29pN/KxVA16XJ2YQP/tyeg5uJGHh9cgNoevmaevqpaWjl+uXBVcXf2ZRMmkSEzSXo1t1D8L5hnOj0Nhhoz21BE3VfjxOOxK5cvSTQkhRtp53P6/F6fIgl8Xqa4aiX7ahZEmkQxkzvsSaq6cDRVJ4W6qdnc2Ijr4BRmxASA0JS/ITo16+QRAEFCm5ONsbolZyqs/h4q0tNcyenNIvHTQenKZ2pHGJSGQjj10+DV+XqS2gddlEwXx0K12bX8bV04FMl4Rh1T0TdrM/P0PX3+HZW7UPiToOVfaUsN1/y/4GXvjgOEZTH1KJgNU2/irwc9fL55OZEseBE224PWJQN5EvRmIzjhgxLlCO13Zg7XOxIESyDU5TG21v/w55chYp1z08YnJBv/AGBJkc0/Y3A77PpKyEUaUb7E3VIEhQpBX4dc1wtJ+r82eSsOgGzAc+xlq5d0zXONXcE1bZBh9JCWqWzMpk4576Uc2aLlaSDUNrRg/3+oXAxt2nhn0v0p/bbTPT+vp/I1HHkXrzo/0uvarsKUi1eqwVuyIaX6jxyTYsKx17giFVJ+Nmz4fMkddRm3EVLzRP4Tu/+4zfv3oAk9k7sTdbnTz15nEq8r9A3PTldG5+mc7NL/tlmDlRmHpmsV1ZH73yDZayLUg0OjST5gx6z7DsdgSJlK6ta0MaQ7D1eX2kGjRcra/BjZT42VcEdK7F5qTH6sCRdQmis4/emgNBjW0sFGXrcbk91LdEvyFUpPDKNmROWNkGoL+DJBKGbEMZsQEhreiVSASWzIp++QZ5Sg69zXU8te5QVEpOfbzrFCaLnS9cGZzEnau7FZl+ZH1eOCvt4Oy68AzZzEe3Ynz/mTM6sCKuHiPG95/BfHRrpEMbE/kZCTS1W+izO+itPoCmaG7AG6BjxbdeNpr6AHB7RJ5+/UhIfztZKXE4XR7au2KGbOMlluiNEeMCZc/xFuQyCbOLRx/wA8XjtNP6+m/A4yH9tu8jUYyc5JFqE9BdchWWo1txdgXW5lWUrafLbKezp2/YY+zNNciTs5H4WbX3wgfHw9J+nnjpXSjSCmh//8+4LKaAzrX0Oujo7iM/PfyJXoDrlhVgtTn57ODpiNw/2rl/dQnnb20o5VLuX10y5PETnXe31vDka4fIS49HcV5VWqQ/t+hx0/b2H3CZO0m79XvI4vT97wkSKZopC+itORCRltpwse1QEzlp/ss2nI/HbqNl7X/RW32A5NVf54oHv87z/3kVGpUMz3k5XLvTzYsfVZJy47eJn3MVph1v0vHRc4iiJwifJPIU5eiRCEStTq+714y1ch/xM1b0b2iciyw+Ed38a7Ec/Rx7a13I4jhSbSReoyAvyGOUp89KqVDJIVchqAL7PjedafeML5yJRKPDWr4zqLGNhaJzDNliDOasbMPiCSvbAGcTve6e8Ov02ptrEOQq5GcqiZuMVtRKKYb40FazLyuNfvkGRUouEpcNlWtgK3g0SE45nG7e2FzNjElJTC8MjsGpy9TeX607EtI4PYJMgct04SV6uza/jOgaON8TXXa6Nr8coYjGR0GmDo8IDWUH8djMYZVtiIRcW3aqd4MqJt8wfmKJ3hiDMB/dSv2fvk7tr26j/k9fn7A7YBczoiiy+1gLpcUpqJTBVWgRRRHjhr/iaD1Jyo3f8dv1M2HRTQgSKaYdbwV0P58xwXCLJFEUcbT4Z8TmdLn5aNep/p3J8wl2+7kgk5N643cQHX20r386oKq3SBixnUtJfiKFmQms33bygqrWCxYzJiUjAlq19/clEQQevm1WVLQCBhNRFFm78QT/985RFs/M4Pf/dinfumM2SQneTRWNSsYjt5dG9HN3ffYvbLWHSL76K6iyBmvcxU1djOi0Y6s5GIHoQk9XTx/Hao0snZU1pkSJ22ah+ZVf0Fd/nJQbv43ukqsAiFPLsfUNXall7LIhCBKSV3+NhEU30LP/Q+8zzuMe8viJhFopIy9DF7U6vZbj28DjIu482YZz0S++GYlSTdeWV0ISgyiKlNUYmVmUFHRpKHPZZ8hEJ59aJ3OycWR9/vNpOqMrn5mmQztlEb1V+yK+wZOepEGrllMVM2QbkgtBtgFAFu9N1Lm6B7vJhxp7czXK9IL+Kr+mdgsZSXEhT5yfK98QrciTvXOTDNngjbtIS05t2ltPZ08fX7giONW8oseNq8eILGH0Ah9BkCDTp+K8ABO9rmE2W4Z7PdrJz/SuA3sq9oJEhqZwdtjuHQmZuswUr+RMY8yQbdzEEr0xBnChtTtcrNS3mmnt7A2JbEPPvg1Yyj7DsOJOtAHsKsriDcTPvhzzkS0Bif8XZiUgCFAzzCLJ3WPEbe0eMdFrs7t4+7Nqvvpfm3hq3SFk0qEnv6FoP1ek5JB42X3Yag5gPvCR3+edOpPozY9QolcQBNYsK6CuuYdjtRNzchRKDlV6DS9+/c1lPHr3JXhEkYzk8JnmhQNRFPnH+uO89GEFl83L4Qf3zUMuk7Jybg7/+OnV5KbHU5KfGNEkr7ViN6YdbxI/+wp0c64c8hhV3nQk6vgLVr5hh0+2YXbgsg0ui4nml36KvfUkabd+b5Bm2mgSJYIgkHjZ/RguvQvLkS20vfU7RJcz8A8RZUzJS6SyvgvP+eXMUYDlyGYUaQUo0/KHPUaqjiNh8U30Vu+nr6Ei6DG0dvbS3mVj1qTgyjaIooeefRuQphVx2p3EkerAkmbN7RYEATKStMSVeDd4Ii3fIAgCRdmjS1BdrFguANkG8G7sS7X6M+un8CG6XTha6/qN2ACajVYyQqjP6+Nc+Ybevuh77p9q7uHXbzcCkCE1DXo/kpJTTpeH1z+tYmqegVnFwXmOuno6QPQg06f6dbxcn4brApRukOmGro4e7vVoJz1Ri0ohRdFchjpvOhKlJmz3joRMnT5OiVYli1X0BoFYojfGAC60doeLlT3HvG1UC6aN3r4TCLb6Y3Rs/Dua4vnol90W8Pn6JTcDYNr5tt/nqJUyslLiqD49dGWPz4jNp012Lj1WB698VMGXH/+Yv717jMzkOH75tcV8+47ZKOUD9Y1C2X6um7cadeEcOja9gMPonxTCyeYetGp5f+VkJFgxJ4s4tZz1209GLIZo5cCJdgzxSvIzdCyYno5cJuHzw42RDitoeDwif3njCG9uqebaJfl85845SM8zBSzJT6TiVOSSYQ7jadreexJlZjHJV39l2OMEiRTt5PlYq/ZdEEnI89l+2CvbEGgLvau7neZ//ifOrhbS7/gR2ikLBh1z/+qSUZ+VgiBgWHYbSVd+CWvFLlrWPRHxKsrxMiXXgLXPFXULDUd7PfbmGuJnrRz12IT5a5Bq9XRufinoXRlHQqTPazt5BGdnE4mL1pCVoqWsJrCkWZPRSrJejUIu9W7waHRYj+8IaoxjoShbT11zD07XxK94DyZuazd9p46hLVkyoWUbfMh0yWFP9DqMpxFdjv5iB7fbQ2tnb39VXKg5K98QPQlDu9PNix8c5zu/20Jtp4c+aRxZsoFrCEGAe6+eGqEIYfP+Btq7bNx5ZeAGqsPh6vYWIMgT/Ev0yvRpOE2tF1zXnmHVPSAdaEYnyJTe1ycgEonArDQ3WocxrLIN4J0DSs77foZark0QBLJS46Ju/jURiSV6YwzgQmt3uFjZe7yVouwEkhKCt+Pm6umg7c3fIjekk3rDtxCEwB8fMl0y8bNWYj70CS6z/22xRdl6ahpNQ75nb64GiRRF2tlqkI5uG8+9c5QvP/4xr358gmkFSfzPt5fzX99cypwpqayal8sjt5eSpA9P+7kgCF7DOoWKtnf+iOgePdl0qrmH/AxdRBc/KoWMqxbmsbOsGaMpsi1u0YTbI3Koso05U1IRBAGNSs4lU1LZcbgpKisAA8Xl9vD7Vw+wYWcdt11WzEO3zBqyPXtqXiJWm5PTbeawx+ix93rN1+RK0m79HoJMPuLx2qmLER02ek8eDlOE4aGrp4+jtUaWlQbmmu3sbKLpxZ/gsprIuOunaApLhzxu5dwcHrm9lBSDGgFIMaiHfVYmLLiO5DXfxFZ7mJZXH8PTZx3LR4oKpuQZAKJOvsF8ZAtIpMRNXz7qsRKFCv2y2+hrKA+6bElZtRF9vJKctLFpQg9Hz74NSLV64koWM7MohWO1Hbjd/ms/NxktZJ6pZBQkUq98Q/X+iG88FOXocblFTjWH/1kZzVgrdp2RbVgc6VCCgiwh/Ine843YWrt6cXvE/t9BqCnJTyROLefJ1w5yw6Pv8ODjH0fU5OxwZTvf+p/NrPukipVzs/nz9y/DkFvIrBRH/zgWr5Ujil7zxvEwVqlDt9vDuk8qKcrRM3eqf0lZf3CZvIlefyt6ZfpURIcNj+3CSqjFz1iBbv7qAa8lXnH/oI6licQlmmYA1EVzw3rfJbMykUoFVArpqHPAYJKZEheTbggCsURvjAFI4xOHfH2itjtcjJjMdipOdbJgWvBkG0SXk9Y3/geP007abd9Hohr7BFK/5GZEjxvTrnf8PmdStp6O7j66zIO1de3NNShS85DIFDS1W3hy7UG+8quNvLetlkUzM3jqP1bxkwcX9jup+1g5N4d//OfVZKVomVWUHPJBSxZvIOXah3C01I7qhi6KIqdazOSN0VgpmKxeko8oiny4sy7SoUQNNadNmHudzJlydjK9rDQTY3df1Bo4+YvT5eaJF/ay5cBp7r+2hAfWTBt2s6GkwPubKq8L72cWRQ9t7z6Js6uV1Fse9Wt8UhfMRKLUXHDyDTvKmhFFWFrqv2yDo+0UTS/+Jx6Xg8x7f4EqZ+SqppVzc3j+J1fx7m9v5PmfXDXis1I3+3JSb/43+horaXr5F7h7J2ZiKyslDq1aTkUU/Z5FjxvL0a1oJl2CVJvg1zm6OVcg06fSueWVoJnliaLIkWojMyclB3Uj0tnVQm/VfuLnXIEglTNrUjI2uysgE7OmdiuZ50joxE1b4pVvqI6sfEPRGa+Bqph8wwAsFTuRJ0182QYf0jMVveGskLQ31yBRapAZvHP+pnbvBlu4pKS2HjyNze7C4fQgAu1dNp5adzjsyd5ui53fvbKfn/x1Bwjw+ENL+O4XLiEhTok8OQdVbyt/+/EVvPvbG3n5F6uZOzWVf24op62rd0z3G4/U4WcHG2np6OXOKyYH9xlqagNB0m8MOBo+07YLUadXKlcBAhl3/8z73+NYt0YDuY4amlx6eoTwyvkdrmrH6fLwwwfm+zUHDBbZKXEYu/vosw/tExHDP2KJ3hgDUA9V1SOVT9h2h4uRfeWtiCLMD6I+r/Gj57A3VZFy/SMoUsb3gJcb0ombuQLzgY9xW/0zWinK9i5qa86TbxBFEXtzDXZdDk+8uJeH/vsTPjtwmqsW5vHXH17Oo3fPHdXMrDBLT22Ahi9jRTtlIfGzr8C0421s9ceGPa6ty4bN7iI/07/FfChJT9IyvySdj3adirWdnuHgCW/VxOzis4YXPvmGbUcmrnxDn93FL/+2m93HWnjo5pncfvlgY7NzyUzWEq9RUFEX3qpH0/Y36a3cS9IVD6DOne7XOYJUjqZ4Hr2VexHdF87EcdvhRnLS4v2WbehrqqbppZ+CRELmfY+hTC8Mekxx05aSftsPcBobaHrpP3GZoydZ6i8SicCUXENUbdzYag/jtnQRP4IJ2/kIUjmGFV/A0XoSa/nOoMTRZLTS2dMXdNmGngMfgUSCbo7XDHBGkXcDx1+dXnOvA4vNOaBlXZU7zSvfUB5Z+Ya0RA1xanlMp/cc+mUbpl4Ysg3g7VoTnXY8feGrRLM3V6PMmNT/b9hk9N47XNINL24ox31eJ5Pd6ebFDeVhub8oinyyt55v/PenbD3YyB1XTOZP/7GK0nPmZ4qUXESnvb/iVRAEvnFrKSLwlzeOjCkxP1apQ7dH5LVNleRn6FgYZB8VV3cbsvhEBKl/JtxygzfR67oAE719TVXIU7LPeDTE0Vt7KNIhjRm3zYKmp46jzhxONoVnvepjZ1kzGpWMWUWjG/wFk6xU70aVz2A1xtiIJXpj9OPus9JbtQ95UibSM+6xCAJyfapfbYIxooM9x1tISlAxKSs4ScKegxsxH9qEfsnNxE0NTnudfsmtiG4Xpt3v+nV84ZnPcm5ljyiKHD9cjqfPytqDdg6eaOPWVcU895Mr+catpaQn+TfJLcxKoK3LhrnXEfDnGAtJV34RmSGN9neexD1Ma3O/EVuAmpuh4rplBZgs9qh2Vg4nByvbmZSdgD5e2f+aT75h+wSVb7DYnPz02Z0cqWrnu1+Yw5ploycABUGgJD+R8jAmenurD9D12b+Im7EC3bxrAzpXO3URnj4LtlNHQxRdeOnq6eNYbQfL/KzmtZ06RvPLP0Oi1JB5/+MokrNDFpumeC7pX/gxru52mv75E2+l0QRjSp6B+paeqDEZMpdtQaKOR1N8SUDnxU1fhjwl11vVG4RNDl/idVYQE70epx3zoU/RTlnYX6FviFeRkxZPmZ+J3qYzen7nVvQKEinaqZGXbxAEgaIcPVXDmMpejPhkG+KmLYl0KEHDV0np6g6PfIPH5cDRVj/QiK3dilopQx+nHOHM4GHsGlrWq/1MwUIoaWq38JNndvCHfx0kOzWOPz66kvuG0JVXpOYC4Gg/W2Wclqjh3mtK2FfeGvDc1uO0DyvRMZrU4Y7DTTS2W7jzyuBW84JXusFf2QY4K/HgvMAM2URRxN5UhTKjGEEiRV1Qiq328ITVIrbVHEQQPRx1ZFN3Zn0YDtxuD7uPtTC/xFvIEk6yUrzjeEy+YXzEEr0x+jFtW4en10z9lLv5Rc9tfLfzfj7wLMXZ0Yj1xIXV7nqh4nC6OXiijQXT0oMygehrrMT40XOoC0sxXHpXECL0okjKRDttCT37PvSrtXfPsRakEoGXP6zgS499xN/eKeMHT21j7dqPAZi1aD7P/+QqHlgzDUN8YOZlviRy7TBmb8FGolCTeuN3cJk76fjouSGP8Q3keRmRl24AKC1OISsljve3xUzZevucVNR1csmUwZPpZbOz6JiA8g3dFjs//vN2qhq6+P7987l8fq7f507NN9DYbqHbEvokirOzmbZ3/oAiLZ/kax8K+BmnLpyNIFddMPINO440+S3b0Fu9n5Z/PY5Ml0zmfY/3t2yGEnXeDDLu/hkem4WmF3/stxFltGB3uPCIcOePP4i47qTbZqH3xB7ipi9HkI6sR30+gkRK4sq7cXW1YD786bhjKas2kqhTBVUD1HL0czx9FnTzBuoqzipK5nhdJ07X6LITvsqfjPPiiiuJHvmG+pYeHM5YZwyApXwH8qRM5Cn+jzfRTuUR7ybi6ef+gwOPP8Ce994K6f0cbfXgcaE4Y8QG3t9BZoo2bFXSyYbhvUDu+ekGfvm3XWzcfSqocwSny8Nrmyp55H83U33axDdvncUTDy8btrPFt6npaK8f8Pr1ywooyk7g2bfLsPhR7OGx92La8RYNT39z2GNGkpLyeETWbjpBTlocS2b6L7fkL84AE70ShRqpNuGCq+h1mVrx2MyosooBUBeU4rZ04Tzv7z9RsFbtRarVY0vIpa4pfIne43Wd9FgdLJ6ZEbZ7+vCN443GWKJ3PMQSvTEAcHQ00r33AyzZi/j9Rx20d9kQgY1d+TS7DTR98HzEzSxijE5ZjZE+h5sFQWgHcllMtL7xP8jiEkm98d8QJNLRTwoAw9JbEZ19dO9ZP+JxW/Y38NS6w/2tYUZTH29vraWx3cL1UwWQyVmzZhladWCLXx++yueaMMk3AKiyJmNYfgeWo1uxHPt80PunmntINajRqMb2mYKNRCKwZmkBJ+q7qKyfWEnMYHO4yojbIzJn8uDJ9IJpaV75hsMTR76ho9vGD5/exul2Cz95cCFLZwW2+CjJ9+r0ngjx98LjsNHy+m9AELw64fLAq5UkciWaokvordyD6Jn4yZZtR5r8km2wlO+gZd1vkCdnk3nfY2HV3FdlTSbj3l+Ax0PTP/8Te8vE2Czasr+B9edsbEVKd9KH9fh2RLeT+Fkrx3S+pngeyqwpdH2+blxzOVEUKasxMqsoePq8oijSs28DitQ8VDkDnbxnFiVjd7ipahj9+dLUbkUiQHqSZsDrqtxpSLUJWMu3ByXeseIzZAtnRVa04rKY6Ks/jrbkwpFt2PPeW2iqvMUHIqAXLOgOvxrSZK/PiE2Vcbai12tIGB59XoD7h6qglUm44/JiVi/Op665hydfO8T9P/+QHz69jXe21tDaOTZdXIDyk5189/db+OeGchZMS+fP37+M1UsKhjSM9SFRapAlpAxK9EqlEh65fTY9Vgd/X3982PPd1m46N79M/Z++Tufml1Ck5ZGw9FaQKgYcJ8iUI0od7j7WzKkWM3dcPnnEeMeC6HbiNnciSwjM3E2mT7vgNHr7DQrPVLprCmcD0Fs78cx4RbcLW81BNEWXUJCRwMnm8K1Vd5U1I5dJuCSIhoH+olLISDGoYxW94ySW6I0BQMfGfyDIlTzbUIz9nGoDDxLesM5DZuuke/d7EYwwhj/sPtaCUiEdd0ul6HbR9ub/4rFZSLvt+0g1wa8sVaTkop26iO59HwwrYQBe/S/7EBUwCrmELIkRZVqB33pUQ5EQpyQ5QRU2nV4f+qW3oMyegnHDs7i62we8V9fSM6q2cLi5fH4OaqWU97dPjERNqDhY2YZaKWVq/mDjyn75hiMTQ76hpcPKD57aRkd3H7/46iLmTg28yrMoR49UIoRUp1cURdrX/xmn8TSpN/078gAqVs5HO3WRVxuyoSKIEYafzjOyDctHqeY1H/6Utrd+jzKziMx7fo5UE/7nijItn4z7HkOQKWh+6af0nT4R9hgC5cUN5TjOqyINp+7k+ZjLtiBPyUUxRk1lQRBIXHUPbksnPfs2jDmOhlYzJrM9qPq89tMVONrq0M1bPSjpN6PQuynhj3xDk9FCskGDXDYw6SRIpGimLKS3+gAex2BD13BRfMaQLRBzuQuV3hNnZBtKJr5sgyiK1Jw2sXXXCX7Xcy0OUUqLWw+AQnAjO/x2yO5tb65BotEhPSMZ4XR5aOvsDWq1/WisnJvDI7eXkmJQIwApBjXfumM29107ja/eNJO//fhK/vBvl3L7FZOx9Dp47p2jfOVXG/nOb7fw6scnONnU7VdLvdXm5M9vHOYHT39Ob5+L/3xwIT98YD5JCcNXFJ+LIiV3yIrOSdl6bloxiY93n6KsZuBzxtndhvGj56h/6iFMO95CXVBK1oO/IeOun5K08m5SrvsGErV3fSTVJpC85iHiZ6wY8v6iKPKvjZVkJGtZPjvLr5gDwSsXIgY8P5LpU3FdYNIN9qYqBJkCxZluAZkuCXlKDrYJqNPb11COx96Lpng++Zk6GtssYekKEUWRnUebmTM5FbVy7Gvs8ZCVHMfp9liidzxE5i8XI6rorT6AreYAiZc/wKl1gwfbKlcGhx25zN7xFvGzVoW1GiiG/4iiyN7jrcyZnIJCPr7q245PXqCvoZyUG7+DMr0gSBEORr/0NqwVu+jZ+wGG5bcPecxw+l+dpl7sLbUBGdMMR2GWntom07ivEwiCRErqDd/m9HOP0vbun8i452cIEilOl5vTbZagmzSMF41KzmXzcvlo1ykevH46CWHSf4s2Dp5oY+aklGH1qpbNzmL3sRYqTnUyrSB6n5X1LT3851934HR5+NU3llCcYxjTdVQKGYVZCSHV6e3e/S7W8h0krroXzVCGoQGgKboEQabAWrETdZ5/Rm7RyM5hZBvMR7fStfllXD0dSJQaPHYr6sJS0m79PhJFYLI2wUSRlEnW/Y/T9PLPaX7ll+jmr8Z69HNcPR3IdEkYVt0z7AI5Egw37gz3eihxdDRib6wk8fIHxlX9qM6bjrpwDqYdbxE/58oxuZCXhUCft3vfBiQqLXFD/P0T4pTkZ+goqzFy55VTRrxOk9E6bIIrrmQJ5gMf01tzIGLJxRSDmniNguoo0+k995kRrt+ipXwn8qSsCSvb4HZ7OF7Xya6yZnYdbaaty4bAFCbJ2thtn8RyVWX/sQmELlHhNWIr6n8utHZa8YjhM2LzsXJuDivnDm3ULAgCk7L1TMrWc+81JTQZLewqa2HX0WZe/biCVz6qID1Jw6IZGSyemcGUvESkEoEt+xt4cUM57V02dFoFbo+Irc/J9csLufeakoCTT/KUHHprDyO6XYOKQ+66egrbjzTx9LpDPPnoKjA1Ydr59pmOO4H4mStIWHwTiqSBCdr4GStQZU2m4c8PY7j07hF/N/vKW6lt7OY7d85GKg1+nZ2z26uDH2hFr1yfhvX4jiH/XSYqfU3VKNIHFgFpCkrp2f8RHqd9TB1hkcJauRdBKkddMIuCvk48ItS3mCnK0Yf0vjWnu2nvsnH3VSOPu6EkKzWOzfsbEEXxgun8CDexit6LHNHtomPT35EnZqCbdw0qxdAJws/lSxE9Ljq3jOwmGiNynGzqwWiysWDa+BKE5rLP6Nn7Abr5a0I+2VemF6Apmkv33vV47EMvoIfT/5picCA6+lCeo002VgqzEmhss9AXYuOI85Eb0km+6sv01R/rr5g/3WbB4xFHbceOBGuWFuBye/h496lIhxIRmo1WWjp6mTNlePdZn3zD9ig2rqs+beKHT29HFOHX31w25iSvj5L8RCrrTbjco+toBort5BE6P30J7dTFJCy+adzXkyjUqAtnY63YjSgGP95w8fnhJnLT48k95zlhProV4/vPnDGJEfHYrSBI0E5bFtEkrw9ZQgqZ9z+ORK2le8db/XG6eowY338G89GtkQ6xn+HGnZH0KEOF5cgWECTEzRi/KW7iqrvx9Fno3vXOmM4/UmMkxaAmLVEz+sF+4DJ3Yq3YRXzp5cMuvmcVJVN+shOna/gqJlEUaW63DJvoPSvfsCMocY8FQRAoztFHVUXv+c+McPwWz8o2LJ5Qi3e7082eYy388V8Huf8XH/GjP29nw8468jMS+PYds/lewgd8S/cxdlFOq1uHr6mnm9DIKHgcfTiNpwfMgX061eGUbgiUzOQ4bllVxG++tZwXfno1D99WSmZKHOu31fKDp7bxxV98xI/+vI0/rj1E+5mNtR6rg16bk7uumsJXb5w5pgpDRUoueFw4O5sHvadSyPjmbaVIO09x+NmfcfrZ72Kt2Ilu3mpyH36alOseHpTk9SHTpyIoVDjahp8Xi6LI2o2VpBrUwybEx4vrjOGpTD/8/HQoZPo0ED3DmstNNES3C0dLLcrM4gGvqwtnI7qd9NUPL9ERbYiiSG/VPlT5M5EoVBRkeud7dWGQb9h5tBmJRGD+OHMK4yErJY7ePhcmc0w6dKzEEr0XOT37P8TZ0UTiFV/ko71N9DncSM/TDVLIJNyweiH6hddjKfuMvsbKYa4WI5LsPtaCIMC8aWM32bG31GL84BlUudNJuvz+IEY3PPplt+OxWejZ/+GQ7w+l/6WUS7l9lneipzxHm2ysFGYl4BGJiHZe3KxVaKcupnPLq9iba/tjyI8y6QaAnLR4SouT+WBHHe4QJPWinQMnvBPpoYzYfGhUcuZOTWXb4eiUbzhW28GP/7IdtVLKE48sC4pEyNT8RBxONyebgjv5dHa30frW75AnZZJy3cNBSwpopy7CbenE3lgVlOuFm86ePo6f7GDZeXrKXZtfRnSdNyEWPZi2rg1jdCMjizPAEG26ostO1+bo2UgeatyRCHDfNVPDGofocWMu24Jm0hzvv904UaYXoi1ZQvee93FZTAGd6/GIlFV3MHNS8PR5ew58DB4PurlXD3vMzKJkHC7PiCaXPVYH1j4XmSlDJ7gEiRTtlEX0Vu2PqHzDpOwE6lvMQ8pRRYLOT18a9MwI9W/xrGzD0pDdI1hYbE627G/g1y/s4d6fbuCx53ezs6yJS6ak8sP75/PyL1fzn19eyJUL81DO8co2FMlbWWtdhEQAtyjgKr0pJLE5WutA9AxM9LYPbUgYrRh0Kq5ZnM8vvrqYl3+5mu/dO5cZk5I4WtMxaONYBD7eM3YzLV8b//k6vaIo0nvyMOn7/syjCR+g7qxBmHMDuY88Q/KVX0KmG7l7QRAkKFLycLTVDXvMocp2TtR3cdtlxchCUM0LZxK9Eimy+MA6yeQG75rxQtHpdbQ3ILocqDIHrg1VudMQpPIJJd/gNJ7GZWpFO3k+AGlJWpQKKSfDYMi2s6yZGYVJEe3ezDoznsfkG8bOhVGjH2NMuK3ddG1di7pwNg2yAp59azuXTE3l0tlZvPRRRf9O6mXzvC05HvutmA9vpuPj58n84n8hCLF9gmhiz/EWJucaMMQHVrl1tm3PCIIEiVJD6s3/HrYWHlVWMerCUky730U3b/WgyjPf7veLG8oxdtlINqi5f3UJBcaPMMtVyJPG71w7KfusIdtQ2quhRBAEkld/nb7TJ2h75w80ZH4JmVQgKzU6KzKuW1bIr/6+h93HWlgSoHHXROfgiTZSEzWjLqKWlmax62h0yDf42h+NXTZ0cQqsNidpiVoef2gJyfrgVCf6DNnK6zrHXR3sw+O00/r6/yB63KTd9gMkyuBVUmqL59EukWGt2IkqO3JtaWNlxzCyDa6ejiGPH+71SOE2D52wi6Y4zx93NGoZVpsLhSK802Zb3VHc5k7irvxS0K5puPQurBW7MG1/g+Srv+z3eadaejD3OoIm2yC6nZgPbkRTdAlyw/BVQzMKkxAEr2zEjElD39uX4BpJm1Q7bQk9Bz6KqHxDcY4et0ekrqmbKXnhnWuci+hxYz2xB7c5/M8MS/mOM7INoals9Jdzx0bfvHLl3Bw6um3sPtbCzrJmyqq95quJOiWr5uawaGYGMyclDyndtOD6m9kDxB96h0Z3IlXOdIrkrZTOH5/c0HDYm88YTp1T7NBstKBVydBpFcOdFrVoVHJWzMlmxZxsrn906I6D8Ujn2Fu9/hJtb/2Ozk9eRL/ybqRyJaYdb2FvrkYaZ0Cz/B5+uVFKSrWB/77Gf28SRWoe1vLtw7aYr91USVKCiisWhE6qxNndhkyXFLBxtlzvTfS6ulohdEp9YcPe5N3AP7+iVyJXosqdRm/tIaJXVG0gvVV7AdAUzQVAKhHIT9eFvCipsd1CQ6uZ1YvzQ3qf0fCtg5vaLcwcZuyPMTKxRO9FTOfWf+Fx9CFffDe/fmEfyXo137tnLnEaBZfNz0UURR7+n0+pOWNSJVGqSbzsXtrfewrL0a3Ez1wZ2Q8Qo5+ObhvVDSbuv7Zk9IPPwde211/RIXrwOO3Y6o6EVS/RsOx2ml78CT0HN6JfeP2g94fS/2r8Rw3KjMKAJzVDkaJXE6+Rh92QzYdUE0/KDY/Q8sovSe5dT3bqspDt+o+X+dPSSTGoeX/7yYsq0etyezhS3c6ll+SMWs3mk2/YdrgpooneLfsbeGrd4f7qsW6LAwG4YXlB0JK8AMl6Ncl6NRV1Xdww/u5yRFHE+OGzOFpqSbvj/6EIwmbOuUhUWtQFs7BW7Bq37mkk2DaEbAN4DUeGar+MNl39iRLnueOO2+3h3/+4lb++eYTS4hTi1PKwxGA5shmJKg5N8bygXVORlEl86WX0HPiYhIXX9S/0R8OnzxssIzZr+S7cVhO6eatHPC5Oo6AwK4EjNUbuGuaYJqO34me4il4AVU4JUq0e6/EdEUv0Tuo3ZItMold0OzGXbaV719s4O5pAIgHP4O4caZw+JPd3Wbroqy9Hv/TWiD53zx8b27ts/OFfB3nlowqaO3oB76bBTZdOYtHMDCbnGJBIRo93wfU3w/U3U/r8bt5rXs33tG/R/v4zZH3p10GZq56LvbkGaXwisvizm6tNRisZKXETbkw7nxSDur/Y6FzGKp1jPrqVjg+f6/9vV48R47t/AkRkhnSSV3+d+FmrEGRy7tfU8/tXD/LhrjquXeJf5lOZlof54Me4zR2DKoDLaowcq+3gqzfNGGQUGUxcpnavDEOASOMTQSK7YCp67U1VSNTxQ/5bqAtn0/nJC/165NGOtWofivRJA2LNz9Sx40hzSHVrd5Z55U0WzcgIyfX9JUWvRi6TcLotVtE7VqIzkxAj5Nhb6zAf3ETcJVfzP+ubsPY5+dEXFxCnObsLLAgCa5YWUtVgorLeW4ETN/NSlBlFdH76Eh5H+E1JYgzN3uPeATpQfd4hW33dzrC30KpySlDlTad71zt4XI5RjxfdLhytdUHR5wXvd70wK4HaRlNQrjcWNAWlJCy4jqm2QyxMaItYHKMhlQhcu6SAI9VGTrWEX+oiUlTUdWKzu7lkBH1eHz75hu0Rlm94cUP5oBZhEXh9c3XQ71WSnxg0Q7ae/R9iObIFw/I70QYxwXUu2qmLcHW342ipDcn1Q0W/bEPpYL1Aw6p7QDqwkkuQKb2vRxGGVfcgyM5rBxQk6FfeHZmA/EAqlfCt22fTbbHzwvvh0fjz9FmxnthN3LSlSGTBrdAzLL8DQRDo2vqa3+ccqTaSnqQh1RAcfd7ufRuQJ2ag9sNgceakZCrquoaVPGgyWpFIhBG1gwWJFO3URfRWR06+IUWvJiEu/IZsHkcf3XvWU//0wxjf/zOCTEnqzf9O8nUPD/4tAqLHE5LEj7Vi9xnZhsgk2n0MNTa6PSLtJhv3rp7K099bxTM/vJwvXjedqXmJfiV5z2X25BROdbqRLr4bR0sN3Xs/CGb4wFkjtnMZyZBwIjGcZNv9qwMrZvEx5FoHEYk6npyHnkR3yVUIMu/m3aq5OcwuTuEf64/T0e3fOleRmg9419bns3bjCfTxSq5elD+m2P3FZWpFnhCYPi94n4tyfQquCyTR29dUjTKzaMgkqM/Mt3cCyDe4rd3YT1cOmgPnZ+gw9zro7AndGLazrImiHD0pEfAkOBeJRCAzWUtjTLphzMQSvRchoijSsfHvSFRa3jPP5PjJTr59x2wKMhMGHbtqbjZqpYz3tnkXw4IgIemqB3FbujDteCvcofezZX8DDz7+MTc8+g4PPv4xW/Y3RCyWaGDP8RZSEzXkpvvfagTR1eprWHY7bksX5kOfjHqsT4MpWIlegMIsPXXN5pAYSvmLYuHtNLn0zO/6ALc1MtXF/nDlglzkMgnvbzsZ6VDCxoETbUgkArOK/JtILyvNorOnL2jJz7EwXJvjeNofh2NqvgGjyTZkFc5omI9upf5PX6f2V7dx6vcP0vHx82iK56FfflvQ4/ShnbwABAnWip0hu0co8Mk2LCsdXOUcP2MFCQuv6/9vmS6Z5DUPhbU7wx/iZ6wgec1DZyqfBCRKjbebxGqKdGgjUpSj54YVk/hwZx3HakM/RlrKdyK6HMTNWhX0a8t0Sejmr8ZS9hmOttF1L90ekaO1HUFrn7Q312BvPIFu3mq/ZMBmFSXjcnuoGOZ52tRuIc2gGbUTRluyGNHloLd6/5jiHi+CIFCUHT5DNrfNTNfnr1H/1EN0bPw7ckMa6V/4CVlf/h/ipi1FN3PlgN+iTJeMfsWd4HHT9OJPcBhPBzUea8UO5MnZEZdtGG4MdLtF7rxiCrnpunFVy5UWe+cJxzyFaIrm0vXZqzhNwdvA9/RZcXY0DZgDO11ujF29UW3E5i8r5+bwyO2lpBjUCHgrfB+5vXTMRmbDrWk8NsugSmtBEPjmbaW43R7++laZX9dXpJ7R/z3PkK2irpPDVUZuvrRoUOI6mHicdtxW05gqesFrKOcK4vczUnjsNpztDYNkG3zIU3KRxhkmhE6vd4wSB3Xz+HI1odLp7ei2UVlvYsnMyFbz+shKjaMplugdM7FE70VI74nd9J06Snv+1by9q4WbLp3EijnZQx6rUcm5fH4O2w410WX27h6psqcQN2MF3bvejUirh6/lqr3Lhoi35eqpdYcv2mRvn8PF4cp2Fk5PD3hiKo0funUwEi0tqrwZKLOnYNr5NqLbOeKx9uYaIDhGbD4mZSXgcntoaDUH7ZqBUm+086J1OTJ3H+3v/wVxCNOiaCAhTsmlc7L5dH8DFtvIf6sLhYOV7UzJNaD1s2V7/hn5hu1HmkIc2fAM1+Y41vbHkfDp9FacCiyxfb7ru7u3G0QRdfG8kOrASzXxqPNnYK3YFbW/s6HYdriJvPR4ctKG3tSTqr2v5333eXK/9deoS/L6iJ+xgtxv/ZXCH79O3qMveg0pP32J3pOHIx3aiNx99VRSDWqeWncIpyu0hlqWsi3Ik7JQZgZvnDsX/eJbEJRqOj97ZdRjTzZ1Y7U5g6bP271vA4Jc5bcE2PTCJCQSoV8+4nyaO6xkpIxeydgv31AeuQ2eomw99a1m+hyukN3DZe6kY9ML1D/1EF1b16LKmkzmA78i877H0EyaM2CueO5vMfdbfyVx+R1k3vdL8Hho+ud/Yg9S14PL0kXfqeNopy6OuLRAqMfG7NQ4EnUqDlUZSb7mq4CAccOzQRtrfH+TcxO9LR29eETI9ON3MBFYOTeH539yFe/+9kae/8lVY07ywvBrmuFez0jWctfVU9lZ1szOstHncBKlBpk+1WuQdw7/2niCeI2C1UvyAw05IFzd7QDIxlDRCyDTp10Q0g3e34U4yIjNhyAIqAtLsdUdQfREhyHmcFir9iGNT0SRPlA+xGegHGzzYx+7okS2wUdWShwtHb0RLcKayMQSvRcZHpeDjk9eQNRn8ZvdGmYVJfPFNdNGPGfN0gJcbg8f7z67U5m46l6QSOj85J+hDnkQQ7Vc2Z1uXtxQHvZYooHDle04XB4WTAt8J1c7bbDrcaRafQVB8Fb19hgxH9ky4rH25mokKi2yEQxcAqUw64wh2+nIVdLWNffQ7DagWnwnvVV7MR/cGLFYRmPNsgLsDjef7h27C/JEodtip+a0iUumpvp9jkYlZ15JWkTlG+65euqg18bT/jgSBZkJKOTSYSvuhmO4lsruba8HL7hh0E5ZhLOzGWf7xPgOd3TbOH6yg6VDyDb4cLSeRBqfiFQ7uEMnWhEEgZTrHkaelEnbW78PauVbsFErZXzj1lJOt1lY90lVyO7j7Gymr6HcqxsZoqSYVBOPfuEN9Fbupa+xcsRjg6nP6+7twXpsG/EzL0Wi8i8ppVHJKcpO4MgQiV5RFGlq969lPRrkG4py9Hg8InUhqMhydjbT/v5fqH/6G3TvWY+2eD7ZX/0d6Xf+CFX24PFgOBSpeWTe/xiCTEHzSz+j73TFuGOzVuwGROKmRVa2ARjSZCiYY6MgCMyenMLhKiOS+GQSV96FrfYg1uPbg3L9oYodfFVvF4J0Q7AZSi5otLXOTZdOoiBTxzNvlmH1o6BBkZo3oKK3qqGL/RVt3HTpJNTK0Foi+RK9/uqtn49cn4bHZsHdZw1mWGGn34hthCIgTeFsPDYL9ubole3yuBzYag+jKZ43aPyPU8tJNahDZsi282gz2alxwxYThJuslDjcHpGWjon93YwUsUTvRUb37vW4TG283DWbuDg1379vHtJRWt2yU+OZMzmFDTvq+ndUZLok9ItvxlqxE9upo+EIvZ/hWq7au2z88OltPP36Yd7ZWsP+ilbaOnsjqpEZDvYcb0WjkjG9MLAFmOh2YqvaizTOgDQ+CV/bXiRbfdWFs1FmTMK0401E9/DVLvbmGpQZk4K6AM5MiUOpkFIbol1Sf6hr7kGrkpGx4kbUBbPo2PQPHB2RqwgdiaJsPVPzDKzffvKC/40drmpHFGHO5MCqJZbOyoyofIOveEinVQSl/XEkZFIJk3P1AX/WSMrHaKYsAAQsFbtCfq9g4DXgGFq2wYe9pRZl2sSzzpYo1aTd9gNEj5vW13+Dx3l+8j96mFeSxoo5Waz7pDJkHSDmI1tAkBAX4rE4YeF1SDQ6Oje/NGK14ZFqI1kpWpISxl/xaD70CaLbiW7eNQGdN3NSMlUNXfTZB84NTBY7NruLDD8TXNqSJRGVbyjqN2QzBe2a9tY6Wt/6HQ3PfBtL2WfEl15Gzjf+ROpN30WRmjema8oTM8m6/3Gk2gSaX/nluKvtreVe2QZFSu64rjNeRFHkUGU7KoWUpARVyMbG0uIUzL0O6pp70M1bjTKjiI6Nz+O2jf+ZYW+uRqZPRao5m4xpMnoTISMZEl6snC8X5M9aRyaV8MjtszGZ+3jhg9F12RWp+Tg7m/vHrrUbK9Gq5Vy3LPTjsU9fd8wVvYa0AdeZqNibzvwuRtjoVheUAgK2KO4e6jt1DNHZN6xHRX5GQkgSvT1WB2U1HSyOEtkG8Eo3ADS1xxK9YyG0W0wxogqXuRPT9tepUxRxyJjCfz8yn4S4wSYMQ3HdskIee343u4+2sPTMIjNh0Q2YD22i4+O/k/Xl3wTdUXY4kodxY1UppHg8ItsONQ5oJ1fIpWSnxJGdFkd2ajw5Z/5vZrIWxTmaSVv2N/DihnKMXTaSDWruX10SkoRIMPF4RPYeb+GSKanIZYHt23Tv24Czs5n0O3+EpmhuiCIMDEEQ0C+7ndZ1T2A59jnxQ2gTelwOHG316BddH9R7SyUCBRk6ahsjl+g91dxDXoYOiURKynWP0PDMtzn97L+Bx4VMl4xh1T1R1Yp93bJC/vfl/RysbGPu1LFVEkwEDpxoI04tpyjHMPrB5zB/WhoKmYRthxuZXhheORS3R2TdJ5UUZibwh3+/NCytsiX5iby5uZo+hwuVwr/phUyXdEa2YfDroUYWZ0CVW4K1YieJK+4M+f3Gy/YjI8s2eJx2nB1NaKcuDnNkwUGRlEnqjd+h9bVfY9zwLCnXPxLxFu/h+OqNMzlQ0cZT6w7x628uC9isaSRE0YOlbAvqglkh/x1IFGoMy26j4+PnsZ08jKZw9qBj3G4Px2o7uPSSoSW+AkH0uOnZ/yGq/JkBJ/xmFaXwxuZqjtd1csmUs90VvgWgv9qkqpypZ+QbdhA3RFdTqElKUKGPV1I1BkM289GtdG1+ud81Pm7WSuzNtdhqDiAo1CQsuoGE+dchiw9srBoOWUIKGfc9Tsurv6Bl7X+RdvOjaKcsCPg6LksXffXH0S+/PShxjYddR1s4Um3k6zfP5LplhSG7T2mxt/jiUGU7hVkJJK/5Bo1/+x6dn7xIynUPj+va3mKHwUZs8Ro58ZrgGjdeKMTPWBHw/HlyroHrlhfy7tZaVl6SzbSC4Z/HirQ8ED042xtoIoXdx1q466opaFT+yX2NB2d3O0hlSMf4u/dVAjtNrSjTQ/ebCDX2piqU2VNGPEaq0aFIL8RWewjDstD5QIyH3qp9CHIlqvyZQ76fn6ljX0UrDqd7QB5jvOw93oLHI0aNbAN4K3oBTrdZWDA9wsFMQGIVvRcRnVtexu1y8WLbTL55aynFASQt5pakkZqoYf32s60OErmSxMvvx9FW55eBVrC475qpnL/2U8qlPHxbKb/51nJeeWw1L/3iGn79zaU8fFspqxfno9cpqTjVxasfV/DfL+7jW/+7mdv/33q+9l+b+MVzu/j5/+3kj2sPTjjd3+rTJrrMdhZMD0zCwG3tpuvzdagnzYmaJK8PTfE8FKn5mLa/OaSGkqP1FHhcKIJoxOajMCuB2kZTRCpURVHkVEsP+Wf0l2z1x7xVzR5v9ZKrx4jx/WcwH90a9tiGY8msTPTxStZfwKZsoihy8EQ7pZNTkAaYzNGo5MwtSWPHkfDLN2w71EiT0codV04OW7Jsan4ibo8YkKO8YdU9IB24MA2nfIx26iKc7Q04OhrDcr+x4pNtWDZ7BNmGtlMgelCmT7yKXh/a4nnol9+BpWwLPfs2RDqcYdHHK/nyDdM5frKTj86RtQoGfaeO4eoxDrnRGQp0c65ClpBC5+ZXEMXBOng1jd3Y7C5mBcGIrbdqH64eIwlzVwd8bklBItIhdHqbjWda1v3UJj0r33AAjyP4xpSj3v+MIVtNgBW95+uZu3qMmLa9Tl9DOYZL7yL3kWdIuuy+oCV5fcji9GTc+0uUaQW0vvE/WI5+HvA1rBW7AJG4kshuQjldbp5/7yi56fFDyjcEk6QENTlpcRyu8rbVK9PyvQUyhz/FVuefyddQuHt7cJnaBpkRN7VbLggjtmjj3mtKSDGoeWrd4RF12ZWp+QDY206xdlMlaqWMG5aHJ2nqMrUiT0gZs6+BL9Hr6pq4Fb0uSxeuHuOwRmznoikspe/0CTxRKFUhiiLWqn2oC0qRyIbetCnI1OHxiEHvKNpZ1kxygoriHH1Qrzse4jUKdFoFjTFDtjERS/ReJPQ1VmE5soVPektYuGgmVywIrJJCKhFYs6SAozUdA9oFtCVLUOWU0PnZq2HT9kmIVyGKXp2aoVquBEEgIU7JjEnJXLM4n6/cOINffHUxf/vxlbz2X2v447+v5Hv3zuXOK6dQlKPHaLKxv6INl3tgImYi6P7uOdaCRCDgasrOLa8gOu0kXfHF0AQ2DrxVvbfh7Gwa0jDFp02mCqIRm49J2XpsdndEtIDau2z09rn6E71dm1/uT/L6EF127+tRglwm4ZpF+eyvaKXZGH0TpmBQ32Kms6dvQAVZICwrzaSzxx5W+QaPR+S1TyrJTY9ncRh35qfkehMMgXzW+BkriJ99NqEVbvkY7ZRFgC8REb34ZBuWzhpetsFxxqDnfPOOiYZh+e1oiubSsekf2OpHb5mNFJfPz2VWUTL/WH+Mju7gJQzNR7YgUWrQTJ4ftGuOhCCTY1hxJ46WmiF/Bz5d3BlF468u7tm3AakuGc3koVtSR0KtlDE51zAo0dtktCKVCKQZNH5f66x8w4GA4wgGRdl6GlrNg2QoRqLzk38OoWfuNYIyLLsNqTp0ST6pOp6Mu3+GKreEtnf+SM+BjwM631q+MypkG97ZWktLRy9fvXHGqLJ1waC0OIVjJzv6E4SG5XcgM6TT/sEzY5anOavPe16i1+ifIWGMwFArZXzz1lIaWs28sbl62ONkhjQEuYrOuip2HGniumUFxIWputplakemH9scFUCi0iJRxeGKYn380bA3ef82Kj8SvepJs0H0hF160h8crXW4e4xohpFtAPrXiSeDqPPeZ3dx8EQbi2ZmRF0nVVZKXCzRO0Ziid6LAFEUaXr//+jxqKlPXcFXbhy6FWA0rlyYi0ImYf22s1W9giCQdNWDeHrNmD5/LVghD4vHI/LiB8dJTdTw4s+vDtiNVaWQUZiVwIo52dx99VS+f988/vQfw1fNDKcHHC3sPtZCSUESOq3/kwl7y0nMhz4hYd5qFMnjb8UMBdqpC5EnZ9O1/fVBFUb25mokGh1SXXDcv8+l35AtAvINdS3eAdvnqBpJ7dJAuGZxHgLwnd9t4YZH3+HBxz+O+kr4QDhwwjvxnTN5bJPo+dPS++UbRsN8dCv1f/o6tb+6jfo/fX3M1ds7jzZT32LmjssnB7WlfDQS4pRkpcRRUdcV0HlucyfS+EQKfrSO3G/9NazyJDJdEsqsyUNuKkUT2w43kp+hG9Egw95yEokqDplubDp90YIgSEi58TvI9am0vfnbqHvm+RAEgYdvK8Xp8vDs22Ov0DsXj92GtWIn2mlLkcj9k9YKBnEzVnjH3M9eHdRJU1ZtJCctHkO8aszXNx/dyqk/fBlbXRmivRfLGE2pZhYlU3XaRG/fWXmupnYraYmagBJ358o3RILiHD0ekVE9AUSPm97q/bSs+2/clqE30Nzm8GwiSpRq0u/8MZqiSzBu+CumXe/4dZ7L7JVt0JZE1oSts6eP1zadYOH0dGaPcTwPlNLiFOwONxWnvGOiRK4kefXXcHW1YBqj4ehQiV67043RZItV9IaIeSVpLJ+dxdqNlZxuG7qKUhAkKFJzaa05gUIu5cYVwe86HA5ndxuyhPF9p2X6NJwTWKPX3lQFgsSvjW5V1mQEhYre2kOhDyxAeqv2AcKI3bYZyXEo5NKg6vQeONGGw+WJKn1eH9mpsUTvWIklei8COg5shvYaPhUX8OgXlwas5eojXqPg0kuy2XLgNJZeR//ryvRC4mdfTve+DSFvf91R1kTN6W7uuXoKclnwdGlSDEMbjCQP83o00NbZS11zDwum+S/bIIoiHRufR6KJR7/8jhBGNz4EQYJh6W042xvoPbFnwHs+bbJQ7DjmpccjlQgR0ek9dWbAzkv3JnqH02YMh3ZpIHirqwRsdteEkj3xl4Mn2shJixv2GTEaaqXML/mGodpyxyLVIYoir22sJCtFO2Kbf6goyU+kvK5zRGOnc/HYe7HVHEI7dfGY2w7Hi3bqIhytJ3F2tUTk/qPR0W2jvK6zXx9/OOwtJ1GkF0RdNcZYkKq0pN32fTyOPlrf/F9E1+iu55EgMyWOu66awo4jzew62jzu61krdiI67cTPWjn+4AJAkEhJXHk3zo4mzEc297/ucns4frKDmZPGPu74nm1uqwnw/ubHKkM0c1ISHo/I8ZNnk5tNRkvABlSRlm+YlO3dVB7OkM3V3U7n1rXUP/1NWtb+F32nKxAUQ49B4ZwTSORK0m77HtqSJXR+8iKdn7066rPeeiI6ZBv++UE5TpeHB28In9DjzEnJSAQ4XNne/5qmoJS4mSsx7XrHK7cTIPbmauRJmUiUZyvYfV1omX4aEsYInK/eNAOlQspT6w4PO5dzxWei6W1h9aI8vz1wxovHYcPT24N8HBW9AHJD6oQ2Y7M3VaNIzfNrg1SQylHnzcRWe8jvuWq46K3aizKrGFmcfthjpBKBvPR4TgbRPHxnWTPxGgXTR9ChjhSZKXGYzHastuicB0YzsUTvBY6rr5eWj1+g3pXE6nvvJlE39ooM8Jov2R1uNu0dmMQxXHoXglxJx8Z/jOv6I+F2e3hpQzm56fFceklwTdLuX12C8jxBc7lMwv2rS4J6n2Cy97g3KbFguv+yDdaKnfTVHyfx0ruQqqJ7QqidtgR5YgZd217vH4g9jj6cxtODWtaChVwmJTc9PmDtvGBQ19xDikGNVu01bjCsugdBdv6ERUC/8u6wxzYSL24oxyNOPNkTf7A73Ryr7WDOGGUbfPgj39C1+eVBbbljkerYe7yV2qZubr98csCawsFgan4i5l5HvwP4aFir9iG6ncRNi1y1l3bqGfmGE7sjFsNIbD/SNKpsg+h24Wg/NaH1ec9HkZJLyvWPYG+sxPjx85EOZ1huXllEfoaOZ948MqDSdCyYj2xBnpiBMmtkQ5lQoJm8AGVmMV1bX8Pj8m7mV9Wb6HO4mVXkf5W4KIq4LCZsp47Rs/8jjBv+GpRnG3ifLzKp0C8nIYoizUbrmBJc2mmRk29ISlCTqFMO0DMX3S6sFbtp/tfj1D/1DUyfr0ORnEXqLf9B3refJXn11wbNCcKpZ95/T6mc1Ju+S3zpZZi2vU7Hpn+MmCyxlu+IuGxDZX0Xm/bWc+OKSWGtetWq5RTnGvp1en0kXfEAEpWW9vf/MqQXxUgMacTmMySMSTeEDEO8igevn86x2g427qkf8phDRhVaiYOb5ieGLS6XyfvdCk5Fb3vA38doQBQ92JurUWb6L+mnLizFZWrDFUUb/C5zJ/bmmhFlG3zkZ+g42dQTlES10+Vh7/EWFkxPC4ukTaD4DNliVb2BE31/zRhBZfvLf0PjseBZcBclheNvdS/MSmBaQSIfbD85YEdTFqfHsOx2bDUHQjZp3rS3gcZ2K/etLgl6AmPl3Bweub20v2pPAAoydH5LQkSC3cdayErRkp06fCvvuXicdjo/eRFFah7xsy8PcXTjR5BI0S+5BUfrSXqr9wPgaD3pNRsKUaIXYFKWntqm7rDv8p5q7umv5gWvdmnymoeQ6ZIBAYlKC4jINLphrxEJhpM3iXbZE384VtOBw+UZsz6vj375hkPDdzwEQ6pDFEX+tfEEaYkaLr0kMrIs0wq8C5zyk/61E1vLdyCNT0SZNTmUYY2IXJ+GIr0wauUbth9uGlW2wWE8DW4XyrSJ65g9FHEli0lYfBPmgx/Tc2hTpMMZEplUwiO3l9LZ08c/Pxj7BpfT1Epf/THiZq6MSFW2IAgkrroHt7mD+j98hdpf3cbnLz0HwIwhKnpFj9urpV+5F9POt2l772ka//EjTv3ui9T/8cs0v/RTjB8+i+joG/J+Y5HkUClkTMlLpKzam9zo7Omjz+EeU6JXle2VbxirjMR4mZStp/p0N87OZjo+/Sf1f/o6rW/8BkfrKfRLbyXn4T+TcddPiStZjCCVD5oThFvP/FwEiZTkNd9AN38NPXvWYxwmYemVbSgnrmRp2GP0IYoiz71zFH28kjuuCP84M7s4hcoG04BqNKlGR9IVX8TeVEXP/o/8vpbL3IXb3DloDuwzJMyISTeElCsX5DJzUjJ/X3+Mrp6Bz7XWzl62nPElVveGL3no7PbKi41HoxfOGLJ5XGGTggkmzs4WPH3WgBK9msLZAFEl3+Bb62qLR9fnL8hMwNzroMs8Nq3vcymrMWLtc7Fk5shdY5EiOzWW6B0rskgHECN07NlzlLTmzzkdP51V16wM2nWvW1rIb17ax4ETbcwrOVtNmjB/NeaDH9Ox6e+oC2YhSIP39XI43fzr4wqm5BlYON1/qYJAWDk3pz+x+88N5az7pJL6lh5y06MrsQbQ2+ekrKaD65b5X73Vvfs9XN3tZNz7CwRJ8GQvQkncjBV0fb4O07bX0RTNPUebLPhGbD4KsxLYtLeezp4+khLCI93hdHk43WZh/nkyHPEzVvQv4kS3k4Znvk3n5pdRF5ZGrM39fJINatqHSOpGs+yJvxysbEMukzC9cHytTP3yDWVNfOWmmUNuVElUWjx9gycxgbTlHjzRTlWDiUduL0UWoV35rJQ44tRyKk51jmr66ZNtiJ97dcS/z9qpi+ja8gquno6okkfp6LZx/GQn91wzdcTjHK3eFeZEN2IbisSVd+NoOYnxw/9DkZKHKmt0s5VwMyUvkTVLC3h/+0kunZvN1LzAK7osRz4DBOJnXhr8AP3EZekCQcBj91YInrDoyJMacR96F7M+DafxNM6ORhwdjTg7m8F91kxMqtUjT84mbtpS5MlZyJOyUSRn0fiPH+E2D07qjvV3NnNSMq9tOoHF5uzvHMgIULoBzsg3lCzGfOgTPA4bkmGkEUKBx+VgkfoUnt6tNPylBQQJmqK5xM++HE3RJcPO0c6dE0QaQZCQdOWXkCg1mLatw+OwkXrjtxGk8v5jrBU7ARFtBGUbth5spLyuk2/fMRuNSj76CUGmtDiFtZsqOVpjZOE55qhxM1ZgOfoZnVteRjtlwZkE/sjYm72GU4Mqeo1WdFoFcerwf76LCUEQePj2Ur71v5t59u0yfnD/2YTc659W0eLxGtLaW0+NqLEaTHwGauOu6DV41/NOUyuyhIml829vqgJAlen/Ro48MQOZPg1b7SES5q0OVWgB0Vu5F1lCKvKU0YvM8jN9hmzd4+7W3lXWjEohZfbk6Py7pydpkAjQ2BZL9AZKdGQKYgSd021mmjb8HUEQmHffI0GtDlk8K4NEnXKAKRt427kSr/gizo4mevZ/GLT7AXyw4yTG7j4euHZaWCpdblheiFIu5bVNVSG/11g4WNmOy+3xO+nt6unAtONNtFMXoc6bEeLogocglaFfcjP2pipsJw9jb6pGGp+ILN4QsntGwpDtdJsZt0fsN2IbCkEqx7DiCzhaT0ZV5eFQsieKKJc98ZcDJ9qYXpCESjH+TavlpVle+YaTgxMe3XvWe5O85z3bAmnL9VXzJuvVXDYvci2yEonA1DM6vaPRL9sQYe1GOFe+YVeEIxnI9iNNwMiyDQD2lloEuRJ5YvQZaYwXQSIl9aZ/QxaXSOsb/4PbGn4NdX+4b3UJSToVT712CKfLM/oJ5yCKHsxlm1Hnz4joIrtr88twpptFFOFu7Xb+PeEDTJ/9i/Z3/ohpx1s42uqR69NJWHAdKdc9TOYXf03eoy+S992/kXnvL0he/TUS5q9BU1iKTJdM4mX3BlVyYFZRMh4RjtUYz7asj1GbVFuy2CvfULV/TOcHiqO9AePGv1P/5FcpOvkaSRILzlk3kvvIM6Tf8UO0k+dPmI14OFMFfukXSLz8fqzlO2hZ9xs8zrMVZtaKnchTclD4kbgIBX12F/9Yf4yi7AQunx+ZcXFqvgGFXMrhM3IjPgRBIPmar4HHg/HD5/zqIrM3Vw9pONXUPjb5khiBk5USx51XTGbb4Sb2nJHQM5psbNpTz9L5RcgSUnG01YUtHld3G4JMgVSbMK7ryPVnEr1dE0+n195UhSBXIU8OzJdCXViK7dRRRHfktV89Tju2ujI0xfP8ynMUnFkv1jWNz5DN4xHZdbSZuVPTUMijc+yRy6SkJWpjFb1jIJbovQDp7XPy4vNvMUtWh3bBjWgSg+suK5NKuGZxAfsr2mg670enKZqLunA2XVvXBm0x1tvn5LVNVcyZnMLMovHLT/hDQpyS1UsK+PzQ6UGfMZJs2d/Ag49/zBMv7EUQvIZs/tC5+SXweEi8/P4QRxh84metQqKKo+W1X2M59jkem2VMJi7+UpCpQxAIqyGbz4itYIREL0Dc9GXIU3K9zujnVFJFkvNlTwCuWJAb1bIn/mA02ahvMTNnSnCSLvOmpaGQSdh+uKn/NVEU6fr8NTo2/h3t1EUkX/fwgKqepNVf9bt6q6zGSHldJ7etKhqz4WawmJpvoL7FjGUU4wTr8R1I45MiKtvgQ5GUhTwlN6o2UQC2HRpdtgHA0XISRWr+hEoSBYJUE0/abd/DYzPT+uZvo+b5dy4alZxv3FrKqRYzb22pDujcvoZyXKY24matClF0/nGunIJdlFHtSqfF5U0gZH/tDxT84BVyvvEn0u/4IUmX3Ud86WWosiaPqPkfbMmBKXkG5DIJR2qMNBstyKQSUgya0U8cAlX2VKRxBizlO8Z0vj94HH2YD39K4ws/4vSz36Vn34eo82eivfGHPNZ9MxUJS6Oqi2As6BfdSPLqr2OrOUjL2l/RfXATp/74Vfrqj+Pu6QjpnG0k3thcjbG7j6/eNBNJBDTrwZukmFGYxKHK9sHvGdIxXPoFeqv2+rXJaG+uQZGSPchwaiyGhDHGzi2rislNj+cPrx7gi499xJce+xiX20NOahyK1LwxmeyNFaepDZk+ddxFUDJdMgiS/grhiYS9qRplxqSA5z+awtmIjj76TleGKDL/sZ08guhyoJk8uj4vQJxGQbJezclxJnpPnOqiy2xn0czoLhLISo2LJXrHQCzRe4EhiiJ/fHU/y+yf49Ekkrny1pDc55pFecikAu/vODngdUEQSLrii3gcfXRu/VdQ7vXWlhrMvQ7uv3ZaUK7nLzdfOgmZVMK6T6KjqnfL/gaeWne4v01eFOHPbxxhy/6GEc/rO30Cy9GtJCy8vn/HdiJhqdiJx9HX3yIquhxjduz2B41KTmayNqyGbHXNPcikAlmpI0/U+53RO5sxH/40TNGNzsq5OTz/k6t4539uICctnqO1HcO6Ek8UDlV6J7vjNWLzoVbKmDctje1HmnB7RERRpPOTF+naupa4WatIvfnf0c1aRe63/krqLf8BgDIA85q1GysxxCu5cmFeUOIdDyX53rb1E6eGr+r12HvprT2ItmRxxGUbfGinLqKvocLbvh4FdHTbKK/rZFnpyNW8oujB3lp3QRmxDYUyvZDkax+ir/4YHZ/+M9LhDMmC6eksLc3kXxtPBLQoMR/egqBQo52yMITRjc65Ccct9mm8bF2KTmJDpktGkZIzoC0/EOJnrCD3W3+l8Mevk/utv45LfkAhl1KSn0hZtZEmo5X0JM2YfRsEiRTt1EXYag7icQRXV97eXEv7hr9y6smv0r7+aTw2M4mXP0Det58l7Zb/IG3GfAw69QBDtomM7pKrSL3xO/TVH6fjg2dwW7zPf4+9N6RztuFo6+rlzc1VrJidxbQIO8mXFifT0Gqmo3vwdyxhwXUo0gro+PA53H3Dm5iKojikEVufw0VHd1+sojeMyGUSls7MwNzrpMN0Vqv3nxsqaPYYcHY09RtahhqXqW3csg3g7aCU6ZJxmSZWRa/ocmJvPRmQPq8Pdf5MECTYokCnt7dqH4JSgzrX/1xHfoaOuubxFSXtPNqMTCowvyS68wOZKVoa260Tfm0ZbqJjdRUjaLz+aRVi1edkyzpJv/qLg3Z9g4VBp2LJrEw+2VOPzT6wskaRkoNu3jWYD27C3lo3rvuYzHbe2VrN0tJMinL047pWoBh0Kq5enM/m/Q20+lk5G0pe3FCO3TnQ7MLudPPihuHNX0TRQ8fHzyONM6BfekuoQwwJXZtfBs/A79hYHbv9pTBLH96K3hYz2anxfumqaornocyaQtfn6wa0SEYDEonAHVdMpr7FzM6jzZEOZ1wcONGOIV5J/ihV1oGwbFYWXWY75TXtGDf8le7d76Kbdy0p131zQCWC6syE1ac7NhrHT3ZwpNrILauKoqL1qjjHgEQijCjfYK3cC24XcSVLwhjZyMRNXQSI9J7YE+lQAPqrv5eOkuh1dbUiOmwo0i7sRC9A/MxL0c2/lp4967Ec/TzS4QzJ126aiUIm4el1h/1qx/Y4+rBW7CCuZDESxfi09saLYdU9/TILVc50sqSdaBXCmGUWQsXMomRONvVQ1WAiY5wJLm3JkqDJN3j6rPTs/4jTf/sejc9/D8uRLWgnzyfjvsfI/vqT6BfdMKDFujhHT3UYN5VDTdyM5UhUccDA732o52xD8Y/1x0EQeOC68BaJDEVpsbcz6Mh58g3g3WxIWfMN3L09dH760rDXcPW04+ntGcKIzSdfEqvoDSeb9g0usrE73WysFkH04Gw/HZY4XN1tyMdpxOZDZkjDOcESvfa2U14j2szAtfslSg2q7Cn01h4OQWT+I4oeeqv2oSmcHdBmakGmjtNtFpyuwUaY/t1XZFdZM7OKU9BGub53dkocDqebju6hzV1jDE0s0XsBcaCijXUbDnNT/BGUOSVoQ7yAvn5ZIdY+F1sODB7MDMvvRKLS0rHxeb8WOsOx7pNK7E4P945iRBMqbl1VhCAIvP5p5Kt6jUMYXo30OoClbCv25moSL7s3rEYjwWQ4Z+6xOHb7S2FWAm1dNsy94dmRr2vuIc9P079+Z3RLZ9C1sIPB8tlZZCZreW1j5bh++5HE7RE5VNnGnCnjb4c7l3nT0lDJBbo/fBrzwY3ol9xC0lUPDqpoleqSkWr19PmZ6F27sRKdVsE1i/KDFut4UCtlFGTqqBgp0Vu+84xsQ/QYa8lTcpEnZkSNTu+2w17ZhuzUkWUb7GeM2C70il4fSZc/gCp3Gu3v/xl7y8nRTwgziToVX7p+OmU1RjbtqR/1eOuJXYiOvojLNsBZmQUxLpU6VwqTtd3jklkIFTMneSVujCbbuBNcqpzxyTeIokjf6Qra3nuKU09+FeOHz4LHTdJVXyb32/9H6g3fRp07tL9EUY6exnYLvX2R14gMFh7b0JXsoZyznc+x2g4+P9TIbauKSB2jrEcwKchMIF6jGFK+AUCZMYmEBWswH/wYW/3xIY/xmRErhjBiA8hIiVX0hpPh1l4nur1/h3Do9Lr7rHj6rMiClOiV69MmXEXvWSO2sZl0qwtn42ipjaj2v72pBrfVhKbYP9kGHwUZCbg9Ig2tY5M0ONViprnDyuIZ0S3bAPR3vDa2myMcycQiluid4Pg0W69/9B1+/txObtQfR+WxkXzVgyE3LZuSZ2BSdgLrt9UOSuhI1XEYVnyBvlPH6D2xe0zXb+vs5YMddVw+L2fUhW6oSEpQc+WCXDbtqcdoCm5bX6AkG4ZO1A73usduo3PzSygzi4mLskVaIAynXRdKTTufIVs4qnotNidGk428DP+/4+q86agL52Da8SaeEVr9IoFUInD75ZOpbepm7/GJNWH0UXPahLnXGTTZBh9KqYdvp2wno+co+pX3kLjqniGf04IgoMwswt40us5nZX0XB060cdOlk1Apx28aFyxK8hKprO/C7R5sSuXps9Jbe5C4KJJtAO+/u3bqYmx1R3H3RnYyaTT5J9sA4GipBYkURQBSHxMZQSoj9eZHkajjaH3jN7ht0Tfxv3JBHtMLk3j+vWN0mUeuQLEc2YJMn4YqJzoMLPfbC/hZ5w24kLLPPZX99ujbQGgxnl3YfrKvflQJq5EQBIn3d19zEI/d/3meu9dM9571nP6/f6PphR9jrdhJ3PTlZH7pv8n6ym9JmH8tUvXISeiibD2iGF5PgFATiTnbubg9Is++XUayXs3Nq8aW/Ak2EonArOJkDle1D7sBblhxJ7KEFIwfPIPoGpz4tzdVg0SGMnWgPJPPRyQm3RBehlt7kZCCIFd6K01DjE9PNxjSDQAyfRpua3fQZWxCib2pGqlWj1Q3Ng8fdUEpIGKrOxLcwPzEfHQrza/8AoDOT18KSOImP/OMIdsY5Rt2HmlCEGDhDP+M3SNJ1hkN8sa2mE5vIETPCitGwJyv2ZoidLOAMizZC1GmF4b8/oIgcN3SAupbzBytGbxTr7vkSuQpuXRsemFMWkWvfnwCQYC7ropMNa+P2y4rRhRF3tgc2areOy8fbFiklEu5f/XQi0PTjjdxW7qGrBicSJzbSupjPI7d/jApjIlenxFboBIBiavuxmOzYNr1bijCGhcr52aTmqjhXxtPTMiq3oMnzujzTg6OERuAx2Gjde1/kdVXzTrrAhrTR958UWYW4zQ2jprIX7uxkji1nDVLoysZMzU/EZvdzamWwUk4a9U+cLtC3nUyFrRTF4HowVoZWfmGHUf8k20AsLecRJGcgyCL7ta7YCKL05N26/dwmTtpe/v3iJ6xtS6GColE4OHbSulzuHnu7aPDHufqbsdWd5T4WStDvjnvD755ZbfFO2frsTp4at3hcSVSg82W/Q385c2y/v+29DrHHaO2ZLFXvqF634jHiaIHW10ZrW//nlNPfoWOjX9HIleRfO03yPv2c6Ss+QaqzCK//5aTsr1zjQtJviESc7Zz+WRvPbWN3XzpummoFNGz+Tm7OIWO7r5htbslCjXJ13wNZ0cjph1vDXrf0VyDIjVv0HO+2WhFH69Eo7p4nv/RwP2rS1CeJ5WllEu5b/V0FCm5OMYpXegPrm7vXDVY0g1yg1endSIZstmbqlBmFo95/FRmFCJRx9EbAZ1e89GtGN9/BvFMYt1t6QxIzzwzWYtCJhmzIdvOo81MzUvEEB9ZySh/SNSpUCulNBqjq7gp2pm42Z8YgzRbb9LswyHK+L+G8LXCLp+TTbxGwfrttYPeEyRSkq/8Eq7uNrp3vxfQdetbevh0Xz1rlhaQMtyuaZhITdRw2bwcPt51iq6eyGnD1DZ1Iwigj1ciACkGNY/cXsrKuTmDjnV2tdC9+z3iZl6KKgoc7cdDsB27/SEhTklygoqa02FI9LZ4B+i8ABO9yvRCtCVL6N6zHpfFFILIxo5MKuH2y4qpajBx8MTQrYrRzMHKdiZlJ5AQFxyNc3efleZXHsN26hj61Q+zxzO9X391OLzGEiL2lsHPVh+1jd3sOd7CDSsmRd0iz2fINpROr7V8B1JdclTJNvhQpBciS0jFWhFZ+QZ/ZRtEUcTRehLFRSLbcC6qrMkkX/0VbLWH6fosOOavwSQnLZ47rpjM1kON7CsfurvBXPYZIBI3c2VYYxuOsXgBhJtQxHhWvmHnkO+7LF2YdrxJw1++RfPLP8dWcxDdnCvJ+spvyfrSE+jmXIFEGfhc1RCvIjlBRXXDhVPRG4k5mw+rzck/PyinJD+R5bOzQn6/QJh9ZuP48DDyDQCaokvQTl9G1443cBjPyuL1G7FlThp0TpPRGqvmjQAr5+bwyO2lpBjUg9ZkitQ8HG2nQl7o4Ax2Re+Z6zgnSKLX3WfF2dE4JiM2H4JEirqgFFutf5r6waRr88uIroFeK4HomUulEnIzdNSNIdHb0mHlZFMPS2ZFv2wDeIsLM1PiYhW9ARI9W50xAsbYZWOuopYrVGVI8ZAmM7PPns8pa/iqQpRyKVctzOWtz2po77INSsqqC2ahmbyArs/X0bNvA26LCZkuCcOqe0ac9L30YQVKhYzbLouORMBtlxfzyd563txSzZdvmOH3eeajW+na/DKung6/PvdwlJ/sZMPOOq5fXshXb5w56vEdn7wIEgmJK6PLQGWsxM9YEXaNwMIsPbVNppDfp66pB61KRop+DIvES+/CWrEL0/Y3SL76yyGIbuxcPj+HtRtP8K+NJ5gzJSXi1Wr+/hZ7+5xU1HVyS5BaPt3Wbppf+SUO42nSbvkPtFMXMr9sL9uPNPHVm2YO6xbvc9a2N1V5nYGHYO2mE2hUMq5fHvoOjkBJMahJ1KmoqOscUG3slW04RMLca6Ky08Ar37CI7r0f4O6zIlWFfwHtk224d/Xo3SxuSxdua/dFo897Pro5V2Jvqsa04016Dm7EY7OMa6wNNrddVsTnh07zlzcO89T3LkN9jryKKIpYyragypsetIqs8TIWL4BwE4oYBUGCIiWX3hO7qf3Vrch0yehX3oVMraPn0CZ6q/aBx40qdxqG5XegnbooaGbHRTl6qk93BeVa0UIk5mwAazdV0m2187OvLIr4nON80pO0pCZqOFTVzpplw4/ZyVc+iK3mEMYPniHjvl8iCBJcXc147L2DjNgAmo0WLpmSFsrQYwzDyrk5QxbbKFLzMB/ahNvShSw+MWT3d3W3ISjUSEaRiPGXsxW9E0N2zd7slTcbixHbuagLSrEe346zvR7FedIooSQYHjQFGTr2HG9BFMWAnnm7zhhmL5oA+rw+slLiqDh1YY2VoSb6Vlkx/OZSw2m+oN1JpqybNJm3PXaWop5LDeFx+vSxekkBiCIbdg5tiqLKLQG3E7elCxBx9RhHbE2orO9iZ1kzN68sClpF3XjJTI5jxSXZbNhZR7fFPvoJnG3JcPUY8edzD4fT5eFP6w6RrFdz7zWja/jZ6sroPbEb/ZJbwqaJdiFSmJVAY5uFPocrpPepa+4hL0M3pkWJIimT+NLL6DnwcdTtwMtlUm69rJjyuk7KagY7TYeTQH6Lh6uMuD1iUPR5XT1Gmv75E5ydTaTf8UO0UxcC3nZ8k9nO8ZPDT+ak6jjkiZn0DaPTe6qlhx1HmrluWSFxUeiWKwgCJfmJgyp6rVV7vbIN05ZGKLLR0U5dBB6XN7kTAbafkW1YVjp6RZqv4luRdnEmegGU2VMAAY/NzHjG2lAgl0l55PbZtHXZePnDigHv2RtP4OxsJj5KqnkhcC+ASBCKGM1Htw4wwXL1GDG++yda1v6KvoZyEhasIfuhJ8m87zHiZ14atCQveHV6G9utWG0XjiFbKmOjDAAAj+RJREFUJGhqt/De5zVcMT+Xohx9pMMZktnFKZRVe+cYwyHVJpB0xQP0NZRjPrgJOGvEpjzPiM1md9HZYyczZsQWVSjSvMnCUMs3uEztyPXBK6SQqOMRFGqcXRMk0XtmfjzUBkggaApnA4RdvkGqHbqTM5C1e36Gjm6LA5PZv9yEj51lzRRk6khPmjjPjqyUONq7enE4o0uqK5rxO9H79ttvM2XKlGH/9/vf/37Ycx0OBzfccANTpkzh9Omhk5Ctra389Kc/5fLLL2fWrFlcffXVPP300zgc4XG9n4hcrz6IQhj4ZVcIHq5XHwxrHGmJGuZPS+ejXaeG/PH17Hl/0GsjtSa8+MFxEuIU3LgiuqrU7rh8Mg6nm3e21vh1/HhbMny8ubmKhlYz37y1dEA10FCIHjcdG/+OLCGVhIXXB3SfGAMpzErAI3oTsaFCFEXqW3oClm04F8PyOxAEga6ta4MYWXC4cmEehnglazdWRjSOQH6LByvbUCulTM0bXxWGs6uFphd/gstiIuPun6KZNKf/vfklaSjkUrYdahzxGsrMIuyNQ2uDr9tUhUoh5YYorOb1MTU/kdbOXjrPkbyxlu/0yjaMswIjlCizipHGJUZMvmH74SYKMnX95hMj4WjxbrAqL+JEr2nrWmBg4mQsY22omFaQxOrF+bz3eQ1VDWerUcyHNyPIVWhLFkcwuoFcN4TW90heAJFgOG3M8cTYtfllcA9OtErU8eR9+1mSLn8ARVJopAB8SckLyZAtEvzt3WPIZVLui6Lv6vnMLk7B2ueiZhRN5rhZq1DlzaDj03/iMndib65BkClQpAysHm0+o1eZmRycis4YwUGRmg+AI8SGbK7uVmQJwavmFgQBuT5t4lT0NlUhT8wc1fRyNGS6JOTJ2djCnOiVqAdLcwWqZ16Q6dV5PxnAWrXL3Ed5XSeLJ1A1L3gTvaJ49rkXY3T8TvSWl3u1r5YuXcr1118/6H8lJcMPrH/84x85ceLEsO+3tLRwxx13sHbtWnQ6HStXrsRqtfLkk0/y5S9/Gacztss9FLI+U0Cvh5LrlxXSY3Ww7fDgxEUgrQmHKts4XGXkjssnR53mZE5aPEtnZbJ+20nMvSNvQLjMXWeqB4d4L4CWjNNtZv61sZLls7OYVzL6YG4+uAlH2ykSL78/qBUnFyM+k5RQ6vS2m2xY+1wBG7Gdi0yXhG7+aixln+Foqw9idONHKZdyy6oijlQbR6xeDTXD/xaNdH72L+wttf3aXAdPtDFzUgpy2dgbXhzt9TS9+BM8jj4y7/kFqpyB46NKKWN+SRo7yppHrOxRZhbjtnQOemY0tVv4/NBprl1SEDVdD0NRkm8AoOJMVa9PtiGuZHHUtdWeiyBI0E5dhK32UNjdp9u7vLIN/piwAdhbTyJPzBiTPuiFQjDaH0PNA2umoY9X8usX9vKlxz7i1kffoP3gVswpM5Eooudv12S0IpUIJCWoRvUCiBQjaWOOleG+Kx6bBUEa2rloUbYegKoGU0jvcyFz4EQbe463cOcVkzHootdYaFZxMgCHRtDpBW/CLeXaryM67TT8+WG6d7+HKHqwHN8+4Lgmo1evMiOm0RtVSFVaZLpk7G11IbuHKIo4Te3I9MEzDQaQGdJwToBEryiK2BurxqXPey6awtn01ZfjcQZWGTtWek8exmk8jXbGinHpmednetePdU3+r1V3H21BFGHRzAmW6E31JvRPD2NoGWMwfmv0Hj/ubWn69a9/TVqa/7tH+/fv5/nnnx/xmJ///Oe0tLTwne98h29+85sA9Pb28vDDD7Njxw7++c9/8uCDD/p9z4sFmS5pyARGJNr1ZxUnk50ax/ptJ7lsXu6gePyJUxRFXvygnBSDmtVL8kMZ7pi588opbDvcxHuf13L31YP1E0XRg/nARjo3vzTsNQSFCo+jD4li5MmoxyPy1LrDKBVSvnrT6LrAbpuFzs9eRZU73dt6HGNcpOjVxGvkIa2yOXVmBzYvfeyJXgD94lvoObiJzs9eIf32HwYjtKBxzaJ81n1SxdpNlfziq+GvXBNdTgSZAtE1xOaMVIZp+xuYtq1DlpCKmDsHbbfInOWXj/l+9qZqmv/1GIJUTuZ9jw2qwPGxtDST7UeaOF7bwcyi5CGP8U1g7U3VA56Xr31SiUwm5aaV42tXCzWFWXrkMgnldZ0smZV5VrahZEmkQxsV7dRF9Oz7gN7qA8SFUWZiR5n/sg3gregN1kJnohJNc6Hh0KrlLCvN5N3PvRXYlygaUAkO/lqdROf+hqhIpJrMdj7d18AVC3J55PbZkQ5nRIbTxhwrkfwOJcQpSTGoR63yjDE0LreH5945SkaSlhuirBPwfBLilBRk6jhc1c4dV4xslOyTbeqfu7hdGN9/BqA/EdTU7q1siyV6ow+fIVuo8NgsiA4bcn1w9Znl+jRsNQcD1nwNN25zJ26rKWjdYerC2XTvWU9f/fEBHXihQBRFuja/jEyXTOqabyLIxr6ZGK9RkJygCqiid+fRZjKStOMqMooEPtPJmCGb//hdslRRUUFycnJASd7e3l5++MMfkp2dTWrq0HqHtbW1bNmyhdzcXB566KH+1zUaDb/61a+QSqW89NLwSbOLGcOqexBkA6u5Ai35DxaCIHDd0gKqGkxU1g8UyvY3zp1lzVQ1mLj7qinIZQPb8qKF/Awdi2ak8+7ntfT2Daw091XxGT98FkV6IYbL7x/0uREkiA4bp5/9N3prRpbY2LinnmO1HTx4/XQM8aNXKHRtW4enz0rSlV+K6sF5oiAIAoVZCdQ2mkJ2D58sxHikGwCkmnj0i26kt3IvfY2RlUk4H5VSxk2XTuJARdugZ0Oo8TjttKx7wrtQkgx8pggyJSnXPUzed54jec03UKTk4Dz6Cd/Wfcy0PY/Tvv5prJV78QyVIB4GW/0xml7+ORKFhsz7Hx82yQtn5Rs+H6ILwociLR8kMuzNZ+UbWjqsbN5/mmsW5fn1XIgkcpmE4hx9f0Wv9fgOZFEu2+BDlTMVqTYh7PIN2w41+i3b4LZZcHW3XbRGbD6GmmMgVURkLjQSO4+29P//C5TVdLi1lPel8OKG8ghGdZb3t5/E6fJw06XRvYEUCiI9ny7K1lMVS/SOiQ076mhoNfPlG6ZH7drhXEqLUzh+snNU/4euzS+DZ6Ac3vmSNE1GC4k65aiybjHCjyItH6exMaA5ZCC4ur2+HLKEIFf06lMRXQ7cFlNQrxts7E3eeXGw5pOq3GkIUnlY5BusJ3Zhb67BsOLOcSV5feRnJlDX5F+i12pzcqSqnUUzMyZcrkCjkpOoU9EYq+j1G78SvQ0NDfT09DB9+vSALv7EE09w+vRpfv3rX6NSDb0g3bZtG6IosmrVKiSSgeFkZmYybdo0Ghsbqa4e2pDmYiZ+xgqS1zw0rpL/YLJqXg5qpYz122qHjFMaf6YyQiIdFKfb7eGlD8vJSYtjVRRUtozEnVdMwWpzsn6btzLH43LQueVVTj/3PZwdTaRc/wgZ9/wcw6IbB/19Um74Fhn3PYYgk9Pyr8dpffv3uIYYTLt6+vj7+mPMmJTElQtyB71/Pg7jaXr2bSB+9uUX/aI/mBRm6alrNuNye0Jy/VPNZpL16qCYaSUsWINUm0Dn5pf6ZQiihTVLC4hTy3ltU/iS0J4+Ky2vPobt5BGS13yTlOsfGfJZKdUmoJt9Bel3/ojXUx/mTa5EO6kUS8UuWtc9wanffYnWN/4Xy9HPcfcN1IUyH91K/Z++Tu2vbqPu91+i+ZVfIotPJPP+x5Eb0keMzyffsPPI8PINEpkCZVreAEO21z+tQiII3LJqYlRxluQnUn26mz5zD70nD6MNk2zDlv0NPPj4x9zw6Ds8+PjHbNnfEND5gkSKZvJCeqsPhK2Vr73LRsWpLv+reVu9Y5AiPbqr2ELNwLmQF90lV0RsLjQcxi4bM+WnSBJ6mCJrRiM4uERxEmNXeOVBhqLP4eL97SdZOD2d7NTBuoEXOpGeTxdl62k2WrHEDNkCosfq4JWPKphdnMKC6SOPudFCaXEKLreH8pOdIx7njyRNU7uVjJg+b1SiSM0D0YPTGBqDdJ8BsywEFb1A1Ov09jVVgUSGMi0/KNeTyJWocqeF3JBN9Ljp2vIq8uRs4mZeGpRr5mfoaGg143SNvlbdW96Kyy1OOH1eH9mpcbFEbwD4tQXo0+dNSkriscceY+vWrbS0tJCZmckNN9zAV77yFZTKgTvhn332GWvXruXBBx9k3rx5w17bl8AtLh56R6awsJCysjIqKyspKpoYC9twEj9jRdQsZjQqOZfPz+HDnad48PoZ6OPPfid8cXbv+5COj/5vkKnF5v0NNLRa+H8PzEcqHbs2ZjgoytEzrySNtz+r4aq8XsybnsPZ2UzczEtJuvwBpNqE/mOH+/tkf+W3mHa8RdeON7DVHCLx8vuIL728PwHy7NtlOJxuHrl9tl9JkY6N/0AiV5J46V3B+6AxKMxKwOX20NBq7he8DyanWnqC1jojUajRL72Vjo+fx3bycL+LbDSgUcm5YcUkXvmogpNN3SH5tzwXd28Pza8+jqOtjtSbvtvfej/Ss9Ll9nCgtodLL1lM2k2liG4ntrqjWCv30HtiD9aKnSCRoc6fjnbyQjyih65P/tlv8ubp7QEEdPOv9bvdd9lsr3zDsVojs4qGrspQZhZjLvsMUfRgNNn5ZG89Vy7MIykhenQ9R2JqfiJvbK7m1J6tyMMk27BlfwNPrTuM/Yw5aHuXjafWHQYIqN1bO3UR5oMfY6s9hHbKwpDEei7bj/hkG/zU540ZsfXjG2tFj5v6J78WVfq8Pi41nGYN26hxpiIRwORW8wXtTuK1ikiHxqf7GjD3Orh55cU7z47kfNpnyFZz2kRpcXAr9C5kXvmogl67i6/cOGPCVKdNL0xCJhU4XNXOnClDd7uCf3IizUYr86cFN9EXIzgoziQgHW2nUIZgMzZkFb0G7/fJaWpBlTNYojBasDdVoUzLD0pFrA914Ww6P3kBV49xwMZxMLGUfYazo5G0W7+HIAlOB0JBpg63R+R02+hr1V1lzRjilUzJMwTl3uEmMyWObYcao15aJFrwK6Pm0+d98803Wb9+PUVFRZSWltLa2sqTTz7JAw88QF/fWVdtk8nEj3/8Y4qKivjud7874rXb2rwPquGkHVJSvA8wo3HwYNfT08Pp06cH/K+lpWXQcTHCx7VLCnC5PXy0u27I9+NnLEeQKeg5uLH/NafLzSsfn6A4R8/iCSIMfueKTK4XNtP52mOIokj63T8l9YZvD0jyjoQgk2NYcQfZX/ktitRcjO//heaXforDeJo9x1vYdriJO6+Y7Ff7bm/1fmy1B9Evv8Pv+8fwj0lZoTNkc7q8CeS89OBVT+nmXIUsIYXOza9EXVXv9csL0ahkrN0Y2qpel7mLpn/+J05jA+m3/cBvfdWKuk5sdjeXTPGOOYJUjmbSHFJWf53c7/wfmQ/8FwkLr8NlasP44bN0fvRcf5L3LCLdO97yO9Z5U73yDdsONw17jDKzGNFhw9nRxJubqxBFuG1V9Esf+JialwhA74mdYZNteHFDeX+S14fd6Q64RV6dNx2JKi5s8g3bDzdSmJlAph/PffBW9ErjE2PP/XMQJFK005Ziqz4wqAI/0lyvPohCcDNF3sxJZzLv2uahENxcrx5ZxinUuD0ib39Ww+RcPdMKEiMay8WKz5AtptPrP3XNPWzYcZJrF+ePW/4qnKiVMqbkJXK4amRDttHkRHr7nJgsdr/HixjhRW5IR5ApcLTWheT6LlMbEpUWqSq4+szexLGAq6stqNcNJqLHjb25Juj+BJrCUgB6aw8H9bo+PC4HnVvXoswoQhPE4gFfwdDJUeQb7E43+ytaWTgjA4lkYiZJs1LisNic9FhDI4lyoeFXotdX0bt69Wo2b97MX/7yF1566SXWr1/P1KlTOXjwIH/4wx/6j//FL35BV1cXTzzxxKBK3/Ox2bwta8NJO/he7+3tHfTeCy+8wOWXXz7gf/fcE12abBcbOWnxzJ6cwoYddbiHaHeXqLRopy3FcuzzfjfzDTvqaO+y8cC106J+d0YURcxlW1C9/zPmK0+y1T2b1C/+L5qC0jFdT5GcTca9vyB5zTdwtNVz+rlHOfbG8xSkabjFj2SO6HbSsfEfyBMzSZh3zZhiiDE8mSlxKBVSagNwM/WXxnYLbo9IfhCrW70bCHfiaKkJu7boaMSp5axZWsCOsibqW/w3DQgEp6mNpn/+BFePkfQv/BhN8Vy/zz1Y2Y5EIgxZWSsIElTZU0i67D6yH/oT2V/7/bDXCaSSUKWUMX/ayPINvolsR80xPtp9isvm5ZCaqPH7HpFGH68kP0lGXFcl2pIlYXnGD9cK395l44kX9vLShnK2HDhN9WkTffbhdRIFqQzN5Pn0Vu1DdIW2pdon27DUz2peAHtLbayadwjipi9HdDvpPbE70qEMQNZnAkAigE1UcNyZTY0ztf/1SLHnWDPNRis3ryyK+jnYhYpOqyA1UUNVgynSoUwIRFHk/94uQ6OSc9cQxsjRzuzJKdQ0do+YrBhNTsRnxJYZM2KLSgSJFEVKTsgM2ZymNmQJw1eEjxWJTIE0PhFnFEs3ODsaER19QS8ckKfkIo0zhEyn13zgY9w9RhJX3RPUsTYrJQ65TNLv+zIchyvb6XO4J0xR3VBkp3o3tmLyDf7hl3TDk08+SUNDA7m5uSgUZ1vMsrOzeeKJJ7j55ptZu3Ytjz76KB999BEffPAB3/jGN5g5c+ao15ZKvWXro33hPZ7BScMHHniAm2++ecBrLS0tsWRvhLl+WSGPPb+bXUdbhly06uZcgeXIZizHtiMruZS1myopLU6mdHJ0t6s5u1owbvgrtpNHUGZNxrriC7zxSh2J+5u4YfnYzUsEQYJu9hVoiuax5x+/59Lu/axQN+JqykSeO7Iudve+DTg7m0i/80cI0uC1r8TwIpUIFGToqG0MfqLXNyAH2/U0bsYKTDvfpuuzV9BOWRC01qBgcOOKSbz3eS2vbariP+71PwnrDw7jaZpf+QWi00HG3T9HlRXYBPDAiTam5BrQjqKXLAgCipRcZLrkoLi0LyvNZPvh4eUb5EmZCEoN1QcO4vZM4/bLR3bqjkYuTW5H2u5BM3VRWO6XbFDTPkSyVyGXcLKpm51lTZybV08xqMlOiSMn7f+3d9/xUdf3H8Bf35u5keQue5K9SCAJyJIhKKCIo6ioFYurRWmtravO/mxFW61aq9iqbR0FcYDiAEVl72UYYYSQTfa+3N7f3x/HHYRckktyM/d+Ph48Wr933+/3E/jk7vt9f9+f9zsUSTFSJMXY/lcWKoQ0dxrUpduhqy2FONO9c/ZiQy3bYDUZYOpsgsRLf6eBRJiQCZ48DuqTuxBaeKWvh+Nw8VLsTH4LwhgtNugm4NGIn3w6ri93VCE2QhywNftGi6wkGSopo9clB062oLSyA/cvGocwPyh9MlSFmdFY8/0ZnKjsGPDh3kDlRJo6bIEOyuj1X4KYVGgqDntkmbm5pw38SNfq+Q8VXx7r1zV6Def7Vrg7o5dhGIjSC6E9+xNYq8Wt909Wgw7de7+AKHUcRGnj3XZcAOByORgTF4qaQZKS9p9ohiSEh3EZnilL4Q0J0bYHW41taoxNG9r9VjByKaNXKBQiMzOzV5DXLi8vD3FxcdBqtSgtLcXzzz+PvLw8/OY3v3FpACKRrc7gxaUfLmbfLhb3zWAKCwtDUlJSrz9xcYFRjH80m5gXi5gIMTburXb6ujAxB/zoZKiObsbXu6qh1Bix9NqxXh6l61iLGd1716Ph3w9D31iByKt/hYSlLyB/YiHy0yOxfnslTGbL4AcaRHUXi5dqJ6AkeQn4jBXNq/8P7d++DYvO+VMri6YHit3rIMoo9mgAItilJ4ajurEH1n4yLoerrlkJLodxqTzHUDAcLiJm3wFTZxNUpdvdeuyRCpcKseDyNOw+1oAmNz6NNbRUo2n1HwGrFQm/eH7IQd4etQFVDQpMyHU9O8JdXdovy42FUMDFnmPOyzcwDAe8mDSw7dW4ojgR8QGYvZNtrUSXRYJuoWduSi61dEEeLl2VJuRz8dvFRXj3qbn4/KXr8M/H5+DJuybhzgW5yE+LhEprxI8H6/CvL0rx9Nt7sfTPP+Dnf9yEP33XAxNHiFPbfsTBk822THwPNGfcM9SyDW11AGv1SO2/QMcwDKT5M6GrPQmzqtvXw3EIn3GL4/8LGCuuER1HjTkGdVm3+mxMZTVdKKvtwo2zMvy+P8Jol5EUjpZOLdRaWpI6EJPZgvc3nMSYuFAsmJbq6+EMS9YYGURC3qDlGwbS3GHL6I2LDJwVPsFGEJsCq1YJi5Om2yPBsizMijbwZe7P6AUAnizGrzN69U0V4AjF4Ee6vgLKVeL0Ilj1ahianccwhqvn4AZYtUrI59zp1uPapcaHDZjRa7FYcfBUCyaNjQOfF7jf9bFyMXhchjJ6XeRSRu9goqKi0NzcjHfffRc9PT1IT0/HU0891es99hq7f/3rXyESibB8+XJkZGQ4avM6q8ELAO3tti/B/mr4Ev/D5TBYeHkqPth4GrXNfZtNMQyDsKK56Nz8AQ5UH8S0cbnIHuOfRcH1jWfR8d3bMLadgzhnCqLm39crY++2udn4v3/vx5ZD57Dg8uEvoTVbrFi59hgiwkKw8NYrEcK5Ft2716Ln4AZoKw4jct69kIyd3uuJcNfOT2A1GRA59+6R/IhkEBlJMny3rxYtnRq3Zk7UNiuRFCP1yBeuOHsyhAlZ6N61FtKCWeDw/CfjZdEVGfh2TzXWbj2L398+YcTH0zeUo+XTF8AIxYi/4zkIhnHhd7yiHSwLFA9hVYE9y6Z7+xqYlZ3ghUVCPmfJkJv5hAh5uCwvFvtPNOP+ReOcBluqDJFI5JzGLVekDunY/sCi10DafRbbjDlQ1nV7JfsoNSEcVhYQh/Cg05sRJRdh6YI8RyM2Po+LMXFhGBPX+7uJZVl0KPRoaFOhoU2N+jYVGtvUKFMmIaO1FM9+cABWcMDjMoiPkiIp5uIsYFsmsEg49Muqtm4tyuu6sfTaPJf3MbbYbkIEcVS6wRlp/gwo9qyDpmwvwidf5+vhAAAYq+2BMFcSDotGiRnRCuzs4mDdKQ5mXM36pGbelzsrIRXxMXfyGK+fm/SWdb4hW2WDAkXZdM/Tn693VaOlU4vnl00L2IcTPC4H4zKicGwEgd6mDg2iwkMQInDLrTzxAEFMKgDA2FYLXqj77nMtmh6wZqNHSjcAAF8WC7WqC1az0a/uH+wMTZUQJmSCYdz/+y9KKwTAQFd9bMhJI/2xaJVQHPwG4pwpCHFzFrJdWkI4th6uR7dKD3lo33Kop2o6odIaMTWAyzYAtuzl+CgJBXpdNOi3g1qtxssvv4yenh78/e9/B4/Xd5eGhgYAgMViu4g9evQojh513lxiy5YtAIDFixcjIyMDWVm2X6LKykqn76+qqgIAZGcH3nLVYDZ3cgrWfH8GG/dU48HFRX1el467Am1bVmMicwZzFizqewAfUJ3c5QjacEPl4EcmQV97AtxQOWJveQKSnMl99inKjkbOGDk+31aBeVNSwBvmRedXO6tQ26zE03dPhjiED4CPyKuWQpo/Ex3fvY22r16HqHQHROmFUB7a6Fj+KUovgiAqaSQ/NhlEur0hW2OPW4NUdS1K5KV6pvENwzCImLMEzWv+BGXJ95BNucEj5xkOeVgIrp6Wim/31uD2eTmIixx+hqquphQt614GL1SO+DueG3b34SPlbQgV85GZPLQLcXd1aZ9ZmIi9x5twsrqzT8d1ldaIHed4WBrCIgadAAJrqZL27CHAakEZkwFjbReuvCzZ4+dcu+UsREIu/vvMPISKXb9JYRgG0XIRouWiXt3QNWd4aP3ib3hlcTTOccY4AsHnWpQ4eKqlV7Z/VHiIrfRD7IUSEMmxoZCHCp0u3dxRUo93viwFAGzcU41omcgRkB6IoaUGnBApeGH+XfLIVwRRSRDEpUN9crdfBHpZloXyyGYIYtOQeN8rjrnwiyMNeHVNCXYda8TsCd79Lm/qUOPAyWbccmXWsB5QEPfKON+QrbKhhwK9l9hRUo9Vm8rQ3q0DA1uj3Is/owNRYVYUDp1uQVuXdlh195va1VS2wc8JYmwP0IxtdRBnFLvtuOYeW6M0nscyemNt51G0+d09ptVkgLGtDrKpN3rk+FxxGARx6dDVHId85mK3HFOxbz1YkwERs+9wy/GcsSfV1TYpIc/pG+jdf6IZAh4HEwP8cxMAEqKkFOh10aBXdhKJBJs3b0Z3dzcOHz6MadOm9Xp9586d6O7uRnZ2Nt57771+jzNv3jycO3cOW7duRVLShQ+NmTNnAgC2b9+Oxx57DBzOhUBZU1MTysrKkJiYiMxMzzwBIZ4RJhHgiglJ2HGkAXcvHAvpJTfbXXoejhnGYIq4FokRvn9aqDq5Cx3fvgPWbAAAWFRdsKi6IEovQuxNj4EjFDndj2EY3DYvG8+/dxDbf6rHvCkpQz53U4can/xwBtPGxfcpkC6MS0PC3X+FsuR7dG1dDV117wcourrTUJ3c5ZZgE3EuJS4UXA6D6sYezCxyz9Jzjc6E9m4dFkzzXKdoWx2oQij2rkdY0VxwhP6zvO/mOZnYtK8Wn2+rcPogyBWas4fRuv5VCCITEPfz58CTyoZ1HJZlcbS8HYVZ0eD6qAvtxLwYCAVc7D3e1CfQ+82ualToIoAQwNBUgZDEwHroqSnbD154NCSRWSir7fL4+epbVdhzvBE3z8kaUpB3IKKMIjB8IeTdp5C9YEav10xmK1o6NahvtQV/G9pUqG9TY+vhc9AZLpT0EYfwetX/TYoJRXOHGmu+PwOj2VYKoktpwFvrbN2eBwv2GltrIIhLo+ZZA5AWzETXlv/B1NUEfoT7l3gOhaG5Csa2WkRd86te/2YzixLxxfYKrPm+DNPHJ3h1SeXXO6vA5XBw3Qwq/+EPQsUCxEWKUUkN2XrZUVKPt9Ydh8Fk+zxlYfuc31FS79JDMX9l70tyvKJ9mPcOmoBuqhQMuKJQcEMj3d6QzaywBXr5nsroldsDva1+F+g1ttYAVovbG7FdTJxeCMX+r2DVa8AJGVm5NLOyA8qfvod03GyP/l06Ar3Nyj4PwViWxYETzSjOiUHIKHiomxQjRcmZVlisrM/u2wLFoFeUDMPg1ltt9cNWrFiB1tYLNVvOnTuH559/HgCwfPnyYQ0gOTkZM2fORHV1Nd544w3Hdq1Wi2effRYWiwX33HPPsI5NfGvh9DQYjBZsOVzf57VPfjyDA8Zs8K0GaE7v9cHoeuvevsYR5L2YqaOh3yCv3WV5schICse6rRVDrt3Isiz+9flx8Hgc3L/IefNChsNF+KSF4IicPLm3GNG9fc2QzkmGxrbMO9StDdnqWmx1lFLc3IjtUhGz74BVp4LiwDcePc9QRYaLMG/yGGw9fM5p06zBqE/uRuvnf4MwNg3xdz4/7CAvAJxrUaFLqfdpdlCIgIdJebHYd6Kp12eIRmfCht1VyC/IADc00tGAIlBY9Bpoq49DkjcNeWmRqGtRQqs3efScn2+rAJ/HxY2zht8g81IcvhDijAnQlh8Ca+1dj53P4yA5NhSXj0/ArXOz8cgdE/H676/AZy8uxIf/Nx8r7p+G+xeNw5yJyQgR8HDsbDtWfVeGv3x4CB9sPO0I8toZTBas2lQ24HhYixmGtjoIqWzDgKR50wEwUJ/c4+uhQHV0Mxi+ENL8mb22czgMll47Fi2dWmw+5Jnu7M70qA3YcrgecyYmISKsb/YP8Y0MasjWx6pNZY4gr53RbB30c9LfjTm/0mM45RvUOhOUGiMSArBmf7ARxqbC0Frr1mNeyOj1zIoee0avqdv/6vReaMTmuUCvKKMIYK3Q1Z4c8bG6d60FCxYRszxbiz9cKkREWIjThmwV9Qp09OhHzYOhxGgpzBYWbV1aXw/F77mUOvDrX/8aEydORFVVFa655hr88pe/xC9/+UssXLgQDQ0NuPfee3HttdcOexDPPfccoqOj8c477+D666/HQw89hPnz52Pv3r2YNWsWfv7znw/72MR3MpJkyEuNwHd7a3otbW1oU2Hr4XPInTwF/MgEKI9t8eEobczKziFtvxjDMLhtbjaaOzXYdaxxSOfd9lM9jld04O6FYxEZPnBAub9i/q6MkYxMRqIMVY0KsKx7GrLVnS+Yf2n9ancTJmRCkjsNPYc2wKJxX6DaHW65MgssC6zfXjGk/ZRHfkTb128gZEwe4u94DlxR6IjGcaTcdsE8wcfLmWYUJqJHbcTJ6gu/zxv3VkOjN+PWudkQJmRC33jWhyMcOlvZBjMkuZcjNzUCLAuU13muOVZLpwY7jjRgwbRUyEKFg+8wBJK8abBoFNA3nHHp/QzDIDJchKLsGFw3Ix0P3DQeLy6fjv89dzU+feFavPa7/ldhdAzy8MPY0QBYzBDGUibmQHhhkQhJyYf61G63fXYPh9Wgg/rUHkjypjvNDpqYG4P89Eh8+mM59EazV8a0aX8tjCYLbrzCfQ9EyMhlJcnQ2qWFUkMN2QBAoTL0+zB4sM9Jf8cwDAqzolFa0THkzyd7M9v4KCrd4O8EMSkwdTaCNbvvIbdJ0QaOOAwcwcD3jcPFlcjA8AQw+2FDNn1TBbihkW6teXypkMRsMIIQaGuOjeg4xo4GqEq3I2ziNcMuLTcUqQnOG7IdONkMDofB5Pw4j4/BG+wla6h8w+BcCvSGhITgww8/xKOPPoqkpCQcPHgQR48eRVFREVauXIknnnhiRINITk7GunXrcNNNN6Grqws7duxAeHg4Hn30Ubz11ltO6wKTwHD9jHQ0d2ocwRQA+Oj7MxAKuFg8NwehRfNgaCiHsf2cD0cJcEOd10q9uPHaQKbkxyMlLhRrt5yFxeraBVuP2oD3vjmJvNQIXD01ddD39zcWV8dIhi89MRw9aiO6lHq3HK+mWQlJCA/RMs9cpF1MfsXtYE1GdO9b7/FzDUVMhBhXXpaMHw7Wufz3qjjwDTo2vQtx5gTE3fbMoNn2rjha3obk2FBEeeHfYiD28g17jjcBAHQGM77eWY3L8mKRmSRDSEIWzN0tsOhUPh3nUNjLNggTMpGTIgfDAGc8WL5h3dYKcDkMbprj/lJP4owJYLh8aM4cGPGxJCI+ssfIES13Puei+tluZ2ytAUCN2FwhzZ8JU1eTo3mdL6hP7wFr0iNswjynrzMMg6XX5qFbZcCG3Z4fp9FkwcY9ts+WlDjPPmwkQ5N5UUO2YNbWpcW760tx34ub+33PYJ+TgaAwKxoKtQF1LUP7Xm/q0AAAEqIpo9ffCWJTAasFxs6hJQINxNzT5rGyDYDtO4knj4XJDwO99kZsnsRw+RCljIOu+tiIHhJ37/wUDF8A+eU3uXF0/UuLD0N9qwqmS1aK7T/RjIL0SLeVM/O1pBgK9LrK5WJgAoEAy5Ytw4YNG3DixAmUlJRg9erVmD9/vkv7b968GeXl5b3q814sPj4ef/3rX7F3716Ulpbi22+/xbJlyyAUujcrh3jX1HHxiAgTYuMe281LZb0Ce4834cZZmZCFChE67gqAw4PyqG+zeqX5M/psY3hCyOcscWl/DofBbXNz0NCmxr7SJpf2+e/XJ6EzmPHg4kKXum3L5ywBw+v9+zCUMZLhu7ghmzvUNSsxJi7MK/U1BVFJCB0/G8qS72HqaRt8By+65aosWCxWfLlj4JIELMuia+en6Nr6P0jyLkfsLY+Dwx/5d4PBZMGp6k4U5/i+oZW9fMP+8+UbNu2rgUprxG3zbDV57Re2gVK+4eKyDQzDQBzCR0pcmMfq9LZ1a7Htp3OYPyXFI0vROUIRROlF0Jw5CJYdWome/ixdkAchn9trm5DPxdIFeQPuZ2ipAcMXgh8xOpbheZIkdyrA5UF9cpfPxqA6uhn86DEDLjUdmxaJy/Ji8cX2Sqi1ns3m3F5Sjx61EYtmUzavv7E3ZKsK0kDvuRYlXv/kCJb9dQs27a/FrKJE3L1w7LA+JwOBvSb/sbNDK9/Q3K4GwwDxI2hmS7xDEGOrv2x0Y/kGs6LNY2Ub7PjhMY5awP7ColXB3N3ilV4VovRCmBVtMHc3D2t/Q1MlNGf2I3zy9eBKwt08OudSE8JhtrC9AqD2/hGXj5KyDYCtD5RUxEdjGwV6B+O9rg8kKPF5HFwzNRUlZ9pw15+/x8P/2AmGAaJkthtxriQcktwpUJ/YCavZN0vVWNYKXU0pOOIwcEMjATDghUUhauEDQ2pydnlhAhKjpVi75WyvUhXOHDnThh1HGnDLldkY42JGTWjBLEQtfAC8sKhhj5EMT1pCGBgGbqnTy7Is6pqVHi/bcDH5zFvBgEH3rnVeO6crEqKkmDUhCZv216JHfaFGturkLpxbeT+qX7wFdW8uQ8unL0CxZx1CC69EzM9+D4bLd8v5T1V1wmi2+rxsg508VIgetRE/+8MGfPjtaYyJC0Vuim21gTA+AwATMIFeR9mGvOmObXmpESg/1+3yqoeh+GKbrQTIzXM8V7dNkjsVFlWn2/4NZk9MxoOLCxEtF4EBEC0X4cHFha41YotJBcPhDvg+AnBFUogzJkB9em+f+sreYGiphqG5CmHF8wZ9sLf02jxo9SasH+TB10hYrSy+3FGFjKRwjMuI8th5yPBIRXzER0lQEWQN2c6e68aLHxzEb17Zjr2lTVg4PQ3/eXoefnd7MW6+MmtYn5OBIFouQmK0BMeHWKe3qUODKJkIAj59B/g7fkQ8GJ7AbQ3ZWNYKU0+7o46up9gzen1Z9uhShmZ7fV7PZvQCgDi9CACgrT4+rP27dnwMjigUsqk3uHFUA0tLsN1XXlyn98BJW6B66igK9DIMg8RoKWX0uoBqIhCPC5PYlgp0KW2BHJYF/vPVSQj5XMyemIywornQnN4LzZkDPglaasr2w9hag+gbfovQcbOHfRwuh8Gtc7Px+idHcPBUS79Fz/UGM/75xXEkRkux+KqhBSVCC2ZRYNcHxCF8xEdK3BLo7VDoodGbPd6I7WK88GiETbwaPYe/g2zqDRBE+88N0q1XZWPnkQZ8tbMKdy0cC9XJXej49h1Hc0SLqhM6VSdE6UWIWrgcDOO+55NHz7aBz+MgP9335U92lNTjhwMXbgRYFmhu1zg6i3OEYvCjEmFoGlpNY19Rn94HXnjM+QC1TW5qBDbtr0V9q8qtDzo6e3TYfOgcrpo0pt9yCO4gzroM4PCgObPfbRklsycmDylgwbJWGFpqbKthiEukBTOhPXsI+nOnIUp13vTUU1RHt4DhCSB14Xs7LSEcs4qS8PWualw3I90jmek/lbWisV2Nx5ZM9MqKEjJ0mUkylNd5rsSNv2BZFscr2rFuawVKKzsgEfFx27xsXD8jHeHS3it2hvo5GUgKs6Kx7ad6mC1W8LiuXd80daipEVuAYDhc8KOSYWyrdcvxLKpuwGIG38M1X/myWLBGPaxapdcyUgdju/5lIIzz/GoUfkQ8eLJY6KqPIfyyBUPaV1d7Arqa44iYexc4QrGHRthXYrQUPC4HtU1KYKJt274TzcgeIxu0D1CgSYyRDvkBWTCijF7icV84yU65uLN4SGoBePI4qI72X4vLU1iLGd07PwE/OrlPN+zhuKI4EfGREqzdUt7vU9CPfyxHW5cWDy4upKfxASQjSeaW5ZR1Ld5pxHYp2eU3geEL0LXzE6+edzDJsaGYPj4B3+6thkprRPf2NY4g78WM7fVuDfICtkZs+WmRCBH4/pnnqk1lMF5SV8tk6d1ZXJiQBUNzpV9lWDhj0amhqymFJG9qr2BSXqotO9nd5RvW76iExcrilis9l80L2LJDRanjoDlzwGf/BubuVrBGHQSxVJ/XVeLMiWAEIqhP7vbqea1GPVSndkOSNw1ckWtNk5ZckwuLxYrPNpd7ZEzrd1QiWi7C9MIEjxyfjFxmkgxt3bpeq1xGE6uVxb7SJjz6xi788d39aGhT4Z7r8vH+s/Nw5zV5fYK8o11RdjT0RsuQGpU2tWuQQI3YAoYgJsVtGb3mHltwy+MZveeP7091evWNFeBHJ7mlP4crROmF0NWdBGtxvZEey7Lo2r4G3NBIhE28xoOj64vH5WBMXKijIVt7tw6V9QpMLRg92bx2idFSdPbooTN4p4FtoKJAL/G4/jrj2rczDAdhRVdBf+60W4vVu0JVuh2mrmZEzF7ilmWwXC4Ht1yVhcqGHpSc6VvbqLJBga93VuLqqSkooGWTASU9MRxt3TqoRlg/0f4FnBIX6o5huYwrCYdsyo3Qlh+E3s+W/982Lwc6gwXf7KqGWdnp9D0WlXuDg509OpxrUaHYT8o2DPY5CQAhCVmwaHocF/r+ylnZBgCIixRDJhW6tSGbQmXA9/vrMHtCEuK8UK9QkjsVZkWboyGatxnOn1dIjdhcxuELIcmdAs2Z/V4tEaUp2wfWoEVYsfMmbM7ER0kwf2oKfjhQh+bzDZfc5ey5bpyq7sQNMzNczhwk3peZfL4nQIN7egL4C7PFii2HzuE3r2zDX/93GGqtCb+5pRD/eXoebpqTCXGIe0oyBZpxGVHgMHA5O02pMUKtM1EjtgAijE2xXbupXQ/m98ceeOV5OqNXbgv0mv0k0MuyLAzNlRDGe/aB/sXE6UVgjXroG866vI/27CEYmiogn3UrODzvNz9LjQ9zlG6wl23ob4VxIEuMpoZsrqArPeJx/XXGvXi7dPwcgMOFyotN2awmA7p3rYUwMce2JNdN5kxMRrRchM82987qtViseGvdMYRLhbj7uny3nY94h70h20jLN9Q1KxEVHgKpD7qfhk+5HoxAhOZVz6L6xVtwbuX9UPmwSZFdanwYphbEYcPuKpikzrMUeGHuLa9wtNz2IMYfGrEBrn1O2ps5+Xv5BnXZ/j5lGwBbXa3cVLlbM3q/2lkJs9mCW+d6vjkHAEhyJgMMB5qy/V4536WMLTUAhwtB9BifnD9QSfNnwmrQQld51GvnVB7dAn5UEoRJuUPa7/Z5OeByOfj4hzNuHc+XOyohCeFh/hSaO/4sI1EGAKhoGHlQyFd2lNTj3hd+xA2Pfo17VvyAv39cgl/9ZQve+OwoeFwOHr9zIt5+4kpcMy016Fe2ScUCZCTJXA70NnfYAhuU0Rs4HA3Z3JDV68jo9XCg15HR2+0fgV5zTxusWiVCvFCf106UUgAwHOiqj7n0ftZqQdeOj8GPTEDo+DmeHVw/0hLC0K0yQKEy4MDJZiTHhiIpxruJRd6QGGP7/GuiQO+AKNBLPM6VzuI8qRyS7ElQndgB1uz6EomRUJZ8D4u6CxFzlri1Vh2fx8EtV2bhTF03Sis6HNs37KlGVUMPli0aB6koODMXAlmGmwK9tc1KpCb4pt6VpuIwWLPx/DIkFmZlBzq+fccvgr23zc2BRm/GoagbgEtKNDA8IeRzlrj1fEfK2xERJvR6CY3+uPI5KYgZA4bL9+uGbBfKNkxz+rmalxqB5g4NFKqRL0tWaoz4dm8NZhQlOp7uexpXHIaQlHxoyn1TvsHQUg1BVDIYHn2HDIUodRy4knCoT3mnfIOx7RwMjeUILZo75OuLiLAQXD8jDTuPNvRqqjISLZ0a7CttwjXTUoM2czJQSER8JEZLUBmgDdl2lNTjrXXH0d6tAwtbX4LtJQ0Q8jl47pdT8eajszGrOAlcyip3KMqORnldN7T6we9/ms5n+sdTjd6AIYhJBeCmQK+iFVyJDBy+Z0uccPhCcCUymBV9V6f6gv26157w4A2cEAlCknJcbsimPrkbpo4GyK/4uc+a5abF2+4vT1R24GR156jM5gVsn38MAzS2UaB3IPQtSzzO1c7ioUVzYdUqoTl7yONjsuo1UOxbD1F6EUQp7s+unTtpDCLCQvDpFludvZZODT76/gwmj43D9PFUGy8QhUuFiAoPGVGg12yxoqFN5fWyDXbd29cAl3SeZ80G23Yfy0yW4bK8WJwobwFYKxiBCAADXlgUohY+4NYmhBYri2Nn21CUHeM3DYlc+ZxkuDwI4tL9OqP3QtmGy52+nnu+Tu8ZNzQb+mZXFfRG72Xz2klypsLU2QRTR71Xz8uyLIytNRBQ2YYhYzhcSPKmQ1vxE6wGrcfPpzy2GeDyht0075YrsyAO4WP1RTW6R+Kb3dVgGAbXz0x3y/GIZ2UkyVAZoKUbVm0qg8Fk6bPdaLbisrxYv/nO9SeFWdGwWFmcqnZeuupiTe0acBh4pVQRcQ+uOBTc0AgYW2tHfCxTT7vH6/Pa8WSxflOj19BUAYYngCDGuytSROlFMLZUw6IZ+POYNZvQvetTCOIyIMmd6qXR9ZWaYEteWbv1LKxWFtNGYX1ewJYIEy0ToYEyegfk+w40JCi40jFXlF4IXng0VEc3Qzp2+oDvHSnFgW9g1akRMdu9WYJ2Aj4XN8/JxH++Pok7n9uEHrURDIDCrCi6yA1g6YkyVDUqhr1/Y5saZgvrsyzS/urf9rfd2267KhNNqz6AiR+KrN+9DY7AMw0XqhoUUGlNflOf186Vz0lhQiZUx7aAtVp8ljEwEHXZPqdlG+wyk2TgcRmcqe0aUYMItc6EDXuqcfn4eKTEeff3SZIzBZ0//BeasgNeLaFgUXfDoumh+rzDJC2YCeVP30Fz5gBCC6/02HmsJgPUJ3ZCkjsVXPHw5qZULMDNczKx6rsynK7pxNi04ZeuUWmN2HywDldMSBp1nbdHq6xkGXYdbYRCZYAsNLCak7lSb570lpcaAQGPg+MVHZg0Nm7A9zZ1qBEtF4PPo1ytQOKuhmxmRSuEid55uM2Xx0Jf756HjSNlaKqEIC4NDNe7oStRWiG6d34CXU0ppAX9N21XHt0Mc0874q59wO2No4fiaHkbOIxt9SiHw6C+VYnMZJnPxuNJidFSKt0wCPqWIH6DYTgILZoLXe0JmLqaPXYes1qBnkMbIcm7HMJ4z2W3iIS2L6Meta35Cwtg1Xdl2FHi3Sww4j7pieFobFNDbxxel09HIzYfBXr7q3Pr7vq3w5WoOolUXge+0U2EifFcDeOj5W1gGKA42z/q8w5FSEIWWJMBxnb/+xwZrGwDYHsIlpEkG3Gd3o17qqHVm3Hb3JwRHWc4eKFyhCTnQlPu3Tq9xhZbIzZBLAV6h0OYkAWeLBbqU3s8eh7NmQOw6jUIK5o7ouNcPyMdslAhVn1XNqIyId/vr4XeaMHPrnD+8IX4n4wkGQBbA99AIw8Pcbq9vzr0xPa9ODYt0qU6vU0dGiRQ2YaAI4xNhbGj8XzptOFhrRaYlZ3gh3snSYEni4FZ2TmiMbsDa7XA0Fzl1bINdsL4dHBEUmhrjvX7HqtRB8XezxGSkg9RWqH3BncJe9kc6/nLBauVxT8/Lx21cYfEGCka29U+KaMWKCjQS/xK6Pg5AMOB8pjnmrIp9n0B1myE/Iqfe+wcAPDJ5vI+2wwmC1a5aSkm8b70xHBY2QsB26Gqa1GCy2F8VhhfPmcJGN4l2UEMA9nsO3wynotZjTp0bf8Ilsg07FYm44cDtR4719Gz7chIDEe4NLAypQBbRi8Av6zTayvbYIG0n7INdnmpEaioV8Bktg7vPHoTvtlVhclj4xxNEr1NkjsVxrZzMHU1ee2chlZboFdIgd5hYRgG0vyZ0NWecEv38/6ojm4GTx6HkJSCER0nRMjD7fNycKq6EyVnhlcn0WS2YMPuahRnRyPNR7XhydBlJIaDYQIz0Jsc07de+qX15klfhdnRqG1Wolul7/c9LMuiuV1N9XkDkCAmBbCaYexoHPYxzKpOwGoBT+adQC9fFguwVph7OgZ/swcZ286BNRu92ojNjuFwIUorhK76eL8BxZ5D38Ki6UHEnDt9umrXWdmc0Rx3SIyWQmewoEvZ/2dmsKNAL/ErvLBIiDMnQl263SNPEE2KNihLfkRo4ZUQRHq2Vi4tXxt9RtqQrbZZicQYqc+W3IUWzELUwgfAC4sCwIAjlAAsCw7H918Fir3rYVF3I/n6ZUiIDsV7X5/E9Y9+jXtf+NGtT6O1ehPO1Hb5XdkGV/HkceCIpH5Zp9detkHQT9kGu9zUCJjMVlQPswzKd/tqodKacNs879bmvZgkZwoAW/amtxhaqsGPiAdHSJlxwyUtmAmwVmjK9nnk+MaOBujryxBWPM8tN3zzp6QgNkKM1d+VwWodetbKziMN6FYZcNMc798gk+ETh/CRECUNuIZsLZ0anKzqRGFW1KB9OUhvhVlRANCrifOllBojNHozErzUfJS4jzsaspkVtoxvbwV6eXJbLWBf1+m1X+/6IqMXsJVvsKi7nf7bWbQqKA58DXH2JIR4qaRGf4It7mBvwtzUrvHxSPyX7+/uCblEWPE8WDQ90FT85PZjd+/+DAzDQD7zVrcf+1L9LVOj5WuBK1ouQqiYj6phNkmpa1Yi1cv1RC8VWjALY377LtKf+Rwpj3wAYXwmOn58HxadymdjMnW3oOfgBkjHXYEDLSK0dWkdS4/au3V4a91xtwV7Sys7YLGyARvoZRgGwvgsvwv0WnQqW9mGsZcPGuDKTZEDAMpqh55VqTeY8dXOSkzIiUH2GPmwxuoOvPBoCBOyoC7zXqDX2FJDZRtGSBCVBEFsGtQnd3vk+KpjWwAOz7Y6yQ34PA7uvCYX1U092HN8aJlgLMti/Y4qpCWEoTAr8MrUBLusZFnAZfSu+f4MuFwOHrljIt5/dj6+ee1GvP/sfAryuiA9UQapiD9g+QZ7QINKNwQefmQCwOXB2FY77GOYzwdcvVW6gS+LO3/e4a0ocRdDUyU4IqnXmtBdSpxeBADQ1Rzv85pi/3qwBh0irvD9yshgizsknl89Qg3Z+keBXuJ3RBlF4IZGQnXUveUbjO3noD6xC2GXLfBKTdKlC/Ig5PdulkTL1wIbwzBITwwfViaiVm9CW7fOZ/V5nWE4XEQtXA6rTo3OLat8No7OrasADhcRs5dg1aYymCy9l/QbTBZ8+N1pt5zrSHkbREIuclMi3HI8XxAmZMLYXg+r0X+WK2nPHraVbcidNuh7I8NFiIkQ48ww6vR+f6AOPWqjT7N57SS5U2FsqYLJCzdBFp0a5p42asTmBtKCmTA0Vbi9FwBrNkFVugOS7EngStxXJmFWcRJS48Pw0fdnYLa4Xu6k5Ewb6ltVWDQ7k5rABqDMZBk6e/ToDpBlqbXNSuw82oAbZqYjIsx5nV7SPy6HwfisKBw9297vEvGmDltAgzJ6Aw/D4UIQlTyijF5TTzsABrzwKPcNbADcUDnA5fk+o7e5AsL4LJ99j/HCIsGPSoKu+liv7WZlJ5Q/fQ/puCsgiPFeY97+BFvcISpcBAGfi8Y2CvT2hwK9xO8wHC5Ci66Crvq4W2+gu3Z8AkYQAtnlN7ntmAOZPTEZDy4upOVro0x6ogy1zaoh3XADQF2zLWM2NcF/Ar2ArUFE+NQboC7dBl3tCa+fX1d7Atryg5Bdvgi8sMh+lxh1KvS4+/kf8Ow7e/Hu+lJ8u6cax8+2o7NH51Ih/h0l9bj3hR+xaV8tLFYWe4eYHedPQhKyANYKQ0u1r4fioD69DzzZ4GUb7PJSIlBW2zWkJgpGkwVf7qjAuIwojE3zfQNBSe5UAICm3PNZvcZWasTmLtKxMwAwbm/Kpik/CKtOhdDieW49LofD4BcL8tDcocGWQ+dc3u/LHZWIDA/BzKJEt46HeEdmgDVkW/1dGcQhfNxMZUKGrTArGh0KHZo7nC9FburQgMNhEBsh9vLIiDsIYlNhbB1J6YY2cEMjwHD5bhxV/xiGA354DMzdvgv0Wo06GNsbIEz0TdkGO3F6EfTnymA1GRzbuvesA2u1Qj7rNh+O7IJgiztwOAwSoiRopIzefvF8PQBCnAkrvBKK3eugOrYVEbNH3jRN33gW2rOHIJ91O7hi7zXCmj0xedR+wAar9MRwmC1W1LeqhtTcprbF1sDN16UbnJHPvBWaMwfQ/t07SPrV38Hhe6dJGWu1oHPzB+CFxyB8yvUAbEuM2p0EeyUhPBRmRaO+VYWtP9VDZzA7XhMJeUiKkSI5NhRJMdLzf0IRHyUBj8txdKK1Nykwmqx4a51tCVYg/n5e3JBNNGasj0dzvmxDbSnCp1zvcsZFXqocO482oL1bhxgXb1o3H6xDl9KAR+6YOJLhug1fHgdBbBo0ZQcgm3KDR89laDnfiC0u3aPnCQa8sEiEjBkL9aldkM24xW1ZQsqjm8GTxUCUNs4tx7vYpLGxyEuNwCc/lmPOZcl9snYuVdmgQGllB+65Lh88LuV0BKJ0e0O2egUmjY3z9XAGdLqmE4dOt2DptXmQigW+Hk7AKjpfYuV4RbvTrN2mdjVi5WL6nQ5QgpgUqEu3w6xWgCeVDXl/c08b+F6qz2vHk8X6NKPX0FINsFafNGK7mCi9CD2HNkJ/7jTEGcUwdjZBdWwrwiZe4/V/k4EEW9whMUY67L45wYACvcQv8cKjIcoohur4Nshn3QqGM/BNzUBYlkXX9jXgiMMQPuU6N46SBKOLG7INKdDb1ANxCA/RflgricMXImrBMrR8/DwUez5HxJwlXjmv6ugWGNvqEHPzY47g8tIFeb2CsoBt6dEDN413XLywLIsupR4NrWo0tKnQ0KZGfZsKxyvase2nC7V8uRwGcZEStCu0MJr6loNYtaksIC+IuJJw8MJj/KZOr6b8kK1sQ97lLu+Tm2ornVFW2+VSoNdktuLz7ZXIS43A+EzvLFt0hSR3Krp3fgKzstOjJYGMrTXghka4tSRAMJMWzETHd+/A2FrjluC5qasJ+rqTkM++Awzj/iAMwzBYem0envrXXny7pxo3zRk4u+mrHVUQCXm4emqK28dCvMP+ALNymD0BvIVlWaz6rgzyUCGun0EPokYiPkqCaLkIxyraseDyvqs3mjs1iI+m+ryBShibCsDWkG04gV6Tog2ilHz3DmoQfHksDE1nvXrOixmaKgEAwnjfBnpDxowFw+VDW30M4oxidO/6FAxPANn0m306rmCXGC3F/hPNMJmtPmt07s/ob4T4rbDiubCou6CtPDKi4+hqSm03YDNuAUfgf0E2ElgSoqUQCrioGuITxLoWFVLiwvy2VqI4rRDS8bOhOPD1iGqIucqiU6Nr5ycIGZMPSc5Ux3ZXlh4xDIPIcBEKs6OxcEY67r9pPF54YDo+/L+r8dmL1+K1383Cwz+fgJvmZGJMXGifIK9dIHeiFSZk+k+gt2y/rWzDEAJmqfFhCBFwXa7Tu+2nc+hQ6HDbvGy/+h26UL7hoEfPY2iphpDKNriNJHcqwOG5rSmb8thWgOEgdPyVbjmeMwUZUZiQG4N1Wyug1pn6fV9btxa7jzfi6qkpkIi8s8SXeEZmkgyVDUNvWulNR8rbcKq6E7fNy0GIkPKHRoJhGBRmRqO0wtY09mIsy6KpXUON2AKYINpWx3U419isxQSLqgs8LzVis+PJYmHVa2DR+WZ5vKGpAjxZjM8fcnP4QoSMGQtd9TEYWqqhOb0X4ZMXDitgT9wnMVoKq5VFS6fzcjfBjgK9xG+JMyeCK5VDdXTzsI/Bsiy6d6wBLzwaYcXz3Tg6Eqy4HAZp8WFDWirCsixqm5V+1YjNmcir7gInRIL2b98Ga7UMvsMIdO9ZB6teg8h59/QJ3M2emDzsjt3iED6yx8hx5WXJWHrtWDx99+R+s6gDuROtMDEL5p52mNUKn47DXrZBknf5kAKwXC4H2WPkKKsbPNBrtlixbmsFMpNlmJDjP0vkAEAQlQR+VBI0ZzxXp9dqMsDU2QQBNWJzG64oFOKMYqhP7RnxZx1rMUFduh3irMvAC5W7aYTOLV2QB7XOhC93VPb7ng27q8EAuGGma/Wyif/KTJKhS2lAZ49/PpS0Wlms+rYMsRFizJ9C2ePuUJgdDbXOhJpLrjEVagN0BjMSoqgRW6DiSsLBlcqHFeg1KzsB1ur1MgF8Wazt/D4q32BorIAwwbf1ee044jCYOhrQ+N7jAMOAGx7t6yEFvaQY2+ch1el1jgK9xG8xXB5Cx8+BtuoozMqOYR1DU34AhuYqyGfdBoZHmS3EPdITw1Hd2AOr1bVGUp09emh0JqTGea8+9HBwxWGInHcPDE0VUJb84LHzGDsaoPxpE0KL5kLoheDVaOxEG3L+wtfQ3H/AxxuGU7bBLi81AjVNyl71lp3ZeaQBrV1a3D7Xv7J57SS5U6GvL4NF45kl1sa2OoC1Un1eN5MWzIRF3QV9fdmIjqM5+xMsmh6EFc9108j6l5Ekw8yiRHy9qwrdKn2f19U6E344UIuZRYl+WSaIDE1msgwAUOWn5Rv2Hm9CdVMP7rwml5bNuklhlq000bGK9l7bm9ptGWsJVLohoAliUmFsrR3yfubzzcl5Xq/RazufO5uju8qs7oZZ2eHoS+FLqpO7ej/QZ1l0/fgBVCd3+W5QxFHLvLGNAr3O0Lcy8WuhxXMB1grVsW1D3pe1WtC942Pwo5IgLZjlgdGRYJWRJIPOYEZLl2tLRWqbbY3Y/D2jFwCk+TMhSi9C1441w37AMpjOLR+Cwxci4orbPXL8S43GTrSC2DSA4fi8fIOmbB94stghlW2wy02NgNXKoqK+/6XJFiuLdVvPIi0hDJPz/bMhkSR3GsBaPVa+wdhSDQCU0etm4qzLwAhCRly+QXVsM7hhURClF7lnYIO485pcmMxWrN3ct27ijwdqoTNY8LMrKJt3NEhPCAeHsTXX8zdmixWrvy9DanwYZhUn+Xo4o4Y8NAQpcaE4frZ3oLe5wxbIoIzewCaITYGxowGsZeAH3Jcy9ZwP9Hq5dIM9g9gXGb32+rwhfpDR2719DWDpXTKJNRts24nPSEV8yKRCyujtBwV6iV/jy2IhSiuE8vjWIS+vVJXugKmzCRGz7xhRMzdCLpV+viGbq1k2decDvakBEOhlGAZRC5YBVis6vv8PWNa1rGVXaStLoKs6CtnMW71ac2sk5SD8EUcQAkH0GMeFsC9YtCroak9AkjdtWJm2uSm2Ze5lA9Tp3XOsEY3tGtw2N8cvs3kBWydtnjwOmnLPlG8wtNSAEyIFL4yWCboThy+EJGcKNGf2gzX3X/N2ICZFK3TVxxFWdJXXrjMSoqWYN3kMvj9Q26sunclsxTe7q1GYFYWMJJlXxkI8K0TIQ1JsKCrqFb4eSh9bDp1Dc4cGv7g2DxyOf342B6rC7GicrumE8aKmtE0dGnA5DGIoUz+gCWNSAasZps7GIe1nVrQBDMejTV+d4YRIwBGFwtTto0AvwxlWIoG7mZWdQ9pOvCcxRkqB3n5QoJf4vdDiebAoO6CrPubyPlazEd27PoMwIQvi7MmeGxwJSilxoeByGJfr9Na2KBEVHgKpWODhkbkHXxYL+RW3Q1vxk1trj7IWEzo3fwh+RALCL7vGbccNVraGbJVuD8a7SnP24PmyDdOHtb9ULEBybCjO1DrP6LVaWazdehbJsaGYNi5+JEP1KIZhIMmdCl3tSVh0Krcf39haA0Fcmt8GugOZNH8mrHoNtFVHh7W/yt6ErfAqN49sYD+fnwMOw+CTH8sd23Yfa0Rnjx6LZvt+mStxH1tDNoXPPuedMZgs+OTHcuSlRmBSXqyvhzPqFGVFw2i29noI2tSuQWyEGFwu3boHMkGMrZa1YYh1es097eCFRfkkcYkvi/VNRm9zBQQxKeDwhV4/96X6C7B7O/BO+kqIklCgtx/0bUH8niT7MnAl4VAe3eLyPsqSH2BRdSJizhK6OSZux+dxMSYu1OVAb10ANGK7VPjk6yCITUPnD/+FRe+ebqY9P22CqasJkfPuBsOlmtkjJUzIglWvhrm7xSfn15TtP1+2YfglBfJSI3CmtstpvesDJ5txrkWFW+dm+33GmCR3GmC1QHv2sFuPy1rMMLad80ot62AkShsPjjgM6lNDL9/AWsxQHd8GcUax12/2IsNFWDgjHdtL6lHXrATLsvhyRyVS4kL9rmEhGZnMJBkUKgO6lH1rMvvKt3uq0aXUY+m1eXSN7QH56ZHgchgcv6hOb1OH2lGPkgQufmQCwOUNuSGbSdEKnsw3q3p48liYvBzoZVkWhqZKv6jPCwDyOUvA8HoHnBmeEPI5S3w0ImKXFCNFj9oItdbo66H4HQr0Er/HcPmQjp8DbcVPMKv6r+VoZzVoodi3HqK0QohSx3lhhCQY2RuyDZZlY7ZYUd+qCoiyDRdjOFxEL1wOi1aJrm0fjfh4Fk0PFLvXQZRRDHHmRDeMkNgvgPVertOrOrkLdW/8CrrqY7BolcMKktnlpcqh1pn6PI1nWRafbTmLhCgJZhYljnTIHieMzwAvLMqtGfAAztfyM0EY6/uli6MRw+FCOnY6tBU/wWrQDWlfbWUJLOpuhBbP89DoBnbLlVng8zh45I2duOGxb1DbrERuagQF3kaZrPMN2fylfINGZ8Ln2yowMTcGBRlRvh7OqCQO4SN7jNwR6GVZFs0dGiREUSO2QMdweRBEJQ+5IZtZ0Q5euG+y5/myWJh72odcQnEkzN3NsOo1fhPoDS2YhaiFD4AXFgWAAS8sClELH0Ao9QDyuUR7QzbK6u2DAr0kIIQVXWVrylY6eFM2xcENsGqViJh9hxdGRoJVRqIMCvXgWTaN7WqYLWzAZfQCtuBV+OSFUB39Ebpzp0d0rK6dn8BqMiBy7t3uGRyBIDoZDD/Eqw3ZVCd3oePbd2BR25aUskYdOr59Z9idh3NTIwD0rdN7uKwV1Y09WHxVNrh+ns0LXCjfoK05DqtB67bjGltrAFAjNk+S5s8EazYOuZme8ugWcKUREGdO8NDIBnbkTCssFhZGk9WxbftP9dhRUu+T8RDPSE0I86uGbF/uqIRKa8IvFuT5eiijWlF2NCrrFVBrjehS6qE3WijQO0oIYlKGlNFrNRthUXeB76uMXlkMYLXArPJePVp9o+261h8asdmFFszCmN++i/RnPseY375LQV4/kRhDgd7+UKCXBAR+RAJCUgqgOroFLGvt930WTQ96Dn4DSe40v3kKSEYnR0O2Qco3BFIjNmfks24HLzwaHd+9M+yGRYaWGqiObkHYZQsgiKLu3O7CcLgQxqd7tSFb9/Y1YM2GXttG0nk4MVqKUDEfZy4K9LIsi882lyMmQozZEwNnvkhypwEWM7QVJW47pqGlBgxfCH6E/9YoDnTCxGzwwmOGlJlu7mmHruooQguv9Fmz11WbymC5pOSJ0WzFqk1lPhkP8YwQAQ9j4sJQ6QcZvd0qPb7eVYWZRYnU8M/DCrOiYWWBE1UdaOqwlc+Kp9INo4IgNgUWdTcsGtfKv5l7OgCcD7j6AF9myyQ2e7Ehm6GpEgw/BHy6ZyCDiI2QgMNh0NjunjKDowkFeknACCueB3NPG3Q1pf2+R7FvPViTEfIrbvfiyEgwSksIA8Ng0Dq9tc1KcDgMkmIC8wKdIwhB1IL7YepsRPe+9UPen2VZdG7+ABxxKOQzFntghMFNmJAJY0sNWMvwgvBD5e7OwwzDIDc1oldG79Gz7Th7ToHFV2aBF0CNZ4RJ2eBK5VCf2e+2YxpbayCISfFZMDEYMAwDaf4M6GpKYVYrXNpHedy2uii0yLtN2C7W0e281ER/20ngykySoaph8FJRnrZ2y1kYzVbceU2uT8cRDLLHyBEi4OJ4RQeazgcwKKN3dLA3ZHM1q9feCI0X7ptAL09uC/R6s06voakCwvh0uvYhg+LzOIiLEKOxjTJ6LxU4d1Ak6ElypoAjCoWqn6Zs5p529JR8j9DxsylrkHicOISP+EjJoIHeumYVEqOl4PMC92JFnFEMaf5MKPauh7GjYUj7as4cgP7cKUTMuh1cUWAGu/2ZMCELrMUEY9s5r5zPE52H81Ij0NCmhlJjdGTzRoWH4KpJycM+pi8wDAeSnCnQVR2F1Tjyxkksa4WhtRbCOKrP62nSglkAa4WmbN+g72WtFqiObYUovRB8H2VYAUCUXDSk7SRwZSaFQ6E2oEPhu4ZsrV1afL+/FvMmj6GmYF7A53FQkBGFY2fb0dyhBo/LQbRc7OthETcQxqQCAAyuBnp7bLWa7Zm13sYLiwIYjtcyelmLCYbWGgj9qGwD8W+JMVIq3eAEBXpJwGB4fISOnw3N2UNOs266d68FAwbymbd6f3AkKGUkyQYt3VDbogzYsg0Xi5x3DzjCELR/+/aA5VMuZjUZ0LX1fxDEpCC0eK6HRxic7CVqvFWnN2zydX22jbTzsL1Ob3ldF05WdeJ0TRduvjIrIB+OSHKngjUboa06OuJjmbtbwRq0EMRSfV5PE0QnQxCT6lL5Bl3VMVhUnQjzURM2u6UL8iDk9/4dEfK5WEq1U0edzPMN2SobBm9I7Ckf/3AGHIbBz+fn+GwMwaYwKxqN7WqcqOpAXKQ4IOrVk8FxJeHgSmQwttW69H6TohXg8MCVyjw6rv4wHC544dFey+g1ttYBFjMFeonLEqOlaGpXw2r17aoXf0OBXhJQQovmAlYL1Cd29Npu7GiAqnQHwiZeDV64b4rVk+CTnhiOti4tVFqj09e1ehPaurSjItDLlYQj8qq7YGg4029W/aV6Dm6AuacdkfPuoeVXHsILiwZXEg69l+r0mjoaAA4XXKkc7uo8nJUsA4fDoKy2C59uLoc8VIh5U1LcN2gvChkzFhxxGDRuKN9gON+ITUiN2LxCWjAThsazMHW3DPg+5dHN4EpkEGdd5qWROTd7YjIeXFyIaLkIDIBouQgPLi7E7ImBlQlPBpeaEA4Oh0Flg2s1Pd2trlmJ7SX1uG5GOiLDKWPcWwqzogAAZ88pkBBFWdSjiSA2xRbQdIG5px288CifXkfz5bEwK9q8ci779WwI9dohLkqMlsJotqJDQaWrLsbz9QAIGQpBVBJCkvOgPLoZ4VNvBMPYnm537/wEDF8I2eU3+XiEJJjYG7JVN/agMKvvA4ZzLSoAgduI7VLS8XOgOrkLndtWQ5x1GXihEf2+16zshGLfeohzpkCUOs6LowwuDMNAGJ/plYxec0+77YFa8TxEXfMrtx33wIlmcBhg3Vbbz3BFcWKfTMVAwXC4kGRPhvr0HljNRnB4gmEfy9hSA3C4EESPceMISX+kY6eja9tqqE/tgXzGLU7fY1Z2QltZAtm0G8FwfX8JPXtiMgV2g4CQz0VKXKjPGrKt3lQGkZCHm6+kDDtvqm3qAcMA7PmmbDtK6un3fZQQxKSi5/C3YC3mQb9LzIo2n5YJAgCeLBaa8oNeOZehuQJciQzcsCivnI8EvsTzfXAa2tWIiaASN3aU0UsCTmjxXJi7W6CvOwnA1plTc+YAZFNuAFcS7uPRkWCScVGg15naZiUAIGWUBHoZhkH0gvsBixmdP7434Hu7tn8EWK2IvGqpl0YXvISJ2TB1NMKq92zHWcX+rwAAsmk/c9sxd5TU4611x2G2XFhutf9EM3aU1LvtHN4myZ0K1qiHrvr4iI5jaKmBICoZDI/vppGRgfDCoxEyZizUp3b32/RKVbodYK221UWEeFFmkgyVDQqvN2Q7U9eFg6dacNOcTIRJhv/gigzNjpJ6/PPzUtj/uXUGM95adzygvxvJBYKYFMBihqmradD3mnvafNaIzY4vi4FVq4TV4PmMSUNjBYQJmY5kLkIGk3i+bjw1ZOuNAr0k4Ehyp4ETIoHy6GYAQNeOj8ERhyF8yvU+HhkJNuFSIaLCQwYM9IqEPMSMouY4/Ih4yGYshubMAWjKDzl9j77xLNQndyF8yvXgy+O8PMLgY6vTy8LQUu2xc5hV3VAd24rQ8bPdWh5n1aYyGEyWXtuMZitWbSpz2zm8TZRaAE6IBJozB4Z9DJZlYWythoDKNniVNH8mTB0NMLbW9nmNZa1QHdsCUeo4+lwjXpeZLINSY0R7t/eWprIsi1XflkEmFeKGmRleOy9x/t1oMFkC+ruRXCCMTQWAQcs3WI16WDQ94PlBRi8Aj9fpteo1MHU2Un1eMiTyUCFEQh6aqCFbLxToJQGHwxdCGJ8Jzem9qH7xZuhqjkOUXgSOcPQE00jgSE/svyFbbbMSKXGho+6ptGzqDRDEjEHHD/+B1aDt9RrLWtH54/vgSuWQTadSKt4gjLfdgHuyfEPPga/AWi2QXb7Ircft6Cdo0d/2QMBw+RBnT4K24jBYi2lYx7Cou2HR9FB9Xi+T5E4DOFynTdl01cdh7mlHqI+bsJHglJkkAwBUNii8ds6jZ9txoqoDt87Nhkjo+1IlwWQ0fjeSC/iRCQCHB8MgDdnMPe229/s40Ms/H+g1ezjQa2iuAnCh0TAhrth5pAEmswUb99bg3hd+pJUP51GglwQc1cld0J073WubpuwAVCd3+WhEJJilJ4ajsU0FvdHcazvLsqhrVo6asg0XY7g8RF27HBZVN7p2fNzrNfWJXTA0VSBizp3gCOjhizdwRaHgR8R7rCGbRdMD5ZEfIS2Y5fZMxqh+st372x4oJDlTYdVroKs9Oaz9jS22RmyCWAr0ehNXHApxRjHUp/aAZa29XlMd2wKOOAyS7Mk+Gh0JZqnxYeByGK8FelmWxervTiMmQoxrpgVmc8xANlq/G4kNw+VDEJUIY9vAGb32Bmi+Lt3Ak3sno9d+HSuMp0Avcc2lJeDau3VU5uY8CvSSgNO9fQ1waZaUxWjbToiXpSeGw8peqMdr16XUQ60zjZpGbJcKScxG2GULoPzpe+gbygEAVqMOXds/gjAhC9Jxs3w8wuAiTMjyWEav4uA3YM0mj2RoL12Q16fxmpDPxdIFeW4/lzeJ0gvBCEKGXb7B0GoL9NqXdxLvkebPhEXVCX39hSXSZnU3NGcPI3TcbKqZTHxCwOciJT4MFV5qyLavtBmVDT1YcnUO+LzAbI4ZyEbrdyO5QBCbOmjpBlPP+UCvjzN6OSFScIRimLs9nNHbVAF+RAK4IqlHz0NGDypz0z8K9JKAY1Z2Dmk7IZ7UX0O20daIzZmI2XeAGxqB1vWvoe7NZah95U5Y1N0QZRSDYejrxZuECZmwqLrc/jlo0aqgLPkekrGXQxCZ6NZjA8Dsicl4cHEhouUiMACi5SI8uLgw4DuLc3gCiDMnQnP2EFirZfAdLmFoqQY/Ih4cIXUP9jZx1mVg+CFQn7xQvkFduh2wWhBaTE3YiO9kJslQ5YWGbBaLFas3lWFMXCiumBDYn8WBarR+N5ILBDEpsKi7YNEq+32PWdEGhicAVyLz3sCcYBgGPFmsxzN6DU2VVLaBDAmVuekfFVwiAYcXFgmzssPpdkK8LVouQqiY3yfQW3c+0DtaM3oBgCMUQTL2cigPbui1vWf/1+BHxCO0gLJ6vcXeuMLQVOnWz8KewxvBGvWQT7/Fbce81OyJyaPy5lWSNw2a03uhP3caotRxQ9rX2FJDNzs+whGEQJIzGZqy/Yi6+j6Aw4Xy2FaEjMn3yMMOQlyVmSzDjwfr0NqlRVykxGPn2fpTPRrb1XjmnsngckZXj4FAMlq/G4mNICYVAGBsq+v3GsHc0wZeeLRf9PrgyWJg6mjw2PHNyk5Y1F3UiI0MSZRc5LRJKZW5oYxeEoDkc5aA4Ql7bWN4QsjnLPHRiEgwYxgG6YnhfRqy1TYrERkeglCxwEcj8w5t2f4+21izgUqpeJkgNtXW2KPZfeUbrHoNlIe/gzhnCgQxY9x23GAhTi8GwxMMuXyDRaeGuaeNGrH5kDR/Jqx6NbRVx6CvPQlzdwtl8xKfy0yyrSCqanDeANYdjCYLPvnhDHJS5JiS796a7ISQCwQxttrXA9XpNSnafF6f144vj4VZ0danfr27GOz1eekhNxkCKnPTPwr0koATWjALUQsfAC8sCgADXlgUohY+QNmDxGfSE2WobVLCbLlw8VPXrBrVZRvsqJSKf+DwBBDGpri1IVvPT5tgNWghn+G5bN7RjCMIgSijGJozB4Z0Y2RspUZsviZKGw+OOAzqU7uhPLYFnBApJLlTfT0sEuRS48PA4zKoqO/22Dm+21eDjh497rp2rF9kERIyWvGkMnAl4TAMUKfX3NMGvo/r89rxZbFgLSZYVJ75/NE3nQU4PFviAiEuojI3/aPSDSQghRbMosAu8RvpieEwW6yob1UhLcH2/8+1qlCUHe3roXkclVLxH8KELKhO7ATLWkdcI9lq1KHn0EaIMydCGJfuphEGH2neNGjLD8LQUI6QZNeyCxyN2Ojv3WcYLg+CmFRoTu+1/ff5xnp03UF8ae/xJgDAF9srsetYI5YuyHPrzaxWb8LaLRUozo7GuMwotx2XEOKcICYVxrZap69ZDVpYdWqfN2Kz48liAQAmRavbr/FVJ3dBeehbwGpGw9u/hXzOEvq+JS6jMjfOUUYvIYSM0KUN2Zra1TBbrEGR0UulVPyHMCETrFEHU2fTiI+lLPkBVp0KshmL3TCy4CXOnAhweVAPoXyDsaUG3NAIcCXhHhwZGYjq5C7o6y90bGaNenR8+w5UJ3f5cFQkmO0oqcdb647DbLE1Ymvv1uGtdcexo6Tebef4ckcVVFojll471m3HJIT0TxCTAlN7vdOmrSZFGwD4TaCXL7cFes1ubsimOrkL7RvfBmsx2Y6v7KDvW0LcgAK9hBAyQgnRUggFXEegt65ZBWB0N2Kzo1Iq/uNCQ7aR1em1mgzoOfgNROmFCEmkphgjwRGKIU4rhPbMAbAs69I+hpZqCKlsg091b18DnL/ptKPa48SXVm0qg8HUOxhkMFmwcu0xvPfNSfxwoBanqjvRozYM6/g9agO+3lWJ6YUJyEyWuWHEhJDBCGJTwFpMTh/Qm+2BXj+p0csLjwbAwNTt3kBv17aPAIux1zb6viVk5Kh0AyGEjBCXwyAtPszRkK22RQkOh0FyrNTHI/MOKqXiH/iRCWAEIhgaKxA6fs6wj6M6uhkWTQ/klM3rFpK8adBWlsDQXIWQQZqMWE0GmDqbqB6sj1HtceJvOpx0FQcAo9mK7/bVwnhREDhULEByrBRJMaFIipEiOdb2v9FyMbic3nV3d5TUY9WmMkfX8mwK8hLiNYKYVAC2hmyC6N5Lz809tkCvv9ToZbh8cMMiHeNyB4umBxYVfd8S4gkU6CWEEDdITwzH9pIGWK0s6pqVSIyWgM/jDr4jIW7CMBwIEzJH1JDNajZCsf9rhKTku1xTlgxMnDUJ4HChObN/0ECvsa0OYK1Un9fHqPY48TdRcpEjGHuxaLkI/316HtoVOjS0qdDQpkZ9q+1/D51qwY8HL2T4CngcJERLkRRjCwKrNAb8eOgcTOYLzSI//qEcEWEhVO+QEC8QRCYCHK7tuz9/Rq/XTIo2MPwQcEShPhpdX3xZrNsyevVNlWj94pV+X6fvW0JGhgK9hBDiBumJMny3rxYtXRrUNiuRPUbu6yGRIBSSkAXFga9hNRvB4QmGvL/6+DZY1F2IueG3HhhdcOKKpBClFkBz5gAi5tw5YCd7Y4utEZsgjko3+JJ8zhJ0fPsOWPOFIBnVHie+tHRBHt5ad7xX+QYhn4ulC/LA4TCIjRAjNkKMibmxvfZTaoxobFOjoU2F+vP/W9XYg32lTbA6qSZjMFmwalMZBXoJ8QKGx4cgKhGG1to+r5l72sCTxQx4zeBtPFksdNVHR3wc5bEt6Pj+P+BJ5ZBd8XP07F1P37eEuBkFegkhxA0ykmyNk05Xd6K1S4t5U8b4eEQkGAkTMgGrBcbWWoQkZg9pX9ZigmLflxAm5iAkdZyHRhicJLnT0PHdOzC21UEYm9rv+wwt1eCESMELi/be4Egf9lI03dvXwKzsBC8skrqAE5+yB15XbSpDR7cOUXIRli7IGzQgGyYRICwtAnlpEb22G00W3PzkRqf79FcmghDifoKYVOjqTvXZbla0gR/uX9cCfHks1OpuWE0GcPjCwXe4BGs2oePH96A6uhmitELE/OxhcMWh4Mti6PuWEDejQC8hhLhBSlwouBwG20saAACpcaO/ERvxPxc3ZBtqoFd1YifMyg5ELbjfrzJIRgNJ9mR0bPo3NGX7Bwz0GltrIIhLo79/P0C1x4m/mT0x2W2ZtgI+F9H9lIOIkovccg5CyOAEMSlQn9wFi04F7vkyDSzLwtTTjpAx+T4eXW98mW3FgFnR1qem8GDMyk60fvEKDE0VkF1+E+RX3A6GYytxR9+3hLgfx9U3fvXVV8jJyen3z+uvv97r/d988w2WLFmCiRMnoqCgAPPnz8fLL7+Mnp4ep8evqanBI488giuuuAKFhYW4/vrrsXr1alitVqfvJ4QQf8LncTEmLhSllba6jinxFOgl3scLjQA3NAKGIdbpZa0WKPauhzA+A6KMYg+NLnhxJeEIGZMHTfmBft/DWswwtp2DMJbKNhBCPG/pgjwI+b17CdjLQRBCvEMQkwLgfI3+86x6NViDFjyZf2X08uS2QK9JMbQ6vbq6k2h47zEYOxoQe/MfEDFniSPISwjxDJczesvKygAA06dPR0RERJ/X8/IuXBT83//9Hz777DMIBAKMHz8eoaGhOHHiBN5//31s2bIFn3zyCaKiohzvP3PmDJYsWQK1Wo0JEyZg3LhxOHjwIF544QUcP34cr7766kh+RkII8QpxyIWP1Kf+tQd3XTuW6twRrxMmZMHQVDGkfdSndsOsaEXk3Lspm9RDJDlT0fnjezC21zvNhDF1NoK1mKgRGyHEK4ZbDoIQ4j6CmFQAgLG1FqKUAgCAWdEOAOCHx/a3m09cnNHrCpZl0XNoA7q2rgY/Ih6xt/wBgqgkTw6REHKey4He06dPAwD++te/Ija2/w+d3bt347PPPkNsbCw++OADZGRkAAC0Wi0ef/xxbNmyBX/5y1/w97//HYDtA+APf/gD1Go1/va3v+HGG28EAHR1deHuu+/Ghg0bMG/ePFx99dXD/iEJIcTTdpTUo7y22/HfHQo93lp3HADopol4VUhCJrTlB3stAxyILZv3CwhiUiDOnuSFEQYnSa4t0Ks5c8BpoNdAjdgIIV7mznIQhJCh40pl4IjDemX0mnpsGbP+ltHLEYeB4Qtdyui1GnVo//ZtaE7vhThnCmKu/y04QioLQ4i3uFy64cyZM4iKihowyAsAX375JQDgd7/7nSPICwBisRgvvvgiOBwONm/eDKPRCADYu3cvysvLMXnyZEeQFwAiIiLwpz/9CQCwevVql38gQgjxhVWbymC+pIW1vXs1Id50oU6va+UbNGcOwNTZBNmMWyib14N4oREQJuVAc8Z5+QZDSzUYvhD8iHgvj4wQQgghvsAwDIQxKb0CvfaMXl54jK+G5RTDMODJYmHuHjjQa+pqQuOHT0FTth8Rc+5E7M2PU5CXEC9zKdBbX18PpVKJ/PzBC4JLJBJkZWWhuLhvjT+ZTAa5XA6j0QiFQgHAlgEMAHPnzu3z/gkTJiAyMhIlJSVQq9WuDJUQQnyivy7V1L2aeJswPgMA41Kgl2Wt6N7zOfhRSZDkTvX84IKcJHcqjG21MHU193nN2FoDQUwK1a0jhBBCgoggNhXG9nqwVgsAwKxoBUcoBlck9fHI+uLLYgfM6NWcPYyG95+ARa1A3M+fhezyRZREQIgPuBTotdfnjYyMxIoVKzBv3jyMGzcOV199Nf75z3/CYDA43rtixQps3LgR6el9a8w1NTWhs7MTQqEQcrkcAFBZabsRzc523h08LS0NVqsVVVVVQ/vJCCHEi/rrUk3dq4m3cYRi8KMSXarTqy0/DFP7Ocim3wyGcXmRDxkmezD90qxelrXC0FpL9XkJIYSQICOISQFrNjoeApt72v0um9eOJ4+FWdEKlu29ipG1WtC18xO0rnsJfHk8Eu/7G8RphT4aJSHEpbs6e33e9evXY+PGjcjMzERhYSFaW1vx5ptv4q677oJerx/0OP/4xz8AAHPmzAGfzwcAtLXZinlHRzuvQWPf3tHR4cpQCSHEJ6h7NfEnwoQsGJor+1yIX4xlWXTvWQeePA7SsdO9OLrgxQ+PgTA+o0+g16xoA2vQQhBL9XkJIYSQYCKISQEAR/kGU08beDL/DPTyZbFgTQZYND2ObRadGi1r/wrFns8hHX8lEu56AXw/DVQTEiyGlNG7YMECbN++HW+//TY++ugjbNy4Ebm5uTh69KgjiNufTz/9FF9//TVEIhEeeughx3adzrasOSQkxOl+9u1arbbPa0qlEg0NDb3+tLS0uPIjEUKIW82emIwHFxciWi4CAyBaLsKDiwupyQnxiZCETFg0PTAr2/t9j67yCIytNZBPv5nKBXiRJHcqDM2VMPdc+LcxtFQDAITUiI0QQggJKoKoZIDhwNhaC5ZlYVb4d6AXAMw9tmQ9Q2stGt//A3Q1JxB1zTJEX/drcHgCXw6REAKA58qb3nzzTdTX12PMmDEQCC784iYlJeGll17CokWL8Nlnn+HRRx91ZOpebO3atfjzn/8MhmHw4osv9mrSxuXabi4Hq91itVr7bPvf//6Ht956y5UfgRBCPI66VxN/4WjI1ljhNKvCkc0bHgNpwSxvDy+oSXKnomv7GmjKDyJ88nUAAGNLDcDhQhA9xsejI4QQQog3MTw++FGJMLbVwapVgjUZwPfTQK+h/RwAoOnDp8ARSWE16sEVhSFh6QqEJDovxUkI8T6XAr1CoRCZmZlOX8vLy0NcXByam5tRW1uLrKysXq+/9dZbWLlyJTgcDlasWIGFCxf2el0kstWv7K/0g327RCLp89pdd92FRYsW9drW0tKCJUuWuPJjEUIIIaOSIGYMGC4fhqZKp2UZdDWlMDRVIGrB/WC4Ll0KEDfhRyRAEJMCddl+R6DX0FIDQVQyGF7fh+WEEEIIGd2EManQ1ZfBpLBlyvpjjV7VyV3o3rXW8d9WnRoAA9n0myjIS4ifcUvnlaioKAAXyjAAgNlsxlNPPYWVK1dCIBDg73//OxYvXtxn35gY24dYfzV429ttSxud1fANCwtDUlJSrz9xcXEj/nkIIYSQQMZw+RDEpfXbkE2xZx24oZEIHT/HyyMjwPnyDQ3lMKu6AQDG1hoIqGwDIYQQEpQEMSmwKDtgbK0BAL/M6O3evgawGC/ZyqJn/1e+GA4hZACDBnrVajX++Mc/4qGHHoLZbHb6noaGBgBwBFmNRiOWL1+O9evXIywsDO+99x4WLFjgdF97BnBlZWWf11iWRXV1Nbhcbq9yD4QQQggZmDAhC4aWarBWS6/turpT0NeXQTbtZ5RB6iOS3GkAWGjKD8Ks6oZFo6D6vIQQQkiQsjdk05w9DMA/M3rNys4hbSeE+M6ggV6JRILNmzfjhx9+wOHDh/u8vnPnTnR3dyM7O9uRnfvoo49i165diImJwZo1azB58uR+jz9z5kwAwNatW/u8duTIEXR1dWHixImQSqUu/1CEEEJIsAtJyAJrMsDYXt9ru2LPOnAlMoQWXeWjkRFBdDL4kYnQnNkP4/lGbIJYCvQSQgghwUgQmwoA0NWWgiMKBUco8u2AnOCFRQ5pOyHEdwYN9DIMg1tvvRUAsGLFCrS2tjpeO3fuHJ5//nkAwPLlywEAn3zyCX788UdIpVKsWrUK2dkD12uZPHkysrKysHfvXqxde6HmS1dXF/785z8DAO65554h/liEEEJIcBMm2GrrG5ourJjRN5RDV3sC4VNvBIcv9NXQCGzlG/TnTkNbcxwAIDx/k0cIIYSQ4MKVysERhQIWs19m8wKAfM4SMLze144MTwj5HOqPRIi/YViWZQd7k16vx7333ouSkhKIxWJMnDgRAHDw4EEYjUbce++9eOKJJ2A2mzFnzhy0tbUhLS0NBQUF/R7z6aefRkREBACgtLQUd911F7RaLQoLCxETE4NDhw6hp6cHt956K1asWOHyD9TQ0ICrrroKW7duRVJSksv7EUIIIaMJy7Ko+/vdkORORfRC28PY5k9fgKG5CmN+8zY4ghAfjzC4GVqq0fje447/5oVFQT5nCUILZvlwVIQQQgjxhfp3fwdTh60kpr9eE6hO7kL39jUwKzvBC4v0yzESQgCXWm2HhITgww8/xIcffogNGzbg4MGDEAgEKCoqwi9+8QvMnz8fAFBeXo62NlunyJqaGtTU1PR7zN///veOQO/48eOxbt06vPnmmzh48CAqKiqQkpKCRx55xGkDN0IIIYQMjGEYCBMyHQ3ZDE2V0FUdhXz2Egry+gHDJSU1zMoOdHz7DgDQTRMhhBASRFQnd8HU2ez4b3+9JggtmOVX4yGEOOdSRm8goYxeQgghxKZr5ydQ7F2P1MdWo+3rf0B/rgxjHnwbHKHY10MLeudW3g+zsqPPdl5YFMb89l0fjIgQQgghvkDXBIQQdxq0Ri8hhBBCApMwIQtgrVCVbof27GGET1pIQV4/Qd2rCSGEEALQNQEhxL0o0EsIIYSMUvbskM4f/guAAUcq8+l4yAXUvZoQQgghAF0TEELciwK9hBBCyCikOrkLXVtWXbSFRdfmD6E6uctnYyIXUPdqQgghhAB0TUAIcS8K9BJCCCGjUPf2NWDNhl7bWLMB3dvX+GhE5GKhBbMQtfAB8MKiADDghUUhauED1OSEEEIICTJ0TUAIcSeerwdACCGEEPejem/+j7pXE0IIIQSgawJCiPtQRi8hhBAyClG9N0IIIYQQQggJLhToJYQQQkYhqvdGCCGEEEIIIcGFSjcQQggho5B9+V/39jUwKzvBC4uEfM4SWhZICCGEEEIIIaMUBXoJIYSQUYrqvRFCCCGEEEJI8KDSDYQQQgghhBBCCCGEEBLgKNBLCCGEEEIIIYQQQgghAY4CvYQQQgghhBBCCCGEEBLgKNBLCCGEEEIIIYQQQgghAY4CvYQQQgghhBBCCCGEEBLgKNBLCCGEEEIIIYQQQgghAY4CvYQQQgghhBBCCCGEEBLgKNBLCCGEEEIIIYQQQgghAY7n6wG4m8ViAQC0tLT4eCSEEEIIIYQQQgghhBDiXnFxceDx+oZ1R12gt729HQCwZMkSH4+EEEIIIYQQQgghhBBC3Gvr1q1ISkrqs51hWZb1wXg8Rq/X4+TJk4iOjgaXy/X1cJxqaWnBkiVLsGbNGsTFxfl6OCTA0PwhI0VziIwUzSEyUjSHyEjQ/CEjRXOIjBTNITJSNIfISAVNRm9ISAguu+wyXw/DJXFxcU6j74S4guYPGSmaQ2SkaA6RkaI5REaC5g8ZKZpDZKRoDpGRojlE3I2asRFCCCGEEEIIIYQQQkiAo0AvIYQQQgghhBBCCCGEBDgK9BJCCCGEEEIIIYQQQkiAo0CvD4SFheHBBx9EWFiYr4dCAhDNHzJSNIfISNEcIiNFc4iMBM0fMlI0h8hI0RwiI0VziHgKw7Is6+tBEEIIIYQQQgghhBBCCBk+yuglhBBCCCGEEEIIIYSQAEeBXkIIIYQQQgghhBBCCAlwoz7Qu379euTk5OCnn35y+vqWLVvwi1/8AhMmTMBll12Gn//859i4caNXzg0AO3fuxH333YfJkyejoKAAc+bMwf/93/+hpaVlyOfT6/V46623cPXVV6OgoABTp07Fr3/9axw5csSl/Q8cOIDc3Fx8/fXXQz73aGKxWPDRRx/h5ptvRnFxMcaPH4+FCxfin//8JwwGQ5/3nzhxAg888ACmT5+O4uJiLF68GBs2bBj2uT/99FP87Gc/Q3FxMaZOnYqHH34YNTU1Lu2/cuVK5OTkDGv+ALY59O6772LhwoUoLCzEjBkz8Oyzz6Ktra3X+w4ePIicnByX/gSjYJ5DF2NZFkuXLsW8efNc3ufcuXMoLi7GlVdeOeLzB6pgnj/Nzc147rnncOWVV6KgoACTJk3CPffcg127drm0/5NPPomxY8cO69yjCc0h1+eQwWDAypUrcc0116CgoAATJ07EXXfdhZ07dw7r/KNFIM0hvV6Pf/3rX7j++utRWFiIoqIi3HTTTVi9ejWsVuuQz69UKvHKK6/g6quvxvjx43HllVfipZdeglqtdmn/b7/9Fjk5OXjyySeHfO7RJpjn0cUMBgMWLFiAu+++e9D30v2YDc0dG1fmDn2PORfMc6iyshKPP/44Zs2a1SsudOzYMafvv+uuuwa8n6+rqxvyGIhvjeoavUePHsW9994LrVaLNWvW4LLLLuv1+t///ne8++67AIDc3FzEx8fj5MmTaG9vx6JFi/Diiy+Cy+V65NwA8O9//xuvvfYaOBwOxo8fj8jISJSVlaGpqQkRERH46KOPkJGR4dL5zGYz7r33Xhw8eBAREREoLCyESqVCSUkJGIbBX/7yFyxatKjf/Wtra3HnnXeivb0df/vb33DjjTcO6+cOdBaLBb/+9a+xY8cOiMViFBYWgsfj4fjx41AqlSgsLMT//vc/iEQiAMDevXtx//33w2q1YtKkSRCJRNi/fz/0ej0eeOABPPzww0M6/1NPPYX169dDLpdj0qRJaG5uxokTJyAWi7FmzZoBAxhbtmzB7373O5jNZuzcuRNxcXFDOrfJZML999+PvXv3Ij4+HuPHj0d1dTUqKioQHR2NtWvXIiEhAQBQVVWFt99+u99jHTlyBI2NjRg7diy+/PLLIY0j0AXzHLrUyy+/jPfffx9jxozB5s2bB32/xWLBkiVLcPToUSQmJmLbtm0jOn8gCub5U1lZiSVLlkChUCAxMRG5ubno6OhAaWkpWJbFH/7wB9x333397v/RRx9hxYoV4HK5OH369JDOPZrQHHJ9DhmNRtx9990oKSmBXC5HcXExlEolSkpKwLIsnnrqKZcCM6NNIM0hjUaDpUuX4uTJk5DJZBg3bhxMJhOOHz8OnU6Hq666CitXrnT5el6tVuOOO+5AeXk50tLSkJ2djVOnTqGhoQGZmZn49NNPERoa2u/+ra2tuP7669HT04NFixbhpZdeGtLPPpoE8zy69O/hsccew3fffYdp06bhww8/7Pe9dD9mQ3Pnwt/DYHOHvsecC+Y5dPDgQfzqV7+CwWBARkYG0tLS0NDQgDNnzoDH4+GVV17Btdde22ufKVOmwGg04qqrrnJ6zCeffBJRUVFD+jsgPsaOUt9//z1bXFzMZmdns9nZ2ezhw4d7vb5v3z42Ozubzc/PZ3/44QfHdp1Oxz7yyCNsdnY2u3r1ao+cm2VZtqKigs3NzWWLiorYI0eOOLYbjUb2T3/6E5udnc3eeuutLp/z3//+N5udnc0uWbKEValUju379+9n8/Pz2aKiIranp8fpvocOHWKnT5/uGO9XX301hJ92dPnkk0/Y7Oxs9vrrr2dbWloc2zs7O9nbbruNzc7OZl999VWWZW1zZdq0aWx+fj67f/9+x3vr6urYWbNmsdnZ2eyJEydcPvcPP/zAZmdns4sWLWKVSqXTMVmtVqf7fvTRR2x+fr7j37C5uXmoPzr7/vvvs9nZ2eyyZctYg8HAsizLWq1W9rXXXmOzs7PZ+++/36XjlJeXs+PGjWMnT548rHEEumCeQ3Z6vZ596qmnHMeaO3euS/u98847jn3mzJkz7PMHsmCeP7feeiubnZ3N/u1vf2PNZrNj+759+9iCggI2Ly+Praio6LOfxWJhX3/9dTYnJ4fNzs5m8/Lyhnzu0YTmkOtzaPXq1Wx2djZ7xx13OL12ys/PZ1tbW4c8jkAXSHPo1VdfZbOzs9m77rqr13VufX09O3/+fDY7O5v95JNPXD7/ihUr2OzsbPbZZ59lLRYLy7IsazKZ2Mcff5zNzs5mn3/++QH3v++++xxz+IknnnD5vKNRMM8jO4VCwS5btswxJ+66665+30v3YxfQ3HF97tD3mHPBOoeMRiM7e/Zsp7Gsr7/+ms3JyWGLi4vZzs5Ox/aGhgY2Ozubvfvuu13+GYn/G3WlG1paWvCHP/wBDz30EKxWa79PHtauXQsAuO+++zB//nzH9pCQEDz//POIiIjAW2+9BbPZ7PZzA8DXX38Nq9WKe+65B8XFxY7tfD4fTz/9NCIiInDs2DE0Nja6dO5vvvkGAPDMM89AKpU6tk+dOhWXX345tFptnxIOCoUCL774Iu6++250d3c7sjWDmT379Omnn0ZsbKxje0REBP70pz8BsC3JA2z/hp2dnbj++usxdepUx3vHjBmDxx57DACwevVql8/9/vvvA7A9Mbs4W+T222/H9OnTUV5ejoMHD/bap6qqCsuWLcPzzz8PqVQKiUQyhJ/2AqvVig8++AAMw+CPf/wjBAIBAIBhGPz+979HWloatm/fjvr6+gGPYzQa8cgjj8BgMOCFF14YcUZoIArWOWS3a9cuLFq0CF988QWSk5Nd3u/MmTNYuXIlJk2aNKLzB7pgnT/V1dU4duwYkpKS8Mgjj/TKWpg2bRp+/vOfw2Kx4Pvvv++13/Hjx3HHHXfg7bffRlJS0rDOPdrQHHJ9Du3ZswcAcO+99zq9drJn1ASbQJpD9rGuWLECYWFhju1JSUl4/PHHe411MEqlEuvWrYNUKsUTTzwBDsd2m8Tj8fDcc88hPDwcn3/+ObRardP9P/74Y+zevTvov8fsgnUeAbbSVRs2bMCNN96IHTt2DHg9RPdjfdHccW3uAPQ91p9gnUMHDhxAU1MTJk2ahDvvvLPXazfccAOuuuoqaDSaXmU9ysrKAAD5+fku/4zE/426QO8//vEPfP311ygoKMBnn32G9PR0p+87e/YsAGD27Nl9XpNIJCgoKEB3dzdOnjzp9nMDtoBuTk6O04tBPp/vuGG9tDZqf9auXYuvvvoKeXl5fV7TaDQA0Cfdf/Xq1Vi1ahUSExPxv//9D1OmTHHpXKOZXC5Heno6xo8f3+e11NRUABf+TXbv3g0ATpc4zJkzB1wu1+W6kkqlEseOHYNMJnNa5sN+jkuP96c//Qk7d+7E9OnTsX79eshkMpfOd6mzZ8+itbUVubm5fYIlHA7HUS91sJ9n1apVqKiowPz584dUl3U0CdY5ZPerX/0KNTU1+MUvfuEojTMYo9GIxx9/HDKZDM8+++yIzh/ognX+KBQKFBUVYdasWU6Xpl36s9s9/PDDOHr0KK677jqsW7duWOcebWgOuT6H7IG81tbWPu/v7u4GAISHhw9rPIEsUOaQRqNBamoqxo8f7zQYkpaW1musgzl8+DD0ej2mTp3aK2AC2O4Npk2bBr1ej8OHD/fZt66uDq+88gpmzJiBxYsXu3S+0S5Y5xEANDY24rHHHkNnZyceeughrFixot/30v1YXzR3XJs7AH2P9SdY55Ber0dBQQFmzJjh9HVn10L2cmcU6B1deL4egLulp6fj5Zdfxg033OD44HPGXtT60gs5O/tNQlVVFYqKitx6bgB46KGH8NBDDzl9TavVorKyEgBczogUiUR9grwsy+Lzzz/HkSNHEBcX1+fCIS4uDs888wxuv/12CAQCfP755y6dazR75513+n3txIkTAC78m1RUVAAAsrOz+7xXKpUiJiYGzc3N6OjoGLSmTVVVFViWRUZGhtO5Y39oYH9AYVdQUIB77rlnxI2r7PMtKyvL6ev9nf9inZ2dePvttyEQCPDEE0+MaDyBLFjnkN3VV1+N3/zmN8jJyUFDQ4NL+7zxxhs4e/Ys3nnnnREHmgNdsM6fCRMm4LPPPuv3dfvPfnFWBmDL1Fy0aJHTi+lgRXPIOWdzaNasWdi6dSveeOMNREZGYvr06dBoNPjvf/+L0tJSjB8/PijnVqDMIYlEgo8++sjlsQ7G1Wuh8vJyXHHFFY7tFosFTzzxBHg8Hl588cU+WevBKljnEWBL2rnllluwfPlyJCUlDTgn6H6sL5o7rs0dgL7H+hOsc2jevHkDJls5uxayB3p7enpw33334dSpUzAYDCgoKMCyZcswc+ZMl85N/MuoC/QuW7bMpfelpaWhuroaP/30U58LOoPBgFOnTgEAurq63H7uwfznP/+BVqvFuHHjEB8fP+T9m5qa8OKLL+LMmTNoaGhAVlYWXn/9dcdyfDvKOHAdy7J44403AMBR6qO9vR0AEB0d7XSf6Ohol78UBjtWTEwMAFsw9WLuCqi68rM4O//FPvjgA6jVatx+++20hNqJ0T6H7N58880hvb+kpATvv/8+brrpJsyZMwctLS1uHc9oESzzx5nq6mps2LABDMP0uXh98cUXPX7+0YLmUN85tHjxYpw+fRpr167t8/D9lltuwZNPPjnog/tg4q9zyBmj0ehoGntxibaRnL+/a6H//ve/OHr0KF5++eWgLFk1VKN9HgG2IIqr3090P+Y6mjt90ffY0ATDHOrP/v37cfDgQYSEhGDWrFmO7fbSDc899xyys7MxadIk1NbW4tChQzh06BCefvpp3HXXXSM+P/GuoP2tX7RoEQBbuYWL69YYjUa8+OKLjnR2o9Ho1XHt3LkT7777LjgcjqMmy1BVVVVhy5Ytjmw6q9XqeFJFhufvf/87Dh8+jKioKPzyl78EAOh0OgC2us7O2Lf3V8vtYvb32Dt/XkooFLp8rOEY7PyD/Sw6nQ5r164Fj8dz/P2Q3kb7HBoOrVaLJ598ErGxsXjmmWd8PRy/FqzzR6FQ4KGHHoLJZMJNN93kNNuCuIbmUN85xOVycfXVVyMzMxNRUVGYM2cOCgsLweVy8eOPP2Lr1q1eHau/C5Q5xJ7vNF9bW4uMjAyXA2nDuRay15e/6qqr8LOf/cyl8wS70T6PiOfQ3OmLvseGJljnUENDgyO2tGzZMkRERACwJTW2tLSAx+Phtddew4YNG7By5Ups2LABr7/+Ong8Hl5++WVHMJgEjqAN9M6bNw+LFy+GQqHA7bffjttuuw3Lly/HvHnzsGHDBkcgmM/ne21MO3bswG9/+1tYLBY8/PDDw67RVFhYiJKSEuzbtw8rVqxAa2srHn744SEVgicXvPHGG/j3v/8NgUCAf/zjH44PRi6XC4ZhwDDMgPvby4QMxF4qxB3HGo6Rnv+bb75BT08PrrnmmiE14AoWwTCHhuOll15CfX09/vKXv/RbRocE7/zp6urCvffei4qKCuTn5+OPf/yj18492tAccj6HPvroI9x7773IysrCli1b8M4772Dt2rX4+OOPweVy8dRTT2Hfvn1eG68/C5Q5ZLFY8PTTT2Pjxo0IDw/HypUr+6xoc9f57fXlpVIpnn/+eZfOEeyCYR4Rz6C54xx9j7kuWOfQuXPnsHTpUrS3t2P27NlYvny547WIiAjs378f3377La677rpe+1177bVYsmQJLBYLPvnkk2Gfn/jGqCvdMBQvvPACCgsLsWbNGpw+fRpSqRSXX345HnroIWzatAkAHJ0S3377bVRVVfU5xvLly5GRkTHisXz++ed47rnnYDab8eCDD/YpA/GXv/zFaRmJp59+2vEhZWfv1iiVSnHrrbdCKpXi4YcfxptvvomFCxeOeKzBwmw24/nnn8dnn30GoVCIlStX9mqeJxKJoFQqYTAYHE/nLqbX6wHA0UHc3rXzUq+++qrjqZ99n0sZDAYAgFgsHtbP0tXVhb/85S99tkdERODpp58e9Pz27f2d3/77Yn9AQmyCaQ4N1c6dO/HZZ5/hjjvuwOWXXz6sMY12wTx/6urqsGzZMtTW1iI/Px/vvfdev9kRpH80h/qfQ93d3XjttdcQGRmJF154oddrRUVFePLJJ/HEE0/g3XffDerPqECaQ1qtFo888gi2b98OmUyG9957r9c1elVVlWMZ7MUyMjKwfPnyIV8L2evL/+Mf/xh0OW+wC6Z5RNyL5k7/6HvMNcE8h0pLS7F8+XJ0dHRgxowZePPNN/uU8oiIiOgTT7KbM2cO/ve//znKmpLAEdSBXsBW18ZZKnx1dTUAICEhAQCwb98+HDp0yOn+Iw30vv7663jnnXfAMAyeeuop3H333X3es2XLFjQ2NvbZ/vvf/77fX0y7q6++GkKhELW1tdBqtcO+yQomGo0Gv/vd77B7926EhYXhX//6V68vBMBWY0epVKK9vd1pTdpLa/Rs2LDB6bleffVVR72ejo4Op++xlxLpr97PYLRardPzJyYm4umnnx70/APVG+rp6cHhw4cRGRmJadOmDWt8o1GwzaGheuWVVxzjuviCyX5h1N3d7dj+6quvDmfIAS2Y58+xY8fwwAMPoLu7G5MnT8bbb79NGd/DQHNo4Dl04sQJaLVaTJs2zXHzdjF7/bpgXq4YSHOoo6MDy5Ytw6lTpxAXF4f//ve/fXpwdHR0OD3/5MmTsXz58iFdC3V0dOD999+HWCzG1q1bey2PtpdO++mnn/DYY48FfQAw2OYRcR+aOwOj77HBBfMc2rp1Kx599FHodDpce+21ePnll4ecGWwfZ3+Ba+K/gjbQ29LSgqqqKmRmZvbp4g0ABw8eBMMwyM/PBwCsXr3a7WNgWRbPPvssPv/8cwgEArz88su49tprnb5327Zt/R5HqVTirbfegl6vd7p0jMPhgMfjwWAwwGKxuG38o1VPTw/uuecenDp1CvHx8fj3v//ttC5kVlYWKisrUVVV1edLQa1Wo62tDREREY4sj/Ly8n7PmZmZCQ6H4zRrHLjw4GG49SmTkpIGPL/9uPaO05eyj8vZ+fft2wez2Yy5c+c6lqsEu2CcQ0Nlr021ZcuWfl+3X8gEW6A3mOfP7t278eCDD0Kv12PhwoV46aWXaLnsMNAcGnwOqVQqAACP5/xS2P59ZjKZhjXeQBdIc6ixsRFLly5FQ0MDsrOz8Z///MdpU7QpU6YMeH77DfVg10I5OTnQ6/WwWq39PoAAgPr6etTX1wd1ADAY5xFxD5o7g6PvsYEF8xz64osv8Mc//hEWiwX33HMPnnjiCaflJPbt24cvv/wS+fn5TpMN7Q8uqdFo4AnaGr07duzAvffei48//rjPa9u3b0dLSwsuu+wyjy7Feumll/D5559DKpXivffe6zfIO5iQkBCsW7cOn332mdOL05KSEmg0GiQnJztKURDnjEaj40laZmYmPv30035vSmfOnAnAeaBq27ZtsFgsuOKKK1w6r1gsxsSJE9HZ2YkjR470ed1+DlePN1QZGRlITEzE6dOn0dzc3Os1q9WKbdu2gWEYx898sdLSUgC2JUIkeOfQUG3btg3l5eV9/uzcuROALUvPvi2YBPP8OX78uCNAd9ddd+G1116jIO8w0BxybQ6lp6cDsD3Yd5apYq9pmJOT47Hx+qtAmkMKhQL33HMPGhoaMGnSJHz88cfDviGdNGkSQkJCsH///j6NcjQaDfbv3+8Yo/3Bg7M/f/vb3wDYylmVl5d7JFkkEATrPCIjR3PHNfQ91r9gnkNbtmzBs88+C6vViieffBJPPvlkvzWD9Xo9vvnmG6xatQpms7nP61999RUAYMaMGcMeD/GNoA30zpo1C3w+Hx9//DHq6uoc26urq/Hcc88BAB566CGPnX/Xrl348MMPwePx8O6772Ly5MnDPpZAIHDURn3mmWegUCgcr1VVVTmWNN57770jGnMwePPNN3Hs2DHEx8dj9erVA37IXn311YiMjMSXX37pCE4BtgyO1157DQzDOH0y1p877rgDAPDnP/+5Vz3mzz77DPv27UN+fv6wG/S54vbbb4fFYsEzzzzT6wbnjTfeQG1tLebNm4cxY8b02e/kyZMAgIKCAo+NLZAE8xwiIxes80ev1+ORRx6BXq/HbbfdhqeffnrQRhbEOZpDrs2hvLw8jB8/HgqFAs888wyMRqPjtTNnzuCvf/0rAGDp0qUeGa8/C6Q59Oc//xl1dXUYO3Ys/vOf/4wooUEsFuNnP/sZenp68Oc//9lx02uv76hUKnHbbbdRKRkXBes8IiNHc8c19D3Wv2CdQ+3t7XjqqadgtVrxyCOP4J577hnw/TNmzEBiYiIaGxvxyiuv9Fr9/cUXX2DTpk2Ijo7GLbfcMuwxEd8I2tINCQkJ+P3vf49XXnkFN954IyZPngyLxYKDBw/CZDLhscceG1HwdTBvvvkmACAyMhKffvopPv30U6fvc7XZ2yOPPIJjx47h2LFjmD9/PiZMmACVSoXS0lIYjUbceuutjg8d4lx3d7cj6yIiIsJp0xe7V199FVKpFCtWrMBDDz2E+++/H5MmTYJEIsGBAweg0+nw8MMPIzc31+XzX3vttfjxxx+xadMmXHPNNZg8eTJaW1tRWlqKsLAwvPzyyyP+GQdy9913Y8eOHdi7d69jDtXU1ODs2bNITEzst+O9fUkHNSKhOURGJpjnzxdffOH4LFEoFP02upg0aRJuu+02j40j0NEcGtoceu2113DnnXdi48aN2L9/P4qLi9Hd3Y3S0lKYTCbceeedw15tFagCaQ5VVFQ4msFKJJJ+r1OG0jT04YcfxsGDB/HVV1+hpKQEY8eOxenTp1FfX4/8/Hz89re/dflnCWbBPo/I8NHcGRr6HusrmOfQBx98AKVSCT6fj/Ly8n6vhebPn4/58+dDIBDg1VdfxX333YcPP/wQ27ZtQ25uLurr61FWVgaxWIyVK1fSw68AFLSBXgD45S9/CblcjtWrV2P//v0IDQ3FlClTcN9993m0M6VCocCJEycAAK2trf3W9gJcb/YmlUrx8ccf47333sO3336LPXv2QCgUoqioCHfccQcWLFjgtvGPVocPH3Ysezl16tSA3SXtNUOvuuoqrF69Gv/85z9x/PhxsCyLnJwc3H333cP6O3/11VdRVFSEzz//HDt37oRcLsfChQvx0EMPITU1dVg/l6sEAgHee+89/Pvf/8bGjRuxfft2REdH47bbbsODDz7oKC5/KfuTyrCwMI+OLxAE+xwiIxPM82f37t2O///DDz/0+z4ej0eB3gHQHLJxdQ6NGTMGX331Ff7zn/9g27Zt2LlzJ4RCIYqLi4P22imQ5tCePXvAsqxj3P0ZStNQmUyGTz/9FG+99Ra2bNmC7du3Iz4+Hr/85S/xwAMPOG14RPoK9nlEho/mztDQ91hfwTyH7NdCJpMJGzdu7Pd9KSkpmD9/PgBgwoQJ+PLLL/H2229j79692L59O+RyOW666Sb8+te/RnJysis/MvEzDGufWYQQQgghhBBCCCGEEEICUtDW6CWEEEIIIYQQQgghhJDRggK9hBBCCCGEEEIIIYQQEuAo0EsIIYQQQgghhBBCCCEBjgK9hBBCCCGEEEIIIYQQEuAo0EsIIYQQQgghhBBCCCEBjgK9hBBCCCGEEEIIIYQQEuAo0EsIIYQQQgghhBBCCCEBjgK9hBBCCCGEEEIIIYQQEuAo0EsIIYQQQgghhBBCCCEBjgK9hBBCCCGEEEIIIYQQEuD+Hy1SnhK8d7HpAAAAAElFTkSuQmCC\n", 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", 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\n", 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\n", 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oTJ48WQ6HQ16vN+T3mOBS2hdeeKF22223iMeFoWPeDGyo82ZXhxxyiC6//PJBAxG7inZ/ELHFXBlYqswV1mSphTkzsKHOmVjFfzwej7766itJPX/v9vZ2ff7552pqalJxcbFmzpypjIyMiMeI0WPUBpyXLFmia665RllZWRE9Licnx7rd0dER8trgMgrBGb2R2n333fXSSy/F/QvXk08+ad3ea6+9Yva8hmGotbVVX331lZ599lk98cQT6urqUk5OjtX/rD8333yz9tlnH041DxM1NTXWifXi4mIrwz24hM2u/S52NW7cOOv2jh07Il4QBL/WhAkTQl5bXl5u3e6vZM4+++yj1157bdCDJUNlvvZg/zaSrIBzdXW1DMMYdG7U1tbq4YcfluTvF7J06dKhDxgxw5wZmoceeijiv29/zFLaBQUFuvbaa2MwMsQD8yW+nn76aeu0+EBrwFjNOSQGcya+Qs2ZjIwMnXXWWfrf//1fdXZ26owzztBll12mb33rW8rIyNBHH32kG2+8UXV1dZKkyy+/3MqWRnKMtPkyGMMwhvzdv66uTl1dXZIG/7dxOBwqKytTdXX1gOMNLqW99957a9myZRGPCYnFvIl+z6yoqEjPPPNMyJKnA0nU/iBih7mSnLkisSYbrpgzsYvJ9CfUa23atEkej0eSv0LA8uXL9fzzz6uzs9O6Jjc3V8cff7wuuOCCXpnZgGnUBpyjPbEU/Cazbt26kD0g1q1bZ90OzoyOVCJOLG7cuNFqLp+RkaEjjjgiZs9977336qabbur1sz333FPXX399yNJb++67b8zGgPi76aabrA+lo446yjrl7na7rWtCZU1JvU9WNTQ0RDyGSF4r+EOxv9cKVRI+Vpqamqx/s8HGK/WMubu7W83NzYNm9zzyyCPWomDp0qUh+4Ug8ZgzQxOLwFdwKe0rr7wyZQIZ6Iv5Ej+1tbW64447rD8fffTR/V5HsHl4Yc7ETzhz5uyzz1ZLS4v+8pe/aOfOnf22NBk/frwuvfRSHXnkkXEdLwY30ubLYB577LFe5RJD7WkMJJLxSv4xV1dXDzhes5R2RkaGbrjhhpAZ00gNzJvI540pLy8v6gAaGc3DD3MlOXPFxJps+GHORD9nBjNY/McsIy6p1/edYK2trXrwwQf173//W3ffffeAvbQxerGKj1Bwj4CHH37YOtm+q46ODj377LPWn83Tv6moublZv/jFL6w38yVLlsR0EVtVVdXnZ9u2bdMzzzzTKwscw9ejjz6q559/XpK/CsA555xj3RdcCWCwgGdwSY7BKgj0J/gxg5X3CB5LNK8VC+3t7dbtcILBwdcEP7Y/XV1d1iIiJydHp5xySpSjRDwwZ5IvuJT2AQccoBNOOCHZQ8IAmC/x09XVpYsuushqvXD44YdTRm4EYM7ETyRz5rzzztPZZ5894HNVVFT0yr5Acoy2+fLZZ59pxYoV1p8vuuiiiB5vivZ7TH/jDS6lfcEFFwzaexDJx7yJbt5g9GGupMZcYU02fDBn4jdnwon/BAecbTabTj75ZD3zzDP65JNP9M477+h3v/ud1X9769at+ulPfzro/jRGHwLOEZo+fbr2228/SdLatWt1/fXX9wk6e71e/b//9/96BVpTtSx0e3u7fvrTn2rz5s2S/H+/WL+5zZ07V9dff71++9vf6r/+67/kdDrV1NSk+++/X4sXL+63QT2Gj1deeUXXXXed9edrrrmm1wdWd3e3dXuwD+ng+4MfF65EvlYseL1e63Y4/S+Crwl+bH9eeOEFqyzQiSeeSJmTFMKcSQ1mKe3c3Fxdf/31yR4OBsB8iR+v16vLL79c77//viR/6wXKyg9/zJn4iWTOfPHFFzr88MP1pz/9SVlZWVq8eLFuvvlm3XzzzTrttNOUk5Oj999/X4sXL7banyDxRtt82bp1q8466yxrM/TEE0/UIYccEuFI/aL9HrPreINLae+5554688wzoxoPEod5E/28wejCXEmNucKabPhgzsRvzoQb/zFbwtpsNv3ud7/Tddddp1mzZikrK0slJSU68sgj9be//c2qPPDll1/qgQceiMuYMXyN2pLaQ3HNNdfoxBNPVFtbm/7617/qww8/1LHHHquxY8equrpaTz31lDZu3KgFCxbok08+UUdHR0qWsm1tbdVPf/pTrV69WpK/RMRtt90W87Eec8wxvf58xhln6Oyzz9aaNWu0ceNG3XzzzWz2D1Mvv/yyLr74YmvDYdmyZVq0aFGva4L7pHs8npAf1MGVAMLZuNjVrq8VSvBrpaenR/xasRDJeKXIxvz4449bt0877bQoRod4YM6khuBS2pdddlmvnjtIHcyX+Onu7tZll12mF198UZL/73bbbbeppKQkySPDUDBn4ieSObNjxw4tXbpU9fX1GjdunO69915NmTLFun/RokU644wzdOaZZ2rr1q267rrrNGHCBH3nO99J2N8Ho2++bNq0SWeccYZ1IPUb3/iGrrrqqojHaQreM4jke8yu4zVLaaenp2vFihVyOBxRjwnxx7wZ2rzB6MFcSY25wpps+GDOxG/ORBL/ueWWW3T99derrq5uwFLZ+fn5+s1vfqPjjjtOkn//+dxzz43L2DE8EXCOwtSpU3XXXXfpvPPOU0NDgz777DN99tlnva7Zf//99bvf/U4LFy6U5J+MprfffjtkiYXZs2eroqIiPoMPcLvdOvfcc7VmzRpJUkFBge6++26rLEKwTZs26auvvhrwuYqKiiLqt1xUVKRbb71Vhx9+uLq6uvTMM8/o8ssvH7QfLVLLE088oWuuucY6vXXcccfpl7/8ZZ/rcnJyrNudnZ1hLwiCP/heeeWVkGP55je/qZKSkj6vFUrw/cELiVhYt25dv6XkTeXl5dpzzz0jGu+u14Qas8vl0kcffSTJ/36SSn0PRzPmzMDCnTOxEFxKe8GCBZSbT1HMl4ENdb60t7frwgsv1BtvvCHJ/6X41ltv1d577x3TcSKxmDMDS/Scuf3221VfXy/J34MueGPTNHHiRN1666067rjj5PP59Ic//IHNzQQabfNlzZo1Ovfcc61+hDNnztSdd97Z72NXr14dsv/g5MmTNXXqVOXm5oY93uBrgl8zuJT2ueeeqxkzZgz6PEge5s3Q5w1GB+ZK6swV1mTDA3MmfnMmkviPKTs7e9C+zLNnz9Yee+yhL774Qtu3b1dNTU1M27NieCPgHKV9991XL774ou655x69+uqr2rFjh7KzszVjxgydcMIJ+uEPf6j6+noZhiFJcjqd1mOvvvpq7dixY8DnXrFihY4//vi4jf3rr7/WWWedpa1bt0ryB4DvvvvuATdaXnjhBd1+++0DPt9+++2nBx98MKIxlJeX66CDDtK//vUveTwerV271grOI/Xdfvvtuu2226w/n3DCCbr++uv7LR0ffJCgoaGh1+GLXQV/iAZnjJx33nkhx/PAAw9owYIFvV7L7K03kOD7Y53R9eCDD+rpp58e8P7jjjtON954o7KyspSRkaGurq5Bxyv1jDk9PT3kv+Prr79ulfo/4ogjIhw94oE5E1q4cyYWzFLa2dnZuuGGG1K25cVoxnwJbSjzZdcvnJmZmbrtttt00EEHxXSMSCzmTGiJnDOGYVgbWdOmTdP+++8/4OvOnDlTCxYs0DvvvKO1a9fK5XKptLQ03L8WojTa5subb76pCy+8UG1tbZKkPffcU3ffffeA7Xb+8Ic/aNWqVQM+3/nnn68LLrig179FJN9jzPEGl9KePn26fvrTnw76HEge5k1s5g1GPuZK6swV1mTDA3MmfnMm0vhPpKZMmaIvvvhCkgg4oxcCzkNQUlKiSy+9VJdeemm/9wc3Wu/vFFUyrFu3TmeffbZcLpckacyYMbr77rs1ffr0hI9l8uTJ1m1zPEhtPp9P11xzjR577DHrZ8uWLdMvf/nLAYM2waemqqqqQp6Sqq6utm5Hk+W/62uFEnx/vCsKhLL77rtr48aNqqysHPRa899nsPG+9tpr1m0CzsnFnEk9L730kiR/xtphhx026PXLly/X8uXLJfV8+UB8MF/ia/v27frJT35ifeHMy8vTn/70J+23335JHhmixZyJr2jmjNvtVlNTkySF9f1q1qxZeuedd6zXY3MzfkbjfHnqqad01VVXWRlD++67r+64446QG7ThGjNmjHJzc9Xa2jro9xiv16va2tpe462qqtLHH38sSdq4caPmzJkz6Gseeuih1u3gvRbED/MmtvMGIxdzJfXmCmuy1Macie+cSUT8Jy2NsCL6x29GHAWfQAk+PRIcDEqkNWvW6Cc/+Ymam5sl+d88//KXvwxaJuGCCy4I+4TZLbfcoo0bNyo9PT1kVrTUu+REcEkupCafz6fLL79czz33nPWzX/ziF/rZz34W8nHBpT2++OKLkJt05skom82mPfbYw/p5uBsKU6ZMkc1mk2EY1nMN5Msvv7Rux/rAxY033hh2NubUqVO1ceNGVVdXq6WlRXl5ef1e19LSYi2YQo3X6/Vai+SpU6f2OtiBxGLOhC+SOYORifkSvmjmy9dff62lS5danyOlpaW66667Yna6GYnHnAlfIueMWd1KCq/MsN1u7/c2Yms0zpdHHnlE1157rfU7+d3vfle///3vBy31GEnlsqlTp2rNmjXatGlTyOs2b95s9WRMxkF3RId5E595g5GHuZKac4U1WepizsR3zkQb/3G73XrhhRdUV1en3XffXccee2zI62tqaqzbY8aMiXicGLkIOEdo586d+s1vfqPa2lqdcMIJA5a+NgxD//rXvyT5y2nPmjUrkcPsY9OmTTrrrLOsN5tZs2bp7rvvjvmJrf/85z9at26dHA7HoCVIzNJ0UupkgGNg11xzjbUYsNvtuvrqq7V48eJBH7f33nsrPT1dHo9H7733npYsWdLvdV6vV++//74kfzmbaHp65+TkaM6cOfr000+1evVq+Xy+AReK7777rvV3iaQHeazNnz9fL774orxer1avXq2DDz643+tWrVplbdTMnz9/wOfbsGGDVZplr732ivl4ET7mTGq67rrr1NHREfKad99911rYL1261Cq/FfxFBbHFfImf2tpanXHGGVbgbPz48brnnntC9m1C6mPOxM9Q5kxxcbHVLmX9+vUyDCNk64bgDaxUyeweiUbbfPnHP/7Ra1Pz2GOP1Q033BDzTJT58+drzZo1crlc+uKLLwZcJ5njNR8j+Q9x/PGPfxz0NR544AG99957kvxrODLOEod5E595g5GHuZKac4U1WepizsRvzgwl/uPxePTrX/9akjRjxoyQAefm5matXbtWkjR27FiNGzdu6IPHiMGRnQgVFRXp1Vdf1erVq/XMM88MeN3LL79snXA5/vjjk9ojsrOzUxdccIHVU2Du3Ll64IEH4vJl7cADD5Tkf3N/9NFHB7xu9erV+uijjyT5e2mw6ZnannrqKavMid1u14oVK8JaDEj+HhtmsOa1114bsOTa888/L7fbLUn6wQ9+EPVYDz/8cElSXV2dXnzxxX6v2b59u15//XVJ0sKFCwfslZEI3/ve96z3h1An18z77HZ7yDLZ5ge+JDLXkog5k7oOOOAAHXbYYSH/mz17tnX97NmzrZ/Huq8o/Jgv8WMYhi655BLt2LFDkjRx4kQ99NBDrLuGOeZM/Ax1zjgcDivboqqqyjqA3J+vvvpK//nPfyT5N4MIpMXHaJsvmzZt0i9/+UtrU/Pkk0/WjTfeGJdNzeDvJAN9j/F6vXr44YclSfn5+TrggAMkSdnZ2YOuxw477LBem/7f/va3rZ8jvpg38Zs3GFmYK6k7V1iTpSbmTPzmzFDjP2PHjrW+82zYsMFqfdKfe+65x0rkWLRo0ZDGjZGHgHOEMjIydMghh0iS3nvvPb388st9rvn000919dVXS/K/Gf74xz9O6Bh3deutt1plriZMmKC77rorqtM94Vi8eLGys7MlSX/+85+1evXqPtd8+eWXuuiii6w/h1uuG8lRV1enG264wfrzFVdcMWhZjV2dccYZkvynpS6++GK1tLT0uv/LL7+0XiM3N1cnnXRS1OM98cQTrf4Xv/71r/uUeGtpadHFF18sj8cjSUmfn2PGjNFRRx0lSXr77bd1zz339Lnmrrvu0sqVKyVJRx55ZMiTY8GnMmfOnBnj0SIczBkgfMyX+HrkkUeszLDCwkLdc889nNgf5pgz8RWLOfOTn/zEun3NNddo3bp1fa7ZuXOnfv7zn1s93M4666whjBoDGY3zZfny5Vbp0MMOO0zXXntt3A6/77XXXtp7770lSU888USfzVjDMHT99ddr8+bNkqRTTz110NKRSD7mTXznDUYO5krqzxXWZKmFORPfOROL+M/SpUut21dccUWvstmm559/Xnfeeackf2Km+f8EMKXeEaRh4Oc//7lee+01dXV16ec//7l++MMfasGCBbLb7frggw/097//XV1dXbLZbPr1r3+d1IyohoYG60Sx5D+F/MEHHwz6uKysLOv0cSTGjRunK664Qtdcc406Ozt1xhln6JhjjtGCBQskSR9++KGefvpp68325JNP1ve///2IXweJc//991sf4BUVFZowYYJeeeWVQR83e/Zsa4PugAMO0BFHHKGXXnpJH330kRYtWqTFixervLxc69ev1yOPPKLW1lZJ0uWXXz6kOVNSUqILL7xQv/71r1VfX6+TTjpJp556qmbNmqWqqio98sgj2r59uyTpqKOOiur3PNYuu+wy/fvf/1ZTU5NuuukmrVq1yjpJ99JLL+mNN96Q5P8gv/zyy0M+17Zt26zbnMpMDuYMED7mS/x4PB7ri6AkHXLIIdq4caM2btw46GPJHktdzJn4idWc+fa3v62TTz5Zjz32mFwul04++WQdeeSRmj9/vrKzs7Vu3Tr97W9/U1NTkyT/3/voo4+O/V8Io26+vPXWW/rkk08k+TO7vvvd7+rVV18d9HXLy8ujrox01VVX6eSTT5bH49FFF12k1157TQcccIA6Ojr0zDPP6MMPP5Tk7x947rnnRvUaSCzmTfznDUYG5krqzxXWZKmFORO/OROr+M8pp5yiF198UatXr9aWLVt0zDHH6OSTT9bMmTPV0tKi1157zcroTktL04033sjeM/qwGWZOP/Tee+9p2bJlkqTzzz8/ZObtq6++qksvvdTqlbqrnJwcXXfddfrhD38Yl7FK/k2QHTt2aPz48Xrttdf6vebRRx/Vr371q4ifO9RzhuPBBx/UjTfeaJ0Q25XdbteZZ56piy++eMAeCP257bbbdPvtt0vy93MyA9mInwMOOEC1tbURP27FihW9epy3t7fr/PPP19tvv93v9TabTeeff77OP//8qMca7He/+53+/Oc/a6C3uIMOOki33nprxKfsZ8yYIUnab7/9QpbAjtTHH3+s//qv/7LKvuyqtLRUd9xxh+bNmxfyeY477jitX79ekr+nutPpjNkYER7mTG/xmjP9ufLKK/X0009L8n9OT5gwIarneeqpp7R8+XJJff+/ILaYL73Fcr689dZbUZ/Q37BhQ1jXxWrOIXzMmd5Sdc54vV7ddNNNuv/++0M+7oQTTtA111yjjIyMqF4XoY22+XLxxRfr+eefj/j1jjvuON14441RjVWSXnnlFV122WUD7o1MmjRJd999tyZOnBjxc/M5k3jMm/AMdd70Z+nSpVq1apWk8NdipnD2BxFbzJXwJHuusCZLHcyZ8EQzZ2IZ/2lqatJFF1004L+v5E+IuuGGGziojn6R4RylQw89VH//+99133336e2331ZlZaXsdrt22203fec739HSpUtTomF6OCfy42Hp0qU68MAD9eCDD+rtt99WdXW1bDabxo4dq29961s69dRTNX369KSMDeFzu91RLQb6k52drb/85S967rnn9Mwzz+izzz5Tc3OzioqKtM8++2jZsmXaZ599YvJaknTRRRfpoIMO0l//+letXr1aLpdL2dnZmjVrlk444QQdc8wxKVX65xvf+IZeeOEF3XfffXrttde0fft2eb1eTZw4UYcccoh+/OMfh3UyzzzJJylupfMxMOYMED7mS3wlaw2I+GHOxFcs54zD4dAvf/lLHXvssXrkkUe0atUq1dTUyOfzqaysTPPnz9ePfvQjffOb34zZa6K30ThfkvW+f9hhh+n555/Xvffeq3//+9/Wd//JkyfriCOO0NKlS5Wbm5uUsSEyzBsgPMyV4YM1WWpgzsRXLF+roKBAf/nLX/Tqq6/qmWee0SeffKL6+nrl5ORowoQJOuSQQ7R48WIymzEgMpwBAAAAAAAAAAAAAFEJv5YxAAAAAAAAAAAAAABBCDgDAAAAAAAAAAAAAKJCwBkAAAAAAAAAAAAAEBUCzgAAAAAAAAAAAACAqBBwBgAAAAAAAAAAAABEhYAzAAAAAAAAAAAAACAqBJwBAAAAAAAAAAAAAFEh4AwAAAAAAAAAAAAAiAoBZwAAAAAAAAAAAABAVAg4AwAAAAAAAAAAAACiQsAZAAAAAAAAAAAAABAVAs4AAAAAAAAAAAAAgKgQcAYAAAAAAAAAAAAARIWAMwAAAAAAAAAAAAAgKgScAQAAAAAAAAAAAABR+f8esY2JBNjaNQAAAABJRU5ErkJggg==", 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\n", 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", 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\n", 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", 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\n", + "image/png": 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", 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\n", 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -291,12440 +298,38 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "8593e4f9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating CDR features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.78% of the records are missing a location. \n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:58:28 to 2020-02-29 19:01:57\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 08:02:22 to 2020-02-29 23:54:24\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 13:39:38 to 2020-02-29 14:37:41\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 09:28:21 to 2020-02-29 20:23:01\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 07:00:19 to 2020-02-29 09:40:21\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 03:21:11 to 2020-02-29 18:02:09\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 09:56:21 to 2020-02-29 23:57:46\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 01:03:12 to 2020-02-29 02:43:26\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:52:24 to 2020-02-29 11:42:57\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 04:44:01 to 2020-02-29 23:06:35\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.49% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-02 03:40:27 to 2020-02-29 00:12:48\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.57% of the records are missing a location. (0 + 1) / 1]\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 03:48:29 to 2020-02-29 05:50:46\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:28:44 to 2020-02-29 17:36:12\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 01:37:47 to 2020-02-29 17:18:33\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 01:46:52 to 2020-02-29 06:19:17\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 03:39:34 to 2020-02-29 00:30:27\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 04:22:25 to 2020-02-28 20:07:26\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 06:09:35 to 2020-02-29 22:25:05\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 12:25:22 to 2020-02-29 14:30:24\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 02:11:34 to 2020-02-29 23:04:17\n", - "\u001b[32m[x]\u001b[0m 202 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 116 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 172 records from 2020-01-01 17:52:09 to 2020-02-28 13:14:49\n", - "\u001b[32m[x]\u001b[0m 157 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 07:20:17 to 2020-02-29 14:03:57\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-02 03:27:13 to 2020-02-29 20:49:05\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 09:47:41 to 2020-02-29 22:38:38\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 13:29:21 to 2020-02-29 18:03:55\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 02:15:58 to 2020-02-29 10:10:34\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 00:52:24 to 2020-02-29 00:14:59\n", - "\u001b[32m[x]\u001b[0m 162 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 01:14:48 to 2020-02-29 13:26:22\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 06:34:07 to 2020-02-29 18:05:49\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.49% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 04:01:14 to 2020-02-29 12:07:56\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 01:00:20 to 2020-02-29 19:04:41\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.48% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 121 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 01:27:12 to 2020-02-29 16:13:13\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 10:05:32 to 2020-02-29 21:16:11\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 11:21:07 to 2020-02-29 23:39:12\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.28% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 07:00:54 to 2020-02-29 21:49:02\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 01:27:12 to 2020-02-29 16:31:12\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 02:34:52 to 2020-02-29 16:38:45\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 03:24:27 to 2020-02-29 21:54:37\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 11:00:45 to 2020-02-29 15:52:57\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 15.77% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 03:09:53 to 2020-02-29 12:21:43\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:58:28 to 2020-02-28 18:44:36\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 231 records from 2020-01-01 03:52:41 to 2020-02-29 22:46:51\n", - "\u001b[32m[x]\u001b[0m 208 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 03:36:59 to 2020-02-29 15:03:57\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 04:27:28 to 2020-02-29 23:46:11\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 12:30:33 to 2020-02-29 23:12:55\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 17:11:28 to 2020-02-29 13:29:33\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.80% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 01:35:55 to 2020-02-29 13:57:38\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:48:08 to 2020-02-29 19:37:22\n", - "\u001b[32m[x]\u001b[0m 162 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 05:31:30 to 2020-02-29 11:42:47\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 17:18:35 to 2020-02-29 22:52:28\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:49:51 to 2020-02-29 23:43:54\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 12:14:48 to 2020-02-29 05:14:09\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 00:02:23 to 2020-02-29 04:30:38\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:38:12 to 2020-02-29 22:40:44\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 11:40:35 to 2020-02-29 14:50:49\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 08:05:52 to 2020-02-29 04:34:58\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.12% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 11:49:11 to 2020-02-29 09:49:15\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.77% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 07:16:45 to 2020-02-29 14:59:55\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 02:31:00 to 2020-02-29 17:04:31\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 02:53:30 to 2020-02-28 21:25:47\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 04:27:06 to 2020-02-29 12:14:27\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 20.28% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:28:33 to 2020-02-29 09:47:41\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 11:32:39 to 2020-02-29 21:26:59\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 03:08:35 to 2020-02-29 10:00:54\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.19% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:55:46 to 2020-02-29 18:51:39\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.13% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:03:05 to 2020-02-29 21:34:29\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 02:49:27 to 2020-02-29 20:21:36\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 09:38:33 to 2020-02-29 19:32:37\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 12:48:26 to 2020-02-29 16:17:36\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 12:45:52 to 2020-02-29 23:23:05\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 07:28:59 to 2020-02-29 05:46:34\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 05:53:19 to 2020-02-29 21:26:59\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:57:13 to 2020-02-29 14:26:48\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:08:25 to 2020-02-29 22:09:30\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 04:07:51 to 2020-02-29 13:52:46\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 05:52:03 to 2020-02-28 18:58:31\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 12:57:16 to 2020-02-29 21:20:09\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 07:13:05 to 2020-02-28 16:09:11\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.34% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:06:09 to 2020-02-29 07:10:32\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 00:31:13 to 2020-02-29 17:31:43\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:41:44 to 2020-02-29 16:58:15\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.28% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:15:52 to 2020-02-29 14:25:57\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.01% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:15:58 to 2020-02-29 17:03:05\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 19.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 04:27:26 to 2020-02-28 01:05:10\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 08:35:50 to 2020-02-29 08:56:49\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 00:51:01 to 2020-02-29 20:49:01\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 00:59:21 to 2020-02-29 15:45:38\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:41:38 to 2020-02-29 19:43:50\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:16:05 to 2020-02-29 21:00:11\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 05:49:59 to 2020-02-29 23:09:21\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 06:06:09 to 2020-02-29 20:06:41\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 152 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 00:07:47 to 2020-02-29 15:59:29\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 07:59:44 to 2020-02-29 20:10:13\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 10:20:32 to 2020-02-29 11:48:07\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.42% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 10:51:08 to 2020-02-29 22:29:32\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 04:41:31 to 2020-02-29 13:01:07\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:51:18 to 2020-02-29 23:28:57\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:10:56 to 2020-02-29 17:14:13\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 06:45:54 to 2020-02-29 23:29:34\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 06:35:29 to 2020-02-28 20:10:44\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:21:35 to 2020-02-29 20:04:42\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 00:53:05 to 2020-02-29 18:09:25\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:00:20 to 2020-02-29 19:50:28\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:57:13 to 2020-02-29 16:14:39\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 20.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 03:22:54 to 2020-02-29 17:47:16\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 01:46:43 to 2020-02-29 14:15:00\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 18:18:39 to 2020-02-28 23:48:47\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.87% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-02 20:01:48 to 2020-02-29 22:22:26\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.88% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:24:24 to 2020-02-29 23:01:18\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 08:07:10 to 2020-02-29 15:07:03\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 01:57:59 to 2020-02-29 19:05:21\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 12:44:09 to 2020-02-29 16:35:23\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.05% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 02:31:00 to 2020-02-29 22:55:09\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 09:23:16 to 2020-02-29 18:26:24\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 11:55:57 to 2020-02-29 16:58:22\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:45:45 to 2020-02-29 23:28:32\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.88% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:02:04 to 2020-02-29 21:57:07\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-03 00:38:24 to 2020-02-29 21:29:26\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 15:06:09 to 2020-02-29 23:04:36\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 03:12:37 to 2020-02-29 19:05:06\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 04:21:10 to 2020-02-28 22:13:13\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 01:21:42 to 2020-02-29 23:25:01\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.51% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 12:53:27 to 2020-02-29 13:48:37\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 14:48:27 to 2020-02-29 15:25:22\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 18:26:42 to 2020-02-29 21:32:05\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 01:18:39 to 2020-02-29 20:23:01\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 06:39:48 to 2020-02-29 13:09:53\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 00:13:51 to 2020-02-29 23:53:09\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 00:24:08 to 2020-02-29 20:19:22\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 01:21:42 to 2020-02-29 20:57:52\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.72% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 153 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 03:29:54 to 2020-02-29 17:26:58\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.25% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 06:36:20 to 2020-02-29 09:36:01\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 10:13:06 to 2020-02-28 22:45:21\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 05:02:14 to 2020-02-29 19:40:07\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 10:44:03 to 2020-02-28 23:39:29\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 17:49:47 to 2020-02-29 21:34:29\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 169 records from 2020-01-01 15:46:08 to 2020-02-29 21:44:00\n", - "\u001b[32m[x]\u001b[0m 159 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:43:25 to 2020-02-29 22:47:34\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 00:13:51 to 2020-02-29 22:06:16\n", - "\u001b[32m[x]\u001b[0m 202 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 03:52:41 to 2020-02-29 19:06:30\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 06:11:54 to 2020-02-29 21:59:04\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 14:12:11 to 2020-02-28 21:31:59\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.70% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 04:52:06 to 2020-02-29 16:18:56\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 00:51:11 to 2020-02-29 09:44:58\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 03:24:30 to 2020-02-29 19:50:13\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 03:13:37 to 2020-02-29 01:17:34\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:26:40 to 2020-02-29 16:59:51\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 11:33:43 to 2020-02-29 07:24:10\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.42% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 11:42:31 to 2020-02-29 12:10:25\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 03:12:37 to 2020-02-29 13:24:21\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:28:41 to 2020-02-29 22:35:18\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 03:35:24 to 2020-02-29 23:56:35\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 20.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 03:04:43 to 2020-02-29 23:10:29\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 07:31:08 to 2020-02-29 18:32:05\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:12:42 to 2020-02-29 18:26:47\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 156 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 03:47:54 to 2020-02-29 13:29:41\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 04:57:30 to 2020-02-29 14:59:14\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 00:37:14 to 2020-02-29 13:01:07\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 02:14:43 to 2020-02-29 22:22:49\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 05:02:37 to 2020-02-29 16:17:49\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:17:53 to 2020-02-29 23:00:24\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 227 records from 2020-01-01 02:52:39 to 2020-02-29 20:29:29\n", - "\u001b[32m[x]\u001b[0m 205 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.72% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 03:09:08 to 2020-02-29 21:49:26\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 07:16:35 to 2020-02-29 19:15:07\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 18:18:39 to 2020-02-29 11:54:35\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:30:00 to 2020-02-29 21:06:10\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 07:27:08 to 2020-02-29 22:51:25\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 01:46:18 to 2020-02-29 17:06:31\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 11:18:53 to 2020-02-29 23:02:01\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.05% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 05:21:34 to 2020-02-29 20:11:21\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 01:59:49 to 2020-02-29 07:33:19\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 02:12:58 to 2020-02-29 17:33:19\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.77% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 03:36:59 to 2020-02-29 17:57:28\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:20:52 to 2020-02-29 14:55:36\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 00:20:30 to 2020-02-29 19:30:39\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.13% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:43:54 to 2020-02-29 18:21:43\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:51:11 to 2020-02-29 06:50:05\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 00:31:13 to 2020-02-29 05:14:29\n", - "\u001b[32m[x]\u001b[0m 206 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 05:55:09 to 2020-02-29 21:07:33\n", - "\u001b[32m[x]\u001b[0m 160 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 08:31:41 to 2020-02-29 20:50:31\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 06:12:59 to 2020-02-29 11:02:17\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.26% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 00:49:14 to 2020-02-29 15:22:57\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 04:29:28 to 2020-02-29 14:23:41\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 15:25:43 to 2020-02-29 19:04:03\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 00:02:04 to 2020-02-29 23:04:36\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 00:20:52 to 2020-02-29 22:22:26\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 04:02:43 to 2020-02-29 21:42:46\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:21:10 to 2020-02-29 19:43:47\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 09:38:10 to 2020-02-29 23:23:38\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 01:46:43 to 2020-02-29 23:19:35\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 05:59:43 to 2020-02-29 12:36:13\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.51% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 07:44:45 to 2020-02-29 21:59:22\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 153 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 230 records from 2020-01-01 00:41:38 to 2020-02-29 23:52:22\n", - "\u001b[32m[x]\u001b[0m 209 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 01:42:54 to 2020-02-29 16:27:03\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 02:12:00 to 2020-02-29 20:31:44\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 13:56:11 to 2020-02-29 22:15:35\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:42:44 to 2020-02-29 13:01:05\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 03:27:45 to 2020-02-29 12:12:52\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 05:34:51 to 2020-02-29 07:53:20\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 06:13:03 to 2020-02-29 23:54:48\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:02:19 to 2020-02-29 16:18:46\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.70% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 09:28:04 to 2020-02-29 22:47:56\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 01:56:40 to 2020-02-29 11:13:46\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 02:14:38 to 2020-02-29 12:38:32\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.99% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 04:42:54 to 2020-02-29 17:13:08\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 11:25:09 to 2020-02-29 19:49:35\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-02 14:23:06 to 2020-02-29 18:15:07\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 228 records from 2020-01-01 01:56:55 to 2020-02-29 14:42:45\n", - "\u001b[32m[x]\u001b[0m 206 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 03:43:48 to 2020-02-29 19:42:36\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 168 records from 2020-01-01 08:04:07 to 2020-02-29 17:16:20\n", - "\u001b[32m[x]\u001b[0m 154 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 09:28:04 to 2020-02-29 15:36:00\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 14:34:53 to 2020-02-29 22:30:03\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 01:07:55 to 2020-02-29 18:43:30\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 03:52:41 to 2020-02-29 15:51:29\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 04:01:48 to 2020-02-29 09:50:09\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.91% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 17:41:50 to 2020-02-29 10:31:31\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 14:43:48 to 2020-02-28 21:08:36\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 15:12:23 to 2020-02-29 15:58:23\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 02:37:02 to 2020-02-29 15:45:51\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 01:56:53 to 2020-02-29 13:32:03\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 19:48:52 to 2020-02-29 19:04:41\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 20.10% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 04:54:05 to 2020-02-29 21:38:34\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.25% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 12:12:42 to 2020-02-29 14:49:18\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.75% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 21:14:06 to 2020-02-29 23:34:14\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 01:26:02 to 2020-02-29 03:42:11\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 13:41:47 to 2020-02-29 16:21:06\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 01:22:53 to 2020-02-29 20:00:32\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 02:25:29 to 2020-02-29 19:50:28\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 04:20:55 to 2020-02-29 12:26:36\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 02:32:52 to 2020-02-29 18:30:02\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.75% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 02:36:50 to 2020-02-29 22:45:42\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 12:54:30 to 2020-02-29 21:20:20\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 13:45:25 to 2020-02-29 11:04:17\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 03:01:27 to 2020-02-28 22:30:35\n", - "\u001b[32m[x]\u001b[0m 207 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 05:49:46 to 2020-02-29 20:45:44\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 08:42:10 to 2020-02-29 19:35:12\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 03:16:50 to 2020-02-28 22:17:05\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 00:19:47 to 2020-02-29 23:01:17\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-02 03:03:59 to 2020-02-29 11:41:10\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 20.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 13:35:55 to 2020-02-29 19:51:37\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 02:02:25 to 2020-02-29 23:48:33\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:49:28 to 2020-02-29 23:05:57\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 08:18:35 to 2020-02-29 23:01:06\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.60% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 04:15:45 to 2020-02-29 10:45:06\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 03:17:17 to 2020-02-29 21:02:26\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 11:58:03 to 2020-02-29 19:14:39\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 226 records from 2020-01-01 01:26:28 to 2020-02-29 22:50:13\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 01:59:49 to 2020-02-29 19:36:19\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 227 records from 2020-01-01 06:09:09 to 2020-02-29 11:02:17\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 00:40:16 to 2020-02-29 23:25:01\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 150 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 226 records from 2020-01-01 07:45:25 to 2020-02-29 22:42:21\n", - "\u001b[32m[x]\u001b[0m 206 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 12:49:39 to 2020-02-29 23:01:06\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 14:15:48 to 2020-02-28 21:09:03\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 00:33:45 to 2020-02-29 11:30:34\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.99% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 09:05:13 to 2020-02-29 18:30:36\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 02:24:32 to 2020-02-29 22:15:35\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:37:55 to 2020-02-29 23:33:35\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 06:42:35 to 2020-02-29 22:10:09\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:54:21 to 2020-02-29 17:54:40\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 03:41:03 to 2020-02-29 20:36:35\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 04:52:45 to 2020-02-29 12:13:03\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 05:36:32 to 2020-02-29 16:30:01\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.05% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 09:46:32 to 2020-02-29 04:15:45\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:35:29 to 2020-02-29 21:19:51\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 10:19:48 to 2020-02-29 16:52:33\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 08:04:07 to 2020-02-29 12:49:56\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:34:48 to 2020-02-29 18:09:17\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:15:52 to 2020-02-29 19:30:39\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 03:10:31 to 2020-02-29 19:44:47\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 04:25:08 to 2020-02-29 07:57:40\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.75% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 04:54:43 to 2020-02-29 17:45:46\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 05:03:52 to 2020-02-29 19:04:03\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 05:25:08 to 2020-02-29 19:32:19\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 03:09:51 to 2020-02-29 18:04:17\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 10:11:12 to 2020-02-29 20:06:56\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 01:14:59 to 2020-02-29 14:43:28\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 05:01:12 to 2020-02-29 17:05:28\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 03:04:21 to 2020-02-29 11:11:20\n", - "\u001b[32m[x]\u001b[0m 203 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 07:10:08 to 2020-02-29 14:04:50\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 7.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 155 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 07:45:28 to 2020-02-29 08:32:41\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 01:14:51 to 2020-02-29 18:05:49\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:14:28 to 2020-02-29 21:08:53\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 04:48:09 to 2020-02-29 20:50:18\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 07:29:29 to 2020-02-29 21:59:04\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 09:46:52 to 2020-02-29 21:08:53\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.51% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 21:47:59 to 2020-02-29 19:23:16\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 04:42:54 to 2020-02-29 14:54:39\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.42% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:52:38 to 2020-02-29 06:41:38\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 00:40:20 to 2020-02-29 23:56:26\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 02:15:22 to 2020-02-29 22:22:49\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 04:17:04 to 2020-02-28 13:27:45\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 11:27:06 to 2020-02-29 22:55:42\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 07:10:08 to 2020-02-29 10:04:48\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 228 records from 2020-01-01 02:42:07 to 2020-02-29 23:41:52\n", - "\u001b[32m[x]\u001b[0m 208 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 03:24:51 to 2020-02-28 17:32:17\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 05:02:29 to 2020-02-29 15:43:55\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 00:40:20 to 2020-02-29 15:28:37\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 02:44:18 to 2020-02-29 22:25:05\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:23:45 to 2020-02-29 18:15:33\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 07:26:11 to 2020-02-29 12:41:38\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.41% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 09:55:38 to 2020-02-29 06:04:11\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 10:36:44 to 2020-02-29 23:40:39\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 00:53:36 to 2020-02-29 20:49:05\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 00:57:30 to 2020-02-29 08:28:26\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.13% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 03:50:23 to 2020-02-29 20:45:50\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.03% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 05:03:02 to 2020-02-29 18:25:09\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 09:05:42 to 2020-02-29 13:29:00\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.48% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 12:48:36 to 2020-02-29 23:11:35\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-02 07:06:24 to 2020-02-29 19:24:08\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:56:29 to 2020-02-29 14:34:24\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 09:46:40 to 2020-02-28 14:29:50\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:10:38 to 2020-02-29 19:49:36\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 14:37:54 to 2020-02-29 20:57:06\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.70% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 06:18:32 to 2020-02-29 23:54:48\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-02 01:32:34 to 2020-02-29 12:14:27\n", - "\u001b[32m[x]\u001b[0m 204 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-02 05:43:59 to 2020-02-29 19:40:07\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 04:36:18 to 2020-02-29 20:44:35\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 16:55:19 to 2020-02-29 04:23:12\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 152 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 00:34:42 to 2020-02-29 19:53:25\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 00:39:49 to 2020-02-29 21:00:48\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 6.77% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 02:16:18 to 2020-02-28 23:20:55\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 09:20:18 to 2020-02-29 20:57:06\n", - "\u001b[32m[x]\u001b[0m 202 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.80% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 22:25:36 to 2020-02-29 22:32:23\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:02:29 to 2020-02-29 19:45:26\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 7.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 16:13:46 to 2020-02-29 15:31:10\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 6.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 01:27:17 to 2020-02-29 13:24:23\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 04:16:42 to 2020-02-29 21:44:21\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 05:09:02 to 2020-02-29 22:18:23\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 02:19:17 to 2020-02-29 23:15:26\n", - "\u001b[32m[x]\u001b[0m 159 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 11:04:47 to 2020-02-29 19:24:08\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:39:57 to 2020-02-29 20:44:35\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 10:16:59 to 2020-02-29 20:32:54\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.88% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 07:23:27 to 2020-02-29 16:44:37\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 12:20:05 to 2020-02-29 15:50:56\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:43:54 to 2020-02-29 19:09:12\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 08:37:11 to 2020-02-29 14:26:32\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:17:50 to 2020-02-29 14:25:57\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 09:46:04 to 2020-02-29 14:00:38\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-02 03:01:33 to 2020-02-29 20:29:29\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 04:46:36 to 2020-02-29 17:36:42\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 04:54:05 to 2020-02-29 23:43:32\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 06:52:20 to 2020-02-29 23:13:39\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.31% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 04:55:46 to 2020-02-29 18:55:25\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 01:56:40 to 2020-02-29 19:32:00\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.28% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:34:01 to 2020-02-29 15:00:03\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 13:21:57 to 2020-02-29 23:11:35\n", - "\u001b[32m[x]\u001b[0m 162 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 06:49:38 to 2020-02-29 22:46:23\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:06:36 to 2020-02-29 11:51:02\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 07:25:46 to 2020-02-29 13:35:25\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 09:41:55 to 2020-02-29 23:14:42\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 10:08:09 to 2020-02-29 17:30:19\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.25% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 06:26:40 to 2020-02-29 20:43:27\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 04:27:03 to 2020-02-29 13:13:13\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 117 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 00:31:38 to 2020-02-29 23:03:09\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 01:24:38 to 2020-02-29 23:27:18\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 07:40:13 to 2020-02-29 20:49:01\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 09:38:39 to 2020-02-29 20:47:56\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 11:11:12 to 2020-02-29 22:08:42\n", - "\u001b[32m[x]\u001b[0m 206 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 02:49:38 to 2020-02-29 19:06:30\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.54% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 03:47:43 to 2020-02-29 21:54:56\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 00:10:34 to 2020-02-29 13:48:25\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 22:57:11 to 2020-02-29 21:01:52\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:08:57 to 2020-02-29 03:55:46\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 05:25:33 to 2020-02-29 23:01:17\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:24:38 to 2020-02-29 22:16:13\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 08:39:19 to 2020-02-29 05:28:25\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.72% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 08:45:53 to 2020-02-29 23:06:38\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.31% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 168 records from 2020-01-01 10:24:00 to 2020-02-29 21:20:09\n", - "\u001b[32m[x]\u001b[0m 149 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 08:05:13 to 2020-02-29 18:32:05\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 02:51:12 to 2020-02-29 16:11:12\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 03:12:20 to 2020-02-29 19:14:39\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 00:21:35 to 2020-02-29 08:26:36\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 00:52:27 to 2020-02-29 23:20:17\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 18:26:54 to 2020-02-29 22:30:56\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:01:32 to 2020-02-29 07:31:49\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 04:35:42 to 2020-02-29 16:52:49\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 09:47:54 to 2020-02-29 00:08:21\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:32:08 to 2020-02-29 08:37:32\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.28% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-02 20:21:35 to 2020-02-29 19:36:08\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 05:51:44 to 2020-02-29 20:57:52\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 00:21:12 to 2020-02-29 21:49:47\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 00:49:14 to 2020-02-29 18:25:09\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 02:44:18 to 2020-02-29 14:37:41\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.19% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 04:29:26 to 2020-02-29 21:20:52\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 03:18:24 to 2020-02-29 15:03:36\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 21:13:20 to 2020-02-29 16:14:22\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:50:07 to 2020-02-29 07:22:58\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 06:28:53 to 2020-02-28 21:11:22\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 04:27:28 to 2020-02-29 20:45:44\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 00:17:50 to 2020-02-29 18:05:58\n", - "\u001b[32m[x]\u001b[0m 157 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 151 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 06:43:37 to 2020-02-29 03:16:14\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 02:29:29 to 2020-02-29 19:45:43\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-02 06:03:36 to 2020-02-29 23:08:55\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 00:18:39 to 2020-02-29 19:03:46\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 229 records from 2020-01-01 07:17:58 to 2020-02-29 19:15:10\n", - "\u001b[32m[x]\u001b[0m 207 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 12:31:51 to 2020-02-29 19:57:09\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-02 12:01:17 to 2020-02-29 14:53:58\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.72% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 02:00:03 to 2020-02-29 20:24:53\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 02:00:13 to 2020-02-29 21:45:47\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 10:12:25 to 2020-02-29 19:51:55\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 02:30:00 to 2020-02-29 18:42:58\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 12:38:32 to 2020-02-29 17:54:48\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 15:35:32 to 2020-02-29 23:41:52\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.19% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 19:02:13 to 2020-02-29 22:44:21\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.05% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 156 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 00:04:26 to 2020-02-28 10:33:16\n", - "\u001b[32m[x]\u001b[0m 209 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:20:28 to 2020-02-29 23:24:06\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 02:51:40 to 2020-02-29 23:13:31\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 229 records from 2020-01-01 01:05:50 to 2020-02-29 10:16:35\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 14:01:49 to 2020-02-29 17:51:26\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 03:06:24 to 2020-02-29 20:19:04\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 02:33:51 to 2020-02-29 21:35:43\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 00:40:02 to 2020-02-29 21:48:29\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 04:44:08 to 2020-02-29 15:03:36\n", - "\u001b[32m[x]\u001b[0m 160 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 18:59:46 to 2020-02-29 22:22:27\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 10:30:02 to 2020-02-29 23:47:12\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 00:33:20 to 2020-02-29 14:30:58\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:00:07 to 2020-02-29 19:15:13\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 174 records from 2020-01-01 03:59:43 to 2020-02-29 16:40:15\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 121 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 04:41:31 to 2020-02-29 15:08:20\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:02:25 to 2020-02-29 22:05:17\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 02:14:07 to 2020-02-29 16:38:34\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.60% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 19:21:48 to 2020-02-28 23:49:23\n", - "\u001b[32m[x]\u001b[0m 162 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 05:49:59 to 2020-02-29 21:20:52\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 08:53:18 to 2020-02-29 23:33:19\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 01:57:59 to 2020-02-29 16:08:32\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 05:51:44 to 2020-02-29 13:00:28\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 06:01:17 to 2020-02-29 23:08:59\n", - "\u001b[32m[x]\u001b[0m 159 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 06:08:39 to 2020-02-29 20:00:39\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 10:47:14 to 2020-02-29 19:02:28\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 12:48:26 to 2020-02-29 15:33:28\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 05:16:39 to 2020-02-29 20:41:23\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:45:54 to 2020-02-29 05:42:10\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.31% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 09:31:02 to 2020-02-29 20:21:01\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.52% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 112 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 165 records from 2020-01-01 00:45:00 to 2020-02-29 23:32:09\n", - "\u001b[32m[x]\u001b[0m 156 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 09:30:34 to 2020-02-29 23:10:39\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 06:33:13 to 2020-02-29 11:48:12\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 19:34:30 to 2020-02-29 11:41:10\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:23:42 to 2020-02-29 23:33:35\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 09:54:49 to 2020-02-29 19:32:19\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 11:12:29 to 2020-02-29 18:08:00\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 05:53:34 to 2020-02-29 21:31:12\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 08:52:46 to 2020-02-29 17:52:22\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 150 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 231 records from 2020-01-01 01:26:02 to 2020-02-29 17:51:26\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 11:27:19 to 2020-02-29 10:10:39\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 15:14:24 to 2020-02-29 22:10:09\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.52% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 16:15:21 to 2020-02-29 17:25:59\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.54% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:43:36 to 2020-02-29 23:36:33\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 08:52:31 to 2020-02-29 23:58:06\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 18:59:46 to 2020-02-29 20:25:52\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 113 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 160 records from 2020-01-01 01:45:45 to 2020-02-29 13:53:41\n", - "\u001b[32m[x]\u001b[0m 150 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:45:49 to 2020-02-29 21:53:05\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 09:05:05 to 2020-02-29 20:19:04\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 226 records from 2020-01-01 09:46:32 to 2020-02-29 23:14:42\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 03:12:43 to 2020-02-29 00:59:07\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.48% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 06:04:12 to 2020-02-29 23:36:16\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 09:43:25 to 2020-02-29 15:30:34\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 08:34:57 to 2020-02-29 07:11:37\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 01:42:54 to 2020-02-29 04:34:58\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 23:46:31 to 2020-02-29 10:01:50\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-02 17:12:47 to 2020-02-29 20:16:07\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.75% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 01:34:53 to 2020-02-29 20:37:41\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 05:01:08 to 2020-02-29 05:49:03\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 231 records from 2020-01-01 00:06:20 to 2020-02-29 21:35:43\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 01:20:16 to 2020-02-29 20:27:31\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 01:03:22 to 2020-02-29 21:09:59\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:27:10 to 2020-02-29 04:40:27\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 23:41:34 to 2020-02-29 19:45:26\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-02 07:08:13 to 2020-02-29 10:54:39\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 10:57:27 to 2020-02-29 17:24:42\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 03:16:07 to 2020-02-29 23:38:58\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 00:28:12 to 2020-02-29 09:15:28\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 09:41:18 to 2020-02-29 17:36:12\n", - "\u001b[32m[x]\u001b[0m 203 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 18:05:39 to 2020-02-29 23:06:56\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 01:24:33 to 2020-02-29 18:30:53\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 02:15:22 to 2020-02-29 21:39:17\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 10:22:05 to 2020-02-29 12:29:06\n", - "\u001b[32m[x]\u001b[0m 204 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 10:22:51 to 2020-02-29 21:49:47\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 07:24:58 to 2020-02-29 23:35:04\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 18:37:00 to 2020-02-29 14:27:07\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 05:39:57 to 2020-02-29 20:58:24\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 17:47:09 to 2020-02-29 13:52:57\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-02 10:24:46 to 2020-02-28 07:19:30\n", - "\u001b[32m[x]\u001b[0m 157 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:08:16 to 2020-02-29 20:59:44\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:14:05 to 2020-02-29 21:39:17\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 16:50:11 to 2020-02-29 20:50:18\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-02 06:06:57 to 2020-02-29 20:19:22\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 01:41:44 to 2020-02-29 10:17:32\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.70% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 04:48:09 to 2020-02-29 01:53:03\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:27:31 to 2020-02-29 16:04:33\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 04:54:43 to 2020-02-29 20:37:41\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 02:24:33 to 2020-02-29 21:07:00\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 221 records from 2020-01-01 13:24:25 to 2020-02-29 18:50:30\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 06:31:59 to 2020-02-29 23:57:46\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 02:41:52 to 2020-02-29 20:24:53\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.70% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 03:16:07 to 2020-02-29 18:34:22\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:56:24 to 2020-02-29 17:47:07\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 04:38:47 to 2020-02-29 10:40:19\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 00:00:42 to 2020-02-29 14:02:32\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.87% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 07:00:54 to 2020-02-29 18:21:47\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 220 records from 2020-01-01 02:17:18 to 2020-02-29 17:40:22\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:05:11 to 2020-02-28 09:23:33\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 01:20:32 to 2020-02-29 10:05:07\n", - "\u001b[32m[x]\u001b[0m 202 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.03% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 02:51:32 to 2020-02-29 18:06:58\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 12:12:31 to 2020-02-29 22:09:30\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 22:56:40 to 2020-02-29 12:12:16\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-02 07:33:58 to 2020-02-29 20:27:25\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 19:56:25 to 2020-02-29 17:44:09\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.51% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 17:11:14 to 2020-02-29 22:01:58\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 170 records from 2020-01-01 09:59:41 to 2020-02-29 03:00:13\n", - "\u001b[32m[x]\u001b[0m 156 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 230 records from 2020-01-01 06:05:22 to 2020-02-29 23:55:45\n", - "\u001b[32m[x]\u001b[0m 203 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 06:54:21 to 2020-02-29 19:17:36\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 01:27:17 to 2020-02-29 23:01:42\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 16:13:38 to 2020-02-29 11:28:03\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 17:33:30 to 2020-02-29 23:52:24\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:16:27 to 2020-02-29 15:49:36\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 17.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:14:51 to 2020-02-28 21:58:10\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 05:35:27 to 2020-02-29 20:21:01\n", - "\u001b[32m[x]\u001b[0m 160 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 116 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 170 records from 2020-01-01 05:41:29 to 2020-02-29 15:17:29\n", - "\u001b[32m[x]\u001b[0m 157 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.41% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 22:26:40 to 2020-02-29 23:16:32\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 06:07:02 to 2020-02-29 05:42:15\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 230 records from 2020-01-01 08:26:20 to 2020-02-29 20:00:32\n", - "\u001b[32m[x]\u001b[0m 207 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:50:00 to 2020-02-29 23:29:34\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 174 records from 2020-01-01 04:59:13 to 2020-02-29 06:16:38\n", - "\u001b[32m[x]\u001b[0m 156 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.49% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 03:47:30 to 2020-02-29 10:31:02\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.25% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 10:36:17 to 2020-02-29 23:56:35\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 20:18:51 to 2020-02-29 14:59:14\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 08:21:41 to 2020-02-29 21:42:46\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 03:13:37 to 2020-02-29 15:56:56\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 04:15:45 to 2020-02-29 15:46:34\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 13:31:48 to 2020-02-29 21:09:59\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 12:56:57 to 2020-02-29 22:57:37\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 06:34:07 to 2020-02-29 22:05:17\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 08:53:09 to 2020-02-29 16:58:22\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 06:52:20 to 2020-02-29 10:45:57\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.01% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 05:15:10 to 2020-02-29 23:55:45\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 01:08:34 to 2020-02-29 22:46:23\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 01:50:19 to 2020-02-29 03:27:44\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 03:07:43 to 2020-02-29 16:06:30\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 04:58:55 to 2020-02-29 20:10:42\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 02:51:12 to 2020-02-29 20:59:55\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.77% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 00:18:39 to 2020-02-29 22:47:34\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 05:29:52 to 2020-02-29 23:23:38\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 115 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 09:09:00 to 2020-02-29 09:47:43\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:56:29 to 2020-02-29 22:21:41\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.31% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:23:42 to 2020-02-29 21:01:09\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:45:00 to 2020-02-29 22:57:56\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.52% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 00:06:20 to 2020-02-29 14:39:58\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 16:53:19 to 2020-02-29 06:58:45\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 14:37:54 to 2020-02-29 17:07:43\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 161 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 233 records from 2020-01-02 07:43:31 to 2020-02-29 23:36:05\n", - "\u001b[32m[x]\u001b[0m 212 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 00:40:16 to 2020-02-29 14:56:21\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 00:03:05 to 2020-02-28 19:35:04\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 20:13:02 to 2020-02-29 22:00:49\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 12:20:59 to 2020-02-29 04:27:44\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 00:50:07 to 2020-02-29 23:03:55\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 04:21:23 to 2020-02-29 22:48:07\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.26% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:33:02 to 2020-02-29 20:32:45\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 02:00:13 to 2020-02-29 04:33:03\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-02 07:04:16 to 2020-02-29 21:24:36\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 00:18:19 to 2020-02-29 17:44:09\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:24:08 to 2020-02-29 14:24:40\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 04:33:16 to 2020-02-29 18:38:00\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.54% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 05:40:48 to 2020-02-29 21:07:00\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 06:22:39 to 2020-02-29 23:52:56\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 14:57:36 to 2020-02-29 22:19:20\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.99% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 03:38:04 to 2020-02-29 21:03:12\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:05:50 to 2020-02-29 15:03:55\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 02:30:00 to 2020-02-29 18:33:05\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 01:16:05 to 2020-02-29 23:26:11\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 09:26:54 to 2020-02-29 21:29:25\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 8.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 11:45:38 to 2020-02-29 12:27:46\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:44:47 to 2020-02-29 23:06:35\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 01:09:21 to 2020-02-29 15:30:34\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 05:37:57 to 2020-02-29 23:53:09\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.10% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 06:35:58 to 2020-02-29 16:57:20\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 01:07:55 to 2020-02-29 23:56:26\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.41% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 09:49:30 to 2020-02-29 22:30:03\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 06:46:08 to 2020-02-29 23:42:20\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 05:45:26 to 2020-02-29 07:52:16\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 16:41:39 to 2020-02-28 12:59:40\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.91% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 08:37:16 to 2020-02-29 13:24:21\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 10:51:03 to 2020-02-29 20:59:55\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:14:05 to 2020-02-29 23:39:12\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:36:16 to 2020-02-29 12:19:19\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.31% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 01:37:03 to 2020-02-29 23:33:19\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 13:33:02 to 2020-02-29 18:49:11\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 02:16:59 to 2020-02-29 04:31:28\n", - "\u001b[32m[x]\u001b[0m 159 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 15.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 02:14:38 to 2020-02-29 12:30:51\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 08:19:42 to 2020-02-29 23:27:18\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 10:07:19 to 2020-02-29 18:30:19\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.48% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 01:20:16 to 2020-02-29 14:42:45\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:04:26 to 2020-02-29 22:18:23\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 04:23:42 to 2020-02-29 17:32:13\n", - "\u001b[32m[x]\u001b[0m 157 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 18:36:37 to 2020-02-29 12:58:01\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 03:39:34 to 2020-02-29 21:03:48\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 02:18:33 to 2020-02-29 14:34:24\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 02:41:52 to 2020-02-29 17:02:43\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 07:37:28 to 2020-02-29 18:50:30\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 11:35:07 to 2020-02-29 19:15:08\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-02 13:12:02 to 2020-02-28 15:35:26\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 00:55:46 to 2020-02-29 22:32:38\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 01:33:02 to 2020-02-28 21:01:34\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 05:51:58 to 2020-02-29 15:14:26\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 02:47:26 to 2020-02-29 20:21:55\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 151 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 11:39:31 to 2020-02-29 14:45:06\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 02:14:43 to 2020-02-29 16:50:28\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-02 07:00:11 to 2020-02-29 21:45:47\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 151 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 227 records from 2020-01-01 12:35:29 to 2020-02-29 23:11:27\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 16.75% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 04:54:00 to 2020-02-29 13:56:16\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 02:36:50 to 2020-02-29 11:55:12\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 05:34:27 to 2020-02-29 23:56:05\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 07:00:54 to 2020-02-29 22:32:41\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 14:31:21 to 2020-02-29 20:32:54\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 01:24:55 to 2020-02-29 08:45:02\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 21:04:29 to 2020-02-29 23:42:31\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 172 records from 2020-01-01 09:29:13 to 2020-02-29 15:38:31\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 14:07:39 to 2020-02-29 18:11:07\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 09:47:54 to 2020-02-28 21:28:57\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.01% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 05:29:31 to 2020-02-29 12:31:33\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 09:18:08 to 2020-02-29 20:03:25\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.12% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 07:48:32 to 2020-02-29 14:54:04\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 07:18:19 to 2020-02-29 21:32:57\n", - "\u001b[32m[x]\u001b[0m 162 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 00:50:00 to 2020-02-29 19:54:43\n", - "\u001b[32m[x]\u001b[0m 207 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 02:26:06 to 2020-02-29 19:15:07\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 07:11:13 to 2020-02-29 23:52:24\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:38:12 to 2020-02-29 23:19:19\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 20:13:02 to 2020-02-29 18:32:03\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 06:06:14 to 2020-02-29 10:14:06\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 00:06:38 to 2020-02-29 06:05:47\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:12:11 to 2020-02-29 21:54:56\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 03:20:52 to 2020-02-29 18:20:39\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 121 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 00:57:30 to 2020-02-29 23:33:15\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 19:40:10 to 2020-02-29 23:47:12\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 22:18:48 to 2020-02-29 11:39:04\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 00:28:41 to 2020-02-29 14:41:12\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:37:14 to 2020-02-29 16:48:02\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 02:31:43 to 2020-02-29 21:00:11\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 152 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 07:04:32 to 2020-02-29 17:13:08\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 112 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 04:21:23 to 2020-02-29 18:03:55\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 233 records from 2020-01-01 05:36:32 to 2020-02-29 23:54:24\n", - "\u001b[32m[x]\u001b[0m 207 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 17:38:47 to 2020-02-29 16:18:38\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 08:20:52 to 2020-02-29 22:57:24\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 01:14:59 to 2020-02-29 22:01:58\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 05:52:03 to 2020-02-29 22:31:40\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 05:09:02 to 2020-02-29 19:36:08\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 09:45:25 to 2020-02-29 21:46:54\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 13:56:46 to 2020-02-28 23:10:24\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.34% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:16:44 to 2020-02-29 23:04:17\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 05:34:15 to 2020-02-29 23:10:39\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 06:13:45 to 2020-02-29 23:27:54\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 04:26:57 to 2020-02-29 07:22:35\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.97% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 06:35:29 to 2020-02-29 20:47:56\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 03:44:48 to 2020-02-29 23:24:58\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 06:19:01 to 2020-02-29 16:50:28\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 01:34:53 to 2020-02-29 23:19:19\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 08:02:05 to 2020-02-29 19:34:49\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 16:23:59 to 2020-02-29 14:52:10\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 01:22:35 to 2020-02-29 15:34:40\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-02 04:59:51 to 2020-02-29 23:58:06\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 00:21:10 to 2020-02-29 23:53:08\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.70% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 00:21:12 to 2020-02-29 15:21:54\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 04:23:42 to 2020-02-29 17:31:43\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 05:41:29 to 2020-02-29 20:58:25\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 08:53:13 to 2020-02-29 14:54:56\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 16.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 18:05:39 to 2020-02-28 21:08:02\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 00:02:12 to 2020-02-29 05:44:32\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 04:19:43 to 2020-02-29 13:40:53\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 07:13:05 to 2020-02-29 23:16:32\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 08:00:59 to 2020-02-29 20:01:55\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 05:31:28 to 2020-02-29 17:32:38\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 06:42:05 to 2020-02-29 16:02:11\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 10:07:02 to 2020-02-29 20:06:41\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 01:24:55 to 2020-02-29 17:41:33\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 03:53:19 to 2020-02-29 23:09:21\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 07:27:08 to 2020-02-29 08:01:45\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 01:22:53 to 2020-02-29 23:10:31\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 21:36:32 to 2020-02-29 17:42:16\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:55:52 to 2020-02-28 20:47:54\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:06:38 to 2020-02-29 23:06:56\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 03:02:40 to 2020-02-29 20:20:16\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 01:10:28 to 2020-02-29 17:24:42\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:08:16 to 2020-02-29 20:06:25\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 07:12:28 to 2020-02-29 21:22:08\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 00:18:19 to 2020-02-29 17:27:34\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 11:15:34 to 2020-02-29 18:25:27\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 06:18:28 to 2020-02-29 22:16:13\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 15:46:54 to 2020-02-29 21:34:41\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-02 00:02:03 to 2020-02-29 22:22:27\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 04:44:01 to 2020-02-29 17:46:38\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 05:13:42 to 2020-02-29 23:56:05\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 05:21:34 to 2020-02-29 13:22:36\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 05:36:25 to 2020-02-28 19:53:35\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 06:44:29 to 2020-02-29 11:06:23\n", - "\u001b[32m[x]\u001b[0m 159 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 7.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 01:22:36 to 2020-02-29 16:14:55\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 08:31:59 to 2020-02-29 07:11:45\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 18:41:14 to 2020-02-29 21:51:24\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 08:42:04 to 2020-02-29 23:01:18\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 01:54:54 to 2020-02-29 18:05:58\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 05:31:30 to 2020-02-29 23:36:16\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 08:10:12 to 2020-02-29 20:36:59\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 08:12:12 to 2020-02-29 23:43:32\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 13.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 07:08:19 to 2020-02-29 16:14:39\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.73% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 01:37:47 to 2020-02-29 21:20:20\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.80% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 09:45:45 to 2020-02-29 16:52:33\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 12:45:52 to 2020-02-29 12:54:21\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 12:38:32 to 2020-02-29 20:45:50\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 117 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 01:09:21 to 2020-02-29 07:02:25\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 00:27:31 to 2020-02-29 21:23:03\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:17:07 to 2020-02-29 20:06:56\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 01:00:12 to 2020-02-29 13:57:49\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 09:02:29 to 2020-02-29 17:26:12\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.51% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 02:43:25 to 2020-02-29 20:58:24\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 05:16:28 to 2020-02-29 16:01:06\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.19% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 07:04:50 to 2020-02-29 16:19:51\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 00:54:21 to 2020-02-29 18:00:54\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 17:52:32 to 2020-02-29 16:41:54\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 06:34:41 to 2020-02-28 14:03:29\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 06:44:34 to 2020-02-29 20:20:14\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 07:48:33 to 2020-02-29 23:08:55\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 08:45:29 to 2020-02-29 12:23:51\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:46:52 to 2020-02-29 00:51:05\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 02:51:53 to 2020-02-29 13:57:49\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 08:34:31 to 2020-02-29 23:26:11\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 21:17:50 to 2020-02-29 16:29:36\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 00:20:30 to 2020-02-29 13:15:15\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.36% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 113 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 01:03:20 to 2020-02-29 20:58:25\n", - "\u001b[32m[x]\u001b[0m 162 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 01:03:22 to 2020-02-29 10:44:06\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:59:21 to 2020-02-29 15:24:35\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 116 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 159 records from 2020-01-01 04:29:41 to 2020-02-29 02:14:42\n", - "\u001b[32m[x]\u001b[0m 152 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 01:14:29 to 2020-02-29 10:17:05\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-01 06:54:58 to 2020-02-29 23:22:42\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 20:24:41 to 2020-02-29 20:21:55\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 05:35:27 to 2020-02-29 14:00:44\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 17:36:44 to 2020-02-29 04:35:26\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 10:05:32 to 2020-02-29 17:01:27\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 08:06:30 to 2020-02-29 15:35:15\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.12% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 04:07:51 to 2020-02-29 20:53:31\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:55:03 to 2020-02-29 20:10:42\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.31% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:40:02 to 2020-02-29 23:59:45\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 01:26:14 to 2020-02-29 14:30:58\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 02:15:49 to 2020-02-29 22:09:05\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 04:12:20 to 2020-02-29 14:06:49\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 227 records from 2020-01-01 05:39:57 to 2020-02-29 16:41:08\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.82% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 02:03:01 to 2020-02-29 23:40:39\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 14:45:51 to 2020-02-29 21:07:33\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 10:14:44 to 2020-02-29 14:01:37\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 00:30:40 to 2020-02-29 13:01:05\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 15:33:36 to 2020-02-29 13:29:03\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 06:42:05 to 2020-02-29 23:29:58\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 10:38:23 to 2020-02-29 22:29:46\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 06:29:23 to 2020-02-29 23:29:58\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 07:02:59 to 2020-02-29 15:52:57\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 12:06:02 to 2020-02-29 20:05:18\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 23:28:27 to 2020-02-29 19:57:00\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 04:16:42 to 2020-02-29 21:53:05\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.10% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 08:32:12 to 2020-02-29 23:52:56\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.16% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 05:03:35 to 2020-02-29 20:37:30\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 15:26:57 to 2020-02-29 23:00:24\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.54% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:52:27 to 2020-02-29 20:37:30\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.80% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 03:47:43 to 2020-02-29 06:41:38\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 05:58:42 to 2020-02-29 22:32:23\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.54% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 10:20:34 to 2020-02-29 18:48:06\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 116 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 13:35:55 to 2020-02-29 09:24:18\n", - "\u001b[32m[x]\u001b[0m 160 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 19.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 06:07:43 to 2020-02-29 21:03:12\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.12% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-02 06:42:32 to 2020-02-29 02:17:22\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:52:38 to 2020-02-29 22:46:51\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:18:39 to 2020-02-29 22:40:44\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 02:33:51 to 2020-02-29 20:59:56\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 03:27:23 to 2020-02-29 16:51:39\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 07:00:19 to 2020-02-29 16:14:22\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 04:22:21 to 2020-02-29 21:57:07\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 03:13:38 to 2020-02-29 09:21:04\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 03:02:40 to 2020-02-29 21:50:19\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 02:52:39 to 2020-02-29 20:03:25\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 07:23:16 to 2020-02-29 14:50:40\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 01:19:28 to 2020-02-29 21:40:42\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 118 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 13:07:52 to 2020-02-29 18:20:39\n", - "\u001b[32m[x]\u001b[0m 159 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-02 06:01:47 to 2020-02-29 17:08:49\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 15:49:17 to 2020-02-29 20:56:26\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.12% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 115 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 170 records from 2020-01-01 01:45:49 to 2020-02-29 23:48:33\n", - "\u001b[32m[x]\u001b[0m 156 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 07:03:05 to 2020-02-28 23:59:41\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 01:19:28 to 2020-02-29 21:46:54\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 04:30:26 to 2020-02-29 23:10:31\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.60% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 04:31:39 to 2020-02-29 16:50:20\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.10% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 117 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 168 records from 2020-01-01 06:59:20 to 2020-02-29 12:03:03\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 7.96% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 07:55:40 to 2020-02-29 17:32:38\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 00:36:16 to 2020-02-29 11:57:21\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.08% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 05:34:15 to 2020-02-29 23:02:01\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 07:55:27 to 2020-02-29 18:57:37\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.42% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 08:06:00 to 2020-02-29 13:00:28\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.66% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-02 03:45:50 to 2020-02-29 23:36:05\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 00:17:53 to 2020-02-29 21:49:02\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:10:35 to 2020-02-29 16:09:28\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 122 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 01:08:16 to 2020-02-28 17:14:45\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.13% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 02:36:24 to 2020-02-29 23:08:59\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 13:09:18 to 2020-02-29 23:46:11\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.91% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 165 records from 2020-01-01 00:10:38 to 2020-02-29 20:03:58\n", - "\u001b[32m[x]\u001b[0m 150 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 7.32% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:53:43 to 2020-02-29 23:01:42\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 01:22:26 to 2020-02-29 14:56:34\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 10:28:44 to 2020-02-29 21:51:46\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 07:04:50 to 2020-02-29 20:03:58\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 15:41:26 to 2020-02-29 22:55:19\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.77% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-01 01:24:33 to 2020-02-29 18:10:45\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 04:07:26 to 2020-02-29 20:27:44\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.99% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 02:50:53 to 2020-02-29 20:37:29\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 01:36:16 to 2020-02-29 11:52:36\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 19:09:23 to 2020-02-29 02:31:03\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 21:27:32 to 2020-02-29 12:46:53\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 00:12:42 to 2020-02-29 15:42:06\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 02:33:21 to 2020-02-29 12:11:47\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 00:55:00 to 2020-02-29 17:17:37\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 09:01:32 to 2020-02-29 14:45:06\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 16:41:24 to 2020-02-29 19:50:13\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 15.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 06:19:51 to 2020-02-29 21:59:22\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.26% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 154 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 18:46:22 to 2020-02-29 18:47:48\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.34% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 19:29:56 to 2020-02-29 07:11:32\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 114 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-01 00:51:01 to 2020-02-29 20:34:18\n", - "\u001b[32m[x]\u001b[0m 153 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 07:19:32 to 2020-02-29 23:20:17\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 03:54:29 to 2020-02-29 22:45:42\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 02:29:54 to 2020-02-28 14:54:50\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.01% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 207 records from 2020-01-01 00:09:47 to 2020-02-29 23:28:32\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.80% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 10:55:58 to 2020-02-29 16:41:08\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 03:12:20 to 2020-02-29 20:21:36\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.49% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 19:06:01 to 2020-02-29 22:39:56\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 00:32:11 to 2020-02-29 19:33:39\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 06:42:06 to 2020-02-29 23:19:35\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 06:55:52 to 2020-02-29 17:32:11\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 04:12:20 to 2020-02-28 08:32:00\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:20:52 to 2020-02-29 18:48:06\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 05:03:23 to 2020-02-29 20:53:31\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.05% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 00:16:27 to 2020-02-29 11:18:44\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 04:48:23 to 2020-02-29 06:06:19\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 01:50:19 to 2020-02-29 10:19:08\n", - "\u001b[32m[x]\u001b[0m 193 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 10:05:43 to 2020-02-29 18:19:47\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.93% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 20:36:42 to 2020-02-29 14:04:50\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 152 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 12:26:49 to 2020-02-29 18:42:32\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.86% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 01:02:19 to 2020-02-29 23:59:45\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 03:21:11 to 2020-02-29 20:26:19\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 00:51:18 to 2020-02-29 10:15:29\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:18:33 to 2020-02-29 19:43:50\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 114 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-01 01:30:42 to 2020-02-29 19:32:00\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 00:19:47 to 2020-02-29 14:36:51\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 08:58:27 to 2020-02-29 20:36:13\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 01:20:32 to 2020-02-29 13:27:52\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 214 records from 2020-01-01 01:08:16 to 2020-02-29 17:51:50\n", - "\u001b[32m[x]\u001b[0m 199 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 12:56:35 to 2020-02-29 20:14:36\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.14% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 01:08:34 to 2020-02-29 09:49:15\n", - "\u001b[32m[x]\u001b[0m 156 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.81% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 222 records from 2020-01-01 12:17:24 to 2020-02-29 16:41:54\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 00:32:08 to 2020-02-29 23:19:01\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.42% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 219 records from 2020-01-01 03:41:08 to 2020-02-29 17:42:16\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 7.91% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 05:47:05 to 2020-02-29 03:54:28\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 10:46:16 to 2020-02-29 23:42:20\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 19:43:59 to 2020-02-29 20:39:23\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.38% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 10:36:44 to 2020-02-29 23:03:55\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:02:23 to 2020-02-29 18:40:40\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 18.13% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 117 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 171 records from 2020-01-01 00:31:38 to 2020-02-29 18:40:40\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 06:49:46 to 2020-02-29 16:38:34\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 04:58:32 to 2020-02-29 21:44:00\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 16:24:15 to 2020-02-29 11:13:37\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 02:49:38 to 2020-02-29 19:13:03\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.47% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 119 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 181 records from 2020-01-01 12:44:17 to 2020-02-29 23:15:26\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:53:51 to 2020-02-29 20:34:18\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 03:06:24 to 2020-02-29 21:49:11\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.88% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 03:37:27 to 2020-02-29 20:20:01\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.63% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 05:13:02 to 2020-02-29 11:51:00\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 05:56:47 to 2020-02-29 11:53:32\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.41% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 179 records from 2020-01-01 03:27:23 to 2020-02-29 22:35:43\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.20% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 02:12:00 to 2020-02-29 20:04:42\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 232 records from 2020-01-01 10:45:23 to 2020-02-29 20:28:04\n", - "\u001b[32m[x]\u001b[0m 203 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 11:58:03 to 2020-02-29 20:20:14\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 150 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 13:54:39 to 2020-02-29 23:32:09\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 19:37:02 to 2020-02-29 22:42:36\n", - "\u001b[32m[x]\u001b[0m 175 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-02 10:55:47 to 2020-02-28 23:55:44\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 02:24:32 to 2020-02-29 23:13:39\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.42% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 146 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 07:43:38 to 2020-02-29 19:13:03\n", - "\u001b[32m[x]\u001b[0m 200 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.15% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 02:29:48 to 2020-02-29 19:06:26\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 08:40:38 to 2020-02-29 19:07:04\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 01:19:13 to 2020-02-29 15:42:13\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:33:20 to 2020-02-29 21:01:24\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 228 records from 2020-01-01 04:14:56 to 2020-02-29 20:29:21\n", - "\u001b[32m[x]\u001b[0m 207 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 00:43:36 to 2020-02-29 03:44:43\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 01:01:37 to 2020-02-29 22:07:35\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.89% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 02:16:15 to 2020-02-29 22:57:56\n", - "\u001b[32m[x]\u001b[0m 163 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 05:14:01 to 2020-02-29 08:37:33\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 09:38:51 to 2020-02-29 15:49:00\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 123 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 01:46:18 to 2020-02-29 20:25:28\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 02:38:57 to 2020-02-29 22:52:28\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.27% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 03:04:43 to 2020-02-29 18:30:53\n", - "\u001b[32m[x]\u001b[0m 204 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.60% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 05:49:46 to 2020-02-29 06:23:10\n", - "\u001b[32m[x]\u001b[0m 197 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 03:41:08 to 2020-02-29 17:41:33\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.55% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 01:26:14 to 2020-02-29 21:51:24\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 12.04% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 06:11:54 to 2020-02-28 11:05:18\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 03:12:25 to 2020-02-29 23:36:33\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 03:49:50 to 2020-02-29 19:51:37\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 05:03:23 to 2020-02-29 23:05:57\n", - "\u001b[32m[x]\u001b[0m 202 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 04:25:08 to 2020-02-29 07:25:51\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 217 records from 2020-01-01 04:29:41 to 2020-02-29 22:31:40\n", - "\u001b[32m[x]\u001b[0m 202 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 02:29:48 to 2020-02-29 15:38:31\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.69% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 177 records from 2020-01-01 01:36:16 to 2020-02-29 15:37:50\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-01 01:56:53 to 2020-02-28 19:55:47\n", - "\u001b[32m[x]\u001b[0m 160 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 06:23:16 to 2020-02-29 20:32:45\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.75% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 02:16:15 to 2020-02-29 22:55:19\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 238 records from 2020-01-01 09:41:15 to 2020-02-29 13:56:16\n", - "\u001b[32m[x]\u001b[0m 210 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.23% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 04:36:18 to 2020-02-29 21:29:25\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-02 04:30:15 to 2020-02-29 23:23:05\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 00:53:43 to 2020-02-29 19:23:16\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 197 records from 2020-01-01 01:37:27 to 2020-02-29 20:39:23\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.94% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 02:00:07 to 2020-02-29 04:28:07\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 10.99% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 03:35:24 to 2020-02-29 23:42:31\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 202 records from 2020-01-01 11:07:04 to 2020-02-29 17:06:58\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 03:24:51 to 2020-02-29 21:01:24\n", - "\u001b[32m[x]\u001b[0m 187 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 6.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 00:08:25 to 2020-02-29 13:38:58\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 00:09:47 to 2020-02-29 17:06:58\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 00:53:43 to 2020-02-29 04:22:55\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 01:42:47 to 2020-02-29 22:00:49\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.10% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 01:19:13 to 2020-02-29 21:26:49\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 01:56:55 to 2020-02-29 16:52:49\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.79% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 03:24:27 to 2020-02-29 11:15:15\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 125 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 04:27:26 to 2020-02-29 21:06:10\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.07% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 06:00:53 to 2020-02-29 10:48:48\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 01:28:18 to 2020-02-29 22:48:07\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 01:55:03 to 2020-02-29 20:43:27\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 6.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 04:08:22 to 2020-02-29 06:56:48\n", - "\u001b[32m[x]\u001b[0m 180 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 04:52:06 to 2020-02-29 11:08:37\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.97% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 05:29:08 to 2020-02-29 23:11:27\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.98% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 173 records from 2020-01-01 05:34:51 to 2020-02-29 11:46:16\n", - "\u001b[32m[x]\u001b[0m 158 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-01 16:37:51 to 2020-02-29 17:33:19\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 15:23:09 to 2020-02-29 18:03:01\n", - "\u001b[32m[x]\u001b[0m 192 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.02% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 01:35:55 to 2020-02-29 11:38:15\n", - "\u001b[32m[x]\u001b[0m 178 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 15.90% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 17:37:55 to 2020-02-29 17:16:20\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.05% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 02:32:52 to 2020-02-29 21:09:16\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 151 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 215 records from 2020-01-01 04:19:47 to 2020-02-29 16:54:59\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.11% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 180 records from 2020-01-01 03:16:50 to 2020-02-29 18:59:25\n", - "\u001b[32m[x]\u001b[0m 165 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.67% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 186 records from 2020-01-01 18:12:20 to 2020-02-29 16:54:52\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 09:43:25 to 2020-02-29 19:03:46\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.62% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 212 records from 2020-01-01 06:46:08 to 2020-02-29 20:25:28\n", - "\u001b[32m[x]\u001b[0m 191 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 112 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 159 records from 2020-01-01 07:15:23 to 2020-02-29 18:26:47\n", - "\u001b[32m[x]\u001b[0m 148 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 16:10:46 to 2020-02-29 18:06:58\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 07:55:27 to 2020-02-29 23:34:24\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 17:45:32 to 2020-02-29 13:38:02\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.26% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 16:09:46 to 2020-02-29 22:35:18\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.37% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 10:26:44 to 2020-02-29 05:02:58\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 19:22:56 to 2020-02-29 10:44:32\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.46% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 114 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 164 records from 2020-01-01 03:51:53 to 2020-02-29 05:24:49\n", - "\u001b[32m[x]\u001b[0m 151 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-02 09:37:16 to 2020-02-29 22:55:09\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 141 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 203 records from 2020-01-01 00:02:12 to 2020-02-29 17:02:43\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 02:19:58 to 2020-02-29 14:45:30\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 9.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 05:30:58 to 2020-02-29 19:08:48\n", - "\u001b[32m[x]\u001b[0m 184 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.33% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 210 records from 2020-01-01 06:18:28 to 2020-02-29 22:13:58\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 124 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 11:39:19 to 2020-02-29 21:03:48\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.39% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 00:30:00 to 2020-02-29 15:10:29\n", - "\u001b[32m[x]\u001b[0m 203 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.44% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 01:00:12 to 2020-02-29 17:32:11\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.30% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 128 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 188 records from 2020-01-01 05:01:12 to 2020-02-29 10:44:32\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.10% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 12:56:16 to 2020-02-29 18:09:25\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.60% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 142 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 218 records from 2020-01-01 00:55:00 to 2020-02-28 18:41:15\n", - "\u001b[32m[x]\u001b[0m 198 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.56% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 04:01:48 to 2020-02-29 07:38:55\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.45% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 147 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 223 records from 2020-01-01 00:53:36 to 2020-02-29 17:27:34\n", - "\u001b[32m[x]\u001b[0m 196 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.58% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 199 records from 2020-01-01 08:21:41 to 2020-02-29 21:49:26\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 04:37:45 to 2020-02-29 23:53:08\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.74% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 06:22:55 to 2020-02-29 21:32:05\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.35% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 06:34:41 to 2020-02-29 23:43:54\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.65% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 134 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 04:01:14 to 2020-02-29 18:02:48\n", - "\u001b[32m[x]\u001b[0m 194 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.09% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 191 records from 2020-01-01 03:59:43 to 2020-02-29 10:27:56\n", - "\u001b[32m[x]\u001b[0m 173 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 05:13:02 to 2020-02-29 17:08:49\n", - "\u001b[32m[x]\u001b[0m 167 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 139 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 195 records from 2020-01-01 00:07:47 to 2020-02-29 17:16:05\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[33mWarning: 14.29% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 189 records from 2020-01-01 03:46:24 to 2020-02-29 23:06:38\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 182 records from 2020-01-01 02:11:34 to 2020-02-29 23:39:27\n", - "\u001b[32m[x]\u001b[0m 164 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.40% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 209 records from 2020-01-01 02:14:07 to 2020-02-29 13:32:26\n", - "\u001b[32m[x]\u001b[0m 186 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.17% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 143 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:32:11 to 2020-02-29 14:46:03\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 10.68% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 127 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 00:28:12 to 2020-02-29 23:39:27\n", - "\u001b[32m[x]\u001b[0m 185 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 6.84% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 190 records from 2020-01-01 09:35:26 to 2020-02-29 18:51:11\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.95% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 03:04:43 to 2020-02-29 15:02:45\n", - "\u001b[32m[x]\u001b[0m 176 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.85% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 132 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 192 records from 2020-01-01 09:05:42 to 2020-02-29 13:27:52\n", - "\u001b[32m[x]\u001b[0m 171 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.91% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 176 records from 2020-01-02 00:29:41 to 2020-02-29 22:55:42\n", - "\u001b[32m[x]\u001b[0m 160 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.92% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 194 records from 2020-01-01 00:10:35 to 2020-02-29 23:33:15\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 193 records from 2020-01-01 06:33:55 to 2020-02-29 20:31:44\n", - "\u001b[32m[x]\u001b[0m 174 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 8.53% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 211 records from 2020-01-01 10:27:25 to 2020-02-29 15:28:39\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.21% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 126 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 15:00:23 to 2020-02-29 19:32:20\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 17.41% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 201 records from 2020-01-01 08:10:12 to 2020-02-29 15:34:52\n", - "\u001b[32m[x]\u001b[0m 182 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.06% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 137 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 208 records from 2020-01-01 00:53:43 to 2020-02-29 21:32:57\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.64% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 198 records from 2020-01-01 02:42:07 to 2020-02-29 19:19:37\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.71% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 205 records from 2020-01-01 07:53:52 to 2020-02-29 10:47:46\n", - "\u001b[32m[x]\u001b[0m 189 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 135 antenna(s) are missing a location.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[0m\u001b[32m[x]\u001b[0m 196 records from 2020-01-01 05:25:33 to 2020-02-29 19:49:35\n", - "\u001b[32m[x]\u001b[0m 172 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.76% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 133 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 185 records from 2020-01-01 06:53:47 to 2020-02-29 07:25:51\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.22% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 01:37:27 to 2020-02-29 15:30:39\n", - "\u001b[32m[x]\u001b[0m 183 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.59% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 145 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 229 records from 2020-01-01 21:45:43 to 2020-02-29 22:47:56\n", - "\u001b[32m[x]\u001b[0m 212 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 129 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 16:41:24 to 2020-02-29 16:33:04\n", - "\u001b[32m[x]\u001b[0m 181 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.13% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 131 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 184 records from 2020-01-01 01:42:47 to 2020-02-29 12:51:23\n", - "\u001b[32m[x]\u001b[0m 166 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.83% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 187 records from 2020-01-01 05:31:28 to 2020-02-29 10:27:08\n", - "\u001b[32m[x]\u001b[0m 168 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 16.99% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 138 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 206 records from 2020-01-01 05:34:47 to 2020-02-29 07:23:10\n", - "\u001b[32m[x]\u001b[0m 190 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 14.61% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 120 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 178 records from 2020-01-02 01:37:40 to 2020-02-29 16:54:52\n", - "\u001b[32m[x]\u001b[0m 170 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.49% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 148 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 213 records from 2020-01-02 09:34:56 to 2020-02-29 22:32:41\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 9.78% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 149 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 225 records from 2020-01-01 00:37:55 to 2020-02-29 11:07:26\n", - "\u001b[32m[x]\u001b[0m 201 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.43% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 115 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 175 records from 2020-01-01 00:00:42 to 2020-02-29 22:08:42\n", - "\u001b[32m[x]\u001b[0m 161 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 12.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 130 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 183 records from 2020-01-01 00:10:34 to 2020-02-29 15:22:18\n", - "\u001b[32m[x]\u001b[0m 169 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.18% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 144 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 224 records from 2020-01-01 02:47:26 to 2020-02-29 21:19:51\n", - "\u001b[32m[x]\u001b[0m 206 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 11.57% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 216 records from 2020-01-01 06:57:41 to 2020-02-29 19:33:39\n", - "\u001b[32m[x]\u001b[0m 195 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.24% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 204 records from 2020-01-01 08:37:16 to 2020-02-29 16:49:58\n", - "\u001b[32m[x]\u001b[0m 179 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 13.50% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 136 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 11:01:00 to 2020-02-29 16:10:43\n", - "\u001b[32m[x]\u001b[0m 188 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n", - "\u001b[33mWarning: 15.00% of the records are missing a location.\n", - " No antennas file was given and records are using antennas for position.\n", - "\u001b[0m\u001b[33mWarning: 140 antenna(s) are missing a location.\n", - "\u001b[0m\u001b[32m[x]\u001b[0m 200 records from 2020-01-01 10:38:25 to 2020-02-29 15:30:39\n", - "\u001b[32m[x]\u001b[0m 177 contacts\n", - "\u001b[32m[ ]\u001b[0m No attribute stored\n", - "\u001b[32m[ ]\u001b[0m No antenna stored\n", - "\u001b[32m[ ]\u001b[0m No recharges\n", - "\u001b[32m[x]\u001b[0m Has home\n", - "\u001b[32m[x]\u001b[0m Has texts\n", - "\u001b[32m[x]\u001b[0m Has calls\n", - "\u001b[32m[ ]\u001b[0m No network\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Successfully exported 1000 object(s) to ./outputs/featurizer//datasets/bandicoot_features/0to1000/0.csv\n", - "\r", - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating international features...\n", - "Calculating spatial features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/luciomelito/Library/Caches/pypoetry/virtualenvs/cider-n28xxEz6-py3.8/lib/python3.8/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=:' syntax is deprecated. ':' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n", - " in_crs_string = _prepare_from_proj_string(in_crs_string)\n", - "../../cider/cider/featurizer.py:325: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.\n", - "Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.\n", - "\n", - "Left CRS: +init=epsg:4326 +type=crs\n", - "Right CRS: EPSG:4326\n", - "\n", - " antennas = gpd.sjoin(antennas, shapefile, op='within', how='left').drop('index_right', axis=1)\n", - "../../cider/cider/featurizer.py:325: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.\n", - "Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.\n", - "\n", - "Left CRS: +init=epsg:4326 +type=crs\n", - "Right CRS: EPSG:4326\n", - "\n", - " antennas = gpd.sjoin(antennas, shapefile, op='within', how='left').drop('index_right', axis=1)\n", - "../../cider/cider/featurizer.py:325: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.\n", - "Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.\n", - "\n", - "Left CRS: +init=epsg:4326 +type=crs\n", - "Right CRS: EPSG:4326\n", - "\n", - " antennas = gpd.sjoin(antennas, shapefile, op='within', how='left').drop('index_right', axis=1)\n", - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating recharges features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating mobile data features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating mobile money features...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], + "execution_count": 26, + "id": "acc99715-7c14-41a2-a269-a7fa57e1fa46", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "featurizer.cdr_features()\n", + "featurizer.cdr_features_spark()\n", "featurizer.international_features()\n", "featurizer.location_features()\n", "featurizer.recharges_features()\n", "featurizer.mobiledata_features()\n", "featurizer.mobilemoney_features()\n", + "\n", "featurizer.all_features()" ] }, + { + "cell_type": "markdown", + "id": "dcefc331-6b93-4d7e-8445-8cf58c75430b", + "metadata": {}, + "source": [ + "Now we read the features back with pandas in to see what the table looks like. This works fine because our synthetic dataset is small, but such files can be too large to fit in memory if the number of subscribers is large; Cider uses [pyspark](https://spark.apache.org/docs/latest/api/python/) to manage large datasets. Another option for working with large datasets is [dask](https://www.dask.org/)." + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 32, "id": "2d077b0c", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -12748,15 +353,15 @@ " \n", " \n", " name\n", - " cdr_reporting__number_of_records\n", - " cdr_active_days__allweek__allday__callandtext\n", - " cdr_active_days__allweek__day__callandtext\n", - " cdr_active_days__allweek__night__callandtext\n", - " cdr_active_days__weekday__allday__callandtext\n", - " cdr_active_days__weekday__day__callandtext\n", - " cdr_active_days__weekday__night__callandtext\n", - " cdr_active_days__weekend__allday__callandtext\n", - " cdr_active_days__weekend__day__callandtext\n", + " active_days_allweek_allday\n", + " active_days_allweek_day\n", + " active_days_allweek_night\n", + " active_days_weekday_allday\n", + " active_days_weekday_day\n", + " active_days_weekday_night\n", + " active_days_weekend_allday\n", + " active_days_weekend_day\n", + " active_days_weekend_night\n", " ...\n", " mobilemoney_outgoing_p2p_amount_min\n", " mobilemoney_outgoing_p2p_amount_max\n", @@ -12773,260 +378,233 @@ " \n", " \n", " 0\n", - " dsBHAdXrrk\n", - " 184\n", - " 58\n", + " AASxnSdfla\n", + " 56\n", " 46\n", " 50\n", - " 41\n", - " 32\n", + " 40\n", + " 31\n", " 35\n", - " 17\n", - " 14\n", + " 16\n", + " 15\n", + " 15\n", " ...\n", - " 26.361252\n", - " 66.175190\n", - " 198.212410\n", - " 144.32855\n", - " 270.12857\n", - " 148.945410\n", - " 78.153360\n", - " 218.63590\n", - " 8.0\n", - " 8.0\n", + " 31.799332\n", + " 58.921886\n", + " 187.554639\n", + " 138.324660\n", + " 228.47707\n", + " 138.780946\n", + " 106.525330\n", + " 180.20863\n", + " 6.0\n", + " 6.0\n", " \n", " \n", " 1\n", - " JGPCbfDGes\n", - " 209\n", - " 59\n", - " 49\n", - " 50\n", - " 42\n", - " 35\n", - " 34\n", - " 17\n", + " ANnpaBqoKb\n", + " 55\n", + " 45\n", + " 47\n", + " 40\n", + " 36\n", + " 33\n", + " 15\n", + " 9\n", " 14\n", " ...\n", - " 33.154373\n", - " 60.508873\n", - " 166.658227\n", - " 83.04488\n", - " 209.16385\n", - " 115.873907\n", - " 22.536007\n", - " 173.27129\n", - " 6.0\n", - " 6.0\n", + " 37.976850\n", + " 57.337646\n", + " 182.190155\n", + " 158.963560\n", + " 223.29039\n", + " 137.548449\n", + " 106.978870\n", + " 185.31354\n", + " 3.0\n", + " 3.0\n", " \n", " \n", " 2\n", - " dYwshzRseD\n", - " 205\n", - " 59\n", - " 50\n", - " 52\n", - " 42\n", - " 36\n", - " 37\n", - " 17\n", - " 14\n", + " AWrbWzWkYp\n", + " 55\n", + " 45\n", + " 46\n", + " 39\n", + " 32\n", + " 33\n", + " 16\n", + " 13\n", + " 13\n", " ...\n", - " 28.580658\n", - " 63.042980\n", - " 192.916470\n", - " 136.88690\n", - " 234.86200\n", - " 152.284147\n", - " 104.606220\n", - " 200.48862\n", + " 53.090126\n", + " 58.511192\n", + " 202.108686\n", + " 108.117546\n", + " 272.34055\n", + " 146.345222\n", + " 52.118343\n", + " 219.25041\n", " 4.0\n", " 4.0\n", " \n", " \n", " 3\n", - " ygMEXUQDbn\n", - " 218\n", + " AaJProAtlR\n", " 56\n", - " 51\n", - " 46\n", + " 48\n", + " 48\n", " 41\n", - " 37\n", + " 36\n", " 34\n", " 15\n", + " 12\n", " 14\n", " ...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 32.858997\n", + " 68.323395\n", + " 235.638050\n", + " 194.502760\n", + " 287.86615\n", + " 188.334709\n", + " 143.178710\n", + " 242.73692\n", + " 4.0\n", + " 4.0\n", " \n", " \n", " 4\n", - " YtvkGlMWwe\n", - " 212\n", - " 57\n", - " 52\n", - " 45\n", - " 41\n", - " 38\n", - " 31\n", + " ApDvhzOUJr\n", + " 56\n", + " 39\n", + " 51\n", + " 40\n", + " 28\n", + " 36\n", " 16\n", - " 14\n", + " 11\n", + " 15\n", " ...\n", - " 35.715935\n", - " 66.577260\n", - " 182.742442\n", - " 151.35474\n", - " 245.24483\n", - " 131.224984\n", - " 91.455580\n", - " 190.52692\n", - " 6.0\n", - " 6.0\n", + " 44.307076\n", + " 52.375270\n", + " 192.653709\n", + " 185.676930\n", + " 199.63050\n", + " 144.312531\n", + " 133.301650\n", + " 155.32341\n", + " 2.0\n", + " 2.0\n", " \n", " \n", "\n", - "

5 rows × 1203 columns

\n", + "

5 rows × 1122 columns

\n", "
" ], "text/plain": [ - " name cdr_reporting__number_of_records \\\n", - "0 dsBHAdXrrk 184 \n", - "1 JGPCbfDGes 209 \n", - "2 dYwshzRseD 205 \n", - "3 ygMEXUQDbn 218 \n", - "4 YtvkGlMWwe 212 \n", + " name active_days_allweek_allday active_days_allweek_day \\\n", + "0 AASxnSdfla 56 46 \n", + "1 ANnpaBqoKb 55 45 \n", + "2 AWrbWzWkYp 55 45 \n", + "3 AaJProAtlR 56 48 \n", + "4 ApDvhzOUJr 56 39 \n", "\n", - " cdr_active_days__allweek__allday__callandtext \\\n", - "0 58 \n", - "1 59 \n", - "2 59 \n", - "3 56 \n", - "4 57 \n", + " active_days_allweek_night active_days_weekday_allday \\\n", + "0 50 40 \n", + "1 47 40 \n", + "2 46 39 \n", + "3 48 41 \n", + "4 51 40 \n", "\n", - " cdr_active_days__allweek__day__callandtext \\\n", - "0 46 \n", - "1 49 \n", - "2 50 \n", - "3 51 \n", - "4 52 \n", + " active_days_weekday_day active_days_weekday_night \\\n", + "0 31 35 \n", + "1 36 33 \n", + "2 32 33 \n", + "3 36 34 \n", + "4 28 36 \n", "\n", - " cdr_active_days__allweek__night__callandtext \\\n", - "0 50 \n", - "1 50 \n", - "2 52 \n", - "3 46 \n", - "4 45 \n", + " active_days_weekend_allday active_days_weekend_day \\\n", + "0 16 15 \n", + "1 15 9 \n", + "2 16 13 \n", + "3 15 12 \n", + "4 16 11 \n", "\n", - " cdr_active_days__weekday__allday__callandtext \\\n", - "0 41 \n", - "1 42 \n", - "2 42 \n", - "3 41 \n", - "4 41 \n", + " active_days_weekend_night ... mobilemoney_outgoing_p2p_amount_min \\\n", + "0 15 ... 31.799332 \n", + "1 14 ... 37.976850 \n", + "2 13 ... 53.090126 \n", + "3 14 ... 32.858997 \n", + "4 15 ... 44.307076 \n", "\n", - " cdr_active_days__weekday__day__callandtext \\\n", - "0 32 \n", - "1 35 \n", - "2 36 \n", - "3 37 \n", - "4 38 \n", - "\n", - " cdr_active_days__weekday__night__callandtext \\\n", - "0 35 \n", - "1 34 \n", - "2 37 \n", - "3 34 \n", - "4 31 \n", - "\n", - " cdr_active_days__weekend__allday__callandtext \\\n", - "0 17 \n", - "1 17 \n", - "2 17 \n", - "3 15 \n", - "4 16 \n", - "\n", - " cdr_active_days__weekend__day__callandtext ... \\\n", - "0 14 ... \n", - "1 14 ... \n", - "2 14 ... \n", - "3 14 ... \n", - "4 14 ... \n", - "\n", - " mobilemoney_outgoing_p2p_amount_min mobilemoney_outgoing_p2p_amount_max \\\n", - "0 26.361252 66.175190 \n", - "1 33.154373 60.508873 \n", - "2 28.580658 63.042980 \n", - "3 NaN NaN \n", - "4 35.715935 66.577260 \n", + " mobilemoney_outgoing_p2p_amount_max \\\n", + "0 58.921886 \n", + "1 57.337646 \n", + "2 58.511192 \n", + "3 68.323395 \n", + "4 52.375270 \n", "\n", " mobilemoney_outgoing_p2p_balance_before_mean \\\n", - "0 198.212410 \n", - "1 166.658227 \n", - "2 192.916470 \n", - "3 NaN \n", - "4 182.742442 \n", + "0 187.554639 \n", + "1 182.190155 \n", + "2 202.108686 \n", + "3 235.638050 \n", + "4 192.653709 \n", "\n", " mobilemoney_outgoing_p2p_balance_before_min \\\n", - "0 144.32855 \n", - "1 83.04488 \n", - "2 136.88690 \n", - "3 NaN \n", - "4 151.35474 \n", + "0 138.324660 \n", + "1 158.963560 \n", + "2 108.117546 \n", + "3 194.502760 \n", + "4 185.676930 \n", "\n", " mobilemoney_outgoing_p2p_balance_before_max \\\n", - "0 270.12857 \n", - "1 209.16385 \n", - "2 234.86200 \n", - "3 NaN \n", - "4 245.24483 \n", + "0 228.47707 \n", + "1 223.29039 \n", + "2 272.34055 \n", + "3 287.86615 \n", + "4 199.63050 \n", "\n", " mobilemoney_outgoing_p2p_balance_after_mean \\\n", - "0 148.945410 \n", - "1 115.873907 \n", - "2 152.284147 \n", - "3 NaN \n", - "4 131.224984 \n", + "0 138.780946 \n", + "1 137.548449 \n", + "2 146.345222 \n", + "3 188.334709 \n", + "4 144.312531 \n", "\n", " mobilemoney_outgoing_p2p_balance_after_min \\\n", - "0 78.153360 \n", - "1 22.536007 \n", - "2 104.606220 \n", - "3 NaN \n", - "4 91.455580 \n", + "0 106.525330 \n", + "1 106.978870 \n", + "2 52.118343 \n", + "3 143.178710 \n", + "4 133.301650 \n", "\n", " mobilemoney_outgoing_p2p_balance_after_max mobilemoney_outgoing_p2p_txns \\\n", - "0 218.63590 8.0 \n", - "1 173.27129 6.0 \n", - "2 200.48862 4.0 \n", - "3 NaN NaN \n", - "4 190.52692 6.0 \n", + "0 180.20863 6.0 \n", + "1 185.31354 3.0 \n", + "2 219.25041 4.0 \n", + "3 242.73692 4.0 \n", + "4 155.32341 2.0 \n", "\n", " mobilemoney_outgoing_p2p_contacts \n", - "0 8.0 \n", - "1 6.0 \n", + "0 6.0 \n", + "1 3.0 \n", "2 4.0 \n", - "3 NaN \n", - "4 6.0 \n", + "3 4.0 \n", + "4 2.0 \n", "\n", - "[5 rows x 1203 columns]" + "[5 rows x 1122 columns]" ] }, - "execution_count": 5, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", - "pd.read_csv('./outputs/featurizer/datasets/features.csv').head()" + "path_to_all_features = datastore.cfg.path.working.directory_path / 'featurizer' / 'datasets' / 'features.csv'\n", + "pd.read_csv(path_to_all_features).head()" ] }, { @@ -13039,15 +617,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 51, "id": "b117b92f", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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VK1bYtxcWFtqeeOIJW3x8vO2kk06q8h5XXk/l3++8efOqXU9DVP7dXHXVVbXuc/z13HbbbbaSkhKbzfbn39/OnTttJ5xwgq1Pnz62JUuWVHnvzp07bUOGDLHFx8fbZs6cWeW14cOH24/73//+137NxcXFtjvuuMP+uVeez2az2caOHWuLj4+3vfXWW7by8nL79rVr19pOOukkW3x8vO2XX36xb3///fdt8fHxtrPPPtu2c+fOKuf/+uuvbSeccIItPj7e9tVXX9m3FxcX20aPHm2Lj4+33XDDDfbrtNlstq+++srWp08fW3x8vO3XX3+tdi3H/4wc//nedttt9m3Z2dm2M844wxYfH2+bPn26LTs72/7a5s2bbUOHDrXFx8fb7r77bpd/XiLiPMrTytMNMWjQIFt8fLzttddea9D+NUlOTrb/jv/3v/9tKygosL/2ww8/2E477TRbfHy87fnnn6/yvsrPpE+fPrZ58+bZ80ZeXp79Ohr699Xcv/+TTz7Z9u2339q3Hz161Hb11Vfb4uPjbZMnT673/CtXrrT//R4+fNi+PS8vz3bzzTfb4uPjbddcc01DPk4RaaDK76qffvpptdd++uknW+/evW29e/e2zZkzx1ZaWmqz2Wy2iooK22effWY78cQTbfHx8bYFCxbUeMy/fsduynf8n376yRYfH28744wzGnxNtd0rHZ83r7jiCtv+/fvtr3333Xe23r17Vzt/XdezefNmW9++fW19+/a1ffDBB/bfvxUVFbYFCxbY+vXrZ+vbt69ty5Yt1a4nPj7eNn78ePv3gcrcffx3hZNPPrlKLOnp6bYLLrjAFh8fb7vuuuuqxHLrrbfa4uPjbRMnTqxyH1RcXGz/XtK7d29bRkaG/bW6vpfUlAeb8vPgyuup6R6uLrfddpstPj7eNmXKlAbtXxNHfs+o67tUQxz/WaekpNS6X00/z8f/25gwYYLtyJEj9uubOnWqLT4+3nbCCSfYzjjjjCrfU//3v//Z33f8v+mUlBTbwIEDbfHx8bYXXnihyueybNky+2vff/99k67VE6hHr9SqZ8+ezJo1yz4M3tfXlzvuuIOgoCDKy8vZunVrvccoLi5mzpw5gLGi5PFD5ENCQpg5cyYDBgzg8OHDfPrpp9Xe7+/vz+23347FYgGocZGq//znP1VWTj7llFPs09E3b95s3378VNQZM2ZUeVLbtm1bbrnlFgD27t1LRUVFvddWk4KCAt5++20AHnvsMft0BICgoCDuu+8+RowYQXFxMa+++mqNx3Cn63GEO+64A39/f+DPv7/169fj5+fH3//+92pPF0844QTGjx8PQFJSUo3HHDx4MHfccYf9mgMCAuxPb/Py8qq8b/fu3YCx2u/x02v+9re/cd1113HBBRdQWlpq3/7TTz/h6+vLbbfdVuXJNsD555/PoEGDqsX27bffsnfvXmJjY3nhhReq/JxecMEF/POf/wSM0XdN8eGHH5KTk0N8fDzPPfccERER9tf69+/Pq6++isViYcmSJSQmJlZ7vzM/LxExj/J043ljns7LywOokhsaa+7cuRQXFzN48GAefPDBKtNChwwZwpNPPgnAO++8Q3Z2drX3X3755Vx11VX2vBEWFsbtt98OGPkyPz+/3hia+nm9/vrrAPzrX/+qMgo7KiqKZ599Fj8/P3766SfS09PrPH9l/hsyZAjt27e3bw8LC+O+++7jnHPOoVevXvVeh4g4xquvvorNZmP8+PHccMMN9tZhFouFSy+9lLvuuguA2bNnU15eXu/xmvId3xn8/PyYPXt2lUWpzjvvPPv0+uPzSF1eeeUV+0jViRMn2n//WiwWLr/8ciZNmkRZWZk9x//Vrbfeap8JWFPuvummm+yt5wDatWtn7xN7fIzFxcVs3rwZi8XCo48+WuVYAQEB3H333QQEBGCz2UhOTm7QtdWkuT8P7nY9jsjdnlIPaKjKRekqR60HBAQwZcoUwJiN9X//939VvqeOHj3a/u9o165d9u1vv/02+fn5XHrppdx+++1VPpcRI0bYf1Zefvllp1+TWVTolVoNHjzYfuNWKSAgwN7PryFf2jdt2kROTg5t27attTdM5S/c1atXV3stPj6+xqnolSp7z/xVZW+942McOHAgGzdu5Lvvvqtx2mHlTU1FRQXFxcV1XFXtfv31V6xWK1FRUbU2VL/66qsB43r/moTc7XqaKyIiolqfQzD6EG3ZsoWnnnqqxvdVxl5bo/mhQ4dW29a+fXv7+47/nCp/+c+YMYOtW7dis9nsr02fPp3nn3++SsJ45ZVX2Lx5M+PGjat2jvLycvvP4/GxrVq1CoALL7yQoKCgau+79dZb+fbbb3n44YdrvJ76/PDDDwBcccUVNfbH7du3L6eeeio2m80ey/Gc+XmJiHmUpxvPG/N0Zd5pSKGjNpV/t1deeWWNr48cOZKOHTtSVFRkn256vJryzPGtlxrys9iUz2vfvn3s27cPPz+/Gqe0tmvXjs8++4wff/yRmJiYOs8fFxcHwKeffsrChQurFLQ7derEW2+9ZW+HIiLOZbVa+fXXX4Hafy+NHz+egIAAMjIy2L59e73HbMp3fGfo3bt3jb+PasojtSkqKmLdunUANa63AdgH06xfv56ysrJqrx/fqqAmNf1erynGwMBAVq9eze+//15jC4Li4mJat25tj7spHPHz4E7XA3/m7pr+bhrKE+oBjdGjRw86duxYZdvx/33OOedUe09lUdhqtdq3Vfbdr+3fxoUXXojFYmHnzp1kZmY2O253pB69UqvavhBX9s5pyFOhytGFBQUFTJgwocZ9jh07BhhPmv6qvj57NS16AnXf9AQFBbF9+3Z27NjBgQMHOHDgALt3765y/qY+8dq3bx9gJPDamrNXjgKyWq0cOXKkyufsbtfTXHX9/VUWJ3788UcSExPtse/YsYOMjAyg9rjr+tksLCys8r477riD2267zd67KSoqirPPPpshQ4YwfPjwKr0UK/n7+9v76yYnJ5OSksLevXvZsWOH/YvA8QXQlJQUgFpH+kRFRdX4pLyhKn+uauqpWKlv3778+uuv9n2P5+zPS0TMoTzdeN6Yp9u2bUt+fn6NI20bIj8/336j07dv31r369OnD4cOHWpwnjm+12JjitCN+bwqF7KJjY0lJCSkxuPF/2URvtqMGDGCAQMGsHnzZh588EEefvhhTjrpJM455xyGDx9eY49qEXGOlJQUysrK7Ote1CQ4OJju3buza9cu9u3bR//+/es9bmO/4ztDfXmkIflg//799hl2999/f435rPI4BQUFpKenExsba38tJCSkxsWrjlfT7/X6cl1qaiq//fYb+/fvJzU1lT179pCQkGCPtam5zhE/D+50PfDn96em5u6/xuqu9YDGqOnvqHJmMBh9imt7vfLfbX5+PocPHwbg+eef57XXXqvxXL6+vpSVldkXI/Q2KvRKrY7/R1WThiTByoRZUFDApk2bGrTv8epryF5fjH/1yy+/8OSTT1Z5ymexWOjSpQsXX3wxX375ZaOO91eVT5LqGt10/I1Ifn5+lV9o7nY9zVXXQgaffPIJr776apWplP7+/vTp04fevXvbF0qoSWN+NkeOHMnHH3/MG2+8wdq1a8nKymLp0qUsXbqUgIAAxo8fzz333GM/ZnFxMf/973+ZP39+lSf6oaGhnHLKKRw5cqTK1BCAnJwcgFpvMpur8ueqri9klec+/mlmJWd+XiJiHuXpxvPGPN2tWzf27dvHnj17GrR/SUkJBw4coGfPnkDVvNGQz6W5eaYujf28HJl/AwICeP/993n77bf5/PPP2b9/P5s3b2bz5s288sor9OrVi0cffZRTTz212ecSkbpV/p4JDg6u9aEc1P176a+a8h3fGepb6K0xuRuMxa/rU9kmoKExQOPy3aFDh5g1axbLly+vEn90dDSjR49mzZo19ofGTeGInwd3uh4wFqSDxrUKSUhIoHv37lWuxd3rAY1RXy7/6yy2mhz/d79jx4569//rvw1voUKvOFXldIFhw4bZe6iZZffu3Vx77bWUlJRw2mmnMWbMGHr37k2PHj0ICwtj7969zf5FWPnLqa4pN8f/Mqnrhqo+rrgeZ1mwYAEzZ84EYMyYMQwdOpRevXrRtWtXAgICmD9/fp2F3sY6+eSTefXVVyksLOSXX37hxx9/ZOXKlfYV5X18fLj//vsB46n40qVLCQkJ4YYbbuCUU06hR48edOrUCR8fH+66665qXwIrnwY7a6pXSEgIubm5df5cVb7miJvdxnxeIuLZlKer87Q8PXToUFauXMmGDRuoqKio8yYY4Oeff2bq1KlER0fz5ZdfVitst2rVqsb3OTLP1KQpn1d9rZ4aKygoiJtvvpmbb76ZvXv38uOPP7Ju3TrWrFnDnj17uO666/jf//5XbxsIEWme49so1PV7rTG/l5ryHd9dVV5vSEgIv/32m6mxFBYWMnnyZPbv30+nTp2YMGECJ554Ij169LCPlKxpyn1jOOPnoTauuB4wcvfTTz/N0aNHSUhIoHfv3nXub7Va+cc//oG/vz+zZ89m8ODBHl0PcJbj1xj48ccfmzWr1pOp0CtOVfmkqq5G5ampqRw5coS4uDh7jxVnmDdvHiUlJZx11lm89dZb1frYpKWlNfsclX1+EhISak1ClU/bgoODa5260xCuuB5neeuttwC47bbbuPXWW6u97qjYy8rKOHDgALm5uZx88skEBwczZMgQhgwZwj333MOLL77Iq6++ypdffsn9999Peno6X331FWAs7nLGGWc0KLauXbuSkJBQ40JoYDxNfOyxx+jTp4+9wN0Y3bp1Y/PmzezYsYPTTjutxn0qf666dOnS6ONXauznJSKeT3na8/P0ueeey6xZs8jOzuaLL77gsssuq3P/BQsWAMb04coboOjoaDIzM9mxYwcdOnSo9h6bzWYfGdOcPFOXpnxelT+/Bw8epKioqMY++Q8//DBpaWlcd911Neb1StnZ2SQnJ9OtWzeioqLo1q0b3bp148orryQ9PZ1//OMfZGZmsmzZMiZOnNi8ixWROsXFxeHr60tpaSm7d++utngaGDNRKqem1/d7qanf8d1VXFwcPj4+FBQUkJaWVmUByUr5+fls376d2NhYYmNjGzQasimWLVvG/v37iYiIYNGiRdWm1xcXF9tnXzSVo38e6uKK6wGjH23Pnj1JTEzk3XfftS96WpvPPvuM0tJSLBaLvcWUJ9cDnCU8PJyoqCiysrJITk6usdBbXl7Ohg0biI2NpVOnTjX2N/Z0WoxNHOL4G6XjpzecdtpphISEcODAAdavX1/jex944AHGjx9f68JcjnLw4EHA6MtX0z/mRYsW2f//8X166hsZc7xTTz2VsLAwsrOz+d///lfjPh9++CEAZ555ZqOO/VdNvR53UBl7TT1ni4qK7F/Emhv3tm3bOP/885k2bVqNDejPPvts4M+eRQcPHrT//NbUpzApKck+Per4xvmDBw8G4Ouvv6akpKTa+7755ht+++23Km0qKr9sNWR6VuXiAQsXLqyxYf+2bdvscf3tb3+r93i1aeznJSKeQ3na4I15OiYmhn/84x8APPfcczX20K20bNkyvv32WwBuuOEG+/YhQ4YA8PHHH9f6vvT0dPz9/e0r0zdVbZ9pUz6vnj170qFDB0pLS1m6dGm192RnZ7NkyRJ++OEH+0jl2s5/9913c+WVV1Y5T6WYmBj74nLu9p1KxBuFhoZy+umnA7X/XlqwYAGlpaVERETYC19Q83fspn7Hdwc1XU9YWJh98Edtn8+7777LNddcwzXXXOPU7+6Vv7s7duxYYw/VL774wt7TtrbcXd/9UHN+HhqrqdfTFLfccgsAn3/+OcuXL691v5SUFF566SXAWJy7snjpyfUAZ6q8d/7kk09qfH3JkiVMmTKFSy+9lIKCAleG5jIq9IpDHD894tChQ/b/HxYWxuTJkwHjC/TxN5FFRUU88cQT/PTTT/j6+jJp0iSnxlg56uPrr79m//799u3Hjh3jiSeeqHKDcHyRq/LaMjIyaiziHS80NJQpU6YAxgiSVatWVTnmk08+yYoVK/D392f69OmmXI87qIz9gw8+qDJ99sCBA9x4443262lu3AMGDCAuLo7c3FzuvffeKr2UsrKyeOWVV4A/C7VdunSxf+l4/fXXqyTEDRs2MG3aNPuXv+Nju/jii4mNjeXAgQPcc889Vab9/u9//+Odd94BsP9bgD9/ro7/91KbCRMmEBUVxe7du7n77rurPEXesmULt912Gzabjb///e/N+mLT2M9LRDyH8rTBW/P0v/71L7p3705mZiYTJkxg0aJFVT6LgoIC5s6dyx133AEYq7GPHj3a/vrUqVMJCgpizZo1PP7441VaIaxevZoHHngAgGuuuYa2bds2K9ba/r6a8nlZLBauv/56AJ566inWrl1r3ycrK4u7776bgoICBg0aZH+4XNv5L774YgBee+21KscB44Htxo0b8fHxcciUXRGp380334yPjw/z58/njTfesH8Ht9lsfP755zz33HMATJ8+vUrP0pq+Yzf1O747qO2e4eabb8ZisTB37lzef/99+zVVfj5z5swBYNKkSU4dsVj5uzshIYEVK1bYt5eWlrJw4UKeeOIJ+7aacjc07H6oqT8Prrqeprjgggu46KKLqKio4LbbbuPFF18kKyvL/npFRQXfffcdEyZMICcnh65du9rz+PGxemI9wJmuu+46AgMDWbJkCc8//3yVa1+zZg2PPfYYAJdffnmt7ao8nVo3iENERETQvn170tLSuOqqq4iLi+Opp56iV69e3HLLLSQnJ/O///2PKVOmEBsbS0REBPv377cX+R599FFOPPFEp8Y4ZcoUlixZQkZGBhdccAHdunXDYrGwb98+SkpKOOGEE0hLSyMnJ4eMjAx7D55evXphsVjIzMxk9OjRtG/fvtYniQA33XQTycnJfPXVV9xwww107NiRNm3akJycjNVqJTg4mMcff7zOla2deT3uYPr06UyfPp3169czePBgunXrhtVqZf/+/dhsNs4880x++uknjhw5gs1ma/JUI4vFwn//+1+uuuoqvv76a1asWEHnzp0BY7Xa4uJiYmNjmTFjBgBt2rRh4sSJzJs3jzfeeINPP/2UDh06kJGRQUZGBn5+fpx++un88ssvZGRk2M8TFBTEyy+/zNSpU+3n6dGjB0ePHrVPmZk+fXqVaWK9e/dm9+7dPPXUUyxevJirrrqKsWPH1ngdUVFRvPzyy9x000188803LF++nF69emG1Wu0jtwYNGsS///3vJn1OTf28RMRzKE//yRvzdFhYGO+++y7Tp0/n999/54EHHuDxxx+3T3dNSkqyFzXHjh1rv8mp1KNHD5555hnuvvtu5s2bx6effkqPHj3Iysqyjxg6//zzq9xgNlVtf19N/byuvPJKduzYwcKFC5k6dSqdOnWy9yaszFvHj0av7fxjxoxhxYoVfPvtt0ydOpX27dvTtm1b+3cAgDvvvNM+sldEnGvQoEE88MADzJo1i+eee4633nqLzp07c/jwYTIzMwGjiPnXViq1fcduynd8d1Db9Zx11lnce++9PPXUU8yaNYtXXnmFuLg40tLS7J/PpZdeyjXXXOPU+EaOHEm/fv3Yvn07N910E3FxcYSHh5OSkkJubi4RERF07tyZhISEKp9tXd9LatLUnwdXXU9TPfnkkwQFBbFo0SJeffVV3njjDWJjY2nVqhUpKSn2gTf9+/fn5ZdfrrI4tyfXA5ypZ8+ePP3008yYMYM5c+Ywb948unXrRnZ2tv07zdlnn83dd99tcqTOoxG94jCzZ8/mpJNOoqioiJSUFA4cOACAn58fL7zwAs8//zx/+9vfsFqtJCQkEBgYyHnnnceHH37I5Zdf7vT44uLi7L3rOnTowL59+zh8+DAnnHAC9913HwsXLrRPe1+5cqX9fd26dePxxx+nc+fOZGZmkpKSwpEjR2o9j6+vL8899xzPP/88Z599tv1627Rpw5VXXslnn33GRRddZNr1uINRo0bx0Ucfcc455xAaGkpCQgJ5eXn87W9/46WXXuK9994jOjqavLw8fvnll2adq3///ixYsIBLLrmEqKgo9u7dS2pqKl27duWWW27hyy+/rLKoygMPPMDTTz9N//79KSsrIyEhAX9/fy6++GIWLFhgvzn+5ZdfqoxG7tu3L1988QWTJk2iXbt27N69G6vVyt/+9jfmzp1rn5pT6d5772XEiBEEBASwd+9ee0+p2px66qksWbKEa665hg4dOrBnzx5ycnI4/fTTeeKJJ3j33XcJDw9v1mfVlM9LRDyH8rTBW/N0TEwMH3/8MS+88AKjRo0iMjKSffv2kZSURHR0NJdccgkffvghTz75ZI2jnUaNGsXnn3/OuHHjiIiIYNeuXRQXFzN48GBeeuklXnjhhWaNkqpU299Xcz6vxx9/nNmzZ3PWWWeRm5tLUlISMTExTJ06lc8++4yOHTvWe36LxcJzzz3HAw88wMknn0x+fj67du3CZrNx3nnn8e6771ZpdyEiznfVVVcxf/58LrzwQvz9/dm5cyc+Pj6MHj2ad999t8Y1I2r7jt3U7/hmq+ueYfLkySxYsIALL7yQgIAAdu3aRWFhIaeffjpPPfUUTz31lNN681by8/Pjgw8+4JZbbqFXr14cOXKE5ORkoqOjufbaa1myZAlXXnklQJURslD795LaNOXnwZXX0xQBAQHMmjWL+fPnc/nll9tz065du/D392fo0KE8++yzfPLJJ9XuwTy5HuBs559/fpXvNAkJCWRnZ3PSSSdx//3388YbbxAQEGB2mE5jsTWkQaSIiIiIiIiIiIiIuC2N6BURERERERERERHxcCr0ioiIiIiIiIiIiHg4FXpFREREREREREREPJwKvSIiIiIiIiIiIiIeToVeJygrKyM1NZWysjKzQxEREfF4yqsiIiKOoZwqIuLdVOh1grS0NEaMGEFaWprZoYiIiHg85VURERHHUE4VEfFuKvSKiIiIiIiIiIiIeDgVekVEREREREREREQ8nAq9IiIiIiIiIiIiIh5OhV4RERERERERERERD6dCr4iIiIiIiIiIiIiHU6FXRERERERERERExMOp0CsiIiIiIiIiIiLi4VToFREREREREREREfFwKvSKiIiIiIiIiIiIeDgVekVEREREREREREQ8nJ/ZAYgHykqAiJ7g42t2JCIiIp7Nmg4+fhDcxuxIRERExNUKsyBrF9gqoFUnCO8CFovZUYmIB1OhVxrn4HpYMBwCWsGw56DfJLMjEhER8Uy2Clg0EnKSIG44XPqlHqKKiIh4u4oy2PUJ/Pos5CRC625g8YX8Q2Dxgb5Xwym3QXhnsyMVEQ+kQq80ztr7YeB042njuoeMJGRRBxAREZFG27MYKsrhkk/hh7th37fQ/QKzoxIRERFnyUmCpeON/H/CBIg57c+HvDYb5KVA8lfw/skw8HY4417wCzQ1ZBHxLKrQScOlrIJjydB1NEQPAL8Q2L/c7KhEREQ8T0U5rH3QmBnjFwxdz4ctr5sdlYiIiDhL6lr4cBB0OBuG/Rc6DKo6k8diMUbxnnwTjHwN9n8Pn/wNcg+YF7OIeBwVeqXhfv0v9B5v9BIEo+C79Q1zYxIREfFEB1YY0zTbn2H8d+fhxgNVa7qZUYmIiIgzpK6FL8bAGfdA/Nj6+/CGxsDf/g3tT4cPz4CM310Spoh4PhV6pWFsFXBwtfHUsVKXkcY008Is8+ISERHxRCkrof1pf97o+YdC7GDY8b65cYmIiIhj5e6HL8caRd72pzf8fRaLMdDq5Jtg0Xlw6EfnxSgiXkOFXmmYI9shoDUEt/1zW0AraHMiHFxjXlwiIiKeKGUlRPevuq3jWcYDVBEREfEOZUXwxWUQf/mfs3gaq9NQOO1u+PwSyNzi2PhExOuo0CsNk7q6+g0pQGQvOLzB9fGIiIh4qtJCyNwMUX2rbo86AdJ/NRZjEREREc/346PGAKn4cc07TodBMOAmWDTaGCEsIlILFXqlYVJWQtt+1bdHnQCHNYVERESkwdJ+htY9wD+46vbgNsZCpzlJ5sQlIiIijpOxGba8AadMr78nb0N0Ptfo7/vZRVCS3/zjiYhXUqFX6mezGe0Z2g6o/lrUCZC+yejhKyIiIvVL/QHanljza1F9IE0zZURERDyarQK+uw76XWs8yHWUXuOgVWf45hrNABKRGqnQK/XLSQJ8ILR99deCIo2pKNl7XB6WiIiIR0pZBdEn1fxaZC849JMroxERERFHS1gIpVbofr5jj2uxwMDbIWsXbJrt2GOLiFdQoVfql/4rtOlT+3STqBOMaagiIiJSN5sNMjYZubMmUSfAYRV6RURcaf369VxzzTUMGjSIgQMHcvXVV7N69epGHWPv3r3ceeedDB06lAEDBnDxxRczb948KipqnvmYnp7Oww8/zIgRI+jfvz+jR4/mlVdeoaSkpMb9ExMTueOOOzjrrLM46aSTOO+883j66afJyclp7OWKs5WXwJp74aSpYHFCycU3AM58EH56DNJ+cfzxRcSjqdAr9Tu6A1rF1f56ZDwcUp9eERGRehVkABYIjKz59cjecHS7cZMoIiJOt3jxYqZMmcJvv/1G//79OeWUU/jtt9+YNm0a8+fPb9Axdu3axbhx4/jqq6/o2LEjgwcPJi0tjccff5wZM2ZU2z8tLY0rrriC+fPnEx4ezrBhw7BarcyePZupU6dSWlpa7fiXX345X3/9NdHR0QwdOhSAt99+m0svvZT09PTmfxDiOFvfhNAYiDnVeecI6win3AZLJxgjh0VE/uARhd7FixfTu3dvfv3110a9r7FPSXNzc3nmmWcYPXo0/fv359xzz+Wpp54iP7+FNzo/sg3CO9f+ukb0ioh4DOVUkx3dAa271T5Lxj8YwmKN3CsiIk6Vnp7OzJkzadWqFZ9++ilz587lrbfe4qOPPiIsLIxZs2bVW0S12WzMmDGD/Px8/vOf//Dxxx/z8ssv8+2339K7d2+WLFnCt99+W+U9jzzyCGlpadx+++189tlnzJ49m++++46zzz6bn3/+mXnz5lXZ/84776SgoID777+fL7/8kpdffpn//e9/jB8/nsOHD/Pss886/LORJiovhZ+fgr7XOP9cccOMlk8r73D+uUTEY7h9ofe3337j3//+d6Pf19inpPn5+Vx11VW8+eabWCwWhg0bhsVi4Z133mH8+PHk5eU56pI8z9EdEN6l9tfDu0B2oprBi4i4OeVUN3B0h7GISl3Cu0BWgmviERFpwT788ENKSkqYPHky8fHx9u39+/dn2rRpFBcX1zuqd926dSQkJHDGGWcwZswY+/aoqCgeeeQRgCqF2+TkZFatWkXnzp258cYb7dtDQkKYNWsWvr6+fPDBB/bt27dvJykpiRNOOIFJkybZt/v6+vJ///d/AKxZs6ZJ1y9OkPAJhLSDNn1dc76Tb4HkpbB/mWvOJyJuz60Lvd9++y1Tp06loKCg0e9t7FPSF154gYSEBK644gq+/vprZs+ezbfffsuYMWNITEzkhRdecNBVeZjyUsjdB2F1tG4ICDf+tzDTJSGJiEjjKae6iaPboVWnuvcJ7WAssiIiIk5VWSAdOXJktdcqt9XXq7euYwwcOJA2bdqwceNG+4yWtWvXYrPZGD58OD4+VW/HO3bsSN++fTl48CCJiYkA9OvXj7Vr1/LSSy9VO77VakzZ9/PzqzNGcRFbBfw0C3r/03XnDAgzFmf79jq1cBARwE0LvWlpacyYMYPp06dTUVFB27ZtG/X+xj4lzc3NZeHChYSFhXHPPffYE66fnx8zZ86kdevWLFq0qEk3xx4vJwlCYsAvsPZ9LJY/Rh/tdl1cIiLSIMqpbubINgjvWvc+reJU6BURcTKbzUZiYiI+Pj5079692utdu3bFx8eHxMREbHXMXKwsyB4/Ivh43bp1o6KigqSkpCr79+rVq8b9K2PZvfvPe6vo6Gg6d646GyQvL4/HH38cgEsuuaTW+MSF9n9v/K8ze/PWpMMgY/H0tQ+49rwi4pbcstD7wgsv8MUXX3DiiScyf/78GhNvXRr7lPSXX36hqKiIM888k7CwsCr7h4aGctZZZ1FUVMQvv7TAFS2z6mnbUKlVJ8jWNFMREXejnOpmsnbVn1dbdYJsFXpFRJzp2LFjlJSUEBERQUBAQLXX/fz8iIyMpLCw0D5ytiYZGRmAUYytSeX2I0eOVNm/Xbt2Ddr/r7744gumTJnCkCFD+OGHHxg3bhx33FFzj9bc3FxSU1Or/ElLS6v1WqSZNj4PPS6pvQ+/M/W/AXbMg8ytrj+3iLgVtyz0du/enaeffpqFCxfSu3fvRr+/sU9JG7p/QkILLGQe3WGMLKpPaAf1ExQRcUPKqW6kMAvKCiC4nlHVreKMGTXqfS8i4jSFhYUABAcH17pPUFAQQJ2F3srjVO5b2zEqZ7I0dv+/+uGHH1i/fj0FBQX4+PiQk5NTa/H2vffeY8SIEVX+TJw4sdZrkWbIToS0X6DzCHPOHxQBfSfBshv1/UGkhXPLZj7XX399s97f2KekmZmZVbbXtv/Ro0ervZabm0tubm6VbV71lPTINmN18PqEx8HhFjo6S0TEjXlSTgUvz6tZO6F19/pH+gS0At9AsB6GsI6uiU1EpIX56yyVutTVusHX1xcASz2/2ysqKpq0/1/de++9PPXUU6SkpPD666/zxRdfsGXLFpYuXUrr1q2r7Dtp0iQuu+yyKtvS0tJU7HWG31+Grn+vu+Whs3W/EPZ+AwkL4ITx5sUhIqZyy0JvczX2KWnl/9b2NLeup6rvvfceL7/8cvMCdmdHd0Dc8Pr3C4uDnI+cH4+IiLiUK3MqeHlePboDWnWufz8w2jtk71ahV0TESUJCQgAoLi6udZ+ioqIq+9akMt9V7lvbMUJDQxu1f23nrHzw2qNHD/7zn/+QnZ3N6tWr+eSTT7jhhhuq7BseHk54eHitsYuDlBXB9vdhhMnfX3x8of91sOZe6HUZ+FZvSSIi3s8rC72ufKrq1U9JbTY4lgxhsfXvGxYLufuhogx8vPLHSkSkRXL1SCWvzqvZeyC0fcP2DYs1WiLFDXNqSCIiLVVYWBghISFkZ2dTVlaGn1/Ve5iysjKys7MJDAyss1jarl07du7cyZEjR+jRo0e11/8606WyUFtbD97K/WubSfNXF198MatXr2bHjh0N2l+cYM9nENnLPR7OxpxqtFX8/VU49f/MjkZETOCWPXqbq7FPSZvzVDU8PJxOnTpV+dO+fQNv4txdURZYfCEgrP59/QKNnoO5+50fl4iIuIwrcyp4eV49lgxhHRq2b1hHY+E2ERFxCovFQs+ePSkvL2ffvn3VXt+7dy8VFRXEx8fXeZzKnvSVPeqPZ7PZSE5OxtfX114Ermt/gKSkJAD7eX/88UceeOABli5dWuP+lQvJlZWV1RmnONGW16Hb382O4k8nTYWfn4TSmmdPiYh388pCb2OfkjZ0/9r6DXqtY3sb91SyVZwWZBMR8TLKqQ50LNkYZdMQreKMnr4iIuI0gwcPBmDZsmXVXqvcNnTo0AYdY/ny5dVe27RpE1lZWZx66qmEhYVV2X/lypXVZrccOnSInTt3EhsbS8+ePQEjny5atIj33nuvxvOvXr0agL59+9YZpzhJThIc2Qod/2Z2JH+K6AFRfYwCtIi0OF5Z6G3sU9KG7t+U1co9Wu6+hk8xBaMonFPzZygiIp5JOdWBcvdDSCNaN+QkOTceEZEWbuzYsQQGBjJ37ly2bdtm375161befPNNgoKCuPLKK+3bDxw4QFJSEnl5efZtZ5xxBr169WLdunUsWLDAvj0rK4tHH30UgClTpti3x8XFMXjwYJKTk3nxxRft2wsKCnjwwQcpLy+vsv+IESNo27YtW7Zs4Y033qgS/4IFC1i8eDFhYWFcccUVDvhEpNG2vQudR7hfP9w+V8LP/zH6B4tIi+KVhd7GPiU9/fTTCQoK4scff6y2OIzVauXHH38kJCSEU0891TUX4C6O7YXghvWGAiA42niPiIh4DeVUBynJN6ZQBkU2bP+Q9pCXavTLFxERp+jUqRP33HMP+fn5/POf/2Tq1KlMnTqVCRMmYLVaeeyxx2jTpo19/8mTJ3PBBRfw/fff27f5+PjwxBNPEBISwkMPPcQVV1zBrbfeyt///ncSEhK44oorOPfcc6ucd+bMmURHRzNnzhwuvvhipk+fzqhRo1i3bh1DhgxhwoQJ9n1DQkJ45plnCAoK4rnnnuOCCy7glltu4fzzz+ehhx4iODiYF154oWXOlDGbrQJ2vAddRpkdSXWR8RDRHXa8b3YkIuJiHl/oPXToEElJSWRlZdm3NfYpaUhICJdeeinHjh3j0Ucftfc3Kisr47HHHiM3N5fx48fbp9u0GDnJEBrT8P1DY1ToFRHxYMqpTpS7z+jPW88idXb+weAXBIWZTg1LRKSlmzhxInPmzGHAgAFs2rSJbdu2MXDgQN555x3GjBnToGP079+fhQsXMnr0aPbv38+6devo2LEjjz76KI888ki1/ePi4li4cCFjx44lKyuLVatW0bp1a+666y5efvnlagvDnX322Xz66adcdNFF5OTksGrVKqxWK2PHjmXx4sX2h7LiYik/gF8wRPY0O5Ka9RoLG5/XQ2ORFsav/l3c2z333MPPP//Mrbfeym233WbfPnPmTCZMmMCcOXNYsWIF3bp1Y9OmTWRmZlZ7Sgpwxx13sGHDBj7//HM2btxI37592bFjBykpKfTr16/KsVuMY0kQN7zh+4fEaDE2EREPppzqRMf2Nbw/b6XQ9pB7AEIaMbtGREQabfjw4QwfXv99z4oVK2p9rWfPnsyePbvB5+zQoQNPPvlkg/fv2bMnzz33XIP3FxfY9g50Hml2FLVrNxDKSyFlFXRuxH29iHg0jx/RW5vGPiWNiIjgk08+4eqrr6asrIyVK1fi4+PDddddx3vvvUdoaKhJV2Ki3P2N69EbEgN5Kc6LR0RETKGc6gDH9ja+YBvSXg9QRURE3FFpASR9YfTndVcWC/S4GDa9YHYkIuJCFptN4/gdLTU1lREjRrB8+XI6depkdjhNY7PBiyFwyWJj+miD3lMBi8+HW7LBP8S58YmISIvhFXl15R1QVgAnTKh/30q/vQIdz4LT7nReXCIi0qJ4RU51Bzs/gt9fgXNmmR1J3UoL4at/wpRdRgspEfF6XjuiV5qpIMPoN9TQIi+AxeePxWM0qldERKSKY0mNb90QokVORURE3NL29xrX5tAs/sEQOwR2fmh2JCLiIir0Ss2O7YWwjo1/X6immYqIiFRzbF/j2iGBFjkVERFxR9Z0OPwTxP7N7EgapssIo5+wJnOLtAgq9ErNcvc1/oYUjP6DKvSKiIhUlXugCSN620PeAefEIyIiIk2z62PoeLYxA9YTRPeH4hzI3Gx2JCLiAir0Ss2O7YPg6Ma/L7itUSQWERERQ/ExqCiFgPDGvU+LnIqIiLifHe+79yJsf2XxgS4jjXYTIuL1VOiVmuUdMHoDNlaIppmKiIhUkZdqtGGwWBr3vsDWUF4MJfnOiUtEREQaJyvBeAjb7hSzI2mcTkNgz2K1bxBpAVTolZrlHjBG5zZWaIwxGlhEREQM+alNmyVjsaj3vYiIiDvZ8QHEnQs+vmZH0jitu4OtAo5sNTsSEXEyFXqlZk29KQ2JUT9BERGR4+WlNu3hKRh9elXoFRERMZ/NBjvnQedzzY6k8SwWo6/wns/MjkREnEyFXqmZ9XDTWjcER0NBBlSUOT4mERERT5SXCkFRTXuvFjkVERFxD4d+NPrdRsabHUnTxJ5ttG8QEa+mQq9UV14KRdkQGNn49/r6Gz0FCzIcH5eIiIgnymtiOyQwCsT5Bx0bj4iIiDTe9vcgbkTje+67izYnGjN39QBZxKup0CvVWQ9DUJum9x0KjtZNqYiISKW81KbNkgEIbmMs+iIiIiLmKSuG3QuhywizI2k6H1+IOR32fWd2JCLiRCr0SnXNuSGFP25KVegVEREBjIefTR3RG9zWyMsiIiJinr1fGwuahbY3O5LmaXcy7PvW7ChExIlU6JXq8g82bSG2SkFtwHrIcfGIiIh4svxDTc+rwW2NmTYiIiJinu3vQufhZkfRfDGnQsoKsFWYHYmIOIkKvVJdfqpRrG2qoCiNPhIREQEoLYRSq9G/vimC2+rhqYiIiJkKjkDKSug01OxImi8kGgIjION3syMRESdRoVeqyz0AwU1cHRzUT1BERKRS/kHjpsrSxK9cAeFQVmgUjEVERMT1dn0EHc6CgDCzI3GM6FNg//dmRyEiTqJCr1TX7B69WoxNREQE+KPQ267p77dYILidRvWKiIiYZds70OU8s6NwnJhT1KdXxIup0CvV5ac0fdEYMN6brxtSERGRZrdDAuPhqx6gioiIuN6RbWBNg3anmB2J47TtD2m/QEWZ2ZGIiBOo0CvVNWfRGDBaN2jhGBEREcg7aOTF5ghqYxxHREREXGvLXOgyEnx8zY7EcQLDISQGMreYHYmIOIEKvVKVrQKs6c0b0RsQDuXFxuIzIiIiLVnegeblVDAWOVXrBhEREdcqK4adH0C3882OxPHa9IND682OQkScQIVeqarwCASEgm9A049hsRj9CNW+QUREWrq8ZrZDAi1yKiIiYobEzyCiB4R1NDsSx2vTB1JXmx2FiDiBCr1SVf7h5t+Qwh99ejXNVEREWjjr4eb36A1uYyyUKiIiIq6z+XXo+nezo3COtifCoXVmRyEiTqBCr1TliBtSMI6hEb0iItLSWdMgOKp5x9DDUxEREdfK3gNHtkDsOWZH4hxhsVBWBLmaMSTibVTolaqsh41egM0VFKWbUhERadlsNijIcMCI3rZ6eCoiIuJKm1+DrqOb19LQnVksf4zqVZ9eEW+jQq9UZT0MQZHNP476CYqISEtXnAM+/uAX1LzjBLWFgnSjcCwiIiLOVVoI29+D7heZHYlzRcRD2gazoxARB1OhV6rKOwiBEc0/TrBaN4iISAtndVDfe79A8A2EouzmH0tERETqtnshRPb2zkXYjhfVC9J+MTsKEXEwFXqlKusho0jbXEFtjBtcERGRlsqa5picCkbBuCDNMccSERGRmtlssOkF6OHlo3nBKGZnbAZbhdmRiIgDqdArVTlsMbYo4wZXRESkpXJU33v4o/e9HqCKiIg41eENUJAJ7QeZHYnzBbaGgDDISTI7EhFxIBV6pSprmuMKvQUZzT+OiIiIp8o/DIEO6HsPf+RVPUAVERFxqk0vQI9LwMfX7EhcI6o3pG80OwoRcSAVeuVPNhtY0yHYAaOP/EOhohRKrc0/loiIiCfKP+iYBU7B6J+vmTIiIiLOk38Y9n4D3c43OxLXad1DfXpFvIwKvfKnklzjyaVfcPOPZbEY/QR1UyoiIi1VvoP63oNRMM4/6JhjiYiISHW/vwKdRxjtDFqKyHgVekW8jAq98qd8B60OXimojQq9IiLSclkPqUeviIiIJygthM1zoOelZkfiWpHxkKkF2US8iQq98qcCB/XnrRSsBdlERKQFs6Y7ttBrVaFXRETEKXZ9ZPSrDe9sdiSuFRQBfkGQe8DsSETEQVTolT/lO3B1cPijn6BuSkVEpIUqSHfcA9RgzZIRERFxCpsNfv1vyxvNW6l1Dziy1ewoRMRBVOiVP1kPO27RGFChV0REWq7SQigvgoBWjjleUJRROBYRERHHSllp5OyY08yOxBytu0LmFrOjEBEHUaFX/uTI1cHBGMWUf8hxxxMREfEU1sMQ1NZYnNQRAlpBqRXKih1zPBERETFUjuZ1VM72NOFdIeN3s6MQEQdRoVf+lO/ARWPA6NGrQq+IiLRE1sNGuwVHsfhoVK+IiIij5STB4fXQ5TyzIzFP6+5q3SDiRVTolT9ZHdyjNyjKWOBNRESkpbGmOTanAgS3VZ9eERERR/rtZej6d2NBspYqvDPk7tOsIREvoUKv/Kkgw8GF3jZgzXDc8URERDxFQbrRq96RgiJV6BUREXGU0gLY/h50v9jsSMzlGwBhsZC1y+xIRMQBVOiVPxVkOH4xtsIjUFHuuGOKiIh4Amua4wu9gZFa5FRERMRREuZDm74Q1sHsSMyn9g0iXkOFXjFUlEHxMQho7bhj+vpDQBgUHXXcMUVERDxB/iHHPjyFP0b0qtArIiLiEJtmQ4+LzI7CPYR3hswtZkchIg7gZ3YAtVm/fj1z5swhISGB0tJS+vXrx7Rp0xgyZEi977366qv5+eef693v1ltv5bbbbrP/98iRI0lJSal1/+3bt+Pn57YfWfMUZEJQBPj4Ova4wdGQfxhC2jn2uCIi0mDKqSawphk3TY4UFAn5Bx17TBERkZYo/TejzVLM6WZH4h5ad4ODa82OQkQcwC3vsBYvXsx9991HQEAAZ555JhUVFWzYsIFp06bx2GOPMX78+Drff/bZZxMTE1Pja1arlRUrVgDQp08f+/a8vDxSU1Np27YtZ511Vo3v9fHx4gHQBemOXzQGjJtSrRAuImIa5VSTOCOvBkUaq4OLiIhI82x7C7qOcvxAJ0/VqjNkJZgdhYg4gNsVetPT05k5cyatWrXio48+Ij4+HoAtW7YwZcoUZs2axbBhw2q96QS46aaban3t7rvvBmDatGmMHDnSvn3nzp3YbDaGDx/O448/7qCr8SDOWB0cjP6EKvSKiJhCOdVEBemOb90QGKWcKiIi0lxlxbDrYzj3ZbMjcR9hHY32UGVF4BdkdjQi0gxuN5zmww8/pKSkhMmTJ9tvSAH69+/PtGnTKC4uZv78+U069pdffsmSJUvo168f//d//1fltR07dgDQr1+/Jsfu0ZyxOjgYx9QK4SIiplBONYnNZrRECnT0iN4osKrQKyIi0izJS4zFx7QI2598/CAsFrJ3mx2JiDST2xV616xZA1BlZFClym2rV69u9HHz8/P5z3/+g4+PD48//ni1voA7d+4EWvBNqdVJhd6gCC0cIyJiEuVUk5Tmg8UH/IMde9ygSCjMdOwxRUREWppt70KXEWZH4X7Cu0DWLrOjEJFmcqtCr81mIzExER8fH7p3717t9a5du+Lj40NiYiI2m61Rx3799dfJzMzk8ssvp2/fvtVe37FjB76+vuzdu5eJEydy2mmncfrpp3PjjTeyZUsLWH3SeshJhd5IjegVETGBcqqJrGkQ3Mbxx/ULNkYLl+Q7/tgiIiItQVEOpK6GjueYHYn7CYuFozvMjkJEmsmtCr3Hjh2jpKSEiIgIAgICqr3u5+dHZGQkhYWFWK3WBh83JyeHefPm4e/vX2OvwZKSEpKTkykvL2fGjBkUFxczaNAgIiMjWblyJVdeeSX/+9//ajx2bm4uqampVf6kpXlgYdOaBsHO6NEbpUKviIgJPDGngpfkVasT+vMCWCxGAVl9ekVERJom6QuIOQUCwsyOxP2Ed4Yj282OQkSaya0WYyssLAQgOLj2qY5BQUZjcKvVSlhYw345z58/n8LCQsaNG0eHDtX78CQkJFBWVkZoaCivvPJKlRXC3333XZ588knuu+8+Tj31VKKjo6u897333uPll72gibs1DWJOc/xxgyKhIMPxxxURkTp5Yk4FL8mrBenOWeAU/uzTG9HDOccXERHxZjs/gtjBZkfhnlp1hsQvzI5CRJrJrQq9Pj4NH2Dc0GmmFRUVfPTRRwBcd911Ne5z0kknsXbtWkpKSoiNja3y2uTJk/nll19YtmwZn332Gddff32V1ydNmsRll11WZVtaWhoTJ05s6KW4B2eNPlKhV0TEFJ6YU8FL8qo1zTntkOCPvKoRvSIiIo1WmAWH1sPA282OxD216gw5SVBRDj6+ZkcjIk3kVoXekJAQAIqLi2vdp6ioqMq+9dm0aRNpaWmccsopdOvWrdb9ahpVVGn48OEsW7aMbdu2VXstPDyc8PDwBsXi1goznDP6KDACirKVLEREXMwTcyp4SV61pkFAa+ccO1C970VERJpk79fQ7hTwb9j3nhbHP9h4oJy7HyKqr+8gIp7BrXr0hoWFERISQnZ2NmVlZdVeLysrIzs7m8DAwAbfBC5fvhyACy+8sMlxVd6wVt4Qe53yUijOhQAn3Fj7+EFgKyg84vhji4hIrZRTTWQ97LzWDYGtVegVERFpisQvoMMgs6Nwb+GdIWuX2VGISDM0qdA7ZcoUvvjiC3v/P0exWCz07NmT8vJy9u3bV+31vXv3UlFRQXx8fIOPuXr1agBGjRpV6z5ff/01d911F0uWLKnx9dTUVADat2/f4PN6lMJMCIpw3ojbIC0cIyJSG+VUL2Q97Jx2SGDMlLEeds6xRUTckLPypLQw5aWw/3vocKbZkbi30FjI2WN2FCLSDE0q9P7444/ce++9nH322cyYMYO1a9c2uL9ffQYPNhqjL1u2rNprlduGDh3aoGPl5+eTlJREbGwsMTExte539OhRli5dyscff1ztNZvNxpdffgnAOeec06DzehyrExeNAeNm16pCr4hITZRTvVCBk/reg5GvCzSiV0RaDmfmSWlBDq2DVrEQ3MbsSNxbWAfISjA7ChFphiYVep966inOOussSkpK+PLLL5k2bRpDhgzh6aefZteu5g3zHzt2LIGBgcydO7dK/76tW7fy5ptvEhQUxJVXXmnffuDAAZKSksjLy6t2rO3bt2Oz2TjxxBPrPOeFF15IWFgYGzdu5N1337Vvt9lsvPLKK/z+++/Ex8dz7rnnNuva3JYzb0jB6CeoEb0iIjVSTvVCBRlOLPTq4amItCzOzJPSgiR+Ae3VtqFereLUukHEwzVpMbZLL72USy+9lCNHjrB06VK+/PJLduzYwTvvvMO7775Lr169GDNmDBdddFGdo35q0qlTJ+655x4ee+wx/vnPfzJokPHLeMOGDZSVlfH000/Tps2fT+EmT57MwYMHefLJJxk7dmyVY1VOD23btm2d54yKiuKJJ57grrvu4sknn2TRokV0796dhIQE9u3bR3R0NC+99BJ+fm61dp3jFKQbxVhnCWytQq+ISC2UU72MzQYFmc6bKRMUaRSSRURaCGfmSWlB9n4Np95pdhTur1UnyFbrBhFP1qzF2Nq2bcvkyZNZvHgxX3/9NTfccAOdOnVi9+7dPPPMM5x77rlMmTKFzz//nIKCggYfd+LEicyZM4cBAwawadMmtm3bxsCBA3nnnXcYM2ZMg4+TlZUFQKtWrerdd/To0XzyySeMGjWKI0eOsGLFCkpLS7n66qv58ssv6dq1a4PP63Gs6UYx1lkCIyFf/QRFROqinOolSvPBYgG/YOccPyhKhV4RaZGclSelBcg/ZNzzRvQ0OxL3F9IeCjOgzIsXzRXxchabExocJSUl8c033/DWW2/ZV9UOCgri/PPP56qrrqJv376OPqVbSU1NZcSIESxfvpxOnTqZHU79Vt4BZYVwwj+dc/y9/4O8FLjwQ+ccX0TEi7X0nAoellezE2HBcLhgnnOOb7PB4vPhlqPgH+qcc4iIeBDlycbxqJzqKDs/hC1vwtkzzY7EM/zvWrhsCbTtZ3YkItIEDp03mZuby3fffceyZcv46aef7Ik2JiaG3NxcFi9ezGeffca4ceOYOXOm907b9DTWNGjd1XnHD4qEtF+cd3wRES+knOqhCpy8wKnFAkFt/hiZ1N155xERcXOOzJPr169nzpw5JCQkUFpaSr9+/ey9gBtq7969vPTSS2zcuJGcnBw6d+7MFVdcwcSJE/HxqT6RNj09nVdeeYV169aRmZlJhw4duOSSS5g2bRoBAQHV9k9MTOT1119nw4YNZGVlERYWxsCBA7n++us5+eSTGxxni7R/GUT3NzsKz9GqE2TvVqFXxEM1+66wqKiI5cuXs3TpUtauXUtZWRk2m42QkBAuvfRSxowZw5lnnklRURFff/01zzzzDIsWLSIoKIgHHnjAEdcgzVWQBjGnOO/4QVqMTUSkIZRTvYCzFzgFCI4yzqNCr4i0MM7Ik4sXL+a+++4jICCAM888k4qKCjZs2MC0adN47LHHGD9+fL1x7dq1i4kTJ5Kfn8/AgQM56aST2LBhA48//jibN2/m2WefrbJ/Wloa48ePJy0tjb59+9KvXz82bdrE7Nmz+emnn3j77bfx9/e3718ZT3FxMT169OCkk04iNTWV5cuX88MPP/DMM89wwQUXNO/D9VY2m1HoPedxsyPxHKEdjEKviHikJhV6y8rKWLt2LUuWLGHFihUUFRVhs9nw8fHhrLPOYsyYMYwaNYrg4D/70wUHB/OPf/yD6Ohorr/+epYsWaKbUndRkOnkxdgioTDTeccXEfFgyqlepiADApzY9x6MvGrVA1QRaRmcmSfT09OZOXMmrVq14qOPPiI+Ph6ALVu2MGXKFGbNmsWwYcPqXOTNZrMxY8YM8vPz+c9//mPvf5+VlcXkyZNZsmQJ5513HqNHj7a/55FHHiEtLY3bb7+dm2++GYCCggJuueUW1q9fz7x587j22msBKC0t5d5776W4uJiHHnqIq666yn6cL7/8khkzZvDggw9y5plnEhXlxBklnurYXigvhladzY7Ec7SKhaxdZkchIk3UpELv3/72N3Jzc6ls79urVy8uueQSLrnkknpXOu3RowcA5eXlTTm1OENBhnNHHwVFQmEW2CrA0qz1/0REvI5yqpdx9gKnAIERmikjIi2GM/Pkhx9+SElJCTfccIO9yAvQv39/pk2bxvPPP8/8+fOZPn16redYt24dCQkJnHHGGVUWOY2KiuKRRx5hwoQJzJs3z17oTU5OZtWqVXTu3Jkbb7zRvn9ISAizZs1i5MiRfPDBB/ZC708//cShQ4c4/fTTqxR5AS655BK+/fZbli1bxg8//MBll11W5+fRIqWshHYnG62PpGHCOsGhn8yOQkSaqEmF3mPHjhEVFcWFF17IpZdeSr9+De/dUl5eznXXXdeo94gTVZRDUbZx0+gsPn4QEAaFRyEk2nnnERHxQMqpXsZ62PmtGwJbq9ArIi2GM/PkmjVrABg5cmS110aOHMnzzz/P6tWr6yz01nWMgQMH0qZNGzZu3Eh+fj5hYWGsXbsWm83G8OHDq/Xu7dixI3379mXr1q0kJibSs2dPioqKOPHEEznnnHNqPH/Xrl0ByMjIqDXGFi11NbTR96RGadUJjiWZHYWINFGTCr2vvfYaQ4YMwdfXt9Hv7dy5M3fffXdTTivOUHTUKML6OHkRn6AoY+SwCr0iIlUop3oZ62Fo58S+92AUkq1pzj2HiIibcFaetNlsJCYm4uPjQ/fu1Xued+3aFR8fHxITE7HZbFhqGRGamJgIUGVE8PG6devG0aNHSUpKYsCAAfb9e/XqVeP+3bt3Z+vWrezevZuePXty3nnncd5559V6jVu3bgWod3Rzi3VoPZxxj9lReJbgtlB8DEqt4B9qdjQi0khNmkf/3Xff8dZbbzVo38cff5yrr766KacRVyjIMFbvdjYtyCYiUiPlVC/j7HZIYMzCsR527jlERNyEs/LksWPHKCkpISIigoCAgGqv+/n5ERkZSWFhIVartdbjVI6kjY6ueUBL5fYjR45U2b9du3YN2r8uP/74Ixs2bCAoKIghQ4ZUez03N5fU1NQqf9LSWtCDwoIjRr4M72Z2JJ7F4mO0b8jRqF4RT9SkQu9nn33GDz/80KB9N2zYwJYtW5pyGnEFqwtWBwctHCMiUgvlVC9TkOHcBU7BmCWjnCoiLYSz8mRhYSFAlUXc/iooKAigzkJv5XEq963tGAUFBU3avzapqan861//AuD666+vcSG29957jxEjRlT5M3HixDqP61UO/wRtTgSfxo8Gb/HCOkJOotlRiEgT1Dtff9++fSxYsKDa9oMHD/Kf//yn1vfZbDYOHTrEnj17aNu2bfOiFOcpSHduf95K6icoIqKc2hIUZDr/AWpQpFFQFhHxMq7Mk3/tj1uXyoXgalLZUqK21g6VKioqmrR/TQ4cOMDkyZPJzMxk2LBh3HTTTTXuN2nSpGoLtKWlpbWcYu+hddDmBLOj8EyhHTSiV8RD1Vvo7dKlCz/99BM7d+60b7NYLKSnp/POO+/U+d7KhHjxxRc3M0xxGpcVeiPUT1BEWjzlVC9XVgTlxeAf5tzzBEZCYaZzzyEiYgJX5smQkBAAiouLa92nqKioyr41qRwRXLlvbccIDQ1t1P61nXPLli3cdNNNHDlyhHPOOYfZs2fXWrQODw8nPDy81ti9Xuoa6DnG7Cg8U2h7yEowOwoRaYJ6C70Wi4XHHnuMDz/80L7ts88+o23btgwePLjO94WEhNC7d2/Gjh3rmGjF8axpxmhbZwuKVD9BEWnxlFO9XEG60VahnlFazRbQCsoKjcKyX81Tf0VEPJEr82RYWBghISFkZ2dTVlaGn1/VW+OysjKys7MJDAyss1jarl07du7cyZEjR+jRo0e11zMzjQdzlb13K3vz1taDt3L/mnr4Ll++nLvuuovCwkIuuOACnn766Rr7CwtQUQYZv8EZ95odiWdqFQvJX5sdhYg0Qb2FXoATTzyRJ5980v7fn332GV26dKmyTTyUNQ1Cal4IwKGCIiFTfSVFRJRTvZgrFmIDo5AcFGWcL7yz888nIuJCrsqTFouFnj17smXLFvbt20fPnj2rvL53714qKiqIj4+v8zi9evXihx9+IDExkUGDBlV5zWazkZycjK+vr70I3KtXLwASE2vuf5qUZEyX/+t5P/30Ux566CHKy8uZMmUK99xzT73tH1q0ozsgNAYCnDzLxluFxap1g4iHatJibMuXL+fFF190dCxiBmuaixZji1A/QRGRGiinehFXLXAK6tMrIi2GM/Nk5SjhZcuWVXutctvQoUMbdIzly5dXe23Tpk1kZWVx6qmnEhYWVmX/lStXVuvDe+jQIXbu3ElsbGyVwvOyZct48MEHqaio4N577+Xee+9Vkbc+6Rshou4ivdQhJMb4nlFWe2sTEXFPTSr0xsbGajEYb1GQ7vzVwcEYeVSoG1IRkb9STvUirsqp8MeIXi1yKiLez5l5cuzYsQQGBjJ37ly2bdtm375161befPNNgoKCuPLKK+3bDxw4QFJSEnl5efZtZ5xxBr169WLdunVVFpLLysri0UcfBWDKlCn27XFxcQwePJjk5OQqBeyCggIefPBB+4jdSpmZmdx3331UVFRw5513VnlN6nD4Z4jsWf9+UjMfP2NEdO4+syMRkUaqt3XDWWedhcViYf78+cTFxdm3NYbFYmH9+vVNi1Ccy1XTTAMjofAo2GzO710oIuKmlFO9XEG6a/rewx+LnKrQKyLexdV5slOnTtxzzz089thj/POf/7S3XtiwYQNlZWU8/fTTtGnTxr7/5MmTOXjwIE8++aS9F7CPjw9PPPEEkyZN4qGHHmLRokW0a9eOn3/+mWPHjnHFFVdw7rnnVjnvzJkzmTBhAnPmzGHFihV069aNTZs2kZmZyZAhQ5gwYYJ933feeYfc3Fz8/f1JSEjg7rvvrvFaRo0axahRoxr1WXm1tJ+h3ySzo/Bsle0bonqbHYmINEK9hd7s7GwsFgtlZWVVtjWGppW4KZvNKL4GRTn/XH6BxlPB4mMQFOH884mIuCHlVC+Xf9gowLpCYGuN6BURr2NGnpw4cSIdO3bkzTffZNOmTQQEBDBw4EBuuummBheZ+/fvz8KFC5k9ezYbNmxgz549dOnShTvvvJPLL7+82v5xcXH2/VevXs3+/fuJi4vjmmuuYdKkSVUWhluzZg0ApaWlLF26tNYYunTpokJvpfJSyNoJEb3MjsSzhXaAnJp7SYuI+6q30Pv+++8D0LFjx2rbxMMVHwPfAOOPKwS1+WNF8gjXnE9ExM0op3q5gnSIdFE/wMAIsB52zblERFzErDw5fPhwhg8fXu9+K1asqPW1nj17Mnv27Aafs0OHDg1aYG7JkiUNPqb84eh2o0jpH2x2JJ4ttD1k7zE7ChFppHoLvWeccUaDtokHKkh3zWjeSpX9BDX1Q0RaKOVUL2dNgw6D6t/PEYIiIXu3a84lIuIiypPiEOkbXffg1ZuFdYSD68yOQkQaqUmLsdUnKSmJ3bt3V1tFVNyMywu9WiFcRKSxlFM9SEGGixdjU04VEVGelGrSfoGIHmZH4flCO8KxZLOjEJFGanKhNy8vj9dee41PPvnEvi09PZ2xY8dy0UUXMWbMGEaNGsUvv/zikEDFCazprlmIrVJgay0cIyJSA+VUL+GqBU7hj9YNKvSKSMugPCmNkrEJInqaHYXnC+0AufvBpocoIp6kSYXeY8eOMW7cOGbPnm1vDg/w8MMPs2PHDmw2G35+fqSmpjJt2jRSUlIcFrA4kCtXB4c/bkrTXHc+EREPoJzqJcpLoSQPAsJdc76gKChUoVdEvJ/ypDSKrQKO7tCIXkfwDwb/MN3Di3iYJhV63333Xfbv30/nzp257LLLADh48CA//PADPj4+vPfee/z2229MmTKFoqIi3nzzTYcGLQ5iTXPd6uBgTGe1HnLd+UREPIByqpcozDQWG/Xxdc35AsKhONcoMIuIeDHlSWmUY3uNHBnQyuxIvEOrWMhJMjsKEWmEJhV6V65ciZ+fH2+//TYjR44EYPny5QAMHDiQQYMG4efnx5133kl4eDjr1693XMTiONbDruslCMZ0Vj0NFBGpQjnVS1hd3Pfex9coLBdmuu6cIiImUJ6URsncAq01mtdhQjqoT6+Ih2lSoTclJYVu3boRGxtr37ZmzRosFgtDhgyxb/P396dTp05kZGhqoVuyprm2R29QpNEuQkRE7JRTvUSBi/veg1FYVu97EfFyypPSKJmboXVXs6PwHqHtITvR7ChEpBGaVOgtKSnB39+/yn//+uuvAJx55plV9rVarfj4NHnNN3EmV9+UBkVqhXARkb9QTvUSBemunSUDeoAqIi2C8qQ0SsYmaN3N7Ci8R1h7yFGhV8STNCkLduzYkQMHDlBUVATAjz/+SGFhIZGRkfTv39++3759+0hNTaVTp06OiVYcy5WrgwMERkFBJthsrjuniIibU071ElYXL3AKf/S+V0skEfFuypPSKJlbIaKn2VF4j9BYFXpFPEyTCr3Dhg0jPz+f+++/n+XLl/PUU09hsVgYNWqUfZ8tW7Zw2223UVFRUWVKjbgJm80ouga6sJ+gfzBYfKA033XnFBFxc8qpXsJ62LULnIJxPo3oFREvpzwpDVaSZ+TFsNj695WGCesAufvMjkJEGsGvKW+64YYb+O677/j666/55ptvsNlsREZGctNNNwHGU9Zrr70Wm81Gly5duO666xwatDhAaT5YLEbx1ZWC2xijj7QKqogIoJzqNaxpru8JGBhhFJhFRLyY8qQ02JFtRtsGH1+zI/EeQW2gJN8oouseXsQjNKnQGxUVxaeffsrcuXPZs2cPnTt3ZsqUKcTExADQrVs3QkJC+Pvf/86MGTNo3drFUxmlftZ045e2qwVFGueO7OX6c4uIuCHlVC9RkAYxp7j2nEGRWglbRLye8qQ02JHtEN7V7Ci8i8VijJA+thei+9e/v4iYrkmFXjAS7j333FPja+3bt+fHH38kICCgyYGJkxWkG6t1u1pQlKaZioj8hXKqF7BmuLYdEhg59dA6155TRMQEypPSIEe3QSv1aHa4sA6Qk6xCr4iHcNqSpEq0bs7VC7FVUj9BEZFGU071AIWZrs+rQZFGgVlEpIVTnhTAWIhNI3odL6S9ZhCJeJAmj+gF2Lp1K5s3byY/P5/y8nJsNlut+956663NOZU4WkE6BJgwrSmgtVYIFxGpgXKqB6soh6JsCHRxXg2KhEIVekWkZVCelHpl7YT+08yOwvuEtoecRLOjEJEGalKht6SkhDvuuIMVK1bUu6/NZsNisSjZuhtrmutvSMG4Kc0/5Przioi4KeVUL1B0FALCwKdZz88bL6A1FOdCRZnrzy0i4iLKk9IgRTlQkgsh7cyOxPuEdYQD9f/7ExH30KS7gnfeeYfly5cD0LlzZ7p160ZgYKBDAxMnsx42p3VDUBRk73H9eUVE3JRyqhewpkNwW9ef18cXgiKgINPonyci4oWUJ6VBsnYabRssTutO2XKFdjAWYxMRj9CkQu+XX36JxWLhgQce4KqrrnJ0TOIK+Yeh/amuP29QpHr0iogcRznVC1jTzFngFIzzWtNU6BURr6U8KQ1yZDuEdzE7Cu8U2gHyU4xWVT6+ZkcjIvVo0uOulJQUOnTooETrycy6KQ2KNBaCExERQDnVKxSkmTNLBvQAVUS8nvKkNMiRrdAqzuwovJNfoLGoulowiniEJhV6Q0NDCQsLc3Qs4koF6RBoQqE3MMpYmVxERADlVK9gTTNngVOAQBV6RcS7KU9KgxzZBq27mh2F9wqLhWNJZkchIg3QpELvaaedxt69e8nKynJ0POIKNptRbDVjRK9/MGCBkjzXn1tExA0pp3oBs/regzHCxppmzrlFRFxAeVIaJGuX0aNXnCOkPeQkmx2FiDRAkwq9t9xyCwAPPPAAJSUlDg2o0vr167nmmmsYNGgQAwcO5Oqrr2b16tUNfv/hw4fp3bt3rX8mTJhQ7T3p6ek8/PDDjBgxgv79+zN69GheeeUVp12jaUrzAcsfRVcTBLfxrJvSozthzX1G3ycREQdzRU4F5VWnyj9sYo/eSM+aSllqhU0vwZY3wVZhdjQi4gFclSfFg5XkQXE2hLQzOxLvFRqjEb0iHqJJi7EdOHCAcePG8fHHHzNkyBDOOOMMYmJi8Pf3r3F/i8XCv/71rwYff/Hixdx3330EBARw5plnUlFRwYYNG5g2bRqPPfYY48ePr/cYO3bsAKB3797Ex8dXe71bt25V/jstLY3x48eTlpZG37596devH5s2bWL27Nn89NNPvP3227Ven8exphnFVrME/VHojexlXgwNteFJ+PU5iB0M8wfDmTPh1NvNjkpEvIizcyoorzqd9TDEmLDAKRiF3uzd5py7sXKS4OOzIaoPFGXDtrfhH99AoEltL0TEI7giT4qHy0qAVl3A0qRxbNIQoR0ge4/ZUYhIAzSp0Dt9+nQsFgsAOTk5fPfdd/b//iubzdaoZJuens7MmTNp1aoVH330kf1mcsuWLUyZMoVZs2YxbNgwYmJi6jzOzp07Abjuuuu45JJL6j3vI488QlpaGrfffjs333wzAAUFBdxyyy2sX7+eefPmce211zboGtyemauDAwRHecaI3iPb4NdnYOTrEBINJ/wTlt8EXUdBmz5mRyciXsKZORWUV13Cmm7iYmweMkvGZoPvroNeY6H3eGM07y/PwPpHYPjzZkcnIm7M2XlSvEDWLgjXQmxOFdYR9n1rdhQi0gBNKvReeumltSbX5vrwww8pKSnhhhtuqDJiqH///kybNo3nn3+e+fPnM3369DqPUznyqF+/fvWeMzk5mVWrVtG5c2duvPFG+/aQkBBmzZrFyJEj+eCDD7znhrQg3dxCb2CkMfrJndkq4Nup0HeyUeQFY7pK7ythxXQY9x046d+AiLQszsypoLzqEoUZJrZu8JCHpzvmQX4aDHrQ+G+LD/SfBt9eBwNuhKje5sYnIm7L2XlSvEDWTmOxMHGesI5wbK/ZUYhIAzSp0PvUU085Og67NWvWADBy5Mhqr40cOZLnn3+e1atX13tDunPnTkJCQqpNJa3J2rVrsdlsDB8+HB+fqtM9OnbsSN++fdm6dSuJiYn07NmzEVfjpqxpxuItZgmMcP9C777voPgY9Lio6vaeY+D762H/Muh6njmxiYhXcWZOBeVVpysvheJcCAg35/xBUVCQYc65G8pWAesfhtPuAh/fP7cHRRmzZVbPgEu/MC8+EXFrzs6T4gWObIfo/mZH4d0CI6G82LhHVsslEbfmVk1sbDYbiYmJ+Pj40L1792qvd+3aFR8fHxITE7HZbLUeJycnh0OHDtGtWzfeeecdLrnkEgYMGMA555zDQw89RHp6epX9ExMTAejVq+aesZWx7N7tIT3w6mM9bO4v56Ao9184Zstc6HZ+9T5Pvv5GsXfrG+bEJSLSCMqrLlCQYbRtOL6A6UoBraCsCEoLzTl/Q6SuBh9/aHNi9de6XwwpK92/WC0iIu4raxeEdzE7Cu9msUCrWMhJNjsSEalHswu9CQkJzJ07l/vvv98+GshqtfLpp582elXUY8eOUVJSQkREBAEBAdVe9/PzIzIyksLCQqxWa63HqewjuH37dp5//nnatGnDoEGDKC8vZ8GCBfzjH/8gOfnPX1AZGcbNRbt2Na/SGR1tTN0/cuRIo67HbeUfMr9HrzsXeguOwIHvofOIml+PGw57vzWeZoqIOJAjcyoor7pEgckLnFosxvkL3Lh9w5Y3oOvomlse+QdDx7/Bzo9cH5eIeBxH50nxAhVlkLsPwjqZHYn3C+0Ix5LMjkJE6tGk1g1g3Dw+8MADLF++HPiz8T1ASkoKDzzwALNnz+aNN96gd++G9V0rLDRGowQHB9e6T1BQEGAk9LCwsBr3qewjGB8fz6uvvkpcnNGYvaCggIceeoilS5dy9913s3jx4irnrTx2becsKCio9lpubi65ublVtqWlufHNFhitGyJMnCobFOXeN6Q750HHsyGg5p8vAlsbq6vvXgQnTXVtbCLilZyRU0F51SWsJve9B6PQa02D1vW31XC5ohxIXgrnz6t9ny4jYft7cOr/uSoqEfEwzsqT4gWO7YXgaPALNDsS7xfaHnJU6BVxd00q9JaUlHDttdeyfft2QkJCOOuss9i6dSuZmZmAkXjDw8NJT0/n6quv5vPPP6djx471HvevffzqUtcU08mTJzNq1ChCQ0OJivrz5iskJITHH3+cX375he3bt/P7779z8skn4+trTLesr8l/RUVFtW3vvfceL7/8coPjdgvWNHNvSoPauPcUzR0fQJ8r696n87mw7R0VekWk2ZyVU0F51SWsaUbfOjMFtXHfBdkSP4d2p9bdMir6ZLA+Y/RYbFv/Yn8i0rI4M0+KF8jaBeGdzY6iZQjtANle0HZLxMs1qXXDBx98wPbt2zn99NNZtmwZr7zyCp06/TlVok+fPqxYsYLTTz+dvLw85s6d26DjhoSEAFBcXFzrPkVFRVX2rYmvry9xcXFVbkYrBQcHc+aZZwLGFNTKbccfuzHnnDRpEsuXL6/y58MPP6w1NrdQkG70EzRLYAQUZkFFuXkx1KYgE3L2QNt6mvl3GASZm402DyIizeCsnArKqy5RkGb+oiTuvMhp0hLocEbd+/j4QuwQSPzMNTGJiEdxZp4UL3B0p9o2uEpYR8hJNDsKEalHkwq9S5cuxc/Pj2effbbGmz6AsLAwnn32Wfz8/OwrftcnLCyMkJAQsrOzKSsrq/Z6WVkZ2dnZBAYGEh7e9NWt27ZtC/w5tbSyh2BtvQIrnxbX1GswPDycTp06VfnTvn37JsfmdDYbFB4xd0Svj69xU1yYaV4Mtdn3rTHyyNe/7v18A6DdyXBguUvCEhHv5aycWvk+5VUnyz9k7sNTMAq9+W5Y6K0oM/Jk+3oKvQDtT4W93zg/JhHxOM7Mk+IFsnYai4SJ84V1NFpliIhba1Khd+/evfTs2ZOYmJg694uJiaF79+7VVuOujcVioWfPnpSXl7Nv374az1tRUUF8fHydx3n55ZeZPn06CQkJNb6empoKYL9xrFwVvHKV8L9KSjL60NR3Xo9QlA1+wUah0kzBbd1zmmnSEqP/bkO0G6ibUhFpNmflVFBedQmz2yGBscip1Q0XOT30o9HPryGL1UUPMGbKFOfWv6+ItCjOzJPiBbJ2Qas4s6NoGUJijLUJyrXwoYg7a1Kh12Kx2Eft1KeioqLGlb5rM3jwYACWLVtW7bXKbUOHDq3zGAkJCXz77bd88031ItzRo0dZt24d/v7+DBo0qMo5V65cWa1f4KFDh9i5cyexsbH07GniAmaOYj1s7urglYKi3G+aaUU5HPjeaMvQEO1Pg/3fGaOkRUSayJk5FZRXnS7/kPmF3qAoIw53s/draH96w/b1C4I2/SBllTMjEhEP5Ow8KR4ue48Kva7i4wch7SB3v9mRiEgdmlTo7datG6mpqfYRPLU5cOAAiYmJdO/evcHHHjt2LIGBgcydO5dt27bZt2/dupU333yToKAgrrzyz4WyDhw4QFJSEnl5efZt48ePB+Cdd95h48aN9u1Wq5X777+f/Px8xo0bR3R0NABxcXEMHjyY5ORkXnzxRfv+BQUFPPjgg5SXlzNlypQGX4Nbsx42Fm0xW1Ck+43oTfvF+GxCohu2f1gnsPjA0R3OjUtEvJozcyoorzpdgRuM6A2Kcr+cCpD8VcPaNlTSTBkRqYGz86R4sKJsKCtyj/vbliIsFnKSzI5CROrQpELvxRdfTHl5Offccw85OTk17pOTk8Pdd98NwPnnn9/gY3fq1Il77rmH/Px8/vnPfzJ16lSmTp3KhAkTsFqtPPbYY7Rp8+cv8smTJ3PBBRfw/fff27edc845TJkyhaKiIq666iomTpzIrbfeyogRI1i1ahWnnXYa99xzT5Xzzpw5k+joaObMmcPFF1/M9OnTGTVqFOvWrWPIkCFMmDChEZ+QG7MeNv+GFNyz0Juywlj5u6EsFog5zejrKyLSRM7MqaC86nTWdPNnygS1MRZadSeFWXAsGdr0afh72p8G+/7nvJhExCM5O0+KB8veDeGdjfsycY2wDlqQTcTN+TXlTVdeeSVfffUVGzdu5IILLuCss86yP2F95513SE5O5rvvvuPYsWP06tWLiRMnNur4EydOpGPHjrz55pts2rSJgIAABg4cyE033cRZZ53VoGPce++9DBgwgA8++IAdO3ZQUVFB586due6665g0aRL+/lUX24qLi2PhwoXMnj2b1atXs3//fuLi4rjmmmuYNGkSfn5N+qjcjzssGgN/LBxz0Owoqkr5AWLPbtx7ovtDyko47U7nxCQiXs/ZORWUV52mJA+wgV+IuXEERRoLrdoqjJkm7uDQeqMVg08j/p5bd4fiHOO7SlhHp4UmIp7FFXlSPFT2bmjVyewoWpbQDsbnLiJuy2KzNa3B6LFjx7jnnntYtWpV1QNaLFQe8vTTT+e5556rcVVtb5aamsqIESNYvnw5nTq5WeJZMd1YBbv3FebGcWAFHNkKYz4zN45Ktgp4OQr+/nbjRjwXZMCym+HmTD1JFpEmU06tm9vm1awEWDQKzn/P7Ejg80th6p6Gtx9yttX3GDmy36TGvW/dQ3DKdOh9uXPiEhGPpDzpOG6bU5ti7QOQdxBOnGx2JC3HwXWQuhrGaVariLtq8nCa1q1bM2fOHLZt28by5ctJSkoiPz+f4OBgunTpwrBhwzjjjEb0ZRPXyD/UuGmUzhLc1r0WYzuyHYIiGt/WIqQd+AYY01ciezklNBHxfsqpHspdFjgFo8BrPew+hd7UHyC+CcXaqD5wcK0KvSJShfKk1OjoTmjbz+woWhb16BVxe82aN1leXk7Hjh25+uqradWqVbVpm+KG8g9B7DlmR2EUevPdqNB7cC20ObFp7217ovFkU4VeEWkG5VQPlO8mC5zCH3n1kNFSyGxlRZC5Bc6c2fj3tj0Rtr/r8JBExPMpT0o12buh62izo2hZwjpCfooxS7gx7ZlExGUa9S+zvLycJUuWsHLlSjZu3MjRo0ervN6pUydOPvlkLrjgAoYNG4ZFU9ndj9ssxvbHwjE2m3u0PEj9Adr0bdp7o/rAwTWaMiQijaKc6gWshyHQDfreg5Hb3aX3fdqvRr9d/+DGvzeyN2TtgtIC8De597GImEp5UupkqzBGlraKMzuSlsU3wLiXzz0AEd3NjkZEatDgQu+GDRt44IEHOHjwILW19U1JSSE1NZWlS5fSs2dPHn/8cQYMGOCwYKWZbDajuOoO00z9AsE3CIqy3COeg2th8JNNe2/bfvDrfx0bj4h4NeVUL5F/0D0WOIU/Cr2HzI7CcHBN0x+e+gVCRE9I+wXihjo2LhHxGMqTUq+8VAhopYeCZmjVyWhdqEKviFtqUKH322+/5e6776a0tJTAwECGDx/OKaecQmxsLKGhoZSUlGC1WklOTua3337jxx9/ZM+ePVx99dW89NJLDB2qL+puoSQPsJi/OnilkGjjptTsQm/+YSi1QlgTFyNo3QPyU6EwC4LdYLS0iLg15VQvkn8IWnczOwpDUBTkpZgdheHQemh3StPfX9mnV4VekRZJeVIaJHs3tOpsdhQtU2hHo9DLKLMjEZEa1FvoTUtL4/7776e0tJRRo0bxyCOPEBVVdzHr8OHD/Pvf/2bFihX861//4ttvvyUy0k1GvLRk1sNGDz93mdYU3AashyD6JHPjSPsFok5o+ufi42vclKb/Cl2V7ESkdsqpXib/ILQ/zewoDMFt4fBPZkdhSN8IJ0xo+vvb9HGfaxERl1KelAbL3m0sDCauF9oBshLMjkJEauFT3w4ff/wxVquVCy+8kNmzZ9ebaAE6dOjAq6++yqhRo8jLy2PhwoUOCVaayZ1WBwejt487TDNN+7n5C6lVTjMVEamDcqqXsaa5R997MPK7O+TU/MNQVmjcBDZVZG+jWCwiLY7ypDRY1i5jYTBxvVaxRqFdRNxSvYXeNWvW4OPjw4wZMxp98DvvvBObzcbq1aubFJw4WP4h91kdHIy+hu5wU3r4J+Omsjkie0HaBsfEIyJeSznVyxSku09eDW5rFJ7NlvaLMculObOHQtsbxeL8w46LS0Q8gvKkNFjWLqNXrLheWOwfrRtExB3VW+g9ePAgHTp0ICYmptEH79q1K9HR0SQnJzcpOHEw62H3WTQGjJvjvFRzY7DZjFFDUSc07zhRGn0kIvVTTvUipYVQXmQsBOMOgqKg6ChUlJsbhyNmyVgsRl5WXhVpcZQnpcGy90CrOLOjaJlCO0LeAagoMzsSEalBvYXegoIC2rRp+miV6OhocnNzm/x+cSB3Wh0cjNFHZi8cc2wv+AY2v6VFaEcoyQdrumPiEhGvpJzqRayHITjaffre+/hBYGtjlLGZDv8EkfHNP45aIom0SMqT0iDlJcbM0Oa0CZKm8ws0vgMd22t2JCJSg3oLvaWlpfj7+zf5BEFBQZSXmzy6RAzu1rqhcjE2M1UuxNZcFssfC7Jp9JGI1E451Yu4W997MB6gmtkSyVGzZMAoFqslkkiLozwpDZKTDKEx4Nv0nxVppladtSCbiJuqt9ArXiT/oHvdlLpDP8G0nyGimVNMK0Vq9JGISIvhbg9PwfxCb+4+x8ySgT9aIm0yisciIiLHy96ttg1mC4uFbBV6RdyRCr0tSf4hY4qFuwiKgsIj5vYTTP+1+b0EK0X0MqasioiI98s/6H6F3sAoc2fKpG9yTNsGML6v2CqMz1lEROR42bshNNbsKFq2VrFwdIfZUYhIDfwaslNJSQmHDjXtxqGkpKRJ7xMHs9n+mGba1uxI/uTjBwHhUJhprLDtajYbZG6Ggbc75niRvWDL6445loh4LeVUL5GXAsFRZkdRVVAk5JlYGM3YBK27O+ZYFotRNM74Tauqi7QwypNSr6yd0Kqj2VG0bK3iYM9is6MQkRo0qNC7bds2RowY4exYxJmKc8DiA/4hZkdSVXD0H430TSj05h0AnwBjZLEjhHaA0nwoyIQQNxo5LSJuRTnVS+QdcNzoVUcJbgP5qeadP+1X6DTYcceL6G4Uentc7LhjiojbU56UemUlQM9LzY6iZWvVGXISzY5CRGrQoNYNNputWX/EDeQfhJAYs6OoLqQt5Jl0U5r+m2Nv0i0Wo31Dxu+OO6aIeB3lVC+Rd9C92iGBMWsnL8W882duhoiejjte6+5GiyURaVGUJ6VeOYnq0Wu24LZQkg/FuWZHIiJ/Ue+I3uXLl7siDnG2vFT3uyEFc29K0zcao4UcqXUPyPwdup7n2OOKiFdQTvUi+W5Y6A1pZ97DU2salBU59qFyZC/Y/p7jjicibk95UupVkgfFxzSD0mwWizGqNzsB2p9udjQicpx6C72xsWpy7hXyDzpmFWxHC2pjTH81Q/qv0PFMxx4zortRQBYRqYFyqpew2aAg3b363oNR6DVr8bKM3yCqt3Hj5yihHaHoKBTlQFCE444rIm5LeVLqlb0HwjsbbQnFXK06GW00VOgVcSv67dhS5KU4rhetI4W0g9z95pw7c7PRasGRInoaN7siIuK9Co+AXzD4BZodSVX+YWCrMEY6uVq6Axdiq+TjCxE9jHwtIiICkL0bwrRIp1sIizUWxhMRt6JCb0uRl+J+I4/AmHKTa8KI3oJMY+G00A6OPW54F6NwXVrg2OOKiIj7yD/onlNGLRajdYIZLZHSNxpFWUerbIkkIiICxgjSsI5mRyEArbvAka1mRyEif6FCb0uRl+qeN6XBJk0zzfjdGM3ryCmmAL7+EN4Njmxz7HFFRMR9uGN/3kpm9enN/N2xC7FVat0d0tQSSUSca/369VxzzTUMGjSIgQMHcvXVV7N69epGHWPv3r3ceeedDB06lAEDBnDxxRczb948Kioqatw/PT2dhx9+mBEjRtC/f39Gjx7NK6+8QklJSb3nKi4u5vzzz2fy5MmNitErZO2CVmrx4RbCu8KRHWZHISJ/oUJvS5F/0H1H9FrTjKmmrpS52fFTTCtFdFf7BhERb5aXavSYd0dmLHJakm/kcmesgB7ZEzI2Of64IiJ/WLx4MVOmTOG3336jf//+nHLKKfz2229MmzaN+fPnN+gYu3btYty4cXz11Vd07NiRwYMHk5aWxuOPP86MGTOq7Z+WlsYVV1zB/PnzCQ8PZ9iwYVitVmbPns3UqVMpLS2t9Vzl5eXce++9JCcnN/maPVp2AoQ5Id9I44V1gvwUYzFWEXEb9S7GJl4i/5B7jj7yDYCAMCjIgND2rjtvxiZo3c05x27dzRgxLCIi3ikv1T0XOAWj0OvqlkhHthoPT318HX/s8G6QkwTlpcasGRERB0pPT2fmzJm0atWKjz76iPj4eAC2bNnClClTmDVrFsOGDSMmJqbWY9hsNmbMmEF+fj7/+c9/GDNmDABZWVlMnjyZJUuWcN555zF69Gj7ex555BHS0tK4/fbbufnmmwEoKCjglltuYf369cybN49rr7222rmOHTvGjBkzWLVqlQM/BQ9is0F2onMeLErj+fobxd7s3RDd3+xoROQPGtHbEpQWQqkVAlubHUnNzOgnmPG7MUrIGSJ6QKZG9IqIeK28A+45Swb+6H3v4kVOM3533iwZv0AI62BM1RURcbAPP/yQkpISJk+ebC/yAvTv359p06ZRXFxc76jedevWkZCQwBlnnGEv8gJERUXxyCOPADBv3jz79uTkZFatWkXnzp258cYb7dtDQkKYNWsWvr6+fPDBB1XOYbPZWLJkCWPGjGHVqlXExbXQQmdBhvFQMTDc7EikUngXOLLd7ChE5Dgq9LYE1kPGjZ/FTf+6Q9q5ttBbVgTHko2eQs7QuruR7FzdjkJERFwjL9U9Z8mAke/zXDyi15mzZOCPBdk2O+/4ItJirVmzBoCRI0dWe61yW329eus6xsCBA2nTpg0bN24kPz8fgLVr12Kz2Rg+fDg+PlXvzzp27Ejfvn05ePAgiYmJ9u0HDx7k7rvv5ujRo0yfPp1///vfjbhKL5K9G1p1NjsKOV6rznBU69OIuBM3rfyJQ7nzDSm4vp/g0R3GdB/fAOccP7A1+IfCsX3OOb6IiJjLXfveg7HIqasXY8v4zZjN4izhXdX7XkQczmazkZiYiI+PD927V5+V0LVrV3x8fEhMTMRms9V6nMqC7PEjgo/XrVs3KioqSEpKqrJ/r169aty/Mpbdu3fbt/n7+zNu3Di++eYbbrnllmoF4hYjKwFadTI7Cjle6y6QucXsKETkOOrR2xK48w0pGAvauHKaaeZmY3SQM0X0NBJehJOmsoqIiHmsh903r4ZEG3nfZgOLxfnnqyiHozud17oBjFy6f5nzji8iLdKxY8coKSkhKiqKgIDqA0D8/PyIjIzk6NGjWK1WwsLCajxORkYGANHRNQ+sqdx+5MiRKvu3a9euQfsDxMTEMGvWrIZcVhW5ubnk5uZW2ZaWltbo47iNrJ0QFmt2FHK88K6w40OzoxCR46jQ2xLkunEvQTBuSl3Zey99E7Tu6txztO4Kmb9Dr0udex4REXGt4mNQUQYBrcyOpGb+oeDjB0VZrlkwLicRgqKMhVWdJaInbHzeeccXkRapsLAQgODg4Fr3CQoKAqiz0Ft5nMp9aztGQUFBk/Zvjvfee4+XX3652cdxG0d3QMezzY5CjhfWCfJTjPaIfjX/TIuIa6nQ2xIcSzb64LqrkHaw/3vXnS/jN+g5pv79mqN1d6NnoYiIeJfcAxDawTWjZZsqtP0fbZtcUOjN3Ozctg1gPKyuKAVrOoTGOPdcItJiNKb9QV2tG3x9fQGw1JMXKioqmrR/c0yaNInLLrusyra0tDQmTpzY7GObIns39B5vdhRyPF//P/r07oCYgWZHIyKoR2/LkLvfvW+MXLkYm80GR7YZo4OcKaKHehWJiHijvAPunVPhj7zqogXZMn537kJsYBTVI3pqQTYRcaiQkBAAiouLa92nqKioyr41qRwRXLlvbccIDQ1t1P51nbOhwsPD6dSpU5U/7du3b/ZxTVFeAvmpENbR7Ejkr7RoqohbUaG3Jcg94N4jeoOjofCokbydLe+AsQhbUKRzzxPWCQoyoCTPuecRERHXyt3v3gucgpHzXbUgaPpG54/oBQjvpptIEXGosLAwQkJCyM7OpqysrNrrZWVlZGdnExgYSHh4eK3Hqey1e3xP3eNlZmYCf/bebej+tfXwbbFykiCkvfMW1Jama93NaI8oIm5BhV5vZ7MZPXNC3PjJrY+v0ac31wWjjzI2Q2TNK9w6lI+v0b4hc6vzzyUiIq5zbJ97970HCImBY0muOdeRrc6fJQMQ0c1ovSQi4iAWi4WePXtSXl7Ovn37qr2+d+9eKioqiI+Pr/M4vXoZ9xaJiYnVXrPZbCQnJ+Pr60uPHj3q3R8gKcn4/V3feVucrASjRYC4n8iealso4kZU6PV2xTnG//qHmhpGvUI7wLG9zj9Pxm/GyqCu0Fqjj0REvE7uXvdv3RDa3hj55GwFR6A03ygsO1tED+NhrYiIAw0ePBiAZcuWVXutctvQoUMbdIzly5dXe23Tpk1kZWVx6qmn2hdzq9x/5cqV1frwHjp0iJ07dxIbG0vPni54iOZJshPUtsFdte5htEeso5e1iLiOCr3eLne/+y8aA8ZNaa4rCr2bXDPFFIyCskYfiYh4l9z9rilsNkdoByNOZ8vcDBG9XPMdI7yrMUq5rPZemiIijTV27FgCAwOZO3cu27Zts2/funUrb775JkFBQVx55ZX27QcOHCApKYm8vD/bs51xxhn06tWLdevWsWDBAvv2rKwsHn30UQCmTJli3x4XF8fgwYNJTk7mxRdftG8vKCjgwQcfpLy8vMr+8ocj26FVnNlRSE2CIsAvGHL3mR2JiAB+ZgcgTpZ7wP1vSMHoJ5iT7PzzZG6BXmOdfx4wCsq7PnLNuURExDXyUt0/r4a2d12h19kLsVXyDTD632ftgnYDXHNOEfF6nTp14p577uGxxx7jn//8J4MGDQJgw4YNlJWV8fTTT9OmTRv7/pMnT+bgwYM8+eSTjB1r3FP4+PjwxBNPMGnSJB566CEWLVpEu3bt+Pnnnzl27BhXXHEF5557bpXzzpw5kwkTJjBnzhxWrFhBt27d2LRpE5mZmQwZMoQJEya47kPwFNm7oP2pZkchtYno5ZoFWkWkXhrR6+1y9xv9b91daHvn9xMszoWCdONG0RUiesDRHWCrqH9fERFxf+UlUHjE/Xv0BoSDrRyKsp17nvSNRj96V4nQqt4i4ngTJ05kzpw5DBgwgE2bNrFt2zYGDhzIO++8w5gxYxp0jP79+7Nw4UJGjx7N/v37WbduHR07duTRRx/lkUceqbZ/XFwcCxcuZOzYsWRlZbFq1Spat27NXXfdxcsvv4yfn8ZjVWGzQfZujeh1Z+qlL+I2lEG8Xe4+CPaAFVtD28O+b517jiNbjRtSH1/nnqdSQCvjZjsn2WhQLyIini3/oFHkdVUeaSqLBcJijYXjgiKdd57MzdBpiPOO/1eVLZH6XeO6c4pIizB8+HCGDx9e734rVqyo9bWePXsye/bsBp+zQ4cOPPnkkw3e/3iDBg0iISGhSe/1SAUZgAUCnZjTpHla94C0DWZHISJoRK/3O7YPQj2h0OuCfoIZv7t25BFo9JGIiDfJ3W88mPQEzu59X1ZsLPjmyimaEd01WkhEpCU6usPIN+6+7kxLFnUCpP2qBdlE3IAKvd4uzwMWjQEIioKSPCi1Ou8c6RuNm0RXat3NKDCLiIjnyz1g9JT3BMExcMyJhd6j26FVJ6N3rqtE9DBm5+gmUkSkZcnaCWFq2+DWKr8fuWKNABGpk9u2bli/fj1z5swhISGB0tJS+vXrx7Rp0xgypOFTBH/44Qfef/99tm7dSkFBAdHR0QwePJibb76Z9u2rjsgpKyvjlFNOoaSkpMZjxcTEsHr16mZdkynyUjyj0Gvx+XNUb5u+zjlHxkY46XrnHLs2rXtA2s+uPaeISA2UVx0gd7/79+etFNrOGHHrLBm/QYSL2xIF/bEgkvUwhHV07blFRMQ8lQ8XxX1ZLMZ9/OEN0Lqr2dGItGhuWehdvHgx9913HwEBAZx55plUVFSwYcMGpk2bxmOPPcb48ePrPcYbb7zBc889h4+PD/3796dNmzbs3LmT+fPn8/333/PBBx/Qo0cP+/6JiYmUlJTQuXNnBgyovppzRESEIy/RNUoLoTjHGC3rCUI7GKOPnFHoLS+BrATXj+iN6Alb57r2nCIif6G86iA5SZ7x8BSMnHpwjfOOn/ar69shWSwQ+ceq3ir0ioi0HEe2QfcLzY5C6hMZD4fWwwn1f68UEedxu0Jveno6M2fOpFWrVnz00UfEx8cDsGXLFqZMmcKsWbMYNmwYMTG132glJiby/PPPExISwttvv80pp5wCQGlpKU888QQfffQR999/P/Pnz7e/Z+fOnQCMHTuWm266yYlX6EK5eyG0o/svGlMp1InTTI/uMG4K/YKdc/zahHUwiu2FWRDsIQV3EfEqyqsOlJMI0SeZHUXDhHYw+vQ7S8ZGOOFK5x2/Nq3/6NPb/QLXn1tERMyRlQADbjY7CqlPm76w6yOzoxBp8dyuR++HH35ISUkJkydPtt+MAvTv359p06ZRXFxc5UayJl988QUVFRVMmTLFfjMK4O/vz/33309UVBS///47Bw8etL+2Y8cOAPr16+fgKzJRTrJnjXgJaQ/Ze5xz7IzfIKKXc45dF4uPcV4tyCYiJlFedaBjfzxA9QRhf7RDslU4/tgV5XBku+tbN4BR6E3f5PrzioiIOYpyoDTfc3rkt2SRvY3R1+U1t+0SEddwu0LvmjXGNMORI0dWe61yW309/fz9/enduzenn356ja916mT098nIyLBvrxx5dOKJJzYtcHd0LMlzVgcHo+9S1k7nHDt9o+unmFZq3R0yfzfn3CLS4imvOkhZERQe8ZwbTb9gCGxt9Op3tOw9ENwGAsIcf+z6RPRUThURaUmydkJ4V6N9j7g3/2BoFadBTiImc6tCr81mIzExER8fH7p3r16U69q1Kz4+PiQmJmKrY8Xl6dOn8+WXX3LWWWdVe62goIDExEQA+8IxNpuNnTt3Eh0dzfLlyxk3bhynnHIKZ555JnfeeSfJyckOukIXy95jjJL1FK3inDeiN30jRJow8giMvsDpG805t4i0aMqrDnRsn/Hw1FPaIYGRV7N2O/64Zs2SAeOarIehJM+c84uIiGsd3Wn87hfP0KYvpDpxjQARqZdbFXqPHTtGSUkJERERBAQEVHvdz8+PyMhICgsLsVqtTTrH3LlzKSgo4KSTTqJDhw4ApKSkkJ+fT2ZmJg8//DCBgYEMGjSIwMBAvvrqK8aNG8fGjR5YqMtJNKZueorQDmA95PipHrYKYwqJGVNMwThvxu/mnFtEWjTlVQc6luRZ7ZDAmCmT7YxC7yZo3c3xx20IH98/ZspsNef8IiLiWke3qdDrSaIHwP7vzY5CpEVzq8XYCgsLAQgOrn3BrKCgIACsVithYY2bMvjDDz/w+uuv4+Pjw7/+9S/79so+gjExMbz++uv06dMHgLKyMp577jnefvtt7rjjDr7//nsCAwOrHDM3N5fc3Nwq29LS0hoVl9PkJENvD1rx0tffGIGckwxtTnDccbP3GNNXA1s77piNEd7VKLqXFYNfYL27i4g4ivKqA+Uke1Y7JDD6CTujJVLaz9DNxNXPW/cw2jfEnm1eDCIi4hoZm6FL9fZT4qaiT4aNz0NFGfi4VblJpMVwq395Pj4NH2Bc1xTTmqxatYrp06dTXl7OXXfdxaBBg+yvjR49mlWrVuHj41Nl1XE/Pz/+9a9/sWHDBrZv386yZcu48MKqNzbvvfceL7/8cqNicQlbBeQdMEbJepJWccboI0cWetN+gSgHHq+x/AKN6zq6HWIGmheHiLQ4yqsOlLPHA3NqJziw3LHHtFUYs1ROvdOxx22MiG5qiSQi0lIc3QH9p5kdhTRUUITxYDx9I3QYVO/uIuJ4blXoDQkJAaC4uLjWfYqKiqrs2xCLFi1i5syZlJWVceutt3L99ddXed1isdinm/6Vj48PQ4cOZfv27Wzbtq3aDemkSZO47LLLqmxLS0tj4sSJDY7PKfIPQUA4+AWZG0djhXWE7ATHHjPtZ4jo4dhjNlZETyPZqdArIi6kvOpA2Xugo4eNIHVG7/vsRAhoZd4sGYDIeNg8x7zzi4iIaxRmQWk+hMTUv6+4j7YDjAfNKvSKmMKtCr1hYWGEhISQnZ1NWVkZfn5VwysrKyM7O5vAwEDCw8MbdMznn3+eOXPmYLFYuO+++5g8eXKj42rbti3w583w8cLDwxsci0vlJEFYrNlRNF5YLGTtcuwx036G+Csce8zGiugBab/qabSIuJTyqgMd2wvx48yOonFC20NBGpQVOe7Bb/qvENnbMcdqqtY9jAK2WiKJiHi3o9uMha0tFrMjkcZo90ef3kH3mx2JSIvkVouxWSwWevbsSXl5Ofv27av2+t69e6moqCA+Pr7eY9lsNh544AHmzJlDQEAA//3vf2u9Gf3www/5v//7P9avX1/j66mpqcCfq4l7hGMe2EsQ/lgh3IGF3ooyY8GWyPp/Zpwqsjek/2JuDCLS4iivOoitAnL3e17rBh8/CI01Hv46yuEN5i1uWskvEMLj4IgWZBMR8WqZWyHcpMU/pemiBxiDnEqbttCviDSPWxV6AQYPHgzAsmXLqr1WuW3o0KH1Huepp55i0aJFhIWF8dZbb3HBBRfUum9KSgrffPMNn332WbXXiouL+fbbbwE455xzGnQNbiEn0XMLvTmJjjve0Z0Q0g4CGrfAkMNF9DQK2OWl5sYhIi2O8qoD5B8G/1Dwb3h7C7fRqpPR+95R0n6GKJNH9AJE9FKfXhERb5e5BcI7mx2FNFZAGLTpC/u+MzsSkRbJ7Qq9Y8eOJTAwkLlz57Jt2zb79q1bt/Lmm28SFBTElVdead9+4MD/t3fn4U2V2QPHv+m+UaAthQIFWkqQlkXKItWRTQQ3RB0BFRR0RMGFGWecH+4KuOHoKAhuw4iKKAijMuiAUnYKFKQIlUKhlB1aSle6b/f3x9sUalto2iQ3Sc/nefIgyc3NuZfY03vu+573BEeOHOHChQvVz23evJnPPvsMNzc3Pv74YwYOHHjZz7z77rtxdXVl1apV1RefAGVlZcyePZvTp08zePBgoqKiLHikVpaV7JitG7wDofQClORaZn9pu/SfYgrg7q1GgmXu1zsSIUQzI3nVAnIOqxuRjsivvfqdwBIqy9VFt96zZEDdQE2TmTJCCOHUzu+FljKi1yGFDILD/9E7CiGaJbvq0QvQsWNHZsyYwaxZs7jnnnuqV/GOj4+nvLycOXPmEBgYWL395MmTOX36NG+88QZ33XUXAPPmzQMgMDCQpUuXsnTp0jo/a9q0aXTt2pWIiAieeeYZXn/9daZPn06vXr1o3749e/fuJS0tjfDwcN58800rH7mFZR6ALqP0jsJ8BhdoGQ7n90MHCyx6czYeWus8xdTENPoo+Gq9IxFCNCOSVy0g84DjFnr9O0PGXsvsK/MA+LTRf5YMQOtukLhQ7yiEEEJYi6apvCOtGxxTh+tg7aNqRquru97RCNGs2F2hF2DChAm0b9+ehQsXkpCQgIeHB9HR0UybNo2YmJjLvjcnJ4fERNWzLT09nVWrVtW77dixY+natSsADzzwAN26dWPhwoXs27eP5ORk2rdvz9SpU3nkkUfw9fW13AFaW2UF5B5R0zUdkX8YnN9nmULvmW1w9bSm78cSTAuy9fqT3pEIIZoZyatNlJnkuDm1ZTgc+cEy+zq7HQIiLbOvpmoVoUYqV5SCq4fe0QghhLC0C6fUz3evVnpHIhrDJ1jNKjq9BToN1zsaIZoVuyz0AgwbNoxhw4Zdcbv169fX+HurVq1ITm7cFMWYmJgrXvA6hLzj4BUAbt56R9I4LbvAuV+bvp/SC2pRulbdmr4vSwgwwv4v9I5CCNFMSV5tgswk6DxC7ygap2WYuvlriYLoqS0QeJVl4moqNy9VfD+fCG376R2NEEIISzu3xz5aBYnGC7kWDi6VQq8QNmZ3PXqFBWQdVFM1HVWrriqxN9XZqgVj7GWqSCsjZB2A8hK9IxFCCGGO7GTHXQzG1UP17M880PR9ndkGgT2bvh9Lad1d5XohhBDO51yC9Od1dF1GwqFvoKxA70iEaFak0OuMsg6An4NOMQWV0DOTQKts2n7OxEGAnYw8ArUgW4tQy/VKFEIIYX1lBVB0Hnza6R1J47UMVy2RmqIwAwrP2deN5ACjKj4LIYRwPum71QAg4bh82qgbxMnf6B2JEM2KFHqdkSP3EgTwbAnuvqoFRVOc2gKBdtJL0CTgKji7Q+8ohBBCNFTWITWa18VV70gaz79L01sindkOQT3t6zwERqpFV4UQQjifc7/aTws+0XhhN8Hej/SOQohmRQq9zigzyXGnmJq0ioCMJow+0iohbRcERlkuJkto3V1GHwkhhCPJOggtHD2nhlug0BsHAT0sEo7F+IdBwRkoztE7EiGEEJZUlAUl2WoxL+HYQmIg7xikJ+gdiRDNhhR6nVH2IWhhR1MrG6Nll6a1OMhMAk9/8GptsZAsIrCHjD4SQghHknVA9bh1ZK26QuZvTdvHqc32N0vGxVXdQE2TPr1CCOFUMn5VC7EZpFzh8FxcwTge4l7UOxIhmg35yelsCs9DZZn9FTjN1TJM9WVqrBMboE0fy8VjKS06qV6Phef1jkQIIURDnN/v+LNkvNuohUAL0hv3/rICNcsmyM5myYBadFVaIgkhhHM5t0fN2hDOoettakSvDHgSwiak0OtsMverpGgw6B1J0wT0UCtpa1rj3n/8Zwi+2qIhWYSLqxoRlSZJTgghHELmfsefJWMwVPWzbWRB9PRWVVB187ZsXJYQcJW0RBJCCGeT9ossxOZMXD2gx32w8W9NX3BdCHFFUuh1Nhl7VS8+R+cbAlo5XDhh/nsry9VCbMF9LR+XJQRcBafj9I5CCCHElZQXq75yjj6iF6pyz9bGvfd4rH3OkoGLC7LJhaMQQjiP9F/UDUbhPMJugdI8SJirdyRCOD03vQMQFpae4BzTXAwGtbr36W3gb+ZIqvQE8GkDXgHWia2pgnrC4e/0jqI2TVMX8799qqZLYYAO10Gvh6H9IL2jE0II28vcr3KQq4fekTRdUBQcWtG49x5fCz0fsmw8luIdBB4tIPOA/bWWuHAKEv8NKd9DeZG6id1jAkROBDcvvaMTQgj7VJwNBWfBv4vekQhLcnGFgTNg3ZMQOsyys281TbWvrKxQ+dXRZzcL0URS6HU2GXug96N6R2EZAT3g9Bboca957zsRC22utkpIFhEYBdteUSPF7OVCrygLYqeqBW263g4D/q5GR6X9AivvBONdMPgf4O6jd6RCCGE7536Flk4wSwZUTs3Yp3r1unk2/H1FWZCTot5vr4J6qd8X7KXQq2mQ9AVs/Ct0HAo9J4OHP1w4qW6m/vI23Po1tLXTmUdCCKGntF3Q+ipVGBTOxa8DRE+H/4yCsevUAChzleSqGUqn49SaPtnJUJCmirymxfv8QtQ1d+gwCL9VLYguRDMihV5nUlEGWYfUQmbOIDAK9n1s/vuO/Qydb7R8PJbi7qP+jdJ2Qcfr9Y5GJcZlQ9SF8oiPaxYAWndTzfN3vwfLb4C7f1Yjp4QQojk4t8d5cqq7j1oQ9Nwe82ZpnNoIQb3B1d1qoTVZYJRahLXPVL0jUUXerc9D8jIYPAdaRVx8rWUYdLhe3ZBeMQJu+wY636BfrEIIYY/O7oAAo95RCGsJHaoGFH0zDIa/D93HX34ErlYJ5/bC0f9B6g9wPlHdfA6MVNfSkRPVorOmAVTlJVCYDrmpcCYOdr2lfv/p/5T6LBcpgQnnJ99yZ5KdDL7t7HOxlMZo3Q1yjkBpPnj4New9Jbnqzt6A/7NubE0VGAWnNutf6C08r4q8Hf4AkffXvY1HCxj0vCr2/ucmuDsW3J3kOyaEEJeTngDGP+odheUE9lALl5lT6D38PbSNtlpIFtGmNxz4UhVZ9Z6uue1lOLQchv4TPFvWft1gUDejfYLhh/Ew5lvoONj2cQohhL06sw3aX6t3FMKaOg1XdYu4F9U1ZtQkaNtPPVdRombAnP9NrbtzapO6Wd12IHS7E2JeufzMJDdPtbaCfydVVL76CTVrddfbKkcPmwfht9joQIXQhyzG5kwy9jrX6qSuHtDaaN4q4an/UwvG2HuLgTa94OQGfWOoLIcfxqqkWl+R18TgAv3+om4ixE5VF9NCCOHMNE316L10RKajC+yhLpgaqrIcjv6gbgbaM78OKta8Y/rGkfyNas0w+B91F3kv1aYPXPMsrBoLeY1YeFYIIZyRpqlZj/bcLkhYRmAkjPgQIm6HI/+FNZNgyQA1i3TT03BiPbTuCsPmwk2fQd/HoN1A89pPgWoB0j5G3YDtNQVip8HKu6Ao0yqHJYQ9kBG9zsRZFmK7VHC0mqLReUTDtj+0Qv0gt3dBvWDnW+rCVK/pI1ueg7JC6Pmnhm1vcIF+f4P1T6qWGvYwRVYIIawl75i6uXWlgp0jadsP9sxveJ/eU1vAp50aYWPPDAY1qvfUZv1abWQmQexjcP0b4NWqYe9p2w+63aV64d+7zfyLVyGEcDa5R9W1kU8bvSMRtuDipkZv22IEt8EAIddAcF+1UOrnvVQLpY52fjNbiEaQEb3O5NweaOUki8aYdIhRq1U3ZARpebHqe9f+OquH1WSeLVWT+LRd+nz+yU2Q9Dlc85x5Cx24e0PMS6pInHXIevEJIYTezu11rtG8AF4B0LKL6rvbEIf/4xg3T0H1ET72sz6fXVEGP94LPR9UbafMYRynZiFtn2md2IQQwpGc3aFGegphLa4ecPU0iP4zrBwDez/ROyIhLE4Kvc5Cq1Qjelt31zsSy2rZVV1AZSZdedvjsap1RUNH0ugtOBqO/WT7zy29AKsnQfRfGjdSzb8T9JgAax5Qq5sKIYQzOrvD+Qq9AO0Gqb67V6JVqhut9t62waRdfzi+Vp/WQjtmg7sfhDWi55/BANFPwb5P9Lv5K4QQ9uLEerWWiRDWFnINDHsP4l+DrS9Ia0LhVKTQ6yyyDoKnP3gH6B2JZRkMajRRyvdX3jbxX9BxqLUjspy2/eDoatt/7pbn1J3ypozS6nYnVJSqKcBCCOGMTm+FICccVdThWtUL70oXNCfWg7sv+HexSVhN5tderbh9PtG2n5uRCL/Oh35PNX4hOO8ANbpo9SR1c1sIIZqrU5ugzdV6RyGaixahqth7+FtY97gUe4XTkEKvszizHQKc8IIUVEHy8LeX3+bCKdWOoKG9fO1BUC+10E9Jru0+M303JC+FPo82bT8GFzXdZftMyD9rmdiEEMJeVJRBxq/OmVdbdAJXdziXcPnt9rwP4bc2vniph7b9bDtTRquEtY9A5GTwDmravkKHg4c/JMy1SGhCCOFwCtKgMEO/XuuiefJqDYPfUjf4Y6dJsVc4BSn0OovTWyHAydo2mLTpA/lnVGuK+uz7BDrfoPrcOQo3T1XsPbHBNp9XWQE/T1GLr1licSH/zhB+C2z4S9P3JYQQ9uT8PvANAQ8/vSOxPIMBOg2HXz+ofxvTzdNON9ouLkuw9UyZxE+hLB+63tb0fRkM0PcJiH8d8k42fX9CCOFoTm1WC2uas36IEJbg4acWUz0TB5v+JsVe4fCk0OsszmyDICftZ+TiBhF3wM45db9eXqwKveEWuNCyteBoOPo/23xW4r/V6KMuoyy3zx4T4PRmtTK7EEI4izPbIaCH3lFYT9cxaqG1grS6X9/7oZoh4+5t27iaKrgvpO2EsgLrf1ZxNmx9Dvo+qWa5WEKLjqpovOlpy+xPCCEcyYkNENRT7yhEc+XuC9e9BkdWqd77QjgwKfQ6g+JsyD+tFi5zVl1vg+M/Q+6x2q/teFX1nHXEaT7tY+DISlWAtabibIh7Aa5+3LLTcN28odcUWP+ELMwmhHAep7dCwFV6R2E9ni1Vq4C62gTkpMKvH4LxbtvH1VTuvmoRH1u0b4h7CdpfC62Nlt3vVfeqUW2n4yy7XyGEsHcnN6qZnELoxdMfrp+jBpHtW6h3NEI0mhR6ncHZeDXyyJmnubj7ql6BG56qWRTNPKCmn/aZpl9sTdGiI3i0hDM7rPs5cS+ponLrbpbfd+gwwAC/LbL8voUQQg9ntjv/qCLjH2Hvx5CVfPE5TYO1j0L3ceDbTr/YmqL9tZC83LqfcX4/HPwKej5o+X27eUOvh2DdE9a/CSyEEPbiwmkoTINWEXpHIpo77wDVxmHrs/osnC6EBUih1xmcWK9GtDq7yAcg7xhs+j81ejRjH3w/Wj3v00bv6Bqvw3VweIX19p+ZBAeXQJQVLkhBjRDuM02NGC69YJ3PEEIIW8k7rvqutuiodyTW5ddBzcj4z02qD355sVqE5MIJxxzNa9LhD+rCrKLUOvvXNDWLpcdEy/S7r0unG0Arh6TF1tm/EELYm6Orod0A5x64JBxHi1CIeQn+NxHO7dU7GiHMJoVeZ3DsJ2jbX+8orM/VA659BY6tgfdbwLIhYBwH3e7QO7Km6fAHOLTCOk3fNQ3WPQlXTQCvVpbfv0lAd9UbMf51632GEELYwrG1Kqdaqu+qPQu/WRUV/x0BHwZD9iEY8o7qje+ovAOhZRc4sc46+09ZCXknoOto6+wf1Hev91TY8qxt+g0LIYTejvxXFXqFsBdBvVTbw+9uVTfEhXAgzeAqxskVpEPeUQh04kVjLuXZEm6YD6NXwE2fQZeRekfUdC3D1Z/nEiy/7yP/VaOgI8ZYft+/1/MhNQ24rj7KQgjhKI7+D9r21TsK24mcCHesgpH/hkEvqpWnHV37ayH5G8vvt7wYNj6lZrFYuxgeFKX6Dde3EK0QQjiL8hLVn7etFHqFnek0HLrcBN/dBmWFekcjRINJodfRnYiF4GjHHn3TGO4+1psyaWsGgxpRlfipZfdbVgTr/wxXP2ab74dPGzW6WlYLF0I4qsoKOLm+ecySuZSLq/oZbsnFOvXUaTikfGf50bC73gL/ztDORt+PXg/Dnvch76RtPk8IIfRweqv62WrN2YdCNFaPCeDTVrVxkN75wkFIodfRHV2tpswLx9ZllFrYpazIcvvc9Ra0Coe2/Sy3zysxjoMzcXBqq+0+UwghLCX9F/AOBu8gvSMRTeEdBIG9LLsoW95x2P0u9JlquX1eiW9b6DoGNv/ddp8phBC2dmSltG0Q9stggH5PQW4qbH1e72iEaBAp9DoyrRKOr7XdyBJhPb7tIOAqNQLJErJTIGEu9H7EMvtrKDcvNQJp/eNqZJwQQjiSo2ugbbTeUQhLCBsF+z62zL40DWIfg253qXxtS1eNh5Ob4HScbT9XCCFsobJC3ZQLHaJ3JELUz9UDYl6BpC/ht8/0jkaIK5JCryM7swPc/dTK2cLxdRkFez9s+n40DdZOgavusf0FKUDocMDVchfYQghhK4dWQMggvaMQlhAyCHJSIPNA0/d1+FvIOgjdxzd9X+Zy84beD0PsNLmBKoRwPqe3qHZ8/p31jkSIy/NqBX94FTb9TfWUFsKOSaHXkR38CkKH6h2FsJT210HOETi7s2n7SfoC8tOg2x8tE5e5DAbo+zjEvQSF5/WJQQghzJV5EIrOqVWWheNzcYOut0P8G03bT3E2rH8Sov+iRvToIXQ4uLhb5mawEELYkwNfQcfBekchRMP4d4ZrnodVYyEzSe9ohKiXFHodVWUFHFouhV5n4uquetxue6Xx+8g7Dhv/Cv3/phbX0UurrmoxnI1/1S8GIYQwR/JS6DhE35+dwrIi7oTUVZCT2vh9xE6DkBhoo+MNAIMB+j4B216G/LP6xSGEEJZUUQaH/6OuGYRwFG2jodcj8J+bIP+M3tEIUScp9DqqU5vBKwBahOodibCk8Fvg3G5I32P+eysr4H/3g3EstI6wfGzminoQTsTC8Vi9IxFCiMvTNDWqSG6eOhcPP+g6GuJfb9z7Dy5Vs2x6T7FsXI3RMgzCb4V1j+kdiRBCWMaR/6oRknq0mhOiKbrcCGE3w/IRUJSldzRC1CKFXkeV9IUaeSSci6sHXHUfrHtcLbZnjq3PQ3mhKvTaA3dv6Psk/DwFSvP1jkYIIep3LkH9/AzooXckwtK6/RFSvof0BPPel5kE656Aa55RC43agx4T4dwe1TNYCCEcXcJ76macEI6o+z0Q1Bu+vQlKL+gdjRA1SKHXERWeV7/kdxmldyTCGsJvg7IC2PtRw9+T/A0kLYZrXrCvacftY1ThZMNTekcihBD12/0ehN2ipsgL5+LZUi1mtmaymibcEMXZ8N3t0PsRaG20anhmcfWA/n+HtVOhIE3vaIQQovEyEiH7EHS4Xu9IhGgcgwH6PAI+7VQbh7ICvSMSopoUeh3Rvo9VUvRqrXckwhpcXKHfUxD3Ipz/7crbp/6oeghe+4paDdTe9H0cjq2GI6v0jkQIIWorSFPTR8Nv1TsSYS2dR4Gbj5r5ciXFObD8BggZCF1GWj00swVFqemiqyeZP/NHCCHsRcI8NbjFxU3vSIRoPIML9PsLeLaCFSOhJE/viIQApNDreCpKYc986HaH3pEIa2rZBfo8phJG7tH6tzvwFax+AK6bDa272Sw8s7j7wsBn4KeHmrYgjhBCWMOvH0LoMDXyUzgng0HloUPfwO53698u7wQsHw4tw6GXHfTlrU/k/VBwFna+qXckQghhvtxjcHgFhEvbBuEEDC5qIXSfYPhmGBRm6B2REMgtNEfz26fg3wla2cFiW8K6Ot8AZfnw1SAYNg+6j7s4rTj3mFp9++RGuH6OfSy+djlBPaH7vbDyDrhvB7j76BuPpqkCesY+yD0CRZlQXqTi8mmnzmfbfiphCyGcV3E27P0AhrytdyTC2rxaqXy56a9qtsyQdy7OgikvgQNLYMsM1dO3+3j7buPh4gaDXlT9/Nv2t4+Rx6UXIH23moqdfwZK88DFXY1y8u8CbXqpVk721F5KCKGPuBcgYox9zkQUojEMLtB3OuxfBEuugT+ugQA7av0kmh0p9DqSklyIewn+8JrekQhbiRijivpxL8CGv0BwHyg8pwq9YTfBiA/UiFlH0O1OyEmB/94Nd/7X9lO1ykvg2BrV3/rYGvVcayP4hoCHv7ogLclRC/Ac+BKyDoJ3GzWdu/t41W/Yni/8hRDm2z4T2l+rVv0Wzs+3LYz4GBIXwscdIPhqlYsyk6BVV7h2JgRG6h1lw/i0gUHPw4/3wdhYdSy2lpkEB5ep1ifZh9RN0hadwCsA3LxBq4ALJ+B4bNVN1SzoNAy63QURd4Knv+1jFkLoKyMRjq6Bmz/XOxIhLMtggJ4PgU9bWHod3PQ5hN+id1SimZJCryPZPhvaDbTfKfrCOoKi4MZP1AiZ3FR1AdUyHNy99Y7MPAYD9P+rulmx5kG46TPbjOzJTFJTsw8sgZZhqqgz9J/g2/7yhVutUhWmz+yA1ferC9bej0DPP6ligRDCsWUdgv1fwKiFekcibMnDT/XT6z0FMvcDLtBnGvi20zsy87XpA32fhP/cDOM32Wb0UGk+HPwKfv1AtY/oOASiJkFgFLi6X/69xdmQtlPNTlv3pLqRevXj0OE6uZEqRHNQWa4Wxox8wHEGqghhrvBb1QCCnx5SrZauexXcPPWOSjQzdlvo3bZtGx999BHJycmUlZURFRXFlClTGDx4cIP3cfToUd5//312795NTk4OnTp1Yty4cUyYMAEXl9rtidPT01mwYAFxcXFkZGQQEhLC7bffzpQpU/Dw8LDk4Znv1FbY/xnc+LG+cQh9GAzQooN6ODIXN4h5EeJehh/Gw61fqVXELU2rVIvU/fJPyPwNwm5Ro5/NuZA3uKgRv62NKklnJ8ORH2HnHIi4A/o/raaiCuEgJK9eoqIU/jcBekxQN89E8+Puq26eO7rQIar10LLBaqqotUb25h6FhLmw/3NVYO5xHwT3M++GrVdr6DJKPUpy4dhaWD1RtXfo/7SaPXOlYrEQVuIIOTIvL4+PP/6Y2NhYzp49S1BQECNHjuSJJ57Az8+vScdvEzvfVNcCXaU3r3ByQT1hxEdqXYAvo2HkQjU7VAgbMWiapukdxO99++23PPvss3h4eDBo0CAqKyuJj4+nrKyMWbNmMX78+Cvu4+DBg0yYMIH8/Hyio6MJDAwkPj6evLw8Ro8ezdtv1+zHl5aWxvjx40lLSyMyMpLQ0FASEhLIyMhg4MCBfPrpp7i7N+yXz1OnTnHDDTewbt06Onbs2KhzUENRJnzRR416kB8QwhlUlEL861BRAqNXWK6AXZqvLkJ3v6sKyN3uhI5DLXvhWJILqT9Aykpo01st8NPpBhmNJOya5NXf2fhXOLsTrpulbuoI4ehOboI982DEh6qnvyVoGpyOg1/egVObVMuormMsO6tFq4Sz8aqtUv6ZqtHWj0pbB2FTjpAj8/Pzue+++0hOTiYsLAyj0cj+/fs5deoUERERLF26lBYtWjToeC2eUxvi2Fr48V418ELWwBDNhabBiXWQ+C/ofCNcOwtahesdlWgG7K7Qm56ezogRI/D09OSrr77CaFTT0Pbt28eDDz5IWVkZa9eupW3b+n/J1DSNMWPGkJyczFtvvcWYMWMAyMrKYvLkySQnJzNv3jxGjRpV/Z6pU6eyYcMG/vznP/PYY48BUFhYyOOPP862bduYMWMGDz30UIOOwaLJsyQXVtyoRhX2erhp+xLCnmiVcPBrVTAd+g70mNj4Yun5/bD3I9Vbt00f1f8vqJd1i68VpSpxH1oBbj4woGo0kpuX9T5TiEaQvPo7CfNg11twwwLwbNm0fQlhT7IPqTZfnYarBQZ92jRuPyV5kLwU9sxXi6p1vUONwrV2y6jsQyqnpu1S7SCi/6xaLglhRY6SI1999VUWL17MuHHjmDlzJi4uLpSXl/Pcc8+xcuVKJk6cyIsvvtigY7Z5offcr7B8BMS8pAZJCNHclBWo/JbyPXQeqfKbrP8irMjuhrEsWbKE0tJSJk+eXJ1oAXr37s2UKVMoKSlh2bJll91HXFwcycnJDBw4sDrRAgQEBPDKK68AsHjx4urnU1NT2bhxI506dWLq1KnVz/v4+PDaa6/h6urKl19+aaEjNENBmkqKfqGqL6gQzsTgoqZNXzcL4t+AL/urBFhR2rD356SqUUaL+8Hy4eqmyIiP4NpX1C+R1k6crh4QdjOM/Jdq7bD3I/gkFDb9HbIPW/ezhTCD5NUqmqZar+x6SxXBpMgrnE1rI9z4EZQXw6KrYMtzapRsQ5Tmw+Hv4Yd7VC478BVEToRRn0K3O2yzLkBrI1zznMrlBemwOBr+cwscWaV6ewphBY6QI/Py8li+fDl+fn7MmDGjug2Em5sbL7/8Mi1btmTFihUUFhY26VxYxbG16no2+kkp8ormy91X3cC8eTF4B6n2YQvD1AyzY2uh9ILeEQonY3c9erds2QLAiBEjar02YsQI3n33XTZv3sz06dMbtQ/TVJrdu3eTn5+Pn58fW7duRdM0hg0bVqt/Uvv27YmMjCQxMZGUlBQiIiKacngNd2oLrBqrCkmRD8jdHuG8Aq6C4e/DmW2w4zX4+WHoNAJCBqmpLZ6t1QVeSQ7kHYP0BDi7Xd0ZbTcIrhpvfp9ASzK4QMhA9bhwElL/B1/FqAXzIieolcX9O+kTmxBIXgWgrBD+ezdcOA5D3pEFFYXzcveFq6dB19vh8ApY1EPl2Y5DVF95n2BwcVd9fS+cUguWpu2EjH0QGKkWRrvpM9VPVy++baHPo+qi+MR62PoC/PQn1ZLCOBY6/EG/nC+cjiPkyF27dlFcXMyIESNq9eL19fUlJiaGNWvWsGvXLoYMGWL2ObCKsiLYMQsSF6r1Odr00TsiIfTn4Qfdx4Lxbsg5DGd3wJYZkHUQ/DpAYA9o1Q38u6hc6N1G5WPPluDRUrU1crG7Ep6wQ3b1LdE0jZSUFFxcXAgPr927pEuXLri4uJCSkoKmaRjqKX6mpKQA1Lgre6mwsDAyMzM5cuQIffr0qd6+W7dudW4fHh5OYmIihw4dsl2hd/d76hfabnfZ5vOE0JOLK3S8Xj0KMyB9t+rZl/ojlF0Ag6u6ePVuo1Yx7TJSJUB7uwHSIlRdnPb6kzqGYz/Dtpng0xa63AgdB6vFf1qEWid2TVPTbAszoDgTirPUSOeSXCjLV8Wu8iKoLAOtQr3H4KZGJ7t5q18+3P3ULxOercE7UN119g6SlhQOSvJqlbSdkJsCwxfIYk+ieWjRQU0N7TMNMvZC5kFI+lLlA61C/dz3CgTfEHXBOegl24zaNYebF4Tfoh4XTsHJDRA7Vc14Cx2mWlSEDFKtmqy1onl5CRSdV4/iTHXTuSRXjb4qK1A5taJE5VUAXNTPGDfvqrzaAjz81YJzXgEXc6pnS/v7HaYZcpQc2ZDtAZKTk/Uv9BZlqQXEd78LAd1V33DvIH1jEsLeGAyXLPr9AFSUqQFDecdUjju5oSrX5FU9LlRdyxWo3FJ9rRak2jT5tleF4hYdwK8jtOio/rRWbrQGrVLl2gunoeAsFKZX5d+qa9qygqp8WzXDx9VdtU/0aKHOhU8QeAerxdf9qs6HhwMsUmkldlXozc3NpbS0lICAgDpXGnVzc6N169ZkZmZSUFBQ7+qi586dA6BNm7p7k5meP3/+fI3tg4Prbgz/++0vlZeXR15eXo3nTp8+Dagm+42WVQIGd0ir/ZlCODcDePdXj/oUoX7o2zNDVwjtCh0mQW4qpCdB8j/Vf1eWgn+YKlr7dVAJ2rOVKrK6eas7tQYXlfAqy1VSKy9SCa4sX11oFmdXPbIu/lmSq0ZqefqrC0sPX3DzBVcvlehdPdTrBteqBag0VRyuLKv6nFKoKIbyQlUULrsAJRfUfl3d1R1l7yB1seoVpBKqV0DVBWzri3ea3asKxh6+6rN00q5dO9zc7CrN2Zzk1Spp56HQDzJyG/d+IRxaGASGQWA9L2tAZgFQYMOYzOUFATerR1EWZCZCwo+QOxfyz6qLupadVbsz33YqN3n4g7sPuHiAadRkZYXKeeXFFy+aS/KgJFs9irPU/ouzVa6tKL2YU919qx4+4OKp8qqL+8WcjQGohMpK9RkVJRfzalmBao9Rlq9yakWpulD3Crjkpmqgyq1erdRFq2crtY27H3j6VeVzT90KxM6YUx0lR2ZkZDRo/5mZtX83ttq1KqjfFQvOQO5ROP8bnNkBmfshOBrCn4BWXSEXyJXrWSGuzB88eqtHfZNqNE3lFFM+Kb2gCsEZuXDyuPrv4ix1nVyUpa7FfNtVPULUw7tNzWs4z5aqWOpihYEQWoWKtSSv6vo1S900LcyAwjTVpqnwLOSnq5g9vNVNaM9WVdeyVbnP3QfcAi7J5xpUlENpGVwogtLzUL5XnY+Sqmvkokx17evTRg268g1R58En+GLOrc61/up62Yb51do51a6ydVFREQDe3vWPKPDyUqPKLpdsTfsxbVvfPkx9jMzd/lKff/458+fPr/N9EyZMqPP5hjsM/NDEfQgh7IupL2g+sL/qYQn+VQ+Tcqp+u27CPg117PdC1eN4E/ZrGzZdTdpOSV79nfefb9r7hRB2xgXoUPXfJ6oeluAJ/L7FS2HVoym8qh4m5UB61cO+OWNOdZQcafqzvjj1u1atS1vgNPCpFfYthGgYn6oHQAnqus2er928gUvzS07Vo7G8uPi7AdhjnrV2TrWrQu/vexRdjqZp9b7m6qpGkNU3vcaksrKyUdtfatKkSdx55501nistLeXkyZN06dKlet/CcaWlpTFhwgSWLFlCu3bt9A5HWIH8Gzsn+beUvCrsj/y8dX7yb+ycnPHf0lFypOTU2uTnTMPIeWo4OVcNI+epYa50nqx97uyq0Ovjo+46lJSU1LtNcXFxjW3rYrrbadq2vn34+vqatX1dn+nv74+/v3+t5+vq8yQcW7t27ZxuJIOoSf6NhbORvCrslfy8dX7ybyzsnaPkSMmp9ZOfMw0j56nh5Fw1jJynhtHrPDX8NqYN+Pn54ePjQ3Z2NuXl5bVeLy8vJzs7G09PzzoTlomp31Fdvf+gdp+jhm5fXx8lIYQQwh5JXhVCCCHq5ig50tz9CyGEaN7sqtBrMBiIiIigoqKCY8eO1Xr96NGjVFZW1ruiqYlpRVLTCqWX0jSN1NRUXF1d6dq16xW3Bzhy5AhQ/0qqQgghhD2SvCqEEELUzVFyZEO37969+2XjFEII0TzYVaEX4PrrrwcgNja21mum54YMGdKgfaxbt67WawkJCWRlZdGvX7/qhvqm7Tds2FCrt9GZM2c4cOAAHTp0ICIiwsyjEUIIIfQleVUIIYSomyPkyAEDBuDl5cX27dtrLbhWUFDA9u3b8fHxoV+/flc8XiGEEM7P7gq9d911F56envzrX//it99+q34+MTGRhQsX4uXlxX333Vf9/IkTJzhy5AgXLlyofm7gwIF069aNuLg4vvnmm+rns7KymDlzJgAPPvhg9fOhoaFcf/31pKamMnfu3OrnCwsLeeGFF6ioqKixvWhe/P39eeKJJy47ZUs4Nvk3Fs5M8qqwJ/Lz1vnJv7FwJI6QI318fLjjjjvIzc1l5syZ1W0mysvLmTVrFnl5eYwfP766kNwcyM+ZhpHz1HByrhpGzlPD6H2eDNrllhDVyZIlS5g1axbu7u5cc801AMTHx1NeXs6cOXMYM2ZM9bbDhw/n9OnTvPHGG9x1113Vz+/bt49JkyZRWFhInz59CA4OZufOneTm5jJu3Dhmz55d4zNPnjzJvffeS0ZGBkajkbCwMBISEsjIyGDw4MF8+OGHuLnZ1dp1QgghRINIXhVCCCHq5gg5Micnh3vuuYejR48SGhpKZGQkSUlJnDx5kqioKBYvXly92JsQQojmzS4LvaCmsixcuJCkpCQ8PDzo3r0706ZNIyYmpsZ29SVbUH2M5s2bR3x8PKWlpXTu3Jl77rmHsWPH4urqWuszz549y7x589i8eTMXLlwgNDSUMWPGMGnSJDw9Pa16vEIIIYQ1SV4VQggh6uYIOTInJ4f58+cTGxtLZmYmISEh3HjjjUydOpUWLVpY9oQIIYRwWHZb6BVCCCGEEEIIIYQQQgjRMDJnUoh6VFRU8PXXX/Pdd9+RmppKRUUFoaGh3HLLLTz88MMyGs3J5OTkMHr0aM6dO0dycrLe4QghhFORnNq8SE4VQljLtm3b+Oijj0hOTqasrIyoqCimTJnC4MGD9Q7NaszJob/88gsTJkyod1+jR4/m7bffrvHc0aNHef/999m9ezc5OTl06tSJcePGMWHCBFxc7G5Zp3p9//33zJgxo97Xp06dylNPPVX998TERBYsWEBiYiKFhYVERETwwAMPMHr06Drf7wznqXv37g3a7osvvqhuZXP27FmGDh1a77bR0dF8/fXXNZ5LT09nwYIFxMXFkZGRQUhICLfffjtTpkzBw8Oj0fHbwrfffsuzzz7LkiVL6N+/f63Xzf0emHsu8vLy+Pjjj4mNjeXs2bMEBQUxcuRInnjiiQb3YpdCrxB1qKio4LHHHmPjxo34+PjQp08f3Nzc2Lt3L/PmzWPTpk18/vnneHt76x2qsJCZM2dy7tw5vcMQQginIzm1+ZGcKoSwBlMBxsPDg0GDBlFZWUl8fDxTpkxh1qxZjB8/Xu8QLc7cHJqUlARA37596dixY639RUdH1/j7wYMHmTBhAvn5+URHR9OrVy/i4+N59dVX2bt3b62isD07cOAAANdddx0BAQG1Xu/Ro0f1f8fFxfHoo49SWVnJgAED8Pb2Zvv27Tz99NOkpKTUKAiD85yn+orYoBab3Lt3Ly1atCA0NLT6edN3qnv37hiNxlrvCwsLq/H3tLQ0xo8fT1paGpGRkURFRZGQkMC8efPYsWMHn376Ke7u7hY6Isvas2dPrZ7qlzL3e2DuucjPz2fixIkkJycTFhbG0KFD2b9/P4sWLWLLli0sXbq0Ya16NCFELV9//bVmNBq10aNHa2lpadXPZ2ZmauPHj9eMRqP29ttv6xihsKRVq1ZpRqOx+iGEEMJyJKc2L5JThRDWkJaWpvXs2VPr16+flpycXP383r17tejoaK1Xr141coyzMDeHPvPMM5rRaNR++eWXK+67srJSGz16tGY0GrXvv/++xr5Nz69Zs8ayB2RFEydO1IxG4xW/B0VFRVpMTIwWFRWlbd++vfr548ePa4MHD9aMRqOWmJhY/byznae6FBQUaKNGjdK6d++ubdy4scZr77//vmY0GrWVK1c2aF+PPvqoZjQatQULFtTY/+TJkzWj0aj9+9//tmjslrJmzRqtb9++1b+/7Nq1q8brjfkemHsuZs+erRmNRu2FF17QKioqNE3TtLKyMu3vf/+7ZjQatVmzZjXoWBxjfLkQNvbdd98B8Nxzz9G2bdvq5wMCAnjllVcA+PHHH/UITVhYeno6s2bNom/fvnUulCGEEKJpJKc2H5JThRDWsmTJEkpLS5k8eXKNUYW9e/dmypQplJSUsGzZMh0jtA5zc2hSUhIuLi41Rq/WJy4ujuTkZAYOHMiYMWPq3PfixYstcBS2cfDgQYKCgmqcp7qsXLmSzMxMRo8ezaBBg6qf79SpE08//TRQ87id7TzV5fXXX+fo0aNMnDiRIUOG1HjNNKI3KirqivtJTU1l48aNdOrUialTp1Y/7+Pjw2uvvYarqytffvmlZYNvorS0NP7v//6P6dOnU1lZSVBQUJ3bmfs9MPdc5OXlsXz5cvz8/JgxY0Z1Gwg3NzdefvllWrZsyYoVKygsLLziMUmhV4g6tG7dmvDwcHr37l3rtS5dugDIlEQn8fzzz1NaWsqcOXP0DkUIIZyS5NTmQ3KqEMJatmzZAsCIESNqvWZ6bvPmzTaNyRbMyaGlpaUcOXKE8PBwfHx8rrjvy53T6OhoAgMD2b17N/n5+U04Ats4efIkeXl5DSpGmo77hhtuqPXasGHDcHV1rfFdcqbzVJd9+/axYsUKQkJC+Otf/1rr9QMHDuDj41OrRUNdtm7diqZpDBs2rFa/2vbt2xMZGcnp06dJSUmxWPxN9d5777Fy5Up69uzJsmXLCA8Pr3M7c78H5p6LXbt2UVxczKBBg2r14vX19SUmJobi4mJ27dp1xWOSQq8Qdfjoo49YvXp1nQkyMTERgHbt2tk6LGFhX331FVu2bOHpp5+mc+fOeocjhBBOSXJq8yA5VQhhLZqmkZKSgouLS51FmC5duuDi4kJKSgqapukQofWYk0MPHz5MWVkZHTp04N133+Xmm2+md+/eDB8+nDlz5pCXl1fj/aYCU119V0H1Xq2srOTIkSOWPCSrMPXnDQwMZPbs2dx444306tWLUaNGsWDBAkpKSqq3PXz4MFD3cfv5+REcHExWVhbnz58HnOs81eX1119H0zSefvrpWt+znJwczpw5Q1hYGIsWLeL222+nT58+/OEPf+DFF18kPT29xvamc9WtW7c6P8v0/++hQ4escCSNEx4ezpw5c1i+fPllF6sz93tg7rlo6PYNWeRWCr1CmEHTNObOnQvAyJEjdY5GNMXx48f5xz/+QUxMzGVXphVCCGEdklOdh+RUIYQ15ebmUlpaSqtWrepcpd7NzY3WrVtTVFREQUGBDhHaXl051DTFftOmTXzxxReEhobSr18/8vLy+PTTTxk7dixZWVnV+zCNBG7Tpk2dn2F63lTwtGemY//222/54YcfiIiIoE+fPqSnpzNv3jwmTZpEcXExABkZGUDDj9uZztPvbd68mT179hAREcGtt95a63VTAX3//v28++67BAYGcs0111BRUcE333zDH//4R1JTU6u3N52r4ODgOj/PHs/VI488wh133FFr1O3vmfs9MPdcNPR7mZmZedk4QQq9Qpjln//8J7t27SIoKIiHH35Y73BEI1VUVFT3vXn99dcxGAx6hySEEM2O5FTnIDlVCGFtRUVFAHh7e9e7jZeXF0CzKfTWlUNNRbmBAweybt06PvnkExYtWsTPP/9MTEwMx44d4+WXX67eh+m8ms7d75meb0hPUL2Zjv3mm29mw4YNfPjhh3z55Zf88MMPXHXVVezZs4f33nsPMP+4nek8/d7nn38OqGJnXfnbVEA3Go2sXr2aRYsW8cknn7Bu3Tpuu+02MjIyqvsag3OfK2t/b0x/1vdzzpxzJ4VeIRpo7ty5fPLJJ3h4ePDee+8REBCgd0iikRYuXMiePXt45plnaN++vd7hCCFEsyM51XlIThVCWNuVRtpdytlaN9Slvhz67LPPsmbNGj788MMaeTUgIIA5c+bg4+PD2rVrq0camhbNvNINusrKSisdieXMmzePH3/8kbfeeqtG+4GOHTvy5ptvYjAYWLZsGWVlZbi6umIwGBp83M50ni6VmppKXFwc7du357bbbqtzm8mTJxMbG8vnn39OaGho9fM+Pj68+uqrtG3blv379/Prr78CznuuwPxjs/b2lyOFXiGuoLy8nJdeeokPPvgAT09P5s+fz4ABA/QOSzTSwYMHef/99xk6dChjx47VOxwhhGhWJKc6F8mpQghbMBXuLu2z+numafkNWYTMUV0ph7q7uxMWFlZrISeAtm3bEhkZiaZp1aM0TSMHTefu90zP+/r6WvpQLM7T05OIiIg6W3v06NGDdu3aUVhYyLFjx/D29kbTtHq/T78/bmc6T5das2YNmqYxevTo6iLj77m6uhIaGlrnDXlvb28GDRoEqNYOpufgyufKEf8/Nfd7YO65sOS5c7viFkI0YwUFBfz5z39my5Yt+Pv788EHH8gFqYN79913KSsro6ysrMY0E7h4d8z0/HPPPSejzIQQwkIkpzofyalCCFvw8/PDx8eH7OxsysvLcXOrWcYoLy8nOzsbT09P/P39dYrSuiyRQ4OCgoCLU8qDg4M5cOAA58+fp2vXrrW2v1LPUEcSFBTE2bNnKSoqIjg4mLy8PDIyMujYsWOtbX9/3M56nmJjYwHq7M3bUHV9p6D+Hrymc1Vf31p7Zu73wNxz0dDtG/I9kxG9QtQjNzeX+++/ny1bthASEsKSJUvkgtQJmHraxMXFsWrVqhoP01Qv098dsXeQEELYI8mpzklyqhDCFgwGAxEREVRUVHDs2LFarx89epTKykqMRqPtg7OBhubQ2bNn8/jjj9e7WNOpU6cAaNeuHQDdunUDICUlpda2mqaRmpqKq6trnUUte5Kfn8+LL77I9OnTKS8vr3ObS4/ddNxHjhypc1/nzp0jICCguojpLOfpUpmZmezfv58uXbrQvXv3erebP38+06dPJzk5uc7XzflOwcVz7oj/r5r7PTD3XDR0+8v9e5lIoVeIOpSWlvLII4+wf/9+IiIiWLp0qUP+MBK1LV68mOTk5Dofpikrpr/XdYdXCCGEeSSnOi/JqUIIW7n++uuBi6MQL2V6bsiQITaNyRbMyaF79uwhNjaW9evX13rt0KFDHDhwgFatWhEVFQVcPKfr1q2rtX1CQgJZWVn069evzlYQ9sTX15e1a9fy008/sWvXrlqvb9q0iezsbIxGI8HBwZf9Lq1fv56Kiooa3yVnOU+X2rdvHwB9+/a97HbJycn89NNPrF69utZrmZmZxMXF4e7uzjXXXANcPFcbNmyo1Uv2zJkzHDhwgA4dOhAREWGJw7Apc78H5p6LAQMG4OXlxfbt22vdHC8oKGD79u34+PjQr1+/K8YqhV4h6jBv3jx+/fVXQkJCWLx4cfUdKiGEEEKYR3KqEEKIprrrrrvw9PTkX//6F7/99lv184mJiSxcuBAvLy/uu+8+HSO0DnNy6Pjx4wHVVufS0apZWVk8++yzVFRU8PDDD1f3sR04cCDdunUjLi6Ob775psb2M2fOBODBBx+0xmFZlMFgYNy4cYAa1Zyenl792okTJ5g1axYA06ZNA2DUqFEEBgby3XffsWnTpuptT548yTvvvIPBYGDy5MnVzzvLebqU6f+hnj17XnY703dq0aJF7N69u/r5goICnnvuOfLz87n77rur2wmEhoZy/fXXk5qayty5c6u3Lyws5IUXXqCiosLhzpWJud8Dc8+Fj48Pd9xxB7m5ucycObN6dHp5eTmzZs0iLy+P8ePHN+iGgkFrDstSCmGG7Oxshg4dSnFxMVFRUYSHh9e77dtvv23DyIS1RUZGUlFRUe/UFCGEEOaRnNp8SU4VQljakiVLmDVrVo0RhPHx8ZSXlzNnzhzGjBmjc4SWZW4Orays5C9/+Qs//fQT7u7u9O/fH29vb+Lj4ykoKODmm2/mnXfeqbHw1r59+5g0aRKFhYX06dOH4OBgdu7cSW5uLuPGjWP27Nm2ONQmKy4u5qGHHmL37t01Rj3Gx8dTWlrKQw89xIwZM6q3X7duHdOnT6eiooIBAwbg6+vLjh07KCoq4qmnnmLq1Kk19u8s58lkxowZfP/998ydO5ebbrrpstu++eabLFq0CBcXF6Kjo2ndujW//PIL2dnZ9O/fn4ULF1YvJAaqYH7vvfeSkZGB0WgkLCyMhIQEMjIyGDx4MB9++GGtPtv25P7772fnzp0sWbKE/v3713jN3O+BueciJyeHe+65h6NHjxIaGkpkZCRJSUmcPHmSqKgoFi9e3KBF/6TQK8Tv/Pzzzzz55JMN2lYuXpyLXJQKIYRlSU5tviSnCiGsYcOGDSxcuJCkpCQ8PDzo3r0706ZNIyYmRu/QLK4xOVTTNJYtW8by5ctJSUnBxcWFiIgIxo0bx913343BYKj13pSUFObNm1ddFO3cuTP33HMPY8eOrVEUtnelpaV89tlnrFq1imPHjuHh4UFkZCT3338/I0eOrLV9QkICCxYsYO/evWiaRkREBJMnT+bmm2+uc//Ocp4ApkyZwubNm1m0aBHXXnvtFbdfvXo1X375JUlJSVRWVtKpUyfGjBnDpEmTcHd3r7X92bNnmTdvHps3b+bChQuEhoZWb+/p6WmNQ7KYyxV6wfzvgbnnIicnh/nz5xMbG0tmZiYhISHceOONTJ06lRYtWjToGKTQK4QQQgghhBBCCCGEEA5OevQKIYQQQgghhBBCCCGEg5NCrxBCCCGEEEIIIYQQQjg4KfQKIYQQQgghhBBCCCGEg5NCrxBCCCGEEEIIIYQQQjg4KfQKIYQQQgghhBBCCCGEg5NCrxBCCCGEEEIIIYQQQjg4KfQKIYQQQgghhBBCCCGEg5NCrxBCCCGEEEIIIYQQQjg4KfQKIYQQQgghhBBCCCGEg5NCrxBCCCGEEEIIIYQQQji4/wcSZfIL9ay2RAAAAABJRU5ErkJggg==\n", + "image/png": 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", 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\n", 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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rlStX6l69eumsrCx98cUXl9s2ZcqUwLELFiyo9BojR44M6rM0dOjQwLlfeumlSvfZvn277tGjh87KytIPPfRQpa9969atun///jorK0uPHDmy3LZly5YFrnHXXXdVOD4nJ0eff/75gX2GDh1abnvZz+qKFSsCz7vdbj1kyBCdlZWl+/btq1evXl0hrv379+vhw4cHvgfLfr60Lp+rHnzwQW2aZoXj+/Xrp7OysvSgQYPKbSt9nx977LGVfjds2LBBd+/eXWdlZenHHnuswvaj1blBZd26dRUeq1ev5ocffuA///kPF1xwQWCIxSWXXEL37t0rPU9SUhIXXHBBpdvefvttABISEvjXv/5V6Z3xmWeeyRVXXAH42zHXrl1b6bn69+/PqFGjKjx/1llnce655wLw9ddfl6saCeX1hw8fTlpaWqXbghXK+M4++2z69+9f4fmLLroo0L64ceNGy+KzWlV/v7y8PLTWNGrUiEsvvbRcNWBZ55xzTuDfe/furfI6d999d4X2epvNxiWXXBL4uWzHzAMHDgT+XVn7d1JSEuPGjePGG2/k2muvLbdt9+7dtGjRgszMTC699NJK4zn99NMDJcqj4/74448xTRObzcaTTz5Z6d/3L3/5S6C3fmm1a7A8Hk+g9N6jRw/uvPPOSvf761//Giitvffee3i93kr3u+2222jWrFmF58v+Luryu7bZbNx+++3ccMMN/OlPfwrUJtbVscceyx//+McKz/fs2TPwHbZ27dpyn5Wzzz478Hf68ssvKxy7Z8+eQJVtVd+3tXHllVdW+vykSZPweDw0a9asyhJ9+/btue222wB/beuGDRvKHQ/QqFEjHn300QrHN27cuNKml5p899137Nq1C/DXqvbo0aPCPk2bNg3UiHm93iqHwyYkJJR7P5c9fvDgwYD/M1K2P9b+/fsB/+ewsmaYrl27ctttt3H77bdz2mmn1fh66pyoL7744gqPSy+9lJtuuol///vf7NmzB/AnwocffrjK83Tv3r3SHr0ejyfwxhoyZEi11WJl3zxVfRlU9QYrfS2l11y4cGFYrt+rV68qjw9GqOMbOHBgpc8nJibSpEkTgHLt+5GOz2pV/f0aNWrEJ598wtKlS/m///u/Ko8v+/rdbnel+zgcjkB15tHatGkT+HfZv0PHjh0D/37ggQdYvnx5hWPPP/987rnnngqfgXvuuYd58+Yxe/ZsbDZbpddVSgW+YI6Oe968eQD07duX1q1bV3p8q1at+PTTT1m2bBm33nprpfvU1sqVKwM30pdeemmVw/XsdjuXXXYZADk5OVXe/FX1nq/qd92uXbvA7+mf//wn8+bNqzAe/JRTTuG+++7j2muvJTExsZavrLwRI0ZU2RmtbNty6XcVQGpqaqDD33fffVehr8SXX36J1hqn08nw4cODiqtly5aV3tgA/PDDD4D/vVC2qeBoZefSKJ2O2jTNwPHDhg2r0MZbqlevXnVueiv9PrHZbNWOs+7Tp0/g3GV/r2Udd9xxVcZW1XumU6dOgP+G/s9//jPbt2+vcOzYsWO58847A8m+OiFpo7bb7aSkpNCiRQt69+7NBRdcUGkprayqSiC7du2iqKgIqLp9u1S3bt1wOBx4PJ4qp+Ds3bt3tceXKp1gI9TXr+p1BivU8VX1RQsEvnCqKplEIj6r1ebvV/rlevjwYX777Te2bdvGr7/+yqpVq1i2bFlgP31kyNnRmjRpUuWXXNkv/bLJoXv37gwYMIDFixezfv16rr76apo3b86gQYMCj5o6NZXG7Xa72bFjB9u2bWPTpk2sXbuWpUuXBm66y3aG0VoHvnRq6rF6dA/+YG3evDnw75reU2W3b9q0qdIbrVatWlV6bFW/64yMDC666CKmTZvGrl27GDNmDGlpaZx66qkMGjSI0047rdrOa7VVXTtlx44dcblclJSUVBjyeuGFFzJ9+nRycnJYsGBBuS/+0lL24MGDg+7jUdVnwOv1Bmrbpk+fXuu/944dOwB/R7zSjpBV1byW6tmzZ7mSeE1Kv086duxY4+egV69ebNiwgc2bN2OaZoWbpareL0C5PkVlvycvvPBCXn31VQ4ePMg333zDN998Q1ZWFqeeeiqnnXYaJ5xwQoUOntWpc6IO1cxBVf3yyvZgLS3RVcVms5GWlsb+/fsr9HwtVdWwFijfI/3gwYNhuX4oeniXFer4qrv7D2aikVDHZ7Wa/n5bt25l4sSJzJkzJ1DdVVZthuvU9m9wdKL/97//zQMPPMCMGTMA/xff1KlTmTp1Kg6Hg1NOOYXrrruuQg9m8PfILR35sGHDhkpnDFNKVbhmdnZ2YN9QN+lUJTs7O/Dvmt5TZbdXttaA0+msthah1NGv+5FHHsFmszF16lRM0yQnJ4cvvviCL774AsMwOOGEE7j66qsDzWnBqC7ZG4ZBampqpZ+V0047jbS0NHJycvjyyy8DiXrz5s2sWbMGqF+1d1Wfgdzc3HI3cbVVOsKkbJNCTe+lmv7uRyv9HdXmBqr03KZpkpeXV+GGJpjPZ0ZGBm+88QZ/+ctfAjcYGzZsYMOGDbzxxhukpqZy5plnctNNN1W6OM3Rom42iapKHVUpfaNUlVSq+lAefa3SavhQXz/Us2qFOr5Qi/b46qq6uL777jv+7//+r1zVcNOmTencuTPHHnss/fr1w263B9rnQi09PZ2XX36ZDRs28OWXXzJ79uzAjbTH42HOnDnMmTOHG2+8kXvuuSdw3IEDBxg9enS5EkpCQgKdO3ema9eu9OrVi1NPPZXRo0dXGI5U0xSgVgsmcdSGy+Xi8ccf5w9/+ANfffUVM2fOZNWqVZimiWma/Pjjj/z44498++23PPfcc0GNp67pM1D62Tq69sXpdHL22WfzwQcfMHPmTEpKSnC5XIHSdKNGjWocDx+MsiXIyy67rEJfiKqUJsKy1fR1/d6oSV3OV/Y9HcrvoW7duvHZZ5+xaNEivv76a+bOnRuopcrNzWXq1Kl89tlnPPfcc5x11lnVnivqEnXZu5my4wkr4/V6A3dnVVXrHD58uMq7sbLnL21LDPX1Q03iiw579+7lr3/9K263m0aNGnHnnXdy1llnVagmnD17dthjycrKIisri7vuuov9+/czf/58ZsyYwaxZs/D5fLzxxhucddZZ9OnTB/CXDkuTdOmEEt27d69wU1tQUFDhWmUnrYjU6nhHv6eqa64p+54LR4m/bdu2/OEPf+APf/gDOTk5LFy4kFmzZjF9+nRKSkr4+uuvOf3004MqwVb3+yw7N0Vlr+vCCy/kgw8+ID8/n/nz5zNs2DC+/fZbwN9fqLr242CV/bvYbLY6z59f9nWUrTWpTF1r3ErPXdN54ff3jN1ur3SCqPpQSjFw4MBAv4hNmzbx/fff8/XXX/PTTz/h8Xh44IEHOPXUU6sdmhuWKUTro23btoGlDFetWlXtvuvWrQvclVU1jrq6qvrSaiH4vT0t1NcPNYkvOnz++eeBziMPPvhgYOzn0Xbv3h2W65umybZt2yp0ImvWrBkXX3wx48eP55lnngk8X9oB7MCBA8ycOROAk08+mX/84x/07Nmz0iR99KyC4C9ZlibKX375pdoYb775Zi644IJ6zyZXdtxtTe+pspOhtG/fvl7XLaW1ZteuXSxatKjc82lpaZxzzjn861//Ktdj+Pvvvw/qOtWNrtiwYUPgs1JZQuzXr1+gR/qcOXPYtWtX4GasPtXe1XG5XIHOVEdPQnO0nTt38t///pfPP/880MehXbt2geRU03up7KI6tVH6ntmyZUuFyU6OVvqeatOmTbU1sHXhdrv55ZdfKvQn6Ny5M6NGjWLKlClcffXVwO+TQFUn6hK13W4P9ICdPXt2oO24MmUngDjppJMq3aeyIQulSifkSElJCVwz1NevTjDVLJGMLxjRHl+obNu2LfDv6jrCfP3114F/16VTXk3uvfdezjzzTEaPHk1xcXGl+5TtaVs6ZGjHjh2B6uHqOoNNnz49UCV4dHV36SxRy5cvr3LIWW5uLosWLWLDhg3lhisF857v2bNnoNft1KlTq6zW9Pl8gZnwSqckDoWXXnqJoUOHcsMNNwQ6Qh2td+/egdqGYIdnlX2vHG3atGmA//dX1bS+pQm5tMkD/B3Bgp3VqzZKZ9tbt25dtcn6zTff5IUXXuDPf/5zoIBks9kCx8+ZM6fSGhzwJ9u6TnhS+jvyer1VLn0L8PPPPweSaSi/g4YPH85FF11U6TS4pcr2HalqREipqEvUQKCto7i4OFC9eLSZM2fy4YcfAv4PSVW9uz/++ONKu91/9dVXTJ8+HYArrriiXA+8UF6/OmWro0p7StdGpOILVrTHFwplOyLOnz+/0n3Gjx8fGIoCVBg6Ux+lYy+Li4sZP358pfuU/eIvTVplqxsXL15caZvz6tWr+cc//hH4+ei4S0sCXq+Xhx56qNLX9eSTTwaeLzsWvOx7vrbD/pxOZ2DqzNWrV1f5ep955pnAl+5VV10VsuresuNcn3322Ur3mT9/fqAZJ9gbhHnz5gUSclmLFi3i/fffD8RSVU1B6RCuvXv3BqajPe+88+o9/3h1rrnmmsDN19/+9rdKb8xLZ7oD/7K1ZeefHzlyJOAvVT7++OMVbsKKi4t54IEH6tyGfcYZZwQ6Er/wwguVltgPHToUmCfcbrcH3tehUHqTvGTJEubOnVvpPqWfT5vNVmOP+ahrowb/+Nvzzz+fL774gh9++IERI0Zwww03kJWVRV5eHt9++y0fffQRpmmSlJRU7kvlaD6fj1tuuYUbbriBIUOG4PV6+fbbbwNvnJYtW1aYZCCU169O2bbzd955h/T0dBwOR41DFSIVX7CiPb5QOOOMM5gwYQLg//I+ePAgJ598Mk6nky1btjB16tQK1dI1VcHVxVlnnUXnzp3ZtGkTr732Gps2beLCCy+kZcuW5OTk8MMPPwTe4+3btw90VunQoQNZWVls2LCBNWvWcNNNN3HNNdfQokULDhw4wJw5c/jkk0/K3VwdHXevXr246qqr+OCDD5gzZw5XXnklN9xwAx06dGDXrl18+OGHgXGsI0aMKDdEqux7fvz48Zxzzjk0btyYdu3aVft6x44dy6xZs9i6dSvjx49n1apVXHbZZWRmZgauWXrDlJWVxR133FGP3255xx9/PCeffDILFizgq6++Yt++fVx55ZW0bduW/Px8li5dyjvvvAP4b4TKzsddFwkJCdx3332sXLmS4cOH43A4mDNnDm+99RZer5eUlBQefPDBKo/v2LEjvXr1YuXKlYEan2Dm9q6Lbt26ccstt/DKK6+wadMmLrroIm688UaOP/54CgsLmT9/fmBSFPA3E5W9gRo4cCBnn30233zzTWD428iRI8nMzGTz5s28/vrrdRqWVcrpdPLEE09wyy23kJeXx1VXXRUYAeF0OgNTiO7btw/wv79qu0BGbdx000188sknlJSUMHbsWK666ipOPvlk0tPT2bNnD5988kmg/8qFF15IZmZmteeLykQN8NRTT6GU4vPPP+fXX3+tdP7c1q1b8+KLL5ab/OFoo0eP5r333uO1117jtddeK7etU6dOVU6iH6rrV6dNmzZ06dKFjRs3Mn/+fObPn0/Lli0rTE5fmUjEVx/RHl999ezZk1tuuYVXX30Vt9vNq6++yquvvlpuH6fTyd/+9jeeffZZCgoKyo0Hri+Hw8H48eMZPXo0e/bsCSyicrQ2bdrw6quvlqsxevLJJ7nhhhsoKChg4cKFldY4DRs2jOTkZD7//PPAikNlJyh64IEHKCkp4eOPP2bNmjXl5jkudcYZZ/Doo4+We27gwIGB8cCvv/46r7/+OhdddBH//Oc/q329ycnJvP3229x6662sW7eOuXPnVlpSGThwIM8991yVqxkF61//+legp/zSpUsrLNAA/puQ8ePHV7kQUU0eeeQR/v73v/P+++8HStCl0tPTeeWVVyqdGa2sCy+8MFAF3aVLlzp38ArGXXfdhcfj4c0332T//v2V3ng7HA4eeOABzjjjjArbnnrqKQ4dOsSPP/7IokWLKvQFOPXUU/F4PCxevLhOtSSnnXYazz33HPfddx9FRUWVfkbtdjt33XVXudXwQqF9+/b861//4i9/+QslJSW88847gZu5sk455RQeeeSRGs8XtYna6XTyzDPPcOmll/Lhhx+yfPlyDh48SOPGjenQoQPnn38+F154YY299M4++2wuuOAC/vOf/7B06VK8Xi8dO3bkoosu4vLLLw90fArX9aujlOKVV17hiSeeCCze4HA4AsMrIvH7CZdojy8U7r77bnr37h1YmjI/P5/ExETatGnDgAEDuPbaa2nXrh0//PBDIJE++OCDdZrooDqdOnXiiy++YPLkycyePZtNmzZRWFhIo0aN6Ny5M2eeeSZXXXVVhfd4z549+fTTT3n11VeZP38++/btwzAMmjZtSvfu3bn00ksZOnQo3333XaDT3Lx588pVWTocDp5++mkuuOACpkyZwooVK8jOziYpKYnjjjuOyy67rNIxxa1ateKVV17hhRdeYMOGDSilqmxjP1pmZiZTp07l448/5quvvuKXX34hNzeX5s2bc8wxx3DZZZcxdOjQsFT1Nm3alKlTp/Lhhx8yffp0fv31V/Ly8khOTqZt27acfvrpjBw5sl6jF3r16sW0adMYP348CxcuJDc3l1atWnH66adz880312pM8FlnncXjjz8OhK8T2dGUUtxzzz2cf/75vP/++/z444/s3bsXrTUtW7bkpJNO4vrrr69yvHBSUhJvv/02H374IZ988gmbNm3C7XbTsWNHLr/8cq666ipuuummwL51ce6559KvXz/eeecd5s2bF+hj0Lp1awYOHBhYQCQchg8fTrdu3Xj33XdZvHgxO3bswOPxkJ6eTs+ePbnwwgs5++yza3UupUM9gC0K1GaNUiGEiDWzZ8/m1ltvRSnFzJkzqx3K1pBccsklrF27lsGDB1coFceDqC1RCyGEqJvSkSwnnnhig0jS//jHP8jOzqZ///5Vzsl96NChwNC1us75HSuiste3EEKIulm6dCnfffcdUP1iRNGkoKCAjz/+mL///e+Bjl1He/bZZwOdG8Mxw1pDICVqIYRooN566y3Wrl2L2+1mzpw5eDweOnToUOu2T6tdcsklfPjhhxQWFnLVVVdx/fXXBxbr2bFjB9OmTQt0Ljv//PPp16+fxRFbQxK1EEI0UMXFxXz66aeBnx0OB48//nilSwhHoz59+nDPPffwz3/+k507dwbWhz7aWWedVe3kIbGuYfw1hRBCVNCjRw+aN29Obm4uxx57LHfddVeVa5tHq9GjRzNw4EAmT57M4sWLAwtXNG/enGOPPZYRI0YwZMgQa4O0WEz2+hZCCCFihXQmE0IIIaKYJGohhBAiikmiFkIIIaKYJGohhBAiikmiFkIIIaKYJGohhBAiikmiFkIIIaKYJGohhBAiikmiFkIIIaKYJGohhBAiikmiFkIIIaKYJGohhBAiikmiFkIIIaKYJGohhBAiikmiFkIIIaKYJGohhBAiikmiFkIIIaKYJGohhBAiikmiFkIIIaKYJGohhBAiikmiFkIIIaKYJGohhBAiikmiFkIIIaKYJGohhBAiikmiFkIIIaKYJGohhBAiikmiFkIIIaKYJGpRKdNnUpxXjOkzrQ5FCCHimt3qAER08hZ72TTnV3J359J5cBeadGqKz+3DsBuYXhN7gh3DJvd5QggRbkprra0OQkQX02uSve0Qb10+EXeBGwBnspMW3VuS1roxyjBIb59Bs6zmNOvanEaZjfAUebA5bDgSHBZHL4QQsUUStaigJL+EGU9OZ+W0n2q1v81hI+uMYzjhugG0Pr4NxXnFuJJdaK3xlnjRponNaZckLoQQQZBELSrwFHtwJDjwur1smLmeGU98Q8GBggr7NctqTu/L+nDMmd1ITE/C9PhAKQy7wYr3l7LwtfkkNE6kUYtUWvVuRY/ze5LePgOf24cz2YlSyoJXJ4QQDYskalEtd6EbZSj2rNnN1gWb2TJ/M3vW7qZ1n7Zc/t+rsLsqb6v2FHv45es1fHHvZ+WeT0xLpMPJneh1SW8yj2t1pJStcSQ6I/SKhBCiYZFELWrN6/biK/HiSHLi8/iwu+zVloo9xR7Gn/Y8xbnFVe6T1jadzqd1oV3/9mT2aEmjzFS8RR6UoXAkSalbCCEkUYuwKckvYfqjX7Hm81W1PsbmsNG0azNaHJtJp1M702FgR2xHbgikjVsIEY8kUYuw0VqzcfYGPrp9Sr3Ok9GxCV2GdOW4i3rRtHMzfw9zp/QwF0LEB0nUIqw8RW5eOOkZvCXekJzPmeykebdMWvZoSZsT2tGqZyuSm6bgKZbhYUKI2CSJWoSVu9DNt3//mlUf/xy2azgSHTTvlklmj0w6n9aFlj1b40hwYPpMbA4bNocNZUhbtxCiYZKZyURYGXaDhMaJYb2Gp8jDzhXb2bliO8veWwJA49ZptOnblqT0JJKbpdC4VWMS0hJJzkgmuWkKiWmJmD4T0+PDkeSUWdaEEFFLErUIK6UUhgWl2cM7czi8M6fafVKaN6JV79a0O6EdHQZ2IqNjE7wlXgy7IVXoQoioIYlahJVhM3AkRmfSy9+Xx4bvfmHDd78A/tJ/i26ZtBvQnh7n96RJ56b4PD5cyS6LIxVCxDNJ1CKslKFIbpJidRi1YnpNdq/exe7Vu1j8+kIS0xLpMqQrHU7pTMseLUlrk47P40WbWqrLhRARI53JRNht/n4jU8ZMtjqMelOGIr19Bs2PaUGL7pkMGD0Qm8NmdVhCiBgnRQIRdhmdmlodQkhoU3Noy0F++WYtS9/9EdPrq9e5SvKKcRe68br9pfSjt3tLvPg8PkJ5L330dYQQ0U+qvkXYpbVOw+a04XMHn9iizUXPjEAFUfWttcZT5GH/+n0sfW8xzhQXziQnziQnNteRj6MGn9tLSYF/nvWkjCRSWzbGZjcozi3GmeTEU+zF9Hgx7DYMh4FSCmUoDJuB6TMpPlxMSvMUMto3QWuNu9BNSX4J+fvySGuTTtsT2knVvRANhCRqEXYl+SVk9mjFzhXbrQ4lJOwuO236tMHmtFNSUFKrzmba9CfL4sNFfP3QF2yZvzkCkVauw8COXPLvy0lITbAsBiFE7UmiFmFVkl+C9pns+2WP1aGEjLfEyz97PYndZefPK+6tdB93oRuA3at20aJ7JnvX7mHha/PZMn8TWFz73P6kjjiSorMnvhCiIknUImw8RR5m/fM7Nsz4BU+Rx+pwQk5rjTZ1uVnPPMUefG4fC1+bz/LJS3AXuC2MsHLdz+uBzS6d4IRoKCRRi5DyFHkwfSaHthzkh5fnsnHOr1aHFDY2hw1tmpT2yXQXutnywya+uPfTqEzQ4F/gJLlJstVhCCHqQBK1CCnTZ/LuNW+yf8M+q0MJu7b92oFSuAvduPNL+PrhL9k4e4PVYVWr+TEtML2m1WEIIepAErUIKVeKi0NbDlodRkQMu+cstNbMn/A9S95chM8T/b3a257QDrtMjypEgyKJWoRUwYH8BpGwQmHl1BWs/mwlBQcKrA6lVgy7Qc+Le8skLUI0MJKoRUjZnPHzllr8xkKrQ6iTToO6WB2CECIIMuOBCClXiixgEa2SMpJQStblFqKhkUQtQkabmkO/HbI6DFGFbYu3omySqIVoaCRRi5BxF7mZ/siXVochqpCzI4dsuZESosGRRC1CxlPg5rfFW60OQ1Tjp/8tx11YYnUYQog6kEQtQsJT5Gbpez9aHYaowXEX9MLukuFZQjQkkqhFSBg2g5XTfrI6DFGNHucfR9OuzWTVLCEaGPnEipAwfWaDGU8cj+wuO2c9dA7OJKfVoQgh6kgStQiJ/P2SpKNZuxPby9AsIRooSdQiJOwume0qmrXu2xaHlKaFaJAkUYuQSExLIqV5I6vDEFVo1au1tE0L0UDJJ1eEhDY1PS44zuowRBUye7S0OgQhRJAkUYuQUDaFYZfq72hkT7CTlJ5kdRhCiCBJohYhoX0mqz7+2eowRCVadMukOK/Y6jCEEEGKn6WORFiYPhNviZevH/qC/H15VocjKtHzkt7YXfJRF6Khkk+vqBdPkYdP7prK5u83Wh2KqETmcS057qJe2ONo+VEhYo1UfYuguQvdHN6Vw9aFm60ORVQiuVkKV742UkrTQjRw8gkWQSnJL+H7l+awfPJSTK9pdTjiKMpQXPHK1TiTnTLRiRANnCRqUWfeEi/FuUUsm7wE0yNJOhoNuHEgGR2aSJW3EDFAPsWizuwuO/+79QNJ0lEq87iWnPrHwTgSZZUsIWKBtFGLOsvfn8/+DfusDkNU4bwnLsSeIPfgQsQKSdSizhIaJ6Bs0u4ZjVp0zyStbbq0SwsRQyRRi6AkZSRbHYKoxAnX9sfmlBnihIglkqhFndmddkpkpqvoo6Db8O7YZCpXIWKKNGSJOik8VMDXD32Bt9hrdSjiKO1ObI9GWx2GECLEpEQt6iR72yE2zFhvdRjiKMqmOPfxC3AmyprTQsQaSdSiTpof04Lm3VpYHYY4SpNOTUlqkowypBOZELFGErWoE5vTzuA/DbU6DHGUjA5N0KZUewsRiyRRizoxbAbtT+pIk05NrQ5FlJHQKAHDJh9nIWKRfLJFndnsNo49t4fVYYgyXCkuSdRCxCj5ZIs6M+wGTTtLiTqaOJKcGHb5OAsRi+STLYKS3i7D6hBEGYlpiVKiFiJGySdbBKVJ56YkpCZYHYY4IrmJzBQnRKySRC2Co2HES1dgc8gsWNFAbpqEiF2SqEVQHIkOWvZsxZ0L/8wZ9w0nMS3R6pDiml2WtBQiZimttQy+FPXiKfZg2Azy9uSy+K2FrJz6k0wxGmE3ffIHmYhGiBgliVqElLvAjTZNFk5cwOLXF2B6TatDigu3zbyDtNZpVochhAgDSdQiLNyFbmwOGz+8PJdNczeyd90eq0OKaXfMv1s6lAkRoyRRi7DylngxvT5y9+Ty+T2fsGf1bqtDikl/XvE3HLIghxAxSTqTibCyu+w4k1006diUa9+9gTb92lkdUkyyu2TFWiFilSRqERHKUNgTHFw+4SqQBZ5CKqV5Cl63z+owhBBhIolaRIxSCrvDxnEX9LQ6lJjSqncbfB5J1ELEKknUIqLsCQ6GP3IeXYZmWR1KzOh4ciecSdI+LUSsks5kIuK01niKPPjcXn6dtYGVH//M9qW/gbwT68zmsHHnwj/jSnFZHYoQIkwkUQtLmT4TT7EHh8vB4jcXsviNhRRlF1odVoPR/fzjOPuR8yRRCxHDJFGLqOEp9mB6TT7/68f8OmuD1eE0CNdPuZHWvdtYHYYQIoykjVpEDUeCA1eKiwufGUFyU5m8oyZ2l50Wx2ZaHYYQIswkUYuoY9gNTr/nLKvDiHrdzu6OT4ZlCRHzJFGLqGN32uk8pKvVYUS94y/vK23TQsQBSdQiKhk2g4yOTawOI6olN0uxOgQhRARIohZRSSlFh4EdrQ4jqrnzS6wOQQgRAZKoRVRyJDrofVkfq8OIas4UmeREiHggiVpEraadmtIoM9XqMKJWRntpGhAiHkiiFlHL6/bJMK0qGA4DbcoUCELEA0nUInopSM6QRF2ZRs1T8RR7rA5DCBEBkqhF1HIlu7j4+UtpfbzMvHW0xq0bo32m1WEIISJAErWIWspQOJKcnPnA2VaHEnUatUjFsNusDkMIEQGSqEVUU0pheqXkeLSkJskoQ1kdhhAiAiRRi6jX4thMWh7Xqtxzrfu0Ia1NWoV9laFoltUcV6PYnrGrcavG2JxSohYiHtitDkCImticNq6bPIp5L81hxQfLuPy/V5N5XEvQmplPf8eaL1bhLnADcPELl9H5tC6YHh+vnPsfCvbnWxt8mDRu1RilpEQtRDyQZS5Fg+EudPPetW9x47RbAs+VFJRgd9pRhsLn8aFNjTPJibfES0leMe+OfIvs3w5ZGHV4XPjMJfQ4v6fVYQghIkCqvkWDoYAbp92Cp+j3YUmuZBc2hw3DZuBIcOBM8s/WZXfZSUxP4tSxgy2KNrw8xV6rQxBCRIgkatFgOI4kYUeio1b7mz6ThNSEcIZkmcYtG1sdghAiQiRRi5hleky+uOcTq8MIiyXvLMZdUILPI+tRCxHrJFGLmGVz2hj65zNics3mTXN/5bXz/8vedXtwF7qtDkcIEUbSmUzENG+JlwMb9/Pmpa9ZHUp4KDjpppM59Y+DsblsGIbcewsRa+RTLWKa3WUno2MTWhybaXUo4aFh0cQFvHPVG+TuOoy7SErXQsQaSdQi5pk+k7R26VaHEVb71u/l1XP/w7qv1kpVuBAxRqq+RczzeX3k783jwKYDfP7XjynKKbI6pLDqeXEvhj98Xq17xwshopskahEXtNaYXpMNM37hk7umWh1O2HU6tTOXvHg5jgSHzAkuRAMnVd8iLiilMH1m3FQLb/5hE+9e/SaF2YV4S2RyFCEaMilRi7hgek0O/XaQxW8sxPT4aNGjJe4CN8kZSaS3b8KhrQf5fvxcCg8WWB1qSCWmJTJi/BVkdm8ZmLVNCNGwSKIWccFT5MHn9eFfx0LhTHKitUYphTIU2tQseWcRM5/+zupQQ0/BwFtO5ZRbB2FPsMtiHkI0MJKoRdxzF7o5sHE/H93+AQUHYqtEXVbr49tw6fgrcKa4cCRIRzMhGgppoxZxzVPk5rdFW3jn6jdiOkkD7F6zi8O7DoPyobVMPSpEQyGJWsQ3pcjZkYP2xX7F0jmPnk/TLmkYjkVovQmtfUiFmhDRTxK1iGt2p53el/WxOoywa9E9k2PP6Y4t4WcANLsw9XKgRErXQkQ5SdQibrkL3exZt5s3R7xqdSjhpeDsh8/DcB4Cyg5PK8DUP6I5KMlaiChmtzoAIayStyeXty6daHUYYZXcNJkzHziHpl0zQC2pZA8TrdcCbYGOKCX37kJEG0nUIm4VHY7tqUR7X96HM+87G9MsxpawDDCr3FezHa3zMegB2GQIlxBRRBK1iFs2u83qEMLC7rJz7hMX0nVYVwzXWgyya3lkNqZejqF6o7VDStdCRAn5JIq45fPGZrvscRf1Iuv0LtgSlkKtk3SpQky9FCiQdmshooQkahGXTJ9J9tZDVocRFj0uOA6b6wDgCfIMHky9HI3/9yPDuISwllR9i7jkKfLw00crrA4j5ByJDlr1aoOmso5jdaHReg0+bQApGOoYtHahlHxlCBFpUqIWccnusrN75U6rwwi5Did3wusuAUpCdEYTyMXUS9D6V7T2SJW4EBEmt8ciLu37ZS8+T+wlnGPP6Y4zqYBwVFRr9qL1AZRqD7o1oKTDmRARIIlaxKWDWw9aHUKdJKYnccptgzjx+gEsfmMB7kI3nQd35fsX57L5+42B/dr2a4dWO8IYiQ+tN6PZhaG6onUaYMhwLiHCSBK1iEspTVOsDqFKSRlJdB7clS3zN5O/L4+mXZtxwwc3glGCz1xLn2t6oL02HEl5XPLvS1n58c989/dvSGqSTFKTZCASNyHFmHoV0PhI+7VT2q+FCBP5ZIm45C4IVRtuaDkSHVz73ihSW6XgLdF8OGYyAMowMFxrgBJszn3gBA3YElz0uaI/856fzaBxg9FmIaqaiU1C7zCm/hFFS6Az/urw2ByfLoRVpIFJxKWWPVtZHUKlTv/bWaS2SsBwLsKZspdr3r6OtHbpFOUUAmmVHFGC6Svgug9G0/Pi3hjODRGO2E+zG1MvRLP7yHCuSN4sCBHblJYBkiIOlRSUMPX2Kfy2eKvVoQRknXEMF/zjYmyJa4HDR55tiq8kC2eiE487B8P+UyVH2lB0RLO/zHFWSsRQXYHGSPu1EPUnJWoRlxwJDjoO6mx1GAE2p41Lx195ZIWrssn2ADbXCtxF+Ri2qmYZ86HZSHQkaYAiTL0SU68GitHaa3VAQjRo0kYt4pOGktxiq6MIaNa1OSV5RdiT11eytQiba2nEY6q/7DLt152Q9mshgiOJWsQlT7GH3N25VocRkNqqMZrYG9cNGs0utN6HUp1At0DGXwtRN/JpEXHJ5rCx+YdNVocRkJSehLLFcgcsL1pvwNTLgDyZ3UyIOpBELeLS4Z05FGUXWh1GwLYlv2EYiUCi1aGEWSGmXoGp16J1ibRfC1ELkqhF3DF9JlvmR09pGuDQloOsnPYTvpLuVocSIQcx9SK0/u3IcC4pYQtRFUnUIu4YNoNjzupOx1M7k9qqsdXhBMx5diZoF9DI6lAiRKPZ7k/Y7JflNIWogoyjFnHJ9Jm4C9wYDgObzcbhXTnMfWE2v3yz1tK4xnx5G+kdDwG7LY3DGikYKgtIkulIhShDStQiLhk2g4TUBJyJTmxOGxkdmnDeExdyyYuXYzis+1j4V/SK1yFM+Zh6Oab+Ba3dUh0uxBGSqIU4wpnspPOgztw5/26waDKt3N25KBKsuXjUOHCk/XqbTEcqBJKohSjHkejEmeIisXHke187k5207t0aTX7Erx19TDS/YerFaA5I+7WIa5KohThK4cECinKKIn7dE64fgD1RA3sifu3o5UbrtZj6J6BAhnOJuCSJWsQlrXWlJTStNduWbLMgIuh4cidsrkisJd0Q5WHqpWj9K1p7pP1axBVJ1CJOaQhUqXqPfPlrvCVe9q6zoESroEmnJkBe5K/dgGj2HhnOtUPar0XckDEQIk5ptM5Dsx5IAhwYqitK2Wndu3XEozn2nB7YE+zAgYhfu+HxofUWNLtRqgvodGQ5TRHLJFGLOGWAsoP2Av7FOUx9COVw0uHkfrQ9oR3bl0agClzBidcPYPBdwzCcG8N/vZhSjNar0TTBUN2J32Ft1svJySE/P/hOkCkpKaSlpYUuoBgjE56IuOSv7t6AZl+FbUofw47lbiZd93ZYYzDsBldOHEmrXi2xJawHqlpvWlRPYajTpERtkZycHM48ayi5h4OfO79x48Z8++23kqyrICVqEacUmsOVbtHqV9r2G+QfSx3G29ghd59Oh5M64nUfBlKBAsAdvgvGLA2YSInaGvn5+eQeLuTdd+4mMzO9zsfv2ZPNddc/S35+viTqKkiiFnHKoOqk2Iyiw0VhTdIAP01Zxv71e2ncJp0uQ7rS8riT8Zk/IyXrYEjFoNUymzemdcu6J2pM6RBYE0nUIk75ULREs6vCFkVTfp29IewRHNp6iENbDwHww/i5/N+Sv2JPlnHCwZFEbTnTDC7pSqKukQzPEnFJKTtKdQZSKmzTZNPplE4kpiWijMi0eyY1Scae6ABKInI9IUTDISVqEccMDNUbUy8Hys5EtgtXagvGff9/KEORtzeXz/7yCTuWbaNZVnOOOasbe9bsYePsDbgauWjVqzVbF21B+4Ir1SlDcef8uykpPIw9QdqogyMdySyntf8RzHGiWpKoRdxSSqG1DUP1wdQ/Ar9XO9tcKwDQGDTK7MTl/70Kd6GbhMYJGLZiTG8Cyz9YSudTO5PePoM1X6zmq/s/DyoOm8PfCcqeIMOzgieJ2nI6yKpvmbSmRpKoRVxTykBrA0VGpUO1/ItDbMSR4sGZ0gjNSsCLzZ5Bnyv6oGxulGMbnQd3Der6NqeNq9+8FndRPjaXzEoWPEnUljN1kG3UUqKuiSRqEdf80wioWqxY9dtR3ZUOYU9aDLgwS/rxw/jvgrp+ZveWZPZohXIsCOp4UUoSteWkM1nYSGcyEZf880TnHVlKcTkQ3GQNig7s27CPnz5cHtTxu1btZPfqXfhK+iIfx2AZSK9vEcvkm0HEqWJMvQytt+KfaCQ4mhJcjRKC7h2ufZr3R7/L7pX78RX3Q0qGwbAhiToKlJaog3mIakmiFnFHa40O2eIXv9G4ZQrnPXkhqa0aB3UGn9vHlJsnc3hnAYpOIYornsjXWFTQQSZp6UxWI3mHizhkgg7VMCiN4VrFMWd14A9f/5EbP76FPlf1IyE1odxezmRnoHc3wJD/G8b1H4ymebcWAPg8PpZNWoKnOC1EccUTKVFHA6U1SptBPORvVxPpTCbikEaHdGKRAgzncsCg6THtGPqXwZxx71n8tngrm+ZtpMf5PWnRPROA7G2H2LF8O8dd0BOb6zCXT7iaNy55la5Ds0hvn4H2ynzVdSfljaggncnCRhK1iEsKIwxlMBPYij1xK+Ci46ntadN3MDanF2X/EYCMTi3I6NgZ1Fo0OSSknsid8+/GU1yIMykZb0lRdRcQlZISmYhtkqhFHLKBSgrz93sJmg3Yk45+fjuo7b9HkrACkwxsCfvwmY1RDpnru+6kRBYVTB3cmGgZR10jSdQi7vhnJEu2OowjvBCYaKXyZTdFTXxIb/koIDOThY0kahGXFIlSYRozTCRRRwGZmSxsJFGLOFWhTlo0WFKijgo6yKFWUqKukXSXFHFKvhxih5TIRGyTErWIO1qbaA5ZHYYIKf+c7cJCMjwrbCRRizhkovUmq4MQISWlastJr++wkUQt4orW3iNJOpQTngirKSUTxVhOen2HjSRqEWcUmoNWByFE7JFe32EjnclE3PCXpn8DQjXPt4gWWvusDkGIsJEStYgjBpq9VgchRGwyTZR0JgsLKVGLOKJRZFgdhAgLqT61nNbBP0S1pEQt4oI+0mFFk2dxJELEKOlMFjaSqEWcUGgOAPlWByLCQkpllpPOZGEjVd8iLiilUHJfKoRogOSbS8QFrX1oLcOyYpeMo7acTHgSNpKoRdyQ8dOxTKYPtZxMIRo2kqhFnDDxr5ilgCKLYxGhZUfm+o4C0pksbCRRizhhw1DdAYWpfwLp/R1D7PhvxKTLjaWCHWolw7NqJO9sEReUMlDKDhgYqovV4YiQclgdgBBhJSVqEVeUUmjdCH/nI5l2MjbYkOFZUUCGZ4WNJGoRh0wgASiwOhAREsVI5WAUiHCv7wULFjBhwgTWr1+Px+OhR48ejBkzhtNOO63W55g7dy7vvPMOq1atorCwkGbNmjFo0CBuv/12MjMzy+3r9Xrp06cPbnflawW0aNGCefPmBfVaaiKJWsQpSdSxo1iWuYwGEez1PW3aNO69916cTicnnXQSpmmyePFixowZw2OPPcaVV15Z4zleffVVnn32WQzDoFevXjRp0oR169YxZcoUvvvuO9577z06d+4c2H/jxo243W7atWtH7969K5wvLS2tzq+jtiRRizikMFQ7TH0Y8FodjAgBrfNQqpHVYcS3CPX63rt3Lw8//DCNGjVi8uTJZGVlAbBy5UpGjx7NE088wZAhQ2jRokWV59i4cSPPP/88SUlJvPHGG/Tp0wcAj8fDk08+yeTJk7nvvvuYMmVK4Jh169YBMGLECG677ba6vsp6kfoiEXf8pa8UDNUfuVeNDVpqR+LGpEmTcLvdjBo1KpCkAXr16sWYMWMoKSkpl2Ar8+mnn2KaJqNHjw4kaQCHw8F9991HRkYGP/30Ezt37gxsW7t2LQA9evQI8SuqmSRqEZf8ydqGornVoYgQUKRYHYIobaMO5lEH33//PQBnnHFGhW2lz9XUVuxwODjmmGM48cQTK93Wpk0bAPbt2xd4vrREfdxxx9Up3lCQ4oSIYwYytCdWJFgdgNA6uMlL6jCOWmvNxo0bMQyDTp06VdjeoUMHDMNg48aNaK1RqvJJcO644w7uuOOOSrcVFhayceNGgECHMq0169ato1mzZsycOZMpU6awadMmXC4XJ598MmPHjq00nlCRErWIYyaaQ1YHIURsiECJ+vDhw7jdbtLS0nA6nRW22+120tPTKSoqoqAguOaQ1157jcLCQnr27EnLli0B2L59O/n5+ezfv5+HHnoIl8vFgAEDcLlcfPnll1x22WUsW7YsqOvVhpSoRRzzIDOUCREi9exMtmfPngqbUlNTSU1NDfxcVOSf/jcxMbHK0yUk+GtXCgoKSEmpW5PI3LlzeeWVVzAMg7/85S+B50vbp1u0aMErr7zCscceC/iHbD377LO88cYb3HXXXXz33Xe4XK46XbM2JFGLOGZHJj4RIjqMHDmywnNjx45l3LhxgZ8No/aVwLqOU5POmTOHO+64A5/Px913382AAQMC24YPH86cOXMwDKNcb3K73c5f/vIXFi9ezJo1a5gxYwbnnXdena5bG5KoRRxTKNUerTdbHYioF4UsyBEF6jnhyaRJkypMMlK2NA2QlJQEQElJSZWnKy4uLrdvbXz00Uc8/PDDeL1exo4dyy233FJuu1IqUA1+NMMwGDx4MGvWrGH16tWSqIUIJaVsoFuj2YJMQdmQpSF/vyhQzwlPMjMzA72tq5KSkkJSUhLZ2dl4vV7s9vIpzOv1kp2djcvlqpDkq/L8888zYcIElFLce++9jBo1qs4voWnTpsDvNwmhJp3JRNxTtLU6BFEPSmXib8IQlopAZzKlFF26dMHn87F169YK27ds2YJpmuXGV1dFa83999/PhAkTcDqdPPfcc1Um6UmTJvGnP/2JBQsWVLp9x44dABVqBEJFErWIcwZKZVgdhAiagaJplcNwRASVDs+q86NutSGDBg0CYMaMGRW2lT43ePDgGs/z9NNP89FHH5GSksLrr7/OueeeW+W+27dv5+uvv+bjjz+usK2kpITp06cDcOqpp9bqNdSVJGoRk7TWZR4+tPagtbdcBxOtTcDE1JusC1TUi6IZUu0dX0aMGIHL5eK1115j9erVgedXrVrFxIkTSUhI4Jprrgk8v23bNjZt2kRe3u8jPObNm8dbb72F3W7nlVdeoX///tVe87LLLsNms/H5558HkjL4pxz9+9//zs6dOznttNPCNmuZtFGLBk9rL/7ORAb+lbFAswd/b24DdCEaNwobqDTQaYATTfaRjmSF1gQu6k2pNkfWGReW00F2JqtjibpNmzbcc889PPbYY1x11VWB3tmLFy/G6/Xyj3/8gyZNmgT2HzVqFDt37uSpp55ixIgRALz44osANGnShA8++IAPPvig0mvddtttdO7cmS5duvC3v/2NJ598kjvuuIOePXvSqlUrfv75Z/bs2UOnTp14+umn6/7aa0ne4aJB85eYt6HZh1LtATda78A/RvqofQH0Xil/xYxEoPY9e0WYRXCZy5EjR9KqVSsmTpzI8uXLcTqd9O3bl9tuu42BAwdWe2xOTg6rVq0C/At8fP7551Xue/nllwdW0Lr++uvp2rUrEydOZOXKlaxfv55WrVpx6623csstt5CcnFzn11FbkqhFA+fDvx5xMVqvtzoYEUFKtUSGZUWRCC5zCTB06FCGDh1a436zZs0q93NaWhrr1wf3XTFw4MAabwTCQRK1aOBsaPZbHYSwgjZBSf1I1IhgiTreSGcy0cD58FeBinij2W51CEJEhCRq0cAplDp6gXgDCP18uyLa+NDsPdJ7X1hOBzF+2tR17kwWj6TqWzRo/tnF2qBUK0p7fPuXrtRACaZeBRRZFp8IL613VnKjJixh6iDbqCVR10RK1KLBU8qGUg6Uch15GP4ETgKGOgFoUtMpRINVALitDkLAkQlPgnyIakmJWsQspQy0NlEqDa0PHrXVOPLwUX7CDBuKTFCN0Hov/mFeTuAwsspWtPq9FOevBtdHbtRERElnsrCRRC1iltY+IB+tt5V7XtEKpTrz+9AeD+DFn7gT8H/xGyjVtMxRXrTegiYbKcFFH/+McyaaQyipQRExRhK1aLD8X87+Um7Z2an8CboYrfeh+e2oozJQqgtKlW31caG1s8x80aWlsbLntKFUVxQKzb4jM5pVnFRFRJ6p16NUM7TeCRSj1AlAitVhxZ8IzUwWjyRRiwbMRLMTtA9oR2mXC1OvBrKrOCa30mdrWtTBv/3Ix0W3wDBa4jN/wF8SF9bKRevf/66aXJQk6siL8IQn8UQStWjYtAfNjiPV26XzfVfdlhyKRRz8bd8aQ52AqRfX+3wixHQRGvOoWhMRdtJGHTaSqEWD5e8w1PxIlac+8qi+w5dSrULS0chfwk4AbUNK1dHFX6KWL/+IMwkyUYc8kpgjt5yigUvGUMdR/ZzPNiAVpboS6kUcFK1QtOb3dm1hPS9SyyFiiZSoRYOmlA2t01C0R7P1qK0OlGp7JJFq/LOYhS6h+od+dQA0iqaYeiWSIKJBIXLjZAHpTBY2kqhFg+ef8KQDWjfG1BvxJ+RMFC0D28Nz3d8rpLROxVCD0OxH641Ij3Ar2Si9MRMRZGq0tFGHhSRqEUNSMVQfSr+gIznpReBauhlKOY6UroWII8HOMiYl6hpJohYxIxpmo/L3CG+Moq2s7mQZL/5OhdIFJ6Kk13fYyDtZiBArrYoXQohQkBK1EGFh4K+Cl9JC5BnIV5sFpEQdNvJuFiIsTPyLeZRYHUgcMvFXfzusDiS+SK/vsJFELUTYyEQo1nEjiTrCpEQdNpKohQiLPGRZTCvJl3+kaZOghmdpmZmsRtKZTIgQ09pXyfrXIrIkUYvYISVqIUJIH2lv0+yxOJJ4J2uGR5xUfYeNJGohQkpj6p+QmclKWdPzXetsIC0qxtbHDelMFjaSqIUIKRN/+7QA15H/R77nuyZHJhCNNClRh40kaiFCLhkosDqIKGDl0LRCQKO1PrIkqQg7mUI0bKQzmRAhl2B1AHEuA0OdBKYPPDKOXTR8kqiFCBGtTfztsTkWRxK/FB0wOBZz1qd4n7wVigoCHfxEePmHZwX3ENWTRC1ECPiTdDGmXoqMn7aKHaXa4n3tMcz5X4LW+P43HjzSAzwiSjuT1fUhN1I1kkQtRD35S2w+TP0zMmWodRTN0fk5sPf3Vcv0ri34Jj2DLsxDm1J0C6tgknSwHdDijCRqIerNxNRrkCRttUTYs7PCs3r7r3jfeBxlGEdqPkQ4SNV3+EiiFqIetPai9VakXdpqDpTKgOLCyjcf2ovnlYfAK/Ovi4ZHErUQ9VKMZnvNu4mwMlRvKHDjWzKz6p32bsP8aR5a2qzDQ6q+w0bGUQsRJP+c3rutDiOG2YAkFGlo9gPFZbY5gcZHJjVJBO3E+9wd+CecqZr5/RcYfQaHKd44p6np11/1caJakqiFCJqJpsjqIGKMDUULlGoJJIPpBq0w7J3RugitC1AqBXBAcYG/gVMpfDMnU6sskZ+DuXQ2Rt/TUE4Z7x5KsnpW+EiiFiJoBkqlofUhqwNpYFJRpKApAUpXGTNQtEWptuiCXMxlMzHnfwXeI9XUhh3VayCqdWd8m9fA+mUQZC9u89vJ4HRhHDdAknUomQRXopZEXSNJ1EIESSkb6NZH2qjjcRGOI6VfmoOygzbRFABFaHKA3KP2Vyg6olRrdPZ+jPRmmHoVYMdQHaHIi3fqc7BlbcVLmV70T9+jf/o+JJGbX7yJ3rYe23mjUA5nSM4pRLhIohainpTqhNbrrQ4jghwo2qBUayjMx/x5ITp7LyohGdWqI6RmYLTsBUqBzkerHLTO8yd1dyLeyU/Bjo0Ywy7DOOUc8JZg/rwY86t3iWTxSq9cgJneAmPgcClZh4ImuPZmaaOukSRqIerBX6puDuSiicWOZQ6Uau0vNQeWrHShs/fj/bJ86bfC922Tlhg9T0J1OBajeTt0fh7etx6FwnwAzFkfYc76KEKvo3Lm3I8huRFGr5MlWdeTNnWQbdSSqWsiiVqIevKvedw+BnuAp2KoXugDezF/+gwzN8c/aci29XD4YM2HH9yNOedj4OOwR1of5lfvAEiyri/p9R02kqiFqCf/MK3NVocRcobqiN6wEt+UFwPPxep3qvnVO2DYpINZPQQ7y5j0+q6ZTHgiRD1o7UOzA80+q0MJsSSgEb4v37E6kIgxv3gL8+f5aE+JTDUqoookaiHqRaP1NquDCDlFBvrQfsjPsTqUCNKYX7+L7+2n4cBudElxzYeI35n1eIhqSaIWIkj+Ku9txOaylglwYI/VQVhC79qC978P4Pv6HbRHFlqpLVmUI3wkUQsRBH/VqEZTcbWm2KDBsFkdhIW0f/jW95+j3ZKsa6W0M1ldH7Ha8SGEJFELUQdam2jtA4ox9TJiszQNUAKNM6wOwnLm4u/AjNW/cYhp0EE8JFHXTBK1ELXkT9C5mHoFpl4CMTzPt6YY1aiR1WFYz1OCOesjtFvaq4V1JFELUQdaHwDyif1igAm2eK76/p25fK6sY10L0kYdPpKohaglpWwoFR/VwQq7rNtcyvTh+2aStFXXRHp9h40kaiHqJF6qgzXqyGrPAvTqhZiLpksVeDW0DrJEHeuVUyEgiVqIOrEBsb/akpaq7wrMOdMw1y6RZF2FYDqSBTqUiWpJohaiTkwgHqqEi0Gm0qzA/PIdkCYBEWGSqIWoBa01Wnsw9WqrQ4mQAv8yld37Wx1IdPF58M2eKqXqypgq+IeoliRqIWrFh6nXATlWBxIxWm/Fft61VocRdfTKhVJfWwnp9R0+kqiFqBVFPCVpAM0uSEhA9TrF6lCii9eNufAb6QV+FH97swriYXXk0U8StRC1Ukj8jSPRaH0Qo1tfqwOJOnrHRpmx7CjS6zt8JFELUQva0g5kBv5lJ0s5gWaAI4zXTEDRCsNoiW/NkjBep4Hyef1t+EJEgN3qAIRoGKzqPGTDUL2BFOAwWh9EqQ5QUgSurph6QRiu2QhD9Ubn5eCd9xasWRSGazRs+rf1UFQArkSrQ4kawbY3Sxt1zaRELUQNtNagrSlRK7qgc914n7oVvWUH5DfG98U7eP85Dv+Y7tCXqg3VAb12Ob4X/oxePifk548V5iJppy4nqPZpBVpqJmoiJWohakPZLJje245SzfFOfhi8bnzvPVt+s7cEZUtHsy+E10wHGuOb91kIzxmb9IHd0k5dhjYVOoihVsEcE2+kRC1EDZRSKBpH/rq0hMJ82L+j0u3mkrkodQyG6g80qffVFFkY6jjMbz+s8pridzo3W9qpy5CZycJHStRC1EoK/g5dhWG9iuIYlGqE1vko1RTzhw+r3NecMQVzxhSMoZdinHwWGDbQ+Wi8gAfIRnOAir3Vk1GkgEoCnYxSCUACFObjffXPkJcdvhcYS7L3gyP2p5MV1pNELUStKAzVBVOvDOM1mqBoijnvS1Tz1njXToM1P9Z4lDl7KubsqdCmC0aXXpCQhGqUhup0DMqZhdbZgIFSLiABtEYX5sGBvegDmzH37UDv3AS7t4bxtcUgnweKiyApxepIokKgzTmI40T1JFELUQtKGWjdGH8bbjhKnAaG6obvmw/QS2YGd4odGzF3bCz/XLtjsJ0wDLwefLu3oLdtgL3b6x+u8JM26gCtg2yjDjJRL1iwgAkTJrB+/Xo8Hg89evRgzJgxnHbaabU+x9y5c3nnnXdYtWoVhYWFNGvWjEGDBnH77beTmZlZYf+9e/fy8ssvM3/+fPbv30/Lli258MILGTNmDE5n+GpXpI1aiFrzJ1N/b+tQUEAykIqiKxTmB5+kq7JtPb5p/8X32UT/uSVJh47DCYnJVkcRNSLZRj1t2jRGjx7NihUr6NWrF3369GHFihWMGTOGKVOm1Oocr776KrfccgsLFiygY8eOgQQ/ZcoULrnkEjZt2lRu/z179nDFFVcwZcoUUlNTGTJkCAUFBbz44ovcdNNNeDyeur+QWpIStRC1pJRCaztKHYPWa+t5NgeGOsE/NEVrcJfgnfhoSOIUkaHadPGvpGWTr1GIXNX33r17efjhh2nUqBGTJ08mKysLgJUrVzJ69GieeOIJhgwZQosWLao8x8aNG3n++edJSkrijTfeoE+fPgB4PB6efPJJJk+ezH333Vcu6T/yyCPs2bOHO++8k9tvvx2AwsJC/vjHP7JgwQLeffddbrzxxrq+/FqRErUQdaCUDUUT/CXhepyHtuhDB/E+fgveJ/6A9193wOGDoQlSREZqBij5Co20SZMm4Xa7GTVqVCBJA/Tq1YsxY8ZQUlJSY6n6008/xTRNRo8eHUjSAA6Hg/vuu4+MjAx++ukndu7cCcDmzZuZM2cO7dq149Zbbw3sn5SUxBNPPIHNZuO9994L8Sv9nbzLhKgz5Z8drD5nUE0xF34bmnCEJZQrAQz5Ci2lTYUZxKOu7drff/89AGeccUaFbaXPzZs3r9pzOBwOjjnmGE488cRKt7Vp0waAffv8cxT88MMPaK0ZOnQoxlF/81atWtG9e3d27tzJxo0bK5wvFORdJkQdKWWgyABS63EWJ3rb+lCFJCygWnVEyfCsgEi0UWut2bhxI4Zh0KlTpwrbO3TogGEYbNy40T+jYBXuuOMOPvvsMwYOHFhhW2FhYSDhlnYoK/25a9eulZ6vNJYNGzbU/sXUgTSuCBEUf8cyU9c8fKqiDEDBgb2hDkpEkM7NRpsmSkrVQP3bqPfs2VNhW2pqKqmpv98QHz58GLfbTUZGRqW9rO12O+np6Rw8eJCCggJSUuo+dO61116jsLCQnj170rJlS+D3knXz5s0rPaZZs2YAHDhwoM7Xqw1J1EIEwd+xzIlSndB6cx2OzMBQ3fF99S7xt2xmjMk5AF4POF1WRxIVNMENtSot944cObLCtrFjxzJu3LjAz0VFRQAkJla9GEpCQgJAUIl67ty5vPLKKxiGwV/+8pcK1y09d1XXLCwMz4RIkqiFCJJSdtCt0ewH8mrY24WiFUq1wfflu7LYRQzQ+TkyjjqEJk2aVGHsctnSNFChfbg61VV9V2bOnDnccccd+Hw+7r77bgYMGBDYZrP5h2SqGqaMNc3w3HxLohaiXgyU6oDWq6rYnnhk7HUK+uAevN88B5vXRDJAES4FNd2cxRdTK8wgStSlx2RmZgY6cVUlKcm/LntJSdWrlhUXF5fbtzY++ugjHn74YbxeL2PHjuWWW24pt720BF967lBcsy4kUQtRD0op0EYVC2spDNUTvfFXfJ++5l9gQ8QMnX9Yen2XEYnVs1JSUkhKSiI7Oxuv14vdXj6Feb1esrOzcblcFUrjVXn++eeZMGECSinuvfdeRo0aVWGf0rbpqtqg9+/fX26/UJN3mRD1VtX9bgLgwvf5m5KkY1FBLthDvx54QxWJXt9KKbp06YLP52Pr1q0Vtm/ZsgXTNMuNr646Xs3999/PhAkTcDqdPPfcc5Umafi9t3dVw69KZzGrzXWDIYlaiHpLAirrUFSM1tnY73oGWlccSiIaOK8b3FVXwcYbfaTqu66PunZAGzRoEAAzZsyosK30ucGDB9d4nqeffpqPPvqIlJQUXn/9dc4999warzl79uwK7dC7du1i3bp1tG7dmi5dutT6ddSFJGoh6k1hqF5HPecvaWlWo3U2tv5nRT4sEX75h62OIO6MGDECl8vFa6+9xurVqwPPr1q1iokTJ5KQkMA111wTeH7btm1s2rSJvLzf+xTMmzePt956C7vdziuvvEL//v2rvWbbtm0ZNGgQmzdv5t///nfg+cLCQh544AF8Ph+jR48O4assT9qohagnpQwgGbQD0EeSdgr+RTd8gMK37SsrQxRhog8fRDVtaXUYUSFSc323adOGe+65h8cee4yrrroq0Dt78eLFeL1e/vGPf9CkSZPA/qNGjWLnzp089dRTjBgxAoAXX3wRgCZNmvDBBx/wwQcfVHqt2267jc6dOwPw8MMPc/XVVzNhwgRmzZpFx44dWb58Ofv37+e0007j6quvrvNrry1J1ELUk9YmUAB4UHRG5xThe/lP/mHSzVpC9n5/NamIOfrgHnSnHjUO24kH9e31XRcjR46kVatWTJw4keXLl+N0Ounbty+33XZbpbONlZWTk8OqVf5RGnv37uXzzz+vct/LL788kKjbtm3L//73P1588UXmzZvHb7/9Rtu2bbn++uu54YYbKnRsCyVJ1ELUm8bUywAbSrXE+8VzUNqOtX+npZGJMMs5AD6vdCoDNEGWqAnuJmfo0KEMHTq0xv1mzZpV7ue0tDTWrw9u+t6WLVvy1FNPBXVsfUiiFqIetPYBRUBTDNUBfWgfbKnvEpiiwbDZQErTgL8CKZjpPmR+vppJohYiSFprlLKhdTKGtwPmqqWYX75jdVgikppkomQ9ahFm8g4TIkhKKTzvPw+//mx1KMIiRmZ7q0OIHkF2JiOYY+KMJGohgqQP7pEkHee0NoNsYY09pg6uY5hZtym545KMoxYiSHr/LqtDEFbbt7POiz/EqtLhWcE8RPUkUQtRDa012uNGe36fgUqbPrS7BHPFXAsjE9FA797qX+pSHClRB/cQ1ZOqbyGqoN0lYPrwTXkR1XMgRs+BoDV6+6/4ZkyBvdutDlFYTHtKQEu/ZRFekqiFALS7GIqLwO5AH9yNymzv//f6VejffkFv2wC7t6L37/T/Wwjwz/UtVd9A5GYmi0eSqIUAOLQP76sPw5EFK42B52KcdiHm+uX+7drEXDbbuvhEdHIlgJIWRPBPXBJM17pgJzyJJ5KohQDIaA6p6ZB7CABz4VeYC2V+blE95UwAw2Z1GFGhrktWlj1OVE9uBYUAsDuwj/yz1VEI0WAFs8RlsPODxxtJ1EIAyrCh9/xmdRhCCFGBVH0LAXg/ex390/dWhyFEg2UG2UYtU8bUTBK1iHvaXQwHd1sdhhANmibINuqQRxJ7pOpbCLsTY+hlVkchRIOmg2yfluFZNZNELeKeMgxUq46o7v2tDkU0OJJkSmlU0A9RPUnUQgDK6cJ24U3QvI3VoYiGxFCSq0XYSaIWopTDgf3av0BCstWRiIZCGaAkU4PM9R1OkqiFOEIpAxIS/SVrIWrDsEmiPqJ0mcu6P6yOPPpJohaiDGV3ojp1R/UYYHUooiGwO/w3eELaqMNI3mFCHEU5E7CdPxoaN7U6FBHtnAlWRxA1dJDV3jKFaM0kUQtRGbsD+9V3yTzOoloqpbHVIYg4IIlaiEoomw3SmmL0G2J1KCKaNUqzOoKoIVXf4SOJWogqKKcLY+A5soyhqJJKb251CFFDen2Hj3wDCVGdpBSME4ZaHYWIVlIYDJDVs8JHErUQ1VDOBIxhl0GijK0WFenDB60OIWroejxE9SRRC1ETmw3jrKutjkJEo6ICqyMQcUAStRA1UHYnRvcTUa06Wh2KiDaSqANkUY7wkUQtRG3YHdguHiPDtUQ5ujDf6hCihlmPh6ierEctRC0oZaBTM7CNvh9MH3r7Rsw508DrsTo0YaWiPLQ2ZXYyjgzPCqJ0LMOzaiaJWohaUs4EVOtOAOjM9qAU5ncfWByVsFRJMfh8YJdEHWzpWErUNZN3lxBBUA4nRt8h0Cjd6lCEhXRxIZg+q8MQMU4StRDBstmw3/4k6pi+2G56CGPICKsjEpFWUgymlAlBJjwJJ6n6FiJIyu5AK/ydzJwuVIt2mIu/hSLpYBQ3SgplVYmAYKcDlTbqmkiJWoh6UDYHypXo70xkerHf/BCq7xCwyT1wXCgpkvWoj5ASdfhIohYiRJQzAZXeHNtZV/l7h8tQrpinJVEHyKIc4SOJWogQU84EVNNW2C662epQRLgVF8kNmQg7SdRChIFyulDH9MEYdKHVoYhwKikCmyRqkKrvcJKGNCHCRDkTME45D9W1F3r1Isxff4bs/VaHJULJ60Y6Q/kFm3QlUddMErUQYaScLlSbLujmbTFOvwLlcOJbNB3z2w+QdYNihNcDTpfVUUQFaW8OD6n6FiIClNOFcjgBMPoOQR3bz+KIRKjo/TvRMumJVH2HkSRqISLN4cR28S0YZ48EZ4LV0Yh68n060T+NqBBhIolaiAhTSgWmILVd8gerwxH1dWAXeu2PVkdhOVk9K3ykjVoIiyi7Azr3gEZpkJdjdTgiGKlNME48HXVsP0zThxHHQ7V0kGtLy3rUNZNELYSVTBPVpRd6xTyrIxF1oE48HduwEf6mC/chlDMx7rtRyepZ4SOJWggLKWcCxnEn4ZNE3TC06YL98tsgKRmduwIObgdlQOYlKBW/pWnwT3keTMcwmSq9ZpKohbCYSmtmdQiiKimNMU48A5XVG9WkuX8O9/z16H1rQR/pQOZs5v93nCdqET6SqIWwmkt6fkcNuxPad8N2/Cmozt3BlQTubHTJDnT2evAcosL4d1sK0i/X/1sJpnAsBeqaSaIWwmquRKsjEIAx7DKMk88G7QX3PnThz5C9x/9zNZQ9VUrTgInCDKJjmBn3rfs1k0QthNV8PlTrzuidm6yOJH517YVx8nD0wdngOVinQ7U3H6W9oBxhCq5hkBJ1+Eh9jRBWcziwXf0njBPPAHt8f9lbxX7e9ej8tXVO0gAUbpISNTIzWThJohbCYkoZqKRGGKdfhv3O51CtO1sdUnzp1ANSGkPBr3U/VtlQzc/5vWOZEGEgiVqIKKGcCZCUgu26v2IMuxQaN7E6pNiXkIT98tvR+b/U2BZdqaROYCSiDKkJ0QQ3K5kUqGsmiVqIKKKUAocDY+A52C6+xepwYpyBfdw/wDwI+WuDOoNK7IAypKsP+MdDB/sQ1ZNELUSUUcpA2eyo1p1Q7btZHU5kJKSAM7K9320j/gB2E529kODKdQocaSGOquEyUUE/RPXkVlCIaOXzQWqG1VFEhP3mB1AZmeicfejsA+D1gmFAQS6+T14N+fWMC25EHXs8+sAMgq58tSX5i4OSZ4AjpeMgjxPVk0QtRDQz4qPSyztzKo7L/4hWO1BNnaCcoE1I6IG9+wTwlIDNgd67A9+bT1Z/spTGGD0GQNOW/oRfXADuYtAa1bYrqn1XSEj0J2lfXvBBKwcyU7WIBEnUQkQrmw2jQzd8K+fHfrGj4DBaayjagTYLf38+bzU4GoORCLZkVMtu2B+YGJiyU+/+Dd8Xb0HjJth6nwJNMlHNWoE3D7z5gALDAerIV50nB12yBg7vCq7zWFlKitJlyaIc4SOJWogopewO6N4ftWQmetcWq8MJK9tZV0H+L1A2SQNggicbyAZAF24EeyNK65tVWlfstzziL30XbQczH733czCLIxC1Qrr5/E7r4JJurN+DhoIkaiGimdbxMcWox13LHTV4c3//6fBSOLw0PDHVxHMISvagXZnS8xuZmSyc5HZQiGjmcGC75m5o1trqSMKrMN9fRd3A6OwfkMpbP5mZLHwkUQsRxZQywOtGOZxWhxJeGc3R3nyrowhORKrZRTyT+hohop1pgjO2l8JUDgdae6wOIzjeArCnWh2F5WR4VvhIohYi2in8naVimTJosK2V3jy0zvTPKhfHIt3re8GCBUyYMIH169fj8Xjo0aMHY8aM4bTTTgvqfJs3b+biiy/m1ltv5fbbb690nzPOOIPt27dXeY41a9Zgt4c+rUqiFiLaKQNd2ECrhWtJ+7xgb5grUGmzEIUm3mc+0QTX3hzM7dm0adO49957cTqdnHTSSZimyeLFixkzZgyPPfYYV155ZZ3Od+jQIcaOHUtJSUmV++Tl5bFjxw6aNm3KwIEDK93HCNO8B5KohYh2dgcc2mt1FGGlN6/D6HMiunATDa9zVnwn6FKR6vW9d+9eHn74YRo1asTkyZPJysoCYOXKlYwePZonnniCIUOG0KJFi1qdb8OGDYwbN46tW7dWu9+6devQWjN06FAef/zxOkZdP9KZTIgoorVGlxSjTdP/f58Xc9F08NVzco4oZ34zGUwbJLa3OpQgKCRZR86kSZNwu92MGjUqkKQBevXqxZgxYygpKWHKlCk1nqeoqIjx48dzxRVXsHXrVtq0aVPt/mvX+hdu6dGjR/1eQBAkUQsRJbQ2oTAf3wcv4H36D3gnPoL3n3/EnPk/q0OLABNz4Xeoxn0hoWENRVNKvkYhcsOzvv/+e8DfXny00ufmzZtX43m+/vprXnrpJVJSUhg/fjwXX3xxtfuvW7cOkEQtRNzSpgk+H95Jz6B/+wW8Hji4xz/HdZww532K7/O3UGkDwWhIvdyNuO9IBqBRQT9qfQ2t2bhxI4Zh0KlTpwrbO3TogGEYbNy40T8lbTXS0tIYN24c06dP58wzz6zx2mvXrsVms7FlyxZGjhzJCSecwIknnsitt97KypUra/0agiFt1EJYRJsm+tefwGZH5xxAr5wPe36zOixL6Z/no08ajko5Bp37s9Xh1JKUd+BI6TiY4wAU7Nmzp8K21NRUUlN/H/p2+PBh3G43GRkZOJ0V5xaw2+2kp6dz8OBBCgoKSElJqfK6w4YNY9iwYbWK0e12s3nzZnw+H3/961/p2bMnAwYM4Ndff2X27Nn88MMPPPPMM5x99tm1Ol9dSaIWwgLa50Vv+Bnf/16yOpTokpCCSkyBpFbQUBK1VH0D9Zjr+8j/R44cWWHb2LFjGTduXODnoqIiABITq55WNyHBXxtTU6Kui/Xr1+P1eklOTubll18u1+v7rbfe4qmnnuLee++lX79+NGvWLCTXLEsStRBWME18Mz+0OoqoY7/lQXCWoA98Z3UotSeJOiQmTZpEZmZmuefKlqahbsOfaqr6rouePXvyww8/4Ha7ad26fB+KUaNGsWTJEmbMmMHHH3/MLbfcErLrlpJELUSE6eJCfF++HfNDroKSmoHePx18DWjcuCRqoP7DszIzM2vseZ2UlARQ7Xjn4uLicvuGSnUl5aFDhzJjxgxWr14d0muWkkQtRARpnxeVkITeHJ4PdIOWkATK1rCSNCBt1H71aqOupZSUFJKSksjOzsbr9VaYBczr9ZKdnY3L5apQGg+n0iReepMQavIOEyICtDbRnhL0umV4JjwARQVWhxR9igvB9IKrpdWR1I2UqAHQ9fivtpRSdOnSBZ/PV+kEJVu2bME0zXLjq0Phq6++4u677+bzzz+vdPuOHTsAKlTdh4q8w4QIM+3zwqF9+N75J75p/4V9O6wOKWr5vp2CSj8FkrpYHUodyNcoRG4c9aBBgwCYMWNGhW2lzw0ePLjer6esgwcP8sUXX/D+++9X2Ka15rPPPgPg1FNPDel1S8k7TIhw83rwTZuA3rnJ6kiinl4yE++H41Gpx4MtND12w07GUEfUiBEjcLlcvPbaa+XahFetWsXEiRNJSEjgmmuuCTy/bds2Nm3aRF5eXtDXPO+880hJSWHZsmW89dZbgee11rz88sv89NNPZGVl1Xq4V11JohYi3JRC9RtqdRQNx4afID8HnE2tjqSW5Gu0lA7iUVdt2rThnnvuIT8/n6uuuoqbbrqJm266iauvvpqCggIee+wxmjRpEth/1KhRnHvuuXz3XfAjCTIyMnjyySdxOBw89dRTnH/++dxxxx2cffbZvPTSSzRr1oyXXnopLCtngXQmEyLslDMB47gB6HVL0JukE1mt+LwNp+23ocQZZpHoTFZq5MiRtGrViokTJ7J8+XKcTid9+/bltttuq3Jlq/oaPnw4rVu35pVXXmHJkiVs3bqV5s2bc91113H77beTkZERlusCKB3KwWZCiEpp08RcNhvz63etDqVBsN/5T7S5BYq2WB1KjVSTYShXc6vDsMyOHTs4/fTTGVZyEUnUvbmikHxmuT5l5syZNQ7PildSohYiApRhoJqEp0doTLLZweezOorakRI14C8ZR6pEHW/kHSZEpNjkvrhW7E5ITAFvrtWR1JJ8jYrwkm8OISJFegfXiup7GpjF4M2xOpTakb8rcKREHURDqim/vhpJohYiAnRJEeasj6wOo0EwOvUA9z6rw6gDKVEDoP0Lc4jQk0QtRJhp04f50/fobRusDqVhSE5FN6RpRKVEDUgbdThJohYi3Nxu9M7NVkfRcGgNNKTk15BiDR8dZIlaCuE1kzobIcJOg8dtdRANh91Ow/r6lq9REV5SohYiErRU8FUr63jsl/4BbA5Aow82oGYCqfoGpOo7nCRRCyEsZ+t9ChgG+tA8cB+gYX19S6IG/7zXwcyfVZfVs+KV1NkIEXYKHC6rg4hqvqmvoLduQGWcBo2OtTqcOpJEDf726WBWzpKe4jWTRC1EuBkGKiHJ6iiim+nFN+lZvO+/gEoK7VrCYSdV30BwC3IEuzBHvJFELUS4ORzQurPVUTQMO7eC4bA6ijqSr1ERXtJGLUSYKWVgdO7RoFpdLdP5ODBLrI6ijqREDb9XZdf5uNCHEnMkUQsRCYkpkJwKBQ1l/moLNErHftFodN4qqyOpG8nTgCTqcJJELUQkGEbExlIbF9yI0eMEdPYBzCWz0MvnROS6QevUA3Zsxv6HR6BkBxRutDqiOpJMDf7e28H04JZe3zWTRC1EJHjcqA7d0Bt+CvuljK7HgXcPJOZhO+cqdPtj8H38StivW3cGtjEPojLbAQaYbvTeZVYHFQRJ1PB7r+86Hxf6UGKOJGohIsGZgNFnML4IJGoSktCHfgRvDrp4J6rHWTB9EhRG0/zZBvZxT0JKAnrPp6A9NNyvbEnUIryku6IQEaCUQnXoRkS+1JVBoOXPmwveXIy+Q8J/3TowTj4bGqWgD8wA7abhJmn/31YcGWqlg3hYHXgDIIlaiAhRrkRISQ3/hbQJ6vchTrp4F6pb3/Bftw5Uyw5Qsh+01+pQ6skIajauWGSig36I6kmiFiICtKcE36LpkH84vBdqlI6yJ4C3zHXce1HNWob3unXRqQfq2D7o4m1WR1J/SiFlQr+gStMyM1mtSBu1EJHg86F//Tn817E70NpXfhEQ90GwO6FxEzh8sPrj05pBzv6whGacfgVG/6Fgc6BzV0DJ7rBcJ7IMJFH7yaIc4SOJWohIMAx09r7wX6ZbX/AcpvzXnwnubIy+QzBnT614UFIKthG3otp39c8K5inGXP495oKvIT8nJHHZrv8rqm0ndM4iKNlLzCQ3ZfMXCaWZWoSRJGohIkFrcNVhvu8WbSEhGX77pW6X8Xr8yePo50t2orJ6w+ypYHeijj0B1awV6ti+qPTmULIHfWgueLIhoTVGn34YA84Abwk65xAcPoTeuxNz+Wyoyw2HYWC78QFUsybo/d+AWVyn1xP1lI2YuemoJ381dhDjqOXXVyNJ1EJEgjIw2nTG3FtDu2xKGvY/PgFOf1L3/n10nS5jtDsGvHkVN5TsRTXrgf3e//qrwX1F4CtEF+9E718KvsLf9y3egS7eARjgbALOdFSLJFTbfhgnnwWHD+Kd+RGs+bHidVq0w2jfDdW6I6p1B391u68AfXBWA5watBYquSmKV0GPo5ZEXSNJ1EJEgHK6UB27w7LZ1e5nDLkEyEfv/gLV4iJstz0B+3bgWz4Xtqyt/iJde/k7aR2aV3Gb5xA6e4G/ROvNOzJuuSYmuPeDe//vZUbDBUmdsF9yM5x+GXr/bnAmoJo0g6RG/n18BeDJQXt2w8GfyndsiznSRl0q2B7c0uu7ZpKohYgQ1bZrzTuZviNFDI0+OBNcrVAdW2A/9i4wfehN6/B9/zns2uzfPyEFY8AZGL1PhtR0fyctdxWdwUp21f9FmCWQvw5dsBGSO6NaJoD2oj2/wP5D4IumSVUiQMnAmVLB9uCWEnXNJFELESmJyf7FOYqqTmbmvM8w+g4CRwZ4DoF3/ZHyhgJXS1TbtthvvA/cxf4OTM5E8BxGF22FvT8cmTwkArQH8n+RspBUfYsIkEQtRKR4PahOPdBrFle9T34OescmVEZ7tOdQmQ0aSnahS3aBWgYJbfylW3csTBrSkEl371JS9R0+Um8jRKS4ErANu6zm/exOsFXTQ1x7oWirfxyyJGlr2VOQZO0nE56Ej5SohYgQpQzMwkp6ZJdhDLsM1aIVev/XEYpK1Is2kUTtp4MsUcsylzWTRC1EBKmM5vi/2DWkNAavBzCgSXOME07H6NkffXB2bA5likVmCTK3lp+pNWYQxeNgjok3kqiFiCSXC/v9E/wzgKEJlMa0F9yH/Em6XNu0iGqGEylRi3CTRC1ERCl0wToo2Cjty7HAlgJKvkbBX4UdTDW2VH3XTN5hQkSSMlDKiZYkHROUMmQ96iNkUY7wkUQtRAQpZaAdGVaHIUJGxlGXks5k4SOJWohIq27olWhYZGayAK11kItySKKuibzLhIggrU1wh3+5SxEhhpR1RPjJu0yISNJedMluq6MQoWIkWB1B1PC3UQczM5moiSRqISLK8K/5LGKD4bI6gqghU4iGjyRqISJNvpdih/ZZHUHU0EdSdTDHiepJohYikpQBZqHVUYgQ0YVbwJGOMhxWh2I56fUdPtKZTIhIkvHTscW9D5mZTISblKiFiCQt1XwxRXtAJjwB/G3NStqow0IStRARorUJxbusDkOElOFfp1FyNSYmKoj2ZlPaqGskiVqISNEm2ldgdRQilFTp4ipCK41WQXQmU/L7q4m0UQsRIcqwoxp1l0UcYolhRxK1nxno9133h6ieJGohIkrqSGOK3HSJCJB3mRARok0vFG+Xnt+xRBJ1gMYMqr1ZxlHXTN5lQkSMiS7aZnUQIpQkUQfIhCfhI+8yISJF2VHOpjLXdywxHEhzhp+pNATRmcyUzmQ1kjZqISJEKQNcmVaHIUJJ2WQc9RFmPf4T1ZNELUQkOdKQElgMUTbka1SEm1R9CxEBWmuUUihlQzvSZAWtmGFDbrz8pDNZ+EiiFiLMtPZB/i9oeyNUYrsjpTARC5Syo6TqGyhNuJKow0EStRDh5j6EzlsFgM5ZCtptcUAiZAz5Ci3ln0K07st+SqKumbzLhAgjbXrQRVvKPCFJOqYoWd6ylJSow0d6QQgRTsqA4p1WRyHCRclXqAg/KVELEU6mG8wSq6MQ4aLrXtUbq0xlooJalENK1DWRRC1EOHkOWR2BCCdfCVqb/jHycc4/M1kwNy6m9Juvgby7hAgTrX3okn1WhyHCSLv3ytztRwS/dpaUqGsiJWohwkX7wFdodRQinNz7ZLjdEab2BdcUoH1SYqyB/H6ECCspLcQ07YOi7Wgtf2cRPpKohQgXZUe5WlodhQgznbcauSGTqu9wkkQtRNhoMJxWByHCzZcPxbvivlRd2pms7o/gfm8LFizg+uuvZ8CAAfTt25frrruOefPmBR3/5s2b6dWrF//5z3+q3Cc3N5d//etfDB8+nF69ejFs2DCefvpp8vPzg75ubUiiFiKEtOn1dyLz5qEPr0Dn/Gh1SCICdPHuuB+qVTrXd13/CyZRT5s2jdGjR7NixQp69epFnz59WLFiBWPGjGHKlCl1Pt+hQ4cYO3YsJSVVD6XMz8/n2muvZeLEiSilGDJkCEop3nzzTa688kry8vLqfN3aks5kQoSAvzSlQXvQ+2eAr8DqkEQkKVmcI/hq7Lods3fvXh5++GEaNWrE5MmTycrKAmDlypWMHj2aJ554giFDhtCiRYtanW/Dhg2MGzeOrVu3VrvfCy+8wPr167niiit49NFHMQwDr9fLfffdx6effsoLL7zAgw8+WKfXUltSohainrTpBc9h9P7p6L2fSpKOR8qQdakjZNKkSbjdbkaNGhVI0gC9evVizJgxlJSU1KpUXVRUxPjx47niiivYunUrbdq0qXLf3Nxc/ve//5GSksI999yDYfhTp91u5+GHH6Zx48Z89NFHFBaGZ5SHJGohQkAf+Ba8uVaHIawiJWq0Nv3NPnV+1K1E/f333wNwxhlnVNhW+lxt2qq//vprXnrpJVJSUhg/fjwXX3xxlfsuWbKE4uJiTjrpJFJSUsptS05OZuDAgRQXF7NkyZI6vJLak0QtRL0dqfYWcUtJog6yhbpua1hrrdm4cSOGYdCpU6cK2zt06IBhGGzcuBGtq/9MpqWlMW7cOKZPn86ZZ55Z7b4bN24EoGvXrpVuL41l/fr1tXkZdSZt1ELUVw1fCCIe2OJ+XWp/D+5gfgf+Tnh79uypsCU1NZXU1NTAz4cPH8btdpORkYHTWXFEhd1uJz09nYMHD1JQUFCh9FvWsGHDGDZsWK0i3L9/PwDNmjWrdHvp8wcPHqzV+epKErUQ9SVLVwqZ67veRo4cWeG5sWPHMm7cuMDPRUVFACQmJlZ5noSEBIAaE3VdlLY9V3Xd0muGq41aErUQ9aC1D4q2WR2GsJg2SyDOF+fQWgc3lvxIjdSkSZPIzMwst6lsaRoIdOKqbTyhYrP5p4mtqdbENMMzll4StRD1oX3okt1WRyGsVrIXUo6N65K1fxx13au+jSNt1JmZmdX2vAZISkoCqHa8c3Fxcbl9Q6G0JF167khcsyxJ1ELUh7KDW5ayjHueQ8T7NKIaX1DdNeqyNGZKSgpJSUlkZ2fj9Xqx28unMK/XS3Z2Ni6Xq0JpvD6aN28OwIEDByrdXlMbdn3F7+2fEKFguon3L2gBoNH564/MTBef74dIzPWtlKJLly74fL5KJyjZsmULpmmWG18dCqW9vUt7fx9t06ZNABxzzDEhvW4pSdRC1IfhBOWwOgoRDfLXoXOXQ5wm6kgZNGgQADNmzKiwrfS5wYMHh/SaJ554IgkJCSxcuLBCh7GCggIWLlxIUlIS/fr1C+l1S0miFqI+tBecTayOQkQFDYWboWiLv5NhnCntTFb3R93qy0eMGIHL5eK1115j9erVgedXrVrFxIkTSUhI4Jprrgk8v23bNjZt2lSvubiTkpK4+OKLOXz4MI8++iherxfwV7U/9thj5ObmcuWVV4asl/nRpI1aiPpQdpSzObqk4hhQEUcS2qIadQdsYHP5ezLH2bBq/zjqujdS13V+8DZt2nDPPffw2GOPcdVVVzFgwAAAFi9ejNfr5R//+AdNmvx+8zxq1Ch27tzJU089xYgRI+ocX6m77rqLxYsX88knn7Bs2TK6d+/O2rVr2b59Oz169Cg3jCzUpEQtRD0oZYArs+YdRexSTlTaAJQjHeVIRRkulBF/ZaDgStNmUG36I0eOZMKECfTu3Zvly5ezevVq+vbty5tvvslFF10Uhlfnn8nsgw8+4LrrrsPr9TJ79mwMw+Dmm2/m7bffJjk5OSzXBVA6lIPNhIgzWmtwH0AfnGl1KMIqjiaoJoNRcbr2+I4dOzj99NM5sMeJ6QtieJZN0zTTzcyZM2scnhWv4u+2T4gQ0doEX4GsOR3vbOEZOytEKUnUQgRLm+DNk2Ut450t+cjqWfEt2AlPlCxoUyNpoxYiSMqwg7M5quUIcEjP73il7I2OrJ4V3yLZRh1vJFELUQ/+TkM2VPrJVocirGJvZHUEUSFS61HHI6n6FqKelFJom8vfVukLz+o5IorZwtfbtyHxzzIWzHEgZcbqyW9HiFDQJtikZBWXbAlWRyBinJSohQgJA5wZ4N5rdSAikmT62AB/1Xcwx4GUGasniVqIEFCGHVKORRf9Vn31t+H0fzNpT+SCE+FjOP21KdKZrJ6JWlRHErUQoaLsqGbD0QW/Qt4aKNtiZ0tGNe77+yxmviL04WUga1k3cFISLKWP/Ff34+JsrtUgSKIWIkSUMkC5ILkbJLRDF20FTFRCG3CkA8q/D6BtyRhNBmPmLIPCX60MW9SHdoOSZA1HOpMFUTyWRF0zSdRChJgy7GCkgr2H/+dKqkWV8n85qcQ2aEnUDVccrpIlIk8StRBhUptJMJSrhczLJGJDEEtW+o+TEnVNJFELYSFdtMPqEIQIEROCuu2URF0TSdRCWMmQ4T0iNmitg5plTGtp46+JJGohrORsCsrp75QkRAPmn5ksiEQdhlhijdzKCGEl7QN7itVRCCGimJSohbCUAnsqeA5ZHYgQ9RLsSlgy4UnNpEQthJWUHZXQ2uoohAgBXz0eojpSohbCQkoptCPN6jCEqLfgO5NJr++aSKIWwkJaa3AftDoMETSNDC8qZR551JX8/moiVd9CWEl70YVbrI5CCBHFpEQthEW01uDLl6UxGzLpCfU7bfofdT5OStQ1kUQthFW0F5231uoohAiJ4FfPkpudmkiiFsIyWiY6afCkjfp3muDaqKUFtiaSqIWwjAJvgdVBCBEaWgfXFCDNBzWSWxkhrKJs4JNE3bDpwJKlQoSLlKiFsIqvEJnpuOHT2kQpKfPY7Aaampd2rew4UT1J1EJYxZNjdQQiJOL7ZislJYXGjRvTrl3w52jcuDEpKTLnfVWUDmqlbyFEfWhtovNWQ770+m7oVOZlKCO+yzw5OTnk5+cHfXxKSgppaWmhCyjGxPe7SwiraC94D1sdhQgJKeukpaVJog0jaRwQwhIKvHlWByFCQBkOq0MQMU4StRBWUDbwBl9VKISIH5KohbCCJ4fgJocQ0UabHqtDEDFOErUQEaZNL7pws9VhCCEaCEnUQkSaUlC8w+oohBANhPT6FiKCtOnzJ2mz2OpQRMhIr28RXlKiFiJCtNagPejDS60ORQjRgEiiFiJStA+dPR+0dD4SQtSeJGohIkBrE9z7wL3f6lBEyEnVtwgvSdRCRIL2ofPXWR2FCAfJ0yLMJFELESm+QqsjEGEhmVqElyRqISIloY3VEYiwkIlrRHhJohYiApThQDXqCbYkq0MRoaZ9VkcgYpwkaiEiRSlU4xOsjkKEmiRqEWaSqIWIEKVs4GxqdRgi1CRRizCTRC1EhGjtg+KdVochQk17rY5AxDhJ1EJEgNYmmCXo3J+sDkWEmimJWoSXJGohIkKjD8wEs8TqQESoSYlahJkkaiEiwXSDr8DqKERYSBu1CC9J1EJEguGyOgIRLlrGUYvwkkQtRESYYCRaHYQIB+n1LcJMErUQEaHAJok6JkmiFmFmtzoAIWKZfw1qL/rwcvAcsjocEQZa+0BrlFJWhyJilCRqIcJEa9OfpA/MAG+u1eGIsDHxL8whiVqEhyRqIUKstBSN9qIPzpYk3ZA5moDhBLS/05jnYMWqbl2aqIUID0nUQoRQoBR9eCkUbUe+wBsyhdHsTLTpPvKjAzD9iVnZf/+3EGEmiVqIENGmF8wifyla1p6OARrtK0KV6wRoA2Wr5N9ChI8kaiHqSZd2JPLmoQ/OlJmqYomUmEUUkEQtRH1pH2bOEij6zepIRKiZxWidiFIyklVYR959QtSX97Ak6RilsxfIOGlhOUnUQtSXr9jqCES4+ArQuSvRpsfqSEQck0QtRH25WqDSBoAjA/lIxaDCjSBV38JC0kYtRD0pw45ObI9KaANof69vT7bVYYmQOTKGWnp4C4vIbaIQIaCUgTIcoByoJkNltaxYI+3UwkKSqIUIIf98zwYktLU6FBFKMuROWEgStRChpmwoW4LVUYhQcDaH9FPBlsRHH03j4MFD5ObmWR2ViDOSqIUINe1D+4qsjkLUV0JbSD+VqV8son//k7n88itp3rwln332udWRiTgjncmECDWljszzLRos5UClncjNY8bxxhtvBJ42TZOmTZtaGJiIR1KiFiKEtOmFgs2g3VaHIuojsQP79h0ol6QBbDYbiYkJ+HzSuUxEjiRqIUJEmx4o2IDOXWF1KOHhbAaNeoEj3epIwsyGatSdJ578Z4UtTz/9JCec0A+bTYZqichRWmtZh0+IENDaRO/7IjZWzrI3BmdTcKSBkYxypOPTiq1bt9O5c0e0ez/krYzN8eKNerO/oDGZLdtX2LRgwTwGDhxoQVAinkmJWogQ0FpDyZ6GnaRdLVAtL0e1vAIyTmd7ThM+n/ELL0/8mMuuHIXD2YiuWd1p3qItH332A6rJMEhobXXUoZXUBZK6cNbwCyrd/K9/PUd+fn6EgxLxTkrUQoSANj3onMVQvMPqUIKX1Jl9xa3p2bMfBw4cqHH366+/jrfefBWdPR9KdkcgwDpytfJPPOM9fKTkX81XnZEIqcejnZmcceZ5zJkzt8pdvd5iqfoWESW9voUIFU+O1RHUj6+IpARXrZI0wDvvvIvT6eTVV17yt8sXbg5zgLXkSIeEdpDUmd27d9O8eU/sDhe6eBcUrAd36etT/qr9xA6opM4sXbacESMGs2PHzipPnZiYiJRtRKRJ1bcQIaFRyV0g5ThI7AgoqwMKgsYw6vaVMHHi65xz7sXolOMh+djwhFUbhgtSuqNaXIyZNpiVG4s586zzadO2C05XY7p27cHn367AbDwI1fIKVIuLUS0vR6cPYfkvuZxy6lD69z+l2iQNkJmZidfro6CgIDKvSwik6luIkNDaPLJwgwKtwXMIfWhew5p60pYCTc/CZk+u86Fnn302X305Fb3308jOi53YEZKzUI5Uftu6lXv+9iAffvi/ag/JzMykS5curF69mpycnDpf8u9/f5QHHrgPrfWRKWOFCC9J1EKEgdY+wEAf+LZh9Iw2XJCchU7sit1R90QN8NvWDbRtmYou3AJmib/dOmw3KgakDcBra87fH3+aF18cT25ubpiu9buTTjqJGTO+ITExsc61D0IES95pQoSBUjaUUhjNhhPd1eDKX2Xc/Hy27HVy3vmXBH2mzl268+W3C9m8P5V827GozEsguVsIYz3ClgTNzmLfYTut23Ti8cefjEiSBhg8eBAul0uStIgoKVELEQZaa//sZKYPfWC6v4QZbQwXpA8ir9hgxIirmDVrdkhPP2DAABYumIMu3g4FG8FzsP4nTWyHanwi30yfwbnnXlj/89VR69atefTRh7jyyitISUmJ+PVFfJJELUSYaLMEvedjq8OoXGJHVOM+LFm6glNOGYLXG54q6rPPPptbb72FC84/G/LXQMEvwZ3IcEH6yfhUY0aN/gOTJ08ObaB1tG3bZtq2laVMRWRIohYiTLQ20XumRrZzVY0MSB+IR2Vw85g/8t57kyJyVX9ns0/QhRsh9yeqHdN8NEcTVMZpLFn2E4MHn0FxcXG4wqyVTp06sXLlcpKTg2vLF6KupKFFiLBSRFUbderxFJvpZLbsELEkDfDNN99w2uAzKDCbo5qdB416U+73omyopmdCxlDKfS05mqCaDOGhR55kwIBTLU/SAHa7HZvNFrZaCCGOJiVqIcJEm15QR5KO9zA650fw5gEKtMeaoJK7sW67jeN69rXk8na7nT/+8TYee/RBUlwlkP29v8ah8QnsyXHhdDpp0iQDXbwDPDmolO78/YlnePjhRyyJtyp9+/Zl9uzvcLlcuFwuq8MRMU4StRAR4P+YmQRKkQUb0CV7/PODR1JCG3J0V5o0bRPZ6x4dRkICv25YTatmCVCwHpV+Msf3GcDKlSs59dRTufPOcfTr25t1v6znvPMusjTWytjtdnJyDkj1t4gISdRCWEBrH2gTnfsTFG6K3IWdzXAn9ScxOSNy16xCUlISK39eSrt2bXjo4cd5+umKy0pGq6ZNm7Jr1zYcDofVoYg4IHN9C2EBpWz+UrbhjOyFtUm0TKZVWFhIl67drQ6jzk444QS++eZLfD6fJGoREZKohbBScfVzS4ec4cTttqh9vIEzDIM5c2bRr18fEhMTZfpQETHS61sIy5hgS4zsJQ0XBYUNeM1sC3Xp0oW+fY8nKSlJkrSIKEnUQlhGgS3CnZGUgd0uFWnBsNlsssSlsIR8YoWwUkQW7FCQPgjlaoYyHOxYsSwC14wtKSkpzJr1rdVhiDglJWohLKPBSAj/ZRLbUaJTGTL0bByORPr0PSn814whSUlJ5OVlk5qaKvN7C0tIohbCMsaRCVDCzJ7G/AWLmTdvnsymFQStNUuWLMXhcJCXF4G/lxBHkUQthGU0+PLDfxlnc+bOnRf+68SooqIi+vcfSGpqBlpraacWESeJWgirmJGYt9pAOdN44423InCt2FZcXMzppw+XUrWIOEnUQlhAaw3uA+G/kL0Rps/Lrl27wn+tOLB06VJeeOFFSkqicH1xEbMkUQthBe1DRyJRpx7Pjz9KL+9Qmjr1YzwemTRGRI4MzxLCCkqBCvf0kwbK1YJb/nBumK8TX447rrv0/hYRJYtyCGER7c1D7/82fEteJnWiwOhKauPm4Tl/nNq0aQMdO3aQ2clExEjVtxBWMZJQKd3Cc25XS1RqH8bcMjY8549jN954M2632+owRByRRC2ERZRhg+RjwNkcEtuDq2WIzmyg0gbwf3f/jSlTPgzROUWpuXPnsXbtOqvDEHFEErUQVlIGqskQVOMTUOknoxr3q/85nc1we7y88MKL9T+XqNTkye9TVFSEx+ORcdUi7KQzmRAWUurIvXLp/5O7ogs2gTcn+JMmdWLmLJngJJyeeeY5SkpKSE9P5957/0ZCgsvqkEQMk85kQkQRbXrRh+b5x1i7WoAjDUwPFG4CavdRVZmXcsKJp7B8+Yqwxir8vvtuOmecMSzws9ZaOpqJkJJELUQU0aYXzCKwJYH2gbKDNsGTjT44ixqTtXKgMi/BMKSEFymXXjqC9957B61NNm7ciNPp4phjsqwOS8QQSdRCNADa9KCz50PJnup3dLWi2NWb5EZNIxOYAKBFixaccEI/li5dxtKli2jTpo3VIYkYIp3JhGgQalnt3eg4pvzv4zDHIo62d+9evvzyK0aPvoHmzZtLJzMRUlKiFqIB0NqL3jcdfEctCGEkQHJXMFzgyKDIbSctvYUsZ2mR3r178/nnH7N69VpOP30oHo+H5ORkq8MSDZwkaiGinDa9ULgJnXtU5zB7I1TGUDZs2s4vv2xg1apVPPvs8+Tk5FgSpyivf//+LF483+owRAyQRC1EFNOmFwrWo/NWld+Q2AHVuB9vvT2JG28cY01woloul4uVK1eQldXV6lBEAydt1EJEKW16oOAXf5J2ZEBqX2hyJjQ5E29Sb6697mZJ0lGspKSESZMmS1u1qDeZ8ESIqKUgpTsqOQvT1Pz00yo++XQSLVq04N577yc/P9/qAEUN0tPT8fl82O3yVSuCJ1XfQkQxn8/H4MGns3jxYukg1sCcdtqg/2/vbmOrqg84jv/OPefc0/vQXkpNTW2EFRCKrcgqMtwKAgWGUGbEx+FAQMKDmzFL0GWJL5zv3UyWGHExCxm6OZHKjGNuSGLCgBca6kZpIyRg9AUYQpj09l5677lnL3BkD1Cg7fz/7+338w5Kyu9Fk2//5557rj74YJ/CMJTruqbnoIxx6RuwWC6X05kzZ4h0Gaqvr1c+n5fjOCoUCsrlcqYnoUwRasByc+e2m56AYejqelsbN27Rtm2/0uHD3QqCQBcuXDA9C2WIS9+AxaIoUm9vn1paZpieghFqbW3VwYP7lUoleRY4rgsnasBijuOoqekbuvvueaanYISOHDmi7u5u7gLHdSPUgOWqqqrU1bVTDQ0NpqdghNat26A33vg9d+zjunDpGygDhUJBr7/+O61du970FIyQ4zj68MNDamtrMz0FZYITNVAGfN/X/fffJ9/3TU/BCEVRpCNHekzPQBkh1ECZKJVKWrXq+6ZnYBQcO3Zc+Xze9AyUCUINlImamhq99NIv9c47b2vy5Mmm52AEdu7cpVKpZHoGygShBspIMpnU0qXfVXf3R7r33u+ZnoNh6uvr06ZNT+j06S8kiWhjSNxMBpSpbDar2bO/raNHj5qegmHq6FiovXvfMz0DluNEDZQp3/e1Zs0PTM/ACLz44s85TeOqOFEDZaxYDHXw4AGdOPGpTp48qZ6eo3r33T8qm82anoZr0NPzNzU3T1MsxpkJV0aogQoRhqGy2aw8z9Nbb3Xp6ad/otOnT5uehSF0de3UsmX3KB6Pm54Ci/FrHFAhXNdVTU2NksmkHn74QfX2/l0dHQtNz8IQDhw4yCNFcVWEGqhA8XhctbW12r17lzZs4Glmtnr55Ve0e/cfeKQohsSlb6DCZbNZbd78Q+3Y8ZrpKbiMcePG6dSpzxUEgekpsBQnaqDCpVIpbdv2kmbPnm16Ci7j3LlzPFIUQyLUwBiQSCS0Z887amxsND0Fl9Hb28tr1bgiLn0DY0ShUNAnn3yimTNnqVgsmp6Dr0yZMkXHjvWangGLEWpgDOnv79cLL/xCzz33vKSLN53F43H5vq94PK7q6mqVSiWlUimlUikdP35cZ86cMby68u3b9xfdeecspdNp01NgIUINjDH5fF6lUqQguPje3TAMVSqVVCqVlEwmlcvlNDg4KMeJ6bPPPlNr6+2GF1c+x3H0+eef6qabGkxPgYV4jRoYY6qqqpRMJuS6rlzXVTwe/+rvkpIuvp6dyWRUU1OtKVMma/r06YYXV74oirRjx2sKw9D0FFiIUAMY0qOPjt5nYPu+r0mTJo3a96sUS5Ys1jPPbOVRorgsfioAXFEQBFq/ft2ofb8ZM2aooYHLu/+tp+eocrmcLly4YHoKLMRr1ACGdP78eS1ZskyHDh0yPaXi3HbbbZo+vVn5fF6TJk3Ss8/+VJlMRp7nmZ4Gi/DTAGBIiURCGzduINSj6Oabb1Ymk9Gbb/5WjY2Nl94u53kekcb/4EQN4Kqy2azGj6/X4OCg6Slly/M8tbd/R88//zPNmtWmwcFBeZ6nVCplehosx69uAK4qDEN1di7Xrl1dpqeUhYaGBlVXV6uzc5lc11V7e7s6OhaoWCwqnU7LdV0lEgnTM1EmOFEDuKooivT++/u0ePFS01Osc+ONN2rFik6NH1+rbHZAS5cu0eLFi+Q4jqIokuM48n1fjuOYnooyRagBXJN8Pq+VKx/Unj1/Mj3FGtu3/1oPPfSAisWigiBQsViU7/u8zoxRRagBXJMoipTNZnX4cLcee2y9Tpw4YXqScWfPfqHa2lrTM1DheB81gGviOI7S6bTmzPmWPv74I82bN9f0JCPGjRunZcvu0auvvsKzufG14EQN4LpFUaTz5/vV1DRFZ8+eNT3n/8L3fa1Y0alp06aqublZs2bdoebmaSoUCsrn86quruZJYvhaEGoAw5LP57V9+2+0efMTpqeMikQioZaWFt1663RNnDhBTz75IwVBXIlEQr7vm56HMYxQAxi2gYGc7rhjtvr6+kxPuW6e56mjY6HWr1+ruXPn6oYb6jQwMKBYLKYgCBSPx01PBCQRagAjEIah9u//q+bP7zA95aoymYxaW1s1bdpUzZvXrpUr71OpVOISNqxHqAGMSH9/vx544GG9996fTU+RdPFjPNva2tTScqtmzrxdbW3f1C233KLq6rQGBnLyPFfJZJI4o2wQagAjEkWRTp78VKtXr5Xrxi59zrXruvI87z/+nMlkLn1C1KlTp7R37/ujsmHChAnq7FyurVt/rKamJp079w95nqtUKsWDRlD2CDWAEctmsyoUClf8+r9i6TjOpZNsLBZTKpUZ1v83ceJELVgwXytWLNeCBfMVBIGiKOK52ahIPD4HwIgNJ5D5fP6a/63rurrrrjnq7FyuVaseUV1dncIwVDqd5sSMikeoARhxLYGdOnWqHn98nbZs2aRSqaRkMslbpTDmEGoARlwp1M3NzVq16hGtXv2o6uvrFYvFVFVV9TWvA+xBqAEY8e+hrqur05o1q7VlyyY1Nt4k13UVBIHBdYA9CDUAI2KxmBYt6tBTTz2pRYs6FIYhN4MBl8Fd3wCM+fLL80qnU7ynGRgCoQYAwGL8GgsAgMUINQAAFiPUAABYjFADAGAxQg0AgMUINQAAFiPUAABYjFADAGAxQg0AgMUINQAAFiPUAABYjFADAGAxQg0AgMUINQAAFiPUAABYjFADAGAxQg0AgMUINQAAFiPUAABYjFADAGAxQg0AgMUINQAAFiPUAABYjFADAGAxQg0AgMUINQAAFiPUAABYjFADAGAxQg0AgMUINQAAFiPUAABYjFADAGAxQg0AgMUINQAAFiPUAABY7J9zOWJB0P5zCAAAAABJRU5ErkJggg==\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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", "text/plain": [ - "
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" ] }, "metadata": {}, "output_type": "display_data" }, + { + "ename": "AnalysisException", + "evalue": "Column 'cdr_active_days__allweek__day__callandtext' does not exist. Did you mean one of the following? [active_days_allweek_allday, active_days_weekday_allday, active_days_allweek_day, active_days_allweek_night, active_days_weekday_night, active_days_weekend_allday, active_days_weekday_day, active_days_weekend_night, call_duration_allweek_day_call_max, call_duration_allweek_day_call_mean, call_duration_allweek_day_call_std, active_days_weekend_day, call_duration_allweek_day_call_median, call_duration_allweek_day_call_min, interevent_time_allweek_day_call_max, interevent_time_allweek_day_call_mean, interevent_time_allweek_day_call_std, balance_of_contacts_allweek_day_call_max, balance_of_contacts_allweek_day_call_mean, balance_of_contacts_allweek_day_call_std, call_duration_allweek_allday_call_max, call_duration_allweek_allday_call_mean, call_duration_allweek_allday_call_std, call_duration_allweek_day_call_kurtosis, call_duration_allweek_day_call_skewness, interevent_time_allweek_day_call_median, interevent_time_allweek_day_call_min, balance_of_contacts_allweek_day_call_median, balance_of_contacts_allweek_day_call_min, call_duration_allweek_allday_call_median, call_duration_allweek_allday_call_min, call_duration_allweek_night_call_max, call_duration_allweek_night_call_mean, call_duration_allweek_night_call_std, interevent_time_allweek_allday_call_max, interevent_time_allweek_allday_call_mean, interevent_time_allweek_allday_call_std, interevent_time_allweek_day_call_kurtosis, interevent_time_allweek_day_call_skewness, number_of_contacts_allweek_day_call, number_of_contacts_allweek_day_text, balance_of_contacts_allweek_allday_call_max, balance_of_contacts_allweek_allday_call_mean, balance_of_contacts_allweek_allday_call_std, balance_of_contacts_allweek_day_call_kurtosis, balance_of_contacts_allweek_day_call_skewness, call_duration_allweek_allday_call_kurtosis, call_duration_allweek_allday_call_skewness, call_duration_allweek_night_call_median, call_duration_allweek_night_call_min, call_duration_weekday_day_call_max, call_duration_weekday_day_call_mean, call_duration_weekday_day_call_std, call_duration_weekend_day_call_max, call_duration_weekend_day_call_mean, call_duration_weekend_day_call_std, entropy_of_contacts_allweek_day_call, entropy_of_contacts_allweek_day_text, entropy_of_contacts_weekday_allday_text, interactions_per_contact_allweek_day_call_max, interactions_per_contact_allweek_day_call_mean, interactions_per_contact_allweek_day_call_std, interevent_time_allweek_allday_call_median, interevent_time_allweek_allday_call_min, interevent_time_allweek_night_call_max, interevent_time_allweek_night_call_mean, interevent_time_allweek_night_call_std, number_of_contacts_allweek_allday_text, number_of_contacts_weekday_allday_text, percent_at_home_allweek_allday, percent_at_home_weekday_allday, percent_nocturnal_weekday_call, percent_nocturnal_weekday_text, response_delay_text_allweek_day_max, response_delay_text_allweek_day_mean, response_delay_text_allweek_day_median, response_delay_text_allweek_day_skewness, response_delay_text_weekday_allday_max, response_delay_text_weekday_allday_mean, response_rate_text_weekday_allday, balance_of_contacts_allweek_allday_call_median, balance_of_contacts_allweek_allday_call_min, balance_of_contacts_allweek_night_call_max, balance_of_contacts_allweek_night_call_mean, balance_of_contacts_allweek_night_call_std, call_duration_allweek_night_call_kurtosis, call_duration_allweek_night_call_skewness, call_duration_weekday_day_call_median, call_duration_weekday_day_call_min, call_duration_weekend_day_call_median, call_duration_weekend_day_call_min, entropy_of_contacts_allweek_allday_text, frequent_antennas_weekday_allday, interactions_per_contact_allweek_day_call_median, interactions_per_contact_allweek_day_call_min, interevent_time_allweek_allday_call_kurtosis, interevent_time_allweek_allday_call_skewness, interevent_time_allweek_day_text_max, interevent_time_allweek_day_text_mean, interevent_time_allweek_day_text_median, interevent_time_allweek_day_text_std, interevent_time_allweek_night_call_median, interevent_time_allweek_night_call_min, number_of_contacts_allweek_allday_call, percent_at_home_allweek_day, response_delay_text_allweek_day_min, response_delay_text_allweek_day_std, response_delay_text_weekday_allday_min, response_delay_text_weekday_allday_std, balance_of_contacts_allweek_allday_call_kurtosis, balance_of_contacts_allweek_allday_call_skewness, balance_of_contacts_allweek_day_text_max, balance_of_contacts_allweek_day_text_mean, balance_of_contacts_allweek_day_text_median, balance_of_contacts_allweek_day_text_std, balance_of_contacts_allweek_night_call_median, balance_of_contacts_allweek_night_call_min, balance_of_contacts_weekday_day_call_max, balance_of_contacts_weekday_day_call_mean, balance_of_contacts_weekday_day_call_std, balance_of_contacts_weekend_day_call_max, balance_of_contacts_weekend_day_call_mean, balance_of_contacts_weekend_day_call_std, call_duration_weekday_allday_call_max, call_duration_weekday_allday_call_mean, call_duration_weekday_allday_call_std, call_duration_weekday_day_call_kurtosis, call_duration_weekday_day_call_skewness, call_duration_weekend_day_call_kurtosis, call_duration_weekend_day_call_skewness, entropy_of_antennas_weekday_allday, entropy_of_contacts_allweek_allday_call, entropy_of_contacts_weekday_day_text, entropy_of_contacts_weekend_allday_text, frequent_antennas_allweek_allday, interactions_per_contact_allweek_allday_call_max, interactions_per_contact_allweek_allday_call_mean, interactions_per_contact_allweek_allday_call_std, interactions_per_contact_allweek_day_call_kurtosis, interactions_per_contact_allweek_day_call_skewness, interevent_time_allweek_day_text_kurtosis, interevent_time_allweek_day_text_min, interevent_time_allweek_day_text_skewness, interevent_time_allweek_night_call_kurtosis, interevent_time_allweek_night_call_skewness, interevent_time_weekday_day_call_max, interevent_time_weekday_day_call_mean, interevent_time_weekday_day_call_std, interevent_time_weekend_day_call_max, interevent_time_weekend_day_call_mean, interevent_time_weekend_day_call_std, number_of_antennas_allweek_allday, number_of_antennas_weekday_allday, number_of_contacts_allweek_night_call, number_of_contacts_allweek_night_text, number_of_contacts_weekday_day_text, number_of_contacts_weekend_allday_text, number_of_interactions_in_allweek_day_call, number_of_interactions_in_allweek_day_text, number_of_interactions_in_weekday_allday_text, number_of_interactions_out_weekday_allday_text, percent_at_home_allweek_night, percent_at_home_weekday_night, percent_at_home_weekend_allday, percent_nocturnal_allweek_call, percent_nocturnal_allweek_text, percent_nocturnal_weekend_call, percent_nocturnal_weekend_text, radius_of_gyration_weekday_allday, response_delay_text_allweek_allday_max, response_delay_text_allweek_allday_mean, response_delay_text_allweek_allday_median, response_delay_text_allweek_allday_skewness, response_delay_text_allweek_day_kurtosis, response_delay_text_weekday_allday_median, response_delay_text_weekday_day_max, response_delay_text_weekday_day_mean, response_delay_text_weekday_day_median, response_delay_text_weekday_day_std, response_delay_text_weekend_allday_max, response_delay_text_weekend_allday_mean, response_rate_text_allweek_allday, response_rate_text_weekday_night, response_rate_text_weekend_allday, balance_of_contacts_allweek_day_text_kurtosis, balance_of_contacts_allweek_day_text_min, balance_of_contacts_allweek_day_text_skewness, balance_of_contacts_allweek_night_call_kurtosis, balance_of_contacts_allweek_night_call_skewness, balance_of_contacts_weekday_allday_text_max, balance_of_contacts_weekday_allday_text_min, balance_of_contacts_weekday_allday_text_std, balance_of_contacts_weekday_day_call_median, balance_of_contacts_weekday_day_call_min, balance_of_contacts_weekend_day_call_median, balance_of_contacts_weekend_day_call_min, call_duration_weekday_allday_call_median, call_duration_weekday_allday_call_min, call_duration_weekday_night_call_max, call_duration_weekday_night_call_mean, call_duration_weekday_night_call_std, call_duration_weekend_allday_call_max, call_duration_weekend_allday_call_mean, call_duration_weekend_allday_call_std, entropy_of_antennas_allweek_allday, entropy_of_contacts_allweek_night_call, entropy_of_contacts_allweek_night_text, entropy_of_contacts_weekday_night_text, frequent_antennas_allweek_day, frequent_antennas_weekday_night, frequent_antennas_weekend_allday, interactions_per_contact_allweek_allday_call_median, interactions_per_contact_allweek_allday_call_min, interactions_per_contact_allweek_night_call_max, interactions_per_contact_allweek_night_call_mean, interactions_per_contact_allweek_night_call_std, interactions_per_contact_weekday_day_call_max, interactions_per_contact_weekday_day_call_mean, interactions_per_contact_weekday_day_call_std, interactions_per_contact_weekend_day_call_max, interactions_per_contact_weekend_day_call_mean, interactions_per_contact_weekend_day_call_std, interevent_time_allweek_allday_text_max, interevent_time_allweek_allday_text_mean, interevent_time_allweek_allday_text_median, interevent_time_allweek_allday_text_min, interevent_time_allweek_allday_text_std, interevent_time_weekday_allday_text_max, interevent_time_weekday_allday_text_min, interevent_time_weekday_allday_text_std, interevent_time_weekday_day_call_median, interevent_time_weekday_day_call_min, interevent_time_weekend_day_call_median, interevent_time_weekend_day_call_min, number_of_antennas_allweek_day, number_of_contacts_weekday_night_text, number_of_interactions_alldir_weekday_allday_text, number_of_interactions_in_allweek_allday_text, number_of_interactions_out_allweek_day_call, number_of_interactions_out_allweek_day_text, percent_at_home_weekday_day, percent_pareto_interactions_weekday_allday_text, radius_of_gyration_allweek_allday, response_delay_text_allweek_allday_min, response_delay_text_allweek_allday_std, response_delay_text_allweek_night_max, response_delay_text_allweek_night_mean, response_delay_text_allweek_night_median, response_delay_text_allweek_night_skewness, response_delay_text_weekday_day_kurtosis, response_delay_text_weekday_day_min, response_delay_text_weekday_day_skewness, response_delay_text_weekday_night_max, response_delay_text_weekday_night_mean, response_delay_text_weekday_night_std, response_delay_text_weekend_allday_min, response_delay_text_weekend_allday_std, response_delay_text_weekend_day_max, response_delay_text_weekend_day_mean, response_delay_text_weekend_day_median, response_delay_text_weekend_day_skewness, response_rate_text_allweek_day, response_rate_text_weekday_day, balance_of_contacts_allweek_allday_text_max, balance_of_contacts_allweek_allday_text_mean, balance_of_contacts_allweek_allday_text_median, balance_of_contacts_allweek_allday_text_min, balance_of_contacts_allweek_allday_text_std, balance_of_contacts_weekday_allday_text_mean, balance_of_contacts_weekday_day_call_kurtosis, balance_of_contacts_weekday_day_call_skewness, balance_of_contacts_weekday_day_text_std, balance_of_contacts_weekend_day_call_kurtosis, balance_of_contacts_weekend_day_call_skewness, call_duration_weekday_allday_call_kurtosis, call_duration_weekday_allday_call_skewness, call_duration_weekday_night_call_median, call_duration_weekday_night_call_min, call_duration_weekend_allday_call_median, call_duration_weekend_allday_call_min, call_duration_weekend_night_call_max, call_duration_weekend_night_call_mean, call_duration_weekend_night_call_std, entropy_of_antennas_allweek_day, entropy_of_antennas_weekday_night, entropy_of_antennas_weekend_allday, entropy_of_contacts_weekday_allday_call, entropy_of_contacts_weekday_day_call, entropy_of_contacts_weekend_day_call, entropy_of_contacts_weekend_day_text, frequent_antennas_allweek_night, frequent_antennas_weekday_day, interactions_per_contact_allweek_allday_call_kurtosis, interactions_per_contact_allweek_allday_call_skewness, interactions_per_contact_allweek_day_text_max, interactions_per_contact_allweek_day_text_mean, interactions_per_contact_allweek_day_text_median, interactions_per_contact_allweek_day_text_std, interactions_per_contact_allweek_night_call_median, interactions_per_contact_allweek_night_call_min, interactions_per_contact_weekday_allday_text_max, interactions_per_contact_weekday_allday_text_min, interactions_per_contact_weekday_allday_text_std, interactions_per_contact_weekday_day_call_median, interactions_per_contact_weekday_day_call_min, interactions_per_contact_weekend_day_call_median, interactions_per_contact_weekend_day_call_min, interevent_time_allweek_allday_text_kurtosis, interevent_time_allweek_allday_text_skewness, interevent_time_allweek_night_text_max, interevent_time_allweek_night_text_mean, interevent_time_allweek_night_text_std, interevent_time_weekday_allday_call_max, interevent_time_weekday_allday_call_mean, interevent_time_weekday_allday_call_std, interevent_time_weekday_allday_text_mean, interevent_time_weekday_day_call_kurtosis, interevent_time_weekday_day_call_skewness, interevent_time_weekday_day_text_std, interevent_time_weekend_day_call_kurtosis, interevent_time_weekend_day_call_skewness, number_of_antennas_allweek_night, number_of_antennas_weekday_night, number_of_antennas_weekend_allday, number_of_contacts_weekday_allday_call, number_of_contacts_weekday_day_call, number_of_contacts_weekend_day_call, number_of_contacts_weekend_day_text, number_of_interactions_in_allweek_allday_call, number_of_interactions_in_weekday_day_text, number_of_interactions_in_weekend_allday_text, number_of_interactions_out_allweek_allday_text, number_of_interactions_out_weekday_day_text, number_of_interactions_out_weekend_allday_text, percent_at_home_weekend_night, percent_pareto_durations_weekday_allday, percent_pareto_interactions_allweek_day_call, percent_pareto_interactions_allweek_day_text, radius_of_gyration_allweek_day, radius_of_gyration_weekday_night, radius_of_gyration_weekend_allday, response_delay_text_allweek_allday_kurtosis, response_delay_text_allweek_night_min, response_delay_text_allweek_night_std, response_delay_text_weekday_allday_kurtosis, response_delay_text_weekday_allday_skewness, response_delay_text_weekday_night_median, response_delay_text_weekday_night_min, response_delay_text_weekend_allday_median, response_delay_text_weekend_day_min, response_delay_text_weekend_day_std, response_rate_text_allweek_night, response_rate_text_weekend_night, balance_of_contacts_allweek_allday_text_kurtosis, balance_of_contacts_allweek_allday_text_skewness, balance_of_contacts_allweek_night_text_max, balance_of_contacts_allweek_night_text_mean, balance_of_contacts_allweek_night_text_std, balance_of_contacts_weekday_allday_call_max, balance_of_contacts_weekday_allday_call_mean, balance_of_contacts_weekday_allday_call_std, balance_of_contacts_weekday_day_text_max, balance_of_contacts_weekday_day_text_mean, balance_of_contacts_weekday_day_text_min, balance_of_contacts_weekday_night_call_max, balance_of_contacts_weekday_night_call_mean, balance_of_contacts_weekday_night_call_std, balance_of_contacts_weekend_allday_call_max, balance_of_contacts_weekend_allday_call_mean, balance_of_contacts_weekend_allday_call_std, balance_of_contacts_weekend_allday_text_max, balance_of_contacts_weekend_allday_text_min, balance_of_contacts_weekend_allday_text_std, call_duration_weekday_night_call_kurtosis, call_duration_weekday_night_call_skewness, call_duration_weekend_allday_call_kurtosis, call_duration_weekend_allday_call_skewness, call_duration_weekend_night_call_median, call_duration_weekend_night_call_min, entropy_of_antennas_allweek_night, entropy_of_antennas_weekday_day, entropy_of_contacts_weekend_night_text, frequent_antennas_weekend_night, interactions_per_contact_allweek_day_text_kurtosis, interactions_per_contact_allweek_day_text_min, interactions_per_contact_allweek_day_text_skewness, interactions_per_contact_allweek_night_call_kurtosis, interactions_per_contact_allweek_night_call_skewness, interactions_per_contact_weekday_allday_text_mean, interactions_per_contact_weekday_day_call_kurtosis, interactions_per_contact_weekday_day_call_skewness, interactions_per_contact_weekday_day_text_std, interactions_per_contact_weekend_day_call_kurtosis, interactions_per_contact_weekend_day_call_skewness, interevent_time_allweek_night_text_median, interevent_time_allweek_night_text_min, interevent_time_weekday_allday_call_median, interevent_time_weekday_allday_call_min, interevent_time_weekday_day_text_max, interevent_time_weekday_day_text_mean, interevent_time_weekday_day_text_min, interevent_time_weekday_night_call_max, interevent_time_weekday_night_call_mean, interevent_time_weekday_night_call_std, interevent_time_weekend_allday_call_max, interevent_time_weekend_allday_call_mean, interevent_time_weekend_allday_call_std, interevent_time_weekend_allday_text_max, interevent_time_weekend_allday_text_min, interevent_time_weekend_allday_text_std, number_of_antennas_weekday_day, number_of_contacts_weekend_night_text, number_of_interactions_alldir_allweek_day_call, number_of_interactions_alldir_allweek_day_text, number_of_interactions_alldir_weekday_day_text, number_of_interactions_alldir_weekend_allday_text, number_of_interactions_in_allweek_night_call, number_of_interactions_in_allweek_night_text, number_of_interactions_in_weekday_night_text, number_of_interactions_out_allweek_allday_call, number_of_interactions_out_weekday_night_text, percent_at_home_weekend_day, percent_pareto_durations_allweek_allday, percent_pareto_interactions_allweek_allday_text, percent_pareto_interactions_weekday_day_text, percent_pareto_interactions_weekend_allday_text, radius_of_gyration_allweek_night, radius_of_gyration_weekday_day, response_delay_text_allweek_night_kurtosis, response_delay_text_weekday_night_kurtosis, response_delay_text_weekday_night_skewness, response_delay_text_weekend_day_kurtosis, response_delay_text_weekend_night_max, response_delay_text_weekend_night_mean, response_delay_text_weekend_night_std, response_rate_text_weekend_day, balance_of_contacts_allweek_night_text_median, balance_of_contacts_allweek_night_text_min, balance_of_contacts_weekday_allday_call_median, balance_of_contacts_weekday_allday_call_min, balance_of_contacts_weekday_allday_text_median, balance_of_contacts_weekday_day_text_median, balance_of_contacts_weekday_night_call_median, balance_of_contacts_weekday_night_call_min, balance_of_contacts_weekday_night_text_max, balance_of_contacts_weekday_night_text_min, balance_of_contacts_weekday_night_text_std, balance_of_contacts_weekend_allday_call_median, balance_of_contacts_weekend_allday_call_min, balance_of_contacts_weekend_allday_text_mean, balance_of_contacts_weekend_day_text_max, balance_of_contacts_weekend_day_text_mean, balance_of_contacts_weekend_day_text_median, balance_of_contacts_weekend_day_text_std, balance_of_contacts_weekend_night_call_max, balance_of_contacts_weekend_night_call_mean, balance_of_contacts_weekend_night_call_std, call_duration_weekend_night_call_kurtosis, call_duration_weekend_night_call_skewness, entropy_of_antennas_weekend_night, entropy_of_contacts_weekend_allday_call, frequent_antennas_weekend_day, interactions_per_contact_allweek_allday_text_max, interactions_per_contact_allweek_allday_text_mean, interactions_per_contact_allweek_allday_text_median, interactions_per_contact_allweek_allday_text_min, interactions_per_contact_allweek_allday_text_std, interactions_per_contact_weekday_allday_call_max, interactions_per_contact_weekday_allday_call_mean, interactions_per_contact_weekday_allday_call_std, interactions_per_contact_weekday_day_text_max, interactions_per_contact_weekday_day_text_mean, interactions_per_contact_weekday_day_text_min, interactions_per_contact_weekday_night_call_max, interactions_per_contact_weekday_night_call_mean, interactions_per_contact_weekday_night_call_std, interactions_per_contact_weekend_allday_call_max, interactions_per_contact_weekend_allday_call_mean, interactions_per_contact_weekend_allday_call_std, interactions_per_contact_weekend_allday_text_max, interactions_per_contact_weekend_allday_text_min, interactions_per_contact_weekend_allday_text_std, interevent_time_allweek_night_text_kurtosis, interevent_time_allweek_night_text_skewness, interevent_time_weekday_allday_call_kurtosis, interevent_time_weekday_allday_call_skewness, interevent_time_weekday_allday_text_median, interevent_time_weekday_day_text_median, interevent_time_weekday_night_call_median, interevent_time_weekday_night_call_min, interevent_time_weekday_night_text_max, interevent_time_weekday_night_text_min, interevent_time_weekday_night_text_std, interevent_time_weekend_allday_call_median, interevent_time_weekend_allday_call_min, interevent_time_weekend_allday_text_mean, interevent_time_weekend_day_text_max, interevent_time_weekend_day_text_mean, interevent_time_weekend_day_text_median, interevent_time_weekend_day_text_std, interevent_time_weekend_night_call_max, interevent_time_weekend_night_call_mean, interevent_time_weekend_night_call_std, number_of_antennas_weekend_night, number_of_contacts_weekend_allday_call, number_of_interactions_alldir_allweek_allday_text, number_of_interactions_alldir_weekday_night_text, number_of_interactions_in_weekday_allday_call, number_of_interactions_in_weekday_day_call, number_of_interactions_in_weekend_day_call, number_of_interactions_in_weekend_day_text, number_of_interactions_out_allweek_night_call, number_of_interactions_out_allweek_night_text, number_of_interactions_out_weekday_allday_call, number_of_interactions_out_weekday_day_call, number_of_interactions_out_weekend_day_call, number_of_interactions_out_weekend_day_text, percent_pareto_durations_allweek_day, percent_pareto_durations_weekday_night, percent_pareto_durations_weekend_allday, percent_pareto_interactions_allweek_allday_call, percent_pareto_interactions_weekday_night_text, radius_of_gyration_weekend_night, response_delay_text_weekend_allday_kurtosis, response_delay_text_weekend_allday_skewness, response_delay_text_weekend_night_median, response_delay_text_weekend_night_min, response_delay_text_weekend_night_skewness, balance_of_contacts_allweek_night_text_kurtosis, balance_of_contacts_allweek_night_text_skewness, balance_of_contacts_weekday_allday_call_kurtosis, balance_of_contacts_weekday_allday_call_skewness, balance_of_contacts_weekday_day_text_kurtosis, balance_of_contacts_weekday_day_text_skewness, balance_of_contacts_weekday_night_call_kurtosis, balance_of_contacts_weekday_night_call_skewness, balance_of_contacts_weekday_night_text_mean, balance_of_contacts_weekend_allday_call_kurtosis, balance_of_contacts_weekend_allday_call_skewness, balance_of_contacts_weekend_day_text_kurtosis, balance_of_contacts_weekend_day_text_min, balance_of_contacts_weekend_day_text_skewness, balance_of_contacts_weekend_night_call_median, balance_of_contacts_weekend_night_call_min, entropy_of_antennas_weekend_day, entropy_of_contacts_weekday_night_call, interactions_per_contact_allweek_allday_text_kurtosis, interactions_per_contact_allweek_allday_text_skewness, interactions_per_contact_allweek_night_text_max, interactions_per_contact_allweek_night_text_mean, interactions_per_contact_allweek_night_text_std, interactions_per_contact_weekday_allday_call_median, interactions_per_contact_weekday_allday_call_min, interactions_per_contact_weekday_allday_text_median, interactions_per_contact_weekday_day_text_median, interactions_per_contact_weekday_night_call_median, interactions_per_contact_weekday_night_call_min, interactions_per_contact_weekday_night_text_max, interactions_per_contact_weekday_night_text_min, interactions_per_contact_weekday_night_text_std, interactions_per_contact_weekend_allday_call_median, interactions_per_contact_weekend_allday_call_min, interactions_per_contact_weekend_allday_text_mean, interactions_per_contact_weekend_day_text_max, interactions_per_contact_weekend_day_text_mean, interactions_per_contact_weekend_day_text_median, interactions_per_contact_weekend_day_text_std, interactions_per_contact_weekend_night_call_max, interactions_per_contact_weekend_night_call_mean, interactions_per_contact_weekend_night_call_std, interevent_time_weekday_day_text_kurtosis, interevent_time_weekday_day_text_skewness, interevent_time_weekday_night_call_kurtosis, interevent_time_weekday_night_call_skewness, interevent_time_weekday_night_text_mean, interevent_time_weekend_allday_call_kurtosis, interevent_time_weekend_allday_call_skewness, interevent_time_weekend_day_text_kurtosis, interevent_time_weekend_day_text_min, interevent_time_weekend_day_text_skewness, interevent_time_weekend_night_call_median, interevent_time_weekend_night_call_min, number_of_antennas_weekend_day, number_of_contacts_weekday_night_call, number_of_interactions_alldir_allweek_allday_call, number_of_interactions_alldir_weekday_allday_call, number_of_interactions_alldir_weekday_day_call, number_of_interactions_alldir_weekend_day_call, number_of_interactions_alldir_weekend_day_text, number_of_interactions_in_weekend_night_text, number_of_interactions_out_weekend_night_text, percent_pareto_durations_allweek_night, percent_pareto_durations_weekday_day, percent_pareto_interactions_allweek_night_call, percent_pareto_interactions_allweek_night_text, percent_pareto_interactions_weekday_allday_call, percent_pareto_interactions_weekday_day_call, percent_pareto_interactions_weekend_day_call, percent_pareto_interactions_weekend_day_text, radius_of_gyration_weekend_day, response_delay_text_weekend_night_kurtosis, balance_of_contacts_weekday_allday_text_kurtosis, balance_of_contacts_weekday_allday_text_skewness, balance_of_contacts_weekend_allday_text_median, balance_of_contacts_weekend_night_call_kurtosis, balance_of_contacts_weekend_night_call_skewness, balance_of_contacts_weekend_night_text_max, balance_of_contacts_weekend_night_text_min, balance_of_contacts_weekend_night_text_std, entropy_of_contacts_weekend_night_call, interactions_per_contact_allweek_night_text_median, interactions_per_contact_allweek_night_text_min, interactions_per_contact_weekday_allday_call_kurtosis, interactions_per_contact_weekday_allday_call_skewness, interactions_per_contact_weekday_day_text_kurtosis, interactions_per_contact_weekday_day_text_skewness, interactions_per_contact_weekday_night_call_kurtosis, interactions_per_contact_weekday_night_call_skewness, interactions_per_contact_weekday_night_text_mean, interactions_per_contact_weekend_allday_call_kurtosis, interactions_per_contact_weekend_allday_call_skewness, interactions_per_contact_weekend_day_text_kurtosis, interactions_per_contact_weekend_day_text_min, interactions_per_contact_weekend_day_text_skewness, interactions_per_contact_weekend_night_call_median, interactions_per_contact_weekend_night_call_min, interevent_time_weekday_allday_text_kurtosis, interevent_time_weekday_allday_text_skewness, interevent_time_weekend_allday_text_median, interevent_time_weekend_night_call_kurtosis, interevent_time_weekend_night_call_skewness, interevent_time_weekend_night_text_max, interevent_time_weekend_night_text_min, interevent_time_weekend_night_text_std, number_of_contacts_weekend_night_call, number_of_interactions_alldir_allweek_night_call, number_of_interactions_alldir_allweek_night_text, number_of_interactions_alldir_weekend_night_text, number_of_interactions_in_weekend_allday_call, number_of_interactions_out_weekend_allday_call, percent_initiated_conversations_allweek_day, percent_initiated_conversations_weekend_day, percent_initiated_interactions_allweek_day, percent_initiated_interactions_weekday_allday, percent_initiated_interactions_weekend_day, percent_pareto_durations_weekend_night, percent_pareto_interactions_weekend_night_text, balance_of_contacts_weekday_night_text_median, balance_of_contacts_weekend_night_text_mean, interactions_per_contact_allweek_night_text_kurtosis, interactions_per_contact_allweek_night_text_skewness, interactions_per_contact_weekday_allday_text_kurtosis, interactions_per_contact_weekday_allday_text_skewness, interactions_per_contact_weekend_allday_text_median, interactions_per_contact_weekend_night_call_kurtosis, interactions_per_contact_weekend_night_call_skewness, interactions_per_contact_weekend_night_text_max, interactions_per_contact_weekend_night_text_min, interactions_per_contact_weekend_night_text_std, interevent_time_weekday_night_text_median, interevent_time_weekend_night_text_mean, number_of_interactions_alldir_weekend_allday_call, number_of_interactions_in_weekday_night_call, number_of_interactions_out_weekday_night_call, percent_initiated_conversations_allweek_night, percent_initiated_conversations_weekday_allday, percent_initiated_conversations_weekend_night, percent_initiated_interactions_allweek_night, percent_initiated_interactions_weekend_night, percent_pareto_durations_weekend_day, percent_pareto_interactions_weekend_allday_call, balance_of_contacts_weekend_allday_text_kurtosis, balance_of_contacts_weekend_allday_text_skewness, interactions_per_contact_weekday_night_text_median, interactions_per_contact_weekend_night_text_mean, interevent_time_weekend_allday_text_kurtosis, interevent_time_weekend_allday_text_skewness, number_of_interactions_alldir_weekday_night_call, number_of_interactions_in_weekend_night_call, number_of_interactions_out_weekend_night_call, percent_initiated_conversations_weekday_day, percent_initiated_conversations_weekday_night, percent_initiated_interactions_allweek_allday, percent_initiated_interactions_weekday_day, percent_initiated_interactions_weekday_night, percent_initiated_interactions_weekend_allday, percent_pareto_interactions_weekday_night_call, balance_of_contacts_weekday_night_text_kurtosis, balance_of_contacts_weekday_night_text_skewness, balance_of_contacts_weekend_night_text_median, interactions_per_contact_weekend_allday_text_kurtosis, interactions_per_contact_weekend_allday_text_skewness, interevent_time_weekday_night_text_kurtosis, interevent_time_weekday_night_text_skewness, interevent_time_weekend_night_text_median, number_of_interactions_alldir_weekend_night_call, percent_initiated_conversations_allweek_allday, percent_initiated_conversations_weekend_allday, percent_pareto_interactions_weekend_night_call, balance_of_contacts_weekend_night_text_kurtosis, balance_of_contacts_weekend_night_text_skewness, interactions_per_contact_weekday_night_text_kurtosis, interactions_per_contact_weekday_night_text_skewness, interactions_per_contact_weekend_night_text_median, interevent_time_weekend_night_text_kurtosis, interevent_time_weekend_night_text_skewness, interactions_per_contact_weekend_night_text_kurtosis, interactions_per_contact_weekend_night_text_skewness, name];\n'Project [name#510300, 'cdr_active_days__allweek__day__callandtext]\n+- Project [name#510300, active_days_allweek_allday#510301, active_days_allweek_day#510302, active_days_allweek_night#510303, active_days_weekday_allday#510304, active_days_weekday_day#510305, active_days_weekday_night#510306, active_days_weekend_allday#510307, active_days_weekend_day#510308, active_days_weekend_night#510309, number_of_contacts_allweek_allday_call#510310, number_of_contacts_allweek_allday_text#510311, number_of_contacts_allweek_day_call#510312, number_of_contacts_allweek_day_text#510313, number_of_contacts_allweek_night_call#510314, number_of_contacts_allweek_night_text#510315, number_of_contacts_weekday_allday_call#510316, number_of_contacts_weekday_allday_text#510317, number_of_contacts_weekday_day_call#510318, number_of_contacts_weekday_day_text#510319, number_of_contacts_weekday_night_call#510320, number_of_contacts_weekday_night_text#510321, number_of_contacts_weekend_allday_call#510322, number_of_contacts_weekend_allday_text#510323, ... 685 more fields]\n +- Join Inner, (name#510300 = name#566678)\n :- Relation [name#510300,active_days_allweek_allday#510301,active_days_allweek_day#510302,active_days_allweek_night#510303,active_days_weekday_allday#510304,active_days_weekday_day#510305,active_days_weekday_night#510306,active_days_weekend_allday#510307,active_days_weekend_day#510308,active_days_weekend_night#510309,number_of_contacts_allweek_allday_call#510310,number_of_contacts_allweek_allday_text#510311,number_of_contacts_allweek_day_call#510312,number_of_contacts_allweek_day_text#510313,number_of_contacts_allweek_night_call#510314,number_of_contacts_allweek_night_text#510315,number_of_contacts_weekday_allday_call#510316,number_of_contacts_weekday_allday_text#510317,number_of_contacts_weekday_day_call#510318,number_of_contacts_weekday_day_text#510319,number_of_contacts_weekday_night_call#510320,number_of_contacts_weekday_night_text#510321,number_of_contacts_weekend_allday_call#510322,number_of_contacts_weekend_allday_text#510323,... 685 more fields] csv\n +- Project [name#566678]\n +- Relation [name#566678,active_days_allweek_allday#566679,active_days_allweek_day#566680,active_days_allweek_night#566681,active_days_weekday_allday#566682,active_days_weekday_day#566683,active_days_weekday_night#566684,active_days_weekend_allday#566685,active_days_weekend_day#566686,active_days_weekend_night#566687,number_of_contacts_allweek_allday_call#566688,number_of_contacts_allweek_allday_text#566689,number_of_contacts_allweek_day_call#566690,number_of_contacts_allweek_day_text#566691,number_of_contacts_allweek_night_call#566692,number_of_contacts_allweek_night_text#566693,number_of_contacts_weekday_allday_call#566694,number_of_contacts_weekday_allday_text#566695,number_of_contacts_weekday_day_call#566696,number_of_contacts_weekday_day_text#566697,number_of_contacts_weekday_night_call#566698,number_of_contacts_weekday_night_text#566699,number_of_contacts_weekend_allday_call#566700,number_of_contacts_weekend_allday_text#566701,... 685 more fields] csv\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAnalysisException\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfeaturizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeature_plots\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/gpl/cider/cider/featurizer.py:739\u001b[0m, in \u001b[0;36mFeaturizer.feature_plots\u001b[0;34m(self, read_from_disk)\u001b[0m\n\u001b[1;32m 737\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m subscribers \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 738\u001b[0m users \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeatures[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcdr\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mjoin(subscribers, how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minner\u001b[39m\u001b[38;5;124m'\u001b[39m, on\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 739\u001b[0m \u001b[38;5;28mslice\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43musers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mname\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[43m[\u001b[49m\u001b[43ma\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtoPandas()\n\u001b[1;32m 740\u001b[0m \u001b[38;5;28mslice\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mslice_name\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m slice_name\n\u001b[1;32m 741\u001b[0m boxplot\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mslice\u001b[39m)\n", + "File \u001b[0;32m~/miniconda3/envs/cider/lib/python3.8/site-packages/pyspark/sql/dataframe.py:2023\u001b[0m, in \u001b[0;36mDataFrame.select\u001b[0;34m(self, *cols)\u001b[0m\n\u001b[1;32m 2002\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mselect\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39mcols: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumnOrName\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataFrame\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 2003\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Projects a set of expressions and returns a new :class:`DataFrame`.\u001b[39;00m\n\u001b[1;32m 2004\u001b[0m \n\u001b[1;32m 2005\u001b[0m \u001b[38;5;124;03m .. versionadded:: 1.3.0\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2021\u001b[0m \u001b[38;5;124;03m [Row(name='Alice', age=12), Row(name='Bob', age=15)]\u001b[39;00m\n\u001b[1;32m 2022\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2023\u001b[0m jdf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_jdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_jcols\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcols\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2024\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DataFrame(jdf, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msparkSession)\n", + "File \u001b[0;32m~/miniconda3/envs/cider/lib/python3.8/site-packages/py4j/java_gateway.py:1321\u001b[0m, in \u001b[0;36mJavaMember.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1315\u001b[0m command \u001b[38;5;241m=\u001b[39m proto\u001b[38;5;241m.\u001b[39mCALL_COMMAND_NAME \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1316\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcommand_header \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1317\u001b[0m args_command \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m 1318\u001b[0m proto\u001b[38;5;241m.\u001b[39mEND_COMMAND_PART\n\u001b[1;32m 1320\u001b[0m answer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client\u001b[38;5;241m.\u001b[39msend_command(command)\n\u001b[0;32m-> 1321\u001b[0m return_value \u001b[38;5;241m=\u001b[39m \u001b[43mget_return_value\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1322\u001b[0m \u001b[43m \u001b[49m\u001b[43manswer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgateway_client\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1324\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m temp_arg \u001b[38;5;129;01min\u001b[39;00m temp_args:\n\u001b[1;32m 1325\u001b[0m temp_arg\u001b[38;5;241m.\u001b[39m_detach()\n", + "File \u001b[0;32m~/miniconda3/envs/cider/lib/python3.8/site-packages/pyspark/sql/utils.py:196\u001b[0m, in \u001b[0;36mcapture_sql_exception..deco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 192\u001b[0m converted \u001b[38;5;241m=\u001b[39m convert_exception(e\u001b[38;5;241m.\u001b[39mjava_exception)\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(converted, UnknownException):\n\u001b[1;32m 194\u001b[0m \u001b[38;5;66;03m# Hide where the exception came from that shows a non-Pythonic\u001b[39;00m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;66;03m# JVM exception message.\u001b[39;00m\n\u001b[0;32m--> 196\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m converted \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "\u001b[0;31mAnalysisException\u001b[0m: Column 'cdr_active_days__allweek__day__callandtext' does not exist. Did you mean one of the following? [active_days_allweek_allday, active_days_weekday_allday, active_days_allweek_day, active_days_allweek_night, active_days_weekday_night, active_days_weekend_allday, active_days_weekday_day, active_days_weekend_night, call_duration_allweek_day_call_max, call_duration_allweek_day_call_mean, call_duration_allweek_day_call_std, active_days_weekend_day, call_duration_allweek_day_call_median, call_duration_allweek_day_call_min, interevent_time_allweek_day_call_max, interevent_time_allweek_day_call_mean, interevent_time_allweek_day_call_std, balance_of_contacts_allweek_day_call_max, balance_of_contacts_allweek_day_call_mean, balance_of_contacts_allweek_day_call_std, call_duration_allweek_allday_call_max, call_duration_allweek_allday_call_mean, call_duration_allweek_allday_call_std, call_duration_allweek_day_call_kurtosis, call_duration_allweek_day_call_skewness, interevent_time_allweek_day_call_median, interevent_time_allweek_day_call_min, balance_of_contacts_allweek_day_call_median, balance_of_contacts_allweek_day_call_min, call_duration_allweek_allday_call_median, call_duration_allweek_allday_call_min, call_duration_allweek_night_call_max, call_duration_allweek_night_call_mean, call_duration_allweek_night_call_std, interevent_time_allweek_allday_call_max, interevent_time_allweek_allday_call_mean, interevent_time_allweek_allday_call_std, interevent_time_allweek_day_call_kurtosis, interevent_time_allweek_day_call_skewness, number_of_contacts_allweek_day_call, number_of_contacts_allweek_day_text, balance_of_contacts_allweek_allday_call_max, balance_of_contacts_allweek_allday_call_mean, balance_of_contacts_allweek_allday_call_std, balance_of_contacts_allweek_day_call_kurtosis, balance_of_contacts_allweek_day_call_skewness, call_duration_allweek_allday_call_kurtosis, call_duration_allweek_allday_call_skewness, call_duration_allweek_night_call_median, call_duration_allweek_night_call_min, call_duration_weekday_day_call_max, call_duration_weekday_day_call_mean, call_duration_weekday_day_call_std, call_duration_weekend_day_call_max, call_duration_weekend_day_call_mean, call_duration_weekend_day_call_std, entropy_of_contacts_allweek_day_call, entropy_of_contacts_allweek_day_text, entropy_of_contacts_weekday_allday_text, interactions_per_contact_allweek_day_call_max, interactions_per_contact_allweek_day_call_mean, interactions_per_contact_allweek_day_call_std, interevent_time_allweek_allday_call_median, interevent_time_allweek_allday_call_min, interevent_time_allweek_night_call_max, interevent_time_allweek_night_call_mean, interevent_time_allweek_night_call_std, number_of_contacts_allweek_allday_text, number_of_contacts_weekday_allday_text, percent_at_home_allweek_allday, percent_at_home_weekday_allday, percent_nocturnal_weekday_call, percent_nocturnal_weekday_text, response_delay_text_allweek_day_max, response_delay_text_allweek_day_mean, response_delay_text_allweek_day_median, response_delay_text_allweek_day_skewness, response_delay_text_weekday_allday_max, response_delay_text_weekday_allday_mean, response_rate_text_weekday_allday, balance_of_contacts_allweek_allday_call_median, balance_of_contacts_allweek_allday_call_min, balance_of_contacts_allweek_night_call_max, balance_of_contacts_allweek_night_call_mean, balance_of_contacts_allweek_night_call_std, call_duration_allweek_night_call_kurtosis, call_duration_allweek_night_call_skewness, call_duration_weekday_day_call_median, call_duration_weekday_day_call_min, call_duration_weekend_day_call_median, call_duration_weekend_day_call_min, entropy_of_contacts_allweek_allday_text, frequent_antennas_weekday_allday, interactions_per_contact_allweek_day_call_median, interactions_per_contact_allweek_day_call_min, interevent_time_allweek_allday_call_kurtosis, interevent_time_allweek_allday_call_skewness, interevent_time_allweek_day_text_max, interevent_time_allweek_day_text_mean, interevent_time_allweek_day_text_median, interevent_time_allweek_day_text_std, interevent_time_allweek_night_call_median, interevent_time_allweek_night_call_min, number_of_contacts_allweek_allday_call, percent_at_home_allweek_day, response_delay_text_allweek_day_min, response_delay_text_allweek_day_std, response_delay_text_weekday_allday_min, response_delay_text_weekday_allday_std, balance_of_contacts_allweek_allday_call_kurtosis, balance_of_contacts_allweek_allday_call_skewness, balance_of_contacts_allweek_day_text_max, balance_of_contacts_allweek_day_text_mean, balance_of_contacts_allweek_day_text_median, balance_of_contacts_allweek_day_text_std, balance_of_contacts_allweek_night_call_median, balance_of_contacts_allweek_night_call_min, balance_of_contacts_weekday_day_call_max, balance_of_contacts_weekday_day_call_mean, balance_of_contacts_weekday_day_call_std, balance_of_contacts_weekend_day_call_max, balance_of_contacts_weekend_day_call_mean, balance_of_contacts_weekend_day_call_std, call_duration_weekday_allday_call_max, call_duration_weekday_allday_call_mean, call_duration_weekday_allday_call_std, call_duration_weekday_day_call_kurtosis, call_duration_weekday_day_call_skewness, call_duration_weekend_day_call_kurtosis, call_duration_weekend_day_call_skewness, entropy_of_antennas_weekday_allday, entropy_of_contacts_allweek_allday_call, entropy_of_contacts_weekday_day_text, entropy_of_contacts_weekend_allday_text, frequent_antennas_allweek_allday, interactions_per_contact_allweek_allday_call_max, interactions_per_contact_allweek_allday_call_mean, interactions_per_contact_allweek_allday_call_std, interactions_per_contact_allweek_day_call_kurtosis, interactions_per_contact_allweek_day_call_skewness, interevent_time_allweek_day_text_kurtosis, interevent_time_allweek_day_text_min, interevent_time_allweek_day_text_skewness, interevent_time_allweek_night_call_kurtosis, interevent_time_allweek_night_call_skewness, interevent_time_weekday_day_call_max, interevent_time_weekday_day_call_mean, interevent_time_weekday_day_call_std, interevent_time_weekend_day_call_max, interevent_time_weekend_day_call_mean, interevent_time_weekend_day_call_std, number_of_antennas_allweek_allday, number_of_antennas_weekday_allday, number_of_contacts_allweek_night_call, number_of_contacts_allweek_night_text, number_of_contacts_weekday_day_text, number_of_contacts_weekend_allday_text, number_of_interactions_in_allweek_day_call, number_of_interactions_in_allweek_day_text, number_of_interactions_in_weekday_allday_text, number_of_interactions_out_weekday_allday_text, percent_at_home_allweek_night, percent_at_home_weekday_night, percent_at_home_weekend_allday, percent_nocturnal_allweek_call, percent_nocturnal_allweek_text, percent_nocturnal_weekend_call, percent_nocturnal_weekend_text, radius_of_gyration_weekday_allday, response_delay_text_allweek_allday_max, response_delay_text_allweek_allday_mean, response_delay_text_allweek_allday_median, response_delay_text_allweek_allday_skewness, response_delay_text_allweek_day_kurtosis, response_delay_text_weekday_allday_median, response_delay_text_weekday_day_max, response_delay_text_weekday_day_mean, response_delay_text_weekday_day_median, response_delay_text_weekday_day_std, response_delay_text_weekend_allday_max, response_delay_text_weekend_allday_mean, response_rate_text_allweek_allday, response_rate_text_weekday_night, response_rate_text_weekend_allday, balance_of_contacts_allweek_day_text_kurtosis, balance_of_contacts_allweek_day_text_min, balance_of_contacts_allweek_day_text_skewness, balance_of_contacts_allweek_night_call_kurtosis, balance_of_contacts_allweek_night_call_skewness, balance_of_contacts_weekday_allday_text_max, balance_of_contacts_weekday_allday_text_min, balance_of_contacts_weekday_allday_text_std, balance_of_contacts_weekday_day_call_median, balance_of_contacts_weekday_day_call_min, balance_of_contacts_weekend_day_call_median, balance_of_contacts_weekend_day_call_min, call_duration_weekday_allday_call_median, call_duration_weekday_allday_call_min, call_duration_weekday_night_call_max, call_duration_weekday_night_call_mean, call_duration_weekday_night_call_std, call_duration_weekend_allday_call_max, call_duration_weekend_allday_call_mean, call_duration_weekend_allday_call_std, entropy_of_antennas_allweek_allday, entropy_of_contacts_allweek_night_call, entropy_of_contacts_allweek_night_text, entropy_of_contacts_weekday_night_text, frequent_antennas_allweek_day, frequent_antennas_weekday_night, frequent_antennas_weekend_allday, interactions_per_contact_allweek_allday_call_median, interactions_per_contact_allweek_allday_call_min, interactions_per_contact_allweek_night_call_max, interactions_per_contact_allweek_night_call_mean, interactions_per_contact_allweek_night_call_std, interactions_per_contact_weekday_day_call_max, interactions_per_contact_weekday_day_call_mean, interactions_per_contact_weekday_day_call_std, interactions_per_contact_weekend_day_call_max, interactions_per_contact_weekend_day_call_mean, interactions_per_contact_weekend_day_call_std, interevent_time_allweek_allday_text_max, interevent_time_allweek_allday_text_mean, interevent_time_allweek_allday_text_median, interevent_time_allweek_allday_text_min, interevent_time_allweek_allday_text_std, interevent_time_weekday_allday_text_max, interevent_time_weekday_allday_text_min, interevent_time_weekday_allday_text_std, interevent_time_weekday_day_call_median, interevent_time_weekday_day_call_min, interevent_time_weekend_day_call_median, interevent_time_weekend_day_call_min, number_of_antennas_allweek_day, number_of_contacts_weekday_night_text, number_of_interactions_alldir_weekday_allday_text, number_of_interactions_in_allweek_allday_text, number_of_interactions_out_allweek_day_call, number_of_interactions_out_allweek_day_text, percent_at_home_weekday_day, percent_pareto_interactions_weekday_allday_text, radius_of_gyration_allweek_allday, response_delay_text_allweek_allday_min, response_delay_text_allweek_allday_std, response_delay_text_allweek_night_max, response_delay_text_allweek_night_mean, response_delay_text_allweek_night_median, response_delay_text_allweek_night_skewness, response_delay_text_weekday_day_kurtosis, response_delay_text_weekday_day_min, response_delay_text_weekday_day_skewness, response_delay_text_weekday_night_max, response_delay_text_weekday_night_mean, response_delay_text_weekday_night_std, response_delay_text_weekend_allday_min, response_delay_text_weekend_allday_std, response_delay_text_weekend_day_max, response_delay_text_weekend_day_mean, response_delay_text_weekend_day_median, response_delay_text_weekend_day_skewness, response_rate_text_allweek_day, response_rate_text_weekday_day, balance_of_contacts_allweek_allday_text_max, balance_of_contacts_allweek_allday_text_mean, balance_of_contacts_allweek_allday_text_median, balance_of_contacts_allweek_allday_text_min, balance_of_contacts_allweek_allday_text_std, balance_of_contacts_weekday_allday_text_mean, balance_of_contacts_weekday_day_call_kurtosis, balance_of_contacts_weekday_day_call_skewness, balance_of_contacts_weekday_day_text_std, balance_of_contacts_weekend_day_call_kurtosis, balance_of_contacts_weekend_day_call_skewness, call_duration_weekday_allday_call_kurtosis, call_duration_weekday_allday_call_skewness, call_duration_weekday_night_call_median, call_duration_weekday_night_call_min, call_duration_weekend_allday_call_median, call_duration_weekend_allday_call_min, call_duration_weekend_night_call_max, call_duration_weekend_night_call_mean, call_duration_weekend_night_call_std, entropy_of_antennas_allweek_day, entropy_of_antennas_weekday_night, entropy_of_antennas_weekend_allday, entropy_of_contacts_weekday_allday_call, entropy_of_contacts_weekday_day_call, entropy_of_contacts_weekend_day_call, entropy_of_contacts_weekend_day_text, frequent_antennas_allweek_night, frequent_antennas_weekday_day, interactions_per_contact_allweek_allday_call_kurtosis, interactions_per_contact_allweek_allday_call_skewness, interactions_per_contact_allweek_day_text_max, interactions_per_contact_allweek_day_text_mean, interactions_per_contact_allweek_day_text_median, interactions_per_contact_allweek_day_text_std, interactions_per_contact_allweek_night_call_median, interactions_per_contact_allweek_night_call_min, interactions_per_contact_weekday_allday_text_max, interactions_per_contact_weekday_allday_text_min, interactions_per_contact_weekday_allday_text_std, interactions_per_contact_weekday_day_call_median, interactions_per_contact_weekday_day_call_min, interactions_per_contact_weekend_day_call_median, interactions_per_contact_weekend_day_call_min, interevent_time_allweek_allday_text_kurtosis, interevent_time_allweek_allday_text_skewness, interevent_time_allweek_night_text_max, interevent_time_allweek_night_text_mean, interevent_time_allweek_night_text_std, interevent_time_weekday_allday_call_max, interevent_time_weekday_allday_call_mean, interevent_time_weekday_allday_call_std, interevent_time_weekday_allday_text_mean, interevent_time_weekday_day_call_kurtosis, interevent_time_weekday_day_call_skewness, interevent_time_weekday_day_text_std, interevent_time_weekend_day_call_kurtosis, interevent_time_weekend_day_call_skewness, number_of_antennas_allweek_night, number_of_antennas_weekday_night, number_of_antennas_weekend_allday, number_of_contacts_weekday_allday_call, number_of_contacts_weekday_day_call, number_of_contacts_weekend_day_call, number_of_contacts_weekend_day_text, number_of_interactions_in_allweek_allday_call, number_of_interactions_in_weekday_day_text, number_of_interactions_in_weekend_allday_text, number_of_interactions_out_allweek_allday_text, number_of_interactions_out_weekday_day_text, number_of_interactions_out_weekend_allday_text, percent_at_home_weekend_night, percent_pareto_durations_weekday_allday, percent_pareto_interactions_allweek_day_call, percent_pareto_interactions_allweek_day_text, radius_of_gyration_allweek_day, radius_of_gyration_weekday_night, radius_of_gyration_weekend_allday, response_delay_text_allweek_allday_kurtosis, response_delay_text_allweek_night_min, response_delay_text_allweek_night_std, response_delay_text_weekday_allday_kurtosis, response_delay_text_weekday_allday_skewness, response_delay_text_weekday_night_median, response_delay_text_weekday_night_min, response_delay_text_weekend_allday_median, response_delay_text_weekend_day_min, response_delay_text_weekend_day_std, response_rate_text_allweek_night, response_rate_text_weekend_night, balance_of_contacts_allweek_allday_text_kurtosis, balance_of_contacts_allweek_allday_text_skewness, balance_of_contacts_allweek_night_text_max, balance_of_contacts_allweek_night_text_mean, balance_of_contacts_allweek_night_text_std, balance_of_contacts_weekday_allday_call_max, balance_of_contacts_weekday_allday_call_mean, balance_of_contacts_weekday_allday_call_std, balance_of_contacts_weekday_day_text_max, balance_of_contacts_weekday_day_text_mean, balance_of_contacts_weekday_day_text_min, balance_of_contacts_weekday_night_call_max, balance_of_contacts_weekday_night_call_mean, balance_of_contacts_weekday_night_call_std, balance_of_contacts_weekend_allday_call_max, balance_of_contacts_weekend_allday_call_mean, balance_of_contacts_weekend_allday_call_std, balance_of_contacts_weekend_allday_text_max, balance_of_contacts_weekend_allday_text_min, balance_of_contacts_weekend_allday_text_std, call_duration_weekday_night_call_kurtosis, call_duration_weekday_night_call_skewness, call_duration_weekend_allday_call_kurtosis, call_duration_weekend_allday_call_skewness, call_duration_weekend_night_call_median, call_duration_weekend_night_call_min, entropy_of_antennas_allweek_night, entropy_of_antennas_weekday_day, entropy_of_contacts_weekend_night_text, frequent_antennas_weekend_night, interactions_per_contact_allweek_day_text_kurtosis, interactions_per_contact_allweek_day_text_min, interactions_per_contact_allweek_day_text_skewness, interactions_per_contact_allweek_night_call_kurtosis, interactions_per_contact_allweek_night_call_skewness, interactions_per_contact_weekday_allday_text_mean, interactions_per_contact_weekday_day_call_kurtosis, interactions_per_contact_weekday_day_call_skewness, interactions_per_contact_weekday_day_text_std, interactions_per_contact_weekend_day_call_kurtosis, interactions_per_contact_weekend_day_call_skewness, interevent_time_allweek_night_text_median, interevent_time_allweek_night_text_min, interevent_time_weekday_allday_call_median, interevent_time_weekday_allday_call_min, interevent_time_weekday_day_text_max, interevent_time_weekday_day_text_mean, interevent_time_weekday_day_text_min, interevent_time_weekday_night_call_max, interevent_time_weekday_night_call_mean, interevent_time_weekday_night_call_std, interevent_time_weekend_allday_call_max, interevent_time_weekend_allday_call_mean, interevent_time_weekend_allday_call_std, interevent_time_weekend_allday_text_max, interevent_time_weekend_allday_text_min, interevent_time_weekend_allday_text_std, number_of_antennas_weekday_day, number_of_contacts_weekend_night_text, number_of_interactions_alldir_allweek_day_call, number_of_interactions_alldir_allweek_day_text, number_of_interactions_alldir_weekday_day_text, number_of_interactions_alldir_weekend_allday_text, number_of_interactions_in_allweek_night_call, number_of_interactions_in_allweek_night_text, number_of_interactions_in_weekday_night_text, number_of_interactions_out_allweek_allday_call, number_of_interactions_out_weekday_night_text, percent_at_home_weekend_day, percent_pareto_durations_allweek_allday, percent_pareto_interactions_allweek_allday_text, percent_pareto_interactions_weekday_day_text, percent_pareto_interactions_weekend_allday_text, radius_of_gyration_allweek_night, radius_of_gyration_weekday_day, response_delay_text_allweek_night_kurtosis, response_delay_text_weekday_night_kurtosis, response_delay_text_weekday_night_skewness, response_delay_text_weekend_day_kurtosis, response_delay_text_weekend_night_max, response_delay_text_weekend_night_mean, response_delay_text_weekend_night_std, response_rate_text_weekend_day, balance_of_contacts_allweek_night_text_median, balance_of_contacts_allweek_night_text_min, balance_of_contacts_weekday_allday_call_median, balance_of_contacts_weekday_allday_call_min, balance_of_contacts_weekday_allday_text_median, balance_of_contacts_weekday_day_text_median, balance_of_contacts_weekday_night_call_median, balance_of_contacts_weekday_night_call_min, balance_of_contacts_weekday_night_text_max, balance_of_contacts_weekday_night_text_min, balance_of_contacts_weekday_night_text_std, balance_of_contacts_weekend_allday_call_median, balance_of_contacts_weekend_allday_call_min, balance_of_contacts_weekend_allday_text_mean, balance_of_contacts_weekend_day_text_max, balance_of_contacts_weekend_day_text_mean, balance_of_contacts_weekend_day_text_median, balance_of_contacts_weekend_day_text_std, balance_of_contacts_weekend_night_call_max, balance_of_contacts_weekend_night_call_mean, balance_of_contacts_weekend_night_call_std, call_duration_weekend_night_call_kurtosis, call_duration_weekend_night_call_skewness, entropy_of_antennas_weekend_night, entropy_of_contacts_weekend_allday_call, frequent_antennas_weekend_day, interactions_per_contact_allweek_allday_text_max, interactions_per_contact_allweek_allday_text_mean, interactions_per_contact_allweek_allday_text_median, interactions_per_contact_allweek_allday_text_min, interactions_per_contact_allweek_allday_text_std, interactions_per_contact_weekday_allday_call_max, interactions_per_contact_weekday_allday_call_mean, interactions_per_contact_weekday_allday_call_std, interactions_per_contact_weekday_day_text_max, interactions_per_contact_weekday_day_text_mean, interactions_per_contact_weekday_day_text_min, interactions_per_contact_weekday_night_call_max, interactions_per_contact_weekday_night_call_mean, interactions_per_contact_weekday_night_call_std, interactions_per_contact_weekend_allday_call_max, interactions_per_contact_weekend_allday_call_mean, interactions_per_contact_weekend_allday_call_std, interactions_per_contact_weekend_allday_text_max, interactions_per_contact_weekend_allday_text_min, interactions_per_contact_weekend_allday_text_std, interevent_time_allweek_night_text_kurtosis, interevent_time_allweek_night_text_skewness, interevent_time_weekday_allday_call_kurtosis, interevent_time_weekday_allday_call_skewness, interevent_time_weekday_allday_text_median, interevent_time_weekday_day_text_median, interevent_time_weekday_night_call_median, interevent_time_weekday_night_call_min, interevent_time_weekday_night_text_max, interevent_time_weekday_night_text_min, interevent_time_weekday_night_text_std, interevent_time_weekend_allday_call_median, interevent_time_weekend_allday_call_min, interevent_time_weekend_allday_text_mean, interevent_time_weekend_day_text_max, interevent_time_weekend_day_text_mean, interevent_time_weekend_day_text_median, interevent_time_weekend_day_text_std, interevent_time_weekend_night_call_max, interevent_time_weekend_night_call_mean, interevent_time_weekend_night_call_std, number_of_antennas_weekend_night, number_of_contacts_weekend_allday_call, number_of_interactions_alldir_allweek_allday_text, number_of_interactions_alldir_weekday_night_text, number_of_interactions_in_weekday_allday_call, number_of_interactions_in_weekday_day_call, number_of_interactions_in_weekend_day_call, number_of_interactions_in_weekend_day_text, number_of_interactions_out_allweek_night_call, number_of_interactions_out_allweek_night_text, number_of_interactions_out_weekday_allday_call, number_of_interactions_out_weekday_day_call, number_of_interactions_out_weekend_day_call, number_of_interactions_out_weekend_day_text, percent_pareto_durations_allweek_day, percent_pareto_durations_weekday_night, percent_pareto_durations_weekend_allday, percent_pareto_interactions_allweek_allday_call, percent_pareto_interactions_weekday_night_text, radius_of_gyration_weekend_night, response_delay_text_weekend_allday_kurtosis, response_delay_text_weekend_allday_skewness, response_delay_text_weekend_night_median, response_delay_text_weekend_night_min, response_delay_text_weekend_night_skewness, balance_of_contacts_allweek_night_text_kurtosis, balance_of_contacts_allweek_night_text_skewness, balance_of_contacts_weekday_allday_call_kurtosis, balance_of_contacts_weekday_allday_call_skewness, balance_of_contacts_weekday_day_text_kurtosis, balance_of_contacts_weekday_day_text_skewness, balance_of_contacts_weekday_night_call_kurtosis, balance_of_contacts_weekday_night_call_skewness, balance_of_contacts_weekday_night_text_mean, balance_of_contacts_weekend_allday_call_kurtosis, balance_of_contacts_weekend_allday_call_skewness, balance_of_contacts_weekend_day_text_kurtosis, balance_of_contacts_weekend_day_text_min, balance_of_contacts_weekend_day_text_skewness, balance_of_contacts_weekend_night_call_median, balance_of_contacts_weekend_night_call_min, entropy_of_antennas_weekend_day, entropy_of_contacts_weekday_night_call, interactions_per_contact_allweek_allday_text_kurtosis, interactions_per_contact_allweek_allday_text_skewness, interactions_per_contact_allweek_night_text_max, interactions_per_contact_allweek_night_text_mean, interactions_per_contact_allweek_night_text_std, interactions_per_contact_weekday_allday_call_median, interactions_per_contact_weekday_allday_call_min, interactions_per_contact_weekday_allday_text_median, interactions_per_contact_weekday_day_text_median, interactions_per_contact_weekday_night_call_median, interactions_per_contact_weekday_night_call_min, interactions_per_contact_weekday_night_text_max, interactions_per_contact_weekday_night_text_min, interactions_per_contact_weekday_night_text_std, interactions_per_contact_weekend_allday_call_median, interactions_per_contact_weekend_allday_call_min, interactions_per_contact_weekend_allday_text_mean, interactions_per_contact_weekend_day_text_max, interactions_per_contact_weekend_day_text_mean, interactions_per_contact_weekend_day_text_median, interactions_per_contact_weekend_day_text_std, interactions_per_contact_weekend_night_call_max, interactions_per_contact_weekend_night_call_mean, interactions_per_contact_weekend_night_call_std, interevent_time_weekday_day_text_kurtosis, interevent_time_weekday_day_text_skewness, interevent_time_weekday_night_call_kurtosis, interevent_time_weekday_night_call_skewness, interevent_time_weekday_night_text_mean, interevent_time_weekend_allday_call_kurtosis, interevent_time_weekend_allday_call_skewness, interevent_time_weekend_day_text_kurtosis, interevent_time_weekend_day_text_min, interevent_time_weekend_day_text_skewness, interevent_time_weekend_night_call_median, interevent_time_weekend_night_call_min, number_of_antennas_weekend_day, number_of_contacts_weekday_night_call, number_of_interactions_alldir_allweek_allday_call, number_of_interactions_alldir_weekday_allday_call, number_of_interactions_alldir_weekday_day_call, number_of_interactions_alldir_weekend_day_call, number_of_interactions_alldir_weekend_day_text, number_of_interactions_in_weekend_night_text, number_of_interactions_out_weekend_night_text, percent_pareto_durations_allweek_night, percent_pareto_durations_weekday_day, percent_pareto_interactions_allweek_night_call, percent_pareto_interactions_allweek_night_text, percent_pareto_interactions_weekday_allday_call, percent_pareto_interactions_weekday_day_call, percent_pareto_interactions_weekend_day_call, percent_pareto_interactions_weekend_day_text, radius_of_gyration_weekend_day, response_delay_text_weekend_night_kurtosis, balance_of_contacts_weekday_allday_text_kurtosis, balance_of_contacts_weekday_allday_text_skewness, balance_of_contacts_weekend_allday_text_median, balance_of_contacts_weekend_night_call_kurtosis, balance_of_contacts_weekend_night_call_skewness, balance_of_contacts_weekend_night_text_max, balance_of_contacts_weekend_night_text_min, balance_of_contacts_weekend_night_text_std, entropy_of_contacts_weekend_night_call, interactions_per_contact_allweek_night_text_median, interactions_per_contact_allweek_night_text_min, interactions_per_contact_weekday_allday_call_kurtosis, interactions_per_contact_weekday_allday_call_skewness, interactions_per_contact_weekday_day_text_kurtosis, interactions_per_contact_weekday_day_text_skewness, interactions_per_contact_weekday_night_call_kurtosis, interactions_per_contact_weekday_night_call_skewness, interactions_per_contact_weekday_night_text_mean, interactions_per_contact_weekend_allday_call_kurtosis, interactions_per_contact_weekend_allday_call_skewness, interactions_per_contact_weekend_day_text_kurtosis, interactions_per_contact_weekend_day_text_min, interactions_per_contact_weekend_day_text_skewness, interactions_per_contact_weekend_night_call_median, interactions_per_contact_weekend_night_call_min, interevent_time_weekday_allday_text_kurtosis, interevent_time_weekday_allday_text_skewness, interevent_time_weekend_allday_text_median, interevent_time_weekend_night_call_kurtosis, interevent_time_weekend_night_call_skewness, interevent_time_weekend_night_text_max, interevent_time_weekend_night_text_min, interevent_time_weekend_night_text_std, number_of_contacts_weekend_night_call, number_of_interactions_alldir_allweek_night_call, number_of_interactions_alldir_allweek_night_text, number_of_interactions_alldir_weekend_night_text, number_of_interactions_in_weekend_allday_call, number_of_interactions_out_weekend_allday_call, percent_initiated_conversations_allweek_day, percent_initiated_conversations_weekend_day, percent_initiated_interactions_allweek_day, percent_initiated_interactions_weekday_allday, percent_initiated_interactions_weekend_day, percent_pareto_durations_weekend_night, percent_pareto_interactions_weekend_night_text, balance_of_contacts_weekday_night_text_median, balance_of_contacts_weekend_night_text_mean, interactions_per_contact_allweek_night_text_kurtosis, interactions_per_contact_allweek_night_text_skewness, interactions_per_contact_weekday_allday_text_kurtosis, interactions_per_contact_weekday_allday_text_skewness, interactions_per_contact_weekend_allday_text_median, interactions_per_contact_weekend_night_call_kurtosis, interactions_per_contact_weekend_night_call_skewness, interactions_per_contact_weekend_night_text_max, interactions_per_contact_weekend_night_text_min, interactions_per_contact_weekend_night_text_std, interevent_time_weekday_night_text_median, interevent_time_weekend_night_text_mean, number_of_interactions_alldir_weekend_allday_call, number_of_interactions_in_weekday_night_call, number_of_interactions_out_weekday_night_call, percent_initiated_conversations_allweek_night, percent_initiated_conversations_weekday_allday, percent_initiated_conversations_weekend_night, percent_initiated_interactions_allweek_night, percent_initiated_interactions_weekend_night, percent_pareto_durations_weekend_day, percent_pareto_interactions_weekend_allday_call, balance_of_contacts_weekend_allday_text_kurtosis, balance_of_contacts_weekend_allday_text_skewness, interactions_per_contact_weekday_night_text_median, interactions_per_contact_weekend_night_text_mean, interevent_time_weekend_allday_text_kurtosis, interevent_time_weekend_allday_text_skewness, number_of_interactions_alldir_weekday_night_call, number_of_interactions_in_weekend_night_call, number_of_interactions_out_weekend_night_call, percent_initiated_conversations_weekday_day, percent_initiated_conversations_weekday_night, percent_initiated_interactions_allweek_allday, percent_initiated_interactions_weekday_day, percent_initiated_interactions_weekday_night, percent_initiated_interactions_weekend_allday, percent_pareto_interactions_weekday_night_call, balance_of_contacts_weekday_night_text_kurtosis, balance_of_contacts_weekday_night_text_skewness, balance_of_contacts_weekend_night_text_median, interactions_per_contact_weekend_allday_text_kurtosis, interactions_per_contact_weekend_allday_text_skewness, interevent_time_weekday_night_text_kurtosis, interevent_time_weekday_night_text_skewness, interevent_time_weekend_night_text_median, number_of_interactions_alldir_weekend_night_call, percent_initiated_conversations_allweek_allday, percent_initiated_conversations_weekend_allday, percent_pareto_interactions_weekend_night_call, balance_of_contacts_weekend_night_text_kurtosis, balance_of_contacts_weekend_night_text_skewness, interactions_per_contact_weekday_night_text_kurtosis, interactions_per_contact_weekday_night_text_skewness, interactions_per_contact_weekend_night_text_median, interevent_time_weekend_night_text_kurtosis, interevent_time_weekend_night_text_skewness, interactions_per_contact_weekend_night_text_kurtosis, interactions_per_contact_weekend_night_text_skewness, name];\n'Project [name#510300, 'cdr_active_days__allweek__day__callandtext]\n+- Project [name#510300, active_days_allweek_allday#510301, active_days_allweek_day#510302, active_days_allweek_night#510303, active_days_weekday_allday#510304, active_days_weekday_day#510305, active_days_weekday_night#510306, active_days_weekend_allday#510307, active_days_weekend_day#510308, active_days_weekend_night#510309, number_of_contacts_allweek_allday_call#510310, number_of_contacts_allweek_allday_text#510311, number_of_contacts_allweek_day_call#510312, number_of_contacts_allweek_day_text#510313, number_of_contacts_allweek_night_call#510314, number_of_contacts_allweek_night_text#510315, number_of_contacts_weekday_allday_call#510316, number_of_contacts_weekday_allday_text#510317, number_of_contacts_weekday_day_call#510318, number_of_contacts_weekday_day_text#510319, number_of_contacts_weekday_night_call#510320, number_of_contacts_weekday_night_text#510321, number_of_contacts_weekend_allday_call#510322, number_of_contacts_weekend_allday_text#510323, ... 685 more fields]\n +- Join Inner, (name#510300 = name#566678)\n :- Relation [name#510300,active_days_allweek_allday#510301,active_days_allweek_day#510302,active_days_allweek_night#510303,active_days_weekday_allday#510304,active_days_weekday_day#510305,active_days_weekday_night#510306,active_days_weekend_allday#510307,active_days_weekend_day#510308,active_days_weekend_night#510309,number_of_contacts_allweek_allday_call#510310,number_of_contacts_allweek_allday_text#510311,number_of_contacts_allweek_day_call#510312,number_of_contacts_allweek_day_text#510313,number_of_contacts_allweek_night_call#510314,number_of_contacts_allweek_night_text#510315,number_of_contacts_weekday_allday_call#510316,number_of_contacts_weekday_allday_text#510317,number_of_contacts_weekday_day_call#510318,number_of_contacts_weekday_day_text#510319,number_of_contacts_weekday_night_call#510320,number_of_contacts_weekday_night_text#510321,number_of_contacts_weekend_allday_call#510322,number_of_contacts_weekend_allday_text#510323,... 685 more fields] csv\n +- Project [name#566678]\n +- Relation [name#566678,active_days_allweek_allday#566679,active_days_allweek_day#566680,active_days_allweek_night#566681,active_days_weekday_allday#566682,active_days_weekday_day#566683,active_days_weekday_night#566684,active_days_weekend_allday#566685,active_days_weekend_day#566686,active_days_weekend_night#566687,number_of_contacts_allweek_allday_call#566688,number_of_contacts_allweek_allday_text#566689,number_of_contacts_allweek_day_call#566690,number_of_contacts_allweek_day_text#566691,number_of_contacts_allweek_night_call#566692,number_of_contacts_allweek_night_text#566693,number_of_contacts_weekday_allday_call#566694,number_of_contacts_weekday_allday_text#566695,number_of_contacts_weekday_day_call#566696,number_of_contacts_weekday_day_text#566697,number_of_contacts_weekday_night_call#566698,number_of_contacts_weekday_night_text#566699,number_of_contacts_weekend_allday_call#566700,number_of_contacts_weekend_allday_text#566701,... 685 more fields] csv\n" + ] + }, { "data": { - "image/png": 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Home Location Inference

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Home Location Inference#

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from datastore import DataStore
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Load some CDR data, antennas data, and (optionally) spatial files, whose paths have been defined in config.yml. Define the geographic granularity for inference (options are ‘antenna_id’, ‘tower_id’ if a tower_id is provided in the antennas files, or any of the spatial files). Optionally load wealth prediction data to construct a poverty map and ground truth location data to evaluate accuracy.

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datastore = DataStore(cfg_dir='./configs/config.yml')
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Loading CDR...
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Filter to a specific date range.

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home_locator.ds.filter_dates('2020-01-05', '2020-02-01')
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Try running the three different home location algorithms, specifying towers as the requested geographic level of aggregation.

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The three algorithms currently implemented are relatively simple; more sophisticate methods to infer users’ home locations do exist.

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home_locator.get_home_locations(algo='count_transactions', geo='tower_id')
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subscriber_idtower_idcount_modal_days
0JGPCbfDGest354
1YtvkGlMWwet394
2dYwshzRseDt264
3dsBHAdXrrkt352
4ygMEXUQDbnt274
............
995amzyXHglBxt73
996dJSvXqUVSYt643
997ptudOnjdRUt355
998uXZrufHOmEt794
999zZkqaZFAtzt804
-

1000 rows × 3 columns

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Generate a population map based on inferred home locations using the unique days algorithm. Use points at each of the tower locations.

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home_locator.map(algo='count_days', geo='tower_id', kind='population', voronoi=False)
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-../../_images/documentation_home_location_10_0.png -
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Evaluate the accuracy of the home location inference using the ground truth data. Generate maps and a table of precision and recall for each tower.

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home_locator.accuracy(algo='count_days', geo='tower_id')
-home_locator.map(algo = 'count_days', geo='tower_id', kind='recall', voronoi=False)
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Observations with inferred home location: 1000 (1000 unique)
-Observations with ground truth home location: 1000 (1000 unique)
-Observations with both: 1000 (1000 unique)
-Overall accuracy: 0.02
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-../../_images/documentation_home_location_12_1.png -
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home_locator.accuracy_tables[('tower_id', 'count_days')].head()
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tower_idrecallprecisionoverall_accuracy
67t70.0769230.1666670.017
15t220.2500000.1428570.017
33t390.2142860.0909090.017
66t690.1000000.0555560.017
37t420.1538460.0555560.017
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Use population density maps to identify which areas are over/under-represented in your sample.

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home_locator.pop_comparison(algo='count_days', geo='tower_id')
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-../../_images/documentation_home_location_15_0.png -
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-

- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
-

-
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- -
- - -
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/quickstart/documentation_home_location.ipynb b/docs/_sources/quickstart/documentation_home_location.ipynb index a9d264d..871e188 100644 --- a/docs/_sources/quickstart/documentation_home_location.ipynb +++ b/docs/_sources/quickstart/documentation_home_location.ipynb @@ -15,8 +15,8 @@ "metadata": {}, "outputs": [], "source": [ - "from datastore import DataStore\n", - "from home_location import HomeLocator" + "from cider.datastore import DataStore\n", + "from cider.home_location import HomeLocator" ] }, { @@ -44,7 +44,11 @@ } ], "source": [ - "datastore = DataStore(cfg_dir='./configs/config.yml')\n", + "# This path should point to your cider installation, where configs and data for this demo are located.\n", + "from pathlib import Path\n", + "cider_installation_directory = Path('../../cider')\n", + "\n", + "datastore = DataStore(config_file_path_string= cider_installation_directory / 'configs' / 'config_quickstart.yml')\n", "home_locator = HomeLocator(datastore=datastore, clean_folders=True)" ] }, @@ -429,7 +433,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.1" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/docs/_sources/quickstart/documentation_ml.html b/docs/_sources/quickstart/documentation_ml.html deleted file mode 100644 index a364d95..0000000 --- a/docs/_sources/quickstart/documentation_ml.html +++ /dev/null @@ -1,888 +0,0 @@ - - - - - - - - - Machine Learning - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Machine Learning

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Machine Learning#

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from datastore import DataStore
-from ml import Learner
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Initialize data store object, then learner, automatically loading feature file produced by featurizer, along with file of data labels, and merging features to labels.

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datastore = DataStore(cfg_dir='./configs/config.yml')
-learner = Learner(datastore=datastore, clean_folders=True)
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Number of observations with features: 1000 (1000 unique)
-Number of observations with labels: 50 (50 unique)
-Number of matched observations: 50 (50 unique)
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[Stage 42:===================>                                      (1 + 2) / 3]
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Experiment quickly with untuned models to get a sense of accuracy. Lasso, Ridge, random forest, and gradient boosting models are implemented natively, other models can be implemented by hand.

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lasso_scores = learner.untuned_model('lasso')
-randomforest_scores = learner.untuned_model('randomforest')
-print('LASSO', lasso_scores)
-print('Random Forest', randomforest_scores)
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LASSO {'train_r2': '1.00 (0.00)', 'test_r2': '-0.19 (0.48)', 'train_rmse': '6.65 (-3.02)', 'test_rmse': '15665.32 (-1859.46)'}
-Random Forest {'train_r2': '0.84 (0.01)', 'test_r2': '-0.02 (0.12)', 'train_rmse': '6200.40 (-319.50)', 'test_rmse': '14937.78 (-1482.62)'}
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Fine-tune a gradient boosting model, tuning hyperparameters over cross validation, and produce predictions for all labeled observations out-of-sample over cross-validation. Also generate predictions for all subscribers in the feature dataset.

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gradientboosting_scores = learner.tuned_model('gradientboosting')
-print('Gradient Boosting (Tuned)', gradientboosting_scores)
-learner.oos_predictions('gradientboosting', kind='tuned')
-learner.population_predictions('gradientboosting', kind='tuned')
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[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
-Gradient Boosting (Tuned) {'train_r2': '0.80 (0.03)', 'test_r2': '0.07 (0.12)', 'train_rmse': '6808.46 (-409.84)', 'test_rmse': '14259.12 (-1920.92)'}
-[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
-[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
-[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
-[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
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namepredicted
0dsBHAdXrrk67249.768675
1JGPCbfDGes69289.940765
2dYwshzRseD85109.109600
3ygMEXUQDbn82018.137664
4YtvkGlMWwe76983.875546
.........
5amzyXHglBx83987.844414
6zZkqaZFAtz82984.849245
7uXZrufHOmE87731.514440
8dJSvXqUVSY76191.160752
9YosNCLWrFL85338.034626
-

1000 rows × 2 columns

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Evaluate the model’s accuracy. Produce a scatterplot of true vs. predicted values with a LOESS fit and a bar plot of the most important features. Generate a table showing the targeting accuracy, precision, and recall of the predictions for nine hypothetical targeting scenarios (targeting between 10% and 90% of the population).

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learner.scatter_plot('gradientboosting', kind='tuned')
-learner.feature_importances_plot('gradientboosting', kind='tuned')
-learner.targeting_table('gradientboosting', kind='tuned')
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-../../_images/documentation_ml_9_0.png -../../_images/documentation_ml_9_1.png -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Proportion of Population TargetedPearsonSpearmanAUCAccuracyPrecisionRecall
010%0.560.50.7393%65%65%
120%0.560.50.7381%53%53%
230%0.560.50.7371%52%52%
340%0.560.50.7367%59%59%
450%0.560.50.7365%65%65%
560%0.560.50.7369%74%74%
670%0.560.50.7379%85%85%
780%0.560.50.7375%84%84%
890%0.560.50.7384%91%91%
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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
-

-
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- -
- - -
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/quickstart/documentation_ml.ipynb b/docs/_sources/quickstart/documentation_ml.ipynb index e2b814b..8bfb1ef 100644 --- a/docs/_sources/quickstart/documentation_ml.ipynb +++ b/docs/_sources/quickstart/documentation_ml.ipynb @@ -10,13 +10,15 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ec77078c", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "from datastore import DataStore\n", - "from ml import Learner" + "from cider.datastore import DataStore\n", + "from cider.ml import Learner" ] }, { @@ -46,15 +48,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "\r", - "[Stage 42:===================> (1 + 2) / 3]\r", - "\r", " \r" ] } ], "source": [ - "datastore = DataStore(cfg_dir='./configs/config.yml')\n", + "# This path should point to your cider installation, where configs and data for this demo are located.\n", + "from pathlib import Path\n", + "cider_installation_directory = Path('../../cider')\n", + "\n", + "datastore = DataStore(config_file_path_string= cider_installation_directory / 'configs' / 'config_quickstart.yml')\n", "learner = Learner(datastore=datastore, clean_folders=True)" ] }, @@ -426,9 +429,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "conda_cider", "language": "python", - "name": "python3" + "name": "conda_cider" }, "language_info": { "codemirror_mode": { @@ -440,7 +443,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.1" + "version": "3.8.16" } }, "nbformat": 4, diff --git a/docs/_sources/quickstart/documentation_satellite.html b/docs/_sources/quickstart/documentation_satellite.html deleted file mode 100644 index 5d4c1d9..0000000 --- a/docs/_sources/quickstart/documentation_satellite.html +++ /dev/null @@ -1,729 +0,0 @@ - - - - - - - - - Satellite - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Satellite#

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We first load some datasets - whose paths have been specified in the config file - using the datastore. The satellite module will by default try to load antenna/tower coordinates, shapefiles defining (administrative) boundaries, the Relative Wealth Index for the country of interest, and population density raster data.

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datastore = DataStore(cfg_dir='../../cider/configs/config.yml')
-satellite = Satellite(datastore=datastore)
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Loading antennas...
-Warning: 10 antennas missing location
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The module can aggregate the RWI - which provides an asset index measure at a resolution of 2.4km x 2.4km - at one of the geographic levels provided by the antennas/towers, in which case their voronoi tessellation will be used - or one of the loaded shapefiles.

-In this case, we have loaded (synthetic) data for the country of Togo, and for aggregations at the tower level we will run:

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satellite.aggregate_scores(dataset='rwi', geo='tower_id')
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-../../_images/documentation_satellite_5_0.png -
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while for cantons, admin level 3 in Togo, we will run:

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satellite.aggregate_scores(dataset='rwi', geo='cantons')
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../../cider/satellite.py:152: RuntimeWarning: invalid value encountered in double_scalars
-  score = (score * pop).sum() / pop.sum()
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-../../_images/documentation_satellite_7_1.png -
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The table is also saved as a geojson, storing the identifier of each region, its score - in this case its Relative Wealth Index - its population, and its geometry.

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import geopandas as gpd
-gpd.read_file('../../cider/outputs/satellite/maps/cantons_rwi.geojson').head()
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regionscorepopgeometry
01-0.28359716017.569922MULTIPOLYGON (((0.75228 6.83786, 0.75137 6.840...
12-0.4135654367.875125MULTIPOLYGON (((0.69026 6.80602, 0.69627 6.806...
23-0.3944289757.760028MULTIPOLYGON (((0.63102 6.74430, 0.63295 6.747...
34-0.4209522884.407900MULTIPOLYGON (((0.67259 6.85123, 0.67714 6.849...
45-0.1121902907.861156MULTIPOLYGON (((0.75269 6.84116, 0.75137 6.840...
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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
-

-
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/quickstart/documentation_satellite.ipynb b/docs/_sources/quickstart/documentation_satellite.ipynb index 87189b7..cb0023b 100644 --- a/docs/_sources/quickstart/documentation_satellite.ipynb +++ b/docs/_sources/quickstart/documentation_satellite.ipynb @@ -15,8 +15,8 @@ "metadata": {}, "outputs": [], "source": [ - "from datastore import DataStore\n", - "from satellite import Satellite" + "from cider.datastore import DataStore\n", + "from cider.satellite import Satellite" ] }, { @@ -43,7 +43,11 @@ } ], "source": [ - "datastore = DataStore(cfg_dir='../../cider/configs/config.yml')\n", + "# This path should point to your cider installation, where configs and data for this demo are located.\n", + "from pathlib import Path\n", + "cider_installation_directory = Path('../../cider')\n", + "\n", + "datastore = DataStore(config_file_path_string= cider_installation_directory / 'configs' / 'config_quickstart.yml')\n", "satellite = Satellite(datastore=datastore)" ] }, @@ -238,7 +242,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.1" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/docs/_sources/quickstart/documentation_targeting.html b/docs/_sources/quickstart/documentation_targeting.html deleted file mode 100644 index 7757909..0000000 --- a/docs/_sources/quickstart/documentation_targeting.html +++ /dev/null @@ -1,801 +0,0 @@ - - - - - - - - - Targeting Evaluations - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Targeting Evaluations

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Targeting Evaluations#

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Set up the configuration file and load some targeting simulation data, using the datastore object.

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Create a table that compares targeting methods to a ground truth poverty measure. Three threshold-agnostic targeting metrics will be calculated: Pearson correlation (between ground truth and proxies), Spearman correlation, and a threshold-agnostic Area Under the Reciever Operating Characteristic Curve Score (AUC). Additionally, provide percentile targeting thresholds for ground truth and proxy to obtain four threshold-specific targeting metrics: accuracy, precision, recall, and threshold-specific AUC. As with all calculations in this module, the table can be calculated with or without sample weights.

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targeting.targeting_table('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], 50, 20)
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Targeting MethodPearsonSpearmanAUC (Threshold-Agnostic)AccuracyPrecisionRecallAUC (Threshold-Specific)
0proxy10.8533700.8568800.8823190.7001.0000.4000.925204
1proxy20.8163230.8182750.8695010.6980.9950.3980.921336
2proxy30.7752430.7726710.8460710.6880.9700.3880.886284
3proxy40.7311960.7249840.8177990.6800.9500.3800.866024
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Draw threshold-agnostic receiver operatoring characteristic (ROC) curves and precision-recall curves. Threshold-agnostic ROC curves are drawn by taking a grid matched targeting thresholds (that is, targeting K% of the population according to a proxy and the same K% of the population according to the ground-truth, for a krid of K between 0 and 100), and calculating the false positive rate and true positive rate of the targeting method for each point in the grid. Threshold-agnostic precision-reccall curves are calculated in the same way, so precision and recall are balanced by construction.

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targeting.roc_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=None, weighted=False)
-targeting.precision_recall_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=None, weighted=False)
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-../../_images/documentation_targeting_7_0.png -../../_images/documentation_targeting_7_1.png -
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Draw threshold-specific ROC curves and precision-recall curves. Threshold-specific ROC curves are calculated by holding the share of the population targeted according to the ground-truth measure constant, and varying the share of the population targeted on the proxy measure to obtain false positive and true positive rate trade-offs.

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targeting.roc_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=50, weighted=False)
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-../../_images/documentation_targeting_9_0.png -../../_images/documentation_targeting_9_1.png -
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Provide a budget (provided as the level of benefits assigned to each individual UBI program, in the same units as the ground truth poverty measure) to draw social welfare curves for a set of targeting methods. Individual welfare is assumed to be a convex function of pre-program poverty and benefits assigned. Generate a table with the share of population targeted and corresponding transfer size that optimizes social welfare for each proxy poverty measure that could be used for targeting.

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targeting.utility_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], .01, weighted=False)
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-../../_images/documentation_targeting_11_0.png -
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ProxyOptimal Share of Population TargetedMaximum UtilityOptimal Transfer Size
0proxy126.255102-1225.9544390.038168
1proxy29.081633-817.0806670.111111
2proxy338.377551-1606.0653290.026110
3proxy435.346939-1582.8120990.028329
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- - By UC Berkeley Global Policy Lab
- - © Copyright 2022.
-

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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/quickstart/documentation_targeting.ipynb b/docs/_sources/quickstart/documentation_targeting.ipynb index 3a53f25..704e4c4 100644 --- a/docs/_sources/quickstart/documentation_targeting.ipynb +++ b/docs/_sources/quickstart/documentation_targeting.ipynb @@ -15,8 +15,8 @@ "metadata": {}, "outputs": [], "source": [ - "from datastore import DataStore\n", - "from targeting import Targeting" + "from cider.datastore import DataStore\n", + "from cider.targeting import Targeting" ] }, { @@ -34,7 +34,11 @@ "metadata": {}, "outputs": [], "source": [ - "datastore = DataStore(cfg_dir='./configs/config.yml')\n", + "# This path should point to your cider installation, where configs and data for this demo are located.\n", + "from pathlib import Path\n", + "cider_installation_directory = Path('../../cider')\n", + "\n", + "datastore = DataStore(config_file_path_string= cider_installation_directory / 'configs' / 'config_quickstart.yml')\n", "targeting = Targeting(datastore=datastore)" ] }, @@ -360,7 +364,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.1" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/docs/_sources/quickstart/installation.html b/docs/_sources/quickstart/installation.html deleted file mode 100644 index 4ae0b20..0000000 --- a/docs/_sources/quickstart/installation.html +++ /dev/null @@ -1,581 +0,0 @@ - - - - - - - - - Installation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Installation#

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To install, and manage dependencies and virtual environments this project uses Poetry. Follow the instructions to install Poetry.

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From the root directory poetry update followed by poetry install - this will establish a venv with all the needed dependencies.

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Once your venv is made you can use poetry run [command] to run a single CLI command inside the venv.

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You can use poetry shell to enter into the venv.

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- - By UC Berkeley Global Policy Lab
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- - - - - - - \ No newline at end of file diff --git a/docs/_sources/quickstart/installation.md b/docs/_sources/quickstart/installation.md index 5835daa..dfccc57 100644 --- a/docs/_sources/quickstart/installation.md +++ b/docs/_sources/quickstart/installation.md @@ -1,9 +1,36 @@ ## Installation +### Using Poetry To install, and manage dependencies and virtual environments this project uses Poetry. Follow the [instructions](https://python-poetry.org/docs/) to install Poetry. -From the root directory `poetry update` followed by `poetry install` - this will establish a venv with all the needed dependencies. +From the root directory `poetry update` followed by `poetry install` - this will establish a virtual environment with all the needed dependencies. -Once your venv is made you can use `poetry run [command]` to run a single CLI command inside the venv. +Once your virtual environment is made you can use `poetry run [command]` to run a single CLI command inside the virtual environment. -You can use `poetry shell` to enter into the venv. \ No newline at end of file +You can use `poetry shell` to enter into the virtual environment. + +### Using Pip +Another option is to install dependencies using Pip: + +1. Run `pip install .` in the top-level directory to automatically install Cider's dependencies. +2. Add Cider's top-level directory to your PYTHONPATH: + +```export PYTHONPATH=$PYTHONPATH:/path/to/cider``` + +You'll likely want to add this line to your .bashrc, .zshrc, or similar. + +### On Apple M1 +The above may not work on Apple M1 processors due to certain dependencies being unavailable or behaving badly. One option to sidestep this issue is to use [conda](https://docs.conda.io/en/latest/) to create a virtual environment which uses x86 architecture (rather than the ARM64 architecture which Apple's M1 chips use). + +[This guide](https://towardsdatascience.com/how-to-manage-conda-environments-on-an-apple-silicon-m1-mac-1e29cb3bad12) contains details on setting up Conda and creating such an environment. Summarized, the steps are as follows: + +1. Install conda, likely [Miniconda](https://docs.conda.io/projects/continuumio-conda/en/latest/user-guide/install/) +2. Run the following commands: +``` +CONDA_SUBDIR=osx-64 conda create -n my_env_name python=3.8 +conda activate my_env_name +conda config --env --set subdir osx-64 +``` +3. Run `conda install swig` + +And then follow either of the installation procedures above from within the environment you just created. \ No newline at end of file diff --git a/docs/_sources/quickstart/poverty_survey.html b/docs/_sources/quickstart/poverty_survey.html deleted file mode 100644 index 9fc8db3..0000000 --- a/docs/_sources/quickstart/poverty_survey.html +++ /dev/null @@ -1,604 +0,0 @@ - - - - - - - - - Poverty Outcome Generation from Survey Data - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Poverty Outcome Generation from Survey Data

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Poverty Outcome Generation from Survey Data#

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Set up the configuration file and load some survey data (see standardized data formats) for file schema).

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datastore = DataStore('/path/to/config/file')
-outcomes_generator = SurveyOutcomesGenerator(datastore=datastore, clean_folders=True)
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Calculate PCA asset index and proxy-means test (PMT). Only use binary and continuous columns in the asset index.

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asset_index = outcomes_generator.asset_index(cols=['con1', 'con2', 'bin1', 'bin2'])
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Select five components to be used in the proxy-means test using forward selection of predictors with a linear regression. Calculate a proxy-means test with these components and obtain out-of-sample PMT predictions for the training survey.

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selected_cols, scores = outcomes_generator\
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-pmt = outcomes_generator.fit_pmt('consumption', 
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[OUT] r2 score: 0.56

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Use the trained proxy-means test on another survey dataset.

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predictions = outcomes_generator.pretrained_pmt('/path/to/other/data.csv', selected_cols, 'linear')
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- - By UC Berkeley Global Policy Lab
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rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem} diff --git a/docs/_static/basic.css b/docs/_static/basic.css index 5d8ae08..d54be80 100644 --- a/docs/_static/basic.css +++ b/docs/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -130,7 +130,7 @@ ul.search li a { font-weight: bold; } -ul.search li div.context { +ul.search li p.context { color: #888; margin: 2px 0 0 30px; text-align: left; @@ -508,6 +508,63 @@ table.hlist td { vertical-align: top; } +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + /* -- other body styles ----------------------------------------------------- */ @@ -634,14 +691,6 @@ dl.glossary dt { font-size: 1.1em; } -.optional { - font-size: 1.3em; -} - -.sig-paren { - font-size: larger; -} - .versionmodified { font-style: italic; } @@ -682,8 +731,9 @@ dl.glossary dt { .classifier:before { font-style: normal; - margin: 0.5em; + margin: 0 0.5em; content: ":"; + display: inline-block; } abbr, acronym { @@ -707,6 +757,7 @@ span.pre { -ms-hyphens: none; -webkit-hyphens: none; hyphens: none; + white-space: nowrap; } div[class*="highlight-"] { @@ -770,8 +821,12 @@ div.code-block-caption code { table.highlighttable td.linenos, span.linenos, -div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */ - user-select: none; +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ } div.code-block-caption span.caption-number { @@ -786,16 +841,6 @@ div.literal-block-wrapper { margin: 1em 0; } -code.descname { - background-color: transparent; - font-weight: bold; - font-size: 1.2em; -} - -code.descclassname { - background-color: transparent; -} - code.xref, a code { background-color: transparent; font-weight: bold; diff --git a/docs/_static/copybutton.css b/docs/_static/copybutton.css index 40eafe5..f1916ec 100644 --- a/docs/_static/copybutton.css +++ b/docs/_static/copybutton.css @@ -35,7 +35,8 @@ div.highlight { position: relative; } -.highlight:hover button.copybtn { +/* Show the copybutton */ +.highlight:hover button.copybtn, button.copybtn.success { opacity: 1; } diff --git a/docs/_static/copybutton.js b/docs/_static/copybutton.js index 40ac331..02c5c82 100644 --- a/docs/_static/copybutton.js +++ b/docs/_static/copybutton.js @@ -102,18 +102,25 @@ const clearSelection = () => { } } -// Changes tooltip text for two seconds, then changes it back +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + const temporarilyChangeTooltip = (el, oldText, newText) => { el.setAttribute('data-tooltip', newText) el.classList.add('success') - setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000) - setTimeout(() => el.classList.remove('success'), 2000) + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) } // Changes the copy button icon for two seconds, then changes it back const temporarilyChangeIcon = (el) => { el.innerHTML = iconCheck; - setTimeout(() => {el.innerHTML = iconCopy}, 2000) + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) } const addCopyButtonToCodeCells = () => { @@ -125,7 +132,8 @@ const addCopyButtonToCodeCells = () => { } // Add copybuttons to all of our code cells - const codeCells = document.querySelectorAll('div.highlight pre') + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) codeCells.forEach((codeCell, index) => { const id = codeCellId(index) codeCell.setAttribute('id', id) @@ -141,10 +149,25 @@ function escapeRegExp(string) { return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string } +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + // Callback when a copy button is clicked. Will be passed the node that was clicked // should then grab the text and replace pieces of text that shouldn't be used in output function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; var match; @@ -199,7 +222,12 @@ function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onl var copyTargetText = (trigger) => { var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - return formatCopyText(target.innerText, '', false, true, true, true, '', '') + + // get filtered text + let exclude = '.linenos, .gp'; + + let text = filterText(target, exclude); + return formatCopyText(text, '', false, true, true, true, '', '') } // Initialize with a callback so we can modify the text before copy diff --git a/docs/_static/copybutton_funcs.js b/docs/_static/copybutton_funcs.js index b9168c5..dbe1aaa 100644 --- a/docs/_static/copybutton_funcs.js +++ b/docs/_static/copybutton_funcs.js @@ -2,10 +2,25 @@ function escapeRegExp(string) { return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string } +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + // Callback when a copy button is clicked. Will be passed the node that was clicked // should then grab the text and replace pieces of text that shouldn't be used in output export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; var match; diff --git a/docs/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/docs/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css new file mode 100644 index 0000000..3225661 --- /dev/null +++ b/docs/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css @@ -0,0 +1 @@ +.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) 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#d3d4d5;--sd-color-muted-highlight: #5c636a;--sd-color-dark-highlight: #1c1f23;--sd-color-black-highlight: black;--sd-color-white-highlight: #d9d9d9;--sd-color-primary-text: #fff;--sd-color-secondary-text: #fff;--sd-color-success-text: #fff;--sd-color-info-text: #fff;--sd-color-warning-text: #212529;--sd-color-danger-text: #fff;--sd-color-light-text: #212529;--sd-color-muted-text: #fff;--sd-color-dark-text: #fff;--sd-color-black-text: #fff;--sd-color-white-text: #212529;--sd-color-shadow: rgba(0, 0, 0, 0.15);--sd-color-card-border: rgba(0, 0, 0, 0.125);--sd-color-card-border-hover: hsla(231, 99%, 66%, 1);--sd-color-card-background: transparent;--sd-color-card-text: inherit;--sd-color-card-header: transparent;--sd-color-card-footer: transparent;--sd-color-tabs-label-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-hover: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-inactive: hsl(0, 0%, 66%);--sd-color-tabs-underline-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-underline-hover: rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem} diff --git a/docs/_static/doctools.js b/docs/_static/doctools.js index 61ac9d2..e1bfd70 100644 --- a/docs/_static/doctools.js +++ b/docs/_static/doctools.js @@ -4,7 +4,7 @@ * * Sphinx JavaScript utilities for all documentation. * - * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -154,9 +154,7 @@ var Documentation = { this.fixFirefoxAnchorBug(); this.highlightSearchWords(); this.initIndexTable(); - if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { - this.initOnKeyListeners(); - } + this.initOnKeyListeners(); }, /** @@ -264,6 +262,16 @@ var Documentation = { hideSearchWords : function() { $('#searchbox .highlight-link').fadeOut(300); $('span.highlighted').removeClass('highlighted'); + var url = new URL(window.location); + url.searchParams.delete('highlight'); + window.history.replaceState({}, '', url); + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar : function() { + $('input[name=q]').first().focus(); }, /** @@ -288,25 +296,54 @@ var Documentation = { }, initOnKeyListeners: function() { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) + return; + $(document).keydown(function(event) { var activeElementType = document.activeElement.tagName; // don't navigate when in search box, textarea, dropdown or button if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' - && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey - && !event.shiftKey) { - switch (event.keyCode) { - case 37: // left - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; - } - case 39: // right - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; - } + && activeElementType !== 'BUTTON') { + if (event.altKey || event.ctrlKey || event.metaKey) + return; + + if (!event.shiftKey) { + switch (event.key) { + case 'ArrowLeft': + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) + break; + var prevHref = $('link[rel="prev"]').prop('href'); + if (prevHref) { + window.location.href = prevHref; + return false; + } + break; + case 'ArrowRight': + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) + break; + var nextHref = $('link[rel="next"]').prop('href'); + if (nextHref) { + window.location.href = nextHref; + return false; + } + break; + case 'Escape': + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) + break; + Documentation.hideSearchWords(); + return false; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case '/': + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) + break; + Documentation.focusSearchBar(); + return false; } } }); diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js index 93b7c24..877e3c3 100644 --- a/docs/_static/documentation_options.js +++ b/docs/_static/documentation_options.js @@ -8,5 +8,7 @@ var DOCUMENTATION_OPTIONS = { LINK_SUFFIX: '.html', HAS_SOURCE: true, SOURCELINK_SUFFIX: '', - NAVIGATION_WITH_KEYS: true + NAVIGATION_WITH_KEYS: true, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, }; \ No newline at end of file diff --git a/docs/_static/language_data.js b/docs/_static/language_data.js index 863704b..ebe2f03 100644 --- a/docs/_static/language_data.js +++ b/docs/_static/language_data.js @@ -5,7 +5,7 @@ * This script contains the language-specific data used by searchtools.js, * namely the list of stopwords, stemmer, scorer and splitter. * - * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ diff --git a/docs/_static/locales/ar/LC_MESSAGES/booktheme.mo b/docs/_static/locales/ar/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..15541a6 Binary files /dev/null and b/docs/_static/locales/ar/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/ar/LC_MESSAGES/booktheme.po b/docs/_static/locales/ar/LC_MESSAGES/booktheme.po index b5c145f..eff38df 100644 --- a/docs/_static/locales/ar/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/ar/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: ar\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "بواسطة" + msgid "repository" msgstr "مخزن" -msgid "open issue" -msgstr "قضية مفتوحة" - -msgid "Contents" -msgstr "محتويات" - msgid "Fullscreen mode" msgstr "وضع ملء الشاشة" -msgid "Download this page" -msgstr "قم بتنزيل هذه الصفحة" +msgid "Contents" +msgstr "محتويات" msgid "Download source file" msgstr "تنزيل ملف المصدر" -msgid "Launch" -msgstr "إطلاق" - msgid "Edit this page" msgstr "قم بتحرير هذه الصفحة" -msgid "Toggle navigation" -msgstr "تبديل التنقل" +msgid "Last updated on" +msgstr "آخر تحديث في" -msgid "Theme by the" -msgstr "موضوع بواسطة" +msgid "Print to PDF" +msgstr "طباعة إلى PDF" -msgid "Source repository" -msgstr "مستودع المصدر" +msgid "suggest edit" +msgstr "أقترح تحرير" -msgid "Last updated on" -msgstr "آخر تحديث في" +msgid "Download this page" +msgstr "قم بتنزيل هذه الصفحة" + +msgid "Toggle navigation" +msgstr "تبديل التنقل" msgid "By the" msgstr "بواسطة" @@ -50,32 +47,29 @@ msgstr "بواسطة" msgid "Sphinx Book Theme" msgstr "موضوع كتاب أبو الهول" -msgid "Open an issue" -msgstr "افتح قضية" - -msgid "next page" -msgstr "الصفحة التالية" +msgid "previous page" +msgstr "الصفحة السابقة" -msgid "Copyright" -msgstr "حقوق النشر" +msgid "Source repository" +msgstr "مستودع المصدر" -msgid "Search this book..." -msgstr "بحث في هذا الكتاب ..." +msgid "open issue" +msgstr "قضية مفتوحة" -msgid "Print to PDF" -msgstr "طباعة إلى PDF" +msgid "Download notebook file" +msgstr "تنزيل ملف دفتر الملاحظات" -msgid "By" -msgstr "بواسطة" +msgid "Copyright" +msgstr "حقوق النشر" -msgid "previous page" -msgstr "الصفحة السابقة" +msgid "Theme by the" +msgstr "موضوع بواسطة" -msgid "Search the docs ..." -msgstr "ابحث في المستندات ..." +msgid "Open an issue" +msgstr "افتح قضية" -msgid "Download notebook file" -msgstr "تنزيل ملف دفتر الملاحظات" +msgid "next page" +msgstr "الصفحة التالية" -msgid "suggest edit" -msgstr "أقترح تحرير" +msgid "Launch" +msgstr "إطلاق" diff --git a/docs/_static/locales/bg/LC_MESSAGES/booktheme.mo b/docs/_static/locales/bg/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..da95120 Binary files /dev/null and b/docs/_static/locales/bg/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/bg/LC_MESSAGES/booktheme.po b/docs/_static/locales/bg/LC_MESSAGES/booktheme.po index 4e1bc06..088f04f 100644 --- a/docs/_static/locales/bg/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/bg/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: bg\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "От" + msgid "repository" msgstr "хранилище" -msgid "open issue" -msgstr "отворен брой" - -msgid "Contents" -msgstr "Съдържание" - msgid "Fullscreen mode" msgstr "Режим на цял екран" -msgid "Download this page" -msgstr "Изтеглете тази страница" +msgid "Contents" +msgstr "Съдържание" msgid "Download source file" msgstr "Изтеглете изходния файл" -msgid "Launch" -msgstr "Стартиране" - msgid "Edit this page" msgstr "Редактирайте тази страница" -msgid "Toggle navigation" -msgstr "Превключване на навигацията" +msgid "Last updated on" +msgstr "Последна актуализация на" -msgid "Theme by the" -msgstr "Тема от" +msgid "Print to PDF" +msgstr "Печат в PDF" -msgid "Source repository" -msgstr "Хранилище на източника" +msgid "suggest edit" +msgstr "предложи редактиране" -msgid "Last updated on" -msgstr "Последна актуализация на" +msgid "Download this page" +msgstr "Изтеглете тази страница" + +msgid "Toggle navigation" +msgstr "Превключване на навигацията" msgid "By the" msgstr "По" @@ -50,32 +47,29 @@ msgstr "По" msgid "Sphinx Book Theme" msgstr "Тема на книгата Sphinx" -msgid "Open an issue" -msgstr "Отворете проблем" - -msgid "next page" -msgstr "Следваща страница" +msgid "previous page" +msgstr "предишна страница" -msgid "Copyright" -msgstr "Авторско право" +msgid "Source repository" +msgstr "Хранилище на източника" -msgid "Search this book..." -msgstr "Търсене в тази книга ..." +msgid "open issue" +msgstr "отворен брой" -msgid "Print to PDF" -msgstr "Печат в PDF" +msgid "Download notebook file" +msgstr "Изтеглете файла на бележника" -msgid "By" -msgstr "От" +msgid "Copyright" +msgstr "Авторско право" -msgid "previous page" -msgstr "предишна страница" +msgid "Theme by the" +msgstr "Тема от" -msgid "Search the docs ..." -msgstr "Търсене в документите ..." +msgid "Open an issue" +msgstr "Отворете проблем" -msgid "Download notebook file" -msgstr "Изтеглете файла на бележника" +msgid "next page" +msgstr "Следваща страница" -msgid "suggest edit" -msgstr "предложи редактиране" +msgid "Launch" +msgstr "Стартиране" diff --git a/docs/_static/locales/bn/LC_MESSAGES/booktheme.mo b/docs/_static/locales/bn/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..6b96639 Binary files /dev/null and b/docs/_static/locales/bn/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/bn/LC_MESSAGES/booktheme.po b/docs/_static/locales/bn/LC_MESSAGES/booktheme.po index 6722be2..80b5808 100644 --- a/docs/_static/locales/bn/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/bn/LC_MESSAGES/booktheme.po @@ -8,32 +8,26 @@ msgstr "" "Language: bn\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "খোলা সমস্যা" - -msgid "Download this page" -msgstr "এই পৃষ্ঠাটি ডাউনলোড করুন" +msgid "By" +msgstr "দ্বারা" msgid "Download source file" msgstr "উত্স ফাইল ডাউনলোড করুন" -msgid "Launch" -msgstr "শুরু করা" - msgid "Edit this page" msgstr "এই পৃষ্ঠাটি সম্পাদনা করুন" -msgid "Toggle navigation" -msgstr "নেভিগেশন টগল করুন" +msgid "Last updated on" +msgstr "সর্বশেষ আপডেট" -msgid "Theme by the" -msgstr "থিম দ্বারা" +msgid "Print to PDF" +msgstr "পিডিএফ প্রিন্ট করুন" -msgid "Source repository" -msgstr "উত্স সংগ্রহস্থল" +msgid "Download this page" +msgstr "এই পৃষ্ঠাটি ডাউনলোড করুন" -msgid "Last updated on" -msgstr "সর্বশেষ আপডেট" +msgid "Toggle navigation" +msgstr "নেভিগেশন টগল করুন" msgid "By the" msgstr "দ্বারা" @@ -41,26 +35,29 @@ msgstr "দ্বারা" msgid "Sphinx Book Theme" msgstr "স্পিনিক্স বুক থিম" -msgid "Open an issue" -msgstr "একটি সমস্যা খুলুন" +msgid "previous page" +msgstr "আগের পৃষ্ঠা" -msgid "next page" -msgstr "পরবর্তী পৃষ্ঠা" +msgid "Source repository" +msgstr "উত্স সংগ্রহস্থল" + +msgid "open issue" +msgstr "খোলা সমস্যা" + +msgid "Download notebook file" +msgstr "নোটবুক ফাইল ডাউনলোড করুন" msgid "Copyright" msgstr "কপিরাইট" -msgid "Search this book..." -msgstr "এই বইটি অনুসন্ধান করুন ..." - -msgid "Print to PDF" -msgstr "পিডিএফ প্রিন্ট করুন" +msgid "Theme by the" +msgstr "থিম দ্বারা" -msgid "By" -msgstr "দ্বারা" +msgid "Open an issue" +msgstr "একটি সমস্যা খুলুন" -msgid "previous page" -msgstr "আগের পৃষ্ঠা" +msgid "next page" +msgstr "পরবর্তী পৃষ্ঠা" -msgid "Download notebook file" -msgstr "নোটবুক ফাইল ডাউনলোড করুন" +msgid "Launch" +msgstr "শুরু করা" diff --git a/docs/_static/locales/ca/LC_MESSAGES/booktheme.mo b/docs/_static/locales/ca/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..a4dd30e Binary files /dev/null and b/docs/_static/locales/ca/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/ca/LC_MESSAGES/booktheme.po b/docs/_static/locales/ca/LC_MESSAGES/booktheme.po index 546e395..0f4bdf5 100644 --- a/docs/_static/locales/ca/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/ca/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: ca\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "número obert" - -msgid "Download this page" -msgstr "Descarregueu aquesta pàgina" +msgid "By" +msgstr "Per" msgid "Download source file" msgstr "Baixeu el fitxer font" -msgid "Launch" -msgstr "Llançament" - msgid "Edit this page" msgstr "Editeu aquesta pàgina" -msgid "Toggle navigation" -msgstr "Commuta la navegació" +msgid "Last updated on" +msgstr "Darrera actualització el" -msgid "Theme by the" -msgstr "Tema del" +msgid "Print to PDF" +msgstr "Imprimeix a PDF" -msgid "Source repository" -msgstr "Dipòsit de fonts" +msgid "suggest edit" +msgstr "suggerir edició" -msgid "Last updated on" -msgstr "Darrera actualització el" +msgid "Download this page" +msgstr "Descarregueu aquesta pàgina" + +msgid "Toggle navigation" +msgstr "Commuta la navegació" msgid "By the" msgstr "Per la" @@ -41,29 +38,29 @@ msgstr "Per la" msgid "Sphinx Book Theme" msgstr "Tema del llibre Esfinx" -msgid "Open an issue" -msgstr "Obriu un número" +msgid "previous page" +msgstr "Pàgina anterior" -msgid "next page" -msgstr "pàgina següent" +msgid "Source repository" +msgstr "Dipòsit de fonts" -msgid "Copyright" -msgstr "Copyright" +msgid "open issue" +msgstr "número obert" -msgid "Search this book..." -msgstr "Cerca en aquest llibre ..." +msgid "Download notebook file" +msgstr "Descarregar fitxer de quadern" -msgid "Print to PDF" -msgstr "Imprimeix a PDF" +msgid "Copyright" +msgstr "Copyright" -msgid "By" -msgstr "Per" +msgid "Theme by the" +msgstr "Tema del" -msgid "previous page" -msgstr "Pàgina anterior" +msgid "Open an issue" +msgstr "Obriu un número" -msgid "Download notebook file" -msgstr "Descarregar fitxer de quadern" +msgid "next page" +msgstr "pàgina següent" -msgid "suggest edit" -msgstr "suggerir edició" +msgid "Launch" +msgstr "Llançament" diff --git a/docs/_static/locales/cs/LC_MESSAGES/booktheme.mo b/docs/_static/locales/cs/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..c39e01a Binary files /dev/null and b/docs/_static/locales/cs/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/cs/LC_MESSAGES/booktheme.po b/docs/_static/locales/cs/LC_MESSAGES/booktheme.po index 4889556..12632b5 100644 --- a/docs/_static/locales/cs/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/cs/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: cs\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Podle" + msgid "repository" msgstr "úložiště" -msgid "open issue" -msgstr "otevřené číslo" - -msgid "Contents" -msgstr "Obsah" - msgid "Fullscreen mode" msgstr "Režim celé obrazovky" -msgid "Download this page" -msgstr "Stáhněte si tuto stránku" +msgid "Contents" +msgstr "Obsah" msgid "Download source file" msgstr "Stáhněte si zdrojový soubor" -msgid "Launch" -msgstr "Zahájení" - msgid "Edit this page" msgstr "Upravit tuto stránku" -msgid "Toggle navigation" -msgstr "Přepnout navigaci" +msgid "Last updated on" +msgstr "Naposledy aktualizováno" -msgid "Theme by the" -msgstr "Téma od" +msgid "Print to PDF" +msgstr "Tisk do PDF" -msgid "Source repository" -msgstr "Zdrojové úložiště" +msgid "suggest edit" +msgstr "navrhnout úpravy" -msgid "Last updated on" -msgstr "Naposledy aktualizováno" +msgid "Download this page" +msgstr "Stáhněte si tuto stránku" + +msgid "Toggle navigation" +msgstr "Přepnout navigaci" msgid "By the" msgstr "Podle" @@ -50,32 +47,29 @@ msgstr "Podle" msgid "Sphinx Book Theme" msgstr "Téma knihy Sfinga" -msgid "Open an issue" -msgstr "Otevřete problém" - -msgid "next page" -msgstr "další strana" +msgid "previous page" +msgstr "předchozí stránka" -msgid "Copyright" -msgstr "autorská práva" +msgid "Source repository" +msgstr "Zdrojové úložiště" -msgid "Search this book..." -msgstr "Hledat v této knize ..." +msgid "open issue" +msgstr "otevřené číslo" -msgid "Print to PDF" -msgstr "Tisk do PDF" +msgid "Download notebook file" +msgstr "Stáhnout soubor poznámkového bloku" -msgid "By" -msgstr "Podle" +msgid "Copyright" +msgstr "autorská práva" -msgid "previous page" -msgstr "předchozí stránka" +msgid "Theme by the" +msgstr "Téma od" -msgid "Search the docs ..." -msgstr "Hledat v dokumentech ..." +msgid "Open an issue" +msgstr "Otevřete problém" -msgid "Download notebook file" -msgstr "Stáhnout soubor poznámkového bloku" +msgid "next page" +msgstr "další strana" -msgid "suggest edit" -msgstr "navrhnout úpravy" +msgid "Launch" +msgstr "Zahájení" diff --git a/docs/_static/locales/da/LC_MESSAGES/booktheme.mo b/docs/_static/locales/da/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..f43157d Binary files /dev/null and b/docs/_static/locales/da/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/da/LC_MESSAGES/booktheme.po b/docs/_static/locales/da/LC_MESSAGES/booktheme.po index 675a05f..145e211 100644 --- a/docs/_static/locales/da/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/da/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: da\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Ved" + msgid "repository" msgstr "lager" -msgid "open issue" -msgstr "åbent nummer" - -msgid "Contents" -msgstr "Indhold" - msgid "Fullscreen mode" msgstr "Fuldskærmstilstand" -msgid "Download this page" -msgstr "Download denne side" +msgid "Contents" +msgstr "Indhold" msgid "Download source file" msgstr "Download kildefil" -msgid "Launch" -msgstr "Start" - msgid "Edit this page" msgstr "Rediger denne side" -msgid "Toggle navigation" -msgstr "Skift navigation" +msgid "Last updated on" +msgstr "Sidst opdateret den" -msgid "Theme by the" -msgstr "Tema af" +msgid "Print to PDF" +msgstr "Udskriv til PDF" -msgid "Source repository" -msgstr "Kildelager" +msgid "suggest edit" +msgstr "foreslå redigering" -msgid "Last updated on" -msgstr "Sidst opdateret den" +msgid "Download this page" +msgstr "Download denne side" + +msgid "Toggle navigation" +msgstr "Skift navigation" msgid "By the" msgstr "Ved" @@ -50,32 +47,29 @@ msgstr "Ved" msgid "Sphinx Book Theme" msgstr "Sphinx bogtema" -msgid "Open an issue" -msgstr "Åbn et problem" - -msgid "next page" -msgstr "Næste side" +msgid "previous page" +msgstr "forrige side" -msgid "Copyright" -msgstr "ophavsret" +msgid "Source repository" +msgstr "Kildelager" -msgid "Search this book..." -msgstr "Søg i denne bog ..." +msgid "open issue" +msgstr "åbent nummer" -msgid "Print to PDF" -msgstr "Udskriv til PDF" +msgid "Download notebook file" +msgstr "Download notesbog-fil" -msgid "By" -msgstr "Ved" +msgid "Copyright" +msgstr "ophavsret" -msgid "previous page" -msgstr "forrige side" +msgid "Theme by the" +msgstr "Tema af" -msgid "Search the docs ..." -msgstr "Søg i dokumenterne ..." +msgid "Open an issue" +msgstr "Åbn et problem" -msgid "Download notebook file" -msgstr "Download notesbog-fil" +msgid "next page" +msgstr "Næste side" -msgid "suggest edit" -msgstr "foreslå redigering" +msgid "Launch" +msgstr "Start" diff --git a/docs/_static/locales/de/LC_MESSAGES/booktheme.mo b/docs/_static/locales/de/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..648b565 Binary files /dev/null and b/docs/_static/locales/de/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/de/LC_MESSAGES/booktheme.po b/docs/_static/locales/de/LC_MESSAGES/booktheme.po index 70eaab5..eb2f605 100644 --- a/docs/_static/locales/de/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/de/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: de\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Durch" + msgid "repository" msgstr "Repository" -msgid "open issue" -msgstr "offenes Thema" - -msgid "Contents" -msgstr "Inhalt" - msgid "Fullscreen mode" msgstr "Vollbildmodus" -msgid "Download this page" -msgstr "Laden Sie diese Seite herunter" +msgid "Contents" +msgstr "Inhalt" msgid "Download source file" msgstr "Quelldatei herunterladen" -msgid "Launch" -msgstr "Starten" - msgid "Edit this page" msgstr "Bearbeite diese Seite" -msgid "Toggle navigation" -msgstr "Navigation umschalten" +msgid "Last updated on" +msgstr "Zuletzt aktualisiert am" -msgid "Theme by the" -msgstr "Thema von der" +msgid "Print to PDF" +msgstr "In PDF drucken" -msgid "Source repository" -msgstr "Quell-Repository" +msgid "suggest edit" +msgstr "vorschlagen zu bearbeiten" -msgid "Last updated on" -msgstr "Zuletzt aktualisiert am" +msgid "Download this page" +msgstr "Laden Sie diese Seite herunter" + +msgid "Toggle navigation" +msgstr "Navigation umschalten" msgid "By the" msgstr "Bis zum" @@ -50,32 +47,29 @@ msgstr "Bis zum" msgid "Sphinx Book Theme" msgstr "Sphinx-Buch-Thema" -msgid "Open an issue" -msgstr "Öffnen Sie ein Problem" - -msgid "next page" -msgstr "Nächste Seite" +msgid "previous page" +msgstr "vorherige Seite" -msgid "Copyright" -msgstr "Urheberrechte ©" +msgid "Source repository" +msgstr "Quell-Repository" -msgid "Search this book..." -msgstr "Dieses Buch durchsuchen ..." +msgid "open issue" +msgstr "offenes Thema" -msgid "Print to PDF" -msgstr "In PDF drucken" +msgid "Download notebook file" +msgstr "Notebook-Datei herunterladen" -msgid "By" -msgstr "Durch" +msgid "Copyright" +msgstr "Urheberrechte ©" -msgid "previous page" -msgstr "vorherige Seite" +msgid "Theme by the" +msgstr "Thema von der" -msgid "Search the docs ..." -msgstr "Durchsuchen Sie die Dokumente ..." +msgid "Open an issue" +msgstr "Öffnen Sie ein Problem" -msgid "Download notebook file" -msgstr "Notebook-Datei herunterladen" +msgid "next page" +msgstr "Nächste Seite" -msgid "suggest edit" -msgstr "vorschlagen zu bearbeiten" +msgid "Launch" +msgstr "Starten" diff --git a/docs/_static/locales/el/LC_MESSAGES/booktheme.mo b/docs/_static/locales/el/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..fca6e93 Binary files /dev/null and b/docs/_static/locales/el/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/el/LC_MESSAGES/booktheme.po b/docs/_static/locales/el/LC_MESSAGES/booktheme.po index 222cfa3..9e19fb2 100644 --- a/docs/_static/locales/el/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/el/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: el\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Με" + msgid "repository" msgstr "αποθήκη" -msgid "open issue" -msgstr "ανοιχτό ζήτημα" - -msgid "Contents" -msgstr "Περιεχόμενα" - msgid "Fullscreen mode" msgstr "ΛΕΙΤΟΥΡΓΙΑ ΠΛΗΡΟΥΣ ΟΘΟΝΗΣ" -msgid "Download this page" -msgstr "Λήψη αυτής της σελίδας" +msgid "Contents" +msgstr "Περιεχόμενα" msgid "Download source file" msgstr "Λήψη αρχείου προέλευσης" -msgid "Launch" -msgstr "Εκτόξευση" - msgid "Edit this page" msgstr "Επεξεργαστείτε αυτήν τη σελίδα" -msgid "Toggle navigation" -msgstr "Εναλλαγή πλοήγησης" +msgid "Last updated on" +msgstr "Τελευταία ενημέρωση στις" -msgid "Theme by the" -msgstr "Θέμα από το" +msgid "Print to PDF" +msgstr "Εκτύπωση σε PDF" -msgid "Source repository" -msgstr "Αποθήκη πηγής" +msgid "suggest edit" +msgstr "προτείνω επεξεργασία" -msgid "Last updated on" -msgstr "Τελευταία ενημέρωση στις" +msgid "Download this page" +msgstr "Λήψη αυτής της σελίδας" + +msgid "Toggle navigation" +msgstr "Εναλλαγή πλοήγησης" msgid "By the" msgstr "Από το" @@ -50,32 +47,29 @@ msgstr "Από το" msgid "Sphinx Book Theme" msgstr "Θέμα βιβλίου Sphinx" -msgid "Open an issue" -msgstr "Ανοίξτε ένα ζήτημα" - -msgid "next page" -msgstr "επόμενη σελίδα" +msgid "previous page" +msgstr "προηγούμενη σελίδα" -msgid "Copyright" -msgstr "Πνευματική ιδιοκτησία" +msgid "Source repository" +msgstr "Αποθήκη πηγής" -msgid "Search this book..." -msgstr "Αναζήτηση σε αυτό το βιβλίο ..." +msgid "open issue" +msgstr "ανοιχτό ζήτημα" -msgid "Print to PDF" -msgstr "Εκτύπωση σε PDF" +msgid "Download notebook file" +msgstr "Λήψη αρχείου σημειωματάριου" -msgid "By" -msgstr "Με" +msgid "Copyright" +msgstr "Πνευματική ιδιοκτησία" -msgid "previous page" -msgstr "προηγούμενη σελίδα" +msgid "Theme by the" +msgstr "Θέμα από το" -msgid "Search the docs ..." -msgstr "Αναζήτηση στα έγγραφα ..." +msgid "Open an issue" +msgstr "Ανοίξτε ένα ζήτημα" -msgid "Download notebook file" -msgstr "Λήψη αρχείου σημειωματάριου" +msgid "next page" +msgstr "επόμενη σελίδα" -msgid "suggest edit" -msgstr "προτείνω επεξεργασία" +msgid "Launch" +msgstr "Εκτόξευση" diff --git a/docs/_static/locales/eo/LC_MESSAGES/booktheme.mo b/docs/_static/locales/eo/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..d1072bb Binary files /dev/null and b/docs/_static/locales/eo/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/eo/LC_MESSAGES/booktheme.po b/docs/_static/locales/eo/LC_MESSAGES/booktheme.po index bdf994b..11d0adc 100644 --- a/docs/_static/locales/eo/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/eo/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: eo\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "De" + msgid "repository" msgstr "deponejo" -msgid "open issue" -msgstr "malferma numero" - -msgid "Contents" -msgstr "Enhavo" - msgid "Fullscreen mode" msgstr "Plenekrana reĝimo" -msgid "Download this page" -msgstr "Elŝutu ĉi tiun paĝon" +msgid "Contents" +msgstr "Enhavo" msgid "Download source file" msgstr "Elŝutu fontodosieron" -msgid "Launch" -msgstr "Lanĉo" - msgid "Edit this page" msgstr "Redaktu ĉi tiun paĝon" -msgid "Toggle navigation" -msgstr "Ŝalti navigadon" +msgid "Last updated on" +msgstr "Laste ĝisdatigita la" -msgid "Theme by the" -msgstr "Temo de la" +msgid "Print to PDF" +msgstr "Presi al PDF" -msgid "Source repository" -msgstr "Fonto-deponejo" +msgid "suggest edit" +msgstr "sugesti redaktadon" -msgid "Last updated on" -msgstr "Laste ĝisdatigita la" +msgid "Download this page" +msgstr "Elŝutu ĉi tiun paĝon" + +msgid "Toggle navigation" +msgstr "Ŝalti navigadon" msgid "By the" msgstr "Per la" @@ -50,32 +47,29 @@ msgstr "Per la" msgid "Sphinx Book Theme" msgstr "Sfinksa Libro-Temo" -msgid "Open an issue" -msgstr "Malfermu numeron" - -msgid "next page" -msgstr "sekva paĝo" +msgid "previous page" +msgstr "antaŭa paĝo" -msgid "Copyright" -msgstr "Kopirajto" +msgid "Source repository" +msgstr "Fonto-deponejo" -msgid "Search this book..." -msgstr "Serĉu ĉi tiun libron ..." +msgid "open issue" +msgstr "malferma numero" -msgid "Print to PDF" -msgstr "Presi al PDF" +msgid "Download notebook file" +msgstr "Elŝutu kajeran dosieron" -msgid "By" -msgstr "De" +msgid "Copyright" +msgstr "Kopirajto" -msgid "previous page" -msgstr "antaŭa paĝo" +msgid "Theme by the" +msgstr "Temo de la" -msgid "Search the docs ..." -msgstr "Serĉu la dokumentojn ..." +msgid "Open an issue" +msgstr "Malfermu numeron" -msgid "Download notebook file" -msgstr "Elŝutu kajeran dosieron" +msgid "next page" +msgstr "sekva paĝo" -msgid "suggest edit" -msgstr "sugesti redaktadon" +msgid "Launch" +msgstr "Lanĉo" diff --git a/docs/_static/locales/es/LC_MESSAGES/booktheme.mo b/docs/_static/locales/es/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..ba2ee4d Binary files /dev/null and b/docs/_static/locales/es/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/es/LC_MESSAGES/booktheme.po b/docs/_static/locales/es/LC_MESSAGES/booktheme.po index b23d3ec..94f936a 100644 --- a/docs/_static/locales/es/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/es/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: es\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Por" + msgid "repository" msgstr "repositorio" -msgid "open issue" -msgstr "Tema abierto" - -msgid "Contents" -msgstr "Contenido" - msgid "Fullscreen mode" msgstr "Modo de pantalla completa" -msgid "Download this page" -msgstr "Descarga esta pagina" +msgid "Contents" +msgstr "Contenido" msgid "Download source file" msgstr "Descargar archivo fuente" -msgid "Launch" -msgstr "Lanzamiento" - msgid "Edit this page" msgstr "Edita esta página" -msgid "Toggle navigation" -msgstr "Navegación de palanca" +msgid "Last updated on" +msgstr "Ultima actualización en" -msgid "Theme by the" -msgstr "Tema por el" +msgid "Print to PDF" +msgstr "Imprimir en PDF" -msgid "Source repository" -msgstr "Repositorio de origen" +msgid "suggest edit" +msgstr "sugerir editar" -msgid "Last updated on" -msgstr "Ultima actualización en" +msgid "Download this page" +msgstr "Descarga esta pagina" + +msgid "Toggle navigation" +msgstr "Navegación de palanca" msgid "By the" msgstr "Por el" @@ -50,32 +47,29 @@ msgstr "Por el" msgid "Sphinx Book Theme" msgstr "Tema del libro de la esfinge" -msgid "Open an issue" -msgstr "Abrir un problema" - -msgid "next page" -msgstr "siguiente página" +msgid "previous page" +msgstr "pagina anterior" -msgid "Copyright" -msgstr "Derechos de autor" +msgid "Source repository" +msgstr "Repositorio de origen" -msgid "Search this book..." -msgstr "Buscar este libro ..." +msgid "open issue" +msgstr "Tema abierto" -msgid "Print to PDF" -msgstr "Imprimir en PDF" +msgid "Download notebook file" +msgstr "Descargar archivo de cuaderno" -msgid "By" -msgstr "Por" +msgid "Copyright" +msgstr "Derechos de autor" -msgid "previous page" -msgstr "pagina anterior" +msgid "Theme by the" +msgstr "Tema por el" -msgid "Search the docs ..." -msgstr "Buscar los documentos ..." +msgid "Open an issue" +msgstr "Abrir un problema" -msgid "Download notebook file" -msgstr "Descargar archivo de cuaderno" +msgid "next page" +msgstr "siguiente página" -msgid "suggest edit" -msgstr "sugerir editar" +msgid "Launch" +msgstr "Lanzamiento" diff --git a/docs/_static/locales/et/LC_MESSAGES/booktheme.mo b/docs/_static/locales/et/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..983b823 Binary files /dev/null and b/docs/_static/locales/et/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/et/LC_MESSAGES/booktheme.po b/docs/_static/locales/et/LC_MESSAGES/booktheme.po index 664c246..e371f3b 100644 --- a/docs/_static/locales/et/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/et/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: et\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Kõrval" + msgid "repository" msgstr "hoidla" -msgid "open issue" -msgstr "avatud küsimus" - -msgid "Contents" -msgstr "Sisu" - msgid "Fullscreen mode" msgstr "Täisekraanirežiim" -msgid "Download this page" -msgstr "Laadige see leht alla" +msgid "Contents" +msgstr "Sisu" msgid "Download source file" msgstr "Laadige alla lähtefail" -msgid "Launch" -msgstr "Käivitage" - msgid "Edit this page" msgstr "Muutke seda lehte" -msgid "Toggle navigation" -msgstr "Lülita navigeerimine sisse" +msgid "Last updated on" +msgstr "Viimati uuendatud" -msgid "Theme by the" -msgstr "Teema" +msgid "Print to PDF" +msgstr "Prindi PDF-i" -msgid "Source repository" -msgstr "Allikahoidla" +msgid "suggest edit" +msgstr "soovita muuta" -msgid "Last updated on" -msgstr "Viimati uuendatud" +msgid "Download this page" +msgstr "Laadige see leht alla" + +msgid "Toggle navigation" +msgstr "Lülita navigeerimine sisse" msgid "By the" msgstr "Autor" @@ -50,32 +47,29 @@ msgstr "Autor" msgid "Sphinx Book Theme" msgstr "Sfinksiraamatu teema" -msgid "Open an issue" -msgstr "Avage probleem" - -msgid "next page" -msgstr "järgmine leht" +msgid "previous page" +msgstr "eelmine leht" -msgid "Copyright" -msgstr "Autoriõigus" +msgid "Source repository" +msgstr "Allikahoidla" -msgid "Search this book..." -msgstr "Otsige sellest raamatust ..." +msgid "open issue" +msgstr "avatud küsimus" -msgid "Print to PDF" -msgstr "Prindi PDF-i" +msgid "Download notebook file" +msgstr "Laadige sülearvuti fail alla" -msgid "By" -msgstr "Kõrval" +msgid "Copyright" +msgstr "Autoriõigus" -msgid "previous page" -msgstr "eelmine leht" +msgid "Theme by the" +msgstr "Teema" -msgid "Search the docs ..." -msgstr "Dokumentidest otsimine ..." +msgid "Open an issue" +msgstr "Avage probleem" -msgid "Download notebook file" -msgstr "Laadige sülearvuti fail alla" +msgid "next page" +msgstr "järgmine leht" -msgid "suggest edit" -msgstr "soovita muuta" +msgid "Launch" +msgstr "Käivitage" diff --git a/docs/_static/locales/fi/LC_MESSAGES/booktheme.mo b/docs/_static/locales/fi/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..d8ac054 Binary files /dev/null and b/docs/_static/locales/fi/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/fi/LC_MESSAGES/booktheme.po b/docs/_static/locales/fi/LC_MESSAGES/booktheme.po index c4b0d30..2ba0faa 100644 --- a/docs/_static/locales/fi/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/fi/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: fi\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Tekijä" + msgid "repository" msgstr "arkisto" -msgid "open issue" -msgstr "avoin ongelma" - -msgid "Contents" -msgstr "Sisällys" - msgid "Fullscreen mode" msgstr "Koko näytön tila" -msgid "Download this page" -msgstr "Lataa tämä sivu" +msgid "Contents" +msgstr "Sisällys" msgid "Download source file" msgstr "Lataa lähdetiedosto" -msgid "Launch" -msgstr "Tuoda markkinoille" - msgid "Edit this page" msgstr "Muokkaa tätä sivua" -msgid "Toggle navigation" -msgstr "Vaihda navigointia" +msgid "Last updated on" +msgstr "Viimeksi päivitetty" -msgid "Theme by the" -msgstr "Teeman tekijä" +msgid "Print to PDF" +msgstr "Tulosta PDF-tiedostoon" -msgid "Source repository" -msgstr "Lähteen arkisto" +msgid "suggest edit" +msgstr "ehdottaa muokkausta" -msgid "Last updated on" -msgstr "Viimeksi päivitetty" +msgid "Download this page" +msgstr "Lataa tämä sivu" + +msgid "Toggle navigation" +msgstr "Vaihda navigointia" msgid "By the" msgstr "Mukaan" @@ -50,32 +47,29 @@ msgstr "Mukaan" msgid "Sphinx Book Theme" msgstr "Sphinx-kirjan teema" -msgid "Open an issue" -msgstr "Avaa ongelma" - -msgid "next page" -msgstr "seuraava sivu" +msgid "previous page" +msgstr "Edellinen sivu" -msgid "Copyright" -msgstr "Tekijänoikeus" +msgid "Source repository" +msgstr "Lähteen arkisto" -msgid "Search this book..." -msgstr "Hae tästä kirjasta ..." +msgid "open issue" +msgstr "avoin ongelma" -msgid "Print to PDF" -msgstr "Tulosta PDF-tiedostoon" +msgid "Download notebook file" +msgstr "Lataa muistikirjatiedosto" -msgid "By" -msgstr "Tekijä" +msgid "Copyright" +msgstr "Tekijänoikeus" -msgid "previous page" -msgstr "Edellinen sivu" +msgid "Theme by the" +msgstr "Teeman tekijä" -msgid "Search the docs ..." -msgstr "Hae dokumenteista ..." +msgid "Open an issue" +msgstr "Avaa ongelma" -msgid "Download notebook file" -msgstr "Lataa muistikirjatiedosto" +msgid "next page" +msgstr "seuraava sivu" -msgid "suggest edit" -msgstr "ehdottaa muokkausta" +msgid "Launch" +msgstr "Tuoda markkinoille" diff --git a/docs/_static/locales/fr/LC_MESSAGES/booktheme.mo b/docs/_static/locales/fr/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..f663d39 Binary files /dev/null and b/docs/_static/locales/fr/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/fr/LC_MESSAGES/booktheme.po b/docs/_static/locales/fr/LC_MESSAGES/booktheme.po index 93762ce..e18714c 100644 --- a/docs/_static/locales/fr/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/fr/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: fr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Par" + msgid "repository" msgstr "dépôt" -msgid "open issue" -msgstr "signaler un problème" - -msgid "Contents" -msgstr "Contenu" - msgid "Fullscreen mode" msgstr "Mode plein écran" -msgid "Download this page" -msgstr "Téléchargez cette page" +msgid "Contents" +msgstr "Contenu" msgid "Download source file" msgstr "Télécharger le fichier source" -msgid "Launch" -msgstr "lancement" - msgid "Edit this page" msgstr "Modifier cette page" -msgid "Toggle navigation" -msgstr "Basculer la navigation" +msgid "Last updated on" +msgstr "Dernière mise à jour le" -msgid "Theme by the" -msgstr "Thème par le" +msgid "Print to PDF" +msgstr "Imprimer au format PDF" -msgid "Source repository" -msgstr "Dépôt source" +msgid "suggest edit" +msgstr "suggestion de modification" -msgid "Last updated on" -msgstr "Dernière mise à jour le" +msgid "Download this page" +msgstr "Téléchargez cette page" + +msgid "Toggle navigation" +msgstr "Basculer la navigation" msgid "By the" msgstr "Par le" @@ -50,32 +47,29 @@ msgstr "Par le" msgid "Sphinx Book Theme" msgstr "Thème du livre Sphinx" -msgid "Open an issue" -msgstr "Ouvrez un problème" - -msgid "next page" -msgstr "page suivante" +msgid "previous page" +msgstr "page précédente" -msgid "Copyright" -msgstr "droits d'auteur" +msgid "Source repository" +msgstr "Dépôt source" -msgid "Search this book..." -msgstr "Rechercher dans ce livre ..." +msgid "open issue" +msgstr "signaler un problème" -msgid "Print to PDF" -msgstr "Imprimer au format PDF" +msgid "Download notebook file" +msgstr "Télécharger le fichier notebook" -msgid "By" -msgstr "Par" +msgid "Copyright" +msgstr "droits d'auteur" -msgid "previous page" -msgstr "page précédente" +msgid "Theme by the" +msgstr "Thème par le" -msgid "Search the docs ..." -msgstr "Rechercher dans les documents ..." +msgid "Open an issue" +msgstr "Ouvrez un problème" -msgid "Download notebook file" -msgstr "Télécharger le fichier notebook" +msgid "next page" +msgstr "page suivante" -msgid "suggest edit" -msgstr "suggestion de modification" +msgid "Launch" +msgstr "lancement" diff --git a/docs/_static/locales/hr/LC_MESSAGES/booktheme.mo b/docs/_static/locales/hr/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..eca4a1a Binary files /dev/null and b/docs/_static/locales/hr/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/hr/LC_MESSAGES/booktheme.po b/docs/_static/locales/hr/LC_MESSAGES/booktheme.po index 7e94599..c1207c3 100644 --- a/docs/_static/locales/hr/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/hr/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: hr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Po" + msgid "repository" msgstr "spremište" -msgid "open issue" -msgstr "otvoreno izdanje" - -msgid "Contents" -msgstr "Sadržaj" - msgid "Fullscreen mode" msgstr "Način preko cijelog zaslona" -msgid "Download this page" -msgstr "Preuzmite ovu stranicu" +msgid "Contents" +msgstr "Sadržaj" msgid "Download source file" msgstr "Preuzmi izvornu datoteku" -msgid "Launch" -msgstr "Pokrenite" - msgid "Edit this page" msgstr "Uredite ovu stranicu" -msgid "Toggle navigation" -msgstr "Uključi / isključi navigaciju" +msgid "Last updated on" +msgstr "Posljednje ažuriranje:" -msgid "Theme by the" -msgstr "Tema autora" +msgid "Print to PDF" +msgstr "Ispis u PDF" -msgid "Source repository" -msgstr "Izvorno spremište" +msgid "suggest edit" +msgstr "predloži uređivanje" -msgid "Last updated on" -msgstr "Posljednje ažuriranje:" +msgid "Download this page" +msgstr "Preuzmite ovu stranicu" + +msgid "Toggle navigation" +msgstr "Uključi / isključi navigaciju" msgid "By the" msgstr "Od strane" @@ -50,32 +47,29 @@ msgstr "Od strane" msgid "Sphinx Book Theme" msgstr "Tema knjige Sphinx" -msgid "Open an issue" -msgstr "Otvorite izdanje" - -msgid "next page" -msgstr "sljedeća stranica" +msgid "previous page" +msgstr "Prethodna stranica" -msgid "Copyright" -msgstr "Autorska prava" +msgid "Source repository" +msgstr "Izvorno spremište" -msgid "Search this book..." -msgstr "Pretražite ovu knjigu ..." +msgid "open issue" +msgstr "otvoreno izdanje" -msgid "Print to PDF" -msgstr "Ispis u PDF" +msgid "Download notebook file" +msgstr "Preuzmi datoteku bilježnice" -msgid "By" -msgstr "Po" +msgid "Copyright" +msgstr "Autorska prava" -msgid "previous page" -msgstr "Prethodna stranica" +msgid "Theme by the" +msgstr "Tema autora" -msgid "Search the docs ..." -msgstr "Pretražite dokumente ..." +msgid "Open an issue" +msgstr "Otvorite izdanje" -msgid "Download notebook file" -msgstr "Preuzmi datoteku bilježnice" +msgid "next page" +msgstr "sljedeća stranica" -msgid "suggest edit" -msgstr "predloži uređivanje" +msgid "Launch" +msgstr "Pokrenite" diff --git a/docs/_static/locales/id/LC_MESSAGES/booktheme.mo b/docs/_static/locales/id/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..d07a06a Binary files /dev/null and b/docs/_static/locales/id/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/id/LC_MESSAGES/booktheme.po b/docs/_static/locales/id/LC_MESSAGES/booktheme.po index fbeca80..ae1b27a 100644 --- a/docs/_static/locales/id/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/id/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: id\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Oleh" + msgid "repository" msgstr "gudang" -msgid "open issue" -msgstr "masalah terbuka" - -msgid "Contents" -msgstr "Isi" - msgid "Fullscreen mode" msgstr "Mode layar penuh" -msgid "Download this page" -msgstr "Unduh halaman ini" +msgid "Contents" +msgstr "Isi" msgid "Download source file" msgstr "Unduh file sumber" -msgid "Launch" -msgstr "Meluncurkan" - msgid "Edit this page" msgstr "Edit halaman ini" -msgid "Toggle navigation" -msgstr "Alihkan navigasi" +msgid "Last updated on" +msgstr "Terakhir diperbarui saat" -msgid "Theme by the" -msgstr "Tema oleh" +msgid "Print to PDF" +msgstr "Cetak ke PDF" -msgid "Source repository" -msgstr "Repositori sumber" +msgid "suggest edit" +msgstr "menyarankan edit" -msgid "Last updated on" -msgstr "Terakhir diperbarui saat" +msgid "Download this page" +msgstr "Unduh halaman ini" + +msgid "Toggle navigation" +msgstr "Alihkan navigasi" msgid "By the" msgstr "Oleh" @@ -50,32 +47,29 @@ msgstr "Oleh" msgid "Sphinx Book Theme" msgstr "Tema Buku Sphinx" -msgid "Open an issue" -msgstr "Buka masalah" - -msgid "next page" -msgstr "halaman selanjutnya" +msgid "previous page" +msgstr "halaman sebelumnya" -msgid "Copyright" -msgstr "hak cipta" +msgid "Source repository" +msgstr "Repositori sumber" -msgid "Search this book..." -msgstr "Telusuri buku ini ..." +msgid "open issue" +msgstr "masalah terbuka" -msgid "Print to PDF" -msgstr "Cetak ke PDF" +msgid "Download notebook file" +msgstr "Unduh file notebook" -msgid "By" -msgstr "Oleh" +msgid "Copyright" +msgstr "hak cipta" -msgid "previous page" -msgstr "halaman sebelumnya" +msgid "Theme by the" +msgstr "Tema oleh" -msgid "Search the docs ..." -msgstr "Telusuri dokumen ..." +msgid "Open an issue" +msgstr "Buka masalah" -msgid "Download notebook file" -msgstr "Unduh file notebook" +msgid "next page" +msgstr "halaman selanjutnya" -msgid "suggest edit" -msgstr "menyarankan edit" +msgid "Launch" +msgstr "Meluncurkan" diff --git a/docs/_static/locales/it/LC_MESSAGES/booktheme.mo b/docs/_static/locales/it/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..53ba476 Binary files /dev/null and b/docs/_static/locales/it/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/it/LC_MESSAGES/booktheme.po b/docs/_static/locales/it/LC_MESSAGES/booktheme.po index f64a72b..9803243 100644 --- a/docs/_static/locales/it/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/it/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: it\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Di" + msgid "repository" msgstr "repository" -msgid "open issue" -msgstr "questione aperta" - -msgid "Contents" -msgstr "Contenuti" - msgid "Fullscreen mode" msgstr "Modalità schermo intero" -msgid "Download this page" -msgstr "Scarica questa pagina" +msgid "Contents" +msgstr "Contenuti" msgid "Download source file" msgstr "Scarica il file sorgente" -msgid "Launch" -msgstr "Lanciare" - msgid "Edit this page" msgstr "Modifica questa pagina" -msgid "Toggle navigation" -msgstr "Attiva / disattiva la navigazione" +msgid "Last updated on" +msgstr "Ultimo aggiornamento il" -msgid "Theme by the" -msgstr "Tema di" +msgid "Print to PDF" +msgstr "Stampa in PDF" -msgid "Source repository" -msgstr "Repository di origine" +msgid "suggest edit" +msgstr "suggerisci modifica" -msgid "Last updated on" -msgstr "Ultimo aggiornamento il" +msgid "Download this page" +msgstr "Scarica questa pagina" + +msgid "Toggle navigation" +msgstr "Attiva / disattiva la navigazione" msgid "By the" msgstr "Dal" @@ -50,32 +47,29 @@ msgstr "Dal" msgid "Sphinx Book Theme" msgstr "Tema del libro della Sfinge" -msgid "Open an issue" -msgstr "Apri un problema" - -msgid "next page" -msgstr "pagina successiva" +msgid "previous page" +msgstr "pagina precedente" -msgid "Copyright" -msgstr "Diritto d'autore" +msgid "Source repository" +msgstr "Repository di origine" -msgid "Search this book..." -msgstr "Cerca in questo libro ..." +msgid "open issue" +msgstr "questione aperta" -msgid "Print to PDF" -msgstr "Stampa in PDF" +msgid "Download notebook file" +msgstr "Scarica il file del taccuino" -msgid "By" -msgstr "Di" +msgid "Copyright" +msgstr "Diritto d'autore" -msgid "previous page" -msgstr "pagina precedente" +msgid "Theme by the" +msgstr "Tema di" -msgid "Search the docs ..." -msgstr "Cerca nei documenti ..." +msgid "Open an issue" +msgstr "Apri un problema" -msgid "Download notebook file" -msgstr "Scarica il file del taccuino" +msgid "next page" +msgstr "pagina successiva" -msgid "suggest edit" -msgstr "suggerisci modifica" +msgid "Launch" +msgstr "Lanciare" diff --git a/docs/_static/locales/iw/LC_MESSAGES/booktheme.mo b/docs/_static/locales/iw/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..a45c657 Binary files /dev/null and b/docs/_static/locales/iw/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/iw/LC_MESSAGES/booktheme.po b/docs/_static/locales/iw/LC_MESSAGES/booktheme.po index a2a09f0..cb646cf 100644 --- a/docs/_static/locales/iw/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/iw/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: iw\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "על ידי" + msgid "repository" msgstr "מאגר" -msgid "open issue" -msgstr "בעיה פתוחה" - -msgid "Contents" -msgstr "תוכן" - msgid "Fullscreen mode" msgstr "מצב מסך מלא" -msgid "Download this page" -msgstr "הורד דף זה" +msgid "Contents" +msgstr "תוכן" msgid "Download source file" msgstr "הורד את קובץ המקור" -msgid "Launch" -msgstr "לְהַשִׁיק" - msgid "Edit this page" msgstr "ערוך דף זה" -msgid "Toggle navigation" -msgstr "החלף ניווט" +msgid "Last updated on" +msgstr "עודכן לאחרונה ב" -msgid "Theme by the" -msgstr "נושא מאת" +msgid "Print to PDF" +msgstr "הדפס לקובץ PDF" -msgid "Source repository" -msgstr "מאגר המקורות" +msgid "suggest edit" +msgstr "מציע לערוך" -msgid "Last updated on" -msgstr "עודכן לאחרונה ב" +msgid "Download this page" +msgstr "הורד דף זה" + +msgid "Toggle navigation" +msgstr "החלף ניווט" msgid "By the" msgstr "דרך" @@ -50,32 +47,29 @@ msgstr "דרך" msgid "Sphinx Book Theme" msgstr "נושא ספר ספינקס" -msgid "Open an issue" -msgstr "פתח גיליון" - -msgid "next page" -msgstr "עמוד הבא" +msgid "previous page" +msgstr "עמוד קודם" -msgid "Copyright" -msgstr "זכויות יוצרים" +msgid "Source repository" +msgstr "מאגר המקורות" -msgid "Search this book..." -msgstr "חפש בספר זה ..." +msgid "open issue" +msgstr "בעיה פתוחה" -msgid "Print to PDF" -msgstr "הדפס לקובץ PDF" +msgid "Download notebook file" +msgstr "הורד קובץ מחברת" -msgid "By" -msgstr "על ידי" +msgid "Copyright" +msgstr "זכויות יוצרים" -msgid "previous page" -msgstr "עמוד קודם" +msgid "Theme by the" +msgstr "נושא מאת" -msgid "Search the docs ..." -msgstr "חפש במסמכים ..." +msgid "Open an issue" +msgstr "פתח גיליון" -msgid "Download notebook file" -msgstr "הורד קובץ מחברת" +msgid "next page" +msgstr "עמוד הבא" -msgid "suggest edit" -msgstr "מציע לערוך" +msgid "Launch" +msgstr "לְהַשִׁיק" diff --git a/docs/_static/locales/ja/LC_MESSAGES/booktheme.mo b/docs/_static/locales/ja/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..1cefd29 Binary files /dev/null and b/docs/_static/locales/ja/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/ja/LC_MESSAGES/booktheme.po b/docs/_static/locales/ja/LC_MESSAGES/booktheme.po index 216d540..4ec4083 100644 --- a/docs/_static/locales/ja/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/ja/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: ja\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "著者" + msgid "repository" msgstr "リポジトリ" -msgid "open issue" -msgstr "未解決の問題" - -msgid "Contents" -msgstr "目次" - msgid "Fullscreen mode" msgstr "全画面モード" -msgid "Download this page" -msgstr "このページをダウンロード" +msgid "Contents" +msgstr "目次" msgid "Download source file" msgstr "ソースファイルをダウンロード" -msgid "Launch" -msgstr "起動" - msgid "Edit this page" msgstr "このページを編集" -msgid "Toggle navigation" -msgstr "ナビゲーションを切り替え" +msgid "Last updated on" +msgstr "最終更新日" -msgid "Theme by the" -msgstr "のテーマ" +msgid "Print to PDF" +msgstr "PDFに印刷" -msgid "Source repository" -msgstr "ソースリポジトリ" +msgid "suggest edit" +msgstr "編集を提案する" -msgid "Last updated on" -msgstr "最終更新日" +msgid "Download this page" +msgstr "このページをダウンロード" + +msgid "Toggle navigation" +msgstr "ナビゲーションを切り替え" msgid "By the" msgstr "によって" @@ -50,32 +47,29 @@ msgstr "によって" msgid "Sphinx Book Theme" msgstr "スフィンクスの本のテーマ" -msgid "Open an issue" -msgstr "問題を報告" - -msgid "next page" -msgstr "次のページ" +msgid "previous page" +msgstr "前のページ" -msgid "Copyright" -msgstr "Copyright" +msgid "Source repository" +msgstr "ソースリポジトリ" -msgid "Search this book..." -msgstr "この本を検索..." +msgid "open issue" +msgstr "未解決の問題" -msgid "Print to PDF" -msgstr "PDFに印刷" +msgid "Download notebook file" +msgstr "ノートブックファイルをダウンロード" -msgid "By" -msgstr "著者" +msgid "Copyright" +msgstr "Copyright" -msgid "previous page" -msgstr "前のページ" +msgid "Theme by the" +msgstr "のテーマ" -msgid "Search the docs ..." -msgstr "ドキュメントを検索..." +msgid "Open an issue" +msgstr "問題を報告" -msgid "Download notebook file" -msgstr "ノートブックファイルをダウンロード" +msgid "next page" +msgstr "次のページ" -msgid "suggest edit" -msgstr "編集を提案する" +msgid "Launch" +msgstr "起動" diff --git a/docs/_static/locales/ko/LC_MESSAGES/booktheme.mo b/docs/_static/locales/ko/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..06c7ec9 Binary files /dev/null and b/docs/_static/locales/ko/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/ko/LC_MESSAGES/booktheme.po b/docs/_static/locales/ko/LC_MESSAGES/booktheme.po index 8c19ab6..d1d1882 100644 --- a/docs/_static/locales/ko/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/ko/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: ko\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "으로" + msgid "repository" msgstr "저장소" -msgid "open issue" -msgstr "열린 문제" - -msgid "Contents" -msgstr "내용" - msgid "Fullscreen mode" msgstr "전체 화면으로보기" -msgid "Download this page" -msgstr "이 페이지 다운로드" +msgid "Contents" +msgstr "내용" msgid "Download source file" msgstr "소스 파일 다운로드" -msgid "Launch" -msgstr "시작하다" - msgid "Edit this page" msgstr "이 페이지 편집" -msgid "Toggle navigation" -msgstr "탐색 전환" +msgid "Last updated on" +msgstr "마지막 업데이트" -msgid "Theme by the" -msgstr "테마별" +msgid "Print to PDF" +msgstr "PDF로 인쇄" -msgid "Source repository" -msgstr "소스 저장소" +msgid "suggest edit" +msgstr "편집 제안" -msgid "Last updated on" -msgstr "마지막 업데이트" +msgid "Download this page" +msgstr "이 페이지 다운로드" + +msgid "Toggle navigation" +msgstr "탐색 전환" msgid "By the" msgstr "에 의해" @@ -50,32 +47,29 @@ msgstr "에 의해" msgid "Sphinx Book Theme" msgstr "스핑크스 도서 테마" -msgid "Open an issue" -msgstr "이슈 열기" - -msgid "next page" -msgstr "다음 페이지" +msgid "previous page" +msgstr "이전 페이지" -msgid "Copyright" -msgstr "저작권" +msgid "Source repository" +msgstr "소스 저장소" -msgid "Search this book..." -msgstr "이 책 검색 ..." +msgid "open issue" +msgstr "열린 문제" -msgid "Print to PDF" -msgstr "PDF로 인쇄" +msgid "Download notebook file" +msgstr "노트북 파일 다운로드" -msgid "By" -msgstr "으로" +msgid "Copyright" +msgstr "저작권" -msgid "previous page" -msgstr "이전 페이지" +msgid "Theme by the" +msgstr "테마별" -msgid "Search the docs ..." -msgstr "문서 검색 ..." +msgid "Open an issue" +msgstr "이슈 열기" -msgid "Download notebook file" -msgstr "노트북 파일 다운로드" +msgid "next page" +msgstr "다음 페이지" -msgid "suggest edit" -msgstr "편집 제안" +msgid "Launch" +msgstr "시작하다" diff --git a/docs/_static/locales/lt/LC_MESSAGES/booktheme.mo b/docs/_static/locales/lt/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..4468ba0 Binary files /dev/null and b/docs/_static/locales/lt/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/lt/LC_MESSAGES/booktheme.po b/docs/_static/locales/lt/LC_MESSAGES/booktheme.po index 368967f..b8efd38 100644 --- a/docs/_static/locales/lt/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/lt/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: lt\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Iki" + msgid "repository" msgstr "saugykla" -msgid "open issue" -msgstr "atviras klausimas" - -msgid "Contents" -msgstr "Turinys" - msgid "Fullscreen mode" msgstr "Pilno ekrano režimas" -msgid "Download this page" -msgstr "Atsisiųskite šį puslapį" +msgid "Contents" +msgstr "Turinys" msgid "Download source file" msgstr "Atsisiųsti šaltinio failą" -msgid "Launch" -msgstr "Paleiskite" - msgid "Edit this page" msgstr "Redaguoti šį puslapį" -msgid "Toggle navigation" -msgstr "Perjungti naršymą" +msgid "Last updated on" +msgstr "Paskutinį kartą atnaujinta" -msgid "Theme by the" -msgstr "Tema" +msgid "Print to PDF" +msgstr "Spausdinti į PDF" -msgid "Source repository" -msgstr "Šaltinio saugykla" +msgid "suggest edit" +msgstr "pasiūlyti redaguoti" -msgid "Last updated on" -msgstr "Paskutinį kartą atnaujinta" +msgid "Download this page" +msgstr "Atsisiųskite šį puslapį" + +msgid "Toggle navigation" +msgstr "Perjungti naršymą" msgid "By the" msgstr "Prie" @@ -50,32 +47,29 @@ msgstr "Prie" msgid "Sphinx Book Theme" msgstr "Sfinkso knygos tema" -msgid "Open an issue" -msgstr "Atidarykite problemą" - -msgid "next page" -msgstr "Kitas puslapis" +msgid "previous page" +msgstr "Ankstesnis puslapis" -msgid "Copyright" -msgstr "Autorių teisės" +msgid "Source repository" +msgstr "Šaltinio saugykla" -msgid "Search this book..." -msgstr "Ieškoti šioje knygoje ..." +msgid "open issue" +msgstr "atviras klausimas" -msgid "Print to PDF" -msgstr "Spausdinti į PDF" +msgid "Download notebook file" +msgstr "Atsisiųsti nešiojamojo kompiuterio failą" -msgid "By" -msgstr "Iki" +msgid "Copyright" +msgstr "Autorių teisės" -msgid "previous page" -msgstr "Ankstesnis puslapis" +msgid "Theme by the" +msgstr "Tema" -msgid "Search the docs ..." -msgstr "Ieškoti dokumentuose ..." +msgid "Open an issue" +msgstr "Atidarykite problemą" -msgid "Download notebook file" -msgstr "Atsisiųsti nešiojamojo kompiuterio failą" +msgid "next page" +msgstr "Kitas puslapis" -msgid "suggest edit" -msgstr "pasiūlyti redaguoti" +msgid "Launch" +msgstr "Paleiskite" diff --git a/docs/_static/locales/lv/LC_MESSAGES/booktheme.mo b/docs/_static/locales/lv/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..74aa4d8 Binary files /dev/null and b/docs/_static/locales/lv/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/lv/LC_MESSAGES/booktheme.po b/docs/_static/locales/lv/LC_MESSAGES/booktheme.po index 066fa4e..c66fe62 100644 --- a/docs/_static/locales/lv/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/lv/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: lv\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Autors" + msgid "repository" msgstr "krātuve" -msgid "open issue" -msgstr "atklāts jautājums" - -msgid "Contents" -msgstr "Saturs" - msgid "Fullscreen mode" msgstr "Pilnekrāna režīms" -msgid "Download this page" -msgstr "Lejupielādējiet šo lapu" +msgid "Contents" +msgstr "Saturs" msgid "Download source file" msgstr "Lejupielādēt avota failu" -msgid "Launch" -msgstr "Uzsākt" - msgid "Edit this page" msgstr "Rediģēt šo lapu" -msgid "Toggle navigation" -msgstr "Pārslēgt navigāciju" +msgid "Last updated on" +msgstr "Pēdējoreiz atjaunināts" -msgid "Theme by the" -msgstr "Autora tēma" +msgid "Print to PDF" +msgstr "Drukāt PDF formātā" -msgid "Source repository" -msgstr "Avota krātuve" +msgid "suggest edit" +msgstr "ieteikt rediģēt" -msgid "Last updated on" -msgstr "Pēdējoreiz atjaunināts" +msgid "Download this page" +msgstr "Lejupielādējiet šo lapu" + +msgid "Toggle navigation" +msgstr "Pārslēgt navigāciju" msgid "By the" msgstr "Ar" @@ -50,32 +47,29 @@ msgstr "Ar" msgid "Sphinx Book Theme" msgstr "Sfinksa grāmatas tēma" -msgid "Open an issue" -msgstr "Atveriet problēmu" - -msgid "next page" -msgstr "nākamā lapaspuse" +msgid "previous page" +msgstr "iepriekšējā lapa" -msgid "Copyright" -msgstr "Autortiesības" +msgid "Source repository" +msgstr "Avota krātuve" -msgid "Search this book..." -msgstr "Meklēt šajā grāmatā ..." +msgid "open issue" +msgstr "atklāts jautājums" -msgid "Print to PDF" -msgstr "Drukāt PDF formātā" +msgid "Download notebook file" +msgstr "Lejupielādēt piezīmju grāmatiņu" -msgid "By" -msgstr "Autors" +msgid "Copyright" +msgstr "Autortiesības" -msgid "previous page" -msgstr "iepriekšējā lapa" +msgid "Theme by the" +msgstr "Autora tēma" -msgid "Search the docs ..." -msgstr "Meklēt dokumentos ..." +msgid "Open an issue" +msgstr "Atveriet problēmu" -msgid "Download notebook file" -msgstr "Lejupielādēt piezīmju grāmatiņu" +msgid "next page" +msgstr "nākamā lapaspuse" -msgid "suggest edit" -msgstr "ieteikt rediģēt" +msgid "Launch" +msgstr "Uzsākt" diff --git a/docs/_static/locales/ml/LC_MESSAGES/booktheme.mo b/docs/_static/locales/ml/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..2736e8f Binary files /dev/null and b/docs/_static/locales/ml/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/ml/LC_MESSAGES/booktheme.po b/docs/_static/locales/ml/LC_MESSAGES/booktheme.po index 2b0fd76..aaa2cfd 100644 --- a/docs/_static/locales/ml/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/ml/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: ml\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "തുറന്ന പ്രശ്നം" - -msgid "Download this page" -msgstr "ഈ പേജ് ഡൗൺലോഡുചെയ്യുക" +msgid "By" +msgstr "എഴുതിയത്" msgid "Download source file" msgstr "ഉറവിട ഫയൽ ഡൗൺലോഡുചെയ്യുക" -msgid "Launch" -msgstr "സമാരംഭിക്കുക" - msgid "Edit this page" msgstr "ഈ പേജ് എഡിറ്റുചെയ്യുക" -msgid "Toggle navigation" -msgstr "നാവിഗേഷൻ ടോഗിൾ ചെയ്യുക" +msgid "Last updated on" +msgstr "അവസാനം അപ്‌ഡേറ്റുചെയ്‌തത്" -msgid "Theme by the" -msgstr "പ്രമേയം" +msgid "Print to PDF" +msgstr "PDF- ലേക്ക് പ്രിന്റുചെയ്യുക" -msgid "Source repository" -msgstr "ഉറവിട ശേഖരം" +msgid "suggest edit" +msgstr "എഡിറ്റുചെയ്യാൻ നിർദ്ദേശിക്കുക" -msgid "Last updated on" -msgstr "അവസാനം അപ്‌ഡേറ്റുചെയ്‌തത്" +msgid "Download this page" +msgstr "ഈ പേജ് ഡൗൺലോഡുചെയ്യുക" + +msgid "Toggle navigation" +msgstr "നാവിഗേഷൻ ടോഗിൾ ചെയ്യുക" msgid "By the" msgstr "എഴുതിയത്" @@ -41,29 +38,29 @@ msgstr "എഴുതിയത്" msgid "Sphinx Book Theme" msgstr "സ്ഫിങ്ക്സ് പുസ്തക തീം" -msgid "Open an issue" -msgstr "ഒരു പ്രശ്നം തുറക്കുക" +msgid "previous page" +msgstr "മുൻപത്തെ താൾ" -msgid "next page" -msgstr "അടുത്ത പേജ്" +msgid "Source repository" +msgstr "ഉറവിട ശേഖരം" -msgid "Copyright" -msgstr "പകർപ്പവകാശം" +msgid "open issue" +msgstr "തുറന്ന പ്രശ്നം" -msgid "Search this book..." -msgstr "ഈ പുസ്തകം തിരയുക ..." +msgid "Download notebook file" +msgstr "നോട്ട്ബുക്ക് ഫയൽ ഡൺലോഡ് ചെയ്യുക" -msgid "Print to PDF" -msgstr "PDF- ലേക്ക് പ്രിന്റുചെയ്യുക" +msgid "Copyright" +msgstr "പകർപ്പവകാശം" -msgid "By" -msgstr "എഴുതിയത്" +msgid "Theme by the" +msgstr "പ്രമേയം" -msgid "previous page" -msgstr "മുൻപത്തെ താൾ" +msgid "Open an issue" +msgstr "ഒരു പ്രശ്നം തുറക്കുക" -msgid "Download notebook file" -msgstr "നോട്ട്ബുക്ക് ഫയൽ ഡൺലോഡ് ചെയ്യുക" +msgid "next page" +msgstr "അടുത്ത പേജ്" -msgid "suggest edit" -msgstr "എഡിറ്റുചെയ്യാൻ നിർദ്ദേശിക്കുക" +msgid "Launch" +msgstr "സമാരംഭിക്കുക" diff --git a/docs/_static/locales/mr/LC_MESSAGES/booktheme.mo b/docs/_static/locales/mr/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..fe53010 Binary files /dev/null and b/docs/_static/locales/mr/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/mr/LC_MESSAGES/booktheme.po b/docs/_static/locales/mr/LC_MESSAGES/booktheme.po index 51d011f..09e4128 100644 --- a/docs/_static/locales/mr/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/mr/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: mr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "खुला मुद्दा" - -msgid "Download this page" -msgstr "हे पृष्ठ डाउनलोड करा" +msgid "By" +msgstr "द्वारा" msgid "Download source file" msgstr "स्त्रोत फाइल डाउनलोड करा" -msgid "Launch" -msgstr "लाँच करा" - msgid "Edit this page" msgstr "हे पृष्ठ संपादित करा" -msgid "Toggle navigation" -msgstr "नेव्हिगेशन टॉगल करा" +msgid "Last updated on" +msgstr "अखेरचे अद्यतनित" -msgid "Theme by the" -msgstr "द्वारा थीम" +msgid "Print to PDF" +msgstr "पीडीएफवर मुद्रित करा" -msgid "Source repository" -msgstr "स्त्रोत भांडार" +msgid "suggest edit" +msgstr "संपादन सुचवा" -msgid "Last updated on" -msgstr "अखेरचे अद्यतनित" +msgid "Download this page" +msgstr "हे पृष्ठ डाउनलोड करा" + +msgid "Toggle navigation" +msgstr "नेव्हिगेशन टॉगल करा" msgid "By the" msgstr "द्वारा" @@ -41,29 +38,29 @@ msgstr "द्वारा" msgid "Sphinx Book Theme" msgstr "स्फिंक्स बुक थीम" -msgid "Open an issue" -msgstr "एक मुद्दा उघडा" +msgid "previous page" +msgstr "मागील पान" -msgid "next page" -msgstr "पुढील पृष्ठ" +msgid "Source repository" +msgstr "स्त्रोत भांडार" -msgid "Copyright" -msgstr "कॉपीराइट" +msgid "open issue" +msgstr "खुला मुद्दा" -msgid "Search this book..." -msgstr "हे पुस्तक शोधा ..." +msgid "Download notebook file" +msgstr "नोटबुक फाईल डाउनलोड करा" -msgid "Print to PDF" -msgstr "पीडीएफवर मुद्रित करा" +msgid "Copyright" +msgstr "कॉपीराइट" -msgid "By" -msgstr "द्वारा" +msgid "Theme by the" +msgstr "द्वारा थीम" -msgid "previous page" -msgstr "मागील पान" +msgid "Open an issue" +msgstr "एक मुद्दा उघडा" -msgid "Download notebook file" -msgstr "नोटबुक फाईल डाउनलोड करा" +msgid "next page" +msgstr "पुढील पृष्ठ" -msgid "suggest edit" -msgstr "संपादन सुचवा" +msgid "Launch" +msgstr "लाँच करा" diff --git a/docs/_static/locales/ms/LC_MESSAGES/booktheme.mo b/docs/_static/locales/ms/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..f02603f Binary files /dev/null and b/docs/_static/locales/ms/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/ms/LC_MESSAGES/booktheme.po b/docs/_static/locales/ms/LC_MESSAGES/booktheme.po index 6f8a250..8b8e61d 100644 --- a/docs/_static/locales/ms/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/ms/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: ms\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "isu terbuka" - -msgid "Download this page" -msgstr "Muat turun halaman ini" +msgid "By" +msgstr "Oleh" msgid "Download source file" msgstr "Muat turun fail sumber" -msgid "Launch" -msgstr "Lancarkan" - msgid "Edit this page" msgstr "Edit halaman ini" -msgid "Toggle navigation" -msgstr "Togol navigasi" +msgid "Last updated on" +msgstr "Terakhir dikemas kini pada" -msgid "Theme by the" -msgstr "Tema oleh" +msgid "Print to PDF" +msgstr "Cetak ke PDF" -msgid "Source repository" -msgstr "Repositori sumber" +msgid "suggest edit" +msgstr "cadangkan edit" -msgid "Last updated on" -msgstr "Terakhir dikemas kini pada" +msgid "Download this page" +msgstr "Muat turun halaman ini" + +msgid "Toggle navigation" +msgstr "Togol navigasi" msgid "By the" msgstr "Oleh" @@ -41,29 +38,29 @@ msgstr "Oleh" msgid "Sphinx Book Theme" msgstr "Tema Buku Sphinx" -msgid "Open an issue" -msgstr "Buka masalah" +msgid "previous page" +msgstr "halaman sebelumnya" -msgid "next page" -msgstr "muka surat seterusnya" +msgid "Source repository" +msgstr "Repositori sumber" -msgid "Copyright" -msgstr "hak cipta" +msgid "open issue" +msgstr "isu terbuka" -msgid "Search this book..." -msgstr "Cari buku ini ..." +msgid "Download notebook file" +msgstr "Muat turun fail buku nota" -msgid "Print to PDF" -msgstr "Cetak ke PDF" +msgid "Copyright" +msgstr "hak cipta" -msgid "By" -msgstr "Oleh" +msgid "Theme by the" +msgstr "Tema oleh" -msgid "previous page" -msgstr "halaman sebelumnya" +msgid "Open an issue" +msgstr "Buka masalah" -msgid "Download notebook file" -msgstr "Muat turun fail buku nota" +msgid "next page" +msgstr "muka surat seterusnya" -msgid "suggest edit" -msgstr "cadangkan edit" +msgid "Launch" +msgstr "Lancarkan" diff --git a/docs/_static/locales/nl/LC_MESSAGES/booktheme.mo b/docs/_static/locales/nl/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..e59e7ec Binary files /dev/null and b/docs/_static/locales/nl/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/nl/LC_MESSAGES/booktheme.po b/docs/_static/locales/nl/LC_MESSAGES/booktheme.po index 4065b58..97b685f 100644 --- a/docs/_static/locales/nl/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/nl/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: nl\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Door" + msgid "repository" msgstr "repository" -msgid "open issue" -msgstr "open probleem" - -msgid "Contents" -msgstr "Inhoud" - msgid "Fullscreen mode" msgstr "Volledig scherm" -msgid "Download this page" -msgstr "Download deze pagina" +msgid "Contents" +msgstr "Inhoud" msgid "Download source file" msgstr "Download het bronbestand" -msgid "Launch" -msgstr "Lancering" - msgid "Edit this page" msgstr "bewerk deze pagina" -msgid "Toggle navigation" -msgstr "Schakel navigatie" +msgid "Last updated on" +msgstr "Laatst geupdate op" -msgid "Theme by the" -msgstr "Thema door de" +msgid "Print to PDF" +msgstr "Afdrukken naar pdf" -msgid "Source repository" -msgstr "Bronopslagplaats" +msgid "suggest edit" +msgstr "suggereren bewerken" -msgid "Last updated on" -msgstr "Laatst geupdate op" +msgid "Download this page" +msgstr "Download deze pagina" + +msgid "Toggle navigation" +msgstr "Schakel navigatie" msgid "By the" msgstr "Door de" @@ -50,32 +47,29 @@ msgstr "Door de" msgid "Sphinx Book Theme" msgstr "Sphinx-boekthema" -msgid "Open an issue" -msgstr "Open een probleem" - -msgid "next page" -msgstr "volgende bladzijde" +msgid "previous page" +msgstr "vorige pagina" -msgid "Copyright" -msgstr "auteursrechten" +msgid "Source repository" +msgstr "Bronopslagplaats" -msgid "Search this book..." -msgstr "Zoek in dit boek ..." +msgid "open issue" +msgstr "open probleem" -msgid "Print to PDF" -msgstr "Afdrukken naar pdf" +msgid "Download notebook file" +msgstr "Download notebookbestand" -msgid "By" -msgstr "Door" +msgid "Copyright" +msgstr "auteursrechten" -msgid "previous page" -msgstr "vorige pagina" +msgid "Theme by the" +msgstr "Thema door de" -msgid "Search the docs ..." -msgstr "Doorzoek de documenten ..." +msgid "Open an issue" +msgstr "Open een probleem" -msgid "Download notebook file" -msgstr "Download notebookbestand" +msgid "next page" +msgstr "volgende bladzijde" -msgid "suggest edit" -msgstr "suggereren bewerken" +msgid "Launch" +msgstr "Lancering" diff --git a/docs/_static/locales/no/LC_MESSAGES/booktheme.mo b/docs/_static/locales/no/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..6cd15c8 Binary files /dev/null and b/docs/_static/locales/no/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/no/LC_MESSAGES/booktheme.po b/docs/_static/locales/no/LC_MESSAGES/booktheme.po index c4391bb..d5f0a84 100644 --- a/docs/_static/locales/no/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/no/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: no\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Av" + msgid "repository" msgstr "oppbevaringssted" -msgid "open issue" -msgstr "åpent nummer" - -msgid "Contents" -msgstr "Innhold" - msgid "Fullscreen mode" msgstr "Fullskjerm-modus" -msgid "Download this page" -msgstr "Last ned denne siden" +msgid "Contents" +msgstr "Innhold" msgid "Download source file" msgstr "Last ned kildefilen" -msgid "Launch" -msgstr "Start" - msgid "Edit this page" msgstr "Rediger denne siden" -msgid "Toggle navigation" -msgstr "Bytt navigasjon" +msgid "Last updated on" +msgstr "Sist oppdatert den" -msgid "Theme by the" -msgstr "Tema av" +msgid "Print to PDF" +msgstr "Skriv ut til PDF" -msgid "Source repository" -msgstr "Kildedepot" +msgid "suggest edit" +msgstr "foreslå redigering" -msgid "Last updated on" -msgstr "Sist oppdatert den" +msgid "Download this page" +msgstr "Last ned denne siden" + +msgid "Toggle navigation" +msgstr "Bytt navigasjon" msgid "By the" msgstr "Ved" @@ -50,32 +47,29 @@ msgstr "Ved" msgid "Sphinx Book Theme" msgstr "Sphinx boktema" -msgid "Open an issue" -msgstr "Åpne et problem" - -msgid "next page" -msgstr "neste side" +msgid "previous page" +msgstr "forrige side" -msgid "Copyright" -msgstr "opphavsrett" +msgid "Source repository" +msgstr "Kildedepot" -msgid "Search this book..." -msgstr "Søk i denne boken ..." +msgid "open issue" +msgstr "åpent nummer" -msgid "Print to PDF" -msgstr "Skriv ut til PDF" +msgid "Download notebook file" +msgstr "Last ned notatbokfilen" -msgid "By" -msgstr "Av" +msgid "Copyright" +msgstr "opphavsrett" -msgid "previous page" -msgstr "forrige side" +msgid "Theme by the" +msgstr "Tema av" -msgid "Search the docs ..." -msgstr "Søk i dokumentene ..." +msgid "Open an issue" +msgstr "Åpne et problem" -msgid "Download notebook file" -msgstr "Last ned notatbokfilen" +msgid "next page" +msgstr "neste side" -msgid "suggest edit" -msgstr "foreslå redigering" +msgid "Launch" +msgstr "Start" diff --git a/docs/_static/locales/pl/LC_MESSAGES/booktheme.mo b/docs/_static/locales/pl/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..9ebb584 Binary files /dev/null and b/docs/_static/locales/pl/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/pl/LC_MESSAGES/booktheme.po b/docs/_static/locales/pl/LC_MESSAGES/booktheme.po index bd03ba4..c674c55 100644 --- a/docs/_static/locales/pl/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/pl/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: pl\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Przez" + msgid "repository" msgstr "magazyn" -msgid "open issue" -msgstr "otwarty problem" - -msgid "Contents" -msgstr "Zawartość" - msgid "Fullscreen mode" msgstr "Pełny ekran" -msgid "Download this page" -msgstr "Pobierz tę stronę" +msgid "Contents" +msgstr "Zawartość" msgid "Download source file" msgstr "Pobierz plik źródłowy" -msgid "Launch" -msgstr "Uruchomić" - msgid "Edit this page" msgstr "Edytuj tę strone" -msgid "Toggle navigation" -msgstr "Przełącz nawigację" +msgid "Last updated on" +msgstr "Ostatnia aktualizacja" -msgid "Theme by the" -msgstr "Motyw autorstwa" +msgid "Print to PDF" +msgstr "Drukuj do PDF" -msgid "Source repository" -msgstr "Repozytorium źródłowe" +msgid "suggest edit" +msgstr "zaproponuj edycję" -msgid "Last updated on" -msgstr "Ostatnia aktualizacja" +msgid "Download this page" +msgstr "Pobierz tę stronę" + +msgid "Toggle navigation" +msgstr "Przełącz nawigację" msgid "By the" msgstr "Przez" @@ -50,32 +47,29 @@ msgstr "Przez" msgid "Sphinx Book Theme" msgstr "Motyw książki Sphinx" -msgid "Open an issue" -msgstr "Otwórz problem" - -msgid "next page" -msgstr "Następna strona" +msgid "previous page" +msgstr "Poprzednia strona" -msgid "Copyright" -msgstr "prawa autorskie" +msgid "Source repository" +msgstr "Repozytorium źródłowe" -msgid "Search this book..." -msgstr "Przeszukaj tę książkę ..." +msgid "open issue" +msgstr "otwarty problem" -msgid "Print to PDF" -msgstr "Drukuj do PDF" +msgid "Download notebook file" +msgstr "Pobierz plik notatnika" -msgid "By" -msgstr "Przez" +msgid "Copyright" +msgstr "prawa autorskie" -msgid "previous page" -msgstr "Poprzednia strona" +msgid "Theme by the" +msgstr "Motyw autorstwa" -msgid "Search the docs ..." -msgstr "Przeszukaj dokumenty ..." +msgid "Open an issue" +msgstr "Otwórz problem" -msgid "Download notebook file" -msgstr "Pobierz plik notatnika" +msgid "next page" +msgstr "Następna strona" -msgid "suggest edit" -msgstr "zaproponuj edycję" +msgid "Launch" +msgstr "Uruchomić" diff --git a/docs/_static/locales/pt/LC_MESSAGES/booktheme.mo b/docs/_static/locales/pt/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..d0ddb87 Binary files /dev/null and b/docs/_static/locales/pt/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/pt/LC_MESSAGES/booktheme.po b/docs/_static/locales/pt/LC_MESSAGES/booktheme.po index 8ac25a1..7ca36d9 100644 --- a/docs/_static/locales/pt/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/pt/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: pt\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "De" + msgid "repository" msgstr "repositório" -msgid "open issue" -msgstr "questão aberta" - -msgid "Contents" -msgstr "Conteúdo" - msgid "Fullscreen mode" msgstr "Modo tela cheia" -msgid "Download this page" -msgstr "Baixe esta página" +msgid "Contents" +msgstr "Conteúdo" msgid "Download source file" msgstr "Baixar arquivo fonte" -msgid "Launch" -msgstr "Lançamento" - msgid "Edit this page" msgstr "Edite essa página" -msgid "Toggle navigation" -msgstr "Alternar de navegação" +msgid "Last updated on" +msgstr "Última atualização em" -msgid "Theme by the" -msgstr "Tema por" +msgid "Print to PDF" +msgstr "Imprimir em PDF" -msgid "Source repository" -msgstr "Repositório fonte" +msgid "suggest edit" +msgstr "sugerir edição" -msgid "Last updated on" -msgstr "Última atualização em" +msgid "Download this page" +msgstr "Baixe esta página" + +msgid "Toggle navigation" +msgstr "Alternar de navegação" msgid "By the" msgstr "Pelo" @@ -50,32 +47,29 @@ msgstr "Pelo" msgid "Sphinx Book Theme" msgstr "Tema do livro Sphinx" -msgid "Open an issue" -msgstr "Abra um problema" - -msgid "next page" -msgstr "próxima página" +msgid "previous page" +msgstr "página anterior" -msgid "Copyright" -msgstr "direito autoral" +msgid "Source repository" +msgstr "Repositório fonte" -msgid "Search this book..." -msgstr "Pesquise este livro ..." +msgid "open issue" +msgstr "questão aberta" -msgid "Print to PDF" -msgstr "Imprimir em PDF" +msgid "Download notebook file" +msgstr "Baixar arquivo de notebook" -msgid "By" -msgstr "De" +msgid "Copyright" +msgstr "direito autoral" -msgid "previous page" -msgstr "página anterior" +msgid "Theme by the" +msgstr "Tema por" -msgid "Search the docs ..." -msgstr "Pesquise os documentos ..." +msgid "Open an issue" +msgstr "Abra um problema" -msgid "Download notebook file" -msgstr "Baixar arquivo de notebook" +msgid "next page" +msgstr "próxima página" -msgid "suggest edit" -msgstr "sugerir edição" +msgid "Launch" +msgstr "Lançamento" diff --git a/docs/_static/locales/ro/LC_MESSAGES/booktheme.mo b/docs/_static/locales/ro/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..3c36ab1 Binary files /dev/null and b/docs/_static/locales/ro/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/ro/LC_MESSAGES/booktheme.po b/docs/_static/locales/ro/LC_MESSAGES/booktheme.po index d0f7d2f..1a86498 100644 --- a/docs/_static/locales/ro/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/ro/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: ro\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "De" + msgid "repository" msgstr "repertoriu" -msgid "open issue" -msgstr "problema deschisă" - -msgid "Contents" -msgstr "Cuprins" - msgid "Fullscreen mode" msgstr "Modul ecran întreg" -msgid "Download this page" -msgstr "Descarcă această pagină" +msgid "Contents" +msgstr "Cuprins" msgid "Download source file" msgstr "Descărcați fișierul sursă" -msgid "Launch" -msgstr "Lansa" - msgid "Edit this page" msgstr "Editați această pagină" -msgid "Toggle navigation" -msgstr "Comutare navigare" +msgid "Last updated on" +msgstr "Ultima actualizare la" -msgid "Theme by the" -msgstr "Tema de" +msgid "Print to PDF" +msgstr "Imprimați în PDF" -msgid "Source repository" -msgstr "Depozit sursă" +msgid "suggest edit" +msgstr "sugerează editare" -msgid "Last updated on" -msgstr "Ultima actualizare la" +msgid "Download this page" +msgstr "Descarcă această pagină" + +msgid "Toggle navigation" +msgstr "Comutare navigare" msgid "By the" msgstr "Langa" @@ -50,32 +47,29 @@ msgstr "Langa" msgid "Sphinx Book Theme" msgstr "Tema Sphinx Book" -msgid "Open an issue" -msgstr "Deschideți o problemă" - -msgid "next page" -msgstr "pagina următoare" +msgid "previous page" +msgstr "pagina anterioară" -msgid "Copyright" -msgstr "Drepturi de autor" +msgid "Source repository" +msgstr "Depozit sursă" -msgid "Search this book..." -msgstr "Căutați în această carte ..." +msgid "open issue" +msgstr "problema deschisă" -msgid "Print to PDF" -msgstr "Imprimați în PDF" +msgid "Download notebook file" +msgstr "Descărcați fișierul notebook" -msgid "By" -msgstr "De" +msgid "Copyright" +msgstr "Drepturi de autor" -msgid "previous page" -msgstr "pagina anterioară" +msgid "Theme by the" +msgstr "Tema de" -msgid "Search the docs ..." -msgstr "Căutați documente ..." +msgid "Open an issue" +msgstr "Deschideți o problemă" -msgid "Download notebook file" -msgstr "Descărcați fișierul notebook" +msgid "next page" +msgstr "pagina următoare" -msgid "suggest edit" -msgstr "sugerează editare" +msgid "Launch" +msgstr "Lansa" diff --git a/docs/_static/locales/ru/LC_MESSAGES/booktheme.mo b/docs/_static/locales/ru/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..6b8ca41 Binary files /dev/null and b/docs/_static/locales/ru/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/ru/LC_MESSAGES/booktheme.po b/docs/_static/locales/ru/LC_MESSAGES/booktheme.po index 9b55bfe..e64ee9f 100644 --- a/docs/_static/locales/ru/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/ru/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: ru\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "По" + msgid "repository" msgstr "хранилище" -msgid "open issue" -msgstr "открытый вопрос" - -msgid "Contents" -msgstr "Содержание" - msgid "Fullscreen mode" msgstr "Полноэкранный режим" -msgid "Download this page" -msgstr "Загрузите эту страницу" +msgid "Contents" +msgstr "Содержание" msgid "Download source file" msgstr "Скачать исходный файл" -msgid "Launch" -msgstr "Запуск" - msgid "Edit this page" msgstr "Редактировать эту страницу" -msgid "Toggle navigation" -msgstr "Переключить навигацию" +msgid "Last updated on" +msgstr "Последнее обновление" -msgid "Theme by the" -msgstr "Тема от" +msgid "Print to PDF" +msgstr "Распечатать в PDF" -msgid "Source repository" -msgstr "Исходный репозиторий" +msgid "suggest edit" +msgstr "предложить редактировать" -msgid "Last updated on" -msgstr "Последнее обновление" +msgid "Download this page" +msgstr "Загрузите эту страницу" + +msgid "Toggle navigation" +msgstr "Переключить навигацию" msgid "By the" msgstr "Посредством" @@ -50,32 +47,29 @@ msgstr "Посредством" msgid "Sphinx Book Theme" msgstr "Тема книги Сфинкс" -msgid "Open an issue" -msgstr "Открыть вопрос" - -msgid "next page" -msgstr "Следующая страница" +msgid "previous page" +msgstr "Предыдущая страница" -msgid "Copyright" -msgstr "авторское право" +msgid "Source repository" +msgstr "Исходный репозиторий" -msgid "Search this book..." -msgstr "Искать в этой книге ..." +msgid "open issue" +msgstr "открытый вопрос" -msgid "Print to PDF" -msgstr "Распечатать в PDF" +msgid "Download notebook file" +msgstr "Скачать файл записной книжки" -msgid "By" -msgstr "По" +msgid "Copyright" +msgstr "авторское право" -msgid "previous page" -msgstr "Предыдущая страница" +msgid "Theme by the" +msgstr "Тема от" -msgid "Search the docs ..." -msgstr "Искать в документах ..." +msgid "Open an issue" +msgstr "Открыть вопрос" -msgid "Download notebook file" -msgstr "Скачать файл записной книжки" +msgid "next page" +msgstr "Следующая страница" -msgid "suggest edit" -msgstr "предложить редактировать" +msgid "Launch" +msgstr "Запуск" diff --git a/docs/_static/locales/sk/LC_MESSAGES/booktheme.mo b/docs/_static/locales/sk/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..59bd0dd Binary files /dev/null and b/docs/_static/locales/sk/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/sk/LC_MESSAGES/booktheme.po b/docs/_static/locales/sk/LC_MESSAGES/booktheme.po index 3e6fd9f..42a959f 100644 --- a/docs/_static/locales/sk/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/sk/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: sk\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Autor:" + msgid "repository" msgstr "Úložisko" -msgid "open issue" -msgstr "otvorené vydanie" - -msgid "Contents" -msgstr "Obsah" - msgid "Fullscreen mode" msgstr "Režim celej obrazovky" -msgid "Download this page" -msgstr "Stiahnite si túto stránku" +msgid "Contents" +msgstr "Obsah" msgid "Download source file" msgstr "Stiahnite si zdrojový súbor" -msgid "Launch" -msgstr "Spustiť" - msgid "Edit this page" msgstr "Upraviť túto stránku" -msgid "Toggle navigation" -msgstr "Prepnúť navigáciu" +msgid "Last updated on" +msgstr "Posledná aktualizácia dňa" -msgid "Theme by the" -msgstr "Téma od" +msgid "Print to PDF" +msgstr "Tlač do PDF" -msgid "Source repository" -msgstr "Zdrojové úložisko" +msgid "suggest edit" +msgstr "navrhnúť úpravu" -msgid "Last updated on" -msgstr "Posledná aktualizácia dňa" +msgid "Download this page" +msgstr "Stiahnite si túto stránku" + +msgid "Toggle navigation" +msgstr "Prepnúť navigáciu" msgid "By the" msgstr "Podľa" @@ -50,32 +47,29 @@ msgstr "Podľa" msgid "Sphinx Book Theme" msgstr "Téma knihy Sfinga" -msgid "Open an issue" -msgstr "Otvorte problém" - -msgid "next page" -msgstr "ďalšia strana" +msgid "previous page" +msgstr "predchádzajúca strana" -msgid "Copyright" -msgstr "Autorské práva" +msgid "Source repository" +msgstr "Zdrojové úložisko" -msgid "Search this book..." -msgstr "Hľadať v tejto knihe ..." +msgid "open issue" +msgstr "otvorené vydanie" -msgid "Print to PDF" -msgstr "Tlač do PDF" +msgid "Download notebook file" +msgstr "Stiahnite si zošit" -msgid "By" -msgstr "Autor:" +msgid "Copyright" +msgstr "Autorské práva" -msgid "previous page" -msgstr "predchádzajúca strana" +msgid "Theme by the" +msgstr "Téma od" -msgid "Search the docs ..." -msgstr "Hľadať v dokumentoch ..." +msgid "Open an issue" +msgstr "Otvorte problém" -msgid "Download notebook file" -msgstr "Stiahnite si zošit" +msgid "next page" +msgstr "ďalšia strana" -msgid "suggest edit" -msgstr "navrhnúť úpravu" +msgid "Launch" +msgstr "Spustiť" diff --git a/docs/_static/locales/sl/LC_MESSAGES/booktheme.mo b/docs/_static/locales/sl/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..87bf26d Binary files /dev/null and b/docs/_static/locales/sl/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/sl/LC_MESSAGES/booktheme.po b/docs/_static/locales/sl/LC_MESSAGES/booktheme.po index dd99bbe..39c6a42 100644 --- a/docs/_static/locales/sl/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/sl/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: sl\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Avtor" + msgid "repository" msgstr "odlagališče" -msgid "open issue" -msgstr "odprto vprašanje" - -msgid "Contents" -msgstr "Vsebina" - msgid "Fullscreen mode" msgstr "Celozaslonski način" -msgid "Download this page" -msgstr "Prenesite to stran" +msgid "Contents" +msgstr "Vsebina" msgid "Download source file" msgstr "Prenesite izvorno datoteko" -msgid "Launch" -msgstr "Kosilo" - msgid "Edit this page" msgstr "Uredite to stran" -msgid "Toggle navigation" -msgstr "Preklopi navigacijo" +msgid "Last updated on" +msgstr "Nazadnje posodobljeno dne" -msgid "Theme by the" -msgstr "Tema avtorja" +msgid "Print to PDF" +msgstr "Natisni v PDF" -msgid "Source repository" -msgstr "Izvorno skladišče" +msgid "suggest edit" +msgstr "predlagajte urejanje" -msgid "Last updated on" -msgstr "Nazadnje posodobljeno dne" +msgid "Download this page" +msgstr "Prenesite to stran" + +msgid "Toggle navigation" +msgstr "Preklopi navigacijo" msgid "By the" msgstr "Avtor" @@ -50,32 +47,29 @@ msgstr "Avtor" msgid "Sphinx Book Theme" msgstr "Tema knjige Sphinx" -msgid "Open an issue" -msgstr "Odprite številko" - -msgid "next page" -msgstr "Naslednja stran" +msgid "previous page" +msgstr "Prejšnja stran" -msgid "Copyright" -msgstr "avtorske pravice" +msgid "Source repository" +msgstr "Izvorno skladišče" -msgid "Search this book..." -msgstr "Poiščite to knjigo ..." +msgid "open issue" +msgstr "odprto vprašanje" -msgid "Print to PDF" -msgstr "Natisni v PDF" +msgid "Download notebook file" +msgstr "Prenesite datoteko zvezka" -msgid "By" -msgstr "Avtor" +msgid "Copyright" +msgstr "avtorske pravice" -msgid "previous page" -msgstr "Prejšnja stran" +msgid "Theme by the" +msgstr "Tema avtorja" -msgid "Search the docs ..." -msgstr "Poiščite dokumente ..." +msgid "Open an issue" +msgstr "Odprite številko" -msgid "Download notebook file" -msgstr "Prenesite datoteko zvezka" +msgid "next page" +msgstr "Naslednja stran" -msgid "suggest edit" -msgstr "predlagajte urejanje" +msgid "Launch" +msgstr "Kosilo" diff --git a/docs/_static/locales/sr/LC_MESSAGES/booktheme.mo b/docs/_static/locales/sr/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..ec740f4 Binary files /dev/null and b/docs/_static/locales/sr/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/sr/LC_MESSAGES/booktheme.po b/docs/_static/locales/sr/LC_MESSAGES/booktheme.po index 12284e0..30a6cc2 100644 --- a/docs/_static/locales/sr/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/sr/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: sr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Од стране" + msgid "repository" msgstr "спремиште" -msgid "open issue" -msgstr "отворено издање" - -msgid "Contents" -msgstr "Садржај" - msgid "Fullscreen mode" msgstr "Режим целог екрана" -msgid "Download this page" -msgstr "Преузмите ову страницу" +msgid "Contents" +msgstr "Садржај" msgid "Download source file" msgstr "Преузми изворну датотеку" -msgid "Launch" -msgstr "Лансирање" - msgid "Edit this page" msgstr "Уредите ову страницу" -msgid "Toggle navigation" -msgstr "Укључи / искључи навигацију" +msgid "Last updated on" +msgstr "Последње ажурирање" -msgid "Theme by the" -msgstr "Тхеме би" +msgid "Print to PDF" +msgstr "Испис у ПДФ" -msgid "Source repository" -msgstr "Изворно спремиште" +msgid "suggest edit" +msgstr "предложи уређивање" -msgid "Last updated on" -msgstr "Последње ажурирање" +msgid "Download this page" +msgstr "Преузмите ову страницу" + +msgid "Toggle navigation" +msgstr "Укључи / искључи навигацију" msgid "By the" msgstr "Од" @@ -50,32 +47,29 @@ msgstr "Од" msgid "Sphinx Book Theme" msgstr "Тема књиге Спхинк" -msgid "Open an issue" -msgstr "Отворите издање" - -msgid "next page" -msgstr "Следећа страна" +msgid "previous page" +msgstr "Претходна страница" -msgid "Copyright" -msgstr "Ауторско право" +msgid "Source repository" +msgstr "Изворно спремиште" -msgid "Search this book..." -msgstr "Претражите ову књигу ..." +msgid "open issue" +msgstr "отворено издање" -msgid "Print to PDF" -msgstr "Испис у ПДФ" +msgid "Download notebook file" +msgstr "Преузмите датотеку бележнице" -msgid "By" -msgstr "Од стране" +msgid "Copyright" +msgstr "Ауторско право" -msgid "previous page" -msgstr "Претходна страница" +msgid "Theme by the" +msgstr "Тхеме би" -msgid "Search the docs ..." -msgstr "Претражите документе ..." +msgid "Open an issue" +msgstr "Отворите издање" -msgid "Download notebook file" -msgstr "Преузмите датотеку бележнице" +msgid "next page" +msgstr "Следећа страна" -msgid "suggest edit" -msgstr "предложи уређивање" +msgid "Launch" +msgstr "Лансирање" diff --git a/docs/_static/locales/sv/LC_MESSAGES/booktheme.mo b/docs/_static/locales/sv/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..be951be Binary files /dev/null and b/docs/_static/locales/sv/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/sv/LC_MESSAGES/booktheme.po b/docs/_static/locales/sv/LC_MESSAGES/booktheme.po index 203c917..8953d7f 100644 --- a/docs/_static/locales/sv/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/sv/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: sv\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Förbi" + msgid "repository" msgstr "förvar" -msgid "open issue" -msgstr "öppet problem" - -msgid "Contents" -msgstr "Innehåll" - msgid "Fullscreen mode" msgstr "Fullskärmsläge" -msgid "Download this page" -msgstr "Ladda ner den här sidan" +msgid "Contents" +msgstr "Innehåll" msgid "Download source file" msgstr "Ladda ner källfil" -msgid "Launch" -msgstr "Lansera" - msgid "Edit this page" msgstr "Redigera den här sidan" -msgid "Toggle navigation" -msgstr "Växla navigering" +msgid "Last updated on" +msgstr "Senast uppdaterad den" -msgid "Theme by the" -msgstr "Tema av" +msgid "Print to PDF" +msgstr "Skriv ut till PDF" -msgid "Source repository" -msgstr "Källförvar" +msgid "suggest edit" +msgstr "föreslå redigering" -msgid "Last updated on" -msgstr "Senast uppdaterad den" +msgid "Download this page" +msgstr "Ladda ner den här sidan" + +msgid "Toggle navigation" +msgstr "Växla navigering" msgid "By the" msgstr "Vid" @@ -50,32 +47,29 @@ msgstr "Vid" msgid "Sphinx Book Theme" msgstr "Sphinx boktema" -msgid "Open an issue" -msgstr "Öppna ett problem" - -msgid "next page" -msgstr "nästa sida" +msgid "previous page" +msgstr "föregående sida" -msgid "Copyright" -msgstr "upphovsrätt" +msgid "Source repository" +msgstr "Källförvar" -msgid "Search this book..." -msgstr "Sök i den här boken ..." +msgid "open issue" +msgstr "öppet problem" -msgid "Print to PDF" -msgstr "Skriv ut till PDF" +msgid "Download notebook file" +msgstr "Ladda ner anteckningsbokfilen" -msgid "By" -msgstr "Förbi" +msgid "Copyright" +msgstr "upphovsrätt" -msgid "previous page" -msgstr "föregående sida" +msgid "Theme by the" +msgstr "Tema av" -msgid "Search the docs ..." -msgstr "Sök i dokumenten ..." +msgid "Open an issue" +msgstr "Öppna ett problem" -msgid "Download notebook file" -msgstr "Ladda ner anteckningsbokfilen" +msgid "next page" +msgstr "nästa sida" -msgid "suggest edit" -msgstr "föreslå redigering" +msgid "Launch" +msgstr "Lansera" diff --git a/docs/_static/locales/ta/LC_MESSAGES/booktheme.mo b/docs/_static/locales/ta/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..29f52e1 Binary files /dev/null and b/docs/_static/locales/ta/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/ta/LC_MESSAGES/booktheme.po b/docs/_static/locales/ta/LC_MESSAGES/booktheme.po index eefce8a..2100227 100644 --- a/docs/_static/locales/ta/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/ta/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: ta\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "திறந்த பிரச்சினை" - -msgid "Download this page" -msgstr "இந்தப் பக்கத்தைப் பதிவிறக்கவும்" +msgid "By" +msgstr "வழங்கியவர்" msgid "Download source file" msgstr "மூல கோப்பைப் பதிவிறக்குக" -msgid "Launch" -msgstr "தொடங்க" - msgid "Edit this page" msgstr "இந்தப் பக்கத்தைத் திருத்தவும்" -msgid "Toggle navigation" -msgstr "வழிசெலுத்தலை நிலைமாற்று" +msgid "Last updated on" +msgstr "கடைசியாக புதுப்பிக்கப்பட்டது" -msgid "Theme by the" -msgstr "வழங்கிய தீம்" +msgid "Print to PDF" +msgstr "PDF இல் அச்சிடுக" -msgid "Source repository" -msgstr "மூல களஞ்சியம்" +msgid "suggest edit" +msgstr "திருத்த பரிந்துரைக்கவும்" -msgid "Last updated on" -msgstr "கடைசியாக புதுப்பிக்கப்பட்டது" +msgid "Download this page" +msgstr "இந்தப் பக்கத்தைப் பதிவிறக்கவும்" + +msgid "Toggle navigation" +msgstr "வழிசெலுத்தலை நிலைமாற்று" msgid "By the" msgstr "மூலம்" @@ -41,29 +38,29 @@ msgstr "மூலம்" msgid "Sphinx Book Theme" msgstr "ஸ்பிங்க்ஸ் புத்தக தீம்" -msgid "Open an issue" -msgstr "சிக்கலைத் திறக்கவும்" +msgid "previous page" +msgstr "முந்தைய பக்கம்" -msgid "next page" -msgstr "அடுத்த பக்கம்" +msgid "Source repository" +msgstr "மூல களஞ்சியம்" -msgid "Copyright" -msgstr "பதிப்புரிமை" +msgid "open issue" +msgstr "திறந்த பிரச்சினை" -msgid "Search this book..." -msgstr "இந்த புத்தகத்தைத் தேடுங்கள் ..." +msgid "Download notebook file" +msgstr "நோட்புக் கோப்பைப் பதிவிறக்கவும்" -msgid "Print to PDF" -msgstr "PDF இல் அச்சிடுக" +msgid "Copyright" +msgstr "பதிப்புரிமை" -msgid "By" -msgstr "வழங்கியவர்" +msgid "Theme by the" +msgstr "வழங்கிய தீம்" -msgid "previous page" -msgstr "முந்தைய பக்கம்" +msgid "Open an issue" +msgstr "சிக்கலைத் திறக்கவும்" -msgid "Download notebook file" -msgstr "நோட்புக் கோப்பைப் பதிவிறக்கவும்" +msgid "next page" +msgstr "அடுத்த பக்கம்" -msgid "suggest edit" -msgstr "திருத்த பரிந்துரைக்கவும்" +msgid "Launch" +msgstr "தொடங்க" diff --git a/docs/_static/locales/te/LC_MESSAGES/booktheme.mo b/docs/_static/locales/te/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..0a5f4b4 Binary files /dev/null and b/docs/_static/locales/te/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/te/LC_MESSAGES/booktheme.po b/docs/_static/locales/te/LC_MESSAGES/booktheme.po index 0373b70..ef56fae 100644 --- a/docs/_static/locales/te/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/te/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: te\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "ఓపెన్ ఇష్యూ" - -msgid "Download this page" -msgstr "ఈ పేజీని డౌన్‌లోడ్ చేయండి" +msgid "By" +msgstr "ద్వారా" msgid "Download source file" msgstr "మూల ఫైల్‌ను డౌన్‌లోడ్ చేయండి" -msgid "Launch" -msgstr "ప్రారంభించండి" - msgid "Edit this page" msgstr "ఈ పేజీని సవరించండి" -msgid "Toggle navigation" -msgstr "నావిగేషన్‌ను టోగుల్ చేయండి" +msgid "Last updated on" +msgstr "చివరిగా నవీకరించబడింది" -msgid "Theme by the" -msgstr "ద్వారా థీమ్" +msgid "Print to PDF" +msgstr "PDF కి ముద్రించండి" -msgid "Source repository" -msgstr "మూల రిపోజిటరీ" +msgid "suggest edit" +msgstr "సవరించమని సూచించండి" -msgid "Last updated on" -msgstr "చివరిగా నవీకరించబడింది" +msgid "Download this page" +msgstr "ఈ పేజీని డౌన్‌లోడ్ చేయండి" + +msgid "Toggle navigation" +msgstr "నావిగేషన్‌ను టోగుల్ చేయండి" msgid "By the" msgstr "ద్వారా" @@ -41,29 +38,29 @@ msgstr "ద్వారా" msgid "Sphinx Book Theme" msgstr "సింహిక పుస్తక థీమ్" -msgid "Open an issue" -msgstr "సమస్యను తెరవండి" +msgid "previous page" +msgstr "ముందు పేజి" -msgid "next page" -msgstr "తరువాతి పేజీ" +msgid "Source repository" +msgstr "మూల రిపోజిటరీ" -msgid "Copyright" -msgstr "కాపీరైట్" +msgid "open issue" +msgstr "ఓపెన్ ఇష్యూ" -msgid "Search this book..." -msgstr "ఈ పుస్తకాన్ని శోధించండి ..." +msgid "Download notebook file" +msgstr "నోట్బుక్ ఫైల్ను డౌన్లోడ్ చేయండి" -msgid "Print to PDF" -msgstr "PDF కి ముద్రించండి" +msgid "Copyright" +msgstr "కాపీరైట్" -msgid "By" -msgstr "ద్వారా" +msgid "Theme by the" +msgstr "ద్వారా థీమ్" -msgid "previous page" -msgstr "ముందు పేజి" +msgid "Open an issue" +msgstr "సమస్యను తెరవండి" -msgid "Download notebook file" -msgstr "నోట్బుక్ ఫైల్ను డౌన్లోడ్ చేయండి" +msgid "next page" +msgstr "తరువాతి పేజీ" -msgid "suggest edit" -msgstr "సవరించమని సూచించండి" +msgid "Launch" +msgstr "ప్రారంభించండి" diff --git a/docs/_static/locales/tg/LC_MESSAGES/booktheme.mo b/docs/_static/locales/tg/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..b21c6c6 Binary files /dev/null and b/docs/_static/locales/tg/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/tg/LC_MESSAGES/booktheme.po b/docs/_static/locales/tg/LC_MESSAGES/booktheme.po index 1807ae2..c4aae01 100644 --- a/docs/_static/locales/tg/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/tg/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: tg\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Бо" + msgid "repository" msgstr "анбор" -msgid "open issue" -msgstr "барориши кушод" - -msgid "Contents" -msgstr "Мундариҷа" - msgid "Fullscreen mode" msgstr "Ҳолати экрани пурра" -msgid "Download this page" -msgstr "Ин саҳифаро зеркашӣ кунед" +msgid "Contents" +msgstr "Мундариҷа" msgid "Download source file" msgstr "Файли манбаъро зеркашӣ кунед" -msgid "Launch" -msgstr "Оғоз" - msgid "Edit this page" msgstr "Ин саҳифаро таҳрир кунед" -msgid "Toggle navigation" -msgstr "Гузаришро иваз кунед" +msgid "Last updated on" +msgstr "Last навсозӣ дар" -msgid "Theme by the" -msgstr "Мавзӯъи аз" +msgid "Print to PDF" +msgstr "Чоп ба PDF" -msgid "Source repository" -msgstr "Анбори манбаъ" +msgid "suggest edit" +msgstr "пешниҳод вироиш" -msgid "Last updated on" -msgstr "Last навсозӣ дар" +msgid "Download this page" +msgstr "Ин саҳифаро зеркашӣ кунед" + +msgid "Toggle navigation" +msgstr "Гузаришро иваз кунед" msgid "By the" msgstr "Бо" @@ -50,32 +47,29 @@ msgstr "Бо" msgid "Sphinx Book Theme" msgstr "Сфинкс Мавзӯи китоб" -msgid "Open an issue" -msgstr "Масъаларо кушоед" - -msgid "next page" -msgstr "саҳифаи оянда" +msgid "previous page" +msgstr "саҳифаи қаблӣ" -msgid "Copyright" -msgstr "Ҳуқуқи муаллиф" +msgid "Source repository" +msgstr "Анбори манбаъ" -msgid "Search this book..." -msgstr "Ин китобро ҷустуҷӯ кунед ..." +msgid "open issue" +msgstr "барориши кушод" -msgid "Print to PDF" -msgstr "Чоп ба PDF" +msgid "Download notebook file" +msgstr "Файли дафтарро зеркашӣ кунед" -msgid "By" -msgstr "Бо" +msgid "Copyright" +msgstr "Ҳуқуқи муаллиф" -msgid "previous page" -msgstr "саҳифаи қаблӣ" +msgid "Theme by the" +msgstr "Мавзӯъи аз" -msgid "Search the docs ..." -msgstr "Ҷустуҷӯи ҳуҷҷатҳо ..." +msgid "Open an issue" +msgstr "Масъаларо кушоед" -msgid "Download notebook file" -msgstr "Файли дафтарро зеркашӣ кунед" +msgid "next page" +msgstr "саҳифаи оянда" -msgid "suggest edit" -msgstr "пешниҳод вироиш" +msgid "Launch" +msgstr "Оғоз" diff --git a/docs/_static/locales/th/LC_MESSAGES/booktheme.mo b/docs/_static/locales/th/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..abede98 Binary files /dev/null and b/docs/_static/locales/th/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/th/LC_MESSAGES/booktheme.po b/docs/_static/locales/th/LC_MESSAGES/booktheme.po index a286feb..4c64505 100644 --- a/docs/_static/locales/th/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/th/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: th\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "โดย" + msgid "repository" msgstr "ที่เก็บ" -msgid "open issue" -msgstr "เปิดปัญหา" - -msgid "Contents" -msgstr "สารบัญ" - msgid "Fullscreen mode" msgstr "โหมดเต็มหน้าจอ" -msgid "Download this page" -msgstr "ดาวน์โหลดหน้านี้" +msgid "Contents" +msgstr "สารบัญ" msgid "Download source file" msgstr "ดาวน์โหลดไฟล์ต้นฉบับ" -msgid "Launch" -msgstr "เปิด" - msgid "Edit this page" msgstr "แก้ไขหน้านี้" -msgid "Toggle navigation" -msgstr "ไม่ต้องสลับช่องทาง" +msgid "Last updated on" +msgstr "ปรับปรุงล่าสุดเมื่อ" -msgid "Theme by the" -msgstr "ธีมโดย" +msgid "Print to PDF" +msgstr "พิมพ์เป็น PDF" -msgid "Source repository" -msgstr "ที่เก็บซอร์ส" +msgid "suggest edit" +msgstr "แนะนำแก้ไข" -msgid "Last updated on" -msgstr "ปรับปรุงล่าสุดเมื่อ" +msgid "Download this page" +msgstr "ดาวน์โหลดหน้านี้" + +msgid "Toggle navigation" +msgstr "ไม่ต้องสลับช่องทาง" msgid "By the" msgstr "โดย" @@ -50,32 +47,29 @@ msgstr "โดย" msgid "Sphinx Book Theme" msgstr "ธีมหนังสือสฟิงซ์" -msgid "Open an issue" +msgid "previous page" +msgstr "หน้าที่แล้ว" + +msgid "Source repository" +msgstr "ที่เก็บซอร์ส" + +msgid "open issue" msgstr "เปิดปัญหา" -msgid "next page" -msgstr "หน้าต่อไป" +msgid "Download notebook file" +msgstr "ดาวน์โหลดไฟล์สมุดบันทึก" msgid "Copyright" msgstr "ลิขสิทธิ์" -msgid "Search this book..." -msgstr "ค้นหาหนังสือเล่มนี้ ..." - -msgid "Print to PDF" -msgstr "พิมพ์เป็น PDF" - -msgid "By" -msgstr "โดย" - -msgid "previous page" -msgstr "หน้าที่แล้ว" +msgid "Theme by the" +msgstr "ธีมโดย" -msgid "Search the docs ..." -msgstr "ค้นหาเอกสาร ..." +msgid "Open an issue" +msgstr "เปิดปัญหา" -msgid "Download notebook file" -msgstr "ดาวน์โหลดไฟล์สมุดบันทึก" +msgid "next page" +msgstr "หน้าต่อไป" -msgid "suggest edit" -msgstr "แนะนำแก้ไข" +msgid "Launch" +msgstr "เปิด" diff --git a/docs/_static/locales/tl/LC_MESSAGES/booktheme.mo b/docs/_static/locales/tl/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..8df1b73 Binary files /dev/null and b/docs/_static/locales/tl/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/tl/LC_MESSAGES/booktheme.po b/docs/_static/locales/tl/LC_MESSAGES/booktheme.po index 2e09439..2deaaab 100644 --- a/docs/_static/locales/tl/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/tl/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: tl\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "bukas na isyu" - -msgid "Download this page" -msgstr "I-download ang pahinang ito" +msgid "By" +msgstr "Ni" msgid "Download source file" msgstr "Mag-download ng file ng pinagmulan" -msgid "Launch" -msgstr "Ilunsad" - msgid "Edit this page" msgstr "I-edit ang pahinang ito" -msgid "Toggle navigation" -msgstr "I-toggle ang pag-navigate" +msgid "Last updated on" +msgstr "Huling na-update noong" -msgid "Theme by the" -msgstr "Tema ng" +msgid "Print to PDF" +msgstr "I-print sa PDF" -msgid "Source repository" -msgstr "Pinagmulan ng imbakan" +msgid "suggest edit" +msgstr "iminumungkahi i-edit" -msgid "Last updated on" -msgstr "Huling na-update noong" +msgid "Download this page" +msgstr "I-download ang pahinang ito" + +msgid "Toggle navigation" +msgstr "I-toggle ang pag-navigate" msgid "By the" msgstr "Sa pamamagitan ng" @@ -41,29 +38,29 @@ msgstr "Sa pamamagitan ng" msgid "Sphinx Book Theme" msgstr "Tema ng Sphinx Book" -msgid "Open an issue" -msgstr "Magbukas ng isyu" +msgid "previous page" +msgstr "Nakaraang pahina" -msgid "next page" -msgstr "Susunod na pahina" +msgid "Source repository" +msgstr "Pinagmulan ng imbakan" -msgid "Copyright" -msgstr "Copyright" +msgid "open issue" +msgstr "bukas na isyu" -msgid "Search this book..." -msgstr "Maghanap sa librong ito ..." +msgid "Download notebook file" +msgstr "Mag-download ng file ng notebook" -msgid "Print to PDF" -msgstr "I-print sa PDF" +msgid "Copyright" +msgstr "Copyright" -msgid "By" -msgstr "Ni" +msgid "Theme by the" +msgstr "Tema ng" -msgid "previous page" -msgstr "Nakaraang pahina" +msgid "Open an issue" +msgstr "Magbukas ng isyu" -msgid "Download notebook file" -msgstr "Mag-download ng file ng notebook" +msgid "next page" +msgstr "Susunod na pahina" -msgid "suggest edit" -msgstr "iminumungkahi i-edit" +msgid "Launch" +msgstr "Ilunsad" diff --git a/docs/_static/locales/tr/LC_MESSAGES/booktheme.mo b/docs/_static/locales/tr/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..029ae18 Binary files /dev/null and b/docs/_static/locales/tr/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/tr/LC_MESSAGES/booktheme.po b/docs/_static/locales/tr/LC_MESSAGES/booktheme.po index fe21499..f80c643 100644 --- a/docs/_static/locales/tr/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/tr/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: tr\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Tarafından" + msgid "repository" msgstr "depo" -msgid "open issue" -msgstr "Açık konu" - -msgid "Contents" -msgstr "İçindekiler" - msgid "Fullscreen mode" msgstr "Tam ekran modu" -msgid "Download this page" -msgstr "Bu sayfayı indirin" +msgid "Contents" +msgstr "İçindekiler" msgid "Download source file" msgstr "Kaynak dosyayı indirin" -msgid "Launch" -msgstr "Başlatmak" - msgid "Edit this page" msgstr "Bu sayfayı düzenle" -msgid "Toggle navigation" -msgstr "Gezinmeyi değiştir" +msgid "Last updated on" +msgstr "Son güncelleme tarihi" -msgid "Theme by the" -msgstr "Tarafından tema" +msgid "Print to PDF" +msgstr "PDF olarak yazdır" -msgid "Source repository" -msgstr "Kaynak kod deposu" +msgid "suggest edit" +msgstr "düzenleme öner" -msgid "Last updated on" -msgstr "Son güncelleme tarihi" +msgid "Download this page" +msgstr "Bu sayfayı indirin" + +msgid "Toggle navigation" +msgstr "Gezinmeyi değiştir" msgid "By the" msgstr "Tarafından" @@ -50,32 +47,29 @@ msgstr "Tarafından" msgid "Sphinx Book Theme" msgstr "Sfenks Kitap Teması" -msgid "Open an issue" -msgstr "Bir sorunu açın" - -msgid "next page" -msgstr "sonraki Sayfa" +msgid "previous page" +msgstr "önceki sayfa" -msgid "Copyright" -msgstr "Telif hakkı" +msgid "Source repository" +msgstr "Kaynak kod deposu" -msgid "Search this book..." -msgstr "Bu kitabı ara ..." +msgid "open issue" +msgstr "Açık konu" -msgid "Print to PDF" -msgstr "PDF olarak yazdır" +msgid "Download notebook file" +msgstr "Defter dosyasını indirin" -msgid "By" -msgstr "Tarafından" +msgid "Copyright" +msgstr "Telif hakkı" -msgid "previous page" -msgstr "önceki sayfa" +msgid "Theme by the" +msgstr "Tarafından tema" -msgid "Search the docs ..." -msgstr "Belgelerde ara ..." +msgid "Open an issue" +msgstr "Bir sorunu açın" -msgid "Download notebook file" -msgstr "Defter dosyasını indirin" +msgid "next page" +msgstr "sonraki Sayfa" -msgid "suggest edit" -msgstr "düzenleme öner" +msgid "Launch" +msgstr "Başlatmak" diff --git a/docs/_static/locales/uk/LC_MESSAGES/booktheme.mo b/docs/_static/locales/uk/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..16ab789 Binary files /dev/null and b/docs/_static/locales/uk/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/uk/LC_MESSAGES/booktheme.po b/docs/_static/locales/uk/LC_MESSAGES/booktheme.po index ae85786..13fc4e3 100644 --- a/docs/_static/locales/uk/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/uk/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: uk\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Автор" + msgid "repository" msgstr "сховище" -msgid "open issue" -msgstr "відкритий випуск" - -msgid "Contents" -msgstr "Зміст" - msgid "Fullscreen mode" msgstr "Повноекранний режим" -msgid "Download this page" -msgstr "Завантажте цю сторінку" +msgid "Contents" +msgstr "Зміст" msgid "Download source file" msgstr "Завантажити вихідний файл" -msgid "Launch" -msgstr "Запуск" - msgid "Edit this page" msgstr "Редагувати цю сторінку" -msgid "Toggle navigation" -msgstr "Переключити навігацію" +msgid "Last updated on" +msgstr "Останнє оновлення:" -msgid "Theme by the" -msgstr "Тема від" +msgid "Print to PDF" +msgstr "Друк у форматі PDF" -msgid "Source repository" -msgstr "Джерело сховища" +msgid "suggest edit" +msgstr "запропонувати редагувати" -msgid "Last updated on" -msgstr "Останнє оновлення:" +msgid "Download this page" +msgstr "Завантажте цю сторінку" + +msgid "Toggle navigation" +msgstr "Переключити навігацію" msgid "By the" msgstr "По" @@ -50,32 +47,29 @@ msgstr "По" msgid "Sphinx Book Theme" msgstr "Тема книги \"Сфінкс\"" -msgid "Open an issue" -msgstr "Відкрийте випуск" - -msgid "next page" -msgstr "Наступна сторінка" +msgid "previous page" +msgstr "Попередня сторінка" -msgid "Copyright" -msgstr "Авторське право" +msgid "Source repository" +msgstr "Джерело сховища" -msgid "Search this book..." -msgstr "Шукати в цій книзі ..." +msgid "open issue" +msgstr "відкритий випуск" -msgid "Print to PDF" -msgstr "Друк у форматі PDF" +msgid "Download notebook file" +msgstr "Завантажте файл блокнота" -msgid "By" -msgstr "Автор" +msgid "Copyright" +msgstr "Авторське право" -msgid "previous page" -msgstr "Попередня сторінка" +msgid "Theme by the" +msgstr "Тема від" -msgid "Search the docs ..." -msgstr "Шукати в документах ..." +msgid "Open an issue" +msgstr "Відкрийте випуск" -msgid "Download notebook file" -msgstr "Завантажте файл блокнота" +msgid "next page" +msgstr "Наступна сторінка" -msgid "suggest edit" -msgstr "запропонувати редагувати" +msgid "Launch" +msgstr "Запуск" diff --git a/docs/_static/locales/ur/LC_MESSAGES/booktheme.mo b/docs/_static/locales/ur/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..de8c84b Binary files /dev/null and b/docs/_static/locales/ur/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/ur/LC_MESSAGES/booktheme.po b/docs/_static/locales/ur/LC_MESSAGES/booktheme.po index 4c4c637..a39526d 100644 --- a/docs/_static/locales/ur/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/ur/LC_MESSAGES/booktheme.po @@ -8,32 +8,29 @@ msgstr "" "Language: ur\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "open issue" -msgstr "کھلا مسئلہ" - -msgid "Download this page" -msgstr "اس صفحے کو ڈاؤن لوڈ کریں" +msgid "By" +msgstr "بذریعہ" msgid "Download source file" msgstr "سورس فائل ڈاؤن لوڈ کریں" -msgid "Launch" -msgstr "لانچ کریں" - msgid "Edit this page" msgstr "اس صفحے میں ترمیم کریں" -msgid "Toggle navigation" -msgstr "نیویگیشن ٹوگل کریں" +msgid "Last updated on" +msgstr "آخری بار تازہ کاری ہوئی" -msgid "Theme by the" -msgstr "کے ذریعہ تھیم" +msgid "Print to PDF" +msgstr "پی ڈی ایف پرنٹ کریں" -msgid "Source repository" -msgstr "ماخذ ذخیرہ" +msgid "suggest edit" +msgstr "ترمیم کی تجویز کریں" -msgid "Last updated on" -msgstr "آخری بار تازہ کاری ہوئی" +msgid "Download this page" +msgstr "اس صفحے کو ڈاؤن لوڈ کریں" + +msgid "Toggle navigation" +msgstr "نیویگیشن ٹوگل کریں" msgid "By the" msgstr "کی طرف" @@ -41,29 +38,29 @@ msgstr "کی طرف" msgid "Sphinx Book Theme" msgstr "سپنکس بک تھیم" -msgid "Open an issue" -msgstr "ایک مسئلہ کھولیں" +msgid "previous page" +msgstr "سابقہ ​​صفحہ" -msgid "next page" -msgstr "اگلا صفحہ" +msgid "Source repository" +msgstr "ماخذ ذخیرہ" -msgid "Copyright" -msgstr "کاپی رائٹ" +msgid "open issue" +msgstr "کھلا مسئلہ" -msgid "Search this book..." -msgstr "اس کتاب کو تلاش کریں…" +msgid "Download notebook file" +msgstr "نوٹ بک فائل ڈاؤن لوڈ کریں" -msgid "Print to PDF" -msgstr "پی ڈی ایف پرنٹ کریں" +msgid "Copyright" +msgstr "کاپی رائٹ" -msgid "By" -msgstr "بذریعہ" +msgid "Theme by the" +msgstr "کے ذریعہ تھیم" -msgid "previous page" -msgstr "سابقہ ​​صفحہ" +msgid "Open an issue" +msgstr "ایک مسئلہ کھولیں" -msgid "Download notebook file" -msgstr "نوٹ بک فائل ڈاؤن لوڈ کریں" +msgid "next page" +msgstr "اگلا صفحہ" -msgid "suggest edit" -msgstr "ترمیم کی تجویز کریں" +msgid "Launch" +msgstr "لانچ کریں" diff --git a/docs/_static/locales/vi/LC_MESSAGES/booktheme.mo b/docs/_static/locales/vi/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..2bb3255 Binary files /dev/null and b/docs/_static/locales/vi/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/vi/LC_MESSAGES/booktheme.po b/docs/_static/locales/vi/LC_MESSAGES/booktheme.po index 2c42502..72a7219 100644 --- a/docs/_static/locales/vi/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/vi/LC_MESSAGES/booktheme.po @@ -8,41 +8,38 @@ msgstr "" "Language: vi\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" +msgid "By" +msgstr "Bởi" + msgid "repository" msgstr "kho" -msgid "open issue" -msgstr "vấn đề mở" - -msgid "Contents" -msgstr "Nội dung" - msgid "Fullscreen mode" msgstr "Chế độ toàn màn hình" -msgid "Download this page" -msgstr "Tải xuống trang này" +msgid "Contents" +msgstr "Nội dung" msgid "Download source file" msgstr "Tải xuống tệp nguồn" -msgid "Launch" -msgstr "Phóng" - msgid "Edit this page" msgstr "chỉnh sửa trang này" -msgid "Toggle navigation" -msgstr "Chuyển đổi điều hướng thành" +msgid "Last updated on" +msgstr "Cập nhật lần cuối vào" -msgid "Theme by the" -msgstr "Chủ đề của" +msgid "Print to PDF" +msgstr "In sang PDF" -msgid "Source repository" -msgstr "Kho nguồn" +msgid "suggest edit" +msgstr "đề nghị chỉnh sửa" -msgid "Last updated on" -msgstr "Cập nhật lần cuối vào" +msgid "Download this page" +msgstr "Tải xuống trang này" + +msgid "Toggle navigation" +msgstr "Chuyển đổi điều hướng thành" msgid "By the" msgstr "Bằng" @@ -50,32 +47,29 @@ msgstr "Bằng" msgid "Sphinx Book Theme" msgstr "Chủ đề sách nhân sư" -msgid "Open an issue" -msgstr "Mở một vấn đề" - -msgid "next page" -msgstr "Trang tiếp theo" +msgid "previous page" +msgstr "trang trước" -msgid "Copyright" -msgstr "Bản quyền" +msgid "Source repository" +msgstr "Kho nguồn" -msgid "Search this book..." -msgstr "Tìm kiếm cuốn sách này ..." +msgid "open issue" +msgstr "vấn đề mở" -msgid "Print to PDF" -msgstr "In sang PDF" +msgid "Download notebook file" +msgstr "Tải xuống tệp sổ tay" -msgid "By" -msgstr "Bởi" +msgid "Copyright" +msgstr "Bản quyền" -msgid "previous page" -msgstr "trang trước" +msgid "Theme by the" +msgstr "Chủ đề của" -msgid "Search the docs ..." -msgstr "Tìm kiếm tài liệu ..." +msgid "Open an issue" +msgstr "Mở một vấn đề" -msgid "Download notebook file" -msgstr "Tải xuống tệp sổ tay" +msgid "next page" +msgstr "Trang tiếp theo" -msgid "suggest edit" -msgstr "đề nghị chỉnh sửa" +msgid "Launch" +msgstr "Phóng" diff --git a/docs/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo b/docs/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..0e3235d Binary files /dev/null and b/docs/_static/locales/zh_CN/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/zh_CN/LC_MESSAGES/booktheme.po b/docs/_static/locales/zh_CN/LC_MESSAGES/booktheme.po index 4098aed..e62bd5a 100644 --- a/docs/_static/locales/zh_CN/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/zh_CN/LC_MESSAGES/booktheme.po @@ -8,71 +8,68 @@ msgstr "" "Language: zh_CN\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "repository" -msgstr "资料库" - -msgid "open issue" -msgstr "公开的问题" +msgid "By" +msgstr "作者：" -msgid "Contents" -msgstr "内容" +msgid "repository" +msgstr "仓库" msgid "Fullscreen mode" msgstr "全屏模式" -msgid "Download this page" -msgstr "下载此页面" +msgid "Contents" +msgstr "目录" msgid "Download source file" msgstr "下载源文件" -msgid "Launch" -msgstr "发射" - msgid "Edit this page" -msgstr "编辑这个页面" +msgstr "编辑此页面" -msgid "Toggle navigation" -msgstr "切换导航" +msgid "Last updated on" +msgstr "上次更新时间：" -msgid "Theme by the" -msgstr "主题由" +msgid "Print to PDF" +msgstr "列印成 PDF" -msgid "Source repository" -msgstr "源库" +msgid "suggest edit" +msgstr "提出修改建议" -msgid "Last updated on" -msgstr "上次更新时间：" +msgid "Download this page" +msgstr "下载此页面" + +msgid "Toggle navigation" +msgstr "显示或隐藏导航栏" msgid "By the" -msgstr "由" +msgstr "作者：" msgid "Sphinx Book Theme" -msgstr "狮身人面像书主题" +msgstr "Sphinx Book 主题" -msgid "Open an issue" -msgstr "打开一个问题" +msgid "previous page" +msgstr "上一页" -msgid "next page" -msgstr "下一页" +msgid "Source repository" +msgstr "源码库" -msgid "Copyright" -msgstr "版权" +msgid "open issue" +msgstr "创建议题" -msgid "Search this book..." -msgstr "搜索这本书..." +msgid "Download notebook file" +msgstr "下载笔记本文件" -msgid "Print to PDF" -msgstr "列印成PDF" +msgid "Copyright" +msgstr "版权" -msgid "By" -msgstr "通过" +msgid "Theme by the" +msgstr "主题作者：" -msgid "previous page" -msgstr "上一页" +msgid "Open an issue" +msgstr "创建议题" -msgid "Search the docs ..." -msgstr "搜索文档..." +msgid "next page" +msgstr "下一页" -msgid "Download notebook file" -msgstr "下载笔记本文件" +msgid "Launch" +msgstr "启动" diff --git a/docs/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo b/docs/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo new file mode 100644 index 0000000..9116fa9 Binary files /dev/null and b/docs/_static/locales/zh_TW/LC_MESSAGES/booktheme.mo differ diff --git a/docs/_static/locales/zh_TW/LC_MESSAGES/booktheme.po b/docs/_static/locales/zh_TW/LC_MESSAGES/booktheme.po index 52cff5e..4a9ced7 100644 --- a/docs/_static/locales/zh_TW/LC_MESSAGES/booktheme.po +++ b/docs/_static/locales/zh_TW/LC_MESSAGES/booktheme.po @@ -8,74 +8,68 @@ msgstr "" "Language: zh_TW\n" "Plural-Forms: nplurals=2; plural=(n != 1);\n" -msgid "repository" -msgstr "資料庫" - -msgid "open issue" -msgstr "公開的問題" +msgid "By" +msgstr "作者：" -msgid "Contents" -msgstr "內容" +msgid "repository" +msgstr "儲存庫" msgid "Fullscreen mode" -msgstr "全屏模式" +msgstr "全螢幕模式" -msgid "Download this page" -msgstr "下載此頁面" +msgid "Contents" +msgstr "目錄" msgid "Download source file" -msgstr "下載源文件" - -msgid "Launch" -msgstr "發射" +msgstr "下載原始檔" msgid "Edit this page" -msgstr "編輯這個頁面" +msgstr "編輯此頁面" -msgid "Toggle navigation" -msgstr "切換導航" +msgid "Last updated on" +msgstr "最後更新時間：" -msgid "Theme by the" -msgstr "主題由" +msgid "Print to PDF" +msgstr "列印成 PDF" -msgid "Source repository" -msgstr "源庫" +msgid "suggest edit" +msgstr "提出修改建議" -msgid "Last updated on" -msgstr "上次更新時間：" +msgid "Download this page" +msgstr "下載此頁面" + +msgid "Toggle navigation" +msgstr "顯示或隱藏導覽列" msgid "By the" -msgstr "由" +msgstr "作者：" msgid "Sphinx Book Theme" -msgstr "獅身人面像書主題" +msgstr "Sphinx Book 佈景主題" -msgid "Open an issue" -msgstr "打開一個問題" - -msgid "next page" -msgstr "下一頁" +msgid "previous page" +msgstr "上一頁" -msgid "Copyright" -msgstr "版權" +msgid "Source repository" +msgstr "來源儲存庫" -msgid "Search this book..." -msgstr "搜索這本書..." +msgid "open issue" +msgstr "公開的問題" -msgid "Print to PDF" -msgstr "列印成PDF" +msgid "Download notebook file" +msgstr "下載 Notebook 檔案" -msgid "By" -msgstr "通過" +msgid "Copyright" +msgstr "Copyright" -msgid "previous page" -msgstr "上一頁" +msgid "Theme by the" +msgstr "佈景主題作者：" -msgid "Search the docs ..." -msgstr "搜索文檔..." +msgid "Open an issue" +msgstr "開啟議題" -msgid "Download notebook file" -msgstr "下載筆記本文件" +msgid "next page" +msgstr "下一頁" -msgid "suggest edit" -msgstr "建議編輯" +msgid "Launch" +msgstr "啟動" diff --git a/docs/_static/logo.png b/docs/_static/logo.png index 991edee..f7498dc 100644 Binary files a/docs/_static/logo.png and b/docs/_static/logo.png differ diff --git a/docs/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css b/docs/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css new file mode 100644 index 0000000..3356631 --- /dev/null +++ b/docs/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css @@ -0,0 +1,2342 @@ +/* Variables */ +:root { + --mystnb-source-bg-color: #f7f7f7; + --mystnb-stdout-bg-color: #fcfcfc; + --mystnb-stderr-bg-color: #fdd; + --mystnb-traceback-bg-color: #fcfcfc; + --mystnb-source-border-color: #ccc; + --mystnb-source-margin-color: green; + --mystnb-stdout-border-color: #f7f7f7; + --mystnb-stderr-border-color: #f7f7f7; + --mystnb-traceback-border-color: #ffd6d6; + --mystnb-hide-prompt-opacity: 70%; + --mystnb-source-border-radius: .4em; + --mystnb-source-border-width: 1px; +} + +/* Whole cell */ +div.container.cell { + padding-left: 0; + margin-bottom: 1em; +} + +/* Removing all background formatting so we can control at the div level */ +.cell_input div.highlight, +.cell_output pre, +.cell_input pre, +.cell_output .output { + border: none; + box-shadow: none; +} + +.cell_output .output pre, +.cell_input pre { + margin: 0px; +} + +/* Input cells */ +div.cell div.cell_input, +div.cell details.above-input>summary { + padding-left: 0em; + padding-right: 0em; + border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid; + background-color: var(--mystnb-source-bg-color); + border-left-color: var(--mystnb-source-margin-color); + border-left-width: medium; + border-radius: var(--mystnb-source-border-radius); +} + +div.cell_input>div, +div.cell_output div.output>div.highlight { + margin: 0em !important; + border: none !important; +} + +/* All cell outputs */ +.cell_output { + padding-left: 1em; + padding-right: 0em; + margin-top: 1em; +} + +/* Text outputs from cells */ +.cell_output .output.text_plain, +.cell_output .output.traceback, +.cell_output .output.stream, +.cell_output .output.stderr { + margin-top: 1em; + margin-bottom: 0em; + box-shadow: none; +} + +.cell_output .output.text_plain, +.cell_output .output.stream { + background: var(--mystnb-stdout-bg-color); + border: 1px solid var(--mystnb-stdout-border-color); +} + +.cell_output .output.stderr { + background: var(--mystnb-stderr-bg-color); + border: 1px solid var(--mystnb-stderr-border-color); +} + +.cell_output .output.traceback { + background: var(--mystnb-traceback-bg-color); + border: 1px solid var(--mystnb-traceback-border-color); +} + +/* Collapsible cell content */ +div.cell details.above-input div.cell_input { + border-top-left-radius: 0; + border-top-right-radius: 0; + border-top: var(--mystnb-source-border-width) var(--mystnb-source-border-color) dashed; +} + +div.cell div.cell_input.above-output-prompt { + border-bottom-left-radius: 0; + border-bottom-right-radius: 0; +} + +div.cell details.above-input>summary { + border-bottom-left-radius: 0; + border-bottom-right-radius: 0; + border-bottom: var(--mystnb-source-border-width) var(--mystnb-source-border-color) dashed; + padding-left: 1em; + margin-bottom: 0; +} + +div.cell details.above-output>summary { + background-color: var(--mystnb-source-bg-color); + padding-left: 1em; + padding-right: 0em; + border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid; + border-radius: var(--mystnb-source-border-radius); + border-left-color: var(--mystnb-source-margin-color); + border-left-width: medium; +} + +div.cell details.below-input>summary { + background-color: var(--mystnb-source-bg-color); + padding-left: 1em; + padding-right: 0em; + border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid; + border-top: none; + border-bottom-left-radius: var(--mystnb-source-border-radius); + border-bottom-right-radius: var(--mystnb-source-border-radius); + border-left-color: var(--mystnb-source-margin-color); + border-left-width: medium; +} + +div.cell details.hide>summary>span { + opacity: var(--mystnb-hide-prompt-opacity); +} + +div.cell details.hide[open]>summary>span.collapsed { + display: none; +} + +div.cell details.hide:not([open])>summary>span.expanded { + display: none; +} + +@keyframes collapsed-fade-in { + 0% { + opacity: 0; + } + + 100% { + opacity: 1; + } +} +div.cell details.hide[open]>summary~* { + -moz-animation: collapsed-fade-in 0.3s ease-in-out; + -webkit-animation: collapsed-fade-in 0.3s ease-in-out; + animation: collapsed-fade-in 0.3s ease-in-out; +} + +/* Math align to the left */ +.cell_output .MathJax_Display { + text-align: left !important; +} + +/* Pandas tables. Pulled from the Jupyter / nbsphinx CSS */ +div.cell_output table { + border: none; + border-collapse: collapse; + border-spacing: 0; + color: black; + font-size: 1em; + table-layout: fixed; +} + +div.cell_output thead { + border-bottom: 1px solid black; + vertical-align: bottom; +} + +div.cell_output tr, +div.cell_output th, +div.cell_output td { + text-align: right; + vertical-align: middle; + padding: 0.5em 0.5em; + line-height: normal; + white-space: normal; + max-width: none; + border: none; +} + +div.cell_output th { + font-weight: bold; +} + +div.cell_output tbody tr:nth-child(odd) { + background: #f5f5f5; +} + +div.cell_output tbody tr:hover { + background: rgba(66, 165, 245, 0.2); +} + +/** source code line numbers **/ +span.linenos { + opacity: 0.5; +} + +/* Inline text from `paste` operation */ + +span.pasted-text { + font-weight: bold; +} + +span.pasted-inline img { + max-height: 2em; +} + +tbody span.pasted-inline img { + max-height: none; +} + +/* Font colors for translated ANSI escape sequences +Color values are copied from Jupyter Notebook +https://github.com/jupyter/notebook/blob/52581f8eda9b319eb0390ac77fe5903c38f81e3e/notebook/static/notebook/less/ansicolors.less#L14-L21 +Background colors from +https://nbsphinx.readthedocs.io/en/latest/code-cells.html#ANSI-Colors +*/ +div.highlight .-Color-Bold { + font-weight: bold; +} + +div.highlight .-Color[class*=-Black] { + color: #3E424D +} + +div.highlight .-Color[class*=-Red] { + color: #E75C58 +} + +div.highlight .-Color[class*=-Green] { + color: #00A250 +} + +div.highlight .-Color[class*=-Yellow] { + color: #DDB62B +} + +div.highlight .-Color[class*=-Blue] { + color: #208FFB +} + +div.highlight .-Color[class*=-Magenta] { + color: #D160C4 +} + +div.highlight .-Color[class*=-Cyan] { + color: #60C6C8 +} + +div.highlight .-Color[class*=-White] { + color: #C5C1B4 +} + +div.highlight .-Color[class*=-BGBlack] { + background-color: #3E424D +} + +div.highlight .-Color[class*=-BGRed] { + background-color: #E75C58 +} + +div.highlight .-Color[class*=-BGGreen] { + background-color: #00A250 +} + +div.highlight .-Color[class*=-BGYellow] { + background-color: #DDB62B +} + +div.highlight .-Color[class*=-BGBlue] { + background-color: #208FFB +} + +div.highlight .-Color[class*=-BGMagenta] { + background-color: #D160C4 +} + +div.highlight .-Color[class*=-BGCyan] { + background-color: #60C6C8 +} + +div.highlight .-Color[class*=-BGWhite] { + background-color: #C5C1B4 +} + +/* Font colors for 8-bit ANSI */ + +div.highlight .-Color[class*=-C0] { + color: #000000 +} + +div.highlight .-Color[class*=-BGC0] { + background-color: #000000 +} + +div.highlight .-Color[class*=-C1] { + color: #800000 +} + +div.highlight .-Color[class*=-BGC1] { + background-color: #800000 +} + +div.highlight .-Color[class*=-C2] { + color: #008000 +} + +div.highlight .-Color[class*=-BGC2] { + background-color: #008000 +} + +div.highlight .-Color[class*=-C3] { + color: #808000 +} + +div.highlight .-Color[class*=-BGC3] { + background-color: #808000 +} + +div.highlight .-Color[class*=-C4] { + color: #000080 +} + +div.highlight .-Color[class*=-BGC4] { + background-color: #000080 +} + +div.highlight .-Color[class*=-C5] { + color: #800080 +} + +div.highlight .-Color[class*=-BGC5] { + background-color: #800080 +} + +div.highlight .-Color[class*=-C6] { + color: #008080 +} + +div.highlight .-Color[class*=-BGC6] { + background-color: #008080 +} + +div.highlight .-Color[class*=-C7] { + color: #C0C0C0 +} + +div.highlight .-Color[class*=-BGC7] { + background-color: #C0C0C0 +} + +div.highlight .-Color[class*=-C8] { + color: #808080 +} + +div.highlight .-Color[class*=-BGC8] { + background-color: #808080 +} + +div.highlight .-Color[class*=-C9] { + color: #FF0000 +} + +div.highlight .-Color[class*=-BGC9] { + background-color: #FF0000 +} + +div.highlight .-Color[class*=-C10] { + color: #00FF00 +} + +div.highlight .-Color[class*=-BGC10] { + background-color: #00FF00 +} + +div.highlight .-Color[class*=-C11] { + color: #FFFF00 +} + +div.highlight .-Color[class*=-BGC11] { + background-color: #FFFF00 +} + +div.highlight .-Color[class*=-C12] { + color: #0000FF +} + +div.highlight .-Color[class*=-BGC12] { + background-color: #0000FF +} + +div.highlight .-Color[class*=-C13] { + color: #FF00FF +} + +div.highlight .-Color[class*=-BGC13] { + background-color: #FF00FF +} + +div.highlight .-Color[class*=-C14] { + color: #00FFFF +} + +div.highlight .-Color[class*=-BGC14] { + background-color: #00FFFF +} + +div.highlight .-Color[class*=-C15] { + color: #FFFFFF +} + +div.highlight .-Color[class*=-BGC15] { + background-color: #FFFFFF +} + +div.highlight .-Color[class*=-C16] { + color: #000000 +} + +div.highlight .-Color[class*=-BGC16] { + background-color: #000000 +} + +div.highlight .-Color[class*=-C17] { + color: #00005F +} + +div.highlight .-Color[class*=-BGC17] { + background-color: #00005F +} + +div.highlight .-Color[class*=-C18] { + color: #000087 +} + +div.highlight .-Color[class*=-BGC18] { + background-color: #000087 +} + +div.highlight .-Color[class*=-C19] { + color: #0000AF +} + +div.highlight .-Color[class*=-BGC19] { + background-color: #0000AF +} + +div.highlight .-Color[class*=-C20] { + color: #0000D7 +} + +div.highlight .-Color[class*=-BGC20] { + background-color: #0000D7 +} + +div.highlight .-Color[class*=-C21] { + color: #0000FF +} + +div.highlight .-Color[class*=-BGC21] { + background-color: #0000FF +} + +div.highlight .-Color[class*=-C22] { + color: #005F00 +} + +div.highlight .-Color[class*=-BGC22] { + background-color: #005F00 +} + +div.highlight .-Color[class*=-C23] { + color: #005F5F +} + +div.highlight .-Color[class*=-BGC23] { + background-color: #005F5F +} + +div.highlight .-Color[class*=-C24] { + color: #005F87 +} + +div.highlight .-Color[class*=-BGC24] { + background-color: #005F87 +} + +div.highlight .-Color[class*=-C25] { + color: #005FAF +} + +div.highlight .-Color[class*=-BGC25] { + background-color: #005FAF +} + +div.highlight .-Color[class*=-C26] { + color: #005FD7 +} + +div.highlight .-Color[class*=-BGC26] { + background-color: #005FD7 +} + +div.highlight .-Color[class*=-C27] { + color: #005FFF +} + +div.highlight .-Color[class*=-BGC27] { + background-color: #005FFF +} + +div.highlight .-Color[class*=-C28] { + color: #008700 +} + +div.highlight .-Color[class*=-BGC28] { + background-color: #008700 +} + +div.highlight .-Color[class*=-C29] { + color: #00875F +} + +div.highlight .-Color[class*=-BGC29] { + background-color: #00875F +} + +div.highlight .-Color[class*=-C30] { + color: #008787 +} + +div.highlight .-Color[class*=-BGC30] { + background-color: #008787 +} + +div.highlight .-Color[class*=-C31] { + color: #0087AF +} + +div.highlight .-Color[class*=-BGC31] { + background-color: #0087AF +} + +div.highlight .-Color[class*=-C32] { + color: #0087D7 +} + +div.highlight .-Color[class*=-BGC32] { + background-color: #0087D7 +} + +div.highlight .-Color[class*=-C33] { + color: #0087FF +} + +div.highlight .-Color[class*=-BGC33] { + background-color: #0087FF +} + +div.highlight .-Color[class*=-C34] { + color: #00AF00 +} + +div.highlight .-Color[class*=-BGC34] { + background-color: #00AF00 +} + +div.highlight .-Color[class*=-C35] { + color: #00AF5F +} + +div.highlight .-Color[class*=-BGC35] { + background-color: #00AF5F +} + +div.highlight .-Color[class*=-C36] { + color: #00AF87 +} + +div.highlight .-Color[class*=-BGC36] { + background-color: #00AF87 +} + +div.highlight .-Color[class*=-C37] { + color: #00AFAF +} + +div.highlight .-Color[class*=-BGC37] { + background-color: #00AFAF +} + +div.highlight .-Color[class*=-C38] { + color: #00AFD7 +} + +div.highlight .-Color[class*=-BGC38] { + background-color: #00AFD7 +} + +div.highlight .-Color[class*=-C39] { + color: #00AFFF +} + +div.highlight .-Color[class*=-BGC39] { + background-color: #00AFFF +} + +div.highlight .-Color[class*=-C40] { + color: #00D700 +} + +div.highlight .-Color[class*=-BGC40] { + background-color: #00D700 +} + +div.highlight .-Color[class*=-C41] { + color: #00D75F +} + +div.highlight .-Color[class*=-BGC41] { + background-color: #00D75F +} + +div.highlight .-Color[class*=-C42] { + color: #00D787 +} + +div.highlight .-Color[class*=-BGC42] { + background-color: #00D787 +} + +div.highlight .-Color[class*=-C43] { + color: #00D7AF +} + +div.highlight .-Color[class*=-BGC43] { + background-color: #00D7AF +} + +div.highlight .-Color[class*=-C44] { + color: #00D7D7 +} + +div.highlight .-Color[class*=-BGC44] { + background-color: #00D7D7 +} + +div.highlight .-Color[class*=-C45] { + color: #00D7FF +} + +div.highlight .-Color[class*=-BGC45] { + background-color: #00D7FF +} + +div.highlight .-Color[class*=-C46] { + color: #00FF00 +} + +div.highlight .-Color[class*=-BGC46] { + background-color: #00FF00 +} + +div.highlight .-Color[class*=-C47] { + color: #00FF5F +} + +div.highlight .-Color[class*=-BGC47] { + background-color: #00FF5F +} + +div.highlight .-Color[class*=-C48] { + color: #00FF87 +} + +div.highlight .-Color[class*=-BGC48] { + background-color: #00FF87 +} + +div.highlight .-Color[class*=-C49] { + color: #00FFAF +} + +div.highlight .-Color[class*=-BGC49] { + background-color: #00FFAF +} + +div.highlight .-Color[class*=-C50] { + color: #00FFD7 +} + +div.highlight .-Color[class*=-BGC50] { + background-color: #00FFD7 +} + +div.highlight .-Color[class*=-C51] { + color: #00FFFF +} + +div.highlight .-Color[class*=-BGC51] { + background-color: #00FFFF +} + +div.highlight .-Color[class*=-C52] { + color: #5F0000 +} + +div.highlight .-Color[class*=-BGC52] { + background-color: #5F0000 +} + +div.highlight .-Color[class*=-C53] { + color: #5F005F +} + +div.highlight .-Color[class*=-BGC53] { + background-color: #5F005F +} + +div.highlight .-Color[class*=-C54] { + color: #5F0087 +} + +div.highlight .-Color[class*=-BGC54] { + background-color: #5F0087 +} + +div.highlight .-Color[class*=-C55] { + color: #5F00AF +} + +div.highlight .-Color[class*=-BGC55] { + background-color: #5F00AF +} + +div.highlight .-Color[class*=-C56] { + color: #5F00D7 +} + +div.highlight .-Color[class*=-BGC56] { + background-color: #5F00D7 +} + +div.highlight .-Color[class*=-C57] { + color: #5F00FF +} + +div.highlight .-Color[class*=-BGC57] { + background-color: #5F00FF +} + +div.highlight .-Color[class*=-C58] { + color: #5F5F00 +} + +div.highlight .-Color[class*=-BGC58] { + background-color: #5F5F00 +} + +div.highlight .-Color[class*=-C59] { + color: #5F5F5F +} + +div.highlight .-Color[class*=-BGC59] { + background-color: #5F5F5F +} + +div.highlight .-Color[class*=-C60] { + color: #5F5F87 +} + +div.highlight .-Color[class*=-BGC60] { + background-color: #5F5F87 +} + +div.highlight .-Color[class*=-C61] { + color: #5F5FAF +} + +div.highlight .-Color[class*=-BGC61] { + background-color: #5F5FAF +} + +div.highlight .-Color[class*=-C62] { + color: #5F5FD7 +} + +div.highlight .-Color[class*=-BGC62] { + background-color: #5F5FD7 +} + +div.highlight .-Color[class*=-C63] { + color: #5F5FFF +} + +div.highlight .-Color[class*=-BGC63] { + background-color: #5F5FFF +} + +div.highlight .-Color[class*=-C64] { + color: #5F8700 +} + +div.highlight .-Color[class*=-BGC64] { + background-color: #5F8700 +} + +div.highlight .-Color[class*=-C65] { + color: #5F875F +} + +div.highlight .-Color[class*=-BGC65] { + background-color: #5F875F +} + +div.highlight .-Color[class*=-C66] { + color: #5F8787 +} + +div.highlight .-Color[class*=-BGC66] { + background-color: #5F8787 +} + +div.highlight .-Color[class*=-C67] { + color: #5F87AF +} + +div.highlight .-Color[class*=-BGC67] { + background-color: #5F87AF +} + +div.highlight .-Color[class*=-C68] { + color: #5F87D7 +} + +div.highlight .-Color[class*=-BGC68] { + background-color: #5F87D7 +} + +div.highlight .-Color[class*=-C69] { + color: #5F87FF +} + +div.highlight .-Color[class*=-BGC69] { + background-color: #5F87FF +} + +div.highlight .-Color[class*=-C70] { + color: #5FAF00 +} + +div.highlight .-Color[class*=-BGC70] { + background-color: #5FAF00 +} + +div.highlight .-Color[class*=-C71] { + color: #5FAF5F +} + +div.highlight .-Color[class*=-BGC71] { + background-color: #5FAF5F +} + +div.highlight .-Color[class*=-C72] { + color: #5FAF87 +} + +div.highlight .-Color[class*=-BGC72] { + background-color: #5FAF87 +} + +div.highlight .-Color[class*=-C73] { + color: #5FAFAF +} + +div.highlight .-Color[class*=-BGC73] { + background-color: #5FAFAF +} + +div.highlight .-Color[class*=-C74] { + color: #5FAFD7 +} + +div.highlight .-Color[class*=-BGC74] { + background-color: #5FAFD7 +} + +div.highlight .-Color[class*=-C75] { + color: #5FAFFF +} + +div.highlight .-Color[class*=-BGC75] { + background-color: #5FAFFF +} + +div.highlight .-Color[class*=-C76] { + color: #5FD700 +} + +div.highlight .-Color[class*=-BGC76] { + background-color: #5FD700 +} + +div.highlight .-Color[class*=-C77] { + color: #5FD75F +} + +div.highlight .-Color[class*=-BGC77] { + background-color: #5FD75F +} + +div.highlight .-Color[class*=-C78] { + color: #5FD787 +} + +div.highlight .-Color[class*=-BGC78] { + background-color: #5FD787 +} + +div.highlight .-Color[class*=-C79] { + color: #5FD7AF +} + +div.highlight .-Color[class*=-BGC79] { + background-color: #5FD7AF +} + +div.highlight .-Color[class*=-C80] { + color: #5FD7D7 +} + +div.highlight .-Color[class*=-BGC80] { + background-color: #5FD7D7 +} + +div.highlight .-Color[class*=-C81] { + color: #5FD7FF +} + +div.highlight .-Color[class*=-BGC81] { + background-color: #5FD7FF +} + +div.highlight .-Color[class*=-C82] { + color: #5FFF00 +} + +div.highlight .-Color[class*=-BGC82] { + background-color: #5FFF00 +} + +div.highlight .-Color[class*=-C83] { + color: #5FFF5F +} + +div.highlight .-Color[class*=-BGC83] { + background-color: #5FFF5F +} + +div.highlight .-Color[class*=-C84] { + color: #5FFF87 +} + +div.highlight .-Color[class*=-BGC84] { + background-color: #5FFF87 +} + +div.highlight .-Color[class*=-C85] { + color: #5FFFAF +} + +div.highlight .-Color[class*=-BGC85] { + background-color: #5FFFAF +} + +div.highlight .-Color[class*=-C86] { + color: #5FFFD7 +} + +div.highlight .-Color[class*=-BGC86] { + background-color: #5FFFD7 +} + +div.highlight .-Color[class*=-C87] { + color: #5FFFFF +} + +div.highlight .-Color[class*=-BGC87] { + background-color: #5FFFFF +} + +div.highlight .-Color[class*=-C88] { + color: #870000 +} + +div.highlight .-Color[class*=-BGC88] { + background-color: #870000 +} + +div.highlight .-Color[class*=-C89] { + color: #87005F +} + +div.highlight .-Color[class*=-BGC89] { + background-color: #87005F +} + +div.highlight .-Color[class*=-C90] { + color: #870087 +} + +div.highlight .-Color[class*=-BGC90] { + background-color: #870087 +} + +div.highlight .-Color[class*=-C91] { + color: #8700AF +} + +div.highlight .-Color[class*=-BGC91] { + background-color: #8700AF +} + +div.highlight .-Color[class*=-C92] { + color: #8700D7 +} + +div.highlight .-Color[class*=-BGC92] { + background-color: #8700D7 +} + +div.highlight .-Color[class*=-C93] { + color: #8700FF +} + +div.highlight .-Color[class*=-BGC93] { + background-color: #8700FF +} + +div.highlight .-Color[class*=-C94] { + color: #875F00 +} + +div.highlight .-Color[class*=-BGC94] { + background-color: #875F00 +} + +div.highlight .-Color[class*=-C95] { + color: #875F5F +} + +div.highlight .-Color[class*=-BGC95] { + background-color: #875F5F +} + +div.highlight .-Color[class*=-C96] { + color: #875F87 +} + +div.highlight .-Color[class*=-BGC96] { + background-color: #875F87 +} + +div.highlight .-Color[class*=-C97] { + color: #875FAF +} + +div.highlight .-Color[class*=-BGC97] { + background-color: #875FAF +} + +div.highlight .-Color[class*=-C98] { + color: #875FD7 +} + +div.highlight .-Color[class*=-BGC98] { + background-color: #875FD7 +} + +div.highlight .-Color[class*=-C99] { + color: #875FFF +} + +div.highlight .-Color[class*=-BGC99] { + background-color: #875FFF +} + +div.highlight .-Color[class*=-C100] { + color: #878700 +} + +div.highlight .-Color[class*=-BGC100] { + background-color: #878700 +} + +div.highlight .-Color[class*=-C101] { + color: #87875F +} + +div.highlight .-Color[class*=-BGC101] { + background-color: #87875F +} + +div.highlight .-Color[class*=-C102] { + color: #878787 +} + +div.highlight .-Color[class*=-BGC102] { + background-color: #878787 +} + +div.highlight .-Color[class*=-C103] { + color: #8787AF +} + +div.highlight .-Color[class*=-BGC103] { + background-color: #8787AF +} + +div.highlight .-Color[class*=-C104] { + color: #8787D7 +} + +div.highlight .-Color[class*=-BGC104] { + background-color: #8787D7 +} + +div.highlight .-Color[class*=-C105] { + color: #8787FF +} + +div.highlight .-Color[class*=-BGC105] { + background-color: #8787FF +} + +div.highlight .-Color[class*=-C106] { + color: #87AF00 +} + +div.highlight .-Color[class*=-BGC106] { + background-color: #87AF00 +} + +div.highlight .-Color[class*=-C107] { + color: #87AF5F +} + +div.highlight .-Color[class*=-BGC107] { + background-color: #87AF5F +} + +div.highlight .-Color[class*=-C108] { + color: #87AF87 +} + +div.highlight .-Color[class*=-BGC108] { + background-color: #87AF87 +} + +div.highlight .-Color[class*=-C109] { + color: #87AFAF +} + +div.highlight .-Color[class*=-BGC109] { + background-color: #87AFAF +} + +div.highlight .-Color[class*=-C110] { + color: #87AFD7 +} + +div.highlight .-Color[class*=-BGC110] { + background-color: #87AFD7 +} + +div.highlight .-Color[class*=-C111] { + color: #87AFFF +} + +div.highlight .-Color[class*=-BGC111] { + background-color: #87AFFF +} + +div.highlight .-Color[class*=-C112] { + color: #87D700 +} + +div.highlight .-Color[class*=-BGC112] { + background-color: #87D700 +} + +div.highlight .-Color[class*=-C113] { + color: #87D75F +} + +div.highlight .-Color[class*=-BGC113] { + background-color: #87D75F +} + +div.highlight .-Color[class*=-C114] { + color: #87D787 +} + +div.highlight .-Color[class*=-BGC114] { + background-color: #87D787 +} + +div.highlight .-Color[class*=-C115] { + color: #87D7AF +} + +div.highlight .-Color[class*=-BGC115] { + background-color: #87D7AF +} + +div.highlight .-Color[class*=-C116] { + color: #87D7D7 +} + +div.highlight .-Color[class*=-BGC116] { + background-color: #87D7D7 +} + +div.highlight .-Color[class*=-C117] { + color: #87D7FF +} + +div.highlight .-Color[class*=-BGC117] { + background-color: #87D7FF +} + +div.highlight .-Color[class*=-C118] { + color: #87FF00 +} + +div.highlight .-Color[class*=-BGC118] { + background-color: #87FF00 +} + +div.highlight .-Color[class*=-C119] { + color: #87FF5F +} + +div.highlight .-Color[class*=-BGC119] { + background-color: #87FF5F +} + +div.highlight .-Color[class*=-C120] { + color: #87FF87 +} + +div.highlight .-Color[class*=-BGC120] { + background-color: #87FF87 +} + +div.highlight .-Color[class*=-C121] { + color: #87FFAF +} + +div.highlight .-Color[class*=-BGC121] { + background-color: #87FFAF +} + +div.highlight .-Color[class*=-C122] { + color: #87FFD7 +} + +div.highlight .-Color[class*=-BGC122] { + background-color: #87FFD7 +} + +div.highlight .-Color[class*=-C123] { + color: #87FFFF +} + +div.highlight .-Color[class*=-BGC123] { + background-color: #87FFFF +} + +div.highlight .-Color[class*=-C124] { + color: #AF0000 +} + +div.highlight .-Color[class*=-BGC124] { + background-color: #AF0000 +} + +div.highlight .-Color[class*=-C125] { + color: #AF005F +} + +div.highlight .-Color[class*=-BGC125] { + background-color: #AF005F +} + +div.highlight .-Color[class*=-C126] { + color: #AF0087 +} + +div.highlight .-Color[class*=-BGC126] { + background-color: #AF0087 +} + +div.highlight .-Color[class*=-C127] { + color: #AF00AF +} + +div.highlight .-Color[class*=-BGC127] { + background-color: #AF00AF +} + +div.highlight .-Color[class*=-C128] { + color: #AF00D7 +} + +div.highlight .-Color[class*=-BGC128] { + background-color: #AF00D7 +} + +div.highlight .-Color[class*=-C129] { + color: #AF00FF +} + +div.highlight .-Color[class*=-BGC129] { + background-color: #AF00FF +} + +div.highlight .-Color[class*=-C130] { + color: #AF5F00 +} + +div.highlight .-Color[class*=-BGC130] { + background-color: #AF5F00 +} + +div.highlight .-Color[class*=-C131] { + color: #AF5F5F +} + +div.highlight .-Color[class*=-BGC131] { + background-color: #AF5F5F +} + +div.highlight .-Color[class*=-C132] { + color: #AF5F87 +} + +div.highlight .-Color[class*=-BGC132] { + background-color: #AF5F87 +} + +div.highlight .-Color[class*=-C133] { + color: #AF5FAF +} + +div.highlight .-Color[class*=-BGC133] { + background-color: #AF5FAF +} + +div.highlight .-Color[class*=-C134] { + color: #AF5FD7 +} + +div.highlight .-Color[class*=-BGC134] { + background-color: #AF5FD7 +} + +div.highlight .-Color[class*=-C135] { + color: #AF5FFF +} + +div.highlight .-Color[class*=-BGC135] { + background-color: #AF5FFF +} + +div.highlight .-Color[class*=-C136] { + color: #AF8700 +} + +div.highlight .-Color[class*=-BGC136] { + background-color: #AF8700 +} + +div.highlight .-Color[class*=-C137] { + color: #AF875F +} + +div.highlight .-Color[class*=-BGC137] { + background-color: #AF875F +} + +div.highlight .-Color[class*=-C138] { + color: #AF8787 +} + +div.highlight .-Color[class*=-BGC138] { + background-color: #AF8787 +} + +div.highlight .-Color[class*=-C139] { + color: #AF87AF +} + +div.highlight .-Color[class*=-BGC139] { + background-color: #AF87AF +} + +div.highlight .-Color[class*=-C140] { + color: #AF87D7 +} + +div.highlight .-Color[class*=-BGC140] { + background-color: #AF87D7 +} + +div.highlight .-Color[class*=-C141] { + color: #AF87FF +} + +div.highlight .-Color[class*=-BGC141] { + background-color: #AF87FF +} + +div.highlight .-Color[class*=-C142] { + color: #AFAF00 +} + +div.highlight .-Color[class*=-BGC142] { + background-color: #AFAF00 +} + +div.highlight .-Color[class*=-C143] { + color: #AFAF5F +} + +div.highlight .-Color[class*=-BGC143] { + background-color: #AFAF5F +} + +div.highlight .-Color[class*=-C144] { + color: #AFAF87 +} + +div.highlight .-Color[class*=-BGC144] { + background-color: #AFAF87 +} + +div.highlight 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t)e.hide();const e=this._getDimension();this._element.classList.remove("collapse"),this._element.classList.add("collapsing"),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i="scroll"+(e[0].toUpperCase()+e.slice(1));this._queueCallback(()=>{this._isTransitioning=!1,this._element.classList.remove("collapsing"),this._element.classList.add("collapse","show"),this._element.style[e]="",fe.trigger(this._element,"shown.bs.collapse")},this._element,!0),this._element.style[e]=this._element[i]+"px"}hide(){if(this._isTransitioning||!this._isShown())return;if(fe.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=this._element.getBoundingClientRect()[t]+"px",Vt(this._element),this._element.classList.add("collapsing"),this._element.classList.remove("collapse","show");for(const t of this._triggerArray){const e=Mt(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0;this._element.style[t]="",this._queueCallback(()=>{this._isTransitioning=!1,this._element.classList.remove("collapsing"),this._element.classList.add("collapse"),fe.trigger(this._element,"hidden.bs.collapse")},this._element,!0)}_isShown(t=this._element){return t.classList.contains("show")}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ft(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren('[data-bs-toggle="collapse"]');for(const e of t){const t=Mt(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=Te.find(":scope .collapse .collapse",this._config.parent);return Te.find(t,this._config.parent).filter(t=>!e.includes(t))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=je.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}fe.on(document,"click.bs.collapse.data-api",'[data-bs-toggle="collapse"]',(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=jt(this),i=Te.find(e);for(const t of i)je.getOrCreateInstance(t,{toggle:!1}).toggle()})),Xt(je);const Me="ArrowUp",He="ArrowDown",We='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Fe=Qt()?"top-end":"top-start",Be=Qt()?"top-start":"top-end",ze=Qt()?"bottom-end":"bottom-start",qe=Qt()?"bottom-start":"bottom-end",Re=Qt()?"left-start":"right-start",Ve=Qt()?"right-start":"left-start",$e={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ke={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Qe extends we{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=Te.next(this._element,".dropdown-menu")[0]||Te.prev(this._element,".dropdown-menu")[0]||Te.findOne(".dropdown-menu",this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return $e}static get DefaultType(){return Ke}static get NAME(){return"dropdown"}toggle(){return this._isShown()?this.hide():this.show()}show(){if(zt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!fe.trigger(this._element,"show.bs.dropdown",t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add("show"),this._element.classList.add("show"),fe.trigger(this._element,"shown.bs.dropdown",t)}}hide(){if(zt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!fe.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._popper&&this._popper.destroy(),this._menu.classList.remove("show"),this._element.classList.remove("show"),this._element.setAttribute("aria-expanded","false"),ve.removeDataAttribute(this._menu,"popper"),fe.trigger(this._element,"hidden.bs.dropdown",t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Wt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError("dropdown".toUpperCase()+': Option "reference" provided type "object" without a required "getBoundingClientRect" method.');return t}_createPopper(){if(void 0===n)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Wt(this._config.reference)?t=Ft(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=It(t,this._menu,e)}_isShown(){return this._menu.classList.contains("show")}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Re;if(t.classList.contains("dropstart"))return Ve;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Be:Fe:e?qe:ze}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(ve.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=Te.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(t=>Bt(t));i.length&&Gt(i,e,t===He,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Qe.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=Te.find('[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled).show');for(const i of e){const e=Qe.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Me,He].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(We)?this:Te.prev(this,We)[0]||Te.next(this,We)[0]||Te.findOne(We,t.delegateTarget.parentNode),o=Qe.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}fe.on(document,"keydown.bs.dropdown.data-api",We,Qe.dataApiKeydownHandler),fe.on(document,"keydown.bs.dropdown.data-api",".dropdown-menu",Qe.dataApiKeydownHandler),fe.on(document,"click.bs.dropdown.data-api",Qe.clearMenus),fe.on(document,"keyup.bs.dropdown.data-api",Qe.clearMenus),fe.on(document,"click.bs.dropdown.data-api",We,(function(t){t.preventDefault(),Qe.getOrCreateInstance(this).toggle()})),Xt(Qe);class Xe{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"padding-right",e=>e+t),this._setElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","padding-right",e=>e+t),this._setElementAttributes(".sticky-top","margin-right",e=>e-t)}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"padding-right"),this._resetElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","padding-right"),this._resetElementAttributes(".sticky-top","margin-right")}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,i(Number.parseFloat(s))+"px")})}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&ve.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,t=>{const i=ve.getDataAttribute(t,e);null!==i?(ve.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)})}_applyManipulationCallback(t,e){if(Wt(t))e(t);else for(const i of Te.find(t,this._element))e(i)}}const Ye={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Ue={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ge extends ye{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Ye}static get DefaultType(){return Ue}static get NAME(){return"backdrop"}show(t){if(!this._config.isVisible)return void Yt(t);this._append();const e=this._getElement();this._config.isAnimated&&Vt(e),e.classList.add("show"),this._emulateAnimation(()=>{Yt(t)})}hide(t){this._config.isVisible?(this._getElement().classList.remove("show"),this._emulateAnimation(()=>{this.dispose(),Yt(t)})):Yt(t)}dispose(){this._isAppended&&(fe.off(this._element,"mousedown.bs.backdrop"),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ft(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),fe.on(t,"mousedown.bs.backdrop",()=>{Yt(this._config.clickCallback)}),this._isAppended=!0}_emulateAnimation(t){Ut(t,this._getElement(),this._config.isAnimated)}}const Je={autofocus:!0,trapElement:null},Ze={autofocus:"boolean",trapElement:"element"};class ti extends ye{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return Je}static get DefaultType(){return Ze}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),fe.off(document,".bs.focustrap"),fe.on(document,"focusin.bs.focustrap",t=>this._handleFocusin(t)),fe.on(document,"keydown.tab.bs.focustrap",t=>this._handleKeydown(t)),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,fe.off(document,".bs.focustrap"))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=Te.focusableChildren(e);0===i.length?e.focus():"backward"===this._lastTabNavDirection?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?"backward":"forward")}}const ei={backdrop:!0,focus:!0,keyboard:!0},ii={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class ni extends we{constructor(t,e){super(t,e),this._dialog=Te.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Xe,this._addEventListeners()}static get Default(){return ei}static get DefaultType(){return ii}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){if(this._isShown||this._isTransitioning)return;fe.trigger(this._element,"show.bs.modal",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add("modal-open"),this._adjustDialog(),this._backdrop.show(()=>this._showElement(t)))}hide(){if(!this._isShown||this._isTransitioning)return;fe.trigger(this._element,"hide.bs.modal").defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove("show"),this._queueCallback(()=>this._hideModal(),this._element,this._isAnimated()))}dispose(){for(const t of[window,this._dialog])fe.off(t,".bs.modal");this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ge({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new ti({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=Te.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),Vt(this._element),this._element.classList.add("show");this._queueCallback(()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,fe.trigger(this._element,"shown.bs.modal",{relatedTarget:t})},this._dialog,this._isAnimated())}_addEventListeners(){fe.on(this._element,"keydown.dismiss.bs.modal",t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()}),fe.on(window,"resize.bs.modal",()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()}),fe.on(this._element,"mousedown.dismiss.bs.modal",t=>{fe.one(this._element,"click.dismiss.bs.modal",e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())})})}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide(()=>{document.body.classList.remove("modal-open"),this._resetAdjustments(),this._scrollBar.reset(),fe.trigger(this._element,"hidden.bs.modal")})}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(fe.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains("modal-static")||(t||(this._element.style.overflowY="hidden"),this._element.classList.add("modal-static"),this._queueCallback(()=>{this._element.classList.remove("modal-static"),this._queueCallback(()=>{this._element.style.overflowY=e},this._dialog)},this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Qt()?"paddingLeft":"paddingRight";this._element.style[t]=e+"px"}if(!i&&t){const t=Qt()?"paddingRight":"paddingLeft";this._element.style[t]=e+"px"}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=ni.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}fe.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=Mt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),fe.one(e,"show.bs.modal",t=>{t.defaultPrevented||fe.one(e,"hidden.bs.modal",()=>{Bt(this)&&this.focus()})});const i=Te.findOne(".modal.show");i&&ni.getInstance(i).hide();ni.getOrCreateInstance(e).toggle(this)})),Ae(ni),Xt(ni);const si=".bs.offcanvas",oi=`load${si}.data-api`,ri=`click${si}.data-api`,ai={backdrop:!0,keyboard:!0,scroll:!1},li={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class ci extends we{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return ai}static get DefaultType(){return li}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){if(this._isShown)return;if(fe.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented)return;this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Xe).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add("showing");this._queueCallback(()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add("show"),this._element.classList.remove("showing"),fe.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})},this._element,!0)}hide(){if(!this._isShown)return;if(fe.trigger(this._element,"hide.bs.offcanvas").defaultPrevented)return;this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add("hiding"),this._backdrop.hide();this._queueCallback(()=>{this._element.classList.remove("show","hiding"),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Xe).reset(),fe.trigger(this._element,"hidden.bs.offcanvas")},this._element,!0)}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ge({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():fe.trigger(this._element,"hidePrevented.bs.offcanvas")}:null})}_initializeFocusTrap(){return new ti({trapElement:this._element})}_addEventListeners(){fe.on(this._element,"keydown.dismiss.bs.offcanvas",t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():fe.trigger(this._element,"hidePrevented.bs.offcanvas"))})}static jQueryInterface(t){return this.each((function(){const e=ci.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}fe.on(document,ri,'[data-bs-toggle="offcanvas"]',(function(t){const e=Mt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),zt(this))return;fe.one(e,"hidden.bs.offcanvas",()=>{Bt(this)&&this.focus()});const i=Te.findOne(".offcanvas.show");i&&i!==e&&ci.getInstance(i).hide();ci.getOrCreateInstance(e).toggle(this)})),fe.on(window,oi,()=>{for(const t of Te.find(".offcanvas.show"))ci.getOrCreateInstance(t).show()}),fe.on(window,"resize.bs.offcanvas",()=>{for(const t of Te.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&ci.getOrCreateInstance(t).hide()}),Ae(ci),Xt(ci);const ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),hi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,di=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,fi=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!ui.has(i)||Boolean(hi.test(t.nodeValue)||di.test(t.nodeValue)):e.filter(t=>t instanceof RegExp).some(t=>t.test(i))},pi={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]};const gi={allowList:pi,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},mi={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},_i={entry:"(string|element|function|null)",selector:"(string|element)"};class bi extends ye{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return gi}static get DefaultType(){return mi}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map(t=>this._resolvePossibleFunction(t)).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},_i)}_setContent(t,e,i){const n=Te.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Wt(e)?this._putElementInTemplate(Ft(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)fi(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const vi=new Set(["sanitize","allowList","sanitizeFn"]),yi={AUTO:"auto",TOP:"top",RIGHT:Qt()?"left":"right",BOTTOM:"bottom",LEFT:Qt()?"right":"left"},wi={allowList:pi,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},Ai={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class Ei extends we{constructor(t,e){if(void 0===n)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return wi}static get DefaultType(){return Ai}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),fe.off(this._element.closest(".modal"),"hide.bs.modal",this._hideModalHandler),this.tip&&this.tip.remove(),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=fe.trigger(this._element,this.constructor.eventName("show")),e=(qt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this.tip&&(this.tip.remove(),this.tip=null);const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),fe.trigger(this._element,this.constructor.eventName("inserted"))),this._popper?this._popper.update():this._popper=this._createPopper(i),i.classList.add("show"),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._queueCallback(()=>{fe.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1},this.tip,this._isAnimated())}hide(){if(!this._isShown())return;if(fe.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented)return;const t=this._getTipElement();if(t.classList.remove("show"),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1,this._isHovered=null;this._queueCallback(()=>{this._isWithActiveTrigger()||(this._isHovered||t.remove(),this._element.removeAttribute("aria-describedby"),fe.trigger(this._element,this.constructor.eventName("hidden")),this._disposePopper())},this.tip,this._isAnimated())}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove("fade","show"),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add("fade"),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new bi({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains("fade")}_isShown(){return this.tip&&this.tip.classList.contains("show")}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=yi[e.toUpperCase()];return It(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)fe.on(this._element,this.constructor.eventName("click"),this._config.selector,t=>{this._initializeOnDelegatedTarget(t).toggle()});else if("manual"!==e){const t="hover"===e?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i="hover"===e?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");fe.on(this._element,t,this._config.selector,t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?"focus":"hover"]=!0,e._enter()}),fe.on(this._element,i,this._config.selector,t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?"focus":"hover"]=e._element.contains(t.relatedTarget),e._leave()})}this._hideModalHandler=()=>{this._element&&this.hide()},fe.on(this._element.closest(".modal"),"hide.bs.modal",this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout(()=>{this._isHovered&&this.show()},this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout(()=>{this._isHovered||this.hide()},this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=ve.getDataAttributes(this._element);for(const t of Object.keys(e))vi.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ft(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=Ei.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Xt(Ei);const Ci={...Ei.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},Ti={...Ei.DefaultType,content:"(null|string|element|function)"};class Oi extends Ei{static get Default(){return Ci}static get DefaultType(){return Ti}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=Oi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Xt(Oi);const xi={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},ki={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Li extends we{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return xi}static get DefaultType(){return ki}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ft(t.target)||document.body,t.rootMargin=t.offset?t.offset+"px 0px -30%":t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map(t=>Number.parseFloat(t))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(fe.off(this._config.target,"click.bs.scrollspy"),fe.on(this._config.target,"click.bs.scrollspy","[href]",t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}}))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver(t=>this._observerCallback(t),t)}_observerCallback(t){const e=t=>this._targetLinks.get("#"+t.target.id),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=Te.find("[href]",this._config.target);for(const e of t){if(!e.hash||zt(e))continue;const t=Te.findOne(e.hash,this._element);Bt(t)&&(this._targetLinks.set(e.hash,e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add("active"),this._activateParents(t),fe.trigger(this._element,"activate.bs.scrollspy",{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))Te.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add("active");else for(const e of Te.parents(t,".nav, .list-group"))for(const t of Te.prev(e,".nav-link, .nav-item > .nav-link, .list-group-item"))t.classList.add("active")}_clearActiveClass(t){t.classList.remove("active");const e=Te.find("[href].active",t);for(const t of e)t.classList.remove("active")}static jQueryInterface(t){return this.each((function(){const e=Li.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(window,"load.bs.scrollspy.data-api",()=>{for(const t of Te.find('[data-bs-spy="scroll"]'))Li.getOrCreateInstance(t)}),Xt(Li);const Di="ArrowLeft",Si="ArrowRight",Ii="ArrowUp",Ni="ArrowDown",Pi='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',ji='.nav-link:not(.dropdown-toggle), .list-group-item:not(.dropdown-toggle), [role="tab"]:not(.dropdown-toggle), '+Pi;class Mi extends we{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),fe.on(this._element,"keydown.bs.tab",t=>this._keydown(t)))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?fe.trigger(e,"hide.bs.tab",{relatedTarget:t}):null;fe.trigger(t,"show.bs.tab",{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){if(!t)return;t.classList.add("active"),this._activate(Mt(t));this._queueCallback(()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),fe.trigger(t,"shown.bs.tab",{relatedTarget:e})):t.classList.add("show")},t,t.classList.contains("fade"))}_deactivate(t,e){if(!t)return;t.classList.remove("active"),t.blur(),this._deactivate(Mt(t));this._queueCallback(()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),fe.trigger(t,"hidden.bs.tab",{relatedTarget:e})):t.classList.remove("show")},t,t.classList.contains("fade"))}_keydown(t){if(![Di,Si,Ii,Ni].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=[Si,Ni].includes(t.key),i=Gt(this._getChildren().filter(t=>!zt(t)),t.target,e,!0);i&&(i.focus({preventScroll:!0}),Mi.getOrCreateInstance(i).show())}_getChildren(){return Te.find(ji,this._parent)}_getActiveElem(){return this._getChildren().find(t=>this._elemIsActive(t))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=Mt(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby","#"+t.id))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=Te.findOne(t,i);s&&s.classList.toggle(n,e)};n(".dropdown-toggle","active"),n(".dropdown-menu","show"),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains("active")}_getInnerElement(t){return t.matches(ji)?t:Te.findOne(ji,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Mi.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(document,"click.bs.tab",Pi,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),zt(this)||Mi.getOrCreateInstance(this).show()})),fe.on(window,"load.bs.tab",()=>{for(const t of Te.find('.active[data-bs-toggle="tab"], .active[data-bs-toggle="pill"], .active[data-bs-toggle="list"]'))Mi.getOrCreateInstance(t)}),Xt(Mi);const Hi={animation:"boolean",autohide:"boolean",delay:"number"},Wi={animation:!0,autohide:!0,delay:5e3};class Fi extends we{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return Wi}static get DefaultType(){return Hi}static get NAME(){return"toast"}show(){if(fe.trigger(this._element,"show.bs.toast").defaultPrevented)return;this._clearTimeout(),this._config.animation&&this._element.classList.add("fade");this._element.classList.remove("hide"),Vt(this._element),this._element.classList.add("show","showing"),this._queueCallback(()=>{this._element.classList.remove("showing"),fe.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()},this._element,this._config.animation)}hide(){if(!this.isShown())return;if(fe.trigger(this._element,"hide.bs.toast").defaultPrevented)return;this._element.classList.add("showing"),this._queueCallback(()=>{this._element.classList.add("hide"),this._element.classList.remove("showing","show"),fe.trigger(this._element,"hidden.bs.toast")},this._element,this._config.animation)}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove("show"),super.dispose()}isShown(){return this._element.classList.contains("show")}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout(()=>{this.hide()},this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){fe.on(this._element,"mouseover.bs.toast",t=>this._onInteraction(t,!0)),fe.on(this._element,"mouseout.bs.toast",t=>this._onInteraction(t,!1)),fe.on(this._element,"focusin.bs.toast",t=>this._onInteraction(t,!0)),fe.on(this._element,"focusout.bs.toast",t=>this._onInteraction(t,!1))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 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software and associated documentation files (the "Software"), to deal - * in the Software without restriction, including without limitation the rights - * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell - * copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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t=this;if(this._touchSupported){var e=function(e){t._pointerEvent&&w[e.originalEvent.pointerType.toUpperCase()]?t.touchStartX=e.originalEvent.clientX:t._pointerEvent||(t.touchStartX=e.originalEvent.touches[0].clientX)},n=function(e){t._pointerEvent&&w[e.originalEvent.pointerType.toUpperCase()]&&(t.touchDeltaX=e.originalEvent.clientX-t.touchStartX),t._handleSwipe(),"hover"===t._config.pause&&(t.pause(),t.touchTimeout&&clearTimeout(t.touchTimeout),t.touchTimeout=setTimeout((function(e){return t.cycle(e)}),500+t._config.interval))};o.default(this._element.querySelectorAll(".carousel-item img")).on("dragstart.bs.carousel",(function(t){return t.preventDefault()})),this._pointerEvent?(o.default(this._element).on("pointerdown.bs.carousel",(function(t){return e(t)})),o.default(this._element).on("pointerup.bs.carousel",(function(t){return n(t)})),this._element.classList.add("pointer-event")):(o.default(this._element).on("touchstart.bs.carousel",(function(t){return 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t=this;o.default(document).off("focusin.bs.modal").on("focusin.bs.modal",(function(e){document!==e.target&&t._element!==e.target&&0===o.default(t._element).has(e.target).length&&t._element.focus()}))},e._setEscapeEvent=function(){var t=this;this._isShown?o.default(this._element).on("keydown.dismiss.bs.modal",(function(e){t._config.keyboard&&27===e.which?(e.preventDefault(),t.hide()):t._config.keyboard||27!==e.which||t._triggerBackdropTransition()})):this._isShown||o.default(this._element).off("keydown.dismiss.bs.modal")},e._setResizeEvent=function(){var t=this;this._isShown?o.default(window).on("resize.bs.modal",(function(e){return t.handleUpdate(e)})):o.default(window).off("resize.bs.modal")},e._hideModal=function(){var t=this;this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._showBackdrop((function(){o.default(document.body).removeClass("modal-open"),t._resetAdjustments(),t._resetScrollbar(),o.default(t._element).trigger("hidden.bs.modal")}))},e._removeBackdrop=function(){this._backdrop&&(o.default(this._backdrop).remove(),this._backdrop=null)},e._showBackdrop=function(t){var e=this,n=o.default(this._element).hasClass("fade")?"fade":"";if(this._isShown&&this._config.backdrop){if(this._backdrop=document.createElement("div"),this._backdrop.className="modal-backdrop",n&&this._backdrop.classList.add(n),o.default(this._backdrop).appendTo(document.body),o.default(this._element).on("click.dismiss.bs.modal",(function(t){e._ignoreBackdropClick?e._ignoreBackdropClick=!1:t.target===t.currentTarget&&("static"===e._config.backdrop?e._triggerBackdropTransition():e.hide())})),n&&d.reflow(this._backdrop),o.default(this._backdrop).addClass("show"),!t)return;if(!n)return void t();var i=d.getTransitionDurationFromElement(this._backdrop);o.default(this._backdrop).one(d.TRANSITION_END,t).emulateTransitionEnd(i)}else if(!this._isShown&&this._backdrop){o.default(this._backdrop).removeClass("show");var r=function(){e._removeBackdrop(),t&&t()};if(o.default(this._element).hasClass("fade")){var 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0===r.default)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this.element=t,this.config=this._getConfig(e),this.tip=null,this._setListeners()}var e=t.prototype;return e.enable=function(){this._isEnabled=!0},e.disable=function(){this._isEnabled=!1},e.toggleEnabled=function(){this._isEnabled=!this._isEnabled},e.toggle=function(t){if(this._isEnabled)if(t){var e=this.constructor.DATA_KEY,n=o.default(t.currentTarget).data(e);n||(n=new this.constructor(t.currentTarget,this._getDelegateConfig()),o.default(t.currentTarget).data(e,n)),n._activeTrigger.click=!n._activeTrigger.click,n._isWithActiveTrigger()?n._enter(null,n):n._leave(null,n)}else{if(o.default(this.getTipElement()).hasClass("show"))return void 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a=this.getTipElement(),s=d.getUID(this.constructor.NAME);a.setAttribute("id",s),this.element.setAttribute("aria-describedby",s),this.setContent(),this.config.animation&&o.default(a).addClass("fade");var l="function"==typeof this.config.placement?this.config.placement.call(this,a,this.element):this.config.placement,u=this._getAttachment(l);this.addAttachmentClass(u);var f=this._getContainer();o.default(a).data(this.constructor.DATA_KEY,this),o.default.contains(this.element.ownerDocument.documentElement,this.tip)||o.default(a).appendTo(f),o.default(this.element).trigger(this.constructor.Event.INSERTED),this._popper=new r.default(this.element,a,this._getPopperConfig(u)),o.default(a).addClass("show"),o.default(a).addClass(this.config.customClass),"ontouchstart"in document.documentElement&&o.default(document.body).children().on("mouseover",null,o.default.noop);var c=function(){t.config.animation&&t._fixTransition();var 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t=this.element.getAttribute("data-original-title");return t||(t="function"==typeof this.config.title?this.config.title.call(this.element):this.config.title),t},e._getPopperConfig=function(t){var e=this;return l({},{placement:t,modifiers:{offset:this._getOffset(),flip:{behavior:this.config.fallbackPlacement},arrow:{element:".arrow"},preventOverflow:{boundariesElement:this.config.boundary}},onCreate:function(t){t.originalPlacement!==t.placement&&e._handlePopperPlacementChange(t)},onUpdate:function(t){return e._handlePopperPlacementChange(t)}},this.config.popperConfig)},e._getOffset=function(){var t=this,e={};return"function"==typeof this.config.offset?e.fn=function(e){return 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- // prevent stemmer from cutting word smaller than two chars - if(word.length < 3 && tmp[i].length >= 3) { - word = tmp[i]; - } var toAppend; // select the correct list if (word[0] == '-') { @@ -276,13 +272,16 @@ var Search = { setTimeout(function() { displayNextItem(); }, 5); - } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) { + } else if (DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY) { $.ajax({url: requestUrl, dataType: "text", complete: function(jqxhr, textstatus) { var data = jqxhr.responseText; if (data !== '' && data !== undefined) { - listItem.append(Search.makeSearchSummary(data, searchterms, hlterms)); + var summary = Search.makeSearchSummary(data, searchterms, hlterms); + if (summary) { + listItem.append(summary); + } } Search.output.append(listItem); setTimeout(function() { @@ -290,7 +289,7 @@ var Search = { }, 5); }}); } else { - // no source available, just display title + // just display title Search.output.append(listItem); setTimeout(function() { displayNextItem(); @@ -325,7 +324,9 @@ var Search = { var results = []; for (var prefix in objects) { - for (var name in objects[prefix]) { + for (var iMatch = 0; iMatch != objects[prefix].length; ++iMatch) { + var match = objects[prefix][iMatch]; + var name = match[4]; var fullname = (prefix ? prefix + '.' : '') + name; var fullnameLower = fullname.toLowerCase() if (fullnameLower.indexOf(object) > -1) { @@ -339,7 +340,6 @@ var Search = { } else if (parts[parts.length - 1].indexOf(object) > -1) { score += Scorer.objPartialMatch; } - var match = objects[prefix][name]; var objname = objnames[match[1]][2]; var title = titles[match[0]]; // If more than one term searched for, we require other words to be @@ -498,6 +498,9 @@ var Search = { */ makeSearchSummary : function(htmlText, keywords, hlwords) { var text = Search.htmlToText(htmlText); + if (text == "") { + return null; + } var textLower = text.toLowerCase(); var start = 0; $.each(keywords, function() { @@ -509,7 +512,7 @@ var Search = { var excerpt = ((start > 0) ? 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').text(excerpt); + var rv = $('

').text(excerpt); $.each(hlwords, function() { rv = rv.highlightText(this, 'highlighted'); }); diff --git a/docs/_static/styles/bootstrap.css b/docs/_static/styles/bootstrap.css new file mode 100644 index 0000000..b258fd9 --- /dev/null +++ b/docs/_static/styles/bootstrap.css @@ -0,0 +1,6 @@ +/*! + * Bootstrap v5.2.2 (https://getbootstrap.com/) + * Copyright 2011-2022 The Bootstrap Authors + * Copyright 2011-2022 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + 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--pst-color-paragraph: var(--pst-color-text-base); - --pst-color-link: 0, 91, 129; - --pst-color-link-hover: 227, 46, 0; - --pst-color-headerlink: 198, 15, 15; - --pst-color-headerlink-hover: 255, 255, 255; - --pst-color-preformatted-text: 34, 34, 34; - --pst-color-preformatted-background: 250, 250, 250; - --pst-color-inline-code: 232, 62, 140; - - --pst-color-active-navigation: 19, 6, 84; - --pst-color-navbar-link: 77, 77, 77; - --pst-color-navbar-link-hover: var(--pst-color-active-navigation); - --pst-color-navbar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-link: 77, 77, 77; - --pst-color-sidebar-link-hover: var(--pst-color-active-navigation); - --pst-color-sidebar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-expander-background-hover: 244, 244, 244; - --pst-color-sidebar-caption: 77, 77, 77; - --pst-color-toc-link: 119, 117, 122; - --pst-color-toc-link-hover: var(--pst-color-active-navigation); - --pst-color-toc-link-active: var(--pst-color-active-navigation); - - /***************************************************************************** - * Icon - **/ - - /* font awesome icons*/ - --pst-icon-check-circle: "\f058"; - --pst-icon-info-circle: "\f05a"; - --pst-icon-exclamation-triangle: "\f071"; - --pst-icon-exclamation-circle: "\f06a"; - --pst-icon-times-circle: "\f057"; - --pst-icon-lightbulb: "\f0eb"; - - /***************************************************************************** - * Admonitions - **/ - - --pst-color-admonition-default: var(--pst-color-info); - --pst-color-admonition-note: var(--pst-color-info); - --pst-color-admonition-attention: var(--pst-color-warning); - --pst-color-admonition-caution: var(--pst-color-warning); - --pst-color-admonition-warning: var(--pst-color-warning); - --pst-color-admonition-danger: var(--pst-color-danger); - --pst-color-admonition-error: var(--pst-color-danger); - --pst-color-admonition-hint: var(--pst-color-success); - --pst-color-admonition-tip: var(--pst-color-success); - --pst-color-admonition-important: var(--pst-color-success); - - --pst-icon-admonition-default: var(--pst-icon-info-circle); - --pst-icon-admonition-note: var(--pst-icon-info-circle); - --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle); - --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-error: var(--pst-icon-times-circle); - --pst-icon-admonition-hint: var(--pst-icon-lightbulb); - --pst-icon-admonition-tip: var(--pst-icon-lightbulb); - --pst-icon-admonition-important: var(--pst-icon-exclamation-circle); - - /***************************************************************************** - * versionmodified - **/ - - --pst-color-versionmodified-default: var(--pst-color-info); - --pst-color-versionmodified-added: var(--pst-color-success); - --pst-color-versionmodified-changed: var(--pst-color-warning); - --pst-color-versionmodified-deprecated: var(--pst-color-danger); - - --pst-icon-versionmodified-default: var(--pst-icon-exclamation-circle); - --pst-icon-versionmodified-added: var(--pst-icon-exclamation-circle); - --pst-icon-versionmodified-changed: var(--pst-icon-exclamation-circle); - --pst-icon-versionmodified-deprecated: var(--pst-icon-exclamation-circle); -} diff --git a/docs/_static/togglebutton.css b/docs/_static/togglebutton.css index b751c3d..54a6787 100644 --- a/docs/_static/togglebutton.css +++ b/docs/_static/togglebutton.css @@ -8,21 +8,39 @@ } /* Toggle buttons inside admonitions so we see the title */ -.toggle.admonition { +.admonition.toggle { position: relative; } /* Titles should cut off earlier to avoid overlapping w/ button */ -.toggle.admonition .admonition-title { +.admonition.toggle .admonition-title { padding-right: 25%; cursor: pointer; } +/* Hovering will cause a slight shift in color to make it feel interactive */ +.admonition.toggle .admonition-title:hover { + box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 1%); +} + +/* Hovering will cause a slight shift in color to make it feel interactive */ +.admonition.toggle .admonition-title:active { + box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 3%); +} + +/* Remove extra whitespace below the admonition title when hidden */ +.admonition.toggle-hidden { + padding-bottom: 0; +} + +.admonition.toggle-hidden .admonition-title { + margin-bottom: 0; +} + /* hides all the content of a page until de-toggled */ -.toggle-hidden.admonition .admonition-title ~ * { +.admonition.toggle-hidden .admonition-title ~ * { height: 0; margin: 0; - float: left; /* so they overlap when hidden */ opacity: 0; visibility: hidden; } @@ -37,14 +55,12 @@ button.toggle-button { border: none; outline: none; - /* Positioning just inside the amonition title */ - margin-right: 0.5em; + /* Positioning just inside the admonition title */ position: absolute; - right: 0em; - top: .5em; - float: right; + right: 0.5em; padding: 0px; - display: flex; + border: none; + outline: none; } /* Display the toggle hint on wide screens */ @@ -64,15 +80,15 @@ button.toggle-button { stroke: currentColor; /* So that we inherit the color of other text */ } -/* Rotate icon with admonitions so that it points down */ -.admonition.toggle .tb-icon { - transform: rotate(-90deg); +/* The icon should point right when closed, down when open. */ +/* Open */ +.admonition.toggle button .tb-icon { + transform: rotate(90deg); } -/* When the admonition is hidden, icon should flip upwards but retain rotation */ -/* We scaleX, in order to flip along Y, because it is rotated */ -.admonition.toggle .toggle-button-hidden .tb-icon { - transform: rotate(-90deg) scaleX(-1); +/* Closed */ +.admonition.toggle button.toggle-button-hidden .tb-icon { + transform: rotate(0deg); } /* With details toggles, we don't rotate the icon so it points right */ @@ -93,29 +109,29 @@ details.toggle-details { margin: 1em 0; } + details.toggle-details summary { display: flex; align-items: center; - width: fit-content; cursor: pointer; list-style: none; - border-radius: .4em; - border: 1px solid #ccc; - background: #f8f8f8; + border-radius: .2em; + border-left: 3px solid #1976d2; + background-color: rgb(204 204 204 / 10%); padding: 0.2em 0.7em 0.3em 0.5em; /* Less padding on left because the SVG has left margin */ - font-size: 0.8em; -} - -.toggle-details__summary-text { - margin-left: 0.2em; + font-size: 0.9em; } details.toggle-details summary:hover { - background: #f6f6f6; + background-color: rgb(204 204 204 / 20%); } details.toggle-details summary:active { - background: #eee; + background: rgb(204 204 204 / 28%); +} + +.toggle-details__summary-text { + margin-left: 0.2em; } details.toggle-details[open] summary { diff --git a/docs/_static/togglebutton.js b/docs/_static/togglebutton.js index 116a836..215a7ee 100644 --- a/docs/_static/togglebutton.js +++ b/docs/_static/togglebutton.js @@ -25,20 +25,24 @@ var initToggleItems = () => { } // This is the button that will be added to each item to trigger the toggle var collapseButton = ` - `; - item.insertAdjacentHTML("afterbegin", collapseButton); + title = item.querySelector(".admonition-title") + title.insertAdjacentHTML("beforeend", collapseButton); thisButton = document.getElementById(buttonID); // Add click handlers for the button + admonition title (if admonition) - thisButton.addEventListener('click', toggleClickHandler); admonitionTitle = document.querySelector(`#${toggleID} > .admonition-title`) if (admonitionTitle) { + // If an admonition, then make the whole title block clickable admonitionTitle.addEventListener('click', toggleClickHandler); admonitionTitle.dataset.target = toggleID admonitionTitle.dataset.button = buttonID + } else { + // If not an admonition then we'll listen for the button click + thisButton.addEventListener('click', toggleClickHandler); } // Now hide the item for this toggle button unless explicitly noted to show @@ -60,6 +64,7 @@ var initToggleItems = () => { // Now move the toggle-able content inside of the details block details = item.previousElementSibling details.appendChild(item) + item.classList.add("toggle-details__container") // Set up a click trigger to change the text as needed details.addEventListener('click', (click) => { @@ -73,9 +78,9 @@ var initToggleItems = () => { } // Update the inner text for the proper hint if (details.open) { - summary.querySelector("span").innerText = toggleHintShow; + summary.querySelector("span.toggle-details__summary-text").innerText = toggleHintShow; } else { - summary.querySelector("span").innerText = toggleHintHide; + summary.querySelector("span.toggle-details__summary-text").innerText = toggleHintHide; } }); @@ -102,12 +107,22 @@ var toggleHidden = (button) => { } var toggleClickHandler = (click) => { + // Be cause the admonition title is clickable and extends to the whole admonition + // We only look for a click event on this title to trigger the toggle. + if (click.target.classList.contains("admonition-title")) { - // If it's an admonition title, the button will be just before - button = click.target.previousElementSibling; + button = click.target.querySelector(".toggle-button"); + } else if (click.target.classList.contains("tb-icon")) { + // We've clicked the icon and need to search up one parent for the button + button = click.target.parentElement; + } else if (click.target.tagName == "polyline") { + // We've clicked the SVG elements inside the button, need to up 2 layers + button = click.target.parentElement.parentElement; + } else if (click.target.classList.contains("toggle-button")) { + // We've clicked the button itself and so don't need to do anything + button = click.target; } else { - // If not, we've clicked the button itself or its content, so search upwards - button = click.currentTarget; + console.log(`[togglebutton]: Couldn't find button for ${click.target}`) } target = document.getElementById(button.dataset['button']); toggleHidden(target); diff --git a/docs/_static/underscore.js b/docs/_static/underscore.js index 166240e..cf177d4 100644 --- a/docs/_static/underscore.js +++ b/docs/_static/underscore.js @@ -1,6 +1,6 @@ -!function(n,r){"object"==typeof exports&&"undefined"!=typeof module?module.exports=r():"function"==typeof define&&define.amd?define("underscore",r):(n=n||self,function(){var t=n._,e=n._=r();e.noConflict=function(){return n._=t,e}}())}(this,(function(){ -// Underscore.js 1.12.0 +!function(n,r){"object"==typeof exports&&"undefined"!=typeof module?module.exports=r():"function"==typeof define&&define.amd?define("underscore",r):(n="undefined"!=typeof globalThis?globalThis:n||self,function(){var t=n._,e=n._=r();e.noConflict=function(){return n._=t,e}}())}(this,(function(){ +// Underscore.js 1.13.1 // https://underscorejs.org -// (c) 2009-2020 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors +// (c) 2009-2021 Jeremy 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\ No newline at end of file diff --git a/docs/api/fairness.html b/docs/api/fairness.html index 75a7849..8f8e71a 100644 --- a/docs/api/fairness.html +++ b/docs/api/fairness.html @@ -1,48 +1,44 @@ + - + + + - - + Fairness API Reference - + + + + + - - + + + - - - - + + + + + - + - + - - - - - - - - - - - - - - - + + - + + @@ -50,27 +46,12 @@ - + - - - - - - - - - - - - - - - - + + - - - - - - + + - - - - - - - - -
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+By UC Berkeley Global Policy Lab

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\ No newline at end of file diff --git a/docs/api/featurizer.html b/docs/api/featurizer.html index c8edd01..89919f1 100644 --- a/docs/api/featurizer.html +++ b/docs/api/featurizer.html @@ -1,48 +1,44 @@ + - + + + - - + Featurizer API Reference - + + + + + - - + + + - - - - + + + + + - + - + - - - - - - - - - - - - - - - + + - + + @@ -50,27 +46,12 @@ - + - - - - - - - - - - - - - - - - + + - - - - - - + + - - - - - - - - -
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\ No newline at end of file diff --git a/docs/api/home_location.html b/docs/api/home_location.html index 2f20027..ca06cb8 100644 --- a/docs/api/home_location.html +++ b/docs/api/home_location.html @@ -1,48 +1,44 @@ + - + + + - - + Home Location API Reference - + + + + + - - + + + - - - - + + + + + - + - + - - - - - - - - - - - - - - - + + - + + @@ -50,27 +46,12 @@ - + - - - - - - - - - - - - - - - - + + - - - - - - + + - - - - - - - - -
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\ No newline at end of file diff --git a/docs/api/ml.html b/docs/api/ml.html index fe21dc8..2f34ff1 100644 --- a/docs/api/ml.html +++ b/docs/api/ml.html @@ -1,48 +1,44 @@ + - + + + - - + ML API Reference - + + + + + - - + + + - - - - + + + + + - + - - - - - - - - - - + - - - - - - + + - + + @@ -50,27 +46,12 @@ - + - - - - - - - - - - - - - - - - + + - - - - - - + + - - - - - - - - -
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\ No newline at end of file diff --git a/docs/api/poverty_survey.html b/docs/api/poverty_survey.html index e800b11..99ef2d1 100644 --- a/docs/api/poverty_survey.html +++ b/docs/api/poverty_survey.html @@ -1,48 +1,44 @@ + - + + + - - + Survey API Reference - + + + + + - - + + + - - - - + + + + + - + - - - - - - - - - - + - - - - - - + + - + + @@ -50,27 +46,12 @@ - + - - - - - - - - - - - - - - - - + + - - - - - - + + - - - - - - - - -
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\ No newline at end of file diff --git a/docs/api/satellite.html b/docs/api/satellite.html index 27807f6..8088189 100644 --- a/docs/api/satellite.html +++ b/docs/api/satellite.html @@ -1,48 +1,44 @@ + - + + + - - + Satellite API Reference - + + + + + - - + + + - - - - + + + + + - + - + - - - - - - - - - - - - - - - + + - + + @@ -50,27 +46,12 @@ - + - - - - - - - - - - - - - - - - + + - - - - - - + + - - - - - - - - -
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\ No newline at end of file diff --git a/docs/api/targeting.html b/docs/api/targeting.html index 7a758cb..5c8cc86 100644 --- a/docs/api/targeting.html +++ b/docs/api/targeting.html @@ -1,48 +1,44 @@ + - + + + - - + Targeting API Reference - + + + + + - - + + + - - - - + + + + + - + - + - - - - - - - - - - - - - - - + + - + + @@ -50,27 +46,12 @@ - + - - - - - - - - - - - - - - - - + + - - - - - - + + - - - - - - - - -
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\ No newline at end of file diff --git a/docs/data_formats/antennas.html b/docs/data_formats/antennas.html index 76535bc..8092a83 100644 --- a/docs/data_formats/antennas.html +++ b/docs/data_formats/antennas.html @@ -1,48 +1,44 @@ + - + + + - - + Antennas - + + + + + - - + + + - - - - + + + + + - + - - - - - - - - - - + - - - - - - + + - + + @@ -50,27 +46,12 @@ - + - - - - - - - - - - - - - - - - + + - - - - - - + + - - - - - - - - -
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+By UC Berkeley Global Policy Lab

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\ No newline at end of file diff --git a/docs/data_formats/mobile_data.html b/docs/data_formats/mobile_data.html index 0704576..17468ac 100644 --- a/docs/data_formats/mobile_data.html +++ b/docs/data_formats/mobile_data.html @@ -1,48 +1,44 @@ + - + + + - - + Mobile Data Usage - + + + + + - - + + + - - - - + + + + + - + - - - - - - - - - - + - - - - - - + + - + + @@ -50,27 +46,12 @@ - + - - - - - - - - - - - - - - - - + + - - - - - - + + - - - - - - - - -
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+ + \ No newline at end of file diff --git a/docs/docs/_build/jupyter_execute/quickstart/documentation_featurizer.html b/docs/docs/_build/jupyter_execute/quickstart/documentation_featurizer.html new file mode 100644 index 0000000..aec85c2 --- /dev/null +++ b/docs/docs/_build/jupyter_execute/quickstart/documentation_featurizer.html @@ -0,0 +1,12982 @@ + + + + + + + + + + + Featurizer + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+ + + + + +
+ + +
+ + + + + + +
+ + +
+ + + + + + + + + + + +
+ +
+ +
+
+ + + +
+

Featurizer

+ +
+
+ +
+
+
+ + + + +
+ +
+
+
import sys
+sys.path.insert(0, '../../cider/cider')
+
+
+
+
+
+

Featurizer#

+
+
+
from datastore import DataStore
+from featurizer import Featurizer
+
+
+
+
+

Load some mobile phone metadata. See standardized data formats for file schemas.

+
+
+
datastore = DataStore(cfg_dir='../../cider/configs/config.yml')
+featurizer = Featurizer(datastore=datastore, clean_folders=True)
+
+
+
+
+
WARNING: An illegal reflective access operation has occurred
+WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/Users/luciomelito/Library/Caches/pypoetry/virtualenvs/cider-n28xxEz6-py3.8/lib/python3.8/site-packages/pyspark/jars/spark-unsafe_2.12-3.1.2.jar) to constructor java.nio.DirectByteBuffer(long,int)
+WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform
+WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
+WARNING: All illegal access operations will be denied in a future release
+21/10/26 10:50:27 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
+Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
+Setting default log level to "WARN".
+To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
+
+
+
Loading CDR...
+
+
+
                                                                                
+
+
+
Loading recharges...
+SUCCESS!
+Loading mobile data...
+Loading mobile data...
+Loading antennas...
+Warning: 10 antennas missing location
+
+
+
+
+

Remove duplicate records, filter to just a specific date range, remove outlier days and spammers based on call and text volumes, and remove duplicate records in CDR, recharges, mobile data records, and mobile money records.

+
+
+
# Deduplication
+featurizer.ds.deduplicate()
+
+# Filter to just January 1 - February 28 (inclusive)
+featurizer.ds.filter_dates('2020-01-01', '2020-02-28')
+
+# Remove transactions involving spammers who place 1.8+ calls/texts per active day
+spammers = featurizer.ds.remove_spammers(spammer_threshold=1.8)
+
+
+
+
+
                                                                                
+
+
+
Number of spammers identified: 20
+
+
+
+
+
+
+
# Remove all records from days more than 2 standard deviations from the mean transaction volume
+outlier_days = featurizer.ds.filter_outlier_days(num_sds=2)
+
+
+
+
+
Outliers removed: 2020-01-24, 2020-02-06
+
+
+
+
+

Produce summary statistics and plots.

+
+
+
print(featurizer.diagnostic_statistics())
+
+
+
+
+
                                                                                
+
+
+
{'CDR': {'Days': 60, 'Transactions': 92719, 'Subscribers': 980, 'Recipients': 980}, 'Recharges': {'Days': 60, 'Transactions': 9452, 'Subscribers': 782}, 'Mobile Data': {'Days': 60, 'Transactions': 9548, 'Subscribers': 591}, 'Mobile Money': {'Days': 60, 'Transactions': 5469, 'Subscribers': 682, 'Recipients': 682}}
+
+
+
+
+
+
+
featurizer.diagnostic_plots()
+
+
+
+
+
                                                                                
+
+
+../../../../_images/0aee49bf0c71f98b4056f639b86b667a33229d7fae9c71776490dccf6a97d6aa.png +../../../../_images/50c77cf9cf067bd961eb966a8b96ba8d21e10fc688c5f2a94830a709bbc5f380.png +../../../../_images/a1b092510b6e921ecb2e2815ad6f8f9ecd8687c1af7da2c95ddedf8c4e295262.png +../../../../_images/06616dbd35de5ea55391dc66ce82b17e7deef672ffb5ebc0fae0f532b17eac28.png +../../../../_images/fee6da41ed4e1d9d52f8c17945bc0b758098e651d8253f159dbf93d5c3604cac.png +../../../../_images/17fc9e6cdddfa984bd8d376d91c1ed607b1a0e5093dc0f3ce1007c08657cc60e.png +../../../../_images/821194cec681cd2afb71468df082a1ad49ce442b8e2158e7a389a73175e6538c.png +../../../../_images/c82a41358bf969d033b73f81e953ea53bcf05a16a76734cd527e5db869bc04b8.png +
+
+

Featurize the data

+
+
+
featurizer.cdr_features()
+featurizer.international_features()
+featurizer.location_features()
+featurizer.recharges_features()
+featurizer.mobiledata_features()
+featurizer.mobilemoney_features()
+featurizer.all_features()
+
+
+
+
+
Calculating CDR features...
+
+
+
Warning: 9.78% of the records are missing a location.                      
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 01:58:28 to 2020-02-29 19:01:57
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.96% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 08:02:22 to 2020-02-29 23:54:24
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.20% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 13:39:38 to 2020-02-29 14:37:41
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.93% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 218 records from 2020-01-01 09:28:21 to 2020-02-29 20:23:01
+[x] 195 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 07:00:19 to 2020-02-29 09:40:21
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.20% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 03:21:11 to 2020-02-29 18:02:09
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 179 records from 2020-01-01 09:56:21 to 2020-02-29 23:57:46
+[x] 164 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.78% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 01:03:12 to 2020-02-29 02:43:26
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.55% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 00:52:24 to 2020-02-29 11:42:57
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 04:44:01 to 2020-02-29 23:06:35
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.49% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 212 records from 2020-01-02 03:40:27 to 2020-02-29 00:12:48
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.57% of the records are missing a location.         (0 + 1) / 1]
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 03:48:29 to 2020-02-29 05:50:46
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 01:28:44 to 2020-02-29 17:36:12
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.47% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 01:37:47 to 2020-02-29 17:18:33
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 01:46:52 to 2020-02-29 06:19:17
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.90% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 03:39:34 to 2020-02-29 00:30:27
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.35% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 04:22:25 to 2020-02-28 20:07:26
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.02% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 06:09:35 to 2020-02-29 22:25:05
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 14.08% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 12:25:22 to 2020-02-29 14:30:24
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.16% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 219 records from 2020-01-01 02:11:34 to 2020-02-29 23:04:17
+[x] 202 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.14% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 116 antenna(s) are missing a location.
+[x] 172 records from 2020-01-01 17:52:09 to 2020-02-28 13:14:49
+[x] 157 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 07:20:17 to 2020-02-29 14:03:57
+[x] 195 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.86% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 210 records from 2020-01-02 03:27:13 to 2020-02-29 20:49:05
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.29% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 149 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 09:47:41 to 2020-02-29 22:38:38
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 13:29:21 to 2020-02-29 18:03:55
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.95% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 02:15:58 to 2020-02-29 10:10:34
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 00:52:24 to 2020-02-29 00:14:59
+[x] 162 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.71% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 01:14:48 to 2020-02-29 13:26:22
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.71% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 06:34:07 to 2020-02-29 18:05:49
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.49% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 04:01:14 to 2020-02-29 12:07:56
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.02% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 01:00:20 to 2020-02-29 19:04:41
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.48% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 121 antenna(s) are missing a location.
+[x] 182 records from 2020-01-01 01:27:12 to 2020-02-29 16:13:13
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 18.81% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 218 records from 2020-01-01 10:05:32 to 2020-02-29 21:16:11
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 217 records from 2020-01-01 11:21:07 to 2020-02-29 23:39:12
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.28% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 07:00:54 to 2020-02-29 21:49:02
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.23% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 01:27:12 to 2020-02-29 16:31:12
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.67% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 118 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 02:34:52 to 2020-02-29 16:38:45
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 03:24:27 to 2020-02-29 21:54:37
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.35% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 219 records from 2020-01-01 11:00:45 to 2020-02-29 15:52:57
+[x] 195 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 15.77% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 222 records from 2020-01-01 03:09:53 to 2020-02-29 12:21:43
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.95% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 01:58:28 to 2020-02-28 18:44:36
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.69% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 154 antenna(s) are missing a location.
+[x] 231 records from 2020-01-01 03:52:41 to 2020-02-29 22:46:51
+[x] 208 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.83% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 03:36:59 to 2020-02-29 15:03:57
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 04:27:28 to 2020-02-29 23:46:11
+[x] 165 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 12:30:33 to 2020-02-29 23:12:55
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.95% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 17:11:28 to 2020-02-29 13:29:33
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.80% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 01:35:55 to 2020-02-29 13:57:38
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.16% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 03:48:08 to 2020-02-29 19:37:22
+[x] 162 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 05:31:30 to 2020-02-29 11:42:47
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 17:18:35 to 2020-02-29 22:52:28
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.39% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 09:49:51 to 2020-02-29 23:43:54
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.65% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 12:14:48 to 2020-02-29 05:14:09
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.20% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 00:02:23 to 2020-02-29 04:30:38
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.45% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 01:38:12 to 2020-02-29 22:40:44
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.23% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 11:40:35 to 2020-02-29 14:50:49
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.08% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 08:05:52 to 2020-02-29 04:34:58
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 12.12% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 11:49:11 to 2020-02-29 09:49:15
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.77% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 07:16:45 to 2020-02-29 14:59:55
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.73% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 02:31:00 to 2020-02-29 17:04:31
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.84% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 02:53:30 to 2020-02-28 21:25:47
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.02% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 04:27:06 to 2020-02-29 12:14:27
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 20.28% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 05:28:33 to 2020-02-29 09:47:41
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 220 records from 2020-01-01 11:32:39 to 2020-02-29 21:26:59
+[x] 200 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 03:08:35 to 2020-02-29 10:00:54
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.19% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 00:55:46 to 2020-02-29 18:51:39
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.13% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 00:03:05 to 2020-02-29 21:34:29
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 02:49:27 to 2020-02-29 20:21:36
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.98% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 09:38:33 to 2020-02-29 19:32:37
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 12:48:26 to 2020-02-29 16:17:36
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 12:45:52 to 2020-02-29 23:23:05
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.23% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 07:28:59 to 2020-02-29 05:46:34
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 05:53:19 to 2020-02-29 21:26:59
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 01:57:13 to 2020-02-29 14:26:48
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 00:08:25 to 2020-02-29 22:09:30
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 04:07:51 to 2020-02-29 13:52:46
+[x] 163 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 05:52:03 to 2020-02-28 18:58:31
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 12:57:16 to 2020-02-29 21:20:09
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 07:13:05 to 2020-02-28 16:09:11
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 10.34% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 06:06:09 to 2020-02-29 07:10:32
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 120 antenna(s) are missing a location.
+[x] 176 records from 2020-01-01 00:31:13 to 2020-02-29 17:31:43
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 01:41:44 to 2020-02-29 16:58:15
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.28% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 00:15:52 to 2020-02-29 14:25:57
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.01% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 02:15:58 to 2020-02-29 17:03:05
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 19.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 04:27:26 to 2020-02-28 01:05:10
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.94% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 08:35:50 to 2020-02-29 08:56:49
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.16% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 00:51:01 to 2020-02-29 20:49:01
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 00:59:21 to 2020-02-29 15:45:38
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 00:41:38 to 2020-02-29 19:43:50
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.90% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 01:16:05 to 2020-02-29 21:00:11
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.35% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 05:49:59 to 2020-02-29 23:09:21
+[x] 163 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.32% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 06:06:09 to 2020-02-29 20:06:41
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.39% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 152 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 00:07:47 to 2020-02-29 15:59:29
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 07:59:44 to 2020-02-29 20:10:13
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 10:20:32 to 2020-02-29 11:48:07
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.42% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 10:51:08 to 2020-02-29 22:29:32
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 10.81% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 04:41:31 to 2020-02-29 13:01:07
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 00:51:18 to 2020-02-29 23:28:57
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.69% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 01:10:56 to 2020-02-29 17:14:13
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.85% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 06:45:54 to 2020-02-29 23:29:34
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.21% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 06:35:29 to 2020-02-28 20:10:44
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.92% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 00:21:35 to 2020-02-29 20:04:42
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 00:53:05 to 2020-02-29 18:09:25
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.85% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 01:00:20 to 2020-02-29 19:50:28
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.36% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 01:57:13 to 2020-02-29 16:14:39
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 20.93% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 03:22:54 to 2020-02-29 17:47:16
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.32% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 01:46:43 to 2020-02-29 14:15:00
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 18:18:39 to 2020-02-28 23:48:47
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.87% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 195 records from 2020-01-02 20:01:48 to 2020-02-29 22:22:26
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.88% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 09:24:24 to 2020-02-29 23:01:18
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.23% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 08:07:10 to 2020-02-29 15:07:03
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.02% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 01:57:59 to 2020-02-29 19:05:21
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.92% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 181 records from 2020-01-01 12:44:09 to 2020-02-29 16:35:23
+[x] 167 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.05% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 02:31:00 to 2020-02-29 22:55:09
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 09:23:16 to 2020-02-29 18:26:24
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 11:55:57 to 2020-02-29 16:58:22
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.86% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 01:45:45 to 2020-02-29 23:28:32
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 10.88% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 00:02:04 to 2020-02-29 21:57:07
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 189 records from 2020-01-03 00:38:24 to 2020-02-29 21:29:26
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 15:06:09 to 2020-02-29 23:04:36
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 18.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 03:12:37 to 2020-02-29 19:05:06
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.90% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 04:21:10 to 2020-02-28 22:13:13
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 01:21:42 to 2020-02-29 23:25:01
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.51% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 218 records from 2020-01-01 12:53:27 to 2020-02-29 13:48:37
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 14:48:27 to 2020-02-29 15:25:22
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.23% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 18:26:42 to 2020-02-29 21:32:05
+[x] 167 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.21% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 01:18:39 to 2020-02-29 20:23:01
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.23% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 06:39:48 to 2020-02-29 13:09:53
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.30% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 00:13:51 to 2020-02-29 23:53:09
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 00:24:08 to 2020-02-29 20:19:22
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 01:21:42 to 2020-02-29 20:57:52
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.72% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 153 antenna(s) are missing a location.
+[x] 218 records from 2020-01-01 03:29:54 to 2020-02-29 17:26:58
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.25% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 06:36:20 to 2020-02-29 09:36:01
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.69% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 10:13:06 to 2020-02-28 22:45:21
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.32% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 05:02:14 to 2020-02-29 19:40:07
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 10:44:03 to 2020-02-28 23:39:29
+[x] 167 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 17:49:47 to 2020-02-29 21:34:29
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 14.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 119 antenna(s) are missing a location.
+[x] 169 records from 2020-01-01 15:46:08 to 2020-02-29 21:44:00
+[x] 159 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.18% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 02:43:25 to 2020-02-29 22:47:34
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 223 records from 2020-01-01 00:13:51 to 2020-02-29 22:06:16
+[x] 202 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 119 antenna(s) are missing a location.
+[x] 182 records from 2020-01-01 03:52:41 to 2020-02-29 19:06:30
+[x] 163 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.20% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 06:11:54 to 2020-02-29 21:59:04
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.29% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 14:12:11 to 2020-02-28 21:31:59
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.70% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 04:52:06 to 2020-02-29 16:18:56
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.38% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 00:51:11 to 2020-02-29 09:44:58
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 03:24:30 to 2020-02-29 19:50:13
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 03:13:37 to 2020-02-29 01:17:34
+[x] 164 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.53% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 06:26:40 to 2020-02-29 16:59:51
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 11:33:43 to 2020-02-29 07:24:10
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 18.42% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 11:42:31 to 2020-02-29 12:10:25
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.16% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 03:12:37 to 2020-02-29 13:24:21
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.07% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 00:28:41 to 2020-02-29 22:35:18
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 03:35:24 to 2020-02-29 23:56:35
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 20.20% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 03:04:43 to 2020-02-29 23:10:29
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.62% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 07:31:08 to 2020-02-29 18:32:05
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 12.98% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 00:12:42 to 2020-02-29 18:26:47
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 156 antenna(s) are missing a location.
+[x] 222 records from 2020-01-01 03:47:54 to 2020-02-29 13:29:41
+[x] 200 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.47% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 04:57:30 to 2020-02-29 14:59:14
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 225 records from 2020-01-01 00:37:14 to 2020-02-29 13:01:07
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.96% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 02:14:43 to 2020-02-29 22:22:49
+[x] 200 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.38% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 05:02:37 to 2020-02-29 16:17:49
+[x] 166 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.73% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 00:17:53 to 2020-02-29 23:00:24
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.45% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 227 records from 2020-01-01 02:52:39 to 2020-02-29 20:29:29
+[x] 205 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.72% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 03:09:08 to 2020-02-29 21:49:26
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 07:16:35 to 2020-02-29 19:15:07
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.14% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 18:18:39 to 2020-02-29 11:54:35
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.96% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 00:30:00 to 2020-02-29 21:06:10
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 07:27:08 to 2020-02-29 22:51:25
+[x] 198 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.61% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 120 antenna(s) are missing a location.
+[x] 176 records from 2020-01-01 01:46:18 to 2020-02-29 17:06:31
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.61% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 222 records from 2020-01-01 11:18:53 to 2020-02-29 23:02:01
+[x] 200 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.05% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 181 records from 2020-01-01 05:21:34 to 2020-02-29 20:11:21
+[x] 164 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 01:59:49 to 2020-02-29 07:33:19
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 11.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 02:12:58 to 2020-02-29 17:33:19
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.77% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 03:36:59 to 2020-02-29 17:57:28
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 00:20:52 to 2020-02-29 14:55:36
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 00:20:30 to 2020-02-29 19:30:39
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.13% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 01:43:54 to 2020-02-29 18:21:43
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.78% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 00:51:11 to 2020-02-29 06:50:05
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 224 records from 2020-01-01 00:31:13 to 2020-02-29 05:14:29
+[x] 206 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 179 records from 2020-01-01 05:55:09 to 2020-02-29 21:07:33
+[x] 160 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.86% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 08:31:41 to 2020-02-29 20:50:31
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.32% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 06:12:59 to 2020-02-29 11:02:17
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.26% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 00:49:14 to 2020-02-29 15:22:57
+[x] 167 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 04:29:28 to 2020-02-29 14:23:41
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 15:25:43 to 2020-02-29 19:04:03
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 00:02:04 to 2020-02-29 23:04:36
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.78% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 00:20:52 to 2020-02-29 22:22:26
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 182 records from 2020-01-01 04:02:43 to 2020-02-29 21:42:46
+[x] 166 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.30% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 00:21:10 to 2020-02-29 19:43:47
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.71% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 09:38:10 to 2020-02-29 23:23:38
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 01:46:43 to 2020-02-29 23:19:35
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 179 records from 2020-01-01 05:59:43 to 2020-02-29 12:36:13
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.51% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 07:44:45 to 2020-02-29 21:59:22
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 153 antenna(s) are missing a location.
+[x] 230 records from 2020-01-01 00:41:38 to 2020-02-29 23:52:22
+[x] 209 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 9.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 01:42:54 to 2020-02-29 16:27:03
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 02:12:00 to 2020-02-29 20:31:44
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 13:56:11 to 2020-02-29 22:15:35
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.39% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 09:42:44 to 2020-02-29 13:01:05
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.82% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 03:27:45 to 2020-02-29 12:12:52
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 05:34:51 to 2020-02-29 07:53:20
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 06:13:03 to 2020-02-29 23:54:48
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 01:02:19 to 2020-02-29 16:18:46
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.70% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 09:28:04 to 2020-02-29 22:47:56
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.24% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 01:56:40 to 2020-02-29 11:13:46
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 02:14:38 to 2020-02-29 12:38:32
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.99% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 04:42:54 to 2020-02-29 17:13:08
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.85% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 11:25:09 to 2020-02-29 19:49:35
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.08% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 213 records from 2020-01-02 14:23:06 to 2020-02-29 18:15:07
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.47% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 228 records from 2020-01-01 01:56:55 to 2020-02-29 14:42:45
+[x] 206 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.39% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 03:43:48 to 2020-02-29 19:42:36
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.71% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 168 records from 2020-01-01 08:04:07 to 2020-02-29 17:16:20
+[x] 154 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 09:28:04 to 2020-02-29 15:36:00
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 10.73% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 177 records from 2020-01-01 14:34:53 to 2020-02-29 22:30:03
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 01:07:55 to 2020-02-29 18:43:30
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.24% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 03:52:41 to 2020-02-29 15:51:29
+[x] 163 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 04:01:48 to 2020-02-29 09:50:09
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.91% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 220 records from 2020-01-01 17:41:50 to 2020-02-29 10:31:31
+[x] 199 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.08% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 14:43:48 to 2020-02-28 21:08:36
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.20% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 15:12:23 to 2020-02-29 15:58:23
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.29% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 02:37:02 to 2020-02-29 15:45:51
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.08% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 01:56:53 to 2020-02-29 13:32:03
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.47% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 181 records from 2020-01-01 19:48:52 to 2020-02-29 19:04:41
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 20.10% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 04:54:05 to 2020-02-29 21:38:34
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.25% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 12:12:42 to 2020-02-29 14:49:18
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.75% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 21:14:06 to 2020-02-29 23:34:14
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.23% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 01:26:02 to 2020-02-29 03:42:11
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.92% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 13:41:47 to 2020-02-29 16:21:06
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 01:22:53 to 2020-02-29 20:00:32
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 02:25:29 to 2020-02-29 19:50:28
+[x] 167 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 04:20:55 to 2020-02-29 12:26:36
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.38% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 02:32:52 to 2020-02-29 18:30:02
+
+
+
[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.75% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 217 records from 2020-01-01 02:36:50 to 2020-02-29 22:45:42
+[x] 198 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 12:54:30 to 2020-02-29 21:20:20
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 13:45:25 to 2020-02-29 11:04:17
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 225 records from 2020-01-01 03:01:27 to 2020-02-28 22:30:35
+[x] 207 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 220 records from 2020-01-01 05:49:46 to 2020-02-29 20:45:44
+[x] 201 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.90% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 217 records from 2020-01-01 08:42:10 to 2020-02-29 19:35:12
+[x] 198 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.30% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 03:16:50 to 2020-02-28 22:17:05
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 00:19:47 to 2020-02-29 23:01:17
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 196 records from 2020-01-02 03:03:59 to 2020-02-29 11:41:10
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 20.30% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 13:35:55 to 2020-02-29 19:51:37
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 02:02:25 to 2020-02-29 23:48:33
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 03:49:28 to 2020-02-29 23:05:57
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.63% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 08:18:35 to 2020-02-29 23:01:06
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.60% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 217 records from 2020-01-01 04:15:45 to 2020-02-29 10:45:06
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 03:17:17 to 2020-02-29 21:02:26
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 11:58:03 to 2020-02-29 19:14:39
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.95% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 226 records from 2020-01-01 01:26:28 to 2020-02-29 22:50:13
+[x] 199 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 12.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 01:59:49 to 2020-02-29 19:36:19
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 18.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 227 records from 2020-01-01 06:09:09 to 2020-02-29 11:02:17
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 00:40:16 to 2020-02-29 23:25:01
+[x] 165 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 150 antenna(s) are missing a location.
+[x] 226 records from 2020-01-01 07:45:25 to 2020-02-29 22:42:21
+[x] 206 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 12:49:39 to 2020-02-29 23:01:06
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.92% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 14:15:48 to 2020-02-28 21:09:03
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.29% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 00:33:45 to 2020-02-29 11:30:34
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.99% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 09:05:13 to 2020-02-29 18:30:36
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.02% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 181 records from 2020-01-01 02:24:32 to 2020-02-29 22:15:35
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 00:37:55 to 2020-02-29 23:33:35
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.67% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 06:42:35 to 2020-02-29 22:10:09
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 00:54:21 to 2020-02-29 17:54:40
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 03:41:03 to 2020-02-29 20:36:35
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 04:52:45 to 2020-02-29 12:13:03
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.65% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 05:36:32 to 2020-02-29 16:30:01
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.05% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 09:46:32 to 2020-02-29 04:15:45
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 06:35:29 to 2020-02-29 21:19:51
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.18% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 10:19:48 to 2020-02-29 16:52:33
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 08:04:07 to 2020-02-29 12:49:56
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 02:34:48 to 2020-02-29 18:09:17
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.78% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 00:15:52 to 2020-02-29 19:30:39
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.62% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 03:10:31 to 2020-02-29 19:44:47
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 11.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 04:25:08 to 2020-02-29 07:57:40
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.75% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 04:54:43 to 2020-02-29 17:45:46
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.36% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 220 records from 2020-01-01 05:03:52 to 2020-02-29 19:04:03
+[x] 195 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 05:25:08 to 2020-02-29 19:32:19
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 03:09:51 to 2020-02-29 18:04:17
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.45% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 220 records from 2020-01-01 10:11:12 to 2020-02-29 20:06:56
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.83% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 01:14:59 to 2020-02-29 14:43:28
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.96% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 05:01:12 to 2020-02-29 17:05:28
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.45% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 223 records from 2020-01-01 03:04:21 to 2020-02-29 11:11:20
+[x] 203 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.86% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 175 records from 2020-01-01 07:10:08 to 2020-02-29 14:04:50
+[x] 163 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 7.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 155 antenna(s) are missing a location.
+[x] 217 records from 2020-01-01 07:45:28 to 2020-02-29 08:32:41
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 01:14:51 to 2020-02-29 18:05:49
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 18.18% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 01:14:28 to 2020-02-29 21:08:53
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.32% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 04:48:09 to 2020-02-29 20:50:18
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 07:29:29 to 2020-02-29 21:59:04
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 220 records from 2020-01-01 09:46:52 to 2020-02-29 21:08:53
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.51% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 21:47:59 to 2020-02-29 19:23:16
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 04:42:54 to 2020-02-29 14:54:39
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 14.42% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 00:52:38 to 2020-02-29 06:41:38
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.24% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 00:40:20 to 2020-02-29 23:56:26
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 02:15:22 to 2020-02-29 22:22:49
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 04:17:04 to 2020-02-28 13:27:45
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 11:27:06 to 2020-02-29 22:55:42
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 07:10:08 to 2020-02-29 10:04:48
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.67% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 228 records from 2020-01-01 02:42:07 to 2020-02-29 23:41:52
+[x] 208 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.73% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 03:24:51 to 2020-02-28 17:32:17
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 05:02:29 to 2020-02-29 15:43:55
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 18.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 00:40:20 to 2020-02-29 15:28:37
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 02:44:18 to 2020-02-29 22:25:05
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 01:23:45 to 2020-02-29 18:15:33
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.62% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 07:26:11 to 2020-02-29 12:41:38
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.41% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 09:55:38 to 2020-02-29 06:04:11
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 10:36:44 to 2020-02-29 23:40:39
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 00:53:36 to 2020-02-29 20:49:05
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.29% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 00:57:30 to 2020-02-29 08:28:26
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 9.13% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 03:50:23 to 2020-02-29 20:45:50
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.03% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 173 records from 2020-01-01 05:03:02 to 2020-02-29 18:25:09
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 09:05:42 to 2020-02-29 13:29:00
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.48% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 12:48:36 to 2020-02-29 23:11:35
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 220 records from 2020-01-02 07:06:24 to 2020-02-29 19:24:08
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 00:56:29 to 2020-02-29 14:34:24
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.96% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 09:46:40 to 2020-02-28 14:29:50
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 00:10:38 to 2020-02-29 19:49:36
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.32% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 14:37:54 to 2020-02-29 20:57:06
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.70% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 06:18:32 to 2020-02-29 23:54:48
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.21% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 223 records from 2020-01-02 01:32:34 to 2020-02-29 12:14:27
+[x] 204 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.96% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 201 records from 2020-01-02 05:43:59 to 2020-02-29 19:40:07
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 04:36:18 to 2020-02-29 20:44:35
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 16:55:19 to 2020-02-29 04:23:12
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 152 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 00:34:42 to 2020-02-29 19:53:25
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.38% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 00:39:49 to 2020-02-29 21:00:48
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 6.77% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 02:16:18 to 2020-02-28 23:20:55
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 13.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 218 records from 2020-01-01 09:20:18 to 2020-02-29 20:57:06
+[x] 202 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.80% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 22:25:36 to 2020-02-29 22:32:23
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.21% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 05:02:29 to 2020-02-29 19:45:26
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 7.32% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 16:13:46 to 2020-02-29 15:31:10
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 6.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 182 records from 2020-01-01 01:27:17 to 2020-02-29 13:24:23
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 04:16:42 to 2020-02-29 21:44:21
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 05:09:02 to 2020-02-29 22:18:23
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.86% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 175 records from 2020-01-01 02:19:17 to 2020-02-29 23:15:26
+[x] 159 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 11:04:47 to 2020-02-29 19:24:08
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 05:39:57 to 2020-02-29 20:44:35
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.29% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 221 records from 2020-01-01 10:16:59 to 2020-02-29 20:32:54
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.88% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 07:23:27 to 2020-02-29 16:44:37
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.85% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 12:20:05 to 2020-02-29 15:50:56
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.78% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 118 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 01:43:54 to 2020-02-29 19:09:12
+[x] 166 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.21% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 08:37:11 to 2020-02-29 14:26:32
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 00:17:50 to 2020-02-29 14:25:57
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 09:46:04 to 2020-02-29 14:00:38
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 199 records from 2020-01-02 03:01:33 to 2020-02-29 20:29:29
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.29% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 04:46:36 to 2020-02-29 17:36:42
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 04:54:05 to 2020-02-29 23:43:32
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 9.55% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 06:52:20 to 2020-02-29 23:13:39
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.31% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 04:55:46 to 2020-02-29 18:55:25
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 180 records from 2020-01-01 01:56:40 to 2020-02-29 19:32:00
+[x] 163 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.28% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 06:34:01 to 2020-02-29 15:00:03
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.84% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 182 records from 2020-01-01 13:21:57 to 2020-02-29 23:11:35
+[x] 162 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.90% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 06:49:38 to 2020-02-29 22:46:23
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 01:06:36 to 2020-02-29 11:51:02
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.30% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 07:25:46 to 2020-02-29 13:35:25
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 09:41:55 to 2020-02-29 23:14:42
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 10:08:09 to 2020-02-29 17:30:19
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.25% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 06:26:40 to 2020-02-29 20:43:27
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 04:27:03 to 2020-02-29 13:13:13
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.29% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 117 antenna(s) are missing a location.
+[x] 179 records from 2020-01-01 00:31:38 to 2020-02-29 23:03:09
+[x] 164 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.18% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 149 antenna(s) are missing a location.
+[x] 220 records from 2020-01-01 01:24:38 to 2020-02-29 23:27:18
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 07:40:13 to 2020-02-29 20:49:01
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 09:38:39 to 2020-02-29 20:47:56
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.67% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 221 records from 2020-01-01 11:11:12 to 2020-02-29 22:08:42
+[x] 206 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.83% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 02:49:38 to 2020-02-29 19:06:30
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.54% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 03:47:43 to 2020-02-29 21:54:56
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.94% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 00:10:34 to 2020-02-29 13:48:25
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.45% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 22:57:11 to 2020-02-29 21:01:52
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 13.93% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 02:08:57 to 2020-02-29 03:55:46
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.24% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 05:25:33 to 2020-02-29 23:01:17
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.69% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 01:24:38 to 2020-02-29 22:16:13
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.63% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 08:39:19 to 2020-02-29 05:28:25
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 18.72% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 08:45:53 to 2020-02-29 23:06:38
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.31% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 168 records from 2020-01-01 10:24:00 to 2020-02-29 21:20:09
+[x] 149 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 08:05:13 to 2020-02-29 18:32:05
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 02:51:12 to 2020-02-29 16:11:12
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.14% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 03:12:20 to 2020-02-29 19:14:39
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.82% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 220 records from 2020-01-01 00:21:35 to 2020-02-29 08:26:36
+[x] 198 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 00:52:27 to 2020-02-29 23:20:17
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.21% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 18:26:54 to 2020-02-29 22:30:56
+[x] 166 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.39% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 09:01:32 to 2020-02-29 07:31:49
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 181 records from 2020-01-01 04:35:42 to 2020-02-29 16:52:49
+[x] 166 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 09:47:54 to 2020-02-29 00:08:21
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.29% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 00:32:08 to 2020-02-29 08:37:32
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.28% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 216 records from 2020-01-02 20:21:35 to 2020-02-29 19:36:08
+[x] 201 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.14% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 05:51:44 to 2020-02-29 20:57:52
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.73% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 00:21:12 to 2020-02-29 21:49:47
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.45% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 220 records from 2020-01-01 00:49:14 to 2020-02-29 18:25:09
+[x] 199 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.98% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 02:44:18 to 2020-02-29 14:37:41
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 10.19% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 154 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 04:29:26 to 2020-02-29 21:20:52
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 03:18:24 to 2020-02-29 15:03:36
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.98% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 21:13:20 to 2020-02-29 16:14:22
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 00:50:07 to 2020-02-29 07:22:58
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 06:28:53 to 2020-02-28 21:11:22
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 218 records from 2020-01-01 04:27:28 to 2020-02-29 20:45:44
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 181 records from 2020-01-01 00:17:50 to 2020-02-29 18:05:58
+[x] 157 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.40% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 151 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 06:43:37 to 2020-02-29 03:16:14
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.86% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 177 records from 2020-01-01 02:29:29 to 2020-02-29 19:45:43
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 180 records from 2020-01-02 06:03:36 to 2020-02-29 23:08:55
+[x] 163 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 120 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 00:18:39 to 2020-02-29 19:03:46
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 229 records from 2020-01-01 07:17:58 to 2020-02-29 19:15:10
+[x] 207 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.18% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 220 records from 2020-01-01 12:31:51 to 2020-02-29 19:57:09
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.36% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 193 records from 2020-01-02 12:01:17 to 2020-02-29 14:53:58
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.72% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 218 records from 2020-01-01 02:00:03 to 2020-02-29 20:24:53
+[x] 201 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.14% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 02:00:13 to 2020-02-29 21:45:47
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 10:12:25 to 2020-02-29 19:51:55
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.93% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 02:30:00 to 2020-02-29 18:42:58
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.14% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 12:38:32 to 2020-02-29 17:54:48
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 15:35:32 to 2020-02-29 23:41:52
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.19% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 19:02:13 to 2020-02-29 22:44:21
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.05% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 156 antenna(s) are missing a location.
+[x] 224 records from 2020-01-01 00:04:26 to 2020-02-28 10:33:16
+[x] 209 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 13.40% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 01:20:28 to 2020-02-29 23:24:06
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 02:51:40 to 2020-02-29 23:13:31
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 229 records from 2020-01-01 01:05:50 to 2020-02-29 10:16:35
+[x] 199 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 180 records from 2020-01-01 14:01:49 to 2020-02-29 17:51:26
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.35% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 03:06:24 to 2020-02-29 20:19:04
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.14% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 217 records from 2020-01-01 02:33:51 to 2020-02-29 21:35:43
+[x] 201 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.21% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 00:40:02 to 2020-02-29 21:48:29
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.40% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 173 records from 2020-01-01 04:44:08 to 2020-02-29 15:03:36
+[x] 160 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 18:59:46 to 2020-02-29 22:22:27
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.35% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 10:30:02 to 2020-02-29 23:47:12
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.47% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 00:33:20 to 2020-02-29 14:30:58
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.94% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 02:00:07 to 2020-02-29 19:15:13
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.07% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 120 antenna(s) are missing a location.
+[x] 174 records from 2020-01-01 03:59:43 to 2020-02-29 16:40:15
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 121 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 04:41:31 to 2020-02-29 15:08:20
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 06:02:25 to 2020-02-29 22:05:17
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 02:14:07 to 2020-02-29 16:38:34
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.60% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 19:21:48 to 2020-02-28 23:49:23
+[x] 162 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 05:49:59 to 2020-02-29 21:20:52
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 13.61% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 08:53:18 to 2020-02-29 23:33:19
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.61% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 01:57:59 to 2020-02-29 16:08:32
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 05:51:44 to 2020-02-29 13:00:28
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.53% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 179 records from 2020-01-01 06:01:17 to 2020-02-29 23:08:59
+[x] 159 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 06:08:39 to 2020-02-29 20:00:39
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 10:47:14 to 2020-02-29 19:02:28
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.92% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 178 records from 2020-01-01 12:48:26 to 2020-02-29 15:33:28
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.98% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 05:16:39 to 2020-02-29 20:41:23
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 06:45:54 to 2020-02-29 05:42:10
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.31% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 221 records from 2020-01-01 09:31:02 to 2020-02-29 20:21:01
+[x] 199 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.52% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 112 antenna(s) are missing a location.
+[x] 165 records from 2020-01-01 00:45:00 to 2020-02-29 23:32:09
+[x] 156 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 09:30:34 to 2020-02-29 23:10:39
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 220 records from 2020-01-01 06:33:13 to 2020-02-29 11:48:12
+[x] 199 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 19:34:30 to 2020-02-29 11:41:10
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 06:23:42 to 2020-02-29 23:33:35
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 09:54:49 to 2020-02-29 19:32:19
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 11:12:29 to 2020-02-29 18:08:00
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 05:53:34 to 2020-02-29 21:31:12
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.39% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 08:52:46 to 2020-02-29 17:52:22
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 150 antenna(s) are missing a location.
+[x] 231 records from 2020-01-01 01:26:02 to 2020-02-29 17:51:26
+[x] 200 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 11.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 11:27:19 to 2020-02-29 10:10:39
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 119 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 15:14:24 to 2020-02-29 22:10:09
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.52% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 16:15:21 to 2020-02-29 17:25:59
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.54% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 00:43:36 to 2020-02-29 23:36:33
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.07% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 176 records from 2020-01-01 08:52:31 to 2020-02-29 23:58:06
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 18:59:46 to 2020-02-29 20:25:52
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.62% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 113 antenna(s) are missing a location.
+[x] 160 records from 2020-01-01 01:45:45 to 2020-02-29 13:53:41
+[x] 150 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 18.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 01:45:49 to 2020-02-29 21:53:05
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.02% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 09:05:05 to 2020-02-29 20:19:04
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.93% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 226 records from 2020-01-01 09:46:32 to 2020-02-29 23:14:42
+[x] 200 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.62% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 03:12:43 to 2020-02-29 00:59:07
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.48% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 06:04:12 to 2020-02-29 23:36:16
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.24% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 09:43:25 to 2020-02-29 15:30:34
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 224 records from 2020-01-01 08:34:57 to 2020-02-29 07:11:37
+[x] 199 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 01:42:54 to 2020-02-29 04:34:58
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 218 records from 2020-01-01 23:46:31 to 2020-02-29 10:01:50
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.69% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 197 records from 2020-01-02 17:12:47 to 2020-02-29 20:16:07
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.75% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+
+
+
[x] 197 records from 2020-01-01 01:34:53 to 2020-02-29 20:37:41
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.85% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 05:01:08 to 2020-02-29 05:49:03
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.69% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 154 antenna(s) are missing a location.
+[x] 231 records from 2020-01-01 00:06:20 to 2020-02-29 21:35:43
+[x] 200 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 01:20:16 to 2020-02-29 20:27:31
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 223 records from 2020-01-01 01:03:22 to 2020-02-29 21:09:59
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 05:27:10 to 2020-02-29 04:40:27
+[x] 198 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.85% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 179 records from 2020-01-01 23:41:34 to 2020-02-29 19:45:26
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 185 records from 2020-01-02 07:08:13 to 2020-02-29 10:54:39
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 10:57:27 to 2020-02-29 17:24:42
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.94% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 03:16:07 to 2020-02-29 23:38:58
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.83% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 00:28:12 to 2020-02-29 09:15:28
+[x] 166 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 219 records from 2020-01-01 09:41:18 to 2020-02-29 17:36:12
+[x] 203 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 18:05:39 to 2020-02-29 23:06:56
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 220 records from 2020-01-01 01:24:33 to 2020-02-29 18:30:53
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.84% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 02:15:22 to 2020-02-29 21:39:17
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.14% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 149 antenna(s) are missing a location.
+[x] 221 records from 2020-01-01 10:22:05 to 2020-02-29 12:29:06
+[x] 204 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 10:22:51 to 2020-02-29 21:49:47
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.14% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 149 antenna(s) are missing a location.
+[x] 217 records from 2020-01-01 07:24:58 to 2020-02-29 23:35:04
+[x] 195 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 13.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 18:37:00 to 2020-02-29 14:27:07
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.85% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 05:39:57 to 2020-02-29 20:58:24
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 17:47:09 to 2020-02-29 13:52:57
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 171 records from 2020-01-02 10:24:46 to 2020-02-28 07:19:30
+[x] 157 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 01:08:16 to 2020-02-29 20:59:44
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 00:14:05 to 2020-02-29 21:39:17
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.84% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 16:50:11 to 2020-02-29 20:50:18
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 182 records from 2020-01-02 06:06:57 to 2020-02-29 20:19:22
+[x] 166 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 01:41:44 to 2020-02-29 10:17:32
+[x] 165 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.70% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 04:48:09 to 2020-02-29 01:53:03
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.90% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 00:27:31 to 2020-02-29 16:04:33
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.95% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 04:54:43 to 2020-02-29 20:37:41
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.08% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 02:24:33 to 2020-02-29 21:07:00
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 221 records from 2020-01-01 13:24:25 to 2020-02-29 18:50:30
+[x] 200 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 06:31:59 to 2020-02-29 23:57:46
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.40% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 02:41:52 to 2020-02-29 20:24:53
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.70% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 03:16:07 to 2020-02-29 18:34:22
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.16% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 03:56:24 to 2020-02-29 17:47:07
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.90% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 04:38:47 to 2020-02-29 10:40:19
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 00:00:42 to 2020-02-29 14:02:32
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 14.87% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 07:00:54 to 2020-02-29 18:21:47
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.18% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 220 records from 2020-01-01 02:17:18 to 2020-02-29 17:40:22
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 06:05:11 to 2020-02-28 09:23:33
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 222 records from 2020-01-01 01:20:32 to 2020-02-29 10:05:07
+[x] 202 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.03% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 02:51:32 to 2020-02-29 18:06:58
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 12:12:31 to 2020-02-29 22:09:30
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.29% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 22:56:40 to 2020-02-29 12:12:16
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.63% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 205 records from 2020-01-02 07:33:58 to 2020-02-29 20:27:25
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 19:56:25 to 2020-02-29 17:44:09
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.51% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 17:11:14 to 2020-02-29 22:01:58
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.71% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 120 antenna(s) are missing a location.
+[x] 170 records from 2020-01-01 09:59:41 to 2020-02-29 03:00:13
+[x] 156 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.65% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 230 records from 2020-01-01 06:05:22 to 2020-02-29 23:55:45
+[x] 203 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 06:54:21 to 2020-02-29 19:17:36
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.94% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 01:27:17 to 2020-02-29 23:01:42
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.67% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 180 records from 2020-01-01 16:13:38 to 2020-02-29 11:28:03
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 17:33:30 to 2020-02-29 23:52:24
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.84% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 00:16:27 to 2020-02-29 15:49:36
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 17.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 01:14:51 to 2020-02-28 21:58:10
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 05:35:27 to 2020-02-29 20:21:01
+[x] 160 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.53% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 116 antenna(s) are missing a location.
+[x] 170 records from 2020-01-01 05:41:29 to 2020-02-29 15:17:29
+[x] 157 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.41% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 22:26:40 to 2020-02-29 23:16:32
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.90% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 06:07:02 to 2020-02-29 05:42:15
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.61% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 149 antenna(s) are missing a location.
+[x] 230 records from 2020-01-01 08:26:20 to 2020-02-29 20:00:32
+[x] 207 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 00:50:00 to 2020-02-29 23:29:34
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.07% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 119 antenna(s) are missing a location.
+[x] 174 records from 2020-01-01 04:59:13 to 2020-02-29 06:16:38
+[x] 156 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.49% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 03:47:30 to 2020-02-29 10:31:02
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.25% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 10:36:17 to 2020-02-29 23:56:35
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.36% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 217 records from 2020-01-01 20:18:51 to 2020-02-29 14:59:14
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 08:21:41 to 2020-02-29 21:42:46
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.02% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 03:13:37 to 2020-02-29 15:56:56
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 04:15:45 to 2020-02-29 15:46:34
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 13:31:48 to 2020-02-29 21:09:59
+[x] 195 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 12:56:57 to 2020-02-29 22:57:37
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 06:34:07 to 2020-02-29 22:05:17
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 08:53:09 to 2020-02-29 16:58:22
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 06:52:20 to 2020-02-29 10:45:57
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 14.01% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 05:15:10 to 2020-02-29 23:55:45
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.95% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 01:08:34 to 2020-02-29 22:46:23
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 01:50:19 to 2020-02-29 03:27:44
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.35% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 03:07:43 to 2020-02-29 16:06:30
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 04:58:55 to 2020-02-29 20:10:42
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.90% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 02:51:12 to 2020-02-29 20:59:55
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.77% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 00:18:39 to 2020-02-29 22:47:34
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.47% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 05:29:52 to 2020-02-29 23:23:38
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.40% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 115 antenna(s) are missing a location.
+[x] 173 records from 2020-01-01 09:09:00 to 2020-02-29 09:47:43
+[x] 158 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.08% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 00:56:29 to 2020-02-29 22:21:41
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.31% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 06:23:42 to 2020-02-29 21:01:09
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 00:45:00 to 2020-02-29 22:57:56
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.52% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 217 records from 2020-01-01 00:06:20 to 2020-02-29 14:39:58
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.02% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 16:53:19 to 2020-02-29 06:58:45
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 14:37:54 to 2020-02-29 17:07:43
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 161 antenna(s) are missing a location.
+[x] 233 records from 2020-01-02 07:43:31 to 2020-02-29 23:36:05
+[x] 212 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 00:40:16 to 2020-02-29 14:56:21
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.55% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 00:03:05 to 2020-02-28 19:35:04
+
+
+
[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 20:13:02 to 2020-02-29 22:00:49
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.95% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 12:20:59 to 2020-02-29 04:27:44
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.93% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 218 records from 2020-01-01 00:50:07 to 2020-02-29 23:03:55
+[x] 198 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.62% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 04:21:23 to 2020-02-29 22:48:07
+[x] 195 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.26% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 01:33:02 to 2020-02-29 20:32:45
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.95% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 02:00:13 to 2020-02-29 04:33:03
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.65% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 206 records from 2020-01-02 07:04:16 to 2020-02-29 21:24:36
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 00:18:19 to 2020-02-29 17:44:09
+[x] 200 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.36% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 00:24:08 to 2020-02-29 14:24:40
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.92% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 04:33:16 to 2020-02-29 18:38:00
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.54% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 05:40:48 to 2020-02-29 21:07:00
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 06:22:39 to 2020-02-29 23:52:56
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 14:57:36 to 2020-02-29 22:19:20
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.99% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 177 records from 2020-01-01 03:38:04 to 2020-02-29 21:03:12
+[x] 166 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 01:05:50 to 2020-02-29 15:03:55
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 02:30:00 to 2020-02-29 18:33:05
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.23% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 01:16:05 to 2020-02-29 23:26:11
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 09:26:54 to 2020-02-29 21:29:25
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 8.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 11:45:38 to 2020-02-29 12:27:46
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 02:44:47 to 2020-02-29 23:06:35
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 01:09:21 to 2020-02-29 15:30:34
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 05:37:57 to 2020-02-29 23:53:09
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.10% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 06:35:58 to 2020-02-29 16:57:20
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 01:07:55 to 2020-02-29 23:56:26
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.41% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 09:49:30 to 2020-02-29 22:30:03
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 06:46:08 to 2020-02-29 23:42:20
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.29% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 182 records from 2020-01-01 05:45:26 to 2020-02-29 07:52:16
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 16:41:39 to 2020-02-28 12:59:40
+[x] 165 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 18.91% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 08:37:16 to 2020-02-29 13:24:21
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 120 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 10:51:03 to 2020-02-29 20:59:55
+[x] 166 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.82% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 00:14:05 to 2020-02-29 23:39:12
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 00:36:16 to 2020-02-29 12:19:19
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.31% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 01:37:03 to 2020-02-29 23:33:19
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 13:33:02 to 2020-02-29 18:49:11
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.85% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 02:16:59 to 2020-02-29 04:31:28
+[x] 159 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 15.24% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 02:14:38 to 2020-02-29 12:30:51
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 18.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 08:19:42 to 2020-02-29 23:27:18
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.81% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 10:07:19 to 2020-02-29 18:30:19
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.48% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 01:20:16 to 2020-02-29 14:42:45
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.14% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 00:04:26 to 2020-02-29 22:18:23
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 118 antenna(s) are missing a location.
+[x] 176 records from 2020-01-01 04:23:42 to 2020-02-29 17:32:13
+[x] 157 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 18:36:37 to 2020-02-29 12:58:01
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.24% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 03:39:34 to 2020-02-29 21:03:48
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.18% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 02:18:33 to 2020-02-29 14:34:24
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 02:41:52 to 2020-02-29 17:02:43
+[x] 198 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.21% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 07:37:28 to 2020-02-29 18:50:30
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 11:35:07 to 2020-02-29 19:15:08
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.08% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 199 records from 2020-01-02 13:12:02 to 2020-02-28 15:35:26
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.92% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 00:55:46 to 2020-02-29 22:32:38
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.47% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 01:33:02 to 2020-02-28 21:01:34
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.83% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 05:51:58 to 2020-02-29 15:14:26
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 02:47:26 to 2020-02-29 20:21:55
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 151 antenna(s) are missing a location.
+[x] 219 records from 2020-01-01 11:39:31 to 2020-02-29 14:45:06
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.78% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 180 records from 2020-01-01 02:14:43 to 2020-02-29 16:50:28
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 213 records from 2020-01-02 07:00:11 to 2020-02-29 21:45:47
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 18.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 151 antenna(s) are missing a location.
+[x] 227 records from 2020-01-01 12:35:29 to 2020-02-29 23:11:27
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 16.75% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 04:54:00 to 2020-02-29 13:56:16
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.02% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 02:36:50 to 2020-02-29 11:55:12
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.47% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 05:34:27 to 2020-02-29 23:56:05
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 222 records from 2020-01-01 07:00:54 to 2020-02-29 22:32:41
+[x] 201 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 14:31:21 to 2020-02-29 20:32:54
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 175 records from 2020-01-01 01:24:55 to 2020-02-29 08:45:02
+[x] 165 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.63% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 21:04:29 to 2020-02-29 23:42:31
+[x] 198 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 119 antenna(s) are missing a location.
+[x] 172 records from 2020-01-01 09:29:13 to 2020-02-29 15:38:31
+[x] 158 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 14:07:39 to 2020-02-29 18:11:07
+[x] 167 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 09:47:54 to 2020-02-28 21:28:57
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.01% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 05:29:31 to 2020-02-29 12:31:33
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.73% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 09:18:08 to 2020-02-29 20:03:25
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.12% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 07:48:32 to 2020-02-29 14:54:04
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 182 records from 2020-01-01 07:18:19 to 2020-02-29 21:32:57
+[x] 162 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.16% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 224 records from 2020-01-01 00:50:00 to 2020-02-29 19:54:43
+[x] 207 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 02:26:06 to 2020-02-29 19:15:07
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 07:11:13 to 2020-02-29 23:52:24
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 01:38:12 to 2020-02-29 23:19:19
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 12.94% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 20:13:02 to 2020-02-29 18:32:03
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.39% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 218 records from 2020-01-01 06:06:14 to 2020-02-29 10:14:06
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 120 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 00:06:38 to 2020-02-29 06:05:47
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.93% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 01:12:11 to 2020-02-29 21:54:56
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 03:20:52 to 2020-02-29 18:20:39
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.18% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 121 antenna(s) are missing a location.
+[x] 173 records from 2020-01-01 00:57:30 to 2020-02-29 23:33:15
+[x] 166 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.47% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 19:40:10 to 2020-02-29 23:47:12
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 22:18:48 to 2020-02-29 11:39:04
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 00:28:41 to 2020-02-29 14:41:12
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 00:37:14 to 2020-02-29 16:48:02
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.71% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 02:31:43 to 2020-02-29 21:00:11
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 152 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 07:04:32 to 2020-02-29 17:13:08
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 112 antenna(s) are missing a location.
+[x] 180 records from 2020-01-01 04:21:23 to 2020-02-29 18:03:55
+[x] 167 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 233 records from 2020-01-01 05:36:32 to 2020-02-29 23:54:24
+[x] 207 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 17:38:47 to 2020-02-29 16:18:38
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.63% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 08:20:52 to 2020-02-29 22:57:24
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.82% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 01:14:59 to 2020-02-29 22:01:58
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.81% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 181 records from 2020-01-01 05:52:03 to 2020-02-29 22:31:40
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 05:09:02 to 2020-02-29 19:36:08
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 9.71% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 09:45:25 to 2020-02-29 21:46:54
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 13:56:46 to 2020-02-28 23:10:24
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.34% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 06:16:44 to 2020-02-29 23:04:17
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 05:34:15 to 2020-02-29 23:10:39
+[x] 165 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 06:13:45 to 2020-02-29 23:27:54
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 04:26:57 to 2020-02-29 07:22:35
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.97% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 179 records from 2020-01-01 06:35:29 to 2020-02-29 20:47:56
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.08% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 03:44:48 to 2020-02-29 23:24:58
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.38% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 06:19:01 to 2020-02-29 16:50:28
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 01:34:53 to 2020-02-29 23:19:19
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.32% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 08:02:05 to 2020-02-29 19:34:49
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.20% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 16:23:59 to 2020-02-29 14:52:10
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.02% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 01:22:35 to 2020-02-29 15:34:40
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 205 records from 2020-01-02 04:59:51 to 2020-02-29 23:58:06
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.83% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 00:21:10 to 2020-02-29 23:53:08
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.70% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 00:21:12 to 2020-02-29 15:21:54
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.08% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 04:23:42 to 2020-02-29 17:31:43
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.73% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 179 records from 2020-01-01 05:41:29 to 2020-02-29 20:58:25
+[x] 163 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 08:53:13 to 2020-02-29 14:54:56
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 16.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 18:05:39 to 2020-02-28 21:08:02
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.78% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 154 antenna(s) are missing a location.
+[x] 225 records from 2020-01-01 00:02:12 to 2020-02-29 05:44:32
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 04:19:43 to 2020-02-29 13:40:53
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 180 records from 2020-01-01 07:13:05 to 2020-02-29 23:16:32
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.81% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 08:00:59 to 2020-02-29 20:01:55
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 05:31:28 to 2020-02-29 17:32:38
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 06:42:05 to 2020-02-29 16:02:11
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 10:07:02 to 2020-02-29 20:06:41
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 01:24:55 to 2020-02-29 17:41:33
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 03:53:19 to 2020-02-29 23:09:21
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.82% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 07:27:08 to 2020-02-29 08:01:45
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.20% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 176 records from 2020-01-01 01:22:53 to 2020-02-29 23:10:31
+[x] 167 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.84% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 21:36:32 to 2020-02-29 17:42:16
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 06:55:52 to 2020-02-28 20:47:54
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 00:06:38 to 2020-02-29 23:06:56
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.69% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 03:02:40 to 2020-02-29 20:20:16
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.16% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 01:10:28 to 2020-02-29 17:24:42
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 01:08:16 to 2020-02-29 20:06:25
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 13.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 07:12:28 to 2020-02-29 21:22:08
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.63% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 00:18:19 to 2020-02-29 17:27:34
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.84% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 11:15:34 to 2020-02-29 18:25:27
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.82% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 06:18:28 to 2020-02-29 22:16:13
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 15:46:54 to 2020-02-29 21:34:41
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.55% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 199 records from 2020-01-02 00:02:03 to 2020-02-29 22:22:27
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 04:44:01 to 2020-02-29 17:46:38
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.38% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 05:13:42 to 2020-02-29 23:56:05
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 05:21:34 to 2020-02-29 13:22:36
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.96% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 05:36:25 to 2020-02-28 19:53:35
+[x] 195 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.73% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 179 records from 2020-01-01 06:44:29 to 2020-02-29 11:06:23
+[x] 159 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 7.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 01:22:36 to 2020-02-29 16:14:55
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 08:31:59 to 2020-02-29 07:11:45
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.69% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 18:41:14 to 2020-02-29 21:51:24
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 08:42:04 to 2020-02-29 23:01:18
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 01:54:54 to 2020-02-29 18:05:58
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 05:31:30 to 2020-02-29 23:36:16
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 08:10:12 to 2020-02-29 20:36:59
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.81% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 08:12:12 to 2020-02-29 23:43:32
+[x] 165 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 13.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 07:08:19 to 2020-02-29 16:14:39
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.73% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 01:37:47 to 2020-02-29 21:20:20
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.80% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 09:45:45 to 2020-02-29 16:52:33
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.36% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 181 records from 2020-01-01 12:45:52 to 2020-02-29 12:54:21
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 12:38:32 to 2020-02-29 20:45:50
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.53% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 117 antenna(s) are missing a location.
+[x] 179 records from 2020-01-01 01:09:21 to 2020-02-29 07:02:25
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.86% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 00:27:31 to 2020-02-29 21:23:03
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 06:17:07 to 2020-02-29 20:06:56
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.95% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 01:00:12 to 2020-02-29 13:57:49
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 09:02:29 to 2020-02-29 17:26:12
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.51% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 02:43:25 to 2020-02-29 20:58:24
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 05:16:28 to 2020-02-29 16:01:06
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.19% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 07:04:50 to 2020-02-29 16:19:51
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.39% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 00:54:21 to 2020-02-29 18:00:54
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 17:52:32 to 2020-02-29 16:41:54
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 06:34:41 to 2020-02-28 14:03:29
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.98% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 06:44:34 to 2020-02-29 20:20:14
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 11.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 07:48:33 to 2020-02-29 23:08:55
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.85% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 08:45:29 to 2020-02-29 12:23:51
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.62% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 01:46:52 to 2020-02-29 00:51:05
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 02:51:53 to 2020-02-29 13:57:49
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 08:34:31 to 2020-02-29 23:26:11
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.20% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 21:17:50 to 2020-02-29 16:29:36
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.36% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 176 records from 2020-01-01 00:20:30 to 2020-02-29 13:15:15
+[x] 165 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.36% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 113 antenna(s) are missing a location.
+[x] 178 records from 2020-01-01 01:03:20 to 2020-02-29 20:58:25
+[x] 162 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.86% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 01:03:22 to 2020-02-29 10:44:06
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.35% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 00:59:21 to 2020-02-29 15:24:35
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.69% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 116 antenna(s) are missing a location.
+[x] 159 records from 2020-01-01 04:29:41 to 2020-02-29 02:14:42
+[x] 152 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 01:14:29 to 2020-02-29 10:17:05
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 119 antenna(s) are missing a location.
+[x] 171 records from 2020-01-01 06:54:58 to 2020-02-29 23:22:42
+[x] 158 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 20:24:41 to 2020-02-29 20:21:55
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.62% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 05:35:27 to 2020-02-29 14:00:44
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 17:36:44 to 2020-02-29 04:35:26
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.47% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 218 records from 2020-01-01 10:05:32 to 2020-02-29 17:01:27
+[x] 195 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 12.63% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 08:06:30 to 2020-02-29 15:35:15
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.12% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 04:07:51 to 2020-02-29 20:53:31
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.62% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 01:55:03 to 2020-02-29 20:10:42
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.31% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 00:40:02 to 2020-02-29 23:59:45
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 01:26:14 to 2020-02-29 14:30:58
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.85% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 02:15:49 to 2020-02-29 22:09:05
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.55% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 04:12:20 to 2020-02-29 14:06:49
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.45% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 227 records from 2020-01-01 05:39:57 to 2020-02-29 16:41:08
+[x] 201 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.82% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 02:03:01 to 2020-02-29 23:40:39
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 14:45:51 to 2020-02-29 21:07:33
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 10:14:44 to 2020-02-29 14:01:37
+[x] 165 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 00:30:40 to 2020-02-29 13:01:05
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 15:33:36 to 2020-02-29 13:29:03
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 06:42:05 to 2020-02-29 23:29:58
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 10:38:23 to 2020-02-29 22:29:46
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 06:29:23 to 2020-02-29 23:29:58
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.16% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 07:02:59 to 2020-02-29 15:52:57
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 119 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 12:06:02 to 2020-02-29 20:05:18
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 9.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 23:28:27 to 2020-02-29 19:57:00
+[x] 165 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 04:16:42 to 2020-02-29 21:53:05
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.10% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 08:32:12 to 2020-02-29 23:52:56
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.16% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 05:03:35 to 2020-02-29 20:37:30
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.93% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 218 records from 2020-01-01 15:26:57 to 2020-02-29 23:00:24
+[x] 200 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.54% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 00:52:27 to 2020-02-29 20:37:30
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.80% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 118 antenna(s) are missing a location.
+[x] 178 records from 2020-01-01 03:47:43 to 2020-02-29 06:41:38
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 05:58:42 to 2020-02-29 22:32:23
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.54% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 10:20:34 to 2020-02-29 18:48:06
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 116 antenna(s) are missing a location.
+[x] 178 records from 2020-01-01 13:35:55 to 2020-02-29 09:24:18
+[x] 160 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 19.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 06:07:43 to 2020-02-29 21:03:12
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.12% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 198 records from 2020-01-02 06:42:32 to 2020-02-29 02:17:22
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 00:52:38 to 2020-02-29 22:46:51
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.93% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 01:18:39 to 2020-02-29 22:40:44
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 02:33:51 to 2020-02-29 20:59:56
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.63% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 03:27:23 to 2020-02-29 16:51:39
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.67% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 180 records from 2020-01-01 07:00:19 to 2020-02-29 16:14:22
+[x] 164 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 10.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 04:22:21 to 2020-02-29 21:57:07
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 03:13:38 to 2020-02-29 09:21:04
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.94% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 03:02:40 to 2020-02-29 21:50:19
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 02:52:39 to 2020-02-29 20:03:25
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 07:23:16 to 2020-02-29 14:50:40
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 01:19:28 to 2020-02-29 21:40:42
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.61% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 118 antenna(s) are missing a location.
+[x] 173 records from 2020-01-01 13:07:52 to 2020-02-29 18:20:39
+[x] 159 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 200 records from 2020-01-02 06:01:47 to 2020-02-29 17:08:49
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.53% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 15:49:17 to 2020-02-29 20:56:26
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.12% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 115 antenna(s) are missing a location.
+[x] 170 records from 2020-01-01 01:45:49 to 2020-02-29 23:48:33
+[x] 156 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.98% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 07:03:05 to 2020-02-28 23:59:41
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 01:19:28 to 2020-02-29 21:46:54
+[x] 164 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 04:30:26 to 2020-02-29 23:10:31
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.60% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 04:31:39 to 2020-02-29 16:50:20
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.10% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 117 antenna(s) are missing a location.
+[x] 168 records from 2020-01-01 06:59:20 to 2020-02-29 12:03:03
+[x] 158 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 7.96% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 07:55:40 to 2020-02-29 17:32:38
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 00:36:16 to 2020-02-29 11:57:21
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.08% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 05:34:15 to 2020-02-29 23:02:01
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.63% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 07:55:27 to 2020-02-29 18:57:37
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.42% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 08:06:00 to 2020-02-29 13:00:28
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.66% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 183 records from 2020-01-02 03:45:50 to 2020-02-29 23:36:05
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.90% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 00:17:53 to 2020-02-29 21:49:02
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 12.20% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 00:10:35 to 2020-02-29 16:09:28
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.30% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 122 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 01:08:16 to 2020-02-28 17:14:45
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.13% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 02:36:24 to 2020-02-29 23:08:59
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 13:09:18 to 2020-02-29 23:46:11
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.91% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 165 records from 2020-01-01 00:10:38 to 2020-02-29 20:03:58
+[x] 150 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 7.32% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 00:53:43 to 2020-02-29 23:01:42
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.53% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 01:22:26 to 2020-02-29 14:56:34
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.90% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 10:28:44 to 2020-02-29 21:51:46
+[x] 166 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 07:04:50 to 2020-02-29 20:03:58
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 15:41:26 to 2020-02-29 22:55:19
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.77% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 119 antenna(s) are missing a location.
+[x] 176 records from 2020-01-01 01:24:33 to 2020-02-29 18:10:45
+[x] 158 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 120 antenna(s) are missing a location.
+[x] 182 records from 2020-01-01 04:07:26 to 2020-02-29 20:27:44
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.99% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 177 records from 2020-01-01 02:50:53 to 2020-02-29 20:37:29
+[x] 163 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.18% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 01:36:16 to 2020-02-29 11:52:36
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 19:09:23 to 2020-02-29 02:31:03
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 21:27:32 to 2020-02-29 12:46:53
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.98% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 00:12:42 to 2020-02-29 15:42:06
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.78% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 02:33:21 to 2020-02-29 12:11:47
+[x] 166 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.81% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 00:55:00 to 2020-02-29 17:17:37
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.85% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 09:01:32 to 2020-02-29 14:45:06
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 16:41:24 to 2020-02-29 19:50:13
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 15.23% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 06:19:51 to 2020-02-29 21:59:22
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.26% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 154 antenna(s) are missing a location.
+[x] 222 records from 2020-01-01 18:46:22 to 2020-02-29 18:47:48
+[x] 195 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.34% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 19:29:56 to 2020-02-29 07:11:32
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 114 antenna(s) are missing a location.
+[x] 171 records from 2020-01-01 00:51:01 to 2020-02-29 20:34:18
+[x] 153 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 07:19:32 to 2020-02-29 23:20:17
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 03:54:29 to 2020-02-29 22:45:42
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 02:29:54 to 2020-02-28 14:54:50
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.01% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 207 records from 2020-01-01 00:09:47 to 2020-02-29 23:28:32
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.80% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 10:55:58 to 2020-02-29 16:41:08
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.69% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 03:12:20 to 2020-02-29 20:21:36
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.49% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 19:06:01 to 2020-02-29 22:39:56
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.20% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 00:32:11 to 2020-02-29 19:33:39
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 06:42:06 to 2020-02-29 23:19:35
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.40% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 06:55:52 to 2020-02-29 17:32:11
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.07% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 219 records from 2020-01-01 04:12:20 to 2020-02-28 08:32:00
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.30% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 03:20:52 to 2020-02-29 18:48:06
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.38% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 05:03:23 to 2020-02-29 20:53:31
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.05% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 00:16:27 to 2020-02-29 11:18:44
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 14.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 04:48:23 to 2020-02-29 06:06:19
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.14% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 01:50:19 to 2020-02-29 10:19:08
+[x] 193 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 10:05:43 to 2020-02-29 18:19:47
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.93% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 20:36:42 to 2020-02-29 14:04:50
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 152 antenna(s) are missing a location.
+[x] 217 records from 2020-01-01 12:26:49 to 2020-02-29 18:42:32
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.86% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 01:02:19 to 2020-02-29 23:59:45
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.23% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 03:21:11 to 2020-02-29 20:26:19
+[x] 167 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 00:51:18 to 2020-02-29 10:15:29
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.95% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 02:18:33 to 2020-02-29 19:43:50
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.62% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 114 antenna(s) are missing a location.
+[x] 171 records from 2020-01-01 01:30:42 to 2020-02-29 19:32:00
+[x] 158 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.55% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 149 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 00:19:47 to 2020-02-29 14:36:51
+[x] 199 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.35% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 08:58:27 to 2020-02-29 20:36:13
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 149 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 01:20:32 to 2020-02-29 13:27:52
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 214 records from 2020-01-01 01:08:16 to 2020-02-29 17:51:50
+[x] 199 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 12:56:35 to 2020-02-29 20:14:36
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.14% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 175 records from 2020-01-01 01:08:34 to 2020-02-29 09:49:15
+[x] 156 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.81% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 222 records from 2020-01-01 12:17:24 to 2020-02-29 16:41:54
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 00:32:08 to 2020-02-29 23:19:01
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.42% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 219 records from 2020-01-01 03:41:08 to 2020-02-29 17:42:16
+[x] 195 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 7.91% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 05:47:05 to 2020-02-29 03:54:28
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 10:46:16 to 2020-02-29 23:42:20
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+
+
+
[x] 208 records from 2020-01-01 19:43:59 to 2020-02-29 20:39:23
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.38% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 10:36:44 to 2020-02-29 23:03:55
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.92% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 00:02:23 to 2020-02-29 18:40:40
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 18.13% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 117 antenna(s) are missing a location.
+[x] 171 records from 2020-01-01 00:31:38 to 2020-02-29 18:40:40
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 06:49:46 to 2020-02-29 16:38:34
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 04:58:32 to 2020-02-29 21:44:00
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 16:24:15 to 2020-02-29 11:13:37
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 02:49:38 to 2020-02-29 19:13:03
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.47% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 119 antenna(s) are missing a location.
+[x] 181 records from 2020-01-01 12:44:17 to 2020-02-29 23:15:26
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 02:53:51 to 2020-02-29 20:34:18
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 03:06:24 to 2020-02-29 21:49:11
+[x] 201 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.88% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 03:37:27 to 2020-02-29 20:20:01
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.63% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 05:13:02 to 2020-02-29 11:51:00
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 05:56:47 to 2020-02-29 11:53:32
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.41% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 179 records from 2020-01-01 03:27:23 to 2020-02-29 22:35:43
+[x] 164 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.20% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 02:12:00 to 2020-02-29 20:04:42
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 232 records from 2020-01-01 10:45:23 to 2020-02-29 20:28:04
+[x] 203 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.71% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 11:58:03 to 2020-02-29 20:20:14
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 10.84% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 150 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 13:54:39 to 2020-02-29 23:32:09
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 19:37:02 to 2020-02-29 22:42:36
+[x] 175 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 204 records from 2020-01-02 10:55:47 to 2020-02-28 23:55:44
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.35% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 02:24:32 to 2020-02-29 23:13:39
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.42% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 146 antenna(s) are missing a location.
+[x] 223 records from 2020-01-01 07:43:38 to 2020-02-29 19:13:03
+[x] 200 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.15% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 02:29:48 to 2020-02-29 19:06:26
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 08:40:38 to 2020-02-29 19:07:04
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 01:19:13 to 2020-02-29 15:42:13
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 00:33:20 to 2020-02-29 21:01:24
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 228 records from 2020-01-01 04:14:56 to 2020-02-29 20:29:21
+[x] 207 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 00:43:36 to 2020-02-29 03:44:43
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 01:01:37 to 2020-02-29 22:07:35
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.89% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 180 records from 2020-01-01 02:16:15 to 2020-02-29 22:57:56
+[x] 163 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.65% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 05:14:01 to 2020-02-29 08:37:33
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 09:38:51 to 2020-02-29 15:49:00
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 123 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 01:46:18 to 2020-02-29 20:25:28
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 02:38:57 to 2020-02-29 22:52:28
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.27% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 224 records from 2020-01-01 03:04:43 to 2020-02-29 18:30:53
+[x] 204 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.60% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 217 records from 2020-01-01 05:49:46 to 2020-02-29 06:23:10
+[x] 197 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 03:41:08 to 2020-02-29 17:41:33
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.55% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 01:26:14 to 2020-02-29 21:51:24
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 12.04% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 06:11:54 to 2020-02-28 11:05:18
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 03:12:25 to 2020-02-29 23:36:33
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.07% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 03:49:50 to 2020-02-29 19:51:37
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 225 records from 2020-01-01 05:03:23 to 2020-02-29 23:05:57
+[x] 202 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.78% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 04:25:08 to 2020-02-29 07:25:51
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 217 records from 2020-01-01 04:29:41 to 2020-02-29 22:31:40
+[x] 202 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.65% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 02:29:48 to 2020-02-29 15:38:31
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.69% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 177 records from 2020-01-01 01:36:16 to 2020-02-29 15:37:50
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.24% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 178 records from 2020-01-01 01:56:53 to 2020-02-28 19:55:47
+[x] 160 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 06:23:16 to 2020-02-29 20:32:45
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.75% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 02:16:15 to 2020-02-29 22:55:19
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.61% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 238 records from 2020-01-01 09:41:15 to 2020-02-29 13:56:16
+[x] 210 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.23% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 04:36:18 to 2020-02-29 21:29:25
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 198 records from 2020-01-02 04:30:15 to 2020-02-29 23:23:05
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.40% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 00:53:43 to 2020-02-29 19:23:16
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 197 records from 2020-01-01 01:37:27 to 2020-02-29 20:39:23
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.94% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 02:00:07 to 2020-02-29 04:28:07
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 10.99% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 03:35:24 to 2020-02-29 23:42:31
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 202 records from 2020-01-01 11:07:04 to 2020-02-29 17:06:58
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.92% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 03:24:51 to 2020-02-29 21:01:24
+[x] 187 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 6.84% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 00:08:25 to 2020-02-29 13:38:58
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 00:09:47 to 2020-02-29 17:06:58
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.92% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 00:53:43 to 2020-02-29 04:22:55
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 01:42:47 to 2020-02-29 22:00:49
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.10% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 01:19:13 to 2020-02-29 21:26:49
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.71% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 01:56:55 to 2020-02-29 16:52:49
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.79% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 149 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 03:24:27 to 2020-02-29 11:15:15
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.35% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 125 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 04:27:26 to 2020-02-29 21:06:10
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.07% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 06:00:53 to 2020-02-29 10:48:48
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 01:28:18 to 2020-02-29 22:48:07
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 01:55:03 to 2020-02-29 20:43:27
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 6.67% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 04:08:22 to 2020-02-29 06:56:48
+[x] 180 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 04:52:06 to 2020-02-29 11:08:37
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.97% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 05:29:08 to 2020-02-29 23:11:27
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.98% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 173 records from 2020-01-01 05:34:51 to 2020-02-29 11:46:16
+[x] 158 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 213 records from 2020-01-01 16:37:51 to 2020-02-29 17:33:19
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.40% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 15:23:09 to 2020-02-29 18:03:01
+[x] 192 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.02% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 01:35:55 to 2020-02-29 11:38:15
+[x] 178 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 15.90% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 17:37:55 to 2020-02-29 17:16:20
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.05% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 02:32:52 to 2020-02-29 21:09:16
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 151 antenna(s) are missing a location.
+[x] 215 records from 2020-01-01 04:19:47 to 2020-02-29 16:54:59
+[x] 198 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.11% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 180 records from 2020-01-01 03:16:50 to 2020-02-29 18:59:25
+[x] 165 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.67% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 186 records from 2020-01-01 18:12:20 to 2020-02-29 16:54:52
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.95% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 09:43:25 to 2020-02-29 19:03:46
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.62% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 212 records from 2020-01-01 06:46:08 to 2020-02-29 20:25:28
+[x] 191 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.84% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 112 antenna(s) are missing a location.
+[x] 159 records from 2020-01-01 07:15:23 to 2020-02-29 18:26:47
+[x] 148 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.24% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 16:10:46 to 2020-02-29 18:06:58
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 07:55:27 to 2020-02-29 23:34:24
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 196 records from 2020-01-01 17:45:32 to 2020-02-29 13:38:02
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.26% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 16:09:46 to 2020-02-29 22:35:18
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.37% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 10:26:44 to 2020-02-29 05:02:58
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.95% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 19:22:56 to 2020-02-29 10:44:32
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.46% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 114 antenna(s) are missing a location.
+[x] 164 records from 2020-01-01 03:51:53 to 2020-02-29 05:24:49
+[x] 151 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 188 records from 2020-01-02 09:37:16 to 2020-02-29 22:55:09
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.29% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 141 antenna(s) are missing a location.
+[x] 203 records from 2020-01-01 00:02:12 to 2020-02-29 17:02:43
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 02:19:58 to 2020-02-29 14:45:30
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 9.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 05:30:58 to 2020-02-29 19:08:48
+[x] 184 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.33% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 210 records from 2020-01-01 06:18:28 to 2020-02-29 22:13:58
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 124 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 11:39:19 to 2020-02-29 21:03:48
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.39% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 224 records from 2020-01-01 00:30:00 to 2020-02-29 15:10:29
+[x] 203 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.44% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 01:00:12 to 2020-02-29 17:32:11
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.30% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 128 antenna(s) are missing a location.
+[x] 188 records from 2020-01-01 05:01:12 to 2020-02-29 10:44:32
+[x] 167 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.10% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 12:56:16 to 2020-02-29 18:09:25
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.60% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 142 antenna(s) are missing a location.
+[x] 218 records from 2020-01-01 00:55:00 to 2020-02-28 18:41:15
+[x] 198 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.56% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 04:01:48 to 2020-02-29 07:38:55
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.45% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 147 antenna(s) are missing a location.
+[x] 223 records from 2020-01-01 00:53:36 to 2020-02-29 17:27:34
+[x] 196 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.58% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 199 records from 2020-01-01 08:21:41 to 2020-02-29 21:49:26
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 04:37:45 to 2020-02-29 23:53:08
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.74% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 06:22:55 to 2020-02-29 21:32:05
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.35% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 06:34:41 to 2020-02-29 23:43:54
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.65% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 134 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 04:01:14 to 2020-02-29 18:02:48
+[x] 194 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.09% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 191 records from 2020-01-01 03:59:43 to 2020-02-29 10:27:56
+[x] 173 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 05:13:02 to 2020-02-29 17:08:49
+[x] 167 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.92% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 139 antenna(s) are missing a location.
+[x] 195 records from 2020-01-01 00:07:47 to 2020-02-29 17:16:05
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Warning: 14.29% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 189 records from 2020-01-01 03:46:24 to 2020-02-29 23:06:38
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 182 records from 2020-01-01 02:11:34 to 2020-02-29 23:39:27
+[x] 164 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.40% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 149 antenna(s) are missing a location.
+[x] 209 records from 2020-01-01 02:14:07 to 2020-02-29 13:32:26
+[x] 186 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.17% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 143 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 00:32:11 to 2020-02-29 14:46:03
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 10.68% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 127 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 00:28:12 to 2020-02-29 23:39:27
+[x] 185 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 6.84% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 190 records from 2020-01-01 09:35:26 to 2020-02-29 18:51:11
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.95% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 03:04:43 to 2020-02-29 15:02:45
+[x] 176 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.85% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 132 antenna(s) are missing a location.
+[x] 192 records from 2020-01-01 09:05:42 to 2020-02-29 13:27:52
+[x] 171 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.91% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 120 antenna(s) are missing a location.
+[x] 176 records from 2020-01-02 00:29:41 to 2020-02-29 22:55:42
+[x] 160 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.92% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 194 records from 2020-01-01 00:10:35 to 2020-02-29 23:33:15
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 193 records from 2020-01-01 06:33:55 to 2020-02-29 20:31:44
+[x] 174 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 8.53% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 211 records from 2020-01-01 10:27:25 to 2020-02-29 15:28:39
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.21% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 126 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 15:00:23 to 2020-02-29 19:32:20
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 17.41% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 201 records from 2020-01-01 08:10:12 to 2020-02-29 15:34:52
+[x] 182 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.06% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 137 antenna(s) are missing a location.
+[x] 208 records from 2020-01-01 00:53:43 to 2020-02-29 21:32:57
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.64% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 198 records from 2020-01-01 02:42:07 to 2020-02-29 19:19:37
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.71% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 205 records from 2020-01-01 07:53:52 to 2020-02-29 10:47:46
+[x] 189 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.24% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 135 antenna(s) are missing a location.
+
+
+
[x] 196 records from 2020-01-01 05:25:33 to 2020-02-29 19:49:35
+[x] 172 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.76% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 133 antenna(s) are missing a location.
+[x] 185 records from 2020-01-01 06:53:47 to 2020-02-29 07:25:51
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.22% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 01:37:27 to 2020-02-29 15:30:39
+[x] 183 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.59% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 145 antenna(s) are missing a location.
+[x] 229 records from 2020-01-01 21:45:43 to 2020-02-29 22:47:56
+[x] 212 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 129 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 16:41:24 to 2020-02-29 16:33:04
+[x] 181 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.13% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 131 antenna(s) are missing a location.
+[x] 184 records from 2020-01-01 01:42:47 to 2020-02-29 12:51:23
+[x] 166 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.83% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 187 records from 2020-01-01 05:31:28 to 2020-02-29 10:27:08
+[x] 168 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 16.99% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 138 antenna(s) are missing a location.
+[x] 206 records from 2020-01-01 05:34:47 to 2020-02-29 07:23:10
+[x] 190 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 14.61% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 120 antenna(s) are missing a location.
+[x] 178 records from 2020-01-02 01:37:40 to 2020-02-29 16:54:52
+[x] 170 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.49% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 148 antenna(s) are missing a location.
+[x] 213 records from 2020-01-02 09:34:56 to 2020-02-29 22:32:41
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 9.78% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 149 antenna(s) are missing a location.
+[x] 225 records from 2020-01-01 00:37:55 to 2020-02-29 11:07:26
+[x] 201 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.43% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 115 antenna(s) are missing a location.
+[x] 175 records from 2020-01-01 00:00:42 to 2020-02-29 22:08:42
+[x] 161 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 12.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 130 antenna(s) are missing a location.
+[x] 183 records from 2020-01-01 00:10:34 to 2020-02-29 15:22:18
+[x] 169 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.18% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 144 antenna(s) are missing a location.
+[x] 224 records from 2020-01-01 02:47:26 to 2020-02-29 21:19:51
+[x] 206 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 11.57% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 216 records from 2020-01-01 06:57:41 to 2020-02-29 19:33:39
+[x] 195 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.24% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 204 records from 2020-01-01 08:37:16 to 2020-02-29 16:49:58
+[x] 179 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 13.50% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 136 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 11:01:00 to 2020-02-29 16:10:43
+[x] 188 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+Warning: 15.00% of the records are missing a location.
+         No antennas file was given and records are using antennas for position.
+Warning: 140 antenna(s) are missing a location.
+[x] 200 records from 2020-01-01 10:38:25 to 2020-02-29 15:30:39
+[x] 177 contacts
+[ ] No attribute stored
+[ ] No antenna stored
+[ ] No recharges
+[x] Has home
+[x] Has texts
+[x] Has calls
+[ ] No network
+
+
+
Successfully exported 1000 object(s) to ./outputs/featurizer//datasets/bandicoot_features/0to1000/0.csv
+
+                                                                                
+
+
+
Calculating international features...
+Calculating spatial features...
+
+
+
/Users/luciomelito/Library/Caches/pypoetry/virtualenvs/cider-n28xxEz6-py3.8/lib/python3.8/site-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6
+  in_crs_string = _prepare_from_proj_string(in_crs_string)
+../../cider/cider/featurizer.py:325: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.
+Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.
+
+Left CRS: +init=epsg:4326 +type=crs
+Right CRS: EPSG:4326
+
+  antennas = gpd.sjoin(antennas, shapefile, op='within', how='left').drop('index_right', axis=1)
+../../cider/cider/featurizer.py:325: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.
+Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.
+
+Left CRS: +init=epsg:4326 +type=crs
+Right CRS: EPSG:4326
+
+  antennas = gpd.sjoin(antennas, shapefile, op='within', how='left').drop('index_right', axis=1)
+../../cider/cider/featurizer.py:325: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.
+Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.
+
+Left CRS: +init=epsg:4326 +type=crs
+Right CRS: EPSG:4326
+
+  antennas = gpd.sjoin(antennas, shapefile, op='within', how='left').drop('index_right', axis=1)
+                                                                                
+
+
+
Calculating recharges features...
+
+
+
                                                                                
+
+
+
Calculating mobile data features...
+
+
+
                                                                                
+
+
+
Calculating mobile money features...
+
+
+
                                                                                
+
+
+
+
+
+
+
import pandas as pd
+pd.read_csv('./outputs/featurizer/datasets/features.csv').head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
namecdr_reporting__number_of_recordscdr_active_days__allweek__allday__callandtextcdr_active_days__allweek__day__callandtextcdr_active_days__allweek__night__callandtextcdr_active_days__weekday__allday__callandtextcdr_active_days__weekday__day__callandtextcdr_active_days__weekday__night__callandtextcdr_active_days__weekend__allday__callandtextcdr_active_days__weekend__day__callandtext...mobilemoney_outgoing_p2p_amount_minmobilemoney_outgoing_p2p_amount_maxmobilemoney_outgoing_p2p_balance_before_meanmobilemoney_outgoing_p2p_balance_before_minmobilemoney_outgoing_p2p_balance_before_maxmobilemoney_outgoing_p2p_balance_after_meanmobilemoney_outgoing_p2p_balance_after_minmobilemoney_outgoing_p2p_balance_after_maxmobilemoney_outgoing_p2p_txnsmobilemoney_outgoing_p2p_contacts
0dsBHAdXrrk1845846504132351714...26.36125266.175190198.212410144.32855270.12857148.94541078.153360218.635908.08.0
1JGPCbfDGes2095949504235341714...33.15437360.508873166.65822783.04488209.16385115.87390722.536007173.271296.06.0
2dYwshzRseD2055950524236371714...28.58065863.042980192.916470136.88690234.86200152.284147104.606220200.488624.04.0
3ygMEXUQDbn2185651464137341514...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4YtvkGlMWwe2125752454138311614...35.71593566.577260182.742442151.35474245.24483131.22498491.455580190.526926.06.0
+

5 rows × 1203 columns

+
+
+

Plot the distributions of some of the features.

+
+
+
featurizer.feature_plots()
+
+
+
+
+../../../../_images/e5bef637a52c1aeb6644bd920c3265588f12236866761700aa4d32ce42c0eb7e.png +../../../../_images/ebc104241d8359e56f33e774fa0fb125687edfa0e54b20c93de671cc9714ee92.png +../../../../_images/9eecd0c2c87d4dbf445c5750c8241750ac0809960a25110acf34061857b83390.png +
                                                                                
+
+
+../../../../_images/fd1020e8c7ba79231843cb87be036ebe2d3030df2a043f8417258a569ec54898.png +../../../../_images/8db61f89b1343a5d32d8362ed2a45baab1936f95e42e9e132ad251b610429a25.png +../../../../_images/a6b0f1552321ce34460e51587acbc6516f5f559bf375f893a9e37743ce48ddb0.png +../../../../_images/7b1aa812aba8406842407ca8dd111f6a40bd618357e935243963d1b8beade058.png +../../../../_images/07b3afcff2d2d5cb0ed1fb82e664aa044715c931d0ea672342fe9b294033b235.png +../../../../_images/5eb329ef54f691c8f797f1ae50548f2422336f5ee92269d9f02e99087dab390f.png +
+
+
+ + + + +
+ + + + +
+ +
+
+
+ +
+ + + + +
+
+
+
+ +
+ +

+By UC Berkeley Global Policy Lab +

+ +
+ +
+ +

+ + © Copyright 2022. +
+ +

+ +
+ +
+ +
+ +
+ +
+ +
+
+ + +
+
+
+ + + + + +
+
+ + \ No newline at end of file diff --git a/docs/docs/_build/jupyter_execute/quickstart/documentation_home_location.html b/docs/docs/_build/jupyter_execute/quickstart/documentation_home_location.html new file mode 100644 index 0000000..45499b1 --- /dev/null +++ b/docs/docs/_build/jupyter_execute/quickstart/documentation_home_location.html @@ -0,0 +1,738 @@ + + + + + + + + + + + Home Location Inference + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+ + + Ctrl+K +
+
+ + + +
+
+ +
+ + + +
+ + + + +
+ +
+
+ + +
+
+
+ + +
+
+ + + + +
+ +
+ + + +
+ +
+
+ +
+
+ +
+ +
+ +
+ + +
+ +
+ +
+ + + + + +
+ + +
+ + + + + + +
+ + +
+ + + + + + + + + + + +
+ +
+ +
+
+ + + +
+

Home Location Inference

+ +
+
+ +
+
+
+ + + + +
+ +
+

Home Location Inference#

+
+
+
from datastore import DataStore
+from home_location import HomeLocator
+
+
+
+
+

Load some CDR data, antennas data, and (optionally) spatial files, whose paths have been defined in config.yml. Define the geographic granularity for inference (options are ‘antenna_id’, ‘tower_id’ if a tower_id is provided in the antennas files, or any of the spatial files). Optionally load wealth prediction data to construct a poverty map and ground truth location data to evaluate accuracy.

+
+
+
datastore = DataStore(cfg_dir='./configs/config.yml')
+home_locator = HomeLocator(datastore=datastore, clean_folders=True)
+
+
+
+
+
Loading CDR...
+Loading antennas...
+Warning: 10 antennas missing location
+
+
+
+
+

Filter to a specific date range.

+
+
+
home_locator.ds.filter_dates('2020-01-05', '2020-02-01')
+
+
+
+
+

Try running the three different home location algorithms, specifying towers as the requested geographic level of aggregation.

+
+

Note

+

The three algorithms currently implemented are relatively simple; more sophisticate methods to infer users’ home locations do exist.

+
+
+
+
home_locator.get_home_locations(algo='count_transactions', geo='tower_id')
+home_locator.get_home_locations(algo='count_days', geo='tower_id')
+home_locator.get_home_locations(algo='count_modal_days', geo='tower_id')
+
+
+
+
+
                                                                                
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
subscriber_idtower_idcount_modal_days
0JGPCbfDGest354
1YtvkGlMWwet394
2dYwshzRseDt264
3dsBHAdXrrkt352
4ygMEXUQDbnt274
............
995amzyXHglBxt73
996dJSvXqUVSYt643
997ptudOnjdRUt355
998uXZrufHOmEt794
999zZkqaZFAtzt804
+

1000 rows × 3 columns

+
+
+

Generate a population map based on inferred home locations using the unique days algorithm. Use points at each of the tower locations.

+
+
+
home_locator.map(algo='count_days', geo='tower_id', kind='population', voronoi=False)
+
+
+
+
+../../../../_images/02106cd4eee328acfe7274cba260d23775534215a1a4a711469b1681d2cd52dd.png +
+
+

Evaluate the accuracy of the home location inference using the ground truth data. Generate maps and a table of precision and recall for each tower.

+
+
+
home_locator.accuracy(algo='count_days', geo='tower_id')
+home_locator.map(algo = 'count_days', geo='tower_id', kind='recall', voronoi=False)
+
+
+
+
+
Observations with inferred home location: 1000 (1000 unique)
+Observations with ground truth home location: 1000 (1000 unique)
+Observations with both: 1000 (1000 unique)
+Overall accuracy: 0.02
+
+
+../../../../_images/5e655a02438d82bffdd4eb22ddd78506d0337a4f72f28433f9786253679f741e.png +
+
+
+
+
home_locator.accuracy_tables[('tower_id', 'count_days')].head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
tower_idrecallprecisionoverall_accuracy
67t70.0769230.1666670.017
15t220.2500000.1428570.017
33t390.2142860.0909090.017
66t690.1000000.0555560.017
37t420.1538460.0555560.017
+
+
+

Use population density maps to identify which areas are over/under-represented in your sample.

+
+
+
home_locator.pop_comparison(algo='count_days', geo='tower_id')
+
+
+
+
+../../../../_images/59b6b042ec7270bd8a0d43d16bd5e8c586f70747ba4717fcfd4c00cdc9de1ee5.png +
+
+
+ + + + +
+ + + + +
+ +
+
+
+ +
+ + + + +
+
+
+
+ +
+ +

+By UC Berkeley Global Policy Lab +

+ +
+ +
+ +

+ + © Copyright 2022. +
+ +

+ +
+ +
+ +
+ +
+ +
+ +
+
+ + +
+
+
+ + + + + +
+
+ + \ No newline at end of file diff --git a/docs/docs/_build/jupyter_execute/quickstart/documentation_ml.html b/docs/docs/_build/jupyter_execute/quickstart/documentation_ml.html new file mode 100644 index 0000000..4afcf99 --- /dev/null +++ b/docs/docs/_build/jupyter_execute/quickstart/documentation_ml.html @@ -0,0 +1,765 @@ + + + + + + + + + + + Machine Learning + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+ + + Ctrl+K +
+
+ + + +
+
+ +
+ + + +
+ + + + +
+ +
+
+ + +
+
+
+ + +
+
+ + + + +
+ +
+ + + +
+ +
+
+ +
+
+ +
+ +
+ +
+ + +
+ +
+ +
+ + + + + +
+ + +
+ + + + + + +
+ + +
+ + + + + + + + + + + +
+ +
+ +
+
+ + + +
+

Machine Learning

+ +
+
+ +
+
+
+ + + + +
+ +
+

Machine Learning#

+
+
+
from datastore import DataStore
+from ml import Learner
+
+
+
+
+

Initialize data store object, then learner, automatically loading feature file produced by featurizer, along with file of data labels, and merging features to labels.

+
+
+
datastore = DataStore(cfg_dir='./configs/config.yml')
+learner = Learner(datastore=datastore, clean_folders=True)
+
+
+
+
+
Number of observations with features: 1000 (1000 unique)
+Number of observations with labels: 50 (50 unique)
+Number of matched observations: 50 (50 unique)
+
+
+
[Stage 42:===================>                                      (1 + 2) / 3]
+
+                                                                                
+
+
+
+
+

Experiment quickly with untuned models to get a sense of accuracy. Lasso, Ridge, random forest, and gradient boosting models are implemented natively, other models can be implemented by hand.

+
+
+
lasso_scores = learner.untuned_model('lasso')
+randomforest_scores = learner.untuned_model('randomforest')
+print('LASSO', lasso_scores)
+print('Random Forest', randomforest_scores)
+
+
+
+
+
LASSO {'train_r2': '1.00 (0.00)', 'test_r2': '-0.19 (0.48)', 'train_rmse': '6.65 (-3.02)', 'test_rmse': '15665.32 (-1859.46)'}
+Random Forest {'train_r2': '0.84 (0.01)', 'test_r2': '-0.02 (0.12)', 'train_rmse': '6200.40 (-319.50)', 'test_rmse': '14937.78 (-1482.62)'}
+
+
+
+
+

Fine-tune a gradient boosting model, tuning hyperparameters over cross validation, and produce predictions for all labeled observations out-of-sample over cross-validation. Also generate predictions for all subscribers in the feature dataset.

+
+
+
gradientboosting_scores = learner.tuned_model('gradientboosting')
+print('Gradient Boosting (Tuned)', gradientboosting_scores)
+learner.oos_predictions('gradientboosting', kind='tuned')
+learner.population_predictions('gradientboosting', kind='tuned')
+
+
+
+
+
[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
+Gradient Boosting (Tuned) {'train_r2': '0.80 (0.03)', 'test_r2': '0.07 (0.12)', 'train_rmse': '6808.46 (-409.84)', 'test_rmse': '14259.12 (-1920.92)'}
+[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
+[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
+[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
+[LightGBM] [Warning] min_data_in_leaf is set=10, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=10
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
namepredicted
0dsBHAdXrrk67249.768675
1JGPCbfDGes69289.940765
2dYwshzRseD85109.109600
3ygMEXUQDbn82018.137664
4YtvkGlMWwe76983.875546
.........
5amzyXHglBx83987.844414
6zZkqaZFAtz82984.849245
7uXZrufHOmE87731.514440
8dJSvXqUVSY76191.160752
9YosNCLWrFL85338.034626
+

1000 rows × 2 columns

+
+
+

Evaluate the model’s accuracy. Produce a scatterplot of true vs. predicted values with a LOESS fit and a bar plot of the most important features. Generate a table showing the targeting accuracy, precision, and recall of the predictions for nine hypothetical targeting scenarios (targeting between 10% and 90% of the population).

+
+
+
learner.scatter_plot('gradientboosting', kind='tuned')
+learner.feature_importances_plot('gradientboosting', kind='tuned')
+learner.targeting_table('gradientboosting', kind='tuned')
+
+
+
+
+../../../../_images/df2223b2c50f4cabb153cab22e195534134e74fac0d7536d96e50306912f623d.png +../../../../_images/55fc51d64fda5244690d51680fa82c57f361d15698ccf63013a77215f73f0e49.png +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Proportion of Population TargetedPearsonSpearmanAUCAccuracyPrecisionRecall
010%0.560.50.7393%65%65%
120%0.560.50.7381%53%53%
230%0.560.50.7371%52%52%
340%0.560.50.7367%59%59%
450%0.560.50.7365%65%65%
560%0.560.50.7369%74%74%
670%0.560.50.7379%85%85%
780%0.560.50.7375%84%84%
890%0.560.50.7384%91%91%
+
+
+
+ + + + +
+ + + + +
+ +
+
+
+ +
+ + + + +
+
+
+
+ +
+ +

+By UC Berkeley Global Policy Lab +

+ +
+ +
+ +

+ + © Copyright 2022. +
+ +

+ +
+ +
+ +
+ +
+ +
+ +
+
+ + +
+
+
+ + + + + +
+
+ + \ No newline at end of file diff --git a/docs/docs/_build/jupyter_execute/quickstart/documentation_satellite.html b/docs/docs/_build/jupyter_execute/quickstart/documentation_satellite.html new file mode 100644 index 0000000..55a717b --- /dev/null +++ b/docs/docs/_build/jupyter_execute/quickstart/documentation_satellite.html @@ -0,0 +1,606 @@ + + + + + + + + + + + Satellite + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Satellite

+ +
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+ +
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+
+ + + + +
+ +
+

Satellite#

+
+
+
from datastore import DataStore
+from satellite import Satellite
+
+
+
+
+

We first load some datasets - whose paths have been specified in the config file - using the datastore. The satellite module will by default try to load antenna/tower coordinates, shapefiles defining (administrative) boundaries, the Relative Wealth Index for the country of interest, and population density raster data.

+
+
+
datastore = DataStore(cfg_dir='../../cider/configs/config.yml')
+satellite = Satellite(datastore=datastore)
+
+
+
+
+
Loading antennas...
+Warning: 10 antennas missing location
+
+
+
+
+

The module can aggregate the RWI - which provides an asset index measure at a resolution of 2.4km x 2.4km - at one of the geographic levels provided by the antennas/towers, in which case their voronoi tessellation will be used - or one of the loaded shapefiles.

+In this case, we have loaded (synthetic) data for the country of Togo, and for aggregations at the tower level we will run:

+
+
+
satellite.aggregate_scores(dataset='rwi', geo='tower_id')
+
+
+
+
+../../../../_images/e8a15b5988fd76c6a079240aac56b6430a8e700954e40040ed2d2dde2fbc6fc2.png +
+
+

while for cantons, admin level 3 in Togo, we will run:

+
+
+
satellite.aggregate_scores(dataset='rwi', geo='cantons')
+
+
+
+
+
../../cider/satellite.py:152: RuntimeWarning: invalid value encountered in double_scalars
+  score = (score * pop).sum() / pop.sum()
+
+
+../../../../_images/79535c38022ede8c2a8cb72c550f861dd5d4ad8e97ffe96e96ac4027e197d67d.png +
+
+

The table is also saved as a geojson, storing the identifier of each region, its score - in this case its Relative Wealth Index - its population, and its geometry.

+
+
+
import geopandas as gpd
+gpd.read_file('../../cider/outputs/satellite/maps/cantons_rwi.geojson').head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
regionscorepopgeometry
01-0.28359716017.569922MULTIPOLYGON (((0.75228 6.83786, 0.75137 6.840...
12-0.4135654367.875125MULTIPOLYGON (((0.69026 6.80602, 0.69627 6.806...
23-0.3944289757.760028MULTIPOLYGON (((0.63102 6.74430, 0.63295 6.747...
34-0.4209522884.407900MULTIPOLYGON (((0.67259 6.85123, 0.67714 6.849...
45-0.1121902907.861156MULTIPOLYGON (((0.75269 6.84116, 0.75137 6.840...
+
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+ + \ No newline at end of file diff --git a/docs/docs/_build/jupyter_execute/quickstart/documentation_targeting.html b/docs/docs/_build/jupyter_execute/quickstart/documentation_targeting.html new file mode 100644 index 0000000..1436ab6 --- /dev/null +++ b/docs/docs/_build/jupyter_execute/quickstart/documentation_targeting.html @@ -0,0 +1,678 @@ + + + + + + + + + + + Targeting Evaluations + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Targeting Evaluations

+ +
+
+ +
+
+
+ + + + +
+ +
+

Targeting Evaluations#

+
+
+
from datastore import DataStore
+from targeting import Targeting
+
+
+
+
+

Set up the configuration file and load some targeting simulation data, using the datastore object.

+
+
+
datastore = DataStore(cfg_dir='./configs/config.yml')
+targeting = Targeting(datastore=datastore)
+
+
+
+
+

Create a table that compares targeting methods to a ground truth poverty measure. Three threshold-agnostic targeting metrics will be calculated: Pearson correlation (between ground truth and proxies), Spearman correlation, and a threshold-agnostic Area Under the Reciever Operating Characteristic Curve Score (AUC). Additionally, provide percentile targeting thresholds for ground truth and proxy to obtain four threshold-specific targeting metrics: accuracy, precision, recall, and threshold-specific AUC. As with all calculations in this module, the table can be calculated with or without sample weights.

+
+
+
targeting.targeting_table('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], 50, 20)
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Targeting MethodPearsonSpearmanAUC (Threshold-Agnostic)AccuracyPrecisionRecallAUC (Threshold-Specific)
0proxy10.8533700.8568800.8823190.7001.0000.4000.925204
1proxy20.8163230.8182750.8695010.6980.9950.3980.921336
2proxy30.7752430.7726710.8460710.6880.9700.3880.886284
3proxy40.7311960.7249840.8177990.6800.9500.3800.866024
+
+
+

Draw threshold-agnostic receiver operatoring characteristic (ROC) curves and precision-recall curves. Threshold-agnostic ROC curves are drawn by taking a grid matched targeting thresholds (that is, targeting K% of the population according to a proxy and the same K% of the population according to the ground-truth, for a krid of K between 0 and 100), and calculating the false positive rate and true positive rate of the targeting method for each point in the grid. Threshold-agnostic precision-reccall curves are calculated in the same way, so precision and recall are balanced by construction.

+
+
+
targeting.roc_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=None, weighted=False)
+targeting.precision_recall_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=None, weighted=False)
+
+
+
+
+../../../../_images/87598737ac4454d1a0f20533c22da6eb040b970020023848313229a5fb240db0.png +../../../../_images/ba1056d7bcb7c86625496fdf93c88cca61a3092f413a1691eb14db109a062932.png +
+
+

Draw threshold-specific ROC curves and precision-recall curves. Threshold-specific ROC curves are calculated by holding the share of the population targeted according to the ground-truth measure constant, and varying the share of the population targeted on the proxy measure to obtain false positive and true positive rate trade-offs.

+
+
+
targeting.roc_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=50, weighted=False)
+targeting.precision_recall_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], p=50, weighted=False)
+
+
+
+
+../../../../_images/228f12239cc16183e099afd9d3109989bd4a5ca97041dee75016d4ec69809166.png +../../../../_images/145650c79c5477ac5672a6a53833dc4778ed80d51b3459ce4a27ec779ce92657.png +
+
+

Provide a budget (provided as the level of benefits assigned to each individual UBI program, in the same units as the ground truth poverty measure) to draw social welfare curves for a set of targeting methods. Individual welfare is assumed to be a convex function of pre-program poverty and benefits assigned. Generate a table with the share of population targeted and corresponding transfer size that optimizes social welfare for each proxy poverty measure that could be used for targeting.

+
+
+
targeting.utility_curves('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], .01, weighted=False)
+targeting.utility_table('consumption', ['proxy1', 'proxy2', 'proxy3', 'proxy4'], .01, weighted=False)
+
+
+
+
+../../../../_images/df7204157563862d4ee46fe97536b4da8950340f54e6f7c5a2d0f5118e4bbd5d.png +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ProxyOptimal Share of Population TargetedMaximum UtilityOptimal Transfer Size
0proxy126.255102-1225.9544390.038168
1proxy29.081633-817.0806670.111111
2proxy338.377551-1606.0653290.026110
3proxy435.346939-1582.8120990.028329
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