diff --git a/.gitignore b/.gitignore index fad710aac..24ab2fee9 100644 --- a/.gitignore +++ b/.gitignore @@ -6,23 +6,21 @@ mysql_config # Notebooks *.ipynb - -!notebooks/00_intro.ipynb -!notebooks/01_spikesorting.ipynb -!notebooks/02_curation.ipynb -!notebooks/03_lfp.ipynb -!notebooks/04_Trodes_position.ipynb -!notebooks/05_DLC_from_scratch.ipynb -!notebooks/06_DLC_from_dir.ipynb -!notebooks/4_position_info.ipynb -!notebooks/07_linearization.ipynb -!notebooks/08_Extract_Mark_indicators.ipynb -!notebooks/09_Decoding_with_GPUs_on_the_GPU_cluster.ipynb -!notebooks/10_1D_Clusterless_Decoding.ipynb -!notebooks/11_2D_Clusterless_Decoding.ipynb -!notebooks/12_Ripple_Detection.ipynb -!notebooks/13_Theta_phase_and_power.ipynb - +!notebooks/00*.ipynb +!notebooks/01*.ipynb +!notebooks/02*.ipynb +!notebooks/10*.ipynb +!notebooks/11*.ipynb +!notebooks/12*.ipynb +!notebooks/14*.ipynb +!notebooks/20*.ipynb +!notebooks/21*.ipynb +!notebooks/22*.ipynb +!notebooks/23*.ipynb +!notebooks/24*.ipynb +!notebooks/30*.ipynb +!notebooks/31*.ipynb +!notebooks/33*.ipynb # Byte-compiled / optimized / DLL files __pycache__/ @@ -167,7 +165,7 @@ temp_nwb/*s *.json *.gz *.pdf -dj_local_con*.json +dj_local_conf* !dj_local_conf_example.json !/.vscode/extensions.json diff --git a/CHANGELOG.md b/CHANGELOG.md index 2b8aafc57..514c172ab 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,30 @@ # Change Log +## [0.4.2] (October 10, 2023) + +### Infrastructure / Support + +- Bumped Python version to 3.9. #583 +- Updated user management helper scripts for MySQL 8. #650 +- Centralized config/path handling to permit setting via datajoint config. #593 +- Fixed Merge Table deletes: error specificity and transaction context. #617 + +### Pipelines + +- Common: + - Added support multiple cameras per epoch. #557 + - Removed `common_backup` schema. #631 + - Added support for multiple position objects per NWB in `common_behav` via + PositionSource.SpatialSeries and RawPosition.PosObject #628, #616. + _Note:_ Existing functions have been made compatible, but column labels for + `RawPosition.fetch1_dataframe` may change. +- Spike sorting: + - Added pipeline populator. #637, #646, #647 + - Fixed curation functionality for `nn_isolation`. #597, #598 +- Position: Added position interval/epoch mapping via PositionIntervalMap. #620, + #621, #627 +- LFP: Refactored pipeline. #594, #588, #605, #606, #607, #608, #615, #629 + ## [0.4.1] (June 30, 2023) - Add mkdocs automated deployment. #527, #537, #549, #551 @@ -62,6 +87,7 @@ - Allow creation and linkage of device metadata from YAML #400 - Move helper functions to utils directory #386 +[0.4.2]: https://github.com/LorenFrankLab/spyglass/releases/tag/0.4.2 [0.4.1]: https://github.com/LorenFrankLab/spyglass/releases/tag/0.4.1 [0.4.0]: https://github.com/LorenFrankLab/spyglass/releases/tag/0.4.0 [0.3.4]: https://github.com/LorenFrankLab/spyglass/releases/tag/0.3.4 diff --git a/dj_local_conf_example.json b/dj_local_conf_example.json index e91261e58..cf57ae7be 100644 --- a/dj_local_conf_example.json +++ b/dj_local_conf_example.json @@ -31,6 +31,10 @@ "database.prefix": "username_", "spyglass_dirs": { "base": "/your/base/path" - } + }, + "kachery_dirs": { + "cloud": "/your/base/path/.kachery_cloud" + }, + "kachery_zone": "franklab.default" } } diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 8da40cad6..0b0051a14 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -47,28 +47,30 @@ nav: - Home: index.md - Installation: installation.md - Miscellaneous: - - FigURL: misc/figurl_views.md - - Session Groups: misc/session_groups.md - - Insert Data: misc/insert_data.md - - Merge Tables: misc/merge_tables.md + - FigURL: misc/figurl_views.md + - Session Groups: misc/session_groups.md + - Insert Data: misc/insert_data.md + - Merge Tables: misc/merge_tables.md - Tutorials: + - General: - Setup: notebooks/00_Setup.ipynb - Insert Data: notebooks/01_Insert_Data.ipynb - - Spike Sorting: notebooks/02_Spike_Sorting.ipynb - - Curation: notebooks/03_Curation.ipynb - - LFP: notebooks/03_lfp.ipynb - - Position: - - Information: notebooks/4_position_info.ipynb - - Pipeline: notebooks/04_Trodes_position.ipynb - - From Scratch: notebooks/05_DLC_from_scratch.ipynb - - From Pre-Trained: notebooks/06_DLC_from_dir.ipynb - - Linearization Pipeline: notebooks/07_linearization.ipynb - - Mark Indicators: notebooks/08_Extract_Mark_indicators.ipynb - - GPU: notebooks/09_Decoding_with_GPUs_on_the_GPU_cluster.ipynb - - 1D Clusterless Decoding: notebooks/10_1D_Clusterless_Decoding.ipynb - - 2D Clusterless Decoding: notebooks/11_2D_Clusterless_Decoding.ipynb - - Ripple Detection: notebooks/12_Ripple_Detection.ipynb - - Spyglass Kachery Setup: notebooks/Spyglass_kachery_setup.ipynb + - Data Sync: notebooks/02_Data_Sync.ipynb + - Ephys: + - Spike Sorting: notebooks/10_Spike_Sorting.ipynb + - Curation: notebooks/11_Curation.ipynb + - LFP: notebooks/12_LFP.ipynb + - Theta: notebooks/14_Theta.ipynb + - Position: + - Position Trodes: notebooks/20_Position_Trodes.ipynb + - Position DLC 1: notebooks/21_Position_DLC_1.ipynb + - Position DLC 2: notebooks/22_Position_DLC_2.ipynb + - Linearization: notebooks/24_Linearization.ipynb + - Combined: + - Ripple Detection: notebooks/30_Ripple_Detection.ipynb + - Extract Mark Indicators: notebooks/31_Extract_Mark_Indicators.ipynb + - Decoding with GPUs: notebooks/32_Decoding_with_GPUs.ipynb + - Decoding Clusterless: notebooks/33_Decoding_Clusterless.ipynb - API Reference: api/ # defer to gen-files + literate-nav - How to Contribute: contribute.md - Change Log: CHANGELOG.md diff --git a/notebooks/00_Setup.ipynb b/notebooks/00_Setup.ipynb index 05ed1ff6c..86beb6b32 100644 --- a/notebooks/00_Setup.ipynb +++ b/notebooks/00_Setup.ipynb @@ -231,19 +231,54 @@ "source": [ "### Loading the config\n", "\n", - "We can check that the paths are correctly set up by loading the config.\n" + "We can check that the paths are correctly set up by loading the config from \n", + "the main Spyglass directory.\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "912ac84b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'debug_mode': True,\n", + " 'prepopulate': True,\n", + " 'SPYGLASS_BASE_DIR': '/stelmo/nwb',\n", + " 'SPYGLASS_RAW_DIR': '/stelmo/nwb/raw',\n", + " 'SPYGLASS_ANALYSIS_DIR': '/stelmo/nwb/analysis',\n", + " 'SPYGLASS_RECORDING_DIR': '/stelmo/nwb/recording',\n", + " 'SPYGLASS_SORTING_DIR': '/stelmo/nwb/spikesorting',\n", + " 'SPYGLASS_WAVEFORMS_DIR': '/stelmo/nwb/waveforms',\n", + " 'SPYGLASS_TEMP_DIR': '/stelmo/nwb/tmp',\n", + " 'SPYGLASS_VIDEO_DIR': '/stelmo/nwb/video',\n", + " 'KACHERY_CLOUD_DIR': '/stelmo/nwb/kachery_storage',\n", + " 'KACHERY_STORAGE_DIR': '/stelmo/nwb/kachery_storage',\n", + " 'KACHERY_TEMP_DIR': '/stelmo/nwb/tmp',\n", + " 'KACHERY_ZONE': 'franklab.default',\n", + " 'FIGURL_CHANNEL': 'franklab2',\n", + " 'DJ_SUPPORT_FILEPATH_MANAGEMENT': 'TRUE',\n", + " 'KACHERY_CLOUD_EPHEMERAL': 'TRUE'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "from spyglass.settings import load_config\n", + "import os\n", + "import datajoint as dj\n", + "\n", + "if os.path.basename(os.getcwd()) == \"notebooks\":\n", + " os.chdir(\"..\")\n", + "dj.config.load(\"dj_local_conf.json\")\n", "\n", - "load_config()" + "from spyglass.settings import config\n", + "\n", + "config" ] }, { @@ -260,10 +295,32 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "afb63913-4e6b-4049-ae1d-55ab1ac8d42c", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-09-28 08:07:06,176][INFO]: Connecting root@localhost:3307\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-09-28 08:07:06,254][INFO]: Connected root@localhost:3307\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populate: Populating table DataAcquisitionDeviceSystem with data {'data_acquisition_device_system': 'SpikeGadgets'} using insert1.\n", + "Populate: Populating table DataAcquisitionDeviceAmplifier with data {'data_acquisition_device_amplifier': 'Intan'} using insert1.\n" + ] + }, { "data": { "text/html": [ @@ -348,23 +405,11 @@ "text/plain": [ "*nwb_file_name nwb_file_a\n", "+------------+ +--------+\n", - "CH101_20210711 =BLOB= \n", - "CH73_20211206_ =BLOB= \n", - "CH65_20211212_ =BLOB= \n", - "J1620210620_.n =BLOB= \n", - "montague202008 =BLOB= \n", - "chimi20200304_ =BLOB= \n", - "Wallie20220913 =BLOB= \n", - "mango20211203_ =BLOB= \n", - "peanut20201108 =BLOB= \n", - "wilbur20210406 =BLOB= \n", - "eliot20221022_ =BLOB= \n", - "Dan20211109_.n =BLOB= \n", - " ...\n", - " (Total: 817)" + "\n", + " (Total: 0)" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -375,6 +420,14 @@ "Nwbfile()" ] }, + { + "cell_type": "markdown", + "id": "13fd64af", + "metadata": {}, + "source": [ + "# Up Next" + ] + }, { "cell_type": "markdown", "id": "c6850095", diff --git a/notebooks/01_Insert_Data.ipynb b/notebooks/01_Insert_Data.ipynb index 4f19895b4..a485bc507 100644 --- a/notebooks/01_Insert_Data.ipynb +++ b/notebooks/01_Insert_Data.ipynb @@ -45,8 +45,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2023-07-18 17:58:44,303][INFO]: Connecting root@localhost:3306\n", - "[2023-07-18 17:58:44,332][INFO]: Connected root@localhost:3306\n" + "[2023-10-05 11:48:12,292][INFO]: Connecting root@localhost:3306\n", + "[2023-10-05 11:48:12,302][INFO]: Connected root@localhost:3306\n" ] } ], @@ -121,72 +121,72 @@ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", - "\n", + "\n", "\n", "\n", "0\n", - "\n", - "0\n", + "\n", + "0\n", "\n", "\n", "\n", "sgc.LFPBandSelection\n", "\n", - "\n", - "sgc.LFPBandSelection\n", + "\n", + "sgc.LFPBandSelection\n", "\n", "\n", "\n", "\n", "\n", "0->sgc.LFPBandSelection\n", - "\n", + "\n", "\n", "\n", "\n", "sgc.TaskEpoch\n", "\n", - "\n", - "sgc.TaskEpoch\n", + "\n", + "sgc.TaskEpoch\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "sgc.VideoFile\n", - "\n", - "\n", - "sgc.VideoFile\n", + "\n", + "\n", + "sgc.VideoFile\n", "\n", "\n", "\n", "\n", "\n", "sgc.TaskEpoch->sgc.VideoFile\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "sgc.StateScriptFile\n", - "\n", - "\n", - "sgc.StateScriptFile\n", + "\n", + "\n", + "sgc.StateScriptFile\n", "\n", "\n", "\n", "\n", "\n", "sgc.TaskEpoch->sgc.StateScriptFile\n", - 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"\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -665,7 +693,11 @@ "metadata": {}, "source": [ "By adding diagrams together, of adding and subtracting levels, we can visualize\n", - "key parts of Spyglass.\n" + "key parts of Spyglass.\n", + "\n", + "_Note:_ Notice the _Selection_ tables. This is a design pattern that selects a\n", + "subset of upstream items for further processing. In some cases, these also pair\n", + "the selected data with processing parameters.\n" ] }, { @@ -685,10 +717,12 @@ "\n", "We offer a few examples:\n", "\n", - "- `minirec20230622.nwb`, .3 GB: minimal recording, on\n", - " [Box](https://ucsf.box.com/s/k3sgql6z475oia848q1rgms4zdh4rkjn)\n", - "- `montague20200802.nwb`, 8 GB: full recording, on\n", - " [DropBox](https://www.dropbox.com/scl/fo/4i5b1z4iapetzxfps0grf/h?dl=0&preview=montague20200802_tutorial_.nwb&rlkey=ctahes9v0r7bxes8yceh86gzg)\n", + "- `minirec20230622.nwb`, .3 GB: minimal recording,\n", + " [Link](https://ucsf.box.com/s/k3sgql6z475oia848q1rgms4zdh4rkjn)\n", + "- `mediumnwb20230802.nwb`, 32 GB: full-featured dataset, \n", + " [Link](https://ucsf.box.com/s/2qbhxghzpttfam4b7q7j8eg0qkut0opa) \n", + "- `montague20200802.nwb`, 8 GB: full experimental recording, \n", + " [Link](https://ucsf.box.com/s/26je2eytjpqepyznwpm92020ztjuaomb)\n", "- For those in the UCSF network, these and many others on `/stelmo/nwb/raw`\n", "\n", "If you are connected to the Frank lab database, please rename any downloaded\n", @@ -698,16 +732,22 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ + "from spyglass.utils.nwb_helper_fn import get_nwb_copy_filename\n", + "\n", "# Define the name of the file that you copied and renamed\n", "nwb_file_name = \"minirec20230622.nwb\"\n", - "filename, file_extension = os.path.splitext(nwb_file_name)\n", - "# By convention, Spyglass will add an underscore before the extension and copy\n", - "# the file, removing raw data.\n", - "nwb_copy_file_name = filename + \"_\" + file_extension" + "nwb_copy_file_name = get_nwb_copy_filename(nwb_file_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Spyglass will create a copy with this name." ] }, { @@ -718,7 +758,7 @@ { "data": { "text/plain": [ - "'minirec20230622.nwb'" + "'minirec20230622_.nwb'" ] }, "execution_count": 4, @@ -727,7 +767,7 @@ } ], "source": [ - "nwb_file_name" + "nwb_copy_file_name" ] }, { @@ -755,7 +795,16 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Please add the Google user ID for Firstname Lastname in the LabMember.LabMemberInfo table to help manage permissions.\n", + "Please add the Google user ID for Firstname2 Lastname2 in the LabMember.LabMemberInfo table to help manage permissions.\n" + ] + } + ], "source": [ "# take a look at the lab members\n", "sgc.LabMember.insert_from_nwbfile(nwb_file_name)" @@ -845,25 +894,22 @@ "

datajoint_user_name

\n", " used for permission to delete entries\n", " \n", - " Alison Comrie\n", - "comrie.alison@gmail.com\n", - "alisonFirstname Lastname\n", + " Firstname Lastname\n", "example1@gmail.com\n", "example1Firstname2 Lastname2\n", "example2@gmail.com\n", "example2 \n", " \n", " \n", - "

Total: 3

\n", + "

Total: 2

\n", " " ], "text/plain": [ "*lab_member_na google_user_na datajoint_user\n", "+------------+ +------------+ +------------+\n", - "Alison Comrie comrie.alison@ alison \n", "Firstname Last example1@gmail example1 \n", "Firstname2 Las example2@gmail example2 \n", - " (Total: 3)" + " (Total: 2)" ] }, "execution_count": 6, @@ -873,7 +919,7 @@ ], "source": [ "sgc.LabMember.LabMemberInfo.insert(\n", - " [ # Full name, Google email address, DataJont username\n", + " [ # Full name, Google email address, DataJoint username\n", " [\"Firstname Lastname\", \"example1@gmail.com\", \"example1\"],\n", " [\"Firstname2 Lastname2\", \"example2@gmail.com\", \"example2\"],\n", " ],\n", @@ -890,14 +936,9 @@ "associated information in the database. This is often a subgroup that\n", "collaborates on the same projects. Data is associated with a given team,\n", "granting members analysis (e.g., curation) and deletion (coming soon)\n", - "privelages.\n" + "privileges.\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "code", "execution_count": 7, @@ -991,26 +1032,24 @@ "

lab_member_name

\n", " \n", " \n", - " Beans\n", - "Alison ComrieFirstname Lastname\n", + " Firstname Lastname\n", "Firstname LastnameMy Team\n", "Firstname LastnameFirstname2 Lastname2\n", "Firstname2 Lastname2My Team\n", "Firstname2 Lastname2 \n", " \n", " \n", - "

Total: 5

\n", + "

Total: 4

\n", " " ], "text/plain": [ "*team_name *lab_member_na\n", "+------------+ +------------+\n", - "Beans Alison Comrie \n", "Firstname Last Firstname Last\n", "My Team Firstname Last\n", "Firstname2 Las Firstname2 Las\n", "My Team Firstname2 Las\n", - " (Total: 5)" + " (Total: 4)" ] }, "execution_count": 8, @@ -1026,7 +1065,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Inserting from NWB\n", + "## Inserting from NWB\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "\n", "`spyglass.data_import.insert_sessions` helps take the many fields of data\n", "present in an NWB file and insert them into various tables across Spyglass. If\n", @@ -1037,12 +1082,13 @@ "- neural activity (extracellular recording of multiple brain areas)\n", "- etc.\n", "\n", - "_Note:_ this may take a while because it makes a copy of the NWB file.\n" + "_Note:_ this may take time as Spyglass creates the copy. You may see a prompt \n", + "about inserting device information." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": { "tags": [] }, @@ -1053,14 +1099,12 @@ "text": [ "Creating a copy of NWB file minirec20230622.nwb with link to raw ephys data: minirec20230622_.nwb\n", "Populate Session...\n", - "No config found at file path /home/cb/wrk/zOther/data/raw/minirec20230622_spyglass_config.yaml\n", + "No config found at file path /home/cb/wrk/data/raw/minirec20230622_spyglass_config.yaml\n", "Institution...\n", "Lab...\n", "LabMember...\n", + "Please add the Google user ID for Firstname2 Lastname2 in the LabMember.LabMemberInfo table to help manage permissions.\n", "Subject...\n", - "Populate DataAcquisitionDevice...\n", - "Inserted or referenced data acquisition device(s): dict_keys(['dataacq_device0'])\n", - "\n", "Populate CameraDevice...\n", "Inserted camera devices ['test camera 1']\n", "\n", @@ -1074,10 +1118,10 @@ "LabMember with name lastname2, firstname2 does not exist. Cannot link Session with LabMember in Session.Experimenter.\n", "Populate ElectrodeGroup...\n", "Populate Electrode...\n", - "No config found at file path /home/cb/wrk/zOther/data/raw/minirec20230622_spyglass_config.yaml\n", + "No config found at file path /home/cb/wrk/data/raw/minirec20230622_spyglass_config.yaml\n", "Populate Raw...\n", "Estimating sampling rate...\n", - "Estimated sampling rate: 30000.0\n", + "Estimated sampling rate for file: 30000.0 Hz\n", "Importing raw data: Sampling rate:\t30000.0 Hz\n", "Number of valid intervals:\t2\n", "Populate SampleCount...\n", @@ -1085,13 +1129,19 @@ "Populate TaskEpochs\n", "Populate StateScriptFile\n", "Populate VideoFile\n", - "No video found corresponding to epoch 01_s1\n", - "No video found corresponding to epoch 02_s2\n", + "No video found corresponding to file minirec20230622_.nwb, epoch 01_s1\n", + "No video found corresponding to file minirec20230622_.nwb, epoch 02_s2\n", "RawPosition...\n", - "Processing raw position data. Estimated sampling rate: 30.0 Hz\n", - "Processing raw position data. Estimated sampling rate: 30.0 Hz\n", - "Processing raw position data. Estimated sampling rate: 30.0 Hz\n", - "Processing raw position data. Estimated sampling rate: 30.0 Hz\n" + "Estimated sampling rate for 12345: 30.0 Hz\n", + "WARNING: Setting minimum valid interval to 5.1912336349487305\n", + "Estimated sampling rate for 12345: 30.0 Hz\n", + "WARNING: Setting minimum valid interval to 5.1912336349487305\n", + "Estimated sampling rate for 12345: 30.0 Hz\n", + "WARNING: Setting minimum valid interval to 5.339195609092712\n", + "Estimated sampling rate for 12345: 30.0 Hz\n", + "WARNING: Setting minimum valid interval to 5.339195609092712\n", + "Populated PosIntervalMap for minirec20230622_.nwb, 01_s1\n", + "Populated PosIntervalMap for minirec20230622_.nwb, 02_s2\n" ] } ], @@ -1119,7 +1169,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1197,7 +1247,7 @@ " (Total: 1)" ] }, - "execution_count": 28, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1215,7 +1265,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1232,7 +1282,7 @@ " 'experiment_description']" ] }, - "execution_count": 29, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1250,7 +1300,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1259,7 +1309,7 @@ "['nwb_file_name']" ] }, - "execution_count": 30, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1277,7 +1327,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1387,7 +1437,7 @@ " (Total: 1)" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1405,7 +1455,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1441,7 +1491,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1480,7 +1530,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1590,7 +1640,7 @@ " (Total: 1)" ] }, - "execution_count": 14, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1608,21 +1658,21 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", - "\n", + "\n", "\n", "\n", "sgc.Raw\n", "\n", - "\n", - "sgc.Raw\n", + "\n", + "sgc.Raw\n", "\n", "\n", "\n", @@ -1630,43 +1680,43 @@ "\n", "sgc.Session\n", "\n", - "\n", - "sgc.Session\n", + "\n", + "sgc.Session\n", "\n", "\n", "\n", "\n", "\n", "sgc.Session->sgc.Raw\n", - "\n", + "\n", "\n", "\n", "\n", "sgc.IntervalList\n", "\n", - "\n", - "sgc.IntervalList\n", + "\n", + "sgc.IntervalList\n", "\n", "\n", "\n", "\n", "\n", "sgc.Session->sgc.IntervalList\n", - "\n", + "\n", "\n", "\n", "\n", "sgc.IntervalList->sgc.Raw\n", - "\n", + "\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 35, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1677,7 +1727,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1775,7 +1825,7 @@ " (Total: 1)" ] }, - "execution_count": 15, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1795,7 +1845,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1818,7 +1868,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1898,15 +1948,11 @@ "=BLOB=
minirec20230622_.nwb\n", "pos 1 valid times\n", "=BLOB=
minirec20230622_.nwb\n", - "pos 2 valid times\n", - "=BLOB=
minirec20230622_.nwb\n", - "pos 3 valid times\n", - "=BLOB=
minirec20230622_.nwb\n", "raw data valid times\n", "=BLOB=
\n", " \n", " \n", - "

Total: 7

\n", + "

Total: 5

\n", " " ], "text/plain": [ @@ -1916,13 +1962,11 @@ "minirec2023062 02_s2 =BLOB= \n", "minirec2023062 pos 0 valid ti =BLOB= \n", "minirec2023062 pos 1 valid ti =BLOB= \n", - "minirec2023062 pos 2 valid ti =BLOB= \n", - "minirec2023062 pos 3 valid ti =BLOB= \n", "minirec2023062 raw data valid =BLOB= \n", - " (Total: 7)" + " (Total: 5)" ] }, - "execution_count": 37, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1939,22 +1983,22 @@ "these with [`fetch`](https://datajoint.com/docs/core/datajoint-python/0.14/query/fetch/)\n", "\n", "_Note:_ like `insert`/`insert1`, `fetch` can be uses as `fetch1` to raise an\n", - "error when many (or no) etries are retrieved. To limit to one entry when there\n", + "error when many (or no) entries are retrieved. To limit to one entry when there\n", "may be many, use `query.fetch(limit=1)[0]`\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([], shape=(0, 2), dtype=float64)" + "array([[1.68747483e+09, 1.68747484e+09]])" ] }, - "execution_count": 17, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1979,17 +2023,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array(['01_s1', '02_s2', 'pos 0 valid times', 'pos 3 valid times',\n", - " 'raw data valid times'], dtype=object)" + "array(['01_s1', '02_s2', 'pos 0 valid times', 'raw data valid times'],\n", + " dtype=object)" ] }, - "execution_count": 18, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -2018,12 +2062,56 @@ "Another neat feature of DataJoint is that it automatically maintains\n", "[data integrity](https://datajoint.com/docs/core/datajoint-python/0.14/design/integrity/)\n", "with _cascading deletes_. For example, if we delete our `Session` entry, all\n", - "associated downstream entries are also deleted (e.g. `Raw`, `IntervalList`).\n" + "associated downstream entries are also deleted (e.g. `Raw`, `IntervalList`).\n", + "\n", + "_Note_: The deletion process can be complicated by \n", + "[Merge Tables](https://lorenfranklab.github.io/spyglass/0.4/misc/merge_tables/)\n", + "when the entry is referenced by a part table. To demo deletion in these cases, \n", + "run the hidden code below.\n", + "\n", + "
\n", + "Quick Merge Insert\n", + "\n", + "```python\n", + "import spyglass.lfp as lfp\n", + "\n", + "sgc.FirFilterParameters().create_standard_filters()\n", + "lfp.lfp_electrode.LFPElectrodeGroup.create_lfp_electrode_group(\n", + " nwb_file_name=nwb_copy_file_name,\n", + " group_name=\"test\",\n", + " electrode_list=[0],\n", + ")\n", + "lfp.v1.LFPSelection.insert1(\n", + " {\n", + " \"nwb_file_name\": nwb_copy_file_name,\n", + " \"lfp_electrode_group_name\": \"test\",\n", + " \"target_interval_list_name\": \"01_s1\",\n", + " \"filter_name\": \"LFP 0-400 Hz\",\n", + " \"filter_sampling_rate\": 30_000,\n", + " },\n", + " skip_duplicates=True,\n", + ")\n", + "lfp.v1.LFPV1().populate()\n", + "```\n", + "
\n", + "
\n", + "Deleting Merge Entries\n", + "\n", + "```python\n", + "from spyglass.utils.dj_merge_tables import delete_downstream_merge\n", + "\n", + "delete_downstream_merge(\n", + " sgc.Nwbfile(),\n", + " restriction={\"nwb_file_name\": nwb_copy_file_name},\n", + " dry_run=False, # True will show Merge Table entries that would be deleted\n", + ") \n", + "```\n", + "
" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -2133,7 +2221,7 @@ " (Total: 1)" ] }, - "execution_count": 48, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -2148,41 +2236,45 @@ "metadata": {}, "source": [ "By default, DataJoint is cautious about deletes and will prompt before deleting.\n", - "To delete, respond `yes` in the prompt.\n" + "To delete, uncomment the cell below and respond `yes` in the prompt.\n" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[2023-07-18 18:51:35,382][INFO]: Deleting 4 rows from `common_behav`.`_raw_position`\n", - "INFO:datajoint:Deleting 4 rows from `common_behav`.`_raw_position`\n", - "[2023-07-18 18:51:35,388][INFO]: Deleting 4 rows from `common_behav`.`position_source`\n", - "INFO:datajoint:Deleting 4 rows from `common_behav`.`position_source`\n", - "[2023-07-18 18:51:35,393][INFO]: Deleting 7 rows from `common_dio`.`_d_i_o_events`\n", + "[2023-09-28 08:29:15,814][INFO]: Deleting 4 rows from `common_behav`.`_raw_position__pos_object`\n", + "INFO:datajoint:Deleting 4 rows from `common_behav`.`_raw_position__pos_object`\n", + "[2023-09-28 08:29:15,822][INFO]: Deleting 2 rows from `common_behav`.`_raw_position`\n", + "INFO:datajoint:Deleting 2 rows from `common_behav`.`_raw_position`\n", + "[2023-09-28 08:29:15,834][INFO]: Deleting 4 rows from `common_behav`.`position_source__spatial_series`\n", + "INFO:datajoint:Deleting 4 rows from `common_behav`.`position_source__spatial_series`\n", + "[2023-09-28 08:29:15,841][INFO]: Deleting 2 rows from `common_behav`.`position_source`\n", + "INFO:datajoint:Deleting 2 rows from `common_behav`.`position_source`\n", + "[2023-09-28 08:29:15,851][INFO]: Deleting 7 rows from `common_dio`.`_d_i_o_events`\n", "INFO:datajoint:Deleting 7 rows from `common_dio`.`_d_i_o_events`\n", - "[2023-07-18 18:51:35,406][INFO]: Deleting 128 rows from `common_ephys`.`_electrode`\n", + "[2023-09-28 08:29:15,871][INFO]: Deleting 128 rows from `common_ephys`.`_electrode`\n", "INFO:datajoint:Deleting 128 rows from `common_ephys`.`_electrode`\n", - "[2023-07-18 18:51:35,408][INFO]: Deleting 1 rows from `common_ephys`.`_electrode_group`\n", + "[2023-09-28 08:29:15,879][INFO]: Deleting 1 rows from `common_ephys`.`_electrode_group`\n", "INFO:datajoint:Deleting 1 rows from `common_ephys`.`_electrode_group`\n", - "[2023-07-18 18:51:35,412][INFO]: Deleting 1 rows from `common_ephys`.`_raw`\n", + "[2023-09-28 08:29:15,887][INFO]: Deleting 1 rows from `common_ephys`.`_raw`\n", "INFO:datajoint:Deleting 1 rows from `common_ephys`.`_raw`\n", - "[2023-07-18 18:51:35,415][INFO]: Deleting 1 rows from `common_ephys`.`_sample_count`\n", + "[2023-09-28 08:29:15,896][INFO]: Deleting 1 rows from `common_ephys`.`_sample_count`\n", "INFO:datajoint:Deleting 1 rows from `common_ephys`.`_sample_count`\n", - "[2023-07-18 18:51:35,426][INFO]: Deleting 2 rows from `common_task`.`_task_epoch`\n", + "[2023-09-28 08:29:15,908][INFO]: Deleting 2 rows from `common_behav`.`__position_interval_map`\n", + "INFO:datajoint:Deleting 2 rows from `common_behav`.`__position_interval_map`\n", + "[2023-09-28 08:29:15,918][INFO]: Deleting 2 rows from `common_task`.`_task_epoch`\n", "INFO:datajoint:Deleting 2 rows from `common_task`.`_task_epoch`\n", - "[2023-07-18 18:51:35,434][INFO]: Deleting 7 rows from `common_interval`.`interval_list`\n", - "INFO:datajoint:Deleting 7 rows from `common_interval`.`interval_list`\n", - "[2023-07-18 18:51:35,441][INFO]: Deleting 1 rows from `common_session`.`_session__data_acquisition_device`\n", - "INFO:datajoint:Deleting 1 rows from `common_session`.`_session__data_acquisition_device`\n", - "[2023-07-18 18:51:35,442][INFO]: Deleting 1 rows from `common_session`.`_session`\n", + "[2023-09-28 08:29:15,924][INFO]: Deleting 5 rows from `common_interval`.`interval_list`\n", + "INFO:datajoint:Deleting 5 rows from `common_interval`.`interval_list`\n", + "[2023-09-28 08:29:15,931][INFO]: Deleting 1 rows from `common_session`.`_session`\n", "INFO:datajoint:Deleting 1 rows from `common_session`.`_session`\n", - "[2023-07-18 18:51:36,986][INFO]: Deletes committed.\n", + "[2023-09-28 08:29:18,765][INFO]: Deletes committed.\n", "INFO:datajoint:Deletes committed.\n" ] }, @@ -2192,7 +2284,7 @@ "1" ] }, - "execution_count": 50, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -2210,7 +2302,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2312,7 +2404,7 @@ " (Total: 0)" ] }, - "execution_count": 51, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2323,7 +2415,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -2407,7 +2499,7 @@ " (Total: 0)" ] }, - "execution_count": 52, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -2460,13 +2552,21 @@ } ], "source": [ - "# Let's delete the entry\n", - "(sgc.Nwbfile & {\"nwb_file_name\": nwb_copy_file_name}).delete()" + "# Uncomment to delete\n", + "# (sgc.Nwbfile & {\"nwb_file_name\": nwb_copy_file_name}).delete()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the file (ends with `_.nwb`) has not been deleted, even if the entry\n", + "was deleted above.\n" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2485,10 +2585,16 @@ } ], "source": [ - "# Note that the file (ends with _.nwb) has not been deleted, even though the entry is\n", "!ls $SPYGLASS_BASE_DIR/raw" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can clean these files with the `cleanup` method\n" + ] + }, { "cell_type": "code", "execution_count": 55, @@ -2503,7 +2609,6 @@ } ], "source": [ - "# We clean it up\n", "sgc.Nwbfile().cleanup(delete_files=True)" ] }, @@ -2527,7 +2632,6 @@ } ], "source": [ - "# Now the file is gone as well\n", "!ls $SPYGLASS_BASE_DIR/raw" ] }, @@ -2535,8 +2639,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In the [next notebook](./02_Spike_Sorting.ipynb), we'll dive into the Spike\n", - "Sorting pipeline.\n" + "## Up Next" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the [next notebook](./02_Data_Sync.ipynb), we'll explore tools for syncing.\n" ] } ], diff --git a/notebooks/02_Data_Sync.ipynb b/notebooks/02_Data_Sync.ipynb new file mode 100644 index 000000000..7acf25306 --- /dev/null +++ b/notebooks/02_Data_Sync.ipynb @@ -0,0 +1,664 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sync Data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook will cover ...\n", + "\n", + "1. [General Kachery information](#kachery) \n", + "2. Setting up Kachery as a [host](#host-setup). If you'll use an existing host, \n", + " skip this.\n", + "3. Setting up Kachery in your [database](#database-setup). If you're using an \n", + " existing database, skip this.\n", + "4. Adding Kachery [data](#data-setup).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_Developer Note:_ if you may make a PR in the future, be sure to copy this\n", + "notebook, and use the `gitignore` prefix `temp` to avoid future conflicts.\n", + "\n", + "This is one notebook in a multi-part series on Spyglass.\n", + "\n", + "- To set up your Spyglass environment and database, see\n", + " [the Setup notebook](./00_Setup.ipynb)\n", + "- To fully demonstrate syncing features, we'll need to run some basic analyses. \n", + " This can either be done with code in this notebook or by running another\n", + " notebook (e.g., [LFP](./12_LFP.ipynb))\n", + "- For additional info on DataJoint syntax, including table definitions and\n", + " inserts, see\n", + " [these additional tutorials](https://github.com/datajoint/datajoint-tutorials)\n", + "\n", + "Let's start by importing the `spyglass` package.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-09-28 09:39:48,974][INFO]: Connecting root@localhost:3307\n", + "[2023-09-28 09:39:49,050][INFO]: Connected root@localhost:3307\n" + ] + } + ], + "source": [ + "import os\n", + "import datajoint as dj\n", + "import pandas as pd\n", + "\n", + "# change to the upper level folder to detect dj_local_conf.json\n", + "if os.path.basename(os.getcwd()) == \"notebooks\":\n", + " os.chdir(\"..\")\n", + "dj.config.load(\"dj_local_conf.json\") # load config for database connection\n", + "\n", + "import spyglass.common as sgc\n", + "import spyglass.sharing as sgs\n", + "from spyglass.settings import config\n", + "\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example analysis files, run the code hidden below.\n", + "\n", + "
\n", + "Quick Analysis\n", + "\n", + "```python\n", + "from spyglass.utils.nwb_helper_fn import get_nwb_copy_filename\n", + "import spyglass.data_import as sgi\n", + "import spyglass.lfp as lfp\n", + "\n", + "nwb_file_name = \"minirec20230622.nwb\"\n", + "nwb_copy_file_name = get_nwb_copy_filename(nwb_file_name)\n", + "\n", + "sgi.insert_sessions(nwb_file_name)\n", + "sgc.FirFilterParameters().create_standard_filters()\n", + "lfp.lfp_electrode.LFPElectrodeGroup.create_lfp_electrode_group(\n", + " nwb_file_name=nwb_copy_file_name,\n", + " group_name=\"test\",\n", + " electrode_list=[0],\n", + ")\n", + "lfp.v1.LFPSelection.insert1(\n", + " {\n", + " \"nwb_file_name\": nwb_copy_file_name,\n", + " \"lfp_electrode_group_name\": \"test\",\n", + " \"target_interval_list_name\": \"01_s1\",\n", + " \"filter_name\": \"LFP 0-400 Hz\",\n", + " \"filter_sampling_rate\": 30_000,\n", + " },\n", + " skip_duplicates=True,\n", + ")\n", + "lfp.v1.LFPV1().populate()\n", + "```\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Kachery\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cloud\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook contains instructions for setting up data sharing/syncing through\n", + "[_Kachery Cloud_](https://github.com/flatironinstitute/kachery-cloud), which\n", + "makes it possible to share analysis results, stored in NWB files. When a user\n", + "tries to access a file, Spyglass does the following:\n", + "\n", + "1. Try to load from the local file system/store. \n", + "2. If unavailable, check if it is in the relevant sharing table (i.e., \n", + " `NwbKachery` or `AnalysisNWBKachery`).\n", + "3. If present, attempt to download from the associated Kachery Resource.\n", + "\n", + "_Note:_ large file downloads may take a long time, so downloading raw data is\n", + "not supported. We suggest direct transfer with\n", + "[globus](https://www.globus.org/data-transfer) or a similar service.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Zone\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A [Kachery Zone](https://github.com/flatironinstitute/kachery-cloud/blob/main/doc/create_kachery_zone.md)\n", + "is a cloud storage host. The Frank laboratory has three separate Kachery zones:\n", + "\n", + "1. `franklab.default`: Internal file sharing, including figurls\n", + "2. `franklab.collaborator`: File sharing with collaborating labs.\n", + "3. `franklab.public`: Public file sharing (not yet active)\n", + "\n", + "Setting your zone can either be done as as an environment variable or an item \n", + "in a DataJoint config.\n", + "\n", + "- Environment variable:\n", + "\n", + " ```bash\n", + " export KACHERY_ZONE=franklab.default\n", + " export KACHERY_CLOUD_DIR=/stelmo/nwb/.kachery_cloud\n", + " ```\n", + "\n", + "- DataJoint Config:\n", + "\n", + " ```json\n", + " \"custom\": {\n", + " \"kachery_zone\": \"franklab.default\",\n", + " \"kachery_dirs\": {\n", + " \"cloud\": \"/your/base/path/.kachery_cloud\"\n", + " }\n", + " }\n", + " ```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Host Setup" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Zones\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See\n", + "[instructions](https://github.com/flatironinstitute/kachery-cloud/blob/main/doc/create_kachery_zone.md)\n", + "for setting up new Kachery Zones, including creating a cloud bucket and\n", + "registering it with the Kachery team. \n", + "\n", + "_Notes:_ \n", + "\n", + "- Bucket names cannot include periods, so we substitute a dash, as in\n", + " `franklab-default`.\n", + "- You only need to create an API token for your first zone." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resources" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "See [instructions](https://github.com/scratchrealm/kachery-resource/blob/main/README.md)\n", + "for setting up zone resources. This allows for sharing files on demand. We \n", + "suggest using the same name for the zone and resource.\n", + "\n", + "_Note:_ For each zone, you need to run the local daemon that listens for\n", + "requests from that zone. An example of the bash script we use is\n", + "\n", + "```bash\n", + " export KACHERY_ZONE=franklab.collaborators\n", + " export KACHERY_CLOUD_DIR=/stelmo/nwb/.kachery_cloud\n", + " cd /stelmo/nwb/franklab_collaborators_resource\n", + " npx kachery-resource@latest share\n", + "```" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Database Setup\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll add zones/resources to the Spyglass database. First, we'll check existing\n", + "Zones." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "

kachery_zone_name

\n", + " the name of the kachery zone. Note that this is the same as the name of the kachery resource.\n", + "
\n", + "

description

\n", + " description of this zone\n", + "
\n", + "

kachery_cloud_dir

\n", + " kachery cloud directory on local machine where files are linked\n", + "
\n", + "

kachery_proxy

\n", + " kachery sharing proxy\n", + "
\n", + "

lab_name

\n", + " \n", + "
\n", + " \n", + "

Total: 0

\n", + " " + ], + "text/plain": [ + "*kachery_zone_ description kachery_cloud_ kachery_proxy lab_name \n", + "+------------+ +------------+ +------------+ +------------+ +----------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sgs.KacheryZone()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check existing file list:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "

kachery_zone_name

\n", + " the name of the kachery zone. Note that this is the same as the name of the kachery resource.\n", + "
\n", + "

analysis_file_name

\n", + " name of the file\n", + "
\n", + "

analysis_file_uri

\n", + " the uri of the file\n", + "
\n", + " \n", + "

Total: 0

\n", + " " + ], + "text/plain": [ + "*kachery_zone_ *analysis_file analysis_file_\n", + "+------------+ +------------+ +------------+\n", + "\n", + " (Total: 0)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sgs.AnalysisNwbfileKachery()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Prepare an entry for the `KacheryZone` table:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "zone_name = config.get(\"KACHERY_ZONE\")\n", + "cloud_dir = config.get(\"KACHERY_CLOUD_DIR\")\n", + "\n", + "zone_key = {\n", + " \"kachery_zone_name\": zone_name,\n", + " \"description\": \" \".join(zone_name.split(\".\")) + \" zone\",\n", + " \"kachery_cloud_dir\": cloud_dir,\n", + " \"kachery_proxy\": \"https://kachery-resource-proxy.herokuapp.com\",\n", + " \"lab_name\": sgc.Lab.fetch(\"lab_name\", limit=1)[0],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use caution when inserting into an active database, as it could interfere with\n", + "ongoing work." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "sgs.KacheryZone().insert1(zone_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Setup" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the zone exists, we can add `AnalysisNWB` files we want to share by adding\n", + "entries to the `AnalysisNwbfileKacherySelection` table.\n", + "\n", + "_Note:_ This step depends on having previously run an analysis on the example \n", + "file." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "nwb_copy_filename = \"minirec20230622_.nwb\"\n", + "\n", + "analysis_file_list = ( # Grab all analysis files for this nwb file\n", + " sgc.AnalysisNwbfile() & {\"nwb_file_name\": nwb_copy_filename}\n", + ").fetch(\"analysis_file_name\")\n", + "\n", + "kachery_selection_key = {\"kachery_zone_name\": zone_name}\n", + "\n", + "for file in analysis_file_list: # Add all analysis to shared list\n", + " kachery_selection_key[\"analysis_file_name\"] = file\n", + " sgs.AnalysisNwbfileKacherySelection.insert1(\n", + " kachery_selection_key, skip_duplicates=True\n", + " )" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "With those files in the selection table, we can add them as links to the zone by\n", + "populating the `AnalysisNwbfileKachery` table:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sgs.AnalysisNwbfileKachery.populate()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "source": [ + "If all of that worked, \n", + "\n", + "1. Go to https://kachery-gateway.figurl.org/admin?zone=your_zone\n", + " (changing your_zone to the name of your zone)\n", + "2. Go to the Admin/Authorization Settings tab\n", + "3. Add the GitHub login names and permissions for the users you want to share \n", + " with. \n", + "\n", + "If those users can connect to your database, they should now be able to use the\n", + "`.fetch_nwb()` method to download any `AnalysisNwbfiles` that have been shared\n", + "through Kachery.\n", + "\n", + "For example:\n", + "\n", + "```python\n", + "from spyglass.spikesorting import CuratedSpikeSorting\n", + "\n", + "test_sort = (\n", + " CuratedSpikeSorting & {\"nwb_file_name\": \"minirec20230622_.nwb\"}\n", + ").fetch()[0]\n", + "sort = (CuratedSpikeSorting & test_sort).fetch_nwb()\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Up Next" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the [next notebook](./10_Spike_Sorting.ipynb), we'll start working with \n", + "ephys data with spike sorting." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "spyglass", + "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": "3.9.16" + }, + "vscode": { + "interpreter": { + "hash": "a172adcc57043bf031ddf85b5016360bc9bbefd0c359647f76b348e9e6923166" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/05_DLC_from_scratch.ipynb b/notebooks/05_DLC_from_scratch.ipynb deleted file mode 100644 index c83137a22..000000000 --- a/notebooks/05_DLC_from_scratch.ipynb +++ /dev/null @@ -1,1587 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a93a1550-8a67-4346-a4bf-e5a136f3d903", - "metadata": {}, - "source": [ - "## Position using DeepLabCut from Scratch\n", - "\n", - "**Note: make a copy of this notebook and run the copy to avoid git conflicts in the future**\n", - "\n", - "This is a tutorial on how to extract position via DeepLabCut (DLC) using the Spyglass pipeline used in Loren Frank's lab, UCSF. It will walk through creating your DLC project, extracting and labeling frames, training your model, executing pose estimation on a novel behavioral video, processing the pose estimation output to extract a centroid and orientation, and inserting the resulting information into the `IntervalPositionInfo` table.
\n", - "-> This tutorial assumes you've completed [tutorial 0](0_intro.ipynb)
\n", - "**Note 2: Make sure you are running this within the spyglass Conda environment**" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0f567531", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-04-20 14:51:05,338][INFO]: Connecting dgramling@lmf-db.cin.ucsf.edu:3306\n", - "[2023-04-20 14:51:05,410][INFO]: Connected dgramling@lmf-db.cin.ucsf.edu:3306\n" - ] - } - ], - "source": [ - "from pathlib import Path, PosixPath, PurePath\n", - "import os\n", - "import glob\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pynwb\n", - "import datajoint as dj\n", - "import spyglass.common as sgc\n", - "import spyglass.position.v1 as sgp\n", - "from spyglass.position import FinalPosition" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a8b531f7", - "metadata": {}, - "source": [ - "#### Here is a schematic showing the tables used in this notebook.
\n", - "![dlc_scratch.png|2000x900](./../notebook-images/dlc_scratch.png)" - ] - }, - { - "cell_type": "markdown", - "id": "0c67d88c-c90e-467b-ae2e-672c49a12f95", - "metadata": {}, - "source": [ - "### Table of Contents\n", - "[`DLCProject`](#DLCProject1)
\n", - "[`DLCModelTraining`](#DLCModelTraining1)
\n", - "[`DLCModel`](#DLCModel1)
\n", - "[`DLCPoseEstimation`](#DLCPoseEstimation1)
\n", - "[`DLCSmoothInterp`](#DLCSmoothInterp1)
\n", - "[`DLCCentroid`](#DLCCentroid1)
\n", - "[`DLCOrientation`](#DLCOrientation1)
\n", - "[`DLCPos`](#DLCPos1)
\n", - "[`DLCPosVideo`](#DLCPosVideo1)
\n", - "[`PosSource`](#PosSource1)
\n", - "[`IntervalPositionInfo`](#IntervalPositionInfo1)
" - ] - }, - { - "cell_type": "markdown", - "id": "5e6221a3-17e5-45c0-aa40-2fd664b02219", - "metadata": {}, - "source": [ - "#### [DLCProject](#TableOfContents) \n", - "__You can click on any header to return to the Table of Contents__" - ] - }, - { - "cell_type": "markdown", - "id": "27aed0e1-3af7-4499-bae8-96a64e81041e", - "metadata": {}, - "source": [ - "
\n", - " Note: The cells within the DLCProject step need to be performed in a local Jupyter notebook to allow for use of the frame labeling GUI.
" - ] - }, - { - "cell_type": "markdown", - "id": "96637cb9-519d-41e1-8bfd-69f68dc66b36", - "metadata": {}, - "source": [ - "Let us begin with visualizing the contents of the BodyPart table. This table will store standard names of body parts used within DLC models throughout the lab with a concise description.
\n", - ">
Please do not add to this table unless necessary.
" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b69f829f-9877-48ae-89d1-f876af2b8835", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

bodypart

\n", - " \n", - "
\n", - "

bodypart_description

\n", - " \n", - "
greenLEDgreen LED on implant LED ring
redLEDredLED
redLED_Ccenter red LED on implant LED ring
redLED_Lleft red LED on implant LED ring
redLED_Rright red LED on implant LED ring
tailBasebase of the tail on subject
whiteLEDwhite LED on headstage
\n", - " \n", - "

Total: 7

\n", - " " - ], - "text/plain": [ - "*bodypart bodypart_descr\n", - "+----------+ +------------+\n", - "greenLED green LED on i\n", - "redLED redLED \n", - "redLED_C center red LED\n", - "redLED_L left red LED o\n", - "redLED_R right red LED \n", - "tailBase base of the ta\n", - "whiteLED white LED on h\n", - " (Total: 7)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sgp.BodyPart()" - ] - }, - { - "cell_type": "markdown", - "id": "ca5a15e2-f087-4bd2-9d4a-ea2ac4becd80", - "metadata": {}, - "source": [ - "
\n", - "If the bodyparts you plan to use in your model are not yet in the table, here is code to add bodyparts:\n", - "
\n", - "\n", - ">```python\n", - "sgp.BodyPart.insert([{'bodypart': 'bodypart_1', 'bodypart_description': 'concise description of bodypart'},\n", - " {'bodypart': 'bodypart_2', 'bodypart_description': 'concise description of bodypart'},],\n", - " skip_duplicates=True)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "78fe7c06-30c9-43e1-9e9a-029a70b0d4dd", - "metadata": {}, - "source": [ - "Next we want to construct a list of videos from which we will extract frames to train the model.
The list can either contain dictionaries identifying behavioral videos for NWB files that have already been added to Spyglass, or absolute file paths to the videos you want to use.
For this tutorial, we'll use two videos for which we already have frames labeled." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e3aa1c2f", - "metadata": {}, - "outputs": [], - "source": [ - "video_list = [\n", - " {\"nwb_file_name\": \"J1620210529_.nwb\", \"epoch\": 2},\n", - " {\"nwb_file_name\": \"peanut20201103_.nwb\", \"epoch\": 4},\n", - "]" - ] - }, - { - "cell_type": "markdown", - "id": "32c023b0-d00d-40b0-9a37-d0d3e4a4ae2a", - "metadata": {}, - "source": [ - "Before creating our project, we need to define a few variables.\n", - ">First, we want to set a team name to one that exists in the `LabTeam` table to ensure proper permission are set.
In this case we'll use \"LorenLab\", as all Frank Lab members are a part of this team.
\n", - "We also need to define a `project_name`, which should be a unique identifier for this project. For the tutorial we'll set it as __\"tutorial_scratch_yourinitials\"__
Next, we need to define a list of `bodyparts` for which we want to extract position. The pre-labeled frames we're using include the bodyparts listed below, but please modify as needed for your own project.
We also want to define how many frames we want to extract and eventually label from each video we're using. I will typically use 200 `frames_per_video`, but we'll keep it to 100 for efficiency's sake." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "347e98f1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "project name: tutorial_scratch_DG is already in use.\n" - ] - } - ], - "source": [ - "team_name = \"LorenLab\"\n", - "project_name = \"tutorial_scratch_DG\"\n", - "frames_per_video = 100\n", - "bodyparts = [\"redLED_C\", \"greenLED\", \"redLED_L\", \"redLED_R\", \"tailBase\"]\n", - "project_key = sgp.DLCProject.insert_new_project(\n", - " project_name=project_name,\n", - " bodyparts=bodyparts,\n", - " lab_team=team_name,\n", - " frames_per_video=frames_per_video,\n", - " video_list=video_list,\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "f5d83452-48eb-4669-89eb-a6beb1f2d051", - "metadata": {}, - "source": [ - "Now that we've intialized our project we'll need to extract and label frames.
While this has already been done for this tutorial, here are the commands in order to pull up the DLC GUI to perform these actions:\n", - ">```python\n", - "sgp.DLCProject().run_extract_frames(project_key)\n", - "sgp.DLCProject().run_label_frames(project_key)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "df257015", - "metadata": {}, - "source": [ - "Typically, in order to use pre-labeled frames to your project you'll need to change the values in the labeled-data files. You can do that using the `import_labeled_frames` method. \n", - "
This function expects the `project_key` from your new project
The absolute path to the project you want to import the labeled frames from
The filename (without file extension) of the videos from which you want the frames.\n", - "
Here we'll use the path to a pre-existing project from tutorial 06" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "520a9526-fcd1-417b-b368-00d17e0284e2", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/dgramling/Src/spyglass/src/spyglass/position/v1/position_dlc_project.py:451: FutureWarning: inplace is deprecated and will be removed in a future version.\n", - " dlc_df.columns.set_levels([team_name], level=0, inplace=True)\n", - "/home/dgramling/Src/spyglass/src/spyglass/position/v1/position_dlc_project.py:451: FutureWarning: inplace is deprecated and will be removed in a future version.\n", - " dlc_df.columns.set_levels([team_name], level=0, inplace=True)\n", - "2023-04-20 14:51:08.706450: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-04-20 14:51:08.897194: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading DLC 2.2.3...\n", - "OpenCV is built with OpenMP support. This usually results in poor performance. For details, see https://github.com/tensorpack/benchmarks/blob/master/ImageNet/benchmark-opencv-resize.py\n" - ] - } - ], - "source": [ - "sgp.DLCProject.import_labeled_frames(\n", - " project_key.copy(),\n", - " import_project_path=\"/nimbus/deeplabcut/projects/tutorial_model-LorenLab-2022-07-15/\",\n", - " video_filenames=[\"20201103_peanut_04_r2\", \"20210529_J16_02_r1\"],\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "7e2e3eab-60c7-4a3c-bc8f-fd4e8dcf52a2", - "metadata": {}, - "source": [ - "
\n", - " This step and beyond should be run on a GPU-enabled machine.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "0e48ecf0", - "metadata": {}, - "source": [ - "#### [DLCModelTraining](#ToC) \n", - "Please make sure you're running this notebook on a GPU-enabled machine.
\n", - "Now that we've imported existing frames, we can get ready to train our model.
\n", - "First, we'll need to define a set of parameters for `DLCModelTrainingParams`, which will get used by DeepLabCut during training
\n", - "Let's start with `gputouse`
\n", - "\n", - "
\n", - "gputouse determines which GPU core to use for pose estimation. Run the cell below to determine which core has space and set the gputouse variable accordingly.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a8fc5bb7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{0: 80383, 1: 35, 2: 35, 3: 35, 4: 35, 5: 35, 6: 35, 7: 35, 8: 35, 9: 35}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sgp.dlc_utils.get_gpu_memory()" - ] - }, - { - "cell_type": "markdown", - "id": "bca035a9", - "metadata": {}, - "source": [ - "
\n", - "Set GPU core here
" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1ff0e393", - "metadata": {}, - "outputs": [], - "source": [ - "gputouse = 1 ## 1-9" - ] - }, - { - "cell_type": "markdown", - "id": "2b047686", - "metadata": {}, - "source": [ - "Now let's define the rest of our parameters and insert the entry.
\n", - "(If you want to see all possible parameters that you can pass, checkout the line below):\n", - ">```python\n", - "sgp.DLCModelTrainingParams.get_accepted_params()\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "399581ee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New param set not added\n", - "A param set with name: tutorial already exists\n" - ] - } - ], - "source": [ - "training_params_name = \"tutorial\"\n", - "sgp.DLCModelTrainingParams.insert_new_params(\n", - " paramset_name=training_params_name,\n", - " params={\n", - " \"trainingsetindex\": 0,\n", - " \"shuffle\": 1,\n", - " \"gputouse\": gputouse,\n", - " \"net_type\": \"resnet_50\",\n", - " \"augmenter_type\": \"imgaug\",\n", - " },\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6b6cc709", - "metadata": {}, - "source": [ - "Next we'll modify the `project_key` from above to include the necessary entries for `DLCModelTraining`" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "7acd150b", - "metadata": {}, - "outputs": [], - "source": [ - "# project_key['project_path'] = os.path.dirname(project_key['config_path'])\n", - "if \"config_path\" in project_key:\n", - " del project_key[\"config_path\"]" - ] - }, - { - "cell_type": "markdown", - "id": "0bc7ddaa", - "metadata": {}, - "source": [ - "And here we can insert an entry into `DLCModelTrainingSelection` and populate `DLCModelTraining` with that entry, which will run training for us.
\n", - "**Note**: You can stop training at any point using `I + I` or interrupt the Kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d56d3c39-7b85-4f6a-b9fb-816a1d1912da", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "# Specification for a DLC model training instance\n", - "project_name : varchar(100) # name of DLC project\n", - "dlc_training_params_name : varchar(50) # descriptive name of parameter set\n", - "training_id : int # unique integer,\n", - "---\n", - "model_prefix=\"\" : varchar(32) # " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sgp.DLCModelTrainingSelection.heading" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "139d2f30", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[20-Apr-23 15:00:16] in /home/dgramling/Src/spyglass/src/spyglass/position/v1/position_dlc_training.py, line 179: creating training dataset\n", - "INFO:DLC_project_{project_name}_training:creating training dataset\n" - ] - }, - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: '/nimbus/deeplabcut/projects/tutorial_scratch_DG-LorenLab-2022-11-03/labeled-data/20201103_peanut_04_r2/img22800.png'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[22], line 7\u001b[0m\n\u001b[1;32m 1\u001b[0m sgp\u001b[38;5;241m.\u001b[39mDLCModelTrainingSelection()\u001b[38;5;241m.\u001b[39minsert1({\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mproject_key,\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdlc_training_params_name\u001b[39m\u001b[38;5;124m\"\u001b[39m: training_params_name,\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtraining_id\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_prefix\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m,})\n\u001b[1;32m 5\u001b[0m model_training_key \u001b[38;5;241m=\u001b[39m (sgp\u001b[38;5;241m.\u001b[39mDLCModelTrainingSelection \u001b[38;5;241m&\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mproject_key,\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdlc_training_params_name\u001b[39m\u001b[38;5;124m\"\u001b[39m:training_params_name,})\u001b[38;5;241m.\u001b[39mfetch1(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKEY\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m \u001b[43msgp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDLCModelTraining\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpopulate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_training_key\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass-position/lib/python3.9/site-packages/datajoint/autopopulate.py:230\u001b[0m, in \u001b[0;36mAutoPopulate.populate\u001b[0;34m(self, suppress_errors, return_exception_objects, reserve_jobs, order, limit, max_calls, display_progress, processes, make_kwargs, *restrictions)\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m processes \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 227\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m (\n\u001b[1;32m 228\u001b[0m tqdm(keys, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m display_progress \u001b[38;5;28;01melse\u001b[39;00m keys\n\u001b[1;32m 229\u001b[0m ):\n\u001b[0;32m--> 230\u001b[0m error \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_populate1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpopulate_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 231\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error \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 232\u001b[0m error_list\u001b[38;5;241m.\u001b[39mappend(error)\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass-position/lib/python3.9/site-packages/datajoint/autopopulate.py:281\u001b[0m, in \u001b[0;36mAutoPopulate._populate1\u001b[0;34m(self, key, jobs, suppress_errors, return_exception_objects, make_kwargs)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m_allow_insert \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 280\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 281\u001b[0m \u001b[43mmake\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmake_kwargs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 282\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m, \u001b[38;5;167;01mSystemExit\u001b[39;00m, \u001b[38;5;167;01mException\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m error:\n\u001b[1;32m 283\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "File \u001b[0;32m~/Src/spyglass/src/spyglass/position/v1/position_dlc_training.py:180\u001b[0m, in \u001b[0;36mDLCModelTraining.make\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 176\u001b[0m training_dataset_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 177\u001b[0m k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m dlc_config\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m training_dataset_input_args\n\u001b[1;32m 178\u001b[0m }\n\u001b[1;32m 179\u001b[0m logger\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreating training dataset\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 180\u001b[0m \u001b[43mcreate_training_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdlc_cfg_filepath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtraining_dataset_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 181\u001b[0m \u001b[38;5;66;03m# ---- Trigger DLC model training job ----\u001b[39;00m\n\u001b[1;32m 182\u001b[0m train_network_input_args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(inspect\u001b[38;5;241m.\u001b[39msignature(train_network)\u001b[38;5;241m.\u001b[39mparameters)\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass-position/lib/python3.9/site-packages/deeplabcut/generate_training_dataset/trainingsetmanipulation.py:992\u001b[0m, in \u001b[0;36mcreate_training_dataset\u001b[0;34m(config, num_shuffles, Shuffles, windows2linux, userfeedback, trainIndices, testIndices, net_type, augmenter_type, posecfg_template)\u001b[0m\n\u001b[1;32m 982\u001b[0m (\n\u001b[1;32m 983\u001b[0m datafilename,\n\u001b[1;32m 984\u001b[0m metadatafilename,\n\u001b[1;32m 985\u001b[0m ) \u001b[38;5;241m=\u001b[39m auxiliaryfunctions\u001b[38;5;241m.\u001b[39mget_data_and_metadata_filenames(\n\u001b[1;32m 986\u001b[0m trainingsetfolder, trainFraction, shuffle, cfg\n\u001b[1;32m 987\u001b[0m )\n\u001b[1;32m 989\u001b[0m \u001b[38;5;66;03m################################################################################\u001b[39;00m\n\u001b[1;32m 990\u001b[0m \u001b[38;5;66;03m# Saving data file (convert to training file for deeper cut (*.mat))\u001b[39;00m\n\u001b[1;32m 991\u001b[0m \u001b[38;5;66;03m################################################################################\u001b[39;00m\n\u001b[0;32m--> 992\u001b[0m data, MatlabData \u001b[38;5;241m=\u001b[39m \u001b[43mformat_training_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 993\u001b[0m \u001b[43m \u001b[49m\u001b[43mData\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrainIndices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnbodyparts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproject_path\u001b[49m\n\u001b[1;32m 994\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 995\u001b[0m sio\u001b[38;5;241m.\u001b[39msavemat(\n\u001b[1;32m 996\u001b[0m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(project_path, datafilename), {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdataset\u001b[39m\u001b[38;5;124m\"\u001b[39m: MatlabData}\n\u001b[1;32m 997\u001b[0m )\n\u001b[1;32m 999\u001b[0m \u001b[38;5;66;03m################################################################################\u001b[39;00m\n\u001b[1;32m 1000\u001b[0m \u001b[38;5;66;03m# Saving metadata (Pickle file)\u001b[39;00m\n\u001b[1;32m 1001\u001b[0m \u001b[38;5;66;03m################################################################################\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass-position/lib/python3.9/site-packages/deeplabcut/generate_training_dataset/trainingsetmanipulation.py:685\u001b[0m, in \u001b[0;36mformat_training_data\u001b[0;34m(df, train_inds, nbodyparts, project_path)\u001b[0m\n\u001b[1;32m 683\u001b[0m filename \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mindex[i]\n\u001b[1;32m 684\u001b[0m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimage\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m filename\n\u001b[0;32m--> 685\u001b[0m img_shape \u001b[38;5;241m=\u001b[39m \u001b[43mread_image_shape_fast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproject_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 686\u001b[0m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m img_shape\n\u001b[1;32m 687\u001b[0m temp \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39miloc[i]\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass-position/lib/python3.9/site-packages/deeplabcut/generate_training_dataset/trainingsetmanipulation.py:667\u001b[0m, in \u001b[0;36mread_image_shape_fast\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m 664\u001b[0m \u001b[38;5;129m@lru_cache\u001b[39m(maxsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 665\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_image_shape_fast\u001b[39m(path):\n\u001b[1;32m 666\u001b[0m \u001b[38;5;66;03m# Blazing fast and does not load the image into memory\u001b[39;00m\n\u001b[0;32m--> 667\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mImage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m img:\n\u001b[1;32m 668\u001b[0m width, height \u001b[38;5;241m=\u001b[39m img\u001b[38;5;241m.\u001b[39msize\n\u001b[1;32m 669\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(img\u001b[38;5;241m.\u001b[39mgetbands()), height, width\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass-position/lib/python3.9/site-packages/PIL/Image.py:3227\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(fp, mode, formats)\u001b[0m\n\u001b[1;32m 3224\u001b[0m filename \u001b[38;5;241m=\u001b[39m fp\n\u001b[1;32m 3226\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filename:\n\u001b[0;32m-> 3227\u001b[0m fp \u001b[38;5;241m=\u001b[39m \u001b[43mbuiltins\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3228\u001b[0m exclusive_fp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 3230\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/nimbus/deeplabcut/projects/tutorial_scratch_DG-LorenLab-2022-11-03/labeled-data/20201103_peanut_04_r2/img22800.png'" - ] - } - ], - "source": [ - "sgp.DLCModelTrainingSelection().insert1(\n", - " {\n", - " **project_key,\n", - " \"dlc_training_params_name\": training_params_name,\n", - " \"training_id\": 0,\n", - " \"model_prefix\": \"\",\n", - " }\n", - ")\n", - "model_training_key = (\n", - " sgp.DLCModelTrainingSelection\n", - " & {\n", - " **project_key,\n", - " \"dlc_training_params_name\": training_params_name,\n", - " }\n", - ").fetch1(\"KEY\")\n", - "sgp.DLCModelTraining.populate(model_training_key)" - ] - }, - { - "cell_type": "markdown", - "id": "da004b3e", - "metadata": {}, - "source": [ - "Here we'll make sure that the entry made it into the table properly!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e5306fd9", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "sgp.DLCModelTraining() & model_training_key" - ] - }, - { - "cell_type": "markdown", - "id": "ac5b7687", - "metadata": {}, - "source": [ - "Populating `DLCModelTraining` automatically inserts the entry into `DLCModelSource`. `DLCModelSource` is a table that is used to switch between the models we train using Spyglass and pre-existing projects." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a349dc3d", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModelSource() & model_training_key" - ] - }, - { - "cell_type": "markdown", - "id": "92cb8969", - "metadata": {}, - "source": [ - "Notice the `source` field in the table above. It will only accept _\"FromImport\"_ or _\"FromUpstream\"_ as entries. Let's checkout the `FromUpstream` part table attached to `DLCModelSource` below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0cc1afa", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModelSource.FromUpstream() & model_training_key" - ] - }, - { - "cell_type": "markdown", - "id": "67a9b2c6", - "metadata": {}, - "source": [ - "#### [DLCModel](#TableOfContents) \n", - "Next we'll get ready to populate the `DLCModel` table, which holds all the relevant information for both pre-trained models and models trained within Spyglass.
First we'll need to determine a set of parameters for our model to select the correct model file.
We can visualize a default set below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bb663861", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModelParams.get_default()" - ] - }, - { - "cell_type": "markdown", - "id": "8b45a6ed", - "metadata": {}, - "source": [ - "> Here is the syntax to add your own parameter set:\n", - ">```python\n", - "dlc_model_params_name = \"make_this_yours\"\n", - "params = {\n", - " \"params\": {},\n", - " \"shuffle\": 1,\n", - " \"trainingsetindex\": 0,\n", - " \"model_prefix\": \"\",\n", - " }\n", - "sgp.DLCModelParams.insert1({\"dlc_model_params_name\": dlc_model_params_name, \"params\": params}, skip_duplicates=True)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "7bce9696", - "metadata": {}, - "source": [ - "Now that we've defined a set of parameters and inserted into `DLCModelParams`, we can insert an entry into `DLCModelSelection` and populate `DLCModel`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eaa23fab", - "metadata": {}, - "outputs": [], - "source": [ - "temp_model_key = (sgp.DLCModelSource & model_training_key).fetch1(\"KEY\")\n", - "sgp.DLCModelSelection().insert1({\n", - " **temp_model_key,\n", - " \"dlc_model_params_name\": \"default\"},\n", - " skip_duplicates=True)\n", - "model_key = (sgp.DLCModelSelection & ).fetch1(\"KEY\")\n", - "sgp.DLCModel.populate(model_key)" - ] - }, - { - "cell_type": "markdown", - "id": "f8f1b839", - "metadata": {}, - "source": [ - "Again, let's make sure that everything looks correct in `DLCModel`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c39f72ca", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModel() & model_key" - ] - }, - { - "cell_type": "markdown", - "id": "53ce4ee4", - "metadata": {}, - "source": [ - "#### [DLCPoseEstimation](#TableOfContents) \n", - "\n", - "Alright, now that we've trained model and populated the `DLCModel` table, we're ready to set-up Pose Estimation on a behavioral video of your choice.
For this tutorial, you can choose to use an epoch of your choice, we can also use the one specified below. If you'd like to use your own video, just specify the `nwb_file_name` and `epoch` number and make sure it's in the `VideoFile` table!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc2a8dab-7caf-4389-8494-9158d2ec5b20", - "metadata": {}, - "outputs": [], - "source": [ - "nwb_file_name = \"J1620210604_.nwb\"\n", - "epoch = 14" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ecfe757-cee8-435a-b902-77b1e6dc1a0e", - "metadata": {}, - "outputs": [], - "source": [ - "sgc.VideoFile() & {\"nwb_file_name\": nwb_file_name, \"epoch\": epoch}" - ] - }, - { - "cell_type": "markdown", - "id": "0f26a081-859d-4dff-bb58-84cec2ff4b3f", - "metadata": {}, - "source": [ - "To set up pose estimation, we need to make sure a few things are in order. Using `insert_estimation_task` will take care of these steps for us!
Briefly, it will convert out video to be in .mp4 format (DLC struggles with .h264) and determine the directory in which we'll store the pose estimation results.
\n", - ">**`task_mode`** determines whether or not populating `DLCPoseEstimation` runs a new pose estimation, or loads an existing. Use _'trigger'_ unless you've already run this specific pose estimation.
**`video_file_num`** will be 0 in almost all cases.
__`gputouse`__ has already been set above during the training step. It may be a good idea to make sure that core is still free before moving forward." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9df5644f-febc-49d7-a60d-6991798c20d7", - "metadata": {}, - "outputs": [], - "source": [ - "pose_estimation_key = sgp.DLCPoseEstimationSelection.insert_estimation_task(\n", - " {\n", - " \"nwb_file_name\": nwb_file_name,\n", - " \"epoch\": epoch,\n", - " \"video_file_num\": 0,\n", - " **model_key,\n", - " },\n", - " task_mode=\"trigger\",\n", - " params={\"gputouse\": gputouse, \"videotype\": \"mp4\"},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "5feb2a26-fae1-41ca-828f-cc6c73ebd24e", - "metadata": {}, - "source": [ - "And now we populate `DLCPoseEstimation`! This might take a bit..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "88f28ecc-d3a4-40f9-a1fb-afb4bdd04497", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPoseEstimation().populate(pose_estimation_key)" - ] - }, - { - "cell_type": "markdown", - "id": "88757488-cfa4-4e7c-b965-7dacac43810a", - "metadata": {}, - "source": [ - "Let's visualize the output from Pose Estimation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45dd4f3b-7bf4-41b7-be5f-820fe3ee9f69", - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCPoseEstimation() & pose_estimation_key).fetch_dataframe()" - ] - }, - { - "cell_type": "markdown", - "id": "52f45ab3-9344-4975-b5ff-f80a5727cdac", - "metadata": {}, - "source": [ - "#### [DLCSmoothInterp](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "id": "0ccd5dbe-097a-4138-a234-da78a5902684", - "metadata": {}, - "source": [ - "Now that we've completed pose estimation, it's time to interpolate over low likelihood periods and smooth the resulting positions.
First we need to define some parameters for smoothing and interpolation. We can see the default parameter set below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f6e44a34-8d6d-4206-b02a-9ca38a68f1c0", - "metadata": {}, - "outputs": [], - "source": [ - "print(sgp.DLCSmoothInterpParams.get_default())\n", - "si_params_name = \"default\"" - ] - }, - { - "cell_type": "markdown", - "id": "a245c9e5-e8f6-4c6f-b9e1-d71ab3e06d59", - "metadata": {}, - "source": [ - "> If you'd like to change any of these parameters, here is the syntax to do that\n", - ">```python\n", - "si_params_name = 'your_unique_param_name'\n", - "params = {\n", - " \"smoothing_params\": {\n", - " \"smoothing_duration\": 0.##,\n", - " \"smooth_method\": \"moving_avg\",\n", - " },\n", - " \"interp_params\": {\n", - " \"likelihood_thresh\": 0.##,\n", - " },\n", - " \"max_plausible_speed\": ###,\n", - " \"speed_smoothing_std_dev\": 0.###,\n", - "}\n", - "sgp.DLCSmoothInterpParams().insert1(\n", - " {\n", - " 'dlc_si_params_name': si_params_name,\n", - " \"params\": params,\n", - " },\n", - " skip_duplicates=True)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "8139036e-ce7e-41ec-be78-aa15a4b0b795", - "metadata": {}, - "source": [ - "Here we'll create a dictionary with the correct set of keys for the `DLCSmoothInterpSelection` table" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec730b91-a974-4f54-9d55-35f52e08487f", - "metadata": {}, - "outputs": [], - "source": [ - "si_key = pose_estimation_key.copy()\n", - "fields = list(sgp.DLCSmoothInterpSelection.fetch().dtype.fields.keys())\n", - "si_key = {key: val for key, val in si_key.items() if key in fields}\n", - "si_key" - ] - }, - { - "cell_type": "markdown", - "id": "9a47a6de-51ff-4980-b105-42a75ef7f7a3", - "metadata": {}, - "source": [ - "And now we can insert all of the bodyparts we want to process into `DLCSmoothInterpSelection`
\n", - "First lets visualize the bodyparts we have available to us.
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6e5fcad0-e211-4bd7-82b1-d69bec0eb3d7", - "metadata": {}, - "outputs": [], - "source": [ - "print((sgp.DLCPoseEstimation.BodyPart & pose_estimation_key).fetch(\"bodypart\"))" - ] - }, - { - "cell_type": "markdown", - "id": "7c6e3ad2-1960-43cd-a223-784c08211013", - "metadata": {}, - "source": [ - "We can use `insert1` to insert a single bodypart, but would suggest using `insert` to insert a list of keys with different bodyparts." - ] - }, - { - "cell_type": "markdown", - "id": "70621afe-a144-47f2-9d70-212fbcd85aba", - "metadata": {}, - "source": [ - ">_Syntax to insert a single bodypart_\n", - ">```python\n", - "sgp.DLCSmoothInterpSelection.insert1(\n", - " {\n", - " **si_key,\n", - " 'bodypart': 'greenLED',\n", - " 'dlc_si_params_name': si_params_name,\n", - " },\n", - " skip_duplicates=True)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "3e2f73cd-2534-40a2-86e6-948ccd902812", - "metadata": {}, - "source": [ - "Lets set a list of bodyparts we want to insert and then insert them into `DLCSmoothInterpSelection`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "819e826d-38ef-4219-8d52-5353c6b4b61a", - "metadata": {}, - "outputs": [], - "source": [ - "bodyparts = [\"greenLED\", \"redLED_C\"]\n", - "sgp.DLCSmoothInterpSelection.insert(\n", - " [\n", - " {\n", - " **si_key,\n", - " \"bodypart\": bodypart,\n", - " \"dlc_si_params_name\": si_params_name,\n", - " }\n", - " for bodypart in bodyparts\n", - " ],\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6dca5640-3e9a-42b7-bc61-7f3e1a219619", - "metadata": {}, - "source": [ - "And to make sure that all of the bodyparts we want made it into the the selection table, we can visualize the table below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3b347b29-1583-4fbc-9b35-8e062b611d59", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCSmoothInterpSelection() & si_key" - ] - }, - { - "cell_type": "markdown", - "id": "af8f0d26-3879-4f50-a076-e60685028083", - "metadata": {}, - "source": [ - "Now we can populate `DLCSmoothInterp`, which will perform smoothing and interpolation on all of the bodyparts we specified.
We can limit the populate using `si_key` since it is bodypart agnostic." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9bf16c32-0f5e-4cd2-b814-56745e836599", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCSmoothInterp().populate(si_key)" - ] - }, - { - "cell_type": "markdown", - "id": "3d3af0a2-16cc-43dc-af9c-0ec606cfe1e1", - "metadata": {}, - "source": [ - "And let's visualize the resulting position data using a scatter plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ced96b05-e6dc-4771-bfb8-bcbddfb8e494", - "metadata": {}, - "outputs": [], - "source": [ - "(\n", - " sgp.DLCSmoothInterp() & {**si_key, \"bodypart\": bodyparts[0]}\n", - ").fetch1_dataframe().plot.scatter(x=\"x\", y=\"y\", s=1, figsize=(5, 5))" - ] - }, - { - "cell_type": "markdown", - "id": "a838e4c4-8ff9-4b73-aee5-00eb91ea899f", - "metadata": {}, - "source": [ - "#### [DLCSmoothInterpCohort](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "id": "3cf3d882-2c24-46ca-bfcc-72f21712e47b", - "metadata": {}, - "source": [ - "Now that we've smoothed and interpolated our position data for each bodypart, we need to form a set of bodyparts from which we want to derive a centroid and orientation (or potentially a second set for orientation). This is the goal of the `DLCSmoothInterpCohort` table." - ] - }, - { - "cell_type": "markdown", - "id": "5017fd46-2bb9-4349-981b-f9789ffec338", - "metadata": {}, - "source": [ - "First, let's make a key that represents the 'cohort' we want to form.\n", - "> We'll set the `dlc_si_cohort_selection_name` to a concise name
We'll also form a dictionary with the bodypart name as the key and the smoothing/interpolation parameter name used for that bodypart as the value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "92fb1af9-20cf-46d9-a518-a7f551334bc8", - "metadata": {}, - "outputs": [], - "source": [ - "cohort_key = si_key.copy()\n", - "if \"bodypart\" in cohort_key:\n", - " del cohort_key[\"bodypart\"]\n", - "if \"dlc_si_params_name\" in cohort_key:\n", - " del cohort_key[\"dlc_si_params_name\"]\n", - "cohort_key[\"dlc_si_cohort_selection_name\"] = \"green_red_led\"\n", - "cohort_key[\"bodyparts_params_dict\"] = {\n", - " \"greenLED\": si_params_name,\n", - " \"redLED_C\": si_params_name,\n", - "}\n", - "print(cohort_key)" - ] - }, - { - "cell_type": "markdown", - "id": "11c6a327-d4b0-4de1-a2c6-10a0443a3f96", - "metadata": {}, - "source": [ - "Here we'll insert the cohort into the `DLCSmoothInterpCohortSelection` table
..and populate `DLCSmoothInterpCohort`, which collates the separately smoothed and interpolated bodyparts into a single entry." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "805f55c1-3c7b-4cf9-bdd7-98743810c671", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCSmoothInterpCohortSelection().insert1(cohort_key, skip_duplicates=True)\n", - "sgp.DLCSmoothInterpCohort.populate(cohort_key)" - ] - }, - { - "cell_type": "markdown", - "id": "a6b7d361-47c5-4748-ac59-f51b897f7fe6", - "metadata": {}, - "source": [ - "And of course, let's make sure that the table populated correctly. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7672b63-6dfc-46db-b8df-95c1e6730b6c", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCSmoothInterpCohort.BodyPart() & cohort_key" - ] - }, - { - "cell_type": "markdown", - "id": "d871bdca-2278-43ec-a70c-52257ad26170", - "metadata": {}, - "source": [ - "#### [DLCCentroid](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "id": "4cc37edb-fdd3-4a05-8cd5-91f3c5f7cbbb", - "metadata": {}, - "source": [ - "We now have a cohort of smoothed and interpolated bodyparts from which to determine a centroid!
To start, we'll need a set of parameters to use for determining the centroid. For this tutorial, we can use the default." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e31c8db-0396-475a-af71-ae38433d2b7d", - "metadata": {}, - "outputs": [], - "source": [ - "# Here is the default set\n", - "print(sgp.DLCCentroidParams.get_default())\n", - "centroid_params_name = \"default\"" - ] - }, - { - "cell_type": "markdown", - "id": "852948f7-e743-4319-be6b-265dadfca713", - "metadata": {}, - "source": [ - ">Here is the syntax to add your own parameters:\n", - ">```python\n", - "centroid_params = {\n", - " 'centroid_method': 'two_pt_centroid',\n", - " 'points' : {\n", - " 'greenLED': 'greenLED',\n", - " 'redLED_C': 'redLED_C',},\n", - " 'speed_smoothing_std_dev': 0.100,\n", - "}\n", - "centroid_params_name = 'your_unique_param_name'\n", - "sgp.DLCCentroidParams.insert1({'dlc_centroid_params_name': centroid_params_name,\n", - " 'params': centroid_params},\n", - " skip_duplicates=True)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "85ad4e53-43dd-4e05-84c4-7d4504766746", - "metadata": {}, - "source": [ - "And now let's make a key to insert into `DLCCentroidSelection`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28ac17cb-4bb3-47b2-b1b9-1c4b37797591", - "metadata": {}, - "outputs": [], - "source": [ - "centroid_key = cohort_key.copy()\n", - "fields = list(sgp.DLCCentroidSelection.fetch().dtype.fields.keys())\n", - "centroid_key = {key: val for key, val in centroid_key.items() if key in fields}\n", - "centroid_key[\"dlc_centroid_params_name\"] = centroid_params_name\n", - "print(centroid_key)" - ] - }, - { - "cell_type": "markdown", - "id": "2674c0d3-d3fd-4cd9-a843-260c442c2d23", - "metadata": {}, - "source": [ - "Let's insert it into `DLCCentroidSelection` and then populate `DLCCentroid` !" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "47fccef4-2fef-4f74-b7a4-8564328b14d4", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCCentroidSelection.insert1(centroid_key, skip_duplicates=True)\n", - "sgp.DLCCentroid.populate(centroid_key)" - ] - }, - { - "cell_type": "markdown", - "id": "6e49c5ad-909f-4f1a-a156-f8f8a84fb78a", - "metadata": {}, - "source": [ - "Here we can visualize the resulting centroid position" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29e7e447-fa6f-4f06-9ec9-4b9838b7255e", - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCCentroid() & centroid_key).fetch1_dataframe().plot.scatter(\n", - " x=\"position_x\",\n", - " y=\"position_y\",\n", - " c=\"speed\",\n", - " colormap=\"viridis\",\n", - " alpha=0.5,\n", - " s=0.5,\n", - " figsize=(10, 10),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "cb513a9d-5250-404c-8887-639f785516c7", - "metadata": {}, - "source": [ - "#### [DLCOrientation](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "id": "509076f0-f0b8-4fd0-8884-32c48ca4a125", - "metadata": {}, - "source": [ - "We'll now go through a similar process to identify the orientation!
To start, we'll need a set of parameters to use for determining the orientation. For this tutorial, we can use the default." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "faf244b3-7295-48ed-90ea-cf878e85e122", - "metadata": {}, - "outputs": [], - "source": [ - "print(sgp.DLCOrientationParams.get_default())\n", - "dlc_orientation_params_name = \"default\"" - ] - }, - { - "cell_type": "markdown", - "id": "8ec170be-7a7a-4a20-986c-d055aee1a08b", - "metadata": {}, - "source": [ - "Here we'll prune the `cohort_key` we used above and add our `dlc_orientation_params_name` to make it suitable for `DLCOrientationSelection`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "09e4a6cf-472e-43e3-90aa-f7ff7fb9dc72", - "metadata": {}, - "outputs": [], - "source": [ - "fields = list(sgp.DLCOrientationSelection.fetch().dtype.fields.keys())\n", - "orient_key = {key: val for key, val in cohort_key.items() if key in fields}\n", - "orient_key[\"dlc_orientation_params_name\"] = dlc_orientation_params_name\n", - "print(orient_key)" - ] - }, - { - "cell_type": "markdown", - "id": "9406d2de-9b71-4591-82f6-ed53f2d4f220", - "metadata": {}, - "source": [ - "And now let's insert into `DLCOrientationSelection` and populate `DLCOrientation` to determine the orientation!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5d23302-02e3-427a-ac35-2f648e3ae674", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCOrientationSelection().insert1(orient_key, skip_duplicates=True)\n", - "sgp.DLCOrientation().populate(orient_key)" - ] - }, - { - "cell_type": "markdown", - "id": "36f62da0-0cc5-4ffb-b2df-7b68c3f6e268", - "metadata": {}, - "source": [ - "We can fetch the output of `DLCOrientation` as a dataframe to make sure everything looks appropriate." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c5eba7f4-0b32-486a-894a-c97404c74d2b", - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCOrientation() & orient_key).fetch1_dataframe()" - ] - }, - { - "cell_type": "markdown", - "id": "dc75aeaf-018a-46ed-83a8-6603ae100791", - "metadata": {}, - "source": [ - "#### [DLCPos](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "id": "21d3f9ba-dc89-4c32-a125-1fa85cd4132d", - "metadata": {}, - "source": [ - "Ok, we're now done with processing the position data! We just have to do some table manipulations to make sure everything ends up in the same format and same location.
\n", - "To summarize, we brought in a pretrained DLC project, used that model to run pose estimation on a new behavioral video, smoothed and interpolated the result, formed a cohort of bodyparts, and determined the centroid and orientation of this cohort. **_Whew!_**
\n", - "Now let's populate `DLCPos` with our centroid and orientation entries from above.
----
\n", - "To begin, we'll make a key that combines the cohort names we used for the orientation and centroid as well as the params names for both." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a166dd6-3863-4349-97ac-19d7d6a841b4", - "metadata": {}, - "outputs": [], - "source": [ - "fields = list(sgp.DLCPos.fetch().dtype.fields.keys())\n", - "dlc_key = {key: val for key, val in centroid_key.items() if key in fields}\n", - "dlc_key[\"dlc_si_cohort_centroid\"] = centroid_key[\"dlc_si_cohort_selection_name\"]\n", - "dlc_key[\"dlc_si_cohort_orientation\"] = orient_key[\n", - " \"dlc_si_cohort_selection_name\"\n", - "]\n", - "dlc_key[\"dlc_orientation_params_name\"] = orient_key[\n", - " \"dlc_orientation_params_name\"\n", - "]\n", - "print(dlc_key)" - ] - }, - { - "cell_type": "markdown", - "id": "551e4c5e-7c32-46b0-a138-80064a212fbe", - "metadata": {}, - "source": [ - "Now we can insert into `DLCPosSelection` and populate `DLCPos` with our `dlc_key`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d7badff-0ad7-48cf-aef6-a4f55df8ded9", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPosSelection().insert1(dlc_key, skip_duplicates=True)\n", - "sgp.DLCPos().populate(dlc_key)" - ] - }, - { - "cell_type": "markdown", - "id": "412f1cff-2ead-4489-8a10-9fa7a5d33292", - "metadata": {}, - "source": [ - "We can also make sure that all of our data made it through by fetching the dataframe attached to this entry.
We should expect 8 columns:\n", - ">time
video_frame_ind
position_x
position_y
orientation
velocity_x
velocity_y
speed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "853db96b-1cd4-4ff6-91ea-aca7f7d3851d", - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCPos() & dlc_key).fetch1_dataframe()" - ] - }, - { - "cell_type": "markdown", - "id": "2d8623a8-1725-4e02-b1a2-d2f993988102", - "metadata": {}, - "source": [ - "And even more, we can fetch the `pose_eval_result` that is calculated during this step. This field contains the percentage of frames that each bodypart was below the likelihood threshold of 0.95 as a means of assessing the quality of the pose estimation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4f06244-9d59-44d4-bcbb-062809b3ea6e", - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCPos() & dlc_key).fetch1(\"pose_eval_result\")" - ] - }, - { - "cell_type": "markdown", - "id": "b2303147-3657-479c-8f72-b3fc6905a596", - "metadata": {}, - "source": [ - "#### [DLCPosVideo](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "id": "af0b081d-f619-4c38-ba48-6ae1c0c5ff2b", - "metadata": {}, - "source": [ - "Here we can create a video with the centroid and orientation overlaid on the animal's behavioral video. This will also plot the likelihood of each bodypart used in the cohort. This is completely optional, but a good idea to make sure everything looks correct." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0a725c08-a616-43a0-8925-4a82bf872ba3", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPosVideoParams.insert_default()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84e2f782-ba45-487a-8e8f-e80dd33d9c31", - "metadata": {}, - "outputs": [], - "source": [ - "params = {\n", - " \"percent_frames\": 0.05,\n", - " \"incl_likelihood\": True,\n", - "}\n", - "sgp.DLCPosVideoParams.insert1(\n", - " {\"dlc_pos_video_params_name\": \"five_percent\", \"params\": params},\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5758e2fc-13e6-46cb-9a93-ae1b4c1f4741", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPosVideoSelection.insert1(\n", - " {**dlc_key, \"dlc_pos_video_params_name\": \"five_percent\"},\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2887c0a5-77c8-421e-935e-0692f3f1fd68", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPosVideo().populate(dlc_key)" - ] - }, - { - "cell_type": "markdown", - "id": "5a68bba8-9871-40ac-84c9-51ac0e76d44e", - "metadata": {}, - "source": [ - "#### [PositionOutput](#TableOfContents) " - ] - }, - { - "cell_type": "markdown", - "id": "25325173-bbaf-4b85-aef6-201384d9933b", - "metadata": {}, - "source": [ - "`PositionOutput` is the final table of the position pipeline and is automatically populated when we populate `DLCPosV1`! Let's make sure that our entry made it in." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59ec40c9-78d8-4edd-8158-be91fb15af3e", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.PositionOutput() & dlc_key" - ] - }, - { - "cell_type": "markdown", - "id": "c414d9e0-e495-42ef-a8b0-1c7d53aed02e", - "metadata": {}, - "source": [ - "`PositionOutput` also has a part table, similar to the `DLCModelSource` table above. Let's check that out as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "50760123-7f09-4a94-a1f7-41a037914fd7", - "metadata": {}, - "outputs": [], - "source": [ - "PositionOutput.DLCPosV1() & dlc_key" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c96daaa9-5e70-4a2c-b0a4-c2849e3a1440", - "metadata": {}, - "outputs": [], - "source": [ - "(PositionOutput.DLCPosV1() & dlc_key).fetch1_dataframe()" - ] - }, - { - "cell_type": "markdown", - "id": "e48c7a4e-0bbc-4101-baf2-e84f1f5739d5", - "metadata": {}, - "source": [ - "#### [PositionVideo](#TableOfContents)" - ] - }, - { - "cell_type": "markdown", - "id": "388e6602-8e80-47fa-be78-4ae120d52e41", - "metadata": {}, - "source": [ - "Bonus points if you made it this far... We can use the `PositionVideo` table to create a video that overlays just the centroid and orientation (regardless of upstream source) on the behavioral video. This table uses the parameter `plot` to determine whether to plot the entry deriving from the DLC arm or from the Trodes arm of the position pipeline. This parameter also accepts 'all', which will plot both (if they exist) in order to compare results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b2a782ce-0a14-4725-887f-ae6f341635f8", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.PositionVideoSelection().insert1(\n", - " {\n", - " \"nwb_file_name\": \"J1620210604_.nwb\",\n", - " \"interval_list_name\": \"pos 13 valid times\",\n", - " \"trodes_position_id\": 0,\n", - " \"dlc_position_id\": 1,\n", - " \"plot\": \"DLC\",\n", - " \"output_dir\": \"/home/dgramling/Src/\",\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c32993e7-5b32-46f9-a2f9-9634aef785f2", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.PositionVideo.populate({\"plot\": \"DLC\"})" - ] - }, - { - "cell_type": "markdown", - "id": "be097052-3789-4d55-aca1-e44d426c39b4", - "metadata": {}, - "source": [ - "### _CONGRATULATIONS!!_\n", - "Please treat yourself to a nice tea break :-)" - ] - }, - { - "cell_type": "markdown", - "id": "c71c90a2", - "metadata": {}, - "source": [ - "### [Return To Table of Contents](#TableOfContents)
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b4c1e1c-0a5f-40fe-8e02-749bb7f0d457", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:spyglass-position] *", - "language": "python", - "name": "conda-env-spyglass-position-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/05_Position_Trodes.ipynb b/notebooks/05_Position_Trodes.ipynb deleted file mode 100644 index b0e24468a..000000000 --- a/notebooks/05_Position_Trodes.ipynb +++ /dev/null @@ -1,1907 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1c8ddac6", - "metadata": {}, - "source": [ - "## Position using Trodes Tracking from NWB file\n", - "\n", - "**Note: make a copy of this notebook and run the copy to avoid git conflicts in the future**\n", - "\n", - "This is a tutorial on how to process position that was extracted through Trodes Tracking (online or offline) using the Spyglass pipeline used in Loren Frank's lab, UCSF. It will walk through defining your parameters and processing the raw position to extract a centroid and orientation, and inserting the resulting information into the `IntervalPositionInfo` table.
\n", - "-> This tutorial assumes you've completed [tutorial 0](0_intro.ipynb)
\n", - "**Note 2: Make sure you are running this within the spyglass Conda environment**" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "2cedca1c-6ae6-407f-91c1-322a34fb2cec", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import datajoint as dj\n", - "import spyglass.position as sgp\n", - "import spyglass.common as sgc" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "c223f03e", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "dj.config[\"filepath_checksum_size_limit\"] = 1 * 1024**3\n", - "dj.config.save_global()" - ] - }, - { - "cell_type": "markdown", - "id": "0cb1b9de-be15-4a79-9b02-ebad3ef456d5", - "metadata": {}, - "source": [ - "\n", - "This data pipeline takes the 2D position (in video pixels) of the green and red LEDs tracked by Trodes, and computes:\n", - "+ head position (in cm)\n", - "+ head orientation (in radians)\n", - "+ head velocity (in cm/s)\n", - "+ head speed (in cm/s)\n", - "\n", - "We can then check the quality of the head position and direction by plotting the them on the video along with the oringal green and red LEDs.\n", - "\n", - "This notebook will take you through this process for one dataset." - ] - }, - { - "cell_type": "markdown", - "id": "6250ed23-6c95-4f72-984d-5b8490c66c9d", - "metadata": {}, - "source": [ - "### 1. Loading the session data\n", - "\n", - "First let us make sure that the session we want to analyze is inserted into the `RawPosition` table" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "3d2a2b0b-5e13-432d-a145-7cb2736b54d0", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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nwb_file_name

\n", - " name of the NWB file\n", - "
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interval_list_name

\n", - " descriptive name of this interval list\n", - "
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raw_position_object_id

\n", - " the object id of the spatial series for this epoch in the NWB file\n", - "
chimi20200216_new_.nwbpos 0 valid times9cdefc88-4d2f-4e52-ad6e-d4f7592e4446
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Total: 7

\n", - " " - ], - "text/plain": [ - "*nwb_file_name *interval_list raw_position_o\n", - "+------------+ +------------+ +------------+\n", - "chimi20200216_ pos 0 valid ti 9cdefc88-4d2f-\n", - "chimi20200216_ pos 1 valid ti db43201b-285b-\n", - "chimi20200216_ pos 2 valid ti d35cbc4d-e3d9-\n", - "chimi20200216_ pos 3 valid ti 39f017a8-2a22-\n", - "chimi20200216_ pos 4 valid ti 6a07a237-a0d5-\n", - "chimi20200216_ pos 5 valid ti 2090528b-c186-\n", - "chimi20200216_ pos 6 valid ti c8e09ed9-848c-\n", - " (Total: 7)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nwb_file_name = \"chimi20200216_new.nwb\"\n", - "nwb_copy_file_name = sgc.nwb_helper_fn.get_nwb_copy_filename(nwb_file_name)\n", - "sgc.RawPosition() & {\"nwb_file_name\": nwb_copy_file_name}" - ] - }, - { - "cell_type": "markdown", - "id": "516e6433-61f2-47a6-8ad9-bdd87cecbfe4", - "metadata": {}, - "source": [ - "### 2. Setting the parameters for running the position pipeline\n", - "\n", - "The parameters for the position pipeline are set by the `TrodesPosParams` table. `default` is the name of the standard set of parameters. As usual, if you want to change the parameters, you can insert your own into the table.\n", - "\n", - "The parameters are as follows:\n", - "\n", - "+ `max_separation` is the maxmium acceptable distance (in cm) between the red and green LEDs. When the distance between the LEDs becomes greater than this number, the times are marked as NaNs and inferred by interpolation. This is useful parameter when the inferred red or green LED position tracks a reflection instead of the true red or green LED position. It is set to 9.0 cm by default.\n", - "+ `max_speed` is the maximum plausible speed (in cm/s) the animal can move at. Times when the speed is greater than this threshold are marked as NaNs and inferred by interpolation. This can be useful in preventing big, sudden jumps in position. It is set to 300.0 cm/s by default.\n", - "+ `position_smoothing_duration` controls how much the red and green LEDs are smoothed before computing the average of their position to get the head position. It is in units of seconds.\n", - "+ `speed_smoothing_std_dev` controls how much the head speed is smoothed. It corresponds to the standard deviation of the Gaussian kernel used to smooth the speed and is in units of seconds. It is set to 0.100 seconds by default.\n", - "+ `front_led1` is either 1 or 0 indicating True or False. It controls which LED is treated as the front LED and the back LED, which is important for calculating the head direction.\n", - " + 1 indicates that the LED corresponding to `xloc`, `yloc` in the `RawPosition` table as the front LED and the LED corresponding to `xloc2`, `yloc2` as the back LED.\n", - " + 0 indicates that `xloc`, `yloc` are treated as the back LED and `xloc2`, `yloc2` are treated as the front LED.\n", - "\n", - "We can get a list of potential parameters using the method `TrodesPosParams.get_accepted_params()`
\n", - "And view the default setting for the parameters using `get_default`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "99be8696-a232-451c-829f-56ef1be9a8ed", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "accepted parameters:\n", - "['max_separation', 'max_speed', 'position_smoothing_duration', 'speed_smoothing_std_dev', 'orient_smoothing_std_dev', 'led1_is_front', 'is_upsampled', 'upsampling_sampling_rate', 'upsampling_interpolation_method']\n", - "default parameters:\n", - "{'max_separation': 9.0, 'max_speed': 300.0, 'position_smoothing_duration': 0.125, 'speed_smoothing_std_dev': 0.1, 'orient_smoothing_std_dev': 0.001, 'led1_is_front': 1, 'is_upsampled': 0, 'upsampling_sampling_rate': None, 'upsampling_interpolation_method': 'linear'}\n" - ] - } - ], - "source": [ - "print(f\"accepted parameters:\\n{sgp.TrodesPosParams.get_accepted_params()}\")\n", - "print(f\"default parameters:\\n{sgp.TrodesPosParams.get_default()['params']}\")" - ] - }, - { - "cell_type": "markdown", - "id": "8dbe4448", - "metadata": {}, - "source": [ - "Now we pair the parameters we chose with an interval from our specified NWB file and insert into `TrodesPosSelection`.
\n", - "We can define the interval we want to use via its `interval_list_name`, which we can see in the `IntervalList` table.
\n", - ">```python\n", - ">sgc.IntervalList & {\"nwb_file_name\": nwb_copy_file_name\n", - ">```\n", - "A cool trick doctors don't want you to know: `interval_list_name` = 'pos (epoch# - 1) valid times' (e.g. epoch 3's interval_list_name: 'pos 2 valid times')" - ] - }, - { - "cell_type": "markdown", - "id": "6d995c46-d6d4-4665-a32b-375a12b76c82", - "metadata": {}, - "source": [ - "Let's choose the interval corresponding to `pos 1 valid times` in `chimi20200216_new_.nwb`.\n", - "\n", - "We first look at the \"raw\" position data now to see the input into the pipeline. The raw position is in the `RawPosition` table and corresponds to the inferred position of the red and green LEDs from the video (using an algorithm in Trodes). It is in units of pixels. The number of time points corresponds to when the position tracking was turned on and off (and so may not have the same number of time points as the video itself).\n", - "\n", - "We can retrieve the data in the `RawPosition` table for a given interval using a special method called `fetch1_dataframe`. It returns the position of the red and green LEDs as a pandas dataframe where time is the index. The columns of the dataframe are:\n", - "+ `xloc`, `yloc` are the x- and y-position of one of the LEDs\n", - "+ `xloc2`, `yloc2` are the x- and y-position of the other LEDs." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "0de9bdcd-79d1-4099-9503-8bec1a3a4014", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " xloc yloc xloc2 yloc2\n", - "time \n", - "1.581887e+09 264 638 285 635\n", - "1.581887e+09 264 672 269 635\n", - "1.581887e+09 264 638 280 642\n", - "1.581887e+09 268 635 284 636\n", - "1.581887e+09 268 674 283 636\n", - "... ... ... ... ...\n", - "1.581888e+09 556 598 543 616\n", - "1.581888e+09 555 598 543 616\n", - "1.581888e+09 556 598 543 616\n", - "1.581888e+09 555 598 543 616\n", - "1.581888e+09 556 598 542 615\n", - "\n", - "[39340 rows x 4 columns]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "interval_list_name = \"pos 1 valid times\"\n", - "raw_position_df = (\n", - " sgc.RawPosition()\n", - " & {\n", - " \"nwb_file_name\": nwb_copy_file_name,\n", - " \"interval_list_name\": interval_list_name,\n", - " }\n", - ").fetch1_dataframe()\n", - "raw_position_df" - ] - }, - { - "cell_type": "markdown", - "id": "6e990ae3-78c7-4ece-9229-9ebb0800276e", - "metadata": {}, - "source": [ - "Let's just quickly plot the two LEDs to get a sense of the inputs to the pipeline:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "df3d7797-6514-4ddc-9e5d-012a2b8a3e0c", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Raw Position')" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", - "ax.plot(raw_position_df.xloc, raw_position_df.yloc, color=\"green\")\n", - "ax.plot(raw_position_df.xloc2, raw_position_df.yloc2, color=\"red\")\n", - "ax.set_xlabel(\"x-position [pixels]\", fontsize=18)\n", - "ax.set_ylabel(\"y-position [pixels]\", fontsize=18)\n", - "ax.set_title(\"Raw Position\", fontsize=28)" - ] - }, - { - "cell_type": "markdown", - "id": "9d4a9905-8ccb-4ab8-8488-a26842f20e50", - "metadata": {}, - "source": [ - "Okay, now that we understand what the inputs to the pipeline are, let's associate a set of parameters with a given interval.
\n", - "To associate parameters with a given interval, we insert them into the `TrodesPosSelection` table.
\n", - "Here we associate the `default` Trodes position parameters with the interval `pos 1 valid times`:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "9757d864-942e-4f4f-b7eb-5e454ff7151b", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "sgp.TrodesPosSelection.insert1(\n", - " {\n", - " \"nwb_file_name\": nwb_copy_file_name,\n", - " \"interval_list_name\": \"pos 1 valid times\",\n", - " \"trodes_pos_params_name\": \"default\",\n", - " },\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "e2050880-e140-43d5-bb19-b847e94685cc", - "metadata": {}, - "source": [ - "Now let's check to see if we've inserted the parameters correctly:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "0fcc42c3-ae95-4d56-be13-a7bc740d0dda", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "sgp.TrodesPosSelection()\n", - "trodes_key = (\n", - " sgp.TrodesPosSelection()\n", - " & {\n", - " \"nwb_file_name\": nwb_copy_file_name,\n", - " \"interval_list_name\": \"pos 1 valid times\",\n", - " \"trodes_pos_params_name\": \"default\",\n", - " }\n", - ").fetch1(\"KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "38ba4acd-2906-4528-9b88-8ed15f7c1c4f", - "metadata": {}, - "source": [ - "### 3. Running the position pipeline and retrieving the results\n", - "\n", - "Now that we have associated the parameters with the interval we want to run, we can finally run the pipeline for that interval.\n", - "\n", - "We run the pipeline using the `populate` method on the `TrodesPos` table." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "75412566-c9ea-4590-a7d0-c6b4af214fcf", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computing position for: {'nwb_file_name': 'chimi20200216_new_.nwb', 'interval_list_name': 'pos 1 valid times', 'trodes_pos_params_name': 'default'}\n", - "Writing new NWB file chimi20200216_new_GP6D6C8SDF.nwb\n" - ] - }, - { - "ename": "OSError", - "evalue": "Unable to create file (unable to open file: name = '/stelmo/nwb/analysis/chimi20200216_new/chimi20200216_new_GP6D6C8SDF.nwb', errno = 13, error message = 'Permission denied', flags = 13, o_flags = 242)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/dgramling/Src/spyglass/notebooks/04_Trodes_position.ipynb Cell 19\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m sgp\u001b[39m.\u001b[39;49mTrodesPos\u001b[39m.\u001b[39;49mpopulate(trodes_key)\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass/lib/python3.8/site-packages/datajoint/autopopulate.py:229\u001b[0m, in \u001b[0;36mAutoPopulate.populate\u001b[0;34m(self, suppress_errors, return_exception_objects, reserve_jobs, order, limit, max_calls, display_progress, processes, make_kwargs, *restrictions)\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[39mif\u001b[39;00m processes \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 226\u001b[0m \u001b[39mfor\u001b[39;00m key \u001b[39min\u001b[39;00m (\n\u001b[1;32m 227\u001b[0m tqdm(keys, desc\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m) \u001b[39mif\u001b[39;00m display_progress \u001b[39melse\u001b[39;00m keys\n\u001b[1;32m 228\u001b[0m ):\n\u001b[0;32m--> 229\u001b[0m error \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_populate1(key, jobs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mpopulate_kwargs)\n\u001b[1;32m 230\u001b[0m \u001b[39mif\u001b[39;00m error \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 231\u001b[0m error_list\u001b[39m.\u001b[39mappend(error)\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass/lib/python3.8/site-packages/datajoint/autopopulate.py:281\u001b[0m, in \u001b[0;36mAutoPopulate._populate1\u001b[0;34m(self, key, jobs, suppress_errors, return_exception_objects, make_kwargs)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m_allow_insert \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 280\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 281\u001b[0m make(\u001b[39mdict\u001b[39;49m(key), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m(make_kwargs \u001b[39mor\u001b[39;49;00m {}))\n\u001b[1;32m 282\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mSystemExit\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m error:\n\u001b[1;32m 283\u001b[0m \u001b[39mtry\u001b[39;00m:\n", - "File \u001b[0;32m~/Src/spyglass/src/spyglass/position/position_trodes_position.py:109\u001b[0m, in \u001b[0;36mTrodesPos.make\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mmake\u001b[39m(\u001b[39mself\u001b[39m, key):\n\u001b[1;32m 108\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mComputing position for: \u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 109\u001b[0m key[\u001b[39m\"\u001b[39m\u001b[39manalysis_file_name\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m AnalysisNwbfile()\u001b[39m.\u001b[39;49mcreate(key[\u001b[39m\"\u001b[39;49m\u001b[39mnwb_file_name\u001b[39;49m\u001b[39m\"\u001b[39;49m])\n\u001b[1;32m 110\u001b[0m raw_position \u001b[39m=\u001b[39m (RawPosition() \u001b[39m&\u001b[39m key)\u001b[39m.\u001b[39mfetch_nwb()[\u001b[39m0\u001b[39m]\n\u001b[1;32m 111\u001b[0m position_info_parameters \u001b[39m=\u001b[39m (TrodesPosParams() \u001b[39m&\u001b[39m key)\u001b[39m.\u001b[39mfetch1(\u001b[39m\"\u001b[39m\u001b[39mparams\u001b[39m\u001b[39m\"\u001b[39m)\n", - "File \u001b[0;32m~/Src/spyglass/src/spyglass/common/common_nwbfile.py:177\u001b[0m, in \u001b[0;36mAnalysisNwbfile.create\u001b[0;34m(self, nwb_file_name)\u001b[0m\n\u001b[1;32m 175\u001b[0m analysis_file_abs_path \u001b[39m=\u001b[39m AnalysisNwbfile\u001b[39m.\u001b[39mget_abs_path(analysis_file_name)\n\u001b[1;32m 176\u001b[0m \u001b[39m# export the new NWB file\u001b[39;00m\n\u001b[0;32m--> 177\u001b[0m \u001b[39mwith\u001b[39;00m pynwb\u001b[39m.\u001b[39;49mNWBHDF5IO(\n\u001b[1;32m 178\u001b[0m path\u001b[39m=\u001b[39;49manalysis_file_abs_path, mode\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mw\u001b[39;49m\u001b[39m\"\u001b[39;49m, manager\u001b[39m=\u001b[39;49mio\u001b[39m.\u001b[39;49mmanager\n\u001b[1;32m 179\u001b[0m ) \u001b[39mas\u001b[39;00m export_io:\n\u001b[1;32m 180\u001b[0m export_io\u001b[39m.\u001b[39mexport(io, nwbf)\n\u001b[1;32m 182\u001b[0m \u001b[39m# change the permissions to only allow owner to write\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass/lib/python3.8/site-packages/hdmf/utils.py:583\u001b[0m, in \u001b[0;36mdocval..dec..func_call\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 581\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfunc_call\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m 582\u001b[0m pargs \u001b[39m=\u001b[39m _check_args(args, kwargs)\n\u001b[0;32m--> 583\u001b[0m \u001b[39mreturn\u001b[39;00m func(args[\u001b[39m0\u001b[39;49m], \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mpargs)\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass/lib/python3.8/site-packages/pynwb/__init__.py:246\u001b[0m, in \u001b[0;36mNWBHDF5IO.__init__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[39melif\u001b[39;00m manager \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 245\u001b[0m manager \u001b[39m=\u001b[39m get_manager()\n\u001b[0;32m--> 246\u001b[0m \u001b[39msuper\u001b[39;49m(NWBHDF5IO, \u001b[39mself\u001b[39;49m)\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(path, manager\u001b[39m=\u001b[39;49mmanager, mode\u001b[39m=\u001b[39;49mmode, file\u001b[39m=\u001b[39;49mfile_obj, comm\u001b[39m=\u001b[39;49mcomm, driver\u001b[39m=\u001b[39;49mdriver)\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass/lib/python3.8/site-packages/hdmf/utils.py:583\u001b[0m, in \u001b[0;36mdocval..dec..func_call\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 581\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfunc_call\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m 582\u001b[0m pargs \u001b[39m=\u001b[39m _check_args(args, kwargs)\n\u001b[0;32m--> 583\u001b[0m \u001b[39mreturn\u001b[39;00m func(args[\u001b[39m0\u001b[39;49m], \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mpargs)\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass/lib/python3.8/site-packages/hdmf/backends/hdf5/h5tools.py:79\u001b[0m, in \u001b[0;36mHDF5IO.__init__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__mode \u001b[39m=\u001b[39m mode\n\u001b[1;32m 78\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__file \u001b[39m=\u001b[39m file_obj\n\u001b[0;32m---> 79\u001b[0m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(manager, source\u001b[39m=\u001b[39;49mpath)\n\u001b[1;32m 80\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__built \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m() \u001b[39m# keep track of each builder for each dataset/group/link for each file\u001b[39;00m\n\u001b[1;32m 81\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__read \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m() \u001b[39m# keep track of which files have been read. Key is the filename value is the builder\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass/lib/python3.8/site-packages/hdmf/utils.py:583\u001b[0m, in \u001b[0;36mdocval..dec..func_call\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 581\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfunc_call\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m 582\u001b[0m pargs \u001b[39m=\u001b[39m _check_args(args, kwargs)\n\u001b[0;32m--> 583\u001b[0m \u001b[39mreturn\u001b[39;00m func(args[\u001b[39m0\u001b[39;49m], \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mpargs)\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass/lib/python3.8/site-packages/hdmf/backends/io.py:22\u001b[0m, in \u001b[0;36mHDMFIO.__init__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__built \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m()\n\u001b[1;32m 21\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__source \u001b[39m=\u001b[39m source\n\u001b[0;32m---> 22\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mopen()\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass/lib/python3.8/site-packages/hdmf/backends/hdf5/h5tools.py:774\u001b[0m, in \u001b[0;36mHDF5IO.open\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 771\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdriver \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 772\u001b[0m kwargs\u001b[39m.\u001b[39mupdate(driver\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdriver)\n\u001b[0;32m--> 774\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__file \u001b[39m=\u001b[39m File(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msource, open_flag, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass/lib/python3.8/site-packages/h5py/_hl/files.py:406\u001b[0m, in \u001b[0;36mFile.__init__\u001b[0;34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, **kwds)\u001b[0m\n\u001b[1;32m 404\u001b[0m \u001b[39mwith\u001b[39;00m phil:\n\u001b[1;32m 405\u001b[0m fapl \u001b[39m=\u001b[39m make_fapl(driver, libver, rdcc_nslots, rdcc_nbytes, rdcc_w0, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n\u001b[0;32m--> 406\u001b[0m fid \u001b[39m=\u001b[39m make_fid(name, mode, userblock_size,\n\u001b[1;32m 407\u001b[0m fapl, fcpl\u001b[39m=\u001b[39;49mmake_fcpl(track_order\u001b[39m=\u001b[39;49mtrack_order),\n\u001b[1;32m 408\u001b[0m swmr\u001b[39m=\u001b[39;49mswmr)\n\u001b[1;32m 410\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(libver, \u001b[39mtuple\u001b[39m):\n\u001b[1;32m 411\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_libver \u001b[39m=\u001b[39m libver\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass/lib/python3.8/site-packages/h5py/_hl/files.py:179\u001b[0m, in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m 177\u001b[0m fid \u001b[39m=\u001b[39m h5f\u001b[39m.\u001b[39mcreate(name, h5f\u001b[39m.\u001b[39mACC_EXCL, fapl\u001b[39m=\u001b[39mfapl, fcpl\u001b[39m=\u001b[39mfcpl)\n\u001b[1;32m 178\u001b[0m \u001b[39melif\u001b[39;00m mode \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mw\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[0;32m--> 179\u001b[0m fid \u001b[39m=\u001b[39m h5f\u001b[39m.\u001b[39;49mcreate(name, h5f\u001b[39m.\u001b[39;49mACC_TRUNC, fapl\u001b[39m=\u001b[39;49mfapl, fcpl\u001b[39m=\u001b[39;49mfcpl)\n\u001b[1;32m 180\u001b[0m \u001b[39melif\u001b[39;00m mode \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39ma\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m 181\u001b[0m \u001b[39m# Open in append mode (read/write).\u001b[39;00m\n\u001b[1;32m 182\u001b[0m \u001b[39m# If that fails, create a new file only if it won't clobber an\u001b[39;00m\n\u001b[1;32m 183\u001b[0m \u001b[39m# existing one (ACC_EXCL)\u001b[39;00m\n\u001b[1;32m 184\u001b[0m \u001b[39mtry\u001b[39;00m:\n", - "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mh5py/h5f.pyx:108\u001b[0m, in \u001b[0;36mh5py.h5f.create\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mOSError\u001b[0m: Unable to create file (unable to open file: name = '/stelmo/nwb/analysis/chimi20200216_new/chimi20200216_new_GP6D6C8SDF.nwb', errno = 13, error message = 'Permission denied', flags = 13, o_flags = 242)" - ] - } - ], - "source": [ - "sgp.TrodesPos.populate(trodes_key)" - ] - }, - { - "cell_type": "markdown", - "id": "3924153b-4692-4952-8b0c-9966c4454097", - "metadata": {}, - "source": [ - "We can see that each NWB file, interval, and parameter set is now associated with a newly created analysis NWB file and object IDs that correspond to our newly computed data. This isn't as useful to work with so we will use another method below to actually retrieve the data for a given interval." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "64355919-a952-4e88-8b5a-5e678922ddde", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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nwb_file_name

\n", - " name of the NWB file\n", - "
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interval_list_name

\n", - " descriptive name of this interval list\n", - "
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trodes_pos_params_name

\n", - " name for this set of parameters\n", - "
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analysis_file_name

\n", - " name of the file\n", - "
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position_object_id

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orientation_object_id

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velocity_object_id

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Total: 0

\n", - " " - ], - "text/plain": [ - "*nwb_file_name *interval_list *trodes_pos_pa analysis_file_ position_objec orientation_ob velocity_objec\n", - "+------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", - "\n", - " (Total: 0)" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sgp.TrodesPos()" - ] - }, - { - "cell_type": "markdown", - "id": "f57a851b-ab28-44fe-8d88-393f01b5bd38", - "metadata": {}, - "source": [ - "In order to retrieve the results of the computation, we use a special method called `fetch1_dataframe` from the `IntervalPositionInfo` table that will retrieve the position pipeline results as a pandas DataFrame. Time is set as the index of the dataframe.\n", - "\n", - "This will only work for a single interval so we need to specify the NWB file and the interval.\n", - "\n", - "This dataframe has the following columns:\n", - "+ `head_position_x`, `head_position_y`: the x,y position of the head position (in cm).\n", - "+ `head_orientation`: The direction of the head relative to the bottom left corner (in radians)\n", - "+ `head_velocity_x`, `head_velocity_y`: the directional change in head position over time (in cm/s)\n", - "+ `head_speed`: the magnitude of the change in head position over time (in cm/s)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "998fda38-17ba-46ff-bb7b-656abb25b162", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/stelmo/nwb/analysis/chimi20200216_new_ZF24K2JHC1.nwb\n" - ] - }, - { - "data": { - "text/html": [ - "
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head_position_xhead_position_yhead_orientationhead_velocity_xhead_velocity_yhead_speed
time
1.581887e+0991.051650211.1270502.9996961.3870742.8488383.168573
1.581887e+0990.844337211.4172873.0783863.1232013.4111114.624939
1.581887e+0990.637025211.707525-3.1145725.4316434.0895976.799085
1.581887e+0990.802875211.596958-3.0331098.0977534.9792629.506138
1.581887e+0991.288579211.482443-3.06255010.8404826.07137312.424880
.....................
1.581888e+09182.158583201.452467-0.9869260.3482760.2185750.411182
1.581888e+09182.158583201.397183-0.9786100.279135-0.0584130.285182
1.581888e+09182.213867201.341900-0.9575890.193798-0.2832000.343162
1.581888e+09182.158583201.341900-0.9700830.110838-0.4173800.431846
1.581888e+09182.158583201.286617-0.9364140.045190-0.4539660.456209
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39340 rows × 6 columns

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" - ], - "text/plain": [ - " head_position_x head_position_y head_orientation \\\n", - "time \n", - "1.581887e+09 91.051650 211.127050 2.999696 \n", - "1.581887e+09 90.844337 211.417287 3.078386 \n", - "1.581887e+09 90.637025 211.707525 -3.114572 \n", - "1.581887e+09 90.802875 211.596958 -3.033109 \n", - "1.581887e+09 91.288579 211.482443 -3.062550 \n", - "... ... ... ... \n", - "1.581888e+09 182.158583 201.452467 -0.986926 \n", - "1.581888e+09 182.158583 201.397183 -0.978610 \n", - "1.581888e+09 182.213867 201.341900 -0.957589 \n", - "1.581888e+09 182.158583 201.341900 -0.970083 \n", - "1.581888e+09 182.158583 201.286617 -0.936414 \n", - "\n", - " head_velocity_x head_velocity_y head_speed \n", - "time \n", - "1.581887e+09 1.387074 2.848838 3.168573 \n", - "1.581887e+09 3.123201 3.411111 4.624939 \n", - "1.581887e+09 5.431643 4.089597 6.799085 \n", - "1.581887e+09 8.097753 4.979262 9.506138 \n", - "1.581887e+09 10.840482 6.071373 12.424880 \n", - "... ... ... ... \n", - "1.581888e+09 0.348276 0.218575 0.411182 \n", - "1.581888e+09 0.279135 -0.058413 0.285182 \n", - "1.581888e+09 0.193798 -0.283200 0.343162 \n", - "1.581888e+09 0.110838 -0.417380 0.431846 \n", - "1.581888e+09 0.045190 -0.453966 0.456209 \n", - "\n", - "[39340 rows x 6 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "position_info = (\n", - " IntervalPositionInfo()\n", - " & {\n", - " \"nwb_file_name\": nwb_copy_file_name,\n", - " \"interval_list_name\": \"pos 1 valid times\",\n", - " \"position_info_param_name\": \"default\",\n", - " }\n", - ").fetch1_dataframe()\n", - "position_info" - ] - }, - { - "cell_type": "markdown", - "id": "4d7b3667-5ec1-42c1-8794-db88155aadec", - "metadata": {}, - "source": [ - "If you are not familiar with pandas, the time variable is set as the index. It can be accessed using `.index` on the dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "3ad382d4-e70b-479c-b20f-ea00c51a6953", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Float64Index([1581886916.3153033, 1581886916.3486283, 1581886916.3819742,\n", - " 1581886916.4152992, 1581886916.448645, 1581886916.4819698,\n", - " 1581886916.5152948, 1581886916.5486405, 1581886916.5819652,\n", - " 1581886916.6152902,\n", - " ...\n", - " 1581888227.3021932, 1581888227.335518, 1581888227.368864,\n", - " 1581888227.4021888, 1581888227.435535, 1581888227.4688597,\n", - " 1581888227.5021844, 1581888227.5355306, 1581888227.5688553,\n", - " 1581888227.60218],\n", - " dtype='float64', name='time', length=39340)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "position_info.index" - ] - }, - { - "cell_type": "markdown", - "id": "b2a3e0cf-1f63-428e-adc9-bc1947f8fcf9", - "metadata": {}, - "source": [ - "### 4. Examining the results\n", - "\n", - "We should always spot check our results to verify that the pipeline worked correctly.\n", - "\n", - "#### Plots\n", - "Let's plot some of the variables first:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "3dba612e-24e5-4b67-b77d-3f1a8dff7cf3", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Head Position')" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", - "ax.plot(position_info.head_position_x, position_info.head_position_y)\n", - "ax.set_xlabel(\"x-position [cm]\", fontsize=18)\n", - "ax.set_ylabel(\"y-position [cm]\", fontsize=18)\n", - "ax.set_title(\"Head Position\", fontsize=28)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "be8679cf-6f14-4726-af03-36430fb4b5a9", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Head Velocity')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n", - "ax.plot(position_info.head_velocity_x, position_info.head_velocity_y)\n", - "ax.set_xlabel(\"x-velocity [cm/s]\", fontsize=18)\n", - "ax.set_ylabel(\"y-velocity [cm/s]\", fontsize=18)\n", - "ax.set_title(\"Head Velocity\", fontsize=28)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "93d440ae-e995-42af-88b3-c1146ccd7d45", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1581886916.3153033, 1581888227.60218)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 1, figsize=(25, 3))\n", - "ax.plot(position_info.index, position_info.head_speed)\n", - "ax.set_xlabel(\"Time\", fontsize=18)\n", - "ax.set_ylabel(\"Speed [cm/s]\", fontsize=18)\n", - "ax.set_title(\"Head Speed\", fontsize=28)\n", - "ax.set_xlim((position_info.index.min(), position_info.index.max()))" - ] - }, - { - "cell_type": "markdown", - "id": "d084d1d8-21ed-406f-a887-1146e644f246", - "metadata": {}, - "source": [ - "### Video\n", - "\n", - "These look reasonable but it is better to evaluate these variables by plotting the results on the video where we can see how they correspond. \n", - "\n", - "The video will appear in the current working directory when it is done." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "9bd9f843-4afd-438b-87fe-b09dee880282", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2022-08-04 15:05:53,003][WARNING]: Skipped checksum for file with hash: 050179d6-42a0-9236-45ee-6069346d0196, and path: /stelmo/nwb/raw/chimi20200216_new_.nwb\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading position data...\n", - "Loading video data...\n" - ] - }, - { - "ename": "KeyError", - "evalue": "'3234c1e5-c992-4fb4-9c82-d1603d90bce7'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [24]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mspyglass\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon_position\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PositionVideo\n\u001b[0;32m----> 3\u001b[0m \u001b[43mPositionVideo\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmake\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnwb_file_name\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mnwb_copy_file_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minterval_list_name\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpos 1 valid times\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mposition_info_param_name\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdefault\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/code/spyglass/src/spyglass/common/common_position.py:631\u001b[0m, in \u001b[0;36mPositionVideo.make\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 628\u001b[0m io \u001b[38;5;241m=\u001b[39m pynwb\u001b[38;5;241m.\u001b[39mNWBHDF5IO(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/stelmo/nwb/raw/\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m\n\u001b[1;32m 629\u001b[0m video_info[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnwb_file_name\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 630\u001b[0m nwb_file \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m--> 631\u001b[0m nwb_video \u001b[38;5;241m=\u001b[39m \u001b[43mnwb_file\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobjects\u001b[49m\u001b[43m[\u001b[49m\u001b[43mvideo_info\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvideo_file_object_id\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 632\u001b[0m video_filename \u001b[38;5;241m=\u001b[39m nwb_video\u001b[38;5;241m.\u001b[39mexternal_file\u001b[38;5;241m.\u001b[39mvalue[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 634\u001b[0m nwb_base_filename \u001b[38;5;241m=\u001b[39m key[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnwb_file_name\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.nwb\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/anaconda3/envs/spyglass/lib/python3.9/site-packages/hdmf/utils.py:932\u001b[0m, in \u001b[0;36mLabelledDict.__getitem__\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m 930\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getitem__\u001b[39m(val)\n\u001b[1;32m 931\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 932\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mKeyError\u001b[0m: '3234c1e5-c992-4fb4-9c82-d1603d90bce7'" - ] - } - ], - "source": [ - "from spyglass.common.common_position import PositionVideo\n", - "\n", - "PositionVideo().make(\n", - " {\n", - " \"nwb_file_name\": nwb_copy_file_name,\n", - " \"interval_list_name\": \"pos 1 valid times\",\n", - " \"position_info_param_name\": \"default\",\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d26dfd72-3931-43c6-b407-c0c56406b756", - "metadata": {}, - "source": [ - "### 4. Upsampling position data\n", - "\n", - "Sometimes you need the position data to be in a different rate than it is sampled in, such as when decoding in 2 ms time bins. You can use the upsampling parameters to get this data:\n", - "+ `is_upsampled` controls whether upsampling happens. If it is 1 then there is upsampling, and if it is 0 then upsampling does not happen.\n", - "+ `upsampling_sampling_rate` is the rate you want to upsample to. For example position is typically recorded at 33 frames per seconds and you may want to upsample up to 500 frames per second.\n", - "+ `upsampling_interpolation_method` is the interpolation method used for upsampling. It is set to linear by default. See the methods available for pandas.DataFrame.interpolate to get a list of the methods." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "a13cdc05-ea4c-4edb-83a6-bb52af28fa75", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n", - "

position_info_param_name

\n", - " name for this set of parameters\n", - "
\n", - "

max_separation

\n", - " max distance (in cm) between head LEDs\n", - "
\n", - "

max_speed

\n", - " max speed (in cm / s) of animal\n", - "
\n", - "

position_smoothing_duration

\n", - " size of moving window (in seconds)\n", - "
\n", - "

speed_smoothing_std_dev

\n", - " smoothing standard deviation (in seconds)\n", - "
\n", - "

head_orient_smoothing_std_dev

\n", - " smoothing std deviation (in seconds)\n", - "
\n", - "

led1_is_front

\n", - " first LED is front LED and second is back LED, else first LED is back\n", - "
\n", - "

is_upsampled

\n", - " upsample the position to higher sampling rate\n", - "
\n", - "

upsampling_sampling_rate

\n", - " The rate to be upsampled to\n", - "
\n", - "

upsampling_interpolation_method

\n", - " see pandas.DataFrame.interpolation for list of methods\n", - "
default9.0300.00.1250.10.00110nanlinear
default_decoding9.0300.00.1250.10.00111500.0linear
default_lfp9.0300.00.1250.10.001111000.0linear
\n", - " \n", - "

Total: 3

\n", - " " - ], - "text/plain": [ - "*position_info max_separation max_speed position_smoot speed_smoothin head_orient_sm led1_is_front is_upsampled upsampling_sam upsampling_int\n", - "+------------+ +------------+ +-----------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+ +------------+\n", - "default 9.0 300.0 0.125 0.1 0.001 1 0 nan linear \n", - "default_decodi 9.0 300.0 0.125 0.1 0.001 1 1 500.0 linear \n", - "default_lfp 9.0 300.0 0.125 0.1 0.001 1 1 1000.0 linear \n", - " (Total: 3)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "PositionInfoParameters.insert1(\n", - " {\n", - " \"position_info_param_name\": \"default_decoding\",\n", - " \"is_upsampled\": 1,\n", - " \"upsampling_sampling_rate\": 500,\n", - " },\n", - " skip_duplicates=True,\n", - ")\n", - "\n", - "PositionInfoParameters()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "2b78e50a-d7b1-4b88-a449-ea3cb4ff8a9a", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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position_info_param_name

\n", - " name for this set of parameters\n", - "
\n", - "

nwb_file_name

\n", - " name of the NWB file\n", - "
\n", - "

interval_list_name

\n", - " descriptive name of this interval list\n", - "
default_decodingCH6120211203_.nwbpos 0 valid times
default_decodingCH6520211125_.nwbpos 0 valid times
default_decodingCH6520211201_.nwbpos 0 valid times
defaultchimi20200216_new_.nwbpos 1 valid times
default_decodingchimi20200216_new_.nwbpos 1 valid times
default_lfpchimi20200216_new_.nwbpos 1 valid times
defaultfern20211007_.nwbpos 0 valid times
default_decodingfern20211007_.nwbpos 0 valid times
defaultfern20211007_.nwbpos 1 valid times
default_decodingfern20211007_.nwbpos 1 valid times
defaultfern20211007_.nwbpos 10 valid times
default_decodingfern20211007_.nwbpos 10 valid times
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...

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Total: 1815

\n", - " " - ], - "text/plain": [ - "*position_info *nwb_file_name *interval_list\n", - "+------------+ +------------+ +------------+\n", - "default_decodi CH6120211203_. pos 0 valid ti\n", - "default_decodi CH6520211125_. pos 0 valid ti\n", - "default_decodi CH6520211201_. pos 0 valid ti\n", - "default chimi20200216_ pos 1 valid ti\n", - "default_decodi chimi20200216_ pos 1 valid ti\n", - "default_lfp chimi20200216_ pos 1 valid ti\n", - "default fern20211007_. pos 0 valid ti\n", - "default_decodi fern20211007_. pos 0 valid ti\n", - "default fern20211007_. pos 1 valid ti\n", - "default_decodi fern20211007_. pos 1 valid ti\n", - "default fern20211007_. pos 10 valid t\n", - "default_decodi fern20211007_. pos 10 valid t\n", - " ...\n", - " (Total: 1815)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "IntervalPositionInfoSelection.insert1(\n", - " {\n", - " \"nwb_file_name\": nwb_copy_file_name,\n", - " \"interval_list_name\": \"pos 1 valid times\",\n", - " \"position_info_param_name\": \"default_decoding\",\n", - " },\n", - " skip_duplicates=True,\n", - ")\n", - "\n", - "IntervalPositionInfoSelection()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "dd40f6d6-cad9-4e8b-b118-85eb7e2b92af", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [ - "IntervalPositionInfo.populate()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "ab65c431-9977-43e7-a0f9-f95f73c309b0", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/stelmo/nwb/analysis/chimi20200216_new_6YC9LPAR7S.nwb\n" - ] - }, - { - "data": { - "text/html": [ - "
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head_position_xhead_position_yhead_orientationhead_velocity_xhead_velocity_yhead_speed
time
1.581887e+0991.051650211.1270502.6800481.7415502.3014782.886139
1.581887e+0991.039455211.1441233.0032411.8275552.3339312.964320
1.581887e+0991.027260211.1611963.0083981.9158002.3666683.044898
1.581887e+0991.015065211.1782683.0128022.0062862.3997053.127901
1.581887e+0991.002871211.1953413.0172422.0990122.4330593.213352
.....................
1.581888e+09182.158583201.299625-0.9443040.057520-0.3560120.360629
1.581888e+09182.158583201.296373-0.9423290.053954-0.3563430.360404
1.581888e+09182.158583201.293121-0.9403570.050477-0.3564070.359964
1.581888e+09182.158583201.289869-0.9530590.047091-0.3562120.359312
1.581888e+09182.158583201.286617-0.5880810.043796-0.3557640.358450
\n", - "

655645 rows × 6 columns

\n", - "
" - ], - "text/plain": [ - " head_position_x head_position_y head_orientation \\\n", - "time \n", - "1.581887e+09 91.051650 211.127050 2.680048 \n", - "1.581887e+09 91.039455 211.144123 3.003241 \n", - "1.581887e+09 91.027260 211.161196 3.008398 \n", - "1.581887e+09 91.015065 211.178268 3.012802 \n", - "1.581887e+09 91.002871 211.195341 3.017242 \n", - "... ... ... ... \n", - "1.581888e+09 182.158583 201.299625 -0.944304 \n", - "1.581888e+09 182.158583 201.296373 -0.942329 \n", - "1.581888e+09 182.158583 201.293121 -0.940357 \n", - "1.581888e+09 182.158583 201.289869 -0.953059 \n", - "1.581888e+09 182.158583 201.286617 -0.588081 \n", - "\n", - " head_velocity_x head_velocity_y head_speed \n", - "time \n", - "1.581887e+09 1.741550 2.301478 2.886139 \n", - "1.581887e+09 1.827555 2.333931 2.964320 \n", - "1.581887e+09 1.915800 2.366668 3.044898 \n", - "1.581887e+09 2.006286 2.399705 3.127901 \n", - "1.581887e+09 2.099012 2.433059 3.213352 \n", - "... ... ... ... \n", - "1.581888e+09 0.057520 -0.356012 0.360629 \n", - "1.581888e+09 0.053954 -0.356343 0.360404 \n", - "1.581888e+09 0.050477 -0.356407 0.359964 \n", - "1.581888e+09 0.047091 -0.356212 0.359312 \n", - "1.581888e+09 0.043796 -0.355764 0.358450 \n", - "\n", - "[655645 rows x 6 columns]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "upsampled_position_info = (\n", - " IntervalPositionInfo()\n", - " & {\n", - " \"nwb_file_name\": nwb_copy_file_name,\n", - " \"interval_list_name\": \"pos 1 valid times\",\n", - " \"position_info_param_name\": \"default_decoding\",\n", - " }\n", - ").fetch1_dataframe()\n", - "\n", - "upsampled_position_info" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "1a1f6d12-3199-4136-ab66-9ac80241d4b5", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Upsampled Head Position')" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(\n", - " 1, 2, figsize=(20, 10), sharex=True, sharey=True, constrained_layout=True\n", - ")\n", - "axes[0].plot(position_info.head_position_x, position_info.head_position_y)\n", - "axes[0].set_xlabel(\"x-position [cm]\", fontsize=18)\n", - "axes[0].set_ylabel(\"y-position [cm]\", fontsize=18)\n", - "axes[0].set_title(\"Head Position\", fontsize=28)\n", - "\n", - "axes[1].plot(\n", - " upsampled_position_info.head_position_x,\n", - " upsampled_position_info.head_position_y,\n", - ")\n", - "axes[1].set_xlabel(\"x-position [cm]\", fontsize=18)\n", - "axes[1].set_ylabel(\"y-position [cm]\", fontsize=18)\n", - "axes[1].set_title(\"Upsampled Head Position\", fontsize=28)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "7e7ee409-a3f3-4692-b07f-bbfb3a8697e7", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Upsampled Head Speed')" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(\n", - " 2, 1, figsize=(25, 6), sharex=True, sharey=True, constrained_layout=True\n", - ")\n", - "axes[0].plot(position_info.index, position_info.head_speed)\n", - "axes[0].set_xlabel(\"Time\", fontsize=18)\n", - "axes[0].set_ylabel(\"Speed [cm/s]\", fontsize=18)\n", - "axes[0].set_title(\"Head Speed\", fontsize=28)\n", - "axes[0].set_xlim((position_info.index.min(), position_info.index.max()))\n", - "\n", - "axes[1].plot(upsampled_position_info.index, upsampled_position_info.head_speed)\n", - "axes[1].set_xlabel(\"Time\", fontsize=18)\n", - "axes[1].set_ylabel(\"Speed [cm/s]\", fontsize=18)\n", - "axes[1].set_title(\"Upsampled Head Speed\", fontsize=28)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "68ae8cf4-cb14-4ce2-9a16-b2faafc73d26", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Upsampled Head Velocity')" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(\n", - " 1, 2, figsize=(20, 10), sharex=True, sharey=True, constrained_layout=True\n", - ")\n", - "axes[0].plot(position_info.head_velocity_x, position_info.head_velocity_y)\n", - "axes[0].set_xlabel(\"x-velocity [cm/s]\", fontsize=18)\n", - "axes[0].set_ylabel(\"y-velocity [cm/s]\", fontsize=18)\n", - "axes[0].set_title(\"Head Velocity\", fontsize=28)\n", - "\n", - "axes[1].plot(\n", - " upsampled_position_info.head_velocity_x,\n", - " upsampled_position_info.head_velocity_y,\n", - ")\n", - "axes[1].set_xlabel(\"x-velocity [cm/s]\", fontsize=18)\n", - "axes[1].set_ylabel(\"y-velocity [cm/s]\", fontsize=18)\n", - "axes[1].set_title(\"Upsampled Head Velocity\", fontsize=28)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "325b0892-e26a-47d9-9a16-8768d7aa9516", - "metadata": { - "vscode": { - "languageId": "python" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "vscode": { - "interpreter": { - "hash": "8a94588eda9d64d9e9a351ab8144e55b1fabf5113b54e67dd26a8c27df0381b3" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/06_DLC_from_dir.ipynb b/notebooks/06_DLC_from_dir.ipynb deleted file mode 100644 index bcb20673e..000000000 --- a/notebooks/06_DLC_from_dir.ipynb +++ /dev/null @@ -1,1295 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6fad7706-ab2b-4157-a62f-6f6ffc373d61", - "metadata": {}, - "source": [ - "## Position using DeepLabCut from a Pre-Trained DLC Project\n", - "\n", - "**Note: make a copy of this notebook and run the copy to avoid git conflicts in the future**\n", - "\n", - "This is a tutorial on how to extract position given a pre-trained DeepLabCut (DLC) model using the Spyglass pipeline used in Loren Frank's lab, UCSF. It will walk through adding your DLC model to Spyglass, executing pose estimation on a novel behavioral video, processing the pose estimation output to extract a centroid and orientation, and inserting the resulting information into the `IntervalPositionInfo` table.
\n", - "-> This tutorial assumes you've completed [tutorial 0](0_intro.ipynb)
\n", - "**Note 2: Make sure you are running this within the spyglass Conda environment**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0f567531", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path, PosixPath, PurePath\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pynwb\n", - "import datajoint as dj\n", - "import spyglass.common as sgc\n", - "import spyglass.position.v1 as sgp\n", - "from spyglass.position import PositionOutput" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7e3e1854-0baf-44f4-a5a6-ddc1fdb4c3e1", - "metadata": {}, - "source": [ - "#### Here is a schematic showing the tables used in this notebook.
\n", - "![dlc_existing.png|2000x900](./../notebook-images/dlc_existing.png)" - ] - }, - { - "cell_type": "markdown", - "id": "0388fc5f", - "metadata": {}, - "source": [ - "### Table of Contents\n", - "[`DLCProject`](#DLCProject)
\n", - "[`DLCModel`](#DLCModel)
\n", - "[`DLCPoseEstimation`](#DLCPoseEstimation)
\n", - "[`DLCSmoothInterp`](#DLCSmoothInterp)
\n", - "[`DLCCentroid`](#DLCCentroid)
\n", - "[`DLCOrientation`](#DLCOrientation)
\n", - "[`DLCPos`](#DLCPos)
\n", - "[`DLCPosVideo`](#DLCPosVideo)
\n", - "[`PositionOutput`](#PositionOutput)
" - ] - }, - { - "cell_type": "markdown", - "id": "6adc175d", - "metadata": {}, - "source": [ - "#### [DLCProject](#ToC) \n", - "__You can click on any header to return to the Table of Contents__" - ] - }, - { - "cell_type": "markdown", - "id": "e7c51888-b05d-4a51-bb9f-b075db4bbf49", - "metadata": {}, - "source": [ - "Let us begin with visualizing the contents of the BodyPart table. This table will store standard names of body parts used within DLC models throughout the lab with a concise description.
\n", - ">*Please do not add to this table unless necessary.*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c938c639", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.BodyPart()" - ] - }, - { - "cell_type": "markdown", - "id": "f422dd98-728b-4b48-877b-f77c2d60872f", - "metadata": {}, - "source": [ - "To use an existing DLC project we can use the `insert_existing_project` method on the `DLCProject` table.
This function will return a dictionary that can be used to query `DLCProject` in the future and expects:
\n", - ">`project_name`: a short, unique, descriptive name of your project that will be referenced throughout the pipeline
`lab_team`: the name of your team from the Spyglass table `LabTeam`
`config_path`: string of the path to your existing DLC project's config.yaml
`bodyparts`: a list of bodyparts used in your project (optional)
`frames_per_video`: number of frames to extract for training from each video (optional)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f20ecce9", - "metadata": {}, - "outputs": [], - "source": [ - "project_name = \"tutorial_DG\"\n", - "lab_team = \"LorenLab\"\n", - "project_key = sgp.DLCProject.insert_existing_project(\n", - " project_name=project_name,\n", - " lab_team=lab_team,\n", - " config_path=\"/nimbus/deeplabcut/projects/tutorial_model-LorenLab-2022-07-15/config.yaml\",\n", - " bodyparts=[\"redLED_C\", \"greenLED\", \"redLED_L\", \"redLED_R\", \"tailBase\"],\n", - " frames_per_video=200,\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d9d4223-63da-462e-8164-7cc63c945760", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCProject() & {\"project_name\": project_name}" - ] - }, - { - "cell_type": "markdown", - "id": "1c7876e7", - "metadata": {}, - "source": [ - "#### [DLCModel](#ToC) " - ] - }, - { - "cell_type": "markdown", - "id": "fa36a042-f13e-4a36-812a-a4efaeb57a09", - "metadata": {}, - "source": [ - "Lets take a look at the `DLCModelInput` table next. This table has `dlc_model_name` and `project_name` as primary keys and `project_path` as a secondary key. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "25f0a45e-5bd9-48bf-a79d-908bd5a17235", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModelInput()" - ] - }, - { - "cell_type": "markdown", - "id": "39ee99ae-586a-4cbb-9255-15ddd594b1b7", - "metadata": {}, - "source": [ - "Next we can modify the `project_key` to replace `config_path` with `project_path` to fit with the fields in `DLCModelInput`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc961e93-8fe8-4069-a945-a9fc1e1ad993", - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"current project_key:\\n{project_key}\")\n", - "if not \"project_path\" in project_key:\n", - " project_key[\"project_path\"] = os.path.dirname(project_key[\"config_path\"])\n", - " del project_key[\"config_path\"]\n", - " print(f\"updated project_key:\\n{project_key}\")" - ] - }, - { - "cell_type": "markdown", - "id": "4b958ef7-160c-4141-a7c2-1177fdfd6eb6", - "metadata": {}, - "source": [ - "Here we can set a unique name for our model using the `dlc_model_name` variable.
We then combine this with the updated `project_key` to insert into `DLCModelInput`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "49650dc2", - "metadata": {}, - "outputs": [], - "source": [ - "dlc_model_name = \"tutorial_model_DG\"\n", - "sgp.DLCModelInput().insert1(\n", - " {\"dlc_model_name\": dlc_model_name, **project_key}, skip_duplicates=True\n", - ")\n", - "sgp.DLCModelInput()" - ] - }, - { - "cell_type": "markdown", - "id": "d04c4785-23b4-4a79-9ef9-3815c1215422", - "metadata": {}, - "source": [ - "Inserting an entry into `DLCModelInput` will also populate `DLCModelSource`. `DLCModelSource` is a table that is used to switch between models trained using Spyglass and pre-existing projects." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "01021925", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModelSource() & project_key" - ] - }, - { - "cell_type": "markdown", - "id": "8d8756c5-0d85-490b-a712-a95faa074b43", - "metadata": {}, - "source": [ - "Notice the `source` field in the table above. It will only accept _\"FromImport\"_ or _\"FromUpstream\"_ as entries. Let's checkout the `FromImport` part table attached to `DLCModelSource` below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "22fb6d58-225f-49fb-86ee-4b3197aa841f", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModelSource.FromImport() & project_key" - ] - }, - { - "cell_type": "markdown", - "id": "02b9297c-49dc-43b8-ad7b-3897c4d442bf", - "metadata": {}, - "source": [ - "Next we'll get ready to populate the `DLCModel` table, which holds all the relevant information for both pre-trained models and models trained within Spyglass.
First we'll need to determine a set of parameters for our model to select the correct model file.
We can visualize a default set below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8e01d109", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModelParams.get_default()" - ] - }, - { - "cell_type": "markdown", - "id": "8aa565b0-37e4-462f-b0d8-fd1b1686b69c", - "metadata": {}, - "source": [ - "> Here is the syntax to add your own parameter set:\n", - ">```python\n", - "dlc_model_params_name = \"make_this_yours\"\n", - "params = {\n", - " \"params\": {},\n", - " \"shuffle\": 1,\n", - " \"trainingsetindex\": 0,\n", - " \"model_prefix\": \"\",\n", - " }\n", - "sgp.DLCModelParams.insert1({\"dlc_model_params_name\": dlc_model_params_name, \"params\": params}, skip_duplicates=True)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "581612b4-f4e5-4438-aeeb-f267eaeb5bad", - "metadata": {}, - "source": [ - "Now let's fetch the primary keys from `DLCModelSource` to make our lives a bit easier when we insert into `DLCModelSelection`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8b81585-5926-43d3-9e1b-de287ef33826", - "metadata": {}, - "outputs": [], - "source": [ - "temp_model_key = (sgp.DLCModelSource.FromImport() & project_key).fetch1(\"KEY\")" - ] - }, - { - "cell_type": "markdown", - "id": "b7fb287f-5c3d-431c-b196-5d327e4eb198", - "metadata": {}, - "source": [ - "And insert into `DLCModelSelection` to allow for population of `DLCModel`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eaa23fab", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModelSelection().insert1(\n", - " {**temp_model_key, \"dlc_model_params_name\": \"default\"}, skip_duplicates=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "ed9b3158-a07e-4630-bb42-a609d1d8ee24", - "metadata": {}, - "source": [ - "Let's populate `DLCModel`!!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8f1b839", - "metadata": {}, - "outputs": [], - "source": [ - "model_key = (sgp.DLCModelSelection & temp_model_key).fetch1(\"KEY\")\n", - "sgp.DLCModel.populate(model_key)" - ] - }, - { - "cell_type": "markdown", - "id": "a920fc2d-5b81-4d4b-817b-d7549d2810ac", - "metadata": {}, - "source": [ - "And of course make sure it populated correctly" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "930df143-c756-4904-b4b6-7eed8c194b9d", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCModel() & model_key" - ] - }, - { - "cell_type": "markdown", - "id": "86db6243", - "metadata": {}, - "source": [ - "#### [DLCPoseEstimation](#ToC) " - ] - }, - { - "cell_type": "markdown", - "id": "6873f0e5-d32c-4c06-a964-56f33b5e7c1d", - "metadata": {}, - "source": [ - "
\n", - "\n", - "The following steps should be run on a GPU cluster
" - ] - }, - { - "cell_type": "markdown", - "id": "a4241c1f-f11a-4701-93cd-bdcc4a437d73", - "metadata": {}, - "source": [ - "Alright, now that we brought our trained model into Spyglass we're ready to set-up Pose Estimation on a behavioral video of your choice.
For this tutorial, you can choose to use an epoch of your choice, we can also use the one specified below. If you'd like to use your own video, just specify the `nwb_file_name` and `epoch` number and make sure it's in the `VideoFile` table!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7d41790-9643-47e4-a916-df8ee1035059", - "metadata": {}, - "outputs": [], - "source": [ - "nwb_file_name = \"J1620210529_.nwb\"\n", - "epoch = 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8a295b1", - "metadata": {}, - "outputs": [], - "source": [ - "sgc.VideoFile() & {\"nwb_file_name\": nwb_file_name, \"epoch\": epoch}" - ] - }, - { - "cell_type": "markdown", - "id": "0420f1fe-56e5-48cb-adb8-09bfc6999d3f", - "metadata": {}, - "source": [ - "
\n", - " Setting up Pose Estimation
\n", - "gputouse determines which GPU core to use for pose estimation. Run the cell below to determine which core has space and set the gputouse variable accordingly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ce5adf5-9631-4ae5-a91f-e0367ebced2f", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.dlc_utils.get_gpu_memory()" - ] - }, - { - "cell_type": "markdown", - "id": "6ddc2410-19cb-40d3-b00d-4716e6945be7", - "metadata": {}, - "source": [ - "
\n", - "Set GPU core here
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e0d1d75f-a4f0-46e7-9726-6e923042a0f1", - "metadata": {}, - "outputs": [], - "source": [ - "gputouse = 0 ## 0-9" - ] - }, - { - "cell_type": "markdown", - "id": "555f7f01-9d06-40ed-91d3-508266888d78", - "metadata": {}, - "source": [ - "To set up pose estimation, we need to make sure a few things are in order. Using `insert_estimation_task` will take care of these steps for us!
Briefly, it will convert out video to be in .mp4 format (DLC struggles with .h264) and determine the directory in which we'll store the pose estimation results.
\n", - ">**`task_mode`** determines whether or not populating `DLCPoseEstimation` runs a new pose estimation, or loads an existing. Use _'trigger'_ unless you've already run this specific pose estimation.
**`video_file_num`** will be 0 in almost all cases.
**`check_crop`** is a boolean True/False and will trigger a prompt for the user to enter the cropping coordinates. A frame of the video with coordinates will be provided for reference." - ] - }, - { - "cell_type": "markdown", - "id": "bc21b30f-739d-44f2-aaa0-8401570d2dfb", - "metadata": {}, - "source": [ - "
\n", - " When prompted for crop, the behavior takes place on the left-hand maze. The coordinates I used were: 50, 500, 50, 800. Feel free to play around with these!
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5913f0f", - "metadata": {}, - "outputs": [], - "source": [ - "pose_estimation_key = sgp.DLCPoseEstimationSelection.insert_estimation_task(\n", - " {\n", - " \"nwb_file_name\": nwb_file_name,\n", - " \"epoch\": epoch,\n", - " \"video_file_num\": 0,\n", - " **model_key,\n", - " },\n", - " task_mode=\"trigger\",\n", - " params={\"gputouse\": gputouse, \"videotype\": \"mp4\", \"cropping\": None},\n", - " check_crop=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "cbd0b2dc-5a57-45d7-94e6-44b0d08554de", - "metadata": {}, - "source": [ - "And now we populate `DLCPoseEstimation`! This might take a bit..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02c30671", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPoseEstimation().populate(pose_estimation_key)" - ] - }, - { - "cell_type": "markdown", - "id": "7ede348f-c6fe-4861-b51f-e41143653673", - "metadata": {}, - "source": [ - "Let's visualize the output from Pose Estimation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb089d35-3d59-403b-b802-242ed4513acf", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "(sgp.DLCPoseEstimation() & pose_estimation_key).fetch_dataframe()" - ] - }, - { - "cell_type": "markdown", - "id": "97b35927", - "metadata": {}, - "source": [ - "#### [DLCSmoothInterp](#ToC) " - ] - }, - { - "cell_type": "markdown", - "id": "a37ca70f-2ce6-435c-bb21-93ebcbba370e", - "metadata": {}, - "source": [ - "Now that we've completed pose estimation, it's time to identify NaNs and optionally interpolate over low likelihood periods and smooth the resulting positions.
First we need to define some parameters for smoothing and interpolation. We can see the default parameter set below.
__Note__: it is recommended to use the `just_nan` parameters here and save interpolation and smoothing for the centroid step as this provides for a better end result." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d079c861-45f3-4428-9d9a-0a299254ee88", - "metadata": {}, - "outputs": [], - "source": [ - "# The default parameter set to interpolate and smooth over each LED individually\n", - "print(sgp.DLCSmoothInterpParams.get_default())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5caf1e8-fb00-4193-8b52-bc69c9a00ca0", - "metadata": {}, - "outputs": [], - "source": [ - "# The just_nan parameter set that identifies NaN indices and leaves smoothing and interpolation to the centroid step\n", - "print(sgp.DLCSmoothInterpParams.get_nan_params())\n", - "si_params_name = \"just_nan\"" - ] - }, - { - "cell_type": "markdown", - "id": "de1c25d7-9cb8-4307-90d9-be5c3b78cfd0", - "metadata": {}, - "source": [ - "> If you'd like to change any of these parameters, here is the syntax to do that\n", - ">```python\n", - "si_params_name = 'your_unique_param_name'\n", - "params = {\n", - " \"smoothing_params\": {\n", - " \"smoothing_duration\": 0.##,\n", - " \"smooth_method\": \"moving_avg\",\n", - " },\n", - " \"interp_params\": {\n", - " \"likelihood_thresh\": 0.##,\n", - " },\n", - " \"max_plausible_speed\": ###,\n", - " \"speed_smoothing_std_dev\": 0.###,\n", - "}\n", - "sgp.DLCSmoothInterpParams().insert1(\n", - " {\n", - " 'dlc_si_params_name': si_params_name,\n", - " \"params\": params,\n", - " },\n", - " skip_duplicates=True)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "bb95887e-71ff-4212-b0f6-bc90fb7dcb96", - "metadata": {}, - "source": [ - "Here we'll create a dictionary with the correct set of keys for the `DLCSmoothInterpSelection` table" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7be6db58", - "metadata": {}, - "outputs": [], - "source": [ - "si_key = pose_estimation_key.copy()\n", - "fields = list(sgp.DLCSmoothInterpSelection.fetch().dtype.fields.keys())\n", - "si_key = {key: val for key, val in si_key.items() if key in fields}\n", - "si_key" - ] - }, - { - "cell_type": "markdown", - "id": "8235561b-5c29-41d5-9bbb-b2a95a497dfe", - "metadata": {}, - "source": [ - "And now we can insert all of the bodyparts we want to process into `DLCSmoothInterpSelection`
\n", - "First lets visualize the bodyparts we have available to us.
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2f987d05", - "metadata": {}, - "outputs": [], - "source": [ - "print((sgp.DLCPoseEstimation.BodyPart & pose_estimation_key).fetch(\"bodypart\"))" - ] - }, - { - "cell_type": "markdown", - "id": "c2520750-94ca-45c3-a841-15bb04d896d7", - "metadata": {}, - "source": [ - "We can use `insert1` to insert a single bodypart, but would suggest using `insert` to insert a list of keys with different bodyparts." - ] - }, - { - "cell_type": "markdown", - "id": "d0beab3d-690c-47b8-9763-3adcfbd9538f", - "metadata": {}, - "source": [ - ">_Syntax to insert a single bodypart_\n", - ">```python\n", - "sgp.DLCSmoothInterpSelection.insert1(\n", - " {\n", - " **si_key,\n", - " 'bodypart': 'greenLED',\n", - " 'dlc_si_params_name': si_params_name,\n", - " },\n", - " skip_duplicates=True)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "88869472-c2a5-45a6-997f-a3d05d262759", - "metadata": {}, - "source": [ - "Lets set a list of bodyparts we want to insert and then insert them into `DLCSmoothInterpSelection`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71bf0e84", - "metadata": {}, - "outputs": [], - "source": [ - "bodyparts = [\"greenLED\", \"redLED_C\"]\n", - "sgp.DLCSmoothInterpSelection.insert(\n", - " [\n", - " {\n", - " **si_key,\n", - " \"bodypart\": bodypart,\n", - " \"dlc_si_params_name\": si_params_name,\n", - " }\n", - " for bodypart in bodyparts\n", - " ],\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "a3527cd1", - "metadata": {}, - "source": [ - "And to make sure that all of the bodyparts we want made it into the the selection table, we can visualize the table below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d5a35e89", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCSmoothInterpSelection() & si_key" - ] - }, - { - "cell_type": "markdown", - "id": "76ccae76-b58c-4239-82fd-361b16327b28", - "metadata": {}, - "source": [ - "Now we can populate `DLCSmoothInterp`, which will perform smoothing and interpolation on all of the bodyparts we specified.
We can limit the populate using `si_key` since it is bodypart agnostic." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d5d0046", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCSmoothInterp().populate(si_key)" - ] - }, - { - "cell_type": "markdown", - "id": "80805fdd-9867-4784-81d1-887f44ee3ccf", - "metadata": {}, - "source": [ - "And let's visualize the resulting position data using a scatter plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5ff5546", - "metadata": {}, - "outputs": [], - "source": [ - "(\n", - " sgp.DLCSmoothInterp() & {**si_key, \"bodypart\": bodyparts[0]}\n", - ").fetch1_dataframe().plot.scatter(x=\"x\", y=\"y\", s=1, figsize=(5, 5))" - ] - }, - { - "cell_type": "markdown", - "id": "5bb5bb2c-b168-4d31-83b6-1d81775415e2", - "metadata": {}, - "source": [ - "#### [DLCSmoothInterpCohort](#ToC) " - ] - }, - { - "cell_type": "markdown", - "id": "1629eb32-fbc6-4828-92b7-d15d5788158e", - "metadata": {}, - "source": [ - "Now that we've smoothed and interpolated our position data for each bodypart, we need to form a set of bodyparts from which we want to derive a centroid and orientation (or potentially a second set for orientation). This is the goal of the `DLCSmoothInterpCohort` table." - ] - }, - { - "cell_type": "markdown", - "id": "1b7ab7da-337f-40f6-8c2a-e38c226fabde", - "metadata": {}, - "source": [ - "First, let's make a key that represents the 'cohort' we want to form.\n", - "> We'll set the `dlc_si_cohort_selection_name` to a concise name
We'll also form a dictionary with the bodypart name as the key and the smoothing/interpolation parameter name used for that bodypart as the value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0dfab362", - "metadata": {}, - "outputs": [], - "source": [ - "cohort_key = si_key.copy()\n", - "if \"bodypart\" in cohort_key:\n", - " del cohort_key[\"bodypart\"]\n", - "if \"dlc_si_params_name\" in cohort_key:\n", - " del cohort_key[\"dlc_si_params_name\"]\n", - "cohort_key[\"dlc_si_cohort_selection_name\"] = \"green_red_led\"\n", - "cohort_key[\"bodyparts_params_dict\"] = {\n", - " \"greenLED\": si_params_name,\n", - " \"redLED_C\": si_params_name,\n", - "}\n", - "print(cohort_key)" - ] - }, - { - "cell_type": "markdown", - "id": "b646b4a5-16b9-48d5-ae44-2b57c220062f", - "metadata": {}, - "source": [ - "Here we'll insert the cohort into the `DLCSmoothInterpCohortSelection` table
..and populate `DLCSmoothInterpCohort`, which collates the separately smoothed and interpolated bodyparts into a single entry." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b9356875", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCSmoothInterpCohortSelection().insert1(cohort_key, skip_duplicates=True)\n", - "sgp.DLCSmoothInterpCohort.populate(cohort_key)" - ] - }, - { - "cell_type": "markdown", - "id": "e952ff54-dbc2-4cf5-88c4-55eb8eb578e7", - "metadata": {}, - "source": [ - "And of course, let's make sure that the table populated correctly. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c5aa8b3", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCSmoothInterpCohort.BodyPart() & cohort_key" - ] - }, - { - "cell_type": "markdown", - "id": "dcd8332f", - "metadata": {}, - "source": [ - "#### [DLCCentroid](#ToC) " - ] - }, - { - "cell_type": "markdown", - "id": "eac0b2e2-e2f7-475f-ae9e-8a9c134881f1", - "metadata": {}, - "source": [ - "We now have a cohort of smoothed and interpolated bodyparts from which to determine a centroid!
To start, we'll need a set of parameters to use for determining the centroid. For this tutorial, we can use the default." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "404ae5be", - "metadata": {}, - "outputs": [], - "source": [ - "# Here is the default set\n", - "print(sgp.DLCCentroidParams.get_default())\n", - "centroid_params_name = \"default\"" - ] - }, - { - "cell_type": "markdown", - "id": "7a9b45da-59da-46f0-aae2-c05c9b88ec39", - "metadata": {}, - "source": [ - ">Here is the syntax to add your own parameters:\n", - ">```python\n", - "centroid_params = {\n", - " 'centroid_method': 'two_pt_centroid',\n", - " 'points' : {\n", - " 'point1': 'greenLED',\n", - " 'point2': 'redLED_C',},\n", - " 'speed_smoothing_std_dev': 0.100,\n", - "}\n", - "centroid_params_name = 'your_unique_param_name'\n", - "sgp.DLCCentroidParams.insert1({'dlc_centroid_params_name': centroid_params_name,\n", - " 'params': centroid_params},\n", - " skip_duplicates=True)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "5683fc3b-e63b-4ba9-ab5a-8227f0a64feb", - "metadata": {}, - "source": [ - "And now let's make a key to insert into `DLCCentroidSelection`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1eed9bc3", - "metadata": {}, - "outputs": [], - "source": [ - "centroid_key = cohort_key.copy()\n", - "fields = list(sgp.DLCCentroidSelection.fetch().dtype.fields.keys())\n", - "centroid_key = {key: val for key, val in centroid_key.items() if key in fields}\n", - "centroid_key[\"dlc_centroid_params_name\"] = centroid_params_name\n", - "print(centroid_key)" - ] - }, - { - "cell_type": "markdown", - "id": "3ecf3415-4f8e-456a-92aa-6a22aa33edee", - "metadata": {}, - "source": [ - "Let's insert it into `DLCCentroidSelection` and then populate `DLCCentroid` !" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3b59e64", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCCentroidSelection.insert1(centroid_key, skip_duplicates=True)\n", - "sgp.DLCCentroid.populate(centroid_key)" - ] - }, - { - "cell_type": "markdown", - "id": "d5ad2907-3791-49c5-911c-b394da20861b", - "metadata": {}, - "source": [ - "Here we can visualize the resulting centroid position" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1a487ea", - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCCentroid() & centroid_key).fetch1_dataframe().plot.scatter(\n", - " x=\"position_x\",\n", - " y=\"position_y\",\n", - " c=\"speed\",\n", - " colormap=\"viridis\",\n", - " alpha=0.5,\n", - " s=0.5,\n", - " figsize=(10, 10),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "90a2b462", - "metadata": {}, - "source": [ - "#### [DLCOrientation](#ToC) " - ] - }, - { - "cell_type": "markdown", - "id": "6bfe9e8a-4805-4916-818d-393ae14d5151", - "metadata": {}, - "source": [ - "We'll now go through a similar process to identify the orientation!
To start, we'll need a set of parameters to use for determining the orientation. For this tutorial, we can use the default." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74efceda", - "metadata": {}, - "outputs": [], - "source": [ - "print(sgp.DLCOrientationParams.get_default())\n", - "dlc_orientation_params_name = \"default\"" - ] - }, - { - "cell_type": "markdown", - "id": "15b2d925-a749-45a8-9b79-8c0935021f88", - "metadata": {}, - "source": [ - "Here we'll prune the `cohort_key` we used above and add our `dlc_orientation_params_name` to make it suitable for `DLCOrientationSelection`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eedfd230", - "metadata": {}, - "outputs": [], - "source": [ - "fields = list(sgp.DLCOrientationSelection.fetch().dtype.fields.keys())\n", - "orient_key = {key: val for key, val in cohort_key.items() if key in fields}\n", - "orient_key[\"dlc_orientation_params_name\"] = dlc_orientation_params_name\n", - "print(orient_key)" - ] - }, - { - "cell_type": "markdown", - "id": "6b4defcf-a4ab-4d87-9183-803776f5eefd", - "metadata": {}, - "source": [ - "And now let's insert into `DLCOrientationSelection` and populate `DLCOrientation` to determine the orientation!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "56793200", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCOrientationSelection().insert1(orient_key, skip_duplicates=True)\n", - "sgp.DLCOrientation().populate(orient_key)" - ] - }, - { - "cell_type": "markdown", - "id": "0170037c-ce84-4241-bcd7-1b2eb7100615", - "metadata": {}, - "source": [ - "We can fetch the output of `DLCOrientation` as a dataframe to make sure everything looks appropriate." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "06c972e8", - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCOrientation() & orient_key).fetch1_dataframe()" - ] - }, - { - "cell_type": "markdown", - "id": "04c4b409", - "metadata": {}, - "source": [ - "#### [DLCPos](#ToC) " - ] - }, - { - "cell_type": "markdown", - "id": "c57bf28e-cc74-4254-84a8-5a6a52d0b559", - "metadata": {}, - "source": [ - "Ok, we're now done with processing the position data! We just have to do some table manipulations to make sure everything ends up in the same format and same location.
\n", - "To summarize, we brought in a pretrained DLC project, used that model to run pose estimation on a new behavioral video, smoothed and interpolated the result, formed a cohort of bodyparts, and determined the centroid and orientation of this cohort. **_Whew!_**
\n", - "Now let's populate `DLCPos` with our centroid and orientation entries from above.
----
\n", - "To begin, we'll make a key that combines the cohort names we used for the orientation and centroid as well as the params names for both." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "947afa29", - "metadata": {}, - "outputs": [], - "source": [ - "fields = list(sgp.DLCPosV1.fetch().dtype.fields.keys())\n", - "dlc_key = {key: val for key, val in centroid_key.items() if key in fields}\n", - "dlc_key[\"dlc_si_cohort_centroid\"] = centroid_key[\"dlc_si_cohort_selection_name\"]\n", - "dlc_key[\"dlc_si_cohort_orientation\"] = orient_key[\n", - " \"dlc_si_cohort_selection_name\"\n", - "]\n", - "dlc_key[\"dlc_orientation_params_name\"] = orient_key[\n", - " \"dlc_orientation_params_name\"\n", - "]\n", - "print(dlc_key)" - ] - }, - { - "cell_type": "markdown", - "id": "69b8f581-e87e-40f3-8eb0-0cb8ac79a425", - "metadata": {}, - "source": [ - "Now we can insert into `DLCPosSelection` and populate `DLCPos` with our `dlc_key`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca7cb9f1", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPosSelection().insert1(dlc_key, skip_duplicates=True)\n", - "sgp.DLCPosV1().populate(dlc_key)" - ] - }, - { - "cell_type": "markdown", - "id": "f13444ea-ea60-4fe8-9ebe-332299122844", - "metadata": {}, - "source": [ - "We can also make sure that all of our data made it through by fetching the dataframe attached to this entry.
We should expect 8 columns:\n", - ">time
video_frame_ind
position_x
position_y
orientation
velocity_x
velocity_y
speed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f57c72cb", - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCPosV1() & dlc_key).fetch1_dataframe()" - ] - }, - { - "cell_type": "markdown", - "id": "bede5081-0506-4bfb-8b3c-88cef1de9f99", - "metadata": {}, - "source": [ - "And even more, we can fetch the `pose_eval_result` that is calculated during this step. This field contains the percentage of frames that each bodypart was below the likelihood threshold of 0.95 as a means of assessing the quality of the pose estimation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f6910c87", - "metadata": {}, - "outputs": [], - "source": [ - "(sgp.DLCPosV1() & dlc_key).fetch1(\"pose_eval_result\")" - ] - }, - { - "cell_type": "markdown", - "id": "ba851a47", - "metadata": {}, - "source": [ - "#### [DLCPosVideo](#ToC) " - ] - }, - { - "cell_type": "markdown", - "id": "1cbeaced-7a63-4730-bf0e-810b66d2f7e4", - "metadata": {}, - "source": [ - "Here we can create a video with the centroid and orientation overlaid on the animal's behavioral video. This will also plot the likelihood of each bodypart used in the cohort. This is completely optional, but a good idea to make sure everything looks correct." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8c39d40c", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPosVideoParams.insert_default()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a42b491", - "metadata": {}, - "outputs": [], - "source": [ - "params = {\n", - " \"percent_frames\": 0.05,\n", - " \"incl_likelihood\": True,\n", - "}\n", - "sgp.DLCPosVideoParams.insert1(\n", - " {\"dlc_pos_video_params_name\": \"five_percent\", \"params\": params},\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee1e4a5b", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPosVideoSelection.insert1(\n", - " {**dlc_key, \"dlc_pos_video_params_name\": \"five_percent\"},\n", - " skip_duplicates=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8aee96f", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.DLCPosVideo().populate(dlc_key)" - ] - }, - { - "cell_type": "markdown", - "id": "745ce032", - "metadata": {}, - "source": [ - "#### [PositionOutput](#ToC) " - ] - }, - { - "cell_type": "markdown", - "id": "b5003cb7-a0ef-445e-8c9f-a2342f2cda13", - "metadata": {}, - "source": [ - "`PositionOutput` is the final table of the position pipeline and is automatically populated when we populate `DLCPos`! Let's make sure that our entry made it in." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "96bcbfb9", - "metadata": {}, - "outputs": [], - "source": [ - "PositionOutput() & dlc_key" - ] - }, - { - "cell_type": "markdown", - "id": "1b74d710-4137-41b0-90ed-6231d36f6e09", - "metadata": {}, - "source": [ - "`PositionOutput` also has a part table, similar to the `DLCModelSource` table above. Let's check that out as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "33ca3928-f51e-480f-94de-f22956017931", - "metadata": {}, - "outputs": [], - "source": [ - "PositionOutput.DLCPosV1() & dlc_key" - ] - }, - { - "cell_type": "markdown", - "id": "1acfbf12-d69c-4412-ab18-6da7f31dd9e8", - "metadata": {}, - "source": [ - "#### [PositionVideo](#ToC)" - ] - }, - { - "cell_type": "markdown", - "id": "ca3bf99a-9c29-47f0-a743-0f1217a51b72", - "metadata": {}, - "source": [ - "Bonus points if you made it this far... We can use the `PositionVideo` table to create a video that overlays just the centroid and orientation (regardless of upstream source) on the behavioral video. This table uses the parameter `plot` to determine whether to plot the entry deriving from the DLC arm or from the Trodes arm of the position pipeline. This parameter also accepts 'all', which will plot both (if they exist) in order to compare results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fed5fd75", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.PositionVideoSelection().insert1(\n", - " {\n", - " \"nwb_file_name\": \"J1620210604_.nwb\",\n", - " \"interval_list_name\": \"pos 13 valid times\",\n", - " \"trodes_position_id\": 0,\n", - " \"dlc_position_id\": 1,\n", - " \"plot\": \"DLC\",\n", - " \"output_dir\": \"/home/dgramling/Src/\",\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9fd2bfac", - "metadata": {}, - "outputs": [], - "source": [ - "sgp.PositionVideo.populate({\"plot\": \"DLC\"})" - ] - }, - { - "cell_type": "markdown", - "id": "bb5f9d5b-4fbf-4831-b275-46e4051ecc09", - "metadata": {}, - "source": [ - "### _CONGRATULATIONS!!_\n", - "Please treat yourself to a nice tea break :-)" - ] - }, - { - "cell_type": "markdown", - "id": "5dbb3e99", - "metadata": {}, - "source": [ - "### [`Return To Table of Contents`](#ToC)
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:spyglass-position] *", - "language": "python", - "name": "conda-env-spyglass-position-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/02_Spike_Sorting.ipynb b/notebooks/10_Spike_Sorting.ipynb similarity index 99% rename from notebooks/02_Spike_Sorting.ipynb rename to notebooks/10_Spike_Sorting.ipynb index 5f42891a1..71abbd3eb 100644 --- a/notebooks/02_Spike_Sorting.ipynb +++ b/notebooks/10_Spike_Sorting.ipynb @@ -162,9 +162,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "_Exercise_ code hidden here\n", + "
\n", + "Code hidden here\n", "\n", - "\n" + "
" ] }, { diff --git a/notebooks/11_2D_Clusterless_Decoding.ipynb b/notebooks/11_2D_Clusterless_Decoding.ipynb deleted file mode 100644 index 4c2ed0d62..000000000 --- a/notebooks/11_2D_Clusterless_Decoding.ipynb +++ /dev/null @@ -1,1424 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "05e6ef1a-25d8-4c66-9abe-5fbe109aa05e", - "metadata": {}, - "source": [ - "## 2D Clusterless Decoding" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1801a7f7", - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8a882cf8-b4c6-40c6-b800-79ac9af9fddb", - "metadata": {}, - "outputs": [], - "source": [ - "# ignore datajoint+jupyter async warnings\n", - "import warnings\n", - "\n", - "warnings.simplefilter(\"ignore\", category=DeprecationWarning)\n", - "warnings.simplefilter(\"ignore\", category=ResourceWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d46d7e2c-e475-459a-ae47-5aac32e806e6", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import xarray as xr\n", - "import logging\n", - "\n", - "FORMAT = \"%(asctime)s %(message)s\"\n", - "\n", - "logging.basicConfig(level=\"INFO\", format=FORMAT, datefmt=\"%d-%b-%y %H:%M:%S\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "90352bb7-f85b-476d-8d07-5978f28a47af", - "metadata": {}, - "outputs": [], - "source": [ - "nwb_copy_file_name = \"chimi20200216_new_.nwb\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ed3e32a4-6640-434c-a4d7-0620216dbe9d", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/edeno/miniconda3/envs/spyglass/lib/python3.8/site-packages/seaborn/rcmod.py:82: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", - " if LooseVersion(mpl.__version__) >= \"3.0\":\n", - "/home/edeno/miniconda3/envs/spyglass/lib/python3.8/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", - " other = LooseVersion(other)\n", - "13-Sep-22 15:42:32 Connected edeno@lmf-db.cin.ucsf.edu:3306\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Connecting edeno@lmf-db.cin.ucsf.edu:3306\n" - ] - }, - { - "data": { - "application/javascript": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function 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url = js_urls[i];\n if (skip.indexOf(url) >= 0) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) >= 0) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [];\n var js_modules = [];\n var css_urls = [];\n var inline_js = [\n function(Bokeh) {\n inject_raw_css(\".bk.alert {\\n padding: 0.75rem 1.25rem;\\n border: 1px solid transparent;\\n border-radius: 0.25rem;\\n /* Don't set margin because that will not render correctly! */\\n /* margin-bottom: 1rem; */\\n margin-top: 15px;\\n margin-bottom: 15px;\\n}\\n.bk.alert a {\\n color: rgb(11, 46, 19); /* #002752; */\\n font-weight: 700;\\n text-decoration: rgb(11, 46, 19);\\n text-decoration-color: rgb(11, 46, 19);\\n text-decoration-line: none;\\n text-decoration-style: solid;\\n text-decoration-thickness: auto;\\n }\\n.bk.alert a:hover {\\n color: rgb(11, 46, 19);\\n font-weight: 700;\\n text-decoration: underline;\\n}\\n\\n.bk.alert-primary {\\n color: #004085;\\n background-color: #cce5ff;\\n border-color: #b8daff;\\n}\\n.bk.alert-primary hr {\\n border-top-color: #9fcdff;\\n}\\n\\n.bk.alert-secondary {\\n color: #383d41;\\n background-color: #e2e3e5;\\n border-color: #d6d8db;\\n }\\n.bk.alert-secondary hr {\\n border-top-color: #c8cbcf;\\n}\\n\\n.bk.alert-success {\\n color: #155724;\\n background-color: #d4edda;\\n border-color: #c3e6cb;\\n }\\n\\n.bk.alert-success hr {\\n border-top-color: #b1dfbb;\\n}\\n\\n.bk.alert-info {\\n color: #0c5460;\\n background-color: #d1ecf1;\\n border-color: #bee5eb;\\n }\\n.bk.alert-info hr {\\n border-top-color: #abdde5;\\n}\\n\\n.bk.alert-warning {\\n color: #856404;\\n background-color: #fff3cd;\\n border-color: #ffeeba;\\n }\\n\\n.bk.alert-warning hr {\\n border-top-color: #ffe8a1;\\n}\\n\\n.bk.alert-danger {\\n color: #721c24;\\n background-color: #f8d7da;\\n border-color: #f5c6cb;\\n}\\n.bk.alert-danger hr {\\n border-top-color: #f1b0b7;\\n}\\n\\n.bk.alert-light {\\n color: #818182;\\n background-color: #fefefe;\\n border-color: #fdfdfe;\\n }\\n.bk.alert-light hr {\\n border-top-color: #ececf6;\\n}\\n\\n.bk.alert-dark {\\n color: #1b1e21;\\n background-color: #d6d8d9;\\n border-color: #c6c8ca;\\n }\\n.bk.alert-dark hr {\\n border-top-color: #b9bbbe;\\n}\\n\\n\\n/* adjf\\u00e6l */\\n\\n.bk.alert-primary a {\\n color: #002752;\\n}\\n\\n.bk.alert-secondary a {\\n color: #202326;\\n}\\n\\n\\n.bk.alert-success a {\\n color: #0b2e13;\\n}\\n\\n\\n.bk.alert-info a {\\n color: #062c33;\\n}\\n\\n\\n.bk.alert-warning a {\\n color: #533f03;\\n}\\n\\n\\n.bk.alert-danger a {\\n color: #491217;\\n}\\n\\n.bk.alert-light a {\\n color: #686868;\\n}\\n\\n.bk.alert-dark a {\\n color: #040505;\\n}\");\n },\n function(Bokeh) {\n inject_raw_css(\".bk.debugger-card {\\n border: 1px solid rgba(0,0,0,1);\\n color: rgba(255,255,255,1);\\n background-color: rgba(0,0,0,1);\\n border-radius: 0rem;\\n}\\n.bk.debugger-card-header {\\n align-items: center;\\n text-align: left;\\n background-color: rgba(0, 0, 0, 1)!important;\\n color: rgba(255, 255, 255, 1);\\n border-radius: 0rem;\\n display: inline-flex;\\n justify-content: start;\\n width: 100%;\\n}\\n.bk.debugger-card-button {\\n background-color: transparent;\\n color: rgba(255, 255, 255, 1);\\n margin-left: 0.5em;\\n}\\n.bk.debugger-card-title {\\n align-items: center;\\n text-align: left;\\n color: rgba(255, 255, 255, 1);\\n font-size: 1em;\\n overflow-wrap: break-word;\\n}\\n\\n/* Special debugger buttons for clearing and saving */\\n.bk button.special_btn {\\n width: 25px;\\n height: 25px;\\n background-color: black;\\n color: white;\\n display: inline-block;\\n}\\n\\n\\n.bk button.special_btn .tooltiptext {\\n visibility: hidden;\\n width: 100px;\\n background-color: darkgray;\\n color: #fff;\\n text-align: center;\\n border-radius: 6px;\\n padding: 5px 0;\\n \\n /* Position the tooltip */\\n position: relative;\\n z-index: 1;\\n top: 100%;\\n left: 100%;\\n margin-left: -100px;\\n display: block;\\n}\\n\\n.bk button.special_btn:hover .tooltiptext {\\n visibility: visible;\\n}\\n\\n\\n\\n.bk button.clear_btn:hover .shown { display: none;}\\n.bk button.clear_btn:hover:before { content: \\\"\\u2611\\\"; }\");\n },\n function(Bokeh) {\n inject_raw_css(\".bk.card {\\n border: 1px solid rgba(0,0,0,.125);\\n border-radius: 0.25rem;\\n}\\n.bk.accordion {\\n border: 1px solid rgba(0,0,0,.125);\\n}\\n.bk.card-header {\\n align-items: center;\\n background-color: rgba(0, 0, 0, 0.03);\\n border-radius: 0.25rem;\\n display: inline-flex;\\n justify-content: start;\\n width: 100%;\\n}\\n.bk.accordion-header {\\n align-items: center;\\n background-color: rgba(0, 0, 0, 0.03);\\n border-radius: 0;\\n display: flex;\\n justify-content: start;\\n width: 100%;\\n}\\n.bk.card-button {\\n background-color: transparent;\\n margin-left: 0.5em;\\n}\\n.bk.card-header-row {\\n position: relative !important;\\n}\\n.bk.card-title {\\n align-items: center;\\n font-size: 1.4em;\\n font-weight: bold;\\n overflow-wrap: break-word;\\n}\\n.bk.card-header-row > .bk {\\n overflow-wrap: break-word;\\n text-align: center;\\n}\\n\");\n },\n function(Bokeh) {\n inject_raw_css(\"table.panel-df {\\n margin-left: auto;\\n margin-right: auto;\\n border: none;\\n border-collapse: collapse;\\n border-spacing: 0;\\n color: black;\\n font-size: 12px;\\n table-layout: fixed;\\n width: 100%;\\n}\\n\\n.panel-df tr, .panel-df th, .panel-df td {\\n text-align: right;\\n vertical-align: middle;\\n padding: 0.5em 0.5em !important;\\n line-height: normal;\\n white-space: normal;\\n max-width: none;\\n border: none;\\n}\\n\\n.panel-df tbody {\\n display: table-row-group;\\n vertical-align: middle;\\n border-color: inherit;\\n}\\n\\n.panel-df tbody tr:nth-child(odd) {\\n background: #f5f5f5;\\n}\\n\\n.panel-df thead {\\n border-bottom: 1px solid black;\\n vertical-align: bottom;\\n}\\n\\n.panel-df tr:hover {\\n background: lightblue !important;\\n cursor: pointer;\\n}\\n\");\n },\n function(Bokeh) {\n inject_raw_css(\".bk.pn-loading:before {\\n position: absolute;\\n height: 100%;\\n width: 100%;\\n content: '';\\n z-index: 1000;\\n background-color: rgb(255,255,255,0.50);\\n border-color: lightgray;\\n background-repeat: no-repeat;\\n background-position: center;\\n background-size: auto 50%;\\n border-width: 1px;\\n cursor: progress;\\n}\\n.bk.pn-loading.arcs:hover:before {\\n cursor: progress;\\n}\\n\");\n },\n function(Bokeh) {\n inject_raw_css(\".json-formatter-row {\\n font-family: monospace;\\n}\\n.json-formatter-row,\\n.json-formatter-row a,\\n.json-formatter-row a:hover {\\n color: black;\\n text-decoration: none;\\n}\\n.json-formatter-row .json-formatter-row {\\n margin-left: 1rem;\\n}\\n.json-formatter-row .json-formatter-children.json-formatter-empty {\\n opacity: 0.5;\\n margin-left: 1rem;\\n}\\n.json-formatter-row .json-formatter-children.json-formatter-empty:after {\\n display: none;\\n}\\n.json-formatter-row .json-formatter-children.json-formatter-empty.json-formatter-object:after {\\n content: \\\"No properties\\\";\\n}\\n.json-formatter-row .json-formatter-children.json-formatter-empty.json-formatter-array:after {\\n content: \\\"[]\\\";\\n}\\n.json-formatter-row .json-formatter-string,\\n.json-formatter-row .json-formatter-stringifiable {\\n color: green;\\n white-space: pre;\\n word-wrap: break-word;\\n}\\n.json-formatter-row .json-formatter-number {\\n color: blue;\\n}\\n.json-formatter-row .json-formatter-boolean {\\n color: red;\\n}\\n.json-formatter-row .json-formatter-null {\\n color: #855A00;\\n}\\n.json-formatter-row .json-formatter-undefined {\\n color: #ca0b69;\\n}\\n.json-formatter-row .json-formatter-function {\\n color: #FF20ED;\\n}\\n.json-formatter-row .json-formatter-date {\\n background-color: rgba(0, 0, 0, 0.05);\\n}\\n.json-formatter-row .json-formatter-url {\\n text-decoration: underline;\\n color: blue;\\n cursor: pointer;\\n}\\n.json-formatter-row .json-formatter-bracket {\\n color: blue;\\n}\\n.json-formatter-row .json-formatter-key {\\n color: #00008B;\\n padding-right: 0.2rem;\\n}\\n.json-formatter-row .json-formatter-toggler-link {\\n cursor: pointer;\\n}\\n.json-formatter-row .json-formatter-toggler {\\n line-height: 1.2rem;\\n font-size: 0.7rem;\\n vertical-align: middle;\\n opacity: 0.6;\\n cursor: pointer;\\n padding-right: 0.2rem;\\n}\\n.json-formatter-row .json-formatter-toggler:after {\\n display: inline-block;\\n transition: transform 100ms ease-in;\\n content: \\\"\\\\25BA\\\";\\n}\\n.json-formatter-row > a > .json-formatter-preview-text {\\n opacity: 0;\\n transition: opacity 0.15s ease-in;\\n font-style: italic;\\n}\\n.json-formatter-row:hover > a > .json-formatter-preview-text {\\n opacity: 0.6;\\n}\\n.json-formatter-row.json-formatter-open > .json-formatter-toggler-link .json-formatter-toggler:after {\\n transform: rotate(90deg);\\n}\\n.json-formatter-row.json-formatter-open > .json-formatter-children:after {\\n display: inline-block;\\n}\\n.json-formatter-row.json-formatter-open > a > .json-formatter-preview-text {\\n display: none;\\n}\\n.json-formatter-row.json-formatter-open.json-formatter-empty:after {\\n display: block;\\n}\\n.json-formatter-dark.json-formatter-row {\\n font-family: monospace;\\n}\\n.json-formatter-dark.json-formatter-row,\\n.json-formatter-dark.json-formatter-row a,\\n.json-formatter-dark.json-formatter-row a:hover {\\n color: white;\\n text-decoration: none;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-row {\\n margin-left: 1rem;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-children.json-formatter-empty {\\n opacity: 0.5;\\n margin-left: 1rem;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-children.json-formatter-empty:after {\\n display: none;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-children.json-formatter-empty.json-formatter-object:after {\\n content: \\\"No properties\\\";\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-children.json-formatter-empty.json-formatter-array:after {\\n content: \\\"[]\\\";\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-string,\\n.json-formatter-dark.json-formatter-row .json-formatter-stringifiable {\\n color: #31F031;\\n white-space: pre;\\n word-wrap: break-word;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-number {\\n color: #66C2FF;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-boolean {\\n color: #EC4242;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-null {\\n color: #EEC97D;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-undefined {\\n color: #ef8fbe;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-function {\\n color: #FD48CB;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-date {\\n background-color: rgba(255, 255, 255, 0.05);\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-url {\\n text-decoration: underline;\\n color: #027BFF;\\n cursor: pointer;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-bracket {\\n color: #9494FF;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-key {\\n color: #23A0DB;\\n padding-right: 0.2rem;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-toggler-link {\\n cursor: pointer;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-toggler {\\n line-height: 1.2rem;\\n font-size: 0.7rem;\\n vertical-align: middle;\\n opacity: 0.6;\\n cursor: pointer;\\n padding-right: 0.2rem;\\n}\\n.json-formatter-dark.json-formatter-row .json-formatter-toggler:after {\\n display: inline-block;\\n transition: transform 100ms ease-in;\\n content: \\\"\\\\25BA\\\";\\n}\\n.json-formatter-dark.json-formatter-row > a > .json-formatter-preview-text {\\n opacity: 0;\\n transition: opacity 0.15s ease-in;\\n font-style: italic;\\n}\\n.json-formatter-dark.json-formatter-row:hover > a > .json-formatter-preview-text {\\n opacity: 0.6;\\n}\\n.json-formatter-dark.json-formatter-row.json-formatter-open > .json-formatter-toggler-link .json-formatter-toggler:after {\\n transform: rotate(90deg);\\n}\\n.json-formatter-dark.json-formatter-row.json-formatter-open > .json-formatter-children:after {\\n display: inline-block;\\n}\\n.json-formatter-dark.json-formatter-row.json-formatter-open > a > .json-formatter-preview-text {\\n display: none;\\n}\\n.json-formatter-dark.json-formatter-row.json-formatter-open.json-formatter-empty:after {\\n display: block;\\n}\\n\");\n },\n function(Bokeh) {\n inject_raw_css(\".bk.panel-widget-box {\\n min-height: 20px;\\n background-color: #f5f5f5;\\n border: 1px solid #e3e3e3;\\n border-radius: 4px;\\n -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.05);\\n box-shadow: inset 0 1px 1px rgba(0,0,0,.05);\\n overflow-x: hidden;\\n overflow-y: hidden;\\n}\\n\\n.scrollable {\\n overflow: scroll;\\n}\\n\\nprogress {\\n appearance: none;\\n -moz-appearance: none;\\n -webkit-appearance: none;\\n border: none;\\n height: 20px;\\n background-color: whiteSmoke;\\n border-radius: 3px;\\n box-shadow: 0 2px 3px rgba(0,0,0,.5) inset;\\n color: royalblue;\\n position: relative;\\n margin: 0 0 1.5em;\\n}\\n\\nprogress[value]::-webkit-progress-bar {\\n background-color: whiteSmoke;\\n border-radius: 3px;\\n box-shadow: 0 2px 3px rgba(0,0,0,.5) inset;\\n}\\n\\nprogress[value]::-webkit-progress-value {\\n position: relative;\\n background-size: 35px 20px, 100% 100%, 100% 100%;\\n border-radius:3px;\\n}\\n\\nprogress.active:not([value])::before {\\n background-position: 10%;\\n animation-name: stripes;\\n animation-duration: 3s;\\n animation-timing-function: linear;\\n animation-iteration-count: infinite;\\n}\\n\\nprogress[value]::-moz-progress-bar {\\n background-size: 35px 20px, 100% 100%, 100% 100%;\\n border-radius:3px;\\n}\\n\\nprogress:not([value])::-moz-progress-bar {\\n border-radius:3px;\\n background: linear-gradient(-45deg, transparent 33%, rgba(0, 0, 0, 0.2) 33%, rgba(0, 0, 0, 0.2) 66%, transparent 66%) left/2.5em 1.5em;\\n}\\n\\nprogress.active:not([value])::-moz-progress-bar {\\n background-position: 10%;\\n animation-name: stripes;\\n animation-duration: 3s;\\n animation-timing-function: linear;\\n animation-iteration-count: infinite;\\n}\\n\\nprogress.active:not([value])::-webkit-progress-bar {\\n background-position: 10%;\\n animation-name: stripes;\\n animation-duration: 3s;\\n animation-timing-function: linear;\\n animation-iteration-count: infinite;\\n}\\n\\nprogress.primary[value]::-webkit-progress-value { background-color: #007bff; }\\nprogress.primary:not([value])::before { background-color: #007bff; }\\nprogress.primary:not([value])::-webkit-progress-bar { background-color: #007bff; }\\nprogress.primary::-moz-progress-bar { background-color: #007bff; }\\n\\nprogress.secondary[value]::-webkit-progress-value { background-color: #6c757d; }\\nprogress.secondary:not([value])::before { background-color: #6c757d; }\\nprogress.secondary:not([value])::-webkit-progress-bar { background-color: #6c757d; }\\nprogress.secondary::-moz-progress-bar { background-color: #6c757d; }\\n\\nprogress.success[value]::-webkit-progress-value { background-color: #28a745; }\\nprogress.success:not([value])::before { background-color: #28a745; }\\nprogress.success:not([value])::-webkit-progress-bar { background-color: #28a745; }\\nprogress.success::-moz-progress-bar { background-color: #28a745; }\\n\\nprogress.danger[value]::-webkit-progress-value { background-color: #dc3545; }\\nprogress.danger:not([value])::before { background-color: #dc3545; }\\nprogress.danger:not([value])::-webkit-progress-bar { background-color: #dc3545; }\\nprogress.danger::-moz-progress-bar { background-color: #dc3545; }\\n\\nprogress.warning[value]::-webkit-progress-value { background-color: #ffc107; }\\nprogress.warning:not([value])::before { background-color: #ffc107; }\\nprogress.warning:not([value])::-webkit-progress-bar { background-color: #ffc107; }\\nprogress.warning::-moz-progress-bar { background-color: #ffc107; }\\n\\nprogress.info[value]::-webkit-progress-value { background-color: #17a2b8; }\\nprogress.info:not([value])::before { background-color: #17a2b8; }\\nprogress.info:not([value])::-webkit-progress-bar { background-color: #17a2b8; }\\nprogress.info::-moz-progress-bar { background-color: #17a2b8; }\\n\\nprogress.light[value]::-webkit-progress-value { background-color: #f8f9fa; }\\nprogress.light:not([value])::before { background-color: #f8f9fa; }\\nprogress.light:not([value])::-webkit-progress-bar { background-color: #f8f9fa; }\\nprogress.light::-moz-progress-bar { background-color: #f8f9fa; }\\n\\nprogress.dark[value]::-webkit-progress-value { background-color: #343a40; }\\nprogress.dark:not([value])::-webkit-progress-bar { background-color: #343a40; }\\nprogress.dark:not([value])::before { background-color: #343a40; }\\nprogress.dark::-moz-progress-bar { background-color: #343a40; }\\n\\nprogress:not([value])::-webkit-progress-bar {\\n border-radius: 3px;\\n background: linear-gradient(-45deg, transparent 33%, rgba(0, 0, 0, 0.2) 33%, rgba(0, 0, 0, 0.2) 66%, transparent 66%) left/2.5em 1.5em;\\n}\\nprogress:not([value])::before {\\n content:\\\" \\\";\\n position:absolute;\\n height: 20px;\\n top:0;\\n left:0;\\n right:0;\\n bottom:0;\\n border-radius: 3px;\\n background: linear-gradient(-45deg, transparent 33%, rgba(0, 0, 0, 0.2) 33%, rgba(0, 0, 0, 0.2) 66%, transparent 66%) left/2.5em 1.5em;\\n}\\n\\n@keyframes stripes {\\n from {background-position: 0%}\\n to {background-position: 100%}\\n}\\n\\n.bk-root .bk.loader {\\n overflow: hidden;\\n}\\n\\n.bk.loader::after {\\n content: \\\"\\\";\\n border-radius: 50%;\\n -webkit-mask-image: radial-gradient(transparent 50%, rgba(0, 0, 0, 1) 54%);\\n width: 100%;\\n height: 100%;\\n left: 0;\\n top: 0;\\n position: absolute;\\n}\\n\\n.bk-root .bk.loader.dark::after {\\n background: #0f0f0f;\\n}\\n\\n.bk-root .bk.loader.light::after {\\n background: #f0f0f0;\\n}\\n\\n.bk-root .bk.loader.spin::after {\\n animation: spin 2s linear infinite;\\n}\\n\\n.bk-root div.bk.loader.spin.primary-light::after {\\n background: linear-gradient(135deg, #f0f0f0 50%, transparent 50%), linear-gradient(45deg, #f0f0f0 50%, #007bff 50%);\\n}\\n\\n.bk-root div.bk.loader.spin.secondary-light::after {\\n background: linear-gradient(135deg, #f0f0f0 50%, transparent 50%), linear-gradient(45deg, #f0f0f0 50%, #6c757d 50%);\\n}\\n\\n.bk-root div.bk.loader.spin.success-light::after {\\n background: linear-gradient(135deg, #f0f0f0 50%, transparent 50%), linear-gradient(45deg, #f0f0f0 50%, #28a745 50%);\\n}\\n\\n.bk-root div.bk.loader.spin.danger-light::after {\\n background: linear-gradient(135deg, #f0f0f0 50%, transparent 50%), linear-gradient(45deg, #f0f0f0 50%, #dc3545 50%);\\n}\\n\\n.bk-root div.bk.loader.spin.warning-light::after {\\n background: linear-gradient(135deg, #f0f0f0 50%, transparent 50%), linear-gradient(45deg, #f0f0f0 50%, #ffc107 50%);\\n}\\n\\n.bk-root div.bk.loader.spin.info-light::after {\\n background: linear-gradient(135deg, #f0f0f0 50%, transparent 50%), linear-gradient(45deg, #f0f0f0 50%, #17a2b8 50%);\\n}\\n\\n.bk-root div.bk.loader.spin.light-light::after {\\n background: linear-gradient(135deg, #f0f0f0 50%, transparent 50%), linear-gradient(45deg, #f0f0f0 50%, #f8f9fa 50%);\\n}\\n\\n.bk-root div.bk.loader.dark-light::after {\\n background: linear-gradient(135deg, #f0f0f0 50%, transparent 50%), linear-gradient(45deg, #f0f0f0 50%, #343a40 50%);\\n}\\n\\n.bk-root div.bk.loader.spin.primary-dark::after {\\n background: linear-gradient(135deg, #0f0f0f 50%, transparent 50%), linear-gradient(45deg, #0f0f0f 50%, #007bff 50%);\\n}\\n\\n.bk-root div.bk.loader.spin.secondary-dark::after {\\n background: linear-gradient(135deg, #0f0f0f 50%, transparent 50%), linear-gradient(45deg, #0f0f0f 50%, #6c757d 50%);\\n}\\n\\n.bk-root div.bk.loader.spin.success-dark::after {\\n background: linear-gradient(135deg, #0f0f0f 50%, transparent 50%), linear-gradient(45deg, #0f0f0f 50%, #28a745 50%);\\n}\\n\\n.bk-root div.bk.loader.spin.danger-dark::after {\\n background: linear-gradient(135deg, #0f0f0f 50%, transparent 50%), linear-gradient(45deg, #0f0f0f 50%, #dc3545 50%)\\n}\\n\\n.bk-root div.bk.loader.spin.warning-dark::after {\\n background: linear-gradient(135deg, #0f0f0f 50%, transparent 50%), linear-gradient(45deg, #0f0f0f 50%, #ffc107 50%);\\n}\\n\\n.bk-root div.bk.loader.spin.info-dark::after {\\n background: linear-gradient(135deg, #0f0f0f 50%, transparent 50%), linear-gradient(45deg, #0f0f0f 50%, #17a2b8 50%);\\n}\\n\\n.bk-root div.bk.loader.spin.light-dark::after {\\n background: linear-gradient(135deg, #0f0f0f 50%, transparent 50%), linear-gradient(45deg, #0f0f0f 50%, #f8f9fa 50%);\\n}\\n\\n.bk-root div.bk.loader.spin.dark-dark::after {\\n background: linear-gradient(135deg, #0f0f0f 50%, transparent 50%), linear-gradient(45deg, #0f0f0f 50%, #343a40 50%);\\n}\\n\\n/* Safari */\\n@-webkit-keyframes spin {\\n 0% { -webkit-transform: rotate(0deg); }\\n 100% { -webkit-transform: rotate(360deg); }\\n}\\n\\n@keyframes spin {\\n 0% { transform: rotate(0deg); }\\n 100% { transform: rotate(360deg); }\\n}\\n\\n.dot div {\\n height: 100%;\\n width: 100%;\\n border: 1px solid #000 !important;\\n background-color: #fff;\\n border-radius: 50%;\\n display: inline-block;\\n}\\n\\n.dot-filled div {\\n height: 100%;\\n width: 100%;\\n border: 1px solid #000 !important;\\n border-radius: 50%;\\n display: inline-block;\\n}\\n\\n.dot-filled.primary div {\\n background-color: #007bff;\\n}\\n\\n.dot-filled.secondary div {\\n background-color: #6c757d;\\n}\\n\\n.dot-filled.success div {\\n background-color: #28a745;\\n}\\n\\n.dot-filled.danger div {\\n background-color: #dc3545;\\n}\\n\\n.dot-filled.warning div {\\n background-color: #ffc107;\\n}\\n\\n.dot-filled.info div {\\n background-color: #17a2b8;\\n}\\n\\n.dot-filled.dark div {\\n background-color: #343a40;\\n}\\n\\n.dot-filled.light div {\\n background-color: #f8f9fa;\\n}\\n\\n/* Slider editor 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bold } /* Generic.Strong */\\n.codehilite .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\\n.codehilite .gt { color: #0044DD } /* Generic.Traceback */\\n.codehilite .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\\n.codehilite .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\\n.codehilite .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\\n.codehilite .kp { color: #008000 } /* Keyword.Pseudo */\\n.codehilite .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\\n.codehilite .kt { color: #B00040 } /* Keyword.Type */\\n.codehilite .m { color: #666666 } /* Literal.Number */\\n.codehilite .s { color: #BA2121 } /* Literal.String */\\n.codehilite .na { color: #7D9029 } /* Name.Attribute */\\n.codehilite .nb { color: #008000 } /* Name.Builtin */\\n.codehilite .nc { color: #0000FF; font-weight: bold } /* Name.Class */\\n.codehilite .no { color: #880000 } /* Name.Constant */\\n.codehilite .nd { color: #AA22FF } 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url(\\\"data:image/svg+xml;base64,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\\\");\\n background-size: auto calc(min(50%, 400px));\\n }\\n \");\n },\n function(Bokeh) {\n /* BEGIN bokeh.min.js */\n /*!\n * Copyright (c) 2012 - 2022, Anaconda, Inc., and Bokeh Contributors\n * All rights reserved.\n * \n * Redistribution and use in source and binary forms, with or without modification,\n * are permitted provided that the following conditions are met:\n * \n * Redistributions of source code must retain the above copyright notice,\n * this list of conditions and the following disclaimer.\n * \n * Redistributions in binary form must reproduce the above copyright notice,\n * this list of conditions and the following disclaimer in the documentation\n * and/or other materials provided with the distribution.\n * \n * Neither the name of Anaconda nor the names of any contributors\n * may be used to endorse or promote products derived from this software\n * without specific prior written permission.\n * \n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF\n * THE POSSIBILITY OF SUCH DAMAGE.\n */\n (function(root, factory) {\n const bokeh = factory();\n bokeh.__bokeh__ = true;\n if (typeof root.Bokeh === \"undefined\" || typeof root.Bokeh.__bokeh__ === \"undefined\") {\n root.Bokeh = bokeh;\n }\n const Bokeh = root.Bokeh;\n Bokeh[bokeh.version] = bokeh;\n })(this, function() {\n let define;\n const parent_require = typeof require === \"function\" && require\n return (function(modules, entry, aliases, externals) {\n if (aliases === undefined) 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a}s(\"embed_items_notebook\",g.embed_items_notebook),s(\"kernels\",g.kernels),s(\"BOKEH_ROOT\",e(398).BOKEH_ROOT),t.embed_item=async function(e,o){const t={},n=(0,_.uuid4)();t[n]=e.doc,null==o&&(o=e.target_id);const s=document.getElementById(o);null!=s&&s.classList.add(m.BOKEH_ROOT);const d={roots:{[e.root_id]:o},root_ids:[e.root_id],docid:n};await(0,a.defer)();const[r]=await w(t,[d]);return r},t.embed_items=async function(e,o,t,n){return await(0,a.defer)(),w(e,o,t,n)}},\n function _(t,_,o,r,n){r();const a=t(1);(0,a.__exportStar)(t(6),o),(0,a.__exportStar)(t(35),o)},\n function _(e,t,s,o,n){o();const i=e(1),r=e(7),l=e(3),_=e(19),a=e(251),c=e(14),d=e(30),h=e(15),f=e(17),u=e(31),m=e(29),g=e(9),v=e(13),p=(0,i.__importStar)(e(77)),w=e(26),b=e(8),y=e(309),k=e(75),M=e(53),j=e(396),z=e(35);class S{constructor(e){this.document=e,this.session=null,this.subscribed_models=new Set}send_event(e){const t=new z.MessageSentEvent(this.document,\"bokeh_event\",e.to_json());this.document._trigger_on_change(t)}trigger(e){for(const t of this.subscribed_models)null!=e.origin&&e.origin!=t||t._process_event(e)}}s.EventManager=S,S.__name__=\"EventManager\",s.documents=[],s.DEFAULT_TITLE=\"Bokeh Application\";class E{constructor(e){var t;s.documents.push(this),this._init_timestamp=Date.now(),this._resolver=null!==(t=null==e?void 0:e.resolver)&&void 0!==t?t:new r.ModelResolver,this._title=s.DEFAULT_TITLE,this._roots=[],this._all_models=new Map,this._all_models_freeze_count=0,this._callbacks=new Map,this._message_callbacks=new Map,this.event_manager=new S(this),this.idle=new h.Signal0(this,\"idle\"),this._idle_roots=new WeakMap,this._interactive_timestamp=null,this._interactive_plot=null}get layoutables(){return this._roots.filter((e=>e instanceof y.LayoutDOM))}get is_idle(){for(const e of this.layoutables)if(!this._idle_roots.has(e))return!1;return!0}notify_idle(e){this._idle_roots.set(e,!0),this.is_idle&&(_.logger.info(`document idle at ${Date.now()-this._init_timestamp} ms`),this.event_manager.send_event(new a.DocumentReady),this.idle.emit())}clear(){this._push_all_models_freeze();try{for(;this._roots.length>0;)this.remove_root(this._roots[0])}finally{this._pop_all_models_freeze()}}interactive_start(e,t=null){null==this._interactive_plot&&(this._interactive_plot=e,this._interactive_plot.trigger_event(new a.LODStart)),this._interactive_finalize=t,this._interactive_timestamp=Date.now()}interactive_stop(){null!=this._interactive_plot&&(this._interactive_plot.trigger_event(new a.LODEnd),null!=this._interactive_finalize&&this._interactive_finalize()),this._interactive_plot=null,this._interactive_timestamp=null,this._interactive_finalize=null}interactive_duration(){return null==this._interactive_timestamp?-1:Date.now()-this._interactive_timestamp}destructively_move(e){if(e===this)throw new Error(\"Attempted to overwrite a document with itself\");e.clear();const t=(0,g.copy)(this._roots);this.clear();for(const e of t)if(null!=e.document)throw new Error(`Somehow we didn't detach ${e}`);if(0!=this._all_models.size)throw new Error(`this._all_models still had stuff in it: ${this._all_models}`);for(const s of t)e.add_root(s);e.set_title(this._title)}_push_all_models_freeze(){this._all_models_freeze_count+=1}_pop_all_models_freeze(){this._all_models_freeze_count-=1,0===this._all_models_freeze_count&&this._recompute_all_models()}_invalidate_all_models(){_.logger.debug(\"invalidating document models\"),0===this._all_models_freeze_count&&this._recompute_all_models()}_recompute_all_models(){let e=new Set;for(const t of this._roots)e=p.union(e,t.references());const t=new Set(this._all_models.values()),s=p.difference(t,e),o=p.difference(e,t),n=new Map;for(const t of e)n.set(t.id,t);for(const e of s)e.detach_document();for(const e of o)e.attach_document(this);this._all_models=n}roots(){return this._roots}add_root(e,t){if(_.logger.debug(`Adding root: ${e}`),!(0,g.includes)(this._roots,e)){this._push_all_models_freeze();try{this._roots.push(e)}finally{this._pop_all_models_freeze()}this._trigger_on_change(new z.RootAddedEvent(this,e,t))}}remove_root(e,t){const s=this._roots.indexOf(e);if(!(s<0)){this._push_all_models_freeze();try{this._roots.splice(s,1)}finally{this._pop_all_models_freeze()}this._trigger_on_change(new z.RootRemovedEvent(this,e,t))}}title(){return this._title}set_title(e,t){e!==this._title&&(this._title=e,this._trigger_on_change(new z.TitleChangedEvent(this,e,t)))}get_model_by_id(e){var t;return null!==(t=this._all_models.get(e))&&void 0!==t?t:null}get_model_by_name(e){const t=[];for(const s of this._all_models.values())s instanceof M.Model&&s.name==e&&t.push(s);switch(t.length){case 0:return null;case 1:return t[0];default:throw new Error(`Multiple models are named '${e}'`)}}on_message(e,t){const s=this._message_callbacks.get(e);null==s?this._message_callbacks.set(e,new Set([t])):s.add(t)}remove_on_message(e,t){var s;null===(s=this._message_callbacks.get(e))||void 0===s||s.delete(t)}_trigger_on_message(e,t){const s=this._message_callbacks.get(e);if(null!=s)for(const e of s)e(t)}on_change(e,t=!1){this._callbacks.has(e)||this._callbacks.set(e,t)}remove_on_change(e){this._callbacks.delete(e)}_trigger_on_change(e){for(const[t,s]of this._callbacks)if(!s&&e instanceof z.DocumentEventBatch)for(const s of e.events)t(s);else t(e)}_notify_change(e,t,s,o,n){this._trigger_on_change(new z.ModelChangedEvent(this,e,t,s,o,null==n?void 0:n.setter_id,null==n?void 0:n.hint))}static _instantiate_object(e,t,s,o){const n=Object.assign(Object.assign({},s),{id:e,__deferred__:!0});return new(o.get(t))(n)}static _instantiate_references_json(e,t,s){var o;const n=new Map;for(const i of e){const e=i.id,r=i.type,l=null!==(o=i.attributes)&&void 0!==o?o:{};let _=t.get(e);null==_&&(_=E._instantiate_object(e,r,l,s),null!=i.subtype&&_.set_subtype(i.subtype)),n.set(_.id,_)}return n}static _resolve_refs(e,t,s,o){function n(e){var i;if((0,f.is_ref)(e)){const o=null!==(i=t.get(e.id))&&void 0!==i?i:s.get(e.id);if(null!=o)return o;throw new Error(`reference ${JSON.stringify(e)} isn't known (not in Document?)`)}if((0,u.is_NDArray_ref)(e)){const{buffer:t,dtype:s,shape:n}=(0,u.decode_NDArray)(e,o);return(0,m.ndarray)(t,{dtype:s,shape:n})}return(0,b.isArray)(e)?function(e){const t=[];for(const s of e)t.push(n(s));return t}(e):(0,b.isPlainObject)(e)?function(e){const t={};for(const[s,o]of(0,v.entries)(e))t[s]=n(o);return t}(e):e}return n(e)}static _initialize_references_json(e,t,s,o){const n=new Map;for(const{id:i,attributes:r}of e){const e=!t.has(i),l=e?s.get(i):t.get(i),_=E._resolve_refs(r,t,s,o);l.setv(_,{silent:!0}),n.set(i,{instance:l,is_new:e})}const i=[],r=new Set;function l(e){if(e instanceof c.HasProps){if(n.has(e.id)&&!r.has(e.id)){r.add(e.id);const{instance:t,is_new:s}=n.get(e.id),{attributes:o}=t;for(const e of(0,v.values)(o))l(e);s&&(t.finalize(),i.push(t))}}else if((0,b.isArray)(e))for(const t of e)l(t);else if((0,b.isPlainObject)(e))for(const t of(0,v.values)(e))l(t)}for(const e of n.values())l(e.instance);for(const e of i)e.connect_signals()}static _event_for_attribute_change(e,t,s,o,n){if(o.get_model_by_id(e.id).property(t).syncable){const i={kind:\"ModelChanged\",model:{id:e.id},attr:t,new:s};return c.HasProps._json_record_references(o,s,n,{recursive:!0}),i}return null}static _events_to_sync_objects(e,t,s,o){const n=Object.keys(e.attributes),i=Object.keys(t.attributes),r=(0,g.difference)(n,i),l=(0,g.difference)(i,n),a=(0,g.intersection)(n,i),c=[];for(const e of r)_.logger.warn(`Server sent key ${e} but we don't seem to have it in our JSON`);for(const n of l){const i=t.attributes[n];c.push(E._event_for_attribute_change(e,n,i,s,o))}for(const n of a){const i=e.attributes[n],r=t.attributes[n];null==i&&null==r||(null==i||null==r?c.push(E._event_for_attribute_change(e,n,r,s,o)):\"data\"==n||(0,w.is_equal)(i,r)||c.push(E._event_for_attribute_change(e,n,r,s,o)))}return c.filter((e=>null!=e))}static _compute_patch_since_json(e,t){const s=t.to_json(!1);function o(e){const t=new Map;for(const s of e.roots.references)t.set(s.id,s);return t}const n=o(e),i=new Map,r=[];for(const t of e.roots.root_ids)i.set(t,n.get(t)),r.push(t);const l=o(s),_=new Map,a=[];for(const e of s.roots.root_ids)_.set(e,l.get(e)),a.push(e);if(r.sort(),a.sort(),(0,g.difference)(r,a).length>0||(0,g.difference)(a,r).length>0)throw new Error(\"Not implemented: computing add/remove of document roots\");const c=new Set;let h=[];for(const e of t._all_models.keys())if(n.has(e)){const s=E._events_to_sync_objects(n.get(e),l.get(e),t,c);h=h.concat(s)}const f=new d.Serializer({include_defaults:!1});return f.to_serializable([...c]),{references:[...f.definitions],events:h}}to_json_string(e=!0){return JSON.stringify(this.to_json(e))}to_json(e=!0){const t=new d.Serializer({include_defaults:e}),s=t.to_serializable(this._roots);return{version:l.version,title:this._title,roots:{root_ids:s.map((e=>e.id)),references:[...t.definitions]}}}static from_json_string(e){const t=JSON.parse(e);return E.from_json(t)}static from_json(e){_.logger.debug(\"Creating Document from JSON\");const t=e.version,s=-1!==t.indexOf(\"+\")||-1!==t.indexOf(\"-\"),o=`Library versions: JS (${l.version}) / Python (${t})`;s||l.version.replace(/-(dev|rc)\\./,\"$1\")==t?_.logger.debug(o):(_.logger.warn(\"JS/Python version mismatch\"),_.logger.warn(o));const n=new r.ModelResolver;null!=e.defs&&(0,j.resolve_defs)(e.defs,n);const i=e.roots,a=i.root_ids,c=i.references,d=E._instantiate_references_json(c,new Map,n);E._initialize_references_json(c,new Map,d,new Map);const h=new E({resolver:n});h._push_all_models_freeze();for(const e of a){const t=d.get(e);null!=t&&h.add_root(t)}return h._pop_all_models_freeze(),h.set_title(e.title),h}replace_with_json(e){E.from_json(e).destructively_move(this)}create_json_patch_string(e){return JSON.stringify(this.create_json_patch(e))}create_json_patch(e){for(const t of e)if(t.document!=this)throw new Error(\"Cannot create a patch using events from a different document\");const t=new d.Serializer,s=t.to_serializable(e);for(const e of this._all_models.values())t.remove_def(e);return{events:s,references:[...t.definitions]}}apply_json_patch(e,t=new Map,s){const o=e.references,n=e.events,i=E._instantiate_references_json(o,this._all_models,this._resolver);t instanceof Map||(t=new Map(t));for(const e of n)switch(e.kind){case\"RootAdded\":case\"RootRemoved\":case\"ModelChanged\":{const t=e.model.id,s=this._all_models.get(t);if(null!=s)i.set(t,s);else if(!i.has(t))throw _.logger.warn(`Got an event for unknown model ${e.model}\"`),new Error(\"event model wasn't known\");break}}const r=new Map(this._all_models),l=new Map;for(const[e,t]of i)r.has(e)||l.set(e,t);E._initialize_references_json(o,r,l,t);for(const e of n)switch(e.kind){case\"MessageSent\":{const{msg_type:s,msg_data:o}=e;let n;if(void 0===o){if(1!=t.size)throw new Error(\"expected exactly one buffer\");{const[[,e]]=t;n=e}}else n=E._resolve_refs(o,r,l,t);this._trigger_on_message(s,n);break}case\"ModelChanged\":{const o=e.model.id,n=this._all_models.get(o);if(null==n)throw new Error(`Cannot apply patch to ${o} which is not in the document`);const i=e.attr,_=E._resolve_refs(e.new,r,l,t);n.setv({[i]:_},{setter_id:s});break}case\"ColumnDataChanged\":{const o=e.column_source.id,n=this._all_models.get(o);if(null==n)throw new Error(`Cannot stream to ${o} which is not in the document`);const i=E._resolve_refs(e.new,new Map,new Map,t);if(null!=e.cols)for(const e in n.data)e in i||(i[e]=n.data[e]);n.setv({data:i},{setter_id:s,check_eq:!1});break}case\"ColumnsStreamed\":{const t=e.column_source.id,o=this._all_models.get(t);if(null==o)throw new Error(`Cannot stream to ${t} which is not in the document`);if(!(o instanceof k.ColumnDataSource))throw new Error(\"Cannot stream to non-ColumnDataSource\");const n=e.data,i=e.rollover;o.stream(n,i,s);break}case\"ColumnsPatched\":{const t=e.column_source.id,o=this._all_models.get(t);if(null==o)throw new Error(`Cannot patch ${t} which is not in the document`);if(!(o instanceof k.ColumnDataSource))throw new Error(\"Cannot patch non-ColumnDataSource\");const n=e.patches;o.patch(n,s);break}case\"RootAdded\":{const t=e.model.id,o=i.get(t);this.add_root(o,s);break}case\"RootRemoved\":{const t=e.model.id,o=i.get(t);this.remove_root(o,s);break}case\"TitleChanged\":this.set_title(e.title,s);break;default:throw new Error(`Unknown patch event ${JSON.stringify(e)}`)}}}s.Document=E,E.__name__=\"Document\"},\n function _(e,o,s,r,t){r();const l=e(1),i=e(8),d=e(13),n=e(14);s.overrides={};const a=new Map;s.Models=e=>{const 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extends Int16Array{constructor(e,s){super(e),this[h]=!0,this.dtype=\"int16\",this.shape=null!=s?s:x(e)?e.shape:[this.length],this.dimension=this.shape.length,null==this[A.equals]&&(this[A.equals]=(e,s)=>m.prototype[A.equals].call(this,e,s)),null==this[q.serialize]&&(this[q.serialize]=e=>m.prototype[q.serialize].call(this,e))}[(h=z,A.equals)](e,s){return s.eq(this.shape,e.shape)&&s.arrays(this,e)}[q.serialize](e){return(0,d.encode_NDArray)(this)}}t.Int16NDArray=m,m.__name__=\"Int16NDArray\";class g extends Uint32Array{constructor(e,s){super(e),this[u]=!0,this.dtype=\"uint32\",this.shape=null!=s?s:x(e)?e.shape:[this.length],this.dimension=this.shape.length,null==this[A.equals]&&(this[A.equals]=(e,s)=>g.prototype[A.equals].call(this,e,s)),null==this[q.serialize]&&(this[q.serialize]=e=>g.prototype[q.serialize].call(this,e))}[(u=z,A.equals)](e,s){return 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Float32Array{constructor(e,s){super(e),this[p]=!0,this.dtype=\"float32\",this.shape=null!=s?s:x(e)?e.shape:[this.length],this.dimension=this.shape.length,null==this[A.equals]&&(this[A.equals]=(e,s)=>U.prototype[A.equals].call(this,e,s)),null==this[q.serialize]&&(this[q.serialize]=e=>U.prototype[q.serialize].call(this,e))}[(p=z,A.equals)](e,s){return s.eq(this.shape,e.shape)&&s.arrays(this,e)}[q.serialize](e){return(0,d.encode_NDArray)(this)}}t.Float32NDArray=U,U.__name__=\"Float32NDArray\";class w extends Float64Array{constructor(e,s){super(e),this[c]=!0,this.dtype=\"float64\",this.shape=null!=s?s:x(e)?e.shape:[this.length],this.dimension=this.shape.length,null==this[A.equals]&&(this[A.equals]=(e,s)=>w.prototype[A.equals].call(this,e,s)),null==this[q.serialize]&&(this[q.serialize]=e=>w.prototype[q.serialize].call(this,e))}[(c=z,A.equals)](e,s){return s.eq(this.shape,e.shape)&&s.arrays(this,e)}[q.serialize](e){return(0,d.encode_NDArray)(this)}}function x(e){return(0,y.isObject)(e)&&void 0!==e[z]}t.Float64NDArray=w,w.__name__=\"Float64NDArray\",t.is_NDArray=x,t.ndarray=function(e,s={}){let{dtype:t}=s;null==t&&(t=e instanceof ArrayBuffer||(0,y.isArray)(e)?\"float64\":(()=>{switch(!0){case e instanceof Uint8Array:return\"uint8\";case e instanceof Int8Array:return\"int8\";case e instanceof Uint16Array:return\"uint16\";case e instanceof Int16Array:return\"int16\";case e instanceof Uint32Array:return\"uint32\";case e instanceof Int32Array:return\"int32\";case e instanceof Float32Array:return\"float32\";case e instanceof Float64Array:return\"float64\";default:(0,_.unreachable)()}})());const{shape:i}=s;switch(t){case\"uint8\":return new D(e,i);case\"int8\":return new N(e,i);case\"uint16\":return new f(e,i);case\"int16\":return new m(e,i);case\"uint32\":return new g(e,i);case\"int32\":return new I(e,i);case\"float32\":return new U(e,i);case\"float64\":return new w(e,i)}}},\n function _(e,r,t,i,s){i();const n=e(11),a=e(13),l=e(8);t.serialize=Symbol(\"serialize\");class o 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t.attributes)t.removeAttributeNode(e)},n.display=function(t){t.style.display=\"\"},n.undisplay=function(t){t.style.display=\"none\"},n.show=function(t){t.style.visibility=\"\"},n.hide=function(t){t.style.visibility=\"hidden\"},n.offset=function(t){const e=t.getBoundingClientRect();return{top:e.top+window.pageYOffset-document.documentElement.clientTop,left:e.left+window.pageXOffset-document.documentElement.clientLeft}},n.matches=d,n.parent=function(t,e){let n=t;for(;n=n.parentElement;)if(d(n,e))return n;return null},n.extents=f,n.size=u,n.scroll_size=function(t){return{width:Math.ceil(t.scrollWidth),height:Math.ceil(t.scrollHeight)}},n.outer_size=function(t){const{margin:{left:e,right:n,top:i,bottom:o}}=f(t),{width:s,height:l}=u(t);return{width:Math.ceil(s+e+n),height:Math.ceil(l+i+o)}},n.content_size=function(t){const{left:e,top:n}=t.getBoundingClientRect(),{padding:i}=f(t);let o=0,s=0;for(const l of t.children){const t=l.getBoundingClientRect();o=Math.max(o,Math.ceil(t.left-e-i.left+t.width)),s=Math.max(s,Math.ceil(t.top-n-i.top+t.height))}return{width:o,height:s}},n.position=function(t,e,n){const{style:i}=t;if(i.left=`${e.x}px`,i.top=`${e.y}px`,i.width=`${e.width}px`,i.height=`${e.height}px`,null==n)i.margin=\"\";else{const{top:t,right:e,bottom:o,left:s}=n;i.margin=`${t}px ${e}px ${o}px ${s}px`}},n.children=function(t){return Array.from(t.children)};class p{constructor(t){this.el=t,this.classList=t.classList}get values(){const t=[];for(let e=0;e{document.addEventListener(\"DOMContentLoaded\",(()=>t()),{once:!0})}))}},\n function _(o,i,t,e,r){e(),t.root=\"bk-root\",t.default=\".bk-root{position:relative;width:auto;height:auto;box-sizing:border-box;font-family:Helvetica, Arial, sans-serif;font-size:13px;}.bk-root .bk,.bk-root .bk:before,.bk-root .bk:after{box-sizing:inherit;margin:0;border:0;padding:0;background-image:none;font-family:inherit;font-size:100%;line-height:1.42857143;}.bk-root pre.bk{font-family:Courier, monospace;}\"},\n function _(e,t,r,a,c){a();const n=e(1),l=e(46);c(\"Line\",l.Line),c(\"LineScalar\",l.LineScalar),c(\"LineVector\",l.LineVector);const i=e(49);c(\"Fill\",i.Fill),c(\"FillScalar\",i.FillScalar),c(\"FillVector\",i.FillVector);const s=e(50);c(\"Text\",s.Text),c(\"TextScalar\",s.TextScalar),c(\"TextVector\",s.TextVector);const o=e(51);c(\"Hatch\",o.Hatch),c(\"HatchScalar\",o.HatchScalar),c(\"HatchVector\",o.HatchVector);const u=(0,n.__importStar)(e(48)),V=e(47);c(\"VisualProperties\",V.VisualProperties),c(\"VisualUniforms\",V.VisualUniforms);class h{constructor(e){this._visuals=[];for(const[t,r]of e.model._mixins){const a=(()=>{switch(r){case u.Line:return new l.Line(e,t);case u.LineScalar:return new l.LineScalar(e,t);case u.LineVector:return new l.LineVector(e,t);case u.Fill:return new i.Fill(e,t);case u.FillScalar:return new i.FillScalar(e,t);case u.FillVector:return new i.FillVector(e,t);case u.Text:return new s.Text(e,t);case u.TextScalar:return new s.TextScalar(e,t);case u.TextVector:return new s.TextVector(e,t);case u.Hatch:return new o.Hatch(e,t);case u.HatchScalar:return new o.HatchScalar(e,t);case u.HatchVector:return new o.HatchVector(e,t);default:throw new Error(\"unknown visual\")}})();a instanceof V.VisualProperties&&a.update(),this._visuals.push(a),Object.defineProperty(this,t+a.type,{get:()=>a,configurable:!1,enumerable:!0})}}*[Symbol.iterator](){yield*this._visuals}}r.Visuals=h,h.__name__=\"Visuals\"},\n function _(e,t,i,l,s){l();const a=e(1),n=e(47),h=(0,a.__importStar)(e(48)),o=e(22),_=e(8);function r(e){if((0,_.isArray)(e))return e;switch(e){case\"solid\":return[];case\"dashed\":return[6];case\"dotted\":return[2,4];case\"dotdash\":return[2,4,6,4];case\"dashdot\":return[6,4,2,4];default:return e.split(\" \").map(Number).filter(_.isInteger)}}i.resolve_line_dash=r;class u extends n.VisualProperties{get doit(){const e=this.line_color.get_value(),t=this.line_alpha.get_value(),i=this.line_width.get_value();return!(null==e||0==t||0==i)}apply(e){const{doit:t}=this;return t&&(this.set_value(e),e.stroke()),t}values(){return{color:this.line_color.get_value(),alpha:this.line_alpha.get_value(),width:this.line_width.get_value(),join:this.line_join.get_value(),cap:this.line_cap.get_value(),dash:this.line_dash.get_value(),offset:this.line_dash_offset.get_value()}}set_value(e){const t=this.line_color.get_value(),i=this.line_alpha.get_value();e.strokeStyle=(0,o.color2css)(t,i),e.lineWidth=this.line_width.get_value(),e.lineJoin=this.line_join.get_value(),e.lineCap=this.line_cap.get_value(),e.lineDash=r(this.line_dash.get_value()),e.lineDashOffset=this.line_dash_offset.get_value()}}i.Line=u,u.__name__=\"Line\";class c extends n.VisualUniforms{get doit(){const e=this.line_color.value,t=this.line_alpha.value,i=this.line_width.value;return!(0==e||0==t||0==i)}apply(e){const{doit:t}=this;return t&&(this.set_value(e),e.stroke()),t}values(){return{color:this.line_color.value,alpha:this.line_alpha.value,width:this.line_width.value,join:this.line_join.value,cap:this.line_cap.value,dash:this.line_dash.value,offset:this.line_dash_offset.value}}set_value(e){const t=this.line_color.value,i=this.line_alpha.value;e.strokeStyle=(0,o.color2css)(t,i),e.lineWidth=this.line_width.value,e.lineJoin=this.line_join.value,e.lineCap=this.line_cap.value,e.lineDash=r(this.line_dash.value),e.lineDashOffset=this.line_dash_offset.value}}i.LineScalar=c,c.__name__=\"LineScalar\";class d extends n.VisualUniforms{get doit(){const{line_color:e}=this;if(e.is_Scalar()&&0==e.value)return!1;const{line_alpha:t}=this;if(t.is_Scalar()&&0==t.value)return!1;const{line_width:i}=this;return!i.is_Scalar()||0!=i.value}apply(e,t){const{doit:i}=this;return i&&(this.set_vectorize(e,t),e.stroke()),i}values(e){return{color:this.line_color.get(e),alpha:this.line_alpha.get(e),width:this.line_width.get(e),join:this.line_join.get(e),cap:this.line_cap.get(e),dash:this.line_dash.get(e),offset:this.line_dash_offset.get(e)}}set_vectorize(e,t){const i=this.line_color.get(t),l=this.line_alpha.get(t),s=this.line_width.get(t),a=this.line_join.get(t),n=this.line_cap.get(t),h=this.line_dash.get(t),_=this.line_dash_offset.get(t);e.strokeStyle=(0,o.color2css)(i,l),e.lineWidth=s,e.lineJoin=a,e.lineCap=n,e.lineDash=r(h),e.lineDashOffset=_}}i.LineVector=d,d.__name__=\"LineVector\",u.prototype.type=\"line\",u.prototype.attrs=Object.keys(h.Line),c.prototype.type=\"line\",c.prototype.attrs=Object.keys(h.LineScalar),d.prototype.type=\"line\",d.prototype.attrs=Object.keys(h.LineVector)},\n function _(t,s,o,i,r){i();class e{constructor(t,s=\"\"){this.obj=t,this.prefix=s;const o=this;this._props=[];for(const i of this.attrs){const r=t.model.properties[s+i];r.change.connect((()=>this.update())),o[i]=r,this._props.push(r)}}*[Symbol.iterator](){yield*this._props}update(){}}o.VisualProperties=e,e.__name__=\"VisualProperties\";class p{constructor(t,s=\"\"){this.obj=t,this.prefix=s;for(const o of this.attrs)Object.defineProperty(this,o,{get:()=>t[s+o]})}*[Symbol.iterator](){for(const t of this.attrs)yield this.obj.model.properties[this.prefix+t]}update(){}}o.VisualUniforms=p,p.__name__=\"VisualUniforms\"},\n function _(e,l,t,a,c){a();const r=e(1),o=(0,r.__importStar)(e(18)),n=e(20),i=(0,r.__importStar)(e(21)),_=e(13);t.Line={line_color:[i.Nullable(i.Color),\"black\"],line_alpha:[i.Alpha,1],line_width:[i.Number,1],line_join:[n.LineJoin,\"bevel\"],line_cap:[n.LineCap,\"butt\"],line_dash:[i.Or(n.LineDash,i.Array(i.Number)),[]],line_dash_offset:[i.Number,0]},t.Fill={fill_color:[i.Nullable(i.Color),\"gray\"],fill_alpha:[i.Alpha,1]},t.Hatch={hatch_color:[i.Nullable(i.Color),\"black\"],hatch_alpha:[i.Alpha,1],hatch_scale:[i.Number,12],hatch_pattern:[i.Nullable(i.Or(n.HatchPatternType,i.String)),null],hatch_weight:[i.Number,1],hatch_extra:[i.Dict(i.AnyRef()),{}]},t.Text={text_color:[i.Nullable(i.Color),\"#444444\"],text_alpha:[i.Alpha,1],text_font:[o.Font,\"helvetica\"],text_font_size:[i.FontSize,\"16px\"],text_font_style:[n.FontStyle,\"normal\"],text_align:[n.TextAlign,\"left\"],text_baseline:[n.TextBaseline,\"bottom\"],text_line_height:[i.Number,1.2]},t.LineScalar={line_color:[o.ColorScalar,\"black\"],line_alpha:[o.NumberScalar,1],line_width:[o.NumberScalar,1],line_join:[o.LineJoinScalar,\"bevel\"],line_cap:[o.LineCapScalar,\"butt\"],line_dash:[o.LineDashScalar,[]],line_dash_offset:[o.NumberScalar,0]},t.FillScalar={fill_color:[o.ColorScalar,\"gray\"],fill_alpha:[o.NumberScalar,1]},t.HatchScalar={hatch_color:[o.ColorScalar,\"black\"],hatch_alpha:[o.NumberScalar,1],hatch_scale:[o.NumberScalar,12],hatch_pattern:[o.NullStringScalar,null],hatch_weight:[o.NumberScalar,1],hatch_extra:[o.AnyScalar,{}]},t.TextScalar={text_color:[o.ColorScalar,\"#444444\"],text_alpha:[o.NumberScalar,1],text_font:[o.FontScalar,\"helvetica\"],text_font_size:[o.FontSizeScalar,\"16px\"],text_font_style:[o.FontStyleScalar,\"normal\"],text_align:[o.TextAlignScalar,\"left\"],text_baseline:[o.TextBaselineScalar,\"bottom\"],text_line_height:[o.NumberScalar,1.2]},t.LineVector={line_color:[o.ColorSpec,\"black\"],line_alpha:[o.NumberSpec,1],line_width:[o.NumberSpec,1],line_join:[o.LineJoinSpec,\"bevel\"],line_cap:[o.LineCapSpec,\"butt\"],line_dash:[o.LineDashSpec,[]],line_dash_offset:[o.NumberSpec,0]},t.FillVector={fill_color:[o.ColorSpec,\"gray\"],fill_alpha:[o.NumberSpec,1]},t.HatchVector={hatch_color:[o.ColorSpec,\"black\"],hatch_alpha:[o.NumberSpec,1],hatch_scale:[o.NumberSpec,12],hatch_pattern:[o.NullStringSpec,null],hatch_weight:[o.NumberSpec,1],hatch_extra:[o.AnyScalar,{}]},t.TextVector={text_color:[o.ColorSpec,\"#444444\"],text_alpha:[o.NumberSpec,1],text_font:[o.FontSpec,\"helvetica\"],text_font_size:[o.FontSizeSpec,\"16px\"],text_font_style:[o.FontStyleSpec,\"normal\"],text_align:[o.TextAlignSpec,\"left\"],text_baseline:[o.TextBaselineSpec,\"bottom\"],text_line_height:[o.NumberSpec,1.2]},t.attrs_of=function(e,l,t,a=!1){const c={};for(const r of(0,_.keys)(t)){const t=`${l}${r}`,o=e[t];c[a?t:r]=o}return c}},\n function _(l,t,e,i,s){i();const a=l(1),o=l(47),r=(0,a.__importStar)(l(48)),_=l(22);class c extends o.VisualProperties{get doit(){const l=this.fill_color.get_value(),t=this.fill_alpha.get_value();return!(null==l||0==t)}apply(l,t){const{doit:e}=this;return e&&(this.set_value(l),l.fill(t)),e}values(){return{color:this.fill_color.get_value(),alpha:this.fill_alpha.get_value()}}set_value(l){const t=this.fill_color.get_value(),e=this.fill_alpha.get_value();l.fillStyle=(0,_.color2css)(t,e)}}e.Fill=c,c.__name__=\"Fill\";class h extends o.VisualUniforms{get doit(){const l=this.fill_color.value,t=this.fill_alpha.value;return!(0==l||0==t)}apply(l,t){const{doit:e}=this;return e&&(this.set_value(l),l.fill(t)),e}values(){return{color:this.fill_color.value,alpha:this.fill_alpha.value}}set_value(l){const t=this.fill_color.value,e=this.fill_alpha.value;l.fillStyle=(0,_.color2css)(t,e)}}e.FillScalar=h,h.__name__=\"FillScalar\";class u extends o.VisualUniforms{get doit(){const{fill_color:l}=this;if(l.is_Scalar()&&0==l.value)return!1;const{fill_alpha:t}=this;return!t.is_Scalar()||0!=t.value}apply(l,t,e){const{doit:i}=this;return i&&(this.set_vectorize(l,t),l.fill(e)),i}values(l){return{color:this.fill_color.get(l),alpha:this.fill_alpha.get(l)}}set_vectorize(l,t){const e=this.fill_color.get(t),i=this.fill_alpha.get(t);l.fillStyle=(0,_.color2css)(e,i)}}e.FillVector=u,u.__name__=\"FillVector\",c.prototype.type=\"fill\",c.prototype.attrs=Object.keys(r.Fill),h.prototype.type=\"fill\",h.prototype.attrs=Object.keys(r.FillScalar),u.prototype.type=\"fill\",u.prototype.attrs=Object.keys(r.FillVector)},\n function _(t,e,l,s,_){s();const i=t(1),a=t(47),o=(0,i.__importStar)(t(48)),n=t(22);class h extends a.VisualProperties{get doit(){const t=this.text_color.get_value(),e=this.text_alpha.get_value();return!(null==t||0==e)}values(){return{color:this.text_color.get_value(),alpha:this.text_alpha.get_value(),font:this.text_font.get_value(),font_size:this.text_font_size.get_value(),font_style:this.text_font_style.get_value(),align:this.text_align.get_value(),baseline:this.text_baseline.get_value(),line_height:this.text_line_height.get_value()}}set_value(t){const e=this.text_color.get_value(),l=this.text_alpha.get_value();t.fillStyle=(0,n.color2css)(e,l),t.font=this.font_value(),t.textAlign=this.text_align.get_value(),t.textBaseline=this.text_baseline.get_value()}font_value(){return`${this.text_font_style.get_value()} ${this.text_font_size.get_value()} ${this.text_font.get_value()}`}}l.Text=h,h.__name__=\"Text\";class x extends a.VisualUniforms{get doit(){const t=this.text_color.value,e=this.text_alpha.value;return!(0==t||0==e)}values(){return{color:this.text_color.value,alpha:this.text_alpha.value,font:this.text_font.value,font_size:this.text_font_size.value,font_style:this.text_font_style.value,align:this.text_align.value,baseline:this.text_baseline.value,line_height:this.text_line_height.value}}set_value(t){const e=this.text_color.value,l=this.text_alpha.value,s=this.font_value(),_=this.text_align.value,i=this.text_baseline.value;t.fillStyle=(0,n.color2css)(e,l),t.font=s,t.textAlign=_,t.textBaseline=i}font_value(){return`${this.text_font_style.value} ${this.text_font_size.value} ${this.text_font.value}`}}l.TextScalar=x,x.__name__=\"TextScalar\";class u extends a.VisualUniforms{values(t){return{color:this.text_color.get(t),alpha:this.text_alpha.get(t),font:this.text_font.get(t),font_size:this.text_font_size.get(t),font_style:this.text_font_style.get(t),align:this.text_align.get(t),baseline:this.text_baseline.get(t),line_height:this.text_line_height.get(t)}}get doit(){const{text_color:t}=this;if(t.is_Scalar()&&0==t.value)return!1;const{text_alpha:e}=this;return!e.is_Scalar()||0!=e.value}set_vectorize(t,e){const l=this.text_color.get(e),s=this.text_alpha.get(e),_=this.font_value(e),i=this.text_align.get(e),a=this.text_baseline.get(e);t.fillStyle=(0,n.color2css)(l,s),t.font=_,t.textAlign=i,t.textBaseline=a}font_value(t){return`${this.text_font_style.get(t)} ${this.text_font_size.get(t)} ${this.text_font.get(t)}`}}l.TextVector=u,u.__name__=\"TextVector\",h.prototype.type=\"text\",h.prototype.attrs=Object.keys(o.Text),x.prototype.type=\"text\",x.prototype.attrs=Object.keys(o.TextScalar),u.prototype.type=\"text\",u.prototype.attrs=Object.keys(o.TextVector)},\n function _(t,e,a,r,i){r();const h=t(1),s=t(47),n=t(52),c=(0,h.__importStar)(t(18)),_=(0,h.__importStar)(t(48));class l extends s.VisualProperties{constructor(){super(...arguments),this._update_iteration=0}update(){if(this._update_iteration++,this._hatch_image=null,!this.doit)return;const t=this.hatch_color.get_value(),e=this.hatch_alpha.get_value(),a=this.hatch_scale.get_value(),r=this.hatch_pattern.get_value(),i=this.hatch_weight.get_value(),h=t=>{this._hatch_image=t},s=this.hatch_extra.get_value()[r];if(null!=s){const r=s.get_pattern(t,e,a,i);if(r instanceof Promise){const{_update_iteration:t}=this;r.then((e=>{this._update_iteration==t&&(h(e),this.obj.request_render())}))}else h(r)}else{const s=this.obj.canvas.create_layer(),c=(0,n.get_pattern)(s,r,t,e,a,i);h(c)}}get doit(){const t=this.hatch_color.get_value(),e=this.hatch_alpha.get_value(),a=this.hatch_pattern.get_value();return!(null==t||0==e||\" \"==a||\"blank\"==a||null==a)}apply(t,e){const{doit:a}=this;return a&&(this.set_value(t),t.layer.undo_transform((()=>t.fill(e)))),a}set_value(t){const e=this.pattern(t);t.fillStyle=null!=e?e:\"transparent\"}pattern(t){const e=this._hatch_image;return null==e?null:t.createPattern(e,this.repetition())}repetition(){const t=this.hatch_pattern.get_value(),e=this.hatch_extra.get_value()[t];if(null==e)return\"repeat\";switch(e.repetition){case\"repeat\":return\"repeat\";case\"repeat_x\":return\"repeat-x\";case\"repeat_y\":return\"repeat-y\";case\"no_repeat\":return\"no-repeat\"}}}a.Hatch=l,l.__name__=\"Hatch\";class o extends s.VisualUniforms{constructor(){super(...arguments),this._static_doit=!1,this._update_iteration=0}_compute_static_doit(){const t=this.hatch_color.value,e=this.hatch_alpha.value,a=this.hatch_pattern.value;return!(null==t||0==e||\" \"==a||\"blank\"==a||null==a)}update(){this._update_iteration++;const t=this.hatch_color.length;if(this._hatch_image=new c.UniformScalar(null,t),this._static_doit=this._compute_static_doit(),!this._static_doit)return;const e=this.hatch_color.value,a=this.hatch_alpha.value,r=this.hatch_scale.value,i=this.hatch_pattern.value,h=this.hatch_weight.value,s=e=>{this._hatch_image=new c.UniformScalar(e,t)},_=this.hatch_extra.value[i];if(null!=_){const t=_.get_pattern(e,a,r,h);if(t instanceof Promise){const{_update_iteration:e}=this;t.then((t=>{this._update_iteration==e&&(s(t),this.obj.request_render())}))}else s(t)}else{const t=this.obj.canvas.create_layer(),c=(0,n.get_pattern)(t,i,e,a,r,h);s(c)}}get doit(){return this._static_doit}apply(t,e){const{doit:a}=this;return a&&(this.set_value(t),t.layer.undo_transform((()=>t.fill(e)))),a}set_value(t){var e;t.fillStyle=null!==(e=this.pattern(t))&&void 0!==e?e:\"transparent\"}pattern(t){const e=this._hatch_image.value;return null==e?null:t.createPattern(e,this.repetition())}repetition(){const t=this.hatch_pattern.value,e=this.hatch_extra.value[t];if(null==e)return\"repeat\";switch(e.repetition){case\"repeat\":return\"repeat\";case\"repeat_x\":return\"repeat-x\";case\"repeat_y\":return\"repeat-y\";case\"no_repeat\":return\"no-repeat\"}}}a.HatchScalar=o,o.__name__=\"HatchScalar\";class u extends s.VisualUniforms{constructor(){super(...arguments),this._static_doit=!1,this._update_iteration=0}_compute_static_doit(){const{hatch_color:t}=this;if(t.is_Scalar()&&0==t.value)return!1;const{hatch_alpha:e}=this;if(e.is_Scalar()&&0==e.value)return!1;const{hatch_pattern:a}=this;if(a.is_Scalar()){const t=a.value;if(\" \"==t||\"blank\"==t||null==t)return!1}return!0}update(){this._update_iteration++;const t=this.hatch_color.length;if(this._hatch_image=new c.UniformScalar(null,t),this._static_doit=this._compute_static_doit(),!this._static_doit)return;const e=(t,e,a,r,i,h)=>{const s=this.hatch_extra.value[t];if(null!=s){const t=s.get_pattern(e,a,r,i);if(t instanceof Promise){const{_update_iteration:e}=this;t.then((t=>{this._update_iteration==e&&(h(t),this.obj.request_render())}))}else h(t)}else{const s=this.obj.canvas.create_layer(),c=(0,n.get_pattern)(s,t,e,a,r,i);h(c)}};if(this.hatch_color.is_Scalar()&&this.hatch_alpha.is_Scalar()&&this.hatch_scale.is_Scalar()&&this.hatch_pattern.is_Scalar()&&this.hatch_weight.is_Scalar()){const a=this.hatch_color.value,r=this.hatch_alpha.value,i=this.hatch_scale.value;e(this.hatch_pattern.value,a,r,i,this.hatch_weight.value,(e=>{this._hatch_image=new c.UniformScalar(e,t)}))}else{const a=new Array(t);a.fill(null),this._hatch_image=new c.UniformVector(a);for(let r=0;r{a[r]=t}))}}}get doit(){return this._static_doit}apply(t,e,a){const{doit:r}=this;return r&&(this.set_vectorize(t,e),t.layer.undo_transform((()=>t.fill(a)))),r}set_vectorize(t,e){var a;t.fillStyle=null!==(a=this.pattern(t,e))&&void 0!==a?a:\"transparent\"}pattern(t,e){const a=this._hatch_image.get(e);return null==a?null:t.createPattern(a,this.repetition(e))}repetition(t){const e=this.hatch_pattern.get(t),a=this.hatch_extra.value[e];if(null==a)return\"repeat\";switch(a.repetition){case\"repeat\":return\"repeat\";case\"repeat_x\":return\"repeat-x\";case\"repeat_y\":return\"repeat-y\";case\"no_repeat\":return\"no-repeat\"}}}a.HatchVector=u,u.__name__=\"HatchVector\",l.prototype.type=\"hatch\",l.prototype.attrs=Object.keys(_.Hatch),o.prototype.type=\"hatch\",o.prototype.attrs=Object.keys(_.HatchScalar),u.prototype.type=\"hatch\",u.prototype.attrs=Object.keys(_.HatchVector)},\n function _(e,o,a,s,r){s();const i=e(22);function l(e,o,a){e.moveTo(0,a+.5),e.lineTo(o,a+.5),e.stroke()}function n(e,o,a){e.moveTo(a+.5,0),e.lineTo(a+.5,o),e.stroke()}function t(e,o){e.moveTo(0,o),e.lineTo(o,0),e.stroke(),e.moveTo(0,0),e.lineTo(o,o),e.stroke()}a.hatch_aliases={\" \":\"blank\",\".\":\"dot\",o:\"ring\",\"-\":\"horizontal_line\",\"|\":\"vertical_line\",\"+\":\"cross\",'\"':\"horizontal_dash\",\":\":\"vertical_dash\",\"@\":\"spiral\",\"/\":\"right_diagonal_line\",\"\\\\\":\"left_diagonal_line\",x:\"diagonal_cross\",\",\":\"right_diagonal_dash\",\"`\":\"left_diagonal_dash\",v:\"horizontal_wave\",\">\":\"vertical_wave\",\"*\":\"criss_cross\"},a.get_pattern=function(e,o,s,r,c,k){return e.resize(c,c),e.prepare(),function(e,o,s,r,c,k){var _;const T=c,v=T/2,h=v/2,d=(0,i.color2css)(s,r);switch(e.strokeStyle=d,e.fillStyle=d,e.lineCap=\"square\",e.lineWidth=k,null!==(_=a.hatch_aliases[o])&&void 0!==_?_:o){case\"blank\":break;case\"dot\":e.arc(v,v,v/2,0,2*Math.PI,!0),e.fill();break;case\"ring\":e.arc(v,v,v/2,0,2*Math.PI,!0),e.stroke();break;case\"horizontal_line\":l(e,T,v);break;case\"vertical_line\":n(e,T,v);break;case\"cross\":l(e,T,v),n(e,T,v);break;case\"horizontal_dash\":l(e,v,v);break;case\"vertical_dash\":n(e,v,v);break;case\"spiral\":{const o=T/30;e.moveTo(v,v);for(let a=0;a<360;a++){const s=.1*a,r=v+o*s*Math.cos(s),i=v+o*s*Math.sin(s);e.lineTo(r,i)}e.stroke();break}case\"right_diagonal_line\":e.moveTo(.5-h,T),e.lineTo(h+.5,0),e.stroke(),e.moveTo(h+.5,T),e.lineTo(3*h+.5,0),e.stroke(),e.moveTo(3*h+.5,T),e.lineTo(5*h+.5,0),e.stroke(),e.stroke();break;case\"left_diagonal_line\":e.moveTo(h+.5,T),e.lineTo(.5-h,0),e.stroke(),e.moveTo(3*h+.5,T),e.lineTo(h+.5,0),e.stroke(),e.moveTo(5*h+.5,T),e.lineTo(3*h+.5,0),e.stroke(),e.stroke();break;case\"diagonal_cross\":t(e,T);break;case\"right_diagonal_dash\":e.moveTo(h+.5,3*h+.5),e.lineTo(3*h+.5,h+.5),e.stroke();break;case\"left_diagonal_dash\":e.moveTo(h+.5,h+.5),e.lineTo(3*h+.5,3*h+.5),e.stroke();break;case\"horizontal_wave\":e.moveTo(0,h),e.lineTo(v,3*h),e.lineTo(T,h),e.stroke();break;case\"vertical_wave\":e.moveTo(h,0),e.lineTo(3*h,v),e.lineTo(h,T),e.stroke();break;case\"criss_cross\":t(e,T),l(e,T,v),n(e,T,v)}}(e.ctx,o,s,r,c,k),e.canvas}},\n function _(e,t,s,n,c){var a;n();const i=e(14),r=e(8),l=e(13),o=e(26),_=e(19);class h extends i.HasProps{constructor(e){super(e)}get is_syncable(){return this.syncable}[o.equals](e,t){return t.eq(this.id,e.id)&&super[o.equals](e,t)}initialize(){super.initialize(),this._js_callbacks=new Map}connect_signals(){super.connect_signals(),this._update_property_callbacks(),this.connect(this.properties.js_property_callbacks.change,(()=>this._update_property_callbacks())),this.connect(this.properties.js_event_callbacks.change,(()=>this._update_event_callbacks())),this.connect(this.properties.subscribed_events.change,(()=>this._update_event_callbacks()))}_process_event(e){var t;for(const s of null!==(t=this.js_event_callbacks[e.event_name])&&void 0!==t?t:[])s.execute(e);null!=this.document&&this.subscribed_events.some((t=>t==e.event_name))&&this.document.event_manager.send_event(e)}trigger_event(e){null!=this.document&&(e.origin=this,this.document.event_manager.trigger(e))}_update_event_callbacks(){null!=this.document?this.document.event_manager.subscribed_models.add(this):_.logger.warn(\"WARNING: Document not defined for updating event callbacks\")}_update_property_callbacks(){const e=e=>{const[t,s=null]=e.split(\":\");return null!=s?this.properties[s][t]:this[t]};for(const[t,s]of this._js_callbacks){const n=e(t);for(const e of s)this.disconnect(n,e)}this._js_callbacks.clear();for(const[t,s]of(0,l.entries)(this.js_property_callbacks)){const n=s.map((e=>()=>e.execute(this)));this._js_callbacks.set(t,n);const c=e(t);for(const e of n)this.connect(c,e)}}_doc_attached(){(0,l.isEmpty)(this.js_event_callbacks)&&0==this.subscribed_events.length||this._update_event_callbacks()}_doc_detached(){this.document.event_manager.subscribed_models.delete(this)}select(e){if((0,r.isString)(e))return[...this.references()].filter((t=>t instanceof h&&t.name===e));if(e.prototype instanceof i.HasProps)return[...this.references()].filter((t=>t instanceof e));throw new Error(\"invalid selector\")}select_one(e){const t=this.select(e);switch(t.length){case 0:return null;case 1:return t[0];default:throw new Error(\"found more than one object matching given selector\")}}}s.Model=h,a=h,h.__name__=\"Model\",a.define((({Any:e,Unknown:t,Boolean:s,String:n,Array:c,Dict:a,Nullable:i})=>({tags:[c(t),[]],name:[i(n),null],js_property_callbacks:[a(c(e)),{}],js_event_callbacks:[a(c(e)),{}],subscribed_events:[c(n),[]],syncable:[s,!0]})))},\n function _(e,t,s,a,r){var c,n;a();const _=e(12),o=e(53),i=e(55),l=e(59),u=e(61),g=e(62),h=e(57),p=e(63),m=e(67);class x{constructor(e,t){this.x_scale=e,this.y_scale=t,this.x_source=this.x_scale.source_range,this.y_source=this.y_scale.source_range,this.ranges=[this.x_source,this.y_source],this.scales=[this.x_scale,this.y_scale]}map_to_screen(e,t){return[this.x_scale.v_compute(e),this.y_scale.v_compute(t)]}map_from_screen(e,t){return[this.x_scale.v_invert(e),this.y_scale.v_invert(t)]}}s.CoordinateTransform=x,x.__name__=\"CoordinateTransform\";class y extends o.Model{constructor(e){super(e)}get x_ranges(){return new Map([[\"default\",this.x_source]])}get y_ranges(){return new Map([[\"default\",this.y_source]])}_get_scale(e,t,s){if(e instanceof m.FactorRange!=t instanceof g.CategoricalScale)throw new Error(`Range ${e.type} is incompatible is Scale ${t.type}`);t instanceof u.LogScale&&e instanceof p.DataRange1d&&(e.scale_hint=\"log\");const a=t.clone();return a.setv({source_range:e,target_range:s}),a}get_transform(e){const{x_source:t,x_scale:s,x_target:a}=this,r=this._get_scale(t,s,a),{y_source:c,y_scale:n,y_target:_}=this,o=this._get_scale(c,n,_),i=new v({source_scale:r,source_range:r.source_range,target_scale:e.x_scale,target_range:e.x_target}),l=new v({source_scale:o,source_range:o.source_range,target_scale:e.y_scale,target_range:e.y_target});return new x(i,l)}}s.CoordinateMapping=y,c=y,y.__name__=\"CoordinateMapping\",c.define((({Ref:e})=>({x_source:[e(h.Range),()=>new p.DataRange1d],y_source:[e(h.Range),()=>new p.DataRange1d],x_scale:[e(i.Scale),()=>new l.LinearScale],y_scale:[e(i.Scale),()=>new l.LinearScale],x_target:[e(h.Range)],y_target:[e(h.Range)]})));class v extends i.Scale{constructor(e){super(e)}get s_compute(){const e=this.source_scale.s_compute,t=this.target_scale.s_compute;return s=>t(e(s))}get s_invert(){const e=this.source_scale.s_invert,t=this.target_scale.s_invert;return s=>e(t(s))}compute(e){return this.s_compute(e)}v_compute(e){const{s_compute:t}=this;return(0,_.map)(e,t)}invert(e){return this.s_invert(e)}v_invert(e){const{s_invert:t}=this;return(0,_.map)(e,t)}}s.CompositeScale=v,n=v,v.__name__=\"CompositeScale\",n.internal((({Ref:e})=>({source_scale:[e(i.Scale)],target_scale:[e(i.Scale)]})))},\n function _(e,t,r,n,s){var _;n();const a=e(56),c=e(57),o=e(58),i=e(24);class u extends a.Transform{constructor(e){super(e)}compute(e){return this.s_compute(e)}v_compute(e){const t=new i.ScreenArray(e.length),{s_compute:r}=this;for(let n=0;n({source_range:[e(c.Range)],target_range:[e(o.Range1d)]})))},\n function _(n,s,o,r,c){r();const e=n(53);class t extends e.Model{constructor(n){super(n)}}o.Transform=t,t.__name__=\"Transform\"},\n function _(e,t,n,i,s){var r;i();const a=e(53);class l extends a.Model{constructor(e){super(e),this.have_updated_interactively=!1}get is_reversed(){return this.start>this.end}get is_valid(){return isFinite(this.min)&&isFinite(this.max)}get span(){return Math.abs(this.end-this.start)}}n.Range=l,r=l,l.__name__=\"Range\",r.define((({Number:e,Tuple:t,Or:n,Auto:i,Nullable:s})=>({bounds:[s(n(t(s(e),s(e)),i)),null],min_interval:[s(e),null],max_interval:[s(e),null]}))),r.internal((({Array:e,AnyRef:t})=>({plots:[e(t()),[]]})))},\n function _(t,e,s,n,r){var a;n();const i=t(57);class _ extends i.Range{constructor(t){super(t)}_set_auto_bounds(){if(\"auto\"==this.bounds){const t=Math.min(this._reset_start,this._reset_end),e=Math.max(this._reset_start,this._reset_end);this.setv({bounds:[t,e]},{silent:!0})}}initialize(){super.initialize(),this._set_auto_bounds()}get min(){return Math.min(this.start,this.end)}get max(){return Math.max(this.start,this.end)}reset(){this._set_auto_bounds();const{_reset_start:t,_reset_end:e}=this;this.start!=t||this.end!=e?this.setv({start:t,end:e}):this.change.emit()}map(t){return new _({start:t(this.start),end:t(this.end)})}widen(t){let{start:e,end:s}=this;return this.is_reversed?(e+=t,s-=t):(e-=t,s+=t),new _({start:e,end:s})}}s.Range1d=_,a=_,_.__name__=\"Range1d\",a.define((({Number:t,Nullable:e})=>({start:[t,0],end:[t,1],reset_start:[e(t),null,{on_update(t,e){e._reset_start=null!=t?t:e.start}}],reset_end:[e(t),null,{on_update(t,e){e._reset_end=null!=t?t:e.end}}]})))},\n function _(t,e,n,r,s){r();const a=t(60);class _ extends a.ContinuousScale{constructor(t){super(t)}get s_compute(){const[t,e]=this._linear_compute_state();return n=>t*n+e}get s_invert(){const[t,e]=this._linear_compute_state();return n=>(n-e)/t}_linear_compute_state(){const t=this.source_range.start,e=this.source_range.end,n=this.target_range.start,r=(this.target_range.end-n)/(e-t);return[r,-r*t+n]}}n.LinearScale=_,_.__name__=\"LinearScale\"},\n function _(n,c,o,s,e){s();const t=n(55);class u extends t.Scale{constructor(n){super(n)}}o.ContinuousScale=u,u.__name__=\"ContinuousScale\"},\n function _(t,e,a,o,s){o();const r=t(60);class n extends r.ContinuousScale{constructor(t){super(t)}get s_compute(){const[t,e,a,o]=this._compute_state();return s=>{if(0==a)return 0;{const r=(Math.log(s)-o)/a;return isFinite(r)?r*t+e:NaN}}}get s_invert(){const[t,e,a,o]=this._compute_state();return s=>{const r=(s-e)/t;return Math.exp(a*r+o)}}_get_safe_factor(t,e){let a=t<0?0:t,o=e<0?0:e;if(a==o)if(0==a)[a,o]=[1,10];else{const t=Math.log(a)/Math.log(10);a=10**Math.floor(t),o=Math.ceil(t)!=Math.floor(t)?10**Math.ceil(t):10**(Math.ceil(t)+1)}return[a,o]}_compute_state(){const t=this.source_range.start,e=this.source_range.end,a=this.target_range.start,o=this.target_range.end-a,[s,r]=this._get_safe_factor(t,e);let n,c;0==s?(n=Math.log(r),c=0):(n=Math.log(r)-Math.log(s),c=Math.log(s));return[o,a,n,c]}}a.LogScale=n,n.__name__=\"LogScale\"},\n function _(t,e,c,a,s){a();const n=t(55),r=t(59),{_linear_compute_state:o}=r.LinearScale.prototype;class l extends n.Scale{constructor(t){super(t)}get s_compute(){const[t,e]=o.call(this),c=this.source_range;return a=>t*c.synthetic(a)+e}get s_invert(){const[t,e]=o.call(this);return c=>(c-e)/t}}c.CategoricalScale=l,l.__name__=\"CategoricalScale\"},\n function _(t,i,n,e,a){e();const s=t(1);var l;const _=t(64),o=t(20),r=t(9),h=t(19),d=(0,s.__importStar)(t(65)),u=t(66);class g extends _.DataRange{constructor(t){super(t),this.have_updated_interactively=!1}initialize(){super.initialize(),this._initial_start=this.start,this._initial_end=this.end,this._initial_range_padding=this.range_padding,this._initial_range_padding_units=this.range_padding_units,this._initial_follow=this.follow,this._initial_follow_interval=this.follow_interval,this._initial_default_span=this.default_span,this._plot_bounds=new Map}get min(){return Math.min(this.start,this.end)}get max(){return Math.max(this.start,this.end)}computed_renderers(){const{renderers:t,names:i}=this,n=(0,r.concat)(this.plots.map((t=>t.data_renderers)));return(0,u.compute_renderers)(0==t.length?\"auto\":t,n,i)}_compute_plot_bounds(t,i){let n=d.empty();for(const e of t){const t=i.get(e);null==t||!e.visible&&this.only_visible||(n=d.union(n,t))}return n}adjust_bounds_for_aspect(t,i){const n=d.empty();let e=t.x1-t.x0;e<=0&&(e=1);let a=t.y1-t.y0;a<=0&&(a=1);const s=.5*(t.x1+t.x0),l=.5*(t.y1+t.y0);return el&&(\"start\"==this.follow?a=e+s*l:\"end\"==this.follow&&(e=a-s*l)),[e,a]}update(t,i,n,e){if(this.have_updated_interactively)return;const a=this.computed_renderers();let s=this._compute_plot_bounds(a,t);null!=e&&(s=this.adjust_bounds_for_aspect(s,e)),this._plot_bounds.set(n,s);const[l,_]=this._compute_min_max(this._plot_bounds.entries(),i);let[o,r]=this._compute_range(l,_);null!=this._initial_start&&(\"log\"==this.scale_hint?this._initial_start>0&&(o=this._initial_start):o=this._initial_start),null!=this._initial_end&&(\"log\"==this.scale_hint?this._initial_end>0&&(r=this._initial_end):r=this._initial_end);let h=!1;\"auto\"==this.bounds&&(this.setv({bounds:[o,r]},{silent:!0}),h=!0);const[d,u]=[this.start,this.end];if(o!=d||r!=u){const t={};o!=d&&(t.start=o),r!=u&&(t.end=r),this.setv(t),h=!1}h&&this.change.emit()}reset(){this.have_updated_interactively=!1,this.setv({range_padding:this._initial_range_padding,range_padding_units:this._initial_range_padding_units,follow:this._initial_follow,follow_interval:this._initial_follow_interval,default_span:this._initial_default_span},{silent:!0}),this.change.emit()}}n.DataRange1d=g,l=g,g.__name__=\"DataRange1d\",l.define((({Boolean:t,Number:i,Nullable:n})=>({start:[i],end:[i],range_padding:[i,.1],range_padding_units:[o.PaddingUnits,\"percent\"],flipped:[t,!1],follow:[n(o.StartEnd),null],follow_interval:[n(i),null],default_span:[i,2],only_visible:[t,!1]}))),l.internal((({Enum:t})=>({scale_hint:[t(\"log\",\"auto\"),\"auto\"]})))},\n function _(e,n,a,r,s){var t;r();const c=e(57);class _ extends c.Range{constructor(e){super(e)}}a.DataRange=_,t=_,_.__name__=\"DataRange\",t.define((({String:e,Array:n,AnyRef:a})=>({names:[n(e),[]],renderers:[n(a()),[]]})))},\n function _(t,i,e,h,r){h();const s=t(24),n=t(26),{min:x,max:y}=Math;e.empty=function(){return{x0:1/0,y0:1/0,x1:-1/0,y1:-1/0}},e.positive_x=function(){return{x0:Number.MIN_VALUE,y0:-1/0,x1:1/0,y1:1/0}},e.positive_y=function(){return{x0:-1/0,y0:Number.MIN_VALUE,x1:1/0,y1:1/0}},e.union=function(t,i){return{x0:x(t.x0,i.x0),x1:y(t.x1,i.x1),y0:x(t.y0,i.y0),y1:y(t.y1,i.y1)}};class o{constructor(t){if(null==t)this.x0=0,this.y0=0,this.x1=0,this.y1=0;else if(\"x0\"in t){const{x0:i,y0:e,x1:h,y1:r}=t;if(!(i<=h&&e<=r))throw new Error(`invalid bbox {x0: ${i}, y0: ${e}, x1: ${h}, y1: ${r}}`);this.x0=i,this.y0=e,this.x1=h,this.y1=r}else if(\"x\"in t){const{x:i,y:e,width:h,height:r}=t;if(!(h>=0&&r>=0))throw new Error(`invalid bbox {x: ${i}, y: ${e}, width: ${h}, height: ${r}}`);this.x0=i,this.y0=e,this.x1=i+h,this.y1=e+r}else{let i,e,h,r;if(\"width\"in t)if(\"left\"in t)i=t.left,e=i+t.width;else if(\"right\"in t)e=t.right,i=e-t.width;else{const h=t.width/2;i=t.hcenter-h,e=t.hcenter+h}else i=t.left,e=t.right;if(\"height\"in t)if(\"top\"in t)h=t.top,r=h+t.height;else if(\"bottom\"in t)r=t.bottom,h=r-t.height;else{const i=t.height/2;h=t.vcenter-i,r=t.vcenter+i}else h=t.top,r=t.bottom;if(!(i<=e&&h<=r))throw new Error(`invalid bbox {left: ${i}, top: ${h}, right: ${e}, bottom: ${r}}`);this.x0=i,this.y0=h,this.x1=e,this.y1=r}}static from_rect({left:t,right:i,top:e,bottom:h}){return new o({x0:Math.min(t,i),y0:Math.min(e,h),x1:Math.max(t,i),y1:Math.max(e,h)})}equals(t){return this.x0==t.x0&&this.y0==t.y0&&this.x1==t.x1&&this.y1==t.y1}[n.equals](t,i){return i.eq(this.x0,t.x0)&&i.eq(this.y0,t.y0)&&i.eq(this.x1,t.x1)&&i.eq(this.y1,t.y1)}toString(){return`BBox({left: ${this.left}, top: ${this.top}, width: ${this.width}, height: ${this.height}})`}get left(){return this.x0}get top(){return this.y0}get right(){return this.x1}get bottom(){return this.y1}get p0(){return[this.x0,this.y0]}get p1(){return[this.x1,this.y1]}get x(){return this.x0}get y(){return this.y0}get width(){return this.x1-this.x0}get height(){return this.y1-this.y0}get size(){return{width:this.width,height:this.height}}get rect(){const{x0:t,y0:i,x1:e,y1:h}=this;return{p0:{x:t,y:i},p1:{x:e,y:i},p2:{x:e,y:h},p3:{x:t,y:h}}}get box(){const{x:t,y:i,width:e,height:h}=this;return{x:t,y:i,width:e,height:h}}get h_range(){return{start:this.x0,end:this.x1}}get v_range(){return{start:this.y0,end:this.y1}}get ranges(){return[this.h_range,this.v_range]}get aspect(){return this.width/this.height}get hcenter(){return(this.left+this.right)/2}get vcenter(){return(this.top+this.bottom)/2}get area(){return this.width*this.height}relative(){const{width:t,height:i}=this;return new o({x:0,y:0,width:t,height:i})}translate(t,i){const{x:e,y:h,width:r,height:s}=this;return new o({x:t+e,y:i+h,width:r,height:s})}relativize(t,i){return[t-this.x,i-this.y]}contains(t,i){return this.x0<=t&&t<=this.x1&&this.y0<=i&&i<=this.y1}clip(t,i){return tthis.x1&&(t=this.x1),ithis.y1&&(i=this.y1),[t,i]}grow_by(t){return new o({left:this.left-t,right:this.right+t,top:this.top-t,bottom:this.bottom+t})}shrink_by(t){return new o({left:this.left+t,right:this.right-t,top:this.top+t,bottom:this.bottom-t})}union(t){return new o({x0:x(this.x0,t.x0),y0:x(this.y0,t.y0),x1:y(this.x1,t.x1),y1:y(this.y1,t.y1)})}intersection(t){return this.intersects(t)?new o({x0:y(this.x0,t.x0),y0:y(this.y0,t.y0),x1:x(this.x1,t.x1),y1:x(this.y1,t.y1)}):null}intersects(t){return!(t.x1this.x1||t.y1this.y1)}get xview(){return{compute:t=>this.left+t,v_compute:t=>{const i=new s.ScreenArray(t.length),e=this.left;for(let h=0;hthis.bottom-t,v_compute:t=>{const i=new s.ScreenArray(t.length),e=this.bottom;for(let h=0;h0&&(r=r.filter((n=>(0,l.includes)(t,n.name)))),r}},\n function _(t,n,e,i,s){var r;i();const a=t(57),o=t(20),g=t(21),p=t(24),c=t(9),l=t(8),u=t(11);function h(t,n,e=0){const i=new Map;for(let s=0;sa.get(t).value)));r.set(t,{value:l/s,mapping:a}),o+=s+n+p}return[r,(a.size-1)*n+g]}function _(t,n,e,i,s=0){var r;const a=new Map,o=new Map;for(const[n,e,i]of t){const t=null!==(r=o.get(n))&&void 0!==r?r:[];o.set(n,[...t,[e,i]])}let g=s,p=0;for(const[t,s]of o){const r=s.length,[o,l]=d(s,e,i,g);p+=l;const u=(0,c.sum)(s.map((([t])=>o.get(t).value)));a.set(t,{value:u/r,mapping:o}),g+=r+n+l}return[a,(o.size-1)*n+p]}e.Factor=(0,g.Or)(g.String,(0,g.Tuple)(g.String,g.String),(0,g.Tuple)(g.String,g.String,g.String)),e.FactorSeq=(0,g.Or)((0,g.Array)(g.String),(0,g.Array)((0,g.Tuple)(g.String,g.String)),(0,g.Array)((0,g.Tuple)(g.String,g.String,g.String))),e.map_one_level=h,e.map_two_levels=d,e.map_three_levels=_;class f extends a.Range{constructor(t){super(t)}get min(){return this.start}get max(){return this.end}initialize(){super.initialize(),this._init(!0)}connect_signals(){super.connect_signals(),this.connect(this.properties.factors.change,(()=>this.reset())),this.connect(this.properties.factor_padding.change,(()=>this.reset())),this.connect(this.properties.group_padding.change,(()=>this.reset())),this.connect(this.properties.subgroup_padding.change,(()=>this.reset())),this.connect(this.properties.range_padding.change,(()=>this.reset())),this.connect(this.properties.range_padding_units.change,(()=>this.reset()))}reset(){this._init(!1),this.change.emit()}_lookup(t){switch(t.length){case 1:{const[n]=t,e=this._mapping.get(n);return null!=e?e.value:NaN}case 2:{const[n,e]=t,i=this._mapping.get(n);if(null!=i){const t=i.mapping.get(e);if(null!=t)return t.value}return NaN}case 3:{const[n,e,i]=t,s=this._mapping.get(n);if(null!=s){const t=s.mapping.get(e);if(null!=t){const n=t.mapping.get(i);if(null!=n)return n.value}}return NaN}default:(0,u.unreachable)()}}synthetic(t){if((0,l.isNumber)(t))return t;if((0,l.isString)(t))return this._lookup([t]);let n=0;const e=t[t.length-1];return(0,l.isNumber)(e)&&(n=e,t=t.slice(0,-1)),this._lookup(t)+n}v_synthetic(t){const n=t.length,e=new p.ScreenArray(n);for(let i=0;i{if((0,c.every)(this.factors,l.isString)){const t=this.factors,[n,e]=h(t,this.factor_padding);return{levels:1,mapping:n,tops:null,mids:null,inside_padding:e}}if((0,c.every)(this.factors,(t=>(0,l.isArray)(t)&&2==t.length&&(0,l.isString)(t[0])&&(0,l.isString)(t[1])))){const t=this.factors,[n,e]=d(t,this.group_padding,this.factor_padding),i=[...n.keys()];return{levels:2,mapping:n,tops:i,mids:null,inside_padding:e}}if((0,c.every)(this.factors,(t=>(0,l.isArray)(t)&&3==t.length&&(0,l.isString)(t[0])&&(0,l.isString)(t[1])&&(0,l.isString)(t[2])))){const t=this.factors,[n,e]=_(t,this.group_padding,this.subgroup_padding,this.factor_padding),i=[...n.keys()],s=[];for(const[t,e]of n)for(const n of e.mapping.keys())s.push([t,n]);return{levels:3,mapping:n,tops:i,mids:s,inside_padding:e}}(0,u.unreachable)()})();this._mapping=e,this.tops=i,this.mids=s;let a=0,o=this.factors.length+r;if(\"percent\"==this.range_padding_units){const t=(o-a)*this.range_padding/2;a-=t,o+=t}else a-=this.range_padding,o+=this.range_padding;this.setv({start:a,end:o,levels:n},{silent:t}),\"auto\"==this.bounds&&this.setv({bounds:[a,o]},{silent:!0})}}e.FactorRange=f,r=f,f.__name__=\"FactorRange\",r.define((({Number:t})=>({factors:[e.FactorSeq,[]],factor_padding:[t,0],subgroup_padding:[t,.8],group_padding:[t,1.4],range_padding:[t,0],range_padding_units:[o.PaddingUnits,\"percent\"],start:[t],end:[t]}))),r.internal((({Number:t,String:n,Array:e,Tuple:i,Nullable:s})=>({levels:[t],mids:[s(e(i(n,n))),null],tops:[s(e(n)),null]})))},\n function _(t,e,s,a,i){a();const n=t(1);var _;const r=t(69),o=t(112),l=t(48),d=t(20),h=t(24),c=t(113),u=(0,n.__importStar)(t(18)),v=t(10);class p extends r.DataAnnotationView{async lazy_initialize(){await super.lazy_initialize();const{start:t,end:e}=this.model;null!=t&&(this.start=await(0,c.build_view)(t,{parent:this})),null!=e&&(this.end=await(0,c.build_view)(e,{parent:this}))}set_data(t){var e,s;super.set_data(t),null===(e=this.start)||void 0===e||e.set_data(t),null===(s=this.end)||void 0===s||s.set_data(t)}remove(){var t,e;null===(t=this.start)||void 0===t||t.remove(),null===(e=this.end)||void 0===e||e.remove(),super.remove()}map_data(){const{frame:t}=this.plot_view;\"data\"==this.model.start_units?(this._sx_start=this.coordinates.x_scale.v_compute(this._x_start),this._sy_start=this.coordinates.y_scale.v_compute(this._y_start)):(this._sx_start=t.bbox.xview.v_compute(this._x_start),this._sy_start=t.bbox.yview.v_compute(this._y_start)),\"data\"==this.model.end_units?(this._sx_end=this.coordinates.x_scale.v_compute(this._x_end),this._sy_end=this.coordinates.y_scale.v_compute(this._y_end)):(this._sx_end=t.bbox.xview.v_compute(this._x_end),this._sy_end=t.bbox.yview.v_compute(this._y_end));const{_sx_start:e,_sy_start:s,_sx_end:a,_sy_end:i}=this,n=e.length,_=this._angles=new h.ScreenArray(n);for(let t=0;t({x_start:[u.XCoordinateSpec,{field:\"x_start\"}],y_start:[u.YCoordinateSpec,{field:\"y_start\"}],start_units:[d.SpatialUnits,\"data\"],start:[e(t(o.ArrowHead)),null],x_end:[u.XCoordinateSpec,{field:\"x_end\"}],y_end:[u.YCoordinateSpec,{field:\"y_end\"}],end_units:[d.SpatialUnits,\"data\"],end:[e(t(o.ArrowHead)),()=>new o.OpenHead]})))},\n function _(t,e,n,s,a){s();const o=t(1);var i;const c=t(40),r=t(70),_=t(75),l=t(78),h=(0,o.__importStar)(t(18));class d extends c.AnnotationView{constructor(){super(...arguments),this._initial_set_data=!1}connect_signals(){super.connect_signals();const t=()=>{this.set_data(this.model.source),this._rerender()};this.connect(this.model.change,t),this.connect(this.model.source.streaming,t),this.connect(this.model.source.patching,t),this.connect(this.model.source.change,t)}_rerender(){this.request_render()}set_data(t){const e=this;for(const n of this.model)if(n instanceof h.VectorSpec||n instanceof h.ScalarSpec)if(n instanceof h.BaseCoordinateSpec){const s=n.array(t);e[`_${n.attr}`]=s}else{const s=n.uniform(t);e[`${n.attr}`]=s}this.plot_model.use_map&&(null!=e._x&&l.inplace.project_xy(e._x,e._y),null!=e._xs&&l.inplace.project_xsys(e._xs,e._ys));for(const t of this.visuals)t.update()}_render(){this._initial_set_data||(this.set_data(this.model.source),this._initial_set_data=!0),this.map_data(),this.paint(this.layer.ctx)}}n.DataAnnotationView=d,d.__name__=\"DataAnnotationView\";class u extends c.Annotation{constructor(t){super(t)}}n.DataAnnotation=u,i=u,u.__name__=\"DataAnnotation\",i.define((({Ref:t})=>({source:[t(r.ColumnarDataSource),()=>new _.ColumnDataSource]})))},\n function _(t,e,n,s,a){var i;s();const r=t(71),l=t(15),c=t(19),o=t(73),h=t(8),u=t(9),g=t(13),d=t(72),_=t(74),m=t(29);class w extends r.DataSource{constructor(t){super(t),this.selection_manager=new o.SelectionManager(this)}get_array(t){let e=this.data[t];return null==e?this.data[t]=e=[]:(0,h.isArray)(e)||(this.data[t]=e=Array.from(e)),e}initialize(){super.initialize(),this._select=new l.Signal0(this,\"select\"),this.inspect=new l.Signal(this,\"inspect\"),this.streaming=new l.Signal0(this,\"streaming\"),this.patching=new l.Signal(this,\"patching\")}get_column(t){const e=this.data[t];return null!=e?e:null}columns(){return(0,g.keys)(this.data)}get_length(t=!0){const e=(0,u.uniq)((0,g.values)(this.data).map((t=>(0,m.is_NDArray)(t)?t.shape[0]:t.length)));switch(e.length){case 0:return null;case 1:return e[0];default:{const n=\"data source has columns of inconsistent lengths\";if(t)return c.logger.warn(n),e.sort()[0];throw new Error(n)}}}get length(){var t;return null!==(t=this.get_length())&&void 0!==t?t:0}clear(){const t={};for(const e of this.columns())t[e]=new this.data[e].constructor(0);this.data=t}}n.ColumnarDataSource=w,i=w,w.__name__=\"ColumnarDataSource\",i.define((({Ref:t})=>({selection_policy:[t(_.SelectionPolicy),()=>new _.UnionRenderers]}))),i.internal((({AnyRef:t})=>({inspected:[t(),()=>new d.Selection]})))},\n function _(e,c,n,t,o){var a;t();const s=e(53),r=e(72);class l extends s.Model{constructor(e){super(e)}}n.DataSource=l,a=l,l.__name__=\"DataSource\",a.define((({Ref:e})=>({selected:[e(r.Selection),()=>new r.Selection]})))},\n function _(i,e,s,t,n){var l;t();const c=i(53),d=i(9),h=i(13);class _ extends c.Model{constructor(i){super(i)}get_view(){return this.view}get selected_glyph(){return this.selected_glyphs.length>0?this.selected_glyphs[0]:null}add_to_selected_glyphs(i){this.selected_glyphs.push(i)}update(i,e=!0,s=\"replace\"){switch(s){case\"replace\":this.indices=i.indices,this.line_indices=i.line_indices,this.multiline_indices=i.multiline_indices,this.image_indices=i.image_indices,this.view=i.view,this.selected_glyphs=i.selected_glyphs;break;case\"append\":this.update_through_union(i);break;case\"intersect\":this.update_through_intersection(i);break;case\"subtract\":this.update_through_subtraction(i)}}clear(){this.indices=[],this.line_indices=[],this.multiline_indices={},this.image_indices=[],this.view=null,this.selected_glyphs=[]}map(i){return new _(Object.assign(Object.assign({},this.attributes),{indices:this.indices.map(i),multiline_indices:(0,h.to_object)((0,h.entries)(this.multiline_indices).map((([e,s])=>[i(Number(e)),s]))),image_indices:this.image_indices.map((e=>Object.assign(Object.assign({},e),{index:i(e.index)})))}))}is_empty(){return 0==this.indices.length&&0==this.line_indices.length&&0==this.image_indices.length}update_through_union(i){this.indices=(0,d.union)(this.indices,i.indices),this.selected_glyphs=(0,d.union)(i.selected_glyphs,this.selected_glyphs),this.line_indices=(0,d.union)(i.line_indices,this.line_indices),this.view=i.view,this.multiline_indices=(0,h.merge)(i.multiline_indices,this.multiline_indices)}update_through_intersection(i){this.indices=(0,d.intersection)(this.indices,i.indices),this.selected_glyphs=(0,d.union)(i.selected_glyphs,this.selected_glyphs),this.line_indices=(0,d.union)(i.line_indices,this.line_indices),this.view=i.view,this.multiline_indices=(0,h.merge)(i.multiline_indices,this.multiline_indices)}update_through_subtraction(i){this.indices=(0,d.difference)(this.indices,i.indices),this.selected_glyphs=(0,d.union)(i.selected_glyphs,this.selected_glyphs),this.line_indices=(0,d.union)(i.line_indices,this.line_indices),this.view=i.view,this.multiline_indices=(0,h.merge)(i.multiline_indices,this.multiline_indices)}}s.Selection=_,l=_,_.__name__=\"Selection\",l.define((({Int:i,Array:e,Dict:s})=>({indices:[e(i),[]],line_indices:[e(i),[]],multiline_indices:[s(e(i)),{}]}))),l.internal((({Int:i,Array:e,AnyRef:s,Struct:t,Nullable:n})=>({selected_glyphs:[e(s()),[]],view:[n(s()),null],image_indices:[e(t({index:i,dim1:i,dim2:i,flat_index:i})),[]]})))},\n function _(e,t,o,s,c){s();const n=e(72);function i(e){return\"GlyphRenderer\"==e.model.type}function l(e){return\"GraphRenderer\"==e.model.type}class r{constructor(e){this.source=e,this.inspectors=new Map}select(e,t,o,s=\"replace\"){const c=[],n=[];for(const t of e)i(t)?c.push(t):l(t)&&n.push(t);let r=!1;for(const e of n){const c=e.model.selection_policy.hit_test(t,e);r=r||e.model.selection_policy.do_selection(c,e.model,o,s)}if(c.length>0){const e=this.source.selection_policy.hit_test(t,c);r=r||this.source.selection_policy.do_selection(e,this.source,o,s)}return r}inspect(e,t){let o=!1;if(i(e)){const s=e.hit_test(t);if(null!=s){o=!s.is_empty();const c=this.get_or_create_inspector(e.model);c.update(s,!0,\"replace\"),this.source.setv({inspected:c},{silent:!0}),this.source.inspect.emit([e.model,{geometry:t}])}}else if(l(e)){const s=e.model.inspection_policy.hit_test(t,e);o=o||e.model.inspection_policy.do_inspection(s,t,e,!1,\"replace\")}return o}clear(e){this.source.selected.clear(),null!=e&&this.get_or_create_inspector(e.model).clear()}get_or_create_inspector(e){let t=this.inspectors.get(e);return null==t&&(t=new n.Selection,this.inspectors.set(e,t)),t}}o.SelectionManager=r,r.__name__=\"SelectionManager\"},\n function _(e,t,n,s,o){s();const r=e(53);class c extends r.Model{do_selection(e,t,n,s){return null!=e&&(t.selected.update(e,n,s),t._select.emit(),!t.selected.is_empty())}}n.SelectionPolicy=c,c.__name__=\"SelectionPolicy\";class l extends c{hit_test(e,t){const n=[];for(const s of t){const t=s.hit_test(e);null!=t&&n.push(t)}if(n.length>0){const e=n[0];for(const t of n)e.update_through_intersection(t);return e}return null}}n.IntersectRenderers=l,l.__name__=\"IntersectRenderers\";class _ extends c{hit_test(e,t){const n=[];for(const s of t){const t=s.hit_test(e);null!=t&&n.push(t)}if(n.length>0){const e=n[0];for(const t of n)e.update_through_union(t);return e}return null}}n.UnionRenderers=_,_.__name__=\"UnionRenderers\"},\n function _(t,n,e,s,o){s();const r=t(1);var l;const c=t(70),i=t(8),a=t(13),u=(0,r.__importStar)(t(76)),h=t(77),d=t(35);function f(t,n,e){if((0,i.isArray)(t)){const s=t.concat(n);return null!=e&&s.length>e?s.slice(-e):s}if((0,i.isTypedArray)(t)){const s=t.length+n.length;if(null!=e&&s>e){const o=s-e,r=t.length;let l;t.length({data:[t(n),{}]})))},\n function _(t,n,o,e,c){e(),o.concat=function(t,...n){let o=t.length;for(const t of n)o+=t.length;const e=new t.constructor(o);e.set(t,0);let c=t.length;for(const t of n)e.set(t,c),c+=t.length;return e}},\n function _(n,o,t,e,f){function c(...n){const o=new Set;for(const t of n)for(const n of t)o.add(n);return o}e(),t.union=c,t.intersection=function(n,...o){const t=new Set;n:for(const e of n){for(const n of o)if(!n.has(e))continue n;t.add(e)}return t},t.difference=function(n,...o){const t=new Set(n);for(const n of c(...o))t.delete(n);return t}},\n function _(n,t,e,o,r){o();const c=n(1),l=(0,c.__importDefault)(n(79)),i=(0,c.__importDefault)(n(80)),u=n(24),a=new i.default(\"GOOGLE\"),s=new i.default(\"WGS84\"),f=(0,l.default)(s,a);e.wgs84_mercator={compute:(n,t)=>isFinite(n)&&isFinite(t)?f.forward([n,t]):[NaN,NaN],invert:(n,t)=>isFinite(n)&&isFinite(t)?f.inverse([n,t]):[NaN,NaN]};const _={lon:[-20026376.39,20026376.39],lat:[-20048966.1,20048966.1]},p={lon:[-180,180],lat:[-85.06,85.06]},{min:g,max:h}=Math;function m(n,t){const o=g(n.length,t.length),r=(0,u.infer_type)(n,t),c=new r(o),l=new r(o);return e.inplace.project_xy(n,t,c,l),[c,l]}e.clip_mercator=function(n,t,e){const[o,r]=_[e];return[h(n,o),g(t,r)]},e.in_bounds=function(n,t){const[e,o]=p[t];return e2?void 0!==e.name&&\"geocent\"===e.name||void 0!==n.name&&\"geocent\"===n.name?\"number\"==typeof o.z?[o.x,o.y,o.z].concat(t.splice(3)):[o.x,o.y,t[2]].concat(t.splice(3)):[o.x,o.y].concat(t.splice(2)):[o.x,o.y]):(a=(0,c.default)(e,n,t,r),2===(i=Object.keys(t)).length||i.forEach((function(r){if(void 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i=(0,m.default)(f.default,a.datumCode);i&&(a.datum_params=a.datum_params||(i.towgs84?i.towgs84.split(\",\"):null),a.ellps=i.ellipse,a.datumName=i.datumName?i.datumName:a.datumCode)}a.k0=a.k0||1,a.axis=a.axis||\"enu\",a.ellps=a.ellps||\"wgs84\",a.lat1=a.lat1||a.lat0;var l=(0,o.sphere)(a.a,a.b,a.rf,a.ellps,a.sphere),d=(0,o.eccentricity)(l.a,l.b,l.rf,a.R_A),_=(0,n.getNadgrids)(a.nadgrids),c=a.datum||(0,p.default)(a.datumCode,a.datum_params,l.a,l.b,d.es,d.ep2,_);(0,r.default)(this,a),(0,r.default)(this,s),this.a=l.a,this.b=l.b,this.rf=l.rf,this.sphere=l.sphere,this.es=d.es,this.e=d.e,this.ep2=d.ep2,this.datum=c,this.init(),e(null,this)}else e(t)}else e(t)}h.projections=d.default,h.projections.start(),a.default=h},\n function _(t,r,n,u,e){u();const f=t(1),i=(0,f.__importDefault)(t(82)),a=(0,f.__importDefault)(t(89)),o=(0,f.__importDefault)(t(84)),l=(0,f.__importDefault)(t(88));var C=[\"PROJECTEDCRS\",\"PROJCRS\",\"GEOGCS\",\"GEOCCS\",\"PROJCS\",\"LOCAL_CS\",\"GEODCRS\",\"GEODETICCRS\",\"GEODETICDATUM\",\"ENGCRS\",\"ENGINEERINGCRS\"];var d=[\"3857\",\"900913\",\"3785\",\"102113\"];n.default=function(t){if(!function(t){return\"string\"==typeof t}(t))return t;if(function(t){return t in i.default}(t))return i.default[t];if(function(t){return C.some((function(r){return t.indexOf(r)>-1}))}(t)){var r=(0,a.default)(t);if(function(t){var r=(0,l.default)(t,\"authority\");if(r){var n=(0,l.default)(r,\"epsg\");return n&&d.indexOf(n)>-1}}(r))return i.default[\"EPSG:3857\"];var n=function(t){var r=(0,l.default)(t,\"extension\");if(r)return(0,l.default)(r,\"proj4\")}(r);return n?(0,o.default)(n):r}return function(t){return\"+\"===t[0]}(t)?(0,o.default)(t):void 0}},\n function _(t,r,i,e,n){e();const f=t(1),a=(0,f.__importDefault)(t(83)),l=(0,f.__importDefault)(t(84)),u=(0,f.__importDefault)(t(89));function o(t){var r=this;if(2===arguments.length){var 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e[r];for(var t,a=Object.keys(e),n=r.toLowerCase().replace(o,\"\"),f=-1;++f0?90:-90),e.lat_ts=e.lat1)}(n),n}},\n function _(t,e,r,i,s){i(),r.default=function(t){return new d(t).output()};var h=/\\s/,o=/[A-Za-z]/,n=/[A-Za-z84]/,a=/[,\\]]/,u=/[\\d\\.E\\-\\+]/;function d(t){if(\"string\"!=typeof t)throw new Error(\"not a string\");this.text=t.trim(),this.level=0,this.place=0,this.root=null,this.stack=[],this.currentObject=null,this.state=1}d.prototype.readCharicter=function(){var t=this.text[this.place++];if(4!==this.state)for(;h.test(t);){if(this.place>=this.text.length)return;t=this.text[this.place++]}switch(this.state){case 1:return this.neutral(t);case 2:return this.keyword(t);case 4:return this.quoted(t);case 5:return this.afterquote(t);case 3:return this.number(t);case-1:return}},d.prototype.afterquote=function(t){if('\"'===t)return this.word+='\"',void(this.state=4);if(a.test(t))return this.word=this.word.trim(),void this.afterItem(t);throw new Error(\"havn't handled \\\"\"+t+'\" in afterquote yet, index '+this.place)},d.prototype.afterItem=function(t){return\",\"===t?(null!==this.word&&this.currentObject.push(this.word),this.word=null,void(this.state=1)):\"]\"===t?(this.level--,null!==this.word&&(this.currentObject.push(this.word),this.word=null),this.state=1,this.currentObject=this.stack.pop(),void(this.currentObject||(this.state=-1))):void 0},d.prototype.number=function(t){if(!u.test(t)){if(a.test(t))return this.word=parseFloat(this.word),void this.afterItem(t);throw new Error(\"havn't handled \\\"\"+t+'\" in number yet, index '+this.place)}this.word+=t},d.prototype.quoted=function(t){'\"'!==t?this.word+=t:this.state=5},d.prototype.keyword=function(t){if(n.test(t))this.word+=t;else{if(\"[\"===t){var e=[];return e.push(this.word),this.level++,null===this.root?this.root=e:this.currentObject.push(e),this.stack.push(this.currentObject),this.currentObject=e,void(this.state=1)}if(!a.test(t))throw new Error(\"havn't handled \\\"\"+t+'\" in keyword yet, index 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l(t){if(0===t.length)return null;var e=\"@\"===t[0];return e&&(t=t.slice(1)),\"null\"===t?{name:\"null\",mandatory:!e,grid:null,isNull:!0}:{name:t,mandatory:!e,grid:u[t]||null,isNull:!1}}function o(t){return t/3600*Math.PI/180}function a(t,e,n){return String.fromCharCode.apply(null,new Uint8Array(t.buffer.slice(e,n)))}function d(t){return t.map((function(t){return[o(t.longitudeShift),o(t.latitudeShift)]}))}function g(t,e,n){return{name:a(t,e+8,e+16).trim(),parent:a(t,e+24,e+24+8).trim(),lowerLatitude:t.getFloat64(e+72,n),upperLatitude:t.getFloat64(e+88,n),lowerLongitude:t.getFloat64(e+104,n),upperLongitude:t.getFloat64(e+120,n),latitudeInterval:t.getFloat64(e+136,n),longitudeInterval:t.getFloat64(e+152,n),gridNodeCount:t.getInt32(e+168,n)}}function s(t,e,n,r){for(var i=e+176,u=[],l=0;l1&&console.log(\"Only single NTv2 subgrids are currently supported, subsequent sub grids are ignored\");var l=function(t,e,n){for(var r=176,i=[],u=0;ua.y||f>a.x||N1e-12&&Math.abs(n.y)>1e-12);if(d<0)return console.log(\"Inverse grid shift iterator failed to converge.\"),a;a.x=(0,u.default)(l.x+t.ll[0]),a.y=l.y+t.ll[1]}else isNaN(l.x)||(a.x=r.x+l.x,a.y=r.y+l.y);return a}function f(r,e){var t,a={x:r.x/e.del[0],y:r.y/e.del[1]},i=Math.floor(a.x),l=Math.floor(a.y),n=a.x-1*i,o=a.y-1*l,u={x:Number.NaN,y:Number.NaN};if(i<0||i>=e.lim[0])return u;if(l<0||l>=e.lim[1])return u;t=l*e.lim[0]+i;var d=e.cvs[t][0],s=e.cvs[t][1];t++;var y=e.cvs[t][0],f=e.cvs[t][1];t+=e.lim[0];var x=e.cvs[t][0],m=e.cvs[t][1];t--;var N=e.cvs[t][0],c=e.cvs[t][1],_=n*o,g=n*(1-o),v=(1-n)*(1-o),S=(1-n)*o;return u.x=v*d+g*y+S*N+_*x,u.y=v*s+g*f+S*c+_*m,u}t.default=function(r,e,t){if((0,o.compareDatums)(r,e))return t;if(r.datum_type===n.PJD_NODATUM||e.datum_type===n.PJD_NODATUM)return t;var a=r.a,i=r.es;if(r.datum_type===n.PJD_GRIDSHIFT){if(0!==s(r,!1,t))return;a=n.SRS_WGS84_SEMIMAJOR,i=n.SRS_WGS84_ESQUARED}var l=e.a,u=e.b,y=e.es;if(e.datum_type===n.PJD_GRIDSHIFT&&(l=n.SRS_WGS84_SEMIMAJOR,u=n.SRS_WGS84_SEMIMINOR,y=n.SRS_WGS84_ESQUARED),i===y&&a===l&&!d(r.datum_type)&&!d(e.datum_type))return t;if(t=(0,o.geodeticToGeocentric)(t,i,a),d(r.datum_type)&&(t=(0,o.geocentricToWgs84)(t,r.datum_type,r.datum_params)),d(e.datum_type)&&(t=(0,o.geocentricFromWgs84)(t,e.datum_type,e.datum_params)),t=(0,o.geocentricToGeodetic)(t,y,l,u),e.datum_type===n.PJD_GRIDSHIFT&&0!==s(e,!0,t))return;return t},t.applyGridShift=s},\n function _(a,t,r,m,s){m();const u=a(85);r.compareDatums=function(a,t){return a.datum_type===t.datum_type&&(!(a.a!==t.a||Math.abs(a.es-t.es)>5e-11)&&(a.datum_type===u.PJD_3PARAM?a.datum_params[0]===t.datum_params[0]&&a.datum_params[1]===t.datum_params[1]&&a.datum_params[2]===t.datum_params[2]:a.datum_type!==u.PJD_7PARAM||a.datum_params[0]===t.datum_params[0]&&a.datum_params[1]===t.datum_params[1]&&a.datum_params[2]===t.datum_params[2]&&a.datum_params[3]===t.datum_params[3]&&a.datum_params[4]===t.datum_params[4]&&a.datum_params[5]===t.datum_params[5]&&a.datum_params[6]===t.datum_params[6]))},r.geodeticToGeocentric=function(a,t,r){var m,s,_,e,n=a.x,d=a.y,i=a.z?a.z:0;if(d<-u.HALF_PI&&d>-1.001*u.HALF_PI)d=-u.HALF_PI;else if(d>u.HALF_PI&&d<1.001*u.HALF_PI)d=u.HALF_PI;else{if(d<-u.HALF_PI)return{x:-1/0,y:-1/0,z:a.z};if(d>u.HALF_PI)return{x:1/0,y:1/0,z:a.z}}return n>Math.PI&&(n-=2*Math.PI),s=Math.sin(d),e=Math.cos(d),_=s*s,{x:((m=r/Math.sqrt(1-t*_))+i)*e*Math.cos(n),y:(m+i)*e*Math.sin(n),z:(m*(1-t)+i)*s}},r.geocentricToGeodetic=function(a,t,r,m){var s,_,e,n,d,i,p,P,y,z,M,o,A,c,x,h=1e-12,f=a.x,I=a.y,F=a.z?a.z:0;if(s=Math.sqrt(f*f+I*I),_=Math.sqrt(f*f+I*I+F*F),s/r1e-24&&A<30);return{x:c,y:Math.atan(M/Math.abs(z)),z:x}},r.geocentricToWgs84=function(a,t,r){if(t===u.PJD_3PARAM)return{x:a.x+r[0],y:a.y+r[1],z:a.z+r[2]};if(t===u.PJD_7PARAM){var m=r[0],s=r[1],_=r[2],e=r[3],n=r[4],d=r[5],i=r[6];return{x:i*(a.x-d*a.y+n*a.z)+m,y:i*(d*a.x+a.y-e*a.z)+s,z:i*(-n*a.x+e*a.y+a.z)+_}}},r.geocentricFromWgs84=function(a,t,r){if(t===u.PJD_3PARAM)return{x:a.x-r[0],y:a.y-r[1],z:a.z-r[2]};if(t===u.PJD_7PARAM){var m=r[0],s=r[1],_=r[2],e=r[3],n=r[4],d=r[5],i=r[6],p=(a.x-m)/i,P=(a.y-s)/i,y=(a.z-_)/i;return{x:p+d*P-n*y,y:-d*p+P+e*y,z:n*p-e*P+y}}}},\n function _(e,a,i,r,s){r(),i.default=function(e,a,i){var r,s,n,c=i.x,d=i.y,f=i.z||0,u={};for(n=0;n<3;n++)if(!a||2!==n||void 0!==i.z)switch(0===n?(r=c,s=-1!==\"ew\".indexOf(e.axis[n])?\"x\":\"y\"):1===n?(r=d,s=-1!==\"ns\".indexOf(e.axis[n])?\"y\":\"x\"):(r=f,s=\"z\"),e.axis[n]){case\"e\":u[s]=r;break;case\"w\":u[s]=-r;break;case\"n\":u[s]=r;break;case\"s\":u[s]=-r;break;case\"u\":void 0!==i[s]&&(u.z=r);break;case\"d\":void 0!==i[s]&&(u.z=-r);break;default:return null}return u}},\n function _(n,t,e,u,f){u(),e.default=function(n){var t={x:n[0],y:n[1]};return n.length>2&&(t.z=n[2]),n.length>3&&(t.m=n[3]),t}},\n function _(e,i,n,t,r){function o(e){if(\"function\"==typeof Number.isFinite){if(Number.isFinite(e))return;throw new TypeError(\"coordinates must be finite numbers\")}if(\"number\"!=typeof e||e!=e||!isFinite(e))throw new TypeError(\"coordinates must be finite numbers\")}t(),n.default=function(e){o(e.x),o(e.y)}},\n function _(e,i,s,t,o){t();const n=e(1);var l,a,r,_,c;const d=e(53),v=e(42),u=(0,n.__importStar)(e(45)),h=e(48),m=(0,n.__importStar)(e(18));class T extends v.View{initialize(){super.initialize(),this.visuals=new u.Visuals(this)}request_render(){this.parent.request_render()}get canvas(){return this.parent.canvas}set_data(e){const i=this;for(const s of this.model){if(!(s instanceof m.VectorSpec||s instanceof m.ScalarSpec))continue;const t=s.uniform(e);i[`${s.attr}`]=t}}}s.ArrowHeadView=T,T.__name__=\"ArrowHeadView\";class p extends d.Model{constructor(e){super(e)}}s.ArrowHead=p,l=p,p.__name__=\"ArrowHead\",l.define((()=>({size:[m.NumberSpec,25]})));class V extends T{clip(e,i){this.visuals.line.set_vectorize(e,i);const s=this.size.get(i);e.moveTo(.5*s,s),e.lineTo(.5*s,-2),e.lineTo(-.5*s,-2),e.lineTo(-.5*s,s),e.lineTo(0,0),e.lineTo(.5*s,s)}render(e,i){if(this.visuals.line.doit){this.visuals.line.set_vectorize(e,i);const s=this.size.get(i);e.beginPath(),e.moveTo(.5*s,s),e.lineTo(0,0),e.lineTo(-.5*s,s),e.stroke()}}}s.OpenHeadView=V,V.__name__=\"OpenHeadView\";class f extends p{constructor(e){super(e)}}s.OpenHead=f,a=f,f.__name__=\"OpenHead\",a.prototype.default_view=V,a.mixins(h.LineVector);class w extends T{clip(e,i){this.visuals.line.set_vectorize(e,i);const s=this.size.get(i);e.moveTo(.5*s,s),e.lineTo(.5*s,-2),e.lineTo(-.5*s,-2),e.lineTo(-.5*s,s),e.lineTo(.5*s,s)}render(e,i){this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(e,i),this._normal(e,i),e.fill()),this.visuals.line.doit&&(this.visuals.line.set_vectorize(e,i),this._normal(e,i),e.stroke())}_normal(e,i){const s=this.size.get(i);e.beginPath(),e.moveTo(.5*s,s),e.lineTo(0,0),e.lineTo(-.5*s,s),e.closePath()}}s.NormalHeadView=w,w.__name__=\"NormalHeadView\";class H extends p{constructor(e){super(e)}}s.NormalHead=H,r=H,H.__name__=\"NormalHead\",r.prototype.default_view=w,r.mixins([h.LineVector,h.FillVector]),r.override({fill_color:\"black\"});class z extends T{clip(e,i){this.visuals.line.set_vectorize(e,i);const s=this.size.get(i);e.moveTo(.5*s,s),e.lineTo(.5*s,-2),e.lineTo(-.5*s,-2),e.lineTo(-.5*s,s),e.lineTo(0,.5*s),e.lineTo(.5*s,s)}render(e,i){this.visuals.fill.doit&&(this.visuals.fill.set_vectorize(e,i),this._vee(e,i),e.fill()),this.visuals.line.doit&&(this.visuals.line.set_vectorize(e,i),this._vee(e,i),e.stroke())}_vee(e,i){const s=this.size.get(i);e.beginPath(),e.moveTo(.5*s,s),e.lineTo(0,0),e.lineTo(-.5*s,s),e.lineTo(0,.5*s),e.closePath()}}s.VeeHeadView=z,z.__name__=\"VeeHeadView\";class x extends p{constructor(e){super(e)}}s.VeeHead=x,_=x,x.__name__=\"VeeHead\",_.prototype.default_view=z,_.mixins([h.LineVector,h.FillVector]),_.override({fill_color:\"black\"});class g extends T{render(e,i){if(this.visuals.line.doit){this.visuals.line.set_vectorize(e,i);const s=this.size.get(i);e.beginPath(),e.moveTo(.5*s,0),e.lineTo(-.5*s,0),e.stroke()}}clip(e,i){}}s.TeeHeadView=g,g.__name__=\"TeeHeadView\";class b extends p{constructor(e){super(e)}}s.TeeHead=b,c=b,b.__name__=\"TeeHead\",c.prototype.default_view=g,c.mixins(h.LineVector)},\n function _(n,e,t,i,o){i();const s=n(9);async function c(n,e,t){const i=new n(Object.assign(Object.assign({},t),{model:e}));return i.initialize(),await i.lazy_initialize(),i}t.build_view=async function(n,e={parent:null},t=(n=>n.default_view)){const i=await c(t(n),n,e);return i.connect_signals(),i},t.build_views=async function(n,e,t={parent:null},i=(n=>n.default_view)){const o=(0,s.difference)([...n.keys()],e);for(const e of o)n.get(e).remove(),n.delete(e);const a=[],f=e.filter((e=>!n.has(e)));for(const e of f){const o=await c(i(e),e,t);n.set(e,o),a.push(o)}for(const n of a)n.connect_signals();return a},t.remove_views=function(n){for(const[e,t]of n)t.remove(),n.delete(e)}},\n function _(e,s,_,i,l){i();const t=e(1);var o;const r=e(115),p=(0,t.__importStar)(e(48));class h extends r.UpperLowerView{paint(e){e.beginPath(),e.moveTo(this._lower_sx[0],this._lower_sy[0]);for(let s=0,_=this._lower_sx.length;s<_;s++)e.lineTo(this._lower_sx[s],this._lower_sy[s]);for(let s=this._upper_sx.length-1;s>=0;s--)e.lineTo(this._upper_sx[s],this._upper_sy[s]);e.closePath(),this.visuals.fill.apply(e),e.beginPath(),e.moveTo(this._lower_sx[0],this._lower_sy[0]);for(let s=0,_=this._lower_sx.length;s<_;s++)e.lineTo(this._lower_sx[s],this._lower_sy[s]);this.visuals.line.apply(e),e.beginPath(),e.moveTo(this._upper_sx[0],this._upper_sy[0]);for(let s=0,_=this._upper_sx.length;s<_;s++)e.lineTo(this._upper_sx[s],this._upper_sy[s]);this.visuals.line.apply(e)}}_.BandView=h,h.__name__=\"BandView\";class n extends r.UpperLower{constructor(e){super(e)}}_.Band=n,o=n,n.__name__=\"Band\",o.prototype.default_view=h,o.mixins([p.Line,p.Fill]),o.override({fill_color:\"#fff9ba\",fill_alpha:.4,line_color:\"#cccccc\",line_alpha:.3})},\n function _(e,t,i,s,o){s();const r=e(1);var n;const p=e(69),a=e(20),_=(0,r.__importStar)(e(18));class h extends p.DataAnnotationView{map_data(){const{frame:e}=this.plot_view,t=this.model.dimension,i=this.coordinates.x_scale,s=this.coordinates.y_scale,o=\"height\"==t?s:i,r=\"height\"==t?i:s,n=\"height\"==t?e.bbox.yview:e.bbox.xview,p=\"height\"==t?e.bbox.xview:e.bbox.yview;let a,_,h;a=\"data\"==this.model.properties.lower.units?o.v_compute(this._lower):n.v_compute(this._lower),_=\"data\"==this.model.properties.upper.units?o.v_compute(this._upper):n.v_compute(this._upper),h=\"data\"==this.model.properties.base.units?r.v_compute(this._base):p.v_compute(this._base);const[d,c]=\"height\"==t?[1,0]:[0,1],u=[a,h],l=[_,h];this._lower_sx=u[d],this._lower_sy=u[c],this._upper_sx=l[d],this._upper_sy=l[c]}}i.UpperLowerView=h,h.__name__=\"UpperLowerView\";class d extends _.CoordinateSpec{get dimension(){return\"width\"==this.obj.dimension?\"x\":\"y\"}get units(){var e;return null!==(e=this.spec.units)&&void 0!==e?e:\"data\"}}i.XOrYCoordinateSpec=d,d.__name__=\"XOrYCoordinateSpec\";class c extends p.DataAnnotation{constructor(e){super(e)}}i.UpperLower=c,n=c,c.__name__=\"UpperLower\",n.define((()=>({dimension:[a.Dimension,\"height\"],lower:[d,{field:\"lower\"}],upper:[d,{field:\"upper\"}],base:[d,{field:\"base\"}]})))},\n function _(t,o,i,n,e){n();const s=t(1);var l;const r=t(40),a=(0,s.__importStar)(t(48)),c=t(20),h=t(65);i.EDGE_TOLERANCE=2.5;class b extends r.AnnotationView{constructor(){super(...arguments),this.bbox=new h.BBox}connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.request_render()))}_render(){const{left:t,right:o,top:i,bottom:n}=this.model;if(null==t&&null==o&&null==i&&null==n)return;const{frame:e}=this.plot_view,s=this.coordinates.x_scale,l=this.coordinates.y_scale,r=(t,o,i,n,e)=>{let s;return s=null!=t?this.model.screen?t:\"data\"==o?i.compute(t):n.compute(t):e,s};this.bbox=h.BBox.from_rect({left:r(t,this.model.left_units,s,e.bbox.xview,e.bbox.left),right:r(o,this.model.right_units,s,e.bbox.xview,e.bbox.right),top:r(i,this.model.top_units,l,e.bbox.yview,e.bbox.top),bottom:r(n,this.model.bottom_units,l,e.bbox.yview,e.bbox.bottom)}),this._paint_box()}_paint_box(){const{ctx:t}=this.layer;t.save();const{left:o,top:i,width:n,height:e}=this.bbox;t.beginPath(),t.rect(o,i,n,e),this.visuals.fill.apply(t),this.visuals.hatch.apply(t),this.visuals.line.apply(t),t.restore()}interactive_bbox(){const t=this.model.line_width+i.EDGE_TOLERANCE;return this.bbox.grow_by(t)}interactive_hit(t,o){if(null==this.model.in_cursor)return!1;return this.interactive_bbox().contains(t,o)}cursor(t,o){const{left:i,right:n,bottom:e,top:s}=this.bbox;return Math.abs(t-i)<3||Math.abs(t-n)<3?this.model.ew_cursor:Math.abs(o-e)<3||Math.abs(o-s)<3?this.model.ns_cursor:this.bbox.contains(t,o)?this.model.in_cursor:null}}i.BoxAnnotationView=b,b.__name__=\"BoxAnnotationView\";class u extends r.Annotation{constructor(t){super(t)}update({left:t,right:o,top:i,bottom:n}){this.setv({left:t,right:o,top:i,bottom:n,screen:!0})}}i.BoxAnnotation=u,l=u,u.__name__=\"BoxAnnotation\",l.prototype.default_view=b,l.mixins([a.Line,a.Fill,a.Hatch]),l.define((({Number:t,Nullable:o})=>({top:[o(t),null],top_units:[c.SpatialUnits,\"data\"],bottom:[o(t),null],bottom_units:[c.SpatialUnits,\"data\"],left:[o(t),null],left_units:[c.SpatialUnits,\"data\"],right:[o(t),null],right_units:[c.SpatialUnits,\"data\"],render_mode:[c.RenderMode,\"canvas\"]}))),l.internal((({Boolean:t,String:o,Nullable:i})=>({screen:[t,!1],ew_cursor:[i(o),null],ns_cursor:[i(o),null],in_cursor:[i(o),null]}))),l.override({fill_color:\"#fff9ba\",fill_alpha:.4,line_color:\"#cccccc\",line_alpha:.3})},\n function _(t,e,i,o,n){o();const a=t(1);var r;const s=t(40),l=t(118),_=t(126),c=t(127),h=t(130),u=t(168),p=t(131),m=t(192),g=t(132),d=t(173),f=t(172),w=t(196),b=t(204),v=t(206),x=t(133),y=t(20),k=(0,a.__importStar)(t(48)),z=t(9),j=t(207),C=t(208),L=t(211),B=t(123),S=t(11),M=t(113),T=t(65),A=t(8);class O extends s.AnnotationView{get orientation(){return this._orientation}initialize(){super.initialize();const{ticker:t,formatter:e,color_mapper:i}=this.model;this._ticker=\"auto\"!=t?t:(()=>{switch(!0){case i instanceof w.LogColorMapper:return new u.LogTicker;case i instanceof w.ScanningColorMapper:return new u.BinnedTicker({mapper:i});case i instanceof w.CategoricalColorMapper:return new u.CategoricalTicker;default:return new u.BasicTicker}})(),this._formatter=\"auto\"!=e?e:(()=>{switch(!0){case this._ticker instanceof u.LogTicker:return new m.LogTickFormatter;case i instanceof w.CategoricalColorMapper:return new m.CategoricalTickFormatter;default:return new m.BasicTickFormatter}})(),this._major_range=(()=>{if(i instanceof w.CategoricalColorMapper){const{factors:t}=i;return new v.FactorRange({factors:t})}if(i instanceof f.ContinuousColorMapper){const{min:t,max:e}=i.metrics;return new v.Range1d({start:t,end:e})}(0,S.unreachable)()})(),this._major_scale=(()=>{if(i instanceof w.LinearColorMapper)return new b.LinearScale;if(i instanceof w.LogColorMapper)return new b.LogScale;if(i instanceof w.ScanningColorMapper){const{binning:t}=i.metrics;return new b.LinearInterpolationScale({binning:t})}if(i instanceof w.CategoricalColorMapper)return new b.CategoricalScale;(0,S.unreachable)()})(),this._minor_range=new v.Range1d({start:0,end:1}),this._minor_scale=new b.LinearScale;const o=k.attrs_of(this.model,\"major_label_\",k.Text,!0),n=k.attrs_of(this.model,\"major_tick_\",k.Line,!0),a=k.attrs_of(this.model,\"minor_tick_\",k.Line,!0),r=k.attrs_of(this.model,\"title_\",k.Text),s=i instanceof w.CategoricalColorMapper?c.CategoricalAxis:i instanceof w.LogColorMapper?c.LogAxis:c.LinearAxis;this._axis=new s(Object.assign(Object.assign(Object.assign({ticker:this._ticker,formatter:this._formatter,major_tick_in:this.model.major_tick_in,major_tick_out:this.model.major_tick_out,minor_tick_in:this.model.minor_tick_in,minor_tick_out:this.model.minor_tick_out,major_label_standoff:this.model.label_standoff,major_label_overrides:this.model.major_label_overrides,major_label_policy:this.model.major_label_policy,axis_line_color:null},o),n),a));const{title:_}=this.model;_&&(this._title=new l.Title(Object.assign({text:_,standoff:this.model.title_standoff},r)))}async lazy_initialize(){await super.lazy_initialize();const t=this,e={get parent(){return t.parent},get root(){return t.root},get frame(){return t._frame},get canvas_view(){return t.parent.canvas_view},request_layout(){t.parent.request_layout()}};this._axis_view=await(0,M.build_view)(this._axis,{parent:e}),null!=this._title&&(this._title_view=await(0,M.build_view)(this._title,{parent:e}))}remove(){var t;null===(t=this._title_view)||void 0===t||t.remove(),this._axis_view.remove(),super.remove()}connect_signals(){super.connect_signals(),this.connect(this._ticker.change,(()=>this.request_render())),this.connect(this._formatter.change,(()=>this.request_render())),this.connect(this.model.color_mapper.metrics_change,(()=>{const t=this._major_range,e=this._major_scale,{color_mapper:i}=this.model;if(i instanceof f.ContinuousColorMapper&&t instanceof v.Range1d){const{min:e,max:o}=i.metrics;t.setv({start:e,end:o})}if(i instanceof w.ScanningColorMapper&&e instanceof b.LinearInterpolationScale){const{binning:t}=i.metrics;e.binning=t}this._set_canvas_image(),this.plot_view.request_layout()}))}_set_canvas_image(){const{orientation:t}=this,e=(()=>{const{palette:e}=this.model.color_mapper;return\"vertical\"==t?(0,z.reversed)(e):e})(),[i,o]=\"vertical\"==t?[1,e.length]:[e.length,1],n=this._image=document.createElement(\"canvas\");n.width=i,n.height=o;const a=n.getContext(\"2d\"),r=a.getImageData(0,0,i,o),s=new w.LinearColorMapper({palette:e}).rgba_mapper.v_compute((0,z.range)(0,e.length));r.data.set(s),a.putImageData(r,0,0)}update_layout(){const{location:t,width:e,height:i,padding:o,margin:n}=this.model,[a,r]=(()=>{if(!(0,A.isString)(t))return[\"end\",\"start\"];switch(t){case\"top_left\":return[\"start\",\"start\"];case\"top\":case\"top_center\":return[\"start\",\"center\"];case\"top_right\":return[\"start\",\"end\"];case\"bottom_left\":return[\"end\",\"start\"];case\"bottom\":case\"bottom_center\":return[\"end\",\"center\"];case\"bottom_right\":return[\"end\",\"end\"];case\"left\":case\"center_left\":return[\"center\",\"start\"];case\"center\":case\"center_center\":return[\"center\",\"center\"];case\"right\":case\"center_right\":return[\"center\",\"end\"]}})(),s=this._orientation=(()=>{const{orientation:t}=this.model;return\"auto\"==t?null!=this.panel?this.panel.is_horizontal?\"horizontal\":\"vertical\":\"start\"==r||\"end\"==r||\"center\"==r&&\"center\"==a?\"vertical\":\"horizontal\":t})(),l=new C.NodeLayout,c=new C.VStack,h=new C.VStack,u=new C.HStack,p=new C.HStack;l.absolute=!0,c.absolute=!0,h.absolute=!0,u.absolute=!0,p.absolute=!0;const[m,g,d,f]=(()=>\"horizontal\"==s?[this._major_scale,this._minor_scale,this._major_range,this._minor_range]:[this._minor_scale,this._major_scale,this._minor_range,this._major_range])();this._frame=new _.CartesianFrame(m,g,d,f),l.on_resize((t=>this._frame.set_geometry(t)));const w=new L.BorderLayout;this._inner_layout=w,w.absolute=!0,w.center_panel=l,w.top_panel=c,w.bottom_panel=h,w.left_panel=u,w.right_panel=p;const b={left:o,right:o,top:o,bottom:o},v=(()=>{if(null==this.panel){if((0,A.isString)(t))return{left:n,right:n,top:n,bottom:n};{const[e,i]=t;return{left:e,right:n,top:n,bottom:i}}}if(!(0,A.isString)(t)){const[e,i]=t;return w.fixup_geometry=(t,o)=>{const n=t,a=this.layout.bbox,{width:r,height:s}=t;if(t=new T.BBox({left:a.left+e,bottom:a.bottom-i,width:r,height:s}),null!=o){const e=t.left-n.left,i=t.top-n.top,{left:a,top:r,width:s,height:l}=o;o=new T.BBox({left:a+e,top:r+i,width:s,height:l})}return[t,o]},{left:e,right:0,top:0,bottom:i}}w.fixup_geometry=(t,e)=>{const i=t;if(\"horizontal\"==s){const{top:e,width:i,height:o}=t;if(\"end\"==r){const{right:n}=this.layout.bbox;t=new T.BBox({right:n,top:e,width:i,height:o})}else if(\"center\"==r){const{hcenter:n}=this.layout.bbox;t=new T.BBox({hcenter:Math.round(n),top:e,width:i,height:o})}}else{const{left:e,width:i,height:o}=t;if(\"end\"==a){const{bottom:n}=this.layout.bbox;t=new T.BBox({left:e,bottom:n,width:i,height:o})}else if(\"center\"==a){const{vcenter:n}=this.layout.bbox;t=new T.BBox({left:e,vcenter:Math.round(n),width:i,height:o})}}if(null!=e){const o=t.left-i.left,n=t.top-i.top,{left:a,top:r,width:s,height:l}=e;e=new T.BBox({left:a+o,top:r+n,width:s,height:l})}return[t,e]}})();let x,y,k,z;if(w.padding=b,null!=this.panel?(x=\"max\",y=void 0,k=void 0,z=void 0):\"auto\"==(\"horizontal\"==s?e:i)?(x=\"fixed\",y=25*this.model.color_mapper.palette.length,k={percent:.3},z={percent:.8}):(x=\"fit\",y=void 0),\"horizontal\"==s){const t=\"auto\"==e?void 0:e,o=\"auto\"==i?25:i;w.set_sizing({width_policy:x,height_policy:\"min\",width:y,min_width:k,max_width:z,halign:r,valign:a,margin:v}),w.center_panel.set_sizing({width_policy:\"auto\"==e?\"fit\":\"fixed\",height_policy:\"fixed\",width:t,height:o})}else{const t=\"auto\"==e?25:e,o=\"auto\"==i?void 0:i;w.set_sizing({width_policy:\"min\",height_policy:x,height:y,min_height:k,max_height:z,halign:r,valign:a,margin:v}),w.center_panel.set_sizing({width_policy:\"fixed\",height_policy:\"auto\"==i?\"fit\":\"fixed\",width:t,height:o})}c.set_sizing({width_policy:\"fit\",height_policy:\"min\"}),h.set_sizing({width_policy:\"fit\",height_policy:\"min\"}),u.set_sizing({width_policy:\"min\",height_policy:\"fit\"}),p.set_sizing({width_policy:\"min\",height_policy:\"fit\"});const{_title_view:S}=this;null!=S&&(\"horizontal\"==s?(S.panel=new B.Panel(\"above\"),S.update_layout(),c.children.push(S.layout)):(S.panel=new B.Panel(\"left\"),S.update_layout(),u.children.push(S.layout)));const{panel:M}=this,O=null!=M&&s==M.orientation?M.side:\"horizontal\"==s?\"below\":\"right\",R=(()=>{switch(O){case\"above\":return c;case\"below\":return h;case\"left\":return u;case\"right\":return p}})(),{_axis_view:F}=this;if(F.panel=new B.Panel(O),F.update_layout(),R.children.push(F.layout),null!=this.panel){const t=new j.Grid([{layout:w,row:0,col:0}]);t.absolute=!0,\"horizontal\"==s?t.set_sizing({width_policy:\"max\",height_policy:\"min\"}):t.set_sizing({width_policy:\"min\",height_policy:\"max\"}),this.layout=t}else this.layout=this._inner_layout;const{visible:I}=this.model;this.layout.sizing.visible=I,this._set_canvas_image()}_render(){var t;const{ctx:e}=this.layer;e.save(),this._paint_bbox(e,this._inner_layout.bbox),this._paint_image(e,this._inner_layout.center_panel.bbox),null===(t=this._title_view)||void 0===t||t.render(),this._axis_view.render(),e.restore()}_paint_bbox(t,e){const{x:i,y:o}=e;let{width:n,height:a}=e;i+n>=this.parent.canvas_view.bbox.width&&(n-=1),o+a>=this.parent.canvas_view.bbox.height&&(a-=1),t.save(),this.visuals.background_fill.doit&&(this.visuals.background_fill.set_value(t),t.fillRect(i,o,n,a)),this.visuals.border_line.doit&&(this.visuals.border_line.set_value(t),t.strokeRect(i,o,n,a)),t.restore()}_paint_image(t,e){const{x:i,y:o,width:n,height:a}=e;t.save(),t.setImageSmoothingEnabled(!1),t.globalAlpha=this.model.scale_alpha,t.drawImage(this._image,i,o,n,a),this.visuals.bar_line.doit&&(this.visuals.bar_line.set_value(t),t.strokeRect(i,o,n,a)),t.restore()}serializable_state(){const t=super.serializable_state(),{children:e=[]}=t,i=(0,a.__rest)(t,[\"children\"]);return null!=this._title_view&&e.push(this._title_view.serializable_state()),e.push(this._axis_view.serializable_state()),Object.assign(Object.assign({},i),{children:e})}}i.ColorBarView=O,O.__name__=\"ColorBarView\";class R extends s.Annotation{constructor(t){super(t)}}i.ColorBar=R,r=R,R.__name__=\"ColorBar\",r.prototype.default_view=O,r.mixins([[\"major_label_\",k.Text],[\"title_\",k.Text],[\"major_tick_\",k.Line],[\"minor_tick_\",k.Line],[\"border_\",k.Line],[\"bar_\",k.Line],[\"background_\",k.Fill]]),r.define((({Alpha:t,Number:e,String:i,Tuple:o,Dict:n,Or:a,Ref:r,Auto:s,Nullable:l})=>({location:[a(y.Anchor,o(e,e)),\"top_right\"],orientation:[a(y.Orientation,s),\"auto\"],title:[l(i),null],title_standoff:[e,2],width:[a(e,s),\"auto\"],height:[a(e,s),\"auto\"],scale_alpha:[t,1],ticker:[a(r(h.Ticker),s),\"auto\"],formatter:[a(r(p.TickFormatter),s),\"auto\"],major_label_overrides:[n(a(i,r(x.BaseText))),{}],major_label_policy:[r(g.LabelingPolicy),()=>new g.NoOverlap],color_mapper:[r(d.ColorMapper)],label_standoff:[e,5],margin:[e,30],padding:[e,10],major_tick_in:[e,5],major_tick_out:[e,0],minor_tick_in:[e,0],minor_tick_out:[e,0]}))),r.override({background_fill_color:\"#ffffff\",background_fill_alpha:.95,bar_line_color:null,border_line_color:null,major_label_text_font_size:\"11px\",major_tick_line_color:\"#ffffff\",minor_tick_line_color:null,title_text_font_size:\"13px\",title_text_font_style:\"italic\"})},\n function _(t,e,i,s,l){s();const o=t(1);var a;const n=t(119),r=t(20),c=t(120),h=(0,o.__importStar)(t(48));class _ extends n.TextAnnotationView{_get_location(){const t=this.model.offset,e=this.model.standoff/2;let i,s;const{bbox:l}=this.layout;switch(this.panel.side){case\"above\":case\"below\":switch(this.model.vertical_align){case\"top\":s=l.top+e;break;case\"middle\":s=l.vcenter;break;case\"bottom\":s=l.bottom-e}switch(this.model.align){case\"left\":i=l.left+t;break;case\"center\":i=l.hcenter;break;case\"right\":i=l.right-t}break;case\"left\":switch(this.model.vertical_align){case\"top\":i=l.left+e;break;case\"middle\":i=l.hcenter;break;case\"bottom\":i=l.right-e}switch(this.model.align){case\"left\":s=l.bottom-t;break;case\"center\":s=l.vcenter;break;case\"right\":s=l.top+t}break;case\"right\":switch(this.model.vertical_align){case\"top\":i=l.right-e;break;case\"middle\":i=l.hcenter;break;case\"bottom\":i=l.left+e}switch(this.model.align){case\"left\":s=l.top+t;break;case\"center\":s=l.vcenter;break;case\"right\":s=l.bottom-t}}return[i,s]}_render(){const{text:t}=this.model;if(null==t||0==t.length)return;this.model.text_baseline=this.model.vertical_align,this.model.text_align=this.model.align;const[e,i]=this._get_location(),s=this.panel.get_label_angle_heuristic(\"parallel\");(\"canvas\"==this.model.render_mode?this._canvas_text.bind(this):this._css_text.bind(this))(this.layer.ctx,t,e,i,s)}_get_size(){const{text:t}=this.model,e=new c.TextBox({text:t});e.visuals=this.visuals.text.values();const{width:i,height:s}=e.size();return{width:i,height:0==s?0:2+s+this.model.standoff}}}i.TitleView=_,_.__name__=\"TitleView\";class d extends n.TextAnnotation{constructor(t){super(t)}}i.Title=d,a=d,d.__name__=\"Title\",a.prototype.default_view=_,a.mixins([h.Text,[\"border_\",h.Line],[\"background_\",h.Fill]]),a.define((({Number:t,String:e})=>({text:[e,\"\"],vertical_align:[r.VerticalAlign,\"bottom\"],align:[r.TextAlign,\"left\"],offset:[t,0],standoff:[t,10]}))),a.prototype._props.text_align.options.internal=!0,a.prototype._props.text_baseline.options.internal=!0,a.override({text_font_size:\"13px\",text_font_style:\"bold\",text_line_height:1,background_fill_color:null,border_line_color:null})},\n function _(e,t,s,i,l){var n;i();const o=e(40),a=e(43),r=e(20),d=e(120),u=e(123),c=e(11);class h extends o.AnnotationView{update_layout(){const{panel:e}=this;this.layout=null!=e?new u.SideLayout(e,(()=>this.get_size()),!0):void 0}initialize(){super.initialize(),\"css\"==this.model.render_mode&&(this.el=(0,a.div)(),this.plot_view.canvas_view.add_overlay(this.el))}remove(){null!=this.el&&(0,a.remove)(this.el),super.remove()}connect_signals(){super.connect_signals(),\"css\"==this.model.render_mode?this.connect(this.model.change,(()=>this.render())):this.connect(this.model.change,(()=>this.request_render()))}render(){this.model.visible||\"css\"!=this.model.render_mode||(0,a.undisplay)(this.el),super.render()}_canvas_text(e,t,s,i,l){const n=new d.TextBox({text:t});n.angle=l,n.position={sx:s,sy:i},n.visuals=this.visuals.text.values();const{background_fill:o,border_line:a}=this.visuals;if(o.doit||a.doit){const{p0:t,p1:s,p2:i,p3:l}=n.rect();e.beginPath(),e.moveTo(t.x,t.y),e.lineTo(s.x,s.y),e.lineTo(i.x,i.y),e.lineTo(l.x,l.y),e.closePath(),this.visuals.background_fill.apply(e),this.visuals.border_line.apply(e)}this.visuals.text.doit&&n.paint(e)}_css_text(e,t,s,i,l){const{el:n}=this;(0,c.assert)(null!=n),(0,a.undisplay)(n),n.textContent=t,this.visuals.text.set_value(e),n.style.position=\"absolute\",n.style.left=`${s}px`,n.style.top=`${i}px`,n.style.color=e.fillStyle,n.style.font=e.font,n.style.lineHeight=\"normal\",n.style.whiteSpace=\"pre\";const[o,r]=(()=>{switch(this.visuals.text.text_align.get_value()){case\"left\":return[\"left\",\"0%\"];case\"center\":return[\"center\",\"-50%\"];case\"right\":return[\"right\",\"-100%\"]}})(),[d,u]=(()=>{switch(this.visuals.text.text_baseline.get_value()){case\"top\":return[\"top\",\"0%\"];case\"middle\":return[\"center\",\"-50%\"];case\"bottom\":return[\"bottom\",\"-100%\"];default:return[\"center\",\"-50%\"]}})();let h=`translate(${r}, ${u})`;l&&(h+=`rotate(${l}rad)`),n.style.transformOrigin=`${o} ${d}`,n.style.transform=h,this.layout,this.visuals.background_fill.doit&&(this.visuals.background_fill.set_value(e),n.style.backgroundColor=e.fillStyle),this.visuals.border_line.doit&&(this.visuals.border_line.set_value(e),n.style.borderStyle=e.lineDash.length<2?\"solid\":\"dashed\",n.style.borderWidth=`${e.lineWidth}px`,n.style.borderColor=e.strokeStyle),(0,a.display)(n)}}s.TextAnnotationView=h,h.__name__=\"TextAnnotationView\";class _ extends o.Annotation{constructor(e){super(e)}}s.TextAnnotation=_,n=_,_.__name__=\"TextAnnotation\",n.define((()=>({render_mode:[r.RenderMode,\"canvas\"]})))},\n function _(t,e,s,i,n){i();const h=t(65),o=t(121),r=t(9),a=t(8),c=t(122),_=t(22);s.text_width=(()=>{const t=document.createElement(\"canvas\").getContext(\"2d\");let e=\"\";return(s,i)=>(i!=e&&(e=i,t.font=i),t.measureText(s).width)})();class l{constructor(){this._position={sx:0,sy:0},this.font_size_scale=1,this.align=\"left\",this._base_font_size=13,this._x_anchor=\"left\",this._y_anchor=\"center\"}set base_font_size(t){null!=t&&(this._base_font_size=t)}get base_font_size(){return this._base_font_size}set position(t){this._position=t}get position(){return this._position}infer_text_height(){return\"ascent_descent\"}bbox(){const{p0:t,p1:e,p2:s,p3:i}=this.rect(),n=Math.min(t.x,e.x,s.x,i.x),o=Math.min(t.y,e.y,s.y,i.y),r=Math.max(t.x,e.x,s.x,i.x),a=Math.max(t.y,e.y,s.y,i.y);return new h.BBox({left:n,right:r,top:o,bottom:a})}size(){const{width:t,height:e}=this._size(),{angle:s}=this;if(s){const i=Math.cos(Math.abs(s)),n=Math.sin(Math.abs(s));return{width:Math.abs(t*i+e*n),height:Math.abs(t*n+e*i)}}return{width:t,height:e}}rect(){const t=this._rect(),{angle:e}=this;if(e){const{sx:s,sy:i}=this.position,n=new c.AffineTransform;return n.translate(s,i),n.rotate(e),n.translate(-s,-i),n.apply_rect(t)}return t}paint_rect(t){const{p0:e,p1:s,p2:i,p3:n}=this.rect();t.save(),t.strokeStyle=\"red\",t.lineWidth=1,t.beginPath();const{round:h}=Math;t.moveTo(h(e.x),h(e.y)),t.lineTo(h(s.x),h(s.y)),t.lineTo(h(i.x),h(i.y)),t.lineTo(h(n.x),h(n.y)),t.closePath(),t.stroke(),t.restore()}paint_bbox(t){const{x:e,y:s,width:i,height:n}=this.bbox();t.save(),t.strokeStyle=\"blue\",t.lineWidth=1,t.beginPath();const{round:h}=Math;t.moveTo(h(e),h(s)),t.lineTo(h(e),h(s+n)),t.lineTo(h(e+i),h(s+n)),t.lineTo(h(e+i),h(s)),t.closePath(),t.stroke(),t.restore()}}s.GraphicsBox=l,l.__name__=\"GraphicsBox\";class x extends l{constructor({text:t}){super(),this.text=t}set visuals(t){const e=t.color,s=t.alpha,i=t.font_style;let n=t.font_size;const h=t.font,{font_size_scale:r,base_font_size:a}=this,c=(0,o.parse_css_font_size)(n);if(null!=c){let{value:t,unit:e}=c;t*=r,\"em\"==e&&a&&(t*=a,e=\"px\"),n=`${t}${e}`}const l=`${i} ${n} ${h}`;this.font=l,this.color=(0,_.color2css)(e,s),this.line_height=t.line_height;const x=t.align;this._x_anchor=x;const u=t.baseline;this._y_anchor=(()=>{switch(u){case\"top\":return\"top\";case\"middle\":return\"center\";case\"bottom\":return\"bottom\";default:return\"baseline\"}})()}infer_text_height(){if(this.text.includes(\"\\n\"))return\"ascent_descent\";{function t(t){for(const e of new Set(t))if(!(\"0\"<=e&&e<=\"9\"))switch(e){case\",\":case\".\":case\"+\":case\"-\":case\"\\u2212\":case\"e\":continue;default:return!1}return!0}return t(this.text)?\"cap\":\"ascent_descent\"}}_text_line(t){var e;const s=null!==(e=this.text_height_metric)&&void 0!==e?e:this.infer_text_height(),i=(()=>{switch(s){case\"x\":case\"x_descent\":return t.x_height;case\"cap\":case\"cap_descent\":return t.cap_height;case\"ascent\":case\"ascent_descent\":return t.ascent}})(),n=(()=>{switch(s){case\"x\":case\"cap\":case\"ascent\":return 0;case\"x_descent\":case\"cap_descent\":case\"ascent_descent\":return t.descent}})();return{height:i+n,ascent:i,descent:n}}get nlines(){return this.text.split(\"\\n\").length}_size(){var t,e;const{font:i}=this,n=(0,o.font_metrics)(i),h=(this.line_height-1)*n.height,a=\"\"==this.text,c=this.text.split(\"\\n\"),_=c.length,l=c.map((t=>(0,s.text_width)(t,i))),x=this._text_line(n).height*_,u=\"%\"==(null===(t=this.width)||void 0===t?void 0:t.unit)?this.width.value:1,p=\"%\"==(null===(e=this.height)||void 0===e?void 0:e.unit)?this.height.value:1;return{width:(0,r.max)(l)*u,height:a?0:(x+h*(_-1))*p,metrics:n}}_computed_position(t,e,s){const{width:i,height:n}=t,{sx:h,sy:o,x_anchor:r=this._x_anchor,y_anchor:c=this._y_anchor}=this.position;return{x:h-(()=>{if((0,a.isNumber)(r))return r*i;switch(r){case\"left\":return 0;case\"center\":return.5*i;case\"right\":return i}})(),y:o-(()=>{var t;if((0,a.isNumber)(c))return c*n;switch(c){case\"top\":return 0;case\"center\":return.5*n;case\"bottom\":return n;case\"baseline\":if(1!=s)return.5*n;switch(null!==(t=this.text_height_metric)&&void 0!==t?t:this.infer_text_height()){case\"x\":case\"x_descent\":return e.x_height;case\"cap\":case\"cap_descent\":return e.cap_height;case\"ascent\":case\"ascent_descent\":return e.ascent}}})()}}_rect(){const{width:t,height:e,metrics:s}=this._size(),i=this.text.split(\"\\n\").length,{x:n,y:o}=this._computed_position({width:t,height:e},s,i);return new h.BBox({x:n,y:o,width:t,height:e}).rect}paint(t){var e,i;const{font:n}=this,h=(0,o.font_metrics)(n),a=(this.line_height-1)*h.height,c=this.text.split(\"\\n\"),_=c.length,l=c.map((t=>(0,s.text_width)(t,n))),x=this._text_line(h),u=x.height*_,p=\"%\"==(null===(e=this.width)||void 0===e?void 0:e.unit)?this.width.value:1,f=\"%\"==(null===(i=this.height)||void 0===i?void 0:i.unit)?this.height.value:1,g=(0,r.max)(l)*p,d=(u+a*(_-1))*f;t.save(),t.fillStyle=this.color,t.font=this.font,t.textAlign=\"left\",t.textBaseline=\"alphabetic\";const{sx:b,sy:m}=this.position,{align:y}=this,{angle:w}=this;w&&(t.translate(b,m),t.rotate(w),t.translate(-b,-m));let{x:v,y:z}=this._computed_position({width:g,height:d},h,_);if(\"justify\"==y)for(let e=0;e<_;e++){let i=v;const h=c[e].split(\" \"),o=h.length,_=h.map((t=>(0,s.text_width)(t,n))),l=(g-(0,r.sum)(_))/(o-1);for(let e=0;e{switch(y){case\"left\":return 0;case\"center\":return.5*(g-l[e]);case\"right\":return g-l[e]}})();t.fillStyle=this.color,t.fillText(c[e],s,z+x.ascent),z+=x.height+a}t.restore()}}s.TextBox=x,x.__name__=\"TextBox\";class u extends l{constructor(t,e){super(),this.base=t,this.expo=e}get children(){return[this.base,this.expo]}set base_font_size(t){super.base_font_size=t,this.base.base_font_size=t,this.expo.base_font_size=t}set position(t){this._position=t;const e=this.base.size(),s=this.expo.size(),i=this._shift_scale()*e.height,n=Math.max(e.height,i+s.height);this.base.position={sx:0,x_anchor:\"left\",sy:n,y_anchor:\"bottom\"},this.expo.position={sx:e.width,x_anchor:\"left\",sy:i,y_anchor:\"bottom\"}}get position(){return this._position}set visuals(t){this.expo.font_size_scale=.7,this.base.visuals=t,this.expo.visuals=t}_shift_scale(){if(this.base instanceof x&&1==this.base.nlines){const{x_height:t,cap_height:e}=(0,o.font_metrics)(this.base.font);return t/e}return 2/3}infer_text_height(){return this.base.infer_text_height()}_rect(){const t=this.base.bbox(),e=this.expo.bbox(),s=t.union(e),{x:i,y:n}=this._computed_position();return s.translate(i,n).rect}_size(){const t=this.base.size(),e=this.expo.size();return{width:t.width+e.width,height:Math.max(t.height,this._shift_scale()*t.height+e.height)}}paint(t){t.save();const{angle:e}=this;if(e){const{sx:s,sy:i}=this.position;t.translate(s,i),t.rotate(e),t.translate(-s,-i)}const{x:s,y:i}=this._computed_position();t.translate(s,i),this.base.paint(t),this.expo.paint(t),t.restore()}paint_bbox(t){super.paint_bbox(t);const{x:e,y:s}=this._computed_position();t.save(),t.translate(e,s);for(const e of this.children)e.paint_bbox(t);t.restore()}_computed_position(){const{width:t,height:e}=this._size(),{sx:s,sy:i,x_anchor:n=this._x_anchor,y_anchor:h=this._y_anchor}=this.position;return{x:s-(()=>{if((0,a.isNumber)(n))return n*t;switch(n){case\"left\":return 0;case\"center\":return.5*t;case\"right\":return t}})(),y:i-(()=>{if((0,a.isNumber)(h))return h*e;switch(h){case\"top\":return 0;case\"center\":return.5*e;case\"bottom\":return e;case\"baseline\":return.5*e}})()}}}s.BaseExpo=u,u.__name__=\"BaseExpo\";class p{constructor(t){this.items=t}set base_font_size(t){for(const e of this.items)e.base_font_size=t}get length(){return this.items.length}set visuals(t){for(const e of this.items)e.visuals=t;const e={x:0,cap:1,ascent:2,x_descent:3,cap_descent:4,ascent_descent:5},s=(0,r.max_by)(this.items.map((t=>t.infer_text_height())),(t=>e[t]));for(const t of this.items)t.text_height_metric=s}set angle(t){for(const e of this.items)e.angle=t}max_size(){let t=0,e=0;for(const s of this.items){const i=s.size();t=Math.max(t,i.width),e=Math.max(e,i.height)}return{width:t,height:e}}}s.GraphicsBoxes=p,p.__name__=\"GraphicsBoxes\"},\n function _(t,e,n,r,l){r();const a=t(11),c=(()=>{try{return\"undefined\"!=typeof OffscreenCanvas&&null!=new OffscreenCanvas(0,0).getContext(\"2d\")}catch(t){return!1}})()?(t,e)=>new OffscreenCanvas(t,e):(t,e)=>{const n=document.createElement(\"canvas\");return n.width=t,n.height=e,n},o=(()=>{const t=c(0,0).getContext(\"2d\");return e=>{t.font=e;const n=t.measureText(\"M\"),r=t.measureText(\"x\"),l=t.measureText(\"\\xc5\\u015ag|\"),c=l.fontBoundingBoxAscent,o=l.fontBoundingBoxDescent;if(null!=c&&null!=o)return{height:c+o,ascent:c,descent:o,cap_height:n.actualBoundingBoxAscent,x_height:r.actualBoundingBoxAscent};const s=l.actualBoundingBoxAscent,u=l.actualBoundingBoxDescent;if(null!=s&&null!=u)return{height:s+u,ascent:s,descent:u,cap_height:n.actualBoundingBoxAscent,x_height:r.actualBoundingBoxAscent};(0,a.unreachable)()}})(),s=(()=>{const t=c(0,0).getContext(\"2d\");return(e,n)=>{t.font=n;const r=t.measureText(e),l=r.actualBoundingBoxAscent,c=r.actualBoundingBoxDescent;if(null!=l&&null!=c)return{width:r.width,height:l+c,ascent:l,descent:c};(0,a.unreachable)()}})(),u=(()=>{const t=document.createElement(\"canvas\"),e=t.getContext(\"2d\");let n=-1,r=-1;return(l,a=1)=>{e.font=l;const{width:c}=e.measureText(\"M\"),o=c*a,s=Math.ceil(o),u=Math.ceil(2*o),i=Math.ceil(1.5*o);n{let e=0;for(let n=0;n<=i;n++)for(let r=0;r{let e=t.length-4;for(let n=u;n>=i;n--)for(let r=0;r{const t=document.createElement(\"canvas\"),e=t.getContext(\"2d\");let n=-1,r=-1;return(l,a,c=1)=>{e.font=a;const{width:o}=e.measureText(\"M\"),s=o*c,u=Math.ceil(s),i=Math.ceil(2*s),f=Math.ceil(1.5*s);(n{let e=0;for(let n=0;n<=f;n++)for(let r=0;r{let e=t.length-4;for(let n=i;n>=f;n--)for(let r=0;r{try{return o(\"normal 10px sans-serif\"),o}catch(t){return u}})(),h=(()=>{try{return s(\"A\",\"normal 10px sans-serif\"),s}catch(t){return i}})(),g=new Map;function d(t){let e=g.get(t);return null==e&&(e={font:f(t),glyphs:new Map},g.set(t,e)),e.font}n.font_metrics=d,n.glyph_metrics=function(t,e){let n=g.get(e);null==n&&(d(e),n=g.get(e));let r=n.glyphs.get(t);return null==r&&(r=h(t,e),n.glyphs.set(t,r)),r},n.parse_css_font_size=function(t){const e=t.match(/^\\s*(\\d+(\\.\\d+)?)(\\w+)\\s*$/);if(null!=e){const[,t,,n]=e,r=Number(t);if(isFinite(r))return{value:r,unit:n}}return null}},\n function _(t,s,r,n,i){n();const{sin:e,cos:a}=Math;class h{constructor(t=1,s=0,r=0,n=1,i=0,e=0){this.a=t,this.b=s,this.c=r,this.d=n,this.e=i,this.f=e}toString(){const{a:t,b:s,c:r,d:n,e:i,f:e}=this;return`matrix(${t}, ${s}, ${r}, ${n}, ${i}, ${e})`}static from_DOMMatrix(t){const{a:s,b:r,c:n,d:i,e,f:a}=t;return new h(s,r,n,i,e,a)}to_DOMMatrix(){const{a:t,b:s,c:r,d:n,e:i,f:e}=this;return new DOMMatrix([t,s,r,n,i,e])}clone(){const{a:t,b:s,c:r,d:n,e:i,f:e}=this;return new h(t,s,r,n,i,e)}get is_identity(){const{a:t,b:s,c:r,d:n,e:i,f:e}=this;return 1==t&&0==s&&0==r&&1==n&&0==i&&0==e}apply_point(t){const[s,r]=this.apply(t.x,t.y);return{x:s,y:r}}apply_rect(t){return{p0:this.apply_point(t.p0),p1:this.apply_point(t.p1),p2:this.apply_point(t.p2),p3:this.apply_point(t.p3)}}apply(t,s){const{a:r,b:n,c:i,d:e,e:a,f:h}=this;return[r*t+i*s+a,n*t+e*s+h]}iv_apply(t,s){const{a:r,b:n,c:i,d:e,e:a,f:h}=this,c=t.length;for(let o=0;o{const h={max:4,fit:3,min:2,fixed:1};return h[i]>h[t]};if(\"fixed\"!=n&&\"fixed\"!=s)if(n==s){const n=t,s=_(t/e),r=_(h*e),g=h;Math.abs(i.width-n)+Math.abs(i.height-s)<=Math.abs(i.width-r)+Math.abs(i.height-g)?(t=n,h=s):(t=r,h=g)}else r(n,s)?h=_(t/e):t=_(h*e);else\"fixed\"==n?h=_(t/e):\"fixed\"==s&&(t=_(h*e))}return{width:t,height:h}}measure(i){if(!this.sizing.visible)return{width:0,height:0};const t=i=>\"fixed\"==this.sizing.width_policy&&null!=this.sizing.width?this.sizing.width:i,h=i=>\"fixed\"==this.sizing.height_policy&&null!=this.sizing.height?this.sizing.height:i,e=new s.Sizeable(i).shrink_by(this.sizing.margin).map(t,h),n=this._measure(e),r=this.clip_size(n,e),g=t(r.width),l=h(r.height),a=this.apply_aspect(e,{width:g,height:l});return Object.assign(Object.assign({},n),a)}compute(i={}){const t=this.measure({width:null!=i.width&&this.is_width_expanding()?i.width:1/0,height:null!=i.height&&this.is_height_expanding()?i.height:1/0}),{width:h,height:e}=t,n=new r.BBox({left:0,top:0,width:h,height:e});let s;if(null!=t.inner){const{left:i,top:n,right:g,bottom:l}=t.inner;s=new r.BBox({left:i,top:n,right:h-g,bottom:e-l})}this.set_geometry(n,s)}get xview(){return this.bbox.xview}get yview(){return this.bbox.yview}clip_size(i,t){function h(i,t,h,e){return null==h?h=0:(0,g.isNumber)(h)||(h=Math.round(h.percent*t)),null==e?e=1/0:(0,g.isNumber)(e)||(e=Math.round(e.percent*t)),a(h,l(i,e))}return{width:h(i.width,t.width,this.sizing.min_width,this.sizing.max_width),height:h(i.height,t.height,this.sizing.min_height,this.sizing.max_height)}}has_size_changed(){const{_dirty:i}=this;return this._dirty=!1,i}}h.Layoutable=o,o.__name__=\"Layoutable\";class d extends o{_measure(i){const{width_policy:t,height_policy:h}=this.sizing;return{width:(()=>{const{width:h}=this.sizing;if(i.width==1/0)return null!=h?h:0;switch(t){case\"fixed\":return null!=h?h:0;case\"min\":return null!=h?l(i.width,h):0;case\"fit\":return null!=h?l(i.width,h):i.width;case\"max\":return null!=h?a(i.width,h):i.width}})(),height:(()=>{const{height:t}=this.sizing;if(i.height==1/0)return null!=t?t:0;switch(h){case\"fixed\":return null!=t?t:0;case\"min\":return null!=t?l(i.height,t):0;case\"fit\":return null!=t?l(i.height,t):i.height;case\"max\":return null!=t?a(i.height,t):i.height}})()}}}h.LayoutItem=d,d.__name__=\"LayoutItem\";class u extends o{_measure(i){const t=this._content_size(),h=i.bounded_to(this.sizing.size).bounded_to(t);return{width:(()=>{switch(this.sizing.width_policy){case\"fixed\":return null!=this.sizing.width?this.sizing.width:t.width;case\"min\":return t.width;case\"fit\":return h.width;case\"max\":return Math.max(t.width,h.width)}})(),height:(()=>{switch(this.sizing.height_policy){case\"fixed\":return null!=this.sizing.height?this.sizing.height:t.height;case\"min\":return t.height;case\"fit\":return h.height;case\"max\":return Math.max(t.height,h.height)}})()}}}h.ContentLayoutable=u,u.__name__=\"ContentLayoutable\"},\n function _(e,t,s,a,_){a();const r=e(62),n=e(61),g=e(58),i=e(63),c=e(67),h=e(65),l=e(13),o=e(11);class x{constructor(e,t,s,a,_={},r={},n={},g={}){this.in_x_scale=e,this.in_y_scale=t,this.x_range=s,this.y_range=a,this.extra_x_ranges=_,this.extra_y_ranges=r,this.extra_x_scales=n,this.extra_y_scales=g,this._bbox=new h.BBox,(0,o.assert)(null==e.source_range&&null==e.target_range),(0,o.assert)(null==t.source_range&&null==t.target_range),this._configure_scales()}get bbox(){return this._bbox}_get_ranges(e,t){return new Map((0,l.entries)(Object.assign(Object.assign({},t),{default:e})))}_get_scales(e,t,s,a){var _;const g=new Map((0,l.entries)(Object.assign(Object.assign({},t),{default:e}))),h=new Map;for(const[t,l]of s){if(l instanceof c.FactorRange!=e instanceof r.CategoricalScale)throw new Error(`Range ${l.type} is incompatible is Scale ${e.type}`);e instanceof n.LogScale&&l instanceof i.DataRange1d&&(l.scale_hint=\"log\");const s=(null!==(_=g.get(t))&&void 0!==_?_:e).clone();s.setv({source_range:l,target_range:a}),h.set(t,s)}return h}_configure_frame_ranges(){const{bbox:e}=this;this._x_target=new g.Range1d({start:e.left,end:e.right}),this._y_target=new g.Range1d({start:e.bottom,end:e.top})}_configure_scales(){this._configure_frame_ranges(),this._x_ranges=this._get_ranges(this.x_range,this.extra_x_ranges),this._y_ranges=this._get_ranges(this.y_range,this.extra_y_ranges),this._x_scales=this._get_scales(this.in_x_scale,this.extra_x_scales,this._x_ranges,this._x_target),this._y_scales=this._get_scales(this.in_y_scale,this.extra_y_scales,this._y_ranges,this._y_target)}_update_scales(){this._configure_frame_ranges();for(const[,e]of this._x_scales)e.target_range=this._x_target;for(const[,e]of this._y_scales)e.target_range=this._y_target}set_geometry(e){this._bbox=e,this._update_scales()}get x_target(){return this._x_target}get y_target(){return this._y_target}get x_ranges(){return this._x_ranges}get y_ranges(){return this._y_ranges}get x_scales(){return this._x_scales}get y_scales(){return this._y_scales}get x_scale(){return this._x_scales.get(\"default\")}get y_scale(){return this._y_scales.get(\"default\")}get xscales(){return(0,l.to_object)(this.x_scales)}get yscales(){return(0,l.to_object)(this.y_scales)}}s.CartesianFrame=x,x.__name__=\"CartesianFrame\"},\n function _(i,s,x,A,o){A(),o(\"Axis\",i(128).Axis),o(\"CategoricalAxis\",i(140).CategoricalAxis),o(\"ContinuousAxis\",i(143).ContinuousAxis),o(\"DatetimeAxis\",i(144).DatetimeAxis),o(\"LinearAxis\",i(145).LinearAxis),o(\"LogAxis\",i(162).LogAxis),o(\"MercatorAxis\",i(165).MercatorAxis)},\n function _(t,e,i,s,a){s();const o=t(1);var l;const n=t(129),_=t(130),r=t(131),h=t(132),c=(0,o.__importStar)(t(48)),b=t(20),u=t(24),m=t(123),d=t(9),x=t(13),f=t(8),g=t(120),p=t(67),v=t(133),w=t(113),j=t(11),k=t(8),y=t(134),{abs:z}=Math;class M extends n.GuideRendererView{constructor(){super(...arguments),this._axis_label_view=null,this._major_label_views=new Map}async lazy_initialize(){await super.lazy_initialize(),await this._init_axis_label(),await this._init_major_labels()}async _init_axis_label(){const{axis_label:t}=this.model;if(null!=t){const e=(0,k.isString)(t)?(0,y.parse_delimited_string)(t):t;this._axis_label_view=await(0,w.build_view)(e,{parent:this})}else this._axis_label_view=null}async _init_major_labels(){const{major_label_overrides:t}=this.model;for(const[e,i]of(0,x.entries)(t)){const t=(0,k.isString)(i)?(0,y.parse_delimited_string)(i):i;this._major_label_views.set(e,await(0,w.build_view)(t,{parent:this}))}}update_layout(){this.layout=new m.SideLayout(this.panel,(()=>this.get_size()),!0),this.layout.on_resize((()=>this._coordinates=void 0))}get_size(){const{visible:t,fixed_location:e}=this.model;if(t&&null==e&&this.is_renderable){const{extents:t}=this;return{width:0,height:Math.round(t.tick+t.tick_label+t.axis_label)}}return{width:0,height:0}}get is_renderable(){const[t,e]=this.ranges;return t.is_valid&&e.is_valid}_render(){var t;if(!this.is_renderable)return;const{tick_coords:e,extents:i}=this,s=this.layer.ctx;s.save(),this._draw_rule(s,i),this._draw_major_ticks(s,i,e),this._draw_minor_ticks(s,i,e),this._draw_major_labels(s,i,e),this._draw_axis_label(s,i,e),null===(t=this._paint)||void 0===t||t.call(this,s,i,e),s.restore()}connect_signals(){super.connect_signals();const{axis_label:t,major_label_overrides:e}=this.model.properties;this.on_change(t,(async()=>{var t;null===(t=this._axis_label_view)||void 0===t||t.remove(),await this._init_axis_label()})),this.on_change(e,(async()=>{for(const t of this._major_label_views.values())t.remove();await this._init_major_labels()})),this.connect(this.model.change,(()=>this.plot_view.request_layout()))}get needs_clip(){return null!=this.model.fixed_location}_draw_rule(t,e){if(!this.visuals.axis_line.doit)return;const[i,s]=this.rule_coords,[a,o]=this.coordinates.map_to_screen(i,s),[l,n]=this.normals,[_,r]=this.offsets;this.visuals.axis_line.set_value(t),t.beginPath();for(let e=0;e0?s+i+3:0}_draw_axis_label(t,e,i){if(null==this._axis_label_view||null!=this.model.fixed_location)return;const[s,a]=(()=>{const{bbox:t}=this.layout;switch(this.panel.side){case\"above\":return[t.hcenter,t.bottom];case\"below\":return[t.hcenter,t.top];case\"left\":return[t.right,t.vcenter];case\"right\":return[t.left,t.vcenter]}})(),[o,l]=this.normals,n=e.tick+e.tick_label+this.model.axis_label_standoff,{vertical_align:_,align:r}=this.panel.get_label_text_heuristics(\"parallel\"),h={sx:s+o*n,sy:a+l*n,x_anchor:r,y_anchor:_},c=this._axis_label_view.graphics();c.visuals=this.visuals.axis_label_text.values(),c.angle=this.panel.get_label_angle_heuristic(\"parallel\"),this.plot_view.base_font_size&&(c.base_font_size=this.plot_view.base_font_size),c.position=h,c.align=r,c.paint(t)}_draw_ticks(t,e,i,s,a){if(!a.doit)return;const[o,l]=e,[n,_]=this.coordinates.map_to_screen(o,l),[r,h]=this.normals,[c,b]=this.offsets,[u,m]=[r*(c-i),h*(b-i)],[d,x]=[r*(c+s),h*(b+s)];a.set_value(t),t.beginPath();for(let e=0;et.bbox())),M=(()=>{const[t]=this.ranges;return t.is_reversed?0==this.dimension?(t,e)=>z[t].left-z[e].right:(t,e)=>z[e].top-z[t].bottom:0==this.dimension?(t,e)=>z[e].left-z[t].right:(t,e)=>z[t].top-z[e].bottom})(),{major_label_policy:O}=this.model,T=O.filter(k,z,M),A=[...T.ones()];if(0!=A.length){const t=this.parent.canvas_view.bbox,e=e=>{const i=z[e];if(i.left<0){const t=-i.left,{position:s}=y[e];y[e].position=Object.assign(Object.assign({},s),{sx:s.sx+t})}else if(i.right>t.width){const s=i.right-t.width,{position:a}=y[e];y[e].position=Object.assign(Object.assign({},a),{sx:a.sx-s})}},i=e=>{const i=z[e];if(i.top<0){const t=-i.top,{position:s}=y[e];y[e].position=Object.assign(Object.assign({},s),{sy:s.sy+t})}else if(i.bottom>t.height){const s=i.bottom-t.height,{position:a}=y[e];y[e].position=Object.assign(Object.assign({},a),{sy:a.sy-s})}},s=A[0],a=A[A.length-1];0==this.dimension?(e(s),e(a)):(i(s),i(a))}for(const e of T){y[e].paint(t)}}_tick_extent(){return this.model.major_tick_out}_tick_label_extents(){const t=this.tick_coords.major,e=this.compute_labels(t[this.dimension]),i=this.model.major_label_orientation,s=this.model.major_label_standoff,a=this.visuals.major_label_text;return[this._oriented_labels_extent(e,i,s,a)]}get extents(){const t=this._tick_label_extents();return{tick:this._tick_extent(),tick_labels:t,tick_label:(0,d.sum)(t),axis_label:this._axis_label_extent()}}_oriented_labels_extent(t,e,i,s){if(0==t.length||!s.doit)return 0;const a=this.panel.get_label_angle_heuristic(e);t.visuals=s.values(),t.angle=a,t.base_font_size=this.plot_view.base_font_size;const o=t.max_size(),l=0==this.dimension?o.height:o.width;return l>0?i+l+3:0}get normals(){return this.panel.normals}get dimension(){return this.panel.dimension}compute_labels(t){const e=this.model.formatter.format_graphics(t,this),{_major_label_views:i}=this,s=new Set;for(let a=0;az(l-n)?(t=r(_(a,o),l),s=_(r(a,o),n)):(t=_(a,o),s=r(a,o)),[t,s]}}get rule_coords(){const t=this.dimension,e=(t+1)%2,[i]=this.ranges,[s,a]=this.computed_bounds,o=[new Array(2),new Array(2)];return o[t][0]=Math.max(s,i.min),o[t][1]=Math.min(a,i.max),o[t][0]>o[t][1]&&(o[t][0]=o[t][1]=NaN),o[e][0]=this.loc,o[e][1]=this.loc,o}get tick_coords(){const t=this.dimension,e=(t+1)%2,[i]=this.ranges,[s,a]=this.computed_bounds,o=this.model.ticker.get_ticks(s,a,i,this.loc),l=o.major,n=o.minor,_=[[],[]],r=[[],[]],[h,c]=[i.min,i.max];for(let i=0;ic||(_[t].push(l[i]),_[e].push(this.loc));for(let i=0;ic||(r[t].push(n[i]),r[e].push(this.loc));return{major:_,minor:r}}get loc(){const{fixed_location:t}=this.model;if(null!=t){if((0,f.isNumber)(t))return t;const[,e]=this.ranges;if(e instanceof p.FactorRange)return e.synthetic(t);(0,j.unreachable)()}const[,e]=this.ranges;switch(this.panel.side){case\"left\":case\"below\":return e.start;case\"right\":case\"above\":return e.end}}serializable_state(){return Object.assign(Object.assign({},super.serializable_state()),{bbox:this.layout.bbox.box})}remove(){var t;null===(t=this._axis_label_view)||void 0===t||t.remove();for(const t of this._major_label_views.values())t.remove();super.remove()}has_finished(){if(!super.has_finished())return!1;if(null!=this._axis_label_view&&!this._axis_label_view.has_finished())return!1;for(const t of this._major_label_views.values())if(!t.has_finished())return!1;return!0}}i.AxisView=M,M.__name__=\"AxisView\";class O extends n.GuideRenderer{constructor(t){super(t)}}i.Axis=O,l=O,O.__name__=\"Axis\",l.prototype.default_view=M,l.mixins([[\"axis_\",c.Line],[\"major_tick_\",c.Line],[\"minor_tick_\",c.Line],[\"major_label_\",c.Text],[\"axis_label_\",c.Text]]),l.define((({Any:t,Int:e,Number:i,String:s,Ref:a,Dict:o,Tuple:l,Or:n,Nullable:c,Auto:u})=>({bounds:[n(l(i,i),u),\"auto\"],ticker:[a(_.Ticker)],formatter:[a(r.TickFormatter)],axis_label:[c(n(s,a(v.BaseText))),null],axis_label_standoff:[e,5],major_label_standoff:[e,5],major_label_orientation:[n(b.TickLabelOrientation,i),\"horizontal\"],major_label_overrides:[o(n(s,a(v.BaseText))),{}],major_label_policy:[a(h.LabelingPolicy),()=>new h.AllLabels],major_tick_in:[i,2],major_tick_out:[i,6],minor_tick_in:[i,0],minor_tick_out:[i,4],fixed_location:[c(n(i,t)),null]}))),l.override({axis_line_color:\"black\",major_tick_line_color:\"black\",minor_tick_line_color:\"black\",major_label_text_font_size:\"11px\",major_label_text_align:\"center\",major_label_text_baseline:\"alphabetic\",axis_label_text_font_size:\"13px\",axis_label_text_font_style:\"italic\"})},\n function _(e,r,d,n,i){var s;n();const _=e(41);class u extends _.RendererView{}d.GuideRendererView=u,u.__name__=\"GuideRendererView\";class c extends _.Renderer{constructor(e){super(e)}}d.GuideRenderer=c,s=c,c.__name__=\"GuideRenderer\",s.override({level:\"guide\"})},\n function _(c,e,n,s,o){s();const r=c(53);class t extends r.Model{constructor(c){super(c)}}n.Ticker=t,t.__name__=\"Ticker\"},\n function _(t,o,r,e,c){e();const n=t(53),a=t(120);class m extends n.Model{constructor(t){super(t)}format_graphics(t,o){return this.doFormat(t,o).map((t=>new a.TextBox({text:t})))}compute(t,o){return this.doFormat([t],null!=o?o:{loc:0})[0]}v_compute(t,o){return this.doFormat(t,null!=o?o:{loc:0})}}r.TickFormatter=m,m.__name__=\"TickFormatter\"},\n function _(e,n,s,t,i){var c,r;t();const l=e(53),o=e(13),a=e(34),u=e(8),d=e(24);class _ extends l.Model{constructor(e){super(e)}}s.LabelingPolicy=_,_.__name__=\"LabelingPolicy\";class f extends _{constructor(e){super(e)}filter(e,n,s){return e}}s.AllLabels=f,f.__name__=\"AllLabels\";class m extends _{constructor(e){super(e)}filter(e,n,s){const{min_distance:t}=this;let i=null;for(const n of e)null!=i&&s(i,n)({min_distance:[e,5]})));class b extends _{constructor(e){super(e)}get names(){return(0,o.keys)(this.args)}get values(){return(0,o.values)(this.args)}get func(){const e=(0,a.use_strict)(this.code);return new d.GeneratorFunction(\"indices\",\"bboxes\",\"distance\",...this.names,e)}filter(e,n,s){const t=Object.create(null),i=this.func.call(t,e,n,s,...this.values);let c=i.next();if(c.done&&void 0!==c.value){const{value:n}=c;return n instanceof d.Indices?n:void 0===n?e:(0,u.isIterable)(n)?d.Indices.from_indices(e.size,n):d.Indices.all_unset(e.size)}{const n=[];do{n.push(c.value),c=i.next()}while(!c.done);return d.Indices.from_indices(e.size,n)}}}s.CustomLabelingPolicy=b,r=b,b.__name__=\"CustomLabelingPolicy\",r.define((({Unknown:e,String:n,Dict:s})=>({args:[s(e),{}],code:[n,\"\"]})))},\n function _(e,s,t,n,a){var _;n();const x=e(53),c=e(42);class i extends c.View{}t.BaseTextView=i,i.__name__=\"BaseTextView\";class o extends x.Model{constructor(e){super(e)}}t.BaseText=o,_=o,o.__name__=\"BaseText\",_.define((({String:e})=>({text:[e]})))},\n function _(n,e,t,i,r){i();const s=n(135),l=n(139),d=[{start:\"$$\",end:\"$$\",inline:!1},{start:\"\\\\[\",end:\"\\\\]\",inline:!1},{start:\"\\\\(\",end:\"\\\\)\",inline:!0}];t.parse_delimited_string=function(n){for(const e of d){const t=n.indexOf(e.start),i=t+e.start.length;if(0==t){const t=n.indexOf(e.end,i),r=t;if(t==n.length-e.end.length)return new s.TeX({text:n.slice(i,r),inline:e.inline});break}}return new l.PlainText({text:n})}},\n function _(t,e,s,i,n){var o,r,a;i();const h=t(8),_=t(136),l=t(22),c=t(120),d=t(121),u=t(122),g=t(65),p=t(133),x=t(137);class m extends p.BaseTextView{constructor(){super(...arguments),this._position={sx:0,sy:0},this.align=\"left\",this._x_anchor=\"left\",this._y_anchor=\"center\",this._base_font_size=13,this.font_size_scale=1,this.svg_image=null}graphics(){return this}infer_text_height(){return\"ascent_descent\"}set base_font_size(t){null!=t&&(this._base_font_size=t)}get base_font_size(){return this._base_font_size}get has_image_loaded(){return null!=this.svg_image}_rect(){const{width:t,height:e}=this._size(),{x:s,y:i}=this._computed_position();return new g.BBox({x:s,y:i,width:t,height:e}).rect}set position(t){this._position=t}get position(){return this._position}get text(){return this.model.text}get provider(){return x.default_provider}async lazy_initialize(){await super.lazy_initialize(),\"not_started\"==this.provider.status&&await this.provider.fetch(),\"not_started\"!=this.provider.status&&\"loading\"!=this.provider.status||this.provider.ready.connect((()=>this.load_image())),\"loaded\"==this.provider.status&&await this.load_image()}connect_signals(){super.connect_signals(),this.on_change(this.model.properties.text,(()=>this.load_image()))}set visuals(t){const e=t.color,s=t.alpha,i=t.font_style;let n=t.font_size;const o=t.font,{font_size_scale:r,_base_font_size:a}=this,h=(0,d.parse_css_font_size)(n);if(null!=h){let{value:t,unit:e}=h;t*=r,\"em\"==e&&a&&(t*=a,e=\"px\"),n=`${t}${e}`}const _=`${i} ${n} ${o}`;this.font=_,this.color=(0,l.color2css)(e,s)}_computed_position(){const{width:t,height:e}=this._size(),{sx:s,sy:i,x_anchor:n=this._x_anchor,y_anchor:o=this._y_anchor}=this.position;return{x:s-(()=>{if((0,h.isNumber)(n))return n*t;switch(n){case\"left\":return 0;case\"center\":return.5*t;case\"right\":return t}})(),y:i-(()=>{if((0,h.isNumber)(o))return o*e;switch(o){case\"top\":return 0;case\"center\":return.5*e;case\"bottom\":return e;case\"baseline\":return.5*e}})()}}size(){const{width:t,height:e}=this._size(),{angle:s}=this;if(s){const i=Math.cos(Math.abs(s)),n=Math.sin(Math.abs(s));return{width:Math.abs(t*i+e*n),height:Math.abs(t*n+e*i)}}return{width:t,height:e}}get_text_dimensions(){return{width:(0,c.text_width)(this.model.text,this.font),height:(0,d.font_metrics)(this.font).height}}get_image_dimensions(){var t,e,s,i;const n=parseFloat(null!==(e=null===(t=this.svg_element.getAttribute(\"height\"))||void 0===t?void 0:t.replace(/([A-z])/g,\"\"))&&void 0!==e?e:\"0\"),o=parseFloat(null!==(i=null===(s=this.svg_element.getAttribute(\"width\"))||void 0===s?void 0:s.replace(/([A-z])/g,\"\"))&&void 0!==i?i:\"0\");return{width:(0,d.font_metrics)(this.font).x_height*o,height:(0,d.font_metrics)(this.font).x_height*n}}_size(){return this.has_image_loaded?this.get_image_dimensions():this.get_text_dimensions()}bbox(){const{p0:t,p1:e,p2:s,p3:i}=this.rect(),n=Math.min(t.x,e.x,s.x,i.x),o=Math.min(t.y,e.y,s.y,i.y),r=Math.max(t.x,e.x,s.x,i.x),a=Math.max(t.y,e.y,s.y,i.y);return new g.BBox({left:n,right:r,top:o,bottom:a})}rect(){const t=this._rect(),{angle:e}=this;if(e){const{sx:s,sy:i}=this.position,n=new u.AffineTransform;return n.translate(s,i),n.rotate(e),n.translate(-s,-i),n.apply_rect(t)}return t}paint_rect(t){const{p0:e,p1:s,p2:i,p3:n}=this.rect();t.save(),t.strokeStyle=\"red\",t.lineWidth=1,t.beginPath();const{round:o}=Math;t.moveTo(o(e.x),o(e.y)),t.lineTo(o(s.x),o(s.y)),t.lineTo(o(i.x),o(i.y)),t.lineTo(o(n.x),o(n.y)),t.closePath(),t.stroke(),t.restore()}paint_bbox(t){const{x:e,y:s,width:i,height:n}=this.bbox();t.save(),t.strokeStyle=\"blue\",t.lineWidth=1,t.beginPath();const{round:o}=Math;t.moveTo(o(e),o(s)),t.lineTo(o(e),o(s+n)),t.lineTo(o(e+i),o(s+n)),t.lineTo(o(e+i),o(s)),t.closePath(),t.stroke(),t.restore()}async load_image(){if(null==this.provider.MathJax)return null;const t=this._process_text(this.model.text);if(null==t)return this._has_finished=!0,null;const e=t.children[0];this.svg_element=e,e.setAttribute(\"font\",this.font),e.setAttribute(\"stroke\",this.color);const s=e.outerHTML,i=new Blob([s],{type:\"image/svg+xml\"}),n=URL.createObjectURL(i);try{this.svg_image=await(0,_.load_image)(n)}finally{URL.revokeObjectURL(n)}return this.parent.request_layout(),this.svg_image}paint(t){t.save();const{sx:e,sy:s}=this.position;this.angle&&(t.translate(e,s),t.rotate(this.angle),t.translate(-e,-s));const{x:i,y:n}=this._computed_position();if(null!=this.svg_image){const{width:e,height:s}=this.get_image_dimensions();t.drawImage(this.svg_image,i,n,e,s)}else t.fillStyle=this.color,t.font=this.font,t.textAlign=\"left\",t.textBaseline=\"alphabetic\",t.fillText(this.model.text,i,n+(0,d.font_metrics)(this.font).ascent);t.restore(),this._has_finished||\"failed\"!=this.provider.status&&!this.has_image_loaded||(this._has_finished=!0,this.parent.notify_finished_after_paint())}}s.MathTextView=m,m.__name__=\"MathTextView\";class f extends p.BaseText{constructor(t){super(t)}}s.MathText=f,f.__name__=\"MathText\";class v extends m{_process_text(t){}}s.AsciiView=v,v.__name__=\"AsciiView\";class y extends f{constructor(t){super(t)}}s.Ascii=y,o=y,y.__name__=\"Ascii\",o.prototype.default_view=v;class w extends m{_process_text(t){var e;return null===(e=this.provider.MathJax)||void 0===e?void 0:e.mathml2svg(t.trim())}}s.MathMLView=w,w.__name__=\"MathMLView\";class b extends f{constructor(t){super(t)}}s.MathML=b,r=b,b.__name__=\"MathML\",r.prototype.default_view=w;class M extends m{_process_text(t){var e;return null===(e=this.provider.MathJax)||void 0===e?void 0:e.tex2svg(t,void 0,this.model.macros)}}s.TeXView=M,M.__name__=\"TeXView\";class T extends f{constructor(t){super(t)}}s.TeX=T,a=T,T.__name__=\"TeX\",a.prototype.default_view=M,a.define((({Boolean:t,Number:e,String:s,Dict:i,Tuple:n,Or:o})=>({macros:[i(o(s,n(s,e))),{}],inline:[t,!1]})))},\n function _(i,e,t,s,o){s();const a=i(19);t.load_image=async function(i,e){return new n(i,e).promise};class n{constructor(i,e={}){this._image=new Image,this._finished=!1;const{attempts:t=1,timeout:s=1}=e;this.promise=new Promise(((o,n)=>{this._image.crossOrigin=\"anonymous\";let r=0;this._image.onerror=()=>{if(++r==t){const s=`unable to load ${i} image after ${t} attempts`;if(a.logger.warn(s),null==this._image.crossOrigin)return void(null!=e.failed&&e.failed());a.logger.warn(`attempting to load ${i} without a cross origin policy`),this._image.crossOrigin=null,r=0}setTimeout((()=>this._image.src=i),s)},this._image.onload=()=>{this._finished=!0,null!=e.loaded&&e.loaded(this._image),o(this._image)},this._image.src=i}))}get finished(){return this._finished}get image(){if(this._finished)return this._image;throw new Error(\"not loaded yet\")}}t.ImageLoader=n,n.__name__=\"ImageLoader\"},\n function _(t,e,a,s,n){var r=this&&this.__createBinding||(Object.create?function(t,e,a,s){void 0===s&&(s=a),Object.defineProperty(t,s,{enumerable:!0,get:function(){return e[a]}})}:function(t,e,a,s){void 0===s&&(s=a),t[s]=e[a]}),i=this&&this.__setModuleDefault||(Object.create?function(t,e){Object.defineProperty(t,\"default\",{enumerable:!0,value:e})}:function(t,e){t.default=e}),d=this&&this.__importStar||function(t){if(t&&t.__esModule)return t;var e={};if(null!=t)for(var a in t)\"default\"!==a&&Object.prototype.hasOwnProperty.call(t,a)&&r(e,t,a);return i(e,t),e};s();const o=t(15),u=t(138);class c{constructor(){this.ready=new o.Signal0(this,\"ready\"),this.status=\"not_started\"}}a.MathJaxProvider=c,c.__name__=\"MathJaxProvider\";class h extends c{get MathJax(){return null}async fetch(){this.status=\"failed\"}}a.NoProvider=h,h.__name__=\"NoProvider\";class l extends c{get MathJax(){return\"undefined\"!=typeof MathJax?MathJax:null}async fetch(){const t=document.createElement(\"script\");t.src=\"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-svg.js\",t.onload=()=>{this.status=\"loaded\",this.ready.emit()},t.onerror=()=>{this.status=\"failed\"},this.status=\"loading\",document.head.appendChild(t)}}a.CDNProvider=l,l.__name__=\"CDNProvider\";class _ extends c{get MathJax(){return this._mathjax}async fetch(){this.status=\"loading\";try{const e=await(0,u.load_module)(Promise.resolve().then((()=>d(t(519)))));this._mathjax=e,this.status=\"loaded\",this.ready.emit()}catch(t){this.status=\"failed\"}}}a.BundleProvider=_,_.__name__=\"BundleProvider\",a.default_provider=new _},\n function _(n,r,o,t,c){t(),o.load_module=async function(n){try{return await n}catch(n){if((r=n)instanceof Error&&\"code\"in r&&\"MODULE_NOT_FOUND\"===n.code)return null;throw n}var r}},\n function _(e,t,i,n,s){var a;n();const x=e(133),_=e(120);class l extends x.BaseTextView{initialize(){super.initialize(),this._has_finished=!0}graphics(){return new _.TextBox({text:this.model.text})}}i.PlainTextView=l,l.__name__=\"PlainTextView\";class r extends x.BaseText{constructor(e){super(e)}}i.PlainText=r,a=r,r.__name__=\"PlainText\",a.prototype.default_view=l},\n function _(t,s,o,e,i){e();const r=t(1);var a;const l=t(128),_=t(141),n=t(142),p=(0,r.__importStar)(t(48)),c=t(20),h=t(120),m=t(8);class u extends l.AxisView{_paint(t,s,o){this._draw_group_separators(t,s,o)}_draw_group_separators(t,s,o){const[e]=this.ranges,[i,r]=this.computed_bounds;if(!e.tops||e.tops.length<2||!this.visuals.separator_line.doit)return;const a=this.dimension,l=(a+1)%2,_=[[],[]];let n=0;for(let t=0;ti&&pnew h.GraphicsBoxes(t.map((t=>(0,m.isString)(t)?new h.TextBox({text:t}):t))),_=t=>l(this.model.formatter.doFormat(t,this));if(1==t.levels){const t=_(i.major);a.push([t,r.major,this.model.major_label_orientation,this.visuals.major_label_text])}else if(2==t.levels){const t=_(i.major.map((t=>t[1])));a.push([t,r.major,this.model.major_label_orientation,this.visuals.major_label_text]),a.push([l(i.tops),r.tops,this.model.group_label_orientation,this.visuals.group_text])}else if(3==t.levels){const t=_(i.major.map((t=>t[2]))),s=i.mids.map((t=>t[1]));a.push([t,r.major,this.model.major_label_orientation,this.visuals.major_label_text]),a.push([l(s),r.mids,this.model.subgroup_label_orientation,this.visuals.subgroup_text]),a.push([l(i.tops),r.tops,this.model.group_label_orientation,this.visuals.group_text])}return a}get tick_coords(){const t=this.dimension,s=(t+1)%2,[o]=this.ranges,[e,i]=this.computed_bounds,r=this.model.ticker.get_ticks(e,i,o,this.loc),a={major:[[],[]],mids:[[],[]],tops:[[],[]],minor:[[],[]]};return a.major[t]=r.major,a.major[s]=r.major.map((()=>this.loc)),3==o.levels&&(a.mids[t]=r.mids,a.mids[s]=r.mids.map((()=>this.loc))),o.levels>1&&(a.tops[t]=r.tops,a.tops[s]=r.tops.map((()=>this.loc))),a}}o.CategoricalAxisView=u,u.__name__=\"CategoricalAxisView\";class d extends l.Axis{constructor(t){super(t)}}o.CategoricalAxis=d,a=d,d.__name__=\"CategoricalAxis\",a.prototype.default_view=u,a.mixins([[\"separator_\",p.Line],[\"group_\",p.Text],[\"subgroup_\",p.Text]]),a.define((({Number:t,Or:s})=>({group_label_orientation:[s(c.TickLabelOrientation,t),\"parallel\"],subgroup_label_orientation:[s(c.TickLabelOrientation,t),\"parallel\"]}))),a.override({ticker:()=>new _.CategoricalTicker,formatter:()=>new n.CategoricalTickFormatter,separator_line_color:\"lightgrey\",separator_line_width:2,group_text_font_style:\"bold\",group_text_font_size:\"11px\",group_text_color:\"grey\",subgroup_text_font_style:\"bold\",subgroup_text_font_size:\"11px\"})},\n function _(t,c,o,s,e){s();const r=t(130);class i extends r.Ticker{constructor(t){super(t)}get_ticks(t,c,o,s){var e,r;return{major:this._collect(o.factors,o,t,c),minor:[],tops:this._collect(null!==(e=o.tops)&&void 0!==e?e:[],o,t,c),mids:this._collect(null!==(r=o.mids)&&void 0!==r?r:[],o,t,c)}}_collect(t,c,o,s){const e=[];for(const r of t){const t=c.synthetic(r);t>o&&tnew _.DatetimeTicker,formatter:()=>new m.DatetimeTickFormatter})},\n function _(e,i,s,n,r){var t;n();const a=e(143),o=e(146),c=e(147);class _ extends a.ContinuousAxisView{}s.LinearAxisView=_,_.__name__=\"LinearAxisView\";class u extends a.ContinuousAxis{constructor(e){super(e)}}s.LinearAxis=u,t=u,u.__name__=\"LinearAxis\",t.prototype.default_view=_,t.override({ticker:()=>new c.BasicTicker,formatter:()=>new o.BasicTickFormatter})},\n function _(i,t,e,n,o){var r;n();const s=i(131),c=i(34);function _(i){let t=\"\";for(const e of i)t+=\"-\"==e?\"\\u2212\":e;return t}e.unicode_replace=_;class a extends s.TickFormatter{constructor(i){super(i),this.last_precision=3}get scientific_limit_low(){return 10**this.power_limit_low}get scientific_limit_high(){return 10**this.power_limit_high}_need_sci(i){if(!this.use_scientific)return!1;const{scientific_limit_high:t}=this,{scientific_limit_low:e}=this,n=i.length<2?0:Math.abs(i[1]-i[0])/1e4;for(const o of i){const i=Math.abs(o);if(!(i<=n)&&(i>=t||i<=e))return!0}return!1}_format_with_precision(i,t,e){return t?i.map((i=>_(i.toExponential(e)))):i.map((i=>_((0,c.to_fixed)(i,e))))}_auto_precision(i,t){const e=new Array(i.length),n=this.last_precision<=15;i:for(let o=this.last_precision;n?o<=15:o>=1;n?o++:o--){if(t){e[0]=i[0].toExponential(o);for(let t=1;t({precision:[n(t,e),\"auto\"],use_scientific:[i,!0],power_limit_high:[t,5],power_limit_low:[t,-3]})))},\n function _(c,e,s,i,n){i();const r=c(148);class t extends r.AdaptiveTicker{constructor(c){super(c)}}s.BasicTicker=t,t.__name__=\"BasicTicker\"},\n function _(t,i,a,s,e){var n;s();const r=t(149),_=t(9),l=t(10);class h extends r.ContinuousTicker{constructor(t){super(t)}get_min_interval(){return this.min_interval}get_max_interval(){var t;return null!==(t=this.max_interval)&&void 0!==t?t:1/0}initialize(){super.initialize();const t=(0,_.nth)(this.mantissas,-1)/this.base,i=(0,_.nth)(this.mantissas,0)*this.base;this.extended_mantissas=[t,...this.mantissas,i],this.base_factor=0===this.get_min_interval()?1:this.get_min_interval()}get_interval(t,i,a){const s=i-t,e=this.get_ideal_interval(t,i,a),n=Math.floor((0,l.log)(e/this.base_factor,this.base)),r=this.base**n*this.base_factor,h=this.extended_mantissas,m=h.map((t=>Math.abs(a-s/(t*r)))),v=h[(0,_.argmin)(m)]*r;return(0,l.clamp)(v,this.get_min_interval(),this.get_max_interval())}}a.AdaptiveTicker=h,n=h,h.__name__=\"AdaptiveTicker\",n.define((({Number:t,Array:i,Nullable:a})=>({base:[t,10],mantissas:[i(t),[1,2,5]],min_interval:[t,0],max_interval:[a(t),null]})))},\n function _(t,n,i,s,e){var o;s();const r=t(130),c=t(9);class _ extends r.Ticker{constructor(t){super(t)}get_ticks(t,n,i,s){return this.get_ticks_no_defaults(t,n,s,this.desired_num_ticks)}get_ticks_no_defaults(t,n,i,s){const e=this.get_interval(t,n,s),o=Math.floor(t/e),r=Math.ceil(n/e);let _;_=isFinite(o)&&isFinite(r)?(0,c.range)(o,r+1):[];const u=_.map((t=>t*e)).filter((i=>t<=i&&i<=n)),a=this.num_minor_ticks,f=[];if(a>0&&u.length>0){const i=e/a,s=(0,c.range)(0,a).map((t=>t*i));for(const i of s.slice(1)){const s=u[0]-i;t<=s&&s<=n&&f.push(s)}for(const i of u)for(const e of s){const s=i+e;t<=s&&s<=n&&f.push(s)}}return{major:u,minor:f}}get_ideal_interval(t,n,i){return(n-t)/i}}i.ContinuousTicker=_,o=_,_.__name__=\"ContinuousTicker\",o.define((({Int:t})=>({num_minor_ticks:[t,5],desired_num_ticks:[t,6]})))},\n function _(s,t,e,n,i){n();var r;const o=(0,s(1).__importDefault)(s(151)),a=s(131),c=s(19),u=s(152),m=s(9),h=s(8);function d(s){return(0,o.default)(s,\"%Y %m %d %H %M %S\").split(/\\s+/).map((s=>parseInt(s,10)))}function l(s,t){if((0,h.isFunction)(t))return t(s);{const e=(0,u.sprintf)(\"$1%06d\",function(s){return Math.round(s/1e3%1*1e6)}(s));return-1==(t=t.replace(/((^|[^%])(%%)*)%f/,e)).indexOf(\"%\")?t:(0,o.default)(s,t)}}const f=[\"microseconds\",\"milliseconds\",\"seconds\",\"minsec\",\"minutes\",\"hourmin\",\"hours\",\"days\",\"months\",\"years\"];class _ extends a.TickFormatter{constructor(s){super(s),this.strip_leading_zeros=!0}initialize(){super.initialize(),this._update_width_formats()}_update_width_formats(){const s=+(0,o.default)(new Date),t=function(t){const e=t.map((t=>l(s,t).length)),n=(0,m.sort_by)((0,m.zip)(e,t),(([s])=>s));return(0,m.unzip)(n)};this._width_formats={microseconds:t(this.microseconds),milliseconds:t(this.milliseconds),seconds:t(this.seconds),minsec:t(this.minsec),minutes:t(this.minutes),hourmin:t(this.hourmin),hours:t(this.hours),days:t(this.days),months:t(this.months),years:t(this.years)}}_get_resolution_str(s,t){const e=1.1*s;switch(!1){case!(e<.001):return\"microseconds\";case!(e<1):return\"milliseconds\";case!(e<60):return t>=60?\"minsec\":\"seconds\";case!(e<3600):return t>=3600?\"hourmin\":\"minutes\";case!(e<86400):return\"hours\";case!(e<2678400):return\"days\";case!(e<31536e3):return\"months\";default:return\"years\"}}doFormat(s,t){if(0==s.length)return[];const e=Math.abs(s[s.length-1]-s[0])/1e3,n=e/(s.length-1),i=this._get_resolution_str(n,e),[,[r]]=this._width_formats[i],o=[],a=f.indexOf(i),u={};for(const s of f)u[s]=0;u.seconds=5,u.minsec=4,u.minutes=4,u.hourmin=3,u.hours=3;for(const t of s){let s,e;try{e=d(t),s=l(t,r)}catch(s){c.logger.warn(`unable to format tick for timestamp value ${t}`),c.logger.warn(` - ${s}`),o.push(\"ERR\");continue}let n=!1,m=a;for(;0==e[u[f[m]]];){let r;if(m+=1,m==f.length)break;if((\"minsec\"==i||\"hourmin\"==i)&&!n){if(\"minsec\"==i&&0==e[4]&&0!=e[5]||\"hourmin\"==i&&0==e[3]&&0!=e[4]){r=this._width_formats[f[a-1]][1][0],s=l(t,r);break}n=!0}r=this._width_formats[f[m]][1][0],s=l(t,r)}if(this.strip_leading_zeros){let t=s.replace(/^0+/g,\"\");t!=s&&isNaN(parseInt(t))&&(t=`0${t}`),o.push(t)}else o.push(s)}return o}}e.DatetimeTickFormatter=_,r=_,_.__name__=\"DatetimeTickFormatter\",r.define((({String:s,Array:t})=>({microseconds:[t(s),[\"%fus\"]],milliseconds:[t(s),[\"%3Nms\",\"%S.%3Ns\"]],seconds:[t(s),[\"%Ss\"]],minsec:[t(s),[\":%M:%S\"]],minutes:[t(s),[\":%M\",\"%Mm\"]],hourmin:[t(s),[\"%H:%M\"]],hours:[t(s),[\"%Hh\",\"%H:%M\"]],days:[t(s),[\"%m/%d\",\"%a%d\"]],months:[t(s),[\"%m/%Y\",\"%b %Y\"]],years:[t(s),[\"%Y\"]]})))},\n function _(e,t,n,r,o){!function(e){\"object\"==typeof t&&t.exports?t.exports=e():\"function\"==typeof define?define(e):this.tz=e()}((function(){function e(e,t,n){var r,o=t.day[1];do{r=new Date(Date.UTC(n,t.month,Math.abs(o++)))}while(t.day[0]<7&&r.getUTCDay()!=t.day[0]);return(r={clock:t.clock,sort:r.getTime(),rule:t,save:6e4*t.save,offset:e.offset})[r.clock]=r.sort+6e4*t.time,r.posix?r.wallclock=r[r.clock]+(e.offset+t.saved):r.posix=r[r.clock]-(e.offset+t.saved),r}function t(t,n,r){var o,a,u,i,l,s,c,f=t[t.zone],h=[],T=new 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w=\"\";if(v=null===($=-1===n.indexOf(\".\")?n.match(/([0-9]+).*/):n.match(/([0-9]+)\\..*/))?-1:$[1].length,-1!==n.indexOf(\"-\")&&(P=!0),n.indexOf(\"(\")>-1?(B=!0,n=n.slice(1,-1)):n.indexOf(\"+\")>-1&&(E=!0,n=n.replace(/\\+/g,\"\")),n.indexOf(\"a\")>-1){if(g=n.split(\".\")[0].match(/[0-9]+/g)||[\"0\"],g=parseInt(g[0],10),U=n.indexOf(\"aK\")>=0,N=n.indexOf(\"aM\")>=0,S=n.indexOf(\"aB\")>=0,j=n.indexOf(\"aT\")>=0,D=U||N||S||j,n.indexOf(\" a\")>-1?(k=\" \",n=n.replace(\" 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x.indexOf(\"-\")>-1&&(x=x.slice(1),R=!0),x.length-1&&(x=x.toString().replace(/(\\d)(?=(\\d{3})+(?!\\d))/g,\"$1\"+o[u].delimiters.thousands)),0===n.indexOf(\".\")&&(x=\"\"),b+(n.indexOf(\"(\")2)&&(o.length<2?!!o[0].match(/^\\d+.*\\d$/)&&!o[0].match(u):1===o[0].length?!!o[0].match(/^\\d+$/)&&!o[0].match(u)&&!!o[1].match(/^\\d+$/):!!o[0].match(/^\\d+.*\\d$/)&&!o[0].match(u)&&!!o[1].match(/^\\d+$/)))))},n.exports={format:function(e,n,t,r){return null!=t&&t!==a.culture()&&a.setCulture(t),p(Number(e),null!=n?n:s,null==r?Math.round:r)}}},\n function _(e,n,t,r,i){!function(){\"use strict\";var e={not_string:/[^s]/,not_bool:/[^t]/,not_type:/[^T]/,not_primitive:/[^v]/,number:/[diefg]/,numeric_arg:/[bcdiefguxX]/,json:/[j]/,not_json:/[^j]/,text:/^[^\\x25]+/,modulo:/^\\x25{2}/,placeholder:/^\\x25(?:([1-9]\\d*)\\$|\\(([^)]+)\\))?(\\+)?(0|'[^$])?(-)?(\\d+)?(?:\\.(\\d+))?([b-gijostTuvxX])/,key:/^([a-z_][a-z_\\d]*)/i,key_access:/^\\.([a-z_][a-z_\\d]*)/i,index_access:/^\\[(\\d+)\\]/,sign:/^[+-]/};function n(e){return i(a(e),arguments)}function r(e,t){return n.apply(null,[e].concat(t||[]))}function i(t,r){var i,s,a,o,p,c,l,u,f,d=1,g=t.length,y=\"\";for(s=0;s=0),o.type){case\"b\":i=parseInt(i,10).toString(2);break;case\"c\":i=String.fromCharCode(parseInt(i,10));break;case\"d\":case\"i\":i=parseInt(i,10);break;case\"j\":i=JSON.stringify(i,null,o.width?parseInt(o.width):0);break;case\"e\":i=o.precision?parseFloat(i).toExponential(o.precision):parseFloat(i).toExponential();break;case\"f\":i=o.precision?parseFloat(i).toFixed(o.precision):parseFloat(i);break;case\"g\":i=o.precision?String(Number(i.toPrecision(o.precision))):parseFloat(i);break;case\"o\":i=(parseInt(i,10)>>>0).toString(8);break;case\"s\":i=String(i),i=o.precision?i.substring(0,o.precision):i;break;case\"t\":i=String(!!i),i=o.precision?i.substring(0,o.precision):i;break;case\"T\":i=Object.prototype.toString.call(i).slice(8,-1).toLowerCase(),i=o.precision?i.substring(0,o.precision):i;break;case\"u\":i=parseInt(i,10)>>>0;break;case\"v\":i=i.valueOf(),i=o.precision?i.substring(0,o.precision):i;break;case\"x\":i=(parseInt(i,10)>>>0).toString(16);break;case\"X\":i=(parseInt(i,10)>>>0).toString(16).toUpperCase()}e.json.test(o.type)?y+=i:(!e.number.test(o.type)||u&&!o.sign?f=\"\":(f=u?\"+\":\"-\",i=i.toString().replace(e.sign,\"\")),c=o.pad_char?\"0\"===o.pad_char?\"0\":o.pad_char.charAt(1):\" \",l=o.width-(f+i).length,p=o.width&&l>0?c.repeat(l):\"\",y+=o.align?f+i+p:\"0\"===c?f+p+i:p+f+i)}return y}var s=Object.create(null);function a(n){if(s[n])return s[n];for(var t,r=n,i=[],a=0;r;){if(null!==(t=e.text.exec(r)))i.push(t[0]);else if(null!==(t=e.modulo.exec(r)))i.push(\"%\");else{if(null===(t=e.placeholder.exec(r)))throw new SyntaxError(\"[sprintf] unexpected placeholder\");if(t[2]){a|=1;var o=[],p=t[2],c=[];if(null===(c=e.key.exec(p)))throw new SyntaxError(\"[sprintf] failed to parse named argument key\");for(o.push(c[1]);\"\"!==(p=p.substring(c[0].length));)if(null!==(c=e.key_access.exec(p)))o.push(c[1]);else{if(null===(c=e.index_access.exec(p)))throw new SyntaxError(\"[sprintf] failed to parse named argument key\");o.push(c[1])}t[2]=o}else a|=2;if(3===a)throw new Error(\"[sprintf] mixing positional and named placeholders is not (yet) supported\");i.push({placeholder:t[0],param_no:t[1],keys:t[2],sign:t[3],pad_char:t[4],align:t[5],width:t[6],precision:t[7],type:t[8]})}r=r.substring(t[0].length)}return s[n]=i}void 0!==t&&(t.sprintf=n,t.vsprintf=r),\"undefined\"!=typeof window&&(window.sprintf=n,window.vsprintf=r,\"function\"==typeof define&&define.amd&&define((function(){return{sprintf:n,vsprintf:r}})))}()},\n function _(e,n,i,a,s){var r;a();const t=e(9),c=e(148),m=e(156),_=e(157),k=e(160),o=e(161),T=e(159);class w extends m.CompositeTicker{constructor(e){super(e)}}i.DatetimeTicker=w,r=w,w.__name__=\"DatetimeTicker\",r.override({num_minor_ticks:0,tickers:()=>[new c.AdaptiveTicker({mantissas:[1,2,5],base:10,min_interval:0,max_interval:500*T.ONE_MILLI,num_minor_ticks:0}),new c.AdaptiveTicker({mantissas:[1,2,5,10,15,20,30],base:60,min_interval:T.ONE_SECOND,max_interval:30*T.ONE_MINUTE,num_minor_ticks:0}),new c.AdaptiveTicker({mantissas:[1,2,4,6,8,12],base:24,min_interval:T.ONE_HOUR,max_interval:12*T.ONE_HOUR,num_minor_ticks:0}),new _.DaysTicker({days:(0,t.range)(1,32)}),new _.DaysTicker({days:(0,t.range)(1,31,3)}),new _.DaysTicker({days:[1,8,15,22]}),new _.DaysTicker({days:[1,15]}),new k.MonthsTicker({months:(0,t.range)(0,12,1)}),new k.MonthsTicker({months:(0,t.range)(0,12,2)}),new k.MonthsTicker({months:(0,t.range)(0,12,4)}),new k.MonthsTicker({months:(0,t.range)(0,12,6)}),new o.YearsTicker({})]})},\n function _(t,e,i,r,s){var n;r();const _=t(149),a=t(9);class l extends _.ContinuousTicker{constructor(t){super(t)}get min_intervals(){return this.tickers.map((t=>t.get_min_interval()))}get max_intervals(){return this.tickers.map((t=>t.get_max_interval()))}get_min_interval(){return this.min_intervals[0]}get_max_interval(){return this.max_intervals[0]}get_best_ticker(t,e,i){const r=e-t,s=this.get_ideal_interval(t,e,i),n=[(0,a.sorted_index)(this.min_intervals,s)-1,(0,a.sorted_index)(this.max_intervals,s)],_=[this.min_intervals[n[0]],this.max_intervals[n[1]]].map((t=>Math.abs(i-r/t)));let l;if((0,a.is_empty)(_.filter((t=>!isNaN(t)))))l=this.tickers[0];else{const t=n[(0,a.argmin)(_)];l=this.tickers[t]}return l}get_interval(t,e,i){return this.get_best_ticker(t,e,i).get_interval(t,e,i)}get_ticks_no_defaults(t,e,i,r){return this.get_best_ticker(t,e,r).get_ticks_no_defaults(t,e,i,r)}}i.CompositeTicker=l,n=l,l.__name__=\"CompositeTicker\",n.define((({Array:t,Ref:e})=>({tickers:[t(e(_.ContinuousTicker)),[]]})))},\n function _(t,e,n,s,o){var a;s();const i=t(158),r=t(159),c=t(9);class _ extends i.SingleIntervalTicker{constructor(t){super(t)}initialize(){super.initialize();const t=this.days;t.length>1?this.interval=(t[1]-t[0])*r.ONE_DAY:this.interval=31*r.ONE_DAY}get_ticks_no_defaults(t,e,n,s){const o=function(t,e){const n=(0,r.last_month_no_later_than)(new Date(t)),s=(0,r.last_month_no_later_than)(new Date(e));s.setUTCMonth(s.getUTCMonth()+1);const o=[],a=n;for(;o.push((0,r.copy_date)(a)),a.setUTCMonth(a.getUTCMonth()+1),!(a>s););return o}(t,e),a=this.days,i=this.interval,_=(0,c.concat)(o.map((t=>((t,e)=>{const n=t.getUTCMonth(),s=[];for(const o of a){const a=(0,r.copy_date)(t);a.setUTCDate(o),new Date(a.getTime()+e/2).getUTCMonth()==n&&s.push(a)}return s})(t,i))));return{major:_.map((t=>t.getTime())).filter((n=>t<=n&&n<=e)),minor:[]}}}n.DaysTicker=_,a=_,_.__name__=\"DaysTicker\",a.define((({Int:t,Array:e})=>({days:[e(t),[]]}))),a.override({num_minor_ticks:0})},\n function _(e,n,t,r,i){var a;r();const l=e(149);class s extends l.ContinuousTicker{constructor(e){super(e)}get_interval(e,n,t){return this.interval}get_min_interval(){return this.interval}get_max_interval(){return this.interval}}t.SingleIntervalTicker=s,a=s,s.__name__=\"SingleIntervalTicker\",a.define((({Number:e})=>({interval:[e]})))},\n function _(t,n,e,_,E){function N(t){return new Date(t.getTime())}function O(t){const n=N(t);return n.setUTCDate(1),n.setUTCHours(0),n.setUTCMinutes(0),n.setUTCSeconds(0),n.setUTCMilliseconds(0),n}_(),e.ONE_MILLI=1,e.ONE_SECOND=1e3,e.ONE_MINUTE=60*e.ONE_SECOND,e.ONE_HOUR=60*e.ONE_MINUTE,e.ONE_DAY=24*e.ONE_HOUR,e.ONE_MONTH=30*e.ONE_DAY,e.ONE_YEAR=365*e.ONE_DAY,e.copy_date=N,e.last_month_no_later_than=O,e.last_year_no_later_than=function(t){const n=O(t);return n.setUTCMonth(0),n}},\n function _(t,e,n,a,r){var s;a();const i=t(158),o=t(159),l=t(9);class _ extends i.SingleIntervalTicker{constructor(t){super(t)}initialize(){super.initialize();const t=this.months;t.length>1?this.interval=(t[1]-t[0])*o.ONE_MONTH:this.interval=12*o.ONE_MONTH}get_ticks_no_defaults(t,e,n,a){const r=function(t,e){const n=(0,o.last_year_no_later_than)(new Date(t)),a=(0,o.last_year_no_later_than)(new Date(e));a.setUTCFullYear(a.getUTCFullYear()+1);const r=[],s=n;for(;r.push((0,o.copy_date)(s)),s.setUTCFullYear(s.getUTCFullYear()+1),!(s>a););return r}(t,e),s=this.months;return{major:(0,l.concat)(r.map((t=>s.map((e=>{const n=(0,o.copy_date)(t);return n.setUTCMonth(e),n}))))).map((t=>t.getTime())).filter((n=>t<=n&&n<=e)),minor:[]}}}n.MonthsTicker=_,s=_,_.__name__=\"MonthsTicker\",s.define((({Int:t,Array:e})=>({months:[e(t),[]]})))},\n function _(e,t,a,i,r){i();const n=e(147),_=e(158),s=e(159);class c extends _.SingleIntervalTicker{constructor(e){super(e)}initialize(){super.initialize(),this.interval=s.ONE_YEAR,this.basic_ticker=new n.BasicTicker({num_minor_ticks:0})}get_ticks_no_defaults(e,t,a,i){const r=(0,s.last_year_no_later_than)(new Date(e)).getUTCFullYear(),n=(0,s.last_year_no_later_than)(new Date(t)).getUTCFullYear();return{major:this.basic_ticker.get_ticks_no_defaults(r,n,a,i).major.map((e=>Date.UTC(e,0,1))).filter((a=>e<=a&&a<=t)),minor:[]}}}a.YearsTicker=c,c.__name__=\"YearsTicker\"},\n function _(e,o,i,s,t){var n;s();const r=e(143),_=e(163),c=e(164);class a extends r.ContinuousAxisView{}i.LogAxisView=a,a.__name__=\"LogAxisView\";class u extends r.ContinuousAxis{constructor(e){super(e)}}i.LogAxis=u,n=u,u.__name__=\"LogAxis\",n.prototype.default_view=a,n.override({ticker:()=>new c.LogTicker,formatter:()=>new _.LogTickFormatter})},\n function _(e,t,n,o,r){var i;o();const a=e(131),s=e(146),c=e(164),l=e(120),{abs:u,log:x,round:_}=Math;class p extends a.TickFormatter{constructor(e){super(e)}initialize(){super.initialize(),this.basic_formatter=new s.BasicTickFormatter}format_graphics(e,t){var n,o;if(0==e.length)return[];const r=null!==(o=null===(n=this.ticker)||void 0===n?void 0:n.base)&&void 0!==o?o:10,i=this._exponents(e,r);return null==i?this.basic_formatter.format_graphics(e,t):i.map((e=>{if(u(e)u(e)({ticker:[n(t(c.LogTicker)),null],min_exponent:[e,0]})))},\n function _(t,o,e,s,n){var r;s();const i=t(148),a=t(9);class c extends i.AdaptiveTicker{constructor(t){super(t)}get_ticks_no_defaults(t,o,e,s){const n=this.num_minor_ticks,r=[],i=this.base,c=Math.log(t)/Math.log(i),f=Math.log(o)/Math.log(i),l=f-c;let h;if(isFinite(l))if(l<2){const e=this.get_interval(t,o,s),i=Math.floor(t/e),c=Math.ceil(o/e);if(h=(0,a.range)(i,c+1).filter((t=>0!=t)).map((t=>t*e)).filter((e=>t<=e&&e<=o)),n>0&&h.length>0){const t=e/n,o=(0,a.range)(0,n).map((o=>o*t));for(const t of o.slice(1))r.push(h[0]-t);for(const t of h)for(const e of o)r.push(t+e)}}else{const t=Math.ceil(.999999*c),o=Math.floor(1.000001*f),e=Math.ceil((o-t)/9);if(h=(0,a.range)(t-1,o+1,e).map((t=>i**t)),n>0&&h.length>0){const t=i**e/n,o=(0,a.range)(1,n+1).map((o=>o*t));for(const t of o)r.push(h[0]/t);r.push(h[0]);for(const t of h)for(const e of o)r.push(t*e)}}else h=[];return{major:h.filter((e=>t<=e&&e<=o)),minor:r.filter((e=>t<=e&&e<=o))}}}e.LogTicker=c,r=c,c.__name__=\"LogTicker\",r.override({mantissas:[1,5]})},\n function _(e,r,t,i,a){var o;i();const s=e(128),c=e(145),n=e(166),_=e(167);class x extends s.AxisView{}t.MercatorAxisView=x,x.__name__=\"MercatorAxisView\";class d extends c.LinearAxis{constructor(e){super(e)}}t.MercatorAxis=d,o=d,d.__name__=\"MercatorAxis\",o.prototype.default_view=x,o.override({ticker:()=>new _.MercatorTicker({dimension:\"lat\"}),formatter:()=>new n.MercatorTickFormatter({dimension:\"lat\"})})},\n function _(r,t,e,o,n){var i;o();const c=r(146),s=r(20),a=r(78);class l extends c.BasicTickFormatter{constructor(r){super(r)}doFormat(r,t){if(null==this.dimension)throw new Error(\"MercatorTickFormatter.dimension not configured\");if(0==r.length)return[];const e=r.length,o=new Array(e);if(\"lon\"==this.dimension)for(let n=0;n({dimension:[r(s.LatLon),null]})))},\n function _(t,o,n,s,r){var e;s();const i=t(147),c=t(20),_=t(78);class a extends i.BasicTicker{constructor(t){super(t)}get_ticks_no_defaults(t,o,n,s){if(null==this.dimension)throw new Error(`${this}.dimension wasn't configured`);return[t,o]=(0,_.clip_mercator)(t,o,this.dimension),\"lon\"==this.dimension?this._get_ticks_lon(t,o,n,s):this._get_ticks_lat(t,o,n,s)}_get_ticks_lon(t,o,n,s){const[r]=_.wgs84_mercator.invert(t,n),[e,i]=_.wgs84_mercator.invert(o,n),c=super.get_ticks_no_defaults(r,e,n,s),a=[];for(const t of c.major)if((0,_.in_bounds)(t,\"lon\")){const[o]=_.wgs84_mercator.compute(t,i);a.push(o)}const m=[];for(const t of c.minor)if((0,_.in_bounds)(t,\"lon\")){const[o]=_.wgs84_mercator.compute(t,i);m.push(o)}return{major:a,minor:m}}_get_ticks_lat(t,o,n,s){const[,r]=_.wgs84_mercator.invert(n,t),[e,i]=_.wgs84_mercator.invert(n,o),c=super.get_ticks_no_defaults(r,i,n,s),a=[];for(const t of c.major)if((0,_.in_bounds)(t,\"lat\")){const[,o]=_.wgs84_mercator.compute(e,t);a.push(o)}const m=[];for(const t of c.minor)if((0,_.in_bounds)(t,\"lat\")){const[,o]=_.wgs84_mercator.compute(e,t);m.push(o)}return{major:a,minor:m}}}n.MercatorTicker=a,e=a,a.__name__=\"MercatorTicker\",e.define((({Nullable:t})=>({dimension:[t(c.LatLon),null]})))},\n function _(e,i,r,c,k){c(),k(\"AdaptiveTicker\",e(148).AdaptiveTicker),k(\"BasicTicker\",e(147).BasicTicker),k(\"CategoricalTicker\",e(141).CategoricalTicker),k(\"CompositeTicker\",e(156).CompositeTicker),k(\"ContinuousTicker\",e(149).ContinuousTicker),k(\"DatetimeTicker\",e(155).DatetimeTicker),k(\"DaysTicker\",e(157).DaysTicker),k(\"FixedTicker\",e(169).FixedTicker),k(\"LogTicker\",e(164).LogTicker),k(\"MercatorTicker\",e(167).MercatorTicker),k(\"MonthsTicker\",e(160).MonthsTicker),k(\"SingleIntervalTicker\",e(158).SingleIntervalTicker),k(\"Ticker\",e(130).Ticker),k(\"YearsTicker\",e(161).YearsTicker),k(\"BinnedTicker\",e(170).BinnedTicker)},\n function _(r,t,e,i,n){var s;i();const _=r(149);class c extends _.ContinuousTicker{constructor(r){super(r)}get_ticks_no_defaults(r,t,e,i){return{major:this.ticks,minor:this.minor_ticks}}get_interval(r,t,e){return 0}get_min_interval(){return 0}get_max_interval(){return 0}}e.FixedTicker=c,s=c,c.__name__=\"FixedTicker\",s.define((({Number:r,Array:t})=>({ticks:[t(r),[]],minor_ticks:[t(r),[]]})))},\n function _(e,n,t,r,i){var o;r();const a=e(130),s=e(171),c=e(12);class m extends a.Ticker{constructor(e){super(e)}get_ticks(e,n,t,r){const{binning:i}=this.mapper.metrics,o=Math.max(0,(0,c.left_edge_index)(e,i)),a=Math.min((0,c.left_edge_index)(n,i)+1,i.length-1),s=[];for(let e=o;e<=a;e++)s.push(i[e]);const{num_major_ticks:m}=this,_=[],h=\"auto\"==m?s.length:m,l=Math.max(1,Math.floor(s.length/h));for(let e=0;e({mapper:[n(s.ScanningColorMapper)],num_major_ticks:[t(e,r),8]})))},\n function _(n,e,i,r,o){r();const t=n(172),a=n(12);class c extends t.ContinuousColorMapper{constructor(n){super(n)}cmap(n,e,i,r,o){if(no.binning[o.binning.length-1])return r;return e[(0,a.left_edge_index)(n,o.binning)]}}i.ScanningColorMapper=c,c.__name__=\"ScanningColorMapper\"},\n function _(t,e,o,n,s){var l;n();const c=t(173),i=t(175),a=t(9),h=t(8);class r extends c.ColorMapper{constructor(t){super(t),this._scan_data=null}connect_signals(){super.connect_signals();const t=()=>{for(const[t]of this.domain)this.connect(t.view.change,(()=>this.update_data())),this.connect(t.data_source.selected.change,(()=>this.update_data()))};this.connect(this.properties.domain.change,(()=>t())),t()}update_data(){const{domain:t,palette:e}=this,o=[...this._collect(t)];this._scan_data=this.scan(o,e.length),this.metrics_change.emit(),this.change.emit()}get metrics(){return null==this._scan_data&&this.update_data(),this._scan_data}*_collect(t){for(const[e,o]of t)for(const t of(0,h.isArray)(o)?o:[o]){let o=e.data_source.get_column(t);o=e.view.indices.select(o);const n=e.view.masked,s=e.data_source.selected.indices;let l;if(null!=n&&s.length>0?l=(0,a.intersection)([...n],s):null!=n?l=[...n]:s.length>0&&(l=s),null!=l&&(o=(0,a.map)(l,(t=>o[t]))),o.length>0&&!(0,h.isNumber)(o[0]))for(const t of o)yield*t;else yield*o}}_v_compute(t,e,o,n){const{nan_color:s}=n;let{low_color:l,high_color:c}=n;null==l&&(l=o[0]),null==c&&(c=o[o.length-1]);const{domain:i}=this,h=(0,a.is_empty)(i)?t:[...this._collect(i)];this._scan_data=this.scan(h,o.length),this.metrics_change.emit();for(let n=0,i=t.length;n({high:[a(t),null],low:[a(t),null],high_color:[a(n),null],low_color:[a(n),null],domain:[c(l(o(i.GlyphRenderer),s(e,c(e)))),[]]})))},\n function _(e,r,t,n,o){var a;n();const c=e(174),i=e(15),_=e(24),l=e(22),s=e(27);function p(e){return(0,l.encode_rgba)((0,l.color2rgba)(e))}function u(e){const r=new Uint32Array(e.length);for(let t=0,n=e.length;te))),r}get rgba_mapper(){const e=this,r=u(this.palette),t=this._colors(p);return{v_compute(n){const o=new _.ColorArray(n.length);return e._v_compute(n,o,r,t),new Uint8ClampedArray((0,s.to_big_endian)(o).buffer)}}}_colors(e){return{nan_color:e(this.nan_color)}}}t.ColorMapper=h,a=h,h.__name__=\"ColorMapper\",a.define((({Color:e,Array:r})=>({palette:[r(e)],nan_color:[e,\"gray\"]})))},\n function _(r,e,n,s,o){s();const p=r(56);class t extends p.Transform{constructor(r){super(r)}compute(r){throw new Error(\"mapping single values is not supported\")}}n.Mapper=t,t.__name__=\"Mapper\"},\n function _(e,t,i,s,l){var h;s();const n=e(176),o=e(177),a=e(186),c=e(187),_=e(189),r=e(179),d=e(70),p=e(190),g=e(24),u=e(12),y=e(13),m=e(113),v=e(67),f={fill:{},line:{}},w={fill:{fill_alpha:.3,fill_color:\"grey\"},line:{line_alpha:.3,line_color:\"grey\"}},b={fill:{fill_alpha:.2},line:{}},V={fill:{fill_alpha:.2},line:{}};class x extends n.DataRendererView{get glyph_view(){return this.glyph}async lazy_initialize(){var e;await super.lazy_initialize();const t=this.model.glyph;this.glyph=await this.build_glyph_view(t);const i=\"fill\"in this.glyph.visuals,s=\"line\"in this.glyph.visuals,l=Object.assign({},t.attributes);function h(e){const h=(0,y.clone)(l);return i&&(0,y.extend)(h,e.fill),s&&(0,y.extend)(h,e.line),new t.constructor(h)}function n(e,t){return t instanceof r.Glyph?t:h(\"auto\"==t?e:{fill:{},line:{}})}delete l.id;let{selection_glyph:o,nonselection_glyph:a,hover_glyph:c,muted_glyph:_}=this.model;o=n(f,o),this.selection_glyph=await this.build_glyph_view(o),a=n(b,a),this.nonselection_glyph=await this.build_glyph_view(a),null!=c&&(this.hover_glyph=await this.build_glyph_view(c)),_=n(V,_),this.muted_glyph=await this.build_glyph_view(_);const d=n(w,\"auto\");this.decimated_glyph=await this.build_glyph_view(d),this.selection_glyph.set_base(this.glyph),this.nonselection_glyph.set_base(this.glyph),null===(e=this.hover_glyph)||void 0===e||e.set_base(this.glyph),this.muted_glyph.set_base(this.glyph),this.decimated_glyph.set_base(this.glyph),this.set_data()}async build_glyph_view(e){return(0,m.build_view)(e,{parent:this})}remove(){var e;this.glyph.remove(),this.selection_glyph.remove(),this.nonselection_glyph.remove(),null===(e=this.hover_glyph)||void 0===e||e.remove(),this.muted_glyph.remove(),this.decimated_glyph.remove(),super.remove()}connect_signals(){super.connect_signals();const e=()=>this.request_render(),t=()=>this.update_data();this.connect(this.model.change,e),this.connect(this.glyph.model.change,t),this.connect(this.selection_glyph.model.change,t),this.connect(this.nonselection_glyph.model.change,t),null!=this.hover_glyph&&this.connect(this.hover_glyph.model.change,t),this.connect(this.muted_glyph.model.change,t),this.connect(this.decimated_glyph.model.change,t),this.connect(this.model.data_source.change,t),this.connect(this.model.data_source.streaming,t),this.connect(this.model.data_source.patching,(e=>this.update_data(e))),this.connect(this.model.data_source.selected.change,e),this.connect(this.model.data_source._select,e),null!=this.hover_glyph&&this.connect(this.model.data_source.inspect,e),this.connect(this.model.properties.view.change,t),this.connect(this.model.view.properties.indices.change,t),this.connect(this.model.view.properties.masked.change,(()=>this.set_visuals())),this.connect(this.model.properties.visible.change,(()=>this.plot_view.invalidate_dataranges=!0));const{x_ranges:i,y_ranges:s}=this.plot_view.frame;for(const[,e]of i)e instanceof v.FactorRange&&this.connect(e.change,t);for(const[,e]of s)e instanceof v.FactorRange&&this.connect(e.change,t);const{transformchange:l,exprchange:h}=this.model.glyph;this.connect(l,t),this.connect(h,t)}_update_masked_indices(){const e=this.glyph.mask_data();return this.model.view.masked=e,e}update_data(e){this.set_data(e),this.request_render()}set_data(e){const t=this.model.data_source;this.all_indices=this.model.view.indices;const{all_indices:i}=this;this.glyph.set_data(t,i,e),this.set_visuals(),this._update_masked_indices();const{lod_factor:s}=this.plot_model,l=this.all_indices.count;this.decimated=new g.Indices(l);for(let e=0;e!n||n.is_empty()?[]:n.selected_glyph?this.model.view.convert_indices_from_subset(i):n.indices.length>0?n.indices:Object.keys(n.multiline_indices).map((e=>parseInt(e))))()),d=(0,u.filter)(i,(e=>r.has(t[e]))),{lod_threshold:p}=this.plot_model;let g,y,m;if(null!=this.model.document&&this.model.document.interactive_duration()>0&&!e&&null!=p&&t.length>p?(i=[...this.decimated],g=this.decimated_glyph,y=this.decimated_glyph,m=this.selection_glyph):(g=this.model.muted?this.muted_glyph:this.glyph,y=this.nonselection_glyph,m=this.selection_glyph),null!=this.hover_glyph&&d.length){const e=new Set(i);for(const t of d)e.delete(t);i=[...e]}if(h.length){const e={};for(const t of h)e[t]=!0;const l=new Array,n=new Array;if(this.glyph instanceof o.LineView)for(const i of t)null!=e[i]?l.push(i):n.push(i);else for(const s of i)null!=e[t[s]]?l.push(s):n.push(s);y.render(s,n),m.render(s,l),null!=this.hover_glyph&&(this.glyph instanceof o.LineView?this.hover_glyph.render(s,this.model.view.convert_indices_from_subset(d)):this.hover_glyph.render(s,d))}else if(this.glyph instanceof o.LineView)this.hover_glyph&&d.length?this.hover_glyph.render(s,this.model.view.convert_indices_from_subset(d)):g.render(s,t);else if(this.glyph instanceof a.PatchView||this.glyph instanceof c.HAreaView||this.glyph instanceof _.VAreaView)if(0==n.selected_glyphs.length||null==this.hover_glyph)g.render(s,t);else for(const e of n.selected_glyphs)e==this.glyph.model&&this.hover_glyph.render(s,t);else g.render(s,i),this.hover_glyph&&d.length&&this.hover_glyph.render(s,d);s.restore()}draw_legend(e,t,i,s,l,h,n,o){0!=this.glyph.data_size&&(null==o&&(o=this.model.get_reference_point(h,n)),this.glyph.draw_legend_for_index(e,{x0:t,x1:i,y0:s,y1:l},o))}hit_test(e){if(!this.model.visible)return null;const t=this.glyph.hit_test(e);return null==t?null:this.model.view.convert_selection_from_subset(t)}}i.GlyphRendererView=x,x.__name__=\"GlyphRendererView\";class G extends n.DataRenderer{constructor(e){super(e)}initialize(){super.initialize(),this.view.source!=this.data_source&&(this.view.source=this.data_source,this.view.compute_indices())}get_reference_point(e,t){if(null!=e){const i=this.data_source.get_column(e);if(null!=i)for(const[e,s]of Object.entries(this.view.indices_map))if(i[parseInt(e)]==t)return s}return 0}get_selection_manager(){return this.data_source.selection_manager}}i.GlyphRenderer=G,h=G,G.__name__=\"GlyphRenderer\",h.prototype.default_view=x,h.define((({Boolean:e,Auto:t,Or:i,Ref:s,Null:l,Nullable:h})=>({data_source:[s(d.ColumnarDataSource)],view:[s(p.CDSView),e=>new p.CDSView({source:e.data_source})],glyph:[s(r.Glyph)],hover_glyph:[h(s(r.Glyph)),null],nonselection_glyph:[i(s(r.Glyph),t,l),\"auto\"],selection_glyph:[i(s(r.Glyph),t,l),\"auto\"],muted_glyph:[i(s(r.Glyph),t,l),\"auto\"],muted:[e,!1]})))},\n function _(e,r,t,a,n){var s;a();const c=e(41);class _ extends c.RendererView{get xscale(){return this.coordinates.x_scale}get yscale(){return this.coordinates.y_scale}}t.DataRendererView=_,_.__name__=\"DataRendererView\";class i extends c.Renderer{constructor(e){super(e)}get selection_manager(){return this.get_selection_manager()}}t.DataRenderer=i,s=i,i.__name__=\"DataRenderer\",s.override({level:\"glyph\"})},\n function _(e,t,i,s,n){s();const l=e(1);var _;const r=e(178),o=e(184),a=(0,l.__importStar)(e(48)),h=(0,l.__importStar)(e(185)),c=e(72);class d extends r.XYGlyphView{async lazy_initialize(){await super.lazy_initialize();const{webgl:t}=this.renderer.plot_view.canvas_view;if(null==t?void 0:t.regl_wrapper.has_webgl){const{LineGL:i}=await Promise.resolve().then((()=>(0,l.__importStar)(e(426))));this.glglyph=new i(t.regl_wrapper,this)}}_render(e,t,i){const{sx:s,sy:n}=null!=i?i:this;let l=null;const _=e=>null!=l&&e-l!=1;let r=!0;e.beginPath();for(const i of t){const t=s[i],o=n[i];isFinite(t+o)?r||_(i)?(e.moveTo(t,o),r=!1):e.lineTo(t,o):r=!0,l=i}this.visuals.line.set_value(e),e.stroke()}_hit_point(e){const t=new c.Selection,i={x:e.sx,y:e.sy};let s=9999;const n=Math.max(2,this.line_width.value/2);for(let e=0,l=this.sx.length-1;e({x:[c.XCoordinateSpec,{field:\"x\"}],y:[c.YCoordinateSpec,{field:\"y\"}]})))},\n function _(e,t,s,i,n){i();const r=e(1),a=(0,r.__importStar)(e(18)),o=(0,r.__importStar)(e(65)),_=(0,r.__importStar)(e(45)),l=e(42),c=e(53),h=e(19),d=e(24),u=e(8),f=e(180),p=e(12),g=e(26),y=e(181),x=e(67),v=e(72),{abs:b,ceil:m}=Math;class w extends l.View{constructor(){super(...arguments),this._index=null,this._data_size=null,this._nohit_warned=new Set}get renderer(){return this.parent}get has_webgl(){return null!=this.glglyph}get index(){const{_index:e}=this;if(null!=e)return e;throw new Error(`${this}.index_data() wasn't called`)}get data_size(){const{_data_size:e}=this;if(null!=e)return e;throw new Error(`${this}.set_data() wasn't called`)}initialize(){super.initialize(),this.visuals=new _.Visuals(this)}request_render(){this.parent.request_render()}get canvas(){return this.renderer.parent.canvas_view}render(e,t,s){var i;null!=this.glglyph&&(this.renderer.needs_webgl_blit=this.glglyph.render(e,t,null!==(i=this.base)&&void 0!==i?i:this),this.renderer.needs_webgl_blit)||this._render(e,t,null!=s?s:this.base)}has_finished(){return!0}notify_finished(){this.renderer.notify_finished()}_bounds(e){return e}bounds(){return this._bounds(this.index.bbox)}log_bounds(){const{x0:e,x1:t}=this.index.bounds(o.positive_x()),{y0:s,y1:i}=this.index.bounds(o.positive_y());return this._bounds({x0:e,y0:s,x1:t,y1:i})}get_anchor_point(e,t,[s,i]){switch(e){case\"center\":case\"center_center\":{const[e,n]=this.scenterxy(t,s,i);return{x:e,y:n}}default:return null}}scenterx(e,t,s){return this.scenterxy(e,t,s)[0]}scentery(e,t,s){return this.scenterxy(e,t,s)[1]}sdist(e,t,s,i=\"edge\",n=!1){const r=t.length,a=new d.ScreenArray(r),o=e.s_compute;if(\"center\"==i)for(let e=0;em(e))),a}draw_legend_for_index(e,t,s){}hit_test(e){switch(e.type){case\"point\":if(null!=this._hit_point)return this._hit_point(e);break;case\"span\":if(null!=this._hit_span)return this._hit_span(e);break;case\"rect\":if(null!=this._hit_rect)return this._hit_rect(e);break;case\"poly\":if(null!=this._hit_poly)return this._hit_poly(e)}return this._nohit_warned.has(e.type)||(h.logger.debug(`'${e.type}' selection not available for ${this.model.type}`),this._nohit_warned.add(e.type)),null}_hit_rect_against_index(e){const{sx0:t,sx1:s,sy0:i,sy1:n}=e,[r,a]=this.renderer.coordinates.x_scale.r_invert(t,s),[o,_]=this.renderer.coordinates.y_scale.r_invert(i,n),l=[...this.index.indices({x0:r,x1:a,y0:o,y1:_})];return new v.Selection({indices:l})}_project_data(){}*_iter_visuals(){for(const e of this.visuals)for(const t of e)(t instanceof a.VectorSpec||t instanceof a.ScalarSpec)&&(yield t)}set_base(e){e!=this&&e instanceof this.constructor&&(this.base=e)}_configure(e,t){Object.defineProperty(this,(0,u.isString)(e)?e:e.attr,Object.assign({configurable:!0,enumerable:!0},t))}set_visuals(e,t){var s;for(const s of this._iter_visuals()){const{base:i}=this;if(null!=i){const e=i.model.properties[s.attr];if(null!=e&&(0,g.is_equal)(s.get_value(),e.get_value())){this._configure(s,{get:()=>i[`${s.attr}`]});continue}}const n=s.uniform(e).select(t);this._configure(s,{value:n})}for(const e of this.visuals)e.update();null===(s=this.glglyph)||void 0===s||s.set_visuals_changed()}set_data(e,t,s){var i;const{x_source:n,y_source:r}=this.renderer.coordinates,o=new Set(this._iter_visuals());this._data_size=t.count;for(const s of this.model)if((s instanceof a.VectorSpec||s instanceof a.ScalarSpec)&&!o.has(s))if(s instanceof a.BaseCoordinateSpec){const i=s.array(e);let o=t.select(i);const _=\"x\"==s.dimension?n:r;if(_ instanceof x.FactorRange)if(s instanceof a.CoordinateSpec)o=_.v_synthetic(o);else if(s instanceof a.CoordinateSeqSpec)for(let e=0;e{const s=new Uint32Array(r);for(let a=0;a>1;t[s]>i?e=s:n=s+1}return t[n]}class r extends d.default{get boxes(){return this._boxes}search_indices(i,t,n,e){if(this._pos!==this._boxes.length)throw new Error(\"Data not yet indexed - call index.finish().\");let s=this._boxes.length-4;const d=[],x=new o.Indices(this.numItems);for(;void 0!==s;){const o=Math.min(s+4*this.nodeSize,h(s,this._levelBounds));for(let h=s;h>2],r=this._boxes[h+0],l=this._boxes[h+1],a=this._boxes[h+2],_=this._boxes[h+3];na||t>_||(s<4*this.numItems?x.set(o):d.push(o)))}s=d.pop()}return x}}r.__name__=\"_FlatBush\";class l{constructor(i){this.index=null,i>0&&(this.index=new r(i))}add_rect(i,t,n,e){var s;isFinite(i+t+n+e)?null===(s=this.index)||void 0===s||s.add(i,t,n,e):this.add_empty()}add_point(i,t){var n;isFinite(i+t)?null===(n=this.index)||void 0===n||n.add(i,t,i,t):this.add_empty()}add_empty(){var i;null===(i=this.index)||void 0===i||i.add(1/0,1/0,-1/0,-1/0)}finish(){var i;null===(i=this.index)||void 0===i||i.finish()}_normalize(i){let{x0:t,y0:n,x1:e,y1:s}=i;return t>e&&([t,e]=[e,t]),n>s&&([n,s]=[s,n]),{x0:t,y0:n,x1:e,y1:s}}get bbox(){if(null==this.index)return(0,x.empty)();{const{minX:i,minY:t,maxX:n,maxY:e}=this.index;return{x0:i,y0:t,x1:n,y1:e}}}indices(i){if(null==this.index)return new o.Indices(0);{const{x0:t,y0:n,x1:e,y1:s}=this._normalize(i);return this.index.search_indices(t,n,e,s)}}bounds(i){const t=(0,x.empty)();if(null==this.index)return t;const{boxes:n}=this.index;for(const e of this.indices(i)){const s=n[4*e+0],d=n[4*e+1],o=n[4*e+2],x=n[4*e+3];s>=i.x0&&st.x1&&(t.x1=o),d>=i.y0&&dt.y1&&(t.y1=x)}return t}}n.SpatialIndex=l,l.__name__=\"SpatialIndex\"},\n function _(t,s,i,e,h){e();const n=(0,t(1).__importDefault)(t(183)),o=[Int8Array,Uint8Array,Uint8ClampedArray,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array];class r{static from(t){if(!(t instanceof ArrayBuffer))throw new Error(\"Data must be an instance of ArrayBuffer.\");const[s,i]=new Uint8Array(t,0,2);if(251!==s)throw new Error(\"Data does not appear to be in a Flatbush format.\");if(i>>4!=3)throw new Error(`Got v${i>>4} 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e=0;e>2]=t,this._boxes[this._pos++]=e,this._boxes[this._pos++]=h,this._boxes[this._pos++]=n,this._boxes[this._pos++]=o}}}search(t,s,i,e,h){if(this._pos!==this._boxes.length)throw new Error(\"Data not yet indexed - call index.finish().\");let n=this._boxes.length-4;const o=[],r=[];for(;void 0!==n;){const a=Math.min(n+4*this.nodeSize,_(n,this._levelBounds));for(let _=n;_>2];ithis._boxes[_+2]||s>this._boxes[_+3]||(n<4*this.numItems?(void 0===h||h(a))&&r.push(a):o.push(a)))}n=o.pop()}return r}neighbors(t,s,i=1/0,e=1/0,h){if(this._pos!==this._boxes.length)throw new Error(\"Data not yet indexed - call index.finish().\");let n=this._boxes.length-4;const o=this._queue,r=[],x=e*e;for(;void 0!==n;){const e=Math.min(n+4*this.nodeSize,_(n,this._levelBounds));for(let i=n;i>2],r=a(t,this._boxes[i],this._boxes[i+2]),_=a(s,this._boxes[i+1],this._boxes[i+3]),x=r*r+_*_;n<4*this.numItems?(void 0===h||h(e))&&o.push(-e-1,x):o.push(e,x)}for(;o.length&&o.peek()<0;){if(o.peekValue()>x)return 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_,s,y,p;\"point\"==n.type?([y,p]=e.yscale.r_invert(o-1,o+1),[_,s]=e.xscale.r_invert(c-1,c+1)):\"v\"==n.direction?([y,p]=e.yscale.r_invert(o,o),[_,s]=[Math.min(a-1,i-1),Math.max(a+1,i+1)]):([_,s]=e.xscale.r_invert(c,c),[y,p]=[Math.min(t-1,r-1),Math.max(t+1,r+1)]);const{x:g,y:h}=l.check_2_segments_intersect(_,y,s,p,a,t,i,r);return[g,h]}},\n function _(t,n,e,i,r){function s(t,n){return(t.x-n.x)**2+(t.y-n.y)**2}function o(t,n,e){const i=s(n,e);if(0==i)return s(t,n);const r=((t.x-n.x)*(e.x-n.x)+(t.y-n.y)*(e.y-n.y))/i;if(r<0)return s(t,n);if(r>1)return s(t,e);return s(t,{x:n.x+r*(e.x-n.x),y:n.y+r*(e.y-n.y)})}i(),e.point_in_poly=function(t,n,e,i){let r=!1,s=e[e.length-1],o=i[i.length-1];for(let u=0;u0&&_<1&&h>0&&h<1,x:t+_*(e-t),y:n+_*(i-n)}}}},\n function _(t,s,e,i,a){i();const l=t(1);var n;const _=t(178),o=t(184),c=(0,l.__importStar)(t(185)),h=(0,l.__importStar)(t(48)),r=t(72);class p extends _.XYGlyphView{_render(t,s,e){const{sx:i,sy:a}=null!=e?e:this;let l=!0;t.beginPath();for(const e of s){const s=i[e],n=a[e];isFinite(s+n)?l?(t.moveTo(s,n),l=!1):t.lineTo(s,n):(t.closePath(),l=!0)}t.closePath(),this.visuals.fill.apply(t),this.visuals.hatch.apply(t),this.visuals.line.apply(t)}draw_legend_for_index(t,s,e){(0,o.generic_area_scalar_legend)(this.visuals,t,s)}_hit_point(t){const s=new r.Selection;return c.point_in_poly(t.sx,t.sy,this.sx,this.sy)&&(s.add_to_selected_glyphs(this.model),s.view=this),s}}e.PatchView=p,p.__name__=\"PatchView\";class d extends _.XYGlyph{constructor(t){super(t)}}e.Patch=d,n=d,d.__name__=\"Patch\",n.prototype.default_view=p,n.mixins([h.LineScalar,h.FillScalar,h.HatchScalar])},\n function _(t,s,e,i,n){i();const h=t(1);var r;const a=t(188),_=(0,h.__importStar)(t(185)),o=(0,h.__importStar)(t(18)),l=t(72);class c extends a.AreaView{_index_data(t){const{min:s,max:e}=Math,{data_size:i}=this;for(let n=0;n=0;s--)t.lineTo(n[s],h[s]);t.closePath(),this.visuals.fill.apply(t),this.visuals.hatch.apply(t)}_hit_point(t){const s=this.sy.length,e=new l.Selection;for(let i=0,n=s-1;i({x1:[o.XCoordinateSpec,{field:\"x1\"}],x2:[o.XCoordinateSpec,{field:\"x2\"}],y:[o.YCoordinateSpec,{field:\"y\"}]})))},\n function _(e,a,r,_,s){_();const n=e(1);var c;const i=e(179),l=e(184),t=(0,n.__importStar)(e(48));class o extends i.GlyphView{draw_legend_for_index(e,a,r){(0,l.generic_area_scalar_legend)(this.visuals,e,a)}}r.AreaView=o,o.__name__=\"AreaView\";class d extends i.Glyph{constructor(e){super(e)}}r.Area=d,c=d,d.__name__=\"Area\",c.mixins([t.FillScalar,t.HatchScalar])},\n function _(t,s,e,i,n){i();const h=t(1);var r;const a=t(188),_=(0,h.__importStar)(t(185)),o=(0,h.__importStar)(t(18)),l=t(72);class c extends a.AreaView{_index_data(t){const{min:s,max:e}=Math,{data_size:i}=this;for(let n=0;n=0;s--)t.lineTo(i[s],h[s]);t.closePath(),this.visuals.fill.apply(t),this.visuals.hatch.apply(t)}scenterxy(t){return[this.sx[t],(this.sy1[t]+this.sy2[t])/2]}_hit_point(t){const s=this.sx.length,e=new l.Selection;for(let i=0,n=s-1;i({x:[o.XCoordinateSpec,{field:\"x\"}],y1:[o.YCoordinateSpec,{field:\"y1\"}],y2:[o.YCoordinateSpec,{field:\"y2\"}]})))},\n function _(e,i,s,t,n){var c;t();const o=e(53),r=e(24),u=e(191),_=e(70);class a extends o.Model{constructor(e){super(e)}initialize(){super.initialize(),this.compute_indices()}connect_signals(){super.connect_signals(),this.connect(this.properties.filters.change,(()=>this.compute_indices()));const e=()=>{const e=()=>this.compute_indices();null!=this.source&&(this.connect(this.source.change,e),this.source instanceof _.ColumnarDataSource&&(this.connect(this.source.streaming,e),this.connect(this.source.patching,e)))};let i=null!=this.source;i?e():this.connect(this.properties.source.change,(()=>{i||(e(),i=!0)}))}compute_indices(){var e;const{source:i}=this;if(null==i)return;const s=null!==(e=i.get_length())&&void 0!==e?e:1,t=r.Indices.all_set(s);for(const e of this.filters)t.intersect(e.compute_indices(i));this.indices=t,this._indices=[...t],this.indices_map_to_subset()}indices_map_to_subset(){this.indices_map={};for(let e=0;ethis._indices[e]))}convert_selection_to_subset(e){return e.map((e=>this.indices_map[e]))}convert_indices_from_subset(e){return e.map((e=>this._indices[e]))}}s.CDSView=a,c=a,a.__name__=\"CDSView\",c.define((({Array:e,Ref:i})=>({filters:[e(i(u.Filter)),[]],source:[i(_.ColumnarDataSource)]}))),c.internal((({Int:e,Dict:i,Ref:s,Nullable:t})=>({indices:[s(r.Indices)],indices_map:[i(e),{}],masked:[t(s(r.Indices)),null]})))},\n function _(e,t,n,s,c){s();const o=e(53);class r extends o.Model{constructor(e){super(e)}}n.Filter=r,r.__name__=\"Filter\"},\n function _(t,r,a,e,c){e(),c(\"BasicTickFormatter\",t(146).BasicTickFormatter),c(\"CategoricalTickFormatter\",t(142).CategoricalTickFormatter),c(\"DatetimeTickFormatter\",t(150).DatetimeTickFormatter),c(\"FuncTickFormatter\",t(193).FuncTickFormatter),c(\"LogTickFormatter\",t(163).LogTickFormatter),c(\"MercatorTickFormatter\",t(166).MercatorTickFormatter),c(\"NumeralTickFormatter\",t(194).NumeralTickFormatter),c(\"PrintfTickFormatter\",t(195).PrintfTickFormatter),c(\"TickFormatter\",t(131).TickFormatter)},\n function _(t,e,n,s,r){var c;s();const i=t(131),a=t(13),u=t(34);class o extends i.TickFormatter{constructor(t){super(t)}get names(){return(0,a.keys)(this.args)}get values(){return(0,a.values)(this.args)}_make_func(){const t=(0,u.use_strict)(this.code);return new Function(\"tick\",\"index\",\"ticks\",...this.names,t)}doFormat(t,e){const n=this._make_func().bind({});return t.map(((t,e,s)=>`${n(t,e,s,...this.values)}`))}}n.FuncTickFormatter=o,c=o,o.__name__=\"FuncTickFormatter\",c.define((({Unknown:t,String:e,Dict:n})=>({args:[n(t),{}],code:[e,\"\"]})))},\n function _(r,n,t,o,e){o();var a;const u=(0,r(1).__importStar)(r(153)),c=r(131),i=r(20);class s extends c.TickFormatter{constructor(r){super(r)}get _rounding_fn(){switch(this.rounding){case\"round\":case\"nearest\":return Math.round;case\"floor\":case\"rounddown\":return Math.floor;case\"ceil\":case\"roundup\":return Math.ceil}}doFormat(r,n){const{format:t,language:o,_rounding_fn:e}=this;return r.map((r=>u.format(r,t,o,e)))}}t.NumeralTickFormatter=s,a=s,s.__name__=\"NumeralTickFormatter\",a.define((({String:r})=>({format:[r,\"0,0\"],language:[r,\"en\"],rounding:[i.RoundingFunction,\"round\"]})))},\n function _(t,r,n,o,a){var e;o();const i=t(131),s=t(152);class c extends i.TickFormatter{constructor(t){super(t)}doFormat(t,r){return t.map((t=>(0,s.sprintf)(this.format,t)))}}n.PrintfTickFormatter=c,e=c,c.__name__=\"PrintfTickFormatter\",e.define((({String:t})=>({format:[t,\"%s\"]})))},\n function _(r,o,a,p,e){p(),e(\"CategoricalColorMapper\",r(197).CategoricalColorMapper),e(\"CategoricalMarkerMapper\",r(199).CategoricalMarkerMapper),e(\"CategoricalPatternMapper\",r(200).CategoricalPatternMapper),e(\"ContinuousColorMapper\",r(172).ContinuousColorMapper),e(\"ColorMapper\",r(173).ColorMapper),e(\"LinearColorMapper\",r(201).LinearColorMapper),e(\"LogColorMapper\",r(202).LogColorMapper),e(\"ScanningColorMapper\",r(171).ScanningColorMapper),e(\"EqHistColorMapper\",r(203).EqHistColorMapper)},\n function _(t,o,r,a,e){var c;a();const s=t(198),l=t(173),n=t(67);class _ extends l.ColorMapper{constructor(t){super(t)}_v_compute(t,o,r,{nan_color:a}){(0,s.cat_v_compute)(t,this.factors,r,o,this.start,this.end,a)}}r.CategoricalColorMapper=_,c=_,_.__name__=\"CategoricalColorMapper\",c.define((({Number:t,Nullable:o})=>({factors:[n.FactorSeq],start:[t,0],end:[o(t),null]})))},\n function _(n,t,e,l,i){l();const c=n(12),u=n(8);function f(n,t){if(n.length!=t.length)return!1;for(let e=0,l=n.length;ef(n,h)))),s=_<0||_>=e.length?r:e[_],l[g]=s}}},\n function _(e,r,a,t,s){var c;t();const l=e(198),n=e(67),u=e(174),o=e(20);class p extends u.Mapper{constructor(e){super(e)}v_compute(e){const r=new Array(e.length);return(0,l.cat_v_compute)(e,this.factors,this.markers,r,this.start,this.end,this.default_value),r}}a.CategoricalMarkerMapper=p,c=p,p.__name__=\"CategoricalMarkerMapper\",c.define((({Number:e,Array:r,Nullable:a})=>({factors:[n.FactorSeq],markers:[r(o.MarkerType)],start:[e,0],end:[a(e),null],default_value:[o.MarkerType,\"circle\"]})))},\n function _(t,e,a,r,n){var s;r();const c=t(198),l=t(67),p=t(174),u=t(20);class o extends p.Mapper{constructor(t){super(t)}v_compute(t){const e=new Array(t.length);return(0,c.cat_v_compute)(t,this.factors,this.patterns,e,this.start,this.end,this.default_value),e}}a.CategoricalPatternMapper=o,s=o,o.__name__=\"CategoricalPatternMapper\",s.define((({Number:t,Array:e,Nullable:a})=>({factors:[l.FactorSeq],patterns:[e(u.HatchPatternType)],start:[t,0],end:[a(t),null],default_value:[u.HatchPatternType,\" \"]})))},\n function _(n,r,o,t,a){t();const e=n(172),i=n(12);class s extends e.ContinuousColorMapper{constructor(n){super(n)}scan(n,r){const o=null!=this.low?this.low:(0,i.min)(n),t=null!=this.high?this.high:(0,i.max)(n);return{max:t,min:o,norm_factor:1/(t-o),normed_interval:1/r}}cmap(n,r,o,t,a){const e=r.length-1;if(n==a.max)return r[e];const i=(n-a.min)*a.norm_factor,s=Math.floor(i/a.normed_interval);return s<0?o:s>e?t:r[s]}}o.LinearColorMapper=s,s.__name__=\"LinearColorMapper\"},\n function _(o,t,n,r,l){r();const a=o(172),s=o(12);class e extends a.ContinuousColorMapper{constructor(o){super(o)}scan(o,t){const n=null!=this.low?this.low:(0,s.min)(o),r=null!=this.high?this.high:(0,s.max)(o);return{max:r,min:n,scale:t/(Math.log(r)-Math.log(n))}}cmap(o,t,n,r,l){const a=t.length-1;if(o>l.max)return r;if(o==l.max)return t[a];if(oa&&(e=a),t[e]}}n.LogColorMapper=e,e.__name__=\"LogColorMapper\"},\n function _(e,n,s,t,l){var i;t();const r=e(171),o=e(12),c=e(9);class a extends r.ScanningColorMapper{constructor(e){super(e)}scan(e,n){let s=null!=this.low?this.low:(0,o.min)(e);const t=null!=this.high?this.high:(0,o.max)(e),l=this.bins,i=(0,c.linspace)(s,t,l+1),r=(0,o.bin_counts)(e,i);let a=0;for(let e=0;e1&&(f=1-n)}const d=(0,c.linspace)(f,1,n+1),g=(0,o.interpolate)(d,p,_);return this.rescale_discrete_levels?s=g[0]:g[0]=s,g[g.length-1]=t,{min:s,max:t,binning:g}}}s.EqHistColorMapper=a,i=a,a.__name__=\"EqHistColorMapper\",i.define((({Boolean:e,Int:n})=>({bins:[n,65536],rescale_discrete_levels:[e,!1]})))},\n function _(a,e,l,c,n){c(),n(\"CategoricalScale\",a(62).CategoricalScale),n(\"ContinuousScale\",a(60).ContinuousScale),n(\"LinearScale\",a(59).LinearScale),n(\"LinearInterpolationScale\",a(205).LinearInterpolationScale),n(\"LogScale\",a(61).LogScale),n(\"Scale\",a(55).Scale)},\n function _(e,r,n,t,a){var i;t();const s=e(55),o=e(59),c=e(12);class _ extends s.Scale{constructor(e){super(e)}connect_signals(){super.connect_signals();const{source_range:e,target_range:r}=this.properties;this.on_change([e,r],(()=>{this.linear_scale=new o.LinearScale({source_range:this.source_range,target_range:this.target_range})}))}get s_compute(){throw new Error(\"not implemented\")}get s_invert(){throw new Error(\"not implemented\")}compute(e){return e}v_compute(e){const{binning:r}=this,{start:n,end:t}=this.source_range,a=n,i=t,s=r.length,o=(t-n)/(s-1),_=new Float64Array(s);for(let e=0;e{if(ei)return i;const n=(0,c.left_edge_index)(e,r);if(-1==n)return a;if(n>=s-1)return i;const t=r[n],o=(e-t)/(r[n+1]-t),l=_[n];return l+o*(_[n+1]-l)}));return this.linear_scale.v_compute(l)}invert(e){return e}v_invert(e){return new Float64Array(e)}}n.LinearInterpolationScale=_,i=_,_.__name__=\"LinearInterpolationScale\",i.internal((({Arrayable:e,Ref:r})=>({binning:[e],linear_scale:[r(o.LinearScale),e=>new o.LinearScale({source_range:e.source_range,target_range:e.target_range})]})))},\n function _(a,n,e,g,R){g(),R(\"DataRange\",a(64).DataRange),R(\"DataRange1d\",a(63).DataRange1d),R(\"FactorRange\",a(67).FactorRange),R(\"Range\",a(57).Range),R(\"Range1d\",a(58).Range1d)},\n function _(a,o,i,t,e){t();var n=a(124);e(\"Sizeable\",n.Sizeable),e(\"SizingPolicy\",n.SizingPolicy);var c=a(125);e(\"Layoutable\",c.Layoutable),e(\"LayoutItem\",c.LayoutItem);var r=a(208);e(\"HStack\",r.HStack),e(\"VStack\",r.VStack);var l=a(209);e(\"Grid\",l.Grid),e(\"Row\",l.Row),e(\"Column\",l.Column);var S=a(210);e(\"ContentBox\",S.ContentBox),e(\"VariadicBox\",S.VariadicBox)},\n function _(t,e,h,i,r){i();const n=t(125),o=t(65);class s extends n.Layoutable{constructor(){super(...arguments),this.children=[]}*[Symbol.iterator](){yield*this.children}}h.Stack=s,s.__name__=\"Stack\";class c extends s{_measure(t){let e=0,h=0;for(const t of this.children){const i=t.measure({width:0,height:0});e+=i.width,h=Math.max(h,i.height)}return{width:e,height:h}}_set_geometry(t,e){super._set_geometry(t,e);const h=this.absolute?t.top:0;let i=this.absolute?t.left:0;const{height:r}=t;for(const t of this.children){const{width:e}=t.measure({width:0,height:0});t.set_geometry(new o.BBox({left:i,width:e,top:h,height:r})),i+=e}}}h.HStack=c,c.__name__=\"HStack\";class a extends s{_measure(t){let e=0,h=0;for(const t of this.children){const i=t.measure({width:0,height:0});e=Math.max(e,i.width),h+=i.height}return{width:e,height:h}}_set_geometry(t,e){super._set_geometry(t,e);const h=this.absolute?t.left:0;let i=this.absolute?t.top:0;const{width:r}=t;for(const t of this.children){const{height:e}=t.measure({width:0,height:0});t.set_geometry(new o.BBox({top:i,height:e,left:h,width:r})),i+=e}}}h.VStack=a,a.__name__=\"VStack\";class l extends n.Layoutable{constructor(){super(...arguments),this.children=[]}*[Symbol.iterator](){yield*this.children}_measure(t){const{width_policy:e,height_policy:h}=this.sizing,{min:i,max:r}=Math;let n=0,o=0;for(const e of this.children){const{width:h,height:i}=e.measure(t);n=r(n,h),o=r(o,i)}return{width:(()=>{const{width:h}=this.sizing;if(t.width==1/0)return\"fixed\"==e&&null!=h?h:n;switch(e){case\"fixed\":return null!=h?h:n;case\"min\":return n;case\"fit\":return null!=h?i(t.width,h):t.width;case\"max\":return null!=h?r(t.width,h):t.width}})(),height:(()=>{const{height:e}=this.sizing;if(t.height==1/0)return\"fixed\"==h&&null!=e?e:o;switch(h){case\"fixed\":return null!=e?e:o;case\"min\":return o;case\"fit\":return null!=e?i(t.height,e):t.height;case\"max\":return null!=e?r(t.height,e):t.height}})()}}_set_geometry(t,e){super._set_geometry(t,e);const h=this.absolute?t:t.relative(),{left:i,right:r,top:n,bottom:s}=h,c=Math.round(h.vcenter),a=Math.round(h.hcenter);for(const e of this.children){const{margin:h,halign:l,valign:d}=e.sizing,{width:u,height:g,inner:_}=e.measure(t),w=(()=>{switch(`${d}_${l}`){case\"start_start\":return new o.BBox({left:i+h.left,top:n+h.top,width:u,height:g});case\"start_center\":return new o.BBox({hcenter:a,top:n+h.top,width:u,height:g});case\"start_end\":return new o.BBox({right:r-h.right,top:n+h.top,width:u,height:g});case\"center_start\":return new o.BBox({left:i+h.left,vcenter:c,width:u,height:g});case\"center_center\":return new o.BBox({hcenter:a,vcenter:c,width:u,height:g});case\"center_end\":return new o.BBox({right:r-h.right,vcenter:c,width:u,height:g});case\"end_start\":return new o.BBox({left:i+h.left,bottom:s-h.bottom,width:u,height:g});case\"end_center\":return new o.BBox({hcenter:a,bottom:s-h.bottom,width:u,height:g});case\"end_end\":return new o.BBox({right:r-h.right,bottom:s-h.bottom,width:u,height:g})}})(),m=null==_?w:new o.BBox({left:w.left+_.left,top:w.top+_.top,right:w.right-_.right,bottom:w.bottom-_.bottom});e.set_geometry(w,m)}}}h.NodeLayout=l,l.__name__=\"NodeLayout\"},\n function _(t,i,s,e,o){e();const n=t(124),l=t(125),r=t(8),h=t(65),c=t(9),{max:a,round:g}=Math;class p{constructor(t){this.def=t,this._map=new Map}get(t){let i=this._map.get(t);return void 0===i&&(i=this.def(),this._map.set(t,i)),i}apply(t,i){const s=this.get(t);this._map.set(t,i(s))}}p.__name__=\"DefaultMap\";class f{constructor(){this._items=[],this._nrows=0,this._ncols=0}get nrows(){return this._nrows}get ncols(){return this._ncols}add(t,i){const{r1:s,c1:e}=t;this._nrows=a(this._nrows,s+1),this._ncols=a(this._ncols,e+1),this._items.push({span:t,data:i})}at(t,i){return this._items.filter((({span:s})=>s.r0<=t&&t<=s.r1&&s.c0<=i&&i<=s.c1)).map((({data:t})=>t))}row(t){return this._items.filter((({span:i})=>i.r0<=t&&t<=i.r1)).map((({data:t})=>t))}col(t){return this._items.filter((({span:i})=>i.c0<=t&&t<=i.c1)).map((({data:t})=>t))}foreach(t){for(const{span:i,data:s}of this._items)t(i,s)}map(t){const i=new f;for(const{span:s,data:e}of this._items)i.add(s,t(s,e));return i}}f.__name__=\"Container\";class _ extends l.Layoutable{constructor(t=[]){super(),this.items=t,this.rows=\"auto\",this.cols=\"auto\",this.spacing=0}*[Symbol.iterator](){for(const{layout:t}of this.items)yield t}is_width_expanding(){if(super.is_width_expanding())return!0;if(\"fixed\"==this.sizing.width_policy)return!1;const{cols:t}=this._state;return(0,c.some)(t,(t=>\"max\"==t.policy))}is_height_expanding(){if(super.is_height_expanding())return!0;if(\"fixed\"==this.sizing.height_policy)return!1;const{rows:t}=this._state;return(0,c.some)(t,(t=>\"max\"==t.policy))}_init(){var t,i,s,e;super._init();const o=new f;for(const{layout:t,row:i,col:s,row_span:e,col_span:n}of this.items)if(t.sizing.visible){const l=i,r=s,h=i+(null!=e?e:1)-1,c=s+(null!=n?n:1)-1;o.add({r0:l,c0:r,r1:h,c1:c},t)}const{nrows:n,ncols:l}=o,h=new Array(n);for(let s=0;s{var t;const i=(0,r.isPlainObject)(this.rows)?null!==(t=this.rows[s])&&void 0!==t?t:this.rows[\"*\"]:this.rows;return null==i?{policy:\"auto\"}:(0,r.isNumber)(i)?{policy:\"fixed\",height:i}:(0,r.isString)(i)?{policy:i}:i})(),n=null!==(t=e.align)&&void 0!==t?t:\"auto\";if(\"fixed\"==e.policy)h[s]={policy:\"fixed\",height:e.height,align:n};else if(\"min\"==e.policy)h[s]={policy:\"min\",align:n};else if(\"fit\"==e.policy||\"max\"==e.policy)h[s]={policy:e.policy,flex:null!==(i=e.flex)&&void 0!==i?i:1,align:n};else{if(\"auto\"!=e.policy)throw new Error(\"unrechable\");(0,c.some)(o.row(s),(t=>t.is_height_expanding()))?h[s]={policy:\"max\",flex:1,align:n}:h[s]={policy:\"min\",align:n}}}const a=new Array(l);for(let t=0;t{var i;const s=(0,r.isPlainObject)(this.cols)?null!==(i=this.cols[t])&&void 0!==i?i:this.cols[\"*\"]:this.cols;return null==s?{policy:\"auto\"}:(0,r.isNumber)(s)?{policy:\"fixed\",width:s}:(0,r.isString)(s)?{policy:s}:s})(),n=null!==(s=i.align)&&void 0!==s?s:\"auto\";if(\"fixed\"==i.policy)a[t]={policy:\"fixed\",width:i.width,align:n};else if(\"min\"==i.policy)a[t]={policy:\"min\",align:n};else if(\"fit\"==i.policy||\"max\"==i.policy)a[t]={policy:i.policy,flex:null!==(e=i.flex)&&void 0!==e?e:1,align:n};else{if(\"auto\"!=i.policy)throw new Error(\"unrechable\");(0,c.some)(o.col(t),(t=>t.is_width_expanding()))?a[t]={policy:\"max\",flex:1,align:n}:a[t]={policy:\"min\",align:n}}}const[g,p]=(0,r.isNumber)(this.spacing)?[this.spacing,this.spacing]:this.spacing;this._state={items:o,nrows:n,ncols:l,rows:h,cols:a,rspacing:g,cspacing:p}}_measure_totals(t,i){const{nrows:s,ncols:e,rspacing:o,cspacing:n}=this._state;return{height:(0,c.sum)(t)+(s-1)*o,width:(0,c.sum)(i)+(e-1)*n}}_measure_cells(t){const{items:i,nrows:s,ncols:e,rows:o,cols:l,rspacing:r,cspacing:h}=this._state,c=new Array(s);for(let t=0;t{const{r0:e,c0:f,r1:d,c1:u}=i,w=(d-e)*r,m=(u-f)*h;let y=0;for(let i=e;i<=d;i++)y+=t(i,f).height;y+=w;let x=0;for(let i=f;i<=u;i++)x+=t(e,i).width;x+=m;const b=s.measure({width:x,height:y});_.add(i,{layout:s,size_hint:b});const z=new n.Sizeable(b).grow_by(s.sizing.margin);z.height-=w,z.width-=m;const v=[];for(let t=e;t<=d;t++){const i=o[t];\"fixed\"==i.policy?z.height-=i.height:v.push(t)}if(z.height>0){const t=g(z.height/v.length);for(const i of v)c[i]=a(c[i],t)}const j=[];for(let t=f;t<=u;t++){const i=l[t];\"fixed\"==i.policy?z.width-=i.width:j.push(t)}if(z.width>0){const t=g(z.width/j.length);for(const i of j)p[i]=a(p[i],t)}}));return{size:this._measure_totals(c,p),row_heights:c,col_widths:p,size_hints:_}}_measure_grid(t){const{nrows:i,ncols:s,rows:e,cols:o,rspacing:n,cspacing:l}=this._state,r=this._measure_cells(((t,i)=>{const s=e[t],n=o[i];return{width:\"fixed\"==n.policy?n.width:1/0,height:\"fixed\"==s.policy?s.height:1/0}}));let h;h=\"fixed\"==this.sizing.height_policy&&null!=this.sizing.height?this.sizing.height:t.height!=1/0&&this.is_height_expanding()?t.height:r.size.height;let c,p=0;for(let t=0;t0)for(let t=0;ti?i:e,t--}}}c=\"fixed\"==this.sizing.width_policy&&null!=this.sizing.width?this.sizing.width:t.width!=1/0&&this.is_width_expanding()?t.width:r.size.width;let f=0;for(let t=0;t0)for(let t=0;ts?s:o,t--}}}const{row_heights:_,col_widths:d,size_hints:u}=this._measure_cells(((t,i)=>({width:r.col_widths[i],height:r.row_heights[t]})));return{size:this._measure_totals(_,d),row_heights:_,col_widths:d,size_hints:u}}_measure(t){const{size:i}=this._measure_grid(t);return i}_set_geometry(t,i){super._set_geometry(t,i);const{nrows:s,ncols:e,rspacing:o,cspacing:n}=this._state,{row_heights:l,col_widths:r,size_hints:c}=this._measure_grid(t),f=this._state.rows.map(((t,i)=>Object.assign(Object.assign({},t),{top:0,height:l[i],get bottom(){return this.top+this.height}}))),_=this._state.cols.map(((t,i)=>Object.assign(Object.assign({},t),{left:0,width:r[i],get right(){return this.left+this.width}}))),d=c.map(((t,i)=>Object.assign(Object.assign({},i),{outer:new h.BBox,inner:new h.BBox})));for(let i=0,e=this.absolute?t.top:0;i{const{layout:r,size_hint:c}=l,{sizing:a}=r,{width:p,height:d}=c,u=function(t,i){let s=(i-t)*n;for(let e=t;e<=i;e++)s+=_[e].width;return s}(i,e),w=function(t,i){let s=(i-t)*o;for(let e=t;e<=i;e++)s+=f[e].height;return s}(t,s),m=i==e&&\"auto\"!=_[i].align?_[i].align:a.halign,y=t==s&&\"auto\"!=f[t].align?f[t].align:a.valign;let x=_[i].left;\"start\"==m?x+=a.margin.left:\"center\"==m?x+=g((u-p)/2):\"end\"==m&&(x+=u-a.margin.right-p);let b=f[t].top;\"start\"==y?b+=a.margin.top:\"center\"==y?b+=g((w-d)/2):\"end\"==y&&(b+=w-a.margin.bottom-d),l.outer=new h.BBox({left:x,top:b,width:p,height:d})}));const u=f.map((()=>({start:new p((()=>0)),end:new p((()=>0))}))),w=_.map((()=>({start:new p((()=>0)),end:new p((()=>0))})));d.foreach((({r0:t,c0:i,r1:s,c1:e},{size_hint:o,outer:n})=>{const{inner:l}=o;null!=l&&(u[t].start.apply(n.top,(t=>a(t,l.top))),u[s].end.apply(f[s].bottom-n.bottom,(t=>a(t,l.bottom))),w[i].start.apply(n.left,(t=>a(t,l.left))),w[e].end.apply(_[e].right-n.right,(t=>a(t,l.right))))})),d.foreach((({r0:t,c0:i,r1:s,c1:e},o)=>{const{size_hint:n,outer:l}=o,r=t=>{const i=this.absolute?l:l.relative(),s=i.left+t.left,e=i.top+t.top,o=i.right-t.right,n=i.bottom-t.bottom;return new h.BBox({left:s,top:e,right:o,bottom:n})};if(null!=n.inner){let h=r(n.inner);if(!1!==n.align){const o=u[t].start.get(l.top),n=u[s].end.get(f[s].bottom-l.bottom),c=w[i].start.get(l.left),a=w[e].end.get(_[e].right-l.right);try{h=r({top:o,bottom:n,left:c,right:a})}catch(t){}}o.inner=h}else o.inner=l})),d.foreach(((t,{layout:i,outer:s,inner:e})=>{i.set_geometry(s,e)}))}}s.Grid=_,_.__name__=\"Grid\";class d extends _{constructor(t){super(),this.items=t.map(((t,i)=>({layout:t,row:0,col:i}))),this.rows=\"fit\"}}s.Row=d,d.__name__=\"Row\";class u extends _{constructor(t){super(),this.items=t.map(((t,i)=>({layout:t,row:i,col:0}))),this.cols=\"fit\"}}s.Column=u,u.__name__=\"Column\"},\n function _(e,t,s,n,i){n();const a=e(125),c=e(124),o=e(43);class r extends a.ContentLayoutable{constructor(e){super(),this.content_size=(0,o.unsized)(e,(()=>new c.Sizeable((0,o.size)(e))))}_content_size(){return this.content_size}}s.ContentBox=r,r.__name__=\"ContentBox\";class _ extends a.Layoutable{constructor(e){super(),this.el=e}_measure(e){const t=new c.Sizeable(e).bounded_to(this.sizing.size);return(0,o.sized)(this.el,t,(()=>{const e=new c.Sizeable((0,o.content_size)(this.el)),{border:t,padding:s}=(0,o.extents)(this.el);return e.grow_by(t).grow_by(s).map(Math.ceil)}))}}s.VariadicBox=_,_.__name__=\"VariadicBox\";class h extends _{constructor(e){super(e),this._cache=new Map}_measure(e){const{width:t,height:s}=e,n=`${t},${s}`;let i=this._cache.get(n);return null==i&&(i=super._measure(e),this._cache.set(n,i)),i}invalidate_cache(){this._cache.clear()}}s.CachedVariadicBox=h,h.__name__=\"CachedVariadicBox\"},\n function _(t,e,i,h,o){h();const s=t(124),r=t(125),n=t(65);class g extends r.Layoutable{constructor(){super(...arguments),this.min_border={left:0,top:0,right:0,bottom:0},this.padding={left:0,top:0,right:0,bottom:0}}*[Symbol.iterator](){yield this.top_panel,yield this.bottom_panel,yield this.left_panel,yield this.right_panel,yield this.center_panel}_measure(t){t=new s.Sizeable({width:\"fixed\"==this.sizing.width_policy||t.width==1/0?this.sizing.width:t.width,height:\"fixed\"==this.sizing.height_policy||t.height==1/0?this.sizing.height:t.height});const e=this.left_panel.measure({width:0,height:t.height}),i=Math.max(e.width,this.min_border.left)+this.padding.left,h=this.right_panel.measure({width:0,height:t.height}),o=Math.max(h.width,this.min_border.right)+this.padding.right,r=this.top_panel.measure({width:t.width,height:0}),n=Math.max(r.height,this.min_border.top)+this.padding.top,g=this.bottom_panel.measure({width:t.width,height:0}),a=Math.max(g.height,this.min_border.bottom)+this.padding.bottom,d=new s.Sizeable(t).shrink_by({left:i,right:o,top:n,bottom:a}),l=this.center_panel.measure(d);return{width:i+l.width+o,height:n+l.height+a,inner:{left:i,right:o,top:n,bottom:a},align:(()=>{const{width_policy:t,height_policy:e}=this.center_panel.sizing;return\"fixed\"!=t&&\"fixed\"!=e})()}}_set_geometry(t,e){super._set_geometry(t,e),this.center_panel.set_geometry(e);const i=this.left_panel.measure({width:0,height:t.height}),h=this.right_panel.measure({width:0,height:t.height}),o=this.top_panel.measure({width:t.width,height:0}),s=this.bottom_panel.measure({width:t.width,height:0}),{left:r,top:g,right:a,bottom:d}=e;this.top_panel.set_geometry(new n.BBox({left:r,right:a,bottom:g,height:o.height})),this.bottom_panel.set_geometry(new n.BBox({left:r,right:a,top:d,height:s.height})),this.left_panel.set_geometry(new n.BBox({top:g,bottom:d,right:r,width:i.width})),this.right_panel.set_geometry(new n.BBox({top:g,bottom:d,left:a,width:h.width}))}}i.BorderLayout=g,g.__name__=\"BorderLayout\"},\n function _(t,e,i,s,l){s();const n=t(1);var o;const a=t(119),_=t(10),d=t(20),h=t(120),r=t(123),u=(0,n.__importStar)(t(48));class c extends a.TextAnnotationView{update_layout(){const{panel:t}=this;this.layout=null!=t?new r.SideLayout(t,(()=>this.get_size()),!1):void 0}_get_size(){const{text:t}=this.model,e=new h.TextBox({text:t}),{angle:i,angle_units:s}=this.model;e.angle=(0,_.resolve_angle)(i,s),e.visuals=this.visuals.text.values();const{width:l,height:n}=e.size();return{width:l,height:n}}_render(){const{angle:t,angle_units:e}=this.model,i=(0,_.resolve_angle)(t,e),s=null!=this.layout?this.layout:this.plot_view.frame,l=this.coordinates.x_scale,n=this.coordinates.y_scale;let o=\"data\"==this.model.x_units?l.compute(this.model.x):s.bbox.xview.compute(this.model.x),a=\"data\"==this.model.y_units?n.compute(this.model.y):s.bbox.yview.compute(this.model.y);o+=this.model.x_offset,a-=this.model.y_offset;(\"canvas\"==this.model.render_mode?this._canvas_text.bind(this):this._css_text.bind(this))(this.layer.ctx,this.model.text,o,a,i)}}i.LabelView=c,c.__name__=\"LabelView\";class x extends a.TextAnnotation{constructor(t){super(t)}}i.Label=x,o=x,x.__name__=\"Label\",o.prototype.default_view=c,o.mixins([u.Text,[\"border_\",u.Line],[\"background_\",u.Fill]]),o.define((({Number:t,String:e,Angle:i})=>({x:[t],x_units:[d.SpatialUnits,\"data\"],y:[t],y_units:[d.SpatialUnits,\"data\"],text:[e,\"\"],angle:[i,0],angle_units:[d.AngleUnits,\"rad\"],x_offset:[t,0],y_offset:[t,0]}))),o.override({background_fill_color:null,border_line_color:null})},\n function _(t,e,s,i,l){i();const o=t(1);var a;const r=t(69),n=(0,o.__importStar)(t(48)),d=t(20),_=t(43),c=t(120),h=(0,o.__importStar)(t(18)),u=t(11);class v extends r.DataAnnotationView{set_data(t){var e;if(super.set_data(t),null===(e=this.els)||void 0===e||e.forEach((t=>(0,_.remove)(t))),\"css\"==this.model.render_mode){const t=this.els=[...this.text].map((()=>(0,_.div)({style:{display:\"none\"}})));for(const e of t)this.plot_view.canvas_view.add_overlay(e)}else delete this.els}remove(){var t;null===(t=this.els)||void 0===t||t.forEach((t=>(0,_.remove)(t))),super.remove()}_rerender(){\"css\"==this.model.render_mode?this.render():this.request_render()}map_data(){const{x_scale:t,y_scale:e}=this.coordinates,s=null!=this.layout?this.layout:this.plot_view.frame;this.sx=\"data\"==this.model.x_units?t.v_compute(this._x):s.bbox.xview.v_compute(this._x),this.sy=\"data\"==this.model.y_units?e.v_compute(this._y):s.bbox.yview.v_compute(this._y)}paint(){const t=\"canvas\"==this.model.render_mode?this._v_canvas_text.bind(this):this._v_css_text.bind(this),{ctx:e}=this.layer;for(let s=0,i=this.text.length;s{switch(this.visuals.text.text_align.get(e)){case\"left\":return[\"left\",\"0%\"];case\"center\":return[\"center\",\"-50%\"];case\"right\":return[\"right\",\"-100%\"]}})(),[d,c]=(()=>{switch(this.visuals.text.text_baseline.get(e)){case\"top\":return[\"top\",\"0%\"];case\"middle\":return[\"center\",\"-50%\"];case\"bottom\":return[\"bottom\",\"-100%\"];default:return[\"center\",\"-50%\"]}})();let h=`translate(${n}, ${c})`;o&&(h+=`rotate(${o}rad)`),a.style.transformOrigin=`${r} ${d}`,a.style.transform=h,this.layout,this.visuals.background_fill.doit&&(this.visuals.background_fill.set_vectorize(t,e),a.style.backgroundColor=t.fillStyle),this.visuals.border_line.doit&&(this.visuals.border_line.set_vectorize(t,e),a.style.borderStyle=t.lineDash.length<2?\"solid\":\"dashed\",a.style.borderWidth=`${t.lineWidth}px`,a.style.borderColor=t.strokeStyle),(0,_.display)(a)}}s.LabelSetView=v,v.__name__=\"LabelSetView\";class x extends r.DataAnnotation{constructor(t){super(t)}}s.LabelSet=x,a=x,x.__name__=\"LabelSet\",a.prototype.default_view=v,a.mixins([n.TextVector,[\"border_\",n.LineVector],[\"background_\",n.FillVector]]),a.define((()=>({x:[h.XCoordinateSpec,{field:\"x\"}],y:[h.YCoordinateSpec,{field:\"y\"}],x_units:[d.SpatialUnits,\"data\"],y_units:[d.SpatialUnits,\"data\"],text:[h.StringSpec,{field:\"text\"}],angle:[h.AngleSpec,0],x_offset:[h.NumberSpec,{value:0}],y_offset:[h.NumberSpec,{value:0}],render_mode:[d.RenderMode,\"canvas\"]}))),a.override({background_fill_color:null,border_line_color:null})},\n function _(t,e,i,l,s){l();const n=t(1);var o;const h=t(40),a=t(215),_=t(20),r=(0,n.__importStar)(t(48)),d=t(15),c=t(123),g=t(121),m=t(65),b=t(9),f=t(8),u=t(11);class x extends h.AnnotationView{update_layout(){const{panel:t}=this;this.layout=null!=t?new c.SideLayout(t,(()=>this.get_size())):void 0}cursor(t,e){return\"none\"==this.model.click_policy?null:\"pointer\"}get legend_padding(){return null!=this.model.border_line_color?this.model.padding:0}connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.request_render())),this.connect(this.model.item_change,(()=>this.request_render()))}compute_legend_bbox(){const t=this.model.get_legend_names(),{glyph_height:e,glyph_width:i}=this.model,{label_height:l,label_width:s}=this.model;this.max_label_height=(0,b.max)([(0,g.font_metrics)(this.visuals.label_text.font_value()).height,l,e]);const{ctx:n}=this.layer;n.save(),this.visuals.label_text.set_value(n),this.text_widths=new Map;for(const e of t)this.text_widths.set(e,(0,b.max)([n.measureText(e).width,s]));this.visuals.title_text.set_value(n),this.title_height=this.model.title?(0,g.font_metrics)(this.visuals.title_text.font_value()).height+this.model.title_standoff:0,this.title_width=this.model.title?n.measureText(this.model.title).width:0,n.restore();const o=Math.max((0,b.max)([...this.text_widths.values()]),0),h=this.model.margin,{legend_padding:a}=this,_=this.model.spacing,{label_standoff:r}=this.model;let d,c;if(\"vertical\"==this.model.orientation)d=t.length*this.max_label_height+Math.max(t.length-1,0)*_+2*a+this.title_height,c=(0,b.max)([o+i+r+2*a,this.title_width+2*a]);else{let e=2*a+Math.max(t.length-1,0)*_;for(const[,t]of this.text_widths)e+=(0,b.max)([t,s])+i+r;c=(0,b.max)([this.title_width+2*a,e]),d=this.max_label_height+this.title_height+2*a}const x=null!=this.layout?this.layout:this.plot_view.frame,[p,w]=x.bbox.ranges,{location:v}=this.model;let y,k;if((0,f.isString)(v))switch(v){case\"top_left\":y=p.start+h,k=w.start+h;break;case\"top\":case\"top_center\":y=(p.end+p.start)/2-c/2,k=w.start+h;break;case\"top_right\":y=p.end-h-c,k=w.start+h;break;case\"bottom_right\":y=p.end-h-c,k=w.end-h-d;break;case\"bottom\":case\"bottom_center\":y=(p.end+p.start)/2-c/2,k=w.end-h-d;break;case\"bottom_left\":y=p.start+h,k=w.end-h-d;break;case\"left\":case\"center_left\":y=p.start+h,k=(w.end+w.start)/2-d/2;break;case\"center\":case\"center_center\":y=(p.end+p.start)/2-c/2,k=(w.end+w.start)/2-d/2;break;case\"right\":case\"center_right\":y=p.end-h-c,k=(w.end+w.start)/2-d/2}else if((0,f.isArray)(v)&&2==v.length){const[t,e]=v;y=x.bbox.xview.compute(t),k=x.bbox.yview.compute(e)-d}else(0,u.unreachable)();return new m.BBox({left:y,top:k,width:c,height:d})}interactive_bbox(){return this.compute_legend_bbox()}interactive_hit(t,e){return this.interactive_bbox().contains(t,e)}on_hit(t,e){let i;const{glyph_width:l}=this.model,{legend_padding:s}=this,n=this.model.spacing,{label_standoff:o}=this.model;let h=i=s;const a=this.compute_legend_bbox(),_=\"vertical\"==this.model.orientation;for(const r of this.model.items){const d=r.get_labels_list_from_label_prop();for(const c of d){const d=a.x+h,g=a.y+i+this.title_height;let b,f;[b,f]=_?[a.width-2*s,this.max_label_height]:[this.text_widths.get(c)+l+o,this.max_label_height];if(new m.BBox({left:d,top:g,width:b,height:f}).contains(t,e)){switch(this.model.click_policy){case\"hide\":for(const t of r.renderers)t.visible=!t.visible;break;case\"mute\":for(const t of r.renderers)t.muted=!t.muted}return!0}_?i+=this.max_label_height+n:h+=this.text_widths.get(c)+l+o+n}}return!1}_render(){if(0==this.model.items.length)return;if(!(0,b.some)(this.model.items,(t=>t.visible)))return;for(const t of this.model.items)t.legend=this.model;const{ctx:t}=this.layer,e=this.compute_legend_bbox();t.save(),this._draw_legend_box(t,e),this._draw_legend_items(t,e),this._draw_title(t,e),t.restore()}_draw_legend_box(t,e){t.beginPath(),t.rect(e.x,e.y,e.width,e.height),this.visuals.background_fill.apply(t),this.visuals.border_line.apply(t)}_draw_legend_items(t,e){const{glyph_width:i,glyph_height:l}=this.model,{legend_padding:s}=this,n=this.model.spacing,{label_standoff:o}=this.model;let h=s,a=s;const _=\"vertical\"==this.model.orientation;for(const r of this.model.items){if(!r.visible)continue;const d=r.get_labels_list_from_label_prop(),c=r.get_field_from_label_prop();if(0==d.length)continue;const g=(()=>{switch(this.model.click_policy){case\"none\":return!0;case\"hide\":return(0,b.every)(r.renderers,(t=>t.visible));case\"mute\":return(0,b.every)(r.renderers,(t=>!t.muted))}})();for(const m of d){const d=e.x+h,b=e.y+a+this.title_height,f=d+i,u=b+l;_?a+=this.max_label_height+n:h+=this.text_widths.get(m)+i+o+n,this.visuals.label_text.set_value(t),t.fillText(m,f+o,b+this.max_label_height/2);for(const e of r.renderers){const i=this.plot_view.renderer_view(e);null==i||i.draw_legend(t,d,f,b,u,c,m,r.index)}if(!g){let l,n;[l,n]=_?[e.width-2*s,this.max_label_height]:[this.text_widths.get(m)+i+o,this.max_label_height],t.beginPath(),t.rect(d,b,l,n),this.visuals.inactive_fill.set_value(t),t.fill()}}}}_draw_title(t,e){const{title:i}=this.model;i&&this.visuals.title_text.doit&&(t.save(),t.translate(e.x0,e.y0+this.title_height),this.visuals.title_text.set_value(t),t.fillText(i,this.legend_padding,this.legend_padding-this.model.title_standoff),t.restore())}_get_size(){const{width:t,height:e}=this.compute_legend_bbox();return{width:t+2*this.model.margin,height:e+2*this.model.margin}}}i.LegendView=x,x.__name__=\"LegendView\";class p extends h.Annotation{constructor(t){super(t)}initialize(){super.initialize(),this.item_change=new d.Signal0(this,\"item_change\")}get_legend_names(){const t=[];for(const e of this.items){const i=e.get_labels_list_from_label_prop();t.push(...i)}return t}}i.Legend=p,o=p,p.__name__=\"Legend\",o.prototype.default_view=x,o.mixins([[\"label_\",r.Text],[\"title_\",r.Text],[\"inactive_\",r.Fill],[\"border_\",r.Line],[\"background_\",r.Fill]]),o.define((({Number:t,String:e,Array:i,Tuple:l,Or:s,Ref:n,Nullable:o})=>({orientation:[_.Orientation,\"vertical\"],location:[s(_.LegendLocation,l(t,t)),\"top_right\"],title:[o(e),null],title_standoff:[t,5],label_standoff:[t,5],glyph_height:[t,20],glyph_width:[t,20],label_height:[t,20],label_width:[t,20],margin:[t,10],padding:[t,10],spacing:[t,3],items:[i(n(a.LegendItem)),[]],click_policy:[_.LegendClickPolicy,\"none\"]}))),o.override({border_line_color:\"#e5e5e5\",border_line_alpha:.5,border_line_width:1,background_fill_color:\"#ffffff\",background_fill_alpha:.95,inactive_fill_color:\"white\",inactive_fill_alpha:.7,label_text_font_size:\"13px\",label_text_baseline:\"middle\",title_text_font_size:\"13px\",title_text_font_style:\"italic\"})},\n function _(e,r,l,n,t){n();const i=e(1);var s;const o=e(53),a=e(175),_=e(70),u=e(216),d=(0,i.__importStar)(e(18)),c=e(19),f=e(9);class h extends o.Model{constructor(e){super(e)}_check_data_sources_on_renderers(){if(null!=this.get_field_from_label_prop()){if(this.renderers.length<1)return!1;const e=this.renderers[0].data_source;if(null!=e)for(const r of this.renderers)if(r.data_source!=e)return!1}return!0}_check_field_label_on_data_source(){const e=this.get_field_from_label_prop();if(null!=e){if(this.renderers.length<1)return!1;const r=this.renderers[0].data_source;if(null!=r&&!(0,f.includes)(r.columns(),e))return!1}return!0}initialize(){super.initialize(),this.legend=null,this.connect(this.change,(()=>{var e;return null===(e=this.legend)||void 0===e?void 0:e.item_change.emit()}));this._check_data_sources_on_renderers()||c.logger.error(\"Non matching data sources on legend item renderers\");this._check_field_label_on_data_source()||c.logger.error(`Bad column name on label: ${this.label}`)}get_field_from_label_prop(){const{label:e}=this;return(0,u.isField)(e)?e.field:null}get_labels_list_from_label_prop(){if(!this.visible)return[];if((0,u.isValue)(this.label)){const{value:e}=this.label;return null!=e?[e]:[]}const e=this.get_field_from_label_prop();if(null!=e){let r;if(!this.renderers[0]||null==this.renderers[0].data_source)return[\"No source found\"];if(r=this.renderers[0].data_source,r instanceof _.ColumnarDataSource){const l=r.get_column(e);return null!=l?(0,f.uniq)(Array.from(l)):[\"Invalid field\"]}}return[]}}l.LegendItem=h,s=h,h.__name__=\"LegendItem\",s.define((({Boolean:e,Int:r,Array:l,Ref:n,Nullable:t})=>({label:[d.NullStringSpec,null],renderers:[l(n(a.GlyphRenderer)),[]],index:[t(r),null],visible:[e,!0]})))},\n function _(i,n,e,t,u){t();const c=i(8);e.isValue=function(i){return(0,c.isPlainObject)(i)&&\"value\"in i},e.isField=function(i){return(0,c.isPlainObject)(i)&&\"field\"in i},e.isExpr=function(i){return(0,c.isPlainObject)(i)&&\"expr\"in i}},\n function _(t,n,e,s,i){s();const o=t(1);var a;const l=t(40),c=(0,o.__importStar)(t(48)),r=t(20);class _ extends l.AnnotationView{connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.request_render()))}_render(){const{xs:t,ys:n}=this.model;if(t.length!=n.length)return;const e=t.length;if(e<3)return;const{frame:s}=this.plot_view,{ctx:i}=this.layer,o=this.coordinates.x_scale,a=this.coordinates.y_scale,{screen:l}=this.model;function c(t,n,e,s){return l?t:\"data\"==n?e.v_compute(t):s.v_compute(t)}const r=c(t,this.model.xs_units,o,s.bbox.xview),_=c(n,this.model.ys_units,a,s.bbox.yview);i.beginPath();for(let t=0;t({xs:[n(t),[]],xs_units:[r.SpatialUnits,\"data\"],ys:[n(t),[]],ys_units:[r.SpatialUnits,\"data\"]}))),a.internal((({Boolean:t})=>({screen:[t,!1]}))),a.override({fill_color:\"#fff9ba\",fill_alpha:.4,line_color:\"#cccccc\",line_alpha:.3})},\n function _(e,t,n,o,i){o();const s=e(1);var l;const r=e(40),c=(0,s.__importStar)(e(48));class a extends r.AnnotationView{connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.request_render()))}_render(){const{gradient:e,y_intercept:t}=this.model;if(null==e||null==t)return;const{frame:n}=this.plot_view,o=this.coordinates.x_scale,i=this.coordinates.y_scale;let s,l,r,c;if(0==e)s=i.compute(t),l=s,r=n.bbox.left,c=r+n.bbox.width;else{s=n.bbox.top,l=s+n.bbox.height;const a=(i.invert(s)-t)/e,_=(i.invert(l)-t)/e;r=o.compute(a),c=o.compute(_)}const{ctx:a}=this.layer;a.save(),a.beginPath(),this.visuals.line.set_value(a),a.moveTo(r,s),a.lineTo(c,l),a.stroke(),a.restore()}}n.SlopeView=a,a.__name__=\"SlopeView\";class _ extends r.Annotation{constructor(e){super(e)}}n.Slope=_,l=_,_.__name__=\"Slope\",l.prototype.default_view=a,l.mixins(c.Line),l.define((({Number:e,Nullable:t})=>({gradient:[t(e),null],y_intercept:[t(e),null]}))),l.override({line_color:\"black\"})},\n function _(e,t,i,o,n){o();const s=e(1);var l;const a=e(40),r=(0,s.__importStar)(e(48)),c=e(20);class d extends a.AnnotationView{connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.plot_view.request_paint(this)))}_render(){const{location:e}=this.model;if(null==e)return;const{frame:t}=this.plot_view,i=this.coordinates.x_scale,o=this.coordinates.y_scale,n=(t,i)=>\"data\"==this.model.location_units?t.compute(e):this.model.for_hover?e:i.compute(e);let s,l,a,r;\"width\"==this.model.dimension?(a=n(o,t.bbox.yview),l=t.bbox.left,r=t.bbox.width,s=this.model.line_width):(a=t.bbox.top,l=n(i,t.bbox.xview),r=this.model.line_width,s=t.bbox.height);const{ctx:c}=this.layer;c.save(),c.beginPath(),this.visuals.line.set_value(c),c.moveTo(l,a),\"width\"==this.model.dimension?c.lineTo(l+r,a):c.lineTo(l,a+s),c.stroke(),c.restore()}}i.SpanView=d,d.__name__=\"SpanView\";class _ extends a.Annotation{constructor(e){super(e)}}i.Span=_,l=_,_.__name__=\"Span\",l.prototype.default_view=d,l.mixins(r.Line),l.define((({Number:e,Nullable:t})=>({render_mode:[c.RenderMode,\"canvas\"],location:[t(e),null],location_units:[c.SpatialUnits,\"data\"],dimension:[c.Dimension,\"width\"]}))),l.internal((({Boolean:e})=>({for_hover:[e,!1]}))),l.override({line_color:\"black\"})},\n function _(i,e,t,o,l){var s;o();const a=i(40),_=i(221),n=i(113),r=i(43),h=i(123),b=i(65);class v extends a.AnnotationView{constructor(){super(...arguments),this._invalidate_toolbar=!0,this._previous_bbox=new b.BBox}update_layout(){this.layout=new h.SideLayout(this.panel,(()=>this.get_size()),!0)}initialize(){super.initialize(),this.el=(0,r.div)(),this.plot_view.canvas_view.add_event(this.el)}async lazy_initialize(){await super.lazy_initialize(),this._toolbar_view=await(0,n.build_view)(this.model.toolbar,{parent:this}),this.plot_view.visibility_callbacks.push((i=>this._toolbar_view.set_visibility(i)))}remove(){this._toolbar_view.remove(),(0,r.remove)(this.el),super.remove()}render(){this.model.visible||(0,r.undisplay)(this.el),super.render()}_render(){const{bbox:i}=this.layout;this._previous_bbox.equals(i)||((0,r.position)(this.el,i),this._previous_bbox=i,this._invalidate_toolbar=!0),this._invalidate_toolbar&&(this.el.style.position=\"absolute\",this.el.style.overflow=\"hidden\",(0,r.empty)(this.el),this.el.appendChild(this._toolbar_view.el),this._toolbar_view.layout.bbox=i,this._toolbar_view.render(),this._invalidate_toolbar=!1),(0,r.display)(this.el)}_get_size(){const{tools:i,logo:e}=this.model.toolbar;return{width:30*i.length+(null!=e?25:0)+15,height:30}}}t.ToolbarPanelView=v,v.__name__=\"ToolbarPanelView\";class d extends a.Annotation{constructor(i){super(i)}}t.ToolbarPanel=d,s=d,d.__name__=\"ToolbarPanel\",s.prototype.default_view=v,s.define((({Ref:i})=>({toolbar:[i(_.Toolbar)]})))},\n function _(t,e,s,i,o){var c;i();const n=t(8),a=t(9),l=t(13),r=t(222),_=t(223),u=t(232),p=t(233);function v(t){switch(t){case\"tap\":return\"active_tap\";case\"pan\":return\"active_drag\";case\"pinch\":case\"scroll\":return\"active_scroll\";case\"multi\":return\"active_multi\"}return null}function h(t){return\"tap\"==t||\"pan\"==t}s.Drag=r.Tool,s.Inspection=r.Tool,s.Scroll=r.Tool,s.Tap=r.Tool;class f extends p.ToolbarBase{constructor(t){super(t)}connect_signals(){super.connect_signals();const{tools:t,active_drag:e,active_inspect:s,active_scroll:i,active_tap:o,active_multi:c}=this.properties;this.on_change([t,e,s,i,o,c],(()=>this._init_tools()))}_init_tools(){if(super._init_tools(),\"auto\"==this.active_inspect);else if(this.active_inspect instanceof u.InspectTool){let t=!1;for(const e of this.inspectors)e!=this.active_inspect?e.active=!1:t=!0;t||(this.active_inspect=null)}else if((0,n.isArray)(this.active_inspect)){const t=(0,a.intersection)(this.active_inspect,this.inspectors);t.length!=this.active_inspect.length&&(this.active_inspect=t);for(const t of this.inspectors)(0,a.includes)(this.active_inspect,t)||(t.active=!1)}else if(null==this.active_inspect)for(const t of this.inspectors)t.active=!1;const t=t=>{t.active?this._active_change(t):t.active=!0};for(const t of(0,l.values)(this.gestures)){t.tools=(0,a.sort_by)(t.tools,(t=>t.default_order));for(const e of t.tools)this.connect(e.properties.active.change,(()=>this._active_change(e)))}for(const[e,s]of(0,l.entries)(this.gestures)){const i=v(e);if(i){const o=this[i];\"auto\"==o?0!=s.tools.length&&h(e)&&t(s.tools[0]):null!=o&&((0,a.includes)(this.tools,o)?t(o):this[i]=null)}}}}s.Toolbar=f,c=f,f.__name__=\"Toolbar\",c.prototype.default_view=p.ToolbarBaseView,c.define((({Or:t,Ref:e,Auto:i,Null:o})=>({active_drag:[t(e(s.Drag),i,o),\"auto\"],active_inspect:[t(e(s.Inspection),i,o),\"auto\"],active_scroll:[t(e(s.Scroll),i,o),\"auto\"],active_tap:[t(e(s.Tap),i,o),\"auto\"],active_multi:[t(e(_.GestureTool),i,o),\"auto\"]})))},\n function _(t,e,n,o,s){var i;o();const a=t(42),r=t(9),l=t(53);class c extends a.View{get plot_view(){return this.parent}get plot_model(){return this.parent.model}connect_signals(){super.connect_signals(),this.connect(this.model.properties.active.change,(()=>{this.model.active?this.activate():this.deactivate()}))}activate(){}deactivate(){}}n.ToolView=c,c.__name__=\"ToolView\";class _ extends l.Model{constructor(t){super(t)}get synthetic_renderers(){return[]}_get_dim_limits([t,e],[n,o],s,i){const a=s.bbox.h_range;let l;\"width\"==i||\"both\"==i?(l=[(0,r.min)([t,n]),(0,r.max)([t,n])],l=[(0,r.max)([l[0],a.start]),(0,r.min)([l[1],a.end])]):l=[a.start,a.end];const c=s.bbox.v_range;let _;return\"height\"==i||\"both\"==i?(_=[(0,r.min)([e,o]),(0,r.max)([e,o])],_=[(0,r.max)([_[0],c.start]),(0,r.min)([_[1],c.end])]):_=[c.start,c.end],[l,_]}static register_alias(t,e){this.prototype._known_aliases.set(t,e)}static from_string(t){const e=this.prototype._known_aliases.get(t);if(null!=e)return e();{const e=[...this.prototype._known_aliases.keys()];throw new Error(`unexpected tool name '${t}', possible tools are ${e.join(\", \")}`)}}}n.Tool=_,i=_,_.__name__=\"Tool\",i.prototype._known_aliases=new Map,i.define((({String:t,Nullable:e})=>({description:[e(t),null]}))),i.internal((({Boolean:t})=>({active:[t,!1]})))},\n function _(e,o,t,s,n){s();const u=e(224),_=e(231);class l extends u.ButtonToolView{}t.GestureToolView=l,l.__name__=\"GestureToolView\";class i extends u.ButtonTool{constructor(e){super(e),this.button_view=_.OnOffButtonView}}t.GestureTool=i,i.__name__=\"GestureTool\"},\n function _(t,e,o,s,i){s();const n=t(1);var l;const r=(0,n.__importDefault)(t(225)),a=t(226),u=t(222),h=t(43),_=t(34),d=t(8),c=t(9),m=(0,n.__importStar)(t(227)),p=m,v=(0,n.__importDefault)(t(228)),f=(0,n.__importDefault)(t(229)),g=t(230);class b extends a.DOMView{initialize(){super.initialize();const t=this.model.menu;if(null!=t){const e=this.parent.model.toolbar_location,o=\"left\"==e||\"above\"==e,s=this.parent.model.horizontal?\"vertical\":\"horizontal\";this._menu=new g.ContextMenu(o?(0,c.reversed)(t):t,{orientation:s,prevent_hide:t=>t.target==this.el})}this._hammer=new r.default(this.el,{touchAction:\"auto\",inputClass:r.default.TouchMouseInput}),this.connect(this.model.change,(()=>this.render())),this._hammer.on(\"tap\",(t=>{var e;(null===(e=this._menu)||void 0===e?void 0:e.is_open)?this._menu.hide():t.target==this.el&&this._clicked()})),this._hammer.on(\"press\",(()=>this._pressed())),this.el.addEventListener(\"keydown\",(t=>{t.keyCode==h.Keys.Enter&&this._clicked()}))}remove(){var t;this._hammer.destroy(),null===(t=this._menu)||void 0===t||t.remove(),super.remove()}styles(){return[...super.styles(),m.default,v.default,f.default]}css_classes(){return super.css_classes().concat(p.toolbar_button)}render(){(0,h.empty)(this.el);const t=this.model.computed_icon;(0,d.isString)(t)&&((0,_.startsWith)(t,\"data:image\")?this.el.style.backgroundImage=`url(\"${t}\")`:this.el.classList.add(t)),this.el.title=this.model.tooltip,this.el.tabIndex=0,null!=this._menu&&this.root.el.appendChild(this._menu.el)}_pressed(){var t;const e=(()=>{switch(this.parent.model.toolbar_location){case\"right\":return{left_of:this.el};case\"left\":return{right_of:this.el};case\"above\":return{below:this.el};case\"below\":return{above:this.el}}})();null===(t=this._menu)||void 0===t||t.toggle(e)}}o.ButtonToolButtonView=b,b.__name__=\"ButtonToolButtonView\";class w extends u.ToolView{}o.ButtonToolView=w,w.__name__=\"ButtonToolView\";class y extends u.Tool{constructor(t){super(t)}_get_dim_tooltip(t){const{description:e,tool_name:o}=this;return null!=e?e:\"both\"==t?o:`${o} (${\"width\"==t?\"x\":\"y\"}-axis)`}get tooltip(){var t;return null!==(t=this.description)&&void 0!==t?t:this.tool_name}get computed_icon(){return this.icon}get menu(){return null}}o.ButtonTool=y,l=y,y.__name__=\"ButtonTool\",l.internal((({Boolean:t})=>({disabled:[t,!1]})))},\n function _(t,e,i,n,r){\n /*! Hammer.JS - v2.0.7 - 2016-04-22\n * http://hammerjs.github.io/\n *\n * Copyright (c) 2016 Jorik Tangelder;\n * Licensed under the MIT license */\n !function(t,i,n,r){\"use strict\";var s,o=[\"\",\"webkit\",\"Moz\",\"MS\",\"ms\",\"o\"],a=i.createElement(\"div\"),h=Math.round,u=Math.abs,c=Date.now;function l(t,e,i){return setTimeout(T(t,i),e)}function p(t,e,i){return!!Array.isArray(t)&&(f(t,i[e],i),!0)}function f(t,e,i){var n;if(t)if(t.forEach)t.forEach(e,i);else if(t.length!==r)for(n=0;n\\s*\\(/gm,\"{anonymous}()@\"):\"Unknown Stack Trace\",s=t.console&&(t.console.warn||t.console.log);return s&&s.call(t.console,r,n),e.apply(this,arguments)}}s=\"function\"!=typeof Object.assign?function(t){if(t===r||null===t)throw new TypeError(\"Cannot convert undefined or null to object\");for(var e=Object(t),i=1;i-1}function S(t){return t.trim().split(/\\s+/g)}function b(t,e,i){if(t.indexOf&&!i)return t.indexOf(e);for(var n=0;ni[e]})):n.sort()),n}function x(t,e){for(var i,n,s=e[0].toUpperCase()+e.slice(1),a=0;a1&&!i.firstMultiple?i.firstMultiple=H(e):1===s&&(i.firstMultiple=!1);var o=i.firstInput,a=i.firstMultiple,h=a?a.center:o.center,l=e.center=L(n);e.timeStamp=c(),e.deltaTime=e.timeStamp-o.timeStamp,e.angle=G(h,l),e.distance=j(h,l),function(t,e){var i=e.center,n=t.offsetDelta||{},r=t.prevDelta||{},s=t.prevInput||{};1!==e.eventType&&4!==s.eventType||(r=t.prevDelta={x:s.deltaX||0,y:s.deltaY||0},n=t.offsetDelta={x:i.x,y:i.y});e.deltaX=r.x+(i.x-n.x),e.deltaY=r.y+(i.y-n.y)}(i,e),e.offsetDirection=V(e.deltaX,e.deltaY);var p=U(e.deltaTime,e.deltaX,e.deltaY);e.overallVelocityX=p.x,e.overallVelocityY=p.y,e.overallVelocity=u(p.x)>u(p.y)?p.x:p.y,e.scale=a?(f=a.pointers,v=n,j(v[0],v[1],W)/j(f[0],f[1],W)):1,e.rotation=a?function(t,e){return G(e[1],e[0],W)+G(t[1],t[0],W)}(a.pointers,n):0,e.maxPointers=i.prevInput?e.pointers.length>i.prevInput.maxPointers?e.pointers.length:i.prevInput.maxPointers:e.pointers.length,function(t,e){var i,n,s,o,a=t.lastInterval||e,h=e.timeStamp-a.timeStamp;if(8!=e.eventType&&(h>25||a.velocity===r)){var c=e.deltaX-a.deltaX,l=e.deltaY-a.deltaY,p=U(h,c,l);n=p.x,s=p.y,i=u(p.x)>u(p.y)?p.x:p.y,o=V(c,l),t.lastInterval=e}else i=a.velocity,n=a.velocityX,s=a.velocityY,o=a.direction;e.velocity=i,e.velocityX=n,e.velocityY=s,e.direction=o}(i,e);var f,v;var d=t.element;_(e.srcEvent.target,d)&&(d=e.srcEvent.target);e.target=d}(t,i),t.emit(\"hammer.input\",i),t.recognize(i),t.session.prevInput=i}function H(t){for(var e=[],i=0;i=u(e)?t<0?2:4:e<0?8:16}function j(t,e,i){i||(i=F);var n=e[i[0]]-t[i[0]],r=e[i[1]]-t[i[1]];return Math.sqrt(n*n+r*r)}function G(t,e,i){i||(i=F);var n=e[i[0]]-t[i[0]],r=e[i[1]]-t[i[1]];return 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e=nt[t.type];if(1===e&&(this.started=!0),this.started){var i=at.call(this,t,e);12&e&&i[0].length-i[1].length==0&&(this.started=!1),this.callback(this.manager,e,{pointers:i[0],changedPointers:i[1],pointerType:N,srcEvent:t})}}});var ht={touchstart:1,touchmove:2,touchend:4,touchcancel:8},ut=\"touchstart touchmove touchend touchcancel\";function ct(){this.evTarget=ut,this.targetIds={},q.apply(this,arguments)}function lt(t,e){var i=P(t.touches),n=this.targetIds;if(3&e&&1===i.length)return n[i[0].identifier]=!0,[i,i];var r,s,o=P(t.changedTouches),a=[],h=this.target;if(s=i.filter((function(t){return _(t.target,h)})),1===e)for(r=0;r-1&&n.splice(t,1)}),2500)}}function dt(t){for(var e=t.srcEvent.clientX,i=t.srcEvent.clientY,n=0;n-1&&this.requireFail.splice(e,1),this},hasRequireFailures:function(){return this.requireFail.length>0},canRecognizeWith:function(t){return!!this.simultaneous[t.id]},emit:function(t){var e=this,i=this.state;function 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this._super.attrTest.call(this,t)&&(Math.abs(t.rotation)>this.options.threshold||2&this.state)}}),g(Xt,Ot,{defaults:{event:\"swipe\",threshold:10,velocity:.3,direction:30,pointers:1},getTouchAction:function(){return Rt.prototype.getTouchAction.call(this)},attrTest:function(t){var e,i=this.options.direction;return 30&i?e=t.overallVelocity:6&i?e=t.overallVelocityX:i&Y&&(e=t.overallVelocityY),this._super.attrTest.call(this,t)&&i&t.offsetDirection&&t.distance>this.options.threshold&&t.maxPointers==this.options.pointers&&u(e)>this.options.velocity&&4&t.eventType},emit:function(t){var e=xt(t.offsetDirection);e&&this.manager.emit(this.options.event+e,t),this.manager.emit(this.options.event,t)}}),g(Yt,Pt,{defaults:{event:\"tap\",pointers:1,taps:1,interval:300,time:250,threshold:9,posThreshold:10},getTouchAction:function(){return[Et]},process:function(t){var e=this.options,i=t.pointers.length===e.pointers,n=t.distance .bk-divider{cursor:default;overflow:hidden;background-color:#e5e5e5;}.bk-root .bk-context-menu.bk-horizontal > .bk-divider{width:1px;margin:5px 0;}.bk-root .bk-context-menu.bk-vertical > .bk-divider{height:1px;margin:0 5px;}.bk-root .bk-context-menu > :not(.bk-divider){border:1px solid transparent;}.bk-root .bk-context-menu > :not(.bk-divider).bk-active{border-color:#26aae1;}.bk-root .bk-context-menu > :not(.bk-divider):hover{background-color:#f9f9f9;}.bk-root .bk-context-menu > :not(.bk-divider):focus,.bk-root .bk-context-menu > :not(.bk-divider):focus-visible{outline:1px dotted #26aae1;outline-offset:-1px;}.bk-root .bk-context-menu > :not(.bk-divider)::-moz-focus-inner{border:0;}.bk-root .bk-context-menu.bk-horizontal > :not(.bk-divider):first-child{border-top-left-radius:4px;border-bottom-left-radius:4px;}.bk-root .bk-context-menu.bk-horizontal > :not(.bk-divider):last-child{border-top-right-radius:4px;border-bottom-right-radius:4px;}.bk-root .bk-context-menu.bk-vertical > :not(.bk-divider):first-child{border-top-left-radius:4px;border-top-right-radius:4px;}.bk-root .bk-context-menu.bk-vertical > :not(.bk-divider):last-child{border-bottom-left-radius:4px;border-bottom-right-radius:4px;}.bk-root .bk-menu{position:absolute;left:0;width:100%;z-index:100;cursor:pointer;font-size:12px;background-color:#fff;border:1px solid #ccc;border-radius:4px;box-shadow:0 6px 12px rgba(0, 0, 0, 0.175);}.bk-root .bk-menu.bk-above{bottom:100%;}.bk-root .bk-menu.bk-below{top:100%;}.bk-root .bk-menu > .bk-divider{height:1px;margin:7.5px 0;overflow:hidden;background-color:#e5e5e5;}.bk-root .bk-menu > :not(.bk-divider){padding:6px 12px;}.bk-root .bk-menu > :not(.bk-divider):hover,.bk-root .bk-menu > :not(.bk-divider).bk-active{background-color:#e6e6e6;}.bk-root .bk-caret{display:inline-block;vertical-align:middle;width:0;height:0;margin:0 5px;}.bk-root .bk-caret.bk-down{border-top:4px solid;}.bk-root .bk-caret.bk-up{border-bottom:4px solid;}.bk-root .bk-caret.bk-down,.bk-root .bk-caret.bk-up{border-right:4px solid transparent;border-left:4px solid transparent;}.bk-root .bk-caret.bk-left{border-right:4px solid;}.bk-root .bk-caret.bk-right{border-left:4px solid;}.bk-root .bk-caret.bk-left,.bk-root .bk-caret.bk-right{border-top:4px solid transparent;border-bottom:4px solid transparent;}\"},\n function _(t,e,i,n,o){n();const s=t(1),l=t(43),h=t(9),r=(0,s.__importStar)(t(229));class d{constructor(t,e={}){var i,n;this.items=t,this.el=(0,l.div)(),this._open=!1,this._item_click=t=>{var e;null===(e=t.handler)||void 0===e||e.call(t),this.hide()},this._on_mousedown=t=>{var e;const{target:i}=t;i instanceof Node&&this.el.contains(i)||(null===(e=this.prevent_hide)||void 0===e?void 0:e.call(this,t))||this.hide()},this._on_keydown=t=>{t.keyCode==l.Keys.Esc&&this.hide()},this._on_blur=()=>{this.hide()},this.orientation=null!==(i=e.orientation)&&void 0!==i?i:\"vertical\",this.reversed=null!==(n=e.reversed)&&void 0!==n&&n,this.prevent_hide=e.prevent_hide,(0,l.undisplay)(this.el)}get is_open(){return this._open}get can_open(){return 0!=this.items.length}remove(){(0,l.remove)(this.el),this._unlisten()}_listen(){document.addEventListener(\"mousedown\",this._on_mousedown),document.addEventListener(\"keydown\",this._on_keydown),window.addEventListener(\"blur\",this._on_blur)}_unlisten(){document.removeEventListener(\"mousedown\",this._on_mousedown),document.removeEventListener(\"keydown\",this._on_keydown),window.removeEventListener(\"blur\",this._on_blur)}_position(t){const e=this.el.parentElement;if(null!=e){const i=(()=>{if(\"left_of\"in t){const{left:e,top:i}=t.left_of.getBoundingClientRect();return{right:e,top:i}}if(\"right_of\"in t){const{top:e,right:i}=t.right_of.getBoundingClientRect();return{left:i,top:e}}if(\"below\"in t){const{left:e,bottom:i}=t.below.getBoundingClientRect();return{left:e,top:i}}if(\"above\"in t){const{left:e,top:i}=t.above.getBoundingClientRect();return{left:e,bottom:i}}return t})(),n=e.getBoundingClientRect();this.el.style.left=null!=i.left?i.left-n.left+\"px\":\"\",this.el.style.top=null!=i.top?i.top-n.top+\"px\":\"\",this.el.style.right=null!=i.right?n.right-i.right+\"px\":\"\",this.el.style.bottom=null!=i.bottom?n.bottom-i.bottom+\"px\":\"\"}}render(){var t;(0,l.empty)(this.el,!0),(0,l.classes)(this.el).add(\"bk-context-menu\",`bk-${this.orientation}`);const e=this.reversed?(0,h.reversed)(this.items):this.items;for(const i of e){let e;if(null==i)e=(0,l.div)({class:r.divider});else{if(null!=i.if&&!i.if())continue;if(null!=i.content)e=i.content;else{const n=null!=i.icon?(0,l.div)({class:[\"bk-menu-icon\",i.icon]}):null,o=[(null===(t=i.active)||void 0===t?void 0:t.call(i))?\"bk-active\":null,i.class];e=(0,l.div)({class:o,title:i.tooltip,tabIndex:0},n,i.label,i.content),e.addEventListener(\"click\",(()=>{this._item_click(i)})),e.addEventListener(\"keydown\",(t=>{t.keyCode==l.Keys.Enter&&this._item_click(i)}))}}this.el.appendChild(e)}}show(t){if(0!=this.items.length&&!this._open){if(this.render(),0==this.el.children.length)return;this._position(null!=t?t:{left:0,top:0}),(0,l.display)(this.el),this._listen(),this._open=!0}}hide(){this._open&&(this._open=!1,this._unlisten(),(0,l.undisplay)(this.el))}toggle(t){this._open?this.hide():this.show(t)}}i.ContextMenu=d,d.__name__=\"ContextMenu\"},\n function _(t,e,i,n,o){n();const s=t(1),c=t(224),l=(0,s.__importStar)(t(227)),a=t(43);class _ extends c.ButtonToolButtonView{render(){super.render(),(0,a.classes)(this.el).toggle(l.active,this.model.active)}_clicked(){const{active:t}=this.model;this.model.active=!t}}i.OnOffButtonView=_,_.__name__=\"OnOffButtonView\"},\n function _(e,o,t,n,s){var c;n();const l=e(224),_=e(231);class i extends l.ButtonToolView{}t.InspectToolView=i,i.__name__=\"InspectToolView\";class a extends l.ButtonTool{constructor(e){super(e),this.event_type=\"move\"}}t.InspectTool=a,c=a,a.__name__=\"InspectTool\",c.prototype.button_view=_.OnOffButtonView,c.define((({Boolean:e})=>({toggleable:[e,!0]}))),c.override({active:!0})},\n function _(t,o,e,l,i){l();const s=t(1);var n,a;const r=t(19),c=t(43),h=t(113),_=t(226),u=t(20),v=t(9),d=t(234),p=t(13),b=t(8),g=t(235),f=t(65),m=t(53),w=t(222),y=t(223),T=t(238),z=t(239),x=t(232),B=t(230),C=(0,s.__importStar)(t(227)),k=C,L=(0,s.__importStar)(t(240)),M=L;class S extends m.Model{constructor(t){super(t)}get visible(){var t;return!this.autohide||null!==(t=this._visible)&&void 0!==t&&t}}e.ToolbarViewModel=S,n=S,S.__name__=\"ToolbarViewModel\",n.define((({Boolean:t})=>({autohide:[t,!1]}))),n.internal((({Boolean:t,Nullable:o})=>({_visible:[o(t),null]})));class $ extends _.DOMView{constructor(){super(...arguments),this.layout={bbox:new f.BBox}}initialize(){super.initialize(),this._tool_button_views=new Map,this._toolbar_view_model=new S({autohide:this.model.autohide});const{toolbar_location:t}=this.model,o=\"left\"==t||\"above\"==t,e=this.model.horizontal?\"vertical\":\"horizontal\";this._overflow_menu=new B.ContextMenu([],{orientation:e,reversed:o})}async lazy_initialize(){await super.lazy_initialize(),await this._build_tool_button_views()}connect_signals(){super.connect_signals(),this.connect(this.model.properties.tools.change,(async()=>{await this._build_tool_button_views(),this.render()})),this.connect(this.model.properties.autohide.change,(()=>{this._toolbar_view_model.autohide=this.model.autohide,this._on_visible_change()})),this.connect(this._toolbar_view_model.properties._visible.change,(()=>this._on_visible_change()))}styles(){return[...super.styles(),C.default,L.default]}remove(){(0,h.remove_views)(this._tool_button_views),super.remove()}async _build_tool_button_views(){const t=null!=this.model._proxied_tools?this.model._proxied_tools:this.model.tools;await(0,h.build_views)(this._tool_button_views,t,{parent:this},(t=>t.button_view))}set_visibility(t){t!=this._toolbar_view_model._visible&&(this._toolbar_view_model._visible=t)}_on_visible_change(){const{visible:t}=this._toolbar_view_model;(0,c.classes)(this.el).toggle(k.toolbar_hidden,!t)}render(){(0,c.empty)(this.el),this.el.classList.add(k.toolbar),this.el.classList.add(k[this.model.toolbar_location]),this._toolbar_view_model.autohide=this.model.autohide,this._on_visible_change();const{horizontal:t}=this.model;let o=0;if(null!=this.model.logo){const e=\"grey\"===this.model.logo?M.grey:null,l=(0,c.a)({href:\"https://bokeh.org/\",target:\"_blank\",class:[M.logo,M.logo_small,e]});this.el.appendChild(l);const{width:i,height:s}=l.getBoundingClientRect();o+=t?i:s}for(const[,t]of this._tool_button_views)t.render();const e=[],l=t=>this._tool_button_views.get(t).el,{gestures:i}=this.model;for(const t of(0,p.values)(i))e.push(t.tools.map(l));e.push(this.model.actions.map(l)),e.push(this.model.inspectors.filter((t=>t.toggleable)).map(l));const s=e.filter((t=>0!=t.length)),n=()=>(0,c.div)({class:k.divider}),{bbox:a}=this.layout;let r=!1;this.root.el.appendChild(this._overflow_menu.el);const h=(0,c.div)({class:k.tool_overflow,tabIndex:0},t?\"\\u22ee\":\"\\u22ef\"),_=()=>{const t=(()=>{switch(this.model.toolbar_location){case\"right\":return{left_of:h};case\"left\":return{right_of:h};case\"above\":return{below:h};case\"below\":return{above:h}}})();this._overflow_menu.toggle(t)};h.addEventListener(\"click\",(()=>{_()})),h.addEventListener(\"keydown\",(t=>{t.keyCode==c.Keys.Enter&&_()}));for(const e of(0,d.join)(s,n))if(r)this._overflow_menu.items.push({content:e,class:t?k.right:k.above});else{this.el.appendChild(e);const{width:l,height:i}=e.getBoundingClientRect();if(o+=t?l:i,r=t?o>a.width-15:o>a.height-15,r){this.el.removeChild(e),this.el.appendChild(h);const{items:t}=this._overflow_menu;t.splice(0,t.length),t.push({content:e})}}}update_layout(){}update_position(){}after_layout(){this._has_finished=!0}export(t,o=!0){const e=\"png\"==t?\"canvas\":\"svg\",l=new g.CanvasLayer(e,o);return l.resize(0,0),l}}function V(){return{pan:{tools:[],active:null},scroll:{tools:[],active:null},pinch:{tools:[],active:null},tap:{tools:[],active:null},doubletap:{tools:[],active:null},press:{tools:[],active:null},pressup:{tools:[],active:null},rotate:{tools:[],active:null},move:{tools:[],active:null},multi:{tools:[],active:null}}}e.ToolbarBaseView=$,$.__name__=\"ToolbarBaseView\";class A extends m.Model{constructor(t){super(t)}initialize(){super.initialize(),this._init_tools()}_init_tools(){const t=function(t,o){if(t.length!=o.length)return!0;const e=new Set(o.map((t=>t.id)));return(0,v.some)(t,(t=>!e.has(t.id)))},o=this.tools.filter((t=>t instanceof x.InspectTool));t(this.inspectors,o)&&(this.inspectors=o);const e=this.tools.filter((t=>t instanceof z.HelpTool));t(this.help,e)&&(this.help=e);const l=this.tools.filter((t=>t instanceof T.ActionTool));t(this.actions,l)&&(this.actions=l);const i=(t,o)=>{t in this.gestures||r.logger.warn(`Toolbar: unknown event type '${t}' for tool: ${o}`)},s={pan:{tools:[],active:null},scroll:{tools:[],active:null},pinch:{tools:[],active:null},tap:{tools:[],active:null},doubletap:{tools:[],active:null},press:{tools:[],active:null},pressup:{tools:[],active:null},rotate:{tools:[],active:null},move:{tools:[],active:null},multi:{tools:[],active:null}};for(const t of this.tools)if(t instanceof y.GestureTool&&t.event_type)if((0,b.isString)(t.event_type))s[t.event_type].tools.push(t),i(t.event_type,t);else{s.multi.tools.push(t);for(const o of t.event_type)i(o,t)}for(const o of Object.keys(s)){const e=this.gestures[o];t(e.tools,s[o].tools)&&(e.tools=s[o].tools),e.active&&(0,v.every)(e.tools,(t=>t.id!=e.active.id))&&(e.active=null)}}get horizontal(){return\"above\"===this.toolbar_location||\"below\"===this.toolbar_location}get vertical(){return\"left\"===this.toolbar_location||\"right\"===this.toolbar_location}_active_change(t){const{event_type:o}=t;if(null==o)return;const e=(0,b.isString)(o)?[o]:o;for(const o of e)if(t.active){const e=this.gestures[o].active;null!=e&&t!=e&&(r.logger.debug(`Toolbar: deactivating tool: ${e} for event type '${o}'`),e.active=!1),this.gestures[o].active=t,r.logger.debug(`Toolbar: activating tool: ${t} for event type '${o}'`)}else this.gestures[o].active=null}}e.ToolbarBase=A,a=A,A.__name__=\"ToolbarBase\",a.prototype.default_view=$,a.define((({Boolean:t,Array:o,Ref:e,Nullable:l})=>({tools:[o(e(w.Tool)),[]],logo:[l(u.Logo),\"normal\"],autohide:[t,!1]}))),a.internal((({Array:t,Struct:o,Ref:e,Nullable:l})=>{const i=o({tools:t(e(y.GestureTool)),active:l(e(w.Tool))});return{gestures:[o({pan:i,scroll:i,pinch:i,tap:i,doubletap:i,press:i,pressup:i,rotate:i,move:i,multi:i}),V],actions:[t(e(T.ActionTool)),[]],inspectors:[t(e(x.InspectTool)),[]],help:[t(e(z.HelpTool)),[]],toolbar_location:[u.Location,\"right\"]}}))},\n function _(n,o,e,t,f){t();const r=n(9);function*i(n,o){const e=n.length;if(o>e)return;const t=(0,r.range)(o);for(yield t.map((o=>n[o]));;){let f;for(const n of(0,r.reversed)((0,r.range)(o)))if(t[n]!=n+e-o){f=n;break}if(null==f)return;t[f]+=1;for(const n of(0,r.range)(f+1,o))t[n]=t[n-1]+1;yield t.map((o=>n[o]))}}e.enumerate=function*(n){let o=0;for(const e of n)yield[e,o++]},e.join=function*(n,o){let 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c.Callback{constructor(e){super(e)}navigate(e){this.same_tab?window.location.href=e:window.open(e)}execute(e,{source:t}){const n=e=>{const n=(0,r.replace_placeholders)(this.url,t,e,void 0,void 0,encodeURI);if(!(0,a.isString)(n))throw new Error(\"HTML output is not supported in this context\");this.navigate(n)},{selected:o}=t;for(const e of o.indices)n(e);for(const e of o.line_indices)n(e)}}n.OpenURL=d,s=d,d.__name__=\"OpenURL\",s.define((({Boolean:e,String:t})=>({url:[t,\"http://\"],same_tab:[e,!1]})))},\n function _(a,n,i,e,r){e(),r(\"Canvas\",a(249).Canvas),r(\"CartesianFrame\",a(126).CartesianFrame),r(\"CoordinateMapping\",a(54).CoordinateMapping)},\n function _(e,t,i,s,a){var l,r=this&&this.__createBinding||(Object.create?function(e,t,i,s){void 0===s&&(s=i),Object.defineProperty(e,s,{enumerable:!0,get:function(){return t[i]}})}:function(e,t,i,s){void 0===s&&(s=i),e[s]=t[i]}),n=this&&this.__setModuleDefault||(Object.create?function(e,t){Object.defineProperty(e,\"default\",{enumerable:!0,value:t})}:function(e,t){e.default=t}),o=this&&this.__importStar||function(e){if(e&&e.__esModule)return e;var t={};if(null!=e)for(var i in e)\"default\"!==i&&Object.prototype.hasOwnProperty.call(e,i)&&r(t,e,i);return n(t,e),t};s();const h=e(14),c=e(28),u=e(226),_=e(19),d=e(43),p=e(20),b=e(13),v=e(250),g=e(65),w=e(138),y=e(235);const f=(()=>{let t;return async()=>void 0!==t?t:t=await async function(){const t=document.createElement(\"canvas\"),i=t.getContext(\"webgl\",{premultipliedAlpha:!0});if(null!=i){const s=await(0,w.load_module)(Promise.resolve().then((()=>o(e(410)))));if(null!=s){const e=s.get_regl(i);if(e.has_webgl)return{canvas:t,regl_wrapper:e};_.logger.trace(\"WebGL is supported, but not the required extensions\")}else _.logger.trace(\"WebGL is supported, but bokehjs(.min).js bundle is not available\")}else _.logger.trace(\"WebGL is not supported\");return null}()})(),m={position:\"absolute\",top:\"0\",left:\"0\",width:\"100%\",height:\"100%\"};class x extends u.DOMView{constructor(){super(...arguments),this.bbox=new g.BBox,this.webgl=null}initialize(){super.initialize(),this.underlays_el=(0,d.div)({style:m}),this.primary=this.create_layer(),this.overlays=this.create_layer(),this.overlays_el=(0,d.div)({style:m}),this.events_el=(0,d.div)({class:\"bk-canvas-events\",style:m});const e=[this.underlays_el,this.primary.el,this.overlays.el,this.overlays_el,this.events_el];(0,b.extend)(this.el.style,m),(0,d.append)(this.el,...e),this.ui_event_bus=new v.UIEventBus(this)}async lazy_initialize(){if(await super.lazy_initialize(),\"webgl\"==this.model.output_backend&&(this.webgl=await f(),c.settings.force_webgl&&null==this.webgl))throw new Error(\"webgl is not available\")}remove(){this.ui_event_bus.destroy(),super.remove()}add_underlay(e){this.underlays_el.appendChild(e)}add_overlay(e){this.overlays_el.appendChild(e)}add_event(e){this.events_el.appendChild(e)}get pixel_ratio(){return this.primary.pixel_ratio}resize(e,t){this.bbox=new g.BBox({left:0,top:0,width:e,height:t}),this.primary.resize(e,t),this.overlays.resize(e,t)}prepare_webgl(e){const{webgl:t}=this;if(null!=t){const{width:i,height:s}=this.bbox;t.canvas.width=this.pixel_ratio*i,t.canvas.height=this.pixel_ratio*s;const[a,l,r,n]=e,{xview:o,yview:h}=this.bbox,c=o.compute(a),u=h.compute(l+n),_=this.pixel_ratio;t.regl_wrapper.set_scissor(_*c,_*u,_*r,_*n),this._clear_webgl()}}blit_webgl(e){const{webgl:t}=this;if(null!=t){if(_.logger.debug(\"Blitting WebGL canvas\"),e.restore(),e.drawImage(t.canvas,0,0),e.save(),this.model.hidpi){const t=this.pixel_ratio;e.scale(t,t),e.translate(.5,.5)}this._clear_webgl()}}_clear_webgl(){const{webgl:e}=this;if(null!=e){const{regl_wrapper:t,canvas:i}=e;t.clear(i.width,i.height)}}compose(){const e=this.create_layer(),{width:t,height:i}=this.bbox;return e.resize(t,i),e.ctx.drawImage(this.primary.canvas,0,0),e.ctx.drawImage(this.overlays.canvas,0,0),e}create_layer(){const{output_backend:e,hidpi:t}=this.model;return new y.CanvasLayer(e,t)}to_blob(){return this.compose().to_blob()}}i.CanvasView=x,x.__name__=\"CanvasView\";class z extends h.HasProps{constructor(e){super(e)}}i.Canvas=z,l=z,z.__name__=\"Canvas\",l.prototype.default_view=x,l.internal((({Boolean:e})=>({hidpi:[e,!0],output_backend:[p.OutputBackend,\"canvas\"]})))},\n function _(t,e,s,n,i){n();const r=t(1),a=(0,r.__importDefault)(t(225)),_=t(15),h=t(19),o=t(43),l=(0,r.__importStar)(t(251)),c=t(252),p=t(9),u=t(8),v=t(27),d=t(230);class g{constructor(t){this.canvas_view=t,this.pan_start=new _.Signal(this,\"pan:start\"),this.pan=new _.Signal(this,\"pan\"),this.pan_end=new _.Signal(this,\"pan:end\"),this.pinch_start=new _.Signal(this,\"pinch:start\"),this.pinch=new _.Signal(this,\"pinch\"),this.pinch_end=new _.Signal(this,\"pinch:end\"),this.rotate_start=new _.Signal(this,\"rotate:start\"),this.rotate=new _.Signal(this,\"rotate\"),this.rotate_end=new _.Signal(this,\"rotate:end\"),this.tap=new _.Signal(this,\"tap\"),this.doubletap=new _.Signal(this,\"doubletap\"),this.press=new _.Signal(this,\"press\"),this.pressup=new _.Signal(this,\"pressup\"),this.move_enter=new _.Signal(this,\"move:enter\"),this.move=new _.Signal(this,\"move\"),this.move_exit=new _.Signal(this,\"move:exit\"),this.scroll=new _.Signal(this,\"scroll\"),this.keydown=new _.Signal(this,\"keydown\"),this.keyup=new _.Signal(this,\"keyup\"),this.hammer=new a.default(this.hit_area,{touchAction:\"auto\",inputClass:a.default.TouchMouseInput}),this._prev_move=null,this._curr_pan=null,this._curr_pinch=null,this._curr_rotate=null,this._configure_hammerjs(),this.hit_area.addEventListener(\"mousemove\",(t=>this._mouse_move(t))),this.hit_area.addEventListener(\"mouseenter\",(t=>this._mouse_enter(t))),this.hit_area.addEventListener(\"mouseleave\",(t=>this._mouse_exit(t))),this.hit_area.addEventListener(\"contextmenu\",(t=>this._context_menu(t))),this.hit_area.addEventListener(\"wheel\",(t=>this._mouse_wheel(t))),document.addEventListener(\"keydown\",this),document.addEventListener(\"keyup\",this),this.menu=new d.ContextMenu([],{prevent_hide:t=>2==t.button&&t.target==this.hit_area}),this.hit_area.appendChild(this.menu.el)}get hit_area(){return this.canvas_view.events_el}destroy(){this.menu.remove(),this.hammer.destroy(),document.removeEventListener(\"keydown\",this),document.removeEventListener(\"keyup\",this)}handleEvent(t){\"keydown\"==t.type?this._key_down(t):\"keyup\"==t.type&&this._key_up(t)}_configure_hammerjs(){this.hammer.get(\"doubletap\").recognizeWith(\"tap\"),this.hammer.get(\"tap\").requireFailure(\"doubletap\"),this.hammer.get(\"doubletap\").dropRequireFailure(\"tap\"),this.hammer.on(\"doubletap\",(t=>this._doubletap(t))),this.hammer.on(\"tap\",(t=>this._tap(t))),this.hammer.on(\"press\",(t=>this._press(t))),this.hammer.on(\"pressup\",(t=>this._pressup(t))),this.hammer.get(\"pan\").set({direction:a.default.DIRECTION_ALL}),this.hammer.on(\"panstart\",(t=>this._pan_start(t))),this.hammer.on(\"pan\",(t=>this._pan(t))),this.hammer.on(\"panend\",(t=>this._pan_end(t))),this.hammer.get(\"pinch\").set({enable:!0}),this.hammer.on(\"pinchstart\",(t=>this._pinch_start(t))),this.hammer.on(\"pinch\",(t=>this._pinch(t))),this.hammer.on(\"pinchend\",(t=>this._pinch_end(t))),this.hammer.get(\"rotate\").set({enable:!0}),this.hammer.on(\"rotatestart\",(t=>this._rotate_start(t))),this.hammer.on(\"rotate\",(t=>this._rotate(t))),this.hammer.on(\"rotateend\",(t=>this._rotate_end(t)))}register_tool(t){const e=t.model.event_type;null!=e&&((0,u.isString)(e)?this._register_tool(t,e):e.forEach(((e,s)=>this._register_tool(t,e,s<1))))}_register_tool(t,e,s=!0){const n=t,{id:i}=n.model,r=t=>e=>{e.id==i&&t(e.e)},a=t=>e=>{t(e.e)};switch(e){case\"pan\":null!=n._pan_start&&n.connect(this.pan_start,r(n._pan_start.bind(n))),null!=n._pan&&n.connect(this.pan,r(n._pan.bind(n))),null!=n._pan_end&&n.connect(this.pan_end,r(n._pan_end.bind(n)));break;case\"pinch\":null!=n._pinch_start&&n.connect(this.pinch_start,r(n._pinch_start.bind(n))),null!=n._pinch&&n.connect(this.pinch,r(n._pinch.bind(n))),null!=n._pinch_end&&n.connect(this.pinch_end,r(n._pinch_end.bind(n)));break;case\"rotate\":null!=n._rotate_start&&n.connect(this.rotate_start,r(n._rotate_start.bind(n))),null!=n._rotate&&n.connect(this.rotate,r(n._rotate.bind(n))),null!=n._rotate_end&&n.connect(this.rotate_end,r(n._rotate_end.bind(n)));break;case\"move\":null!=n._move_enter&&n.connect(this.move_enter,r(n._move_enter.bind(n))),null!=n._move&&n.connect(this.move,r(n._move.bind(n))),null!=n._move_exit&&n.connect(this.move_exit,r(n._move_exit.bind(n)));break;case\"tap\":null!=n._tap&&n.connect(this.tap,r(n._tap.bind(n))),null!=n._doubletap&&n.connect(this.doubletap,r(n._doubletap.bind(n)));break;case\"press\":null!=n._press&&n.connect(this.press,r(n._press.bind(n))),null!=n._pressup&&n.connect(this.pressup,r(n._pressup.bind(n)));break;case\"scroll\":null!=n._scroll&&n.connect(this.scroll,r(n._scroll.bind(n)));break;default:throw new Error(`unsupported event_type: ${e}`)}s&&(null!=n._keydown&&n.connect(this.keydown,a(n._keydown.bind(n))),null!=n._keyup&&n.connect(this.keyup,a(n._keyup.bind(n))),v.is_mobile&&null!=n._scroll&&\"pinch\"==e&&(h.logger.debug(\"Registering scroll on touch screen\"),n.connect(this.scroll,r(n._scroll.bind(n)))))}_hit_test_renderers(t,e,s){var n;const i=t.get_renderer_views();for(const t of(0,p.reversed)(i))if(null===(n=t.interactive_hit)||void 0===n?void 0:n.call(t,e,s))return t;return null}set_cursor(t=\"default\"){this.hit_area.style.cursor=t}_hit_test_frame(t,e,s){return t.frame.bbox.contains(e,s)}_hit_test_canvas(t,e,s){return t.layout.bbox.contains(e,s)}_hit_test_plot(t,e){for(const s of this.canvas_view.plot_views)if(s.layout.bbox.relative().contains(t,e))return s;return null}_trigger(t,e,s){var n;const{sx:i,sy:r}=e,a=this._hit_test_plot(i,r),_=t=>{const[s,n]=[i,r];return Object.assign(Object.assign({},e),{sx:s,sy:n})};if(\"panstart\"==e.type||\"pan\"==e.type||\"panend\"==e.type){let n;if(\"panstart\"==e.type&&null!=a?(this._curr_pan={plot_view:a},n=a):\"pan\"==e.type&&null!=this._curr_pan?n=this._curr_pan.plot_view:\"panend\"==e.type&&null!=this._curr_pan?(n=this._curr_pan.plot_view,this._curr_pan=null):n=null,null!=n){const e=_();this.__trigger(n,t,e,s)}}else if(\"pinchstart\"==e.type||\"pinch\"==e.type||\"pinchend\"==e.type){let n;if(\"pinchstart\"==e.type&&null!=a?(this._curr_pinch={plot_view:a},n=a):\"pinch\"==e.type&&null!=this._curr_pinch?n=this._curr_pinch.plot_view:\"pinchend\"==e.type&&null!=this._curr_pinch?(n=this._curr_pinch.plot_view,this._curr_pinch=null):n=null,null!=n){const e=_();this.__trigger(n,t,e,s)}}else if(\"rotatestart\"==e.type||\"rotate\"==e.type||\"rotateend\"==e.type){let n;if(\"rotatestart\"==e.type&&null!=a?(this._curr_rotate={plot_view:a},n=a):\"rotate\"==e.type&&null!=this._curr_rotate?n=this._curr_rotate.plot_view:\"rotateend\"==e.type&&null!=this._curr_rotate?(n=this._curr_rotate.plot_view,this._curr_rotate=null):n=null,null!=n){const e=_();this.__trigger(n,t,e,s)}}else if(\"mouseenter\"==e.type||\"mousemove\"==e.type||\"mouseleave\"==e.type){const h=null===(n=this._prev_move)||void 0===n?void 0:n.plot_view;if(null!=h&&(\"mouseleave\"==e.type||h!=a)){const{sx:t,sy:e}=_();this.__trigger(h,this.move_exit,{type:\"mouseleave\",sx:t,sy:e,shiftKey:!1,ctrlKey:!1},s)}if(null!=a&&(\"mouseenter\"==e.type||h!=a)){const{sx:t,sy:e}=_();this.__trigger(a,this.move_enter,{type:\"mouseenter\",sx:t,sy:e,shiftKey:!1,ctrlKey:!1},s)}if(null!=a&&\"mousemove\"==e.type){const e=_();this.__trigger(a,t,e,s)}this._prev_move={sx:i,sy:r,plot_view:a}}else if(null!=a){const e=_();this.__trigger(a,t,e,s)}}__trigger(t,e,s,n){var i,r,a;const _=t.model.toolbar.gestures,h=e.name.split(\":\")[0],o=this._hit_test_renderers(t,s.sx,s.sy),l=this._hit_test_canvas(t,s.sx,s.sy);switch(h){case\"move\":{const n=_.move.active;null!=n&&this.trigger(e,s,n.id);const r=t.model.toolbar.inspectors.filter((t=>t.active));let a=\"default\";null!=o?(a=null!==(i=o.cursor(s.sx,s.sy))&&void 0!==i?i:a,(0,p.is_empty)(r)||(e=this.move_exit)):this._hit_test_frame(t,s.sx,s.sy)&&((0,p.is_empty)(r)||(a=\"crosshair\")),this.set_cursor(a),t.set_toolbar_visibility(l),r.map((t=>this.trigger(e,s,t.id)));break}case\"tap\":{const{target:i}=n;if(null!=i&&i!=this.hit_area)return;if(null===(r=null==o?void 0:o.on_hit)||void 0===r||r.call(o,s.sx,s.sy),this._hit_test_frame(t,s.sx,s.sy)){const t=_.tap.active;null!=t&&this.trigger(e,s,t.id)}break}case\"doubletap\":if(this._hit_test_frame(t,s.sx,s.sy)){const t=null!==(a=_.doubletap.active)&&void 0!==a?a:_.tap.active;null!=t&&this.trigger(e,s,t.id)}break;case\"scroll\":{const t=_[v.is_mobile?\"pinch\":\"scroll\"].active;null!=t&&(n.preventDefault(),n.stopPropagation(),this.trigger(e,s,t.id));break}case\"pan\":{const t=_.pan.active;null!=t&&(n.preventDefault(),this.trigger(e,s,t.id));break}default:{const t=_[h].active;null!=t&&this.trigger(e,s,t.id)}}this._trigger_bokeh_event(t,s)}trigger(t,e,s=null){t.emit({id:s,e})}_trigger_bokeh_event(t,e){const s=(()=>{const{sx:s,sy:n}=e,i=t.frame.x_scale.invert(s),r=t.frame.y_scale.invert(n);switch(e.type){case\"wheel\":return new l.MouseWheel(s,n,i,r,e.delta);case\"mousemove\":return new l.MouseMove(s,n,i,r);case\"mouseenter\":return new l.MouseEnter(s,n,i,r);case\"mouseleave\":return new l.MouseLeave(s,n,i,r);case\"tap\":return new l.Tap(s,n,i,r);case\"doubletap\":return new l.DoubleTap(s,n,i,r);case\"press\":return new l.Press(s,n,i,r);case\"pressup\":return new l.PressUp(s,n,i,r);case\"pan\":return new l.Pan(s,n,i,r,e.deltaX,e.deltaY);case\"panstart\":return new l.PanStart(s,n,i,r);case\"panend\":return new l.PanEnd(s,n,i,r);case\"pinch\":return new l.Pinch(s,n,i,r,e.scale);case\"pinchstart\":return new l.PinchStart(s,n,i,r);case\"pinchend\":return new l.PinchEnd(s,n,i,r);case\"rotate\":return new l.Rotate(s,n,i,r,e.rotation);case\"rotatestart\":return new l.RotateStart(s,n,i,r);case\"rotateend\":return new l.RotateEnd(s,n,i,r);default:return}})();null!=s&&t.model.trigger_event(s)}_get_sxy(t){const{pageX:e,pageY:s}=function(t){return\"undefined\"!=typeof TouchEvent&&t instanceof TouchEvent}(t)?(0!=t.touches.length?t.touches:t.changedTouches)[0]:t,{left:n,top:i}=(0,o.offset)(this.hit_area);return{sx:e-n,sy:s-i}}_pan_event(t){return Object.assign(Object.assign({type:t.type},this._get_sxy(t.srcEvent)),{deltaX:t.deltaX,deltaY:t.deltaY,shiftKey:t.srcEvent.shiftKey,ctrlKey:t.srcEvent.ctrlKey})}_pinch_event(t){return Object.assign(Object.assign({type:t.type},this._get_sxy(t.srcEvent)),{scale:t.scale,shiftKey:t.srcEvent.shiftKey,ctrlKey:t.srcEvent.ctrlKey})}_rotate_event(t){return Object.assign(Object.assign({type:t.type},this._get_sxy(t.srcEvent)),{rotation:t.rotation,shiftKey:t.srcEvent.shiftKey,ctrlKey:t.srcEvent.ctrlKey})}_tap_event(t){return Object.assign(Object.assign({type:t.type},this._get_sxy(t.srcEvent)),{shiftKey:t.srcEvent.shiftKey,ctrlKey:t.srcEvent.ctrlKey})}_move_event(t){return Object.assign(Object.assign({type:t.type},this._get_sxy(t)),{shiftKey:t.shiftKey,ctrlKey:t.ctrlKey})}_scroll_event(t){return Object.assign(Object.assign({type:t.type},this._get_sxy(t)),{delta:(0,c.getDeltaY)(t),shiftKey:t.shiftKey,ctrlKey:t.ctrlKey})}_key_event(t){return{type:t.type,keyCode:t.keyCode}}_pan_start(t){const e=this._pan_event(t);e.sx-=t.deltaX,e.sy-=t.deltaY,this._trigger(this.pan_start,e,t.srcEvent)}_pan(t){this._trigger(this.pan,this._pan_event(t),t.srcEvent)}_pan_end(t){this._trigger(this.pan_end,this._pan_event(t),t.srcEvent)}_pinch_start(t){this._trigger(this.pinch_start,this._pinch_event(t),t.srcEvent)}_pinch(t){this._trigger(this.pinch,this._pinch_event(t),t.srcEvent)}_pinch_end(t){this._trigger(this.pinch_end,this._pinch_event(t),t.srcEvent)}_rotate_start(t){this._trigger(this.rotate_start,this._rotate_event(t),t.srcEvent)}_rotate(t){this._trigger(this.rotate,this._rotate_event(t),t.srcEvent)}_rotate_end(t){this._trigger(this.rotate_end,this._rotate_event(t),t.srcEvent)}_tap(t){this._trigger(this.tap,this._tap_event(t),t.srcEvent)}_doubletap(t){this._trigger(this.doubletap,this._tap_event(t),t.srcEvent)}_press(t){this._trigger(this.press,this._tap_event(t),t.srcEvent)}_pressup(t){this._trigger(this.pressup,this._tap_event(t),t.srcEvent)}_mouse_enter(t){this._trigger(this.move_enter,this._move_event(t),t)}_mouse_move(t){this._trigger(this.move,this._move_event(t),t)}_mouse_exit(t){this._trigger(this.move_exit,this._move_event(t),t)}_mouse_wheel(t){this._trigger(this.scroll,this._scroll_event(t),t)}_context_menu(t){!this.menu.is_open&&this.menu.can_open&&t.preventDefault();const{sx:e,sy:s}=this._get_sxy(t);this.menu.toggle({left:e,top:s})}_key_down(t){this.trigger(this.keydown,this._key_event(t))}_key_up(t){this.trigger(this.keyup,this._key_event(t))}}s.UIEventBus=g,g.__name__=\"UIEventBus\"},\n function _(e,t,s,n,_){n();var a=this&&this.__decorate||function(e,t,s,n){var _,a=arguments.length,o=a<3?t:null===n?n=Object.getOwnPropertyDescriptor(t,s):n;if(\"object\"==typeof Reflect&&\"function\"==typeof Reflect.decorate)o=Reflect.decorate(e,t,s,n);else for(var r=e.length-1;r>=0;r--)(_=e[r])&&(o=(a<3?_(o):a>3?_(t,s,o):_(t,s))||o);return a>3&&o&&Object.defineProperty(t,s,o),o};function o(e){return function(t){t.prototype.event_name=e}}class r{to_json(){const{event_name:e}=this;return{event_name:e,event_values:this._to_json()}}}s.BokehEvent=r,r.__name__=\"BokehEvent\";class c extends r{constructor(){super(...arguments),this.origin=null}_to_json(){return{model:this.origin}}}s.ModelEvent=c,c.__name__=\"ModelEvent\";let l=class extends r{_to_json(){return{}}};s.DocumentReady=l,l.__name__=\"DocumentReady\",s.DocumentReady=l=a([o(\"document_ready\")],l);let i=class extends c{};s.ButtonClick=i,i.__name__=\"ButtonClick\",s.ButtonClick=i=a([o(\"button_click\")],i);let u=class extends 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u=s(178),o=s(24),_=s(48),d=(0,n.__importStar)(s(18)),h=s(27),c=s(72);class l extends u.XYGlyphView{_map_data(){\"data\"==this.model.properties.inner_radius.units?this.sinner_radius=this.sdist(this.renderer.xscale,this._x,this.inner_radius):this.sinner_radius=(0,o.to_screen)(this.inner_radius),\"data\"==this.model.properties.outer_radius.units?this.souter_radius=this.sdist(this.renderer.xscale,this._x,this.outer_radius):this.souter_radius=(0,o.to_screen)(this.outer_radius)}_render(s,e,i){const{sx:r,sy:t,sinner_radius:n,souter_radius:a}=null!=i?i:this;for(const i of e){const e=r[i],u=t[i],o=n[i],_=a[i];if(isFinite(e+u+o+_)){if(s.beginPath(),h.is_ie)for(const i of[!1,!0])s.moveTo(e,u),s.arc(e,u,o,0,Math.PI,i),s.moveTo(e+_,u),s.arc(e,u,_,Math.PI,0,!i);else 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a=n+1,u=new Array(a);u[n]=(e+r)/2;const o=new Array(a);o[n]=(i+t)/2;const _=.5*Math.min(Math.abs(r-e),Math.abs(t-i)),d=new Array(a);d[n]=.4*_;const h=new Array(a);h[n]=.8*_,this._render(s,[n],{sx:u,sy:o,sinner_radius:d,souter_radius:h})}}i.AnnulusView=l,l.__name__=\"AnnulusView\";class x extends u.XYGlyph{constructor(s){super(s)}}i.Annulus=x,a=x,x.__name__=\"Annulus\",a.prototype.default_view=l,a.mixins([_.LineVector,_.FillVector,_.HatchVector]),a.define((({})=>({inner_radius:[d.DistanceSpec,{field:\"inner_radius\"}],outer_radius:[d.DistanceSpec,{field:\"outer_radius\"}]})))},\n function _(e,i,s,t,n){t();const r=e(1);var a;const c=e(178),d=e(184),l=e(48),_=e(24),o=e(20),u=(0,r.__importStar)(e(18));class h extends c.XYGlyphView{_map_data(){\"data\"==this.model.properties.radius.units?this.sradius=this.sdist(this.renderer.xscale,this._x,this.radius):this.sradius=(0,_.to_screen)(this.radius)}_render(e,i,s){if(this.visuals.line.doit){const{sx:t,sy:n,sradius:r,start_angle:a,end_angle:c}=null!=s?s:this,d=\"anticlock\"==this.model.direction;for(const s of i){const i=t[s],l=n[s],_=r[s],o=a.get(s),u=c.get(s);isFinite(i+l+_+o+u)&&(e.beginPath(),e.arc(i,l,_,o,u,d),this.visuals.line.set_vectorize(e,s),e.stroke())}}}draw_legend_for_index(e,i,s){(0,d.generic_line_vector_legend)(this.visuals,e,i,s)}}s.ArcView=h,h.__name__=\"ArcView\";class g extends c.XYGlyph{constructor(e){super(e)}}s.Arc=g,a=g,g.__name__=\"Arc\",a.prototype.default_view=h,a.mixins(l.LineVector),a.define((({})=>({direction:[o.Direction,\"anticlock\"],radius:[u.DistanceSpec,{field:\"radius\"}],start_angle:[u.AngleSpec,{field:\"start_angle\"}],end_angle:[u.AngleSpec,{field:\"end_angle\"}]})))},\n function _(e,t,i,n,s){n();const o=e(1);var c;const r=e(48),a=e(179),_=e(184),d=e(78),l=(0,o.__importStar)(e(18));function x(e,t,i,n,s,o,c,r){const a=[],_=[[],[]];for(let _=0;_<=2;_++){let d,l,x;if(0===_?(l=6*e-12*i+6*s,d=-3*e+9*i-9*s+3*c,x=3*i-3*e):(l=6*t-12*n+6*o,d=-3*t+9*n-9*o+3*r,x=3*n-3*t),Math.abs(d)<1e-12){if(Math.abs(l)<1e-12)continue;const e=-x/l;0({x0:[l.XCoordinateSpec,{field:\"x0\"}],y0:[l.YCoordinateSpec,{field:\"y0\"}],x1:[l.XCoordinateSpec,{field:\"x1\"}],y1:[l.YCoordinateSpec,{field:\"y1\"}],cx0:[l.XCoordinateSpec,{field:\"cx0\"}],cy0:[l.YCoordinateSpec,{field:\"cy0\"}],cx1:[l.XCoordinateSpec,{field:\"cx1\"}],cy1:[l.YCoordinateSpec,{field:\"cy1\"}]}))),c.mixins(r.LineVector)},\n function _(s,i,e,t,r){t();const a=s(1);var n;const h=s(178),d=s(48),l=s(24),_=s(20),c=(0,a.__importStar)(s(185)),u=(0,a.__importStar)(s(18)),o=s(9),x=s(12),m=s(72);class p extends h.XYGlyphView{async lazy_initialize(){await super.lazy_initialize();const{webgl:i}=this.renderer.plot_view.canvas_view;if(null!=i&&i.regl_wrapper.has_webgl){const{CircleGL:e}=await Promise.resolve().then((()=>(0,a.__importStar)(s(423))));this.glglyph=new e(i.regl_wrapper,this)}}get use_radius(){return!(this.radius.is_Scalar()&&isNaN(this.radius.value))}_set_data(s){super._set_data(s);const i=(()=>{if(this.use_radius)return 2*this.max_radius;{const{size:s}=this;return s.is_Scalar()?s.value:(0,x.max)(s.array)}})();this._configure(\"max_size\",{value:i})}_map_data(){if(this.use_radius)if(\"data\"==this.model.properties.radius.units)switch(this.model.radius_dimension){case\"x\":this.sradius=this.sdist(this.renderer.xscale,this._x,this.radius);break;case\"y\":this.sradius=this.sdist(this.renderer.yscale,this._y,this.radius);break;case\"max\":{const s=this.sdist(this.renderer.xscale,this._x,this.radius),i=this.sdist(this.renderer.yscale,this._y,this.radius);this.sradius=(0,x.map)(s,((s,e)=>Math.max(s,i[e])));break}case\"min\":{const s=this.sdist(this.renderer.xscale,this._x,this.radius),i=this.sdist(this.renderer.yscale,this._y,this.radius);this.sradius=(0,x.map)(s,((s,e)=>Math.min(s,i[e])));break}}else this.sradius=(0,l.to_screen)(this.radius);else{const s=l.ScreenArray.from(this.size);this.sradius=(0,x.map)(s,(s=>s/2))}}_mask_data(){const{frame:s}=this.renderer.plot_view,i=s.x_target,e=s.y_target;let t,r;return this.use_radius&&\"data\"==this.model.properties.radius.units?(t=i.map((s=>this.renderer.xscale.invert(s))).widen(this.max_radius),r=e.map((s=>this.renderer.yscale.invert(s))).widen(this.max_radius)):(t=i.widen(this.max_size).map((s=>this.renderer.xscale.invert(s))),r=e.widen(this.max_size).map((s=>this.renderer.yscale.invert(s)))),this.index.indices({x0:t.start,x1:t.end,y0:r.start,y1:r.end})}_render(s,i,e){const{sx:t,sy:r,sradius:a}=null!=e?e:this;for(const e of i){const i=t[e],n=r[e],h=a[e];isFinite(i+n+h)&&(s.beginPath(),s.arc(i,n,h,0,2*Math.PI,!1),this.visuals.fill.apply(s,e),this.visuals.hatch.apply(s,e),this.visuals.line.apply(s,e))}}_hit_point(s){const{sx:i,sy:e}=s,t=this.renderer.xscale.invert(i),r=this.renderer.yscale.invert(e),{hit_dilation:a}=this.model;let n,h,d,l;if(this.use_radius&&\"data\"==this.model.properties.radius.units)n=t-this.max_radius*a,h=t+this.max_radius*a,d=r-this.max_radius*a,l=r+this.max_radius*a;else{const s=i-this.max_size*a,t=i+this.max_size*a;[n,h]=this.renderer.xscale.r_invert(s,t);const r=e-this.max_size*a,_=e+this.max_size*a;[d,l]=this.renderer.yscale.r_invert(r,_)}const _=this.index.indices({x0:n,x1:h,y0:d,y1:l}),c=[];if(this.use_radius&&\"data\"==this.model.properties.radius.units)for(const s of _){const i=(this.sradius[s]*a)**2,[e,n]=this.renderer.xscale.r_compute(t,this._x[s]),[h,d]=this.renderer.yscale.r_compute(r,this._y[s]);(e-n)**2+(h-d)**2<=i&&c.push(s)}else for(const s of _){const t=(this.sradius[s]*a)**2;(this.sx[s]-i)**2+(this.sy[s]-e)**2<=t&&c.push(s)}return new m.Selection({indices:c})}_hit_span(s){const{sx:i,sy:e}=s,t=this.bounds();let r,a,n,h;if(\"h\"==s.direction){let s,e;if(n=t.y0,h=t.y1,this.use_radius&&\"data\"==this.model.properties.radius.units)s=i-this.max_radius,e=i+this.max_radius,[r,a]=this.renderer.xscale.r_invert(s,e);else{const t=this.max_size/2;s=i-t,e=i+t,[r,a]=this.renderer.xscale.r_invert(s,e)}}else{let s,i;if(r=t.x0,a=t.x1,this.use_radius&&\"data\"==this.model.properties.radius.units)s=e-this.max_radius,i=e+this.max_radius,[n,h]=this.renderer.yscale.r_invert(s,i);else{const t=this.max_size/2;s=e-t,i=e+t,[n,h]=this.renderer.yscale.r_invert(s,i)}}const d=[...this.index.indices({x0:r,x1:a,y0:n,y1:h})];return new m.Selection({indices:d})}_hit_rect(s){const{sx0:i,sx1:e,sy0:t,sy1:r}=s,[a,n]=this.renderer.xscale.r_invert(i,e),[h,d]=this.renderer.yscale.r_invert(t,r),l=[...this.index.indices({x0:a,x1:n,y0:h,y1:d})];return new m.Selection({indices:l})}_hit_poly(s){const{sx:i,sy:e}=s,t=(0,o.range)(0,this.sx.length),r=[];for(let s=0,a=t.length;s({angle:[u.AngleSpec,0],size:[u.ScreenSizeSpec,{value:4}],radius:[u.NullDistanceSpec,null],radius_dimension:[_.RadiusDimension,\"x\"],hit_dilation:[s,1]})))},\n function _(e,l,s,i,_){var p;i();const t=e(274);class a extends t.EllipseOvalView{}s.EllipseView=a,a.__name__=\"EllipseView\";class n extends t.EllipseOval{constructor(e){super(e)}}s.Ellipse=n,p=n,n.__name__=\"Ellipse\",p.prototype.default_view=a},\n function _(t,s,e,i,h){i();const n=t(1),r=t(275),a=(0,n.__importStar)(t(185)),l=t(24),_=t(72),o=(0,n.__importStar)(t(18));class d extends r.CenterRotatableView{_map_data(){\"data\"==this.model.properties.width.units?this.sw=this.sdist(this.renderer.xscale,this._x,this.width,\"center\"):this.sw=(0,l.to_screen)(this.width),\"data\"==this.model.properties.height.units?this.sh=this.sdist(this.renderer.yscale,this._y,this.height,\"center\"):this.sh=(0,l.to_screen)(this.height)}_render(t,s,e){const{sx:i,sy:h,sw:n,sh:r,angle:a}=null!=e?e:this;for(const e of s){const s=i[e],l=h[e],_=n[e],o=r[e],d=a.get(e);isFinite(s+l+_+o+d)&&(t.beginPath(),t.ellipse(s,l,_/2,o/2,d,0,2*Math.PI),this.visuals.fill.apply(t,e),this.visuals.hatch.apply(t,e),this.visuals.line.apply(t,e))}}_hit_point(t){let s,e,i,h,n,r,l,o,d;const{sx:c,sy:p}=t,w=this.renderer.xscale.invert(c),x=this.renderer.yscale.invert(p);\"data\"==this.model.properties.width.units?(s=w-this.max_width,e=w+this.max_width):(r=c-this.max_width,l=c+this.max_width,[s,e]=this.renderer.xscale.r_invert(r,l)),\"data\"==this.model.properties.height.units?(i=x-this.max_height,h=x+this.max_height):(o=p-this.max_height,d=p+this.max_height,[i,h]=this.renderer.yscale.r_invert(o,d));const m=this.index.indices({x0:s,x1:e,y0:i,y1:h}),y=[];for(const t of m)n=a.point_in_ellipse(c,p,this.angle.get(t),this.sh[t]/2,this.sw[t]/2,this.sx[t],this.sy[t]),n&&y.push(t);return new _.Selection({indices:y})}draw_legend_for_index(t,{x0:s,y0:e,x1:i,y1:h},n){const r=n+1,a=new Array(r);a[n]=(s+i)/2;const l=new Array(r);l[n]=(e+h)/2;const _=this.sw[n]/this.sh[n],d=.8*Math.min(Math.abs(i-s),Math.abs(h-e)),c=new Array(r),p=new Array(r);_>1?(c[n]=d,p[n]=d/_):(c[n]=d*_,p[n]=d);const w=new o.UniformScalar(0,r);this._render(t,[n],{sx:a,sy:l,sw:c,sh:p,angle:w})}}e.EllipseOvalView=d,d.__name__=\"EllipseOvalView\";class c extends r.CenterRotatable{constructor(t){super(t)}}e.EllipseOval=c,c.__name__=\"EllipseOval\"},\n function _(e,t,i,a,n){a();const s=e(1);var r;const h=e(178),o=e(48),_=(0,s.__importStar)(e(18));class c extends h.XYGlyphView{get max_w2(){return\"data\"==this.model.properties.width.units?this.max_width/2:0}get max_h2(){return\"data\"==this.model.properties.height.units?this.max_height/2:0}_bounds({x0:e,x1:t,y0:i,y1:a}){const{max_w2:n,max_h2:s}=this;return{x0:e-n,x1:t+n,y0:i-s,y1:a+s}}}i.CenterRotatableView=c,c.__name__=\"CenterRotatableView\";class l extends h.XYGlyph{constructor(e){super(e)}}i.CenterRotatable=l,r=l,l.__name__=\"CenterRotatable\",r.mixins([o.LineVector,o.FillVector,o.HatchVector]),r.define((({})=>({angle:[_.AngleSpec,0],width:[_.DistanceSpec,{field:\"width\"}],height:[_.DistanceSpec,{field:\"height\"}]})))},\n function _(t,e,s,i,r){i();const h=t(1);var a;const n=t(277),_=t(24),o=(0,h.__importStar)(t(18));class l extends n.BoxView{async lazy_initialize(){await super.lazy_initialize();const{webgl:e}=this.renderer.plot_view.canvas_view;if(null!=e&&e.regl_wrapper.has_webgl){const{LRTBGL:s}=await Promise.resolve().then((()=>(0,h.__importStar)(t(427))));this.glglyph=new s(e.regl_wrapper,this)}}scenterxy(t){return[(this.sleft[t]+this.sright[t])/2,this.sy[t]]}_lrtb(t){const e=this._left[t],s=this._right[t],i=this._y[t],r=this.height.get(t)/2;return[Math.min(e,s),Math.max(e,s),i+r,i-r]}_map_data(){this.sy=this.renderer.yscale.v_compute(this._y),this.sh=this.sdist(this.renderer.yscale,this._y,this.height,\"center\"),this.sleft=this.renderer.xscale.v_compute(this._left),this.sright=this.renderer.xscale.v_compute(this._right);const t=this.sy.length;this.stop=new _.ScreenArray(t),this.sbottom=new _.ScreenArray(t);for(let e=0;e({left:[o.XCoordinateSpec,{value:0}],y:[o.YCoordinateSpec,{field:\"y\"}],height:[o.NumberSpec,{value:1}],right:[o.XCoordinateSpec,{field:\"right\"}]})))},\n function _(t,e,s,r,i){var n;r();const a=t(48),h=t(179),o=t(184),c=t(72);class _ extends h.GlyphView{get_anchor_point(t,e,s){const r=Math.min(this.sleft[e],this.sright[e]),i=Math.max(this.sright[e],this.sleft[e]),n=Math.min(this.stop[e],this.sbottom[e]),a=Math.max(this.sbottom[e],this.stop[e]);switch(t){case\"top_left\":return{x:r,y:n};case\"top\":case\"top_center\":return{x:(r+i)/2,y:n};case\"top_right\":return{x:i,y:n};case\"bottom_left\":return{x:r,y:a};case\"bottom\":case\"bottom_center\":return{x:(r+i)/2,y:a};case\"bottom_right\":return{x:i,y:a};case\"left\":case\"center_left\":return{x:r,y:(n+a)/2};case\"center\":case\"center_center\":return{x:(r+i)/2,y:(n+a)/2};case\"right\":case\"center_right\":return{x:i,y:(n+a)/2}}}_index_data(t){const{min:e,max:s}=Math,{data_size:r}=this;for(let i=0;i(0,n.__importStar)(e(425))));this.glglyph=new s(t.regl_wrapper,this)}}scenterxy(e){return[this.sx[e],this.sy[e]]}_set_data(){const{orientation:e,size:t,aspect_scale:s}=this.model,{q:i,r}=this,n=this.q.length;this._x=new Float64Array(n),this._y=new Float64Array(n);const{_x:a,_y:l}=this,o=Math.sqrt(3);if(\"pointytop\"==e)for(let e=0;e({r:[c.NumberSpec,{field:\"r\"}],q:[c.NumberSpec,{field:\"q\"}],scale:[c.NumberSpec,1],size:[e,1],aspect_scale:[e,1],orientation:[_.HexTileOrientation,\"pointytop\"]}))),a.override({line_color:null})},\n function _(e,a,t,_,r){var n;_();const s=e(280),o=e(173),i=e(201);class p extends s.ImageBaseView{connect_signals(){super.connect_signals(),this.connect(this.model.color_mapper.change,(()=>this._update_image()))}_update_image(){null!=this.image_data&&(this._set_data(null),this.renderer.request_render())}_flat_img_to_buf8(e){return this.model.color_mapper.rgba_mapper.v_compute(e)}}t.ImageView=p,p.__name__=\"ImageView\";class m extends s.ImageBase{constructor(e){super(e)}}t.Image=m,n=m,m.__name__=\"Image\",n.prototype.default_view=p,n.define((({Ref:e})=>({color_mapper:[e(o.ColorMapper),()=>new i.LinearColorMapper({palette:[\"#000000\",\"#252525\",\"#525252\",\"#737373\",\"#969696\",\"#bdbdbd\",\"#d9d9d9\",\"#f0f0f0\",\"#ffffff\"]})]})))},\n function _(e,t,i,s,a){s();const h=e(1);var n;const r=e(178),_=e(24),d=(0,h.__importStar)(e(18)),l=e(72),g=e(9),o=e(29),c=e(11);class m extends r.XYGlyphView{connect_signals(){super.connect_signals(),this.connect(this.model.properties.global_alpha.change,(()=>this.renderer.request_render()))}_render(e,t,i){const{image_data:s,sx:a,sy:h,sw:n,sh:r,global_alpha:_}=null!=i?i:this,d=e.getImageSmoothingEnabled();e.setImageSmoothingEnabled(!1);const l=_.is_Scalar();l&&(e.globalAlpha=_.value);for(const i of t){const t=s[i],_=a[i],d=h[i],g=n[i],o=r[i],c=this.global_alpha.get(i);if(null==t||!isFinite(_+d+g+o+c))continue;l||(e.globalAlpha=c);const m=d;e.translate(0,m),e.scale(1,-1),e.translate(0,-m),e.drawImage(t,0|_,0|d,g,o),e.translate(0,m),e.scale(1,-1),e.translate(0,-m)}e.setImageSmoothingEnabled(d)}_set_data(e){this._set_width_heigh_data();for(let t=0,i=this.image.length;t({image:[d.NDArraySpec,{field:\"image\"}],dw:[d.DistanceSpec,{field:\"dw\"}],dh:[d.DistanceSpec,{field:\"dh\"}],global_alpha:[d.NumberSpec,{value:1}],dilate:[e,!1]})))},\n function _(e,a,t,r,_){var n;r();const s=e(280),m=e(8);class i extends s.ImageBaseView{_flat_img_to_buf8(e){let a;return a=(0,m.isArray)(e)?new Uint32Array(e):e,new Uint8ClampedArray(a.buffer)}}t.ImageRGBAView=i,i.__name__=\"ImageRGBAView\";class g extends s.ImageBase{constructor(e){super(e)}}t.ImageRGBA=g,n=g,g.__name__=\"ImageRGBA\",n.prototype.default_view=i},\n function _(e,t,s,r,a){r();const i=e(1);var n;const o=e(178),c=e(24),_=e(20),h=(0,i.__importStar)(e(18)),l=e(12),d=e(136);class m extends o.XYGlyphView{constructor(){super(...arguments),this._images_rendered=!1,this._set_data_iteration=0}connect_signals(){super.connect_signals(),this.connect(this.model.properties.global_alpha.change,(()=>this.renderer.request_render()))}_index_data(e){const{data_size:t}=this;for(let s=0;s{this._set_data_iteration==r&&(this.image[a]=e,this.renderer.request_render())},attempts:t+1,timeout:s})}const a=\"data\"==this.model.properties.w.units,i=\"data\"==this.model.properties.h.units,n=this._x.length,o=new c.ScreenArray(a?2*n:n),_=new c.ScreenArray(i?2*n:n),{anchor:h}=this.model;function m(e,t){switch(h){case\"top_left\":case\"bottom_left\":case\"left\":case\"center_left\":return[e,e+t];case\"top\":case\"top_center\":case\"bottom\":case\"bottom_center\":case\"center\":case\"center_center\":return[e-t/2,e+t/2];case\"top_right\":case\"bottom_right\":case\"right\":case\"center_right\":return[e-t,e]}}function g(e,t){switch(h){case\"top_left\":case\"top\":case\"top_center\":case\"top_right\":return[e,e-t];case\"bottom_left\":case\"bottom\":case\"bottom_center\":case\"bottom_right\":return[e+t,e];case\"left\":case\"center_left\":case\"center\":case\"center_center\":case\"right\":case\"center_right\":return[e+t/2,e-t/2]}}if(a)for(let e=0;e({url:[h.StringSpec,{field:\"url\"}],anchor:[_.Anchor,\"top_left\"],global_alpha:[h.NumberSpec,{value:1}],angle:[h.AngleSpec,0],w:[h.NullDistanceSpec,null],h:[h.NullDistanceSpec,null],dilate:[e,!1],retry_attempts:[t,0],retry_timeout:[t,0]})))},\n function _(e,t,s,i,n){i();const o=e(1);var r;const l=e(78),_=e(48),c=(0,o.__importStar)(e(185)),h=(0,o.__importStar)(e(18)),a=e(12),d=e(13),x=e(179),y=e(184),g=e(72);class p extends x.GlyphView{_project_data(){l.inplace.project_xy(this._xs.array,this._ys.array)}_index_data(e){const{data_size:t}=this;for(let s=0;s0&&o.set(e,s)}return new g.Selection({indices:[...o.keys()],multiline_indices:(0,d.to_object)(o)})}get_interpolation_hit(e,t,s){const i=this._xs.get(e),n=this._ys.get(e),o=i[t],r=n[t],l=i[t+1],_=n[t+1];return(0,y.line_interpolation)(this.renderer,s,o,r,l,_)}draw_legend_for_index(e,t,s){(0,y.generic_line_vector_legend)(this.visuals,e,t,s)}scenterxy(){throw new Error(`${this}.scenterxy() is not implemented`)}}s.MultiLineView=p,p.__name__=\"MultiLineView\";class u extends x.Glyph{constructor(e){super(e)}}s.MultiLine=u,r=u,u.__name__=\"MultiLine\",r.prototype.default_view=p,r.define((({})=>({xs:[h.XCoordinateSeqSpec,{field:\"xs\"}],ys:[h.YCoordinateSeqSpec,{field:\"ys\"}]}))),r.mixins(_.LineVector)},\n function _(t,e,s,n,i){n();const o=t(1);var r;const l=t(181),h=t(179),a=t(184),_=t(12),c=t(12),d=t(48),x=(0,o.__importStar)(t(185)),y=(0,o.__importStar)(t(18)),f=t(72),g=t(11);class p extends h.GlyphView{_project_data(){}_index_data(t){const{min:e,max:s}=Math,{data_size:n}=this;for(let i=0;i1&&c.length>1)for(let 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o.CenterRotatableView{async lazy_initialize(){await super.lazy_initialize();const{webgl:e}=this.renderer.plot_view.canvas_view;if(null==e?void 0:e.regl_wrapper.has_webgl){const{RectGL:s}=await Promise.resolve().then((()=>l(t(429))));this.glglyph=new s(e.regl_wrapper,this)}}_map_data(){if(\"data\"==this.model.properties.width.units)[this.sw,this.sx0]=this._map_dist_corner_for_data_side_length(this._x,this.width,this.renderer.xscale);else{this.sw=(0,_.to_screen)(this.width);const t=this.sx.length;this.sx0=new _.ScreenArray(t);for(let e=0;e({dilate:[t,!1]})))},\n function _(e,t,r,a,s){a();const i=e(1);var n;const l=e(292),_=e(293),c=(0,i.__importStar)(e(18));class o extends l.MarkerView{async lazy_initialize(){await super.lazy_initialize();const{webgl:t}=this.renderer.plot_view.canvas_view;if(null!=t&&t.regl_wrapper.has_webgl){const{MultiMarkerGL:r}=await Promise.resolve().then((()=>(0,i.__importStar)(e(428))));this.glglyph=new r(t.regl_wrapper,this)}}_render(e,t,r){const{sx:a,sy:s,size:i,angle:n,marker:l}=null!=r?r:this;for(const r of t){const t=a[r],c=s[r],o=i.get(r),g=n.get(r),w=l.get(r);if(!isFinite(t+c+o+g)||null==w)continue;const p=o/2;e.beginPath(),e.translate(t,c),g&&e.rotate(g),_.marker_funcs[w](e,r,p,this.visuals),g&&e.rotate(-g),e.translate(-t,-c)}}draw_legend_for_index(e,{x0:t,x1:r,y0:a,y1:s},i){const n=i+1,l=this.marker.get(i),_=Object.assign(Object.assign({},this._get_legend_args({x0:t,x1:r,y0:a,y1:s},i)),{marker:new c.UniformScalar(l,n)});this._render(e,[i],_)}}r.ScatterView=o,o.__name__=\"ScatterView\";class g extends l.Marker{constructor(e){super(e)}}r.Scatter=g,n=g,g.__name__=\"Scatter\",n.prototype.default_view=o,n.define((()=>({marker:[c.MarkerSpec,{value:\"circle\"}]})))},\n function _(e,t,s,n,i){n();const r=e(1);var a;const c=e(178),o=e(48),_=(0,r.__importStar)(e(185)),h=(0,r.__importStar)(e(18)),l=e(9),x=e(72);class d extends c.XYGlyphView{_render(e,t,s){const{sx:n,sy:i,size:r,angle:a}=null!=s?s:this;for(const s of t){const t=n[s],c=i[s],o=r.get(s),_=a.get(s);if(!isFinite(t+c+o+_))continue;const h=o/2;e.beginPath(),e.translate(t,c),_&&e.rotate(_),this._render_one(e,s,h,this.visuals),_&&e.rotate(-_),e.translate(-t,-c)}}_mask_data(){const{x_target:e,y_target:t}=this.renderer.plot_view.frame,s=e.widen(this.max_size).map((e=>this.renderer.xscale.invert(e))),n=t.widen(this.max_size).map((e=>this.renderer.yscale.invert(e)));return this.index.indices({x0:s.start,x1:s.end,y0:n.start,y1:n.end})}_hit_point(e){const{sx:t,sy:s}=e,{max_size:n}=this,{hit_dilation:i}=this.model,r=t-n*i,a=t+n*i,[c,o]=this.renderer.xscale.r_invert(r,a),_=s-n*i,h=s+n*i,[l,d]=this.renderer.yscale.r_invert(_,h),y=this.index.indices({x0:c,x1:o,y0:l,y1:d}),g=[];for(const e of y){const n=this.size.get(e)/2*i;Math.abs(this.sx[e]-t)<=n&&Math.abs(this.sy[e]-s)<=n&&g.push(e)}return new x.Selection({indices:g})}_hit_span(e){const{sx:t,sy:s}=e,n=this.bounds(),i=this.max_size/2;let r,a,c,o;if(\"h\"==e.direction){c=n.y0,o=n.y1;const e=t-i,s=t+i;[r,a]=this.renderer.xscale.r_invert(e,s)}else{r=n.x0,a=n.x1;const e=s-i,t=s+i;[c,o]=this.renderer.yscale.r_invert(e,t)}const _=[...this.index.indices({x0:r,x1:a,y0:c,y1:o})];return new x.Selection({indices:_})}_hit_rect(e){const{sx0:t,sx1:s,sy0:n,sy1:i}=e,[r,a]=this.renderer.xscale.r_invert(t,s),[c,o]=this.renderer.yscale.r_invert(n,i),_=[...this.index.indices({x0:r,x1:a,y0:c,y1:o})];return new x.Selection({indices:_})}_hit_poly(e){const{sx:t,sy:s}=e,n=(0,l.range)(0,this.sx.length),i=[];for(let e=0,r=n.length;e({size:[h.ScreenSizeSpec,{value:4}],angle:[h.AngleSpec,0],hit_dilation:[e,1]})))},\n function _(l,o,n,t,i){t();const e=Math.sqrt(3),a=Math.sqrt(5),c=(a+1)/4,p=Math.sqrt((5-a)/8),r=(a-1)/4,h=Math.sqrt((5+a)/8);function u(l,o){l.rotate(Math.PI/4),s(l,o),l.rotate(-Math.PI/4)}function f(l,o){const 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i=2*n;l.rect(-n,-n,i,i),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)}function q(l,o,n,t){!function(l,o){const n=Math.sqrt(5-2*a)*o;l.moveTo(0,-o),l.lineTo(n*r,n*h-o),l.lineTo(n*(1+r),n*h-o),l.lineTo(n*(1+r-c),n*(h+p)-o),l.lineTo(n*(1+2*r-c),n*(2*h+p)-o),l.lineTo(0,2*n*h-o),l.lineTo(-n*(1+2*r-c),n*(2*h+p)-o),l.lineTo(-n*(1+r-c),n*(h+p)-o),l.lineTo(-n*(1+r),n*h-o),l.lineTo(-n*r,n*h-o),l.closePath()}(l,n),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)}function M(l,o,n,t){y(l,n),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)}n.marker_funcs={asterisk:function(l,o,n,t){s(l,n),u(l,n),t.line.apply(l,o)},circle:v,circle_cross:function(l,o,n,t){l.arc(0,0,n,0,2*Math.PI,!1),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.doit&&(t.line.set_vectorize(l,o),s(l,n),l.stroke())},circle_dot:function(l,o,n,t){v(l,o,n,t),_(l,o,n,t)},circle_y:function(l,o,n,t){l.arc(0,0,n,0,2*Math.PI,!1),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.doit&&(t.line.set_vectorize(l,o),f(l,n),l.stroke())},circle_x:function(l,o,n,t){l.arc(0,0,n,0,2*Math.PI,!1),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.doit&&(t.line.set_vectorize(l,o),u(l,n),l.stroke())},cross:function(l,o,n,t){s(l,n),t.line.apply(l,o)},diamond:d,diamond_dot:function(l,o,n,t){d(l,o,n,t),_(l,o,n,t)},diamond_cross:function(l,o,n,t){T(l,n),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.doit&&(t.line.set_vectorize(l,o),l.moveTo(0,n),l.lineTo(0,-n),l.moveTo(-n/1.5,0),l.lineTo(n/1.5,0),l.stroke())},dot:_,hex:P,hex_dot:function(l,o,n,t){P(l,o,n,t),_(l,o,n,t)},inverted_triangle:function(l,o,n,t){l.rotate(Math.PI),y(l,n),l.rotate(-Math.PI),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)},plus:function(l,o,n,t){const i=3*n/8,e=[i,i,n,n,i,i,-i,-i,-n,-n,-i,-i],a=[n,i,i,-i,-i,-n,-n,-i,-i,i,i,n];l.beginPath();for(let o=0;o<12;o++)l.lineTo(e[o],a[o]);l.closePath(),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)},square:m,square_cross:function(l,o,n,t){const i=2*n;l.rect(-n,-n,i,i),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.doit&&(t.line.set_vectorize(l,o),s(l,n),l.stroke())},square_dot:function(l,o,n,t){m(l,o,n,t),_(l,o,n,t)},square_pin:function(l,o,n,t){const i=3*n/8;l.moveTo(-n,-n),l.quadraticCurveTo(0,-i,n,-n),l.quadraticCurveTo(i,0,n,n),l.quadraticCurveTo(0,i,-n,n),l.quadraticCurveTo(-i,0,-n,-n),l.closePath(),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)},square_x:function(l,o,n,t){const i=2*n;l.rect(-n,-n,i,i),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.doit&&(t.line.set_vectorize(l,o),l.moveTo(-n,n),l.lineTo(n,-n),l.moveTo(-n,-n),l.lineTo(n,n),l.stroke())},star:q,star_dot:function(l,o,n,t){q(l,o,n,t),_(l,o,n,t)},triangle:M,triangle_dot:function(l,o,n,t){M(l,o,n,t),_(l,o,n,t)},triangle_pin:function(l,o,n,t){const i=n*e,a=i/3,c=3*a/8;l.moveTo(-n,a),l.quadraticCurveTo(0,c,n,a),l.quadraticCurveTo(e*c/2,c/2,0,a-i),l.quadraticCurveTo(-e*c/2,c/2,-n,a),l.closePath(),t.fill.apply(l,o),t.hatch.apply(l,o),t.line.apply(l,o)},dash:function(l,o,n,t){!function(l,o){l.moveTo(-o,0),l.lineTo(o,0)}(l,n),t.line.apply(l,o)},x:function(l,o,n,t){u(l,n),t.line.apply(l,o)},y:function(l,o,n,t){f(l,n),t.line.apply(l,o)}}},\n function _(e,t,s,i,n){i();const r=e(1);var o;const _=(0,r.__importStar)(e(185)),h=(0,r.__importStar)(e(18)),c=e(48),a=e(78),d=e(179),x=e(184),l=e(72);class y extends d.GlyphView{_project_data(){a.inplace.project_xy(this._x0,this._y0),a.inplace.project_xy(this._x1,this._y1)}_index_data(e){const{min:t,max:s}=Math,{_x0:i,_x1:n,_y0:r,_y1:o,data_size:_}=this;for(let h=0;h<_;h++){const _=i[h],c=n[h],a=r[h],d=o[h];e.add_rect(t(_,c),t(a,d),s(_,c),s(a,d))}}_render(e,t,s){if(this.visuals.line.doit){const{sx0:i,sy0:n,sx1:r,sy1:o}=null!=s?s:this;for(const s of t){const t=i[s],_=n[s],h=r[s],c=o[s];isFinite(t+_+h+c)&&(e.beginPath(),e.moveTo(t,_),e.lineTo(h,c),this.visuals.line.set_vectorize(e,s),e.stroke())}}}_hit_point(e){const{sx:t,sy:s}=e,i={x:t,y:s},[n,r]=this.renderer.xscale.r_invert(t-2,t+2),[o,h]=this.renderer.yscale.r_invert(s-2,s+2),c=this.index.indices({x0:n,y0:o,x1:r,y1:h}),a=[];for(const e of c){const t=Math.max(2,this.line_width.get(e)/2)**2,s={x:this.sx0[e],y:this.sy0[e]},n={x:this.sx1[e],y:this.sy1[e]};_.dist_to_segment_squared(i,s,n)({x0:[h.XCoordinateSpec,{field:\"x0\"}],y0:[h.YCoordinateSpec,{field:\"y0\"}],x1:[h.XCoordinateSpec,{field:\"x1\"}],y1:[h.YCoordinateSpec,{field:\"y1\"}]}))),o.mixins(c.LineVector)},\n function _(t,e,s,i,n){i();const o=t(1);var _;const l=t(178),a=(0,o.__importStar)(t(48)),c=t(296);class r extends l.XYGlyphView{_set_data(){const{tension:t,closed:e}=this.model;[this._xt,this._yt]=(0,c.catmullrom_spline)(this._x,this._y,20,t,e)}_map_data(){const{x_scale:t,y_scale:e}=this.renderer.coordinates;this.sxt=t.v_compute(this._xt),this.syt=e.v_compute(this._yt)}_render(t,e,s){const{sxt:i,syt:n}=null!=s?s:this;let o=!0;t.beginPath();const _=i.length;for(let e=0;e<_;e++){const s=i[e],_=n[e];isFinite(s+_)?o?(t.moveTo(s,_),o=!1):t.lineTo(s,_):o=!0}this.visuals.line.set_value(t),t.stroke()}}s.SplineView=r,r.__name__=\"SplineView\";class h extends l.XYGlyph{constructor(t){super(t)}}s.Spline=h,_=h,h.__name__=\"Spline\",_.prototype.default_view=r,_.mixins(a.LineScalar),_.define((({Boolean:t,Number:e})=>({tension:[e,.5],closed:[t,!1]})))},\n function _(n,t,e,o,s){o();const c=n(24),l=n(11);e.catmullrom_spline=function(n,t,e=10,o=.5,s=!1){(0,l.assert)(n.length==t.length);const r=n.length,f=s?r+1:r,w=(0,c.infer_type)(n,t),i=new w(f+2),u=new w(f+2);i.set(n,1),u.set(t,1),s?(i[0]=n[r-1],u[0]=t[r-1],i[f]=n[0],u[f]=t[0],i[f+1]=n[1],u[f+1]=t[1]):(i[0]=n[0],u[0]=t[0],i[f+1]=n[r-1],u[f+1]=t[r-1]);const g=new w(4*(e+1));for(let n=0,t=0;n<=e;n++){const o=n/e,s=o**2,c=o*s;g[t++]=2*c-3*s+1,g[t++]=-2*c+3*s,g[t++]=c-2*s+o,g[t++]=c-s}const h=new w((f-1)*(e+1)),_=new w((f-1)*(e+1));for(let n=1,t=0;n1&&(e.stroke(),o=!1)}o?(e.lineTo(t,r),e.lineTo(a,c)):(e.beginPath(),e.moveTo(s[n],i[n]),o=!0),l=n}e.lineTo(s[a-1],i[a-1]),e.stroke()}}draw_legend_for_index(e,t,n){(0,r.generic_line_scalar_legend)(this.visuals,e,t)}}n.StepView=f,f.__name__=\"StepView\";class u extends a.XYGlyph{constructor(e){super(e)}}n.Step=u,l=u,u.__name__=\"Step\",l.prototype.default_view=f,l.mixins(c.LineScalar),l.define((()=>({mode:[_.StepMode,\"before\"]})))},\n function _(t,e,s,i,n){i();const o=t(1);var _;const h=t(178),l=t(48),r=(0,o.__importStar)(t(185)),a=(0,o.__importStar)(t(18)),c=t(121),x=t(11),u=t(72);class f extends h.XYGlyphView{_rotate_point(t,e,s,i,n){return[(t-s)*Math.cos(n)-(e-i)*Math.sin(n)+s,(t-s)*Math.sin(n)+(e-i)*Math.cos(n)+i]}_text_bounds(t,e,s,i){return[[t,t+s,t+s,t,t],[e,e,e-i,e-i,e]]}_render(t,e,s){const{sx:i,sy:n,x_offset:o,y_offset:_,angle:h,text:l}=null!=s?s:this;this._sys=[],this._sxs=[];for(const s of e){const e=this._sxs[s]=[],r=this._sys[s]=[],a=i[s],x=n[s],u=o.get(s),f=_.get(s),p=h.get(s),g=l.get(s);if(isFinite(a+x+u+f+p)&&null!=g&&this.visuals.text.doit){const i=`${g}`;t.save(),t.translate(a+u,x+f),t.rotate(p),this.visuals.text.set_vectorize(t,s);const n=this.visuals.text.font_value(s),{height:o}=(0,c.font_metrics)(n),_=this.text_line_height.get(s)*o;if(-1==i.indexOf(\"\\n\")){t.fillText(i,0,0);const s=a+u,n=x+f,o=t.measureText(i).width,[h,l]=this._text_bounds(s,n,o,_);e.push(h),r.push(l)}else{const n=i.split(\"\\n\"),o=_*n.length,h=this.text_baseline.get(s);let l;switch(h){case\"top\":l=0;break;case\"middle\":l=-o/2+_/2;break;case\"bottom\":l=-o+_;break;default:l=0,console.warn(`'${h}' baseline not supported with multi line text`)}for(const s of n){t.fillText(s,0,l);const i=a+u,n=l+x+f,o=t.measureText(s).width,[h,c]=this._text_bounds(i,n,o,_);e.push(h),r.push(c),l+=_}}t.restore()}}}_hit_point(t){const{sx:e,sy:s}=t,i=[];for(let t=0;t({text:[a.NullStringSpec,{field:\"text\"}],angle:[a.AngleSpec,0],x_offset:[a.NumberSpec,0],y_offset:[a.NumberSpec,0]})))},\n function _(t,e,s,i,r){i();const h=t(1);var o;const a=t(277),n=t(24),_=(0,h.__importStar)(t(18));class l extends a.BoxView{async lazy_initialize(){await super.lazy_initialize();const{webgl:e}=this.renderer.plot_view.canvas_view;if(null!=e&&e.regl_wrapper.has_webgl){const{LRTBGL:s}=await Promise.resolve().then((()=>(0,h.__importStar)(t(427))));this.glglyph=new s(e.regl_wrapper,this)}}scenterxy(t){return[this.sx[t],(this.stop[t]+this.sbottom[t])/2]}_lrtb(t){const e=this.width.get(t)/2,s=this._x[t],i=this._top[t],r=this._bottom[t];return[s-e,s+e,Math.max(i,r),Math.min(i,r)]}_map_data(){this.sx=this.renderer.xscale.v_compute(this._x),this.sw=this.sdist(this.renderer.xscale,this._x,this.width,\"center\"),this.stop=this.renderer.yscale.v_compute(this._top),this.sbottom=this.renderer.yscale.v_compute(this._bottom);const t=this.sx.length;this.sleft=new n.ScreenArray(t),this.sright=new n.ScreenArray(t);for(let e=0;e({x:[_.XCoordinateSpec,{field:\"x\"}],bottom:[_.YCoordinateSpec,{value:0}],width:[_.NumberSpec,{value:1}],top:[_.YCoordinateSpec,{field:\"top\"}]})))},\n function _(e,s,t,i,n){i();const r=e(1);var a;const c=e(178),d=e(184),l=e(48),h=e(24),o=e(20),_=(0,r.__importStar)(e(18)),u=e(10),g=e(72),x=e(12);class p extends c.XYGlyphView{_map_data(){\"data\"==this.model.properties.radius.units?this.sradius=this.sdist(this.renderer.xscale,this._x,this.radius):this.sradius=(0,h.to_screen)(this.radius),this.max_sradius=(0,x.max)(this.sradius)}_render(e,s,t){const{sx:i,sy:n,sradius:r,start_angle:a,end_angle:c}=null!=t?t:this,d=\"anticlock\"==this.model.direction;for(const t of s){const s=i[t],l=n[t],h=r[t],o=a.get(t),_=c.get(t);isFinite(s+l+h+o+_)&&(e.beginPath(),e.arc(s,l,h,o,_,d),e.lineTo(s,l),e.closePath(),this.visuals.fill.apply(e,t),this.visuals.hatch.apply(e,t),this.visuals.line.apply(e,t))}}_hit_point(e){let s,t,i,n,r;const{sx:a,sy:c}=e,d=this.renderer.xscale.invert(a),l=this.renderer.yscale.invert(c);t=a-this.max_sradius,i=a+this.max_sradius;const[h,o]=this.renderer.xscale.r_invert(t,i);n=c-this.max_sradius,r=c+this.max_sradius;const[_,x]=this.renderer.yscale.r_invert(n,r),p=[];for(const e of this.index.indices({x0:h,x1:o,y0:_,y1:x})){const a=this.sradius[e]**2;[t,i]=this.renderer.xscale.r_compute(d,this._x[e]),[n,r]=this.renderer.yscale.r_compute(l,this._y[e]),s=(t-i)**2+(n-r)**2,s<=a&&p.push(e)}const y=\"anticlock\"==this.model.direction,m=[];for(const e of p){const s=Math.atan2(c-this.sy[e],a-this.sx[e]);(0,u.angle_between)(-s,-this.start_angle.get(e),-this.end_angle.get(e),y)&&m.push(e)}return new g.Selection({indices:m})}draw_legend_for_index(e,s,t){(0,d.generic_area_vector_legend)(this.visuals,e,s,t)}scenterxy(e){const s=this.sradius[e]/2,t=(this.start_angle.get(e)+this.end_angle.get(e))/2;return[this.sx[e]+s*Math.cos(t),this.sy[e]+s*Math.sin(t)]}}t.WedgeView=p,p.__name__=\"WedgeView\";class y extends c.XYGlyph{constructor(e){super(e)}}t.Wedge=y,a=y,y.__name__=\"Wedge\",a.prototype.default_view=p,a.mixins([l.LineVector,l.FillVector,l.HatchVector]),a.define((({})=>({direction:[o.Direction,\"anticlock\"],radius:[_.DistanceSpec,{field:\"radius\"}],start_angle:[_.AngleSpec,{field:\"start_angle\"}],end_angle:[_.AngleSpec,{field:\"end_angle\"}]})))},\n function _(t,_,r,o,a){o();const e=t(1);(0,e.__exportStar)(t(302),r),(0,e.__exportStar)(t(303),r),(0,e.__exportStar)(t(304),r)},\n function _(e,t,d,n,s){n();const o=e(53),r=e(12),_=e(9),i=e(72);class c extends o.Model{constructor(e){super(e)}_hit_test(e,t,d){if(!t.model.visible)return null;const n=d.glyph.hit_test(e);return null==n?null:d.model.view.convert_selection_from_subset(n)}}d.GraphHitTestPolicy=c,c.__name__=\"GraphHitTestPolicy\";class a extends c{constructor(e){super(e)}hit_test(e,t){return this._hit_test(e,t,t.edge_view)}do_selection(e,t,d,n){if(null==e)return!1;const s=t.edge_renderer.data_source.selected;return s.update(e,d,n),t.edge_renderer.data_source._select.emit(),!s.is_empty()}do_inspection(e,t,d,n,s){if(null==e)return!1;const{edge_renderer:o}=d.model,r=o.get_selection_manager().get_or_create_inspector(d.edge_view.model);return r.update(e,n,s),d.edge_view.model.data_source.setv({inspected:r},{silent:!0}),d.edge_view.model.data_source.inspect.emit([d.edge_view.model,{geometry:t}]),!r.is_empty()}}d.EdgesOnly=a,a.__name__=\"EdgesOnly\";class l extends c{constructor(e){super(e)}hit_test(e,t){return this._hit_test(e,t,t.node_view)}do_selection(e,t,d,n){if(null==e)return!1;const s=t.node_renderer.data_source.selected;return s.update(e,d,n),t.node_renderer.data_source._select.emit(),!s.is_empty()}do_inspection(e,t,d,n,s){if(null==e)return!1;const{node_renderer:o}=d.model,r=o.get_selection_manager().get_or_create_inspector(d.node_view.model);return r.update(e,n,s),d.node_view.model.data_source.setv({inspected:r},{silent:!0}),d.node_view.model.data_source.inspect.emit([d.node_view.model,{geometry:t}]),!r.is_empty()}}d.NodesOnly=l,l.__name__=\"NodesOnly\";class u extends c{constructor(e){super(e)}hit_test(e,t){return this._hit_test(e,t,t.node_view)}get_linked_edges(e,t,d){let n=[];\"selection\"==d?n=e.selected.indices.map((t=>e.data.index[t])):\"inspection\"==d&&(n=e.inspected.indices.map((t=>e.data.index[t])));const s=[];for(let e=0;e(0,r.indexOf)(e.data.index,t)));return new i.Selection({indices:o})}do_selection(e,t,d,n){if(null==e)return!1;const s=t.edge_renderer.data_source.selected;s.update(e,d,n);const o=t.node_renderer.data_source.selected,r=this.get_linked_nodes(t.node_renderer.data_source,t.edge_renderer.data_source,\"selection\");return o.update(r,d,n),t.edge_renderer.data_source._select.emit(),!s.is_empty()}do_inspection(e,t,d,n,s){if(null==e)return!1;const o=d.edge_view.model.data_source.selection_manager.get_or_create_inspector(d.edge_view.model);o.update(e,n,s),d.edge_view.model.data_source.setv({inspected:o},{silent:!0});const r=d.node_view.model.data_source.selection_manager.get_or_create_inspector(d.node_view.model),_=this.get_linked_nodes(d.node_view.model.data_source,d.edge_view.model.data_source,\"inspection\");return r.update(_,n,s),d.node_view.model.data_source.setv({inspected:r},{silent:!0}),d.edge_view.model.data_source.inspect.emit([d.edge_view.model,{geometry:t}]),!o.is_empty()}}d.EdgesAndLinkedNodes=m,m.__name__=\"EdgesAndLinkedNodes\"},\n function _(e,o,t,r,n){var s;r();const a=e(53),d=e(260);class _ extends a.Model{constructor(e){super(e)}get node_coordinates(){return new u({layout:this})}get edge_coordinates(){return new i({layout:this})}}t.LayoutProvider=_,_.__name__=\"LayoutProvider\";class c extends d.CoordinateTransform{constructor(e){super(e)}}t.GraphCoordinates=c,s=c,c.__name__=\"GraphCoordinates\",s.define((({Ref:e})=>({layout:[e(_)]})));class u extends c{constructor(e){super(e)}_v_compute(e){const[o,t]=this.layout.get_node_coordinates(e);return{x:o,y:t}}}t.NodeCoordinates=u,u.__name__=\"NodeCoordinates\";class i extends c{constructor(e){super(e)}_v_compute(e){const[o,t]=this.layout.get_edge_coordinates(e);return{x:o,y:t}}}t.EdgeCoordinates=i,i.__name__=\"EdgeCoordinates\"},\n function _(t,a,l,e,n){var o;e();const r=t(303);class u extends r.LayoutProvider{constructor(t){super(t)}get_node_coordinates(t){var a;const l=null!==(a=t.data.index)&&void 0!==a?a:[],e=l.length,n=new Float64Array(e),o=new Float64Array(e);for(let t=0;t({graph_layout:[l(a(t,t)),{}]})))},\n function _(i,d,n,r,G){r(),G(\"Grid\",i(306).Grid)},\n function _(i,e,n,s,t){s();const r=i(1);var o;const d=i(127),_=i(129),a=i(130),l=(0,r.__importStar)(i(48)),h=i(8);class c extends _.GuideRendererView{_render(){const i=this.layer.ctx;i.save(),this._draw_regions(i),this._draw_minor_grids(i),this._draw_grids(i),i.restore()}connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.request_render()))}_draw_regions(i){if(!this.visuals.band_fill.doit&&!this.visuals.band_hatch.doit)return;const[e,n]=this.grid_coords(\"major\",!1);for(let s=0;sn[1]&&(t=n[1]);else{[s,t]=n;for(const i of this.plot_view.axis_views)i.dimension==this.model.dimension&&i.model.x_range_name==this.model.x_range_name&&i.model.y_range_name==this.model.y_range_name&&([s,t]=i.computed_bounds)}return[s,t]}grid_coords(i,e=!0){const n=this.model.dimension,s=(n+1)%2,[t,r]=this.ranges();let[o,d]=this.computed_bounds();[o,d]=[Math.min(o,d),Math.max(o,d)];const _=[[],[]],a=this.model.get_ticker();if(null==a)return _;const l=a.get_ticks(o,d,t,r.min)[i],h=t.min,c=t.max,u=r.min,m=r.max;e||(l[0]!=h&&l.splice(0,0,h),l[l.length-1]!=c&&l.push(c));for(let i=0;i({bounds:[r(t(i,i),e),\"auto\"],dimension:[n(0,1),0],axis:[o(s(d.Axis)),null],ticker:[o(s(a.Ticker)),null]}))),o.override({level:\"underlay\",band_fill_color:null,band_fill_alpha:0,grid_line_color:\"#e5e5e5\",minor_grid_line_color:null})},\n function _(o,a,x,B,e){B(),e(\"Box\",o(308).Box),e(\"Column\",o(310).Column),e(\"GridBox\",o(311).GridBox),e(\"HTMLBox\",o(312).HTMLBox),e(\"LayoutDOM\",o(309).LayoutDOM),e(\"Panel\",o(313).Panel),e(\"Row\",o(314).Row),e(\"Spacer\",o(315).Spacer),e(\"Tabs\",o(316).Tabs),e(\"WidgetBox\",o(319).WidgetBox)},\n function _(e,n,s,t,c){var i;t();const o=e(309);class r extends o.LayoutDOMView{connect_signals(){super.connect_signals(),this.connect(this.model.properties.children.change,(()=>this.rebuild()))}get child_models(){return this.model.children}}s.BoxView=r,r.__name__=\"BoxView\";class a extends o.LayoutDOM{constructor(e){super(e)}}s.Box=a,i=a,a.__name__=\"Box\",i.define((({Number:e,Array:n,Ref:s})=>({children:[n(s(o.LayoutDOM)),[]],spacing:[e,0]})))},\n function _(t,i,e,s,o){var l;s();const n=t(53),h=t(20),a=t(43),r=t(19),_=t(8),c=t(22),u=t(121),d=t(113),p=t(226),m=t(207),g=t(44),w=t(235);class f extends p.DOMView{constructor(){super(...arguments),this._offset_parent=null,this._viewport={}}get is_layout_root(){return this.is_root||!(this.parent instanceof f)}get base_font_size(){const t=getComputedStyle(this.el).fontSize,i=(0,u.parse_css_font_size)(t);if(null!=i){const{value:t,unit:e}=i;if(\"px\"==e)return t}return null}initialize(){super.initialize(),this.el.style.position=this.is_layout_root?\"relative\":\"absolute\",this._child_views=new Map}async lazy_initialize(){await super.lazy_initialize(),await this.build_child_views()}remove(){for(const t of this.child_views)t.remove();this._child_views.clear(),super.remove()}connect_signals(){super.connect_signals(),this.is_layout_root&&(this._on_resize=()=>this.resize_layout(),window.addEventListener(\"resize\",this._on_resize),this._parent_observer=setInterval((()=>{const t=this.el.offsetParent;this._offset_parent!=t&&(this._offset_parent=t,null!=t&&(this.compute_viewport(),this.invalidate_layout()))}),250));const t=this.model.properties;this.on_change([t.width,t.height,t.min_width,t.min_height,t.max_width,t.max_height,t.margin,t.width_policy,t.height_policy,t.sizing_mode,t.aspect_ratio,t.visible],(()=>this.invalidate_layout())),this.on_change([t.background,t.css_classes],(()=>this.invalidate_render()))}disconnect_signals(){null!=this._parent_observer&&clearTimeout(this._parent_observer),null!=this._on_resize&&window.removeEventListener(\"resize\",this._on_resize),super.disconnect_signals()}css_classes(){return super.css_classes().concat(this.model.css_classes)}get child_views(){return this.child_models.map((t=>this._child_views.get(t)))}async build_child_views(){await(0,d.build_views)(this._child_views,this.child_models,{parent:this})}render(){super.render(),(0,a.empty)(this.el);const{background:t}=this.model;this.el.style.backgroundColor=null!=t?(0,c.color2css)(t):\"\",(0,a.classes)(this.el).clear().add(...this.css_classes());for(const t of this.child_views)this.el.appendChild(t.el),t.render()}update_layout(){for(const t of this.child_views)t.update_layout();this._update_layout()}update_position(){this.el.style.display=this.model.visible?\"block\":\"none\";const t=this.is_layout_root?this.layout.sizing.margin:void 0;(0,a.position)(this.el,this.layout.bbox,t);for(const t of this.child_views)t.update_position()}after_layout(){for(const t of this.child_views)t.after_layout();this._has_finished=!0}compute_viewport(){this._viewport=this._viewport_size()}renderTo(t){t.appendChild(this.el),this._offset_parent=this.el.offsetParent,this.compute_viewport(),this.build(),this.notify_finished()}build(){if(!this.is_layout_root)throw new Error(`${this.toString()} is not a root layout`);return this.render(),this.update_layout(),this.compute_layout(),this}async rebuild(){await this.build_child_views(),this.invalidate_render()}compute_layout(){const t=Date.now();this.layout.compute(this._viewport),this.update_position(),this.after_layout(),r.logger.debug(`layout computed in ${Date.now()-t} ms`)}resize_layout(){this.root.compute_viewport(),this.root.compute_layout()}invalidate_layout(){this.root.update_layout(),this.root.compute_layout()}invalidate_render(){this.render(),this.invalidate_layout()}has_finished(){if(!super.has_finished())return!1;for(const t of this.child_views)if(!t.has_finished())return!1;return!0}_width_policy(){return null!=this.model.width?\"fixed\":\"fit\"}_height_policy(){return null!=this.model.height?\"fixed\":\"fit\"}box_sizing(){let{width_policy:t,height_policy:i,aspect_ratio:e}=this.model;\"auto\"==t&&(t=this._width_policy()),\"auto\"==i&&(i=this._height_policy());const{sizing_mode:s}=this.model;if(null!=s)if(\"fixed\"==s)t=i=\"fixed\";else if(\"stretch_both\"==s)t=i=\"max\";else if(\"stretch_width\"==s)t=\"max\";else if(\"stretch_height\"==s)i=\"max\";else switch(null==e&&(e=\"auto\"),s){case\"scale_width\":t=\"max\",i=\"min\";break;case\"scale_height\":t=\"min\",i=\"max\";break;case\"scale_both\":t=\"max\",i=\"max\"}const o={width_policy:t,height_policy:i},{min_width:l,min_height:n}=this.model;null!=l&&(o.min_width=l),null!=n&&(o.min_height=n);const{width:h,height:a}=this.model;null!=h&&(o.width=h),null!=a&&(o.height=a);const{max_width:r,max_height:c}=this.model;null!=r&&(o.max_width=r),null!=c&&(o.max_height=c),\"auto\"==e&&null!=h&&null!=a?o.aspect=h/a:(0,_.isNumber)(e)&&(o.aspect=e);const{margin:u}=this.model;if(null!=u)if((0,_.isNumber)(u))o.margin={top:u,right:u,bottom:u,left:u};else if(2==u.length){const[t,i]=u;o.margin={top:t,right:i,bottom:t,left:i}}else{const[t,i,e,s]=u;o.margin={top:t,right:i,bottom:e,left:s}}o.visible=this.model.visible;const{align:d}=this.model;return(0,_.isArray)(d)?[o.halign,o.valign]=d:o.halign=o.valign=d,o}_viewport_size(){return(0,a.undisplayed)(this.el,(()=>{let t=this.el;for(;t=t.parentElement;){if(t.classList.contains(g.root))continue;if(t==document.body){const{margin:{left:t,right:i,top:e,bottom:s}}=(0,a.extents)(document.body);return{width:Math.ceil(document.documentElement.clientWidth-t-i),height:Math.ceil(document.documentElement.clientHeight-e-s)}}const{padding:{left:i,right:e,top:s,bottom:o}}=(0,a.extents)(t),{width:l,height:n}=t.getBoundingClientRect(),h=Math.ceil(l-i-e),r=Math.ceil(n-s-o);if(h>0||r>0)return{width:h>0?h:void 0,height:r>0?r:void 0}}return{}}))}export(t,i=!0){const e=\"png\"==t?\"canvas\":\"svg\",s=new w.CanvasLayer(e,i),{width:o,height:l}=this.layout.bbox;s.resize(o,l);for(const e of this.child_views){const o=e.export(t,i),{x:l,y:n}=e.layout.bbox;s.ctx.drawImage(o.canvas,l,n)}return s}serializable_state(){return Object.assign(Object.assign({},super.serializable_state()),{bbox:this.layout.bbox.box,children:this.child_views.map((t=>t.serializable_state()))})}}e.LayoutDOMView=f,f.__name__=\"LayoutDOMView\";class y extends n.Model{constructor(t){super(t)}}e.LayoutDOM=y,l=y,y.__name__=\"LayoutDOM\",l.define((t=>{const{Boolean:i,Number:e,String:s,Auto:o,Color:l,Array:n,Tuple:a,Or:r,Null:_,Nullable:c}=t,u=a(e,e),d=a(e,e,e,e);return{width:[c(e),null],height:[c(e),null],min_width:[c(e),null],min_height:[c(e),null],max_width:[c(e),null],max_height:[c(e),null],margin:[c(r(e,u,d)),[0,0,0,0]],width_policy:[r(m.SizingPolicy,o),\"auto\"],height_policy:[r(m.SizingPolicy,o),\"auto\"],aspect_ratio:[r(e,o,_),null],sizing_mode:[c(h.SizingMode),null],visible:[i,!0],disabled:[i,!1],align:[r(h.Align,a(h.Align,h.Align)),\"start\"],background:[c(l),null],css_classes:[n(s),[]]}}))},\n function _(o,s,t,i,e){var n;i();const a=o(308),l=o(209);class u extends a.BoxView{_update_layout(){const o=this.child_views.map((o=>o.layout));this.layout=new l.Column(o),this.layout.rows=this.model.rows,this.layout.spacing=[this.model.spacing,0],this.layout.set_sizing(this.box_sizing())}}t.ColumnView=u,u.__name__=\"ColumnView\";class _ extends a.Box{constructor(o){super(o)}}t.Column=_,n=_,_.__name__=\"Column\",n.prototype.default_view=u,n.define((({Any:o})=>({rows:[o,\"auto\"]})))},\n function _(s,o,t,i,e){var n;i();const l=s(309),a=s(209);class r extends l.LayoutDOMView{connect_signals(){super.connect_signals();const{children:s,rows:o,cols:t,spacing:i}=this.model.properties;this.on_change([s,o,t,i],(()=>this.rebuild()))}get child_models(){return this.model.children.map((([s])=>s))}_update_layout(){this.layout=new a.Grid,this.layout.rows=this.model.rows,this.layout.cols=this.model.cols,this.layout.spacing=this.model.spacing;for(const[s,o,t,i,e]of this.model.children){const n=this._child_views.get(s);this.layout.items.push({layout:n.layout,row:o,col:t,row_span:i,col_span:e})}this.layout.set_sizing(this.box_sizing())}}t.GridBoxView=r,r.__name__=\"GridBoxView\";class c extends l.LayoutDOM{constructor(s){super(s)}}t.GridBox=c,n=c,c.__name__=\"GridBox\",n.prototype.default_view=r,n.define((({Any:s,Int:o,Number:t,Tuple:i,Array:e,Ref:n,Or:a,Opt:r})=>({children:[e(i(n(l.LayoutDOM),o,o,r(o),r(o))),[]],rows:[s,\"auto\"],cols:[s,\"auto\"],spacing:[a(t,i(t,t)),0]})))},\n function _(t,e,o,s,n){s();const _=t(309),i=t(207);class a extends _.LayoutDOMView{get child_models(){return[]}_update_layout(){this.layout=new i.ContentBox(this.el),this.layout.set_sizing(this.box_sizing())}}o.HTMLBoxView=a,a.__name__=\"HTMLBoxView\";class u extends _.LayoutDOM{constructor(t){super(t)}}o.HTMLBox=u,u.__name__=\"HTMLBox\"},\n function _(e,n,l,a,o){var t;a();const s=e(53),c=e(309);class d extends s.Model{constructor(e){super(e)}}l.Panel=d,t=d,d.__name__=\"Panel\",t.define((({Boolean:e,String:n,Ref:l})=>({title:[n,\"\"],child:[l(c.LayoutDOM)],closable:[e,!1],disabled:[e,!1]})))},\n function _(o,s,t,i,e){var a;i();const n=o(308),l=o(209);class _ extends n.BoxView{_update_layout(){const o=this.child_views.map((o=>o.layout));this.layout=new l.Row(o),this.layout.cols=this.model.cols,this.layout.spacing=[0,this.model.spacing],this.layout.set_sizing(this.box_sizing())}}t.RowView=_,_.__name__=\"RowView\";class c extends n.Box{constructor(o){super(o)}}t.Row=c,a=c,c.__name__=\"Row\",a.prototype.default_view=_,a.define((({Any:o})=>({cols:[o,\"auto\"]})))},\n function _(e,t,a,s,_){var o;s();const i=e(309),n=e(207);class u extends i.LayoutDOMView{get child_models(){return[]}_update_layout(){this.layout=new n.LayoutItem,this.layout.set_sizing(this.box_sizing())}}a.SpacerView=u,u.__name__=\"SpacerView\";class c extends i.LayoutDOM{constructor(e){super(e)}}a.Spacer=c,o=c,c.__name__=\"Spacer\",o.prototype.default_view=u},\n function _(e,t,s,i,l){i();const h=e(1);var a;const o=e(207),d=e(43),r=e(9),c=e(10),n=e(20),_=e(309),p=e(313),b=(0,h.__importStar)(e(317)),m=b,u=(0,h.__importStar)(e(318)),g=u,v=(0,h.__importStar)(e(229)),w=v;class f extends _.LayoutDOMView{constructor(){super(...arguments),this._scroll_index=0}connect_signals(){super.connect_signals(),this.connect(this.model.properties.tabs.change,(()=>this.rebuild())),this.connect(this.model.properties.active.change,(()=>this.on_active_change()))}styles(){return[...super.styles(),u.default,v.default,b.default]}get child_models(){return this.model.tabs.map((e=>e.child))}_update_layout(){const e=this.model.tabs_location,t=\"above\"==e||\"below\"==e,{scroll_el:s,headers_el:i}=this;this.header=new class extends o.ContentBox{_measure(e){const l=(0,d.size)(s),h=(0,d.children)(i).slice(0,3).map((e=>(0,d.size)(e))),{width:a,height:o}=super._measure(e);if(t){const t=l.width+(0,r.sum)(h.map((e=>e.width)));return{width:e.width!=1/0?e.width:t,height:o}}{const t=l.height+(0,r.sum)(h.map((e=>e.height)));return{width:a,height:e.height!=1/0?e.height:t}}}}(this.header_el),t?this.header.set_sizing({width_policy:\"fit\",height_policy:\"fixed\"}):this.header.set_sizing({width_policy:\"fixed\",height_policy:\"fit\"});let l=1,h=1;switch(e){case\"above\":l-=1;break;case\"below\":l+=1;break;case\"left\":h-=1;break;case\"right\":h+=1}const a={layout:this.header,row:l,col:h},c=this.child_views.map((e=>({layout:e.layout,row:1,col:1})));this.layout=new o.Grid([a,...c]),this.layout.set_sizing(this.box_sizing())}update_position(){super.update_position(),this.header_el.style.position=\"absolute\",(0,d.position)(this.header_el,this.header.bbox);const e=this.model.tabs_location,t=\"above\"==e||\"below\"==e,s=(0,d.size)(this.scroll_el),i=(0,d.scroll_size)(this.headers_el);if(t){const{width:e}=this.header.bbox;i.width>e?(this.wrapper_el.style.maxWidth=e-s.width+\"px\",(0,d.display)(this.scroll_el),this.do_scroll(this.model.active)):(this.wrapper_el.style.maxWidth=\"\",(0,d.undisplay)(this.scroll_el))}else{const{height:e}=this.header.bbox;i.height>e?(this.wrapper_el.style.maxHeight=e-s.height+\"px\",(0,d.display)(this.scroll_el),this.do_scroll(this.model.active)):(this.wrapper_el.style.maxHeight=\"\",(0,d.undisplay)(this.scroll_el))}const{child_views:l}=this;for(const e of l)(0,d.hide)(e.el);const h=l[this.model.active];null!=h&&(0,d.show)(h.el)}render(){super.render();const{active:e}=this.model,t=this.model.tabs.map(((t,s)=>{const i=(0,d.div)({class:[m.tab,s==e?m.active:null]},t.title);if(i.addEventListener(\"click\",(e=>{this.model.disabled||e.target==e.currentTarget&&this.change_active(s)})),t.closable){const e=(0,d.div)({class:m.close});e.addEventListener(\"click\",(e=>{if(e.target==e.currentTarget){this.model.tabs=(0,r.remove_at)(this.model.tabs,s);const e=this.model.tabs.length;this.model.active>e-1&&(this.model.active=e-1)}})),i.appendChild(e)}return(this.model.disabled||t.disabled)&&i.classList.add(m.disabled),i}));this.headers_el=(0,d.div)({class:[m.headers]},t),this.wrapper_el=(0,d.div)({class:m.headers_wrapper},this.headers_el),this.left_el=(0,d.div)({class:[g.btn,g.btn_default],disabled:\"\"},(0,d.div)({class:[w.caret,m.left]})),this.right_el=(0,d.div)({class:[g.btn,g.btn_default]},(0,d.div)({class:[w.caret,m.right]})),this.left_el.addEventListener(\"click\",(()=>this.do_scroll(\"left\"))),this.right_el.addEventListener(\"click\",(()=>this.do_scroll(\"right\"))),this.scroll_el=(0,d.div)({class:g.btn_group},this.left_el,this.right_el);const s=this.model.tabs_location;this.header_el=(0,d.div)({class:[m.tabs_header,m[s]]},this.scroll_el,this.wrapper_el),this.el.appendChild(this.header_el)}do_scroll(e){const t=this.model.tabs.length;\"left\"==e?this._scroll_index-=1:\"right\"==e?this._scroll_index+=1:this._scroll_index=e,this._scroll_index=(0,c.clamp)(this._scroll_index,0,t-1),0==this._scroll_index?this.left_el.setAttribute(\"disabled\",\"\"):this.left_el.removeAttribute(\"disabled\"),this._scroll_index==t-1?this.right_el.setAttribute(\"disabled\",\"\"):this.right_el.removeAttribute(\"disabled\");const s=(0,d.children)(this.headers_el).slice(0,this._scroll_index).map((e=>e.getBoundingClientRect())),i=this.model.tabs_location;if(\"above\"==i||\"below\"==i){const e=-(0,r.sum)(s.map((e=>e.width)));this.headers_el.style.left=`${e}px`}else{const e=-(0,r.sum)(s.map((e=>e.height)));this.headers_el.style.top=`${e}px`}}change_active(e){e!=this.model.active&&(this.model.active=e)}on_active_change(){const e=this.model.active,t=(0,d.children)(this.headers_el);for(const e of t)e.classList.remove(m.active);t[e].classList.add(m.active);const{child_views:s}=this;for(const e of s)(0,d.hide)(e.el);(0,d.show)(s[e].el)}}s.TabsView=f,f.__name__=\"TabsView\";class x extends _.LayoutDOM{constructor(e){super(e)}}s.Tabs=x,a=x,x.__name__=\"Tabs\",a.prototype.default_view=f,a.define((({Int:e,Array:t,Ref:s})=>({tabs:[t(s(p.Panel)),[]],tabs_location:[n.Location,\"above\"],active:[e,0]})))},\n function _(e,r,b,o,t){o(),b.root=\"bk-root\",b.tabs_header=\"bk-tabs-header\",b.btn_group=\"bk-btn-group\",b.btn=\"bk-btn\",b.headers_wrapper=\"bk-headers-wrapper\",b.above=\"bk-above\",b.right=\"bk-right\",b.below=\"bk-below\",b.left=\"bk-left\",b.headers=\"bk-headers\",b.tab=\"bk-tab\",b.active=\"bk-active\",b.close=\"bk-close\",b.disabled=\"bk-disabled\",b.default='.bk-root .bk-tabs-header{display:flex;flex-wrap:nowrap;align-items:center;overflow:hidden;user-select:none;-ms-user-select:none;-moz-user-select:none;-webkit-user-select:none;}.bk-root .bk-tabs-header .bk-btn-group{height:auto;margin-right:5px;}.bk-root .bk-tabs-header .bk-btn-group > .bk-btn{flex-grow:0;height:auto;padding:4px 4px;}.bk-root .bk-tabs-header .bk-headers-wrapper{flex-grow:1;overflow:hidden;color:#666666;}.bk-root .bk-tabs-header.bk-above .bk-headers-wrapper{border-bottom:1px solid #e6e6e6;}.bk-root .bk-tabs-header.bk-right .bk-headers-wrapper{border-left:1px solid #e6e6e6;}.bk-root .bk-tabs-header.bk-below .bk-headers-wrapper{border-top:1px solid #e6e6e6;}.bk-root .bk-tabs-header.bk-left .bk-headers-wrapper{border-right:1px solid #e6e6e6;}.bk-root .bk-tabs-header.bk-above,.bk-root .bk-tabs-header.bk-below{flex-direction:row;}.bk-root .bk-tabs-header.bk-above .bk-headers,.bk-root .bk-tabs-header.bk-below .bk-headers{flex-direction:row;}.bk-root .bk-tabs-header.bk-left,.bk-root .bk-tabs-header.bk-right{flex-direction:column;}.bk-root .bk-tabs-header.bk-left .bk-headers,.bk-root .bk-tabs-header.bk-right .bk-headers{flex-direction:column;}.bk-root .bk-tabs-header .bk-headers{position:relative;display:flex;flex-wrap:nowrap;align-items:center;}.bk-root .bk-tabs-header .bk-tab{padding:4px 8px;border:solid transparent;white-space:nowrap;cursor:pointer;}.bk-root .bk-tabs-header .bk-tab:hover{background-color:#f2f2f2;}.bk-root .bk-tabs-header .bk-tab.bk-active{color:#4d4d4d;background-color:white;border-color:#e6e6e6;}.bk-root .bk-tabs-header .bk-tab .bk-close{margin-left:10px;}.bk-root .bk-tabs-header .bk-tab.bk-disabled{cursor:not-allowed;pointer-events:none;opacity:0.65;}.bk-root .bk-tabs-header.bk-above .bk-tab{border-width:3px 1px 0px 1px;border-radius:4px 4px 0 0;}.bk-root .bk-tabs-header.bk-right .bk-tab{border-width:1px 3px 1px 0px;border-radius:0 4px 4px 0;}.bk-root .bk-tabs-header.bk-below .bk-tab{border-width:0px 1px 3px 1px;border-radius:0 0 4px 4px;}.bk-root .bk-tabs-header.bk-left .bk-tab{border-width:1px 0px 1px 3px;border-radius:4px 0 0 4px;}.bk-root .bk-close{display:inline-block;width:10px;height:10px;vertical-align:middle;background-image:url(\\'data:image/svg+xml;utf8, \\');}.bk-root .bk-close:hover{background-image:url(\\'data:image/svg+xml;utf8, \\');}'},\n function _(o,b,r,t,e){t(),r.root=\"bk-root\",r.btn=\"bk-btn\",r.active=\"bk-active\",r.btn_default=\"bk-btn-default\",r.btn_primary=\"bk-btn-primary\",r.btn_success=\"bk-btn-success\",r.btn_warning=\"bk-btn-warning\",r.btn_danger=\"bk-btn-danger\",r.btn_light=\"bk-btn-light\",r.btn_group=\"bk-btn-group\",r.vertical=\"bk-vertical\",r.horizontal=\"bk-horizontal\",r.dropdown_toggle=\"bk-dropdown-toggle\",r.default=\".bk-root .bk-btn{height:100%;display:inline-block;text-align:center;vertical-align:middle;white-space:nowrap;cursor:pointer;padding:6px 12px;font-size:12px;border:1px solid transparent;border-radius:4px;outline:0;user-select:none;-ms-user-select:none;-moz-user-select:none;-webkit-user-select:none;}.bk-root .bk-btn:hover,.bk-root .bk-btn:focus{text-decoration:none;}.bk-root .bk-btn:active,.bk-root .bk-btn.bk-active{background-image:none;box-shadow:inset 0 3px 5px rgba(0, 0, 0, 0.125);}.bk-root .bk-btn[disabled]{cursor:not-allowed;pointer-events:none;opacity:0.65;box-shadow:none;}.bk-root .bk-btn-default{color:#333;background-color:#fff;border-color:#ccc;}.bk-root .bk-btn-default:hover{background-color:#f5f5f5;border-color:#b8b8b8;}.bk-root .bk-btn-default.bk-active{background-color:#ebebeb;border-color:#adadad;}.bk-root .bk-btn-default[disabled],.bk-root .bk-btn-default[disabled]:hover,.bk-root .bk-btn-default[disabled]:focus,.bk-root .bk-btn-default[disabled]:active,.bk-root .bk-btn-default[disabled].bk-active{background-color:#e6e6e6;border-color:#ccc;}.bk-root .bk-btn-primary{color:#fff;background-color:#428bca;border-color:#357ebd;}.bk-root .bk-btn-primary:hover{background-color:#3681c1;border-color:#2c699e;}.bk-root .bk-btn-primary.bk-active{background-color:#3276b1;border-color:#285e8e;}.bk-root .bk-btn-primary[disabled],.bk-root .bk-btn-primary[disabled]:hover,.bk-root .bk-btn-primary[disabled]:focus,.bk-root .bk-btn-primary[disabled]:active,.bk-root .bk-btn-primary[disabled].bk-active{background-color:#506f89;border-color:#357ebd;}.bk-root .bk-btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c;}.bk-root .bk-btn-success:hover{background-color:#4eb24e;border-color:#409240;}.bk-root .bk-btn-success.bk-active{background-color:#47a447;border-color:#398439;}.bk-root .bk-btn-success[disabled],.bk-root .bk-btn-success[disabled]:hover,.bk-root .bk-btn-success[disabled]:focus,.bk-root .bk-btn-success[disabled]:active,.bk-root .bk-btn-success[disabled].bk-active{background-color:#667b66;border-color:#4cae4c;}.bk-root .bk-btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236;}.bk-root .bk-btn-warning:hover{background-color:#eea43b;border-color:#e89014;}.bk-root .bk-btn-warning.bk-active{background-color:#ed9c28;border-color:#d58512;}.bk-root .bk-btn-warning[disabled],.bk-root .bk-btn-warning[disabled]:hover,.bk-root .bk-btn-warning[disabled]:focus,.bk-root .bk-btn-warning[disabled]:active,.bk-root .bk-btn-warning[disabled].bk-active{background-color:#c89143;border-color:#eea236;}.bk-root .bk-btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a;}.bk-root .bk-btn-danger:hover{background-color:#d5433e;border-color:#bd2d29;}.bk-root .bk-btn-danger.bk-active{background-color:#d2322d;border-color:#ac2925;}.bk-root .bk-btn-danger[disabled],.bk-root .bk-btn-danger[disabled]:hover,.bk-root .bk-btn-danger[disabled]:focus,.bk-root .bk-btn-danger[disabled]:active,.bk-root .bk-btn-danger[disabled].bk-active{background-color:#a55350;border-color:#d43f3a;}.bk-root .bk-btn-light{color:#333;background-color:#fff;border-color:#ccc;border-color:transparent;}.bk-root .bk-btn-light:hover{background-color:#f5f5f5;border-color:#b8b8b8;}.bk-root .bk-btn-light.bk-active{background-color:#ebebeb;border-color:#adadad;}.bk-root .bk-btn-light[disabled],.bk-root .bk-btn-light[disabled]:hover,.bk-root .bk-btn-light[disabled]:focus,.bk-root .bk-btn-light[disabled]:active,.bk-root .bk-btn-light[disabled].bk-active{background-color:#e6e6e6;border-color:#ccc;}.bk-root .bk-btn-group{height:100%;display:flex;flex-wrap:nowrap;align-items:center;}.bk-root .bk-btn-group:not(.bk-vertical),.bk-root .bk-btn-group.bk-horizontal{flex-direction:row;}.bk-root .bk-btn-group.bk-vertical{flex-direction:column;}.bk-root .bk-btn-group > .bk-btn{flex-grow:1;}.bk-root .bk-btn-group:not(.bk-vertical) > .bk-btn + .bk-btn{margin-left:-1px;}.bk-root .bk-btn-group.bk-vertical > .bk-btn + .bk-btn{margin-top:-1px;}.bk-root .bk-btn-group:not(.bk-vertical) > .bk-btn:first-child:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;}.bk-root .bk-btn-group.bk-vertical > .bk-btn:first-child:not(:last-child){border-bottom-left-radius:0;border-bottom-right-radius:0;}.bk-root .bk-btn-group:not(.bk-vertical) > .bk-btn:not(:first-child):last-child{border-bottom-left-radius:0;border-top-left-radius:0;}.bk-root .bk-btn-group.bk-vertical > .bk-btn:not(:first-child):last-child{border-top-left-radius:0;border-top-right-radius:0;}.bk-root .bk-btn-group > .bk-btn:not(:first-child):not(:last-child){border-radius:0;}.bk-root .bk-btn-group.bk-vertical > .bk-btn{width:100%;}.bk-root .bk-btn-group .bk-dropdown-toggle{flex:0 0 0;padding:6px 6px;}\"},\n function _(e,t,o,n,_){var i;n();const s=e(310);class d extends s.ColumnView{}o.WidgetBoxView=d,d.__name__=\"WidgetBoxView\";class a extends s.Column{constructor(e){super(e)}}o.WidgetBox=a,i=a,a.__name__=\"WidgetBox\",i.prototype.default_view=d},\n function _(t,a,i,e,M){e();var T=t(135);M(\"MathText\",T.MathText),M(\"Ascii\",T.Ascii),M(\"MathML\",T.MathML),M(\"TeX\",T.TeX),M(\"PlainText\",t(139).PlainText)},\n function _(r,o,t,e,n){e(),n(\"CustomJSTransform\",r(322).CustomJSTransform),n(\"Dodge\",r(323).Dodge),n(\"Interpolator\",r(325).Interpolator),n(\"Jitter\",r(326).Jitter),n(\"LinearInterpolator\",r(327).LinearInterpolator),n(\"StepInterpolator\",r(328).StepInterpolator),n(\"Transform\",r(56).Transform)},\n function _(r,t,s,n,e){var a;n();const u=r(56),o=r(13),m=r(34);class _ extends u.Transform{constructor(r){super(r)}get names(){return(0,o.keys)(this.args)}get values(){return(0,o.values)(this.args)}_make_transform(r,t){return new Function(...this.names,r,(0,m.use_strict)(t))}get scalar_transform(){return this._make_transform(\"x\",this.func)}get vector_transform(){return this._make_transform(\"xs\",this.v_func)}compute(r){return this.scalar_transform(...this.values,r)}v_compute(r){return this.vector_transform(...this.values,r)}}s.CustomJSTransform=_,a=_,_.__name__=\"CustomJSTransform\",a.define((({Unknown:r,String:t,Dict:s})=>({args:[s(r),{}],func:[t,\"\"],v_func:[t,\"\"]})))},\n function _(e,n,r,o,s){var t;o();const u=e(324);class a extends u.RangeTransform{constructor(e){super(e)}_compute(e){return e+this.value}}r.Dodge=a,t=a,a.__name__=\"Dodge\",t.define((({Number:e})=>({value:[e,0]})))},\n function _(e,n,t,r,a){var s;r();const c=e(56),o=e(57),i=e(67),u=e(24),h=e(8),l=e(11);class g extends c.Transform{constructor(e){super(e)}v_compute(e){let n;this.range instanceof i.FactorRange?n=this.range.v_synthetic(e):(0,h.isArrayableOf)(e,h.isNumber)?n=e:(0,l.unreachable)();const t=new((0,u.infer_type)(n))(n.length);for(let e=0;e({range:[n(e(o.Range)),null]})))},\n function _(t,e,r,n,s){var o;n();const i=t(56),a=t(70),h=t(24),l=t(9),d=t(8);class c extends i.Transform{constructor(t){super(t),this._sorted_dirty=!0}connect_signals(){super.connect_signals(),this.connect(this.change,(()=>this._sorted_dirty=!0))}v_compute(t){const e=new((0,h.infer_type)(t))(t.length);for(let r=0;ro*(e[t]-e[r]))),this._x_sorted=new((0,h.infer_type)(e))(n),this._y_sorted=new((0,h.infer_type)(r))(n);for(let t=0;t({x:[o(r,s(e))],y:[o(r,s(e))],data:[i(n(a.ColumnarDataSource)),null],clip:[t,!0]})))},\n function _(t,s,e,i,r){i();const n=t(1);var o;const a=t(324),u=t(67),h=t(20),c=t(8),m=t(12),f=(0,n.__importStar)(t(10)),_=t(11);class p extends a.RangeTransform{constructor(t){super(t)}v_compute(t){var s;let e;this.range instanceof u.FactorRange?e=this.range.v_synthetic(t):(0,c.isArrayableOf)(t,c.isNumber)?e=t:(0,_.unreachable)();const i=e.length;(null===(s=this.previous_offsets)||void 0===s?void 0:s.length)!=i&&(this.previous_offsets=new Array(i),this.previous_offsets=(0,m.map)(this.previous_offsets,(()=>this._compute())));const r=this.previous_offsets;return(0,m.map)(e,((t,s)=>r[s]+t))}_compute(){switch(this.distribution){case\"uniform\":return this.mean+(f.random()-.5)*this.width;case\"normal\":return f.rnorm(this.mean,this.width)}}}e.Jitter=p,o=p,p.__name__=\"Jitter\",o.define((({Number:t})=>({mean:[t,0],width:[t,1],distribution:[h.Distribution,\"uniform\"]})))},\n function _(t,s,_,r,e){r();const i=t(9),o=t(325);class n extends o.Interpolator{constructor(t){super(t)}compute(t){if(this.sort(!1),this.clip){if(tthis._x_sorted[this._x_sorted.length-1])return NaN}else{if(tthis._x_sorted[this._x_sorted.length-1])return this._y_sorted[this._y_sorted.length-1]}if(t==this._x_sorted[0])return this._y_sorted[0];const s=(0,i.find_last_index)(this._x_sorted,(s=>sthis._x_sorted[this._x_sorted.length-1])return NaN}else{if(tthis._x_sorted[this._x_sorted.length-1])return this._y_sorted[this._y_sorted.length-1]}let e;switch(this.mode){case\"after\":e=(0,d.find_last_index)(this._x_sorted,(e=>t>=e));break;case\"before\":e=(0,d.find_index)(this._x_sorted,(e=>t<=e));break;case\"center\":{const s=(0,d.map)(this._x_sorted,(e=>Math.abs(e-t))),r=(0,d.min)(s);e=(0,d.find_index)(s,(t=>r===t));break}default:throw new Error(`unknown mode: ${this.mode}`)}return-1!=e?this._y_sorted[e]:NaN}}s.StepInterpolator=h,_=h,h.__name__=\"StepInterpolator\",_.define((()=>({mode:[n.StepMode,\"after\"]})))},\n function _(p,o,t,a,n){a(),n(\"MapOptions\",p(330).MapOptions),n(\"GMapOptions\",p(330).GMapOptions),n(\"GMapPlot\",p(330).GMapPlot),n(\"Plot\",p(331).Plot)},\n function _(e,t,n,o,a){var s,p,_;o();const i=e(331),r=e(53),l=e(58),c=e(336);a(\"GMapPlotView\",c.GMapPlotView);class d extends r.Model{constructor(e){super(e)}}n.MapOptions=d,s=d,d.__name__=\"MapOptions\",s.define((({Int:e,Number:t})=>({lat:[t],lng:[t],zoom:[e,12]})));class u extends d{constructor(e){super(e)}}n.GMapOptions=u,p=u,u.__name__=\"GMapOptions\",p.define((({Boolean:e,Int:t,String:n})=>({map_type:[n,\"roadmap\"],scale_control:[e,!1],styles:[n],tilt:[t,45]})));class M extends i.Plot{constructor(e){super(e),this.use_map=!0}}n.GMapPlot=M,_=M,M.__name__=\"GMapPlot\",_.prototype.default_view=c.GMapPlotView,_.define((({String:e,Ref:t})=>({map_options:[t(u)],api_key:[e],api_version:[e,\"weekly\"]}))),_.override({x_range:()=>new l.Range1d,y_range:()=>new l.Range1d})},\n function _(e,t,r,n,i){n();const o=e(1);var a;const s=(0,o.__importStar)(e(48)),l=(0,o.__importStar)(e(18)),_=e(15),c=e(20),h=e(9),d=e(13),u=e(8),g=e(309),p=e(128),f=e(306),b=e(40),w=e(118),y=e(59),m=e(221),x=e(57),v=e(55),A=e(75),S=e(41),R=e(176),D=e(175),L=e(63),P=e(332);i(\"PlotView\",P.PlotView);class k extends g.LayoutDOM{constructor(e){super(e),this.use_map=!1}_doc_attached(){super._doc_attached(),this._push_changes([[this.properties.inner_height,null,this.inner_height],[this.properties.inner_width,null,this.inner_width]])}initialize(){super.initialize(),this.reset=new _.Signal0(this,\"reset\");for(const e of(0,d.values)(this.extra_x_ranges).concat(this.x_range)){let t=e.plots;(0,u.isArray)(t)&&(t=t.concat(this),e.setv({plots:t},{silent:!0}))}for(const e of(0,d.values)(this.extra_y_ranges).concat(this.y_range)){let t=e.plots;(0,u.isArray)(t)&&(t=t.concat(this),e.setv({plots:t},{silent:!0}))}}add_layout(e,t=\"center\"){const r=this.properties[t].get_value();this.setv({[t]:[...r,e]})}remove_layout(e){const t=t=>{(0,h.remove_by)(t,(t=>t==e))};t(this.left),t(this.right),t(this.above),t(this.below),t(this.center)}get data_renderers(){return this.renderers.filter((e=>e instanceof R.DataRenderer))}add_renderers(...e){this.renderers=this.renderers.concat(e)}add_glyph(e,t=new A.ColumnDataSource,r={}){const n=new D.GlyphRenderer(Object.assign(Object.assign({},r),{data_source:t,glyph:e}));return this.add_renderers(n),n}add_tools(...e){this.toolbar.tools=this.toolbar.tools.concat(e)}get panels(){return[...this.side_panels,...this.center]}get side_panels(){const{above:e,below:t,left:r,right:n}=this;return(0,h.concat)([e,t,r,n])}}r.Plot=k,a=k,k.__name__=\"Plot\",a.prototype.default_view=P.PlotView,a.mixins([[\"outline_\",s.Line],[\"background_\",s.Fill],[\"border_\",s.Fill]]),a.define((({Boolean:e,Number:t,String:r,Array:n,Dict:i,Or:o,Ref:a,Null:s,Nullable:_})=>({toolbar:[a(m.Toolbar),()=>new m.Toolbar],toolbar_location:[_(c.Location),\"right\"],toolbar_sticky:[e,!0],plot_width:[l.Alias(\"width\")],plot_height:[l.Alias(\"height\")],frame_width:[_(t),null],frame_height:[_(t),null],title:[o(a(w.Title),r,s),\"\",{convert:e=>(0,u.isString)(e)?new w.Title({text:e}):e}],title_location:[_(c.Location),\"above\"],above:[n(o(a(b.Annotation),a(p.Axis))),[]],below:[n(o(a(b.Annotation),a(p.Axis))),[]],left:[n(o(a(b.Annotation),a(p.Axis))),[]],right:[n(o(a(b.Annotation),a(p.Axis))),[]],center:[n(o(a(b.Annotation),a(f.Grid))),[]],renderers:[n(a(S.Renderer)),[]],x_range:[a(x.Range),()=>new L.DataRange1d],y_range:[a(x.Range),()=>new L.DataRange1d],x_scale:[a(v.Scale),()=>new y.LinearScale],y_scale:[a(v.Scale),()=>new y.LinearScale],extra_x_ranges:[i(a(x.Range)),{}],extra_y_ranges:[i(a(x.Range)),{}],extra_x_scales:[i(a(v.Scale)),{}],extra_y_scales:[i(a(v.Scale)),{}],lod_factor:[t,10],lod_interval:[t,300],lod_threshold:[_(t),2e3],lod_timeout:[t,500],hidpi:[e,!0],output_backend:[c.OutputBackend,\"canvas\"],min_border:[_(t),5],min_border_top:[_(t),null],min_border_left:[_(t),null],min_border_bottom:[_(t),null],min_border_right:[_(t),null],inner_width:[t,0],inner_height:[t,0],outer_width:[t,0],outer_height:[t,0],match_aspect:[e,!1],aspect_scale:[t,1],reset_policy:[c.ResetPolicy,\"standard\"]}))),a.override({width:600,height:600,outline_line_color:\"#e5e5e5\",border_fill_color:\"#ffffff\",background_fill_color:\"#ffffff\"})},\n function _(e,t,i,s,a){s();const n=e(1),o=e(126),l=e(249),r=e(309),_=e(40),h=e(118),d=e(128),u=e(220),c=e(251),p=e(113),v=e(45),g=e(19),b=e(251),m=e(333),y=e(8),w=e(9),f=e(235),x=e(208),z=e(211),k=e(209),q=e(123),M=e(65),R=e(334),V=e(335),S=e(28);class O extends r.LayoutDOMView{constructor(){super(...arguments),this._outer_bbox=new M.BBox,this._inner_bbox=new M.BBox,this._needs_paint=!0,this._needs_layout=!1,this._invalidated_painters=new Set,this._invalidate_all=!0,this._needs_notify=!1}get canvas(){return this.canvas_view}get state(){return this._state_manager}set invalidate_dataranges(e){this._range_manager.invalidate_dataranges=e}renderer_view(e){const t=this.renderer_views.get(e);if(null==t)for(const[,t]of this.renderer_views){const i=t.renderer_view(e);if(null!=i)return i}return t}get is_paused(){return null!=this._is_paused&&0!==this._is_paused}get child_models(){return[]}pause(){null==this._is_paused?this._is_paused=1:this._is_paused+=1}unpause(e=!1){if(null==this._is_paused)throw new Error(\"wasn't paused\");this._is_paused-=1,0!=this._is_paused||e||this.request_paint(\"everything\")}notify_finished_after_paint(){this._needs_notify=!0}request_render(){this.request_paint(\"everything\")}request_paint(e){this.invalidate_painters(e),this.schedule_paint()}invalidate_painters(e){if(\"everything\"==e)this._invalidate_all=!0;else if((0,y.isArray)(e))for(const t of e)this._invalidated_painters.add(t);else this._invalidated_painters.add(e)}schedule_paint(){if(!this.is_paused){const e=this.throttled_paint();this._ready=this._ready.then((()=>e))}}request_layout(){this._needs_layout=!0,this.request_paint(\"everything\")}reset(){\"standard\"==this.model.reset_policy&&(this.state.clear(),this.reset_range(),this.reset_selection()),this.model.trigger_event(new c.Reset)}remove(){(0,p.remove_views)(this.renderer_views),(0,p.remove_views)(this.tool_views),this.canvas_view.remove(),super.remove()}render(){super.render(),this.el.appendChild(this.canvas_view.el),this.canvas_view.render()}initialize(){this.pause(),super.initialize(),this.lod_started=!1,this.visuals=new v.Visuals(this),this._initial_state={selection:new Map,dimensions:{width:0,height:0}},this.visibility_callbacks=[],this.renderer_views=new Map,this.tool_views=new Map,this.frame=new o.CartesianFrame(this.model.x_scale,this.model.y_scale,this.model.x_range,this.model.y_range,this.model.extra_x_ranges,this.model.extra_y_ranges,this.model.extra_x_scales,this.model.extra_y_scales),this._range_manager=new R.RangeManager(this),this._state_manager=new V.StateManager(this,this._initial_state),this.throttled_paint=(0,m.throttle)((()=>this.repaint()),1e3/60);const{title_location:e,title:t}=this.model;null!=e&&null!=t&&(this._title=t instanceof h.Title?t:new h.Title({text:t}));const{toolbar_location:i,toolbar:s}=this.model;null!=i&&null!=s&&(this._toolbar=new u.ToolbarPanel({toolbar:s}),s.toolbar_location=i)}async lazy_initialize(){await super.lazy_initialize();const{hidpi:e,output_backend:t}=this.model,i=new l.Canvas({hidpi:e,output_backend:t});this.canvas_view=await(0,p.build_view)(i,{parent:this}),this.canvas_view.plot_views=[this],await this.build_renderer_views(),await this.build_tool_views(),this._range_manager.update_dataranges(),this.unpause(!0),g.logger.debug(\"PlotView initialized\")}_width_policy(){return null==this.model.frame_width?super._width_policy():\"min\"}_height_policy(){return null==this.model.frame_height?super._height_policy():\"min\"}_update_layout(){var e,t,i,s,a;this.layout=new z.BorderLayout,this.layout.set_sizing(this.box_sizing());const n=(0,w.copy)(this.model.above),o=(0,w.copy)(this.model.below),l=(0,w.copy)(this.model.left),r=(0,w.copy)(this.model.right),d=e=>{switch(e){case\"above\":return n;case\"below\":return o;case\"left\":return l;case\"right\":return r}},{title_location:c,title:p}=this.model;null!=c&&null!=p&&d(c).push(this._title);const{toolbar_location:v,toolbar:g}=this.model;if(null!=v&&null!=g){const e=d(v);let t=!0;if(this.model.toolbar_sticky)for(let i=0;i{var i;const s=this.renderer_view(t);return s.panel=new q.Panel(e),null===(i=s.update_layout)||void 0===i||i.call(s),s.layout},m=(e,t)=>{const i=\"above\"==e||\"below\"==e,s=[];for(const a of t)if((0,y.isArray)(a)){const t=a.map((t=>{const s=b(e,t);if(t instanceof u.ToolbarPanel){const e=i?\"width_policy\":\"height_policy\";s.set_sizing(Object.assign(Object.assign({},s.sizing),{[e]:\"min\"}))}return s}));let n;i?(n=new k.Row(t),n.set_sizing({width_policy:\"max\",height_policy:\"min\"})):(n=new k.Column(t),n.set_sizing({width_policy:\"min\",height_policy:\"max\"})),n.absolute=!0,s.push(n)}else s.push(b(e,a));return s},f=null!==(e=this.model.min_border)&&void 0!==e?e:0;this.layout.min_border={left:null!==(t=this.model.min_border_left)&&void 0!==t?t:f,top:null!==(i=this.model.min_border_top)&&void 0!==i?i:f,right:null!==(s=this.model.min_border_right)&&void 0!==s?s:f,bottom:null!==(a=this.model.min_border_bottom)&&void 0!==a?a:f};const M=new x.NodeLayout,R=new x.VStack,V=new x.VStack,S=new x.HStack,O=new x.HStack;M.absolute=!0,R.absolute=!0,V.absolute=!0,S.absolute=!0,O.absolute=!0,M.children=this.model.center.filter((e=>e instanceof _.Annotation)).map((e=>{var t;const i=this.renderer_view(e);return null===(t=i.update_layout)||void 0===t||t.call(i),i.layout})).filter((e=>null!=e));const{frame_width:P,frame_height:j}=this.model;M.set_sizing(Object.assign(Object.assign({},null!=P?{width_policy:\"fixed\",width:P}:{width_policy:\"fit\"}),null!=j?{height_policy:\"fixed\",height:j}:{height_policy:\"fit\"})),M.on_resize((e=>this.frame.set_geometry(e))),R.children=(0,w.reversed)(m(\"above\",n)),V.children=m(\"below\",o),S.children=(0,w.reversed)(m(\"left\",l)),O.children=m(\"right\",r),R.set_sizing({width_policy:\"fit\",height_policy:\"min\"}),V.set_sizing({width_policy:\"fit\",height_policy:\"min\"}),S.set_sizing({width_policy:\"min\",height_policy:\"fit\"}),O.set_sizing({width_policy:\"min\",height_policy:\"fit\"}),this.layout.center_panel=M,this.layout.top_panel=R,this.layout.bottom_panel=V,this.layout.left_panel=S,this.layout.right_panel=O}get axis_views(){const e=[];for(const[,t]of this.renderer_views)t instanceof d.AxisView&&e.push(t);return e}set_toolbar_visibility(e){for(const t of this.visibility_callbacks)t(e)}update_range(e,t){this.pause(),this._range_manager.update(e,t),this.unpause()}reset_range(){this.update_range(null),this.trigger_ranges_update_event()}trigger_ranges_update_event(){const{x_range:e,y_range:t}=this.model;this.model.trigger_event(new b.RangesUpdate(e.start,e.end,t.start,t.end))}get_selection(){const e=new Map;for(const t of this.model.data_renderers){const{selected:i}=t.selection_manager.source;e.set(t,i)}return e}update_selection(e){for(const t of this.model.data_renderers){const i=t.selection_manager.source;if(null!=e){const s=e.get(t);null!=s&&i.selected.update(s,!0)}else i.selection_manager.clear()}}reset_selection(){this.update_selection(null)}_invalidate_layout(){(()=>{var e;for(const t of this.model.side_panels){const i=this.renderer_views.get(t);if(null===(e=i.layout)||void 0===e?void 0:e.has_size_changed())return this.invalidate_painters(i),!0}return!1})()&&this.root.compute_layout()}get_renderer_views(){return this.computed_renderers.map((e=>this.renderer_views.get(e)))}*_compute_renderers(){const{above:e,below:t,left:i,right:s,center:a,renderers:n}=this.model;yield*n,yield*e,yield*t,yield*i,yield*s,yield*a,null!=this._title&&(yield this._title),null!=this._toolbar&&(yield this._toolbar);for(const e of this.model.toolbar.tools)null!=e.overlay&&(yield e.overlay),yield*e.synthetic_renderers}async build_renderer_views(){this.computed_renderers=[...this._compute_renderers()],await(0,p.build_views)(this.renderer_views,this.computed_renderers,{parent:this})}async build_tool_views(){const e=this.model.toolbar.tools;(await(0,p.build_views)(this.tool_views,e,{parent:this})).map((e=>this.canvas_view.ui_event_bus.register_tool(e)))}connect_signals(){super.connect_signals();const{x_ranges:e,y_ranges:t}=this.frame;for(const[,t]of e)this.connect(t.change,(()=>{this._needs_layout=!0,this.request_paint(\"everything\")}));for(const[,e]of t)this.connect(e.change,(()=>{this._needs_layout=!0,this.request_paint(\"everything\")}));const{above:i,below:s,left:a,right:n,center:o,renderers:l}=this.model.properties;this.on_change([i,s,a,n,o,l],(async()=>await this.build_renderer_views())),this.connect(this.model.toolbar.properties.tools.change,(async()=>{await this.build_renderer_views(),await this.build_tool_views()})),this.connect(this.model.change,(()=>this.request_paint(\"everything\"))),this.connect(this.model.reset,(()=>this.reset()))}has_finished(){if(!super.has_finished())return!1;if(this.model.visible)for(const[,e]of this.renderer_views)if(!e.has_finished())return!1;return!0}after_layout(){var e;super.after_layout();for(const[,t]of this.renderer_views)t instanceof _.AnnotationView&&(null===(e=t.after_layout)||void 0===e||e.call(t));if(this._needs_layout=!1,this.model.setv({inner_width:Math.round(this.frame.bbox.width),inner_height:Math.round(this.frame.bbox.height),outer_width:Math.round(this.layout.bbox.width),outer_height:Math.round(this.layout.bbox.height)},{no_change:!0}),!1!==this.model.match_aspect&&(this.pause(),this._range_manager.update_dataranges(),this.unpause(!0)),!this._outer_bbox.equals(this.layout.bbox)){const{width:e,height:t}=this.layout.bbox;this.canvas_view.resize(e,t),this._outer_bbox=this.layout.bbox,this._invalidate_all=!0,this._needs_paint=!0}const{inner_bbox:t}=this.layout;this._inner_bbox.equals(t)||(this._inner_bbox=t,this._needs_paint=!0),this._needs_paint&&this.paint()}repaint(){this._needs_layout&&this._invalidate_layout(),this.paint()}paint(){this.is_paused||(this.model.visible&&(g.logger.trace(`${this.toString()}.paint()`),this._actual_paint()),this._needs_notify&&(this._needs_notify=!1,this.notify_finished()))}_actual_paint(){var e;const{document:t}=this.model;if(null!=t){const e=t.interactive_duration();e>=0&&e{t.interactive_duration()>this.model.lod_timeout&&t.interactive_stop(),this.request_paint(\"everything\")}),this.model.lod_timeout):t.interactive_stop()}this._range_manager.invalidate_dataranges&&(this._range_manager.update_dataranges(),this._invalidate_layout());let i=!1,s=!1;if(this._invalidate_all)i=!0,s=!0;else for(const e of this._invalidated_painters){const{level:t}=e.model;if(\"overlay\"!=t?i=!0:s=!0,i&&s)break}this._invalidated_painters.clear(),this._invalidate_all=!1;const a=[this.frame.bbox.left,this.frame.bbox.top,this.frame.bbox.width,this.frame.bbox.height],{primary:n,overlays:o}=this.canvas_view;i&&(n.prepare(),this.canvas_view.prepare_webgl(a),this._map_hook(n.ctx,a),this._paint_empty(n.ctx,a),this._paint_outline(n.ctx,a),this._paint_levels(n.ctx,\"image\",a,!0),this._paint_levels(n.ctx,\"underlay\",a,!0),this._paint_levels(n.ctx,\"glyph\",a,!0),this._paint_levels(n.ctx,\"guide\",a,!1),this._paint_levels(n.ctx,\"annotation\",a,!1),n.finish()),(s||S.settings.wireframe)&&(o.prepare(),this._paint_levels(o.ctx,\"overlay\",a,!1),S.settings.wireframe&&this._paint_layout(o.ctx,this.layout),o.finish()),null==this._initial_state.range&&(this._initial_state.range=null!==(e=this._range_manager.compute_initial())&&void 0!==e?e:void 0),this._needs_paint=!1}_paint_levels(e,t,i,s){for(const a of this.computed_renderers){if(a.level!=t)continue;const n=this.renderer_views.get(a);e.save(),(s||n.needs_clip)&&(e.beginPath(),e.rect(...i),e.clip()),n.render(),e.restore(),n.has_webgl&&n.needs_webgl_blit&&this.canvas_view.blit_webgl(e)}}_paint_layout(e,t){const{x:i,y:s,width:a,height:n}=t.bbox;e.strokeStyle=\"blue\",e.strokeRect(i,s,a,n);for(const a of t)e.save(),t.absolute||e.translate(i,s),this._paint_layout(e,a),e.restore()}_map_hook(e,t){}_paint_empty(e,t){const[i,s,a,n]=[0,0,this.layout.bbox.width,this.layout.bbox.height],[o,l,r,_]=t;this.visuals.border_fill.doit&&(this.visuals.border_fill.set_value(e),e.fillRect(i,s,a,n),e.clearRect(o,l,r,_)),this.visuals.background_fill.doit&&(this.visuals.background_fill.set_value(e),e.fillRect(o,l,r,_))}_paint_outline(e,t){if(this.visuals.outline_line.doit){e.save(),this.visuals.outline_line.set_value(e);let[i,s,a,n]=t;i+a==this.layout.bbox.width&&(a-=1),s+n==this.layout.bbox.height&&(n-=1),e.strokeRect(i,s,a,n),e.restore()}}to_blob(){return this.canvas_view.to_blob()}export(e,t=!0){const i=\"png\"==e?\"canvas\":\"svg\",s=new f.CanvasLayer(i,t),{width:a,height:n}=this.layout.bbox;s.resize(a,n);const{canvas:o}=this.canvas_view.compose();return s.ctx.drawImage(o,0,0),s}serializable_state(){const e=super.serializable_state(),{children:t}=e,i=(0,n.__rest)(e,[\"children\"]),s=this.get_renderer_views().map((e=>e.serializable_state())).filter((e=>null!=e.bbox));return Object.assign(Object.assign({},i),{children:[...null!=t?t:[],...s]})}}i.PlotView=O,O.__name__=\"PlotView\"},\n function _(t,n,e,o,u){o(),e.throttle=function(t,n){let e=null,o=0,u=!1;return function(){return new Promise(((r,i)=>{const l=function(){o=Date.now(),e=null,u=!1;try{t(),r()}catch(t){i(t)}},a=Date.now(),c=n-(a-o);c<=0&&!u?(null!=e&&clearTimeout(e),u=!0,requestAnimationFrame(l)):e||u?r():e=setTimeout((()=>requestAnimationFrame(l)),c)}))}}},\n function _(t,n,e,a,s){a();const o=t(63),r=t(19);class l{constructor(t){this.parent=t,this.invalidate_dataranges=!0}get frame(){return this.parent.frame}update(t,n){const{x_ranges:e,y_ranges:a}=this.frame;if(null==t){for(const[,t]of e)t.reset();for(const[,t]of a)t.reset();this.update_dataranges()}else{const s=[];for(const[n,a]of e)s.push([a,t.xrs.get(n)]);for(const[n,e]of a)s.push([e,t.yrs.get(n)]);(null==n?void 0:n.scrolling)&&this._update_ranges_together(s),this._update_ranges_individually(s,n)}}reset(){this.update(null)}_update_dataranges(t){const n=new Map,e=new Map;let a=!1;for(const[,n]of t.x_ranges)n instanceof o.DataRange1d&&\"log\"==n.scale_hint&&(a=!0);for(const[,n]of t.y_ranges)n instanceof o.DataRange1d&&\"log\"==n.scale_hint&&(a=!0);for(const t of this.parent.model.data_renderers){const s=this.parent.renderer_view(t);if(null==s)continue;const o=s.glyph_view.bounds();if(null!=o&&n.set(t,o),a){const n=s.glyph_view.log_bounds();null!=n&&e.set(t,n)}}let s=!1,l=!1;const i=t.x_target.span,d=t.y_target.span;let u;!1!==this.parent.model.match_aspect&&0!=i&&0!=d&&(u=1/this.parent.model.aspect_scale*(i/d));for(const[,a]of t.x_ranges){if(a instanceof o.DataRange1d){const t=\"log\"==a.scale_hint?e:n;a.update(t,0,this.parent.model,u),a.follow&&(s=!0)}null!=a.bounds&&(l=!0)}for(const[,a]of t.y_ranges){if(a instanceof o.DataRange1d){const t=\"log\"==a.scale_hint?e:n;a.update(t,1,this.parent.model,u),a.follow&&(s=!0)}null!=a.bounds&&(l=!0)}if(s&&l){r.logger.warn(\"Follow enabled so bounds are unset.\");for(const[,n]of t.x_ranges)n.bounds=null;for(const[,n]of t.y_ranges)n.bounds=null}}update_dataranges(){this._update_dataranges(this.frame);for(const t of this.parent.model.renderers){const{coordinates:n}=t;null!=n&&this._update_dataranges(n)}null!=this.compute_initial()&&(this.invalidate_dataranges=!1)}compute_initial(){let t=!0;const{x_ranges:n,y_ranges:e}=this.frame,a=new Map,s=new Map;for(const[e,s]of n){const{start:n,end:o}=s;if(null==n||null==o||isNaN(n+o)){t=!1;break}a.set(e,{start:n,end:o})}if(t)for(const[n,a]of e){const{start:e,end:o}=a;if(null==e||null==o||isNaN(e+o)){t=!1;break}s.set(n,{start:e,end:o})}return t?{xrs:a,yrs:s}:(r.logger.warn(\"could not set initial ranges\"),null)}_update_ranges_together(t){let n=1;for(const[e,a]of t)n=Math.min(n,this._get_weight_to_constrain_interval(e,a));if(n<1)for(const[e,a]of t)a.start=n*a.start+(1-n)*e.start,a.end=n*a.end+(1-n)*e.end}_update_ranges_individually(t,n){const e=!!(null==n?void 0:n.panning),a=!!(null==n?void 0:n.scrolling);let s=!1;for(const[n,o]of t){if(!a){const t=this._get_weight_to_constrain_interval(n,o);t<1&&(o.start=t*o.start+(1-t)*n.start,o.end=t*o.end+(1-t)*n.end)}if(null!=n.bounds&&\"auto\"!=n.bounds){const[t,r]=n.bounds,l=Math.abs(o.end-o.start);n.is_reversed?(null!=t&&t>o.end&&(s=!0,o.end=t,(e||a)&&(o.start=t+l)),null!=r&&ro.start&&(s=!0,o.start=t,(e||a)&&(o.end=t+l)),null!=r&&r0&&r0&&r>a&&(s=(a-o)/(r-o)),s=Math.max(0,Math.min(1,s))}return s}}e.RangeManager=l,l.__name__=\"RangeManager\"},\n function _(t,i,s,e,n){e();const h=t(15);class a{constructor(t,i){this.parent=t,this.initial_state=i,this.changed=new h.Signal0(this.parent,\"state_changed\"),this.history=[],this.index=-1}_do_state_change(t){const i=null!=this.history[t]?this.history[t].state:this.initial_state;return null!=i.range&&this.parent.update_range(i.range),null!=i.selection&&this.parent.update_selection(i.selection),i}push(t,i){const{history:s,index:e}=this,n=null!=s[e]?s[e].state:{},h=Object.assign(Object.assign(Object.assign({},this.initial_state),n),i);this.history=this.history.slice(0,this.index+1),this.history.push({type:t,state:h}),this.index=this.history.length-1,this.changed.emit()}clear(){this.history=[],this.index=-1,this.changed.emit()}undo(){if(this.can_undo){this.index-=1;const t=this._do_state_change(this.index);return this.changed.emit(),t}return null}redo(){if(this.can_redo){this.index+=1;const t=this._do_state_change(this.index);return this.changed.emit(),t}return null}get can_undo(){return this.index>=0}get can_redo(){return this.indexm.emit();const s=encodeURIComponent,o=document.createElement(\"script\");o.type=\"text/javascript\",o.src=`https://maps.googleapis.com/maps/api/js?v=${s(e)}&key=${s(t)}&callback=_bokeh_gmaps_callback`,document.body.appendChild(o)}(t,e)}m.connect((()=>this.request_paint(\"everything\")))}this.unpause()}remove(){(0,p.remove)(this.map_el),super.remove()}update_range(t,e){var s,o;if(null==t)this.map.setCenter({lat:this.initial_lat,lng:this.initial_lng}),this.map.setOptions({zoom:this.initial_zoom}),super.update_range(null,e);else if(null!=t.sdx||null!=t.sdy)this.map.panBy(null!==(s=t.sdx)&&void 0!==s?s:0,null!==(o=t.sdy)&&void 0!==o?o:0),super.update_range(t,e);else if(null!=t.factor){if(10!==this.zoom_count)return void(this.zoom_count+=1);this.zoom_count=0,this.pause(),super.update_range(t,e);const s=t.factor<0?-1:1,o=this.map.getZoom();if(null!=o){const t=o+s;if(t>=2){this.map.setZoom(t);const[e,s]=this._get_projected_bounds();s-e<0&&this.map.setZoom(o)}}this.unpause()}this._set_bokeh_ranges()}_build_map(){const{maps:t}=google;this.map_types={satellite:t.MapTypeId.SATELLITE,terrain:t.MapTypeId.TERRAIN,roadmap:t.MapTypeId.ROADMAP,hybrid:t.MapTypeId.HYBRID};const e=this.model.map_options,s={center:new t.LatLng(e.lat,e.lng),zoom:e.zoom,disableDefaultUI:!0,mapTypeId:this.map_types[e.map_type],scaleControl:e.scale_control,tilt:e.tilt};null!=e.styles&&(s.styles=JSON.parse(e.styles)),this.map_el=(0,p.div)({style:{position:\"absolute\"}}),this.canvas_view.add_underlay(this.map_el),this.map=new t.Map(this.map_el,s),t.event.addListener(this.map,\"idle\",(()=>this._set_bokeh_ranges())),t.event.addListener(this.map,\"bounds_changed\",(()=>this._set_bokeh_ranges())),t.event.addListenerOnce(this.map,\"tilesloaded\",(()=>this._render_finished())),this.connect(this.model.properties.map_options.change,(()=>this._update_options())),this.connect(this.model.map_options.properties.styles.change,(()=>this._update_styles())),this.connect(this.model.map_options.properties.lat.change,(()=>this._update_center(\"lat\"))),this.connect(this.model.map_options.properties.lng.change,(()=>this._update_center(\"lng\"))),this.connect(this.model.map_options.properties.zoom.change,(()=>this._update_zoom())),this.connect(this.model.map_options.properties.map_type.change,(()=>this._update_map_type())),this.connect(this.model.map_options.properties.scale_control.change,(()=>this._update_scale_control())),this.connect(this.model.map_options.properties.tilt.change,(()=>this._update_tilt()))}_render_finished(){this._tiles_loaded=!0,this.notify_finished()}has_finished(){return super.has_finished()&&!0===this._tiles_loaded}_get_latlon_bounds(){const t=this.map.getBounds(),e=t.getNorthEast(),s=t.getSouthWest();return[s.lng(),e.lng(),s.lat(),e.lat()]}_get_projected_bounds(){const[t,e,s,o]=this._get_latlon_bounds(),[i,a]=l.wgs84_mercator.compute(t,s),[n,p]=l.wgs84_mercator.compute(e,o);return[i,n,a,p]}_set_bokeh_ranges(){const[t,e,s,o]=this._get_projected_bounds();this.frame.x_range.setv({start:t,end:e}),this.frame.y_range.setv({start:s,end:o})}_update_center(t){var e;const s=null===(e=this.map.getCenter())||void 0===e?void 0:e.toJSON();null!=s&&(s[t]=this.model.map_options[t],this.map.setCenter(s),this._set_bokeh_ranges())}_update_map_type(){this.map.setOptions({mapTypeId:this.map_types[this.model.map_options.map_type]})}_update_scale_control(){this.map.setOptions({scaleControl:this.model.map_options.scale_control})}_update_tilt(){this.map.setOptions({tilt:this.model.map_options.tilt})}_update_options(){this._update_styles(),this._update_center(\"lat\"),this._update_center(\"lng\"),this._update_zoom(),this._update_map_type()}_update_styles(){this.map.setOptions({styles:JSON.parse(this.model.map_options.styles)})}_update_zoom(){this.map.setOptions({zoom:this.model.map_options.zoom}),this._set_bokeh_ranges()}_map_hook(t,e){if(null==this.map&&\"undefined\"!=typeof google&&null!=google.maps&&this._build_map(),null!=this.map_el){const[t,s,o,i]=e;this.map_el.style.top=`${s}px`,this.map_el.style.left=`${t}px`,this.map_el.style.width=`${o}px`,this.map_el.style.height=`${i}px`}}_paint_empty(t,e){const s=this.layout.bbox.width,o=this.layout.bbox.height,[i,a,n,p]=e;t.clearRect(0,0,s,o),t.beginPath(),t.moveTo(0,0),t.lineTo(0,o),t.lineTo(s,o),t.lineTo(s,0),t.lineTo(0,0),t.moveTo(i,a),t.lineTo(i+n,a),t.lineTo(i+n,a+p),t.lineTo(i,a+p),t.lineTo(i,a),t.closePath(),null!=this.model.border_fill_color&&(t.fillStyle=(0,_.color2css)(this.model.border_fill_color),t.fill())}}s.GMapPlotView=d,d.__name__=\"GMapPlotView\"},\n function _(t,_,n,o,r){o();(0,t(1).__exportStar)(t(132),n)},\n function _(e,r,d,n,R){n(),R(\"GlyphRenderer\",e(175).GlyphRenderer),R(\"GraphRenderer\",e(339).GraphRenderer),R(\"GuideRenderer\",e(129).GuideRenderer);var G=e(41);R(\"Renderer\",G.Renderer),R(\"RendererGroup\",G.RendererGroup)},\n function _(e,r,i,n,t){var o;n();const s=e(176),d=e(175),a=e(303),p=e(302),l=e(113),_=e(178),h=e(283),y=e(286);class c extends s.DataRendererView{get glyph_view(){return this.node_view.glyph}async lazy_initialize(){await super.lazy_initialize(),this.apply_coordinates();const{parent:e}=this,{edge_renderer:r,node_renderer:i}=this.model;this.edge_view=await(0,l.build_view)(r,{parent:e}),this.node_view=await(0,l.build_view)(i,{parent:e})}connect_signals(){super.connect_signals(),this.connect(this.model.layout_provider.change,(()=>{this.apply_coordinates(),this.edge_view.set_data(),this.node_view.set_data(),this.request_render()}))}apply_coordinates(){const{edge_renderer:e,node_renderer:r}=this.model;if(!(e.glyph instanceof h.MultiLine||e.glyph instanceof y.Patches))throw new Error(`${this}.edge_renderer.glyph must be a MultiLine glyph`);if(!(r.glyph instanceof _.XYGlyph))throw new Error(`${this}.node_renderer.glyph must be a XYGlyph glyph`);const i=this.model.layout_provider.edge_coordinates,n=this.model.layout_provider.node_coordinates;e.glyph.properties.xs.internal=!0,e.glyph.properties.ys.internal=!0,r.glyph.properties.x.internal=!0,r.glyph.properties.y.internal=!0,e.glyph.xs={expr:i.x},e.glyph.ys={expr:i.y},r.glyph.x={expr:n.x},r.glyph.y={expr:n.y}}remove(){this.edge_view.remove(),this.node_view.remove(),super.remove()}_render(){this.edge_view.render(),this.node_view.render()}renderer_view(e){if(e instanceof d.GlyphRenderer){if(e==this.edge_view.model)return this.edge_view;if(e==this.node_view.model)return this.node_view}return super.renderer_view(e)}}i.GraphRendererView=c,c.__name__=\"GraphRendererView\";class g extends s.DataRenderer{constructor(e){super(e)}get_selection_manager(){return this.node_renderer.data_source.selection_manager}}i.GraphRenderer=g,o=g,g.__name__=\"GraphRenderer\",o.prototype.default_view=c,o.define((({Ref:e})=>({layout_provider:[e(a.LayoutProvider)],node_renderer:[e(d.GlyphRenderer)],edge_renderer:[e(d.GlyphRenderer)],selection_policy:[e(p.GraphHitTestPolicy),()=>new p.NodesOnly],inspection_policy:[e(p.GraphHitTestPolicy),()=>new p.NodesOnly]})))},\n function _(e,t,n,o,c){o();(0,e(1).__exportStar)(e(74),n),c(\"Selection\",e(72).Selection)},\n function _(a,e,S,o,r){o(),r(\"ServerSentDataSource\",a(342).ServerSentDataSource),r(\"AjaxDataSource\",a(344).AjaxDataSource),r(\"ColumnDataSource\",a(75).ColumnDataSource),r(\"ColumnarDataSource\",a(70).ColumnarDataSource),r(\"CDSView\",a(190).CDSView),r(\"DataSource\",a(71).DataSource),r(\"GeoJSONDataSource\",a(345).GeoJSONDataSource),r(\"WebDataSource\",a(343).WebDataSource)},\n function _(e,t,i,a,s){a();const n=e(343);class r extends n.WebDataSource{constructor(e){super(e),this.initialized=!1}setup(){if(!this.initialized){this.initialized=!0;new EventSource(this.data_url).onmessage=e=>{var t;this.load_data(JSON.parse(e.data),this.mode,null!==(t=this.max_size)&&void 0!==t?t:void 0)}}}}i.ServerSentDataSource=r,r.__name__=\"ServerSentDataSource\"},\n function _(e,t,a,n,r){var s;n();const l=e(75),o=e(20);class c extends l.ColumnDataSource{constructor(e){super(e)}get_column(e){const t=this.data[e];return null!=t?t:[]}get_length(){var e;return null!==(e=super.get_length())&&void 0!==e?e:0}initialize(){super.initialize(),this.setup()}load_data(e,t,a){const{adapter:n}=this;let r;switch(r=null!=n?n.execute(this,{response:e}):e,t){case\"replace\":this.data=r;break;case\"append\":{const e=this.data;for(const t of this.columns()){const n=Array.from(e[t]),s=Array.from(r[t]),l=n.concat(s);r[t]=null!=a?l.slice(-a):l}this.data=r;break}}}}a.WebDataSource=c,s=c,c.__name__=\"WebDataSource\",s.define((({Any:e,Int:t,String:a,Nullable:n})=>({max_size:[n(t),null],mode:[o.UpdateMode,\"replace\"],adapter:[n(e),null],data_url:[a]})))},\n function _(t,e,i,s,a){var n;s();const r=t(343),o=t(20),l=t(19),d=t(13);class h extends r.WebDataSource{constructor(t){super(t),this.interval=null,this.initialized=!1}destroy(){null!=this.interval&&clearInterval(this.interval),super.destroy()}setup(){if(!this.initialized&&(this.initialized=!0,this.get_data(this.mode),null!=this.polling_interval)){const t=()=>this.get_data(this.mode,this.max_size,this.if_modified);this.interval=setInterval(t,this.polling_interval)}}get_data(t,e=null,i=!1){const s=this.prepare_request();s.addEventListener(\"load\",(()=>this.do_load(s,t,null!=e?e:void 0))),s.addEventListener(\"error\",(()=>this.do_error(s))),s.send()}prepare_request(){const t=new XMLHttpRequest;t.open(this.method,this.data_url,!0),t.withCredentials=!1,t.setRequestHeader(\"Content-Type\",this.content_type);const e=this.http_headers;for(const[i,s]of(0,d.entries)(e))t.setRequestHeader(i,s);return t}do_load(t,e,i){if(200===t.status){const s=JSON.parse(t.responseText);this.load_data(s,e,i)}}do_error(t){l.logger.error(`Failed to fetch JSON from ${this.data_url} with code ${t.status}`)}}i.AjaxDataSource=h,n=h,h.__name__=\"AjaxDataSource\",n.define((({Boolean:t,Int:e,String:i,Dict:s,Nullable:a})=>({polling_interval:[a(e),null],content_type:[i,\"application/json\"],http_headers:[s(i),{}],method:[o.HTTPMethod,\"POST\"],if_modified:[t,!1]})))},\n function _(e,t,o,r,n){var s;r();const a=e(70),i=e(19),l=e(9),c=e(13);function _(e){return null!=e?e:NaN}const{hasOwnProperty:g}=Object.prototype;class u extends a.ColumnarDataSource{constructor(e){super(e)}initialize(){super.initialize(),this._update_data()}connect_signals(){super.connect_signals(),this.connect(this.properties.geojson.change,(()=>this._update_data()))}_update_data(){this.data=this.geojson_to_column_data()}_get_new_list_array(e){return(0,l.range)(0,e).map((e=>[]))}_get_new_nan_array(e){return(0,l.range)(0,e).map((e=>NaN))}_add_properties(e,t,o,r){var n;const s=null!==(n=e.properties)&&void 0!==n?n:{};for(const[e,n]of(0,c.entries)(s))g.call(t,e)||(t[e]=this._get_new_nan_array(r)),t[e][o]=_(n)}_add_geometry(e,t,o){function r(e,t){return e.concat([[NaN,NaN,NaN]]).concat(t)}switch(e.type){case\"Point\":{const[r,n,s]=e.coordinates;t.x[o]=r,t.y[o]=n,t.z[o]=_(s);break}case\"LineString\":{const{coordinates:r}=e;for(let e=0;e1&&i.logger.warn(\"Bokeh does not support Polygons with holes in, only exterior ring used.\");const r=e.coordinates[0];for(let e=0;e1&&i.logger.warn(\"Bokeh does not support Polygons with holes in, only exterior ring used.\"),n.push(t[0]);const s=n.reduce(r);for(let e=0;e({geojson:[e]}))),s.internal((({Dict:e,Arrayable:t})=>({data:[e(t),{}]})))},\n function _(e,r,T,o,S){o(),S(\"BBoxTileSource\",e(347).BBoxTileSource),S(\"MercatorTileSource\",e(348).MercatorTileSource),S(\"QUADKEYTileSource\",e(351).QUADKEYTileSource),S(\"TileRenderer\",e(352).TileRenderer),S(\"TileSource\",e(349).TileSource),S(\"TMSTileSource\",e(355).TMSTileSource),S(\"WMTSTileSource\",e(353).WMTSTileSource)},\n function _(e,t,r,o,l){var i;o();const n=e(348);class s extends n.MercatorTileSource{constructor(e){super(e)}get_image_url(e,t,r){const o=this.string_lookup_replace(this.url,this.extra_url_vars);let l,i,n,s;return this.use_latlon?[i,s,l,n]=this.get_tile_geographic_bounds(e,t,r):[i,s,l,n]=this.get_tile_meter_bounds(e,t,r),o.replace(\"{XMIN}\",i.toString()).replace(\"{YMIN}\",s.toString()).replace(\"{XMAX}\",l.toString()).replace(\"{YMAX}\",n.toString())}}r.BBoxTileSource=s,i=s,s.__name__=\"BBoxTileSource\",i.define((({Boolean:e})=>({use_latlon:[e,!1]})))},\n function _(t,e,i,_,s){var r;_();const o=t(349),n=t(9),l=t(350);class u extends o.TileSource{constructor(t){super(t)}initialize(){super.initialize(),this._resolutions=(0,n.range)(this.min_zoom,this.max_zoom+1).map((t=>this.get_resolution(t)))}_computed_initial_resolution(){return null!=this.initial_resolution?this.initial_resolution:2*Math.PI*6378137/this.tile_size}is_valid_tile(t,e,i){return!(!this.wrap_around&&(t<0||t>=2**i))&&!(e<0||e>=2**i)}parent_by_tile_xyz(t,e,i){const _=this.tile_xyz_to_quadkey(t,e,i),s=_.substring(0,_.length-1);return this.quadkey_to_tile_xyz(s)}get_resolution(t){return this._computed_initial_resolution()/2**t}get_resolution_by_extent(t,e,i){return[(t[2]-t[0])/i,(t[3]-t[1])/e]}get_level_by_extent(t,e,i){const _=(t[2]-t[0])/i,s=(t[3]-t[1])/e,r=Math.max(_,s);let o=0;for(const t of this._resolutions){if(r>t){if(0==o)return 0;if(o>0)return o-1}o+=1}return o-1}get_closest_level_by_extent(t,e,i){const _=(t[2]-t[0])/i,s=(t[3]-t[1])/e,r=Math.max(_,s),o=this._resolutions.reduce((function(t,e){return Math.abs(e-r)e?(u=o-s,a*=t):(u*=e,a=n-r)}const h=(u-(o-s))/2,c=(a-(n-r))/2;return[s-h,r-c,o+h,n+c]}tms_to_wmts(t,e,i){return[t,2**i-1-e,i]}wmts_to_tms(t,e,i){return[t,2**i-1-e,i]}pixels_to_meters(t,e,i){const _=this.get_resolution(i);return[t*_-this.x_origin_offset,e*_-this.y_origin_offset]}meters_to_pixels(t,e,i){const _=this.get_resolution(i);return[(t+this.x_origin_offset)/_,(e+this.y_origin_offset)/_]}pixels_to_tile(t,e){let i=Math.ceil(t/this.tile_size);i=0===i?i:i-1;return[i,Math.max(Math.ceil(e/this.tile_size)-1,0)]}pixels_to_raster(t,e,i){return[t,(this.tile_size<=l;t--)for(let i=n;i<=u;i++)this.is_valid_tile(i,t,e)&&h.push([i,t,e,this.get_tile_meter_bounds(i,t,e)]);return this.sort_tiles_from_center(h,[n,l,u,a]),h}quadkey_to_tile_xyz(t){let e=0,i=0;const _=t.length;for(let s=_;s>0;s--){const r=1<0;s--){const i=1<0;)if(s=s.substring(0,s.length-1),[t,e,i]=this.quadkey_to_tile_xyz(s),[t,e,i]=this.denormalize_xyz(t,e,i,_),this.tiles.has(this.tile_xyz_to_key(t,e,i)))return[t,e,i];return[0,0,0]}normalize_xyz(t,e,i){if(this.wrap_around){const _=2**i;return[(t%_+_)%_,e,i]}return[t,e,i]}denormalize_xyz(t,e,i,_){return[t+_*2**i,e,i]}denormalize_meters(t,e,i,_){return[t+2*_*Math.PI*6378137,e]}calculate_world_x_by_tile_xyz(t,e,i){return Math.floor(t/2**i)}}i.MercatorTileSource=u,r=u,u.__name__=\"MercatorTileSource\",r.define((({Boolean:t})=>({snap_to_zoom:[t,!1],wrap_around:[t,!0]}))),r.override({x_origin_offset:20037508.34,y_origin_offset:20037508.34,initial_resolution:156543.03392804097})},\n function _(e,t,r,i,n){var l;i();const a=e(53),s=e(13);class c extends a.Model{constructor(e){super(e)}initialize(){super.initialize(),this.tiles=new Map,this._normalize_case()}connect_signals(){super.connect_signals(),this.connect(this.change,(()=>this._clear_cache()))}string_lookup_replace(e,t){let r=e;for(const[e,i]of(0,s.entries)(t))r=r.replace(`{${e}}`,i);return r}_normalize_case(){const e=this.url.replace(\"{x}\",\"{X}\").replace(\"{y}\",\"{Y}\").replace(\"{z}\",\"{Z}\").replace(\"{q}\",\"{Q}\").replace(\"{xmin}\",\"{XMIN}\").replace(\"{ymin}\",\"{YMIN}\").replace(\"{xmax}\",\"{XMAX}\").replace(\"{ymax}\",\"{YMAX}\");this.url=e}_clear_cache(){this.tiles=new Map}tile_xyz_to_key(e,t,r){return`${e}:${t}:${r}`}key_to_tile_xyz(e){const[t,r,i]=e.split(\":\").map((e=>parseInt(e)));return[t,r,i]}sort_tiles_from_center(e,t){const[r,i,n,l]=t,a=(n-r)/2+r,s=(l-i)/2+i;e.sort((function(e,t){return Math.sqrt((a-e[0])**2+(s-e[1])**2)-Math.sqrt((a-t[0])**2+(s-t[1])**2)}))}get_image_url(e,t,r){return this.string_lookup_replace(this.url,this.extra_url_vars).replace(\"{X}\",e.toString()).replace(\"{Y}\",t.toString()).replace(\"{Z}\",r.toString())}}r.TileSource=c,l=c,c.__name__=\"TileSource\",l.define((({Number:e,String:t,Dict:r,Nullable:i})=>({url:[t,\"\"],tile_size:[e,256],max_zoom:[e,30],min_zoom:[e,0],extra_url_vars:[r(t),{}],attribution:[t,\"\"],x_origin_offset:[e],y_origin_offset:[e],initial_resolution:[i(e),null]})))},\n function _(t,e,r,n,o){n();const c=t(78);function _(t,e){return c.wgs84_mercator.compute(t,e)}function g(t,e){return c.wgs84_mercator.invert(t,e)}r.geographic_to_meters=_,r.meters_to_geographic=g,r.geographic_extent_to_meters=function(t){const[e,r,n,o]=t,[c,g]=_(e,r),[i,u]=_(n,o);return[c,g,i,u]},r.meters_extent_to_geographic=function(t){const[e,r,n,o]=t,[c,_]=g(e,r),[i,u]=g(n,o);return[c,_,i,u]}},\n function _(e,t,r,s,_){s();const o=e(348);class c extends o.MercatorTileSource{constructor(e){super(e)}get_image_url(e,t,r){const s=this.string_lookup_replace(this.url,this.extra_url_vars),[_,o,c]=this.tms_to_wmts(e,t,r),i=this.tile_xyz_to_quadkey(_,o,c);return s.replace(\"{Q}\",i)}}r.QUADKEYTileSource=c,c.__name__=\"QUADKEYTileSource\"},\n function _(t,e,i,s,_){s();const n=t(1);var a;const o=t(349),r=t(353),h=t(41),l=t(58),d=t(43),m=t(136),c=t(9),u=t(8),p=(0,n.__importStar)(t(354));class g extends h.RendererView{initialize(){this._tiles=[],super.initialize()}connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.request_render())),this.connect(this.model.tile_source.change,(()=>this.request_render()))}remove(){null!=this.attribution_el&&(0,d.removeElement)(this.attribution_el),super.remove()}styles(){return[...super.styles(),p.default]}get_extent(){return[this.x_range.start,this.y_range.start,this.x_range.end,this.y_range.end]}get map_plot(){return this.plot_model}get map_canvas(){return this.layer.ctx}get map_frame(){return this.plot_view.frame}get x_range(){return this.map_plot.x_range}get y_range(){return this.map_plot.y_range}_set_data(){this.extent=this.get_extent(),this._last_height=void 0,this._last_width=void 0}_update_attribution(){null!=this.attribution_el&&(0,d.removeElement)(this.attribution_el);const{attribution:t}=this.model.tile_source;if((0,u.isString)(t)&&t.length>0){const{layout:e,frame:i}=this.plot_view,s=e.bbox.width-i.bbox.right,_=e.bbox.height-i.bbox.bottom,n=i.bbox.width;this.attribution_el=(0,d.div)({class:p.tile_attribution,style:{position:\"absolute\",right:`${s}px`,bottom:`${_}px`,\"max-width\":n-4+\"px\",padding:\"2px\",\"background-color\":\"rgba(255,255,255,0.5)\",\"font-size\":\"9px\",\"line-height\":\"1.05\",\"white-space\":\"nowrap\",overflow:\"hidden\",\"text-overflow\":\"ellipsis\"}}),this.plot_view.canvas_view.add_event(this.attribution_el),this.attribution_el.innerHTML=t,this.attribution_el.title=this.attribution_el.textContent.replace(/\\s*\\n\\s*/g,\" \")}}_map_data(){this.initial_extent=this.get_extent();const t=this.model.tile_source.get_level_by_extent(this.initial_extent,this.map_frame.bbox.height,this.map_frame.bbox.width),e=this.model.tile_source.snap_to_zoom_level(this.initial_extent,this.map_frame.bbox.height,this.map_frame.bbox.width,t);this.x_range.start=e[0],this.y_range.start=e[1],this.x_range.end=e[2],this.y_range.end=e[3],this.x_range instanceof l.Range1d&&(this.x_range.reset_start=e[0],this.x_range.reset_end=e[2]),this.y_range instanceof l.Range1d&&(this.y_range.reset_start=e[1],this.y_range.reset_end=e[3]),this._update_attribution()}_create_tile(t,e,i,s,_=!1){const n=this.model.tile_source.tile_xyz_to_quadkey(t,e,i),a=this.model.tile_source.tile_xyz_to_key(t,e,i);if(this.model.tile_source.tiles.has(a))return;const[o,r,h]=this.model.tile_source.normalize_xyz(t,e,i),l=this.model.tile_source.get_image_url(o,r,h),d={img:void 0,tile_coords:[t,e,i],normalized_coords:[o,r,h],quadkey:n,cache_key:a,bounds:s,loaded:!1,finished:!1,x_coord:s[0],y_coord:s[3]};this.model.tile_source.tiles.set(a,d),this._tiles.push(d),new m.ImageLoader(l,{loaded:t=>{Object.assign(d,{img:t,loaded:!0}),_?(d.finished=!0,this.notify_finished()):this.request_render()},failed(){d.finished=!0}})}_enforce_aspect_ratio(){if(this._last_height!==this.map_frame.bbox.height||this._last_width!==this.map_frame.bbox.width){const t=this.get_extent(),e=this.model.tile_source.get_level_by_extent(t,this.map_frame.bbox.height,this.map_frame.bbox.width),i=this.model.tile_source.snap_to_zoom_level(t,this.map_frame.bbox.height,this.map_frame.bbox.width,e);this.x_range.setv({start:i[0],end:i[2]}),this.y_range.setv({start:i[1],end:i[3]}),this.extent=i,this._last_height=this.map_frame.bbox.height,this._last_width=this.map_frame.bbox.width}}has_finished(){if(!super.has_finished())return!1;if(0==this._tiles.length)return!1;for(const t of this._tiles)if(!t.finished)return!1;return!0}_render(){null==this.map_initialized&&(this._set_data(),this._map_data(),this.map_initialized=!0),this._enforce_aspect_ratio(),this._update(),null!=this.prefetch_timer&&clearTimeout(this.prefetch_timer),this.prefetch_timer=setTimeout(this._prefetch_tiles.bind(this),500),this.has_finished()&&this.notify_finished()}_draw_tile(t){const e=this.model.tile_source.tiles.get(t);if(null!=e&&e.loaded){const[[t],[i]]=this.coordinates.map_to_screen([e.bounds[0]],[e.bounds[3]]),[[s],[_]]=this.coordinates.map_to_screen([e.bounds[2]],[e.bounds[1]]),n=s-t,a=_-i,o=t,r=i,h=this.map_canvas.getImageSmoothingEnabled();this.map_canvas.setImageSmoothingEnabled(this.model.smoothing),this.map_canvas.drawImage(e.img,o,r,n,a),this.map_canvas.setImageSmoothingEnabled(h),e.finished=!0}}_set_rect(){const t=this.plot_model.outline_line_width,e=this.map_frame.bbox.left+t/2,i=this.map_frame.bbox.top+t/2,s=this.map_frame.bbox.width-t,_=this.map_frame.bbox.height-t;this.map_canvas.rect(e,i,s,_),this.map_canvas.clip()}_render_tiles(t){this.map_canvas.save(),this._set_rect(),this.map_canvas.globalAlpha=this.model.alpha;for(const e of t)this._draw_tile(e);this.map_canvas.restore()}_prefetch_tiles(){const{tile_source:t}=this.model,e=this.get_extent(),i=this.map_frame.bbox.height,s=this.map_frame.bbox.width,_=this.model.tile_source.get_level_by_extent(e,i,s),n=this.model.tile_source.get_tiles_by_extent(e,_);for(let e=0,i=Math.min(10,n.length);ei&&(s=this.extent,o=i,r=!0),r&&(this.x_range.setv({start:s[0],end:s[2]}),this.y_range.setv({start:s[1],end:s[3]})),this.extent=s;const h=t.get_tiles_by_extent(s,o),l=[],d=[],m=[],u=[];for(const e of h){const[i,s,n]=e,a=t.tile_xyz_to_key(i,s,n),o=t.tiles.get(a);if(null!=o&&o.loaded)d.push(a);else if(this.model.render_parents){const[e,a,o]=t.get_closest_parent_by_tile_xyz(i,s,n),r=t.tile_xyz_to_key(e,a,o),h=t.tiles.get(r);if(null!=h&&h.loaded&&!(0,c.includes)(m,r)&&m.push(r),_){const e=t.children_by_tile_xyz(i,s,n);for(const[i,s,_]of e){const e=t.tile_xyz_to_key(i,s,_);t.tiles.has(e)&&u.push(e)}}}null==o&&l.push(e)}this._render_tiles(m),this._render_tiles(u),this._render_tiles(d),null!=this.render_timer&&clearTimeout(this.render_timer),this.render_timer=setTimeout((()=>this._fetch_tiles(l)),65)}}i.TileRendererView=g,g.__name__=\"TileRendererView\";class b extends h.Renderer{constructor(t){super(t)}}i.TileRenderer=b,a=b,b.__name__=\"TileRenderer\",a.prototype.default_view=g,a.define((({Boolean:t,Number:e,Ref:i})=>({alpha:[e,1],smoothing:[t,!0],tile_source:[i(o.TileSource),()=>new r.WMTSTileSource],render_parents:[t,!0]}))),a.override({level:\"image\"})},\n function _(t,e,r,o,s){o();const c=t(348);class i extends c.MercatorTileSource{constructor(t){super(t)}get_image_url(t,e,r){const o=this.string_lookup_replace(this.url,this.extra_url_vars),[s,c,i]=this.tms_to_wmts(t,e,r);return o.replace(\"{X}\",s.toString()).replace(\"{Y}\",c.toString()).replace(\"{Z}\",i.toString())}}r.WMTSTileSource=i,i.__name__=\"WMTSTileSource\"},\n function _(t,o,i,b,r){b(),i.root=\"bk-root\",i.tile_attribution=\"bk-tile-attribution\",i.default=\".bk-root .bk-tile-attribution a{color:black;}\"},\n function _(e,r,t,c,o){c();const i=e(348);class l extends i.MercatorTileSource{constructor(e){super(e)}get_image_url(e,r,t){return this.string_lookup_replace(this.url,this.extra_url_vars).replace(\"{X}\",e.toString()).replace(\"{Y}\",r.toString()).replace(\"{Z}\",t.toString())}}t.TMSTileSource=l,l.__name__=\"TMSTileSource\"},\n function _(e,t,u,a,r){a(),r(\"CanvasTexture\",e(357).CanvasTexture),r(\"ImageURLTexture\",e(359).ImageURLTexture),r(\"Texture\",e(358).Texture)},\n function _(t,e,n,c,s){var r;c();const o=t(358),a=t(34);class u extends o.Texture{constructor(t){super(t)}get func(){const t=(0,a.use_strict)(this.code);return new Function(\"ctx\",\"color\",\"scale\",\"weight\",t)}get_pattern(t,e,n){const c=document.createElement(\"canvas\");c.width=e,c.height=e;const s=c.getContext(\"2d\");return this.func.call(this,s,t,e,n),c}}n.CanvasTexture=u,r=u,u.__name__=\"CanvasTexture\",r.define((({String:t})=>({code:[t]})))},\n function _(e,t,n,r,o){var i;r();const s=e(53),u=e(20);class c extends s.Model{constructor(e){super(e)}}n.Texture=c,i=c,c.__name__=\"Texture\",i.define((()=>({repetition:[u.TextureRepetition,\"repeat\"]})))},\n function _(e,t,i,r,n){var a;r();const s=e(358),o=e(136);class u extends s.Texture{constructor(e){super(e)}initialize(){super.initialize(),this._loader=new o.ImageLoader(this.url)}get_pattern(e,t,i){const{_loader:r}=this;return this._loader.finished?r.image:r.promise}}i.ImageURLTexture=u,a=u,u.__name__=\"ImageURLTexture\",a.define((({String:e})=>({url:[e]})))},\n function _(o,l,T,e,t){e(),t(\"ActionTool\",o(238).ActionTool),t(\"CustomAction\",o(361).CustomAction),t(\"HelpTool\",o(239).HelpTool),t(\"RedoTool\",o(362).RedoTool),t(\"ResetTool\",o(363).ResetTool),t(\"SaveTool\",o(364).SaveTool),t(\"UndoTool\",o(365).UndoTool),t(\"ZoomInTool\",o(366).ZoomInTool),t(\"ZoomOutTool\",o(369).ZoomOutTool),t(\"ButtonTool\",o(224).ButtonTool),t(\"EditTool\",o(370).EditTool),t(\"BoxEditTool\",o(371).BoxEditTool),t(\"FreehandDrawTool\",o(372).FreehandDrawTool),t(\"PointDrawTool\",o(373).PointDrawTool),t(\"PolyDrawTool\",o(374).PolyDrawTool),t(\"PolyTool\",o(375).PolyTool),t(\"PolyEditTool\",o(376).PolyEditTool),t(\"BoxSelectTool\",o(377).BoxSelectTool),t(\"BoxZoomTool\",o(379).BoxZoomTool),t(\"GestureTool\",o(223).GestureTool),t(\"LassoSelectTool\",o(380).LassoSelectTool),t(\"LineEditTool\",o(382).LineEditTool),t(\"PanTool\",o(384).PanTool),t(\"PolySelectTool\",o(381).PolySelectTool),t(\"RangeTool\",o(385).RangeTool),t(\"SelectTool\",o(378).SelectTool),t(\"TapTool\",o(386).TapTool),t(\"WheelPanTool\",o(387).WheelPanTool),t(\"WheelZoomTool\",o(388).WheelZoomTool),t(\"CrosshairTool\",o(389).CrosshairTool),t(\"CustomJSHover\",o(390).CustomJSHover),t(\"HoverTool\",o(391).HoverTool),t(\"InspectTool\",o(232).InspectTool),t(\"Tool\",o(222).Tool),t(\"ToolProxy\",o(394).ToolProxy),t(\"Toolbar\",o(221).Toolbar),t(\"ToolbarBase\",o(233).ToolbarBase),t(\"ProxyToolbar\",o(395).ProxyToolbar),t(\"ToolbarBox\",o(395).ToolbarBox)},\n function _(t,o,e,s,n){var c;s();const i=t(238);class u extends i.ActionToolButtonView{css_classes(){return super.css_classes().concat(\"bk-toolbar-button-custom-action\")}}e.CustomActionButtonView=u,u.__name__=\"CustomActionButtonView\";class l extends i.ActionToolView{doit(){var t;null===(t=this.model.callback)||void 0===t||t.execute(this.model)}}e.CustomActionView=l,l.__name__=\"CustomActionView\";class a extends i.ActionTool{constructor(t){super(t),this.tool_name=\"Custom Action\",this.button_view=u}}e.CustomAction=a,c=a,a.__name__=\"CustomAction\",c.prototype.default_view=l,c.define((({Any:t,String:o,Nullable:e})=>({callback:[e(t)],icon:[o]}))),c.override({description:\"Perform a Custom Action\"})},\n function _(e,o,t,i,s){var n;i();const l=e(238),_=e(228);class d extends l.ActionToolView{connect_signals(){super.connect_signals(),this.connect(this.plot_view.state.changed,(()=>this.model.disabled=!this.plot_view.state.can_redo))}doit(){const e=this.plot_view.state.redo();null!=(null==e?void 0:e.range)&&this.plot_view.trigger_ranges_update_event()}}t.RedoToolView=d,d.__name__=\"RedoToolView\";class a extends l.ActionTool{constructor(e){super(e),this.tool_name=\"Redo\",this.icon=_.tool_icon_redo}}t.RedoTool=a,n=a,a.__name__=\"RedoTool\",n.prototype.default_view=d,n.override({disabled:!0}),n.register_alias(\"redo\",(()=>new a))},\n function _(e,o,t,s,i){var _;s();const n=e(238),l=e(228);class c extends n.ActionToolView{doit(){this.plot_view.reset()}}t.ResetToolView=c,c.__name__=\"ResetToolView\";class r extends n.ActionTool{constructor(e){super(e),this.tool_name=\"Reset\",this.icon=l.tool_icon_reset}}t.ResetTool=r,_=r,r.__name__=\"ResetTool\",_.prototype.default_view=c,_.register_alias(\"reset\",(()=>new r))},\n function _(e,o,t,a,i){var s;a();const c=e(238),n=e(228);class l extends c.ActionToolView{async copy(){const e=await this.plot_view.to_blob(),o=new ClipboardItem({[e.type]:Promise.resolve(e)});await navigator.clipboard.write([o])}async save(e){const o=await this.plot_view.to_blob(),t=document.createElement(\"a\");t.href=URL.createObjectURL(o),t.download=e,t.target=\"_blank\",t.dispatchEvent(new MouseEvent(\"click\"))}doit(e=\"save\"){switch(e){case\"save\":this.save(\"bokeh_plot\");break;case\"copy\":this.copy()}}}t.SaveToolView=l,l.__name__=\"SaveToolView\";class r extends c.ActionTool{constructor(e){super(e),this.tool_name=\"Save\",this.icon=n.tool_icon_save}get menu(){return[{icon:\"bk-tool-icon-copy-to-clipboard\",tooltip:\"Copy image to clipboard\",if:()=>\"undefined\"!=typeof ClipboardItem,handler:()=>{this.do.emit(\"copy\")}}]}}t.SaveTool=r,s=r,r.__name__=\"SaveTool\",s.prototype.default_view=l,s.register_alias(\"save\",(()=>new r))},\n function _(o,e,t,n,i){var s;n();const l=o(238),_=o(228);class d extends l.ActionToolView{connect_signals(){super.connect_signals(),this.connect(this.plot_view.state.changed,(()=>this.model.disabled=!this.plot_view.state.can_undo))}doit(){const o=this.plot_view.state.undo();null!=(null==o?void 0:o.range)&&this.plot_view.trigger_ranges_update_event()}}t.UndoToolView=d,d.__name__=\"UndoToolView\";class a extends l.ActionTool{constructor(o){super(o),this.tool_name=\"Undo\",this.icon=_.tool_icon_undo}}t.UndoTool=a,s=a,a.__name__=\"UndoTool\",s.prototype.default_view=d,s.override({disabled:!0}),s.register_alias(\"undo\",(()=>new a))},\n function _(o,n,e,i,s){var t;i();const _=o(367),m=o(228);class a extends _.ZoomBaseToolView{}e.ZoomInToolView=a,a.__name__=\"ZoomInToolView\";class l extends _.ZoomBaseTool{constructor(o){super(o),this.sign=1,this.tool_name=\"Zoom In\",this.icon=m.tool_icon_zoom_in}}e.ZoomInTool=l,t=l,l.__name__=\"ZoomInTool\",t.prototype.default_view=a,t.register_alias(\"zoom_in\",(()=>new l({dimensions:\"both\"}))),t.register_alias(\"xzoom_in\",(()=>new l({dimensions:\"width\"}))),t.register_alias(\"yzoom_in\",(()=>new l({dimensions:\"height\"})))},\n function _(o,t,e,i,s){var n;i();const a=o(238),_=o(20),l=o(368);class m extends a.ActionToolView{doit(){var o;const t=this.plot_view.frame,e=this.model.dimensions,i=\"width\"==e||\"both\"==e,s=\"height\"==e||\"both\"==e,n=(0,l.scale_range)(t,this.model.sign*this.model.factor,i,s);this.plot_view.state.push(\"zoom_out\",{range:n}),this.plot_view.update_range(n,{scrolling:!0,maintain_focus:this.model.maintain_focus}),null===(o=this.model.document)||void 0===o||o.interactive_start(this.plot_model),this.plot_view.trigger_ranges_update_event()}}e.ZoomBaseToolView=m,m.__name__=\"ZoomBaseToolView\";class h extends a.ActionTool{constructor(o){super(o),this.maintain_focus=!0}get tooltip(){return this._get_dim_tooltip(this.dimensions)}}e.ZoomBaseTool=h,n=h,h.__name__=\"ZoomBaseTool\",n.define((({Percent:o})=>({factor:[o,.1],dimensions:[_.Dimensions,\"both\"]})))},\n function _(n,t,o,r,s){r();const c=n(10);function e(n,t,o){const[r,s]=[n.start,n.end],c=null!=o?o:(s+r)/2;return[r-(r-c)*t,s-(s-c)*t]}function a(n,[t,o]){const r=new Map;for(const[s,c]of n){const[n,e]=c.r_invert(t,o);r.set(s,{start:n,end:e})}return r}o.scale_highlow=e,o.get_info=a,o.scale_range=function(n,t,o=!0,r=!0,s){t=(0,c.clamp)(t,-.9,.9);const l=o?t:0,[u,i]=e(n.bbox.h_range,l,null!=s?s.x:void 0),_=a(n.x_scales,[u,i]),f=r?t:0,[g,x]=e(n.bbox.v_range,f,null!=s?s.y:void 0);return{xrs:_,yrs:a(n.y_scales,[g,x]),factor:t}}},\n function _(o,e,t,i,s){var n;i();const _=o(367),a=o(228);class m extends _.ZoomBaseToolView{}t.ZoomOutToolView=m,m.__name__=\"ZoomOutToolView\";class l extends _.ZoomBaseTool{constructor(o){super(o),this.sign=-1,this.tool_name=\"Zoom Out\",this.icon=a.tool_icon_zoom_out}}t.ZoomOutTool=l,n=l,l.__name__=\"ZoomOutTool\",n.prototype.default_view=m,n.define((({Boolean:o})=>({maintain_focus:[o,!0]}))),n.register_alias(\"zoom_out\",(()=>new l({dimensions:\"both\"}))),n.register_alias(\"xzoom_out\",(()=>new l({dimensions:\"width\"}))),n.register_alias(\"yzoom_out\",(()=>new l({dimensions:\"height\"})))},\n function _(e,t,s,o,n){var r;o();const i=e(9),c=e(8),a=e(11),_=e(175),l=e(223);class d extends l.GestureToolView{constructor(){super(...arguments),this._mouse_in_frame=!0}_select_mode(e){const{shiftKey:t,ctrlKey:s}=e;return t||s?t&&!s?\"append\":!t&&s?\"intersect\":t&&s?\"subtract\":void(0,a.unreachable)():\"replace\"}_move_enter(e){this._mouse_in_frame=!0}_move_exit(e){this._mouse_in_frame=!1}_map_drag(e,t,s){if(!this.plot_view.frame.bbox.contains(e,t))return null;const o=this.plot_view.renderer_view(s);if(null==o)return null;return[o.coordinates.x_scale.invert(e),o.coordinates.y_scale.invert(t)]}_delete_selected(e){const t=e.data_source,s=t.selected.indices;s.sort();for(const e of t.columns()){const o=t.get_array(e);for(let e=0;e({custom_icon:[n(t),null],empty_value:[e],renderers:[s(o(_.GlyphRenderer)),[]]})))},\n function _(e,t,s,i,_){var o;i();const n=e(43),a=e(20),d=e(370),l=e(228);class r extends d.EditToolView{_tap(e){null==this._draw_basepoint&&null==this._basepoint&&this._select_event(e,this._select_mode(e),this.model.renderers)}_keyup(e){if(this.model.active&&this._mouse_in_frame)for(const t of this.model.renderers)if(e.keyCode===n.Keys.Backspace)this._delete_selected(t);else if(e.keyCode==n.Keys.Esc){t.data_source.selection_manager.clear()}}_set_extent([e,t],[s,i],_,o=!1){const n=this.model.renderers[0],a=this.plot_view.renderer_view(n);if(null==a)return;const d=n.glyph,l=n.data_source,[r,h]=a.coordinates.x_scale.r_invert(e,t),[p,u]=a.coordinates.y_scale.r_invert(s,i),[c,m]=[(r+h)/2,(p+u)/2],[f,b]=[h-r,u-p],[y,x]=[d.x.field,d.y.field],[w,v]=[d.width.field,d.height.field];if(_)this._pop_glyphs(l,this.model.num_objects),y&&l.get_array(y).push(c),x&&l.get_array(x).push(m),w&&l.get_array(w).push(f),v&&l.get_array(v).push(b),this._pad_empty_columns(l,[y,x,w,v]);else{const e=l.data[y].length-1;y&&(l.data[y][e]=c),x&&(l.data[x][e]=m),w&&(l.data[w][e]=f),v&&(l.data[v][e]=b)}this._emit_cds_changes(l,!0,!1,o)}_update_box(e,t=!1,s=!1){if(null==this._draw_basepoint)return;const i=[e.sx,e.sy],_=this.plot_view.frame,o=this.model.dimensions,n=this.model._get_dim_limits(this._draw_basepoint,i,_,o);if(null!=n){const[e,i]=n;this._set_extent(e,i,t,s)}}_doubletap(e){this.model.active&&(null!=this._draw_basepoint?(this._update_box(e,!1,!0),this._draw_basepoint=null):(this._draw_basepoint=[e.sx,e.sy],this._select_event(e,\"append\",this.model.renderers),this._update_box(e,!0,!1)))}_move(e){this._update_box(e,!1,!1)}_pan_start(e){if(e.shiftKey){if(null!=this._draw_basepoint)return;this._draw_basepoint=[e.sx,e.sy],this._update_box(e,!0,!1)}else{if(null!=this._basepoint)return;this._select_event(e,\"append\",this.model.renderers),this._basepoint=[e.sx,e.sy]}}_pan(e,t=!1,s=!1){if(e.shiftKey){if(null==this._draw_basepoint)return;this._update_box(e,t,s)}else{if(null==this._basepoint)return;this._drag_points(e,this.model.renderers)}}_pan_end(e){if(this._pan(e,!1,!0),e.shiftKey)this._draw_basepoint=null;else{this._basepoint=null;for(const e of this.model.renderers)this._emit_cds_changes(e.data_source,!1,!0,!0)}}}s.BoxEditToolView=r,r.__name__=\"BoxEditToolView\";class h extends d.EditTool{constructor(e){super(e),this.tool_name=\"Box Edit Tool\",this.icon=l.tool_icon_box_edit,this.event_type=[\"tap\",\"pan\",\"move\"],this.default_order=1}}s.BoxEditTool=h,o=h,h.__name__=\"BoxEditTool\",o.prototype.default_view=r,o.define((({Int:e})=>({dimensions:[a.Dimensions,\"both\"],num_objects:[e,0]})))},\n function _(e,t,a,s,r){var _;s();const d=e(43),o=e(8),n=e(370),i=e(228);class l extends n.EditToolView{_draw(e,t,a=!1){if(!this.model.active)return;const s=this.model.renderers[0],r=this._map_drag(e.sx,e.sy,s);if(null==r)return;const[_,d]=r,n=s.data_source,i=s.glyph,[l,h]=[i.xs.field,i.ys.field];if(\"new\"==t)this._pop_glyphs(n,this.model.num_objects),l&&n.get_array(l).push([_]),h&&n.get_array(h).push([d]),this._pad_empty_columns(n,[l,h]);else if(\"add\"==t){if(l){const e=n.data[l].length-1;let t=n.get_array(l)[e];(0,o.isArray)(t)||(t=Array.from(t),n.data[l][e]=t),t.push(_)}if(h){const e=n.data[h].length-1;let t=n.get_array(h)[e];(0,o.isArray)(t)||(t=Array.from(t),n.data[h][e]=t),t.push(d)}}this._emit_cds_changes(n,!0,!0,a)}_pan_start(e){this._draw(e,\"new\")}_pan(e){this._draw(e,\"add\")}_pan_end(e){this._draw(e,\"add\",!0)}_tap(e){this._select_event(e,this._select_mode(e),this.model.renderers)}_keyup(e){if(this.model.active&&this._mouse_in_frame)for(const t of this.model.renderers)e.keyCode===d.Keys.Esc?t.data_source.selection_manager.clear():e.keyCode===d.Keys.Backspace&&this._delete_selected(t)}}a.FreehandDrawToolView=l,l.__name__=\"FreehandDrawToolView\";class h extends n.EditTool{constructor(e){super(e),this.tool_name=\"Freehand Draw Tool\",this.icon=i.tool_icon_freehand_draw,this.event_type=[\"pan\",\"tap\"],this.default_order=3}}a.FreehandDrawTool=h,_=h,h.__name__=\"FreehandDrawTool\",_.prototype.default_view=l,_.define((({Int:e})=>({num_objects:[e,0]}))),_.register_alias(\"freehand_draw\",(()=>new h))},\n function _(e,t,s,o,a){var i;o();const n=e(43),_=e(370),r=e(228);class d extends _.EditToolView{_tap(e){if(this._select_event(e,this._select_mode(e),this.model.renderers).length||!this.model.add)return;const t=this.model.renderers[0],s=this._map_drag(e.sx,e.sy,t);if(null==s)return;const o=t.glyph,a=t.data_source,[i,n]=[o.x.field,o.y.field],[_,r]=s;this._pop_glyphs(a,this.model.num_objects),i&&a.get_array(i).push(_),n&&a.get_array(n).push(r),this._pad_empty_columns(a,[i,n]),a.change.emit(),a.data=a.data,a.properties.data.change.emit()}_keyup(e){if(this.model.active&&this._mouse_in_frame)for(const t of this.model.renderers)e.keyCode===n.Keys.Backspace?this._delete_selected(t):e.keyCode==n.Keys.Esc&&t.data_source.selection_manager.clear()}_pan_start(e){this.model.drag&&(this._select_event(e,\"append\",this.model.renderers),this._basepoint=[e.sx,e.sy])}_pan(e){this.model.drag&&null!=this._basepoint&&this._drag_points(e,this.model.renderers)}_pan_end(e){if(this.model.drag){this._pan(e);for(const e of this.model.renderers)this._emit_cds_changes(e.data_source,!1,!0,!0);this._basepoint=null}}}s.PointDrawToolView=d,d.__name__=\"PointDrawToolView\";class l extends _.EditTool{constructor(e){super(e),this.tool_name=\"Point Draw Tool\",this.icon=r.tool_icon_point_draw,this.event_type=[\"tap\",\"pan\",\"move\"],this.default_order=2}}s.PointDrawTool=l,i=l,l.__name__=\"PointDrawTool\",i.prototype.default_view=d,i.define((({Boolean:e,Int:t})=>({add:[e,!0],drag:[e,!0],num_objects:[t,0]})))},\n function _(e,t,s,i,a){var r;i();const o=e(43),n=e(8),d=e(375),_=e(228);class h extends d.PolyToolView{constructor(){super(...arguments),this._drawing=!1,this._initialized=!1}_tap(e){this._drawing?this._draw(e,\"add\",!0):this._select_event(e,this._select_mode(e),this.model.renderers)}_draw(e,t,s=!1){const i=this.model.renderers[0],a=this._map_drag(e.sx,e.sy,i);if(this._initialized||this.activate(),null==a)return;const[r,o]=this._snap_to_vertex(e,...a),d=i.data_source,_=i.glyph,[h,l]=[_.xs.field,_.ys.field];if(\"new\"==t)this._pop_glyphs(d,this.model.num_objects),h&&d.get_array(h).push([r,r]),l&&d.get_array(l).push([o,o]),this._pad_empty_columns(d,[h,l]);else if(\"edit\"==t){if(h){const e=d.data[h][d.data[h].length-1];e[e.length-1]=r}if(l){const e=d.data[l][d.data[l].length-1];e[e.length-1]=o}}else if(\"add\"==t){if(h){const e=d.data[h].length-1;let t=d.get_array(h)[e];const s=t[t.length-1];t[t.length-1]=r,(0,n.isArray)(t)||(t=Array.from(t),d.data[h][e]=t),t.push(s)}if(l){const e=d.data[l].length-1;let t=d.get_array(l)[e];const s=t[t.length-1];t[t.length-1]=o,(0,n.isArray)(t)||(t=Array.from(t),d.data[l][e]=t),t.push(s)}}this._emit_cds_changes(d,!0,!1,s)}_show_vertices(){if(!this.model.active)return;const e=[],t=[];for(let s=0;sthis._show_vertices()))}this._initialized=!0}}deactivate(){this._drawing&&(this._remove(),this._drawing=!1),this.model.vertex_renderer&&this._hide_vertices()}}s.PolyDrawToolView=h,h.__name__=\"PolyDrawToolView\";class l extends d.PolyTool{constructor(e){super(e),this.tool_name=\"Polygon Draw Tool\",this.icon=_.tool_icon_poly_draw,this.event_type=[\"pan\",\"tap\",\"move\"],this.default_order=3}}s.PolyDrawTool=l,r=l,l.__name__=\"PolyDrawTool\",r.prototype.default_view=h,r.define((({Boolean:e,Int:t})=>({drag:[e,!0],num_objects:[t,0]})))},\n function _(e,r,t,s,o){var _;s();const d=e(8),i=e(370);class l extends i.EditToolView{_set_vertices(e,r){const t=this.model.vertex_renderer.glyph,s=this.model.vertex_renderer.data_source,[o,_]=[t.x.field,t.y.field];o&&((0,d.isArray)(e)?s.data[o]=e:t.x={value:e}),_&&((0,d.isArray)(r)?s.data[_]=r:t.y={value:r}),this._emit_cds_changes(s,!0,!0,!1)}_hide_vertices(){this._set_vertices([],[])}_snap_to_vertex(e,r,t){if(this.model.vertex_renderer){const s=this._select_event(e,\"replace\",[this.model.vertex_renderer]),o=this.model.vertex_renderer.data_source,_=this.model.vertex_renderer.glyph,[d,i]=[_.x.field,_.y.field];if(s.length){const e=o.selected.indices[0];d&&(r=o.data[d][e]),i&&(t=o.data[i][e]),o.selection_manager.clear()}}return[r,t]}}t.PolyToolView=l,l.__name__=\"PolyToolView\";class n extends i.EditTool{constructor(e){super(e)}}t.PolyTool=n,_=n,n.__name__=\"PolyTool\",_.define((({AnyRef:e})=>({vertex_renderer:[e()]})))},\n function _(e,t,s,r,i){var _;r();const d=e(43),n=e(8),l=e(375),a=e(228);class c extends l.PolyToolView{constructor(){super(...arguments),this._drawing=!1,this._cur_index=null}_doubletap(e){if(!this.model.active)return;const t=this._map_drag(e.sx,e.sy,this.model.vertex_renderer);if(null==t)return;const[s,r]=t,i=this._select_event(e,\"replace\",[this.model.vertex_renderer]),_=this.model.vertex_renderer.data_source,d=this.model.vertex_renderer.glyph,[n,l]=[d.x.field,d.y.field];if(i.length&&null!=this._selected_renderer){const e=_.selected.indices[0];this._drawing?(this._drawing=!1,_.selection_manager.clear()):(_.selected.indices=[e+1],n&&_.get_array(n).splice(e+1,0,s),l&&_.get_array(l).splice(e+1,0,r),this._drawing=!0),_.change.emit(),this._emit_cds_changes(this._selected_renderer.data_source)}else this._show_vertices(e)}_show_vertices(e){if(!this.model.active)return;const t=this.model.renderers[0],s=()=>this._update_vertices(t),r=null==t?void 0:t.data_source,i=this._select_event(e,\"replace\",this.model.renderers);if(!i.length)return this._set_vertices([],[]),this._selected_renderer=null,this._drawing=!1,this._cur_index=null,void(null!=r&&r.disconnect(r.properties.data.change,s));null!=r&&r.connect(r.properties.data.change,s),this._cur_index=i[0].data_source.selected.indices[0],this._update_vertices(i[0])}_update_vertices(e){const t=e.glyph,s=e.data_source,r=this._cur_index,[i,_]=[t.xs.field,t.ys.field];if(this._drawing)return;if(null==r&&(i||_))return;let d,l;i&&null!=r?(d=s.data[i][r],(0,n.isArray)(d)||(s.data[i][r]=d=Array.from(d))):d=t.xs.value,_&&null!=r?(l=s.data[_][r],(0,n.isArray)(l)||(s.data[_][r]=l=Array.from(l))):l=t.ys.value,this._selected_renderer=e,this._set_vertices(d,l)}_move(e){if(this._drawing&&null!=this._selected_renderer){const t=this.model.vertex_renderer,s=t.data_source,r=t.glyph,i=this._map_drag(e.sx,e.sy,t);if(null==i)return;let[_,d]=i;const n=s.selected.indices;[_,d]=this._snap_to_vertex(e,_,d),s.selected.indices=n;const[l,a]=[r.x.field,r.y.field],c=n[0];l&&(s.data[l][c]=_),a&&(s.data[a][c]=d),s.change.emit(),this._selected_renderer.data_source.change.emit()}}_tap(e){const t=this.model.vertex_renderer,s=this._map_drag(e.sx,e.sy,t);if(null==s)return;if(this._drawing&&this._selected_renderer){let[r,i]=s;const _=t.data_source,d=t.glyph,[n,l]=[d.x.field,d.y.field],a=_.selected.indices;[r,i]=this._snap_to_vertex(e,r,i);const c=a[0];if(_.selected.indices=[c+1],n){const e=_.get_array(n),t=e[c];e[c]=r,e.splice(c+1,0,t)}if(l){const e=_.get_array(l),t=e[c];e[c]=i,e.splice(c+1,0,t)}return _.change.emit(),void this._emit_cds_changes(this._selected_renderer.data_source,!0,!1,!0)}const r=this._select_mode(e);this._select_event(e,r,[t]),this._select_event(e,r,this.model.renderers)}_remove_vertex(){if(!this._drawing||!this._selected_renderer)return;const e=this.model.vertex_renderer,t=e.data_source,s=e.glyph,r=t.selected.indices[0],[i,_]=[s.x.field,s.y.field];i&&t.get_array(i).splice(r,1),_&&t.get_array(_).splice(r,1),t.change.emit(),this._emit_cds_changes(this._selected_renderer.data_source)}_pan_start(e){this._select_event(e,\"append\",[this.model.vertex_renderer]),this._basepoint=[e.sx,e.sy]}_pan(e){null!=this._basepoint&&(this._drag_points(e,[this.model.vertex_renderer]),this._selected_renderer&&this._selected_renderer.data_source.change.emit())}_pan_end(e){null!=this._basepoint&&(this._drag_points(e,[this.model.vertex_renderer]),this._emit_cds_changes(this.model.vertex_renderer.data_source,!1,!0,!0),this._selected_renderer&&this._emit_cds_changes(this._selected_renderer.data_source),this._basepoint=null)}_keyup(e){if(!this.model.active||!this._mouse_in_frame)return;let t;t=this._selected_renderer?[this.model.vertex_renderer]:this.model.renderers;for(const s of t)e.keyCode===d.Keys.Backspace?(this._delete_selected(s),this._selected_renderer&&this._emit_cds_changes(this._selected_renderer.data_source)):e.keyCode==d.Keys.Esc&&(this._drawing?(this._remove_vertex(),this._drawing=!1):this._selected_renderer&&this._hide_vertices(),s.data_source.selection_manager.clear())}deactivate(){this._selected_renderer&&(this._drawing&&(this._remove_vertex(),this._drawing=!1),this._hide_vertices())}}s.PolyEditToolView=c,c.__name__=\"PolyEditToolView\";class o extends l.PolyTool{constructor(e){super(e),this.tool_name=\"Poly Edit Tool\",this.icon=a.tool_icon_poly_edit,this.event_type=[\"tap\",\"pan\",\"move\"],this.default_order=4}}s.PolyEditTool=o,_=o,o.__name__=\"PolyEditTool\",_.prototype.default_view=c},\n function _(e,t,o,s,i){var l;s();const n=e(378),_=e(116),c=e(20),r=e(228);class a extends n.SelectToolView{_compute_limits(e){const t=this.plot_view.frame,o=this.model.dimensions;let s=this._base_point;if(\"center\"==this.model.origin){const[t,o]=s,[i,l]=e;s=[t-(i-t),o-(l-o)]}return this.model._get_dim_limits(s,e,t,o)}_pan_start(e){const{sx:t,sy:o}=e;this._base_point=[t,o]}_pan(e){const{sx:t,sy:o}=e,s=[t,o],[i,l]=this._compute_limits(s);this.model.overlay.update({left:i[0],right:i[1],top:l[0],bottom:l[1]}),this.model.select_every_mousemove&&this._do_select(i,l,!1,this._select_mode(e))}_pan_end(e){const{sx:t,sy:o}=e,s=[t,o],[i,l]=this._compute_limits(s);this._do_select(i,l,!0,this._select_mode(e)),this.model.overlay.update({left:null,right:null,top:null,bottom:null}),this._base_point=null,this.plot_view.state.push(\"box_select\",{selection:this.plot_view.get_selection()})}_do_select([e,t],[o,s],i,l=\"replace\"){const n={type:\"rect\",sx0:e,sx1:t,sy0:o,sy1:s};this._select(n,i,l)}}o.BoxSelectToolView=a,a.__name__=\"BoxSelectToolView\";const h=()=>new _.BoxAnnotation({level:\"overlay\",top_units:\"screen\",left_units:\"screen\",bottom_units:\"screen\",right_units:\"screen\",fill_color:\"lightgrey\",fill_alpha:.5,line_color:\"black\",line_alpha:1,line_width:2,line_dash:[4,4]});class m extends n.SelectTool{constructor(e){super(e),this.tool_name=\"Box Select\",this.icon=r.tool_icon_box_select,this.event_type=\"pan\",this.default_order=30}get tooltip(){return this._get_dim_tooltip(this.dimensions)}}o.BoxSelectTool=m,l=m,m.__name__=\"BoxSelectTool\",l.prototype.default_view=a,l.define((({Boolean:e,Ref:t})=>({dimensions:[c.Dimensions,\"both\"],select_every_mousemove:[e,!1],overlay:[t(_.BoxAnnotation),h],origin:[c.BoxOrigin,\"corner\"]}))),l.register_alias(\"box_select\",(()=>new m)),l.register_alias(\"xbox_select\",(()=>new m({dimensions:\"width\"}))),l.register_alias(\"ybox_select\",(()=>new m({dimensions:\"height\"})))},\n function _(e,t,s,n,r){var o;n();const c=e(223),i=e(175),a=e(339),l=e(176),d=e(66),_=e(20),h=e(43),p=e(251),u=e(15),m=e(11);class v extends c.GestureToolView{connect_signals(){super.connect_signals(),this.model.clear.connect((()=>this._clear()))}get computed_renderers(){const{renderers:e,names:t}=this.model,s=this.plot_model.data_renderers;return(0,d.compute_renderers)(e,s,t)}_computed_renderers_by_data_source(){var e;const t=new Map;for(const s of this.computed_renderers){let n;if(s instanceof i.GlyphRenderer)n=s.data_source;else{if(!(s instanceof a.GraphRenderer))continue;n=s.node_renderer.data_source}const r=null!==(e=t.get(n))&&void 0!==e?e:[];t.set(n,[...r,s])}return t}_select_mode(e){const{shiftKey:t,ctrlKey:s}=e;return t||s?t&&!s?\"append\":!t&&s?\"intersect\":t&&s?\"subtract\":void(0,m.unreachable)():this.model.mode}_keyup(e){e.keyCode==h.Keys.Esc&&this._clear()}_clear(){for(const e of this.computed_renderers)e.get_selection_manager().clear();const e=this.computed_renderers.map((e=>this.plot_view.renderer_view(e)));this.plot_view.request_paint(e)}_select(e,t,s){const n=this._computed_renderers_by_data_source();for(const[,r]of n){const n=r[0].get_selection_manager(),o=[];for(const e of r){const t=this.plot_view.renderer_view(e);null!=t&&o.push(t)}n.select(o,e,t,s)}null!=this.model.callback&&this._emit_callback(e),this._emit_selection_event(e,t)}_emit_selection_event(e,t=!0){const{x_scale:s,y_scale:n}=this.plot_view.frame;let r;switch(e.type){case\"point\":{const{sx:t,sy:o}=e,c=s.invert(t),i=n.invert(o);r=Object.assign(Object.assign({},e),{x:c,y:i});break}case\"span\":{const{sx:t,sy:o}=e,c=s.invert(t),i=n.invert(o);r=Object.assign(Object.assign({},e),{x:c,y:i});break}case\"rect\":{const{sx0:t,sx1:o,sy0:c,sy1:i}=e,[a,l]=s.r_invert(t,o),[d,_]=n.r_invert(c,i);r=Object.assign(Object.assign({},e),{x0:a,y0:d,x1:l,y1:_});break}case\"poly\":{const{sx:t,sy:o}=e,c=s.v_invert(t),i=n.v_invert(o);r=Object.assign(Object.assign({},e),{x:c,y:i});break}}this.plot_model.trigger_event(new p.SelectionGeometry(r,t))}}s.SelectToolView=v,v.__name__=\"SelectToolView\";class b extends c.GestureTool{constructor(e){super(e)}initialize(){super.initialize(),this.clear=new u.Signal0(this,\"clear\")}get menu(){return[{icon:\"bk-tool-icon-replace-mode\",tooltip:\"Replace the current selection\",active:()=>\"replace\"==this.mode,handler:()=>{this.mode=\"replace\",this.active=!0}},{icon:\"bk-tool-icon-append-mode\",tooltip:\"Append to the current selection (Shift)\",active:()=>\"append\"==this.mode,handler:()=>{this.mode=\"append\",this.active=!0}},{icon:\"bk-tool-icon-intersect-mode\",tooltip:\"Intersect with the current selection (Ctrl)\",active:()=>\"intersect\"==this.mode,handler:()=>{this.mode=\"intersect\",this.active=!0}},{icon:\"bk-tool-icon-subtract-mode\",tooltip:\"Subtract from the current selection (Shift+Ctrl)\",active:()=>\"subtract\"==this.mode,handler:()=>{this.mode=\"subtract\",this.active=!0}},null,{icon:\"bk-tool-icon-clear-selection\",tooltip:\"Clear the current selection (Esc)\",handler:()=>{this.clear.emit()}}]}}s.SelectTool=b,o=b,b.__name__=\"SelectTool\",o.define((({String:e,Array:t,Ref:s,Or:n,Auto:r})=>({renderers:[n(t(s(l.DataRenderer)),r),\"auto\"],names:[t(e),[]],mode:[_.SelectionMode,\"replace\"]})))},\n function _(t,o,e,s,i){var n;s();const _=t(223),a=t(116),l=t(20),r=t(228);class h extends _.GestureToolView{_match_aspect(t,o,e){const s=e.bbox.aspect,i=e.bbox.h_range.end,n=e.bbox.h_range.start,_=e.bbox.v_range.end,a=e.bbox.v_range.start;let l=Math.abs(t[0]-o[0]),r=Math.abs(t[1]-o[1]);const h=0==r?0:l/r,[c]=h>=s?[1,h/s]:[s/h,1];let m,p,d,b;return t[0]<=o[0]?(m=t[0],p=t[0]+l*c,p>i&&(p=i)):(p=t[0],m=t[0]-l*c,m_&&(d=_)):(d=t[1],b=t[1]-l/s,bnew a.BoxAnnotation({level:\"overlay\",top_units:\"screen\",left_units:\"screen\",bottom_units:\"screen\",right_units:\"screen\",fill_color:\"lightgrey\",fill_alpha:.5,line_color:\"black\",line_alpha:1,line_width:2,line_dash:[4,4]});class m extends _.GestureTool{constructor(t){super(t),this.tool_name=\"Box Zoom\",this.icon=r.tool_icon_box_zoom,this.event_type=\"pan\",this.default_order=20}get tooltip(){return this._get_dim_tooltip(this.dimensions)}}e.BoxZoomTool=m,n=m,m.__name__=\"BoxZoomTool\",n.prototype.default_view=h,n.define((({Boolean:t,Ref:o})=>({dimensions:[l.Dimensions,\"both\"],overlay:[o(a.BoxAnnotation),c],match_aspect:[t,!1],origin:[l.BoxOrigin,\"corner\"]}))),n.register_alias(\"box_zoom\",(()=>new m({dimensions:\"both\"}))),n.register_alias(\"xbox_zoom\",(()=>new m({dimensions:\"width\"}))),n.register_alias(\"ybox_zoom\",(()=>new m({dimensions:\"height\"})))},\n function _(s,e,t,o,_){var l;o();const i=s(378),a=s(217),c=s(381),n=s(43),h=s(228);class r extends i.SelectToolView{constructor(){super(...arguments),this.sxs=[],this.sys=[]}connect_signals(){super.connect_signals(),this.connect(this.model.properties.active.change,(()=>this._active_change()))}_active_change(){this.model.active||this._clear_overlay()}_keyup(s){s.keyCode==n.Keys.Enter&&this._clear_overlay()}_pan_start(s){this.sxs=[],this.sys=[];const{sx:e,sy:t}=s;this._append_overlay(e,t)}_pan(s){const[e,t]=this.plot_view.frame.bbox.clip(s.sx,s.sy);this._append_overlay(e,t),this.model.select_every_mousemove&&this._do_select(this.sxs,this.sys,!1,this._select_mode(s))}_pan_end(s){const{sxs:e,sys:t}=this;this._clear_overlay(),this._do_select(e,t,!0,this._select_mode(s)),this.plot_view.state.push(\"lasso_select\",{selection:this.plot_view.get_selection()})}_append_overlay(s,e){const{sxs:t,sys:o}=this;t.push(s),o.push(e),this.model.overlay.update({xs:t,ys:o})}_clear_overlay(){this.sxs=[],this.sys=[],this.model.overlay.update({xs:this.sxs,ys:this.sys})}_do_select(s,e,t,o){const _={type:\"poly\",sx:s,sy:e};this._select(_,t,o)}}t.LassoSelectToolView=r,r.__name__=\"LassoSelectToolView\";class y extends i.SelectTool{constructor(s){super(s),this.tool_name=\"Lasso Select\",this.icon=h.tool_icon_lasso_select,this.event_type=\"pan\",this.default_order=12}}t.LassoSelectTool=y,l=y,y.__name__=\"LassoSelectTool\",l.prototype.default_view=r,l.define((({Boolean:s,Ref:e})=>({select_every_mousemove:[s,!0],overlay:[e(a.PolyAnnotation),c.DEFAULT_POLY_OVERLAY]}))),l.register_alias(\"lasso_select\",(()=>new y))},\n function _(e,t,s,l,o){var i;l();const a=e(378),_=e(217),c=e(43),n=e(9),h=e(228);class y extends a.SelectToolView{initialize(){super.initialize(),this.data={sx:[],sy:[]}}connect_signals(){super.connect_signals(),this.connect(this.model.properties.active.change,(()=>this._active_change()))}_active_change(){this.model.active||this._clear_data()}_keyup(e){e.keyCode==c.Keys.Enter&&this._clear_data()}_doubletap(e){this._do_select(this.data.sx,this.data.sy,!0,this._select_mode(e)),this.plot_view.state.push(\"poly_select\",{selection:this.plot_view.get_selection()}),this._clear_data()}_clear_data(){this.data={sx:[],sy:[]},this.model.overlay.update({xs:[],ys:[]})}_tap(e){const{sx:t,sy:s}=e;this.plot_view.frame.bbox.contains(t,s)&&(this.data.sx.push(t),this.data.sy.push(s),this.model.overlay.update({xs:(0,n.copy)(this.data.sx),ys:(0,n.copy)(this.data.sy)}))}_do_select(e,t,s,l){const o={type:\"poly\",sx:e,sy:t};this._select(o,s,l)}}s.PolySelectToolView=y,y.__name__=\"PolySelectToolView\";s.DEFAULT_POLY_OVERLAY=()=>new _.PolyAnnotation({level:\"overlay\",xs_units:\"screen\",ys_units:\"screen\",fill_color:\"lightgrey\",fill_alpha:.5,line_color:\"black\",line_alpha:1,line_width:2,line_dash:[4,4]});class d extends a.SelectTool{constructor(e){super(e),this.tool_name=\"Poly Select\",this.icon=h.tool_icon_polygon_select,this.event_type=\"tap\",this.default_order=11}}s.PolySelectTool=d,i=d,d.__name__=\"PolySelectTool\",i.prototype.default_view=y,i.define((({Ref:e})=>({overlay:[e(_.PolyAnnotation),s.DEFAULT_POLY_OVERLAY]}))),i.register_alias(\"poly_select\",(()=>new d))},\n function _(e,t,s,i,r){var n;i();const _=e(20),d=e(383),o=e(228);class l extends d.LineToolView{constructor(){super(...arguments),this._drawing=!1}_doubletap(e){if(!this.model.active)return;const t=this.model.renderers;for(const s of t){1==this._select_event(e,\"replace\",[s]).length&&(this._selected_renderer=s)}this._show_intersections(),this._update_line_cds()}_show_intersections(){if(!this.model.active)return;if(null==this._selected_renderer)return;if(!this.model.renderers.length)return this._set_intersection([],[]),this._selected_renderer=null,void(this._drawing=!1);const e=this._selected_renderer.data_source,t=this._selected_renderer.glyph,[s,i]=[t.x.field,t.y.field],r=e.get_array(s),n=e.get_array(i);this._set_intersection(r,n)}_tap(e){const t=this.model.intersection_renderer;if(null==this._map_drag(e.sx,e.sy,t))return;if(this._drawing&&this._selected_renderer){const s=this._select_mode(e);if(0==this._select_event(e,s,[t]).length)return}const s=this._select_mode(e);this._select_event(e,s,[t]),this._select_event(e,s,this.model.renderers)}_update_line_cds(){if(null==this._selected_renderer)return;const e=this.model.intersection_renderer.glyph,t=this.model.intersection_renderer.data_source,[s,i]=[e.x.field,e.y.field];if(s&&i){const e=t.data[s],r=t.data[i];this._selected_renderer.data_source.data[s]=e,this._selected_renderer.data_source.data[i]=r}this._emit_cds_changes(this._selected_renderer.data_source,!0,!0,!1)}_pan_start(e){this._select_event(e,\"append\",[this.model.intersection_renderer]),this._basepoint=[e.sx,e.sy]}_pan(e){null!=this._basepoint&&(this._drag_points(e,[this.model.intersection_renderer],this.model.dimensions),this._selected_renderer&&this._selected_renderer.data_source.change.emit())}_pan_end(e){null!=this._basepoint&&(this._drag_points(e,[this.model.intersection_renderer]),this._emit_cds_changes(this.model.intersection_renderer.data_source,!1,!0,!0),this._selected_renderer&&this._emit_cds_changes(this._selected_renderer.data_source),this._basepoint=null)}activate(){this._drawing=!0}deactivate(){this._selected_renderer&&(this._drawing&&(this._drawing=!1),this._hide_intersections())}}s.LineEditToolView=l,l.__name__=\"LineEditToolView\";class h extends d.LineTool{constructor(e){super(e),this.tool_name=\"Line Edit Tool\",this.icon=o.tool_icon_line_edit,this.event_type=[\"tap\",\"pan\",\"move\"],this.default_order=4}get tooltip(){return this._get_dim_tooltip(this.dimensions)}}s.LineEditTool=h,n=h,h.__name__=\"LineEditTool\",n.prototype.default_view=l,n.define((()=>({dimensions:[_.Dimensions,\"both\"]})))},\n function _(e,i,n,t,s){var o;t();const r=e(8),_=e(370);class d extends _.EditToolView{_set_intersection(e,i){const n=this.model.intersection_renderer.glyph,t=this.model.intersection_renderer.data_source,[s,o]=[n.x.field,n.y.field];s&&((0,r.isArray)(e)?t.data[s]=e:n.x={value:e}),o&&((0,r.isArray)(i)?t.data[o]=i:n.y={value:i}),this._emit_cds_changes(t,!0,!0,!1)}_hide_intersections(){this._set_intersection([],[])}}n.LineToolView=d,d.__name__=\"LineToolView\";class a extends _.EditTool{constructor(e){super(e)}}n.LineTool=a,o=a,a.__name__=\"LineTool\",o.define((({AnyRef:e})=>({intersection_renderer:[e()]})))},\n function _(t,s,n,e,i){e();const o=t(1);var a;const _=t(223),l=t(20),r=(0,o.__importStar)(t(228));function h(t,s,n){const e=new Map;for(const[i,o]of t){const[t,a]=o.r_invert(s,n);e.set(i,{start:t,end:a})}return e}n.update_ranges=h;class d extends _.GestureToolView{_pan_start(t){var s;this.last_dx=0,this.last_dy=0;const{sx:n,sy:e}=t,i=this.plot_view.frame.bbox;if(!i.contains(n,e)){const t=i.h_range,s=i.v_range;(nt.end)&&(this.v_axis_only=!0),(es.end)&&(this.h_axis_only=!0)}null===(s=this.model.document)||void 0===s||s.interactive_start(this.plot_model)}_pan(t){var s;this._update(t.deltaX,t.deltaY),null===(s=this.model.document)||void 0===s||s.interactive_start(this.plot_model)}_pan_end(t){this.h_axis_only=!1,this.v_axis_only=!1,null!=this.pan_info&&this.plot_view.state.push(\"pan\",{range:this.pan_info}),this.plot_view.trigger_ranges_update_event()}_update(t,s){const n=this.plot_view.frame,e=t-this.last_dx,i=s-this.last_dy,o=n.bbox.h_range,a=o.start-e,_=o.end-e,l=n.bbox.v_range,r=l.start-i,d=l.end-i,p=this.model.dimensions;let c,u,m,v,x,g;\"width\"!=p&&\"both\"!=p||this.v_axis_only?(c=o.start,u=o.end,m=0):(c=a,u=_,m=-e),\"height\"!=p&&\"both\"!=p||this.h_axis_only?(v=l.start,x=l.end,g=0):(v=r,x=d,g=-i),this.last_dx=t,this.last_dy=s;const{x_scales:w,y_scales:y}=n,f=h(w,c,u),b=h(y,v,x);this.pan_info={xrs:f,yrs:b,sdx:m,sdy:g},this.plot_view.update_range(this.pan_info,{panning:!0})}}n.PanToolView=d,d.__name__=\"PanToolView\";class p extends _.GestureTool{constructor(t){super(t),this.tool_name=\"Pan\",this.event_type=\"pan\",this.default_order=10}get tooltip(){return this._get_dim_tooltip(this.dimensions)}}n.PanTool=p,a=p,p.__name__=\"PanTool\",a.prototype.default_view=d,a.define((()=>({dimensions:[l.Dimensions,\"both\",{on_update(t,s){switch(t){case\"both\":s.icon=r.tool_icon_pan;break;case\"width\":s.icon=r.tool_icon_xpan;break;case\"height\":s.icon=r.tool_icon_ypan}}}]}))),a.register_alias(\"pan\",(()=>new p({dimensions:\"both\"}))),a.register_alias(\"xpan\",(()=>new p({dimensions:\"width\"}))),a.register_alias(\"ypan\",(()=>new p({dimensions:\"height\"})))},\n function _(e,t,i,s,n){var l;s();const a=e(116),r=e(58),o=e(19),_=e(223),h=e(228);function d(e){switch(e){case 1:return 2;case 2:return 1;case 4:return 5;case 5:return 4;default:return e}}function u(e,t,i,s){if(null==t)return!1;const n=i.compute(t);return Math.abs(e-n)n.right)&&(l=!1)}if(null!=n.bottom&&null!=n.top){const e=s.invert(t);(en.top)&&(l=!1)}return l}function g(e,t,i){let s=0;return e>=i.start&&e<=i.end&&(s+=1),t>=i.start&&t<=i.end&&(s+=1),s}function y(e,t,i,s){const n=t.compute(e),l=t.invert(n+i);return l>=s.start&&l<=s.end?l:e}function f(e,t,i){return e>t.start?(t.end=e,i):(t.end=t.start,t.start=e,d(i))}function v(e,t,i){return e=o&&(e.start=a,e.end=r)}i.flip_side=d,i.is_near=u,i.is_inside=c,i.sides_inside=g,i.compute_value=y,i.update_range_end_side=f,i.update_range_start_side=v,i.update_range=m;class p extends _.GestureToolView{initialize(){super.initialize(),this.side=0,this.model.update_overlay_from_ranges()}connect_signals(){super.connect_signals(),null!=this.model.x_range&&this.connect(this.model.x_range.change,(()=>this.model.update_overlay_from_ranges())),null!=this.model.y_range&&this.connect(this.model.y_range.change,(()=>this.model.update_overlay_from_ranges()))}_pan_start(e){this.last_dx=0,this.last_dy=0;const t=this.model.x_range,i=this.model.y_range,{frame:s}=this.plot_view,n=s.x_scale,l=s.y_scale,r=this.model.overlay,{left:o,right:_,top:h,bottom:d}=r,g=this.model.overlay.line_width+a.EDGE_TOLERANCE;null!=t&&this.model.x_interaction&&(u(e.sx,o,n,g)?this.side=1:u(e.sx,_,n,g)?this.side=2:c(e.sx,e.sy,n,l,r)&&(this.side=3)),null!=i&&this.model.y_interaction&&(0==this.side&&u(e.sy,d,l,g)&&(this.side=4),0==this.side&&u(e.sy,h,l,g)?this.side=5:c(e.sx,e.sy,n,l,this.model.overlay)&&(3==this.side?this.side=7:this.side=6))}_pan(e){const t=this.plot_view.frame,i=e.deltaX-this.last_dx,s=e.deltaY-this.last_dy,n=this.model.x_range,l=this.model.y_range,a=t.x_scale,r=t.y_scale;if(null!=n)if(3==this.side||7==this.side)m(n,a,i,t.x_range);else if(1==this.side){const e=y(n.start,a,i,t.x_range);this.side=v(e,n,this.side)}else if(2==this.side){const e=y(n.end,a,i,t.x_range);this.side=f(e,n,this.side)}if(null!=l)if(6==this.side||7==this.side)m(l,r,s,t.y_range);else if(4==this.side){const e=y(l.start,r,s,t.y_range);this.side=v(e,l,this.side)}else if(5==this.side){const e=y(l.end,r,s,t.y_range);this.side=f(e,l,this.side)}this.last_dx=e.deltaX,this.last_dy=e.deltaY}_pan_end(e){this.side=0,this.plot_view.trigger_ranges_update_event()}}i.RangeToolView=p,p.__name__=\"RangeToolView\";const x=()=>new a.BoxAnnotation({level:\"overlay\",fill_color:\"lightgrey\",fill_alpha:.5,line_color:\"black\",line_alpha:1,line_width:.5,line_dash:[2,2]});class w extends _.GestureTool{constructor(e){super(e),this.tool_name=\"Range Tool\",this.icon=h.tool_icon_range,this.event_type=\"pan\",this.default_order=1}initialize(){super.initialize(),this.overlay.in_cursor=\"grab\",this.overlay.ew_cursor=null!=this.x_range&&this.x_interaction?\"ew-resize\":null,this.overlay.ns_cursor=null!=this.y_range&&this.y_interaction?\"ns-resize\":null}update_overlay_from_ranges(){null==this.x_range&&null==this.y_range&&(this.overlay.left=null,this.overlay.right=null,this.overlay.bottom=null,this.overlay.top=null,o.logger.warn(\"RangeTool not configured with any Ranges.\")),null==this.x_range?(this.overlay.left=null,this.overlay.right=null):(this.overlay.left=this.x_range.start,this.overlay.right=this.x_range.end),null==this.y_range?(this.overlay.bottom=null,this.overlay.top=null):(this.overlay.bottom=this.y_range.start,this.overlay.top=this.y_range.end)}}i.RangeTool=w,l=w,w.__name__=\"RangeTool\",l.prototype.default_view=p,l.define((({Boolean:e,Ref:t,Nullable:i})=>({x_range:[i(t(r.Range1d)),null],x_interaction:[e,!0],y_range:[i(t(r.Range1d)),null],y_interaction:[e,!0],overlay:[t(a.BoxAnnotation),x]})))},\n function _(e,t,s,o,i){var l;o();const a=e(378),n=e(20),c=e(228);class _ extends a.SelectToolView{_tap(e){\"tap\"==this.model.gesture&&this._handle_tap(e)}_doubletap(e){\"doubletap\"==this.model.gesture&&this._handle_tap(e)}_handle_tap(e){const{sx:t,sy:s}=e,o={type:\"point\",sx:t,sy:s};this._select(o,!0,this._select_mode(e))}_select(e,t,s){const{callback:o}=this.model;if(\"select\"==this.model.behavior){const i=this._computed_renderers_by_data_source();for(const[,l]of i){const i=l[0].get_selection_manager(),a=l.map((e=>this.plot_view.renderer_view(e))).filter((e=>null!=e));if(i.select(a,e,t,s)&&null!=o){const t=a[0].coordinates.x_scale.invert(e.sx),s=a[0].coordinates.y_scale.invert(e.sy),l={geometries:Object.assign(Object.assign({},e),{x:t,y:s}),source:i.source};o.execute(this.model,l)}}this._emit_selection_event(e),this.plot_view.state.push(\"tap\",{selection:this.plot_view.get_selection()})}else for(const t of this.computed_renderers){const s=this.plot_view.renderer_view(t);if(null==s)continue;const i=t.get_selection_manager();if(i.inspect(s,e)&&null!=o){const t=s.coordinates.x_scale.invert(e.sx),l=s.coordinates.y_scale.invert(e.sy),a={geometries:Object.assign(Object.assign({},e),{x:t,y:l}),source:i.source};o.execute(this.model,a)}}}}s.TapToolView=_,_.__name__=\"TapToolView\";class r extends a.SelectTool{constructor(e){super(e),this.tool_name=\"Tap\",this.icon=c.tool_icon_tap_select,this.event_type=\"tap\",this.default_order=10}}s.TapTool=r,l=r,r.__name__=\"TapTool\",l.prototype.default_view=_,l.define((({Any:e,Enum:t,Nullable:s})=>({behavior:[n.TapBehavior,\"select\"],gesture:[t(\"tap\",\"doubletap\"),\"tap\"],callback:[s(e)]}))),l.register_alias(\"click\",(()=>new r({behavior:\"inspect\"}))),l.register_alias(\"tap\",(()=>new r)),l.register_alias(\"doubletap\",(()=>new r({gesture:\"doubletap\"})))},\n function _(e,t,s,n,i){var a;n();const o=e(223),l=e(20),_=e(228),r=e(384);class h extends o.GestureToolView{_scroll(e){let t=this.model.speed*e.delta;t>.9?t=.9:t<-.9&&(t=-.9),this._update_ranges(t)}_update_ranges(e){var t;const{frame:s}=this.plot_view,n=s.bbox.h_range,i=s.bbox.v_range,[a,o]=[n.start,n.end],[l,_]=[i.start,i.end];let h,d,p,c;switch(this.model.dimension){case\"height\":{const t=Math.abs(_-l);h=a,d=o,p=l-t*e,c=_-t*e;break}case\"width\":{const t=Math.abs(o-a);h=a-t*e,d=o-t*e,p=l,c=_;break}}const{x_scales:g,y_scales:u}=s,w={xrs:(0,r.update_ranges)(g,h,d),yrs:(0,r.update_ranges)(u,p,c),factor:e};this.plot_view.state.push(\"wheel_pan\",{range:w}),this.plot_view.update_range(w,{scrolling:!0}),null===(t=this.model.document)||void 0===t||t.interactive_start(this.plot_model,(()=>this.plot_view.trigger_ranges_update_event()))}}s.WheelPanToolView=h,h.__name__=\"WheelPanToolView\";class d extends o.GestureTool{constructor(e){super(e),this.tool_name=\"Wheel Pan\",this.icon=_.tool_icon_wheel_pan,this.event_type=\"scroll\",this.default_order=12}get tooltip(){return this._get_dim_tooltip(this.dimension)}}s.WheelPanTool=d,a=d,d.__name__=\"WheelPanTool\",a.prototype.default_view=h,a.define((()=>({dimension:[l.Dimension,\"width\"]}))),a.internal((({Number:e})=>({speed:[e,.001]}))),a.register_alias(\"xwheel_pan\",(()=>new d({dimension:\"width\"}))),a.register_alias(\"ywheel_pan\",(()=>new d({dimension:\"height\"})))},\n function _(e,o,t,s,i){var n;s();const l=e(223),_=e(368),h=e(20),a=e(27),r=e(228);class m extends l.GestureToolView{_pinch(e){const{sx:o,sy:t,scale:s,ctrlKey:i,shiftKey:n}=e;let l;l=s>=1?20*(s-1):-20/s,this._scroll({type:\"wheel\",sx:o,sy:t,delta:l,ctrlKey:i,shiftKey:n})}_scroll(e){var o;const{frame:t}=this.plot_view,s=t.bbox.h_range,i=t.bbox.v_range,{sx:n,sy:l}=e,h=this.model.dimensions,a=(\"width\"==h||\"both\"==h)&&s.startthis.plot_view.trigger_ranges_update_event()))}}t.WheelZoomToolView=m,m.__name__=\"WheelZoomToolView\";class d extends l.GestureTool{constructor(e){super(e),this.tool_name=\"Wheel Zoom\",this.icon=r.tool_icon_wheel_zoom,this.event_type=a.is_mobile?\"pinch\":\"scroll\",this.default_order=10}get tooltip(){return this._get_dim_tooltip(this.dimensions)}}t.WheelZoomTool=d,n=d,d.__name__=\"WheelZoomTool\",n.prototype.default_view=m,n.define((({Boolean:e,Number:o})=>({dimensions:[h.Dimensions,\"both\"],maintain_focus:[e,!0],zoom_on_axis:[e,!0],speed:[o,1/600]}))),n.register_alias(\"wheel_zoom\",(()=>new d({dimensions:\"both\"}))),n.register_alias(\"xwheel_zoom\",(()=>new d({dimensions:\"width\"}))),n.register_alias(\"ywheel_zoom\",(()=>new d({dimensions:\"height\"})))},\n function _(i,e,s,t,o){var n;t();const l=i(232),a=i(219),h=i(20),r=i(13),_=i(228);class c extends l.InspectToolView{_move(i){if(!this.model.active)return;const{sx:e,sy:s}=i;this.plot_view.frame.bbox.contains(e,s)?this._update_spans(e,s):this._update_spans(null,null)}_move_exit(i){this._update_spans(null,null)}_update_spans(i,e){const s=this.model.dimensions;\"width\"!=s&&\"both\"!=s||(this.model.spans.width.location=e),\"height\"!=s&&\"both\"!=s||(this.model.spans.height.location=i)}}s.CrosshairToolView=c,c.__name__=\"CrosshairToolView\";class p extends l.InspectTool{constructor(i){super(i),this.tool_name=\"Crosshair\",this.icon=_.tool_icon_crosshair}get tooltip(){return this._get_dim_tooltip(this.dimensions)}get synthetic_renderers(){return(0,r.values)(this.spans)}}s.CrosshairTool=p,n=p,p.__name__=\"CrosshairTool\",(()=>{function i(i,e){return new a.Span({for_hover:!0,dimension:e,location_units:\"screen\",level:\"overlay\",line_color:i.line_color,line_width:i.line_width,line_alpha:i.line_alpha})}n.prototype.default_view=c,n.define((({Alpha:i,Number:e,Color:s})=>({dimensions:[h.Dimensions,\"both\"],line_color:[s,\"black\"],line_width:[e,1],line_alpha:[i,1]}))),n.internal((({Struct:e,Ref:s})=>({spans:[e({width:s(a.Span),height:s(a.Span)}),e=>({width:i(e,\"width\"),height:i(e,\"height\")})]}))),n.register_alias(\"crosshair\",(()=>new p))})()},\n function _(e,s,t,r,n){var o;r();const a=e(53),u=e(13),c=e(34);class i extends a.Model{constructor(e){super(e)}get values(){return(0,u.values)(this.args)}_make_code(e,s,t,r){return new Function(...(0,u.keys)(this.args),e,s,t,(0,c.use_strict)(r))}format(e,s,t){return this._make_code(\"value\",\"format\",\"special_vars\",this.code)(...this.values,e,s,t)}}t.CustomJSHover=i,o=i,i.__name__=\"CustomJSHover\",o.define((({Unknown:e,String:s,Dict:t})=>({args:[t(e),{}],code:[s,\"\"]})))},\n function _(e,t,n,s,i){s();const o=e(1);var r;const l=e(232),a=e(390),c=e(241),_=e(175),d=e(339),p=e(176),h=e(177),u=e(283),m=e(186),y=e(187),f=e(189),x=(0,o.__importStar)(e(185)),v=e(152),w=e(43),g=e(22),b=e(13),k=e(234),C=e(8),S=e(113),T=e(20),$=e(228),R=e(15),A=e(66),M=(0,o.__importStar)(e(242)),V=e(392);function G(e,t,n,s,i,o){const r={x:i[e],y:o[e]},l={x:i[e+1],y:o[e+1]};let a,c;if(\"span\"==t.type)\"h\"==t.direction?(a=Math.abs(r.x-n),c=Math.abs(l.x-n)):(a=Math.abs(r.y-s),c=Math.abs(l.y-s));else{const e={x:n,y:s};a=x.dist_2_pts(r,e),c=x.dist_2_pts(l,e)}return adelete this._template_el)),this.on_change([e,t,n],(async()=>await this._update_ttmodels()))}async _update_ttmodels(){const{_ttmodels:e,computed_renderers:t}=this;e.clear();const{tooltips:n}=this.model;if(null!=n)for(const t of this.computed_renderers){const s=new c.Tooltip({custom:(0,C.isString)(n)||(0,C.isFunction)(n),attachment:this.model.attachment,show_arrow:this.model.show_arrow});t instanceof _.GlyphRenderer?e.set(t,s):t instanceof d.GraphRenderer&&(e.set(t.node_renderer,s),e.set(t.edge_renderer,s))}const s=await(0,S.build_views)(this._ttviews,[...e.values()],{parent:this.plot_view});for(const e of s)e.render();const i=[...function*(){for(const e of t)e instanceof _.GlyphRenderer?yield e:e instanceof d.GraphRenderer&&(yield e.node_renderer,yield e.edge_renderer)}()],o=this._slots.get(this._update);if(null!=o){const e=new Set(i.map((e=>e.data_source)));R.Signal.disconnect_receiver(this,o,e)}for(const e of i)this.connect(e.data_source.inspect,this._update)}get computed_renderers(){const{renderers:e,names:t}=this.model,n=this.plot_model.data_renderers;return(0,A.compute_renderers)(e,n,t)}get ttmodels(){return this._ttmodels}_clear(){this._inspect(1/0,1/0);for(const[,e]of this.ttmodels)e.clear()}_move(e){if(!this.model.active)return;const{sx:t,sy:n}=e;this.plot_view.frame.bbox.contains(t,n)?this._inspect(t,n):this._clear()}_move_exit(){this._clear()}_inspect(e,t){let n;if(\"mouse\"==this.model.mode)n={type:\"point\",sx:e,sy:t};else{n={type:\"span\",direction:\"vline\"==this.model.mode?\"h\":\"v\",sx:e,sy:t}}for(const e of this.computed_renderers){const t=e.get_selection_manager(),s=this.plot_view.renderer_view(e);null!=s&&t.inspect(s,n)}this._emit_callback(n)}_update([e,{geometry:t}]){var n,s;if(!this.model.active)return;if(\"point\"!=t.type&&\"span\"!=t.type)return;if(!(e instanceof _.GlyphRenderer))return;if(\"ignore\"==this.model.muted_policy&&e.muted)return;const i=this.ttmodels.get(e);if(null==i)return;const o=e.get_selection_manager(),r=o.inspectors.get(e),l=e.view.convert_selection_to_subset(r);if(r.is_empty()&&null==r.view)return void i.clear();const a=o.source,c=this.plot_view.renderer_view(e);if(null==c)return;const{sx:d,sy:p}=t,x=c.coordinates.x_scale,v=c.coordinates.y_scale,g=x.invert(d),k=v.invert(p),{glyph:C}=c,S=[];if(C instanceof m.PatchView){const[t,n]=[d,p],s={x:g,y:k,sx:d,sy:p,rx:t,ry:n,name:e.name};S.push([t,n,this._render_tooltips(a,-1,s)])}if(C instanceof y.HAreaView)for(const t of l.line_indices){const n=C._x1,s=C._x2,i=C._y,[o,r]=[d,p],c={index:t,x:g,y:k,sx:d,sy:p,data_x1:n,data_x2:s,data_y:i,rx:o,ry:r,indices:l.line_indices,name:e.name};S.push([o,r,this._render_tooltips(a,t,c)])}if(C instanceof f.VAreaView)for(const t of l.line_indices){const n=C._x,s=C._y1,i=C._y2,[o,r]=[d,p],c={index:t,x:g,y:k,sx:d,sy:p,data_x:n,data_y1:s,data_y2:i,rx:o,ry:r,indices:l.line_indices,name:e.name};S.push([o,r,this._render_tooltips(a,t,c)])}if(C instanceof h.LineView)for(const n of l.line_indices){let s,i,o=C._x[n+1],r=C._y[n+1],c=n;switch(this.model.line_policy){case\"interp\":[o,r]=C.get_interpolation_hit(n,t),s=x.compute(o),i=v.compute(r);break;case\"prev\":[[s,i],c]=H(C.sx,C.sy,n);break;case\"next\":[[s,i],c]=H(C.sx,C.sy,n+1);break;case\"nearest\":[[s,i],c]=G(n,t,d,p,C.sx,C.sy),o=C._x[c],r=C._y[c];break;default:[s,i]=[d,p]}const _={index:c,x:g,y:k,sx:d,sy:p,data_x:o,data_y:r,rx:s,ry:i,indices:l.line_indices,name:e.name};S.push([s,i,this._render_tooltips(a,c,_)])}for(const t of r.image_indices){const n={index:t.index,x:g,y:k,sx:d,sy:p,name:e.name},s=this._render_tooltips(a,t,n);S.push([d,p,s])}for(const i of l.indices)if(C instanceof u.MultiLineView&&!(0,b.isEmpty)(l.multiline_indices))for(const n of l.multiline_indices[i.toString()]){let s,o,r,c=C._xs.get(i)[n],h=C._ys.get(i)[n],u=n;switch(this.model.line_policy){case\"interp\":[c,h]=C.get_interpolation_hit(i,n,t),s=x.compute(c),o=v.compute(h);break;case\"prev\":[[s,o],u]=H(C.sxs.get(i),C.sys.get(i),n);break;case\"next\":[[s,o],u]=H(C.sxs.get(i),C.sys.get(i),n+1);break;case\"nearest\":[[s,o],u]=G(n,t,d,p,C.sxs.get(i),C.sys.get(i)),c=C._xs.get(i)[u],h=C._ys.get(i)[u];break;default:throw new Error(\"shouldn't have happened\")}r=e instanceof _.GlyphRenderer?e.view.convert_indices_from_subset([i])[0]:i;const m={index:r,x:g,y:k,sx:d,sy:p,data_x:c,data_y:h,segment_index:u,indices:l.multiline_indices,name:e.name};S.push([s,o,this._render_tooltips(a,r,m)])}else{const t=null===(n=C._x)||void 0===n?void 0:n[i],o=null===(s=C._y)||void 0===s?void 0:s[i];let r,c,h;if(\"snap_to_data\"==this.model.point_policy){let e=C.get_anchor_point(this.model.anchor,i,[d,p]);if(null==e&&(e=C.get_anchor_point(\"center\",i,[d,p]),null==e))continue;r=e.x,c=e.y}else[r,c]=[d,p];h=e instanceof _.GlyphRenderer?e.view.convert_indices_from_subset([i])[0]:i;const u={index:h,x:g,y:k,sx:d,sy:p,data_x:t,data_y:o,indices:l.indices,name:e.name};S.push([r,c,this._render_tooltips(a,h,u)])}if(0==S.length)i.clear();else{const{content:e}=i;(0,w.empty)(i.content);for(const[,,t]of S)null!=t&&e.appendChild(t);const[t,n]=S[S.length-1];i.setv({position:[t,n]},{check_eq:!1})}}_emit_callback(e){const{callback:t}=this.model;if(null!=t)for(const n of this.computed_renderers){if(!(n instanceof _.GlyphRenderer))continue;const s=this.plot_view.renderer_view(n);if(null==s)continue;const{x_scale:i,y_scale:o}=s.coordinates,r=i.invert(e.sx),l=o.invert(e.sy),a=n.data_source.inspected;t.execute(this.model,{geometry:Object.assign({x:r,y:l},e),renderer:n,index:a})}}_create_template(e){const t=(0,w.div)({style:{display:\"table\",borderSpacing:\"2px\"}});for(const[n]of e){const e=(0,w.div)({style:{display:\"table-row\"}});t.appendChild(e);const s=(0,w.div)({style:{display:\"table-cell\"},class:M.tooltip_row_label},0!=n.length?`${n}: `:\"\");e.appendChild(s);const i=(0,w.span)();i.dataset.value=\"\";const o=(0,w.span)({class:M.tooltip_color_block},\" \");o.dataset.swatch=\"\",(0,w.undisplay)(o);const r=(0,w.div)({style:{display:\"table-cell\"},class:M.tooltip_row_value},i,o);e.appendChild(r)}return t}_render_template(e,t,n,s,i){const o=e.cloneNode(!0),r=o.querySelectorAll(\"[data-value]\"),l=o.querySelectorAll(\"[data-swatch]\"),a=/\\$color(\\[.*\\])?:(\\w*)/,c=/\\$swatch:(\\w*)/;for(const[[,e],o]of(0,k.enumerate)(t)){const t=e.match(c),_=e.match(a);if(null!=t||null!=_){if(null!=t){const[,e]=t,i=n.get_column(e);if(null==i)r[o].textContent=`${e} unknown`;else{const e=(0,C.isNumber)(s)?i[s]:null;null!=e&&(l[o].style.backgroundColor=(0,g.color2css)(e),(0,w.display)(l[o]))}}if(null!=_){const[,e=\"\",t]=_,i=n.get_column(t);if(null==i){r[o].textContent=`${t} unknown`;continue}const a=e.indexOf(\"hex\")>=0,c=e.indexOf(\"swatch\")>=0,d=(0,C.isNumber)(s)?i[s]:null;if(null==d){r[o].textContent=\"(null)\";continue}r[o].textContent=a?(0,g.color2hex)(d):(0,g.color2css)(d),c&&(l[o].style.backgroundColor=(0,g.color2css)(d),(0,w.display)(l[o]))}}else{const t=(0,v.replace_placeholders)(e.replace(\"$~\",\"$data_\"),n,s,this.model.formatters,i);if((0,C.isString)(t))r[o].textContent=t;else for(const e of t)r[o].appendChild(e)}}return o}_render_tooltips(e,t,n){var s;const{tooltips:i}=this.model;if((0,C.isString)(i)){const s=(0,v.replace_placeholders)({html:i},e,t,this.model.formatters,n);return(0,w.div)(s)}if((0,C.isFunction)(i))return i(e,n);if(i instanceof V.Template)return this._template_view.update(e,t,n),this._template_view.el;if(null!=i){const o=null!==(s=this._template_el)&&void 0!==s?s:this._template_el=this._create_template(i);return this._render_template(o,i,e,t,n)}return null}}n.HoverToolView=z,z.__name__=\"HoverToolView\";class P extends l.InspectTool{constructor(e){super(e),this.tool_name=\"Hover\",this.icon=$.tool_icon_hover}}n.HoverTool=P,r=P,P.__name__=\"HoverTool\",r.prototype.default_view=z,r.define((({Any:e,Boolean:t,String:n,Array:s,Tuple:i,Dict:o,Or:r,Ref:l,Function:c,Auto:_,Nullable:d})=>({tooltips:[d(r(l(V.Template),n,s(i(n,n)),c())),[[\"index\",\"$index\"],[\"data (x, y)\",\"($x, $y)\"],[\"screen (x, y)\",\"($sx, $sy)\"]]],formatters:[o(r(l(a.CustomJSHover),v.FormatterType)),{}],renderers:[r(s(l(p.DataRenderer)),_),\"auto\"],names:[s(n),[]],mode:[T.HoverMode,\"mouse\"],muted_policy:[T.MutedPolicy,\"show\"],point_policy:[T.PointPolicy,\"snap_to_data\"],line_policy:[T.LinePolicy,\"nearest\"],show_arrow:[t,!0],anchor:[T.Anchor,\"center\"],attachment:[T.TooltipAttachment,\"horizontal\"],callback:[d(e)]}))),r.register_alias(\"hover\",(()=>new P))},\n function _(e,t,s,n,a){n();const l=e(1);var i,_,o,r,c,d,p,u,m,w,f,h,x;const v=e(53),y=e(309),V=e(393);a(\"Styles\",V.Styles);const g=e(43),T=e(42),b=e(226),R=e(113),D=e(8),M=e(13),S=(0,l.__importStar)(e(242)),O=e(152);class C extends b.DOMView{}s.DOMNodeView=C,C.__name__=\"DOMNodeView\";class z extends v.Model{constructor(e){super(e)}}s.DOMNode=z,z.__name__=\"DOMNode\",z.__module__=\"bokeh.models.dom\";class P extends C{render(){super.render(),this.el.textContent=this.model.content}_createElement(){return document.createTextNode(\"\")}}s.TextView=P,P.__name__=\"TextView\";class A extends z{constructor(e){super(e)}}s.Text=A,i=A,A.__name__=\"Text\",i.prototype.default_view=P,i.define((({String:e})=>({content:[e,\"\"]})));class N extends C{}s.PlaceholderView=N,N.__name__=\"PlaceholderView\",N.tag_name=\"span\";class E extends z{constructor(e){super(e)}}s.Placeholder=E,_=E,E.__name__=\"Placeholder\",_.define((({})=>({})));class G extends N{update(e,t,s){this.el.textContent=t.toString()}}s.IndexView=G,G.__name__=\"IndexView\";class I extends E{constructor(e){super(e)}}s.Index=I,o=I,I.__name__=\"Index\",o.prototype.default_view=G,o.define((({})=>({})));class k extends N{update(e,t,s){const n=(0,O._get_column_value)(this.model.field,e,t),a=null==n?\"???\":`${n}`;this.el.textContent=a}}s.ValueRefView=k,k.__name__=\"ValueRefView\";class $ extends E{constructor(e){super(e)}}s.ValueRef=$,r=$,$.__name__=\"ValueRef\",r.prototype.default_view=k,r.define((({String:e})=>({field:[e]})));class B extends k{render(){super.render(),this.value_el=(0,g.span)(),this.swatch_el=(0,g.span)({class:S.tooltip_color_block},\" \"),this.el.appendChild(this.value_el),this.el.appendChild(this.swatch_el)}update(e,t,s){const n=(0,O._get_column_value)(this.model.field,e,t),a=null==n?\"???\":`${n}`;this.el.textContent=a}}s.ColorRefView=B,B.__name__=\"ColorRefView\";class L extends ${constructor(e){super(e)}}s.ColorRef=L,c=L,L.__name__=\"ColorRef\",c.prototype.default_view=B,c.define((({Boolean:e})=>({hex:[e,!0],swatch:[e,!0]})));class j extends C{constructor(){super(...arguments),this.child_views=new Map}async lazy_initialize(){await super.lazy_initialize();const e=this.model.children.filter((e=>e instanceof v.Model));await(0,R.build_views)(this.child_views,e,{parent:this})}render(){super.render();const{style:e}=this.model;if(null!=e)if(e instanceof V.Styles)for(const t of e){const e=t.get_value();if((0,D.isString)(e)){const s=t.attr.replace(/_/g,\"-\");this.el.style.hasOwnProperty(s)&&this.el.style.setProperty(s,e)}}else for(const[t,s]of(0,M.entries)(e)){const e=t.replace(/_/g,\"-\");this.el.style.hasOwnProperty(e)&&this.el.style.setProperty(e,s)}for(const e of this.model.children)if((0,D.isString)(e)){const t=document.createTextNode(e);this.el.appendChild(t)}else{this.child_views.get(e).renderTo(this.el)}}}s.DOMElementView=j,j.__name__=\"DOMElementView\";class q extends z{constructor(e){super(e)}}s.DOMElement=q,d=q,q.__name__=\"DOMElement\",d.define((({String:e,Array:t,Dict:s,Or:n,Nullable:a,Ref:l})=>({style:[a(n(l(V.Styles),s(e))),null],children:[t(n(e,l(z),l(y.LayoutDOM))),[]]})));class F extends T.View{}s.ActionView=F,F.__name__=\"ActionView\";class H extends v.Model{constructor(e){super(e)}}s.Action=H,p=H,H.__name__=\"Action\",H.__module__=\"bokeh.models.dom\",p.define((({})=>({})));class J extends j{constructor(){super(...arguments),this.action_views=new Map}async lazy_initialize(){await super.lazy_initialize(),await(0,R.build_views)(this.action_views,this.model.actions,{parent:this})}remove(){(0,R.remove_views)(this.action_views),super.remove()}update(e,t,s={}){!function n(a){for(const l of a.child_views.values())l instanceof N?l.update(e,t,s):l instanceof j&&n(l)}(this);for(const n of this.action_views.values())n.update(e,t,s)}}s.TemplateView=J,J.__name__=\"TemplateView\",J.tag_name=\"div\";class K extends 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function _(t,o,e,n,s){var i;n();const l=t(15),c=t(53),r=t(224),a=t(232),u=t(234);class h extends c.Model{constructor(t){super(t)}get button_view(){return this.tools[0].button_view}get event_type(){return this.tools[0].event_type}get tooltip(){return this.tools[0].tooltip}get tool_name(){return this.tools[0].tool_name}get icon(){return this.tools[0].computed_icon}get computed_icon(){return this.icon}get toggleable(){const t=this.tools[0];return t instanceof a.InspectTool&&t.toggleable}initialize(){super.initialize(),this.do=new l.Signal0(this,\"do\")}connect_signals(){super.connect_signals(),this.connect(this.do,(()=>this.doit())),this.connect(this.properties.active.change,(()=>this.set_active()));for(const t of this.tools)this.connect(t.properties.active.change,(()=>{this.active=t.active}))}doit(){for(const t of this.tools)t.do.emit()}set_active(){for(const t of this.tools)t.active=this.active}get menu(){const{menu:t}=this.tools[0];if(null==t)return null;const o=[];for(const[e,n]of(0,u.enumerate)(t))if(null==e)o.push(null);else{const t=()=>{var t,o,e;for(const s of this.tools)null===(e=null===(o=null===(t=s.menu)||void 0===t?void 0:t[n])||void 0===o?void 0:o.handler)||void 0===e||e.call(o)};o.push(Object.assign(Object.assign({},e),{handler:t}))}return o}}e.ToolProxy=h,i=h,h.__name__=\"ToolProxy\",i.define((({Boolean:t,Array:o,Ref:e})=>({tools:[o(e(r.ButtonTool)),[]],active:[t,!1],disabled:[t,!1]})))},\n function _(o,t,s,e,i){var n,r;e();const l=o(20),c=o(9),h=o(13),a=o(233),_=o(221),p=o(394),u=o(309),f=o(207);class y extends a.ToolbarBase{constructor(o){super(o)}initialize(){super.initialize(),this._merge_tools()}_merge_tools(){this._proxied_tools=[];const o={},t={},s={},e=[],i=[];for(const o of this.help)(0,c.includes)(i,o.redirect)||(e.push(o),i.push(o.redirect));this._proxied_tools.push(...e),this.help=e;for(const[o,t]of(0,h.entries)(this.gestures)){o in s||(s[o]={});for(const e of t.tools)e.type in s[o]||(s[o][e.type]=[]),s[o][e.type].push(e)}for(const t of this.inspectors)t.type in o||(o[t.type]=[]),o[t.type].push(t);for(const o of this.actions)o.type in t||(t[o.type]=[]),t[o.type].push(o);const n=(o,t=!1)=>{const s=new p.ToolProxy({tools:o,active:t});return this._proxied_tools.push(s),s};for(const o of(0,h.keys)(s)){const t=this.gestures[o];t.tools=[];for(const e of(0,h.keys)(s[o])){const i=s[o][e];if(i.length>0)if(\"multi\"==o)for(const o of i){const s=n([o]);t.tools.push(s),this.connect(s.properties.active.change,(()=>this._active_change(s)))}else{const o=n(i);t.tools.push(o),this.connect(o.properties.active.change,(()=>this._active_change(o)))}}}this.actions=[];for(const[o,s]of(0,h.entries)(t))if(\"CustomAction\"==o)for(const o of s)this.actions.push(n([o]));else s.length>0&&this.actions.push(n(s));this.inspectors=[];for(const t of(0,h.values)(o))t.length>0&&this.inspectors.push(n(t,!0));for(const[o,t]of(0,h.entries)(this.gestures))0!=t.tools.length&&(t.tools=(0,c.sort_by)(t.tools,(o=>o.default_order)),\"pinch\"!=o&&\"scroll\"!=o&&\"multi\"!=o&&(t.tools[0].active=!0))}}s.ProxyToolbar=y,n=y,y.__name__=\"ProxyToolbar\",n.define((({Array:o,Ref:t})=>({toolbars:[o(t(_.Toolbar)),[]]})));class d extends u.LayoutDOMView{initialize(){this.model.toolbar.toolbar_location=this.model.toolbar_location,super.initialize()}get child_models(){return[this.model.toolbar]}_update_layout(){this.layout=new f.ContentBox(this.child_views[0].el);const{toolbar:o}=this.model;o.horizontal?this.layout.set_sizing({width_policy:\"fit\",min_width:100,height_policy:\"fixed\"}):this.layout.set_sizing({width_policy:\"fixed\",height_policy:\"fit\",min_height:100})}after_layout(){super.after_layout();const o=this.child_views[0];o.layout.bbox=this.layout.bbox,o.render()}}s.ToolbarBoxView=d,d.__name__=\"ToolbarBoxView\";class b extends u.LayoutDOM{constructor(o){super(o)}}s.ToolbarBox=b,r=b,b.__name__=\"ToolbarBox\",r.prototype.default_view=d,r.define((({Ref:o})=>({toolbar:[o(a.ToolbarBase)],toolbar_location:[l.Location,\"right\"]})))},\n function _(e,n,r,t,o){t();const s=e(1),u=e(53),c=(0,s.__importStar)(e(21)),a=e(8),l=e(13);r.resolve_defs=function(e,n){var r,t,o,s;function i(e){return null!=e.module?`${e.module}.${e.name}`:e.name}function f(e){if((0,a.isString)(e))switch(e){case\"Any\":return c.Any;case\"Unknown\":return c.Unknown;case\"Boolean\":return c.Boolean;case\"Number\":return c.Number;case\"Int\":return c.Int;case\"String\":return c.String;case\"Null\":return c.Null}else switch(e[0]){case\"Nullable\":{const[,n]=e;return c.Nullable(f(n))}case\"Or\":{const[,...n]=e;return c.Or(...n.map(f))}case\"Tuple\":{const[,n,...r]=e;return c.Tuple(f(n),...r.map(f))}case\"Array\":{const[,n]=e;return c.Array(f(n))}case\"Struct\":{const[,...n]=e,r=n.map((([e,n])=>[e,f(n)]));return c.Struct((0,l.to_object)(r))}case\"Dict\":{const[,n]=e;return c.Dict(f(n))}case\"Map\":{const[,n,r]=e;return c.Map(f(n),f(r))}case\"Enum\":{const[,...n]=e;return c.Enum(...n)}case\"Ref\":{const[,r]=e,t=n.get(i(r));if(null!=t)return c.Ref(t);throw new Error(`${i(r)} wasn't defined before referencing it`)}case\"AnyRef\":return c.AnyRef()}}for(const c of e){const e=(()=>{if(null==c.extends)return u.Model;{const e=n.get(i(c.extends));if(null!=e)return e;throw new Error(`base model ${i(c.extends)} of ${i(c)} is not defined`)}})(),a=((s=class extends e{}).__name__=c.name,s.__module__=c.module,s);for(const e of null!==(r=c.properties)&&void 0!==r?r:[]){const n=f(null!==(t=e.kind)&&void 0!==t?t:\"Unknown\");a.define({[e.name]:[n,e.default]})}for(const e of null!==(o=c.overrides)&&void 0!==o?o:[])a.override({[e.name]:e.default});n.register(a)}}},\n function _(n,e,t,o,i){o();const d=n(5),c=n(226),s=n(113),a=n(43),l=n(398);t.index={},t.add_document_standalone=async function(n,e,o=[],i=!1){const u=new Map;async function f(i){let d;const f=n.roots().indexOf(i),r=o[f];null!=r?d=r:e.classList.contains(l.BOKEH_ROOT)?d=e:(d=(0,a.div)({class:l.BOKEH_ROOT}),e.appendChild(d));const w=await(0,s.build_view)(i,{parent:null});return w instanceof c.DOMView&&w.renderTo(d),u.set(i,w),t.index[i.id]=w,w}for(const e of n.roots())await f(e);return i&&(window.document.title=n.title()),n.on_change((n=>{n instanceof d.RootAddedEvent?f(n.model):n instanceof d.RootRemovedEvent?function(n){const e=u.get(n);null!=e&&(e.remove(),u.delete(n),delete t.index[n.id])}(n.model):i&&n instanceof d.TitleChangedEvent&&(window.document.title=n.title)})),[...u.values()]}},\n function _(o,e,n,t,r){t();const l=o(43),d=o(44);function u(o){let e=document.getElementById(o);if(null==e)throw new Error(`Error rendering Bokeh model: could not find #${o} HTML tag`);if(!document.body.contains(e))throw new Error(`Error rendering Bokeh model: element #${o} must be under `);if(\"SCRIPT\"==e.tagName){const o=(0,l.div)({class:n.BOKEH_ROOT});(0,l.replaceWith)(e,o),e=o}return e}n.BOKEH_ROOT=d.root,n._resolve_element=function(o){const{elementid:e}=o;return null!=e?u(e):document.body},n._resolve_root_elements=function(o){const e=[];if(null!=o.root_ids&&null!=o.roots)for(const n of o.root_ids)e.push(u(o.roots[n]));return e}},\n function _(n,o,t,s,e){s();const c=n(400),r=n(19),a=n(397);t._get_ws_url=function(n,o){let t,s=\"ws:\";return\"https:\"==window.location.protocol&&(s=\"wss:\"),null!=o?(t=document.createElement(\"a\"),t.href=o):t=window.location,null!=n?\"/\"==n&&(n=\"\"):n=t.pathname.replace(/\\/+$/,\"\"),`${s}//${t.host}${n}/ws`};const i={};t.add_document_from_session=async function(n,o,t,s=[],e=!1){const l=window.location.search.substr(1);let d;try{d=await function(n,o,t){const s=(0,c.parse_token)(o).session_id;n in i||(i[n]={});const e=i[n];return s in e||(e[s]=(0,c.pull_session)(n,o,t)),e[s]}(n,o,l)}catch(n){const t=(0,c.parse_token)(o).session_id;throw r.logger.error(`Failed to load Bokeh session ${t}: ${n}`),n}return(0,a.add_document_standalone)(d.document,t,s,e)}},\n function _(e,s,n,t,o){t();const r=e(19),i=e(5),c=e(401),l=e(402),_=e(403);n.DEFAULT_SERVER_WEBSOCKET_URL=\"ws://localhost:5006/ws\",n.DEFAULT_TOKEN=\"eyJzZXNzaW9uX2lkIjogImRlZmF1bHQifQ\";let h=0;function a(e){let s=e.split(\".\")[0];const n=s.length%4;return 0!=n&&(s+=\"=\".repeat(4-n)),JSON.parse(atob(s.replace(/_/g,\"/\").replace(/-/g,\"+\")))}n.parse_token=a;class d{constructor(e=n.DEFAULT_SERVER_WEBSOCKET_URL,s=n.DEFAULT_TOKEN,t=null){this.url=e,this.token=s,this.args_string=t,this._number=h++,this.socket=null,this.session=null,this.closed_permanently=!1,this._current_handler=null,this._pending_replies=new Map,this._pending_messages=[],this._receiver=new l.Receiver,this.id=a(s).session_id.split(\".\")[0],r.logger.debug(`Creating websocket ${this._number} to '${this.url}' session '${this.id}'`)}async connect(){if(this.closed_permanently)throw new Error(\"Cannot connect() a closed ClientConnection\");if(null!=this.socket)throw new Error(\"Already connected\");this._current_handler=null,this._pending_replies.clear(),this._pending_messages=[];try{let e=`${this.url}`;return null!=this.args_string&&this.args_string.length>0&&(e+=`?${this.args_string}`),this.socket=new WebSocket(e,[\"bokeh\",this.token]),new Promise(((e,s)=>{this.socket.binaryType=\"arraybuffer\",this.socket.onopen=()=>this._on_open(e,s),this.socket.onmessage=e=>this._on_message(e),this.socket.onclose=e=>this._on_close(e,s),this.socket.onerror=()=>this._on_error(s)}))}catch(e){throw r.logger.error(`websocket creation failed to url: ${this.url}`),r.logger.error(` - ${e}`),e}}close(){this.closed_permanently||(r.logger.debug(`Permanently closing websocket connection ${this._number}`),this.closed_permanently=!0,null!=this.socket&&this.socket.close(1e3,`close method called on ClientConnection ${this._number}`),this.session._connection_closed())}_schedule_reconnect(e){setTimeout((()=>{this.closed_permanently||r.logger.info(`Websocket connection ${this._number} disconnected, will not attempt to reconnect`)}),e)}send(e){if(null==this.socket)throw new Error(`not connected so cannot send ${e}`);e.send(this.socket)}async send_with_reply(e){const s=await new Promise(((s,n)=>{this._pending_replies.set(e.msgid(),{resolve:s,reject:n}),this.send(e)}));if(\"ERROR\"===s.msgtype())throw new Error(`Error reply ${s.content.text}`);return s}async _pull_doc_json(){const e=c.Message.create(\"PULL-DOC-REQ\",{}),s=await this.send_with_reply(e);if(!(\"doc\"in s.content))throw new Error(\"No 'doc' field in PULL-DOC-REPLY\");return s.content.doc}async _repull_session_doc(e,s){var n;r.logger.debug(this.session?\"Repulling session\":\"Pulling session for first time\");try{const n=await this._pull_doc_json();if(null==this.session)if(this.closed_permanently)r.logger.debug(\"Got new document after connection was already closed\"),s(new Error(\"The connection has been closed\"));else{const s=i.Document.from_json(n),t=i.Document._compute_patch_since_json(n,s);if(t.events.length>0){r.logger.debug(`Sending ${t.events.length} changes from model construction back to server`);const e=c.Message.create(\"PATCH-DOC\",{},t);this.send(e)}this.session=new _.ClientSession(this,s,this.id);for(const e of this._pending_messages)this.session.handle(e);this._pending_messages=[],r.logger.debug(\"Created a new session from new pulled doc\"),e(this.session)}else this.session.document.replace_with_json(n),r.logger.debug(\"Updated existing session with new pulled doc\")}catch(e){null===(n=console.trace)||void 0===n||n.call(console,e),r.logger.error(`Failed to repull session ${e}`),s(e instanceof Error?e:`${e}`)}}_on_open(e,s){r.logger.info(`Websocket connection ${this._number} is now 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t.def(e.shared.vao,\".currentVAO?\",e.shared.vao,\".currentVAO.count:-1\")}));return m}return null}(),h=l(br,!1);return{elements:s,primitive:m,count:p,instances:h,offset:d,vao:f,vaoActive:i,elementsActive:u,static:a}}(e,d),A=function(e,t){var r=e.static,n=e.dynamic,i={};return k.forEach((function(e){var o=E(e);function f(t,a){if(e in r){var f=t(r[e]);i[o]=an((function(){return f}))}else if(e in n){var u=n[e];i[o]=on(u,(function(e,t){return a(e,t,e.invoke(t,u))}))}}switch(e){case Nt:case It:case Vt:case er:case Mt:case ir:case Xt:case Kt:case Jt:case Gt:return f((function(r){return _.commandType(r,\"boolean\",e,t.commandStr),r}),(function(t,r,n){return _.optional((function(){t.assert(r,\"typeof \"+n+'===\"boolean\"',\"invalid flag \"+e,t.commandStr)})),n}));case Wt:return f((function(r){return _.commandParameter(r,$r,\"invalid \"+e,t.commandStr),$r[r]}),(function(t,r,n){var a=t.constants.compareFuncs;return _.optional((function(){t.assert(r,n+\" in \"+a,\"invalid \"+e+\", must be one of 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_.optional((function(){e.assert(t,e.shared.isArrayLike+\"(\"+r+\")&&\"+r+\".length===4\",\"blend.color must be a 4d array\")})),V(4,(function(e){return t.def(\"+\",r,\"[\",e,\"]\")}))}));case tr:return f((function(e){return _.commandType(e,\"number\",o,t.commandStr),0|e}),(function(e,t,r){return _.optional((function(){e.assert(t,\"typeof \"+r+'===\"number\"',\"invalid stencil.mask\")})),t.def(r,\"|0\")}));case rr:return f((function(r){_.commandType(r,\"object\",o,t.commandStr);var n=r.cmp||\"keep\",a=r.ref||0,i=\"mask\"in r?r.mask:-1;return _.commandParameter(n,$r,e+\".cmp\",t.commandStr),_.commandType(a,\"number\",e+\".ref\",t.commandStr),_.commandType(i,\"number\",e+\".mask\",t.commandStr),[$r[n],a,i]}),(function(e,t,r){var n=e.constants.compareFuncs;return _.optional((function(){function a(){e.assert(t,Array.prototype.join.call(arguments,\"\"),\"invalid stencil.func\")}a(r+\"&&typeof \",r,'===\"object\"'),a('!(\"cmp\" in ',r,\")||(\",r,\".cmp in \",n,\")\")})),[t.def('\"cmp\" in 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_.commandType(r,\"number\",o+\".factor\",t.commandStr),_.commandType(n,\"number\",o+\".units\",t.commandStr),[r,n]}),(function(t,r,n){return _.optional((function(){t.assert(r,n+\"&&typeof \"+n+'===\"object\"',\"invalid \"+e)})),[r.def(n,\".factor|0\"),r.def(n,\".units|0\")]}));case qt:return f((function(e){var r=0;return\"front\"===e?r=Hr:\"back\"===e&&(r=Nr),_.command(!!r,o,t.commandStr),r}),(function(e,t,r){return _.optional((function(){e.assert(t,r+'===\"front\"||'+r+'===\"back\"',\"invalid cull.face\")})),t.def(r,'===\"front\"?',Hr,\":\",Nr)}));case Yt:return f((function(e){return _.command(\"number\"==typeof e&&e>=a.lineWidthDims[0]&&e<=a.lineWidthDims[1],\"invalid line width, must be a positive number between \"+a.lineWidthDims[0]+\" and \"+a.lineWidthDims[1],t.commandStr),e}),(function(e,t,r){return _.optional((function(){e.assert(t,\"typeof \"+r+'===\"number\"&&'+r+\">=\"+a.lineWidthDims[0]+\"&&\"+r+\"<=\"+a.lineWidthDims[1],\"invalid line width\")})),r}));case Qt:return f((function(e){return _.commandParameter(e,Zr,o,t.commandStr),Zr[e]}),(function(e,t,r){return _.optional((function(){e.assert(t,r+'===\"cw\"||'+r+'===\"ccw\"',\"invalid frontFace, must be one of cw,ccw\")})),t.def(r+'===\"cw\"?2304:'+qr)}));case Ht:return f((function(e){return _.command(me(e)&&4===e.length,\"color.mask must be length 4 array\",t.commandStr),e.map((function(e){return!!e}))}),(function(e,t,r){return _.optional((function(){e.assert(t,e.shared.isArrayLike+\"(\"+r+\")&&\"+r+\".length===4\",\"invalid color.mask\")})),V(4,(function(e){return\"!!\"+r+\"[\"+e+\"]\"}))}));case Zt:return f((function(e){_.command(\"object\"==typeof e&&e,o,t.commandStr);var r=\"value\"in e?e.value:1,n=!!e.invert;return _.command(\"number\"==typeof r&&r>=0&&r<=1,\"sample.coverage.value must be a number between 0 and 1\",t.commandStr),[r,n]}),(function(e,t,r){return _.optional((function(){e.assert(t,r+\"&&typeof \"+r+'===\"object\"',\"invalid sample.coverage\")})),[t.def('\"value\" in 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t=x[e];t&&(A[e]=t)}O(fr),O(E(or));var T=Object.keys(A).length>0,D={framebuffer:y,draw:w,shader:S,state:A,dirty:T,scopeVAO:null,drawVAO:null,useVAO:!1,attributes:{}};if(D.profile=function(e){var t,r=e.static,n=e.dynamic;if(ur in r){var a=!!r[ur];(t=an((function(e,t){return a}))).enable=a}else if(ur in n){var i=n[ur];t=on(i,(function(e,t){return e.invoke(t,i)}))}return t}(e),D.uniforms=function(e,t){var r=e.static,n=e.dynamic,a={};return Object.keys(r).forEach((function(e){var n,i=r[e];if(\"number\"==typeof i||\"boolean\"==typeof i)n=an((function(){return i}));else if(\"function\"==typeof i){var o=i._reglType;\"texture2d\"===o||\"textureCube\"===o?n=an((function(e){return e.link(i)})):\"framebuffer\"===o||\"framebufferCube\"===o?(_.command(i.color.length>0,'missing color attachment for framebuffer sent to uniform \"'+e+'\"',t.commandStr),n=an((function(e){return e.link(i.color[0])}))):_.commandRaise('invalid data for uniform \"'+e+'\"',t.commandStr)}else me(i)?n=an((function(t){return 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a=this._map.get(e);if(null==a){const s=(0,n.gcd)(t);if(s>1){t=(0,o.map)(t,(t=>t/s)),a=this._get_or_create(t);const[r,n,_]=a;a=[r,n,s],this._map.set(e,a)}else{const[r,n]=this._create_texture(t);a=[r,n,s],this._map.set(e,a)}}return a}get(t){return t.length%2==1&&(t=(0,_.concat)([t,t])),this._get_or_create(t)}}a.DashCache=c,c.__name__=\"DashCache\"},\n 414: function _(n,t,e,r,o){function u(n,t){let e,r;n>t?(e=n,r=t):(e=t,r=n);let o=e%r;for(;0!=o;)e=r,r=o,o=e%r;return r}r(),e.gcd=function(n){let t=n[0];for(let e=1;e= 0.0 ? 1.0 : -1.0;\\n}\\n\\nvoid main()\\n{\\n if (a_show_curr < 0.5) {\\n // Line segment has non-finite value at one or both ends, do not render.\\n gl_Position = vec4(-2.0, -2.0, 0.0, 1.0);\\n return;\\n }\\n\\n const float min_miter_factor_round_join_mesh = sqrt(2.0);\\n\\n int join_type = int(u_line_join + 0.5);\\n int cap_type = int(u_line_cap + 0.5);\\n float halfwidth = 0.5*(u_linewidth + u_antialias);\\n vec2 segment_along = normalize(a_point_end - a_point_start); // unit vector.\\n v_segment_length = length(a_point_end - a_point_start);\\n vec2 segment_right = right_vector(segment_along); // unit vector.\\n vec2 xy;\\n\\n bool miter_too_large_start = false;\\n bool miter_too_large_end = false;\\n\\n v_coords.y = a_position.y*halfwidth; // Overwritten later for end points.\\n\\n bool has_start_cap = a_show_prev < 0.5;\\n bool has_end_cap = a_show_next < 0.5;\\n\\n vec2 point_normal_start;\\n float cos_theta_start;\\n float turn_right_start;\\n if (has_start_cap)\\n point_normal_start = segment_right;\\n else {\\n vec2 prev_right = right_vector(normalize(a_point_start - a_point_prev));\\n point_normal_start = normalize(segment_right + prev_right);\\n cos_theta_start = dot(segment_right, point_normal_start); // Always +ve\\n turn_right_start = sign_no_zero(dot(segment_right, a_point_prev - a_point_start));\\n }\\n\\n vec2 point_normal_end;\\n float cos_theta_end;\\n float turn_right_end;\\n if (has_end_cap)\\n point_normal_end = segment_right;\\n else {\\n vec2 next_right = right_vector(normalize(a_point_next - a_point_end));\\n point_normal_end = normalize(segment_right + next_right);\\n cos_theta_end = dot(segment_right, point_normal_end); // Always +ve\\n turn_right_end = sign_no_zero(dot(segment_right, a_point_next - a_point_end));\\n }\\n\\n float miter_factor_start = 1.0 / dot(segment_right, point_normal_start);\\n float miter_factor_end = 1.0 / dot(segment_right, point_normal_end);\\n if (join_type == miter_join) {\\n // If miter too large, use bevel join instead.\\n miter_too_large_start = (miter_factor_start > u_miter_limit);\\n miter_too_large_end = (miter_factor_end > u_miter_limit);\\n }\\n\\n float sign_at_start = -sign(a_position.x); // +ve at segment start, -ve end.\\n vec2 point = sign_at_start > 0.0 ? a_point_start : a_point_end;\\n vec2 adjacent_point =\\n sign_at_start > 0.0 ? (has_start_cap ? a_point_start : a_point_prev)\\n : (has_end_cap ? a_point_end : a_point_next);\\n\\n if ( (has_start_cap && sign_at_start > 0.0) ||\\n (has_end_cap && sign_at_start < 0.0) ) {\\n // Cap.\\n xy = point - segment_right*(halfwidth*a_position.y);\\n if (cap_type == butt_cap)\\n xy -= sign_at_start*0.5*u_antialias*segment_along;\\n else\\n xy -= sign_at_start*halfwidth*segment_along;\\n }\\n else { // Join.\\n // +ve if turning to right, -ve if to left.\\n float turn_sign = sign_at_start > 0.0 ? turn_right_start : turn_right_end;\\n\\n vec2 adjacent_right = sign_at_start*normalize(right_vector(point - adjacent_point));\\n vec2 point_right = normalize(segment_right + adjacent_right);\\n float miter_factor = sign_at_start > 0.0 ? miter_factor_start : miter_factor_end;\\n bool miter_too_large = sign_at_start > 0.0 ? miter_too_large_start : miter_too_large_end;\\n\\n if (abs(a_position.x) > 1.5) {\\n // Outer point, meets prev/next segment.\\n float factor; // multiplied by halfwidth...\\n\\n if (join_type == bevel_join || (join_type == miter_join && miter_too_large))\\n factor = 1.0 / miter_factor; // cos_theta.\\n else if (join_type == round_join &&\\n miter_factor > min_miter_factor_round_join_mesh)\\n factor = 1.0;\\n else // miter, or round (small angle only).\\n factor = miter_factor;\\n\\n xy = point - point_right*(halfwidth*turn_sign*factor);\\n v_coords.y = turn_sign*halfwidth*factor / miter_factor;\\n }\\n else if (turn_sign*a_position.y < 0.0) {\\n // Inner point, meets prev/next segment.\\n float len = halfwidth*miter_factor;\\n float segment_len = v_segment_length;\\n float adjacent_len = distance(point, adjacent_point);\\n\\n if (len <= min(segment_len, adjacent_len))\\n // Normal behaviour.\\n xy = point - point_right*(len*a_position.y);\\n else\\n // For short wide line segments the inner point using the above\\n // calculation can be outside of the line. Here clipping it.\\n xy = point + segment_right*(halfwidth*turn_sign);\\n }\\n else {\\n // Point along outside edge.\\n xy = point - segment_right*(halfwidth*a_position.y);\\n if (join_type == round_join &&\\n miter_factor > min_miter_factor_round_join_mesh) {\\n xy = line_intersection(xy, segment_along,\\n point - turn_sign*point_right*halfwidth,\\n right_vector(point_right));\\n }\\n }\\n }\\n\\n vec2 pos = xy + 0.5; // Bokeh's offset.\\n pos /= u_canvas_size / u_pixel_ratio; // in 0..1\\n gl_Position = vec4(2.0*pos.x - 1.0, 1.0 - 2.0*pos.y, 0.0, 1.0);\\n\\n v_coords.x = dot(xy - a_point_start, segment_along);\\n v_flags = float(int(has_start_cap) +\\n 2*int(has_end_cap) +\\n 4*int(miter_too_large_start) +\\n 8*int(miter_too_large_end));\\n v_cos_theta_turn_right_start = cos_theta_start*turn_right_start;\\n v_cos_theta_turn_right_end = cos_theta_end*turn_right_end;\\n\\n#ifdef DASHED\\n v_length_so_far = a_length_so_far;\\n#endif\\n}\\n\"},\n 416: function _(n,t,a,i,e){i();a.default=\"\\nprecision mediump float;\\n\\nconst int butt_cap = 0;\\nconst int round_cap = 1;\\nconst int square_cap = 2;\\n\\nconst int miter_join = 0;\\nconst int round_join = 1;\\nconst int bevel_join = 2;\\n\\nuniform float u_linewidth;\\nuniform float u_antialias;\\nuniform float u_line_join;\\nuniform float u_line_cap;\\nuniform vec4 u_line_color;\\n#ifdef DASHED\\nuniform sampler2D u_dash_tex;\\nuniform vec4 u_dash_tex_info;\\nuniform float u_dash_scale;\\nuniform float u_dash_offset;\\n#endif\\n\\nvarying float v_segment_length;\\nvarying vec2 v_coords;\\nvarying float v_flags;\\nvarying float v_cos_theta_turn_right_start;\\nvarying float v_cos_theta_turn_right_end;\\n#ifdef DASHED\\nvarying float v_length_so_far;\\n#endif\\n\\nfloat cross_z(in vec2 v0, in vec2 v1)\\n{\\n return v0.x*v1.y - v0.y*v1.x;\\n}\\n\\nfloat point_line_side(in vec2 point, in vec2 start, in vec2 end)\\n{\\n // +ve if point to right of line.\\n // Alternatively could do dot product with right_vector.\\n return cross_z(point - start, end - start);\\n}\\n\\nfloat point_line_distance(in vec2 point, in vec2 start, in vec2 end)\\n{\\n return point_line_side(point, start, end) / distance(start, end);\\n}\\n\\nvec2 right_vector(in vec2 v)\\n{\\n return vec2(v.y, -v.x);\\n}\\n\\nfloat bevel_join_distance(in float sign_start, in float halfwidth)\\n{\\n float cos_theta_turn_right = sign_start > 0.0 ? v_cos_theta_turn_right_start\\n : v_cos_theta_turn_right_end;\\n float cos_theta = abs(cos_theta_turn_right);\\n float turn_right = sign(cos_theta_turn_right);\\n float distance_along = sign_start > 0.0 ? 0.0 : v_segment_length;\\n\\n // In v_coords reference frame (x is along segment, y across).\\n vec2 line_start = vec2(distance_along, halfwidth*turn_right);\\n float sin_alpha = cos_theta;\\n float cos_alpha = sqrt(1.0 - sin_alpha*sin_alpha);\\n vec2 line_along = vec2(-sign_start*turn_right*sin_alpha, -cos_alpha);\\n\\n return halfwidth + sign_start*point_line_distance(\\n v_coords, line_start, line_start+line_along);\\n}\\n\\nfloat cap(in int cap_type, in float x, in float y)\\n{\\n // x is distance along segment in direction away from end of segment,\\n // y is distance across segment.\\n if (cap_type == butt_cap)\\n return max(0.5*u_linewidth - x, abs(y));\\n else if (cap_type == square_cap)\\n return max(-x, abs(y));\\n else // cap_type == round_cap\\n return distance(vec2(min(x, 0.0), y), vec2(0.0, 0.0));\\n}\\n\\nfloat distance_to_alpha(in float dist)\\n{\\n return 1.0 - smoothstep(0.5*(u_linewidth - u_antialias),\\n 0.5*(u_linewidth + u_antialias), dist);\\n}\\n\\n#ifdef DASHED\\nfloat dash_distance(in float x)\\n{\\n // x is in direction of v_coords.x, i.e. along segment.\\n float tex_length = u_dash_tex_info.x;\\n float tex_offset = u_dash_tex_info.y;\\n float tex_dist_min = u_dash_tex_info.z;\\n float tex_dist_max = u_dash_tex_info.w;\\n\\n // Apply offset.\\n x += v_length_so_far - u_dash_scale*tex_offset + u_dash_offset;\\n\\n // Interpolate within texture to obtain distance to dash.\\n float dist = texture2D(u_dash_tex,\\n vec2(x / (tex_length*u_dash_scale), 0.0)).a;\\n\\n // Scale distance within min and max limits.\\n dist = tex_dist_min + dist*(tex_dist_max - tex_dist_min);\\n\\n return u_dash_scale*dist;\\n}\\n\\nfloat clip_dash_distance(in float x, in float offset, in float sign_along)\\n{\\n // Return clipped dash distance, sign_along is +1.0 if looking forward\\n // into next segment and -1.0 if looking backward into previous segment.\\n float half_antialias = 0.5*u_antialias;\\n\\n if (sign_along*x > half_antialias) {\\n // Outside antialias region, use usual dash distance.\\n return dash_distance(offset + x);\\n }\\n else {\\n // Inside antialias region.\\n // Dash distance at edge of antialias region clipped to half_antialias.\\n float edge_dist = min(dash_distance(offset + sign_along*half_antialias), half_antialias);\\n\\n // Physical distance from dash distance at edge of antialias region.\\n return edge_dist + sign_along*x - half_antialias;\\n }\\n}\\n\\nmat2 rotation_matrix(in float sign_start)\\n{\\n // Rotation matrix for v_coords from this segment to prev or next segment.\\n float cos_theta_turn_right = sign_start > 0.0 ? v_cos_theta_turn_right_start\\n : v_cos_theta_turn_right_end;\\n float cos_theta = abs(cos_theta_turn_right);\\n float turn_right = sign(cos_theta_turn_right);\\n\\n float sin_theta = sqrt(1.0 - cos_theta*cos_theta)*sign_start*turn_right;\\n float cos_2theta = 2.0*cos_theta*cos_theta - 1.0;\\n float sin_2theta = 2.0*sin_theta*cos_theta;\\n return mat2(cos_2theta, -sin_2theta, sin_2theta, cos_2theta);\\n}\\n#endif\\n\\nvoid main()\\n{\\n int join_type = int(u_line_join + 0.5);\\n int cap_type = int(u_line_cap + 0.5);\\n float halfwidth = 0.5*(u_linewidth + u_antialias);\\n float half_antialias = 0.5*u_antialias;\\n\\n // Extract flags.\\n int flags = int(v_flags + 0.5);\\n bool miter_too_large_end = (flags / 8 > 0);\\n flags -= 8*int(miter_too_large_end);\\n bool miter_too_large_start = (flags / 4 > 0);\\n flags -= 4*int(miter_too_large_start);\\n bool has_end_cap = (flags / 2 > 0);\\n flags -= 2*int(has_end_cap);\\n bool has_start_cap = flags > 0;\\n\\n float dist = v_coords.y; // For straight segment, and miter join.\\n\\n // Along-segment coords with respect to end of segment, +ve inside segment\\n // so equivalent to v_coords.x at start of segment.\\n float end_coords_x = v_segment_length - v_coords.x;\\n\\n if (v_coords.x <= half_antialias) {\\n // At start of segment, either cap or join.\\n if (has_start_cap)\\n dist = cap(cap_type, v_coords.x, v_coords.y);\\n else if (join_type == round_join)\\n dist = distance(v_coords, vec2(0.0, 0.0));\\n else if (join_type == bevel_join ||\\n (join_type == miter_join && miter_too_large_start))\\n dist = max(abs(dist), bevel_join_distance(1.0, halfwidth));\\n // else a miter join which uses the default dist calculation.\\n }\\n else if (end_coords_x <= half_antialias) {\\n // At end of segment, either cap or join.\\n if (has_end_cap)\\n dist = cap(cap_type, end_coords_x, v_coords.y);\\n else if (join_type == round_join)\\n dist = distance(v_coords, vec2(v_segment_length, 0));\\n else if ((join_type == bevel_join ||\\n (join_type == miter_join && miter_too_large_end)))\\n dist = max(abs(dist), bevel_join_distance(-1.0, halfwidth));\\n // else a miter join which uses the default dist calculation.\\n }\\n\\n float alpha = distance_to_alpha(abs(dist));\\n\\n#ifdef DASHED\\n if (u_dash_tex_info.x >= 0.0) {\\n // Dashes in straight segments (outside of joins) are easily calculated.\\n dist = dash_distance(v_coords.x);\\n\\n if (!has_start_cap && cap_type == butt_cap) {\\n if (v_coords.x < half_antialias) {\\n // Outer of start join rendered solid color or not at all\\n // depending on whether corner point is in dash or gap, with\\n // antialiased ends.\\n if (dash_distance(0.0) > 0.0) {\\n // Corner is solid color.\\n dist = max(dist, min(half_antialias, -v_coords.x));\\n // Avoid visible antialiasing band between corner and dash.\\n dist = max(dist, dash_distance(half_antialias));\\n }\\n else {\\n // Use large negative value so corner not colored.\\n dist = -halfwidth;\\n\\n if (v_coords.x > -half_antialias) {\\n // Consider antialias region of dash after start region.\\n float edge_dist = min(dash_distance(half_antialias), half_antialias);\\n dist = max(dist, edge_dist + v_coords.x - half_antialias);\\n }\\n }\\n }\\n\\n vec2 prev_coords = rotation_matrix(1.0)*v_coords;\\n\\n if (abs(prev_coords.y) < halfwidth && prev_coords.x < half_antialias) {\\n // Extend dashes across from end of previous segment, with antialiased end.\\n float new_dist = clip_dash_distance(prev_coords.x, 0.0, -1.0);\\n new_dist = min(new_dist, 0.5*u_linewidth - abs(prev_coords.y));\\n dist = max(dist, new_dist);\\n }\\n }\\n\\n if (!has_end_cap && cap_type == butt_cap) {\\n if (end_coords_x < half_antialias) {\\n // Similar for end join.\\n if (dash_distance(v_segment_length) > 0.0) {\\n // Corner is solid color.\\n dist = max(dist, min(half_antialias, -end_coords_x));\\n // Avoid visible antialiasing band between corner and dash.\\n dist = max(dist, dash_distance(v_segment_length - half_antialias));\\n }\\n else {\\n // Use large negative value so corner not colored.\\n dist = -halfwidth;\\n\\n if (end_coords_x > -half_antialias) {\\n // Consider antialias region of dash before end region.\\n float edge_dist = min(dash_distance(v_segment_length - half_antialias),\\n half_antialias);\\n dist = max(dist, edge_dist + end_coords_x - half_antialias);\\n }\\n }\\n }\\n\\n vec2 next_coords = rotation_matrix(-1.0)*(v_coords - vec2(v_segment_length, 0.0));\\n\\n if (abs(next_coords.y) < halfwidth && next_coords.x > -half_antialias) {\\n // Extend dashes across from next segment, with antialiased end.\\n float new_dist = clip_dash_distance(next_coords.x, v_segment_length, 1.0);\\n new_dist = min(new_dist, 0.5*u_linewidth - abs(next_coords.y));\\n dist = max(dist, new_dist);\\n }\\n }\\n\\n dist = cap(cap_type, dist, v_coords.y);\\n\\n float dash_alpha = distance_to_alpha(dist);\\n alpha = min(alpha, dash_alpha);\\n }\\n#endif\\n\\n alpha = u_line_color.a*alpha;\\n gl_FragColor = vec4(u_line_color.rgb*alpha, alpha); // Premultiplied alpha.\\n}\\n\"},\n 417: function _(n,i,e,t,a){t();e.default=\"\\nprecision mediump float;\\n\\nattribute vec2 a_position;\\nattribute vec2 a_center;\\nattribute float a_width;\\nattribute float a_height;\\nattribute float a_angle; // In radians\\nattribute float a_linewidth;\\nattribute vec4 a_line_color;\\nattribute vec4 a_fill_color;\\nattribute float a_line_cap;\\nattribute float a_line_join;\\nattribute float a_show;\\n#ifdef HATCH\\nattribute float a_hatch_pattern;\\nattribute float a_hatch_scale;\\nattribute float a_hatch_weight;\\nattribute vec4 a_hatch_color;\\n#endif\\n\\nuniform float u_pixel_ratio;\\nuniform vec2 u_canvas_size;\\nuniform float u_antialias;\\nuniform float u_size_hint;\\n\\nvarying float v_linewidth;\\nvarying vec2 v_size; // 2D size for rects compared to 1D for markers.\\nvarying vec4 v_line_color;\\nvarying vec4 v_fill_color;\\nvarying float v_line_cap;\\nvarying float v_line_join;\\nvarying vec2 v_coords;\\n#ifdef HATCH\\nvarying float v_hatch_pattern;\\nvarying float v_hatch_scale;\\nvarying float v_hatch_weight;\\nvarying vec4 v_hatch_color;\\nvarying vec2 v_hatch_coords;\\n#endif\\n\\nvoid main()\\n{\\n if (a_show < 0.5) {\\n // Do not show this rect.\\n gl_Position = vec4(-2.0, -2.0, 0.0, 1.0);\\n return;\\n }\\n\\n v_size = vec2(a_width, a_height);\\n v_linewidth = a_linewidth;\\n v_line_color = a_line_color;\\n v_fill_color = a_fill_color;\\n v_line_cap = a_line_cap;\\n v_line_join = a_line_join;\\n\\n if (v_linewidth < 1.0) {\\n // Linewidth less than 1 is implemented as 1 but with reduced alpha.\\n v_line_color.a *= v_linewidth;\\n v_linewidth = 1.0;\\n }\\n\\n#ifdef HATCH\\n v_hatch_pattern = a_hatch_pattern;\\n v_hatch_scale = a_hatch_scale;\\n v_hatch_weight = a_hatch_weight;\\n v_hatch_color = a_hatch_color;\\n#endif\\n\\n vec2 enclosing_size;\\n // Need extra size of (v_linewidth+u_antialias) if edge of marker parallel to\\n // edge of bounding box. If symmetric spike towards edge then multiply by\\n // 1/cos(theta) where theta is angle between spike and bbox edges.\\n int size_hint = int(u_size_hint + 0.5);\\n if (size_hint == 1) // Dash\\n enclosing_size = vec2(v_size.x + v_linewidth + u_antialias,\\n v_linewidth + u_antialias);\\n else if (size_hint == 2) // Dot\\n enclosing_size = 0.25*v_size + u_antialias;\\n else if (size_hint == 3) // Diamond\\n enclosing_size = vec2(v_size.x*(2.0/3.0) + (v_linewidth + u_antialias)*1.20185,\\n v_size.y + (v_linewidth + u_antialias)*1.80278);\\n else if (size_hint == 4) // Hex\\n enclosing_size = v_size + (v_linewidth + u_antialias)*vec2(2.0/sqrt(3.0), 1.0);\\n else if (size_hint == 5) // Square pin\\n enclosing_size = v_size + (v_linewidth + u_antialias)*3.1;\\n else if (size_hint == 6) // Triangle\\n enclosing_size = vec2(v_size.x + (v_linewidth + u_antialias)*sqrt(3.0),\\n v_size.y*(2.0/sqrt(3.0)) + (v_linewidth + u_antialias)*2.0);\\n else if (size_hint == 7) // Triangle pin\\n enclosing_size = v_size + (v_linewidth + u_antialias)*vec2(4.8, 6.0);\\n else if (size_hint == 8) // Star\\n enclosing_size = vec2(v_size.x*0.95106 + (v_linewidth + u_antialias)*3.0,\\n v_size.y + (v_linewidth + u_antialias)*3.2);\\n else\\n enclosing_size = v_size + v_linewidth + u_antialias;\\n\\n // Coordinates in rotated frame with respect to center of marker, used for\\n // distance functions in fragment shader.\\n v_coords = a_position*enclosing_size;\\n\\n float c = cos(-a_angle);\\n float s = sin(-a_angle);\\n mat2 rotation = mat2(c, -s, s, c);\\n\\n vec2 pos = a_center + rotation*v_coords;\\n#ifdef HATCH\\n // Coordinates for hatching in unrotated frame of reference.\\n v_hatch_coords = pos - 0.5;\\n#endif\\n pos += 0.5; // Make up for Bokeh's offset.\\n pos /= u_canvas_size / u_pixel_ratio; // 0 to 1.\\n gl_Position = vec4(2.0*pos.x - 1.0, 1.0 - 2.0*pos.y, 0.0, 1.0);\\n}\\n\"},\n 418: function _(n,i,e,t,a){t();e.default=\"\\nprecision mediump float;\\n\\nconst float SQRT2 = sqrt(2.0);\\nconst float SQRT3 = sqrt(3.0);\\nconst float PI = 3.14159265358979323846;\\n\\nconst int butt_cap = 0;\\nconst int round_cap = 1;\\nconst int square_cap = 2;\\n\\nconst int miter_join = 0;\\nconst int round_join = 1;\\nconst int bevel_join = 2;\\n\\n#ifdef HATCH\\nconst int hatch_dot = 1;\\nconst int hatch_ring = 2;\\nconst int hatch_horizontal_line = 3;\\nconst int hatch_vertical_line = 4;\\nconst int hatch_cross = 5;\\nconst int hatch_horizontal_dash = 6;\\nconst int hatch_vertical_dash = 7;\\nconst int hatch_spiral = 8;\\nconst int hatch_right_diagonal_line = 9;\\nconst int hatch_left_diagonal_line = 10;\\nconst int hatch_diagonal_cross = 11;\\nconst int hatch_right_diagonal_dash = 12;\\nconst int hatch_left_diagonal_dash = 13;\\nconst int hatch_horizontal_wave = 14;\\nconst int hatch_vertical_wave = 15;\\nconst int hatch_criss_cross = 16;\\n#endif\\n\\nuniform float u_antialias;\\n\\nvarying float v_linewidth;\\nvarying vec2 v_size;\\nvarying vec4 v_line_color;\\nvarying vec4 v_fill_color;\\nvarying float v_line_cap;\\nvarying float v_line_join;\\nvarying vec2 v_coords;\\n#ifdef HATCH\\nvarying float v_hatch_pattern;\\nvarying float v_hatch_scale;\\nvarying float v_hatch_weight;\\nvarying vec4 v_hatch_color;\\nvarying vec2 v_hatch_coords;\\n#endif\\n\\n// Lines within the marker (dot, cross, x and y) are added at the end as they are\\n// on top of the fill rather than astride it.\\n#if defined(USE_CIRCLE_DOT) || defined(USE_DIAMOND_DOT) || defined(USE_DOT) || defined(USE_HEX_DOT) || defined(USE_SQUARE_DOT) || defined(USE_STAR_DOT) || defined(USE_TRIANGLE_DOT)\\n #define APPEND_DOT\\n#endif\\n\\n#if defined(USE_CIRCLE_CROSS) || defined(USE_SQUARE_CROSS)\\n #define APPEND_CROSS\\n#endif\\n\\n#ifdef USE_DIAMOND_CROSS\\n #define APPEND_CROSS_2\\n#endif\\n\\n#ifdef USE_CIRCLE_X\\n #define APPEND_X\\n #define APPEND_X_LEN (0.5*v_size.x)\\n#endif\\n\\n#ifdef USE_SQUARE_X\\n #define APPEND_X\\n #define APPEND_X_LEN (v_size.x/SQRT2)\\n#endif\\n\\n#ifdef USE_CIRCLE_Y\\n #define APPEND_Y\\n#endif\\n\\n#if defined(USE_ASTERISK) || defined(USE_CROSS) || defined(USE_DASH) || defined(USE_DOT) || defined(USE_X) || defined(USE_Y)\\n // No fill.\\n #define LINE_ONLY\\n#endif\\n\\n#if defined(LINE_ONLY) || defined(APPEND_CROSS) || defined(APPEND_CROSS_2) || defined(APPEND_X) || defined(APPEND_Y)\\nfloat end_cap_distance(in vec2 p, in vec2 end_point, in vec2 unit_direction, in int line_cap)\\n{\\n vec2 offset = p - end_point;\\n if (line_cap == butt_cap)\\n return dot(offset, unit_direction) + 0.5*v_linewidth;\\n else if (line_cap == square_cap)\\n return dot(offset, unit_direction);\\n else if (line_cap == round_cap && dot(offset, unit_direction) > 0.0)\\n return length(offset);\\n else\\n // Default is outside of line and should be -0.5*(v_linewidth+u_antialias) or less,\\n // so here avoid the multiplication.\\n return -v_linewidth-u_antialias;\\n}\\n#endif\\n\\n#if !(defined(LINE_ONLY) || defined(USE_SQUARE_PIN) || defined(USE_TRIANGLE_PIN))\\n// For line join at a vec2 corner where 2 line segments meet, consider bevel points which are the 2\\n// points obtained by moving half a linewidth away from the corner point in the directions normal to\\n// the line segments. The line through these points is the bevel line, characterised by a vec2\\n// unit_normal and offset distance from the corner point. Edge of bevel join straddles this line,\\n// round join occurs outside of this line centred on the corner point. In general\\n// offset = (linewidth/2)*sin(alpha/2)\\n// where alpha is the angle between the 2 line segments at the corner.\\nfloat line_join_distance_no_miter(\\n in vec2 p, in vec2 corner, in vec2 unit_normal, in float offset, in int line_join)\\n{\\n // Simplified version of line_join_distance ignoring miter which most markers do implicitly\\n // as they are composed of straight line segments.\\n float dist_outside = dot((p - corner), unit_normal) - offset;\\n\\n if (line_join == bevel_join && dist_outside > -0.5*u_antialias)\\n return dist_outside + 0.5*v_linewidth;\\n else if (dist_outside > 0.0) // round_join\\n return distance(p, corner);\\n else\\n // Default is outside of line and should be -0.5*(v_linewidth+u_antialias) or less,\\n // so here avoid the multiplication.\\n return -v_linewidth-u_antialias;\\n}\\n#endif\\n\\n#if defined(USE_SQUARE_PIN) || defined(USE_TRIANGLE_PIN)\\n// Line join distance including miter but only one-sided check as assuming use of symmetry in\\n// calling function.\\nfloat line_join_distance_incl_miter(\\n in vec2 p, in vec2 corner, in vec2 unit_normal, in float offset, in int line_join,\\n vec2 miter_unit_normal)\\n{\\n float dist_outside = dot((p - corner), unit_normal) - offset;\\n\\n if (line_join == miter_join && dist_outside > 0.0)\\n return dot((p - corner), miter_unit_normal);\\n else if (line_join == bevel_join && dist_outside > -0.5*u_antialias)\\n return dist_outside + 0.5*v_linewidth;\\n else if (dist_outside > 0.0) // round_join\\n return distance(p, corner);\\n else\\n return -v_linewidth-u_antialias;\\n}\\n#endif\\n\\n#if defined(APPEND_CROSS) || defined(APPEND_X) || defined(USE_ASTERISK) || defined(USE_CROSS) || defined(USE_X)\\nfloat one_cross(in vec2 p, in int line_cap, in float len)\\n{\\n p = abs(p);\\n p = (p.y > p.x) ? p.yx : p.xy;\\n float dist = p.y;\\n float end_dist = end_cap_distance(p, vec2(len, 0.0), vec2(1.0, 0.0), line_cap);\\n return max(dist, end_dist);\\n}\\n#endif\\n\\n#ifdef APPEND_CROSS_2\\nfloat one_cross_2(in vec2 p, in int line_cap, in vec2 lengths)\\n{\\n // Cross with different length in x and y directions.\\n p = abs(p);\\n bool switch_xy = (p.y > p.x);\\n p = switch_xy ? p.yx : p.xy;\\n float len = switch_xy ? lengths.y : lengths.x;\\n float dist = p.y;\\n float end_dist = end_cap_distance(p, vec2(len, 0.0), vec2(1.0, 0.0), line_cap);\\n return max(dist, end_dist);\\n}\\n#endif\\n\\n#if defined(APPEND_Y) || defined(USE_Y)\\nfloat one_y(in vec2 p, in int line_cap, in float len)\\n{\\n p = vec2(abs(p.x), -p.y);\\n\\n // End point of line to right is (1/2, 1/3)*len*SQRT3.\\n // Unit vector along line is (1/2, 1/3)*k where k = 6/SQRT13.\\n const float k = 6.0/sqrt(13.0);\\n vec2 unit_along = vec2(0.5*k, k/3.0);\\n vec2 end_point = vec2(0.5*len*SQRT3, len*SQRT3/3.0);\\n float dist = max(abs(dot(p, vec2(-unit_along.y, unit_along.x))),\\n end_cap_distance(p, end_point, unit_along, line_cap));\\n\\n if (p.y < 0.0) {\\n // Vertical line.\\n float vert_dist = max(p.x,\\n end_cap_distance(p, vec2(0.0, -len), vec2(0.0, -1.0), line_cap));\\n dist = min(dist, vert_dist);\\n }\\n return dist;\\n}\\n#endif\\n\\n// One marker_distance function per marker type.\\n// Distance is zero on edge of marker, +ve outside and -ve inside.\\n\\n#ifdef USE_ASTERISK\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n // Assuming v_size.x == v.size_y\\n vec2 p_diag = vec2((p.x + p.y)/SQRT2, (p.x - p.y)/SQRT2);\\n float len = 0.5*v_size.x;\\n return min(one_cross(p, line_cap, len), // cross\\n one_cross(p_diag, line_cap, len)); // x\\n}\\n#endif\\n\\n#if defined(USE_CIRCLE) || defined(USE_CIRCLE_CROSS) || defined(USE_CIRCLE_DOT) || defined(USE_CIRCLE_X) || defined(USE_CIRCLE_Y)\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n // Assuming v_size.x == v.size_y\\n return length(p) - 0.5*v_size.x;\\n}\\n#endif\\n\\n#ifdef USE_CROSS\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n // Assuming v_size.x == v.size_y\\n return one_cross(p, line_cap, 0.5*v_size.x);\\n}\\n#endif\\n\\n#ifdef USE_DASH\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n p = abs(p);\\n float dist = p.y;\\n float end_dist = end_cap_distance(p, vec2(0.5*v_size.x, 0.0), vec2(1.0, 0.0), line_cap);\\n return max(dist, end_dist);\\n}\\n#endif\\n\\n#if defined(USE_DIAMOND) || defined(USE_DIAMOND_CROSS) || defined(USE_DIAMOND_DOT)\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n // Assuming v_size.x == v.size_y\\n // Only need to consider +ve quadrant, the 2 end points are (2r/3, 0) and (0, r)\\n // where r = radius = v_size.x/2.\\n // Line has outward-facing unit normal vec2(1, 2/3)/k where k = SQRT13/3\\n // hence vec2(3, 2)/SQRT13, and distance from origin of 2r/(3k) = 2r/SQRT13.\\n p = abs(p);\\n float r = 0.5*v_size.x;\\n const float SQRT13 = sqrt(13.0);\\n float dist = dot(p, vec2(3.0, 2.0))/SQRT13 - 2.0*r/SQRT13;\\n\\n if (line_join != miter_join) {\\n dist = max(dist, line_join_distance_no_miter(\\n p, vec2(0.0, r), vec2(0.0, 1.0), v_linewidth/SQRT13, line_join));\\n\\n dist = max(dist, line_join_distance_no_miter(\\n p, vec2(r*2.0/3.0, 0.0), vec2(1.0, 0.0), v_linewidth*(1.5/SQRT13), line_join));\\n }\\n\\n return dist;\\n}\\n#endif\\n\\n#ifdef USE_DOT\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n // Dot is always appended.\\n return v_linewidth+u_antialias;\\n}\\n#endif\\n\\n#if defined(USE_HEX) || defined(USE_HEX_DOT)\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n // A regular hexagon has v_size.x == v.size_y = r where r is the length of\\n // each of the 3 sides of the 6 equilateral triangles that comprise the hex.\\n // Only consider +ve quadrant, the 3 corners are at (0, h), (rx/2, h), (rx, 0)\\n // where rx = 0.5*v_size.x, ry = 0.5*v_size.y and h = ry*SQRT3/2.\\n // Sloping line has outward normal vec2(h, rx/2). Length of this is\\n // len = sqrt(h**2 + rx**2/4) to give unit normal (h, rx/2)/len and distance\\n // from origin of this line is rx*h/len.\\n p = abs(p);\\n float rx = v_size.x/2.0;\\n float h = v_size.y*(SQRT3/4.0);\\n float len_normal = sqrt(h*h + 0.25*rx*rx);\\n vec2 unit_normal = vec2(h, 0.5*rx) / len_normal;\\n float dist = max(dot(p, unit_normal) - rx*h/len_normal, // Distance from sloping line.\\n p.y - h); // Distance from horizontal line.\\n\\n if (line_join != miter_join) {\\n dist = max(dist, line_join_distance_no_miter(\\n p, vec2(rx, 0.0), vec2(1.0, 0.0), 0.5*v_linewidth*unit_normal.x, line_join));\\n\\n unit_normal = normalize(unit_normal + vec2(0.0, 1.0)); // At (rx/2, h) corner.\\n dist = max(dist, line_join_distance_no_miter(\\n p, vec2(0.5*rx, h), unit_normal, 0.5*v_linewidth*unit_normal.y, line_join));\\n }\\n return dist;\\n}\\n#endif\\n\\n#ifdef USE_PLUS\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n // Assuming v_size.x == v.size_y\\n // Only need to consider one octant, the +ve quadrant with x >= y.\\n p = abs(p);\\n p = (p.y > p.x) ? p.yx : p.xy;\\n\\n // 3 corners are (r, 0), (r, 3r/8) and (3r/8, 3r/8).\\n float r = 0.5*v_size.x;\\n p = p - vec2(r, 0.375*r); // Distance with respect to outside corner\\n float dist = max(p.x, p.y);\\n\\n if (line_join != miter_join) {\\n // Outside corner\\n dist = max(dist, line_join_distance_no_miter(\\n p, vec2(0.0, 0.0), vec2(1.0/SQRT2, 1.0/SQRT2), v_linewidth/(2.0*SQRT2), line_join));\\n\\n // Inside corner\\n dist = min(dist, -line_join_distance_no_miter(\\n p, vec2(-5.0*r/8.0, 0.0), vec2(-1.0/SQRT2, -1.0/SQRT2), v_linewidth/(2.0*SQRT2), line_join));\\n }\\n\\n return dist;\\n}\\n#endif\\n\\n#if defined(USE_SQUARE) || defined(USE_SQUARE_CROSS) || defined(USE_SQUARE_DOT) || defined(USE_SQUARE_X)\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n vec2 p2 = abs(p) - v_size/2.0; // Offset from corner\\n float dist = max(p2.x, p2.y);\\n\\n if (line_join != miter_join)\\n dist = max(dist, line_join_distance_no_miter(\\n p2, vec2(0.0, 0.0), vec2(1.0/SQRT2, 1.0/SQRT2), v_linewidth/(2.0*SQRT2), line_join));\\n\\n return dist;\\n}\\n#endif\\n\\n#ifdef USE_SQUARE_PIN\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n // Assuming v_size.x == v.size_y\\n p = abs(p);\\n p = (p.y > p.x) ? p.yx : p.xy;\\n // p is in octant between y=0 and y=x.\\n // Quadratic bezier curve passes through (r, r), (11r/16, 0) and (r, -r).\\n // Circular arc that passes through the same points has center at\\n // x = r + 231r/160 = 2.44275r and y = 0 and hence radius is\\n // x - 11r/16 = 1.75626 precisely.\\n float r = 0.5*v_size.x;\\n float center_x = r*2.44375;\\n float radius = r*1.75626;\\n float dist = radius - distance(p, vec2(center_x, 0.0));\\n\\n // Magic number is 0.5*sin(atan(8/5) - pi/4)\\n dist = max(dist, line_join_distance_incl_miter(\\n p, vec2(r, r), vec2(1.0/SQRT2, 1.0/SQRT2), v_linewidth*0.1124297533493792, line_join,\\n vec2(8.0/sqrt(89.0), -5.0/sqrt(89.0))));\\n\\n return dist;\\n}\\n#endif\\n\\n#if defined(USE_STAR) || defined(USE_STAR_DOT)\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n // Assuming v_size.x == v.size_y\\n const float SQRT5 = sqrt(5.0);\\n const float COS72 = 0.25*(SQRT5 - 1.0);\\n const float SIN72 = sqrt((5.0+SQRT5) / 8.0);\\n\\n float angle = atan(p.x, p.y); // In range -pi to +pi clockwise from +y direction.\\n angle = mod(angle, 0.4*PI) - 0.2*PI; // In range -pi/5 to +pi/5 clockwise from +y direction.\\n p = length(p)*vec2(cos(angle), abs(sin(angle))); // (x,y) in pi/10 (36 degree) sector.\\n\\n // 2 corners are at (r, 0) and (r-a*SIN72, a*COS72) where a = r sqrt(5-2*sqrt(5)).\\n // Line has outward-facing unit normal vec2(COS72, SIN72) and distance from\\n // origin of dot(vec2(r, 0), vec2(COS72, SIN72)) = r*COS72\\n float r = 0.5*v_size.x;\\n float a = r*sqrt(5.0 - 2.0*SQRT5);\\n float dist = dot(p, vec2(COS72, SIN72)) - r*COS72;\\n\\n if (line_join != miter_join) {\\n // Outside corner\\n dist = max(dist, line_join_distance_no_miter(\\n p, vec2(r, 0.0), vec2(1.0, 0.0), v_linewidth*(0.5*COS72), line_join));\\n\\n // Inside corner\\n const float COS36 = sqrt(0.5 + COS72/2.0);\\n const float SIN36 = sqrt(0.5 - COS72/2.0);\\n dist = min(dist, -line_join_distance_no_miter(\\n p, vec2(r-a*SIN72, a*COS72), vec2(-COS36, -SIN36), v_linewidth*(0.5*COS36), line_join));\\n }\\n\\n return dist;\\n}\\n#endif\\n\\n#if defined(USE_TRIANGLE) || defined(USE_TRIANGLE_DOT) || defined(USE_INVERTED_TRIANGLE)\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n // Assuming v_size.x == v.size_y\\n // For normal triangle 3 corners are at (-r, a), (r, a), (0, a-h)=(0, -2h/3)\\n // given r = radius = v_size.x/2, h = SQRT3*r, a = h/3.\\n // Sloping line has outward-facing unit normal vec2(h, -r)/2r = vec2(SQRT3, -1)/2\\n // and distance from origin of a. Horizontal line has outward-facing unit normal\\n // vec2(0, 1) and distance from origin of a.\\n float r = 0.5*v_size.x;\\n float a = r*SQRT3/3.0;\\n\\n // Only need to consider +ve x.\\n#ifdef USE_INVERTED_TRIANGLE\\n p = vec2(abs(p.x), -p.y);\\n#else\\n p = vec2(abs(p.x), p.y);\\n#endif\\n\\n float dist = max(0.5*dot(p, vec2(SQRT3, -1.0)) - a, // Distance from sloping line.\\n p.y - a); // Distance from horizontal line.\\n\\n if (line_join != miter_join) {\\n dist = max(dist, line_join_distance_no_miter(\\n p, vec2(0.0, -(2.0/SQRT3)*r), vec2(0.0, -1.0), v_linewidth*0.25, line_join));\\n\\n dist = max(dist, line_join_distance_no_miter(\\n p, vec2(r, a), vec2(SQRT3/2.0, 0.5), v_linewidth*0.25, line_join));\\n }\\n\\n return dist;\\n}\\n#endif\\n\\n#ifdef USE_TRIANGLE_PIN\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n // Assuming v_size.x == v.size_y\\n float angle = atan(p.x, -p.y); // In range -pi to +pi.\\n angle = mod(angle, PI*2.0/3.0) - PI/3.0; // In range -pi/3 to pi/3.\\n p = length(p)*vec2(cos(angle), abs(sin(angle))); // (x,y) in range 0 to pi/3.\\n // Quadratic bezier curve passes through (a, r), ((a+b)/2, 0) and (a, -r) where\\n // a = r/SQRT3, b = 3a/8 = r SQRT3/8. Circular arc that passes through the same points has\\n // center at (a+x, 0) and radius x+c where c = (a-b)/2 and x = (r**2 - c**2) / (2c).\\n // Ignore r factor until the end so can use const.\\n const float a = 1.0/SQRT3;\\n const float b = SQRT3/8.0;\\n const float c = (a-b)/2.0;\\n const float x = (1.0 - c*c) / (2.0*c);\\n const float center_x = x + a;\\n const float radius = x + c;\\n float r = 0.5*v_size.x;\\n float dist = r*radius - distance(p, vec2(r*center_x, 0.0));\\n\\n // Magic number is 0.5*sin(atan(8*sqrt(3)/5) - pi/3)\\n dist = max(dist, line_join_distance_incl_miter(\\n p, vec2(a*r, r), vec2(0.5, 0.5*SQRT3), v_linewidth*0.0881844526878324, line_join,\\n vec2(8.0*SQRT3, -5.0)/sqrt(217.0)));\\n\\n return dist;\\n}\\n#endif\\n\\n#ifdef USE_X\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n // Assuming v_size.x == v.size_y\\n p = vec2((p.x + p.y)/SQRT2, (p.x - p.y)/SQRT2);\\n return one_cross(p, line_cap, 0.5*v_size.x);\\n}\\n#endif\\n\\n#ifdef USE_Y\\nfloat marker_distance(in vec2 p, in int line_cap, in int line_join)\\n{\\n // Assuming v_size.x == v.size_y\\n return one_y(p, line_cap, 0.5*v_size.x);\\n}\\n#endif\\n\\n// Convert distance from edge of marker to fraction in range 0 to 1, depending\\n// on antialiasing width.\\nfloat distance_to_fraction(in float dist)\\n{\\n return 1.0 - smoothstep(-0.5*u_antialias, 0.5*u_antialias, dist);\\n}\\n\\n// Return fraction from 0 (no fill color) to 1 (full fill color).\\nfloat fill_fraction(in float dist)\\n{\\n return distance_to_fraction(dist);\\n}\\n\\n// Return fraction in range 0 (no line color) to 1 (full line color).\\nfloat line_fraction(in float dist)\\n{\\n return distance_to_fraction(abs(dist) - 0.5*v_linewidth);\\n}\\n\\n// Return fraction (in range 0 to 1) of a color, with premultiplied alpha.\\nvec4 fractional_color(in vec4 color, in float fraction)\\n{\\n color.a *= fraction;\\n color.rgb *= color.a;\\n return color;\\n}\\n\\n// Blend colors that have premultiplied alpha.\\nvec4 blend_colors(in vec4 src, in vec4 dest)\\n{\\n return (1.0 - src.a)*dest + src;\\n}\\n\\n#ifdef APPEND_DOT\\nfloat dot_fraction(in vec2 p)\\n{\\n // Assuming v_size.x == v_size.y\\n float radius = 0.125*v_size.x;\\n float dot_distance = max(length(p) - radius, -0.5*u_antialias);\\n return fill_fraction(dot_distance);\\n}\\n#endif\\n\\n#ifdef HATCH\\n// Wrap coordinate(s) by removing integer part to give distance from center of\\n// repeat, in the range -0.5 to +0.5.\\nfloat wrap(in float x)\\n{\\n return fract(x) - 0.5;\\n}\\n\\nvec2 wrap(in vec2 xy)\\n{\\n return fract(xy) - 0.5;\\n}\\n\\n// Return fraction from 0 (no hatch color) to 1 (full hatch color).\\nfloat hatch_fraction(in vec2 coords, in int hatch_pattern)\\n{\\n float scale = v_hatch_scale; // Hatch repeat distance.\\n\\n // Coordinates and linewidth/halfwidth are scaled to hatch repeat distance.\\n coords = coords / scale;\\n float halfwidth = 0.5*v_hatch_weight / scale; // Half the hatch linewidth.\\n\\n // Default is to return fraction of zero, i.e. no pattern.\\n float dist = u_antialias;\\n\\n if (hatch_pattern == hatch_dot) {\\n const float dot_radius = 0.25;\\n dist = length(wrap(coords)) - dot_radius;\\n }\\n else if (hatch_pattern == hatch_ring) {\\n const float ring_radius = 0.25;\\n dist = abs(length(wrap(coords)) - ring_radius) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_horizontal_line) {\\n dist = abs(wrap(coords.y)) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_vertical_line) {\\n dist = abs(wrap(coords.x)) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_cross) {\\n dist = min(abs(wrap(coords.x)), abs(wrap(coords.y))) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_horizontal_dash) {\\n // Dashes have square caps.\\n const float halflength = 0.25;\\n dist = max(abs(wrap(coords.y)),\\n abs(wrap(coords.x) + 0.25) - halflength) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_vertical_dash) {\\n const float halflength = 0.25;\\n dist = max(abs(wrap(coords.x)),\\n abs(wrap(coords.y) + 0.25) - halflength) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_spiral) {\\n vec2 wrap2 = wrap(coords);\\n float angle = wrap(atan(wrap2.y, wrap2.x) / (2.0*PI));\\n // Canvas spiral radius increases by scale*pi/15 each rotation.\\n const float dr = PI/15.0;\\n float radius = length(wrap2);\\n // At any angle, spiral lines are equally spaced dr apart.\\n // Find distance to nearest of these lines.\\n float frac = fract((radius - dr*angle) / dr); // 0 to 1.\\n dist = dr*(abs(frac - 0.5));\\n dist = min(dist, radius) - halfwidth; // Consider center point also.\\n }\\n else if (hatch_pattern == hatch_right_diagonal_line) {\\n dist = abs(wrap(2.0*coords.x + coords.y))/sqrt(5.0) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_left_diagonal_line) {\\n dist = abs(wrap(2.0*coords.x - coords.y))/sqrt(5.0) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_diagonal_cross) {\\n coords = vec2(coords.x + coords.y + 0.5, coords.x - coords.y + 0.5);\\n dist = min(abs(wrap(coords.x)), abs(wrap(coords.y))) / SQRT2 - halfwidth;\\n }\\n else if (hatch_pattern == hatch_right_diagonal_dash) {\\n float across = coords.x + coords.y + 0.5;\\n dist = abs(wrap(across)) / SQRT2; // Distance to nearest solid line.\\n\\n across = floor(across); // Offset for dash.\\n float along = wrap(0.5*(coords.x - coords.y + across));\\n const float halflength = 0.25;\\n along = abs(along) - halflength; // Distance along line.\\n\\n dist = max(dist, along) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_left_diagonal_dash) {\\n float across = coords.x - coords.y + 0.5;\\n dist = abs(wrap(across)) / SQRT2; // Distance to nearest solid line.\\n\\n across = floor(across); // Offset for dash.\\n float along = wrap(0.5*(coords.x + coords.y + across));\\n const float halflength = 0.25;\\n along = abs(along) - halflength; // Distance along line.\\n\\n dist = max(dist, along) - halfwidth;\\n }\\n else if (hatch_pattern == hatch_horizontal_wave) {\\n float wrapx = wrap(coords.x);\\n float wrapy = wrap(coords.y - 0.25 + abs(wrapx));\\n dist = abs(wrapy) / SQRT2 - halfwidth;\\n }\\n else if (hatch_pattern == hatch_vertical_wave) {\\n float wrapy = wrap(coords.y);\\n float wrapx = wrap(coords.x - 0.25 + abs(wrapy));\\n dist = abs(wrapx) / SQRT2 - halfwidth;\\n }\\n else if (hatch_pattern == hatch_criss_cross) {\\n float plus = min(abs(wrap(coords.x)), abs(wrap(coords.y)));\\n\\n coords = vec2(coords.x + coords.y + 0.5, coords.x - coords.y + 0.5);\\n float X = min(abs(wrap(coords.x)), abs(wrap(coords.y))) / SQRT2;\\n\\n dist = min(plus, X) - halfwidth;\\n }\\n\\n return distance_to_fraction(dist*scale);\\n}\\n#endif\\n\\nvoid main()\\n{\\n int line_cap = int(v_line_cap + 0.5);\\n int line_join = int(v_line_join + 0.5);\\n#ifdef HATCH\\n int hatch_pattern = int(v_hatch_pattern + 0.5);\\n#endif\\n\\n float dist = marker_distance(v_coords, line_cap, line_join);\\n\\n#ifdef LINE_ONLY\\n vec4 color = vec4(0.0, 0.0, 0.0, 0.0);\\n#else\\n float fill_frac = fill_fraction(dist);\\n vec4 color = fractional_color(v_fill_color, fill_frac);\\n#endif\\n\\n#if defined(HATCH) && !defined(LINE_ONLY)\\n if (hatch_pattern > 0 && fill_frac > 0.0) {\\n float hatch_frac = hatch_fraction(v_hatch_coords, hatch_pattern);\\n vec4 hatch_color = fractional_color(v_hatch_color, hatch_frac*fill_frac);\\n color = blend_colors(hatch_color, color);\\n }\\n#endif\\n\\n float line_frac = line_fraction(dist);\\n\\n#ifdef APPEND_DOT\\n line_frac = max(line_frac, dot_fraction(v_coords));\\n#endif\\n#ifdef APPEND_CROSS\\n line_frac = max(line_frac, line_fraction(one_cross(v_coords, line_cap, 0.5*v_size.x)));\\n#endif\\n#ifdef APPEND_CROSS_2\\n vec2 lengths = vec2(v_size.x/3.0, v_size.x/2.0);\\n line_frac = max(line_frac, line_fraction(one_cross_2(v_coords, line_cap, lengths)));\\n#endif\\n#ifdef APPEND_X\\n vec2 p = vec2((v_coords.x + v_coords.y)/SQRT2, (v_coords.x - v_coords.y)/SQRT2);\\n line_frac = max(line_frac, line_fraction(one_cross(p, line_cap, APPEND_X_LEN)));\\n#endif\\n#ifdef APPEND_Y\\n line_frac = max(line_frac, line_fraction(one_y(v_coords, line_cap, 0.5*v_size.x)));\\n#endif\\n\\n if (line_frac > 0.0) {\\n vec4 line_color = fractional_color(v_line_color, line_frac);\\n color = blend_colors(line_color, color);\\n }\\n\\n gl_FragColor = color;\\n}\\n\"},\n 419: function _(t,_,i,h,e){h();const s=t(420),a=t(421),r=t(422);class l extends s.BaseGLGlyph{constructor(t,_){super(t,_),this.glyph=_,this._antialias=1.5,this._show_all=!1}_draw_one_marker_type(t,_,i){const h={scissor:this.regl_wrapper.scissor,viewport:this.regl_wrapper.viewport,canvas_size:[_.width,_.height],pixel_ratio:_.pixel_ratio,center:i._centers,width:i._widths,height:i._heights,angle:i._angles,size_hint:(0,r.marker_type_to_size_hint)(t),nmarkers:i.nvertices,antialias:this._antialias,linewidth:this._linewidths,line_color:this._line_rgba,fill_color:this._fill_rgba,line_cap:this._line_caps,line_join:this._line_joins,show:this._show};if(this._have_hatch){const _=Object.assign(Object.assign({},h),{hatch_pattern:this._hatch_patterns,hatch_scale:this._hatch_scales,hatch_weight:this._hatch_weights,hatch_color:this._hatch_rgba});this.regl_wrapper.marker_hatch(t)(_)}else this.regl_wrapper.marker_no_hatch(t)(h)}_set_visuals(){const t=this._get_visuals(),_=t.fill,i=t.line;if(null==this._linewidths&&(this._linewidths=new a.Float32Buffer(this.regl_wrapper),this._line_caps=new a.Uint8Buffer(this.regl_wrapper),this._line_joins=new a.Uint8Buffer(this.regl_wrapper),this._line_rgba=new a.NormalizedUint8Buffer(this.regl_wrapper),this._fill_rgba=new a.NormalizedUint8Buffer(this.regl_wrapper)),this._linewidths.set_from_prop(i.line_width),this._line_caps.set_from_line_cap(i.line_cap),this._line_joins.set_from_line_join(i.line_join),this._line_rgba.set_from_color(i.line_color,i.line_alpha),this._fill_rgba.set_from_color(_.fill_color,_.fill_alpha),this._have_hatch=t.hatch.doit,this._have_hatch){const _=t.hatch;null==this._hatch_patterns&&(this._hatch_patterns=new a.Uint8Buffer(this.regl_wrapper),this._hatch_scales=new a.Float32Buffer(this.regl_wrapper),this._hatch_weights=new a.Float32Buffer(this.regl_wrapper),this._hatch_rgba=new a.NormalizedUint8Buffer(this.regl_wrapper)),this._hatch_patterns.set_from_hatch_pattern(_.hatch_pattern),this._hatch_scales.set_from_prop(_.hatch_scale),this._hatch_weights.set_from_prop(_.hatch_weight),this._hatch_rgba.set_from_color(_.hatch_color,_.hatch_alpha)}}}i.BaseMarkerGL=l,l.__name__=\"BaseMarkerGL\",l.missing_point=-1e4},\n 420: function _(e,t,s,i,h){i();class a{constructor(e,t){this.glyph=t,this.nvertices=0,this.size_changed=!1,this.data_changed=!1,this.visuals_changed=!1,this.regl_wrapper=e}set_data_changed(){const{data_size:e}=this.glyph;e!=this.nvertices&&(this.nvertices=e,this.size_changed=!0),this.data_changed=!0}set_visuals_changed(){this.visuals_changed=!0}render(e,t,s){if(0==t.length)return!0;const{width:i,height:h}=this.glyph.renderer.plot_view.canvas_view.webgl.canvas,a={pixel_ratio:this.glyph.renderer.plot_view.canvas_view.pixel_ratio,width:i,height:h};return this.draw(t,s,a),!0}}s.BaseGLGlyph=a,a.__name__=\"BaseGLGlyph\"},\n 421: function _(r,t,a,e,s){e();const i=r(422),_=r(22);class n{constructor(r){this.regl_wrapper=r,this.is_scalar=!0}get_sized_array(r){return null!=this.array&&this.array.length==r||(this.array=this.new_array(r)),this.array}is_normalized(){return!1}get length(){return null!=this.array?this.array.length:0}set_from_array(r){const t=r.length,a=this.get_sized_array(t);for(let e=0;e0}_set_data(){const s=this.glyph.sx.length,i=s-1;this._is_closed=s>2&&this.glyph.sx[0]==this.glyph.sx[s-1]&&this.glyph.sy[0]==this.glyph.sy[s-1]&&isFinite(this.glyph.sx[0])&&isFinite(this.glyph.sy[0]),null==this._points&&(this._points=new o.Float32Buffer(this.regl_wrapper));const t=this._points.get_sized_array(2*(s+2));for(let i=1;is/255)),this._linewidth=s.line_width.value,this._linewidth<1&&(this._color[3]*=this._linewidth,this._linewidth=1),this._line_dash=(0,a.resolve_line_dash)(s.line_dash.value),this._is_dashed()&&([this._dash_tex_info,this._dash_tex,this._dash_scale]=this.regl_wrapper.get_dash(this._line_dash),this._dash_offset=s.line_dash_offset.value)}}t.LineGL=r,r.__name__=\"LineGL\"},\n 427: function _(s,t,i,e,r){e();const h=s(421),a=s(424);class n extends a.SingleMarkerGL{constructor(s,t){super(s,t),this.glyph=t}draw(s,t,i){this._draw_impl(s,i,t.glglyph,\"square\")}_get_visuals(){return this.glyph.visuals}_set_data(){const s=this.nvertices;null==this._centers&&(this._centers=new h.Float32Buffer(this.regl_wrapper),this._widths=new h.Float32Buffer(this.regl_wrapper),this._heights=new h.Float32Buffer(this.regl_wrapper),this._angles=new h.Float32Buffer(this.regl_wrapper),this._angles.set_from_scalar(0));const t=this._centers.get_sized_array(2*s),i=this._heights.get_sized_array(s),e=this._widths.get_sized_array(s);for(let r=0;r1||s.length<_){this._show_all=!1,n.fill(0),r=0;for(const e of s)1!=h&&i._marker_types.get(e)!=t||(n[e]=255,r++)}else this._show_all&&a==_||(this._show_all=!0,n.fill(255));this._show.update(),0!=r&&this._draw_one_marker_type(t,e,i)}}_get_visuals(){return this.glyph.visuals}_set_data(){const s=this.nvertices;null==this._centers&&(this._centers=new r.Float32Buffer(this.regl_wrapper),this._widths=new r.Float32Buffer(this.regl_wrapper),this._heights=this._widths,this._angles=new r.Float32Buffer(this.regl_wrapper));const t=this._centers.get_sized_array(2*s);for(let e=0;ethis.render()))}remove(){null!=this.icon_view&&this.icon_view.remove(),super.remove()}styles(){return[...super.styles(),d.default]}_render_button(...t){return(0,c.button)({type:\"button\",disabled:this.model.disabled,class:[h.btn,h[`btn_${this.model.button_type}`]]},...t)}render(){super.render(),this.button_el=this._render_button(this.model.label),this.button_el.addEventListener(\"click\",(()=>this.click())),null!=this.icon_view&&(\"\"!=this.model.label?(0,c.prepend)(this.button_el,this.icon_view.el,(0,c.nbsp)()):(0,c.prepend)(this.button_el,this.icon_view.el),this.icon_view.render()),this.group_el=(0,c.div)({class:h.btn_group},this.button_el),this.el.appendChild(this.group_el)}click(){}}n.AbstractButtonView=b,b.__name__=\"AbstractButtonView\";class p extends _.Control{constructor(t){super(t)}}n.AbstractButton=p,o=p,p.__name__=\"AbstractButton\",o.define((({String:t,Ref:e,Nullable:n})=>({label:[t,\"Button\"],icon:[n(e(a.AbstractIcon)),null],button_type:[r.ButtonType,\"default\"]})))},\n 442: function _(t,e,o,s,n){s();const i=t(512),l=t(43);class c extends i.WidgetView{connect_signals(){super.connect_signals();const t=this.model.properties;this.on_change(t.disabled,(()=>{for(const t of this.controls())(0,l.toggle_attribute)(t,\"disabled\",this.model.disabled)}))}}o.ControlView=c,c.__name__=\"ControlView\";class r extends i.Widget{constructor(t){super(t)}}o.Control=r,r.__name__=\"Control\"},\n 512: function _(i,e,t,n,o){var r;n();const s=i(312);class _ extends s.HTMLBoxView{get orientation(){return\"horizontal\"}get default_size(){return this.model.default_size}_width_policy(){return\"horizontal\"==this.orientation?super._width_policy():\"fixed\"}_height_policy(){return\"horizontal\"==this.orientation?\"fixed\":super._height_policy()}box_sizing(){const i=super.box_sizing();return\"horizontal\"==this.orientation?null==i.width&&(i.width=this.default_size):null==i.height&&(i.height=this.default_size),i}}t.WidgetView=_,_.__name__=\"WidgetView\";class h extends s.HTMLBox{constructor(i){super(i)}}t.Widget=h,r=h,h.__name__=\"Widget\",r.define((({Number:i})=>({default_size:[i,300]}))),r.override({margin:[5,5,5,5]})},\n 444: function _(c,t,s,n,e){n();const o=c(53),_=c(226);class a extends _.DOMView{}s.AbstractIconView=a,a.__name__=\"AbstractIconView\";class r extends o.Model{constructor(c){super(c)}}s.AbstractIcon=r,r.__name__=\"AbstractIcon\"},\n 445: function _(e,t,n,s,i){s();const h=e(1);var o;const _=e(446),u=e(43),r=e(10),c=(0,h.__importStar)(e(229)),a=c;class l extends _.TextInputView{constructor(){super(...arguments),this._open=!1,this._last_value=\"\",this._hover_index=0}styles(){return[...super.styles(),c.default]}render(){super.render(),this.input_el.addEventListener(\"keydown\",(e=>this._keydown(e))),this.input_el.addEventListener(\"keyup\",(e=>this._keyup(e))),this.menu=(0,u.div)({class:[a.menu,a.below]}),this.menu.addEventListener(\"click\",(e=>this._menu_click(e))),this.menu.addEventListener(\"mouseover\",(e=>this._menu_hover(e))),this.el.appendChild(this.menu),(0,u.undisplay)(this.menu)}change_input(){this._open&&this.menu.children.length>0?(this.model.value=this.menu.children[this._hover_index].textContent,this.input_el.focus(),this._hide_menu()):this.model.restrict||super.change_input()}_update_completions(e){(0,u.empty)(this.menu);for(const t of e){const e=(0,u.div)(t);this.menu.appendChild(e)}e.length>0&&this.menu.children[0].classList.add(a.active)}_show_menu(){if(!this._open){this._open=!0,this._hover_index=0,this._last_value=this.model.value,(0,u.display)(this.menu);const e=t=>{const{target:n}=t;n instanceof HTMLElement&&!this.el.contains(n)&&(document.removeEventListener(\"click\",e),this._hide_menu())};document.addEventListener(\"click\",e)}}_hide_menu(){this._open&&(this._open=!1,(0,u.undisplay)(this.menu))}_menu_click(e){e.target!=e.currentTarget&&e.target instanceof Element&&(this.model.value=e.target.textContent,this.input_el.focus(),this._hide_menu())}_menu_hover(e){if(e.target!=e.currentTarget&&e.target instanceof Element){let t=0;for(t=0;t0&&(this.menu.children[this._hover_index].classList.remove(a.active),this._hover_index=(0,r.clamp)(e,0,t-1),this.menu.children[this._hover_index].classList.add(a.active))}_keydown(e){}_keyup(e){switch(e.keyCode){case u.Keys.Enter:this.change_input();break;case u.Keys.Esc:this._hide_menu();break;case u.Keys.Up:this._bump_hover(this._hover_index-1);break;case u.Keys.Down:this._bump_hover(this._hover_index+1);break;default:{const e=this.input_el.value;if(e.lengthe:e=>e.toLowerCase();for(const n of this.model.completions)s(n).startsWith(s(e))&&t.push(n);this._update_completions(t),0==t.length?this._hide_menu():this._show_menu()}}}}n.AutocompleteInputView=l,l.__name__=\"AutocompleteInputView\";class d extends _.TextInput{constructor(e){super(e)}}n.AutocompleteInput=d,o=d,d.__name__=\"AutocompleteInput\",o.prototype.default_view=l,o.define((({Boolean:e,Int:t,String:n,Array:s})=>({completions:[s(n),[]],min_characters:[t,2],case_sensitive:[e,!0],restrict:[e,!0]})))},\n 446: function _(t,e,n,p,_){p();const u=t(1);var i;const s=t(447),r=t(43),x=(0,u.__importStar)(t(449));class a extends s.TextLikeInputView{_render_input(){this.input_el=(0,r.input)({type:\"text\",class:x.input})}}n.TextInputView=a,a.__name__=\"TextInputView\";class c extends s.TextLikeInput{constructor(t){super(t)}}n.TextInput=c,i=c,c.__name__=\"TextInput\",i.prototype.default_view=a},\n 447: function _(e,t,n,i,l){var s;i();const h=e(448);class a extends h.InputWidgetView{connect_signals(){super.connect_signals(),this.connect(this.model.properties.name.change,(()=>{var e;return this.input_el.name=null!==(e=this.model.name)&&void 0!==e?e:\"\"})),this.connect(this.model.properties.value.change,(()=>this.input_el.value=this.model.value)),this.connect(this.model.properties.value_input.change,(()=>this.input_el.value=this.model.value_input)),this.connect(this.model.properties.disabled.change,(()=>this.input_el.disabled=this.model.disabled)),this.connect(this.model.properties.placeholder.change,(()=>this.input_el.placeholder=this.model.placeholder)),this.connect(this.model.properties.max_length.change,(()=>{const{max_length:e}=this.model;null!=e?this.input_el.maxLength=e:this.input_el.removeAttribute(\"maxLength\")}))}render(){var e;super.render(),this._render_input();const{input_el:t}=this;t.name=null!==(e=this.model.name)&&void 0!==e?e:\"\",t.value=this.model.value,t.disabled=this.model.disabled,t.placeholder=this.model.placeholder,null!=this.model.max_length&&(t.maxLength=this.model.max_length),t.addEventListener(\"change\",(()=>this.change_input())),t.addEventListener(\"input\",(()=>this.change_input_value())),this.group_el.appendChild(t)}change_input(){this.model.value=this.input_el.value,super.change_input()}change_input_value(){this.model.value_input=this.input_el.value,super.change_input()}}n.TextLikeInputView=a,a.__name__=\"TextLikeInputView\";class u extends h.InputWidget{constructor(e){super(e)}}n.TextLikeInput=u,s=u,u.__name__=\"TextLikeInput\",s.define((({Int:e,String:t,Nullable:n})=>({value:[t,\"\"],value_input:[t,\"\"],placeholder:[t,\"\"],max_length:[n(e),null]})))},\n 448: function _(e,t,n,s,l){s();const i=e(1);var o;const r=e(442),_=e(43),p=(0,i.__importStar)(e(449)),a=p;class c extends r.ControlView{*controls(){yield this.input_el}connect_signals(){super.connect_signals(),this.connect(this.model.properties.title.change,(()=>{this.label_el.textContent=this.model.title}))}styles(){return[...super.styles(),p.default]}render(){super.render();const{title:e}=this.model;this.label_el=(0,_.label)({style:{display:0==e.length?\"none\":\"\"}},e),this.group_el=(0,_.div)({class:a.input_group},this.label_el),this.el.appendChild(this.group_el)}change_input(){}}n.InputWidgetView=c,c.__name__=\"InputWidgetView\";class d extends r.Control{constructor(e){super(e)}}n.InputWidget=d,o=d,d.__name__=\"InputWidget\",o.define((({String:e})=>({title:[e,\"\"]})))},\n 449: function _(o,p,t,n,i){n(),t.root=\"bk-root\",t.input=\"bk-input\",t.input_group=\"bk-input-group\",t.inline=\"bk-inline\",t.spin_wrapper=\"bk-spin-wrapper\",t.spin_btn=\"bk-spin-btn\",t.spin_btn_up=\"bk-spin-btn-up\",t.spin_btn_down=\"bk-spin-btn-down\",t.default='.bk-root .bk-input{display:inline-block;width:100%;flex-grow:1;min-height:31px;padding:0 12px;background-color:#fff;border:1px solid #ccc;border-radius:4px;}.bk-root .bk-input:focus{border-color:#66afe9;outline:0;box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 8px rgba(102, 175, 233, 0.6);}.bk-root .bk-input::placeholder,.bk-root .bk-input:-ms-input-placeholder,.bk-root .bk-input::-moz-placeholder,.bk-root .bk-input::-webkit-input-placeholder{color:#999;opacity:1;}.bk-root .bk-input[disabled]{cursor:not-allowed;background-color:#eee;opacity:1;}.bk-root select:not([multiple]).bk-input,.bk-root select:not([size]).bk-input{height:auto;appearance:none;-webkit-appearance:none;background-image:url(\\'data:image/svg+xml;utf8,\\');background-position:right 0.5em center;background-size:8px 6px;background-repeat:no-repeat;}.bk-root select[multiple].bk-input,.bk-root select[size].bk-input,.bk-root textarea.bk-input{height:auto;}.bk-root .bk-input-group{width:100%;height:100%;display:inline-flex;flex-wrap:nowrap;align-items:start;flex-direction:column;white-space:nowrap;}.bk-root .bk-input-group.bk-inline{flex-direction:row;}.bk-root .bk-input-group.bk-inline > *:not(:first-child){margin-left:5px;}.bk-root .bk-input-group input[type=\"checkbox\"] + span,.bk-root .bk-input-group input[type=\"radio\"] + span{position:relative;top:-2px;margin-left:3px;}.bk-root .bk-input-group > .bk-spin-wrapper{display:inherit;width:inherit;height:inherit;position:relative;overflow:hidden;padding:0;vertical-align:middle;}.bk-root .bk-input-group > .bk-spin-wrapper input{padding-right:20px;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn{position:absolute;display:block;height:50%;min-height:0;min-width:0;width:30px;padding:0;margin:0;right:0;border:none;background:none;cursor:pointer;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn:before{content:\"\";display:inline-block;transform:translateY(-50%);border-left:5px solid transparent;border-right:5px solid transparent;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn.bk-spin-btn-up{top:0;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn.bk-spin-btn-up:before{border-bottom:5px solid black;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn.bk-spin-btn-up:disabled:before{border-bottom-color:grey;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn.bk-spin-btn-down{bottom:0;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn.bk-spin-btn-down:before{border-top:5px solid black;}.bk-root .bk-input-group > .bk-spin-wrapper > .bk-spin-btn.bk-spin-btn-down:disabled:before{border-top-color:grey;}'},\n 450: function _(t,e,n,o,c){var s;o();const u=t(441),r=t(251);class i extends u.AbstractButtonView{click(){this.model.trigger_event(new r.ButtonClick),super.click()}}n.ButtonView=i,i.__name__=\"ButtonView\";class _ extends u.AbstractButton{constructor(t){super(t)}}n.Button=_,s=_,_.__name__=\"Button\",s.prototype.default_view=i,s.override({label:\"Button\"})},\n 451: function _(t,e,o,c,a){c();const s=t(1);var n;const i=t(452),r=t(43),u=(0,s.__importStar)(t(318));class _ extends i.ButtonGroupView{get active(){return new Set(this.model.active)}change_active(t){const{active:e}=this;e.has(t)?e.delete(t):e.add(t),this.model.active=[...e].sort()}_update_active(){const{active:t}=this;this._buttons.forEach(((e,o)=>{(0,r.classes)(e).toggle(u.active,t.has(o))}))}}o.CheckboxButtonGroupView=_,_.__name__=\"CheckboxButtonGroupView\";class h extends i.ButtonGroup{constructor(t){super(t)}}o.CheckboxButtonGroup=h,n=h,h.__name__=\"CheckboxButtonGroup\",n.prototype.default_view=_,n.define((({Int:t,Array:e})=>({active:[e(t),[]]})))},\n 452: function _(t,e,n,s,i){s();const o=t(1);var r;const a=t(453),l=t(20),d=t(43),u=(0,o.__importStar)(t(318)),_=u;class c extends a.OrientedControlView{get default_size(){return\"horizontal\"==this.orientation?this.model.default_size:void 0}*controls(){yield*this._buttons}connect_signals(){super.connect_signals();const t=this.model.properties;this.on_change(t.button_type,(()=>this.render())),this.on_change(t.labels,(()=>this.render())),this.on_change(t.active,(()=>this._update_active()))}styles(){return[...super.styles(),u.default]}render(){super.render(),this._buttons=this.model.labels.map(((t,e)=>{const n=(0,d.div)({class:[_.btn,_[`btn_${this.model.button_type}`]],disabled:this.model.disabled},t);return n.addEventListener(\"click\",(()=>this.change_active(e))),n})),this._update_active();const t=\"horizontal\"==this.model.orientation?_.horizontal:_.vertical,e=(0,d.div)({class:[_.btn_group,t]},this._buttons);this.el.appendChild(e)}}n.ButtonGroupView=c,c.__name__=\"ButtonGroupView\";class h extends a.OrientedControl{constructor(t){super(t)}}n.ButtonGroup=h,r=h,h.__name__=\"ButtonGroup\",r.define((({String:t,Array:e})=>({labels:[e(t),[]],button_type:[l.ButtonType,\"default\"]})))},\n 453: function _(n,t,e,o,r){var i;o();const a=n(442),l=n(20);class s extends a.ControlView{get orientation(){return this.model.orientation}}e.OrientedControlView=s,s.__name__=\"OrientedControlView\";class _ extends a.Control{constructor(n){super(n)}}e.OrientedControl=_,i=_,_.__name__=\"OrientedControl\",i.define((()=>({orientation:[l.Orientation,\"horizontal\"]})))},\n 454: function _(e,t,n,i,s){i();const o=e(1);var a;const c=e(455),l=e(43),d=e(9),p=(0,o.__importStar)(e(449));class r extends c.InputGroupView{render(){super.render();const e=(0,l.div)({class:[p.input_group,this.model.inline?p.inline:null]});this.el.appendChild(e);const{active:t,labels:n}=this.model;this._inputs=[];for(let i=0;ithis.change_active(i))),this._inputs.push(s),this.model.disabled&&(s.disabled=!0),(0,d.includes)(t,i)&&(s.checked=!0);const o=(0,l.label)(s,(0,l.span)(n[i]));e.appendChild(o)}}change_active(e){const t=new Set(this.model.active);t.has(e)?t.delete(e):t.add(e),this.model.active=[...t].sort()}}n.CheckboxGroupView=r,r.__name__=\"CheckboxGroupView\";class h extends c.InputGroup{constructor(e){super(e)}}n.CheckboxGroup=h,a=h,h.__name__=\"CheckboxGroup\",a.prototype.default_view=r,a.define((({Boolean:e,Int:t,String:n,Array:i})=>({active:[i(t),[]],labels:[i(n),[]],inline:[e,!1]})))},\n 455: function _(n,t,e,s,o){s();const r=n(1),u=n(442),c=(0,r.__importDefault)(n(449));class _ extends u.ControlView{*controls(){yield*this._inputs}connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.render()))}styles(){return[...super.styles(),c.default]}}e.InputGroupView=_,_.__name__=\"InputGroupView\";class i extends u.Control{constructor(n){super(n)}}e.InputGroup=i,i.__name__=\"InputGroup\"},\n 456: function _(e,t,i,n,o){n();const s=e(1);var l;const r=e(448),c=e(43),a=e(22),d=(0,s.__importStar)(e(449));class h extends r.InputWidgetView{connect_signals(){super.connect_signals(),this.connect(this.model.properties.name.change,(()=>{var e;return this.input_el.name=null!==(e=this.model.name)&&void 0!==e?e:\"\"})),this.connect(this.model.properties.color.change,(()=>this.input_el.value=(0,a.color2hexrgb)(this.model.color))),this.connect(this.model.properties.disabled.change,(()=>this.input_el.disabled=this.model.disabled))}render(){super.render(),this.input_el=(0,c.input)({type:\"color\",class:d.input,name:this.model.name,value:this.model.color,disabled:this.model.disabled}),this.input_el.addEventListener(\"change\",(()=>this.change_input())),this.group_el.appendChild(this.input_el)}change_input(){this.model.color=this.input_el.value,super.change_input()}}i.ColorPickerView=h,h.__name__=\"ColorPickerView\";class p extends r.InputWidget{constructor(e){super(e)}}i.ColorPicker=p,l=p,p.__name__=\"ColorPicker\",l.prototype.default_view=h,l.define((({Color:e})=>({color:[e,\"#000000\"]})))},\n 457: function _(e,t,i,n,s){n();const a=e(1);var l;const o=(0,a.__importDefault)(e(458)),d=e(448),r=e(43),c=e(20),u=e(8),h=(0,a.__importStar)(e(449)),_=(0,a.__importDefault)(e(459));function p(e){const t=[];for(const i of e)if((0,u.isString)(i))t.push(i);else{const[e,n]=i;t.push({from:e,to:n})}return t}class m extends d.InputWidgetView{connect_signals(){super.connect_signals();const{value:e,min_date:t,max_date:i,disabled_dates:n,enabled_dates:s,position:a,inline:l}=this.model.properties;this.connect(e.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.setDate(this.model.value)})),this.connect(t.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.set(\"minDate\",this.model.min_date)})),this.connect(i.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.set(\"maxDate\",this.model.max_date)})),this.connect(n.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.set(\"disable\",this.model.disabled_dates)})),this.connect(s.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.set(\"enable\",this.model.enabled_dates)})),this.connect(a.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.set(\"position\",this.model.position)})),this.connect(l.change,(()=>{var e;return null===(e=this._picker)||void 0===e?void 0:e.set(\"inline\",this.model.inline)}))}remove(){var e;null===(e=this._picker)||void 0===e||e.destroy(),super.remove()}styles(){return[...super.styles(),_.default]}render(){var e,t;null==this._picker&&(super.render(),this.input_el=(0,r.input)({type:\"text\",class:h.input,disabled:this.model.disabled}),this.group_el.appendChild(this.input_el),this._picker=(0,o.default)(this.input_el,{defaultDate:this.model.value,minDate:null!==(e=this.model.min_date)&&void 0!==e?e:void 0,maxDate:null!==(t=this.model.max_date)&&void 0!==t?t:void 0,inline:this.model.inline,position:this.model.position,disable:p(this.model.disabled_dates),enable:p(this.model.enabled_dates),onChange:(e,t,i)=>this._on_change(e,t,i)}))}_on_change(e,t,i){this.model.value=t,this.change_input()}}i.DatePickerView=m,m.__name__=\"DatePickerView\";class v extends d.InputWidget{constructor(e){super(e)}}i.DatePicker=v,l=v,v.__name__=\"DatePicker\",l.prototype.default_view=m,l.define((({Boolean:e,String:t,Array:i,Tuple:n,Or:s,Nullable:a})=>{const l=i(s(t,n(t,t)));return{value:[t],min_date:[a(t),null],max_date:[a(t),null],disabled_dates:[l,[]],enabled_dates:[l,[]],position:[c.CalendarPosition,\"auto\"],inline:[e,!1]}}))},\n 458: function _(e,n,t,a,i){\n /* flatpickr v4.6.6, @license MIT */var o,r;o=this,r=function(){\"use strict\";\n /*! *****************************************************************************\n Copyright (c) Microsoft Corporation.\n \n Permission to use, copy, modify, and/or distribute this software for any\n purpose with or without fee is hereby granted.\n \n THE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\n REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\n AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\n INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\n LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\n OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\n PERFORMANCE OF THIS SOFTWARE.\n ***************************************************************************** */var e=function(){return e=Object.assign||function(e){for(var n,t=1,a=arguments.length;t\",noCalendar:!1,now:new Date,onChange:[],onClose:[],onDayCreate:[],onDestroy:[],onKeyDown:[],onMonthChange:[],onOpen:[],onParseConfig:[],onReady:[],onValueUpdate:[],onYearChange:[],onPreCalendarPosition:[],plugins:[],position:\"auto\",positionElement:void 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0,!0).getTime(),a=Math.min(n,w.selectedDates[0].getTime()),i=Math.max(n,w.selectedDates[0].getTime()),o=!1,r=0,l=0,c=a;ca&&cr)?r=c:c>t&&(!l||c0&&m0&&m>l;return g?(f.classList.add(\"notAllowed\"),[\"inRange\",\"startRange\",\"endRange\"].forEach((function(e){f.classList.remove(e)})),\"continue\"):o&&!g?\"continue\":([\"startRange\",\"inRange\",\"endRange\",\"notAllowed\"].forEach((function(e){f.classList.remove(e)})),void(void 0!==e&&(e.classList.add(n<=w.selectedDates[0].getTime()?\"startRange\":\"endRange\"),tn&&m===t&&f.classList.add(\"endRange\"),m>=r&&(0===l||m<=l)&&(d=t,u=n,(c=m)>Math.min(d,u)&&c0||t.getMinutes()>0||t.getSeconds()>0),w.selectedDates&&(w.selectedDates=w.selectedDates.filter((function(e){return X(e)})),w.selectedDates.length||\"min\"!==e||S(t),be()),w.daysContainer&&(de(),void 0!==t?w.currentYearElement[e]=t.getFullYear().toString():w.currentYearElement.removeAttribute(e),w.currentYearElement.disabled=!!a&&void 0!==t&&a.getFullYear()===t.getFullYear())}}function re(){return w.config.wrap?p.querySelector(\"[data-input]\"):p}function le(){\"object\"!=typeof w.config.locale&&void 0===k.l10ns[w.config.locale]&&w.config.errorHandler(new Error(\"flatpickr: invalid locale \"+w.config.locale)),w.l10n=e(e({},k.l10ns.default),\"object\"==typeof w.config.locale?w.config.locale:\"default\"!==w.config.locale?k.l10ns[w.config.locale]:void 0),D.K=\"(\"+w.l10n.amPM[0]+\"|\"+w.l10n.amPM[1]+\"|\"+w.l10n.amPM[0].toLowerCase()+\"|\"+w.l10n.amPM[1].toLowerCase()+\")\",void 0===e(e({},v),JSON.parse(JSON.stringify(p.dataset||{}))).time_24hr&&void 0===k.defaultConfig.time_24hr&&(w.config.time_24hr=w.l10n.time_24hr),w.formatDate=b(w),w.parseDate=C({config:w.config,l10n:w.l10n})}function ce(e){if(void 0!==w.calendarContainer){pe(\"onPreCalendarPosition\");var n=e||w._positionElement,t=Array.prototype.reduce.call(w.calendarContainer.children,(function(e,n){return e+n.offsetHeight}),0),a=w.calendarContainer.offsetWidth,i=w.config.position.split(\" \"),o=i[0],r=i.length>1?i[1]:null,l=n.getBoundingClientRect(),c=window.innerHeight-l.bottom,s=\"above\"===o||\"below\"!==o&&ct,u=window.pageYOffset+l.top+(s?-t-2:n.offsetHeight+2);if(d(w.calendarContainer,\"arrowTop\",!s),d(w.calendarContainer,\"arrowBottom\",s),!w.config.inline){var f=window.pageXOffset+l.left,m=!1,g=!1;\"center\"===r?(f-=(a-l.width)/2,m=!0):\"right\"===r&&(f-=a-l.width,g=!0),d(w.calendarContainer,\"arrowLeft\",!m&&!g),d(w.calendarContainer,\"arrowCenter\",m),d(w.calendarContainer,\"arrowRight\",g);var p=window.document.body.offsetWidth-(window.pageXOffset+l.right),h=f+a>window.document.body.offsetWidth,v=p+a>window.document.body.offsetWidth;if(d(w.calendarContainer,\"rightMost\",h),!w.config.static)if(w.calendarContainer.style.top=u+\"px\",h)if(v){var D=function(){for(var e=null,n=0;nw.currentMonth+w.config.showMonths-1)&&\"range\"!==w.config.mode;if(w.selectedDateElem=t,\"single\"===w.config.mode)w.selectedDates=[a];else if(\"multiple\"===w.config.mode){var o=ve(a);o?w.selectedDates.splice(parseInt(o),1):w.selectedDates.push(a)}else\"range\"===w.config.mode&&(2===w.selectedDates.length&&w.clear(!1,!1),w.latestSelectedDateObj=a,w.selectedDates.push(a),0!==M(a,w.selectedDates[0],!0)&&w.selectedDates.sort((function(e,n){return e.getTime()-n.getTime()})));if(I(),i){var r=w.currentYear!==a.getFullYear();w.currentYear=a.getFullYear(),w.currentMonth=a.getMonth(),r&&(pe(\"onYearChange\"),K()),pe(\"onMonthChange\")}if(De(),J(),be(),i||\"range\"===w.config.mode||1!==w.config.showMonths?void 0!==w.selectedDateElem&&void 0===w.hourElement&&w.selectedDateElem&&w.selectedDateElem.focus():L(t),void 0!==w.hourElement&&void 0!==w.hourElement&&w.hourElement.focus(),w.config.closeOnSelect){var l=\"single\"===w.config.mode&&!w.config.enableTime,c=\"range\"===w.config.mode&&2===w.selectedDates.length&&!w.config.enableTime;(l||c)&&se()}A()}}w.parseDate=C({config:w.config,l10n:w.l10n}),w._handlers=[],w.pluginElements=[],w.loadedPlugins=[],w._bind=N,w._setHoursFromDate=S,w._positionCalendar=ce,w.changeMonth=G,w.changeYear=Q,w.clear=function(e,n){if(void 0===e&&(e=!0),void 0===n&&(n=!0),w.input.value=\"\",void 0!==w.altInput&&(w.altInput.value=\"\"),void 0!==w.mobileInput&&(w.mobileInput.value=\"\"),w.selectedDates=[],w.latestSelectedDateObj=void 0,!0===n&&(w.currentYear=w._initialDate.getFullYear(),w.currentMonth=w._initialDate.getMonth()),!0===w.config.enableTime){var t=_(),a=t.hours,i=t.minutes,o=t.seconds;O(a,i,o)}w.redraw(),e&&pe(\"onChange\")},w.close=function(){w.isOpen=!1,w.isMobile||(void 0!==w.calendarContainer&&w.calendarContainer.classList.remove(\"open\"),void 0!==w._input&&w._input.classList.remove(\"active\")),pe(\"onClose\")},w._createElement=s,w.destroy=function(){void 0!==w.config&&pe(\"onDestroy\");for(var e=w._handlers.length;e--;){var n=w._handlers[e];n.element.removeEventListener(n.event,n.handler,n.options)}if(w._handlers=[],w.mobileInput)w.mobileInput.parentNode&&w.mobileInput.parentNode.removeChild(w.mobileInput),w.mobileInput=void 0;else if(w.calendarContainer&&w.calendarContainer.parentNode)if(w.config.static&&w.calendarContainer.parentNode){var t=w.calendarContainer.parentNode;if(t.lastChild&&t.removeChild(t.lastChild),t.parentNode){for(;t.firstChild;)t.parentNode.insertBefore(t.firstChild,t);t.parentNode.removeChild(t)}}else w.calendarContainer.parentNode.removeChild(w.calendarContainer);w.altInput&&(w.input.type=\"text\",w.altInput.parentNode&&w.altInput.parentNode.removeChild(w.altInput),delete w.altInput),w.input&&(w.input.type=w.input._type,w.input.classList.remove(\"flatpickr-input\"),w.input.removeAttribute(\"readonly\")),[\"_showTimeInput\",\"latestSelectedDateObj\",\"_hideNextMonthArrow\",\"_hidePrevMonthArrow\",\"__hideNextMonthArrow\",\"__hidePrevMonthArrow\",\"isMobile\",\"isOpen\",\"selectedDateElem\",\"minDateHasTime\",\"maxDateHasTime\",\"days\",\"daysContainer\",\"_input\",\"_positionElement\",\"innerContainer\",\"rContainer\",\"monthNav\",\"todayDateElem\",\"calendarContainer\",\"weekdayContainer\",\"prevMonthNav\",\"nextMonthNav\",\"monthsDropdownContainer\",\"currentMonthElement\",\"currentYearElement\",\"navigationCurrentMonth\",\"selectedDateElem\",\"config\"].forEach((function(e){try{delete w[e]}catch(e){}}))},w.isEnabled=X,w.jumpToDate=P,w.open=function(e,n){if(void 0===n&&(n=w._positionElement),!0===w.isMobile){if(e){e.preventDefault();var t=g(e);t&&t.blur()}return void 0!==w.mobileInput&&(w.mobileInput.focus(),w.mobileInput.click()),void pe(\"onOpen\")}if(!w._input.disabled&&!w.config.inline){var a=w.isOpen;w.isOpen=!0,a||(w.calendarContainer.classList.add(\"open\"),w._input.classList.add(\"active\"),pe(\"onOpen\"),ce(n)),!0===w.config.enableTime&&!0===w.config.noCalendar&&(!1!==w.config.allowInput||void 0!==e&&w.timeContainer.contains(e.relatedTarget)||setTimeout((function(){return w.hourElement.select()}),50))}},w.redraw=de,w.set=function(e,n){if(null!==e&&\"object\"==typeof e)for(var a in Object.assign(w.config,e),e)void 0!==fe[a]&&fe[a].forEach((function(e){return e()}));else w.config[e]=n,void 0!==fe[e]?fe[e].forEach((function(e){return e()})):t.indexOf(e)>-1&&(w.config[e]=c(n));w.redraw(),be(!0)},w.setDate=function(e,n,t){if(void 0===n&&(n=!1),void 0===t&&(t=w.config.dateFormat),0!==e&&!e||e instanceof Array&&0===e.length)return w.clear(n);me(e,t),w.latestSelectedDateObj=w.selectedDates[w.selectedDates.length-1],w.redraw(),P(void 0,n),S(),0===w.selectedDates.length&&w.clear(!1),be(n),n&&pe(\"onChange\")},w.toggle=function(e){if(!0===w.isOpen)return w.close();w.open(e)};var fe={locale:[le,z],showMonths:[q,E,$],minDate:[P],maxDate:[P]};function me(e,n){var t=[];if(e instanceof Array)t=e.map((function(e){return w.parseDate(e,n)}));else if(e instanceof Date||\"number\"==typeof e)t=[w.parseDate(e,n)];else if(\"string\"==typeof e)switch(w.config.mode){case\"single\":case\"time\":t=[w.parseDate(e,n)];break;case\"multiple\":t=e.split(w.config.conjunction).map((function(e){return w.parseDate(e,n)}));break;case\"range\":t=e.split(w.l10n.rangeSeparator).map((function(e){return w.parseDate(e,n)}))}else w.config.errorHandler(new Error(\"Invalid date supplied: \"+JSON.stringify(e)));w.selectedDates=w.config.allowInvalidPreload?t:t.filter((function(e){return e instanceof Date&&X(e,!1)})),\"range\"===w.config.mode&&w.selectedDates.sort((function(e,n){return e.getTime()-n.getTime()}))}function ge(e){return e.slice().map((function(e){return\"string\"==typeof e||\"number\"==typeof e||e instanceof Date?w.parseDate(e,void 0,!0):e&&\"object\"==typeof e&&e.from&&e.to?{from:w.parseDate(e.from,void 0),to:w.parseDate(e.to,void 0)}:e})).filter((function(e){return e}))}function pe(e,n){if(void 0!==w.config){var t=w.config[e];if(void 0!==t&&t.length>0)for(var a=0;t[a]&&a1||\"static\"===w.config.monthSelectorType?w.monthElements[n].textContent=h(t.getMonth(),w.config.shorthandCurrentMonth,w.l10n)+\" \":w.monthsDropdownContainer.value=t.getMonth().toString(),e.value=t.getFullYear().toString()})),w._hidePrevMonthArrow=void 0!==w.config.minDate&&(w.currentYear===w.config.minDate.getFullYear()?w.currentMonth<=w.config.minDate.getMonth():w.currentYearw.config.maxDate.getMonth():w.currentYear>w.config.maxDate.getFullYear()))}function we(e){return w.selectedDates.map((function(n){return w.formatDate(n,e)})).filter((function(e,n,t){return\"range\"!==w.config.mode||w.config.enableTime||t.indexOf(e)===n})).join(\"range\"!==w.config.mode?w.config.conjunction:w.l10n.rangeSeparator)}function be(e){void 0===e&&(e=!0),void 0!==w.mobileInput&&w.mobileFormatStr&&(w.mobileInput.value=void 0!==w.latestSelectedDateObj?w.formatDate(w.latestSelectedDateObj,w.mobileFormatStr):\"\"),w.input.value=we(w.config.dateFormat),void 0!==w.altInput&&(w.altInput.value=we(w.config.altFormat)),!1!==e&&pe(\"onValueUpdate\")}function Ce(e){var n=g(e),t=w.prevMonthNav.contains(n),a=w.nextMonthNav.contains(n);t||a?G(t?-1:1):w.yearElements.indexOf(n)>=0?n.select():n.classList.contains(\"arrowUp\")?w.changeYear(w.currentYear+1):n.classList.contains(\"arrowDown\")&&w.changeYear(w.currentYear-1)}return function(){w.element=w.input=p,w.isOpen=!1,function(){var n=[\"wrap\",\"weekNumbers\",\"allowInput\",\"allowInvalidPreload\",\"clickOpens\",\"time_24hr\",\"enableTime\",\"noCalendar\",\"altInput\",\"shorthandCurrentMonth\",\"inline\",\"static\",\"enableSeconds\",\"disableMobile\"],i=e(e({},JSON.parse(JSON.stringify(p.dataset||{}))),v),o={};w.config.parseDate=i.parseDate,w.config.formatDate=i.formatDate,Object.defineProperty(w.config,\"enable\",{get:function(){return w.config._enable},set:function(e){w.config._enable=ge(e)}}),Object.defineProperty(w.config,\"disable\",{get:function(){return w.config._disable},set:function(e){w.config._disable=ge(e)}});var r=\"time\"===i.mode;if(!i.dateFormat&&(i.enableTime||r)){var l=k.defaultConfig.dateFormat||a.dateFormat;o.dateFormat=i.noCalendar||r?\"H:i\"+(i.enableSeconds?\":S\":\"\"):l+\" H:i\"+(i.enableSeconds?\":S\":\"\")}if(i.altInput&&(i.enableTime||r)&&!i.altFormat){var d=k.defaultConfig.altFormat||a.altFormat;o.altFormat=i.noCalendar||r?\"h:i\"+(i.enableSeconds?\":S K\":\" K\"):d+\" h:i\"+(i.enableSeconds?\":S\":\"\")+\" K\"}Object.defineProperty(w.config,\"minDate\",{get:function(){return w.config._minDate},set:oe(\"min\")}),Object.defineProperty(w.config,\"maxDate\",{get:function(){return w.config._maxDate},set:oe(\"max\")});var s=function(e){return function(n){w.config[\"min\"===e?\"_minTime\":\"_maxTime\"]=w.parseDate(n,\"H:i:S\")}};Object.defineProperty(w.config,\"minTime\",{get:function(){return w.config._minTime},set:s(\"min\")}),Object.defineProperty(w.config,\"maxTime\",{get:function(){return w.config._maxTime},set:s(\"max\")}),\"time\"===i.mode&&(w.config.noCalendar=!0,w.config.enableTime=!0),Object.assign(w.config,o,i);for(var u=0;u-1?w.config[m]=c(f[m]).map(x).concat(w.config[m]):void 0===i[m]&&(w.config[m]=f[m])}i.altInputClass||(w.config.altInputClass=re().className+\" \"+w.config.altInputClass),pe(\"onParseConfig\")}(),le(),w.input=re(),w.input?(w.input._type=w.input.type,w.input.type=\"text\",w.input.classList.add(\"flatpickr-input\"),w._input=w.input,w.config.altInput&&(w.altInput=s(w.input.nodeName,w.config.altInputClass),w._input=w.altInput,w.altInput.placeholder=w.input.placeholder,w.altInput.disabled=w.input.disabled,w.altInput.required=w.input.required,w.altInput.tabIndex=w.input.tabIndex,w.altInput.type=\"text\",w.input.setAttribute(\"type\",\"hidden\"),!w.config.static&&w.input.parentNode&&w.input.parentNode.insertBefore(w.altInput,w.input.nextSibling)),w.config.allowInput||w._input.setAttribute(\"readonly\",\"readonly\"),w._positionElement=w.config.positionElement||w._input):w.config.errorHandler(new Error(\"Invalid input element specified\")),function(){w.selectedDates=[],w.now=w.parseDate(w.config.now)||new Date;var e=w.config.defaultDate||(\"INPUT\"!==w.input.nodeName&&\"TEXTAREA\"!==w.input.nodeName||!w.input.placeholder||w.input.value!==w.input.placeholder?w.input.value:null);e&&me(e,w.config.dateFormat),w._initialDate=w.selectedDates.length>0?w.selectedDates[0]:w.config.minDate&&w.config.minDate.getTime()>w.now.getTime()?w.config.minDate:w.config.maxDate&&w.config.maxDate.getTime()0&&(w.latestSelectedDateObj=w.selectedDates[0]),void 0!==w.config.minTime&&(w.config.minTime=w.parseDate(w.config.minTime,\"H:i\")),void 0!==w.config.maxTime&&(w.config.maxTime=w.parseDate(w.config.maxTime,\"H:i\")),w.minDateHasTime=!!w.config.minDate&&(w.config.minDate.getHours()>0||w.config.minDate.getMinutes()>0||w.config.minDate.getSeconds()>0),w.maxDateHasTime=!!w.config.maxDate&&(w.config.maxDate.getHours()>0||w.config.maxDate.getMinutes()>0||w.config.maxDate.getSeconds()>0)}(),w.utils={getDaysInMonth:function(e,n){return void 0===e&&(e=w.currentMonth),void 0===n&&(n=w.currentYear),1===e&&(n%4==0&&n%100!=0||n%400==0)?29:w.l10n.daysInMonth[e]}},w.isMobile||function(){var e=window.document.createDocumentFragment();if(w.calendarContainer=s(\"div\",\"flatpickr-calendar\"),w.calendarContainer.tabIndex=-1,!w.config.noCalendar){if(e.appendChild((w.monthNav=s(\"div\",\"flatpickr-months\"),w.yearElements=[],w.monthElements=[],w.prevMonthNav=s(\"span\",\"flatpickr-prev-month\"),w.prevMonthNav.innerHTML=w.config.prevArrow,w.nextMonthNav=s(\"span\",\"flatpickr-next-month\"),w.nextMonthNav.innerHTML=w.config.nextArrow,q(),Object.defineProperty(w,\"_hidePrevMonthArrow\",{get:function(){return w.__hidePrevMonthArrow},set:function(e){w.__hidePrevMonthArrow!==e&&(d(w.prevMonthNav,\"flatpickr-disabled\",e),w.__hidePrevMonthArrow=e)}}),Object.defineProperty(w,\"_hideNextMonthArrow\",{get:function(){return w.__hideNextMonthArrow},set:function(e){w.__hideNextMonthArrow!==e&&(d(w.nextMonthNav,\"flatpickr-disabled\",e),w.__hideNextMonthArrow=e)}}),w.currentYearElement=w.yearElements[0],De(),w.monthNav)),w.innerContainer=s(\"div\",\"flatpickr-innerContainer\"),w.config.weekNumbers){var n=function(){w.calendarContainer.classList.add(\"hasWeeks\");var e=s(\"div\",\"flatpickr-weekwrapper\");e.appendChild(s(\"span\",\"flatpickr-weekday\",w.l10n.weekAbbreviation));var n=s(\"div\",\"flatpickr-weeks\");return e.appendChild(n),{weekWrapper:e,weekNumbers:n}}(),t=n.weekWrapper,a=n.weekNumbers;w.innerContainer.appendChild(t),w.weekNumbers=a,w.weekWrapper=t}w.rContainer=s(\"div\",\"flatpickr-rContainer\"),w.rContainer.appendChild($()),w.daysContainer||(w.daysContainer=s(\"div\",\"flatpickr-days\"),w.daysContainer.tabIndex=-1),J(),w.rContainer.appendChild(w.daysContainer),w.innerContainer.appendChild(w.rContainer),e.appendChild(w.innerContainer)}w.config.enableTime&&e.appendChild(function(){w.calendarContainer.classList.add(\"hasTime\"),w.config.noCalendar&&w.calendarContainer.classList.add(\"noCalendar\"),w.timeContainer=s(\"div\",\"flatpickr-time\"),w.timeContainer.tabIndex=-1;var e=s(\"span\",\"flatpickr-time-separator\",\":\"),n=m(\"flatpickr-hour\",{\"aria-label\":w.l10n.hourAriaLabel});w.hourElement=n.getElementsByTagName(\"input\")[0];var t=m(\"flatpickr-minute\",{\"aria-label\":w.l10n.minuteAriaLabel});if(w.minuteElement=t.getElementsByTagName(\"input\")[0],w.hourElement.tabIndex=w.minuteElement.tabIndex=-1,w.hourElement.value=o(w.latestSelectedDateObj?w.latestSelectedDateObj.getHours():w.config.time_24hr?w.config.defaultHour:function(e){switch(e%24){case 0:case 12:return 12;default:return e%12}}(w.config.defaultHour)),w.minuteElement.value=o(w.latestSelectedDateObj?w.latestSelectedDateObj.getMinutes():w.config.defaultMinute),w.hourElement.setAttribute(\"step\",w.config.hourIncrement.toString()),w.minuteElement.setAttribute(\"step\",w.config.minuteIncrement.toString()),w.hourElement.setAttribute(\"min\",w.config.time_24hr?\"0\":\"1\"),w.hourElement.setAttribute(\"max\",w.config.time_24hr?\"23\":\"12\"),w.minuteElement.setAttribute(\"min\",\"0\"),w.minuteElement.setAttribute(\"max\",\"59\"),w.timeContainer.appendChild(n),w.timeContainer.appendChild(e),w.timeContainer.appendChild(t),w.config.time_24hr&&w.timeContainer.classList.add(\"time24hr\"),w.config.enableSeconds){w.timeContainer.classList.add(\"hasSeconds\");var a=m(\"flatpickr-second\");w.secondElement=a.getElementsByTagName(\"input\")[0],w.secondElement.value=o(w.latestSelectedDateObj?w.latestSelectedDateObj.getSeconds():w.config.defaultSeconds),w.secondElement.setAttribute(\"step\",w.minuteElement.getAttribute(\"step\")),w.secondElement.setAttribute(\"min\",\"0\"),w.secondElement.setAttribute(\"max\",\"59\"),w.timeContainer.appendChild(s(\"span\",\"flatpickr-time-separator\",\":\")),w.timeContainer.appendChild(a)}return w.config.time_24hr||(w.amPM=s(\"span\",\"flatpickr-am-pm\",w.l10n.amPM[r((w.latestSelectedDateObj?w.hourElement.value:w.config.defaultHour)>11)]),w.amPM.title=w.l10n.toggleTitle,w.amPM.tabIndex=-1,w.timeContainer.appendChild(w.amPM)),w.timeContainer}()),d(w.calendarContainer,\"rangeMode\",\"range\"===w.config.mode),d(w.calendarContainer,\"animate\",!0===w.config.animate),d(w.calendarContainer,\"multiMonth\",w.config.showMonths>1),w.calendarContainer.appendChild(e);var i=void 0!==w.config.appendTo&&void 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tt(t,e){e.handle&&(d(e.handle,r.cssClasses.active),M-=1),e.listeners.forEach((function(t){D.removeEventListener(t[0],t[1])})),0===M&&(d(w,r.cssClasses.drag),pt(),t.cursor&&(O.style.cursor=\"\",O.removeEventListener(\"selectstart\",s))),e.handleNumbers.forEach((function(t){st(\"change\",t),st(\"set\",t),st(\"end\",t)}))}function et(t,e){if(!e.handleNumbers.some(R)){var n;1===e.handleNumbers.length&&(n=p[e.handleNumbers[0]].children[0],M+=1,f(n,r.cssClasses.active)),t.stopPropagation();var 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at(t,e,n,i,o,s){var a;return p.length>1&&!r.events.unconstrained&&(i&&e>0&&(a=C.getAbsoluteDistance(t[e-1],r.margin,!1),n=Math.max(n,a)),o&&e1&&r.limit&&(i&&e>0&&(a=C.getAbsoluteDistance(t[e-1],r.limit,!1),n=Math.min(n,a)),o&&e1?n.forEach((function(t,r){var n=at(o,t,o[t]+e,a[r],l[r],!1);!1===n?e=0:(e=n-o[t],o[t]=n)})):a=l=[!0];var u=!1;n.forEach((function(t,n){u=ft(t,r[t]+e,a[n],l[n])||u})),u&&(n.forEach((function(t){st(\"update\",t),st(\"slide\",t)})),null!=i&&st(\"drag\",s))}function ct(t,e){return r.dir?100-t-e:t}function pt(){k.forEach((function(t){var e=V[t]>50?-1:1,r=3+(p.length+e*t);p[t].style.zIndex=String(r)}))}function ft(t,e,n,i,o){return o||(e=at(V,t,e,n,i,!1)),!1!==e&&(function(t,e){V[t]=e,N[t]=C.fromStepping(e);var n=\"translate(\"+lt(10*(ct(e,0)-L)+\"%\",\"0\")+\")\";p[t].style[r.transformRule]=n,dt(t),dt(t+1)}(t,e),!0)}function dt(t){if(m[t]){var e=0,n=100;0!==t&&(e=V[t-1]),t!==m.length-1&&(n=V[t]);var 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null!==o&&!1!==o&&(o=Number(o.toFixed(a))),null!==s&&!1!==s&&(s=Number(s.toFixed(a))),[s,o]}f(b=w,r.cssClasses.target),0===r.dir?f(b,r.cssClasses.ltr):f(b,r.cssClasses.rtl),0===r.ort?f(b,r.cssClasses.horizontal):f(b,r.cssClasses.vertical),f(b,\"rtl\"===getComputedStyle(b).direction?r.cssClasses.textDirectionRtl:r.cssClasses.textDirectionLtr),a=T(b,r.cssClasses.base),function(t,e){var n=T(e,r.cssClasses.connects);p=[],(m=[]).push(z(n,t[0]));for(var i=0;i=0&&t .noUi-tooltip{-webkit-transform:translate(50%, 0);transform:translate(50%, 0);left:auto;bottom:10px;}.bk-root .noUi-vertical .noUi-origin > .noUi-tooltip{-webkit-transform:translate(0, -18px);transform:translate(0, -18px);top:auto;right:28px;}.bk-root .noUi-handle{cursor:grab;cursor:-webkit-grab;}.bk-root .noUi-handle.noUi-active{cursor:grabbing;cursor:-webkit-grabbing;}.bk-root .noUi-handle:after,.bk-root .noUi-handle:before{display:none;}.bk-root .noUi-tooltip{display:none;white-space:nowrap;}.bk-root .noUi-handle:hover .noUi-tooltip{display:block;}.bk-root .noUi-horizontal{width:100%;height:10px;}.bk-root .noUi-vertical{width:10px;height:100%;}.bk-root .noUi-horizontal .noUi-handle{width:14px;height:18px;right:-7px;top:-5px;}.bk-root .noUi-vertical .noUi-handle{width:18px;height:14px;right:-5px;top:-7px;}.bk-root .noUi-target.noUi-horizontal{margin:5px 0px;}.bk-root .noUi-target.noUi-vertical{margin:0px 5px;}'},\n 465: function _(e,t,r,a,i){a();var s;const d=(0,e(1).__importDefault)(e(151)),o=e(461),_=e(8);class n extends o.AbstractSliderView{}r.DateSliderView=n,n.__name__=\"DateSliderView\";class c extends o.AbstractSlider{constructor(e){super(e),this.behaviour=\"tap\",this.connected=[!0,!1]}_formatter(e,t){return(0,_.isString)(t)?(0,d.default)(e,t):t.compute(e)}}r.DateSlider=c,s=c,c.__name__=\"DateSlider\",s.prototype.default_view=n,s.override({format:\"%d %b %Y\"})},\n 466: function _(e,t,r,a,i){a();var n;const s=(0,e(1).__importDefault)(e(151)),d=e(461),o=e(8);class _ extends d.AbstractRangeSliderView{}r.DatetimeRangeSliderView=_,_.__name__=\"DatetimeRangeSliderView\";class c extends d.AbstractSlider{constructor(e){super(e),this.behaviour=\"drag\",this.connected=[!1,!0,!1]}_formatter(e,t){return(0,o.isString)(t)?(0,s.default)(e,t):t.compute(e)}}r.DatetimeRangeSlider=c,n=c,c.__name__=\"DatetimeRangeSlider\",n.prototype.default_view=_,n.override({format:\"%d %b %Y %H:%M:%S\",step:36e5})},\n 467: function _(e,t,s,r,i){var _;r();const n=e(468);class a extends n.MarkupView{render(){super.render(),this.model.render_as_text?this.markup_el.textContent=this.model.text:this.markup_el.innerHTML=this.has_math_disabled()?this.model.text:this.process_tex()}}s.DivView=a,a.__name__=\"DivView\";class d extends n.Markup{constructor(e){super(e)}}s.Div=d,_=d,d.__name__=\"Div\",_.prototype.default_view=a,_.define((({Boolean:e})=>({render_as_text:[e,!1]})))},\n 468: function _(t,e,s,i,r){i();const a=t(1);var n;const o=t(210),d=t(43),h=t(137),l=t(512),_=(0,a.__importStar)(t(469));class u extends l.WidgetView{get provider(){return h.default_provider}async lazy_initialize(){await super.lazy_initialize(),\"not_started\"==this.provider.status&&await this.provider.fetch(),\"not_started\"!=this.provider.status&&\"loading\"!=this.provider.status||this.provider.ready.connect((()=>{this.contains_tex_string()&&this.rerender()}))}after_layout(){super.after_layout(),\"loading\"===this.provider.status&&(this._has_finished=!1)}rerender(){this.layout.invalidate_cache(),this.render(),this.root.compute_layout()}connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>{this.rerender()}))}styles(){return[...super.styles(),_.default]}_update_layout(){this.layout=new o.CachedVariadicBox(this.el),this.layout.set_sizing(this.box_sizing())}render(){super.render();const t=Object.assign(Object.assign({},this.model.style),{display:\"inline-block\"});this.markup_el=(0,d.div)({class:_.clearfix,style:t}),this.el.appendChild(this.markup_el),\"failed\"!=this.provider.status&&\"loaded\"!=this.provider.status||(this._has_finished=!0)}has_math_disabled(){return this.model.disable_math||!this.contains_tex_string()}process_tex(){if(!this.provider.MathJax)return this.model.text;const{text:t}=this.model,e=this.provider.MathJax.find_tex(t),s=[];let i=0;for(const r of e)s.push(t.slice(i,r.start.n)),s.push(this.provider.MathJax.tex2svg(r.math,{display:r.display}).outerHTML),i=r.end.n;return i0}}s.MarkupView=u,u.__name__=\"MarkupView\";class p extends l.Widget{constructor(t){super(t)}}s.Markup=p,n=p,p.__name__=\"Markup\",n.define((({Boolean:t,String:e,Dict:s})=>({text:[e,\"\"],style:[s(e),{}],disable_math:[t,!1]})))},\n 469: function _(o,r,e,t,a){t(),e.root=\"bk-root\",e.clearfix=\"bk-clearfix\",e.default='.bk-root .bk-clearfix:before,.bk-root 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n.addEventListener(\"click\",(()=>this._item_click(t))),n}}));this.menu=(0,_.div)({class:[m.menu,m.below]},t),this.el.appendChild(this.menu),(0,_.undisplay)(this.menu)}_show_menu(){if(!this._open){this._open=!0,(0,_.display)(this.menu);const e=t=>{const{target:i}=t;i instanceof HTMLElement&&!this.el.contains(i)&&(document.removeEventListener(\"click\",e),this._hide_menu())};document.addEventListener(\"click\",e)}}_hide_menu(){this._open&&(this._open=!1,(0,_.undisplay)(this.menu))}_toggle_menu(){this._open?this._hide_menu():this._show_menu()}click(){this.model.is_split?(this._hide_menu(),this.model.trigger_event(new d.ButtonClick),super.click()):this._toggle_menu()}_item_click(e){this._hide_menu();const t=this.model.menu[e];if(null!=t){const i=(0,u.isString)(t)?t:t[1];(0,u.isString)(i)?this.model.trigger_event(new d.MenuItemClick(i)):i.execute(this.model,{index:e})}}}i.DropdownView=p,p.__name__=\"DropdownView\";class a extends r.AbstractButton{constructor(e){super(e)}get is_split(){return this.split}}i.Dropdown=a,l=a,a.__name__=\"Dropdown\",l.prototype.default_view=p,l.define((({Null:e,Boolean:t,String:i,Array:n,Tuple:s,Or:o})=>({split:[t,!1],menu:[n(o(i,s(i,o(i)),e)),[]]}))),l.override({label:\"Dropdown\"})},\n 471: function _(e,l,i,t,s){var n;t();const a=e(43),o=e(512);class d extends o.WidgetView{connect_signals(){super.connect_signals(),this.connect(this.model.change,(()=>this.render()))}render(){const{multiple:e,accept:l,disabled:i,width:t}=this.model;null==this.dialog_el&&(this.dialog_el=(0,a.input)({type:\"file\",multiple:e}),this.dialog_el.onchange=()=>{const{files:e}=this.dialog_el;null!=e&&this.load_files(e)},this.el.appendChild(this.dialog_el)),null!=l&&\"\"!=l&&(this.dialog_el.accept=l),this.dialog_el.style.width=`${t}px`,this.dialog_el.disabled=i}async load_files(e){const l=[],i=[],t=[];for(const s of e){const e=await this._read_file(s),[,n=\"\",,a=\"\"]=e.split(/[:;,]/,4);l.push(a),i.push(s.name),t.push(n)}this.model.multiple?this.model.setv({value:l,filename:i,mime_type:t}):this.model.setv({value:l[0],filename:i[0],mime_type:t[0]})}_read_file(e){return new Promise(((l,i)=>{const t=new FileReader;t.onload=()=>{var s;const{result:n}=t;null!=n?l(n):i(null!==(s=t.error)&&void 0!==s?s:new Error(`unable to read '${e.name}'`))},t.readAsDataURL(e)}))}}i.FileInputView=d,d.__name__=\"FileInputView\";class r extends o.Widget{constructor(e){super(e)}}i.FileInput=r,n=r,r.__name__=\"FileInput\",n.prototype.default_view=d,n.define((({Boolean:e,String:l,Array:i,Or:t})=>({value:[t(l,i(l)),\"\"],mime_type:[t(l,i(l)),\"\"],filename:[t(l,i(l)),\"\"],accept:[l,\"\"],multiple:[e,!1]})))},\n 472: function _(e,t,i,s,n){s();const l=e(1);var o;const r=e(43),c=e(8),h=e(448),p=(0,l.__importStar)(e(449));class d extends h.InputWidgetView{connect_signals(){super.connect_signals(),this.connect(this.model.properties.value.change,(()=>this.render_selection())),this.connect(this.model.properties.options.change,(()=>this.render())),this.connect(this.model.properties.name.change,(()=>this.render())),this.connect(this.model.properties.title.change,(()=>this.render())),this.connect(this.model.properties.size.change,(()=>this.render())),this.connect(this.model.properties.disabled.change,(()=>this.render()))}render(){super.render();const e=this.model.options.map((e=>{let t,i;return(0,c.isString)(e)?t=i=e:[t,i]=e,(0,r.option)({value:t},i)}));this.input_el=(0,r.select)({multiple:!0,class:p.input,name:this.model.name,disabled:this.model.disabled},e),this.input_el.addEventListener(\"change\",(()=>this.change_input())),this.group_el.appendChild(this.input_el),this.render_selection()}render_selection(){const e=new Set(this.model.value);for(const t of 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p.Markup{constructor(e){super(e)}}r.Paragraph=h,n=h,h.__name__=\"Paragraph\",n.prototype.default_view=i},\n 474: function _(e,s,t,n,r){var p;n();const u=e(446);class a extends u.TextInputView{render(){super.render(),this.input_el.type=\"password\"}}t.PasswordInputView=a,a.__name__=\"PasswordInputView\";class o extends u.TextInput{constructor(e){super(e)}}t.PasswordInput=o,p=o,o.__name__=\"PasswordInput\",p.prototype.default_view=a},\n 475: function _(e,t,i,l,s){l();const o=e(1);var n;const h=(0,o.__importDefault)(e(476)),a=e(43),u=e(8),c=e(210),_=(0,o.__importStar)(e(449)),d=(0,o.__importDefault)(e(477)),r=e(448);class m extends r.InputWidgetView{constructor(){super(...arguments),this._last_height=null}connect_signals(){super.connect_signals(),this.connect(this.model.properties.disabled.change,(()=>this.set_disabled()));const{value:e,max_items:t,option_limit:i,search_option_limit:l,delete_button:s,placeholder:o,options:n,name:h,title:a}=this.model.properties;this.on_change([e,t,i,l,s,o,n,h,a],(()=>this.render()))}styles(){return[...super.styles(),d.default]}_update_layout(){this.layout=new c.CachedVariadicBox(this.el),this.layout.set_sizing(this.box_sizing())}render(){super.render(),this.input_el=(0,a.select)({multiple:!0,class:_.input,name:this.model.name,disabled:this.model.disabled}),this.group_el.appendChild(this.input_el);const e=new Set(this.model.value),t=this.model.options.map((t=>{let i,l;return(0,u.isString)(t)?i=l=t:[i,l]=t,{value:i,label:l,selected:e.has(i)}})),i=this.model.solid?\"solid\":\"light\",l=`choices__item ${i}`,s=`choices__button ${i}`,o={choices:t,duplicateItemsAllowed:!1,removeItemButton:this.model.delete_button,classNames:{item:l,button:s}};null!=this.model.placeholder&&(o.placeholderValue=this.model.placeholder),null!=this.model.max_items&&(o.maxItemCount=this.model.max_items),null!=this.model.option_limit&&(o.renderChoiceLimit=this.model.option_limit),null!=this.model.search_option_limit&&(o.searchResultLimit=this.model.search_option_limit),this.choice_el=new h.default(this.input_el,o);const n=()=>this.choice_el.containerOuter.element.getBoundingClientRect().height;null!=this._last_height&&this._last_height!=n()&&this.root.invalidate_layout(),this._last_height=n(),this.input_el.addEventListener(\"change\",(()=>this.change_input()))}set_disabled(){this.model.disabled?this.choice_el.disable():this.choice_el.enable()}change_input(){const e=null!=this.el.querySelector(\"select:focus\"),t=[];for(const e of this.el.querySelectorAll(\"option\"))e.selected&&t.push(e.value);this.model.value=t,super.change_input(),e&&this.input_el.focus()}}i.MultiChoiceView=m,m.__name__=\"MultiChoiceView\";class p extends r.InputWidget{constructor(e){super(e)}}i.MultiChoice=p,n=p,p.__name__=\"MultiChoice\",n.prototype.default_view=m,n.define((({Boolean:e,Int:t,String:i,Array:l,Tuple:s,Or:o,Nullable:n})=>({value:[l(i),[]],options:[l(o(i,s(i,i))),[]],max_items:[n(t),null],delete_button:[e,!0],placeholder:[n(i),null],option_limit:[n(t),null],search_option_limit:[n(t),null],solid:[e,!0]})))},\n 476: function _(e,t,i,n,s){\n /*! choices.js v9.0.1 | © 2019 Josh Johnson | https://github.com/jshjohnson/Choices#readme */\n var r,o;r=window,o=function(){return function(e){var t={};function i(n){if(t[n])return t[n].exports;var s=t[n]={i:n,l:!1,exports:{}};return e[n].call(s.exports,s,s.exports,i),s.l=!0,s.exports}return i.m=e,i.c=t,i.d=function(e,t,n){i.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:n})},i.r=function(e){\"undefined\"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:\"Module\"}),Object.defineProperty(e,\"__esModule\",{value:!0})},i.t=function(e,t){if(1&t&&(e=i(e)),8&t)return e;if(4&t&&\"object\"==typeof e&&e&&e.__esModule)return e;var n=Object.create(null);if(i.r(n),Object.defineProperty(n,\"default\",{enumerable:!0,value:e}),2&t&&\"string\"!=typeof e)for(var s in e)i.d(n,s,function(t){return e[t]}.bind(null,s));return n},i.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return i.d(t,\"a\",t),t},i.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},i.p=\"/public/assets/scripts/\",i(i.s=4)}([function(e,t,i){\"use strict\";var n=function(e){return function(e){return!!e&&\"object\"==typeof e}(e)&&!function(e){var t=Object.prototype.toString.call(e);return\"[object RegExp]\"===t||\"[object Date]\"===t||function(e){return e.$$typeof===s}(e)}(e)},s=\"function\"==typeof Symbol&&Symbol.for?Symbol.for(\"react.element\"):60103;function r(e,t){return!1!==t.clone&&t.isMergeableObject(e)?l((i=e,Array.isArray(i)?[]:{}),e,t):e;var i}function o(e,t,i){return e.concat(t).map((function(e){return r(e,i)}))}function a(e){return Object.keys(e).concat(function(e){return Object.getOwnPropertySymbols?Object.getOwnPropertySymbols(e).filter((function(t){return e.propertyIsEnumerable(t)})):[]}(e))}function c(e,t,i){var n={};return i.isMergeableObject(e)&&a(e).forEach((function(t){n[t]=r(e[t],i)})),a(t).forEach((function(s){(function(e,t){try{return t in e&&!(Object.hasOwnProperty.call(e,t)&&Object.propertyIsEnumerable.call(e,t))}catch(e){return!1}})(e,s)||(i.isMergeableObject(t[s])&&e[s]?n[s]=function(e,t){if(!t.customMerge)return l;var i=t.customMerge(e);return\"function\"==typeof i?i:l}(s,i)(e[s],t[s],i):n[s]=r(t[s],i))})),n}function l(e,t,i){(i=i||{}).arrayMerge=i.arrayMerge||o,i.isMergeableObject=i.isMergeableObject||n,i.cloneUnlessOtherwiseSpecified=r;var s=Array.isArray(t);return s===Array.isArray(e)?s?i.arrayMerge(e,t,i):c(e,t,i):r(t,i)}l.all=function(e,t){if(!Array.isArray(e))throw new Error(\"first argument should be an array\");return e.reduce((function(e,i){return l(e,i,t)}),{})};var h=l;e.exports=h},function(e,t,i){\"use strict\";(function(e,n){var s,r=i(3);s=\"undefined\"!=typeof self?self:\"undefined\"!=typeof window?window:void 0!==e?e:n;var o=Object(r.a)(s);t.a=o}).call(this,i(5),i(6)(e))},function(e,t,i){\n /*!\n * Fuse.js v3.4.5 - Lightweight fuzzy-search (http://fusejs.io)\n *\n * Copyright (c) 2012-2017 Kirollos Risk (http://kiro.me)\n * All Rights Reserved. Apache Software License 2.0\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n */\n e.exports=function(e){var t={};function i(n){if(t[n])return t[n].exports;var s=t[n]={i:n,l:!1,exports:{}};return e[n].call(s.exports,s,s.exports,i),s.l=!0,s.exports}return i.m=e,i.c=t,i.d=function(e,t,n){i.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:n})},i.r=function(e){\"undefined\"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:\"Module\"}),Object.defineProperty(e,\"__esModule\",{value:!0})},i.t=function(e,t){if(1&t&&(e=i(e)),8&t)return e;if(4&t&&\"object\"==typeof e&&e&&e.__esModule)return e;var n=Object.create(null);if(i.r(n),Object.defineProperty(n,\"default\",{enumerable:!0,value:e}),2&t&&\"string\"!=typeof e)for(var s in e)i.d(n,s,function(t){return e[t]}.bind(null,s));return n},i.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return i.d(t,\"a\",t),t},i.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},i.p=\"\",i(i.s=1)}([function(e,t){e.exports=function(e){return Array.isArray?Array.isArray(e):\"[object Array]\"===Object.prototype.toString.call(e)}},function(e,t,i){function n(e){return(n=\"function\"==typeof Symbol&&\"symbol\"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&\"function\"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?\"symbol\":typeof e})(e)}function s(e,t){for(var i=0;i1&&void 0!==arguments[1]?arguments[1]:{limit:!1};this._log('---------\\nSearch pattern: \"'.concat(e,'\"'));var i=this._prepareSearchers(e),n=i.tokenSearchers,s=i.fullSearcher,r=this._search(n,s),o=r.weights,a=r.results;return this._computeScore(o,a),this.options.shouldSort&&this._sort(a),t.limit&&\"number\"==typeof t.limit&&(a=a.slice(0,t.limit)),this._format(a)}},{key:\"_prepareSearchers\",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:\"\",t=[];if(this.options.tokenize)for(var i=e.split(this.options.tokenSeparator),n=0,s=i.length;n0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1?arguments[1]:void 0,i=this.list,n={},s=[];if(\"string\"==typeof i[0]){for(var r=0,o=i.length;r1)throw new Error(\"Key weight has to be > 0 and <= 1\");p=p.name}else a[p]={weight:1};this._analyze({key:p,value:this.options.getFn(h,p),record:h,index:c},{resultMap:n,results:s,tokenSearchers:e,fullSearcher:t})}return{weights:a,results:s}}},{key:\"_analyze\",value:function(e,t){var i=e.key,n=e.arrayIndex,s=void 0===n?-1:n,r=e.value,o=e.record,c=e.index,l=t.tokenSearchers,h=void 0===l?[]:l,u=t.fullSearcher,d=void 0===u?[]:u,p=t.resultMap,m=void 0===p?{}:p,f=t.results,v=void 0===f?[]:f;if(null!=r){var g=!1,_=-1,b=0;if(\"string\"==typeof r){this._log(\"\\nKey: \".concat(\"\"===i?\"-\":i));var y=d.search(r);if(this._log('Full text: \"'.concat(r,'\", score: ').concat(y.score)),this.options.tokenize){for(var E=r.split(this.options.tokenSeparator),I=[],S=0;S-1&&(P=(P+_)/2),this._log(\"Score average:\",P);var D=!this.options.tokenize||!this.options.matchAllTokens||b>=h.length;if(this._log(\"\\nCheck Matches: \".concat(D)),(g||y.isMatch)&&D){var M=m[c];M?M.output.push({key:i,arrayIndex:s,value:r,score:P,matchedIndices:y.matchedIndices}):(m[c]={item:o,output:[{key:i,arrayIndex:s,value:r,score:P,matchedIndices:y.matchedIndices}]},v.push(m[c]))}}else if(a(r))for(var N=0,F=r.length;N-1&&(o.arrayIndex=r.arrayIndex),t.matches.push(o)}}})),this.options.includeScore&&s.push((function(e,t){t.score=e.score}));for(var r=0,o=e.length;ri)return s(e,this.pattern,n);var o=this.options,a=o.location,c=o.distance,l=o.threshold,h=o.findAllMatches,u=o.minMatchCharLength;return r(e,this.pattern,this.patternAlphabet,{location:a,distance:c,threshold:l,findAllMatches:h,minMatchCharLength:u})}}])&&n(t.prototype,i),a&&n(t,a),e}();e.exports=a},function(e,t){var i=/[\\-\\[\\]\\/\\{\\}\\(\\)\\*\\+\\?\\.\\\\\\^\\$\\|]/g;e.exports=function(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:/ +/g,s=new RegExp(t.replace(i,\"\\\\$&\").replace(n,\"|\")),r=e.match(s),o=!!r,a=[];if(o)for(var c=0,l=r.length;c=P;N-=1){var F=N-1,j=i[e.charAt(F)];if(j&&(E[F]=1),M[N]=(M[N+1]<<1|1)&j,0!==T&&(M[N]|=(O[N+1]|O[N])<<1|1|O[N+1]),M[N]&L&&(C=n(t,{errors:T,currentLocation:F,expectedLocation:v,distance:l}))<=_){if(_=C,(b=F)<=v)break;P=Math.max(1,2*v-b)}}if(n(t,{errors:T+1,currentLocation:v,expectedLocation:v,distance:l})>_)break;O=M}return{isMatch:b>=0,score:0===C?.001:C,matchedIndices:s(E,f)}}},function(e,t){e.exports=function(e,t){var i=t.errors,n=void 0===i?0:i,s=t.currentLocation,r=void 0===s?0:s,o=t.expectedLocation,a=void 0===o?0:o,c=t.distance,l=void 0===c?100:c,h=n/e.length,u=Math.abs(a-r);return l?h+u/l:u?1:h}},function(e,t){e.exports=function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:[],t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:1,i=[],n=-1,s=-1,r=0,o=e.length;r=t&&i.push([n,s]),n=-1)}return e[r-1]&&r-n>=t&&i.push([n,r-1]),i}},function(e,t){e.exports=function(e){for(var t={},i=e.length,n=0;n/g,\"&rt;\").replace(/-1?e.map((function(e){var i=e;return i.id===parseInt(t.choiceId,10)&&(i.selected=!0),i})):e;case\"REMOVE_ITEM\":return t.choiceId>-1?e.map((function(e){var i=e;return i.id===parseInt(t.choiceId,10)&&(i.selected=!1),i})):e;case\"FILTER_CHOICES\":return e.map((function(e){var i=e;return i.active=t.results.some((function(e){var t=e.item,n=e.score;return t.id===i.id&&(i.score=n,!0)})),i}));case\"ACTIVATE_CHOICES\":return e.map((function(e){var i=e;return i.active=t.active,i}));case\"CLEAR_CHOICES\":return v;default:return e}},general:_}),A=function(e,t){var i=e;if(\"CLEAR_ALL\"===t.type)i=void 0;else if(\"RESET_TO\"===t.type)return O(t.state);return C(i,t)};function L(e,t){for(var i=0;i\"'+I(e)+'\"'},maxItemText:function(e){return\"Only \"+e+\" values can be added\"},valueComparer:function(e,t){return e===t},fuseOptions:{includeScore:!0},callbackOnInit:null,callbackOnCreateTemplates:null,classNames:{containerOuter:\"choices\",containerInner:\"choices__inner\",input:\"choices__input\",inputCloned:\"choices__input--cloned\",list:\"choices__list\",listItems:\"choices__list--multiple\",listSingle:\"choices__list--single\",listDropdown:\"choices__list--dropdown\",item:\"choices__item\",itemSelectable:\"choices__item--selectable\",itemDisabled:\"choices__item--disabled\",itemChoice:\"choices__item--choice\",placeholder:\"choices__placeholder\",group:\"choices__group\",groupHeading:\"choices__heading\",button:\"choices__button\",activeState:\"is-active\",focusState:\"is-focused\",openState:\"is-open\",disabledState:\"is-disabled\",highlightedState:\"is-highlighted\",selectedState:\"is-selected\",flippedState:\"is-flipped\",loadingState:\"is-loading\",noResults:\"has-no-results\",noChoices:\"has-no-choices\"}},D=\"showDropdown\",M=\"hideDropdown\",N=\"change\",F=\"choice\",j=\"search\",K=\"addItem\",R=\"removeItem\",H=\"highlightItem\",B=\"highlightChoice\",V=\"ADD_CHOICE\",G=\"FILTER_CHOICES\",q=\"ACTIVATE_CHOICES\",U=\"CLEAR_CHOICES\",z=\"ADD_GROUP\",W=\"ADD_ITEM\",X=\"REMOVE_ITEM\",$=\"HIGHLIGHT_ITEM\",J=46,Y=8,Z=13,Q=65,ee=27,te=38,ie=40,ne=33,se=34,re=\"text\",oe=\"select-one\",ae=\"select-multiple\",ce=function(){function e(e){var t=e.element,i=e.type,n=e.classNames,s=e.position;this.element=t,this.classNames=n,this.type=i,this.position=s,this.isOpen=!1,this.isFlipped=!1,this.isFocussed=!1,this.isDisabled=!1,this.isLoading=!1,this._onFocus=this._onFocus.bind(this),this._onBlur=this._onBlur.bind(this)}var t=e.prototype;return t.addEventListeners=function(){this.element.addEventListener(\"focus\",this._onFocus),this.element.addEventListener(\"blur\",this._onBlur)},t.removeEventListeners=function(){this.element.removeEventListener(\"focus\",this._onFocus),this.element.removeEventListener(\"blur\",this._onBlur)},t.shouldFlip=function(e){if(\"number\"!=typeof e)return!1;var t=!1;return\"auto\"===this.position?t=!window.matchMedia(\"(min-height: \"+(e+1)+\"px)\").matches:\"top\"===this.position&&(t=!0),t},t.setActiveDescendant=function(e){this.element.setAttribute(\"aria-activedescendant\",e)},t.removeActiveDescendant=function(){this.element.removeAttribute(\"aria-activedescendant\")},t.open=function(e){this.element.classList.add(this.classNames.openState),this.element.setAttribute(\"aria-expanded\",\"true\"),this.isOpen=!0,this.shouldFlip(e)&&(this.element.classList.add(this.classNames.flippedState),this.isFlipped=!0)},t.close=function(){this.element.classList.remove(this.classNames.openState),this.element.setAttribute(\"aria-expanded\",\"false\"),this.removeActiveDescendant(),this.isOpen=!1,this.isFlipped&&(this.element.classList.remove(this.classNames.flippedState),this.isFlipped=!1)},t.focus=function(){this.isFocussed||this.element.focus()},t.addFocusState=function(){this.element.classList.add(this.classNames.focusState)},t.removeFocusState=function(){this.element.classList.remove(this.classNames.focusState)},t.enable=function(){this.element.classList.remove(this.classNames.disabledState),this.element.removeAttribute(\"aria-disabled\"),this.type===oe&&this.element.setAttribute(\"tabindex\",\"0\"),this.isDisabled=!1},t.disable=function(){this.element.classList.add(this.classNames.disabledState),this.element.setAttribute(\"aria-disabled\",\"true\"),this.type===oe&&this.element.setAttribute(\"tabindex\",\"-1\"),this.isDisabled=!0},t.wrap=function(e){!function(e,t){void 0===t&&(t=document.createElement(\"div\")),e.nextSibling?e.parentNode.insertBefore(t,e.nextSibling):e.parentNode.appendChild(t),t.appendChild(e)}(e,this.element)},t.unwrap=function(e){this.element.parentNode.insertBefore(e,this.element),this.element.parentNode.removeChild(this.element)},t.addLoadingState=function(){this.element.classList.add(this.classNames.loadingState),this.element.setAttribute(\"aria-busy\",\"true\"),this.isLoading=!0},t.removeLoadingState=function(){this.element.classList.remove(this.classNames.loadingState),this.element.removeAttribute(\"aria-busy\"),this.isLoading=!1},t._onFocus=function(){this.isFocussed=!0},t._onBlur=function(){this.isFocussed=!1},e}();function le(e,t){for(var i=0;i0?this.element.scrollTop+o-s:e.offsetTop;requestAnimationFrame((function(){i._animateScroll(a,t)}))}},t._scrollDown=function(e,t,i){var n=(i-e)/t,s=n>1?n:1;this.element.scrollTop=e+s},t._scrollUp=function(e,t,i){var n=(e-i)/t,s=n>1?n:1;this.element.scrollTop=e-s},t._animateScroll=function(e,t){var i=this,n=this.element.scrollTop,s=!1;t>0?(this._scrollDown(n,4,e),ne&&(s=!0)),s&&requestAnimationFrame((function(){i._animateScroll(e,t)}))},e}();function de(e,t){for(var i=0;i0?\"treeitem\":\"option\"),Object.assign(g.dataset,{choice:\"\",id:l,value:h,selectText:i}),m?(g.classList.add(a),g.dataset.choiceDisabled=\"\",g.setAttribute(\"aria-disabled\",\"true\")):(g.classList.add(r),g.dataset.choiceSelectable=\"\"),g},input:function(e,t){var i=e.input,n=e.inputCloned,s=Object.assign(document.createElement(\"input\"),{type:\"text\",className:i+\" \"+n,autocomplete:\"off\",autocapitalize:\"off\",spellcheck:!1});return s.setAttribute(\"role\",\"textbox\"),s.setAttribute(\"aria-autocomplete\",\"list\"),s.setAttribute(\"aria-label\",t),s},dropdown:function(e){var t=e.list,i=e.listDropdown,n=document.createElement(\"div\");return n.classList.add(t,i),n.setAttribute(\"aria-expanded\",\"false\"),n},notice:function(e,t,i){var n=e.item,s=e.itemChoice,r=e.noResults,o=e.noChoices;void 0===i&&(i=\"\");var a=[n,s];return\"no-choices\"===i?a.push(o):\"no-results\"===i&&a.push(r),Object.assign(document.createElement(\"div\"),{innerHTML:t,className:a.join(\" \")})},option:function(e){var t=e.label,i=e.value,n=e.customProperties,s=e.active,r=e.disabled,o=new Option(t,i,!1,s);return n&&(o.dataset.customProperties=n),o.disabled=r,o}},be=function(e){return void 0===e&&(e=!0),{type:q,active:e}},ye=function(e,t){return{type:$,id:e,highlighted:t}},Ee=function(e){var t=e.value,i=e.id,n=e.active,s=e.disabled;return{type:z,value:t,id:i,active:n,disabled:s}},Ie=function(e){return{type:\"SET_IS_LOADING\",isLoading:e}};function Se(e,t){for(var i=0;i=0?this._store.getGroupById(s):null;return this._store.dispatch(ye(i,!0)),t&&this.passedElement.triggerEvent(H,{id:i,value:o,label:c,groupValue:l&&l.value?l.value:null}),this},r.unhighlightItem=function(e){if(!e)return this;var t=e.id,i=e.groupId,n=void 0===i?-1:i,s=e.value,r=void 0===s?\"\":s,o=e.label,a=void 0===o?\"\":o,c=n>=0?this._store.getGroupById(n):null;return this._store.dispatch(ye(t,!1)),this.passedElement.triggerEvent(H,{id:t,value:r,label:a,groupValue:c&&c.value?c.value:null}),this},r.highlightAll=function(){var e=this;return this._store.items.forEach((function(t){return e.highlightItem(t)})),this},r.unhighlightAll=function(){var e=this;return this._store.items.forEach((function(t){return e.unhighlightItem(t)})),this},r.removeActiveItemsByValue=function(e){var t=this;return this._store.activeItems.filter((function(t){return t.value===e})).forEach((function(e){return t._removeItem(e)})),this},r.removeActiveItems=function(e){var t=this;return this._store.activeItems.filter((function(t){return t.id!==e})).forEach((function(e){return t._removeItem(e)})),this},r.removeHighlightedItems=function(e){var t=this;return void 0===e&&(e=!1),this._store.highlightedActiveItems.forEach((function(i){t._removeItem(i),e&&t._triggerChange(i.value)})),this},r.showDropdown=function(e){var t=this;return this.dropdown.isActive||requestAnimationFrame((function(){t.dropdown.show(),t.containerOuter.open(t.dropdown.distanceFromTopWindow),!e&&t._canSearch&&t.input.focus(),t.passedElement.triggerEvent(D,{})})),this},r.hideDropdown=function(e){var t=this;return this.dropdown.isActive?(requestAnimationFrame((function(){t.dropdown.hide(),t.containerOuter.close(),!e&&t._canSearch&&(t.input.removeActiveDescendant(),t.input.blur()),t.passedElement.triggerEvent(M,{})})),this):this},r.getValue=function(e){void 0===e&&(e=!1);var t=this._store.activeItems.reduce((function(t,i){var n=e?i.value:i;return t.push(n),t}),[]);return this._isSelectOneElement?t[0]:t},r.setValue=function(e){var t=this;return this.initialised?(e.forEach((function(e){return t._setChoiceOrItem(e)})),this):this},r.setChoiceByValue=function(e){var t=this;return!this.initialised||this._isTextElement||(Array.isArray(e)?e:[e]).forEach((function(e){return t._findAndSelectChoiceByValue(e)})),this},r.setChoices=function(e,t,i,n){var s=this;if(void 0===e&&(e=[]),void 0===t&&(t=\"value\"),void 0===i&&(i=\"label\"),void 0===n&&(n=!1),!this.initialised)throw new ReferenceError(\"setChoices was called on a non-initialized instance of Choices\");if(!this._isSelectElement)throw new TypeError(\"setChoices can't be used with INPUT based Choices\");if(\"string\"!=typeof t||!t)throw new TypeError(\"value parameter must be a name of 'value' field in passed objects\");if(n&&this.clearChoices(),\"function\"==typeof e){var r=e(this);if(\"function\"==typeof Promise&&r instanceof Promise)return new Promise((function(e){return requestAnimationFrame(e)})).then((function(){return s._handleLoadingState(!0)})).then((function(){return r})).then((function(e){return s.setChoices(e,t,i,n)})).catch((function(e){s.config.silent||console.error(e)})).then((function(){return s._handleLoadingState(!1)})).then((function(){return s}));if(!Array.isArray(r))throw new TypeError(\".setChoices first argument function must return either array of choices or Promise, got: \"+typeof r);return this.setChoices(r,t,i,!1)}if(!Array.isArray(e))throw new TypeError(\".setChoices must be called either with array of choices with a function resulting into Promise of array of choices\");return this.containerOuter.removeLoadingState(),this._startLoading(),e.forEach((function(e){e.choices?s._addGroup({id:parseInt(e.id,10)||null,group:e,valueKey:t,labelKey:i}):s._addChoice({value:e[t],label:e[i],isSelected:e.selected,isDisabled:e.disabled,customProperties:e.customProperties,placeholder:e.placeholder})})),this._stopLoading(),this},r.clearChoices=function(){return this._store.dispatch({type:U}),this},r.clearStore=function(){return this._store.dispatch({type:\"CLEAR_ALL\"}),this},r.clearInput=function(){var e=!this._isSelectOneElement;return this.input.clear(e),!this._isTextElement&&this._canSearch&&(this._isSearching=!1,this._store.dispatch(be(!0))),this},r._render=function(){if(!this._store.isLoading()){this._currentState=this._store.state;var e=this._currentState.choices!==this._prevState.choices||this._currentState.groups!==this._prevState.groups||this._currentState.items!==this._prevState.items,t=this._isSelectElement,i=this._currentState.items!==this._prevState.items;e&&(t&&this._renderChoices(),i&&this._renderItems(),this._prevState=this._currentState)}},r._renderChoices=function(){var e=this,t=this._store,i=t.activeGroups,n=t.activeChoices,s=document.createDocumentFragment();if(this.choiceList.clear(),this.config.resetScrollPosition&&requestAnimationFrame((function(){return e.choiceList.scrollToTop()})),i.length>=1&&!this._isSearching){var r=n.filter((function(e){return!0===e.placeholder&&-1===e.groupId}));r.length>=1&&(s=this._createChoicesFragment(r,s)),s=this._createGroupsFragment(i,n,s)}else n.length>=1&&(s=this._createChoicesFragment(n,s));if(s.childNodes&&s.childNodes.length>0){var o=this._store.activeItems,a=this._canAddItem(o,this.input.value);a.response?(this.choiceList.append(s),this._highlightChoice()):this.choiceList.append(this._getTemplate(\"notice\",a.notice))}else{var c,l;this._isSearching?(l=\"function\"==typeof this.config.noResultsText?this.config.noResultsText():this.config.noResultsText,c=this._getTemplate(\"notice\",l,\"no-results\")):(l=\"function\"==typeof this.config.noChoicesText?this.config.noChoicesText():this.config.noChoicesText,c=this._getTemplate(\"notice\",l,\"no-choices\")),this.choiceList.append(c)}},r._renderItems=function(){var e=this._store.activeItems||[];this.itemList.clear();var t=this._createItemsFragment(e);t.childNodes&&this.itemList.append(t)},r._createGroupsFragment=function(e,t,i){var n=this;return void 0===i&&(i=document.createDocumentFragment()),this.config.shouldSort&&e.sort(this.config.sorter),e.forEach((function(e){var s=function(e){return t.filter((function(t){return n._isSelectOneElement?t.groupId===e.id:t.groupId===e.id&&(\"always\"===n.config.renderSelectedChoices||!t.selected)}))}(e);if(s.length>=1){var r=n._getTemplate(\"choiceGroup\",e);i.appendChild(r),n._createChoicesFragment(s,i,!0)}})),i},r._createChoicesFragment=function(e,t,i){var n=this;void 0===t&&(t=document.createDocumentFragment()),void 0===i&&(i=!1);var s=this.config,r=s.renderSelectedChoices,o=s.searchResultLimit,a=s.renderChoiceLimit,c=this._isSearching?w:this.config.sorter,l=function(e){if(\"auto\"!==r||n._isSelectOneElement||!e.selected){var i=n._getTemplate(\"choice\",e,n.config.itemSelectText);t.appendChild(i)}},h=e;\"auto\"!==r||this._isSelectOneElement||(h=e.filter((function(e){return!e.selected})));var u=h.reduce((function(e,t){return t.placeholder?e.placeholderChoices.push(t):e.normalChoices.push(t),e}),{placeholderChoices:[],normalChoices:[]}),d=u.placeholderChoices,p=u.normalChoices;(this.config.shouldSort||this._isSearching)&&p.sort(c);var m=h.length,f=this._isSelectOneElement?[].concat(d,p):p;this._isSearching?m=o:a&&a>0&&!i&&(m=a);for(var v=0;v=n){var o=s?this._searchChoices(e):0;this.passedElement.triggerEvent(j,{value:e,resultCount:o})}else r&&(this._isSearching=!1,this._store.dispatch(be(!0)))}},r._canAddItem=function(e,t){var i=!0,n=\"function\"==typeof this.config.addItemText?this.config.addItemText(t):this.config.addItemText;if(!this._isSelectOneElement){var s=function(e,t,i){return void 0===i&&(i=\"value\"),e.some((function(e){return\"string\"==typeof t?e[i]===t.trim():e[i]===t}))}(e,t);this.config.maxItemCount>0&&this.config.maxItemCount<=e.length&&(i=!1,n=\"function\"==typeof this.config.maxItemText?this.config.maxItemText(this.config.maxItemCount):this.config.maxItemText),!this.config.duplicateItemsAllowed&&s&&i&&(i=!1,n=\"function\"==typeof this.config.uniqueItemText?this.config.uniqueItemText(t):this.config.uniqueItemText),this._isTextElement&&this.config.addItems&&i&&\"function\"==typeof this.config.addItemFilter&&!this.config.addItemFilter(t)&&(i=!1,n=\"function\"==typeof this.config.customAddItemText?this.config.customAddItemText(t):this.config.customAddItemText)}return{response:i,notice:n}},r._searchChoices=function(e){var t=\"string\"==typeof e?e.trim():e,i=\"string\"==typeof this._currentValue?this._currentValue.trim():this._currentValue;if(t.length<1&&t===i+\" \")return 0;var n=this._store.searchableChoices,r=t,o=[].concat(this.config.searchFields),a=Object.assign(this.config.fuseOptions,{keys:o}),c=new s.a(n,a).search(r);return this._currentValue=t,this._highlightPosition=0,this._isSearching=!0,this._store.dispatch(function(e){return{type:G,results:e}}(c)),c.length},r._addEventListeners=function(){var e=document.documentElement;e.addEventListener(\"touchend\",this._onTouchEnd,!0),this.containerOuter.element.addEventListener(\"keydown\",this._onKeyDown,!0),this.containerOuter.element.addEventListener(\"mousedown\",this._onMouseDown,!0),e.addEventListener(\"click\",this._onClick,{passive:!0}),e.addEventListener(\"touchmove\",this._onTouchMove,{passive:!0}),this.dropdown.element.addEventListener(\"mouseover\",this._onMouseOver,{passive:!0}),this._isSelectOneElement&&(this.containerOuter.element.addEventListener(\"focus\",this._onFocus,{passive:!0}),this.containerOuter.element.addEventListener(\"blur\",this._onBlur,{passive:!0})),this.input.element.addEventListener(\"keyup\",this._onKeyUp,{passive:!0}),this.input.element.addEventListener(\"focus\",this._onFocus,{passive:!0}),this.input.element.addEventListener(\"blur\",this._onBlur,{passive:!0}),this.input.element.form&&this.input.element.form.addEventListener(\"reset\",this._onFormReset,{passive:!0}),this.input.addEventListeners()},r._removeEventListeners=function(){var e=document.documentElement;e.removeEventListener(\"touchend\",this._onTouchEnd,!0),this.containerOuter.element.removeEventListener(\"keydown\",this._onKeyDown,!0),this.containerOuter.element.removeEventListener(\"mousedown\",this._onMouseDown,!0),e.removeEventListener(\"click\",this._onClick),e.removeEventListener(\"touchmove\",this._onTouchMove),this.dropdown.element.removeEventListener(\"mouseover\",this._onMouseOver),this._isSelectOneElement&&(this.containerOuter.element.removeEventListener(\"focus\",this._onFocus),this.containerOuter.element.removeEventListener(\"blur\",this._onBlur)),this.input.element.removeEventListener(\"keyup\",this._onKeyUp),this.input.element.removeEventListener(\"focus\",this._onFocus),this.input.element.removeEventListener(\"blur\",this._onBlur),this.input.element.form&&this.input.element.form.removeEventListener(\"reset\",this._onFormReset),this.input.removeEventListeners()},r._onKeyDown=function(e){var t,i=e.target,n=e.keyCode,s=e.ctrlKey,r=e.metaKey,o=this._store.activeItems,a=this.input.isFocussed,c=this.dropdown.isActive,l=this.itemList.hasChildren(),h=String.fromCharCode(n),u=J,d=Y,p=Z,m=Q,f=ee,v=te,g=ie,_=ne,b=se,y=s||r;!this._isTextElement&&/[a-zA-Z0-9-_ ]/.test(h)&&this.showDropdown();var E=((t={})[m]=this._onAKey,t[p]=this._onEnterKey,t[f]=this._onEscapeKey,t[v]=this._onDirectionKey,t[_]=this._onDirectionKey,t[g]=this._onDirectionKey,t[b]=this._onDirectionKey,t[d]=this._onDeleteKey,t[u]=this._onDeleteKey,t);E[n]&&E[n]({event:e,target:i,keyCode:n,metaKey:r,activeItems:o,hasFocusedInput:a,hasActiveDropdown:c,hasItems:l,hasCtrlDownKeyPressed:y})},r._onKeyUp=function(e){var t=e.target,i=e.keyCode,n=this.input.value,s=this._store.activeItems,r=this._canAddItem(s,n),o=J,a=Y;if(this._isTextElement)if(r.notice&&n){var c=this._getTemplate(\"notice\",r.notice);this.dropdown.element.innerHTML=c.outerHTML,this.showDropdown(!0)}else this.hideDropdown(!0);else{var l=(i===o||i===a)&&!t.value,h=!this._isTextElement&&this._isSearching,u=this._canSearch&&r.response;l&&h?(this._isSearching=!1,this._store.dispatch(be(!0))):u&&this._handleSearch(this.input.value)}this._canSearch=this.config.searchEnabled},r._onAKey=function(e){var t=e.hasItems;e.hasCtrlDownKeyPressed&&t&&(this._canSearch=!1,this.config.removeItems&&!this.input.value&&this.input.element===document.activeElement&&this.highlightAll())},r._onEnterKey=function(e){var t=e.event,i=e.target,n=e.activeItems,s=e.hasActiveDropdown,r=Z,o=i.hasAttribute(\"data-button\");if(this._isTextElement&&i.value){var a=this.input.value;this._canAddItem(n,a).response&&(this.hideDropdown(!0),this._addItem({value:a}),this._triggerChange(a),this.clearInput())}if(o&&(this._handleButtonAction(n,i),t.preventDefault()),s){var c=this.dropdown.getChild(\".\"+this.config.classNames.highlightedState);c&&(n[0]&&(n[0].keyCode=r),this._handleChoiceAction(n,c)),t.preventDefault()}else this._isSelectOneElement&&(this.showDropdown(),t.preventDefault())},r._onEscapeKey=function(e){e.hasActiveDropdown&&(this.hideDropdown(!0),this.containerOuter.focus())},r._onDirectionKey=function(e){var t,i,n,s=e.event,r=e.hasActiveDropdown,o=e.keyCode,a=e.metaKey,c=ie,l=ne,h=se;if(r||this._isSelectOneElement){this.showDropdown(),this._canSearch=!1;var u,d=o===c||o===h?1:-1,p=\"[data-choice-selectable]\";if(a||o===h||o===l)u=d>0?this.dropdown.element.querySelector(\"[data-choice-selectable]:last-of-type\"):this.dropdown.element.querySelector(p);else{var m=this.dropdown.element.querySelector(\".\"+this.config.classNames.highlightedState);u=m?function(e,t,i){if(void 0===i&&(i=1),e instanceof Element&&\"string\"==typeof t){for(var n=(i>0?\"next\":\"previous\")+\"ElementSibling\",s=e[n];s;){if(s.matches(t))return s;s=s[n]}return s}}(m,p,d):this.dropdown.element.querySelector(p)}u&&(t=u,i=this.choiceList.element,void 0===(n=d)&&(n=1),t&&(n>0?i.scrollTop+i.offsetHeight>=t.offsetTop+t.offsetHeight:t.offsetTop>=i.scrollTop)||this.choiceList.scrollToChildElement(u,d),this._highlightChoice(u)),s.preventDefault()}},r._onDeleteKey=function(e){var t=e.event,i=e.target,n=e.hasFocusedInput,s=e.activeItems;!n||i.value||this._isSelectOneElement||(this._handleBackspace(s),t.preventDefault())},r._onTouchMove=function(){this._wasTap&&(this._wasTap=!1)},r._onTouchEnd=function(e){var t=(e||e.touches[0]).target;this._wasTap&&this.containerOuter.element.contains(t)&&((t===this.containerOuter.element||t===this.containerInner.element)&&(this._isTextElement?this.input.focus():this._isSelectMultipleElement&&this.showDropdown()),e.stopPropagation()),this._wasTap=!0},r._onMouseDown=function(e){var t=e.target;if(t instanceof HTMLElement){if(we&&this.choiceList.element.contains(t)){var i=this.choiceList.element.firstElementChild,n=\"ltr\"===this._direction?e.offsetX>=i.offsetWidth:e.offsetX0&&this.unhighlightAll(),this.containerOuter.removeFocusState(),this.hideDropdown(!0))},r._onFocus=function(e){var t,i=this,n=e.target;this.containerOuter.element.contains(n)&&((t={}).text=function(){n===i.input.element&&i.containerOuter.addFocusState()},t[\"select-one\"]=function(){i.containerOuter.addFocusState(),n===i.input.element&&i.showDropdown(!0)},t[\"select-multiple\"]=function(){n===i.input.element&&(i.showDropdown(!0),i.containerOuter.addFocusState())},t)[this.passedElement.element.type]()},r._onBlur=function(e){var t=this,i=e.target;if(this.containerOuter.element.contains(i)&&!this._isScrollingOnIe){var n,s=this._store.activeItems.some((function(e){return e.highlighted}));((n={}).text=function(){i===t.input.element&&(t.containerOuter.removeFocusState(),s&&t.unhighlightAll(),t.hideDropdown(!0))},n[\"select-one\"]=function(){t.containerOuter.removeFocusState(),(i===t.input.element||i===t.containerOuter.element&&!t._canSearch)&&t.hideDropdown(!0)},n[\"select-multiple\"]=function(){i===t.input.element&&(t.containerOuter.removeFocusState(),t.hideDropdown(!0),s&&t.unhighlightAll())},n)[this.passedElement.element.type]()}else this._isScrollingOnIe=!1,this.input.element.focus()},r._onFormReset=function(){this._store.dispatch({type:\"RESET_TO\",state:this._initialState})},r._highlightChoice=function(e){var t=this;void 0===e&&(e=null);var i=Array.from(this.dropdown.element.querySelectorAll(\"[data-choice-selectable]\"));if(i.length){var n=e;Array.from(this.dropdown.element.querySelectorAll(\".\"+this.config.classNames.highlightedState)).forEach((function(e){e.classList.remove(t.config.classNames.highlightedState),e.setAttribute(\"aria-selected\",\"false\")})),n?this._highlightPosition=i.indexOf(n):(n=i.length>this._highlightPosition?i[this._highlightPosition]:i[i.length-1])||(n=i[0]),n.classList.add(this.config.classNames.highlightedState),n.setAttribute(\"aria-selected\",\"true\"),this.passedElement.triggerEvent(B,{el:n}),this.dropdown.isActive&&(this.input.setActiveDescendant(n.id),this.containerOuter.setActiveDescendant(n.id))}},r._addItem=function(e){var t=e.value,i=e.label,n=void 0===i?null:i,s=e.choiceId,r=void 0===s?-1:s,o=e.groupId,a=void 0===o?-1:o,c=e.customProperties,l=void 0===c?null:c,h=e.placeholder,u=void 0!==h&&h,d=e.keyCode,p=void 0===d?null:d,m=\"string\"==typeof t?t.trim():t,f=p,v=l,g=this._store.items,_=n||m,b=r||-1,y=a>=0?this._store.getGroupById(a):null,E=g?g.length+1:1;return this.config.prependValue&&(m=this.config.prependValue+m.toString()),this.config.appendValue&&(m+=this.config.appendValue.toString()),this._store.dispatch(function(e){var t=e.value,i=e.label,n=e.id,s=e.choiceId,r=e.groupId,o=e.customProperties,a=e.placeholder,c=e.keyCode;return{type:W,value:t,label:i,id:n,choiceId:s,groupId:r,customProperties:o,placeholder:a,keyCode:c}}({value:m,label:_,id:E,choiceId:b,groupId:a,customProperties:l,placeholder:u,keyCode:f})),this._isSelectOneElement&&this.removeActiveItems(E),this.passedElement.triggerEvent(K,{id:E,value:m,label:_,customProperties:v,groupValue:y&&y.value?y.value:void 0,keyCode:f}),this},r._removeItem=function(e){if(!e||!E(\"Object\",e))return this;var t=e.id,i=e.value,n=e.label,s=e.choiceId,r=e.groupId,o=r>=0?this._store.getGroupById(r):null;return this._store.dispatch(function(e,t){return{type:X,id:e,choiceId:t}}(t,s)),o&&o.value?this.passedElement.triggerEvent(R,{id:t,value:i,label:n,groupValue:o.value}):this.passedElement.triggerEvent(R,{id:t,value:i,label:n}),this},r._addChoice=function(e){var t=e.value,i=e.label,n=void 0===i?null:i,s=e.isSelected,r=void 0!==s&&s,o=e.isDisabled,a=void 0!==o&&o,c=e.groupId,l=void 0===c?-1:c,h=e.customProperties,u=void 0===h?null:h,d=e.placeholder,p=void 0!==d&&d,m=e.keyCode,f=void 0===m?null:m;if(null!=t){var v=this._store.choices,g=n||t,_=v?v.length+1:1,b=this._baseId+\"-\"+this._idNames.itemChoice+\"-\"+_;this._store.dispatch(function(e){var t=e.value,i=e.label,n=e.id,s=e.groupId,r=e.disabled,o=e.elementId,a=e.customProperties,c=e.placeholder,l=e.keyCode;return{type:V,value:t,label:i,id:n,groupId:s,disabled:r,elementId:o,customProperties:a,placeholder:c,keyCode:l}}({id:_,groupId:l,elementId:b,value:t,label:g,disabled:a,customProperties:u,placeholder:p,keyCode:f})),r&&this._addItem({value:t,label:g,choiceId:_,customProperties:u,placeholder:p,keyCode:f})}},r._addGroup=function(e){var t=this,i=e.group,n=e.id,s=e.valueKey,r=void 0===s?\"value\":s,o=e.labelKey,a=void 0===o?\"label\":o,c=E(\"Object\",i)?i.choices:Array.from(i.getElementsByTagName(\"OPTION\")),l=n||Math.floor((new Date).valueOf()*Math.random()),h=!!i.disabled&&i.disabled;c?(this._store.dispatch(Ee({value:i.label,id:l,active:!0,disabled:h})),c.forEach((function(e){var i=e.disabled||e.parentNode&&e.parentNode.disabled;t._addChoice({value:e[r],label:E(\"Object\",e)?e[a]:e.innerHTML,isSelected:e.selected,isDisabled:i,groupId:l,customProperties:e.customProperties,placeholder:e.placeholder})}))):this._store.dispatch(Ee({value:i.label,id:i.id,active:!1,disabled:i.disabled}))},r._getTemplate=function(e){var t;if(!e)return null;for(var i=this.config.classNames,n=arguments.length,s=new Array(n>1?n-1:0),r=1;r{var e;return this.input_el.name=null!==(e=this.model.name)&&void 0!==e?e:\"\"})),this.connect(this.model.properties.value.change,(()=>{this.input_el.value=this.format_value,this.old_value=this.input_el.value})),this.connect(this.model.properties.low.change,(()=>{const{value:e,low:t,high:l}=this.model;null!=t&&null!=l&&(0,p.assert)(t<=l,\"Invalid bounds, low must be inferior to high\"),null!=e&&null!=t&&e{const{value:e,low:t,high:l}=this.model;null!=t&&null!=l&&(0,p.assert)(l>=t,\"Invalid bounds, high must be superior to low\"),null!=e&&null!=l&&e>l&&(this.model.value=l)})),this.connect(this.model.properties.high.change,(()=>this.input_el.placeholder=this.model.placeholder)),this.connect(this.model.properties.disabled.change,(()=>this.input_el.disabled=this.model.disabled)),this.connect(this.model.properties.placeholder.change,(()=>this.input_el.placeholder=this.model.placeholder))}get format_value(){return null!=this.model.value?this.model.pretty(this.model.value):\"\"}_set_input_filter(e){this.input_el.addEventListener(\"input\",(()=>{const{selectionStart:t,selectionEnd:l}=this.input_el;if(e(this.input_el.value))this.old_value=this.input_el.value;else{const e=this.old_value.length-this.input_el.value.length;this.input_el.value=this.old_value,t&&l&&this.input_el.setSelectionRange(t-1,l+e)}}))}render(){super.render(),this.input_el=(0,r.input)({type:\"text\",class:_.input,name:this.model.name,value:this.format_value,disabled:this.model.disabled,placeholder:this.model.placeholder}),this.old_value=this.format_value,this.set_input_filter(),this.input_el.addEventListener(\"change\",(()=>this.change_input())),this.input_el.addEventListener(\"focusout\",(()=>this.input_el.value=this.format_value)),this.group_el.appendChild(this.input_el)}set_input_filter(){\"int\"==this.model.mode?this._set_input_filter((e=>m.test(e))):\"float\"==this.model.mode&&this._set_input_filter((e=>c.test(e)))}bound_value(e){let t=e;const{low:l,high:i}=this.model;return t=null!=l?Math.max(l,t):t,t=null!=i?Math.min(i,t):t,t}get value(){let e=\"\"!=this.input_el.value?Number(this.input_el.value):null;return null!=e&&(e=this.bound_value(e)),e}change_input(){null==this.value?this.model.value=null:Number.isNaN(this.value)||(this.model.value=this.value)}}l.NumericInputView=v,v.__name__=\"NumericInputView\";class g extends o.InputWidget{constructor(e){super(e)}_formatter(e,t){return(0,d.isString)(t)?h.format(e,t):t.doFormat([e],{loc:0})[0]}pretty(e){return null!=this.format?this._formatter(e,this.format):`${e}`}}l.NumericInput=g,u=g,g.__name__=\"NumericInput\",u.prototype.default_view=v,u.define((({Number:e,String:t,Enum:l,Ref:i,Or:n,Nullable:s})=>({value:[s(e),null],placeholder:[t,\"\"],mode:[l(\"int\",\"float\"),\"int\"],format:[s(n(t,i(a.TickFormatter))),null],low:[s(e),null],high:[s(e),null]})))},\n 479: function _(e,t,r,s,n){var a;s();const o=e(468),_=e(43);class p extends o.MarkupView{render(){super.render();const e=(0,_.pre)({style:{overflow:\"auto\"}},this.model.text);this.markup_el.appendChild(e)}}r.PreTextView=p,p.__name__=\"PreTextView\";class u extends o.Markup{constructor(e){super(e)}}r.PreText=u,a=u,u.__name__=\"PreText\",a.prototype.default_view=p},\n 480: function _(t,o,e,a,i){a();const n=t(1);var u;const s=t(452),c=t(43),_=(0,n.__importStar)(t(318));class r extends s.ButtonGroupView{change_active(t){this.model.active!==t&&(this.model.active=t)}_update_active(){const{active:t}=this.model;this._buttons.forEach(((o,e)=>{(0,c.classes)(o).toggle(_.active,t===e)}))}}e.RadioButtonGroupView=r,r.__name__=\"RadioButtonGroupView\";class l extends s.ButtonGroup{constructor(t){super(t)}}e.RadioButtonGroup=l,u=l,l.__name__=\"RadioButtonGroup\",u.prototype.default_view=r,u.define((({Int:t,Nullable:o})=>({active:[o(t),null]})))},\n 481: function _(e,n,i,t,a){t();const s=e(1);var l;const o=e(43),d=e(34),p=e(455),r=(0,s.__importStar)(e(449));class u extends p.InputGroupView{render(){super.render();const e=(0,o.div)({class:[r.input_group,this.model.inline?r.inline:null]});this.el.appendChild(e);const n=(0,d.uniqueId)(),{active:i,labels:t}=this.model;this._inputs=[];for(let a=0;athis.change_active(a))),this._inputs.push(s),this.model.disabled&&(s.disabled=!0),a==i&&(s.checked=!0);const l=(0,o.label)(s,(0,o.span)(t[a]));e.appendChild(l)}}change_active(e){this.model.active=e}}i.RadioGroupView=u,u.__name__=\"RadioGroupView\";class c extends p.InputGroup{constructor(e){super(e)}}i.RadioGroup=c,l=c,c.__name__=\"RadioGroup\",l.prototype.default_view=u,l.define((({Boolean:e,Int:n,String:i,Array:t,Nullable:a})=>({active:[a(n),null],labels:[t(i),[]],inline:[e,!1]})))},\n 482: function _(e,r,t,a,i){a();var n;const o=(0,e(1).__importStar)(e(153)),s=e(461),_=e(8);class d extends s.AbstractRangeSliderView{}t.RangeSliderView=d,d.__name__=\"RangeSliderView\";class c extends s.AbstractSlider{constructor(e){super(e),this.behaviour=\"drag\",this.connected=[!1,!0,!1]}_formatter(e,r){return(0,_.isString)(r)?o.format(e,r):r.compute(e)}}t.RangeSlider=c,n=c,c.__name__=\"RangeSlider\",n.prototype.default_view=d,n.override({format:\"0[.]00\"})},\n 483: function _(e,t,n,s,i){s();const l=e(1);var u;const a=e(43),o=e(8),p=e(13),_=e(448),r=(0,l.__importStar)(e(449));class c extends _.InputWidgetView{constructor(){super(...arguments),this._known_values=new Set}connect_signals(){super.connect_signals();const{value:e,options:t}=this.model.properties;this.on_change(e,(()=>{this._update_value()})),this.on_change(t,(()=>{(0,a.empty)(this.input_el),(0,a.append)(this.input_el,...this.options_el()),this._update_value()}))}options_el(){const{_known_values:e}=this;function t(t){return t.map((t=>{let n,s;return(0,o.isString)(t)?n=s=t:[n,s]=t,e.add(n),(0,a.option)({value:n},s)}))}e.clear();const{options:n}=this.model;return(0,o.isArray)(n)?t(n):(0,p.entries)(n).map((([e,n])=>(0,a.optgroup)({label:e},t(n))))}render(){super.render(),this.input_el=(0,a.select)({class:r.input,name:this.model.name,disabled:this.model.disabled},this.options_el()),this._update_value(),this.input_el.addEventListener(\"change\",(()=>this.change_input())),this.group_el.appendChild(this.input_el)}change_input(){const e=this.input_el.value;this.model.value=e,super.change_input()}_update_value(){const{value:e}=this.model;this._known_values.has(e)?this.input_el.value=e:this.input_el.removeAttribute(\"value\")}}n.SelectView=c,c.__name__=\"SelectView\";class h extends _.InputWidget{constructor(e){super(e)}}n.Select=h,u=h,h.__name__=\"Select\",u.prototype.default_view=c,u.define((({String:e,Array:t,Tuple:n,Dict:s,Or:i})=>{const l=t(i(e,n(e,e)));return{value:[e,\"\"],options:[i(l,s(l)),[]]}}))},\n 484: function _(e,t,r,i,a){i();var o;const s=(0,e(1).__importStar)(e(153)),_=e(461),n=e(8);class c extends _.AbstractSliderView{}r.SliderView=c,c.__name__=\"SliderView\";class d extends _.AbstractSlider{constructor(e){super(e),this.behaviour=\"tap\",this.connected=[!0,!1]}_formatter(e,t){return(0,n.isString)(t)?s.format(e,t):t.compute(e)}}r.Slider=d,o=d,d.__name__=\"Slider\",o.prototype.default_view=c,o.override({format:\"0[.]00\"})},\n 485: function _(e,t,i,n,s){var l;n();const o=e(478),r=e(43),{min:a,max:h,floor:_,abs:u}=Math;function d(e){return _(e)!==e?e.toFixed(16).replace(/0+$/,\"\").split(\".\")[1].length:0}class p extends o.NumericInputView{*buttons(){yield this.btn_up_el,yield this.btn_down_el}initialize(){super.initialize(),this._handles={interval:void 0,timeout:void 0},this._interval=200}connect_signals(){super.connect_signals();const e=this.model.properties;this.on_change(e.disabled,(()=>{for(const e of this.buttons())(0,r.toggle_attribute)(e,\"disabled\",this.model.disabled)}))}render(){super.render(),this.wrapper_el=(0,r.div)({class:\"bk-spin-wrapper\"}),this.group_el.replaceChild(this.wrapper_el,this.input_el),this.btn_up_el=(0,r.button)({class:\"bk-spin-btn bk-spin-btn-up\"}),this.btn_down_el=(0,r.button)({class:\"bk-spin-btn bk-spin-btn-down\"}),this.wrapper_el.appendChild(this.input_el),this.wrapper_el.appendChild(this.btn_up_el),this.wrapper_el.appendChild(this.btn_down_el);for(const e of this.buttons())(0,r.toggle_attribute)(e,\"disabled\",this.model.disabled),e.addEventListener(\"mousedown\",(e=>this._btn_mouse_down(e))),e.addEventListener(\"mouseup\",(()=>this._btn_mouse_up())),e.addEventListener(\"mouseleave\",(()=>this._btn_mouse_leave()));this.input_el.addEventListener(\"keydown\",(e=>this._input_key_down(e))),this.input_el.addEventListener(\"keyup\",(()=>this.model.value_throttled=this.model.value)),this.input_el.addEventListener(\"wheel\",(e=>this._input_mouse_wheel(e))),this.input_el.addEventListener(\"wheel\",function(e,t,i=!1){let n;return function(...s){const l=this,o=i&&void 0===n;void 0!==n&&clearTimeout(n),n=setTimeout((function(){n=void 0,i||e.apply(l,s)}),t),o&&e.apply(l,s)}}((()=>{this.model.value_throttled=this.model.value}),this.model.wheel_wait,!1))}get precision(){const{low:e,high:t,step:i}=this.model,n=d;return h(n(u(null!=e?e:0)),n(u(null!=t?t:0)),n(u(i)))}remove(){this._stop_incrementation(),super.remove()}_start_incrementation(e){clearInterval(this._handles.interval),this._counter=0;const{step:t}=this.model,i=e=>{if(this._counter+=1,this._counter%5==0){const t=Math.floor(this._counter/5);t<10?(clearInterval(this._handles.interval),this._handles.interval=setInterval((()=>i(e)),this._interval/(t+1))):t>=10&&t<=13&&(clearInterval(this._handles.interval),this._handles.interval=setInterval((()=>i(2*e)),this._interval/10))}this.increment(e)};this._handles.interval=setInterval((()=>i(e*t)),this._interval)}_stop_incrementation(){clearTimeout(this._handles.timeout),this._handles.timeout=void 0,clearInterval(this._handles.interval),this._handles.interval=void 0,this.model.value_throttled=this.model.value}_btn_mouse_down(e){e.preventDefault();const t=e.currentTarget===this.btn_up_el?1:-1;this.increment(t*this.model.step),this.input_el.focus(),this._handles.timeout=setTimeout((()=>this._start_incrementation(t)),this._interval)}_btn_mouse_up(){this._stop_incrementation()}_btn_mouse_leave(){this._stop_incrementation()}_input_mouse_wheel(e){if(document.activeElement===this.input_el){e.preventDefault();const t=e.deltaY>0?-1:1;this.increment(t*this.model.step)}}_input_key_down(e){switch(e.keyCode){case r.Keys.Up:return e.preventDefault(),this.increment(this.model.step);case r.Keys.Down:return e.preventDefault(),this.increment(-this.model.step);case r.Keys.PageUp:return e.preventDefault(),this.increment(this.model.page_step_multiplier*this.model.step);case r.Keys.PageDown:return e.preventDefault(),this.increment(-this.model.page_step_multiplier*this.model.step)}}adjust_to_precision(e){return this.bound_value(Number(e.toFixed(this.precision)))}increment(e){const{low:t,high:i}=this.model;null==this.model.value?e>0?this.model.value=null!=t?t:null!=i?a(0,i):0:e<0&&(this.model.value=null!=i?i:null!=t?h(t,0):0):this.model.value=this.adjust_to_precision(this.model.value+e)}change_input(){super.change_input(),this.model.value_throttled=this.model.value}}i.SpinnerView=p,p.__name__=\"SpinnerView\";class m extends o.NumericInput{constructor(e){super(e)}}i.Spinner=m,l=m,m.__name__=\"Spinner\",l.prototype.default_view=p,l.define((({Number:e,Nullable:t})=>({value_throttled:[t(e),null],step:[e,1],page_step_multiplier:[e,10],wheel_wait:[e,100]}))),l.override({mode:\"float\"})},\n 486: function _(e,t,s,n,i){n();const o=e(1);var r;const c=e(447),l=e(43),p=(0,o.__importStar)(e(449));class _ extends c.TextLikeInputView{connect_signals(){super.connect_signals(),this.connect(this.model.properties.rows.change,(()=>this.input_el.rows=this.model.rows)),this.connect(this.model.properties.cols.change,(()=>this.input_el.cols=this.model.cols))}_render_input(){this.input_el=(0,l.textarea)({class:p.input})}render(){super.render(),this.input_el.cols=this.model.cols,this.input_el.rows=this.model.rows}}s.TextAreaInputView=_,_.__name__=\"TextAreaInputView\";class a extends c.TextLikeInput{constructor(e){super(e)}}s.TextAreaInput=a,r=a,a.__name__=\"TextAreaInput\",r.prototype.default_view=_,r.define((({Int:e})=>({cols:[e,20],rows:[e,2]}))),r.override({max_length:500})},\n 487: function _(e,t,s,c,i){c();const o=e(1);var a;const n=e(441),l=e(43),_=(0,o.__importStar)(e(318));class r extends n.AbstractButtonView{connect_signals(){super.connect_signals(),this.connect(this.model.properties.active.change,(()=>this._update_active()))}render(){super.render(),this._update_active()}click(){this.model.active=!this.model.active,super.click()}_update_active(){(0,l.classes)(this.button_el).toggle(_.active,this.model.active)}}s.ToggleView=r,r.__name__=\"ToggleView\";class g extends n.AbstractButton{constructor(e){super(e)}}s.Toggle=g,a=g,g.__name__=\"Toggle\",a.prototype.default_view=r,a.define((({Boolean:e})=>({active:[e,!1]}))),a.override({label:\"Toggle\"})},\n }, 439, {\"models/widgets/main\":439,\"models/widgets/index\":440,\"models/widgets/abstract_button\":441,\"models/widgets/control\":442,\"models/widgets/widget\":512,\"models/widgets/abstract_icon\":444,\"models/widgets/autocomplete_input\":445,\"models/widgets/text_input\":446,\"models/widgets/text_like_input\":447,\"models/widgets/input_widget\":448,\"styles/widgets/inputs.css\":449,\"models/widgets/button\":450,\"models/widgets/checkbox_button_group\":451,\"models/widgets/button_group\":452,\"models/widgets/oriented_control\":453,\"models/widgets/checkbox_group\":454,\"models/widgets/input_group\":455,\"models/widgets/color_picker\":456,\"models/widgets/date_picker\":457,\"styles/widgets/flatpickr.css\":459,\"models/widgets/date_range_slider\":460,\"models/widgets/abstract_slider\":461,\"styles/widgets/sliders.css\":463,\"styles/widgets/nouislider.css\":464,\"models/widgets/date_slider\":465,\"models/widgets/datetime_range_slider\":466,\"models/widgets/div\":467,\"models/widgets/markup\":468,\"styles/clearfix.css\":469,\"models/widgets/dropdown\":470,\"models/widgets/file_input\":471,\"models/widgets/multiselect\":472,\"models/widgets/paragraph\":473,\"models/widgets/password_input\":474,\"models/widgets/multichoice\":475,\"styles/widgets/choices.css\":477,\"models/widgets/numeric_input\":478,\"models/widgets/pretext\":479,\"models/widgets/radio_button_group\":480,\"models/widgets/radio_group\":481,\"models/widgets/range_slider\":482,\"models/widgets/selectbox\":483,\"models/widgets/slider\":484,\"models/widgets/spinner\":485,\"models/widgets/textarea_input\":486,\"models/widgets/toggle\":487}, {});});\n\n /* END bokeh-widgets.min.js */\n },\n \n function(Bokeh) {\n /* BEGIN bokeh-tables.min.js */\n /*!\n * Copyright (c) 2012 - 2022, Anaconda, Inc., and Bokeh Contributors\n * All rights reserved.\n * \n * Redistribution and use in source and binary forms, with or without modification,\n * are permitted provided that the following conditions are met:\n * \n * Redistributions of source code must retain the above copyright notice,\n * this list of conditions and the following disclaimer.\n * \n * Redistributions in binary form must reproduce the above copyright notice,\n * this list of conditions and the following disclaimer in the documentation\n * and/or other materials provided with the distribution.\n * \n * Neither the name of Anaconda nor the names of any contributors\n * may be used to endorse or promote products derived from this software\n * without specific prior written permission.\n * \n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF\n * THE POSSIBILITY OF SUCH DAMAGE.\n */\n (function(root, factory) {\n factory(root[\"Bokeh\"], \"2.4.3\");\n })(this, function(Bokeh, version) {\n let define;\n return (function(modules, entry, aliases, externals) {\n const bokeh = typeof Bokeh !== \"undefined\" && (version != null ? Bokeh[version] : Bokeh);\n if (bokeh != null) {\n return bokeh.register_plugin(modules, entry, aliases);\n } else {\n throw new Error(\"Cannot find Bokeh \" + version + \". You have to load it prior to loading plugins.\");\n }\n })\n ({\n 488: function _(t,e,o,r,s){r();const _=(0,t(1).__importStar)(t(489));o.Tables=_;(0,t(7).register_models)(_)},\n 489: function _(g,a,r,e,t){e();const o=g(1);(0,o.__exportStar)(g(490),r),(0,o.__exportStar)(g(493),r),t(\"DataTable\",g(496).DataTable),t(\"TableColumn\",g(514).TableColumn),t(\"TableWidget\",g(513).TableWidget);var n=g(516);t(\"AvgAggregator\",n.AvgAggregator),t(\"MinAggregator\",n.MinAggregator),t(\"MaxAggregator\",n.MaxAggregator),t(\"SumAggregator\",n.SumAggregator);var A=g(517);t(\"GroupingInfo\",A.GroupingInfo),t(\"DataCube\",A.DataCube)},\n 490: function _(e,t,i,s,a){s();const r=e(1);var l,n,u,d,o,p,_,c,h;const E=e(43),V=e(226),m=e(53),f=e(491),v=(0,r.__importStar)(e(492));class w extends V.DOMView{constructor(e){const{model:t,parent:i}=e.column;super(Object.assign({model:t,parent:i},e)),this.args=e,this.initialize(),this.render()}get emptyValue(){return null}initialize(){super.initialize(),this.inputEl=this._createInput(),this.defaultValue=null}async lazy_initialize(){throw new Error(\"unsupported\")}css_classes(){return super.css_classes().concat(v.cell_editor)}render(){super.render(),this.args.container.append(this.el),this.el.appendChild(this.inputEl),this.renderEditor(),this.disableNavigation()}renderEditor(){}disableNavigation(){this.inputEl.addEventListener(\"keydown\",(e=>{switch(e.keyCode){case E.Keys.Left:case E.Keys.Right:case E.Keys.Up:case E.Keys.Down:case E.Keys.PageUp:case E.Keys.PageDown:e.stopImmediatePropagation()}}))}destroy(){this.remove()}focus(){this.inputEl.focus()}show(){}hide(){}position(){}getValue(){return this.inputEl.value}setValue(e){this.inputEl.value=e}serializeValue(){return this.getValue()}isValueChanged(){return!(\"\"==this.getValue()&&null==this.defaultValue)&&this.getValue()!==this.defaultValue}applyValue(e,t){const i=this.args.grid.getData(),s=i.index.indexOf(e[f.DTINDEX_NAME]);i.setField(s,this.args.column.field,t)}loadValue(e){const t=e[this.args.column.field];this.defaultValue=null!=t?t:this.emptyValue,this.setValue(this.defaultValue)}validateValue(e){if(this.args.column.validator){const t=this.args.column.validator(e);if(!t.valid)return t}return{valid:!0,msg:null}}validate(){return this.validateValue(this.getValue())}}i.CellEditorView=w,w.__name__=\"CellEditorView\";class g extends m.Model{}i.CellEditor=g,g.__name__=\"CellEditor\";class x extends w{get emptyValue(){return\"\"}_createInput(){return(0,E.input)({type:\"text\"})}renderEditor(){this.inputEl.focus(),this.inputEl.select()}loadValue(e){super.loadValue(e),this.inputEl.defaultValue=this.defaultValue,this.inputEl.select()}}i.StringEditorView=x,x.__name__=\"StringEditorView\";class y extends g{}i.StringEditor=y,l=y,y.__name__=\"StringEditor\",l.prototype.default_view=x,l.define((({String:e,Array:t})=>({completions:[t(e),[]]})));class I extends w{_createInput(){return(0,E.textarea)()}renderEditor(){this.inputEl.focus(),this.inputEl.select()}}i.TextEditorView=I,I.__name__=\"TextEditorView\";class b extends g{}i.TextEditor=b,n=b,b.__name__=\"TextEditor\",n.prototype.default_view=I;class N extends w{_createInput(){return(0,E.select)()}renderEditor(){for(const e of this.model.options)this.inputEl.appendChild((0,E.option)({value:e},e));this.focus()}}i.SelectEditorView=N,N.__name__=\"SelectEditorView\";class C extends g{}i.SelectEditor=C,u=C,C.__name__=\"SelectEditor\",u.prototype.default_view=N,u.define((({String:e,Array:t})=>({options:[t(e),[]]})));class D extends w{_createInput(){return(0,E.input)({type:\"text\"})}}i.PercentEditorView=D,D.__name__=\"PercentEditorView\";class S extends g{}i.PercentEditor=S,d=S,S.__name__=\"PercentEditor\",d.prototype.default_view=D;class k extends w{_createInput(){return(0,E.input)({type:\"checkbox\"})}renderEditor(){this.focus()}loadValue(e){this.defaultValue=!!e[this.args.column.field],this.inputEl.checked=this.defaultValue}serializeValue(){return this.inputEl.checked}}i.CheckboxEditorView=k,k.__name__=\"CheckboxEditorView\";class z extends g{}i.CheckboxEditor=z,o=z,z.__name__=\"CheckboxEditor\",o.prototype.default_view=k;class P extends w{_createInput(){return(0,E.input)({type:\"text\"})}renderEditor(){this.inputEl.focus(),this.inputEl.select()}remove(){super.remove()}serializeValue(){var e;return null!==(e=parseInt(this.getValue(),10))&&void 0!==e?e:0}loadValue(e){super.loadValue(e),this.inputEl.defaultValue=this.defaultValue,this.inputEl.select()}validateValue(e){return isNaN(e)?{valid:!1,msg:\"Please enter a valid integer\"}:super.validateValue(e)}}i.IntEditorView=P,P.__name__=\"IntEditorView\";class T extends g{}i.IntEditor=T,p=T,T.__name__=\"IntEditor\",p.prototype.default_view=P,p.define((({Int:e})=>({step:[e,1]})));class K extends w{_createInput(){return(0,E.input)({type:\"text\"})}renderEditor(){this.inputEl.focus(),this.inputEl.select()}remove(){super.remove()}serializeValue(){var e;return null!==(e=parseFloat(this.getValue()))&&void 0!==e?e:0}loadValue(e){super.loadValue(e),this.inputEl.defaultValue=this.defaultValue,this.inputEl.select()}validateValue(e){return isNaN(e)?{valid:!1,msg:\"Please enter a valid number\"}:super.validateValue(e)}}i.NumberEditorView=K,K.__name__=\"NumberEditorView\";class A extends g{}i.NumberEditor=A,_=A,A.__name__=\"NumberEditor\",_.prototype.default_view=K,_.define((({Number:e})=>({step:[e,.01]})));class M extends w{_createInput(){return(0,E.input)({type:\"text\"})}}i.TimeEditorView=M,M.__name__=\"TimeEditorView\";class O extends g{}i.TimeEditor=O,c=O,O.__name__=\"TimeEditor\",c.prototype.default_view=M;class F extends w{_createInput(){return(0,E.input)({type:\"text\"})}get emptyValue(){return new Date}renderEditor(){this.inputEl.focus(),this.inputEl.select()}destroy(){super.destroy()}show(){super.show()}hide(){super.hide()}position(){return super.position()}getValue(){}setValue(e){}}i.DateEditorView=F,F.__name__=\"DateEditorView\";class L extends g{}i.DateEditor=L,h=L,L.__name__=\"DateEditor\",h.prototype.default_view=F},\n 491: function _(_,n,i,t,d){t(),i.DTINDEX_NAME=\"__bkdt_internal_index__\"},\n 492: function _(e,l,o,t,r){t(),o.root=\"bk-root\",o.data_table=\"bk-data-table\",o.cell_special_defaults=\"bk-cell-special-defaults\",o.cell_select=\"bk-cell-select\",o.cell_index=\"bk-cell-index\",o.header_index=\"bk-header-index\",o.cell_editor=\"bk-cell-editor\",o.cell_editor_completion=\"bk-cell-editor-completion\",o.default='.bk-root .bk-data-table{box-sizing:content-box;font-size:11px;}.bk-root .bk-data-table input[type=\"checkbox\"]{margin-left:4px;margin-right:4px;}.bk-root 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