diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index 0262a44..f3a20b2 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -1,55 +1,39 @@
-// For format details, see https://aka.ms/devcontainer.json. For config options, see the
-// README at: https://github.com/devcontainers/templates/tree/main/src/python
{
- "name": "Python 3",
- // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
- "image": "mcr.microsoft.com/devcontainers/python:1-3.12-bullseye",
- "features": {
- "ghcr.io/devcontainers/features/python:1": {},
- "ghcr.io/devcontainers-contrib/features/ruff:1": {},
- "ghcr.io/devcontainers-contrib/features/pre-commit:2": {}
- },
-
- // Use 'forwardPorts' to make a list of ports inside the container available locally.
- // "forwardPorts": [],
-
- // Use 'postCreateCommand' to run commands after the container is created.
- "postCreateCommand": "pip3 install -r requirements.txt && pip3 install -r requirements-dev.txt",
-
- // Configure tool-specific properties.
- // "customizations": {},
-
- // Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
- // "remoteUser": "root",
-
- "customizations": {
- "vscode": {
- "extensions": [
- "ms-toolsai.jupyter",
- "charliermarsh.ruff",
- "ms-python.pylint",
- "ms-python.vscode-pylance",
- "visualstudioexptteam.vscodeintellicode",
- "esbenp.prettier-vscode",
- "GitHub.vscode-pull-request-github"
- ],
- "settings": {
- "python.pythonPath": "/usr/local/bin/python",
- "python.testing.pytestArgs": ["--no-cov"],
- "editor.formatOnPaste": false,
- "editor.formatOnSave": true,
- "editor.formatOnType": true,
- "files.trimTrailingWhitespace": true,
- "terminal.integrated.profiles.linux": {
- "zsh": {
- "path": "/usr/bin/zsh"
- }
- },
- "terminal.integrated.defaultProfile.linux": "zsh",
- "[python]": {
- "editor.defaultFormatter": "charliermarsh.ruff"
- }
- }
- }
- }
+ "name": "Python 3",
+ "image": "mcr.microsoft.com/devcontainers/python:1.1.9-3.11-bookworm",
+ "features": {},
+ "postCreateCommand": "zsh -l .devcontainer/post-install.sh",
+ // "postStartCommand": "",
+ "remoteUser": "root",
+ "customizations": {
+ "vscode": {
+ "extensions": [
+ "ms-toolsai.jupyter",
+ "charliermarsh.ruff",
+ "ms-python.pylint",
+ "ms-python.vscode-pylance",
+ "visualstudioexptteam.vscodeintellicode",
+ "esbenp.prettier-vscode",
+ "GitHub.vscode-pull-request-github",
+ "mhutchie.git-graph"
+ ],
+ "settings": {
+ "python.pythonPath": "/usr/local/bin/python",
+ "python.testing.pytestArgs": ["--no-cov"],
+ "editor.formatOnPaste": false,
+ "editor.formatOnSave": true,
+ "editor.formatOnType": true,
+ "files.trimTrailingWhitespace": true,
+ "terminal.integrated.profiles.linux": {
+ "zsh": {
+ "path": "/usr/bin/zsh"
+ }
+ },
+ "terminal.integrated.defaultProfile.linux": "zsh",
+ "[python]": {
+ "editor.defaultFormatter": "charliermarsh.ruff"
+ }
+ }
+ }
+ }
}
diff --git a/.devcontainer/post-install.sh b/.devcontainer/post-install.sh
new file mode 100644
index 0000000..cd03f72
--- /dev/null
+++ b/.devcontainer/post-install.sh
@@ -0,0 +1,24 @@
+#!/bin/zsh -l
+# Add the current directory to Git's safe directory list
+git config --global --add safe.directory /workspaces/OpenEnergyID
+
+# Install Python tools
+pipx install ruff poetry pre-commit
+
+# Install project dependencies using Poetry
+poetry install
+
+# Install pre-commit hooks
+pre-commit install
+
+# Install Node.js and Pure prompt
+# nvm install node
+# npm install --global pure-prompt
+
+# # Configure Zsh to use Pure prompt
+# "autoload -U promptinit; promptinit; prompt pure"
+# "echo "autoload -U promptinit; promptinit; prompt pure" >> ~/.zshrc"
+
+# # Uncomment the following line to install zsh-syntax-highlighting
+# # sudo apt-get install zsh-syntax-highlighting
+# # echo "source /usr/share/zsh-syntax-highlighting/zsh-syntax-highlighting.zsh" >> ${ZDOTDIR:-$HOME}/.zshrc
diff --git a/DEVELOPERS.md b/DEVELOPERS.md
index 10a97f6..3b23208 100644
--- a/DEVELOPERS.md
+++ b/DEVELOPERS.md
@@ -1,9 +1,13 @@
# Development Environment Setup
This guide outlines the setup process for our development environment, focusing on packaging and dependency management.
+
## Info on dev setup
+
### Commit practices (pre-commit)
-we use [pre-commit ](https://pre-commit.com/)to run RUFF.
+
+we use [pre-commit ](https://pre-commit.com/)to run RUFF.
+
### Poetry for Packaging and Dependency Management
We use [Poetry](https://python-poetry.org/docs/main/#installation) as our primary tool for packaging and managing dependencies. Poetry provides a simple yet powerful way to manage project dependencies and publish packages.
@@ -18,31 +22,42 @@ To ensure that Python CLI applications are installed globally on your system whi
1. First, [install pipx](https://pipx.pypa.io/stable/installation/) following the instructions on the official website.
2. Once pipx is installed, you can easily install Poetry by running the following command in your terminal:
- ```pipx install poetry```
-3. (*optional*) [install](vscode:extension/zeshuaro.vscode-python-poetry) vscode extension for poetry
+ `pipx install poetry`
+3. (_optional_) [install](vscode:extension/zeshuaro.vscode-python-poetry) vscode extension for poetry
+
### Setup with Poetry
+
```shell
poetry install
```
+
### publishing with poetry
+
https://python-poetry.org/docs/repositories/
+
## Remarks for devcontainer
-You can also work inside a docker container(devcontainer).
-To make sure you have access/permissions inside the devcontainer.
-From inside the devcontainer after launch, check user and take ownership:
+You can also work inside a docker container(devcontainer).
```bash
whoami
sudo chown -R $USER:$USER /workspaces/ -R
```
-other option is to run as root from within the `devcontainer.json` `"remoteUser": "root"`
+### some info about devcontainers:
+
+- [poetry in a devcontainer](https://marioscalas.medium.com/using-python-and-poetry-inside-a-dev-container-33c80bc5a22c)
+
+- [our python/container version](https://github.com/devcontainers/images/blob/main/src/python/history/1.1.9.md#variant-311-bookworm)
+
+- notes on [devcontainer git authentication](https://code.visualstudio.com/remote/advancedcontainers/sharing-git-credentials). dont forget to ssh-add if you use keys.
# Useful commands for dev work
+
## clean up
-[vulture](https://github.com/jendrikseipp/vulture) for finding dead python 🐍
-```pipx run vulture . --exclude venv```
-[deptry](https://github.com/fpgmaas/deptry) for checking dependencies
-```pipx run deptry . --ignore-notebooks```
\ No newline at end of file
+[vulture](https://github.com/jendrikseipp/vulture) for finding dead python 🐍
+`pipx run vulture . --exclude venv`
+
+[deptry](https://github.com/fpgmaas/deptry) for checking dependencies
+`pipx run deptry . --ignore-notebooks`
diff --git a/data/dyntar/sample_output.json b/data/dyntar/sample_output.json
index 1a15025..9406013 100644
--- a/data/dyntar/sample_output.json
+++ b/data/dyntar/sample_output.json
@@ -10468,8 +10468,8 @@
"electricity_delivered_smr2",
"electricity_exported_smr2",
"cost_electricity_delivered_smr2",
- "cost_electricity_exported_smr2",
"cost_electricity_delivered_smr3",
+ "cost_electricity_exported_smr2",
"cost_electricity_exported_smr3",
"rlp_weighted_price_delivered",
"spp_weighted_price_exported",
@@ -10491,8 +10491,8 @@
0.025193268242791556,
null,
0.00010334278633193095,
- null,
0.000094346,
+ null,
0.0,
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null,
@@ -10513,8 +10513,8 @@
0.0248176869408186,
null,
0.00010180215183123789,
- null,
0.000106652,
+ null,
0.0,
0.004102,
null,
@@ -10535,8 +10535,8 @@
0.024258973626416307,
null,
0.00009951030981555968,
- null,
0.00008204,
+ null,
0.0,
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null,
@@ -10557,8 +10557,8 @@
0.02373007118997354,
null,
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- null,
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+ null,
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null,
@@ -10579,14 +10579,14 @@
0.13333455088053722,
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+ 0.0,
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@@ -10601,14 +10601,14 @@
0.12910030800802066,
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0.12616920946807492,
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0.11656131836346734,
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[
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[
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[
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[
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[
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],
[
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[
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0.04002372381624863,
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@@ -240523,17 +240523,18 @@
0.03848674306444068,
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+ -0.0,
+ -0.0034693182175038915,
1.0,
0.0,
1.0
]
- ]
+ ],
+ "summary": null
}
\ No newline at end of file
diff --git a/demo_dyntar_analysis.ipynb b/demo_dyntar_analysis.ipynb
index 4e14ae9..1a835f7 100644
--- a/demo_dyntar_analysis.ipynb
+++ b/demo_dyntar_analysis.ipynb
@@ -34,6 +34,7 @@
" DynamicTariffAnalysisInput,\n",
" DynamicTariffAnalysisOutput,\n",
" calculate_dyntar_columns,\n",
+ " summarize_result,\n",
")\n",
"\n",
"import seaborn as sns\n",
@@ -318,8 +319,8 @@
" | electricity_delivered_smr2 | \n",
" electricity_exported_smr2 | \n",
" cost_electricity_delivered_smr2 | \n",
- " cost_electricity_exported_smr2 | \n",
" cost_electricity_delivered_smr3 | \n",
+ " cost_electricity_exported_smr2 | \n",
" cost_electricity_exported_smr3 | \n",
" rlp_weighted_price_delivered | \n",
" spp_weighted_price_exported | \n",
@@ -343,8 +344,8 @@
" 0.025193 | \n",
" NaN | \n",
" 0.000103 | \n",
- " NaN | \n",
" 0.000094 | \n",
+ " NaN | \n",
" 0.0 | \n",
" 0.004102 | \n",
" NaN | \n",
@@ -366,8 +367,8 @@
" 0.024818 | \n",
" NaN | \n",
" 0.000102 | \n",
- " NaN | \n",
" 0.000107 | \n",
+ " NaN | \n",
" 0.0 | \n",
" 0.004102 | \n",
" NaN | \n",
@@ -389,8 +390,8 @@
" 0.024259 | \n",
" NaN | \n",
" 0.000100 | \n",
- " NaN | \n",
" 0.000082 | \n",
+ " NaN | \n",
" 0.0 | \n",
" 0.004102 | \n",
" NaN | \n",
@@ -412,8 +413,8 @@
" 0.023730 | \n",
" NaN | \n",
" 0.000097 | \n",
- " NaN | \n",
" 0.000119 | \n",
+ " NaN | \n",
" 0.0 | \n",
" 0.004102 | \n",
" NaN | \n",
@@ -435,14 +436,14 @@
" 0.133335 | \n",
" 0.0 | \n",
" 0.000535 | \n",
- " 0.0 | \n",
" 0.000080 | \n",
" 0.0 | \n",
+ " 0.0 | \n",
" 0.086561 | \n",
" 0.062453 | \n",
- " -0.810623 | \n",
+ " -0.009356 | \n",
" 0.0 | \n",
- " -0.810623 | \n",
+ " -0.009356 | \n",
" 4 | \n",
" 0 | \n",
" 4 | \n",
@@ -481,14 +482,14 @@
" 0.039762 | \n",
" 0.0 | \n",
" 0.003802 | \n",
- " -0.0 | \n",
" 0.010997 | \n",
" -0.0 | \n",
+ " -0.0 | \n",
" 0.046355 | \n",
" 0.019040 | \n",
- " -2.011276 | \n",
- " 0.0 | \n",
- " -2.011276 | \n",
+ " -0.003707 | \n",
+ " -0.0 | \n",
+ " -0.003707 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
@@ -504,14 +505,14 @@
" 0.040024 | \n",
" 0.0 | \n",
" 0.003827 | \n",
- " -0.0 | \n",
" 0.013388 | \n",
" -0.0 | \n",
+ " -0.0 | \n",
" 0.046355 | \n",
" 0.019040 | \n",
- " -2.655088 | \n",
- " 0.0 | \n",
- " -2.655088 | \n",
+ " -0.004926 | \n",
+ " -0.0 | \n",
+ " -0.004926 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
@@ -527,14 +528,14 @@
" 0.039903 | \n",
" 0.0 | \n",
" 0.003816 | \n",
- " -0.0 | \n",
" 0.011093 | \n",
" -0.0 | \n",
+ " -0.0 | \n",
" 0.046355 | \n",
" 0.019040 | \n",
- " -2.027059 | \n",
- " 0.0 | \n",
- " -2.027059 | \n",
+ " -0.003749 | \n",
+ " -0.0 | \n",
+ " -0.003749 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
@@ -550,14 +551,14 @@
" 0.038904 | \n",
" 0.0 | \n",
" 0.003720 | \n",
- " -0.0 | \n",
" 0.012527 | \n",
" -0.0 | \n",
+ " -0.0 | \n",
" 0.046355 | \n",
" 0.019040 | \n",
- " -2.516225 | \n",
- " 0.0 | \n",
- " -2.516225 | \n",
+ " -0.004538 | \n",
+ " -0.0 | \n",
+ " -0.004538 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
@@ -573,14 +574,14 @@
" 0.038487 | \n",
" 0.0 | \n",
" 0.003227 | \n",
- " -0.0 | \n",
" 0.010985 | \n",
" -0.0 | \n",
+ " -0.0 | \n",
" 0.046355 | \n",
" 0.019040 | \n",
- " -1.944626 | \n",
- " 0.0 | \n",
- " -1.944626 | \n",
+ " -0.003469 | \n",
+ " -0.0 | \n",
+ " -0.003469 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
@@ -682,19 +683,6 @@
"2024-04-18 20:45:00+00:00 0.003720 \n",
"2024-04-18 21:00:00+00:00 0.003227 \n",
"\n",
- " cost_electricity_exported_smr2 \\\n",
- "2023-12-31 23:00:00+00:00 NaN \n",
- "2023-12-31 23:15:00+00:00 NaN \n",
- "2023-12-31 23:30:00+00:00 NaN \n",
- "2023-12-31 23:45:00+00:00 NaN \n",
- "2024-01-01 00:00:00+00:00 0.0 \n",
- "... ... \n",
- "2024-04-18 20:00:00+00:00 -0.0 \n",
- "2024-04-18 20:15:00+00:00 -0.0 \n",
- "2024-04-18 20:30:00+00:00 -0.0 \n",
- "2024-04-18 20:45:00+00:00 -0.0 \n",
- "2024-04-18 21:00:00+00:00 -0.0 \n",
- "\n",
" cost_electricity_delivered_smr3 \\\n",
"2023-12-31 23:00:00+00:00 0.000094 \n",
"2023-12-31 23:15:00+00:00 0.000107 \n",
@@ -708,6 +696,19 @@
"2024-04-18 20:45:00+00:00 0.012527 \n",
"2024-04-18 21:00:00+00:00 0.010985 \n",
"\n",
+ " cost_electricity_exported_smr2 \\\n",
+ "2023-12-31 23:00:00+00:00 NaN \n",
+ "2023-12-31 23:15:00+00:00 NaN \n",
+ "2023-12-31 23:30:00+00:00 NaN \n",
+ "2023-12-31 23:45:00+00:00 NaN \n",
+ "2024-01-01 00:00:00+00:00 0.0 \n",
+ "... ... \n",
+ "2024-04-18 20:00:00+00:00 -0.0 \n",
+ "2024-04-18 20:15:00+00:00 -0.0 \n",
+ "2024-04-18 20:30:00+00:00 -0.0 \n",
+ "2024-04-18 20:45:00+00:00 -0.0 \n",
+ "2024-04-18 21:00:00+00:00 -0.0 \n",
+ "\n",
" cost_electricity_exported_smr3 \\\n",
"2023-12-31 23:00:00+00:00 0.0 \n",
"2023-12-31 23:15:00+00:00 0.0 \n",
@@ -739,26 +740,26 @@
"2023-12-31 23:15:00+00:00 NaN 0.000000 \n",
"2023-12-31 23:30:00+00:00 NaN -0.000000 \n",
"2023-12-31 23:45:00+00:00 NaN 0.000000 \n",
- "2024-01-01 00:00:00+00:00 0.062453 -0.810623 \n",
+ "2024-01-01 00:00:00+00:00 0.062453 -0.009356 \n",
"... ... ... \n",
- "2024-04-18 20:00:00+00:00 0.019040 -2.011276 \n",
- "2024-04-18 20:15:00+00:00 0.019040 -2.655088 \n",
- "2024-04-18 20:30:00+00:00 0.019040 -2.027059 \n",
- "2024-04-18 20:45:00+00:00 0.019040 -2.516225 \n",
- "2024-04-18 21:00:00+00:00 0.019040 -1.944626 \n",
+ "2024-04-18 20:00:00+00:00 0.019040 -0.003707 \n",
+ "2024-04-18 20:15:00+00:00 0.019040 -0.004926 \n",
+ "2024-04-18 20:30:00+00:00 0.019040 -0.003749 \n",
+ "2024-04-18 20:45:00+00:00 0.019040 -0.004538 \n",
+ "2024-04-18 21:00:00+00:00 0.019040 -0.003469 \n",
"\n",
" heatmap_exported heatmap_total \\\n",
"2023-12-31 23:00:00+00:00 0.0 0.000000 \n",
"2023-12-31 23:15:00+00:00 0.0 0.000000 \n",
"2023-12-31 23:30:00+00:00 0.0 0.000000 \n",
"2023-12-31 23:45:00+00:00 0.0 0.000000 \n",
- "2024-01-01 00:00:00+00:00 0.0 -0.810623 \n",
+ "2024-01-01 00:00:00+00:00 0.0 -0.009356 \n",
"... ... ... \n",
- "2024-04-18 20:00:00+00:00 0.0 -2.011276 \n",
- "2024-04-18 20:15:00+00:00 0.0 -2.655088 \n",
- "2024-04-18 20:30:00+00:00 0.0 -2.027059 \n",
- "2024-04-18 20:45:00+00:00 0.0 -2.516225 \n",
- "2024-04-18 21:00:00+00:00 0.0 -1.944626 \n",
+ "2024-04-18 20:00:00+00:00 -0.0 -0.003707 \n",
+ "2024-04-18 20:15:00+00:00 -0.0 -0.004926 \n",
+ "2024-04-18 20:30:00+00:00 -0.0 -0.003749 \n",
+ "2024-04-18 20:45:00+00:00 -0.0 -0.004538 \n",
+ "2024-04-18 21:00:00+00:00 -0.0 -0.003469 \n",
"\n",
" heatmap_delivered_description \\\n",
"2023-12-31 23:00:00+00:00 0 \n",
@@ -815,6 +816,33 @@
"cell_type": "code",
"execution_count": 5,
"metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "cost_electricity_delivered_smr2 75.351600\n",
+ "cost_electricity_delivered_smr3 83.939318\n",
+ "cost_electricity_exported_smr2 -9.485280\n",
+ "cost_electricity_exported_smr3 -8.439498\n",
+ "cost_electricity_total_smr2 65.866320\n",
+ "cost_electricity_total_smr3 75.499819\n",
+ "ratio 0.113553\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "summarize_result(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
"outputs": [],
"source": [
"output_model = DynamicTariffAnalysisOutput.from_pandas(df)\n",
@@ -831,7 +859,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -861,7 +889,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -873,7 +901,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -913,7 +941,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -952,7 +980,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -1021,12 +1049,12 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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",
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",
"text/plain": [
""
]
@@ -1078,12 +1106,12 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
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Wfl7bONZ+Xusc4Ldv4zneY2Ji4OjoiKioKCntyZMn2LJlCzp16oTZs2dL6UlJSRgwYAB69eqlcT5MTXSnpKTA2dkZp0+fltJycnLw008/oV27dnjttdekid+HDx8iKCgILi4uSE9Pl+VNEpZ+luXO2s+rm7WfVzdrP8t2hnUby2vMsS53XmOOdbmLsQt/4wfW5c5rzLEud4FAUHnEoqKBor7x+frrr2FnZ4dLly4BgMZBtevXr4eVlRXu3LlT7vdqsl/9N06dOgUXFxf88MMPGk8mAcCuXbvQrFkzXLhwQWtfadQeFm7AMMqdNzdrP69u1n5DcLNqZ1i2c4ZQ7qzjnbe+jed4V3Pnzh20atUK33zzDYB/yyQrKwtr165F586dcejQIenzV65cQUpKSo13P336FM7OzuXeFCgoKMCePXvg7u6ODz/8UEpPTEyUJoZqup9lubP28+pm7efVzdrPsp1h3cbyGnOsy53XmGPlFmMXfscurMudt5hjXe4CgaDqiEVFA0OlUpXrLHv16oX27dvj8ePHADQbVVtbW42bJ20pLCxEUVGRRsPcu3dvvfgLCwtRWFiokTZixAjY2dnhypUr5T7v6OiIZcuWyeLOy8tDfn6+9MQLAIwaNQrNmzfXuRtgW+8s65zneOe1zgF+886yjQPYtnO81rnaz2PfxnO8lyUjIwN9+vSBv78/0tLSNK5lZWXB09MTU6dOrXXuvLw8BAYGol+/foiIiNC4lp+fj5EjR2LEiBE6cQNAbm4uAgMD0b9/f737WZY7az+vbtZ+Xt2s/SzbOZZtHMBvzPHct/EU72Lswt/YRcxN8RnvAoGg+hiRwGCIjY2l2bNn09ChQyk8PJyOHj1KRER79+6l4uJi6t+/P927d4/q1KlDRERZWVlka2tLTZs2lcUfFxdHM2bMIF9fX1qwYAGdP3+eiIh27dpFRKRTf0xMDAUGBlLv3r1p1qxZknP//v1kb29Po0ePpnPnzlFxcTERERUVFZGdnR01b95ca/f169fpzTffpO7du9OUKVPowIEDRES0b98+at26tU7dRGzrnWWd8xzvvNY5Eb95Z9nGEbFt53itcyJ++zae452IKCMjg27dukVpaWmUnZ1N5ubmtHHjRjpy5AitWLGCsrKypM82bNiQhgwZQvHx8dK/RxvS09Pp6tWrFB0dTY8fPyZzc3PavHmzXtwPHz6kK1euUFxcHKWnp1O9evVo/vz5FBcXRytXrqSbN29KnzUxMaE+ffrQ7du3KScnR2s3EVFqaiqdPn2aTp06Rffv36f69evTggULKDY2llatWqVTP8s6J2Jb77zGOxG/eec53lm2cyzbOCJ+453nvo3XeBdjF/7GLmJuis94FwgEWsJ6VVNQwvXr19GoUSOMGTMGEyZMgKenJ9q1a4fw8HAAwI0bN+Du7g4HBwds374dP/30E0JDQ2FlZYXExESt/VFRUbCyskJAQABCQkLQvn17LFiwQLoeHR2NTp066cR/48YNNGrUCIGBgZgzZw5ee+01NG7cWDoLIS8vD76+vrC1tcXChQuxY8cOzJ07F40aNcLNmze1cl+7dg0WFhaYOXMm1q5dCz8/P0yYMAE5OTkAgOzsbPTp00cnboBtvbOsc57jndc6B/jNO8s2DmDbzvFa5wC/fRvP8Q4AkZGR6NChA5ycnGBvb4+RI0fi6tWrAICDBw/CxMQEkydPRlxcnPQ7kyZNwvjx41FcXKyVOyoqCp06dULbtm1hb2+Pt99+G48ePQIAHD58WKfuyMhItGnTBo6OjrCzs4OHhwd+//13AMDVq1dhbm6O4cOH4/jx49LvTJ8+HYMHD0Z+fr5WbqAk725ubnBzc4O9vT0GDhwobXV39epVmJmZ6czPss4B9vXOY7wD/Oad93hn1c6xbOMAvuOd576Nx3gXYxf+xi5iborPeBcIBNojFhUNAKVSiZCQELz++uvSK983b97EypUrYWVlhffeew9AyWvhb775Jjw8PODg4IAuXbpUuAVAVblz5w4cHBywePFiKS08PBwTJ05EVlaWlKYr/6pVqzB48GDpdfuHDx9i586dqFevHoKDg6XPhYaGws/PDy4uLhVuuVFVkpOT0aZNG41879q1CyNGjMDjx4/x5MkTnbkBtvXOss55jnde6xzgO++s2jiAbTvHc50D/PZtvMY7ANy9exdNmzbF3LlzceHCBXz00Ufw8/ODmZkZzpw5AwA4efIkrK2t0aNHD/j6+uL1119Hw4YNERUVpZU7JiYGVlZWWLBgAa5fv46NGzfCwcEBCQkJ0md+/fVXnbj/+ecftGzZEgsXLkR8fDwOHjyIiRMnwtjYGF9++SWAkokDLy8veHh4wM3NDcOGDYOZmZk0Ka0N169fR+PGjREaGoqkpCTs3bsXbdq0kc5iAUomIXXhZ1nnANt65zXeAX7zznO8s2znWLZxAL/xznPfxmu8i7ELf2MXMTfFb7wLBALtEYuKBsKQIUMwZswYjbTU1FSsX78ezZs3x/bt26X0Bw8eIDU1VaNzrS5KpRLffPMNZs2ahdTUVCk9JCQEXl5ecHFxwbhx4/Dxxx/rxA8AkydPRp8+fTTS1AduN2jQQGOP9Ly8PDx58kRjj/XqcuzYMcyePVvj0PZFixbByckJDg4O6N27N8LCwqRrubm5srnVsKh3Q6hznuOd1zoH+M07qzYOYN/O8VrnAL99G8/xfurUKXh5eUlvUABAQkICJk6ciHr16uH8+fMASgbs//3vfzF58mQsWrQIMTExWnnT09PRvXt3hISESGlKpRL9+vXDkSNH8PfffyM5ORlAyRPHcrqBksm9du3aaUzy5uXlITQ0FHXq1MG+ffsAlEwaHTp0CHPnzsWWLVs03iqpLhkZGejduzdmzZqlkd6vXz/s3r0bBw8eRGxsLAAgKSlJdj+rOgfY1zuv8Q7wm3ee451VO8e6jQP4jXee+zae412MXfgbu4i5KT7jXSAQaI9YVDQQNm7ciF69euHGjRsa6SkpKZg6dSoGDRqkcSMvJ3fv3sW1a9ekn5ctW4b69evjP//5D7Zv347g4GB07NgR586d04l/z549cHR0xJ9//qmRnpWVhTVr1sDT07NcucjBkydPEB8fL/28evVqmJqa4tNPP8X333+PDRs2oGnTpjhw4IDsbjWs6p11nfMc77zWOcBv3lm1cQD7do7XOgf47dt4jveffvoJRkZGuH//vkb6/fv3MW7cODg4OODWrVsa19RPBmtDSkoKPvzwQ42YX716NYyMjODi4oKOHTvC3t6+3FsbcrgB4MyZM1AoFFLZq59wB4DZs2ejYcOGiI6OlsVVlqysLHz11Ve4ePGilKbOu7u7O7y8vGBsbKyz7zqrOgfY1zuv8Q7wm3ee451VO8e6jQP4jXee+zae412MXfgbu4i5KT7jXSAQaI9YVDQQfvvtNzg4OGDFihVIT0/XuHby5EkYGxvr7TXvBQsW4Oeff5Z+joqKgpmZGb7//nud+CIjI9GjRw9Mmzat3BNmly9fRoMGDXDixAmduEuzfv16HDlyRPo5OTkZLVq0wLZt23TmNJR613edG0q+AX7zru98A/zm3VDaOED/7RyvdQ4YTr3ru84NJd+A/vOempqKrl27YtGiRXj69KnGtb///hve3t7YvXs3AMhytlhp0tLSpP/fuXOn9CZBamoqIiIi0LdvXwQGBqKgoEB2t1KphK+vL8aMGYOHDx8C+HdS9969e/D19cXy5cuhUqlkdwPQ2Cbphx9+gLW1NQ4cOIAnT54gPT0d/v7+6NOnD3JycjQmRuWAZZ0DbOud13gH+M07z/HOsp1j2cYB/MY7z30bz/Euxi7s7+F5Ha8CYm5KjT7iXSAQaI9YVGRI2RuwDz74AMbGxli7di3u3bsnpaekpKBDhw6yN+Zl/WV/Vt+kJScno1u3bjh9+rTO3Lt370bLli3xzjvvIDIyUkrPzMyEh4cHjh07pjN3YWGhxs/qG+aHDx/ilVdekf2JKJb1bkh1znO881LnFfl4yTvLNq4ivz7bOV7rvCIfL30bz/FeloULF8LDwwNffPEFcnNzNa55eXlh+vTpsvoqeiMjLS1N2o5Ojb+/P0aOHCmruzTbtm1D165dsWzZsnJbEw0dOhRjx46V3VlR3h8/flzufJ0ZM2ZgwIABsvvV6LvOAcOpd17jHeA37zzHu77bOUNp4wB+453nvo2neBdjl3/hdewi5qb4iHeBQCAvdUjADGNjYwJAZ8+epV69elFISAgplUpauXIlpaSk0PDhw6lDhw60detWysjIIFtbW534z58/T927dydjY2MiIgJACoWCjIyMiIho+/btlJubS87OzrK7f/zxRxo9ejS98cYbpFKpKDw8nJKSkmj06NHk4eFBu3fvpvv375Obm5vs7p9++olGjhxJdevWJaJ/861QKIiI6IMPPqC0tDTq3LmzbO7Sfhb1bgh1znO881Tn6r/PIu9l3frMe1m3vtu4sn59tXPqv0uk/3ivyM3ie17az1vfxjLfpf0s8q5UKsnY2JhUKhUZGRnRhg0bKDExkd5//33Kzc2lgIAAatCgARER2dvbU7NmzWR3l6a4uJgaN25MjRs3JqKSMgBAZmZmZG9vTwCIiKQyqS7q/Kr/O2PGDEpMTKRffvmFcnNzaenSpWRhYUFERJaWlmRmZkZKpZKMjIy0dpf2l6a4uJgsLCwkr7r+i4qKyNXVlYqLi8nY2FhrP8s6L+0vjb7qndd4L+3nLe88xzvLdo5lGweAVCoVk3ov7S4ND3Ve2l8afdU7r/FOxOdYvayf17GLmJviK94FAoHMVH89UqAN6icyAgMD4eTkhLNnz0rXvv76a7z66qto2LAh2rdvj5YtW+rsyRi1v+xTfwAQFxeHhQsXolGjRrh69aqsfgBYunQp7OzssH//fintl19+weTJk2Fqaop27drB2dlZJ6/bq90HDx4sd+3atWuYO3cuLCwsZM93UVERADb1XtatzzrnLd7VZQ38+7SVvvL+PLc+6ryiw7P1lffnuXWd97S0tHJP2OqzjXueX9ft3J07d6QtatRPmOqrzp/nZtGvAfz1bep2hlW+y/r13a8DJU9SL126FL/++quUNnnyZHh4eKBv375Yv349AgMD8fLLL+P69es6cZ85c6bC60qlEsuWLYOdnZ3s5+CoVCpMmzYN3333nZS2YsUKdOvWDW3atMG8efMwduxYnZ29pPb/+OOPFV7Pz8/HsmXLYGNjg9jYWNndrOq8tJ9VvfMY76X9vOWd93hn1c7pu43Lzs4u59dXvT/LzVudl/az6tt4iXc1LOcpDGFODuBv7MJyTq4iPy/zcoYS7wKBQD7EoqKeuH//Pv7++28cPXpU4xXv2NhYBAcHl5sMT0tLw7Vr13D58mU8ePBA7/6bN28iMDAQXbt21boxV/vK7nmflJSEVatW4fHjxxrpBQUFuHv3LhITE7U+FLiq7sTERCxcuBB9+vTR2O6hujx69AixsbHlOswbN27ovN6r6pazzu/evYvjx4/jq6++0og3feS7On458x4XF4dFixZpHDb+PLecea+qW858A0BERAR69uxZ7rujj7xX1S1n3q9duwYXFxds27ZNo61JSkrCypUrddrGVccvZzt37do11KlTB+3bt9dI10edV9Utd7wnJCTgP//5D+bNm4c//vhD2g5MH31bVd1y1vm9e/dw4cIF/Pzzz8jPz5cWs2/fvq2XeK+qX+5+vSKOHz+Opk2bYuXKlcjLy5PSv/76a7z55pvw8fHByJEjdeIv7S79UAlQchbKzJkzYW1trZOJoMuXL8PLywvvvPOOxnZ4Z86cwdy5czF48GAEBATg2rVrsrvL+stumXXq1ClMnToVNjY2Osn7yZMnmdV5Wb8u672i87L0lfcXuXUd7+np6RrnuZX16zLvL3LrMu/x8fG4cOHCM926jvcX+XWd9/3796OgoEBK01c79yK3rtu4uLg4TJo0CSkpKVKavur9RW5d1nlOTg4eP36skb+LFy/qrW97kV/ffduFCxeY9eul3brMN8t5OZZzcgC/83Is5+Sq468t83Is5+QEAoF+EIuKeiAyMhKtWrWCs7MzzM3N0bZtW+zZswepqakAUO5G2VD8165dk2XS19fXF8nJyQD+vYlQdyoV7Z0vF9V1JyQkyDLxGRUVhS5dusDFxQVNmjTBwIEDNa7r4mBxbd1y1HlUVBScnZ3h6ekJU1NTeHp6agwMdJlvbfza5l2lUiE3Nxfe3t5QKBQIDg4uF3u6indt3HLUOQBcvXoVdevWxYIFCyq8rst6r65bjrzHxsbCwsIC8+bNQ1JSUrnrZSdE5aa6fjnauYiICJiammLIkCFwdHTE7t27Afzbp+iyzqvrliveo6Ki0KRJE/j5+aF169awt7fXGPzoun2vjluOOo+MjIS9vT28vb1ha2sLe3t7fPLJJ/jnn38A6D7eq+uXq1+/ceMGFi5ciMmTJ+ODDz7AzZs3pWsnT56UJkTKtrf5+fla3+tV1l2a3bt3Y8aMGVq/QZKamlrhm+BAyXcxIyOjwmsqlUqWfq86/sOHDyMsLEzrtxkSExPx/vvvY968edi7d6/GtVOnTum0zqviL41c9X7jxg2EhIRgyJAhWLVqlcZ3SNd5r6y7NHLlGyhpM1q3bo1ly5ZpnC9U1q+LvFfWXRq58h4REQEzMzPs2LGj3DV9xHtl/aWRK++RkZFo3Lgxpk6dWq7cL1++rNN2rjpuudo4oOQ+un79+lAoFPjyyy81rum63ivrLo1cdR4dHY2hQ4fC1dUV/v7+OHz4sHRNH31bdfxy1XtcXBxCQ0MxadIkbNq0SePcwosXL+o079Vxy5VvlvNyLOfk1H+Hx3k5lnNy2vhr+rwcqzk5gUCgX8Sioo5JS0tD27ZtsWTJEiQkJODevXsYN24cXF1dsWLFinJPoW7duhX79u1j6v/+++9lcd++fRtOTk5QKBRo06aN9PThszqQ999/H5s2bWLm3rhxoyxuoORm2draGqGhoTh//jyOHTuG1q1bY/HixRV+Xs56r45brjqPjY2FtbU1wsLCkJSUhMTERFhbW2sMksq65Yz36vjlyruaJUuWICAgAPXr18frr7+O27dvP9MtZ96r6pYz39HR0ahfvz6WL18OoGRgkJ6ejsTExGf65cp7ddxy5V2pVGLatGkICAiQfj5z5gy++OIL3Lhxo9wkuJxtXHX9crVzV69eRYMGDbBs2TIUFhaiW7dueOONN575eTnrvDpuOeP9/v37cHV1xcqVK6XBsJubGz7++OMKPy9nvVfHLVedp6SkwMnJCatWrcL9+/ehUqkwcuRI1KtXD3PmzCk3ESp3vFfHL2e/HhMTA3NzcwwaNAijRo2Cubk5+vfvj08++aTCz9+5c8cg3Dk5OVq5r1+/jpdeegmjR4/G06dPpfRn3U/JvfWhNv78/Hyt3FFRUWjevDn69euH7t27w8jI6LkxJWeda+vXtt7VDy+MHj0a06dPx0svvYSVK1dWyq0t2ri1zbeaTz75BAqFAh4eHli7dq304MKL/Czd2uZd3bfOmzevUp+XO9/a+LXNe1JSElq2bPnMh9PKImc7p41b2zYO+HdRb+HChZg/fz569eqFf/7555ltrJz1ro1b2zqPiYmBhYUFZs6ciU8++QQ9evTAhAkTnrm4IXffpo1f23qPiYlBo0aNMGbMGAQHB6NFixbw9PTERx999EK3tmjj1jbfLOflWM7JAfzOy7Gck6uuvzbMyxnCnJxAINAPYlFRx8TExMDe3h6XLl3SSF+0aBE6dOiAjRs3SjfF6enpcHBwwKBBg5CVlVWj/Xl5eQgLC8OIESPw66+/4pVXXkGrVq2eeRPx9OlT9O/fH76+vhU+kVhT3ACQlZWFsWPHYsaMGVKaUqnErFmzMHz48HKfl7PcWbozMjIwePBgzJkzRyN94MCB+Oyzz/D+++/j+vXr0lYqjx49kjXeWfvVcRUSEoJt27YhJiYGJiYmePPNN5GTk4NNmzZJg2G5v+ss3Y8ePYKTkxM8PDyktICAAHh5ecHW1havvPIKIiIipEGynH6WbqDk6cqePXti165dAIDevXvDy8sL5ubmcHR0xPTp06UnMjMyMmRtZ1j64+PjoVAosHTpUinthx9+gImJCU6dOlXu83KWO0u3mrNnz6J9+/Yab4qNGzcO8+fPx6RJk/DFF19I5f7kyRNZ652l++jRo+jatSsePnwobc928eJFWFtbw8PDAytWrJC275K7X2XtLygowKRJkzB16lQpLT4+HuPGjUO3bt3w3//+V+PzmzdvRv/+/cvde+nTXXb7wOrw4MEDdO/eHX379oW1tTXGjBmjsbBXlh07dsDZ2RknTpzQ2q2N/9ixY1q779y5AycnJyxcuFDqY3fu3AkbGxuN758aOetcG78c9Z6YmAh7e3uNSa+VK1dixowZ5bakK+2WI+/VdcuR79JERkbirbfewpo1a9CsWTOsXr26wrdl5a736rjlyPvNmzdhYmIi9a2FhYU4dOgQduzYgYMHD5brP+XOd3X9ctX7zz//jMGDB0vupUuXwt/fH0FBQdI9FlDy4Jrc7Vx13HK0cQBw6dIlmJmZYcmSJQCAb7/9Fubm5tIZW2XHzHLWe3XdctR5bm4u/P39ERISIqUdPHgQI0aMQGpqqka86aLOq+uXo96zsrIwcOBALFy4UEq7e/curKysYGNjgzVr1mh8Xs68V9ctV7yznJdj6eZ1Xo7lvBhrP8t5MdZzcgKBQL/UIYFOKSwspKKiIsrNzSUiory8PKpfvz6Fh4dTXl4ebd++nQYOHEgdO3YkS0tLOnXqFCmVSmrYsGGN9terV4/c3Nyoffv21LdvX3J0dKQ33niDevbsSWfPnqXmzZuTSqUiIyMjAkBmZma0a9cuUqlUZGFhUWPdakxNTcnd3V362cjIiHr27Em///47FRYWkkKhoLp16xIAsrS0pN9++41UKpUs9c7KbW5uTkOHDtVwr1mzhn799VcqLCykBw8e0IYNG2jr1q00duxYsrKykjXeWfvVDBo0iPbt20czZsygP/74g3r16kVnzpyhoqIiGjVqFBGRTr7rrNxWVlY0YMAAioyMpJUrV9Ivv/xCVlZWNH36dGrcuDFt3LiR/P396eTJk+Tk5CSrn6WbiMjY2JgaN25MGRkZtHz5cjIxMaGdO3dSq1ataNu2bfT999/Tl19+SWFhYWRubk67d+8mpVIpWzvDyl+vXj36+OOPKTg4mIiIAJCPjw917tyZDh06RL6+vhptrNx9Cyu3midPnlBqaiolJCRQy5YtaevWrbR//34KDg6m9PR02r59O0VERNC6deuoUaNGstY7S/edO3coMTGRrK2tpbTs7Gzy8fEhCwsL2rFjBwUGBlLLli3JzMxM9nhn6X/ppZcoNTWVHBwciKgk7pycnGjjxo20YsUK2rdvH7Vu3ZqGDh1KRETW1tZUWFhINjY2zNy2trZauyMiIsje3p7mzp1LKpWK/Pz8KCgoiD7//HMyMzMr9/nmzZtTx44dydHRUWu3Nv42bdpo5VWpVLR3715ycnKiJUuWkJGREREReXt7U926dUmlUpX7HTnrXBu/tvWuVCrpxx9/JD8/PwoNDZXS7969SzExMdSjRw/y8vKiwYMH07BhwzTc2uZdG7cc8V4aAHTu3Dn68ssvSalU0o4dO+jll1+m06dPk6urK61du1bDL0e9V9etbd6Li4vpo48+ooYNG1KnTp2IiMjf35/u3r1LmZmZlJycTKNGjaLFixeTh4eHhluOfGvjl6ver1y5Qo8fPyYiosGDB1NxcTG5u7vT9evX6dKlSxQXF0fr1q0jhUJBLVq0kLWdq45b2zaOiCgnJ4d69+5N06ZNk2Jq/Pjx9Pnnn9Py5cvp2LFjVKeO5lSRXPWujVuOOjcxMaH09HQpnoiI/vjjD4qIiCBPT09ycXGhLl260Pr160mhUMjet1XXL0e9GxkZ0ePHj6XvWm5uLtnZ2VHfvn3p8ePH9Msvv5Cnpyf5+fkREVHLli1ly3t13XLkm4ioqKiIiouLmczLsXTXq1eP2rdvTx06dGAyL8fKTUT08ssvS/FGpN85OZZ+c3NzGj58OHXs2FFK09e8GEu3QCBggP7XMWs/SqVS41yfnj174pVXXpF+Lr11Q+fOnTF+/HgA8p1FxNKvVCorfJJYpVIhISFBejrp7t270r/l8uXLyMzMrNFutV+9F37p7VnUb0l999136NChg8bvZGdn1wq3+q2R0pw5cwaOjo44dOiQ9OTd8OHD0blzZ+n3arq/7HcNAI4fPw4XFxfp6Ss/Pz8YGRnBz8/vuVtY1SS32l+6LVmyZAmsra0xZMiQcvvgt2vXDm+99RYA+doZlu7SfyckJATt27fHxIkT8emnn2p8dv78+XB1dUVhYaFs51Sw9BcXF5c776P0z8uXL4eFhYV09oXaKcd3jaVb/XdKl/ugQYPQuHFj9OvXDyYmJjhy5Ih0LTw8HC1btqzwjMua7P7nn3/QqlUrTJgwAbdu3cLZs2fRoEEDhIeHAwBcXFywevVqALq5n2HhV/+twsJCBAQEYPTo0cjPz4dKpZJiKyEhAT4+Phg3bpzG7z3vjbqa4AZKtusq/Qbw+fPnYWlpiTFjxmiceVS6vOXafpK1//Tp0wgNDdVIUyqVsLe3r/CtaEC+cmftT0lJwfnz56WfV69eDWNjYyxduhRbt26Ft7c3+vbti/v379cqd1kGDBggbSO/ceNGmJqawtzcvNxbM7rw69t98+ZNTJs2Dd26dUOLFi0wePBgxMbGIjc3F5cuXYKdnR3efPNNnbgNwX/ixAn07dsXn3/+OV599VVprJiRkYFVq1ahW7duuHbtmvR5Ods5lu7SxySo29HPPvsMzs7OuHz5MoCK3ySqyW6lUomnT59i4MCBGDFiBLZt24bFixejfv36+PLLL3HkyBGsWrUKnp6eOHDggPR7cpU7S79KpUJqaiqaNWumsbVlSkoK3NzcsGvXLnTs2BFBQUEav1fT3aXx9vZGnz59pJ91PS9nKO7S6GtujLVbqVTqfV7MEPwVjfn1NS/H0i0QCNggFhVlJiYmBhMnTkS/fv0QFBSE33//HZcvX4ajoyPGjBkjfU49GTpv3jwMGzasVvhLu6dPn66xZ7a6g7l165Z0E5GYmIiZM2eic+fOFW7rU1PcZf0zZszQ8KvL+ocffkC7du2k9Hnz5mHo0KFa3zgairtsud++fRsJCQka/45Nmzaha9euFS7+1jR/aXdwcDAOHz4MpVKJrKwsDBo0CEDJVpzNmzfHV199hYYNG2L48OHSzXNNdZf1q9sZAPj000+xd+9e6Tunjq9Ro0Zh9OjRtco9bdo0nD59GtnZ2ejZsycUCoW0hZOa48ePw93dXZY2hrW/ophTo17Uf/jwIVxdXREaGirbIiprd1l/UFAQ/vzzTwAlW9Tt27cPXl5eePTokdTOnDt3Dk5OTrhx40atcU+fPh0HDhzA/v374ebmBktLS1haWmqcfdWzZ89yiyA12V+2f/z9999hbGyssd2o+jO///47jIyMEB0dLdsiuqG41aj/9l9//SUt7D19+hSFhYX4+OOPcfToUQAVTyjUFP+z3KUfUnBwcMDx48elaydPnpTtoR2W/me5Hz16hDlz5mg8vHD9+nUoFAqNtJrqfp7f19dX2n5yypQpMDMzQ9OmTbFx48ZyZ7jWBvetW7fwxhtvYMiQIYiLi9O4dujQISgUCty4cUO2CW+W/rJ/IzY2Fs2aNYObmxv69++vcS05ORkNGjTAnj17tPaydpf1V9ReZmVloUWLFpg5c6ZGulwPpxmCGyjpSwYNGoQJEybAxcUFO3fulK49ePAALVu2xPr167X2GoK/rPujjz6CQqFAYGAgwsLC0LBhQ2mL9R9++AH29vYa95Y11Z2dnY3MzEyNxegrV66gSZMmeP3116U0XcyLsXQ/yw9oLuDoam7M0N26mhdj7X+WGyjZZliX82Is3QKBgC1iUVFG4uLiYG5ujvHjxyM0NBTu7u7w9vbG22+/jT179qB169bw9/dHYWGh1LFMmjQJ48ePR1FRkdY3zCz9Fbk7d+6ssZe2+u8nJCTA19cXCoUCpqamWp+NwNJdWT8A/N///R9cXFwAQHoisfTT2LXFXfp8iLIDgilTpiAwMFCWgQJL/7Pc7777LgoLCzFs2DA0btwYNjY2uHjxIoCSp7RsbGy0ngxi6X6Wv1OnTs88cFylUmH06NFYvny59HNtcs+fPx/nzp1D9+7d0aJFCxw9elR62vDdd99F7969ZXnKlqW/Mu2M+m3pgIAA+Pj4yDZIYOl+lt/d3R3z588HAPz5559wdXXV+J358+ejc+fOWp8FYkjujh07wsfHB3PnzkVmZiZiY2OlNwmAkieMBw0ahG3btgHQfgKQtf/GjRvYvHmzxttQQMmZTkZGRvjss8800i9fvgxXV1eNNy9qk7ssf//9NywtLTF27FgEBASgbt26uHXrVo32V+QuHUdFRUXIzs6Gk5MT/vrrLwAl91MKhUKWvpWl/0Xlru5D1G/JRkVFwdPTE1FRUVp5Wbuf5Vf3IYsWLcLXX3+NWbNmoVmzZkhMTMS6devQoEEDbNmyResJQENzA0BSUhKOHDki/TvUMbhv3z60bdtWtoekWPqf5T58+DDq1KmDJk2a4Ny5c1J6QUEB+vbtKz24UFPdz/OrUcfVtm3b4OjoKOtZoYbozs7ORnFxMXx8fPDdd99J6YWFhXj11Vele4qa7K/IrVQq8dVXX8Hb2xuDBg3Chg0bpGsffvghPDw8ZFnIZemOiYnBgAED4OHhgWbNmuGbb74BUHLG37fffgtra2uMHj1aJ/NiLN3P81f0d+WeG6sJbl3Mi7H2V6Xc1cg1L8bSLRAI2CMWFWVCpVJhyZIlGDt2rJSWmZmJ9957D126dMGECRNw4MABODs7w9nZGf7+/hg7dixMTU01tjSpif5nudesWYNOnTpJT5+pKSgowPjx42FpaYmYmJga666qf//+/ejWrRuWLFmCl156SWNCtLa5g4KCNG4kCgsLERYWBmtra8TGxmrlZu1/nrtDhw4ICAjA0qVLMWTIEOnmWD1QzsvLq7HuF/k7duyIqVOnlpsEDQsLg62tLeLj42ude/Xq1fD09MSUKVMQFRWFnj17onnz5nB3d8ewYcPQqFEjXL16VSs3a39V29jExEQoFIpyW7HWNPeL/O7u7njnnXfw5MkTtGnTBt27d8eyZcswZcoUWFlZ6bTcWblXr16NDh064O2339b4fGZmJkJDQ9GkSRPpSdSa7I+Pj4elpSUUCgUWL16Mhw8fStdycnKwatUqKBQKhIWF4cqVK0hPT0doaCicnJyQlpZWK90VcfbsWSgUClhaWmp9T8HaXxm3UqlEXl6eNNn93nvvyfZwGkv/89zqPrXsxNCSJUvQtWtXncacrt0v8gPAF198AYVCAVtbW+khLQDYsGEDbt68WWvdFU0Ezp8/HwMHDpRl+0mW/he5v/32WxgZGWHgwIH49ttvER8fj9DQUDRr1gzJyck11l0Zf2nUW87KtahmqG6lUons7Gx07doVy5Ytw5MnT5CVlYVly5bB1tYWiYmJNdr/onLPy8vT2IITAN555x2MHj0aeXl5Wi1usXTHxMTAysoKc+fOxf/+9z/MmzcPdevWxZUrVwCU3E8dOnQIzZs3R9u2bWWdF2Ppfp4/IiKiws/LOTdWU9wHDx6UdV6Mtb+q5S7nvBhLt0AgMAzEoqKMTJ48WePsQqBksmvTpk3w8fHBxo0bkZmZiUWLFiEoKAjvvPOOLAtbhuB/lnvz5s3o3LmzdOaRSqXC1q1bYWxsLN1c1WR3ZfzqrUu+++47KBQKWFhYyPb0pSG71eV+8uRJjBo1Cs2bN9druevS/7zvmq+vL2bPnl3hmwNyPHnJ0v08f9lyP3HiBIYNG4amTZvq/buub7e3tze2bt0KoOQ8mOXLlyM8PFyWLSgNwV+VNjYzMxOzZs3SetLTENzP8mdlZWHz5s3w9PTEli1bEB0djT59+sDHxwdjxozRab/K0l1R33L16lUEBwejWbNmsgzKWfuzs7MRGBiIyZMnY9u2bVAoFFiwYIHGAoZSqcSuXbvQtGlT2NnZoW3btrL4DdH9rInfgoICBAcH4+WXX5Yl5lj6q+r28PCAt7c3XnrpJY3Fnpror6o7JiYGYWFhMDMzQ2RkZI11V9Z/48YNhIWFSZNjcp37Y+ju0veL0dHRWLp0KczMzGR5O5Slv7Ixd/LkSfj4+MDGxgZt27aFs7Oz1veSLN1V8ZfmrbfegouLi9bnctcEt3qc7OzsjK5du6JVq1Z6LXdd+Kv6XYuNjcWcOXPw8ssv6+W7pit3eno6BgwYgNmzZ2uk+/r6YtasWRppmZmZWLhwoWzzYizdlfWXLnelUokPP/xQlrmxmuSWe16Mpb+qbjnnxVi6BQKB4SAWFWVA3Vhu3boVPXr0KHcOxOPHjxEUFISuXbuW60xrur8y7qlTp6J79+7IysoCUHIuhhyTvizdVfUXFBTg1q1b6NWrlyyD8prizsjIQEJCAlauXFnuczXR/yJ3eno6goKC0KtXLynm5IKluzL+suUeHx+PRYsWyfZmqqG7u3TpIts5Q4bir0q5Z2ZmSullnzyuae6q+Hv06CH1pQUFBdIZj7Xd3b17d+nt5x9//FGWp/kNwZ+bm4tt27Zh7969AP4d+Jdd3ANKzu09ffo0jhw5Ist5tYbqrmji98KFC2jXrp0sb+mx9lfWXVxcjPT0dJibm8PY2Fi27TdZ+qtS7klJSRgxYgRcXV1leQOfpbsq/tLbh8v1cFZNcAMl7cygQYPQunXrZ751UJP8VXE/evQIN2/eRERExAvfmjZ0d1X96lj766+/ZOlba4r77NmzWLNmDT755BNZthNn7a+KOzMzE1u3bkXv3r31/l2T2/3gwQN06dIFZ86cAfDvfFdAQAAmTpwI4N/ttEsjx7wYS3dl/WWRa26sJrnlnBdj7a+KW6VSyTovxtItEAgMB7GoKCO3bt2CtbU1AgMDpUl99c1xcnIyFAoF/u///k/6vFwDREPwV8b9yy+/yOYzFHdl/UeOHJG2OOHJrT4DRK6bZUPxV7bcdQFLd2X96nKXe5HL0N2l2xkW7buu/CLen+8/fPiwcNcif9m+cu/evVAoFJg/f740GVZUVISkpCRu3I8ePQJQ0peqt+HT9txOQ/JXxl1UVISHDx/i6NGjiI6Ols3N2l8Zd3FxMVJTU5GSkoKUlJRa4X6RX72Qr1QqZXtooSa4S5d7Wloabt++LXt7w9Jf2e+aXItKhuKurF+pVMqyjXhNcav71cLCQtkWcA3JX5U2tqioSO/9qq7cpReq1OezhoWF4Y033tD4XOntlOUaN7F0V8Vf+oFMntzqMYXc82Is/ZV1q51yzouxdAsEAsNALCrKzG+//QYTExPMnDlT4+bwn3/+gbu7u8bh67XNz6u7Mv4///yTSzfrcq+tMWfo5S7ivfb5eXWz9vPqNgR/cXGxNNHz7bffSk/Z37t3D3PnzsXIkSORnZ0t+wMEhu729/dHbm6u7F5D8L/IPWLECI23x2qTvzLlLsfZzIbmrox/5MiRzMqdpZt1ubOMudravlfWn5OTwyzvrNwjRoxgWu669Btyv6pLd+kFjKVLl2LgwIHSz+vWrcOWLVtQVFRU69ys/Ybs3rx5s052FjIEvyGXu67jXSAQsEMsKuqAQ4cOwcTEBCNHjsTevXtx/fp1hIaGwtbWVvanbA3Nz6ubtZ9XN2s/r27Wfl7drP28uln7eXUbgr/0NlV79+5F3bp14eLigjp16si2HWBNc+vjHBSW/me5jY2NdV7urP2GWO6GEHPiu147/YZa7izbmdqed1Huhvdd04cbKFno8PPzAwAsW7YMCoVCtu20DdHN2s+rm7WfV7dAIGCHWFTUEZcvX0bv3r3RqlUrODo6ynbQek3w8+pm7efVzdrPq5u1n1c3az+vbtZ+Xt2G4FepVNJAuW/fvrC0tJTtHBbhNky/yDt/btZ+Xt2s/by6Wft5dbP28+hWL2auWLEC06ZNw6ZNm2BiYoLLly/XajdrP69u1n5e3QKBgB1iUVGHPH36FLdv30ZUVJTO9uk3VD+vbtZ+Xt2s/by6Wft5dbP28+pm7efVbQj+4uJizJ07FwqFApGRkcLNgV/knT83az+vbtZ+Xt2s/by6Wft5da9ZswYKhQLm5ua4ePEiN27Wfl7drP28ugUCgf5RAAAJBAKBQCAQCASCciiVSvrqq6/Iy8uLOnXqJNwc+EXe+XOz9vPqZu3n1c3az6ubtZ9X96VLl6hLly4UHR1Nbm5u3LhZ+3l1s/bz6hYIBPpHLCoKBAKBQCAQCATPAQApFArh5sgv8s6fm7WfVzdrP69u1n5e3az9vLpzcnLI1NSUOzdrP69u1n5e3QKBQL+IRUWBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQPBcjFj/AwQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgWEjFhUFAoFAIBAIBAKBQCAQCAQCgUAgEAgEAsFzEYuKAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoHguYhFRYFAIBAIBAKBQCAQCAQCgUAgEAgEAoFA8FzEoqJAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIHguYlFRIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBA8F7GoKBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAInotYVBQIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBM9FLCoKBAKBQCAQCAQCgUAgEAgEAoFAIBAIBILn8v+RFEgIoCSxUAAAAABJRU5ErkJggg==",
"text/plain": [
""
]
@@ -1135,12 +1163,12 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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wYcPc//h68uRJ8/DDD5tOnTqZf/3rX1fkjAo78+0cd7vzfTXb7nxfzbY7387jjN3HWF/d5+wed1/d5+wed367+N7vB7vH3Vf3ObvHHWhsmNi7RtV++fjjH/9obrzxRrNnzx5jjPG4eejs2bNNZGSk+fTTTy+oa8z5tW38z//8j+nUqZNZuXKlx/+hY4wxb7zxhmnZsqX561//6nXet9Xm2JFtzLUx7r6WbXe+r2bbnX8tZNt1nLHzOHctjLvd+7uvfbb58v5e69NPPzXx8fEmKyvLGPN/Y1JRUWFmzZpl7rjjDvOnP/3Jvf2HH35oPv/880afXVZWZjp27HjB/zFfXV1tsrOzzW233Wb++7//f3vnHRfVsf7/Z0HFClIsKCoIgmBBQFQsEUsUbF9ULLFFsBE7XkWioBhNVDS5uSZEY2Isub9oEmPUeG0xscRooiKCVBEUsYGCSFVg9/P7g9ee7AIqsGd3Fs68/0k4Z+HtnOfZmXNm5sx8JhxPTU0VOmdqu5/ldWftl6qbtV+qbtZ+lvUM6zpWqjnH+rpLNedYufmzi3SfXVhfd6nlHOvrzuHUVvjAnp6hUCgqNFgDBgxA165dkZ2dDUC9YrO0tFS7gdGU4uJilJSUqFWOAwcO1Im/uLgYxcXFasfGjh2Ltm3b4vr16xU+b2tri9DQUFHcRUVFePHihTDzAwDGjx8PKysrrbsBtnFnGXMp57tUYw5It+ws6ziAbT0n1Zgr/VJs26Sc7+XJycnBoEGD4OPjg8zMTLVzeXl5cHV1xZw5c+qcu6ioCP7+/hgyZAiioqLUzr148QLjxo3D2LFjteIGgMLCQvj7+2Po0KE697O87qz9UnWz9kvVzdrPsp5jWccB0s05KbdtUsp3/uwivWcX3jclzXzncGo7BsTRGxISEmjx4sU0atQo2rRpE508eZKIiA4cOEClpaU0dOhQevDgAdWrV4+IiPLy8sjS0pJat24tij8xMZHmz59Pnp6etGLFCrp8+TIREe3du5eISKv+uLg48vf3p4EDB9KiRYsE56FDh8ja2pp8fX3p0qVLVFpaSkREJSUl1LZtW7KystLYHR8fTzNmzKC+ffvSrFmz6PDhw0REdPDgQerYsaNW3URs484y5lLOd6nGnEi6ZWdZxxGxreekGnMi6bZtUs53IqKcnBy6ffs2ZWZmUn5+PpmYmFB4eDidOHGC1q5dS3l5ecJnmzZtSiNHjqTk5GTh36MJWVlZdOPGDYqNjaXs7GwyMTGhrVu36sT95MkTun79OiUmJlJWVhY1bNiQli9fTomJiRQWFka3bt0SPmtkZESDBg2iO3fuUEFBgcZuIqKMjAw6f/48nT17lh4+fEiNGjWiFStWUEJCAq1bt06rfpYxJ2Ibd6nmO5F0yy7lfGdZz7Gs44ikm+9Sbtukmu/82UV6zy68b0qa+c7h1AlYjyxyyoiPj0fz5s0xYcIETJkyBa6urujSpQs2bdoEAEhKSoKzszNsbGywfft2/PzzzwgODoa5uTlSU1M19sfExMDc3Bx+fn5YsmQJunbtihUrVgjnY2Nj0aNHD634k5KS0Lx5c/j7+2Pp0qX4v//7P7Ro0UJYG7+oqAienp6wtLREUFAQdu7cicDAQDRv3hy3bt3SyH3z5k2YmppiwYIF+PDDD+Ht7Y0pU6agoKAAAJCfn49BgwZpxQ2wjTvLmEs536Uac0C6ZWdZxwFs6zmpxhyQbtsm5XwHgOjoaHTr1g12dnawtrbGuHHjcOPGDQDAkSNHYGRkhJkzZyIxMVH4nWnTpmHy5MkoLS3VyB0TE4MePXqgc+fOsLa2xnvvvYenT58CAI4dO6ZVd3R0NDp16gRbW1u0bdsWLi4uOHfuHADgxo0bMDExwZgxY3D69Gnhd+bNm4cRI0bgxYsXGrmBsrI7OTnByckJ1tbWGD58uLDs2o0bN2BsbKw1P8uYA+zjLsV8B6RbdqnnO6t6jmUdB0g736Xctkkx3/mzi/SeXXjflDTzncOpK/CBPT1ALpdjyZIleOedd4TXj2/duoWwsDCYm5vjgw8+AFD2ivKMGTPg4uICGxsb9OrVq9LX0avL3bt3YWNjg/fff184tmnTJkydOhV5eXnCMW35161bhxEjRgivfj958gS7du1Cw4YNERAQIHwuODgY3t7ecHBwqHT5h+py7949dOrUSa3ce/fuxdixY5GdnY1nz55pzQ2wjTvLmEs536Uac0DaZWdVxwFs6zkpxxyQbtsm1XwHgPv376N169YIDAzElStX8Pnnn8Pb2xvGxsa4cOECAODMmTOwsLBAv3794OnpiXfeeQdNmzZFTEyMRu64uDiYm5tjxYoViI+PR3h4OGxsbNQ2k//tt9+04n706BHat2+PoKAgJCcn48iRI5g6dSoMDQ2xe/duAGUP725ubnBxcYGTkxNGjx4NY2NjoWNYE+Lj49GiRQsEBwcjLS0NBw4cQKdOnYS9OYCyjkBt+FnGHGAbd6nmOyDdsks531nWcyzrOEC6+S7ltk2q+c6fXaT37ML7pqSb7xxOXYEP7OkJI0eOxIQJE9SOZWRkYOPGjbCyssL27duF448fP0ZGRoZaA1dT5HI5/vvf/2LRokXIyMgQji9ZsgRubm5wcHDApEmT8MUXX2jFDwAzZ87EoEGD1I4pN0Fu3Lix2prZRUVFePbsmdqa2zXl1KlTWLx4sdpG2itXroSdnR1sbGwwcOBAhISECOcKCwtFcythEXd9iLmU812qMQekW3ZWdRzAvp6TaswB6bZtUs73s2fPws3NTXiTAABSUlIwdepUNGzYEJcvXwZQ9tD8n//8BzNnzsTKlSsRFxenkTcrKwt9+/bFkiVLhGNyuRxDhgzBiRMn8Pfff+PevXsAymbeiukGyjrYunTpotbRWlRUhODgYNSrVw8HDx4EUNZxc/ToUQQGBuLjjz9We7uipuTk5GDgwIFYtGiR2vEhQ4Zg3759OHLkCBISEgAAaWlpovtZxRxgH3ep5jsg3bJLOd9Z1XOs6zhAuvku5bZNyvnOn12k9+zC+6akme8cTl2BD+zpCeHh4RgwYACSkpLUjqenp2POnDnw8vJSu5kWk/v37+PmzZvCz6GhoWjUqBH+/e9/Y/v27QgICED37t1x6dIlrfi/++472Nra4s8//1Q7npeXhw0bNsDV1bXCdRGDZ8+eITk5Wfh5/fr1aNKkCb788kv88MMP2Lx5M1q3bo3Dhw+L7lbCKu6sYy7lfJdqzAHplp1VHQewr+ekGnNAum2blPP9559/hoGBAR4+fKh2/OHDh5g0aRJsbGxw+/ZttXOqG8XXlPT0dHz22WdqOb9+/XoYGBjAwcEB3bt3h7W1dYW3F8RwA8CFCxcgk8mEa6+c6Q0AixcvRtOmTREbGyuKqzx5eXnYs2cPrl69KhxTlt3Z2Rlubm4wNDTU2nedVcwB9nGXar4D0i27lPOdVT3Huo4DpJvvUm7bpJzv/NlFes8uvG9KmvnO4dQV+MCenvD777/DxsYGa9euRVZWltq5M2fOwNDQUGevHK9YsQK//PKL8HNMTAyMjY3xww8/aMUXHR2Nfv36Ye7cuRVmWkVGRqJx48b49ddfteJWZePGjThx4oTw871799CuXTtERERozakvcdd1zPWl3IB0y67rcgPSLbu+1HGA7us5qcYc0J+46zrm+lJuQPdlz8jIQO/evbFy5Uo8f/5c7dzff/8Nd3d37Nu3DwBE2WtKlczMTOH/d+3aJcyoz8jIQFRUFAYPHgx/f3+8fPlSdLdcLoenpycmTJiAJ0+eAPinY/XBgwfw9PTEmjVroFAoRHcDUFuy58cff4SFhQUOHz6MZ8+eISsrCz4+Phg0aBAKCgrUOifFgGXMAbZxl2q+A9Itu5TznWU9x7KOA6Sb71Ju26Sc7/zZhf09vFSfVwHeN6VEF/nO4dQV+MAeQ8rfBH366acwNDTEhx9+iAcPHgjH09PT0a1bN9Er1PL+8j8rb5Tu3buHPn364Pz581pz79u3D+3bt8fChQsRHR0tHM/NzYWLiwtOnTqlNXdxcbHaz8qb1idPnuCtt94SfWYQy7jrU8ylnO9SiXllPqmUnWUdV5lfl/WcVGNemU8qbZuU8708QUFBcHFxwTfffIPCwkK1c25ubpg3b56ovsreTMjMzBSWRlPi4+ODcePGiepWJSIiAr1790ZoaGiFZXJGjRqFiRMniu6srOzZ2dkV9luZP38+hg0bJrpfia5jDuhP3KWa74B0yy7lfNd1PacvdRwg3XyXctsmpXznzy7/INVnF943JY1853DqIvWIwwxDQ0MCQBcvXqQBAwbQkiVLSC6XU1hYGKWnp9OYMWOoW7dutG3bNsrJySFLS0ut+C9fvkx9+/YlQ0NDIiICQDKZjAwMDIiIaPv27VRYWEj29vaiu3/66Sfy9fWl6dOnk0KhoE2bNlFaWhr5+vqSi4sL7du3jx4+fEhOTk6iu3/++WcaN24c1a9fn4j+KbdMJiMiok8//ZQyMzOpZ8+eorlV/Szirg8xl3K+Synmyr/Pouzl3bose3m3ruu48n5d1XPKv0uk+3yvzM3ie67ql1rbxrLcqn4WZZfL5WRoaEgKhYIMDAxo8+bNlJqaSp988gkVFhaSn58fNW7cmIiIrK2tqU2bNqK7VSktLaUWLVpQixYtiKjsGgAgY2Njsra2JgBERMI1qSnK8ir/O3/+fEpNTaXjx49TYWEhrV69mkxNTYmIyMzMjIyNjUkul5OBgYHGblW/KqWlpWRqaip4lfEvKSkhR0dHKi0tJUNDQ439LGOu6ldFV3GXar6r+qVWdinnO8t6jmUdB4AUCgWTuKu6VZFCzFX9qugq7lLNdyJpPquX90v12YX3TUkr3zmcOknNxwQ5mqCcmeDv7w87OztcvHhROPftt9/i7bffRtOmTdG1a1e0b99eazNElP7ys98AIDExEUFBQWjevDlu3Lghqh8AVq9ejbZt2+LQoUPCsePHj2PmzJlo0qQJunTpAnt7e628+q10HzlypMK5mzdvIjAwEKampqKXu6SkBACbuJd36zLmUst35bUG/pl1pKuyv86ti5hXtqGxrsr+Ore2y56ZmVlhpqku67jX+bVdz929e1dYLkU501JXMX+dm0W7BkivbVPWM6zKXd6v63YdKJtRvHr1avz222/CsZkzZ8LFxQWDBw/Gxo0b4e/vj2bNmiE+Pl4r7gsXLlR6Xi6XIzQ0FG3bthV9XxSFQoG5c+fi+++/F46tXbsWffr0QadOnbBs2TJMnDhRa3vxKP0//fRTpedfvHiB0NBQtGrVCgkJCaK7WcVc1c8q7lLMd1W/1Mou9XxnVc/puo7Lz8+v4NdV3F/lllrMVf2s2jap5LsSlv0U+tAnB0jv2YVln1xlfqn0y+lLvnM4dQ0+sKcjHj58iL///hsnT55Ue904ISEBAQEBFTqkMzMzcfPmTURGRuLx48c699+6dQv+/v7o3bu3xhWq0ld+DfS0tDSsW7cO2dnZasdfvnyJ+/fvIzU1VeONWqvrTk1NRVBQEAYNGqS29EBNefr0KRISEio0WklJSVqPe3XdYsb8/v37OH36NPbs2aOWb7ood038YpY9MTERK1euVNsA+nVuMcteXbeY5QaAqKgo9O/fv8J3Rxdlr65bzLLfvHkTDg4OiIiIUKtr0tLSEBYWptU6riZ+Meu5mzdvol69eujatavacV3EvLpusfM9JSUF//73v7Fs2TL88ccfwtJUumjbqusWM+YPHjzAlStX8Msvv+DFixfCgPKdO3d0ku/V9YvdrlfG6dOn0bp1a4SFhaGoqEg4/u2332LGjBnw8PDAuHHjtOJXdatO7ADK9sZYsGABLCwstNIZExkZCTc3NyxcuFBtabYLFy4gMDAQI0aMgJ+fH27evCm6u7y//PJNZ8+exZw5c9CqVSutlP3MmTPMYl7er824V7Z/kq7K/ia3tvM9KytLbX+v8n5tlv1Nbm2WPTk5GVeuXHmlW9v5/ia/tst+6NAhvHz5Ujimq3ruTW5t13GJiYmYNm0a0tPThWO6ivub3NqMeUFBAbKzs9XKd/XqVZ21bW/y67ptu3LlCrN2XdWtzXKz7Jdj2ScHSLdfjmWfXE38daVfjmWfHIcjJfjAng6Ijo5Ghw4dYG9vDxMTE3Tu3BnfffcdMjIyAKDCzaq++G/evClKx6unpyfu3bsH4J+GXFmxV7aWuljU1J2SkiJK52NMTAx69eoFBwcHtGzZEsOHD1c7r43NnjV1ixHzmJgY2Nvbw9XVFU2aNIGrq6vazbk2y62JX9OyKxQKFBYWwt3dHTKZDAEBARVyT1v5rolbjJgDwI0bN1C/fn2sWLGi0vPajHtN3WKUPSEhAaampli2bBnS0tIqnBd7Q3mx/GLUc1FRUWjSpAlGjhwJW1tb7Nu3D8A/bYo2Y15Tt1j5HhMTg5YtW8Lb2xsdO3aEtbW12gOItuv3mrjFiHl0dDSsra3h7u4OS0tLWFtbY8eOHXj06BEA7ed7Tf1itetJSUkICgrCzJkz8emnn+LWrVvCuTNnzgidEuXr2xcvXmh8r1dVtyr79u3D/PnzNX6TIiMjo9I3ooGy72JOTk6l5xQKhSjtXk38x44dQ0hIiMaz+lNTU/HJJ59g2bJlOHDggNq5s2fPajXm1fGrIlbck5KSsGTJEowcORLr1q1T+w5pu+xVdasiVrmBsjqjY8eOCA0NVdtvprxfG2WvqlsVscoeFRUFY2Nj7Ny5s8I5XeR7Vf2qiFX26OhotGjRAnPmzKlw3SMjI7Vaz9XELVYdB5TdRzdq1AgymQy7d+9WO6ftuFfVrYpYMY+NjcWoUaPg6OgIHx8fHDt2TDini7atJn6x4p6YmIjg4GBMmzYNW7ZsUdvH7urVq1ote03cYpWbZb8cyz455d+RYr8cyz45Tfy1vV+OVZ8chyNF+MCelsnMzETnzp2xatUqpKSk4MGDB5g0aRIcHR2xdu3aCrMxt23bhoMHDzL1//DDD6K479y5Azs7O8hkMnTq1EmYhfeqSvyTTz7Bli1bmLnDw8NFcQNlN6wWFhYIDg7G5cuXcerUKXTs2BHvv/9+pZ8XM+41cYsV84SEBFhYWCAkJARpaWlITU2FhYWF2oNKebeY+V4Tv1hlV7Jq1Sr4+fmhUaNGeOedd3Dnzp1XusUse3XdYpY7NjYWjRo1wpo1awCU3ZxnZWUhNTX1lX6xyl4Tt1hll8vlmDt3Lvz8/ISfL1y4gG+++QZJSUkVOqLFrONq6hernrtx4wYaN26M0NBQFBcXo0+fPpg+fforPy9mzGviFjPfHz58CEdHR4SFhQkPpE5OTvjiiy8q/byYca+JW6yYp6enw87ODuvWrcPDhw+hUCgwbtw4NGzYEEuXLq3QGSl2vtfEL2a7HhcXBxMTE3h5eWH8+PEwMTHB0KFDsWPHjko/f/fuXb1wFxQUaOSOj49HgwYN4Ovri+fPnwvHX3U/JfYyfJr4X7x4oZE7JiYGVlZWGDJkCPr27QsDA4PX5pSYMdfUr2nclRMIfH19MW/ePDRo0ABhYWFVcmuKJm5Ny61kx44dkMlkcHFxwYcffihMHniTn6Vb07Ir29Zly5ZV6fNil1sTv6ZlT0tLQ/v27V85Qaw8YtZzmrg1reOAfwbWgoKCsHz5cgwYMACPHj16ZR0rZtw1cWsa87i4OJiammLBggXYsWMH+vXrhylTprxygEHstk0Tv6Zxj4uLQ/PmzTFhwgQEBASgXbt2cHV1xeeff/5Gt6Zo4ta03Cz75Vj2yQHS7Zdj2SdXU39d6JfThz45DkdK8IE9LRMXFwdra2tcu3ZN7fjKlSvRrVs3hIeHCzemWVlZsLGxgZeXF/Ly8mq1v6ioCCEhIRg7dix+++03vPXWW+jQocMrG/Lnz59j6NCh8PT0rHRmXm1xA0BeXh4mTpyI+fPnC8fkcjkWLVqEMWPGVPi8mNedpTsnJwcjRozA0qVL1Y4PHz4cX331FT755BPEx8cLy3o8ffpU1Hxn7Vfm1ZIlSxAREYG4uDgYGRlhxowZKCgowJYtW4QHUrG/6yzdT58+hZ2dHVxcXIRjfn5+cHNzg6WlJd566y1ERUUJD6pi+lm6gbJZhv3798fevXsBAAMHDoSbmxtMTExga2uLefPmCTMTc3JyRK1nWPqTk5Mhk8mwevVq4diPP/4IIyMjnD17tsLnxbzuLN1KLl68iK5du6q9MTVp0iQsX74c06ZNwzfffCNc92fPnokad5bukydPonfv3njy5ImwVNjVq1dhYWEBFxcXrF27VlhKSux2lbX/5cuXmDZtGubMmSMcS05OxqRJk9CnTx/85z//Ufv81q1bMXTo0Ar3Xrp0l1/KriY8fvwYffv2xeDBg2FhYYEJEyaoDa6VZ+fOnbC3t8evv/6qsVsT/6lTpzR23717F3Z2dggKChLa2F27dqFVq1Zq3z8lYsZcE78YcU9NTYW1tbVax1NYWBjmz59fYXk0VbcYZa+pW4xyqxIdHY13330XGzZsQJs2bbB+/fpK3xoVO+41cYtR9lu3bsHIyEhoW4uLi3H06FHs3LkTR44cqdB+il3umvrFivsvv/yCESNGCO7Vq1fDx8cHs2fPFu6xgLLJY2LXczVxi1HHAcC1a9dgbGyMVatWAQD2798PExMTYc+l8s/MYsa9pm4xYl5YWAgfHx8sWbJEOHbkyBGMHTsWGRkZavmmjZjX1C9G3PPy8jB8+HAEBQUJx+7fvw9zc3O0atUKGzZsUPu8mGWvqVusfGfZL8fSLdV+OZb9Yqz9LPvFWPfJcThSpB5xtEpxcTGVlJRQYWEhEREVFRVRo0aNaNOmTVRUVETbt2+n4cOHU/fu3cnMzIzOnj1LcrmcmjZtWqv9DRs2JCcnJ+ratSsNHjyYbG1tafr06dS/f3+6ePEiWVlZkUKhIAMDAwJAxsbGtHfvXlIoFGRqalpr3UqaNGlCzs7Ows8GBgbUv39/OnfuHBUXF5NMJqP69esTADIzM6Pff/+dFAqFKHFn5TYxMaFRo0apuTds2EC//fYbFRcX0+PHj2nz5s20bds2mjhxIpmbm4ua76z9Sry8vOjgwYM0f/58+uOPP2jAgAF04cIFKikpofHjxxMRaeW7zsptbm5Ow4YNo+joaAoLC6Pjx4+Tubk5zZs3j1q0aEHh4eHk4+NDZ86cITs7O1H9LN1ERIaGhtSiRQvKycmhNWvWkJGREe3atYs6dOhAERER9MMPP9Du3bspJCSETExMaN++fSSXy0WrZ1j5GzZsSF988QUFBAQQEREA8vDwoJ49e9LRo0fJ09NTrY4Vu21h5Vby7NkzysjIoJSUFGrfvj1t27aNDh06RAEBAZSVlUXbt2+nqKgo+uijj6h58+aixp2l++7du5SamkoWFhbCsfz8fPLw8CBTU1PauXMn+fv7U/v27cnY2Fj0fGfpb9CgAWVkZJCNjQ0RleWdnZ0dhYeH09q1a+ngwYPUsWNHGjVqFBERWVhYUHFxMbVq1YqZ29LSUmN3VFQUWVtbU2BgICkUCvL29qbZs2fT119/TcbGxhU+b2VlRd27dydbW1uN3Zr4O3XqpJFXoVDQgQMHyM7OjlatWkUGBgZEROTu7k7169cnhUJR4XfEjLkmfk3jLpfL6aeffiJvb28KDg4Wjt+/f5/i4uKoX79+5ObmRiNGjKDRo0eruTUtuyZuMfJdFQB06dIl2r17N8nlctq5cyc1a9aMzp8/T46OjvThhx+q+cWIe03dmpa9tLSUPv/8c2ratCn16NGDiIh8fHzo/v37lJubS/fu3aPx48fT+++/Ty4uLmpuMcqtiV+suF+/fp2ys7OJiGjEiBFUWlpKzs7OFB8fT9euXaPExET66KOPSCaTUbt27USt52ri1rSOIyIqKCiggQMH0ty5c4Wcmjx5Mn399de0Zs0aOnXqFNWrp95VJFbcNXGLEXMjIyPKysoS8omI6I8//qCoqChydXUlBwcH6tWrF23cuJFkMpnobVtN/WLE3cDAgLKzs4XvWmFhIbVt25YGDx5M2dnZdPz4cXJ1dSVvb28iImrfvr1oZa+pW4xyExGVlJRQaWkpk345lu6GDRtS165dqVu3bkz65Vi5iYiaNWsm5BuRbvvkWPpNTExozJgx1L17d+GYrvrFWLo5HMmi+7HEuo9cLlfb56V///546623hJ9VlxHo2bMnJk+eDEC8vWlY+uVyeaUzahUKBVJSUoRZOvfv3xf+LZGRkcjNza3VbqVfuTa66lIhyreFvv/+e3Tr1k3td/Lz8+uEW3WjdyUXLlyAra0tjh49KsxAGzNmDHr27Cn8Xm33l/+uAcDp06fh4OAgzELy9vaGgYEBvL29X7ucUm1yK/2qdcmqVatgYWGBkSNHVlgXvUuXLnj33XcBiFfPsHSr/p0lS5aga9eumDp1Kr788ku1zy5fvhyOjo4oLi4Wbd8Clv7S0tIK+z+o/rxmzRqYmpoKeyEonWJ811i6lX9H9bp7eXmhRYsWGDJkCIyMjHDixAnh3KZNm9C+fftK9zysze5Hjx6hQ4cOmDJlCm7fvo2LFy+icePG2LRpEwDAwcEB69evB6Cd+xkWfuXfKi4uhp+fH3x9ffHixQsoFAoht1JSUuDh4YFJkyap/d7r3iyrDW6gbOko1TdhL1++DDMzM0yYMEFtDxzV6y3WUois/efPn0dwcLDaMblcDmtr60rfDgbEu+6s/enp6bh8+bLw8/r162FoaIjVq1dj27ZtcHd3x+DBg/Hw4cM65S7PsGHDhCXNw8PD0aRJE5iYmFR4e0Qbfl27b926hblz56JPnz5o164dRowYgYSEBBQWFuLatWto27YtZsyYoRW3Pvh//fVXDB48GF9//TXefvtt4VkxJycH69atQ58+fXDz5k3h82LWcyzdqkv2K+vRr776Cvb29oiMjARQ+Rs1tdktl8vx/PlzDB8+HGPHjkVERATef/99NGrUCLt378aJEyewbt06uLq64vDhw8LviXXdWfoVCgUyMjLQpk0btWUW09PT4eTkhL1796J79+6YPXu22u/Vdrcq7u7uGDRokPCztvvl9MWtiq76xli75XK5zvvF9MFf2TO/rvrlWLo5HCnDB/ZEJi4uDlOnTsWQIUMwe/ZsnDt3DpGRkbC1tcWECROEzyk7JJctW4bRo0fXCb+qe968eWprKCsr+du3bwsNeWpqKhYsWICePXtWusRMbXGX98+fP1/Nr7zWP/74I7p06SIcX7ZsGUaNGqXxzZu+uMtf9zt37iAlJUXt37Flyxb07t270gHY2uZXdQcEBODYsWOQy+XIy8uDl5cXgLJlIa2srLBnzx40bdoUY8aMEW5ga6u7vF9ZzwDAl19+iQMHDgjfOWV+jR8/Hr6+vnXKPXfuXJw/fx75+fno378/ZDKZsJyQktOnT8PZ2VmUOoa1v7KcU6IcWH/y5AkcHR0RHBws6gbsLN3l/bNnz8aff/4JoGy5tIMHD8LNzQ1Pnz4V6plLly7Bzs4OSUlJdcY9b948HD58GIcOHYKTkxPMzMxgZmamthdS//79KwxE1GZ/+fbx3LlzMDQ0VFv6UvmZc+fOwcDAALGxsaINZOuLW4nyb//111/C4Nrz589RXFyML774AidPngRQ+UN9bfG/yq06UcDGxganT58Wzp05c0a0iTMs/a9yP336FEuXLlWbQBAfHw+ZTKZ2rLa6X+f39PQUlkKcNWsWjI2N0bp1a4SHh1fY07MuuG/fvo3p06dj5MiRSExMVDt39OhRyGQyJCUlidbpzNJf/m8kJCSgTZs2cHJywtChQ9XO3bt3D40bN8Z3332nsZe1u7y/svoyLy8P7dq1w4IFC9SOizVBTB/cQFlb4uXlhSlTpsDBwQG7du0Szj1+/Bjt27fHxo0bNfbqg7+8+/PPP4dMJoO/vz9CQkLQtGlTYbnvH3/8EdbW1mr3lrXVnZ+fj9zcXLUB4evXr6Nly5Z45513hGPa6Bdj6X6VH1AfRNFW35i+u7XVL8ba/yo3ULbkrTb7xVi6ORwOH9gTlcTERJiYmGDy5MkIDg6Gs7Mz3N3d8d577+G7775Dx44d4ePjg+LiYqFynzZtGiZPnoySkhKNb1pZ+itz9+zZU21tZeXfT0lJgaenJ2QyGZo0aaLxWvks3VX1A8D//vc/ODg4AIAwM091VnJdcavuF1D+pnzWrFnw9/cX5Wadpf9V7n/9618oLi7G6NGj0aJFC7Rq1QpXr14FUDZbqVWrVhp3yLB0v8rfo0ePV24CrVAo4OvrizVr1gg/1yX38uXLcenSJfTt2xft2rXDyZMnhVl3//rXvzBw4EBRZpuy9FelnlG+Nezn5wcPDw/RbtRZul/ld3Z2xvLlywEAf/75JxwdHdV+Z/ny5ejZs6fGe0Pok7t79+7w8PBAYGAgcnNzkZCQIMyoB8pm2np5eSEiIgKA5p1wrP1JSUnYunWr2ltBQNkePwYGBvjqq6/UjkdGRsLR0VHtDYS65C7P33//DTMzM0ycOBF+fn6oX78+bt++Xav9lblV86ikpAT5+fmws7PDX3/9BaDsfkomk4nStrL0v+m6K9sQ5duiMTExcHV1RUxMjEZe1u5X+ZVtyMqVK/Htt99i0aJFaNOmDVJTU/HRRx+hcePG+PjjjzXuhNM3NwCkpaXhxIkTwr9DmYMHDx5E586dRZuoxNL/KvexY8dQr149tGzZEpcuXRKOv3z5EoMHDxYmD9RW9+v8SpR5FRERAVtbW1H3jtRHd35+PkpLS+Hh4YHvv/9eOF5cXIy3335buKeozf7K3HK5HHv27IG7uzu8vLywefNm4dxnn30GFxcXUQZTWbrj4uIwbNgwuLi4oE2bNvjvf/8LoGzPt/3798PCwgK+vr5a6Rdj6X6dv7K/K3bfWG1wa6NfjLW/OtddiVj9YizdHA6nDD6wJxIKhQKrVq3CxIkThWO5ubn44IMP0KtXL0yZMgWHDx+Gvb097O3t4ePjg4kTJ6JJkyZqy2vURv+r3Bs2bECPHj2EWVhKXr58icmTJ8PMzAxxcXG11l1d/6FDh9CnTx+sWrUKDRo0UOuUrGvu2bNnqzXmxcXFCAkJgYWFBRISEjRys/a/zt2tWzf4+flh9erVGDlypHCDqnxYLSoqqrXuN/m7d++OOXPmVOiIDAkJgaWlJZKTk+uce/369XB1dcWsWbMQExOD/v37w8rKCs7Ozhg9ejSaN2+OGzduaORm7a9uHZuamgqZTFZhWdDa5n6T39nZGQsXLsSzZ8/QqVMn9O3bF6GhoZg1axbMzc21et1ZudevX49u3brhvffeU/t8bm4ugoOD0bJlS2FGZm32Jycnw8zMDDKZDO+//z6ePHkinCsoKMC6desgk8kQEhKC69evIysrC8HBwbCzs0NmZmaddFfGxYsXIZPJYGZmpvE9BWt/VdxyuRxFRUVCh/MHH3wg2gQxlv7XuZVtavnOmVWrVqF3795azTltu9/kB4BvvvkGMpkMlpaWwkQpANi8eTNu3bpVZ92VdcYtX74cw4cPF2UpRJb+N7n3798PAwMDDB8+HPv370dycjKCg4PRpk0b3Lt3r9a6q+JXRbn8qVgDW/rqlsvlyM/PR+/evREaGopnz54hLy8PoaGhsLS0RGpqaq32v+m6FxUVqS0HCQALFy6Er68vioqKNBpgYumOi4uDubk5AgMD8f/+3//DsmXLUL9+fVy/fh1A2f3U0aNHYWVlhc6dO4vaL8bS/Tp/VFRUpZ8Xs2+striPHDkiar8Ya391r7uY/WIs3RwO5x/4wJ6IzJw5U20vO6Csw2nLli3w8PBAeHg4cnNzsXLlSsyePRsLFy4UZXBJH/yvcm/duhU9e/YU9sBRKBTYtm0bDA0NhRuc2uyuil+5jMb3338PmUwGU1NT0WYh6rNbed3PnDmD8ePHw8rKSqfXXZv+133XPD09sXjx4kpn0IsxA5Gl+3X+8tf9119/xejRo9G6dWudf9d17XZ3d8e2bdsAlO0PsmbNGmzatEmU5RD1wV+dOjY3NxeLFi3SuONRH9yv8ufl5WHr1q1wdXXFxx9/jNjYWAwaNAgeHh6YMGGCVttVlu7K2pYbN24gICAAbdq0EeXBmLU/Pz8f/v7+mDlzJiIiIiCTybBixQq1QQS5XI69e/eidevWaNu2LTp37iyKXx/dr+p8ffnyJQICAtCsWTNRco6lv7puFxcXuLu7o0GDBmoDLrXRX113XFwcQkJCYGxsjOjo6Frrrqo/KSkJISEhQgeVWPvA6Ltb9X4xNjYWq1evhrGxsShvSbL0VzXnzpw5Aw8PD7Rq1QqdO3eGvb29xveSLN3V8avy7rvvwsHBQeN9mmuDW/mcbG9vj969e6NDhw46ve7a8Ff3u5aQkIClS5eiWbNmOvmuacudlZWFYcOGYfHixWrHPT09sWjRIrVjubm5CAoKEq1fjKW7qn7V6y6Xy/HZZ5+J0jdWm9xi94ux9FfXLWa/GEs3h8NRhw/siYCywtq2bRv69etXYV+A7OxszJ49G717967QoNV2f1Xcc+bMQd++fZGXlwegbJ8EMTpeWbqr63/58iVu376NAQMGiPJgXFvcOTk5SElJQVhYWIXP1Ub/m9xZWVmYPXs2BgwYIOScWLB0V8Vf/ronJydj5cqVor2hqe/uXr16aWWzc5b+6lx31c3Oy8/ArW3u6vj79esntKUvX74U9vyr6+6+ffsKbwH/9NNPosxq1wd/YWEhIiIicODAAQD/PHyXH2ADyvZxPX/+PE6cOCHK/qX66q6s8/XKlSvo0qWLKG+rsfZX1V1aWoqsrCyYmJjA0NBQtKUgWfqrc93T0tIwduxYODo6ivImOkt3dfyqS1mLNUGqNriBsnrGy8sLHTt2fOXs+9rkr4776dOnuHXrFqKiot749rC+u6vrV+baX3/9JUrbWlvcFy9exIYNG7Bjxw5RlrZm7a+OOzc3F9u2bcPAgQN1/l0T2/348WP06tU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",
"text/plain": [
""
]
@@ -1189,13 +1217,6 @@
"\n",
"plt.show()"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -1214,7 +1235,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.0"
+ "version": "3.11.9"
}
},
"nbformat": 4,
diff --git a/openenergyid/__init__.py b/openenergyid/__init__.py
index 63cd72a..4889080 100644
--- a/openenergyid/__init__.py
+++ b/openenergyid/__init__.py
@@ -1,6 +1,6 @@
"""Open Energy ID Python SDK."""
-__version__ = "0.1.19"
+__version__ = "0.1.20"
from .enums import Granularity
from .models import TimeDataFrame, TimeSeries
diff --git a/openenergyid/dyntar/__init__.py b/openenergyid/dyntar/__init__.py
index 8a24c23..dd077c7 100644
--- a/openenergyid/dyntar/__init__.py
+++ b/openenergyid/dyntar/__init__.py
@@ -1,9 +1,10 @@
"""Dynamic Tariff Analysis module."""
-from .main import calculate_dyntar_columns
+from .main import calculate_dyntar_columns, summarize_result
from .models import (
DynamicTariffAnalysisInput,
DynamicTariffAnalysisOutput,
+ DynamicTariffAnalysisOutputSummary,
OutputColumns,
RequiredColumns,
)
@@ -12,6 +13,8 @@
"calculate_dyntar_columns",
"DynamicTariffAnalysisInput",
"DynamicTariffAnalysisOutput",
+ "DynamicTariffAnalysisOutputSummary",
"OutputColumns",
"RequiredColumns",
+ "summarize_result",
]
diff --git a/openenergyid/dyntar/const.py b/openenergyid/dyntar/const.py
index 7786743..558b900 100644
--- a/openenergyid/dyntar/const.py
+++ b/openenergyid/dyntar/const.py
@@ -1,5 +1,7 @@
"""Constants for the dyntar analysis."""
+from enum import Enum
+
ELECTRICITY_DELIVERED_SMR3 = "electricity_delivered_smr3"
ELECTRICITY_EXPORTED_SMR3 = "electricity_exported_smr3"
ELECTRICITY_DELIVERED_SMR2 = "electricity_delivered_smr2"
@@ -20,3 +22,10 @@
HEATMAP_DELIVERED_DESCRIPTION = "heatmap_delivered_description"
HEATMAP_EXPORTED_DESCRIPTION = "heatmap_exported_description"
HEATMAP_TOTAL_DESCRIPTION = "heatmap_total_description"
+
+
+class Register(Enum):
+ """Register for dynamic tariff analysis."""
+
+ DELIVERY = "delivery"
+ EXPORT = "export"
diff --git a/openenergyid/dyntar/main.py b/openenergyid/dyntar/main.py
index f916c71..a96863d 100644
--- a/openenergyid/dyntar/main.py
+++ b/openenergyid/dyntar/main.py
@@ -1,6 +1,6 @@
"""Main module of the DynTar package."""
-import numpy as np
+from typing import cast
import pandas as pd
from openenergyid.const import (
@@ -29,6 +29,7 @@
HEATMAP_DELIVERED_DESCRIPTION,
HEATMAP_EXPORTED_DESCRIPTION,
HEATMAP_TOTAL_DESCRIPTION,
+ Register,
)
@@ -40,15 +41,28 @@ def weigh_by_monthly_profile(df: pd.DataFrame, series_name, profile_name) -> pd.
)
-def extend_dataframe_with_smr2(df: pd.DataFrame, inplace: bool = False) -> pd.DataFrame | None:
+def extend_dataframe_with_smr2(
+ df: pd.DataFrame,
+ inplace: bool = False,
+ registers: list[Register] | None = None,
+) -> pd.DataFrame | None:
"""Extend a DataFrame with the SMR2 columns."""
if not inplace:
result_df = df.copy()
else:
result_df = df
- result_df[ELECTRICITY_DELIVERED_SMR2] = weigh_by_monthly_profile(df, ELECTRICITY_DELIVERED, RLP)
- result_df[ELECTRICITY_EXPORTED_SMR2] = weigh_by_monthly_profile(df, ELECTRICITY_EXPORTED, SPP)
+ if registers is None:
+ registers = [Register.DELIVERY, Register.EXPORT]
+
+ if Register.DELIVERY in registers:
+ result_df[ELECTRICITY_DELIVERED_SMR2] = weigh_by_monthly_profile(
+ df, ELECTRICITY_DELIVERED, RLP
+ )
+ if Register.EXPORT in registers:
+ result_df[ELECTRICITY_EXPORTED_SMR2] = weigh_by_monthly_profile(
+ df, ELECTRICITY_EXPORTED, SPP
+ )
result_df.rename(
columns={
@@ -56,6 +70,7 @@ def extend_dataframe_with_smr2(df: pd.DataFrame, inplace: bool = False) -> pd.Da
ELECTRICITY_EXPORTED: ELECTRICITY_EXPORTED_SMR3,
},
inplace=True,
+ errors="ignore",
)
if not inplace:
@@ -63,26 +78,33 @@ def extend_dataframe_with_smr2(df: pd.DataFrame, inplace: bool = False) -> pd.Da
return None
-def extend_dataframe_with_costs(df: pd.DataFrame, inplace: bool = False) -> pd.DataFrame | None:
+def extend_dataframe_with_costs(
+ df: pd.DataFrame, inplace: bool = False, registers: list[Register] | None = None
+) -> pd.DataFrame | None:
"""Extend a DataFrame with the cost columns."""
if not inplace:
result_df = df.copy()
else:
result_df = df
- result_df[COST_ELECTRICITY_DELIVERED_SMR2] = (
- df[ELECTRICITY_DELIVERED_SMR2] * df[PRICE_ELECTRICITY_DELIVERED]
- )
- result_df[COST_ELECTRICITY_EXPORTED_SMR2] = (
- df[ELECTRICITY_EXPORTED_SMR2] * df[PRICE_ELECTRICITY_EXPORTED] * -1
- )
+ if registers is None:
+ registers = [Register.DELIVERY, Register.EXPORT]
- result_df[COST_ELECTRICITY_DELIVERED_SMR3] = (
- df[ELECTRICITY_DELIVERED_SMR3] * df[PRICE_ELECTRICITY_DELIVERED]
- )
- result_df[COST_ELECTRICITY_EXPORTED_SMR3] = (
- df[ELECTRICITY_EXPORTED_SMR3] * df[PRICE_ELECTRICITY_EXPORTED] * -1
- )
+ if Register.DELIVERY in registers:
+ result_df[COST_ELECTRICITY_DELIVERED_SMR2] = (
+ df[ELECTRICITY_DELIVERED_SMR2] * df[PRICE_ELECTRICITY_DELIVERED]
+ )
+ result_df[COST_ELECTRICITY_DELIVERED_SMR3] = (
+ df[ELECTRICITY_DELIVERED_SMR3] * df[PRICE_ELECTRICITY_DELIVERED]
+ )
+
+ if Register.EXPORT in registers:
+ result_df[COST_ELECTRICITY_EXPORTED_SMR2] = (
+ df[ELECTRICITY_EXPORTED_SMR2] * df[PRICE_ELECTRICITY_EXPORTED] * -1
+ )
+ result_df[COST_ELECTRICITY_EXPORTED_SMR3] = (
+ df[ELECTRICITY_EXPORTED_SMR3] * df[PRICE_ELECTRICITY_EXPORTED] * -1
+ )
if not inplace:
return result_df
@@ -90,63 +112,70 @@ def extend_dataframe_with_costs(df: pd.DataFrame, inplace: bool = False) -> pd.D
def extend_dataframe_with_weighted_prices(
- df: pd.DataFrame, inplace: bool = False
+ df: pd.DataFrame, inplace: bool = False, registers: list[Register] | None = None
) -> pd.DataFrame | None:
"""Extend a DataFrame with the weighted price columns."""
if not inplace:
df = df.copy()
- rlp_weighted_price_delivered = (df[PRICE_ELECTRICITY_DELIVERED] * df[RLP]).resample(
- "MS"
- ).sum() / df[RLP].resample("MS").sum()
- df[RLP_WEIGHTED_PRICE_DELIVERED] = rlp_weighted_price_delivered.reindex_like(
- df[RLP], method="ffill"
- )
- spp_weighted_price_exported = (df[PRICE_ELECTRICITY_EXPORTED] * df[SPP]).resample(
- "MS"
- ).sum() / df[SPP].resample("MS").sum()
- df[SPP_WEIGHTED_PRICE_EXPORTED] = spp_weighted_price_exported.reindex_like(
- df[SPP], method="ffill"
- )
+ if registers is None:
+ registers = [Register.DELIVERY, Register.EXPORT]
+
+ if Register.DELIVERY in registers:
+ rlp_weighted_price_delivered = (df[PRICE_ELECTRICITY_DELIVERED] * df[RLP]).resample(
+ "MS"
+ ).sum() / df[RLP].resample("MS").sum()
+ df[RLP_WEIGHTED_PRICE_DELIVERED] = rlp_weighted_price_delivered.reindex_like(
+ df[RLP], method="ffill"
+ )
+
+ if Register.EXPORT in registers:
+ spp_weighted_price_exported = (df[PRICE_ELECTRICITY_EXPORTED] * df[SPP]).resample(
+ "MS"
+ ).sum() / df[SPP].resample("MS").sum()
+ df[SPP_WEIGHTED_PRICE_EXPORTED] = spp_weighted_price_exported.reindex_like(
+ df[SPP], method="ffill"
+ )
if not inplace:
return df
return None
-def extend_dataframe_with_heatmap(df: pd.DataFrame, inplace: bool = False) -> pd.DataFrame | None:
+def extend_dataframe_with_heatmap(
+ df: pd.DataFrame, inplace: bool = False, registers: list[Register] | None = None
+) -> pd.DataFrame | None:
"""Extend a DataFrame with the heatmap columns."""
if not inplace:
df = df.copy()
- normalized_energy_delta_delivered = (
- df[ELECTRICITY_DELIVERED_SMR2] - df[ELECTRICITY_DELIVERED_SMR3]
- ) / df[ELECTRICITY_DELIVERED_SMR2]
- normalized_price_delta_delivered = (
- df[RLP_WEIGHTED_PRICE_DELIVERED] - df[PRICE_ELECTRICITY_DELIVERED]
- ) / df[RLP_WEIGHTED_PRICE_DELIVERED]
- heatmap_score_delivered = normalized_energy_delta_delivered * normalized_price_delta_delivered
-
- normalized_energy_delta_exported = (
- df[ELECTRICITY_EXPORTED_SMR2] - df[ELECTRICITY_EXPORTED_SMR3]
- ) / df[ELECTRICITY_EXPORTED_SMR2]
- normalized_energy_delta_exported = normalized_energy_delta_exported.replace(
- [np.inf, -np.inf], np.nan
- )
- normalized_price_delta_exported = (
- df[SPP_WEIGHTED_PRICE_EXPORTED] - df[PRICE_ELECTRICITY_EXPORTED]
- ) / df[SPP_WEIGHTED_PRICE_EXPORTED]
- heatmap_score_exported = normalized_energy_delta_exported * normalized_price_delta_exported
-
- heatmap_score_delivered.fillna(0, inplace=True)
- heatmap_score_exported.fillna(0, inplace=True)
-
- # Invert scores so that positive values indicate a positive impact
- heatmap_score_delivered = -heatmap_score_delivered
- heatmap_score_combined = heatmap_score_delivered + heatmap_score_exported
-
- df[HEATMAP_DELIVERED] = heatmap_score_delivered
- df[HEATMAP_EXPORTED] = heatmap_score_exported
+ if registers is None:
+ registers = [Register.DELIVERY, Register.EXPORT]
+
+ if Register.DELIVERY in registers:
+ energy_delta_delivered = df[ELECTRICITY_DELIVERED_SMR2] - df[ELECTRICITY_DELIVERED_SMR3]
+ price_delta_delivered = df[RLP_WEIGHTED_PRICE_DELIVERED] - df[PRICE_ELECTRICITY_DELIVERED]
+ heatmap_score_delivered = energy_delta_delivered * price_delta_delivered
+ heatmap_score_delivered.fillna(0, inplace=True)
+ # Invert score so that positive values indicate a positive impact
+ heatmap_score_delivered = -heatmap_score_delivered
+ df[HEATMAP_DELIVERED] = heatmap_score_delivered
+
+ if Register.EXPORT in registers:
+ energy_delta_exported = df[ELECTRICITY_EXPORTED_SMR2] - df[ELECTRICITY_EXPORTED_SMR3]
+ price_delta_exported = df[SPP_WEIGHTED_PRICE_EXPORTED] - df[PRICE_ELECTRICITY_EXPORTED]
+ heatmap_score_exported = energy_delta_exported * price_delta_exported
+ heatmap_score_exported.fillna(0, inplace=True)
+ df[HEATMAP_EXPORTED] = heatmap_score_exported
+
+ if Register.DELIVERY in registers and Register.EXPORT in registers:
+ heatmap_score_delivered = cast(pd.Series, df[HEATMAP_DELIVERED])
+ heatmap_score_exported = cast(pd.Series, df[HEATMAP_EXPORTED])
+ heatmap_score_combined = heatmap_score_delivered + heatmap_score_exported
+ elif Register.DELIVERY in registers:
+ heatmap_score_combined = heatmap_score_delivered
+ else:
+ heatmap_score_combined = heatmap_score_exported
df[HEATMAP_TOTAL] = heatmap_score_combined
if not inplace:
@@ -194,56 +223,90 @@ def map_total_description(
def extend_dataframe_with_heatmap_description(
- df: pd.DataFrame, inplace: bool = False
+ df: pd.DataFrame, inplace: bool = False, registers: list[Register] | None = None
) -> pd.DataFrame | None:
"""Extend a DataFrame with the heatmap description columns."""
if not inplace:
df = df.copy()
- df[HEATMAP_DELIVERED_DESCRIPTION] = list(
- map(
- map_delivery_description,
- df[PRICE_ELECTRICITY_DELIVERED],
- df[RLP_WEIGHTED_PRICE_DELIVERED],
- df[ELECTRICITY_DELIVERED_SMR3],
- df[ELECTRICITY_DELIVERED_SMR2],
+ if registers is None:
+ registers = [Register.DELIVERY, Register.EXPORT]
+
+ if Register.DELIVERY in registers:
+ df[HEATMAP_DELIVERED_DESCRIPTION] = list(
+ map(
+ map_delivery_description,
+ df[PRICE_ELECTRICITY_DELIVERED],
+ df[RLP_WEIGHTED_PRICE_DELIVERED],
+ df[ELECTRICITY_DELIVERED_SMR3],
+ df[ELECTRICITY_DELIVERED_SMR2],
+ )
)
- )
- df[HEATMAP_EXPORTED_DESCRIPTION] = list(
- map(
- map_export_description,
- df[PRICE_ELECTRICITY_EXPORTED],
- df[SPP_WEIGHTED_PRICE_EXPORTED],
- df[ELECTRICITY_EXPORTED_SMR3],
- df[ELECTRICITY_EXPORTED_SMR2],
+
+ if Register.EXPORT in registers:
+ df[HEATMAP_EXPORTED_DESCRIPTION] = list(
+ map(
+ map_export_description,
+ df[PRICE_ELECTRICITY_EXPORTED],
+ df[SPP_WEIGHTED_PRICE_EXPORTED],
+ df[ELECTRICITY_EXPORTED_SMR3],
+ df[ELECTRICITY_EXPORTED_SMR2],
+ )
)
- )
- df[HEATMAP_TOTAL_DESCRIPTION] = list(
- map(
- map_total_description,
- df[HEATMAP_DELIVERED].abs(),
- df[HEATMAP_EXPORTED].abs(),
- df[HEATMAP_DELIVERED_DESCRIPTION],
- df[HEATMAP_EXPORTED_DESCRIPTION],
+ if Register.DELIVERY in registers and Register.EXPORT in registers:
+ df[HEATMAP_TOTAL_DESCRIPTION] = list(
+ map(
+ map_total_description,
+ df[HEATMAP_DELIVERED].abs(),
+ df[HEATMAP_EXPORTED].abs(),
+ df[HEATMAP_DELIVERED_DESCRIPTION],
+ df[HEATMAP_EXPORTED_DESCRIPTION],
+ )
)
- )
+ elif Register.DELIVERY in registers:
+ df[HEATMAP_TOTAL_DESCRIPTION] = df[HEATMAP_DELIVERED_DESCRIPTION]
+ else:
+ df[HEATMAP_TOTAL_DESCRIPTION] = df[HEATMAP_EXPORTED_DESCRIPTION]
if not inplace:
return df
-def calculate_dyntar_columns(df: pd.DataFrame, inplace: bool = False) -> pd.DataFrame | None:
+def calculate_dyntar_columns(
+ df: pd.DataFrame,
+ inplace: bool = False,
+ registers: list[Register] | None = None,
+) -> pd.DataFrame | None:
"""Calculate all columns required for the dynamic tariff analysis."""
if not inplace:
df = df.copy()
- extend_dataframe_with_smr2(df, inplace=True)
- extend_dataframe_with_costs(df, inplace=True)
- extend_dataframe_with_weighted_prices(df, inplace=True)
- extend_dataframe_with_heatmap(df, inplace=True)
- extend_dataframe_with_heatmap_description(df, inplace=True)
+ if registers is None:
+ registers = [Register.DELIVERY, Register.EXPORT]
+
+ extend_dataframe_with_smr2(df, inplace=True, registers=registers)
+ extend_dataframe_with_costs(df, inplace=True, registers=registers)
+ extend_dataframe_with_weighted_prices(df, inplace=True, registers=registers)
+ extend_dataframe_with_heatmap(df, inplace=True, registers=registers)
+ extend_dataframe_with_heatmap_description(df, inplace=True, registers=registers)
if not inplace:
return df
return None
+
+
+def summarize_result(df: pd.DataFrame) -> pd.Series:
+ """Summarize the dynamic tariff analysis result."""
+ summary = df.filter(like="cost").sum()
+
+ abs_smr2 = summary.filter(like="smr2").abs().sum()
+
+ summary["cost_electricity_total_smr2"] = summary.filter(like="smr2").sum()
+ summary["cost_electricity_total_smr3"] = summary.filter(like="smr3").sum()
+
+ summary["ratio"] = (
+ summary["cost_electricity_total_smr3"] - summary["cost_electricity_total_smr2"]
+ ) / abs_smr2
+
+ return summary
diff --git a/openenergyid/dyntar/models.py b/openenergyid/dyntar/models.py
index 862b54b..190a8c7 100644
--- a/openenergyid/dyntar/models.py
+++ b/openenergyid/dyntar/models.py
@@ -1,9 +1,10 @@
"""Models for dynamic tariff analysis."""
from typing import Literal
-from pydantic import Field, conlist, confloat
+from pydantic import Field, conlist, confloat, BaseModel
from openenergyid.models import TimeDataFrame
+from .const import Register
RequiredColumns = Literal[
@@ -43,18 +44,53 @@ class DynamicTariffAnalysisInput(TimeDataFrame):
"""Input frame for dynamic tariff analysis."""
columns: list[RequiredColumns] = Field(
- min_length=len(RequiredColumns.__args__),
+ min_length=3,
max_length=len(RequiredColumns.__args__),
examples=[RequiredColumns.__args__],
)
data: list[
conlist(
item_type=confloat(allow_inf_nan=True),
- min_length=len(RequiredColumns.__args__),
+ min_length=3,
max_length=len(RequiredColumns.__args__),
) # type: ignore
] = Field(examples=[[0.0] * len(RequiredColumns.__args__)])
+ @property
+ def registers(self) -> list[Register]:
+ """Check which registers are present in the input data."""
+ registers = []
+ columns = list(self.columns)
+ # if "electricity_delivered", "price_electricity_delivered" and "RLP" are present
+ if all(
+ column in columns
+ for column in [
+ "electricity_delivered",
+ "price_electricity_delivered",
+ "RLP",
+ ]
+ ):
+ registers.append(Register.DELIVERY)
+ # if "electricity_exported", "price_electricity_exported" and "SPP" are present
+ if all(
+ column in columns
+ for column in ["electricity_exported", "price_electricity_exported", "SPP"]
+ ):
+ registers.append(Register.EXPORT)
+ return registers
+
+
+class DynamicTariffAnalysisOutputSummary(BaseModel):
+ """Summary of the dynamic tariff analysis output."""
+
+ cost_electricity_delivered_smr2: float | None = None
+ cost_electricity_delivered_smr3: float | None = None
+ cost_electricity_exported_smr2: float | None = None
+ cost_electricity_exported_smr3: float | None = None
+ cost_electricity_total_smr2: float | None = None
+ cost_electricity_total_smr3: float | None = None
+ ratio: float | None = None
+
class DynamicTariffAnalysisOutput(TimeDataFrame):
"""Output frame for dynamic tariff analysis."""
@@ -71,3 +107,4 @@ class DynamicTariffAnalysisOutput(TimeDataFrame):
max_length=len(OutputColumns.__args__),
) # type: ignore
] = Field(examples=[[0.0] * len(OutputColumns.__args__)])
+ summary: DynamicTariffAnalysisOutputSummary | None = None
diff --git a/poetry.lock b/poetry.lock
index 3259dbf..b523296 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,5 +1,28 @@
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+[[package]]
+name = "altair"
+version = "5.4.1"
+description = "Vega-Altair: A declarative statistical visualization library for Python."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "altair-5.4.1-py3-none-any.whl", hash = "sha256:0fb130b8297a569d08991fb6fe763582e7569f8a04643bbd9212436e3be04aef"},
+ {file = "altair-5.4.1.tar.gz", hash = "sha256:0ce8c2e66546cb327e5f2d7572ec0e7c6feece816203215613962f0ec1d76a82"},
+]
+
+[package.dependencies]
+jinja2 = "*"
+jsonschema = ">=3.0"
+narwhals = ">=1.5.2"
+packaging = "*"
+typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""}
+
+[package.extras]
+all = ["altair-tiles (>=0.3.0)", "anywidget (>=0.9.0)", "numpy", "pandas (>=0.25.3)", "pyarrow (>=11)", "vega-datasets (>=0.9.0)", "vegafusion[embed] (>=1.6.6)", "vl-convert-python (>=1.6.0)"]
+dev = ["geopandas", "hatch", "ibis-framework[polars]", "ipython[kernel]", "mistune", "mypy", "pandas (>=0.25.3)", "pandas-stubs", "polars (>=0.20.3)", "pytest", "pytest-cov", "pytest-xdist[psutil] (>=3.5,<4.0)", "ruff (>=0.6.0)", "types-jsonschema", "types-setuptools"]
+doc = ["docutils", "jinja2", "myst-parser", "numpydoc", "pillow (>=9,<10)", "pydata-sphinx-theme (>=0.14.1)", "scipy", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinxext-altair"]
+
[[package]]
name = "annotated-types"
version = "0.7.0"
@@ -13,13 +36,13 @@ files = [
[[package]]
name = "anyio"
-version = "4.4.0"
+version = "4.6.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
- {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
+ {file = "anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"},
+ {file = "anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb"},
]
[package.dependencies]
@@ -29,9 +52,9 @@ sniffio = ">=1.1"
typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
[package.extras]
-doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
-trio = ["trio (>=0.23)"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
+trio = ["trio (>=0.26.1)"]
[[package]]
name = "appnope"
@@ -226,89 +249,89 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
[[package]]
name = "certifi"
-version = "2024.7.4"
+version = "2024.8.30"
description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
files = [
- {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
- {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
+ {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
+ {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
]
[[package]]
name = "cffi"
-version = "1.17.0"
+version = "1.17.1"
description = "Foreign Function Interface for Python calling C code."
optional = false
python-versions = ">=3.8"
files = [
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[package.dependencies]
@@ -316,101 +339,116 @@ pycparser = "*"
[[package]]
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description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
optional = false
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testing = ["pytest", "pytest-benchmark"]
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+]
+
+[package.dependencies]
+numpy = ">=1.22.3,<3"
packaging = ">=21.3"
pandas = ">=1.4,<2.1.0 || >2.1.0"
patsy = ">=0.5.6"
scipy = ">=1.8,<1.9.2 || >1.9.2"
[package.extras]
-build = ["cython (>=0.29.33)"]
-develop = ["colorama", "cython (>=0.29.33)", "cython (>=3.0.10,<4)", "flake8", "isort", "joblib", "matplotlib (>=3)", "pytest (>=7.3.0,<8)", "pytest-cov", "pytest-randomly", "pytest-xdist", "pywinpty", "setuptools-scm[toml] (>=8.0,<9.0)"]
+build = ["cython (>=3.0.10)"]
+develop = ["colorama", "cython (>=3.0.10)", "cython (>=3.0.10,<4)", "flake8", "isort", "joblib", "matplotlib (>=3)", "pytest (>=7.3.0,<8)", "pytest-cov", "pytest-randomly", "pytest-xdist", "pywinpty", "setuptools-scm[toml] (>=8.0,<9.0)"]
docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"]
+[[package]]
+name = "tenacity"
+version = "9.0.0"
+description = "Retry code until it succeeds"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"},
+ {file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"},
+]
+
+[package.extras]
+doc = ["reno", "sphinx"]
+test = ["pytest", "tornado (>=4.5)", "typeguard"]
+
[[package]]
name = "terminado"
version = "0.18.1"
@@ -3047,13 +3218,13 @@ test = ["pytest", "ruff"]
[[package]]
name = "tomli"
-version = "2.0.1"
+version = "2.0.2"
description = "A lil' TOML parser"
optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
files = [
- {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
- {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
+ {file = "tomli-2.0.2-py3-none-any.whl", hash = "sha256:2ebe24485c53d303f690b0ec092806a085f07af5a5aa1464f3931eec36caaa38"},
+ {file = "tomli-2.0.2.tar.gz", hash = "sha256:d46d457a85337051c36524bc5349dd91b1877838e2979ac5ced3e710ed8a60ed"},
]
[[package]]
@@ -3111,13 +3282,13 @@ test = ["coverage[toml] (>=7)", "mypy (>=1.2.0)", "pytest (>=7)"]
[[package]]
name = "types-python-dateutil"
-version = "2.9.0.20240316"
+version = "2.9.0.20241003"
description = "Typing stubs for python-dateutil"
optional = false
python-versions = ">=3.8"
files = [
- {file = "types-python-dateutil-2.9.0.20240316.tar.gz", hash = "sha256:5d2f2e240b86905e40944dd787db6da9263f0deabef1076ddaed797351ec0202"},
- {file = "types_python_dateutil-2.9.0.20240316-py3-none-any.whl", hash = "sha256:6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b"},
+ {file = "types-python-dateutil-2.9.0.20241003.tar.gz", hash = "sha256:58cb85449b2a56d6684e41aeefb4c4280631246a0da1a719bdbe6f3fb0317446"},
+ {file = "types_python_dateutil-2.9.0.20241003-py3-none-any.whl", hash = "sha256:250e1d8e80e7bbc3a6c99b907762711d1a1cdd00e978ad39cb5940f6f0a87f3d"},
]
[[package]]
@@ -3148,13 +3319,13 @@ typing-extensions = ">=3.7.4"
[[package]]
name = "tzdata"
-version = "2024.1"
+version = "2024.2"
description = "Provider of IANA time zone data"
optional = false
python-versions = ">=2"
files = [
- {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
- {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
+ {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
+ {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
]
[[package]]
@@ -3173,13 +3344,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
[[package]]
name = "urllib3"
-version = "2.2.2"
+version = "2.2.3"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.8"
files = [
- {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
- {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
+ {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
+ {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
]
[package.extras]
@@ -3243,13 +3414,13 @@ test = ["websockets"]
[[package]]
name = "widgetsnbextension"
-version = "4.0.11"
+version = "4.0.13"
description = "Jupyter interactive widgets for Jupyter Notebook"
optional = false
python-versions = ">=3.7"
files = [
- {file = "widgetsnbextension-4.0.11-py3-none-any.whl", hash = "sha256:55d4d6949d100e0d08b94948a42efc3ed6dfdc0e9468b2c4b128c9a2ce3a7a36"},
- {file = "widgetsnbextension-4.0.11.tar.gz", hash = "sha256:8b22a8f1910bfd188e596fe7fc05dcbd87e810c8a4ba010bdb3da86637398474"},
+ {file = "widgetsnbextension-4.0.13-py3-none-any.whl", hash = "sha256:74b2692e8500525cc38c2b877236ba51d34541e6385eeed5aec15a70f88a6c71"},
+ {file = "widgetsnbextension-4.0.13.tar.gz", hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6"},
]
[[package]]
@@ -3334,4 +3505,4 @@ files = [
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
-content-hash = "e38985f71f6a6d66c80a7920ba143a1da53fe68bc20387e3ee06018923f02924"
+content-hash = "5bfd5211b5e93829295f3aecfab6f7aced8ad5a5c13746e04ecbdda898005b9e"
diff --git a/pyproject.toml b/pyproject.toml
index 3934390..c79ad35 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -41,7 +41,7 @@ line-length = 100
[tool.poetry]
name = "openenergyid"
-version = "0.1.18" # You'll need to set this manually or use a different method for dynamic versioning
+version = "0.1.20" # You'll need to set this manually or use a different method for dynamic versioning
description = "Open Source Python library for energy analytics and simulations"
authors = [
"Jan Pecinovsky ",
@@ -49,6 +49,7 @@ authors = [
"Oscar Swyns ",
]
dependencies.python = "^3.10"
+dependencies.polars = "^1.9.0"
dependencies.pandera = "^0.20.3"
dependencies.numpy = "^2.0.0"
dependencies.patsy = "^0.5.6"
@@ -57,6 +58,7 @@ dependencies.pydantic = "^2.8.2"
dependencies.pandas = "^2.2.2"
[tool.poetry.group.dev.dependencies]
+altair = "^5.4.1"
matplotlib = "^3.9.1"
jupyter = "^1.0.0"
ipykernel = "^6.29.5"
@@ -65,3 +67,4 @@ pytest = "^8.3.2"
entsoe-py = "^0.6.8"
energyid = "^0.0.17"
snakeviz = "^2.2.0"
+plotly = "^5.24.1"