diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 31b8351..bfc7dbb 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,56 +1,58 @@ +--- +default_language_version: + python: python3 repos: -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v3.2.0 + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.6.0 hooks: - - id: check-ast - - id: check-byte-order-marker - - id: check-case-conflict - - id: check-docstring-first - - id: check-executables-have-shebangs - - id: check-json - - id: check-yaml - exclude: ^chart/ - - id: debug-statements - - id: end-of-file-fixer + - id: check-ast + - id: check-byte-order-marker + - id: check-case-conflict + - id: check-docstring-first + - id: check-json + - id: check-yaml + exclude: ^(chart/|docs/) + - id: debug-statements + - id: end-of-file-fixer exclude: ^(docs/|gdocs/) - - id: pretty-format-json + - id: pretty-format-json args: ['--autofix'] - - id: trailing-whitespace + - id: trailing-whitespace + args: ['--markdown-linebreak-ext=md'] exclude: ^(docs/|gdocs/) - - id: mixed-line-ending + - id: mixed-line-ending args: ['--fix=lf'] exclude: ^(docs/|gdocs/) -- repo: https://github.com/psf/black - rev: 22.3.0 + - id: check-added-large-files + args: ['--maxkb=500'] + - id: no-commit-to-branch + args: ['--branch', 'master', '--branch', 'develop'] + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.5.2 hooks: - - id: black - args: [--line-length=120] -- repo: https://github.com/pre-commit/mirrors-mypy - rev: 'v0.941' + - id: ruff + args: [--fix] + - id: ruff-format + - repo: https://github.com/pre-commit/mirrors-mypy + rev: 'v1.10.1' hooks: - - id: mypy - args: [--ignore-missing-imports, --show-error-codes] - files: eurybia - additional_dependencies: [types-PyYAML] -- repo: https://github.com/PyCQA/flake8 - rev: 4.0.1 + - id: mypy + args: [--config-file=pyproject.toml] + files: src +# Décommentez si vous utilisez pydantic (+ ajustez la version): +# additional_dependencies: [pydantic~=1.0] + - repo: https://github.com/pypa/pip-audit + rev: v2.7.3 hooks: - - id: flake8 - exclude: ^tests/ - args: ['--ignore=E501,D2,D3,D4,D104,D100,D106,D107,W503,D105,E203'] - additional_dependencies: [flake8-docstrings] -- repo: https://github.com/pre-commit/mirrors-isort - rev: v5.4.2 + - id: pip-audit + args: [--skip-editable, --fix] +# - repo: https://github.com/gitleaks/gitleaks +# rev: v8.18.2 +# hooks: +# - id: gitleaks + - repo: https://github.com/compilerla/conventional-pre-commit + rev: v3.3.0 hooks: - - id: isort - args: ["--profile", "black", "-l", "120"] -- repo: https://github.com/asottile/pyupgrade - rev: v2.7.2 - hooks: - - id: pyupgrade - args: [--py37-plus] -- repo: https://github.com/asottile/blacken-docs - rev: v1.8.0 - hooks: - - id: blacken-docs - additional_dependencies: [black==21.12b0] + - id: conventional-pre-commit + stages: [commit-msg] + args: [] # optional: list of Conventional Commits types to allow e.g. [feat, fix, ci, chore, test] diff --git a/.readthedocs.yml b/.readthedocs.yml index c07d291..e699d33 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -21,7 +21,7 @@ build: os: ubuntu-20.04 tools: python: "3.10" - + # Optionally set the version of Python and requirements required to build your docs python: install: diff --git a/LICENSE b/LICENSE index 17bde2e..c11903c 100644 --- a/LICENSE +++ b/LICENSE @@ -173,4 +173,4 @@ incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. - END OF TERMS AND CONDITIONS \ No newline at end of file + END OF TERMS AND CONDITIONS diff --git a/MANIFEST.in b/MANIFEST.in index 75fc5e4..4b4ca84 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -7,4 +7,4 @@ include .pre-commit-config.yaml global-exclude *.py[cod] __pycache__/* *.so *.dylib -recursive-include eurybia/assets * \ No newline at end of file +recursive-include eurybia/assets * diff --git a/README.md b/README.md index 2ca09a5..86daf96 100644 --- a/README.md +++ b/README.md @@ -19,6 +19,11 @@ doc + + + pre-commit + +

@@ -189,7 +194,7 @@ One of the schedulers you can use is Apache Airflow. To use it, you can read the ## 🔬 Built With This section list libraries used in Eurybia. - [Shapash](https://github.com/MAIF/shapash/tree/master/shapash) -- [Datapane](https://github.com/datapane/datapane) +- [Panel](https://github.com/holoviz/panel) - [Plotly](https://github.com/plotly/plotly.py) - [Catboost](https://github.com/catboost/catboost) diff --git a/docs/source/tutorials/common/tuto-common01-colors.ipynb b/docs/source/tutorials/common/tuto-common01-colors.ipynb index 1881a0f..5af5db6 100644 --- a/docs/source/tutorials/common/tuto-common01-colors.ipynb +++ b/docs/source/tutorials/common/tuto-common01-colors.ipynb @@ -1,748 +1,748 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "8131b447", - "metadata": {}, - "source": [ - "# Eurybia with custom colors\n", - "\n", - "With this tutorial you will understand how to manipulate colors with Eurybia plots\n", - "\n", - "Contents:\n", - "- Compile Eurybia SmartDrift\n", - "- Use `palette_name` parameter\n", - "- Use `colors_dict` parameter\n", - "- Change the colors after compiling SmartDrift\n", - "\n", - "Data from Kaggle [House Prices](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "255f6f01", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from category_encoders import OrdinalEncoder\n", - "from lightgbm import LGBMRegressor\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "markdown", - "id": "e5d4a5cb", - "metadata": {}, - "source": [ - "## Building Supervized Model" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "14ff4476", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/data_tech/users/users_envs/78257d/eurybia39/lib/python3.9/site-packages/papermill/iorw.py:50: FutureWarning:\n", - "\n", - "pyarrow.HadoopFileSystem is deprecated as of 2.0.0, please use pyarrow.fs.HadoopFileSystem instead.\n", - "\n" - ] - } - ], - "source": [ - "from eurybia.data.data_loader import data_loading\n", - "house_df, house_dict = data_loading('house_prices')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d8733ac7", - "metadata": {}, - "outputs": [], - "source": [ - "#For the purpose of this tutorial and to better represent a common use case of Eurybia, \n", - "#the house_prices dataset was split in two smaller sets : \"training\" and \"production\"\n", - "# To see an interesting analysis, let's test for a bias in the date of construction of training and production dataset\n", - "house_df_learning = house_df.loc[house_df['YearBuilt'] < 1980]\n", - "house_df_production = house_df.loc[house_df['YearBuilt'] >= 1980]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5f68fbba", - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", - "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YearBuilt'])]\n", - "\n", - "y_df_production=house_df_production['SalePrice'].to_frame()\n", - "X_df_production=house_df_production[house_df_production.columns.difference(['SalePrice','YearBuilt'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f756d9ea", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "1b72571f", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "da6116d5", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:The variable BldgType\n", - " has mismatching possible values: \n", - "\n", - " [] ['Two-family Conversion; originally built as one-family dwelling']\n", - "INFO:root:The variable BsmtCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Poor -Severe cracking, settling, or wetness']\n", - "INFO:root:The variable CentralAir\n", - " has mismatching possible values: \n", - "\n", - " [] ['No']\n", - "INFO:root:The variable Condition1\n", - " has mismatching possible values: \n", - "\n", - " [\"Within 200' of East-West Railroad\"] ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", - "INFO:root:The variable Condition2\n", - " has mismatching possible values: \n", - "\n", - " ['Near positive off-site feature--park, greenbelt, etc.'] ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", - "INFO:root:The variable Electrical\n", - " has mismatching possible values: \n", - "\n", - " [] ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "INFO:root:The variable ExterCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Fair', 'Poor', 'Excellent']\n", - "INFO:root:The variable ExterQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Fair']\n", - "INFO:root:The variable Exterior1st\n", - " has mismatching possible values: \n", - "\n", - " ['Imitation Stucco'] ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", - "INFO:root:The variable Exterior2nd\n", - " has mismatching possible values: \n", - "\n", - " ['Other'] ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", - "INFO:root:The variable Foundation\n", - " has mismatching possible values: \n", - "\n", - " ['Wood'] ['Brick & Tile', 'Stone']\n", - "INFO:root:The variable Functional\n", - " has mismatching possible values: \n", - "\n", - " [] ['Major Deductions 2', 'Severely Damaged']\n", - "INFO:root:The variable GarageCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Poor', 'Excellent']\n", - "INFO:root:The variable GarageQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Excellent', 'Poor']\n", - "INFO:root:The variable GarageType\n", - " has mismatching possible values: \n", - "\n", - " [] ['Car Port']\n", - "INFO:root:The variable Heating\n", - " has mismatching possible values: \n", - "\n", - " [] ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", - "INFO:root:The variable HeatingQC\n", - " has mismatching possible values: \n", - "\n", - " [] ['Fair', 'Poor']\n", - "INFO:root:The variable HouseStyle\n", - " has mismatching possible values: \n", - "\n", - " [] ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", - "INFO:root:The variable KitchenQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Fair']\n", - "INFO:root:The variable LandSlope\n", - " has mismatching possible values: \n", - "\n", - " [] ['Severe Slope']\n", - "INFO:root:The variable MSSubClass\n", - " has mismatching possible values: \n", - "\n", - " [] ['2-Story 1945 & Older', '2 Family Conversion - All Styles and Ages', '1-1/2 Story - Unfinished All Ages', '1-Story 1945 & Older', '2-1/2 Story All Ages', '1-Story w/Finished Attic All Ages']\n", - "INFO:root:The variable MSZoning\n", - " has mismatching possible values: \n", - "\n", - " ['Floating Village Residential'] ['Commercial']\n", - "INFO:root:The variable MasVnrType\n", - " has mismatching possible values: \n", - "\n", - " [] ['Brick Common']\n", - "INFO:root:The variable Neighborhood\n", - " has mismatching possible values: \n", - "\n", - " ['Northridge', 'Somerset', 'Northridge Heights', 'Stone Brook', 'Bloomington Heights', 'Bluestem'] ['Brookside', 'Iowa DOT and Rail Road', 'Meadow Village', 'Northpark Villa', 'Briardale', 'South & West of Iowa State University']\n", - "INFO:root:The variable PavedDrive\n", - " has mismatching possible values: \n", - "\n", - " [] ['Partial Pavement']\n", - "INFO:root:The variable RoofMatl\n", - " has mismatching possible values: \n", - "\n", - " ['Clay or Tile'] ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", - "INFO:root:The variable RoofStyle\n", - " has mismatching possible values: \n", - "\n", - " [] ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", - "INFO:root:The variable SaleCondition\n", - " has mismatching possible values: \n", - "\n", - " [] ['Adjoining Land Purchase']\n", - "INFO:root:The variable SaleType\n", - " has mismatching possible values: \n", - "\n", - " ['Contract 15% Down payment regular terms'] []\n", - "INFO:root:The variable Utilities\n", - " has mismatching possible values: \n", - "\n", - " [] ['Electricity and Gas Only']\n" - ] + "cell_type": "markdown", + "id": "8131b447", + "metadata": {}, + "source": [ + "# Eurybia with custom colors\n", + "\n", + "With this tutorial you will understand how to manipulate colors with Eurybia plots\n", + "\n", + "Contents:\n", + "- Compile Eurybia SmartDrift\n", + "- Use `palette_name` parameter\n", + "- Use `colors_dict` parameter\n", + "- Change the colors after compiling SmartDrift\n", + "\n", + "Data from Kaggle [House Prices](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n", - "CPU times: user 3min 52s, sys: 1min 36s, total: 5min 28s\n", - "Wall time: 10.6 s\n" - ] - } - ], - "source": [ - "%time SD.compile(full_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a46db42b", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "id": "255f6f01", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from category_encoders import OrdinalEncoder\n", + "from lightgbm import LGBMRegressor\n", + "from sklearn.model_selection import train_test_split" + ] + }, { - "data": { - "text/html": [ - " \n", - " " + "cell_type": "markdown", + "id": "e5d4a5cb", + "metadata": {}, + "source": [ + "## Building Supervized Model" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "code", + "execution_count": 2, + "id": "14ff4476", + "metadata": {}, + "outputs": [ { - "marker": { - "color": [ - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)", - "rgba(244, 192, 0, 1.0)" - ], - "line": { - "color": "rgba(52, 55, 54, 0.8)", - "width": 0.5 - } - }, - "name": "Global", - "orientation": "h", - "type": "bar", - "x": [ - 0.0046, - 0.0048, - 0.0049, - 0.0049, - 0.0054, - 0.0056, - 0.0063, - 0.0067, - 0.008, - 0.0098, - 0.0102, - 0.0149, - 0.0233, - 0.0277, - 0.0538, - 0.0791, - 0.1015, - 0.1112, - 0.2184, - 0.2384 - ], - "y": [ - "LotArea", - "LotShape", - "WoodDeckSF", - "1stFlrSF", - "BsmtFinSF1", - "Functional", - "Exterior2nd", - "Fireplaces", - "KitchenQual", - "ExterQual", - "MSSubClass", - "BsmtFinType1", - "MSZoning", - "OverallCond", - "GarageFinish", - "Foundation", - "YearRemodAdd", - "Neighborhood", - "GarageYrBlt", - "BsmtQual" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/data_tech/users/users_envs/78257d/eurybia39/lib/python3.9/site-packages/papermill/iorw.py:50: FutureWarning:\n", + "\n", + "pyarrow.HadoopFileSystem is deprecated as of 2.0.0, please use pyarrow.fs.HadoopFileSystem instead.\n", + "\n" + ] } - ], - "layout": { - "autosize": false, - "barmode": "group", - "height": 500, - "hovermode": "closest", - "margin": { - "b": 50, - "l": 160, - "r": 0, - "t": 95 - }, - "template": { - "data": { - "scatter": [ - { - "type": "scatter" - } + ], + "source": [ + "from eurybia.data.data_loader import data_loading\n", + "house_df, house_dict = data_loading('house_prices')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d8733ac7", + "metadata": {}, + "outputs": [], + "source": [ + "#For the purpose of this tutorial and to better represent a common use case of Eurybia, \n", + "#the house_prices dataset was split in two smaller sets : \"training\" and \"production\"\n", + "# To see an interesting analysis, let's test for a bias in the date of construction of training and production dataset\n", + "house_df_learning = house_df.loc[house_df['YearBuilt'] < 1980]\n", + "house_df_production = house_df.loc[house_df['YearBuilt'] >= 1980]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5f68fbba", + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", + "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YearBuilt'])]\n", + "\n", + "y_df_production=house_df_production['SalePrice'].to_frame()\n", + "X_df_production=house_df_production[house_df_production.columns.difference(['SalePrice','YearBuilt'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f756d9ea", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1b72571f", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "da6116d5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:The variable BldgType\n", + " has mismatching possible values: \n", + "\n", + " [] ['Two-family Conversion; originally built as one-family dwelling']\n", + "INFO:root:The variable BsmtCond\n", + " has mismatching possible values: \n", + "\n", + " [] ['Poor -Severe cracking, settling, or wetness']\n", + "INFO:root:The variable CentralAir\n", + " has mismatching possible values: \n", + "\n", + " [] ['No']\n", + "INFO:root:The variable Condition1\n", + " has mismatching possible values: \n", + "\n", + " [\"Within 200' of East-West Railroad\"] ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", + "INFO:root:The variable Condition2\n", + " has mismatching possible values: \n", + "\n", + " ['Near positive off-site feature--park, greenbelt, etc.'] ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", + "INFO:root:The variable Electrical\n", + " has mismatching possible values: \n", + "\n", + " [] ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "INFO:root:The variable ExterCond\n", + " has mismatching possible values: \n", + "\n", + " [] ['Fair', 'Poor', 'Excellent']\n", + "INFO:root:The variable ExterQual\n", + " has mismatching possible values: \n", + "\n", + " [] ['Fair']\n", + "INFO:root:The variable Exterior1st\n", + " has mismatching possible values: \n", + "\n", + " ['Imitation Stucco'] ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", + "INFO:root:The variable Exterior2nd\n", + " has mismatching possible values: \n", + "\n", + " ['Other'] ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", + "INFO:root:The variable Foundation\n", + " has mismatching possible values: \n", + "\n", + " ['Wood'] ['Brick & Tile', 'Stone']\n", + "INFO:root:The variable Functional\n", + " has mismatching possible values: \n", + "\n", + " [] ['Major Deductions 2', 'Severely Damaged']\n", + "INFO:root:The variable GarageCond\n", + " has mismatching possible values: \n", + "\n", + " [] ['Poor', 'Excellent']\n", + "INFO:root:The variable GarageQual\n", + " has mismatching possible values: \n", + "\n", + " [] ['Excellent', 'Poor']\n", + "INFO:root:The variable GarageType\n", + " has mismatching possible values: \n", + "\n", + " [] ['Car Port']\n", + "INFO:root:The variable Heating\n", + " has mismatching possible values: \n", + "\n", + " [] ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", + "INFO:root:The variable HeatingQC\n", + " has mismatching possible values: \n", + "\n", + " [] ['Fair', 'Poor']\n", + "INFO:root:The variable HouseStyle\n", + " has mismatching possible values: \n", + "\n", + " [] ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", + "INFO:root:The variable KitchenQual\n", + " has mismatching possible values: \n", + "\n", + " [] ['Fair']\n", + "INFO:root:The variable LandSlope\n", + " has mismatching possible values: \n", + "\n", + " [] ['Severe Slope']\n", + "INFO:root:The variable MSSubClass\n", + " has mismatching possible values: \n", + "\n", + " [] ['2-Story 1945 & Older', '2 Family Conversion - All Styles and Ages', '1-1/2 Story - Unfinished All Ages', '1-Story 1945 & Older', '2-1/2 Story All Ages', '1-Story w/Finished Attic All Ages']\n", + "INFO:root:The variable MSZoning\n", + " has mismatching possible values: \n", + "\n", + " ['Floating Village Residential'] ['Commercial']\n", + "INFO:root:The variable MasVnrType\n", + " has mismatching possible values: \n", + "\n", + " [] ['Brick Common']\n", + "INFO:root:The variable Neighborhood\n", + " has mismatching possible values: \n", + "\n", + " ['Northridge', 'Somerset', 'Northridge Heights', 'Stone Brook', 'Bloomington Heights', 'Bluestem'] ['Brookside', 'Iowa DOT and Rail Road', 'Meadow Village', 'Northpark Villa', 'Briardale', 'South & West of Iowa State University']\n", + "INFO:root:The variable PavedDrive\n", + " has mismatching possible values: \n", + "\n", + " [] ['Partial Pavement']\n", + "INFO:root:The variable RoofMatl\n", + " has mismatching possible values: \n", + "\n", + " ['Clay or Tile'] ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", + "INFO:root:The variable RoofStyle\n", + " has mismatching possible values: \n", + "\n", + " [] ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", + "INFO:root:The variable SaleCondition\n", + " has mismatching possible values: \n", + "\n", + " [] ['Adjoining Land Purchase']\n", + "INFO:root:The variable SaleType\n", + " has mismatching possible values: \n", + "\n", + " ['Contract 15% Down payment regular terms'] []\n", + "INFO:root:The variable Utilities\n", + " has mismatching possible values: \n", + "\n", + " [] ['Electricity and Gas Only']\n" ] - } }, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial", - "size": 24 - }, - "text": "Features Importance
Response: Current dataset - Total number of features: 71", - "x": 0.5, - "xanchor": "center", - "y": 0.9, - "yanchor": "middle" - }, - "width": 900, - "xaxis": { - "automargin": true, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backend: Shap TreeExplainer\n", + "CPU times: user 3min 52s, sys: 1min 36s, total: 5min 28s\n", + "Wall time: 10.6 s\n" + ] + } + ], + "source": [ + "%time SD.compile(full_validation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a46db42b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] }, - "text": "Contribution" - } + "metadata": {}, + "output_type": "display_data" }, - "yaxis": { - "automargin": true, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 - } - } + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": [ + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)" + ], + "line": { + "color": "rgba(52, 55, 54, 0.8)", + "width": 0.5 + } + }, + "name": "Global", + "orientation": "h", + "type": "bar", + "x": [ + 0.0046, + 0.0048, + 0.0049, + 0.0049, + 0.0054, + 0.0056, + 0.0063, + 0.0067, + 0.008, + 0.0098, + 0.0102, + 0.0149, + 0.0233, + 0.0277, + 0.0538, + 0.0791, + 0.1015, + 0.1112, + 0.2184, + 0.2384 + ], + "y": [ + "LotArea", + "LotShape", + "WoodDeckSF", + "1stFlrSF", + "BsmtFinSF1", + "Functional", + "Exterior2nd", + "Fireplaces", + "KitchenQual", + "ExterQual", + "MSSubClass", + "BsmtFinType1", + "MSZoning", + "OverallCond", + "GarageFinish", + "Foundation", + "YearRemodAdd", + "Neighborhood", + "GarageYrBlt", + "BsmtQual" + ] + } + ], + "layout": { + "autosize": false, + "barmode": "group", + "height": 500, + "hovermode": "closest", + "margin": { + "b": 50, + "l": 160, + "r": 0, + "t": 95 + }, + "template": { + "data": { + "scatter": [ + { + "type": "scatter" + } + ] + } + }, + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial", + "size": 24 + }, + "text": "Features Importance
Response: Current dataset - Total number of features: 71", + "x": 0.5, + "xanchor": "center", + "y": 0.9, + "yanchor": "middle" + }, + "width": 900, + "xaxis": { + "automargin": true, + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial Black", + "size": 16 + }, + "text": "Contribution" + } + }, + "yaxis": { + "automargin": true, + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial Black", + "size": 16 + } + } + } + } + }, + "text/html": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" } - } + ], + "source": [ + "SD.xpl.plot.features_importance()" + ] + }, + { + "cell_type": "markdown", + "id": "29de37f0", + "metadata": {}, + "source": [ + "## Eurybia with different colors" + ] + }, + { + "cell_type": "markdown", + "id": "e78bd32f", + "metadata": {}, + "source": [ + "### Option 1 : define user-specific colors with `colors_dict` parameter\n", + "\n", + "The colors declared will replace the one in the palette used.\n", + "\n", + "In the example below, we replace the colors used in the features importance bar plot:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f705934a", + "metadata": {}, + "outputs": [], + "source": [ + "# first, let's print the colors used in the previous explainer: \n", + "SD.colors_dict['featureimp_bar']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b872827b", + "metadata": {}, + "outputs": [], + "source": [ + "# Now we replace these colors using the colors_dict parameter\n", + "SD2 = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning,\n", + " colors_dict=dict(\n", + " featureimp_bar={\n", + " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", + " \"2\": \"rgba(52, 55, 54, 0.7)\"\n", + " },\n", + " univariate_cat_bar={\n", + " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", + " \"2\": \"rgba(52, 55, 54, 0.7)\"\n", + " },\n", + " univariate_cont_bar={\n", + " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", + " \"2\": \"rgba(52, 55, 54, 0.7)\" \n", + " })\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe066d87", + "metadata": {}, + "outputs": [], + "source": [ + "%time SD2.compile(full_validation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71e96ab3", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "SD2.xpl.plot.features_importance()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8f05061", + "metadata": {}, + "outputs": [], + "source": [ + "SD2.plot.generate_fig_univariate('BsmtQual')" + ] + }, + { + "cell_type": "markdown", + "id": "c362da29", + "metadata": {}, + "source": [ + "### Option 2 : redefine colors after compiling Eurybia" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53b1c6b0", + "metadata": {}, + "outputs": [], + "source": [ + "SD3 = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e94ad9c9", + "metadata": {}, + "outputs": [], + "source": [ + "%time SD3.compile(full_validation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0efde838", + "metadata": {}, + "outputs": [], + "source": [ + "SD3.xpl.plot.features_importance()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f19d2155", + "metadata": { + "scrolled": false }, - "text/html": [ - "
" + "outputs": [], + "source": [ + "SD3.plot.generate_fig_univariate('BsmtQual')" + ] + }, + { + "cell_type": "markdown", + "id": "d73a0ae6", + "metadata": {}, + "source": [ + "- **We redefine the colors with custom colors for the features importance plot**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc7f7bc7", + "metadata": {}, + "outputs": [], + "source": [ + "SD3.define_style( \n", + " colors_dict=dict(\n", + " featureimp_bar={\n", + " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", + " \"2\": \"rgba(52, 55, 54, 0.7)\"\n", + " },\n", + " univariate_cat_bar={\n", + " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", + " \"2\": \"rgba(52, 55, 54, 0.7)\"\n", + " },\n", + " univariate_cont_bar={\n", + " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", + " \"2\": \"rgba(52, 55, 54, 0.7)\" \n", + " }))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5a3f5c1", + "metadata": {}, + "outputs": [], + "source": [ + "SD3.xpl.plot.features_importance()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90300522", + "metadata": {}, + "outputs": [], + "source": [ + "SD3.plot.generate_fig_univariate('BsmtQual')" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "SD.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "markdown", - "id": "29de37f0", - "metadata": {}, - "source": [ - "## Eurybia with different colors" - ] - }, - { - "cell_type": "markdown", - "id": "e78bd32f", - "metadata": {}, - "source": [ - "### Option 1 : define user-specific colors with `colors_dict` parameter\n", - "\n", - "The colors declared will replace the one in the palette used.\n", - "\n", - "In the example below, we replace the colors used in the features importance bar plot:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f705934a", - "metadata": {}, - "outputs": [], - "source": [ - "# first, let's print the colors used in the previous explainer: \n", - "SD.colors_dict['featureimp_bar']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b872827b", - "metadata": {}, - "outputs": [], - "source": [ - "# Now we replace these colors using the colors_dict parameter\n", - "SD2 = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning,\n", - " colors_dict=dict(\n", - " featureimp_bar={\n", - " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", - " \"2\": \"rgba(52, 55, 54, 0.7)\"\n", - " },\n", - " univariate_cat_bar={\n", - " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", - " \"2\": \"rgba(52, 55, 54, 0.7)\"\n", - " },\n", - " univariate_cont_bar={\n", - " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", - " \"2\": \"rgba(52, 55, 54, 0.7)\" \n", - " })\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe066d87", - "metadata": {}, - "outputs": [], - "source": [ - "%time SD2.compile(full_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71e96ab3", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "SD2.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8f05061", - "metadata": {}, - "outputs": [], - "source": [ - "SD2.plot.generate_fig_univariate('BsmtQual')" - ] - }, - { - "cell_type": "markdown", - "id": "c362da29", - "metadata": {}, - "source": [ - "### Option 2 : redefine colors after compiling Eurybia" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53b1c6b0", - "metadata": {}, - "outputs": [], - "source": [ - "SD3 = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e94ad9c9", - "metadata": {}, - "outputs": [], - "source": [ - "%time SD3.compile(full_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0efde838", - "metadata": {}, - "outputs": [], - "source": [ - "SD3.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f19d2155", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "SD3.plot.generate_fig_univariate('BsmtQual')" - ] - }, - { - "cell_type": "markdown", - "id": "d73a0ae6", - "metadata": {}, - "source": [ - "- **We redefine the colors with custom colors for the features importance plot**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc7f7bc7", - "metadata": {}, - "outputs": [], - "source": [ - "SD3.define_style( \n", - " colors_dict=dict(\n", - " featureimp_bar={\n", - " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", - " \"2\": \"rgba(52, 55, 54, 0.7)\"\n", - " },\n", - " univariate_cat_bar={\n", - " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", - " \"2\": \"rgba(52, 55, 54, 0.7)\"\n", - " },\n", - " univariate_cont_bar={\n", - " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", - " \"2\": \"rgba(52, 55, 54, 0.7)\" \n", - " }))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e5a3f5c1", - "metadata": {}, - "outputs": [], - "source": [ - "SD3.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90300522", - "metadata": {}, - "outputs": [], - "source": [ - "SD3.plot.generate_fig_univariate('BsmtQual')" - ] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "eurybia39", - "language": "python", - "name": "eurybia39" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "eurybia39", + "language": "python", + "name": "eurybia39" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/source/tutorials/common/tuto-common02-shapash-webapp.ipynb b/docs/source/tutorials/common/tuto-common02-shapash-webapp.ipynb index 2843959..1b3eb6f 100644 --- a/docs/source/tutorials/common/tuto-common02-shapash-webapp.ipynb +++ b/docs/source/tutorials/common/tuto-common02-shapash-webapp.ipynb @@ -1,696 +1,696 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "2e6c1448", - "metadata": {}, - "source": [ - "# Use Shapash Webapp with Eurybia\n" - ] - }, - { - "cell_type": "markdown", - "id": "97444094", - "metadata": {}, - "source": [ - "With this tutorial you:
\n", - "Understand how use Eurybia and Shapash web app to understand datadrift classifier
\n", - "\n", - "Contents:\n", - "- Build a model to deploy\n", - "- Do data validation between learning dataset and production dataset\n", - "- Generate Report \n", - "- Run Webapp\n", - "\n", - "\n", - "Data from Kaggle [Titanic](https://www.kaggle.com/c/titanic)
" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a3dccf5a", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from category_encoders import OrdinalEncoder\n", - "import catboost\n", - "from eurybia.core.smartdrift import SmartDrift\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "markdown", - "id": "e3724750", - "metadata": {}, - "source": [ - "## Building Supervized Model\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b2e18d96", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9dec2e88", - "metadata": {}, - "outputs": [], - "source": [ - "titan_df = data_loading('titanic')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "dcb7ca66", - "metadata": {}, - "outputs": [], - "source": [ - "features = ['Pclass', 'Age', 'Embarked', 'Sex', 'SibSp', 'Parch', 'Fare']\n", - "features_to_encode = ['Pclass', 'Embarked', 'Sex']" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e879e07c", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/maitrejinx/anaconda3/envs/eurybia39/lib/python3.9/site-packages/category_encoders/utils.py:21: FutureWarning:\n", - "\n", - "is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\n", - "\n" - ] + "cell_type": "markdown", + "id": "2e6c1448", + "metadata": {}, + "source": [ + "# Use Shapash Webapp with Eurybia\n" + ] }, { - "data": { - "text/plain": [ - "OrdinalEncoder(cols=['Pclass', 'Embarked', 'Sex'],\n", - " mapping=[{'col': 'Pclass', 'data_type': dtype('O'),\n", - " 'mapping': Third class 1\n", - "First class 2\n", - "Second class 3\n", - "NaN -2\n", - "dtype: int64},\n", - " {'col': 'Embarked', 'data_type': dtype('O'),\n", - " 'mapping': Southampton 1\n", - "Cherbourg 2\n", - "Queenstown 3\n", - "NaN -2\n", - "dtype: int64},\n", - " {'col': 'Sex', 'data_type': dtype('O'),\n", - " 'mapping': male 1\n", - "female 2\n", - "NaN -2\n", - "dtype: int64}])" + "cell_type": "markdown", + "id": "97444094", + "metadata": {}, + "source": [ + "With this tutorial you:
\n", + "Understand how use Eurybia and Shapash web app to understand datadrift classifier
\n", + "\n", + "Contents:\n", + "- Build a model to deploy\n", + "- Do data validation between learning dataset and production dataset\n", + "- Generate Report \n", + "- Run Webapp\n", + "\n", + "\n", + "Data from Kaggle [Titanic](https://www.kaggle.com/c/titanic)
" ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "encoder = OrdinalEncoder(cols=features_to_encode)\n", - "encoder.fit(titan_df[features]) " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "ab90ab21", - "metadata": {}, - "outputs": [], - "source": [ - "titan_df_encoded = encoder.transform(titan_df[features])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8ecca1ca", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(\n", - " titan_df_encoded,\n", - " titan_df['Survived'].to_frame(),\n", - " test_size=0.2,\n", - " random_state=11\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a623ae46", - "metadata": {}, - "outputs": [], - "source": [ - "i=0\n", - "indice_cat = []\n", - "for feature in titan_df_encoded:\n", - " if feature in features_to_encode:\n", - " indice_cat.append(i)\n", - " i=i+1" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "bc519ae1", - "metadata": {}, - "outputs": [], - "source": [ - "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", - " learning_rate=0.143852,\n", - " iterations=500,\n", - " l2_leaf_reg=15,\n", - " max_depth = 4)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e191e6be", - "metadata": {}, - "outputs": [], - "source": [ - "train_pool_cat = catboost.Pool(data=X_train, label= y_train, cat_features = indice_cat)\n", - "test_pool_cat = catboost.Pool(data=X_test, label=y_test, cat_features = indice_cat) " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2756067d", - "metadata": {}, - "outputs": [], - "source": [ - "model.fit(train_pool_cat, eval_set=test_pool_cat, silent=True)\n", - "y_pred = model.predict(X_test)" - ] - }, - { - "cell_type": "markdown", - "id": "a1ede851", - "metadata": {}, - "source": [ - "## Creating a fake dataset as a production dataset\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "5c70edbf", - "metadata": {}, - "outputs": [], - "source": [ - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "317e93eb", - "metadata": {}, - "outputs": [], - "source": [ - "df_production = titan_df.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "bea39460", - "metadata": {}, - "outputs": [], - "source": [ - "df_production['Age'] = df_production['Age'].apply(lambda x: random.randrange(10, 76)).astype(float)\n", - "df_production['Fare'] = df_production['Fare'].apply(lambda x: random.randrange(1, 100)).astype(float)\n", - "list_sex= [\"male\", \"female\"]\n", - "df_production['Sex'] = df_production['Sex'].apply(lambda x: random.choice(list_sex))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "144207df", - "metadata": {}, - "outputs": [], - "source": [ - "df_baseline = titan_df[features]\n", - "df_current = df_production[features]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "7e8d5593", - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a3dccf5a", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from category_encoders import OrdinalEncoder\n", + "import catboost\n", + "from eurybia.core.smartdrift import SmartDrift\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "id": "e3724750", + "metadata": {}, + "source": [ + "## Building Supervized Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b2e18d96", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9dec2e88", + "metadata": {}, + "outputs": [], + "source": [ + "titan_df = data_loading('titanic')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dcb7ca66", + "metadata": {}, + "outputs": [], + "source": [ + "features = ['Pclass', 'Age', 'Embarked', 'Sex', 'SibSp', 'Parch', 'Fare']\n", + "features_to_encode = ['Pclass', 'Embarked', 'Sex']" + ] + }, { - "data": { - "text/html": [ - "
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" + "cell_type": "code", + "execution_count": 5, + "id": "e879e07c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/maitrejinx/anaconda3/envs/eurybia39/lib/python3.9/site-packages/category_encoders/utils.py:21: FutureWarning:\n", + "\n", + "is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "OrdinalEncoder(cols=['Pclass', 'Embarked', 'Sex'],\n", + " mapping=[{'col': 'Pclass', 'data_type': dtype('O'),\n", + " 'mapping': Third class 1\n", + "First class 2\n", + "Second class 3\n", + "NaN -2\n", + "dtype: int64},\n", + " {'col': 'Embarked', 'data_type': dtype('O'),\n", + " 'mapping': Southampton 1\n", + "Cherbourg 2\n", + "Queenstown 3\n", + "NaN -2\n", + "dtype: int64},\n", + " {'col': 'Sex', 'data_type': dtype('O'),\n", + " 'mapping': male 1\n", + "female 2\n", + "NaN -2\n", + "dtype: int64}])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " Pclass Age Embarked Sex SibSp Parch Fare\n", - "PassengerId \n", - "1 Third class 56.0 Southampton female 1 0 38.0\n", - "2 First class 51.0 Cherbourg female 1 0 84.0\n", - "3 Third class 24.0 Southampton female 0 0 45.0\n", - "4 First class 41.0 Southampton male 1 0 38.0\n", - "5 Third class 32.0 Southampton female 0 0 1.0" + "source": [ + "encoder = OrdinalEncoder(cols=features_to_encode)\n", + "encoder.fit(titan_df[features]) " ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_current.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "a02abe4f", - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ab90ab21", + "metadata": {}, + "outputs": [], + "source": [ + "titan_df_encoded = encoder.transform(titan_df[features])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8ecca1ca", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " titan_df_encoded,\n", + " titan_df['Survived'].to_frame(),\n", + " test_size=0.2,\n", + " random_state=11\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a623ae46", + "metadata": {}, + "outputs": [], + "source": [ + "i=0\n", + "indice_cat = []\n", + "for feature in titan_df_encoded:\n", + " if feature in features_to_encode:\n", + " indice_cat.append(i)\n", + " i=i+1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "bc519ae1", + "metadata": {}, + "outputs": [], + "source": [ + "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", + " learning_rate=0.143852,\n", + " iterations=500,\n", + " l2_leaf_reg=15,\n", + " max_depth = 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e191e6be", + "metadata": {}, + "outputs": [], + "source": [ + "train_pool_cat = catboost.Pool(data=X_train, label= y_train, cat_features = indice_cat)\n", + "test_pool_cat = catboost.Pool(data=X_test, label=y_test, cat_features = indice_cat) " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2756067d", + "metadata": {}, + "outputs": [], + "source": [ + "model.fit(train_pool_cat, eval_set=test_pool_cat, silent=True)\n", + "y_pred = model.predict(X_test)" + ] + }, { - "data": { - "text/html": [ - "
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" + "cell_type": "markdown", + "id": "a1ede851", + "metadata": {}, + "source": [ + "## Creating a fake dataset as a production dataset\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5c70edbf", + "metadata": {}, + "outputs": [], + "source": [ + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "317e93eb", + "metadata": {}, + "outputs": [], + "source": [ + "df_production = titan_df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bea39460", + "metadata": {}, + "outputs": [], + "source": [ + "df_production['Age'] = df_production['Age'].apply(lambda x: random.randrange(10, 76)).astype(float)\n", + "df_production['Fare'] = df_production['Fare'].apply(lambda x: random.randrange(1, 100)).astype(float)\n", + "list_sex= [\"male\", \"female\"]\n", + "df_production['Sex'] = df_production['Sex'].apply(lambda x: random.choice(list_sex))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "144207df", + "metadata": {}, + "outputs": [], + "source": [ + "df_baseline = titan_df[features]\n", + "df_current = df_production[features]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "7e8d5593", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Pclass Age Embarked Sex SibSp Parch Fare\n", + "PassengerId \n", + "1 Third class 56.0 Southampton female 1 0 38.0\n", + "2 First class 51.0 Cherbourg female 1 0 84.0\n", + "3 Third class 24.0 Southampton female 0 0 45.0\n", + "4 First class 41.0 Southampton male 1 0 38.0\n", + "5 Third class 32.0 Southampton female 0 0 1.0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " Pclass Age Embarked Sex SibSp Parch Fare\n", - "PassengerId \n", - "1 Third class 22.0 Southampton male 1 0 7.25\n", - "2 First class 38.0 Cherbourg female 1 0 71.28\n", - "3 Third class 26.0 Southampton female 0 0 7.92\n", - "4 First class 35.0 Southampton female 1 0 53.10\n", - "5 Third class 35.0 Southampton male 0 0 8.05" + "source": [ + "df_current.head()" ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_baseline.head()" - ] - }, - { - "cell_type": "markdown", - "id": "54202f12", - "metadata": {}, - "source": [ - "## Use Eurybia for data validation" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "b66f2c47", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "260c36c3", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=df_current, df_baseline=df_baseline, deployed_model=model, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "c74e56bb", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n", - "CPU times: user 5.47 s, sys: 490 ms, total: 5.96 s\n", - "Wall time: 3.16 s\n" - ] - } - ], - "source": [ - "%time SD.compile(full_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "ec907e9e", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 17, + "id": "a02abe4f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Pclass Age Embarked Sex SibSp Parch Fare\n", + "PassengerId \n", + "1 Third class 22.0 Southampton male 1 0 7.25\n", + "2 First class 38.0 Cherbourg female 1 0 71.28\n", + "3 Third class 26.0 Southampton female 0 0 7.92\n", + "4 First class 35.0 Southampton female 1 0 53.10\n", + "5 Third class 35.0 Southampton male 0 0 8.05" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_baseline.head()" + ] + }, + { + "cell_type": "markdown", + "id": "54202f12", + "metadata": {}, + "source": [ + "## Use Eurybia for data validation" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:datapane:Bokeh version 2.4.2 is not supported, these plots may not display correctly, please install version ~=2.2.0\n" - ] + "cell_type": "code", + "execution_count": 18, + "id": "b66f2c47", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "260c36c3", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=df_current, df_baseline=df_baseline, deployed_model=model, encoding=encoder)" + ] }, { - "data": { - "text/markdown": [ - "Report saved to ./../output/report_titanic.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "code", + "execution_count": 20, + "id": "c74e56bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backend: Shap TreeExplainer\n", + "CPU times: user 5.47 s, sys: 490 ms, total: 5.96 s\n", + "Wall time: 3.16 s\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "%time SD.compile(full_validation=True)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='../output/report_titanic.html', \n", - " title_story=\"Data validation\",\n", - " title_description=\"\"\"Titanic Data validation\"\"\" \n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "582afac8", - "metadata": {}, - "source": [ - "## Launch WebApp Shapash from SmartDrift" - ] - }, - { - "cell_type": "markdown", - "id": "230cc56f", - "metadata": {}, - "source": [ - "After compile step, you can launch a WebApp Shapash directly from your object SmartDrift. It allows you to access several dynamic plots that will help you to understand where drift has been detected in your data.
\n", - "For information on Shapash Webapp : (https://github.com/MAIF/shapash)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "d6966349", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dash is running on http://0.0.0.0:8050/\n", - "\n" - ] + "cell_type": "code", + "execution_count": 21, + "id": "ec907e9e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:datapane:Bokeh version 2.4.2 is not supported, these plots may not display correctly, please install version ~=2.2.0\n" + ] + }, + { + "data": { + "text/markdown": [ + "Report saved to ./../output/report_titanic.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='../output/report_titanic.html', \n", + " title_story=\"Data validation\",\n", + " title_description=\"\"\"Titanic Data validation\"\"\" \n", + " )" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/maitrejinx/anaconda3/envs/eurybia39/lib/python3.9/site-packages/shapash/webapp/smart_app.py:307: FutureWarning:\n", - "\n", - "Using short name for 'orient' is deprecated. Only the options: ('dict', list, 'series', 'split', 'records', 'index') will be used in a future version. Use one of the above to silence this warning.\n", - "\n", - "INFO:root:Your Shapash application run on http://maitrejinx-Latitude-E5570:8050/\n", - "INFO:root:Use the method .kill() to down your app.\n", - "INFO:shapash.webapp.smart_app:Dash is running on http://0.0.0.0:8050/\n", - "\n" - ] + "cell_type": "markdown", + "id": "582afac8", + "metadata": {}, + "source": [ + "## Launch WebApp Shapash from SmartDrift" + ] + }, + { + "cell_type": "markdown", + "id": "230cc56f", + "metadata": {}, + "source": [ + "After compile step, you can launch a WebApp Shapash directly from your object SmartDrift. It allows you to access several dynamic plots that will help you to understand where drift has been detected in your data.
\n", + "For information on Shapash Webapp : (https://github.com/MAIF/shapash)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - " * Serving Flask app \"shapash.webapp.smart_app\" (lazy loading)\n", - " * Environment: production\n", - "\u001b[31m WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n", - "\u001b[2m Use a production WSGI server instead.\u001b[0m\n", - " * Debug mode: off\n" - ] + "cell_type": "code", + "execution_count": 22, + "id": "d6966349", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dash is running on http://0.0.0.0:8050/\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/maitrejinx/anaconda3/envs/eurybia39/lib/python3.9/site-packages/shapash/webapp/smart_app.py:307: FutureWarning:\n", + "\n", + "Using short name for 'orient' is deprecated. Only the options: ('dict', list, 'series', 'split', 'records', 'index') will be used in a future version. Use one of the above to silence this warning.\n", + "\n", + "INFO:root:Your Shapash application run on http://maitrejinx-Latitude-E5570:8050/\n", + "INFO:root:Use the method .kill() to down your app.\n", + "INFO:shapash.webapp.smart_app:Dash is running on http://0.0.0.0:8050/\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " * Serving Flask app \"shapash.webapp.smart_app\" (lazy loading)\n", + " * Environment: production\n", + "\u001b[31m WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n", + "\u001b[2m Use a production WSGI server instead.\u001b[0m\n", + " * Debug mode: off\n" + ] + } + ], + "source": [ + "app = SD.xpl.run_app(title_story='Eurybia datadrift classifier')" + ] + }, + { + "cell_type": "markdown", + "id": "8f49fb5a", + "metadata": {}, + "source": [ + "**Stop the WebApp after using it**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "99534aea", + "metadata": {}, + "outputs": [], + "source": [ + "app.kill()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "eurybia39", + "language": "python", + "name": "eurybia39" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } - ], - "source": [ - "app = SD.xpl.run_app(title_story='Eurybia datadrift classifier')" - ] - }, - { - "cell_type": "markdown", - "id": "8f49fb5a", - "metadata": {}, - "source": [ - "**Stop the WebApp after using it**" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "99534aea", - "metadata": {}, - "outputs": [], - "source": [ - "app.kill()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "eurybia39", - "language": "python", - "name": "eurybia39" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/source/tutorials/data_drift/tutorial01-datadrift-over-years.ipynb b/docs/source/tutorials/data_drift/tutorial01-datadrift-over-years.ipynb index 1331557..99737a6 100644 --- a/docs/source/tutorials/data_drift/tutorial01-datadrift-over-years.ipynb +++ b/docs/source/tutorials/data_drift/tutorial01-datadrift-over-years.ipynb @@ -1,6461 +1,6461 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Monitor Data Drift over years\n", - "With this tutorial you:
\n", - "Understand how to use Eurybia to detect datadrift\n", - "\n", - "Contents:\n", - "- Detect data drift \n", - "- Compile Drift over years\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from category_encoders import OrdinalEncoder\n", - "from lightgbm import LGBMRegressor\n", - "from eurybia import SmartDrift\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_squared_log_error" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Dataset and split in training and production dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "house_df, house_dict = data_loading('house_prices')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Let us consider that the column \"YrSold\" corresponds to the reference date. \n", - "#In 2006, a model was trained using data. And in 2007, we want to detect data drift on new data in production to predict\n", - "#house price\n", - "house_df_learning = house_df.loc[house_df['YrSold'] == 2006]\n", - "house_df_2007 = house_df.loc[house_df['YrSold'] == 2007]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", - "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YrSold'])]\n", - "\n", - "y_df_2007=house_df_2007['SalePrice'].to_frame()\n", - "X_df_2007=house_df_2007[house_df_2007.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building Supervized Model" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from category_encoders import OrdinalEncoder\n", - "\n", - "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", - "\n", - "encoder = OrdinalEncoder(\n", - " cols=categorical_features,\n", - " handle_unknown='ignore',\n", - " return_df=True).fit(X_df_learning)\n", - "\n", - "X_df_learning_encoded=encoder.transform(X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use Eurybia for data drift" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2007, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:The variable BsmtCond\n", - " has mismatching possible values: \n", - "\n", - " ['Poor -Severe cracking, settling, or wetness'] []\n", - "INFO:root:The variable Condition2\n", - " has mismatching possible values: \n", - "\n", - " ['Near positive off-site feature--park, greenbelt, etc.', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad'] ['Adjacent to feeder street']\n", - "INFO:root:The variable Electrical\n", - " has mismatching possible values: \n", - "\n", - " ['Mixed'] ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "INFO:root:The variable ExterQual\n", - " has mismatching possible values: \n", - "\n", - " ['Fair'] []\n", - "INFO:root:The variable Exterior1st\n", - " has mismatching possible values: \n", - "\n", - " [] ['Stone', 'Imitation Stucco']\n", - "INFO:root:The variable Exterior2nd\n", - " has mismatching possible values: \n", - "\n", - " ['Asphalt Shingles', 'Brick Common'] ['Other']\n", - "INFO:root:The variable Foundation\n", - " has mismatching possible values: \n", - "\n", - " [] ['Stone', 'Wood']\n", - "INFO:root:The variable Functional\n", - " has mismatching possible values: \n", - "\n", - " ['Major Deductions 2', 'Severely Damaged'] ['Moderate Deductions']\n", - "INFO:root:The variable GarageQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Excellent']\n", - "INFO:root:The variable Heating\n", - " has mismatching possible values: \n", - "\n", - " [] ['Wall furnace']\n", - "INFO:root:The variable HeatingQC\n", - " has mismatching possible values: \n", - "\n", - " ['Poor'] []\n", - "INFO:root:The variable LotConfig\n", - " has mismatching possible values: \n", - "\n", - " [] ['Frontage on 3 sides of property']\n", - "INFO:root:The variable MSSubClass\n", - " has mismatching possible values: \n", - "\n", - " ['1-Story w/Finished Attic All Ages'] []\n", - "INFO:root:The variable Neighborhood\n", - " has mismatching possible values: \n", - "\n", - " ['Northpark Villa'] []\n", - "INFO:root:The variable RoofMatl\n", - " has mismatching possible values: \n", - "\n", - " ['Roll'] ['Metal']\n", - "INFO:root:The variable RoofStyle\n", - " has mismatching possible values: \n", - "\n", - " ['Mansard', 'Shed'] []\n", - "INFO:root:The variable SaleType\n", - " has mismatching possible values: \n", - "\n", - " ['Warranty Deed - Cash'] ['Contract Low Interest', 'Contract Low Down', 'Contract Low Down payment and low interest']\n", - "INFO:root:The variable Street\n", - " has mismatching possible values: \n", - "\n", - " ['Gravel'] []\n", - "INFO:root:\n", - " The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.626082251082251\n", - " \n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Monitor Data Drift over years\n", + "With this tutorial you:
\n", + "Understand how to use Eurybia to detect datadrift\n", + "\n", + "Contents:\n", + "- Detect data drift \n", + "- Compile Drift over years\n", + "\n" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n", - "CPU times: user 1min 48s, sys: 4min 43s, total: 6min 32s\n", - "Wall time: 16.4 s\n" - ] - } - ], - "source": [ - "%time SD.compile(full_validation=True, date_compile_auc = '01/01/2007', datadrift_file = \"house_price_auc.csv\")\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", - "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", - "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from category_encoders import OrdinalEncoder\n", + "from lightgbm import LGBMRegressor\n", + "from eurybia import SmartDrift\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_log_error" + ] + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_datadrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." - ], - "text/plain": [ - "" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import Dataset and split in training and production dataset" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_datadrift_2007.html', \n", - " title_story=\"Data drift\",\n", - " title_description=\"\"\"House price Data drift 2007\"\"\",\n", - " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" \n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", - "However, to illustrate functionalities, we will detail results with notebook mode analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## First Analysis of results of the data drift" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Data driftn methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", - "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", - "Then a classification model (catboost) is learned to predict this target.\n", - "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Detection data drift performance" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "0.626082251082251" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading" ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Performance of data drift classifier\n", - "SD.auc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An Auc close to 0.5 means that there is little drift" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Importance of features in data drift" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "house_df, house_dict = data_loading('house_prices')" + ] + }, { - "data": { - "text/html": [ - " \n", - " " + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Let us consider that the column \"YrSold\" corresponds to the reference date. \n", + "#In 2006, a model was trained using data. And in 2007, we want to detect data drift on new data in production to predict\n", + "#house price\n", + "house_df_learning = house_df.loc[house_df['YrSold'] == 2006]\n", + "house_df_2007 = house_df.loc[house_df['YrSold'] == 2007]" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", + "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YrSold'])]\n", + "\n", + "y_df_2007=house_df_2007['SalePrice'].to_frame()\n", + "X_df_2007=house_df_2007[house_df_2007.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building Supervized Model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from category_encoders import OrdinalEncoder\n", + "\n", + "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", + "\n", + "encoder = OrdinalEncoder(\n", + " cols=categorical_features,\n", + " handle_unknown='ignore',\n", + " return_df=True).fit(X_df_learning)\n", + "\n", + "X_df_learning_encoded=encoder.transform(X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Eurybia for data drift" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2007, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ { - "marker": { - "color": [ - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)" - ], - "line": { - "color": "rgba(52, 55, 54, 0.8)", - "width": 0.5 - } - }, - "name": "Global", - "orientation": "h", - "type": "bar", - "x": [ - 0.0172, - 0.0183, - 0.0184, - 0.0187, - 0.0211, - 0.0211, - 0.0213, - 0.0214, - 0.0215, - 0.023, - 0.0237, - 0.0237, - 0.0241, - 0.0249, - 0.0276, - 0.0282, - 0.0381, - 0.0454, - 0.0564, - 0.1321 - ], - "y": [ - "YearBuilt", - "BsmtQual", - "Condition1", - "Exterior1st", - "OpenPorchSF", - "1stFlrSF", - "GarageFinish", - "GrLivArea", - "GarageArea", - "BldgType", - "GarageYrBlt", - "OverallCond", - "TotalBsmtSF", - "LandContour", - "HeatingQC", - "SaleType", - "LotArea", - "BsmtUnfSF", - "SaleCondition", - "YearRemodAdd" - ] - } - ], - "layout": { - "autosize": false, - "barmode": "group", - "height": 500, - "hovermode": "closest", - "margin": { - "b": 50, - "l": 160, - "r": 0, - "t": 95 - }, - "template": { - "data": { - "scatter": [ - { - "type": "scatter" - } + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:The variable BsmtCond\n", + " has mismatching possible values: \n", + "\n", + " ['Poor -Severe cracking, settling, or wetness'] []\n", + "INFO:root:The variable Condition2\n", + " has mismatching possible values: \n", + "\n", + " ['Near positive off-site feature--park, greenbelt, etc.', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad'] ['Adjacent to feeder street']\n", + "INFO:root:The variable Electrical\n", + " has mismatching possible values: \n", + "\n", + " ['Mixed'] ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "INFO:root:The variable ExterQual\n", + " has mismatching possible values: \n", + "\n", + " ['Fair'] []\n", + "INFO:root:The variable Exterior1st\n", + " has mismatching possible values: \n", + "\n", + " [] ['Stone', 'Imitation Stucco']\n", + "INFO:root:The variable Exterior2nd\n", + " has mismatching possible values: \n", + "\n", + " ['Asphalt Shingles', 'Brick Common'] ['Other']\n", + "INFO:root:The variable Foundation\n", + " has mismatching possible values: \n", + "\n", + " [] ['Stone', 'Wood']\n", + "INFO:root:The variable Functional\n", + " has mismatching possible values: \n", + "\n", + " ['Major Deductions 2', 'Severely Damaged'] ['Moderate Deductions']\n", + "INFO:root:The variable GarageQual\n", + " has mismatching possible values: \n", + "\n", + " [] ['Excellent']\n", + "INFO:root:The variable Heating\n", + " has mismatching possible values: \n", + "\n", + " [] ['Wall furnace']\n", + "INFO:root:The variable HeatingQC\n", + " has mismatching possible values: \n", + "\n", + " ['Poor'] []\n", + "INFO:root:The variable LotConfig\n", + " has mismatching possible values: \n", + "\n", + " [] ['Frontage on 3 sides of property']\n", + "INFO:root:The variable MSSubClass\n", + " has mismatching possible values: \n", + "\n", + " ['1-Story w/Finished Attic All Ages'] []\n", + "INFO:root:The variable Neighborhood\n", + " has mismatching possible values: \n", + "\n", + " ['Northpark Villa'] []\n", + "INFO:root:The variable RoofMatl\n", + " has mismatching possible values: \n", + "\n", + " ['Roll'] ['Metal']\n", + "INFO:root:The variable RoofStyle\n", + " has mismatching possible values: \n", + "\n", + " ['Mansard', 'Shed'] []\n", + "INFO:root:The variable SaleType\n", + " has mismatching possible values: \n", + "\n", + " ['Warranty Deed - Cash'] ['Contract Low Interest', 'Contract Low Down', 'Contract Low Down payment and low interest']\n", + "INFO:root:The variable Street\n", + " has mismatching possible values: \n", + "\n", + " ['Gravel'] []\n", + "INFO:root:\n", + " The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.626082251082251\n", + " \n" ] - } - }, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial", - "size": 24 - }, - "text": "Features Importance
Response: Current dataset - Total number of features: 71", - "x": 0.5, - "xanchor": "center", - "y": 0.9, - "yanchor": "middle" }, - "width": 900, - "xaxis": { - "automargin": true, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 - }, - "text": "Contribution" - } - }, - "yaxis": { - "automargin": true, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 - } - } + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backend: Shap TreeExplainer\n", + "CPU times: user 1min 48s, sys: 4min 43s, total: 6min 32s\n", + "Wall time: 16.4 s\n" + ] } - } - }, - "text/html": [ - "
" + ], + "source": [ + "%time SD.compile(full_validation=True, date_compile_auc = '01/01/2007', datadrift_file = \"house_price_auc.csv\")\n", + " " ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We get the features with most gaps, those that are most important to analyse.\n", - "With date bias introduced, it is normal that date features are the most impacted. We will then decide to remove them.\n", - "Let's analyse other important variables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Feature importance overview" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", + "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", + "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." + ] + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ { - "hovertemplate": "%{hovertext}", - "hovertext": [ - "Feature: 1stFlrSF
Deployed Model Importance: 7.9%
Datadrift test: K-Smirnov - pvalue: 0.64372
Datadrift model Importance: 2.1", - "Feature: 2ndFlrSF
Deployed Model Importance: 4.2%
Datadrift test: K-Smirnov - pvalue: 0.98064
Datadrift model Importance: 0.6", - "Feature: 3SsnPorch
Deployed Model Importance: 0.0%
Datadrift test: K-Smirnov - pvalue: 1.00000
Datadrift model Importance: 0.0", - "Feature: BedroomAbvGr
Deployed Model Importance: 0.8%
Datadrift test: K-Smirnov - pvalue: 0.65800
Datadrift model Importance: 1.7", - "Feature: BldgType
Deployed Model Importance: 0.5%
Datadrift test: Chi-Square - pvalue: 0.52085
Datadrift model Importance: 2.3", - "Feature: BsmtCond
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.16576
Datadrift model Importance: 1.4", - "Feature: BsmtExposure
Deployed Model Importance: 2.6%
Datadrift test: Chi-Square - pvalue: 0.51913
Datadrift model Importance: 1.0", - "Feature: BsmtFinSF1
Deployed Model Importance: 6.0%
Datadrift test: K-Smirnov - pvalue: 0.31147
Datadrift model Importance: 1.5", - "Feature: BsmtFinSF2
Deployed Model Importance: 0.1%
Datadrift test: K-Smirnov - pvalue: 1.00000
Datadrift model Importance: 0.0", - "Feature: BsmtFinType1
Deployed Model Importance: 1.0%
Datadrift test: Chi-Square - pvalue: 0.92015
Datadrift model Importance: 1.7", - "Feature: BsmtFinType2
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.86205
Datadrift model Importance: 1.4", - "Feature: BsmtFullBath
Deployed Model Importance: 0.7%
Datadrift test: K-Smirnov - pvalue: 0.17420
Datadrift model Importance: 0.0", - "Feature: BsmtHalfBath
Deployed Model Importance: 0.0%
Datadrift test: K-Smirnov - pvalue: 1.00000
Datadrift model Importance: 0.0", - "Feature: BsmtQual
Deployed Model Importance: 0.9%
Datadrift test: Chi-Square - pvalue: 0.34916
Datadrift model Importance: 1.8", - "Feature: BsmtUnfSF
Deployed Model Importance: 3.7%
Datadrift test: K-Smirnov - pvalue: 0.03430
Datadrift model Importance: 4.5", - "Feature: CentralAir
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.51560
Datadrift model Importance: 0.0", - "Feature: Condition1
Deployed Model Importance: 0.1%
Datadrift test: Chi-Square - pvalue: 0.57738
Datadrift model Importance: 1.8", - "Feature: Condition2
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.41740
Datadrift model Importance: 0.5", - "Feature: Electrical
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.47334
Datadrift model Importance: 1.1", - "Feature: EnclosedPorch
Deployed Model Importance: 0.0%
Datadrift test: K-Smirnov - pvalue: 0.99938
Datadrift model Importance: 1.0", - "Feature: ExterCond
Deployed Model Importance: 0.3%
Datadrift test: Chi-Square - pvalue: 0.57309
Datadrift model Importance: 1.4", - "Feature: ExterQual
Deployed Model Importance: 0.1%
Datadrift test: Chi-Square - pvalue: 0.17470
Datadrift model Importance: 0.5", - "Feature: Exterior1st
Deployed Model Importance: 1.1%
Datadrift test: Chi-Square - pvalue: 0.25082
Datadrift model Importance: 1.9", - "Feature: Exterior2nd
Deployed Model Importance: 1.1%
Datadrift test: Chi-Square - pvalue: 0.44102
Datadrift model Importance: 0.8", - "Feature: Fireplaces
Deployed Model Importance: 1.0%
Datadrift test: K-Smirnov - pvalue: 1.00000
Datadrift model Importance: 0.2", - "Feature: Foundation
Deployed Model Importance: 0.5%
Datadrift test: Chi-Square - pvalue: 0.72182
Datadrift model Importance: 0.7", - "Feature: FullBath
Deployed Model Importance: 0.3%
Datadrift test: K-Smirnov - pvalue: 1.00000
Datadrift model Importance: 0.0", - "Feature: Functional
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.60242
Datadrift model Importance: 0.6", - "Feature: GarageArea
Deployed Model Importance: 9.0%
Datadrift test: K-Smirnov - pvalue: 0.82472
Datadrift model Importance: 2.1", - "Feature: GarageCond
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.98895
Datadrift model Importance: 0.4", - "Feature: GarageFinish
Deployed Model Importance: 0.8%
Datadrift test: Chi-Square - pvalue: 0.95863
Datadrift model Importance: 2.1", - "Feature: GarageQual
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.58646
Datadrift model Importance: 0.6", - "Feature: GarageType
Deployed Model Importance: 0.5%
Datadrift test: Chi-Square - pvalue: 0.88454
Datadrift model Importance: 0.8", - "Feature: GarageYrBlt
Deployed Model Importance: 1.9%
Datadrift test: K-Smirnov - pvalue: 0.12702
Datadrift model Importance: 2.4", - "Feature: GrLivArea
Deployed Model Importance: 11.0%
Datadrift test: K-Smirnov - pvalue: 0.63414
Datadrift model Importance: 2.1", - "Feature: HalfBath
Deployed Model Importance: 0.3%
Datadrift test: K-Smirnov - pvalue: 1.00000
Datadrift model Importance: 0.0", - "Feature: Heating
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.77724
Datadrift model Importance: 0.6", - "Feature: HeatingQC
Deployed Model Importance: 1.3%
Datadrift test: Chi-Square - pvalue: 0.27229
Datadrift model Importance: 2.8", - "Feature: HouseStyle
Deployed Model Importance: 0.4%
Datadrift test: Chi-Square - pvalue: 0.46592
Datadrift model Importance: 1.6", - "Feature: KitchenAbvGr
Deployed Model Importance: 0.0%
Datadrift test: K-Smirnov - pvalue: 1.00000
Datadrift model Importance: 0.0", - "Feature: KitchenQual
Deployed Model Importance: 1.4%
Datadrift test: Chi-Square - pvalue: 0.98477
Datadrift model Importance: 1.1", - "Feature: LandContour
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.27068
Datadrift model Importance: 2.5", - "Feature: LandSlope
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.84370
Datadrift model Importance: 1.0", - "Feature: LotArea
Deployed Model Importance: 6.5%
Datadrift test: K-Smirnov - pvalue: 0.23317
Datadrift model Importance: 3.8", - "Feature: LotConfig
Deployed Model Importance: 1.0%
Datadrift test: Chi-Square - pvalue: 0.32681
Datadrift model Importance: 0.8", - "Feature: LotShape
Deployed Model Importance: 0.1%
Datadrift test: Chi-Square - pvalue: 0.50748
Datadrift model Importance: 0.9", - "Feature: LowQualFinSF
Deployed Model Importance: 0.0%
Datadrift test: K-Smirnov - pvalue: 0.99999
Datadrift model Importance: 0.3", - "Feature: MSSubClass
Deployed Model Importance: 1.2%
Datadrift test: Chi-Square - pvalue: 0.63831
Datadrift model Importance: 1.2", - "Feature: MSZoning
Deployed Model Importance: 0.4%
Datadrift test: Chi-Square - pvalue: 0.75517
Datadrift model Importance: 1.4", - "Feature: MasVnrArea
Deployed Model Importance: 2.1%
Datadrift test: K-Smirnov - pvalue: 0.24980
Datadrift model Importance: 1.5", - "Feature: MasVnrType
Deployed Model Importance: 0.1%
Datadrift test: Chi-Square - pvalue: 0.26491
Datadrift model Importance: 1.7", - "Feature: MiscVal
Deployed Model Importance: 0.0%
Datadrift test: K-Smirnov - pvalue: 1.00000
Datadrift model Importance: 0.0", - "Feature: MoSold
Deployed Model Importance: 2.7%
Datadrift test: K-Smirnov - pvalue: 0.88478
Datadrift model Importance: 1.3", - "Feature: Neighborhood
Deployed Model Importance: 4.8%
Datadrift test: K-Smirnov - pvalue: 0.71619
Datadrift model Importance: 0.2", - "Feature: OpenPorchSF
Deployed Model Importance: 2.8%
Datadrift test: K-Smirnov - pvalue: 0.82019
Datadrift model Importance: 2.1", - "Feature: OverallCond
Deployed Model Importance: 1.9%
Datadrift test: K-Smirnov - pvalue: 0.27317
Datadrift model Importance: 2.4", - "Feature: OverallQual
Deployed Model Importance: 3.0%
Datadrift test: K-Smirnov - pvalue: 1.00000
Datadrift model Importance: 0.5", - "Feature: PavedDrive
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.78580
Datadrift model Importance: 1.0", - "Feature: PoolArea
Deployed Model Importance: 0.0%
Datadrift test: K-Smirnov - pvalue: 1.00000
Datadrift model Importance: 0.0", - "Feature: RoofMatl
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.66025
Datadrift model Importance: 0.2", - "Feature: RoofStyle
Deployed Model Importance: 0.3%
Datadrift test: Chi-Square - pvalue: 0.25208
Datadrift model Importance: 0.7", - "Feature: SaleCondition
Deployed Model Importance: 0.1%
Datadrift test: Chi-Square - pvalue: 0.02537
Datadrift model Importance: 5.6", - "Feature: SaleType
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.36016
Datadrift model Importance: 2.8", - "Feature: ScreenPorch
Deployed Model Importance: 0.0%
Datadrift test: K-Smirnov - pvalue: 0.96628
Datadrift model Importance: 1.0", - "Feature: Street
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.49945
Datadrift model Importance: 0.0", - "Feature: TotRmsAbvGrd
Deployed Model Importance: 2.1%
Datadrift test: K-Smirnov - pvalue: 0.80104
Datadrift model Importance: 0.8", - "Feature: TotalBsmtSF
Deployed Model Importance: 5.4%
Datadrift test: K-Smirnov - pvalue: 0.65902
Datadrift model Importance: 2.4", - "Feature: Utilities
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 1.00000
Datadrift model Importance: 0.0", - "Feature: WoodDeckSF
Deployed Model Importance: 3.0%
Datadrift test: K-Smirnov - pvalue: 0.57206
Datadrift model Importance: 1.7", - "Feature: YearBuilt
Deployed Model Importance: 1.2%
Datadrift test: K-Smirnov - pvalue: 0.18464
Datadrift model Importance: 1.7", - "Feature: YearRemodAdd
Deployed Model Importance: 1.9%
Datadrift test: K-Smirnov - pvalue: 0.00938
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DataDrift Test
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" + ], + "source": [ + "#Performance of data drift classifier\n", + "SD.auc" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.scatter_feature_importance()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Putting importance of the drift into perspective according to the importance of the model to be deployed, can help the data scientist to validate that his model can be deployed.
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Univariate analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An Auc close to 0.5 means that there is little drift" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importance of features in data drift" + ] + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { - "hovertemplate": "%{y:.2f}", - "legendgroup": "df_current", - "marker": { - "color": "rgba(0,154,203,255)" - }, - "mode": "lines", - 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" + ], + "source": [ + "SD.xpl.plot.features_importance()" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('YearRemodAdd')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Distribution of predicted values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This graph shows distributions of the production model outputs on both baseline and current datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We get the features with most gaps, those that are most important to analyse.\n", + "With date bias introduced, it is normal that date features are the most impacted. We will then decide to remove them.\n", + "Let's analyse other important variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature importance overview" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." + ] + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ { - "hovertemplate": "%{y:.2f}", - "legendgroup": "Baseline dataset", - "marker": { - "color": "rgba(0,154,203,255)" - }, - "mode": "lines", - "name": "Baseline dataset", - "showlegend": true, - "type": "scatter", - "x": [ - 46448.02140714701, - 47329.18982230834, - 48210.35823746966, - 49091.52665263099, - 49972.69506779232, - 50853.863482953646, - 51735.031898114976, - 52616.200313276306, - 53497.368728437636, - 54378.53714359896, - 55259.70555876029, - 56140.87397392162, - 57022.04238908294, - 57903.21080424427, - 58784.3792194056, - 59665.54763456692, - 60546.71604972825, - 61427.88446488958, - 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Deployed Model Importance: 0.0%
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Datadrift model Importance: 2.8", + "Feature: ScreenPorch
Deployed Model Importance: 0.0%
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Deployed Model Importance: 0.0%
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Datadrift model Importance: 0.0", + "Feature: TotRmsAbvGrd
Deployed Model Importance: 2.1%
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Datadrift model Importance: 0.8", + "Feature: TotalBsmtSF
Deployed Model Importance: 5.4%
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Deployed Model Importance: 0.0%
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Deployed Model Importance: 3.0%
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Deployed Model Importance: 1.2%
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Datadrift model Importance: 1.7", + "Feature: YearRemodAdd
Deployed Model Importance: 1.9%
Datadrift test: K-Smirnov - pvalue: 0.00938
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DataDrift Test
Pvalue" + } + }, + "colorscale": [ + [ + 0, + "rgb(217, 93, 26)" + ], + [ + 1e-06, + "rgb(245, 104, 33)" + ], + [ + 0.0001, + "rgb(245, 127, 67)" + ], + [ + 0.001, + "rgb(242, 153, 90)" + ], + [ + 0.05, + "rgb(240, 195, 162)" + ], + [ + 0.1, + "rgb(161, 221, 254)" + ], + [ + 0.2, + "rgb(103, 208, 255)" + ], + [ + 1, + "rgb(0, 154, 203)" + ] + ] + }, + "height": 600, + "hovermode": "closest", + "template": { + "data": { + "scatter": [ + { + "type": "scatter" + } + ] + } + }, + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial", + "size": 24 + }, + "text": "Datadrift Vs Feature Importance", + "x": 0.5, + "xanchor": "center", + "y": 0.9, + "yanchor": "middle" + }, + "width": 900, + "xaxis": { + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial Black", + "size": 16 + }, + "text": "Datadrift Importance" + } + }, + "yaxis": { + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial Black", + "size": 16 + }, + "text": "Feature Importance - Deployed Model" + } + } + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.scatter_feature_importance()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Putting importance of the drift into perspective according to the importance of the model to be deployed, can help the data scientist to validate that his model can be deployed.
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Univariate analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ { - "hovertemplate": "%{y:.2f}", - "legendgroup": "Current dataset", - "marker": { - "color": "rgba(223, 103, 0, 0.8)" - }, - "mode": "lines", - "name": "Current dataset", - "showlegend": true, - "type": "scatter", - "x": [ - 74854.1516603411, - 75672.97081591707, - 76491.78997149304, - 77310.60912706901, - 78129.42828264498, - 78948.24743822095, - 79767.06659379692, - 80585.88574937289, - 81404.70490494886, - 82223.52406052483, - 83042.3432161008, - 83861.16237167676, - 84679.98152725275, - 85498.80068282872, - 86317.61983840469, - 87136.43899398066, - 87955.25814955663, - 88774.0773051326, - 89592.89646070857, - 90411.71561628453, - 91230.5347718605, - 92049.35392743647, - 92868.17308301244, - 93686.99223858841, - 94505.81139416438, - 95324.63054974035, - 96143.44970531632, - 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" + ], + "source": [ + "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\")" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compile Drift over years" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2008" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "house_df_2008 = house_df.loc[house_df['YrSold'] == 2008]\n", - "\n", - "y_df_2008=house_df_2008['SalePrice'].to_frame()\n", - "X_df_2008=house_df_2008[house_df_2008.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2008, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:The variable Condition1\n", - " has mismatching possible values: \n", - "\n", - " [\"Within 200' of East-West Railroad\"] []\n", - "INFO:root:The variable Condition2\n", - " has mismatching possible values: \n", - "\n", - " ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to postive off-site feature', 'Near positive off-site feature--park, greenbelt, etc.'] []\n", - "INFO:root:The variable Electrical\n", - " has mismatching possible values: \n", - "\n", - " ['Mixed'] []\n", - "INFO:root:The variable ExterCond\n", - " has mismatching possible values: \n", - "\n", - " ['Excellent'] []\n", - "INFO:root:The variable ExterQual\n", - " has mismatching possible values: \n", - "\n", - " ['Fair'] []\n", - "INFO:root:The variable Exterior1st\n", - " has mismatching possible values: \n", - "\n", - 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" + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric()" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "------" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2009" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "house_df_2009 = house_df.loc[house_df['YrSold'] == 2009]\n", - "\n", - "y_df_2009=house_df_2009['SalePrice'].to_frame()\n", - "X_df_2009=house_df_2009[house_df_2009.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2009, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:The variable BsmtCond\n", - " has mismatching possible values: \n", - "\n", - " ['Poor -Severe cracking, settling, or wetness'] []\n", - "INFO:root:The variable Condition1\n", - " has mismatching possible values: \n", - "\n", - " [] ['Adjacent to East-West Railroad']\n", - "INFO:root:The variable Condition2\n", - " has mismatching possible values: \n", - "\n", - " ['Adjacent to arterial street'] []\n", - "INFO:root:The variable Electrical\n", - " has mismatching possible values: \n", - "\n", - " [] ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "INFO:root:The variable ExterCond\n", - " has mismatching possible values: \n", - "\n", - " ['Excellent'] []\n", - "INFO:root:The variable ExterQual\n", - " has mismatching possible values: \n", - "\n", - " ['Fair'] []\n", - "INFO:root:The variable Exterior1st\n", - " has mismatching possible values: \n", - "\n", - " ['Brick Common', 'Cinder Block'] ['Stone', 'Imitation Stucco']\n", - "INFO:root:The variable Exterior2nd\n", - " has mismatching possible values: \n", - "\n", - " ['Brick Common', 'Cinder Block'] ['Other']\n", - "INFO:root:The variable Functional\n", - " has mismatching possible values: \n", - "\n", - " ['Major Deductions 2'] []\n", - "INFO:root:The variable GarageCond\n", - " has mismatching possible values: \n", - "\n", - " ['Excellent'] ['Good']\n", - "INFO:root:The variable GarageQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Poor']\n", - "INFO:root:The variable GarageType\n", - " has mismatching possible values: \n", - "\n", - " [] ['More than one type of garage']\n", - "INFO:root:The variable LotConfig\n", - " has mismatching possible values: \n", - "\n", - " [] ['Frontage on 3 sides of property']\n", - "INFO:root:The variable MSSubClass\n", - " has mismatching possible values: \n", - "\n", - " ['1-Story w/Finished Attic All Ages'] []\n", - "INFO:root:The variable Neighborhood\n", - " has mismatching possible values: \n", - "\n", - " ['Northpark Villa', 'Bluestem'] ['Veenker']\n", - "INFO:root:The variable RoofMatl\n", - " has mismatching possible values: \n", - "\n", - " [] ['Metal', 'Wood Shakes']\n", - "INFO:root:The variable RoofStyle\n", - " has mismatching possible values: \n", - "\n", - " ['Mansard'] []\n", - "INFO:root:The variable SaleCondition\n", - " has mismatching possible values: \n", - "\n", - " [] ['Adjoining Land Purchase']\n", - "INFO:root:The variable SaleType\n", - " has mismatching possible values: \n", - "\n", - " [] ['Other']\n", - "INFO:root:The variable Utilities\n", - " has mismatching possible values: \n", - "\n", - " ['Electricity and Gas Only'] []\n", - "INFO:root:\n", - " The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.5405695039804042\n", - " \n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "------" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True,\n", - " date_compile_auc = '01/01/2009', #optionnal, by default date of compile\n", - " datadrift_file = \"house_price_auc.csv\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2009" + ] + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "house_df_2009 = house_df.loc[house_df['YrSold'] == 2009]\n", + "\n", + "y_df_2009=house_df_2009['SalePrice'].to_frame()\n", + "X_df_2009=house_df_2009[house_df_2009.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2009, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ { - "hovertemplate": "date=%{x}
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" + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric()" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2010" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "house_df_2010 = house_df.loc[house_df['YrSold'] == 2010]\n", - "\n", - "y_df_2010=house_df_2010['SalePrice'].to_frame()\n", - "X_df_2010=house_df_2010[house_df_2010.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2010, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:The variable Condition1\n", - " has mismatching possible values: \n", - "\n", - " [\"Within 200' of East-West Railroad\"] []\n", - "INFO:root:The variable Electrical\n", - " has mismatching possible values: \n", - "\n", - " [] ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "INFO:root:The variable ExterCond\n", - " has mismatching possible values: \n", - "\n", - " ['Poor'] []\n", - "INFO:root:The variable ExterQual\n", - " has mismatching possible values: \n", - "\n", - " ['Fair'] []\n", - "INFO:root:The variable Exterior1st\n", - " has mismatching possible values: \n", - "\n", - " ['Asphalt Shingles'] ['Stone', 'Imitation Stucco']\n", - "INFO:root:The variable Exterior2nd\n", - " has mismatching possible values: \n", - "\n", - " ['Asphalt Shingles', 'Brick Common'] ['Other', 'Stone']\n", - "INFO:root:The variable Functional\n", - " has mismatching possible values: \n", - "\n", - " [] ['Major Deductions 1']\n", - "INFO:root:The variable GarageCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Poor', 'Good']\n", - "INFO:root:The variable GarageQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Good', 'Excellent', 'Poor']\n", - "INFO:root:The variable GarageType\n", - " has mismatching possible values: \n", - "\n", - " [] ['More than one type of garage']\n", - "INFO:root:The variable Heating\n", - " has mismatching possible values: \n", - "\n", - " [] ['Gas hot water or steam heat', 'Wall furnace']\n", - "INFO:root:The variable HouseStyle\n", - " has mismatching possible values: \n", - "\n", - " [] ['Two and one-half story: 2nd level finished', 'One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished']\n", - "INFO:root:The variable LotConfig\n", - " has mismatching possible values: \n", - "\n", - " [] ['Frontage on 3 sides of property']\n", - "INFO:root:The variable LotShape\n", - " has mismatching possible values: \n", - "\n", - " [] ['Irregular']\n", - "INFO:root:The variable MSSubClass\n", - " has mismatching possible values: \n", - "\n", - " ['1-Story w/Finished Attic All Ages'] ['2-1/2 Story All Ages', '1-1/2 Story - Unfinished All Ages']\n", - "INFO:root:The variable MSZoning\n", - " has mismatching possible values: \n", - "\n", - " [] ['Residential High Density']\n", - "INFO:root:The variable Neighborhood\n", - " has mismatching possible values: \n", - "\n", - " ['Northpark Villa'] ['Veenker']\n", - "INFO:root:The variable RoofMatl\n", - " has mismatching possible values: \n", - "\n", - " [] ['Wood Shingles', 'Metal', 'Gravel & Tar']\n", - "INFO:root:The variable RoofStyle\n", - " has mismatching possible values: \n", - "\n", - " ['Mansard', 'Shed'] ['Flat']\n", - "INFO:root:The variable SaleCondition\n", - " has mismatching possible values: \n", - "\n", - " [] ['Adjoining Land Purchase']\n", - "INFO:root:The variable SaleType\n", - " has mismatching possible values: \n", - "\n", - " ['Contract 15% Down payment regular terms'] ['Contract Low Down', 'Contract Low Down payment and low interest', 'Other']\n", - "INFO:root:The variable Street\n", - " has mismatching possible values: \n", - "\n", - " ['Gravel'] []\n", - "INFO:root:\n", - " The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6978632478632478\n", - " \n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2010" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True,\n", - " date_compile_auc = '01/01/2010', #optionnal, by default date of compile\n", - " datadrift_file = \"house_price_auc.csv\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "house_df_2010 = house_df.loc[house_df['YrSold'] == 2010]\n", + "\n", + "y_df_2010=house_df_2010['SalePrice'].to_frame()\n", + "X_df_2010=house_df_2010[house_df_2010.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2010, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" + ] + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ { - "hovertemplate": "date=%{x}
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Unfinished All Ages']\n", + "INFO:root:The variable MSZoning\n", + " has mismatching possible values: \n", + "\n", + " [] ['Residential High Density']\n", + "INFO:root:The variable Neighborhood\n", + " has mismatching possible values: \n", + "\n", + " ['Northpark Villa'] ['Veenker']\n", + "INFO:root:The variable RoofMatl\n", + " has mismatching possible values: \n", + "\n", + " [] ['Wood Shingles', 'Metal', 'Gravel & Tar']\n", + "INFO:root:The variable RoofStyle\n", + " has mismatching possible values: \n", + "\n", + " ['Mansard', 'Shed'] ['Flat']\n", + "INFO:root:The variable SaleCondition\n", + " has mismatching possible values: \n", + "\n", + " [] ['Adjoining Land Purchase']\n", + "INFO:root:The variable SaleType\n", + " has mismatching possible values: \n", + "\n", + " ['Contract 15% Down payment regular terms'] ['Contract Low Down', 'Contract Low Down payment and low interest', 'Other']\n", + "INFO:root:The variable Street\n", + " has mismatching possible values: \n", + "\n", + " ['Gravel'] []\n", + "INFO:root:\n", + " The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6978632478632478\n", + " \n" ] - 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" + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric()" ] - }, - "metadata": {}, - "output_type": "display_data" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "eurybia_datapane", + "language": "python", + "name": "eurybia_datapane" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "336px" + }, + "toc_section_display": true, + "toc_window_display": true } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "eurybia_datapane", - "language": "python", - "name": "eurybia_datapane" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "336px" - }, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/source/tutorials/data_drift/tutorial02-datadrift-high-datadrift.ipynb b/docs/source/tutorials/data_drift/tutorial02-datadrift-high-datadrift.ipynb index 22149e5..59c1992 100644 --- a/docs/source/tutorials/data_drift/tutorial02-datadrift-high-datadrift.ipynb +++ b/docs/source/tutorials/data_drift/tutorial02-datadrift-high-datadrift.ipynb @@ -1,1292 +1,1292 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "aa841847", - "metadata": {}, - "source": [ - "# Detect High Data Drift \n", - "With this tutorial you:
\n", - "Understand how to use Eurybia to detect datadrift\n", - "\n", - "Contents:\n", - "- Detect data drift \n", - "- Compile Drift over years\n", - "\n", - "This public dataset comes from :\n", - "\n", - "https://www.kaggle.com/sobhanmoosavi/us-accidents/version/10\n", - "\n", - "---\n", - "Acknowledgements\n", - "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. “A Countrywide Traffic Accident Dataset.”, 2019.\n", - "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. \"Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights.\" In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.\n", - "---\n", - "\n", - "In this tutorial, the data are not loaded raw, a data preparation to facilitate the use of the tutorial has been done. You can find it here : \n", - "https://github.com/MAIF/eurybia/blob/master/eurybia/data/dataprep_US_car_accidents.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "77dc51f7", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from category_encoders import OrdinalEncoder\n", - "import catboost\n", - "from eurybia import SmartDrift\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn import metrics\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "id": "ccd3e315", - "metadata": {}, - "source": [ - "## Import Dataset and split in training and production dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "66cf30ab", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "2fdb7449", - "metadata": {}, - "outputs": [], - "source": [ - "df_car_accident = data_loading(\"us_car_accident\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2373fdc0", - "metadata": {}, - "outputs": [ + "cells": [ + { + "cell_type": "markdown", + "id": "aa841847", + "metadata": {}, + "source": [ + "# Detect High Data Drift \n", + "With this tutorial you:
\n", + "Understand how to use Eurybia to detect datadrift\n", + "\n", + "Contents:\n", + "- Detect data drift \n", + "- Compile Drift over years\n", + "\n", + "This public dataset comes from :\n", + "\n", + "https://www.kaggle.com/sobhanmoosavi/us-accidents/version/10\n", + "\n", + "---\n", + "Acknowledgements\n", + "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. \u201cA Countrywide Traffic Accident Dataset.\u201d, 2019.\n", + "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. \"Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights.\" In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.\n", + "---\n", + "\n", + "In this tutorial, the data are not loaded raw, a data preparation to facilitate the use of the tutorial has been done. You can find it here : \n", + "https://github.com/MAIF/eurybia/blob/master/eurybia/data/dataprep_US_car_accidents.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "77dc51f7", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from category_encoders import OrdinalEncoder\n", + "import catboost\n", + "from eurybia import SmartDrift\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import metrics\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "ccd3e315", + "metadata": {}, + "source": [ + "## Import Dataset and split in training and production dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "66cf30ab", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading" + ] + }, { - "data": { - "text/html": [ - "
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_accNautical_Twilightseason_acctargettarget_multiyear_accDescription
033.0-117.10.040.093.02.03Daywinter022019At Carmel Mountain Rd - Accident.
129.5-98.50.083.065.010.04Daysummer132017At TX-345-SP/Woodlawn Ave/Exit 567B - Accident.
232.7-96.80.088.057.010.00Nightsummer022021Incident on RUGGED DR near BERKLEY AVE Expect ...
340.0-76.30.061.058.010.04Dayspring022020At PA-741/Rohrerstown Rd - Accident.
441.5-81.81.071.053.010.00Daysummer022020At 117th St/Exit 166 - Accident.
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_accNautical_Twilightseason_acctargettarget_multiyear_accDescription
033.0-117.10.040.093.02.03Daywinter022019At Carmel Mountain Rd - Accident.
129.5-98.50.083.065.010.04Daysummer132017At TX-345-SP/Woodlawn Ave/Exit 567B - Accident.
232.7-96.80.088.057.010.00Nightsummer022021Incident on RUGGED DR near BERKLEY AVE Expect ...
340.0-76.30.061.058.010.04Dayspring022020At PA-741/Rohrerstown Rd - Accident.
441.5-81.81.071.053.010.00Daysummer022020At 117th St/Exit 166 - Accident.
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target01
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" + ], + "text/plain": [ + "target 0 1\n", + "year_acc \n", + "2016 71.406287 28.593713\n", + "2017 67.254620 32.745380\n", + "2018 66.634662 33.365338\n", + "2019 79.551182 20.448818\n", + "2020 89.944804 10.055196\n", + "2021 98.259930 1.740070" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "target 0 1\n", - "year_acc \n", - "2016 71.406287 28.593713\n", - "2017 67.254620 32.745380\n", - "2018 66.634662 33.365338\n", - "2019 79.551182 20.448818\n", - "2020 89.944804 10.055196\n", - "2021 98.259930 1.740070" + "source": [ + "#We will train a classification model to predict the severity of an accident. 0 for a less severe accident and 1 for a severe accident.\n", + "#Let's check percentage in class 0 and 1\n", + "pd.crosstab(df_car_accident.year_acc, df_car_accident.target, normalize = 'index')*100" ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#We will train a classification model to predict the severity of an accident. 0 for a less severe accident and 1 for a severe accident.\n", - "#Let's check percentage in class 0 and 1\n", - "pd.crosstab(df_car_accident.year_acc, df_car_accident.target, normalize = 'index')*100" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c957b0e6", - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=df_accident_baseline['target'].to_frame()\n", - "X_df_learning=df_accident_baseline[df_accident_baseline.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2017=df_accident_2017['target'].to_frame()\n", - "X_df_2017=df_accident_2017[df_accident_2017.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2018=df_accident_2018['target'].to_frame()\n", - "X_df_2018=df_accident_2018[df_accident_2018.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2019=df_accident_2019['target'].to_frame()\n", - "X_df_2019=df_accident_2019[df_accident_2019.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2020=df_accident_2020['target'].to_frame()\n", - "X_df_2020=df_accident_2020[df_accident_2020.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2021=df_accident_2021['target'].to_frame()\n", - "X_df_2021=df_accident_2021[df_accident_2021.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]" - ] - }, - { - "cell_type": "markdown", - "id": "3147cd9a", - "metadata": {}, - "source": [ - "## Building Supervized Model" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2cf9d37a", - "metadata": {}, - "outputs": [], - "source": [ - "features = ['Start_Lat', 'Start_Lng', 'Distance(mi)', 'Temperature(F)',\n", - " 'Humidity(%)', 'Visibility(mi)', 'day_of_week_acc', 'Nautical_Twilight',\n", - " 'season_acc']" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e869af2a", - "metadata": {}, - "outputs": [], - "source": [ - "features_to_encode = [col for col in X_df_learning[features].columns if X_df_learning[col].dtype not in ('float64','int64')]\n", - "\n", - "encoder = OrdinalEncoder(cols=features_to_encode)\n", - "encoder = encoder.fit(X_df_learning[features])\n", - "\n", - "X_df_learning_encoded=encoder.transform(X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "0660e57f", - "metadata": {}, - "outputs": [], - "source": [ - "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "9504b5a8", - "metadata": {}, - "outputs": [], - "source": [ - "train_pool_cat = catboost.Pool(data=Xtrain, label= ytrain, cat_features = features_to_encode)\n", - "test_pool_cat = catboost.Pool(data=Xtest, label= ytest, cat_features = features_to_encode)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "265f74bc", - "metadata": {}, - "outputs": [ + }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a12b4348b3774bdeae49bd6f24a54188", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))" + "cell_type": "code", + "execution_count": 10, + "id": "c957b0e6", + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=df_accident_baseline['target'].to_frame()\n", + "X_df_learning=df_accident_baseline[df_accident_baseline.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2017=df_accident_2017['target'].to_frame()\n", + "X_df_2017=df_accident_2017[df_accident_2017.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2018=df_accident_2018['target'].to_frame()\n", + "X_df_2018=df_accident_2018[df_accident_2018.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2019=df_accident_2019['target'].to_frame()\n", + "X_df_2019=df_accident_2019[df_accident_2019.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2020=df_accident_2020['target'].to_frame()\n", + "X_df_2020=df_accident_2020[df_accident_2020.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2021=df_accident_2021['target'].to_frame()\n", + "X_df_2021=df_accident_2021[df_accident_2021.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]" ] - 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"146:\tlearn: 0.4976199\ttest: 0.5040287\tbest: 0.5036157 (140)\ttotal: 5.88s\tremaining: 120ms\n", - "147:\tlearn: 0.4974101\ttest: 0.5041713\tbest: 0.5036157 (140)\ttotal: 5.91s\tremaining: 79.9ms\n", - "148:\tlearn: 0.4971250\ttest: 0.5041060\tbest: 0.5036157 (140)\ttotal: 5.93s\tremaining: 39.8ms\n", - "149:\tlearn: 0.4966463\ttest: 0.5039708\tbest: 0.5036157 (140)\ttotal: 5.96s\tremaining: 0us\n", - "\n", - "bestTest = 0.5036156698\n", - "bestIteration = 140\n", - "\n", - "Shrink model to first 141 iterations.\n" - ] - } - ], - "source": [ - "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", - " learning_rate=0.143852,\n", - " iterations=150,\n", - " l2_leaf_reg=15,\n", - " max_depth = 4,\n", - " use_best_model=True,\n", - " custom_loss=['Accuracy', 'AUC', 'Logloss'])\n", - "\n", - "model = model.fit(train_pool_cat, plot=True,eval_set=test_pool_cat)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "40eb9cb3", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "3147cd9a", + "metadata": {}, + "source": [ + "## Building Supervized Model" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7634385095163502\n" - ] - } - ], - "source": [ - "proba = model.predict_proba(Xtest)\n", - "print(metrics.roc_auc_score(ytest,proba[:,1]))" - ] - }, - { - "cell_type": "markdown", - "id": "f3957436", - "metadata": {}, - "source": [ - "## Use Eurybia for data validation" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "6f739f9d", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "1e9122e7", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2017, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "8367b447", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 11, + "id": "2cf9d37a", + "metadata": {}, + "outputs": [], + "source": [ + "features = ['Start_Lat', 'Start_Lng', 'Distance(mi)', 'Temperature(F)',\n", + " 'Humidity(%)', 'Visibility(mi)', 'day_of_week_acc', 'Nautical_Twilight',\n", + " 'season_acc']" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6585689489728102\n", - "CPU times: total: 1min 16s\n", - "Wall time: 24.3 s\n" - ] - } - ], - "source": [ - "%time SD.compile(full_validation=True, date_compile_auc = '01/01/2017', datadrift_file = \"car_accident_auc.csv\")\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "86ffdccf", - "metadata": {}, - "source": [ - "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", - "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", - "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "01233de8", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 12, + "id": "e869af2a", + "metadata": {}, + "outputs": [], + "source": [ + "features_to_encode = [col for col in X_df_learning[features].columns if X_df_learning[col].dtype not in ('float64','int64')]\n", + "\n", + "encoder = OrdinalEncoder(cols=features_to_encode)\n", + "encoder = encoder.fit(X_df_learning[features])\n", + "\n", + "X_df_learning_encoded=encoder.transform(X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0660e57f", + "metadata": {}, + "outputs": [], + "source": [ + "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9504b5a8", + "metadata": {}, + "outputs": [], + "source": [ + "train_pool_cat = catboost.Pool(data=Xtrain, label= ytrain, cat_features = features_to_encode)\n", + "test_pool_cat = catboost.Pool(data=Xtest, label= ytest, cat_features = features_to_encode)" + ] + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_car_accident_datadrift_2017.html. 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iterations=150,\n", + " l2_leaf_reg=15,\n", + " max_depth = 4,\n", + " use_best_model=True,\n", + " custom_loss=['Accuracy', 'AUC', 'Logloss'])\n", + "\n", + "model = model.fit(train_pool_cat, plot=True,eval_set=test_pool_cat)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_car_accident_datadrift_2017.html', \n", - " title_story=\"Data drift\",\n", - " title_description=\"\"\"Car accident Data drift 2017\"\"\",\n", - " project_info_file=\"../../../../eurybia/data/project_info_car_accident.yml\" \n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "362b134f", - "metadata": {}, - "source": [ - "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", - "However, to illustrate functionalities, we will detail results with notebook mode analysis." - ] - }, - { - "cell_type": "markdown", - "id": "088b4f61", - "metadata": {}, - "source": [ - "## First Analysis of results of the data drift" - ] - }, - { - "cell_type": "markdown", - "id": "327cdf45", - "metadata": {}, - "source": [ - "Data driftn methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", - "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", - "Then a classification model (catboost) is learned to predict this target.\n", - "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" - ] - }, - { - "cell_type": "markdown", - "id": "664a090a", - "metadata": {}, - "source": [ - "### Detection data drift performance" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "8d53819b", - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "0.6585689489728102" + "cell_type": "code", + "execution_count": 16, + "id": "40eb9cb3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7634385095163502\n" + ] + } + ], + "source": [ + "proba = model.predict_proba(Xtest)\n", + "print(metrics.roc_auc_score(ytest,proba[:,1]))" ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Performance of data drift classifier\n", - "SD.auc" - ] - }, - { - "cell_type": "markdown", - "id": "335aa0c3", - "metadata": {}, - "source": [ - "An Auc close to 0.5 means that there is little drift" - ] - }, - { - "cell_type": "markdown", - "id": "6738e258", - "metadata": {}, - "source": [ - "### Importance of features in data drift" - ] - }, - { - "cell_type": "markdown", - "id": "2f4d21ff", - "metadata": {}, - "source": [ - "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "5271a9c4", - "metadata": {}, - "outputs": [ + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "markdown", - "id": "e419d134", - "metadata": {}, - "source": [ - "We get the features with most gaps, those that are most important to analyse.\n", - "With date bias introduced, it is normal that date features are the most impacted. We will then decide to remove them.\n", - "Let's analyse other important variables" - ] - }, - { - "cell_type": "markdown", - "id": "9b746cf7", - "metadata": {}, - "source": [ - "### Feature importance overview" - ] - }, - { - "cell_type": "markdown", - "id": "56bf2287", - "metadata": {}, - "source": [ - "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "5e24ae92", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "f3957436", + "metadata": {}, + "source": [ + "## Use Eurybia for data validation" + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.scatter_feature_importance()" - ] - }, - { - "cell_type": "markdown", - "id": "8b02726b", - "metadata": {}, - "source": [ - "Putting importance of the drift into perspective according to the importance of the model to be deployed, can help the data scientist to validate that his model can be deployed.
\n" - ] - }, - { - "cell_type": "markdown", - "id": "61392c46", - "metadata": {}, - "source": [ - "### Univariate analysis" - ] - }, - { - "cell_type": "markdown", - "id": "9081041c", - "metadata": {}, - "source": [ - "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "1d38878c", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 17, + "id": "6f739f9d", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('season_acc')" - ] - }, - { - "cell_type": "markdown", - "id": "f9ffecb5", - "metadata": {}, - "source": [ - "### Distribution of predicted values" - ] - }, - { - "cell_type": "markdown", - "id": "b32be6ad", - "metadata": {}, - "source": [ - "This graph shows distributions of the production model outputs on both baseline and current datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "b5d31bc1", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 18, + "id": "1e9122e7", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2017, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\")" - ] - }, - { - "cell_type": "markdown", - "id": "0190d633", - "metadata": {}, - "source": [ - "## Compile Drift over years" - ] - }, - { - "cell_type": "markdown", - "id": "64962082", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2018" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "e07c46d4", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2018, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "e359a75c", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 19, + "id": "8367b447", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6585689489728102\n", + "CPU times: total: 1min 16s\n", + "Wall time: 24.3 s\n" + ] + } + ], + "source": [ + "%time SD.compile(full_validation=True, date_compile_auc = '01/01/2017', datadrift_file = \"car_accident_auc.csv\")\n", + " " + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7036329129677259\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True,\n", - " date_compile_auc = '01/01/2018', #optionnal, by default date of compile\n", - " datadrift_file = \"car_accident_auc.csv\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "feda5ce2", - "metadata": {}, - "source": [ - "----" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "d94cc30b", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "86ffdccf", + "metadata": {}, + "source": [ + "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", + "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", + "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric()" - ] - }, - { - "cell_type": "markdown", - "id": "bed0a0d5", - "metadata": {}, - "source": [ - "------" - ] - }, - { - "cell_type": "markdown", - "id": "6b0aefd4", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2019" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "1d18e162", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2019, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "06d918ad", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 20, + "id": "01233de8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_car_accident_datadrift_2017.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_car_accident_datadrift_2017.html', \n", + " title_story=\"Data drift\",\n", + " title_description=\"\"\"Car accident Data drift 2017\"\"\",\n", + " project_info_file=\"../../../../eurybia/data/project_info_car_accident.yml\" \n", + " )" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7856527709300022\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True,\n", - " date_compile_auc = '01/01/2019', #optionnal, by default date of compile\n", - " datadrift_file = \"car_accident_auc.csv\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "d3fc185d", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "362b134f", + "metadata": {}, + "source": [ + "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", + "However, to illustrate functionalities, we will detail results with notebook mode analysis." + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric()" - ] - }, - { - "cell_type": "markdown", - "id": "f0f5b5f4", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2020" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "4c11bc6f", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2020, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "4c3e4f9e", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "088b4f61", + "metadata": {}, + "source": [ + "## First Analysis of results of the data drift" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7902450838961592\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True,\n", - " date_compile_auc = '01/01/2020', #optionnal, by default date of compile\n", - " datadrift_file = \"car_accident_auc.csv\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "a1ccc557", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "327cdf45", + "metadata": {}, + "source": [ + "Data driftn methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", + "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", + "Then a classification model (catboost) is learned to predict this target.\n", + "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" + ] + }, { - "data": { - "image/png": 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L6XYZ1UPQv/KVr0iqd+u8x0XkGwi1zfm013YgzGWXUa9FrktGtd1r1qxxNo057bTTOuyO6t3N1LvLqLt00r8DrTvm7s6x//73vwPNEOZbX7ovBfe8zD/84Q8yd+5cueyyy9pdmq59BELDby4URwABBIogQCAsAjK3QACBaAvYCoSqpGfH6Tl4eti7ntWW6oy9fM71SxcI3Zkz3cDkmmuucc7m0/Cpxxb8z//8j7ObqX7SBUL/TpqFOIcw3TLcXDaV0T4U6xxCfR70OBBdAjphwgRnp1ed6dV3+vR8SD2Kwn8OoTsO27dvd47RuOiii5xx0ACpx4foM/Hoo486Y5FqhlA3INJzKocNG+Zck299mb5SdWx11lOfTW2jLmvVNuqspv7y4r777uvQvkIFQn9/o/0dht4hgAAChRUgEBbWl9oRQCAGArkeO6Ek6WaC3B/kdcZFDyjXH7x79uzZQbG5udkJBs8995yz2Yi+Z6aHtOuf62Yko0aNcoKHe0ZhukCo1+oSRw1/+qmqqnKOBtB26NECH/jAB5xjL/yB0P1B3723vquoO32m2yQk1/ZqW2wuGdX6tK+33367cxyH/rsesK7n4qm1hhrtiwZcPSLBnZHNZ4ZQ77Vp0ybn3Mh//OMfyfHRENja2uoY6/0GDx6c3GVU23PrrbfKz372M2cc3LZt3brVKTN06FB5z3ve4/zCwHsMh/fZ04Ps9TmYPn26U3c+9WX6cvW+l+j66TEU+qzop7Ky0tkRtZC7jKbrrxuEY/Dthi4igAAC1gUIhNZJqRABBOImYDMQepfm6REIOsOU7qOzeDoLdc8998jzzz/vXPbud79b9OgKnWHSgON+0gVCNyjp7NMvfvELp56ysjLneIHPfe5z8vrrrzvLVv2BUJdgani56667nNkud/MaXS6Z6nq9Ty7tLUQg1Do11Og5gBqs9BB4DWYapHQJpPZh0KBB7bjzDYRuf/UohjvvvNM5sF3v86lPfcq5l4ZF/XiPndCxf+CBB+RXv/qVMw66sc6pp57qLB8eO3as6FmAuhS1urq63VJUDdsLFy50jqXQoOaeZZlvfdlCofrpLxH0mdFZTw2q+u6rhkH9BUYhA6G2LV1/4/Z9h/4igAACtgQIhLYkqQcBBBBAAAEEEEAAAQQQCJkAgTBkA0ZzEUAAAQQQQAABBBBAAAFbAgRCW5LUgwACCCCAAAIIIIAAAgiETIBAGLIBo7kIIIAAAggggAACCCCAgC0BAqEtSepBAAEEEEAAAQQQQAABBEImQCAM2YDRXAQQQAABBBBAAAEEEEDAlgCB0JYk9SCAAAIIIIAAAggggAACIRMgEIZswGguAggggAACCCCAAAIIIGBLgEBoS5J6EEAAAQQQQAABBBBAAIGQCRAIQzZgNBcBBBBAAAEEEEAAAQQQsCVAILQlST0IIIAAAggggAACCCCAQMgECIQhGzCaiwACCCCAAAIIIIAAAgjYEiAQ2pKkHgQQQAABBBBAAAEEEEAgZAIEwpANGM1FAAEEEEAAAQQQQAABBGwJEAhtSVIPAggggAACCCCAAAIIIBAyAQJhyAaM5iKAAAIIIIAAAggggAACtgQIhLYkqQcBBBBAAAEEEEAAAQQQCJkAgTBkA0ZzEUAAAQQQQAABBBBAAAFbAgRCW5LUgwACCCCAAAIIIIAAAgiETIBAGLIBo7kIIIAAAggggAACCCCAgC0BAqEtSepBAAEEEEAAAQQQQAABBEImQCAM2YDRXAQQQAABBBBAAAEEEEDAlgCB0JYk9SCAAAIIIIAAAggggAACIRMgEIZswGguAggggAACCCCAAAIIIGBLgEBoS5J6EEAAAQQQQAABBBBAAIGQCRAIQzZgNBcBBBBAAAEEEEAAAQQQsCVAILQlST0IIIAAAggggAACCCCAQMgECISeAVu2bJk0NzfLtGnTQjaMNBcBBBBAAAEEsgmsXr1aJk2a5Fw2YsQIWbRokZSXl6cstmXLFqmvr5c1a9YEuj7bvfl7BBBAoFQFCIQi4v0/CP3mTyAs1ceVdiGAAAIIIJCfQFNTk8yYMUPmz58v1dXVor8EXrlypcyZM0fKyso6VKo/G6xfv17Gjx/v/F226/NrFaUQQACBzhcgEHrGgBnCzn8gaQECCCCAAAKFEPD/f7w/IGa7pwbEBQsWZJxVzFYHf48AAgiUogCBkEBYis8lbUIAAQQQQMCqwMKFC5363FVA7pLQ6dOnS01NTdZ7afkNGzaknVHMWgEXIIAAAiUqQCAMGAiXLFnSYQivuOKKEh1WmoUAAggggEC8BH7/+9/L9u3b23X68ssvl969ezt/poGuqqoquQQ0aCDUmcVZs2ZlfecwXtr0FgEEoiRAIAwYCBcvXtxh3K+77jppbGyM0vNAXxBAAAEEEAidwGWXXeYEvW7dunUIhH369EkGQv2XfGcIWTIauseCBiOAQEABAmHAQJjKc/jw4QTCgA8alyGAAAIIIFAoAQ2EP/nJT2TYsGFpb2H6DqHOKOry0pkzZzqb0vBBAAEEoiJAICQQRuVZph8IIIAAAjEVCBIIs+0yqn9fV1fnLC3Vdwo1QA4ePDj5fqH+9x133MGmMjF9xug2AlEWIBD6jp1wB3vp0qVZXzJnhjDKXxr0DQEEEEAgLAJBAqH2JdM5hP5A6P53a2urw5Dt3MKwWNFOBBBAwC9AIDR4JgiEBngURQABBBBAwJJA0EBo6XZUgwACCERKgEBoMJwEQgM8iiKAAAIIIGBJgEBoCZJqEEAglgIEQoNhJxAa4FEUAQQQQAABSwIEQkuQVIMAArEUIBAaDDuB0ACPoggggAACCFgSIBBagqQaBBCIpQCB0GDYCYQGeBRFAAEEEEDAkgCB0BIk1SCAQCwFCIQGw04gNMCjKAIIIIAAApYECISWIDu5Gj3rsb6+3jnvUY/+8H/a2tpk9uzZMnr0aBk/frzz13pMyKJFi4x3gfUfS2KTwl+324/ly5dLbW2t9O/fX7p06SLTpk2zeVvqQiCwAIEwMFXHCwmEBngURQABBBBAwJIAgdAOpJ61OGvWrHaVaUALGlRMQ1WugVCPEVmwYEHybEiT+5uUzaaf7QxMDbX6Ceqc7X7u37vj6T9KTf985cqVMmfOHCkrK3MuT2XvDa5unbk8D0HbyXWdL0AgNBgDAqEBHkURQAABBBCwJBCnQLi9bb/c96/X5ZWtbfLOit7yvneeYElRxB8U3ECgN/CGh3Q3NA1V2QKh/77+9prc36RsrgNQqADobYeO3bx585w/6tWrV7uwGSQQumMxcuTIdmVvueUWGTt2rFRXV+faba4vYQECocHgEAgN8CiKAAIIIICAJYG4BMKXt7bJh258WLbvOZCU+8TZg+R740dYkUwVFPzLNN2gsGbNGueeuuRRw6J+dDmnLoN0PzozpcFBZ5X817szUxrE6urqpLW1tV05XTKqwWnnzp2yY8cOp95rrrlGnnzySWfJqH68s5mf//znZdOmTR3un2rpqb8Pc+fOlTPPPFNmzJgh8+fPd9rsb5d3ZiydgfbJXcKq7auoqJCGhganrW7dd911lzOj6X703s3Nzc5/ujOE/vrdGT73z9X8tttuc8po/anCmbZ/yZIl8olPfEJuvPFGZya1vLzcKRMkEKa6xspDRiUlKUAgNBgWAqEBHkURQAABBBCwJBDWQHjXU69K86ZdgRUeevENefrlNztc/7lzh0ifHl0C1/OxswbJ4L49O1yfLgR4//yf//ynU06DlhtQJk6c6LzTl2qWTZd1prveX94/Q6jh6u67706GHn84zWeG0H9PrfPBBx8U/ZnOGwjvvfde58+84VDb4wbVqqqq5HuMixcvlnHjxjn9/81vfpOcTXX7rkHMW7d/htD736lMdaZPA51+NJhWVlZmnbFVG/1ouzSof/KTn0y+l5ktEJ5++ukd3tUM/HBxYSgFCIQGw0YgNMCjKAIIIIAAApYEwhoIP9OwWh5u3Bhc4dAhkSOO6HD9IRHp+Kfpq/3lF2rkwpP7Bw6EGmy8Qcdb0Btmgiy79F7vfwcwVSDUe7kzZzYCof+ebl8ytd17XzdgnXjiiR3e+dOgdccddyTfaUxXd6ZA6G+fe28NdO5sa7pNd9z7uctFr7jiCqeMPwAGDYTeEBn8IeXKMAoQCA1GjUBogEdRBBBAAAEELAmENRCGcYbQvyxSh9BdTpkuVHmXUXqv9wfNYgXCVOE2006g7mOqyzt1JjTVklP/rqdaxl1O++qrrwaeIVSTSZMmdfjK8C6/zRYI/a7ZNrXRm2mftN6ZM2fKwIEDmSG09L0pLNUQCA1GikBogEdRBBBAAAEELAmENRDm2n19h/CDNz4sOzzvEH78rEHy/QnFe4dQw92GDRuSSxazzRBmur5UZwjdQOTOAqY67iLd7J93ls49IsP/fmK2GcJ0s7FBN93xB3B/oE014xskNOb6vHJ9eAQIhAZjRSA0wKMoAggggAAClgTiEgiVS3cZ/bPuMrplt7PL6GWnDbCkGGyXUW+YcYOSG5xSBZZcrndnx9xNVPzBKduS0SCBKdV7i/qe4jnnnJOcxfPPkHnL6JLR3/72tzJhwgTnyAZvkGpsbEy+d+htay6B0N8+HVz3XcQgS0bTGXhDoLZNZ3Xddz/1Hv7gzi6j1r6sQlERgdBgmAiEBngURQABBBBAwJJAnAKhJbKU1QQ5h9C7+6buotmvXz8ZNWpU8n06bx0a7HRDFXcX0VTXe5dI6hLLlpaW5MH0uQZC7ZT//ql2GfXvIJpql1Fvu9x26zt1ujTUPwPnBlj/cs90S2kzzRBqH/xLUkeMGJHcmVTrzLRkNN07kt4loal2UXWXt7q7v2o7OIewkF9tpVU3gdBgPAiEBngURQABBBBAwJIAgdASJNUggEAsBQiEBsNOIDTAoygCCCCAAAKWBAiEliCpBgEEYilAIDQYdgKhAR5FEUAAAQQQsCRAILQESTUIIBBLAQKhwbATCA3wKIoAAggggIAlAQKhJUiqQQCBWAoQCA2GnUBogEdRBBBAAAEELAkQCC1BUg0CCMRSgEBoMOwEQgM8iiKAAAIIIGBJgEBoCZJqEEAglgIEQoNhJxAa4FEUAQQQQAABSwIEQkuQVIMAArEUIBAaDDuB0ACPoggggAACCFgSIBBagqQaBBCIpQCB0GDYCYQGeBRFAAEEEEDAkgCB0BIk1SCAQCwFCIQGw04gNMCjKAIIIIAAApYECISWIGNQzcKFC51eTps2LQa9zb2LbW1tMnv2bBk9erSMGzcu+e/jx4/PqbLVq1fLggULZNGiRVJeXp5TWdOLvX3Itd2m9w5reQKhwcgRCA3wKIoAAggggIAlAQKhHUj9IX7SpElOZfX19aEMTVu2bHHavmbNmiRKbW2tzJkzR8rKyiQKgdA7TtrJiooKaWhokOrqauMHIZ9A6JpPnz5dampqnDYUKhC67Vu+fHm7vi5dujR5bwJh7o8BgTB3s2QJAqEBHkURQAABBBCwJBCrQLhnm8gL94hsaxEZcLrIKR+ypPh2NcuWLZPm5ubQBcJ0QeCWW26RsWPHOoEpKoHQO/um4UtnPG2EwnzCVKpAaP2hPFxhqvY1NTVJXV2dTJ48WZgRzE+eQJifm1OKQGiAR1EEEEAAAQQsCcQmEG5bL/LT80X2vPm23Ls/LfKRWyxJJqrxB0L/rJs7G+P+uc7A3XbbbdLa2ire2Th/uUx/F7TOTB3VYDBjxgyZP39+2tkyDYQ7d+6UHTt2iM4y+WfXtO+zZs1K3iZou9x7f/jDH5brr7/eKe+fZdV76xJK/9/pPVeuXCnHHHOM3H777c7fe2e8/H32z765IemTn/ykrF+/vl1dbhvSjaHWnWpWde7cuSmXjPqv1ev0lwduv7Q+/bPBgwe3WzKa6f7ZxsTb/3SB1Tv2AwcOTC51HTNmjDMO3tlL/3PinXEdMWJEcpmrf0zdv9O+aADV5939eMfL+wx5n31gVzwAACAASURBVPls4+yf/fQ+P+nqtPWFTyA0kCQQGuBRFAEEEEAAAUsCoQ2Ez9whsvml4AqNfxFpfbLj9SO/LNLj2OD1jJgoUj407fXeQOj+ID9x4kRn9kV/SJ43b57zw74bbCorK50lmfpx3z/Ta/UH/aqqquSszeLFi52Q4ZbLp85MnXTb6rZHl4j6P9qmu+++Ozmb5v6Q7rb/t7/9rUyYMMFZXqp/d8cdd7QLcen66s5Saf90ts4/a+a/jzqdeOKJzrXuD/tuqMi23NL/9957aSDUQOsNKJnGUPvpbUumJaP+evTaBx98UEaOHNkhdHnb6N5D30t0nyENVDoWusQ005j4xzBdIMzUbv8vOLz/7Z9d9f6df0y94dl9dlONs/vM6LuT3q+BTOPsHwe917333isXXXSR87ymqzP4F33mKwmEBpIEQgM8iiKAAAIIIGBJILSBcMnHRNauCK5w6JDIEUcEvz7dlf/xO5HqSwMFwkyzUboE0z/74v4A7G5I4oYe783yrTPIcsB0s11uWf+SUW3Lb37zm+Q7ht52emeS9If7dH11Q453dtI7a3f66ad32JzFa7BixQpnVs99z9EbulNtyOL38wZXf13an0zeWn+qdqfaVCZdUM32DqH+vX/m1jsOuYxJpiWt/mfPG0DdX2K4wUtnU90wqkbuJkNe+1TtzhTG042z+3xpsEs3zqnupe1K1d9Mz2y+3xwIhPnKsWTUQI6iCCCAAAII2BMIbSAMwQyhfwMTd9R0BipTINSQlGp5of65SZ25PjXuvdwZs2zhw50VcpcDuktKbQRCN4RoH7zBI59A6G7+o3V5lzl6ZyLd2bVM3tovNyzpv2eaaUsXRIIEQu89tM3embhsY+Id83xmCL3h3N9f7zJe9z7epaH+IOs38PbdDYT+DW/cZaPZAqHfyBsI09WZahY8168RvZ5AmI/a4TLMEBrgURQBBBBAAAFLAqENhLn2X98hvOU8kb3b3y454lMiH/1prjVlvN6/pC7dDFqqIOBfJureyDvbpuVs1Bmk0/4AkSl8vPrqq867Ye5SxrDMEHod0gXCILOgGvCzBcJUR0kECYSFniFM9w6hOzPsPtO6hFk/3hlj77Jmr2Wqd1KDzBB6g3+msck2G+kNhOnqDPI1EOQaAmEQpTTXEAgN8CiKAAIIIICAJYHYBEL1cnYZ/aPI1haRE3WX0cQ7ebY+/gDlf29M76M/FOsn25JR7/t4qcKV+x5W0DqzLRnVeyxZskRmzpzpvAOoH/8P9dkCoTe4eN8vM5kh1KWJ3mWd/vfF/CEu1yWj2QJhpjH0L3P07tjpP4fQX4/+t8566bh43x11x9MNj/7+uvfwvkOoZdxlm5mWRAbZZTTVNdpW3Vjmrbfect6xdI/oSLVDq/uua6plnP7w65+B9o6zO+Pqfh1kmiFM9S6nt5z3HULtn/dry8bXPoHQQJFAaIBHUQQQQAABBCwJxCoQWjLzV5NpF0P/0k93SZ3Wkem9Ov9yvFQbnbjnBQatM1P3U51R599FNNvyRG+bzzjjDOd2umupaSDUerLtMprvO4TZAqH+fbox1H55l8leeOGFTnX6NZXqYHr/klrdUdS/DDifXUZzDYT5nEOo/hs2bOjwvqh/Sa27u2e6XWu91+ty0O3btzu/hHBDpn+nWtcoW/D3j1G6XUbVyq3T1rcDAqGBJIHQAI+iCCCAAAIIWBIgEFqCpBoEEMhJINtsbk6VdeLFBEIDfAKhAR5FEUAAAQQQsCRAILQEWeLV+GdevM31zwSWeFdoXogFvDO9UXnuCIQGDySB0ACPoggggAACCFgSIBBagqQaBBCIpQCB0GDYCYQGeBRFAAEEEEDAkgCB0BIk1SCAQCwFCIQGw04gNMCjKAIIIIAAApYECISWIKkGAQRiKUAgNBh2AqEBHkURQAABBBCwJEAgtARJNQggEEsBAqHBsBMIDfAoigACCCCAgCUBAqElSKpBAIFYChAIDYadQGiAR1EEEEAAAQQsCRAILUFSDQIIxFKAQGgw7ARCAzyKIoAAAgggYEmAQGgJkmoQQCCWAgRCg2EnEBrgURQBBBBAAAFLAgRCS5BUgwACsRQgEBoMO4HQAI+iCCCAAAIIWBIgEFqCpBoEEIilAIHQYNgJhAZ4FEUAAQQQQMCSAIHQEiTVIIBALAUIhAbDTiA0wKMoAggggAAClgQIhJYgqQYBBGIpQCA0GHYCoQEeRRFAAAEEELAkQCC0BEk1CCAQSwECocGwEwgN8CiKAAIIIICAJQECoSVIqkEAgVgKEAgNhp1AaIBHUQQQQAABBCwJEAgtQVINAgjEUoBAaDDsBEIDPIoigAACCCBgSYBAaAmSahBAIJYCBEKDYScQGuBRFAEEEEAAAUsCBEJLkFSDAAKxFCAQGgw7gdAAj6IIIIAAAghYEohiIFy4cKEsWrTIEZo7d67zv7NmzXL+ffz48ZbkqAYBBBAQIRAaPAUEQgM8iiKAAAIIIGBJIGqB0BsG3UA4ZswYqa+vl8rKSpkzZ46UlZVZ0qMaBBCIuwCB0OAJIBAa4FEUAQQQQAABSwJRCoRbtmxJBr+pU6fKlClTZOLEiTJu3DiZPXu2tLS0ODOH5eXllvSoBgEE4i5AIDR4AgiEBngURQABBBBAwJJAFAOhhkB3VpBAaOlBoRoEEEgpQCA0eDAIhAZ4FEUAAQQQQMCSQJQCYVtbmzMTqB/vDOHgwYNl0qRJUltby5JRS88N1SCAQEKAQGjwJBAIDfAoigACCCCAgCWBKAVCJVm9erUT/lJ9li5dKjU1NZbkqAYBBBAgEBo9AwRCIz4KI4AAAgggYEUgaoFQUZqamqSurk5aW1sdo4qKCmloaJDq6morZlSCAAIIuALMEBo8CwRCAzyKIoAAAgggYEkgioHQEg3VIIAAAlkFCIRZidJfQCA0wKMoAggggAAClgSiFgjdYyf0mIlp06Y5Sqn+zBIf1SCAQMwFCIQGDwCB0ACPoggggAACCFgSCBoIve/mjRgxIuPxDf4lm/7r/X+vXclWZ5DuupvK+I+X8B5HwTmEQSS5BgEEggoQCINKpbiOQGiAR1EEEEAAAQQsCQQJhBrgZsyYIfPnz3few1u2bJmsXLky7Y6dGh7Xr18v48ePd1qpM3QbNmxIXu+vz1JXJF3wSxcUbd2XehBAIL4CBEKDsScQGuBRFAEEEEAAAUsCQQKhBsDm5ubkEsxcA50GxAULFiRnFXMtH7SrBMKgUlyHAAK2BAiEBpIEQgM8iiKAAAIIIGBJIEgg1Bk+/bjv5LnBa/r06YGOcfDPKGZbUmrSNfd9wblz5yZnKN3lrt73Ck3uQVkEEEDAFSAQGjwLBEIDPIoigAACCCBgSUAD4YQJE6Rbt27tarz88suld+/ezp9pyKqqqkoGrFwCYZDZQP+SUpOupXo/Uevj6AkTVcoigEA6AQKhwbNBIDTAoygCCCCAAAKWBDQQ6rt+qQJhnz59koFQ/yXXGUI3nGngy3QgvF43b948Z1lpeXm5cc/cwLpmzRqnLsKgMSkVIIBAGgECocGjQSA0wKMoAggggAAClgSCLBnN5x3CoGFQu2E7EFqioRoEEEAgqwCBMCtR+gsIhAZ4FEUAAQQQQMCSQJBAmG2XUX/4y7ZM9N577xX9OUB3LNWP/x1FS12jGgQQQKDgAgRCA2ICoQEeRRFAAAEEELAkECQQ6q0ynUPoD4Q6ozhr1qwOLVy6dKmzdNRbl15UW1ub9giLXLvpXy7qLW/jrMNc28P1CCAQbQECocH4EggN8CiKAAIIIICAJYGggdDS7QpejbvLaKobEQgLzs8NEIidAIHQYMgJhAZ4FEUAAQQQQMCSQJQCoXd20J2NtMRENQgggEBKAQKhwYNBIDTAoygCCCCAAAKWBKIYCJVm0aJFVnYstcRMNQggEFEBAqHBwBIIDfAoigACCCCAgCWBKAVCJdElo6tWrSIQWno+qAYBBDILEAgNnhACoQEeRRFAAAEEELAkELVAmMtxF5YIqQYBBGIsEMlA2NbWJrNnz5bly5c7Qzt37lznwNp0H/9uXtmud+shEMb4K4euI4AAAgiUjECUAmGmHUYVnE1lSuaxoyEIREYgkoHQexaQ+411+vTpzjbR/o8bHkePHu2Exlx+K0cgjMzXAR1BAAEEEAixAIEwxINH0xFAoNMFIhcINQBq+Js5c2agw2L1HKEFCxa0W6cf9HBZAmGnP780AAEEEEAAAYlSIGQ4EUAAgWILRC4Q6gzfjBkzZP78+clAqIfLrly5MuWBsakCYabrvQNEICz248r9EEAAAQQQ6ChAIOSpQAABBPIXiGQgnDdvnjPrV15e7shkCniplpSmun7JkiUdlK+99lppbGzMX5+SCCCAAAIIIGAsEMVA6D2cXvc20M+sWbOy7otgjEkFCCAQO4FIBsJcZgh1xHWWcNKkSe0Gv76+XqZNm5b8s8WLF3d4OK677joCYey+ZOgwAggggECpCUQtEHrDoFprIBwzZozozyaVlZUpVzyV2pjQHgQQCI9A5AJhru8Qphoq/UZ84YUXptyExns9S0bD86DTUgQQQACB6ApEKRC6K5c0+E2dOlWmTJkiEydOlHHjxjk7qLe0tHA+YXQfZXqGQKcIRC4QqmKmXUbdb7T6zTXVURRB3x/U+xAIO+WZ5aYIIIAAAgi0E4hiINSfU9xZQQIhDzwCCBRSIJKBMNM5hKkCoYZAXZevH/9S0Uz4BMJCPprUjQACCCCAQDCBKAVC92cY7bl3hnDw4MHO6y21tbUsGQ32WHAVAggEFIhkIAzYd+PLCITGhFSAAAIIIICAsUCUAqFipNrbwEVaunRp1ldajEGpAAEEYiVAIDQYbgKhAR5FEUAAAQQQsCQQtUCoLHqMVl1dnbS2tjpKFRUV0tDQkDxSyxId1SCAAAJCIDR4CAiEBngURQABBBBAwJJAFAOhJRqqQQABBLIKEAizEqW/gEBogEdRBBBAAAEELAkQCC1BUg0CCMRSgEBoMOwEQgM8iiKAAAIIIGBJIEqB0N38bs2aNRl1eJfQ0sNDNQggwJJRk2eAQGiiR1kEEEAAAQTsCMQxEKqcHlif6ggtO6rUggACcRFghtBgpAmEBngURQABBBBAwJJAlAKhkuh5yhs2bGh3vIS7yYx71jJHUFh6eKgGAQSYITR5BgiEJnqURQABBBBAwI5AlAKhu2S0srKyXSD0/vm3vvUtuf7666WlpUUWLVok5eXldiCpBQEEYinADKHBsBMIDfAoigACCCCAgCWBKAZCpfGGPQKhpYeFahBAoIMAgdDgoSAQGuBRFAEEEEAAAUsCUQqEbW1tMnv2bFm+fHm7dwSXLVsms2bNktraWpk6dapMmTLF0WOG0NJDRDUIxFiAQGgw+ARCAzyKIoAAAgggYEkgSoFQSVavXi36jmCqj+4uqktE9dD6s88+u92yUkucVIMAAjETIBAaDDiB0ACPoggggAACCFgSiFogVBZ3E5nW1takEkdNWHpgqAYBBNoJEAgNHggCoQEeRRFAAAEEELAkEMVAaImGahBAAIGsAgTCrETpLyAQGuBRFAEEEEAAAUsCUQqE3oPpmRG09IBQDQIIZBQgEBo8IARCAzyKIoAAAgggYEmAQGgJkmoQQCCWAgRCg2EnEBrgURQBBBBAAAFLAlEKhEqih8/r7qHMEFp6QKgGAQSYISzUM0AgLJQs9SKAAAIIIBBcIGqB0N1QZvLkyTJ+/PjgEFyJAAII5CHADGEeaG4RAqEBHkURQAABBBCwJBClQOh9hzAVz4gRIzh70NJzQzUIIJAQIBAaPAkEQgM8iiKAAAIIIGBJgEBoCZJqEEAglgIEQoNhJxAa4FEUAQQQQAABSwJRCoSWSKgGAQQQCCxAIAxM1fFCAqEBHkURQAABBBCwJEAgtARJNQggEEsBAqHBsBMIDfAoigACCCCAgCWBKAZCd6dRJZo7d64jNWvWLOff2WjG0oNDNQgg4AgQCA0eBAKhAR5FEUAAAQQQsCQQtUDoDYNuIBwzZozU19dLZWWlzJkzR8rKyizpUQ0CCMRdgEBo8AQQCA3wKIoAAggggIAlgSgFQneXUQ1+U6dOlSlTpsjEiRNl3LhxMnv2bGlpaWGXUUvPDdUggEBCgEBo8CQQCA3wKIoAAggggIAlgSgGQg2B7qwggdDSg0I1CCCQUoBAaPBgEAgN8CiKAAIIIICAJYEoBcK2tjZnJlA/3hnCwYMHy6RJk6S2tpYlo5aeG6pBAAFmCI2fAQKhMSEVIIAAAgggYCwQpUCoGKtXr3bCX6rP0qVLpaamxtiMChBAAAFXgBlCg2eBQGiAR1EEEEAAAQQsCUQtECpLU1OT1NXVSWtrq6NUUVEhDQ0NUl1dbUmNahBAAIGEAIHQ4EkgEBrgURQBBBBAAAFLAlEMhJZoqAYBBBDIKkAgzEqU/gICoQEeRRFAAAEEELAkEKVA6O4yOn36dJaGWno+qAYBBDILEAgNnhACoQEeRRFAAAEEELAkEMVAuGbNGkdnxIgRHDNh6TmhGgQQSC1AIDR4MgiEBngURQABBBBAwJJAlAOhl2ju3Lkyfvx4S2pUgwACCCQErAVCd0cs/0vP3peio7YzFoGQLyMEEEAAAQQ6XyBKgdCvuXDhQmeG0P0wY9j5zxstQCBqAlYCoXtmzvLlyyXVb6+WLVsms2bNitzZOQTCqH050B8EEEAAgTAKRDkQesdDf5664447WEIaxoeUNiNQwgJWAqH7ArT2U3+LVV5e3q7L2f6+hH0yNo1AGNaRo90IIIAAAlESiGogdH9+ct8n1DFjhjBKTy59QaA0BAiEBuNAIDTAoygCCCCAAAKWBKIUCFOFQJeJdwgtPTBUgwAC7QSsBEJ3yeiTTz6Z8tBU9/3C2tpamTNnjpSVlUViGAiEkRhGOoEAAgggEHKBKAdCZgRD/nDSfARCIGAlEGo/3fcEM20qE7XfbBEIQ/CE00QEEEAAgcgLRDEQcg5h5B9bOohAyQhYC4TejWVS9S5qs4PaRwJhyTzHNAQBBBBAIMYCUQqEMR5Guo4AAp0kYC0Quu13Zwq9/YnazKDbNwJhJz213BYBBBBAAAGPAIGQxwEBBBDIX8B6IMy/KeErSSAM35jRYgQQQACB6AmEPRC6G8lUVlbK1KlTZcqUKeLdWdQ7YrxTGL3nlx4h0NkCBEKDESAQGuBRFAEEEEAAAUsCBEJLkFSDAAKxFLASCDNtkeyqLl26VGpqaiKFTCCM1HDSGQQQQACBkAqEPRCGlJ1mI4BARASKFgjVK2qhkEAYka8CuoEAAgggEGoBAmGoh4/GI4BAJwtYCYTZ+uBuNFNfXy/Tpk3Ldnlo/p5AGJqhoqEIIIAAAhEWiFogdM9vdofMf6RXhIeSriGAQCcIFCUQuktKtX+LFi2S8vLyTuiq/VsSCO2bUiMCCCCAAAK5CkQpEC5cuND5WSnVJ6q7tuc63lyPAAJ2BQiEBp4EQgM8iiKAAAIIIGBJICqB0Dsz6H3NJt2fW+KjGgQQiLkAgdDgASAQGuBRFAEEEEAAAUsCQQOhN1hlO76hqalJ6urqpLW11WlltuttdMWdHUw1ExjV129suFEHAgiYCRQlEEb1mxiB0OzhozQCCCCAAAI2BIIEQg14M2bMkPnz50t1dbXozyYrV66UOXPmSFlZWYdmaHhcv369jB8/3vk7DWsbNmxIe71pP9ra2mT27Nny5JNPSkNDg9NG78cNqGeffXbB2mDaB8ojgEA4BawEwiDHTigPu4yG8yGh1QgggAACCJSyQJBAqAGwubk5ubmdPyBm658GxAULFhRsL4Rs+y1k+/ts7efvEUAAgXQCRQuEUQuDCsoMIV9YCCCAAAIIdL5AkECoM3z6cXc7dwPW9OnTA52TnG1G0VTBbU9lZWXKGUACoakw5RFAoKCBMK68BMK4jjz9RgABBBAoJQENhBMmTJBu3bq1a9bll18uvXv3dv5MA2FVVVVyCWgugTDX2cR8bAiE+ahRBgEEbAhYmSG00ZAw1kEgDOOo0WYEEEAAgagJaCDUd/1SBcI+ffokA6H+S64zhO67exooa2pqCkYX9PWbYmxuU7BOUjECCJSkQFECobupTNS+iREIS/KZplEIIIAAAjETCLJkNJ93CIsVBnW4CIQxe2jpLgIlJFCwQOjulrV8+fJkdwmEJTTyNAUBBBBAAIGICAQJhNl2GfWHv2IsE40IP91AAIGQC1gPhN4zfvw2qc7VCbMfM4RhHj3ajgACCCAQFYEggVD7mukcQn8gdFc3+Y2iuEleVJ4D+oEAAvkJWAmEqWYDvc2J2syg2zcCYX4PHaUQQAABBBCwKRA0ENq8J3UhgAACURGwEgj9697dAKgHveohqy0tLQU7t6czB4JA2Jn63BsBBBBAAIGEAIGQJwEBBBDIX8BKIGSGMP8BoCQCCCCAAAIImAkQCM38KI0AAvEWsBIIvYSZ3iGM2rp7Zgjj/cVD7xFAAAEESkOAQFga40ArEEAgnALWA6HLwC6j4XwgaDUCCCCAAAJhEyAQhm3EaC8CCJSSQMECobeTnENYSkNOWxBAAAEEEIiWQNQCYaYzCaO6UV+0nkh6g0C4BIoSCMNFEry1LBkNbsWVCCCAAAIIFEogaoFw4cKFzmZ8qT4EwkI9RdSLQHwFCIQGY08gNMCjKAIIIIAAApYEohQIvbODUdt7wdJwUw0CCFgWIBAagBIIDfAoigACCCCAgCWBKAZCpdFZwvLycktKVIMAAgikFiAQGjwZBEIDPIoigAACCCBgSSBKgVBJdMnoqlWrCISWng+qQQCBzAIEQoMnhEBogEdRBBBAAAEELAlELRA2NTVJXV2dEwxramosKVENAgggwAyh9WeAQGidlAoRQAABBBDIWSBKgTDTDqMKw6YyOT8eFEAAgSwCVmcI4/YiNIGQry8EEEAAAQQ6X4BA2PljQAsQQCC8AgRCg7EjEBrgURQBBBBAAAFLAlEKhJZIqAYBBBAILGA1EOpd3bNz4rBVMoEw8HPGhQgggAACCBRMIIqB0H2PsLW11XGrqKiQhoYGqa6uLpgjFSOAQDwFrAdC9xvY5MmTZfz48ZFWJRBGenjpHAIIIIBASASiFghXr14tkyZNSqkfh1+4h+Sxo5kIREbAaiCM24vQBMLIfB3QEQQQQACBEAtEKRC2tbXJ7NmzZfny5eINf25IrK2tlTlz5khZWVmIR4ymI4BAKQkQCA1Gg0BogEdRBBBAAAEELAlEKRC6v1yvrKxsF/zcoNjS0sL5hJaeG6pBAIGEgNVAGDdUAmHcRpz+IoAAAgiUogCBsBRHhTYhgEBYBCIZCL3LLXQg5s6dm/F9Rv9S1/r6epk2bVrWMSQQZiXiAgQQQAABBAouEKVAyJLRgj8u3AABBHwCBQmE7k6jbhjT/501a1bWYGZrdPT++tFQ54a96dOnS01NTYdbuN94R48e7YRG/39nahOB0NaIUQ8CCCCAAAL5C0QpEKoCm8rk/yxQEgEEchewHgi9YdANhGPGjBGddfOvh8+9udlLaADU8Ddz5szk1szegOivIVVgzHS9tzyBMPt4cAUCCCCAAAKFFohaIFQvjp0o9FND/Qgg4ApYDYTeF6GnTp0qU6ZMkYkTJ8q4ceOcHbOK8SK0fgOdMWOGzJ8/PxkIly1bJitXrky7K5f+vc5g6m5eer6PP1Cme1wIhHwhIYAAAggg0PkCUQyEna9KCxBAIC4CBQmEGgLdWcHOCITz5s2TBQsWSHl5uTOO2QKhGyL12meeecaZzfS/Q7hkyZIOz8S1114rjY2NcXlW6CcCCCCAAAIlKUAgLMlhoVEIIBASAauB0H3/TvvunSEcPHiwc8BqMc7OyXWG0L/E1O3DiSee2C4ULl68uMOQXnfddQTCkDzoNBMBBBBAILoCBMLoji09QwCBwgtYDYTa3M5+ETrXdwg1QOY6o+gOC0tGC/+AcgcEEEAAAQSyCYQ9EKZ65WbNmjUpuz1ixAjOIcz2QPD3CCCQk4D1QKh37+wXoTPtMup+09WlrLqrqP+/080QplIlEOb0rHExAggggAACBREgEBaElUoRQCAmAgUJhJ1tl+kcQn8ATBVgOYews0eQ+yOAAAIIIBBcIOyBMHhPuRIBBBCwLxDJQGifKXWNzBAWS5r7IIAAAgggkF4gSoHQ/cX1yJEj2+1l4N2nYc6cOVJWVsYjgQACCFgRIBAaMBIIDfAoigACCCAQOYE7n3xF9B/9DDquTK669GQ56bjCB5c4BEI11VdiVq1axTuEkfvKoUMIdK6AcSCM84vQBMLOfXi5OwIIIIBA6Qj8+bnX5EtLnmzXoHee2Fvuuer8gjcyDoHQnSEsxpnOBR8wboAAAiUlQCA0GA4CoQEeRRFAAAEEiiqwc+8B2X/wkOw7cFD2HXhL9h58S/YfOCT7Dib+2/nH8/eJa/XPDsreA28dLqvXHZR9B/V/3y6r1z7x0mbZuGNvhz79ccr5clpF74L2NQqB0L8hXzqwYhzhVdDBonIEECg5AeNAWHI9KmKDCIRFxOZWCCCAQEgEdu1zA5YGp8OhyglUbuh663DAOvzfyTD29t8nr9Xg5iubCGlvl213rRPy3v57DYFF+xw6JHLEER1u9+v6UTJ6aN+CNiMugbCiokIaGhqkurq6oJ5UjgAC8RIwDoSploz6X4SOKimBMKojS78QiLfAc63b5cb7X3RmfAYd11Ped9oAufrS4SWHomGnXTgKGJK8ZZIzZofLtgtfnmB18K1D0rb/4NuhzhfUNASW+qdH1yOlW5ejpPtRR0rXLkdIt6MS/931qCOke5fEv+ufef9O/1z/vtvhv+/R5Ujpov99uKz+uVv+kabNcuffX27HcEyPLvLPay4rOE0UAqGLlG5TmYIjcgMEEIitAIHQYOgJhAZ4FEUAgZIVOG/+Dyk+jgAAIABJREFUA/LK1rZ27au/YKjUnTfEeKmhBjANXe4yxeSyxAO+ZYkHDzrLGXVZY2I5o4axQ1LUGa88R6is61GJAOWEprf/6Xr4vzWQJYLU23+XCGm+Mp6/T1571JFOePOWTd4rxfV6rf5TjM/2tv3yn4v/LqvWbXFup2Hw/9WeJuPPHlTw20cpEBYcixsggAACPgHjQOg/8y+T8IgRIyK1MxaBkK8nBBCImsDLW9vk/PkPdOxWmuWAndn/fIJXMqR5QllidswX0ko4eHWmeZB7azDU56jQ7w162xK1QKi7iS5atEi85yKn+rMg48E1CCCAQDYB40CoNwj6IjSBMNtw8PcIIIBA5wls271fvv+XF2XJyuYOjTjqyCOkvGe3tDNeKWesPDNe+vdBg1fy2lQzaUWc8eq8keDOuQpEKRCm203U+4oO5xDm+oRwPQIIZBKwEgjdG8Rt3TszhHxxIYBAFAQ27twrP31wrSxdtV727H9L5JCI+PYGuWrMcPn62JOj0F36EEGBKAXCdMGPYyci+ODSJQRKRMBqICyRPhWtGQTColFzIwQQKIDAhjf3yI//2iS/emJ9snZd5veRdw+SXzy+Tl7dlniP8ONnDZLv1L5Tepd1LUArqBIBcwECobkhNSCAQHwFCIQGY08gNMCjKAIIdJpA8+bd8uMHGmXZk68k23BW5bFy5SXD5eJTju+0dnFjBPIViFIgVAP3fcG5c+fK+PHjHZbVq1fLpEmT2r1XmK8X5RBAAAGvgHEgTHXsxJo1a1Iq8w4hDx8CCCDQeQKNr++UHz3QKMvXtCYboefDXTlmuJwzrLDnxHVer7lzHASiFgjT7c3AOYRxeJrpIwLFFyAQGpgzQ2iAR1EEECiagHuu4H3/ej15z4ve0V+mjDlZdGaQDwJhF4haINTxcH/h7v6SnTAY9qeU9iNQugLGgbB0u1b4lhEIC2/MHRBAIH+Bf7Rscw6Yf+jFjclKLjttgLM5zCkDjsm/YkoiUGICUQyEJUZMcxBAIMICBEKDwSUQGuBRFAEECibwaNMmuWlFY/KAcL3Rh0dUyFWXnizD+h9dsPtSMQKdJUAg7Cx57osAAlEQIBAajCKB0ACPogggYF1gxfNvyE0PNMrTL29L1j3hPSfJlZdUy0nlPa3fjwoRKBWBsAfCOO/HUCrPEO1AIM4C1gOhuzPW0qVLpby8XOrq6qS1tVVqa2slagepEgjj/KVD3xEoHYF7/rlBbn6gSf61YXuyUVeMGixfvbhaTuzTo3QaSksQKJAAgbBAsFSLAAKxELAaCN1DU1VOw9/NN98sixYtSkLW19fLtGnTIgNLIIzMUNIRBEIp8L//eFV+8tcmadq402l/WdejZNKoSvnShcOkf6/uoewTjUYgH4GwB8J8+kwZBBBAwJaA1UDoLnkYOXKkMzOoAXDjxo3S0NAgd911l6xatcoJiDpzGIUPgTAKo0gfEAifwK9Xt8hPHlwrL2/Z7TS+V/cu8tlzquQ/zx8qx/bk8PjwjSgtNhUgEJoKUh4BBOIsULBA+NGPftQJhf3793dCoIZCAmGcHzX6jgACJgJ7D7wlGgR/+uBaeW37HqeqPmVdpe68IfK5c4dI7x5dTKqnLAKhFgh7IPQfMZFpMKJ2pnOoHzwaj0BEBKwGQjVx3yF0fXSWcPLkyTJ79mznj6L0HiEzhBH5KqAbCJSwwO59B2Xxyma59ZGXZNPOfU5L+/Xq5swGXjG6Snp2O6qEW0/TECiOAIGwOM7cBQEEoilgPRC67xEuX75c3N9irVixQmbNmuUsIeUdwmg+SPQKAQTsCmzfc0B+/tg6ue3RdfJm236nct0gRt8P/FRNpXTvcqTdG1IbAiEWCHsg9NPrL9c3bNjQ7pfoTU1Nzsor/SX7+PHjQzxaNB0BBEpNwHogLLUOFrI9zBAWUpe6EYinwLbd+2XRw2tl8cr1snPvAQdBj4z46kWJIMgHAQQ6CkQpEHqPoPCuqvJv3FdWVsajgAACCFgRIBAaMBIIDfAoigAC7QQ27twrt/x1rdy+er3s2f+W83fV/XvJ1y6plo+eORAtBBDIIBDFQKjd9W7E5wZF/5/zYCCAAAKmAtYDIecQmg4J5RFAIE4Cr25rc46OWLqqJdntd57YWyZfUi0fPP3EOFHQVwTyFohSIPS+ejN37tzk8tDVq1fLpEmTInmuc94DT0EEELAiYDUQcg6hlTGhEgQQiIFA8+bdcvMDjXLnk68ke/vuk46VKy8ZLmNOPT4GAnQRAXsCUQqEquK+L9ja2toByRsS7QlSEwIIxFnAaiDkHMI4P0r0HQEEggg0vr5Tblzxotz9zIbk5SOHlMuVY4bLedX9glTBNQgg4BOIWiDU7qU6imLp0qVSU1PD+COAAAJWBQoWCDmH0Oo4URkCCIRc4LnW7XLD/S/KX/71erInFwzvL1ePPVnOqjw25L2j+Qh0rkAUA2HninJ3BBCIk4DVQKhwnEMYp8eHviKAQDaBf7Rskxv+8qI83LgxeenYd54gV196spxW0Ttbcf4eAQQCCBAIAyBxCQIIIJBGwHog5BxCnjUEEEBA5JHGTXLTA42yet2WJEftiAqZcslwGX5CL4gQQMCiQBQDofcX7PreoH70TGfeIbT44FAVAgg4AtYDYZxcOXYiTqNNXxEIJnD/86/LTSuaZM0r25IFPnH2IJl8yXCp6tszWCVchQACOQlELRD6V1tpCBwzZozU19dLZWVluwPrc4LiYgQQQCCFAIHQ4LEgEBrgURSBiAn88ZkNcvNfG+X5DTuSPZs0slK+enG1DDyWA6QjNtx0p8QEohQIvQfTT506VaZMmSITJ06UcePGyezZs6WlpaXd+YQlNhQ0BwEEQihgPRCm2hXLdRkxYkSkvokRCEP4xNNkBCwL/O4frzjnCK7duMupuUfXI+XTNYPlyxcNk+OP6W75blSHAAKpBIIGQvcsP60j6M8k+nPN9OnTZebMmVJdXZ28faqjIYLWmWkU3Z+jNAS6s4IEQp57BBAopID1QOhf5uBtvI1vlIXEyLVuAmGuYlyPQHQEbl/VIrc82CQvb21zOnV0t6PkM+dUyZcuGCbH9uwanY7SEwRCIBAkEGqAmzFjhsyfP98JdsuWLZOVK1emXX7p3ROhoqJCGhoaOgRCb322mLxnOntnCAcPHszB9LaQqQcBBNoJWA2E3mUOn//852Xy5MnOrqP6jVfXvetvuMaPHx+ZISAQRmYo6QgCgQT2HnhLNAj+9KG18vr2PU6Z3mVd5QvnDpEvnDdEevfoEqgeLkIAAbsCQQKhBsDm5maZNm2ac3N/QEzXokwzhIUIhNoO70ymv12cRWj32aE2BBCwvKlMqoPpdZmFHqKqwXDVqlUsGeWpQwCB0Ans3ndQfvl4s9z6yEuyedc+p/19j+4mXzx/qHz2nCrp2e2o0PWJBiMQJYEggVB/DtGPGwjdn1ncn1PyCYR1dXXS2trqFLW9Csq/JDXVLGWUxpC+IIBA5wkUbIbQXeYwcuRIZ6Ywii9CM0PYeQ8ud0agGALb9xyQ2x5dJ7c9tk62t+13bnlC7x7ypQuHyqSRg6V7lyOL0QzugQACWQQ0EE6YMEG6devW7srLL79cevdOnPepgbCqqiq5Usk0EPqbpPVv2LCBHUB5WhFAIHQCVgOh+w337rvvdtba33XXXc6MoPupra2N1DdKAmHonncajEAgAZ0F1NnAxY83y659B50yJx1XJl+5qFo+PbIyUB1chAACxRPQQKivpKQKhH369EkGQv0XWzOE/t7pjN68efNkwYIFUl5ennfnvZvzsTw0b0YKIoBADgLWA6H33t5valFc6kAgzOFJ41IEQiDwxo69zkYxv17dInv2v+W0eGi/o+Vrl1TLx88aFIIe0EQE4ikQZMmo7XcICYTxfNboNQJRFChoIIwimLdPBMKojzD9i4vAq9va5Md/bXI2jHE/pww4xjlMftwZJ8aFgX4iEFqBIIEw2y6j7jt7uvRT9z5wP+k2lbn33ntFfw5wj6Lwv6Nogunu2M4MoYkiZRFAIKgAgTCoVIrrCIQGeBRFoAQEmjfvlptWNIqeJeh+zhjUR668ZLiMfecJJdBCmoAAAkEEggRCrSfTOYT+QOg9dsJtg/fVF/9OoDZfi3HbonswRGl39iBjyTUIIFB8AQKhgTmB0ACPogh0okDj6zvlhhUvyh+f2ZBsxXuryuXKMdVywfD+ndgybo0AAvkIBA2E+dRd7DLe121S3dv2bqbF7h/3QwCB0hMwDoTZvnF5uxy1b2IEwtJ7oGkRApkEnmvdLj/8y4ty//OvJy87r7qfXDlmuIwckv8mEKgjgMBhgf/7isjTtyf+49hKkY/8RKTq/ILzEAgLTswNEEAgwgIEQoPBJRAa4FEUgSIKrFq3RW5+oFEeadyUvOuYU4+Xb4x9h5xWkdiSng8CCBgKPPETkT99s30lPfqIzHz73VzDO6QtHqVAWCgj6kUAAQTSCRgHwjjTEgjjPPr0PQwCDzdulJtWNMnfmrckm/uhM06Uq8ecLMNP6BWGLtBGBMIj8PMPiqx/rGN7P3d3wWcJCYTheUxoKQIIlJ6A9UDo7oxVX1+fPOsn1Z+VHkXuLSIQ5m5GCQSKIfCXf70uNz3QKM+88mbydh87a6BMGXOyVPXtWYwmcA8E4iWwbb3Izy4V2fVGx35/+RGRAWcU1INAWFBeKkcAgYgLWA2E7o5cLS0tzoH07sGs7nuGlZWVHEwf8QeK7iHQmQJ3P7PBWRr6wms7ks34VE2lTL6kWgYeW9aZTePeCERTYP9ukUd+IPL4j0QO7O3Yxz4niXz92YL3PUqB0P0luqJFbe+Fgj8I3AABBPISsBoI0wW/dEExrxaXUCFmCEtoMGhKrAXufPIV+clfm+SlTbsch+5djhQNgl+9uFqOP6Z7rG3oPAIFE3j2dyL3fVtk+6uJW7zr4yK9ThRZ96DIthaRqvNELv5mwWcH9dZRCYTLli2TWbNmtRsyQmHBnmAqRgCBwwIEQoNHgUBogEdRBCwILF3VIj99sEle3trm1Naz21Fyxegqqb9gqPQ9upuFO1AFAgh0EHjjeZHlV4m8vCrxV/1PEam9UaRyVKdhRSEQes89dA+kd2cL586dy3mEnfZ0cWMEoi9gNRAqV6pvXu7hrd73CqNASyCMwijSh7AJ7D3wlixdtV7+56GX5PXte5zm9+7RRT537hD54nlDpHdZ17B1ifYiEA6BPW+KrLhW5G+3JtpbdpzIJd8See8XO739UQiE7iorxXRfu4nqz0+d/sDQAAQQaCdgPRA2NTVJXV2dtLa2trtRRUWFNDQ0SHV1dWSGgEAYmaGkIyEQ2Ln3gCxeuV5ufeQl2bJrn9Pi8qO7Sd15Q+Sz51RJr+5dQtALmohASAU0BD5wvUjb1kQH3vufImO+LaLHSpTAJ0qB0Lvfgvsz1dlnnx2pPRhK4JGhCQgg4BGwHgi1bv9h9VEMg9pPAiFfSwgUXmB7235peHSd/PzxZtF/14++F/ilC4fJpJGDpUfXIwvfCO6AQFwFXvm7yO+/JrLxhYTASSMTy0OPP7WkRAiEJTUcNAYBBEImUJBAGDKDvJtLIMybjoIIZBXYvGuf/Ozhl2TJymbZte+gc/2g48rkyxcOk/8YNThreS5AAAEDgR2vifx5tsizdyYq6T1Q5LLrRU77mEGlhSsapUC4Zs2ajFBsMlO454iaEYirAIHQYOQJhAZ4FEUgjcAbO/Y6O4b+enWL6PuC+hnS72hnx9DxZw/CDQEECi3wyPdEHv6+iB4p0aW7yDlXiVzwXyJdehT6znnXTyDMm46CCCCAgBAIDR4CAqEBHkUR8Am8uq1Nbn4gEQTdz8knHOOcIfjhERV4IYBAoQX+fY/In74psrU5cadTa0UumytybGWh72xcfxQCoTECFSCAAAJ5ChAI84TTYgRCAzyKInBYoHnzbvnRihflf/9x+CwzETl9YB8nCF522gCcEECg0AKb14rc/XWRdQ8l7tRvuMi4GxLnCIbkQyAMyUDRTAQQKEkBAqHBsBAIDfAoGnuBxtd3yg/vf1Hu+eeGpMXZg4+TKy8ZLhe9o3/sfQBAoOAC+3aJ/HWuyMqbE7fSHUMv+qbIqK8U/Na2b0AgtC1KfQggECcBAqHBaBMIDfAoGluBZ155U25c8aKseP6NpME5w/rKlWOGy+ihfWPrQscRKKrA00tF/vIdkV0bE7c967Mil35HpGc4vwYJhEV9ergZAghETIBAaDCgBEIDPIrGTuCJl7bIzQ80yqNNm5J9v+SU4+XqS0+WMwaVxllmsRsUOhw/gdanRJZfLbLh6UTfB56dOEZiwOmhtiAQhnr4aDwCCHSygNVA6J4/OHLkSJk2bVqya21tbTJ79mznv+fMmSNlZWWd3G07tycQ2nGklmgLPPTiRrnpgUb5e/PhA61F5APvGiBXXXqynDLgmGh3nt4hUCoCuzeL3PdtEZ0Z1M8xA0TGXityxsRSaaFROwiERnwURgCBmAsUJRCq8cKFC2XVqlWyaNEiKS8vjwQ7gTASw0gnCiRw379el5tWNMo/X30zeYePnDnQeUdwWP+jC3RXqkUAgQ4C+o7gg/NE9u5I/NW5V4tcOF2kW3S+DqMWCN1fsKc6k5BzCPkaRwAB2wJFCYTuDGFLSwuB0PYIUh8CJSbwhzWt8uO/Nsm/Xzv8w6eITHzvSTL54mo5qbxnibWW5iAQYYHmR0X+cKXIlpcSnTz5MpEPzBc5bkjkOh21QKi/RNdfoKf6EAgj9/jSIQQ6XcBKIGxqapK6ujppbW3N2KHa2lqWjHb6kNMABAojsOzJV5wD5ddt2uXcoHuXI2XieyvlKxcNkxP7lO6B1oXRoFYEOlFgW4vIn2aKvPDHRCOOq0q8Jzj0ok5sVGFvHaVA6J0dXLp0qdTU1BQWj9oRQCD2AkULhBUVFdLQ0CDV1dWRQWfJaGSGko4YCPzqifXy04fWyitb25xaenY7SiaNGixfvnCY9D26m0HNFEUAgZwE9u8WeeQHIo//SOTAXpFuvRJLQ8+9KqdqwnhxFAOhjkOUXrMJ43NFmxGIi4BxIHR/k1VZWSlf/OIX5VOf+pRzYPudd94ZeUMCYeSHmA6mEdiz/y3RILjo4bXyxo69zlW9e3SRz5xTJfXnD5XeZV2xQwCBYgo8+zuR+74lsv3wSp0RnxR53/UiR8fjTM8oBUJ9bKK470Ixvxy4FwII5CZgNRBOnTpVpkyZIv5dRnNrUniuJhCGZ6xoqR2BnXsPyC8fb5aGR9fJll37nEqP69lV6s4fKp87p0p6de9i50bUggACwQTeeF5k+VUiL69KXH/Cu0Quv1mk4sxg5SNyVdQCofsqjgZDloxG5CGlGwiUsIBxIHQ3jFm+fHnWbkbtRWgCYdYh54KICGxv2y+3PrpOfvHYOtm+54DTq/7HdJf6C4bKFaOqpEfXIyPSU7qBQEgE9rwpsuJakb/dmmjw0f1ELr1G5MwrQtIBu82MUiDMtMOoqkXtZym7TwK1IYBAPgLGgVBvGnRTmah9EyMQ5vPIUSZMApt37ZP/eWitszx0976DTtMrju0hX76wWj4zenCYukJbEYiOgIbAB64XaTt8tueor4pcPEuke3zP9SQQRufxpicIIFB8ASuB0G12uoPpi9+t4tyRQFgcZ+5SfAF9L1CPjvjN6hbZe+AtpwGD+/aUr15cLRPfc1LxG8QdEUBApOWJxPLQjS8kNAafK/Lhm0T6Dou9TpQCYewHEwAEECi6gNVAWPTWd/INCYSdPADc3rrAq9va5KYHGuU3q19O1j38hF7OGYKXv3ug9ftRIQIIBBDY8ZrIn2eJ6MYx+tFjJC6bK3LKhwIUjsclBMJ4jDO9RACBwggQCA1cCYQGeBQtKYG1G3c5QfD/nno12a7TKnrLlZcMl/e/a0BJtZXGIBArgYcXJo6S0CMluvYUOf8bIhdMixVBkM5GMRD6X8eJ4vFdQcaWaxBAoPACBEIDYwKhAR5FS0Lghdd2yI33vyj3Pvtasj1nDT5Orry4Wi4+5fiSaCONQCCWAnqovM4Kbm1OdP9dH0/MCh7DL2hSPQ9RC4SrV6+WSZMmpXz0Oaw+lt8R6DQCBRUwDoTecwjdYyfWrFmTstFsKlPQsaRyBAILPPPKm3LD/S/KAy+8kSxzzrC+MvmS4aL/ywcBBDpJYPNakbu/LrLuoUQD+p8iUnujSOWoTmpQOG4bpUDo3b3dG/7ckFhbWytz5syRsrKycAwOrUQAgZIXIBAaDBEzhAZ4FC2KwA33N8ptj77kHBUx6LgymfDek2TVS1vksaZNyftf/I7+8vWx75AzBvUpSpu4CQIIpBDYu0PkwXkiK29O/GXZcSJjvi3ynjq4AghEKRB6f9HuDX5uUGxpaZFFixZJeXl5ABkuQQABBLILGAfC7Lco/hX+sxHnzp0r48ePT9mQdOf9BJnNJBAWf2y5Y3CBZU++ItOW+WbrDx2SQ0ccKUfIIXn/aQPk6rEnyykD4rtVfXBNrkSggAJPLxX5y3dEdm1M3OS9/5kIgz34JU1QdQJhUCmuQwABBDoKWA2E3nDVmWvcFy5c6PR02rRp4rZp+vTpUlNTE+gZWLZsmTQ3NzvlM30IhIE4uagTBHbuPSBX/fopWeFZEuo249zqfvLdD58m1cf36oSWcUsEEEgKtD4lsvxqkQ1PJ/7opJGJ5aHHnwpSjgJRCoQsGc1x8LkcAQSMBSIXCDUAavibOXOmVFdXO0DegJhNLFX5dGUIhNk0+ftiCqx8abOzFPSxxk3y1Mvb5NChQ3LEEUd0aMKv60fJ6KG8J1jMseFeCLQT2L1Z5L5vi+jMoH76DBJ53/Uip30UqDwFohQIlYBNZfJ8ECiGAAJ5CVgNhG740rXtnTVDqNs0z5gxQ+bPn58MhDrjt3LlykAvYQedHdS+EgjzeuYoZEngudbtTgB8tHGTrG7eLHv2Jw6Qdz/H9+4ub2zf2+7PjunRRR6bcYn0LutqqRVUgwACOQnoO4L6rqC+M9ilu8i5VyeOkujSI6dquLi9QNQCofaOYyd4yhFAoFgC1gOh+w1s8uTJad/bK2Tn9P7z5s2TBQsWJF+4DhoIM80OLlmypEOzr732WmlsbCxkd6gbgaSAHhr/8Isb5dGmTfJ40ybZunt/O53+vbrLOdX95NzqvnLhyf3lhN49pOHRdXLnky/L8xt2yMgh5c47g8wO8lAh0AkCzY+K/OFKkS0vJW5+am3iGIljKzuhMdG7ZRQDYfRGiR4hgECpClgNhOk2aHE7H2SjFlMokxnCTEtLFy9e3KFp1113HYHQdMAon1Zg2+79iRnAw/+8vGV3u2uP7naUjBzaV/SdwPOq+8k72ByGpwmB0hPY1iJy7wyRf9+TaFu/4SLjbhCpOq/02hriFhEIQzx4NB0BBDpdIHKBMN93CFMFyWyjw5LRbEL8fS4CuuRTl37qElANgrok1P95T9Vxcl51fycA6r/zQQCBEhXYv1vk4e+LPPK9RAN1x9CLZ4mM/HKJNjjczYpaIMz0C/Zi/HI93E8DrUcAgVwFrAbCXG9eqOsz7TLqfpOdOHFiuyWtuWw847abQFioEYxPvbr5i24CowFQN4Xxf3TWT8Of/qOzgT27HRUfHHqKQFgFnr0zsWnM9tZED876rMjY7ybOFuRTEIGoBUL9mUT3Y0j1IRAW5BGiUgRiLRDJQJjpHMJUgVB389J3DnM96JVAGOuvnbw6v3bjLnmsaaMzC6gBcMeeA+3qqTi2hzMD6CwDHd5P+h7dLa/7UAgBBDpB4I3nRZZfJfLyqsTNB56dOEZiwOmd0Jh43TJKgbBUjvCK1xNEbxGIt4BxIMz23qCXN2q/1SIQxvuLJ0jvN+3cJ480JgKgzgK+tn1Pu2K62+c5w95+D3BIv6ODVMs1CCBQSgJ73hRZca3I325NtOqYASJjrxU5Y2IptTLSbYliINQBy/UX1ZEeZDqHAAIFEyAQGtASCA3wIlp0176D8sTazfKozgI2bZLG13e262n3LkfK2YOPk/OGJ94DPGNQn4hK0C0EYiKgIfCB60XatiY6fN7XRS6cLtK1Z0wASqObUQqEKqpLRletWkUgLI3Hi1YgEHkB40DoF9JvYhs2bGh35l9nH0VRqFEkEBZKNlz1rl63JbkT6D/WH/6h0NOF0wf2cZZ/agDUpaB8EEAgAgItTySWh258IdGZky8T+cB8keOGRKBz4etC1AKh+3OT/kxVU1MTvgGhxQggECoBq4HQXT5aWVnZLhC67/SpzJw5c6SsrCxUSOkaSyCMxDDm3Ak9009nAPUswCde2iJt+w+2q6Oqb8/kDKAuB+UQ+JyJKYBA6QrseE3kz7NEnv1doo3lQ0XG/VBk6EWl2+YYtCxKgTDbqzhRe/0mBo8nXUSg5AUKEgi119517+43N/+fl7xOlgYSCMM+gsHav+HNPfJw48bkbqCbd+1rV7Bfr27OzJ8eCn/B8P5yYp8ewSrmKgQQCJfAwwtFHvmBiB4p0a2XyEUzRM6ZEq4+RLS1BMKIDizdQgCBoghYDYTe3T3nzp2bPNZBd/GcNGmS1NbWMkNYlGHlJiYC29v2y2P6HqBuBtO0SdZvbn8gvB79MHJIuZyr5wEO7yencCC8CTdlESh9gRf+mJgV3NqcaOuIT4m87zqRo/uXfttj0sIoBcKYDBndRACBEhKwGgi1X+6699bWw+cveTrrDYklZJB3U5ghzJuupAruPfCW/L358HuAjZvkn6++2aF9zkYwh4+CeG9VeUm1n8YggECBBDavFbn76yLrHkrc4MR3i9TeIFJxZoFuSLX5ChAI85WjHAIIICBiPRAqaqr170uXLo3ci9EEwvB+CT3zypuJjWAaN8qT67eKhkLv5+QTDh8IP7yfjOJA+PAONC1HIB+BvTtEHvxvkZU/TpQ+up9g3Mj2AAAgAElEQVTIpdeInHlFPrVRpggCUQuEmd4j5B3CIjxQ3AKBmAkUJBDGxZBAGJ6RXrdplxMA9SzAx9duFl0W6v3oe3/uLqDnn9yfA+HDM7S0FAG7Ak/9SuT+a0R2bUzUO/prIhd9U6T7MXbvQ21WBaIWCHV3Ud2LIdWHQGj10aEyBBCQAs0QxkWWQFi6I60bv7iHweuOoK3bUhwIP7SvsxGMvgc4lAPhS3cwaRkCxRBofUpk+dUiG55O3G3IhYndQ/sOK8bduYehQJQCoXd2MIqrqwyHmuIIIFAAAeszhHFa5kAgLMATmWeVu/cdlFUv6YHwm5x//v3ajnY1uQfC626gGgBHDDo2zztRDAEEIiWwe7PIfd8WeXppolvHVYlcNlfklA9FqptR70wUA6GOmXfH9qiPIf1DAIHOE7AeCOO0zIFA2HkPrt75781b5bG1+h7gJvlb85YOjXmXHgjv2QhGQyEfBBBAICmw8maRB+eJ6DuDXXuKnP8NkQumARRCgSgFQuXXn6VWrVpFIAzhs0iTEQijgNVA6D2Y/vOf/7xMnjzZ+aZWXV0t9fX1MnHixORRFGHE8reZQFjcUXzxdT0QPhEAdTZw1772B8IP1gPhnQDYX87lQPjiDg53QyBMAi89mNg9dMtLiVa/6xMil80ROWZAmHpBWz0CUQuE7o7t+jNUTU0NY40AAggUVKAggXDkyJFSV1fnhMDp06c738yi+NsuAmFBn015fbseCJ8IgI83bZKNO/e2u2HfoxMHwus/F5zMgfCFHQ1qRyACAttaRO6dIfLvexKd6X+KyOU/Fhn0ngh0Lt5diFIgzPTqjY4ym8rE+1mn9wgUQqAggbCyslKmTp0qU6ZMEQ2HOlM4e/ZsaWlpidTyBwKh3Udy+54DsnJtYidQDYEvbdrV7gZlXY+SkUP1QPh+cl51fzn1RHb9szsC1IZARAX27xZ5+Psij3wv0cGy40TGfFvkPXUR7XD8ukUgjN+Y02MEELAnYDUQarN0JvDuu++WhoYGueuuu9ptm1xbWytz5syRsrIyez3oxJoIhOb4egSEGwDXvLKtQ4VnuQfCV/eTmiEcCG8uTg0IxEzg2TsTm8Zsb010vKZe5JJvifToEzOIaHc3SoEw2iNF7xBAoBQFrAdCbye9yx4qKiqckKjvE0blQyDMfSSfffXNRABs2iSr123pcCD88BN6Jd4DrO4vo4b1laO7HZX7TSiBAAIIvPG8yPKrRF5elbA4aaTI5TeL9DsZmwgKEAgjOKh0CQEEiiZQ0EBYtF500o0IhNnhW7bsdpZ/agB8fO0m2ba7/YHwA3r3cI6B0GWg5w/vL/16dcteKVcggAAC6QTatoqsuE7k7w2JK/oMEnnf9SKnfRSzCAtELRDG6QivCD+WdA2B0AgQCA2GikDYEW/r7v3yaOPGRABs2iQvb21rd1HvHl1k1NC+h0NgfxnW/2iDEaAoAggg4BH4289EHpgjoqGwS3eRc69OHCXRpQdMEReIWiCM0xFeEX806R4CoRCwGgjd32jpRjLTpr19llNbW5uzqYx+eIcwFM9F4Ebu2f+WrFqXOBD+scZN8q8N2zuUHT20r5w7XJeB9pN3n8SB8IFxuRABBIIJtDyRWB668YXE9ad+WOQD80R6DwxWnqtCLxA0EK5evVomTZrk9Dfobp36s43umD5z5syivPYStyO8Qv/w0QEEIiBQlECoThw7EYGn5XAX/tGyTR5r2pg4D3BdxwPhT6vo7ZwFqAFQN4LhQPjojD09QaCkBHa8JvLnWSLP/i7RrH7DRcbdIFJ1Xkk1k8YUXiBIINSz/WbMmCHz5893gt2yZctk5cqVaX9R7f4ye/ny5VLMfRC8v1yPwxFehX86uAMCCGQTKEogdL+pcuxEtuEozb9vemNnciOYJ17aLDv2HGjX0Mryns4SUA2A5wzrJ8f27FqaHaFVCCAQHYGHF4o88n2R/W2JHUMvniUy8svR6R89yUkgSCDUANjc3JxcweQPiOlu2JkzhHE4wiungeZiBBAoiICVQKjfVPW3WK2th7f1TtNUjp0oyBhar3Tjjr3yiLMRzEZ5rGmzc0C891PuHgg/rJ+cf3I/GXhsNI4RsQ5JhQggYF/ghT8mZgW3NifqPuuzImO/mzhbkE9sBYIEQl2ppB/3lRZ3Jk6Xg9bU1KS1K3Yg1IbE6Qiv2D60dByBEhIoWiAs5nKLYvlGZVOZXfsOOhvAOO8BNm0SnRH0fvRAeF36qTOA+i7gO0/sXSxi7oMAAggkBDavFfnDlSLrH0v898CzRWpvFBlwOkIIiAbCCRMmSLdu7Xeqvvzyy6V378T/Z2nIqqqqkvHjxzv/XcqB0DukUT/Ci8cXAQQ6X8BKIHS7kW5Tmc7vZmFaEOZAqO/+6TuA+i6gvhPo/5xVeaycW93fWQo6kgPhC/MAUSsCCGQX2LtD5MH/Fln548S1xwwQGXudyBkTspflitgIaCDUoJcqEPbp0ycZCPVfwjBDGJuBo6MIIFASAlYDYUn0qIiNCFMg1N0/dRdQ90D4tv0H20lVH3/4QPjh/WT0sH4cCF/E54hbIYBAGoGnfiVy/zUiuzYmLjjv6yIXThfp2hMyBNoJBFkyGpZ3CBlaBBBAoNgC1gNhnA5TLeVA+Oq2NnnkxU3y2NrEMtAtu/a1e7ZO0APhdQlodT+54GQOhC/2Fx73QwCBDAKtT4ksv1pkw9OJi05+f+IYieOGwIZASoEggTDbLqPufgi6tNT7TmFnvEMYp5+leKQRQKDzBawHwjgdplrsQPhc63bpXdZVTjqu4yYu29v2O7N/zj+Nm6Rly+52T9cx7oHwh98DrO7fq/OfPlqAAAIIeAV2bxa571siT9+e+NPyoSLjfigy9CKcEMgoECQQagWZziH0B0LvsRPuzYu1OV6cfpbi0UYAgc4XsBoI43aYarEC4cqXNsuXFv9dth8+7kE3dblp0lnSurUtuRHMs6++2eFpGjW0b3IjmDM5EL7zv9poAQIIpBd4/CaRh+aL6DuD3XqJXDRD5JwpiCEQSCBoIAxUWSdf5J0dXLp0acYdUDu5qdweAQQiIlCQQDhy5EjnGIr6+vr/3979B9txFfYBP/1hY/HDMg88wbKRBBEtnYYqGRiBC4SmTtM0keoyE1lmVP/RaqqZtq5TppZkJJphbCQkuWTCjMlk1Gpa7FHrQZ06U7lxpi0pJskIu2GIIG1JrYARIEP8kx+xoLSTzrnv7fO+1f2x955z7967+7kzjIW05+zZzzlv3/3u2d0Titc5W5h+8hHzrmO/Fb7+/KWRFcQF4f/qluX1AN/+hteEq674syPL2IAAAQKNCnz50yE8/P4QnvvycjO2vi+En7knhFdc22iz7HyxBNoYCGMPnDhxIiwtLS1WZ2gtAQILJzCVQLhx48bQhcVUZzVDuPmu/9R3YL0+Lgi/8hxg/K8F4Rfu50+DCXRX4IULITxyIIQ//I1lg+t+PIQdvxLChp/orokjn1igTYEwIrTxIvrEnasgAQJTF8gaCIuT2MMPPxxOnjwZHnrood7VreIzq3vvp662soMmA+GP37A+/Prt75rVodoPAQIE8gj88MUQPvPREH77XyzXF2cCf/pDIfzE381Tv1o6KdC2QDjoBTed7FwHTYDA1AWyB8Jyi9u+mOqsAuE/uP/3wn/5n99aMxh+8aY3hff/jb8w9QFiBwQIEMgm8Af/PoT//M9D+M7F5Spv/Mch/LUPhPCyV2XbhYq6KbDogXDYW0WrPbp161a3knZzmDtqAlMTmGognFqr56TiWQXC+AbRk7/zlfDZLz/bO/JfeNvrw8633jAnCppBgACBEQJ//L9COPOLIXztseUN3/Ce5beHvuZH0RHIIiAQZmFUCQECHRUQCBM6flaBMKGJihIgQKA5gUvPh/Cpe0L4vZPLbXj15hB+9iMh/MWfa65N9txKgUUPhK3sFAdFgMDCCGQPhOU1fqLChg0bes8TbtmyZWFQ6jZUIKwrZTsCBDon8N//ZQi/dTiEGAqveHkIP/nPQnj3nZ1jcMCzERAIZ+NsLwQItFMgayActpDqkSNHws6dO1ulKBC2qjsdDAECOQQufHb59tCnv7Rc24/9Qgh/83AIr3pdjtrVQaCvQFsC4enTp8PBgwdD+SV85e9WbfwuZUgTINC8QLZAWJ4ZLC+kOujvmz/09BYIhOmGaiBAoCUC3/1mCL/5gRD+x39YPqBr3xzCzR8P4Ya3teQAHcY8C7QhEF66dCkcOnQonDlzJhTfo6p3XcU+sFj9PI9EbSOwmALZAmFxBavf1aviildcqH7fvn2LKdWn1QJha7rSgRAgkCLwmeMh/PYvh/DDSyGse3UIN/1SCG/7+yk1KktgLIE2BMLiTaPxwIsF6YvvVjEExgXq9+zZE9761reGw4cPh3Xr1o1lZGMCBAgMEsgSCIurWp/73Of6Pi9YrKfTtpOYQOgHiwCBTgt86eEQfvNgCC98dZlh294Q/voHQ7hqfadZHPzsBdoUCDdu3NgLfPG7VbyQ/vTTT/e+W11//fW9GcQLFy5YdmL2Q8weCbRaIEsg7HdVq6w26t8XVVggXNSe024CBJIEnv2jEP7jPwnhq7+7XM3r3x7CzfeF8Fproya5KjyxQBsD4Te+8Y01M4IRRyCceIgoSIDAEIGsgbC4qlW9jUEgNAYJECDQAoEffDeET38khLMfXz6Y9TeE8DMfDuEvv7cFB+cQFlmgDYGwerfV5z//+d4LZorHbYq7ra699lozhIs8WLWdwBwKCIQJnWKGMAFPUQIE5lfg+y+E8Pv/LoR4S+g1G0N488+HcOmFEP7rh0L4k6dD+PNXhfDOfxrCu9+//GcfAg0LtCEQRsJhL5Fp6/sYGh46dk+AQAghayA8d+7cUNStW7e26qqWQOhniACBVgr8m58P4cnf6X9of+lvh/C3joZw9fWtPHQHtZgCbQmEUb8IfvHPxYv6itnBixcvesvoYg5RrSYw1wICYUL3CIQJeIoSINC8wIvPhfDiMyH8yTMr/306hO98PYTP/PLlbYszgbtPh/CGn2y+3VpAoCLQpkCocwkQIDBrgSyBcNaNnpf9CYTz0hPaQYBATyDezrka7lZC3ovPhvC9IvDF8Pf0SyGwL9ufxptHLv+XTe8M4e/9BmgCcynQtUBYvJth//79Ydu2bXPZJxpFgMDiCAiECX0lECbgKUqAwGiB731rOeCthrhnQ4gBbzX4rfw5zvLF2b5xP3HW7xWvDeHlr13572uW//v4vwrh//1gbW1b3xfCe39t3D3YnsBMBATCmTDbCQECLRUQCBM6ViBMwFOUQBcFvvtUZQYvzt6VZuxWb998NoRLz48vdOUr1wa7Iui94tpS8HvNcgCML4sZ9Pn9UyE8clcIP/jO8hY/8mMhvO/fhnDNpvHbpASBGQgIhDNAtgsCBForIBAmdK1AmICnKIE2CHz766Vn8OJsXfl5vGJmb+Xvi3A1znHHBd5fvjJrtzqL99oQXnntyqzeSriLs3q5X/IS3zT6zS8uLzL/ur8yTqttS2DmAgLhzMntkACBFgkIhAmdKRAm4ClKYN4EfvhiafaudCtmNeT1btl8JoT/873xj2Ddqyu3aK7cqtmbwSuFuxj+XvW68etXgkBHBQTCjna8wyZAIIuAQJjAKBAm4ClKYNoCcRH13i2Y8bm7PjN31b//4aXxW7Tm+bviObw4g1d+Lq8U+sbfgxIECNQQEAhrINmEAAECAwQEwoShIRAm4ClKYFyB73/78jdorpm9K4JffCbv2RD+b+WlKHX298of6TODF2/ZLD+DtxL2Xr5Up0bbECAwAwGBcAbIdkGAQGsFBMKErhUIE/AUJRBfmlJeIqG8HEJ19u6735zM6+oNpTdoxpm6lefxYsArP5MX//6qaybbh1IECDQuIBA23gUaQIDAAgsIhAmdJxAm4CnaPoHq+ner/7942UrpbZox/I37uWLd5SFu2Fs0X/aqcfdgewIEFlRAIFzQjtNsAgTmQkAgTOgGgTABT9HZCHz6aAif/dUQ4u2WcZmBn/1ICG/eXm/f3/vjlWfvyoudr4S76jN58RbNcT8Dl0iIM3l93qJ5xcvH3YPtCRDoiIBA2JGOdpgECExFQCBMYBUIE/AUnb5AXEvu1//R2v3EELb9oyH8mT+3/JxdzjXwBi2REF+8Un2LZu4lEqavaQ8ECMyxQNcC4Rx3haYRILCAAgJhQqcJhAl4iuYViLdgfvtrIcR18eL/XvhaCH/4SAjPf2Xy/QxcIqH0Ns3iLZuWSJjcWUkCBJIF2hYIn3vuubB3795w7ty5y2y2bt0aTpw4EZaWvNgqeeCogACBnoBAmDAQBMIEPEXHE3j2j14KfDHsrYa/r4XwnW/0f6Pmn8af8D67ed1bQlj60dLbNPu8RTPO6PkQIEBgQQTaFgjvvffeXujr9xEIF2RQaiaBBRIQCBM6SyBMwFP0JYFidq8X9OIMX2mmL/45vpxl1OdlV4ew/oYQrnl9COvj/24IIdZ79uNrS8bt3v9Fb9Qc5enfCRBYKIE2BcLy7OCpU6fCtm3bFqovNJYAgcUTEAgT+kwgTMDrUtFnz5dm98qBb8jsXtWnCHnFf6+54aXgd82mEK58RX/R//aREL70cAjf+oMQNr0zhJ/6QAib390lfcdKgEAHBNoYCGO3uTW0A4PXIRKYAwGBMKETBMIEvLYUjW/irM7oFc/x1Z3diy9jiTN6vf+tzO5V/9wWL8dBgACBKQi0KRBGnnjL6GOPPSYQTmGsqJIAgcsFBMKEUSEQJuAtStFpz+69enMIllNYlNGgnQQIzKlA2wLh+fPnw549e3rB0C2jczroNItAiwQEwoTOFAgT8OahaL/ZvfJzfHXW1hs0uxfX/IuzfJZXmIee1gYCBFou0KZAOOwNo7EbvVSm5YPZ4RFoQEAgTEAXCBPwZlF0zexe+c2cXw/huS/Xa8Flz+6VXtry6k1m9+op2ooAAQJTFRAIp8qrcgIEWi4gECZ0sECYgJdadHV2b+WNnKszeysvbRl7dm9jCL0XtZSe4zO7l9pLyhMgQGAmAm0KhDMBsxMCBAiUBATChOEgECbgjSr6zBODX9YyzuxebxmG8staVv6/2b1RPeDfCRAgsDACAuHCdJWGEiAwhwICYUKnCIQT4n3vW2vX25todu+atW/mNLs3YWcoRoAAgcUXaGMgLC9Of+TIkV4nHTx4MMQ/79y5c/E7zREQIDA3AgJhQlcIhAPwcszuFS9l6Tu7F9/MuS6h5xQlQIAAgTYJtC0QlsNg7KcYAm+66aawd+/esHHjxnD48OGwbp3fg20aw46FQJMCAmGCficDYd/ZvZXn+OK6ey8+N1r0KrN7o5FsQYAAAQJ1BdoUCIu3jMbgd+edd4Y77rgj7Nq1K2zfvj0cOnQoXLhwwfqEdQeG7QgQqCUgENZi6r9RKwNheXavdytnEfa+HsLzX6mnNWh2Lz7Pd018M6ermvUgbUWAAAECdQTaGAhjCCxmBQXCOqPANgQITCogEE4qF0KYeSD8/gshxNm1ST/F7N4LF9Y+w/ft4s2ck87ulZZiuHrDpK1TjgABAgQITCTQpkB46dKl3kxg/JRnCDdt2hR2794dduzY4ZbRiUaJQgQIDBIQCBPGxswC4Te/EMKDu0OIQS5+Nr8rhL/zq8uzbeXPM//7pRm9F1ZCXi/sTTq7V1mKwexewmhRlAABAgSmJdCmQBiNHn/88V746/c5depU2LZt27Qo1UuAQAcFBMKETp9ZIPyVt7wUBov2vuZNIbz2TSF895vLt3Jeen70kVy1vrQEQ7x9s1h3b2MI668Pwbp7ow1tQYAAAQJzJ9C2QBiBz58/H/bs2RMuXrzY896wYUM4efJk2LJly9z5axABAostIBAm9N/MAuGH1tdrZZzB672Vc2XdvfJSDGb36hnaigABAgQWTqCNgXDhOkGDCRBYWAGBMKHrGg2EMeD99C+FsN7sXkIXKkqAAAECLRAQCFvQiQ6BAIHGBFoZCIsHss+cOdODrbOIa/nWjLq3ZcwsEP7rnwvhq7+7dpC8564QfuoDjQ0cOyZAgAABAvMiIBDOS09oBwECiyjQykAYF3SNn3379oViPZ/9+/cPfAg7hsEDBw6EY8eOjXVv/swCYXy76CN3lV4q8+4QbvyHaW8cXcTRqs0ECBAgQKCPQNsCYbEwfXyBzNLS0uqzhN4wavgTIDANgdYFwhgAY/i76667VsNdOSBWEYvZxFtvvXXst3bNLBBOo+fVSYAAAQIEWiLQpkBYXnbi8OHD4b777ustRF989u7d27vg7UOAAIFcAq0LhP1m+06fPh3Onj3bd92eYgbx3Llzq6Z1r8AJhLmGoXoIECBAgMDkAm0KhMX3kre//e29mcEYAJ9++uneG0Yfeuih8Nhjj/UCYpw59CFAgEAOgVYGwqNHj4bjx4+vniyHBcIYIMvbF1fmrrvuujVX4B544IHLvO++++7wxBNP5OgHdRAgQIAAAQITCrQ1EL73ve/thcJrr722FwJjKBQIJxwkihEgMFCglYGw+jzgOIEwSsUFYWOgLF+Bu//++y9DvOeeewRCP1wECBAgQKBhgTYFwkhZPENYsMZZwttvvz0cOnSo91fxVtJ169Y1rG73BAi0RaB1gXDcZwj7bR8D4YMPPjjyhOuW0bb8GDgOAgQIEFhkgbYFwvLb0rdu3dq7QP2pT30qHDx4sHcLqWcIF3m0ajuB+RNoXSAsrqzF//Z7y2hxb/6uXbvCzp07ez0Sr8Q99dRTvQAYP/EK3I033rj674O6TSCcvwGtRQQIECDQPYG2BcLu9aAjJkCgSYFWBsJh6xD2C4TV7etefRMImxy69k2AAAECBJYFBEIjgQABApMLtDIQTs4xXkmBcDwvWxMgQIAAgWkItC0Q9nsDeuFW3ELqLaPTGEnqJNBNAYEwod8FwgQ8RQkQIECAQCaBtgXC6ktlykwCYaZBoxoCBFYFBMKEwSAQJuApSoAAAQIEMgm0KRCWZwdPnToVtm3blklJNQQIEOgvIBAmjAyBMAFPUQIECBAgkEmgjYEw0liAPtMAUQ0BAkMFBMKEASIQJuApSoAAAQIEMgm0KRBGknjLqAXoMw0O1RAgMFJAIBxJNHgDgTABT1ECBAgQIJBJoG4gjOsM7969u7fXOs/iDdv+/PnzYc+ePeHixYurR1GnzjqHXNQdg6FbRuuI2YYAgRQBgTBBTyBMwFOUAAECBAhkEqgTCGPIOnDgQDh27FjYsmVLOH36dDh79mxvDeJ169Zd1pJR21f/PdOhhGFvGK0bZHO1RT0ECHRDQCBM6GeBMAFPUQIECBAgkEmgTiCMAfDJJ58M+/bt6+11VKAbtf2o8pMemkA4qZxyBAhMKiAQTioXQhAIE/AUJUCAAAECmQTqBMJ4+2X8FIGwCF779+/ve1vmqO2rt4zmul00E4lqCBAgUFtAIKxNdfmGAmECnqIECBAgQCCTQAyEt9xyS7jyyivX1HjzzTeHq6++uvd3MeBt3rw57Ny5s/f/6wTCcbd/6qmnBt6CmulQVUOAAIHsAgJhAqlAmICnKAECBAgQyCQQA2EMev0C4fr161cDYfxDrhnCatPjjOHRo0fD8ePHw9LSUvKRlRenP3LkSK++gwcPhvjnItQm70QFBAgQCCEIhAnDQCBMwFOUAAECBAhkEqhzy+ioZwKrTRl3+5yBsBwGY7tiCLzpppvC3r17w8aNG81CZho3qiFAYFlAIEwYCQJhAp6iBAgQIEAgk0CdQFjnraFxGYliqYdR2z/yyCO9dwnEN5bGT/WZw0kPrbiVNQa/O++8M9xxxx1h165dYfv27eHQoUPhwoULFqyfFFc5AgT6CgiECQNDIEzAU5QAAQIECGQSqBMI467qrCtYXvtv2Pblf4t179ixI8vMXREIYwgsZgUFwkwDRTUECAiEuceAQJhbVH0ECBAgQGB8gbqBcPyaZ1/i0qVLvZnA+CnPEG7atCns3r07W/Cc/ZHZIwEC8ypghjChZwTCBDxFCRAgQIBAJoE2BcJIUp19LDOdOnWq7zIZmShVQ4BABwUEwoROFwgT8BQlQIAAAQKZBNoWCCNLdZ3DDRs2hJMnT64+s5iJTjUECBDwUpmUMSAQpugpS4AAAQIE8gi0MRDmkVELAQIERguYIRxtNHALgTABT1ECBAgQIJBJQCDMBKkaAgQ6KSAQJnS7QJiApygBAgQIEMgk0IZAWLxd9Ny5cwNVLEqfacCohgCBNQICYcKAEAgT8BQlQIAAAQKZBLoSCCOXl8pkGjSqIUBgVUAgTBgMAmECnqIECBAgQCCTQJsCYVyQ/vDhw2HdunVrdIo3j+Za7zATvWoIEGiBgECY0IkCYQKeogQIECBAIJNAFwJhcUtpJDtx4kRYWlrKpKcaAgS6LiAQJowAgTABT1ECBAgQIJBJQCDMBKkaAgQ6KSAQJnS7QJiApygBAgQIEMgk0IZAOIrCDOEoIf9OgMCkAgLhpHIhBIEwAU9RAgQIECCQSaALgdAzhJkGi2oIELhMQCBMGBQCYQKeogQIECBAIJNAFwJhJirVECBAQCDMOQYEwpya6iJAgAABApMJCISTuSlFgACBKGCGMGEcCIQJeIoSIECAAIFMAgJhJkjVECDQSQGBMKHbBcIEPEUJECBAgEAmAYEwE6RqCBDopIBAmNDtAmECnqIECBAgQCCTgECYCVI1BAh0UkAgTOh2gTABT1ECBAgQIJBJQCDMBKkaAgQ6KSAQJnS7QJiApygBAgQIEMgkIBBmglQNAQKdFBAIE7pdIEzAU5QAAQIECGQSEAgzQaqGAIFOCgiECd0uECbgKUqAAAECBDIJCISZIFVDgEAnBQTChG4XCBPwFCVAgAABApkEBMJMkKohQKCTAgJhQrcLhAl4ihIgQIAAgUwCArCzWf4AABNxSURBVGEmSNUQINBJAYEwodsFwgQ8RQkQIECAQCYBgTATpGoIEOikgECY0O0CYQKeogQIECBAIJOAQJgJUjUECHRSQCBM6HaBMAFPUQIECBAgkElAIMwEqRoCBDopIBAmdLtAmICnKAECBAgQyCQgEGaCVA0BAp0UEAgTul0gTMBTlAABAgQIZBIQCDNBqoYAgU4KCIQJ3S4QJuApSoAAAQIEMgkIhJkgVUOAQCcFBMKEbhcIE/AUJUCAAAECmQQEwkyQqiFAoJMCAmFCtwuECXiKEiBAgACBTAICYSZI1RAg0EkBgTCh2wXCBDxFCRAgQIBAJgGBMBOkaggQ6KSAQJjQ7QJhAp6iBAgQIEAgk4BAmAlSNQQIdFJAIEzodoEwAU9RAgQIECCQSUAgzASpGgIEOikgECZ0u0CYgKcoAQIECBDIJCAQZoJUDQECnRQQCBO6XSBMwFOUAAECBAhkEhAIM0GqhgCBTgoIhAndLhAm4ClKgAABAgQyCQiEmSBVQ4BAJwUEwoRuFwgT8BQlQIAAAQKZBATCTJCqIUCgkwICYUK3C4QJeIoSIECAAIFMAgJhJkjVECDQSQGBMKHbBcIEPEUJECBAgEAmAYEwE6RqCBDopIBAmNDtAmECnqIECBAgQCCTgECYCVI1BAh0UkAgTOh2gTABT1ECBAgQIJBJQCDMBKkaAgQ6KSAQJnS7QJiApygBAgQIEMgkIBBmglQNAQKdFBAIE7pdIEzAU5QAAQIECGQSEAgzQaqGAIFOCgiECd0uECbgKUqAAAECBDIJCISZIFVDgEAnBQTChG4XCBPwFCVAgAABApkEBMJMkKohQKCTAgJhQrcLhAl4ihIgQIAAgUwCAmEmSNUQINBJAYEwodsFwgQ8RQkQIECAQCYBgTATpGoIEOikgECY0O0CYQKeogQIECBAIJOAQJgJUjUECHRSQCBM6HaBMAFPUQIECBAgkElAIMwEqRoCBDopIBAmdLtAmICnKAECBAgQyCQgEGaCVA0BAp0UEAgTul0gTMBTlAABAgQIZBIQCDNBqoYAgU4KCIQJ3S4QJuApSoAAAQIEMgkIhJkgVUOAQCcFBMKEbhcIE/AUJUCAAAECmQQEwkyQqiFAoJMCAmFCtwuECXiKEiBAgACBTAICYSZI1RAg0EkBgTCh2wXCBDxFCRAgQIBAJgGBMBOkaggQ6KSAQJjQ7QJhAp6iBAgQIEAgk4BAmAlSNQQIdFJAIEzodoEwAU9RAgQIECCQSUAgzASpGgIEOikgECZ0u0CYgKcoAQIECBDIJCAQZoJUDQECnRQQCBO6XSBMwFOUAAECBAhkEhAIM0GqhgCBTgoIhAndLhAm4ClKgAABAgQyCQiEmSBVQ4BAJwVaGQgvXboUDh06FM6cOdPr1CNHjoSdO3cO7ODTp0+HgwcPrvn3vXv3hn379g0dFAJhJ39mHDQBAgQIzJlA3UD4+OOPh927d/dav3Xr1nDixImwtLQ0Z0ejOQQIEJitQCsD4b333ttTjIHuueeeCzHc7d+/P2zbtq2vbgyEZ8+eDYcPHw7r1q2r3QMCYW0qGxIgQIAAgakJ1AmE58+fDwcOHAjHjh0LW7ZsCZP+7p/aQaiYAAECDQm0LhDGABjD31133dU74cdPOSD2c570l4JA2NCotVsCBAgQIFASqBMI4+/6J598cvXun2pABEqAAIGuCrQuEPY7wY8KfNVbRuvcLhoHjEDY1R8bx02AAAEC8yRQJxBWLw7XuYNono5RWwgQIDAtgVYGwqNHj4bjx4+vPhcwKhCWcYtfELt27Vrz3OEDDzxwWR/cfffd4YknnphW36iXAAECBAgQqCEQA+Ett9wSrrzyyjVb33zzzeHqq6/u/V0MhJs3b1793S4Q1oC1CQECnRBoZSAsPyMQe3GcQFhsX76tJP7d/ffff9mAuOeeezoxSBwkAQIECBCYd4H4qMgVV1xxWSBcv379aiCMfyheGCcQznuPah8BArMSaF0gnOQZwip29TmDWXXGqP187GMfC2984xvDjh07Rm3q3wmsCly8eLH3TG2/ixqYCAwT+OAHPxi2b98e3vGOd4AiUFvgC1/4QvjEJz4RPvrRj9YuM4sNPUM4C2X7IEBgEQVaFwhjJwx7y2j1ltC4RMUnP/nJ3q0m8Q2j83zFUCBcxB+x5tssEDbfB4vaAoFwUXuu2XbPayD0ltFmx4W9EyAwvwKtDITD1iHs94xgDJBxLaLiM2rdwqa6UyBsSn6x9ysQLnb/Ndl6gbBJ/cXd97wGwihqHcLFHVdaToDA9ARaGQinx9VszQJhs/6LuneBcFF7rvl2C4TN98EitmCeA+EiemozAQIEpi0gEE5bOGP9AmFGzA5VJRB2qLMzH6pAmBm0I9UJhB3paIdJgEBrBATC1nSlAyFAgAABAgQIECBAgMB4AgLheF62JkCAAAECBAgQIECAQGsEBMLWdKUDIUCAAAECBAgQIECAwHgCAuF4XrYmQIAAAQIECBAgQIBAawQEwhl1ZVwK4+jRo+G2224LW7ZsybLXuKbSAw880Ft0PK6h6NNOgfia9EcffTTs27cvywFOYyxmaZhKsgpM4/yQeyxmPWCVZROISzG95z3vCdu2bctSp3NOFkaVECBAYGoCAmEN2mHrGhbF4/qG+/fv74WzfoGv+uWsWA/x3LlzvSpOnTp12S/fWCaGyOPHj4elpaXLWnr69One3+3cuXPN2krx73bs2BEOHz68GhRTjqG6TmPRkH5trsHZqU3qrHkV+/HJJ58cGPjKX87q9GMEjmU2b97cGxvVT3ksxn87dOhQOHPmzOpm1X5NOYa4rz179oT4ptP4mdc1PudtUNY5P4w655TPD/H46vTjqHNOeSyW68t9zinGcLE+bPV8Nm/9NU/tif1+8ODBXpMGuQ0758Rx9eEPfzjEN8zG3zt1zjmjxuK455xhxzBq3eA643ye+ktbCBAgMA8CAmGNXoi/gOInztAUX9Ri+ItXT8u/LDds2BBOnjzZNxCWv5wVZW688cbeF/b4y/LAgQPh2LFjvbLlL4Nbt24N8UtRNRBWr7jG+jdt2rSmTdddd91qyMhxDAXVqC+NNUg7sUm1X2MfnT17djWol7+47N27t28grH45G9aPEbX8RWpQ+CqPxVh/HLO333577+JBbFMc58U4TjmG6s9K9f93YhBMcJCjzg91zjnV88OofqxzzqmOxWmec8o/K8VFi/L5bALWThSJP7/xAmLxO6N8vqheFBh0zqnOAuf43THOOWfYMcRxfd999/UuMsXficUFp9jG+Pt41DjvxCBwkAQIEJhAQCAcgdbvymf1l2ysYtgV0n5fzsozf9UvgHWC16jbwcpfqGL91dnLcY+hzDRs9mmCMdjaItWr8NUvK8WBD7taX/5yVncsxnoH9dGoW7eqoS3lGKrHO2ict3YATHhg1Qsug9yGnXOq54e6/TjsYs+o20VznXP6XTiohoQJaVtfrPpzP8ht2DmnPAtc95wzzu+/aidU+7vuMcR6qj8bdcd56weCAyRAgMCYAgLhCLB+X+KrMz2jAmH1y1m/X9L9AtqwL2fV28Gqh1GuL8cx1AmpY4691m9e7dNBM2R1v5zV7cdhgXDUhYTqPlKPIZZ/+OGHezOO8TPsFujWD4iaB1j3/DDsS3j1/FC3H4edc0Y9V5brnNPv52TQxZSapJ3YrN+Fg3EvQlVngeuec8a5OFHtjPI+rr/++t4t7MXdM3HbYX3fL0zGMsXz1u5K6MTQd5AECGQQEAhrBMLql9hxA2G/K54PPvjgmmf8xgmEo57XqH6h7Pclb9xjKJjMDtb/qatajRsIq/1Wtx+HBcJh/dfvC2XqMcSxGG+FfuaZZ3rPEXqGcPT4iWZ1zg+DzgODZnXKz5QOGouDAuGo28Rzn3Oq50OBcPS4KX5+b7311tXn0ccNhP1m2HL//isfSfWcM84xFOe5cgCse74arWkLAgQIdEtAIKwRCMvP98XNxwlT/b6c1Z0BGPQlrN8XxuIwqs+ADbrCOs4xlOsuP5/SrR+V8Y+27qzMoBnCOrc/9evHQYFw1G1d8cp89TmtlGOojt8ihOzatavvy27GF25nibrnh0H9WSdQjhsIR93WXH7uNMc5p/xMY9HLg56nbucoGP+oUmcI64axcX53jHvOGecY4rnpqaeeGnph1Qzh+ONICQIEuikgEI7o99RnKPp9Oav7jNCgQDholqdfGIyHl3oMsQ7Pf41/gqgT6IoLDNW3jPb7cla3HwcFwkEXEop99XtpR8ox1Akm46u2v0Td88OgL9v9zg91+7HfOaffWBx2ASrXOafc06OeX2z/qKh3hHWfv+sX8Pv1fd1zzjgXJ8q/T/qdc+ocQ78w2O9cama53rixFQECBATCGmOgPEsy6IrjOF/ORr1FsGhS3V/QcftRL11IOYY69ddg7Nwm1S8jg2bz6n45K4Je/G+/N96WgfuFgn5/Nyropx5DfBtg3G98A6AZwno/AnXPD/3OOYPOQ3X7sd85Z9idCsPuGEg955TPg9W7NOpJdm+rUW8ZLUT6nXMGzQKn9OMk55xRx9Dv8YpBY2XQObd7I8MREyBAYLiAQFhjhAxbh6n6b7G6Yu2n+G/l9ZzKu6reElVe+63f7VLFK8IHXSmPvySLNbuK/ZSXwZj0GOJSBG67qTFIBmwybE2s6jpusYpiHAx6adCwfozlY7liDbL4/4sxEF/R3m8sxi/75XUCi8Mov5J+0mMoLiTs3r17VcczhPXG0rDzw7Bzzhe/+MXw6KOP9l3CZFg/DjvnDBqL0zznlMelW0XrjZly2Bu0DuGgc85b3vKW3gufbrvttsuWTZr0d8eg3391zjnl81h5LcV+47T8O7dYOqc45xg7440dWxMg0F0BgXCKfT+N25xGvelvioej6hkJjFoaYpJmTGMsTtIOZaYrkPv8MI2xOF0BtU8iMOrtw5PU6ZwziZoyBAgQaEZAIJyie+4vZ/Hq6KAZxykehqpnLDCNL2e5x+KMSeyuhsA0zg/TGIs1DsUmMxYYtYzRJM1xzplETRkCBAg0IyAQNuNurwQIECBAgAABAgQIEGhcQCBsvAs0gAABAgQIECBAgAABAs0ICITNuNsrAQIECBAgQIAAAQIEGhcQCBvvAg0gQIAAAQIECBAgQIBAMwICYTPu9kqAAAECBAgQIECAAIHGBQTCxrtAAwgQIECAAAECBAgQINCMgEDYjLu9EiBAgAABAgQIECBAoHEBgbDxLtAAAgQIECBAgAABAgQINCMgEDbjbq8ECBAgQIAAAQIECBBoXEAgbLwLNIAAAQIECBAgQIAAAQLNCAiEzbjbKwECBAgQIECAAAECBBoXEAgb7wINIECAAAECBAgQIECAQDMCAmEz7vZKgAABAgQIECBAgACBxgUEwsa7QAMIECBAgAABAgQIECDQjIBA2Iy7vRIgQIAAAQIECBAgQKBxAYGw8S7QAAIECBAgQIAAAQIECDQjIBA2426vBAgQIECAAAECBAgQaFxAIGy8CzSAAAECBAgQIECAAAECzQgIhM242ysBAgQIECBAgAABAgQaFxAIG+8CDSBAgAABAgQIECBAgEAzAgJhM+72SoAAAQIECBAgQIAAgcYFBMLGu0ADCBAgQIAAAQIECBAg0IyAQNiMu70SIECAAAECBAgQIECgcQGBsPEu0AACBAgQIECAAAECBAg0IyAQNuNurwQIECBAgAABAgQIEGhcQCBsvAs0gAABAgQIECBAgAABAs0ICITNuNsrAQIECBAgQIAAAQIEGhcQCBvvAg0gQIAAAQIECBAgQIBAMwICYTPu9kqAAAECBAgQIECAAIHGBQTCxrtAAwgQIECAAAECBAgQINCMgEDYjLu9EiBAgAABAgQIECBAoHEBgbDxLtAAAgQIECBAgAABAgQINCMgEDbjbq8ECBAgQIAAAQIECBBoXEAgbLwLNIAAAQIECBAgQIAAAQLNCAiEzbjbKwECBAgQIECAAAECBBoXEAgb7wINIECAAAECBAgQIECAQDMCAmEz7vZKgAABAgQIECBAgACBxgUEwsa7QAMIECBAgAABAgQIECDQjIBA2Iy7vRIgQIAAAQIECBAgQKBxAYGw8S7QAAIECBAgQIAAAQIECDQjIBA2426vBAgQIECAAAECBAgQaFxAIGy8CzSAAAECBAgQIECAAAECzQgIhM242ysBAgQIECBAgAABAgQaFxAIG+8CDSBAgAABAgQIECBAgEAzAgJhM+72SoAAAQIECBAgQIAAgcYFBMLGu0ADCBAgQIAAAQIECBAg0IyAQNiMu70SIECAAAECBAgQIECgcQGBsPEu0AACBAgQIECAAAECBAg0IyAQNuNurwQIECBAgAABAgQIEGhcQCBsvAs0gAABAgQIECBAgAABAs0ICITNuNsrAQIECBAgQIAAAQIEGhcQCBvvAg0gQIAAAQIECBAgQIBAMwICYTPu9kqAAAECBAgQIECAAIHGBQTCxrtAAwgQIECAAAECBAgQINCMgEDYjLu9EiBAgAABAgQIECBAoHEBgbDxLtAAAgQIECBAgAABAgQINCMgEDbjbq8ECBAgQIAAAQIECBBoXEAgbLwLNIAAAQIECBAgQIAAAQLNCPx/Zr+tPwZOQ8YAAAAASUVORK5CYII=" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7193852a", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2021" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "940fe45e", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2021, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "ff881c13", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "664a090a", + "metadata": {}, + "source": [ + "### Detection data drift performance" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7500011519622525\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True,\n", - " date_compile_auc = '01/01/2021', #optionnal, by default date of compile\n", - " datadrift_file = \"car_accident_auc.csv\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "7a36e381", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 21, + "id": "8d53819b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6585689489728102" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Performance of data drift classifier\n", + "SD.auc" + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "a06ecc4b", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "335aa0c3", + "metadata": {}, + "source": [ + "An Auc close to 0.5 means that there is little drift" + ] + }, + { + "cell_type": "markdown", + "id": "6738e258", + "metadata": {}, + "source": [ + "### Importance of features in data drift" + ] + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_car_accident_datadrift_2021.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "markdown", + "id": "2f4d21ff", + "metadata": {}, + "source": [ + "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "5271a9c4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "SD.xpl.plot.features_importance()" + ] + }, + { + "cell_type": "markdown", + "id": "e419d134", + "metadata": {}, + "source": [ + "We get the features with most gaps, those that are most important to analyse.\n", + "With date bias introduced, it is normal that date features are the most impacted. We will then decide to remove them.\n", + "Let's analyse other important variables" + ] + }, + { + "cell_type": "markdown", + "id": "9b746cf7", + "metadata": {}, + "source": [ + "### Feature importance overview" + ] + }, + { + "cell_type": "markdown", + "id": "56bf2287", + "metadata": {}, + "source": [ + "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "5e24ae92", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.scatter_feature_importance()" + ] + }, + { + "cell_type": "markdown", + "id": "8b02726b", + "metadata": {}, + "source": [ + "Putting importance of the drift into perspective according to the importance of the model to be deployed, can help the data scientist to validate that his model can be deployed.
\n" + ] + }, + { + "cell_type": "markdown", + "id": "61392c46", + "metadata": {}, + "source": [ + "### Univariate analysis" + ] + }, + { + "cell_type": "markdown", + "id": "9081041c", + "metadata": {}, + "source": [ + "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "1d38878c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate('season_acc')" + ] + }, + { + "cell_type": "markdown", + "id": "f9ffecb5", + "metadata": {}, + "source": [ + "### Distribution of predicted values" + ] + }, + { + "cell_type": "markdown", + "id": "b32be6ad", + "metadata": {}, + "source": [ + "This graph shows distributions of the production model outputs on both baseline and current datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "b5d31bc1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\")" + ] + }, + { + "cell_type": "markdown", + "id": "0190d633", + "metadata": {}, + "source": [ + "## Compile Drift over years" + ] + }, + { + "cell_type": "markdown", + "id": "64962082", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2018" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e07c46d4", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2018, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "e359a75c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7036329129677259\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True,\n", + " date_compile_auc = '01/01/2018', #optionnal, by default date of compile\n", + " datadrift_file = \"car_accident_auc.csv\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "feda5ce2", + "metadata": {}, + "source": [ + "----" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "d94cc30b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric()" + ] + }, + { + "cell_type": "markdown", + "id": "bed0a0d5", + "metadata": {}, + "source": [ + "------" + ] + }, + { + "cell_type": "markdown", + "id": "6b0aefd4", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2019" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "1d18e162", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2019, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "06d918ad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7856527709300022\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True,\n", + " date_compile_auc = '01/01/2019', #optionnal, by default date of compile\n", + " datadrift_file = \"car_accident_auc.csv\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "d3fc185d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric()" + ] + }, + { + "cell_type": "markdown", + "id": "f0f5b5f4", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2020" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "4c11bc6f", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2020, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "4c3e4f9e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7902450838961592\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True,\n", + " date_compile_auc = '01/01/2020', #optionnal, by default date of compile\n", + " datadrift_file = \"car_accident_auc.csv\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a1ccc557", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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L6XYZ1UPQv/KVr0iqd+u8x0XkGwi1zfm013YgzGWXUa9FrktGtd1r1qxxNo057bTTOuyO6t3N1LvLqLt00r8DrTvm7s6x//73vwPNEOZbX7ovBfe8zD/84Q8yd+5cueyyy9pdmq59BELDby4URwABBIogQCAsAjK3QACBaAvYCoSqpGfH6Tl4eti7ntWW6oy9fM71SxcI3Zkz3cDkmmuucc7m0/Cpxxb8z//8j7ObqX7SBUL/TpqFOIcw3TLcXDaV0T4U6xxCfR70OBBdAjphwgRnp1ed6dV3+vR8SD2Kwn8OoTsO27dvd47RuOiii5xx0ACpx4foM/Hoo486Y5FqhlA3INJzKocNG+Zck299mb5SdWx11lOfTW2jLmvVNuqspv7y4r777uvQvkIFQn9/o/0dht4hgAAChRUgEBbWl9oRQCAGArkeO6Ek6WaC3B/kdcZFDyjXH7x79uzZQbG5udkJBs8995yz2Yi+Z6aHtOuf62Yko0aNcoKHe0ZhukCo1+oSRw1/+qmqqnKOBtB26NECH/jAB5xjL/yB0P1B3723vquoO32m2yQk1/ZqW2wuGdX6tK+33367cxyH/rsesK7n4qm1hhrtiwZcPSLBnZHNZ4ZQ77Vp0ybn3Mh//OMfyfHRENja2uoY6/0GDx6c3GVU23PrrbfKz372M2cc3LZt3brVKTN06FB5z3ve4/zCwHsMh/fZ04Ps9TmYPn26U3c+9WX6cvW+l+j66TEU+qzop7Ky0tkRtZC7jKbrrxuEY/Dthi4igAAC1gUIhNZJqRABBOImYDMQepfm6REIOsOU7qOzeDoLdc8998jzzz/vXPbud79b9OgKnWHSgON+0gVCNyjp7NMvfvELp56ysjLneIHPfe5z8vrrrzvLVv2BUJdgani56667nNkud/MaXS6Z6nq9Ty7tLUQg1Do11Og5gBqs9BB4DWYapHQJpPZh0KBB7bjzDYRuf/UohjvvvNM5sF3v86lPfcq5l4ZF/XiPndCxf+CBB+RXv/qVMw66sc6pp57qLB8eO3as6FmAuhS1urq63VJUDdsLFy50jqXQoOaeZZlvfdlCofrpLxH0mdFZTw2q+u6rhkH9BUYhA6G2LV1/4/Z9h/4igAACtgQIhLYkqQcBBBBAAAEEEEAAAQQQCJkAgTBkA0ZzEUAAAQQQQAABBBBAAAFbAgRCW5LUgwACCCCAAAIIIIAAAgiETIBAGLIBo7kIIIAAAggggAACCCCAgC0BAqEtSepBAAEEEEAAAQQQQAABBEImQCAM2YDRXAQQQAABBBBAAAEEEEDAlgCB0JYk9SCAAAIIIIAAAggggAACIRMgEIZswGguAggggAACCCCAAAIIIGBLgEBoS5J6EEAAAQQQQAABBBBAAIGQCRAIQzZgNBcBBBBAAAEEEEAAAQQQsCVAILQlST0IIIAAAggggAACCCCAQMgECIQhGzCaiwACCCCAAAIIIIAAAgjYEiAQ2pKkHgQQQAABBBBAAAEEEEAgZAIEwpANGM1FAAEEEEAAAQQQQAABBGwJEAhtSVIPAggggAACCCCAAAIIIBAyAQJhyAaM5iKAAAIIIIAAAggggAACtgQIhLYkqQcBBBBAAAEEEEAAAQQQCJkAgTBkA0ZzEUAAAQQQQAABBBBAAAFbAgRCW5LUgwACCCCAAAIIIIAAAgiETIBAGLIBo7kIIIAAAggggAACCCCAgC0BAqEtSepBAAEEEEAAAQQQQAABBEImQCAM2YDRXAQQQAABBBBAAAEEEEDAlgCB0JYk9SCAAAIIIIAAAggggAACIRMgEIZswGguAggggAACCCCAAAIIIGBLgEBoS5J6EEAAAQQQQAABBBBAAIGQCRAIQzZgNBcBBBBAAAEEEEAAAQQQsCVAILQlST0IIIAAAggggAACCCCAQMgECISeAVu2bJk0NzfLtGnTQjaMNBcBBBBAAAEEsgmsXr1aJk2a5Fw2YsQIWbRokZSXl6cstmXLFqmvr5c1a9YEuj7bvfl7BBBAoFQFCIQi4v0/CP3mTyAs1ceVdiGAAAIIIJCfQFNTk8yYMUPmz58v1dXVor8EXrlypcyZM0fKyso6VKo/G6xfv17Gjx/v/F226/NrFaUQQACBzhcgEHrGgBnCzn8gaQECCCCAAAKFEPD/f7w/IGa7pwbEBQsWZJxVzFYHf48AAgiUogCBkEBYis8lbUIAAQQQQMCqwMKFC5363FVA7pLQ6dOnS01NTdZ7afkNGzaknVHMWgEXIIAAAiUqQCAMGAiXLFnSYQivuOKKEh1WmoUAAggggEC8BH7/+9/L9u3b23X68ssvl969ezt/poGuqqoquQQ0aCDUmcVZs2ZlfecwXtr0FgEEoiRAIAwYCBcvXtxh3K+77jppbGyM0vNAXxBAAAEEEAidwGWXXeYEvW7dunUIhH369EkGQv2XfGcIWTIauseCBiOAQEABAmHAQJjKc/jw4QTCgA8alyGAAAIIIFAoAQ2EP/nJT2TYsGFpb2H6DqHOKOry0pkzZzqb0vBBAAEEoiJAICQQRuVZph8IIIAAAjEVCBIIs+0yqn9fV1fnLC3Vdwo1QA4ePDj5fqH+9x133MGmMjF9xug2AlEWIBD6jp1wB3vp0qVZXzJnhjDKXxr0DQEEEEAgLAJBAqH2JdM5hP5A6P53a2urw5Dt3MKwWNFOBBBAwC9AIDR4JgiEBngURQABBBBAwJJA0EBo6XZUgwACCERKgEBoMJwEQgM8iiKAAAIIIGBJgEBoCZJqEEAglgIEQoNhJxAa4FEUAQQQQAABSwIEQkuQVIMAArEUIBAaDDuB0ACPoggggAACCFgSIBBagqQaBBCIpQCB0GDYCYQGeBRFAAEEEEDAkgCB0BIk1SCAQCwFCIQGw04gNMCjKAIIIIAAApYECISWIDu5Gj3rsb6+3jnvUY/+8H/a2tpk9uzZMnr0aBk/frzz13pMyKJFi4x3gfUfS2KTwl+324/ly5dLbW2t9O/fX7p06SLTpk2zeVvqQiCwAIEwMFXHCwmEBngURQABBBBAwJIAgdAOpJ61OGvWrHaVaUALGlRMQ1WugVCPEVmwYEHybEiT+5uUzaaf7QxMDbX6Ceqc7X7u37vj6T9KTf985cqVMmfOHCkrK3MuT2XvDa5unbk8D0HbyXWdL0AgNBgDAqEBHkURQAABBBCwJBCnQLi9bb/c96/X5ZWtbfLOit7yvneeYElRxB8U3ECgN/CGh3Q3NA1V2QKh/77+9prc36RsrgNQqADobYeO3bx585w/6tWrV7uwGSQQumMxcuTIdmVvueUWGTt2rFRXV+faba4vYQECocHgEAgN8CiKAAIIIICAJYG4BMKXt7bJh258WLbvOZCU+8TZg+R740dYkUwVFPzLNN2gsGbNGueeuuRRw6J+dDmnLoN0PzozpcFBZ5X817szUxrE6urqpLW1tV05XTKqwWnnzp2yY8cOp95rrrlGnnzySWfJqH68s5mf//znZdOmTR3un2rpqb8Pc+fOlTPPPFNmzJgh8+fPd9rsb5d3ZiydgfbJXcKq7auoqJCGhganrW7dd911lzOj6X703s3Nzc5/ujOE/vrdGT73z9X8tttuc8po/anCmbZ/yZIl8olPfEJuvPFGZya1vLzcKRMkEKa6xspDRiUlKUAgNBgWAqEBHkURQAABBBCwJBDWQHjXU69K86ZdgRUeevENefrlNztc/7lzh0ifHl0C1/OxswbJ4L49O1yfLgR4//yf//ynU06DlhtQJk6c6LzTl2qWTZd1prveX94/Q6jh6u67706GHn84zWeG0H9PrfPBBx8U/ZnOGwjvvfde58+84VDb4wbVqqqq5HuMixcvlnHjxjn9/81vfpOcTXX7rkHMW7d/htD736lMdaZPA51+NJhWVlZmnbFVG/1ouzSof/KTn0y+l5ktEJ5++ukd3tUM/HBxYSgFCIQGw0YgNMCjKAIIIIAAApYEwhoIP9OwWh5u3Bhc4dAhkSOO6HD9IRHp+Kfpq/3lF2rkwpP7Bw6EGmy8Qcdb0Btmgiy79F7vfwcwVSDUe7kzZzYCof+ebl8ytd17XzdgnXjiiR3e+dOgdccddyTfaUxXd6ZA6G+fe28NdO5sa7pNd9z7uctFr7jiCqeMPwAGDYTeEBn8IeXKMAoQCA1GjUBogEdRBBBAAAEELAmENRCGcYbQvyxSh9BdTpkuVHmXUXqv9wfNYgXCVOE2006g7mOqyzt1JjTVklP/rqdaxl1O++qrrwaeIVSTSZMmdfjK8C6/zRYI/a7ZNrXRm2mftN6ZM2fKwIEDmSG09L0pLNUQCA1GikBogEdRBBBAAAEELAmENRDm2n19h/CDNz4sOzzvEH78rEHy/QnFe4dQw92GDRuSSxazzRBmur5UZwjdQOTOAqY67iLd7J93ls49IsP/fmK2GcJ0s7FBN93xB3B/oE014xskNOb6vHJ9eAQIhAZjRSA0wKMoAggggAAClgTiEgiVS3cZ/bPuMrplt7PL6GWnDbCkGGyXUW+YcYOSG5xSBZZcrndnx9xNVPzBKduS0SCBKdV7i/qe4jnnnJOcxfPPkHnL6JLR3/72tzJhwgTnyAZvkGpsbEy+d+htay6B0N8+HVz3XcQgS0bTGXhDoLZNZ3Xddz/1Hv7gzi6j1r6sQlERgdBgmAiEBngURQABBBBAwJJAnAKhJbKU1QQ5h9C7+6buotmvXz8ZNWpU8n06bx0a7HRDFXcX0VTXe5dI6hLLlpaW5MH0uQZC7ZT//ql2GfXvIJpql1Fvu9x26zt1ujTUPwPnBlj/cs90S2kzzRBqH/xLUkeMGJHcmVTrzLRkNN07kt4loal2UXWXt7q7v2o7OIewkF9tpVU3gdBgPAiEBngURQABBBBAwJIAgdASJNUggEAsBQiEBsNOIDTAoygCCCCAAAKWBAiEliCpBgEEYilAIDQYdgKhAR5FEUAAAQQQsCRAILQESTUIIBBLAQKhwbATCA3wKIoAAggggIAlAQKhJUiqQQCBWAoQCA2GnUBogEdRBBBAAAEELAkQCC1BUg0CCMRSgEBoMOwEQgM8iiKAAAIIIGBJgEBoCZJqEEAglgIEQoNhJxAa4FEUAQQQQAABSwIEQkuQVIMAArEUIBAaDDuB0ACPoggggAACCFgSIBBagqQaBBCIpQCB0GDYCYQGeBRFAAEEEEDAkgCB0BIk1SCAQCwFCIQGw04gNMCjKAIIIIAAApYECISWIGNQzcKFC51eTps2LQa9zb2LbW1tMnv2bBk9erSMGzcu+e/jx4/PqbLVq1fLggULZNGiRVJeXp5TWdOLvX3Itd2m9w5reQKhwcgRCA3wKIoAAggggIAlAQKhHUj9IX7SpElOZfX19aEMTVu2bHHavmbNmiRKbW2tzJkzR8rKyiQKgdA7TtrJiooKaWhokOrqauMHIZ9A6JpPnz5dampqnDYUKhC67Vu+fHm7vi5dujR5bwJh7o8BgTB3s2QJAqEBHkURQAABBBCwJBCrQLhnm8gL94hsaxEZcLrIKR+ypPh2NcuWLZPm5ubQBcJ0QeCWW26RsWPHOoEpKoHQO/um4UtnPG2EwnzCVKpAaP2hPFxhqvY1NTVJXV2dTJ48WZgRzE+eQJifm1OKQGiAR1EEEEAAAQQsCcQmEG5bL/LT80X2vPm23Ls/LfKRWyxJJqrxB0L/rJs7G+P+uc7A3XbbbdLa2ire2Th/uUx/F7TOTB3VYDBjxgyZP39+2tkyDYQ7d+6UHTt2iM4y+WfXtO+zZs1K3iZou9x7f/jDH5brr7/eKe+fZdV76xJK/9/pPVeuXCnHHHOM3H777c7fe2e8/H32z765IemTn/ykrF+/vl1dbhvSjaHWnWpWde7cuSmXjPqv1ev0lwduv7Q+/bPBgwe3WzKa6f7ZxsTb/3SB1Tv2AwcOTC51HTNmjDMO3tlL/3PinXEdMWJEcpmrf0zdv9O+aADV5939eMfL+wx5n31gVzwAACAASURBVPls4+yf/fQ+P+nqtPWFTyA0kCQQGuBRFAEEEEAAAUsCoQ2Ez9whsvml4AqNfxFpfbLj9SO/LNLj2OD1jJgoUj407fXeQOj+ID9x4kRn9kV/SJ43b57zw74bbCorK50lmfpx3z/Ta/UH/aqqquSszeLFi52Q4ZbLp85MnXTb6rZHl4j6P9qmu+++Ozmb5v6Q7rb/t7/9rUyYMMFZXqp/d8cdd7QLcen66s5Saf90ts4/a+a/jzqdeOKJzrXuD/tuqMi23NL/9957aSDUQOsNKJnGUPvpbUumJaP+evTaBx98UEaOHNkhdHnb6N5D30t0nyENVDoWusQ005j4xzBdIMzUbv8vOLz/7Z9d9f6df0y94dl9dlONs/vM6LuT3q+BTOPsHwe917333isXXXSR87ymqzP4F33mKwmEBpIEQgM8iiKAAAIIIGBJILSBcMnHRNauCK5w6JDIEUcEvz7dlf/xO5HqSwMFwkyzUboE0z/74v4A7G5I4oYe783yrTPIcsB0s11uWf+SUW3Lb37zm+Q7ht52emeS9If7dH11Q453dtI7a3f66ad32JzFa7BixQpnVs99z9EbulNtyOL38wZXf13an0zeWn+qdqfaVCZdUM32DqH+vX/m1jsOuYxJpiWt/mfPG0DdX2K4wUtnU90wqkbuJkNe+1TtzhTG042z+3xpsEs3zqnupe1K1d9Mz2y+3xwIhPnKsWTUQI6iCCCAAAII2BMIbSAMwQyhfwMTd9R0BipTINSQlGp5of65SZ25PjXuvdwZs2zhw50VcpcDuktKbQRCN4RoH7zBI59A6G7+o3V5lzl6ZyLd2bVM3tovNyzpv2eaaUsXRIIEQu89tM3embhsY+Id83xmCL3h3N9f7zJe9z7epaH+IOs38PbdDYT+DW/cZaPZAqHfyBsI09WZahY8168RvZ5AmI/a4TLMEBrgURQBBBBAAAFLAqENhLn2X98hvOU8kb3b3y454lMiH/1prjVlvN6/pC7dDFqqIOBfJureyDvbpuVs1Bmk0/4AkSl8vPrqq867Ye5SxrDMEHod0gXCILOgGvCzBcJUR0kECYSFniFM9w6hOzPsPtO6hFk/3hlj77Jmr2Wqd1KDzBB6g3+msck2G+kNhOnqDPI1EOQaAmEQpTTXEAgN8CiKAAIIIICAJYHYBEL1cnYZ/aPI1haRE3WX0cQ7ebY+/gDlf29M76M/FOsn25JR7/t4qcKV+x5W0DqzLRnVeyxZskRmzpzpvAOoH/8P9dkCoTe4eN8vM5kh1KWJ3mWd/vfF/CEu1yWj2QJhpjH0L3P07tjpP4fQX4/+t8566bh43x11x9MNj/7+uvfwvkOoZdxlm5mWRAbZZTTVNdpW3Vjmrbfect6xdI/oSLVDq/uua6plnP7w65+B9o6zO+Pqfh1kmiFM9S6nt5z3HULtn/dry8bXPoHQQJFAaIBHUQQQQAABBCwJxCoQWjLzV5NpF0P/0k93SZ3Wkem9Ov9yvFQbnbjnBQatM1P3U51R599FNNvyRG+bzzjjDOd2umupaSDUerLtMprvO4TZAqH+fbox1H55l8leeOGFTnX6NZXqYHr/klrdUdS/DDifXUZzDYT5nEOo/hs2bOjwvqh/Sa27u2e6XWu91+ty0O3btzu/hHBDpn+nWtcoW/D3j1G6XUbVyq3T1rcDAqGBJIHQAI+iCCCAAAIIWBIgEFqCpBoEEMhJINtsbk6VdeLFBEIDfAKhAR5FEUAAAQQQsCRAILQEWeLV+GdevM31zwSWeFdoXogFvDO9UXnuCIQGDySB0ACPoggggAACCFgSIBBagqQaBBCIpQCB0GDYCYQGeBRFAAEEEEDAkgCB0BIk1SCAQCwFCIQGw04gNMCjKAIIIIAAApYECISWIKkGAQRiKUAgNBh2AqEBHkURQAABBBCwJEAgtARJNQggEEsBAqHBsBMIDfAoigACCCCAgCUBAqElSKpBAIFYChAIDYadQGiAR1EEEEAAAQQsCRAILUFSDQIIxFKAQGgw7ARCAzyKIoAAAgggYEmAQGgJkmoQQCCWAgRCg2EnEBrgURQBBBBAAAFLAgRCS5BUgwACsRQgEBoMO4HQAI+iCCCAAAIIWBIgEFqCpBoEEIilAIHQYNgJhAZ4FEUAAQQQQMCSAIHQEiTVIIBALAUIhAbDTiA0wKMoAggggAAClgQIhJYgqQYBBGIpQCA0GHYCoQEeRRFAAAEEELAkQCC0BEk1CCAQSwECocGwEwgN8CiKAAIIIICAJQECoSVIqkEAgVgKEAgNhp1AaIBHUQQQQAABBCwJEAgtQVINAgjEUoBAaDDsBEIDPIoigAACCCBgSYBAaAmSahBAIJYCBEKDYScQGuBRFAEEEEAAAUsCBEJLkFSDAAKxFCAQGgw7gdAAj6IIIIAAAghYEohiIFy4cKEsWrTIEZo7d67zv7NmzXL+ffz48ZbkqAYBBBAQIRAaPAUEQgM8iiKAAAIIIGBJIGqB0BsG3UA4ZswYqa+vl8rKSpkzZ46UlZVZ0qMaBBCIuwCB0OAJIBAa4FEUAQQQQAABSwJRCoRbtmxJBr+pU6fKlClTZOLEiTJu3DiZPXu2tLS0ODOH5eXllvSoBgEE4i5AIDR4AgiEBngURQABBBBAwJJAFAOhhkB3VpBAaOlBoRoEEEgpQCA0eDAIhAZ4FEUAAQQQQMCSQJQCYVtbmzMTqB/vDOHgwYNl0qRJUltby5JRS88N1SCAQEKAQGjwJBAIDfAoigACCCCAgCWBKAVCJVm9erUT/lJ9li5dKjU1NZbkqAYBBBAgEBo9AwRCIz4KI4AAAgggYEUgaoFQUZqamqSurk5aW1sdo4qKCmloaJDq6morZlSCAAIIuALMEBo8CwRCAzyKIoAAAgggYEkgioHQEg3VIIAAAlkFCIRZidJfQCA0wKMoAggggAAClgSiFgjdYyf0mIlp06Y5Sqn+zBIf1SCAQMwFCIQGDwCB0ACPoggggAACCFgSCBoIve/mjRgxIuPxDf4lm/7r/X+vXclWZ5DuupvK+I+X8B5HwTmEQSS5BgEEggoQCINKpbiOQGiAR1EEEEAAAQQsCQQJhBrgZsyYIfPnz3few1u2bJmsXLky7Y6dGh7Xr18v48ePd1qpM3QbNmxIXu+vz1JXJF3wSxcUbd2XehBAIL4CBEKDsScQGuBRFAEEEEAAAUsCQQKhBsDm5ubkEsxcA50GxAULFiRnFXMtH7SrBMKgUlyHAAK2BAiEBpIEQgM8iiKAAAIIIGBJIEgg1Bk+/bjv5LnBa/r06YGOcfDPKGZbUmrSNfd9wblz5yZnKN3lrt73Ck3uQVkEEEDAFSAQGjwLBEIDPIoigAACCCBgSUAD4YQJE6Rbt27tarz88suld+/ezp9pyKqqqkoGrFwCYZDZQP+SUpOupXo/Uevj6AkTVcoigEA6AQKhwbNBIDTAoygCCCCAAAKWBDQQ6rt+qQJhnz59koFQ/yXXGUI3nGngy3QgvF43b948Z1lpeXm5cc/cwLpmzRqnLsKgMSkVIIBAGgECocGjQSA0wKMoAggggAAClgSCLBnN5x3CoGFQu2E7EFqioRoEEEAgqwCBMCtR+gsIhAZ4FEUAAQQQQMCSQJBAmG2XUX/4y7ZM9N577xX9OUB3LNWP/x1FS12jGgQQQKDgAgRCA2ICoQEeRRFAAAEEELAkECQQ6q0ynUPoD4Q6ozhr1qwOLVy6dKmzdNRbl15UW1ub9giLXLvpXy7qLW/jrMNc28P1CCAQbQECocH4EggN8CiKAAIIIICAJYGggdDS7QpejbvLaKobEQgLzs8NEIidAIHQYMgJhAZ4FEUAAQQQQMCSQJQCoXd20J2NtMRENQgggEBKAQKhwYNBIDTAoygCCCCAAAKWBKIYCJVm0aJFVnYstcRMNQggEFEBAqHBwBIIDfAoigACCCCAgCWBKAVCJdElo6tWrSIQWno+qAYBBDILEAgNnhACoQEeRRFAAAEEELAkELVAmMtxF5YIqQYBBGIsEMlA2NbWJrNnz5bly5c7Qzt37lznwNp0H/9uXtmud+shEMb4K4euI4AAAgiUjECUAmGmHUYVnE1lSuaxoyEIREYgkoHQexaQ+411+vTpzjbR/o8bHkePHu2Exlx+K0cgjMzXAR1BAAEEEAixAIEwxINH0xFAoNMFIhcINQBq+Js5c2agw2L1HKEFCxa0W6cf9HBZAmGnP780AAEEEEAAAYlSIGQ4EUAAgWILRC4Q6gzfjBkzZP78+clAqIfLrly5MuWBsakCYabrvQNEICz248r9EEAAAQQQ6ChAIOSpQAABBPIXiGQgnDdvnjPrV15e7shkCniplpSmun7JkiUdlK+99lppbGzMX5+SCCCAAAIIIGAsEMVA6D2cXvc20M+sWbOy7otgjEkFCCAQO4FIBsJcZgh1xHWWcNKkSe0Gv76+XqZNm5b8s8WLF3d4OK677joCYey+ZOgwAggggECpCUQtEHrDoFprIBwzZozozyaVlZUpVzyV2pjQHgQQCI9A5AJhru8Qphoq/UZ84YUXptyExns9S0bD86DTUgQQQACB6ApEKRC6K5c0+E2dOlWmTJkiEydOlHHjxjk7qLe0tHA+YXQfZXqGQKcIRC4QqmKmXUbdb7T6zTXVURRB3x/U+xAIO+WZ5aYIIIAAAgi0E4hiINSfU9xZQQIhDzwCCBRSIJKBMNM5hKkCoYZAXZevH/9S0Uz4BMJCPprUjQACCCCAQDCBKAVC92cY7bl3hnDw4MHO6y21tbUsGQ32WHAVAggEFIhkIAzYd+PLCITGhFSAAAIIIICAsUCUAqFipNrbwEVaunRp1ldajEGpAAEEYiVAIDQYbgKhAR5FEUAAAQQQsCQQtUCoLHqMVl1dnbS2tjpKFRUV0tDQkDxSyxId1SCAAAJCIDR4CAiEBngURQABBBBAwJJAFAOhJRqqQQABBLIKEAizEqW/gEBogEdRBBBAAAEELAkQCC1BUg0CCMRSgEBoMOwEQgM8iiKAAAIIIGBJIEqB0N38bs2aNRl1eJfQ0sNDNQggwJJRk2eAQGiiR1kEEEAAAQTsCMQxEKqcHlif6ggtO6rUggACcRFghtBgpAmEBngURQABBBBAwJJAlAKhkuh5yhs2bGh3vIS7yYx71jJHUFh6eKgGAQSYITR5BgiEJnqURQABBBBAwI5AlAKhu2S0srKyXSD0/vm3vvUtuf7666WlpUUWLVok5eXldiCpBQEEYinADKHBsBMIDfAoigACCCCAgCWBKAZCpfGGPQKhpYeFahBAoIMAgdDgoSAQGuBRFAEEEEAAAUsCUQqEbW1tMnv2bFm+fHm7dwSXLVsms2bNktraWpk6dapMmTLF0WOG0NJDRDUIxFiAQGgw+ARCAzyKIoAAAgggYEkgSoFQSVavXi36jmCqj+4uqktE9dD6s88+u92yUkucVIMAAjETIBAaDDiB0ACPoggggAACCFgSiFogVBZ3E5nW1takEkdNWHpgqAYBBNoJEAgNHggCoQEeRRFAAAEEELAkEMVAaImGahBAAIGsAgTCrETpLyAQGuBRFAEEEEAAAUsCUQqE3oPpmRG09IBQDQIIZBQgEBo8IARCAzyKIoAAAgggYEmAQGgJkmoQQCCWAgRCg2EnEBrgURQBBBBAAAFLAlEKhEqih8/r7qHMEFp6QKgGAQSYISzUM0AgLJQs9SKAAAIIIBBcIGqB0N1QZvLkyTJ+/PjgEFyJAAII5CHADGEeaG4RAqEBHkURQAABBBCwJBClQOh9hzAVz4gRIzh70NJzQzUIIJAQIBAaPAkEQgM8iiKAAAIIIGBJgEBoCZJqEEAglgIEQoNhJxAa4FEUAQQQQAABSwJRCoSWSKgGAQQQCCxAIAxM1fFCAqEBHkURQAABBBCwJEAgtARJNQggEEsBAqHBsBMIDfAoigACCCCAgCWBKAZCd6dRJZo7d64jNWvWLOff2WjG0oNDNQgg4AgQCA0eBAKhAR5FEUAAAQQQsCQQtUDoDYNuIBwzZozU19dLZWWlzJkzR8rKyizpUQ0CCMRdgEBo8AQQCA3wKIoAAggggIAlgSgFQneXUQ1+U6dOlSlTpsjEiRNl3LhxMnv2bGlpaWGXUUvPDdUggEBCgEBo8CQQCA3wKIoAAggggIAlgSgGQg2B7qwggdDSg0I1CCCQUoBAaPBgEAgN8CiKAAIIIICAJYEoBcK2tjZnJlA/3hnCwYMHy6RJk6S2tpYlo5aeG6pBAAFmCI2fAQKhMSEVIIAAAgggYCwQpUCoGKtXr3bCX6rP0qVLpaamxtiMChBAAAFXgBlCg2eBQGiAR1EEEEAAAQQsCUQtECpLU1OT1NXVSWtrq6NUUVEhDQ0NUl1dbUmNahBAAIGEAIHQ4EkgEBrgURQBBBBAAAFLAlEMhJZoqAYBBBDIKkAgzEqU/gICoQEeRRFAAAEEELAkEKVA6O4yOn36dJaGWno+qAYBBDILEAgNnhACoQEeRRFAAAEEELAkEMVAuGbNGkdnxIgRHDNh6TmhGgQQSC1AIDR4MgiEBngURQABBBBAwJJAlAOhl2ju3Lkyfvx4S2pUgwACCCQErAVCd0cs/0vP3peio7YzFoGQLyMEEEAAAQQ6XyBKgdCvuXDhQmeG0P0wY9j5zxstQCBqAlYCoXtmzvLlyyXVb6+WLVsms2bNitzZOQTCqH050B8EEEAAgTAKRDkQesdDf5664447WEIaxoeUNiNQwgJWAqH7ArT2U3+LVV5e3q7L2f6+hH0yNo1AGNaRo90IIIAAAlESiGogdH9+ct8n1DFjhjBKTy59QaA0BAiEBuNAIDTAoygCCCCAAAKWBKIUCFOFQJeJdwgtPTBUgwAC7QSsBEJ3yeiTTz6Z8tBU9/3C2tpamTNnjpSVlUViGAiEkRhGOoEAAgggEHKBKAdCZgRD/nDSfARCIGAlEGo/3fcEM20qE7XfbBEIQ/CE00QEEEAAgcgLRDEQcg5h5B9bOohAyQhYC4TejWVS9S5qs4PaRwJhyTzHNAQBBBBAIMYCUQqEMR5Guo4AAp0kYC0Quu13Zwq9/YnazKDbNwJhJz213BYBBBBAAAGPAIGQxwEBBBDIX8B6IMy/KeErSSAM35jRYgQQQACB6AmEPRC6G8lUVlbK1KlTZcqUKeLdWdQ7YrxTGL3nlx4h0NkCBEKDESAQGuBRFAEEEEAAAUsCBEJLkFSDAAKxFLASCDNtkeyqLl26VGpqaiKFTCCM1HDSGQQQQACBkAqEPRCGlJ1mI4BARASKFgjVK2qhkEAYka8CuoEAAgggEGoBAmGoh4/GI4BAJwtYCYTZ+uBuNFNfXy/Tpk3Ldnlo/p5AGJqhoqEIIIAAAhEWiFogdM9vdofMf6RXhIeSriGAQCcIFCUQuktKtX+LFi2S8vLyTuiq/VsSCO2bUiMCCCCAAAK5CkQpEC5cuND5WSnVJ6q7tuc63lyPAAJ2BQiEBp4EQgM8iiKAAAIIIGBJICqB0Dsz6H3NJt2fW+KjGgQQiLkAgdDgASAQGuBRFAEEEEAAAUsCQQOhN1hlO76hqalJ6urqpLW11WlltuttdMWdHUw1ExjV129suFEHAgiYCRQlEEb1mxiB0OzhozQCCCCAAAI2BIIEQg14M2bMkPnz50t1dbXozyYrV66UOXPmSFlZWYdmaHhcv369jB8/3vk7DWsbNmxIe71pP9ra2mT27Nny5JNPSkNDg9NG78cNqGeffXbB2mDaB8ojgEA4BawEwiDHTigPu4yG8yGh1QgggAACCJSyQJBAqAGwubk5ubmdPyBm658GxAULFhRsL4Rs+y1k+/ts7efvEUAAgXQCRQuEUQuDCsoMIV9YCCCAAAIIdL5AkECoM3z6cXc7dwPW9OnTA52TnG1G0VTBbU9lZWXKGUACoakw5RFAoKCBMK68BMK4jjz9RgABBBAoJQENhBMmTJBu3bq1a9bll18uvXv3dv5MA2FVVVVyCWgugTDX2cR8bAiE+ahRBgEEbAhYmSG00ZAw1kEgDOOo0WYEEEAAgagJaCDUd/1SBcI+ffokA6H+S64zhO67exooa2pqCkYX9PWbYmxuU7BOUjECCJSkQFECobupTNS+iREIS/KZplEIIIAAAjETCLJkNJ93CIsVBnW4CIQxe2jpLgIlJFCwQOjulrV8+fJkdwmEJTTyNAUBBBBAAIGICAQJhNl2GfWHv2IsE40IP91AAIGQC1gPhN4zfvw2qc7VCbMfM4RhHj3ajgACCCAQFYEggVD7mukcQn8gdFc3+Y2iuEleVJ4D+oEAAvkJWAmEqWYDvc2J2syg2zcCYX4PHaUQQAABBBCwKRA0ENq8J3UhgAACURGwEgj9697dAKgHveohqy0tLQU7t6czB4JA2Jn63BsBBBBAAIGEAIGQJwEBBBDIX8BKIGSGMP8BoCQCCCCAAAIImAkQCM38KI0AAvEWsBIIvYSZ3iGM2rp7Zgjj/cVD7xFAAAEESkOAQFga40ArEEAgnALWA6HLwC6j4XwgaDUCCCCAAAJhEyAQhm3EaC8CCJSSQMECobeTnENYSkNOWxBAAAEEEIiWQNQCYaYzCaO6UV+0nkh6g0C4BIoSCMNFEry1LBkNbsWVCCCAAAIIFEogaoFw4cKFzmZ8qT4EwkI9RdSLQHwFCIQGY08gNMCjKAIIIIAAApYEohQIvbODUdt7wdJwUw0CCFgWIBAagBIIDfAoigACCCCAgCWBKAZCpdFZwvLycktKVIMAAgikFiAQGjwZBEIDPIoigAACCCBgSSBKgVBJdMnoqlWrCISWng+qQQCBzAIEQoMnhEBogEdRBBBAAAEELAlELRA2NTVJXV2dEwxramosKVENAgggwAyh9WeAQGidlAoRQAABBBDIWSBKgTDTDqMKw6YyOT8eFEAAgSwCVmcI4/YiNIGQry8EEEAAAQQ6X4BA2PljQAsQQCC8AgRCg7EjEBrgURQBBBBAAAFLAlEKhJZIqAYBBBAILGA1EOpd3bNz4rBVMoEw8HPGhQgggAACCBRMIIqB0H2PsLW11XGrqKiQhoYGqa6uLpgjFSOAQDwFrAdC9xvY5MmTZfz48ZFWJRBGenjpHAIIIIBASASiFghXr14tkyZNSqkfh1+4h+Sxo5kIREbAaiCM24vQBMLIfB3QEQQQQACBEAtEKRC2tbXJ7NmzZfny5eINf25IrK2tlTlz5khZWVmIR4ymI4BAKQkQCA1Gg0BogEdRBBBAAAEELAlEKRC6v1yvrKxsF/zcoNjS0sL5hJaeG6pBAIGEgNVAGDdUAmHcRpz+IoAAAgiUogCBsBRHhTYhgEBYBCIZCL3LLXQg5s6dm/F9Rv9S1/r6epk2bVrWMSQQZiXiAgQQQAABBAouEKVAyJLRgj8u3AABBHwCBQmE7k6jbhjT/501a1bWYGZrdPT++tFQ54a96dOnS01NTYdbuN94R48e7YRG/39nahOB0NaIUQ8CCCCAAAL5C0QpEKoCm8rk/yxQEgEEchewHgi9YdANhGPGjBGddfOvh8+9udlLaADU8Ddz5szk1szegOivIVVgzHS9tzyBMPt4cAUCCCCAAAKFFohaIFQvjp0o9FND/Qgg4ApYDYTeF6GnTp0qU6ZMkYkTJ8q4ceOcHbOK8SK0fgOdMWOGzJ8/PxkIly1bJitXrky7K5f+vc5g6m5eer6PP1Cme1wIhHwhIYAAAggg0PkCUQyEna9KCxBAIC4CBQmEGgLdWcHOCITz5s2TBQsWSHl5uTOO2QKhGyL12meeecaZzfS/Q7hkyZIOz8S1114rjY2NcXlW6CcCCCCAAAIlKUAgLMlhoVEIIBASAauB0H3/TvvunSEcPHiwc8BqMc7OyXWG0L/E1O3DiSee2C4ULl68uMOQXnfddQTCkDzoNBMBBBBAILoCBMLoji09QwCBwgtYDYTa3M5+ETrXdwg1QOY6o+gOC0tGC/+AcgcEEEAAAQSyCYQ9EKZ65WbNmjUpuz1ixAjOIcz2QPD3CCCQk4D1QKh37+wXoTPtMup+09WlrLqrqP+/080QplIlEOb0rHExAggggAACBREgEBaElUoRQCAmAgUJhJ1tl+kcQn8ATBVgOYews0eQ+yOAAAIIIBBcIOyBMHhPuRIBBBCwLxDJQGifKXWNzBAWS5r7IIAAAgggkF4gSoHQ/cX1yJEj2+1l4N2nYc6cOVJWVsYjgQACCFgRIBAaMBIIDfAoigACCCAQOYE7n3xF9B/9DDquTK669GQ56bjCB5c4BEI11VdiVq1axTuEkfvKoUMIdK6AcSCM84vQBMLOfXi5OwIIIIBA6Qj8+bnX5EtLnmzXoHee2Fvuuer8gjcyDoHQnSEsxpnOBR8wboAAAiUlQCA0GA4CoQEeRRFAAAEEiiqwc+8B2X/wkOw7cFD2HXhL9h58S/YfOCT7Dib+2/nH8/eJa/XPDsreA28dLqvXHZR9B/V/3y6r1z7x0mbZuGNvhz79ccr5clpF74L2NQqB0L8hXzqwYhzhVdDBonIEECg5AeNAWHI9KmKDCIRFxOZWCCCAQEgEdu1zA5YGp8OhyglUbuh663DAOvzfyTD29t8nr9Xg5iubCGlvl213rRPy3v57DYFF+xw6JHLEER1u9+v6UTJ6aN+CNiMugbCiokIaGhqkurq6oJ5UjgAC8RIwDoSploz6X4SOKimBMKojS78QiLfAc63b5cb7X3RmfAYd11Ped9oAufrS4SWHomGnXTgKGJK8ZZIzZofLtgtfnmB18K1D0rb/4NuhzhfUNASW+qdH1yOlW5ejpPtRR0rXLkdIt6MS/931qCOke5fEv+ufef9O/1z/vtvhv+/R5Ujpov99uKz+uVv+kabNcuffX27HcEyPLvLPay4rOE0UAqGLlG5TmYIjcgMEEIitAIHQYOgJhAZ4FEUAgZIVOG/+Dyk+jgAAIABJREFUA/LK1rZ27au/YKjUnTfEeKmhBjANXe4yxeSyxAO+ZYkHDzrLGXVZY2I5o4axQ1LUGa88R6is61GJAOWEprf/6Xr4vzWQJYLU23+XCGm+Mp6/T1571JFOePOWTd4rxfV6rf5TjM/2tv3yn4v/LqvWbXFup2Hw/9WeJuPPHlTw20cpEBYcixsggAACPgHjQOg/8y+T8IgRIyK1MxaBkK8nBBCImsDLW9vk/PkPdOxWmuWAndn/fIJXMqR5QllidswX0ko4eHWmeZB7azDU56jQ7w162xK1QKi7iS5atEi85yKn+rMg48E1CCCAQDYB40CoNwj6IjSBMNtw8PcIIIBA5wls271fvv+XF2XJyuYOjTjqyCOkvGe3tDNeKWesPDNe+vdBg1fy2lQzaUWc8eq8keDOuQpEKRCm203U+4oO5xDm+oRwPQIIZBKwEgjdG8Rt3TszhHxxIYBAFAQ27twrP31wrSxdtV727H9L5JCI+PYGuWrMcPn62JOj0F36EEGBKAXCdMGPYyci+ODSJQRKRMBqICyRPhWtGQTColFzIwQQKIDAhjf3yI//2iS/emJ9snZd5veRdw+SXzy+Tl7dlniP8ONnDZLv1L5Tepd1LUArqBIBcwECobkhNSCAQHwFCIQGY08gNMCjKAIIdJpA8+bd8uMHGmXZk68k23BW5bFy5SXD5eJTju+0dnFjBPIViFIgVAP3fcG5c+fK+PHjHZbVq1fLpEmT2r1XmK8X5RBAAAGvgHEgTHXsxJo1a1Iq8w4hDx8CCCDQeQKNr++UHz3QKMvXtCYboefDXTlmuJwzrLDnxHVer7lzHASiFgjT7c3AOYRxeJrpIwLFFyAQGpgzQ2iAR1EEECiagHuu4H3/ej15z4ve0V+mjDlZdGaQDwJhF4haINTxcH/h7v6SnTAY9qeU9iNQugLGgbB0u1b4lhEIC2/MHRBAIH+Bf7Rscw6Yf+jFjclKLjttgLM5zCkDjsm/YkoiUGICUQyEJUZMcxBAIMICBEKDwSUQGuBRFAEECibwaNMmuWlFY/KAcL3Rh0dUyFWXnizD+h9dsPtSMQKdJUAg7Cx57osAAlEQIBAajCKB0ACPogggYF1gxfNvyE0PNMrTL29L1j3hPSfJlZdUy0nlPa3fjwoRKBWBsAfCOO/HUCrPEO1AIM4C1gOhuzPW0qVLpby8XOrq6qS1tVVqa2slagepEgjj/KVD3xEoHYF7/rlBbn6gSf61YXuyUVeMGixfvbhaTuzTo3QaSksQKJAAgbBAsFSLAAKxELAaCN1DU1VOw9/NN98sixYtSkLW19fLtGnTIgNLIIzMUNIRBEIp8L//eFV+8tcmadq402l/WdejZNKoSvnShcOkf6/uoewTjUYgH4GwB8J8+kwZBBBAwJaA1UDoLnkYOXKkMzOoAXDjxo3S0NAgd911l6xatcoJiDpzGIUPgTAKo0gfEAifwK9Xt8hPHlwrL2/Z7TS+V/cu8tlzquQ/zx8qx/bk8PjwjSgtNhUgEJoKUh4BBOIsULBA+NGPftQJhf3793dCoIZCAmGcHzX6jgACJgJ7D7wlGgR/+uBaeW37HqeqPmVdpe68IfK5c4dI7x5dTKqnLAKhFgh7IPQfMZFpMKJ2pnOoHzwaj0BEBKwGQjVx3yF0fXSWcPLkyTJ79mznj6L0HiEzhBH5KqAbCJSwwO59B2Xxyma59ZGXZNPOfU5L+/Xq5swGXjG6Snp2O6qEW0/TECiOAIGwOM7cBQEEoilgPRC67xEuX75c3N9irVixQmbNmuUsIeUdwmg+SPQKAQTsCmzfc0B+/tg6ue3RdfJm236nct0gRt8P/FRNpXTvcqTdG1IbAiEWCHsg9NPrL9c3bNjQ7pfoTU1Nzsor/SX7+PHjQzxaNB0BBEpNwHogLLUOFrI9zBAWUpe6EYinwLbd+2XRw2tl8cr1snPvAQdBj4z46kWJIMgHAQQ6CkQpEHqPoPCuqvJv3FdWVsajgAACCFgRIBAaMBIIDfAoigAC7QQ27twrt/x1rdy+er3s2f+W83fV/XvJ1y6plo+eORAtBBDIIBDFQKjd9W7E5wZF/5/zYCCAAAKmAtYDIecQmg4J5RFAIE4Cr25rc46OWLqqJdntd57YWyZfUi0fPP3EOFHQVwTyFohSIPS+ejN37tzk8tDVq1fLpEmTInmuc94DT0EEELAiYDUQcg6hlTGhEgQQiIFA8+bdcvMDjXLnk68ke/vuk46VKy8ZLmNOPT4GAnQRAXsCUQqEquK+L9ja2toByRsS7QlSEwIIxFnAaiDkHMI4P0r0HQEEggg0vr5Tblzxotz9zIbk5SOHlMuVY4bLedX9glTBNQgg4BOIWiDU7qU6imLp0qVSU1PD+COAAAJWBQoWCDmH0Oo4URkCCIRc4LnW7XLD/S/KX/71erInFwzvL1ePPVnOqjw25L2j+Qh0rkAUA2HninJ3BBCIk4DVQKhwnEMYp8eHviKAQDaBf7Rskxv+8qI83LgxeenYd54gV196spxW0Ttbcf4eAQQCCBAIAyBxCQIIIJBGwHog5BxCnjUEEEBA5JHGTXLTA42yet2WJEftiAqZcslwGX5CL4gQQMCiQBQDofcX7PreoH70TGfeIbT44FAVAgg4AtYDYZxcOXYiTqNNXxEIJnD/86/LTSuaZM0r25IFPnH2IJl8yXCp6tszWCVchQACOQlELRD6V1tpCBwzZozU19dLZWVluwPrc4LiYgQQQCCFAIHQ4LEgEBrgURSBiAn88ZkNcvNfG+X5DTuSPZs0slK+enG1DDyWA6QjNtx0p8QEohQIvQfTT506VaZMmSITJ06UcePGyezZs6WlpaXd+YQlNhQ0BwEEQihgPRCm2hXLdRkxYkSkvokRCEP4xNNkBCwL/O4frzjnCK7duMupuUfXI+XTNYPlyxcNk+OP6W75blSHAAKpBIIGQvcsP60j6M8k+nPN9OnTZebMmVJdXZ28faqjIYLWmWkU3Z+jNAS6s4IEQp57BBAopID1QOhf5uBtvI1vlIXEyLVuAmGuYlyPQHQEbl/VIrc82CQvb21zOnV0t6PkM+dUyZcuGCbH9uwanY7SEwRCIBAkEGqAmzFjhsyfP98JdsuWLZOVK1emXX7p3ROhoqJCGhoaOgRCb322mLxnOntnCAcPHszB9LaQqQcBBNoJWA2E3mUOn//852Xy5MnOrqP6jVfXvetvuMaPHx+ZISAQRmYo6QgCgQT2HnhLNAj+9KG18vr2PU6Z3mVd5QvnDpEvnDdEevfoEqgeLkIAAbsCQQKhBsDm5maZNm2ac3N/QEzXokwzhIUIhNoO70ymv12cRWj32aE2BBCwvKlMqoPpdZmFHqKqwXDVqlUsGeWpQwCB0Ans3ndQfvl4s9z6yEuyedc+p/19j+4mXzx/qHz2nCrp2e2o0PWJBiMQJYEggVB/DtGPGwjdn1ncn1PyCYR1dXXS2trqFLW9Csq/JDXVLGWUxpC+IIBA5wkUbIbQXeYwcuRIZ6Ywii9CM0PYeQ8ud0agGALb9xyQ2x5dJ7c9tk62t+13bnlC7x7ypQuHyqSRg6V7lyOL0QzugQACWQQ0EE6YMEG6devW7srLL79cevdOnPepgbCqqiq5Usk0EPqbpPVv2LCBHUB5WhFAIHQCVgOh+w337rvvdtba33XXXc6MoPupra2N1DdKAmHonncajEAgAZ0F1NnAxY83y659B50yJx1XJl+5qFo+PbIyUB1chAACxRPQQKivpKQKhH369EkGQv0XWzOE/t7pjN68efNkwYIFUl5ennfnvZvzsTw0b0YKIoBADgLWA6H33t5valFc6kAgzOFJ41IEQiDwxo69zkYxv17dInv2v+W0eGi/o+Vrl1TLx88aFIIe0EQE4ikQZMmo7XcICYTxfNboNQJRFChoIIwimLdPBMKojzD9i4vAq9va5Md/bXI2jHE/pww4xjlMftwZJ8aFgX4iEFqBIIEw2y6j7jt7uvRT9z5wP+k2lbn33ntFfw5wj6Lwv6Nogunu2M4MoYkiZRFAIKgAgTCoVIrrCIQGeBRFoAQEmjfvlptWNIqeJeh+zhjUR668ZLiMfecJJdBCmoAAAkEEggRCrSfTOYT+QOg9dsJtg/fVF/9OoDZfi3HbonswRGl39iBjyTUIIFB8AQKhgTmB0ACPogh0okDj6zvlhhUvyh+f2ZBsxXuryuXKMdVywfD+ndgybo0AAvkIBA2E+dRd7DLe121S3dv2bqbF7h/3QwCB0hMwDoTZvnF5uxy1b2IEwtJ7oGkRApkEnmvdLj/8y4ty//OvJy87r7qfXDlmuIwckv8mEKgjgMBhgf/7isjTtyf+49hKkY/8RKTq/ILzEAgLTswNEEAgwgIEQoPBJRAa4FEUgSIKrFq3RW5+oFEeadyUvOuYU4+Xb4x9h5xWkdiSng8CCBgKPPETkT99s30lPfqIzHz73VzDO6QtHqVAWCgj6kUAAQTSCRgHwjjTEgjjPPr0PQwCDzdulJtWNMnfmrckm/uhM06Uq8ecLMNP6BWGLtBGBMIj8PMPiqx/rGN7P3d3wWcJCYTheUxoKQIIlJ6A9UDo7oxVX1+fPOsn1Z+VHkXuLSIQ5m5GCQSKIfCXf70uNz3QKM+88mbydh87a6BMGXOyVPXtWYwmcA8E4iWwbb3Izy4V2fVGx35/+RGRAWcU1INAWFBeKkcAgYgLWA2E7o5cLS0tzoH07sGs7nuGlZWVHEwf8QeK7iHQmQJ3P7PBWRr6wms7ks34VE2lTL6kWgYeW9aZTePeCERTYP9ukUd+IPL4j0QO7O3Yxz4niXz92YL3PUqB0P0luqJFbe+Fgj8I3AABBPISsBoI0wW/dEExrxaXUCFmCEtoMGhKrAXufPIV+clfm+SlTbsch+5djhQNgl+9uFqOP6Z7rG3oPAIFE3j2dyL3fVtk+6uJW7zr4yK9ThRZ96DIthaRqvNELv5mwWcH9dZRCYTLli2TWbNmtRsyQmHBnmAqRgCBwwIEQoNHgUBogEdRBCwILF3VIj99sEle3trm1Naz21Fyxegqqb9gqPQ9upuFO1AFAgh0EHjjeZHlV4m8vCrxV/1PEam9UaRyVKdhRSEQes89dA+kd2cL586dy3mEnfZ0cWMEoi9gNRAqV6pvXu7hrd73CqNASyCMwijSh7AJ7D3wlixdtV7+56GX5PXte5zm9+7RRT537hD54nlDpHdZ17B1ifYiEA6BPW+KrLhW5G+3JtpbdpzIJd8See8XO739UQiE7iorxXRfu4nqz0+d/sDQAAQQaCdgPRA2NTVJXV2dtLa2trtRRUWFNDQ0SHV1dWSGgEAYmaGkIyEQ2Ln3gCxeuV5ufeQl2bJrn9Pi8qO7Sd15Q+Sz51RJr+5dQtALmohASAU0BD5wvUjb1kQH3vufImO+LaLHSpTAJ0qB0Lvfgvsz1dlnnx2pPRhK4JGhCQgg4BGwHgi1bv9h9VEMg9pPAiFfSwgUXmB7235peHSd/PzxZtF/14++F/ilC4fJpJGDpUfXIwvfCO6AQFwFXvm7yO+/JrLxhYTASSMTy0OPP7WkRAiEJTUcNAYBBEImUJBAGDKDvJtLIMybjoIIZBXYvGuf/Ozhl2TJymbZte+gc/2g48rkyxcOk/8YNThreS5AAAEDgR2vifx5tsizdyYq6T1Q5LLrRU77mEGlhSsapUC4Zs2ajFBsMlO454iaEYirAIHQYOQJhAZ4FEUgjcAbO/Y6O4b+enWL6PuC+hnS72hnx9DxZw/CDQEECi3wyPdEHv6+iB4p0aW7yDlXiVzwXyJdehT6znnXTyDMm46CCCCAgBAIDR4CAqEBHkUR8Am8uq1Nbn4gEQTdz8knHOOcIfjhERV4IYBAoQX+fY/In74psrU5cadTa0UumytybGWh72xcfxQCoTECFSCAAAJ5ChAI84TTYgRCAzyKInBYoHnzbvnRihflf/9x+CwzETl9YB8nCF522gCcEECg0AKb14rc/XWRdQ8l7tRvuMi4GxLnCIbkQyAMyUDRTAQQKEkBAqHBsBAIDfAoGnuBxtd3yg/vf1Hu+eeGpMXZg4+TKy8ZLhe9o3/sfQBAoOAC+3aJ/HWuyMqbE7fSHUMv+qbIqK8U/Na2b0AgtC1KfQggECcBAqHBaBMIDfAoGluBZ155U25c8aKseP6NpME5w/rKlWOGy+ihfWPrQscRKKrA00tF/vIdkV0bE7c967Mil35HpGc4vwYJhEV9ergZAghETIBAaDCgBEIDPIrGTuCJl7bIzQ80yqNNm5J9v+SU4+XqS0+WMwaVxllmsRsUOhw/gdanRJZfLbLh6UTfB56dOEZiwOmhtiAQhnr4aDwCCHSygNVA6J4/OHLkSJk2bVqya21tbTJ79mznv+fMmSNlZWWd3G07tycQ2nGklmgLPPTiRrnpgUb5e/PhA61F5APvGiBXXXqynDLgmGh3nt4hUCoCuzeL3PdtEZ0Z1M8xA0TGXityxsRSaaFROwiERnwURgCBmAsUJRCq8cKFC2XVqlWyaNEiKS8vjwQ7gTASw0gnCiRw379el5tWNMo/X30zeYePnDnQeUdwWP+jC3RXqkUAgQ4C+o7gg/NE9u5I/NW5V4tcOF2kW3S+DqMWCN1fsKc6k5BzCPkaRwAB2wJFCYTuDGFLSwuB0PYIUh8CJSbwhzWt8uO/Nsm/Xzv8w6eITHzvSTL54mo5qbxnibWW5iAQYYHmR0X+cKXIlpcSnTz5MpEPzBc5bkjkOh21QKi/RNdfoKf6EAgj9/jSIQQ6XcBKIGxqapK6ujppbW3N2KHa2lqWjHb6kNMABAojsOzJV5wD5ddt2uXcoHuXI2XieyvlKxcNkxP7lO6B1oXRoFYEOlFgW4vIn2aKvPDHRCOOq0q8Jzj0ok5sVGFvHaVA6J0dXLp0qdTU1BQWj9oRQCD2AkULhBUVFdLQ0CDV1dWRQWfJaGSGko4YCPzqifXy04fWyitb25xaenY7SiaNGixfvnCY9D26m0HNFEUAgZwE9u8WeeQHIo//SOTAXpFuvRJLQ8+9KqdqwnhxFAOhjkOUXrMJ43NFmxGIi4BxIHR/k1VZWSlf/OIX5VOf+pRzYPudd94ZeUMCYeSHmA6mEdiz/y3RILjo4bXyxo69zlW9e3SRz5xTJfXnD5XeZV2xQwCBYgo8+zuR+74lsv3wSp0RnxR53/UiR8fjTM8oBUJ9bKK470Ixvxy4FwII5CZgNRBOnTpVpkyZIv5dRnNrUniuJhCGZ6xoqR2BnXsPyC8fb5aGR9fJll37nEqP69lV6s4fKp87p0p6de9i50bUggACwQTeeF5k+VUiL69KXH/Cu0Quv1mk4sxg5SNyVdQCofsqjgZDloxG5CGlGwiUsIBxIHQ3jFm+fHnWbkbtRWgCYdYh54KICGxv2y+3PrpOfvHYOtm+54DTq/7HdJf6C4bKFaOqpEfXIyPSU7qBQEgE9rwpsuJakb/dmmjw0f1ELr1G5MwrQtIBu82MUiDMtMOoqkXtZym7TwK1IYBAPgLGgVBvGnRTmah9EyMQ5vPIUSZMApt37ZP/eWitszx0976DTtMrju0hX76wWj4zenCYukJbEYiOgIbAB64XaTt8tueor4pcPEuke3zP9SQQRufxpicIIFB8ASuB0G12uoPpi9+t4tyRQFgcZ+5SfAF9L1CPjvjN6hbZe+AtpwGD+/aUr15cLRPfc1LxG8QdEUBApOWJxPLQjS8kNAafK/Lhm0T6Dou9TpQCYewHEwAEECi6gNVAWPTWd/INCYSdPADc3rrAq9va5KYHGuU3q19O1j38hF7OGYKXv3ug9ftRIQIIBBDY8ZrIn2eJ6MYx+tFjJC6bK3LKhwIUjsclBMJ4jDO9RACBwggQCA1cCYQGeBQtKYG1G3c5QfD/nno12a7TKnrLlZcMl/e/a0BJtZXGIBArgYcXJo6S0CMluvYUOf8bIhdMixVBkM5GMRD6X8eJ4vFdQcaWaxBAoPACBEIDYwKhAR5FS0Lghdd2yI33vyj3Pvtasj1nDT5Orry4Wi4+5fiSaCONQCCWAnqovM4Kbm1OdP9dH0/MCh7DL2hSPQ9RC4SrV6+WSZMmpXz0Oaw+lt8R6DQCBRUwDoTecwjdYyfWrFmTstFsKlPQsaRyBAILPPPKm3LD/S/KAy+8kSxzzrC+MvmS4aL/ywcBBDpJYPNakbu/LrLuoUQD+p8iUnujSOWoTmpQOG4bpUDo3b3dG/7ckFhbWytz5syRsrKycAwOrUQAgZIXIBAaDBEzhAZ4FC2KwA33N8ptj77kHBUx6LgymfDek2TVS1vksaZNyftf/I7+8vWx75AzBvUpSpu4CQIIpBDYu0PkwXkiK29O/GXZcSJjvi3ynjq4AghEKRB6f9HuDX5uUGxpaZFFixZJeXl5ABkuQQABBLILGAfC7Lco/hX+sxHnzp0r48ePT9mQdOf9BJnNJBAWf2y5Y3CBZU++ItOW+WbrDx2SQ0ccKUfIIXn/aQPk6rEnyykD4rtVfXBNrkSggAJPLxX5y3dEdm1M3OS9/5kIgz34JU1QdQJhUCmuQwABBDoKWA2E3nDVmWvcFy5c6PR02rRp4rZp+vTpUlNTE+gZWLZsmTQ3NzvlM30IhIE4uagTBHbuPSBX/fopWeFZEuo249zqfvLdD58m1cf36oSWcUsEEEgKtD4lsvxqkQ1PJ/7opJGJ5aHHnwpSjgJRCoQsGc1x8LkcAQSMBSIXCDUAavibOXOmVFdXO0DegJhNLFX5dGUIhNk0+ftiCqx8abOzFPSxxk3y1Mvb5NChQ3LEEUd0aMKv60fJ6KG8J1jMseFeCLQT2L1Z5L5vi+jMoH76DBJ53/Uip30UqDwFohQIlYBNZfJ8ECiGAAJ5CVgNhG740rXtnTVDqNs0z5gxQ+bPn58MhDrjt3LlykAvYQedHdS+EgjzeuYoZEngudbtTgB8tHGTrG7eLHv2Jw6Qdz/H9+4ub2zf2+7PjunRRR6bcYn0LutqqRVUgwACOQnoO4L6rqC+M9ilu8i5VyeOkujSI6dquLi9QNQCofaOYyd4yhFAoFgC1gOh+w1s8uTJad/bK2Tn9P7z5s2TBQsWJF+4DhoIM80OLlmypEOzr732WmlsbCxkd6gbgaSAHhr/8Isb5dGmTfJ40ybZunt/O53+vbrLOdX95NzqvnLhyf3lhN49pOHRdXLnky/L8xt2yMgh5c47g8wO8lAh0AkCzY+K/OFKkS0vJW5+am3iGIljKzuhMdG7ZRQDYfRGiR4hgECpClgNhOk2aHE7H2SjFlMokxnCTEtLFy9e3KFp1113HYHQdMAon1Zg2+79iRnAw/+8vGV3u2uP7naUjBzaV/SdwPOq+8k72ByGpwmB0hPY1iJy7wyRf9+TaFu/4SLjbhCpOq/02hriFhEIQzx4NB0BBDpdIHKBMN93CFMFyWyjw5LRbEL8fS4CuuRTl37qElANgrok1P95T9Vxcl51fycA6r/zQQCBEhXYv1vk4e+LPPK9RAN1x9CLZ4mM/HKJNjjczYpaIMz0C/Zi/HI93E8DrUcAgVwFrAbCXG9eqOsz7TLqfpOdOHFiuyWtuWw847abQFioEYxPvbr5i24CowFQN4Xxf3TWT8Of/qOzgT27HRUfHHqKQFgFnr0zsWnM9tZED876rMjY7ybOFuRTEIGoBUL9mUT3Y0j1IRAW5BGiUgRiLRDJQJjpHMJUgVB389J3DnM96JVAGOuvnbw6v3bjLnmsaaMzC6gBcMeeA+3qqTi2hzMD6CwDHd5P+h7dLa/7UAgBBDpB4I3nRZZfJfLyqsTNB56dOEZiwOmd0Jh43TJKgbBUjvCK1xNEbxGIt4BxIMz23qCXN2q/1SIQxvuLJ0jvN+3cJ480JgKgzgK+tn1Pu2K62+c5w95+D3BIv6ODVMs1CCBQSgJ73hRZca3I325NtOqYASJjrxU5Y2IptTLSbYliINQBy/UX1ZEeZDqHAAIFEyAQGtASCA3wIlp0176D8sTazfKozgI2bZLG13e262n3LkfK2YOPk/OGJ94DPGNQn4hK0C0EYiKgIfCB60XatiY6fN7XRS6cLtK1Z0wASqObUQqEKqpLRletWkUgLI3Hi1YgEHkB40DoF9JvYhs2bGh35l9nH0VRqFEkEBZKNlz1rl63JbkT6D/WH/6h0NOF0wf2cZZ/agDUpaB8EEAgAgItTySWh258IdGZky8T+cB8keOGRKBz4etC1AKh+3OT/kxVU1MTvgGhxQggECoBq4HQXT5aWVnZLhC67/SpzJw5c6SsrCxUSOkaSyCMxDDm3Ak9009nAPUswCde2iJt+w+2q6Oqb8/kDKAuB+UQ+JyJKYBA6QrseE3kz7NEnv1doo3lQ0XG/VBk6EWl2+YYtCxKgTDbqzhRe/0mBo8nXUSg5AUKEgi119517+43N/+fl7xOlgYSCMM+gsHav+HNPfJw48bkbqCbd+1rV7Bfr27OzJ8eCn/B8P5yYp8ewSrmKgQQCJfAwwtFHvmBiB4p0a2XyEUzRM6ZEq4+RLS1BMKIDizdQgCBoghYDYTe3T3nzp2bPNZBd/GcNGmS1NbWMkNYlGHlJiYC29v2y2P6HqBuBtO0SdZvbn8gvB79MHJIuZyr5wEO7yencCC8CTdlESh9gRf+mJgV3NqcaOuIT4m87zqRo/uXfttj0sIoBcKYDBndRACBEhKwGgi1X+6699bWw+cveTrrDYklZJB3U5ghzJuupAruPfCW/L358HuAjZvkn6++2aF9zkYwh4+CeG9VeUm1n8YggECBBDavFbn76yLrHkrc4MR3i9TeIFJxZoFuSLX5ChAI85WjHAIIICBiPRAqaqr170uXLo3ci9EEwvB+CT3zypuJjWAaN8qT67eKhkLv5+QTDh8IP7yfjOJA+PAONC1HIB+BvTtEHvxvkZU/TpQ+up9g3Mj2AAAgAElEQVTIpdeInHlFPrVRpggCUQuEmd4j5B3CIjxQ3AKBmAkUJBDGxZBAGJ6RXrdplxMA9SzAx9duFl0W6v3oe3/uLqDnn9yfA+HDM7S0FAG7Ak/9SuT+a0R2bUzUO/prIhd9U6T7MXbvQ21WBaIWCHV3Ud2LIdWHQGj10aEyBBCQAs0QxkWWQFi6I60bv7iHweuOoK3bUhwIP7SvsxGMvgc4lAPhS3cwaRkCxRBofUpk+dUiG55O3G3IhYndQ/sOK8bduYehQJQCoXd2MIqrqwyHmuIIIFAAAeszhHFa5kAgLMATmWeVu/cdlFUv6YHwm5x//v3ajnY1uQfC626gGgBHDDo2zztRDAEEIiWwe7PIfd8WeXppolvHVYlcNlfklA9FqptR70wUA6GOmXfH9qiPIf1DAIHOE7AeCOO0zIFA2HkPrt75781b5bG1+h7gJvlb85YOjXmXHgjv2QhGQyEfBBBAICmw8maRB+eJ6DuDXXuKnP8NkQumARRCgSgFQuXXn6VWrVpFIAzhs0iTEQijgNVA6D2Y/vOf/7xMnjzZ+aZWXV0t9fX1MnHixORRFGHE8reZQFjcUXzxdT0QPhEAdTZw1772B8IP1gPhnQDYX87lQPjiDg53QyBMAi89mNg9dMtLiVa/6xMil80ROWZAmHpBWz0CUQuE7o7t+jNUTU0NY40AAggUVKAggXDkyJFSV1fnhMDp06c738yi+NsuAmFBn015fbseCJ8IgI83bZKNO/e2u2HfoxMHwus/F5zMgfCFHQ1qRyACAttaRO6dIfLvexKd6X+KyOU/Fhn0ngh0Lt5diFIgzPTqjY4ym8rE+1mn9wgUQqAggbCyslKmTp0qU6ZMEQ2HOlM4e/ZsaWlpidTyBwKh3Udy+54DsnJtYidQDYEvbdrV7gZlXY+SkUP1QPh+cl51fzn1RHb9szsC1IZARAX27xZ5+Psij3wv0cGy40TGfFvkPXUR7XD8ukUgjN+Y02MEELAnYDUQarN0JvDuu++WhoYGueuuu9ptm1xbWytz5syRsrIyez3oxJoIhOb4egSEGwDXvLKtQ4VnuQfCV/eTmiEcCG8uTg0IxEzg2TsTm8Zsb010vKZe5JJvifToEzOIaHc3SoEw2iNF7xBAoBQFrAdCbye9yx4qKiqckKjvE0blQyDMfSSfffXNRABs2iSr123pcCD88BN6Jd4DrO4vo4b1laO7HZX7TSiBAAIIvPG8yPKrRF5elbA4aaTI5TeL9DsZmwgKEAgjOKh0CQEEiiZQ0EBYtF500o0IhNnhW7bsdpZ/agB8fO0m2ba7/YHwA3r3cI6B0GWg5w/vL/16dcteKVcggAAC6QTatoqsuE7k7w2JK/oMEnnf9SKnfRSzCAtELRDG6QivCD+WdA2B0AgQCA2GikDYEW/r7v3yaOPGRABs2iQvb21rd1HvHl1k1NC+h0NgfxnW/2iDEaAoAggg4BH4289EHpgjoqGwS3eRc69OHCXRpQdMEReIWiCM0xFeEX806R4CoRCwGgjd32jpRjLTpr19llNbW5uzqYx+eIcwFM9F4Ebu2f+WrFqXOBD+scZN8q8N2zuUHT20r5w7XJeB9pN3n8SB8IFxuRABBIIJtDyRWB668YXE9ad+WOQD80R6DwxWnqtCLxA0EK5evVomTZrk9Dfobp36s43umD5z5syivPYStyO8Qv/w0QEEIiBQlECoThw7EYGn5XAX/tGyTR5r2pg4D3BdxwPhT6vo7ZwFqAFQN4LhQPjojD09QaCkBHa8JvLnWSLP/i7RrH7DRcbdIFJ1Xkk1k8YUXiBIINSz/WbMmCHz5893gt2yZctk5cqVaX9R7f4ye/ny5VLMfRC8v1yPwxFehX86uAMCCGQTKEogdL+pcuxEtuEozb9vemNnciOYJ17aLDv2HGjX0Mryns4SUA2A5wzrJ8f27FqaHaFVCCAQHYGHF4o88n2R/W2JHUMvniUy8svR6R89yUkgSCDUANjc3JxcweQPiOlu2JkzhHE4wiungeZiBBAoiICVQKjfVPW3WK2th7f1TtNUjp0oyBhar3Tjjr3yiLMRzEZ5rGmzc0C891PuHgg/rJ+cf3I/GXhsNI4RsQ5JhQggYF/ghT8mZgW3NifqPuuzImO/mzhbkE9sBYIEQl2ppB/3lRZ3Jk6Xg9bU1KS1K3Yg1IbE6Qiv2D60dByBEhIoWiAs5nKLYvlGZVOZXfsOOhvAOO8BNm0SnRH0fvRAeF36qTOA+i7gO0/sXSxi7oMAAggkBDavFfnDlSLrH0v898CzRWpvFBlwOkIIiAbCCRMmSLdu7Xeqvvzyy6V378T/Z2nIqqqqkvHjxzv/XcqB0DukUT/Ci8cXAQQ6X8BKIHS7kW5Tmc7vZmFaEOZAqO/+6TuA+i6gvhPo/5xVeaycW93fWQo6kgPhC/MAUSsCCGQX2LtD5MH/Fln548S1xwwQGXudyBkTspflitgIaCDUoJcqEPbp0ycZCPVfwjBDGJuBo6MIIFASAlYDYUn0qIiNCFMg1N0/dRdQ90D4tv0H20lVH3/4QPjh/WT0sH4cCF/E54hbIYBAGoGnfiVy/zUiuzYmLjjv6yIXThfp2hMyBNoJBFkyGpZ3CBlaBBBAoNgC1gNhnA5TLeVA+Oq2NnnkxU3y2NrEMtAtu/a1e7ZO0APhdQlodT+54GQOhC/2Fx73QwCBDAKtT4ksv1pkw9OJi05+f+IYieOGwIZASoEggTDbLqPufgi6tNT7TmFnvEMYp5+leKQRQKDzBawHwjgdplrsQPhc63bpXdZVTjqu4yYu29v2O7N/zj+Nm6Rly+52T9cx7oHwh98DrO7fq/OfPlqAAAIIeAV2bxa571siT9+e+NPyoSLjfigy9CKcEMgoECQQagWZziH0B0LvsRPuzYu1OV6cfpbi0UYAgc4XsBoI43aYarEC4cqXNsuXFv9dth8+7kE3dblp0lnSurUtuRHMs6++2eFpGjW0b3IjmDM5EL7zv9poAQIIpBd4/CaRh+aL6DuD3XqJXDRD5JwpiCEQSCBoIAxUWSdf5J0dXLp0acYdUDu5qdweAQQiIlCQQDhy5EjnGIr6+vr/3979B9txFfYBP/1hY/HDMg88wbKRBBEtnYYqGRiBC4SmTtM0keoyE1lmVP/RaqqZtq5TppZkJJphbCQkuWTCjMlk1Gpa7FHrQZ06U7lxpi0pJskIu2GIIG1JrYARIEP8kx+xoLSTzrnv7fO+1f2x955z7967+7kzjIW05+zZzzlv3/3u2d0Titc5W5h+8hHzrmO/Fb7+/KWRFcQF4f/qluX1AN/+hteEq674syPL2IAAAQKNCnz50yE8/P4QnvvycjO2vi+En7knhFdc22iz7HyxBNoYCGMPnDhxIiwtLS1WZ2gtAQILJzCVQLhx48bQhcVUZzVDuPmu/9R3YL0+Lgi/8hxg/K8F4Rfu50+DCXRX4IULITxyIIQ//I1lg+t+PIQdvxLChp/orokjn1igTYEwIrTxIvrEnasgAQJTF8gaCIuT2MMPPxxOnjwZHnrood7VreIzq3vvp662soMmA+GP37A+/Prt75rVodoPAQIE8gj88MUQPvPREH77XyzXF2cCf/pDIfzE381Tv1o6KdC2QDjoBTed7FwHTYDA1AWyB8Jyi9u+mOqsAuE/uP/3wn/5n99aMxh+8aY3hff/jb8w9QFiBwQIEMgm8Af/PoT//M9D+M7F5Spv/Mch/LUPhPCyV2XbhYq6KbDogXDYW0WrPbp161a3knZzmDtqAlMTmGognFqr56TiWQXC+AbRk7/zlfDZLz/bO/JfeNvrw8633jAnCppBgACBEQJ//L9COPOLIXztseUN3/Ce5beHvuZH0RHIIiAQZmFUCQECHRUQCBM6flaBMKGJihIgQKA5gUvPh/Cpe0L4vZPLbXj15hB+9iMh/MWfa65N9txKgUUPhK3sFAdFgMDCCGQPhOU1fqLChg0bes8TbtmyZWFQ6jZUIKwrZTsCBDon8N//ZQi/dTiEGAqveHkIP/nPQnj3nZ1jcMCzERAIZ+NsLwQItFMgayActpDqkSNHws6dO1ulKBC2qjsdDAECOQQufHb59tCnv7Rc24/9Qgh/83AIr3pdjtrVQaCvQFsC4enTp8PBgwdD+SV85e9WbfwuZUgTINC8QLZAWJ4ZLC+kOujvmz/09BYIhOmGaiBAoCUC3/1mCL/5gRD+x39YPqBr3xzCzR8P4Ya3teQAHcY8C7QhEF66dCkcOnQonDlzJhTfo6p3XcU+sFj9PI9EbSOwmALZAmFxBavf1aviildcqH7fvn2LKdWn1QJha7rSgRAgkCLwmeMh/PYvh/DDSyGse3UIN/1SCG/7+yk1KktgLIE2BMLiTaPxwIsF6YvvVjEExgXq9+zZE9761reGw4cPh3Xr1o1lZGMCBAgMEsgSCIurWp/73Of6Pi9YrKfTtpOYQOgHiwCBTgt86eEQfvNgCC98dZlh294Q/voHQ7hqfadZHPzsBdoUCDdu3NgLfPG7VbyQ/vTTT/e+W11//fW9GcQLFy5YdmL2Q8weCbRaIEsg7HdVq6w26t8XVVggXNSe024CBJIEnv2jEP7jPwnhq7+7XM3r3x7CzfeF8Fproya5KjyxQBsD4Te+8Y01M4IRRyCceIgoSIDAEIGsgbC4qlW9jUEgNAYJECDQAoEffDeET38khLMfXz6Y9TeE8DMfDuEvv7cFB+cQFlmgDYGwerfV5z//+d4LZorHbYq7ra699lozhIs8WLWdwBwKCIQJnWKGMAFPUQIE5lfg+y+E8Pv/LoR4S+g1G0N488+HcOmFEP7rh0L4k6dD+PNXhfDOfxrCu9+//GcfAg0LtCEQRsJhL5Fp6/sYGh46dk+AQAghayA8d+7cUNStW7e26qqWQOhniACBVgr8m58P4cnf6X9of+lvh/C3joZw9fWtPHQHtZgCbQmEUb8IfvHPxYv6itnBixcvesvoYg5RrSYw1wICYUL3CIQJeIoSINC8wIvPhfDiMyH8yTMr/306hO98PYTP/PLlbYszgbtPh/CGn2y+3VpAoCLQpkCocwkQIDBrgSyBcNaNnpf9CYTz0hPaQYBATyDezrka7lZC3ovPhvC9IvDF8Pf0SyGwL9ufxptHLv+XTe8M4e/9BmgCcynQtUBYvJth//79Ydu2bXPZJxpFgMDiCAiECX0lECbgKUqAwGiB731rOeCthrhnQ4gBbzX4rfw5zvLF2b5xP3HW7xWvDeHlr13572uW//v4vwrh//1gbW1b3xfCe39t3D3YnsBMBATCmTDbCQECLRUQCBM6ViBMwFOUQBcFvvtUZQYvzt6VZuxWb998NoRLz48vdOUr1wa7Iui94tpS8HvNcgCML4sZ9Pn9UyE8clcIP/jO8hY/8mMhvO/fhnDNpvHbpASBGQgIhDNAtgsCBForIBAmdK1AmICnKIE2CHz766Vn8OJsXfl5vGJmb+Xvi3A1znHHBd5fvjJrtzqL99oQXnntyqzeSriLs3q5X/IS3zT6zS8uLzL/ur8yTqttS2DmAgLhzMntkACBFgkIhAmdKRAm4ClKYN4EfvhiafaudCtmNeT1btl8JoT/873xj2Ddqyu3aK7cqtmbwSuFuxj+XvW68etXgkBHBQTCjna8wyZAIIuAQJjAKBAm4ClKYNoCcRH13i2Y8bm7PjN31b//4aXxW7Tm+bviObw4g1d+Lq8U+sbfgxIECNQQEAhrINmEAAECAwQEwoShIRAm4ClKYFyB73/78jdorpm9K4JffCbv2RD+b+WlKHX298of6TODF2/ZLD+DtxL2Xr5Up0bbECAwAwGBcAbIdkGAQGsFBMKErhUIE/AUJRBfmlJeIqG8HEJ19u6735zM6+oNpTdoxpm6lefxYsArP5MX//6qaybbh1IECDQuIBA23gUaQIDAAgsIhAmdJxAm4CnaPoHq+ner/7942UrpbZox/I37uWLd5SFu2Fs0X/aqcfdgewIEFlRAIFzQjtNsAgTmQkAgTOgGgTABT9HZCHz6aAif/dUQ4u2WcZmBn/1ICG/eXm/f3/vjlWfvyoudr4S76jN58RbNcT8Dl0iIM3l93qJ5xcvH3YPtCRDoiIBA2JGOdpgECExFQCBMYBUIE/AUnb5AXEvu1//R2v3EELb9oyH8mT+3/JxdzjXwBi2REF+8Un2LZu4lEqavaQ8ECMyxQNcC4Rx3haYRILCAAgJhQqcJhAl4iuYViLdgfvtrIcR18eL/XvhaCH/4SAjPf2Xy/QxcIqH0Ns3iLZuWSJjcWUkCBJIF2hYIn3vuubB3795w7ty5y2y2bt0aTpw4EZaWvNgqeeCogACBnoBAmDAQBMIEPEXHE3j2j14KfDHsrYa/r4XwnW/0f6Pmn8af8D67ed1bQlj60dLbNPu8RTPO6PkQIEBgQQTaFgjvvffeXujr9xEIF2RQaiaBBRIQCBM6SyBMwFP0JYFidq8X9OIMX2mmL/45vpxl1OdlV4ew/oYQrnl9COvj/24IIdZ79uNrS8bt3v9Fb9Qc5enfCRBYKIE2BcLy7OCpU6fCtm3bFqovNJYAgcUTEAgT+kwgTMDrUtFnz5dm98qBb8jsXtWnCHnFf6+54aXgd82mEK58RX/R//aREL70cAjf+oMQNr0zhJ/6QAib390lfcdKgEAHBNoYCGO3uTW0A4PXIRKYAwGBMKETBMIEvLYUjW/irM7oFc/x1Z3diy9jiTN6vf+tzO5V/9wWL8dBgACBKQi0KRBGnnjL6GOPPSYQTmGsqJIAgcsFBMKEUSEQJuAtStFpz+69enMIllNYlNGgnQQIzKlA2wLh+fPnw549e3rB0C2jczroNItAiwQEwoTOFAgT8OahaL/ZvfJzfHXW1hs0uxfX/IuzfJZXmIee1gYCBFou0KZAOOwNo7EbvVSm5YPZ4RFoQEAgTEAXCBPwZlF0zexe+c2cXw/huS/Xa8Flz+6VXtry6k1m9+op2ooAAQJTFRAIp8qrcgIEWi4gECZ0sECYgJdadHV2b+WNnKszeysvbRl7dm9jCL0XtZSe4zO7l9pLyhMgQGAmAm0KhDMBsxMCBAiUBATChOEgECbgjSr6zBODX9YyzuxebxmG8staVv6/2b1RPeDfCRAgsDACAuHCdJWGEiAwhwICYUKnCIQT4n3vW2vX25todu+atW/mNLs3YWcoRoAAgcUXaGMgLC9Of+TIkV4nHTx4MMQ/79y5c/E7zREQIDA3AgJhQlcIhAPwcszuFS9l6Tu7F9/MuS6h5xQlQIAAgTYJtC0QlsNg7KcYAm+66aawd+/esHHjxnD48OGwbp3fg20aw46FQJMCAmGCficDYd/ZvZXn+OK6ey8+N1r0KrN7o5FsQYAAAQJ1BdoUCIu3jMbgd+edd4Y77rgj7Nq1K2zfvj0cOnQoXLhwwfqEdQeG7QgQqCUgENZi6r9RKwNheXavdytnEfa+HsLzX6mnNWh2Lz7Pd018M6ermvUgbUWAAAECdQTaGAhjCCxmBQXCOqPANgQITCogEE4qF0KYeSD8/gshxNm1ST/F7N4LF9Y+w/ft4s2ck87ulZZiuHrDpK1TjgABAgQITCTQpkB46dKl3kxg/JRnCDdt2hR2794dduzY4ZbRiUaJQgQIDBIQCBPGxswC4Te/EMKDu0OIQS5+Nr8rhL/zq8uzbeXPM//7pRm9F1ZCXi/sTTq7V1mKwexewmhRlAABAgSmJdCmQBiNHn/88V746/c5depU2LZt27Qo1UuAQAcFBMKETp9ZIPyVt7wUBov2vuZNIbz2TSF895vLt3Jeen70kVy1vrQEQ7x9s1h3b2MI668Pwbp7ow1tQYAAAQJzJ9C2QBiBz58/H/bs2RMuXrzY896wYUM4efJk2LJly9z5axABAostIBAm9N/MAuGH1tdrZZzB672Vc2XdvfJSDGb36hnaigABAgQWTqCNgXDhOkGDCRBYWAGBMKHrGg2EMeD99C+FsN7sXkIXKkqAAAECLRAQCFvQiQ6BAIHGBFoZCIsHss+cOdODrbOIa/nWjLq3ZcwsEP7rnwvhq7+7dpC8564QfuoDjQ0cOyZAgAABAvMiIBDOS09oBwECiyjQykAYF3SNn3379oViPZ/9+/cPfAg7hsEDBw6EY8eOjXVv/swCYXy76CN3lV4q8+4QbvyHaW8cXcTRqs0ECBAgQKCPQNsCYbEwfXyBzNLS0uqzhN4wavgTIDANgdYFwhgAY/i76667VsNdOSBWEYvZxFtvvXXst3bNLBBOo+fVSYAAAQIEWiLQpkBYXnbi8OHD4b777ustRF989u7d27vg7UOAAIFcAq0LhP1m+06fPh3Onj3bd92eYgbx3Llzq6Z1r8AJhLmGoXoIECBAgMDkAm0KhMX3kre//e29mcEYAJ9++uneG0Yfeuih8Nhjj/UCYpw59CFAgEAOgVYGwqNHj4bjx4+vniyHBcIYIMvbF1fmrrvuujVX4B544IHLvO++++7wxBNP5OgHdRAgQIAAAQITCrQ1EL73ve/thcJrr722FwJjKBQIJxwkihEgMFCglYGw+jzgOIEwSsUFYWOgLF+Bu//++y9DvOeeewRCP1wECBAgQKBhgTYFwkhZPENYsMZZwttvvz0cOnSo91fxVtJ169Y1rG73BAi0RaB1gXDcZwj7bR8D4YMPPjjyhOuW0bb8GDgOAgQIEFhkgbYFwvLb0rdu3dq7QP2pT30qHDx4sHcLqWcIF3m0ajuB+RNoXSAsrqzF//Z7y2hxb/6uXbvCzp07ez0Sr8Q99dRTvQAYP/EK3I033rj674O6TSCcvwGtRQQIECDQPYG2BcLu9aAjJkCgSYFWBsJh6xD2C4TV7etefRMImxy69k2AAAECBJYFBEIjgQABApMLtDIQTs4xXkmBcDwvWxMgQIAAgWkItC0Q9nsDeuFW3ELqLaPTGEnqJNBNAYEwod8FwgQ8RQkQIECAQCaBtgXC6ktlykwCYaZBoxoCBFYFBMKEwSAQJuApSoAAAQIEMgm0KRCWZwdPnToVtm3blklJNQQIEOgvIBAmjAyBMAFPUQIECBAgkEmgjYEw0liAPtMAUQ0BAkMFBMKEASIQJuApSoAAAQIEMgm0KRBGknjLqAXoMw0O1RAgMFJAIBxJNHgDgTABT1ECBAgQIJBJoG4gjOsM7969u7fXOs/iDdv+/PnzYc+ePeHixYurR1GnzjqHXNQdg6FbRuuI2YYAgRQBgTBBTyBMwFOUAAECBAhkEqgTCGPIOnDgQDh27FjYsmVLOH36dDh79mxvDeJ169Zd1pJR21f/PdOhhGFvGK0bZHO1RT0ECHRDQCBM6GeBMAFPUQIECBAgkEmgTiCMAfDJJ58M+/bt6+11VKAbtf2o8pMemkA4qZxyBAhMKiAQTioXQhAIE/AUJUCAAAECmQTqBMJ4+2X8FIGwCF779+/ve1vmqO2rt4zmul00E4lqCBAgUFtAIKxNdfmGAmECnqIECBAgQCCTQAyEt9xyS7jyyivX1HjzzTeHq6++uvd3MeBt3rw57Ny5s/f/6wTCcbd/6qmnBt6CmulQVUOAAIHsAgJhAqlAmICnKAECBAgQyCQQA2EMev0C4fr161cDYfxDrhnCatPjjOHRo0fD8ePHw9LSUvKRlRenP3LkSK++gwcPhvjnItQm70QFBAgQCCEIhAnDQCBMwFOUAAECBAhkEqhzy+ioZwKrTRl3+5yBsBwGY7tiCLzpppvC3r17w8aNG81CZho3qiFAYFlAIEwYCQJhAp6iBAgQIEAgk0CdQFjnraFxGYliqYdR2z/yyCO9dwnEN5bGT/WZw0kPrbiVNQa/O++8M9xxxx1h165dYfv27eHQoUPhwoULFqyfFFc5AgT6CgiECQNDIEzAU5QAAQIECGQSqBMI467qrCtYXvtv2Pblf4t179ixI8vMXREIYwgsZgUFwkwDRTUECAiEuceAQJhbVH0ECBAgQGB8gbqBcPyaZ1/i0qVLvZnA+CnPEG7atCns3r07W/Cc/ZHZIwEC8ypghjChZwTCBDxFCRAgQIBAJoE2BcJIUp19LDOdOnWq7zIZmShVQ4BABwUEwoROFwgT8BQlQIAAAQKZBNoWCCNLdZ3DDRs2hJMnT64+s5iJTjUECBDwUpmUMSAQpugpS4AAAQIE8gi0MRDmkVELAQIERguYIRxtNHALgTABT1ECBAgQIJBJQCDMBKkaAgQ6KSAQJnS7QJiApygBAgQIEMgk0IZAWLxd9Ny5cwNVLEqfacCohgCBNQICYcKAEAgT8BQlQIAAAQKZBLoSCCOXl8pkGjSqIUBgVUAgTBgMAmECnqIECBAgQCCTQJsCYVyQ/vDhw2HdunVrdIo3j+Za7zATvWoIEGiBgECY0IkCYQKeogQIECBAIJNAFwJhcUtpJDtx4kRYWlrKpKcaAgS6LiAQJowAgTABT1ECBAgQIJBJQCDMBKkaAgQ6KSAQJnS7QJiApygBAgQIEMgk0IZAOIrCDOEoIf9OgMCkAgLhpHIhBIEwAU9RAgQIECCQSaALgdAzhJkGi2oIELhMQCBMGBQCYQKeogQIECBAIJNAFwJhJirVECBAQCDMOQYEwpya6iJAgAABApMJCISTuSlFgACBKGCGMGEcCIQJeIoSIECAAIFMAgJhJkjVECDQSQGBMKHbBcIEPEUJECBAgEAmAYEwE6RqCBDopIBAmNDtAmECnqIECBAgQCCTgECYCVI1BAh0UkAgTOh2gTABT1ECBAgQIJBJQCDMBKkaAgQ6KSAQJnS7QJiApygBAgQIEMgkIBBmglQNAQKdFBAIE7pdIEzAU5QAAQIECGQSEAgzQaqGAIFOCgiECd0uECbgKUqAAAECBDIJCISZIFVDgEAnBQTChG4XCBPwFCVAgAABApkEBMJMkKohQKCTAgJhQrcLhAl4ihIgQIAAgUwCArCzWf4AABNxSURBVGEmSNUQINBJAYEwodsFwgQ8RQkQIECAQCYBgTATpGoIEOikgECY0O0CYQKeogQIECBAIJOAQJgJUjUECHRSQCBM6HaBMAFPUQIECBAgkElAIMwEqRoCBDopIBAmdLtAmICnKAECBAgQyCQgEGaCVA0BAp0UEAgTul0gTMBTlAABAgQIZBIQCDNBqoYAgU4KCIQJ3S4QJuApSoAAAQIEMgkIhJkgVUOAQCcFBMKEbhcIE/AUJUCAAAECmQQEwkyQqiFAoJMCAmFCtwuECXiKEiBAgACBTAICYSZI1RAg0EkBgTCh2wXCBDxFCRAgQIBAJgGBMBOkaggQ6KSAQJjQ7QJhAp6iBAgQIEAgk4BAmAlSNQQIdFJAIEzodoEwAU9RAgQIECCQSUAgzASpGgIEOikgECZ0u0CYgKcoAQIECBDIJCAQZoJUDQECnRQQCBO6XSBMwFOUAAECBAhkEhAIM0GqhgCBTgoIhAndLhAm4ClKgAABAgQyCQiEmSBVQ4BAJwUEwoRuFwgT8BQlQIAAAQKZBATCTJCqIUCgkwICYUK3C4QJeIoSIECAAIFMAgJhJkjVECDQSQGBMKHbBcIEPEUJECBAgEAmAYEwE6RqCBDopIBAmNDtAmECnqIECBAgQCCTgECYCVI1BAh0UkAgTOh2gTABT1ECBAgQIJBJQCDMBKkaAgQ6KSAQJnS7QJiApygBAgQIEMgkIBBmglQNAQKdFBAIE7pdIEzAU5QAAQIECGQSEAgzQaqGAIFOCgiECd0uECbgKUqAAAECBDIJCISZIFVDgEAnBQTChG4XCBPwFCVAgAABApkEBMJMkKohQKCTAgJhQrcLhAl4ihIgQIAAgUwCAmEmSNUQINBJAYEwodsFwgQ8RQkQIECAQCYBgTATpGoIEOikgECY0O0CYQKeogQIECBAIJOAQJgJUjUECHRSQCBM6HaBMAFPUQIECBAgkElAIMwEqRoCBDopIBAmdLtAmICnKAECBAgQyCQgEGaCVA0BAp0UEAgTul0gTMBTlAABAgQIZBIQCDNBqoYAgU4KCIQJ3S4QJuApSoAAAQIEMgkIhJkgVUOAQCcFBMKEbhcIE/AUJUCAAAECmQQEwkyQqiFAoJMCAmFCtwuECXiKEiBAgACBTAICYSZI1RAg0EkBgTCh2wXCBDxFCRAgQIBAJgGBMBOkaggQ6KSAQJjQ7QJhAp6iBAgQIEAgk4BAmAlSNQQIdFJAIEzodoEwAU9RAgQIECCQSUAgzASpGgIEOikgECZ0u0CYgKcoAQIECBDIJCAQZoJUDQECnRQQCBO6XSBMwFOUAAECBAhkEhAIM0GqhgCBTgoIhAndLhAm4ClKgAABAgQyCQiEmSBVQ4BAJwVaGQgvXboUDh06FM6cOdPr1CNHjoSdO3cO7ODTp0+HgwcPrvn3vXv3hn379g0dFAJhJ39mHDQBAgQIzJlA3UD4+OOPh927d/dav3Xr1nDixImwtLQ0Z0ejOQQIEJitQCsD4b333ttTjIHuueeeCzHc7d+/P2zbtq2vbgyEZ8+eDYcPHw7r1q2r3QMCYW0qGxIgQIAAgakJ1AmE58+fDwcOHAjHjh0LW7ZsCZP+7p/aQaiYAAECDQm0LhDGABjD31133dU74cdPOSD2c570l4JA2NCotVsCBAgQIFASqBMI4+/6J598cvXun2pABEqAAIGuCrQuEPY7wY8KfNVbRuvcLhoHjEDY1R8bx02AAAEC8yRQJxBWLw7XuYNono5RWwgQIDAtgVYGwqNHj4bjx4+vPhcwKhCWcYtfELt27Vrz3OEDDzxwWR/cfffd4YknnphW36iXAAECBAgQqCEQA+Ett9wSrrzyyjVb33zzzeHqq6/u/V0MhJs3b1793S4Q1oC1CQECnRBoZSAsPyMQe3GcQFhsX76tJP7d/ffff9mAuOeeezoxSBwkAQIECBCYd4H4qMgVV1xxWSBcv379aiCMfyheGCcQznuPah8BArMSaF0gnOQZwip29TmDWXXGqP187GMfC2984xvDjh07Rm3q3wmsCly8eLH3TG2/ixqYCAwT+OAHPxi2b98e3vGOd4AiUFvgC1/4QvjEJz4RPvrRj9YuM4sNPUM4C2X7IEBgEQVaFwhjJwx7y2j1ltC4RMUnP/nJ3q0m8Q2j83zFUCBcxB+x5tssEDbfB4vaAoFwUXuu2XbPayD0ltFmx4W9EyAwvwKtDITD1iHs94xgDJBxLaLiM2rdwqa6UyBsSn6x9ysQLnb/Ndl6gbBJ/cXd97wGwihqHcLFHVdaToDA9ARaGQinx9VszQJhs/6LuneBcFF7rvl2C4TN98EitmCeA+EiemozAQIEpi0gEE5bOGP9AmFGzA5VJRB2qLMzH6pAmBm0I9UJhB3paIdJgEBrBATC1nSlAyFAgAABAgQIECBAgMB4AgLheF62JkCAAAECBAgQIECAQGsEBMLWdKUDIUCAAAECBAgQIECAwHgCAuF4XrYmQIAAAQIECBAgQIBAawQEwhl1ZVwK4+jRo+G2224LW7ZsybLXuKbSAw880Ft0PK6h6NNOgfia9EcffTTs27cvywFOYyxmaZhKsgpM4/yQeyxmPWCVZROISzG95z3vCdu2bctSp3NOFkaVECBAYGoCAmEN2mHrGhbF4/qG+/fv74WzfoGv+uWsWA/x3LlzvSpOnTp12S/fWCaGyOPHj4elpaXLWnr69One3+3cuXPN2krx73bs2BEOHz68GhRTjqG6TmPRkH5trsHZqU3qrHkV+/HJJ58cGPjKX87q9GMEjmU2b97cGxvVT3ksxn87dOhQOHPmzOpm1X5NOYa4rz179oT4ptP4mdc1PudtUNY5P4w655TPD/H46vTjqHNOeSyW68t9zinGcLE+bPV8Nm/9NU/tif1+8ODBXpMGuQ0758Rx9eEPfzjEN8zG3zt1zjmjxuK455xhxzBq3eA643ye+ktbCBAgMA8CAmGNXoi/gOInztAUX9Ri+ItXT8u/LDds2BBOnjzZNxCWv5wVZW688cbeF/b4y/LAgQPh2LFjvbLlL4Nbt24N8UtRNRBWr7jG+jdt2rSmTdddd91qyMhxDAXVqC+NNUg7sUm1X2MfnT17djWol7+47N27t28grH45G9aPEbX8RWpQ+CqPxVh/HLO333577+JBbFMc58U4TjmG6s9K9f93YhBMcJCjzg91zjnV88OofqxzzqmOxWmec8o/K8VFi/L5bALWThSJP7/xAmLxO6N8vqheFBh0zqnOAuf43THOOWfYMcRxfd999/UuMsXficUFp9jG+Pt41DjvxCBwkAQIEJhAQCAcgdbvymf1l2ysYtgV0n5fzsozf9UvgHWC16jbwcpfqGL91dnLcY+hzDRs9mmCMdjaItWr8NUvK8WBD7taX/5yVncsxnoH9dGoW7eqoS3lGKrHO2ict3YATHhg1Qsug9yGnXOq54e6/TjsYs+o20VznXP6XTiohoQJaVtfrPpzP8ht2DmnPAtc95wzzu+/aidU+7vuMcR6qj8bdcd56weCAyRAgMCYAgLhCLB+X+KrMz2jAmH1y1m/X9L9AtqwL2fV28Gqh1GuL8cx1AmpY4691m9e7dNBM2R1v5zV7cdhgXDUhYTqPlKPIZZ/+OGHezOO8TPsFujWD4iaB1j3/DDsS3j1/FC3H4edc0Y9V5brnNPv52TQxZSapJ3YrN+Fg3EvQlVngeuec8a5OFHtjPI+rr/++t4t7MXdM3HbYX3fL0zGMsXz1u5K6MTQd5AECGQQEAhrBMLql9hxA2G/K54PPvjgmmf8xgmEo57XqH6h7Pclb9xjKJjMDtb/qatajRsIq/1Wtx+HBcJh/dfvC2XqMcSxGG+FfuaZZ3rPEXqGcPT4iWZ1zg+DzgODZnXKz5QOGouDAuGo28Rzn3Oq50OBcPS4KX5+b7311tXn0ccNhP1m2HL//isfSfWcM84xFOe5cgCse74arWkLAgQIdEtAIKwRCMvP98XNxwlT/b6c1Z0BGPQlrN8XxuIwqs+ADbrCOs4xlOsuP5/SrR+V8Y+27qzMoBnCOrc/9evHQYFw1G1d8cp89TmtlGOojt8ihOzatavvy27GF25nibrnh0H9WSdQjhsIR93WXH7uNMc5p/xMY9HLg56nbucoGP+oUmcI64axcX53jHvOGecY4rnpqaeeGnph1Qzh+ONICQIEuikgEI7o99RnKPp9Oav7jNCgQDholqdfGIyHl3oMsQ7Pf41/gqgT6IoLDNW3jPb7cla3HwcFwkEXEop99XtpR8ox1Akm46u2v0Td88OgL9v9zg91+7HfOaffWBx2ASrXOafc06OeX2z/qKh3hHWfv+sX8Pv1fd1zzjgXJ8q/T/qdc+ocQ78w2O9cama53rixFQECBATCGmOgPEsy6IrjOF/ORr1FsGhS3V/QcftRL11IOYY69ddg7Nwm1S8jg2bz6n45K4Je/G+/N96WgfuFgn5/Nyropx5DfBtg3G98A6AZwno/AnXPD/3OOYPOQ3X7sd85Z9idCsPuGEg955TPg9W7NOpJdm+rUW8ZLUT6nXMGzQKn9OMk55xRx9Dv8YpBY2XQObd7I8MREyBAYLiAQFhjhAxbh6n6b7G6Yu2n+G/l9ZzKu6reElVe+63f7VLFK8IHXSmPvySLNbuK/ZSXwZj0GOJSBG67qTFIBmwybE2s6jpusYpiHAx6adCwfozlY7liDbL4/4sxEF/R3m8sxi/75XUCi8Mov5J+0mMoLiTs3r17VcczhPXG0rDzw7Bzzhe/+MXw6KOP9l3CZFg/DjvnDBqL0zznlMelW0XrjZly2Bu0DuGgc85b3vKW3gufbrvttsuWTZr0d8eg3391zjnl81h5LcV+47T8O7dYOqc45xg7440dWxMg0F0BgXCKfT+N25xGvelvioej6hkJjFoaYpJmTGMsTtIOZaYrkPv8MI2xOF0BtU8iMOrtw5PU6ZwziZoyBAgQaEZAIJyie+4vZ/Hq6KAZxykehqpnLDCNL2e5x+KMSeyuhsA0zg/TGIs1DsUmMxYYtYzRJM1xzplETRkCBAg0IyAQNuNurwQIECBAgAABAgQIEGhcQCBsvAs0gAABAgQIECBAgAABAs0ICITNuNsrAQIECBAgQIAAAQIEGhcQCBvvAg0gQIAAAQIECBAgQIBAMwICYTPu9kqAAAECBAgQIECAAIHGBQTCxrtAAwgQIECAAAECBAgQINCMgEDYjLu9EiBAgAABAgQIECBAoHEBgbDxLtAAAgQIECBAgAABAgQINCMgEDbjbq8ECBAgQIAAAQIECBBoXEAgbLwLNIAAAQIECBAgQIAAAQLNCAiEzbjbKwECBAgQIECAAAECBBoXEAgb7wINIECAAAECBAgQIECAQDMCAmEz7vZKgAABAgQIECBAgACBxgUEwsa7QAMIECBAgAABAgQIECDQjIBA2Iy7vRIgQIAAAQIECBAgQKBxAYGw8S7QAAIECBAgQIAAAQIECDQjIBA2426vBAgQIECAAAECBAgQaFxAIGy8CzSAAAECBAgQIECAAAECzQgIhM242ysBAgQIECBAgAABAgQaFxAIG+8CDSBAgAABAgQIECBAgEAzAgJhM+72SoAAAQIECBAgQIAAgcYFBMLGu0ADCBAgQIAAAQIECBAg0IyAQNiMu70SIECAAAECBAgQIECgcQGBsPEu0AACBAgQIECAAAECBAg0IyAQNuNurwQIECBAgAABAgQIEGhcQCBsvAs0gAABAgQIECBAgAABAs0ICITNuNsrAQIECBAgQIAAAQIEGhcQCBvvAg0gQIAAAQIECBAgQIBAMwICYTPu9kqAAAECBAgQIECAAIHGBQTCxrtAAwgQIECAAAECBAgQINCMgEDYjLu9EiBAgAABAgQIECBAoHEBgbDxLtAAAgQIECBAgAABAgQINCMgEDbjbq8ECBAgQIAAAQIECBBoXEAgbLwLNIAAAQIECBAgQIAAAQLNCAiEzbjbKwECBAgQIECAAAECBBoXEAgb7wINIECAAAECBAgQIECAQDMCAmEz7vZKgAABAgQIECBAgACBxgUEwsa7QAMIECBAgAABAgQIECDQjIBA2Iy7vRIgQIAAAQIECBAgQKBxAYGw8S7QAAIECBAgQIAAAQIECDQjIBA2426vBAgQIECAAAECBAgQaFxAIGy8CzSAAAECBAgQIECAAAECzQgIhM242ysBAgQIECBAgAABAgQaFxAIG+8CDSBAgAABAgQIECBAgEAzAgJhM+72SoAAAQIECBAgQIAAgcYFBMLGu0ADCBAgQIAAAQIECBAg0IyAQNiMu70SIECAAAECBAgQIECgcQGBsPEu0AACBAgQIECAAAECBAg0IyAQNuNurwQIECBAgAABAgQIEGhcQCBsvAs0gAABAgQIECBAgAABAs0ICITNuNsrAQIECBAgQIAAAQIEGhcQCBvvAg0gQIAAAQIECBAgQIBAMwICYTPu9kqAAAECBAgQIECAAIHGBQTCxrtAAwgQIECAAAECBAgQINCMgEDYjLu9EiBAgAABAgQIECBAoHEBgbDxLtAAAgQIECBAgAABAgQINCMgEDbjbq8ECBAgQIAAAQIECBBoXEAgbLwLNIAAAQIECBAgQIAAAQLNCPx/Zr+tPwZOQ8YAAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "7193852a", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2021" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "940fe45e", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2021, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "ff881c13", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7500011519622525\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True,\n", + " date_compile_auc = '01/01/2021', #optionnal, by default date of compile\n", + " datadrift_file = \"car_accident_auc.csv\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "7a36e381", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "a06ecc4b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_car_accident_datadrift_2021.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_car_accident_datadrift_2021.html', \n", + " title_story=\"Data drift\",\n", + " title_description=\"\"\"Car accident Data drift 2021\"\"\",\n", + " project_info_file=\"../../../../eurybia/data/project_info_car_accident.yml\" \n", + " )" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_car_accident_datadrift_2021.html', \n", - " title_story=\"Data drift\",\n", - " title_description=\"\"\"Car accident Data drift 2021\"\"\",\n", - " project_info_file=\"../../../../eurybia/data/project_info_car_accident.yml\" \n", - " )" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "eurybia_3_9", - "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" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "336px" - }, - "toc_section_display": true, - "toc_window_display": true + ], + "metadata": { + "kernelspec": { + "display_name": "eurybia_3_9", + "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" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "336px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "vscode": { + "interpreter": { + "hash": "36c4204cc0170e083c18487e195263df35fcafba9d65a5415ab6b0958d51e154" + } + } }, - "vscode": { - "interpreter": { - "hash": "36c4204cc0170e083c18487e195263df35fcafba9d65a5415ab6b0958d51e154" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/source/tutorials/data_validation/tutorial01-data-validation.ipynb b/docs/source/tutorials/data_validation/tutorial01-data-validation.ipynb index 24d66a1..23784b8 100644 --- a/docs/source/tutorials/data_validation/tutorial01-data-validation.ipynb +++ b/docs/source/tutorials/data_validation/tutorial01-data-validation.ipynb @@ -1,8213 +1,8213 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Validate Data for model deployment\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With this tutorial you:
\n", - "Understand how use Eurybia to do data validation in a simple use case
\n", - "\n", - "Contents:\n", - "- Build a model to deploy\n", - "- Do data validation between learning dataset and production dataset\n", - "- Generate Report \n", - "- Analysis of results\n", - "\n", - "Data from Kaggle [Titanic](https://www.kaggle.com/c/titanic)
" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from category_encoders import OrdinalEncoder\n", - "import catboost\n", - "from eurybia.core.smartdrift import SmartDrift\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building Supervized Model\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "titan_df = data_loading('titanic')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "features = ['Pclass', 'Age', 'Embarked', 'Sex', 'SibSp', 'Parch', 'Fare']\n", - "features_to_encode = ['Pclass', 'Embarked', 'Sex']" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "text/plain": [ - "OrdinalEncoder(cols=['Pclass', 'Embarked', 'Sex'],\n", - " mapping=[{'col': 'Pclass', 'data_type': dtype('O'),\n", - " 'mapping': Third class 1\n", - "First class 2\n", - "Second class 3\n", - "NaN -2\n", - "dtype: int64},\n", - " {'col': 'Embarked', 'data_type': dtype('O'),\n", - " 'mapping': Southampton 1\n", - "Cherbourg 2\n", - "Queenstown 3\n", - "NaN -2\n", - "dtype: int64},\n", - " {'col': 'Sex', 'data_type': dtype('O'),\n", - " 'mapping': male 1\n", - "female 2\n", - "NaN -2\n", - "dtype: int64}])" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Validate Data for model deployment\n" ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "encoder = OrdinalEncoder(cols=features_to_encode)\n", - "encoder.fit(titan_df[features]) " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "titan_df_encoded = encoder.transform(titan_df[features])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(\n", - " titan_df_encoded,\n", - " titan_df['Survived'].to_frame(),\n", - " test_size=0.2,\n", - " random_state=11\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "i=0\n", - "indice_cat = []\n", - "for feature in titan_df_encoded:\n", - " if feature in features_to_encode:\n", - " indice_cat.append(i)\n", - " i=i+1" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", - " learning_rate=0.143852,\n", - " iterations=500,\n", - " l2_leaf_reg=15,\n", - " max_depth = 4)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "train_pool_cat = catboost.Pool(data=X_train, label= y_train, cat_features = indice_cat)\n", - "test_pool_cat = catboost.Pool(data=X_test, label=y_test, cat_features = indice_cat) " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "model.fit(train_pool_cat, eval_set=test_pool_cat, silent=True)\n", - "y_pred = model.predict(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating a fake dataset as a production dataset\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "df_production = titan_df.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "df_production['Age'] = df_production['Age'].apply(lambda x: random.randrange(10, 76)).astype(float)\n", - "df_production['Fare'] = df_production['Fare'].apply(lambda x: random.randrange(1, 100)).astype(float)\n", - "list_sex= [\"male\", \"female\"]\n", - "df_production['Sex'] = df_production['Sex'].apply(lambda x: random.choice(list_sex))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "df_baseline = titan_df[features]\n", - "df_current = df_production[features]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "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", - " \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", - " \n", - " \n", - " \n", - "
PclassAgeEmbarkedSexSibSpParchFare
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1Third class19.0Southamptonfemale1093.0
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" - ], - "text/plain": [ - " Pclass Age Embarked Sex SibSp Parch Fare\n", - "PassengerId \n", - "1 Third class 19.0 Southampton female 1 0 93.0\n", - "2 First class 24.0 Cherbourg female 1 0 57.0\n", - "3 Third class 51.0 Southampton male 0 0 44.0\n", - "4 First class 35.0 Southampton male 1 0 30.0\n", - "5 Third class 54.0 Southampton male 0 0 92.0" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this tutorial you:
\n", + "Understand how use Eurybia to do data validation in a simple use case
\n", + "\n", + "Contents:\n", + "- Build a model to deploy\n", + "- Do data validation between learning dataset and production dataset\n", + "- Generate Report \n", + "- Analysis of results\n", + "\n", + "Data from Kaggle [Titanic](https://www.kaggle.com/c/titanic)
" ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_current.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "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", - " \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", - " \n", - " \n", - " \n", - "
PclassAgeEmbarkedSexSibSpParchFare
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1Third class22.0Southamptonmale107.25
2First class38.0Cherbourgfemale1071.28
3Third class26.0Southamptonfemale007.92
4First class35.0Southamptonfemale1053.10
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\n", - "
" - ], - "text/plain": [ - " Pclass Age Embarked Sex SibSp Parch Fare\n", - "PassengerId \n", - "1 Third class 22.0 Southampton male 1 0 7.25\n", - "2 First class 38.0 Cherbourg female 1 0 71.28\n", - "3 Third class 26.0 Southampton female 0 0 7.92\n", - "4 First class 35.0 Southampton female 1 0 53.10\n", - "5 Third class 35.0 Southampton male 0 0 8.05" + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from category_encoders import OrdinalEncoder\n", + "import catboost\n", + "from eurybia.core.smartdrift import SmartDrift\n", + "from sklearn.model_selection import train_test_split" ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_baseline.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use Eurybia for data validation" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=df_current, df_baseline=df_baseline, deployed_model=model, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n", - "CPU times: user 30.8 s, sys: 23.3 s, total: 54.2 s\n", - "Wall time: 1.65 s\n" - ] - } - ], - "source": [ - "%time SD.compile(full_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building Supervized Model\n" + ] + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_titanic.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." - ], - "text/plain": [ - "" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_titanic.html', \n", - " title_story=\"Data validation\",\n", - " title_description=\"\"\"Titanic Data validation\"\"\",\n", - " project_info_file=\"../../eurybia/data/project_info_titanic.yml\" \n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", - "However, to illustrate functionalities, we will detail results with notebook mode analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analysis of results of the data validation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Data validation methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", - "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", - "Then a classification model (catboost) is learned to predict this target.\n", - "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Detection data drift performance" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "0.9124436936936937" + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "titan_df = data_loading('titanic')" ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Performance of data drift classifier\n", - "SD.auc" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "such a high auc means that datasets are not similar.The differences should be analysed before deploying model in production" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Importance of features in data drift" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/html": [ - " \n", - " " + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "features = ['Pclass', 'Age', 'Embarked', 'Sex', 'SibSp', 'Parch', 'Fare']\n", + "features_to_encode = ['Pclass', 'Embarked', 'Sex']" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "marker": { - "color": [ - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)", - "rgba(0,154,203,255)" - ], - "line": { - "color": "rgba(52, 55, 54, 0.8)", - "width": 0.5 - } - }, - "name": "Global", - "orientation": "h", - "type": "bar", - "x": [ - 0.0123, - 0.0338, - 0.0472, - 0.0862, - 0.1312, - 0.2517, - 0.4377 - ], - "y": [ - "Embarked", - "Parch", - "SibSp", - "Sex", - "Pclass", - "Age", - "Fare" - ] - } - ], - "layout": { - "autosize": false, - "barmode": "group", - "height": 500, - "hovermode": "closest", - "margin": { - "b": 50, - "l": 160, - "r": 0, - "t": 95 - }, - "template": { - "data": { - "scatter": [ - { - "type": "scatter" - } - ] - } - }, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial", - "size": 24 - }, - "text": "Features Importance
Response: Current dataset", - "x": 0.5, - "xanchor": "center", - "y": 0.9, - "yanchor": "middle" - }, - "width": 900, - "xaxis": { - "automargin": true, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 + "data": { + "text/plain": [ + "OrdinalEncoder(cols=['Pclass', 'Embarked', 'Sex'],\n", + " mapping=[{'col': 'Pclass', 'data_type': dtype('O'),\n", + " 'mapping': Third class 1\n", + "First class 2\n", + "Second class 3\n", + "NaN -2\n", + "dtype: int64},\n", + " {'col': 'Embarked', 'data_type': dtype('O'),\n", + " 'mapping': Southampton 1\n", + "Cherbourg 2\n", + "Queenstown 3\n", + "NaN -2\n", + "dtype: int64},\n", + " {'col': 'Sex', 'data_type': dtype('O'),\n", + " 'mapping': male 1\n", + "female 2\n", + "NaN -2\n", + "dtype: int64}])" + ] }, - "text": "Contribution" - } - }, - "yaxis": { - "automargin": true, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 - } - } + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" } - } - }, - "text/html": [ - "
" + ], + "source": [ + "encoder = OrdinalEncoder(cols=features_to_encode)\n", + "encoder.fit(titan_df[features]) " ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Features that explain most differences are fare, age and sex. This makes sense because it is features that have been altered\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Feature importance overview" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "titan_df_encoded = encoder.transform(titan_df[features])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " titan_df_encoded,\n", + " titan_df['Survived'].to_frame(),\n", + " test_size=0.2,\n", + " random_state=11\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "i=0\n", + "indice_cat = []\n", + "for feature in titan_df_encoded:\n", + " if feature in features_to_encode:\n", + " indice_cat.append(i)\n", + " i=i+1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", + " learning_rate=0.143852,\n", + " iterations=500,\n", + " l2_leaf_reg=15,\n", + " max_depth = 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "train_pool_cat = catboost.Pool(data=X_train, label= y_train, cat_features = indice_cat)\n", + "test_pool_cat = catboost.Pool(data=X_test, label=y_test, cat_features = indice_cat) " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "model.fit(train_pool_cat, eval_set=test_pool_cat, silent=True)\n", + "y_pred = model.predict(X_test)" + ] + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating a fake dataset as a production dataset\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "df_production = titan_df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df_production['Age'] = df_production['Age'].apply(lambda x: random.randrange(10, 76)).astype(float)\n", + "df_production['Fare'] = df_production['Fare'].apply(lambda x: random.randrange(1, 100)).astype(float)\n", + "list_sex= [\"male\", \"female\"]\n", + "df_production['Sex'] = df_production['Sex'].apply(lambda x: random.choice(list_sex))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "df_baseline = titan_df[features]\n", + "df_current = df_production[features]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ { - "hovertemplate": "%{hovertext}", - "hovertext": [ - "Feature: Pclass
Deployed Model Importance: 15.8%
Datadrift test: Chi-Square - pvalue: 1.00000
Datadrift model Importance: 13.1", - "Feature: Age
Deployed Model Importance: 16.7%
Datadrift test: K-Smirnov - pvalue: 0.00000
Datadrift model Importance: 25.2", - "Feature: Embarked
Deployed Model Importance: 6.2%
Datadrift test: Chi-Square - pvalue: 1.00000
Datadrift model Importance: 1.2", - "Feature: Sex
Deployed Model Importance: 35.0%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 8.6", - "Feature: SibSp
Deployed Model Importance: 3.8%
Datadrift test: K-Smirnov - pvalue: 1.00000
Datadrift model Importance: 4.7", - "Feature: Parch
Deployed Model Importance: 1.8%
Datadrift test: K-Smirnov - pvalue: 1.00000
Datadrift model Importance: 3.4", - "Feature: Fare
Deployed Model Importance: 20.7%
Datadrift test: K-Smirnov - pvalue: 0.00000
Datadrift model Importance: 43.8" - ], - "marker": { - "color": [ - 1, - 3.7722856358158184e-57, - 1, - 2.5897685255536254e-10, - 1, - 1, - 2.1971740093214243e-80 - ], - "coloraxis": "coloraxis", - "line": { - "color": "white", - "width": 0.8 + "data": { + "text/html": [ + "
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" + ], + "source": [ + "df_baseline.head()" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.scatter_feature_importance()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Features that have the most difference are quite important for the deployed model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Univariate analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "alignmentgroup": "True", - "cliponaxis": false, - "hovertemplate": "target=df_baseline
Percent=%{x}
Sex=%{y}
Percent_displayed=%{text}", - "legendgroup": "df_baseline", - "marker": { - "color": "rgba(0,154,203,255)" - }, - "name": "df_baseline", - "offsetgroup": "df_baseline", - "orientation": "h", - "showlegend": true, - "text": [ - "35.24 %", - "64.76 %" - ], - "textposition": "outside", - "type": "bar", - "x": [ - 35.24130190796858, - 64.75869809203142 - ], - "xaxis": "x", - "y": [ - "female", - "male" - ], - "yaxis": "y" - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Eurybia for data validation" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=df_current, df_baseline=df_baseline, deployed_model=model, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ { - "alignmentgroup": "True", - "cliponaxis": false, - "hovertemplate": "target=df_current
Percent=%{x}
Sex=%{y}
Percent_displayed=%{text}", - "legendgroup": "df_current", - "marker": { - "color": "rgba(223, 103, 0, 0.8)" - }, - "name": "df_current", - "offsetgroup": "df_current", - "orientation": "h", - "showlegend": true, - "text": [ - "50.17 %", - "49.83 %" - ], - "textposition": "outside", - "type": "bar", - "x": [ - 50.168350168350166, - 49.831649831649834 - ], - "xaxis": "x", - "y": [ - "female", - "male" - ], - "yaxis": "y" - } - ], - "layout": { - "barmode": "group", - "height": 600, - "hovermode": "closest", - "legend": { - "title": { - "text": "" - }, - "tracegroupgap": 0 - }, - "margin": { - "t": 60 - }, - "template": { - "data": { - "scatter": [ - { - "type": "scatter" - } + "name": "stdout", + "output_type": "stream", + "text": [ + "Backend: Shap TreeExplainer\n", + "CPU times: user 30.8 s, sys: 23.3 s, total: 54.2 s\n", + "Wall time: 1.65 s\n" ] - } - }, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial", - "size": 24 - }, - "x": 0.5, - "xanchor": "center", - "y": 0.9, - "yanchor": "middle" - }, - "width": 900, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "range": [ - 0, - 74.75869809203142 - ], - "showgrid": false, - "showticklabels": true, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 + } + ], + "source": [ + "%time SD.compile(full_validation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_titanic.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] }, - "text": "Percent" - } - }, - "yaxis": { - "anchor": "x", - "automargin": true, - "domain": [ - 0, - 1 - ], - "showgrid": false, - "showticklabels": true, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_titanic.html', \n", + " title_story=\"Data validation\",\n", + " title_description=\"\"\"Titanic Data validation\"\"\",\n", + " project_info_file=\"../../eurybia/data/project_info_titanic.yml\" \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", + "However, to illustrate functionalities, we will detail results with notebook mode analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis of results of the data validation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data validation methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", + "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", + "Then a classification model (catboost) is learned to predict this target.\n", + "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Detection data drift performance" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9124436936936937" + ] }, - "text": "Density" - } + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" } - } - }, - "text/html": [ - "
" + ], + "source": [ + "#Performance of data drift classifier\n", + "SD.auc" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('Sex')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Distribution of predicted values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This graph shows distributions of the production model outputs on both baseline and current datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "such a high auc means that datasets are not similar.The differences should be analysed before deploying model in production" + ] + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importance of features in data drift" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ { - 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Response: Current dataset", + "x": 0.5, + "xanchor": "center", + "y": 0.9, + "yanchor": "middle" + }, + "width": 900, + "xaxis": { + "automargin": true, + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial Black", + "size": 16 + }, + "text": "Contribution" + } + }, + "yaxis": { + "automargin": true, + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial Black", + "size": 16 + } + } + } + } + }, + "text/html": [ + "
" + ] }, - "text": "Density" - } + "metadata": {}, + "output_type": "display_data" } - } - }, - "text/html": [ - "
" + ], + "source": [ + "SD.xpl.plot.features_importance()" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Differences between 2 datasets generate a difference in the distribution of the predictions of the deployed model. These differences can have important impacts on the performance of the model in production" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Feature contribution on data drift's detection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This graph represents the contribution of a variable to the data drift detection. This graph can help to understand the drift when the analysis of the dataset, either numerical or graphical, does not allow a clear understanding. In the drop-down menu, the variables are sorted by importance of the variables in the data drift detection." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Features that explain most differences are fare, age and sex. This makes sense because it is features that have been altered\n" + ] + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature importance overview" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ { - "customdata": [ - 824, - 1471, - 233, - 798, - 777, - 270, - 416, - 1435, - 1345, - 111, - 736, - 1084, - 173, - 741, - 1169, - 1347, - 188, - 1378, - 1715, - 1168, - 582, - 988, - 1497, - 352, - 593, - 926, - 943, - 1258, - 602, - 464, - 383, - 1208, - 1573, - 462, - 1108, - 1501, - 1271, - 65, - 1140, - 857, - 733, - 610, - 237, - 426, - 1146, - 1600, - 374, - 1662, - 1644, - 29, - 239, - 1691, - 162, - 757, - 1743, - 1034, - 1733, - 1204, - 1137, - 1317, - 846, - 585, - 135, - 1110, - 70, - 1543, - 530, - 705, - 422, - 952, - 485, - 527, - 869, - 324, - 23, - 506, - 756, - 1240, - 331, - 1117, - 413, - 893, - 1468, - 778, - 398, - 631, - 1619, - 1610, - 220, - 351, - 1552, - 693, - 680, - 1723, - 1477, - 576, - 203, - 115, - 1132, - 584, - 332, - 799, - 637, - 548, - 350, - 1339, - 650, - 1197, - 109, - 1220, - 1458, - 479, - 1736, - 1667, - 495, - 212, - 1505, - 342, - 1178, - 1151, - 764, - 371, - 481, - 518, - 1255, - 710, - 588, - 1385, - 590, - 289, - 1780, - 613, - 124, - 316, - 1278, - 49, - 1480, - 1561, - 1073, - 1424, - 1182, - 1235, - 254, - 322, - 1747, - 432, - 78, - 1738, - 1540, - 73, - 69, - 609, - 1111, - 628, - 1061, - 394, - 1555, - 936, - 654, - 914, - 1642, - 1190, - 76, - 1489, - 1309, - 1757, - 561, - 1759, - 305, - 439, - 1320, - 1004, - 339, - 904, - 30, - 1777, - 1701, - 720, - 1043, - 1491, - 1379, - 99, - 429, - 1391, - 939, - 1565, - 365, - 994, - 168, - 1393, - 886, - 617, - 1106, - 1709, - 471, - 1719, - 535, - 817, - 32, - 1310, - 1268, - 251, - 210, - 1460, - 247, - 185, - 514, - 965, - 1284, - 44, - 1712, - 889, - 382, - 1226, - 196, - 1487, - 1328, - 1079, - 59, - 1720, - 415, - 526, - 1074, - 1113, - 651, - 583, - 1436, - 1296, - 123, - 838, - 1519, - 298, - 782, - 275, - 483, - 1457, - 1618, - 1142, - 1249, - 482, - 1361, - 771, - 1114, - 781, - 1262, - 1544, - 1530, - 867, - 1334, - 1417, - 1527, - 1758, - 1177, - 453, - 67, - 344, - 842, - 1145, - 816, - 1253, - 1018, - 1138, - 721, - 297, - 1556, - 1627, - 1211, - 1587, - 1588, - 1481, - 1421, - 354, - 450, - 1593, - 941, - 1611, - 261, - 780, - 240, - 660, - 544, - 1641, - 1657, - 596, - 1033, - 71, - 534, - 259, - 51, - 366, - 1009, - 946, - 1531, - 1539, - 1366, - 567, - 411, - 303, - 1676, - 1682, - 551, - 1771, - 1247, - 478, - 494, - 1193, - 141, - 898, - 607, - 1663, - 1744, - 425, - 1717, - 271, - 184, - 759, - 244, - 1040, - 1053, - 1661, - 529, - 1769, - 1272, - 892, - 1654, - 1583, - 1292, - 250, - 1589, - 1164, - 836, - 923, - 1120, - 367, - 1558, - 618, - 1464, - 1055, - 1509, - 1523, - 170, - 497, - 1274, - 1722, - 300, - 1669, - 1475, - 433, - 1469, - 1716, - 1052, - 1029, - 818, - 532, - 381, - 844, - 557, - 175, - 629, - 1105, - 1741, - 1013, - 1259, - 427, - 1425, - 1748, - 198, - 15, - 265, - 819, - 679, - 802, - 420, - 1205, - 937, - 1293, - 1602, - 408, - 199, - 715, - 1216, - 1242, - 1418, - 950, - 552, - 1159, - 1384, - 1125, - 589, - 1360, - 915, - 310, - 56, - 538, - 414, - 1470, - 43, - 100, - 274, - 1286, - 353, - 101, - 774, - 107, - 554, - 1762, - 745, - 1054, - 611, - 1356, - 1449, - 962, - 1175, - 128, - 486, - 1728, - 727, - 938, - 808, - 1463, - 905, - 925, - 834, - 438, - 599, - 555, - 1365, - 1047, - 1112, - 901, - 964, - 405, - 1704, - 1474, - 861, - 1387, - 614, - 1058, - 226, - 231, - 1760, - 1517, - 1370, - 163, - 669, - 643 - ], - "hovertemplate": "%{hovertext}
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In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ { - "customdata": [ - 824, - 1471, - 233, - 798, - 777, - 270, - 416, - 1435, - 1345, - 111, - 736, - 1084, - 173, - 741, - 1169, - 1347, - 188, - 1378, - 1715, - 1168, - 582, - 988, - 1497, - 352, - 593, - 926, - 943, - 1258, - 602, - 464, - 383, - 1208, - 1573, - 462, - 1108, - 1501, - 1271, - 65, - 1140, - 857, - 733, - 610, - 237, - 426, - 1146, - 1600, - 374, - 1662, - 1644, - 29, - 239, - 1691, - 162, - 757, - 1743, - 1034, - 1733, - 1204, - 1137, - 1317, - 846, - 585, - 135, - 1110, - 70, - 1543, - 530, - 705, - 422, - 952, - 485, - 527, - 869, - 324, - 23, - 506, - 756, - 1240, - 331, - 1117, - 413, - 893, - 1468, - 778, - 398, - 631, - 1619, - 1610, - 220, - 351, - 1552, - 693, - 680, - 1723, - 1477, - 576, - 203, - 115, - 1132, - 584, - 332, - 799, - 637, - 548, - 350, - 1339, - 650, - 1197, - 109, - 1220, - 1458, - 479, - 1736, - 1667, - 495, - 212, - 1505, - 342, - 1178, - 1151, - 764, - 371, - 481, - 518, - 1255, - 710, - 588, - 1385, - 590, - 289, - 1780, - 613, - 124, - 316, - 1278, - 49, - 1480, - 1561, - 1073, - 1424, - 1182, - 1235, - 254, - 322, - 1747, - 432, - 78, - 1738, - 1540, - 73, - 69, - 609, - 1111, - 628, - 1061, - 394, - 1555, - 936, - 654, - 914, - 1642, - 1190, - 76, - 1489, - 1309, - 1757, - 561, - 1759, - 305, - 439, - 1320, - 1004, - 339, - 904, - 30, - 1777, - 1701, - 720, - 1043, - 1491, - 1379, - 99, - 429, - 1391, - 939, - 1565, - 365, - 994, - 168, - 1393, - 886, - 617, - 1106, - 1709, - 471, - 1719, - 535, - 817, - 32, - 1310, - 1268, - 251, - 210, - 1460, - 247, - 185, - 514, - 965, - 1284, - 44, - 1712, - 889, - 382, - 1226, - 196, - 1487, - 1328, - 1079, - 59, - 1720, - 415, - 526, - 1074, - 1113, - 651, - 583, - 1436, - 1296, - 123, - 838, - 1519, - 298, - 782, - 275, - 483, - 1457, - 1618, - 1142, - 1249, - 482, - 1361, - 771, - 1114, - 781, - 1262, - 1544, - 1530, - 867, - 1334, - 1417, - 1527, - 1758, - 1177, - 453, - 67, - 344, - 842, - 1145, - 816, - 1253, - 1018, - 1138, - 721, - 297, - 1556, - 1627, - 1211, - 1587, - 1588, - 1481, - 1421, - 354, - 450, - 1593, - 941, - 1611, - 261, - 780, - 240, - 660, - 544, - 1641, - 1657, - 596, - 1033, - 71, - 534, - 259, - 51, - 366, - 1009, - 946, - 1531, - 1539, - 1366, - 567, - 411, - 303, - 1676, - 1682, - 551, - 1771, - 1247, - 478, - 494, - 1193, - 141, - 898, - 607, - 1663, - 1744, - 425, - 1717, - 271, - 184, - 759, - 244, - 1040, - 1053, - 1661, - 529, - 1769, - 1272, - 892, - 1654, - 1583, - 1292, - 250, - 1589, - 1164, - 836, - 923, - 1120, - 367, - 1558, - 618, - 1464, - 1055, - 1509, - 1523, - 170, - 497, - 1274, - 1722, - 300, - 1669, - 1475, - 433, - 1469, - 1716, - 1052, - 1029, - 818, - 532, - 381, - 844, - 557, - 175, - 629, - 1105, - 1741, - 1013, - 1259, - 427, - 1425, - 1748, - 198, - 15, - 265, - 819, - 679, - 802, - 420, - 1205, - 937, - 1293, - 1602, - 408, - 199, - 715, - 1216, - 1242, - 1418, - 950, - 552, - 1159, - 1384, - 1125, - 589, - 1360, - 915, - 310, - 56, - 538, - 414, - 1470, - 43, - 100, - 274, - 1286, - 353, - 101, - 774, - 107, - 554, - 1762, - 745, - 1054, - 611, - 1356, - 1449, - 962, - 1175, - 128, - 486, - 1728, - 727, - 938, - 808, - 1463, - 905, - 925, - 834, - 438, - 599, - 555, - 1365, - 1047, - 1112, - 901, - 964, - 405, - 1704, - 1474, - 861, - 1387, - 614, - 1058, - 226, - 231, - 1760, - 1517, - 1370, - 163, - 669, - 643 - ], - "hovertemplate": "%{hovertext}
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"family": "Arial", - "size": 24 - }, - "text": "Sex - Feature Contribution
Response: Current dataset", - "x": 0.5, - "xanchor": "center", - "y": 0.9, - "yanchor": "middle" - }, - "violingap": 0.05, - "violingroupgap": 0, - "violinmode": "overlay", - "width": 900, - "xaxis": { - "automargin": true, - "range": [ - -0.6, - 1.6 - ], - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 - }, - "text": "Sex" - }, - "type": "category" - }, - "yaxis": { - "automargin": true, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 + ], + "source": [ + "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Differences between 2 datasets generate a difference in the distribution of the predictions of the deployed model. These differences can have important impacts on the performance of the model in production" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature contribution on data drift's detection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This graph represents the contribution of a variable to the data drift detection. This graph can help to understand the drift when the analysis of the dataset, either numerical or graphical, does not allow a clear understanding. In the drop-down menu, the variables are sorted by importance of the variables in the data drift detection." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + 824, + 1471, + 233, + 798, + 777, + 270, + 416, + 1435, + 1345, + 111, + 736, + 1084, + 173, + 741, + 1169, + 1347, + 188, + 1378, + 1715, + 1168, + 582, + 988, + 1497, + 352, + 593, + 926, + 943, + 1258, + 602, + 464, + 383, + 1208, + 1573, + 462, + 1108, + 1501, + 1271, + 65, + 1140, + 857, + 733, + 610, + 237, + 426, + 1146, + 1600, + 374, + 1662, + 1644, + 29, + 239, + 1691, + 162, + 757, + 1743, + 1034, + 1733, + 1204, + 1137, + 1317, + 846, + 585, + 135, + 1110, + 70, + 1543, + 530, + 705, + 422, + 952, + 485, + 527, + 869, + 324, + 23, + 506, + 756, + 1240, + 331, + 1117, + 413, + 893, + 1468, + 778, + 398, + 631, + 1619, + 1610, + 220, + 351, + 1552, + 693, + 680, + 1723, + 1477, + 576, + 203, + 115, + 1132, + 584, + 332, + 799, + 637, + 548, + 350, + 1339, + 650, + 1197, + 109, + 1220, + 1458, + 479, + 1736, + 1667, + 495, + 212, + 1505, + 342, + 1178, + 1151, + 764, + 371, + 481, + 518, + 1255, + 710, + 588, + 1385, + 590, + 289, + 1780, + 613, + 124, + 316, + 1278, + 49, + 1480, + 1561, + 1073, + 1424, + 1182, + 1235, + 254, + 322, + 1747, + 432, + 78, + 1738, + 1540, + 73, + 69, + 609, + 1111, + 628, + 1061, + 394, + 1555, + 936, + 654, + 914, + 1642, + 1190, + 76, + 1489, + 1309, + 1757, + 561, + 1759, + 305, + 439, + 1320, + 1004, + 339, + 904, + 30, + 1777, + 1701, + 720, + 1043, + 1491, + 1379, + 99, + 429, + 1391, + 939, + 1565, + 365, + 994, + 168, + 1393, + 886, + 617, + 1106, + 1709, + 471, + 1719, + 535, + 817, + 32, + 1310, + 1268, + 251, + 210, + 1460, + 247, + 185, + 514, + 965, + 1284, + 44, + 1712, + 889, + 382, + 1226, + 196, + 1487, + 1328, + 1079, + 59, + 1720, + 415, + 526, + 1074, + 1113, + 651, + 583, + 1436, + 1296, + 123, + 838, + 1519, + 298, + 782, + 275, + 483, + 1457, + 1618, + 1142, + 1249, + 482, + 1361, + 771, + 1114, + 781, + 1262, + 1544, + 1530, + 867, + 1334, + 1417, + 1527, + 1758, + 1177, + 453, + 67, + 344, + 842, + 1145, + 816, + 1253, + 1018, + 1138, + 721, + 297, + 1556, + 1627, + 1211, + 1587, + 1588, + 1481, + 1421, + 354, + 450, + 1593, + 941, + 1611, + 261, + 780, + 240, + 660, + 544, + 1641, + 1657, + 596, + 1033, + 71, + 534, + 259, + 51, + 366, + 1009, + 946, + 1531, + 1539, + 1366, + 567, + 411, + 303, + 1676, + 1682, + 551, + 1771, + 1247, + 478, + 494, + 1193, + 141, + 898, + 607, + 1663, + 1744, + 425, + 1717, + 271, + 184, + 759, + 244, + 1040, + 1053, + 1661, + 529, + 1769, + 1272, + 892, + 1654, + 1583, + 1292, + 250, + 1589, + 1164, + 836, + 923, + 1120, + 367, + 1558, + 618, + 1464, + 1055, + 1509, + 1523, + 170, + 497, + 1274, + 1722, + 300, + 1669, + 1475, + 433, + 1469, + 1716, + 1052, + 1029, + 818, + 532, + 381, + 844, + 557, + 175, + 629, + 1105, + 1741, + 1013, + 1259, + 427, + 1425, + 1748, + 198, + 15, + 265, + 819, + 679, + 802, + 420, + 1205, + 937, + 1293, + 1602, + 408, + 199, + 715, + 1216, + 1242, + 1418, + 950, + 552, + 1159, + 1384, + 1125, + 589, + 1360, + 915, + 310, + 56, + 538, + 414, + 1470, + 43, + 100, + 274, + 1286, + 353, + 101, + 774, + 107, + 554, + 1762, + 745, + 1054, + 611, + 1356, + 1449, + 962, + 1175, + 128, + 486, + 1728, + 727, + 938, + 808, + 1463, + 905, + 925, + 834, + 438, + 599, + 555, + 1365, + 1047, + 1112, + 901, + 964, + 405, + 1704, + 1474, + 861, + 1387, + 614, + 1058, + 226, + 231, + 1760, + 1517, + 1370, + 163, + 669, + 643 + ], + "hovertemplate": "%{hovertext}
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" + ] }, - "text": "Contribution" - } + "metadata": {}, + "output_type": "display_data" } - } - }, - "text/html": [ - "
" + ], + "source": [ + "SD.xpl.plot.contribution_plot('Sex')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This graph is more complex and is usefull for few use case. It provides an understanding of interpretation of the datadrift classifier feature by feature" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "SD.xpl.plot.contribution_plot('Sex')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This graph is more complex and is usefull for few use case. It provides an understanding of interpretation of the datadrift classifier feature by feature" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "eurybia39", - "language": "python", - "name": "eurybia39" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" + ], + "metadata": { + "kernelspec": { + "display_name": "eurybia39", + "language": "python", + "name": "eurybia39" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/source/tutorials/data_validation/tutorial02-data-validation_iteration.ipynb b/docs/source/tutorials/data_validation/tutorial02-data-validation_iteration.ipynb index 3bb2350..7c5bcb5 100644 --- a/docs/source/tutorials/data_validation/tutorial02-data-validation_iteration.ipynb +++ b/docs/source/tutorials/data_validation/tutorial02-data-validation_iteration.ipynb @@ -1,11525 +1,11525 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "4d78bd42", - "metadata": {}, - "source": [ - "# Iterate on Data validation with display analysis\n" - ] - }, - { - "cell_type": "markdown", - "id": "09531f30", - "metadata": {}, - "source": [ - "With this tutorial you:
\n", - "Understand how to use Eurybia to iterate on different phases of data validation
\n", - "We propose to go into more detail about the use of Eurybia
\n", - "\n", - "Contents:\n", - "- Do data validation \n", - "- Generate Report \n", - "- Iterate on analysis of results, data validation, data preparation\n", - "\n", - "Data from Kaggle [House Prices](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "1151a856", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from category_encoders import OrdinalEncoder\n", - "from lightgbm import LGBMRegressor\n", - "from eurybia.core.smartdrift import SmartDrift\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "markdown", - "id": "bb42feda", - "metadata": {}, - "source": [ - "## Import Dataset and split in training and production dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7fd49d89", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e3c453c3", - "metadata": {}, - "outputs": [], - "source": [ - "house_df, house_dict = data_loading('house_prices')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2cf3b7a4", - "metadata": {}, - "outputs": [ + "cells": [ { - "data": { - "text/html": [ - "
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MSSubClassMSZoningLotAreaStreetLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhood...EnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
Id
14562-Story 1946 & NewerResidential Low Density7917PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeGilbert...0000082007Warranty Deed - ConventionalNormal Sale175000
14571-Story 1946 & Newer All StylesResidential Low Density13175PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeNorthwest Ames...0000022010Warranty Deed - ConventionalNormal Sale210000
14582-Story 1945 & OlderResidential Low Density9042PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeCrawford...0000250052010Warranty Deed - ConventionalNormal Sale266500
14591-Story 1946 & Newer All StylesResidential Low Density9717PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeNorth Ames...112000042010Warranty Deed - ConventionalNormal Sale142125
14601-Story 1946 & Newer All StylesResidential Low Density9937PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeEdwards...0000062008Warranty Deed - ConventionalNormal Sale147500
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" - ], - "text/plain": [ - " MSSubClass MSZoning LotArea \\\n", - "Id \n", - "1456 2-Story 1946 & Newer Residential Low Density 7917 \n", - "1457 1-Story 1946 & Newer All Styles Residential Low Density 13175 \n", - "1458 2-Story 1945 & Older Residential Low Density 9042 \n", - "1459 1-Story 1946 & Newer All Styles Residential Low Density 9717 \n", - "1460 1-Story 1946 & Newer All Styles Residential Low Density 9937 \n", - "\n", - " Street LotShape LandContour Utilities \\\n", - "Id \n", - "1456 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", - "1457 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", - "1458 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", - "1459 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", - "1460 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", - "\n", - " LotConfig LandSlope Neighborhood ... EnclosedPorch 3SsnPorch \\\n", - "Id ... \n", - "1456 Inside lot Gentle slope Gilbert ... 0 0 \n", - "1457 Inside lot Gentle slope Northwest Ames ... 0 0 \n", - "1458 Inside lot Gentle slope Crawford ... 0 0 \n", - "1459 Inside lot Gentle slope North Ames ... 112 0 \n", - "1460 Inside lot Gentle slope Edwards ... 0 0 \n", - "\n", - " ScreenPorch PoolArea MiscVal MoSold YrSold \\\n", - "Id \n", - "1456 0 0 0 8 2007 \n", - "1457 0 0 0 2 2010 \n", - "1458 0 0 2500 5 2010 \n", - "1459 0 0 0 4 2010 \n", - "1460 0 0 0 6 2008 \n", - "\n", - " SaleType SaleCondition SalePrice \n", - "Id \n", - "1456 Warranty Deed - Conventional Normal Sale 175000 \n", - "1457 Warranty Deed - Conventional Normal Sale 210000 \n", - "1458 Warranty Deed - Conventional Normal Sale 266500 \n", - "1459 Warranty Deed - Conventional Normal Sale 142125 \n", - "1460 Warranty Deed - Conventional Normal Sale 147500 \n", - "\n", - "[5 rows x 73 columns]" + "cell_type": "markdown", + "id": "4d78bd42", + "metadata": {}, + "source": [ + "# Iterate on Data validation with display analysis\n" ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "house_df.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f22f2520", - "metadata": {}, - "outputs": [], - "source": [ - "# For the purpose of the tutorial split dataset in training and production dataset\n", - "# To see an interesting analysis, let's test for a bias between date of construction of training and production dataset\n", - "house_df_learning = house_df.loc[house_df['YearBuilt'] < 1980]\n", - "house_df_production = house_df.loc[house_df['YearBuilt'] >= 1980]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "7d28382e", - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", - "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YearBuilt'])]\n", - "\n", - "y_df_production=house_df_production['SalePrice'].to_frame()\n", - "X_df_production=house_df_production[house_df_production.columns.difference(['SalePrice','YearBuilt'])]" - ] - }, - { - "cell_type": "markdown", - "id": "b2138620", - "metadata": {}, - "source": [ - "## Use Eurybia for data validation" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d41bf9e4", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "802ac54d", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "67e91660", - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:The variable BldgType\n", - " has mismatching possible values: \n", - "\n", - " [] ['Two-family Conversion; originally built as one-family dwelling']\n", - "INFO:root:The variable BsmtCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Poor -Severe cracking, settling, or wetness']\n", - "INFO:root:The variable CentralAir\n", - " has mismatching possible values: \n", - "\n", - " [] ['No']\n", - "INFO:root:The variable Condition1\n", - " has mismatching possible values: \n", - "\n", - " [\"Within 200' of East-West Railroad\"] ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", - "INFO:root:The variable Condition2\n", - " has mismatching possible values: \n", - "\n", - " ['Near positive off-site feature--park, greenbelt, etc.'] ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", - "INFO:root:The variable Electrical\n", - " has mismatching possible values: \n", - "\n", - " [] ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "INFO:root:The variable ExterCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Fair', 'Poor', 'Excellent']\n", - "INFO:root:The variable ExterQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Fair']\n", - "INFO:root:The variable Exterior1st\n", - " has mismatching possible values: \n", - "\n", - " ['Imitation Stucco'] ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", - "INFO:root:The variable Exterior2nd\n", - " has mismatching possible values: \n", - "\n", - " ['Other'] ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", - "INFO:root:The variable Foundation\n", - " has mismatching possible values: \n", - "\n", - " ['Wood'] ['Brick & Tile', 'Stone']\n", - "INFO:root:The variable Functional\n", - " has mismatching possible values: \n", - "\n", - " [] ['Major Deductions 2', 'Severely Damaged']\n", - "INFO:root:The variable GarageCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Poor', 'Excellent']\n", - "INFO:root:The variable GarageQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Excellent', 'Poor']\n", - "INFO:root:The variable GarageType\n", - " has mismatching possible values: \n", - "\n", - " [] ['Car Port']\n", - "INFO:root:The variable Heating\n", - " has mismatching possible values: \n", - "\n", - " [] ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", - "INFO:root:The variable HeatingQC\n", - " has mismatching possible values: \n", - "\n", - " [] ['Fair', 'Poor']\n", - "INFO:root:The variable HouseStyle\n", - " has mismatching possible values: \n", - "\n", - " [] ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", - "INFO:root:The variable KitchenQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Fair']\n", - "INFO:root:The variable LandSlope\n", - " has mismatching possible values: \n", - "\n", - " [] ['Severe Slope']\n", - "INFO:root:The variable MSSubClass\n", - " has mismatching possible values: \n", - "\n", - " [] ['2-Story 1945 & Older', '2 Family Conversion - All Styles and Ages', '1-1/2 Story - Unfinished All Ages', '1-Story 1945 & Older', '2-1/2 Story All Ages', '1-Story w/Finished Attic All Ages']\n", - "INFO:root:The variable MSZoning\n", - " has mismatching possible values: \n", - "\n", - " ['Floating Village Residential'] ['Commercial']\n", - "INFO:root:The variable MasVnrType\n", - " has mismatching possible values: \n", - "\n", - " [] ['Brick Common']\n", - "INFO:root:The variable Neighborhood\n", - " has mismatching possible values: \n", - "\n", - " ['Northridge', 'Somerset', 'Northridge Heights', 'Stone Brook', 'Bloomington Heights', 'Bluestem'] ['Brookside', 'Iowa DOT and Rail Road', 'Meadow Village', 'Northpark Villa', 'Briardale', 'South & West of Iowa State University']\n", - "INFO:root:The variable PavedDrive\n", - " has mismatching possible values: \n", - "\n", - " [] ['Partial Pavement']\n", - "INFO:root:The variable RoofMatl\n", - " has mismatching possible values: \n", - "\n", - " ['Clay or Tile'] ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", - "INFO:root:The variable RoofStyle\n", - " has mismatching possible values: \n", - "\n", - " [] ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", - "INFO:root:The variable SaleCondition\n", - " has mismatching possible values: \n", - "\n", - " [] ['Adjoining Land Purchase']\n", - "INFO:root:The variable SaleType\n", - " has mismatching possible values: \n", - "\n", - " ['Contract 15% Down payment regular terms'] []\n", - "INFO:root:The variable Utilities\n", - " has mismatching possible values: \n", - "\n", - " [] ['Electricity and Gas Only']\n" - ] + "cell_type": "markdown", + "id": "09531f30", + "metadata": {}, + "source": [ + "With this tutorial you:
\n", + "Understand how to use Eurybia to iterate on different phases of data validation
\n", + "We propose to go into more detail about the use of Eurybia
\n", + "\n", + "Contents:\n", + "- Do data validation \n", + "- Generate Report \n", + "- Iterate on analysis of results, data validation, data preparation\n", + "\n", + "Data from Kaggle [House Prices](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n", - "CPU times: user 2min 39s, sys: 32.4 s, total: 3min 11s\n", - "Wall time: 11.1 s\n" - ] - } - ], - "source": [ - "%time SD.compile(full_validation=True)" - ] - }, - { - "cell_type": "markdown", - "id": "25f49e4f", - "metadata": {}, - "source": [ - "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", - "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", - "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "69eabf2a", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "id": "1151a856", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from category_encoders import OrdinalEncoder\n", + "from lightgbm import LGBMRegressor\n", + "from eurybia.core.smartdrift import SmartDrift\n", + "from sklearn.model_selection import train_test_split" + ] + }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "85b0770962c04d0baa45f6952486914b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Executing: 0%| | 0/27 [00:00\n", - " window.PlotlyConfig = {MathJaxConfig: 'local'};\n", - " if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n", - " if (typeof require !== 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t.y0+e>=r&&t.y1+e<=n&&(t.y0=t.y0+e,t.y1=t.y1+e,t.targetLinks.forEach((function(t){t.y1=t.y1+e})),t.sourceLinks.forEach((function(t){t.y0=t.y0+e}))),t}function j(t,e,r,n){t.nodes.forEach((function(i){n&&i.y+(i.y1-i.y0)>e&&(i.y=i.y-(i.y+(i.y1-i.y0)-e));var a=t.links.filter((function(t){return b(t.source,r)==b(i,r)})),o=a.length;o>1&&a.sort((function(t,e){if(!t.circular&&!e.circular){if(t.target.column==e.target.column)return t.y1-e.y1;if(!V(t,e))return t.y1-e.y1;if(t.target.column>e.target.column){var r=R(e,t);return t.y1-r}if(e.target.column>t.target.column)return R(t,e)-e.y1}return t.circular&&!e.circular?\"top\"==t.circularLinkType?-1:1:e.circular&&!t.circular?\"top\"==e.circularLinkType?1:-1:t.circular&&e.circular?t.circularLinkType===e.circularLinkType&&\"top\"==t.circularLinkType?t.target.column===e.target.column?t.target.y1-e.target.y1:e.target.column-t.target.column:t.circularLinkType===e.circularLinkType&&\"bottom\"==t.circularLinkType?t.target.column===e.target.column?e.target.y1-t.target.y1:t.target.column-e.target.column:\"top\"==t.circularLinkType?-1:1:void 0}));var s=i.y0;a.forEach((function(t){t.y0=s+t.width/2,s+=t.width})),a.forEach((function(t,e){if(\"bottom\"==t.circularLinkType){for(var r=e+1,n=0;r1&&n.sort((function(t,e){if(!t.circular&&!e.circular){if(t.source.column==e.source.column)return t.y0-e.y0;if(!V(t,e))return t.y0-e.y0;if(e.source.column0?\"up\":\"down\"}function H(t,e){return b(t.source,e)==b(t.target,e)}function G(t,r,n){var i=t.nodes,a=t.links,o=!1,s=!1;if(a.forEach((function(t){\"top\"==t.circularLinkType?o=!0:\"bottom\"==t.circularLinkType&&(s=!0)})),0==o||0==s){var l=e.min(i,(function(t){return t.y0})),c=(n-r)/(e.max(i,(function(t){return t.y1}))-l);i.forEach((function(t){var e=(t.y1-t.y0)*c;t.y0=(t.y0-l)*c,t.y1=t.y0+e})),a.forEach((function(t){t.y0=(t.y0-l)*c,t.y1=(t.y1-l)*c,t.width=t.width*c}))}}t.sankeyCircular=function(){var t,n,i=0,a=0,b=1,T=1,M=24,A=m,E=o,C=v,L=y,P=32,I=2,z=null;function O(){var t={nodes:C.apply(null,arguments),links:L.apply(null,arguments)};D(t),_(t,A,z),R(t),B(t),w(t,A),N(t,P,A),V(t);for(var e=4,r=0;r0?r+25+10:r,bottom:n=n>0?n+25+10:n,left:a=a>0?a+25+10:a,right:i=i>0?i+25+10:i}}(o),h=function(t,r){var n=e.max(t.nodes,(function(t){return t.column})),o=b-i,s=T-a,l=o/(o+r.right+r.left),c=s/(s+r.top+r.bottom);return 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a=0,o=0;o>26,this.words[o]=67108863&e;for(;0!==a&&o>26,this.words[o]=67108863&e;if(0===a&&o>>13,p=0|o[1],d=8191&p,g=p>>>13,m=0|o[2],v=8191&m,y=m>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,M=0|o[5],A=8191&M,S=M>>>13,E=0|o[6],C=8191&E,L=E>>>13,P=0|o[7],I=8191&P,z=P>>>13,O=0|o[8],D=8191&O,R=O>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,q=0|s[1],H=8191&q,G=q>>>13,Y=0|s[2],W=8191&Y,Z=Y>>>13,X=0|s[3],J=8191&X,K=X>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ht=8191&ut,ft=ut>>>13,pt=0|s[9],dt=8191&pt,gt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var mt=(c+(n=Math.imul(h,U))|0)+((8191&(i=(i=Math.imul(h,V))+Math.imul(f,U)|0))<<13)|0;c=((a=Math.imul(f,V))+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(g,U)|0,a=Math.imul(g,V);var vt=(c+(n=n+Math.imul(h,H)|0)|0)+((8191&(i=(i=i+Math.imul(h,G)|0)+Math.imul(f,H)|0))<<13)|0;c=((a=a+Math.imul(f,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(g,H)|0,a=a+Math.imul(g,G)|0;var yt=(c+(n=n+Math.imul(h,W)|0)|0)+((8191&(i=(i=i+Math.imul(h,Z)|0)+Math.imul(f,W)|0))<<13)|0;c=((a=a+Math.imul(f,Z)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,H)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,H)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,Z)|0)+Math.imul(g,W)|0,a=a+Math.imul(g,Z)|0;var xt=(c+(n=n+Math.imul(h,J)|0)|0)+((8191&(i=(i=i+Math.imul(h,K)|0)+Math.imul(f,J)|0))<<13)|0;c=((a=a+Math.imul(f,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,H)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,H)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,Z)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,Z)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(g,J)|0,a=a+Math.imul(g,K)|0;var bt=(c+(n=n+Math.imul(h,$)|0)|0)+((8191&(i=(i=i+Math.imul(h,tt)|0)+Math.imul(f,$)|0))<<13)|0;c=((a=a+Math.imul(f,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(A,U),i=(i=Math.imul(A,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,H)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,Z)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,Z)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(g,$)|0,a=a+Math.imul(g,tt)|0;var _t=(c+(n=n+Math.imul(h,rt)|0)|0)+((8191&(i=(i=i+Math.imul(h,nt)|0)+Math.imul(f,rt)|0))<<13)|0;c=((a=a+Math.imul(f,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(C,U),i=(i=Math.imul(C,V))+Math.imul(L,U)|0,a=Math.imul(L,V),n=n+Math.imul(A,H)|0,i=(i=i+Math.imul(A,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,Z)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,Z)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(g,rt)|0,a=a+Math.imul(g,nt)|0;var wt=(c+(n=n+Math.imul(h,at)|0)|0)+((8191&(i=(i=i+Math.imul(h,ot)|0)+Math.imul(f,at)|0))<<13)|0;c=((a=a+Math.imul(f,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(z,U)|0,a=Math.imul(z,V),n=n+Math.imul(C,H)|0,i=(i=i+Math.imul(C,G)|0)+Math.imul(L,H)|0,a=a+Math.imul(L,G)|0,n=n+Math.imul(A,W)|0,i=(i=i+Math.imul(A,Z)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,Z)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(g,at)|0,a=a+Math.imul(g,ot)|0;var Tt=(c+(n=n+Math.imul(h,lt)|0)|0)+((8191&(i=(i=i+Math.imul(h,ct)|0)+Math.imul(f,lt)|0))<<13)|0;c=((a=a+Math.imul(f,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,H)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(z,H)|0,a=a+Math.imul(z,G)|0,n=n+Math.imul(C,W)|0,i=(i=i+Math.imul(C,Z)|0)+Math.imul(L,W)|0,a=a+Math.imul(L,Z)|0,n=n+Math.imul(A,J)|0,i=(i=i+Math.imul(A,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(g,lt)|0,a=a+Math.imul(g,ct)|0;var kt=(c+(n=n+Math.imul(h,ht)|0)|0)+((8191&(i=(i=i+Math.imul(h,ft)|0)+Math.imul(f,ht)|0))<<13)|0;c=((a=a+Math.imul(f,ft)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,H)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,Z)|0)+Math.imul(z,W)|0,a=a+Math.imul(z,Z)|0,n=n+Math.imul(C,J)|0,i=(i=i+Math.imul(C,K)|0)+Math.imul(L,J)|0,a=a+Math.imul(L,K)|0,n=n+Math.imul(A,$)|0,i=(i=i+Math.imul(A,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ht)|0,i=(i=i+Math.imul(d,ft)|0)+Math.imul(g,ht)|0,a=a+Math.imul(g,ft)|0;var Mt=(c+(n=n+Math.imul(h,dt)|0)|0)+((8191&(i=(i=i+Math.imul(h,gt)|0)+Math.imul(f,dt)|0))<<13)|0;c=((a=a+Math.imul(f,gt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,Z)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,Z)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(z,J)|0,a=a+Math.imul(z,K)|0,n=n+Math.imul(C,$)|0,i=(i=i+Math.imul(C,tt)|0)+Math.imul(L,$)|0,a=a+Math.imul(L,tt)|0,n=n+Math.imul(A,rt)|0,i=(i=i+Math.imul(A,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ht)|0,i=(i=i+Math.imul(v,ft)|0)+Math.imul(y,ht)|0,a=a+Math.imul(y,ft)|0;var At=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((a=a+Math.imul(g,gt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,Z))+Math.imul(N,W)|0,a=Math.imul(N,Z),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(z,$)|0,a=a+Math.imul(z,tt)|0,n=n+Math.imul(C,rt)|0,i=(i=i+Math.imul(C,nt)|0)+Math.imul(L,rt)|0,a=a+Math.imul(L,nt)|0,n=n+Math.imul(A,at)|0,i=(i=i+Math.imul(A,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ht)|0,i=(i=i+Math.imul(b,ft)|0)+Math.imul(_,ht)|0,a=a+Math.imul(_,ft)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,gt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(z,rt)|0,a=a+Math.imul(z,nt)|0,n=n+Math.imul(C,at)|0,i=(i=i+Math.imul(C,ot)|0)+Math.imul(L,at)|0,a=a+Math.imul(L,ot)|0,n=n+Math.imul(A,lt)|0,i=(i=i+Math.imul(A,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ht)|0,i=(i=i+Math.imul(T,ft)|0)+Math.imul(k,ht)|0,a=a+Math.imul(k,ft)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,gt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(z,at)|0,a=a+Math.imul(z,ot)|0,n=n+Math.imul(C,lt)|0,i=(i=i+Math.imul(C,ct)|0)+Math.imul(L,lt)|0,a=a+Math.imul(L,ct)|0,n=n+Math.imul(A,ht)|0,i=(i=i+Math.imul(A,ft)|0)+Math.imul(S,ht)|0,a=a+Math.imul(S,ft)|0;var Ct=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,gt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,gt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(z,lt)|0,a=a+Math.imul(z,ct)|0,n=n+Math.imul(C,ht)|0,i=(i=i+Math.imul(C,ft)|0)+Math.imul(L,ht)|0,a=a+Math.imul(L,ft)|0;var Lt=(c+(n=n+Math.imul(A,dt)|0)|0)+((8191&(i=(i=i+Math.imul(A,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,gt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ht)|0,i=(i=i+Math.imul(I,ft)|0)+Math.imul(z,ht)|0,a=a+Math.imul(z,ft)|0;var Pt=(c+(n=n+Math.imul(C,dt)|0)|0)+((8191&(i=(i=i+Math.imul(C,gt)|0)+Math.imul(L,dt)|0))<<13)|0;c=((a=a+Math.imul(L,gt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ht)|0,i=(i=i+Math.imul(D,ft)|0)+Math.imul(R,ht)|0,a=a+Math.imul(R,ft)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,gt)|0)+Math.imul(z,dt)|0))<<13)|0;c=((a=a+Math.imul(z,gt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ht),i=(i=Math.imul(B,ft))+Math.imul(N,ht)|0,a=Math.imul(N,ft);var zt=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,gt)|0)+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863;var Ot=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,gt))+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,l[0]=mt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=Mt,l[10]=At,l[11]=St,l[12]=Et,l[13]=Ct,l[14]=Lt,l[15]=Pt,l[16]=It,l[17]=zt,l[18]=Ot,0!==c&&(l[19]=c,r.length++),r};function d(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(p=f),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?p(this,t,e):r<63?f(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):d(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o>>=1)i++;return 1<>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<o)for(this.length-=o,c=0;c=0&&(0!==u||c>=i);c--){var h=0|this.words[c];this.words[c]=u<<26-a|h>>>a,u=h&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c=0;h--){var f=67108864*(0|n.words[i.length+h])+(0|n.words[i.length+h-1]);for(f=Math.min(f/o|0,67108863),n._ishlnsubmul(i,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(i,1,h),n.isZero()||(n.negative^=1);s&&(s.words[h]=f)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new 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r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),h=e.clone();!e.isZero();){for(var f=0,p=1;0==(e.words[0]&p)&&f<26;++f,p<<=1);if(f>0)for(e.iushrn(f);f-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(h)),i.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(h)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var h=0,f=1;0==(r.words[0]&f)&&h<26;++h,f<<=1);if(h>0)for(r.iushrn(h);h-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:it.length)return 1;if(this.length=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){ni&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new w(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return 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a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function y(){v.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function x(){v.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function b(){v.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function _(){v.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function w(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function T(t){w.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}v.prototype._tmp=function(){var t=new 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HALF_PI :\\n 0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n float b = positive_angle(a);\\n float div = TWO_PI / float(n);\\n float c = roundTo(b, div);\\n return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n return\\n (option > 2) ? look_round_n_directions(rawAngle + delta, option) : // option 3-n: round to n directions\\n (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n (option == 1) ? rawAngle + delta : // use free angle, and flip to align with one direction of the axis\\n (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n (option ==-1) ? 0.0 : // useful for backward compatibility, all texts remains horizontal\\n rawAngle; // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n (axis.y == 0.0) &&\\n (axis.z == 0.0);\\n\\nvoid main() {\\n //Compute world offset\\n float axisDistance = position.z;\\n vec3 dataPosition = axisDistance * axis + offset;\\n\\n float beta = angle; // i.e. user defined attributes for each tick\\n\\n float axisAngle;\\n float clipAngle;\\n float flip;\\n\\n if (enableAlign) {\\n axisAngle = (isAxisTitle) ? HALF_PI :\\n computeViewAngle(dataPosition, dataPosition + axis);\\n clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n beta += applyAlignOption(clipAngle, flip * PI);\\n }\\n\\n //Compute plane offset\\n vec2 planeCoord = position.xy * pixelScale;\\n\\n mat2 planeXform = scale * mat2(\\n cos(beta), sin(beta),\\n -sin(beta), cos(beta)\\n );\\n\\n vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n //Compute clip position\\n vec3 clipPosition = project(dataPosition);\\n\\n //Apply text offset in clip coordinates\\n clipPosition += vec3(viewOffset, 0.0);\\n\\n //Done\\n gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n vec3 realNormal = signAxis * normal;\\n\\n if(dot(realNormal, enable) > 0.0) {\\n vec3 minRange = min(bounds[0], bounds[1]);\\n vec3 maxRange = max(bounds[0], bounds[1]);\\n vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n } else {\\n gl_Position = vec4(0,0,0,0);\\n }\\n\\n colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n gl_FragColor = colorChannel.x * colors[0] +\\n colorChannel.y * colors[1] +\\n colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":312,glslify:413}],255:[function(t,e,r){(function(r){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),h=i(t,[{buffer:u,size:3}]),f=o(t);f.attributes.position.location=0;var p=new c(t,f,u,h);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,h=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,h[0]=this.gl.drawingBufferWidth,h[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=h},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var h=(n||12)/12,f=u.positions,p=u.cells,d=0,g=p.length;d=0;--v){var y=f[m[v]];o.push(h*y[0],-h*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],h=[0,0,0],f=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){h[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),f[d]=(o.length/3|0)-h[d],c[d]=o.length/3|0;for(var g=0;g=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=\"\"+l;if(o<0&&(u=\"-\"+u),i){for(var h=\"\"+c;h.length=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;nr)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var h;h=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?h:h.subarray(0,t.length),e),n.free(h)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:469,\"ndarray-ops\":464,\"typedarray-pool\":567}],259:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,h=1/0,f=-1/0,p=null,d=null,g=[],m=1/0,v=!1,y=0;yo&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(m=Math.min(m,_),v=!1):v=!0}v||(p=x,d=b),g.push(b)}var w=[s,c,h],T=[l,u,f];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(m)||(m=1),a.vectorScale=m;var M=t.coneSize||.5;t.absoluteConeSize&&(M=t.absoluteConeSize*k),a.coneScale=M;y=0;for(var A=0;y=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],h=[];this.cells=r,this.positions=n,this.vectors=i;var f=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,g=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],g=+t.vertexIntensityBounds[1];else for(var m=0;m0){var g=this.triShader;g.bind(),g.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||h,n=t.view||h,i=t.projection||h,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=g(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var h=i(t),p=i(t),m=i(t),v=i(t),y=i(t),x=a(t,[{buffer:h,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new f(t,u,s,l,h,p,y,m,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:131,\"gl-buffer\":258,\"gl-mat4/invert\":278,\"gl-mat4/multiply\":280,\"gl-shader\":312,\"gl-texture2d\":327,\"gl-vao\":332,ndarray:469}],261:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have ||a|| > 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n float index = rawIndex - floor(rawIndex /\\n (segmentCount * 6.0)) *\\n (segmentCount * 6.0);\\n\\n float segment = floor(0.001 + index/6.0);\\n float segmentIndex = index - (segment*6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n float nextAngle = (\\n (segmentIndex > 0.99 && segmentIndex < 1.01) ||\\n (segmentIndex > 4.99 && segmentIndex < 5.01)\\n ) ? 1.0 : 0.0;\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)*0.25;\\n vec3 y = v * sin(angle) * length(d)*0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex < 3.0) {\\n vec3 tx = u * sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * conePosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n // vec4 m_position = model * vec4(conePosition, 1.0);\\n vec4 t_position = view * conePosition;\\n gl_Position = projection * t_position;\\n\\n f_color = color;\\n f_data = conePosition.xyz;\\n f_position = position.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have ||a|| > 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n float index = rawIndex - floor(rawIndex /\\n (segmentCount * 6.0)) *\\n (segmentCount * 6.0);\\n\\n float segment = floor(0.001 + index/6.0);\\n float segmentIndex = index - (segment*6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n float nextAngle = (\\n (segmentIndex > 0.99 && segmentIndex < 1.01) ||\\n (segmentIndex > 4.99 && segmentIndex < 5.01)\\n ) ? 1.0 : 0.0;\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)*0.25;\\n vec3 y = v * sin(angle) * length(d)*0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex < 3.0) {\\n vec3 tx = u * sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n gl_Position = projection * view * conePosition;\\n f_id = id;\\n f_position = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if 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MATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},{}],263:[function(t,e,r){var n=t(\"./1.0/numbers\");e.exports=function(t){return n[t]}},{\"./1.0/numbers\":262}],264:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders/index\"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var h=0;h<3;++h)e.lineWidth(this.lineWidth[h]*this.pixelRatio),r.capSize=this.capSize[h]*u,this.lineCount[h]&&e.drawArrays(e.LINES,this.lineOffset[h],this.lineCount[h]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function h(t,e,r,n){for(var i=u[n],a=0;a0)(g=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,g[0],g[1],g[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,g),o+=2+h(i,g,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{\"./shaders/index\":265,\"gl-buffer\":258,\"gl-vao\":332}],265:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n vec4 worldPosition = model * vec4(position, 1.0);\\n worldPosition = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n gl_Position = projection * view * worldPosition;\\n fragColor = color;\\n fragPosition = position;\\n}\"]),o=n([\"precision highp float;\\n#define GLSLIFY 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n=t(\"gl-texture2d\");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;au||r<0||r>u)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var h=1;if(\"color\"in(n=n||{})){if((h=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(h>1){if(!c)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(h>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+h+\" draw buffers\")}}var f=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&h>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");f=t.FLOAT}else 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s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=f(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g&&(t.depth=f(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):g&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):g?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;vi||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var g=i.attributes;return this.positionBuffer.bind(),g.position.pointer(),this.weightBuffer.bind(),g.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),g.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),h.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},h.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,h,p,d=t.colorLevels||[0],g=t.colorValues||[0,0,0,1],m=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],h=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,h=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(h-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(f.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),M=a.mallocUint8(2*w),A=a.mallocUint32(w),S=0,E=l?b-1:b,C=l?_-1:_,L=0;L max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float dashScale;\\nuniform float opacity;\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (\\n outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n fragColor.a * opacity == 0.\\n ) discard;\\n\\n float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n if(dashWeight < 0.5) {\\n discard;\\n }\\n gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX 1.70141184e38\\n#define FLOAT_MIN 1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n float av = abs(v);\\n\\n //Handle special cases\\n if(av < FLOAT_MIN) {\\n return vec4(0.0, 0.0, 0.0, 0.0);\\n } else if(v > FLOAT_MAX) {\\n return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n } else if(v < -FLOAT_MAX) {\\n return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n }\\n\\n vec4 c = vec4(0,0,0,0);\\n\\n //Compute exponent and mantissa\\n float e = floor(log2(av));\\n float m = av * pow(2.0, -e) - 1.0;\\n\\n //Unpack mantissa\\n c[1] = floor(128.0 * m);\\n m -= c[1] / 128.0;\\n c[2] = floor(32768.0 * m);\\n m -= c[2] / 32768.0;\\n c[3] = floor(8388608.0 * m);\\n\\n //Unpack exponent\\n float ebias = e + 127.0;\\n c[0] = floor(ebias / 2.0);\\n ebias -= c[0] * 2.0;\\n c[1] += floor(ebias) * 128.0;\\n\\n //Unpack sign bit\\n c[0] += 128.0 * step(0.0, -v);\\n\\n //Scale back to range\\n return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":312,glslify:413}],271:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=h(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=f(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var g=new v(e,r,o,s,l,d);return g.update(t),g};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),h=u.createShader,f=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function g(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function m(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:g(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:g(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],f=t.position||t.positions;if(f){var p=t.color||t.colors||[0,0,0,1],g=t.lineWidth||1,m=!1;t:for(e=1;e0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,m=!0}continue t}h[0][r]=Math.min(h[0][r],b[r],_[r]),h[1][r]=Math.max(h[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(g)?g.length>e-1?g[e-1]:g.length>0?g[g.length-1]:[0,0,0,1]:g;var T=s;if(s+=d(b,_),m){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,m=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(f[f.length-1].slice()),this.bounds=h,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e1.0001)return null;v+=m[h]}if(Math.abs(v-1)>.001)return null;return[f,s(t,m),m]}},{barycentric:78,\"polytope-closest-point/lib/closest_point_2d.js\":499}],291:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n , view\\n , projection\\n , inverseModel;\\nuniform vec3 eyePosition\\n , lightPosition;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n gl_Position = project(position);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * vec4(position , 1.0);\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (f_color.a == 0.0 ||\\n outOfRange(clipBounds[0], clipBounds[1], f_data)\\n ) discard;\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n }\\n gl_PointSize = pointSize;\\n f_color = color;\\n f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n if(dot(pointR, pointR) > 0.25) {\\n discard;\\n }\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_id = id;\\n f_position = position;\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute float pointSize;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n gl_PointSize = pointSize;\\n }\\n f_id = id;\\n f_position = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:h,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:f,fragment:h,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:413}],292:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),h=t(\"colormap\"),f=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),g=t(\"./lib/closest-point\"),m=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g,m,v,y,x,b,_,T,k,M,A,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=f,this.triangleUVs=h,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=m,this.edgeUVs=v,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=M,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=A,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function M(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;rt&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function A(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function C(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function L(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=f(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l0&&((h=this.triShader).bind(),h.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((h=this.lineShader).bind(),h.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((h=this.pointShader).bind(),h.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((h=this.contourShader).bind(),h.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;ai[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[M]-i[k]))),y[t]&&T&&(u[1^t]-=A*p*x[t],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=A*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=A*p*g[t+2],ki[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=m[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[M]-i[k]))),y[t+2]&&T&&(u[1^t]+=A*p*x[t+2],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),g.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),g.bind=(f=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],h=a[o],g=a[o+2]-h,m=i[o],v=i[o+2]-m;p[o]=2*l/u*g/v,f[o]=2*(s-c)/u*g/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=f,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),g.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],h=[-1/0],f=l[o];for(e=0;e=0){var g=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(g,e[1],g,e[3],p[d],f[d]):o.drawLine(e[0],g,e[2],g,p[d],f[d])}}for(d=0;d=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;rMath.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,h*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":54,\"has-passive-events\":415,\"mouse-change\":457,\"mouse-event-offset\":458,\"mouse-wheel\":460,\"right-now\":514}],300:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n uv = position;\\n gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":312,glslify:413}],301:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),h=t(\"gl-mat4/perspective\"),f=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function g(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function m(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement(\"canvas\"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error(\"webgl not supported\");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new g,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},M=i(r,k);M.enable=!k.disable;var A=t.spikes||{},S=o(r,A),E=[],C=[],L=[],P=[],I=!0,z=!0,O=new Array(16),D=new Array(16),R={view:null,projection:O,model:D,_ortho:!1},F=(z=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:M,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,z=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n0&&0===L[e-1];)L.pop(),P.pop().dispose()}function q(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener(\"resize\",U),N.update=function(t){N._stopped||(t=t||{},I=!0,z=!0)},N.add=function(t){N._stopped||(t.axes=M,E.push(t),C.push(-1),I=!0,z=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),C.pop(),I=!0,z=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",q),N.mouseListener.enabled=!1,!N.contextLost)){M.dispose(),S.dispose();for(var t=0;tx.distance)continue;for(var c=0;c 1.0) {\\n discard;\\n }\\n baseColor = mix(borderColor, color, step(radius, centerFraction));\\n gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n gl_PointSize = pointSize;\\n\\n vec4 id = pickId + pickOffset;\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) * 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:413}],303:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),h=new s(t,a,l,c,u);return h.update(e),t.addObject(h),h};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e>>1;for(r=0;r=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var h=n.getParameter(n.BLEND),f=n.getParameter(n.DITHER);return h&&!this.blend&&n.disable(n.BLEND),f&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),h&&!this.blend&&n.enable(n.BLEND),f&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":302,\"gl-buffer\":258,\"gl-shader\":312,\"typedarray-pool\":567}],304:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],h=e[2],f=e[3],p=r[0],d=r[1],g=r[2],m=r[3];(a=c*p+u*d+h*g+f*m)<0&&(a=-a,p=-p,d=-d,g=-g,m=-m);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*h+l*g,t[3]=s*f+l*m,t}},{}],305:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],306:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = 1.0;\\n if(distance(highlightId, id) < 0.0001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1);\\n vec4 viewPosition = view * worldPosition;\\n viewPosition = viewPosition / viewPosition.w;\\n vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = pixelRatio;\\n if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n scale *= highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1.0);\\n vec4 viewPosition = view * worldPosition;\\n vec4 clipPosition = projection * viewPosition;\\n clipPosition /= clipPosition.w;\\n\\n gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float lscale = pixelRatio * scale;\\n if(distance(highlightId, id) < 0.0001) {\\n lscale *= highlightScale;\\n }\\n\\n vec4 clipCenter = projection * view * model * vec4(position, 1);\\n vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = dataPosition;\\n }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (\\n outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n interpColor.a * opacity == 0.\\n ) discard;\\n gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],h={vertex:a,fragment:l,attributes:u},f={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},m={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,h)},r.createOrtho=function(t){return v(t,f)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,g)},r.createPickProject=function(t){return v(t,m)}},{\"gl-shader\":312,glslify:413}],308:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function f(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return f(n,n),f(n,n),f(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function g(t){return!0===t||t>1?1:t}function m(t,e,r,n,i,a,o,s,l,c,u,h){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=h,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),h=i(e),f=i(e),p=i(e),d=i(e),g=a(e,[{buffer:h,size:3,type:e.FLOAT},{buffer:f,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new m(e,r,n,o,h,f,p,d,g,s,c,u);return v.update(t),v};var v=m.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=h.slice(),k=[0,0,0],M=[[0,0,0],[0,0,0]];function A(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function C(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||h,u=r.view||h,f=r.projection||h,d=e.axesBounds,g=function(t){for(var e=M,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=f,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=g,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var m=0;m<3;++m)if(a[m]){l.scale=e.projectScale[m],l.opacity=e.projectOpacity[m];for(var v=T,C=0;C<16;++C)v[C]=0;for(C=0;C<4;++C)v[5*C]=1;v[5*m]=0,i[m]<0?v[12+m]=d[0][m]:v[12+m]=d[1][m],s(v,c,v),l.model=v;var L=(m+1)%3,P=(m+2)%3,I=A(x),z=A(b);I[L]=1,z[P]=1;var O=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,z));if(Math.abs(O[1])>Math.abs(D[1])){var R=O;O=D,D=R,R=I,I=z,z=R;var F=L;L=P,P=F}O[0]<0&&(I[L]=-1),D[1]>0&&(z[P]=-1);var B=0,N=0;for(C=0;C<4;++C)B+=Math.pow(c[4*L+C],2),N+=Math.pow(c[4*P+C],2);I[L]/=Math.sqrt(B),z[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=z,l.fragClipBounds[0]=E(k,g[0],m,-1e8),l.fragClipBounds[1]=E(k,g[1],m,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var L=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&C(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||h,l.view=n.view||h,l.projection=n.projection||h,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=L,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=g(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=g(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n0){var z=0,O=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;h[T]=Math.max(h[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(f,n,l,this.pixelRatio)).mesh,M=N.lines,A=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n0?1-A[0][0]:Y<0?1+A[1][0]:1,W*=W>0?1-A[0][1]:W<0?1+A[1][1]:1],X=k.cells||[],J=k.positions||[];for(T=0;T0){var v=r*u;o.drawBox(h-v,f-v,p+v,f+v,a),o.drawBox(h-v,d-v,p+v,d+v,a),o.drawBox(h-v,f-v,h+v,d+v,a),o.drawBox(p-v,f-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":309,\"gl-buffer\":258,\"gl-shader\":312}],311:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t(\"gl-fbo\"),i=t(\"typedarray-pool\"),a=t(\"ndarray\"),o=t(\"bit-twiddle\").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var c=l.prototype;Object.defineProperty(c,\"shape\",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;ar)for(t=r;te)for(t=e;t=0){for(var T=0|w.type.charAt(w.type.length-1),k=new Array(T),M=0;M=0;)A+=1;_[y]=A}var S=new Array(r.length);function E(){f.program=o.program(p,f._vref,f._fref,b,_);for(var t=0;t=0){if((d=f.charCodeAt(f.length-1)-48)<2||d>4)throw new n(\"\",\"Invalid data type for attribute \"+h+\": \"+f);o(t,e,p[0],i,d,a,h)}else{if(!(f.indexOf(\"mat\")>=0))throw new n(\"\",\"Unknown data type for attribute \"+h+\": \"+f);var 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t(e,r){if(\"object\"!=typeof r)return[[e,r]];var n=[];for(var i in r){var a=r[i],o=e;parseInt(i)+\"\"===i?o+=\"[\"+i+\"]\":o+=\".\"+i,\"object\"==typeof a?n.push.apply(n,t(o,a)):n.push([o,a])}return n}(\"\",e),a=0;a4)throw new i(\"\",\"Invalid data type\");return\"b\"===t.charAt(0)?o(r,!1):o(r,0)}if(0===t.indexOf(\"mat\")&&4===t.length){var r;if((r=t.charCodeAt(t.length-1)-48)<2||r>4)throw new i(\"\",\"Invalid uniform dimension type for matrix \"+name+\": \"+t);return o(r*r,0)}throw new i(\"\",\"Unknown uniform data type for \"+name+\": \"+t)}}(r[u].type);var p}function h(t){var e;if(Array.isArray(t)){e=new Array(t.length);for(var r=0;r1){s[0]in a||(a[s[0]]=[]),a=a[s[0]];for(var l=1;l1)for(var l=0;l 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n float segmentCount = 8.0;\\n\\n float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d);\\n vec3 y = v * sin(angle) * length(d);\\n vec3 v3 = x + y;\\n\\n normal = normalize(v3);\\n\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * tubePosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n // vec4 m_position = model * vec4(tubePosition, 1.0);\\n vec4 t_position = view * tubePosition;\\n gl_Position = projection * t_position;\\n\\n f_color = color;\\n f_data = tubePosition.xyz;\\n f_position = position.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have ||a|| > 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n float segmentCount = 8.0;\\n\\n float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d);\\n vec3 y = v * sin(angle) * length(d);\\n vec3 v3 = x + y;\\n\\n normal = normalize(v3);\\n\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n vec3 normal;\\n vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n gl_Position = projection * view * tubePosition;\\n f_id = id;\\n f_position = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:413}],323:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,a){for(var o=0,s=0;s0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(f[T],p[T],p[k],p[k],f[k],f[T]),h.push(y,v,v,v,y,y),d.push(g,m,m,m,g,g);var M=c.length;u.push([M-6,M-5,M-4],[M-3,M-2,M-1])}var A=f;f=p,p=A;var S=y;y=v,v=S;var E=g;g=m,m=E}return{positions:c,cells:u,vectors:h,vertexIntensity:d}}(t,r,a,o)})),h=[],f=[],p=[],d=[];for(s=0;se)return r-1}return r},l=function(t,e,r){return tr?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;nh-1||y>f-1||x>p-1)return n.create();var b,_,w,T,k,M,A=a[0][d],S=a[0][v],E=a[1][g],C=a[1][y],L=a[2][m],P=(o-A)/(S-A),I=(c-E)/(C-E),z=(u-L)/(a[2][x]-L);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(z)||(z=.5),r.reversedX&&(d=h-1-d,v=h-1-v),r.reversedY&&(g=f-1-g,y=f-1-y),r.reversedZ&&(m=p-1-m,x=p-1-x),r.filled){case 5:k=m,M=x,w=g*p,T=y*p,b=d*p*f,_=v*p*f;break;case 4:k=m,M=x,b=d*p,_=v*p,w=g*p*h,T=y*p*h;break;case 3:w=g,T=y,k=m*f,M=x*f,b=d*f*p,_=v*f*p;break;case 2:w=g,T=y,b=d*f,_=v*f,k=m*f*h,M=x*f*h;break;case 1:b=d,_=v,k=m*h,M=x*h,w=g*h*p,T=y*h*p;break;default:b=d,_=v,w=g*h,T=y*h,k=m*h*f,M=x*h*f}var O=i[b+w+k],D=i[b+w+M],R=i[b+T+k],F=i[b+T+M],B=i[_+w+k],N=i[_+w+M],j=i[_+T+k],U=i[_+T+M],V=n.create(),q=n.create(),H=n.create(),G=n.create();n.lerp(V,O,B,P),n.lerp(q,D,N,P),n.lerp(H,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,H,I),n.lerp(W,q,G,I);var Z=n.create();return n.lerp(Z,Y,W,z),Z}(e,t,p)},g=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},m=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(eb||r_||nw)},k=10*n.distance(e[0],e[1])/i,M=k*k,A=1,S=0,E=r.length;E>1&&(A=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;lS&&(S=F),D.push(F),m.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.lengthM&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,L),z=d(N),n.squaredDistance(O,N)-M>-1e-4*M){P.push(N),O=N,I.push(z);R=g(N,z),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}L=N}}var U=o(m,t.colormap,S,A);return h?U.tubeScale=h:(0===S&&(S=1),U.tubeScale=.5*u*A/S),U};var u=t(\"./lib/shaders\"),h=t(\"gl-cone3d\").createMesh;e.exports.createTubeMesh=function(t,e){return h(t,e,{shaders:u,traceType:\"streamtube\"})}},{\"./lib/shaders\":322,\"gl-cone3d\":259,\"gl-vec3\":351,\"gl-vec4\":387}],324:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n vec3 localCoordinate = vec3(uv.zw, f.x);\\n worldCoordinate = objectOffset + localCoordinate;\\n vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n vec4 clipPosition = projection * view * worldPosition;\\n gl_Position = clipPosition;\\n kill = f.y;\\n value = f.z;\\n planeCoordinate = uv.xy;\\n\\n vColor = texture2D(colormap, vec2(value, value));\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * worldPosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n lightDirection = lightPosition - cameraCoordinate.xyz;\\n eyeDirection = eyePosition - cameraCoordinate.xyz;\\n surfaceNormal = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness) {\\n return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n if (\\n kill > 0.0 ||\\n vColor.a == 0.0 ||\\n outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n ) discard;\\n\\n vec3 N = normalize(surfaceNormal);\\n vec3 V = normalize(eyeDirection);\\n vec3 L = normalize(lightDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n //decide how to interpolate color \\u2014 in vertex or in fragment\\n vec4 surfaceColor =\\n step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n step(.5, vertexColor) * vColor;\\n\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n worldCoordinate = objectOffset + dataCoordinate;\\n vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n vec4 clipPosition = projection * view * worldPosition;\\n clipPosition.z += zOffset;\\n\\n gl_Position = clipPosition;\\n value = f + objectOffset.z;\\n kill = -1.0;\\n planeCoordinate = uv.zw;\\n\\n vColor = texture2D(colormap, vec2(value, value));\\n\\n //Don't do lighting for contours\\n surfaceNormal = vec3(1,0,0);\\n eyeDirection = vec3(0,1,0);\\n lightDirection = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n float vh = 255.0 * v;\\n float upper = floor(vh);\\n float lower = fract(vh);\\n return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n if ((kill > 0.0) ||\\n (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":312,glslify:413}],325:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),h=i(e),f=a(e,[{buffer:h,size:4,stride:20,offset:0},{buffer:h,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),g=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);g.minFilter=e.LINEAR,g.magFilter=e.LINEAR;var m=new A(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,g,s,l,h,f,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||\"jet\",m.update(v),m};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),h=t(\"ndarray\"),f=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),g=t(\"binary-search-bounds\"),m=t(\"ndarray-gradient\"),v=t(\"./lib/shaders\"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function A(t,e,r,n,i,a,o,l,c,u,f,p,d,g,m){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=m,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=f,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=g,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[h(s.mallocFloat(1024),[0,0]),h(s.mallocFloat(1024),[0,0]),h(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=A.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;rt&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],C={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function L(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=C.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=C.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return C.showSurface=o,C.showContour=s,C}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),z=[1,0,0,0,1,0,0,0,1];function O(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=z,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=L(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var h=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,h.bind(),h.uniforms=n;var f=this._contourVAO;for(f.bind(),i=0;i<3;++i)for(h.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var h=u?a:1-a,f=0;f<2;++f)for(var p=i+u,d=s+f,m=h*(f?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*m;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=g.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=h(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=h(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=h(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=h(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var g=[0,0];g[o]=1,this._field[o]=h(this._field[o].data,[a[0]+2,a[1]+2],g,0)}this._field[0].set(0,0,0);for(var v=0;v0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=h(t.viewport),T.normalViewport&&(this.viewport.y=this.canvas.height-this.viewport.y-this.viewport.height),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:u.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),f=0;f2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),M=0,A=0;M1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],T.normalViewport||(i*=-1),i}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var H;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;H=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.normalViewport=!1,T.maxAtlasSize=1024,T.atlasCanvas=document.createElement(\"canvas\"),T.atlasContext=T.atlasCanvas.getContext(\"2d\",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{\"bit-twiddle\":97,\"color-normalize\":125,\"css-font\":144,\"detect-kerning\":172,\"es6-weak-map\":233,\"flatten-vertex-data\":244,\"font-atlas\":245,\"font-measure\":246,\"gl-util/context\":328,\"is-plain-obj\":443,\"object-assign\":473,\"parse-rect\":478,\"parse-unit\":480,\"pick-by-alias\":485,regl:512,\"to-px\":550,\"typedarray-pool\":567}],327:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o||c(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var h=function(t,e){i.muls(t,e,255)};function f(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function g(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function m(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new p(t,o,r,n,i,a)}function x(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=g(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var u,f,d=0;if(2===o.length)d=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])d=t.ALPHA;else if(2===o[2])d=t.LUMINANCE_ALPHA;else if(3===o[2])d=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");d=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,l=!1);var v=e.size;if(l)u=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var y=[o[2],o[2]*o[0],1];f=a.malloc(v,r);var x=n(f,o,y,0);\"float32\"!==r&&\"float64\"!==r||c!==t.UNSIGNED_BYTE?i.assign(x,e):h(x,e),u=f.subarray(0,v)}var b=m(t);return t.texImage2D(t.TEXTURE_2D,0,d,o[0],o[1],0,d,c,u),l||a.free(f),new p(t,b,o[0],o[1],d,c)}Object.defineProperties(d,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var e=0;e<2;++e)if(l.indexOf(t[e])<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);this._wrapS=t[0],this._wrapT=t[1];var r=this.gl;return this.bind(),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_S,this._wrapS),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_T,this._wrapT),t}},shape:{get:function(){return this._shapeVector},set:function(t){if(Array.isArray(t)){if(2!==t.length)throw new Error(\"gl-texture2d: Invalid texture shape\")}else t=[0|t,0|t];return f(this,0|t[0],0|t[1]),[0|t[0],0|t[1]]}},width:{get:function(){return this._shape[0]},set:function(t){return f(this,t|=0,this._shape[1]),t}},height:{get:function(){return this._shape[1]},set:function(t){return t|=0,f(this,this._shape[0],t),t}}}),d.bind=function(t){var e=this.gl;return void 0!==t&&e.activeTexture(e.TEXTURE0+(0|t)),e.bindTexture(e.TEXTURE_2D,this.handle),void 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ci=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};ci.prototype.possiblyEvaluate=function(t,e,r){for(var n=new fi(this._properties),i=0,a=Object.keys(this._values);in.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(di),mi=function(t){this.specification=t};mi.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new ii(Math.floor(e.zoom-1),e)),t.expression.evaluate(new ii(Math.floor(e.zoom),e)),t.expression.evaluate(new ii(Math.floor(e.zoom+1),e)),e)}},mi.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},mi.prototype.interpolate=function(t){return t};var vi=function(t){this.specification=t};vi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},vi.prototype.interpolate=function(){return!1};var yi=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new ai(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new oi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Dn(\"DataDrivenProperty\",di),Dn(\"DataConstantProperty\",pi),Dn(\"CrossFadedDataDrivenProperty\",gi),Dn(\"CrossFadedProperty\",mi),Dn(\"ColorRampProperty\",vi);var xi=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(this.metadata=(e=e).metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new ui(r.layout)),r.paint)){for(var n in this._transitionablePaint=new si(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new fi(r.paint)}}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){void 0===r&&(r={}),null!=e&&this._validate(En,\"layers.\"+this.id+\".layout.\"+t,t,e,r)||(\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e)},e.prototype.getPaintProperty=function(t){return m(t,\"-transition\")?this._transitionablePaint.getTransition(t.slice(0,-\"-transition\".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e&&this._validate(Sn,\"layers.\"+this.id+\".paint.\"+t,t,e,r))return!1;if(m(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var n=this._transitionablePaint._values[t],i=\"cross-faded-data-driven\"===n.property.specification[\"property-type\"],a=n.value.isDataDriven(),o=n.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var s=this._transitionablePaint._values[t].value;return s.isDataDriven()||a||i||this._handleOverridablePaintPropertyUpdate(t,o,s)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t=this.maxzoom)||\"none\"===this.visibility},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&Cn(this,t.call(Mn,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:At,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof hi&&Lr(e.property.specification)&&(\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent)return!0}return!1},e}(Mt),bi={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},_i=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},wi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ti(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i=bi[t.type].BYTES_PER_ELEMENT,a=r=ki(r,Math.max(e,i)),o=t.components||1;return n=Math.max(n,i),r+=i*o,{name:t.name,type:t.type,components:o,offset:a}})),size:ki(r,Math.max(n,e)),alignment:e}}function ki(t,e){return Math.ceil(t/e)*e}wi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},wi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},wi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},wi.prototype.clear=function(){this.length=0},wi.prototype.resize=function(t){this.reserve(t),this.length=t},wi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},wi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var Mi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(wi);Mi.prototype.bytesPerElement=4,Dn(\"StructArrayLayout2i4\",Mi);var Ai=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(wi);Ai.prototype.bytesPerElement=8,Dn(\"StructArrayLayout4i8\",Ai);var Si=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(wi);Si.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2i4i12\",Si);var Ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(wi);Ei.prototype.bytesPerElement=8,Dn(\"StructArrayLayout2i4ub8\",Ei);var Ci=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var h=9*t,f=18*t;return this.uint16[h+0]=e,this.uint16[h+1]=r,this.uint16[h+2]=n,this.uint16[h+3]=i,this.uint16[h+4]=a,this.uint16[h+5]=o,this.uint16[h+6]=s,this.uint16[h+7]=l,this.uint8[f+16]=c,this.uint8[f+17]=u,t},e}(wi);Ci.prototype.bytesPerElement=18,Dn(\"StructArrayLayout8ui2ub18\",Ci);var Li=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,h){var f=this.length;return this.resize(f+1),this.emplace(f,t,e,r,n,i,a,o,s,l,c,u,h)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,h,f){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=h,this.int16[p+11]=f,t},e}(wi);Li.prototype.bytesPerElement=24,Dn(\"StructArrayLayout4i4ui4i24\",Li);var Pi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(wi);Pi.prototype.bytesPerElement=12,Dn(\"StructArrayLayout3f12\",Pi);var Ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){return this.uint32[1*t+0]=e,t},e}(wi);Ii.prototype.bytesPerElement=4,Dn(\"StructArrayLayout1ul4\",Ii);var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,h=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[h+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(wi);zi.prototype.bytesPerElement=20,Dn(\"StructArrayLayout6i1ul2ui20\",zi);var Oi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(wi);Oi.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2i2i2i12\",Oi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(wi);Di.prototype.bytesPerElement=16,Dn(\"StructArrayLayout2f1f2i16\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(wi);Ri.prototype.bytesPerElement=12,Dn(\"StructArrayLayout2ub2f12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(wi);Fi.prototype.bytesPerElement=6,Dn(\"StructArrayLayout3ui6\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g,m){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g,m)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g,m,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=h,this.float32[x+8]=f,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=g,this.uint32[x+10]=m,this.int16[y+22]=v,t},e}(wi);Bi.prototype.bytesPerElement=48,Dn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g,m,v,y,x,b,_,w,T,k,M,A,S,E){var C=34*t,L=17*t;return this.int16[C+0]=e,this.int16[C+1]=r,this.int16[C+2]=n,this.int16[C+3]=i,this.int16[C+4]=a,this.int16[C+5]=o,this.int16[C+6]=s,this.int16[C+7]=l,this.uint16[C+8]=c,this.uint16[C+9]=u,this.uint16[C+10]=h,this.uint16[C+11]=f,this.uint16[C+12]=p,this.uint16[C+13]=d,this.uint16[C+14]=g,this.uint16[C+15]=m,this.uint16[C+16]=v,this.uint16[C+17]=y,this.uint16[C+18]=x,this.uint16[C+19]=b,this.uint16[C+20]=_,this.uint16[C+21]=w,this.uint16[C+22]=T,this.uint32[L+12]=k,this.float32[L+13]=M,this.float32[L+14]=A,this.float32[L+15]=S,this.float32[L+16]=E,t},e}(wi);Ni.prototype.bytesPerElement=68,Dn(\"StructArrayLayout8i15ui1ul4f68\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){return this.float32[1*t+0]=e,t},e}(wi);ji.prototype.bytesPerElement=4,Dn(\"StructArrayLayout1f4\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(wi);Ui.prototype.bytesPerElement=6,Dn(\"StructArrayLayout3i6\",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=4*t;return this.uint32[2*t+0]=e,this.uint16[i+2]=r,this.uint16[i+3]=n,t},e}(wi);Vi.prototype.bytesPerElement=8,Dn(\"StructArrayLayout1ul2ui8\",Vi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(wi);qi.prototype.bytesPerElement=4,Dn(\"StructArrayLayout2ui4\",qi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){return this.uint16[1*t+0]=e,t},e}(wi);Hi.prototype.bytesPerElement=2,Dn(\"StructArrayLayout1ui2\",Hi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(wi);Gi.prototype.bytesPerElement=8,Dn(\"StructArrayLayout2f8\",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(wi);Yi.prototype.bytesPerElement=16,Dn(\"StructArrayLayout4f16\",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(_i);Wi.prototype.size=20;var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.get=function(t){return new Wi(this,t)},e}(zi);Dn(\"CollisionBoxArray\",Zi);var Xi=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(_i);Xi.prototype.size=48;var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.get=function(t){return new Xi(this,t)},e}(Bi);Dn(\"PlacedSymbolArray\",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return 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l=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,l),o.primitiveLength++),i?this.e2=l:this.e1=l},Cs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Es-1):this.distance},Cs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Dn(\"LineBucket\",Cs,{omit:[\"layers\",\"patternFeatures\"]});var Ls=new yi({\"line-cap\":new pi(At.layout_line[\"line-cap\"]),\"line-join\":new di(At.layout_line[\"line-join\"]),\"line-miter-limit\":new pi(At.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new pi(At.layout_line[\"line-round-limit\"]),\"line-sort-key\":new di(At.layout_line[\"line-sort-key\"])}),Ps={paint:new yi({\"line-opacity\":new di(At.paint_line[\"line-opacity\"]),\"line-color\":new di(At.paint_line[\"line-color\"]),\"line-translate\":new 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r=0;r=128&&Xs(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,Gs.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,Js,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,Ks,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,tl,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,Qs,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,$s,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,el,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,rl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,nl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,il,e)},writeBytesField:function(t,e){this.writeTag(t,Gs.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,Gs.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,Gs.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,Gs.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,Gs.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,Gs.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};var fl=function(t,e){var r=e.pixelRatio,n=e.version,i=e.stretchX,a=e.stretchY,o=e.content;this.paddedRect=t,this.pixelRatio=r,this.stretchX=i,this.stretchY=a,this.content=o,this.version=n},pl={tl:{configurable:!0},br:{configurable:!0},tlbr:{configurable:!0},displaySize:{configurable:!0}};pl.tl.get=function(){return[this.paddedRect.x+1,this.paddedRect.y+1]},pl.br.get=function(){return[this.paddedRect.x+this.paddedRect.w-1,this.paddedRect.y+this.paddedRect.h-1]},pl.tlbr.get=function(){return this.tl.concat(this.br)},pl.displaySize.get=function(){return[(this.paddedRect.w-2)/this.pixelRatio,(this.paddedRect.h-2)/this.pixelRatio]},Object.defineProperties(fl.prototype,pl);var dl=function(t,e){var r={},n={};this.haveRenderCallbacks=[];var i=[];this.addImages(t,r,i),this.addImages(e,n,i);var a=hl(i),o=new po({width:a.w||1,height:a.h||1});for(var s in t){var l=t[s],c=r[s].paddedRect;po.copy(l.data,o,{x:0,y:0},{x:c.x+1,y:c.y+1},l.data)}for(var u in e){var 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C=ql(f,v,g),L=ql(p,y,m),P=0;P0&&(d=Math.max(10,d),this.circleDiameter=d)}else{var g=o.top*s-l,m=o.bottom*s+l,v=o.left*s-l,y=o.right*s+l,x=o.collisionPadding;if(x&&(v-=x[0]*s,g-=x[1]*s,y+=x[2]*s,m+=x[3]*s),u){var b=new i(v,g),_=new i(y,g),w=new i(v,m),T=new i(y,m),k=u*Math.PI/180;b._rotate(k),_._rotate(k),w._rotate(k),T._rotate(k),v=Math.min(b.x,_.x,w.x,T.x),y=Math.max(b.x,_.x,w.x,T.x),g=Math.min(b.y,_.y,w.y,T.y),m=Math.max(b.y,_.y,w.y,T.y)}t.emplaceBack(e.x,e.y,v,g,y,m,r,n,a)}this.boxEndIndex=t.length},Wl=function(t,e){if(void 0===t&&(t=[]),void 0===e&&(e=Zl),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function Zl(t,e){return te?1:0}function Xl(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,a=1/0,o=-1/0,s=-1/0,l=t[0],c=0;co)&&(o=u.x),(!c||u.y>s)&&(s=u.y)}var h=Math.min(o-n,s-a),f=h/2,p=new Wl([],Jl);if(0===h)return new i(n,a);for(var d=n;dm.d||!m.d)&&(m=y,r&&console.log(\"found best %d after %d 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i=t-1>>1,a=e[i];if(r(n,a)>=0)break;e[t]=a,t=i}e[t]=n},Wl.prototype._down=function(t){for(var e=this.data,r=this.compare,n=this.length>>1,i=e[t];t=0)break;e[t]=o,t=a}e[t]=i};var Ql=Number.POSITIVE_INFINITY;function $l(t,e){return e[1]!==Ql?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case\"top-right\":case\"top-left\":case\"top\":i=r-7;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":i=7-r}switch(t){case\"top-right\":case\"bottom-right\":case\"right\":n=-e;break;case\"top-left\":case\"bottom-left\":case\"left\":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case\"top-right\":case\"top-left\":n=i-7;break;case\"bottom-right\":case\"bottom-left\":n=7-i;break;case\"bottom\":n=7-e;break;case\"top\":n=e-7}switch(t){case\"top-right\":case\"bottom-right\":r=-i;break;case\"top-left\":case\"bottom-left\":r=i;break;case\"left\":r=e;break;case\"right\":r=-e}return[r,n]}(t,e[0])}function 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this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},hc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),i.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,i.verticalPlacedTextSymbolIndex),i.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,i.placedIconSymbolIndex),i.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,i.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Dn(\"SymbolBucket\",hc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),hc.MAX_GLYPHS=65535,hc.addDynamicAttributes=sc;var fc=new yi({\"symbol-placement\":new pi(At.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new pi(At.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new pi(At.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new di(At.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new pi(At.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new pi(At.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new pi(At.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new pi(At.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new pi(At.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new di(At.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new pi(At.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new pi(At.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new di(At.layout_symbol[\"icon-image\"]),\"icon-rotate\":new di(At.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new pi(At.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new pi(At.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new di(At.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new di(At.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new pi(At.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new pi(At.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new pi(At.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new di(At.layout_symbol[\"text-field\"]),\"text-font\":new di(At.layout_symbol[\"text-font\"]),\"text-size\":new di(At.layout_symbol[\"text-size\"]),\"text-max-width\":new di(At.layout_symbol[\"text-max-width\"]),\"text-line-height\":new pi(At.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new di(At.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new di(At.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new di(At.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new pi(At.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new di(At.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new pi(At.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new pi(At.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new di(At.layout_symbol[\"text-rotate\"]),\"text-padding\":new pi(At.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new pi(At.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new di(At.layout_symbol[\"text-transform\"]),\"text-offset\":new di(At.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new pi(At.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new pi(At.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new pi(At.layout_symbol[\"text-optional\"])}),pc={paint:new yi({\"icon-opacity\":new di(At.paint_symbol[\"icon-opacity\"]),\"icon-color\":new di(At.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new di(At.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new di(At.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new di(At.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new pi(At.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new pi(At.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new di(At.paint_symbol[\"text-opacity\"]),\"text-color\":new di(At.paint_symbol[\"text-color\"],{runtimeType:Bt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new di(At.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new di(At.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new di(At.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new pi(At.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new pi(At.paint_symbol[\"text-translate-anchor\"])}),layout:fc},dc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ot,this.defaultValue=t};dc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},dc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},dc.prototype.outputDefined=function(){return!1},dc.prototype.serialize=function(){return null},Dn(\"FormatSectionOverride\",dc,{omit:[\"defaultValue\"]});var gc=function(t){function e(e){t.call(this,e,pc)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(this.layout._values[\"icon-rotation-alignment\"]=\"point\"!==this.layout.get(\"symbol-placement\")?\"map\":\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(this.layout._values[\"text-rotation-alignment\"]=\"point\"!==this.layout.get(\"symbol-placement\")?\"map\":\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a\",targetMapId:n,sourceMapId:a.mapId})}}},Cc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Cc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Cc.prototype.processTask=function(t,e){var r=this;if(\"\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(jn(e.error)):n(null,jn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"\",sourceMapId:r.mapId,error:e?Nn(e):null,data:Nn(n,a)},a)}:function(t){i=!0},s=null,l=jn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Cc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Pc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Pc.prototype.setNorthEast=function(t){return this._ne=t instanceof Ic?new Ic(t.lng,t.lat):Ic.convert(t),this},Pc.prototype.setSouthWest=function(t){return this._sw=t instanceof Ic?new Ic(t.lng,t.lat):Ic.convert(t),this},Pc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Ic)e=t,r=t;else{if(!(t instanceof Pc))return Array.isArray(t)?4===t.length||t.every(Array.isArray)?this.extend(Pc.convert(t)):this.extend(Ic.convert(t)):this;if(r=t._ne,!(e=t._sw)||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Ic(e.lng,e.lat),this._ne=new Ic(r.lng,r.lat)),this},Pc.prototype.getCenter=function(){return new Ic((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Pc.prototype.getSouthWest=function(){return this._sw},Pc.prototype.getNorthEast=function(){return this._ne},Pc.prototype.getNorthWest=function(){return new Ic(this.getWest(),this.getNorth())},Pc.prototype.getSouthEast=function(){return new Ic(this.getEast(),this.getSouth())},Pc.prototype.getWest=function(){return this._sw.lng},Pc.prototype.getSouth=function(){return this._sw.lat},Pc.prototype.getEast=function(){return this._ne.lng},Pc.prototype.getNorth=function(){return this._ne.lat},Pc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Pc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Pc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Pc.prototype.contains=function(t){var e=Ic.convert(t),r=e.lng,n=e.lat,i=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(i=this._sw.lng>=r&&r>=this._ne.lng),this._sw.lat<=n&&n<=this._ne.lat&&i},Pc.convert=function(t){return!t||t instanceof Pc?t:new Pc(t)};var Ic=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Ic.prototype.wrap=function(){return new Ic(c(this.lng,-180,180),this.lat)},Ic.prototype.toArray=function(){return[this.lng,this.lat]},Ic.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Ic.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Ic.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Pc(new Ic(this.lng-r,this.lat-e),new Ic(this.lng+r,this.lat+e))},Ic.convert=function(t){if(t instanceof Ic)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Ic(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Ic(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: , lat: }, an object {lon: , lat: }, or an array of [, ]\")};var zc=2*Math.PI*6371008.8;function Oc(t){return zc*Math.cos(t*Math.PI/180)}function Dc(t){return(180+t)/360}function Rc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Fc(t,e){return t/Oc(e)}function Bc(t){return 360/Math.PI*Math.atan(Math.exp((180-360*t)*Math.PI/180))-90}var Nc=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Nc.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Ic.convert(t);return new Nc(Dc(r.lng),Rc(r.lat),Fc(e,r.lat))},Nc.prototype.toLngLat=function(){return new Ic(360*this.x-180,Bc(this.y))},Nc.prototype.toAltitude=function(){return this.z*Oc(Bc(this.y))},Nc.prototype.meterInMercatorCoordinateUnits=function(){return 1/zc*(t=Bc(this.y),1/Math.cos(t*Math.PI/180));var t};var jc=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=qc(0,t,t,e,r)};jc.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},jc.prototype.url=function(t,e){var r,n,i,a,o,s=(n=this.y,i=this.z,a=Lc(256*(r=this.x),256*(n=Math.pow(2,i)-n-1),i),o=Lc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<this.canonical.z?new Vc(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Vc(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Vc.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?qc(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):qc(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Vc.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ>e&&t.canonical.y===this.canonical.y>>e},Vc.prototype.children=function(t){if(this.overscaledZ>=t)return[new Vc(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Vc(e,this.wrap,e,r,n),new Vc(e,this.wrap,e,r+1,n),new Vc(e,this.wrap,e,r,n+1),new Vc(e,this.wrap,e,r+1,n+1)]},Vc.prototype.isLessThan=function(t){return this.wrapt.wrap)&&(this.overscaledZt.overscaledZ)&&(this.canonical.xt.canonical.x)&&this.canonical.y=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},Hc.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Hc.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Hc.prototype.getPixels=function(){return new po({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Hc.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},Xc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new gs.VectorTile(new Hs(this.rawTileData)).layers,this.sourceLayerCoder=new Gc(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Xc.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=rn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,h=Kc(c),f=this.grid.query(h.minX-u,h.minY-u,h.maxX+u,h.maxY+u),p=Kc(t.cameraQueryGeometry),d=0,g=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,h=c;u=0)return!0;return!1}(a,h)){var f=this.sourceLayerCoder.decode(r),p=this.vtLayers[f].feature(n);if(i.filter(new ii(this.tileID.overscaledZ),p))for(var d=this.getId(p,f),g=0;gn)i=!1;else if(e)if(this.expirationTimeot&&(t.getActor().send(\"enforceCacheSizeLimit\",at),ut=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete(\"mapbox-tiles\");t&&e.catch(t).then((function(){return t()}))},t.clipLine=jl,t.clone=function(t){var e=new to(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=x,t.clone$2=function(t){var e=new to(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ns,t.config=F,t.create=function(){var t=new to(16);return to!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new to(9);return to!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new to(4);return to!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=qr,t.createLayout=Ti,t.createStyleLayer=function(t){return\"custom\"===t.type?new bc(t):new _c[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=Ue,t.offscreenCanvasSupported=ht,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t},t.parseGlyphPBF=function(t){return new Hs(t).readFields(ll,[])},t.pbf=Hs,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays(),t.tilePixelRatio=8192/(512*t.overscaling),t.compareText={},t.iconsNeedLinear=!1;var s=t.layers[0].layout,l=t.layers[0]._unevaluatedLayout._values,c={};if(\"composite\"===t.textSizeData.kind){var u=t.textSizeData,h=u.maxZoom;c.compositeTextSizes=[l[\"text-size\"].possiblyEvaluate(new ii(u.minZoom),o),l[\"text-size\"].possiblyEvaluate(new ii(h),o)]}if(\"composite\"===t.iconSizeData.kind){var f=t.iconSizeData,p=f.maxZoom;c.compositeIconSizes=[l[\"icon-size\"].possiblyEvaluate(new ii(f.minZoom),o),l[\"icon-size\"].possiblyEvaluate(new ii(p),o)]}c.layoutTextSize=l[\"text-size\"].possiblyEvaluate(new ii(t.zoom+1),o),c.layoutIconSize=l[\"icon-size\"].possiblyEvaluate(new ii(t.zoom+1),o),c.textMaxSize=l[\"text-size\"].possiblyEvaluate(new ii(18));for(var d=24*s.get(\"text-line-height\"),g=\"map\"===s.get(\"text-rotation-alignment\")&&\"point\"!==s.get(\"symbol-placement\"),m=s.get(\"text-keep-upright\"),v=s.get(\"text-size\"),y=function(){var a=b[x],l=s.get(\"text-font\").evaluate(a,{},o).join(\",\"),u=v.evaluate(a,{},o),h=c.layoutTextSize.evaluate(a,{},o),f=c.layoutIconSize.evaluate(a,{},o),p={horizontal:{},vertical:void 0},y=a.text,w=[0,0];if(y){var T=y.toString(),k=24*s.get(\"text-letter-spacing\").evaluate(a,{},o),M=function(t){for(var e=0,r=t;e=8192||h.y<0||h.y>=8192||function(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g,m,v,y,x,b,w,T,k,M){var A,S,E,C,L,P=t.addToLineVertexArray(e,r),I=0,z=0,O=0,D=0,R=-1,F=-1,B={},N=ca(\"\"),j=0,U=0;if(void 0===s._unevaluatedLayout.getValue(\"text-radial-offset\")?(j=(A=s.layout.get(\"text-offset\").evaluate(b,{},k).map((function(t){return 24*t})))[0],U=A[1]):(j=24*s.layout.get(\"text-radial-offset\").evaluate(b,{},k),U=Ql),t.allowVerticalPlacement&&n.vertical){var V=s.layout.get(\"text-rotate\").evaluate(b,{},k)+90;C=new Yl(l,e,c,u,h,n.vertical,f,p,d,V),o&&(L=new Yl(l,e,c,u,h,o,m,v,d,V))}if(i){var q=s.layout.get(\"icon-rotate\").evaluate(b,{}),H=\"none\"!==s.layout.get(\"icon-text-fit\"),G=Ul(i,q,T,H),Y=o?Ul(o,q,T,H):void 0;E=new Yl(l,e,c,u,h,i,m,v,!1,q),I=4*G.length;var W=t.iconSizeData,Z=null;\"source\"===W.kind?(Z=[128*s.layout.get(\"icon-size\").evaluate(b,{})])[0]>32640&&_(t.layerIds[0]+': Value for \"icon-size\" is >= 255. 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C=t.mapObject(m.glyphDependencies,(function(t){return Object.keys(t).map(Number)}));Object.keys(C).length?a.send(\"getGlyphs\",{uid:this.uid,stacks:C},(function(t,e){h||(h=t,f=e,I.call(l))})):f={};var L=Object.keys(m.iconDependencies);L.length?a.send(\"getImages\",{icons:L,source:this.source,tileID:this.tileID,type:\"icons\"},(function(t,e){h||(h=t,p=e,I.call(l))})):p={};var P=Object.keys(m.patternDependencies);function I(){if(h)return s(h);if(f&&p&&d){var e=new i(f),r=new t.ImageAtlas(p,d);for(var a in g){var l=g[a];l instanceof t.SymbolBucket?(o(l.layers,this.zoom,n),t.performSymbolLayout(l,f,e.positions,p,r.iconPositions,this.showCollisionBoxes,this.tileID.canonical)):l.hasPattern&&(l instanceof t.LineBucket||l instanceof t.FillBucket||l instanceof 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r=this.loading,n=t.uid;r&&r[n]&&r[n].abort&&(r[n].abort(),delete r[n]),e()},l.prototype.removeTile=function(t,e){var r=this.loaded,n=t.uid;r&&r[n]&&delete r[n],e()};var c=t.window.ImageBitmap,u=function(){this.loaded={}};function h(t,e){if(0!==t.length){f(t[0],e);for(var r=1;r=0!=!!e&&t.reverse()}u.prototype.loadTile=function(e,r){var n=e.uid,i=e.encoding,a=e.rawImageData,o=c&&a instanceof c?this.getImageData(a):a,s=new t.DEMData(n,o,i);this.loaded=this.loaded||{},this.loaded[n]=s,r(null,s)},u.prototype.getImageData=function(e){this.offscreenCanvas&&this.offscreenCanvasContext||(this.offscreenCanvas=new OffscreenCanvas(e.width,e.height),this.offscreenCanvasContext=this.offscreenCanvas.getContext(\"2d\")),this.offscreenCanvas.width=e.width,this.offscreenCanvas.height=e.height,this.offscreenCanvasContext.drawImage(e,0,0,e.width,e.height);var r=this.offscreenCanvasContext.getImageData(-1,-1,e.width+2,e.height+2);return 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p=Math.floor((h+u)/2),d=e[2*p],g=e[2*p+1];I(d,g,r,n)<=l&&s.push(t[p]);var m=(c+1)%2;(0===c?r-i<=d:n-i<=g)&&(o.push(h),o.push(p-1),o.push(m)),(0===c?r+i>=d:n+i>=g)&&(o.push(p+1),o.push(u),o.push(m))}}return s}(this.ids,this.coords,t,e,r,this.nodeSize)};var R={minZoom:0,maxZoom:16,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(t){return t}},F=function(t){this.options=H(Object.create(R),t),this.trees=new Array(this.options.maxZoom+1)};function B(t,e,r,n,i){return{x:t,y:e,zoom:1/0,id:r,parentId:-1,numPoints:n,properties:i}}function N(t,e){var r=t.geometry.coordinates,n=r[1];return{x:V(r[0]),y:q(n),zoom:1/0,index:e,parentId:-1}}function j(t){return{type:\"Feature\",id:t.id,properties:U(t),geometry:{type:\"Point\",coordinates:[(n=t.x,360*(n-.5)),(e=t.y,r=(180-360*e)*Math.PI/180,360*Math.atan(Math.exp(r))/Math.PI-90)]}};var e,r,n}function U(t){var e=t.numPoints,r=e>=1e4?Math.round(e/1e3)+\"k\":e>=1e3?Math.round(e/100)/10+\"k\":e;return 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e=[];return e.size=t.size,e.start=t.start,e.end=t.end,e}function ot(t,e,r,n,i,a){for(var o=0;oo.maxX&&(o.maxX=u),h>o.maxY&&(o.maxY=h)}return o}function gt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if(\"Point\"===a||\"MultiPoint\"===a)for(var s=0;s0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;lo)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n0===e)for(n=0,i=t.length;n24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new D(s,G,Y,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},F.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,h=l.range(V(r),q(a),V(i),q(n));u1?this._map(s,!0):null,d=(o<<5)+(e+1)+this.points.length,g=0,m=c;g>5},F.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},F.prototype._map=function(t,e){if(t.numPoints)return e?H({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?H({},n):n},vt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},vt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<1&&console.time(\"creation\"),f=this.tiles[h]=dt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,f.numFeatures,f.numPoints,f.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(f.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<1&&console.time(\"clipping\");var g,m,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;g=m=v=y=null,x=rt(t,u,r-_,r+T,0,f.minX,f.maxX,l),b=rt(t,u,r+w,r+k,0,f.minX,f.maxX,l),t=null,x&&(g=rt(x,u,n-_,n+T,1,f.minY,f.maxY,l),m=rt(x,u,n+w,n+k,1,f.minY,f.maxY,l),x=null),b&&(v=rt(b,u,n-_,n+T,1,f.minY,f.maxY,l),y=rt(b,u,n+w,n+k,1,f.minY,f.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(g||[],e+1,2*r,2*n),s.push(m||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},vt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,h=r;!l&&c>0;)c--,u=Math.floor(u/2),h=Math.floor(h/2),l=this.tiles[yt(c,u,h)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,h),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,h,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?ft(this.tiles[s],i):null):null};var bt=function(e){function r(t,r,n,i){e.call(this,t,r,n,xt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));!function t(e,r){var n,i=e&&e.type;if(\"FeatureCollection\"===i)for(n=0;n=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var f=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i1&&(s=t[++o]);var c=Math.abs(l-s.left),u=Math.abs(l-s.right),h=Math.min(c,u),f=void 0,p=i/r*(n+1);if(s.isDash){var d=n-Math.abs(p);f=Math.sqrt(h*h+d*d)}else f=n-Math.sqrt(h*h+p*p);this.data[a+l]=Math.max(0,Math.min(255,f+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c1&&(l=t[++s]);var u=Math.abs(c-l.left),h=Math.abs(c-l.right),f=Math.min(u,h);this.data[o+c]=Math.max(0,Math.min(255,(l.isDash?f:-f)+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o=n&&e.x=i&&e.y0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+10&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor,e.request=this.actor.send(i,{type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId},(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;or.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),O=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),(r.prototype=Object.create(e&&e.prototype)).constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];tthis.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},N.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},N.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=Math.ceil(t.width/this._source.tileSize)+1,r=Math.ceil(t.height/this._source.tileSize)+1,n=Math.floor(e*r*5),i=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,n):n;this._cache.setMaxSize(i)},r.prototype.handleWrapJump=function(t){var e=Math.round((t-(void 0===this._prevLng?t:this._prevLng))/360);if(this._prevLng=t,e){var r={};for(var n in this._tiles){var i=this._tiles[n];i.tileID=i.tileID.unwrapTo(i.tileID.wrap+e),r[i.tileID.key]=i}for(var a in this._tiles=r,this._timers)clearTimeout(this._timers[a]),delete this._timers[a];for(var o in this._tiles)this._setTileReloadTimer(o,this._tiles[o])}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},h=0,f=Object.keys(l);hthis._source.maxzoom){var m=d.children(this._source.maxzoom)[0],v=this.getTile(m);if(v&&v.hasData()){n[m.key]=m;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=g.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(g=this.getTile(_))&&x&&(g=this._addTile(_)),g&&(n[_.key]=_,x=g.wasRequested(),g.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),h=1/0,f=1/0,p=-1/0,d=-1/0,g=0,m=c;g=0&&v[1].y+m>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:g})}}},x=0;x=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){this._state.updateState(t=t||\"_geojsonTileLayer\",e,r)},r.prototype.removeFeatureState=function(t,e,r){this._state.removeFeatureState(t=t||\"_geojsonTileLayer\",e,r)},r.prototype.getFeatureState=function(t,e){return this._state.getState(t=t||\"_geojsonTileLayer\",e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function zt(){return new t.window.Worker(Yi.workerUrl)}Lt.maxOverzooming=10,Lt.maxUnderzooming=3;var Ot=\"mapboxgl_preloaded_worker_pool\",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Jt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function re(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),h=[256/n.width*2+1,256/n.height*2+1],f=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;f.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,g=n.transform.width/n.transform.height,m=!1,v=0;vMath.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.yn.x)?{needsFlipping:!0}:null}function ae(e,r,n,i,a,o,s,l,c,u,h,f,p,d){var g,m=r/24,v=e.lineOffsetX*m,y=e.lineOffsetY*m;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ne(m,l,v,y,n,h,f,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=$t(w.first.point,s).point,k=$t(w.last.point,s).point;if(i&&!n){var M=ie(e.writingMode,T,k,d);if(M)return M}g=[w.first];for(var A=e.glyphStartIndex+1;A0?L.point:oe(f,C,S,1,a),I=ie(e.writingMode,S,P,d);if(I)return I}var z=se(m*l.getoffsetX(e.glyphStartIndex),v,y,n,h,f,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!z)return{notEnoughRoom:!0};g=[z]}for(var O=0,D=g;O0?1:-1,g=0;i&&(d*=-1,g=Math.PI),d<0&&(g+=Math.PI);for(var m=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((m+=d)=c)return null;if(y=v,w.push(v),void 0===(v=f[m])){var T=new t.Point(u.getx(m),u.gety(m)),k=$t(T,h);if(k.signedDistanceFromCamera>0)v=f[m]=k.point;else{var M=m-d;v=oe(0===x?o:new t.Point(u.getx(M),u.gety(M)),T,y,_-x+1,h)}}x+=b,b=y.dist(v)}var A=(_-x)/b,S=v.sub(y),E=S.mult(A)._add(y);E._add(S._unit()._perp()._mult(n*d));var C=g+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:C,path:w}}Jt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Jt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Jt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Jt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Jt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Jt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s0:o},Jt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[];return this._forEachCell(a,s,o,l,this._queryCellCircle,c,{hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}},i),n?c.length>0:c},Jt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Jt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Jt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Jt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,h=0,f=c;h=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var g=this.circleCells[i];if(null!==g)for(var m=this.circles,v=0,y=g;vo*o+s*s},Jt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var h=l-s,f=u-c;return h*h+f*f<=r*r};var le=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ce(t,e){for(var r=0;r=1;P--)L.push(E.path[P]);for(var I=1;I0){for(var R=L[0].clone(),F=L[0].clone(),B=1;B=M.x&&F.x<=A.x&&R.y>=M.y&&F.y<=A.y?[L]:F.xA.x||F.yA.y?[]:t.clipLine([L],M.x,M.y,A.x,A.y)}for(var N=0,j=D;N=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t=0&&e0?(this.prevPlacement&&this.prevPlacement.variableOffsets[h.crossTileID]&&this.prevPlacement.placements[h.crossTileID]&&this.prevPlacement.placements[h.crossTileID].text&&(g=this.prevPlacement.variableOffsets[h.crossTileID].anchor),this.variableOffsets[h.crossTileID]={textOffset:m,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:g},this.markUsedJustification(f,t,h,p),f.allowVerticalPlacement&&(this.markUsedOrientation(f,p,h),this.placedOrientations[h.crossTileID]=p),{shift:v,placedGlyphBoxes:y}):void 0},_e.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,h=a.textPixelRatio,f=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,g=a.collisionGroup,m=s.get(\"text-optional\"),v=s.get(\"icon-optional\"),y=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=y&&(x||!o.hasIconData()||v),M=x&&(y||!o.hasTextData()||m);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var A=function(e,a){if(!r[e.crossTileID])if(f)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,A=!1,S=!0,E=null,C={box:null,offscreen:null},L={box:null,offscreen:null},P=null,I=null,z=0,O=0,D=0;a.textFeatureIndex?z=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(z=e.featureIndex),a.verticalTextFeatureIndex&&(O=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,i.markUsedOrientation(o,n=a,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,f={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d=N.length,e,o,n,u);if(m&&(f=m.placedGlyphBoxes)&&f.box&&f.box.length){T=!0,E=m.shift;break}}return f};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&!(C&&C.box&&C.box.length)&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),C&&(T=C.box,S=C.offscreen);var V=F(C&&C.box);if(!T&&i.prevPlacement){var q=i.prevPlacement.variableOffsets[e.crossTileID];q&&(i.variableOffsets[e.crossTileID]=q,i.markUsedJustification(o,q.anchor,e,V))}}else{var H=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,h,l,g.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return H(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?H(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(C&&C.box&&C.box.length)}}if(T=(p=C)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\");P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,g.predicate,e.collisionCircleDiameter,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?be(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,h,l,g.predicate)};A=L&&L.box&&L.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var X=m||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,J=v||0===e.numIconVertices;if(X||J?J?X||(A=A&&T):T=A&&T:A=T=A&&T,T&&p&&p.box&&i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,L&&L.box&&O?O:z,g.ID),A&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,g.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,g.ID),n)){var K=o.bucketInstanceId,Q=i.collisionCircleArrays[K];void 0===Q&&(Q=i.collisionCircleArrays[K]=new me);for(var $=0;$=0;--E){var C=S[E];A(o.symbolInstances.get(C),o.collisionArrays[C])}else for(var L=e.symbolInstanceStart;L=0&&(e.text.placedSymbolArray.get(l).crossTileID=a>=0&&l!==a?0:n.crossTileID)}},_e.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o0,y=i.placedOrientations[a.crossTileID],x=y===t.WritingMode.vertical,b=y===t.WritingMode.horizontal||y===t.WritingMode.horizontalOnly;if(s>0||l>0){var _=Le(m.text);d(e.text,s,x?Pe:_),d(e.text,l,b?Pe:_);var w=m.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=w||x?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=w||b?1:0);var T=i.variableOffsets[a.crossTileID];T&&i.markUsedJustification(e,T.anchor,a,y);var k=i.placedOrientations[a.crossTileID];k&&(i.markUsedJustification(e,\"left\",a,k),i.markUsedOrientation(e,k,a))}if(v){var M=Le(m.icon),A=!(f&&a.verticalPlacedIconSymbolIndex&&x);a.placedIconSymbolIndex>=0&&(d(e.icon,a.numIconVertices,A?M:Pe),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=m.icon.isHidden()),a.verticalPlacedIconSymbolIndex>=0&&(d(e.icon,a.numVerticalIconVertices,A?Pe:M),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=m.icon.isHidden())}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var S=e.collisionArrays[n];if(S){var E=new t.Point(0,0);if(S.textBox||S.verticalTextBox){var C=!0;if(c){var L=i.variableOffsets[g];L?(E=xe(L.anchor,L.width,L.height,L.textOffset,L.textBoxScale),u&&E._rotate(h?i.transform.angle:-i.transform.angle)):C=!1}S.textBox&&we(e.textCollisionBox.collisionVertexArray,m.text.placed,!C||x,E.x,E.y),S.verticalTextBox&&we(e.textCollisionBox.collisionVertexArray,m.text.placed,!C||b,E.x,E.y)}var P=Boolean(!b&&S.verticalIconBox);S.iconBox&&we(e.iconCollisionBox.collisionVertexArray,m.icon.placed,P,f?E.x:0,f?E.y:0),S.verticalIconBox&&we(e.iconCollisionBox.collisionVertexArray,m.icon.placed,!P,f?E.x:0,f?E.y:0)}}},m=0;mt},_e.prototype.setStale=function(){this.stale=!0};var Te=Math.pow(2,25),ke=Math.pow(2,24),Me=Math.pow(2,17),Ae=Math.pow(2,16),Se=Math.pow(2,9),Ee=Math.pow(2,8),Ce=Math.pow(2,1);function Le(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Te+e*ke+r*Me+e*Ae+r*Se+e*Ee+r*Ce+e}var Pe=0,Ie=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Ie.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Ie(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Oe=512/t.EXTENT/2,De=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;nt.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,h=l.x,f=l.y,p=l.sdf,d=l.pixelRatio,g=l.stretchX,m=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:h,y:f},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:g,stretchY:m,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Lt(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(Ne(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s=0;p--){var d=this._order[p];if(r(d))for(var g=i.length-1;g>=0;g--){var m=i[g].feature;if(n[m.layer.id] 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),$e=vr(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),tr=vr(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),er=vr(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),rr=vr(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),nr=vr(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ir=vr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),ar=vr(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),or=vr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),sr=vr(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),lr=vr(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),cr=vr(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),ur=vr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),hr=vr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),fr=vr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),pr=vr(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),dr=vr(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),gr=vr(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),mr=vr(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function vr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n \"+a+\" = a_\"+a+\";\\n#else\\n \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var yr=Object.freeze({__proto__:null,prelude:Ge,background:Ye,backgroundPattern:We,circle:Ze,clippingMask:Xe,heatmap:Je,heatmapTexture:Ke,collisionBox:Qe,collisionCircle:$e,debug:tr,fill:er,fillOutline:rr,fillOutlinePattern:nr,fillPattern:ir,fillExtrusion:ar,fillExtrusionPattern:or,hillshadePrepare:sr,hillshade:lr,line:cr,lineGradient:ur,linePattern:hr,lineSDF:fr,raster:pr,symbolIcon:dr,symbolSDF:gr,symbolTextAndIcon:mr}),xr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};xr.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}br.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,g){var m,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,h,{zoom:f});for(var x=(m={},m[v.LINES]=2,m[v.TRIANGLES]=3,m[v.LINE_STRIP]=1,m)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new xr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,g),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var wr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},Tr=function(e,r,n,i,a,o,s){return t.extend(wr(e,r,n,i),_r(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},kr=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(kr(e),_r(n,r,i))},Ar=function(t,e){return{u_matrix:t,u_world:e}},Sr=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Er=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=fe(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=fe(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Lr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Pr=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Ir=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:fe(e,1,r),u_intensity:n}},Or=function(e,r,n){var i=e.transform;return{u_matrix:Nr(e,r,n),u_ratio:1/fe(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Dr=function(e,r,n){return t.extend(Or(e,r,n),{u_image:0})},Rr=function(e,r,n,i){var a=e.transform,o=Br(r,a);return{u_matrix:Nr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/fe(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Fr=function(e,r,n,i,a){var o=e.lineAtlas,s=Br(r,e.transform),l=\"round\"===n.layout.get(\"line-cap\"),c=o.getDash(i.from,l),u=o.getDash(i.to,l),h=c.width*a.fromScale,f=u.width*a.toScale;return t.extend(Or(e,r,n),{u_patternscale_a:[s/h,-c.height/2],u_patternscale_b:[s/f,-u.height/2],u_sdfgamma:o.width/(256*Math.min(h,f)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:c.y,u_tex_y_b:u.y,u_mix:a.t})};function Br(t,e){return 1/fe(t,1,e.tileZoom)}function Nr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var jr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Ur(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Ur(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var Vr,qr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Hr=function(e,r,n,i,a,o,s,l,c,u,h){var f=a.transform;return t.extend(qr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(f._pitch)*f.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+h})},Gr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Hr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Yr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Wr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,h=u*(n.tileID.canonical.x+n.tileID.wrap*c),f=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/fe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[h>>16,f>>16],u_pixel_coord_lower:[65535&h,65535&f]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Zr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Xr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),h=[],f=0,p=0,d=0;d0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),h.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=f+=b.length/4}x&&u.draw(l,c.LINES,Mt.disabled,At.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,m),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&h.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*f),k._trim();for(var M=0,A=0,S=h;A=0&&(g[v.associatedIconIndex]={shiftedAnchor:k,angle:M})}else ce(v.numGlyphs,p)}if(h){d.clear();for(var S=e.icon.placedSymbolArray,E=0;E0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),h=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),f=!r||Math.abs(r.tileID.overscaledZ-h)>Math.abs(e.tileID.overscaledZ-h),p=f&&e.refreshedUponExpiration?1:t.clamp(f?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var ln=new t.Color(1,0,0,1),cn=new t.Color(0,1,0,1),un=new t.Color(0,0,1,1),hn=new t.Color(1,0,1,1),fn=new t.Color(0,1,1,1);function pn(t,e,r,n){gn(t,0,e+r/2,t.transform.width,r,n)}function dn(t,e,r,n){gn(t,e-r/2,0,r,t.transform.height,n)}function gn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function mn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=Mt.disabled,c=At.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Pr(o,t.Color.red),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var h=r.getTileByID(n.key).latestRawTileData,f=Math.floor((h&&h.byteLength||0)/1024),p=r.getTile(n).tileSize,d=512/Math.min(p,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+\" \"+f+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Pr(o,t.Color.transparent,d),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var vn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=At.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,h=0,f=e;h256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new At({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},yn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new At({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},yn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s=0;this.currentLayer--){var b=this.style._layers[i[this.currentLayer]],_=a[b.source],w=u[b.source];this._renderTileClippingMasks(b,w),this.renderLayer(this,_,b,w)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer0?e.pop():null},yn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},yn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new br(this.context,yr[t],e,Zr[t],this._showOverdrawInspector)),this.cache[r]},yn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},yn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},yn.prototype.initDebugOverlayCanvas=function(){null==this.debugOverlayCanvas&&(this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512,this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,this.context.gl.RGBA))},yn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var xn=function(t,e){this.points=t,this.planes=e};xn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new xn(a,o)};var bn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};bn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,h=0;hthis.max[l]-this.min[l])return 0}return 1};var _n=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};_n.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},_n.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},_n.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},_n.prototype.clone=function(){return new _n(this.top,this.bottom,this.left,this.right)},_n.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var wn=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new _n,this._posMatrixCache={},this._alignedPosMatrixCache={}},Tn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};wn.prototype.clone=function(){var t=new wn(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Tn.minZoom.get=function(){return this._minZoom},Tn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Tn.maxZoom.get=function(){return this._maxZoom},Tn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Tn.minPitch.get=function(){return this._minPitch},Tn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Tn.maxPitch.get=function(){return this._maxPitch},Tn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Tn.renderWorldCopies.get=function(){return this._renderWorldCopies},Tn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Tn.worldSize.get=function(){return this.tileSize*this.scale},Tn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Tn.size.get=function(){return new t.Point(this.width,this.height)},Tn.bearing.get=function(){return-this.angle/Math.PI*180},Tn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Tn.pitch.get=function(){return this._pitch/Math.PI*180},Tn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Tn.fov.get=function(){return this._fov/Math.PI*180},Tn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Tn.zoom.get=function(){return this._zoom},Tn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Tn.center.get=function(){return this._center},Tn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Tn.padding.get=function(){return this._edgeInsets.toJSON()},Tn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Tn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},wn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},wn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},wn.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},wn.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},wn.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&re.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=xn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new bn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],h=[],f=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var g=u.pop(),m=g.x,v=g.y,y=g.fullyVisible;if(!y){var x=g.aabb.intersects(s);if(0===x)continue;y=2===x}var b=g.aabb.distanceX(o),_=g.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_));if(g.zoom===f||w>3+(1<=l)h.push({tileID:new t.OverscaledTileID(g.zoom===f?p:g.zoom,g.wrap,g.zoom,m,v),distanceSq:t.sqrLen([o[0]-.5-m,o[1]-.5-v])});else for(var T=0;T<4;T++){var k=(m<<1)+T%2,M=(v<<1)+(T>>1);u.push({aabb:g.aabb.quadrant(T),zoom:g.zoom+1,x:k,y:M,wrap:g.wrap,fullyVisible:y})}}return h.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},wn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Tn.unmodified.get=function(){return this._unmodified},wn.prototype.zoomScale=function(t){return Math.pow(2,t)},wn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},wn.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},wn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Tn.point.get=function(){return this.project(this.center)},wn.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},wn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},wn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},wn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},wn.prototype.coordinateLocation=function(t){return t.toLngLat()},wn.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[1]/i,s=n[1]/a,l=r[2]/i,c=n[2]/a,u=l===c?0:(0-l)/(c-l);return new t.MercatorCoordinate(t.number(r[0]/i,n[0]/a,u)/this.worldSize,t.number(o,s,u)/this.worldSize)},wn.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},wn.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},wn.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},wn.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},wn.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},wn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},wn.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var h=this.latRange;a=t.mercatorYfromLat(h[1])*this.worldSize,e=(o=t.mercatorYfromLat(h[0])*this.worldSize)-ao&&(i=o-m)}if(this.lngRange){var v=p.x,y=c.x/2;v-yl&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},wn.prototype._calcMatrices=function(){if(this.height){var e=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(this._fov/2)*this.height;var r=Math.PI/2+this._pitch,n=this._fov*(.5+e.y/this.height),i=Math.sin(n)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-r-n,.01,Math.PI-.01)),a=this.point,o=a.x,s=a.y,l=1.01*(Math.cos(Math.PI/2-this._pitch)*i+this.cameraToCenterDistance),c=this.height/50,u=new Float64Array(16);t.perspective(u,this._fov,this.width/this.height,c,l),u[8]=2*-e.x/this.width,u[9]=2*e.y/this.height,t.scale(u,u,[1,-1,1]),t.translate(u,u,[0,0,-this.cameraToCenterDistance]),t.rotateX(u,u,this._pitch),t.rotateZ(u,u,this.angle),t.translate(u,u,[-o,-s,0]),this.mercatorMatrix=t.scale([],u,[this.worldSize,this.worldSize,this.worldSize]),t.scale(u,u,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=u,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,f=this.height%2/2,p=Math.cos(this.angle),d=Math.sin(this.angle),g=o-Math.round(o)+p*h+d*f,m=s-Math.round(s)+p*f+d*h,v=new Float64Array(u);if(t.translate(v,v,[g>.5?g-1:g,m>.5?m-1:m,0]),this.alignedProjMatrix=v,u=t.create(),t.scale(u,u,[this.width/2,-this.height/2,1]),t.translate(u,u,[1,-1,0]),this.labelPlaneMatrix=u,u=t.create(),t.scale(u,u,[1,-1,1]),t.translate(u,u,[-1,-1,0]),t.scale(u,u,[2/this.width,2/this.height,1]),this.glCoordMatrix=u,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(u=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=u,this._posMatrixCache={},this._alignedPosMatrixCache={}}},wn.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},wn.prototype.getCameraPoint=function(){var e=Math.tan(this._pitch)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,e))},wn.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},kn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var Mn={linearity:.3,easing:t.bezier(0,0,.3,1)},An=t.extend({deceleration:2500,maxSpeed:1400},Mn),Sn=t.extend({deceleration:20,maxSpeed:1400},Mn),En=t.extend({deceleration:1e3,maxSpeed:360},Mn),Cn=t.extend({deceleration:1e3,maxSpeed:90},Mn),Ln=function(t){this._map=t,this.clear()};function Pn(t,e){(!t.duration||t.duration0&&r-e[0].time>160;)e.shift()},Ln.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n=this._clickTolerance||this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.dblclick=function(t){return this._firePreventable(new zn(t.type,this._map,t))},Rn.prototype.mouseover=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.mouseout=function(t){this._map.fire(new zn(t.type,this._map,t))},Rn.prototype.touchstart=function(t){return this._firePreventable(new On(t.type,this._map,t))},Rn.prototype.touchmove=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype.touchend=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype.touchcancel=function(t){this._map.fire(new On(t.type,this._map,t))},Rn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Rn.prototype.isEnabled=function(){return!0},Rn.prototype.isActive=function(){return!1},Rn.prototype.enable=function(){},Rn.prototype.disable=function(){};var Fn=function(t){this._map=t};Fn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Fn.prototype.mousemove=function(t){this._map.fire(new zn(t.type,this._map,t))},Fn.prototype.mousedown=function(){this._delayContextMenu=!0},Fn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new zn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Fn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new zn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},Fn.prototype.isEnabled=function(){return!0},Fn.prototype.isActive=function(){return!1},Fn.prototype.enable=function(){},Fn.prototype.disable=function(){};var Bn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Nn(t,e){for(var r={},n=0;nthis.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n30)&&(this.aborted=!0)}}},jn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var Un=function(t){this.singleTap=new jn(t),this.numTaps=t.numTaps,this.reset()};Un.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Un.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Un.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Un.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Vn=function(){this._zoomIn=new Un({numTouches:1,numTaps:2}),this._zoomOut=new Un({numTouches:2,numTaps:1}),this.reset()};Vn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Vn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Vn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Vn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Vn.prototype.touchcancel=function(){this.reset()},Vn.prototype.enable=function(){this._enabled=!0},Vn.prototype.disable=function(){this._enabled=!1,this.reset()},Vn.prototype.isEnabled=function(){return this._enabled},Vn.prototype.isActive=function(){return this._active};var qn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};qn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},qn.prototype._correctButton=function(t,e){return!1},qn.prototype._move=function(t,e){return{}},qn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},qn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)0&&(this._active=!0);var i=Nn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(sMath.abs(t.x)}var ei=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ti(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ti(t)&&ti(e)&&a}},e}(Zn),ri={panStep:100,bearingStep:15,pitchStep:10},ni=function(){var t=ri;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function ii(t){return t*(2-t)}ni.prototype.reset=function(){this._active=!1},ni.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:ii,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},ni.prototype.enable=function(){this._enabled=!0},ni.prototype.disable=function(){this._enabled=!1,this.reset()},ni.prototype.isEnabled=function(){return this._enabled},ni.prototype.isActive=function(){return this._active};var ai=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};ai.prototype.setZoomRate=function(t){this._defaultZoomRate=t},ai.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},ai.prototype.isEnabled=function(){return!!this._enabled},ai.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},ai.prototype.isZooming=function(){return!!this._zooming},ai.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},ai.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},ai.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},ai.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},ai.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},ai.prototype.renderFrame=function(){return this._onScrollFrame()},ai.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var h=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),f=c(h);o=t.number(l,s,f),h<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},ai.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},ai.prototype.reset=function(){this._active=!1};var oi=function(t,e){this._clickZoom=t,this._tapZoom=e};oi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},oi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},oi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},oi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var si=function(){this.reset()};si.prototype.reset=function(){this._active=!1},si.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},si.prototype.enable=function(){this._enabled=!0},si.prototype.disable=function(){this._enabled=!1,this.reset()},si.prototype.isEnabled=function(){return this._enabled},si.prototype.isActive=function(){return this._active};var li=function(){this._tap=new Un({numTouches:1,numTaps:1}),this.reset()};li.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},li.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},li.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},li.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},li.prototype.touchcancel=function(){this.reset()},li.prototype.enable=function(){this._enabled=!0},li.prototype.disable=function(){this._enabled=!1,this.reset()},li.prototype.isEnabled=function(){return this._enabled},li.prototype.isActive=function(){return this._active};var ci=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};ci.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},ci.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},ci.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},ci.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var ui=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};ui.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},ui.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},ui.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},ui.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var hi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};hi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},hi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},hi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},hi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},hi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},hi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var fi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},pi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),(e.prototype=Object.create(t&&t.prototype)).constructor=e,e}(t.Event);function di(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var gi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Ln(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;aa?Math.min(2,_):Math.max(.5,_),w=Math.pow(m,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(g));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,h=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,f=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),g=t.Point.convert(e.offset),m=a.centerPoint.add(g),v=a.pointLocation(m),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var M=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),A=w/a.zoomScale(M-o);_=Math.sqrt(A/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function C(t){return(Math.exp(t)-Math.exp(-t))/2}function L(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return L(P)/L(P+_*t)},z=function(t){return w*((L(P)*(C(e=P+_*t)/L(e))-C(P))/S)/k;var e},O=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(O)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=Te.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==h,this._pitching=f!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*O,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,h,e)),n._pitching&&(a.pitch=t.number(l,f,e)),n._padding&&(a.interpolatePadding(c,p,e),m=a.centerPoint.add(g));var v=1===e?y:a.unproject(x.add(b.mult(z(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,m),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)180?-360:r<-180?360:0}},r}(t.Evented),vi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};vi.prototype.getDefaultPosition=function(){return\"bottom-right\"},vi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},vi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},vi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},vi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var yi=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};yi.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},yi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},yi.prototype.getDefaultPosition=function(){return\"bottom-left\"},yi.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},yi.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},yi.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var xi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};xi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},xi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;re.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new wn(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new xi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},bi,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof wi))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new gi(this,e),this._hash=e.hash&&new kn(\"string\"==typeof e.hash&&e.hash||void 0).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new vi({customAttribution:e.customAttribution})),this.addControl(new yi,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),(i.prototype=Object.create(n&&n.prototype)).constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()60)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new zn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new zn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new zn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;return void 0===r?n.prototype.off.call(this,t,e):(this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ci.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ci.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ci.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ci.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ci.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ci.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ci.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ci.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ci.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ci.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)e.getEast()||r.latitudee.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Fi)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Oi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Oi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||e.originalEvent&&\"resize\"===e.originalEvent.type||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ri--,Fi=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Ri>1?(e={maximumAge:6e5,timeout:0},Fi=!0):(e=this.options.positionOptions,Fi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Ni={maxWidth:100,unit:\"metric\"},ji=function(e){this.options=t.extend({},Ni,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Ui(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;l>5280?Vi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\")):Vi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?Vi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?Vi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Vi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Vi(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))*(o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o)));t.style.width=e*(s/r)+\"px\",t.innerHTML=s+\" \"+n}ji.prototype.getDefaultPosition=function(){return\"bottom-left\"},ji.prototype._onMove=function(){Ui(this._map,this._container,this.options)},ji.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},ji.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},ji.prototype.setUnit=function(t){this.options.unit=t,Ui(this._map,this._container,this.options)};var qi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};qi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},qi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},qi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},qi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},qi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},qi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},qi.prototype._isFullscreen=function(){return this._fullscreen},qi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},qi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Hi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Gi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Hi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),(n.prototype=Object.create(e&&e.prototype)).constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"×\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this;if(this._map&&(this._lngLat||this._trackPointer)&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Li(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var i=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),a=this.options.anchor,o=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),\"top-left\":new t.Point(n,n),\"top-right\":new t.Point(-n,n),bottom:new t.Point(0,-r),\"bottom-left\":new t.Point(n,-n),\"bottom-right\":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),\"top-left\":t.Point.convert(r[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(r[\"top-right\"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),\"bottom-left\":t.Point.convert(r[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(r[\"bottom-right\"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!a){var s,l=this._container.offsetWidth,c=this._container.offsetHeight;s=i.y+o.bottom.ythis._map.transform.height-c?[\"bottom\"]:[],i.xthis._map.transform.width-l/2&&s.push(\"right\"),a=0===s.length?\"bottom\":s.join(\"-\")}var u=i.add(o[a]).round();r.setTransform(this._container,Pi[a]+\" translate(\"+u.x+\"px,\"+u.y+\"px)\"),Ii(this._container,a,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented),Yi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Mi,NavigationControl:Ei,GeolocateControl:Bi,AttributionControl:vi,ScaleControl:ji,FullscreenControl:qi,Popup:Gi,Marker:Oi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(Ot)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(Ot),Rt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return Yi})),r}))},{}],448:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=1<p[1][2]&&(v[0]=-v[0]),p[0][2]>p[2][0]&&(v[1]=-v[1]),p[1][0]>p[0][1]&&(v[2]=-v[2]),!0}},{\"./normalize\":450,\"gl-mat4/clone\":272,\"gl-mat4/create\":273,\"gl-mat4/determinant\":274,\"gl-mat4/invert\":278,\"gl-mat4/transpose\":289,\"gl-vec3/cross\":339,\"gl-vec3/dot\":344,\"gl-vec3/length\":354,\"gl-vec3/normalize\":361}],450:[function(t,e,r){e.exports=function(t,e){var r=e[15];if(0===r)return!1;for(var n=1/r,i=0;i<16;i++)t[i]=e[i]*n;return!0}},{}],451:[function(t,e,r){var n=t(\"gl-vec3/lerp\"),i=t(\"mat4-recompose\"),a=t(\"mat4-decompose\"),o=t(\"gl-mat4/determinant\"),s=t(\"quat-slerp\"),l=h(),c=h(),u=h();function h(){return{translate:f(),scale:f(1),skew:f(),perspective:[0,0,0,1],quaternion:[0,0,0,1]}}function f(t){return[t||0,t||0,t||0]}e.exports=function(t,e,r,h){if(0===o(e)||0===o(r))return!1;var f=a(e,l.translate,l.scale,l.skew,l.perspective,l.quaternion),p=a(r,c.translate,c.scale,c.skew,c.perspective,c.quaternion);return!(!f||!p)&&(n(u.translate,l.translate,c.translate,h),n(u.skew,l.skew,c.skew,h),n(u.scale,l.scale,c.scale,h),n(u.perspective,l.perspective,c.perspective,h),s(u.quaternion,l.quaternion,c.quaternion,h),i(t,u.translate,u.scale,u.skew,u.perspective,u.quaternion),!0)}},{\"gl-mat4/determinant\":274,\"gl-vec3/lerp\":355,\"mat4-decompose\":449,\"mat4-recompose\":452,\"quat-slerp\":501}],452:[function(t,e,r){var n={identity:t(\"gl-mat4/identity\"),translate:t(\"gl-mat4/translate\"),multiply:t(\"gl-mat4/multiply\"),create:t(\"gl-mat4/create\"),scale:t(\"gl-mat4/scale\"),fromRotationTranslation:t(\"gl-mat4/fromRotationTranslation\")},i=(n.create(),n.create());e.exports=function(t,e,r,a,o,s){return n.identity(t),n.fromRotationTranslation(t,s,e),t[3]=o[0],t[7]=o[1],t[11]=o[2],t[15]=o[3],n.identity(i),0!==a[2]&&(i[9]=a[2],n.multiply(t,t,i)),0!==a[1]&&(i[9]=0,i[8]=a[1],n.multiply(t,t,i)),0!==a[0]&&(i[8]=0,i[4]=a[0],n.multiply(t,t,i)),n.scale(t,t,r),t}},{\"gl-mat4/create\":273,\"gl-mat4/fromRotationTranslation\":276,\"gl-mat4/identity\":277,\"gl-mat4/multiply\":280,\"gl-mat4/scale\":287,\"gl-mat4/translate\":288}],453:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],454:[function(t,e,r){\"use strict\";var n=t(\"binary-search-bounds\"),i=t(\"mat4-interpolate\"),a=t(\"gl-mat4/invert\"),o=t(\"gl-mat4/rotateX\"),s=t(\"gl-mat4/rotateY\"),l=t(\"gl-mat4/rotateZ\"),c=t(\"gl-mat4/lookAt\"),u=t(\"gl-mat4/translate\"),h=(t(\"gl-mat4/scale\"),t(\"gl-vec3/normalize\")),f=[0,0,0];function 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n=t(\"array-bounds\"),i=t(\"color-normalize\"),a=t(\"update-diff\"),o=t(\"pick-by-alias\"),s=t(\"object-assign\"),l=t(\"flatten-vertex-data\"),c=t(\"to-float32\"),u=c.float32,h=c.fract32;e.exports=function(t,e){\"function\"==typeof t?(e||(e={}),e.regl=t):e=t;e.length&&(e.positions=e);if(!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");var r,c,p,d,g,m,v=t._gl,y={color:\"black\",capSize:5,lineWidth:1,opacity:1,viewport:null,range:null,offset:0,count:0,bounds:null,positions:[],errors:[]},x=[];return d=t.buffer({usage:\"dynamic\",type:\"uint8\",data:new Uint8Array(0)}),c=t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)}),p=t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)}),g=t.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array(0)}),m=t.buffer({usage:\"static\",type:\"float\",data:f}),T(e),r=t({vert:\"\\n\\t\\tprecision highp float;\\n\\n\\t\\tattribute vec2 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1);\\n\\t\\t}\\n\\t\\t\",frag:\"\\n\\t\\tprecision highp float;\\n\\n\\t\\tvarying vec4 fragColor;\\n\\n\\t\\tuniform float opacity;\\n\\n\\t\\tvoid main() {\\n\\t\\t\\tgl_FragColor = fragColor;\\n\\t\\t\\tgl_FragColor.a *= opacity;\\n\\t\\t}\\n\\t\\t\",uniforms:{range:t.prop(\"range\"),lineWidth:t.prop(\"lineWidth\"),capSize:t.prop(\"capSize\"),opacity:t.prop(\"opacity\"),scale:t.prop(\"scale\"),translate:t.prop(\"translate\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{color:{buffer:d,offset:function(t,e){return 4*e.offset},divisor:1},position:{buffer:c,offset:function(t,e){return 8*e.offset},divisor:1},positionFract:{buffer:p,offset:function(t,e){return 8*e.offset},divisor:1},error:{buffer:g,offset:function(t,e){return 16*e.offset},divisor:1},direction:{buffer:m,stride:24,offset:0},lineOffset:{buffer:m,stride:24,offset:8},capOffset:{buffer:m,stride:24,offset:16}},primitive:\"triangles\",blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\"),stencil:!1,instances:t.prop(\"count\"),count:f.length}),s(b,{update:T,draw:_,destroy:k,regl:t,gl:v,canvas:v.canvas,groups:x}),b;function b(t){t?T(t):null===t&&k(),_()}function _(e){if(\"number\"==typeof e)return w(e);e&&!Array.isArray(e)&&(e=[e]),t._refresh(),x.forEach((function(t,r){t&&(e&&(e[r]?t.draw=!0:t.draw=!1),t.draw?w(r):t.draw=!0)}))}function w(t){\"number\"==typeof t&&(t=x[t]),null!=t&&t&&t.count&&t.color&&t.opacity&&t.positions&&t.positions.length>1&&(t.scaleRatio=[t.scale[0]*t.viewport.width,t.scale[1]*t.viewport.height],r(t),t.after&&t.after(t))}function T(t){if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var e=0,r=0;if(b.groups=x=t.map((function(t,c){var u=x[c];return t?(\"function\"==typeof t?t={after:t}:\"number\"==typeof t[0]&&(t={positions:t}),t=o(t,{color:\"color colors fill\",capSize:\"capSize cap capsize cap-size\",lineWidth:\"lineWidth line-width width line thickness\",opacity:\"opacity alpha\",range:\"range dataBox\",viewport:\"viewport viewBox\",errors:\"errors error\",positions:\"positions position data points\"}),u||(x[c]=u={id:c,scale:null,translate:null,scaleFract:null,translateFract:null,draw:!0},t=s({},y,t)),a(u,t,[{lineWidth:function(t){return.5*+t},capSize:function(t){return.5*+t},opacity:parseFloat,errors:function(t){return t=l(t),r+=t.length,t},positions:function(t,r){return t=l(t,\"float64\"),r.count=Math.floor(t.length/2),r.bounds=n(t,2),r.offset=e,e+=r.count,t}},{color:function(t,e){var r=e.count;if(t||(t=\"transparent\"),!Array.isArray(t)||\"number\"==typeof t[0]){var n=t;t=Array(r);for(var a=0;a 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x) / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x) / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D dashPattern;\\nuniform float dashSize, pixelRatio, thickness, opacity, id, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashSize) * .5 + .25;\\n\\tfloat dash = texture2D(dashPattern, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:\"triangle\",elements:function(t,e){return e.triangles},offset:0,vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position, positionFract;\\n\\nuniform vec4 color;\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio, id;\\nuniform vec4 viewport;\\nuniform float opacity;\\n\\nvarying vec4 fragColor;\\n\\nconst float MAX_LINES = 256.;\\n\\nvoid main() {\\n\\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\\n\\n\\tvec2 position = position * scale + translate\\n + positionFract * scale + translateFract\\n + position * scaleFract\\n + positionFract * scaleFract;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n\\tfragColor.a *= opacity;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n\\tgl_FragColor = fragColor;\\n}\\n\"]),uniforms:{scale:t.prop(\"scale\"),color:t.prop(\"fill\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8},positionFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},m.defaults={dashes:null,join:\"miter\",miterLimit:1,thickness:10,cap:\"square\",color:\"black\",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},m.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},m.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>m.precisionThreshold||e.scale[1]*e.viewport.height>m.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=m.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},m.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,h){var d=e.passes[h];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],h=e[a+8];if(n){var f=Math.abs(s),p=Math.abs(l),d=Math.abs(h),g=Math.max(f,p,d);f===g?(s=s<0?-1:1,l=h=0):d===g?(h=h<0?-1:1,s=l=0):(l=l<0?-1:1,s=h=0)}else{var m=c(s,l,h);s/=m,l/=m,h/=m}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*h,T=c(x-=s*w,b-=l*w,_-=h*w),k=l*(_/=T)-h*(b/=T),M=h*(x/=T)-s*_,A=s*b-l*x,S=c(k,M,A);if(k/=S,M/=S,A/=S,this.center.jump(t,H,G,Y),this.radius.idle(t),this.up.jump(t,s,l,h),this.right.jump(t,x,b,_),2===a){var E=e[1],C=e[5],L=e[9],P=E*x+C*b+L*_,I=E*k+C*M+L*A;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var z=e[2],O=e[6],D=e[10],R=z*s+O*l+D*h,F=z*x+O*b+D*_,B=z*k+O*M+D*A;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var q=V[15],H=V[12]/q,G=V[13]/q,Y=V[14]/q,W=Math.exp(this.computedRadius[0]);this.center.jump(t,H-N*W,G-j*W,Y-U*W)},p.lastT=function(){return 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a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=tthis.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},{\"../main\":593,\"object-assign\":473}],584:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Islamic\",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Islamic\",epochs:[\"BH\",\"AH\"],monthNames:[\"Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' al-thani\",\"Jumada al-awwal\",\"Jumada al-thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-ahad\",\"Yawm al-ithnayn\",\"Yawm ath-thulaathaa'\",\"Yawm al-arbi'aa'\",\"Yawm al-kham\\u012bs\",\"Yawm al-jum'a\",\"Yawm as-sabt\"],dayNamesShort:[\"Aha\",\"Ith\",\"Thu\",\"Arb\",\"Kha\",\"Jum\",\"Sab\"],dayNamesMin:[\"Ah\",\"It\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{\"../main\":593,\"object-assign\":473}],585:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Julian\",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Julian\",epochs:[\"BC\",\"AD\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{\"../main\":593,\"object-assign\":473}],586:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Mayan\",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{\"\":{name:\"Mayan\",epochs:[\"\",\"\"],monthNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],monthNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],dayNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesMin:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],digits:null,dateFormat:\"YYYY.m.d\",firstDay:0,isRTL:!1,haabMonths:[\"Pop\",\"Uo\",\"Zip\",\"Zotz\",\"Tzec\",\"Xul\",\"Yaxkin\",\"Mol\",\"Chen\",\"Yax\",\"Zac\",\"Ceh\",\"Mac\",\"Kankin\",\"Muan\",\"Pax\",\"Kayab\",\"Cumku\",\"Uayeb\"],tzolkinMonths:[\"Imix\",\"Ik\",\"Akbal\",\"Kan\",\"Chicchan\",\"Cimi\",\"Manik\",\"Lamat\",\"Muluc\",\"Oc\",\"Chuen\",\"Eb\",\"Ben\",\"Ix\",\"Men\",\"Cib\",\"Caban\",\"Etznab\",\"Cauac\",\"Ahau\"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{\"../main\":593,\"object-assign\":473}],587:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar;var o=n.instance(\"gregorian\");i(a.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{\"../main\":593,\"object-assign\":473}],588:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),\"undefined\"==typeof this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),\"undefined\"==typeof this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var c=t-this.toJD(l,1,1)+1,u=c<=186?Math.ceil(c/31):Math.ceil((c-6)/30),h=t-this.toJD(l,u,1)+1;return this.newDate(l,u,h)}}),n.calendars.persian=a,n.calendars.jalali=a},{\"../main\":593,\"object-assign\":473}],590:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\"),a=n.instance();function o(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}o.prototype=new 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(e.year());return a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);t=this._t2gYear(i.year());return a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=this._t2gYear(i.year());return a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),n.calendars.thai=o},{\"../main\":593,\"object-assign\":473}],592:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var 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a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\\{0\\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return 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n=t[e],i=\"x\"===n._id.charAt(0),a=n.range;0===d&&a&&a[0]>a[1]!==i&&(h=-1),p[d++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(i?_:1)/2,pmin:0,pmax:i?r.width:r.height}]})),p.sort((function(t,e){return t[0].posref-e[0].posref||h*(e[0].traceIndex-t[0].traceIndex)}));for(;!n&&u<=f;){for(u++,n=!0,o=0;o.01&&y.pmin===x.pmin&&y.pmax===x.pmax){for(s=v.length-1;s>=0;s--)v[s].dp+=i;for(m.push.apply(m,v),p.splice(o+1,1),c=0,s=m.length-1;s>=0;s--)c+=m[s].dp;for(a=c/m.length,s=m.length-1;s>=0;s--)m[s].dp-=a;n=!1}else o++}p.forEach(g)}for(o=p.length-1;o>=0;o--){var b=p[o];for(s=b.length-1;s>=0;s--){var w=b[s],T=w.datum;T.offset=w.dp,T.del=w.del}}}(St,kt?\"xa\":\"ya\",u),L(St,kt));if(e.target&&e.target.tagName){var Et=d.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,bt);c(n.select(e.target),Et?\"pointer\":\"\")}if(!e.target||a||!function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,xt))return;xt&&t.emit(\"plotly_unhover\",{event:e,points:xt});t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:b,yaxes:w,xvals:O,yvals:D})}(t,e,r,a)}))},r.loneHover=function(t,e){var r=!0;Array.isArray(t)||(r=!1,t=[t]);var i=t.map((function(t){return{color:t.color||h.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,eventData:t.eventData||!1,hovertemplateLabels:t.hovertemplateLabels||!1}})),a=n.select(e.container),o=e.outerContainer?n.select(e.outerContainer):a,s={hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||h.background,container:a,outerContainer:o},l=E(i,s,e.gd),c=0,u=0;return l.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5([\\s\\S]*)<\\/extra>/;function E(t,e,r){var i=r._fullLayout,a=e.hovermode,s=e.rotateLabels,c=e.bgColor,f=e.container,p=e.outerContainer,d=e.commonLabelOpts||{},b=e.fontFamily||m.HOVERFONT,_=e.fontSize||m.HOVERFONTSIZE,w=t[0],T=w.xa,S=w.ya,E=\"y\"===a.charAt(0)?\"yLabel\":\"xLabel\",L=w[E],P=(String(L)||\"\").split(\" \")[0],I=p.node().getBoundingClientRect(),z=I.top,O=I.width,D=I.height,R=void 0!==L&&w.distance<=e.hoverdistance&&(\"x\"===a||\"y\"===a);if(R){var F,B,N=!0;for(F=0;Fi.width-E?(v=i.width-E,s.attr(\"d\",\"M\"+(E-k)+\",0L\"+E+\",\"+A+k+\"v\"+A+(2*M+x.height)+\"H-\"+E+\"V\"+A+k+\"H\"+(E-2*k)+\"Z\")):s.attr(\"d\",\"M0,0L\"+k+\",\"+A+k+\"H\"+(M+x.width/2)+\"v\"+A+(2*M+x.height)+\"H-\"+(M+x.width/2)+\"V\"+A+k+\"H-\"+k+\"Z\")}else{var C,P,I;\"right\"===S.side?(C=\"start\",P=1,I=\"\",v=T._offset+T._length):(C=\"end\",P=-1,I=\"-\",v=T._offset),y=S._offset+(w.y0+w.y1)/2,c.attr(\"text-anchor\",C),s.attr(\"d\",\"M0,0L\"+I+k+\",\"+k+\"V\"+(M+x.height/2)+\"h\"+I+(2*M+x.width)+\"V-\"+(M+x.height/2)+\"H\"+I+k+\"V-\"+k+\"Z\");var O,D=x.height/2,R=z-x.top-D,F=\"clip\"+i._uid+\"commonlabel\"+S._id;if(v=0?$-=rt:$+=2*M;var nt=et.height+2*M,it=Q+nt>=D;return nt<=D&&(Q<=z?Q=S._offset+2*M:it&&(Q=D-nt)),tt.attr(\"transform\",\"translate(\"+$+\",\"+Q+\")\"),tt}var at=f.selectAll(\"g.hovertext\").data(t,(function(t){return A(t)}));return at.enter().append(\"g\").classed(\"hovertext\",!0).each((function(){var t=n.select(this);t.append(\"rect\").call(h.fill,h.addOpacity(c,.8)),t.append(\"text\").classed(\"name\",!0),t.append(\"path\").style(\"stroke-width\",\"1px\"),t.append(\"text\").classed(\"nums\",!0).call(u.font,b,_)})),at.exit().remove(),at.each((function(t){var e=n.select(this).attr(\"transform\",\"\"),o=t.color;Array.isArray(o)&&(o=o[t.eventData[0].pointNumber]);var f=t.bgcolor||o,p=h.combine(h.opacity(f)?f:h.defaultLine,c),d=h.combine(h.opacity(o)?o:h.defaultLine,c),g=t.borderColor||h.contrast(p),m=C(t,R,a,i,L,e),v=m[0],y=m[1],w=e.select(\"text.nums\").call(u.font,t.fontFamily||b,t.fontSize||_,t.fontColor||g).text(v).attr(\"data-notex\",1).call(l.positionText,0,0).call(l.convertToTspans,r),T=e.select(\"text.name\"),A=0,S=0;if(y&&y!==v){T.call(u.font,t.fontFamily||b,t.fontSize||_,d).text(y).attr(\"data-notex\",1).call(l.positionText,0,0).call(l.convertToTspans,r);var E=T.node().getBoundingClientRect();A=E.width+2*M,S=E.height+2*M}else T.remove(),e.select(\"rect\").remove();e.select(\"path\").style({fill:p,stroke:g});var P,I,F=w.node().getBoundingClientRect(),B=t.xa._offset+(t.x0+t.x1)/2,N=t.ya._offset+(t.y0+t.y1)/2,j=Math.abs(t.x1-t.x0),U=Math.abs(t.y1-t.y0),V=F.width+k+M+A;if(t.ty0=z-F.top,t.bx=F.width+2*M,t.by=Math.max(F.height+2*M,S),t.anchor=\"start\",t.txwidth=F.width,t.tx2width=A,t.offset=0,s)t.pos=B,P=N+U/2+V<=D,I=N-U/2-V>=0,\"top\"!==t.idealAlign&&P||!I?P?(N+=U/2,t.anchor=\"start\"):t.anchor=\"middle\":(N-=U/2,t.anchor=\"end\");else if(t.pos=N,P=B+j/2+V<=O,I=B-j/2-V>=0,\"left\"!==t.idealAlign&&P||!I)if(P)B+=j/2,t.anchor=\"start\";else{t.anchor=\"middle\";var q=V/2,H=B+q-O,G=B-q;H>0&&(B-=H),G<0&&(B+=-G)}else B-=j/2,t.anchor=\"end\";w.attr(\"text-anchor\",t.anchor),A&&T.attr(\"text-anchor\",t.anchor),e.attr(\"transform\",\"translate(\"+B+\",\"+N+\")\"+(s?\"rotate(\"+x+\")\":\"\"))})),at}function C(t,e,r,n,i,a){var s=\"\",l=\"\";void 0!==t.nameOverride&&(t.name=t.nameOverride),t.name&&(t.trace._meta&&(t.name=o.templateString(t.name,t.trace._meta)),s=O(t.name,t.nameLength)),void 0!==t.zLabel?(void 0!==t.xLabel&&(l+=\"x: \"+t.xLabel+\"
\"),void 0!==t.yLabel&&(l+=\"y: \"+t.yLabel+\"
\"),\"choropleth\"!==t.trace.type&&\"choroplethmapbox\"!==t.trace.type&&(l+=(l?\"z: \":\"\")+t.zLabel)):e&&t[r.charAt(0)+\"Label\"]===i?l=t[(\"x\"===r.charAt(0)?\"y\":\"x\")+\"Label\"]||\"\":void 0===t.xLabel?void 0!==t.yLabel&&\"scattercarpet\"!==t.trace.type&&(l=t.yLabel):l=void 0===t.yLabel?t.xLabel:\"(\"+t.xLabel+\", \"+t.yLabel+\")\",!t.text&&0!==t.text||Array.isArray(t.text)||(l+=(l?\"
\":\"\")+t.text),void 0!==t.extraText&&(l+=(l?\"
\":\"\")+t.extraText),a&&\"\"===l&&!t.hovertemplate&&(\"\"===s&&a.remove(),l=s);var c=n._d3locale,u=t.hovertemplate||!1,h=t.hovertemplateLabels||t,f=t.eventData[0]||{};return u&&(l=(l=o.hovertemplateString(u,h,c,f,t.trace._meta)).replace(S,(function(e,r){return s=O(r,t.nameLength),\"\"}))),[l,s]}function L(t,e){t.each((function(t){var r=n.select(this);if(t.del)return r.remove();var i=r.select(\"text.nums\"),a=t.anchor,o=\"end\"===a?-1:1,s={start:1,end:-1,middle:0}[a],c=s*(k+M),h=c+s*(t.txwidth+M),f=0,p=t.offset;\"middle\"===a&&(c-=t.tx2width/2,h+=t.txwidth/2+M),e&&(p*=-T,f=t.offset*w),r.select(\"path\").attr(\"d\",\"middle\"===a?\"M-\"+(t.bx/2+t.tx2width/2)+\",\"+(p-t.by/2)+\"h\"+t.bx+\"v\"+t.by+\"h-\"+t.bx+\"Z\":\"M0,0L\"+(o*k+f)+\",\"+(k+p)+\"v\"+(t.by/2-k)+\"h\"+o*t.bx+\"v-\"+t.by+\"H\"+(o*k+f)+\"V\"+(p-k)+\"Z\");var d=c+f,g=p+t.ty0-t.by/2+M,m=t.textAlign||\"auto\";\"auto\"!==m&&(\"left\"===m&&\"start\"!==a?(i.attr(\"text-anchor\",\"start\"),d=\"middle\"===a?-t.bx/2-t.tx2width/2+M:-t.bx-M):\"right\"===m&&\"end\"!==a&&(i.attr(\"text-anchor\",\"end\"),d=\"middle\"===a?t.bx/2-t.tx2width/2-M:t.bx+M)),i.call(l.positionText,d,g),t.tx2width&&(r.select(\"text.name\").call(l.positionText,h+s*M+f,p+t.ty0-t.by/2+M),r.select(\"rect\").call(u.setRect,h+(s-1)*t.tx2width/2+f,p-t.by/2-1,t.tx2width,t.by+2))}))}function P(t,e){var r=t.index,n=t.trace||{},a=t.cd[0],s=t.cd[r]||{};function l(t){return t||i(t)&&0===t}var c=Array.isArray(r)?function(t,e){var i=o.castOption(a,r,t);return l(i)?i:o.extractOption({},n,\"\",e)}:function(t,e){return o.extractOption(s,n,t,e)};function u(e,r,n){var i=c(r,n);l(i)&&(t[e]=i)}if(u(\"hoverinfo\",\"hi\",\"hoverinfo\"),u(\"bgcolor\",\"hbg\",\"hoverlabel.bgcolor\"),u(\"borderColor\",\"hbc\",\"hoverlabel.bordercolor\"),u(\"fontFamily\",\"htf\",\"hoverlabel.font.family\"),u(\"fontSize\",\"hts\",\"hoverlabel.font.size\"),u(\"fontColor\",\"htc\",\"hoverlabel.font.color\"),u(\"nameLength\",\"hnl\",\"hoverlabel.namelength\"),u(\"textAlign\",\"hta\",\"hoverlabel.align\"),t.posref=\"y\"===e||\"closest\"===e&&\"h\"===n.orientation?t.xa._offset+(t.x0+t.x1)/2:t.ya._offset+(t.y0+t.y1)/2,t.x0=o.constrain(t.x0,0,t.xa._length),t.x1=o.constrain(t.x1,0,t.xa._length),t.y0=o.constrain(t.y0,0,t.ya._length),t.y1=o.constrain(t.y1,0,t.ya._length),void 0!==t.xLabelVal&&(t.xLabel=\"xLabel\"in t?t.xLabel:p.hoverLabelText(t.xa,t.xLabelVal),t.xVal=t.xa.c2d(t.xLabelVal)),void 0!==t.yLabelVal&&(t.yLabel=\"yLabel\"in t?t.yLabel:p.hoverLabelText(t.ya,t.yLabelVal),t.yVal=t.ya.c2d(t.yLabelVal)),void 0!==t.zLabelVal&&void 0===t.zLabel&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)||\"log\"===t.xa.type&&t.xerr<=0)){var h=p.tickText(t.xa,t.xa.c2l(t.xerr),\"hover\").text;void 0!==t.xerrneg?t.xLabel+=\" +\"+h+\" / -\"+p.tickText(t.xa,t.xa.c2l(t.xerrneg),\"hover\").text:t.xLabel+=\" \\xb1 \"+h,\"x\"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)||\"log\"===t.ya.type&&t.yerr<=0)){var f=p.tickText(t.ya,t.ya.c2l(t.yerr),\"hover\").text;void 0!==t.yerrneg?t.yLabel+=\" +\"+f+\" / -\"+p.tickText(t.ya,t.ya.c2l(t.yerrneg),\"hover\").text:t.yLabel+=\" \\xb1 \"+f,\"y\"===e&&(t.distance+=1)}var d=t.hoverinfo||t.trace.hoverinfo;return d&&\"all\"!==d&&(-1===(d=Array.isArray(d)?d:d.split(\"+\")).indexOf(\"x\")&&(t.xLabel=void 0),-1===d.indexOf(\"y\")&&(t.yLabel=void 0),-1===d.indexOf(\"z\")&&(t.zLabel=void 0),-1===d.indexOf(\"text\")&&(t.text=void 0),-1===d.indexOf(\"name\")&&(t.name=void 0)),t}function I(t,e,r){var n,i,o=r.container,s=r.fullLayout,l=s._size,c=r.event,f=!!e.hLinePoint,d=!!e.vLinePoint;if(o.selectAll(\".spikeline\").remove(),d||f){var g=h.combine(s.plot_bgcolor,s.paper_bgcolor);if(f){var m,v,y=e.hLinePoint;n=y&&y.xa,\"cursor\"===(i=y&&y.ya).spikesnap?(m=c.pointerX,v=c.pointerY):(m=n._offset+y.x,v=i._offset+y.y);var x,b,_=a.readability(y.color,g)<1.5?h.contrast(g):y.color,w=i.spikemode,T=i.spikethickness,k=i.spikecolor||_,M=p.getPxPosition(t,i);if(-1!==w.indexOf(\"toaxis\")||-1!==w.indexOf(\"across\")){if(-1!==w.indexOf(\"toaxis\")&&(x=M,b=m),-1!==w.indexOf(\"across\")){var A=i._counterDomainMin,S=i._counterDomainMax;\"free\"===i.anchor&&(A=Math.min(A,i.position),S=Math.max(S,i.position)),x=l.l+A*l.w,b=l.l+S*l.w}o.insert(\"line\",\":first-child\").attr({x1:x,x2:b,y1:v,y2:v,\"stroke-width\":T,stroke:k,\"stroke-dasharray\":u.dashStyle(i.spikedash,T)}).classed(\"spikeline\",!0).classed(\"crisp\",!0),o.insert(\"line\",\":first-child\").attr({x1:x,x2:b,y1:v,y2:v,\"stroke-width\":T+2,stroke:g}).classed(\"spikeline\",!0).classed(\"crisp\",!0)}-1!==w.indexOf(\"marker\")&&o.insert(\"circle\",\":first-child\").attr({cx:M+(\"right\"!==i.side?T:-T),cy:v,r:T,fill:k}).classed(\"spikeline\",!0)}if(d){var E,C,L=e.vLinePoint;n=L&&L.xa,i=L&&L.ya,\"cursor\"===n.spikesnap?(E=c.pointerX,C=c.pointerY):(E=n._offset+L.x,C=i._offset+L.y);var P,I,z=a.readability(L.color,g)<1.5?h.contrast(g):L.color,O=n.spikemode,D=n.spikethickness,R=n.spikecolor||z,F=p.getPxPosition(t,n);if(-1!==O.indexOf(\"toaxis\")||-1!==O.indexOf(\"across\")){if(-1!==O.indexOf(\"toaxis\")&&(P=F,I=C),-1!==O.indexOf(\"across\")){var B=n._counterDomainMin,N=n._counterDomainMax;\"free\"===n.anchor&&(B=Math.min(B,n.position),N=Math.max(N,n.position)),P=l.t+(1-N)*l.h,I=l.t+(1-B)*l.h}o.insert(\"line\",\":first-child\").attr({x1:E,x2:E,y1:P,y2:I,\"stroke-width\":D,stroke:R,\"stroke-dasharray\":u.dashStyle(n.spikedash,D)}).classed(\"spikeline\",!0).classed(\"crisp\",!0),o.insert(\"line\",\":first-child\").attr({x1:E,x2:E,y1:P,y2:I,\"stroke-width\":D+2,stroke:g}).classed(\"spikeline\",!0).classed(\"crisp\",!0)}-1!==O.indexOf(\"marker\")&&o.insert(\"circle\",\":first-child\").attr({cx:E,cy:F-(\"top\"!==n.side?D:-D),r:D,fill:R}).classed(\"spikeline\",!0)}}}function z(t,e){return!e||(e.vLinePoint!==t._spikepoints.vLinePoint||e.hLinePoint!==t._spikepoints.hLinePoint)}function O(t,e){return l.plainText(t||\"\",{len:e,allowedTags:[\"br\",\"sub\",\"sup\",\"b\",\"i\",\"em\"]})}},{\"../../lib\":750,\"../../lib/events\":739,\"../../lib/override_cursor\":761,\"../../lib/svg_text_utils\":774,\"../../plots/cartesian/axes\":799,\"../../registry\":882,\"../color\":615,\"../dragelement\":634,\"../drawing\":637,\"../legend/defaults\":667,\"../legend/draw\":668,\"./constants\":649,\"./helpers\":651,d3:169,\"fast-isnumeric\":241,tinycolor2:548}],653:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\"),a=t(\"./helpers\").isUnifiedHover;e.exports=function(t,e,r,o){function 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s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 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a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":773}],849:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var h=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var f=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/f)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d/g,\" \"));l[c]=p,u.tickmode=h}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;ar.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect(),a=i.width,o=i.height;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+a+\" \"+o),n.setAttributeNS(null,\"width\",a),n.setAttributeNS(null,\"height\",o),x(e),e.glplot.axes.update(e.axesOptions);for(var s,l=Object.keys(e.traces),c=null,u=e.glplot.selection,d=0;d\")):\"isosurface\"===t.type||\"volume\"===t.type?(w.valueLabel=f.tickText(e._mockAxis,e._mockAxis.d2l(u.traceCoordinate[3]),\"hover\").text,A.push(\"value: \"+w.valueLabel),u.textLabel&&A.push(u.textLabel),y=A.join(\"
\")):y=u.textLabel;var S={x:u.traceCoordinate[0],y:u.traceCoordinate[1],z:u.traceCoordinate[2],data:b._input,fullData:b,curveNumber:b.index,pointNumber:_};p.appendArrayPointValue(S,b,_),t._module.eventData&&(S=b._module.eventData(S,u,b,{},_));var E={points:[S]};e.fullSceneLayout.hovermode&&p.loneHover({trace:b,x:(.5+.5*v[0]/v[3])*a,y:(.5-.5*v[1]/v[3])*o,xLabel:w.xLabel,yLabel:w.yLabel,zLabel:w.zLabel,text:y,name:c.name,color:p.castHoverOption(b,_,\"bgcolor\")||c.color,borderColor:p.castHoverOption(b,_,\"bordercolor\"),fontFamily:p.castHoverOption(b,_,\"font.family\"),fontSize:p.castHoverOption(b,_,\"font.size\"),fontColor:p.castHoverOption(b,_,\"font.color\"),nameLength:p.castHoverOption(b,_,\"namelength\"),textAlign:p.castHoverOption(b,_,\"align\"),hovertemplate:h.castOption(b,_,\"hovertemplate\"),hovertemplateLabels:h.extendFlat({},S,w),eventData:[S]},{container:n,gd:r}),u.buttons&&u.distance<5?r.emit(\"plotly_click\",E):r.emit(\"plotly_hover\",E),s=E}else p.loneUnhover(n),r.emit(\"plotly_unhover\",s);e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):h.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var T=[\"xaxis\",\"yaxis\",\"zaxis\"];function k(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=T[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+\"calendar\"],u=e[\"_\"+o+\"length\"];if(h.isArrayOrTypedArray(l))for(var f,p=0;p<(u||l.length);p++)if(h.isArrayOrTypedArray(l[p]))for(var d=0;dm[1][a])m[0][a]=-1,m[1][a]=1;else{var C=m[1][a]-m[0][a];m[0][a]-=C/32,m[1][a]+=C/32}if(\"reversed\"===s.autorange){var L=m[0][a];m[0][a]=m[1][a],m[1][a]=L}}else{var P=s.range;m[0][a]=s.r2l(P[0]),m[1][a]=s.r2l(P[1])}m[0][a]===m[1][a]&&(m[0][a]-=1,m[1][a]+=1),v[a]=m[1][a]-m[0][a],this.glplot.setBounds(a,{min:m[0][a]*f[a],max:m[1][a]*f[a]})}var I=c.aspectmode;if(\"cube\"===I)g=[1,1,1];else if(\"manual\"===I){var z=c.aspectratio;g=[z.x,z.y,z.z]}else{if(\"auto\"!==I&&\"data\"!==I)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var O=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[T[a]]).type];O[a]=Math.pow(D.acc,1/D.count)/f[a]}g=\"data\"===I||Math.max.apply(null,O)/Math.min.apply(null,O)<=4?O:[1,1,1]}c.aspectratio.x=u.aspectratio.x=g[0],c.aspectratio.y=u.aspectratio.y=g[1],c.aspectratio.z=u.aspectratio.z=g[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position=\"absolute\",B.left=F.l+R.x[0]*F.w+\"px\",B.top=F.t+(1-R.y[1])*F.h+\"px\",B.width=F.w*(R.x[1]-R.x[0])+\"px\",B.height=F.h*(R.y[1]-R.y[0])+\"px\"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),\"orthographic\"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=h.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=h.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),f=l||c;if(f){var p={};if(l&&(e=this.getCamera(),n=(r=h.nestedProperty(t,this.id+\".camera\")).get(),p[this.id+\".camera\"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=h.nestedProperty(t,this.id+\".aspectratio\")).get(),p[this.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,s._preGUI,p),l)r.set(e),h.nestedProperty(s,this.id+\".camera\").set(e);if(c)a.set(i),h.nestedProperty(s,this.id+\".aspectratio\").set(i),this.glplot.redraw()}return f},w.updateFx=function(t,e){var r=this.camera;if(r)if(\"orbit\"===t)r.mode=\"orbit\",r.keyBindingMode=\"rotate\";else if(\"turntable\"===t){r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+\".camera.up\",f={x:0,y:0,z:1},p={};p[c]=f;var d=n.layout;u.call(\"_storeDirectGUIEdit\",d,i._preGUI,p),a.up=f,h.nestedProperty(d,c).set(f)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t=\"png\"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement(\"canvas\");o.width=r,o.height=i;var s,l=o.getContext(\"2d\"),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case\"jpeg\":s=o.toDataURL(\"image/jpeg\");break;case\"webp\":s=o.toDataURL(\"image/webp\");break;default:s=o.toDataURL(\"image/png\")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[T[t]];f.setConvert(e,this.fullLayout),e.setScale=h.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},f.setConvert(this._mockAxis,t)},e.exports=_},{\"../../components/fx\":655,\"../../lib\":750,\"../../lib/show_no_webgl_msg\":771,\"../../lib/str2rgbarray\":773,\"../../plots/cartesian/axes\":799,\"../../registry\":882,\"./layout/convert\":845,\"./layout/spikes\":848,\"./layout/tick_marks\":849,\"./project\":850,\"gl-plot3d\":301,\"has-passive-events\":415,\"is-mobile\":441,\"webgl-context\":578}],852:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a\\xa9 OpenStreetMap',tiles:[\"https://a.tile.openstreetmap.org/{z}/{x}/{y}.png\",\"https://b.tile.openstreetmap.org/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-osm-tiles\",type:\"raster\",source:\"plotly-osm-tiles\",minzoom:0,maxzoom:22}]},\"white-bg\":{id:\"white-bg\",version:8,sources:{},layers:[{id:\"white-bg\",type:\"background\",paint:{\"background-color\":\"#FFFFFF\"},minzoom:0,maxzoom:22}]},\"carto-positron\":{id:\"carto-positron\",version:8,sources:{\"plotly-carto-positron\":{type:\"raster\",attribution:'\\xa9 CARTO',tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-positron\",type:\"raster\",source:\"plotly-carto-positron\",minzoom:0,maxzoom:22}]},\"carto-darkmatter\":{id:\"carto-darkmatter\",version:8,sources:{\"plotly-carto-darkmatter\":{type:\"raster\",attribution:'\\xa9 CARTO',tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-darkmatter\",type:\"raster\",source:\"plotly-carto-darkmatter\",minzoom:0,maxzoom:22}]},\"stamen-terrain\":{id:\"stamen-terrain\",version:8,sources:{\"plotly-stamen-terrain\":{type:\"raster\",attribution:'Map tiles by Stamen Design, under CC BY 3.0 | Data by OpenStreetMap, under ODbL.',tiles:[\"https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-terrain\",type:\"raster\",source:\"plotly-stamen-terrain\",minzoom:0,maxzoom:22}]},\"stamen-toner\":{id:\"stamen-toner\",version:8,sources:{\"plotly-stamen-toner\":{type:\"raster\",attribution:'Map tiles by Stamen Design, under CC BY 3.0 | Data by OpenStreetMap, under ODbL.',tiles:[\"https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-toner\",type:\"raster\",source:\"plotly-stamen-toner\",minzoom:0,maxzoom:22}]},\"stamen-watercolor\":{id:\"stamen-watercolor\",version:8,sources:{\"plotly-stamen-watercolor\":{type:\"raster\",attribution:'Map tiles by Stamen Design, under CC BY 3.0 | Data by OpenStreetMap, under CC BY SA.',tiles:[\"https://stamen-tiles.a.ssl.fastly.net/watercolor/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-stamen-watercolor\",type:\"raster\",source:\"plotly-stamen-watercolor\",minzoom:0,maxzoom:22}]}},i=Object.keys(n);e.exports={requiredVersion:\"1.10.1\",styleUrlPrefix:\"mapbox://styles/mapbox/\",styleUrlSuffix:\"v9\",styleValuesMapbox:[\"basic\",\"streets\",\"outdoors\",\"light\",\"dark\",\"satellite\",\"satellite-streets\"],styleValueDflt:\"basic\",stylesNonMapbox:n,styleValuesNonMapbox:i,traceLayerPrefix:\"plotly-trace-layer-\",layoutLayerPrefix:\"plotly-layout-layer-\",wrongVersionErrorMsg:[\"Your custom plotly.js bundle is not using the correct mapbox-gl version\",\"Please install mapbox-gl@1.10.1.\"].join(\"\\n\"),noAccessTokenErrorMsg:[\"Missing Mapbox access token.\",\"Mapbox trace type require a Mapbox access token to be registered.\",\"For example:\",\" Plotly.plot(gd, data, layout, { mapboxAccessToken: 'my-access-token' });\",\"More info here: https://www.mapbox.com/help/define-access-token/\"].join(\"\\n\"),missingStyleErrorMsg:[\"No valid mapbox style found, please set `mapbox.style` to one of:\",i.join(\", \"),\"or register a Mapbox access token to use a Mapbox-served style.\"].join(\"\\n\"),multipleTokensErrorMsg:[\"Set multiple mapbox access token across different mapbox subplot,\",\"using first token found as mapbox-gl does not allow multipleaccess tokens on the same page.\"].join(\"\\n\"),mapOnErrorMsg:\"Mapbox error.\",mapboxLogo:{path0:\"m 10.5,1.24 c -5.11,0 -9.25,4.15 -9.25,9.25 0,5.1 4.15,9.25 9.25,9.25 5.1,0 9.25,-4.15 9.25,-9.25 0,-5.11 -4.14,-9.25 -9.25,-9.25 z m 4.39,11.53 c -1.93,1.93 -4.78,2.31 -6.7,2.31 -0.7,0 -1.41,-0.05 -2.1,-0.16 0,0 -1.02,-5.64 2.14,-8.81 0.83,-0.83 1.95,-1.28 3.13,-1.28 1.27,0 2.49,0.51 3.39,1.42 1.84,1.84 1.89,4.75 0.14,6.52 z\",path1:\"M 10.5,-0.01 C 4.7,-0.01 0,4.7 0,10.49 c 0,5.79 4.7,10.5 10.5,10.5 5.8,0 10.5,-4.7 10.5,-10.5 C 20.99,4.7 16.3,-0.01 10.5,-0.01 Z m 0,19.75 c -5.11,0 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parseFloat(t.toPrecision(12))})),s=n.scale.linear().domain(C.slice(0,2)).range(\"clockwise\"===f.direction?[0,360]:[360,0]),u.layout.angularAxis.domain=s.domain(),u.layout.angularAxis.endPadding=A?E:0,\"undefined\"==typeof(t=n.select(this).select(\"svg.chart-root\"))||t.empty()){var z=(new DOMParser).parseFromString(\"' + '' + '' + '' + '' + '' + '' + '' + '' + '' + '' + '' + '' + '' + '' + '\",\"application/xml\"),O=this.appendChild(this.ownerDocument.importNode(z.documentElement,!0));t=n.select(O)}t.select(\".guides-group\").style({\"pointer-events\":\"none\"}),t.select(\".angular.axis-group\").style({\"pointer-events\":\"none\"}),t.select(\".radial.axis-group\").style({\"pointer-events\":\"none\"});var D,R=t.select(\".chart-group\"),F={fill:\"none\",stroke:f.tickColor},B={\"font-size\":f.font.size,\"font-family\":f.font.family,fill:f.font.color,\"text-shadow\":[\"-1px 0px\",\"1px -1px\",\"-1px 1px\",\"1px 1px\"].map((function(t,e){return\" \"+t+\" 0 \"+f.font.outlineColor})).join(\",\")};if(f.showLegend){D=t.select(\".legend-group\").attr({transform:\"translate(\"+[x,f.margin.top]+\")\"}).style({display:\"block\"});var N=p.map((function(t,e){var r=o.util.cloneJson(t);return r.symbol=\"DotPlot\"===t.geometry?t.dotType||\"circle\":\"LinePlot\"!=t.geometry?\"square\":\"line\",r.visibleInLegend=\"undefined\"==typeof t.visibleInLegend||t.visibleInLegend,r.color=\"LinePlot\"===t.geometry?t.strokeColor:t.color,r}));o.Legend().config({data:p.map((function(t,e){return t.name||\"Element\"+e})),legendConfig:i({},o.Legend.defaultConfig().legendConfig,{container:D,elements:N,reverseOrder:f.legend.reverseOrder})})();var j=D.node().getBBox();x=Math.min(f.width-j.width-f.margin.left-f.margin.right,f.height-f.margin.top-f.margin.bottom)/2,x=Math.max(10,x),_=[f.margin.left+x,f.margin.top+x],r.range([0,x]),u.layout.radialAxis.domain=r.domain(),D.attr(\"transform\",\"translate(\"+[_[0]+x,_[1]-x]+\")\")}else D=t.select(\".legend-group\").style({display:\"none\"});t.attr({width:f.width,height:f.height}).style({opacity:f.opacity}),R.attr(\"transform\",\"translate(\"+_+\")\").style({cursor:\"crosshair\"});var U=[(f.width-(f.margin.left+f.margin.right+2*x+(j?j.width:0)))/2,(f.height-(f.margin.top+f.margin.bottom+2*x))/2];if(U[0]=Math.max(0,U[0]),U[1]=Math.max(0,U[1]),t.select(\".outer-group\").attr(\"transform\",\"translate(\"+U+\")\"),f.title&&f.title.text){var V=t.select(\"g.title-group text\").style(B).text(f.title.text),q=V.node().getBBox();V.attr({x:_[0]-q.width/2,y:_[1]-x-20})}var H=t.select(\".radial.axis-group\");if(f.radialAxis.gridLinesVisible){var G=H.selectAll(\"circle.grid-circle\").data(r.ticks(5));G.enter().append(\"circle\").attr({class:\"grid-circle\"}).style(F),G.attr(\"r\",r),G.exit().remove()}H.select(\"circle.outside-circle\").attr({r:x}).style(F);var Y=t.select(\"circle.background-circle\").attr({r:x}).style({fill:f.backgroundColor,stroke:f.stroke});function W(t,e){return s(t)%360+f.orientation}if(f.radialAxis.visible){var Z=n.svg.axis().scale(r).ticks(5).tickSize(5);H.call(Z).attr({transform:\"rotate(\"+f.radialAxis.orientation+\")\"}),H.selectAll(\".domain\").style(F),H.selectAll(\"g>text\").text((function(t,e){return this.textContent+f.radialAxis.ticksSuffix})).style(B).style({\"text-anchor\":\"start\"}).attr({x:0,y:0,dx:0,dy:0,transform:function(t,e){return\"horizontal\"===f.radialAxis.tickOrientation?\"rotate(\"+-f.radialAxis.orientation+\") translate(\"+[0,B[\"font-size\"]]+\")\":\"translate(\"+[0,B[\"font-size\"]]+\")\"}}),H.selectAll(\"g>line\").style({stroke:\"black\"})}var X=t.select(\".angular.axis-group\").selectAll(\"g.angular-tick\").data(I),J=X.enter().append(\"g\").classed(\"angular-tick\",!0);X.attr({transform:function(t,e){return\"rotate(\"+W(t)+\")\"}}).style({display:f.angularAxis.visible?\"block\":\"none\"}),X.exit().remove(),J.append(\"line\").classed(\"grid-line\",!0).classed(\"major\",(function(t,e){return e%(f.minorTicks+1)==0})).classed(\"minor\",(function(t,e){return!(e%(f.minorTicks+1)==0)})).style(F),J.selectAll(\".minor\").style({stroke:f.minorTickColor}),X.select(\"line.grid-line\").attr({x1:f.tickLength?x-f.tickLength:0,x2:x}).style({display:f.angularAxis.gridLinesVisible?\"block\":\"none\"}),J.append(\"text\").classed(\"axis-text\",!0).style(B);var K=X.select(\"text.axis-text\").attr({x:x+f.labelOffset,dy:a+\"em\",transform:function(t,e){var r=W(t),n=x+f.labelOffset,i=f.angularAxis.tickOrientation;return\"horizontal\"==i?\"rotate(\"+-r+\" \"+n+\" 0)\":\"radial\"==i?r<270&&r>90?\"rotate(180 \"+n+\" 0)\":null:\"rotate(\"+(r<=180&&r>0?-90:90)+\" \"+n+\" 0)\"}}).style({\"text-anchor\":\"middle\",display:f.angularAxis.labelsVisible?\"block\":\"none\"}).text((function(t,e){return e%(f.minorTicks+1)!=0?\"\":w?w[t]+f.angularAxis.ticksSuffix:t+f.angularAxis.ticksSuffix})).style(B);f.angularAxis.rewriteTicks&&K.text((function(t,e){return e%(f.minorTicks+1)!=0?\"\":f.angularAxis.rewriteTicks(this.textContent,e)}));var Q=n.max(R.selectAll(\".angular-tick text\")[0].map((function(t,e){return t.getCTM().e+t.getBBox().width})));D.attr({transform:\"translate(\"+[x+Q,f.margin.top]+\")\"});var $=t.select(\"g.geometry-group\").selectAll(\"g\").size()>0,tt=t.select(\"g.geometry-group\").selectAll(\"g.geometry\").data(p);if(tt.enter().append(\"g\").attr({class:function(t,e){return\"geometry geometry\"+e}}),tt.exit().remove(),p[0]||$){var et=[];p.forEach((function(t,e){var n={};n.radialScale=r,n.angularScale=s,n.container=tt.filter((function(t,r){return r==e})),n.geometry=t.geometry,n.orientation=f.orientation,n.direction=f.direction,n.index=e,et.push({data:t,geometryConfig:n})}));var rt=n.nest().key((function(t,e){return\"undefined\"!=typeof t.data.groupId||\"unstacked\"})).entries(et),nt=[];rt.forEach((function(t,e){\"unstacked\"===t.key?nt=nt.concat(t.values.map((function(t,e){return[t]}))):nt.push(t.values)})),nt.forEach((function(t,e){var r;r=Array.isArray(t)?t[0].geometryConfig.geometry:t.geometryConfig.geometry;var n=t.map((function(t,e){return i(o[r].defaultConfig(),t)}));o[r]().config(n)()}))}var it,at,ot=t.select(\".guides-group\"),st=t.select(\".tooltips-group\"),lt=o.tooltipPanel().config({container:st,fontSize:8})(),ct=o.tooltipPanel().config({container:st,fontSize:8})(),ut=o.tooltipPanel().config({container:st,hasTick:!0})();if(!k){var ht=ot.select(\"line\").attr({x1:0,y1:0,y2:0}).style({stroke:\"grey\",\"pointer-events\":\"none\"});R.on(\"mousemove.angular-guide\",(function(t,e){var r=o.util.getMousePos(Y).angle;ht.attr({x2:-x,transform:\"rotate(\"+r+\")\"}).style({opacity:.5});var n=(r+180+360-f.orientation)%360;it=s.invert(n);var i=o.util.convertToCartesian(x+12,r+180);lt.text(o.util.round(it)).move([i[0]+_[0],i[1]+_[1]])})).on(\"mouseout.angular-guide\",(function(t,e){ot.select(\"line\").style({opacity:0})}))}var ft=ot.select(\"circle\").style({stroke:\"grey\",fill:\"none\"});R.on(\"mousemove.radial-guide\",(function(t,e){var n=o.util.getMousePos(Y).radius;ft.attr({r:n}).style({opacity:.5}),at=r.invert(o.util.getMousePos(Y).radius);var i=o.util.convertToCartesian(n,f.radialAxis.orientation);ct.text(o.util.round(at)).move([i[0]+_[0],i[1]+_[1]])})).on(\"mouseout.radial-guide\",(function(t,e){ft.style({opacity:0}),ut.hide(),lt.hide(),ct.hide()})),t.selectAll(\".geometry-group .mark\").on(\"mouseover.tooltip\",(function(e,r){var i=n.select(this),a=this.style.fill,s=\"black\",l=this.style.opacity||1;if(i.attr({\"data-opacity\":l}),a&&\"none\"!==a){i.attr({\"data-fill\":a}),s=n.hsl(a).darker().toString(),i.style({fill:s,opacity:1});var c={t:o.util.round(e[0]),r:o.util.round(e[1])};k&&(c.t=w[e[0]]);var u=\"t: \"+c.t+\", r: \"+c.r,h=this.getBoundingClientRect(),f=t.node().getBoundingClientRect(),p=[h.left+h.width/2-U[0]-f.left,h.top+h.height/2-U[1]-f.top];ut.config({color:s}).text(u),ut.move(p)}else a=this.style.stroke||\"black\",i.attr({\"data-stroke\":a}),s=n.hsl(a).darker().toString(),i.style({stroke:s,opacity:1})})).on(\"mousemove.tooltip\",(function(t,e){if(0!=n.event.which)return!1;n.select(this).attr(\"data-fill\")&&ut.show()})).on(\"mouseout.tooltip\",(function(t,e){ut.hide();var r=n.select(this),i=r.attr(\"data-fill\");i?r.style({fill:i,opacity:r.attr(\"data-opacity\")}):r.style({stroke:r.attr(\"data-stroke\"),opacity:r.attr(\"data-opacity\")})}))}))}(c),this},f.config=function(t){if(!arguments.length)return l;var e=o.util.cloneJson(t);return e.data.forEach((function(t,e){l.data[e]||(l.data[e]={}),i(l.data[e],o.Axis.defaultConfig().data[0]),i(l.data[e],t)})),i(l.layout,o.Axis.defaultConfig().layout),i(l.layout,e.layout),this},f.getLiveConfig=function(){return u},f.getinputConfig=function(){return c},f.radialScale=function(t){return r},f.angularScale=function(t){return s},f.svg=function(){return t},n.rebind(f,h,\"on\"),f},o.Axis.defaultConfig=function(t,e){return{data:[{t:[1,2,3,4],r:[10,11,12,13],name:\"Line1\",geometry:\"LinePlot\",color:null,strokeDash:\"solid\",strokeColor:null,strokeSize:\"1\",visibleInLegend:!0,opacity:1}],layout:{defaultColorRange:n.scale.category10().range(),title:null,height:450,width:500,margin:{top:40,right:40,bottom:40,left:40},font:{size:12,color:\"gray\",outlineColor:\"white\",family:\"Tahoma, sans-serif\"},direction:\"clockwise\",orientation:0,labelOffset:10,radialAxis:{domain:null,orientation:-45,ticksSuffix:\"\",visible:!0,gridLinesVisible:!0,tickOrientation:\"horizontal\",rewriteTicks:null},angularAxis:{domain:[0,360],ticksSuffix:\"\",visible:!0,gridLinesVisible:!0,labelsVisible:!0,tickOrientation:\"horizontal\",rewriteTicks:null,ticksCount:null,ticksStep:null},minorTicks:0,tickLength:null,tickColor:\"silver\",minorTickColor:\"#eee\",backgroundColor:\"none\",needsEndSpacing:null,showLegend:!0,legend:{reverseOrder:!1},opacity:1}}},o.util={},o.DATAEXTENT=\"dataExtent\",o.AREA=\"AreaChart\",o.LINE=\"LinePlot\",o.DOT=\"DotPlot\",o.BAR=\"BarChart\",o.util._override=function(t,e){for(var r in t)r in e&&(e[r]=t[r])},o.util._extend=function(t,e){for(var r in t)e[r]=t[r]},o.util._rndSnd=function(){return 2*Math.random()-1+(2*Math.random()-1)+(2*Math.random()-1)},o.util.dataFromEquation2=function(t,e){var r=e||6;return n.range(0,360+r,r).map((function(e,r){var n=e*Math.PI/180;return[e,t(n)]}))},o.util.dataFromEquation=function(t,e,r){var i=e||6,a=[],o=[];n.range(0,360+i,i).forEach((function(e,r){var n=e*Math.PI/180,i=t(n);a.push(e),o.push(i)}));var s={t:a,r:o};return r&&(s.name=r),s},o.util.ensureArray=function(t,e){if(\"undefined\"==typeof t)return null;var r=[].concat(t);return n.range(e).map((function(t,e){return r[e]||r[0]}))},o.util.fillArrays=function(t,e,r){return e.forEach((function(e,n){t[e]=o.util.ensureArray(t[e],r)})),t},o.util.cloneJson=function(t){return JSON.parse(JSON.stringify(t))},o.util.validateKeys=function(t,e){\"string\"==typeof e&&(e=e.split(\".\"));var r=e.shift();return t[r]&&(!e.length||objHasKeys(t[r],e))},o.util.sumArrays=function(t,e){return n.zip(t,e).map((function(t,e){return n.sum(t)}))},o.util.arrayLast=function(t){return t[t.length-1]},o.util.arrayEqual=function(t,e){for(var r=Math.max(t.length,e.length,1);r-- >=0&&t[r]===e[r];);return-2===r},o.util.flattenArray=function(t){for(var e=[];!o.util.arrayEqual(e,t);)e=t,t=[].concat.apply([],t);return t},o.util.deduplicate=function(t){return t.filter((function(t,e,r){return r.indexOf(t)==e}))},o.util.convertToCartesian=function(t,e){var r=e*Math.PI/180;return[t*Math.cos(r),t*Math.sin(r)]},o.util.round=function(t,e){var r=e||2,n=Math.pow(10,r);return Math.round(t*n)/n},o.util.getMousePos=function(t){var e=n.mouse(t.node()),r=e[0],i=e[1],a={};return a.x=r,a.y=i,a.pos=e,a.angle=180*(Math.atan2(i,r)+Math.PI)/Math.PI,a.radius=Math.sqrt(r*r+i*i),a},o.util.duplicatesCount=function(t){for(var e,r={},n={},i=0,a=t.length;i0)){var l=n.select(this.parentNode).selectAll(\"path.line\").data([0]);l.enter().insert(\"path\"),l.attr({class:\"line\",d:u(s),transform:function(t,r){return\"rotate(\"+(e.orientation+90)+\")\"},\"pointer-events\":\"none\"}).style({fill:function(t,e){return d.fill(r,i,a)},\"fill-opacity\":0,stroke:function(t,e){return d.stroke(r,i,a)},\"stroke-width\":function(t,e){return d[\"stroke-width\"](r,i,a)},\"stroke-dasharray\":function(t,e){return d[\"stroke-dasharray\"](r,i,a)},opacity:function(t,e){return d.opacity(r,i,a)},display:function(t,e){return d.display(r,i,a)}})}};var h=e.angularScale.range(),f=Math.abs(h[1]-h[0])/o[0].length*Math.PI/180,p=n.svg.arc().startAngle((function(t){return-f/2})).endAngle((function(t){return f/2})).innerRadius((function(t){return e.radialScale(l+(t[2]||0))})).outerRadius((function(t){return e.radialScale(l+(t[2]||0))+e.radialScale(t[1])}));c.arc=function(t,r,i){n.select(this).attr({class:\"mark arc\",d:p,transform:function(t,r){return\"rotate(\"+(e.orientation+s(t[0])+90)+\")\"}})};var d={fill:function(e,r,n){return t[n].data.color},stroke:function(e,r,n){return t[n].data.strokeColor},\"stroke-width\":function(e,r,n){return t[n].data.strokeSize+\"px\"},\"stroke-dasharray\":function(e,n,i){return r[t[i].data.strokeDash]},opacity:function(e,r,n){return t[n].data.opacity},display:function(e,r,n){return\"undefined\"==typeof t[n].data.visible||t[n].data.visible?\"block\":\"none\"}},g=n.select(this).selectAll(\"g.layer\").data(o);g.enter().append(\"g\").attr({class:\"layer\"});var m=g.selectAll(\"path.mark\").data((function(t,e){return t}));m.enter().append(\"path\").attr({class:\"mark\"}),m.style(d).each(c[e.geometryType]),m.exit().remove(),g.exit().remove()}))}return a.config=function(e){return arguments.length?(e.forEach((function(e,r){t[r]||(t[r]={}),i(t[r],o.PolyChart.defaultConfig()),i(t[r],e)})),this):t},a.getColorScale=function(){},n.rebind(a,e,\"on\"),a},o.PolyChart.defaultConfig=function(){return{data:{name:\"geom1\",t:[[1,2,3,4]],r:[[1,2,3,4]],dotType:\"circle\",dotSize:64,dotVisible:!1,barWidth:20,color:\"#ffa500\",strokeSize:1,strokeColor:\"silver\",strokeDash:\"solid\",opacity:1,index:0,visible:!0,visibleInLegend:!0},geometryConfig:{geometry:\"LinePlot\",geometryType:\"arc\",direction:\"clockwise\",orientation:0,container:\"body\",radialScale:null,angularScale:null,colorScale:n.scale.category20()}}},o.BarChart=function(){return o.PolyChart()},o.BarChart.defaultConfig=function(){return{geometryConfig:{geometryType:\"bar\"}}},o.AreaChart=function(){return o.PolyChart()},o.AreaChart.defaultConfig=function(){return{geometryConfig:{geometryType:\"arc\"}}},o.DotPlot=function(){return o.PolyChart()},o.DotPlot.defaultConfig=function(){return{geometryConfig:{geometryType:\"dot\",dotType:\"circle\"}}},o.LinePlot=function(){return o.PolyChart()},o.LinePlot.defaultConfig=function(){return{geometryConfig:{geometryType:\"line\"}}},o.Legend=function(){var t=o.Legend.defaultConfig(),e=n.dispatch(\"hover\");function r(){var e=t.legendConfig,a=t.data.map((function(t,r){return[].concat(t).map((function(t,n){var a=i({},e.elements[r]);return a.name=t,a.color=[].concat(e.elements[r].color)[n],a}))})),o=n.merge(a);o=o.filter((function(t,r){return e.elements[r]&&(e.elements[r].visibleInLegend||\"undefined\"==typeof e.elements[r].visibleInLegend)})),e.reverseOrder&&(o=o.reverse());var s=e.container;(\"string\"==typeof s||s.nodeName)&&(s=n.select(s));var l=o.map((function(t,e){return t.color})),c=e.fontSize,u=null==e.isContinuous?\"number\"==typeof o[0]:e.isContinuous,h=u?e.height:c*o.length,f=s.classed(\"legend-group\",!0).selectAll(\"svg\").data([0]),p=f.enter().append(\"svg\").attr({width:300,height:h+c,xmlns:\"http://www.w3.org/2000/svg\",\"xmlns:xlink\":\"http://www.w3.org/1999/xlink\",version:\"1.1\"});p.append(\"g\").classed(\"legend-axis\",!0),p.append(\"g\").classed(\"legend-marks\",!0);var d=n.range(o.length),g=n.scale[u?\"linear\":\"ordinal\"]().domain(d).range(l),m=n.scale[u?\"linear\":\"ordinal\"]().domain(d)[u?\"range\":\"rangePoints\"]([0,h]);if(u){var v=f.select(\".legend-marks\").append(\"defs\").append(\"linearGradient\").attr({id:\"grad1\",x1:\"0%\",y1:\"0%\",x2:\"0%\",y2:\"100%\"}).selectAll(\"stop\").data(l);v.enter().append(\"stop\"),v.attr({offset:function(t,e){return e/(l.length-1)*100+\"%\"}}).style({\"stop-color\":function(t,e){return t}}),f.append(\"rect\").classed(\"legend-mark\",!0).attr({height:e.height,width:e.colorBandWidth,fill:\"url(#grad1)\"})}else{var y=f.select(\".legend-marks\").selectAll(\"path.legend-mark\").data(o);y.enter().append(\"path\").classed(\"legend-mark\",!0),y.attr({transform:function(t,e){return\"translate(\"+[c/2,m(e)+c/2]+\")\"},d:function(t,e){var r,i,a,o=t.symbol;return a=3*(i=c),\"line\"===(r=o)?\"M\"+[[-i/2,-i/12],[i/2,-i/12],[i/2,i/12],[-i/2,i/12]]+\"Z\":-1!=n.svg.symbolTypes.indexOf(r)?n.svg.symbol().type(r).size(a)():n.svg.symbol().type(\"square\").size(a)()},fill:function(t,e){return g(e)}}),y.exit().remove()}var x=n.svg.axis().scale(m).orient(\"right\"),b=f.select(\"g.legend-axis\").attr({transform:\"translate(\"+[u?e.colorBandWidth:c,c/2]+\")\"}).call(x);return b.selectAll(\".domain\").style({fill:\"none\",stroke:\"none\"}),b.selectAll(\"line\").style({fill:\"none\",stroke:u?e.textColor:\"none\"}),b.selectAll(\"text\").style({fill:e.textColor,\"font-size\":e.fontSize}).text((function(t,e){return o[e].name})),r}return r.config=function(e){return arguments.length?(i(t,e),this):t},n.rebind(r,e,\"on\"),r},o.Legend.defaultConfig=function(t,e){return{data:[\"a\",\"b\",\"c\"],legendConfig:{elements:[{symbol:\"line\",color:\"red\"},{symbol:\"square\",color:\"yellow\"},{symbol:\"diamond\",color:\"limegreen\"}],height:150,colorBandWidth:30,fontSize:12,container:\"body\",isContinuous:null,textColor:\"grey\",reverseOrder:!1}}},o.tooltipPanel=function(){var t,e,r,a={container:null,hasTick:!1,fontSize:12,color:\"white\",padding:5},s=\"tooltip-\"+o.tooltipPanel.uid++,l=10,c=function(){var n=(t=a.container.selectAll(\"g.\"+s).data([0])).enter().append(\"g\").classed(s,!0).style({\"pointer-events\":\"none\",display:\"none\"});return r=n.append(\"path\").style({fill:\"white\",\"fill-opacity\":.9}).attr({d:\"M0 0\"}),e=n.append(\"text\").attr({dx:a.padding+l,dy:.3*+a.fontSize}),c};return c.text=function(i){var o=n.hsl(a.color).l,s=o>=.5?\"#aaa\":\"white\",u=o>=.5?\"black\":\"white\",h=i||\"\";e.style({fill:u,\"font-size\":a.fontSize+\"px\"}).text(h);var f=a.padding,p=e.node().getBBox(),d={fill:a.color,stroke:s,\"stroke-width\":\"2px\"},g=p.width+2*f+l,m=p.height+2*f;return r.attr({d:\"M\"+[[l,-m/2],[l,-m/4],[a.hasTick?0:l,0],[l,m/4],[l,m/2],[g,m/2],[g,-m/2]].join(\"L\")+\"Z\"}).style(d),t.attr({transform:\"translate(\"+[l,-m/2+2*f]+\")\"}),t.style({display:\"block\"}),c},c.move=function(e){if(t)return t.attr({transform:\"translate(\"+[e[0],e[1]]+\")\"}).style({display:\"block\"}),c},c.hide=function(){if(t)return t.style({display:\"none\"}),c},c.show=function(){if(t)return t.style({display:\"block\"}),c},c.config=function(t){return i(a,t),c},c},o.tooltipPanel.uid=1,o.adapter={},o.adapter.plotly=function(){var t={convert:function(t,e){var r={};if(t.data&&(r.data=t.data.map((function(t,r){var n=i({},t);return[[n,[\"marker\",\"color\"],[\"color\"]],[n,[\"marker\",\"opacity\"],[\"opacity\"]],[n,[\"marker\",\"line\",\"color\"],[\"strokeColor\"]],[n,[\"marker\",\"line\",\"dash\"],[\"strokeDash\"]],[n,[\"marker\",\"line\",\"width\"],[\"strokeSize\"]],[n,[\"marker\",\"symbol\"],[\"dotType\"]],[n,[\"marker\",\"size\"],[\"dotSize\"]],[n,[\"marker\",\"barWidth\"],[\"barWidth\"]],[n,[\"line\",\"interpolation\"],[\"lineInterpolation\"]],[n,[\"showlegend\"],[\"visibleInLegend\"]]].forEach((function(t,r){o.util.translator.apply(null,t.concat(e))})),e||delete n.marker,e&&delete n.groupId,e?(\"LinePlot\"===n.geometry?(n.type=\"scatter\",!0===n.dotVisible?(delete n.dotVisible,n.mode=\"lines+markers\"):n.mode=\"lines\"):\"DotPlot\"===n.geometry?(n.type=\"scatter\",n.mode=\"markers\"):\"AreaChart\"===n.geometry?n.type=\"area\":\"BarChart\"===n.geometry&&(n.type=\"bar\"),delete n.geometry):(\"scatter\"===n.type?\"lines\"===n.mode?n.geometry=\"LinePlot\":\"markers\"===n.mode?n.geometry=\"DotPlot\":\"lines+markers\"===n.mode&&(n.geometry=\"LinePlot\",n.dotVisible=!0):\"area\"===n.type?n.geometry=\"AreaChart\":\"bar\"===n.type&&(n.geometry=\"BarChart\"),delete n.mode,delete n.type),n})),!e&&t.layout&&\"stack\"===t.layout.barmode)){var a=o.util.duplicates(r.data.map((function(t,e){return t.geometry})));r.data.forEach((function(t,e){var n=a.indexOf(t.geometry);-1!=n&&(r.data[e].groupId=n)}))}if(t.layout){var s=i({},t.layout);if([[s,[\"plot_bgcolor\"],[\"backgroundColor\"]],[s,[\"showlegend\"],[\"showLegend\"]],[s,[\"radialaxis\"],[\"radialAxis\"]],[s,[\"angularaxis\"],[\"angularAxis\"]],[s.angularaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularaxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularaxis,[\"nticks\"],[\"ticksCount\"]],[s.angularaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularaxis,[\"range\"],[\"domain\"]],[s.angularaxis,[\"endpadding\"],[\"endPadding\"]],[s.radialaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialaxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularAxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularAxis,[\"nticks\"],[\"ticksCount\"]],[s.angularAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularAxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"endpadding\"],[\"endPadding\"]],[s.radialAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialAxis,[\"range\"],[\"domain\"]],[s.font,[\"outlinecolor\"],[\"outlineColor\"]],[s.legend,[\"traceorder\"],[\"reverseOrder\"]],[s,[\"labeloffset\"],[\"labelOffset\"]],[s,[\"defaultcolorrange\"],[\"defaultColorRange\"]]].forEach((function(t,r){o.util.translator.apply(null,t.concat(e))})),e?(\"undefined\"!=typeof s.tickLength&&(s.angularaxis.ticklen=s.tickLength,delete s.tickLength),s.tickColor&&(s.angularaxis.tickcolor=s.tickColor,delete s.tickColor)):(s.angularAxis&&\"undefined\"!=typeof s.angularAxis.ticklen&&(s.tickLength=s.angularAxis.ticklen),s.angularAxis&&\"undefined\"!=typeof s.angularAxis.tickcolor&&(s.tickColor=s.angularAxis.tickcolor)),s.legend&&\"boolean\"!=typeof s.legend.reverseOrder&&(s.legend.reverseOrder=\"normal\"!=s.legend.reverseOrder),s.legend&&\"boolean\"==typeof s.legend.traceorder&&(s.legend.traceorder=s.legend.traceorder?\"reversed\":\"normal\",delete s.legend.reverseOrder),s.margin&&\"undefined\"!=typeof s.margin.t){var l=[\"t\",\"r\",\"b\",\"l\",\"pad\"],c=[\"top\",\"right\",\"bottom\",\"left\",\"pad\"],u={};n.entries(s.margin).forEach((function(t,e){u[c[l.indexOf(t.key)]]=t.value})),s.margin=u}e&&(delete s.needsEndSpacing,delete s.minorTickColor,delete s.minorTicks,delete s.angularaxis.ticksCount,delete s.angularaxis.ticksCount,delete s.angularaxis.ticksStep,delete s.angularaxis.rewriteTicks,delete s.angularaxis.nticks,delete s.radialaxis.ticksCount,delete s.radialaxis.ticksCount,delete s.radialaxis.ticksStep,delete s.radialaxis.rewriteTicks,delete s.radialaxis.nticks),r.layout=s}return r}};return t}},{\"../../../constants/alignment\":717,\"../../../lib\":750,d3:169}],872:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../../lib\"),a=t(\"../../../components/color\"),o=t(\"./micropolar\"),s=t(\"./undo_manager\"),l=i.extendDeepAll,c=e.exports={};c.framework=function(t){var e,r,i,a,u,h=new s;function f(r,s){return s&&(u=s),n.select(n.select(u).node().parentNode).selectAll(\".svg-container>*:not(.chart-root)\").remove(),e=e?l(e,r):r,i||(i=o.Axis()),a=o.adapter.plotly().convert(e),i.config(a).render(u),t.data=e.data,t.layout=e.layout,c.fillLayout(t),e}return f.isPolar=!0,f.svg=function(){return i.svg()},f.getConfig=function(){return e},f.getLiveConfig=function(){return o.adapter.plotly().convert(i.getLiveConfig(),!0)},f.getLiveScales=function(){return{t:i.angularScale(),r:i.radialScale()}},f.setUndoPoint=function(){var t,n,i=this,a=o.util.cloneJson(e);t=a,n=r,h.add({undo:function(){n&&i(n)},redo:function(){i(t)}}),r=o.util.cloneJson(a)},f.undo=function(){h.undo()},f.redo=function(){h.redo()},f},c.fillLayout=function(t){var e=n.select(t).selectAll(\".plot-container\"),r=e.selectAll(\".svg-container\"),i=t.framework&&t.framework.svg&&t.framework.svg(),o={width:800,height:600,paper_bgcolor:a.background,_container:e,_paperdiv:r,_paper:i};t._fullLayout=l(o,t.layout)}},{\"../../../components/color\":615,\"../../../lib\":750,\"./micropolar\":871,\"./undo_manager\":873,d3:169}],873:[function(t,e,r){\"use strict\";e.exports=function(){var t,e=[],r=-1,n=!1;function i(t,e){return t?(n=!0,t[e](),n=!1,this):this}return{add:function(t){return n||(e.splice(r+1,e.length-r),e.push(t),r=e.length-1),this},setCallback:function(e){t=e},undo:function(){var n=e[r];return n?(i(n,\"undo\"),r-=1,t&&t(n.undo),this):this},redo:function(){var n=e[r+1];return n?(i(n,\"redo\"),r+=1,t&&t(n.redo),this):this},clear:function(){e=[],r=-1},hasUndo:function(){return-1!==r},hasRedo:function(){return r=90||s>90&&l>=450?1:u<=0&&f<=0?0:Math.max(u,f);e=s<=180&&l>=180||s>180&&l>=540?-1:c>=0&&h>=0?0:Math.min(c,h);r=s<=270&&l>=270||s>270&&l>=630?-1:u>=0&&f>=0?0:Math.min(u,f);n=l>=360?1:c<=0&&h<=0?0:Math.max(c,h);return[e,r,n,i]}(f),x=y[2]-y[0],b=y[3]-y[1],_=h/u,w=Math.abs(b/x);_>w?(p=u,v=(h-(d=u*w))/n.h/2,g=[o[0],o[1]],m=[c[0]+v,c[1]-v]):(d=h,v=(u-(p=h/w))/n.w/2,g=[o[0]+v,o[1]-v],m=[c[0],c[1]]),this.xLength2=p,this.yLength2=d,this.xDomain2=g,this.yDomain2=m;var T=this.xOffset2=n.l+n.w*g[0],k=this.yOffset2=n.t+n.h*(1-m[1]),M=this.radius=p/x,A=this.innerRadius=e.hole*M,S=this.cx=T-M*y[0],L=this.cy=k+M*y[3],P=this.cxx=S-T,I=this.cyy=L-k;this.radialAxis=this.mockAxis(t,e,i,{_id:\"x\",side:{counterclockwise:\"top\",clockwise:\"bottom\"}[i.side],domain:[A/n.w,M/n.w]}),this.angularAxis=this.mockAxis(t,e,a,{side:\"right\",domain:[0,Math.PI],autorange:!1}),this.doAutoRange(t,e),this.updateAngularAxis(t,e),this.updateRadialAxis(t,e),this.updateRadialAxisTitle(t,e),this.xaxis=this.mockCartesianAxis(t,e,{_id:\"x\",domain:g}),this.yaxis=this.mockCartesianAxis(t,e,{_id:\"y\",domain:m});var z=this.pathSubplot();this.clipPaths.forTraces.select(\"path\").attr(\"d\",z).attr(\"transform\",R(P,I)),r.frontplot.attr(\"transform\",R(T,k)).call(l.setClipUrl,this._hasClipOnAxisFalse?null:this.clipIds.forTraces,this.gd),r.bg.attr(\"d\",z).attr(\"transform\",R(S,L)).call(s.fill,e.bgcolor)},I.mockAxis=function(t,e,r,n){var i=o.extendFlat({},r,n);return f(i,e,t),i},I.mockCartesianAxis=function(t,e,r){var n=this,i=r._id,a=o.extendFlat({type:\"linear\"},r);h(a,t);var s={x:[0,2],y:[1,3]};return a.setRange=function(){var t=n.sectorBBox,r=s[i],o=n.radialAxis._rl,l=(o[1]-o[0])/(1-e.hole);a.range=[t[r[0]]*l,t[r[1]]*l]},a.isPtWithinRange=\"x\"===i?function(t){return n.isPtInside(t)}:function(){return!0},a.setRange(),a.setScale(),a},I.doAutoRange=function(t,e){var r=this.gd,n=this.radialAxis,i=e.radialaxis;n.setScale(),p(r,n);var a=n.range;i.range=a.slice(),i._input.range=a.slice(),n._rl=[n.r2l(a[0],null,\"gregorian\"),n.r2l(a[1],null,\"gregorian\")]},I.updateRadialAxis=function(t,e){var r=this,n=r.gd,i=r.layers,a=r.radius,l=r.innerRadius,c=r.cx,h=r.cy,f=e.radialaxis,p=E(e.sector[0],360),d=r.radialAxis,g=l90&&p<=270&&(d.tickangle=180);var m=function(t){return\"translate(\"+(d.l2p(t.x)+l)+\",0)\"},v=z(f);if(r.radialTickLayout!==v&&(i[\"radial-axis\"].selectAll(\".xtick\").remove(),r.radialTickLayout=v),g){d.setScale();var y=u.calcTicks(d),x=u.clipEnds(d,y),b=u.getTickSigns(d)[2];u.drawTicks(n,d,{vals:y,layer:i[\"radial-axis\"],path:u.makeTickPath(d,0,b),transFn:m,crisp:!1}),u.drawGrid(n,d,{vals:x,layer:i[\"radial-grid\"],path:function(t){return r.pathArc(d.r2p(t.x)+l)},transFn:o.noop,crisp:!1}),u.drawLabels(n,d,{vals:y,layer:i[\"radial-axis\"],transFn:m,labelFns:u.makeLabelFns(d,0)})}var _=r.radialAxisAngle=r.vangles?L(O(C(f.angle),r.vangles)):f.angle,w=R(c,h),T=w+F(-_);D(i[\"radial-axis\"],g&&(f.showticklabels||f.ticks),{transform:T}),D(i[\"radial-grid\"],g&&f.showgrid,{transform:w}),D(i[\"radial-line\"].select(\"line\"),g&&f.showline,{x1:l,y1:0,x2:a,y2:0,transform:T}).attr(\"stroke-width\",f.linewidth).call(s.stroke,f.linecolor)},I.updateRadialAxisTitle=function(t,e,r){var n=this.gd,i=this.radius,a=this.cx,o=this.cy,s=e.radialaxis,c=this.id+\"title\",u=void 0!==r?r:this.radialAxisAngle,h=C(u),f=Math.cos(h),p=Math.sin(h),d=0;if(s.title){var g=l.bBox(this.layers[\"radial-axis\"].node()).height,m=s.title.font.size;d=\"counterclockwise\"===s.side?-g-.4*m:g+.8*m}this.layers[\"radial-axis-title\"]=v.draw(n,c,{propContainer:s,propName:this.id+\".radialaxis.title\",placeholder:S(n,\"Click to enter radial axis title\"),attributes:{x:a+i/2*f+d*p,y:o-i/2*p+d*f,\"text-anchor\":\"middle\"},transform:{rotate:-u}})},I.updateAngularAxis=function(t,e){var r=this,n=r.gd,i=r.layers,a=r.radius,l=r.innerRadius,c=r.cx,h=r.cy,f=e.angularaxis,p=r.angularAxis;r.fillViewInitialKey(\"angularaxis.rotation\",f.rotation),p.setGeometry(),p.setScale();var d=function(t){return p.t2g(t.x)};\"linear\"===p.type&&\"radians\"===p.thetaunit&&(p.tick0=L(p.tick0),p.dtick=L(p.dtick));var g=function(t){return R(c+a*Math.cos(t),h-a*Math.sin(t))},m=u.makeLabelFns(p,0).labelStandoff,v={xFn:function(t){var e=d(t);return Math.cos(e)*m},yFn:function(t){var e=d(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(m+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*k)},anchorFn:function(t){var e=d(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},heightFn:function(t,e,r){var n=d(t);return-.5*(1+Math.sin(n))*r}},y=z(f);r.angularTickLayout!==y&&(i[\"angular-axis\"].selectAll(\".\"+p._id+\"tick\").remove(),r.angularTickLayout=y);var x,b=u.calcTicks(p);if(\"linear\"===e.gridshape?(x=b.map(d),o.angleDelta(x[0],x[1])<0&&(x=x.slice().reverse())):x=null,r.vangles=x,\"category\"===p.type&&(b=b.filter((function(t){return o.isAngleInsideSector(d(t),r.sectorInRad)}))),p.visible){var _=\"inside\"===p.ticks?-1:1,w=(p.linewidth||1)/2;u.drawTicks(n,p,{vals:b,layer:i[\"angular-axis\"],path:\"M\"+_*w+\",0h\"+_*p.ticklen,transFn:function(t){var e=d(t);return g(e)+F(-L(e))},crisp:!1}),u.drawGrid(n,p,{vals:b,layer:i[\"angular-grid\"],path:function(t){var e=d(t),r=Math.cos(e),n=Math.sin(e);return\"M\"+[c+l*r,h-l*n]+\"L\"+[c+a*r,h-a*n]},transFn:o.noop,crisp:!1}),u.drawLabels(n,p,{vals:b,layer:i[\"angular-axis\"],repositionOnUpdate:!0,transFn:function(t){return g(d(t))},labelFns:v})}D(i[\"angular-line\"].select(\"path\"),f.showline,{d:r.pathSubplot(),transform:R(c,h)}).attr(\"stroke-width\",f.linewidth).call(s.stroke,f.linecolor)},I.updateFx=function(t,e){this.gd._context.staticPlot||(this.updateAngularDrag(t),this.updateRadialDrag(t,e,0),this.updateRadialDrag(t,e,1),this.updateMainDrag(t))},I.updateMainDrag=function(t){var e=this,r=e.gd,o=e.layers,s=t._zoomlayer,l=M.MINZOOM,c=M.OFFEDGE,u=e.radius,h=e.innerRadius,f=e.cx,p=e.cy,v=e.cxx,_=e.cyy,w=e.sectorInRad,T=e.vangles,k=e.radialAxis,S=A.clampTiny,E=A.findXYatLength,C=A.findEnclosingVertexAngles,L=M.cornerHalfWidth,P=M.cornerLen/2,I=d.makeDragger(o,\"path\",\"maindrag\",\"crosshair\");n.select(I).attr(\"d\",e.pathSubplot()).attr(\"transform\",R(f,p));var z,O,D,F,B,N,j,U,V,q={element:I,gd:r,subplot:e.id,plotinfo:{id:e.id,xaxis:e.xaxis,yaxis:e.yaxis},xaxes:[e.xaxis],yaxes:[e.yaxis]};function H(t,e){return Math.sqrt(t*t+e*e)}function G(t,e){return H(t-v,e-_)}function Y(t,e){return Math.atan2(_-e,t-v)}function W(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function Z(t,r){if(0===t)return e.pathSector(2*L);var n=P/t,i=r-n,a=r+n,o=Math.max(0,Math.min(t,u)),s=o-L,l=o+L;return\"M\"+W(s,i)+\"A\"+[s,s]+\" 0,0,0 \"+W(s,a)+\"L\"+W(l,a)+\"A\"+[l,l]+\" 0,0,1 \"+W(l,i)+\"Z\"}function X(t,r,n){if(0===t)return e.pathSector(2*L);var i,a,o=W(t,r),s=W(t,n),l=S((o[0]+s[0])/2),c=S((o[1]+s[1])/2);if(l&&c){var u=c/l,h=-1/u,f=E(L,u,l,c);i=E(P,h,f[0][0],f[0][1]),a=E(P,h,f[1][0],f[1][1])}else{var p,d;c?(p=P,d=L):(p=L,d=P),i=[[l-p,c-d],[l+p,c-d]],a=[[l-p,c+d],[l+p,c+d]]}return\"M\"+i.join(\"L\")+\"L\"+a.reverse().join(\"L\")+\"Z\"}function J(t,e){return e=Math.max(Math.min(e,u),h),tl?(t-1&&1===t&&x(n,r,[e.xaxis],[e.yaxis],e.id,q),i.indexOf(\"event\")>-1&&m.click(r,n,e.id)}q.prepFn=function(t,n,a){var o=r._fullLayout.dragmode,l=I.getBoundingClientRect();if(z=n-l.left,O=a-l.top,T){var c=A.findPolygonOffset(u,w[0],w[1],T);z+=v+c[0],O+=_+c[1]}switch(o){case\"zoom\":q.moveFn=T?tt:Q,q.clickFn=nt,q.doneFn=et,function(){D=null,F=null,B=e.pathSubplot(),N=!1;var t=r._fullLayout[e.id];j=i(t.bgcolor).getLuminance(),(U=d.makeZoombox(s,j,f,p,B)).attr(\"fill-rule\",\"evenodd\"),V=d.makeCorners(s,f,p),b(r)}();break;case\"select\":case\"lasso\":y(t,n,a,q,o)}},I.onmousemove=function(t){m.hover(r,t,e.id),r._fullLayout._lasthover=I,r._fullLayout._hoversubplot=e.id},I.onmouseout=function(t){r._dragging||g.unhover(r,t)},g.init(q)},I.updateRadialDrag=function(t,e,r){var i=this,s=i.gd,l=i.layers,c=i.radius,u=i.innerRadius,h=i.cx,f=i.cy,p=i.radialAxis,m=M.radialDragBoxSize,v=m/2;if(p.visible){var 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n=t(\"d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=o._,l=t(\"../../components/color\"),c=t(\"../../components/drawing\"),u=t(\"../cartesian/set_convert\"),h=t(\"../../lib/extend\").extendFlat,f=t(\"../plots\"),p=t(\"../cartesian/axes\"),d=t(\"../../components/dragelement\"),g=t(\"../../components/fx\"),m=t(\"../../components/dragelement/helpers\"),v=m.freeMode,y=m.rectMode,x=t(\"../../components/titles\"),b=t(\"../cartesian/select\").prepSelect,_=t(\"../cartesian/select\").selectOnClick,w=t(\"../cartesian/select\").clearSelect,T=t(\"../cartesian/select\").clearSelectionsCache,k=t(\"../cartesian/constants\");function M(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}e.exports=M;var A=M.prototype;A.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},A.plot=function(t,e){var 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t=a.numbersScaler(h);z+=t[2];var e,r=k(f,\"numbersScale\",1,t[0],z,Math.min);f._scaleNumbers||(r=1),e=f._isAngular?x-r*h.bottom:x-r*(h.top+h.bottom)/2,f._numbersTop=r*h.top+e;var n=h[w];\"center\"===w&&(n=(h.left+h.right)/2);var i=p-r*n;return _(i=k(f,\"numbersTranslate\",0,i,z,Math.max),e)+\" scale(\"+r+\")\"}))}(t,L,e,{numbersX:h,numbersY:M,numbersScaler:A,transitionOpts:r,onComplete:f}),P&&(S={range:C.gauge.axis.range,color:C.gauge.bgcolor,line:{color:C.gauge.bordercolor,width:0},thickness:1},E={range:C.gauge.axis.range,color:\"rgba(0, 0, 0, 0)\",line:{color:C.gauge.bordercolor,width:C.gauge.borderwidth},thickness:1});var q=L.selectAll(\"g.angular\").data(I?e:[]);q.exit().remove();var H=L.selectAll(\"g.angularaxis\").data(I?e:[]);H.exit().remove(),I&&function(t,e,r,i){var s,l,c,h,f=r[0].trace,p=i.size,d=i.radius,g=i.innerRadius,m=i.gaugeBg,v=i.gaugeOutline,w=[p.l+p.w/2,p.t+p.h/2+d/2],T=i.gauge,k=i.layer,M=i.transitionOpts,A=i.onComplete,S=Math.PI/2;function E(t){var e=f.gauge.axis.range[0],r=(t-e)/(f.gauge.axis.range[1]-e)*Math.PI-S;return r<-S?-S:r>S?S:r}function C(t){return n.svg.arc().innerRadius((g+d)/2-t/2*(d-g)).outerRadius((g+d)/2+t/2*(d-g)).startAngle(-S)}function L(t){t.attr(\"d\",(function(t){return C(t.thickness).startAngle(E(t.range[0])).endAngle(E(t.range[1]))()}))}T.enter().append(\"g\").classed(\"angular\",!0),T.attr(\"transform\",_(w[0],w[1])),k.enter().append(\"g\").classed(\"angularaxis\",!0).classed(\"crisp\",!0),k.selectAll(\"g.xangularaxistick,path,text\").remove(),(s=b(t,f.gauge.axis)).type=\"linear\",s.range=f.gauge.axis.range,s._id=\"xangularaxis\",s.setScale();var P=function(t){return(s.range[0]-t.x)/(s.range[1]-s.range[0])*Math.PI+Math.PI},I={},z=u.makeLabelFns(s,0).labelStandoff;I.xFn=function(t){var e=P(t);return Math.cos(e)*z},I.yFn=function(t){var e=P(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*o)},I.anchorFn=function(t){var e=P(t),r=Math.cos(e);return 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j=N.select(\"path\");y(M)?(j.transition().duration(M.duration).ease(M.easing).each(\"end\",(function(){A&&A()})).each(\"interrupt\",(function(){A&&A()})).attrTween(\"d\",(U=B,V=E(r[0].lastY),q=E(r[0].y),function(){var t=n.interpolate(V,q);return function(e){return U.endAngle(t(e))()}})),f._lastValue=r[0].y):j.attr(\"d\",\"number\"==typeof r[0].y?B.endAngle(E(r[0].y)):\"M0,0Z\");var U,V,q;j.call(x),N.exit().remove(),R=[];var H=f.gauge.threshold.value;H&&R.push({range:[H,H],color:f.gauge.threshold.color,line:{color:f.gauge.threshold.line.color,width:f.gauge.threshold.line.width},thickness:f.gauge.threshold.thickness});var G=T.selectAll(\"g.threshold-arc\").data(R);G.enter().append(\"g\").classed(\"threshold-arc\",!0).append(\"path\"),G.select(\"path\").call(L).call(x),G.exit().remove();var Y=T.selectAll(\"g.gauge-outline\").data([v]);Y.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),Y.select(\"path\").call(L).call(x),Y.exit().remove()}(t,0,e,{radius:B,innerRadius:N,gauge:q,layer:H,size:D,gaugeBg:S,gaugeOutline:E,transitionOpts:r,onComplete:f});var G=L.selectAll(\"g.bullet\").data(z?e:[]);G.exit().remove();var Y=L.selectAll(\"g.bulletaxis\").data(z?e:[]);Y.exit().remove(),z&&function(t,e,r,n){var i,a,o,s,c,h=r[0].trace,f=n.gauge,p=n.layer,g=n.gaugeBg,m=n.gaugeOutline,v=n.size,_=h.domain,w=n.transitionOpts,T=n.onComplete;f.enter().append(\"g\").classed(\"bullet\",!0),f.attr(\"transform\",\"translate(\"+v.l+\", \"+v.t+\")\"),p.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),p.selectAll(\"g.xbulletaxistick,path,text\").remove();var k=v.h,M=h.gauge.bar.thickness*k,A=_.x[0],S=_.x[0]+(_.x[1]-_.x[0])*(h._hasNumber||h._hasDelta?1-l.bulletNumberDomainSize:1);(i=b(t,h.gauge.axis))._id=\"xbulletaxis\",i.domain=[A,S],i.setScale(),a=u.calcTicks(i),o=u.makeTransFn(i),s=u.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(u.drawTicks(t,i,{vals:\"inside\"===i.ticks?u.clipEnds(i,a):a,layer:p,path:u.makeTickPath(i,c,s),transFn:o}),u.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:u.makeLabelFns(i,c)}));function E(t){t.attr(\"width\",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*k})).attr(\"height\",(function(t){return t.thickness*k}))}var C=[g].concat(h.gauge.steps),L=f.selectAll(\"g.bg-bullet\").data(C);L.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),L.select(\"rect\").call(E).call(x),L.exit().remove();var P=f.selectAll(\"g.value-bullet\").data([h.gauge.bar]);P.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),P.select(\"rect\").attr(\"height\",M).attr(\"y\",(k-M)/2).call(x),y(w)?P.select(\"rect\").transition().duration(w.duration).ease(w.easing).each(\"end\",(function(){T&&T()})).each(\"interrupt\",(function(){T&&T()})).attr(\"width\",Math.max(0,i.c2p(Math.min(h.gauge.axis.range[1],r[0].y)))):P.select(\"rect\").attr(\"width\",\"number\"==typeof r[0].y?Math.max(0,i.c2p(Math.min(h.gauge.axis.range[1],r[0].y))):0);P.exit().remove();var I=r.filter((function(){return h.gauge.threshold.value})),z=f.selectAll(\"g.threshold-bullet\").data(I);z.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),z.select(\"line\").attr(\"x1\",i.c2p(h.gauge.threshold.value)).attr(\"x2\",i.c2p(h.gauge.threshold.value)).attr(\"y1\",(1-h.gauge.threshold.thickness)/2*k).attr(\"y2\",(1-(1-h.gauge.threshold.thickness)/2)*k).call(d.stroke,h.gauge.threshold.line.color).style(\"stroke-width\",h.gauge.threshold.line.width),z.exit().remove();var O=f.selectAll(\"g.gauge-outline\").data([m]);O.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"rect\"),O.select(\"rect\").call(E).call(x),O.exit().remove()}(t,0,e,{gauge:G,layer:Y,size:D,gaugeBg:S,gaugeOutline:E,transitionOpts:r,onComplete:f});var W=L.selectAll(\"text.title\").data(e);W.exit().remove(),W.enter().append(\"text\").classed(\"title\",!0),W.attr(\"text-anchor\",(function(){return 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/template_attributes\").hovertemplateAttrs,a=t(\"../mesh3d/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/edit_types\").overrideAll;var c=e.exports=l(s({x:{valType:\"data_array\"},y:{valType:\"data_array\"},z:{valType:\"data_array\"},value:{valType:\"data_array\"},isomin:{valType:\"number\"},isomax:{valType:\"number\"},surface:{show:{valType:\"boolean\",dflt:!0},count:{valType:\"integer\",dflt:2,min:1},fill:{valType:\"number\",min:0,max:1,dflt:1},pattern:{valType:\"flaglist\",flags:[\"A\",\"B\",\"C\",\"D\",\"E\"],extras:[\"all\",\"odd\",\"even\"],dflt:\"all\"}},spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}}},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:i(),showlegend:s({},o.showlegend,{dflt:!1})},n(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:a.opacity,lightposition:a.lightposition,lighting:a.lighting,flatshading:a.flatshading,contour:a.contour,hoverinfo:s({},o.hoverinfo)}),\"calc\",\"nested\");c.flatshading.dflt=!0,c.lighting.facenormalsepsilon.dflt=0,c.x.editType=c.y.editType=c.z.editType=c.value.editType=\"calc+clearAxisTypes\",c.transforms=void 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/template_attributes\").hovertemplateAttrs,a=t(\"../surface/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../lib/extend\").extendFlat;e.exports=s({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:i({editType:\"calc\"}),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"},transforms:void 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t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),w(I),A.exit().remove(),M.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var z=e._fullLayout.paper_bgcolor;k.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return f(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",z+\" -1px 1px 2px, \"+z+\" 1px 1px 2px, \"+z+\" 1px -1px 2px, \"+z+\" -1px -1px 2px\").style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return f(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){s.font(n.select(this),t.parcatsViewModel.categorylabelfont),c.convertToTspans(n.select(this),e)})),M.append(\"text\").attr(\"class\",\"dimlabel\"),k.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){s.font(n.select(this),t.parcatsViewModel.labelfont)})),k.selectAll(\"rect.bandrect\").on(\"mouseover\",S).on(\"mouseout\",E),k.exit().remove(),T.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",C).on(\"drag\",L).on(\"dragend\",P)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),u.exit().remove()}function h(t){return t.key}function f(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function p(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor\"),C=n.mouse(h)[0];a.loneHover({trace:f,x:_-d.left+g.left,y:w-d.top+g.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:C<_?\"right\":\"left\",hovertemplate:(f.line||{}).hovertemplate,hovertemplateLabels:A,eventData:[{data:f._input,fullData:f,count:k,probability:M}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:h})}}}function g(t){if(!t.parcatsViewModel.dragDimension&&(x(n.select(this)),a.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(p),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=m(t),r=v(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function m(t){for(var e=[],r=I(t.parcatsViewModel),n=0;n1&&c.displayInd===l.dimensions.length-1?(r=o.left,i=\"left\"):(r=o.left+o.width,i=\"right\");var f=s.model.count,p=s.model.categoryLabel,d=f/s.parcatsViewModel.model.count,g={countLabel:f,categoryLabel:p,probabilityLabel:d.toFixed(3)},m=[];-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&m.push([\"Count:\",g.countLabel].join(\" \")),-1!==s.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&m.push([\"P(\"+g.categoryLabel+\"):\",g.probabilityLabel].join(\" \"));var v=m.join(\"
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\"),k=l.mostReadable(o.color,[\"black\",\"white\"]);return{trace:h,x:r-t.left,y:f-t.top,text:T,color:o.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:k,fontSize:10,idealAlign:i,hovertemplate:h.hovertemplate,hovertemplateLabels:_,eventData:[{data:h._input,fullData:h,category:p,count:d,probability:y,categorycount:m,colorcount:v,bandcolorcount:g}]}}(s,this):\"dimension\"===c&&(e=function(t,e){var r=[];return n.select(e.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){r.push(A(t,this))})),r}(s,this)),e&&a.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function E(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(x(e.pathSelection),_(e.dimensionSelection.selectAll(\"g.category\")),w(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),a.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(p),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?M(this,\"plotly_unhover\",n.event):k(this,\"plotly_unhover\",n.event)}}function C(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,o.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.yh.y+h.height/2&&(o.model.displayInd=h.model.displayInd,h.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var f=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==f&&a.model.dragXp.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}B(t.parcatsViewModel),F(t.parcatsViewModel),O(t.parcatsViewModel),z(t.parcatsViewModel)}}function P(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=I(t.parcatsViewModel),a=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==a[e]}));o&&a.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),h=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[h],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):k(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,B(t.parcatsViewModel),F(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){O(t.parcatsViewModel,!0),z(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&i.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function I(t){for(var e,r=t.graphDiv._fullData,n=0;n=0;s--)u+=\"C\"+c[s]+\",\"+(e[s+1]+i)+\" \"+l[s]+\",\"+(e[s]+i)+\" \"+(t[s]+r[s])+\",\"+(e[s]+i),u+=\"l-\"+r[s]+\",0 \";return u+=\"Z\"}function F(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function h(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=h(e),i=h(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),ni?1:0}));for(var f=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),g=0;g0?d*(v.count/p):0;for(var y,x=new Array(n.length),b=0;b1?(t.width-80-16)/(n-1):0)*i;var a,o,s,l,c,u=[],h=t.model.maxCats,f=e.categories.length,p=e.count,d=t.height-8*(h-1),g=8*(h-f)/2,m=e.categories.map((function(t){return{displayInd:t.displayInd,categoryInd:t.categoryInd}}));for(m.sort((function(t,e){return t.displayInd-e.displayInd})),c=0;c0?o.count/p*d:0,s={key:o.valueInds[0],model:o,width:16,height:a,y:null!==o.dragY?o.dragY:g,bands:[],parcatsViewModel:t},g=g+a+8,u.push(s);return{key:e.dimensionInd,x:null!==e.dragX?e.dragX:r,y:0,width:16,model:e,categories:u,parcatsViewModel:t,dragCategoryDisplayInd:null,dragDimensionDisplayInd:null,initialDragDimensionDisplayInds:null,initialDragCategoryDisplayInds:null,dragHasMoved:null,potentialClickBand:null}}e.exports=function(t,e,r,n){u(r,t,n,e)}},{\"../../components/drawing\":637,\"../../components/fx\":655,\"../../lib\":750,\"../../lib/svg_text_utils\":774,\"../../plot_api/plot_api\":785,d3:169,tinycolor2:548}],1119:[function(t,e,r){\"use strict\";var n=t(\"./parcats\");e.exports=function(t,e,r,i){var a=t._fullLayout,o=a._paper,s=a._size;n(t,o,e,{width:s.w,height:s.h,margin:{t:s.t,r:s.r,b:s.b,l:s.l}},r,i)}},{\"./parcats\":1118}],1120:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/layout_attributes\"),a=t(\"../../plots/font_attributes\"),o=t(\"../../plots/domain\").attributes,s=t(\"../../lib/extend\").extendFlat,l=t(\"../../plot_api/plot_template\").templatedArray;e.exports={domain:o({name:\"parcoords\",trace:!0,editType:\"plot\"}),labelangle:{valType:\"angle\",dflt:0,editType:\"plot\"},labelside:{valType:\"enumerated\",values:[\"top\",\"bottom\"],dflt:\"top\",editType:\"plot\"},labelfont:a({editType:\"plot\"}),tickfont:a({editType:\"plot\"}),rangefont:a({editType:\"plot\"}),dimensions:l(\"dimension\",{label:{valType:\"string\",editType:\"plot\"},tickvals:s({},i.tickvals,{editType:\"plot\"}),ticktext:s({},i.ticktext,{editType:\"plot\"}),tickformat:s({},i.tickformat,{editType:\"plot\"}),visible:{valType:\"boolean\",dflt:!0,editType:\"plot\"},range:{valType:\"info_array\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},constraintrange:{valType:\"info_array\",freeLength:!0,dimensions:\"1-2\",items:[{valType:\"number\",editType:\"plot\"},{valType:\"number\",editType:\"plot\"}],editType:\"plot\"},multiselect:{valType:\"boolean\",dflt:!0,editType:\"plot\"},values:{valType:\"data_array\",editType:\"calc\"},editType:\"calc\"}),line:s({editType:\"calc\"},n(\"line\",{colorscaleDflt:\"Viridis\",autoColorDflt:!1,editTypeOverride:\"calc\"}))}},{\"../../components/colorscale/attributes\":622,\"../../lib/extend\":740,\"../../plot_api/plot_template\":788,\"../../plots/cartesian/layout_attributes\":813,\"../../plots/domain\":826,\"../../plots/font_attributes\":827}],1121:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"d3\"),a=t(\"../../lib/gup\").keyFun,o=t(\"../../lib/gup\").repeat,s=t(\"../../lib\").sorterAsc,l=n.bar.snapRatio;function c(t,e){return t*(1-l)+e*l}var u=n.bar.snapClose;function h(t,e){return t*(1-u)+e*u}function f(t,e,r,n){if(function(t,e){for(var r=0;r=e[r][0]&&t<=e[r][1])return!0;return!1}(r,n))return r;var i=t?-1:1,a=0,o=e.length-1;if(i<0){var s=a;a=o,o=s}for(var l=e[a],u=l,f=a;i*fe){f=r;break}}if(a=u,isNaN(a)&&(a=isNaN(h)||isNaN(f)?isNaN(h)?f:h:e-c[h][1]t[1]+r||e=.9*t[1]+.1*t[0]?\"n\":e<=.9*t[0]+.1*t[1]?\"s\":\"ns\"}(d,e);g&&(o.interval=l[a],o.intervalPix=d,o.region=g)}}if(t.ordinal&&!o.region){var v=t.unitTickvals,y=t.unitToPaddedPx.invert(e);for(r=0;r=x[0]&&y<=x[1]){o.clickableOrdinalRange=x;break}}}return o}function _(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.brush.svgBrush;a.wasDragged=!0,a._dragging=!0,a.grabbingBar?a.newExtent=[r-a.grabPoint,r+a.barLength-a.grabPoint].map(e.unitToPaddedPx.invert):a.newExtent=[a.startExtent,e.unitToPaddedPx.invert(r)].sort(s),e.brush.filterSpecified=!0,a.extent=a.stayingIntervals.concat([a.newExtent]),a.brushCallback(e),x(t.parentNode)}function w(t,e){var r=b(e,e.height-i.mouse(t)[1]-2*n.verticalPadding),a=\"crosshair\";r.clickableOrdinalRange?a=\"pointer\":r.region&&(a=r.region+\"-resize\"),i.select(document.body).style(\"cursor\",a)}function T(t){t.on(\"mousemove\",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||w(this,t)})).on(\"mouseleave\",(function(t){t.parent.inBrushDrag||v()})).call(i.behavior.drag().on(\"dragstart\",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=b(e,r),l=s.interval,c=o.svgBrush;if(c.wasDragged=!1,c.grabbingBar=\"ns\"===s.region,c.grabbingBar){var u=l.map(e.unitToPaddedPx);c.grabPoint=r-u[0]-n.verticalPadding,c.barLength=u[1]-u[0]}c.clickableOrdinalRange=s.clickableOrdinalRange,c.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(c.stayingIntervals=c.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),c.startExtent=s.region?l[\"s\"===s.region?1:0]:a,e.parent.inBrushDrag=!0,c.brushStartCallback()}(this,t)})).on(\"drag\",(function(t){_(this,t)})).on(\"dragend\",(function(t){!function(t,e){var r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(w(t,e),_(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,v(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&M(r)):M(r),a.brushCallback(e),x(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]a.newExtent[0];a.extent=a.stayingIntervals.concat(c?[a.newExtent]:[]),a.extent.length||M(r),a.brushCallback(e),c?x(t.parentNode,s):(s(),x(t.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function k(t,e){return t[0]-e[0]}function M(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function A(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}e.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(k)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=A(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e).slice();e.filter.set(r),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t){var e=t.selectAll(\".\"+n.cn.axisBrush).data(o,a);e.enter().append(\"g\").classed(n.cn.axisBrush,!0),function(t){var e=t.selectAll(\".background\").data(o);e.enter().append(\"rect\").classed(\"background\",!0).call(p).call(d).style(\"pointer-events\",\"auto\").attr(\"transform\",\"translate(0 \"+n.verticalPadding+\")\"),e.call(T).attr(\"height\",(function(t){return t.height-n.verticalPadding}));var r=t.selectAll(\".highlight-shadow\").data(o);r.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",n.bar.strokeColor).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),r.attr(\"y1\",(function(t){return t.height})).call(y);var i=t.selectAll(\".highlight\").data(o);i.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),i.attr(\"y1\",(function(t){return t.height})).call(y)}(e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?A(t.sort(k)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[f(0,r,t[0],[]),f(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},{\"../../lib\":750,\"../../lib/gup\":747,\"./constants\":1124,d3:169}],1122:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"../../plots/get_data\").getModuleCalcData,a=t(\"./plot\"),o=t(\"../../constants/xmlns_namespaces\");r.name=\"parcoords\",r.plot=function(t){var e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":t,preserveAspectRatio:\"none\",x:0,y:0,width:this.width,height:this.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},{\"../../constants/xmlns_namespaces\":726,\"../../plots/get_data\":836,\"./plot\":1131,d3:169}],1123:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").isArrayOrTypedArray,i=t(\"../../components/colorscale\"),a=t(\"../../lib/gup\").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,\"line\")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:\"line\",cLetter:\"c\"})):(r=function(t){for(var e=new Array(t),r=0;rh&&(n.log(\"parcoords traces support up to \"+h+\" dimensions at the moment\"),d.splice(h));var g=s(t,e,{name:\"dimensions\",layout:l,handleItemDefaults:p}),m=function(t,e,r,o,s){var l=s(\"line.color\",r);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,u);o(e,l,u),Array.isArray(g)&&g.length||(e.visible=!1),f(e,g,\"values\",m);var v={family:l.font.family,size:Math.round(l.font.size/1.2),color:l.font.color};n.coerceFont(u,\"labelfont\",v),n.coerceFont(u,\"tickfont\",v),n.coerceFont(u,\"rangefont\",v),u(\"labelangle\"),u(\"labelside\")}},{\"../../components/colorscale/defaults\":625,\"../../components/colorscale/helpers\":626,\"../../lib\":750,\"../../plots/array_container_defaults\":794,\"../../plots/cartesian/axes\":799,\"../../plots/domain\":826,\"./attributes\":1120,\"./axisbrush\":1121,\"./constants\":1124,\"./merge_length\":1129}],1126:[function(t,e,r){\"use strict\";var n=t(\"../../lib\").isTypedArray;r.convertTypedArray=function(t){return 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loC, hiC, loD, hiD;\\n\\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\\nuniform sampler2D mask, palette;\\nuniform float maskHeight;\\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\\nuniform vec4 contextColor;\\n\\nbool isPick = (drwLayer > 1.5);\\nbool isContext = (drwLayer < 0.5);\\n\\nconst vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\\nconst vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\\n\\nfloat val(mat4 p, mat4 v) {\\n return dot(matrixCompMult(p, v) * UNITS, UNITS);\\n}\\n\\nfloat axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\\n float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\\n float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\\n return y1 * (1.0 - ratio) + y2 * ratio;\\n}\\n\\nint iMod(int a, int b) {\\n return a - b * (a / b);\\n}\\n\\nbool fOutside(float p, float lo, float hi) {\\n return (lo < hi) && (lo > p || p > hi);\\n}\\n\\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\\n return (\\n fOutside(p[0], lo[0], hi[0]) ||\\n fOutside(p[1], lo[1], hi[1]) ||\\n fOutside(p[2], lo[2], hi[2]) ||\\n fOutside(p[3], lo[3], hi[3])\\n );\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n return (\\n vOutside(p[0], lo[0], hi[0]) ||\\n vOutside(p[1], lo[1], hi[1]) ||\\n vOutside(p[2], lo[2], hi[2]) ||\\n vOutside(p[3], lo[3], hi[3])\\n );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n return mOutside(A, loA, hiA) ||\\n mOutside(B, loB, hiB) ||\\n mOutside(C, loC, hiC) ||\\n mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n mat4 pnts[4];\\n pnts[0] = A;\\n pnts[1] = B;\\n pnts[2] = C;\\n pnts[3] = D;\\n\\n for(int i = 0; i < 4; ++i) {\\n for(int j = 0; j < 4; ++j) {\\n for(int k = 0; k < 4; ++k) {\\n if(0 == iMod(\\n int(255.0 * texture2D(mask,\\n vec2(\\n (float(i * 2 + j / 2) + 0.5) / 8.0,\\n (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\\n ))[3]\\n ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\\n 2\\n )) return true;\\n }\\n }\\n }\\n return false;\\n}\\n\\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\\n float x = 0.5 * sign(v) + 0.5;\\n float y = axisY(x, A, B, C, D);\\n float z = 1.0 - abs(v);\\n\\n z += isContext ? 0.0 : 2.0 * float(\\n outsideBoundingBox(A, B, C, D) ||\\n outsideRasterMask(A, B, C, D)\\n );\\n\\n return vec4(\\n 2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\\n z,\\n 1.0\\n );\\n}\\n\\nvoid main() {\\n mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\\n mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\\n mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\\n mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\\n\\n float v = colors[3];\\n\\n gl_Position = position(isContext, v, A, B, C, D);\\n\\n fragColor =\\n isContext ? vec4(contextColor) :\\n isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\\n}\\n\"]),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n gl_FragColor = fragColor;\\n}\\n\"]),o=t(\"./constants\").maxDimensionCount,s=t(\"../../lib\"),l=new Uint8Array(4),c=new Uint8Array(4),u={shape:[256,1],format:\"rgba\",type:\"uint8\",mag:\"nearest\",min:\"nearest\"};function h(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function f(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(!function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var c=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],h(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*c,a.offset=2*l*n,e(a),l*n+c>>8*e)%256/255}function g(t,e,r){for(var n=new Array(8*e),i=0,a=0;au&&(u=t[i].dim1.canvasX,o=i);0===s&&h(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&ni._length&&(S=S.slice(0,i._length));var E,C=i.tickvals;function L(t,e){return{val:t,text:E[e]}}function P(t,e){return t.val-e.val}if(Array.isArray(C)&&C.length){E=i.ticktext,Array.isArray(E)&&E.length?E.length>C.length?E=E.slice(0,C.length):C.length>E.length&&(C=C.slice(0,E.length)):E=C.map(n.format(i.tickformat));for(var I=1;I=r||l>=a)return;var c=t.lineLayer.readPixel(s,a-1-l),u=0!==c[3],h=u?c[2]+256*(c[1]+256*c[0]):null,f={x:s,y:l,clientX:e.clientX,clientY:e.clientY,dataIndex:t.model.key,curveNumber:h};h!==O&&(u?i.hover(f):i.unhover&&i.unhover(f),O=h)}})),z.style(\"opacity\",(function(t){return t.pick?0:1})),u.style(\"background\",\"rgba(255, 255, 255, 0)\");var D=u.selectAll(\".\"+g.cn.parcoords).data(k,h);D.exit().remove(),D.enter().append(\"g\").classed(g.cn.parcoords,!0).style(\"shape-rendering\",\"crispEdges\").style(\"pointer-events\",\"none\"),D.attr(\"transform\",(function(t){return\"translate(\"+t.model.translateX+\",\"+t.model.translateY+\")\"}));var R=D.selectAll(\".\"+g.cn.parcoordsControlView).data(f,h);R.enter().append(\"g\").classed(g.cn.parcoordsControlView,!0),R.attr(\"transform\",(function(t){return\"translate(\"+t.model.pad.l+\",\"+t.model.pad.t+\")\"}));var F=R.selectAll(\".\"+g.cn.yAxis).data((function(t){return t.dimensions}),h);F.enter().append(\"g\").classed(g.cn.yAxis,!0),R.each((function(t){L(F,t)})),z.each((function(t){if(t.viewModel){!t.lineLayer||i?t.lineLayer=v(this,t):t.lineLayer.update(t),(t.key||0===t.key)&&(t.viewModel[t.key]=t.lineLayer);var e=!t.context||i;t.lineLayer.render(t.viewModel.panels,e)}})),F.attr(\"transform\",(function(t){return\"translate(\"+t.xScale(t.xIndex)+\", 0)\"})),F.call(n.behavior.drag().origin((function(t){return t})).on(\"drag\",(function(t){var e=t.parent;T.linePickActive(!1),t.x=Math.max(-g.overdrag,Math.min(t.model.width+g.overdrag,n.event.x)),t.canvasX=t.x*t.model.canvasPixelRatio,F.sort((function(t,e){return t.x-e.x})).each((function(e,r){e.xIndex=r,e.x=t===e?e.x:e.xScale(e.xIndex),e.canvasX=e.x*e.model.canvasPixelRatio})),L(F,e),F.filter((function(e){return 0!==Math.abs(t.xIndex-e.xIndex)})).attr(\"transform\",(function(t){return\"translate(\"+t.xScale(t.xIndex)+\", 0)\"})),n.select(this).attr(\"transform\",\"translate(\"+t.x+\", 0)\"),F.each((function(r,n,i){i===t.parent.key&&(e.dimensions[n]=r)})),e.contextLayer&&e.contextLayer.render(e.panels,!1,!M(e)),e.focusLayer.render&&e.focusLayer.render(e.panels)})).on(\"dragend\",(function(t){var e=t.parent;t.x=t.xScale(t.xIndex),t.canvasX=t.x*t.model.canvasPixelRatio,L(F,e),n.select(this).attr(\"transform\",(function(t){return\"translate(\"+t.x+\", 0)\"})),e.contextLayer&&e.contextLayer.render(e.panels,!1,!M(e)),e.focusLayer&&e.focusLayer.render(e.panels),e.pickLayer&&e.pickLayer.render(e.panels,!0),T.linePickActive(!0),i&&i.axesMoved&&i.axesMoved(e.key,e.dimensions.map((function(t){return t.crossfilterDimensionIndex})))}))),F.exit().remove();var B=F.selectAll(\".\"+g.cn.axisOverlays).data(f,h);B.enter().append(\"g\").classed(g.cn.axisOverlays,!0),B.selectAll(\".\"+g.cn.axis).remove();var N=B.selectAll(\".\"+g.cn.axis).data(f,h);N.enter().append(\"g\").classed(g.cn.axis,!0),N.each((function(t){var e=t.model.height/t.model.tickDistance,r=t.domainScale,i=r.domain();n.select(this).call(n.svg.axis().orient(\"left\").tickSize(4).outerTickSize(2).ticks(e,t.tickFormat).tickValues(t.ordinal?i:null).tickFormat((function(e){return d.isOrdinal(t)?e:P(t.model.dimensions[t.visibleIndex],e)})).scale(r)),l.font(N.selectAll(\"text\"),t.model.tickFont)})),N.selectAll(\".domain, .tick>line\").attr(\"fill\",\"none\").attr(\"stroke\",\"black\").attr(\"stroke-opacity\",.25).attr(\"stroke-width\",\"1px\"),N.selectAll(\"text\").style(\"text-shadow\",\"1px 1px 1px #fff, -1px -1px 1px #fff, 1px -1px 1px #fff, -1px 1px 1px #fff\").style(\"cursor\",\"default\");var j=B.selectAll(\".\"+g.cn.axisHeading).data(f,h);j.enter().append(\"g\").classed(g.cn.axisHeading,!0);var U=j.selectAll(\".\"+g.cn.axisTitle).data(f,h);U.enter().append(\"text\").classed(g.cn.axisTitle,!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"ew-resize\").style(\"pointer-events\",\"auto\"),U.text((function(t){return t.label})).each((function(e){var r=n.select(this);l.font(r,e.model.labelFont),s.convertToTspans(r,t)})).attr(\"transform\",(function(t){var e=C(t.model.labelAngle,t.model.labelSide),r=g.axisTitleOffset;return(e.dir>0?\"\":\"translate(0,\"+(2*r+t.model.height)+\")\")+\"rotate(\"+e.degrees+\")translate(\"+-r*e.dx+\",\"+-r*e.dy+\")\"})).attr(\"text-anchor\",(function(t){var e=C(t.model.labelAngle,t.model.labelSide);return 2*Math.abs(e.dx)>Math.abs(e.dy)?e.dir*e.dx<0?\"start\":\"end\":\"middle\"}));var V=B.selectAll(\".\"+g.cn.axisExtent).data(f,h);V.enter().append(\"g\").classed(g.cn.axisExtent,!0);var q=V.selectAll(\".\"+g.cn.axisExtentTop).data(f,h);q.enter().append(\"g\").classed(g.cn.axisExtentTop,!0),q.attr(\"transform\",\"translate(0,\"+-g.axisExtentOffset+\")\");var H=q.selectAll(\".\"+g.cn.axisExtentTopText).data(f,h);H.enter().append(\"text\").classed(g.cn.axisExtentTopText,!0).call(E),H.text((function(t){return I(t,!0)})).each((function(t){l.font(n.select(this),t.model.rangeFont)}));var G=V.selectAll(\".\"+g.cn.axisExtentBottom).data(f,h);G.enter().append(\"g\").classed(g.cn.axisExtentBottom,!0),G.attr(\"transform\",(function(t){return\"translate(0,\"+(t.model.height+g.axisExtentOffset)+\")\"}));var Y=G.selectAll(\".\"+g.cn.axisExtentBottomText).data(f,h);Y.enter().append(\"text\").classed(g.cn.axisExtentBottomText,!0).attr(\"dy\",\"0.75em\").call(E),Y.text((function(t){return I(t,!1)})).each((function(t){l.font(n.select(this),t.model.rangeFont)})),m.ensureAxisBrush(B)}},{\"../../components/colorscale\":627,\"../../components/drawing\":637,\"../../lib\":750,\"../../lib/gup\":747,\"../../lib/svg_text_utils\":774,\"../../plots/cartesian/axes\":799,\"./axisbrush\":1121,\"./constants\":1124,\"./helpers\":1126,\"./lines\":1128,\"color-rgba\":127,d3:169}],1131:[function(t,e,r){\"use strict\";var n=t(\"./parcoords\"),i=t(\"../../lib/prepare_regl\"),a=t(\"./helpers\").isVisible;function o(t,e,r){var n=e.indexOf(r),i=t.indexOf(n);return-1===i&&(i+=e.length),i}e.exports=function(t,e){var r=t._fullLayout;if(i(t)){var s={},l={},c={},u={},h=r._size;e.forEach((function(e,r){var n=e[0].trace;c[r]=n.index;var i=u[r]=n._fullInput.index;s[r]=t.data[i].dimensions,l[r]=t.data[i].dimensions.slice()}));n(t,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{filterChanged:function(e,n,i){var a=l[e][n],o=i.map((function(t){return t.slice()})),s=\"dimensions[\"+n+\"].constraintrange\",h=r._tracePreGUI[t._fullData[c[e]]._fullInput.uid];if(void 0===h[s]){var f=a.constraintrange;h[s]=f||null}var p=t._fullData[c[e]].dimensions[n];o.length?(1===o.length&&(o=o[0]),a.constraintrange=o,p.constraintrange=o.slice(),o=[o]):(delete a.constraintrange,delete p.constraintrange,o=null);var d={};d[s]=o,t.emit(\"plotly_restyle\",[d,[u[e]]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){var n=function(t,e){return function(r,n){return o(t,e,r)-o(t,e,n)}}(r,l[e].filter(a));s[e].sort(n),l[e].filter((function(t){return!a(t)})).sort((function(t){return l[e].indexOf(t)})).forEach((function(t){s[e].splice(s[e].indexOf(t),1),s[e].splice(l[e].indexOf(t),0,t)})),t.emit(\"plotly_restyle\",[{dimensions:[s[e]]},[u[e]]])}})}}},{\"../../lib/prepare_regl\":763,\"./helpers\":1126,\"./parcoords\":1130}],1132:[function(t,e,r){\"use strict\";var n=t(\"../../plots/attributes\"),i=t(\"../../plots/domain\").attributes,a=t(\"../../plots/font_attributes\"),o=t(\"../../components/color/attributes\"),s=t(\"../../plots/template_attributes\").hovertemplateAttrs,l=t(\"../../plots/template_attributes\").texttemplateAttrs,c=t(\"../../lib/extend\").extendFlat,u=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});e.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:c({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:c({},u,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:c({},u,{}),outsidetextfont:c({},u,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:c({},u,{}),position:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"plot\"},editType:\"plot\"},domain:i({name:\"pie\",trace:!0,editType:\"calc\"}),hole:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},sort:{valType:\"boolean\",dflt:!0,editType:\"calc\"},direction:{valType:\"enumerated\",values:[\"clockwise\",\"counterclockwise\"],dflt:\"counterclockwise\",editType:\"calc\"},rotation:{valType:\"number\",min:-360,max:360,dflt:0,editType:\"calc\"},pull:{valType:\"number\",min:0,max:1,dflt:0,arrayOk:!0,editType:\"calc\"},_deprecated:{title:{valType:\"string\",dflt:\"\",editType:\"calc\"},titlefont:c({},u,{}),titleposition:{valType:\"enumerated\",values:[\"top left\",\"top center\",\"top right\",\"middle center\",\"bottom left\",\"bottom center\",\"bottom right\"],editType:\"calc\"}}}},{\"../../components/color/attributes\":614,\"../../lib/extend\":740,\"../../plots/attributes\":795,\"../../plots/domain\":826,\"../../plots/font_attributes\":827,\"../../plots/template_attributes\":877}],1133:[function(t,e,r){\"use strict\";var n=t(\"../../plots/plots\");r.name=\"pie\",r.plot=function(t,e,i,a){n.plotBasePlot(r.name,t,e,i,a)},r.clean=function(t,e,i,a){n.cleanBasePlot(r.name,t,e,i,a)}},{\"../../plots/plots\":862}],1134:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"tinycolor2\"),a=t(\"../../components/color\"),o={};function s(t){return function(e,r){return!!e&&(!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e))}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c(\"labels\"),c(\"values\")),h=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),h){e._length=h,c(\"marker.line.width\")&&c(\"marker.line.color\"),c(\"marker.colors\"),c(\"scalegroup\");var f,p=c(\"text\"),d=c(\"texttemplate\");if(d||(f=c(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),d||f&&\"none\"!==f){var g=c(\"textposition\");s(t,e,n,c,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||\"auto\"===g||\"outside\"===g)&&c(\"automargin\"),(\"inside\"===g||\"auto\"===g||Array.isArray(g))&&c(\"insidetextorientation\")}o(e,n,c);var m=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",m?\"middle center\":\"top center\");m||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(c,\"title.font\",n.font)}c(\"sort\"),c(\"direction\"),c(\"rotation\"),c(\"pull\")}else e.visible=!1}}},{\"../../lib\":750,\"../../plots/domain\":826,\"../bar/defaults\":896,\"./attributes\":1132,\"fast-isnumeric\":241}],1136:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},{\"../../components/fx/helpers\":651}],1137:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r\"),name:u.hovertemplate||-1!==h.indexOf(\"name\")?u.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:d.castOption(b.bgcolor,t.pts)||t.color,borderColor:d.castOption(b.bordercolor,t.pts),fontFamily:d.castOption(_.family,t.pts),fontSize:d.castOption(_.size,t.pts),fontColor:d.castOption(_.color,t.pts),nameLength:d.castOption(b.namelength,t.pts),textAlign:d.castOption(b.align,t.pts),hovertemplate:d.castOption(u.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[g(t,u)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e}),o._hasHoverLabel=!0}o._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[g(t,u)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[o.index],s=n.select(this).datum();o._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[g(s,i)],event:n.event}),o._hasHoverEvent=!1),o._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),o._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[o.index];e._dragging||!1===r.hovermode||(e._hoverdata=[g(t,i)],a.click(e,n.event))}))}function y(t,e,r){var n=d.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=d.castOption(t._input.textfont.color,e.pts));var i=d.castOption(t.insidetextfont.family,e.pts)||d.castOption(t.textfont.family,e.pts)||r.family,a=d.castOption(t.insidetextfont.size,e.pts)||d.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function x(t,e){for(var r,n,i=0;ie&&e>n||r=-4;m-=2)v(Math.PI*m,\"tan\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+1),\"tan\")}if(h||p){for(m=4;m>=-4;m-=2)v(Math.PI*(m+1.5),\"rad\");for(m=4;m>=-4;m-=2)v(Math.PI*(m+.5),\"rad\")}}if(s||d||h){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;g.push(a)}(d||p)&&((a=_(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a)),(d||f)&&((a=w(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,g.push(a));for(var x=0,b=0,T=0;T=1)break}return g[x]}function _(t,e,r,n,i){e=Math.max(0,e-2*p);var a=t.width/t.height,o=M(a,n,e,r);return{scale:2*o/t.height,rCenter:T(a,o/e),rotate:k(i)}}function w(t,e,r,n,i){e=Math.max(0,e-2*p);var a=t.height/t.width,o=M(a,n,e,r);return{scale:2*o/t.width,rCenter:T(a,o/e),rotate:k(i+Math.PI/2)}}function T(t,e){return Math.cos(e)-t*e}function k(t){return(180/Math.PI*t+720)%180-90}function M(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function A(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function S(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function E(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=L(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),h=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(h+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?h*=2:-1!==a.title.position.indexOf(\"right\")&&(h+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=h/t.titleBox.width,n=C(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function C(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function L(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;er&&(r=t.pull[e]);return r}function P(t,e){for(var r=[],n=0;n1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:d.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:d.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=d.getFirstFilled(i.text,e.pts);(m(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function O(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;f(\"pie\",r),x(e,t),P(e,a);var u=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var u=n.select(this),f=e[0],p=f.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=d.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;ei.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=A(r,i))}(e),u.attr(\"stroke-linejoin\",\"round\"),u.each((function(){var g=n.select(this).selectAll(\"g.slice\").data(e);g.enter().append(\"g\").classed(\"slice\",!0),g.exit().remove();var m=[[[],[]],[[],[]]],x=!1;g.each((function(i,a){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=p.index,m[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=f.cx,u=f.cy,g=n.select(this),_=g.selectAll(\"path.surface\").data([i]);if(_.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),g.call(v,t,e),p.pull){var w=+d.castOption(p.pull,i.pts)||0;w>0&&(o+=w*i.pxmid[0],u+=w*i.pxmid[1])}i.cxFinal=o,i.cyFinal=u;var T=p.hole;if(i.v===f.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(u+i.px0[1])+L(i.px0,i.pxmid,!0,1)+L(i.pxmid,i.px0,!0,1)+\"Z\";T?_.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(u+T*i.px0[1])+L(i.px0,i.pxmid,!1,T)+L(i.pxmid,i.px0,!1,T)+\"Z\"+k):_.attr(\"d\",k)}else{var M=L(i.px0,i.px1,!0,1);if(T){var A=1-T;_.attr(\"d\",\"M\"+(o+T*i.px1[0])+\",\"+(u+T*i.px1[1])+L(i.px1,i.px0,!1,T)+\"l\"+A*i.px0[0]+\",\"+A*i.px0[1]+M+\"Z\")}else _.attr(\"d\",\"M\"+o+\",\"+u+\"l\"+i.px0[0]+\",\"+i.px0[1]+M+\"Z\")}z(t,i,f);var E=d.castOption(p.textposition,i.pts),C=g.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==E?[0]:[]);C.enter().append(\"g\").classed(\"slicetext\",!0),C.exit().remove(),C.each((function(){var g=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),m=l.ensureUniformFontSize(t,\"outside\"===E?function(t,e,r){var n=d.castOption(t.outsidetextfont.color,e.pts)||d.castOption(t.textfont.color,e.pts)||r.color,i=d.castOption(t.outsidetextfont.family,e.pts)||d.castOption(t.textfont.family,e.pts)||r.family,a=d.castOption(t.outsidetextfont.size,e.pts)||d.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(p,i,r.font):y(p,i,r.font));g.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,m).call(c.convertToTspans,t);var v,_=s.bBox(g.node());if(\"outside\"===E)v=S(_,i);else if(v=b(_,i,f),\"auto\"===E&&v.scale<1){var w=l.ensureUniformFontSize(t,p.outsidetextfont);g.call(s.font,w),v=S(_=s.bBox(g.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:I(f.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=u+k[1]*v.rCenter+(v.y||0),O(v,_),v.outside){var 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n=t(\"d3\"),i=t(\"../../lib\"),a=t(\"../../lib/topojson_utils\").getTopojsonFeatures,o=t(\"../../lib/geojson_utils\"),s=t(\"../../lib/geo_location_utils\"),l=t(\"../../plots/cartesian/autorange\").findExtremes,c=t(\"../../constants/numerical\").BADNUM,u=t(\"../scatter/calc\").calcMarkerSize,h=t(\"../scatter/subtypes\"),f=t(\"./style\");e.exports={calcGeoJSON:function(t,e){var r,n,i=t[0].trace,o=e[i.geo],h=o._subplot,f=i._length;if(Array.isArray(i.locations)){var p=i.locationmode,d=\"geojson-id\"===p?s.extractTraceFeature(t):a(i,h.topojson);for(r=0;r=m,k=2*w,M={},A=x.makeCalcdata(e,\"x\"),S=b.makeCalcdata(e,\"y\"),E=s(e,x,\"x\",A),C=s(e,b,\"y\",S);e._x=E,e._y=C,e.xperiodalignment&&(e._origX=A),e.yperiodalignment&&(e._origY=S);var L=new Array(k);for(r=0;r1&&i.extendFlat(s.line,p.linePositions(t,r,n));if(s.errorX||s.errorY){var 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MSSubClassMSZoningLotAreaStreetLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhood...EnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
Id
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14571-Story 1946 & Newer All StylesResidential Low Density13175PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeNorthwest Ames...0000022010Warranty Deed - ConventionalNormal Sale210000
14582-Story 1945 & OlderResidential Low Density9042PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeCrawford...0000250052010Warranty Deed - ConventionalNormal Sale266500
14591-Story 1946 & Newer All StylesResidential Low Density9717PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeNorth Ames...112000042010Warranty Deed - ConventionalNormal Sale142125
14601-Story 1946 & Newer All StylesResidential Low Density9937PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeEdwards...0000062008Warranty Deed - ConventionalNormal Sale147500
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5 rows \u00d7 73 columns

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EnclosedPorch 3SsnPorch \\\n", + "Id ... \n", + "1456 Inside lot Gentle slope Gilbert ... 0 0 \n", + "1457 Inside lot Gentle slope Northwest Ames ... 0 0 \n", + "1458 Inside lot Gentle slope Crawford ... 0 0 \n", + "1459 Inside lot Gentle slope North Ames ... 112 0 \n", + "1460 Inside lot Gentle slope Edwards ... 0 0 \n", + "\n", + " ScreenPorch PoolArea MiscVal MoSold YrSold \\\n", + "Id \n", + "1456 0 0 0 8 2007 \n", + "1457 0 0 0 2 2010 \n", + "1458 0 0 2500 5 2010 \n", + "1459 0 0 0 4 2010 \n", + "1460 0 0 0 6 2008 \n", + "\n", + " SaleType SaleCondition SalePrice \n", + "Id \n", + "1456 Warranty Deed - Conventional Normal Sale 175000 \n", + "1457 Warranty Deed - Conventional Normal Sale 210000 \n", + "1458 Warranty Deed - Conventional Normal Sale 266500 \n", + "1459 Warranty Deed - Conventional Normal Sale 142125 \n", + "1460 Warranty Deed - Conventional Normal Sale 147500 \n", + "\n", + "[5 rows x 73 columns]" + ] }, - "text": "Contribution" - }, - "type": "linear" - }, - 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" + ], + "source": [ + "house_df.tail()" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "markdown", - "id": "790e425a", - "metadata": {}, - "source": [ - "We get the features with most gaps, those that are most important to analyse.\n", - "With date bias introduced, it is normal that date features are the most impacted. We will then decide to remove them.\n", - "Let's analyse other important variables" - ] - }, - { - "cell_type": "markdown", - "id": "ec8dd347", - "metadata": {}, - "source": [ - "### Univariate analysis" - ] - }, - { - "cell_type": "markdown", - "id": "c2c25dca", - "metadata": {}, - "source": [ - "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "13194b27", - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f22f2520", + "metadata": {}, + "outputs": [], + "source": [ + "# For the purpose of the tutorial split dataset in training and production dataset\n", + "# To see an interesting analysis, let's test for a bias between date of construction of training and production dataset\n", + "house_df_learning = house_df.loc[house_df['YearBuilt'] < 1980]\n", + "house_df_production = house_df.loc[house_df['YearBuilt'] >= 1980]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7d28382e", + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", + "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YearBuilt'])]\n", + "\n", + "y_df_production=house_df_production['SalePrice'].to_frame()\n", + "X_df_production=house_df_production[house_df_production.columns.difference(['SalePrice','YearBuilt'])]" + ] + }, + { + "cell_type": "markdown", + "id": "b2138620", + "metadata": {}, + "source": [ + "## Use Eurybia for data validation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d41bf9e4", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "802ac54d", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning)" + ] + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "code", + "execution_count": 9, + "id": "67e91660", + "metadata": {}, + "outputs": [ { - "alignmentgroup": "True", - "cliponaxis": false, - "hovertemplate": "target=df_baseline
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All Styles and Ages', '1-1/2 Story - Unfinished All Ages', '1-Story 1945 & Older', '2-1/2 Story All Ages', '1-Story w/Finished Attic All Ages']\n", + "INFO:root:The variable MSZoning\n", + " has mismatching possible values: \n", + "\n", + " ['Floating Village Residential'] ['Commercial']\n", + "INFO:root:The variable MasVnrType\n", + " has mismatching possible values: \n", + "\n", + " [] ['Brick Common']\n", + "INFO:root:The variable Neighborhood\n", + " has mismatching possible values: \n", + "\n", + " ['Northridge', 'Somerset', 'Northridge Heights', 'Stone Brook', 'Bloomington Heights', 'Bluestem'] ['Brookside', 'Iowa DOT and Rail Road', 'Meadow Village', 'Northpark Villa', 'Briardale', 'South & West of Iowa State University']\n", + "INFO:root:The variable PavedDrive\n", + " has mismatching possible values: \n", + "\n", + " [] ['Partial Pavement']\n", + "INFO:root:The variable RoofMatl\n", + " has mismatching possible values: \n", + "\n", + " ['Clay or Tile'] ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", + "INFO:root:The variable RoofStyle\n", + " has mismatching possible values: \n", + "\n", + " [] ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", + "INFO:root:The variable SaleCondition\n", + " has mismatching possible values: \n", + "\n", + " [] ['Adjoining Land Purchase']\n", + "INFO:root:The variable SaleType\n", + " has mismatching possible values: \n", + "\n", + " ['Contract 15% Down payment regular terms'] []\n", + "INFO:root:The variable Utilities\n", + " has mismatching possible values: \n", + "\n", + " [] ['Electricity and Gas Only']\n" + ] }, { - 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Percent_displayed=%{text}", - "legendgroup": "df_current", - "marker": { - "color": "rgba(223, 103, 0, 0.8)" - }, - "name": "df_current", - "offsetgroup": "df_current", - "orientation": "h", - "showlegend": true, - "text": [ - "4.08 %", - "14.05 %", - "2.29 %", - "0.65 %", - "3.1 %", - "8.01 %", - "0.33 %", - "12.42 %", - "0.49 %", - "20.92 %", - "0.33 %", - "6.7 %", - "12.58 %", - "14.05 %" - ], - "textposition": "outside", - "type": "bar", - "x": [ - 4.084967320261438, - 14.052287581699346, - 2.287581699346405, - 0.6535947712418301, - 3.104575163398693, - 8.006535947712418, - 0.32679738562091504, - 12.418300653594772, - 0.49019607843137253, - 20.915032679738562, - 0.32679738562091504, - 6.699346405228758, - 12.581699346405228, - 14.052287581699346 - ], - "xaxis": "x", - "y": [ - "Mitchell", - "Other", - "Northwest Ames", - "Crawford", - "Edwards", - "Sawyer West", - "Sawyer", - "Gilbert", - "Old Town", - "College Creek", - "North Ames", - "Northridge", - "Northridge Heights", - "Somerset" - ], - "yaxis": "y" - } - ], - "layout": { - "barmode": "group", - "height": 600, - "hovermode": "closest", - "legend": { - "title": { - "text": "" - }, - "tracegroupgap": 0 - }, - "margin": { - "t": 60 - }, - "template": { - "data": { - "scatter": [ - { - "type": "scatter" - } - ] - } - }, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial", - "size": 24 - }, - "x": 0.5, - "xanchor": "center", - "y": 0.9, - "yanchor": "middle" - }, - "width": 900, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "range": [ - 0, - 36.29716981132076 - ], - "showgrid": false, - "showticklabels": true, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 - }, - "text": "Percent" - }, - "type": "linear" - }, - "yaxis": { - "anchor": "x", - "automargin": true, - "autorange": true, - "domain": [ - 0, - 1 - ], - "range": [ - -0.5, - 14.5 - ], - "showgrid": false, - "showticklabels": true, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "85b0770962c04d0baa45f6952486914b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Executing: 0%| | 0/27 [00:00
" + ], + "source": [ + "SD.generate_report( \n", + " output_file='../output/report_house_price_v1.html', \n", + " title_story=\"Data validation V1\",\n", + " title_description=\"\"\"House price Data validation V1\"\"\" \n", + " )" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('Neighborhood')" - ] - }, - { - "cell_type": "markdown", - "id": "da874b83", - "metadata": {}, - "source": [ - "This feature on neighborhood seems to be correlated with the date of build.To avoid creating too much bias, the data scientist decides to remove it from his learning." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "f22587f0", - "metadata": {}, - "outputs": [ + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "alignmentgroup": "True", - "cliponaxis": false, - "hovertemplate": "target=df_baseline
Percent=%{x}
Foundation=%{y}
Percent_displayed=%{text}", - "legendgroup": "df_baseline", - "marker": { - "color": "rgba(0,154,203,255)" - }, - "name": "df_baseline", - "offsetgroup": "df_baseline", - "orientation": "h", - "showlegend": true, - "text": [ - "2.71 %", - "68.4 %", - "10.97 %", - "17.22 %", - "0.71 %" - ], - "textposition": "outside", - "type": "bar", - "x": [ - 2.7122641509433962, - 68.39622641509433, - 10.966981132075471, - 17.21698113207547, - 0.7075471698113207 - ], - "xaxis": "x", - "y": [ - "Slab", - "Cinder Block", - "Poured Contrete", - "Brick & Tile", - "Stone" - ], - "yaxis": "y" - }, + "cell_type": "markdown", + "id": "2fe75388", + "metadata": {}, + "source": [ + "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", + "However, to illustrate functionalities, we will detail results with notebook mode analysis." + ] + }, + { + "cell_type": "markdown", + "id": "bec526c7", + "metadata": {}, + "source": [ + "## First Analysis of results of the data validation" + ] + }, + { + "cell_type": "markdown", + "id": "1187c9d0", + "metadata": {}, + "source": [ + "Data validation methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", + "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", + "Then a classification model (catboost) is learned to predict this target.\n", + "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" + ] + }, + { + "cell_type": "markdown", + "id": "a3d3adb1", + "metadata": {}, + "source": [ + "### Detection data drift performance" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "26482069", + "metadata": {}, + "outputs": [ { - "alignmentgroup": "True", - "cliponaxis": false, - "hovertemplate": "target=df_current
Percent=%{x}
Foundation=%{y}
Percent_displayed=%{text}", - "legendgroup": "df_current", - "marker": { - "color": "rgba(223, 103, 0, 0.8)" - }, - "name": "df_current", - "offsetgroup": "df_current", - "orientation": "h", - "showlegend": true, - "text": [ - "0.16 %", - "8.82 %", - "90.52 %", - "0.49 %" - ], - "textposition": "outside", - "type": "bar", - "x": [ - 0.16339869281045752, - 8.823529411764707, - 90.52287581699346, - 0.49019607843137253 - ], - "xaxis": "x", - "y": [ - "Slab", - "Cinder Block", - "Poured Contrete", - "Wood" - ], - "yaxis": "y" + "data": { + "text/plain": [ + "0.9976525821596245" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" } - ], - "layout": { - "barmode": "group", - "height": 600, - "hovermode": "closest", - "legend": { - "title": { - "text": "" - }, - "tracegroupgap": 0 - }, - "margin": { - "t": 60 - }, - "template": { - "data": { - "scatter": [ - { - "type": "scatter" - } - ] - } - }, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial", - "size": 24 - }, - "x": 0.5, - "xanchor": "center", - "y": 0.9, - "yanchor": "middle" - }, - "width": 900, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "range": [ - 0, - 100.52287581699346 - ], - "showgrid": false, - "showticklabels": true, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 + ], + "source": [ + "#Performance of data drift classifier\n", + "SD.auc" + ] + }, + { + "cell_type": "markdown", + "id": "dd56716b", + "metadata": {}, + "source": [ + "such a high auc means that datasets are not similar.The differences should be analysed before deploying model in production" + ] + }, + { + "cell_type": "markdown", + "id": "9427ec9d", + "metadata": {}, + "source": [ + "### Importance of features in data drift" + ] + }, + { + "cell_type": "markdown", + "id": "f03e8593", + "metadata": {}, + "source": [ + "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "765a5598", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] }, - "text": "Percent" - }, - "type": "linear" + "metadata": {}, + "output_type": "display_data" }, - "yaxis": { - "anchor": "x", - "automargin": true, - "autorange": true, - "domain": [ - 0, - 1 - ], - "range": [ - -0.5, - 5.5 - ], - "showgrid": false, - "showticklabels": true, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": [ + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)", + "rgba(244, 192, 0, 1.0)" + ], + "line": { + "color": "rgba(52, 55, 54, 0.8)", + "width": 0.5 + } + }, + "name": "Global", + "orientation": "h", + "type": "bar", + "x": [ + 0.0046, + 0.0048, + 0.0049, + 0.0049, + 0.0054, + 0.0056, + 0.0063, + 0.0067, + 0.008, + 0.0098, + 0.0102, + 0.0149, + 0.0233, + 0.0277, + 0.0538, + 0.0791, + 0.1015, + 0.1112, + 0.2184, + 0.2384 + ], + "y": [ + "LotArea", + "LotShape", + "WoodDeckSF", + "1stFlrSF", + "BsmtFinSF1", + "Functional", + "Exterior2nd", + "Fireplaces", + "KitchenQual", + "ExterQual", + "MSSubClass", + "BsmtFinType1", + "MSZoning", + "OverallCond", + "GarageFinish", + "Foundation", + "YearRemodAdd", + "Neighborhood", + "GarageYrBlt", + "BsmtQual" + ] + } + ], + "layout": { + "autosize": false, + "barmode": "group", + "height": 500, + "hovermode": "closest", + "margin": { + "b": 50, + "l": 160, + "r": 0, + "t": 95 + }, + "template": { + "data": { + "scatter": [ + { + "type": "scatter" + } + ] + } + }, + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial", + "size": 24 + }, + "text": "Features Importance
Response: Current dataset - Total number of features: 71", + "x": 0.5, + "xanchor": "center", + "y": 0.9, + "yanchor": "middle" + }, + "width": 900, + "xaxis": { + "automargin": true, + "autorange": true, + "range": [ + 0, + 0.25094736842105264 + ], + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial Black", + "size": 16 + }, + "text": "Contribution" + }, + "type": "linear" + }, + "yaxis": { + "automargin": true, + "autorange": true, + "range": [ + -0.5, + 19.5 + ], + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial Black", + "size": 16 + } + }, + "type": "category" + } + } + }, + "image/png": 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" + ], + "source": [ + "SD.xpl.plot.features_importance()" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('Foundation')" - ] - }, - { - "cell_type": "markdown", - "id": "1e1700fa", - "metadata": {}, - "source": [ - "This feature on foundation seems to be correlated with the date of build.To avoid creating too much bias, the data scientist decides to remove it from his learning." - ] - }, - { - "cell_type": "markdown", - "id": "06f54fd4", - "metadata": {}, - "source": [ - "Data scientist thus discards all features that will not be similar to the production training" - ] - }, - { - "cell_type": "markdown", - "id": "a38117de", - "metadata": {}, - "source": [ - "## Second data validation after cleaning data preparation" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "5386c6f4", - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", - "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YearBuilt','BsmtQual',\n", - " 'Neighborhood','Foundation','GarageYrBlt','YearRemodAdd',\n", - " 'GarageFinish','OverallCond','MSZoning','BsmtFinType1','MSSubClass',\n", - " 'ExterQual','KitchenQual','Exterior2nd','Exterior1st','OverallQual',\n", - " 'HeatingQC','FullBath','OpenPorchSF','GarageType','GrLivArea','GarageArea'])]\n", - "\n", - "y_df_production=house_df_production['SalePrice'].to_frame()\n", - "X_df_production=house_df_production[house_df_production.columns.difference(['SalePrice','YearBuilt','BsmtQual',\n", - " 'Neighborhood','Foundation','GarageYrBlt','YearRemodAdd',\n", - " 'GarageFinish','OverallCond','MSZoning','BsmtFinType1','MSSubClass',\n", - " 'ExterQual','KitchenQual','Exterior2nd','Exterior1st','OverallQual',\n", - " 'HeatingQC','FullBath','OpenPorchSF','GarageType','GrLivArea','GarageArea'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "bf9be8de", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "4fee6acd", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:The variable BldgType\n", - " has mismatching possible values: \n", - "\n", - " [] ['Two-family Conversion; originally built as one-family dwelling']\n", - "INFO:root:The variable BsmtCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Poor -Severe cracking, settling, or wetness']\n", - "INFO:root:The variable CentralAir\n", - " has mismatching possible values: \n", - "\n", - " [] ['No']\n", - "INFO:root:The variable Condition1\n", - " has mismatching possible values: \n", - "\n", - " [\"Within 200' of East-West Railroad\"] ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", - "INFO:root:The variable Condition2\n", - " has mismatching possible values: \n", - "\n", - " ['Near positive off-site feature--park, greenbelt, etc.'] ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", - "INFO:root:The variable Electrical\n", - " has mismatching possible values: \n", - "\n", - " [] ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "INFO:root:The variable ExterCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Fair', 'Poor', 'Excellent']\n", - "INFO:root:The variable Functional\n", - " has mismatching possible values: \n", - "\n", - " [] ['Major Deductions 2', 'Severely Damaged']\n", - "INFO:root:The variable GarageCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Poor', 'Excellent']\n", - "INFO:root:The variable GarageQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Excellent', 'Poor']\n", - "INFO:root:The variable Heating\n", - " has mismatching possible values: \n", - "\n", - " [] ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", - "INFO:root:The variable HouseStyle\n", - " has mismatching possible values: \n", - "\n", - " [] ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", - "INFO:root:The variable LandSlope\n", - " has mismatching possible values: \n", - "\n", - " [] ['Severe Slope']\n", - "INFO:root:The variable MasVnrType\n", - " has mismatching possible values: \n", - "\n", - " [] ['Brick Common']\n", - "INFO:root:The variable PavedDrive\n", - " has mismatching possible values: \n", - "\n", - " [] ['Partial Pavement']\n", - "INFO:root:The variable RoofMatl\n", - " has mismatching possible values: \n", - "\n", - " ['Clay or Tile'] ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", - "INFO:root:The variable RoofStyle\n", - " has mismatching possible values: \n", - "\n", - " [] ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", - "INFO:root:The variable SaleCondition\n", - " has mismatching possible values: \n", - "\n", - " [] ['Adjoining Land Purchase']\n", - "INFO:root:The variable SaleType\n", - " has mismatching possible values: \n", - "\n", - " ['Contract 15% Down payment regular terms'] []\n", - "INFO:root:The variable Utilities\n", - " has mismatching possible values: \n", - "\n", - " [] ['Electricity and Gas Only']\n" - ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n", - "CPU times: user 1min 52s, sys: 23.2 s, total: 2min 15s\n", - "Wall time: 7.71 s\n" - ] - } - ], - "source": [ - "%time SD.compile(full_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "029cd7db", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "790e425a", + "metadata": {}, + "source": [ + "We get the features with most gaps, those that are most important to analyse.\n", + "With date bias introduced, it is normal that date features are the most impacted. We will then decide to remove them.\n", + "Let's analyse other important variables" + ] + }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a8bb6b09705e4e238f3e74ba700664c5", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Executing: 0%| | 0/27 [00:00
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" + ], + "source": [ + "SD.plot.generate_fig_univariate('Foundation')" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('2ndFlrSF')" - ] - }, - { - "cell_type": "markdown", - "id": "4b71c4c8", - "metadata": {}, - "source": [ - "Let's assume that the datascientist is ok with these distribution gaps. \n" - ] - }, - { - "cell_type": "markdown", - "id": "0c9d86a8", - "metadata": {}, - "source": [ - "Let's look at the impact on the deployed model. To do this, let's first build the model." - ] - }, - { - "cell_type": "markdown", - "id": "7ddc4642", - "metadata": {}, - "source": [ - "## Building Supervized Model\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "c34b803e", - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\n" - ] - } - ], - "source": [ - "from category_encoders import OrdinalEncoder\n", - "\n", - "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", - "\n", - "encoder = OrdinalEncoder(\n", - " cols=categorical_features,\n", - " handle_unknown='ignore',\n", - " return_df=True).fit(X_df_learning)\n", - "\n", - "X_df_learning_encoded=encoder.transform(X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "43900238", - "metadata": {}, - "outputs": [], - "source": [ - "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "6664824f", - "metadata": {}, - "outputs": [], - "source": [ - "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" - ] - }, - { - "cell_type": "markdown", - "id": "88352c5c", - "metadata": {}, - "source": [ - "## Third Analysis of results of the data validation" - ] - }, - { - "cell_type": "markdown", - "id": "11b8ef7b", - "metadata": {}, - "source": [ - "Let's add model to be deployed to the SmartDrift to put into perspective differences in dataset distributions with importance of the features on model.
\n", - "To get the predicted probability distribution, we also need to add encoding used" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "85072e55", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning,\n", - " deployed_model=regressor, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "abd7d249", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "1e1700fa", + "metadata": {}, + "source": [ + "This feature on foundation seems to be correlated with the date of build.To avoid creating too much bias, the data scientist decides to remove it from his learning." + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:The variable BldgType\n", - " has mismatching possible values: \n", - "\n", - " [] ['Two-family Conversion; originally built as one-family dwelling']\n", - "INFO:root:The variable BsmtCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Poor -Severe cracking, settling, or wetness']\n", - "INFO:root:The variable CentralAir\n", - " has mismatching possible values: \n", - "\n", - " [] ['No']\n", - "INFO:root:The variable Condition1\n", - " has mismatching possible values: \n", - "\n", - " [\"Within 200' of East-West Railroad\"] ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", - "INFO:root:The variable Condition2\n", - " has mismatching possible values: \n", - "\n", - " ['Near positive off-site feature--park, greenbelt, etc.'] ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", - "INFO:root:The variable Electrical\n", - " has mismatching possible values: \n", - "\n", - " [] ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "INFO:root:The variable ExterCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Fair', 'Poor', 'Excellent']\n", - "INFO:root:The variable Functional\n", - " has mismatching possible values: \n", - "\n", - " [] ['Major Deductions 2', 'Severely Damaged']\n", - "INFO:root:The variable GarageCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Poor', 'Excellent']\n", - "INFO:root:The variable GarageQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Excellent', 'Poor']\n", - "INFO:root:The variable Heating\n", - " has mismatching possible values: \n", - "\n", - " [] ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", - "INFO:root:The variable HouseStyle\n", - " has mismatching possible values: \n", - "\n", - " [] ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", - "INFO:root:The variable LandSlope\n", - " has mismatching possible values: \n", - "\n", - " [] ['Severe Slope']\n", - "INFO:root:The variable MasVnrType\n", - " has mismatching possible values: \n", - "\n", - " [] ['Brick Common']\n", - "INFO:root:The variable PavedDrive\n", - " has mismatching possible values: \n", - "\n", - " [] ['Partial Pavement']\n", - "INFO:root:The variable RoofMatl\n", - " has mismatching possible values: \n", - "\n", - " ['Clay or Tile'] ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", - "INFO:root:The variable RoofStyle\n", - " has mismatching possible values: \n", - "\n", - " [] ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", - "INFO:root:The variable SaleCondition\n", - " has mismatching possible values: \n", - "\n", - " [] ['Adjoining Land Purchase']\n", - "INFO:root:The variable SaleType\n", - " has mismatching possible values: \n", - "\n", - " ['Contract 15% Down payment regular terms'] []\n", - "INFO:root:The variable Utilities\n", - " has mismatching possible values: \n", - "\n", - " [] ['Electricity and Gas Only']\n" - ] + "cell_type": "markdown", + "id": "06f54fd4", + "metadata": {}, + "source": [ + "Data scientist thus discards all features that will not be similar to the production training" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n", - "CPU times: user 1min 51s, sys: 24.7 s, total: 2min 15s\n", - "Wall time: 7.91 s\n" - ] - } - ], - "source": [ - "%time SD.compile(full_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "91629f11", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "a38117de", + "metadata": {}, + "source": [ + "## Second data validation after cleaning data preparation" + ] + }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "98b0e5a4055f4bde9a75f242d7a9c654", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Executing: 0%| | 0/27 [00:00", - "hovertext": [ - "Feature: 1stFlrSF
Deployed Model Importance: 14.1%
Datadrift test: K-Smirnov - pvalue: 0.00000
Datadrift model Importance: 2.1", - "Feature: 2ndFlrSF
Deployed Model Importance: 8.0%
Datadrift test: K-Smirnov - pvalue: 0.00000
Datadrift model Importance: 11.7", - "Feature: 3SsnPorch
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.19913
Datadrift model Importance: 0.1", - "Feature: BedroomAbvGr
Deployed Model Importance: 1.9%
Datadrift test: Chi-Square - pvalue: 0.00079
Datadrift model Importance: 3.5", - "Feature: BldgType
Deployed Model Importance: 0.2%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 3.4", - "Feature: BsmtCond
Deployed Model Importance: 0.6%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 0.4", - "Feature: BsmtExposure
Deployed Model Importance: 0.7%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 2.9", - "Feature: BsmtFinSF1
Deployed Model Importance: 7.5%
Datadrift test: K-Smirnov - pvalue: 0.00000
Datadrift model Importance: 4.4", - "Feature: BsmtFinSF2
Deployed Model Importance: 0.4%
Datadrift test: K-Smirnov - pvalue: 0.00009
Datadrift model Importance: 0.9", - "Feature: BsmtFinType2
Deployed Model Importance: 0.2%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 1.5", - "Feature: BsmtFullBath
Deployed Model Importance: 1.1%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 2.2", - "Feature: BsmtHalfBath
Deployed Model Importance: 0.5%
Datadrift test: Chi-Square - pvalue: 0.00009
Datadrift model Importance: 0.5", - "Feature: BsmtUnfSF
Deployed Model Importance: 9.8%
Datadrift test: K-Smirnov - pvalue: 0.00000
Datadrift model Importance: 3.6", - "Feature: CentralAir
Deployed Model Importance: 1.0%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 0.1", - "Feature: Condition1
Deployed Model Importance: 0.6%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 2.5", - "Feature: Condition2
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.09400
Datadrift model Importance: 0.0", - "Feature: Electrical
Deployed Model Importance: 0.3%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 1.8", - "Feature: EnclosedPorch
Deployed Model Importance: 1.2%
Datadrift test: K-Smirnov - pvalue: 0.00000
Datadrift model Importance: 1.8", - "Feature: ExterCond
Deployed Model Importance: 0.7%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 2.7", - "Feature: Fireplaces
Deployed Model Importance: 1.9%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 3.6", - "Feature: Functional
Deployed Model Importance: 0.8%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 1.0", - "Feature: GarageCond
Deployed Model Importance: 0.4%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 0.2", - "Feature: GarageQual
Deployed Model Importance: 0.4%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 0.5", - "Feature: HalfBath
Deployed Model Importance: 0.9%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 3.8", - "Feature: Heating
Deployed Model Importance: 0.3%
Datadrift test: Chi-Square - pvalue: 0.00026
Datadrift model Importance: 0.0", - "Feature: HouseStyle
Deployed Model Importance: 1.8%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 4.0", - "Feature: KitchenAbvGr
Deployed Model Importance: 0.3%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 0.6", - "Feature: LandContour
Deployed Model Importance: 0.5%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 0.8", - "Feature: LandSlope
Deployed Model Importance: 0.5%
Datadrift test: Chi-Square - pvalue: 0.00081
Datadrift model Importance: 0.6", - "Feature: LotArea
Deployed Model Importance: 13.5%
Datadrift test: K-Smirnov - pvalue: 0.00212
Datadrift model Importance: 2.6", - "Feature: LotConfig
Deployed Model Importance: 0.2%
Datadrift test: Chi-Square - pvalue: 0.00236
Datadrift model Importance: 0.7", - "Feature: LotShape
Deployed Model Importance: 0.9%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 3.5", - "Feature: LowQualFinSF
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.58508
Datadrift model Importance: 0.0", - "Feature: MasVnrArea
Deployed Model Importance: 2.0%
Datadrift test: K-Smirnov - pvalue: 0.00000
Datadrift model Importance: 1.3", - "Feature: MasVnrType
Deployed Model Importance: 0.1%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 3.3", - "Feature: MiscVal
Deployed Model Importance: 0.1%
Datadrift test: Chi-Square - pvalue: 0.69736
Datadrift model Importance: 0.1", - "Feature: MoSold
Deployed Model Importance: 4.6%
Datadrift test: Chi-Square - pvalue: 0.75318
Datadrift model Importance: 0.4", - "Feature: PavedDrive
Deployed Model Importance: 0.6%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 1.6", - "Feature: PoolArea
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.49590
Datadrift model Importance: 0.0", - "Feature: RoofMatl
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.07879
Datadrift model Importance: 0.0", - "Feature: RoofStyle
Deployed Model Importance: 0.7%
Datadrift test: Chi-Square - pvalue: 0.00018
Datadrift model Importance: 2.9", - "Feature: SaleCondition
Deployed Model Importance: 1.2%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 3.2", - "Feature: SaleType
Deployed Model Importance: 0.4%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 0.8", - "Feature: ScreenPorch
Deployed Model Importance: 1.0%
Datadrift test: K-Smirnov - pvalue: 0.40255
Datadrift model Importance: 0.7", - "Feature: Street
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.40003
Datadrift model Importance: 0.0", - "Feature: TotRmsAbvGrd
Deployed Model Importance: 2.9%
Datadrift test: Chi-Square - pvalue: 0.00000
Datadrift model Importance: 1.0", - "Feature: TotalBsmtSF
Deployed Model Importance: 9.1%
Datadrift test: K-Smirnov - pvalue: 0.00000
Datadrift model Importance: 10.6", - "Feature: Utilities
Deployed Model Importance: 0.0%
Datadrift test: Chi-Square - pvalue: 0.86985
Datadrift model Importance: 0.0", - "Feature: WoodDeckSF
Deployed Model Importance: 3.1%
Datadrift test: K-Smirnov - pvalue: 0.00000
Datadrift model Importance: 5.6", - "Feature: YrSold
Deployed Model Importance: 3.5%
Datadrift test: Chi-Square - pvalue: 0.68551
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" + ], + "source": [ + "SD.auc" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.scatter_feature_importance()" - ] - }, - { - "cell_type": "markdown", - "id": "b95f3355", - "metadata": {}, - "source": [ - "Putting importance of the drift into perspective according to the importance of the model to be deployed, can help the data scientist to validate that his model can be deployed.
\n", - "Here we see that some features are necessary to analyse" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "6569b08e", - "metadata": {}, - "outputs": [ + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "code", + "execution_count": 21, + "id": "34615404", + "metadata": {}, + "outputs": [ { - "hovertemplate": "%{y:.2f}", - "legendgroup": "df_current", - "marker": { - "color": "rgba(0,154,203,255)" - }, - "mode": "lines", - "name": "df_current", - "showlegend": true, - "type": "scatter", - "x": [ - 1300, - 1425.174, - 1550.348, - 1675.522, - 1800.696, - 1925.87, - 2051.044, - 2176.218, - 2301.392, - 2426.566, - 2551.74, - 2676.9139999999998, - 2802.0879999999997, - 2927.2619999999997, - 3052.4359999999997, - 3177.6099999999997, - 3302.784, - 3427.958, - 3553.132, - 3678.306, - 3803.48, - 3928.654, - 4053.828, - 4179.002, - 4304.1759999999995, - 4429.35, - 4554.523999999999, - 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" + ] + }, + "metadata": {}, + "output_type": "display_data" } - ], - "layout": { - "barmode": "overlay", - "height": 600, - "hovermode": "closest", - "legend": { - "traceorder": "reversed" - }, - "template": { - "data": { - "scatter": [ - { - "type": "scatter" - } + ], + "source": [ + "SD.plot.generate_fig_univariate('2ndFlrSF')" + ] + }, + { + "cell_type": "markdown", + "id": "4b71c4c8", + "metadata": {}, + "source": [ + "Let's assume that the datascientist is ok with these distribution gaps. \n" + ] + }, + { + "cell_type": "markdown", + "id": "0c9d86a8", + "metadata": {}, + "source": [ + "Let's look at the impact on the deployed model. To do this, let's first build the model." + ] + }, + { + "cell_type": "markdown", + "id": "7ddc4642", + "metadata": {}, + "source": [ + "## Building Supervized Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c34b803e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\n" ] - } - }, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial", - "size": 24 - }, - "x": 0.5, - "xanchor": "center", - "y": 0.9, - "yanchor": "middle" - }, - "width": 900, - "xaxis": { - "anchor": "y2", - "autorange": true, - "domain": [ - 0, - 1 - ], - "linecolor": "#BCCCDC", - "range": [ - 1300, - 214817.464 - ], - "showgrid": false, - "showspikes": true, - "spikecolor": "#999999", - "spikedash": "dot", - "spikemode": "across", - "spikethickness": 2, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 - } - }, - "type": "linear", - "zeroline": false - }, - "yaxis": { - "anchor": "free", - "autorange": true, - "domain": [ - 0, - 1 - ], - "position": 0, - "range": [ - -0.000006287665653346401, - 0.00011946564741358167 - ], - "showgrid": false, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 - }, - "text": "Density" - }, - "type": "linear" } - } - }, - "image/png": 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- "text/html": [ - "
" + ], + "source": [ + "from category_encoders import OrdinalEncoder\n", + "\n", + "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", + "\n", + "encoder = OrdinalEncoder(\n", + " cols=categorical_features,\n", + " handle_unknown='ignore',\n", + " return_df=True).fit(X_df_learning)\n", + "\n", + "X_df_learning_encoded=encoder.transform(X_df_learning)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('LotArea')" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "664a80f8", - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "43900238", + "metadata": {}, + "outputs": [], + "source": [ + "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "6664824f", + "metadata": {}, + "outputs": [], + "source": [ + "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" + ] + }, + { + "cell_type": "markdown", + "id": "88352c5c", + "metadata": {}, + "source": [ + "## Third Analysis of results of the data validation" + ] + }, + { + "cell_type": "markdown", + "id": "11b8ef7b", + "metadata": {}, + "source": [ + "Let's add model to be deployed to the SmartDrift to put into perspective differences in dataset distributions with importance of the features on model.
\n", + "To get the predicted probability distribution, we also need to add encoding used" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "85072e55", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning,\n", + " deployed_model=regressor, encoding=encoder)" + ] + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "code", + "execution_count": 27, + "id": "abd7d249", + "metadata": {}, + "outputs": [ { - "hovertemplate": "%{y:.2f}", - "legendgroup": "df_current", - "marker": { - "color": "rgba(0,154,203,255)" - }, - "mode": "lines", - "name": "df_current", - "showlegend": true, - "type": "scatter", - "x": [ - 495, - 503.394, - 511.788, - 520.182, - 528.576, - 536.97, - 545.364, - 553.758, - 562.152, - 570.546, - 578.94, - 587.3340000000001, - 595.728, - 604.122, - 612.516, - 620.91, - 629.304, - 637.698, - 646.092, - 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"spikethickness": 2, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 - } - }, - "type": "linear", - "zeroline": false - }, - "yaxis": { - "anchor": "free", - "autorange": true, - "domain": [ - 0, - 1 - ], - "position": 0, - "range": [ - -0.00008118777508865604, - 0.00154256772797466 - ], - "showgrid": false, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 + } + ], + "source": [ + "%time SD.compile(full_validation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "91629f11", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "98b0e5a4055f4bde9a75f242d7a9c654", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Executing: 0%| | 0/27 [00:00
" + ], + "source": [ + "SD.generate_report( \n", + " output_file='../output/report_house_price_v3.html', \n", + " title_story=\"Data validation V3\",\n", + " title_description=\"\"\"House price Data validation V3\"\"\" \n", + " )" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('1stFlrSF')" - ] - }, - { - "cell_type": "markdown", - "id": "94376f57", - "metadata": {}, - "source": [ - "We see that for important features, the data in production will not be similar in distributions to that in training" - ] - }, - { - "cell_type": "markdown", - "id": "a78ea0a7", - "metadata": {}, - "source": [ - "### Distribution of predicted values" - ] - }, - { - "cell_type": "markdown", - "id": "ae62b69d", - "metadata": {}, - "source": [ - "This graph shows distributions of the production model outputs on both baseline and current datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "77a720dc", - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "markdown", + "id": "134a4f8b", + "metadata": {}, + "source": [ + "### Feature importance overview" + ] + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "markdown", + "id": "f2150418", + "metadata": {}, + "source": [ + "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "a1e4ca16", + "metadata": {}, + "outputs": [ { - "hovertemplate": "%{y:.2f}", - "legendgroup": "Baseline dataset", - "marker": { - "color": "rgba(0,154,203,255)" - }, - "mode": "lines", - "name": "Baseline dataset", - "showlegend": true, - "type": "scatter", - "x": [ - 39614.85744525062, - 40259.14501114568, - 40903.43257704074, - 41547.7201429358, - 42192.00770883086, - 42836.29527472592, - 43480.58284062098, - 44124.870406516035, - 44769.15797241109, - 45413.44553830616, - 46057.733104201216, - 46702.02067009627, - 47346.30823599133, - 47990.59580188639, - 48634.883367781455, - 49279.17093367651, - 49923.45849957157, - 50567.74606546663, - 51212.033631361686, - 51856.32119725675, - 52500.6087631518, - 53144.89632904687, - 53789.183894941925, - 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", 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\n", + "Here we see that some features are necessary to analyse" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "6569b08e", + "metadata": {}, + "outputs": [ { - "hovertemplate": "%{y:.2f}", - "legendgroup": "Current dataset", - "marker": { - "color": "rgba(223, 103, 0, 0.8)" - }, - "mode": "lines", - "name": "Current dataset", - "showlegend": true, - "type": "scatter", - "x": [ - 89889.14324500837, - 90377.75656183274, - 90866.36987865709, - 91354.98319548146, - 91843.59651230581, - 92332.20982913018, - 92820.82314595453, - 93309.4364627789, - 93798.04977960326, - 94286.66309642763, - 94775.27641325198, - 95263.88973007635, - 95752.5030469007, - 96241.11636372507, - 96729.72968054943, - 97218.3429973738, - 97706.95631419815, - 98195.56963102252, - 98684.18294784689, - 99172.79626467124, - 99661.4095814956, - 100150.02289831996, - 100638.63621514433, - 101127.24953196869, - 101615.86284879305, - 102104.47616561741, - 102593.08948244178, - 103081.70279926613, - 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rIRpMxYMIIIAAAggggAACCCCAAAImCBBCmIBsbxVDktK0LbtEW2N6aUhYYIOLyyipUPc39yoi0Ffp4/o0+D0eRAABBBBAAAEEEEAAAQQQQMAZAoQQzlB1cJlNDSGMZrRYnWJpTfWkAQ5uFcUhgAACCCCAAAIIIIAAAggg0DgBQojGeTXL04QQzcJOpQgggAACCCCAAAIIIIAAAg4WIIRwMKgziiOEcIYqZSKAAAIIIIAAAggggAACCJgtQAhhtngT6jPOdTDOd9gztrcuDWnbqBIi3vhGh0orm/RuoyriYQQQQAABBBBAAAEEEEAAAQTqESCEcIMpYs+5DvasonADGpqIAAIIIIAAAggggAACCCDgRgKEEG4wWIQQbjBINBEBBBBAAAEEEEAAAQQQQKBeAUKIeoma/wFCiOYfA1qAAAIIIIAAAggggAACCCBgvwAhhP2GTi/BnhBi2vZMJabm6OmBXTQtspPT20oFCCCAAAIIIIAAAggggAACCJxJgBDCxeeGcSClcTBltwAfZdz2m0a3dv6uLC3YnaX4fuGa3z+80e/zAgIIIIAAAggggAACCCCAAAKOEiCEcJSkk8pJzi7R0KQ0RYcFKjmmV6NrIYRoNBkvIIAAAggggAACCCCAAAIIOEmAEMJJsI4q9kwhRHXlMVVXn1JL34CzVkUI4aiRoBwEEEAAAQQQQAABBBBAAAF7BQgh7BV08vu1Q4jKH3erNOVNlX//qfx7j1DwmCfO2gJ7V1I4uXsUjwACCCCAAAIIIIAAAggg4EUChBAuPtg1Q4StI7ooZ8UNOlVRamv1OTcvkV+voWfsBSGEiw8wzUMAAQQQQAABBBBAAAEEvEiAEMLFB3vt/jzdtS1DEy4I0bNtv1TRx0vl07WffLv2V8k/V6tlu3MUOvldtfRvX2dPCCFcfIBpHgIIIIAAAggggAACCCDgRQKEEC4+2DXPdLh/x32qKsySdfVD3iv3qDLzK7Uf8oACBt2Vho6eAAAgAElEQVRVZ0++yj+mvuv36dKQttoztreL95bmIYAAAggggAACCCCAAAIIeLIAIYSLj641hHi+00HdlLpYrYLC1en+jVKLlpZzIY6uny2fLpeq4x0vnrEnLVanWH5WPWmAi/eW5iGAAAIIIIAAAggggAACCHiyACGEi4+uNYTYWPWKLjv6uToMe1jtosb/HCpUHlP2U1db/t15RvIZb8oghHDxQaZ5CCCAAAIIIIAAAggggICXCBBCuPhAW0OIbXlz1bP6iM695w21Cf21rdX5r92nikMpCo5NkP9Fw+vsDSGEiw8yzUMAAQQQQAABBBBAAAEEvESAEMLFB3ritnS9832mvvzPVHUMaKewWZ9ZtmJYP6U7XlXxJ8vU9jc3KSgmvs7eBK3bo6LKKqWP66OIQF8X7zHNQwABBBBAAAEEEEAAAQQQ8FQBQggXH9khSWkqPPCl3ipappDu/dTxzr+c1uKTeenKXXWLWrYNVudpH9fZG6OMbdkl2hrTS0PCAl28xzQPAQQQQAABBBBAAAEEEEDAUwUIIVx8ZI0AoWPqG3q8YpM6D77DciZE7U/Os6NUVZSl0HvfU+uQbr/4OSGEiw8yzUMAAQQQQAABBBBAAAEEvESAEMLFB9oIEAb963FNaLlXXW9+Qv6R1/+ixQWb/lfHv/1IwaMW1vlzQggXH2SahwACCCCAAAIIIIAAAgh4iQAhhIsPtBEg3PnZPRrctlTnP7RJrc/p+osWW8+FCIgar/Z1rJQwzpVYtz9fa6IjNPGCji7eY5qHAAIIIIAAAggggAACCCDgqQKEEC4+sr1f26E/7bpLfTp3VMTsf9TZWuN2DOOWDJ+u/dTx9lW/eMZ6w0Z8v3DN7x/u4j2meQgggAACCCCAAAIIIIAAAp4qQAjh4iN72dN/0bzMpRo0IFoh45+rs7WnKkp1eOkQtWjjp7BZ/ySEcPExpXkIIIAAAggggAACCCCAgLcKEEK4+MjfljBH44+sV/RN96v90IfO2Nrc58boZMFPCr1v/S+2bLASwsUHmeYhgAACCCCAAAIIIIAAAl4iQAjh4gM9d97vFVW6R8Mmr5D/hdeesbUFGx7R8e/+ruDRj8v/4utOe25jRqFiPz6g0d2CtHFETxfvMc1DAAEEEEAAAQQQQAABBBDwVAFCCBcf2VdnXa4OVSW6If4TteoQdsbWlm5fp+KtKxRw+e1qf+20055Lzi7R0KQ0RYcFKjmml4v3mOYhgAACCCCAAAIIIIAAAgh4qgAhhAuPbMbh/2jv4uE64ROosU/uOGtLK9J3KP+NB+QbEaWQ8SsJIVx4XGkaAggggAACCCCAAAIIIOCtAoQQLjzyX27/m468M0OHQwdo0iMvn7WlVYVZylk5Sq3ad1anB5MIIVx4XGkaAggggAACCCCAAAIIIOCtAoQQLjzyu95/WlnJq7X7/HGKf2De2VtafUpZT14hVZ9S+KM7pRYtT3u+xeoUy9fVkwa4cI9pGgIIIIAAAggggAACCCCAgCcLEEK48Oh+85d7dejbz/TBpXO06s7x9bbUdkPGve+pdUg3Qoh6xXgAAQQQQAABBBBAAAEEEEDATAFCCDO1G1nXd4uv1YHD2XrhyjVKGnt5vW/nv/GgKtK/VMjvlsv3/EGEEPWK8QACCCCAAAIIIIAAAggggICZAoQQZmo3oq7qijKlPj5Y+8uq9cGojVob3b3et4s2P6my3e+qw4hZajfgd3WGEAUT+irIp1W9ZfEAAggggAACCCCAAAIIIIAAAo4W8JoQoqCgQDNmzFBmZqbFMCYmRnFxcTbP2j9PSEjQgAHNd37CicPf6etnxumLqjAd/e1Lmt8/vN6xL/3yFRV/mqh2l92mDsNnnPb8kKQ0bcsu0daYXhoSFlhvWTyAAAIIIIAAAggggAACCCCAgKMFvCKEKC8v18KFCy2hQmxsrOr7+uDBg1q0aJHmzJmjHj16ONq8QeUd37dF374+S++1ukS+o55sUAhRnrZVR9+bJb+eV+mcW58mhGiQNA8hgAACCCCAAAIIIIAAAgiYJeAVIYR1lcOUKVNsqxsSExMtxsZqCCN0WL58ueLj4xUcHPyLkMJ4dvDgwbZ3jfKeeuopzZw50/K8Mz4ln7+ktL8l6ln/Eeo2cnqDQogTuf/WkRdvU+uO3RU6+R1CCGcMDGUigAACCCCAAAIIIIAAAgg0WcArQghDZ8OGDVq5cqWMbRbnn3++FixYoKlTp1pWOqSkpFh+tnTpUluoUDOkaI4QojBpgQ58uV5zOtyhK64Z36AQovpkpbIXD1KL1j4Km/3FaZNizJYD2nSoUBuG99SYiKAmTxheRAABBBBAAAEEEEAAAQQQQKCpAl4TQli3WBhQxrkQNc+EMEIII6SYO3eu/Pz8LJbNHULkvXKP9u/bofs6zdbDN1yviRd0bNAYH04coVNlR9XpoQ/VKjDU9s78XVlasDtL8f3CGxRoNKgyHkIAAQQQQAABBBBAAAEEEECgEQJeEUIY2ydqrnywngkRGhpq2Y7hiishDidep++z/qNbuy3V27GDGnyYZN66u1X5n2/U8fZV8unajxCiEb8MPIoAAggggAACCCCAAAIIIOBcAa8IIWqf+WCQGisfjPDBWP2QlZXlUmdCVFdVKvtPg/R9caWuPf+lRt1oUfD+PB1P/VBBN85T20tGEUI49/eH0hFAAAEEEEAAAQQQQAABBBoh4BUhhPVgyptuuum02zGsKyFq35ZR+3aMmlszDNu6Vk40wrzeR0/k7teRF8fr8xMddWv4okaFECWfrZLxv4BBd6v9kCm2utbuz9Nd2zI04YIQrY3uXm8beAABBBBAAAEEEEAAAQQQQAABRwt4RQhhoBnBwuzZs1VUVGQxrHkmhPG1NagwzoswPsYBlsaVnsbHGkIkJSVZvo6KirL811m3Y5R//6mOrp+tpFaRuvecBxsVQhirIIzVEP69Ryh4zBO2+ZKcXaKhSWmKDgtUckwvR88jykMAAQQQQAABBBBAAAEEEECgXgGvCSHqlTjLA7Vvx7CnrIa8W7p9nYq3rtCyNkO1JOh3Sh/XRxGBvg15VZU/fa28l/+gNmG9de5dLxNCNEiNhxBAAAEEEEAAAQQQQAABBMwQIIRogLLZIUTR5gSV7X5Pk1vfrL8GD1f1pJ9XZDTkY9yMYdyQ0dK/gzpP/4QQoiFoPIMAAggggAACCCCAAAIIIGCKACGEKcyNq+ToOw+r/N//0G/97tH2wAGNCiGMmrIXD1L1yUqFzdimFr7tLJUXVlYpeN0eBfm0UsGEvo1rEE8jgAACCCCAAAIIIIAAAggg4AABQggHIDq6iCMv3a4Th7/X0IDZ2u/fo9EhxJEXx+lE7gGde/cratP5IlvzWqxOsfy7MSsrHN03ykMAAQQQQAABBBBAAAEEEPBeAUIIFxz7w4nX6VRZvn4TnKD81sGNDg2OvjtD5fu3KTg2Qf4XDSeEcMExpkkIIIAAAggggAACCCCAgDcKEEK43KhXK+uJKB2rOqULOj+vrgG+yrjtN41qZfEnT6t0x2tqP+QBBQy6ixCiUXo8jAACCCCAAAIIIIAAAggg4CwBQghnyTax3KriHOU8c6OK2gSpd9CTTbpSs2zXOyr66E9qe8loBd0419aSS9fv09f5xxp15WcTu8FrCCCAAAIIIIAAAggggAACCPxCgBDCxSZFZVaq8tZO1NGgX6tPmxlNCiEqDm5X/psPybdbf4X8/gVbD4ckpWlbdgkhhIuNOc1BAAEEEEAAAQQQQAABBLxFgBDCxUa6PO1THX1vto6ED9SlJyY0KYQ4WZCp3Odi1ap9J3V68K+EEC42xjQHAQQQQAABBBBAAAEEEPBWAUIIFxv5spS3VLRliXJ+fZP6Fd7YpBBC1aeUlRBl6Vn4ozulFi0t/2YlhIsNNs1BAAEEEEAAAQQQQAABBLxMgBDCxQa8eOszKt2+Vl/2vF03F12puMhOWjawS6NbmbNylKoKsxR6/wa1Dv75/fm7srRgd5bi+4Vrfv/wRpfJCwgggAACCCCAAAIIIIAAAgjYI0AIYY+eE94teH+ejqd+qL9d+KDuyY9scmCQ9+pkVf64WyG/f16+3QYQQjhhrCgSAQQQQAABBBBAAAEEEECgcQKEEI3zcvrT+a/fr4qMf2lD3/l68KfOTQ4hCjbN0fFvNyvopgVq2+dGQginjxwVIIAAAggggAACCCCAAAII1CdACFGfkMk/z33htzqZn6E3Bq7QzANtmhxCFH+6XKVfvqz2Q6YoYNDdll4sS83R9O2ZTd7iYTIF1SGAAAIIIIAAAggggAACCHiYACGEiw1o9lNXq7rymNZc87rm7C1ucghRlvK2irYsVrt+N6vDyEctvUzOLtHQpLSmHXbpYk40BwEEEEAAAQQQQAABBBBAwP0ECCFcaMyqT5Qre8mVUstWmtLvNW06VKgNw3tqTERQo1tZvj9ZR9+dKb+eV+mcW58mhGi0IC8ggAACCCCAAAIIIIAAAgg4WoAQwtGidpR3Mv+Qcl+42XKbxa1dntS27BJtjemlIWGBjS71RPY+HVlzp9qEXqBz73mdEKLRgryAAAIIIIAAAggggAACCCDgaAFCCEeL2lGecSClcTClT9d++m3QDLtCiFNl+TqceJ1a+ndQ5+mfWFqVUVKh7m/uVUSgr9LH9bGjpbyKAAIIIIAAAggggAACCCCAQOMFCCEab+a0N4yrOY0rOv0vvk6xre60K4SQqpX15BXSqSqFP7LDssXD+LRYnWL5b/Wkn6/t5IMAAggggAACCCCAAAIIIICAWQKEEGZJN6Ce0u3rVLx1hQIuv12jjl9vZwgh5Tw7SlVFWQq9b71an9OVEKIBY8AjCCCAAAIIIIAAAggggAACzhMghHCebaNLLtqyRGUpb6n9sIfV5buLVFhZpYIJfRXk8/MqhsZ+8l65R5WZXylk/HPyjbiMEKKxgDyPAAIIIIAAAggggAACCCDgUAFCCIdy2lfY0fWzVf79pwqOTVDbfwZbCrNn20TBxj/q+L4tCrppvtr2ibGUF/HGNzpUWqk9Y3vr0pC29jWYtxFAAAEEEEAAAQQQQAABBBBohAAhRCOwnP1o3rq7VPmfvep4x4vy3XzS7hCi+NPlKv3yZQVGT1Hg4Lst5Q1JSrN7m4ezHSgfAQQQQAABBBBAAAEEEEDAMwUIIVxoXHOeuVFVxTnqNOV9tX4ny+4Qouxfb6ro46fUtu9YBV3/R0IIFxprmoIAAggggAACCCCAAAIIeKMAIYTLjPrpt1m0+Mseu0OI8rRPdfS92fLreaXOuXUZIYTLjDUNQQABBBBAAAEEEEAAAQS8U4AQwkXG/VTZUR1OHKGWbYN1+I5N6rt+ny4JaauvxvZucgtPHP5OR166Q206XaBz//C6pZxp2zOVmJqjpwd20bTITk0umxcRQAABBBBAAAEEEEAAAQQQaKwAIURjxZz0/InD3+vIS7erTeiv9e2NqzQ0KU3RYYFKjunV5BpPleXrcOJ1atk2SJ2n/d1SzvxdWVqwO0vx/cI1v394k8vmRQQQQAABBBBAAAEEEEAAAQQaK0AI0VgxJz1ffuAzHX17unzPH6S9Vz/ukBBCqlbWE1HGHRsKf2SH1LIVIYSTxo9iEUAAAQQQQAABBBBAAAEE6hcghKjfyJQnyvasV9HfnlDbS0brq37THBRCSDUPu2wVFE4IYcpoUgkCCCCAAAIIIIAAAggggEBdAoQQLjIvSv7xvEr++aICr5ykXb8e77AQoua1nz5dLlVydonDynYROpqBAAIIIIAAAggggAACCCDgJgKEEC4yUIV/XahjX2+yXKW5qs1gTd+eqbjITlo2sItdLTy6frbKv/9UwbEJ8r9oOCGEXZq8jAACCCCAAAIIIIAAAgggYI8AIYQ9eg58N//Nqao4+IXOufVpPVl0vsMOjyz6+CmV/etNtR/2sAKixhNCOHDMKAoBBBBAAAEEEEAAAQQQQKBxAoQQjfNy2tNHXhynE7kHdO4fXtPjPwU6LIQo/fJlFX+6XAGX3672106zhRCXhrTVHjuu/3QaBAUjgAACCCCAAAIIIIAAAgh4rAAhhIsM7eGnr9Wp40XqHLdFj31f7rAQ4njqhyp4f578e49Q8JgnLL1tsTrF8t/qSQNcpPc0AwEEEEAAAQQQQAABBBBAwBsECCFcYZRPVSnrycstV2iGP/Kl5u/KdlgIUXEoRfmv3SefLn3V8Y7VhBCuMN60AQEEEEAAAQQQQAABBBDwUgFCCBcY+KrCLOWsHKVW7Tur04NJGrPlgDYdKtSG4T01JiLIrhaePPqjcp8fq1ZBv1KnKZsIIezS5GUEEEAAAQQQQAABBBBAAAF7BAgh7NFz0LuVmV8p75V75POrPuo4YY2GJKVpW3aJtsb00pCwQLtqqT5RruwlV/7/KosdlrKC1u1RUWWV0sf1UUSgr13l8zICCCCAAAIIIIAAAggggAACDRUghGiolBOfO75viwo2/lF+F16jc8YudmgIYTT78NIhOlVRqs7TP1FL/w4OL9+JNBSNAAIIIIAAAggggAACCCDgQQKEEC4wmKU7X1fx3/+sdpeNU4fhMx0eEuSuulUn8w7q3HveUJvQXzu8fBcgpAkIIIAAAggggAACCCCAAAJuIEAI4QKDVPzJMpXueFXthz6kgIETHB4S5L/xgCrSdyjkd8vle/4gh5fvAoQ0AQEEEEAAAQQQQAABBBBAwA0ECCFcYJCMrRjGlozgUY/JP/IGBa/bo8LKKhVM6Ksgn1Z2t7AwaYGOffOBgm6Yo7aXjtHEbelatz9fa6IjNPGCjnaXTwEIIIAAAggggAACCCCAAAIINESAEKIhSk5+Ju/Vyar8cbdCfv+8fLsNUIvVKZYaqycNcEjNJdtWquTzlxR49b0KvHKS5u/KctgVoA5pIIUggAACCCCAAAIIIIAAAgh4hYDpIURhYaHi4uI0duxYjR492iuQ6+tk7nNjdLLgJ4Xe+55ah3RzeAhRtvtdFW1+Um37jlXQ9X8khKhvQPg5AggggAACCCCAAAIIIICAUwSaLYRITU21dWjixIl66KGHnNJBdyg068nLpVNVCpv1T7Vo4+fwEKL83//Q0Xcell/PK3XOrcsIIdxhUtBGBBBAAAEEEEAAAQQQQMADBVwihKjp6m2BxKnyYh3+8zVq4dtOYTO2WSgcvR3jRPY+HVlzp9p0ukDn/uF1bcwoVOzHBzS6W5A2jujpgdOaLiGAAAIIIIAAAggggAACCLiigOkhhBVh165dmjx58llNVq1apf79+7uim8PadPLID8pd/Tu1DolQ6L3v6qv8Y+q7fp8uCWmrr8b2dkg9VaV5ylk+Ui3bBqvztI+VnF2ioUlpig4LVHJML4fUQSEIIIAAAggggAACCCCAAAII1CfQbCFE7YZZz4qouU3DeGbevHkefXZExcHtyn/zIflGRClk/EonBQTVynoiyjjqUuGP7FByzjFCiPp+M/g5AggggAACCCCAAAIIIICAwwWaLYTYtGmTHnvssXo7NHLkSM2dO1d+fn71PuuODxz75n0VJj2mtn1uVNBNC5wUQkg5K25QVUmuOj3wvj47FkgI4Y6ThTYjgAACCCCAAAIIIIAAAm4uYHoIcaYVD1ZH6xYM63aNyMhIJSYmKigoyM2p625+yed/Ucm25xQwcKLaD33QaSFE3tqJqsxKVcc7/6JjoZEKXrfH0iBHXQPqkYNDpxBAAAEEEEAAAQQQQAABBBwq4BIhRF1nP6Snp1tuzAgJCfHoEMK4OtO4QrPDiNlqN+BWp4UQR9+brfK0TxUcmyD/i4Y7/PBLh85KCkMAAQQQQAABBBBAAAEEEPBIgWYLIcaOHXvWsx6sKyE8fTuGcXWmcYXmOTcvll+va7QsNUfTt2cqLrKTlg3s4rBJV7RlicpS3lL7YQ8rIGo8IYTDZCkIAQQQQAABBBBAAAEEEECgoQKmhxANbZi3PHfkpTt04vB36jhxrXzCIzV/V5YW7M5SfL9wze8f7jCG0u3rVLx1hQIuv13tr51mCyEKJvRVkE8rh9VDQQgggAACCCCAAAIIIIAAAgicScD0EKLmmRC1t2F4y+qHmoNhXJ1pXKHZ6aEP1Sow1GkhxPHUD1Xw/jz5X3ydgkc/riFJadqWXaKtMb00JCyQ3xAEEEAAAQQQQAABBBBAAAEEnC7gUiGEt5wD8d9RtV6dKYX/caekFk4LISoOpSj/tfvk07WfOt6+ihDC6b9aVIAAAggggAACCCCAAAIIIFBbwJQQory8XAsXLtTmzZsbNAKefiOGFcG4MtO4OrNluxB1jvvI8m1nbcc4efRH5T4/Vq2Dz1Po/RsJIRo0E3kIAQQQQAABBBBAAAEEEEDAkQKmhBBGg62rHLKzs+ttv6cfRmkFqMz6VnlrJ6hN54t07t2vWL49ZssBbTpUqA3De2pMhOOuJa0+Ua7sJVdKLVsp/JEdTqun3sHlAQQQQAABBBBAAAEEEEAAAa8VMC2EMIRXrFihtWvXnhU7LCzM8lz37t09flDK07bq6Huz5Pfrq3XOLX+29NeZZzVkL41WdUWZOk//VI/tK3XKAZgeP2h0EAEEEEAAAQQQQAABBBBAoMkCpoYQRivPdjBlk3vhpi+Wpbytoi2L1a7fzeow8lGnhxC5q27Rybx0nXvPm3o8sy0hhJvOG5qNAAIIIIAAAggggAACCLirgOkhRHNCHTx4ULNnz1ZRUZE6dOigxYsXq0ePHpYmFRQUaMaMGcrMzLR8nZCQoAEDBji1ucXJz6r0izUKjL5fgYP/4PQQIv+NKapI36mQccuVUBBBCOHU0aVwBBBAAAEEEEAAAQQQQACB2gKmhBDW1Q9G5QsWLFB8fLxSU1PPOBrOOJjSCCAWLVqkOXPm2IIHawOsB2caoUNsbKzO9qwjp1DhB/E6tvevCrpxntpeMsrpIUThB/N1bG+Sgm6cq7f9B+uubRka3S1IG0f0dGS3KAsBBBBAAAEEEEAAAQQQQACBOgW8IoSwhgxGwFDX6gYjdFi+fLklHAkODlbtUCIxMVGDBw+2vWusmnjqqac0c+ZMy/NN/eS/PkUVGcbKhBXy7THQUkzwuj0qrKxSwYS+CvJp1dSi63yvOHmlSr94SYFX36td54/T0KQ0RYcFKjmml0ProTAEEEAAAQQQQAABBBBAAAEE6hLwihCi9lYLAyIqKkpz586Vn5+fUlJStHLlSi1dutQWKhjBg/GJi4uTs0II6xkNoZPeUutzz7fU12J1iuW/1ZMcvxWkbNc7KvroT2rbd6y+uvQhQgj+PwEBBBBAAAEEEEAAAQQQQMBUAVNCCFN7VEdlZ1rpEBoaagkZjBBiw4YNtlDCKMKMEKLmbRUt/ds7PYQo379NR9+dIb+eV+mbqx4jhGjuiUn9CCCAAAIIIIAAAggggICXCXhlCGGMcc3VDz/88IPpKyGqT5Qre8mVUstWCn9kh23aOXMlxInsfTqy5k616dRLbe542bL1w/g4Y9WFl/0e0V0EEEAAAQQQQAABBBBAAIEGCDRbCLFixQoNGjRI/fv316ZNm/TYY49Zmjtx4kQ99NBDDWh6wx8xtmMYB2JOnTrVdihlzdUPWVlZpp8JcfLoj8p9fqxaBf1KnaZsMiWEqCrNU87ykWrZ7hx1jtvi1K0fDR8dnkQAAQQQQAABBBBAAAEEEPAWgWYJIYwAYu3atVq1apXFefLkyad5z5s3T6NHj3boGBjbK3Jzcy1bLozPwoULLQdNGodV1nc7Rs2tGca7dZ0h0djGVv64W3mvTpZPl77qeMdqy+tf5R9T3/X7dElIW301tndji2zQ81lP/HzWRPgfUwghGiTGQwgggAACCCCAAAIIIIAAAo4SMD2EsP7Bv3nzZhlhg/GxroKwdmrkyJGnnc/giM5a6925c6eluJiYGMt5ENZP7cMrExISbLdhWEOIpKQky+PGoZbGx57bMY5/+5EKNv2v/C8apuDYJy3lJWeXOP2chpwVN6iqJFedHvhA5394RIdKK5U+ro8iAn0dwUwZCCCAAAIIIIAAAggggAACCJxRwPQQorCw0PLH/3nnnadZs2ZpyZIlMgIJYxvGpEmTLCsUfvrpJ8vBkEFBQS4xdLVvx3BEo0p3vq7iv/9Z7Qb8Th1GzDIthMhbO0GVWd+q450vacRXvtqWXaKtMb00JCzQEd2iDAQQQAABBBBAAAEEEEAAAQRcM4Qwzn74n//5H6WmplpWRVx33XVeE0IUf7pcpV++rPZDpihg0N2mhRBH35ul8rStltUX1//QhRCC/3NAAAEEEEAAAQQQQAABBBAwTcD0lRA1t2PU7OXy5cv14YcfWlZFOGM7hmmiDayo8IN4Hdv7VwXdOE9tLxllWghRtGWxylLeVodhD+vG3P6EEA0cLx5DAAEEEEAAAQQQQAABBBCwX8D0EMJosvVgSmvzIyMjLdsvXnnlFcuBlc44mNJ+KseWkP/Gg6pI/1Ln/C5RfucPthS+LDVH07dnKi6yk5YN7OLYCv+/tNLta1W89RkFXHGH/tzhFi3YnaX4fuGa3z/cKfVRKAIIIIAAAggggAACCCCAAAJWgWYJIWquhggLC7OEEt27d7dc1fnjjz86/IpOVxzuIy/ephO5/9a5d7+qNp0vtDRx/q4sp4cCx1M/VMH78+R/8XVKPO8Bp9fniva0CQEEEEAAAQQQQAABBBBAoHkEmiWEaJ6uulathxNH6FTZUXWa+je1CjjXtBCiIuNfyn/9fvl07adnLppPCOFa04LWIIAAAggggAACCCCAAAIeLUAI0SzDW62sJ36+5jP8j8aVoS1MCyFO5h9S7gs3q3VwF31x/RrFfnxAo7sFaeOIns0iQaUIIIAAAggggAACCCCAAALeI9AsIcSZDqe0slvPiHCVKzodPR2qSvOUs3ykWrYNVudpH9uKn7gtXfq31KUAACAASURBVOv252tNdIQmXtDR0dVayqs+Ua7sJVdKLVtp/4S/a2hSmqLDApUc08sp9VEoAggggAACCCCAAAIIIIAAAlaBZgkhah9MWXs4PD2EOHH4ex156Xa1Cf21zr3nDVv3hySlmXJbRfbSaFVXlOnA+PcV/XEWIQT/f4AAAggggAACCCCAAAIIIGCKgOkhRGFhoeLi4pSamnrGDnp6CFH+w+c6+lacfLtfoZDbnjE9hMh94bc6mZ+hvFvW6pLkcl0a0lZ7xvY2ZcJRCQIIIIAAAggggAACCCCAgPcKNGsIMXHiRK+4CaP29Dr29fsq/OtjatvnRgXdtMD0ECL/9SmqyNipkHEr5PdJG0v91ZMGeO9vAT1HAAEEEEAAAQQQQAABBBAwRcD0EKLmeRDz5s3T6NGjTemoK1VS+sVLKk5eqYAr7lT7a6bamtb9zb3KKKmwrEowVic461P4QbyO7f2rgm6cp3Y7wwkhnAVNuQgggAACCCCAAAIIIIAAAqcJmB5CGLWnp6dbVkCEhIQoMTFRnnoA5ZnmWtGWJSpLeUvthz2sgKjxtsdarE4xJRAoTn5WpV+sUeDV96n9d5eaUie/dwgggAACCCCAAAIIIIAAAgiYHkJwJoRUsOERHf/u7woe/bj8L77O9BCibNfbKvposdr2HasxVTebchgmv2oIIIAAAggggAACCCCAAAIIEEI0wxzIe2WSKjP3KOT3z8u323/PYjBrJUT5/mQdfXem/HpepbFt7yOEaIY5QJUIIIAAAggggAACCCCAgDcKEEI0w6jnPj9WJ4/+qNDJ76h1x+6WFiRnl2hoUpop12WeyN6nI2vuVJtOvXRLp3mEEM0wB6gSAQQQQAABBBBAAAEEEPBGAdNDCG9Ert3n7KXRqq4oU+cZyWrpG2B6CFFVmqec5SPVst05euTSF7Ruf77WREdo4gUdGR4EEEAAAQQQQAABBBBAAAEEnCbQrCHEihUrtHbtWkvnVq1apZ9++knnnXee+vfv77QON3fB1SeOK3vJVVLLVgp/ZIetOWauhJCqlfVElKXu1SPWa/6eHMX3C9f8/j/flMEHAQQQQAABBBBAAAEEEEAAAWcINEsIUdfhlNYQYvXq1TLCie7df96m4GkfYxuGsR2jVdCv1GnKJlv3lqXmaPr2TMVFdtKygV2c3u2cFTeoqiRXr1z1oh75/iQhhNPFqQABBBBAAAEEEEAAAQQQQKBZQohNmzbpscceO03fCCG++OILy8qIiRMnWq7w9MRP5Y+7lffqZPmcd4k63vkXWxfn78rSgt1ZpoUBeWsnqDLrW20e/JT+sD/AtPDDE8eUPiGAAAIIIIAAAggggAACCDRMwPQQory8XAsXLtTmzZs1b948y/aLyZMnW7ZjGB/j3yNHjtTcuXPl5+fXsF640VPH921RwcY/yv+iYQqOfbLZQgjjdgzjlowfo+M1cF+YKQdiutEw0VQEEEAAAQQQQAABBBBAAAEnCJgeQli3YhjhgxE0fPvtt7YQ4uKLL7YEFMbZEImJiQoKCnJCl5u3yNIdr6n4k6fV7rLb1GH4DFtjxmw5oE2HCk07ILLoo8Uq2/W2/nPZA4rK6EMI0bzTgtoRQAABBBBAAAEEEEAAAa8QaLYQwtA1goYffvjBFkKcf/75iouLs8B7aghR/Mkyle54Ve2vmaqAK+60TbIhSWmmXpVZ+sUaFSc/q8O9b1X/I9cQQnjFrzudRAABBBBAAAEEEEAAAQSaV8D0EOJs2zGMFRDGWRGevB2jYNP/6vi3Hyl41GPyj7yh2UKIY3v/qsIP4tXiwusUnh9raUf1pAHNOxupHQEEEEAAAQQQQAABBBBAwKMFTA8hDM26DqasqezJB1Pmv3avKg7tUsj4lfKN+PmaTOPT/c29yiipUPq4PooI9HX6pKvI2Kn816fIp2s/nXt8MiGE08WpAAEEEEAAAQQQQAABBBBAoFlCiLqu6LQORWRkpMduxTD6aFzPaVzTGTr5HbXu+N9rSFusTjE1CDiZn6HcF36r1sFd1Kn1/5paN792CCCAAAIIIIAAAggggAAC3inQLCGElXrFihWWKzmtH09eAWHtY/aSq1R94rg6P7xVLf0CbX03O4Sorjym7Keullq20qBuq3WotNK0VRje+atGrxFAAAEEEEAAAQQQQAABBJo1hPA2/pp/+Ic/ssPW/cLKKgWv26MOPq1UOKGvaSzZS65U9Yly3d17lT46ckpbY3ppSNh/gxHTGkJFCCCAAAIIIIAAAggggAACXiFACGHiMBvbMIztGK2Dz1Po/RttNSdnl2hoUprpN1QY2zGMbRnTej6pd4qCCCFMnAtUhQACCCCAAAIIIIAAAgh4o4DpIUR6eroeeughZWdnn+YdFhYmY3tG9+7/PSfB0wbEOJDSOJjSp0tfdbxjdbOHEMbBlMYBlYt7PKLEkghtGN5TYyKCPI2d/iCAAAIIIIAAAggggAACCLiIgKkhRO0zIOoy8ORzIY5/u1kFm+bI/6LhCo5NsHV/Y0ahYj8+YPpKiIL35+l46of624UP6p78SMX3C9f8/uEuMjVpBgIIIIAAAggggAACCCCAgKcJmBZC1HctZ03YefPmafTo0Z5mrdIvX1Hxp4lqFzVeHYY9bOvf/F1ZWrA7y/QQoHjrMyrdvlZbI27T7WXRptfvcQNMhxBAAAEEEEAAAQQQQAABBM4qYEoIUV5eroULF2rz5s2WxtS12qFmSDFy5EjNnTtXfn5+HjV8xZ88rdIdr6n9NVMVcMWdzR5ClKW8raIti7W90wj99tRYQgiPmm10BgEEEEAAAQQQQAABBBBwPQFTQojCwkLFxcUpNTVVZwoYagYVkZGRSkxMVFCQZ51PULDxjzq+b4uCRy2Uf+T1zR5ClKdt1dH3Zuk/nS5X1Km7NLpbkDaO6Ol6s5QWIYAAAggggAACCCCAAAIIeISA6SHE2c58sJ4Z4akhRN6rk1X5426FjH9OvhGX2SbQkKQ0bcsuMf12isqsb5W3doKOdjhffXxmmX4mhUf8BtEJBBBAAAEEEEAAAQQQQACBBgsQQjSYyv4Hc5+L1cmCTIXe+65ah0Q0ewhRVZKrnBU3qKh1B/UO/hMhhP1DTAkIIIAAAggggAACCCCAAAJnETA9hGjIaHjqSojsJVep+sRxhc3Ypha+7Zo9hJCqlfVElCpPnVLP0JXq0r6t0sf1acgQ8QwCCCCAAAIIIIAAAggggAACjRYghGg0WdNeqK4oU/bSaLVo46+wWZ+dVkjwuj0qrKxSwYS+CvJp1bQKmvhWzvLrVVV6RJcGPa4jbUJUPWlAE0viNQQQQAABBBBAAAEEEEAAAQTOLkAIYdIMOZmfodwXfqvWwV0Uev+G02ptsTrF8nVzBABH1typE9n7dE3ATKX592yWNpg0BFSDAAIIIIAAAggggAACCCDQzAKmhBDN3EeXqL4i41/Kf/1++XTtp463r3KZEOLouzNVvj9Zt/j9QV8EXkYI4RKzhUYggAACCCCAAAIIIIAAAp4pQAhh0rgeT/2bCt6fK//eIxQ85glbrV/lH1Pf9fvULcBHGbf9xqTW/Leaoo8Wq2zX20rsOF6LW1xt+g0dpneYChFAAAEEEEAAAQQQQAABBJpNgBDCJPrSL19W8afLFXD579X+2um2WpOzSzQ0Ka3ZbqYo/eIlFSev1JrAkZrjN4YQwqT5QDUIIIAAAggggAACCCCAgDcKEEKYNOpFf/+zyna+rvbXxCngijtcJoQ4tjdJhR/M17u+UYprfzchhEnzgWoQQAABBBBAAAEEEEAAAW8UIIQwadQLNjyq4999rODRi+R/8UhbrWv35+mubRmacEGI1kZ3N6k1/62mIn2n8t+Yol1+F2pU4DQ9PbCLpkV2Mr0dVIgAAggggAACCCCAAAIIIOD5AoQQJo1x3iuTVJm5RyG/f0G+3frbap2/K0sLdmcpvl+45vcPN6k1/63mZF66clfdon+3PFdDQhY2WztM7zgVIoAAAggggAACCCCAAAIImC5ACGESee5zY3Sy4CeF3rderc/p6jIhRHVFmbKXRiuzoqWuOG8lIYRJ84FqEEAAAQQQQAABBBBAAAFvFCCEMGnUs568XDpVpbCZ/1ALn7a2WockpWlbdkmznsWQveRK5RSX6MpfrdA9l3TXsoFdTFKhGgQQQAABBBBAAAEEEEAAAW8SIIQwYbRPlZfo8J+HqkUbf4XN+uy0Gl0hhMh94WYVHD6oESHz1T3iQiXH9DJBhSoQQAABBBBAAAEEEEAAAQS8TYAQwoQRt567YGzDMLZj1PwEr9ujwsoqFUzoqyCfVia05pdV5L9+v/L+/aVu6zBV7XsOIoRollGgUgQQQAABBBBAAAEEEEDA8wUIIUwY44qMncp/fYrlQErjYErrxwgfjBDC+FRPGmBCS+quouD9ecrd84EeaHunTl50AyFEs40EFSOAAAIIIIAAAggggAACni1ACGHC+B5P/VDGH/r+F1+n4NGP22pMzi7R0KQ0RYcFNusf/sVbn1Hp9rWa3uIGvd1xVLMGIiYMB1UggAACCCCAAAIIIIAAAgg0kwAhhAnwpV++rOJPlyvg8tvV/tppthrX7s/TXdsyNLpbkDaO6GlCS+quoizlbRVtWaxFVVfouc4TCSGabSSoGAEEEEAAAQQQQAABBBDwbAFCCBPGt+jjpSr71xtqf+10BVz+e1uN83dlacHurGa/FrM87VMdfW+2nivvpUVdphNCmDAnqAIBBBBAAAEEEEAAAQQQ8EYBQggTRr1gwyM6/t3fFTzmCfn3HmGrccyWA9p0qFAbhvfUmIggE1pSdxWVWanKWztRfy3vrMld5it9XB9FBPo2W3uoGAEEEEAAAQQQQAABBBBAwDMFCCFMGNe8l/+gyp++VsfbV8mnaz9bjX3X79NX+ce0NaaXhoQFmtCSuquoKslVzoobtPu4n27quqzZ29NsEFSMAAIIIIAAAggggAACCCDgVAFCCKfy/lx4zsrRqir8j+V6TuOaTuunxeoUyz+b82aMn9tSrawnopRWVK5re76kT2MubNZQxIQhoQoEEEAAAQQQQAABBBBAAIFmECCEMAE968nLpVNVCpv1mVq08bfUmFFSoe5v7lW3AB9l3PYbE1px9ipylo/U/qz/aGzXpVp9Q1Szbg9pdgwagAACCCCAAAIIIIAAAggg4BQBQginsP630FPlxTr852vUwqetwmb+w/YDV7me09qgI2vu1KEfvtbtoY9q3OCrNb9/uJNlKB4BBBBAAAEEEEAAAQQQQMDbBLwyhEhMTNTXX3+tpUuXKjg42DLmBQUFmjFjhjIzMy1fJyQkaMCAAXbPh5NHDip39a1qHdJNofe+ZyvPejNGXGQnLRvYxe567C3g6LszdPCrvyvunPs0PHo0IYS9oLyPAAIIIIAAAggggAACCCDwCwGvCyGMACIpKUldunSxhRDl5eVauHChJXSIjY3VwYMHtWjRIs2ZM0c9evSwa9pUpO9U/htT5NttgEJ+/7ytrGnbM5WYmtPs13NaG1T00Z/0789e14LAceo/bAIhhF2jzssIIIAAAggggAACCCCAAAJ1CXhVCLFhwwb9+OOPGjx4sFauXGkLIYzQYfny5YqPj7esjKgdShjBhfGOdWWEsWriqaee0syZM20rKc40vY7t/asKP4iXf+T1Ch610PbYkKQ0bcsucZmbKEq/eEk/blmhxW2GKeOSe5Qc04vfGAQQQAABBBBAAAEEEEAAAQQcKuA1IYQRQKSkpGju3LlKTU09LYQwvl8zlDCEjeDB+MTFxVn+3dQQonT7WhVvfUYBV9yh9tfE2QYveN0eFVZWKX1cH0UE+jp0UJtS2LG9ScpcP1er1F+7+s8ihGgKIu8ggAACCCCAAAIIIIAAAgicVcArQggjZDBCCCOA8PPzs4QRNUOH2j93ZAhRtGWJylLeUvthDysgarxlMKw3Y3TwaaXCCX1dYopWpO9Qxsv36b0TPbTliicIIVxiVGgEAggggAACCCCAAAIIIOBZAl4RQhgBhBE61P5Yz4X44YcfnLYSomD9/+j4958oeMwT8u89wtKEtfvzdNe2DI3uFqSNI3q6xIw6mXdQPz13izaXddCfLk20rNDggwACCCCAAAIIIIAAAggggIAjBbwihKgNVnslhDPPhMh7+W5V/vSNOt6xWj5dfl71MGbLAW06VKg10RGaeEFHR45nk8s6VVGqw0uH6Iv8k7rlwhdVPcn+m0Ga3BheRAABBBBAAAEEEEAAAQQQ8EgBQog6DqKsfTtGzfMhjFlQ1xkSZ5odOc+OUlVRlkLv36DWwV0s50AY50EYn4IJfRXk08plJlb2kiuVkn1U43o9r7J7r3aZdtEQBBBAAAEEEEAAAQQQQAABzxAghAgO/jkQKCjQjBkzlJmZafk6ISHBdhuGNYQwrvY0PlFRUZb/NuR2jKwnL5dOVSn8kR1Sy1YuuRXDOpVznx+rHfu/1wM9EvTN5BiXCkg849eNXiCAAAIIIIAAAggggAAC3i3glSFEY4e89u0YDX3/1PFiHX76GrX0DVDnGcmW11xxK4a1P/mv3ae93/xT08Nm6unf3awhYYEN7SrPIYAAAggggAACCCCAAAIIIFCvACFEvUQ/X9dZ84rOBrxieeTkkR+Uu/p3at2xu0Inv+PSWzGM9ha8P09ff75eizr+QXNuv4cQoqEDzXMIIIAAAggggAACCCCAAAINEiCEaBBT0x6qSP9S+W88KN+IyxQy/jmX3oph9LB46wrt3vyCngsaq/snziKEaNqw8xYCCCCAAAIIIIAAAggggMAZBAghnDg1jn3zgQqTFsg/8gYFj3rMpbdiGAxlKW8pdcPjWusXrV6xczQtspMTdSgaAQQQQAABBBBAAAEEEEDA2wQIIZw44qVfrFFx8rMKuOJOHb3sXnV/c6+lNle7FcNKUJ72qVJfma6Nrfqo9ajFmt8/3Ik6FI0AAggggAACCCCAAAIIIOBtAoQQThzxoi2LVZbytjoMn6HV/tdo+vZMje4WpI0jejqx1qYXXZmVqm9W/l6fnTpPRTevJoRoOiVvIoAAAggggAACCCCAAAII1CFACOHEaXH0vdkyVhcExybotszu2nSoUGuiIzTxgo5OrLXpRVcV52jvkhH6pqKdPr3xNa2N7t70wngTAQQQQAABBBBAAAEEEEAAgVoChBBOnBJ56+5S5X/2ynfcKnX85JSlJlfdivEzQ7XSFvRXWlG5/nzNeiXHXOhEHYpGAAEEEEAAAQQQQAABBBDwNgFCCCeOeM6zN6mqKFvbRq7R+F0Vig4LVHJMLyfWaH/R3y0ZpgPZWVo9+EW9f/Mg+wukBAQQQAABBBBAAAEEEEAAAQT+X4AQwolTIevJy6VTVZoz6E2t+Xehnh7YxeVvnEh9dpzSf/hGb122WK/eFuNEHYpGAAEEEEAAAQQQQAABBBDwNgFCCCeN+KljhTq8bJha+gXqopA/q7CySunj+igi0NdJNTqm2KPvPKzPt29WwnlT9cXD9zmmUEpBAAEEEEAAAQQQQAABBBBAQBIhhJOmwYncf+vIi7epIKCLIv3/V5eE/F97dwIeVXW/cfyd7AkEEkAwKBBBwaIoCuLWFpRKVUCBtu4KatGWWoGqFBT+gKDggrIoSm0L1gXrAlTRooKAoiiigKIVVFZlDwQC2Ugy/+dMmDgZQpI7c2cyd+Y7Pj6E5J7fOedz7iSZlzv3pGl1v/Yh6s2+svsXTNT77/xLf2t2o9746wj7ClMJAQQQQAABBBBAAAEEEEAg5gUIIUJ0ChRtWK6cl/6sT1Pbq0/9OzX49GaafH6LEPVmX9m8D/+pJa89qtca99Ss+x6xrzCVEEAAAQQQQAABBBBAAAEEYl6AECJEp0D+F68rd/79esZ1jsY0uVWr+rVXx8ZpIerNvrL5X7yhj54fof/WO0833jHVEWO2b/ZUQgABBBBAAAEEEEAAAQQQCKUAIUSIdM0VBTsXPaHx8d0178TrlNv/rBD1ZG/Zoo2faPnTt+rDxFN14Z9mqltWur0dUA0BBBBAAAEEEEAAAQQQQCBmBQghQrT0+99+WFs+fFH31btKcWddpXk9Tg5RT/aWLdmzQcseuULfuJrq1MH/IYSwl5dqCCCAAAIIIIAAAggggEBMCxBChGj59742TN9+vkB/afxH/a5Hv4jfmtPL4C46pOVjztO24ngdvH2hBrRtEiIhyiKAAAIIIIAAAggggAACCMSaACFEiFZ89z9v1FfrVun2E0Zpxu8ud9QVBZ+M7qJdeQe15urXNfJcZ1zBEaJlpCwCCCCAAAIIIIAAAggggICNAoQQNmL6ltoxpYfWbP1R17aZos9v+IWy05ND1JP9ZZdOvEwHdm3Wuitm6u5u59rfARURQAABBBBAAAEEEEAAAQRiUoAQIhTLXlaqbRPP1Yo9her3s5kqG3hOKHoJWc2PnuyvnO8/1cc/f1AP9OsTsn4ojAACCCCAAAIIIIAAAgggEFsChBAhWO+SfVv1wxN9tPBgfY3vOE2brj0jBL2EruTqF4Zp62fztei0wZp86+2h64jKCCCAAAIIIIAAAggggAACMSVACBGC5S7a9Kk2PXub5h7O1oLzJmpJr3Yh6CV0JT+f94h+fH+mVpxyg8b98d7QdURlBBBAAAEEEEAAAQQQQACBmBIghAjBcud/OV/fvzJSs+I7K6fbfZrV9aQQ9BK6kruWv6hPXhmvxU0u0WP3TgldR1RGAAEEEEAAAQQQQAABBBCIKQFCiBAsd96yv2vdgmmalnqpTrp0sMZ0ah6CXkJXsvCb9/Tu3+7QJ+lna/zY50PXEZURQAABBBBAAAEEEEAAAQRiSoAQIgTLnfvWA/ry/Zc0odGNuqrPLRrQtkkIegldyeIfv9TCx67WN8knqd+IOY7a2SN0KlRGAAEEEEAAAQQQQAABBBAIVoAQIljBKtrnvPRnfblqse45fqgeueYqdctKD0EvoStZemCnPniguza7G6jV0LcdN/7QyVAZAQQQQAABBBBAAAEEEEAgGAFCiGD0jtF219+u0sp1azXopAe0pP/lzruSoKxUi0d0VN7hUjW452N1y2oQAiVKIoAAAggggAACCCCAAAIIxJoAIUQIVnz7wxdq5Y59uqbd0zp0+y9D0EPoSy4d21UH9u/WV/3+reE/7xD6DukBAQQQQAABBBBAAAEEEEAg6gUIIWxe4rLCPG14uKvW7C/VXZ2f06Zrz7C5h/CUW/Tob5S/7X/6/vInNeRXF4WnU3pBAAEEEEAAAQQQQAABBBCIagFCCJuX9/Cub7Xhqau0sLCJXrnwCS3p1c7mHsJTbuFTt6vg2w/02XmjNOaqa8PTKb0ggAACCCCAAAIIIIAAAghEtQAhhM3Lm7/mda1/dZTmutprc/cHNavrSTb3EJ5yq165Xz8sf0nv/+x2PTJwcHg6pRcEEEAAAQQQQAABBBBAAIGoFiCEsHl5Dy6fpW/efFz/SP6lml0+QmM6Nbe5h/CUW/nmk9q+6Emtaf1bjbzj/vB0Si8IIIAAAggggAACCCCAAAJRLUAIYfPy5r45Tl9+8LImZV6tPn3/oAFtm9jcQ3jK7f5snj5+4V4tbnihHhv9THg6pRcEEEAAAQQQQAABBBBAAIGoFiCEsHl59zx/m9Z++ZH+evxgPXTNNeqWlW5zD+EpV7TxY70z7RZ9kdZe941/NTyd0gsCCCCAAAIIIIAAAggggEBUCxBC2Ly8O5/oqc82btTtrR/SBzf1UHZ6ss09hKdcSc5mLZpwuVaknqYbhz3n2HmER4teEEAAAQQQQAABBBBAAAEEaiNACFEbpVoe4y4t1vaHLtBne/LV52ezVDqwSy1bRuBh7jItGX6G9h+WGt79obo1bxiBg2RICCCAAAIIIIAAAggggAACThIghLBxtUp2f69NT/1O7x+qp6nnztDqfu1trB7+UnNHd1dC3nZl3vaqfn6qs+cSfj16RAABBBBAAAEEEEAAAQQQ8BcghLDxnChcv0SbZw/VKyVttPDcB7SkVzsbq4e/1H+n3qySTZ9oa/cHNahnn/APgB4RQAABBBBAAAEEEEAAAQSiSoAQwsblPPjxc/ruzUf1TOLPlXjJcE0+v4WN1cNf6tV/jlTy2jnae94d6n/VoPAPgB4RQAABBBBAAAEEEEAAAQSiSoAQwsblzP3vg/ruw3/rwfq/0RmX/F5jOjW3sXr4S731+jMqXfK4dpxyhQb+cWL4B0CPCCCAAAIIIIAAAggggAACUSVACGHjcua8OEhff/GBhjW9Q3/td7X6ZGfYWD38pZavWKg9L92pXU3P1q3Dnw//AOgRAQQQQAABBBBAAAEEEEAgqgQIIWxczp1P9tb/tmzUgBbjNes33dUtK93G6uEvtWzdN9o3o59K0rPUd+yi8A+AHhFAAAEEEEAAAQQQQAABBKJKgBDCruV0l2nbhC5anZOvPu3+qV0DzlFGUrxd1eumjrtM/7nrDMWpTL0nrZVccXUzDnpFAAEEEEAAAQQQQAABBBCICgFCCJuWsSRnk3bN+K3ezk3WLadMkXtgZ5sq122Zp4dfrBOKd+iy+95SQuPsuh0MvSOAAAIIIIAAAggggAACCDhagBDCpuUr/O4D/fDiYL1cnK2Xzx6v1f3a21S5bss8Ou4Gtdv3uTr0n6bsM7vX7WDoHQEEEEAAAQQQQAABBBBAwNEChBA2Ld+hT2frh7ce1jTX+Vp79mAt6dXOpsp1W2bC1BFqu/ktndhntM79Rb+6HQy9I4AAAggggAACCCCAAAIIOFqAEMKm5dv/9sPauOwFja/XVyd2vVWTz29hU+W6LTP0H0/p4q+mqUXnK9TxOrbprNvVoHcEEEAAAQQQQAABBBBAwNkChBA2rV/O7Du08asPNLjRH3RJ1ys1plNzmyrXbZnHF72vk9/8g+of/JBzCwAAIABJREFU31YXDZtXt4OhdwQQQAABBBBAAAEEEEAAAUcLEELYtHw7p1+p9Vs2qP8JY/VY74vVJzvDpsp1W+b+T7eo4+zL1DwtUZ3GfipXfGLdDojeEUAAAQQQQAABBBBAAAEEHCtACGHH0pntOSeep/W5+erR5u96p/dp6paVbkflOq8xa/0eHZp5rc7SDp0z9DUlNm1b52NiAAgggAACCCCAAAIIIIAAAs4UIISwYd1K9m7Rrqf7aWFukvqfMlX7+p+ljKR4GyrXfYkl2/M0f8YQ9Sr4RF1ueEhpHXrW/aAYAQIIIIAAAggggAACCCCAgCMFCCFsWLai7z9Szr/v1L/yT9SIViPlHtjZhqqRUcKEENOefUx/3veKzu5xqxp0HxoZA2MUCCCAAAIIIIAAAggggAACjhMghLBhyQ6t/Ld+eHOipquLlnYYrNX92ttQNXJKnD15psZueUQXdO6mxtdNj5yBMRIEEEAAAQQQQAABBBBAAAFHCRBC2LBc+999VDs+ekGjknprV4frtaRXOxuqRk6JzKcW6V/f/lnnnNhMx/9lceQMjJEggAACCCCAAAIIIIAAAgg4SoAQwobl2vvvwdrw5RINzbxNnS64QpPPb2FD1cgpkT37C0387Bad19CtFkMXKL7+cZEzOEaCAAIIIIAAAggggAACCCDgGAFCCBuWytyUcssP3+vqZv+nGy84X2M6NbehauSU6DZ/nS7+ZJSuTvhW2Tc8qeQ2F0TO4BgJAggggAACCCCAAAIIIICAYwQIIWxYqm0TulRsz/lyj1PVJzvDhqqRU8KEECeteUbDSxbphB6DVf/8AZEzOEaCAAIIIIAAAggggAACCCDgGIGYCSGmTJmi+fPnVyzMoEGD1Ldv34q/79u3T3fddZe2bt3q+dyECRPUuXPNu1yU5m7TzulXaFV+snq1mqLFvdqpW1a6Y06A2gx0zGfbtHTxq3ok9x9q07mnMvs8WJtmHIMAAggggAACCCCAAAIIIIBAJYGYCCEKCws1Y8YM3XTTTcrMzNSGDRs0bNgwDR8+3BM0mK+PGzfO87EJJszXx48fr5EjR6p169bVnjJFGz9Rzuw/6YVDJ2hY9qio2p7TO3ETQsxavkKv7hitli3bqultr/A0QgABBBBAAAEEEEAAAQQQQMCyQEyEEP4qVYUOU6dO1ejRoz0hhf/XzVUUF154YcWVEeaqiUcffVR33323kjYu0v4FE/XI4bM0ufntURlCzFq/R7cs3aj3Nt6mtumJyhr2oVzxiZZPNhoggAACCCCAAAIIIIAAAgjEtkBMhhDet16Yt2SYqx9Wrlyp6dOna9KkSZ4QwjxM8GAegwcP9nx8rBAi/vNZ2r7sXxqd1Es7OtwQddtzGoMl2/N00fx1enHHWHWN367jbv6XErPax/Yzh9kjgAACCCCAAAIIIIAAAghYFojJEMI3YDBiJoSYO3euRo0apZSUFEshhHvhOO3+erEG1r9Vce0uieoQ4sHcWep/+GNl9ByltDOvtHyy0QABBBBAAAEEEEAAAQQQQCC2BWIuhDABxK5duyoFDsFcCXH4ldu1Zcs6XdtspK6/4MKo257T+/RwPbNSvfe+radL56pe56vVsMc9sf3MYfYIIIAAAggggAACCCCAAAKWBWIqhKgqgDBi5kaUgd4TovCZnvrxwCF1bTFN93Q+OapDiNPzv9HbhyYruVUnNb5+huWTjQYIIIAAAggggAACCCCAAAKxLRAzIYT/WzB8l72m3TGqevuG9x4SBU9dotWHEtUze1pUbs/pdcqe/YX27M/V6l1DVC81TVn3LIvtZw6zRwABBBBAAAEEEEAAAQQQsCwQEyGE90aUW7durQTUpUuXirdl+B8zYcKEit0wvCHE/PnzPe1NO/Mwu2OYEOK9sha6sdl9UR1CdJu/Tku35+mrA/cpoyhHzf70huIbZlk+4WiAAAIIIIAAAggggAACCCAQuwIxEUIEu7z+u2P41isrzFPLfyzRj0lZUbk9p3eu3hBiRdxMnbDzEzX63WNKOeWXwdLSHgEEEEAAAQQQQAABBBBAIIYECCFqsdjVhRCmublpo3m4B3auRTVnHjLms20a+/k2zU55V7/c+prSf/k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" + ], + "source": [ + "SD.plot.generate_fig_univariate('1stFlrSF')" + ] + }, + { + "cell_type": "markdown", + "id": "94376f57", + "metadata": {}, + "source": [ + "We see that for important features, the data in production will not be similar in distributions to that in training" + ] + }, + { + "cell_type": "markdown", + "id": "a78ea0a7", + "metadata": {}, + "source": [ + "### Distribution of predicted values" + ] + }, + { + "cell_type": "markdown", + "id": "ae62b69d", + "metadata": {}, + "source": [ + "This graph shows distributions of the production model outputs on both baseline and current datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "77a720dc", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "%{y:.2f}", + "legendgroup": "Baseline dataset", + "marker": { + "color": "rgba(0,154,203,255)" + }, + "mode": "lines", + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\")" + ] + }, + { + "cell_type": "markdown", + "id": "23d2aa4f", + "metadata": {}, + "source": [ + "Differences between 2 datasets generate a difference in the distribution of the predictions of the deployed model. These differences can have important impacts on the performance of the model in production.
\n", + "Such differences in predicted probabilities may call into question the decision to deploy the model as is." + ] + }, + { + "cell_type": "markdown", + "id": "01ea277a", + "metadata": {}, + "source": [ + "With this tutorial, we hope to have detailed how Eurybia can be used in a data validation phase before deploying a model." ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\")" - ] - }, - { - "cell_type": "markdown", - "id": "23d2aa4f", - "metadata": {}, - "source": [ - "Differences between 2 datasets generate a difference in the distribution of the predictions of the deployed model. These differences can have important impacts on the performance of the model in production.
\n", - "Such differences in predicted probabilities may call into question the decision to deploy the model as is." - ] - }, - { - "cell_type": "markdown", - "id": "01ea277a", - "metadata": {}, - "source": [ - "With this tutorial, we hope to have detailed how Eurybia can be used in a data validation phase before deploying a model." - ] - } - ], - "metadata": { - "interpreter": { - "hash": "d08e6294e2d60f50397263035a337d71f3055486232bc02b45ce2785f62e7d8b" - }, - "kernelspec": { - "display_name": "eurybia_devenv_nro", - "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": "3.9.7" + ], + "metadata": { + "interpreter": { + "hash": "d08e6294e2d60f50397263035a337d71f3055486232bc02b45ce2785f62e7d8b" + }, + "kernelspec": { + "display_name": "eurybia_devenv_nro", + "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": "3.9.7" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/source/tutorials/model_drift/tutorial01-modeldrift.ipynb b/docs/source/tutorials/model_drift/tutorial01-modeldrift.ipynb index 4694eac..c5da22b 100644 --- a/docs/source/tutorials/model_drift/tutorial01-modeldrift.ipynb +++ b/docs/source/tutorials/model_drift/tutorial01-modeldrift.ipynb @@ -1,1916 +1,1916 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Modeldrift with Eurybia\n", - "With this tutorial you:
\n", - "Understand how to use Eurybia to detect model drift\n", - "\n", - "Contents:\n", - "- Detect data drift \n", - "- Display model drift over years\n", - "\n", - "This tutorial contains only additional features of model drift.\n", - "For more detailed information on data drift, you can consult these tutorials :\n", - "(https://github.com/MAIF/eurybia/tree/master/tutorial/data_drift)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from category_encoders import OrdinalEncoder\n", - "from lightgbm import LGBMRegressor\n", - "from eurybia import SmartDrift\n", - "from eurybia.data.data_loader import data_loading\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_squared_log_error" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Dataset and split in training and production dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "house_df, house_dict = data_loading('house_prices')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Let us consider that the column \"YrSold\" corresponds to the reference date. \n", - "#In 2006, a model was trained using data. And in 2007, we want to detect data drift on new data in production to predict\n", - "#house price\n", - "house_df_learning = house_df.loc[house_df['YrSold'] == 2006]\n", - "house_df_2007 = house_df.loc[house_df['YrSold'] == 2007]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", - "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YrSold'])]\n", - "\n", - "y_df_2007=house_df_2007['SalePrice'].to_frame()\n", - "X_df_2007=house_df_2007[house_df_2007.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building Supervized Model\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from category_encoders import OrdinalEncoder\n", - "\n", - "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", - "\n", - "encoder = OrdinalEncoder(\n", - " cols=categorical_features,\n", - " handle_unknown='ignore',\n", - " return_df=True).fit(X_df_learning)\n", - "\n", - "X_df_learning_encoded=encoder.transform(X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use Eurybia for data drift" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2007, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:The variable BsmtCond\n", - " has mismatching possible values: \n", - "\n", - " ['Poor -Severe cracking, settling, or wetness'] []\n", - "INFO:root:The variable Condition2\n", - " has mismatching possible values: \n", - "\n", - " ['Near positive off-site feature--park, greenbelt, etc.', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad'] ['Adjacent to feeder street']\n", - "INFO:root:The variable Electrical\n", - " has mismatching possible values: \n", - "\n", - " ['Mixed'] ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "INFO:root:The variable ExterQual\n", - " has mismatching possible values: \n", - "\n", - " ['Fair'] []\n", - "INFO:root:The variable Exterior1st\n", - " has mismatching possible values: \n", - "\n", - " [] ['Stone', 'Imitation Stucco']\n", - "INFO:root:The variable Exterior2nd\n", - " has mismatching possible values: \n", - "\n", - " ['Asphalt Shingles', 'Brick Common'] ['Other']\n", - "INFO:root:The variable Foundation\n", - " has mismatching possible values: \n", - "\n", - " [] ['Stone', 'Wood']\n", - "INFO:root:The variable Functional\n", - " has mismatching possible values: \n", - "\n", - " ['Major Deductions 2', 'Severely Damaged'] ['Moderate Deductions']\n", - "INFO:root:The variable GarageQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Excellent']\n", - "INFO:root:The variable Heating\n", - " has mismatching possible values: \n", - "\n", - " [] ['Wall furnace']\n", - "INFO:root:The variable HeatingQC\n", - " has mismatching possible values: \n", - "\n", - " ['Poor'] []\n", - "INFO:root:The variable LotConfig\n", - " has mismatching possible values: \n", - "\n", - " [] ['Frontage on 3 sides of property']\n", - "INFO:root:The variable MSSubClass\n", - " has mismatching possible values: \n", - "\n", - " ['1-Story w/Finished Attic All Ages'] []\n", - "INFO:root:The variable Neighborhood\n", - " has mismatching possible values: \n", - "\n", - " ['Northpark Villa'] []\n", - "INFO:root:The variable RoofMatl\n", - " has mismatching possible values: \n", - "\n", - " ['Roll'] ['Metal']\n", - "INFO:root:The variable RoofStyle\n", - " has mismatching possible values: \n", - "\n", - " ['Mansard', 'Shed'] []\n", - "INFO:root:The variable SaleType\n", - " has mismatching possible values: \n", - "\n", - " ['Warranty Deed - Cash'] ['Contract Low Interest', 'Contract Low Down', 'Contract Low Down payment and low interest']\n", - "INFO:root:The variable Street\n", - " has mismatching possible values: \n", - "\n", - " ['Gravel'] []\n", - "INFO:root:\n", - " The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.626082251082251\n", - " \n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modeldrift with Eurybia\n", + "With this tutorial you:
\n", + "Understand how to use Eurybia to detect model drift\n", + "\n", + "Contents:\n", + "- Detect data drift \n", + "- Display model drift over years\n", + "\n", + "This tutorial contains only additional features of model drift.\n", + "For more detailed information on data drift, you can consult these tutorials :\n", + "(https://github.com/MAIF/eurybia/tree/master/tutorial/data_drift)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n", - "CPU times: user 2min 11s, sys: 5min 38s, total: 7min 49s\n", - "Wall time: 12.4 s\n" - ] - } - ], - "source": [ - "%time SD.compile(full_validation=True, date_compile_auc = '01/01/2007', datadrift_file = \"house_price_auc.csv\")\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", - "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", - "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add model drift in report" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the moment, the model drift part of eurybia only consists of displaying performance of deployed model. \n", - "(We hope to bring new features in the future on this part)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Put model performance in DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = regressor.predict(Xtest)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from category_encoders import OrdinalEncoder\n", + "from lightgbm import LGBMRegressor\n", + "from eurybia import SmartDrift\n", + "from eurybia.data.data_loader import data_loading\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_log_error" + ] + }, { - "data": { - "text/plain": [ - "0.031487" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import Dataset and split in training and production dataset" ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "performance_test = mean_squared_log_error(ytest, y_pred).round(6)\n", - "performance_test" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "#Create Dataframe to track performance over the years\n", - "df_performance = pd.DataFrame({'annee': [2006], 'mois':[1], 'performance': [performance_test]})" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "0.03309" + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "house_df, house_dict = data_loading('house_prices')" ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_2007_encode=encoder.transform(X_df_2007)\n", - "y_pred_2007 = regressor.predict(df_2007_encode)\n", - "performance_2007 = mean_squared_log_error(y_df_2007, y_pred_2007).round(6)\n", - "performance_2007" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Let us consider that the column \"YrSold\" corresponds to the reference date. \n", + "#In 2006, a model was trained using data. And in 2007, we want to detect data drift on new data in production to predict\n", + "#house price\n", + "house_df_learning = house_df.loc[house_df['YrSold'] == 2006]\n", + "house_df_2007 = house_df.loc[house_df['YrSold'] == 2007]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", + "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YrSold'])]\n", + "\n", + "y_df_2007=house_df_2007['SalePrice'].to_frame()\n", + "X_df_2007=house_df_2007[house_df_2007.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building Supervized Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from category_encoders import OrdinalEncoder\n", + "\n", + "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", + "\n", + "encoder = OrdinalEncoder(\n", + " cols=categorical_features,\n", + " handle_unknown='ignore',\n", + " return_df=True).fit(X_df_learning)\n", + "\n", + "X_df_learning_encoded=encoder.transform(X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Eurybia for data drift" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_276700/378754283.py:1: FutureWarning:\n", - "\n", - "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - "\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2007, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" + ] }, { - "data": { - "text/html": [ - "
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anneemoisperformance
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\n", - "
" + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:The variable BsmtCond\n", + " has mismatching possible values: \n", + "\n", + " ['Poor -Severe cracking, settling, or wetness'] []\n", + "INFO:root:The variable Condition2\n", + " has mismatching possible values: \n", + "\n", + " ['Near positive off-site feature--park, greenbelt, etc.', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad'] ['Adjacent to feeder street']\n", + "INFO:root:The variable Electrical\n", + " has mismatching possible values: \n", + "\n", + " ['Mixed'] ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "INFO:root:The variable ExterQual\n", + " has mismatching possible values: \n", + "\n", + " ['Fair'] []\n", + "INFO:root:The variable Exterior1st\n", + " has mismatching possible values: \n", + "\n", + " [] ['Stone', 'Imitation Stucco']\n", + "INFO:root:The variable Exterior2nd\n", + " has mismatching possible values: \n", + "\n", + " ['Asphalt Shingles', 'Brick Common'] ['Other']\n", + "INFO:root:The variable Foundation\n", + " has mismatching possible values: \n", + "\n", + " [] ['Stone', 'Wood']\n", + "INFO:root:The variable Functional\n", + " has mismatching possible values: \n", + "\n", + " ['Major Deductions 2', 'Severely Damaged'] ['Moderate Deductions']\n", + "INFO:root:The variable GarageQual\n", + " has mismatching possible values: \n", + "\n", + " [] ['Excellent']\n", + "INFO:root:The variable Heating\n", + " has mismatching possible values: \n", + "\n", + " [] ['Wall furnace']\n", + "INFO:root:The variable HeatingQC\n", + " has mismatching possible values: \n", + "\n", + " ['Poor'] []\n", + "INFO:root:The variable LotConfig\n", + " has mismatching possible values: \n", + "\n", + " [] ['Frontage on 3 sides of property']\n", + "INFO:root:The variable MSSubClass\n", + " has mismatching possible values: \n", + "\n", + " ['1-Story w/Finished Attic All Ages'] []\n", + "INFO:root:The variable Neighborhood\n", + " has mismatching possible values: \n", + "\n", + " ['Northpark Villa'] []\n", + "INFO:root:The variable RoofMatl\n", + " has mismatching possible values: \n", + "\n", + " ['Roll'] ['Metal']\n", + "INFO:root:The variable RoofStyle\n", + " has mismatching possible values: \n", + "\n", + " ['Mansard', 'Shed'] []\n", + "INFO:root:The variable SaleType\n", + " has mismatching possible values: \n", + "\n", + " ['Warranty Deed - Cash'] ['Contract Low Interest', 'Contract Low Down', 'Contract Low Down payment and low interest']\n", + "INFO:root:The variable Street\n", + " has mismatching possible values: \n", + "\n", + " ['Gravel'] []\n", + "INFO:root:\n", + " The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.626082251082251\n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backend: Shap TreeExplainer\n", + "CPU times: user 2min 11s, sys: 5min 38s, total: 7min 49s\n", + "Wall time: 12.4 s\n" + ] + } ], - "text/plain": [ - " annee mois performance\n", - "0 2006.0 1.0 0.031487\n", - "1 2007.0 1.0 0.033090" + "source": [ + "%time SD.compile(full_validation=True, date_compile_auc = '01/01/2007', datadrift_file = \"house_price_auc.csv\")\n", + " " ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_performance = df_performance.append({'annee': 2007, 'mois':1, 'performance': performance_2007}, ignore_index=True)\n", - "df_performance" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add performance Dataframe in Smartdrift" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", + "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", + "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." + ] + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_modeldrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add model drift in report" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the moment, the model drift part of eurybia only consists of displaying performance of deployed model. \n", + "(We hope to bring new features in the future on this part)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Put model performance in DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = regressor.predict(Xtest)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.031487" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "performance_test = mean_squared_log_error(ytest, y_pred).round(6)\n", + "performance_test" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_modeldrift_2007.html', \n", - " title_story=\"Data drift\",\n", - " title_description=\"\"\"House price model drift 2007\"\"\",\n", - " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" \n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", - "However, to illustrate functionalities, we will detail results with notebook mode analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This tutorial contains only anlysis on additional features of model drift. For more detailed information on data drift, you can consult these tutorials : (https://github.com/MAIF/eurybia/tree/master/tutorial/data_drift)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Display model drift" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/html": [ - " \n", - " " + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "#Create Dataframe to track performance over the years\n", + "df_performance = pd.DataFrame({'annee': [2006], 'mois':[1], 'performance': [performance_test]})" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ { - "customdata": [ - [], - [] - ], - "hovertemplate": "Date=%{x}
performance=%{text}", - "legendgroup": "", - "line": { - "color": "rgba(0,154,203,255)", - "dash": "solid" - }, - "marker": { - "color": "rgba(0,154,203,255)" - }, - "mode": "lines+markers+text", - "name": "", - "orientation": "v", - "showlegend": false, - "text": [ - 0.03, - 0.03 - ], - "textposition": "top right", - "type": "scatter", - "x": [ - "01/01/2006", - "01/01/2007" - ], - "xaxis": "x", - "y": [ - 0.03, - 0.03 - ], - "yaxis": "y" + "data": { + "text/plain": [ + "0.03309" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" } - ], - "layout": { - "height": 600, - "hovermode": "closest", - "legend": { - "tracegroupgap": 0 - }, - "template": { - "data": { - "scatter": [ - { - "type": "scatter" - } + ], + "source": [ + "df_2007_encode=encoder.transform(X_df_2007)\n", + "y_pred_2007 = regressor.predict(df_2007_encode)\n", + "performance_2007 = mean_squared_log_error(y_df_2007, y_pred_2007).round(6)\n", + "performance_2007" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_276700/378754283.py:1: FutureWarning:\n", + "\n", + "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "\n" ] - } }, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial", - "size": 24 - }, - "text": "Performance's Evolution on deployed model", - "x": 0.5, - "xanchor": "center", - "y": 0.9, - "yanchor": "middle" - }, - "width": 900, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "showgrid": false, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 - }, - "text": "Date" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 + { + "data": { + "text/html": [ + "
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anneemoisperformance
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\n", + "
" + ], + "text/plain": [ + " annee mois performance\n", + "0 2006.0 1.0 0.031487\n", + "1 2007.0 1.0 0.033090" + ] }, - "text": "Density" - } + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" } - } - }, - "text/html": [ - "
" + ], + "source": [ + "df_performance = df_performance.append({'annee': 2007, 'mois':1, 'performance': performance_2007}, ignore_index=True)\n", + "df_performance" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_modeldrift_data()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Display model drift with multiple indicators" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you have several metrics or indicators for performance monitoring, it is possible to have reference columns.\n", - "Let's create a dummy performance table to show the use." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/html": [ - "
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indicatorlevel_1anneemoisperformance
0rmse02006.01.00.031487
1rmse12007.01.00.033090
2mse02006.01.01.031988
3mse12007.01.01.033644
\n", - "
" - ], - "text/plain": [ - " indicator level_1 annee mois performance\n", - "0 rmse 0 2006.0 1.0 0.031487\n", - "1 rmse 1 2007.0 1.0 0.033090\n", - "2 mse 0 2006.0 1.0 1.031988\n", - "3 mse 1 2007.0 1.0 1.033644" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add performance Dataframe in Smartdrift" ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_performance_mse = df_performance.copy()\n", - "df_performance_mse['performance']= np.exp(df_performance_mse['performance'])\n", - "df_performance2 = pd.concat([df_performance, df_performance_mse], keys=[\"rmse\", \"mse\"]).reset_index().rename(columns={\"level_0\": \"indicator\"})\n", - "df_performance2" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance2,metric='performance',reference_columns=['indicator']) " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_modeldrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_modeldrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_modeldrift_2007.html', \n", + " title_story=\"Data drift\",\n", + " title_description=\"\"\"House price model drift 2007\"\"\",\n", + " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" \n", + " )" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_modeldrift_2007.html', \n", - " title_story=\"Data drift\",\n", - " title_description=\"\"\"House price model drift 2007\"\"\",\n", - " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" \n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compile Drift over years" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2008" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "house_df_2008 = house_df.loc[house_df['YrSold'] == 2008]\n", - "\n", - "y_df_2008=house_df_2008['SalePrice'].to_frame()\n", - "X_df_2008=house_df_2008[house_df_2008.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "0.028883" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", + "However, to illustrate functionalities, we will detail results with notebook mode analysis." ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_2008_encode=encoder.transform(X_df_2008)\n", - "y_pred_2008 = regressor.predict(df_2008_encode)\n", - "performance_2008 = mean_squared_log_error(y_df_2008, y_pred_2008).round(6)\n", - "performance_2008" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_276700/911963945.py:1: FutureWarning:\n", - "\n", - "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - "\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial contains only anlysis on additional features of model drift. For more detailed information on data drift, you can consult these tutorials : (https://github.com/MAIF/eurybia/tree/master/tutorial/data_drift)" + ] }, { - "data": { - "text/html": [ - "
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anneemoisperformance
02006.01.00.031487
12007.01.00.033090
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\n", - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Display model drift" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + [], + [] + ], + "hovertemplate": "Date=%{x}
performance=%{text}", + "legendgroup": "", + "line": { + "color": "rgba(0,154,203,255)", + "dash": "solid" + }, + "marker": { + "color": "rgba(0,154,203,255)" + }, + "mode": "lines+markers+text", + "name": "", + "orientation": "v", + "showlegend": false, + "text": [ + 0.03, + 0.03 + ], + "textposition": "top right", + "type": "scatter", + "x": [ + "01/01/2006", + "01/01/2007" + ], + "xaxis": "x", + "y": [ + 0.03, + 0.03 + ], + "yaxis": "y" + } + ], + "layout": { + "height": 600, + "hovermode": "closest", + "legend": { + "tracegroupgap": 0 + }, + "template": { + "data": { + "scatter": [ + { + "type": "scatter" + } + ] + } + }, + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial", + "size": 24 + }, + "text": "Performance's Evolution on deployed model", + "x": 0.5, + "xanchor": "center", + "y": 0.9, + "yanchor": "middle" + }, + "width": 900, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "showgrid": false, + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial Black", + "size": 16 + }, + "text": "Date" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "font": { + "color": "rgb(50, 50, 50)", + "family": "Arial Black", + "size": 16 + }, + "text": "Density" + } + } + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - " annee mois performance\n", - "0 2006.0 1.0 0.031487\n", - "1 2007.0 1.0 0.033090\n", - "2 2008.0 1.0 0.028883" + "source": [ + "SD.plot.generate_modeldrift_data()" ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_performance = df_performance.append({'annee': 2008, 'mois':1, 'performance': performance_2008}, ignore_index=True)\n", - "df_performance" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2008, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:The variable Condition1\n", - " has mismatching possible values: \n", - "\n", - " [\"Within 200' of East-West Railroad\"] []\n", - "INFO:root:The variable Condition2\n", - " has mismatching possible values: \n", - "\n", - " ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to postive off-site feature', 'Near positive off-site feature--park, greenbelt, etc.'] []\n", - "INFO:root:The variable Electrical\n", - " has mismatching possible values: \n", - "\n", - " ['Mixed'] []\n", - "INFO:root:The variable ExterCond\n", - " has mismatching possible values: \n", - "\n", - " ['Excellent'] []\n", - "INFO:root:The variable ExterQual\n", - " has mismatching possible values: \n", - "\n", - " ['Fair'] []\n", - "INFO:root:The variable Exterior1st\n", - " has mismatching possible values: \n", - "\n", - " [] ['Imitation Stucco']\n", - "INFO:root:The variable Exterior2nd\n", - " has mismatching possible values: \n", - "\n", - " [] ['Other', 'Stone']\n", - "INFO:root:The variable Foundation\n", - " has mismatching possible values: \n", - "\n", - " [] ['Slab', 'Wood']\n", - "INFO:root:The variable Functional\n", - " has mismatching possible values: \n", - "\n", - " ['Major Deductions 2'] []\n", - "INFO:root:The variable GarageCond\n", - " has mismatching possible values: \n", - "\n", - " ['Excellent'] ['Poor']\n", - "INFO:root:The variable GarageQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Poor']\n", - "INFO:root:The variable GarageType\n", - " has mismatching possible values: \n", - "\n", - " [] ['More than one type of garage']\n", - "INFO:root:The variable Heating\n", - " has mismatching possible values: \n", - "\n", - " ['Hot water or steam heat other than gas', 'Floor Furnace'] ['Wall furnace']\n", - "INFO:root:The variable MSSubClass\n", - " has mismatching possible values: \n", - "\n", - " ['1-Story w/Finished Attic All Ages'] []\n", - "INFO:root:The variable Neighborhood\n", - " has mismatching possible values: \n", - "\n", - " ['Northpark Villa', 'Bluestem'] []\n", - "INFO:root:The variable RoofMatl\n", - " has mismatching possible values: \n", - "\n", - " ['Membrane', 'Clay or Tile'] ['Metal']\n", - "INFO:root:The variable SaleCondition\n", - " has mismatching possible values: \n", - "\n", - " [] ['Sale between family members']\n", - "INFO:root:The variable SaleType\n", - " has mismatching possible values: \n", - "\n", - " ['Contract 15% Down payment regular terms', 'Warranty Deed - Cash'] ['Contract Low Interest', 'Other']\n", - "INFO:root:The variable Street\n", - " has mismatching possible values: \n", - "\n", - " ['Gravel'] []\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Display model drift with multiple indicators" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you have several metrics or indicators for performance monitoring, it is possible to have reference columns.\n", + "Let's create a dummy performance table to show the use." + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:\n", - " The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6877714667557634\n", - " \n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True,\n", - " date_compile_auc = '01/01/2008', #optionnal, by default date of compile\n", - " datadrift_file = \"house_price_auc.csv\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indicatorlevel_1anneemoisperformance
0rmse02006.01.00.031487
1rmse12007.01.00.033090
2mse02006.01.01.031988
3mse12007.01.01.033644
\n", + "
" + ], + "text/plain": [ + " indicator level_1 annee mois performance\n", + "0 rmse 0 2006.0 1.0 0.031487\n", + "1 rmse 1 2007.0 1.0 0.033090\n", + "2 mse 0 2006.0 1.0 1.031988\n", + "3 mse 1 2007.0 1.0 1.033644" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_performance_mse = df_performance.copy()\n", + "df_performance_mse['performance']= np.exp(df_performance_mse['performance'])\n", + "df_performance2 = pd.concat([df_performance, df_performance_mse], keys=[\"rmse\", \"mse\"]).reset_index().rename(columns={\"level_0\": \"indicator\"})\n", + "df_performance2" + ] + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_modeldrift_2008.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance2,metric='performance',reference_columns=['indicator']) " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_modeldrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_modeldrift_2007.html', \n", + " title_story=\"Data drift\",\n", + " title_description=\"\"\"House price model drift 2007\"\"\",\n", + " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" \n", + " )" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_modeldrift_2008.html', \n", - " title_story=\"Model drift\",\n", - " title_description=\"\"\"House price model drift 2008\"\"\",\n", - " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" \n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2009" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "house_df_2009 = house_df.loc[house_df['YrSold'] == 2009]\n", - "\n", - "y_df_2009=house_df_2009['SalePrice'].to_frame()\n", - "X_df_2009=house_df_2009[house_df_2009.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "0.031778" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compile Drift over years" ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_2009_encode=encoder.transform(X_df_2009)\n", - "y_pred_2009 = regressor.predict(df_2009_encode)\n", - "performance_2009 = mean_squared_log_error(y_df_2009, y_pred_2009).round(6)\n", - "performance_2009" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2009, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:The variable BsmtCond\n", - " has mismatching possible values: \n", - "\n", - " ['Poor -Severe cracking, settling, or wetness'] []\n", - "INFO:root:The variable Condition1\n", - " has mismatching possible values: \n", - "\n", - " [] ['Adjacent to East-West Railroad']\n", - "INFO:root:The variable Condition2\n", - " has mismatching possible values: \n", - "\n", - " ['Adjacent to arterial street'] []\n", - "INFO:root:The variable Electrical\n", - " has mismatching possible values: \n", - "\n", - " [] ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "INFO:root:The variable ExterCond\n", - " has mismatching possible values: \n", - "\n", - " ['Excellent'] []\n", - "INFO:root:The variable ExterQual\n", - " has mismatching possible values: \n", - "\n", - " ['Fair'] []\n", - "INFO:root:The variable Exterior1st\n", - " has mismatching possible values: \n", - "\n", - " ['Brick Common', 'Cinder Block'] ['Stone', 'Imitation Stucco']\n", - "INFO:root:The variable Exterior2nd\n", - " has mismatching possible values: \n", - "\n", - " ['Brick Common', 'Cinder Block'] ['Other']\n", - "INFO:root:The variable Functional\n", - " has mismatching possible values: \n", - "\n", - " ['Major Deductions 2'] []\n", - "INFO:root:The variable GarageCond\n", - " has mismatching possible values: \n", - "\n", - " ['Excellent'] ['Good']\n", - "INFO:root:The variable GarageQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Poor']\n", - "INFO:root:The variable GarageType\n", - " has mismatching possible values: \n", - "\n", - " [] ['More than one type of garage']\n", - "INFO:root:The variable LotConfig\n", - " has mismatching possible values: \n", - "\n", - " [] ['Frontage on 3 sides of property']\n", - "INFO:root:The variable MSSubClass\n", - " has mismatching possible values: \n", - "\n", - " ['1-Story w/Finished Attic All Ages'] []\n", - "INFO:root:The variable Neighborhood\n", - " has mismatching possible values: \n", - "\n", - " ['Northpark Villa', 'Bluestem'] ['Veenker']\n", - "INFO:root:The variable RoofMatl\n", - " has mismatching possible values: \n", - "\n", - " [] ['Metal', 'Wood Shakes']\n", - "INFO:root:The variable RoofStyle\n", - " has mismatching possible values: \n", - "\n", - " ['Mansard'] []\n", - "INFO:root:The variable SaleCondition\n", - " has mismatching possible values: \n", - "\n", - " [] ['Adjoining Land Purchase']\n", - "INFO:root:The variable SaleType\n", - " has mismatching possible values: \n", - "\n", - " [] ['Other']\n", - "INFO:root:The variable Utilities\n", - " has mismatching possible values: \n", - "\n", - " ['Electricity and Gas Only'] []\n", - "INFO:root:\n", - " The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.5405695039804042\n", - " \n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2008" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True,\n", - " date_compile_auc = '01/01/2009', #optionnal, by default date of compile\n", - " datadrift_file = \"house_price_auc.csv\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "house_df_2008 = house_df.loc[house_df['YrSold'] == 2008]\n", + "\n", + "y_df_2008=house_df_2008['SalePrice'].to_frame()\n", + "X_df_2008=house_df_2008[house_df_2008.columns.difference(['SalePrice','YrSold'])]" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_276700/248515887.py:1: FutureWarning:\n", - "\n", - "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - "\n" - ] + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.028883" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2008_encode=encoder.transform(X_df_2008)\n", + "y_pred_2008 = regressor.predict(df_2008_encode)\n", + "performance_2008 = mean_squared_log_error(y_df_2008, y_pred_2008).round(6)\n", + "performance_2008" + ] }, { - "data": { - "text/html": [ - "
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anneemoisperformance
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" + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_276700/911963945.py:1: FutureWarning:\n", + "\n", + "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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anneemoisperformance
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" + ], + "text/plain": [ + " annee mois performance\n", + "0 2006.0 1.0 0.031487\n", + "1 2007.0 1.0 0.033090\n", + "2 2008.0 1.0 0.028883" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " annee mois performance\n", - "0 2006.0 1.0 0.031487\n", - "1 2007.0 1.0 0.033090\n", - "2 2008.0 1.0 0.028883\n", - "3 2009.0 1.0 0.031778" + "source": [ + "df_performance = df_performance.append({'annee': 2008, 'mois':1, 'performance': performance_2008}, ignore_index=True)\n", + "df_performance" ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_performance = df_performance.append({'annee': 2009, 'mois':1, 'performance': performance_2009}, ignore_index=True)\n", - "df_performance" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_modeldrift_2009.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2008, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:The variable Condition1\n", + " has mismatching possible values: \n", + "\n", + " [\"Within 200' of East-West Railroad\"] []\n", + "INFO:root:The variable Condition2\n", + " has mismatching possible values: \n", + "\n", + " ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to postive off-site feature', 'Near positive off-site feature--park, greenbelt, etc.'] []\n", + "INFO:root:The variable Electrical\n", + " has mismatching possible values: \n", + "\n", + " ['Mixed'] []\n", + "INFO:root:The variable ExterCond\n", + " has mismatching possible values: \n", + "\n", + " ['Excellent'] []\n", + "INFO:root:The variable ExterQual\n", + " has mismatching possible values: \n", + "\n", + " ['Fair'] []\n", + "INFO:root:The variable Exterior1st\n", + " has mismatching possible values: \n", + "\n", + " [] ['Imitation Stucco']\n", + "INFO:root:The variable Exterior2nd\n", + " has mismatching possible values: \n", + "\n", + " [] ['Other', 'Stone']\n", + "INFO:root:The variable Foundation\n", + " has mismatching possible values: \n", + "\n", + " [] ['Slab', 'Wood']\n", + "INFO:root:The variable Functional\n", + " has mismatching possible values: \n", + "\n", + " ['Major Deductions 2'] []\n", + "INFO:root:The variable GarageCond\n", + " has mismatching possible values: \n", + "\n", + " ['Excellent'] ['Poor']\n", + "INFO:root:The variable GarageQual\n", + " has mismatching possible values: \n", + "\n", + " [] ['Poor']\n", + "INFO:root:The variable GarageType\n", + " has mismatching possible values: \n", + "\n", + " [] ['More than one type of garage']\n", + "INFO:root:The variable Heating\n", + " has mismatching possible values: \n", + "\n", + " ['Hot water or steam heat other than gas', 'Floor Furnace'] ['Wall furnace']\n", + "INFO:root:The variable MSSubClass\n", + " has mismatching possible values: \n", + "\n", + " ['1-Story w/Finished Attic All Ages'] []\n", + "INFO:root:The variable Neighborhood\n", + " has mismatching possible values: \n", + "\n", + " ['Northpark Villa', 'Bluestem'] []\n", + "INFO:root:The variable RoofMatl\n", + " has mismatching possible values: \n", + "\n", + " ['Membrane', 'Clay or Tile'] ['Metal']\n", + "INFO:root:The variable SaleCondition\n", + " has mismatching possible values: \n", + "\n", + " [] ['Sale between family members']\n", + "INFO:root:The variable SaleType\n", + " has mismatching possible values: \n", + "\n", + " ['Contract 15% Down payment regular terms', 'Warranty Deed - Cash'] ['Contract Low Interest', 'Other']\n", + "INFO:root:The variable Street\n", + " has mismatching possible values: \n", + "\n", + " ['Gravel'] []\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backend: Shap TreeExplainer\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:\n", + " The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6877714667557634\n", + " \n" + ] + } ], - "text/plain": [ - "" + "source": [ + "SD.compile(full_validation=True,\n", + " date_compile_auc = '01/01/2008', #optionnal, by default date of compile\n", + " datadrift_file = \"house_price_auc.csv\"\n", + " )" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_modeldrift_2009.html', \n", - " title_story=\"Model drift\",\n", - " title_description=\"\"\"House price model drift 2009\"\"\",\n", - " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" \n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2010" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "house_df_2010 = house_df.loc[house_df['YrSold'] == 2010]\n", - "\n", - "y_df_2010=house_df_2010['SalePrice'].to_frame()\n", - "X_df_2010=house_df_2010[house_df_2010.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "0.023441" + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_2010_encode=encoder.transform(X_df_2010)\n", - "y_pred_2010 = regressor.predict(df_2010_encode)\n", - "performance_2010 = mean_squared_log_error(y_df_2010, y_pred_2010).round(6)\n", - "performance_2010" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_modeldrift_2008.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_modeldrift_2008.html', \n", + " title_story=\"Model drift\",\n", + " title_description=\"\"\"House price model drift 2008\"\"\",\n", + " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2009" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_276700/2445109678.py:1: FutureWarning:\n", - "\n", - "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", - "\n" - ] + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "house_df_2009 = house_df.loc[house_df['YrSold'] == 2009]\n", + "\n", + "y_df_2009=house_df_2009['SalePrice'].to_frame()\n", + "X_df_2009=house_df_2009[house_df_2009.columns.difference(['SalePrice','YrSold'])]" + ] }, { - "data": { - "text/html": [ - "
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anneemoisperformance
02006.01.00.031487
12007.01.00.033090
22008.01.00.028883
32009.01.00.031778
42010.01.00.023441
\n", - "
" + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.031778" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " annee mois performance\n", - "0 2006.0 1.0 0.031487\n", - "1 2007.0 1.0 0.033090\n", - "2 2008.0 1.0 0.028883\n", - "3 2009.0 1.0 0.031778\n", - "4 2010.0 1.0 0.023441" + "source": [ + "df_2009_encode=encoder.transform(X_df_2009)\n", + "y_pred_2009 = regressor.predict(df_2009_encode)\n", + "performance_2009 = mean_squared_log_error(y_df_2009, y_pred_2009).round(6)\n", + "performance_2009" ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_performance = df_performance.append({'annee': 2010, 'mois':1, 'performance': performance_2010}, ignore_index=True)\n", - "df_performance" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2010, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:The variable Condition1\n", - " has mismatching possible values: \n", - "\n", - " [\"Within 200' of East-West Railroad\"] []\n", - "INFO:root:The variable Electrical\n", - " has mismatching possible values: \n", - "\n", - " [] ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "INFO:root:The variable ExterCond\n", - " has mismatching possible values: \n", - "\n", - " ['Poor'] []\n", - "INFO:root:The variable ExterQual\n", - " has mismatching possible values: \n", - "\n", - " ['Fair'] []\n", - "INFO:root:The variable Exterior1st\n", - " has mismatching possible values: \n", - "\n", - " ['Asphalt Shingles'] ['Stone', 'Imitation Stucco']\n", - "INFO:root:The variable Exterior2nd\n", - " has mismatching possible values: \n", - "\n", - " ['Asphalt Shingles', 'Brick Common'] ['Other', 'Stone']\n", - "INFO:root:The variable Functional\n", - " has mismatching possible values: \n", - "\n", - " [] ['Major Deductions 1']\n", - "INFO:root:The variable GarageCond\n", - " has mismatching possible values: \n", - "\n", - " [] ['Poor', 'Good']\n", - "INFO:root:The variable GarageQual\n", - " has mismatching possible values: \n", - "\n", - " [] ['Good', 'Excellent', 'Poor']\n", - "INFO:root:The variable GarageType\n", - " has mismatching possible values: \n", - "\n", - " [] ['More than one type of garage']\n", - "INFO:root:The variable Heating\n", - " has mismatching possible values: \n", - "\n", - " [] ['Gas hot water or steam heat', 'Wall furnace']\n", - "INFO:root:The variable HouseStyle\n", - " has mismatching possible values: \n", - "\n", - " [] ['Two and one-half story: 2nd level finished', 'One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished']\n", - "INFO:root:The variable LotConfig\n", - " has mismatching possible values: \n", - "\n", - " [] ['Frontage on 3 sides of property']\n", - "INFO:root:The variable LotShape\n", - " has mismatching possible values: \n", - "\n", - " [] ['Irregular']\n", - "INFO:root:The variable MSSubClass\n", - " has mismatching possible values: \n", - "\n", - " ['1-Story w/Finished Attic All Ages'] ['2-1/2 Story All Ages', '1-1/2 Story - Unfinished All Ages']\n", - "INFO:root:The variable MSZoning\n", - " has mismatching possible values: \n", - "\n", - " [] ['Residential High Density']\n", - "INFO:root:The variable Neighborhood\n", - " has mismatching possible values: \n", - "\n", - " ['Northpark Villa'] ['Veenker']\n", - "INFO:root:The variable RoofMatl\n", - " has mismatching possible values: \n", - "\n", - " [] ['Wood Shingles', 'Metal', 'Gravel & Tar']\n", - "INFO:root:The variable RoofStyle\n", - " has mismatching possible values: \n", - "\n", - " ['Mansard', 'Shed'] ['Flat']\n", - "INFO:root:The variable SaleCondition\n", - " has mismatching possible values: \n", - "\n", - " [] ['Adjoining Land Purchase']\n", - "INFO:root:The variable SaleType\n", - " has mismatching possible values: \n", - "\n", - " ['Contract 15% Down payment regular terms'] ['Contract Low Down', 'Contract Low Down payment and low interest', 'Other']\n", - "INFO:root:The variable Street\n", - " has mismatching possible values: \n", - "\n", - " ['Gravel'] []\n" - ] + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2009, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n" - ] + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:The variable BsmtCond\n", + " has mismatching possible values: \n", + "\n", + " ['Poor -Severe cracking, settling, or wetness'] []\n", + "INFO:root:The variable Condition1\n", + " has mismatching possible values: \n", + "\n", + " [] ['Adjacent to East-West Railroad']\n", + "INFO:root:The variable Condition2\n", + " has mismatching possible values: \n", + "\n", + " ['Adjacent to arterial street'] []\n", + "INFO:root:The variable Electrical\n", + " has mismatching possible values: \n", + "\n", + " [] ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "INFO:root:The variable ExterCond\n", + " has mismatching possible values: \n", + "\n", + " ['Excellent'] []\n", + "INFO:root:The variable ExterQual\n", + " has mismatching possible values: \n", + "\n", + " ['Fair'] []\n", + "INFO:root:The variable Exterior1st\n", + " has mismatching possible values: \n", + "\n", + " ['Brick Common', 'Cinder Block'] ['Stone', 'Imitation Stucco']\n", + "INFO:root:The variable Exterior2nd\n", + " has mismatching possible values: \n", + "\n", + " ['Brick Common', 'Cinder Block'] ['Other']\n", + "INFO:root:The variable Functional\n", + " has mismatching possible values: \n", + "\n", + " ['Major Deductions 2'] []\n", + "INFO:root:The variable GarageCond\n", + " has mismatching possible values: \n", + "\n", + " ['Excellent'] ['Good']\n", + "INFO:root:The variable GarageQual\n", + " has mismatching possible values: \n", + "\n", + " [] ['Poor']\n", + "INFO:root:The variable GarageType\n", + " has mismatching possible values: \n", + "\n", + " [] ['More than one type of garage']\n", + "INFO:root:The variable LotConfig\n", + " has mismatching possible values: \n", + "\n", + " [] ['Frontage on 3 sides of property']\n", + "INFO:root:The variable MSSubClass\n", + " has mismatching possible values: \n", + "\n", + " ['1-Story w/Finished Attic All Ages'] []\n", + "INFO:root:The variable Neighborhood\n", + " has mismatching possible values: \n", + "\n", + " ['Northpark Villa', 'Bluestem'] ['Veenker']\n", + "INFO:root:The variable RoofMatl\n", + " has mismatching possible values: \n", + "\n", + " [] ['Metal', 'Wood Shakes']\n", + "INFO:root:The variable RoofStyle\n", + " has mismatching possible values: \n", + "\n", + " ['Mansard'] []\n", + "INFO:root:The variable SaleCondition\n", + " has mismatching possible values: \n", + "\n", + " [] ['Adjoining Land Purchase']\n", + "INFO:root:The variable SaleType\n", + " has mismatching possible values: \n", + "\n", + " [] ['Other']\n", + "INFO:root:The variable Utilities\n", + " has mismatching possible values: \n", + "\n", + " ['Electricity and Gas Only'] []\n", + "INFO:root:\n", + " The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.5405695039804042\n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backend: Shap TreeExplainer\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True,\n", + " date_compile_auc = '01/01/2009', #optionnal, by default date of compile\n", + " datadrift_file = \"house_price_auc.csv\"\n", + " )" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:root:\n", - " The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6978632478632478\n", - " \n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True,\n", - " date_compile_auc = '01/01/2010', #optionnal, by default date of compile\n", - " datadrift_file = \"house_price_auc.csv\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_276700/248515887.py:1: FutureWarning:\n", + "\n", + "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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anneemoisperformance
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" + ], + "text/plain": [ + " annee mois performance\n", + "0 2006.0 1.0 0.031487\n", + "1 2007.0 1.0 0.033090\n", + "2 2008.0 1.0 0.028883\n", + "3 2009.0 1.0 0.031778" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_performance = df_performance.append({'annee': 2009, 'mois':1, 'performance': performance_2009}, ignore_index=True)\n", + "df_performance" + ] + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_modeldrift_2010.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_modeldrift_2009.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_modeldrift_2009.html', \n", + " title_story=\"Model drift\",\n", + " title_description=\"\"\"House price model drift 2009\"\"\",\n", + " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" \n", + " )" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_modeldrift_2010.html', \n", - " title_story=\"Model drift\",\n", - " title_description=\"\"\"House price model drift 2010\"\"\",\n", - " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" \n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2010" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "house_df_2010 = house_df.loc[house_df['YrSold'] == 2010]\n", + "\n", + "y_df_2010=house_df_2010['SalePrice'].to_frame()\n", + "X_df_2010=house_df_2010[house_df_2010.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ { - "customdata": [ - [], - [], - [], - [], - [] - ], - "hovertemplate": "Date=%{x}
performance=%{text}", - "legendgroup": "", - "line": { - "color": "rgba(0,154,203,255)", - "dash": "solid" - }, - "marker": { - "color": "rgba(0,154,203,255)" - }, - "mode": "lines+markers+text", - "name": "", - "orientation": "v", - "showlegend": false, - "text": [ - 0.03, - 0.03, - 0.03, - 0.03, - 0.02 - ], - "textposition": "top right", - "type": "scatter", - "x": [ - "01/01/2006", - "01/01/2007", - "01/01/2008", - "01/01/2009", - "01/01/2010" - ], - "xaxis": "x", - "y": [ - 0.03, - 0.03, - 0.03, - 0.03, - 0.02 - ], - "yaxis": "y" + "data": { + "text/plain": [ + "0.023441" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" } - ], - "layout": { - "height": 600, - "hovermode": "closest", - "legend": { - "tracegroupgap": 0 + ], + "source": [ + "df_2010_encode=encoder.transform(X_df_2010)\n", + "y_pred_2010 = regressor.predict(df_2010_encode)\n", + "performance_2010 = mean_squared_log_error(y_df_2010, y_pred_2010).round(6)\n", + "performance_2010" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_276700/2445109678.py:1: FutureWarning:\n", + "\n", + "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n", + "\n" + ] }, - "template": { - "data": { - "scatter": [ - { - "type": "scatter" - } + { + "data": { + "text/html": [ + "
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anneemoisperformance
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" + ], + "text/plain": [ + " annee mois performance\n", + "0 2006.0 1.0 0.031487\n", + "1 2007.0 1.0 0.033090\n", + "2 2008.0 1.0 0.028883\n", + "3 2009.0 1.0 0.031778\n", + "4 2010.0 1.0 0.023441" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_performance = df_performance.append({'annee': 2010, 'mois':1, 'performance': performance_2010}, ignore_index=True)\n", + "df_performance" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2010, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:The variable Condition1\n", + " has mismatching possible values: \n", + "\n", + " [\"Within 200' of East-West Railroad\"] []\n", + "INFO:root:The variable Electrical\n", + " has mismatching possible values: \n", + "\n", + " [] ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "INFO:root:The variable ExterCond\n", + " has mismatching possible values: \n", + "\n", + " ['Poor'] []\n", + "INFO:root:The variable ExterQual\n", + " has mismatching possible values: \n", + "\n", + " ['Fair'] []\n", + "INFO:root:The variable Exterior1st\n", + " has mismatching possible values: \n", + "\n", + " ['Asphalt Shingles'] ['Stone', 'Imitation Stucco']\n", + "INFO:root:The variable Exterior2nd\n", + " has mismatching possible values: \n", + "\n", + " ['Asphalt Shingles', 'Brick Common'] ['Other', 'Stone']\n", + "INFO:root:The variable Functional\n", + " has mismatching possible values: \n", + "\n", + " [] ['Major Deductions 1']\n", + "INFO:root:The variable GarageCond\n", + " has mismatching possible values: \n", + "\n", + " [] ['Poor', 'Good']\n", + "INFO:root:The variable GarageQual\n", + " has mismatching possible values: \n", + "\n", + " [] ['Good', 'Excellent', 'Poor']\n", + "INFO:root:The variable GarageType\n", + " has mismatching possible values: \n", + "\n", + " [] ['More than one type of garage']\n", + "INFO:root:The variable Heating\n", + " has mismatching possible values: \n", + "\n", + " [] ['Gas hot water or steam heat', 'Wall furnace']\n", + "INFO:root:The variable HouseStyle\n", + " has mismatching possible values: \n", + "\n", + " [] ['Two and one-half story: 2nd level finished', 'One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished']\n", + "INFO:root:The variable LotConfig\n", + " has mismatching possible values: \n", + "\n", + " [] ['Frontage on 3 sides of property']\n", + "INFO:root:The variable LotShape\n", + " has mismatching possible values: \n", + "\n", + " [] ['Irregular']\n", + "INFO:root:The variable MSSubClass\n", + " has mismatching possible values: \n", + "\n", + " ['1-Story w/Finished Attic All Ages'] ['2-1/2 Story All Ages', '1-1/2 Story - Unfinished All Ages']\n", + "INFO:root:The variable MSZoning\n", + " has mismatching possible values: \n", + "\n", + " [] ['Residential High Density']\n", + "INFO:root:The variable Neighborhood\n", + " has mismatching possible values: \n", + "\n", + " ['Northpark Villa'] ['Veenker']\n", + "INFO:root:The variable RoofMatl\n", + " has mismatching possible values: \n", + "\n", + " [] ['Wood Shingles', 'Metal', 'Gravel & Tar']\n", + "INFO:root:The variable RoofStyle\n", + " has mismatching possible values: \n", + "\n", + " ['Mansard', 'Shed'] ['Flat']\n", + "INFO:root:The variable SaleCondition\n", + " has mismatching possible values: \n", + "\n", + " [] ['Adjoining Land Purchase']\n", + "INFO:root:The variable SaleType\n", + " has mismatching possible values: \n", + "\n", + " ['Contract 15% Down payment regular terms'] ['Contract Low Down', 'Contract Low Down payment and low interest', 'Other']\n", + "INFO:root:The variable Street\n", + " has mismatching possible values: \n", + "\n", + " ['Gravel'] []\n" ] - } }, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial", - "size": 24 - }, - "text": "Performance's Evolution on deployed model", - "x": 0.5, - "xanchor": "center", - "y": 0.9, - "yanchor": "middle" + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backend: Shap TreeExplainer\n" + ] }, - "width": 900, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "showgrid": false, - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:root:\n", + " The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6978632478632478\n", + " \n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True,\n", + " date_compile_auc = '01/01/2010', #optionnal, by default date of compile\n", + " datadrift_file = \"house_price_auc.csv\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_modeldrift_2010.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] }, - "text": "Date" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "font": { - "color": "rgb(50, 50, 50)", - "family": "Arial Black", - "size": 16 + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_modeldrift_2010.html', \n", + " title_story=\"Model drift\",\n", + " title_description=\"\"\"House price model drift 2010\"\"\",\n", + " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" \n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + [], + [], + [], + [], + [] + ], + "hovertemplate": "Date=%{x}
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" + ], + "source": [ + "SD.plot.generate_modeldrift_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "SD.plot.generate_modeldrift_data()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "----" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "d08e6294e2d60f50397263035a337d71f3055486232bc02b45ce2785f62e7d8b" - }, - "kernelspec": { - "display_name": "eurybia39", - "language": "python", - "name": "eurybia39" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" + ], + "metadata": { + "interpreter": { + "hash": "d08e6294e2d60f50397263035a337d71f3055486232bc02b45ce2785f62e7d8b" + }, + "kernelspec": { + "display_name": "eurybia39", + "language": "python", + "name": "eurybia39" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/source/tutorials/model_drift/tutorial02-modeldrift-high-datadrift.ipynb b/docs/source/tutorials/model_drift/tutorial02-modeldrift-high-datadrift.ipynb index d107139..9f643d1 100644 --- a/docs/source/tutorials/model_drift/tutorial02-modeldrift-high-datadrift.ipynb +++ b/docs/source/tutorials/model_drift/tutorial02-modeldrift-high-datadrift.ipynb @@ -1,955 +1,955 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Detect High Model Drift \n", - "With this tutorial you:
\n", - "Understand how to use Eurybia to detect datadrift\n", - "\n", - "Contents:\n", - "- Detect data drift \n", - "- Compile Drift over years\n", - "\n", - "This public dataset comes from :\n", - "\n", - "https://www.kaggle.com/sobhanmoosavi/us-accidents/version/10\n", - "\n", - "---\n", - "Acknowledgements\n", - "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. “A Countrywide Traffic Accident Dataset.”, 2019.\n", - "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. \"Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights.\" In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.\n", - "---\n", - "\n", - "In this tutorial, the data are not loaded raw, a data preparation to facilitate the use of the tutorial has been done. You can find it here : \n", - "https://github.com/MAIF/eurybia/blob/master/eurybia/data/dataprep_US_car_accidents.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from category_encoders import OrdinalEncoder\n", - "import catboost\n", - "from eurybia import SmartDrift\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn import metrics\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Dataset and split in training and production dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "df_car_accident = data_loading(\"us_car_accident\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_accNautical_Twilightseason_acctargettarget_multiyear_accDescription
033.0-117.10.040.093.02.03Daywinter022019At Carmel Mountain Rd - Accident.
129.5-98.50.083.065.010.04Daysummer132017At TX-345-SP/Woodlawn Ave/Exit 567B - Accident.
232.7-96.80.088.057.010.00Nightsummer022021Incident on RUGGED DR near BERKLEY AVE Expect ...
340.0-76.30.061.058.010.04Dayspring022020At PA-741/Rohrerstown Rd - Accident.
441.5-81.81.071.053.010.00Daysummer022020At 117th St/Exit 166 - Accident.
\n", - "
" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Detect High Model Drift \n", + "With this tutorial you:
\n", + "Understand how to use Eurybia to detect datadrift\n", + "\n", + "Contents:\n", + "- Detect data drift \n", + "- Compile Drift over years\n", + "\n", + "This public dataset comes from :\n", + "\n", + "https://www.kaggle.com/sobhanmoosavi/us-accidents/version/10\n", + "\n", + "---\n", + "Acknowledgements\n", + "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. \u201cA Countrywide Traffic Accident Dataset.\u201d, 2019.\n", + "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. \"Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights.\" In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.\n", + "---\n", + "\n", + "In this tutorial, the data are not loaded raw, a data preparation to facilitate the use of the tutorial has been done. You can find it here : \n", + "https://github.com/MAIF/eurybia/blob/master/eurybia/data/dataprep_US_car_accidents.ipynb" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from category_encoders import OrdinalEncoder\n", + "import catboost\n", + "from eurybia import SmartDrift\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import metrics\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import Dataset and split in training and production dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df_car_accident = data_loading(\"us_car_accident\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_accNautical_Twilightseason_acctargettarget_multiyear_accDescription
033.0-117.10.040.093.02.03Daywinter022019At Carmel Mountain Rd - Accident.
129.5-98.50.083.065.010.04Daysummer132017At TX-345-SP/Woodlawn Ave/Exit 567B - Accident.
232.7-96.80.088.057.010.00Nightsummer022021Incident on RUGGED DR near BERKLEY AVE Expect ...
340.0-76.30.061.058.010.04Dayspring022020At PA-741/Rohrerstown Rd - Accident.
441.5-81.81.071.053.010.00Daysummer022020At 117th St/Exit 166 - Accident.
\n", + "
" + ], + "text/plain": [ + " Start_Lat Start_Lng Distance(mi) Temperature(F) Humidity(%) \\\n", + "0 33.0 -117.1 0.0 40.0 93.0 \n", + "1 29.5 -98.5 0.0 83.0 65.0 \n", + "2 32.7 -96.8 0.0 88.0 57.0 \n", + "3 40.0 -76.3 0.0 61.0 58.0 \n", + "4 41.5 -81.8 1.0 71.0 53.0 \n", + "\n", + " Visibility(mi) day_of_week_acc Nautical_Twilight season_acc target \\\n", + "0 2.0 3 Day winter 0 \n", + "1 10.0 4 Day summer 1 \n", + "2 10.0 0 Night summer 0 \n", + "3 10.0 4 Day spring 0 \n", + "4 10.0 0 Day summer 0 \n", + "\n", + " target_multi year_acc Description \n", + "0 2 2019 At Carmel Mountain Rd - Accident. \n", + "1 3 2017 At TX-345-SP/Woodlawn Ave/Exit 567B - Accident. \n", + "2 2 2021 Incident on RUGGED DR near BERKLEY AVE Expect ... \n", + "3 2 2020 At PA-741/Rohrerstown Rd - Accident. \n", + "4 2 2020 At 117th St/Exit 166 - Accident. " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " Start_Lat Start_Lng Distance(mi) Temperature(F) Humidity(%) \\\n", - "0 33.0 -117.1 0.0 40.0 93.0 \n", - "1 29.5 -98.5 0.0 83.0 65.0 \n", - "2 32.7 -96.8 0.0 88.0 57.0 \n", - "3 40.0 -76.3 0.0 61.0 58.0 \n", - "4 41.5 -81.8 1.0 71.0 53.0 \n", - "\n", - " Visibility(mi) day_of_week_acc Nautical_Twilight season_acc target \\\n", - "0 2.0 3 Day winter 0 \n", - "1 10.0 4 Day summer 1 \n", - "2 10.0 0 Night summer 0 \n", - "3 10.0 4 Day spring 0 \n", - "4 10.0 0 Day summer 0 \n", - "\n", - " target_multi year_acc Description \n", - "0 2 2019 At Carmel Mountain Rd - Accident. \n", - "1 3 2017 At TX-345-SP/Woodlawn Ave/Exit 567B - Accident. \n", - "2 2 2021 Incident on RUGGED DR near BERKLEY AVE Expect ... \n", - "3 2 2020 At PA-741/Rohrerstown Rd - Accident. \n", - "4 2 2020 At 117th St/Exit 166 - Accident. " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_car_accident.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(50000, 13)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_car_accident.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Let us consider that the column \"year_acc\" corresponds to the reference date. \n", - "#In 2016, a model was trained using data. And in next years, we want to detect data drift on new data in production to predict\n", - "df_accident_baseline = df_car_accident.loc[df_car_accident['year_acc'] == 2016]\n", - "df_accident_2017 = df_car_accident.loc[df_car_accident['year_acc'] == 2017]\n", - "df_accident_2018 = df_car_accident.loc[df_car_accident['year_acc'] == 2018]\n", - "df_accident_2019 = df_car_accident.loc[df_car_accident['year_acc'] == 2019]\n", - "df_accident_2020 = df_car_accident.loc[df_car_accident['year_acc'] == 2020]\n", - "df_accident_2021 = df_car_accident.loc[df_car_accident['year_acc'] == 2021]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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target01
year_acc
201671.40628728.593713
201767.25462032.745380
201866.63466233.365338
201979.55118220.448818
202089.94480410.055196
202198.2599301.740070
\n", - "
" + "source": [ + "df_car_accident.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(50000, 13)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "target 0 1\n", - "year_acc \n", - "2016 71.406287 28.593713\n", - "2017 67.254620 32.745380\n", - "2018 66.634662 33.365338\n", - "2019 79.551182 20.448818\n", - "2020 89.944804 10.055196\n", - "2021 98.259930 1.740070" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#We will train a classification model to predict the severity of an accident. 0 for a less severe accident and 1 for a severe accident.\n", - "#Let's check percentage in class 0 and 1\n", - "pd.crosstab(df_car_accident.year_acc, df_car_accident.target, normalize = 'index')*100" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=df_accident_baseline['target'].to_frame()\n", - "X_df_learning=df_accident_baseline[df_accident_baseline.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2017=df_accident_2017['target'].to_frame()\n", - "X_df_2017=df_accident_2017[df_accident_2017.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2018=df_accident_2018['target'].to_frame()\n", - "X_df_2018=df_accident_2018[df_accident_2018.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2019=df_accident_2019['target'].to_frame()\n", - "X_df_2019=df_accident_2019[df_accident_2019.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2020=df_accident_2020['target'].to_frame()\n", - "X_df_2020=df_accident_2020[df_accident_2020.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2021=df_accident_2021['target'].to_frame()\n", - "X_df_2021=df_accident_2021[df_accident_2021.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building Supervized Model" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "features = ['Start_Lat', 'Start_Lng', 'Distance(mi)', 'Temperature(F)',\n", - " 'Humidity(%)', 'Visibility(mi)', 'day_of_week_acc', 'Nautical_Twilight',\n", - " 'season_acc']" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "features_to_encode = [col for col in X_df_learning[features].columns if X_df_learning[col].dtype not in ('float64','int64')]\n", - "\n", - "encoder = OrdinalEncoder(cols=features_to_encode)\n", - "encoder = encoder.fit(X_df_learning[features])\n", - "\n", - "X_df_learning_encoded=encoder.transform(X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "train_pool_cat = catboost.Pool(data=Xtrain, label= ytrain, cat_features = features_to_encode)\n", - "test_pool_cat = catboost.Pool(data=Xtest, label= ytest, cat_features = features_to_encode)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3ca6a823b83a4df3b8eb6a87c4e8da9c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))" + "source": [ + "df_car_accident.shape" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", - " learning_rate=0.143852,\n", - " iterations=150,\n", - " l2_leaf_reg=15,\n", - " max_depth = 4,\n", - " use_best_model=True,\n", - " custom_loss=['Accuracy', 'AUC', 'Logloss'])\n", - "\n", - "model = model.fit(train_pool_cat, plot=True,eval_set=test_pool_cat, verbose=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7634385095163502\n" - ] - } - ], - "source": [ - "proba = model.predict_proba(Xtest)\n", - "print(metrics.roc_auc_score(ytest,proba[:,1]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use Eurybia for data validation" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2017, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6585689489728102\n", - "CPU times: total: 1min 23s\n", - "Wall time: 26.5 s\n" - ] - } - ], - "source": [ - "%time SD.compile(full_validation=True, date_compile_auc = '01/01/2017', datadrift_file = \"car_accident_auc.csv\")\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", - "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", - "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add model drift in report" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the moment, the model drift part of eurybia only consists of displaying performance of deployed model. \n", - "(We hope to bring new features in the future on this part)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Put model performance in DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "proba = model.predict_proba(X_df_2017)\n", - "performance = metrics.roc_auc_score(y_df_2017,proba[:,1]).round(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "#Create Dataframe to track performance over the years\n", - "df_performance = pd.DataFrame({'annee': [2017], 'mois':[1], 'performance': [performance]})" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Report saved to ./report_car_accident_modeldrift_2017.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Let us consider that the column \"year_acc\" corresponds to the reference date. \n", + "#In 2016, a model was trained using data. And in next years, we want to detect data drift on new data in production to predict\n", + "df_accident_baseline = df_car_accident.loc[df_car_accident['year_acc'] == 2016]\n", + "df_accident_2017 = df_car_accident.loc[df_car_accident['year_acc'] == 2017]\n", + "df_accident_2018 = df_car_accident.loc[df_car_accident['year_acc'] == 2018]\n", + "df_accident_2019 = df_car_accident.loc[df_car_accident['year_acc'] == 2019]\n", + "df_accident_2020 = df_car_accident.loc[df_car_accident['year_acc'] == 2020]\n", + "df_accident_2021 = df_car_accident.loc[df_car_accident['year_acc'] == 2021]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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target01
year_acc
201671.40628728.593713
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201866.63466233.365338
201979.55118220.448818
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" + ], + "text/plain": [ + "target 0 1\n", + "year_acc \n", + "2016 71.406287 28.593713\n", + "2017 67.254620 32.745380\n", + "2018 66.634662 33.365338\n", + "2019 79.551182 20.448818\n", + "2020 89.944804 10.055196\n", + "2021 98.259930 1.740070" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "#We will train a classification model to predict the severity of an accident. 0 for a less severe accident and 1 for a severe accident.\n", + "#Let's check percentage in class 0 and 1\n", + "pd.crosstab(df_car_accident.year_acc, df_car_accident.target, normalize = 'index')*100" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_car_accident_modeldrift_2017.html', \n", - " title_story=\"Model drift Report\",\n", - " title_description=\"\"\"US Car accident model drift 2017\"\"\",\n", - " project_info_file=\"../../../../eurybia/data/project_info_car_accident.yml\" \n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This tutorial contains only anlysis on additional features of model drift. For more detailed information on data drift, you can consult these tutorials : (https://github.com/MAIF/eurybia/tree/master/tutorial/model_drift/tutorial02-datadrift-high-datadrift.ipynb)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compile Drift over years" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2018" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2018, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7036329129677259\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True,\n", - " date_compile_auc = '01/01/2018', #optionnal, by default date of compile\n", - " datadrift_file = \"car_accident_auc.csv\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "proba = model.predict_proba(X_df_2018)\n", - "performance = metrics.roc_auc_score(y_df_2018,proba[:,1]).round(5)\n", - "df_performance = df_performance.append({'annee': 2018, 'mois':1, 'performance': performance}, ignore_index=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2019" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2019, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7856527709300022\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True,\n", - " date_compile_auc = '01/01/2019', #optionnal, by default date of compile\n", - " datadrift_file = \"car_accident_auc.csv\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "proba = model.predict_proba(X_df_2019)\n", - "performance = metrics.roc_auc_score(y_df_2019,proba[:,1]).round(5)\n", - "df_performance = df_performance.append({'annee': 2019, 'mois':1, 'performance': performance}, ignore_index=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2020" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2020, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7902450838961592\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True,\n", - " date_compile_auc = '01/01/2020', #optionnal, by default date of compile\n", - " datadrift_file = \"car_accident_auc.csv\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "proba = model.predict_proba(X_df_2020)\n", - "performance = metrics.roc_auc_score(y_df_2020,proba[:,1]).round(5)\n", - "df_performance = df_performance.append({'annee': 2020, 'mois':1, 'performance': performance}, ignore_index=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "5fbfd60a", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2021" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "0812df83", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2021,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "d15400e0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7500011519622525\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2021', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "f5ef03b1", - "metadata": {}, - "outputs": [], - "source": [ - "proba = model.predict_proba(X_df_2021)\n", - "performance = metrics.roc_auc_score(y_df_2021,proba[:,1]).round(5)\n", - "df_performance = df_performance.append({'annee': 2021, 'mois':1, 'performance': performance}, ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In 2019 and 2020, data drift is very high. Is there any impact on the performance of the model?" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_modeldrift_data()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While data drift was high in 2019, the impact on model performance is low. In 2020, data drift leads to a decrease in model performance." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Report saved to ./report_car_accident_modeldrift_2021.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=df_accident_baseline['target'].to_frame()\n", + "X_df_learning=df_accident_baseline[df_accident_baseline.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2017=df_accident_2017['target'].to_frame()\n", + "X_df_2017=df_accident_2017[df_accident_2017.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2018=df_accident_2018['target'].to_frame()\n", + "X_df_2018=df_accident_2018[df_accident_2018.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2019=df_accident_2019['target'].to_frame()\n", + "X_df_2019=df_accident_2019[df_accident_2019.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2020=df_accident_2020['target'].to_frame()\n", + "X_df_2020=df_accident_2020[df_accident_2020.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2021=df_accident_2021['target'].to_frame()\n", + "X_df_2021=df_accident_2021[df_accident_2021.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building Supervized Model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "features = ['Start_Lat', 'Start_Lng', 'Distance(mi)', 'Temperature(F)',\n", + " 'Humidity(%)', 'Visibility(mi)', 'day_of_week_acc', 'Nautical_Twilight',\n", + " 'season_acc']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "features_to_encode = [col for col in X_df_learning[features].columns if X_df_learning[col].dtype not in ('float64','int64')]\n", + "\n", + "encoder = OrdinalEncoder(cols=features_to_encode)\n", + "encoder = encoder.fit(X_df_learning[features])\n", + "\n", + "X_df_learning_encoded=encoder.transform(X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "train_pool_cat = catboost.Pool(data=Xtrain, label= ytrain, cat_features = features_to_encode)\n", + "test_pool_cat = catboost.Pool(data=Xtest, label= ytest, cat_features = features_to_encode)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3ca6a823b83a4df3b8eb6a87c4e8da9c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", + " learning_rate=0.143852,\n", + " iterations=150,\n", + " l2_leaf_reg=15,\n", + " max_depth = 4,\n", + " use_best_model=True,\n", + " custom_loss=['Accuracy', 'AUC', 'Logloss'])\n", + "\n", + "model = model.fit(train_pool_cat, plot=True,eval_set=test_pool_cat, verbose=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7634385095163502\n" + ] + } + ], + "source": [ + "proba = model.predict_proba(Xtest)\n", + "print(metrics.roc_auc_score(ytest,proba[:,1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Eurybia for data validation" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2017, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6585689489728102\n", + "CPU times: total: 1min 23s\n", + "Wall time: 26.5 s\n" + ] + } + ], + "source": [ + "%time SD.compile(full_validation=True, date_compile_auc = '01/01/2017', datadrift_file = \"car_accident_auc.csv\")\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", + "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", + "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add model drift in report" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the moment, the model drift part of eurybia only consists of displaying performance of deployed model. \n", + "(We hope to bring new features in the future on this part)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Put model performance in DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "proba = model.predict_proba(X_df_2017)\n", + "performance = metrics.roc_auc_score(y_df_2017,proba[:,1]).round(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "#Create Dataframe to track performance over the years\n", + "df_performance = pd.DataFrame({'annee': [2017], 'mois':[1], 'performance': [performance]})" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_car_accident_modeldrift_2017.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_car_accident_modeldrift_2017.html', \n", + " title_story=\"Model drift Report\",\n", + " title_description=\"\"\"US Car accident model drift 2017\"\"\",\n", + " project_info_file=\"../../../../eurybia/data/project_info_car_accident.yml\" \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tutorial contains only anlysis on additional features of model drift. For more detailed information on data drift, you can consult these tutorials : (https://github.com/MAIF/eurybia/tree/master/tutorial/model_drift/tutorial02-datadrift-high-datadrift.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compile Drift over years" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2018" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2018, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7036329129677259\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True,\n", + " date_compile_auc = '01/01/2018', #optionnal, by default date of compile\n", + " datadrift_file = \"car_accident_auc.csv\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "proba = model.predict_proba(X_df_2018)\n", + "performance = metrics.roc_auc_score(y_df_2018,proba[:,1]).round(5)\n", + "df_performance = df_performance.append({'annee': 2018, 'mois':1, 'performance': performance}, ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2019" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2019, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7856527709300022\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True,\n", + " date_compile_auc = '01/01/2019', #optionnal, by default date of compile\n", + " datadrift_file = \"car_accident_auc.csv\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "proba = model.predict_proba(X_df_2019)\n", + "performance = metrics.roc_auc_score(y_df_2019,proba[:,1]).round(5)\n", + "df_performance = df_performance.append({'annee': 2019, 'mois':1, 'performance': performance}, ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2020" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2020, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7902450838961592\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True,\n", + " date_compile_auc = '01/01/2020', #optionnal, by default date of compile\n", + " datadrift_file = \"car_accident_auc.csv\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "proba = model.predict_proba(X_df_2020)\n", + "performance = metrics.roc_auc_score(y_df_2020,proba[:,1]).round(5)\n", + "df_performance = df_performance.append({'annee': 2020, 'mois':1, 'performance': performance}, ignore_index=True)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "5fbfd60a", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2021" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "0812df83", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2021,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "d15400e0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7500011519622525\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2021', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "f5ef03b1", + "metadata": {}, + "outputs": [], + "source": [ + "proba = model.predict_proba(X_df_2021)\n", + "performance = metrics.roc_auc_score(y_df_2021,proba[:,1]).round(5)\n", + "df_performance = df_performance.append({'annee': 2021, 'mois':1, 'performance': performance}, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In 2019 and 2020, data drift is very high. Is there any impact on the performance of the model?" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OM/bZCtFWr43WZhWE4v2+UWUjebr59yAVgdBN/VRdnGPTzVjVx0QKhG7GidsvGt2ci2MQQMCdAIHQnRNHIZC0QCoCYazl0SNVLF4Ai/bBPZE7FfZwEO968X6eaCC0r4yn75yp7TXUHQpVL/WskloeP1WBMF54032g25lof8UbXOpuw8cffyyffvpp/cIeqozblUbdfIiLVwc9NvQUST1d1FmHRO7Y2KcmZysQprK+yQbCbL3v7PV1c0dLP2cbKdglundqtIV/IhnGCgjJ9p+6jvMut75r5pySaV+h2O1CTsl6ZioQJnqHUD/D7eYOof7d4AyEsR59SPbfsXi/t/g5Agi4EyAQunPiKASSFjAJhG6eSUv2H9JsPEOYim+2VXv1h0DnynjOYGYaCN08Q6juKLRo0cJaAXb9+vXW6oiJfvBJZHBFW9wj1jlSEQi1hbqbovZ4VMvyR1o4JtlnupIJhPqZNue4ihY2UvkMWrw7i/F+bu+veM9lqWNjPUMY7X0V74sY55iJF670+RJ9hjDS2Iw3TtL9DKGuk/352C+++MLacsP5e8XNM3CR2piMZ6YCof1LwUjPbut6ONsQaRw66+z8gi9Zv0THbyK/RzkWAQTOFiAQMiIQSLOASSCMt8qlqrr6xlZtKKyCiX2LBucdsmj/aNvv8JiudpjsB9NE7xDG+7Ctp32ZBsJ4q4Y6P7Qm21/OvlFBR33wiraKqptv6e3nTEUg1EFc/f93v/tdK/hGuhOa7KqPkfo03hcikfpZ1S+RQJjK+trN441R+7HZet85x12slTfVsdo70iqjzq1o7OdW5dTLPs3Z/uyhfcscXS7dq4zq69i/6FBf6KgtUSKt2Bqp7fY2RlpxNxnPTAbCZFYZjbZqrNNT/TnSKqPRpt9H8iMQpvnDCadHwCZAIGQ4IJBmAZNAqKqm/9F27p2nfpbIfl1uAqH9w3Qy+xBmKhDqb62dy6WrDyvPPPNM/bRK00CoA7fae9HpYd9jLdIdk0T6y9k39uXe7fsyquPs+8tlcsqo3ULXN9IH52T3hYsUoKJtSWLfg1PVJdodQuc2JpE+YKayvskGwmy975zjzh5M7XfJnN728W6/Y53I7wz7oknO97H+vabeQyos6hV2U9l/uu36+Vh1t1ttqRFthWX771q3e/Yl45nJQJjsPoT63yRnf9v3LnQGwmT8CIRp/nDC6REgEDIGEMicgGkgtG+armqt/hFu1qyZ6JX71N+5+eDrNhBGu97ChQutU0TaDD7eP9zxfp7oHUL74i3qg5x6dlCv8qfqN3LkSNEhTj/zE28RhWg/d3roVQa1h3N6aDL9FWk02j8w6z63r2QYbe+weOdyO/IjrTxoN4o1LdYeWnVgs9fduam2OibaHTV9lyXS2Fd3KFX/OgOhnkqqV+dV01v79+8fddXCVNZX+yZyh1CVycb7LtJYsD+fq99b+neN6jd1B815lyeSnzp3rN8ZenzrBaB0X+lVkxP5vZZM/9nbrgNOpGvaj7OPRW1jr2+k92QyntHeo6n+PaquY69fpD6Its/mT3/6U2ssON/f6hwqwKtw7fwdkqhfvPa6/V3GcQggEF+AO4TxjTgCASMB00CoL67+4VZ3ZOwfsq6//noZNWrUOdsQuPmHNNYHVvXh9J133pGlS5fW/6OvPgANHjxYbrnllnM2n453vXg/TzQQ6g8yCxYsOKt+6kO/er6tcePGojZvVy99FyvZQKj91Tfcdg/14fi6666rn6brHCSJ9Fe0AaauqfpB97k6Tt3BGDRokOsVRlUZN1ufOOsQbSn6eBtM6/Oou3vLly+37mKrD4fqpcyGDx9ujSPnK9Z4VOPn7bffru9rZXD77bdbIWrChAnnBEL190888UT9nWJ91yfWOExlfVXbEg2Eqkym33fRxp16ryxatKjeT7331fRl9cWL8o407U+vimvvJ1VOvyed+2japzGr8+oVg/UXOmqcqP+2v1LZf/bz6sVyYk171cdrm1WrVll369VLfSGhv3SIZJqMZ6TzpOP3qLqOqp8Kd+rRA92meL9nIq2CrMp885vftL6gVL+zon2ppMaWG7947TX6h5nCCCBwlgCBkAGBAAIIIIAAAhkVSNVzramotA6Eidx1T8V1OQcCCCDgFQECoVd6gnoggAACCCAQEgEvBUI9ZdTtM7kh6SKaiQACIRIgEIaos2kqAggggAACXhDIdiBUUx4bNWpUvzBXpC1UvOBEHRBAAIFMCBAIM6HMNRBAAAEEEECgXiDbgVCvVKwrxN1BBicCCIRZgEAY5t6n7QgggAACCGRBINuBUK94qe4MTpo0yVr8hhcCCCAQVgECYVh7nnYjgAACCCCAAAIIIIBA6AUIhKEfAgAggAACCCCAAAIIIIBAWAUIhGHtedqNAAIIIIAAAggggAACoRcgEIZ+CACAAAIIIIAAAggggAACYRUgEIa152k3AggggAACCCCAAAIIhF6AQBj6IQAAAggggAACCCCAAAIIhFWAQBjWnqfdCCCAAAIIIIAAAgggEHoBAmHohwAACCCAAAIIIIAAAgggEFYBAmFYe552I4AAAggggAACCCCAQOgFCIShHwIAIIAAAggggAACCCCAQFgFCIRh7XnajQACCCCAAAIIIIAAAqEXIBCGfggAgAACCCCAAAIIIIAAAmEVIBCGtedpNwIIIIAAAggggAACCIRegEAY+iEAAAIIIIAAAggggAACCIRVgEAY1p6n3QgggAACCCCAAAIIIBB6AQJh6IcAAAgggAACCCCAAAIIIBBWAQJhWHuediOAAAIIIIAAAggggEDoBQiEoR8CACCAAAIIIIAAAggggEBYBQiEYe152o0AAggggAACCCCAAAKhFyAQhn4IAIAAAggggAACCCCAAAJhFSAQhrXnaTcCCCCAAAIIIIAAAgiEXoBAGPohAAACCCCAAAIIIIAAAgiEVYBAGNaep90IIIAAAggggAACCCAQegECYeiHAAAIIIAAAggggAACCCAQVgECYVh7nnYjgAACCCCAAAIIIIBA6AUIhKEfAgAggAACCCCAAAIIIIBAWAUIhGHtedqNAAIIIIAAAggggAACoRcgEBoMgV69eklZWZnBGSiKAAIIIIAAAggggAACCGRPgEBoYE8gNMCjKAIIIIAAAggggAACCGRdgEBo0AUEQgM8iiKAAAIIIIAAAggggEDWBQiEBl1AIDTAoygCCCCAAAIIIIAAAghkXYBAaNAFBEIDPIoigAACCCCAAAIIIIBA1gUIhAZdQCA0wKMoAggggAACCCCAAAIIZF2AQGjQBQRCAzyKIoAAAggggAACCCCAQNYFCIQGXUAgNMCjKAIIIIAAAggggAACCGRdgEBo0AUEQgM8iiKAAAIeFzh48KD079+/vpaLFi2SPn36RKz16tWrZeLEief8bOzYsfLwww+L8+f673WBWbNmycyZM+vLz5gxQ8aNG+dxIaqHAAIIIBAEAQKhQS8SCA3wKIoAAgh4XGD8+PEyZcoUGTBggKxfv15GjRolZWVlrmv9wAMPyIgRI6zyKvBNmDBBWrVqZZVX51ahUIc+dawKjuqlg2isAOq6EhyIAAIIIIBAHAECocEQIRAa4FEUAQQQ8LBApABoD4jxqq7KT5s2TebNmxfxUBUQ1UsFzkivRK4Vry78HAEEEEAAgVgCBEKD8UEgNMCjKAIIIOBhATXFU4U2e6BTd/H69evnaiqn/e5gpGbGO5f694U7hB4eIFQNAQQQCJAAgdCgMwmEBngURQABBDwsMH/+fFmwYME5gbBDhw5R7+rp5sS7O6ifJ4w2/VQF0V27dtVPIfUwE1VDAAEEEAiAAIHQoBMJhAZ4FEUAAQQ8LGByhzDWdE8dBtesWVP/PKGdQYVBdUy0qaYeJqNqCCCAAAI+FSAQGnQcgdAAj6IIIICAhwWSfYYwUpDUzSQMerjDqRoCCCAQYgECoUHnEwgN8CiKAAIIeFwg3iqj6t8A5/YQ0e4OqimoU6dOjbpKqXqmUL30SqMep6F6CCCAAAIBEiAQGnQmgdAAj6IIIICAxwXi7UPoDISxpnuqoLh27dpzWqymjqqXfb9DfZBawIapox4fJFQPAQQQCIAAgdCgEwmEBngURQABBBBAAAEEEEAAgawLEAgNuoBAaIBHUQQQQAABBBBAAAEEEMi6AIHQoAsIhAZ4FEUAAQQQQAABBBBAAIGsCxAIDbqAQGiAR1EEEEAAAQQQQAABBBDIugCB0KAL/B4I9RLomiDaJsnq52qxhJkzZ56j5VxhT62kpxZOcK6Up5dw1ydwljPoBooigAACSQt8cuiUHKuskevbNU36HBREAAEEEEDAzwIEQoPe83Mg1Kvn6c2RowW5WDyq/bq8PVyOHTv2rECor7Vo0SLp06ePOP9s0AUURQABBJISUEHwlhUbZWt5pVW+a1GBvDSkp1xa3Dip81EIAQQQQAABvwoQCA16zs+B0BkAnQExHosqv2/fPpkyZcpZh6o7ibt27TorECa7wXO8OvBzBBBAIFmBS//6uaw7XHFW8TFdmsvLQ3ome0rKIYAAAggg4EsBAqFBt/k5EKrgpl72QKfao+/ixWOx3x20HxspEKqfq02XFyxYYN1RVOFz2rRp7K8VD5mfI4BA2gRyZn8Y8dx1k65I2zU5MQIIIIAAAl4UIBAa9IqfA6EKaB06dDgnEM6ZM0cGDBgQUyXa3UFVKFogVGVUINQbM/MMocHAoygCCBgLNJ6zVipqas86T5eiAtl6xyXG5+YECCCAAAII+EnAt4HQvsiJ85k1ZweMHz++PojYf6bvhjkXTHEbVvwcCJO9Qxhvamm0KaPOO4LKzq2zn95Q1BUBBLwv8LMPd8kvPt4tIjlnVXZav/byX5e3934DqCECCCCAAAIpFPBlIFQLmKjgMW/ePItC3e3q16+fjBs3zhWNeqbNHlBUeb0qZiILnvg5ECb7DGGkIGlHjxQIIy1YE+kOpavO4yAEEEAgSYHaujr5j9Xb5KkNB60zjOp8nuTm5MiSHcekqrZWXhjUQyb0aJnk2SmGAAIIIICAPwV8GQidAdAZEON1hSo/YsSIqFMj1R1F9WxdvKmTfg6E8VYZ1QvB2J8pjHd3ULnHWlTGucqom+mp8fqSnyOAAAJuBM7U1MptKzfJqzuOSW6OyJ9v6CZ39Sy2iv7qkz3ywAe75JKWjWTdbRe6OR3HIIAAAgggEBgBXwZCZ2CLtIpltB5y3h2MdJzbxVX8HAhVu2PtQxgpECp3FZKdK4tGOpf6O3uYdF6LMBiY3yE0BAHPC5yorJFhy0rl3X0npTAvRxYP7SWDOzSrr7f6eeu/fCKna+rk7VF9ZEDbIs+3iQoigAACCCCQKgFfBkIVxOyBQocXvSdeLJx4dwejLYry8ccfn3Pa22+/XWJt5p6qTuI8CCCAAALJCeyvqJIbXy2Vz45USLMGefL6zSVyxflNzjnZlHe2y++/2C9f79Lc2o+QFwIIIIAAAmER8GUgTPYOYbyppSoMqmP0s4n2QXD//fefMyZeeuklAmFY3im0EwEEfCew8dhpGbykVLaVV0qHJg3k9RG9pXfzhhHbseX4Gek+7x/WMjMbx18s3ZsV+q69VBgBBBBAAIFkBHwZCJN9hjDWs4GxwmA0WL9PGU1mwFAGAQQQ8IPAxwdPyeAlG+TwmRopOa9QVo3sI20bN4hZ9a+v2CivbDsq372wtfzums5+aCZ1RAABBBBAwFjAl4Ew3iqjalXLqVOnnnX3LtbdQRUw1UuvNOpWlUDoVorjEEAAgcwJvLn7uIxcvlFOVdfK5a0aW9NEzyvIj1uBVXtOyA2LN0ijvFzZd3dfaVqQF7cMByCAAAIIIOB3AV8GQoUeax9CZyDUq2NGWshE/8zZkWobi0hTR+3HeT0QHq2skXWHTsl5BXlyaXFjv49V6o8AAgjEFZi76bB8480tUl1XJ8M6NpOXbuopDfNz45bTB1zy14UxOoUAACAASURBVM/lH4cr5Ndf7Sj3923ruhwHIoAAAggg4FcB3wZCL4B7ORD+qfSg3LNqaz3TpcWN5M2RfaQ533h7YehQBwQQSIPArM/3y3ff3W6d+c4eLeXZgd2sfQYTeT238ZDc/eYW65nDrRMukXy1RwUvBBBAAAEEAixAIDToXK8GQnVnsMUz566Keu9FreW3/XkuxqDLKYoAAh4VeOD9nfKrdXut2j1waTuZcWWHpGpaXVsn7Z9bJwdOV8vzg7rLHWxUn5QjhRBAAAEE/CNAIDToK68Gwrf2nJCBizec07Lr2zWVt0b2NmgxRRFAAAFvCdTW1VmzIf5cdsiq2B+u7SzfvqC1USV/vW6v/L/3d7JRvZEihRFAAAEE/CJAIDToKa8Gwq0nKqXb3E/PadmQDs1k+YgSgxZTFAEEEPCOQEVNrYxbuUle3XHMqtS8G7vL7d1bGlfwyJkaaf/clxvVrx7VR77GRvXGppwAAQQQQMC7AgRCg77xaiBUTVIr5akV8+yvvByxpoxOudDs23MDMooigAACKRE4eqZahiwtlQ8OnLJWBV06vJeoWRCpeumN6m/p2lxevImN6lPlynkQQAABBLwnQCA06BMvB0L1HOGfNhyUl7cdla5FBdbCCHM2HJQ6EflWSbHMHtCVxRIM+p6iCCCQPYE9pypl4OJS2XDstLRqmC8rR5SkfCVlNqrPXv9yZQQQQACBzAoQCA28vRwIIzVryfajMva1zaKmWfVv3UReHdZLWhTG35vLgIiiCCCAQEoF1h+tkJuWlMrOk1XSuahA3h7ZW7o0LUzpNfTJ2Kg+LaycFAEEEEDAYwIEQoMO8VsgVE397HCFDF/25YepLkUFsmx4L+nTvJGBAkURQACBzAi8v/+kNU30WGWNXNC8obVI1vmNGqTt4mxUnzZaTowAAggg4CEBAqFBZ/gxEKrmHjpdLaOWl8ma/SelSX6u/O+N3WVE5+YGEhRFAAEE0iuwYucxGbNio7XQy9Wtm8iK4SXSNAP7quqN6h/5akf5ERvVp7eTOTsCCCCAQFYECIQG7H4NhKrJaq+tb761RZ7fdFhyReRXfNgxGAkURQCBdArM3XRY7n5zs9TUiYzu0tz6EqswT/3mSv+LjerTb8wVEEAAAQSyK0AgNPD3cyDUzf7vf+yTH723w/qgdWePlvL09V2lIEMftAzoKYoAAiEReHTdXvnx+zutBbHuKSmWP17XVXJycjLWejaqzxg1F0IAAQQQyJIAgdAAPgiBUDX/tV3H5daVG+VEVa1c0aqxtdhM6zQ+l2NATlEEEAiJQF1dnXz33e3y+y8OWC2efnl7ebBf+6y0no3qs8LORRFAAAEEMiRAIDSADkogVAQbj52WwUtKZVt5pbRr3MB6Pueiliw2YzA8KIoAAkkKqLtyaorovM1HRN0LfOq6rjKxd6skz2Ze7ERljbT+CxvVm0tyBgQQQAABLwoQCA16JUiBUDEcOVMtapn1t/eWWxs9vzCom4zp2sJAiKIIIIBAYgInq2rklpWbZOWu41KQmyMvD+kpwzudl9hJ0nA0G9WnAZVTIoAAAgh4QoBAaNANQQuEikI9S3ivNU1rv/XNvJqm9bMsTdMy6BqKIoCADwXUCsiDl2yQTw5VSFGDXGumQv82RZ5oCRvVe6IbqAQCCCCAQBoECIQGqEEMhJrj6Q0H5f+s3iq1/1zVb96g7tIwPzOr+hl0CUURQMCnAjvKK2Xgqxtk0/Ez0qZRvrx+c2+5sIW3pq2zUb1PBxfVRgABBBCIKUAgNBggQQ6EikVtyqw+AB2trJFLixvJq0N7SfsmBQZiFEUAAQTOFVh/tEIGLi6VvRVV0r1poawa2Vs6Fnnvd83be07I9Ys3WFPq993dNyP7IDJeEEAAAQQQSLcAgdBAOOiBUNFsPXFGhi0tkw3HTsv5DfOtFUivPL+JgRpFEUAAgX8JvLO3XG5eXibHKmukb8tG8sbNvaVlw3zPErFRvWe7hoohgAACCCQpQCBMEk4VC0MgVO0sr6qR21/fLEt3HJPCvBx59oZuMq57SwM5iiKAAAIii7cfldtWbpLK2jq5sX1TWTi0pzTOz/M0DRvVe7p7qBwCCCCAQBICBMIk0HSRsARC1V61KfSP39spj36612r+A5e2k19e2cFaeIYXAgggkKjAM6UH5Z5VW63fLWO7tZC5g7pLXq73f6OwUX2iPc3xCCCAAAJeFyAQGvRQmAKhZlLfjqsPcVW1ddZS8AsGd/f8N/oGXUxRBBBIg8AvP94jP/1wl3XmyV85X574Wpc0XCV9p2Sj+vTZcmYEEEAAgcwLEAgNzMMYCBXXBwdOys3LyuTA6Wq5sEVDWTqsRDp5cAEIg66lKAIIpEGgrq5O/n31Nnlqw0FrdsGvv9pRftS3bRqulN5TslF9en05OwIIIIBAZgUIhAbeYQ2Eimz3yUoZsrRUPj9yWloW5smiob3kGo/sF2bQpRRFAIE0CVTV1sr41zfLS1uPipoZqqaI+vlZZDaqT9NA4bQIIIAAAhkXIBAakIc5ECq2U9U1cscbW2ThtqOSlyPyzA3d5K6exQaiFEUAgSAKnKyqkZHLN8pbe05YC1MtHtpLBndo5uumslG9r7uPyiOAAAII2AQIhAbDIeyBUNNN+2i3/HztbmtxiP+8qI08fnUn6w4ALwQQQGB/RZUMXVoqnxyqkBaFebJieIlcEZCta/RG9d+7sLXMvKYznY0AAggggIAvBQiEBt1GIPwX3itbj8j4NzbLmZovl49/8aae0qzA28vHG3Q9RRFAwIWA2sf0hsUbZFt5pXRo0sDacL5Hs4YuSvrjEDaq90c/UUsEEEAAgdgCBEKDEUIgPBvv44OnrA2m95yqkh7NCuW1ESXStWmhgTBFEUDArwL/OHxKbny11Fp8quS8Qlk1so+0bdzAr82JWm82qg9cl9IgBBBAIHQCBEKDLicQnounpoepFUg/PHhKmhfkyctDesr17ZoaKFMUAQT8JvDO3nIZtqxUyqtq5fJWjeX1m0vkvIJ8vzXDVX3ZqN4VEwchgAACCHhYgEBo0DkEwsh4lTW1ctebW2TBliOSnyPy2/6d5TsXtjaQpigCCPhF4MUtR+SONzZLZW2dDOvYTF66qac0zM/1S/UTrqd9o/oXBnWXCT1aJnwOCiCAAAIIIJBNAQKhgT6BMDberz7ZIz/5YJfUisi3SorlqQFdJY/VZgxGHEUR8LbA7PUHrH0G1evOHi3l2YHdJDcn+CtMsVG9t8cltUMAAQQQiC1AIDQYIQTC+HhLth+V21/fLCera+W6tkXWFNIWhcGcOhZfgyMQCK6AWm34obW7rQZOvbSd/PLKDsFtrKNlbFQfmq6moQgggEAgBQiEBt1KIHSHt/5ohdy0pFR2nqySLkUFsmx4L+nTvJG7whyFAAKeFqitq5P/WL1Nntpw0KrnH67tLN++IHxTxL/77naZ9fl+uaVrc2uVZV4IIIAAAgj4RYBAaNBTBEL3eIdOV8uo5WWyZv9JaZKfa90p9PvG1O5bz5EIBFPgTE2t3LZyk7y645jk5+TIgsHdZUzXFsFsbJxWsVF9KLudRiOAAAKBECAQGnQjgTAxPLX4wqTVW+VPpYdELTHx66s6yg8vaZvYSTgaAQQ8IaCmSaqVRN/dd1Ia5eXK0uG9Qr+iMBvVe2JoUgkEEEAAgQQFCIQJgtkPJxAmh/e7z/bL9/++XWrqvlx44unru0pBXnBXIUxOiVIIeFdAbS+j9hj87EiFtGqYLytHlMilxY29W+EM1YyN6jMEzWUQQAABBFIqQCA04CQQJo/32q7jcuvKjXKiqlauaNVYlg0vkeKGLDaTvCglEciMwMZjp2XwklLZVl4pnYsK5O2RvaVL08LMXNwHV2Gjeh90ElVEAAEEEDhLgEBoMCAIhAZ4IqI+WA5bViabjp+Rjk0ayNJhJXJRSxabMVOlNALpE/j44CkZvGSDHD5TIxc0byhvjewt5zdqkL4L+vDMz288LHe9uVk6NGkg2++4JBTbbviwm6gyAggggIBNgEBoMBwIhAZ4/yx6vLLGWmzm7b3l1nNILwzqFtpFKcw1OQMC6RN4c/dxGbl8o5z65xYyi4f2kqYFeem7oE/PzEb1Pu04qo0AAgiEWIBAaND5BEIDPFtR9Szh99dsl999vl/UFtbTL28vP+vXPjUn5ywIIGAsMHfTYfnGm1ukuq5ORndpbq0m2iCX536jwbJRvfGQ4wQIIIAAAhkUIBAaYBMIDfAiFH16w0FrFVIVEMd2ayHP3tBNGubzoTO1ypwNgcQE1N56ao899bqnpFj+eF1XyclRX93wiibARvWMDQQQQAABPwkQCA16i0BogBel6Lv7yuXmZWVytLJGLi1uJK8O7SXtmxSk/kKcEQEE4go88P5O+dW6vdZxD3HnPq6X/QC9Uf2tXZvLX9moPiE7DkYAAQQQyKwAgdDAm0BogBej6NYTZ2TY0jLZcOy0nN8wX5YPL5HLWrGkfXq0OSsC5wrU1tXJPau2yp/LDlnTuJ8d2E3u6lkMVQICbFSfABaHIoAAAghkVYBAaMBPIDTAi1O0vKpGbn99syzdcUwK83Ks6aPjurdM3wU5MwIIWAIV1bUy7rVN8uqOY1KQmyMvD+kpwzudh04SAmxUnwQaRRBAAAEEMi5AIDQgJxAa4LkoWiciasqaWqBBvR64tJ388soO1h0LXgggkHqBo2eqZcjSUvngwClp1iBPlg3vJf3bFKX+QiE5IxvVh6SjaSYCCCDgcwECoUEHEggN8BIoOn+z2tdri1TV1ll3KtQKh43zWe4+AUIORSCuwJ5TlTJwcak1VbtNo3xZNbKP9G7eMG45DogtoDeqf/SqjvLDS9rChQACCCCAgOcECIQGXUIgNMBLsOgHB05ai80cOF0tF7ZoaG1i36mIxWYSZORwBCIKrD9aITctKZWdJ6uke9NCWTWyt3Tk/ZWS0cJG9Slh5CQIIIAAAmkUIBAa4BIIDfCSKLr7ZKXcvLxMPjlUIS0L82TR0F5yDdPZkpCkCAL/Enh//0lrmuixyhrp27KRvHFzb2nZMB+iFAmwUX2KIDkNAggggEDaBAiEBrQEQgO8JIuerq6V8W9sloXbjkpejsgzN7D6YZKUFENAVuw8JmNWbJTTNXVyY/umsnBoT6Zjp2FcsFF9GlA5JQIIIIBAygQIhAaUBEIDPMOi09fulukf7Ra18Mx9F7eRx67qJLmsNmOoSvEwCczddFjufnOz1NSJjO3WQuYO6i55vInSMgTsG9X/bXQfuZaZDWlx5qQIIIAAAskJEAiTc7NKEQgN8FJQ9JWtR+SON7ZIRU2tdXfjxZt6SrMCFptJAS2nCLjAo+v2yo/f32l9oTL5K+fLE1/rEvAWZ795bFSf/T6gBggggAACkQUIhAYjg0BogJeiop8drrCef9pzqkp6NCuU10aUSNemhSk6O6dBIFgCdXV1ooLJ7784YG3f8ggrX2asg9moPmPUXAgBBBBAIEEBAmGCYPbDCYQGeCksur+iylqB9MODp6R5QZ61kfb17Zqm8AqcCgH/C6jFTdQU0Xmbj1jTq9UU0XHdW/q/YT5qwS0rNsrL247K9y5sLTOv6eyjmlNVBBBAAIEgCxAIDXqXQGiAl+KilTW1cs+qrfL8psOSnyPy2/6d5TsXtk7xVTgdAv4UOFlVI7es3CQrdx2XwrwcWTy0lwzu0MyfjfFxrdmo3sedR9URQACBAAsQCA06l0BogJemoo+s2ysPvL9TakXkWyXF8tSAriyUkSZrTusPgUOnq2Xwkg3Wdi0tCvNkxfASueL8Jv6ofABryUb1AexUmoQAAgj4XIBAaNCBBEIDvDQWXbL9qNz++mY5WV0r17UtsqaQtihkX7U0knNqjwrsKK+Uga9ukE3Hz0iHJg2sDed7NGvo0dqGo1psVB+OfqaVCCCAgJ8ECIQGvUUgNMBLc9H1Rytk2NIy2VZeKV2KCmTZ8F7Sp3mjNF+V0yPgHQH1Hhi4uFT2VlRJyXmFsmpkH2nbuIF3KhjSmqhnObu88KnsPlUlLwzqLhN68BxnSIcCzUYAAQQ8IxDYQDh+/HhZu3atBT1jxgwZN25cRPSDBw9K//79I/6srKwsZkcRCD0zjiNW5MiZamuxmTX7T0qT/FzrTiHPTXm7z6hdagTe2VsuNy8vk2OVNXJ16ybWFyLnFXCXPDW65mdRU9vVth+XtGwk62670PyEnAEBBBBAAAEDgUAGwlmzZlkkU6ZMsf5fBbdFixZJnz59XFHNnz9f9u3bV18+WiECoSvOrB6kvo2ftHqr/Kn0kOSKyK9ZZj+r/cHF0y+wePtRuW3lJqmsrZNhHZtZX4QU5qnRz8srAmxU75WeoB4IIIAAAkogkIHQGQCdATFe16vya9askVatWsU8lEAYT9I7P5/1+X65b812qa4TubNHS3n6+q5SwIdk73QQNUmJwDOlB63VdtWG82qcPzuwm+TmqB0HeXlNgI3qvdYj1AcBBBAIr0DgAqGeAmoPdOqOn5o++vDDD8ftabd3B9WJCIRxOT11wKo9J2TU8jI5UVUrV7RqLMuGl0hxQ6bReaqTqEzSAr/8eI/89MNdVvmfXNZOfnFFh6TPRcH0C7BRffqNuQICCCCAgDuBwAXC9evXy6hRo866w6dC3oIFC2TevHlxVaLdHVy6dOk5Zb/3ve9JvOcM416QAzIqsPHYaRm2rMxadbFjkwaydFiJXNSSxWYy2glcLKUCdXV1ou42/f6LA9Z5/3hdV5nYO/bshpRWgJMlLaA3qr/3otbW3qm8EEAAAQQQyIZA4AKhyR3CWFNLH3/88XP658knnyQQZmPUGl7zeGWN3Lpyo7y++4Q0ysuVFwZ1kzFdWxieleIIZF6gqrZWxr++WV7aelTyc3JkweDujOXMd0PSV1y9t1yuW7Te+j207+6+0rQgL+lzURABBBBAAIFkBQIXCBVEMs8QRgqS8VCZMhpPyLs/r60T+cHfd8hvP9sn6gmr6Ze3l5/1a+/dClMzBBwCJ6tqZOTyjfLWni+/2Fg6vJdc364pTj4TYKN6n3UY1UUAAQQCKBDIQBhvlVEV5JxbUSS68IwOnkwZ9fe74ukNB61VSGvqRMZ2ayHP3tBNGuazIqO/ezX4td9fUSVDl5bKJ4cqpFXDfFk5okQuLW4c/IYHsIVsVB/ATqVJCCCAgM8EAhkIVR/E2ofQGQhXr14tEydOdLWyqL1/uUPos9Eepbrv7iu3Fps5fKZGLi1uJK8O7SXtmxQEo3G0InACW0+ckRsWb5Bt5ZXSuahA3h7ZW7o0LQxcO8PSIPtG9XMHdZfxbFQflq6nnQgggIBnBAIbCDMhTCDMhHJmrrGjvFJuWlIqG46dlvMb5svy4SVyWSvuuGRGn6u4FfjH4VNy46ulcuB0tVzQvKG8NbK3nN+ogdviHOdRATaq92jHUC0EEEAgJAIEQoOOJhAa4HmwaHlVjdz++mZZuuOYFOblWNNHx3Vv6cGaUqUwCryzt1yGLSuV8qpaua5tkSwe2otFSAIyENioPiAdSTMQQAABnwoQCA06jkBogOfRompD7598sEse/mSPVcOpl7aTX1zZwVp4hhcC2RJ4ccsRueONzVJZWyejuzS3VhNtkMuzrtnqj3Rcl43q06HKORFAAAEE3AgQCN0oRTmGQGiA5/Gi8zcflm+8tUXO1NTJ8E7nWR/AG+ezJLzHuy2Q1Zu9/oD8++ptVtvuKSm29hnMyeEriqB1tn2j+u13XCIdi3iOOWh9THsQQAABrwoQCA16hkBogOeDoh8fPGWt5Kie17qwRUNrE/tOfEjzQc8Fp4r/9dFumb52t9Wgn1/RQX56WbvgNI6WnCPARvUMCgQQQACBbAgQCA3UCYQGeD4puvtkpdy8vMxa3r9lYZ4sGtpLrmlT5JPaU02/CtTW1cl/rN4mT204aE1XfnZgN7mrZ7Ffm0O9XQqwUb1LKA5DAAEEEEipAIHQgJNAaIDno6Knq2ut6aMLthyRvByRZ27gw7mPus93VT1TUyu3rdwkr+44JgW5OfLykJ7WtGVe4RBgo/pw9DOtRAABBLwkQCA06A0CoQGeD4v+fO1umfbRblELz9x3cRt57KpOksujXD7sSe9WWa02qVYSfXffSWnWIE+WDe8l/bkj7d0OS0PN2Kg+DaicEgEEEEAgpgCB0GCAEAgN8Hxa9JWtarXHLVJRUys3tm8qL97UU5oVsNiMT7vTU9XeX1Fl7TH42ZEKadMoX1aN7CO9mzf0VB2pTPoF2Kg+/cZcAQEEEEDgbAECocGIIBAa4Pm46GeHK2T4slLZebJKejQrlNdGlEjXpoU+bhFVz7bAxmOnZfCSUtlWXindmxbKqpG9WWUy252SxeuzUX0W8bk0AgggEEIBAqFBpxMIDfB8XvTQ6WoZtrRUPjx4SpoX5FnPeV3frqnPW0X1syGgVrMdvGSDHD5TI5e3aiwrhpdIy4b52agK1/SIABvVe6QjqAYCCCAQEgECoUFHEwgN8AJQtLKmVu5ZtVWe33RY8nNEftu/s3znwtYBaBlNyJTAm7uPy8jlG+VU9ZdTkBcP7SUN89lwPlP+Xr6O3qj+tm4tZMHgHl6uKnVDAAEEEPC5AIHQoAMJhAZ4ASr62Kd75cfv7ZRaEflWSbE8NaCr5LHaTIB6OD1NmbvpsHzjzS1SXVcnY7u1kLmDujNu0kPty7OyUb0vu41KI4AAAr4UIBAadBuB0AAvYEVf23Vcvr5io5ysrpXr2hZZU0hbFDLtL2DdnLLmzPp8v6g7QOr1w0vayqNXdUzZuTlRcATYqD44fUlLEEAAAS8LEAgNeodAaIAXwKLrj1bIsKVl1sIgXYoKrC0D+jRvFMCW0iQTgakf7JKHP9ljneIP13aWb1/ANGMTzyCXZaP6IPcubUMAAQS8I0AgNOgLAqEBXkCLHjlTbd0pfHtvuTTJz7XuFA7u0CygraVZiQjU1tVZz5z+ueyQtX+lmiI6rnvLRE7BsSEU0BvVP3ZVR/nBJW1DKECTEUAAAQTSLUAgNBAmEBrgBbhoTW2d/J/VW+VPpYdELQ/yCB/kAtzb7ppWUV0r417bJK/uOCaFeTnW4jF8UeDOLuxHsVF92EcA7UcAAQTSL0AgNDAmEBrghaDoH77YL/e+u12q60Tu7NFSnr6+qxTksYJkCLr+rCYePVMtQ5aWygcHTkmLwjxrW4krzm8SNgbam6QAG9UnCUcxBBBAAAHXAgRC11TnHkggNMALSdFVe05YU0iPVtbIFa0ay7LhJVLMHnMh6X2RPacqZeDiUtlw7LR0aNLA2nC+R7OGoWk/DU2NABvVp8aRsyCAAAIIRBYgEBqMDAKhAV6Iim49cUYGLymVTcfPSMcmDWTpsBK5qCWLzQR9CGw8dloGvrpBdp6skpLzCmXVyD7StnGDoDeb9qVBwL5R/Tuj+8g1bYrScBVOiQACCCAQVgECoUHPEwgN8EJW9Hhljdy6cqO8vvuENMrLlRcGdZMxXVuETCE8zX1//0kZvqxUDp+pkatbN7FWnD2vgG1IwjMCUt/S7727Xf7n8/3CRvWpt+WMCCCAQNgFCIQGI4BAaIAXwqK1dSI/em+H/OYf+yRHRB66ooP89LJ2IZQIdpNX7DwmY1ZslNM1dTKsYzNrpdlCnh0NdqdnoHVsVJ8BZC6BAAIIhFSAQGjQ8QRCA7wQF31u4yH55ltbpKZOZGy3FvLsDd2kYT6LzQRhSMzddFjufnOz1bdqIaFnB3aT3BwV/3khYC7ARvXmhpwBAQQQQOBcAQKhwaggEBrghbzou/vKZdTyMmtK4aXFjeTVob2kfZOCkKv4u/mPfbpX7n9vp9SJWHd+f35FB383iNp7ToCN6j3XJVQIAQQQCIQAgdCgGwmEBngUlR3lldZzZp8fOS3nN8yX5cNL5LJWjZHxmUBdXZ18993t8vsvDlg1/+N1XWVi71Y+awXV9YsAG9X7paeoJwIIIOAfAQKhQV8RCA3wKGoJnKqukbGvbZal/9ywXE0fHde9JTo+EVB7xKkpovM2H5H8nBxZMLg7iwX5pO/8Ws0XNh2WO9/YbG1jsv2OS5iS7NeOpN4IIICAhwQIhAadQSA0wKNovYCaYvizD3fJLz/eY/3d1EvbyS+u7GAtPMPLuwIV1bXW4jErdx2Xoga5snhoL7m+XVPvVpiaBUKAjeoD0Y00AgEEEPCUAIHQoDsIhAZ4FD1HYP7mw/KNt7bImZo6Gd7pPOtuU+P8PKQ8KHDodLU13feDA6ekVcN8eWtkb7mwBXtLerCrAlklvVH9V89vIu99/SuBbCONQgABBBDInACB0MCaQGiAR9GIAh8fPCU3Ly+TPaeq5MIWDa1N7DsVsdiMl4aLevZTbTi/6fgZ6VxUIG+P7C1dmhZ6qYrUJeACbFQf8A6meQgggECGBQiEBuAEQgM8ikYV2F9RJUOXlsonhyqkZWGeLBraS65pU4SYBwTWH62QgYtLZW9FlfRt2UhWjiiR8xs18EDNqELYBNioPmw9TnsRQACB9AkQCA1sCYQGeBSNKXC6utaaPrpgyxHJyxF55oZuclfPYtSyKPDO3nLr7u2xyhq5rm2RLB3eiym9WeyPsF9abVTfY94/LAa1uExHZhKEfUjQfgQQQCBpAQJh0nQiBEIDPIq6ElALzagFZ9TCM/dd3EYeu6qT5LLajCu7VB60ePtRuW3lJqmsrZPRXZpbz3c2yM1N5SU4FwIJC+iN6v/zojby3/07JVyeAggggAACCCgBAqHBOCAQGuBR1LXAku1Hra0pKmpq5cb2TeXFm3pKswIWm3ENaHjgM6UH5Z5VW61QPvkr58sfru0sOTmkckNWiqdAgI3qU4DIKRBAAAEECIQmY4BAaKJH2UQEPjtcYa1qufNklfRoViivjSiRrixkkghhUsfO+HiP/OTDXVbZ5fIFFAAAIABJREFUX17RQaZe1i6p81AIgXQJsFF9umQ5LwIIIBAeAe4QGvQ1gdAAj6IJC6itDkYtL5M1+09K84I8eXlIT/a9S1jRXYG6ujr57rvb5fdfHLD2g3x2IM9wupPjqEwLsFF9psW5HgIIIBA8AQKhQZ8SCA3wKJqUgNqU+ptvbZHnNx2W/ByRmdd0lv97QeukzkWhyAJVtbUy/vXN8tLWo1KQm2MFb7UvJC8EvChg36h+3o3d5fbuLb1YTeqEAAIIIOBhAQKhQecQCA3wKGok8Jt/7JP739shNXUi3yoplqcGdJU8VpsxMlWFT1bVyMjlG+WtPSekWYM8WTa8l/Rnyw9jV06QXgE2qk+vL2dHAAEEgi5AIDToYQKhAR5FjQVe23Vcbl25UU5U1VrbIKg7WS0K843PG9YT2Pd/bNMoX1aN7CO9mzcMKwft9pEAG9X7qLOoKgIIIOBBAQKhQacQCA3wKJoSgY3HTsvgJaWyrbxSuhQVWHe0+jRvlJJzh+kkW0+ckRsWb7AcuzctlFUje7OvW5gGQADaykb1AehEmoAAAghkSYBAaABPIDTAo2jKBI6cqZavr9gob+8tlyb5udadwsEdmqXs/EE/0T8On5IbXy2VA6er5fJWjWXF8BJp2ZA7rUHv96C1j43qg9ajtAcBBBDInACB0MCaQGiAR9GUCqhnCe+1VsXcL2q79Eeu6ig/uKRtSq8RxJO9s7dchi0rlfKqL/d4XDy0lzTMZ8P5IPZ1GNqkppCrxZDYqD4MvU0bEUAAgdQJEAgNLAmEBngUTYvA0xsOyv9ZvVVq60Tu7NFSnr6+qxTkEXAiYb+45Yjc8cZmqaytk7HdWsjcQd1ZmCcto5KTZkqAjeozJc11EEAAgWAJEAgN+pNAaIBH0bQJrNpzwppCerSyRq5o1ViWDS+RYqZAnuU9e/0B+ffV26y/+9Elba07qrwQCIIAG9UHoRdpAwIIIJBZAQKhgTeB0ACPomkVUIukDFtaJhuOnZaOTRrI0mElclFLFptR6P/10W6Zvna35f+HazvLt9nHMa1jkZNnVoCN6jPrzdUQQACBIAgQCA16kUBogEfRtAuUV9VYdwpf331CGuXlyguDusmYri3Sfl2vXqC2rk7+Y/U2eWrDQVFbNqopouPYxNur3UW9khRgo/ok4SiGAAIIhFiAQGjQ+QRCAzyKZkRAPUv44/d3ymOf7pUcEXnoig7y08vaZeTaXrrImZpauW3lJnl1xzErHC8cykqsXuof6pJagUfX7ZX7398pXz2/ibz39a+k9uScDQEEEEAgcAIEQoMuJRAa4FE0owLPbTwk96zaKlX/XEDl2Ru6hWY1TbVpt1pJ9N19J6VFYZ68cXNvubS4cUb9uRgCmRRgo/pManMtBBBAwP8CBEKDPiQQGuBRNOMCHxw4KcOWlsrhMzVyaXEjeXVoL2nfpCDj9cjkBfdXVFl7DH52pEI6NGlgbTjfo1nDTFaBayGQFQG9Ub1aQXf+4B5ZqQMXRQABBBDwhwCB0KCfCIQGeBTNisCO8koZvqxUPj9yWs5vmC/Lh5fIZa2Cebds47HTMnhJqWwrr5QLmjeU12/uLW0bN8iKOxdFINMCbFSfaXGuhwACCPhXgEBo0HcEQgM8imZN4FR1jdzxxhZZuO2oFObliJo+GrTFVT4+eEoGL9lg3Q29unUTWTG8RJoW5GXNnAsjkA0BNqrPhjrXRAABBPwnQCA06DMCoQEeRbMu8OCHu+TnH++x6vGTy9rJz6/oYC084/fXm7uPy8jlG+VUda0M69hMXh7SUwrzcv3eLOqPQMICf9tbLgMWrbcWUtp3d1++FElYkAIIIIBAOAQIhAb9TCA0wKOoJwTmbz4s33hri5ypqZPhnc6TBYO7S+N8/95Je3HLERn/+maprquTe0qK5anrukpuThBirieGC5XwoYDeqP7xqzvJ9y9u48MWUGUEEEAAgXQLEAgNhAmEBngU9YyAml558/Iy2XOqSi5s0dDaxL5Tkf8Wm5n1+X757rvbLdefXdbO2mKDFwJhF2Cj+rCPANqPAAIIxBcgEMY3inoEgdAAj6KeElCrcd68rEw+PHhKWhbmyaKhveSaNkWeqmOsykz9YJc8/MmX01//eF1Xmdi7lW/qTkURSKcAG9WnU5dzI4AAAsEQSDgQTp8+XRYuXGi1ftasWfUK/fv3D4ZIAq0gECaAxaGeF6isqZW73twiC7YckbwckWdu6CZ39Sz2dL1r1dTQVVvlz2WHJD8nx5ryOqZrC0/XmcohkGkBNqrPtDjXQwABBPwlkFAgvPzyy89qnQqE+/fvl4ceekjmzp0rKiCF6UUgDFNvh6et6k7bTz7YJXUi1jNHj17VSXI9+BheRXWtjHttk7y645gUNciVxUN7yfXtmoano2gpAi4F7BvVvzu6j/T30d1/l03kMAQQQAABAwHXgXD27Nny5JNPnhMIP/vsM+vvR48eLdOmTTOoSmJFVRidOXOmVWjs2LHy8MMPxz3B+PHjZe3atdZxc+bMkQEDBtSXsYdZt+cjEMYl5wCfCizZflRuf32znKyulRvbN5UXb+opzTy0bcPRM9UyZGmpfHDglLRqmC9vjewtF7Zo5FNtqo1A+gXYqD79xlwBAQQQ8KuA60A4ceJEWbduXf000SlTplj/3apVK5kwYYL07dvXClmZeK1evdq69rx586zLPfDAA9KvXz8ZN25c1Mur8DZjxoyIx6igqEKgLu/8c7STEggz0dtcI1sC649WyE1LSmXnySrp0axQXhtRIl2bFqa1Om6+6NlzqlIGLi6VDcdOS2c5JQWP/Ft9ndasWWP9TlK/I9TvLOfL+WWP/Xr33nuvqN9rvBAIogAb1QexV2kTAgggkBoB14FQTRfVoU996NKBUD07qMPiRx99lJpaxTmLMwA6A6Kz+Pz582Xfvn1RP+ypYLdo0SLp06ePVVQ/GxnvwyGBMCPdzUWyKHDodLWMWl4ma/aflOYFedaefumalunmi56Nx07LwFc3WCH1ojN75fTM+85678aiUr83RowYUT8zQP1ZvdzMLshiF3BpBFImwEb1KaPkRAgggECgBFISCPWzhZkKhOoOngpresrn+vXrZdSoUVJWVhaxc9QHv82bN9dPF1UH6TsJ6r9VYJw6dWr9NFIV9Ow/j9bjBMJAvRdoTBQBtUrhpNVb5U+lauEWkZnXdJb/e0HrlHvF+6Ln/f0nZfiyUjl8pkaua1skX33nTzL65pvPmvodrVLqd4Sa0q5nFTj/nPLGcEIEPCigN6pXz9zuvpON6j3YRVQJAQQQyIqA60Co7wI++OCD0rp16/o7hHpRmUxOGVVBzP4MoA6E0UKccwqoDoA6QOryatqpesYw0jOEzz777DkdpBbTiRZCs9KbXBSBNArM/Gyf/ODvO6SmTuRbatP3AV0lL4WrzcT6omfFzmMyZsVGOV1TJ6O7NLdWE72gd29rqrh+Llj9tw58Tgbn3UH1O2DBggVnfUnkfK44jZScGoGsCbBRfdbouTACCCDgWQHXgfCVV16xVhON9po8ebJMmjQpIw1N9A6h8/iDBw+KmuqqA6TzjmCkqWR//vOfz2nbz3/+cwJhRnqci3hF4LVdx0VNOztRVWvdpVNTSFsU5qeketG+6Ln/hSXy7U+OWUF08lfOlz9c21kOHTpkvYftU72jTQGNdDdQTQvftWtX/XRR/aWQ/XwpaRQnQcBjAmxU77EOoToIIICABwRcB0JVV32XMFK9MzVdVF073tSySHcH7IvO2AOh+m/ndFN99yDa3QZ9fqaMemAEU4WMC6jn+IYtK5NNx89Il6ICWTa8l/Rpbr7CZ7Qvejbd/4K1BcaMKzvIA5e2s9rr/FJH/V20Z4md51XHRnpOONJxGcflggikWYCN6tMMzOkRQAABHwokFAhV++wb06s/Z3q7iUgf/JwB0TklVK84qKd3Ou8OOFcgVR8M1fOJLCrjwxFNlTMicLyyxlps5u295dIkP9e6Uzi4QzOja9vfx3V1dXLH//dX+dvcp2X3XdPl2YHd5K6exWed37kYVKRAGC0kqt8RaqqpfUEZAqFR91HYRwJsVO+jzqKqCCCAQAYEEg6EGaiTq0vEWp7eGQjVCfXfqf92Pmuk7zboC7tdfp47hK66ioMCKqCmcN63Zrv8z+f7JVdEHrmqo/zgkrZJt1aHt+demCt3v7lZ3vz9r6SqY4k896N/l+GdzrNmBqiXDnHOL3YibRcTLeTp97yeIur80ijpRlAQAR8IsFG9DzqJKiKAAAIZFEgoEKo7bGrPQT09VD3HM2TIkPo/Z7DenrgUgdAT3UAlsizw9IaD1iqkKiDe2aOlPH19VynIUxEx8dd//+5/5A//8zurYEXfgfLyE/8tV5zfxPpzpGcE1d+pxWHUy/lFjgqMKuhFm/qtnxvUtXSzsnDiLaIEAt4UuPfd7fK7z/fL2G4tZP7gHt6sJLVCAAEEEMiIgOtAqMOgqtWKFSukuLjYWlBFBUT732Wk1h65CIHQIx1BNbIu8O6+crl5WZkcrayRK1o1lmXDS6S4YWKLzag9D9W2Eh8cOCVtGuXLqpF9pHfzhllvGxVAIIgCbFQfxF6lTQgggEByAq4Dof3Zwblz54pemVM/Z5fJVUaTa2rqSxEIU2/KGf0rsPXEGRm2tEw2HDstHZs0kKXDSuSilu4Wm9lRXmltOK8Wqik5r1BeH9FbOhYV+BeDmiPgAwG9Uf19F7eR31zdyQc1pooIIIAAAukQcB0I7fsQjhkzpr4us2fPlieffFIyuQ9hOiCSOSeBMBk1ygRZoLyqRm5/fbMs3XFMGuXlyguDusmYri1iNnn90QoZuLhU9lZUyeWtGsvrN5fIeQWJ3V0MsiltQyBdAmxUny5ZzosAAgj4S8B1ILz88sutlum7g7qZ9mmjmdx6wgvMBEIv9AJ18JqA2iLi/72/Ux5Zt1dyROTnV3SQn1z25XYRztc7e8vl5uVlcqyyRm5s31QWD+0lDfOTe/7Qaw7UBwE/CLBRvR96iToigAAC6RUgEBr4EggN8CgaeIHnNh6Se1ZtlaraOmvhimdv6HZW2Fu8/ajctnKTVNbWWYvR/PmGbpKXqyIkLwQQyJQAG9VnSprrIIAAAt4VcB0I9ZRR59TQaH/v3SanrmYEwtRZcqZgCnxw4KS12MyB09XStlEDaZCbIztOVkrnogay/USVqFuI9/dtK7/+asdgAtAqBDwuYN+o/n9v7CHjusee4u3x5lA9BBBAAIEkBFwHQv2sYLRrsKhMEvoUQSAEArtOVsmAhetlS/mZc1r74GXtZfoV7UOgQBMR8K7A45/ulR++t1OuOr+J/P3rX/FuRakZAggggEBaBFwHQnV1fTcwUk3C9vygMuAOYVrGJCcNoMBPPtglMz7Zc07L3hzZW25o1zSALaZJCPhHQD3D2+G5dXKyulb+PuYrclXrL/f+5IUAAgggEA6BhAKhIrFvP6H+HMbVRfXQIBCG401CK80F/uuj3TJ97W4CoTklZ0AgLQJsVJ8WVk6KAAII+EIg4UDoi1ZlqJIEwgxBcxnfC7y89ajcsnLjWe04r0GefHLbhdK1KfsN+r6DaYDvBbacOCPd5/7DasfWOy6RLuwD6vs+pQEIIICAW4GEAqHz7qDzImGbNkogdDvMOA4BEXWX8E+lB2VbeaX0bdlI/uvyDvL1rs2hQQABjwiwUb1HOoJqIIAAAhkWcB0I4y0qo+pNIMxw73E5BBBAAAEEUiTARvUpguQ0CCCAgM8EXAdCvTF9rPYRCH3W+1QXAQQQQAABmwAb1TMcEEAAgfAJJBwIR48eLdOmTQufVIQWM2WUYYAAAgggECSBuZsOyx1vbJYOTRrI9jsukdycnCA1j7YggAACCEQQcB0I9fODDz74oIwZMwZMtp1gDCCAAAIIBEygtq5OOj3/qew+VSVsVB+wzqU5CCCAQBQB14GwrKxMJkyYEOptJpyG3CHkfYUAAgggEDSBR9ftlfvf3ylfPb+JvMdG9UHrXtqDAAIInCPgOhDyDOG5o4dAyDsKAQQQQCBoAicqa6T1Xz6R0zV18u7oPtK/TVHQmkh7EEAAAQRsAgRCg+FAIDTAoygCCCCAgGcF9Eb147q1kP8d3MOz9aRiCCCAAALmAgRCA0MCoQEeRRFAAAEEPCuw5fgZ6THvy43q1eIyHdmo3rN9RcUQQAABUwHXgdD0QkEsTyAMYq/SJgQQQAABJcBG9YwDBBBAIBwCBEKDfiYQGuBRFAEEEEDA0wJsVO/p7qFyCCCAQMoEEgqEs2fPlieffDLqxdmYPmX9wokQQAABBBDIuoDeqP43V3eS+y5uk/X6UAEEEEAAgdQLuA6Er7zyijz00EMxa0AgTH0HcUYEEEAAAQSyJcBG9dmS57oIIIBA5gRcB8KJEyfKunXrCIQ2AaaMZm6gciUEEEAAgcwLsFF95s25IgIIIJBpAdeBUO9DqO4CTp8+3arntGnTZM2aNTJlyhSZNWuW9O/fP9P1z+r1CIRZ5efiCCCAAAIZENAb1V/duomsGfOVDFyRSyCAAAIIZFIgoUDYt29fmTNnjujpoytWrJDi4mIrIG7bts36WZheBMIw9TZtRQABBMIpYN+oXgVCFQx5IYAAAggERyChQKiarULg4cOHZcKECfLggw/KmDFjRE8n5RnC4AwMWoIAAggggIAWYKN6xgICCCAQXAHXgVCHPn2XUE8htdMQCIM7UGgZAggggEB4BdioPrx9T8sRQCD4Aq4DoZ4mOnr0aOvZQTVNdOHChfVC+u+DT/avFjJlNEy9TVsRQACBcAvctnKjvLj1qLX9hNqGghcCCCCAQDAEXAdC1VwVAtu3by+TJk2SQ4cOyZAhQ+oVwnZ3UDWcQBiMNwGtQAABBBCIL8BG9fGNOAIBBBDwo0BCgdCPDUxnnQmE6dTl3AgggAACXhNgo3qv9Qj1QQABBMwFCIQGhgRCAzyKIoAAAgj4ToCN6n3XZVQYAQQQiCtAIIxLFP0AAqEBHkURQAABBHwnwEb1vusyKowAAgjEFUgoEOqVRqOdNWzPERII444vDkAAAQQQCJjAY5/ulR+9t1P6t24i77JRfcB6l+YggEAYBVwHQueqopGwCIRhHEK0GQEEEEAgTAL2jer/PqaPXNW6KEzNp60IIIBA4ARcB8JI+w46NQiEgRsfNAgBBBBAAIFzBNionkGBAAIIBEfAdSDU00UnT55sbTvBi20nGAMIIIAAAuEUYKP6cPY7rUYAgWAKuA6Ea9askSlTpkgYN6CP1vU8QxjMNwWtQgABBBCIL6A3qv/+xW3kcTaqjw/GEQgggIBHBVwHQlX/eNNGmTLq0V6mWggggAACCKRY4J195fK1heulqEGu7L6zrzQtyEvxFTgdAggggEAmBFwHwngrjKrKEggz0WVcAwEEEEAAAW8IsFG9N/qBWiCAAAImAq4DYby7gwRCk26gLAIIIIAAAv4TYKN6//UZNUYAAQScAq4DIYvKnDt4eIaQNxQCCCCAQJgF7BvVzx/cQ8Z2axFmDtqOAAII+FLAdSBkURkCoS9HOJVGAAEEEEirABvVp5WXkyOAAAJpF3AdCJkySiBM+2jkAggggAACvhNgo3rfdRkVRgABBM4SIBAaDAimjBrgURQBBBBAIDACbFQfmK6kIQggEEIBAqFBpxMIDfAoigACCCAQGAE2qg9MV9IQBBAIoYDrQBhCm7hNJhDGJeIABBBAAIGQCLBRfUg6mmYigEDgBAiEBl1KIDTAoygCCCCAQKAE2Kg+UN1JYxBAIEQCrgOh2naif//+MmnSpBDxxG4qgZChgAACCCCAwL8E2Kie0YAAAgj4T8B1INSrjE6ePJlQ+M9+JhD6b8BTYwQQQACB9AmwUX36bDkzAgggkC4B14Fw9uzZ8uSTT1r1+Oijj9JVH1+dl0Doq+6isggggAACaRZgo/o0A3N6BBBAIA0CrgMh+xCeq08gTMOI5JQIIIAAAr4WYKN6X3cflUcAgRAKEAgNOp1AaIBHUQQQQACBQAqwUX0gu5VGIYBAgAUIhAadSyA0wKMoAggggEBgBf5zzXaZ+dl+ub17C5l3Y4/AtpOGIYAAAkEQcB0Ig9DYVLeBQJhqUc6HAAIIIBAEATaqD0Iv0gYEEAiLgG8D4axZs2TmzJlWP40dO1YefvjhuH02fvx4Wbt2rXXcnDlzZMCAAfVlDh48aG2roV9r1qyRVq1axTwngTAuOQcggAACCIRUgI3qQ9rxNBsBBHwnkHAgnD59uixcuLC+oaNHj5Zp06ZltOGrV68WFQjnzZtnXfeBBx6Qfv36ybhx46LWQ4W3GTNmRDxm/fr1MmrUKFm0aJH06dPHdVsIhK6pOBABBBBAIGQCbFQfsg6nuQgg4FuBhAJhrJVGM7kVhTMAOgOiszfmz58v+/btkylTpkTsKHW+ESNGnHXH0E2PEgjdKHEMAggggEBYBfRG9f99dSf5z4vbhJWBdiOAAAKeFnAdCO37EEZqUSY3rFdTP1W401M+9R2+srKyqIFv8+bN9dNF1UH2KaEq2Kk7jHo6qfpvffcxVu8RCD09tqkcAggggECWBeZtOiwT3tgsHZo0kO13XCK5OTlZrhGXRwABBBBwCrgOhBMnTpR169aJM/jpoNi3b1/rubxMvFQQsz8DqANhtOf+VIBUzxnqKaXqjuHUqVNFBUj97KB9uqi6Y6he9ucSf/Ob35zTtCeeeMI6By8EEEAAAQQQOFeAjeoZFQgggID3BVwHQj1ddO7cuaICmX6pQDRhwgTrj5maNproHULn8ToEqgCpXmoxGXuYjDQFdcmSJef05r333ksg9P4Yp4YIIIAAAlkUYKP6LOJzaQQQQMCFgOtA6KU7hIk+Q+g83h4I1UqiKuDa7xDGeyZRuzJl1MUI4xAEEEAAgVALsFF9qLufxiOAgA8EXAdCLz1DGG+VUfuUUNUH6ngVaPX0TrVC6a5du+qnhDr/7JxiGq0fCYQ+GOFUEQEEEEAg6wJsVJ/1LqACCCCAQFQB14FQncErq4yqusTah9AZCNXx+u/Uf0daNEbdRVywYIEFpaaCRluR1C5JIOSdhQACCCCAQHyBXScrpdPzn1oHqsVlOhYVxC/EEQgggAACGRFIKBCqGnlhH8KMyLi4CIHQBRKHIIAAAgggICJsVM8wQAABBLwpkHAg9GYzslMrAmF23LkqAggggID/BOwb1e+7u680zs/zXyOoMQIIIBBAgZiBUC8ko1YW1SuJqqmaalVOXmItRsO2E4wEBBBAAAEE3AmwUb07J45CAAEEMikQMxDat5ogEJ7bLQTCTA5VroUAAggg4HcBNqr3ew9SfwQQCKKAq0DotuGZ2ofQbX3SfRyBMN3CnB8BBBBAIEgCbFQfpN6kLQggEBQBV1NG3TaWQOhWiuMQQAABBBAIp4DeqP6aNkXyzug+4USg1QgggICHBGIGQvV8nJ4q6qbOBEI3ShyDAAIIIIBAeAXYqD68fU/LEUDAmwKuVxnVzxOyqMy/OpIpo94c1NQKAQQQQMDbAmxU7+3+oXYIIBAuAdeBUK04qlYXnTRpUriEYrSWQMhQQAABBBBAIHEBNqpP3IwSCCCAQLoEXAdCfYdw8uTJhMJ/9gaBMF3DkvMigAACCARdQG9U/4OL28hjV3cKenNpHwIIIOBZAdeBcPbs2fLkk09aDQnbs4LReo9A6NlxTcUQQAABBDwuwEb1Hu8gqocAAqERcB0I9R3CWDJhC4oEwtC8T2goAggggEAaBNioPg2onBIBBBBIUIBAmCCY/XACoQEeRRFAAAEEQi/ARvWhHwIAIICABwQIhAadQCA0wKMoAggggEDoBewb1S8Y3ENu69Yi9CYAIIAAApkWcB0IdcWmT58uCxcutP6otqDQL7UCadheBMKw9TjtRQABBBBItQAb1adalPMhgAACiQkkFAidzxGqQLh//3556KGHZO7cuaICUpheBMIw9TZtRQABBBBIhwAb1adDlXMigAAC7gVcB0L7KqP69CoQfvbZZ9bqo6NHj5Zp06a5v3IAjiQQBqATaQICCCCAQNYF2Kg+611ABRBAIMQCrgOh2ph+3bp19dNEp0yZYv13q1atZMKECdK3b1+ZM2dOqCgJhKHqbhqLAAIIIJAmATaqTxMsp0UAAQRcCLgOhGq6qA59a9asER0I1bODOiyy7YQLcQ5BAAEEEEAAgXMExr62Sf665YiwUT2DAwEEEMisQEoCoX62kECY2c7jaggggAACCARFgI3qg9KTtAMBBPwm4DoQ6ruADz74oLRu3br+DqFeVIYpo37reuqLAAIIIICAtwTYqN5b/UFtEEAgHAKuA+Err7xirSYa7TV58mSZNGlSONT+2UqeIQxVd9NYBBBAAIE0C7BRfZqBOT0CCCAQQcB1IFRl9V3CSJJhmy6qDAiEvKcQQAABBBBInQAb1afOkjMhgAACbgUSCoTqpPaN6dWfw7jdhMYlELodZhyHAAIIIICAO4HHP90rP3xvp1zTpkjeGd3HXSGOQgABBBBIWiDhQJj0lQJYkEAYwE6lSQgggAACWRVgo/qs8nNxBBAIoQCB0KDTCYQGeBRFAAEEEEAgioDeqH5895Yy98buOCGAAAIIpFEg4UCot5iw12nFihVSXFycxmp689QEQm/2C7VCAAEEEPC3ABvV+7v/qD0CCPhLwHUg1JvRR2verFmzRG1SH6YXgTBMvU1bEUAAAQQyKcBG9ZnU5loIIBBmAdeBMNYKowqQfQjDPIxoOwIIIIAAAqkVeHdfuVy7cL0UNciVfXf3lcb5eam9AGdDAAEEELAEXAdCPVXUuaqofdXRsG09wR1C3kUIIIAAAgikT0BvVP/b/p3k3ovapO9CnBkBBBAIsYDrQKjvEDqnhuqppNwhDPEooukIIIAAAgikQYCN6tOAyikRQABYk6naAAAgAElEQVQBh4DrQKiDn/MOYbSgGAZp7hCGoZdpIwIIIIBAtgTYqD5b8lwXAQTCJOA6EEZaXTQWVBimjxIIw/RWoa0IIIAAAtkQYKP6bKhzTQQQCJMAgdCgtwmEBngURQABBBBAwIWAfaP6T269QPoWN3ZRikMQQAABBNwKEAjdSkU4jkBogEdRBBBAAAEEXAqwUb1LKA5DAAEEkhBwHQiTOHfgixAIA9/FNBABBBBAwAMCbFTvgU6gCgggEFgBAqFB1xIIDfAoigACCCCAQAICbFSfABaHIoAAAgkIEAgTwHIeSiA0wKMoAggggAACCQiwUX0CWByKAAIIJCBAIEwAi0BogEVRBBBAAAEEDAXYqN4QkOIIIIBABAECocGw4A6hAR5FEUAAAQQQSFCAjeoTBONwBBBAwIUAgdAFUrRDCIQGeBRFAAEEEEAgQQE2qk8QjMMRQAABFwIEQhdIBEIDJIoigAACCCCQQgG9Uf21bYrkb6P7pPDMnAoBBBAIpwCB0KDfuUNogEdRBBBAAAEEkhBgo/ok0CiCAAIIxBAgEBoMDwKhAR5FEUAAAQQQSFLgvjU75Lef7ZPx3VvK3Bu7J3kWiiGAAAIIKAECocE4IBAa4FEUAQQQQACBJAXYqD5JOIohgAACEQQIhAbDgkBogEdRBBBAAAEEDAT0RvU/vKStPHpVR4MzURQBBBAItwCB0KD/CYQGeBRFAAEEEEDAQICN6g3wKIoAAgjYBAiEBsOBQGiAR1EEEEAAAQQMBb768hfywYFT8tv+neTei9oYno3iCCCAQDgFCIQG/U4gNMCjKAIIIIAAAoYC/7v5sIx/fbN0aNJAtt9xieTm5BiekeIIIIBA+AQIhAZ9TiA0wKMoAggggAAChgL2jer/OriH3NqtheEZKY4AAgiET4BAaNDnBEIDPIoigAACCCCQAgE2qk8BIqdAAIFQCxAIDbqfQGiAR1EEEEAAAQRSIMBG9SlA5BQIIBBqAQKhQfcTCA3wKIoAAggggECKBNioPkWQnAYBBEIpQCA06HYCoQEeRRFAAAEEEEiRABvVpwiS0yCAQCgFCIQG3U4gNMCjKAIIIIAAAikUYKP6FGJyKgQQCJUAgdCguwmEBngURQABBBBAIIUCbFSfQkxOhQACoRLwbSCcNWuWzJw50+qssWPHysMPPxy348aPHy9r1661jpszZ44MGDDgnDL6vGVlZXHPRyCMS8QBCCCAAAIIZEyAjeozRs2FEEAgQAK+DISrV68WFdzmzZtndcUDDzwg/fr1k3HjxkXtGhXeZsyYEfMYdU51bhUaCYQBGuU0BQEEEEAgFAJ6o/puTQtk4/iL2ag+FL1OIxFAwFTAl4HQGQCdAdGJMn/+fNm3b59MmTIlqpc+ZvDgwTJq1CgCoenIojwCCCCAAAIZFmCj+gyDczkEEAiEgC8DoZr6qcKdnvK5fv36mCFOBcjNmzfXTxdVPbdmzRpp1aqV1YkqDKq7gmraabxz2XudKaOBeA/QCAQQQACBAAmwUX2AOpOmIIBARgR8GQhVELM/A6hDnD3k2fVUgFTPGeoppSoATp061boL6Ly7GC0Q3n///ed0yEsvveTqTmJGepKLIIAAAggggICwUT2DAAEEEEhMwJeBMNE7hM7jDx48KP3797fuEs6dO7d+cRonnT1g6sVonEHTzbOGiXUJRyOAAAIIIICAiYDeqH5Cj5bywqDuJqeiLAIIIBB4AV8GwkSfIXQebw+Eetqo7mmmjAZ+zNNABBBAAIGAC7BRfcA7mOYhgEBKBXwZCOOtMmqfEqq01PETJ06sn96pVhPdtWtXxK0qCIQpHV+cDAEEEEAAgawIjHttkyzYckR+eElbefSqjlmpAxdFAAEE/CDgy0CoYGPtQ+gMhOp4/Xfqv9UWFXrLCmcnEQj9MGypIwIIIIAAArEF2KieEYIAAgi4E/BtIHTXvPQexSqj6fXl7AgggAACCJgI6I3qZ/bvLN+7qLXJqSiLAAIIBFaAQGjQtQRCAzyKIoAAAgggkGYBNqr//9u7//iq6jvP45/85FfCrwSSAAVFkAiSCHZt4w7jjO44s7RoxwWhrtZpOs7omIpVmBb6eOjY2SW2qC0tzup0ZUt9+IOG6VTp6o7b7jilfYTOWApENBoEKz+SCyES+REhCezje8K5czjcc+8993vPvefHK/+0kvs95/t9fr733PvO+fH1GJjNI4BAKAQIhBplJBBq4NEUAQQQQAABjwVYqN5jYDaPAAKhECAQapSRQKiBR1MEEEAAAQRyIMBC9TlAZhcIIBBoAQKhRvkIhBp4NEUAAQQQQCAHAixUnwNkdoEAAoEWIBBqlI9AqIFHUwQQQAABBHIkwEL1OYJmNwggEEgBAqFG2QiEGng0RQABBBBAIEcCLFSfI2h2gwACgRQgEGqUjUCogUdTBBBAAAEEcijAQvU5xGZXCCAQKAECoUa5CIQaeDRFAAEEEEAghwIsVJ9DbHYVGYGlS5fK9u3bjfGuWbNGlixZknTs3d3d0tDQEH9Na2urVFZWGv+tvldbfzo6Oi7aVnt7uyxatEgS/S4y6B4MlECogUog1MCjKQIIIIAAAjkWYKH6HIOzu1ALrF+/3hhfU1NTPNBt2bJFamtrE47bDHOJXqN+19bWFg+ULS0tsnnzZtm0aZOxLXuQJBBmd2oRCDU8CYQaeDRFAAEEEEAgxwIsVJ9jcHYXagH1Pdga7uwB0T74VatWycKFC2XBggUpXZzOBG7dulUaGxs5Q5hS0N0LCITuvC54NYFQA4+mCCCAAAII5FiAhepzDM7uQitgnrGzXvKpzuqpy0ebm5sTjlt9b54/f378ElP1/80zgPYGKlyq8Gf/PYHQmylFINRwJRBq4NEUAQQQQACBPAg80RaTB7ftl9+rKpOtNyW+tC0P3WKXCARKwDyDZw+E1ss8rQMyA6T1jKI6Y6h+rAFShcrVq1cb/57oslACoTfThECo4Uog1MCjKQIIIIAAAnkQYKH6PKCzy9AJuD1DmOj1KtypM4GJzhI6BT8CoTdTiUCo4Uog1MCjKQIIIIAAAnkSYKH6PMGz21AJuL2H0P76ZIEwUYBUeARCb6YQgVDDlUCogUdTBBBAAAEE8iTAQvV5gme3oRJI9ZRR+yWh6vUHDx6MXyKqlqxYvHix8WRRdano3Llz408oNS8dtV82SiD0ZgoRCDVcCYQaeDRFAAEEEEAgjwLmQvUr66rlW5+akseesGsEgiuQbB3CRPcIqn9T9xmqn+XLl8eXrDDvSbRKWMOgfdkJ9bp01j0Mrmxue04g1PAmEGrg0RQBBBBAAIE8CrTGTsi1L7dLWUmhxG6vl5HFRXnsDbtGAAEE8idAINSwJxBq4NEUAQQQQACBPAuwUH2eC8DuEUDAFwIEQo0yEAg18GiKAAIIIIBAngVYqD7PBWD3CCDgCwECoUYZCIQaeDRFAAEEEEAgzwLWhep//Ecz5E8vGZvnHrF7BBBAIPcCBEINcwKhBh5NEUAAAQQQ8IEAC9X7oAh0AQEE8ipAINTgJxBq4NEUAQQQQAABHwioheonPb9TTvSflR23zJb6ipE+6BVdQCD/Ave3fiAb3z0qx84MylUVI+R/XXepXMX7I/+F8aAHBEINVAKhBh5NEUAAAQQQ8IkAC9X7pBB0wzcC32mLyVe27b+gP2NLi+TDO+f5po90JHsCBEINSwKhBh5NEUAAAQQQ8ImAuVB9YYHI+8vqZEpZqU96RjcQyI/AdT99R37Refyinf/zZ2fJH9SU56dT7NUzAQKhBi2BUAOPpggggAACCPhIgIXqfVQMupI3gd4zg/Lk7sPyje2H5PTZcxf147e3zOay0bxVx7sdEwg1bAmEGng0RQABBBBAwEcCLFTvo2LQlZwLHDh5Rh7b1SXPtHfLiYGzIqLCYMEF/ZhWVirvf74u531jh94LEAg1jAmEGng0RQABBBBAwGcCLFTvs4LQHc8F2nr6pHlHp7Ts7ZGB8ycEp44qlfvnVsmRjwfk+T1H5XcnzsjN08bK31w9ibODnlckPzsgEGq4Ewg18GiKAAIIIICAzwRYqN5nBaE7ngm8duAjWburS3528KP4PtSTRFfW1cjSy8ZL0YUnBz3rBxv2hwCBUKMOBEINPJoigAACCCDgMwEWqvdZQehOVgUGzp2TF/f0GEFwV09ffNs3Th4tK+ur5T9NHp3V/bGx4AgQCDVqRSDUwKMpAggggAACPhRgoXofFoUuaQmoNTb/vv2IqKUk9p88Y2yrpKBAls0YL1+rr5HZ44ZrbZ/GwRcgEGrUkECogUdTBBBAAAEEfCjAQvU+LApdykigq6/fCIFPvXVEevsHjW2MLimUu2onyAN11TJpZElG26VR+AQIhBo1JRBq4NEUAQQQQAABnwo8sG2/fLstJp+/bLw8f/10n/aSbiGQWOCd3o/lmzu65Lk9R+XM+aUjJo8skeVXVslfzp4go0uKoEPgAgECocaEIBBq4NEUAQQQQAABnwqwUL1PC0O3kgr8S+dxWbuzS17Z32ssGqF+rhw3QlbUVcttM8ZLSSFPimEKJRYgEGrMDAKhBh5NEUAAAQQQ8LEAC9X7uDh0LS6gTgD+w74PZe2uTvm3I6fi/379pHJZWVctf/KJMWghkFKAQJiSyPkFBEINPJoigAACCCDgYwEWqvdxceia9A2elQ3t3aIegrT3+GlDpLhAZMn08bLqqhqZO34ESgikLUAgTJvq4hcSCDXwaIoAAggggIDPBcyF6r977VT58pyJPu8t3YuCwNHTA/LdNw/Lk7tjcvT00INiyooL5Uu1lcaloVNGlUaBgTFmWYBAqAFKINTAoykCCCCAAAI+F2jZ+6Hc+vP35NLyUtmzdK4UFnAPls9LFtruqbOA6v7AH7zbLR8PDt0hWD2iWO67skr+avZEGVPKg2JCW/wcDIxAqIFMINTAoykCCCCAAAI+F2Chep8XKALde+PIKWnecUh+8v4xOXt+vFeMHS4P1lXLHTMrpJQHxURgFng/RAKhhjGBUAOPpggggAACCARAgIXqA1CkkHVRnf/b8rtjxhnBX8ZOxEf3+9VlsrK+Rj4zdYxwrjpkRc/zcAiEGgUgEGrg0RQBBBBAAIEACLBQfQCKFJIuqjUDf/juUXl8V5e0935sjKpQRG65dJysnlcj8ypGhmSkDMNvAgRCjYoQCDXwaIoAAggggEBABFioPiCFCmg3e88MypO7D8v3dsekq2/AGMXIokL54qxKWVlfLdPKeFBMQEsbmG4TCDVKRSDUwKMpAggggAACARFgofqAFCpg3Txw8ow8tqtLnmnvlhMDQ3cIThheLE1zJsqX51TJuGE8KCZgJQ1sdwmEGqUjEGrg0RQBBBBAAIEACbBQfYCK5fOutvX0SfOOTmnZ2yMDQw8MlZmjhxkPivmzyytlWBF3CPq8hKHrHoFQo6QEQg08miKAAAIIIBAgARaqD1CxfNrVfzrwkazd2Sk/P3Q83sOGiaOMB8V87pKxPCjGp3WLQrcIhBpVJhBq4NEUAQQQQACBgAmwUH3ACuaD7g6cOycv7OkxLg3d1dNn9Eg9KOamaWON+wOvrSrzQS/pQtQFCIQaM4BAqIFHUwQQQAABBAImwEL1AStYHrt7ov+sPP32YfnOmzE5cLLf6MnwogL5wsxK+epV1TK9fFgee8euEbhQgECoMSMIhBp4NEUAAQQQQCBgAixUH7CC5aG7XX398u22mDz91hHp7R80elAxrEj+avZEWT63SiqGFeehV+wSgeQCBEKNGUIg1MCjKQIIIIAAAgEUMBeqX1BdJr9YVBvAEdBlLwTe6f1YHt3RKc/v6RG1nqD6UWcBvzK3Sr5UWykjitSFovwg4E8BAqFGXQiEGng0RQABBBBAIIACLFQfwKJ52OXXO4/L2p1d8ur+Xjn/wFD5DxNGysq6Gvkvl46TQh4Y6qE+m86WAIFQQ5JAqIFHUwQQQAABBAIqwEL1AS1clrqtTgBu3jf0oJh/O3LK2KrKfZ+ZOkZW1FXLdTXlWdoTm0EgNwIEQg1nAqEGHk0RQAABBBAIqAAL1Qe0cJrd7hs8aywir+4R3Hv8tLG10sICuX1mhfx1fbXMGjNccw80RyA/AgRCDXcCoQYeTRFAAAEEEAiwwK0/e09a9n1oBIFvXjMlwCOh66kEjp4ekHVtMfm7tw7L0dNDD4oZW1ok98yeaNwjOGE4D4pJZcjv/S1AINSoD4FQA4+mCCCAAAIIBFiAheoDXLw0u67OAqr7A3/wbrd8PDh0h+C0slK5/8oq+YsrJsjIYh4UkyYlL/O5AIFQo0AEQg08miKAAAIIIBBwARaqD3gBHbr/xpFT0rzjkPzk/WNy9vxr5lWMNBaSv3X6eCniQTHhLHyER0Ug1Cg+gVADj6YIIIAAAggEXICF6gNeQEv31fm/Lb87ZpwR/GXsRPw3fzxltKysq5YbJo8Oz2AZCQI2gcAGwvXr18u6deuM4SxevFiam5tTFnfp0qWyfft243UbNmyQBQsWGP/fui3132vWrJElS5ak3B6BMCURL0AAAQQQQCC0AtaF6v/xj2bI5y4ZG9qxhnVgas3AH7571HhiqFpLUP2UFBTI52eMl6/W18jscTwoJqy1Z1z/LhDIQLh161YjxG3atMkYyapVq2T+/PlJQ5wKb05BT7U3A2V3d7c0NDTIli1bpLY2+YKzBELeSggggAACCERbQD1xUi1DwUL1wZoHvWcG5cndh+W7u2MS6xswOj+6pFD+4oqhB8VMGlkSrAHRWwQ0BAIZCO0B0B4Q7R4tLS0Si8WkqakpLSp1JlG91jyD6NSIQJgWJy9CAAEEEEAgtAIsVB+s0h44ecY4G/g/27vl5MDQHYJTRpXI8iur5O4rJkpZCQ+KCVZF6W02BAIZCO2Brb29XRYtWiQdHR0JTVSA3Lt3b/xyUfWi1tZWqaysTPh6FfQ4Q5iN6cU2EEAAAQQQCL8AC9X7v8ZtPX3SvKNTWvb2yMDQA0Nl7rgRsqK+Wm6bMV6KC3hSjP+rSA+9EghkIFSBzXoPoBkInUKeCpDqPkPzvkB1xnD16tUJA6S6FPXgwYMX3ZPY2Nh4UQ3UmUmnEOpVwdguAggggAACCPhLgIXq/VUPa2/+6cBHsnZnp/z80PH4P98wqVxW1teIemAMPwggIBLIQOj2DKH99eZ9gvYAqcKgCnnmvYnWCfLBBx9cNF9uuOEGAiHvIgQQQAABBBAQFqr3zyQYOHdOXtjTY1wauqunz+hYcYEYS0Z87aoamTt+hH86S08Q8IFAIAOh23sI7a9PFAiThUGnOnEPoQ9mMF1AAAEEEEDABwLbDp+UhpfeNu5Bi91eLyOLi3zQq2h14UT/WXn67cPynTdjcuBkvzH4suJC+fPaCfJgXZVMGVUaLRBGi0CaAoEMhKmeMmq/JFS9Xl3yaV7eab8sVAVG9ZPO0hVWVwJhmrOMlyGAAAIIIBABARaqz0+Ru/r6RT3t9em3jkhv/6DRiZoRJXLflRPlntkTZUwp4Tw/lWGvQREIZCBUuMnWIUx0j6D5b6qtWqLCvCzUPFtoL5j1NU7FJBAGZZrTTwQQQAABBLwXsC5U/97SuVLAg0o8RVfrBj66o1Oe39Mjaj1B9TN77HB5sK5a7phZISWFPCjG0wKw8dAIBDYQ+qECBEI/VIE+IIAAAggg4A8BFqrPTR1e7zwua3d2yav7e+X8A0PluppyWVlXLZ+ZOiY3nWAvCIRIgECoUUwCoQYeTRFAAAEEEAihAAvVe1NUdQJw874eIwi+0X3K2ElRgcgtl4yTVfNqZF7FSG92zFYRiIAAgVCjyARCDTyaIoAAAgggEEIBFqrPblH7Bs/KM+3d8kRbl+w7fsbY+MiiQmmsrZQVddUyrYwHxWRXnK1FUYBAqFF1AqEGHk0RQAABBBAIqYC5UP1tl42X566fHtJRejuso6cHZF1bTP7urcNy9PTQg2ImDi+WpjkTpWlOlYwbxoNivK0AW4+SAIFQo9oEQg08miKAAAIIIBBSARaqz7ywe4+flm/t7JKN73bLx4NDdwhePmaYPDi3Wu68vFKGqetE+UEAgawKEAg1OAmEGng0RQABBBBAIMQCLFTvrrhvHDkla3YckpfePyZnzzf9j1VlxmWhN18yVoiB7jx5NQJuBAiEbrRsryUQauDRFAEEEEAAgRAL/Ovhk/IpFqpPWmF1/u/l3x0zHhTzq9gJ47WFIkYAXH3VJPnkBB4UE+K3CEPzkQCBUKMYBEINPJoigAACCCAQcoGGl96WbYdPyveunWrc+8bPkIBaM1BdEvr4rpiotQTVz/CiAvmzyytlZX21TC8fBhUCCORQgECogU0g1MCjKQIIIIAAAiEXYKH6Cwvce2ZQ1u8+LN/bHZNY34Dxy4phRXLvnCq578qJUjGsOOQzguEh4E8BAqFGXQiEGng0RQABBBBAIOQCLFQ/VOADJ88Yl4U+8063nBwYukPwsvJh8kBdlXxxVqWMKFIXivKDAAL5EiAQasgTCDXwaIoAAggggEAEBKK8UH1bT5+s2dEpLXt75PwDQ+WaCaOMy0LVgvKFPCkmAu8AhhgEAQKhRpUIhBp4NEUAAQQQQCACAlFcqP7/7O+Vtbu65P8dOm5UWOW+z04dIyvra2RBdVkEqs4QEQiWAIFQo14EQg08miKAAAIIIBARgSgsVD9w7pw8v6dHHtvZJW0f9hmVHVZYILfPrJCvXVUjM0bzoJiITHeGGUABAqFG0QiEGng0RQABBBBAICICYV6o/kT/WXnq7cOy7s2YHDjZb1R0XGmR3DN7otw/t0omDOdBMRGZ5gwzwAIEQo3iEQg18GiKAAIIIIBAhATCtlB9V1+/qPsjn37riPT2DxqVnFZWKl+ZWyV31U6QkcU8KCZC05uhBlyAQKhRQAKhBh5NEUAAAQQQiJBAWBaqV+sGPrqj07g8VK0nqH7mV4w0HhSzZPp4KeJBMRGa1Qw1LAIEQo1KEgg18GiKAAIIIIBAxASufeltaQ3oQvWvdx43lo54dX+vDMVAkf/8iTGyoq5arp9UHrFKMlwEwiVAINSoJ4FQA4+mCCCAAAIIREwgaAvVqxOAm/f1GEHwje5TRrVKCgrkthnjZdW8Gpk1ZnjEKshwEQinAIFQo64EQg08miKAAAIIIBAxgaAsVN83eFaeae+WJ9q6ZN/xM0aVxpQUyV/OnmDcI1g9oiRilWO4CIRbgECoUV8CoQYeTRFAAAEEEIiggLlQ/e9Xl8m/LKp1JbB161ZpbGyMt+no6EjZfunSpbJ9+3bjdRs2bJAFCxbE21h/t2bNGrn+pj+VdW0x+fstr0n582su2vbixYulubnZ+PdVq1bJ5s2bXfUlZWd5AQII5EWAQKjBTiDUwKMpAggggAACERTIdKH67u5uaWhokNbWVqmsrJSWlhYj6JkBLRGl+p6igt6SJUsu+vX69euNf2tqapK9x0/LH8+/UroavyknKqca/143foSsrKuWZTPGS3FBgREAFy5caARK1fbgwYPxfdv/O4JlZcgIBFqAQKhRPgKhBh5NEUAAAQQQiKjAg9v2yxNtMbntsvHy3PXT01KwB0B7QLRvRL0+FosZgc8pLD668Ufy3Mlyeen9YzLmV/9gvOzqW79oBMEbp4yON2tvb5eHH35YNm3aZPybCofz58+PB0115lKFQvP3aQ2IFyGAgG8ECIQapSAQauDRFAEEEEAAgYgKZLJQvfWMnsmmvods2bJFamsvvvRUhba9e/fGLxdVbdTZxYrKSnnut+/JI7f+iey793/I4KixUlwg8oed/yrTP3xfnnriWxdVxXp2UP1SBcRFixbJ8uXLjcBp/31Ey8qwEQisAIFQo3QEQg08miKAAAIIIBBhAXOh+q/WV8uj10xJKaFC1+TJky8446e+h9jvCzQ3pO4PVPf8mZeLvvCjH8lDX/+6FP33f5R9He/I1B98TQ4vf1q+9MmZxtIRv/rfPzHuCbSf5bOfHbSGUXWW0Lw/MZ37GVMOkhcggEBeBAiEGuwEQg08miKAAAIIIBBhAbcL1bs9Q6gCoTp7V/epa2X97sPyvV+/I+WP32WcFZw4vERGPf7n8tovfimX1lQZVXC6J9HcjvVhNPYzgqrt6tWrhVAY4QnN0AMtQCDUKB+BUAOPpggggAACCERcwM1C9W7vIbxv5Vdl//hL5f9WXyMnB85K0cljcumT98h9z/5U7r7mcpk96/ILLjdNFDid7g20X6pqXkJqPvAm4mVl+AgEToBAqFEyAqEGHk0RQAABBBCIuMDmfR/Kkp+9J5eWl8p7S+dKQUGBo0iqp4yaZ+l+/OtdsmZHp/z05/8s1T96VPb89QvyBzXlMnfnFhl14ugFTwZVOzMfOpPofsREZwdVG3WGUP2YTzhVYVKFRx4qE/EJzfADK0Ag1CgdgVADj6YIIIAAAghEXMC6UP1PbpwhN08bm1Qk2TqEDz39Q3nhsb81AqD6KSoQue7Qr+WDZ79j/Le6388e2OzrEFqXp0gV8qxtE2074qVl+AgESoBAqFEuAqEGHk0RQAABBBBAQHQWqh84d06e39Mjj+3skrYP+wzNUcWF0jirUh6sq5ZpZaUII4AAAikFCIQpiZxfQCDUwKMpAggggAACCEgmC9Wf6D8rT719WNa9GZMDJ/sNxaoRxdI0p0q+PGeijCktQhYBBBBIW4BAmDbVxS8kEGrg0RQBBBBAAAEEDIF0F6rv6uuXJ3bF5Om3D8tH/WeNtrPGDJcH66rkzssrpbTQ+R5EqBFAAAEnAQKhxtwgEGrg0RQBBBBAAAEEDAHrQvWH/mu9TBxRcoHMO70fS/NvO43LQ/vPnTN+93tVZbKivlpumjZWiIFMJAQQ0BEgEGroEQg18GiKAAIIIIAAAnGBz7zaIa/s75Wp5aVy/aRy44yfyn5rd3XJq/t7jQbXZqkAABNqSURBVNcVisjnLhkrq66aJJ+cMBI9BBBAICsCBEINRgKhBh5NEUAAAQQQQMAQOHZmUOa0vCmHTg3dD/jvP+ps4ND5v3uumGA8KOay0cNQQwABBLIqQCDU4CQQauDRFAEEEEAAAQQMgdc7j8sf/vSdizRGFhUal4WqB8VUDi9GCwEEEPBEgECowUog1MCjKQIIIIAAAggkDYRfn1cj/+2Tk1FCAAEEPBUgEGrwEgg18GiKAAIIIIAAAobAjqOnZN6P37pI49uf/oTcP7cKJQQQQMBTAQKhBi+BUAOPpggggAACCCAQF/ib3xySR7Yfiv/3dTXl8vpnZyGEAAIIeC5AINQgJhBq4NEUAQQQQAABBC4QUA+XUWcLLykbJpeUl6KDAAII5ESAQKjBTCDUwKMpAggggAACCCCAAAII5F2AQKhRAgKhBh5NEUAAAQQQQAABBBBAIO8CBEKNEhAINfBoigACCCCAAAIIIIAAAnkXIBBqlIBAqIFHUwQQQAABBBBAAAEEEMi7AIFQowQEQg08miKAAAIIIIAAAggggEDeBQiEGiUgEGrg0RQBBBBAAAEEEEAAAQTyLkAg1CgBgVADj6YIIIAAAggggAACCCCQdwECoUYJCIQaeDRFAAEEEEAAAQQQQACBvAsQCDVKQCDUwKMpAggggAACCCCAAAII5F2AQKhRAgKhBh5NEUAAAQQQQAABBBBAIO8CBEKNEhAINfBoigACCCCAAAIIIIAAAnkXIBBqlIBAqIFHUwQQQAABBBBAAAEEEMi7AIFQowQEQg08miKAAAIIIIAAAggggEDeBQiEGiUgEGrg0RQBBBBAAAEEEEAAAQTyLkAg1CgBgVADj6YIIIAAAggggAACCCCQdwECoUYJCIQaeDRFAAEEEEAAAQQQQACBvAsQCDVKQCDUwKMpAggggAACCCCAAAII5F2AQKhRAgKhBh5NEUAAAQQQQAABBBBAIO8CBEKNEhAINfBoigACCCCAAAIIIIAAAnkXIBBqlIBAqIFHUwQQQAABBBBAAAEEEMi7AIFQowQEQg08miKAAAIIIIAAAggggEDeBQiEGiVQgZAfBBBAAIHgCnz605+Wbdu2BXcA9BwBBBBAQDo6OlDQECAQauAFoemKFSvkjjvukPr6+iB0lz6GXCAWi8kDDzwgzz33XMhHyvCCIvDss8/KuXPn5Atf+EJQukw/Qy5w++23y2OPPSbV1dUhHynDC4LArl27ZOPGjfL4448Hobv0MUMBAmGGcEFpRiAMSqWi0U8CYTTqHKRREgiDVK1o9JVAGI06B2WUBMKgVEqvnwRCPT/ftyYQ+r5EkeoggTBS5Q7EYAmEgShTpDpJIIxUuX0/WAKh70uUlQ4SCLPC6N+NEAj9W5so9oxAGMWq+3vMBEJ/1yeKvSMQRrHq/h0zgdC/tclmzwiE2dT04bYIhD4sSoS7RCCMcPF9OnQCoU8LE+FuEQgjXHwfDp1A6MOieNAlAqEHqGwSAQQQQAABBBBAAAEEEAiCAIEwCFWijwgggAACCCCAAAIIIICABwIEQg9Q2SQCCCCAAAIIIIAAAgggEAQBAmEQqkQfEUAAAQQQQAABBBBAAAEPBAiEHqBma5Pt7e2yaNEi6ejoyNYmpaWlRbZv3y7Nzc1Z2yYbio6AF/Nn1apVMn/+fFmyZEl0IBlp1gRmzpwpW7Zskdra2qxs04vjblY6xkYCIeDF/PHiuBsITDqZFQEvPmOzfdzNykDZiJYAgVCLz7lxd3e3NDQ0xF/g9IUl2ZtKfQioH/OL8tatW6WxsTG+zURBMdUHhzow3HnnncaXJ/v2Fi9efEFQzHQM9nZWpWyGW49KF9rNZnv+KKilS5caf2BQP2vWrEkY6lJ9GKn3gDkvrNtT27S/b3TGYH5RMwvs1N/QTgAfDkx3/qiabty4MX7cSueYleoLu/W4m+hYZj+G6YxBvTc2b95sVMZ+/PVhuULfpWzPHwW2fv16WbduXdIau/nctm4v0XE32RhSfeane0wP/UTw0QCzMX+sn7Hp1jjZd1P7cTfV53ayMaSaz2YpCKDeT0oCoUfG6g3S1NQkCxYskERfQKxvIKewqF7zyCOPGOHNPMi3trZKZWXlRWf6rAd6py8W5jbMLzTqjbhs2TJje+ZBQrU1A2g2xmDyqg88teSAMuEn9wJezR81ErOm9gO29UDvFL7UvH3llVeML/Sqjy+++GJ8e+acNuerzhjMtuZ7zf7fua8Ie1TzQ3f+qG1UVVWldcyyf1F2+uOU9bir5qD6Ucdx88v9wYMH4wFUZwyqrXVbar9qPxwj8/feSPaZl+n8UXXetGmTMSj7H8cy+dxW2zCv8El0HEs2hlSf+anmc/4qE809q/mhO3+sn7HmMSzZcTed76bW426qz+1UY0hnPpt/dM7mlSDRnFHJR00g9GBWOAVAMyCau0z2pVRt4+GHH45/kNj/gmj/cmxu0/4lwzq8VKHM+mGQjTFY963CghlmPSBnkykEvJg/iQKg9YPG7JL6gLH+ocHaVfVhsHDhwvgXbuvvUgVAN++BdOczEyl3AtmYP9bjSro1tv+hwTpi+3HXrmH/cqMzBntb+7ZzVwn2pAS8mD+JAqD1C342PrfT+cOt/buHdb/JwoE9IDJTciuQjflj/4xN55iV6g+myb7P2T+X0x2D9fuCm+/Kua1IuPdGIPSgvok+2BNdNpfsTWf/y3eiA3OiU+jJAqH1L9+Jhm3tYzbGYO4jVRD1oARs0iaQ7fmTKIw5XfbkFAjtZ6ztRbN/QdMdg3l5nvrDhNq39Q8uTJjcCmRj/tj/8p3uMStZILQfd+0q1jmuOwb78TvVpay5rVD09ubF/LGGNafQqf5d53PbOo/SHYNZXetnvpv5HL3ZkZ8R684f+2dsujVO9t3UftxN9bmd7hjM7ST6XpsqoOanOuHbK4HQg5qqLw3qvhDzMhG1C3XgnTx58gWXAyWb5OpN9OSTT8Yv50zUXr1xNmzYcMHZFacPFrWve++994I+WYdu/5KUjTFY3+CcHfRgornYZLbnj/nl1VrXRHNGddEpEKo5t3PnTsdL5OztdMdg9i/VPY8uWHlphgLZmD/qWFdfXx8//qV7zEoWCO3HXevw7Mdr3THY5zOBMMPJlKVmXswf+2d0ojmTLBCm+ty2f96nOwa1T/v7wM18zhI5m0khoDt/7J+x6dY41ckK63HXPgT753a6Y0j1PlDP5OCSUW/fMgRCD3zT/Sud05vOfsOu+UZR/2u9v8TNGUL7A2oShUHrl3vdMZjb55ITDyZYBpvUPbtmnz/p/qUxWSC0PuAo0YeK/X4qnTEkuhRQvX94sEwGkykLTbIxf+wPSkj3mOUUCBMdd82hmv21/gEuW2Owcqqn7Vr/kJgFajaRpoAX8yfdsyNOf8hN9rmt2qg+W+eL2zFYP/PdzOc0SXmZpoDu/LF/xqZb42SB0H7ctQ4x0X3QbsZgn8/24y+BUHNCpWhOIPTAN917EZzedIkuW9K9B8zpclHzy5H9DJ7uGBSr0z1eHpCzyRQCXsyfdO5FcAqEyS4XdXq4hs4YEl3OmuiMIxMpdwI68yfRZUvpHrOcAqHT5aKJwqCppDMGuzSX1udu7iXakxfzJ937p5wCodPndqIwqMaUzhicPvNV+3Tnc34rFZ2968wfp8/YdGrs9N002eWiTp/b6YzBaT4TCHM71wmEHnmnutHbGpjsf/VIdMOuPVw53a+V6IPF6UEJahurV692XOdQZwxqfJwd9GhyZbBZL+ZPuk+kS3TJqNOX32Rn7XTHoNb0tD9l1H7JdQa0NMlQQGf+OD2MKJ1jllMgTHTcNb9gO/1lWmcMVjanSwkzpKVZhgLZnj+pnrBodtPN57aa++rHaS3hZGNI9Zmf7nzOkJdmLgV05o/TZ2w6NXYKhE7H3WSf26nGkGo+J/uu7JKTl6cQIBB6NEXMN5S5efsXCuujfdVrzKUikv0FxvwiY27T+th0++/Ua8x9Ov3l294Hc7vm2cJMx6C2k+yvkB6Rs9kUAtmeP2p31jlkv/xSzTtz/S37nE30l+9Ec1i1W758+UVLUbh9D1jnpNmWMJj/t0wm8yfZ2eVkxyz779TozTnrdNxNNIdVO+vcyWQMahv2dTFZozX/8zHb80eNyDqH7EtCuf3cTjSH1T6slxonG0Oqz/xUx/T8Vyh6Pch0/iR7iGCyY5bTd1On4246n9tOY0hnPjv1J3ozwfsREwi9N3a1B/uDElw1dnhxsgclZGP7bCPcAtmeP6kelBBuTUanK5DqYUSZbN+L424m/aBNMAW8mD/ZPu4GU5ZeZyLgxWesF8fdTMZGG+8ECITe2brecrK/fLve2PkGyc44ZrpN2kVHwIv5k+rR/tHRZaSZCCT7y3cm2/PiuJtJP2gTTAEv5o8Xx91g6tLrTAS8+IzN9nE3k3HRxlsBAqG3vmwdAQQQQAABBBBAAAEEEPCtAIHQt6WhYwgggAACCCCAAAIIIICAtwIEQm992ToCCCCAAAIIIIAAAggg4FsBAqFvS0PHEEAAAQQQQAABBBBAAAFvBQiE3vqydQQQQAABBBBAAAEEEEDAtwIEQt+Who4hgAACCCCAAAIIIIAAAt4KEAi99WXrCCCAAAIIIIAAAggggIBvBQiEvi0NHUMAAQQQQAABBBBAAAEEvBUgEHrry9YRQAABBBBAAAEEEEAAAd8KEAh9Wxo6hgACCCCAAAIIIIAAAgh4K0Ag9NaXrSOAAAIIIIAAAggggAACvhUgEPq2NHQMAQQQQAABBBBAAAEEEPBWgEDorS9bRwABBBBAAAEEEEAAAQR8K0Ag9G1p6BgCCCCAAAIIIIAAAggg4K0AgdBbX7aOAAIIIIAAAggggAACCPhWgEDo29LQMQQQQAABJ4Grr77aEae+vl42bNigjffII4/IjTfeKA0NDdrbYgMIIIAAAgj4VYBA6NfK0C8EEEAAAUeBZIHQbPTaa69JRUWFa0UVBF9++WWj3fr16wmErgVpgAACCCAQJAECYZCqRV8RQAABBAwBayC8++675a677rro32+66SZ5+OGHXYs1NjbKzp07CYSu5WiAAAIIIBBEAQJhEKtGnxFAAIGICzgFwtbWVmlqaorrWM8S2n9nvsgMlEePHjUuEU30k2o7mYbPiJeR4SOAAAII+ECAQOiDItAFBBBAAAF3Ak6B0B7qzEs+ncKgNRTecsstKQPh97//fXnqqaccO/ub3/zG3UB4NQIIIIAAAnkWIBDmuQDsHgEEEEDAvUC6gfChhx6Sm2++WRJdBtrR0SHLli0zdm59EI3TJaPW11vPCL700kvyjW98w9iO9fJV96OiBQIIIIAAArkXIBDm3pw9IoAAAghoCrgNhPbdWR8cY/7OPLvnFAhTnR20B0vNIdIcAQQQQACBnAgQCHPCzE4QQAABBLIpkG4gNC8ZtZ7dc+oHgTCbFWJbCCCAAAJBESAQBqVS9BMBBBBAIC7gFAjtwe/FF1+UmTNnXvBUUvPyUPtr3QRCLg1lMiKAAAIIhEWAQBiWSjIOBBBAIEICToHQerlnquBnvwQ0VSB0CpDWy08JihGahAwVAQQQCIkAgTAkhWQYCCCAQJQE3C5Mn87rzUCY6F5B83eJ7j003a0PpolSLRgrAggggECwBQiEwa4fvUcAAQQiKZAs4CUKZonWGFQhz3pG0by8VIHat29dh9D6VFETnzODkZyGDBoBBBAIhQCBMBRlZBAIIIAAAggggAACCCCAgHsBAqF7M1oggAACCCCAAAIIIIAAAqEQIBCGoowMAgEEEEAAAQQQQAABBBBwL0AgdG9GCwQQQAABBBBAAAEEEEAgFAIEwlCUkUEggAACCCCAAAIIIIAAAu4FCITuzWiBAAIIIIAAAggggAACCIRCgEAYijIyCAQQQAABBBBAAAEEEEDAvQCB0L0ZLRBAAAEEEEAAAQQQQACBUAgQCENRRgaBAAIIIIAAAggggAACCLgXIBC6N6MFAggggAACCCCAAAIIIBAKAQJhKMrIIBBAAAEEEEAAAQQQQAAB9wIEQvdmtEAAAQQQQAABBBBAAAEEQiFAIAxFGRkEAggggAACCCCAAAIIIOBegEDo3owWCCCAAAIIIIAAAggggEAoBAiEoSgjg0AAAQQQQAABBBBAAAEE3AsQCN2b0QIBBBBAAAEEEEAAAQQQCIUAgTAUZWQQCCCAAAIIIIAAAggggIB7AQKhezNaIIAAAggggAACCCCAAAKhECAQhqKMDAIBBBBAAAEEEEAAAQQQcC9AIHRvRgsEEEAAAQQQQAABBBBAIBQCBMJQlJFBIIAAAggggAACCCCAAALuBQiE7s1ogQACCCCAAAIIIIAAAgiEQoBAGIoyMggEEEAAAQQQQAABBBBAwL0AgdC9GS0QQAABBBBAAAEEEEAAgVAIEAhDUUYGgQACCCCAAAIIIIAAAgi4FyAQujejBQIIIIAAAggggAACCCAQCgECYSjKyCAQQAABBBBAAAEEEEAAAfcCBEL3ZrRAAAEEEEAAAQQQQAABBEIhQCAMRRkZBAIIIIAAAggggAACCCDgXoBA6N6MFggggAACCCCAAAIIIIBAKAQIhKEoI4NAAAEEEEAAAQQQQAABBNwL/H9MMojHTbRpqgAAAABJRU5ErkJggg==" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_modeldrift_data()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While data drift was high in 2019, the impact on model performance is low. In 2020, data drift leads to a decrease in model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_car_accident_modeldrift_2021.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_car_accident_modeldrift_2021.html', \n", + " title_story=\"Model drift Report\",\n", + " title_description=\"\"\"US Car accident model drift 2021\"\"\",\n", + " project_info_file=\"../../../../eurybia/data/project_info_car_accident.yml\" \n", + " )" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_car_accident_modeldrift_2021.html', \n", - " title_story=\"Model drift Report\",\n", - " title_description=\"\"\"US Car accident model drift 2021\"\"\",\n", - " project_info_file=\"../../../../eurybia/data/project_info_car_accident.yml\" \n", - " )" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "eurybia_3_9", - "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" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "336px" - }, - "toc_section_display": true, - "toc_window_display": true + ], + "metadata": { + "kernelspec": { + "display_name": "eurybia_3_9", + "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" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "336px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "vscode": { + "interpreter": { + "hash": "36c4204cc0170e083c18487e195263df35fcafba9d65a5415ab6b0958d51e154" + } + } }, - "vscode": { - "interpreter": { - "hash": "36c4204cc0170e083c18487e195263df35fcafba9d65a5415ab6b0958d51e154" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/source/tutorials/tutorial01-Eurybia-overview.ipynb b/docs/source/tutorials/tutorial01-Eurybia-overview.ipynb index b53653e..8ba3bc8 100644 --- a/docs/source/tutorials/tutorial01-Eurybia-overview.ipynb +++ b/docs/source/tutorials/tutorial01-Eurybia-overview.ipynb @@ -1,248 +1,248 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "fd9f3425", - "metadata": {}, - "source": [ - "# Eurybia - Overview\n", - "This tutorial will help you understand how Eurybia works with a simple use case\n", - "\n", - "Contents:\n", - "- Compile Eurybia \n", - "- Generate report\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b28aa5ec", - "metadata": {}, - "outputs": [], - "source": [ - "from category_encoders import OrdinalEncoder\n", - "from lightgbm import LGBMRegressor\n", - "from eurybia import SmartDrift\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "markdown", - "id": "62afc6bc", - "metadata": {}, - "source": [ - "## Import Dataset and split in training and production dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "205aab5a", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading\n", - "house_df, house_dict = data_loading('house_prices')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f84d2459", - "metadata": {}, - "outputs": [], - "source": [ - "# Let us consider that the column \"YrSold\" corresponds to the reference date. \n", - "#In 2006, a model was trained using data. And in 2007, we want to detect data drift on new data in production to predict\n", - "#house price\n", - "house_df_learning = house_df.loc[house_df['YrSold'] == 2006]\n", - "house_df_2007 = house_df.loc[house_df['YrSold'] == 2007]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ac6ca577", - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", - "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YrSold'])]\n", - "\n", - "y_df_2007=house_df_2007['SalePrice'].to_frame()\n", - "X_df_2007=house_df_2007[house_df_2007.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "markdown", - "id": "c48aadd5", - "metadata": {}, - "source": [ - "## Building Supervized Model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "89edc92c", - "metadata": {}, - "outputs": [], - "source": [ - "from category_encoders import OrdinalEncoder\n", - "\n", - "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", - "\n", - "encoder = OrdinalEncoder(\n", - " cols=categorical_features,\n", - " handle_unknown='ignore',\n", - " return_df=True).fit(X_df_learning)\n", - "\n", - "X_df_learning_encoded=encoder.transform(X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "78a06e16", - "metadata": {}, - "outputs": [], - "source": [ - "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d533dfb9", - "metadata": {}, - "outputs": [], - "source": [ - "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" - ] - }, - { - "cell_type": "markdown", - "id": "17a7e6fa", - "metadata": {}, - "source": [ - "## Use Eurybia for data drift" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e3bd740d", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6096b305", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2007, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c9ff2c6c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Backend: Shap TreeExplainer\n", - "CPU times: user 2min 6s, sys: 5min 38s, total: 7min 44s\n", - "Wall time: 12.3 s\n" - ] - } - ], - "source": [ - "%time SD.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8e53feb7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_datadrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cells": [ + { + "cell_type": "markdown", + "id": "fd9f3425", + "metadata": {}, + "source": [ + "# Eurybia - Overview\n", + "This tutorial will help you understand how Eurybia works with a simple use case\n", + "\n", + "Contents:\n", + "- Compile Eurybia \n", + "- Generate report\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b28aa5ec", + "metadata": {}, + "outputs": [], + "source": [ + "from category_encoders import OrdinalEncoder\n", + "from lightgbm import LGBMRegressor\n", + "from eurybia import SmartDrift\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "id": "62afc6bc", + "metadata": {}, + "source": [ + "## Import Dataset and split in training and production dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "205aab5a", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading\n", + "house_df, house_dict = data_loading('house_prices')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f84d2459", + "metadata": {}, + "outputs": [], + "source": [ + "# Let us consider that the column \"YrSold\" corresponds to the reference date. \n", + "#In 2006, a model was trained using data. And in 2007, we want to detect data drift on new data in production to predict\n", + "#house price\n", + "house_df_learning = house_df.loc[house_df['YrSold'] == 2006]\n", + "house_df_2007 = house_df.loc[house_df['YrSold'] == 2007]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ac6ca577", + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", + "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YrSold'])]\n", + "\n", + "y_df_2007=house_df_2007['SalePrice'].to_frame()\n", + "X_df_2007=house_df_2007[house_df_2007.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "markdown", + "id": "c48aadd5", + "metadata": {}, + "source": [ + "## Building Supervized Model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "89edc92c", + "metadata": {}, + "outputs": [], + "source": [ + "from category_encoders import OrdinalEncoder\n", + "\n", + "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", + "\n", + "encoder = OrdinalEncoder(\n", + " cols=categorical_features,\n", + " handle_unknown='ignore',\n", + " return_df=True).fit(X_df_learning)\n", + "\n", + "X_df_learning_encoded=encoder.transform(X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "78a06e16", + "metadata": {}, + "outputs": [], + "source": [ + "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d533dfb9", + "metadata": {}, + "outputs": [], + "source": [ + "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" + ] + }, + { + "cell_type": "markdown", + "id": "17a7e6fa", + "metadata": {}, + "source": [ + "## Use Eurybia for data drift" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e3bd740d", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6096b305", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2007, df_baseline=X_df_learning, deployed_model=regressor, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c9ff2c6c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backend: Shap TreeExplainer\n", + "CPU times: user 2min 6s, sys: 5min 38s, total: 7min 44s\n", + "Wall time: 12.3 s\n" + ] + } + ], + "source": [ + "%time SD.compile()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8e53feb7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_datadrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_datadrift_2007.html', \n", + " title_story=\"Data drift\",\n", + " title_description=\"\"\"House price Data drift 2007\"\"\",\n", + " project_info_file=\"../eurybia/data/project_info_house_price.yml\" \n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "bca2474b", + "metadata": {}, + "source": [ + "For a more detailed tutorial on :\n", + "- Data validation : (https://github.com/MAIF/eurybia/tree/master/tutorial/data_validation)\n", + "- Data drift : (https://github.com/MAIF/eurybia/tree/master/tutorial/data_drift)\n", + "- Model drift : (https://github.com/MAIF/eurybia/tree/master/tutorial/model_drift)" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_datadrift_2007.html', \n", - " title_story=\"Data drift\",\n", - " title_description=\"\"\"House price Data drift 2007\"\"\",\n", - " project_info_file=\"../eurybia/data/project_info_house_price.yml\" \n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "bca2474b", - "metadata": {}, - "source": [ - "For a more detailed tutorial on :\n", - "- Data validation : (https://github.com/MAIF/eurybia/tree/master/tutorial/data_validation)\n", - "- Data drift : (https://github.com/MAIF/eurybia/tree/master/tutorial/data_drift)\n", - "- Model drift : (https://github.com/MAIF/eurybia/tree/master/tutorial/model_drift)" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "d08e6294e2d60f50397263035a337d71f3055486232bc02b45ce2785f62e7d8b" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.1" + ], + "metadata": { + "interpreter": { + "hash": "d08e6294e2d60f50397263035a337d71f3055486232bc02b45ce2785f62e7d8b" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/eurybia/__init__.py b/eurybia/__init__.py index 0b188a5..2cdea35 100644 --- a/eurybia/__init__.py +++ b/eurybia/__init__.py @@ -5,6 +5,8 @@ from eurybia.core.smartdrift import SmartDrift -from .__version__ import __version__ +VERSION = (1, 2, 0) + +__version__ = ".".join(map(str, VERSION)) __all__ = ["SmartDrift"] diff --git a/eurybia/__version__.py b/eurybia/__version__.py deleted file mode 100644 index 5b3343a..0000000 --- a/eurybia/__version__.py +++ /dev/null @@ -1,3 +0,0 @@ -VERSION = (1, 1, 1) - -__version__ = ".".join(map(str, VERSION)) diff --git a/eurybia/core/smartdrift.py b/eurybia/core/smartdrift.py index ec21f75..3387c8e 100644 --- a/eurybia/core/smartdrift.py +++ b/eurybia/core/smartdrift.py @@ -1,6 +1,7 @@ """ - SmartDrift module """ + import copy import datetime import io @@ -9,7 +10,7 @@ import shutil import tempfile from pathlib import Path -from typing import Dict, Text +from typing import Dict import catboost import pandas as pd @@ -467,7 +468,7 @@ def _analyze_consistency(self, full_validation=False, ignore_cols: list = list() and will not be analyzed: \n {err_dtypes}""" ) # Feature values - err_mods: Dict[Text, Dict] = {} + err_mods: Dict[str, Dict] = {} if full_validation is True: invalid_cols = ignore_cols + new_cols + removed_cols + err_dtypes for column in self.df_baseline.columns: @@ -743,7 +744,7 @@ def _compute_datadrift_stat_test(self, max_size=50000, categ_max=20): max_size : int Sets the maximum number of rows. If the datasets are larger there is sampling categ_max: int - Maximum number of values ​​per feature to apply the chi square test + Maximum number of values \u200b\u200bper feature to apply the chi square test Returns : ------- @@ -765,12 +766,10 @@ def _compute_datadrift_stat_test(self, max_size=50000, categ_max=20): test = ksmirnov_test(current[features].to_numpy(), baseline[features].to_numpy()) except BaseException as e: raise Exception( - """ - There is a problem with the format of {} column between the two datasets. + f""" + There is a problem with the format of {str(features)} column between the two datasets. Error: - """.format( - str(features) - ) + """ + str(e) ) test_results[features] = test diff --git a/eurybia/core/smartplotter.py b/eurybia/core/smartplotter.py index aba87d7..3c25004 100644 --- a/eurybia/core/smartplotter.py +++ b/eurybia/core/smartplotter.py @@ -114,7 +114,6 @@ def generate_fig_univariate_continuous( width: Optional[str] = None, hovermode: Optional[str] = None, ) -> plotly.graph_objs._figure.Figure: - """ Returns a plotly figure containing the distribution of a continuous feature. @@ -148,11 +147,15 @@ def generate_fig_univariate_continuous( """ df_all.loc[:, col].fillna(0, inplace=True) datasets = [df_all[df_all[hue] == val][col].values.tolist() for val in df_all[hue].unique()] + group_labels = [str(val) for val in df_all[hue].unique()] + colors = list(self._style_dict["univariate_cont_bar"].values()) + if group_labels[0] == "Current dataset": + group_labels = ["Baseline dataset", "Current dataset"] fig = ff.create_distplot( datasets, - group_labels=[str(val) for val in df_all[hue].unique()], - colors=list(self._style_dict["univariate_cont_bar"].values()), + group_labels=group_labels, + colors=list(colors), show_hist=False, show_curve=True, show_rug=False, @@ -285,7 +288,7 @@ def generate_fig_univariate_categorical( color=hue, text="Percent_displayed", ) - fig1.update_traces(marker_color=list(self._style_dict["univariate_cat_bar"].values())[0], showlegend=True) + fig1.update_traces(marker_color=list(self._style_dict["univariate_cat_bar"].values())[1], showlegend=True) fig2 = px.bar( df_cat[df_cat[hue] == modalities[1]], @@ -296,7 +299,7 @@ def generate_fig_univariate_categorical( color=hue, text="Percent_displayed", ) - fig2.update_traces(marker_color=list(self._style_dict["univariate_cat_bar"].values())[1], showlegend=True) + fig2.update_traces(marker_color=list(self._style_dict["univariate_cat_bar"].values())[0], showlegend=True) fig = fig1.add_trace(fig2.data[0]) @@ -489,7 +492,6 @@ def generate_historical_datadrift_metric( datadrift_historical["auc_displayed"] = datadrift_historical["auc"].round(2) if self.smartdrift.deployed_model is not None: - fig = make_subplots(specs=[[{"secondary_y": True}]]) fig.add_trace( go.Scatter( diff --git a/eurybia/data/data_loader.py b/eurybia/data/data_loader.py index a538a73..55671dc 100644 --- a/eurybia/data/data_loader.py +++ b/eurybia/data/data_loader.py @@ -1,6 +1,7 @@ """ Data loader module """ + import json import os from urllib.request import urlretrieve diff --git a/eurybia/data/dataprep_US_car_accidents.ipynb b/eurybia/data/dataprep_US_car_accidents.ipynb index e40a35d..9e0f569 100644 --- a/eurybia/data/dataprep_US_car_accidents.ipynb +++ b/eurybia/data/dataprep_US_car_accidents.ipynb @@ -1,973 +1,973 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "9140cc7d", - "metadata": {}, - "source": [ - "# Eurybia - dataprep for US car accidents\n", - "This notebook describes the data preparation leading to the dataset in \"US_Accidents_extract.csv\", used in some of our tutorials. \n" - ] - }, - { - "cell_type": "markdown", - "id": "96701724", - "metadata": {}, - "source": [ - "The original dataset was taken from the Kaggle [US car accidents dataset](https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents).\n", - "\n", - "---\n", - "Acknowledgements\n", - "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. “A Countrywide Traffic Accident Dataset.”, 2019.\n", - "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. \"Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights.\" In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "a4773b01", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import pickle\n", - "import category_encoders as ce" - ] - }, - { - "cell_type": "markdown", - "id": "099f6255", - "metadata": {}, - "source": [ - "### Extract the zipped dataset if you haven't already done so" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8339fc70", - "metadata": {}, - "outputs": [], - "source": [ - "# from zipfile import ZipFile\n", - "# with ZipFile('/tmp/archive.zip', 'r') as zipObj:\n", - "# zipObj.extractall()" - ] - }, - { - "cell_type": "markdown", - "id": "3592a9f4", - "metadata": {}, - "source": [ - "### Load it up" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e2a399d4", - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.read_csv('/tmp/US_Accidents_Dec21_updated.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d5aa6e54", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2845342, 47)\n", - "Index(['ID', 'Severity', 'Start_Time', 'End_Time', 'Start_Lat', 'Start_Lng',\n", - " 'End_Lat', 'End_Lng', 'Distance(mi)', 'Description', 'Number', 'Street',\n", - " 'Side', 'City', 'County', 'State', 'Zipcode', 'Country', 'Timezone',\n", - " 'Airport_Code', 'Weather_Timestamp', 'Temperature(F)', 'Wind_Chill(F)',\n", - " 'Humidity(%)', 'Pressure(in)', 'Visibility(mi)', 'Wind_Direction',\n", - " 'Wind_Speed(mph)', 'Precipitation(in)', 'Weather_Condition', 'Amenity',\n", - " 'Bump', 'Crossing', 'Give_Way', 'Junction', 'No_Exit', 'Railway',\n", - " 'Roundabout', 'Station', 'Stop', 'Traffic_Calming', 'Traffic_Signal',\n", - " 'Turning_Loop', 'Sunrise_Sunset', 'Civil_Twilight', 'Nautical_Twilight',\n", - " 'Astronomical_Twilight'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "print(data.shape)\n", - "print(data.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "6975a335", - "metadata": {}, - "outputs": [], - "source": [ - "feats_to_keep = ['Start_Lat','Start_Lng','Distance(mi)','Temperature(F)','Humidity(%)','Visibility(mi)',\n", - " 'day_of_week_acc','Nautical_Twilight','season_acc','target','target_multi','year_acc','Description']" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "676e1dae", - "metadata": {}, - "source": [ - "### Create targets column \n", - "Here we regroup the severity modalities into two classes to create a binary target column : benign to moderate severity (<= 2) on one side, serious and above on the other (>2)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "372047ed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2 2532991\n", - "3 155105\n", - "4 131193\n", - "1 26053\n", - "Name: Severity, dtype: int64\n" - ] - }, - { - "data": { - "text/plain": [ - "0 89.938011\n", - "1 10.061989\n", - "Name: target, dtype: float64" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(data.Severity.value_counts())\n", - "cond = [data.Severity <= 2]\n", - "choice = ['0']\n", - "data['target'] = np.select(cond, choice, default = '1')\n", - "data['target'].value_counts(normalize = True)*100" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c7b625ad", - "metadata": {}, - "outputs": [], - "source": [ - "data = data.rename(columns={'Severity':'target_multi'})" - ] - }, - { - "cell_type": "markdown", - "id": "938c88cf", - "metadata": {}, - "source": [ - "### Rework the dates \n", - "Here we build a \"day of week\", a \"season\" and a \"year\" feature. This will help us detect and analyze bias or trends that occur on those timescales. \n", - "For example, we can then measure the drift between two same seasons of consecutive years to avoid seasonal bias. \n", - "We could also aggregate by year and mesure the drift from year to year." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a3566f02", - "metadata": {}, - "outputs": [], - "source": [ - "date = ['Start_Time', 'End_Time']\n", - "for d in date:\n", - " data[d] = pd.to_datetime(data[d])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "19eae7e4", - "metadata": {}, - "outputs": [], - "source": [ - "data['month_acc'] = data['Start_Time'].dt.month\n", - "data['day_of_week_acc'] = data['Start_Time'].dt.dayofweek\n", - "data['year_acc'] = data['Start_Time'].dt.year\n", - "cond = [data.month_acc.isin([12,1,2]), \n", - " data.month_acc.isin([3,4,5]), \n", - " data.month_acc.isin([6,7,8]), \n", - " data.month_acc.isin([9,10,11])]\n", - "choix = ['winter','spring','summer','autumn']\n", - "data['season_acc'] = np.select(cond, choix, default = 'NR')" - ] - }, - { - "cell_type": "markdown", - "id": "2de1cdeb", - "metadata": {}, - "source": [ - "### Managing missing values \n", - "The following short analysis, using the \"year\" feature we just created, lets us see how the ratio of missing values evolved over time. " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b8516a2a", - "metadata": {}, - "outputs": [], - "source": [ - "missing_val = pd.DataFrame()\n", - "year = np.unique(data.year_acc)\n", - "for y in year:\n", - " sub = data[data.year_acc == y]\n", - " missing_val_y = pd.DataFrame(sub.isnull().sum().sort_values(ascending=False)/sub.shape[0]*100)\n", - " missing_val_y.columns = ['taux_miss_'+str(y)]\n", - " missing_val = pd.concat([missing_val, missing_val_y], axis = 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8e176f6f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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taux_miss_2016taux_miss_2017taux_miss_2018taux_miss_2019taux_miss_2020taux_miss_2021
Precipitation(in)89.89133386.92517085.58734120.8936066.2187634.277838
Wind_Chill(F)87.97039982.55347277.96305812.7034393.9129912.805896
Number80.74968980.03147978.59182774.64029562.02785353.230340
Wind_Speed(mph)17.97515216.97007018.9929896.5672913.4906942.538126
Visibility(mi)2.3946112.6983002.9526402.7330203.0124762.147254
Weather_Condition2.3692062.7251433.0464042.7639542.9648612.156647
Humidity(%)2.2167772.3603272.6860572.8671963.2684422.266520
Temperature(F)2.0905722.2675972.5720692.7593143.0661612.148709
Pressure(in)1.6398411.9662272.4611462.2009552.5670111.865526
Wind_Direction0.9268671.1591161.6626223.0137463.4932512.538391
Weather_Timestamp0.9170331.1530161.6571061.9101752.3540261.676870
Airport_Code0.1573460.1659370.3370590.3518740.2762580.390013
Timezone0.0729360.0817480.1997840.2849800.1254270.105044
Zipcode0.0622830.0664970.0907000.0823620.0412230.034067
Nautical_Twilight0.0106540.0091510.0030640.0019330.0067110.184356
City0.0106540.0091510.0030640.0019330.0060720.004035
Sunrise_Sunset0.0106540.0091510.0030640.0019330.0067110.184356
Civil_Twilight0.0106540.0091510.0030640.0019330.0067110.184356
Astronomical_Twilight0.0106540.0091510.0030640.0019330.0067110.184356
Street0.0000000.0000000.0000000.0000000.0000000.000132
\n", - "
" + "cells": [ + { + "cell_type": "markdown", + "id": "9140cc7d", + "metadata": {}, + "source": [ + "# Eurybia - dataprep for US car accidents\n", + "This notebook describes the data preparation leading to the dataset in \"US_Accidents_extract.csv\", used in some of our tutorials. \n" + ] + }, + { + "cell_type": "markdown", + "id": "96701724", + "metadata": {}, + "source": [ + "The original dataset was taken from the Kaggle [US car accidents dataset](https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents).\n", + "\n", + "---\n", + "Acknowledgements\n", + "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. \u201cA Countrywide Traffic Accident Dataset.\u201d, 2019.\n", + "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. \"Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights.\" In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a4773b01", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pickle\n", + "import category_encoders as ce" + ] + }, + { + "cell_type": "markdown", + "id": "099f6255", + "metadata": {}, + "source": [ + "### Extract the zipped dataset if you haven't already done so" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8339fc70", + "metadata": {}, + "outputs": [], + "source": [ + "# from zipfile import ZipFile\n", + "# with ZipFile('/tmp/archive.zip', 'r') as zipObj:\n", + "# zipObj.extractall()" + ] + }, + { + "cell_type": "markdown", + "id": "3592a9f4", + "metadata": {}, + "source": [ + "### Load it up" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e2a399d4", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('/tmp/US_Accidents_Dec21_updated.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d5aa6e54", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2845342, 47)\n", + "Index(['ID', 'Severity', 'Start_Time', 'End_Time', 'Start_Lat', 'Start_Lng',\n", + " 'End_Lat', 'End_Lng', 'Distance(mi)', 'Description', 'Number', 'Street',\n", + " 'Side', 'City', 'County', 'State', 'Zipcode', 'Country', 'Timezone',\n", + " 'Airport_Code', 'Weather_Timestamp', 'Temperature(F)', 'Wind_Chill(F)',\n", + " 'Humidity(%)', 'Pressure(in)', 'Visibility(mi)', 'Wind_Direction',\n", + " 'Wind_Speed(mph)', 'Precipitation(in)', 'Weather_Condition', 'Amenity',\n", + " 'Bump', 'Crossing', 'Give_Way', 'Junction', 'No_Exit', 'Railway',\n", + " 'Roundabout', 'Station', 'Stop', 'Traffic_Calming', 'Traffic_Signal',\n", + " 'Turning_Loop', 'Sunrise_Sunset', 'Civil_Twilight', 'Nautical_Twilight',\n", + " 'Astronomical_Twilight'],\n", + " dtype='object')\n" + ] + } ], - "text/plain": [ - " taux_miss_2016 taux_miss_2017 taux_miss_2018 \\\n", - "Precipitation(in) 89.891333 86.925170 85.587341 \n", - "Wind_Chill(F) 87.970399 82.553472 77.963058 \n", - "Number 80.749689 80.031479 78.591827 \n", - "Wind_Speed(mph) 17.975152 16.970070 18.992989 \n", - "Visibility(mi) 2.394611 2.698300 2.952640 \n", - "Weather_Condition 2.369206 2.725143 3.046404 \n", - "Humidity(%) 2.216777 2.360327 2.686057 \n", - "Temperature(F) 2.090572 2.267597 2.572069 \n", - "Pressure(in) 1.639841 1.966227 2.461146 \n", - "Wind_Direction 0.926867 1.159116 1.662622 \n", - "Weather_Timestamp 0.917033 1.153016 1.657106 \n", - "Airport_Code 0.157346 0.165937 0.337059 \n", - "Timezone 0.072936 0.081748 0.199784 \n", - "Zipcode 0.062283 0.066497 0.090700 \n", - "Nautical_Twilight 0.010654 0.009151 0.003064 \n", - "City 0.010654 0.009151 0.003064 \n", - "Sunrise_Sunset 0.010654 0.009151 0.003064 \n", - "Civil_Twilight 0.010654 0.009151 0.003064 \n", - "Astronomical_Twilight 0.010654 0.009151 0.003064 \n", - "Street 0.000000 0.000000 0.000000 \n", - "\n", - " taux_miss_2019 taux_miss_2020 taux_miss_2021 \n", - "Precipitation(in) 20.893606 6.218763 4.277838 \n", - "Wind_Chill(F) 12.703439 3.912991 2.805896 \n", - "Number 74.640295 62.027853 53.230340 \n", - "Wind_Speed(mph) 6.567291 3.490694 2.538126 \n", - "Visibility(mi) 2.733020 3.012476 2.147254 \n", - "Weather_Condition 2.763954 2.964861 2.156647 \n", - "Humidity(%) 2.867196 3.268442 2.266520 \n", - "Temperature(F) 2.759314 3.066161 2.148709 \n", - "Pressure(in) 2.200955 2.567011 1.865526 \n", - "Wind_Direction 3.013746 3.493251 2.538391 \n", - "Weather_Timestamp 1.910175 2.354026 1.676870 \n", - "Airport_Code 0.351874 0.276258 0.390013 \n", - "Timezone 0.284980 0.125427 0.105044 \n", - "Zipcode 0.082362 0.041223 0.034067 \n", - "Nautical_Twilight 0.001933 0.006711 0.184356 \n", - "City 0.001933 0.006072 0.004035 \n", - "Sunrise_Sunset 0.001933 0.006711 0.184356 \n", - "Civil_Twilight 0.001933 0.006711 0.184356 \n", - "Astronomical_Twilight 0.001933 0.006711 0.184356 \n", - "Street 0.000000 0.000000 0.000132 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "missing_val['filtre'] = missing_val.taux_miss_2016+missing_val.taux_miss_2017+missing_val.taux_miss_2018+missing_val.taux_miss_2019+missing_val.taux_miss_2020+missing_val.taux_miss_2021\n", - "missing_val[missing_val.filtre > 0][['taux_miss_2016','taux_miss_2017','taux_miss_2018','taux_miss_2019','taux_miss_2020','taux_miss_2021']]" - ] - }, - { - "cell_type": "markdown", - "id": "2c9f89bf", - "metadata": {}, - "source": [ - "$\\require{color}$\n", - "$\\colorbox{red}{The percentage of missing values, aggregated by year, is far from constant. This is a preliminary sign of data drift.}$" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "eee9e18c", - "metadata": {}, - "outputs": [], - "source": [ - "data = data.dropna(subset=[\"Nautical_Twilight\"])" - ] - }, - { - "cell_type": "markdown", - "id": "7690ac29", - "metadata": {}, - "source": [ - "### Final dataset features" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d8713d00", - "metadata": {}, - "outputs": [], - "source": [ - "data = data[feats_to_keep]" - ] - }, - { - "cell_type": "markdown", - "id": "72e2beca", - "metadata": {}, - "source": [ - "### Quantitative features " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "d075f53f", - "metadata": {}, - "outputs": [], - "source": [ - "for v in ['Distance(mi)','Temperature(F)','Humidity(%)','Visibility(mi)']:\n", - " data[v] = np.round(data[v],0)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "3d90b58e", - "metadata": {}, - "outputs": [], - "source": [ - "data['Start_Lat'] = np.round(data['Start_Lat'],1)\n", - "data['Start_Lng'] = np.round(data['Start_Lng'],1)" - ] - }, - { - "cell_type": "markdown", - "id": "ac192b04", - "metadata": {}, - "source": [ - "### Sampling \n", - "For the purpose of our tutorials, a sample size of ~50000 is sufficient. \n", - "The following few steps reduce the sample size down to about this number, and balance the number of samples per year, in an effort to reduce this source of bias before training a model or producing a quantitative analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "c97d715f", - "metadata": {}, - "outputs": [], - "source": [ - "sampled_data = pd.DataFrame()\n", - "annee = np.unique(data.year_acc)\n", - "for a in annee:\n", - " sub = data[data.year_acc == a]\n", - " sub = sub.reset_index(drop = True)\n", - " tir = np.random.choice(a = sub.shape[0], size = 50000//len(annee)+1, replace = False)\n", - " sampled_data = pd.concat([sampled_data, sub.iloc[tir,:]], axis = 0)\n", - " sampled_data = sampled_data.reset_index(drop = True)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "82247c82", - "metadata": {}, - "outputs": [], - "source": [ - "sampled_data = sampled_data.iloc[np.random.choice(size = 50000, a = sampled_data.index, replace = False),:]\n", - "sampled_data = sampled_data.reset_index(drop = True)" - ] - }, - { - "cell_type": "markdown", - "id": "62f6cb9c", - "metadata": {}, - "source": [ - "### Let us have a final look at our data :" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "990107c2", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_accNautical_Twilightseason_acctargettarget_multiyear_accDescription
033.0-117.10.040.093.02.03Daywinter022019At Carmel Mountain Rd - Accident.
129.5-98.50.083.065.010.04Daysummer132017At TX-345-SP/Woodlawn Ave/Exit 567B - Accident.
232.7-96.80.088.057.010.00Nightsummer022021Incident on RUGGED DR near BERKLEY AVE Expect ...
\n", - "
" + "source": [ + "print(data.shape)\n", + "print(data.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6975a335", + "metadata": {}, + "outputs": [], + "source": [ + "feats_to_keep = ['Start_Lat','Start_Lng','Distance(mi)','Temperature(F)','Humidity(%)','Visibility(mi)',\n", + " 'day_of_week_acc','Nautical_Twilight','season_acc','target','target_multi','year_acc','Description']" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "676e1dae", + "metadata": {}, + "source": [ + "### Create targets column \n", + "Here we regroup the severity modalities into two classes to create a binary target column : benign to moderate severity (<= 2) on one side, serious and above on the other (>2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "372047ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 2532991\n", + "3 155105\n", + "4 131193\n", + "1 26053\n", + "Name: Severity, dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "0 89.938011\n", + "1 10.061989\n", + "Name: target, dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " Start_Lat Start_Lng Distance(mi) Temperature(F) Humidity(%) \\\n", - "0 33.0 -117.1 0.0 40.0 93.0 \n", - "1 29.5 -98.5 0.0 83.0 65.0 \n", - "2 32.7 -96.8 0.0 88.0 57.0 \n", - "\n", - " Visibility(mi) day_of_week_acc Nautical_Twilight season_acc target \\\n", - "0 2.0 3 Day winter 0 \n", - "1 10.0 4 Day summer 1 \n", - "2 10.0 0 Night summer 0 \n", - "\n", - " target_multi year_acc Description \n", - "0 2 2019 At Carmel Mountain Rd - Accident. \n", - "1 3 2017 At TX-345-SP/Woodlawn Ave/Exit 567B - Accident. \n", - "2 2 2021 Incident on RUGGED DR near BERKLEY AVE Expect ... " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sampled_data.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "6bbefb6b", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_acctarget_multiyear_acc
count50000.00000050000.00000050000.00000048753.00000048682.0000048675.00000050000.00000050000.00000050000.00000
mean37.039702-98.1017120.61606061.27493764.190059.1882902.5529602.2940802018.49996
std5.13442618.3746571.75628918.67305422.958873.0638031.7906810.6350791.70787
min24.800000-124.5000000.000000-19.0000003.000000.0000000.0000001.0000002016.00000
25%33.900000-118.2000000.00000049.00000048.0000010.0000001.0000002.0000002017.00000
50%37.500000-93.7000000.00000063.00000066.0000010.0000002.0000002.0000002018.50000
75%40.800000-81.0000001.00000075.00000083.0000010.0000004.0000002.0000002020.00000
max49.000000-67.800000100.000000118.000000100.00000100.0000006.0000004.0000002021.00000
\n", - "
" + "source": [ + "print(data.Severity.value_counts())\n", + "cond = [data.Severity <= 2]\n", + "choice = ['0']\n", + "data['target'] = np.select(cond, choice, default = '1')\n", + "data['target'].value_counts(normalize = True)*100" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c7b625ad", + "metadata": {}, + "outputs": [], + "source": [ + "data = data.rename(columns={'Severity':'target_multi'})" + ] + }, + { + "cell_type": "markdown", + "id": "938c88cf", + "metadata": {}, + "source": [ + "### Rework the dates \n", + "Here we build a \"day of week\", a \"season\" and a \"year\" feature. This will help us detect and analyze bias or trends that occur on those timescales. \n", + "For example, we can then measure the drift between two same seasons of consecutive years to avoid seasonal bias. \n", + "We could also aggregate by year and mesure the drift from year to year." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a3566f02", + "metadata": {}, + "outputs": [], + "source": [ + "date = ['Start_Time', 'End_Time']\n", + "for d in date:\n", + " data[d] = pd.to_datetime(data[d])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "19eae7e4", + "metadata": {}, + "outputs": [], + "source": [ + "data['month_acc'] = data['Start_Time'].dt.month\n", + "data['day_of_week_acc'] = data['Start_Time'].dt.dayofweek\n", + "data['year_acc'] = data['Start_Time'].dt.year\n", + "cond = [data.month_acc.isin([12,1,2]), \n", + " data.month_acc.isin([3,4,5]), \n", + " data.month_acc.isin([6,7,8]), \n", + " data.month_acc.isin([9,10,11])]\n", + "choix = ['winter','spring','summer','autumn']\n", + "data['season_acc'] = np.select(cond, choix, default = 'NR')" + ] + }, + { + "cell_type": "markdown", + "id": "2de1cdeb", + "metadata": {}, + "source": [ + "### Managing missing values \n", + "The following short analysis, using the \"year\" feature we just created, lets us see how the ratio of missing values evolved over time. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b8516a2a", + "metadata": {}, + "outputs": [], + "source": [ + "missing_val = pd.DataFrame()\n", + "year = np.unique(data.year_acc)\n", + "for y in year:\n", + " sub = data[data.year_acc == y]\n", + " missing_val_y = pd.DataFrame(sub.isnull().sum().sort_values(ascending=False)/sub.shape[0]*100)\n", + " missing_val_y.columns = ['taux_miss_'+str(y)]\n", + " missing_val = pd.concat([missing_val, missing_val_y], axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8e176f6f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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taux_miss_2016taux_miss_2017taux_miss_2018taux_miss_2019taux_miss_2020taux_miss_2021
Precipitation(in)89.89133386.92517085.58734120.8936066.2187634.277838
Wind_Chill(F)87.97039982.55347277.96305812.7034393.9129912.805896
Number80.74968980.03147978.59182774.64029562.02785353.230340
Wind_Speed(mph)17.97515216.97007018.9929896.5672913.4906942.538126
Visibility(mi)2.3946112.6983002.9526402.7330203.0124762.147254
Weather_Condition2.3692062.7251433.0464042.7639542.9648612.156647
Humidity(%)2.2167772.3603272.6860572.8671963.2684422.266520
Temperature(F)2.0905722.2675972.5720692.7593143.0661612.148709
Pressure(in)1.6398411.9662272.4611462.2009552.5670111.865526
Wind_Direction0.9268671.1591161.6626223.0137463.4932512.538391
Weather_Timestamp0.9170331.1530161.6571061.9101752.3540261.676870
Airport_Code0.1573460.1659370.3370590.3518740.2762580.390013
Timezone0.0729360.0817480.1997840.2849800.1254270.105044
Zipcode0.0622830.0664970.0907000.0823620.0412230.034067
Nautical_Twilight0.0106540.0091510.0030640.0019330.0067110.184356
City0.0106540.0091510.0030640.0019330.0060720.004035
Sunrise_Sunset0.0106540.0091510.0030640.0019330.0067110.184356
Civil_Twilight0.0106540.0091510.0030640.0019330.0067110.184356
Astronomical_Twilight0.0106540.0091510.0030640.0019330.0067110.184356
Street0.0000000.0000000.0000000.0000000.0000000.000132
\n", + "
" + ], + "text/plain": [ + " taux_miss_2016 taux_miss_2017 taux_miss_2018 \\\n", + "Precipitation(in) 89.891333 86.925170 85.587341 \n", + "Wind_Chill(F) 87.970399 82.553472 77.963058 \n", + "Number 80.749689 80.031479 78.591827 \n", + "Wind_Speed(mph) 17.975152 16.970070 18.992989 \n", + "Visibility(mi) 2.394611 2.698300 2.952640 \n", + "Weather_Condition 2.369206 2.725143 3.046404 \n", + "Humidity(%) 2.216777 2.360327 2.686057 \n", + "Temperature(F) 2.090572 2.267597 2.572069 \n", + "Pressure(in) 1.639841 1.966227 2.461146 \n", + "Wind_Direction 0.926867 1.159116 1.662622 \n", + "Weather_Timestamp 0.917033 1.153016 1.657106 \n", + "Airport_Code 0.157346 0.165937 0.337059 \n", + "Timezone 0.072936 0.081748 0.199784 \n", + "Zipcode 0.062283 0.066497 0.090700 \n", + "Nautical_Twilight 0.010654 0.009151 0.003064 \n", + "City 0.010654 0.009151 0.003064 \n", + "Sunrise_Sunset 0.010654 0.009151 0.003064 \n", + "Civil_Twilight 0.010654 0.009151 0.003064 \n", + "Astronomical_Twilight 0.010654 0.009151 0.003064 \n", + "Street 0.000000 0.000000 0.000000 \n", + "\n", + " taux_miss_2019 taux_miss_2020 taux_miss_2021 \n", + "Precipitation(in) 20.893606 6.218763 4.277838 \n", + "Wind_Chill(F) 12.703439 3.912991 2.805896 \n", + "Number 74.640295 62.027853 53.230340 \n", + "Wind_Speed(mph) 6.567291 3.490694 2.538126 \n", + "Visibility(mi) 2.733020 3.012476 2.147254 \n", + "Weather_Condition 2.763954 2.964861 2.156647 \n", + "Humidity(%) 2.867196 3.268442 2.266520 \n", + "Temperature(F) 2.759314 3.066161 2.148709 \n", + "Pressure(in) 2.200955 2.567011 1.865526 \n", + "Wind_Direction 3.013746 3.493251 2.538391 \n", + "Weather_Timestamp 1.910175 2.354026 1.676870 \n", + "Airport_Code 0.351874 0.276258 0.390013 \n", + "Timezone 0.284980 0.125427 0.105044 \n", + "Zipcode 0.082362 0.041223 0.034067 \n", + "Nautical_Twilight 0.001933 0.006711 0.184356 \n", + "City 0.001933 0.006072 0.004035 \n", + "Sunrise_Sunset 0.001933 0.006711 0.184356 \n", + "Civil_Twilight 0.001933 0.006711 0.184356 \n", + "Astronomical_Twilight 0.001933 0.006711 0.184356 \n", + "Street 0.000000 0.000000 0.000132 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "missing_val['filtre'] = missing_val.taux_miss_2016+missing_val.taux_miss_2017+missing_val.taux_miss_2018+missing_val.taux_miss_2019+missing_val.taux_miss_2020+missing_val.taux_miss_2021\n", + "missing_val[missing_val.filtre > 0][['taux_miss_2016','taux_miss_2017','taux_miss_2018','taux_miss_2019','taux_miss_2020','taux_miss_2021']]" + ] + }, + { + "cell_type": "markdown", + "id": "2c9f89bf", + "metadata": {}, + "source": [ + "$\\require{color}$\n", + "$\\colorbox{red}{The percentage of missing values, aggregated by year, is far from constant. This is a preliminary sign of data drift.}$" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "eee9e18c", + "metadata": {}, + "outputs": [], + "source": [ + "data = data.dropna(subset=[\"Nautical_Twilight\"])" + ] + }, + { + "cell_type": "markdown", + "id": "7690ac29", + "metadata": {}, + "source": [ + "### Final dataset features" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d8713d00", + "metadata": {}, + "outputs": [], + "source": [ + "data = data[feats_to_keep]" + ] + }, + { + "cell_type": "markdown", + "id": "72e2beca", + "metadata": {}, + "source": [ + "### Quantitative features " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d075f53f", + "metadata": {}, + "outputs": [], + "source": [ + "for v in ['Distance(mi)','Temperature(F)','Humidity(%)','Visibility(mi)']:\n", + " data[v] = np.round(data[v],0)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3d90b58e", + "metadata": {}, + "outputs": [], + "source": [ + "data['Start_Lat'] = np.round(data['Start_Lat'],1)\n", + "data['Start_Lng'] = np.round(data['Start_Lng'],1)" + ] + }, + { + "cell_type": "markdown", + "id": "ac192b04", + "metadata": {}, + "source": [ + "### Sampling \n", + "For the purpose of our tutorials, a sample size of ~50000 is sufficient. \n", + "The following few steps reduce the sample size down to about this number, and balance the number of samples per year, in an effort to reduce this source of bias before training a model or producing a quantitative analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c97d715f", + "metadata": {}, + "outputs": [], + "source": [ + "sampled_data = pd.DataFrame()\n", + "annee = np.unique(data.year_acc)\n", + "for a in annee:\n", + " sub = data[data.year_acc == a]\n", + " sub = sub.reset_index(drop = True)\n", + " tir = np.random.choice(a = sub.shape[0], size = 50000//len(annee)+1, replace = False)\n", + " sampled_data = pd.concat([sampled_data, sub.iloc[tir,:]], axis = 0)\n", + " sampled_data = sampled_data.reset_index(drop = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "82247c82", + "metadata": {}, + "outputs": [], + "source": [ + "sampled_data = sampled_data.iloc[np.random.choice(size = 50000, a = sampled_data.index, replace = False),:]\n", + "sampled_data = sampled_data.reset_index(drop = True)" + ] + }, + { + "cell_type": "markdown", + "id": "62f6cb9c", + "metadata": {}, + "source": [ + "### Let us have a final look at our data :" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "990107c2", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_accNautical_Twilightseason_acctargettarget_multiyear_accDescription
033.0-117.10.040.093.02.03Daywinter022019At Carmel Mountain Rd - Accident.
129.5-98.50.083.065.010.04Daysummer132017At TX-345-SP/Woodlawn Ave/Exit 567B - Accident.
232.7-96.80.088.057.010.00Nightsummer022021Incident on RUGGED DR near BERKLEY AVE Expect ...
\n", + "
" + ], + "text/plain": [ + " Start_Lat Start_Lng Distance(mi) Temperature(F) Humidity(%) \\\n", + "0 33.0 -117.1 0.0 40.0 93.0 \n", + "1 29.5 -98.5 0.0 83.0 65.0 \n", + "2 32.7 -96.8 0.0 88.0 57.0 \n", + "\n", + " Visibility(mi) day_of_week_acc Nautical_Twilight season_acc target \\\n", + "0 2.0 3 Day winter 0 \n", + "1 10.0 4 Day summer 1 \n", + "2 10.0 0 Night summer 0 \n", + "\n", + " target_multi year_acc Description \n", + "0 2 2019 At Carmel Mountain Rd - Accident. \n", + "1 3 2017 At TX-345-SP/Woodlawn Ave/Exit 567B - Accident. \n", + "2 2 2021 Incident on RUGGED DR near BERKLEY AVE Expect ... " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " Start_Lat Start_Lng Distance(mi) Temperature(F) Humidity(%) \\\n", - "count 50000.000000 50000.000000 50000.000000 48753.000000 48682.00000 \n", - "mean 37.039702 -98.101712 0.616060 61.274937 64.19005 \n", - "std 5.134426 18.374657 1.756289 18.673054 22.95887 \n", - "min 24.800000 -124.500000 0.000000 -19.000000 3.00000 \n", - "25% 33.900000 -118.200000 0.000000 49.000000 48.00000 \n", - "50% 37.500000 -93.700000 0.000000 63.000000 66.00000 \n", - "75% 40.800000 -81.000000 1.000000 75.000000 83.00000 \n", - "max 49.000000 -67.800000 100.000000 118.000000 100.00000 \n", - "\n", - " Visibility(mi) day_of_week_acc target_multi year_acc \n", - "count 48675.000000 50000.000000 50000.000000 50000.00000 \n", - "mean 9.188290 2.552960 2.294080 2018.49996 \n", - "std 3.063803 1.790681 0.635079 1.70787 \n", - "min 0.000000 0.000000 1.000000 2016.00000 \n", - "25% 10.000000 1.000000 2.000000 2017.00000 \n", - "50% 10.000000 2.000000 2.000000 2018.50000 \n", - "75% 10.000000 4.000000 2.000000 2020.00000 \n", - "max 100.000000 6.000000 4.000000 2021.00000 " - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" + "source": [ + "sampled_data.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6bbefb6b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_acctarget_multiyear_acc
count50000.00000050000.00000050000.00000048753.00000048682.0000048675.00000050000.00000050000.00000050000.00000
mean37.039702-98.1017120.61606061.27493764.190059.1882902.5529602.2940802018.49996
std5.13442618.3746571.75628918.67305422.958873.0638031.7906810.6350791.70787
min24.800000-124.5000000.000000-19.0000003.000000.0000000.0000001.0000002016.00000
25%33.900000-118.2000000.00000049.00000048.0000010.0000001.0000002.0000002017.00000
50%37.500000-93.7000000.00000063.00000066.0000010.0000002.0000002.0000002018.50000
75%40.800000-81.0000001.00000075.00000083.0000010.0000004.0000002.0000002020.00000
max49.000000-67.800000100.000000118.000000100.00000100.0000006.0000004.0000002021.00000
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" + ], + "text/plain": [ + " Start_Lat Start_Lng Distance(mi) Temperature(F) Humidity(%) \\\n", + "count 50000.000000 50000.000000 50000.000000 48753.000000 48682.00000 \n", + "mean 37.039702 -98.101712 0.616060 61.274937 64.19005 \n", + "std 5.134426 18.374657 1.756289 18.673054 22.95887 \n", + "min 24.800000 -124.500000 0.000000 -19.000000 3.00000 \n", + "25% 33.900000 -118.200000 0.000000 49.000000 48.00000 \n", + "50% 37.500000 -93.700000 0.000000 63.000000 66.00000 \n", + "75% 40.800000 -81.000000 1.000000 75.000000 83.00000 \n", + "max 49.000000 -67.800000 100.000000 118.000000 100.00000 \n", + "\n", + " Visibility(mi) day_of_week_acc target_multi year_acc \n", + "count 48675.000000 50000.000000 50000.000000 50000.00000 \n", + "mean 9.188290 2.552960 2.294080 2018.49996 \n", + "std 3.063803 1.790681 0.635079 1.70787 \n", + "min 0.000000 0.000000 1.000000 2016.00000 \n", + "25% 10.000000 1.000000 2.000000 2017.00000 \n", + "50% 10.000000 2.000000 2.000000 2018.50000 \n", + "75% 10.000000 4.000000 2.000000 2020.00000 \n", + "max 100.000000 6.000000 4.000000 2021.00000 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sampled_data.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "61d45a70", + "metadata": {}, + "source": [ + "### Write the sample to disk" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1c8de28d", + "metadata": {}, + "outputs": [], + "source": [ + "# sampled_data.to_csv('US_Accidents_extract.csv', index = False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "eurybia_3_9", + "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": "36c4204cc0170e083c18487e195263df35fcafba9d65a5415ab6b0958d51e154" + } } - ], - "source": [ - "sampled_data.describe()" - ] - }, - { - "cell_type": "markdown", - "id": "61d45a70", - "metadata": {}, - "source": [ - "### Write the sample to disk" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "1c8de28d", - "metadata": {}, - "outputs": [], - "source": [ - "# sampled_data.to_csv('US_Accidents_extract.csv', index = False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "eurybia_3_9", - "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": "36c4204cc0170e083c18487e195263df35fcafba9d65a5415ab6b0958d51e154" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/eurybia/data/project_info_car_accident.yml b/eurybia/data/project_info_car_accident.yml index f9cbb4e..44207e3 100644 --- a/eurybia/data/project_info_car_accident.yml +++ b/eurybia/data/project_info_car_accident.yml @@ -6,13 +6,13 @@ General information: Contributors: Thomas Bouche, Nicolas Roux, Johann Martin Description: Make sure that both datasets has the same properties Source code: https://github.com/MAIF/eurybia - + Dataset information: Path: https://www.kaggle.com/sobhanmoosavi/us-accidents/version/10 Origin: Kaggle datasets Description: Dataset of Car accident to predict severity of accident Depth: accidents between 2016 and 2020 - Perimeter: Samples from both learning and production datasets. + Perimeter: Samples from both learning and production datasets. Target feature: Cible by transforming Severity in two class 0 and 1 Target description: 0 for a less severe accident and 1 for a severe accident @@ -28,4 +28,4 @@ Model training: Hyperparameter optimisazion: None Metrics: Mean Squared Error (MSE) Validation strategy: Train (75%), Test (25%) - Path to script: https://github.com/MAIF/eurybia \ No newline at end of file + Path to script: https://github.com/MAIF/eurybia diff --git a/eurybia/report/common.py b/eurybia/report/common.py index 460c61d..30770be 100644 --- a/eurybia/report/common.py +++ b/eurybia/report/common.py @@ -1,13 +1,16 @@ """ Common functions used in report """ + import os from enum import Enum from numbers import Number from typing import Callable, Dict, Optional, Union import pandas as pd -from pandas.api.types import is_bool_dtype, is_categorical_dtype, is_numeric_dtype, is_string_dtype + +# from pandas.api.types import is_bool_dtype, is_categorical_dtype, is_numeric_dtype, is_string_dtype +from pandas.api.types import infer_dtype, is_numeric_dtype class VarType(Enum): @@ -19,7 +22,7 @@ class VarType(Enum): TYPE_NUM = "Numeric" TYPE_UNSUPPORTED = "Unsupported" - def __str__(self): + def __str__(self) -> str: return str(self.value) @@ -76,11 +79,11 @@ def series_dtype(s: pd.Series) -> VarType: ------- VarType """ - if is_bool_dtype(s): + if infer_dtype(s) == "boolean": return VarType.TYPE_CAT - elif is_string_dtype(s): + elif infer_dtype(s, skipna=True) == "string": return VarType.TYPE_CAT - elif is_categorical_dtype(s): + elif isinstance(s.dtype, pd.CategoricalDtype): return VarType.TYPE_CAT elif is_numeric_dtype(s): if numeric_is_continuous(s): diff --git a/eurybia/report/generation.py b/eurybia/report/generation.py index cdecd0c..82476ca 100644 --- a/eurybia/report/generation.py +++ b/eurybia/report/generation.py @@ -1,45 +1,31 @@ """ Report generation helper module. """ + from datetime import datetime from typing import Optional -import datapane as dp import pandas as pd +import panel as pn +from plotly.graph_objects import Violin from shapash.explainer.smart_explainer import SmartExplainer from eurybia import SmartDrift from eurybia.report.project_report import DriftReport +from eurybia.report.properties import report_css, report_jscallback, report_text, select_callback +pn.extension("plotly") -def _get_index(dr: DriftReport, project_info_file: str, config_report: Optional[dict]) -> dp.Page: - """ - This function generates and returns a Datapane page containing the Eurybia report index - Parameters - ---------- - dr : DriftReport - DriftReport object - project_info_file : str - Path to the file used to display some information about the project in the report. - config_report : dict, optional - Report configuration options. - Returns - ---------- - datapane.Page - """ - - eurybia_logo = """ - - - - """ - - # main block - index_block = [] - - # Title and logo - index_block += [dp.Group(dp.HTML(eurybia_logo), dp.Text(f"# {dr.title_story}"), columns=2)] +def get_index_panel(dr: DriftReport, project_info_file: str, config_report: Optional[dict]) -> pn.Column: + parts = [] + header_logo = pn.pane.PNG( + "https://eurybia.readthedocs.io/en/latest/_images/eurybia-fond-clair.png?raw=true", + styles={"max-width": "150px", "height": "auto"}, + ) + header_title = pn.pane.Markdown(f"# {dr.title_story}") + header = pn.Row(header_logo, header_title) + parts.append(header) if ( config_report is not None @@ -47,24 +33,23 @@ def _get_index(dr: DriftReport, project_info_file: str, config_report: Optional[ and config_report["title_description"] != "" ): raw_title = config_report["title_description"] - index_block += [dp.Text(f"## {raw_title}")] - index_str = "## Eurybia Report contents \n" + parts.append(pn.pane.Markdown(f"## {raw_title}")) - # Tabs index + content_parts = ["## Eurybia Report contents"] if project_info_file is not None: - index_str += "- Project information: report context and information \n" - index_str += "- Consistency Analysis: highlighting differences between the two datasets \n" - index_str += "- Data drift: In-depth data drift analysis \n" - + content_parts.append(report_text["Index"]["01"]) + content_parts.append(report_text["Index"]["02"]) + content_parts.append(report_text["Index"]["03"]) if dr.smartdrift.data_modeldrift is not None: - index_str += "- Model drift: In-depth model drift analysis" - - index_block += [dp.Text(index_str)] + content_parts.append(report_text["Index"]["04"]) + content = pn.pane.Markdown("\n".join(content_parts)) + parts.append(content) # AUC auc_block = dr.smartdrift.plot.generate_indicator( fig_value=dr.smartdrift.auc, height=280, width=500, title="Datadrift classifier AUC" ) + auc_indicator = pn.pane.Plotly(auc_block) # Jensen-Shannon if dr.smartdrift.deployed_model is not None: @@ -76,17 +61,20 @@ def _get_index(dr: DriftReport, project_info_file: str, config_report: Optional[ min_gauge=0, max_gauge=0.2, ) - index_block += [dp.Group(auc_block, JS_block, columns=3)] + js_indicator = pn.pane.Plotly(JS_block) + indicators = pn.Row(auc_indicator, js_indicator) + else: - index_block += [dp.Group(auc_block, columns=2)] + indicators = pn.Row(auc_indicator) + parts.append(indicators) - page_index = dp.Page(title="Index", blocks=index_block) - return page_index + return pn.Column(*parts, name="Index", css_classes=["index"]) -def _dict_to_text_blocks(text_dict, level=1): +def dict_to_text_blocks(text_dict: dict, level: int = 1) -> pn.Column: """ - This function recursively explores the dict and returns a Datapane Group containing other groups and text blocks fed with the dict + This function recursively explores the dict and returns a Panel Column containing + other groups and text blocks fed with the dict Parameters ---------- text_dict: dict @@ -95,257 +83,210 @@ def _dict_to_text_blocks(text_dict, level=1): Recursion level, starting at 1 to allow for direct string manipulation Returns ---------- - datapane.Group - Group of blocks + pn.Column + Column of blocks """ blocks = [] text = "" for k, v in text_dict.items(): if isinstance(v, (str, int, float)) or v is None: - if k.lower() == "date" and v.lower() == "auto": + if k.lower() == "date" and isinstance(v, str) and v.lower() == "auto": v = str(datetime.now())[:-7] text += f"**{k}** : {v} \n" elif isinstance(v, dict): if text != "": - blocks.append(dp.Text(text)) + blocks.append(pn.pane.Markdown(text)) text = "" blocks.append( - dp.Group(dp.Text("#" * min(level, 6) + " " + str(k)), _dict_to_text_blocks(v, level + 1), columns=1) + pn.Column(pn.pane.Markdown("#" * min(level, 6) + " " + str(k)), dict_to_text_blocks(v, level + 1)) ) if text != "": - blocks.append(dp.Text(text)) - return dp.Group(blocks=blocks, columns=1) + blocks.append(pn.pane.Markdown(text)) + return pn.Column(*blocks) -def _get_project_info(dr: DriftReport) -> dp.Page: - """ - This function generates and returns a Datapane page from a dict containing dicts and strings - - Parameters - ---------- - dr : DriftReport - DriftReport object - - Returns - ---------- - datapane.Page - """ +def get_project_information_panel(dr: DriftReport) -> Optional[pn.Column]: if dr.metadata is None: return None - page_info = dp.Page( - title="Project information", - blocks=[_dict_to_text_blocks(dr.metadata)], - ) - return page_info - - -def _get_consistency_analysis(dr: DriftReport) -> dp.Page: - """ - This function generates and returns a Datapane page containing the Eurybia consistency analysis + blocks = dict_to_text_blocks(dr.metadata) + return pn.Column(*blocks, name="Project information", styles=dict(display="none")) - Parameters - ---------- - dr : DriftReport - DriftReport object - Returns - ---------- - datapane.Page - """ +def get_consistency_analysis_panel(dr: DriftReport) -> pn.Column: # Title - blocks = [dp.Text("# Consistency Analysis")] + blocks = [pn.pane.Markdown("# Consistency Analysis")] # Manually ignored coluumns ignore_cols = pd.DataFrame({"ignore_cols": dr.smartdrift.ignore_cols}).rename( columns={"ignore_cols": "Ignored columns"} ) blocks += [ - dp.Text("## Ignored columns in the report (manually excluded)"), + pn.pane.Markdown("## Ignored columns in the report (manually excluded)"), ] if len(ignore_cols) > 0: - blocks += [dp.Table(data=ignore_cols)] + blocks += [pn.pane.DataFrame(ignore_cols)] else: - blocks += [dp.Text("- Ignored columns : None.")] + blocks += [pn.pane.Markdown("- Ignored columns : None.")] # Column mismatches blocks += [ - dp.Text("## Consistency checks: column match between the 2 datasets."), - dp.Text( - """ - The columns identified in this section have been automatically removed from this analysis. - Their presence would always be sufficient for the datadrift classifier to perfectly discriminate the two datasets (maximal data drift, AUC=1). - """ - ), + pn.pane.Markdown("## Consistency checks: column match between the 2 datasets."), + pn.pane.Markdown(report_text["Consistency analysis"]["01"]), ] for k, v in dr.smartdrift.pb_cols.items(): if len(v) > 0: - blocks += [dp.Table(data=pd.DataFrame(v).transpose())] + blocks += [pn.pane.DataFrame(pd.DataFrame(v).transpose())] else: - blocks += [dp.Text(f"- No {k.lower()} have been detected.")] + blocks += [pn.pane.Markdown(f"- No {k.lower()} have been detected.")] blocks += [ - dp.Text("### Unique values identified:"), - dp.Text( - """ - This section displays categorical features in which unique values differ. - This analysis has been performed on unstratified samples of the baseline and current datasets. - Missing or added unique values can be caused by this sampling. - Columns identified in this section have been kept for the analysis. - """ - ), + pn.pane.Markdown("### Unique values identified"), + pn.pane.Markdown(report_text["Consistency analysis"]["02"]), ] if len(dr.smartdrift.err_mods) > 0: blocks += [ - dp.Table( - data=pd.DataFrame(dr.smartdrift.err_mods) + pn.pane.DataFrame( + pd.DataFrame(dr.smartdrift.err_mods) .rename(columns={"err_mods": "Modalities present in one dataset and absent in the other :"}) - .transpose() + .transpose(), ) ] else: - blocks += [dp.Text("- No modalities have been detected as present in one dataset and absent in the other.")] - - page_consistency = dp.Page(title="Consistency Analysis", blocks=blocks) - return page_consistency - + blocks += [ + pn.pane.Markdown("- No modalities have been detected as present in one dataset and absent in the other.") + ] -def _get_datadrift(dr: DriftReport) -> dp.Page: - """ - This function generates and returns a Datapane page containing the Eurybia data drift analysis + return pn.Column(*blocks, name="Consistency Analysis", styles=dict(display="none"), css_classes=["information"]) - Parameters - ---------- - dr : DriftReport - DriftReport object - Returns - ---------- - datapane.Page - """ - # Loop for save in list plots of display analysis - plot_dataset_analysis = [] - table_dataset_analysis = [] - fig_list, labels, table_list = dr.display_dataset_analysis(global_analysis=False)["univariate"] +def get_select_plots(labels: list, key: str, tab: str, figures: list) -> list: + blocks = [] + select = pn.widgets.Select(value=labels[0], options=labels) + select.jscallback(args={"key": f".{key}", "tab": tab}, value=select_callback) + blocks += [select] for i in range(len(labels)): - plot_dataset_analysis.append(dp.Plot(fig_list[i], label=labels[i])) - table_dataset_analysis.append(dp.Table(table_list[i], label=labels[i])) - - # Loop for save in list plots of display analysis - plot_datadrift_contribution = [] - fig_list, labels = dr.display_model_contribution() + f_class = labels[i].replace(" ", "-").lower() + css_classes = [f_class, key] + if labels[i] != labels[0]: + css_classes.append("hidden") + for figure_trace in figures[i].data: + if isinstance(figure_trace, Violin): + figure_trace.update(side="both") + figures[i].update_layout(width=1240) + node = pn.pane.Plotly(figures[i], name=labels[i], css_classes=css_classes) + blocks += [node] + return blocks + + +def get_select_tables(labels: list, key: str, tab: str, tables: list) -> list: + blocks = [] + select = pn.widgets.Select(value=labels[0], options=labels) + select.jscallback(args={"key": f".{key}", "tab": tab}, value=select_callback) + blocks += [select] for i in range(len(labels)): - plot_datadrift_contribution.append(dp.Plot(fig_list[i], label=labels[i])) + f_class = labels[i].replace(" ", "-").lower() + css_classes = [f_class, key] + if i > 0: + css_classes.append("hidden") + node = pn.pane.DataFrame(tables[i], css_classes=css_classes) + blocks += [node] + return blocks + + +def get_data_drift_panel(dr: DriftReport) -> pn.Column: blocks = [ - dp.Text("# Data drift"), - dp.Text( - """The data drift detection methodology is based on the ability of a model classifier to identify whether - a sample belongs to one or another dataset. - For this purpose a target (0) is assigned to the baseline dataset and a second target (1) to the current dataset. - A classification model (catboost) is trained to predict this target. - As such, the data drift classifier performance is directly related to the difference between two datasets. - A marked difference will lead to an easy classification (final AUC close to 1). - Oppositely, highly similars datasets will lead to poor data drift classifier performance (final AUC close to 0.5).""" - ), - dp.Text("## Detecting data drift"), - dp.Text("### Datadrift classifier model perfomances"), - dp.Text( - """The closer your AUC is from 0.5 the less your data drifted. - The closer your AUC is from 1 the more your data drifted""" - ), - dp.Plot( - dr.smartdrift.plot.generate_indicator( - fig_value=dr.smartdrift.auc, height=300, width=500, title="Datadrift classifier AUC" - ) - ), - dp.Text("## Importance of features in data drift"), - dp.Text("### Global feature importance plot"), - dp.Text( - """Bar chart representing the feature importance of each feature for the datadrift classifier. - This parameter is a direct measure of the importance of a feature to perform the classification.""" - ), - dp.Plot(dr.explainer.plot.features_importance()), + pn.pane.Markdown("# Data drift"), + pn.pane.Markdown(report_text["Data drift"]["01"]), + pn.pane.Markdown("## Detecting data drift"), + pn.pane.Markdown("### Datadrift classifier model perfomances"), + pn.pane.Markdown(report_text["Data drift"]["02"]), ] + auc = dr.smartdrift.plot.generate_indicator( + fig_value=dr.smartdrift.auc, height=300, width=500, title="Datadrift classifier AUC" + ) + blocks += [pn.pane.Plotly(auc)] + + blocks += [ + pn.pane.Markdown("## Importance of features in data drift"), + pn.pane.Markdown("### Global feature importance plot"), + pn.pane.Markdown(report_text["Data drift"]["03"]), + ] + + fig_features_importance = dr.explainer.plot.features_importance() + fig_features_importance.update_layout(width=1240) + blocks += [pn.pane.Plotly(fig_features_importance)] if dr.smartdrift.deployed_model is not None: + fig_scatter_feature_importance = dr.smartdrift.plot.scatter_feature_importance() + fig_scatter_feature_importance.update_layout(width=1240) blocks += [ - dp.Text("### Feature importance overview"), - dp.Text( - """Scatter plot depicting, for each feature, the feature importance of the deployed model as a function of the datadrift classifier - feature importance. This graph thus highlight the real importance of a data drift for the deployed model classification. - Interpretation based on graphical feature location: - - Top left : Feature highly important for the deployed model and with low data drift - - Bottom left : Feature with moderated importance for the deployed model and with low data drift - - Bottom right : Feature with moderated importance for the deployed model but with high data drift. - This feature might require your attention. - - Top right : Feature highly important for the deployed model and high drift. This feature requires your attention. - """ - ), - dp.Plot(dr.smartdrift.plot.scatter_feature_importance()), + pn.pane.Markdown("### Feature importance overview"), + pn.pane.Markdown(report_text["Data drift"]["04"]), + pn.pane.Plotly(fig_scatter_feature_importance), ] + blocks += [ - dp.Text("## Dataset analysis"), - dp.Text( - """This section provides numerical and graphical analysis of the 2 datasets distributions, - making easier the study of the most important variable for drift detection.""" - ), - dp.Text("### Global analysis"), - dp.Table(dr._display_dataset_analysis_global()), - dp.Text("### Univariate analysis"), - dp.Text( - """Bar chart showing the unique values distribution of a feature. - Using the drop-down menu, it is possible to select the feature of interest. - Features are sorted according to their respective importance in the datadrift classifier. - For categorical features, the possible values are sorted by descending difference between the two datasets.""" - ), - dp.Select(blocks=plot_dataset_analysis, type=dp.SelectType.DROPDOWN), - dp.Select(blocks=table_dataset_analysis, type=dp.SelectType.DROPDOWN), + pn.pane.Markdown("## Dataset analysis"), + pn.pane.Markdown(report_text["Data drift"]["05"]), + pn.pane.Markdown("### Global analysis"), + pn.pane.DataFrame(dr._display_dataset_analysis_global()), + pn.pane.Markdown("### Univariate analysis"), + pn.pane.Markdown(report_text["Data drift"]["07"]), ] + contribution_figures, contribution_labels = dr.display_model_contribution() + + distribution_figures, labels, distribution_tables = dr.display_dataset_analysis(global_analysis=False)["univariate"] + distribution_plots_blocks = get_select_plots( + labels=labels, key="distribution-plot", tab=".data-drift", figures=distribution_figures + ) + blocks += distribution_plots_blocks + distribute_tables_blocks = get_select_tables( + labels=labels, key="distribution-table", tab=".data-drift", tables=distribution_tables + ) + blocks += distribute_tables_blocks + if dr.smartdrift.deployed_model is not None: + fig_01 = dr.smartdrift.plot.generate_fig_univariate(df_all=dr.smartdrift.df_predict, col="Score", hue="dataset") + fig_01.update_layout(width=1240) blocks += [ - dp.Text("### Distribution of predicted values"), - dp.Text( - "Histogram density showing the distributions of the production model outputs on both baseline and current datasets." - ), - dp.Plot( - dr.smartdrift.plot.generate_fig_univariate(df_all=dr.smartdrift.df_predict, col="Score", hue="dataset") - ), - dp.Text( - """Jensen Shannon Divergence (JSD). The JSD measures the effect of a data drift on the deployed model performance. - A value close to 0 indicates similar data distributions, while a value close to 1 tend to indicate distinct data distributions - with a negative effect on the deployed model performance.""" - ), - dr.smartdrift.plot.generate_indicator( - fig_value=dr.smartdrift.js_divergence, - height=280, - width=500, - title="Jensen Shannon Datadrift", - min_gauge=0, - max_gauge=0.2, - ), + pn.pane.Markdown("### Distribution of predicted values"), + pn.pane.Markdown(report_text["Data drift"]["06"]), + pn.pane.Plotly(fig_01), + pn.pane.Markdown(report_text["Data drift"]["08"]), ] + js_fig = dr.smartdrift.plot.generate_indicator( + fig_value=dr.smartdrift.js_divergence, + height=280, + width=500, + title="Jensen Shannon Datadrift", + min_gauge=0, + max_gauge=0.2, + ) + blocks += [pn.pane.Plotly(js_fig)] blocks += [ - dp.Text("## Feature contribution on data drift's detection"), - dp.Text( - """This graph represents the contribution of a variable to the data drift detection. - This representation constitutes a support to understand the drift when the analysis of the dataset is unclear. - In the drop-down menu, features are sorted by importance in the data drift detection.""" - ), - dp.Select(blocks=plot_datadrift_contribution, type=dp.SelectType.DROPDOWN), + pn.pane.Markdown("## Feature contribution on data drift's detection"), + pn.pane.Markdown(report_text["Data drift"]["09"]), ] + contribution_plots_blocks = get_select_plots( + labels=contribution_labels, + key="contribution-plot", + tab=".data-drift", + figures=contribution_figures, + ) + blocks += contribution_plots_blocks if dr.smartdrift.historical_auc is not None: + fig = dr.smartdrift.plot.generate_historical_datadrift_metric() + fig.update_layout(width=1240) blocks += [ - dp.Text("## Historical Data drift"), - dp.Text( - "Line chart showing the metrics evolution of the datadrift classifier over the given period of time." - ), - dp.Plot(dr.smartdrift.plot.generate_historical_datadrift_metric()), + pn.pane.Markdown("## Historical Data drift"), + pn.pane.Markdown(report_text["Data drift"]["10"]), + pn.pane.Plotly(fig), ] - page_datadrift = dp.Page(title="Data drift", blocks=blocks) - return page_datadrift + return pn.Column(*blocks, name="Data drift", styles=dict(display="none"), css_classes=["data-drift"]) -def _get_modeldrift(dr: DriftReport) -> dp.Page: +def get_model_drift_panel(dr: DriftReport) -> pn.Column: """ - This function generates and returns a Datapane page containing the Eurybia model drift analysis + This function generates and returns a Panel Column page containing the Eurybia model drift analysis Parameters ---------- @@ -354,33 +295,28 @@ def _get_modeldrift(dr: DriftReport) -> dp.Page: Returns ---------- - datapane.Page + pn.Column """ - # Loop for save in list plots of display model drift - if dr.smartdrift.data_modeldrift is not None: - plot_modeldrift = [] - fig_list, labels = dr.display_data_modeldrift() - if labels == []: - plot_modeldrift = dp.Plot(fig_list[0]) - modeldrift_plot = plot_modeldrift - else: - for i in range(len(labels)): - plot_modeldrift.append(dp.Plot(fig_list[i], label=labels[i])) - modeldrift_plot = dp.Select(blocks=plot_modeldrift, label="reference_columns", type=dp.SelectType.DROPDOWN) - else: - modeldrift_plot = dp.Text("## Smartdrift.data_modeldrift is None") + blocks = [ - dp.Text("# Model drift"), - dp.Text( - """This section provides support to monitor the production model's performance over time. - This requires the performance history as input.""" - ), - dp.Text("## Performance evolution of the deployed model"), - dp.Text("Line chart of deployed model performances as a function of time"), - modeldrift_plot, + pn.pane.Markdown("# Model drift"), + pn.pane.Markdown(report_text["Model drift"]["01"]), + pn.pane.Markdown("## Performance evolution of the deployed model"), + pn.pane.Markdown(report_text["Model drift"]["02"]), ] - page_modeldrift = dp.Page(title="Model drift", blocks=blocks) - return page_modeldrift + + if dr.smartdrift.data_modeldrift is None: + blocks += [pn.pane.Markdown("## Smartdrift.data_modeldrift is None")] + else: + figures, labels = dr.display_data_modeldrift() + if labels == []: + figures[0].update_layout(width=1240) + blocks += [pn.pane.Plotly(figures[0])] + else: + list_blocks = get_select_plots(labels=labels, key="modeldrift-plot", tab=".model-drift", figures=figures) + blocks += list_blocks + + return pn.Column(*blocks, name="Model drift", styles=dict(display="none"), css_classes=["model-drift"]) def execute_report( @@ -388,8 +324,8 @@ def execute_report( explainer: SmartExplainer, project_info_file: str, output_file: str, - config_report: Optional[dict] = None, -): + config_report: Optional[dict] = {}, +) -> None: """ Creates the report @@ -406,25 +342,23 @@ def execute_report( output_file : str Path to the HTML file to write """ - - if config_report is None: - config_report = {} - dr = DriftReport( smartdrift=smartdrift, - explainer=explainer, # rename to match kwarg + explainer=explainer, project_info_file=project_info_file, config_report=config_report, ) - pages = [] - pages.append(_get_index(dr, project_info_file, config_report)) + tab_list = [] + tab_list.append(get_index_panel(dr, project_info_file, config_report)) if project_info_file is not None: - pages.append(_get_project_info(dr)) - pages.append(_get_consistency_analysis(dr)) - pages.append(_get_datadrift(dr)) + tab_list.append(get_project_information_panel(dr)) + tab_list.append(get_consistency_analysis_panel(dr)) + tab_list.append(get_data_drift_panel(dr)) if dr.smartdrift.data_modeldrift is not None: - pages.append(_get_modeldrift(dr)) + tab_list.append(get_model_drift_panel(dr)) - report = dp.View(blocks=pages) - dp.save_report(report, path=output_file, open=False, name="report.html") + pn.config.raw_css.append(report_css) + report = pn.Tabs(*tab_list, css_classes=["main-report"]) + report.jscallback(args={"active": report}, active=report_jscallback) + report.save(output_file, embed=True) diff --git a/eurybia/report/project_report.py b/eurybia/report/project_report.py index d1b9780..e7b838c 100644 --- a/eurybia/report/project_report.py +++ b/eurybia/report/project_report.py @@ -1,6 +1,7 @@ """ Module used in the base_report notebook to generate report """ + import copy import logging import os @@ -127,12 +128,16 @@ def _create_data_drift( return None return pd.concat( [ - df_current.assign(data_drift_split=dataset_names["df_current"].values[0]) - if df_current is not None - else None, - df_baseline.assign(data_drift_split=dataset_names["df_baseline"].values[0]) - if df_baseline is not None - else None, + ( + df_current.assign(data_drift_split=dataset_names["df_current"].values[0]) + if df_current is not None + else None + ), + ( + df_baseline.assign(data_drift_split=dataset_names["df_baseline"].values[0]) + if df_baseline is not None + else None + ), ] ).reset_index(drop=True) @@ -221,12 +226,10 @@ def _perform_and_display_analysis_univariate( except BaseException as e: raise Exception( - """ - There is a problem with the format of {} column between the two datasets. + f""" + There is a problem with the format of {str(col)} column between the two datasets. Error: - """.format( - str(col) - ) + """ + str(e) ) return plot_list, labels, table_list @@ -247,11 +250,10 @@ def display_model_contribution(self): Displays explainability of the model as computed in SmartPlotter object """ multiclass = True if (self.explainer._classes and len(self.explainer._classes) > 2) else False - c_list = self.explainer._classes if multiclass else [0, 1] # list just used for multiclass + c_list = self.explainer._classes if multiclass else [1] # list just used for multiclass plot_list = [] labels = [] for index_label, label in enumerate(c_list): # Iterating over all labels in multiclass case - for feature in self.features_imp_list: fig = self.explainer.plot.contribution_plot(feature, label=label, max_points=200) plot_list.append(fig) diff --git a/eurybia/report/properties.py b/eurybia/report/properties.py new file mode 100644 index 0000000..06e8c66 --- /dev/null +++ b/eurybia/report/properties.py @@ -0,0 +1,261 @@ +from typing import Any, Dict + +report_text: Dict[str, Any] = { + "Index": { + "01": "- Project information: report context and information", + "02": "- Consistency Analysis: highlighting differences between the two datasets", + "03": "- Data drift: In-depth data drift analysis", + "04": "- Model drift: In-depth model drift analysis", + }, + "Consistency analysis": { + "01": ( + "The columns identified in this section have been automatically removed from this analysis. " + "Their presence would always be sufficient for the datadrift classifier to perfectly discriminate " + "the two datasets (maximal data drift, AUC=1)." + ), + "02": ( + "This section displays categorical features in which unique values differ. " + "This analysis has been performed on unstratified samples of the baseline and current datasets. " + "Missing or added unique values can be caused by this sampling. " + "Columns identified in this section have been kept for the analysis." + ), + }, + "Data drift": { + "01": ( + "The data drift detection methodology is based on the ability of a model classifier to identify " + "whether a sample belongs to one or another dataset. For this purpose a target (0) is assigned " + "to the baseline dataset and a second target (1) to the current dataset. " + "A classification model (catboost) is trained to predict this target. As such, the data drift " + "classifier performance is directly related to the difference between two datasets. A marked difference " + " will lead to an easy classification (final AUC close to 1). Oppositely, highly similars datasets " + " will lead to poor data drift classifier performance (final AUC close to 0.5)." + ), + "02": ( + "The closer your AUC is from 0.5 the less your data drifted. " + "The closer your AUC is from 1 the more your data drifted." + ), + "03": ( + "Bar chart representing the feature importance of each feature for the datadrift classifier. " + "This parameter is a direct measure of the importance of a feature to perform the classification." + ), + "04": ( + "Scatter plot depicting, for each feature, the feature importance of the deployed model " + "as a function of the datadrift classifier " + "feature importance. This graph thus highlight the real importance of a data drift " + "for the deployed model classification. " + "Interpretation based on graphical feature location:\n" + "- Top left : Feature highly important for the deployed model and with low data drift\n" + "- Bottom left : Feature with moderated importance for the deployed model and with low data drift.\n" + "- Bottom right : Feature with moderated importance for the deployed model but with high data drift. " + "This feature might require your attention.\n" + "- Top right : Feature highly important for the deployed model and high drift. " + "This feature requires your attention." + ), + "05": ( + "This section provides numerical and graphical analysis of the 2 datasets distributions, " + "making easier the study of the most important variable for drift detection." + ), + "06": ( + "Histogram density showing the distributions of the production model outputs on " + "both baseline and current datasets." + ), + "08": ( + "Jensen Shannon Divergence (JSD). " + "The JSD measures the effect of a data drift on the deployed model performance. " + "A value close to 0 indicates similar data distributions, while a value close to 1 " + "tend to indicate distinct data distributions with a negative effect on the deployed model performance." + ), + "07": ( + "Bar chart showing the unique values distribution of a feature. " + "Using the drop-down menu, it is possible to select the feature of interest. " + "Features are sorted according to their respective importance in the datadrift classifier. " + "For categorical features, the possible values are sorted by descending difference " + "between the two datasets." + ), + "09": ( + "This graph represents the contribution of a variable to the data drift detection. " + "This representation constitutes a support to understand the drift " + "when the analysis of the dataset is unclear." + ), + "10": ("Line chart showing the metrics evolution of the datadrift classifier over the given period of time."), + }, + "Model drift": { + "01": ( + "This section provides support to monitor the production model's performance over time. " + "This requires the performance history as input." + ), + "02": ("Line chart of deployed model performances as a function of time."), + }, +} + +report_css: str = """ +.bk-tab { + background-color: white !important; + font-size: 0.875rem; + padding-top: 1em; + padding-bottom: 1em; + border-width: 3px !important; + border-style: solid !important; + border-color: white !important; + color: #6b7280; + margin-left: 1.5em; + margin-right: 1.5em; + text-align: left; +} + +.bk-tab.bk-active { + border-bottom-color: #4e46e5 !important; + color: #101010; +} + +.bk-header { + position: sticky; + top: 0; + z-index: 100; + width: 100%; + background-color: white; +} + +:host(.bk-above) .bk-tab:first-child { + margin-left: 5rem !important; +} + +.bk-above { + width: 100%; +} + +.bk-Row { + width: 100%; + display: grid; + grid-template-columns: auto auto; +} + +.bk-panel-models-layout-Column { + margin-left: auto; + margin-right: auto; + width: 100%; + max-width: 1280px; + padding-left: 1rem; + padding-right: 1rem; +} + +.bk-panel-models-markup-HTML { + width: 100%; +} + +.bk-panel-models-plotly-PlotlyPlot { + width: 100%; + display: flex; + justify-content: center; +} + +h1 { + font-size: 2.25rem; + line-height: 1.33; + font-weight: 700; +} + +h2 { + font-size: 1.5rem; + line-height: 1.33; + font-weight: 700; +} + +h3 { + font-size: 1.25rem; + line-height: 1.6; + font-weight: 600; +} + +h4 { + font-size: 1rem; + line-height: 1.6; + font-weight: 600; +} + +ul { + padding-left: 1.625em; +} + +li { + font-size: 1rem; + line-height: 1.75; + padding-left: 0.375em; + margin-top: 0.5em; + margin-bottom: 0.5em; +} + +p { + font-size: 1rem; +} + +th { + vertical-align: bottom; + text-align: center !important; + font-weight: 600; + font-size: 1rem; +} + +td { + vertical-align: top !important; + text-align: center !important; + font-size: 1rem; +} + +select { + font-size: 1rem; + font-weight: 700; +} + + +.hidden { + display: none; +} + +""" + +report_jscallback: str = """ +console.log("callback called"); +var active_tab = active.active; +var top = document.getElementsByTagName('div')[1]; +var elements = top.shadowRoot.children; +var current_tab = 0; +for (var i=0 ; i dict: @@ -16,7 +17,7 @@ def ksmirnov_test(obs_a: np.array, obs_b: np.array) -> dict: obs_a : np.array 1D array containing the feature values in the first sample obs_b : np.array - 1D array containing the feature values ​​in the second sample + 1D array containing the feature values \u200b\u200bin the second sample Returns ------- @@ -37,7 +38,7 @@ def chisq_test(obs_a: np.array, obs_b: np.array) -> dict: obs_a : np.array 1D array containing the feature values in the first sample obs_b : np.array - 1D array containing the feature values ​​in the second sample + 1D array containing the feature values \u200b\u200bin the second sample Returns ------- @@ -58,11 +59,10 @@ def chisq_test(obs_a: np.array, obs_b: np.array) -> dict: return output - def prob_mass_fun(data, n, range): """ Computing the probability mass function using NumPy’s histogram. - + Parameters ---------- data: pandas.Series @@ -100,7 +100,7 @@ def compute_js_divergence(df_1, df_2, n_bins=30): and 1 (The two score distributions maximally different). """ a = np.concatenate((df_1, df_2), axis=0) - e, p = prob_mass_fun(df_1, n = n_bins, range = (a.min(), a.max())) - _, q = prob_mass_fun(df_2, n = e, range = (a.min(), a.max())) + e, p = prob_mass_fun(df_1, n=n_bins, range=(a.min(), a.max())) + _, q = prob_mass_fun(df_2, n=e, range=(a.min(), a.max())) return scipy.spatial.distance.jensenshannon(p, q) diff --git a/eurybia/utils/utils.py b/eurybia/utils/utils.py index 2d7f693..930e25c 100644 --- a/eurybia/utils/utils.py +++ b/eurybia/utils/utils.py @@ -1,6 +1,7 @@ """ Utils is a group of function for the library """ + from pathlib import Path import pandas as pd diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..ed9c707 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,99 @@ +[build-system] +requires = ["setuptools", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "eurybia" +version = "1.2.0" +authors = [ + {name = "Nicolas Roux"}, + {name = "Thomas Bouché", email = "thomas.bouche@maif.fr"}, + {name = "Johann Martin"}, +] +description = "Eurybia monitor model drift over time and securize model deployment with data validation" +readme = "README.md" +requires-python = ">=3.8" +license = {text = "Apache Software License 2.0"} +classifiers = [ + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "License :: OSI Approved :: Apache Software License", + "Operating System :: OS Independent", +] +dependencies = [ + "pandas", + "catboost>=1.0.1", + "panel>=1.4.1", + "ipywidgets>=7.4.2", + "jinja2>=2.11.0", + "scipy>=1.4.0", + "seaborn>=0.10.1", + "shapash>=2.0.0", + "jupyter", + "plotly", +] + +[project.optional-dependencies] # Optional +all = ["eurybia[dev, test, mypy, ruff]"] +dev = ["pre-commit", "mypy", "ruff"] +test = ["pytest", "pytest-cov"] +mypy = ["mypy"] +ruff = ["ruff"] + + +[tool.setuptools] +package-dir = {"" = "."} + +[tool.setuptools.packages.find] +where = ["."] + + +[tool.setuptools.package-data] +"eurybia" = ["*.csv", "*json", "*.yml", "*.css", "*.js", "*.png"] + +[tool.pytest.ini_options] +testpaths = ["tests"] + +[tool.mypy] +exclude = ["tests", "tutorial"] +ignore_missing_imports = true + +[tool.ruff] +line-length = 120 +exclude = [ + "tests", + "docs", + "tutorial", +] + +[tool.ruff.lint] +select = [ + "E", # pycodestyle errors + "F", # pyflakes + "W", # pycodestyle warnings + "A", # flake8-builtins + "PLC", # pylint conventions + "PLE", # pylint errors + "PLW", # pylint warnings + "UP", # pyupgrade + "S", # flake8-bandit, + "B", # flake8-bugbear + "I", # isort + "D", # pydocstyle + "NPY", # NumPy-specific rules +] +ignore = ["E501", "D2", "D3", "D4", "D104", "D100", "D106", "S311"] +exclude = ["tests/*"] + +[tool.ruff.lint.per-file-ignores] +"eurybia/core/smartdrift.py" = ["S301", "B006", "B008", "B904"] +"eurybia/core/smartplotter.py" = ["D107", "B006"] +"eurybia/data/data_loader.py" = ["S310"] +"eurybia/report/common.py" = ["D105", "S301"] +"eurybia/report/generation.py" = ["D103", "PLW2901", "B006"] +"eurybia/report/project_report.py" = ["S701", "B904", "B007"] +"eurybia/utils/io.py" = ["S301"] +"eurybia/utils/statistical_tests.py" = ["A002"] +"eurybia/utils/utils.py" = ["UP031"] diff --git a/requirements.dev.txt b/requirements.dev.txt index 108db69..7282767 100644 --- a/requirements.dev.txt +++ b/requirements.dev.txt @@ -3,7 +3,7 @@ catboost>=1.0.1 category-encoders>=2.6.0 lightgbm>=2.3.1 numpy>=1.18.0 -pandas>=1.0.2 +pandas plotly>=4.12.0 shapash>=2.0.0 Sphinx==4.5.0 @@ -28,5 +28,5 @@ seaborn>=0.12.2 notebook>=6.0.0 Jinja2>=2.11.0 scipy>=1.1.0 -datapane>=0.16.7 +panel>=1.4.1 pre-commit==2.18.1 diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 8b2c055..0000000 --- a/setup.cfg +++ /dev/null @@ -1,18 +0,0 @@ -[bdist_wheel] -universal = 1 - -[flake8] -exclude = docs -max-line-length = 100 - -[aliases] -test = pytest - -[tool:pytest] -collect_ignore = ['setup.py'] -pep8maxlinelength = 100 -filterwarnings = - ignore::DeprecationWarning - -[pep8] -max-line-length = 100 diff --git a/setup.py b/setup.py deleted file mode 100755 index b142d0a..0000000 --- a/setup.py +++ /dev/null @@ -1,94 +0,0 @@ -#!/usr/bin/env python - -"""The setup script.""" -import os - -from setuptools import setup - -here = os.path.abspath(os.path.dirname(__file__)) - -with open("README.md", encoding="utf8") as readme_file: - long_description = readme_file.read() - -# Load the package's __version__.py module as a dictionary. -version_d: dict = {} -with open(os.path.join(here, "eurybia", "__version__.py")) as f: - exec(f.read(), version_d) - -requirements = [ - "catboost>=1.0.1", - "datapane>=0.16.7", - "ipywidgets>=7.4.2", - "jinja2>=2.11.0", - "scipy>=1.4.0", - "seaborn>=0.10.1", - "shapash>=2.0.0", - "jupyter", -] - - -setup_requirements = [ - "pytest-runner", -] - -test_requirements = [ - "pytest", -] - -setup( - name="eurybia", # Replace with your own username - version=version_d["__version__"], - python_requires=">3.7, < 3.11", - url="https://github.com/MAIF/eurybia", - author="Nicolas Roux, Johann Martin, Thomas Bouché", - author_email="thomas.bouche@maif.fr", - description="Eurybia monitor model drift over time and securize model deployment with data validation", - long_description=long_description, - long_description_content_type="text/markdown", - classifiers=[ - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "License :: OSI Approved :: Apache Software License", - "Operating System :: OS Independent", - ], - install_requires=requirements, - license="Apache Software License 2.0", - keywords="eurybia", - package_dir={ - "eurybia": "eurybia", - "eurybia.data": "eurybia/data", - "eurybia.core": "eurybia/core", - "eurybia.report": "eurybia/report", - "eurybia.assets": "eurybia/assets", - "eurybia.style": "eurybia/style", - "eurybia.utils": "eurybia/utils", - }, - packages=["eurybia", "eurybia.data", "eurybia.core", "eurybia.report", "eurybia.style", "eurybia.utils"], - data_files=[ - ("data", ["eurybia/data/house_prices_dataset.csv"]), - ("data", ["eurybia/data/house_prices_labels.json"]), - ("data", ["eurybia/data/titanicdata.csv"]), - ("data", ["eurybia/data/project_info_car_accident.yml"]), - ("data", ["eurybia/data/project_info_house_price.yml"]), - ("data", ["eurybia/data/project_info_titanic.yml"]), - ("data", ["eurybia/data/titanic_altered.csv"]), - ("data", ["eurybia/data/titanic_original.csv"]), - ("data", ["eurybia/data/US_Accidents_extract.csv"]), - ("style", ["eurybia/style/colors.json"]), - ( - "assets", - [ - "eurybia/assets/local-report-base.css", - "eurybia/assets/local-report-base.js", - "eurybia/assets/logo_eurybia_dp.png", - ], - ), - ], - include_package_data=True, - setup_requires=setup_requirements, - test_suite="tests", - tests_require=test_requirements, - zip_safe=False, -) diff --git a/tests/integration_tests/test_report_generation.py b/tests/integration_tests/test_report_generation.py index d463c7f..c29007a 100644 --- a/tests/integration_tests/test_report_generation.py +++ b/tests/integration_tests/test_report_generation.py @@ -9,7 +9,6 @@ import pandas as pd from category_encoders import OrdinalEncoder -from datapane.client import config from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split @@ -24,11 +23,10 @@ class TestGeneration(unittest.TestCase): Unit test generation.py """ - def setUp(self): + def setUp(self) -> None: """ Initialize data for testing part """ - config.init() script_path = Path(path.abspath(__file__)).parent.parent.parent titanic_original = path.join(script_path, "eurybia/data/titanicdata.csv") titan_df = pd.read_csv(titanic_original, index_col=0) @@ -50,13 +48,13 @@ def setUp(self): self.xpl = smartdrift.xpl self.script_path = script_path - def tearDown(self): + def tearDown(self) -> None: """ method that tidies up after the test method has been run """ os.remove("./report.html") - def test_execute_report_1(self): + def test_execute_report_1(self) -> None: """ Test execute_report() method """ @@ -69,7 +67,7 @@ def test_execute_report_1(self): assert os.path.exists("./report.html") - def test_execute_report_2(self): + def test_execute_report_2(self) -> None: """ Test execute_report() method """ @@ -83,7 +81,7 @@ def test_execute_report_2(self): assert os.path.exists("./report.html") - def test_execute_report_3(self): + def test_execute_report_3(self) -> None: """ Test execute_report() method """ @@ -97,7 +95,7 @@ def test_execute_report_3(self): assert os.path.exists("./report.html") - def test_execute_report_modeldrift_1(self): + def test_execute_report_modeldrift_1(self) -> None: """ Test execute_report() method """ @@ -113,26 +111,37 @@ def test_execute_report_modeldrift_1(self): assert os.path.exists("./report.html") - def test_execute_report_modeldrift_2(self): + def test_execute_report_modeldrift_2(self) -> None: """ Test execute_report() method """ + + import random + + annees = [2020, 2020, 2021, 2021, 2021] * 6 + mois = [11, 12, 1, 2, 3] * 6 + historical_range = [3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1] * 2 + metric = ["lift_devis"] * 15 + ["lift_souscription"] * 15 + metric_value = [random.uniform(3.0, 7.0) for _ in range(30)] + df_perf2 = pd.DataFrame( { - "mois": [1, 2, 3], - "annee": [2018, 2019, 2020], - "performance": [2, 3.46, 2.5], - "reference_column1": [1, 2, 3], - "reference_column2": ["2018", "2019", "2020"], + "annee": annees, + "mois": mois, + "historical_range": historical_range, + "metric": metric, + "metric_value": metric_value, } ) self.smartdrift.add_data_modeldrift( - dataset=df_perf2, metric="performance", reference_columns=["reference_column1", "reference_column2"] + dataset=df_perf2, metric="metric_value", reference_columns=["historical_range", "metric"] ) + script_path = Path(path.abspath(__file__)).parent.parent.parent + titanic_original = path.join(script_path, "eurybia/data/project_info_titanic.yml") execute_report( smartdrift=self.smartdrift, explainer=self.xpl, - project_info_file=os.path.join(current_path, "../data/project_info.yml"), + project_info_file=titanic_original, output_file="./report.html", config_report=dict(title_story="Test integration", title_description="Title of test integration"), ) diff --git a/tutorial/Eurybia_report_example.py b/tutorial/Eurybia_report_example.py index 85b5ec5..43a33c0 100644 --- a/tutorial/Eurybia_report_example.py +++ b/tutorial/Eurybia_report_example.py @@ -12,7 +12,7 @@ from category_encoders import OrdinalEncoder from sklearn import metrics from sklearn.model_selection import train_test_split - + sys.path.insert(0, "../..") from eurybia import SmartDrift diff --git a/tutorial/common/tuto-common01-colors.ipynb b/tutorial/common/tuto-common01-colors.ipynb index 76871e2..76d9014 100644 --- a/tutorial/common/tuto-common01-colors.ipynb +++ b/tutorial/common/tuto-common01-colors.ipynb @@ -1,634 +1,634 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "fb5b09ec", - "metadata": {}, - "source": [ - "# Eurybia with custom colors\n", - "\n", - "With this tutorial, you will understand how to manipulate colors with Eurybia plots\n", - "\n", - "Contents:\n", - "- Compile Eurybia SmartDrift\n", - "- Use `palette_name` parameter\n", - "- Use `colors_dict` parameter\n", - "- Change the colors after compiling SmartDrift\n", - "\n", - "Data from Kaggle [House Prices](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)" - ] - }, - { - "cell_type": "markdown", - "id": "1eed0849", - "metadata": {}, - "source": [ - "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", - "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e001c39b", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from category_encoders import OrdinalEncoder\n", - "from lightgbm import LGBMRegressor\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "markdown", - "id": "9491888b", - "metadata": {}, - "source": [ - "## Building a Supervized Model" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1e6be6b2", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading\n", - "house_df, house_dict = data_loading('house_prices')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b8cb0847", - "metadata": {}, - "outputs": [], - "source": [ - "#For the purpose of this tutorial and to better represent a common use case of Eurybia, \n", - "#the house_prices dataset was split in two smaller sets : \"training\" and \"production\"\n", - "# To see an interesting analysis, let's test for a bias in the date of construction of training and production dataset\n", - "house_df_learning = house_df.loc[house_df['YearBuilt'] < 1980]\n", - "house_df_production = house_df.loc[house_df['YearBuilt'] >= 1980]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9071ee71", - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", - "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YearBuilt'])]\n", - "\n", - "y_df_production=house_df_production['SalePrice'].to_frame()\n", - "X_df_production=house_df_production[house_df_production.columns.difference(['SalePrice','YearBuilt'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c0ec51bb", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d58a1cf8", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "5b0dd030", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "The variable BldgType has mismatching unique values:\n", - "[] | ['Two-family Conversion; originally built as one-family dwelling']\n", - "\n", - "The variable BsmtCond has mismatching unique values:\n", - "[] | ['Poor -Severe cracking, settling, or wetness']\n", - "\n", - "The variable CentralAir has mismatching unique values:\n", - "[] | ['No']\n", - "\n", - "The variable Condition1 has mismatching unique values:\n", - "[\"Within 200' of East-West Railroad\"] | ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", - "\n", - "The variable Condition2 has mismatching unique values:\n", - "['Near positive off-site feature--park, greenbelt, etc.'] | ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "[] | ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "\n", - "The variable ExterCond has mismatching unique values:\n", - "[] | ['Fair', 'Poor', 'Excellent']\n", - "\n", - "The variable ExterQual has mismatching unique values:\n", - "[] | ['Fair']\n", - "\n", - "The variable Exterior1st has mismatching unique values:\n", - "['Imitation Stucco'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", - "\n", - "The variable Exterior2nd has mismatching unique values:\n", - "['Other'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", - "\n", - "The variable Foundation has mismatching unique values:\n", - "['Wood'] | ['Brick & Tile', 'Stone']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "[] | ['Major Deductions 2', 'Severely Damaged']\n", - "\n", - "The variable GarageCond has mismatching unique values:\n", - "[] | ['Poor', 'Excellent']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Excellent', 'Poor']\n", - "\n", - "The variable GarageType has mismatching unique values:\n", - "[] | ['Car Port']\n", - "\n", - "The variable Heating has mismatching unique values:\n", - "[] | ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", - "\n", - "The variable HeatingQC has mismatching unique values:\n", - "[] | ['Fair', 'Poor']\n", - "\n", - "The variable HouseStyle has mismatching unique values:\n", - "[] | ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", - "\n", - "The variable KitchenQual has mismatching unique values:\n", - "[] | ['Fair']\n", - "\n", - "The variable LandSlope has mismatching unique values:\n", - "[] | ['Severe Slope']\n", - "\n", - "The variable MSSubClass has mismatching unique values:\n", - "[] | ['2-Story 1945 & Older', '2 Family Conversion - All Styles and Ages', '1-1/2 Story - Unfinished All Ages', '1-Story 1945 & Older', '2-1/2 Story All Ages', '1-Story w/Finished Attic All Ages']\n", - "\n", - "The variable MSZoning has mismatching unique values:\n", - "['Floating Village Residential'] | ['Commercial']\n", - "\n", - "The variable MasVnrType has mismatching unique values:\n", - "[] | ['Brick Common']\n", - "\n", - "The variable Neighborhood has mismatching unique values:\n", - "['Northridge', 'Somerset', 'Northridge Heights', 'Stone Brook', 'Bloomington Heights', 'Bluestem'] | ['Brookside', 'Iowa DOT and Rail Road', 'Meadow Village', 'Northpark Villa', 'Briardale', 'South & West of Iowa State University']\n", - "\n", - "The variable PavedDrive has mismatching unique values:\n", - "[] | ['Partial Pavement']\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "['Clay or Tile'] | ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", - "\n", - "The variable RoofStyle has mismatching unique values:\n", - "[] | ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", - "\n", - "The variable SaleCondition has mismatching unique values:\n", - "[] | ['Adjoining Land Purchase']\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "['Contract 15% Down payment regular terms'] | []\n", - "\n", - "The variable Utilities has mismatching unique values:\n", - "[] | ['Electricity and Gas Only']\n", - "\n", - "CPU times: user 2min 59s, sys: 33.8 s, total: 3min 33s\n", - "Wall time: 10.7 s\n" - ] - } - ], - "source": [ - "%time SD.compile(full_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "3b31e6d5", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "fb5b09ec", + "metadata": {}, + "source": [ + "# Eurybia with custom colors\n", + "\n", + "With this tutorial, you will understand how to manipulate colors with Eurybia plots\n", + "\n", + "Contents:\n", + "- Compile Eurybia SmartDrift\n", + "- Use `palette_name` parameter\n", + "- Use `colors_dict` parameter\n", + "- Change the colors after compiling SmartDrift\n", + "\n", + "Data from Kaggle [House Prices](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)" + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "markdown", - "id": "38e581f8", - "metadata": {}, - "source": [ - "## Eurybia with different colors" - ] - }, - { - "cell_type": "markdown", - "id": "81a849c1", - "metadata": {}, - "source": [ - "### Option 1 : define user-specific colors with `colors_dict` parameter\n", - "\n", - "The colors declared will replace the one in the default palette.\n", - "\n", - "In the example below, we replace the colors used in the features importance bar plot:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "06fc35d0", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "1eed0849", + "metadata": {}, + "source": [ + "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", + "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." + ] + }, { - "data": { - "text/plain": [ - "{'1': 'rgba(0,154,203,255)', '2': 'rgba(223, 103, 0, 0.8)'}" + "cell_type": "code", + "execution_count": 2, + "id": "e001c39b", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from category_encoders import OrdinalEncoder\n", + "from lightgbm import LGBMRegressor\n", + "from sklearn.model_selection import train_test_split" ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# first, let's print the colors used in the previous explainer: \n", - "SD.colors_dict['featureimp_bar']" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "cb2b65f3", - "metadata": {}, - "outputs": [], - "source": [ - "# Now we replace these colors using the colors_dict parameter\n", - "SD2 = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning,\n", - " colors_dict=dict(\n", - " featureimp_bar={\n", - " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", - " \"2\": \"rgba(52, 55, 54, 0.7)\"\n", - " },\n", - " univariate_cat_bar={\n", - " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", - " \"2\": \"rgba(52, 55, 54, 0.7)\"\n", - " },\n", - " univariate_cont_bar={\n", - " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", - " \"2\": \"rgba(52, 55, 54, 0.7)\" \n", - " })\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "90948cc0", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The variable BldgType has mismatching unique values:\n", - "[] | ['Two-family Conversion; originally built as one-family dwelling']\n", - "\n", - "The variable BsmtCond has mismatching unique values:\n", - "[] | ['Poor -Severe cracking, settling, or wetness']\n", - "\n", - "The variable CentralAir has mismatching unique values:\n", - "[] | ['No']\n", - "\n", - "The variable Condition1 has mismatching unique values:\n", - "[\"Within 200' of East-West Railroad\"] | ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", - "\n", - "The variable Condition2 has mismatching unique values:\n", - "['Near positive off-site feature--park, greenbelt, etc.'] | ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "[] | ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "\n", - "The variable ExterCond has mismatching unique values:\n", - "[] | ['Fair', 'Poor', 'Excellent']\n", - "\n", - "The variable ExterQual has mismatching unique values:\n", - "[] | ['Fair']\n", - "\n", - "The variable Exterior1st has mismatching unique values:\n", - "['Imitation Stucco'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", - "\n", - "The variable Exterior2nd has mismatching unique values:\n", - "['Other'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", - "\n", - "The variable Foundation has mismatching unique values:\n", - "['Wood'] | ['Brick & Tile', 'Stone']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "[] | ['Major Deductions 2', 'Severely Damaged']\n", - "\n", - "The variable GarageCond has mismatching unique values:\n", - "[] | ['Poor', 'Excellent']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Excellent', 'Poor']\n", - "\n", - "The variable GarageType has mismatching unique values:\n", - "[] | ['Car Port']\n", - "\n", - "The variable Heating has mismatching unique values:\n", - "[] | ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", - "\n", - "The variable HeatingQC has mismatching unique values:\n", - "[] | ['Fair', 'Poor']\n", - "\n", - "The variable HouseStyle has mismatching unique values:\n", - "[] | ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", - "\n", - "The variable KitchenQual has mismatching unique values:\n", - "[] | ['Fair']\n", - "\n", - "The variable LandSlope has mismatching unique values:\n", - "[] | ['Severe Slope']\n", - "\n", - "The variable MSSubClass has mismatching unique values:\n", - "[] | ['2-Story 1945 & Older', '2 Family Conversion - All Styles and Ages', '1-1/2 Story - Unfinished All Ages', '1-Story 1945 & Older', '2-1/2 Story All Ages', '1-Story w/Finished Attic All Ages']\n", - "\n", - "The variable MSZoning has mismatching unique values:\n", - "['Floating Village Residential'] | ['Commercial']\n", - "\n", - "The variable MasVnrType has mismatching unique values:\n", - "[] | ['Brick Common']\n", - "\n", - "The variable Neighborhood has mismatching unique values:\n", - "['Northridge', 'Somerset', 'Northridge Heights', 'Stone Brook', 'Bloomington Heights', 'Bluestem'] | ['Brookside', 'Iowa DOT and Rail Road', 'Meadow Village', 'Northpark Villa', 'Briardale', 'South & West of Iowa State University']\n", - "\n", - "The variable PavedDrive has mismatching unique values:\n", - "[] | ['Partial Pavement']\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "['Clay or Tile'] | ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", - "\n", - "The variable RoofStyle has mismatching unique values:\n", - "[] | ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", - "\n", - "The variable SaleCondition has mismatching unique values:\n", - "[] | ['Adjoining Land Purchase']\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "['Contract 15% Down payment regular terms'] | []\n", - "\n", - "The variable Utilities has mismatching unique values:\n", - "[] | ['Electricity and Gas Only']\n", - "\n", - "CPU times: user 2min 58s, sys: 33.5 s, total: 3min 31s\n", - "Wall time: 10.8 s\n" - ] - } - ], - "source": [ - "%time SD2.compile(full_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "f4f06e55", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "9491888b", + "metadata": {}, + "source": [ + "## Building a Supervized Model" + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD2.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b9e34480", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 3, + "id": "1e6be6b2", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading\n", + "house_df, house_dict = data_loading('house_prices')" + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD2.plot.generate_fig_univariate('BsmtQual')" - ] - }, - { - "cell_type": "markdown", - "id": "e11fdd92", - "metadata": {}, - "source": [ - "### Option 2 : redefine colors after compiling Eurybia" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "15c2df84", - "metadata": {}, - "outputs": [], - "source": [ - "SD3 = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "9e1ecf16", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 4, + "id": "b8cb0847", + "metadata": {}, + "outputs": [], + "source": [ + "#For the purpose of this tutorial and to better represent a common use case of Eurybia, \n", + "#the house_prices dataset was split in two smaller sets : \"training\" and \"production\"\n", + "# To see an interesting analysis, let's test for a bias in the date of construction of training and production dataset\n", + "house_df_learning = house_df.loc[house_df['YearBuilt'] < 1980]\n", + "house_df_production = house_df.loc[house_df['YearBuilt'] >= 1980]" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The variable BldgType has mismatching unique values:\n", - "[] | ['Two-family Conversion; originally built as one-family dwelling']\n", - "\n", - "The variable BsmtCond has mismatching unique values:\n", - "[] | ['Poor -Severe cracking, settling, or wetness']\n", - "\n", - "The variable CentralAir has mismatching unique values:\n", - "[] | ['No']\n", - "\n", - "The variable Condition1 has mismatching unique values:\n", - "[\"Within 200' of East-West Railroad\"] | ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", - "\n", - "The variable Condition2 has mismatching unique values:\n", - "['Near positive off-site feature--park, greenbelt, etc.'] | ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "[] | ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "\n", - "The variable ExterCond has mismatching unique values:\n", - "[] | ['Fair', 'Poor', 'Excellent']\n", - "\n", - "The variable ExterQual has mismatching unique values:\n", - "[] | ['Fair']\n", - "\n", - "The variable Exterior1st has mismatching unique values:\n", - "['Imitation Stucco'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", - "\n", - "The variable Exterior2nd has mismatching unique values:\n", - "['Other'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", - "\n", - "The variable Foundation has mismatching unique values:\n", - "['Wood'] | ['Brick & Tile', 'Stone']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "[] | ['Major Deductions 2', 'Severely Damaged']\n", - "\n", - "The variable GarageCond has mismatching unique values:\n", - "[] | ['Poor', 'Excellent']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Excellent', 'Poor']\n", - "\n", - "The variable GarageType has mismatching unique values:\n", - "[] | ['Car Port']\n", - "\n", - "The variable Heating has mismatching unique values:\n", - "[] | ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", - "\n", - "The variable HeatingQC has mismatching unique values:\n", - "[] | ['Fair', 'Poor']\n", - "\n", - "The variable HouseStyle has mismatching unique values:\n", - "[] | ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", - "\n", - "The variable KitchenQual has mismatching unique values:\n", - "[] | ['Fair']\n", - "\n", - "The variable LandSlope has mismatching unique values:\n", - "[] | ['Severe Slope']\n", - "\n", - "The variable MSSubClass has mismatching unique values:\n", - "[] | ['2-Story 1945 & Older', '2 Family Conversion - All Styles and Ages', '1-1/2 Story - Unfinished All Ages', '1-Story 1945 & Older', '2-1/2 Story All Ages', '1-Story w/Finished Attic All Ages']\n", - "\n", - "The variable MSZoning has mismatching unique values:\n", - "['Floating Village Residential'] | ['Commercial']\n", - "\n", - "The variable MasVnrType has mismatching unique values:\n", - "[] | ['Brick Common']\n", - "\n", - "The variable Neighborhood has mismatching unique values:\n", - "['Northridge', 'Somerset', 'Northridge Heights', 'Stone Brook', 'Bloomington Heights', 'Bluestem'] | ['Brookside', 'Iowa DOT and Rail Road', 'Meadow Village', 'Northpark Villa', 'Briardale', 'South & West of Iowa State University']\n", - "\n", - "The variable PavedDrive has mismatching unique values:\n", - "[] | ['Partial Pavement']\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "['Clay or Tile'] | ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", - "\n", - "The variable RoofStyle has mismatching unique values:\n", - "[] | ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", - "\n", - "The variable SaleCondition has mismatching unique values:\n", - "[] | ['Adjoining Land Purchase']\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "['Contract 15% Down payment regular terms'] | []\n", - "\n", - "The variable Utilities has mismatching unique values:\n", - "[] | ['Electricity and Gas Only']\n", - "\n", - "CPU times: user 3min 1s, sys: 33.1 s, total: 3min 34s\n", - "Wall time: 10.7 s\n" - ] - } - ], - "source": [ - "%time SD3.compile(full_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c625009b", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 5, + "id": "9071ee71", + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", + "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YearBuilt'])]\n", + "\n", + "y_df_production=house_df_production['SalePrice'].to_frame()\n", + "X_df_production=house_df_production[house_df_production.columns.difference(['SalePrice','YearBuilt'])]" + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD3.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "1e83f9d7", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 6, + "id": "c0ec51bb", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d58a1cf8", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5b0dd030", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The variable BldgType has mismatching unique values:\n", + "[] | ['Two-family Conversion; originally built as one-family dwelling']\n", + "\n", + "The variable BsmtCond has mismatching unique values:\n", + "[] | ['Poor -Severe cracking, settling, or wetness']\n", + "\n", + "The variable CentralAir has mismatching unique values:\n", + "[] | ['No']\n", + "\n", + "The variable Condition1 has mismatching unique values:\n", + "[\"Within 200' of East-West Railroad\"] | ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", + "\n", + "The variable Condition2 has mismatching unique values:\n", + "['Near positive off-site feature--park, greenbelt, etc.'] | ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "[] | ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "\n", + "The variable ExterCond has mismatching unique values:\n", + "[] | ['Fair', 'Poor', 'Excellent']\n", + "\n", + "The variable ExterQual has mismatching unique values:\n", + "[] | ['Fair']\n", + "\n", + "The variable Exterior1st has mismatching unique values:\n", + "['Imitation Stucco'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", + "\n", + "The variable Exterior2nd has mismatching unique values:\n", + "['Other'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", + "\n", + "The variable Foundation has mismatching unique values:\n", + "['Wood'] | ['Brick & Tile', 'Stone']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "[] | ['Major Deductions 2', 'Severely Damaged']\n", + "\n", + "The variable GarageCond has mismatching unique values:\n", + "[] | ['Poor', 'Excellent']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Excellent', 'Poor']\n", + "\n", + "The variable GarageType has mismatching unique values:\n", + "[] | ['Car Port']\n", + "\n", + "The variable Heating has mismatching unique values:\n", + "[] | ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", + "\n", + "The variable HeatingQC has mismatching unique values:\n", + "[] | ['Fair', 'Poor']\n", + "\n", + "The variable HouseStyle has mismatching unique values:\n", + "[] | ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", + "\n", + "The variable KitchenQual has mismatching unique values:\n", + "[] | ['Fair']\n", + "\n", + "The variable LandSlope has mismatching unique values:\n", + "[] | ['Severe Slope']\n", + "\n", + "The variable MSSubClass has mismatching unique values:\n", + "[] | ['2-Story 1945 & Older', '2 Family Conversion - All Styles and Ages', '1-1/2 Story - Unfinished All Ages', '1-Story 1945 & Older', '2-1/2 Story All Ages', '1-Story w/Finished Attic All Ages']\n", + "\n", + "The variable MSZoning has mismatching unique values:\n", + "['Floating Village Residential'] | ['Commercial']\n", + "\n", + "The variable MasVnrType has mismatching unique values:\n", + "[] | ['Brick Common']\n", + "\n", + "The variable Neighborhood has mismatching unique values:\n", + "['Northridge', 'Somerset', 'Northridge Heights', 'Stone Brook', 'Bloomington Heights', 'Bluestem'] | ['Brookside', 'Iowa DOT and Rail Road', 'Meadow Village', 'Northpark Villa', 'Briardale', 'South & West of Iowa State University']\n", + "\n", + "The variable PavedDrive has mismatching unique values:\n", + "[] | ['Partial Pavement']\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "['Clay or Tile'] | ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", + "\n", + "The variable RoofStyle has mismatching unique values:\n", + "[] | ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", + "\n", + "The variable SaleCondition has mismatching unique values:\n", + "[] | ['Adjoining Land Purchase']\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "['Contract 15% Down payment regular terms'] | []\n", + "\n", + "The variable Utilities has mismatching unique values:\n", + "[] | ['Electricity and Gas Only']\n", + "\n", + "CPU times: user 2min 59s, sys: 33.8 s, total: 3min 33s\n", + "Wall time: 10.7 s\n" + ] + } + ], + "source": [ + "%time SD.compile(full_validation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3b31e6d5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.xpl.plot.features_importance()" + ] + }, + { + "cell_type": "markdown", + "id": "38e581f8", + "metadata": {}, + "source": [ + "## Eurybia with different colors" + ] + }, + { + "cell_type": "markdown", + "id": "81a849c1", + "metadata": {}, + "source": [ + "### Option 1 : define user-specific colors with `colors_dict` parameter\n", + "\n", + "The colors declared will replace the one in the default palette.\n", + "\n", + "In the example below, we replace the colors used in the features importance bar plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "06fc35d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'1': 'rgba(0,154,203,255)', '2': 'rgba(223, 103, 0, 0.8)'}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# first, let's print the colors used in the previous explainer: \n", + "SD.colors_dict['featureimp_bar']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cb2b65f3", + "metadata": {}, + "outputs": [], + "source": [ + "# Now we replace these colors using the colors_dict parameter\n", + "SD2 = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning,\n", + " colors_dict=dict(\n", + " featureimp_bar={\n", + " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", + " \"2\": \"rgba(52, 55, 54, 0.7)\"\n", + " },\n", + " univariate_cat_bar={\n", + " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", + " \"2\": \"rgba(52, 55, 54, 0.7)\"\n", + " },\n", + " univariate_cont_bar={\n", + " \"1\": \"rgba(244, 192, 0, 1.0)\",\n", + " \"2\": \"rgba(52, 55, 54, 0.7)\" \n", + " })\n", + " )" + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" + "cell_type": "code", + "execution_count": 12, + "id": "90948cc0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The variable BldgType has mismatching unique values:\n", + "[] | ['Two-family Conversion; originally built as one-family dwelling']\n", + "\n", + "The variable BsmtCond has mismatching unique values:\n", + "[] | ['Poor -Severe cracking, settling, or wetness']\n", + "\n", + "The variable CentralAir has mismatching unique values:\n", + "[] | ['No']\n", + "\n", + "The variable Condition1 has mismatching unique values:\n", + "[\"Within 200' of East-West Railroad\"] | ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", + "\n", + "The variable Condition2 has mismatching unique values:\n", + "['Near positive off-site feature--park, greenbelt, etc.'] | ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "[] | ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "\n", + "The variable ExterCond has mismatching unique values:\n", + "[] | ['Fair', 'Poor', 'Excellent']\n", + "\n", + "The variable ExterQual has mismatching unique values:\n", + "[] | ['Fair']\n", + "\n", + "The variable Exterior1st has mismatching unique values:\n", + "['Imitation Stucco'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", + "\n", + "The variable Exterior2nd has mismatching unique values:\n", + "['Other'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", + "\n", + "The variable Foundation has mismatching unique values:\n", + "['Wood'] | ['Brick & Tile', 'Stone']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "[] | ['Major Deductions 2', 'Severely Damaged']\n", + "\n", + "The variable GarageCond has mismatching unique values:\n", + "[] | ['Poor', 'Excellent']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Excellent', 'Poor']\n", + "\n", + "The variable GarageType has mismatching unique values:\n", + "[] | ['Car Port']\n", + "\n", + "The variable Heating has mismatching unique values:\n", + "[] | ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", + "\n", + "The variable HeatingQC has mismatching unique values:\n", + "[] | ['Fair', 'Poor']\n", + "\n", + "The variable HouseStyle has mismatching unique values:\n", + "[] | ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", + "\n", + "The variable KitchenQual has mismatching unique values:\n", + "[] | ['Fair']\n", + "\n", + "The variable LandSlope has mismatching unique values:\n", + "[] | ['Severe Slope']\n", + "\n", + "The variable MSSubClass has mismatching unique values:\n", + "[] | ['2-Story 1945 & Older', '2 Family Conversion - All Styles and Ages', '1-1/2 Story - Unfinished All Ages', '1-Story 1945 & Older', '2-1/2 Story All Ages', '1-Story w/Finished Attic All Ages']\n", + "\n", + "The variable MSZoning has mismatching unique values:\n", + "['Floating Village Residential'] | ['Commercial']\n", + "\n", + "The variable MasVnrType has mismatching unique values:\n", + "[] | ['Brick Common']\n", + "\n", + "The variable Neighborhood has mismatching unique values:\n", + "['Northridge', 'Somerset', 'Northridge Heights', 'Stone Brook', 'Bloomington Heights', 'Bluestem'] | ['Brookside', 'Iowa DOT and Rail Road', 'Meadow Village', 'Northpark Villa', 'Briardale', 'South & West of Iowa State University']\n", + "\n", + "The variable PavedDrive has mismatching unique values:\n", + "[] | ['Partial Pavement']\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "['Clay or Tile'] | ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", + "\n", + "The variable RoofStyle has mismatching unique values:\n", + "[] | ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", + "\n", + "The variable SaleCondition has mismatching unique values:\n", + "[] | ['Adjoining Land Purchase']\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "['Contract 15% Down payment regular terms'] | []\n", + "\n", + "The variable Utilities has mismatching unique values:\n", + "[] | ['Electricity and Gas Only']\n", + "\n", + "CPU times: user 2min 58s, sys: 33.5 s, total: 3min 31s\n", + "Wall time: 10.8 s\n" + ] + } + ], + "source": [ + "%time SD2.compile(full_validation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f4f06e55", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD2.plot.generate_fig_univariate('BsmtQual')" + ] + }, + { + "cell_type": "markdown", + "id": "e11fdd92", + "metadata": {}, + "source": [ + "### Option 2 : redefine colors after compiling Eurybia" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "15c2df84", + "metadata": {}, + "outputs": [], + "source": [ + "SD3 = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9e1ecf16", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The variable BldgType has mismatching unique values:\n", + "[] | ['Two-family Conversion; originally built as one-family dwelling']\n", + "\n", + "The variable BsmtCond has mismatching unique values:\n", + "[] | ['Poor -Severe cracking, settling, or wetness']\n", + "\n", + "The variable CentralAir has mismatching unique values:\n", + "[] | ['No']\n", + "\n", + "The variable Condition1 has mismatching unique values:\n", + "[\"Within 200' of East-West Railroad\"] | ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", + "\n", + "The variable Condition2 has mismatching unique values:\n", + "['Near positive off-site feature--park, greenbelt, etc.'] | ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "[] | ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "\n", + "The variable ExterCond has mismatching unique values:\n", + "[] | ['Fair', 'Poor', 'Excellent']\n", + "\n", + "The variable ExterQual has mismatching unique values:\n", + "[] | ['Fair']\n", + "\n", + "The variable Exterior1st has mismatching unique values:\n", + "['Imitation Stucco'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", + "\n", + "The variable Exterior2nd has mismatching unique values:\n", + "['Other'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", + "\n", + "The variable Foundation has mismatching unique values:\n", + "['Wood'] | ['Brick & Tile', 'Stone']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "[] | ['Major Deductions 2', 'Severely Damaged']\n", + "\n", + "The variable GarageCond has mismatching unique values:\n", + "[] | ['Poor', 'Excellent']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Excellent', 'Poor']\n", + "\n", + "The variable GarageType has mismatching unique values:\n", + "[] | ['Car Port']\n", + "\n", + "The variable Heating has mismatching unique values:\n", + "[] | ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", + "\n", + "The variable HeatingQC has mismatching unique values:\n", + "[] | ['Fair', 'Poor']\n", + "\n", + "The variable HouseStyle has mismatching unique values:\n", + "[] | ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", + "\n", + "The variable KitchenQual has mismatching unique values:\n", + "[] | ['Fair']\n", + "\n", + "The variable LandSlope has mismatching unique values:\n", + "[] | ['Severe Slope']\n", + "\n", + "The variable MSSubClass has mismatching unique values:\n", + "[] | ['2-Story 1945 & Older', '2 Family Conversion - All Styles and Ages', '1-1/2 Story - Unfinished All Ages', '1-Story 1945 & Older', '2-1/2 Story All Ages', '1-Story w/Finished Attic All Ages']\n", + "\n", + "The variable MSZoning has mismatching unique values:\n", + "['Floating Village Residential'] | ['Commercial']\n", + "\n", + "The variable MasVnrType has mismatching unique values:\n", + "[] | ['Brick Common']\n", + "\n", + "The variable Neighborhood has mismatching unique values:\n", + "['Northridge', 'Somerset', 'Northridge Heights', 'Stone Brook', 'Bloomington Heights', 'Bluestem'] | ['Brookside', 'Iowa DOT and Rail Road', 'Meadow Village', 'Northpark Villa', 'Briardale', 'South & West of Iowa State University']\n", + "\n", + "The variable PavedDrive has mismatching unique values:\n", + "[] | ['Partial Pavement']\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "['Clay or Tile'] | ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", + "\n", + "The variable RoofStyle has mismatching unique values:\n", + "[] | ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", + "\n", + "The variable SaleCondition has mismatching unique values:\n", + "[] | ['Adjoining Land Purchase']\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "['Contract 15% Down payment regular terms'] | []\n", + "\n", + "The variable Utilities has mismatching unique values:\n", + "[] | ['Electricity and Gas Only']\n", + "\n", + "CPU times: user 3min 1s, sys: 33.1 s, total: 3min 34s\n", + "Wall time: 10.7 s\n" + ] + } + ], + "source": [ + "%time SD3.compile(full_validation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c625009b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD3.plot.generate_fig_univariate('BsmtQual')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "dev_eurybia", + "language": "python", + "name": "dev_eurybia" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" } - ], - "source": [ - "SD3.plot.generate_fig_univariate('BsmtQual')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dev_eurybia", - "language": "python", - "name": "dev_eurybia" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/tutorial/common/tuto-common02-shapash-webapp.ipynb b/tutorial/common/tuto-common02-shapash-webapp.ipynb index ebff7f0..d6fc501 100644 --- a/tutorial/common/tuto-common02-shapash-webapp.ipynb +++ b/tutorial/common/tuto-common02-shapash-webapp.ipynb @@ -1,625 +1,625 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "e0428e7d", - "metadata": {}, - "source": [ - "# Use Shapash Webapp with Eurybia\n" - ] - }, - { - "cell_type": "markdown", - "id": "a38202ed", - "metadata": {}, - "source": [ - "**With this tutorial, you will**\n", - "learn to use Eurybia and the Shapash webapp to understand your datadrift classifier
\n", - "\n", - "Contents:\n", - "- Build a model to deploy\n", - "- Do data validation between learning dataset and production dataset\n", - "- Generate Report \n", - "- Run Webapp\n", - "\n", - "\n", - "Data from Kaggle [Titanic](https://www.kaggle.com/c/titanic)
" - ] - }, - { - "cell_type": "markdown", - "id": "8e317052", - "metadata": {}, - "source": [ - "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", - "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "17e0ebcd", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from category_encoders import OrdinalEncoder\n", - "import catboost\n", - "from eurybia.core.smartdrift import SmartDrift\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "markdown", - "id": "bec988d5", - "metadata": {}, - "source": [ - "## Building Supervized Model\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "53ab6c7e", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "6e06664a", - "metadata": {}, - "outputs": [], - "source": [ - "titan_df = data_loading('titanic')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "3dc1a8aa", - "metadata": {}, - "outputs": [], - "source": [ - "features = ['Pclass', 'Age', 'Embarked', 'Sex', 'SibSp', 'Parch', 'Fare']\n", - "features_to_encode = ['Pclass', 'Embarked', 'Sex']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6e36c91f", - "metadata": {}, - "outputs": [], - "source": [ - "encoder = OrdinalEncoder(cols=features_to_encode)\n", - "encoder.fit(titan_df[features], verbose=False) " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "76bff26a", - "metadata": {}, - "outputs": [], - "source": [ - "titan_df_encoded = encoder.transform(titan_df[features])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "eb1a6d0c", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(\n", - " titan_df_encoded,\n", - " titan_df['Survived'].to_frame(),\n", - " test_size=0.2,\n", - " random_state=11\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "02e96d8b", - "metadata": {}, - "outputs": [], - "source": [ - "i=0\n", - "indice_cat = []\n", - "for feature in titan_df_encoded:\n", - " if feature in features_to_encode:\n", - " indice_cat.append(i)\n", - " i=i+1" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "cbe55ad2", - "metadata": {}, - "outputs": [], - "source": [ - "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", - " learning_rate=0.143852,\n", - " iterations=500,\n", - " l2_leaf_reg=15,\n", - " max_depth = 4)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "920f68e7", - "metadata": {}, - "outputs": [], - "source": [ - "train_pool_cat = catboost.Pool(data=X_train, label= y_train, cat_features = indice_cat)\n", - "test_pool_cat = catboost.Pool(data=X_test, label=y_test, cat_features = indice_cat) " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6bc34d41", - "metadata": {}, - "outputs": [], - "source": [ - "model.fit(train_pool_cat, eval_set=test_pool_cat, silent=True)\n", - "y_pred = model.predict(X_test)" - ] - }, - { - "cell_type": "markdown", - "id": "afa288f0", - "metadata": {}, - "source": [ - "## Creating a fake dataset as a production dataset\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "7b5bdd9a", - "metadata": {}, - "outputs": [], - "source": [ - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "c324e6a0", - "metadata": {}, - "outputs": [], - "source": [ - "df_production = titan_df.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "3dc18f14", - "metadata": {}, - "outputs": [], - "source": [ - "df_production['Age'] = df_production['Age'].apply(lambda x: random.randrange(10, 76)).astype(float)\n", - "df_production['Fare'] = df_production['Fare'].apply(lambda x: random.randrange(1, 100)).astype(float)\n", - "list_sex= [\"male\", \"female\"]\n", - "df_production['Sex'] = df_production['Sex'].apply(lambda x: random.choice(list_sex))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "61d65879", - "metadata": {}, - "outputs": [], - "source": [ - "df_baseline = titan_df[features]\n", - "df_current = df_production[features]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "ec99ad68", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" + "cells": [ + { + "cell_type": "markdown", + "id": "e0428e7d", + "metadata": {}, + "source": [ + "# Use Shapash Webapp with Eurybia\n" + ] + }, + { + "cell_type": "markdown", + "id": "a38202ed", + "metadata": {}, + "source": [ + "**With this tutorial, you will**\n", + "learn to use Eurybia and the Shapash webapp to understand your datadrift classifier
\n", + "\n", + "Contents:\n", + "- Build a model to deploy\n", + "- Do data validation between learning dataset and production dataset\n", + "- Generate Report \n", + "- Run Webapp\n", + "\n", + "\n", + "Data from Kaggle [Titanic](https://www.kaggle.com/c/titanic)
" + ] + }, + { + "cell_type": "markdown", + "id": "8e317052", + "metadata": {}, + "source": [ + "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", + "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "17e0ebcd", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from category_encoders import OrdinalEncoder\n", + "import catboost\n", + "from eurybia.core.smartdrift import SmartDrift\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "id": "bec988d5", + "metadata": {}, + "source": [ + "## Building Supervized Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "53ab6c7e", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6e06664a", + "metadata": {}, + "outputs": [], + "source": [ + "titan_df = data_loading('titanic')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3dc1a8aa", + "metadata": {}, + "outputs": [], + "source": [ + "features = ['Pclass', 'Age', 'Embarked', 'Sex', 'SibSp', 'Parch', 'Fare']\n", + "features_to_encode = ['Pclass', 'Embarked', 'Sex']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e36c91f", + "metadata": {}, + "outputs": [], + "source": [ + "encoder = OrdinalEncoder(cols=features_to_encode)\n", + "encoder.fit(titan_df[features], verbose=False) " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "76bff26a", + "metadata": {}, + "outputs": [], + "source": [ + "titan_df_encoded = encoder.transform(titan_df[features])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "eb1a6d0c", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " titan_df_encoded,\n", + " titan_df['Survived'].to_frame(),\n", + " test_size=0.2,\n", + " random_state=11\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "02e96d8b", + "metadata": {}, + "outputs": [], + "source": [ + "i=0\n", + "indice_cat = []\n", + "for feature in titan_df_encoded:\n", + " if feature in features_to_encode:\n", + " indice_cat.append(i)\n", + " i=i+1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cbe55ad2", + "metadata": {}, + "outputs": [], + "source": [ + "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", + " learning_rate=0.143852,\n", + " iterations=500,\n", + " l2_leaf_reg=15,\n", + " max_depth = 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "920f68e7", + "metadata": {}, + "outputs": [], + "source": [ + "train_pool_cat = catboost.Pool(data=X_train, label= y_train, cat_features = indice_cat)\n", + "test_pool_cat = catboost.Pool(data=X_test, label=y_test, cat_features = indice_cat) " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6bc34d41", + "metadata": {}, + "outputs": [], + "source": [ + "model.fit(train_pool_cat, eval_set=test_pool_cat, silent=True)\n", + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "markdown", + "id": "afa288f0", + "metadata": {}, + "source": [ + "## Creating a fake dataset as a production dataset\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7b5bdd9a", + "metadata": {}, + "outputs": [], + "source": [ + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c324e6a0", + "metadata": {}, + "outputs": [], + "source": [ + "df_production = titan_df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "3dc18f14", + "metadata": {}, + "outputs": [], + "source": [ + "df_production['Age'] = df_production['Age'].apply(lambda x: random.randrange(10, 76)).astype(float)\n", + "df_production['Fare'] = df_production['Fare'].apply(lambda x: random.randrange(1, 100)).astype(float)\n", + "list_sex= [\"male\", \"female\"]\n", + "df_production['Sex'] = df_production['Sex'].apply(lambda x: random.choice(list_sex))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "61d65879", + "metadata": {}, + "outputs": [], + "source": [ + "df_baseline = titan_df[features]\n", + "df_current = df_production[features]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "ec99ad68", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Pclass Age Embarked Sex SibSp Parch Fare\n", + "PassengerId \n", + "1 Third class 22.0 Southampton male 1 0 7.25\n", + "2 First class 38.0 Cherbourg female 1 0 71.28\n", + "3 Third class 26.0 Southampton female 0 0 7.92\n", + "4 First class 35.0 Southampton female 1 0 53.10\n", + "5 Third class 35.0 Southampton male 0 0 8.05" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " Pclass Age Embarked Sex SibSp Parch Fare\n", - "PassengerId \n", - "1 Third class 22.0 Southampton male 1 0 7.25\n", - "2 First class 38.0 Cherbourg female 1 0 71.28\n", - "3 Third class 26.0 Southampton female 0 0 7.92\n", - "4 First class 35.0 Southampton female 1 0 53.10\n", - "5 Third class 35.0 Southampton male 0 0 8.05" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_baseline.head()" - ] - }, - { - "cell_type": "markdown", - "id": "f58587d7", - "metadata": {}, - "source": [ - "## Use Eurybia for data validation" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7fff0997", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "202f0930", - "metadata": {}, - "outputs": [], - "source": [ - "sd = SmartDrift(df_current=df_current, df_baseline=df_baseline, deployed_model=model, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "eae66775", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 35.9 s, sys: 5.03 s, total: 40.9 s\n", - "Wall time: 1.97 s\n" - ] - } - ], - "source": [ - "%time sd.compile(full_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "b09782dc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Report saved to ./report_titanic.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "source": [ + "df_baseline.head()" + ] + }, + { + "cell_type": "markdown", + "id": "f58587d7", + "metadata": {}, + "source": [ + "## Use Eurybia for data validation" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "7fff0997", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "202f0930", + "metadata": {}, + "outputs": [], + "source": [ + "sd = SmartDrift(df_current=df_current, df_baseline=df_baseline, deployed_model=model, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "eae66775", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 35.9 s, sys: 5.03 s, total: 40.9 s\n", + "Wall time: 1.97 s\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "%time sd.compile(full_validation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "b09782dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_titanic.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sd.generate_report( \n", + " output_file='report_titanic.html', \n", + " title_story=\"Data validation\",\n", + " title_description=\"\"\"Titanic Data validation\"\"\" \n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "5f3e9edc", + "metadata": {}, + "source": [ + "## Launch WebApp Shapash from SmartDrift" + ] + }, + { + "cell_type": "markdown", + "id": "945db78a", + "metadata": {}, + "source": [ + "After compile step, you can launch a WebApp Shapash directly from your object SmartDrift. It allows you to access several dynamic plots that will help you to understand where drift has been detected in your data.
\n", + "For information on Shapash Webapp : (https://github.com/MAIF/shapash)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ac7db48", + "metadata": {}, + "outputs": [], + "source": [ + "app = sd.xpl.run_app(title_story='Eurybia datadrift classifier')" + ] + }, + { + "cell_type": "markdown", + "id": "7e4051a4", + "metadata": {}, + "source": [ + "**Stop the WebApp after using it**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66b73619", + "metadata": {}, + "outputs": [], + "source": [ + "app.kill()" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "sd.generate_report( \n", - " output_file='report_titanic.html', \n", - " title_story=\"Data validation\",\n", - " title_description=\"\"\"Titanic Data validation\"\"\" \n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "5f3e9edc", - "metadata": {}, - "source": [ - "## Launch WebApp Shapash from SmartDrift" - ] - }, - { - "cell_type": "markdown", - "id": "945db78a", - "metadata": {}, - "source": [ - "After compile step, you can launch a WebApp Shapash directly from your object SmartDrift. It allows you to access several dynamic plots that will help you to understand where drift has been detected in your data.
\n", - "For information on Shapash Webapp : (https://github.com/MAIF/shapash)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ac7db48", - "metadata": {}, - "outputs": [], - "source": [ - "app = sd.xpl.run_app(title_story='Eurybia datadrift classifier')" - ] - }, - { - "cell_type": "markdown", - "id": "7e4051a4", - "metadata": {}, - "source": [ - "**Stop the WebApp after using it**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "66b73619", - "metadata": {}, - "outputs": [], - "source": [ - "app.kill()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dev_eurybia", - "language": "python", - "name": "dev_eurybia" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" + ], + "metadata": { + "kernelspec": { + "display_name": "dev_eurybia", + "language": "python", + "name": "dev_eurybia" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/tutorial/data_drift/tutorial01-datadrift-over-years.ipynb b/tutorial/data_drift/tutorial01-datadrift-over-years.ipynb index 6608ba2..916b2f8 100644 --- a/tutorial/data_drift/tutorial01-datadrift-over-years.ipynb +++ b/tutorial/data_drift/tutorial01-datadrift-over-years.ipynb @@ -1,1015 +1,1015 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "d078def3", - "metadata": {}, - "source": [ - "# Monitor Data Drift over years\n", - "With this tutorial you:
\n", - "Understand how to use Eurybia to detect datadrift\n", - "\n", - "Contents:\n", - "- Detect data drift \n", - "- Compile Drift over years\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "c8f46b06", - "metadata": {}, - "source": [ - "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", - "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "197aa24c", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from category_encoders import OrdinalEncoder\n", - "from lightgbm import LGBMRegressor\n", - "from eurybia import SmartDrift\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_squared_log_error" - ] - }, - { - "cell_type": "markdown", - "id": "2a9f1c0c", - "metadata": {}, - "source": [ - "## Import Dataset and split in training and production dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7afa8a19", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4307a970", - "metadata": {}, - "outputs": [], - "source": [ - "house_df, house_dict = data_loading('house_prices')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "9ad61fd7", - "metadata": {}, - "outputs": [], - "source": [ - "# Let us consider that the column \"YrSold\" corresponds to the reference date. \n", - "#In 2006, a model was trained using data. And in 2007, we want to detect data drift on new data in production to predict\n", - "#house price\n", - "house_df_learning = house_df.loc[house_df['YrSold'] == 2006]\n", - "house_df_2007 = house_df.loc[house_df['YrSold'] == 2007]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f9f3df4b", - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", - "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YrSold'])]\n", - "\n", - "y_df_2007=house_df_2007['SalePrice'].to_frame()\n", - "X_df_2007=house_df_2007[house_df_2007.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "markdown", - "id": "f96e3de5", - "metadata": {}, - "source": [ - "## Building Supervized Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3b25a871", - "metadata": {}, - "outputs": [], - "source": [ - "from category_encoders import OrdinalEncoder\n", - "\n", - "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", - "\n", - "encoder = OrdinalEncoder(\n", - " cols=categorical_features,\n", - " handle_unknown='ignore',\n", - " return_df=True).fit(X_df_learning)\n", - "\n", - "X_df_learning_encoded=encoder.transform(X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c1f3b29c", - "metadata": {}, - "outputs": [], - "source": [ - "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7215288c", - "metadata": {}, - "outputs": [], - "source": [ - "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" - ] - }, - { - "cell_type": "markdown", - "id": "c0a66eb2", - "metadata": {}, - "source": [ - "## Use Eurybia for data drift" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6dce41f0", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "725a6088", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2007,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "344e3d46", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 6 µs, sys: 2 µs, total: 8 µs\n", - "Wall time: 17.2 µs\n", - "The variable BsmtCond has mismatching unique values:\n", - "['Poor -Severe cracking, settling, or wetness'] | []\n", - "\n", - "The variable Condition2 has mismatching unique values:\n", - "['Near positive off-site feature--park, greenbelt, etc.', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad'] | ['Adjacent to feeder street']\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "['Mixed'] | ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "\n", - "The variable ExterQual has mismatching unique values:\n", - "['Fair'] | []\n", - "\n", - "The variable Exterior1st has mismatching unique values:\n", - "[] | ['Stone', 'Imitation Stucco']\n", - "\n", - "The variable Exterior2nd has mismatching unique values:\n", - "['Asphalt Shingles', 'Brick Common'] | ['Other']\n", - "\n", - "The variable Foundation has mismatching unique values:\n", - "[] | ['Stone', 'Wood']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "['Major Deductions 2', 'Severely Damaged'] | ['Moderate Deductions']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Excellent']\n", - "\n", - "The variable Heating has mismatching unique values:\n", - "[] | ['Wall furnace']\n", - "\n", - "The variable HeatingQC has mismatching unique values:\n", - "['Poor'] | []\n", - "\n", - "The variable LotConfig has mismatching unique values:\n", - "[] | ['Frontage on 3 sides of property']\n", - "\n", - "The variable MSSubClass has mismatching unique values:\n", - "['1-Story w/Finished Attic All Ages'] | []\n", - "\n", - "The variable Neighborhood has mismatching unique values:\n", - "['Northpark Villa'] | []\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "['Roll'] | ['Metal']\n", - "\n", - "The variable RoofStyle has mismatching unique values:\n", - "['Mansard', 'Shed'] | []\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "['Warranty Deed - Cash'] | ['Contract Low Interest', 'Contract Low Down', 'Contract Low Down payment and low interest']\n", - "\n", - "The variable Street has mismatching unique values:\n", - "['Gravel'] | []\n", - "\n", - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.626082251082251\n", - "house_price_auc.csv did not exist and is created. \n" - ] - } - ], - "source": [ - "%time \n", - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2007', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "12ee98ba", - "metadata": {}, - "source": [ - "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", - "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", - "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "36ca3084", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_datadrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cells": [ + { + "cell_type": "markdown", + "id": "d078def3", + "metadata": {}, + "source": [ + "# Monitor Data Drift over years\n", + "With this tutorial you:
\n", + "Understand how to use Eurybia to detect datadrift\n", + "\n", + "Contents:\n", + "- Detect data drift \n", + "- Compile Drift over years\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c8f46b06", + "metadata": {}, + "source": [ + "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", + "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "197aa24c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from category_encoders import OrdinalEncoder\n", + "from lightgbm import LGBMRegressor\n", + "from eurybia import SmartDrift\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_log_error" + ] + }, + { + "cell_type": "markdown", + "id": "2a9f1c0c", + "metadata": {}, + "source": [ + "## Import Dataset and split in training and production dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7afa8a19", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4307a970", + "metadata": {}, + "outputs": [], + "source": [ + "house_df, house_dict = data_loading('house_prices')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9ad61fd7", + "metadata": {}, + "outputs": [], + "source": [ + "# Let us consider that the column \"YrSold\" corresponds to the reference date. \n", + "#In 2006, a model was trained using data. And in 2007, we want to detect data drift on new data in production to predict\n", + "#house price\n", + "house_df_learning = house_df.loc[house_df['YrSold'] == 2006]\n", + "house_df_2007 = house_df.loc[house_df['YrSold'] == 2007]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f9f3df4b", + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", + "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YrSold'])]\n", + "\n", + "y_df_2007=house_df_2007['SalePrice'].to_frame()\n", + "X_df_2007=house_df_2007[house_df_2007.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "markdown", + "id": "f96e3de5", + "metadata": {}, + "source": [ + "## Building Supervized Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b25a871", + "metadata": {}, + "outputs": [], + "source": [ + "from category_encoders import OrdinalEncoder\n", + "\n", + "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", + "\n", + "encoder = OrdinalEncoder(\n", + " cols=categorical_features,\n", + " handle_unknown='ignore',\n", + " return_df=True).fit(X_df_learning)\n", + "\n", + "X_df_learning_encoded=encoder.transform(X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c1f3b29c", + "metadata": {}, + "outputs": [], + "source": [ + "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7215288c", + "metadata": {}, + "outputs": [], + "source": [ + "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" + ] + }, + { + "cell_type": "markdown", + "id": "c0a66eb2", + "metadata": {}, + "source": [ + "## Use Eurybia for data drift" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6dce41f0", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "725a6088", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2007,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "344e3d46", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 6 \u00b5s, sys: 2 \u00b5s, total: 8 \u00b5s\n", + "Wall time: 17.2 \u00b5s\n", + "The variable BsmtCond has mismatching unique values:\n", + "['Poor -Severe cracking, settling, or wetness'] | []\n", + "\n", + "The variable Condition2 has mismatching unique values:\n", + "['Near positive off-site feature--park, greenbelt, etc.', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad'] | ['Adjacent to feeder street']\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "['Mixed'] | ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "\n", + "The variable ExterQual has mismatching unique values:\n", + "['Fair'] | []\n", + "\n", + "The variable Exterior1st has mismatching unique values:\n", + "[] | ['Stone', 'Imitation Stucco']\n", + "\n", + "The variable Exterior2nd has mismatching unique values:\n", + "['Asphalt Shingles', 'Brick Common'] | ['Other']\n", + "\n", + "The variable Foundation has mismatching unique values:\n", + "[] | ['Stone', 'Wood']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "['Major Deductions 2', 'Severely Damaged'] | ['Moderate Deductions']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Excellent']\n", + "\n", + "The variable Heating has mismatching unique values:\n", + "[] | ['Wall furnace']\n", + "\n", + "The variable HeatingQC has mismatching unique values:\n", + "['Poor'] | []\n", + "\n", + "The variable LotConfig has mismatching unique values:\n", + "[] | ['Frontage on 3 sides of property']\n", + "\n", + "The variable MSSubClass has mismatching unique values:\n", + "['1-Story w/Finished Attic All Ages'] | []\n", + "\n", + "The variable Neighborhood has mismatching unique values:\n", + "['Northpark Villa'] | []\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "['Roll'] | ['Metal']\n", + "\n", + "The variable RoofStyle has mismatching unique values:\n", + "['Mansard', 'Shed'] | []\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "['Warranty Deed - Cash'] | ['Contract Low Interest', 'Contract Low Down', 'Contract Low Down payment and low interest']\n", + "\n", + "The variable Street has mismatching unique values:\n", + "['Gravel'] | []\n", + "\n", + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.626082251082251\n", + "house_price_auc.csv did not exist and is created. \n" + ] + } ], - "text/plain": [ - "" + "source": [ + "%time \n", + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2007', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "12ee98ba", + "metadata": {}, + "source": [ + "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", + "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", + "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "36ca3084", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_datadrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_datadrift_2007.html', \n", + " title_story=\"Data drift\",\n", + " title_description=\"\"\"House price Data drift 2007\"\"\", # Optional: add a subtitle to describe report\n", + " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" # Optional: add information on report\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "6b87062a", + "metadata": {}, + "source": [ + "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", + "However, to illustrate functionalities, we will detail results with notebook mode analysis." + ] + }, + { + "cell_type": "markdown", + "id": "79088691", + "metadata": {}, + "source": [ + "## First Analysis of results of the data drift" + ] + }, + { + "cell_type": "markdown", + "id": "0893b964", + "metadata": {}, + "source": [ + "Data driftn methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", + "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", + "Then a classification model (catboost) is learned to predict this target.\n", + "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" + ] + }, + { + "cell_type": "markdown", + "id": "ae3264d5", + "metadata": {}, + "source": [ + "### Detection data drift performance" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "53b9e7f0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Performance of datadrift classifier\n", + "SD.plot.generate_indicator(fig_value=SD.auc, height=300, width=500, title=\"Datadrift classifier AUC\")" + ] + }, + { + "cell_type": "markdown", + "id": "71510d9b", + "metadata": {}, + "source": [ + "An Auc close to 0.5 means that there is little drift" + ] + }, + { + "cell_type": "markdown", + "id": "6d44c81e", + "metadata": {}, + "source": [ + "### Importance of features in data drift" + ] + }, + { + "cell_type": "markdown", + "id": "254bab01", + "metadata": {}, + "source": [ + "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0515dfb4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.xpl.plot.features_importance()" + ] + }, + { + "cell_type": "markdown", + "id": "02b42aa6", + "metadata": {}, + "source": [ + "We get the features with most gaps, those that are most important to analyse.\n", + "With date bias introduced, it is normal that date features are the most impacted. We will then decide to remove them.\n", + "Let's analyse other important variables" + ] + }, + { + "cell_type": "markdown", + "id": "298da0cc", + "metadata": {}, + "source": [ + "### Feature importance overview" + ] + }, + { + "cell_type": "markdown", + "id": "de122753", + "metadata": {}, + "source": [ + "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "94307417", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8uP9m3mMChZD3mA0ePNg3BIFzi2WET8c19gpu2LBBteOfeC76z9FALk2bNs1woi+XZeHjuW0k3kfI+045BUaxWHY4gui/FzbY3Z7+7xlWxsbK3Ak1V83M08BnC4wAssxx5DWr98Onn35a7cU2Eks57yf2398a7Dl1zMFQfPA6CICADAEIoQxX1AoCYSdg5sOa0WiwJaOB99BxRI0/qHEUaNu2bXT06FF1IAQvtwqWWLgaNGjge8nsh2cu4KSthx56iHbt2uVr1z8SYPxw69atxAesZPUBiH8e+CGKv/XmfXRZJbcKIR+ow3LvP068F+jWW29Ve7WWL1/u69Itt9yi9toFpsArM/h1PlDmkksuUdFSFi3uP//jn1tJVuapf70cweG+WU2LFy/2LRt1Ms+4XbNz2ozsmcljZU6Gg08otmbGLlAIWQZZCo3E0Xr/E22rVauW4RAmjkD75+dyvAqADzMyUiAXrpMPEvJPfEjWCy+8kOFn/F7G0a277rqLDh065HuNf8+NKJfxQ36vC9w3yHvlONJp5/3C7Nxx+n4Y7Nn8n5tf5y/y/JeR88/4iyTee8uJ3z/892PzqgNeQhoq6ZiDoZ4Br4MACMgQgBDKcEWtIBB2AmY+rBmNBltm6f+hgT948/Ip/+V3oR54+PDhGU4cNfsByGlb/EGQZdVIgcsE+ee89ItP1vRPoSKELEosTFkltwohPy+Pnf99kyyDfNoofxD2X+aWVR85D0skR1qz+gKA2+GoC+fjU1zNJivz1L9OXroc7FCMUO2yGJQqVUrJpK45HUz2eBkkL0c00o4dO9RJm/4p1Cmj2c1Jp3xCceTXzYxdoBDyIUx8GJORuA+fffaZ77/5lGJ+7zASixrPU/9kjKHxs8DfPT75N7BdPh2Zl5H6J+Ngn0BWHLXkiHNgaty4cYb9jRw5a968ucoW+Ayh3i90vR8Ge7ZAPvw7Xa9evQzd9RdCXv3Bh+4YifcdB0Ztg80XHXPQzDxFHhAAgfATgBCGnylqBAERAmY+rHHD/GGAvxFft26d7zkCr50YOHAg8YcnKynwA5HZD0BO25KKEPKBJLy3KavkZiEMXBrKe/94T6X/vipegsdL6rJaEsv95igB7y3iZXy7d+9WV48EnuTKpxPycjuzyew8DazPbvSBo9wc2XQ6z/h5zM5p5mREkox+BEoNH2gSGJUKJYTZzUmnfMyMn5mxC3bthBF54jYChZC/0OEIn5FY3Lkv/imUELohQhjq/cLs3AnHPA313sRssxsn3RFC43fUzBxEHhAAgcgQgBBGhjtaBQHLBMx8WONK+aATPvDEPwUeKMMHj/C9e0bi6yj4Pi+OtLBA8HIq/mbffw9hoBAGLg0Ldlog1++0rcD9LpdeeqkSFP89hPxhjffB+adQEUL/b8yDDUaoD11mD5Uxy8nKhOCDHm6//fYM48PjlpKS4quGD5LhA2WsJpYdjpLwATVGCrxjL7s6zc7TwDoCT0dlwQ2MAGXXrtN5xnWbHatgEelAYeArD/iDsH8KJYTZzUmnfMzMAzNjFwkhDHz/4r4EilV2ewiDvWdY3UMY6v3C7NwJxzwN9d7EfLIbp8B9nmb3EOqYg2bmKfKAAAiEnwCEMPxMUSMIiBDI6sMaH0LAH1D5InpeqsWXQ/snPtmP7/ry36PDSyX5InIj8f47vssqX758aj8hR5p+/fXXDPUECiELGH8INhKf7sj7DFkq/WXNaVvBlr/yZdIsDBwF44vVeR9T4HJDtwihWU5WJ02oY+P5+gw+Fj9Y4qskeN8lLwHj6xt4jvCVCfxFAO9B5AM7/E8qDYzgSAghy5L/PiY+0Za/pOD9ZjynLl68qC4k5yWHfCk2X1DOe1rbtm2rHsfpPOM6zI4VR+H5CxP/6xR42SRLIO9h48NNmHHgclwnQuiUj5n55VYh5PcTPr2UTxdmpnyReiBf/73FwWSPI7odOnRQB6fweyQfvsTzyUihThkNJYRm50445qlTIczulNGaNWuqOcynTfN7K89x4/RWHXPQzDxFHhAAgfATgBCGnylqBAERAlZObzQegPc08WXm/EfeP5m9d82/TKAQBjva3T8/R5k4uue0LRYT7jvfwWcluUUIzXKy0jfOG+xDnVEHn+jIl9H7i3ngWPrv88qubb7QniNdWdUVWNaMVGTVHn/wDPwiIrtn4z2D/EUGJ6fzjOuwMlZ8oAmLspXkRAi5HSd8zDynmbGLRIQw1LPz9TO8H5avzuHE0sgCyfsMzSQ+OImvjPH/AsWMdPnXbXbuhGOemnm2UOMUeHpzVpwC7yGUnoNmxgt5QAAEwk8AQhh+pqgRBEQIWBVCjv7w/X0lSpTI9Dx84AXvM/SPEvpn4oghLxM8ceKE78eBQsjRJM6X1cE0hhCGoy3e38Yf/LM6hbJLly6Z7iVzixCa5WR10rAo8/4s/zEy6njiiSfUB+KsUuA+r6zy8b1lb7/9tqN7CINdip1Ve3xKKN+JyFFhM8lfCMMxz6yMFS+t5d9J/7vsjGdmweD5GngVilMhdMLHDE+3CiHPZ17l4L+E3egPR7P4S6/AuzL5LkS++9D/epVgDFgm+ToePmjFP5mRLv/8ZudOOOapmWcLJYTMcujQoTRjxoxsp0agEErPQTPzFHlAAATCTwBCGH6mqBEERAgEE0KO2vCHT76LjpdClS1blqpXr058bxcv4cwu8eEhfDodH9vPS6e4Dl4+yJLHp1byv+yEkOvmI+THjx+vLjk/fPhwhmWGhhByvnC0xc/C9w3y/VkshnxZee3atYmXivEpmIEf6NwihFY4WZ04gfuWjPJ87D/fvZadeK1atUpFUPjwIR47PsmV9yAy1woVKqgP2I888oharmklmZGKUPXxJfQcleSloSxevByYn4sj3rz0mcedP/DyoUB8/6KRwjHPzM5pbpNPG+X5zxd4sxjy7+B1111HLDB80A/vzfVPToXQqMsun1DczYxdKNGQOFSGl2uyuPHvP89Z3v9ssOZloHxVSlaJl0BztJyX1PM8ZxHipea8r5C/NOPl54mJiZmKm5GuwEJm547TeWrm2UKNk/HsmzZtUkucV69erb7cY+Hj3zN+D+C/AXwibDA+UnMw1BzF6yAAAjIEIIQyXFErCIAACIAACICADQJmhMdGtSgCAiAAAiCQBQEIIaYGCIAACIAACICAawhACF0zFHgQEAABjxCAEHpkoNFNEAABEAABEMgJBCCEOWGU8IwgAAK5iQCEMDeNJvoCAiAAAiAAAjmcAIQwhw8gHh8EQCDHEYAQ5rghwwODAAiAAAiAQO4lACHMvWOLnoEACLiTAITQneOCpwIBEAABEAABEAABEAABEAABcQIQQnHEaAAEQAAEQAAEQAAEQAAEQAAE3EkAQujOccFTgQAIgAAIgAAIgAAIgAAIgIA4AQihOGI0AAIgAAIgAAIgAAIgAAIgAALuJAAhdOe44KlAAARAAARAAARAAARAAARAQJwAhFAcMRoAARAAARAAARAAARAAARAAAXcSgBC6c1zwVCAAAiAAAiAAAiAAAiAAAiAgTgBCKI4YDYAACIAACIAACIAACIAACICAOwlACN05LngqEAABEAABEAABEAABEAABEBAnACEUR4wGQAAEQAAEQAAEQAAEQAAEQMCdBCCE7hwXPBUIgAAIgAAIgAAIgAAIgAAIiBOAEIojRgMgAAIgAAIgAAIgAAIgAAIg4E4CEEJ3jgueCgRAAARAAARAAARAAARAAATECUAIxRGjARAAARAAARAAARAAARAAARBwJwEIoTvHBU8FAiAAAiAAAiAAAiAAAiAAAuIEIITiiNEACIAACIAACIAACIAACIAACLiTAITQneOCpwIBEAABEAABEAABEAABEAABcQIQQnHEaAAEQAAEQAAEQAAEQAAEQAAE3EkAQujOccFTgQAIgAAIgAAIgAAIgAAIgIA4AQihOGI0AAIgAAIgAAIgAAIgAAIgAALuJAAhdOe44KlAAARAAARAAARAAARAAARAQJwAhFAcMRoAARAAARAAARAAARAAARAAAXcSgBC6c1zwVCAAAiAAAiAAAiAAAiAAAiAgTgBCKI4YDYAACIAACIAACIAACIAACICAOwlACN05LngqEAABEAABEAABEAABEAABEBAnACEUR4wGQAAEQAAEQAAEQAAEQAAEQMCdBCCE7hwXPBUIgAAIgAAIgAAIgAAIgAAIiBOAEIojdkcDf/zxB02YMIFGjhzpjgfKpU9x+vRpSkhIoLi4uFzaw8h36+LFi3TmzBkqVKhQ5B8mFz8BM+Z5HB8fn4t7GdmupaWl0alTpygxMTGyD5LLWz979izFxMSo92YkGQLp6el04sQJKly4sEwDqFUROHfuHEVFRVGePHlABATCSgBCGFac7q0MQqhnbCCE8pwhhPKMuQUIoTxnCKE8Y24BQijPGUIozxhCqIexV1uBEHpk5CGEegYaQijPGUIozxhCqIcxhFAPZwihPGcIoTxjCKEexl5tBULokZGHEOoZaAihPGcIoTxjCKEexhBCPZwhhPKcIYTyjCGEehh7tRUIoUdGHkKoZ6AhhPKcIYTyjCGEehhDCPVwhhDKc4YQyjOGEOph7NVWIIQeGXkIoZ6BhhDKc4YQyjOGEOphDCHUwxlCKM8ZQijPGEKoh7FXW4EQemTkIYR6BhpCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGMIoR7GXm0FQuiRkYcQ6hloCKE8ZwihPGMIoR7GEEI9nCGE8pwhhPKMIYR6GHu1FQihR0YeQqhnoCGE8pwhhPKMIYR6GEMI9XCGEMpzhhDKM4YQ6mHs1VYghB4ZeQihnoGGEMpzhhDKM4YQ6mEMIdTDGUIozxlCKM8YQqiHsVdbgRB6ZOQhhHoGGkIozxlCKM8YQqiHMYRQD2cIoTxnCKE8YwihHsZebQVC6JGRhxDqGWgIoTxnCKE8YwihHsYQQj2cIYTynCGE8owhhHoYe7UVCKFHRh5CqGegIYTynCGE8owhhHoYQwj1cIYQynOGEMozzo1COH36dJozZw5NmDBBHOCDDz5Iffr0oZtuukm8rZzYAIQwJ46ajWeGENqAZqMIhNAGNItFIIQWgdnMfubMGYqLi6P4+HibNaBYKAIQwlCEwvM6hDA8HLOrBUIoz9gtQtijRw+qVKkS9e3bN0OnFy5cSEOGDKHZs2eb/ruxYsUKWrduHbVr104c4DvvvENNmzalK664wlRbX375JXGfxo8fbyp/Ts8EIczpI2jy+SGEJkE5zAYhdAjQRHEIoQlIYcgCIQwDxBBVQAjlGXMLEEJ5zhBCeca5UQh1UEtJSVFfblpNEEKrxJA/RxCAEOoZJgihPGcIoTxjbgFCKM8ZQijPGEKohzGEUA/nc+fOUVRUFOXJk0dPg0FaMRsh/PTTT2np0qV0yy230LRp09TflKuvvlot2yxYsKCq2X/J6KxZs2jixIn0+eefU0xMjK/lwYMH0/nz52nQoEG0fft2tbz0r7/+ouTkZKpYsSK1b99e1WukZ555hsqXL0/Mitu/9NJLadiwYeS/ZJTrGzVqFK1du5aOHz9OpUqVogceeEDl4TRv3jxVxj/17NmT7rvvPtWP//znP7Ro0SL1XJdddhl17NiRqlevHrExCUfDiBCGg2IOqANCqGeQIITynCGE8owhhHoYQwj1cEaEUJ4zhFCeMbeQ04Twk08+oXvvvVdJG0fqBgwYQNWqVaPu3btnEsJTp07Rv//9byV+9evXV6+zcD3yyCPUr18/aty4Mf3555+0c+dOqlq1qlqW+tNPP9GUKVPoww8/pJIlS6oyLIQser169aImTZpQamoq5c2bN4MQ8me1b775RrVTqFAhJZhvv/22WgbbqFEjVU9WEULOwxHHNm3aUGJiIv3888/E8suiymKZUxOEMKeOnMXnhhBaBGYzO4TQJjgLxSCEFmA5yIoIoQN4JotCCE2CcpgNQrPCYCwAACAASURBVOgQoIniEEITkMKQJacJIUcGv/jiC4qNjVW9nzlzJs2YMUMJHKfAQ2VeeuklJW8sjpx4D9/o0aOVnGW1n50jd7feeqsvusdCyGno0KEZiIc6VOaDDz6gPXv2ED9DVkLIosnP9tVXX1G+fPl89T/99NNUt25dat68eRhGOTJVQAgjw117qxBCPcghhPKcIYTyjLkFCKE8ZwihPGNuAUIozxlCKM+YW8hpQrh48WIaM2aMDw5H0zgSx1IYTAj59bfeeksJIC+LHThwIBUrVoxYuDjxZywWt2XLltHRo0eJ30M5/etf/1LLNjmxEPKhN127ds1WCKdOnUpz586lgwcPquglJz5wxnjeYBFCFtxx48YFHWw+sCbwoB09syI8rUAIw8PR9bVACPUMEYRQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xm4Rwv79+6vomBFJM3r+9ddf0/vvv69OGeV9jsYeQo7wGYmFb8SIEcR5gwnhhQsX1LJRjvpdc8019Oijj6qTS+vUqaPy8//nKF7v3r3pkksuUdLIy0krV67sE0AWQha7J598MkshZBHkZ33uuefU8tP8+fMTL2/95ZdffKeKBhNC/hnvcWQxzG0JQpjbRjSL/kAI9Qw0hFCeM4RQnjGEUA9jCKEezhBCec4QQnnGbhFCjqAtWbKEJk2aRNHR0b6Ov/nmm7Rt2zYlWpzsCCGX48NckpKSqF69esRRPK7HaKd169b02GOP0d13363a4P2BvEyTD64xIoJmhHD48OHE8skRSCO9/PLLtH//fp8QchST70jkA2SMtHr1aiWgI0eOpBo1augZdE2tQAg1gY50MxBCPSMAIZTnDCGUZwwh1MMYQqiHM4RQnjOEUJ6xW4Rw79691KFDB3X4Cp+6mZCQQL/99psSRF7aefvttzsSwlWrVql9euXKlaMGDRqotoz0/PPPq/ZY5DgKyfLJ0UY+IdSKEHKkj0815eglHyrDh9Ow0FaoUMEnhCy9fMIpL3EtUaKE2tvIh8mwEB44cIA6d+5MVapUUaeU8hLW2rVrZzjtVM+MCF8rEMLwsXR1TRBCPcMDIZTnDCGUZwwh1MMYQqiHM4RQnjOEUJ6xW4SQn2PDhg308ccfq4ggX//A8tasWTN1qqeR7EYI+X2xZcuWdOTIESV8vBzUSCxiHN3bsWOHWi568803q2coW7asJSHkPYN87QSLHEseXxlRpkwZJbbGRfT8WYMPpuGf8Wc749oJ3sfJfeflryyDRYoUUdHCdu3aqefIqQlCmFNHzuJzQwgtArOZ3V8Iz6Sm064zqXTofCr9dSKFki+m0xWJcVQhfyyVyhNDRRP+f8+OzeY8WQxCqGfYcaiMPGcIoTxjbgFCKM8ZQijP2E1CqKe3aEUnAQihTtoRbAtCqAe+IYQn0mLoy+2n6JfD54M2fHnBOOpRLZGKQwotDwyE0DIyWwUghLawWSoEIbSEy3ZmCKFtdKYLQghNo3KU0Q2njDrqAAq7lgCE0LVDE94HgxCGl2dWtbEQno/NSwNWHaOzqX8fh5xdGlSnGFUp+Pf9PEjmCEAIzXFymgtC6JRg6PIQwtCMwpEDQhgOitnXASGUZ8wtQAj1cPZiKxBCj4w6hFDPQB85fZ7Gbj1LG0/+fadNqFQkPppev7oYFY7//0ldocp4/XUIoZ4ZACGU5wwhlGfMLUAI5TlDCOUZQwj1MPZqKxBCj4w8hFDPQH+z4yR9vuusOv3KbLqpZB7qfGWi2eyezwch1DMFIITynCGE8owhhHoYQwj1cEaEUA9nL7YCIfTIqEMI9Qz06D+P0+IjyZaE8NICcdSnRmEqiiihqUGCEJrC5DgThNAxwpAVQAhDIgpLBkQIw4Ix20oghPKMESHUw9irrUAIPTLyEEL5gT5+IY3eWneUtp+5aEkI88ZE0dC6xagYDpgxNUgQQlOYHGeCEDpGGLICCGFIRGHJACEMC0YIoTzGkC0gQhgSETLYJAAhtAkupxWDEMqP2JHzF2nAisN0Jo0sCSE/2et1ilLlgnHyD5kLWoAQ6hlECKE8ZwihPGNuAUIozxkRQnnGiBCGl/FLL71ExYsXpx49eqiK+f2YL6H/9ddf1b2DY8aMoSuuuCK8jbq4NgihiwcnnI8GIQwnzeB1nUxJoxF/HKO/TqVaEsLCcdE06OqiuILC5BBBCE2CcpgNQugQoIniEEITkMKQBUIYBoghqoAQyjN2ixDyZe0LFixQHY6OjqaCBQtSpUqVqHHjxnT33XdTTIz5O5Y3bdpE3bp1oxkzZlCBAgVMQ+QL6h9//HFffr6kvkSJElSrVi166KGH1POEStwmP3uTJk1U1kWLFtFbb72lLqwvVqwY7dmzR8lids/Gl9h/9dVX2TY1depUKlq0aKjHyfS6XTaWG/qnAITQLrkcVg5CqGfAxm88Tj8esraHsFpiPPWsWogS482/ierpjTtbgRDqGRcIoTxnCKE8Y24BQijPGUIoz9hNQrhz507q378/GeO+Zs0amjZtGlWuXJmGDBlC8fHxpoDYlR5DCJ955hm68sor6cKFC0rg5syZQ+vWraPnnnuObrzxxqDPkJqaSrGxma/7+vLLL+m///0vvffee6qcmWc7evQoJSUl+drp27cv3X777dS0aVPfz1hOrUiyUdBM+6Ygm8wEITQJKqdngxDqGcFlh87QyI2nLEUIm1fKT/eVN//NmJ6euLcVCKGesYEQynOGEMozhhDqYQwh1MPZDXsIOUJ48OBBGj58eIZOb926lbp27UpPPPEEPfroo+q1Tz75hH744QdigUtMTKSGDRvSk08+SRzRC4zycX6OMj7//PNq2eYXX3xBLJ4sUzVq1FCRxNKlS6t6jbLDhg2j2rVrZ3iOV199lVavXk2ffvqpaof/d+nSpartb7/9lo4dO0bz5s0j/yWjr7zyimrTSBxtPHz4cIZ6jWfLbqSbNWtG//73v+mxxx5T2fjv6H/+8x8VfTx//jxddtll1LFjR6pevbp6fe/evfTuu+/Shg0biEWV+9ehQweqWLFihgioPxupmQYhlCLrsnohhHoG5OTZ8zRtXzIt2H/eVIMcHexVLZEKxeEeQlPAiAhCaJaUs3wQQmf8zJSGEJqh5DwPIoTOGYaqAUIYilB4XnezEHIPWeY4YsaSw+nzzz9X8lOyZEk6dOiQ+vlVV11F3bt3V69nFQX7/vvvKW/evEqMWKSmTJmiIoAcveNlqtkJoVHn66+/Ttdee60SwsmTJ9Odd96pZIyvBcuXL18GITSe9eeff6axY8dm+2xWhJAjhnFxcdSmTRslxFw/P8+ECROoVKlS1Lt3b7U8tW3btiofCzD3m5e+IkIYnt8Z1BJAAEKoZ0rwRuSTUfH05a4ztOzIhWwbLZ8vlp6qXpjK5MVSUSujAyG0Qst+XgihfXZmS0IIzZJylg9C6IyfmdIQQjOUnOdxuxCy6MydOzfLfXUrVqygwYMH0/Tp0y1JF0vhAw88QOPGjVPLUrMTQl4+es8996j9f/fff78SMBZTXhLKEUMjBR4qw3nCKYRr166lAQMGKBYsoEZ6+umnqW7dutS8eXNq0aKF+t/77rsv0+SAEDr/fUENQQhACPVMCxbChIQEOpseQ6uOJdOU7afpdGpahsbzxUbTraXy0B2X5KPieSCDVkcGQmiVmL38EEJ73KyUghBaoWU/L4TQPjuzJSGEZkk5y+d2IeTlkfPnz1fyxWnJkiUqurdr1y61l9dIM2fOVHKWlfTs2LGDPvjgA7WU8sSJE75yr732Gl133XXZCmFycjLde++91LNnTyVaLIQsenwAjH+SFkLeU8kCGyzxHkOOHnKe999/X0VRWRJvuOEGJbycIITOfldQOgsCEEI9U8MQQg79czpw7iKl8zrxMymUnEZUPn8ssQMWjo+h+OgoPQ+Vy1qBEOoZUAihPGcIoTxjbgFCKM8ZQijPmFtwuxDyklEWuNGjR9Pu3bvVfjje+9eoUSMqVKgQrV+/nvr06eM7uTOY9PBcatWqlRIkjqDx1RC8TJRPMH3hhReUNJlZMsqRyPr16/v2EPIz6RRClmKOOrL0ZZf279+v9jiuXLmSli9fTl26dKEHH3wQQqjnV8p7rUAI9Yx5oBDqadVbrUAI9Yw3hFCeM4RQnjGEUA9jCKEezm4WQuNQmfbt26uDVRYuXKj2yrEUGYmvcOA9esZVDtu2baNOnTopaWJh5MSHubAIfvzxx1S2bFn1MyPfyy+/HFIIBw0aRHzqaeChMlaFMNizhRpl/0Nl+GCbfv360ciRI9WhOGYS8+JltRxZtNO+mTayyoNDZZzQy0FlIYR6BgtCKM8ZQijPmFuAEMpzhhDKM4YQ6mEMIdTD2S1C6H/txMmTJ9WpnoHXTnD0j5dtvvPOO+qC9+3bt6tDZ/hwGUMIT506RY888ohaPskHwPB1FfzvX//6lzplkwWL8/ApoLwnj5d5+kcIjWsnUlJS1KEzs2fPVvmMSCKPinHKqFUhDPZs/nsBg424vxDy7wQLIUczO3fuTFWqVKHjx4/TsmXL1MmoV199tWLDp5eWKVNG9ZPvQOT/P3DgQPXfgWxCte9kFkIIndDLQWUhhHoGC0IozxlCKM8YQqiHMYRQD2csGZXnDCGUZ8wtuEUI/S+m5wvled8bi81dd92V4Y4/lkTj4nY+aZRP+nz77bczXPbOSys5D18HYVzt8Pvvv9OYMWPUF5P58+dXS0hHjBihDmnxF0KDOkskXxXBosXLLY19eE6EkMsGe7bsRjrw2gkeL4508h5GlsEiRYqoaGG7du1U9JPvbOR7E7nv3E9e4spLRgsWLKiasdq+k1kIIXRCLweVhRDqGSwIoTxnCKE8YwihHsYQQj2cIYTynCGE8ozdIoR6eopWdBOAEOomHqH2IIR6wEMI5TlDCOUZQwj1MIYQ6uEMIZTnDCGUZwwh1MPYq61ACD0y8hBCPQMNIZTnDCGUZwwh1MMYQqiHM4RQnjOEUJ4xhFAPY6+2AiH0yMhDCPUMNIRQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xhBCPYy92gqE0CMjDyHUM9AQQnnOEEJ5xhBCPYwhhHo4QwjlOUMI5RlDCPUw9morEEKPjDyEUM9AQwjlOUMI5RlDCPUwhhDq4QwhlOcMIZRnDCHUw9irrUAIPTLyEEI9Aw0hlOcMIZRnDCHUwxhCqIczhFCeM4RQnjGEUA9jr7YCIfTIyEMI9Qw0hFCeM4RQnjGEUA9jCKEezhBCec4QQnnGXhLC6dOn05w5c2jChAl6wKIVghB6ZBJACPUMNIRQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xm4RwqFDh5L/xfR84fzNN99Mjz/+OPEF8eFIEMJwULRWB4TQGq8cmxtCqGfoIITynCGE8owhhHoYQwj1cIYQynOGEMozdpMQ7ty5k/r370/89/jPP/+kMWPG0H333UddunQJCwgIYVgwWqoEQmgJV87NDCHUM3YQQnnOEEJ5xhBCPYwhhHo4QwjlOUMI5Rm7SQgPHjxIw4cP93V65MiRtHLlSpo4cSK1aNGCHnvsMXrwwQd9r+/Zs4fatWtH7733HlWpUoU++eQT+uGHH+jAgQOUmJhIDRs2pCeffJLy5MmjygQK4dixY2nfvn302muv+eqcOXMmffnllzRp0iTfz5YtW6b+e/v27VSkSBFq3LgxtW3bNmyRSz2jHJlWIISR4a69VQihHuQQQnnOEEJ5xhBCPYwhhHo4QwjlOUMI5Rm7WQhZ9H788UeaOnUqvf/++7Ru3ToaPXq0DwqL4s8//0wffPCB+tnnn39O1atXJ15ueujQIXr33Xfpqquuou7du9sWwlWrVtGLL76oopR16tShY8eOqWeoWbMm9ejRQ88A5eBWIIQ5ePCsPDqE0Aot+3khhPbZmS0JITRLylm+M2fOUFxcHL5ZdYYx29IQQkG4flVDCOU5QwjlGbtRCPk9bMOGDUrE6tWrRwMHDqStW7dS586d6eOPP6ayZcsqMG3atKGmTZtS8+bNg4JasWIFDR48WEUGOdmJED799NNUrVo1at++va+NtWvX0oABA2jWrFkUHR2tZ5ByaCsQwhw6cFYfG0JolZi9/BBCe9yslIIQWqFlPy+E0D47syUhhGZJOcsHIXTGz0xpCKEZSs7znDt3jqKionxLK53XaL0G41AZFiwed/533XXXEQtZ4cKFVYW8/JOXa/JBMyyMPXv2VMtES5UqpV5fsmQJTZkyhXbt2kX8+2kkXgbKy0btCOEDDzyQoS7/nnFbJUqUsN5ZD5WAEHpksCGEegYaQijPGUIoz5hbgBDKc4YQyjPmFiCE8pwhhPKMuQW3CCEfKsMCGBsbq0TL2PtnUPjss89o3rx5KkrIB85w1HDEiBHq5d27d1OHDh2oW7du1KhRIypUqBCtX7+e+vTpQzNmzKACBQpkEkJekrp3794Mewi/+eYbmjZtmm8PIR9qw9FB/72LekYld7QCIcwd4xiyFxDCkIjCkgFCGBaM2VYCIZRnDCHUwxhCqIczhFCeM4RQnrGbhDDwUJnA3vO+wJYtW9I777yjlpPygTJ33323yrZw4UJ1vyDvIzQSiyAfHJOVEHLeRYsWZdiXyKJpHCLD9bBQ8jaHIUOG6BmMXNYKhDCXDWhW3YEQ6hloCKE8ZwihPGMIoR7GEEI9nCGE8pwhhPKMc5IQ8rP27t1bRec5IvjFF1+oyB+nTZs2qSWkLItXXHGFOhH0+eefV4fLZCWEf/31lyrDp5nyPkE+tIZFs2DBgr4IIR8qw1dhcKSQ5TMhIUHVzfsIORqJlD0BCKFHZgiEUM9AQwjlOUMI5RlDCPUwhhDq4QwhlOcMIZRnnNOEcPbs2UrgbrzxRnrppZcyAOKlnl999ZX6GZ80euedd9Lbb7+dpRByPt5XyNdMpKamUo0aNeiyyy6jBQsWZLh2gqWQr53YvHmzOkSmXLly1KRJE2rWrJmeAcrBrXhSCPmPA69lXrp0qfrGgsPa/I1CsPT777+rjbA8uYoXL04fffRRjhxuCKGeYYMQynOGEMozhhDqYQwh1MMZQijPGUIoz9gtQqinp2hFNwFtQnjDDTdY6huvFZZKLIN8wSWHqDmU/eyzz6rjbmvVqpWpST4daf/+/eo+E/62A0IoNSq5o14Iofw4QgjlGUMI9TCGEOrhDCGU5wwhlGcMIdTD2KutaBNC3kBqJfGRtRKJQ80PPfQQvf766+oSTE7Dhw9X/9u3b98sm+QLNVkGIYQSo5J76oQQyo8lhFCeMYRQD2MIoR7OEEJ5zhBCecYQQj2MvdqKNiF0C2A+trZt27b09ddfU/78+dVj8f//73//qza4ZpWyEkL+cJoTEi8ZZZk15DcnPHNOfEb+4BEfH6+OYkaSIcC/c3z0trFBXaYV1MqMeR7zqW1IMgRYCPl6Dz4YAUmOAM/lmJgY9d6MJEOAhfDUqVPqCgEkOQLnz59X9xDygSlZJZ7rSCBglYDnhHDLli3UpUsXmj9/vvql4sSbUqdOnaqOwbUqhCdOnLDKPCL5eenrp59+muEOl4g8SC5vlD/g8bwy5lYu727EusececM4khwBzGU5tv41Yy7Lc2ZZ4YT3ZVnWmMuyfLl2M3M5MTFR/kFc0MKRI0fUoTV8AmlSUpL6HF+0aFEXPFnOfISICSFfQskCtmbNGmKpWrlypSLI+/tat26tDnCRSOGOEEo8o0SdOFRGgmrmOrFkVJ4zlozKM+YWcDG9PGcsGZVnzC1gyag8ZywZlWfMLbjhYno9PQ3dytGjR2nx4sVUtmxZdd0EhDA0s+xyREQIly9fTt27d1eHuNStW1eJoSGEHMVi6+/Vq5eznmVRmvcQPvjgg/Tmm29SzZo1fRLKb2bYQyiC3FOVQgjlhxtCKM8YQqiHMYRQD2cIoTxnCKE8YwhhcMYcVHrkkUcghA6nYESEkPfwNWzYkDp27Kgen6XQEMIdO3ZQjx49aObMmQ67lnVx3kfHF2DyKaN79uyhAQMGqKWULKjGxZgdOnRQS9L4DzZL5K+//qruNnn//ffVspOctq8GEUKx6ZShYgihPGcIoTxjCKEexhBCPZwhhPKcIYTyjN0ghByh5H+RSHnz5iX+F5gghOEZjYgIYYMGDWjevHlkrHP2F0LeMNu4cWNatmxZeHoYpBb/ewj5YJlWrVr57iHkvXY9e/akuXPnqk3ofA8hh6L9U9WqVWn06NFizydRMYRQgmrmOiGE8pwhhPKMIYR6GEMI9XCGEMpzhhDKM3aDEHJg5OPPplJqjN7DxmJSU6hdi0fVljIIocxci4gQsvBNmTJFrfvl5C+E27ZtU5HDhQsXyvTYo7VCCPUMPIRQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xm4Rwld+30fbajTR0+F/Wrl0/ff00jVlIISC1CMihL1791ZHxr/88ssqCmcIIX/Q42WcvCSTL4pHCh8BCGH4WGZXE4RQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xhBCCKHkLIuIEG7evFndBVi6dGm66aab1N68Tp060U8//US7d++myZMnU8WKFSX77bm6IYR6hhxCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGM3CeF2zRHCyllECC9cuEAnT56k5s2b0yeffEJFihTBfaM2p2JEhJCfddOmTTRq1ChasWKFOrSFD3DhSCGf9Hn55Zfb7A6KZUUAQqhnbkAI5TlDCOUZQwj1MIYQ6uEMIZTnDCGUZwwhzBgh5M8CTZs2zQR+9uzZkEIb0zFiQmg8a0pKirJ7XkKakJBgowsoYoYAhNAMJed5IITOGYaqAUIYilB4Xsc9hOHhmF0tEEJ5xtwChFCeM4RQnrFbhHDQ7/tpe029ewgrr19IL14dfMmoHvK5v5WIC2HuR+yOHkII9YwDhFCeM4RQnjEihHoYQwj1cIYQynOGEMozhhBCCCVnmTYhtHrRPC8nRQofAQhh+FhmVxOEUJ4zhFCeMYRQD2MIoR7OEEJ5zhBCecYQQgih5CzTJoSdO3fO0A/+4Lxx40YqX748FS1alI4dO0a7du2iatWqqeWj48aNk+y35+qGEOoZcgihPGcIoTxjCKEexhBCPZwhhPKcIYTyjN0khDs0LxmthCWj4hNMmxD694Qvfx8/fjz16dOHKlSo4HuJhXD48OHUpUsX4svfkcJHAEIYPpaIEOphmVUrEEI9/LGHUJ4zhFCeMbcAIZTnDCGUZwwhRIRQcpZFRAhbtWpFr776KlWuXDlT37Zu3UqDBg2iiRMnSvbbc3VDCPUMOSKE8pwhhPKMESHUwxhCqIczhFCeM4RQnrFrhHDVftIeIfwDh8pIz7CICGGDBg1o7ty5VLhw4Uz9S0pKorvuuouWLFki3XdP1Q8h1DPcEEJ5zhBCecYQQj2MIYR6OEMI5TlDCOUZu0UIX1VCeLueDv/TSqU/FtILV5em1q1ba23XS41FRAibNWtGd9xxBwXuK2TwY8eOpe+//56mTZvmpXEQ7yuEUByxagBCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGMIIYRQcpZFRAjnz59PAwcOpDp16lDDhg2pSJEi6lCZRYsW0dq1a2nIkCHUpIneO04kIbuhbgihnlGAEMpzhhDKM4YQ6mEMIdTDGUIozxlCKM/YTUK4U3OEsCIihOITLCJCyL1avny5OliGRYUvp4+Li6NatWqpqGHdunXFO+61BiCEekYcQijPGUIozxhCqIcxhFAPZwihPGcIoTxjCCEihJKzLGJCaHTKeBNJTEykqKgoyb56um4IoZ7hhxDKc4YQyjOGEOphDCHUwxlCKM8ZQijP2FVCWEvvHkIVIawDIZScZREXQsnOoe7/E4AQ6pkNEEJ5zhBCecYQQj2MIYR6OEMI5TlDCOUZu0UIX1t1gHZqF8IF9DyEUHSSRUwI9+/fr66WWLlyJZ04cYI4QlivXj1q06YNlS5dWrTTXqwcQqhn1CGE8pwhhPKMIYR6GEMI9XCGEMpzhhDKM4YQIkIoOcsiIoTbtm2jdu3aUWpqqjpYpmjRoupQmdWrV1N8fDx99NFHVKlSJcl+e65uCKGeIYcQynOGEMozhhDqYQwh1MMZQijPGUIozxhCCCGUnGUREcKnnnpKHSQzePBgFRk0EkcKn332WXXAzMiRIyX77bm6IYR6hhxCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGM3CeEuzUtGK/yBJaPSMywiQtioUSP67LPPqFy5cpn6t2fPHmrRogX9/PPP0n33VP0QQj3DHSiEp1LS6EJaOqWlp9PGE8lUvXAe4j+cheJjKT5azzPltlYghHpG9MyZM+rLOV61gSRDAEIowzWwVgihPGcIoTxj1wjh6gOkXQjXQQilZ1hEhPD666+nWbNmUbFixTL17+jRo3TffffR4sWLpfvuqfohhHqG218ID51LpfEbj9GWU8m0/2yq7wEuyR9HVQvFU8eqxahoQoyeB8tFrUAI9QwmhFCeM4RQnjG3ACGU5wwhlGfsFiF8XQnhHXo6/E8rFdYtoOfqlKLWrVtrbddLjUVECHn/YPXq1alfv36ZWA8fPpzWr19PH374oZfGQbyvEEJxxKoBQwjXnbxIr60+RKdTLmbZcPE8sfRCnZJUrXCCnofLJa1ACPUMJIRQnjOEUJ4xhFAPYwihHs7nzp1TV7TlyZNHT4MBrUyaNIkghBFBL95oRIRw6dKl1KNHD7rsssuIl48WKVKEjh8/Tr/++itt2bKFxowZQ9dee614573UAIRQz2izEJ6NSaBWP+0x1WCemGj6sFFZYjlEMkcAQmiOk9NcEEKnBEOXhxCGZhSOHIgQhoNi9nVACOUZcwsQQkQIpWZaRISQO8NSOG7cOGJR4TcS/sajZs2a1LVrV8igwGhDCAWgBqny4Olz9Pra47TxZLLpBhuVzkddgZPW5AAAIABJREFUqxanYnmwfNQMNAihGUrO80AInTMMVQOEMBSh8LwOIQwPx+xqgRDKM3aTEO7WvGS0PJaMik+wiAmh0bPk5GQ6deoUFSxYkBISsHROasQhhFJkM9a77cQ56rR4v/qCw0qacnMFKg4hNIUMQmgKk+NMEELHCENWACEMiSgsGSCEYcGYbSUQQnnGrhLCq/TuIVRCWBt7CCVnWcSFULJzqPv/BCCEembDj3tP0utrj1gWwtfrlab6xfPqecgc3gqEUM8AQgjlOUMI5RlzCxBCec4QQnnG7hHCg7RbuxDOhxAKTzFtQrhixQpLXalXr56l/MicPQEIoZ4ZMmT1QVq4/4xlIfxX5cLU4coieh4yh7cCIdQzgBBCec4QQnnGEEI9jCGEeji7YQ/h4NWREcJnESEUnWTahLBu3bqWOrJy5UpL+ZEZQuiGOTBp01GavPWEZSHsW6sE3Vm2gBu64PpngBDqGSIIoTxnCKE8YwihHsYQQj2cIYQ4VEZqpmkVQj4mt3HjxnTzzTdT3rzZL4/j00eRwkcAEcLwscyupmUHT9Pzvx+yLIRjbyhLVQriAnAzowQhNEPJeR4IoXOGoWqAEIYiFJ7XsWQ0PByzqwVCKM+YW3CLEO7RvGS03Lr5hAih7BzTJoSLFi2i7777jn744QfVI5bCpk2bEl9SHxuLI/dlh5nUaa4TJkygkSNHSjfl6fq3JZ2lTksOWBLCuJhoGnt9GapYAEJoZvJACM1Qcp4HQuicYagaIIShCIXndQhheDhCCOU5hmrBFUK45iBpF8K1EMJQc8Pp69qE0HjQ8+fPKymcO3cuLVmyRJ0u2qRJE7rrrruodu3alj5IO+28l8pDCPWMdtKZszRrXzJN2ppkusGnahSnu8sXNJ3f6xkhhHpmAIRQnjOEUJ4xtwAhlOeMCKE8Y24BQoglo1IzTbsQ+nckKSmJ5s+fryKHa9eupTJlytCsWbOk+urpeiGEeoafL6Y/F5NAI/44SiuPngvZ6C1lClDnakWpSDzuIAwJ658MEEKzpJzlgxA642emNITQDCXneSCEzhmGqgFCGIpQeF53gxC+oSKEd4anQyZrKbd2Pg2sXZJat4YQmkRmOVtEhZCflkWQhXDOnDnqm4/ffvvNcidQIDQBCGFoRuHIwULI92keS42mL3Yk0cydJ7Os9l+XFqa7yhagcvnjwtG0Z+qAEOoZagihPGcIoTxjbgFCKM8ZQijPmFuAEEIIpWZaRIRw+/btSgJ52ejevXupVq1aasnoHXfcQUWK4Oh9icGGEEpQzVynIYRxcXGUnJZOa44l04ak87TtZDLtPnOBKhZIoMoF46l64QSqWTSB8kRH63mwXNQKhFDPYEII5TlDCOUZQwj1MIYQ6uEMIYQQSs00bUJ46NAhmjdvnhLBv/76iypXrqwOlWERLFu2rFT/UO8/BCCEeqaCvxD6t3g6NY3OpqRRvrgYKhAbpedhcmkrEEI9AwshlOcMIZRnDCHUwxhCqIezW4Rwr+Ylo2WxZFR8gmkTQr5onpfS3XTTTUoCr7zyymw7V6pUKfHOe6kBCKGe0c5KCPW07o1WIIR6xhlCKM8ZQijPGEKohzGEUA9n1whhbb17CJUQXoU9hJKzTJsQ4mJ6yWEMXTeEMDSjcOSAEIaDYvZ1QAjlGXMLEEJ5zhBCecYQQj2MIYR6OLtBCN9cc4j2ahfCeTQAQig6ybQJ4dSpUy115NFHH7WUH5mzJwAh1DNDIITynCGE8owhhHoYQwj1cMahMvKcIYTyjLkFCCH2EErNNG1CKNUB1GuOAITQHCenuSCETgmGLg8hDM0oHDkQIQwHxezrgBDKM0aEUA9jCKEezhBCCKHUTIMQSpF1Wb0QQj0DAiGU5wwhlGeMCKEexhBCPZwRIZTnDCGUZ+ymCOE+zUtGL1mLJaPSMwxCKE3YJfVDCPUMBIRQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xq4RwrWHSLsQroEQSs8wCKE0YZfUDyHUMxAQQnnOEEJ5xhBCPYwhhHo4QwjlOUMI5Rm7Swib6unwP61cooSwBLVujSWjUuAhhFJkXVYvhFDPgEAI5TlDCOUZQwj1MIYQ6uEMIZTnDCGUZ+wWIRyiIoT6hbA/hFB0kkEIRfG6p3IIoZ6xgBDKc4YQyjOGEOphDCHUwxlCKM8ZQijPGEKICKHkLIMQStJ1Ud0QQj2DASGU5wwhlGcMIdTDGEKohzOEUJ4zhFCesZuEcL/mCGGZNfMIEULZOaZNCHv16mWpJ6NGjbKUH5mzJwAh1DNDIITynCGE8owhhHoYQwj1cIYQynOGEMozdpUQ1tG7ZFQJYS1ECCVnmTYh7Ny5c4Z+8AfnjRs3Uvny5alo0aJ07Ngx2rVrF1WrVo0KFChA48aNk+y35+qGEOoZcgihPGcIoTxjCKEexhBCPZwhhPKcIYTyjN0jhIdpv3YhnAshFJ5i2oTQvx8bNmyg8ePHU58+fahChQq+l1gIhw8fTl26dKGqVasKd91b1UMI9Yw3hFCeM4RQnjGEUA9jCKEezhBCec4QQnnGbhHCoWsjI4TPIEIoOskiIoStWrWiV199lSpXrpypc1u3bqVBgwbRxIkTRTvutcohhHpGHEIozxlCKM8YQqiHMYRQD2cIoTxnCKE8YwghloxKzrKICGGDBg1o7ty5VLhw4Ux9S0pKorvuuouWLFki2W/P1Q0h1DPkEEJ5zhBCecYQQj2MIYR6OEMI5TlDCOUZQwghhJKzLCJC2KxZM7rjjjsocF8hd3Ts2LH0/fff07Rp0yT77bm6IYR6hhxCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGPXCOG6w3RA8x7C0qvnEpaMys6xiAjh/PnzaeDAgVSnTh1q2LAhFSlSRB0qs2jRIlq7di0NGTKEmjRpIttzj9UOIdQz4BBCec4QQnnGEEI9jCGEejhDCOU5QwjlGUMIESGUnGUREULu0PLly9XBMiwqKSkpFBcXR7Vq1VJRw7p160r22ZN1Qwj1DDuEUJ4zhFCeMYRQD2MIoR7OEEJ5zhBCecZuEcK3VITwLj0d/qcVjhD2q1WcWrdurbVdLzUWMSE0IBtvIomJiRQVFeUl9lr7CiHUgxtCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGMIIYRQcpZFXAglO4e6/08AQqhnNkAI5TlDCOUZQwj1MIYQ6uEMIZTnDCGUZwwhhBBKzrKICeH69etpwoQJtGbNGjpx4gStXLlS9XPEiBEqJFy8eHHJfnuubgihniGHEMpzhhDKM4YQ6mEMIdTDGUIozxlCKM/YTUJ48Gq9S0ZL8ZLRmhBCyVkWESHk/YPdu3dXewZ5vyCLoSGEn376KR05coR69erlqN+TJk2ir7/+mvjD42233UbdunWjmJiYoHXu2rWLhg0bRlu2bKGyZcuqtmvWrKny8h+Sd955h3777TfiP958EA6/zgfh5KQEIdQzWv5CeCIljdLS0ul8GlFCNFFMdBTFRkVT/lg9z5JbW4EQ6hnZM2fOqL3d8fHxehr0YCsQQj2DDiGU5wwhlGcMIYQQSs6yiAhh27Zt1emiHTt2VH1jKTSEcMeOHdSjRw+aOXOm7X7ztRV8YA2fVpo/f3569tln6ZZbbqGWLVtmqpP/ILdv3149T4sWLWjBggXEMjl58mRV9r333qN169bRq6++qj4YGXXyKak5KUEI9YwWC2F0fB7adCaN5uw5TfvOXqQjyRcpmojK5I2lYnliqH2VQlQqb/AvJ/Q8Zc5uBUKoZ/wghPKcIYTyjI0vdvkL4YSEBD0NerAVCKGeQT937pw6byNPnjx6GgxohT8fv7XuCOmPEH6HCKHwiEdECPli+nnz5hEfJBMohOfPn6fGjRvTsmXLbHe9f//+KsL3+OOPqzoWLlyoJI//BSZeusr5+d5D448FL1nlf3z1xUsvvURVqlTx1cWy+cUXXyjhzEkJQqhntPaePEfzD6bQvP1ns22wR9XCVKNwAhWOx0FKVkcGQmiVmL38EEJ73KyUghBaoWU/LyKE9tmZLQkhNEvKWT43COGwCAnh01gy6mzyhCgdESFk4ZsyZYpanhkohNu2bVORQ5Y4u+mxxx6jnj17qqgfp+3bt6s6Z8+enWn505w5c+jbb7+lcePG+Zp75ZVXqFy5cipyuGLFCvrkk0/ohRde8EUIL730UnriiSfsPl5EykEI5bGfTkmjz7edpAUH/v4GL1R6qnphalA8Mt/yhXo2N78OIdQzOhBCec4QQnnG3AKEUJ4zhFCeMbcAIcS1E1IzLSJC2Lt3bypQoAC9/PLLal+fsWSUP+g9//zz6sP04MGDbff5gQceoEGDBlHt2rVVHYcOHVLLRb/66itfVNKonCODixcvpuHDh/va4/2EHC3kpatJSUlqmSiLIadq1aqp/86bN6/6b349J6QNGzYoCeelr0jhJ8DzeOPZaBq6PokotAuqB4iJiqJR9YtRQsq58D9QLq+RP3yYke5cjkG0e2AsitdXOTjLc2bGnPCeIcsac1mWL9duZi4XLlxY7EF4pd2wP47QIc2HypRc9R0hQig2rH+/P6Ybs0u2nQy1b968mXgfYenSpemmm25SSzk7depEP/30E+3evVvt36tYsaLtJwpnhJD3CvLewT59+qgDFvgAnD179tDQoUPV8/E3vDkhcYTwww8/VKe4IoWfwInUdBq9IYn+OJasPnSY/eDRrGIBeqRCvvA/UC6ukb844m/8CxYsmIt7GfmuMePY2FgcKiM4FPz3g/cdFypUSLAVVM1RFf7SDgckyc0F/ih58uTJTF+6y7XozZp5WxV/vshuP2x0NJ9aIJMghDJc3VBrRISQO75p0yYaNWqUirylpqYST2COFPbt25cuv/xyR2x4T+BVV13lO0SG9/1NnDgxyz2EAwYMoOnTpyvh48Sy2qpVK7WHkOXyqaeeIt73yMlYfvrdd9+pD0s5JWHJqOxIHUlOo5dWH6XDZ1MoKtq8ENYrlkBdryxM+WJNhhVlu5EjaseSUT3DhCWj8pyxZFSeMbeAJaPynLFkVJ4xt+CKJaMqQni3ng7/08rfEcJi6nwPJBkCERNCozspKSnqWyVeQhquE8B4/yFH8t566y3Kly8fcZSP9y0ap4zyvsESJUpQ/fr1VYSP9wPy682bN1d7FzmSZpwyyvsJObGosgByvatWraIPPvhAZkSEaoUQCoH9p9p9Z1Opz4ojlHYxzZIQls4bQy/VLkZF4uW+0ZPtuf7aIYR6mEMI5TlDCOUZQwj1MIYQ6uHsBiEcHiEh7AshFJ1kERFCFrNmzZpR06ZN1dUOEokjgt98803QewhZEK+44gpq166danrnzp3qHsKtW7fSJZdcoiKCxj2Ex44do3fffZdWr16t5JFPHOU7DStXrizx2GJ1QgjF0KqKjyVfpK7LDlsWwkoFYmlArWJUOA4RQrMjBCE0S8pZPgihM35mSkMIzVByngcRQucMQ9UAIQxFKDyvQwgzRggXLVqkDoU8evSoWhnYr18/KlasWFDYfF4IB3z2799PxYsXVysBb7/99vAMTC6oJSJC2LVrV3XRO9+jcuedd9JDDz3kE7BcwNSVXYAQyg7LseQ0GrT2KO07bW3JaMMSeajLlYkUFw0hNDtCEEKzpJzlgxA642emNITQDCXneSCEzhmGqgFCGIpQeF53jRBeo3/JaN8aGZeMHjhwQN0GwNvErrnmGho9ejQdP37cd8aHP/ETJ06oLWC9evWi2267jdasWaNuD+Ar5CpUqBCewcnhtURECJkZGzpH8PgCeh5U3jfIUcO7775bLR9FCi8BCGF4eQbWdiY1nUb8mUTrjp6ztGT0X5UK0sMVZKLksj2OXO0QQj3sIYTynCGE8oy5BQihPGcIoTxjbsEtQnhYsxCWWPUdBQohn5z/+++/qxV+nIwbBfjnvC3MP/EKQA5GzZ0713foX5s2bdSVdDfccIOewXN5KxETQoML/0HkS+hnzJihThnlfXocwn344YepVq1aLseXcx4PQig/VvvPpVKvpYdMC2GpPH/vHyyagP2DVkYHQmiFlv28EEL77MyWhBCaJeUsH4TQGT8zpSGEZig5zwMh/P+S0TfeeIP4io0uXbr4wHJg6dlnn6V69eplgM3vtXyA5K233qoOjORtYFyezwORvKbD+YjrqyHiQmh0la9y+Pjjj5UYGolDwHxXoXGBvT4sua8lCKH8mF5II/ph32n6cOspU9dOvH51Mbqs4N8n2yKZJwAhNM/KSU4IoRN65spCCM1xcpoLQuiUYOjyEMLQjMKRww1COOKPo6Q/QjiH+gQsGX3xxRfVuR7+J48+/vjj1KFDB3WlXWDiGwfeeecdtWKAg0+835AFEelvAhEVwuTkZOIB4qWjfP0Eb/LkS+V5TyEL4pgxY9RDsigiOSMAIXTGz2zp3UlnaOs5onGbT2VZpGSeGOpZNZEuyR9H+WKwd9AsWyMfhNAqMXv5IYT2uFkpBSG0Qst+XgihfXZmS0IIzZJylg9CaC9CuGHDBnVbwKBBg6hOnTq0bds2eu6559Q+Qj6MBilCQrhx40YlgXz9A3/o4Dv+eIloo0aNMtztl5SUpA6d4SWlSM4IQAid8TNbmi+Z5sOSTl6MogX7ztKOM6m092wq5Y2JpjL5Yqhsvli6rUw+KoprJswizZQPQmgbnaWCEEJLuGxlhhDawma5EITQMjLLBSCElpHZKuAKIVwfgQjh75kjhLxXkJd+Dh06VLE8fPgwtWjRgoLtIWTfmDVrFo0dO9bHna+V4wNljBsHbA1ILioUkQghX0DP0cD7779fHSRTpkyZLJF27txZHSmL5IwAhNAZP7OlWQj5Ps24uL+XgvJhM2dT09QpovliowkeaJZk1vkghM4ZmqkBQmiGkrM8EEJn/MyWhhCaJWU/H4TQPjsrJd0ihEc0HypTPIgQ8uGUvDyUI30c9eMr4lgKDUH0v3OcD5XhE0ZfffVVql27tooQ8umkPXv2VPeQI0UoQvjDDz9kigZiMGQJQAhl+Rq1Bwqhnla91QqEUM94QwjlOUMI5RlzCxBCec4QQnnG3AKEMOM9hL/++qu6OiLYPYSBd44vWLBARQ/5NFI+SIbvQuc9h0h/E4hIhBDw9ROAEOphDiGU5wwhlGfMLUAI5TlDCOUZQwj1MIYQ6uHsHiG8R0+H/2nl7whh0QwHyGh9AA80FjEh5FDvxIkTaeXKlcQXRiYmJqpjYvlekNKlS3sAvd4uQgj18IYQynOGEMozhhDqYQwh1MMZEUJ5zhBCecZuiRC+vf4oHblGvxD2hhCKTrKICCGv3eVNnKmpqWrdb9GiRenYsWNqc2h8fDx99NFHVKlSJdGOe61yCKGeEYcQynOGEMozhhDqYQwh1MMZQijPGUIoz9hVQlg3AkJYHRFCyVkWESF86qmnKCUlhQYPHqwig0biSCFfKMkHcowcOVKy356rG0KoZ8ghhPKcIYTyjCGEehhDCPVwhhDKc4YQyjN2kxAe1SyExX6fQ70hhKKTLCJCyNdLfPbZZ1SuXLlMneP7B/nY2J9//lm0416rHEKoZ8QhhPKcIYTyjCGEehhDCPVwhhDKc4YQyjOGECJCKDnLIiKE119/vboPpFixYpn6xicF3XfffbR48WLJfnuubgihniGHEMpzhhDKM4YQ6mEMIdTDGUIozxlCKM/YPUJ4jPRHCGcjQig8xSIihLx/sHr16tSvX79M3Rs+fDitX7+ePvzwQ+Gue6t6CKGe8YYQynOGEMozhhDqYQwh1MMZQijPGUIoz9gtQjhyfWSE8CksGRWdZBERwqVLl1KPHj3osssuU/cRFilShI4fP058n8iWLVtozJgxdO2114p23GuVQwj1jDiEUJ4zhFCeMYRQD2MIoR7OEEJ5zhBCecauEcI/IyCEK2cThFB2jkVECLlLLIXjxo0jFhV+I4mKiqKaNWtS165dIYMCYw4hFIAapEoIoTxnCKE8YwihHsYQQj2cIYTynCGE8owhhNhDKDnLIiaERqeSk5Pp1KlTVLBgQUpISJDsq6frhhDqGX4IoTxnCKE8YwihHsYQQj2cIYTynCGE8ozdJITHNJ8yWhQRQvEJFnEhFO8hGlAEIIR6JgKEUJ4zhFCeMYRQD2MIoR7OEEJ5zhBCecbuEsJ79XT4n1b+FsIi1Lp1a63teqkxbUJ4ww03WOK6aNEiS/mROXsCEEI9MwRCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGO3COGoP4/Rsbr6hbAXhFB0kmkTwgkTJljqyJNPPmkpPzJDCN0wByCE8qMAIZRnDCHUwxhCqIczhFCeM4RQnrGrhLBeBISwGiKEkrNMmxBKdgJ1hyaACGFoRuHIASEMB8Xs64AQyjOGEOphDCHUwxlCKM8ZQijPGEIIIZScZREVwtTUVNq/fz8dOnSISpYsSWXKlKHY2FjJ/nq2bgihnqGHEMpzhhDKM4YQ6mEMIdTDGUIozxlCKM/YTUJ4XHOEsMjK2dQLEULRSRYxIZw2bRq9//77dOTIEV8HixcvTp06daJmzZqJdtqLlUMI9Yw6hFCeM4RQnjGEUA9jCKEezhBCec4QQnnGEEJECCVnWUSEcPLkyfTuu+/SvffeSzfeeKPvYvpffvmFZs2aRb169aKWLVtK9ttzdUMI9Qw5hFCeM4RQnjGEUA9jCKEezhBCec4QQnnG7hHC46Q/QjgLEULhKRYRIbzrrrtUJPDBBx/M1L3p06fTBx98QLNnzxbuureqhxDqGW8IoTxnCKE8YwihHsYQQj2cIYTynCGE8ozdIoTvbIiAEK6YRT2xZFR0kkVECBs2bEjz58+nAgUKZOocX1LftGlTwrUT4R13CGF4eWZVG4RQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xhBCLBmVnGUREcJu3bpRhw4dqE6dOpn6tmrVKhUh5CWlSOEjACEMH8vsaoIQynOGEMozhhDqYQwh1MMZQijPGUIoz9hNQpik+VCZwogQik+wiAjhwYMHadiwYWoPIUcL4+LiKCUlRUUFeQ9h//79qUSJEuKd91IDEEI9ow0hlOcMIZRnDCHUwxhCqIczhFCeM4RQnjGEEBFCyVkWESG8/fbbif8QJiUlqb7x0lH+IM2pSJEiFBUVlaHPCxYskGTgibohhHqGGUIozxlCKM8YQqiHMYRQD2cIoTxnCKE8Y3cJ4X16OvxPK39HCAtT69attbbrpcYiIoRvv/22Jca9e/e2lB+ZMxOAEOqZFRBCec4QQnnGEEI9jCGEejhDCOU5QwjlGbtFCEdvOE5J9fQLYQ8Ioegki4gQivYIlQclACHUMzEghPKcIYTyjCGEehhDCPVwhhDKc4YQyjN2lRDWj4AQVkWEUHKWQQgl6bqobgihnsGAEMpzhhDKM4YQ6mEMIdTDGUIozxlCKM8YQgghlJxlERPC9evX04QJE2jNmjV04sQJWrlyperniBEj1Brh4sWLS/bbc3VDCPUMOYRQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xm4SwhOaI4SJK2ZRD0QIRSdZRIRw+fLl1L17d6pVqxbVrVtXiaEhhJ9++ikdOXKEevXqJdpxr1UOIdQz4hBCec4QQnnGEEI9jCGEejhDCOU5QwjlGbtHCJNIvxDOhBAKT7GICGHbtm3VdRMdO3ZU3WMpNIRwx44d1KNHD5o5c6Zw171VPYRQz3hDCOU5QwjlGUMI9TCGEOrhDCGU5wwhlGfsFiF8d0NkhLA7IoSikywiQtigQQOaN28eJSYmZhLC8+fPU+PGjWnZsmWiHfda5RBCPSMOIbTP+VQqUToRnUohSogmSogh4gtoCsRmrBNCaJ+xlZJnzpxRd8TGx8dbKYa8FghACC3AcpAVQugAnsmiEEKToBxmO3funLqaLU+ePA5rsld80qRJ9O7GCAjh8pkEIbQ3ZmZLRUQIWfimTJlCZcuWzSSE27ZtU5HDhQsXmu0D8pkgACE0ASkMWSCE1iGeSyPaeYbolyNpdDyVKPni33XkiyFKjIuiO0oSlckbRXH/XE8KIbTO2E4JCKEdatbKQAit8bKbG0Jol5z5chBC86yc5IQQ4h5CJ/Mnu7IREUK+V5Avo3/55ZcpJibGt2SUP+g9//zz6tuPwYMHS/XZk/VCCPUMO4TQGudDF4iWHUun1UkcG8w63VwiimonRlGhWCIIoTXGdnNDCO2SM18OQmielZOcEEIn9MyVhRCa4+Q0l1uE8KTmQ2UKIULodOqELB8RIdy8eTPxPsLSpUvTTTfdRByC7tSpE/3000+0e/dumjx5MlWsWDHkwyODeQIQQvOsnOSEEJqnd/4i0feH0+n3EDJo1HhHySi6unAUxaRfJJaVQoUKmW8MOS0TgBBaRma5AITQMjJbBSCEtrBZKgQhtITLdmYIISKEtidPiIIREUJ+pk2bNtGoUaNoxYoVlJqaStHR0SpS2LdvX7r88sul+uvZeiGEeoYeQmie84ZT6fTV3uwjg4G1da4cTUVjIYTmKdvPCSG0z85sSQihWVLO8kEInfEzUxpCaIaS8zzuEcL7nXfGQg1/RwgT1bV0SDIEIiaERndSUlLo5MmTaglpQkKCTC9RK0EI9UwCCKE5zhwdnLEvjbacMZffyFWvcBTdWTKdmDMihNbYWc0NIbRKzHp+CKF1ZnZKQAjtULNWBkJojZfd3G4QwjEbk+jktfqFsNuVEEK788ZMuYgIIZ8k+ueff6r7BjmVKFGCqlWrFrFTk8yAyul5IIR6RhBCaI4znyj68c40Skoxl9/IdWl+ogdKE6WdgxBaI2c9N4TQOjOrJSCEVonZyw8htMfNSikIoRVa9vNCCBEhtD97si+pVQiTk5PpnXfeoenTp9OFCxcyPBkfbf7www+rOwgRKQz/cEMIw880WI0QQnOcDyWn0/jt1paLcs0FY4kj1zZ8AAAgAElEQVRalSOKvwAhNEfafi4IoX12ZktCCM2ScpYPQuiMn5nSEEIzlJzncYsQntIcISy4fCYhQuh8/mRXgzYh5DeLbt26qT2DfO3EtddeqyKD/PPDhw/T8uXL1aEy9evXp3fffVedNIoUPgIQwvCxzK4mCKE5zidSiN7bnkYpaebyG7lKJURR8/JE6WdPYcmoNXSWc0MILSOzXABCaBmZrQIQQlvYLBWCEFrCZTszhBARQtuTJ0RBbUK4YMECeuWVV+i9996jWrVqBX2stWvXUteuXVW+2267TarPnqwXQqhn2CGE5jifTiX6bHcaHUg2l9/IVa0g0f2liM6fRYTQGjnruSGE1plZLQEhtErMXn4IoT1uVkpBCK3Qsp/XHUJ4gvRHCL9FhND+tDFVUpsQPv3000oE27Rpk+2Dffzxx+oAlGHDhpnqADKZIwAhNMfJaS4IoTmCHBmcti+NNp82l9/IdX3RKLq1OA6VsUbNXm4IoT1uVkpBCK3Qsp8XQmifndmSEEKzpJzlc4MQjv0rAkL427fUFYfKOJs8IUprE8J77rmHxo4dG/J+wR07dlD37t1p1qxZoh33WuUQQj0jDiE0z/lkKtHoremUlm5uL2GxeKKWFaKpQBSunTBP2X5OCKF9dmZLQgjNknKWD0LojJ+Z0hBCM5Sc54EQYsmo81kUvAZtQtiwYUP68ccfiQ+PyS7xwTO33norLVq0SKrPnqwXQqhn2CGE1jhvOp1OU/eYE8KOlaKoVJ4oungRQmiNsr3cEEJ73KyUghBaoWU/L4TQPjuzJSGEZkk5ywchhBA6m0FZl9YmhHzp/MqVK031w0peUxUiE+4h1DQHIITWQCenEe05l07f7kun0xeDly2VQPRIuWjKH0OUEE0QQmuIbeeGENpGZ7oghNA0KkcZIYSO8JkqDCE0hclxJrcI4WnNp4wWwJJRx3MnVAVahXDatGmhnke9ztdPmJVHUxUiE4RQ0xyAENoDzfcRbjuTTnvOER1JJoqLTqei8VHEMli1YBQViP1/vYgQ2mNstRSE0Cox6/khhNaZ2SkBIbRDzVoZCKE1XnZzu0cIH7DbBVvl/hbCQtS6NSKEtgCaKKRVCE08jy8LhNAKrdB5sWQ0NKNw5IAQOqfIewt5EWminwT61wohdM7YTA0QQjOUnOWBEDrjZ7Y0hNAsKfv5IIT22Vkp6QYhfO+vE3T6Ov1C2OUKCKGVuWI1rzYhnDp1qqVne/TRRy3lR+bsCUAI9cwQCKE8ZwihPGNuAUIozxlCKM+YW4AQynOGEMoz5hYghIgQSs00bUIo1QHUa44AhNAcJ6e5IIROCYYuDyEMzSgcOSCE4aCYfR0QQnnGEEI9jCGEejhDCCGEUjMNQihF1mX1el0ID51Po2MpRLvPpdOes2lULl80lc8bRcXio6hEQlTYRgtCGDaUWVYEIZRnjAihHsYQQj2cESGU5wwhlGfspgjhGc1LRvP/9i1hyajsHIMQyvJ1Te1eFULej7b51EWavjeLIyyJT7CMoUvzx1BinPPhghA6ZxiqBghhKELheR0RwvBwzK4WCKE8Y0QI9TCGEOrh7I4I4UnSL4TfQAiFp1iuFcJJkybR119/rY6ov+2226hbt24UExMTFOeuXbto2LBhtGXLFipbtiz16tWLatas6cu7ceNGeu+992jz5s1UoEABatu2Ld19993CQxPe6r0ohOcuEv169CL9eChrGTQo31oqlq4vGkX5YpxFCyGE4Z23wWqDEMozRoRQD2MIoR7OiBDKc4YQyjN2TYRwUwSEcBmEUHqG5Uoh/P7772n8+PE0ZMgQyp8/Pz377LN0yy23UMuWLTPx5D/I7du3p4YNG1KLFi1owYIFxDI5efJkVfbo0aP05JNPqqNuGzVqROfPn1cb1K+44grpsQlr/V4UwvUnLtKU3aFl0ADdqkIsVSsU7Yg7hNARPlOFIYSmMDnOhAihY4QhK4AQhkQUlgwQwrBgzLYSCKE8Y7cI4bgICWFnnDIqOslypRD2799fRfgef/xxBW/hwoVK8vhfYFq/fj1xfr4jMSEhQb3M8sf/mjRpoiKDp06domeeeUZ0IKQr95oQHklOp9FbUyg1zTzZ+GiibpfFUXEHewohhOZ5280JIbRLzlo5CKE1XnZyQwjtULNeBkJonZnVEhBCq8Ts5XfDklEIob2xc3upXCmEjz32GPXs2VNF/Tht376dOnbsSLNnz6b4+PgMYzJnzhz69ttvady4cb6fv/LKK1SuXDkVOeR6atSoQStWrKAjR46o/88/K1mypNvHNsPzeU0I955Pp7FbUiyPEQvhJXntLxuFEFpGbrkAhNAyMlsFIIS2sFkqBCG0hMt2ZgihbXSmC0IITaNylNEtQnhW86Ey+ZZ9Q4gQOpo6IQu7Qgjr1q1L4byI/oEHHqBBgwZR7dq1FYBDhw6p5aJfffUVJSYmZoDCkcHFixfT8OHDfT/n/YQcLezRo4daRpqSkkJvvPGG2l84atQoVd+IESNUfv7QlBPSn3/+qZbBcj9yQuI/LnYT7xVdeyaGZuyzEB78p7Fml0RTrfwX1d5TOyk1NVXtVY2Ksi+Vdtr1UhmeGzw+sbFZ3FzvJRiCfWXGPI+jo50toxZ8xFxRNf99iYsLw4lWuYKGTCcwl2W4BtbqZC7jb6a5MeLPGMwqqzMxuBbe7iSVeKUdRwghhFKEI1dvrhTCcEYI+QCZ6667jrp06aJGad++fdSmTRsVVcybNy8lJydHbvQstMxLYydOnEhDhw61UCpyWZ38ceCy0w8QrU6yLoR1CkdTs9JEdoWU95iyqEBW5OYOR1WYc758+eQaQc2KMeay7ETAXJbla9TOf6f5iw2Itxxv/pvJkVi7MmL3b65cj9xZM0s3p+zmsrH9SaIHPiFs8KBE9VnWqSKElxdU27mQZAjkSiHkPYFXXXWV7xAZPmSGZSirPYQDBgyg6dOn+37BWAJbtWql9hBypLFEiRJZCqHMsIS/Vq8tGbV6oIxBvEX5GKqRGPw0WjOjgiWjZig5y4Mlo874mS2NJaNmSdnPhyWj9tlZKYklo1Zo2cuLJaP2uFkt5YYlo+M5QhgBIewEIbQ6XSzlz5VCyIfITJgwgd566y0VRRg4cCA1btzYJ4i8b5Alr379+sR/kJ944gn1evPmzdUBNB9++KHvlNFly5apKyk4snbJJZf4lozyz3JS8poQHj6fTiNt7CHsfXk8Ff/7bCFbCUJoC5ulQhBCS7hsZ4YQ2kZnuiCE0DQqRxkhhI7wmSoMITSFyXEmCCEihI4nURYVuEII161bR7Vq1QprHzki+M033wS9h5AFka+NaNeunWpz586dSvq2bt2qpO+pp57KcA8hRw+nTp2qlody5JEPlSlevHhYn1e6Mq8J4cmUdJqwI5WOJpvfi8iniz5RKZYS4+zv/4MQSs9kUr/TLCuFChWSb8zDLUAI5QcfQijPmFuAEMpzhhDKM+YWIIQQQqmZ5gohlOoc6v0/Aa8JIff8UHI6jdps/qTRpy6PoxIOrpzgNiGE8r91EEJ5xtwChFCeM4RQnjGEUA9jCKEezm4RwnOal4zmXfYNYcmo7ByDEMrydU3tXhTC8xeJNp9Oo893p4Ych8fKx9KVhaIp3n5wULUBIQyJ2nEGCKFjhKYqgBCawuQoE4TQET7ThREhNI3KdkYIoW10lgq6Qgg3nyLtQrj0awihpZliPTOE0DqzHFnCi0LIA5WcRnQ6NZ0+2ZVKh85nXj5aKiGKWlaMpYJxUY5lEEKo51cDQqiHM4RQnjOEUJ4xIoR6GEMI9XB2gxC+HyEh7IhDZUQnGYRQFK97KveqEBojkJRKFJ2eTsdS0mn/uXQqkzeKisZHUVo6UWEHewYDRxgRQvk5DyGUZ8wtQAjlOUMI5RlDCPUwhhDq4QwhzLiHcNGiRTRu3Dg6evSoOuOjX79+VKxYsaCDwZ8dPvroI5o3b57aV1y5cmV1fkiePHn0DJ7LW4EQunyAwvV4XhfCcHEMVQ+EMBQh569DCJ0zNFMDhNAMJWd5IITO+JktjSWjZknZzwchtM/OSkkI4f+F8MCBA9S+fXviq+auueYaGj16NB0/fjzL+7bHjx//P/bOA0qqIv3bv849OQ8TnSFLkCCgAgqisoY16xpQMK34x4QYEYVVdxcVA8b91l3DypjBLEFAFAElKqiI5AEGJufUub9TFwYm9723u+reZt46hyMyFZ+qGfrhrQB2iSW7GDI1NVW6SJJJJL1PengFkhAq+U4M47wkhGImj4SQP2cSQv6MWQskhPw5kxDyZ8xaICHkz5mEkD9j1oJehNAh+FIZ+9rP0HrL6HvvvYeffvpJivKxVFJSIj0vx/6cPS3XPFVVVUnvi//nP/+RXhOg1JaAZkK4detW6a3ALVu2oLq6Gps2bZJ69/zzz2PSpElh96yD3hcXCaGYGQqFEFY4ffDBj0avH1FmI+xGAyLNQd52I2b4QlohIRSCmYRQAGYSQgGQSQiFQCYhFIJZP0I48jIxAz7SiiSEvaIlP2hKTz75JOLj4zFlypSjf3b55ZdjxowZGD58eIv+bdy4UXpH/PTTT8fixYsRFxeHv/zlL7jwwguFjkPPjWkihBs2bMCdd94pvT04bNgwSQybhPDdd99FWVkZpk6dqmduYdc3EkIxUxaMEJY6vPhwby0KHV4UNnhQ7/GhW4QZ6REmnNEtEkMTbYgiMaR3CMUsZRJCAZxJCAVAJiEUApmEUAhmEsJmQjhr1iz06tWrhSROnDgRt956K8aMGdNiQpYsWYLnnnsOl156qbTNlG0XnT59Ov7+979jyJAhYiZP561oIoQ33ngjRo0ahcmTJ0t4mBQ2CWF+fj7uuusufPnllzpHF17dIyEUM19qhLDB7UN+vQfPba1EncfXYUfHdovAxSdEIyvSLGYwOm2FIoRiJoa2jPLnTELInzFrgbaM8udMQsifMWtBD1tG/7uzFg4NIoS3BhEhXLFiBVhE8bPPPkNUVJQ0WU899RQSExOPuoiYGdRvK5oI4WmnnSbd8sNCtq2F0OFwYOzYsVi3bp1+qYVhz0gIxUyaGiHcW+fG9E1lsjo4PNmOyX3iERfCm1FlNayjTCSEYiaDhJA/ZxJC/oxJCMUwJiEUw5mE8NiWUXZWcPPmzUcvkSktLcWECRPaPUPIgk0sckhC2PE61UQImfCxiczMzGwjhHv27JFsffny5WK+u7pIKySEYiZaqRCWO734+y8V0hZRuWlK3zicmRYpN/txl4+EUMyUkhDy50xCyJ8xCaEYxiSEYjjrRQidgiOEtrWfoXWEsLCwUJK8Rx55RNr2+corr4BJ4Zw5c6TJWLRokXS5zIgRI6T/Z0fVBgwYgL/+9a/SltEHH3xQ2jI6ePBgMZOn81Y0EcJp06YhOjoajz32GEwm09Eto+yD3qOPPgqDwYDZs2frHF14dY+EUMx8KRXCH0udeOH3CkWdS4sw4/HBSYi3GRWVO14ykxCKmUkSQv6cSQj5MyYhFMOYhFAMZ10I4a46CBfCHz9tI4SM+OrVq8Gek2jvHcKHH34Yffr0wU033SRNTnFxsXSOkF1qybaKXnvttbjgggvETFwYtKKJEO7cuRPsHGFaWpp08HPevHm47bbbsHLlShw4cAB5eXnIyckJA3zh00USQjFzpVQI39pVgyUH6xV37v+dlopEm0lxueOhAAmhmFkkIeTPmYSQP2MSQjGMSQjFcCYhbPkwvRjqXaMVTYSQod2xY4d0BSy7Ctbj8cBoNEqRwvvuuw+9e/fuGvQFjpKEUAxsJULY4PXjud8q8VuVU3HnZg5OwsB4q+Jyx0MBEkIxs0hCyJ8zCSF/xiSEYhiTEIrhrAchfF2jCOFfW10qI4Z412lFMyFsQux2u1FTUyNtIbXZbF2HvOCRkhCKAa5ICD3Ay9sq8VOFQ3HnnhiShL5xJISKwVEB2QRICGWjUp2RhFA1OkUF6ZZRRbhUZSYhVIVNcSESQooQKl40MgtoLoQy+0nZgiRAQhgkQJnFlQghq/L9vbX4bH+dzNoPZzMbDXjplFQk0RlCRdwoszICJITKeKnJTUKohpryMiSEypkpLUFCqJSYuvwkhCSE6lZO4FKaCOG2bduwYMECzJw5s00Pn3jiCfzlL39Bv379AveecsgmQEIoG1VQGZUK4ZqSRry0rUpRm+xSmb8NTqQzhLGxirhRZmUESAiV8VKTm4RQDTXlZUgIlTNTWoKEUCkxdfn1IoSuUZepG4DKUtYfP8Vfe0a3eIReZVVUrAMCmgjh7bffLl0qc8opp7TpFnt/kF0qw66PpRQ6AiSEoWPZWU1KhbDS5cOzWyuxq8Ylu4MPDEgAe4+wqyY6Qyhm5kkI+XMmIeTPmLVAQsifMwkhf8asBf0I4eViBnyklcNCGEVCyJG6JkJ4xhlnYMmSJYiKimozNPYhhF0Dy24cpRQ6AiSEoWMZSiFkdRU3enH3+hJZHTw/Kxp/yYlGlNkgK//xmImEUMyskhDy50xCyJ8xCaEYxiSEYjjrQQjf2FUH1yjxQngLCSHXRaaJEJ555pn43//+h9zc3DaD27t3rxQ9JCEM7byTEIaWZ0e1KY0QNtVT2OjFs79VoKCTB+ovOSEaZ6RGIDvKLGYwOm2FhFDMxJAQ8udMQsifMQmhGMYkhGI4kxDSGUJeK00TIWRPS7DH55966imYzcc+3LLnJ6ZPnw72gW/u3Lm8xtwl6yUhFDPtaoWQ9a7M6cOignoUN3pQ1OgB206aHWlGeqQZo7tFoG+sBVZj140MNs0gCaGYtUxCyJ8zCSF/xiSEYhiTEIrhTEJIQshrpWkihE0P06ekpOCss85CcnIyysrKsGLFCpSWlkrRQ3qLMLRTTkIYWp6hjhA2r6/e44fHD9S4PIiymJBoNYrpfJi0QkIoZqJICPlzJiHkz5iEUAxjEkIxnHUhhLvr4Ba8ZdTyw6egLaN815gmQsiGxG4aZQ/Tb9q0CewvRZPJhJNPPhlTp06lG0Y5zDkJIQeo7VQZTIRQTA/DvxUSQjFzSELInzMJIX/GJIRiGJMQiuGsDyGs10AIPyEh5LzENBPCpnG5XC7U1tYiJiYGVmvXfGib8xxL1ZMQiqAMkBDy50xCyJ8xa4GEkD9nEkL+jEkIxTAmIRTDmYSQtozyWmmaCyGvgVG9LQmQEIpZESSE/DmTEPJnTEIohjEJoRjO9OwEf84khPwZsxb0IIRv7tYmQngz3TLKdZFpJoS//PILvvzySxw6dEiKqrROb7/9NteBd7XKSQjFzDgJIX/OJIT8GZMQimFMQiiGMwkhf84khPwZkxDSO4Q8V5kmQvj+++/j2WefRWZmpvT0RHvvET755JM8x93l6iYhFDPlJIT8OZMQ8mdMQiiGMQmhGM4khPw5kxDyZ6wnIfQIvlTG/MMnoAgh3zWmiRCee+65uP766zFx4kS+o6PajxIgIRSzGEgI+XMmIeTPmIRQDGMSQjGcSQj5cyYh5M9YV0I4+goxAz7SiiSEPSIxaRKdIeQFXhMhHD16NL7++mtER0fzGhfV24pAe0JY5vCg2u1Hfp0Lm8udSIu0oHeMGfE2M3KizLCZ6M07pQuJhFApMeX5SQiVM1NTgi6VUUNNWRkSQmW81OYmIVRLTn45EkL5rILJqZczhB4SwmCmUZdlNRFC9jA9ixAOHTpUl1COx041F8IGjw/VLh/m/FaB/Dp3u8O95IQYXHpCNOLpDTxFy4GEUBEuVZlJCFVhU1yIhFAxMsUFSAgVI1NVgIRQFTZFhUgIFeFSnVkPQvjW7npoIYQ3UYRQ9bqRU1ATIayqqpLOEJ5//vk49dRTYTab5fSV8gRBoLkQbq5w4vHNZQFr6x9vw7QBiUi20cPoAWEdyUBCKJeU+nwkhOrZKSlJQqiElrq8JITquCktRUKolJjy/CSEypmpKUFCSFtG1awbOWU0EcLx48eD/fCorKyE0WhEXFwcDIaW2xOXLVsmp/+URyaBJiF89Klncde6Uji8Plklz82MxvU9YxFtpu2jcoCREMqhFFweEsLg+MktTUIol5T6fCSE6tkpKUlCqISWurwkhOq4KS1FQkhCqHTNyM2viRDOnTs3YP+mTZsWMA9lkE+ACeH6X7ai5KRzsK7UIb8ggPsHJmJ0aoSiMl01Mwkh/5knIeTPmLVAQsifMwkhf8asBRJC/pxJCPkzZi3oQgj31MMr+Ayhac0noC2jfNeYJkLId0hUe3sEmBDur6jB55YeKGn0KoJ0VfdYXNs9RlGZrpqZhJD/zJMQ8mdMQiiGMQmhGM4khPw5kxDyZ6wfIWzQQAg/JiHkvMRICDkD1kv1W7duRbnLh7mViYq7NDzJjqkDEhBtprOEgeCREAYiFPzXSQiDZyinBooQyqEUXB4SwuD4yS1NQiiXlPp8JITq2SkpqYcI4f/2aCOEN9KlMkqWiuK8mgmhx+PB9u3bcfDgQbDft04XXHCB4sFQgY4JMNYrC6qx2JCpGFNmpBmPDU2hy2VkkCMhlAEpyCwkhEEClFmchFAmqCCykRAGAU9BURJCBbBUZiUhVAlOYTESQjpDqHDJyM6uiRAWFRXhnnvuwc6dOzvs6KZNm2QPgjIGJsC2jFZ7gWfLEwJnbpVjZEoEbu8XTxFCGeRICGVACjILCWGQAGUWJyGUCSqIbCSEQcBTUJSEUAEslVlJCFWCU1iMhJCEUOGSkZ1dEyGcOXMmKioq8OCDD+Lyyy/H4sWLUVhYKD1WX1ZWhvvvvx+pqamyB0EZAxNgQphfUoElkX1xsKFtRLazGtj5QXaOkFJgAiSEgRkFm4OEMFiC8sqTEMrjFEwuEsJg6MkvS0Ion5XanCSEaskpK6cXIfSdfoWyjgeZ27jmY9zYPRKTJpEQBomyw+KaCCHbDvrqq6+ie/fuGDZsGJpHA7/44gv8/vvvmD59Oq8xd8l6mRD+uGEjGk+7BN8WNihiMPvkZPSLtykq01UzkxDyn3kSQv6MWQskhPw5kxDyZ8xaICHkz5mEkD9j1oJ+hPBKMQM+0sphIYwgIeRIXRMhHD58ONauXSs9SD969GgpMhgdHS0N0+FwgAnjihUrOA6761Xd9A7hrKefx4yfSlAs86bR63rE4qLsKNhMdKGMnFVDQiiHUnB5SAiD4ye3NAmhXFLq85EQqmenpCQJoRJa6vKSEKrjprQUCSFFCJWuGbn5NRHC5lHBK6+8EuzNQSaGLG3btg1TpkzBd999J3cMlE8GgSYhfOGFF7C92oXZv5Sjxt354/TnZETiypxYdIswyWiBsjACJIT81wEJIX/GrAUSQv6cSQj5M2YtkBDy50xCyJ8xa0EPQvj2ngb4ThcfIbyBIoRcF5nmQvjmm2/inXfewVVXXQWr1YoFCxZgyJAhmD17NteBd7XKmwshG3upw4u83TVYU9IAn78lDbvJiMl94zAkwYYEG8mgkrVCQqiElrq8JITquCktRUKolJjy/CSEypmpKUFCqIaasjIkhMp4qc1NQkgRQrVrJ1A5TYTw008/xWWXXSb1zeVyYe7cuVi6dKm0XXTUqFF45JFHEB8fH6jv9HUFBFoLISta5/HD6fWh2OHB71UuZESYwZ6YMBqA7CiLgtopaxMBEkL+a4GEkD9j1gIJIX/OJIT8GbMWSAj5cyYh5M+YtaALIdzbAL/gCKFh9cegCCHfNaaJEHY0JPYDxWAw8B1xF629PSHsoii4DpuEkCteqXISQv6MSQjFMCYhFMOZhJA/ZxJC/oz1I4SNGgjhAhJCzktMEyE877zzsGTJkg6HFujrnJkcl9WTEIqZVhJC/pxJCPkzJiEUw5iEUAxnEkL+nEkI+TMmIaRbRnmuMk2EsPVTE80HyP6CHDFiRIunKHgC6Cp18xZCpw+odXnQ6PHDYgJsRmOXPH9IQsj/O4qEkD9jEkIxjEkIxXAmIeTPmYSQP2O9COG8vdpECCfRpTJcF5nuhHDDhg3Sg/Xffvst14F3tcp5CaHT68eW8np8vq8K++vc2FvrRLzVhBOibciKsuDGvknIiLR2GdwkhPynmoSQP2MSQjGMSQjFcCYh5M+ZhJA/YxJCihDyXGVChXDs2LHSWNiH5qZ3B5sPzu12w+l04pJLLsGsWbN4jrvL1c1DCPPrXPgsvxLv7azokGeS3YxnTs1C91gboszH/1uGJIT8v7VICPkzJiEUw5iEUAxnEkL+nEkI+TPWkxDiDLHPTmD1AkzKJSHkucqECuGrr74qjYU9NXHzzTe3GVdERAS6d+8OJo5G4/EvDzwntnXdoRbCWrcPr20rxUe7O5bB5n3435nd0T/BLnLImrRFQsgfOwkhf8YkhGIYkxCK4UxCyJ8zCSF/xvoSwr+IGXBTK5IQ2jFpEj07wQu8UCFsGsSzzz6L+++/n9eYqN52CIRaCL8vrMX9awtks86OtuD/nZGLVLtZdplwzEhCyH/WSAj5MyYhFMOYhFAMZxJC/pxJCPkzJiEkIeS5yjQRwqeffhoPPfQQz3FR3a0IhFIIixvduPfHA9hZ7VTE+eEhabise4KiMuGWmYSQ/4yREPJnTEIohjEJoRjOJIT8OZMQ8mesFyHM29sInCE+QjiRIoRcF5kmQjh69GisXLkSZvPxHS3iOnMKKw+tEHpw9fLdaPD4FPXistx4PDw0XVGZcMtMQsh/xkgI+TMmIRTDmIRQDGcSQv6cSQj5MyYhpAghz1WmiRDec889uOGGGzB06FCeY6O6mxEIpRDuqHbg+hV7FfM9OTkSs0/JQqLNpLhsuBQgIeQ/UySE/BmTEIphTEIohjMJIX/OJIT8GetGCPM1iBCuWgCKEPJdY5oIYVlZGebMmYMLLrgAo0aNgtXadZ4l4DudHddOQiiGPAkhf84khPwZkxCKYUxCKIYzCSF/ziSE/BnrSQgNgreM+kkIuS8wTYRw/PjxYH8RVlVVSYnmEGIAACAASURBVAOMjY1ts3102bJl3AfflRoIpRCyM4TXLN+Detoy2mYJkRDy/64iIeTPmIRQDGMSQjGcSQj5cyYh5M9YP0LogHghnE8RQs5LTBMhnDt3bsBhTZs2LWAeyiCfQKiFUNWlMkPTwc4RHs+JhJD/7JIQ8mdMQiiGMQmhGM4khPw5kxDyZ6wXIXwnXxshvJ4uleG6yDQRQq4jOlL5vHnz8Nlnn4F9eDz77LNxxx13wGRq/+za/v37wZ7C2LVrFzIzMzF16lQMHDiwRTdZPVOmTEFBQQEWLVokYgghbSOUQsg6trqoTrppVG7KibHildE56BZxfF8kREIod0Woz0dCqJ6dkpL19fWwWCy0pV8JNIV5SQgVAlOZnYRQJTgFxUgIFcAKImtjYyMMBgPsdm3edWafrUkIg5hAHRc9LoXwm2++wWuvvQb2vEVUVBRmzJiBcePG4brrrmszFewv5FtuuUU6yzhhwgSwrapswefl5Ullm9L8+fPxww8/YPv27SSEAGpcXry+vRwf7CqXtbznjcvFifERsvKGcyYSQv6zR0LInzFrgYSQP2cSQv6MWQskhPw5kxDyZ8xa0I0QjhH77IR/1Xxcn0O3jPJcZZoJYWFhId5++21s2rQJ1dXViIuLw/Dhw6XbR9PS0oIaM3vjkEX4Jk6cKNWzfPlySfLYr9Zp69at0puIH3/8MWw2m/TlSZMmSb/OOecc6f9LS0vxwAMP4K677sLMmTNJCI9A3F3jxML9VXhnZ0WH85USYcacU7OQE21DtMUY1LyGQ2ESQv6zRELInzEJoRjGJIRiOJMQ8udMQsifsZ6E0ChYCH0khNwXmCZCuGfPHtx0003weDwYMmQIEhMTUVFRgc2bN0vbk9566y3k5uaqHvw111yDu+++W4r6sbR3715MnjwZCxcubLP9iW3//OKLL/Dvf//7aHuPP/44srKypMghS+z/zzjjDKSnp+O+++4jIWw2M04f8GtFA77Ir0R+rQt7ap2It5qQG2NDZpQFE3snIyvKonouw60gCSH/GSMh5M+YhFAMYxJCMZxJCPlzJiHkz1hfQniVmAEfaeWwENqkYA0lPgQ0EUL2DqHb7cbs2bOlyGBTYpFCtr2TnVt54YUXVI/4kksuwRNPPIHBgwdLdZSUlEjbRRcsWNCiPfY1FhlkW0Gfe+65o+2x84QsWsgiguvXr8dHH30knTHctm1bGyF0Op2q+ymyIIuEsogse+4j1IntZ3f5gTqPD41ewGY0wGwAEqxG6TbZrpTYejCbzR2eV+1KLHiNla0pxjki4vjfgsyLoZx6GWN27pqtZ0p8CNBa5sO1da0ulwtGo5HWMkfcTAjZdsbIyEiOrVDVbC2zz1zsc3JHqWm3Gw9aTWcIjWNICHnw1bJOTYSQRdvef/99KQrXOrFLW9hZvu+//141l1BFCNmW09tuuw1/+9vfpIhle0LIztmEQ/r999+lc5FPPvlkOHQ3bPvI/qGDfYhmHz4o8SHAPniw3QWd/YXIp+WuVStjzNYxrWW+884+4NFbvHwZs7XMPkR3dLEc39a7Tu20lvnPNdshw1Jna7n5/Reh7hETwnfzHdBCCK+jCGGop7NFfZoI4ciRI/HVV18hKSmpzeDKy8tx0UUXSVE7tYmdCRw0aNDRS2TYJTMsOtbRGcLp06fjk08+OfoB88Ybb8T111+PPn364NZbb5XeSWSJfSPW1tYiPj5eim727t1bbReFlwv1LaPCBxAmDdKWUf4TRVtG+TNmLdClMvw505ZR/oxZC7RllD9n2jLKnzFrQQ+Xyry7TwMh/H4+SAj5rjFNhJCdH+zfv790UUvrxLZusu2Nb775puqRs0tkXn/9dTzzzDPS9oWHH34YY8eOPSqI7NxgSkoKRowYIW1pvPnmm6WvX3vttdIFNKxtFk1jW9KqqqqO9mPnzp3SecJ33nlHksRw2kpFQqh6OSkqSEKoCJeqzLyE0O9qAOCHr74C3qoimKKTYIhMhMFogCHi2NZ2VZ0Ow0IkhPwnjYSQP2MSQjGMSQjFcNaLEJoEbxn1khByX2CaCOHatWul83k9e/aULmtJSEhAZWUlVq9eLb0F+Oqrr+KUU04JavAsIvj555+3+w4hE0QW/WNiytK+ffukM4K7d+9GRkYG2BnH1u8QsnztbRkNqpMCC5MQioFNQsifMw8h9Dvr4Pj9G3irDrUZgMFkha3fOJgTMgHz4ZuIu0IiIeQ/yySE/BmTEIphTEIohrM+hNAJ8UL4EUUIOS8xTYSQjYlJIbvZk4kK+0HC9vczCbv99tuDlkHOzMKyehJCMdNGQsifc6iF0Fd1CA2bvwL8h89mdJRsucNgzugPgy2a/yB10AIJIf9JICHkz5iEUAxjEkIxnEkI6ZZRXitNMyFsGhC7yY6dy4uJiTn6DiCvwXblekkIxcw+CSF/zqEUQhYZbFj/EfweebcF2/qOhSWjP/9B6qAFEkL+k0BCyJ8xCaEYxiSEYjjrQQjf26dNhHACXSrDdZFpKoTs5i/2QD17FiI1NVV65y+czuVxnZkQV05CGGKgHVRHQsifc8iE0OOEY9u38JTtVdRp+8mXwRyXpqhMOGYmIeQ/aySE/BmTEIphTEIohrNuhHCs2GcnvN9/hAkn0DuEPFeZZkLI3v/7z3/+g7KysqPjS05Olp55uPzyy3mOuUvWTUIoZtpJCPlzDpUQ+p31aNgwH353o6JO2048C5b0vorKhGNmEkL+s0ZCyJ8xCaEYxiSEYjiTENKWUV4rTRMhZDd4vvLKK7jwwgtx+umnH71UZtWqVdJzFFOnTj16IyivgXe1ekkIxcw4CSF/ziETwsZq1K99T3GHLWknSpfMHO+JhJD/DJMQ8mdMQiiGMQmhGM56EUKz4AihhyKE3BeYJkJ4/vnnS5HASy+9tM0A2XuAb7zxBhYuXMh98F2pARJCMbNNQsifc6iE0F34B5x/fKu4w6aYVNgH/gkGe4zisuFUgISQ/2yREPJnTEIohjEJoRjO+hHCq8UM+Egrh4XQikmTKELIC7wmQjhq1CgsXboU0dFtb+tjF8ycd955WLNmDa8xd8l6SQjFTDsJIX/OoRJCX30lGtZ/oLjDlrS+sPU7S3G5cCtAQsh/xkgI+TMmIRTDmIRQDGc9COH7+5wwjxUvhNeSEHJdZJoI4R133IFbb70VQ4YMaTO4n3/+WYoQsi2llEJHgIQwdCw7q4mEkD/nUAmh31GLhg0L4Pc4FHXa3ucMmDMHKioTjplJCPnPGgkhf8YkhGIYkxCK4awLIdyvgRCu/AjtCSELHrEn7MrLyzFo0CA88MADSEpK6nQyvvvuO/zzn//ELbfcgmuuuUbMxIVBK5oIYXFxsfQQPDtDyKKFFosFbrdbigqyM4QPPfQQUlJSwgBf+HSRhFDMXJEQ8uccKiGEx4WGLV/BV1OsqNP2wRfCnJitqEw4ZiYh5D9rJIT8GZMQimFMQiiGMwnhsS2jRUVFktQxZzj55JPx8ssvo7KyEnPmzOlwMhoaGnDnnXdK3jFu3DgSwmakNBHC8ePHg/1FWFVVJXWFbR1lH6RZSkhIkB6pb56WLVsm5jvtOG6FhFDM5JIQ8uccMiEEwG4arf9hnuxOW7ufAmv2IMBkkV0mXDOSEPKfORJC/oxJCMUwJiEUw1kvQmgRvGXU3U6E8L333sNPP/0kBZhYYk/YXXfddWB/3lFQ6V//+pf0zN2mTZswePBgEkKthXDu3LmKvnOmTZumKD9lbkuAhFDMqiAh5M85lELIeust3YvG35YE7LgppRfsvUfCYGt79jlg4TDMQELIf9JICPkzJiEUw5iEUAxnfQihC+KF8MM2W0affPJJxMfHY8qUKUfhs2frZsyYgeHDh7eZkN27d0vRQyaFjz76KAlhK0KaRAjFfNtQK80JkBCKWQ8khPw5h1oI2dZRn7Mejl+XwNd4eNdC62TtMRKWtF5dRgbZ+EkI+a9lEkL+jEkIxTAmIRTDmYTw2JbRWbNmoVevXi1uHp04caJ0R8mYMWNaTAhbn+xJO7bFlEUGH374YRJCEkIx37R6a4WEUMyMkBDy5xxyIWzqsrNeeqTeW1sOd/k+mKISYIrtBqM9BoaIGMBo5j84HbVAQsh/MkgI+TMmIRTDmIRQDGc9COEH+12wnCn2llH3yg9xTXbLZyeURAgXLVqEzZs3S9FDlkgI265XTSOE7MNzYWHh0fODzbs3dOhQMd9dXaQVEkIxE01CyJ8zNyHk3/WwaoGEkP90kRDyZ0xCKIYxCaEYziSExyKE7Kwgk7ymS2RKS0sxYcKEds8Q/uMf/8DGjRuly2RYYp/VzGYzRo4ceVQSxcygflvRRAjZzUBPP/00Vq1aBfZDpL3EDnxSCh0BEsLQseysJhJC/pxJCPkzZi2QEPLnTELInzEJoRjGJIRiOOtFCK2CI4SudiKELKDEtoc+8sgj0jN27Lk6JoVNgsiiguxymREjRkh/nzmdzqOTNHv2bAwYMABXXnklYmJixEyezlvRRAgnT54s3QbETD4nJweRkZFtMJ100kk6Rxde3SMhFDNfJIT8OZMQ8mdMQiiGMQmhGM7sqnmTyQSbzSamwS7YCgmhmEnXjxCKfb/vsBBaWpwXZMRXr16N1157rd13CNm20D59+uCmm25qMzm0ZbTtetVECE877TTMnz8f2dnH/1teYn5EBG6FhDAwo1DkICEMBcXO6yAh5M+YhFAMYxJCMZxJCPlzJiHkz5i1QEJ4bMuoGOJdpxVNhJCFaJveAuk6qLUdKQmhGP4khPw5kxDyZ0xCKIYxCaEYziSE/DmTEPJnrBch/PCAC9YzBUcIv/sQV7cTIRRDvWu0ookQsrODK1askA5yNh3w7Bq4tRslCaEY9iSE/DmTEPJnTEIohjEJoRjOJIT8OZMQ8mdMQth2y6gY6l2jFU2EkKFdv349/vnPf6J///5ITEyEwWBoQfz+++/vGjMgaJQkhGJAkxDy50xCyJ8xCaEYxiSEYjiTEPLnTELInzEJIQkhz1WmiRCyq1+Z8NXW1iIuLg4RERFtxrhw4UKe4+5ydZMQiplyEkL+nEkI+TMmIRTDmIRQDGcSQv6cSQj5M9aPELphE7xl1PndB7RllPMS00QIr7jiCgwbNgy333474uPjOQ+RqmcEeAih0+tDrduLapcPkWYjrEYDkuzh+3i32w/Uu31w+gCLETAbDIi1tIxcB1pNJISBCAX/dRLC4BnKqYGenZBDKbg8JITB8ZNbmoRQLin1+UgI1bNTUlIPl8p8eICEUMmchUteTYRw9OjRWLJkCb39IXCVhFIIHV4/NpY14Kt9ldhf58LeWicSrWbkxFiRFW3D5BOTkRxGYujyATtqXFhV4kRRoweFjT5EmY1IsxvRLcKEC7MikGwzyZotEkJZmILKREIYFD7ZhUkIZaNSnZGEUDU6RQVJCBXhUpWZhFAVNsWF9CCEH2kkhFfRpTKK14uSApoI4dSpU6XHJAcOHKikr5Q3CAKhEsJ9dU58sKsCH+2t7LA33ewWPHdaFrJjbIg2G4PoNf+iTP5WFjdiyaHGDhuLtRhxe99Y5ESZERHAC0kI+c8ZCSF/xqwFEkL+nEkI+TNmLZAQ8udMQsifMWtBN0I4Tuwto2zL6FVZdIaQ5yrTRAjLysrw4osv4sILL8Tw4cOlB2Mp8SUQCiGsdfvw4m/F+DS/YxlsPoqPx/dEbrR+HwKu8/jweUEjlnUig83HM2tQPHpEd74lloSQ7zpmtZMQ8mdMQiiGMQmhGM4khPw5kxDyZ0xCSELIc5VpIoTjx48H++FRWVkJo9EoXSzT+pbRZcuW8Rx3l6s7FEL4zcEaPLi+QDa7XrE2vDo6R7fbRzdXuvHCtmrZ40mxmTDjpDgkWDuOepIQysapOiMJoWp0igpShFARLlWZSQhVYVNciIRQMTLFBUgIFSNTVUAvEUK74AihgyKEqtaLkkKaCOHcuXMD9nHatGkB81AG+QSCFcJShwf/tyof+XUu+Y0CePKUbPwpM0ZRGRGZK11+vLCtCvvqvYqau6FHNMal2TssQ0KoCKeqzCSEqrApLkRCqBiZ4gIkhIqRqSpAQqgKm6JCJISKcKnOrB8hvFb1GNQUPCyEZkyaNElNcSojg4AmQiijX5QlxASCFcKiRjcu+3oXXH6/op5d0zMBDwxKV1RGROZypw+ztlSh3uNT1NyYVBtu7tWx4JIQKsKpKjMJoSpsiguRECpGprgACaFiZKoKkBCqwqaoEAmhIlyqM5MQkhCqXjwBCpIQ8iKrs3qDFcJfyhtx0/d7FY/qtNRoPDE8A0k2fT1HcaDBi5mb5Z2FbD7oPjEWTDkxFgkdPEdBQqh4iSguQEKoGJmqAiSEqrApKkRCqAiX6swkhKrRyS5IQigbVVAZ9SCE8wvcsI8THCH89gP8hSKEQa2dQIWFCWF+fn6gvrT4em5urqL8lLlzAloJ4cgjQpjYxYTQZzKjweOD1+fHjmoXesdZYTQYYDMZdH/zqt6/l0gIxcwQCSF/ziSE/BmzFkgI+XMmIeTPmLVAQkgRQl4rTZgQsofolaRNmzYpyU55AxAIVgjZ+3yXLd0Jl0/ZltFreybg/uNoy+jYVBtu6mTLKPsQXWew4oP8GrCoakGDG027bFMjzMiItOCBk5KREkbvNOrtm4uEUMyMkBDy50xCyJ8xCaEYxiSEYjjrRQgjBEcIGylCyH2BCRPCDz/8UNFgrr76akX5KTPfCKHqS2VGZOFPWbFCpqfc6ZXkq97jxa4aFwYl2mEAEG02wW5mvzuWKl0+vLCtBvvqPYr6dkPPaIzr1vGlMtsqGvDMb+UoaOi4XrPRgL8PS0O/OBsiW/VLUWe6aGYSQjETT0LInzMJIX/GJIRiGJMQiuGsDyH0QLwQvk9bRjkvMWFCyHkcVD3nCCGrfsWhGjywTv6zE33j7Hhp1Ancn51o9PhR4fLgiZ+KUNjoQbXr2M2hLCI3IiUS1/dKQGqrqNyWSjfmKnh2opvdhOkDO352osLpxZQ1BWD/bf2MSnvT89/TM5ETbaW1q5AACaFCYCqzkxCqBKegGAmhAlhBZKUto0HAk1mUhFAmqCCzkRDSltEgl1CHxUkIeZHVWb3Bbhllw2EP07+ytQQL9lbIGt0n43txFx52S+iG0gb87aeiTvt0QpQV04ekYkD8segeK/tFgQNfH2qQNZ6/DYpH9w4epnd4/fjXtnIs3F8tnRWUI4RZURY8e2o6Eq0mWe1TpsMESAjFrAQSQv6cSQj5M2YtkBDy50xCyJ8xa0EPQrigwIOIs8ReKtP47fu4MpOeneC5ykgIedLVUd2hEEI2nH11LszfU4n3d5d3OLr0SAueOTUb2VFWRFs6fsQ9FHg2VzRi6o8HZVWVGWnBiyMzW5zfO1jvxZoyBxYdbOywjjiLEVP7xSLdbkJEB1s82Zbav646iDq3R7YQsgafPSVd2tpKST4BEkL5rILJSUIYDD15ZUkI5XEKNhcJYbAEA5cnIQzMKBQ5SAgpQhiKddReHSSEvMjqrN5QCSEbltPnx6bSeikall/rxJ5aJ9gtornRVmRH23BL32SkRKh/ZqLG7cP2Gg/6x1kR1Uk1bHvo/60+gKpmW0QDYb8oOxZ3DkiB3XTsTKHLD+yq8WBVSSMKG7041OCVbgJNjzAixW7CBZkRSLV3HsXbVevC7WsOwuvzKRLC205MxBW5cYG6TV9vRoCEUMxyICHkz5mEkD9j1gIJIX/OJIT8GbMWSAhJCHmtNBJCXmR1Vm8ohbBpaE6vH7VuLyqdHkRbTLCZjEi0Bbf9kV328r/d9fi9xoPTU2y4JMuOeGv7UcbVRXV4ZFPnW0VbTwPr32unZ7c5T8jysRtU690+OH2AxWQACway6KCctLigFnN/K1MshCO7ReLxod3kNEF5jhAgIRSzFEgI+XMmIeTPmIRQDGMSQjGc9SKEkWdNEDPgI600SFtGTZg0iYSQF3gSQl5kdVYvDyEM5RBdPmBvvQev/FEHR7OnLZJsJtzRJwrd7EZYjS1vCv3v9gq8s0veecbmfX3nzBxkR1lC2X2sK23EzE1FioXwz9kxmDogOaR9Od4rIyEUM8MkhPw5kxDyZ0xCKIYxCaEYziSEJIS8VhoJIS+yOqtXz0JY5fZjWaEDSwsdHVKbkBuJYYkWxB6J2LELbmZuKsTP5R2f/euoshmDu+HcrJiQzhB7p3HSygOKhfDRId0wJi0ypH053isjIRQzwySE/DmTEPJnTEIohjEJoRjOehDCjw96IDxCuOJ9XEERQq6LTDMh3Lp1K15//XVs2bIF1dXVaHqI/vnnn5dCwsnJFDUJ5czrVQgrXX68sr0O+zt5t6+JQ/9YM27oEXl0W+o/fy7G0kO1ijG9MjILJ4X4IpcD9S7cu64IFQ63ojOET5+SjqEh7otiIGFWgIRQzISREPLnTELInzEJoRjGJIRiOJMQUoSQ10rTRAg3bNiAO++8EyeddBKGDRsmiWGTEL777rsoKyvD1KlTeY25S9arRyHc3+DF33+tUTQf7DKYv50Ui2SbEZ/kV+PFraWKyrNH4d8fl9PuGUJFFbWTeXVxPf62qUi2EF6UE4vJfRNha7UVNth+HO/lSQjFzDAJIX/OJIT8GZMQimFMQiiGMwkhCSGvlaaJEN54440YNWoUJk+eLI2LSWGTEObn5+Ouu+7Cl19+yWvMXbJePQohu0Dmmd9rUcpucZGZ+sSacVuvKGnrKBOwRzYWyix5OFtWpAWvjM5CAoe3/+o8fuRtL8cn+2sCvkPYK8aKJ4alITnA7aWKBtdFMpMQiploEkL+nEkI+TMmIRTDmIRQDGd9CKEXUYIvlalf8R5tGeW8xDQRwtNOOw1ff/014uIOX7ffXAgdDgfGjh2LdevWcR5616pej0LIZuD9fY1YUdTx2cHWs3RNTiTOTrNJf8yep/jv9nJ8sa9a9mTy2C7avPH8qgasL3fi9Z1VHfaJnV+8tkc8MiLVP80he8DHYUYSQjGTSkLInzMJIX/GJIRiGJMQiuFMQkgRQl4rTRMhZML33nvvITMzs40Q7tmzR4ocLl++nNeYu2S9ehXCnbUezPld/jnA2YNjpbcBm1JxoxszNhZhV40z4LxO6ZeMS3PjYOe4RbOurg6w2FDg8OGn8kbsrnYhv86F9EgLsqIsODHOhuEpkYju4IH7gIOgDCAhFLMISAj5cyYh5M+YhFAMYxJCMZx1I4Rni312QooQZtCzEzxXmSZCOG3aNERHR+Oxxx6DyWQ6GiFkH/QeffRRabvd7NmzeY67y9WtVyEsd/okIaxg704ESD2izbizTxRiWr0NWNTgwbxdFVh4oP3ziOyxihlDukkXyaRHhPa5idZdZkJos9lgsRxuh73VWO32ItJkRLTMNw0DcejqXychFLMCSAj5cyYh5M+YhFAMYxJCMZz1IISfHPQiSgMhvJyEkOsi00QId+7cCXaOMC0tDWPGjMG8efNw2223YeXKlThw4ADy8vKQk5PDdeBdrXK9CiHzwHf31uOHMlfAKbnyhAicm25vNx97hmJTWQP21bmkaGFxowc9YqwYlBiBgQkROCGarwg2daq1EAYcFGVQTICEUDEyVQVICFVhU1SIhFARLtWZGxoapH98Zv9YR4kPARJCPlxb10pCSFtGea00TYSQDWbHjh148cUXsXHjRng8HhiNRilSeN9996F37968xttl69WrELIJ+aPGg+e2Bd42+s/BcUi1GwPOIZPDercPaRqc0SMhDDg9QWcgIQwaoawKSAhlYQoqEwlhUPhkFyYhlI1KdUYSQtXoFBXUixBGC44Q1q14DxQhVLRUFGfWTAibeup2u1FTUyNtIaV/vVM8f7IL6FkIK5w+/HNrrXRJTEcpJ8qMe06M1v3ZOxJC2UtSdUYSQtXoFBUkIVSES1VmEkJV2BQXIiFUjExxARJCxchUFdCPEF6nqv9qCx0WQqP0TjklPgQ0EcKnn34aDz30EJ8RUa3tEtCzELJtowUNXtR7/B3Ons0IsCcn9J5ICPnPEAkhf8asBRJC/pxJCPkzZi2QEPLnTELInzFrQRdCeMiL6LMFC+E3JIS8V5gmQjh69GjpvKDZrP8P+LwnQFT9ehZCUQxEtENCyJ8yCSF/xiSEYhiTEIrhTELInzMJIX/GehHCTzUSwssoQsh1kWkihPfccw9uuOEGDB06lOvgqPJjBPQmhC6fH3UeHxI5PBAfaN7rPACLOPK49JOEMBD94L9OQhg8Qzk1UIRQDqXg8pAQBsdPbmkSQrmk1OcjIVTPTklJPUQISQiVzFj45NVECMvKyjBnzhxccMEFGDVqFKxWa/gQC9Oe6kEI2WUvWyocKGn0YHuNEyUOD3pEWzEkKUK6ETST4yUwFS4/ip0G7K1n/wVizH5k2oF4iwG50QZEBL6rRtbMkxDKwhRUJhLCoPDJLkxCKBuV6owkhKrRKSpIQqgIl6rMJISqsCkuREJIZwgVLxqZBTQRwvHjx4P9RVhVVSV1MzY2ts320WXLlskcAmWTQ0BrIaxwepG3uwqLC9q/TdRmNODhwanoE2tBoi10W4lZNLDIAXx8yAunt31Sg+ONGJsExFvZi4XBJRLC4PjJKU1CKIdS8HlICINnGKgGEsJAhELzdRLC0HDsrBYSQv6MWQv6EEIfYgSfIaz95l3QllG+a0wTIZw7d27AUbHH6ymFjoCWQljp8mLmpmLsqg381uC0gckYmxYFuzF4OXP7gW01fnx2KPCj94lWYNIJJsQG+VwhCWHo1mxHNZEQ8mfMWiAh5M+ZhJA/Y9YCCSF/ziSE/BnrSgjPEXupjCSE6XTLKM9VpokQ8hwQ1d0+Aa2EkG0TfXNnZYeRwfZ6+5/RmTghKkgzA1DuAl7d3UFYsJ2GB8UbcF6qEXaT+lVEQqiendySJIRySQWXj4QwOH5ySpMQoKAHkAAAIABJREFUyqEUfB4SwuAZBqqBhDAQodB8XQ8RQvaP7DEaCOGlJIShWUQd1EJCyBWvfirXSghXFzfgH1tKFIHIjbbiyeFpSLCqP9jHtoq+f8CLQoeipnFlphH9Y9VHJ0kIlfFWk5uEUA015WVICJUzU1qChFApMXX5SQjVcVNSioRQCS31eUkI6Qyh+tXTeUlNhPDee+8NOJ7nn38+YB7KIJ+AVkL4SX4N/rOjQn5HAdhNRrw+OgPJdvVnCavdfvxnrw+N8gOEUh/PTDFiTLJ2Qsj6bTQYYDEwDoqwdZnMJIRippqEkD9nEkL+jFkLJIT8OZMQ8mfMWiAhJCHktdI0EcJbb721xXjYDxJ28+jBgwfRt29fRERE4L///W9QY543bx4+++wzsA+PZ599Nu644w6YTO1/wt6/fz+effZZ7Nq1C5mZmZg6dSoGDhwotc/qWLx4sdS3uLg4/PnPf8aECROC6psWhbUQQva0xHO/lWFlUb3iIb8yMgO9YtTfPlvq9OP/7Ql8drB1x06MMeDiDCPsKoOTaiKEtR7glyofSlx+lDmBOq8fqTYD0u1G9I8xIM2uGN9xXYCEUMz0khDy50xCyJ8xCaEYxiSEYjjrRQhjz7lezICPtFLzzbu4NN2ASZNICHmB10QIOxrMgQMH8NJLL+HOO+9ETk6O6jF/8803eO211/D0008jKioKM2bMwLhx43DddW0PwbK/kG+55Rbp+Qsmeux2UyaTeXl5Utk33ngDQ4YMQY8ePcDE8fHHH8eUKVPAbkoNp6SFELKbRdl20d+rnIpRPXBSMs5Oj1ZcrqnAbzV+fHJQuRCmRxhxVaYBcSqPMCoVwhoP8OEBLwocfvj9gL/ZiJvu1bk43YRe0QbEqQ+Yquaox4IkhGJmhYSQP2cSQv6MSQjFMD5ehdBXXwWf2wWD2QyjPRoGs/p/qA7FTOhCCAt9EC6Ey0kIQ7F+OqtDV0LIOrpz507pjcJgIoQPPfSQFOGbOHGiNPbly5dLksd+tU5bt24Fy//xxx/DZrNJX2b/AsF+nXPOOW3ysxtSWaTx7rvv5j03Ia1fCyFkA3j593Is7OCpic4G+ObpWcgI4l1CJlov7FS4XxTAkHgDLk5XGR4EoEQIWWTw2Z0e+PyA0wd4/ZB+z36ZDId/2UwA6834biaclnh4K2lXTySEYlYACSF/ziSE/BmTEIphfDwJod/tgKs4H/U/L4O7rED6vTkuGebkLFiSshA9/HyYY5PFgG3VCgkhRQh5LTzdCWFNTQ3OPfdc/Pjjj6rHfM0110jCxqJ+LO3duxeTJ0/GwoULYbW2/NedRYsW4YsvvsC///3vo+2xKGBWVpYUOWye2A+82267DRdddJH0iyX24TQcEhPCt956C88995zQ7q4pdWD2llJFbSbbTXhuRBqSbUGImc+Ieft8qHArahqXpBtwUkzzOJ2y8uysCltjZnPn4bwGnwEfHfRjb4Mf7AIcJoEdpQgTYDECt+YYkWVX3zdlI9FvbvY9x/5SjI5WH0HW7+j00zPGmK1ji0VluFw/Q9FtT5gQMvGOiYnRbR+Ph46xtcz+Ibf13//Hw9j0Mgb2+ai2tlZ6Vzqck6+qCPW/fY+q5W93OAxbZi+kXPUIDHGpwofqcDhgMBiOBjDa60BHx6NC0VkWWPlcowgh+3xGW0ZDMYvt16ErIWQfpl9++WX88MMP+Pzzz1WP+pJLLsETTzyBwYMHS3WUlJRI20UXLFggnQNsnlhkkLXXXJTYeUIWLbzrrrta5GXbRzdu3Chta236kFRdXa26nyILbtu2De+++y7+8Y9/iGwWFX4L7lpXBGdnxtOqR3/OjsUNuZEwej3q+2qyYFW1BRsq5QsU26J5W64BEZ4G1e2yD3jshzX71VEyGo3Y7bZjfiFQ45YX8mNSmB3hx7VpXli8yrfgqh6QTgsyzowjJX4E5Kxlfq13nZppLfOfayYrLHX2c5l/L47/FsJ9LZu9LjSu/Rg1az6WNVkZ9+XBaYqQlTdUmeSs5dafc0PVNquHhDCUNPVVlyZCeP31bQ+jMhksKiqSIm6zZ8+WLoJRm3hECD/44AMsXboU7PbT+Ph4tV3TrJxWW0bZgNeWNuKxn4tljT0z0oKnh3cL6obRpobYttF3D/hQ6pAnhZdnmjAwyH/clLtldFmJD4uKfNI2Ubkp2QZM7WlCXBffN0pbRuWumODy0ZbR4PjJKU1bRuVQCj4P3TIaPMNANRwPW0Ybt69Dybt/CzTUo1+35w5C8tUzYIoS95lQD1tGWYQwTvClMtXL35V2cFGEUPbyVJxREyF89NFH23SUXeDCbvg877zzkJoaXBienQkcNGjQ0Utk2CUzb7/9dodnCKdPn45PPvnkaNTvxhtvBJPWpjOELLLItpWy84NJSUmKIeuhgJZCWOn0Yl1ZA17YWt4pCiaD9w9MRr/4w2c5Q5HYO4SfHvJKt3d2ls5NNaJPrAEJQe6OkyOEDt/hNxJ/rlJggzh8pvCB3iZkRMiLKoaCnx7rICEUMyskhPw5kxDyZ8xaICHkzznchdBTU4aSNx+Cu+KgIlgp1zyKyP6nKyoTTGZ9CKEfcePF3jJavfwdXJJGQhjM2glUVhMhDNSpYL/OLpF5/fXX8cwzzyAyMhIPP/wwxo4de1QQ2bnBlJQUjBgxAuwv5Jtvvln6+rXXXitdQPPmm28evWWUPTvBooNsG2mTqLKtaoHOiAU7hlCX11II2VgcPj8O1bsx59dSFDV64fAeuwE0xW7CKSlRuLZ7bEgig63Z1bj9WFMObK/zoabZmUJ2Li/eAlycYUKqDSG5sEWOENZ7gTfzvdhZp0wImQbe3sOEvjEkhExWwv2sSqi/x0NdHwlhqIm2rY+EkD9jEkIxjMNdCL01pTj4/I3w+5TdCxE/7nrEjRMnRySEdKkMr+9oTYSQRQGXLFnS4ZgCfV0ODBYRZOcQ23uHkAlinz59cNNNN0lV7du3TxK+3bt3IyMjA/fcc8/RdwjZ2UN2BrF5YpfVsItnwilpLYRNrCpcXri9ftR6fNhX50K/ODvMBiDOaoKNhcA4JbcPYCLGUoEDSLawh9/9MBsMiA7hcw5yhNDtB+YXeLG2QqEQGgx4rJ8p6CgmJ8TCqqUIoRjUJIT8OZMQ8mdMQiiGcbgLoXPfVhS9cZ9iWJH9RiLp0vtgjBBzyZkehPCLQm0ihBdThFDx+lRSQBMhHDZsGDZt2tRuP9lfkCxy19HXlQyO8h4joBchPN7nRI4QOrzAugo/Pj6k7F8iU2wGTOluRLKNnziHw/yQEIqZJRJC/pxJCPkzJiEUwzjchbDhj7Uofe8xxbDsPYYi5ZpHpDcKRSQSQooQ8lpnuhPCDRs24MEHH8S3337La8xdsl4SQjHTLkcIWU921QFv7vOiziM/SnheNyPOSDIiJshzjmJI8GuFhJAf2+Y1kxDy50xCyJ8xCaEYxuEuhN7qMhQ8p3zrZ8zIS5F4/v+JgQxITy6x23LtdruwNps3xG4ZpQihJui5NypUCNk5PZbYh+b23hBzu91wOp1gz0bMmjWL++C7UgMkhGJmW64QHmj0o6ABeL9AXpQwN9KA8alG9IoxIKKLv7ZAQihmLZMQ8udMQsifMQmhGMbhL4SlKHrzQXgqCxUBS756JqIGjFZUJpjMuhDCIj/ix08MZhiKy1YtewcXp4FuGVVMTn4BoUL46quvSj1jl7awi1xap4iICHTv3l264IXeGJM/iXJykhDKoRR8HrlCWO+BdIbQZPDjk0PHLthprwc9ogw4I9GIFLsBJ0QG38dwr4GEUMwMkhDy50xCyJ8xCaEYxuEuhH6XA3Wbv0HFVy/LBmZOykS3G5+COS5FdplgM5IQ0pbRYNdQR+WFCmFTJ9gFLvfffz+vMVG97RAgIRSzLOQKIetNscOP36qB7EhgYZFXugG1/MgtqBEmA5KtQN8YI7rZABMMGBjHLsIRMw49t0JCKGZ2SAj5cyYh5M+YhFAM43AXQkbJV1+F0vlz4NjzkyxoaZNfhC2rr6y8ocqkByH8UqMI4UUUIQzVMmq3Hk2EkOuIqPJ2CZAQilkYSoSQHR8scQA/lvmRHgFEmoBIM1DugnROsNEDSRITrAb0jAbiuvjZwaYZJCEUs5ZJCPlzJiHkz5iEUAzj40EIGSl3WQEql/wXjTvWdQjOaItEyoRZsGX0gcEmdtsOCSFFCHl9R2smhB6PB9u3b8fBgwfBft86XXDBBbzG3CXrPV6EsMLpQXGDG/0SInQ5j0qEsGkAjV5gY4UfDq8BdW4/jEaAvZEYbQJ6xBjQzR6aNxJ1CUxFp0gIVUBTUYSEUAU0hUVICBUCU5mdHqZXCU5BseNFCNmQvXUVaNz2I+q3rpIE0VtTBqPFDnNyJizJ2Uj40y0wCdwm2nwaSAhJCBV8WyrKqokQFhUVSW/97dy5s8PO0rMTiuYxYObjQQhLHR7M2nAQG8saMPPkdIxOi0GSTV97KNUIofQXEPuXSR/g87P/+mE1GCQxtHXxC2TaW9gkhAG/3UOSgYQwJBg7rYSEkD9j1gIJIX/Ox5MQNtHy1lUBXje8DTUwsXcGzRYYbVEwWGz8gXbQgj6EEEj4k9hLZSqX5eGibnSpDM+Fp4kQzpw5ExUVFdLzEpdffjkWL16MwsJCfP311ygrK5POF6ampvIcd5erO5yF0On1Y0tFA+5bewCOZs80nJsdi7sGpKJbhH72UqoVwi63IIMYMAlhEPAUFCUhVABLZVYSQpXgFBYjIVQITEX241EIVWDgXoSEkCKEvBaZJkLItoOyG0fZjaKtH6n/4osv8Pvvv2P69Om8xtwl6w1XISxzePD2znJ8sKui3XlLtJkw59Qs9I23w27SPpxGQsj/24uEkD9j1gIJIX/OJIT8GVOEUAxjEkIxnEkISQh5rTRNhHD48OFYu3YtzGYzRo8eLUUGm94ldDgcYMK4YsUKXmPukvWGoxAW1LvwwLoC7Kp2Bpyz/+ufgstzE5Cg8RZSPQgh23p6yOFHggWItRgCsgu3DCSEYmaMhJA/ZxJC/oxJCMUwJiEUw1kPQvhVkTZbRi9sZ8vomjVr8O9//xvl5eUYNGgQHnjgASQlJbWZjEOHDuGNN97AL7/8Ir13fuKJJ+L2229Hbm6umIkLg1Y0EcLmUcErr7wS06ZNk8SQpW3btmHKlCn47rvvwgBf+HQx3ISQnROcsmqfIsAjUqLw5KlZiGM3smiUtBbCei/wa7UPi4t9SLIYMCHbKEmhVTskIZ8JEsKQI223QhJC/pxJCPkzJiEUw5iEUAxnEsJjEUJ2H8ktt9yChx56CCeffDJefvllVFZWYs6cOW0mg30G3rp1K0aOHImoqCi8/fbb+Pnnn5GXlydm4sKgFc2FkD1S/8477+Cqq66C1WrFggULMGTIEMyePTsM8IVPF8NNCAsb3bh4yS5FgP+UFYtZJ2fAZtIuKqalENZ5gPkHvchv8LfgdnG6CX2iDNJTFsdDIiEUM4skhPw5kxDyZ0xCKIYxCaEYzroQwmIgUfClMhVL89A6Qvjee+/hp59+AnvbnKWSkhJcd911YH+ekpLS6YSwiOI111yD+fPnIz4+Xszk6bwVTYTw008/xWWXXSahcblcmDt3LpYuXQq2XXTUqFF45JFHaIJCvHDCTQgrnF48vL4AP5U1dEoizmpCndsLrx94eXQ2TkuNDjE5ZdVpIYRsi+i+RuCDAx64W7rg0c4PiDHg3DQT4szKxqPH3CSEYmaFhJA/ZxJC/oxJCMUwJiEUw1kPQrhQIyH8c6sto08++aTkCmxXYVNiF1XOmDED7GhaZ2nVqlVSRPHDDz+EwaBdEEHMqpHXiiZC2FHX2A8Umhh5E6c0V7gJIRvfB7sr8dwvRR0OdXBSFMZlxiLGYsTCfRV4bHgm0kNw42il+3CTFS6gxAlkRUA6j8d2XbKH4ztLooWQRQVXlfuwtsIXcElEGIEJ2Sak2cN7CykJYcCpDkkGEsKQYOy0EhJC/oxJCMUwJiEUw1kPQrioGEgSHCEsX5qHC1oJ4axZs9CrVy9MmnRsG+nEiRNx6623YsyYMR1OSHFxMe6++27pDOHYsWPFTFwYtKIrIQwDXmHbxXAUwoP1Lly6dHe7zC/vkQifwYT5++qlr9/TLw4XZscgOYhLZdgD8cUuYGkR0MAeBmyV0uzARWlAVCdSKFIIWR8XHPJhd11gGWw+lAlZJvSNCd9/ESMhFPNjiISQP2cSQv6MSQjFMCYhFMNZD0K4uBhIPlfsO4RlS/NwfmrLdwjVRAjZVtF7770Xl1566dGdimJmTv+taCaE7HDn66+/ji1btqC6uhpND9E///zzku0nJyfrn14Y9TAchbDS6cV9a/fj1wrHUdJpkRZM7JOMRQcbsbnSdfTPMyMtuDg7GlflRKm6abTRB/xaBfzQ/usWLWb6onQgzQbsqvNjQJwB5mZuJVIIWaeWlfixurwde+1kbd7TyyxFPMM1kRCKmTkSQv6cSQj5MyYhFMOYhFAMZ70IYYpgISxtRwjZWcHNmzcfvUSmtLQUEyZM6PAMIXv/nMngeeedJ50fpNSSgCZCuGHDBtx555046aSTpHcImRg2CeG7774rPU4/depUmqsQEghHIWTDz9tZhpd+K5VInJ4WgwGJUfjfnjrUeY5FxdhWzh6xNliMBilCeH//OHSLMMOu4HKZbbXA18WBgbMjeuyc3vgUP74o9OGURCNGJxoQe0SwRAshe17itb3yhfCECAOuzTYh0hR4rHrNQUIoZmZICPlzJiHkz5iEUAxjEkIxnPUghEuKgVTBQliyNA/ntYoQFhYWSttD2b0j7DLKV155BUwKm24ZXbRokXS5zIgRI1BVVYX77rtPuqeEbSttShaLhY6qHYGhiRDeeOON0qRMnjxZ6kbzZyjy8/Nx11134csvvxTz3dVFWtGbEFY6ffix3IMzUi2IaR5iazUf++tcuGLZbkzqk4yDDj+WHGpsM2MxZiPSIy0wGY+F6m7qFYtTk22ynqAocwHv7A+8ENjFNXUev7SdNMMO5EYBm6t8SLEbcE2mAYlWA0QLYY3bj//t86G8o9tkWg3r3FQjRiWF9xsUJISB12oocpAQhoJi53WQEPJnTEIohjEJoRjOehDCr0uAboKFsPjrPJzbSggZ8dWrV+O1115r9x3Chx9+GH369MFNN90kvXfedBtp85libxj27NlTzOTpvBVNhPC0006TJicuLq6NELKbRtkhz3Xr1ukcXXh1Ty9C6GI3YtZ78d9djegebYTXb8DEHhEd3n5Z7vCguNGDJYWN+KawrQyyWWCRwQhzW8m5NDsKV+cGvnV0Vz3wVWHn8+nxA5UuP9h/D7cJXJoOLCw6Fqm8JMOIbIsLsRYj2L86iUpfFvqwsUreOcKpPc1ItIrqGZ92SAj5cG1dKwkhf84khPwZkxCKYUxCKIazHoRwaQmQJlgIi77Ow5/aEUIx1LtGK5oIIRM+tvc3MzOzjRDu2bNHihwuX768a8yAoFHqQQirXT78UO7F4kMOnJFqxUEHcGqCET+UuXF+hg29ok0dPqD+Y6kDL/1RrYjWY4MS0DcusP18Wwps6aBqnx9w+PyoPnLzaPMOXJIBrCnztbiApn+0D+NTjYgP4nIbRYMEsK/Bjzf3Bd42mmkHJuaYwW4bDedEQihm9kgI+XMmIeTPmIRQDGMSQjGc9SCEy5gQnif2UhkmhONTWl4qI4Z412lFEyGcNm0aoqOj8dhjj8FkMh3dMso+6D366KPSfl56mD60i1BrIaxy+fHf3Y1o9PpxVpoVS4u92FN3ONx2UYYJDo8P0WYDzkmztLvNs5xdMLOpAk6vvEhYks2IxwYlItne+WE5dhTx00OQ5LS91Ojzo+rY3TUtsoxIYE9T+FHoOPb4H/uA1zPaiCsyTZ3eRhrK2a1yA2/le1Dl6bzWcclGnJkS5jYIgIQwlKun47pICPlzJiHkz5iEUAxjEkIxnPUghMtLgHTBQlj4dR7OISHkusg0EcKdO3eCnSNMS0uT3gqZN28ebrvtNqxcuRIHDhxAXl4ecnJyuA68q1UeSAgLG9worHfj96oGbChtQEakBaemRqNbpBm5MTZEmNSJRJ3bhz9qfZi3txEnxZuRGmHGZwc9YI+pN0+D44wYHG/A1moPJubakWxr2V69x4d/ba/GT+xxQBlpTLcI3NIzFlYZl6d8Uwr82ipC6PP7wdq0mYxgZwzbe6ThwjRgfaVfOlfYlNgHPPYAvMhzeozlV0VebK7u4FX6I527vbsJ3ezh+9xEE2MSQhnfACHIQkIYAogBqiAh5M+YhFAMYxJCMZz1IITflAAZgoXw0Nd5OJuEkOsi00QI2Yh27NiBF198ERs3boTH44HRaJQihewWoN69e3MddFesvCMhrPd4savaiYfWF6DU0X6I6Y7+qbg4Nx7JtgCvsrcDdm+dFy/taMBZ3WzIbwTWdfJEAjvbdmmGGbvqvLgy2yZd0tI8rS5x4NXt8raNzhqUiH5x8s7x7agDFhUda8nj96PM4UG1y4tEmxleGOGDoYUUMj++MgNg5/eaJ/YB765epjZCy3vNsShlnadj2TPDh0SbETKR8O5uUPWTEAaFT3ZhEkLZqFRnJCFUjU5RwYaGBmk3ks1mU1SOMssnQEIon1UwOXUhhKVApmAhPLiEhDCYdSOnrDAh/OGHH6SbRVsnt9uNmpoaaQsp/bCWM2Xq8rQnhA6vH6uLaiUZDJTGpcfgoSHpSLErk8Iqtx/ba7xYUOBpsbWys/auyDLjzBQzEloJYaXLh7vXl4EJW2cp3mrEP4YkgW0blZMOOYCPCgAf/PD4/Ciod8PNDg8CiDQbYTeb0OA1SJfXSH/qB1LswIAYYGNlSyFMt/lxbZYBMXJCk3I6R3naECAhFLMoSAj5cyYh5M+YtUBCyJ8zCSF/xqwFPQjhilIgS7AQFizJw1kUIeS6yIQJYfOnJZpGdMMNN+Dtt9/mOkCq/DCB9oTwQL0bly7dKRvRpN5JuK1/KuzNnncIVLjS7cfzO1zYWyfv7B+rL9VuwN29rciNbLtt9KVt1filo0N9RzozOsWOyb3ZdlF52yPZzaebqvz4usgr3WjKpK+ppMEApEVYUO1hMmhAk4temQl8W+oDe4qieRqTCIxJhvSv0ZT4ECAh5MO1da0khPw5kxDyZ0xCKIYxCaEYznoQQnYRX7ZgITywJA/jSAi5LjJNhbA9SeQ62i5ceWshZM853P3DfvxR3cFtKh2wemVUDkZ2i1JE8vNDbsw/EODGkyM1MtfsHmXEfX2siLW0FbqVxQ78e0fn20YfOSkBA+MD3y7aNIhDDW58V9SIUrcVv9X4jwqh0cCeszCgW4QVTGyZOLI0NtmAP+p82Ffvl7a1Wo3HBHJyjh8pVsBsVhZJVQS0i2cmIRSzAEgI+XMmIeTPmIRQDGMSQjGc9SCE3zEhPF/sLaNMCM9MpltGea4yEkKedHVUd2shLG5048rlu9HArtlUkGadnIFLcuIVlAAONPjxyG8OHNmF2WnZSDOLsJlweZYFke1E+AodXlQ7O+4zE8oEqxEpAW4XbepEaaMHc34pwbu7K/F//VOQEBGJn6sPR/eS7GaYjeytRPbuoBFGgx9nJBmwqtyLg41+mFn4EECMBYgyGZAVAVzWzYd4C9q8Q+jw+mBXeTGPIthdIDMJoZhJJiHkz5mEkD9jEkIxjEkIxXDWgxCuLAVOECyE+5fkYSwJIddFRkLIFa9+Km8thH9UOXDdt3sUd/DC7Dg8Pvzw+5FyE4uuPbfDhXwZ20ZT7Abc0t2GgbHytnvK7UN7+dhTGB/l10hXxszaeEjKwqKf47PjMSgxGvsbDNK5x24RBpwUa8COeuC7Eo/0JiHbjmpsds3M4YfqjTgjziVtF23+MD27rOf+tQfwyMkZyI6yIkLmVtZgxnY8lyUhFDO7JIT8OZMQ8mdMQiiGMQmhGM56EMLvy4AcwUK4b3GedBxn0qRJYkB3wVaECuHHH3/cAvEVV1yB1n/WlCE3N7cLTge/IecfKsbPv/yCi/40HmYj8Pm+Kjzx02EJUpIGJ0ZgzqnZSFZ4ucwnB934pKDzbaMsupcTZcT9fayIa2e7qJJ+Bsrr9Pqx8GA9Ptxbi2tyozBzYyEqnIf7NzYtGn86IQk764EoswHlTp90Y2lulBHshQnWT/YMx5EA4dGmWJSwb4QHVpNREkIWFfy5vBHT1x+Ufs/SXQNScUF2rHR7KSV1BEgI1XFTWoqEUCkx5flJCJUzU1OCLpVRQ01ZGRJCZbzU5taLEOZqIIRnkBCqXTayygkVQlk9OpJp06ZNSrJT3nYINHiBggagyAEU1jRiX3EZhvTMRrodiDC4cPU3uxRzuyI3ATOGpisut6/Bh5m/OTvdNsq2i45OMuPqEyywy7sgVHE/mgrsrnVjxk9l0v+OTLbj57JaLDtYK/0/k73Zp2Ti7X3Htqay7tjNBlgMBun5hvaEeGKuBRlGpySDNT4j5u0oxwd7Ktr0cXhyJB4blqH4xlbVgz3OCpIQiplQEkL+nEkI+TNmLZAQ8udMQsifMWtBD0K4qgzoLlgI8xfn4XQSQq6LTJgQfvjhh4oGcvXVVyvKT5lbEqjzACtKgILGw3/OfoiUlZUhOztb+v/BsR489ONusHcIFVwaiqdOycL4zFjFuMucfryZ70aps/MnI64/wYLB8ZxtEMD6Mgee21opjSM9wox+sWY83ixies/AVOxyWFHQ0LK/7OmJ7jFW2Fpt+4w0AdP62GB2N6LcZ8L0DYewq8bZISd2nvCpUzJxclJkm7oUw+1iBUgIxUw4CSF/ziSE/BmTEIphTEIohrMehHB1GdDjArGXyuxdnIfRSbRllOcqEyaEPAdBdbckUO8F3tsPuJvdvdJaCLMjfJi/4xA2lNaAyUyLV9c7AfrR2T3RM1bd474LizwfG4rZAAAgAElEQVT4oczbYe1mo0HaLhojYDflW7tqsORg/dG+XNcjBg+uO4CaI1eJnp4WhaGpSVjVqr/sXcKcaAtYX5unQXEmjEs1YGNRDf6xpRiG1vtJOxj11T0ScXWPBGRGWWgZyyRAQigTVJDZSAiDBCijOAmhDEghyEIRwhBADFAFCSF/xqwFPQjhGg2EcA8JIfcFRkLIHbHYBhpYZLAU2N/Qst3WQsi+OijGgymrd8Po90rnCgOlx0/OwIUKbxhtXmely4/OmjEdkaxoAUL4zG+V2Fh+7MmN0al2rC2qwXeFdVKXYyxG/G1YJt7Z3/JG0ySbCRntyNvEHCvS7X5M/n4v2PuOcoWQtfX5n3ohLULAoANNcJh8nYRQzESREPLnTELInzFrgYSQP2cSQv6M9SKEP5QBPQVHCHcvzsMoihByXWQkhFzxiq98Tz2wpKhtu+0JIYsMxhga8Pim/TDD1+aSlOa1TB3QDeOzY5EecXxEshYW1GPe7pqjQ8yKNCMn0oi5vxYf/bN7B3XDlhoLCh3HpJBtF41iV4o2S+y84719bIizGvDclkJ8uLdKthAOSIjA86dlId5KD9nL/W4hIZRLKrh8JITB8ZNTmoRQDqXg85AQBs8wUA0khIEIhebreogQ/lgO9BIshLsW5WEkCWFoFlEHtZAQcsUrvvL1FcDGw0fjWqT2hJBlYFJ4YrQbH+0sxrrSWjjYo3vNUje7GTOHZRw56yYjjChzyCUOD2IsJs2eYNhW7cJjm8tb9HZ8RgQyI8zSw/QssbcMdzUY8X3JkW2uBsBmNLQ58zcg1oQrs83Su4m/lNbhr2sOyBbCW09Mxl/7JsukRtkYARJCMeuAhJA/ZxJC/oxZCySE/DmTEPJnzFrQgxCuLQd6CxbCnYvycBoJIddFRkLIFa/YypnLLS0C9rbaLtr0Q6T5pTKte3ZJug8meMHezNte7UB6pEU6K9jo8UuXqIQyFdS7ce+6g5jQMwFj0mOQaA2daMrtZ4nDKwlhubPjM41s2+jlObH4qOBwnigTu13UhNbPCF6WacZJ8YcjfAdqGnH32oM41Nj5ExtN/Xx/XHf0UHkmU+5Yj7d8JIRiZpSEkD9nEkL+jEkIxTAmIRTDWS9C2OfPYi+VYUJ4aiJdKsNzlZEQ8qQruO56D/B18eFnJlqnjiKETfnOTQN6RvHrcJnDC6/fj41lDZjzS8nRhtgTDA8NTkWqwncNQ9HTnbVuPHrk6YmO6huVGon+cYeFeGCCDdbWV7IaDNL5y7gjRwCr6xvx8vZKfHng2HbUjuruG2fD06dkIcluQb3XjwTOby+Ggpke6iAhFDMLJIT8OZMQ8mdMQiiGMQmhGM56EMJ15UBfwUK4Y1EeTiEh5LrISAi54hVf+cpSYGs7LhJICG/IZREwPv2tdHqxvqwRn+VX4ddKR5sIG7u588kRGTgxzgZ76/Abny5JtTZ6fNhS6cLc39vZY9usXRYpnNY/EQPiA5+fZB+itzcAU344ELDnN/ZOwqQ+yfjyoAO/Vrlwz4nRSLFxmoSAvQmfDCSEYuaKhJA/ZxJC/oxJCMUwJiEUw1kPQri+HDhRsBBuX5SHESSEXBcZCSFXvOIr3113OErYOnUmhLFm4NJMINS3e9Z5fNhd48bP5Q149tdSVLm8YLtD2Y2a7D2/1unqHvFgv5JsfG7cPNjgQWZk27qLGj14YVsVihu9aPAcu0CGXfTSL86KCT2iZUcw6+rqUGOw4rY1B1DS6O50AbwxJhcLC13YVXts2+rNPSIxKNGKKIFiLH6VBtciCWFw/OSWJiGUS0p9PhJC9eyUlKQzhEpoqctLQqiOm9JSehDCDRXihfCPhSSESteK0vwkhEqJ6Tx/tRtYUAA4W76W0OZh+ubDGB4PnJwEhFLDKpxerCxuxJqiWnywp6oNtVS7CSwy2Np7+sTa8MTwdOmx+FAlJqbrSxqkSODU/okYlmxHQqtbPatcPnj8ftR7/Mivc6NPrBXsMlG7yaRIlJkQGq02vLajEmtLjr1z2HwsPj+kbaJnZSVgWZGrzTBHJVtxSXYkEq1tpTlUTMK5HhJCMbNHQsifMwkhf8asBRJC/pxJCPkzZi3oQQg3VgD9BEcIty3Mw3CKEHJdZCSEXPFqU/n+RuCrQy3b7ihCmGIDzk8LXXTQ6fOj1OHF37eU4pSkCHyxvwqbyhrbBRFtNoC969f6kfe3zsgO2UUrlS4f3t5ZiW8Kj920MzYtEjf0ikOK3Qy3H2Dy2s0emq2aTAhtNhvq/UYw8Wud2C2ue+o8YG8yrix1odrVytyPFEiwGnBX3xh0sxvbnlvUZlnpplUSQjFTQULInzMJIX/GJIRiGJMQiuGsByHcVAH0v1DspTK/L8zDsAS6VIbnKiMh5ElXo7qZ5Oyvb7l1lP0Q8dVVISol/WivutmAsSlAsi00Ha1x+7G2tAH/+uPwmTwWZYu1GPC/HRVweNsXHxYhZNFA25FQYa9YG/45PA1pQb536PT6UdDgwT82l6KsnZtEE2wmPDAwCcUuAxYWOHB7n0jkRJmlLa3BpCYhtFjanjc80ODFS9trUeVqxxQ7aPSqEyJwWooNMWaKFjYhIiEMZoXKL0tCKJ+V2pwkhGrJKStHEUJlvNTkJiFUQ015Gb0I4QANhPBkEkLlC0ZBCRJCBbDCLSvbPrq7xgOz0Su9kbe7yoGMGBvcfgOS7FYkWUMXGWRsXD4/XttehW8KD2+VZG41a3AKfihtwJvby8DO6rWXMiLNR98jvL5XAm7tmxQU6mqnF8sK6/H2rmr44YfbB+nMYnOlcvv9KHP4cFlOnPS8xqpSD85Js2Jcmg3xQdz22ZkQ7qjx4JlttYrGdmqSBTf0iJa2r1I6TICEUMxKICHkz5mEkD9j1gIJIX/OJIT8GbMW9CCEP1UAAwUL4daFeRhKQsh1kZEQcsWrbeUHG9yY/H0+Cupd2FntgM/nR3KkBb1jbLikewKu6pGAjMjQvjH4W6UTj/5cim4RZlyRE4ff6sz4o8aD0QkefHOwGisOtRQiFhjMjrTAdER4Xj8jG72DeJePvS84d2s5WD/YMxfsTGCFy4cEqxFsi6r0A9UL6c+a0oB4Ky7PjceaUg+6RZhwW+9I1U9AdCaEFU4fnvq9Fmwbq9x0b78Y9GO3/lA6SoCEUMxiICHkz5mEkD9jEkIxjEkIxXDWgxD+XAmcJFgIf/sqD0NICLkuMhJCrni1qbzC5cWqwhpM+GYPmjYnsh/Wnv/f3nlASVWkbfjtPDkxMwxxyCASxKyYkEUR16yAOee4a173N+e85rgqqy6uqBgwIoKYUUCCwCCZgWFyjh3+89XMHXpi9+2ee2mm3zqHsy5T996qp2qafu5X9ZXbDf+ljEf0SsITB/XF8JTYLmuo7B+cubECWXEx+KJAooQWWCxAUa0bkrzG6qnFUyt3nkOYYLciM9amoncDEpx4ZP/eyAwjoYxk9rx1cSG2VzegoNaL2qaNfCKeIoUigzWelks25bD560emY872nVlBzxkYg9GpDhVZ1VM6E0KJSv5nfTV+LGybSKa9ZyQ4LLhjdHJYEUs9bd9d6lIIzRkpCqHxnCmExjOmEJrDmEJoDudIEMKlu0AIl1MIDZ9gFELDEZv/gPnbyjHl87UtHtyeEEqFAzMT8O8jBioZ64pS7gbe39qAefmNRymIDErimM2VjRLUNxY4Mt2Hf63Ix5qyOrV/MK4pcjd9UCou3yO85aKSUfS5VSV4c315swyL0yXarSoqKH4oB8lbpWFNZWKveOyRGo+lJS2XtB6c4cRxfVxqH2SwpTMhlHtItPSxIJeN7pvmwPmD4uHUKaXBtnV3rUchNGfkKITGc6YQGs+YQmgOYwqhOZwjQQh/LwHGmBwhXPbJfzCWEUJDJxmF0FC85t9cloce9vFq5FW3PAOvIyGUFt4+rjcu2zMTcu5eOKWswYeZW73YUOXDtuqdcpXstEIOp/dPLDMx3Yc1JZVYVFAJLV/KS4f0xfDkmJCbIEdHzN9Ri6oGDx5cXth8H9G5NKcVO2p3LtWUYxBtlsbo5S1jMrCq3IeiVmd1jEmx46xBsbrOBAwkhCV1XtyzshwVkvknQLlsWAL2SW2bnCbQdd395xRCc0aYQmg8Zwqh8YwphOYwphCawzkShHB5sQd7/fVMczrc9JSlc97G6FQrzjnnHFOfG00PoxB2s9H+3/pinDd/Q5tedSaEQ5Jd+PjoYcgOI0r4Z6UPb21pFC63D9ha1QBtZWas3aKkr8rv0Hept1+qBanWOqwsrlaH0Z89LA0ZrtCkNL/Og6dWlUMyeZ7SLw5PrCxskV002WFFRYMX/tv3RAZluehtYzLxUW7LZZyZMVYc2ycG+6bp278XSAhl2ehrf1ZhUXHnh9bH2oA7Riep6CpLSwIUQnNmBIXQeM4UQuMZUwjNYUwhNIdzJAjhyuIG7HPsdHM63PSUxXNmYmSag0JoIHUKoYFwd8Wtr/txM15aVaBLCKVyztTR6BuGEObW+PDseq/KhikROUnuUinpPZtKvN3SYpmm/PVRPW04oXfXCI8kbHn0jzLk1niwXw8X1pbVYF5TtlN5VozNorKeytEYzcUC/KV3PMb1SMRvfoI2MsWuBHVSlgM9dB4OH0gI5dkryxrw5OrKTqfHuFQ7zhucoHsP466Yc2Y/k0IYBHGvG57irbAmZsDiig/igrZVKIQhYdN1EYVQF66QKzPLaMjogr6QQhg0qrAqRoIQriqqxX5TTgurH3ov/vXTdzGiRwyFUC84HfUphDpgRXpV2ad38bcbsTCv7dEG8mEtX6Tt9vYjXjMmDMSpA9NC6mK1BxAhfGOTFzXexqMs6j0+lPkJYXs3vmmYHQPiu+48hXc2VePT3GpkxNgwNsWOh/2WjcpTUpxW5LdaNnrT6AxsrBaBbZTXv2S5UNBgxe9lPuyfZsOJva1I7sI9hPIMkdL3tlRDzkrsqByS4cKoFC4XbY8PhTDAr2l9Feo3/Kr+2LOGwznsUFjjknX/blMIdSPTfQGFUDeykC6gEIaETddFFEJduEKuHAlCmFNYhQOmnBJyH0K58OfP3sewHnEUwlDgBXkNhTBIULtDtcJaD674biM+2Vzaprlen08dO2GzSd7PtuXb40Zg3wz9kQQ56/CzPJEtkUIvlpT6IPvzkh2yR68xWtheka1x5w2wofWJCrIP0GexhHTsw8ZKN+5Y1tj3qf3j8ODyAsj9tCLLRssbvOpcQilyOP2d4zLx4dZ69I61YmKvGHya58XWGkmEY4FENV1W4PR+NvSJsajoZ5XHhxUl9RiR7GxXFIOJEO4OcymS20gh7GR0aitQs3g2POU7M/lanHGIGXc8bEmZgC34lwwUQuN/CyiExjOWJ1AIjedMITSesTwhEoTwz4IKHHzMieZ0uOkpP34+G4PTEymEBlKnEBoId1fc+s7ftuHh37erR0v8SQ6Lz3A5cFx2Cqb0S8YLq/IxN7dcSZsc1i4l1m7FilNHoVdc8F8W633A+krg66bvnRIVHBQPvLfNA9kqKJk8ZR+cUzJ6tgNi7xQLjsmyNieUkXauKXfjhbVVcPt8uGp4AkYkOaAjOKfO93tgRRl21HpwYLoLbo8HG5qym6p+NmXrrJBNjgCGJbkQ53AAFiuSnFZ8mtfYUKHSO86qhFYrB/ewQtr83qYq/FBQq+4/bUC8SlbjXyiExs96CmFbxr6GWrjz16Fu+ecdDoBz6Hg4+oyCJSYhqEGiEAaFKaxKFMKw8AV9MYUwaFQhV6QQhoxO14WRIITrC0pxyOTjdLU73Mrff/4xBmakUAjDBdnJ9RRCA+Huilv/d10xLliwQR2vcPqQNJw/LB394l2o8/rwS0EV9suMhwM+rCuvx7Gf5yDBYcX9+/fDkOR4jE5zwg4ftlQ2IKesBsOSY9EvwQEJqGXF7pRFSRizqLAOFns8cmsbrUkikBMzfXhzswduWCH/ODR4fXBYrYizt5XC87Ot6B/XeK0sLf1sWx2+2VGLDJcVQxIsKrlMSW0DLBYf+sQ50C/egSSnHWVuC9KcjVlD2ytvbajCl9tr1I/GpDjh8XkR0xQVdVot2C89Bp9vr0OMTZa22tA/3oY1lRa1RFQr8rMerp1C6POJWAM2eHB0pgUf51ZhR40HEnG8engi+ifY4WqSayOEULY9bq/xIclpQYq+HDeodPvwR4UXIxKsuo7P2BVzN9hnUghbkvLVlKPuzx/gzl0ZEKEtpTdixh0HiyuwFFIIA+IMuwKFMGyEQd2AQhgUprAqUQjDwhf0xZEghJvyi3DY0ccG3eauqPjtF58iOzONQtgVMDu4B4XQQLi74ta5lfW4cOFGXDYyE1Yf8FNhNY7qk4Qrvt+CTZX1uGB4Oib3TcQzK3fghUOysbykFp/m1mJ7jRsD4m2QoxbeyClERUPjOYJSBic48ehB/ZHksmFLRQMuXLgFO2rcOG94Osb3SkF+vR2D4zz4tbASNYjBumoL4uTcvzovEuwWJDhsKlKoHS+R5AAuyLapZaX5dV48n1OFrdVujEuxo7qhDs+szEd5vUdJX7Xbq7KTxtqsOH1ICs4bloFZW904Z2AssuOtEMnzLznlDbhvRRncXh9yq92o83iR7LQhxWlHpQc4Y0A8Shu8qn1jU5yYk+dDgYQ7/UqM1YLYJvESsZYjIkrqd/I4ubcVX22rVMtPpZyWHY/De8Yg0W5BVwthpRtYUubF3HyvSjAzvY8VvWItimegIseAvLWpARurPdgrxYYpvRzd4pB7CuHOkfdWlaB20bvw1rbdN9zx/LAgbvyZsCb27HQKUQgD/YaF/3MKYfgMg7kDhTAYSuHVoRCGxy/YqyNBCLfsKMARR08OtsldUm/+l5+jX2YGhbBLaLZ/EwqhgXB3xa3XV9SipM6Hz7aUIDsxRh3/cO+SprWQTQ0alRqDfx8+AKtK6/DK2nJYLBbI+YWiVpkxNlw4JBFzc8uwtKi6uQserw/Xje6Jz7ZW4LfC2uZ9eJeMSMPe6Qm4b1kR+sbb8Nf+qfgwr/GMP0nuUuMBPD4fesWKFFrUM8akWHBUphXLSurx+voqFc0cHG/F5vIqvP1noYroyWHw+bVulZxGirZ8c0iSC8+Mz8Z3RT4MT7JjQs+We/nyajz459JirCzdeYyE3N9mtaBXrAM2ixX7pjtx1sB4JDms+Hi7B2sq20/u4vECBer8xJ0/Fyk7qQ/w5rqWX8CHJTpwxfBEuNy1cDgc6k+4pcINvLvVg001Ldt3ZLoVe6dakdhBtFCimX9WejBjU71iqxWR83OynegVa1V7I3fXQiHcOXK+umrULpsDT9HmoIfTGpOI2AOmwxKbRCEMmpoxFSmExnBtfVcKofGcKYTGM5YnRIIQ5ublYeJRk8zpcNNT5n31FXr3zKIQGkidQhgE3BkzZmD27NkqS+fEiRNx5ZVXwmbrmuMSgni8riqbK+qxsrQWaS4b7luah6+3tTzeQITMbrUoUXho/15IdLowc2OFih76x9pO7R8Ph9WHWeuLVLRtW1WDitY9e0h/vLS6GMX1Xlw2Ih1LS+qwIK8K2fFOSETqb6MyUOG1N5/3J4lbZK+iRNNkGWiiw4pRyRYlo+9ubhTOPrE2VNdV44U/8lUmUFmiWVjnVv/rX0QK5a9ECt8+chAWFXuRV+vBpUPjVeSrqNaDedsrUdoA9fdyfetEnhkxdvRPcMJpd8FukeWnXnyUtzPxjPY8Cf5tq/bAp564s4xMkrMTG/BdQW2bcZH7/d+e8ao/4QihbHFcX+XDO1tk+W37pW+MBdP6WZGkhV2bqpXW+zC/wI0fijq6Epic5cABPeyIj8wpHHC+UwhbInJvXoraP74OyE2rYO8zEjF7TgKsna8/ZoQwaKQhV6QQhoxO14UUQl24QqpMIQwJm+6LIkEI8/JyMWnSRN1tD+eCr76ah6ys3hTCcCAGuJZCGADQ119/jRdffBEPPfQQ4uPj8Y9//AMTJkzAmWeeaeCwhH7rNeV1WF5Ug6t+2KIOYvcvsrpSpEWStmiydfLAVFw6Ih0PrijGpsqWh6WPTXXhmN4xuOGnLShrWjLZJ8GBv43KQrzTjudXl6C4zoMUp00Jos9nUZk790h2tnjugEQHpvRJxBd5tRiSYMdf+8Yit9qLf+VUqsjf6ETgtkVbke6yorS+5fmF/jcSkRU5FUWb3C8ZN4zpiUqPFbO31uP0bBeWFFTiw02l2DMlRmUq9Vvl2aI9Q5JdOLxXEv6odGD/NODtLW61nFUOqteKRAclOY0ku/Evx2ZZsaSoCrly1karMiDBjksHxiIjxhqyEMoSUYl+/ljc9v7tzYrT+9qQHW9BrBUorPfh9Q11yK/r+DgL7R7ZcTacne3YLfcVUghbzgRfbTmq5r8c9IdG7H5TYevRL2B9CmFARGFXoBCGjTCoG1AIg8IUViUKYVj4gr44EoQwf/sWHP2XI4Juc1dU/GLufGT26kch7AqYHdyDQhgA7s0334xRo0bh7LPPVjXnzp0LiRjKn0grdW4vPsutwJnfbGzTNC0yKELVuoztEYsH9++HK39qubRUomsS0ZueHYvbftmqLhNp+uToobjqpx2o9njVAe6FdTvFU6RTlou2jsyJzN01LhPP5VRgWJID949Lw4N/lKt9gpV1NZi1rhCFNe42AubfVok0aoLWN96Ba0dnqb2BLocTL66txKn9XPhtRxmWFNfDr0ntDlN6jB137tMbsDqwuMSDHXU+lWjGv0i0TQRVKy4bMK2PBW+0Wi6q/fzEfvE4JsOiosehRAjlKMT3cj3I6WAJa0fzbWofG9KcPjyRU6drSsqcuGmECz2C2ZCo687GVqYQtuJbX4OaJR/CU5IbELzFFYe4g84OKtMohTAgzrArUAjDRhjUDSiEQWEKqxKFMCx8QV8cCUJYuG0jpvzl0KDb3BUVP/t6IXr0GkAh7AqYHdyDQhgA7vTp03HNNdfg4IMPVjU3bNiASy65BHPmzIHT2TISZuA4BXVryfT5Z3kd9v0wB7Lnr3WRPXz+0UHt52cPTcNhvVPwyPIi9XOtyH+OTXMh0erGjJxC9dcSn3t2fDbm5dXi2x3V6BljR0mDVx01IUVkUKSx9eP36hGDQ7IS8PHWGhzTOxZXDE/GmxuqUOf14vvcYvy4owL1Xi/K/c4N9G+/iKYITNOJEeq/Z0wYiMxYBxaV+PDlthpcNsSFf68uVIIqkbbOyqFZ8ThhYCaqPVbYrT78XOxRR2X4F3HBbTU7bzQ80YJMhxvf7mjMYtq63Ds2BWmoU/MiFCGU+31f5MWX+W2XsHbWl2sG21SE86X1dSgIIjqo3WtQvA1nZTtU4p/dqVAI245Ww8bfULd6fsBhtPcajpgxkwFL4HS1FMKAOMOuQCEMG2FQN6AQBoUprEoUwrDwBX1xJAhhce46/HXi+KDb3BUV58z7Aam9B1EIuwJmB/egEAaAe8IJJ+Duu+/G2LFjVc38/Hy1XHTWrFlITk6GCGOklDuefVklIHlqRQEW5LXcOyhtbEzMIvsHW8ri20cOwCe5tVhWWI2Smp1RJpvNjrMGJ+KdnDysLG7c72e1WnH6sAzsl5mEh5cXqQyeYoCVcjYCGs82bB0dlL8/d2gyNlU0qD8Xptdi+8JPMGDCcVhnS8PMdQXYXFmvjsDYUetu3CjYqtisgNcLeJt/aMHte2dhcu9EPLYkH3Hx8dgzthZP/piD9MwslHk6z5py4+gMVJTXYUdVPU4fk6WWWpYUNkqvVlJ69ECpzw4t4eqkTB++XbsV60p2JtvR6kpCneMdBXh/xqsqSU8oJTY2FmdcdjXeLOo82Yf/vXvFAOMqc7B5zUrY9z8WP5QG/+xJaW7kzp2FNWvWhNLcXXaNfPGQPzIXd3WRduzqIi8g/n75hbD/PitgUxqGTsR/5nyLbdu2Bayr9S3U+RzwAaygCAhnoxgbdd/dbeg4l80ZsXDmMudqcGMUzFyeOXNmcDcLoZasjivLzcHxRx4YwtWhX/LRvJ+R3GcohTB0hAGvpBAGQBQoQljYSiICEjewQkxyKv63oVQd2XDbr42H0/uX9paNytLLlw/Lxn3LSlCgsnrujE65bBbcMDIFFyxY3+I+N47Jwpj0BFz38w7E2CxKCovqvCoyKHsUWwuhPPfxA7Lw+B/lyIq14el9kmGpqYA3NhHv5dbjy82F+GZbBXrG2LCt2q2ykrYu/stF5Wdyzy+nDEO1x4L3NtfisJ4uLN1RjJ/zq8RaUeG2NGdCbX0vafNz4/tjUXGjPE3oacVPRR6sr2q959KCUrcP5fU+FT08qy/wSk5puyM4pU8sjsu0oqSkJKwIYYMjDrN2WJHXNmdNu889LN2CA+Nr4Xa7UWqLwzPrAoRG/e5y0zAHHHVtXxwYOEW75NYSVZE3/gkJgc/S65IHdnKTSPkSI8udPcs/gbes7e+91nyLPQaug85ClTu4lwbCuKsy5ho9Drvr/WUuy1E1SUnBvwTS09dIeGGhp71G1ZWoiizlj7RVPUb1d1fcV+ZaeXm5elEeSuFcDY5abW2teoHkcrk6vCA9PT24m4VQS4SwYutqnDRhvxCuDv2S2d8sQkLfERTC0BEGvJJCGACR7CEcM2ZMcxIZSTLzxhtvROQeQunKsyvzcWivRBz0UU67PWu9bHTa4FQc1DMRs7dUqyidf9k7zYWBcRY8/HvLL5lPHtwfTpsVz64uRX6NGz1j7UoIRdPaWy46KtWFib3lGTWY1CsG141MQawNqPb48EluDX7YXorX1xSqg95rPd52k+H4LxeVNsozPz56KOYXeLCqzI0bRsbjjkW5yK1uTIwjOV+qOnCjgzLjcNqQnlhf1RhhmtoXKHf7UKWtR/WDUOPxYW1FAxLtVmyorMeiwvZN7e6xKciOt3fJOYTz8r1YUBTcstErBtnQ09X4Bb+8wYfn19WjqINlt/5jmx1nxSYs2jQAACAASURBVHkDnIjfzZaLSh+4ZLT9Dy138VagoW30WqttccbBlpAOOGIC/sMgFbhkNChMYVXiktGw8AV9MZeMBo0q5IpcMhoyOl0XRsKS0aotK3HKhH10tTvcyu/PX4y4viMphOGC7OR6CmEAuJJE5pVXXsEjjzyCuLg43HrrrTj88MMjNsvohxtLlWi9nlOE73ZUtemdfP/3+S0bnXHEQHy0tVod4r6jpmWW0XMGJ2JRXhnmbitHbZMsxdgtuGPv3lhT3oBaj0Ud0C4RQrmneIjsHWwd3ztrcAqKGoD1lW78fY9k/EXWOTaVnwvr8NmWMjy5PA8SkZRz/lovG5UzBL1eH/wVaXzPeDx+UD/M2Nigjl64Ylgcnv2jAAu2N0a8JEpZ3NJvm5/599E9kRiTgKJ6IMkBnNAbiOvgCAY5SuO1dRVqX+LWKnd7q1nVGYs3jUxGmsvaJUIoCW6eWx84y2hWjAVn9LUh2e/Iw1lbG/BLceAooRxSf0RG4H1kBn72hHxrCmHI6HRdSCHUhSukyhTCkLDpvohCqBuZ7gsohLqRhXRBJAhh7eZlOO2IvUJqf6gXvbvgd8T0G00hDBVgENdRCIOAJBHBDz/8cLc4h7CgtgEL86qwtrwOdy9umTVUuuq/bFQSsrw9YQDuWVaCbdUNLZZqioTdOSYVdy/ORVGtGwVNyVX+MS4Ly4prUVLvwZR+qXh0ZRFibVZ19ITIU3vLRR/ZvyeezalUGUWfPyAdPfxORZfI4tZqN15fnY8PN5ciwW7F5qqGNklpGvyy1EgfFvx1GMo8Nry/pQ6Tslw4qZ8L3+ZV4c6mPkv1soadSWi0YZZsp68e2h+LShtlaEQiMD5dlrp2PBFyKtyQSGFHJdZqwbCkxvvJ8i9ZyhFqUhm5h3B8c4s34PERh6XbMDGjZcM3VHnx/LrA2UZvGhGDdGdwSweD+BUxtQqF0BzcFELjOVMIjWcsT6AQGs+ZQmg8Y3lCJAhh3aalmHr4GHM63PSU/327HK7+YymEBlKnEBoId1fdWs4ilL2AB37Y8bLRBp8PpwxIxXHZKXj1zwrkVrUMp0l20ZFJVsxaX4x6jw/ry+tw3rB0JDptmJtbruTxspE98dzqEnU0Q794p4qitTr6ECNSnJjSNwmzNldjYlYsrt0jGYmOliIi5yWuKK7GP3/NxcaKehXhLPNb9uivYnLlHfv0xqQ+Sfip2IsVpW7cPDIe2fE2tXz1goVbUN2U8rS9ZaP7ZcThzKE98WfTctHjewN+Acuwh6wrhFAaIZlGJeNoZ+XygTZIlNC/lNTLstE6lDYl+Wnv+r6xVlw0yNVhVDRsCAbfgEJoMOCm21MIjedMITSeMYXQHMYUQnM4R4QQblyMqYfuaU6HNSFcuBKuAXtTCA2kTiE0EO6uvPUfpXUorG27DFTaJPsInTYL0l02xDkcat+eJKJZV17XvCRSfv7B+iJsqapXyzVv2asXBiQ6ZZeg6paoitVqUQfcy947SfoixX8bnvxNnN2KGJsVdT4fku225khaazb5NQ3YVFmPV1cXYn5epbpv69I/wYm79u2tsqUmOBxw2GywWnxIdtiQ4bIoMd1R40FVU1pQkdPWCy9lyWvvuBgVyZT2Jdgbl412VekqIZTD5Ts7OkOO90i2+5DSKspX5wF21Hk7PYdRxj87bveMDso4UQi7arZ2fh8KofGcKYTGM6YQmsOYQmgO54gQwg2/Yuohe5jTYU0Iv1sF18B9KYQGUqcQGgh3V99alnWuK6tFcb0HK4trVBRwdI84DE1yoV+CU0X1/MuWynq1j3BNaR1WltYgO96JUWmx6BVnx6Ck4BJRhNtnacOf5dKOevxaWK1Edf/MeIxKjUV2ohO9YrvQ3sJtbDvXd5UQGtC0bnNLCqE5Q0khNJ4zhdB4xhRCcxhTCM3hHBFCuO4XTB0/3JwOa0L4wxq4Bu1PITSQOoXQQLiRdGtJVSz7KNLS0iKpWd2uLRRC44eUQmg8Y3kChdB4zhRC4xlTCM1hTCE0h3NECOGfP2HqwUPN6bAmhD+uhWvwgRRCA6lTCA2EG0m3XrFihcqW+uSTT0ZSs7pdWyiExg8phdB4xhRCcxhTCM3hzKQyxnOmEBrPWJ4QEUK49ntMPXCwOR3WhPCn9XANPZhCaCB1CqGBcCPp1hRCc0aDQmg8Zwqh8YwphOYwphCaw5lCaDxnCqHxjCNGCHMWYuoBA83psCaEP2+Ea9ghFEIDqVMIDYQbSbemEJozGhRC4zlTCI1nTCE0hzGF0BzOFELjOVMIjWccMUK4egGm7p9tToc1IVy0Ga7hh1EIDaROITQQbiTdmkJozmhQCI3nTCE0njGF0BzGFEJzOFMIjedMITSeccQI4apvMG2/vuZ0WBPCX7fCOWIChdBA6hRCA+FG0q0phOaMBoXQeM4UQuMZUwjNYUwhNIczhdB4zhRC4xlHjBD+8TWm7dPbnA5rQvjbNjhHTqQQGkidQmgg3Ei6NYXQnNGgEBrPmUJoPGMKoTmMKYTmcKYQGs+ZQmg844gRwpVfYdreWeZ0WBPCxXlw7jmJQmggdQqhgXAj6dYUQnNGg0JoPGcKofGMKYTmMKYQmsOZQmg8Zwqh8YwjRgiXf4Fp4zLN6bAmhEvz4Rx1NIXQQOoUQgPhRtKtKYTmjAaF0HjOFELjGVMIzWFMITSHM4XQeM4UQuMZR4wQLvsU0/ZKN6fDmhD+Xgjn6CkUQgOpUwgNhBtJt6YQmjMaFELjOVMIjWdMITSHMYXQHM4UQuM5UwiNZxwxQvj7J5g2Js2cDmtCuKwYzrF/pRAaSJ1CaCDcSLo1hdCc0aAQGs+ZQmg8YwqhOYwphOZwphAaz5lCaDzjiBHCpR9h2ugUczqsCeHyUjj3Op5CaCB1CqGBcCPp1hRCc0aDQmg8Zwqh8YwphOYwphCaw5lCaDxnCqHxjCNGCBfPxrRRieZ0WBPClRVwjjuRQmggdQqhgXAj6dYUQnNGg0JoPGcKofGMKYTmMKYQmsOZQmg8Zwqh8YwjRQiXfD4Te2Y6zelw01NW5tdj3OTpFEIDqVMIDYQbSbemEJozGhRC4zlTCI1nTCE0hzGF0BzOFELjOVMIjWccCUL4+++/Q/7sijJ27FjIHxZjCFAIjeEacXelEJozJBRC4zlTCI1nTCE0hzGF0BzOFELjOVMIjWccCUJoTi/5lF1BgEK4K6jvgmdSCM2BTiE0njOF0HjGFEJzGFMIzeFMITSeM4XQeMYUQnMYR+tTKIRRMvIUQnMGmkJoPGcKofGMKYTmMKYQmsOZQmg8Zwqh8YwphOYwjtanUAijZOQphOYMNIXQeM4UQuMZUwjNYUwhNIczhdB4zhRC4xlTCM1hHK1PoRBGychTCM0ZaAqh8ZwphMYzphCaw5hCaA5nCqHxnCmExjOmEJrDOFqfQiGMkpGnEJoz0BRC4zlTCI1nTCE0hzGF0BzOFELjOVMIjWdMITSHcbQ+hUIYJSNPITRnoCmExnOmEBrPmEJoDmMKoTmcKYTGc6YQGs+YQmgO42h9CoUwSkaeQmjOQFMIjedMITSeMYXQHMYUQnM4UwiN50whNJ4xhdAcxtH6FAphlIw8hdCcgaYQGs+ZQmg8YwqhOYwphOZwphAaz5lCaDxjCqE5jKP1KRTCaB159psESIAESIAESIAESIAESCDqCVAIo34KEAAJkAAJkAAJkAAJkAAJkEC0EqAQRuvIs98kQAIkQAIkQAIkQAIkQAJRT4BCGPVTgABIgARIgARIgARIgARIgASilQCFMApG/vvvv8cLL7yAoqIijBkzBjfeeCN69OgRBT3vmi7q4SfJCx5//HH89NNPSEhIwJlnnonjjjtONeTXX3/FzJkzsXbtWjgcDhxwwAG4/PLLVb1oL51xa49NMGOSl5eHCy+8UM35Bx54INoRq/4Hw00DFWhMiouL8dxzz+Hnn3+G1WrFEUccgb/97W9RzzkQt9aAOhuTTZs24emnn8aaNWsQGxuLiRMn4uKLL1a8WVoS0DO3H330USxbtgzbt2/HP/7xD0yYMIE42yGgZy4XFhbiySefVHO1tLQU77zzDtLS0prvOnv2bHz22WfIzc1FcnIyjj32WJxxxhnkrvNz+f3338eXX36JDRs24JhjjsF1113XLsP58+fjvvvuU/8GTp8+nZxJICABCmFARLt3Be1L8c0334y9995bfbkoKSnBww8/vHt3zKTW6+UnMrht2zb885//xJYtW9SXjfvvvx+jR4/Gp59+CpfLpf67trYWjzzyCLKzs3HDDTeY1JvIfUxn3Fq3OtgxkTGoqqpCTEwMhRBAsNw03p2NiWQUvOqqqzBw4ECcfvrpal7LfB83blzkTjKTWtaVc/myyy7D0KFDceWVV6oXejfddJN6yTRlyhSTerN7PEbv3BY5kbn7xBNP4Nxzz6UQdjDMeuayzM8ffvgBffr0gXzfaC2Er776Kvbaay8MGjQImzdvxl133aVeiE6aNGn3mGQGtVLv3F24cCHsdjtE+OQlUXtCKCIvn8/y4lledlAIDRq8bnZbCmE3G9DW3Xn77bexePFiyBtRKfn5+eoLhfx9RkZGN+99+N3Tw8/tduOkk05Sb+UkKiXlscceU/97/fXXt2nMvHnz8NZbb0H+oYzmopdbMGMi0YLPP/8ce+65J37//XcKIaB+54P9LAg0JvLF7/nnn8cbb7zBaJXfL28gbno/n0888UTcfffdzZ8n8gVdXnBcccUV0fyR0abveua2/8UXXXSR+veQEcK200nvXNbuUFZWhlNPPbWNELZ+gsi4zWbDNddcE9VzOdS5Ky/35Qim9oRQVm5kZmbit99+w9ixYymEUT3Dgu88hTB4VrtlTVkql5KSot7EaeXkk09Wkat99913t+yTmY3Ww0+Wwpx33nmQt8/x8fGqmfLfIn5PPfVUm2Y/88wzammNRLKiuejlFmhMJPoqkRWpt2DBAgph0+QKxM1/DgYaE3mJsXXrVtTX12P58uXo16+fYi7R72gugbi1ZhNoTP7zn/+goKBACaBEYG655Rb1BXq//faLZsxt+h6IY0ewKIQdTyO9c1mPEMoKg0svvVRtp9C2VETrhA517nYkhOvWrVMrwEQK5bsFhTBaZ5b+flMI9TPbra64/fbbMWTIEJxzzjnN7T777LPVPpTDDjtst+rLrmisHn5//vmnEm9Z32+xWFRzv/rqK/Wm9JVXXmnR/F9++QUPPvigWsIrS2yiuejhJpwCjcnLL7+soigyz2XPJiOEjbMrEDf/ORhoTB566CHMnTsXt956Kw455BAVjX3ttdcwY8YMJCYmRu10DsStNZhAY5KTk6NebIh8SznhhBPUUjCWlgQCcaQQ6p8xeueyHiGUF0qyp15elMqyxmguoc7d9oRQRPvaa69V+wZFBOXzmUIYzbNLX98phPp47Xa1Q337tNt11KAG6+EX7BvVpUuX4t5771V7KGRJY7SXYLlpnDobE1kGfccdd+Cll16C0+mkEPpNrq6cy7LcS5JHSLIqrcheQlm+JMmSorV05VweNWqUSrohy+/kj6wmkM8N4SvLHFl2EtAzt/25MULY8SzSO5eDFUJ5SScvTWX5s6xeivYS6txtTwglT4F8v5AVYFIohNE+u/T1n0Koj9duV1vWp8sHhJZERpYfyZcM7iEMbij18JM9F7LnRyJ/8mVOivyjJ2/ttD2EsrzuzjvvVNEaeXPHAgTDzZ9TZ2Mi2V1ffPFFtdleiiwflftLtjvZrxnNpSvn8ocffqgyBlIIW86orpzLshxXlqB/9NFHzfNZlqBLMgnJ5siyk4CeuU0hDG7m6J3LwQjhrFmz1HyWF0rMdN5ILNS5254QygsjibxqUdfKykqVgOaggw5qlsTgRp+1opEAhbCbj7qk1ZblobfddpvK8CX71kQKmWU0uIEPxE/eyElUStvTI0lkJHGPrN2XZV6y50c+pGVv1apVq9Q4SFZRbf+mLC2N9iUzMhKdcROeH3zwQXO6/c7GpK6uTmUW1YqIy8qVKxX31NTU4Aa9m9YKNJflxZEccyDLEgONiWQqluyM8qJj/Pjx+OKLL1RyJEkyE81LRgNxk58Lp8mTJ6ul4p2NiSSMkOyAp5xyiooQSrKOe+65R2VpjPZEHK1/RQPN7daf0w0NDepFnSzxF8aHH364+uLM4zxaku3sc7n1XJb/Ly8xysvLVebhN998U33mykoNKfIyQ6KDkuBOEp5IEd7CPZpLoLnb+nNZPhfkjyT1kv+VJeSSnEf+yL998m+gViTDuaxCks+PaP9cjuY5FmzfKYTBktqN63333XcqasJzCEMbxM74yZKMYcOG4fzzz1c39z+3SRLLnHXWWc2b5kXCZU+hf5E68g9ltJfOuIlIyxdg2acm/+hJCXZOcw9hy5nVGTdhJRFWLfrU2ZjIXZcsWaJeMEna9AEDBqjEJ1wC3flngHCTIyPkJZEcAxRoLsvclyisnDkmX6z32WcfXH311Ty7tJ0PTD2f07K0WV4U+Rf58sxkPS3BBvoM8J/LIifyoqN1mTNnjpq7ssxZXu75l4MPPlhtnYj2oudzWfZqS1TRv0ybNg2y/Ll14ZLRaJ9Z+vpPIdTHi7VJgARIgARIgARIgARIgARIoNsQoBB2m6FkR0iABEiABEiABEiABEiABEhAHwEKoT5erE0CJEACJEACJEACJEACJEAC3YYAhbDbDCU7QgIkQAIkQAIkQAIkQAIkQAL6CFAI9fFibRIgARIgARIgARIgARIgARLoNgQohN1mKNkREiABEiABEiABEiABEiABEtBHgEKojxdrkwAJkAAJkAAJkAAJkAAJkEC3IUAh7DZDyY6QAAmQAAmQAAmQAAmQAAmQgD4CFEJ9vFibBEiABEiABEiABEiABEiABLoNAQphtxlKdoQESIAESIAESIAESIAESIAE9BGgEOrjxdokQAIkQAIkQAIkQAIkQAIk0G0IUAi7zVCyIyRAAiRAAiRAAiRAAiRAAiSgjwCFUB8v1iYBEiABEiABEiABEiABEiCBbkOAQththpIdIQESIAESIAESIAESIAESIAF9BCiE+nixNgmQAAmQAAmQAAmQAAmQAAl0GwIUwm4zlOwICZAACZAACZAACZAACZAACegjQCHUx4u1SYAESIAESIAESIAESIAESKDbEKAQdpuhZEdIgARIgARIgARIgARIgARIQB8BCqE+XqxNAiRAAiRAAiRAAiRAAiRAAt2GAIWw2wwlO0ICJEACJEACJEACJEACJEAC+ghQCPXxYm0SIAESIAESIAESIAESIAES6DYEKITdZijZERIgARIgARIgARIgARIgARLQR4BCqI8Xa5MACZAACZAACZAACZAACZBAtyFAIew2Q8mOkAAJkAAJkAAJkAAJkAAJkIA+AhRCfbxYmwRIgARIgARIgARIgARIgAS6DQEKYbcZSnaEBEiABEiABEiABEiABEiABPQRoBDq48XaJEACJEACJEACJEACJEACJNBtCFAIu81QsiMkQAIkQAIkQAIkQAIkQAIkoI8AhVAfL9YmARIgARIgARIgARIgARIggW5DgELYbYaSHSEBEiABEiABEiABEiABEiABfQQohPp4sTYJkAAJkAAJkAAJkAAJkAAJdBsCFMJuM5TsCAmQAAmQAAmQAAmQAAmQAAnoI0Ah1MeLtUmABEiABEiABEiABEiABEig2xCgEHaboWRHSIAEzCbwzjvv4OGHH25+bExMDFJTUzFixAhMnjwZEydOhMViMbtZHT7vsssug8vlwr/+9a+AbXrllVfw2muv4fvvv2+u+/HHH+PVV1/F9u3bIX1dsGBBwPv4V9B4vffeexgwYICua1mZBEiABEiABEjAGAIUQmO48q4kQAJRQKC14NTV1SlZmj9/PkSoxo4di8cff1xJmJ7y7LPP4n//+59u4Qr0jHCEsKioCMcccwyuvfZaTJs2DXa7XT1OT1t3dyHU09dAY8GfkwAJkAAJkECkEKAQRspIsB0kQAK7HYHOBGfZsmW46KKLcNppp+HGG2/U1TejxEOPELZu8JIlS1R/3nrrLRUB1YqetlIIdU0DViYBEiABEiABUwhQCE3BzIeQAAl0RwKBBEdE8LvvvsM333yjllh+8MEHuPfeexUKWUras2dP7L333rjyyiuRlZWl/v7RRx/Ff//73xa4MjIy8Pnnnwd1vXbh3Llz8fzzz2Pbtm0YNmwYbr31Vjz55JMtlow+8cQT+PTTT9Xz7rvvPixatAjHHXccbr75ZhXh1JaM3nHHHfjkk09atOn0009X/7+jtrY33u3x0towc+ZM3H333fj111/Rq1cv3HbbbRg3bhwWLlyIp556Clu3bsWYMWNwzz33IDMzs/n22vVvv/027rrrLoi4pqSk4IwzzsCZZ57Zohm//PILXnjhBaxevVpFOIX9dddd12L5akdMbDZb2OOi3fvdd99V/fjpp5+QmJiIc889FxpPrcFr167Fiy++iMWLF6OhoQGjR4/G1VdfjT322KO5T1LnueeeU3Xq6+sxfPhwXHPNNapfLCRAAiRAAiQQLAEKYbCkWI8ESIAEWhEIJITyxf/BBx/Eyy+/3OZLutvtxqZNm5QAlpSU4M0339S1DLOz63/77TdccskluPDCC5UYyXJPkSr53x49ejTvIRRBmTNnjmqbCIlIh7YUtPUeQhG1Sy+9FCJeIh5aCTdCqLXhoIMOUktR+/fvj6effhpfffWV2p/52Wef4fLLL4csxxUhGjx4MB577LEWQih92GuvvZQADhkyRF0r3G+55RacfPLJqq7IoIj31KlTcf7556Ompgb333+/kkORWk3IO2MSTF87Gxft3gceeCBOOeUUDB06FLKfUsZG5FuEV8qqVatUNFaYSJtFgFesWIEvvvgCt99+u6qTk5Oj+nH44YfjiiuuUGIp81H2eL7++ustxJG/uCRAAiRAAiTQGQEKIecHCZAACYRIIJAQyl7C66+/Hg888ACOOuqodp8iUijSMmPGDOy5556qTjDiod2svesvvvhiiJiIZGhl8+bNOOmkk3DIIYe0EEIRURGsI444okX7zBRCaYNEug444ADVhtLSUpWQp2/fvpg1axYcDof6e9lXKZIoXBMSEtTfiWS11weJNkpCHJFFkdzzzjsPtbW1kEikVuQ5xx57LI4//ngVFe3sfl0xLlpbRQDHjx/f3A6JyookSlRUiiztzcvLU33XBL315BFRlDoyB/3riEgmJSWpvassJEACJEACJBAMAQphMJRYhwRIgATaIRBICGWp6A033NAshLKsT+RFlmnKl3mJUmnFXxo7EsJgrvf5fCqyJGIgf/yLCKFE4LQso5qg/Pjjj3A6nS3qmimEEnWU5ZOyLFMrRx55pBJE4aKVn3/+WUXDROokuuYvcHK9Jo7y9xr72bNnqwibCJhE1ESk/Ivcr6CgABLN9b9fe0zCGRft3tJXube/xMkyT4/Ho14EyBiLtJ911llq+Wd7RZaQSh2J/kqSH/8iy4SlL/PmzePvLAmQAAmQAAkERYBCGBQmViIBEiCBtgQCCaFEtB566CG1H0/2w8l/y7I/iV7JEsf4+HglI5K9U/aUTZkyRT2kI/EI5notuibRJm25pNZyWUIqkTV/Ifzoo4+UPLUuZgqhRPFkz6N/kcidRC39E/IsX75cRfo0nppkifS1PgJj6dKlasnsSy+9hOzsbBx99NEqWisS5V/+7//+DyKaX375ZbMQdsQknHHR2tpeX2V/p+z1fOONN9SyXokmt9dWrd1anc5+J2XZMAsJkAAJkAAJBEOAQhgMJdYhARIggXYIBBLC1kllZBmkZB2VJYFakWykErkKRgiDuV6LEMqyUREi/9JehFCilbLnblcKYXttECGcMGGCirBqpSMhlKhrqBFCiRjm5+e3iBB2xKQjIQxmXDQhbO/e/kIYTIRQ9lNKhFDGWPaKspAACZAACZBAOAQohOHQ47UkQAJRTaAzIRR5ESHTjp0QUZMv8fJ3F1xwQTM32RMn9/EXwn//+98qCvbDDz8019NzvSwVlfqSYEQrsodQIoaydNI/QhiuELbX1o4mRWdZRltLqV4hlD1zkmBFK8JTMrz67yEUkfLPilpWVqaisrKHTxLQdCZt8rNwx0XLMtq6r/5CKM8JZg+h1KmoqFB7T/2X2kb1LyQ7TwIkQAIkEBIBCmFI2HgRCZAACUCJnAidZIocMGCA2v+lHUwvmUVbH0wv0S7JICny0rt3b5VBU7J3fv311y2EUNv/Jkck7LPPPrBarQp3sNfL8REiDBJBmj59uspiKhJYWFjYJstouELYUVvbmx9GCaEciSGZUrUso7L8VPYe3nTTTSqbpxSJIEqWUuEhy05l/6bUkeydsidRjroIJIThjkuwQuifZfSqq66CHDuycuVKtdxYlrhKkSyj8mLh0EMPVVFCab/sS5WXCPK/f//73/krSgIkQAIkQAJBEaAQBoWJlUiABEigLQFNcLSfuFwupKamqpT/kydPVpky5bxBrYiYiUDKl3aRPNkrJhHD1nsIvV6vEkQREIkCaecQBnu9PE/2xIlQyt40OYpB9hSKFEobuzJC2FFbzRRCkVpZNip7M+UcwuTkZLVX8Oyzz27RDJFCOdtPO4dQZFsStwwaNKi5XkfSJhXCHZdghVATPsm8Kv2RIkeCyPJW/3MIN27cqPojR2pUV1erlwxaQhqZMywkQAIkQAIkEAwBCmEwlFiHBEiABEggIgl0JnAR2WA2igRIgARIgAQijACFMMIGhM0hARIgARIIngCFMHhWrEkCJEACJEAC7RGgEHJekAAJkAAJ7LYEKIS77dCx4SRAAiRAAhFCgEIYIQPBZpAACZAACZAACZAACZAACZCA2QQohGYT5/NIgARIgARIgARIgARIgARIIEIIUAgjZCDYDBIgFsBrpwAAAaxJREFUARIgARIgARIgARIgARIwmwCF0GzifB4JkAAJkAAJkAAJkAAJkAAJRAgBCmGEDASbQQIkQAIkQAIkQAIkQAIkQAJmE6AQmk2czyMBEiABEiABEiABEiABEiCBCCFAIYyQgWAzSIAESIAESIAESIAESIAESMBsAhRCs4nzeSRAAiRAAiRAAiRAAiRAAiQQIQQohBEyEGwGCZAACZAACZAACZAACZAACZhNgEJoNnE+jwRIgARIgARIgARIgARIgAQihACFMEIGgs0gARIgARIgARIgARIgARIgAbMJUAjNJs7nkQAJkAAJkAAJkAAJkAAJkECEEKAQRshAsBkkQAIkQAIkQAIkQAIkQAIkYDYBCqHZxPk8EiABEiABEiABEiABEiABEogQAhTCCBkINoMESIAESIAESIAESIAESIAEzCZAITSbOJ9HAiRAAiRAAiRAAiRAAiRAAhFCgEIYIQPBZpAACZAACZAACZAACZAACZCA2QQohGYT5/NIgARIgARIgARIgARIgARIIEIIUAgjZCDYDBIgARIgARIgARIgARIgARIwm8D/A/VGXR097PgoAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.scatter_feature_importance() # works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "4ce4db18", + "metadata": {}, + "source": [ + "Putting importance of the drift into perspective according to the importance of the model to be deployed, can help the data scientist to validate that his model can be deployed.
\n" + ] + }, + { + "cell_type": "markdown", + "id": "fdbc35f4", + "metadata": {}, + "source": [ + "### Univariate analysis" + ] + }, + { + "cell_type": "markdown", + "id": "d3f6c8f1", + "metadata": {}, + "source": [ + "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b9aa98df", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate('YearRemodAdd')" + ] + }, + { + "cell_type": "markdown", + "id": "6c914576", + "metadata": {}, + "source": [ + "### Distribution of predicted values" + ] + }, + { + "cell_type": "markdown", + "id": "30c84fc4", + "metadata": {}, + "source": [ + "This graph shows distributions of the production model outputs on both baseline and current datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "51c99e4c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\") # works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "9b50bcb0", + "metadata": {}, + "source": [ + "Jensen Shannon Divergence (JSD). The JSD measures the effect of a data drift on the deployed model performance. A value close to 0 indicates similar data distributions, while a value close to 1 tend to indicate distinct data distributions with a negative effect on the deployed model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "ceffb5da", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_indicator(\n", + " fig_value=SD.js_divergence,\n", + " height=280,\n", + " width=500,\n", + " title=\"Jensen Shannon Datadrift\",\n", + " min_gauge=0,\n", + " max_gauge=0.2,\n", + " ) #works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "f7f93151", + "metadata": {}, + "source": [ + "## Compile Drift over years" + ] + }, + { + "cell_type": "markdown", + "id": "b2fcad1b", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2008" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3c658ba9", + "metadata": {}, + "outputs": [], + "source": [ + "house_df_2008 = house_df.loc[house_df['YrSold'] == 2008]\n", + "\n", + "y_df_2008=house_df_2008['SalePrice'].to_frame()\n", + "X_df_2008=house_df_2008[house_df_2008.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5aa0896b", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2008,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "92ca7c35", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The variable Condition1 has mismatching unique values:\n", + "[\"Within 200' of East-West Railroad\"] | []\n", + "\n", + "The variable Condition2 has mismatching unique values:\n", + "['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to postive off-site feature', 'Near positive off-site feature--park, greenbelt, etc.'] | []\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "['Mixed'] | []\n", + "\n", + "The variable ExterCond has mismatching unique values:\n", + "['Excellent'] | []\n", + "\n", + "The variable ExterQual has mismatching unique values:\n", + "['Fair'] | []\n", + "\n", + "The variable Exterior1st has mismatching unique values:\n", + "[] | ['Imitation Stucco']\n", + "\n", + "The variable Exterior2nd has mismatching unique values:\n", + "[] | ['Other', 'Stone']\n", + "\n", + "The variable Foundation has mismatching unique values:\n", + "[] | ['Slab', 'Wood']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "['Major Deductions 2'] | []\n", + "\n", + "The variable GarageCond has mismatching unique values:\n", + "['Excellent'] | ['Poor']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Poor']\n", + "\n", + "The variable GarageType has mismatching unique values:\n", + "[] | ['More than one type of garage']\n", + "\n", + "The variable Heating has mismatching unique values:\n", + "['Hot water or steam heat other than gas', 'Floor Furnace'] | ['Wall furnace']\n", + "\n", + "The variable MSSubClass has mismatching unique values:\n", + "['1-Story w/Finished Attic All Ages'] | []\n", + "\n", + "The variable Neighborhood has mismatching unique values:\n", + "['Northpark Villa', 'Bluestem'] | []\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "['Membrane', 'Clay or Tile'] | ['Metal']\n", + "\n", + "The variable SaleCondition has mismatching unique values:\n", + "[] | ['Sale between family members']\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "['Contract 15% Down payment regular terms', 'Warranty Deed - Cash'] | ['Contract Low Interest', 'Other']\n", + "\n", + "The variable Street has mismatching unique values:\n", + "['Gravel'] | []\n", + "\n", + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6877714667557634\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2008', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "b87cbbee", + "metadata": {}, + "source": [ + "----" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9400fdc8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" + ] + }, + { + "cell_type": "markdown", + "id": "04f0d6b9", + "metadata": {}, + "source": [ + "------" + ] + }, + { + "cell_type": "markdown", + "id": "03bca0b9", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2009" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "f28c44f9", + "metadata": {}, + "outputs": [], + "source": [ + "house_df_2009 = house_df.loc[house_df['YrSold'] == 2009]\n", + "\n", + "y_df_2009=house_df_2009['SalePrice'].to_frame()\n", + "X_df_2009=house_df_2009[house_df_2009.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a6f46c7e", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2009,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "2618c106", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The variable BsmtCond has mismatching unique values:\n", + "['Poor -Severe cracking, settling, or wetness'] | []\n", + "\n", + "The variable Condition1 has mismatching unique values:\n", + "[] | ['Adjacent to East-West Railroad']\n", + "\n", + "The variable Condition2 has mismatching unique values:\n", + "['Adjacent to arterial street'] | []\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "[] | ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "\n", + "The variable ExterCond has mismatching unique values:\n", + "['Excellent'] | []\n", + "\n", + "The variable ExterQual has mismatching unique values:\n", + "['Fair'] | []\n", + "\n", + "The variable Exterior1st has mismatching unique values:\n", + "['Brick Common', 'Cinder Block'] | ['Stone', 'Imitation Stucco']\n", + "\n", + "The variable Exterior2nd has mismatching unique values:\n", + "['Brick Common', 'Cinder Block'] | ['Other']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "['Major Deductions 2'] | []\n", + "\n", + "The variable GarageCond has mismatching unique values:\n", + "['Excellent'] | ['Good']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Poor']\n", + "\n", + "The variable GarageType has mismatching unique values:\n", + "[] | ['More than one type of garage']\n", + "\n", + "The variable LotConfig has mismatching unique values:\n", + "[] | ['Frontage on 3 sides of property']\n", + "\n", + "The variable MSSubClass has mismatching unique values:\n", + "['1-Story w/Finished Attic All Ages'] | []\n", + "\n", + "The variable Neighborhood has mismatching unique values:\n", + "['Northpark Villa', 'Bluestem'] | ['Veenker']\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "[] | ['Metal', 'Wood Shakes']\n", + "\n", + "The variable RoofStyle has mismatching unique values:\n", + "['Mansard'] | []\n", + "\n", + "The variable SaleCondition has mismatching unique values:\n", + "[] | ['Adjoining Land Purchase']\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "[] | ['Other']\n", + "\n", + "The variable Utilities has mismatching unique values:\n", + "['Electricity and Gas Only'] | []\n", + "\n", + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.5405695039804042\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2009', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "6a045bbc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" + ] + }, + { + "cell_type": "markdown", + "id": "63c9c1d9", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2010" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "214b36a2", + "metadata": {}, + "outputs": [], + "source": [ + "house_df_2010 = house_df.loc[house_df['YrSold'] == 2010]\n", + "\n", + "y_df_2010=house_df_2010['SalePrice'].to_frame()\n", + "X_df_2010=house_df_2010[house_df_2010.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "e0d62327", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2010,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "860b4906", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The variable Condition1 has mismatching unique values:\n", + "[\"Within 200' of East-West Railroad\"] | []\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "[] | ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "\n", + "The variable ExterCond has mismatching unique values:\n", + "['Poor'] | []\n", + "\n", + "The variable ExterQual has mismatching unique values:\n", + "['Fair'] | []\n", + "\n", + "The variable Exterior1st has mismatching unique values:\n", + "['Asphalt Shingles'] | ['Stone', 'Imitation Stucco']\n", + "\n", + "The variable Exterior2nd has mismatching unique values:\n", + "['Asphalt Shingles', 'Brick Common'] | ['Other', 'Stone']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "[] | ['Major Deductions 1']\n", + "\n", + "The variable GarageCond has mismatching unique values:\n", + "[] | ['Poor', 'Good']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Good', 'Excellent', 'Poor']\n", + "\n", + "The variable GarageType has mismatching unique values:\n", + "[] | ['More than one type of garage']\n", + "\n", + "The variable Heating has mismatching unique values:\n", + "[] | ['Gas hot water or steam heat', 'Wall furnace']\n", + "\n", + "The variable HouseStyle has mismatching unique values:\n", + "[] | ['Two and one-half story: 2nd level finished', 'One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished']\n", + "\n", + "The variable LotConfig has mismatching unique values:\n", + "[] | ['Frontage on 3 sides of property']\n", + "\n", + "The variable LotShape has mismatching unique values:\n", + "[] | ['Irregular']\n", + "\n", + "The variable MSSubClass has mismatching unique values:\n", + "['1-Story w/Finished Attic All Ages'] | ['2-1/2 Story All Ages', '1-1/2 Story - Unfinished All Ages']\n", + "\n", + "The variable MSZoning has mismatching unique values:\n", + "[] | ['Residential High Density']\n", + "\n", + "The variable Neighborhood has mismatching unique values:\n", + "['Northpark Villa'] | ['Veenker']\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "[] | ['Wood Shingles', 'Metal', 'Gravel & Tar']\n", + "\n", + "The variable RoofStyle has mismatching unique values:\n", + "['Mansard', 'Shed'] | ['Flat']\n", + "\n", + "The variable SaleCondition has mismatching unique values:\n", + "[] | ['Adjoining Land Purchase']\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "['Contract 15% Down payment regular terms'] | ['Contract Low Down', 'Contract Low Down payment and low interest', 'Other']\n", + "\n", + "The variable Street has mismatching unique values:\n", + "['Gravel'] | []\n", + "\n", + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6978632478632478\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2010', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1ee11cf8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_datadrift_2007.html', \n", - " title_story=\"Data drift\",\n", - " title_description=\"\"\"House price Data drift 2007\"\"\", # Optional: add a subtitle to describe report\n", - " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" # Optional: add information on report\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "6b87062a", - "metadata": {}, - "source": [ - "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", - "However, to illustrate functionalities, we will detail results with notebook mode analysis." - ] - }, - { - "cell_type": "markdown", - "id": "79088691", - "metadata": {}, - "source": [ - "## First Analysis of results of the data drift" - ] - }, - { - "cell_type": "markdown", - "id": "0893b964", - "metadata": {}, - "source": [ - "Data driftn methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", - "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", - "Then a classification model (catboost) is learned to predict this target.\n", - "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" - ] - }, - { - "cell_type": "markdown", - "id": "ae3264d5", - "metadata": {}, - "source": [ - "### Detection data drift performance" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "53b9e7f0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Performance of datadrift classifier\n", - "SD.plot.generate_indicator(fig_value=SD.auc, height=300, width=500, title=\"Datadrift classifier AUC\")" - ] - }, - { - "cell_type": "markdown", - "id": "71510d9b", - "metadata": {}, - "source": [ - "An Auc close to 0.5 means that there is little drift" - ] - }, - { - "cell_type": "markdown", - "id": "6d44c81e", - "metadata": {}, - "source": [ - "### Importance of features in data drift" - ] - }, - { - "cell_type": "markdown", - "id": "254bab01", - "metadata": {}, - "source": [ - "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "0515dfb4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Hwfnyyy/jrkG2CFnFXbt2+b0P585BIHAwuT8hROYP34+/m6nvpih//vmnIEsIufBXsCMmjoBACUUIQx07jibA0Q6JlR49egimzAZTnBZCrAXFsQqQmfglQ4YMKnjxz5NEjMBj9+7dCQ41oeyzGRFDpYGelXCfRTP98JchRN+uXbumO4dit9WESvwseqjPXTDPEq8lARIggaRMgEKYlKPLsZEACYRFIBQhNBrct2+fboePrBumteFDK9YW4kB3ZIuwtT6yYb4FB9pj2igOREdmEOfQIdOHTB5kCB+UkSVL6Ow8TCtFRm3//v2CzVnQZvxdMnEGHNYo4jxDZFqwaQ2Od8A6PWTmkJEKVQhDHTv6hP7gz5EjRzR7hmMVcuTIocdPYHrlgw8+GHQsnRZCbO6CA+tnzpypzwF2BkW2D7u9YjoxjrTwVzB2iCQyhYcOHdKNgHAfWODIj4ceesjvfWZEzLgx0LMSzrNoph8JCaHRP/yyBFNiscENDp/Hc4r3As57rFWrlr6X4pdQ3nNBP1S8gQRIgASSIAEKYRIMKodEAiRAAiTgHAEzQuRc79gyCZAACZAACdxMgELIJ4IESIAESIAELCRAIbQQJqsiARIgARKwnQCF0HbEbIAESIAESCCaCFAIoynaHCsJkAAJeJ8AhdD7MeQISIAESIAEXESAQuiiYLArJEACJEACAQlQCAMi4gUkQAIkQAIkYJ4AhdA8K15JAiRAAiTgPAEKofMxYA9IgARIgARIgARIgARIgARIwBECFEJHsLNREiABEiABEiABEiABEiABEnCeAIXQ+RiwByRAAiRAAiRAAiRAAiRAAiTgCAEKoSPY2SgJkAAJkAAJkAAJkAAJkAAJOE+AQuh8DNgDEiABEiABEiABEiABEiABEnCEAIXQEexslARIgARIgARIgARIgARIgAScJ0AhdD4G7AEJkAAJkAAJkAAJkAAJkAAJOEKAQugIdjZKAiRAAiRAAiRAAiRAAiRAAs4ToBA6HwP2gARIgARIgARIgARIgARIgAQcIUAhdAQ7GyUBEiABEiABEiABEiABEiAB5wlQCJ2PAXtAAiRAAiRAAiRAAiRAAiRAAo4QoBA6gp2NkgAJkAAJkAAJkAAJkAAJkIDzBCiEzseAPSABEiABEiABEiABEiABEiABRwhQCB3BzkZJgARIgARIgARIgARIgARIwHkCFELnY8AekAAJkAAJkAAJkAAJkAAJkIAjBCiEjmBnoyRAAiRAAiRAAiRAAiRAAiTgPAEKofMxYA9IgARIwFMEJk+eLO+++640aNBA+vTp46m+u7Gz1atXl7Fjx8q9997rePf+9a9/ybRp06RgwYIR78vJkyf1edq/f7907dpVmjdvflMfAr0e8Q6zQRIgARJIIgQohEkkkBwGCThFoH79+nL48GFtPnPmzFK6dGn9UJclSxanuhTxdnfv3i1z5syR//znPxIbGyuZMmXSD9QTJ06U1KlTR7w/djcYLUJ4+fJlKVeuXII4hw8fLtWqVbvl9aefflpef/1104JHIfwb4fjx4+Xq1avSq1cvSZEixS1cA71u9rkPNj5m6w33Ooy9TJkyt1TTuHFjZYLy4osvyubNm/XfderUkQEDBoTbLO8nARIgAaEQ8iEgARIIiwCEsFWrVvrB+NSpU9K3b1/Jnz+/DB48OKx6vXLz6tWrZdCgQSqBQ4cOlYceekiOHz8uixYtUhYxMTGuHcrzzz8ve/fuVXGtWLGi6X6GIoShtmW6U34utLrNKlWqyKhRo/x+aPdtPljhoBD+TQ/SU758ealdu7bfsAd63eyzEmx8zNZr9XUXL16UJ598UqZMmaI/V3zL6NGjVZ4phFZTZ30kEJ0EKITRGXeOmgQsI+ArhKgUmbJPPvlE/v3vf8vZs2dlzJgx8sUXX0i6dOmkSZMm0rRpU217+vTp8t577+mHGnzoeemllyRt2rSCKWu4buXKlXo/vu7du7ekSpVK7/vpp5/k1Vdfle+++05y5swp7du3l6eeekpfw7XNmjWTjz76SH799VepUKGCSpqRpUuozcT6mRioCxcuCD7M4++EskX/+9//5PHHH9dqtm7dqoI4btw4WbhwobRo0UK6deumryGzev369bjmcuTIIWvWrEnw+5BviNnXX38tly5dkhIlSugH6nz58vmtL02aNFrXyJEjJX369JpdOHbs2E3D69Kli8p9/AJpHDFihE7lu3HjRtzLvlNGwf7cuXP6WtasWeWBBx7QmObJkyfRthK7z3hOPvzwQ/n999+lQIECguft2WeflWTJkukvIBJiEMz4zL4ZfIUwoefw5Zdflo0bN8ZViWe3YcOGYsgpnuPChQtLv379pFixYnpdYkIY6JlGTD/++GPJnj271oVnC89Y586d494TdevWlfXr1yuvqlWrCjJOeF4PHTokJUuWVMnFM2G8hxJ7/yX2XjH6umLFCn3v+nIwgCTEDc/e9u3b47gtXrz4pmmr/l7HLISEfr4kxDuh+JjhiJ8tvmML5eeb2WcN1+Hn2MyZM/Xv+IVCGAxJXksCJBCIAIUwECG+TgIkkCgBXyGEhGG6aNGiRfVvyAU+9Hbs2FFloUePHioJ+PCKf8+YMUPlYd26dSoOjz76qEodXscHW3zox/VPPPGEtGnTRq5du6br1h577DFp3bq1TtHEB7xZs2Zpm7j3nnvuUQnElDO0iw+GtWrV0kyYvzbLli2bYD+RNYNA4Tf1EKL45bPPPpOePXvqtzGNy/hQ7XudrxB+/vnnKr2JCWHlypU1u5ohQwatxhBF3+9jKmOjRo1UjpE9ADust0Ld77//vvbDuK9GjRrKEH927dqlAgoRRTGTQYNs1qxZUz/gv/nmm5IrVy5p2bKlYFz+1hBC8D/99FPNjiKrgQ+0Ztryd99XX32lwo8xQKCQecUvGvA9CHMgBmbGF8zb2xDCUqVKJfocJpaBAk9Ixbx582TZsmX6nAYSwoSeaeP5CCSEEGk8v3/++ac+6xAp/FIFzw2mIOL9ZKzXS+z9h18GJPSexnsF9+bNm1cFE3XHL4Hev+gL6kkoQ+j7eqC+GG374+0vPmaE0HdsibWf2M+3xH6exOfVrl07wbOGvymEwbxTeS0JkECwBCiEwRLj9SRAAjcR8F1DiBcefvhheeONN/TDe9u2bTVLYKwHWrJkiXz//fcqIhApZBONzIZRKT5U4sM/PnyjQC6mTp2q2UTc26lTJ81ApkyZUl+H/GXMmFHrw734MIoPUSi4Dx8IIUMHDx702+aBAwcS7CfqTuwD3PLlyzXTggykb3bDF5CvEG7btk2QqUtMCCE8vpuLGGLn+30wQTYQWcHZs2drc2AGsR44cKB+oDbug3hAysHinXfeuWndkRlhMtrCmkhkbVBeeeUVwVTZxDaVgdz/9ddfcVlRM22hbt/7kFmGBECIICKFChXSLGHy5Mn1uQjEwGybZt/ShhBCuBN7Ds1MSUSWE++Tu+++O6AQJvRMmxVC3/vBE788MSRjwYIF+ssSPMcoib3/Ar1XcC/eMwmtuwz0/g1GCAP1JX5MfXmHKoS+Y0us/cR+vpkVwp9//lmee+45QXb89ttvpxCafZPyOhIggZAIUAhDwsabSIAEDAJGhvCZZ57RaZzdu3fXbMSVK1f0wzw+vOO36cZUQ4jKW2+9pVNCIVTIGmDdED5EGVNGMQ3QmE6HaYr48IoM3KZNmzRLBbE0CjJQP/74o4pg/B0S8RqylsY6G39t7ty5M9F+JhbpYDOEZoQQooUsnFEMsfP9/vz583XTEn8FmR7EIP59mC779ttv64dMrHlEMSNMYI3paUbccB/aRh8MIUQMkanEtMTffvtNY2+UDz74QO666y6/bQW6DxzwDO3YsSOuPmSe8PzgeQjEwMz48IsGPDso+GUGstYJFUMIkTFO7Dn0JxxLly5VZr/88otOk0ZBTPDLi0AZQt9dP+M/02YyW7734xcHDz74oE5jRcH48R7ALqcoeA8l9P4D88Te07gXXCDu/kqg928wQhioL4nxDlUIfccWqP2Efr6Z/T8H3quYDo5nxF/hlFGzJHkdCZCAGQIUQjOUeA0JkECCBOKvIcSHXojesGHDdIom1sFh6mdCBbtyYookpAFbzYebIUzsw7PRB9820R4EKlA//fUfawexmQ6m4iW0hhDXVKpUSW/fsmWLrqWEoKI9f2sIzQihkR3znZIZv39WCaGZDCH6jKxhkSJFVAgw3RVZIogPprAiC+ZPzszch3FBMvft26dTgzFNGNOF8QsDZAgTY2BGCIN5a5vNEOKZQBbYyPQePXpUY40sLWQJWWJMgwUz7CoZjhDilymQHyOLhAwxpNl3DWGwQphQhh4xSOy9EujICiszhIn1JRDv+PHBMxAsx0As/P2swc83swW/uME0eWQ2KYRmqfE6EiCBUAlQCEMlx/tIgASUQHwhRPYEH7ggeZgSev/99+t0P2QK8Vt1rH9D5ufIkSP6QRhZQUzFwvewHhAfKrFZDDIWEEl86IdQIUsYfw3St99+q68jawJBSCxDiLb9tYnppC+88ILffiIDFmiKF9aDDRkyRNdCYhzIvuC8NIgx1lFigw+sgcSmKJAEiC/agyiGKoSYBouNQbCGEJtqYJolPgQj24MPkOiDGSE0NuoAd6zH81fQFurEGtCE1hBiWht+AVC8eHGZNGmSrFq1Ki57Zwihv7YC3XfmzBlBVhUfjiE5GCt+2YCYY1psIAZmxhfM2zihNYTxn0OIKJ4dY/MbTMmEoGF6L9Y+Ik7gBJ7hCiHWc6IOvMdw/AmeZ2zcFI4QJvT+w6ZHib1XAglhoPdvMBnCxPqCnzmJ8Y4fHzwDwXJMrP3Efr4F+nmCviCOkEdk3BPapZgZwmDeubyWBEggEAEKYSBCfJ0ESCBRAvGFEBfjgy42BIHUTZgwIW7KHzavwKYmkEBke7B7HoQD8oLMBs4xxIdKfNA3dhnFDqQQK2OnUOyMiSl+xi6jEEVMAUNJTAjRTkJtYsMUf/1Ef8x8gMNYcVA7xADZR2TIkAnC1Dv0G1PlIIOYLuhbQhVC1IEdIzFNE21DLrG2DrtJQsaxvtKMEH755Zc6vRfrlfABF2va/K3/+uGHH/Q6rJvyt8sopohCCJFNxC8EfIshhP7aQh8Tu++OO+7QjVfwB3HHWlFIGcQBu3UGYmB2fGbf4r67jCb2HGLdLOQVQmvsMornCzKLZx+/4MCHfohtuEKIrBt+IYGp0eCJDHTu3LnDEsLE3n+JvVcCCSE4J8YtGCFEXYn1JTHe/uITLMfE2k/s55uZnyd4T+D9iLjGL8Za4IR+jph9lnkdCZAACfgSoBDyeSABEnAVATMfKl3VYXaGBEiABEiABEiABDxMgELo4eCx6ySQFAlQCJNiVDkmEiABEiABEiABtxKgELo1MuwXCUQpAQphlAaewyYBEiABEiABEnCEAIXQEexslARIgARIgARIgARIgARIgAScJ0AhdD4G7AEJkAAJkAAJkAAJkAAJkAAJOEKAQugIdjZKAiRAAiRAAiRAAiRAAiRAAs4ToBA6HwP2gARIgARIgARIgARIgARIgAQcIUAhdAQ7GyUBEiABEiABEiABEiABEiAB5wlQCJ2PAXtAAiRAAiRAAiRAAiRAAiRAAo4QoBA6gp2NkgAJkAAJkAAJkAAJkAAJkIDzBCiEzseAPSABEiABEiABEiABEiABEiABRwhQCB3BzkZJgARIgARIgARIgARIgARIwHkCFELnY8AekAAJkAAJkAAJkAAJkAAJkIAjBCiEjmBnoyRAAiRAAiRAAiRAAiRAAiTgPAEKofMxiNoe/Pvf/5YbN25I8+bNo5aBFwaOGP3xxx+SOXNmL3Q3qvv4559/SqpUqSR16tRRzcHtg7927ZogVhkzZnR7V6O+f//73/8kJiZGUqZMGfUs3Azgr7/+kitXrshtt93m5m6ybyLy+++/68++5MmTk4eLCEfcWDEAACAASURBVFAIXRSMaOsKhPDTg8el1COPRNvQPTfeSxcvSdqYtJ7rd7R1GB+KUqRIoX+8UPDLhmgsN67fkL+u/CVp0qRxbPgpU6aQa1evOda+Vxr+6/JfkjJVyiT34TWpvfPwS5br165LqtSpvPJoOdrPZMmSOdY+Pk+kSZtGnOyDY4MXkSt//SVtn64s6dKlc7Ibt7RNIXRVOKKrMxDCLgevycWsd0TXwDlaEiABEnCYAFw8WXLnPhQ6PHw2H6W/jGHgScBpAjHnTsiOJmWkWNFiTnflpvYphK4Khzs6s2PHDpk/f7688cYbIXWoU6dO0qpVKylduvQt91+8eFFq1qwpq1atksWLF0uHE+kkNnu+kNrhTSRAAiRAAiRAAiRAAiTgFQLpfjsmX9W+l0Lo9oDt3btX+vXrJ9OnT5dcuXJpdzGt6eWXX5ZixYpJ69atLRtCmzZt5NixY1of5lM//PDD0q1bN8mQIYNlbYRSkT8hvHTpktSvX1/uv/9+efXVVxOtlkIYCnXeQwIkQAIkQAIkQAIkkJQJUAg9FN23335bfvzxRxk9erT2Gtms9957TyXRis0aMNcda3wghA0bNpQqVarImTNnZOTIkVKgQAHp0aOHo7T8CeG6devkrbfe0o0QFixYINmyZUuwjxRCR8PHxkmABEiABEiABEiABFxIgELowqAk1CVszNCuXTtp1KiRlCpVStq2bSsjRoyQwoULy9y5c+WTTz4RXFO+fHnp2LGjpE2bVne3wjXff/+9XL16VbOJPXv2lJw5c2ozyK7VrVtXNm3apPe+8847KoRo48knn9RrFi1aJFu3bpUpU6bo1wcOHJA333xTjh49qvWgrYceeiiuvmbNmsmyZcvk7NmzUr16dalTp4689tprcvDgQXnggQdk4MCB2jeUL7/8UmbMmCGnT5+Wu+++W7p27aryiQLJGz9+vOzatUvbefzxx2X79u03TRnt1auX3HvvvfLFF1/IE088oeMxypYtWwQSjXqefvpp+eabb+KmjCK7+u6778rKlSt1A4WmTZvKhAkTOGXUQ+8HdpUESIAESIAESIAESCB8AhTC8BlGtIbvvvtOBg0aJPfcc4/+QdZr5syZsn//funfv79uQ40MYp48eVQeIYSQvYoVK2o/IXXYqn/48OFxAgehHDJkiG5fjd2VfIUQGUJci/owZRXb8r7wwgvSvXt3rRP9wb2zZs2SLFmyqJDlz59fBgwYILGxsSqLd911l2YXUUffvn2lUqVKKomnTp3Stl555RUpWbKkfPTRR7JkyRKZM2eOCuO4ceMEW2uj3f/+97/Su3dvPWLAWEMIiYTIYfwQwvXr16sAohh1Q4YxnRTZw3nz5qkcYw0hrsXmMWPHjtVpsciC7ty5k0IY0aeZjZEACZAACZAACZAACThNgELodARCaH/SpEmaKTPEqXbt2pqBK1KkiNZ25MgRzcJhA5b4BaLUoUMHWb58eZwQ9unTRzOORvFdQ4jvod5hw4ZJ1qxZNfP39ddfq0AZZejQoVK2bFmpWrWqCiEEDusOUSB7EFds5oKCKa6HDx9WuYP8ffvtt3Fyitdx9h8kF/XVqFFDMFYjY4jrkfUzhBDj++yzz3TKLLKRjRs31sxlwYIFdWOYH374QdA3FGRH69Wrp6IKIYQ8Y8wQUxQIdZcuXSiEITyPvIUESIAESIAESIAESMC7BCiEHowdZA5Zv4kTJ+p0yFq1aknevHnjzk7BdEh8f+nSpXL9+nWZPXu2fP7554KdNJEBRNZv7dq1en4RBG7MmDGa1fMVQkwZxRTMffv2qQwiSwjRmjZtmmDdnu9h4NjYBWKFuuLXh4xc8eLFtY8oK1as0CmggwcP1rqwbhEiZhSI4mOPPabTQyG6GKtxoCtkEFJoCGHLli2lWrVq0qBBA70d00eNrCnEEGNFhtIomGKLrCmEsH379tKiRQspV66cvnz+/HmdOstdRj34hmCXSYAESIAESIAESIAEQiZAIQwZnXM3+gohegHZQiYtX75bj0lYvXq1fPzxxyp0kDhjmuWaNWt0A5nEhNBYQ/j+++/rGkKsscO/kXlD5s9fCUYI/WUIIWmQOCNDiOwfZBcFU0ox1RNCiDWRmIaaPn16neqKAuHFlFmseTQykchWGgV9g3AaGcIKFSqoUKJgV1VkRimEzj3XbJkESIAESIAESIAESCDyBCiEkWcedovxhRCbshw6dEgzZDly5NDpk5iWCfFBlhDyhHV+KBAsyFIwQogMYJMmTVQCsR4QWTZs/gKhQjYSR2Lkzp1bN34JRgh/+eUX3RgH2UJMMUX2EDJnTIXF+r506dJJ586dBX2AAKZKlUqFEHKK6a+Y7moUbIqD+jAdFHKMzCMyhejbxo0bZdSoUXo0BbggywnBxKY12FQG9UGeKYRhP56sgARIgARIgARIgARIwEMEKIQeCpbR1fhCiI1jsGnKhg0bdMOY7Nmz6+6emAKJqaNY73fu3Dnd9AWZN2wsE4wQol1I2n/+8x95/fXXdbdQiCWOwMC0U6wxxDmFEK9ghBD1YiMXbAqDzCUkDvVgaioKNpRBexA/bPxy33336a6kkDhME4UMPvroozdFcPLkyboBDSRz8+bNupFMpkyZdB3inj179LxGCKExlRZrMbE2ElNHp06dSiH04PuBXSYBEiABEiABEiABEgidAIUwdHa8M4kSwO6jHU6kk9jst07BTaJD5rBIgARIgARIgARIgASilACFMEoDz2EnTIBCyKeDBEiABEiABEiABEggWghQCKMl0hynaQIQwu7fnpfLmXOZvocXkgAJkAAJhE/ghiSTZHIj/IpYAwmQAAmQgGkCaX7/Vba1flKK/v8RdqZvtPnCZDewWwkLCThAAEJ4+sxpPQeRxb0E8CPiwoULkiFDBvd2kj1TAtgBGLsBY1MoFvcSwNpqxMo46seJniZLnkJuXL/mRNOeajM2NlY3RMNu4SzuJYA9HnAOMnZAZ3E3AexbgZ3rcWRZtJYihf8+z9xNhULopmhEWV8ghJCN5s2bR9nIvTVcxAibKPmeiemtEURPb7G5FWQwderU0TNoD44U58IiVtjEi8XdBPDhFZJhHLvk7t5Gb++w+zmk0MlfskQv/eBG/vvvv+vPPmyWyOIeAhRC98Qi6npCIfRGyCmE3ogTekkh9EasKITeiBN6SSH0RqwohN6IE3pJIXRnrCiE7oxLVPQKQjj7q/2Sr0ChqBivdwd5Q65euSopOQ3R9SG8du2qJE+WXJLxN6+Wx+rq5UvSvcbjUqpY+FN9KISWh8e2CimEtqG1tGIKoaU4ba2MQmgr3pArpxCGjI43hktAdxn9KYbHToQLkveTAAnYTiDNH6dlQZksUuexcmG3RSEMG2HEKqAQRgx1WA1RCMPCF9GbKYQRxW26MQqhaVTWXNi0aVM9zL1w4cLWVGhRLdOnT9dF823atNED6tu2bSsrVqzwW3ug1812icdOmCXF60iABJwmoEL4SGYKodOBiHD7FMIIAw+xOQphiOAcuI1C6AB0E01SCE1Ain/J3r175e2335ZDhw6pROXLl09at24tJUqUCFhbMEKI3yIvXrxY1q1bJ6dPn5YsWbLIww8/LM2aNZOcOXMGbCuYC3yFELuqffrpp1K9enWtolGjRjJs2LA4iY3/ejDt+F5LIQyVHO8jARKINAEKYaSJu6M9CqE74hCoFxTCQITc8zqF0D2x8O0JhTDIuOCHDgSpQYMG8uyzzwq2D//hhx90Z6vixYsHrC0YIXz11Vdl//790qVLFylatKhcvnxZPvvsM22jTp06AdsK5gJfIYx/X3whDKbexK6lEFpFkvWQAAnYTYBCaDdhd9ZPIXRnXOL3ikLojTihlxRCd8aKQhhkXI4fP67ZwFWrVvnd2h3bHo8YMUK+//57PROnWLFi0rNnz7iMnq8Q4tq5c+fKJ598IvhhVr58eenYsaOkTZtWkIXs0aOHZiKRgfQt2PUR57ecPHlSJk2aJAcOHJCsWbNKixYtpFKlSnoppntu375dMmXKJD/++KMg29i9e/e4LOaxY8dk7Nix8tNPP8n999+v2UdcG3/K6OTJk3WsOHIA2263bNlSr/edUhpqPyiEQT58vJwESMAxAhRCx9A72jCF0FH8phunEJpG5fiFFELHQ+C3AxTCIOOCHzo4Nw/ZwKefflqKFCly04HdkLxNmzZJxYoVteYpU6boGW7Dhw/Xr32FcObMmZoB7N+/v55zNHr0aMmTJ4+0a9dO5s+fL1u2bJG33nrLbw+RmYSUoR3UuW/fPq1nwoQJUrBgQRVCtD1x4kSV0m3btqlczpkzR7OaL7zwgvYfmc5du3bJoEGDNOvobw1h/Ayh7xrCcPpBIQzy4ePlJEACjhGgEDqG3tGGKYSO4jfdOIXQNCrHL6QQOh4CCqFVIfj11191bd/XX3+tG7CULFlSs3n+1vXh9Q4dOsjy5ctvEcLatWvLa6+9plKJcuTIERk4cKDK4LRp0zQDiGyjvwKR7Nu3r7z33nu6jhFl/Pjxkj59emnfvr0K4ebNm2XcuHH6GkS1WrVq8tFHHwmyg5BH3GscDDpgwADJnz9/0EIYTj/QfocT6bjLqFUPJushARKwjQCF0Da0rq6YQujq8MR1jkLojTihlxRCd8aKGcIw43L27FmdeokCuUPGbPbs2fL555/LxYsXdWrnmTNnZO3atSpfRoYwb968UqtWLcHfuAYFU0FxsPTSpUsDZgiR8UM7M2bMiBsBRPLo0aMCuYMQIvOHHU2NUrVqVVm4cKFOR0V2zjf7iGwipqoGmyEMpx+YikohDPMB5O0kQAIRIUAhjAhm1zVCIXRdSPx2iELojThRCN0bJwqhBbFBJm7q1KmyZMkSWb16tXz88cc6RRTr7rA7KCRwzZo1msnznTIKIcQawPhrBNElYw0hhO+uu+66qZcQR6wbDJQhTEgI0achQ4bIokWL4upFfzFd1Z8QNmnSRK83jsrwnTJqJkOYUD8ohBY8fKyCBEggIgQohBHB7LpGKISuCwmF0BshSbCXzBC6M4AUwiDjAhnCMRCPP/645M6dW7N/WKeXLl06GTp0qGb3sKEMBAoFu3diaqQ/IYTs4eiKXr16SY4cOQTZxsOHD0vp0qX1XuwyCvHz3WUU8okCmYS8Va5cWRo3bhy3hhDTRgsVKpRohhCi2qpVK12DWKFCBZ2aimmmRp3xzxns1KmT1K9fX9tCib+GMNR+UAiDfPh4OQmQgGMEKISOoXe0YQqho/hNN84MoWlUjl9IIXQ8BH47QCEMMi7nz5/XqZZ79uyRc+fO6YYyWEMIoYJoYcrnyJEj9TXs3Fm2bFnd3MWfEGJd34IFC2TDhg268Uz27Nn17L+6detqr3zPIcS6RdRXqlQpef7553W94okTJzTDePDgQX0N5xNWqVJF701syih2JMV6RWxAg2ms+BrTRfG3vwzh1q1bNQOKKbDY8AZnIfruMhpqPyiEQT58vJwESMAxAhRCx9A72jCF0FH8phunEJpG5fiFFELHQ0AhdGcIordX3GU0emPPkZOA1whQCL0WMWv6SyG0hqPdtVAI7SZsXf0UQutYWlkTM4RW0mRdQRGgEAaFixeTAAk4SIBC6CB8B5umEDoIP4imKYRBwHL4UgqhwwFIoHkKoTvjEhW9ghCO3n1CMt+RLyrG6+VBXrt2XVKkSO7lIURF37HLcTL8l/zvnYtZrCNwI/ZPGVa1pDxR+uGwK8VyACwvyJgxY9h1sQJ7CVAI7eVrVe0UQqtI2l8PhdB+xqG0QCEMhRrvsYQAhBAfiho0aGBJfazEHgLY1RZrZzNlymRPA6zVMgKxsbGSKlUq/cNiPQG8B4xzX8OpnUIYDr3I3kshjCzvUFujEIZKLvL3UQgjz9xMixRCM5R4jS0EIISQjebNm9tSPyu1hgBihE2PsGkSi7sJ4BcskMHUqVO7u6NR3jsKoXceAAqhN2JFIfRGnNBLCqE7Y0UhdGdcoqJXmiGM/VOPtGBxLwEIIT4UcXqbe2Nk9MyJDGGaNGkk/W3p3Q/HRT2kELooGAG6QiH0RqwohN6IE4XQvXGiELo3Nkm+ZxDCMbtPSua8XEPo9mBfu35NUiRP4fZuRn3/rmENYbJkkjxZ5NYQ3pfmskxq21gghizmCFAIzXFyw1UUQjdEIXAfKISBGbnlCmYI3RKJm/tBIXRnXKKiV9xlNCrCzEEmcQK1Y/fK/Hb1JCYmJomP1LrhUQitY2l3TRRCuwlbUz+F0BqOkaiFQhgJysG3QSEMnlnU34EPMyNGjJA9e/boIfUDBw4MiQmFMCRsvIkEXEWAQhh8OCiEwTNz6g4KoVPkg2uXQhgcLyevphA6ST/htimEDsWlTZs2cuzYMW0dU60KFSok3bp1k/z581vao1GjRknBggXj1ukdOHBAunbtKmvXrr2pHUhdsWLFpGnTpgHb37ZtmyxYsEAmT54syZMnF3y4eeedd2Tjxo26+UjWrFmlZMmS0rNnT63Ld6xG5VOnTpWdO3dKhxPpJDY7p4wGhM4LSMClBCiEwQeGQhg8M6fuoBA6RT64dimEwfFy8moKoZP0KYSuow9JatiwoVSpUkWwEcTs2bPl+++/l+nTp1vaVzuEcPny5bJ3717p16+f9nXJkiWyZs0aeeWVVyRv3rzy66+/yq5du+S5556LE0JsHFO5cuW4sWEnxHnz5lEILY02KyOByBOgEAbPnEIYPDOn7qAQOkU+uHYphMHxcvJqCqGT9CmErqMPIWzUqJE8+eST2rd9+/ZJjx49VKxQtm7dKjNnztTtedOmTSvPP/+81KhRQ1asWCFfffWVZMiQQTZv3iw5cuRQEdu9e7csXLhQM3bIAFaoUEHWr1+vWbyUKVNKunTppEyZMvL0008HzBAeP35c+1KvXj3ZsmWLXLhwQerUqSO1a9fW9pENvHr1qu462bJlS8305c6dWzOB/kr8sRrXcMqo6x5LdogEgiZAIQwamc6q4MH0wXNz4g4KoRPUg2+TQhg8M6fuoBA6RT7xdjll1KG4+ErSxYsXVbJ+/PFHef311/Vsvlq1asmYMWOkSJEiuuX/b7/9ptNJIWSYbtm/f3959NFHVRo///xzqVixorRq1Uq++OILmTBhgmbtcIByKBlCCGHr1q2lXbt2OtUUbePrGTNmSM6cOWXZsmWyf//+uAwhMobz58+XZs2ayf333y9333237nRoFAqhQw8ZmyWBCBCgEAYPmUIYPDOn7qAQOkU+uHYphMHxcvJqCqGT9BNum0LoUFzir6tDxm/48OFy3333qRDWrVtXM26VKlWS2267La6XEMJPPvlEJk6cqN87ePCgdOnSRUXROIy6Zs2a8vbbb2vWLlQhhAyuWrVKpRKle/fu0rhxYylbtuwtQoj+btiwQTOSmEqK3QYhh88++6zei3FgGimmiaLky5dPpZUZQocePjZLAhYSoBAGD5NCGDwzp+6gEDpFPrh2KYTB8XLyagqhk/QphK6j75s1u3LliuzYsUPGjx+vGb/s2bPLd999p1k3/I0sYfv27XXjGYgf1ucNHjxYx2RM70TWzijI6iG7iIxifCFEFrJDhw66qQymlxqlb9++8uCDD+o0Vn919u7dW5555hldBxg/Q+gL9/r16zrNFO1iPBBcjBXTXcuXL6+XYgprlixZKISueyrZIRIIngCFMHhmFMLgmTl1B4XQKfLBtUshDI6Xk1dTCJ2kTyF0HX1/0ygbNGggHTt2vGnzFfyQW7x4sUoWsn7BCuHo0aPlnnvuidtlFP9zw3rAuXPnSp48eeK4tGjRQtcDQvjCEUKjQkwxxZpDiCCnjLru8WOHSMAyAhTC4FFSCINn5tQdFEKnyAfXLoUwOF5OXk0hdJI+hdB19H13GTUyhMiqvfnmmypqyAKWKlVKN5TBGr1169bpa8EKIXYtxRpFbBJjFGQDMa0TU01R/+rVq3VDmjlz5uhGMcEKITKGt99+ux5bgSM0sCEOsoPY0AZZTQqh6x4/dogELCNAIQweJYUweGZO3UEhdIp8cO1SCIPj5eTVFEIn6VMIXUffdw0hplBCqJAhrFq1qu4+hymhhw4d0s1Z7rrrLj2jsECBAkELIeQOh8ifPn1aypUrJ5j6+d///lfeeustlU78EEW9WDNYtGhR5RSsEOL8wQ8//FDPVcQHHRw9gSM1jGMmKISue/zYIRKwjACFMHiUFMLgmTl1B4XQKfLBtUshDI6Xk1dTCJ2kTyF0J/0o7xU3lYnyB4DDTxIEKITBh5FCGDwzp+6gEDpFPrh2KYTB8XLyagqhk/QphO6kH+W9UiE8nlZis+eLchIcPgl4l0Dti/tkfvv6Og2dxRwBCqE5Tm64ikLohigE7gOFMDAjt1xBIXRLJG7uB4+dcGdcoqJXEMI5Xx+QfAUKRsV4vTrIGzdErl69KqlSpfTqEKKm31evXtPdg5Mn/+ccULsHnz9LOunXqLbuHsxijgCF0BwnN1xFIXRDFAL3gUIYmJFbrqAQuiUSFEJ3RiIKewUhxBmGzZs3j8LRe2fIiNEff/whmTNn9k6no7SnWH+M8z6NM0mjFIPrh00hdH2I4jpIIfRGrCiE3ogTekkhdGesmCF0Z1yiolcUQm+EmULojTihlxRCb8SKQuiNOKGXFEJvxIpC6I04UQjdGycKoXtjk+R7BiH89fRpPauQxb0EIIQXLlyQDBkyuLeTSbxn6WJidLfhQIVCGIiQO16nELojDmZ6QSE0Q8n5ayiEzsfAbA+YITRLKrLXUQgjy5ut+RCAEHb/9rxczpKbXEiABBIhUOKvX2VF5waSPXv2RDlRCL3xGFEIvREn9JJC6I1YUQi9ESf0kkLozlhRCN0Zl6joFY+diIowc5AWECj3v4PyYfN/UQgtYOmGKiiEboiCuT5QCM1xcvoqCqHTETDfPoXQPKtIXkkhjCRtm9rChh/16tWTBQsWSI4cOUy3cvLkSenQoYMedh+/7NmzR3r16iU4VB6HzNtRKIR2UGWdSZEAhTBpRZVC6J14Ugi9ESsKoTfixAyhe+NEIXRvbEz3zA4hHDt2rGzfvl2yZMkis2bNSrAv+GCTIkUK0331vZBCGBI23hSFBCiESSvoFELvxJNC6I1YUQi9EScKoXvjRCF0b2xM9ywxIYyNjZU333xTdu7cqeeEPfXUU9KiRQs9q6xt27Zy9OhRyZkzp7Y1evRoueOOO+TSpUtSv359efHFF/V7kydPlsKFC+s1yCZCFDNmzCj79++XOnXqyNNPPy1z586VTz75RPBDuXz58tKxY0dJmzatXLlyRUaMGCHff/+9nmVXrFgx6dmzp7ZJITQdYl4Y5QQohEnrAaAQeieeFEJvxIpC6I04UQjdGycKoXtjY7pniQnhhAkT5PTp0zJgwACBHPbr10+qV6+uIpfQlNH169fLjBkzZNGiRTJo0CDJkyePdO7cOU4I33jjDRk3bpyUKFFCzxFEBhFy2L9/f4mJiVGJxD3t2rVTIdy0aZNUrFhR758yZYqeaTd8+HAKoekI88JoJ0AhTFpPAIXQO/GkEHojVhRCb8SJQujeOFEI3Rsb0z1LTAifffZZef3116VQoUJa34YNG2TZsmWaNUxICHv37i133323dOrUSTZu3ChTp06VxYsXa4YRGUIII7KGRqldu7a89tprUqRIEf3WkSNHZODAgTJ//vxbxnDq1Cldt7h8+XIKoekI88JoJ0AhTFpPAIXQO/GkEHojVhRCb8SJQujeOFEI3Rsb0z1LSAixBX2tWrXk/fff1ymeKN99950MGzZMlixZ4lcIkU18/vnnBVlACJ4xfbRv3746FRRCuGvXLhk8eLDWZ7SRN29eSZYsmX4PWUN8f+nSpXL9+nWZPXu2fP7553Lx4kW95syZM7J27VoVxg4n0kls9nymx8oLSSAaCVAIk1bUKYTeiSeF0BuxohB6I04UQvfGiULo3tiY7lmoGcJffvlFp3X67jKKnUohcJkzZ45r//z58/Loo4/KkCFDbhFCXATpnDRpkuTLd6vYrV69Wj7++GOdIoo6IZxNmzaVNWvW6K6oFELTYeaFUUyAQpi0gk8h9E48KYTeiBWF0BtxohC6N04UQvfGxnTPDCF89913bzqnDFM8sYbw3Llzur4Pawjx9zPPPKNrCPE1ZG7hwoWSLVs2ba9Vq1ZSuXJlqVGjRlz7+/btU6HDtNHNmzfflCHERVhveOjQIT2mAsdenD17Vg4fPiylS5fWLCE2lIFMokyfPl3ee+89CqHp6PJCEhChECatp4BC6J14Ugi9ESsKoTfiRCF0b5wohO6NjemeGUIY/wYIWoUKFXS94BdffKHHQ/zrX/+Sli1bxh0VgQ1hkMXDB5Tu3bsLjptA5s43Q4h6W7duLViPiDp8p4ziNWwcg3uwPhF9yZ49u25cU7duXZ06OnLkSJVSHGFRtmxZ3ViGGULT4eWFJEAhTGLPAIXQOwGlEHojVhRCb8SJQujeOFEI3RubJN8zHjuR5EPMAVpEgBlCi0C6pBoKoUsCYaIbFEITkFxwCYXQBUEw2YXff/9d97XA8Wcs7iFAIXRPLKKuJxTCqAs5BxwiAQphiOBcehuF0KWB8dMtCqE3YkUh9EacmCF0b5wohO6NTZLvGYSwy8GrcjHrHUl+rBwgCYRDoFTscfmo3XM3rRH2Vx+maKdKlUpSp04dTnO812YCFEKbAVtYPYXQQpg2VkUhtBGuxVUzQ2gxUIuqoxBaBJLVBE8AQrjp0AkpXfqR4G/mHRElcPHSRYlJGxPRNtnYPwQypk0lDR4vp2eBJlYohN54aiiE3ogTekkh9EasKITeiBN6SSF0Z6wohO6MS1T0CkKIMwubN28eFeP16iARI2wWFH+jIa+OJyn3m0LojehSCL0RJwqhd+JEIfROrCiE7owVhdCdcYmKXlEIvRFm3b53nQAAIABJREFUCqE34oReUgi9ESsKoTfiRCH0TpwohN6JFYXQnbGiELozLlHRKwjhpwePS+kyZaJivF4e5MWLlyQmJq0nhnD1r7+k+RPlJVPGTJ7or5WdpBBaSdO+uiiE9rG1umZOGbWaqD31UQjt4WpHrRRCO6iGXyeFMHyGrCFEAn9vKnONm8qEyI+3+SeQ5vdT8vGz90mFkg9FHSIKoTdCTiH0RpyYIfROnCiE3okVhdCdsaIQujMulvbq1KlT0rZtW1mxYoWl9YZbGY+dCJcg7/dHIO25n2XDk3dKeQohHxCXEqAQujQwfrrFDKE3YkUh9Eac0EsKoTtjRSF0Z1z89mr9+vWyaNEi+eWXX+S2226TIkWKyIsvvihZs2ZNdBRmhXDmzJmyePFiv3XlyZNH5s6dayktCqGlOFnZ/xOgEPLYCbe/GSiEbo/QP/2jEHojVhRCb8SJQujeOFEI3Rubm3q2b98+efnll2XgwIHy0EMP6VbYX331lZQqVUqyZctmiRDiQwr+oKxcuVI2b94sY8eO1a+TJUum55tZWSiEVtJkXQYBCiGF0O3vBgqh2yNEIfROhP7uKYXQOxFjhtCdsaIQujMut/QK0z3XrVsnb7zxht8ef/bZZwLBQjYwU6ZMUr9+fXnuuef02vgZwv/+978yZcoU2bNnj6RJk0bq1KkjdevWvane5cuXy6ZNm2TixImyZMkS2bt3rwwePDjuGtyfPHly6dSpU1xbX3zxhe5yeP/990vnzp3jBNLIbJ49e1YKFy6sWc3cuXNrfzucSCex2fN5JArsphcIUAgphG5/TimEbo8QhdA7EaIQei1WFEJ3RoxC6M643NKrH3/8Ubp06SINGzaUMmXKSIECBSR16tRx1yFbmDNnTrnzzjtl//790qdPH83uQcB8hRBHCHTv3l3uu+8+adWqlUAOcW3Hjh21XqP4CiFErkWLFjpdNX369JpFRD9effVVrR/yWahQIRk+fLjePmjQILn33nuladOmmsUcN26cjBw5Uu6++25ZtmyZiiaEct68eRRCjzx/XuomhZBC6PbnlULo9ghRCL0TIQqh12JFIXRnxCiE7oyL315999138sEHH8h//vMfuXTpkjz11FPSoUMHv1M5x4wZIwULFtTsn68QQiyRoUM9yPChQP4OHDggvXv39iuE+Ga/fv2kYsWKUq1aNdmxY4fMmDFDZs2apddDCDGd9ZFHHtGvIYHTpk3T15FVhHw2aNBAX4OQ1qtXT6ZOnSrIHDJD6KEH0CNdpRBSCN3+qFII3R4hCqF3IkQh9FqsKITujBiF0J1xCdgrCNzQoUOlRo0a0rhxY53SOXv2bDl58qTeizWGkMGWLVveJITbtm2TESNGSK5cueLauHr1qmYchwwZkqAQfvrpp7pL6euvv67343q0awghsoXIEqJAOl966SUVTQgr3vwxMTFxdV+4cEGGDRum4kghDBhqXhAkAQohhTDIRybil1MII4485Aa5qUzI6CJ6I9cQRhR3WI1RCMPCZ9vNFELb0Npf8eTJk1X8BgwYoHLWpk0bqVy5smb+ME0Tm81gWqhvhvDQoUOa7cO6QGwUk1DxnTKKa/DDFlm+CRMmSLdu3TT7hymqhhDie8ggomzdulXlFNdg+ig2vqlZs+YtTXENof3PSDS2QCGkELr9uacQuj1C//SPQuiNWFEIvREn9JJC6M5YUQjdGZdbeoVpmufOnVO5ypIlixw8eFCzbJC02rVr6wYy2ADmnnvuUQHEZi/PPvvsLUJ4/fp16dGjhxQvXlyaNWumm8r89NNPcvHiRSlatGhcu/GFEC8gO4jdTjNmzKjCaRRMGcWxFOgPZBTCWbZsWa0fG81MmjRJp44igxgbG6uZwUqVKnFTGY88e17rJoWQQuj2Z5ZC6PYIUQi9E6G/e0oh9E7EKITujBWF0J1xuaVXmCKKcwCxYQx28kT274knnpDmzZurhG3cuFE3acH3cS4hsn+YFho/Q4iKsZHMW2+9Jbt27ZIrV67oRjTYNAayaRR/Qog1jD179pRevXpJ1apVbxJCZCg//PBDzVgiU4gNcIxjKjDddOHChSqqOD/xwQcf1I1smCH0yMPnsW5SCCmEbn9kKYRujxCF0DsRohB6LVYUQndGjELozri4slenT59WwcR0U4idb4YQm9jkz58/qH5TCIPCxYtNEqAQUghNPiqOXUYhdAx90A1zymjQyBy5gRlCR7CH1CiFMCRstt9EIbQdcdJoAFNNkVXElE9kCH0LpoxSCJNGnJPCKCiEFEK3P8cUQrdHiBlC70SIGUKvxYpC6M6IUQjdGRdX9QpHXED6sInMqFGj4jaTsSJD2P3b83I58z87nrpq4OyMJwmkPn9GPqpfViqWfMiT/Q+n05hOjqnavmeUhlMf77WHAIXQHq521MoMoR1Ura+TGULrmdpVI4XQLrLh1UshDI8f7w6DAKaMnj5zWo/OYHEvAZwdiaNCMmTI4N5OxutZwQIFJUWKFJ7pr1UdpRBaRdLeeiiE9vK1snYKoZU07auLQmgfW6trphBaTdSa+iiE1nBkLSEQgBBCNrAxDot7CSBGf/zxh2TOnNm9nWTPlACF0BsPAoXQG3FCLymE3ogVhdAbcUIvKYTujBWF0J1xiYpeUQi9EWYKoTfiRCH0TpwohN6JFYXQG7GiEHojThRC98aJQuje2CT5nkEIZ3+1X/IVKJTkx+rtAd6Qq1euSspUqVw7jDTXr8roF+pJpoyZXNvHSHSMGcJIUA6/DQph+AwjVQOFMFKkw2uHQhgev0jezQxhJGmbb4tCaJ4Vr7SYgB478VOMxGbPZ3HNrC7aCBT4/Yh80qiM5Lsrup8lCqE3nnwKoTfihF5SCL0RKwqhN+LEDKF740QhdG9sLO/Z2rVrZcuWLTJixAjL6w6lQp5DGAo13uOPgAphw0cohNxl1BNvEAqhJ8KknaQQeiNWFEJvxIlC6N44UQgdjM0bb7wh+CH20ksvxfXi3Llz0rBhQ1m8eLFkzZo1rN41atRIhg0bJoULF9Z6jh8/Lr/88ouUKVMmrHqNmw8cOCCzZs2SH374Qb/1wAMPSNu2bSVfvn+yNJgaMGfOHNmxY4ecP39ecufOLVWqVJF69erJ0qVLpcOJdMwQWhKN6K6EQvh3/Jkh9Mb7gELojThRCL0TJwqhd2LFKaPujBWF0MG4RFoIrRzqjz/+KD169JAGDRpItWrVdLfQ5cuXy8qVK+XNN9+U22+/XQ+x79y5s+TKlUteeOEFufPOO+XMmTPy4YcfqhTu2rWLQmhlUKK4LgohhdBLjz+F0DvRYobQG7GiEHojTswQujdOFEIHY2NGCK9cuSJz586VTz75RLOJ5cuXl44dO0ratGnl2LFjMnHiRDly5IgeRl2hQgXp1KmT/nvy5MmyatUqPSogZcqU0rJlS7l+/fpNU0Zx2HzdunX1ezhn7r777tNsJc5vw7WzZ8+Wjz/+WNKkSSNNmzaVCRMmaJ049Lpfv35ad58+fW4i+Morr8htt90mffv2lUWLFqkgoh70ybdAIOfNm0chdPD5S0pNUwgphF56nimE3okWhdAbsaIQeiNOFEL3xolC6GBszAjhzJkzZf/+/dK/f3+JiYmR0aNHS548eaRdu3Zy9OhRPR8OIoe/Bw4cKE888YROx0SJP2U0/hpCCGHx4sVlwIABej0yfnXq1NHsHa5duHChjBkzRjJmzKjtbt26VYUQgonD5AcNGiRly5a9ieDGjRtl2rRpOh20d+/ekj9/fhVYf4VrCB18+JJY0xRCCqGXHmkKoXeiRSH0RqwohN6IE4XQvXGiEDoYGwghMnDI9vkW/A/IWENYu3Ztee2116RIkSJ6CbKBEL/58+ff0nNI3LZt22To0KGmhRAZvRIlSuj177zzjiAj2b59exXQRx55RGrVqqWvYYpohw4dVAjxgxf9mjp1atz6RKMze/bskZdfflnWrVun10NQIZ4UQgcftChomkJIIfTSY04h9E60KITeiBWF0BtxohC6N04UQgdjAyHE/2ywEYtRkOlDRg1CiKmaELK8efNKsmTJ9BJMtcTGEcjAYQOa6dOny969e+Xq1asqalinh6mdKGYyhMgAIouHAsnEGj9kCiGFLVq0kHLlyulr2BAG00uNDGH16tVl8ODBzBA6+Pyw6X8IUAgphF56P1AIvRMtCqE3YkUh9EacKITujROF0MHYmJkyCiGcNGnSTTt3Gl0eNWqUpE+fXqePQh7Xr1+vwoZ1hShNmjSRIUOGxGXx/E0ZTUgIsUYQu5H6yxAGWkOI15F5xBpC9AeZR64hdPBBi4KmKYQUQi895hRC70SLQuiNWFEIvREnCqF740QhdDA2ZoRwxowZcujQIenVq5fkyJFDzp49K4cPH5bSpUvr1NGSJUvq9M1Lly7pRi/IIBpCiA1mMF2zcuXKOspghNBYQzh27FjJkCHDTWsIIXw4cuLFF1/UIzKQLTR2GV22bJluaFOgQAHdZRR9wI6jrVq1kjvuuIO7jDr4vCXlpimEFEIvPd8UQu9Ei0LojVhRCL0RJwqhe+NEIXQwNmaEEGv6FixYIBs2bNCNY7Jnz64ChumbWNcHYcNmM9jZs1ChQrJ79+44IcQmMFjnd/HiRc0iYvdQ34PpIYsJZQixyygye2vWrNHsI8RvypQp+nXy5MmV2r59+/QanEN4+fJlPTcRG81gkxuj4LwZ7DKKcwjxP1aeQ+jgA5eEm6YQUgi99HhTCL0TLQqhN2JFIfRGnCiE7o0ThdC9sXFVzw4ePBg3DdRfx3AEBjKGOLbCWHcYaADcZTQQIb5ulgCFkEJo9llxw3UUQjdEwVwfKITmODl9FYXQ6QiYb58H05tnFckrKYSRpO2htpCZRLaxVKlSOvXz1Vdf1eMuunbtmuAokCn89ttvNXuJoykCFQphIEJ83SwBCiGF0Oyz4obrKIRuiIK5PlAIzXFy+ioKodMRMN8+hdA8q0heSSGMJG0PtQUh7N69u/z888+6IQzEsEuXLjo11aoCIRy9+4RkviOfVVWyHpsIXLt2XVKk+HuqsBtL+st/ytw2z0nOHDnd2L2I9Qk7EOP9inW+LO4lQCF0b2zi94xC6I1YUQi9ESf0kkLozlhRCN0Zl6joFYQQH2AbNGgQFeP16iCxYRCOHcmUKZNrh5A8RQrJ7OL+RQochTBSpMNrh0IYHr9I3k0hjCTt0NuiEIbOLtJ3UggjTdxcexRCc5x4lQ0EIISQjebNm9tQO6u0igBihA2NMmfObFWVrMcmAhRCm8BaXC2F0GKgNlZHIbQRroVVUwgthGlzVRRCmwGHWD2FMERwvC18ApohjP1Tj8ZgcS8BCCE+FGXMmNH2TqaLSae75rKERoBCGBq3SN9FIYw08dDboxCGzi6Sd1III0k7vLYohOHxs+tuCqFdZFlvQAIQwjG7T0rmvFxDGBCWwxdcu35NUiRPYXsvHrntqozv0Mz2dpJqAxRCb0SWQuiNOKGXFEJvxIpC6I04oZcUQnfGikLozrhERa+4y2hUhDmoQbaI/VbmdG8V1D28+B8CFEJvPA0UQm/EiULonThRCL0TKwqhO2NFIXRnXBLsFQ6Zr1mzpqxatcrzOwlSCD328EWguxTC8CBTCMPjF6m7KYSRIh1+O8wQhs8wEjVQCCNB2Zo2KITWcLS6FgqhRUSx6Ua9evVkwYIFkiNHDotqvbUaf0K4ZcsWWbJkiRw+fFjSpk0rRYsWlaZNm8q9994bVj9Wr14t27ZtkxEjRoRVT0I3UwhtwerpSimE4YWPQhgev0jdTSGMFOnw26EQhs8wEjVQCCNB2Zo2KITWcLS6FgqhRUSdEkJkCqdPny7t27eXMmXKqBDu2rVL//To0SOs0dkphPhABHnucCKdxGbnGsKwApWEbqYQhhdMCmF4/CJ1N4UwUqTDb4dCGD7DSNRAIYwEZWvaoBBaw9HqWiiEFhFNTAiPHTsmEydOlCNHjuih0RUqVJBOnTrpv48fP67ihuwiMn0XLlyQOnXqSO3atbVn2OHx3XfflZUrV0qaNGk08zdhwgSdMorXGjZsKG3btpXq1avfNBK8lixZMr1m6dKl8tFHH0lsbKw8/PDD0q1bN90xMrG2T5w4odddvnxZjxvIkCGDvPXWW3Ly5EmZNGmSHDhwQLJmzSotWrSQSpUqadvz58+XM2fOxIkoxoJxrFmzRlKkSKG7idatW1c2bdok+OFduXJlCqFFz19SqYZCGF4kKYTh8YvU3RTCSJEOvx0KYfgMI1EDhTASlK1pg0JoDUera6EQWkQ0MSE8evSonuN233336d8DBw6UJ554QiUQUta6dWtp166dCtNvv/2mX8+YMUNy5swp69evF0ytHDt2rErcyJEjZefOnSqEkLKePXvK8uXL5bbbbvM7ko0bN8qsWbPktdde06ms48aNkytXrsjQoUMDth0/Q3j9+nWVz4oVK6qY7tu3T/r376+CWrBgQVNCWLhwYRkyZIikTJlS5s2bRyG06PlLKtVQCMOLJIUwPH6RuptCGCnS4bdDIQyfYSRqoBBGgrI1bVAIreFodS0UQouIBjNldO3atbo2z5AyyCAED1k0lO7du0vjxo2lbNmyKlylSpXSrCHK/v37pUuXLnr9V199JaNGjZIVK1YkOIoBAwbIQw89pPKJggxekyZNNGOIfyfWdnwhRNt9+/aV9957L66v48ePl/Tp0+uUVTMZwj59+uh4ULiG0KKHLwlVQyEML5gUwvD4RepuCmGkSIffDoUwfIaRqIFCGAnK1rRBIbSGo9W1UAgtIpqYEJ47d07X+e3du1euXr2q0yXvvPNOzawZ0zaXLVsW15PevXvLM888o1MqIVqYllmuXDl9/fz58zrt0myGsEOHDprNQ1bPKFWrVpWZM2fqdFJMV02o7fhCCImdPXu2Zi+NAglEBhTiaUYIx4wZI/nz56cQWvTcJbVqKIThRZRCGB6/SN1NIYwU6fDboRCGzzASNVAII0HZmjYohNZwtLoWCqFFRBMTQmTxkEVDNg7rADENFEKHdYWBhBAZQqw5rFatmvYU6xHbtGlz0xpC1Gu8bgzHWEMYKEOYmBBi7d/WrVvjdhkNlCF8//33dafTl19+Wbvx888/q8z6riGkEFr0wCXRaiiE4QWWQhgev0jdTSGMFOnw26EQhs8wEjVQCCNB2Zo2KITWcLS6FgqhRUQNIcQGMNmzZ4+rFWvlBg0aJCVLltQNVi5duiT9+vXT7JwZIVy3bp1O78TUTMgksorI3BnnEGKzGWTsjF1Gcc3u3bt1l1FMPd2wYYPMmTNHRo8eLdmyZdP70QdjumpiQrhjxw5df4jNZDCdFWsIIaPIXGJKq7GGEH0rVKiQfPPNN/L666/L22+/rbudTp06Vdc3UggtesiioBoKYXhBphCGxy9Sd1MII0U6/HYohOEzjEQNFMJIULamDQqhNRytroVCaBFRQwjjV9erVy/dcAWbwsTExOjmL5AnSJsZIYSEYZrm9u3bdVdPTB2FaPkeTP/ZZ5/pOYTYxRQiVqxYMV0niHMIIZ6LFy/WdYY4wxDrCbt27ao7hwbKTmLzGcgsxA8ZTqz5w+6j2GX04MGDkiVLFmnWrJlUqVIlbtjo25dffqlSjDWQmCpLIbToIYuCaiiE4QWZQhgev0jdTSGMFOnw26EQhs8wEjVQCCNB2Zo2KITWcLS6Fgqh1URZn2kC3FTGNKqouZBCGF6oKYTh8YvU3RTCSJEOvx0KYfgMI1EDhTASlK1pg0JoDUera6EQWk2U9ZkmoEJ4PC0PpjdNLOlf2OLidzKnxwtJf6A2jZBCaBNYi6ulEFoM1MbqKIQ2wrWwagqhhTBtropCaDPgEKunEIYIjreFTwBCOOfrA5KvQMHwK2MNthG4cUN0d9xUqVLa1oZR8f05s0jPhjVtbyepNkAh9EZkKYTeiBN6SSH0RqwohN6IE3pJIXRnrCiE7oxLVPQKQog1js2bN4+K8Xp1kIgR1shi3SmLuwlQCN0dH6N3FEJvxIlC6J04UQi9EysKoTtjRSF0Z1yiolcUQm+EmULojTihlxRCb8SKQuiNOFEIvRMnCqF3YkUhdGesKITujEtU9ApC+Ovp01KjRo2oGK9XBwkhvHDhgmTIkMHyIdx1552SLl06y+uN1gophN6IPIXQG3GiEHonThRC78SKQujOWFEI3RmXqOgVhLD7t+flcpbcUTFeDvJmAikuXpBhJbJJj3r8hYBVzwaF0CqS9tZDIbSXr5W1cw2hlTTtq4tCaB9bq2umEFpN1Jr6KITWcHSkljFjxkj+/Pmlfv36jrQfbqM8diJcgt6+P9Wff8jEe65Jp5pPeXsgLuo9hdBFwUikKxRCb8SJGULvxIlC6J1YUQjdGSsKoQ1xmTlzph4G76/kyZNH5s6d6/e1UaNG6SH2ZgXPVwhxOP3w4cPj6s2WLZs888wz0qJFC0tHePLkSenQoYMedG8UfLh55513ZOPGjbr5SNasWaVkyZLSs2dPvaRNmzZy7Nixm/qBA+x37twpHU6k47ETlkbIO5VRCK2PFYXQeqZ21EghtIOqPXUyQ2gPV6trpRBaTdS++iiE9rENp2YKYTj0ErgX/7PHH5SVK1fK5s2bZezYsfp1smTJJFWqVLYIIUR09uzZcv36dTl8+LD069dPXnrpJSlfvrxlo/QnhEuWLJE1a9bIK6+8Innz5pVff/1Vdu3aJc8991ycEEJyK1euHNcPMJg3bx6F0LLIeK8iCqH1MaMQWs/UjhophHZQtadOCqE9XK2ulUJoNVH76qMQ2sc2nJophOHQM3Hv8uXLZdOmTTJx4kS9+ssvv5QZM2bI6dOn5e6775auXbtKgQIFZP369TJ58mRJmTKlbrJRpkwZ6datm8yZM0dfO3/+vMpWp06dpESJElpX/AwhhNA3+9i/f38pXry4NGnSRC5evKhS+s033+hRD7fffruMHz9e0qZNqxnJZs2aybJly+Ts2bNSvXp1qVOnjrz22mty8OBBeeCBB2TgwIF6bdu2beXo0aOSM2dO7cPo0aO1j7lz59ZMoL+C7zdq1EiefPLJm17mlFETD1ASvoRCaH1wKYTWM7WjRgqhHVTtqZNCaA9Xq2ulEFpN1L76KIT2sQ2nZgphOPRM3OsrhKdOnVJpQiYNUyo/+ugjQXYNQgXZ8jdlFNMwH3roIcmUKZNm4ZABRGYtTZo0CQohMoRHjhyR3r17q8jh/qVLl8r3338vAwYMkBQpUsihQ4d0/SEydRBC/BuvxcbGSseOHeWuu+6SHj16CKa49u3bVypVqqSS6C9DiDHOnz9fpfL+++9X0UUm1CgUQhMPShReQiG0PugUQuuZ2lEjhdAOqvbUSSG0h6vVtVIIrSZqX30UQvvYhlMzhTAceibu9RVCyN+3335701o/HMqOrF/ZsmX9CmH8Jpo2bSrDhg3TrGJiawhxX5UqVaRPnz6SPHly+eCDD3TqqpGR9K0XQojppQ8//LB+G8J6zz33SKtWrfTr9957T6egQjD9CSEyjhs2bNBM5t69eyUmJkbl8Nlnn9X7IYSYRmpMlc2XL59MmDBBmCE08QAl4UsohNYHl0JoPVM7aqQQ2kHVnjophPZwtbpWCqHVRO2rj0JoH9twaqYQhkPPxL2+Qjht2jRdW9ilS5e4OyFZjz32mJ7F5y9DCMlCHefOnVOxw5ROXIesX2JTRs+cOaNTRJH5Q8YPPywhYJi+in9XrVpVWrZsqXVCCI260LERI0boVNNatWppP7GBDNYEDh482K8Q+mJAdnLLli3aR0xJve+++1QIMT5jLSOmxWbJkoVCaOL5ScqXUAitjy6F0HqmdtRIIbSDqj11Ugjt4Wp1rRRCq4naVx+F0D624dRMIQyHnol7A2UIsQsohA0ZQqzHQ2bO2GUU2bjOnTtrNg1ih4LMG3bvRDYv0BpCbGjz/vvv6zRT33L8+HHB+kLsFlqhQoWghPCXX36Rdu3a3bTLqD8MrVu3ltq1a6sIcsqoiQclCi+hEFofdAqh9UztqJFCaAdVe+qkENrD1epaKYRWE7WvPgqhfWzDqZlCGA49E/f6CiFkCpuyINMGoUPmbdGiRXFrCKdPn66bv2DtHsqBAwdk0KBBulFM6tSpZfv27fo1xDEhITTk77ffftMMYYYMGbS93bt360Yw2EwGG9R0795dhRAiGkyGEGsMkTlcuHCh4GgLFGxGg3qLFSumaxu3bt2q2UFsklOoUCEKoYnnJBovoRBaH3UKofVM7aiRQmgHVXvqpBDaw9XqWimEVhO1rz4KoX1sw6mZQhgOPRP3xt9lFGfvYTdQ7DKKtXTYSRRnD6Igc4fpmnitXLlyumYPkoh7cuXKpdft2LFDM4r+hND3HEKI4IMPPqgZRojb6tWrVeJwTiB2MX3qqad0jSA2fwlGCNHPWbNmaX34UAPpwwY1H374oZ41iO9hN9SGDRvGHTPBDKGJByUKL6EQWh90CqH1TO2okUJoB1V76qQQ2sPV6lophFYTta8+CqF9bMOpmUIYDj3eGxYBbioTFj7P30whtD6EFELrmdpRI4XQDqr21EkhtIer1bVSCK0mal99FEL72IZTM4UwHHq8NywCFMKw8Hn+Zgqh9SGkEFrP1I4aKYR2ULWnTgqhPVytrpVCaDVR++qjENrHNpyaKYTh0OO9YRGAEHY5eFUuZr0jrHp4szcJpIw9L6OLxkjXOtW8OQAX9ppC6MKg+OkShdAbcUIvKYTeiBWF0BtxQi8phO6MFYXQnXGJil7pMRiHTkjp0o9ExXi9PMiLly5KTNoYS4dw7fo1qXx/Ebk3fz5L643myiiE3og+hdAbcaIQeidOFELvxIpC6M5YUQjdGZeo6BWEEIfaN2/ePCrG69VBIkbYjChz5sxeHULU9JtC6I1QUwi9EScKoXfiRCH0TqwohO4VCJuhAAAgAElEQVSMFYXQnXGJil5RCL0RZgqhN+KEXlIIvRErCqE34kQh9E6cKITeiRWF0J2xohC6My5R0SsI4acHj0vpMmWiYrxeHuTFi5ckJiatJUO4cuUvafpYWcmWNasl9bGSfwhQCL3xNFAIvREnCqF34kQh9E6sKITujBWF0J1xiYpe/b2pzDVuKhMV0f5nkKnPn5HFle+W6hUfjbKR2z9cCqH9jK1ogUJoBcXI1MFNZSLDOdxWKIThEozc/RTCyLEOpiUKYTC0PHzt2rVrZcuWLXrw/alTp6Rt27ayYsWKiI/o4sWLUrNmTVm1apUsXrxYOpxIJ7HZualIxAPhYINpfv9VPiifXZ4px8yw1WGgEFpN1J76KIT2cLWjVgqhHVStr5NCaD1Tu2qkENpFNrx6KYTh8Uv07gMHDsisWbPkhx9+0OtKlCghbdq0kfz589vYqv+qAwkhZHHJkiVy+PBhSZs2rRQtWlSaNm0q9957r6V9pRBaitOTlVEI7QsbhdA+tlbWTCG0kqa9dVEI7eVrVe0UQqtI2l8PhdB+xqG0QCEMhZqJe3788Ufp0aOHNGjQQKpVq6a7aS5fvlxWrlwpU6dOlbx585qoxdwl+HCRIkWKRC9OTAiRrZs+fbq0b99eypQpo0K4a9cu/YMxWFkohFbS9GZdFEL74kYhtI+tlTVTCK2kaW9dFEJ7+VpVO4XQKpL210MhtJ9xKC1QCEOhZuKefv366Tb9ffr0uenqV155RWJiYuTll1+W+vXry4QJE+IyhniTNGnSRBYsWKD3IsP45ptvytGjRyVnzpzSsWNHeeihh7Q+3Fu3bl3ZtGmT4AfhO++8I3PmzJH169fL+fPnVTg7deqkWUmUhITw8uXL0rBhQ51CWr169Zv6ColNliyZyuzSpUvlo48+ktjYWHn44YelW7dukjFjRjl+/LhKY7169XRK6oULF6ROnTpSu3ZtrQv3vvvuuyrCadKk0awjxswpoyYeoiR6CYXQvsBSCO1ja2XNFEIradpbF4XQXr5W1U4htIqk/fVQCO1nHEoLFMJQqAW45/r161KjRg0ZNGiQlC1b9qarN27cqBnC999/X8aNGydZs2aVF154Qa/58MMPZceOHTJq1CjBGwbf7969u1SsWFG+++47GTJkiE5BzZIliwph4cKF9XspU6ZUcUPdEMZMmTLJmjVrZPbs2TJv3jwVsYSEEPX27NlTs5e33Xab35GhXrT72muvSY4cObTfV65ckaFDh6oQtm7dWtq1a6d9+u233/TrGTNmqMRCULF5zNixY1UgR44cKTt37qQQ2vDceaVKCqF9kaIQ2sfWypophFbStLcuCqG9fK2qnUJoFUn766EQ2s84lBYohKFQC3APsmTIkEH8IG2+Zc+ePdKrVy8VJUzJRLYMwoQC+YNI/utf/5Jly5bJ119/rQJlFAgYBLNq1aoqX8g+lipVKsHeIBs3bNgwKVCgQIJCuG3bNhXQxDaYGTBggIomsoAoZ86c0UwmMob4N2QQGT9j2irG0bhxY+1r//79tY/IGqLs379funTpQiG04bnzSpUUQvsiRSG0j62VNVMIraRpb10UQnv5WlU7hdAqkvbXQyG0n3EoLVAIQ6EW4B5kCDH9cvDgwYlmCHFdo0aNNNOGTCEya9jYJV26dDJt2jRZt26dTh01yqVLl1SsIIP4M2bMmJs2qIFkItN37tw5SZ48uZw9e1ZlDzIXToawQ4cOOtUTmUqjQEpnzpypU0IxZRQCa5TevXvLM888I5UrV9Z1iS1atJBy5crpy5jOiqmunDJqw4PnkSophPYFikJoH1sra6YQWknT3roohPbytap2CqFVJO2vh0JoP+NQWqAQhkLNxD2JrSHEFM6BAwdqLcgiYronpoFiIxrj+5hSit1JsebQX4kvhCdPnpTOnTvftCaxWbNmOh0Ua/4CrSFElg+b3/gWYw1hoAxhYkKIDGGFChXi6j527JjutEohNPEQJdFLKIT2BZZCaB9bK2umEFpJ0966KIT28rWqdgqhVSTtr4dCaD/jUFqgEIZCzcQ92BDmxRdf1A1bkC00dhnFNMspU6bInXfeqbXs3btXM4lYX4c1g0YmDdk9SFrXrl1VqHA/rs2dO7euzYsvhGgPaxbnzp0rqVOnlu3bt+vXo0ePTlQI0Qds+II1f8YuoxDW3bt365RWTP/csGGDbliDurJly6bSiWylsYYwMSFElhNjHj9+vK5lxL2rV6+mEJp4hpLqJRRC+yJLIbSPrZU1UwitpGlvXRRCe/laVTuF0CqS9tdDIbSfcSgtUAhDoWbynn379unun8j0IQtYvHhx3c3znnvuuamG5s2bC/6ng+miqVKlinvt4MGDehwEMoeYAlqkSBHd3RNS6G/KKK7Fhi25cuWSggUL6gY12Jk0sQyh0dhnn32m7R85ckSPnShWrJiuE8Q5hJBRHCKPdYY4NgJTUCGqmM5q7DKa0JRRTIvF5jYQVEyLhfAiK8oMocmHKAleRiG0L6gUQvvYWlkzhdBKmvbWRSG0l69VtVMIrSJpfz0UQvsZh9IChTAUarzHEgLYTKfDiXQSmz2fJfWxEm8QoBDaFycKoX1srayZQmglTXvrohDay9eq2imEVpG0vx4Kof2MQ2mBQhgKNd5jCQEKoSUYPVcJhdC+kFEI7WNrZc0UQitp2lsXhdBevlbVTiG0iqT99VAI7WccSgsUwlCo8R5LCEAIu397Xi5nzmVJfazEGwRSXzgr86o/INXL33xGpzd67+5eUgjdHR+jdxRCb8QJvaQQeiNWFEJvxAm9pBC6M1YUQnfGJSp6BSE8fea0nr3I4l4CWEOKszUzZMhgWScL3FNAUqZMaVl9rOhvAhRCbzwJFEJvxIlC6J04UQi9EysKoTtjRSF0Z1yiolcQQsgGNtVhcS8BxOiPP/646UxM9/Y2untGIfRG/CmE3ogThdA7caIQeidWFEJ3xopC6M64REWvKITeCDOF0BtxYobQO3GiEHonVpwy6o1YUQi9ESf0kkLozlhRCN0Zl6joFYRw9lf7JV+BQlExXu8O8oZcvXJVUvociRLOWK5cvii9n/uXlChcIJxqeK8fAswQeuOxoBB6I07MEHonThRC78SKQujOWFEI3RmXqOiV7jL6UwyPnYiKaP8zSN1ltEIOeaZcmSgbuf3DpRDaz9iKFiiEVlCMTB3MEEaGc7itUAjDJRi5+ymEkWMdTEsUwmBo8dqgCTRt2lQGDx4shQsXvuVeHjsRNM4kcQOPnbAvjBRC+9haWTOF0Eqa9tZFIbSXr1W1UwitIml/PRRC+xmH0gKFMBRqQdyDzTjeffdd2b59u86bzp49uzzwwAPSsGFDufPOO4Ooyd5L9+7dK/369ZPp06dLrlx/HwOBtWMvv/yyFCtWTFq3bn1LBzC2evXqxX0fu1CWLFlSunfvLunTp9fv+wrhqFGj5P/aOw8oKYus/V8Ycs6iSA6iBFFBWNBlWXHJOUuWHCQqCCpIzpKVnA4ZRSSJgoCCBJcFXXEBBZEomZXlG2DB8X+e+r7u/8wwM/129xuqpp/3HI4wXXXr1vP0jP2bW6FYsWLSrFkz9RqB0FlPdY1OIHTOGQKhc9raGZlAaKeazsYiEDqrr13RCYR2Kel8HAKh8xqHMgKBMBTVLPbBh7NevXpJ3rx5pWPHjgoAcXy/Dw7bt29vMdL/NgOg4U/KlCmD6me18bx58+TUqVMyYcIE1WXLli3y4YcfKkhMkyZNnDD4QIO5AAiXL1+uQPfatWsyZswYKVmypPTs2ZNAaFX4CGtHIHTOcAKhc9raGZlAaKeazsYiEDqrr13RCYR2Kel8HAKh8xqHMgKBMBTVLPZZvXq1gqpFixZJ6kQO5Lh//76MHj1afvjhB3nw4IGqxvXv31/y5MmjRsFr2bNnl9OnT8ulS5dk1KhR8uWXX8r27dvl1q1bki9fPgVfZcuWVe1jYmJk8eLF8umnn0ratGlVhW7q1KkqD0DdzZs3ZdasWfLdd9+p1xs3bixNmjRRffEDtWvXrtKyZUspX768dOnSRY1fqlQpOXv2rPTr108B4K5du1Slr3v37urfK1eulNy5c6sYa9askcOHD/uh0lchPHPmjMyYMUPdPZchQwapWLGimlf38xm4h9Di+ym5NCMQOuckgdA5be2MTCC0U01nYxEIndXXrugEQruUdD4OgdB5jUMZgUAYimoW+wwaNEiKFCmiwCmxB0C4e/duefHFF1UTwBqWYgL8fECI5ZyAqZw5c6oKIYDsmWeekaxZs8q2bdsUAKJKB8D77LPPZNWqVTJx4kTJkiWLArO9e/cqIASUYjknAA8VS8Dh4MGDpUePHgrQ8Bw9elSGDRum8sYfX6UPQIhlo6hqtmnTRuUBII0NhNevX1cAWa5cOdUOD5eMWnyzRFAzAqFzZhMIndPWzsgEQjvVdDYWgdBZfe2KTiC0S0nn4xAIndc4lBEIhKGoZrFPt27dpHr16v49c6icYR8dnsKFCytoi/+gCgiA3LBhgx8IH3300QT38Pn6ArpGjhwpRYsWlaFDh8rzzz8vDRs2VC9jCSjiAQjPnTsnAwYMkI8//ti/7BTj/PjjjwJ49T3Tp09Xy1qXLFki6dKlU18GEHbu3Fk2b97sXz4afw+hb16TJk1SsEogtPhGibBmBELnDCcQOqetnZEJhHaq6WwsAqGz+toVnUBol5LOxyEQOq9xKCMQCENRzWIfHMgCSPNVCFENxP9c9uzZI1u3blV783xLPL/++mu5c+eOpEiRQq5evaoqfdgriIpb6dKl/YCHobFcFCB348YN1QaVOYAmqoaAUFTnKleurLJEFQ9LQgGEhw4dUvF8h8bgdSxTRY7vvvuuf1aIjarltGnT/F/zLRldv369/2s+IPQtGcWH0RUrVsi3334rs2fPVnNhhdDimyWCmhEInTObQOictnZGJhDaqaazsQiEzuprV3QCoV1KOh+HQOi8xqGMQCAMRTWLfQBKWNKJPYTYO+d7duzYIevWrVNACDDEfj8sEc2WLZtcuXJFQRT6RUVFPQSEFy5cUAfVYF8gqox42rZtq/YdPvvss+qkUCz/TKhCCKjD62vXrlWwltgTKhAiHqqQr776qtpLmCNHjjhAiOWrWIbKU0YtvoGSaTMCoXPGEgid09bOyARCO9V0NhaB0Fl97YpOILRLSefjEAid1ziUEQiEoahmsQ8+nGEPHg5+6dChgzz++OMSHR2t9vjhEJk5c+YoMMTffRU6QCJO9kwMCLG8E3v8li1bppZuYmkn/g3YAhD69hBi2SaugYi9hxBQioNhUHEERGLPIQAOlUmcDOp7ggVC3ymjmBv+7gNeVC9jVwgxN4yFHPDw2gmLb6Rk1oxA6JyhBELntLUzMoHQTjWdjUUgdFZfu6ITCO1S0vk4BELnNQ5lBAJhKKoF0QdvfOzFO3DggDrEBQe9YI/fK6+8okARH+BwVQOWf+LUzUqVKqmDZRIDQgwNsDp48KBa+onTPhEbB8MACLEEFRVJ9Afw4b5DXzwAGnIAiGI/I5aw4ioMLDHFqaKhAqGvH/YbIh+cTvrUU0+pL8cGQlQosWQVVVAsacXeSJ4yGsSbKZk0JRA6ZySB0Dlt7YxMILRTTWdjEQid1deu6ARCu5R0Pg6B0HmNQxmBQBiKagb1+emnn+Sdd94RXIGh28MKoW6OuJMPgdA5nQmEzmlrZ2QCoZ1qOhuLQOisvnZFJxDapaTzcQiEzmscyggEwlBU07gPqn5HjhxRFT8s4Rw7dqyqxL322mvaZU0g1M4SVxIiEDonM4HQOW3tjEwgtFNNZ2MRCJ3V167oBEK7lHQ+DoHQeY1DGYFAGIpqGvcBEOKuwYsXL6p7BwGGvXv3lowZM2qXNYBwwpHzku3xgtrlxoTiKvD77zESFZXSFln+iP6PTKj9J3nh2bK2xGOQ/68AgdCMdwOB0AyfkCWB0AyvCIRm+IQsCYR6ekUg1NOXiMgKQIgPsM2bN4+I+Zo6yT/++ENdX+K7W9KOeeBEXexp5WOvAgRCe/V0KhqB0Cll7Y9LILRfUyciEgidUNWZmARCZ3QNNyqBMFwF2T9kBQCEgI127dqFHIMdnVcAHuHOSUAcH70VIBDq7Y8vOwKhGT4hSwKhGV4RCM3wCVkSCPX0ikCopy8RkZWqEEb/j/9ewoiYtIGTBBDiQxFOyA31wZLldGnThdqd/SwqQCC0KJTHzQiEHhsQxPAEwiDE8rApgdBD8YMcmkAYpGAuNScQuiQ0h3lYAQDhxCMXJFs+7iHU/f3xe8zvEpUyKqQ0//j9gdTJn0mGtGoUUn92sq4AgdC6Vl62JBB6qX5wYxMIg9PLq9YEQq+UD35cAmHwmrnRg0DohsocI0EFeMpoZLwxUt6/J/3TnpfJnVpExoQ9nCWB0EPxgxiaQBiEWB43JRB6bIDF4QmEFoXSoBmBUAMTEkiBQKinL0FldenSJXUZ/KZNmxLsh4vrV6xYITNnzgwqrtONCYROK6xHfAKhez4QCN3TOpyRCIThqOduXwKhu3qHOhqBMFTl3O9HIHRfcysjEgitqORxGxzo0bRpU38WmTNnlueee05dL5EpUyaxCwgXLFgga9asSXC2uMtw2bJltipBILRVTm2DEQjds4ZA6J7W4YxEIAxHPXf7Egjd1TvU0QiEoSrnfj8CofuaWxmRQGhFJY/b+IBw+fLlkitXLrl27ZqMGTNGSpYsKT179rQNCPEhBX/wbN68Wb788kuZNGmS+neKFCnUvYZ2PgRCO9XUNxaB0D1vCITuaR3OSATCcNRzty+B0F29Qx2NQBiqcu73IxC6r7mVEQmEVlTyuI0PCFeuXCm5c+dW2aCSd/jwYZkwYcJDQIgPhVOmTFGv58mTR/7yl7/I/v37/UtGAXqoBqJdzZo15dtvv5WOHTtKhQoV/DPdsGGD7N69W6ZNmyZr166VY8eOyfDhw/2vz5o1S90jByBt1qyZNGjQQL755hsVs0yZMtKrVy8/QG7fvl1Wr14t169flxIlSsiAAQMkb968QiD0+I3l0vAEQpeEFlHff/jFTZo0adwblCMFrQCBMGjJPOtAIPRM+qAGJhAGJZenjQmEnsqf6OAEQj19iZNVfCAEWI0ePVrKlSsn7du3fwgIJ0+erK4JGDJkiNy8eVMGDRqk7pDDHsKLFy9Kt27dZOzYsVKqVCkFe4sXL1bxEgNCjIdxAHVYoooPMy1atFAxAHgAwuLFi8uoUaNU3sOGDZOnnnpKWrduLYcOHRLkg4pmoUKFZP369Qo0AZSoeHY/n0Gic/GUUQPehiGnSCAMWbqgOxIIg5bMkw4EQk9kD2lQAmFIsrneiUDouuQhD0ggDFk6RzsSCB2V157g8fcQImrhwoXVcs6sWbM+BIR169aV6dOnS9GiRVUCgL49e/YoIERl8fjx4/5qX0xMjNqfCHhMDAgRA6+/+OKLUrt2bcEhNfPnz5eFCxeq+ADCN954Q55//nn1b0DgBx98oF5HVRHg2bx5c/Ua7rTDeLNnzxZUDgmE9rxHdI5CIHTPHQKhe1qHMxKBMBz13O1LIHRX71BHIxCGqpz7/QiE7mtuZUQCoRWVPG4Tv0KID304NRRLPQFWly9f9p8yevv2bWnUqJFgyScuA8cDGAQUAgjff/99tR+wR48e/lnhhNKuXbsmCYS7du1Sp5i+9957qpoI2GzVqpUfCFEtRJUQz6lTp2TgwIEqh+7duwu++dOnT+8fDzmOHDlSgSOB0OM3lwvDEwhdEPn/hiAQuqd1OCMRCMNRz92+BEJ39Q51NAJhqMq5349A6L7mVkYkEFpRyeM2Ce0hPHfunLz66quq4ocfhLGvnUCFcO7cuZIvXz6V+caNG1U1DkCIZZ8nTpwIukKIMVDlmzp1qvTp00dV/7A/EQ8qhPgaKoh49u7dq5ahog2Wj5YvX17q16//kIrcQ+jxG8ul4QmELgnNPYTuCR3mSATCMAV0sTuB0EWxwxiKQBiGeC53JRC6LLjF4QiEFoXysln8U0ajo6PV/rsdO3bIunXr5MqVK3GAEEtJM2TIoA52uXv3rvTr108dNAEgvHDhgtpDOG7cOMt7CH1zR3UQy02zZMmi9gX6HgAhrqVA1Q8HzWB5aaVKlaRt27bqoBksX8XSUVQQkTsqg1WrVuWhMl6+qVwcm0DontisELqndTgjEQjDUc/dvgRCd/UOdTQCYajKud+PQOi+5lZGJBBaUcnjNvH3EKZLl06KFSumIBCHt8S/hxD/AwO84euAN+zh+/vf/+4/ZXTnzp2qguc7ZRSAhtNCcUiN74l9yqjva0ePHpX+/fvL66+/LjVq1IgDhFg++sknn6jDbFAp7N27t/+UUSw3XbVqlcoHy1gxzuDBgwmEHr+v3BqeQOiW0jxl1D2lwxuJQBiefm72JhC6qXboYxEIQ9fO7Z4EQrcVtzYegdCaTsm2FT6Y4JAXnPrpW2Ka2GRRicT1FNiP6NufiLaoEE6cOFEddBPMwyWjwahlblsCoXvesULontbhjEQgDEc9d/sSCN3VO9TRCIShKud+PwKh+5pbGZFAaEWlZNbm4MGD8swzz6jlnTicBnv+5s2bpw6bSezBaaRz5sxRSz5RIYz9hAWEZ9Px2olk9v6KPx0FhOkuyOTOLZP5TL2fHoHQew+sZEAgtKKSHm0IhHr4ECgLAmEghfR5nUCojxexMyEQ6umLo1nhYJivvvpKjYHTQnEgTIECBRIdE/sQAX04RAZ7D32Hyfg6hAOES/7xoxQsWszR+TJ4eAr88YfIgwcPJHXqVCEF+iMmRqo+UUA61HwppP7sZF0BAqF1rbxsSSD0Uv3gxiYQBqeXV60JhF4pH/y4BMLgNXOjB4HQDZU5RoIKYMko7iVs164dFdJYAXiEfazZsmXTOEumBgUIhGa8DwiEZviELAmEZnhFIDTDJ2RJINTTKwKhnr5ERFYEQjNsJhCa4ROB0ByfCITmeEUgNMMrAqEZPhEI9fWJQKivN8k+MwDh5StXBPcm8tFXAQDh7du3JXPmzIkmmTdvXsmWNau+k4iQzFghNMNoAqEZPrFCaI5PBEJzvGKFUE+vCIR6+hIRWQEI+35/S+5lzxsR8022k3zwX+mY/a7M6t0x2U7RlIkRCM1wikBohk8EQnN8IhCa4xWBUE+vCIR6+hIRWfHaieRhc4oH96Rv6vMytXOL5DEhg2dBIDTDPAKhGT4RCM3xiUBojlcEQj29IhC64AsO5Fi6dKns379fbabNlSuXPP3009KiRQvJnz+/CxkEP8R3332nrpfo3LmzytOJh0DohKruxyQQuq95YiMSCPXxIqlMCIRm+EQgNMcnAqE5XhEI9fSKQOiwL/iA1qtXL8EeK1zqDgDEfiwfHLZv3z6oDPBBIioqKqg+oTSeNGmSyjF79uyycOHCREOEkw+BMBRn9OtDINTHEwKhPl4QCM3wIlCWPFQmkEJ6vE4g1MMHK1kQCK2o5H4bAqHDmq9evVq2bNkiixYtktSpUyc42v3792X06NHyww8/qPvennzySenfv7//vj/c89ekSRPZvXu34IceYi1ZskS2b98ut27dknz58knPnj2lbNmyKj4ukV+8eLF8+umnkjZtWmndurXg7kHkkSZNGrl586bMmjVLUAXE640bN1bxfY/v3sEBAwbIhAkTZMaMGVKiRAn18qZNmxQoZsmSRU6cOKH61qxZU5YtWyZffPGFyq9KlSrSo0cPSZcunSQ1NwKhw28+l8ITCF0S2sIwBEILImnQhBVCDUywmAKB0KJQHjcjEHpsQBDDEwiDEMvFpgRCh8UeNGiQFClSRLp3757oSIAmwN6LL76o2gDWsMx01KhR6t8AQgDZu+++K6lSpZIUKVLIzp075ZlnnpGsWbPKtm3bFAAuX75cAd5nn30mq1atkokTJypwA9Tt3btXASGgtG/fvlKqVClVsQQcDh48WAFcxYoV1XgAzfnz5wtgdtiwYfLoo4+qKqcPCGfOnCmTJ09WAIoTKFFBBBwOHTpU0qdPr8ZDn65duyogTGxuBEKH33wuhScQuiS0hWEIhBZE0qAJgVADEyymQCC0KJTHzQiEHhsQxPAEwiDEcrEpgdBhsbt16ybVq1dXUIfn8OHDMm7cOPX3woULK2iL/1y6dEkB5IYNG/xACGgrX758otmiCjhy5EgpWrSoArPnn39eGjZsqNqfOnVKxQMQnjt3TlD5+/jjjyVlypTqdYzz448/CuAVD/5bqFAhVXUEeM6ePVvWrFmjYBQVQgAjqoa+p1GjRjJ+/Hh54okn1JdOnz4tb7/9tqxYsSLJuREIHX7zuRSeQOiS0BaGIRBaEEmDJgRCDUywmAKB0KJQHjcjEHpsQBDDEwiDEMvFpgRCh8V+4403FKT5KoSomOF/MHv27JGtW7fK3Llz/Us8v/76a7lz546qAF69elVV+gBtgEmAIwDS9wDKAHI3btxQba5fv65AE1VDQCj2JlauXFk1x7JSLAkFEB46dEgtT33kkUf8sbBMFTmiAnnlyhVp06aNoAoIwPMtH33zzTfVUlAAIaB2+PDhqj8+gAI8sWwVeeNB1RBfX7duXZJzAzB2P59BonMVdNgFhndSAQKhk+oGF5tAGJxeXrUmEHqlfPDjEgiD18yLHgRCL1QPbUwCYWi6Od2LQOiwwitXrlRLOrHvDxU237Njxw4FTABCgCH2+2GJaLZs2RSUoeKHfjhAJj4QXrhwQS3hxL5AHyS2bdtW7Tt89tlnZciQIWr5Z0IVwrNnz6rX165d6we42BIgXyw/RR6+B0D5pz/9SQFjfCBEG4wzffp0KVjwYbBLam4Yi0Do8BvQhfAEQhdEtjgEgdCiUB43IxB6bEAQwxMIgxDLw6YEQg/FD3JoAmGQgrnUnEDosND4gIall6igdejQQR5//HGJjo5We/xwiOIf9QwAACAASURBVMycOXMUGOLvAC48gMQPP/wwUSDE8k7s7cNBLjgkBoe84N/Yuwcg9O0hxEmhmTNnjrOHEFDar18/KV26tAAisecQy0hRmSxZsqTaV1itWjWpW7euX5njx48rWMWy0S+//DJOhRCNsN/w5MmT6pqK3Llzq2rlzz//LBUqVEhybgRCh998LoUnELoktIVhCIQWRNKgCYFQAxMspkAgtCiUx80IhB4bEMTwBMIgxHKxKYHQBbHx5sepoAcOHFCHuOCgF+zxe+WVVxQo4kPcmDFj1PJPXPNQqVIldbBMYhVCHzQePHhQLf0sVqyYio2DYQCEOGUUFUn0B/DhHkFfPCwvRQ4AUSz9xBJWXIWBJaYZM2aUgQMHCkAtdoUQ43Xq1Enq1aunKpaxl4ziNcRAH1Q9cRgO7lmsU6eOWqaa1NwIhC68+VwYgkDogsgWhyAQWhTK42YEQo8NCGJ4AmEQYnnYlEDoofhBDk0gDFIwl5oTCF0S2sthfvrpJ3nnnXfUqaE6PTxURic3Qs+FQBi6dnb3JBDaragz8QiEzujqRFQCoROq2h+TQGi/pk5FJBA6pWx4cQmE4emnZW9U7I4cOaJOJcXy1LFjx6prIF577TWt8iUQamVHyMkQCEOWzvaOBELbJXUkIIHQEVkdCUogdERW24MSCG2X1LGABELHpA0rMIEwLPn07AwgxF2DFy9eVPcOAgx79+6tloTq9AAIe//0QO7keFyntJhLkAqk+P2/0i3DTZnRo02QPdncbgUIhHYr6kw8AqEzujoRlUDohKr2xyQQ2q+pUxEJhE4pG15cAmF4+rF3GAoACHefPC8VKjwfRhR2dUOBO3fvSPp06RMcKuaPP6RMgcfkxaefciMVjpGEAgRCM94eBEIzfEKWBEIzvCIQmuETsiQQ6ukVgVBPXyIiKwAh7ixs165dRMzX1EnCIxwWFP+gIVPnk5zzJhCa4S6B0AyfCITm+EQgNMcrAqGeXhEI9fQlIrIiEJphM4HQDJ+QJYHQDK8IhGb4RCA0xycCoTleEQj19IpAqKcvEZEVgHDXT2elQsWKETFfkyd5585dSZ8+XYJTSPnHH9K++ouSLl3Cr5s8b9NyJxCa4RiB0AyfCITm+EQgNMcrAqGeXhEI9fQlIrL630NlfuehMoa7XfA/52VH+2pSsEBBw2difvoEQjM8JBCa4ROB0ByfCITmeEUg1NMrAqGevtieVevWrWX48OFSokQJ22OHGpDXToSqnF79iv77tHzR4nkCoQa2EAg1MMFCCgRCCyJp0oSHymhiRIA0CIRm+IQsCYR6ekUg1NMX2bt3r6xbt05OnTolqVKlkvz580utWrWkZs2akjJlygSznjlzpuCH4sCBAx96ffv27VKhQgXLB4N899138vrrr0vnzp2lRYsWjqhEIHREVteDEghdlzzRAQmE+niRVCYEQjN8YoXQHJ8IhOZ4RSDU0ysCoYa+bN68WebPn69grGLFipI5c2Y5efKkrF+/Xvr06SPZs2ePkzUO/cCf2bNnJwqEwU5z0qRJsn//fjXWwoULE+2ODzZRUVHBhlftCYQhyaZdJwKhPpYQCPXxgkBohheBsmSFMJBCerxOINTDBytZEAitqOR+GwKh+5onOeK9e/dURa5bt26qIpjYM3r0aAVrp0+flkuXLsmoUaMEIJlYhdC3ZBQAN2zYMFmzZo2/0ohq5LJly2TevHlquLt370qzZs1kwIABMmHCBJkxY4Z/qemmTZsUKGbJkkVOnDghjRs3VlVL9P/iiy/U+FWqVJEePXqoQ0bu378vyPWHH36QBw8eyJNPPin9+/eXPHnyEAg1e++Fmg6BMFTl7O9HILRfUyciskLohKrOxCQQOqOr3VEJhHYr6lw8AqFz2oYTmUAYjnoO9D169KgCpk8++UQyZMiQJBAeO3ZMwVrOnDlVhXDWrFkBgRB7CNu2bSv9+vWT5557TsUfOXKkAr6WLVuqf2N5KSqUq1evVvD46KOPSq9evdRrAEIsTZ08ebKULVtWjYsKIuBw6NChkj59egWR6NO1a1cFhLt375YXX3xR9UeOuNMOAMsKoQNvIA9CEgg9ED2RIQmE+niRVCYEQjN8QpYEQjO8IhCa4ROyJBDq6RWBUDNfvv76axk/frwCL98zZMgQtWQUz6BBg9ReQFTdAF2dOnXyt0tqD2HsQ2UWL14s169fV3sEo6OjpXnz5grqHnnkEf8YhQoVkp49e8rOnTvVUlRUFLGXEXkBGAGivqdRo0Yq5yeeeEJ9CVXLt99+W1asWPGQuqhmdu/eXTZs2EAg1Oy9F2o6BMJQlbO/H4HQfk2diEggdEJVZ2ISCJ3R1e6oBEK7FXUuHoHQOW3DiUwgDEc9B/p+//33aqlm7ArhrVu31HJL7B/EUkwsyQQQli5dWho2bBg0EJ45c0b69u2rDq3ZtWuXfPrppzJ16lQV58qVK9KmTRtVBQTg+ZaPvvnmm2pcAOHhw4fViaV48AEUOeTLl09SpEihvoaqIb6O+DExMQIABejeuXNHtbl69ap89tlnChi7n88g0bl4XYEDbyXXQhIIXZM64EAEwoASadGAQKiFDZaSIBBaksnzRgRCzy2wnACB0LJUrjYkELoqd+DBfHsIUUXD3rzYT7t27dTewnCBEDGxnLN9+/YK8BCvXr16aqiVK1cqgMuWLZt/aADpn/70J3n33XcfAkI0AhBOnz5dChZ8GOy2bt2qgBNLRBETwIlq5bZt29RYBMLA7wndWxAI9XGIQKiPF0llQiA0wydkSSA0wysCoRk+IUsCoZ5eEQg19GXjxo2yaNEi6dKli1oemilTJrUME/v5cKVE5cqVE60QoqKH6p/vQUUuderUCsJi30OI/YGHDh2Sf/3rX7Jq1SrJmjWr6tKxY0epVq2a1K1b1x/j+PHjCuiwbPTLL7+MUyFEI+w3xJJWLEHNnTu3Wo76888/q9xRJcSBMoBJPHPnzpUPP/yQQKjh+y7UlAiEoSpnfz8Cof2aOhGRQOiEqs7EJBA6o6vdUQmEdivqXDwCoXPahhOZQBiOeg72/eqrrxQ44R5CLMHEnr4GDRpI9erV1TUPiS0ZBUzGfnCaJ5ZmxgdCX6Xu+eeflzFjxqguOKQGwInKXewKIV7DXkVUETF27CWjeA0Hx6DPjh071IExuXLlkjp16kiTJk3U0lHEv3HjhjoVtVKlSupgGVYIHXzzuByaQOiy4EkMRyDUx4ukMiEQmuETsiQQmuEVgdAMn5AlgVBPrwiEevoSEVnxlNHkYTOBUB8fCYT6eEEgNMOLQFkSCAMppMfrBEI9fLCSBYHQikrutyEQuq85R/w/BQiEyeOtQCDUx0cCoT5eEAjN8CJQlgTCQArp8TqBUA8frGRBILSikvttCITua84RYwFh3+9vyb1s/3vdBR8zFXg8+ops71ZXCuQvYOYEklHWBEIzzOSSUTN8QpYEQjO8IhCa4ROyJBDq6RWBUE9fIiIrVAivXL0S5wCbiJi4YZPEHtbbt29L5syZE8wchxYVKVzEsFklz3QJhGb4SiA0wycCoTk+EQjN8YpAqKdXBEI9fYmIrACEgA1cp8FHXwXgEQ4Lin/QkL4ZR25mBEIzvCcQmuETgdAcnwiE5nhFINTTKwKhnr5ERFYEQjNsJhCa4ROyJBCa4RWB0AyfCITm+EQgNMcrAqGeXhEI9fQlIrICEC4+dEIKFi0eEfM1d5J/yIP7DyRV6tQPTSHm9wdSs0wxaVW9qrnTS0aZEwjNMJNAaIZPBEJzfCIQmuMVgVBPrwiEevoSEVmpU0bPpZfoXAUjYr7JcZJR/70jb2W/LiNaN0qO0zNuTgRCMywjEJrhE4HQHJ8IhOZ4RSDU0ysCoZ6+eJLVpUuXpEuXLrJp06aA41tti1jxL7L3Bee1EwFl1r5B1D0A4TUCoSZOEQg1MSJAGgRCM3wiEJrjE4HQHK8IhHp6RSC00Ze9e/fKunXr5NSpU5IqVSrJnz+/1KpVS2rWrCkpU6a0caTwQuGAkKZNm8YJki5dOlmzZo3s2rVL6tSpE3CA6OhoS20JhAGlNLoBgVAv+wiEevmRWDYEQjN8IhCa4xOB0ByvCIR6ekUgtMmXzZs3y/z586Vz585SsWJFdUT/yZMnZf369dKnTx/Jnj17UCPhA0NUVFRQfaw29gHh0qVLJVeuXKpbihQpBNcH2P0QCO1WVK94BEK9/CAQ6uUHgdAMP5LKkvcQmuEhgdAMn5AlgVBPrwiENvhy7949adGihXTr1k1VBBN7vvrqK8EySSy3zJo1qzRr1kwaNGigmgOc9u/fL1myZJETJ05I48aNpWzZsjJt2jQ5ffq0grUXXnhBevbs6Qe3L7/8UhYsWKBOFkQV8ttvv5WOHTtKhQoVVMzt27fL6tWr5fr161KiRAkZMGCA5M2bV10hgArhypUrJXfu3P504y8DHT16tALZM2fOqBjI7a233lIQGb8tqqPIBd/oqDa2adNG3S+IeR06dEhy5MghO3bskJw5c6o8MDcuGbXhzedxCAKhxwbEG55AqJcfBEIz/CAQmu8TgdAcDwmEenpFILTBl6NHj0r//v3lk08+kQwZMiQaEWCUJ08etZQU0Dd48GCZNGmSgjWA08yZM2Xy5MkKlnDUP0AM8FaqVCn137ffflteeuklBXMXL15UADp27Fj1+tq1a2Xx4sUCiAMQYizEGjNmjBQqVEhVKnfv3i2zZs2SW7duWQbCH3/8UWbMmKHuoPvggw/k7t27aq6xgRC5NmzYUCZOnChPPPGE4Deq165dk8KFC6t5zZ49WwYNGiR//vOfBZXUDRs2yJIlSwiENrz3vA5BIPTagbjjEwj18oNAaIYfBELzfSIQmuMhgVBPrwiENvjy9ddfy/jx4+McxjJkyBC1ZBQPYMhXtYs9HACqWLFiqhoIcEJFD/CV2PPZZ5/Jvn37ZMSIEWq/3/Hjx2X48OGqeUxMjII8jIux8HWAYvPmzdXrgDa8DjhLnz69+numTJnUUlE8tWvXVhW92IfKAC4LFiwobdu2VW3+8Y9/yMKFC+X9999/CAibNGmilstWrVpVMmbM6J9C/Hnhhzb2KG7cuFE+/PBD6X4+A08ZteE96FUIAqFXyic8LoFQLz8IhGb4QSA03ycCoTkeEgj19IpAaIMv33//vVoGGbtCiCrcgwcP1P7BHj16SJUqVeTYsWOqinfhwgU1KippgMEOHTooIIx/GueNGzdk7ty5qh9i4QceqotTp05VUAaYQ2zfA5jr2rWrAsLu3bur5ZuAP99z+/ZtGTlypDz22GN+OPTtbUQ7vB4fCEuXLq2qf3hQCZ0yZYqaQ/wlo3htxYoVqg2qhKheFi9ePMF51ahRQ1atWiVbtmwhENrw/vMyBIHQS/UfHptAqJcfBEIz/CAQmu8TgdAcDwmEenpFILTBF98eQkAY9vLFftq1a6fgCEDYqlUrVUWrVq2aOnUUSzqxpw77/hICwnHjxqkqHiAvbdq0qoIIiMK+QuwNxLLTxCqEw4YNk/Lly0v9+vUfmmEwewitAqFvEPxQRvVyz549Mm/ePAKhDe8vnUMQCPVyh0Colx8EQjP8IBCa7xOB0BwPCYR6ekUgtMkXLIFctGiRqrChQgeQw2EwALOBAwfKn/70J3WADGCuSJEiqsKGA2Lq1auXKBBiz+Bzzz0njRo1Unv3sBwUSz8RA1VGgCagMaE9hN98841Mnz5dASMqdbgmAvsKsaTTbiBEbFQ3AaA4UAZ7BD///HNVxUwIdFkhtOlNp0EYAqEGJsRKgUColx8EQjP8IBCa7xOB0BwPCYR6ekUgtNEXnCKKfXG4hxDghsNcAIHVq1dXV0js3LlTli9frqqCOHUTSz4feeSRRIEQcXDoDJZzYl8ewO7IkSMKCPEgHpZv+k4ZBfABMsuVK6dex52CWJoJ+ER/fB0H2dgNhBgf4Ik9k5hTgQIF1FLZokWLEghtfH/pGIpAqJcrBEK9/CAQmuEHgdB8nwiE5nhIINTTKwKhnr4EnRXuLcRBMThFNF++fEH396IDr53wQnV7xyQQ2qtnuNEIhOEq6E5/Xkzvjs52jMJ7CO1Q0fkYBELnNbZrBAKhXUraG4dAaK+erkY7ePCgPPPMM2o/Ig50wV2A2LfnOznU1WRCGIxAGIJomnUhEOplCIFQLz9YITTDD1YIzfeJQGiOhwRCPb0iEOrpi6WscNoolqniwfJMLNPEck1THgDhhCPnJdvjBU1JOWLz/P33GImKSvnw/B/cl2ZFckrfpnUjVhudJk4g1MmNxHNhhdAMn5AlK4RmeEUgNMMnZEkg1NMrAqGevkREVgBCfID13ZUYEZM2cJLYD4trVLJmzZpg9jhAKU2aNAbOLPmlTCA0w1MCoRk+EQjN8YlAaI5XBEI9vSIQ6ulLRGQFIARs4GoOPvoqAI9wEFG2bNn0TZKZKQUIhGa8EQiEZvhEIDTHJwKhOV4RCPX0ikCopy8RkZWqEEb/jzRr1iwi5mvqJAGEWDaVJUuWh6aQJXMWSZ06talTS3Z5EwjNsJRAaIZPBEJzfCIQmuMVgVBPrwiEevoSEVkBCCceuSDZ8nEPoe6G/x7zu0SljIqT5h//vSsdyuSXznWr655+xORHIDTDagKhGT4RCM3xiUBojlcEQj29IhDq6UtEZMVTRs22OVX0LRn3+F15vUltsyeSjLInEJphJoHQDJ8IhOb4RCA0xysCoZ5eEQj19EWrrCZOnCiFCxe2fWkngVArm4NOhkAYtGSOdyAQOi6xLQMQCG2R0ZUgPGXUFZnDHoRAGLaErgUgELomdVADEQiDkku/xpMnT5Z//vOf8uuvv8rQoUOlWrVqAZPcunWr7Nu3T0aPHu1vO3PmTNm4cWOcvl27dlUQGAgI8eFm0aJFsnPnTnX4SI4cOeS5556T/v37q3idO3eWM2fOxIk9e/ZswT2K3c9nkOhcXDIa0DQNGxAI9TOFQKifJwllRCA0wydkSSA0wysCoRk+IUsCoZ5eEQj19MVyVhs2bFDVO9xJ2L59+7CA8O7du9K3b1//2KlSpVKX3icFhPhg89FHH8m2bdvknXfekXz58snly5fl8OHD0qBBAz8QAixjwyoOIlm+fDmB0LLT+jUkEOrnCYFQP08IhGZ4kliWBEIz/CMQmuETgVBfnwiE+noTVGaowrVu3ToOdO3du1cWLFigfhuTLl06adOmjZQrV05dYH/v3j11jUDmzJllzpw5ggohfqAOHDjwoXFjA+GmTZtk//796sTJEydOSOPGjeW7776TvHnzqkpgQg++3rJlS6lePe7hI1wyGpTF2jUmEGpnCa+d0M+SBDNihdAQo1ghNMYoAqExVrFCqKlVBEJNjQk2rfhAiKsCGjZsqKp7TzzxhFr2cu3aNVVNTGzJqFUgBDxiqWrZsmXVPYKffPKJrFixQtq2bStlypSRQoUKSYoUKfxTIBAG66YZ7QmE+vnECqF+nrBCaIYnrBCa7ROB0Bz/uGRUT68IhHr6EnRWCQFhkyZNVNWuatWqkjFjRn/MxIDw008/VZVE37N06VJVQYxfIdy+fbvMmDHD3w5QuGPHDsHXjx07JunTp1dwWK9ePdUGOWAZqe++uoIFC6olrqwQBm2zVh0IhFrZoZIhEOrnCYHQDE8IhGb7RCA0xz8CoZ5eEQj19CXorBJaMnr06FFVucN/USXs1q2bFC9ePNEKIaqIXbp08Y+dM2fOh/YQYsko9gcOHz48wRxjYmJkz549Mm7cOJkyZYqUKlVKAWHdunWlSpUqqg/2JmbPnp1AGLTLenUgEOrlB4FQPz8Sy4hLRs3xinsIzfCKQGiGT8iSQKinVwRCPX0JOquEgNAXBD8o16xZo0Bt3rx56gAY7C+Mf8qo1SWjSQGhb8xOnTpJo0aNFAhyyWjQdhrRgUCon02sEOrnCSuEZnjCCqHZPhEIzfGPQKinVwRCPX2xnNX9+/fVPr4ePXqog1uwPBQVOJwYCnArX768WgaK00g///xzef/99+XAgQOycOFCdZhMVFSUGiuYQ2XiA+H69evlsccekyeffFLSpk2rYBPVQSwrRUWSQGjZTqMaEgj1s4tAqJ8nBEIzPCEQmu0TgdAc/wiEenpFINTTF8tZ9evXT3744Yc47ceOHStPPfWUWtZ58uRJdcBLgQIF1OmiRYsWFUDksGHD5Pjx45IpUya1dDMcIMT9gzhYBncNYikUrp5o0aKF/8RTAqFlO41qSCDUzy4CoX6eEAjN8IRAaLZPBEJz/CMQ6ukVgVBPXyIiKx4qY7bNBEL9/CMQ6ucJgdAMTwiEZvtEIDTHPwKhnl4RCPX0JSKyUkB4Np1E5yoYEfNNbpNUQFjgv/J6k9rJbWrGzodAaIZ1PFTGDJ+QJQ+VMcMrAqEZPiFLAqGeXhEI9fQlIrICEC75x49SsGixiJivqZP84w+RBw8eSOrUqeJMIebBA2lSsYzUq/y8qVNLdnkTCM2wlEBohk8EQnN8IhCa4xWBUE+vCIR6+hIRWQEIcSBOu3btImK+pk4SHv3222+SLVs2U6cQMXkTCM2wmkBohk8EQnN8IhCa4xWBUE+vCIR6+hIRWREIzbCZQGiGT8iSQGiGVwRCM3wiEJrjE4HQHK8IhHp6RSDU05eIyApAePnKFXVXIR99FQAQ3r59WzJnzvxQkvnzPy4ZM2TUN/kIy4xAaIbhBEIzfCIQmuMTgdAcrwiEenpFINTTl4jICkDY9/tbci973oiYb3KbZNS9/5EhT2SRIa0aJrepGTsfAqEZ1hEIzfCJQGiOTwRCc7wiEOrpFYFQT1+0y6pZs2YyceJEKVy4sG258doJ26T0JBCvnfBE9iQHJRDq50lCGREIzfCJQGiOTwRCc7wiEOrpFYHQBl9u3rwp8+fPl4MHD8qdO3ekUKFC0qZNG6lcubIN0ZMO8dVXX8moUaNUo1SpUsljjz2mDmmpWrWqrWMnBYT4cLNo0SLBBfU4fCRHjhzy3HPPSf/+/VUOuJgel9bHfmbPnq306n4+A6+dsNUp94IRCN3T2upIBEKrSnnbjkDorf7BjM5rJ4JRy7u2BELvtA92ZAJhsIq5055AGKbO0dHR0rNnTwWBAJ+sWbPK/v37ZebMmTJw4ED5y1/+EuYIgYFwwYIFsnjxYrl//758/vnn8v7778uyZcskT548lsfGB5SoqKhE2ycFhGvXrpVt27bJO++8I/ny5ZPLly/L4cOHpUGDBn4gRP9q1ar546dOnVqWL19OILTskH4NCYT6eUIg1M+ThDIiEJrhE7IkEJrhFYHQDJ+QJYFQT68IhGH6snr1atmyZYssWbIkDlB9+OGHgj8rV66U8+fPS79+/aR+/fpy6NAhVUXE333AhBS2b98uiHX9+nUpUaKEDBgwQPLmzStnz55VfZs2bSp79uxRh3s0btxYGjVqpDJHhRBACAD0PbVr15YRI0ZIqVKlFByiEofq4d/+9jdp3769pEyZUjZt2qTANUuWLHLixAkVs2bNmrJ06VLZtWuX+p8gIHfs2LGSKVMmAdB16NBBPvroI5VjlSpVFPACIkePHq1yBRAn9ODrLVu2lOrVq8d5mUtGw3zzedydQOixAQkMTyDUzxMCoRmeJJYlgdAM/wiEZvhEINTXJwJhmN4MGjRIihYtKt26dYsTCdAECMJSUgBYp06dpGPHjvLKK6+o34706NFD3nrrLSldurSCxMmTJ8uYMWMUhK1fv152794ts2bNknPnzqm+Xbt2VVB27do19W/ERQUwPhAeOHBAhg0bpiqGqNxduXJFjYNK5pAhQ6ROnToK/gCEqGJi3LJly6r7AOfOnSvHjh1T7XPlyiU//fSTFChQQNKnT6/GLliwoAwdOlTNB8tB27ZtK3/9619lw4YNsmLFCvXvMmXKqDmkSJHCrweBMMw3mabdCYT6GUMg1M8TAqEZnhAIzfaJQGiOf6wQ6ukVgTBMXwCCqHwBmGI/MTExUqNGDZkyZYq60BsQBwhLly6darZw4UJ1Z1ifPn1k+PDhqprXvHlz9RrgDBVB7LPDDznAIKqQviWdffv2lVatWkmlSpUUEAIkUenDklHAGqqAqD7Wq1dP3nvvPSlevLiKu2PHDgWbqBoiF1QlZ8yY4U8bfcaPHy9PPvnkQ6pgfoMHD5by5cv783/w4IECYeSL2IgHoARAAg4xPh4AIZaRYpkoHoDl1KlThRXCMN98HncnEHpsQALDEwj184RAaIYnBEKzfSIQmuMfgVBPrwiEYfpitULYu3dv2bhxo380gNl3332nlnZ2795dVQ0BUr4HS0NHjhwpGTNmVEtG0d73YMxatWqpPXkAwnnz5imwA3D57orDB8OGDRuqJZ6ARTxHjx5VMVE5BBBinx9gFI+vPZa5Yh9k/Cf+HkJUBK9evapyi/0AhLG0ddy4cQqGAboAQtw1iGWmeLB8NXv27ATCMN97XncnEHrtwMPjEwj184RAaIYnBEKzfSIQmuMfgVBPrwiEYfpidQ8hKoSxYQvVPxwsgAohlnii8oZ9hfEf3x7CpIAw/h5CX4xAFcLYQIg+AEiAXGIVwtjXTiQGhL6xMV/scwQIcslomG8yTbsTCPUzhkConycEQjM8IRCa7ROB0Bz/CIR6ekUgDNMXfADDKaNFihRRy0KxPBSHtaBih3122GMHqAMUoaqHSiH+/frrr6vqIPbvffPNNzJ9+nRVrcPyTuz3w75CXB0RDhCiQnfjxg217w8x8V/k4NtDGB8I58yZow6YwV7DhPYQJgaEgFVcdwGQTJs2rezdu1dVB6EB5kMgDPNNpml3AqF+xhAI9fOEQGiGJwRCs30iEJrjH4FQT68IhDb4ggNkUKXz3UOIPXK4h/CFF15Q0X1QhwNl1qxZo/YCtmjRwn9SKNrgZM9Vq1bJpUuX1DLRcuXKqT174QAhPhxivyCAE2O+Q08aegAAIABJREFU/PLL6qRQ/D3+klHkgB+oOC0VuQAgcTgM9if6ThlNDAhx/+Ann3yi7hpE1RNXT2B+vmsmCIQ2vMk0DEEg1M8UAqF+nhAIzfCEQGi2TwRCc/wjEOrpFYHQBV8SgjoXhtV+CB4qo71FSSZIINTPPwKhfp4QCM3whEBotk8EQnP8IxDq6RWB0AVfCIQJi0wgdOHN5+AQBEIHxQ0xNIEwROFc7saL6V0WPIzheA9hGOK52JVA6KLYYQ5FIAxTQIe6EwgdEjZ2WAJh4kDY+6cHcifH4y64wCHsViDVnf/I8CKpZHCLhw9DsnssxrOmAIHQmk5etyIQeu2A9fEJhNa18rIlgdBL9YMbm0AYnF5utSYQuqU0x3lIAVQId588LxUqPE91NFfgzt07kj7d/78WBen+HvO7VC1VQkoXLaR59pGTHoHQDK8JhGb4hCwJhGZ4RSA0wydkSSDU0ysCoZ6+RERWAEJcat+uXbuImK+pk4RHv/32mzpBl4/eChAI9fbHlx2B0AyfCITm+EQgNMcrAqGeXhEI9fQlIrIiEJphM4HQDJ+QJYHQDK8IhGb4RCA0xycCoTleEQj19IpAqKcvEZEVgHDXT2elQsWKETFf3SdZKGdWqVWpwkNpEgh1d+7/50cgNMMrAqEZPhEIzfGJQGiOVwRCPb0iEOrpS0RkBSDs/dPvPFRGE7ebPTglywd2lRQpUsTJiECoiUEW0iAQWhBJgyYEQg1MsJgC9xBaFMrjZgRCjw0IYngCYRBiudiUQOii2LoMdenSJenSpYu6nN7Lh9dOeKn+w2O3jf5elvbpQCDUy5agsiEQBiWXZ40JhJ5JH/TABMKgJfOkA4HQE9lDGpRAGJJsjnciEDouccIDdO7cWc6cORPnxYoVK8ro0aOTzOjChQvSvXv3sGAuOjpadu3aJXXq1Alp9teuXZNp06bJiRMn1GlRa9askRw5cgSMNW7cOClWrJg0a9ZMtSUQBpTM1QYEQlfldmQwAqEjstoelEBou6SOBSQQOiatrYEJhLbK6WgwAqGj8oYcnEAYsnThdQQQAoyqVavmD5QyZUpJlSqVo0CIDyJRUVEhJ4/++Gbet2+f5MuXTwYPHkwgDFlNvToSCPXyI5RsCIShqOZ+HwKh+5qHOiKBMFTl3O1HIHRX73BGIxCGo55zfQmEzmmbZGQAYcuWLaV69eoPtdu9e7csXLhQ5syZIxkzZpRvvvlGJk2aJPPnz5c33nhDfvnlF8mTJ4/qN2HCBHn88cdl+/btsnr1arl+/bqUKFFCBgwYIHnz5pWzZ89Kv379pGnTpqoqiApd+/bt4ywZRcXw/fffl4MHDyog/dvf/qbaAFCxrHT//v2SJUsWVRFs3Lix1KtXT42NqwgQN36FcNmyZbJ582bBD2hcVfDmm2/KuXPnZMaMGSp+hgwZBNXQ7NmzS/fzGSQ6V0GPXOCwsRUgEJr/fiAQmuEhgdAMn5AlgdAMrwiEZviELAmEenpFIPTIl6SAEClheSUqeVgeiv1+/fv3l0qVKklCS0YPHTokkydPljFjxkihQoVk/fr1AqicNWuWArFOnTopwGvTpo269+/y5ctxgHDq1Kly5coVeeuttwRwOGTIELWcFPAHIJw5c6aKX7ZsWdXfd+hIQkD4448/yogRI2T27NkKBrFfEfPInTu3mhOXjHr0hrMwLIHQgkiaNyEQam7Q/6VHIDTDJwKhOT4RCM3xikCop1cEQo98ARACzFKnTu3PAAAGaMNz+/Zt6dq1q6oQPvXUUwoI8SQEhMOHD5dSpUpJ8+bNVRtAGyp3gDL8kMRYqNilSZNGvR7/UBlU/N577z0pXry4en3Hjh0KKlE1BBCi+ojqXvwnISA8deqUWkY6dOhQKVOmTJz5EQg9erNZHJZAaFEojZsRCDU2J1ZqBEIzfCIQmuMTgdAcrwiEenpFIPTIF0Ba3bp1pUqVKv4MsJQSAOh7sGT0o48+ksWLF6tloYkBIaqI+AZLnz69vy+AcuTIkSoelowC8HxPbCDEB8iGDRuqcbAsFM/Ro0dV37Vr1yogPHz4sAA6rQAh2mzbtk31O3/+vKpq9ujRQ1ULCYQevdksDksgtCiUxs0IhBqbQyA0w5x4WXLJqBm2EQjN8AlZEgj19IpA6JEvgZaMotI2cOBAef7559UeBsAUnl9//VVVDmNfGTFs2DApX7681K9f/6HZ+PYQJgaE6BCoQhgsEPqSQAURex8fffRR6dWrl9rvWKRIEZ4y6tF7LtCwBMJACun/OoFQf4+QISuEZviELAmEZnhFIDTDJwKhvj4RCD3yJqFTRrE3D0tI8YOtZ8+e0qBBA6lZs6b6O6ANwIc9fqjorVq1SnLmzKmyx6Ez06dPV1U8LPtEG+wrrFq1qv9QmaSAcMqUKXLjxg21zBN98d9atWr59xAmBITI8datW9KqVStZvny5OiAGS1Jx4A0+lJYsWVJiYmJk4sSJkitXLunWrZvMnTtX7ty5oyqWeHjthEdvvkSGJRDq5Uco2RAIQ1HN/T4EQvc1D3VEAmGoyrnbj0Dort7hjMYKYTjqOdeXQOictklGTugeQlTPAE0ffPCBAjlfVRDVwtdff10dEoOrHnAC6datW9VvmbG3r0CBAuoEUUAiloNimWi5cuXUXj4rFUJ8iMR+QYAlDoB5+eWXpUOHDurvCS0ZxbgA1fjPli1bBLkCTi9evKjg9umnn1b7HzNnzqxywT2LOMCmcuXKqnLIU0Y9egMmMCyBUB8vQs2EQBiqcu72IxC6q3c4oxEIw1HPvb4EQve0DnckAmG4CjrTn0DojK6MakEBVggtiORiEwKhi2I7NBSB0CFhbQ5LILRZUAfDEQgdFNfG0ARCG8V0OBSB0GGBQwxPIAxROHYLXwECYfga2hmBQGinmt7EIhB6o3uwoxIIg1XMu/YEQu+0D2ZkAmEwannblkDorf6JjU4g1NOXiMgKQNj3+1tyL9sjETFf3SfZMNUVWf5GD/89k758cY0JDgjCSbF89FaAQKi3P77sCIRm+IQsCYRmeEUgNMMnZEkg1NMrAqGevkREVgDCK1evqOs3+HivQM6cuSTX/x1UFDsbAqH33ljNgEBoVSlv2xEIvdU/mNEJhMGo5V1bAqF32gc7MoEwWMXcaU8gdEdnjpKAAgBCwEa7du2oj8YKEAg1NideagRCM7wiEJrhEyuE5vhEIDTHKwKhnl4RCPX0JSKyIhCaYTOB0AyfkCWB0AyvCIRm+EQgNMcnAqE5XhEI9fSKQKinLxGRFYBw8aETUrBo8YiYr66TTPn7f2Vyx2aSPZE9ggRCXZ17OC8CoRleEQjN8IlAaI5PBEJzvCIQ6ukVgVBPXyIiK3XK6Ln0Ep2rYETMV9dJ5rt5Wva0qCCFCxVOMEUCoa7OEQjNcSZupgRCc5zjHkIzvCIQmuETsiQQ6ukVgVBPX2zP6s6dO1K/fn3B5fFp0qSR1q1by/Dhw6VEiRIJjhXodTsS5LUTdqgYfgwFhM3LEwjDl9LzCKwQem6BpQQIhJZk0qIRgVALGwImQSAMKJE2DQiE2lgRJxECoce+7NmzR9auXSs///yzpEuXTkqWLKlg7amnnrI1s/hAuH37dqlQoYK6SmDr1q2yb98+GT16tH/M2K+Hk8jhw4dl+fLl8tNPP0muXLlk8eLF/nAEwnCUta8vgdA+Lb2ORCD02gFr4xMIremkQysCoQ4uBM6BQBhYI11aEAh1cSJuHgRCD31BtW7u3LnSrVs3qVixogJCABT+9OvXz9bM4gNh7OAJAaFdgx87dkx+/fVXuXHjhqpOEgjtUta+OARC+7T0OhKB0GsHrI1PILSmkw6tCIQ6uBA4BwJhYI10aUEg1MUJAqEWTty7d09atGghXbp0kTp16sTJCXu2UqRIoa5kWLdunWzcuFGio6Pl2WeflT59+kiWLFnk7NmzChqbNm0qqDLevn1bGjduLI0aNVKx0Hfp0qWyefNmSZs2rao6Tp069aEloxkyZFAxkQ+qhZkzZ5Y5c+bEWVIaTh6+iX311VcKBgmEWrz94iRBINTPk1AzIhCGqpy7/QiE7uodzmgEwnDUc68vgdA9rcMdiUAYroLO9GeF0BldA0Y9evSo9O/fXzZs2CAZM2ZMsP3OnTtl4cKFMn78eMmdO7dMnjxZ7t+/LyNGjFBA2KlTJ+natas0a9ZMrl27pv49f/58yZMnj2DJJ5ZkTpo0SQHkmDFj5ODBgwnuIUyoQhh7D2E4eRAIA74VPG9AIPTcAtsSIBDaJqWjgQiEjspra3ACoa1yOhaMQOiYtLYHJhDaLqktAQmEtsgYfBDs2Rs3bpxs2rQp0c5vvfWWPPPMM6oKiOfq1avyyiuvqIoh/g4YxDLMqKgo9Xrfvn2lVatWUqlSJRk6dKiUL19eVQ3xnDhxQnr37h0SEIaTB4Ew+PeG2z0IhG4r7tx4BELntLUzMoHQTjWdjUUgdFZfu6ITCO1S0vk4BELnNQ5lBAJhKKrZ0MdKhbB79+5q6eaLL77oH7FGjRqyYMECtSQUS0bXr1/vf23QoEFSq1YtqVatmtqX2L59e6lcubJ6/datW9KkSZOQgDCcPAiENrxZHA5BIHRYYBfDEwhdFDuMoQiEYYjnclcCocuChzgcgTBE4TzoRiD0QHQLQxIILYjkRBPfHkJU+WrXrh1nCN8ewkCVuaSAEBXCF154wR/7zJkz0rlz5wSBcNu2bbJ37944p4zGXjIaTh4EQifePfbGJBDaq6eX0QiEXqpvfWwCoXWtvG5JIPTaAWvjEwit6aRDKwKhDi48nAOB0ENfcOAL9vz5ThnF4S9HjhxRp4xi+eeOHTtkyZIlMmHCBMmZM6c6FObu3bv+PYRJAeHnn3+ulpZOmTJFHSqDvtgrmNA9hAcOHFB7FXGYjG/5aWwgDCePmJgYefDggQLOZcuWybx589SBOalTp1Z7HLufz8CL6T18D2JoAqHHBtg4PIHQRjEdDEUgdFBcm0MTCG0W1KFwBEKHhHUgLIHQAVFtCEkgtEHEcELg9E3cQ3j69Gl17cSTTz6p9gniHkJUCtesWaP2GeLaCOwnfO2119RpoL5TRhNbMgoQw4me+/fvlxw5cqilo7Nnz04QCHFQzbBhw+T48eOSKVMmBWqxgTCcPAC3gwcPjiMR7lqcOXMmgTCcN46NfQmENorpcSgCoccGWByeQGhRKA2aEQg1MMFCCgRCCyJp0oRAqIkR8dIgEOrpS0RkxQqhHjYTCPXwwY4sCIR2qOh8DAKh8xrbNQKB0C4lnY1DIHRWXzujEwjtVNO+WARC+7RkpCAVIBAGKZhDzQmEDgnrQVgCoQeihzAkgTAE0TzqQiD0SPgghyUQBimYh80JhB6Kn8TQBEI9fYmIrACEE46cl2yPF4yI+eo6ybTRt2RFl0aS95FHEkwRS4Z/++03tVSZj94KEAj19seXHYHQDJ+QJYHQDK8IhGb4hCwJhHp6RSDU05eIyApAiA+wzZs3j4j56jrJFClTSvYkYI9AqKtzD+dFIDTDKwKhGT4RCM3xiUBojlcEQj29IhDq6UtEZAUgBGy0a9cuIuZr6iQJhOY4RyA0wysCoRk+EQjN8YlAaI5XBEI9vSIQ6ulLRGSlKoTR/yPNmjWLiPnqOsmMGTKqE24TewiEujrHCqE5zsTNlEBojnNcMmqGVwRCM3xClgRCPb0iEOrpS0RkBSCceOSCZMvHPYReGR7z4L7UK5hV3mzZgEDolQk2jssKoY1iOhiKQOiguDaHJhDaLKhD4QiEDgnrQFgCoQOi2hCSQGiDiAwRmgI8ZTQ03ezslfK/d+X1jJdkQoemBEI7hfUoFoHQI+GDHJZAGKRgHjYnEHoofhBDEwiDEMvjpgRCjw1IZHgCoZ6+JNusLl26JF26dJFNmzbxYnoNXCYQamCCjSkQCG0U08FQBEIHxbU5NIHQZkEdCkcgdEhYB8ISCB0Q1YaQBEIbRLQ7xFdffSWjRo3yh82ZM6fUqlVL2rdvb+tQW7dulX379sno0aPjxP3xxx9l4cKF8q9//Ut9/emnn1YQV7Bg+Es7CYS2Whh2MAJh2BJqFYBAqJUdiSZDIDTDJ2RJIDTDKwKhGT4hSwKhnl4RCDX0BUC4YMECWbx4scTExMjPP/8sQ4YMkYEDB0qVKlVsyzghIDx16pT069dPXQVRu3ZtdQrohg0bZPPmzfL+++/LY489Ftb4BMKw5LO9M4HQdkk9DUgg9FR+y4MTCC1L5XlDAqHnFlhKgEBoSSYtGhEItbDhoSQIhBr64gPCZcuW+bMbOnSolC5dWl555RX55ZdfZOrUqXLmzBlJmTKlgkTA4tmzZxXM4RqH5cuXq749e/aU7Nmzy4wZM+TmzZtSr1496dSpk5w/f1769Okj9+7dUxeOZ86cWebMmaPAE/8ePHhwHGXeeecdyZgxo7z55pty9OhRmTJligJW39OtWzfBn2effVaQP/YHAv6yZs2qThFt0OB/Dy0hEOr1hiMQ6uVHuNkQCMNV0J3+BEJ3dLZjFAKhHSo6H4NA6LzGdo1AILRLSXvjEAjt1dOWaLGBEBXC06dPy6BBg+Ttt9+WZ555Rv0XyzgBWvfv3xdU9UqWLKmAEEs7mzRpIh06dJBvvvlGJk+erNr2799f7ty5Iz169FAwV7RoUYlfIcRYdevWlWHDhkmlSpXizGXnzp3ywQcfyLp16wIC4aFDhyRPnjySP39+OXHihILLSZMmSYkSJQiEtrxD7AtCILRPSx0iEQh1cCFwDgTCwBrp0oJAqIsTSedBIDTDJ2RJINTTKwKhhr7E30OIFP/6178qsEJF8N1331VVPFQLAV6+xweEOLAlTZo06sv169dX7VG5w4NK3wsvvCA1atR4CAhv374tjRo1ktmzZyt4i/1899138sYbb8jnn38eEAjjSzpx4kQpVqyYNG7cmECo2fuNQKiZIWGmQyAMU0CXuhMIXRLahmEIhDaI6EIIAqELIts0BIHQJiFtDkMgtFlQO8LFXzJ69epVVWErXLiwqvBduXJFlixZIgcOHJAcOXIoMAQw+paMrl+/3p8GqogAMvTFgwNksPS0YcOGCVYI69SpI8OHDw+rQnjs2DG1nPTChQtqTPwPFTCIqiWXjNrxDrEvBoHQPi11iEQg1MGFwDkQCANrpEsLAqEuTiSdB4HQDJ+QJYFQT68IhBr6ktAeQhzq8tFHH8XZt4clnocPH1ZLSFevXi23bt1SewitAuG2bdtk7969cU4ZTWoPIaqOqDCePHlSVR19+xQhYatWrVQFEZVI/L1z585SrVo1VdHEslWclNqxY0cCoWbvNwKhZoaEmQ6BMEwBXepOIHRJaBuGIRDaIKILIQiELohs0xAEQpuEtDkMgdBmQe0IF/uUUcS7du2aqhDi4BdU7/B62bJl1bJR7B/s3bu32tt348aNoIAQFUZcL4HDZKKiolTquHJiwIAB0qJFC0G10HfKKCATB9Ng72F0dLSCvpkzZ0qBAgVkz549MnLkSJkwYYLa44gDZKZNmyZFihRRAIiDbXCYDYHQjneHvTEIhPbq6XU0AqHXDlgbn0BoTScdWhEIdXAhcA4EwsAa6dKCQKiLE3HzIBBq6Ev8PYQAwXLlykmvXr1UpQ1gBgjDgTI4QRSniqIaF+ySUfTHATLHjx+XTJkyqZNB8eDfixYtUvcQ4hRSLEtFu1KlSvnV2rFjh6oQYvzixYvLt99+K927d1cVQhxAg9eQK/qmSJFCHnnkEQKhhu81AqGGpoSREoEwDPFc7EogdFHsMIciEIYpoEvdCYQuCW3DMARCG0R0IASB0AFRk1NIXG2BiiGutahcubKtUwOAdj+fQaJzhX/hva2JRVAwAmHyMptAaIafBEIzfEKWBEIzvCIQmuETsiQQ6ukVgVBPX7TKCpXC77//Xl1nkSpVKttyU0B4Nh2B0DZFgw+kgDDTZZnQsVminbFs+LffflNLlPnorQCBUG9/fNkRCM3wiUBojk8EQnO8IhDq6RWBUE9fIiIrAOGSf/woBYsWi4j56jjJP2JipFrJAtKuxksEQh0NCjInAmGQgnnUnEDokfAhDMsKYQiiedCFQOiB6CEOSSAMUTiHuxEIHRaY4RNXAECI6hP2QPLRVwFWCPX1Jn5mBEIzvCIQmuETK4Tm+EQgNMcrAqGeXhEI9fQlIrIiEJphM4HQDJ+QJYHQDK8IhGb4RCA0xycCoTleEQj19IpAqKcvEZEVgPDylStSt27diJiv15PMlTOn5MqVK+g0CIRBS+ZZBwKhZ9IHNTCBMCi5PG3MJaOeym95cAKhZak8b0gg9NyCBBMgEOrpS0RkBSDs+/0tuZc9b0TM1+tJ1k9xSVYO7qmuAQnmIRAGo5a3bQmE3upvdXQCoVWlvG9HIPTeAysZEAitqKRHGwKhHj7Ez4JAqKcvxmaFi+i7dOkimzZtUnNo3bq1DB8+XEqUKPHQnHjthLs2t4n+Xpb16UAgdFd2V0cjELoqd8iDEQhDls71jgRC1yUPaUACYUiyedKJQOiJ7AEHJRAGlEiPBseOHZMhQ4bI3Llz1SXveFC5eeONN+TJJ5+UTp062ZIorhdo2rSpP1bmzJnlueeek759+6rL6wM90dHRsmvXLqlTp85DQDhu3DgpVqyYNGv2v1ccEAgDqWnv6wRCe/XUMRqBUEdXHs6JQGiGT8iSQGiGVwRCM3xClgRCPb0iEOrpS4JZzZs3T06dOiUTJkxQr2/ZskU+/PBDBYlp0qQJeyb4kHL79m0FhMuXL1f7za5duyZjxoyRkiVLSs+ePYMeI3aFkEAYtHy2diAQ2iqnlsEIhFra8lBSBEIzfCIQmuMTgdAcrwiEenpFINTTlwSzwg+8rl27SsuWLaV8+fJqaebo0aPlsccek1mzZsl3330nadOmlcaNG6tL5PGcOXNGpk2bJqdPn5bUqVPLCy+8oMAOfz979qz069dPASCqeqjede/eXf175cqVkjt3bhVjzZo1cvjwYT+I1qhRQ1atWiU5cuRQr7///vuSPn166dixoyS2ZBR5zJgxQ11snyFDBqlYsaJkz55dup/PwIvpXXoPEghdEtrDYQiEHoofxNAEwiDE8rgpK4QeG2BxeAKhRaE0aEYg1MCEBFIgEOrpS6JZHT16VIYNGyZFihRRf3r06KGWc5YqVUoB2c2bN2Xw4MHq64CuX375RbAMFK/jv2+//ba89NJLCvoAhFhq2r59e2nTpo1agnrr1q04QHj9+nUFneXKlVPt8IQChNhDyAqht282AqG3+rsxOoHQDZXDH4NAGL6GbkUgELqldHjjEAjD08/N3gRCN9W2PhaB0LpW2rScPn267N+/X5YsWSIXLlyQAQMGyMcffywpU6ZUOW7YsEF+/PFHGTRo0EM5f/bZZ7Jv3z4ZMWKEAsLOnTvL5s2b/UtO4+8hRIDChQvLpEmTJGvWrARCbd4FwSdCIAxeM9N6EAjNcIxAaIZPyJJAaIZXBEIzfEKWBEI9vSIQ6ulLklkB+Hbv3q2WggLuUMHzHTSDjg8ePJCiRYvKu+++Kzdu3FB7DHEoDb6OH5r58+eXqVOn+peMrl+/3j+eDwh9S0bxAXPFihXy7bffyuzZs9UJlawQGvimERECoZm+BZM1gTAYtbxrSyD0TvtgRyYQBquYN+0JhN7oHsqoBMJQVHO+D4HQeY1tHyE2EJ48eVKdPrp27doErxPAMk2cDoq9h9hfuH37dnUYDWDSt4cwKSBE8ufOnZNXX31V7SXEvsEGDRoIDrjxQej48ePV35PaQ4glozgMB8tcecqo7W8JSwEJhJZkMroRgdAM+wiEZvjECqE5PhEIzfGKQKinVwRCPX2xXCGMiYlRB8OULl1a2rZtq6APAHfnzh11Mij2DOLaiEaNGsndu3cVPGKvYCAg9J0yimsk8PcdO3bIunXr1LJULFHFPkRcLXHx4kV1SA0gMRAQolKJvJAvHl474e6bj0Dort5ejEYg9EL14MckEAavmVc9WCH0SvngxiUQBqeXl60JhF6qn/jYBEI9fbEMhGiIg2TmzJmjTgK9f/++WhKKA2BwEimuqcD+P5wCmjFjRilevLgcOXIkIBD6EkiXLp06fRQnmj711FPqy6hKTp48WaKioiRPnjxq/2HevHkDAiEqkljeeuXKFalcubI8+uijPGXUxfcfgdBFsT0aikDokfBBDksgDFIwD5sTCD0UP4ihCYRBiOVxUwKhxwYkMjyBUE9fIiIrVgjdtZlA6K7eXoxGIPRC9eDHJBAGr5lXPQiEXikf3LgEwuD08rI1gdBL9RMfm0Copy8RkRWB0F2bCYTu6u3FaARCL1QPfkwCYfCaedWDQOiV8sGNSyAMTi8vWxMIvVSfQKin+hGeFYCw908P5E6OxyNcCXem3/TBz7JiYNcEDx9KKgPsOcXps9myZXMnUY4SsgIEwpClc7UjgdBVucMajEAYlnyudSYQuiZ12AMRCMOW0JEArBA6IiuDWlEAQLj75HmpUOF5K83ZJkwFCuXJLjWffzboKATCoCXzrAOB0DPpgxqYQBiUXJ42JhB6Kr/lwQmElqXyvCGB0HMLEkyAQKinLxGRFYAQsNGuXbuImK+pkyQQmuMcgdAMrwiEZviELAmEZnhFIDTDJ2RJINTTKwKhnr4wKypABagAFaACVIAKUAEqQAWogOMKEAgdl5gDUAEqQAWoABWgAlSAClABKkAF9FSAQKinL8yKClABKkAFqAAVoAJUgApQASrguAIEQscl5gBUgApQASpABagAFaACVIAKUAE9FSAQ6ulLss9q2bJlsmHDBsHhCi+99JL06tVLoqKikv28dZlgdHS0vPfee3LgwAHJlCmTtG7dWurVq5doel9//bXMmTNHrl+/LmXLlpU33nhDcubMqdrDx08//VQuXLggWbNmlTp16sgrr7yiy1Ql4mzeAAAQOElEQVSNzyMp7eNPzqqv06dPl82bN8uMGTPkySefNF4jHSZgVXtfroF83b59u6xYsUIuX74sjzzyiAwZMkSeeOIJHaZqfA6BtI89wbNnz8rkyZPl5MmTki9fPunbt6+ULl3a3wQe4XsJh88UKVJEevbsKSVLljReIx0nEMiL2DmvX79ePv/8czl9+rTUqlVL+vXrp+OUkk1OwXymw/fTP//5T/n1119l6NChUq1aNb8O/Dzh3VuCQOid9hE78hdffCFz586VCRMmSMaMGf0/EAAlfNxRADB48eJFefvtt+XcuXPKg7Fjx0qZMmUeSuDSpUvSqVMnGTx4sDz77LMyc+ZMuXnzpkycOFG1XbhwoZQrV059GML/sEeMGCE9evSQl19+2Z3JJONRAmkff+pWfD1x4oTMnj1bfvrpJ/VLAQKhPW8gK9r7RgrkK35RM2XKFBkwYIDy58qVK5IlSxbJmzevPclGcJRA2seWJiYmRv3sq1y5svolFyAdH3xxQjb+37Vv3z71/7FJkyZJ4cKFZfXq1bJlyxb1Xz72KhDIi/ij7dmzR1KlSiW7d++W9OnTEwjttSNOtGA/0wH68P0ydepUad++fRwg5OcJB40KEJpA6J32ETsywAK/YW3btq3SYMeOHep/svjDx3kFHjx4II0aNZIxY8aoah8efPjEM3DgwIcSWLlypRw+fFj9lhwPPpwC3vH13LlzP9QeP+RR7e3Tp4/zk0nmIwSjvRVf8aHqtddeU1UO/MYcvhMIw38TWdE+9iiBfO3evbv6Hq1Ro0b4yTFCHAUCaR+78Q8//KB+EfbRRx9J2rRp1Uu4Jgl/qlevLqhCAQp9PxuvXbsmrVq1ko8//litvOBjnwKBvEhsJPwCEyuRWCG0z4v4kUL9TNe5c2f1WSJ2hTB+bH6ecM63+JEJhO5pzZH+T4GWLVsqWMBvXfFgSUfXrl3Vb1bTpElDnRxWAEs7O3TooJZ64rfcePD3nTt3qiWE8Z9x48ZJtmzZVNXP9zRu3FhVFcuXLx+nOe4s7Natm1p+mtQSVIenmGzCB6O9FV/hM77f+vfvL7Vr1yYQ2vROsaJ97KGS8vXpp59W3qAyBb8A8VWrVpUuXbrw56MNfgXzPbV161bZuHGjWi7ve7AC4vHHH1f+AADxYRh/fBXCQ4cOCZZk87FXgUBeEAjt1TuYaKF+pgsEhPw8EYwL4bclEIavISMEqUCDBg1k5MiRgg8+eHwVpw8//FDtQePjrALYCwO4w/6KFClSqMGwFGrNmjWyYMGChwYfNmyYFCtWTP1W3PeguosPqH/+85/jtMdyD3wgAlimTp3a2YlEQPRgtA/k640bN1RlEMtFsfyQQGjfGyiQ9vFHSsrX4sWLq+81rKJAu/v376ul3VWqVFHLq/iEp0Aw31OoDKIC6FtBgZFRDUS1EJV2VJ7wM2/dunUqqRw5csj48eMVHPKxV4FAXhAI7dU7mGihfqYLBIT8PBGMC+G3JRCGryEjBKlAqL9NCnIYNk9EATurGbErhNg3A8jEXipUFPmEr0Aw1YxAviIWlgjj0B88BMLw/fFFCKR9/JGS8hUwgZ+RqET5VlHg0CYcXAKY5xOeAsF8TwWqSuFn3rZt22TUqFFqfyf2q82bN08WLVokmTNnDi9R9o6jQCAvCITevWFC/UyXFBDy84T7fhII3dc84kfE8hp8MPUdIoMNyUuXLuUeQpfeGdjv1LBhQ/WbbN9peYA4LM9IbA/ht99+6z9E5urVq+qAhdh7CFHdxdIqrPf3nT7q0nSS9TDQOJD2PgEC+dqsWbM4Wv373/9WH1pRjcL7gU/oCgTSPn7kQL42adJEfS8SCEP3JLGegbSP3Q/71t588021V9C34gHL7du0aaP2EAIuURXEMnnfg+X0qOjiAC4+9ikQyAsCoX1aBxsp1M90iQEhP08E64A97QmE9ujIKEEogENksDQRJ7NlyJBBHaeOPTI8ZTQIEcNsiiVQWKqLDy7nz59XH3pGjx7tP2UUSzVq1qypjlnH0dBYHvrWW2+p00RnzZolgELfKaPY54Tf5mEpVZ48eVRmKVOmVCe88QlPgUDaAxbPnDkjWLKDJylfAYDYj+Z78KF2+PDhaul2unTpwkuUvZPUHt9rOGgE30f43gjkK6pMx44dk3fffVcAm/jew5JR30FclDt0BQJpj0oUDsuqUKGC+n559dVX1f+fcFgM/t+F6p/vlFH87MMfnNDsqxDi/2s4IC2hA7dCz5o9A3kR/2chlvPizwcffKD+27t3b3XYGa+3sv+9FOgzXezvKYyOZfD4BTS2rqC6iO8vfF7Az0Z+nrDfH6sRCYRWlWI7WxVARfCTTz7hPYS2qmo9WOw703CwDOAg9iEwWE4IQPT9lnvv3r3qqpCE7iEEyOMDb+wHlQ0seeMTvgJJaQ8QxxUF06ZNUwMF8jV2NlwyGr43sSMkpT3gDgdpYXmh7wNpUr7iAxNOR/zyyy/VQTI4hQ8wyX259niWlPb4BWWJEiWkY8eOajD8wgW/7Dp16pQ89thj6rRK38oKQAp+uYkDuW7fvi2PPvqo2uf5wgsv2JMoo8RRICkv4v8sXLx4sVrFEvtp0aKFoCrFx34FkvpMF/97Ct9DqPjGfvBLFfwShp8n7PfGakQCoVWl2I4KUAEqQAWoABWgAlSAClABKpDMFCAQJjNDOR0qQAWoABWgAlSAClABKkAFqIBVBQiEVpViOypABagAFaACVIAKUAEqQAWoQDJTgECYzAzldKgAFaACVIAKUAEqQAWoABWgAlYVIBBaVYrtqAAVoAJUgApQASpABagAFaACyUwBAmEyM5TToQJUgApQASpABagAFaACVIAKWFWAQGhVKbajAlSAClABKkAFqAAVoAJUgAokMwUIhMnMUE6HClABKkAFqAAVoAJUgApQASpgVQECoVWl2I4KUAEqQAWoABWgAlSAClABKpDMFCAQJjNDOR0qQAWoABWgAlSAClABKkAFqIBVBQiEVpViOypABagAFaACVIAKUAEqQAWoQDJTgECYzAzldKgAFaACVIAKUAEqQAWoABWgAlYVIBBaVYrtqAAVoAJUgApQASpABagAFaACyUwBAmEyM5TToQJUgApQASpABagAFaACVIAKWFWAQGhVKbajAlSAClABKkAFqAAVoAJUgAokMwUIhMnMUE6HClABKkAFqAAVoAJUgApQASpgVQECoVWl2I4KUAEqQAWoABWgAlSAClABKpDMFCAQJjNDOR0qQAWoABWgAlSAClABKkAFqIBVBQiEVpViOypABagAFaACVIAKUAEqQAWoQDJTgECYzAzldKgAFaACVIAKUAEqQAWoABWgAlYVIBBaVYrtqAAVoAJUgApQASpABagAFaACyUwBAmEyM5TToQJUgApQATMV+P7772XFihVy5MgR+fe//y05c+aUggULSoMGDaR69eqSKlUqLSa2YMECWbx4sXz99dcqn6lTp8rWrVtl+/btjuYXf1xHB2NwKkAFqEAEKUAgjCCzOVUqQAWoABXQU4E1a9bIpEmTpFmzZtK8eXPJly+f3LhxQ7Zs2SIAobFjx0q1atVsT3727Nmydu1a+fLLLy3HdhoIE8uJQGjZIjakAlSACgSlAIEwKLnYmApQASpABaiAvQr885//lFdffVU6deokPXr0eCg4Kod3796VChUq2DuwiIQChPGTsLtCaEdOtgvFgFSAClCBZKwAgTAZm8upUQEqQAWogP4KDBw4UA4dOiSff/65pE2bNmDC33zzjcyZM0eOHz+ulpE+++yz0q9fPylUqJC/rw/S1q1bJ6NGjZIDBw5I5syZpX379tKqVSvVbvLkybJq1ao44+XOnVu2bdvmXwaK18eMGSN///vfpV69ejJ48GBVsUxoyejKlStlxIgRaslrtmzZ5JVXXpHWrVv747/99tvyyy+/yPLly+OM2aVLF5Xbe++9l2ROCVUI7dIioOhsQAWoABVIxgoQCJOxuZwaFaACVIAK6K/An//8ZwV106ZNC5gsAKhXr15qWWnHjh3lzp07ajkp4BDwljdvXhUDQIjlppUqVZImTZpI8eLF5aOPPpIZM2YomCtbtqxql1g1ztcfeQEgy5Qp49/DmBAQYqxy5copACxWrJjaTzh+/Hh58803pXHjxmosK0CYVE7xx7Vbi4DiswEVoAJUIJkqQCBMpsZyWlSAClABKqC/Ardv35aqVasqaBs6dGjAhDt06KCWj65evdrfFgfQ1KlTR+rXr68qeD4gRCUOAFilShV/W1T5AIlvvfVWQCBE/ylTpshf/vKXOHklBIQJtR05cqQ6eAawiEqm3UBotxYBxWcDKkAFqEAyVYBAmEyN5bSoABWgAlRAfwV8QNi0aVMZMmRIkgnfu3dPwR0qg6gSxn569uwpV69eFSwR9QEhlnDu378/zumkffr0kd9//11VBvEkVSEE5KF/mjRpLAEhlqWmTp3a33bXrl3y+uuvy4YNGyR//vy2AqETWuj/bmGGVIAKUAFnFCAQOqMro1IBKkAFqAAVsKSA1SWj165dkxo1agj2HGJ/XuznnXfekYMHD6p9iD4gRGVux44dcdoBOi9evChLly4NCIQbN24UQF38J6EKIaAv/kml3377rTooZ968efLcc88lCoSdO3eWLFmyqD2ESUFq7HGd0MKSWWxEBagAFUiGChAIk6GpnBIVoAJUgAqYo4DVQ2WSqoqhYnjlypU4FcKE7gYMBggTu1swsSWjgSqE48aNU4fTrF+/Po45DRs2lCJFigQFhE5oYc47hplSASpABexVgEBor56MRgWoABWgAlQgKAV810507dpV8Cf+c/ToUXV4DK6dwL45wFDs00F/++03qV27tjoFFIe44EnsKoj4QLho0SJ1aui+ffviDJvUVRKJASEqfNgP6XtwuunevXv9ewiXLVumTkf94osvJH369KrZuXPn1KEzL774oh8IE8sp/rh2axGUaWxMBagAFUhGChAIk5GZnAoVoAJUgAqYqQAOicE1EC1atFCX0+Ni+uvXrwuqdPPnz/dfTI8q3GuvvSYtW7ZUcAhQROUN0IgYjz76aFBA6NvnB1DDss6UKVMm2R8vJgSEmzdvViel+k4ZxVJV5DVo0CB1YA6ey5cvq4Nv8G/ct3jp0iW1nPTXX3+VPHny+IEwsZzij2u3Fma+c5g1FaACVCB8BQiE4WvICFSAClABKkAFwlYAF9DjIBfsvcPJoTly5FB3CwKiXn75Zf/hMAChuXPn+u8hBMjhsBgsu/Q9ViuEMTEx6p5CQNh//vMfiX8PIa6PiP8kdg8hcsfJoriHMGvWrGqfY9u2beN0R3UQJ58CDkuXLq32FeKeQ989hGicWE4J3UNopxZhG8gAVIAKUAFDFSAQGmoc06YCVIAKUAEqQAWoABWgAlSACoSrAIEwXAXZnwpQASpABagAFaACVIAKUAEqYKgCBEJDjWPaVIAKUAEqQAWoABWgAlSAClCBcBUgEIarIPtTASpABagAFaACVIAKUAEqQAUMVeD/ASBCdtJ/SfOSAAAAAElFTkSuQmCC" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "markdown", - "id": "02b42aa6", - "metadata": {}, - "source": [ - "We get the features with most gaps, those that are most important to analyse.\n", - "With date bias introduced, it is normal that date features are the most impacted. We will then decide to remove them.\n", - "Let's analyse other important variables" - ] - }, - { - "cell_type": "markdown", - "id": "298da0cc", - "metadata": {}, - "source": [ - "### Feature importance overview" - ] - }, - { - "cell_type": "markdown", - "id": "de122753", - "metadata": {}, - "source": [ - "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "94307417", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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8uP9m3mMChZD3mA0ePNg3BIFzi2WET8c19gpu2LBBteOfeC76z9FALk2bNs1woi+XZeHjuW0k3kfI+045BUaxWHY4gui/FzbY3Z7+7xlWxsbK3Ak1V83M08BnC4wAssxx5DWr98Onn35a7cU2Eks57yf2398a7Dl1zMFQfPA6CICADAEIoQxX1AoCYSdg5sOa0WiwJaOB99BxRI0/qHEUaNu2bXT06FF1IAQvtwqWWLgaNGjge8nsh2cu4KSthx56iHbt2uVr1z8SYPxw69atxAesZPUBiH8e+CGKv/XmfXRZJbcKIR+ow3LvP068F+jWW29Ve7WWL1/u69Itt9yi9toFpsArM/h1PlDmkksuUdFSFi3uP//jn1tJVuapf70cweG+WU2LFy/2LRt1Ms+4XbNz2ozsmcljZU6Gg08otmbGLlAIWQZZCo3E0Xr/E22rVauW4RAmjkD75+dyvAqADzMyUiAXrpMPEvJPfEjWCy+8kOFn/F7G0a277rqLDh065HuNf8+NKJfxQ36vC9w3yHvlONJp5/3C7Nxx+n4Y7Nn8n5tf5y/y/JeR88/4iyTee8uJ3z/892PzqgNeQhoq6ZiDoZ4Br4MACMgQgBDKcEWtIBB2AmY+rBmNBltm6f+hgT948/Ip/+V3oR54+PDhGU4cNfsByGlb/EGQZdVIgcsE+ee89ItP1vRPoSKELEosTFkltwohPy+Pnf99kyyDfNoofxD2X+aWVR85D0skR1qz+gKA2+GoC+fjU1zNJivz1L9OXroc7FCMUO2yGJQqVUrJpK45HUz2eBkkL0c00o4dO9RJm/4p1Cmj2c1Jp3xCceTXzYxdoBDyIUx8GJORuA+fffaZ77/5lGJ+7zASixrPU/9kjKHxs8DfPT75N7BdPh2Zl5H6J+Ngn0BWHLXkiHNgaty4cYb9jRw5a968ucoW+Ayh3i90vR8Ge7ZAPvw7Xa9evQzd9RdCXv3Bh+4YifcdB0Ztg80XHXPQzDxFHhAAgfATgBCGnylqBAERAmY+rHHD/GGAvxFft26d7zkCr50YOHAg8YcnKynwA5HZD0BO25KKEPKBJLy3KavkZiEMXBrKe/94T6X/vipegsdL6rJaEsv95igB7y3iZXy7d+9WV48EnuTKpxPycjuzyew8DazPbvSBo9wc2XQ6z/h5zM5p5mREkox+BEoNH2gSGJUKJYTZzUmnfMyMn5mxC3bthBF54jYChZC/0OEIn5FY3Lkv/imUELohQhjq/cLs3AnHPA313sRssxsn3RFC43fUzBxEHhAAgcgQgBBGhjtaBQHLBMx8WONK+aATPvDEPwUeKMMHj/C9e0bi6yj4Pi+OtLBA8HIq/mbffw9hoBAGLg0Ldlog1++0rcD9LpdeeqkSFP89hPxhjffB+adQEUL/b8yDDUaoD11mD5Uxy8nKhOCDHm6//fYM48PjlpKS4quGD5LhA2WsJpYdjpLwATVGCrxjL7s6zc7TwDoCT0dlwQ2MAGXXrtN5xnWbHatgEelAYeArD/iDsH8KJYTZzUmnfMzMAzNjFwkhDHz/4r4EilV2ewiDvWdY3UMY6v3C7NwJxzwN9d7EfLIbp8B9nmb3EOqYg2bmKfKAAAiEnwCEMPxMUSMIiBDI6sMaH0LAH1D5InpeqsWXQ/snPtmP7/ry36PDSyX5InIj8f47vssqX758aj8hR5p+/fXXDPUECiELGH8INhKf7sj7DFkq/WXNaVvBlr/yZdIsDBwF44vVeR9T4HJDtwihWU5WJ02oY+P5+gw+Fj9Y4qskeN8lLwHj6xt4jvCVCfxFAO9B5AM7/E8qDYzgSAghy5L/PiY+0Za/pOD9ZjynLl68qC4k5yWHfCk2X1DOe1rbtm2rHsfpPOM6zI4VR+H5CxP/6xR42SRLIO9h48NNmHHgclwnQuiUj5n55VYh5PcTPr2UTxdmpnyReiBf/73FwWSPI7odOnRQB6fweyQfvsTzyUihThkNJYRm50445qlTIczulNGaNWuqOcynTfN7K89x4/RWHXPQzDxFHhAAgfATgBCGnylqBAERAlZObzQegPc08WXm/EfeP5m9d82/TKAQBjva3T8/R5k4uue0LRYT7jvfwWcluUUIzXKy0jfOG+xDnVEHn+jIl9H7i3ngWPrv88qubb7QniNdWdUVWNaMVGTVHn/wDPwiIrtn4z2D/EUGJ6fzjOuwMlZ8oAmLspXkRAi5HSd8zDynmbGLRIQw1LPz9TO8H5avzuHE0sgCyfsMzSQ+OImvjPH/AsWMdPnXbXbuhGOemnm2UOMUeHpzVpwC7yGUnoNmxgt5QAAEwk8AQhh+pqgRBEQIWBVCjv7w/X0lSpTI9Dx84AXvM/SPEvpn4oghLxM8ceKE78eBQsjRJM6X1cE0hhCGoy3e38Yf/LM6hbJLly6Z7iVzixCa5WR10rAo8/4s/zEy6njiiSfUB+KsUuA+r6zy8b1lb7/9tqN7CINdip1Ve3xKKN+JyFFhM8lfCMMxz6yMFS+t5d9J/7vsjGdmweD5GngVilMhdMLHDE+3CiHPZ17l4L+E3egPR7P4S6/AuzL5LkS++9D/epVgDFgm+ToePmjFP5mRLv/8ZudOOOapmWcLJYTMcujQoTRjxoxsp0agEErPQTPzFHlAAATCTwBCGH6mqBEERAgEE0KO2vCHT76LjpdClS1blqpXr058bxcv4cwu8eEhfDodH9vPS6e4Dl4+yJLHp1byv+yEkOvmI+THjx+vLjk/fPhwhmWGhhByvnC0xc/C9w3y/VkshnxZee3atYmXivEpmIEf6NwihFY4WZ04gfuWjPJ87D/fvZadeK1atUpFUPjwIR47PsmV9yAy1woVKqgP2I888oharmklmZGKUPXxJfQcleSloSxevByYn4sj3rz0mcedP/DyoUB8/6KRwjHPzM5pbpNPG+X5zxd4sxjy7+B1111HLDB80A/vzfVPToXQqMsun1DczYxdKNGQOFSGl2uyuPHvP89Z3v9ssOZloHxVSlaJl0BztJyX1PM8ZxHipea8r5C/NOPl54mJiZmKm5GuwEJm547TeWrm2UKNk/HsmzZtUkucV69erb7cY+Hj3zN+D+C/AXwibDA+UnMw1BzF6yAAAjIEIIQyXFErCIAACIAACICADQJmhMdGtSgCAiAAAiCQBQEIIaYGCIAACIAACICAawhACF0zFHgQEAABjxCAEHpkoNFNEAABEAABEMgJBCCEOWGU8IwgAAK5iQCEMDeNJvoCAiAAAiAAAjmcAIQwhw8gHh8EQCDHEYAQ5rghwwODAAiAAAiAQO4lACHMvWOLnoEACLiTAITQneOCpwIBEAABEAABEAABEAABEAABcQIQQnHEaAAEQAAEQAAEQAAEQAAEQAAE3EkAQujOccFTgQAIgAAIgAAIgAAIgAAIgIA4AQihOGI0AAIgAAIgAAIgAAIgAAIgAALuJAAhdOe44KlAAARAAARAAARAAARAAARAQJwAhFAcMRoAARAAARAAARAAARAAARAAAXcSgBC6c1zwVCAAAiAAAiAAAiAAAiAAAiAgTgBCKI4YDYAACIAACIAACIAACIAACICAOwlACN05LngqEAABEAABEAABEAABEAABEBAnACEUR4wGQAAEQAAEQAAEQAAEQAAEQMCdBCCE7hwXPBUIgAAIgAAIgAAIgAAIgAAIiBOAEIojRgMgAAIgAAIgAAIgAAIgAAIg4E4CEEJ3jgueCgRAAARAAARAAARAAARAAATECUAIxRGjARAAARAAARAAARAAARAAARBwJwEIoTvHBU8FAiAAAiAAAiAAAiAAAiAAAuIEIITiiNEACIAACIAACIAACIAACIAACLiTAITQneOCpwIBEAABEAABEAABEAABEAABcQIQQnHEaAAEQAAEQAAEQAAEQAAEQAAE3EkAQujOccFTgQAIgAAIgAAIgAAIgAAIgIA4AQihOGI0AAIgAAIgAAIgAAIgAAIgAALuJAAhdOe44KlAAARAAARAAARAAARAAARAQJwAhFAcMRoAARAAARAAARAAARAAARAAAXcSgBC6c1zwVCAAAiAAAiAAAiAAAiAAAiAgTgBCKI4YDYAACIAACIAACIAACIAACICAOwlACN05LngqEAABEAABEAABEAABEAABEBAnACEUR4wGQAAEQAAEQAAEQAAEQAAEQMCdBCCE7hwXPBUIgAAIgAAIgAAIgAAIgAAIiBOAEIojdkcDf/zxB02YMIFGjhzpjgfKpU9x+vRpSkhIoLi4uFzaw8h36+LFi3TmzBkqVKhQ5B8mFz8BM+Z5HB8fn4t7GdmupaWl0alTpygxMTGyD5LLWz979izFxMSo92YkGQLp6el04sQJKly4sEwDqFUROHfuHEVFRVGePHlABATCSgBCGFac7q0MQqhnbCCE8pwhhPKMuQUIoTxnCKE8Y24BQijPGUIozxhCqIexV1uBEHpk5CGEegYaQijPGUIozxhCqIcxhFAPZwihPGcIoTxjCKEexl5tBULokZGHEOoZaAihPGcIoTxjCKEexhBCPZwhhPKcIYTyjCGEehh7tRUIoUdGHkKoZ6AhhPKcIYTyjCGEehhDCPVwhhDKc4YQyjOGEOph7NVWIIQeGXkIoZ6BhhDKc4YQyjOGEOphDCHUwxlCKM8ZQijPGEKoh7FXW4EQemTkIYR6BhpCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGMIoR7GXm0FQuiRkYcQ6hloCKE8ZwihPGMIoR7GEEI9nCGE8pwhhPKMIYR6GHu1FQihR0YeQqhnoCGE8pwhhPKMIYR6GEMI9XCGEMpzhhDKM4YQ6mHs1VYghB4ZeQihnoGGEMpzhhDKM4YQ6mEMIdTDGUIozxlCKM8YQqiHsVdbgRB6ZOQhhHoGGkIozxlCKM8YQqiHMYRQD2cIoTxnCKE8YwihHsZebQVC6JGRhxDqGWgIoTxnCKE8YwihHsYQQj2cIYTynCGE8owhhHoYe7UVCKFHRh5CqGegIYTynCGE8owhhHoYQwj1cIYQynOGEMozzo1COH36dJozZw5NmDBBHOCDDz5Iffr0oZtuukm8rZzYAIQwJ46ajWeGENqAZqMIhNAGNItFIIQWgdnMfubMGYqLi6P4+HibNaBYKAIQwlCEwvM6hDA8HLOrBUIoz9gtQtijRw+qVKkS9e3bN0OnFy5cSEOGDKHZs2eb/ruxYsUKWrduHbVr104c4DvvvENNmzalK664wlRbX375JXGfxo8fbyp/Ts8EIczpI2jy+SGEJkE5zAYhdAjQRHEIoQlIYcgCIQwDxBBVQAjlGXMLEEJ5zhBCeca5UQh1UEtJSVFfblpNEEKrxJA/RxCAEOoZJgihPGcIoTxjbgFCKM8ZQijPGEKohzGEUA/nc+fOUVRUFOXJk0dPg0FaMRsh/PTTT2np0qV0yy230LRp09TflKuvvlot2yxYsKCq2X/J6KxZs2jixIn0+eefU0xMjK/lwYMH0/nz52nQoEG0fft2tbz0r7/+ouTkZKpYsSK1b99e1WukZ555hsqXL0/Mitu/9NJLadiwYeS/ZJTrGzVqFK1du5aOHz9OpUqVogceeEDl4TRv3jxVxj/17NmT7rvvPtWP//znP7Ro0SL1XJdddhl17NiRqlevHrExCUfDiBCGg2IOqANCqGeQIITynCGE8owhhHoYQwj1cEaEUJ4zhFCeMbeQ04Twk08+oXvvvVdJG0fqBgwYQNWqVaPu3btnEsJTp07Rv//9byV+9evXV6+zcD3yyCPUr18/aty4Mf3555+0c+dOqlq1qlqW+tNPP9GUKVPoww8/pJIlS6oyLIQser169aImTZpQamoq5c2bN4MQ8me1b775RrVTqFAhJZhvv/22WgbbqFEjVU9WEULOwxHHNm3aUGJiIv3888/E8suiymKZUxOEMKeOnMXnhhBaBGYzO4TQJjgLxSCEFmA5yIoIoQN4JotCCE2CcpgNQrPCYCwAACAASURBVOgQoIniEEITkMKQJacJIUcGv/jiC4qNjVW9nzlzJs2YMUMJHKfAQ2VeeuklJW8sjpx4D9/o0aOVnGW1n50jd7feeqsvusdCyGno0KEZiIc6VOaDDz6gPXv2ED9DVkLIosnP9tVXX1G+fPl89T/99NNUt25dat68eRhGOTJVQAgjw117qxBCPcghhPKcIYTyjLkFCKE8ZwihPGNuAUIozxlCKM+YW8hpQrh48WIaM2aMDw5H0zgSx1IYTAj59bfeeksJIC+LHThwIBUrVoxYuDjxZywWt2XLltHRo0eJ30M5/etf/1LLNjmxEPKhN127ds1WCKdOnUpz586lgwcPquglJz5wxnjeYBFCFtxx48YFHWw+sCbwoB09syI8rUAIw8PR9bVACPUMEYRQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xm4Rwv79+6vomBFJM3r+9ddf0/vvv69OGeV9jsYeQo7wGYmFb8SIEcR5gwnhhQsX1LJRjvpdc8019Oijj6qTS+vUqaPy8//nKF7v3r3pkksuUdLIy0krV67sE0AWQha7J598MkshZBHkZ33uuefU8tP8+fMTL2/95ZdffKeKBhNC/hnvcWQxzG0JQpjbRjSL/kAI9Qw0hFCeM4RQnjGEUA9jCKEezhBCec4QQnnGbhFCjqAtWbKEJk2aRNHR0b6Ov/nmm7Rt2zYlWpzsCCGX48NckpKSqF69esRRPK7HaKd169b02GOP0d13363a4P2BvEyTD64xIoJmhHD48OHE8skRSCO9/PLLtH//fp8QchST70jkA2SMtHr1aiWgI0eOpBo1augZdE2tQAg1gY50MxBCPSMAIZTnDCGUZwwh1MMYQqiHM4RQnjOEUJ6xW4Rw79691KFDB3X4Cp+6mZCQQL/99psSRF7aefvttzsSwlWrVql9euXKlaMGDRqotoz0/PPPq/ZY5DgKyfLJ0UY+IdSKEHKkj0815eglHyrDh9Ow0FaoUMEnhCy9fMIpL3EtUaKE2tvIh8mwEB44cIA6d+5MVapUUaeU8hLW2rVrZzjtVM+MCF8rEMLwsXR1TRBCPcMDIZTnDCGUZwwh1MMYQqiHM4RQnjOEUJ6xW4SQn2PDhg308ccfq4ggX//A8tasWTN1qqeR7EYI+X2xZcuWdOTIESV8vBzUSCxiHN3bsWOHWi568803q2coW7asJSHkPYN87QSLHEseXxlRpkwZJbbGRfT8WYMPpuGf8Wc749oJ3sfJfeflryyDRYoUUdHCdu3aqefIqQlCmFNHzuJzQwgtArOZ3V8Iz6Sm064zqXTofCr9dSKFki+m0xWJcVQhfyyVyhNDRRP+f8+OzeY8WQxCqGfYcaiMPGcIoTxjbgFCKM8ZQijP2E1CqKe3aEUnAQihTtoRbAtCqAe+IYQn0mLoy+2n6JfD54M2fHnBOOpRLZGKQwotDwyE0DIyWwUghLawWSoEIbSEy3ZmCKFtdKYLQghNo3KU0Q2njDrqAAq7lgCE0LVDE94HgxCGl2dWtbEQno/NSwNWHaOzqX8fh5xdGlSnGFUp+Pf9PEjmCEAIzXFymgtC6JRg6PIQwtCMwpEDQhgOitnXASGUZ8wtQAj1cPZiKxBCj4w6hFDPQB85fZ7Gbj1LG0/+fadNqFQkPppev7oYFY7//0ldocp4/XUIoZ4ZACGU5wwhlGfMLUAI5TlDCOUZQwj1MPZqKxBCj4w8hFDPQH+z4yR9vuusOv3KbLqpZB7qfGWi2eyezwch1DMFIITynCGE8owhhHoYQwj1cEaEUA9nL7YCIfTIqEMI9Qz06D+P0+IjyZaE8NICcdSnRmEqiiihqUGCEJrC5DgThNAxwpAVQAhDIgpLBkQIw4Ix20oghPKMESHUw9irrUAIPTLyEEL5gT5+IY3eWneUtp+5aEkI88ZE0dC6xagYDpgxNUgQQlOYHGeCEDpGGLICCGFIRGHJACEMC0YIoTzGkC0gQhgSETLYJAAhtAkupxWDEMqP2JHzF2nAisN0Jo0sCSE/2et1ilLlgnHyD5kLWoAQ6hlECKE8ZwihPGNuAUIozxkRQnnGiBCGl/FLL71ExYsXpx49eqiK+f2YL6H/9ddf1b2DY8aMoSuuuCK8jbq4NgihiwcnnI8GIQwnzeB1nUxJoxF/HKO/TqVaEsLCcdE06OqiuILC5BBBCE2CcpgNQugQoIniEEITkMKQBUIYBoghqoAQyjN2ixDyZe0LFixQHY6OjqaCBQtSpUqVqHHjxnT33XdTTIz5O5Y3bdpE3bp1oxkzZlCBAgVMQ+QL6h9//HFffr6kvkSJElSrVi166KGH1POEStwmP3uTJk1U1kWLFtFbb72lLqwvVqwY7dmzR8lids/Gl9h/9dVX2TY1depUKlq0aKjHyfS6XTaWG/qnAITQLrkcVg5CqGfAxm88Tj8esraHsFpiPPWsWogS482/ierpjTtbgRDqGRcIoTxnCKE8Y24BQijPGUIoz9hNQrhz507q378/GeO+Zs0amjZtGlWuXJmGDBlC8fHxpoDYlR5DCJ955hm68sor6cKFC0rg5syZQ+vWraPnnnuObrzxxqDPkJqaSrGxma/7+vLLL+m///0vvffee6qcmWc7evQoJSUl+drp27cv3X777dS0aVPfz1hOrUiyUdBM+6Ygm8wEITQJKqdngxDqGcFlh87QyI2nLEUIm1fKT/eVN//NmJ6euLcVCKGesYEQynOGEMozhhDqYQwh1MPZDXsIOUJ48OBBGj58eIZOb926lbp27UpPPPEEPfroo+q1Tz75hH744QdigUtMTKSGDRvSk08+SRzRC4zycX6OMj7//PNq2eYXX3xBLJ4sUzVq1FCRxNKlS6t6jbLDhg2j2rVrZ3iOV199lVavXk2ffvqpaof/d+nSpartb7/9lo4dO0bz5s0j/yWjr7zyimrTSBxtPHz4cIZ6jWfLbqSbNWtG//73v+mxxx5T2fjv6H/+8x8VfTx//jxddtll1LFjR6pevbp6fe/evfTuu+/Shg0biEWV+9ehQweqWLFihgioPxupmQYhlCLrsnohhHoG5OTZ8zRtXzIt2H/eVIMcHexVLZEKxeEeQlPAiAhCaJaUs3wQQmf8zJSGEJqh5DwPIoTOGYaqAUIYilB4XnezEHIPWeY4YsaSw+nzzz9X8lOyZEk6dOiQ+vlVV11F3bt3V69nFQX7/vvvKW/evEqMWKSmTJmiIoAcveNlqtkJoVHn66+/Ttdee60SwsmTJ9Odd96pZIyvBcuXL18GITSe9eeff6axY8dm+2xWhJAjhnFxcdSmTRslxFw/P8+ECROoVKlS1Lt3b7U8tW3btiofCzD3m5e+IkIYnt8Z1BJAAEKoZ0rwRuSTUfH05a4ztOzIhWwbLZ8vlp6qXpjK5MVSUSujAyG0Qst+XgihfXZmS0IIzZJylg9C6IyfmdIQQjOUnOdxuxCy6MydOzfLfXUrVqygwYMH0/Tp0y1JF0vhAw88QOPGjVPLUrMTQl4+es8996j9f/fff78SMBZTXhLKEUMjBR4qw3nCKYRr166lAQMGKBYsoEZ6+umnqW7dutS8eXNq0aKF+t/77rsv0+SAEDr/fUENQQhACPVMCxbChIQEOpseQ6uOJdOU7afpdGpahsbzxUbTraXy0B2X5KPieSCDVkcGQmiVmL38EEJ73KyUghBaoWU/L4TQPjuzJSGEZkk5y+d2IeTlkfPnz1fyxWnJkiUqurdr1y61l9dIM2fOVHKWlfTs2LGDPvjgA7WU8sSJE75yr732Gl133XXZCmFycjLde++91LNnTyVaLIQsenwAjH+SFkLeU8kCGyzxHkOOHnKe999/X0VRWRJvuOEGJbycIITOfldQOgsCEEI9U8MQQg79czpw7iKl8zrxMymUnEZUPn8ssQMWjo+h+OgoPQ+Vy1qBEOoZUAihPGcIoTxjbgFCKM8ZQijPmFtwuxDyklEWuNGjR9Pu3bvVfjje+9eoUSMqVKgQrV+/nvr06eM7uTOY9PBcatWqlRIkjqDx1RC8TJRPMH3hhReUNJlZMsqRyPr16/v2EPIz6RRClmKOOrL0ZZf279+v9jiuXLmSli9fTl26dKEHH3wQQqjnV8p7rUAI9Yx5oBDqadVbrUAI9Yw3hFCeM4RQnjGEUA9jCKEezm4WQuNQmfbt26uDVRYuXKj2yrEUGYmvcOA9esZVDtu2baNOnTopaWJh5MSHubAIfvzxx1S2bFn1MyPfyy+/HFIIBw0aRHzqaeChMlaFMNizhRpl/0Nl+GCbfv360ciRI9WhOGYS8+JltRxZtNO+mTayyoNDZZzQy0FlIYR6BgtCKM8ZQijPmFuAEMpzhhDKM4YQ6mEMIdTD2S1C6H/txMmTJ9WpnoHXTnD0j5dtvvPOO+qC9+3bt6tDZ/hwGUMIT506RY888ohaPskHwPB1FfzvX//6lzplkwWL8/ApoLwnj5d5+kcIjWsnUlJS1KEzs2fPVvmMSCKPinHKqFUhDPZs/nsBg424vxDy7wQLIUczO3fuTFWqVKHjx4/TsmXL1MmoV199tWLDp5eWKVNG9ZPvQOT/P3DgQPXfgWxCte9kFkIIndDLQWUhhHoGC0IozxlCKM8YQqiHMYRQD2csGZXnDCGUZ8wtuEUI/S+m5wvled8bi81dd92V4Y4/lkTj4nY+aZRP+nz77bczXPbOSys5D18HYVzt8Pvvv9OYMWPUF5P58+dXS0hHjBihDmnxF0KDOkskXxXBosXLLY19eE6EkMsGe7bsRjrw2gkeL4508h5GlsEiRYqoaGG7du1U9JPvbOR7E7nv3E9e4spLRgsWLKiasdq+k1kIIXRCLweVhRDqGSwIoTxnCKE8YwihHsYQQj2cIYTynCGE8ozdIoR6eopWdBOAEOomHqH2IIR6wEMI5TlDCOUZQwj1MIYQ6uEMIZTnDCGUZwwh1MPYq61ACD0y8hBCPQMNIZTnDCGUZwwh1MMYQqiHM4RQnjOEUJ4xhFAPY6+2AiH0yMhDCPUMNIRQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xhBCPYy92gqE0CMjDyHUM9AQQnnOEEJ5xhBCPYwhhHo4QwjlOUMI5RlDCPUw9morEEKPjDyEUM9AQwjlOUMI5RlDCPUwhhDq4QwhlOcMIZRnDCHUw9irrUAIPTLyEEI9Aw0hlOcMIZRnDCHUwxhCqIczhFCeM4RQnjGEUA9jr7YCIfTIyEMI9Qw0hFCeM4RQnjGEUA9jCKEezhBCec4QQnnGXhLC6dOn05w5c2jChAl6wKIVghB6ZBJACPUMNIRQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xm4RwqFDh5L/xfR84fzNN99Mjz/+OPEF8eFIEMJwULRWB4TQGq8cmxtCqGfoIITynCGE8owhhHoYQwj1cIYQynOGEMozdpMQ7ty5k/r370/89/jPP/+kMWPG0H333UddunQJCwgIYVgwWqoEQmgJV87NDCHUM3YQQnnOEEJ5xhBCPYwhhHo4QwjlOUMI5Rm7SQgPHjxIw4cP93V65MiRtHLlSpo4cSK1aNGCHnvsMXrwwQd9r+/Zs4fatWtH7733HlWpUoU++eQT+uGHH+jAgQOUmJhIDRs2pCeffJLy5MmjygQK4dixY2nfvn302muv+eqcOXMmffnllzRp0iTfz5YtW6b+e/v27VSkSBFq3LgxtW3bNmyRSz2jHJlWIISR4a69VQihHuQQQnnOEEJ5xhBCPYwhhHo4QwjlOUMI5Rm7WQhZ9H788UeaOnUqvf/++7Ru3ToaPXq0DwqL4s8//0wffPCB+tnnn39O1atXJ15ueujQIXr33Xfpqquuou7du9sWwlWrVtGLL76oopR16tShY8eOqWeoWbMm9ejRQ88A5eBWIIQ5ePCsPDqE0Aot+3khhPbZmS0JITRLylm+M2fOUFxcHL5ZdYYx29IQQkG4flVDCOU5QwjlGbtRCPk9bMOGDUrE6tWrRwMHDqStW7dS586d6eOPP6ayZcsqMG3atKGmTZtS8+bNg4JasWIFDR48WEUGOdmJED799NNUrVo1at++va+NtWvX0oABA2jWrFkUHR2tZ5ByaCsQwhw6cFYfG0JolZi9/BBCe9yslIIQWqFlPy+E0D47syUhhGZJOcsHIXTGz0xpCKEZSs7znDt3jqKionxLK53XaL0G41AZFiwed/533XXXEQtZ4cKFVYW8/JOXa/JBMyyMPXv2VMtES5UqpV5fsmQJTZkyhXbt2kX8+2kkXgbKy0btCOEDDzyQoS7/nnFbJUqUsN5ZD5WAEHpksCGEegYaQijPGUIoz5hbgBDKc4YQyjPmFiCE8pwhhPKMuQW3CCEfKsMCGBsbq0TL2PtnUPjss89o3rx5KkrIB85w1HDEiBHq5d27d1OHDh2oW7du1KhRIypUqBCtX7+e+vTpQzNmzKACBQpkEkJekrp3794Mewi/+eYbmjZtmm8PIR9qw9FB/72LekYld7QCIcwd4xiyFxDCkIjCkgFCGBaM2VYCIZRnDCHUwxhCqIczhFCeM4RQnrGbhDDwUJnA3vO+wJYtW9I777yjlpPygTJ33323yrZw4UJ1vyDvIzQSiyAfHJOVEHLeRYsWZdiXyKJpHCLD9bBQ8jaHIUOG6BmMXNYKhDCXDWhW3YEQ6hloCKE8ZwihPGMIoR7GEEI9nCGE8pwhhPKMc5IQ8rP27t1bRec5IvjFF1+oyB+nTZs2qSWkLItXXHGFOhH0+eefV4fLZCWEf/31lyrDp5nyPkE+tIZFs2DBgr4IIR8qw1dhcKSQ5TMhIUHVzfsIORqJlD0BCKFHZgiEUM9AQwjlOUMI5RlDCPUwhhDq4QwhlOcMIZRnnNOEcPbs2UrgbrzxRnrppZcyAOKlnl999ZX6GZ80euedd9Lbb7+dpRByPt5XyNdMpKamUo0aNeiyyy6jBQsWZLh2gqWQr53YvHmzOkSmXLly1KRJE2rWrJmeAcrBrXhSCPmPA69lXrp0qfrGgsPa/I1CsPT777+rjbA8uYoXL04fffRRjhxuCKGeYYMQynOGEMozhhDqYQwh1MMZQijPGUIoz9gtQqinp2hFNwFtQnjDDTdY6huvFZZKLIN8wSWHqDmU/eyzz6rjbmvVqpWpST4daf/+/eo+E/62A0IoNSq5o14Iofw4QgjlGUMI9TCGEOrhDCGU5wwhlGcMIdTD2KutaBNC3kBqJfGRtRKJQ80PPfQQvf766+oSTE7Dhw9X/9u3b98sm+QLNVkGIYQSo5J76oQQyo8lhFCeMYRQD2MIoR7OEEJ5zhBCecYQQj2MvdqKNiF0C2A+trZt27b09ddfU/78+dVj8f//73//qza4ZpWyEkL+cJoTEi8ZZZk15DcnPHNOfEb+4BEfH6+OYkaSIcC/c3z0trFBXaYV1MqMeR7zqW1IMgRYCPl6Dz4YAUmOAM/lmJgY9d6MJEOAhfDUqVPqCgEkOQLnz59X9xDygSlZJZ7rSCBglYDnhHDLli3UpUsXmj9/vvql4sSbUqdOnaqOwbUqhCdOnLDKPCL5eenrp59+muEOl4g8SC5vlD/g8bwy5lYu727EusececM4khwBzGU5tv41Yy7Lc2ZZ4YT3ZVnWmMuyfLl2M3M5MTFR/kFc0MKRI0fUoTV8AmlSUpL6HF+0aFEXPFnOfISICSFfQskCtmbNGmKpWrlypSLI+/tat26tDnCRSOGOEEo8o0SdOFRGgmrmOrFkVJ4zlozKM+YWcDG9PGcsGZVnzC1gyag8ZywZlWfMLbjhYno9PQ3dytGjR2nx4sVUtmxZdd0EhDA0s+xyREQIly9fTt27d1eHuNStW1eJoSGEHMVi6+/Vq5eznmVRmvcQPvjgg/Tmm29SzZo1fRLKb2bYQyiC3FOVQgjlhxtCKM8YQqiHMYRQD2cIoTxnCKE8YwhhcMYcVHrkkUcghA6nYESEkPfwNWzYkDp27Kgen6XQEMIdO3ZQjx49aObMmQ67lnVx3kfHF2DyKaN79uyhAQMGqKWULKjGxZgdOnRQS9L4DzZL5K+//qruNnn//ffVspOctq8GEUKx6ZShYgihPGcIoTxjCKEexhBCPZwhhPKcIYTyjN0ghByh5H+RSHnz5iX+F5gghOEZjYgIYYMGDWjevHlkrHP2F0LeMNu4cWNatmxZeHoYpBb/ewj5YJlWrVr57iHkvXY9e/akuXPnqk3ofA8hh6L9U9WqVWn06NFizydRMYRQgmrmOiGE8pwhhPKMIYR6GEMI9XCGEMpzhhDKM3aDEHJg5OPPplJqjN7DxmJSU6hdi0fVljIIocxci4gQsvBNmTJFrfvl5C+E27ZtU5HDhQsXyvTYo7VCCPUMPIRQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xm4Rwld+30fbajTR0+F/Wrl0/ff00jVlIISC1CMihL1791ZHxr/88ssqCmcIIX/Q42WcvCSTL4pHCh8BCGH4WGZXE4RQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xhBCCKHkLIuIEG7evFndBVi6dGm66aab1N68Tp060U8//US7d++myZMnU8WKFSX77bm6IYR6hhxCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGM3CeF2zRHCyllECC9cuEAnT56k5s2b0yeffEJFihTBfaM2p2JEhJCfddOmTTRq1ChasWKFOrSFD3DhSCGf9Hn55Zfb7A6KZUUAQqhnbkAI5TlDCOUZQwj1MIYQ6uEMIZTnDCGUZwwhzBgh5M8CTZs2zQR+9uzZkEIb0zFiQmg8a0pKirJ7XkKakJBgowsoYoYAhNAMJed5IITOGYaqAUIYilB4Xsc9hOHhmF0tEEJ5xtwChFCeM4RQnrFbhHDQ7/tpe029ewgrr19IL14dfMmoHvK5v5WIC2HuR+yOHkII9YwDhFCeM4RQnjEihHoYQwj1cIYQynOGEMozhhBCCCVnmTYhtHrRPC8nRQofAQhh+FhmVxOEUJ4zhFCeMYRQD2MIoR7OEEJ5zhBCecYQQgih5CzTJoSdO3fO0A/+4Lxx40YqX748FS1alI4dO0a7du2iatWqqeWj48aNk+y35+qGEOoZcgihPGcIoTxjCKEexhBCPZwhhPKcIYTyjN0khDs0LxmthCWj4hNMmxD694Qvfx8/fjz16dOHKlSo4HuJhXD48OHUpUsX4svfkcJHAEIYPpaIEOphmVUrEEI9/LGHUJ4zhFCeMbcAIZTnDCGUZwwhRIRQcpZFRAhbtWpFr776KlWuXDlT37Zu3UqDBg2iiRMnSvbbc3VDCPUMOSKE8pwhhPKMESHUwxhCqIczhFCeM4RQnrFrhHDVftIeIfwDh8pIz7CICGGDBg1o7ty5VLhw4Uz9S0pKorvuuouWLFki3XdP1Q8h1DPcEEJ5zhBCecYQQj2MIYR6OEMI5TlDCOUZu0UIX1VCeLueDv/TSqU/FtILV5em1q1ba23XS41FRAibNWtGd9xxBwXuK2TwY8eOpe+//56mTZvmpXEQ7yuEUByxagBCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGMIIYRQcpZFRAjnz59PAwcOpDp16lDDhg2pSJEi6lCZRYsW0dq1a2nIkCHUpIneO04kIbuhbgihnlGAEMpzhhDKM4YQ6mEMIdTDGUIozxlCKM/YTUK4U3OEsCIihOITLCJCyL1avny5OliGRYUvp4+Li6NatWqpqGHdunXFO+61BiCEekYcQijPGUIozxhCqIcxhFAPZwihPGcIoTxjCCEihJKzLGJCaHTKeBNJTEykqKgoyb56um4IoZ7hhxDKc4YQyjOGEOphDCHUwxlCKM8ZQijP2FVCWEvvHkIVIawDIZScZREXQsnOoe7/E4AQ6pkNEEJ5zhBCecYQQj2MIYR6OEMI5TlDCOUZu0UIX1t1gHZqF8IF9DyEUHSSRUwI9+/fr66WWLlyJZ04cYI4QlivXj1q06YNlS5dWrTTXqwcQqhn1CGE8pwhhPKMIYR6GEMI9XCGEMpzhhDKM4YQIkIoOcsiIoTbtm2jdu3aUWpqqjpYpmjRoupQmdWrV1N8fDx99NFHVKlSJcl+e65uCKGeIYcQynOGEMozhhDqYQwh1MMZQijPGUIozxhCCCGUnGUREcKnnnpKHSQzePBgFRk0EkcKn332WXXAzMiRIyX77bm6IYR6hhxCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGM3CeEuzUtGK/yBJaPSMywiQtioUSP67LPPqFy5cpn6t2fPHmrRogX9/PPP0n33VP0QQj3DHSiEp1LS6EJaOqWlp9PGE8lUvXAe4j+cheJjKT5azzPltlYghHpG9MyZM+rLOV61gSRDAEIowzWwVgihPGcIoTxj1wjh6gOkXQjXQQilZ1hEhPD666+nWbNmUbFixTL17+jRo3TffffR4sWLpfvuqfohhHqG218ID51LpfEbj9GWU8m0/2yq7wEuyR9HVQvFU8eqxahoQoyeB8tFrUAI9QwmhFCeM4RQnjG3ACGU5wwhlGfsFiF8XQnhHXo6/E8rFdYtoOfqlKLWrVtrbddLjUVECHn/YPXq1alfv36ZWA8fPpzWr19PH374oZfGQbyvEEJxxKoBQwjXnbxIr60+RKdTLmbZcPE8sfRCnZJUrXCCnofLJa1ACPUMJIRQnjOEUJ4xhFAPYwihHs7nzp1TV7TlyZNHT4MBrUyaNIkghBFBL95oRIRw6dKl1KNHD7rsssuIl48WKVKEjh8/Tr/++itt2bKFxowZQ9dee614573UAIRQz2izEJ6NSaBWP+0x1WCemGj6sFFZYjlEMkcAQmiOk9NcEEKnBEOXhxCGZhSOHIgQhoNi9nVACOUZcwsQQkQIpWZaRISQO8NSOG7cOGJR4TcS/sajZs2a1LVrV8igwGhDCAWgBqny4Olz9Pra47TxZLLpBhuVzkddgZPW5AAAIABJREFUqxanYnmwfNQMNAihGUrO80AInTMMVQOEMBSh8LwOIQwPx+xqgRDKM3aTEO7WvGS0PJaMik+wiAmh0bPk5GQ6deoUFSxYkBISsHROasQhhFJkM9a77cQ56rR4v/qCw0qacnMFKg4hNIUMQmgKk+NMEELHCENWACEMiSgsGSCEYcGYbSUQQnnGrhLCq/TuIVRCWBt7CCVnWcSFULJzqPv/BCCEembDj3tP0utrj1gWwtfrlab6xfPqecgc3gqEUM8AQgjlOUMI5RlzCxBCec4QQnnG7hHCg7RbuxDOhxAKTzFtQrhixQpLXalXr56l/MicPQEIoZ4ZMmT1QVq4/4xlIfxX5cLU4coieh4yh7cCIdQzgBBCec4QQnnGEEI9jCGEeji7YQ/h4NWREcJnESEUnWTahLBu3bqWOrJy5UpL+ZEZQuiGOTBp01GavPWEZSHsW6sE3Vm2gBu64PpngBDqGSIIoTxnCKE8YwihHsYQQj2cIYQ4VEZqpmkVQj4mt3HjxnTzzTdT3rzZL4/j00eRwkcAEcLwscyupmUHT9Pzvx+yLIRjbyhLVQriAnAzowQhNEPJeR4IoXOGoWqAEIYiFJ7XsWQ0PByzqwVCKM+YW3CLEO7RvGS03Lr5hAih7BzTJoSLFi2i7777jn744QfVI5bCpk2bEl9SHxuLI/dlh5nUaa4TJkygkSNHSjfl6fq3JZ2lTksOWBLCuJhoGnt9GapYAEJoZvJACM1Qcp4HQuicYagaIIShCIXndQhheDhCCOU5hmrBFUK45iBpF8K1EMJQc8Pp69qE0HjQ8+fPKymcO3cuLVmyRJ0u2qRJE7rrrruodu3alj5IO+28l8pDCPWMdtKZszRrXzJN2ppkusGnahSnu8sXNJ3f6xkhhHpmAIRQnjOEUJ4xtwAhlOeMCKE8Y24BQoglo1IzTbsQ+nckKSmJ5s+fryKHa9eupTJlytCsWbOk+urpeiGEeoafL6Y/F5NAI/44SiuPngvZ6C1lClDnakWpSDzuIAwJ658MEEKzpJzlgxA642emNITQDCXneSCEzhmGqgFCGIpQeF53gxC+oSKEd4anQyZrKbd2Pg2sXZJat4YQmkRmOVtEhZCflkWQhXDOnDnqm4/ffvvNcidQIDQBCGFoRuHIwULI92keS42mL3Yk0cydJ7Os9l+XFqa7yhagcvnjwtG0Z+qAEOoZagihPGcIoTxjbgFCKM8ZQijPmFuAEEIIpWZaRIRw+/btSgJ52ejevXupVq1aasnoHXfcQUWK4Oh9icGGEEpQzVynIYRxcXGUnJZOa44l04ak87TtZDLtPnOBKhZIoMoF46l64QSqWTSB8kRH63mwXNQKhFDPYEII5TlDCOUZQwj1MIYQ6uEMIYQQSs00bUJ46NAhmjdvnhLBv/76iypXrqwOlWERLFu2rFT/UO8/BCCEeqaCvxD6t3g6NY3OpqRRvrgYKhAbpedhcmkrEEI9AwshlOcMIZRnDCHUwxhCqIezW4Rwr+Ylo2WxZFR8gmkTQr5onpfS3XTTTUoCr7zyymw7V6pUKfHOe6kBCKGe0c5KCPW07o1WIIR6xhlCKM8ZQijPGEKohzGEUA9n1whhbb17CJUQXoU9hJKzTJsQ4mJ6yWEMXTeEMDSjcOSAEIaDYvZ1QAjlGXMLEEJ5zhBCecYQQj2MIYR6OLtBCN9cc4j2ahfCeTQAQig6ybQJ4dSpUy115NFHH7WUH5mzJwAh1DNDIITynCGE8owhhHoYQwj1cMahMvKcIYTyjLkFCCH2EErNNG1CKNUB1GuOAITQHCenuSCETgmGLg8hDM0oHDkQIQwHxezrgBDKM0aEUA9jCKEezhBCCKHUTIMQSpF1Wb0QQj0DAiGU5wwhlGeMCKEexhBCPZwRIZTnDCGUZ+ymCOE+zUtGL1mLJaPSMwxCKE3YJfVDCPUMBIRQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xq4RwrWHSLsQroEQSs8wCKE0YZfUDyHUMxAQQnnOEEJ5xhBCPYwhhHo4QwjlOUMI5Rm7Swib6unwP61cooSwBLVujSWjUuAhhFJkXVYvhFDPgEAI5TlDCOUZQwj1MIYQ6uEMIZTnDCGUZ+wWIRyiIoT6hbA/hFB0kkEIRfG6p3IIoZ6xgBDKc4YQyjOGEOphDCHUwxlCKM8ZQijPGEKICKHkLIMQStJ1Ud0QQj2DASGU5wwhlGcMIdTDGEKohzOEUJ4zhFCesZuEcL/mCGGZNfMIEULZOaZNCHv16mWpJ6NGjbKUH5mzJwAh1DNDIITynCGE8owhhHoYQwj1cIYQynOGEMozdpUQ1tG7ZFQJYS1ECCVnmTYh7Ny5c4Z+8AfnjRs3Uvny5alo0aJ07Ngx2rVrF1WrVo0KFChA48aNk+y35+qGEOoZcgihPGcIoTxjCKEexhBCPZwhhPKcIYTyjN0jhIdpv3YhnAshFJ5i2oTQvx8bNmyg8ePHU58+fahChQq+l1gIhw8fTl26dKGqVasKd91b1UMI9Yw3hFCeM4RQnjGEUA9jCKEezhBCec4QQnnGbhHCoWsjI4TPIEIoOskiIoStWrWiV199lSpXrpypc1u3bqVBgwbRxIkTRTvutcohhHpGHEIozxlCKM8YQqiHMYRQD2cIoTxnCKE8YwghloxKzrKICGGDBg1o7ty5VLhw4Ux9S0pKorvuuouWLFki2W/P1Q0h1DPkEEJ5zhBCecYQQj2MIYR6OEMI5TlDCOUZQwghhJKzLCJC2KxZM7rjjjsocF8hd3Ts2LH0/fff07Rp0yT77bm6IYR6hhxCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGPXCOG6w3RA8x7C0qvnEpaMys6xiAjh/PnzaeDAgVSnTh1q2LAhFSlSRB0qs2jRIlq7di0NGTKEmjRpIttzj9UOIdQz4BBCec4QQnnGEEI9jCGEejhDCOU5QwjlGUMIESGUnGUREULu0PLly9XBMiwqKSkpFBcXR7Vq1VJRw7p160r22ZN1Qwj1DDuEUJ4zhFCeMYRQD2MIoR7OEEJ5zhBCecZuEcK3VITwLj0d/qcVjhD2q1WcWrdurbVdLzUWMSE0IBtvIomJiRQVFeUl9lr7CiHUgxtCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGMIIYRQcpZFXAglO4e6/08AQqhnNkAI5TlDCOUZQwj1MIYQ6uEMIZTnDCGUZwwhhBBKzrKICeH69etpwoQJtGbNGjpx4gStXLlS9XPEiBEqJFy8eHHJfnuubgihniGHEMpzhhDKM4YQ6mEMIdTDGUIozxlCKM/YTUJ48Gq9S0ZL8ZLRmhBCyVkWESHk/YPdu3dXewZ5vyCLoSGEn376KR05coR69erlqN+TJk2ir7/+mvjD42233UbdunWjmJiYoHXu2rWLhg0bRlu2bKGyZcuqtmvWrKny8h+Sd955h3777TfiP958EA6/zgfh5KQEIdQzWv5CeCIljdLS0ul8GlFCNFFMdBTFRkVT/lg9z5JbW4EQ6hnZM2fOqL3d8fHxehr0YCsQQj2DDiGU5wwhlGcMIYQQSs6yiAhh27Zt1emiHTt2VH1jKTSEcMeOHdSjRw+aOXOm7X7ztRV8YA2fVpo/f3569tln6ZZbbqGWLVtmqpP/ILdv3149T4sWLWjBggXEMjl58mRV9r333qN169bRq6++qj4YGXXyKak5KUEI9YwWC2F0fB7adCaN5uw5TfvOXqQjyRcpmojK5I2lYnliqH2VQlQqb/AvJ/Q8Zc5uBUKoZ/wghPKcIYTyjI0vdvkL4YSEBD0NerAVCKGeQT937pw6byNPnjx6GgxohT8fv7XuCOmPEH6HCKHwiEdECPli+nnz5hEfJBMohOfPn6fGjRvTsmXLbHe9f//+KsL3+OOPqzoWLlyoJI//BSZeusr5+d5D448FL1nlf3z1xUsvvURVqlTx1cWy+cUXXyjhzEkJQqhntPaePEfzD6bQvP1ns22wR9XCVKNwAhWOx0FKVkcGQmiVmL38EEJ73KyUghBaoWU/LyKE9tmZLQkhNEvKWT43COGwCAnh01gy6mzyhCgdESFk4ZsyZYpanhkohNu2bVORQ5Y4u+mxxx6jnj17qqgfp+3bt6s6Z8+enWn505w5c+jbb7+lcePG+Zp75ZVXqFy5cipyuGLFCvrkk0/ohRde8EUIL730UnriiSfsPl5EykEI5bGfTkmjz7edpAUH/v4GL1R6qnphalA8Mt/yhXo2N78OIdQzOhBCec4QQnnG3AKEUJ4zhFCeMbcAIcS1E1IzLSJC2Lt3bypQoAC9/PLLal+fsWSUP+g9//zz6sP04MGDbff5gQceoEGDBlHt2rVVHYcOHVLLRb/66itfVNKonCODixcvpuHDh/va4/2EHC3kpatJSUlqmSiLIadq1aqp/86bN6/6b349J6QNGzYoCeelr0jhJ8DzeOPZaBq6PokotAuqB4iJiqJR9YtRQsq58D9QLq+RP3yYke5cjkG0e2AsitdXOTjLc2bGnPCeIcsac1mWL9duZi4XLlxY7EF4pd2wP47QIc2HypRc9R0hQig2rH+/P6Ybs0u2nQy1b968mXgfYenSpemmm25SSzk7depEP/30E+3evVvt36tYsaLtJwpnhJD3CvLewT59+qgDFvgAnD179tDQoUPV8/E3vDkhcYTwww8/VKe4IoWfwInUdBq9IYn+OJasPnSY/eDRrGIBeqRCvvA/UC6ukb844m/8CxYsmIt7GfmuMePY2FgcKiM4FPz3g/cdFypUSLAVVM1RFf7SDgckyc0F/ih58uTJTF+6y7XozZp5WxV/vshuP2x0NJ9aIJMghDJc3VBrRISQO75p0yYaNWqUirylpqYST2COFPbt25cuv/xyR2x4T+BVV13lO0SG9/1NnDgxyz2EAwYMoOnTpyvh48Sy2qpVK7WHkOXyqaeeIt73yMlYfvrdd9+pD0s5JWHJqOxIHUlOo5dWH6XDZ1MoKtq8ENYrlkBdryxM+WJNhhVlu5EjaseSUT3DhCWj8pyxZFSeMbeAJaPynLFkVJ4xt+CKJaMqQni3ng7/08rfEcJi6nwPJBkCERNCozspKSnqWyVeQhquE8B4/yFH8t566y3Kly8fcZSP9y0ap4zyvsESJUpQ/fr1VYSP9wPy682bN1d7FzmSZpwyyvsJObGosgByvatWraIPPvhAZkSEaoUQCoH9p9p9Z1Opz4ojlHYxzZIQls4bQy/VLkZF4uW+0ZPtuf7aIYR6mEMI5TlDCOUZQwj1MIYQ6uHsBiEcHiEh7AshFJ1kERFCFrNmzZpR06ZN1dUOEokjgt98803QewhZEK+44gpq166danrnzp3qHsKtW7fSJZdcoiKCxj2Ex44do3fffZdWr16t5JFPHOU7DStXrizx2GJ1QgjF0KqKjyVfpK7LDlsWwkoFYmlArWJUOA4RQrMjBCE0S8pZPgihM35mSkMIzVByngcRQucMQ9UAIQxFKDyvQwgzRggXLVqkDoU8evSoWhnYr18/KlasWFDYfF4IB3z2799PxYsXVysBb7/99vAMTC6oJSJC2LVrV3XRO9+jcuedd9JDDz3kE7BcwNSVXYAQyg7LseQ0GrT2KO07bW3JaMMSeajLlYkUFw0hNDtCEEKzpJzlgxA642emNITQDCXneSCEzhmGqgFCGIpQeF53jRBeo3/JaN8aGZeMHjhwQN0GwNvErrnmGho9ejQdP37cd8aHP/ETJ06oLWC9evWi2267jdasWaNuD+Ar5CpUqBCewcnhtURECJkZGzpH8PgCeh5U3jfIUcO7775bLR9FCi8BCGF4eQbWdiY1nUb8mUTrjp6ztGT0X5UK0sMVZKLksj2OXO0QQj3sIYTynCGE8oy5BQihPGcIoTxjbsEtQnhYsxCWWPUdBQohn5z/+++/qxV+nIwbBfjnvC3MP/EKQA5GzZ0713foX5s2bdSVdDfccIOewXN5KxETQoML/0HkS+hnzJihThnlfXocwn344YepVq1aLseXcx4PQig/VvvPpVKvpYdMC2GpPH/vHyyagP2DVkYHQmiFlv28EEL77MyWhBCaJeUsH4TQGT8zpSGEZig5zwMh/P+S0TfeeIP4io0uXbr4wHJg6dlnn6V69eplgM3vtXyA5K233qoOjORtYFyezwORvKbD+YjrqyHiQmh0la9y+Pjjj5UYGolDwHxXoXGBvT4sua8lCKH8mF5II/ph32n6cOspU9dOvH51Mbqs4N8n2yKZJwAhNM/KSU4IoRN65spCCM1xcpoLQuiUYOjyEMLQjMKRww1COOKPo6Q/QjiH+gQsGX3xxRfVuR7+J48+/vjj1KFDB3WlXWDiGwfeeecdtWKAg0+835AFEelvAhEVwuTkZOIB4qWjfP0Eb/LkS+V5TyEL4pgxY9RDsigiOSMAIXTGz2zp3UlnaOs5onGbT2VZpGSeGOpZNZEuyR9H+WKwd9AsWyMfhNAqMXv5IYT2uFkpBSG0Qst+XgihfXZmS0IIzZJylg9CaC9CuGHDBnVbwKBBg6hOnTq0bds2eu6559Q+Qj6MBilCQrhx40YlgXz9A3/o4Dv+eIloo0aNMtztl5SUpA6d4SWlSM4IQAid8TNbmi+Z5sOSTl6MogX7ztKOM6m092wq5Y2JpjL5Yqhsvli6rUw+KoprJswizZQPQmgbnaWCEEJLuGxlhhDawma5EITQMjLLBSCElpHZKuAKIVwfgQjh75kjhLxXkJd+Dh06VLE8fPgwtWjRgoLtIWTfmDVrFo0dO9bHna+V4wNljBsHbA1ILioUkQghX0DP0cD7779fHSRTpkyZLJF27txZHSmL5IwAhNAZP7OlWQj5Ps24uL+XgvJhM2dT09QpovliowkeaJZk1vkghM4ZmqkBQmiGkrM8EEJn/MyWhhCaJWU/H4TQPjsrJd0ihEc0HypTPIgQ8uGUvDyUI30c9eMr4lgKDUH0v3OcD5XhE0ZfffVVql27tooQ8umkPXv2VPeQI0UoQvjDDz9kigZiMGQJQAhl+Rq1Bwqhnla91QqEUM94QwjlOUMI5RlzCxBCec4QQnnG3AKEMOM9hL/++qu6OiLYPYSBd44vWLBARQ/5NFI+SIbvQuc9h0h/E4hIhBDw9ROAEOphDiGU5wwhlGfMLUAI5TlDCOUZQwj1MIYQ6uHsHiG8R0+H/2nl7whh0QwHyGh9AA80FjEh5FDvxIkTaeXKlcQXRiYmJqpjYvlekNKlS3sAvd4uQgj18IYQynOGEMozhhDqYQwh1MMZEUJ5zhBCecZuiRC+vf4oHblGvxD2hhCKTrKICCGv3eVNnKmpqWrdb9GiRenYsWNqc2h8fDx99NFHVKlSJdGOe61yCKGeEYcQynOGEMozhhDqYQwh1MMZQijPGUIoz9hVQlg3AkJYHRFCyVkWESF86qmnKCUlhQYPHqwig0biSCFfKMkHcowcOVKy356rG0KoZ8ghhPKcIYTyjCGEehhDCPVwhhDKc4YQyjN2kxAe1SyExX6fQ70hhKKTLCJCyNdLfPbZZ1SuXLlMneP7B/nY2J9//lm0416rHEKoZ8QhhPKcIYTyjCGEehhDCPVwhhDKc4YQyjOGECJCKDnLIiKE119/vboPpFixYpn6xicF3XfffbR48WLJfnuubgihniGHEMpzhhDKM4YQ6mEMIdTDGUIozxlCKM/YPUJ4jPRHCGcjQig8xSIihLx/sHr16tSvX79M3Rs+fDitX7+ePvzwQ+Gue6t6CKGe8YYQynOGEMozhhDqYQwh1MMZQijPGUIoz9gtQjhyfWSE8CksGRWdZBERwqVLl1KPHj3osssuU/cRFilShI4fP058n8iWLVtozJgxdO2114p23GuVQwj1jDiEUJ4zhFCeMYRQD2MIoR7OEEJ5zhBCecauEcI/IyCEK2cThFB2jkVECLlLLIXjxo0jFhV+I4mKiqKaNWtS165dIYMCYw4hFIAapEoIoTxnCKE8YwihHsYQQj2cIYTynCGE8owhhNhDKDnLIiaERqeSk5Pp1KlTVLBgQUpISJDsq6frhhDqGX4IoTxnCKE8YwihHsYQQj2cIYTynCGE8ozdJITHNJ8yWhQRQvEJFnEhFO8hGlAEIIR6JgKEUJ4zhFCeMYRQD2MIoR7OEEJ5zhBCecbuEsJ79XT4n1b+FsIi1Lp1a63teqkxbUJ4ww03WOK6aNEiS/mROXsCEEI9MwRCKM8ZQijPGEKohzGEUA9nCKE8ZwihPGO3COGoP4/Rsbr6hbAXhFB0kmkTwgkTJljqyJNPPmkpPzJDCN0wByCE8qMAIZRnDCHUwxhCqIczhFCeM4RQnrGrhLBeBISwGiKEkrNMmxBKdgJ1hyaACGFoRuHIASEMB8Xs64AQyjOGEOphDCHUwxlCKM8ZQijPGEIIIZScZREVwtTUVNq/fz8dOnSISpYsSWXKlKHY2FjJ/nq2bgihnqGHEMpzhhDKM4YQ6mEMIdTDGUIozxlCKM/YTUJ4XHOEsMjK2dQLEULRSRYxIZw2bRq9//77dOTIEV8HixcvTp06daJmzZqJdtqLlUMI9Yw6hFCeM4RQnjGEUA9jCKEezhBCec4QQnnGEEJECCVnWUSEcPLkyfTuu+/SvffeSzfeeKPvYvpffvmFZs2aRb169aKWLVtK9ttzdUMI9Qw5hFCeM4RQnjGEUA9jCKEezhBCec4QQnnG7hHC46Q/QjgLEULhKRYRIbzrrrtUJPDBBx/M1L3p06fTBx98QLNnzxbuureqhxDqGW8IoTxnCKE8YwihHsYQQj2cIYTynCGE8ozdIoTvbIiAEK6YRT2xZFR0kkVECBs2bEjz58+nAgUKZOocX1LftGlTwrUT4R13CGF4eWZVG4RQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xhBCLBmVnGUREcJu3bpRhw4dqE6dOpn6tmrVKhUh5CWlSOEjACEMH8vsaoIQynOGEMozhhDqYQwh1MMZQijPGUIoz9hNQpik+VCZwogQik+wiAjhwYMHadiwYWoPIUcL4+LiKCUlRUUFeQ9h//79qUSJEuKd91IDEEI9ow0hlOcMIZRnDCHUwxhCqIczhFCeM4RQnjGEEBFCyVkWESG8/fbbif8QJiUlqb7x0lH+IM2pSJEiFBUVlaHPCxYskGTgibohhHqGGUIozxlCKM8YQqiHMYRQD2cIoTxnCKE8Y3cJ4X16OvxPK39HCAtT69attbbrpcYiIoRvv/22Jca9e/e2lB+ZMxOAEOqZFRBCec4QQnnGEEI9jCGEejhDCOU5QwjlGbtFCEdvOE5J9fQLYQ8Ioegki4gQivYIlQclACHUMzEghPKcIYTyjCGEehhDCPVwhhDKc4YQyjN2lRDWj4AQVkWEUHKWQQgl6bqobgihnsGAEMpzhhDKM4YQ6mEMIdTDGUIozxlCKM8YQgghlJxlERPC9evX04QJE2jNmjV04sQJWrlyperniBEj1Brh4sWLS/bbc3VDCPUMOYRQnjOEUJ4xhFAPYwihHs4QQnnOEEJ5xm4SwhOaI4SJK2ZRD0QIRSdZRIRw+fLl1L17d6pVqxbVrVtXiaEhhJ9++ikdOXKEevXqJdpxr1UOIdQz4hBCec4QQnnGEEI9jCGEejhDCOU5QwjlGbtHCJNIvxDOhBAKT7GICGHbtm3VdRMdO3ZU3WMpNIRwx44d1KNHD5o5c6Zw171VPYRQz3hDCOU5QwjlGUMI9TCGEOrhDCGU5wwhlGfsFiF8d0NkhLA7IoSikywiQtigQQOaN28eJSYmZhLC8+fPU+PGjWnZsmWiHfda5RBCPSMOIbTP+VQqUToRnUohSogmSogh4gtoCsRmrBNCaJ+xlZJnzpxRd8TGx8dbKYa8FghACC3AcpAVQugAnsmiEEKToBxmO3funLqaLU+ePA5rsld80qRJ9O7GCAjh8pkEIbQ3ZmZLRUQIWfimTJlCZcuWzSSE27ZtU5HDhQsXmu0D8pkgACE0ASkMWSCE1iGeSyPaeYbolyNpdDyVKPni33XkiyFKjIuiO0oSlckbRXH/XE8KIbTO2E4JCKEdatbKQAit8bKbG0Jol5z5chBC86yc5IQQ4h5CJ/Mnu7IREUK+V5Avo3/55ZcpJibGt2SUP+g9//zz6tuPwYMHS/XZk/VCCPUMO4TQGudDF4iWHUun1UkcG8w63VwiimonRlGhWCIIoTXGdnNDCO2SM18OQmielZOcEEIn9MyVhRCa4+Q0l1uE8KTmQ2UKIULodOqELB8RIdy8eTPxPsLSpUvTTTfdRByC7tSpE/3000+0e/dumjx5MlWsWDHkwyODeQIQQvOsnOSEEJqnd/4i0feH0+n3EDJo1HhHySi6unAUxaRfJJaVQoUKmW8MOS0TgBBaRma5AITQMjJbBSCEtrBZKgQhtITLdmYIISKEtidPiIIREUJ+pk2bNtGoUaNoxYoVlJqaStHR0SpS2LdvX7r88sul+uvZeiGEeoYeQmie84ZT6fTV3uwjg4G1da4cTUVjIYTmKdvPCSG0z85sSQihWVLO8kEInfEzUxpCaIaS8zzuEcL7nXfGQg1/RwgT1bV0SDIEIiaERndSUlLo5MmTaglpQkKCTC9RK0EI9UwCCKE5zhwdnLEvjbacMZffyFWvcBTdWTKdmDMihNbYWc0NIbRKzHp+CKF1ZnZKQAjtULNWBkJojZfd3G4QwjEbk+jktfqFsNuVEEK788ZMuYgIIZ8k+ueff6r7BjmVKFGCqlWrFrFTk8yAyul5IIR6RhBCaI4znyj68c40Skoxl9/IdWl+ogdKE6WdgxBaI2c9N4TQOjOrJSCEVonZyw8htMfNSikIoRVa9vNCCBEhtD97si+pVQiTk5PpnXfeoenTp9OFCxcyPBkfbf7www+rOwgRKQz/cEMIw880WI0QQnOcDyWn0/jt1paLcs0FY4kj1zZ8AAAgAElEQVRalSOKvwAhNEfafi4IoX12ZktCCM2ScpYPQuiMn5nSEEIzlJzncYsQntIcISy4fCYhQuh8/mRXgzYh5DeLbt26qT2DfO3EtddeqyKD/PPDhw/T8uXL1aEy9evXp3fffVedNIoUPgIQwvCxzK4mCKE5zidSiN7bnkYpaebyG7lKJURR8/JE6WdPYcmoNXSWc0MILSOzXABCaBmZrQIQQlvYLBWCEFrCZTszhBARQtuTJ0RBbUK4YMECeuWVV+i9996jWrVqBX2stWvXUteuXVW+2267TarPnqwXQqhn2CGE5jifTiX6bHcaHUg2l9/IVa0g0f2liM6fRYTQGjnruSGE1plZLQEhtErMXn4IoT1uVkpBCK3Qsp/XHUJ4gvRHCL9FhND+tDFVUpsQPv3000oE27Rpk+2Dffzxx+oAlGHDhpnqADKZIwAhNMfJaS4IoTmCHBmcti+NNp82l9/IdX3RKLq1OA6VsUbNXm4IoT1uVkpBCK3Qsp8XQmifndmSEEKzpJzlc4MQjv0rAkL427fUFYfKOJs8IUprE8J77rmHxo4dG/J+wR07dlD37t1p1qxZoh33WuUQQj0jDiE0z/lkKtHoremUlm5uL2GxeKKWFaKpQBSunTBP2X5OCKF9dmZLQgjNknKWD0LojJ+Z0hBCM5Sc54EQYsmo81kUvAZtQtiwYUP68ccfiQ+PyS7xwTO33norLVq0SKrPnqwXQqhn2CGE1jhvOp1OU/eYE8KOlaKoVJ4oungRQmiNsr3cEEJ73KyUghBaoWU/L4TQPjuzJSGEZkk5ywchhBA6m0FZl9YmhHzp/MqVK031w0peUxUiE+4h1DQHIITWQCenEe05l07f7kun0xeDly2VQPRIuWjKH0OUEE0QQmuIbeeGENpGZ7oghNA0KkcZIYSO8JkqDCE0hclxJrcI4WnNp4wWwJJRx3MnVAVahXDatGmhnke9ztdPmJVHUxUiE4RQ0xyAENoDzfcRbjuTTnvOER1JJoqLTqei8VHEMli1YBQViP1/vYgQ2mNstRSE0Cox6/khhNaZ2SkBIbRDzVoZCKE1XnZzu0cIH7DbBVvl/hbCQtS6NSKEtgCaKKRVCE08jy8LhNAKrdB5sWQ0NKNw5IAQOqfIewt5EWminwT61wohdM7YTA0QQjOUnOWBEDrjZ7Y0hNAsKfv5IIT22Vkp6QYhfO+vE3T6Ov1C2OUKCKGVuWI1rzYhnDp1qqVne/TRRy3lR+bsCUAI9cwQCKE8ZwihPGNuAUIozxlCKM+YW4AQynOGEMoz5hYghIgQSs00bUIo1QHUa44AhNAcJ6e5IIROCYYuDyEMzSgcOSCE4aCYfR0QQnnGEEI9jCGEejhDCCGEUjMNQihF1mX1el0ID51Po2MpRLvPpdOes2lULl80lc8bRcXio6hEQlTYRgtCGDaUWVYEIZRnjAihHsYQQj2cESGU5wwhlGfspgjhGc1LRvP/9i1hyajsHIMQyvJ1Te1eFULej7b51EWavjeLIyyJT7CMoUvzx1BinPPhghA6ZxiqBghhKELheR0RwvBwzK4WCKE8Y0QI9TCGEOrh7I4I4UnSL4TfQAiFp1iuFcJJkybR119/rY6ov+2226hbt24UExMTFOeuXbto2LBhtGXLFipbtiz16tWLatas6cu7ceNGeu+992jz5s1UoEABatu2Ld19993CQxPe6r0ohOcuEv169CL9eChrGTQo31oqlq4vGkX5YpxFCyGE4Z23wWqDEMozRoRQD2MIoR7OiBDKc4YQyjN2TYRwUwSEcBmEUHqG5Uoh/P7772n8+PE0ZMgQyp8/Pz377LN0yy23UMuWLTPx5D/I7du3p4YNG1KLFi1owYIFxDI5efJkVfbo0aP05JNPqqNuGzVqROfPn1cb1K+44grpsQlr/V4UwvUnLtKU3aFl0ADdqkIsVSsU7Yg7hNARPlOFIYSmMDnOhAihY4QhK4AQhkQUlgwQwrBgzLYSCKE8Y7cI4bgICWFnnDIqOslypRD2799fRfgef/xxBW/hwoVK8vhfYFq/fj1xfr4jMSEhQb3M8sf/mjRpoiKDp06domeeeUZ0IKQr95oQHklOp9FbUyg1zTzZ+GiibpfFUXEHewohhOZ5280JIbRLzlo5CKE1XnZyQwjtULNeBkJonZnVEhBCq8Ts5XfDklEIob2xc3upXCmEjz32GPXs2VNF/Tht376dOnbsSLNnz6b4+PgMYzJnzhz69ttvady4cb6fv/LKK1SuXDkVOeR6atSoQStWrKAjR46o/88/K1mypNvHNsPzeU0I955Pp7FbUiyPEQvhJXntLxuFEFpGbrkAhNAyMlsFIIS2sFkqBCG0hMt2ZgihbXSmC0IITaNylNEtQnhW86Ey+ZZ9Q4gQOpo6IQu7Qgjr1q1L4byI/oEHHqBBgwZR7dq1FYBDhw6p5aJfffUVJSYmZoDCkcHFixfT8OHDfT/n/YQcLezRo4daRpqSkkJvvPGG2l84atQoVd+IESNUfv7QlBPSn3/+qZbBcj9yQuI/LnYT7xVdeyaGZuyzEB78p7Fml0RTrfwX1d5TOyk1NVXtVY2Ksi+Vdtr1UhmeGzw+sbFZ3FzvJRiCfWXGPI+jo50toxZ8xFxRNf99iYsLw4lWuYKGTCcwl2W4BtbqZC7jb6a5MeLPGMwqqzMxuBbe7iSVeKUdRwghhFKEI1dvrhTCcEYI+QCZ6667jrp06aJGad++fdSmTRsVVcybNy8lJydHbvQstMxLYydOnEhDhw61UCpyWZ38ceCy0w8QrU6yLoR1CkdTs9JEdoWU95iyqEBW5OYOR1WYc758+eQaQc2KMeay7ETAXJbla9TOf6f5iw2Itxxv/pvJkVi7MmL3b65cj9xZM0s3p+zmsrH9SaIHPiFs8KBE9VnWqSKElxdU27mQZAjkSiHkPYFXXXWV7xAZPmSGZSirPYQDBgyg6dOn+37BWAJbtWql9hBypLFEiRJZCqHMsIS/Vq8tGbV6oIxBvEX5GKqRGPw0WjOjgiWjZig5y4Mlo874mS2NJaNmSdnPhyWj9tlZKYklo1Zo2cuLJaP2uFkt5YYlo+M5QhgBIewEIbQ6XSzlz5VCyIfITJgwgd566y0VRRg4cCA1btzYJ4i8b5Alr379+sR/kJ944gn1evPmzdUBNB9++KHvlNFly5apKyk4snbJJZf4lozyz3JS8poQHj6fTiNt7CHsfXk8Ff/7bCFbCUJoC5ulQhBCS7hsZ4YQ2kZnuiCE0DQqRxkhhI7wmSoMITSFyXEmCCEihI4nURYVuEII161bR7Vq1QprHzki+M033wS9h5AFka+NaNeunWpz586dSvq2bt2qpO+pp57KcA8hRw+nTp2qlody5JEPlSlevHhYn1e6Mq8J4cmUdJqwI5WOJpvfi8iniz5RKZYS4+zv/4MQSs9kUr/TLCuFChWSb8zDLUAI5QcfQijPmFuAEMpzhhDKM+YWIIQQQqmZ5gohlOoc6v0/Aa8JIff8UHI6jdps/qTRpy6PoxIOrpzgNiGE8r91EEJ5xtwChFCeM4RQnjGEUA9jCKEezm4RwnOal4zmXfYNYcmo7ByDEMrydU3tXhTC8xeJNp9Oo893p4Ych8fKx9KVhaIp3n5wULUBIQyJ2nEGCKFjhKYqgBCawuQoE4TQET7ThREhNI3KdkYIoW10lgq6Qgg3nyLtQrj0awihpZliPTOE0DqzHFnCi0LIA5WcRnQ6NZ0+2ZVKh85nXj5aKiGKWlaMpYJxUY5lEEKo51cDQqiHM4RQnjOEUJ4xIoR6GEMI9XB2gxC+HyEh7IhDZUQnGYRQFK97KveqEBojkJRKFJ2eTsdS0mn/uXQqkzeKisZHUVo6UWEHewYDRxgRQvk5DyGUZ8wtQAjlOUMI5RlDCPUwhhDq4QwhzLiHcNGiRTRu3Dg6evSoOuOjX79+VKxYsaCDwZ8dPvroI5o3b57aV1y5cmV1fkiePHn0DJ7LW4EQunyAwvV4XhfCcHEMVQ+EMBQh569DCJ0zNFMDhNAMJWd5IITO+JktjSWjZknZzwchtM/OSkkI4f+F8MCBA9S+fXviq+auueYaGj16NB0/fjzL+7bHjx//P/bOA0qqIv3bv849OQ8TnSFLkCCgAgqisoY16xpQMK34x4QYEYVVdxcVA8b91l3DypjBLEFAFAElKqiI5AEGJufUub9TFwYm9723u+reZt46hyMyFZ+qGfrhrQB2iSW7GDI1NVW6SJJJJL1PengFkhAq+U4M47wkhGImj4SQP2cSQv6MWQskhPw5kxDyZ8xaICHkz5mEkD9j1oJehNAh+FIZ+9rP0HrL6HvvvYeffvpJivKxVFJSIj0vx/6cPS3XPFVVVUnvi//nP/+RXhOg1JaAZkK4detW6a3ALVu2oLq6Gps2bZJ69/zzz2PSpElh96yD3hcXCaGYGQqFEFY4ffDBj0avH1FmI+xGAyLNQd52I2b4QlohIRSCmYRQAGYSQgGQSQiFQCYhFIJZP0I48jIxAz7SiiSEvaIlP2hKTz75JOLj4zFlypSjf3b55ZdjxowZGD58eIv+bdy4UXpH/PTTT8fixYsRFxeHv/zlL7jwwguFjkPPjWkihBs2bMCdd94pvT04bNgwSQybhPDdd99FWVkZpk6dqmduYdc3EkIxUxaMEJY6vPhwby0KHV4UNnhQ7/GhW4QZ6REmnNEtEkMTbYgiMaR3CMUsZRJCAZxJCAVAJiEUApmEUAhmEsJmQjhr1iz06tWrhSROnDgRt956K8aMGdNiQpYsWYLnnnsOl156qbTNlG0XnT59Ov7+979jyJAhYiZP561oIoQ33ngjRo0ahcmTJ0t4mBQ2CWF+fj7uuusufPnllzpHF17dIyEUM19qhLDB7UN+vQfPba1EncfXYUfHdovAxSdEIyvSLGYwOm2FIoRiJoa2jPLnTELInzFrgbaM8udMQsifMWtBD1tG/7uzFg4NIoS3BhEhXLFiBVhE8bPPPkNUVJQ0WU899RQSExOPuoiYGdRvK5oI4WmnnSbd8sNCtq2F0OFwYOzYsVi3bp1+qYVhz0gIxUyaGiHcW+fG9E1lsjo4PNmOyX3iERfCm1FlNayjTCSEYiaDhJA/ZxJC/oxJCMUwJiEUw5mE8NiWUXZWcPPmzUcvkSktLcWECRPaPUPIgk0sckhC2PE61UQImfCxiczMzGwjhHv27JFsffny5WK+u7pIKySEYiZaqRCWO734+y8V0hZRuWlK3zicmRYpN/txl4+EUMyUkhDy50xCyJ8xCaEYxiSEYjjrRQidgiOEtrWfoXWEsLCwUJK8Rx55RNr2+corr4BJ4Zw5c6TJWLRokXS5zIgRI6T/Z0fVBgwYgL/+9a/SltEHH3xQ2jI6ePBgMZOn81Y0EcJp06YhOjoajz32GEwm09Eto+yD3qOPPgqDwYDZs2frHF14dY+EUMx8KRXCH0udeOH3CkWdS4sw4/HBSYi3GRWVO14ykxCKmUkSQv6cSQj5MyYhFMOYhFAMZ10I4a46CBfCHz9tI4SM+OrVq8Gek2jvHcKHH34Yffr0wU033SRNTnFxsXSOkF1qybaKXnvttbjgggvETFwYtKKJEO7cuRPsHGFaWpp08HPevHm47bbbsHLlShw4cAB5eXnIyckJA3zh00USQjFzpVQI39pVgyUH6xV37v+dlopEm0lxueOhAAmhmFkkIeTPmYSQP2MSQjGMSQjFcCYhbPkwvRjqXaMVTYSQod2xY4d0BSy7Ctbj8cBoNEqRwvvuuw+9e/fuGvQFjpKEUAxsJULY4PXjud8q8VuVU3HnZg5OwsB4q+Jyx0MBEkIxs0hCyJ8zCSF/xiSEYhiTEIrhrAchfF2jCOFfW10qI4Z412lFMyFsQux2u1FTUyNtIbXZbF2HvOCRkhCKAa5ICD3Ay9sq8VOFQ3HnnhiShL5xJISKwVEB2QRICGWjUp2RhFA1OkUF6ZZRRbhUZSYhVIVNcSESQooQKl40MgtoLoQy+0nZgiRAQhgkQJnFlQghq/L9vbX4bH+dzNoPZzMbDXjplFQk0RlCRdwoszICJITKeKnJTUKohpryMiSEypkpLUFCqJSYuvwkhCSE6lZO4FKaCOG2bduwYMECzJw5s00Pn3jiCfzlL39Bv379AveecsgmQEIoG1VQGZUK4ZqSRry0rUpRm+xSmb8NTqQzhLGxirhRZmUESAiV8VKTm4RQDTXlZUgIlTNTWoKEUCkxdfn1IoSuUZepG4DKUtYfP8Vfe0a3eIReZVVUrAMCmgjh7bffLl0qc8opp7TpFnt/kF0qw66PpRQ6AiSEoWPZWU1KhbDS5cOzWyuxq8Ylu4MPDEgAe4+wqyY6Qyhm5kkI+XMmIeTPmLVAQsifMwkhf8asBf0I4eViBnyklcNCGEVCyJG6JkJ4xhlnYMmSJYiKimozNPYhhF0Dy24cpRQ6AiSEoWMZSiFkdRU3enH3+hJZHTw/Kxp/yYlGlNkgK//xmImEUMyskhDy50xCyJ8xCaEYxiSEYjjrQQjf2FUH1yjxQngLCSHXRaaJEJ555pn43//+h9zc3DaD27t3rxQ9JCEM7byTEIaWZ0e1KY0QNtVT2OjFs79VoKCTB+ovOSEaZ6RGIDvKLGYwOm2FhFDMxJAQ8udMQsifMQmhGMYkhGI4kxDSGUJeK00TIWRPS7DH55966imYzcc+3LLnJ6ZPnw72gW/u3Lm8xtwl6yUhFDPtaoWQ9a7M6cOignoUN3pQ1OgB206aHWlGeqQZo7tFoG+sBVZj140MNs0gCaGYtUxCyJ8zCSF/xiSEYhiTEIrhTEJIQshrpWkihE0P06ekpOCss85CcnIyysrKsGLFCpSWlkrRQ3qLMLRTTkIYWp6hjhA2r6/e44fHD9S4PIiymJBoNYrpfJi0QkIoZqJICPlzJiHkz5iEUAxjEkIxnHUhhLvr4Ba8ZdTyw6egLaN815gmQsiGxG4aZQ/Tb9q0CewvRZPJhJNPPhlTp06lG0Y5zDkJIQeo7VQZTIRQTA/DvxUSQjFzSELInzMJIX/GJIRiGJMQiuGsDyGs10AIPyEh5LzENBPCpnG5XC7U1tYiJiYGVmvXfGib8xxL1ZMQiqAMkBDy50xCyJ8xa4GEkD9nEkL+jEkIxTAmIRTDmYSQtozyWmmaCyGvgVG9LQmQEIpZESSE/DmTEPJnTEIohjEJoRjO9OwEf84khPwZsxb0IIRv7tYmQngz3TLKdZFpJoS//PILvvzySxw6dEiKqrROb7/9NteBd7XKSQjFzDgJIX/OJIT8GZMQimFMQiiGMwkhf84khPwZkxDSO4Q8V5kmQvj+++/j2WefRWZmpvT0RHvvET755JM8x93l6iYhFDPlJIT8OZMQ8mdMQiiGMQmhGM4khPw5kxDyZ6wnIfQIvlTG/MMnoAgh3zWmiRCee+65uP766zFx4kS+o6PajxIgIRSzGEgI+XMmIeTPmIRQDGMSQjGcSQj5cyYh5M9YV0I4+goxAz7SiiSEPSIxaRKdIeQFXhMhHD16NL7++mtER0fzGhfV24pAe0JY5vCg2u1Hfp0Lm8udSIu0oHeMGfE2M3KizLCZ6M07pQuJhFApMeX5SQiVM1NTgi6VUUNNWRkSQmW81OYmIVRLTn45EkL5rILJqZczhB4SwmCmUZdlNRFC9jA9ixAOHTpUl1COx041F8IGjw/VLh/m/FaB/Dp3u8O95IQYXHpCNOLpDTxFy4GEUBEuVZlJCFVhU1yIhFAxMsUFSAgVI1NVgIRQFTZFhUgIFeFSnVkPQvjW7npoIYQ3UYRQ9bqRU1ATIayqqpLOEJ5//vk49dRTYTab5fSV8gRBoLkQbq5w4vHNZQFr6x9vw7QBiUi20cPoAWEdyUBCKJeU+nwkhOrZKSlJQqiElrq8JITquCktRUKolJjy/CSEypmpKUFCSFtG1awbOWU0EcLx48eD/fCorKyE0WhEXFwcDIaW2xOXLVsmp/+URyaBJiF89Klncde6Uji8Plklz82MxvU9YxFtpu2jcoCREMqhFFweEsLg+MktTUIol5T6fCSE6tkpKUlCqISWurwkhOq4KS1FQkhCqHTNyM2viRDOnTs3YP+mTZsWMA9lkE+ACeH6X7ai5KRzsK7UIb8ggPsHJmJ0aoSiMl01Mwkh/5knIeTPmLVAQsifMwkhf8asBRJC/pxJCPkzZi3oQgj31MMr+Ayhac0noC2jfNeYJkLId0hUe3sEmBDur6jB55YeKGn0KoJ0VfdYXNs9RlGZrpqZhJD/zJMQ8mdMQiiGMQmhGM4khPw5kxDyZ6wfIWzQQAg/JiHkvMRICDkD1kv1W7duRbnLh7mViYq7NDzJjqkDEhBtprOEgeCREAYiFPzXSQiDZyinBooQyqEUXB4SwuD4yS1NQiiXlPp8JITq2SkpqYcI4f/2aCOEN9KlMkqWiuK8mgmhx+PB9u3bcfDgQbDft04XXHCB4sFQgY4JMNYrC6qx2JCpGFNmpBmPDU2hy2VkkCMhlAEpyCwkhEEClFmchFAmqCCykRAGAU9BURJCBbBUZiUhVAlOYTESQjpDqHDJyM6uiRAWFRXhnnvuwc6dOzvs6KZNm2QPgjIGJsC2jFZ7gWfLEwJnbpVjZEoEbu8XTxFCGeRICGVACjILCWGQAGUWJyGUCSqIbCSEQcBTUJSEUAEslVlJCFWCU1iMhJCEUOGSkZ1dEyGcOXMmKioq8OCDD+Lyyy/H4sWLUVhYKD1WX1ZWhvvvvx+pqamyB0EZAxNgQphfUoElkX1xsKFtRLazGtj5QXaOkFJgAiSEgRkFm4OEMFiC8sqTEMrjFEwuEsJg6MkvS0Ion5XanCSEaskpK6cXIfSdfoWyjgeZ27jmY9zYPRKTJpEQBomyw+KaCCHbDvrqq6+ie/fuGDZsGJpHA7/44gv8/vvvmD59Oq8xd8l6mRD+uGEjGk+7BN8WNihiMPvkZPSLtykq01UzkxDyn3kSQv6MWQskhPw5kxDyZ8xaICHkz5mEkD9j1oJ+hPBKMQM+0sphIYwgIeRIXRMhHD58ONauXSs9SD969GgpMhgdHS0N0+FwgAnjihUrOA6761Xd9A7hrKefx4yfSlAs86bR63rE4qLsKNhMdKGMnFVDQiiHUnB5SAiD4ye3NAmhXFLq85EQqmenpCQJoRJa6vKSEKrjprQUCSFFCJWuGbn5NRHC5lHBK6+8EuzNQSaGLG3btg1TpkzBd999J3cMlE8GgSYhfOGFF7C92oXZv5Sjxt354/TnZETiypxYdIswyWiBsjACJIT81wEJIX/GrAUSQv6cSQj5M2YtkBDy50xCyJ8xa0EPQvj2ngb4ThcfIbyBIoRcF5nmQvjmm2/inXfewVVXXQWr1YoFCxZgyJAhmD17NteBd7XKmwshG3upw4u83TVYU9IAn78lDbvJiMl94zAkwYYEG8mgkrVCQqiElrq8JITquCktRUKolJjy/CSEypmpKUFCqIaasjIkhMp4qc1NQkgRQrVrJ1A5TYTw008/xWWXXSb1zeVyYe7cuVi6dKm0XXTUqFF45JFHEB8fH6jv9HUFBFoLISta5/HD6fWh2OHB71UuZESYwZ6YMBqA7CiLgtopaxMBEkL+a4GEkD9j1gIJIX/OJIT8GbMWSAj5cyYh5M+YtaALIdzbAL/gCKFh9cegCCHfNaaJEHY0JPYDxWAw8B1xF629PSHsoii4DpuEkCteqXISQv6MSQjFMCYhFMOZhJA/ZxJC/oz1I4SNGgjhAhJCzktMEyE877zzsGTJkg6HFujrnJkcl9WTEIqZVhJC/pxJCPkzJiEUw5iEUAxnEkL+nEkI+TMmIaRbRnmuMk2EsPVTE80HyP6CHDFiRIunKHgC6Cp18xZCpw+odXnQ6PHDYgJsRmOXPH9IQsj/O4qEkD9jEkIxjEkIxXAmIeTPmYSQP2O9COG8vdpECCfRpTJcF5nuhHDDhg3Sg/Xffvst14F3tcp5CaHT68eW8np8vq8K++vc2FvrRLzVhBOibciKsuDGvknIiLR2GdwkhPynmoSQP2MSQjGMSQjFcCYh5M+ZhJA/YxJCihDyXGVChXDs2LHSWNiH5qZ3B5sPzu12w+l04pJLLsGsWbN4jrvL1c1DCPPrXPgsvxLv7azokGeS3YxnTs1C91gboszH/1uGJIT8v7VICPkzJiEUw5iEUAxnEkL+nEkI+TPWkxDiDLHPTmD1AkzKJSHkucqECuGrr74qjYU9NXHzzTe3GVdERAS6d+8OJo5G4/EvDzwntnXdoRbCWrcPr20rxUe7O5bB5n3435nd0T/BLnLImrRFQsgfOwkhf8YkhGIYkxCK4UxCyJ8zCSF/xvoSwr+IGXBTK5IQ2jFpEj07wQu8UCFsGsSzzz6L+++/n9eYqN52CIRaCL8vrMX9awtks86OtuD/nZGLVLtZdplwzEhCyH/WSAj5MyYhFMOYhFAMZxJC/pxJCPkzJiEkIeS5yjQRwqeffhoPPfQQz3FR3a0IhFIIixvduPfHA9hZ7VTE+eEhabise4KiMuGWmYSQ/4yREPJnTEIohjEJoRjOJIT8OZMQ8mesFyHM29sInCE+QjiRIoRcF5kmQjh69GisXLkSZvPxHS3iOnMKKw+tEHpw9fLdaPD4FPXistx4PDw0XVGZcMtMQsh/xkgI+TMmIRTDmIRQDGcSQv6cSQj5MyYhpAghz1WmiRDec889uOGGGzB06FCeY6O6mxEIpRDuqHbg+hV7FfM9OTkSs0/JQqLNpLhsuBQgIeQ/UySE/BmTEIphTEIohjMJIX/OJIT8GetGCPM1iBCuWgCKEPJdY5oIYVlZGebMmYMLLrgAo0aNgtXadZ4l4DudHddOQiiGPAkhf84khPwZkxCKYUxCKIYzCSF/ziSE/BnrSQgNgreM+kkIuS8wTYRw/PjxYH8RVlVVSYnmEGIAACAASURBVAOMjY1ts3102bJl3AfflRoIpRCyM4TXLN+Detoy2mYJkRDy/64iIeTPmIRQDGMSQjGcSQj5cyYh5M9YP0LogHghnE8RQs5LTBMhnDt3bsBhTZs2LWAeyiCfQKiFUNWlMkPTwc4RHs+JhJD/7JIQ8mdMQiiGMQmhGM4khPw5kxDyZ6wXIXwnXxshvJ4uleG6yDQRQq4jOlL5vHnz8Nlnn4F9eDz77LNxxx13wGRq/+za/v37wZ7C2LVrFzIzMzF16lQMHDiwRTdZPVOmTEFBQQEWLVokYgghbSOUQsg6trqoTrppVG7KibHildE56BZxfF8kREIod0Woz0dCqJ6dkpL19fWwWCy0pV8JNIV5SQgVAlOZnYRQJTgFxUgIFcAKImtjYyMMBgPsdm3edWafrUkIg5hAHRc9LoXwm2++wWuvvQb2vEVUVBRmzJiBcePG4brrrmszFewv5FtuuUU6yzhhwgSwrapswefl5Ullm9L8+fPxww8/YPv27SSEAGpcXry+vRwf7CqXtbznjcvFifERsvKGcyYSQv6zR0LInzFrgYSQP2cSQv6MWQskhPw5kxDyZ8xa0I0QjhH77IR/1Xxcn0O3jPJcZZoJYWFhId5++21s2rQJ1dXViIuLw/Dhw6XbR9PS0oIaM3vjkEX4Jk6cKNWzfPlySfLYr9Zp69at0puIH3/8MWw2m/TlSZMmSb/OOecc6f9LS0vxwAMP4K677sLMmTNJCI9A3F3jxML9VXhnZ0WH85USYcacU7OQE21DtMUY1LyGQ2ESQv6zRELInzEJoRjGJIRiOJMQ8udMQsifsZ6E0ChYCH0khNwXmCZCuGfPHtx0003weDwYMmQIEhMTUVFRgc2bN0vbk9566y3k5uaqHvw111yDu+++W4r6sbR3715MnjwZCxcubLP9iW3//OKLL/Dvf//7aHuPP/44srKypMghS+z/zzjjDKSnp+O+++4jIWw2M04f8GtFA77Ir0R+rQt7ap2It5qQG2NDZpQFE3snIyvKonouw60gCSH/GSMh5M+YhFAMYxJCMZxJCPlzJiHkz1hfQniVmAEfaeWwENqkYA0lPgQ0EUL2DqHb7cbs2bOlyGBTYpFCtr2TnVt54YUXVI/4kksuwRNPPIHBgwdLdZSUlEjbRRcsWNCiPfY1FhlkW0Gfe+65o+2x84QsWsgiguvXr8dHH30knTHctm1bGyF0Op2q+ymyIIuEsogse+4j1IntZ3f5gTqPD41ewGY0wGwAEqxG6TbZrpTYejCbzR2eV+1KLHiNla0pxjki4vjfgsyLoZx6GWN27pqtZ0p8CNBa5sO1da0ulwtGo5HWMkfcTAjZdsbIyEiOrVDVbC2zz1zsc3JHqWm3Gw9aTWcIjWNICHnw1bJOTYSQRdvef/99KQrXOrFLW9hZvu+//141l1BFCNmW09tuuw1/+9vfpIhle0LIztmEQ/r999+lc5FPPvlkOHQ3bPvI/qGDfYhmHz4o8SHAPniw3QWd/YXIp+WuVStjzNYxrWW+884+4NFbvHwZs7XMPkR3dLEc39a7Tu20lvnPNdshw1Jna7n5/Reh7hETwnfzHdBCCK+jCGGop7NFfZoI4ciRI/HVV18hKSmpzeDKy8tx0UUXSVE7tYmdCRw0aNDRS2TYJTMsOtbRGcLp06fjk08+OfoB88Ybb8T111+PPn364NZbb5XeSWSJfSPW1tYiPj5eim727t1bbReFlwv1LaPCBxAmDdKWUf4TRVtG+TNmLdClMvw505ZR/oxZC7RllD9n2jLKnzFrQQ+Xyry7TwMh/H4+SAj5rjFNhJCdH+zfv790UUvrxLZusu2Nb775puqRs0tkXn/9dTzzzDPS9oWHH34YY8eOPSqI7NxgSkoKRowYIW1pvPnmm6WvX3vttdIFNKxtFk1jW9KqqqqO9mPnzp3SecJ33nlHksRw2kpFQqh6OSkqSEKoCJeqzLyE0O9qAOCHr74C3qoimKKTYIhMhMFogCHi2NZ2VZ0Ow0IkhPwnjYSQP2MSQjGMSQjFcNaLEJoEbxn1khByX2CaCOHatWul83k9e/aULmtJSEhAZWUlVq9eLb0F+Oqrr+KUU04JavAsIvj555+3+w4hE0QW/WNiytK+ffukM4K7d+9GRkYG2BnH1u8QsnztbRkNqpMCC5MQioFNQsifMw8h9Dvr4Pj9G3irDrUZgMFkha3fOJgTMgHz4ZuIu0IiIeQ/yySE/BmTEIphTEIohrM+hNAJ8UL4EUUIOS8xTYSQjYlJIbvZk4kK+0HC9vczCbv99tuDlkHOzMKyehJCMdNGQsifc6iF0Fd1CA2bvwL8h89mdJRsucNgzugPgy2a/yB10AIJIf9JICHkz5iEUAxjEkIxnEkI6ZZRXitNMyFsGhC7yY6dy4uJiTn6DiCvwXblekkIxcw+CSF/zqEUQhYZbFj/EfweebcF2/qOhSWjP/9B6qAFEkL+k0BCyJ8xCaEYxiSEYjjrQQjf26dNhHACXSrDdZFpKoTs5i/2QD17FiI1NVV65y+czuVxnZkQV05CGGKgHVRHQsifc8iE0OOEY9u38JTtVdRp+8mXwRyXpqhMOGYmIeQ/aySE/BmTEIphTEIohrNuhHCs2GcnvN9/hAkn0DuEPFeZZkLI3v/7z3/+g7KysqPjS05Olp55uPzyy3mOuUvWTUIoZtpJCPlzDpUQ+p31aNgwH353o6JO2048C5b0vorKhGNmEkL+s0ZCyJ8xCaEYxiSEYjiTENKWUV4rTRMhZDd4vvLKK7jwwgtx+umnH71UZtWqVdJzFFOnTj16IyivgXe1ekkIxcw4CSF/ziETwsZq1K99T3GHLWknSpfMHO+JhJD/DJMQ8mdMQiiGMQmhGM56EUKz4AihhyKE3BeYJkJ4/vnnS5HASy+9tM0A2XuAb7zxBhYuXMh98F2pARJCMbNNQsifc6iE0F34B5x/fKu4w6aYVNgH/gkGe4zisuFUgISQ/2yREPJnTEIohjEJoRjO+hHCq8UM+Egrh4XQikmTKELIC7wmQjhq1CgsXboU0dFtb+tjF8ycd955WLNmDa8xd8l6SQjFTDsJIX/OoRJCX30lGtZ/oLjDlrS+sPU7S3G5cCtAQsh/xkgI+TMmIRTDmIRQDGc9COH7+5wwjxUvhNeSEHJdZJoI4R133IFbb70VQ4YMaTO4n3/+WYoQsi2llEJHgIQwdCw7q4mEkD/nUAmh31GLhg0L4Pc4FHXa3ucMmDMHKioTjplJCPnPGgkhf8YkhGIYkxCK4awLIdyvgRCu/AjtCSELHrEn7MrLyzFo0CA88MADSEpK6nQyvvvuO/zzn//ELbfcgmuuuUbMxIVBK5oIYXFxsfQQPDtDyKKFFosFbrdbigqyM4QPPfQQUlJSwgBf+HSRhFDMXJEQ8uccKiGEx4WGLV/BV1OsqNP2wRfCnJitqEw4ZiYh5D9rJIT8GZMQimFMQiiGMwnhsS2jRUVFktQxZzj55JPx8ssvo7KyEnPmzOlwMhoaGnDnnXdK3jFu3DgSwmakNBHC8ePHg/1FWFVVJXWFbR1lH6RZSkhIkB6pb56WLVsm5jvtOG6FhFDM5JIQ8uccMiEEwG4arf9hnuxOW7ufAmv2IMBkkV0mXDOSEPKfORJC/oxJCMUwJiEUw1kvQmgRvGXU3U6E8L333sNPP/0kBZhYYk/YXXfddWB/3lFQ6V//+pf0zN2mTZswePBgEkKthXDu3LmKvnOmTZumKD9lbkuAhFDMqiAh5M85lELIeust3YvG35YE7LgppRfsvUfCYGt79jlg4TDMQELIf9JICPkzJiEUw5iEUAxnfQihC+KF8MM2W0affPJJxMfHY8qUKUfhs2frZsyYgeHDh7eZkN27d0vRQyaFjz76KAlhK0KaRAjFfNtQK80JkBCKWQ8khPw5h1oI2dZRn7Mejl+XwNd4eNdC62TtMRKWtF5dRgbZ+EkI+a9lEkL+jEkIxTAmIRTDmYTw2JbRWbNmoVevXi1uHp04caJ0R8mYMWNaTAhbn+xJO7bFlEUGH374YRJCEkIx37R6a4WEUMyMkBDy5xxyIWzqsrNeeqTeW1sOd/k+mKISYIrtBqM9BoaIGMBo5j84HbVAQsh/MkgI+TMmIRTDmIRQDGc9COEH+12wnCn2llH3yg9xTXbLZyeURAgXLVqEzZs3S9FDlkgI265XTSOE7MNzYWHh0fODzbs3dOhQMd9dXaQVEkIxE01CyJ8zNyHk3/WwaoGEkP90kRDyZ0xCKIYxCaEYziSExyKE7Kwgk7ymS2RKS0sxYcKEds8Q/uMf/8DGjRuly2RYYp/VzGYzRo4ceVQSxcygflvRRAjZzUBPP/00Vq1aBfZDpL3EDnxSCh0BEsLQseysJhJC/pxJCPkzZi2QEPLnTELInzEJoRjGJIRiOOtFCK2CI4SudiKELKDEtoc+8sgj0jN27Lk6JoVNgsiiguxymREjRkh/nzmdzqOTNHv2bAwYMABXXnklYmJixEyezlvRRAgnT54s3QbETD4nJweRkZFtMJ100kk6Rxde3SMhFDNfJIT8OZMQ8mdMQiiGMQmhGM7sqnmTyQSbzSamwS7YCgmhmEnXjxCKfb/vsBBaWpwXZMRXr16N1157rd13CNm20D59+uCmm25qMzm0ZbTtetVECE877TTMnz8f2dnH/1teYn5EBG6FhDAwo1DkICEMBcXO6yAh5M+YhFAMYxJCMZxJCPlzJiHkz5i1QEJ4bMuoGOJdpxVNhJCFaJveAuk6qLUdKQmhGP4khPw5kxDyZ0xCKIYxCaEYziSE/DmTEPJnrBch/PCAC9YzBUcIv/sQV7cTIRRDvWu0ookQsrODK1askA5yNh3w7Bq4tRslCaEY9iSE/DmTEPJnTEIohjEJoRjOJIT8OZMQ8mdMQth2y6gY6l2jFU2EkKFdv349/vnPf6J///5ITEyEwWBoQfz+++/vGjMgaJQkhGJAkxDy50xCyJ8xCaEYxiSEYjiTEPLnTELInzEJIQkhz1WmiRCyq1+Z8NXW1iIuLg4RERFtxrhw4UKe4+5ydZMQiplyEkL+nEkI+TMmIRTDmIRQDGcSQv6cSQj5M9aPELphE7xl1PndB7RllPMS00QIr7jiCgwbNgy333474uPjOQ+RqmcEeAih0+tDrduLapcPkWYjrEYDkuzh+3i32w/Uu31w+gCLETAbDIi1tIxcB1pNJISBCAX/dRLC4BnKqYGenZBDKbg8JITB8ZNbmoRQLin1+UgI1bNTUlIPl8p8eICEUMmchUteTYRw9OjRWLJkCb39IXCVhFIIHV4/NpY14Kt9ldhf58LeWicSrWbkxFiRFW3D5BOTkRxGYujyATtqXFhV4kRRoweFjT5EmY1IsxvRLcKEC7MikGwzyZotEkJZmILKREIYFD7ZhUkIZaNSnZGEUDU6RQVJCBXhUpWZhFAVNsWF9CCEH2kkhFfRpTKK14uSApoI4dSpU6XHJAcOHKikr5Q3CAKhEsJ9dU58sKsCH+2t7LA33ewWPHdaFrJjbIg2G4PoNf+iTP5WFjdiyaHGDhuLtRhxe99Y5ESZERHAC0kI+c8ZCSF/xqwFEkL+nEkI+TNmLZAQ8udMQsifMWtBN0I4Tuwto2zL6FVZdIaQ5yrTRAjLysrw4osv4sILL8Tw4cOlB2Mp8SUQCiGsdfvw4m/F+DS/YxlsPoqPx/dEbrR+HwKu8/jweUEjlnUig83HM2tQPHpEd74lloSQ7zpmtZMQ8mdMQiiGMQmhGM4khPw5kxDyZ0xCSELIc5VpIoTjx48H++FRWVkJo9EoXSzT+pbRZcuW8Rx3l6s7FEL4zcEaPLi+QDa7XrE2vDo6R7fbRzdXuvHCtmrZ40mxmTDjpDgkWDuOepIQysapOiMJoWp0igpShFARLlWZSQhVYVNciIRQMTLFBUgIFSNTVUAvEUK74AihgyKEqtaLkkKaCOHcuXMD9nHatGkB81AG+QSCFcJShwf/tyof+XUu+Y0CePKUbPwpM0ZRGRGZK11+vLCtCvvqvYqau6FHNMal2TssQ0KoCKeqzCSEqrApLkRCqBiZ4gIkhIqRqSpAQqgKm6JCJISKcKnOrB8hvFb1GNQUPCyEZkyaNElNcSojg4AmQiijX5QlxASCFcKiRjcu+3oXXH6/op5d0zMBDwxKV1RGROZypw+ztlSh3uNT1NyYVBtu7tWx4JIQKsKpKjMJoSpsiguRECpGprgACaFiZKoKkBCqwqaoEAmhIlyqM5MQkhCqXjwBCpIQ8iKrs3qDFcJfyhtx0/d7FY/qtNRoPDE8A0k2fT1HcaDBi5mb5Z2FbD7oPjEWTDkxFgkdPEdBQqh4iSguQEKoGJmqAiSEqrApKkRCqAiX6swkhKrRyS5IQigbVVAZ9SCE8wvcsI8THCH89gP8hSKEQa2dQIWFCWF+fn6gvrT4em5urqL8lLlzAloJ4cgjQpjYxYTQZzKjweOD1+fHjmoXesdZYTQYYDMZdH/zqt6/l0gIxcwQCSF/ziSE/BmzFkgI+XMmIeTPmLVAQkgRQl4rTZgQsofolaRNmzYpyU55AxAIVgjZ+3yXLd0Jl0/ZltFreybg/uNoy+jYVBtu6mTLKPsQXWew4oP8GrCoakGDG027bFMjzMiItOCBk5KREkbvNOrtm4uEUMyMkBDy50xCyJ8xCaEYxiSEYjjrRQgjBEcIGylCyH2BCRPCDz/8UNFgrr76akX5KTPfCKHqS2VGZOFPWbFCpqfc6ZXkq97jxa4aFwYl2mEAEG02wW5mvzuWKl0+vLCtBvvqPYr6dkPPaIzr1vGlMtsqGvDMb+UoaOi4XrPRgL8PS0O/OBsiW/VLUWe6aGYSQjETT0LInzMJIX/GJIRiGJMQiuGsDyH0QLwQvk9bRjkvMWFCyHkcVD3nCCGrfsWhGjywTv6zE33j7Hhp1Ancn51o9PhR4fLgiZ+KUNjoQbXr2M2hLCI3IiUS1/dKQGqrqNyWSjfmKnh2opvdhOkDO352osLpxZQ1BWD/bf2MSnvT89/TM5ETbaW1q5AACaFCYCqzkxCqBKegGAmhAlhBZKUto0HAk1mUhFAmqCCzkRDSltEgl1CHxUkIeZHVWb3Bbhllw2EP07+ytQQL9lbIGt0n43txFx52S+iG0gb87aeiTvt0QpQV04ekYkD8segeK/tFgQNfH2qQNZ6/DYpH9w4epnd4/fjXtnIs3F8tnRWUI4RZURY8e2o6Eq0mWe1TpsMESAjFrAQSQv6cSQj5M2YtkBDy50xCyJ8xa0EPQrigwIOIs8ReKtP47fu4MpOeneC5ykgIedLVUd2hEEI2nH11LszfU4n3d5d3OLr0SAueOTUb2VFWRFs6fsQ9FHg2VzRi6o8HZVWVGWnBiyMzW5zfO1jvxZoyBxYdbOywjjiLEVP7xSLdbkJEB1s82Zbav646iDq3R7YQsgafPSVd2tpKST4BEkL5rILJSUIYDD15ZUkI5XEKNhcJYbAEA5cnIQzMKBQ5SAgpQhiKddReHSSEvMjqrN5QCSEbltPnx6bSeikall/rxJ5aJ9gtornRVmRH23BL32SkRKh/ZqLG7cP2Gg/6x1kR1Uk1bHvo/60+gKpmW0QDYb8oOxZ3DkiB3XTsTKHLD+yq8WBVSSMKG7041OCVbgJNjzAixW7CBZkRSLV3HsXbVevC7WsOwuvzKRLC205MxBW5cYG6TV9vRoCEUMxyICHkz5mEkD9j1gIJIX/OJIT8GbMWSAhJCHmtNBJCXmR1Vm8ohbBpaE6vH7VuLyqdHkRbTLCZjEi0Bbf9kV328r/d9fi9xoPTU2y4JMuOeGv7UcbVRXV4ZFPnW0VbTwPr32unZ7c5T8jysRtU690+OH2AxWQACway6KCctLigFnN/K1MshCO7ReLxod3kNEF5jhAgIRSzFEgI+XMmIeTPmIRQDGMSQjGc9SKEkWdNEDPgI600SFtGTZg0iYSQF3gSQl5kdVYvDyEM5RBdPmBvvQev/FEHR7OnLZJsJtzRJwrd7EZYjS1vCv3v9gq8s0veecbmfX3nzBxkR1lC2X2sK23EzE1FioXwz9kxmDogOaR9Od4rIyEUM8MkhPw5kxDyZ0xCKIYxCaEYziSEJIS8VhoJIS+yOqtXz0JY5fZjWaEDSwsdHVKbkBuJYYkWxB6J2LELbmZuKsTP5R2f/euoshmDu+HcrJiQzhB7p3HSygOKhfDRId0wJi0ypH053isjIRQzwySE/DmTEPJnTEIohjEJoRjOehDCjw96IDxCuOJ9XEERQq6LTDMh3Lp1K15//XVs2bIF1dXVaHqI/vnnn5dCwsnJFDUJ5czrVQgrXX68sr0O+zt5t6+JQ/9YM27oEXl0W+o/fy7G0kO1ijG9MjILJ4X4IpcD9S7cu64IFQ63ojOET5+SjqEh7otiIGFWgIRQzISREPLnTELInzEJoRjGJIRiOJMQUoSQ10rTRAg3bNiAO++8EyeddBKGDRsmiWGTEL777rsoKyvD1KlTeY25S9arRyHc3+DF33+tUTQf7DKYv50Ui2SbEZ/kV+PFraWKyrNH4d8fl9PuGUJFFbWTeXVxPf62qUi2EF6UE4vJfRNha7UVNth+HO/lSQjFzDAJIX/OJIT8GZMQimFMQiiGMwkhCSGvlaaJEN54440YNWoUJk+eLI2LSWGTEObn5+Ouu+7Cl19+yWvMXbJePQohu0Dmmd9rUcpucZGZ+sSacVuvKGnrKBOwRzYWyix5OFtWpAWvjM5CAoe3/+o8fuRtL8cn+2sCvkPYK8aKJ4alITnA7aWKBtdFMpMQiploEkL+nEkI+TMmIRTDmIRQDGd9CKEXUYIvlalf8R5tGeW8xDQRwtNOOw1ff/014uIOX7ffXAgdDgfGjh2LdevWcR5616pej0LIZuD9fY1YUdTx2cHWs3RNTiTOTrNJf8yep/jv9nJ8sa9a9mTy2C7avPH8qgasL3fi9Z1VHfaJnV+8tkc8MiLVP80he8DHYUYSQjGTSkLInzMJIX/GJIRiGJMQiuFMQkgRQl4rTRMhZML33nvvITMzs40Q7tmzR4ocLl++nNeYu2S9ehXCnbUezPld/jnA2YNjpbcBm1JxoxszNhZhV40z4LxO6ZeMS3PjYOe4RbOurg6w2FDg8OGn8kbsrnYhv86F9EgLsqIsODHOhuEpkYju4IH7gIOgDCAhFLMISAj5cyYh5M+YhFAMYxJCMZx1I4Rni312QooQZtCzEzxXmSZCOG3aNERHR+Oxxx6DyWQ6GiFkH/QeffRRabvd7NmzeY67y9WtVyEsd/okIaxg704ESD2izbizTxRiWr0NWNTgwbxdFVh4oP3ziOyxihlDukkXyaRHhPa5idZdZkJos9lgsRxuh73VWO32ItJkRLTMNw0DcejqXychFLMCSAj5cyYh5M+YhFAMYxJCMZz1IISfHPQiSgMhvJyEkOsi00QId+7cCXaOMC0tDWPGjMG8efNw2223YeXKlThw4ADy8vKQk5PDdeBdrXK9CiHzwHf31uOHMlfAKbnyhAicm25vNx97hmJTWQP21bmkaGFxowc9YqwYlBiBgQkROCGarwg2daq1EAYcFGVQTICEUDEyVQVICFVhU1SIhFARLtWZGxoapH98Zv9YR4kPARJCPlxb10pCSFtGea00TYSQDWbHjh148cUXsXHjRng8HhiNRilSeN9996F37968xttl69WrELIJ+aPGg+e2Bd42+s/BcUi1GwPOIZPDercPaRqc0SMhDDg9QWcgIQwaoawKSAhlYQoqEwlhUPhkFyYhlI1KdUYSQtXoFBXUixBGC44Q1q14DxQhVLRUFGfWTAibeup2u1FTUyNtIaV/vVM8f7IL6FkIK5w+/HNrrXRJTEcpJ8qMe06M1v3ZOxJC2UtSdUYSQtXoFBUkIVSES1VmEkJV2BQXIiFUjExxARJCxchUFdCPEF6nqv9qCx0WQqP0TjklPgQ0EcKnn34aDz30EJ8RUa3tEtCzELJtowUNXtR7/B3Ons0IsCcn9J5ICPnPEAkhf8asBRJC/pxJCPkzZi2QEPLnTELInzFrQRdCeMiL6LMFC+E3JIS8V5gmQjh69GjpvKDZrP8P+LwnQFT9ehZCUQxEtENCyJ8yCSF/xiSEYhiTEIrhTELInzMJIX/GehHCTzUSwssoQsh1kWkihPfccw9uuOEGDB06lOvgqPJjBPQmhC6fH3UeHxI5PBAfaN7rPACLOPK49JOEMBD94L9OQhg8Qzk1UIRQDqXg8pAQBsdPbmkSQrmk1OcjIVTPTklJPUQISQiVzFj45NVECMvKyjBnzhxccMEFGDVqFKxWa/gQC9Oe6kEI2WUvWyocKGn0YHuNEyUOD3pEWzEkKUK6ETST4yUwFS4/ip0G7K1n/wVizH5k2oF4iwG50QZEBL6rRtbMkxDKwhRUJhLCoPDJLkxCKBuV6owkhKrRKSpIQqgIl6rMJISqsCkuREJIZwgVLxqZBTQRwvHjx4P9RVhVVSV1MzY2ts320WXLlskcAmWTQ0BrIaxwepG3uwqLC9q/TdRmNODhwanoE2tBoi10W4lZNLDIAXx8yAunt31Sg+ONGJsExFvZi4XBJRLC4PjJKU1CKIdS8HlICINnGKgGEsJAhELzdRLC0HDsrBYSQv6MWQv6EEIfYgSfIaz95l3QllG+a0wTIZw7d27AUbHH6ymFjoCWQljp8mLmpmLsqg381uC0gckYmxYFuzF4OXP7gW01fnx2KPCj94lWYNIJJsQG+VwhCWHo1mxHNZEQ8mfMWiAh5M+ZhJA/Y9YCCSF/ziSE/BnrSgjPEXupjCSE6XTLKM9VpokQ8hwQ1d0+Aa2EkG0TfXNnZYeRwfZ6+5/RmTghKkgzA1DuAl7d3UFYsJ2GB8UbcF6qEXaT+lVEQqiendySJIRySQWXj4QwOH5ySpMQoKAHkAAAIABJREFUyqEUfB4SwuAZBqqBhDAQodB8XQ8RQvaP7DEaCOGlJIShWUQd1EJCyBWvfirXSghXFzfgH1tKFIHIjbbiyeFpSLCqP9jHtoq+f8CLQoeipnFlphH9Y9VHJ0kIlfFWk5uEUA015WVICJUzU1qChFApMXX5SQjVcVNSioRQCS31eUkI6Qyh+tXTeUlNhPDee+8NOJ7nn38+YB7KIJ+AVkL4SX4N/rOjQn5HAdhNRrw+OgPJdvVnCavdfvxnrw+N8gOEUh/PTDFiTLJ2Qsj6bTQYYDEwDoqwdZnMJIRippqEkD9nEkL+jFkLJIT8OZMQ8mfMWiAhJCHktdI0EcJbb721xXjYDxJ28+jBgwfRt29fRERE4L///W9QY543bx4+++wzsA+PZ599Nu644w6YTO1/wt6/fz+effZZ7Nq1C5mZmZg6dSoGDhwotc/qWLx4sdS3uLg4/PnPf8aECROC6psWhbUQQva0xHO/lWFlUb3iIb8yMgO9YtTfPlvq9OP/7Ql8drB1x06MMeDiDCPsKoOTaiKEtR7glyofSlx+lDmBOq8fqTYD0u1G9I8xIM2uGN9xXYCEUMz0khDy50xCyJ8xCaEYxiSEYjjrRQhjz7lezICPtFLzzbu4NN2ASZNICHmB10QIOxrMgQMH8NJLL+HOO+9ETk6O6jF/8803eO211/D0008jKioKM2bMwLhx43DddW0PwbK/kG+55Rbp+Qsmeux2UyaTeXl5Utk33ngDQ4YMQY8ePcDE8fHHH8eUKVPAbkoNp6SFELKbRdl20d+rnIpRPXBSMs5Oj1ZcrqnAbzV+fHJQuRCmRxhxVaYBcSqPMCoVwhoP8OEBLwocfvj9gL/ZiJvu1bk43YRe0QbEqQ+Yquaox4IkhGJmhYSQP2cSQv6MSQjFMD5ehdBXXwWf2wWD2QyjPRoGs/p/qA7FTOhCCAt9EC6Ey0kIQ7F+OqtDV0LIOrpz507pjcJgIoQPPfSQFOGbOHGiNPbly5dLksd+tU5bt24Fy//xxx/DZrNJX2b/AsF+nXPOOW3ysxtSWaTx7rvv5j03Ia1fCyFkA3j593Is7OCpic4G+ObpWcgI4l1CJlov7FS4XxTAkHgDLk5XGR4EoEQIWWTw2Z0e+PyA0wd4/ZB+z36ZDId/2UwA6834biaclnh4K2lXTySEYlYACSF/ziSE/BmTEIphfDwJod/tgKs4H/U/L4O7rED6vTkuGebkLFiSshA9/HyYY5PFgG3VCgkhRQh5LTzdCWFNTQ3OPfdc/Pjjj6rHfM0110jCxqJ+LO3duxeTJ0/GwoULYbW2/NedRYsW4YsvvsC///3vo+2xKGBWVpYUOWye2A+82267DRdddJH0iyX24TQcEhPCt956C88995zQ7q4pdWD2llJFbSbbTXhuRBqSbUGImc+Ieft8qHArahqXpBtwUkzzOJ2y8uysCltjZnPn4bwGnwEfHfRjb4Mf7AIcJoEdpQgTYDECt+YYkWVX3zdlI9FvbvY9x/5SjI5WH0HW7+j00zPGmK1ji0VluFw/Q9FtT5gQMvGOiYnRbR+Ph46xtcz+Ibf13//Hw9j0Mgb2+ai2tlZ6Vzqck6+qCPW/fY+q5W93OAxbZi+kXPUIDHGpwofqcDhgMBiOBjDa60BHx6NC0VkWWPlcowgh+3xGW0ZDMYvt16ErIWQfpl9++WX88MMP+Pzzz1WP+pJLLsETTzyBwYMHS3WUlJRI20UXLFggnQNsnlhkkLXXXJTYeUIWLbzrrrta5GXbRzdu3Chta236kFRdXa26nyILbtu2De+++y7+8Y9/iGwWFX4L7lpXBGdnxtOqR3/OjsUNuZEwej3q+2qyYFW1BRsq5QsU26J5W64BEZ4G1e2yD3jshzX71VEyGo3Y7bZjfiFQ45YX8mNSmB3hx7VpXli8yrfgqh6QTgsyzowjJX4E5Kxlfq13nZppLfOfayYrLHX2c5l/L47/FsJ9LZu9LjSu/Rg1az6WNVkZ9+XBaYqQlTdUmeSs5dafc0PVNquHhDCUNPVVlyZCeP31bQ+jMhksKiqSIm6zZ8+WLoJRm3hECD/44AMsXboU7PbT+Ph4tV3TrJxWW0bZgNeWNuKxn4tljT0z0oKnh3cL6obRpobYttF3D/hQ6pAnhZdnmjAwyH/clLtldFmJD4uKfNI2Ubkp2QZM7WlCXBffN0pbRuWumODy0ZbR4PjJKU1bRuVQCj4P3TIaPMNANRwPW0Ybt69Dybt/CzTUo1+35w5C8tUzYIoS95lQD1tGWYQwTvClMtXL35V2cFGEUPbyVJxREyF89NFH23SUXeDCbvg877zzkJoaXBienQkcNGjQ0Utk2CUzb7/9dodnCKdPn45PPvnkaNTvxhtvBJPWpjOELLLItpWy84NJSUmKIeuhgJZCWOn0Yl1ZA17YWt4pCiaD9w9MRr/4w2c5Q5HYO4SfHvJKt3d2ls5NNaJPrAEJQe6OkyOEDt/hNxJ/rlJggzh8pvCB3iZkRMiLKoaCnx7rICEUMyskhPw5kxDyZ8xaICHkzznchdBTU4aSNx+Cu+KgIlgp1zyKyP6nKyoTTGZ9CKEfcePF3jJavfwdXJJGQhjM2glUVhMhDNSpYL/OLpF5/fXX8cwzzyAyMhIPP/wwxo4de1QQ2bnBlJQUjBgxAuwv5Jtvvln6+rXXXitdQPPmm28evWWUPTvBooNsG2mTqLKtaoHOiAU7hlCX11II2VgcPj8O1bsx59dSFDV64fAeuwE0xW7CKSlRuLZ7bEgig63Z1bj9WFMObK/zoabZmUJ2Li/eAlycYUKqDSG5sEWOENZ7gTfzvdhZp0wImQbe3sOEvjEkhExWwv2sSqi/x0NdHwlhqIm2rY+EkD9jEkIxjMNdCL01pTj4/I3w+5TdCxE/7nrEjRMnRySEdKkMr+9oTYSQRQGXLFnS4ZgCfV0ODBYRZOcQ23uHkAlinz59cNNNN0lV7du3TxK+3bt3IyMjA/fcc8/RdwjZ2UN2BrF5YpfVsItnwilpLYRNrCpcXri9ftR6fNhX50K/ODvMBiDOaoKNhcA4JbcPYCLGUoEDSLawh9/9MBsMiA7hcw5yhNDtB+YXeLG2QqEQGgx4rJ8p6CgmJ8TCqqUIoRjUJIT8OZMQ8mdMQiiGcbgLoXPfVhS9cZ9iWJH9RiLp0vtgjBBzyZkehPCLQm0ihBdThFDx+lRSQBMhHDZsGDZt2tRuP9lfkCxy19HXlQyO8h4joBchPN7nRI4QOrzAugo/Pj6k7F8iU2wGTOluRLKNnziHw/yQEIqZJRJC/pxJCPkzJiEUwzjchbDhj7Uofe8xxbDsPYYi5ZpHpDcKRSQSQooQ8lpnuhPCDRs24MEHH8S3337La8xdsl4SQjHTLkcIWU921QFv7vOiziM/SnheNyPOSDIiJshzjmJI8GuFhJAf2+Y1kxDy50xCyJ8xCaEYxuEuhN7qMhQ8p3zrZ8zIS5F4/v+JgQxITy6x23LtdruwNps3xG4ZpQihJui5NypUCNk5PZbYh+b23hBzu91wOp1gz0bMmjWL++C7UgMkhGJmW64QHmj0o6ABeL9AXpQwN9KA8alG9IoxIKKLv7ZAQihmLZMQ8udMQsifMQmhGMbhL4SlKHrzQXgqCxUBS756JqIGjFZUJpjMuhDCIj/ix08MZhiKy1YtewcXp4FuGVVMTn4BoUL46quvSj1jl7awi1xap4iICHTv3l264IXeGJM/iXJykhDKoRR8HrlCWO+BdIbQZPDjk0PHLthprwc9ogw4I9GIFLsBJ0QG38dwr4GEUMwMkhDy50xCyJ8xCaEYxuEuhH6XA3Wbv0HFVy/LBmZOykS3G5+COS5FdplgM5IQ0pbRYNdQR+WFCmFTJ9gFLvfffz+vMVG97RAgIRSzLOQKIetNscOP36qB7EhgYZFXugG1/MgtqBEmA5KtQN8YI7rZABMMGBjHLsIRMw49t0JCKGZ2SAj5cyYh5M+YhFAM43AXQkbJV1+F0vlz4NjzkyxoaZNfhC2rr6y8ocqkByH8UqMI4UUUIQzVMmq3Hk2EkOuIqPJ2CZAQilkYSoSQHR8scQA/lvmRHgFEmoBIM1DugnROsNEDSRITrAb0jAbiuvjZwaYZJCEUs5ZJCPlzJiHkz5iEUAzj40EIGSl3WQEql/wXjTvWdQjOaItEyoRZsGX0gcEmdtsOCSFFCHl9R2smhB6PB9u3b8fBgwfBft86XXDBBbzG3CXrPV6EsMLpQXGDG/0SInQ5j0qEsGkAjV5gY4UfDq8BdW4/jEaAvZEYbQJ6xBjQzR6aNxJ1CUxFp0gIVUBTUYSEUAU0hUVICBUCU5mdHqZXCU5BseNFCNmQvXUVaNz2I+q3rpIE0VtTBqPFDnNyJizJ2Uj40y0wCdwm2nwaSAhJCBV8WyrKqokQFhUVSW/97dy5s8PO0rMTiuYxYObjQQhLHR7M2nAQG8saMPPkdIxOi0GSTV97KNUIofQXEPuXSR/g87P/+mE1GCQxtHXxC2TaW9gkhAG/3UOSgYQwJBg7rYSEkD9j1gIJIX/Ox5MQNtHy1lUBXje8DTUwsXcGzRYYbVEwWGz8gXbQgj6EEEj4k9hLZSqX5eGibnSpDM+Fp4kQzpw5ExUVFdLzEpdffjkWL16MwsJCfP311ygrK5POF6ampvIcd5erO5yF0On1Y0tFA+5bewCOZs80nJsdi7sGpKJbhH72UqoVwi63IIMYMAlhEPAUFCUhVABLZVYSQpXgFBYjIVQITEX241EIVWDgXoSEkCKEvBaZJkLItoOyG0fZjaKtH6n/4osv8Pvvv2P69Om8xtwl6w1XISxzePD2znJ8sKui3XlLtJkw59Qs9I23w27SPpxGQsj/24uEkD9j1gIJIX/OJIT8GVOEUAxjEkIxnEkISQh5rTRNhHD48OFYu3YtzGYzRo8eLUUGm94ldDgcYMK4YsUKXmPukvWGoxAW1LvwwLoC7Kp2Bpyz/+ufgstzE5Cg8RZSPQgh23p6yOFHggWItRgCsgu3DCSEYmaMhJA/ZxJC/oxJCMUwJiEUw1kPQvhVkTZbRi9sZ8vomjVr8O9//xvl5eUYNGgQHnjgASQlJbWZjEOHDuGNN97AL7/8Ir13fuKJJ+L2229Hbm6umIkLg1Y0EcLmUcErr7wS06ZNk8SQpW3btmHKlCn47rvvwgBf+HQx3ISQnROcsmqfIsAjUqLw5KlZiGM3smiUtBbCei/wa7UPi4t9SLIYMCHbKEmhVTskIZ8JEsKQI223QhJC/pxJCPkzJiEUw5iEUAxnEsJjEUJ2H8ktt9yChx56CCeffDJefvllVFZWYs6cOW0mg30G3rp1K0aOHImoqCi8/fbb+Pnnn5GXlydm4sKgFc2FkD1S/8477+Cqq66C1WrFggULMGTIEMyePTsM8IVPF8NNCAsb3bh4yS5FgP+UFYtZJ2fAZtIuKqalENZ5gPkHvchv8LfgdnG6CX2iDNJTFsdDIiEUM4skhPw5kxDyZ0xCKIYxCaEYzroQwmIgUfClMhVL89A6Qvjee+/hp59+AnvbnKWSkhJcd911YH+ekpLS6YSwiOI111yD+fPnIz4+Xszk6bwVTYTw008/xWWXXSahcblcmDt3LpYuXQq2XXTUqFF45JFHaIJCvHDCTQgrnF48vL4AP5U1dEoizmpCndsLrx94eXQ2TkuNDjE5ZdVpIYRsi+i+RuCDAx64W7rg0c4PiDHg3DQT4szKxqPH3CSEYmaFhJA/ZxJC/oxJCMUwJiEUw1kPQrhQIyH8c6sto08++aTkCmxXYVNiF1XOmDED7GhaZ2nVqlVSRPHDDz+EwaBdEEHMqpHXiiZC2FHX2A8Umhh5E6c0V7gJIRvfB7sr8dwvRR0OdXBSFMZlxiLGYsTCfRV4bHgm0kNw42il+3CTFS6gxAlkRUA6j8d2XbKH4ztLooWQRQVXlfuwtsIXcElEGIEJ2Sak2cN7CykJYcCpDkkGEsKQYOy0EhJC/oxJCMUwJiEUw1kPQrioGEgSHCEsX5qHC1oJ4axZs9CrVy9MmnRsG+nEiRNx6623YsyYMR1OSHFxMe6++27pDOHYsWPFTFwYtKIrIQwDXmHbxXAUwoP1Lly6dHe7zC/vkQifwYT5++qlr9/TLw4XZscgOYhLZdgD8cUuYGkR0MAeBmyV0uzARWlAVCdSKFIIWR8XHPJhd11gGWw+lAlZJvSNCd9/ESMhFPNjiISQP2cSQv6MSQjFMCYhFMNZD0K4uBhIPlfsO4RlS/NwfmrLdwjVRAjZVtF7770Xl1566dGdimJmTv+taCaE7HDn66+/ji1btqC6uhpND9E///zzku0nJyfrn14Y9TAchbDS6cV9a/fj1wrHUdJpkRZM7JOMRQcbsbnSdfTPMyMtuDg7GlflRKm6abTRB/xaBfzQ/usWLWb6onQgzQbsqvNjQJwB5mZuJVIIWaeWlfixurwde+1kbd7TyyxFPMM1kRCKmTkSQv6cSQj5MyYhFMOYhFAMZ70IYYpgISxtRwjZWcHNmzcfvUSmtLQUEyZM6PAMIXv/nMngeeedJ50fpNSSgCZCuGHDBtx555046aSTpHcImRg2CeG7774rPU4/depUmqsQEghHIWTDz9tZhpd+K5VInJ4WgwGJUfjfnjrUeY5FxdhWzh6xNliMBilCeH//OHSLMMOu4HKZbbXA18WBgbMjeuyc3vgUP74o9OGURCNGJxoQe0SwRAshe17itb3yhfCECAOuzTYh0hR4rHrNQUIoZmZICPlzJiHkz5iEUAxjEkIxnPUghEuKgVTBQliyNA/ntYoQFhYWSttD2b0j7DLKV155BUwKm24ZXbRokXS5zIgRI1BVVYX77rtPuqeEbSttShaLhY6qHYGhiRDeeOON0qRMnjxZ6kbzZyjy8/Nx11134csvvxTz3dVFWtGbEFY6ffix3IMzUi2IaR5iazUf++tcuGLZbkzqk4yDDj+WHGpsM2MxZiPSIy0wGY+F6m7qFYtTk22ynqAocwHv7A+8ENjFNXUev7SdNMMO5EYBm6t8SLEbcE2mAYlWA0QLYY3bj//t86G8o9tkWg3r3FQjRiWF9xsUJISB12oocpAQhoJi53WQEPJnTEIohjEJoRjOehDCr0uAboKFsPjrPJzbSggZ8dWrV+O1115r9x3Chx9+GH369MFNN90kvXfedBtp85libxj27NlTzOTpvBVNhPC0006TJicuLq6NELKbRtkhz3Xr1ukcXXh1Ty9C6GI3YtZ78d9djegebYTXb8DEHhEd3n5Z7vCguNGDJYWN+KawrQyyWWCRwQhzW8m5NDsKV+cGvnV0Vz3wVWHn8+nxA5UuP9h/D7cJXJoOLCw6Fqm8JMOIbIsLsRYj2L86iUpfFvqwsUreOcKpPc1ItIrqGZ92SAj5cG1dKwkhf84khPwZkxCKYUxCKIazHoRwaQmQJlgIi77Ow5/aEUIx1LtGK5oIIRM+tvc3MzOzjRDu2bNHihwuX768a8yAoFHqQQirXT78UO7F4kMOnJFqxUEHcGqCET+UuXF+hg29ok0dPqD+Y6kDL/1RrYjWY4MS0DcusP18Wwps6aBqnx9w+PyoPnLzaPMOXJIBrCnztbiApn+0D+NTjYgP4nIbRYMEsK/Bjzf3Bd42mmkHJuaYwW4bDedEQihm9kgI+XMmIeTPmIRQDGMSQjGc9SCEy5gQnif2UhkmhONTWl4qI4Z412lFEyGcNm0aoqOj8dhjj8FkMh3dMso+6D366KPSfl56mD60i1BrIaxy+fHf3Y1o9PpxVpoVS4u92FN3ONx2UYYJDo8P0WYDzkmztLvNs5xdMLOpAk6vvEhYks2IxwYlItne+WE5dhTx00OQ5LS91Ojzo+rY3TUtsoxIYE9T+FHoOPb4H/uA1zPaiCsyTZ3eRhrK2a1yA2/le1Dl6bzWcclGnJkS5jYIgIQwlKun47pICPlzJiHkz5iEUAxjEkIxnPUghMtLgHTBQlj4dR7OISHkusg0EcKdO3eCnSNMS0uT3gqZN28ebrvtNqxcuRIHDhxAXl4ecnJyuA68q1UeSAgLG9worHfj96oGbChtQEakBaemRqNbpBm5MTZEmNSJRJ3bhz9qfZi3txEnxZuRGmHGZwc9YI+pN0+D44wYHG/A1moPJubakWxr2V69x4d/ba/GT+xxQBlpTLcI3NIzFlYZl6d8Uwr82ipC6PP7wdq0mYxgZwzbe6ThwjRgfaVfOlfYlNgHPPYAvMhzeozlV0VebK7u4FX6I527vbsJ3ezh+9xEE2MSQhnfACHIQkIYAogBqiAh5M+YhFAMYxJCMZz1IITflAAZgoXw0Nd5OJuEkOsi00QI2Yh27NiBF198ERs3boTH44HRaJQihewWoN69e3MddFesvCMhrPd4savaiYfWF6DU0X6I6Y7+qbg4Nx7JtgCvsrcDdm+dFy/taMBZ3WzIbwTWdfJEAjvbdmmGGbvqvLgy2yZd0tI8rS5x4NXt8raNzhqUiH5x8s7x7agDFhUda8nj96PM4UG1y4tEmxleGOGDoYUUMj++MgNg5/eaJ/YB765epjZCy3vNsShlnadj2TPDh0SbETKR8O5uUPWTEAaFT3ZhEkLZqFRnJCFUjU5RwYaGBmk3ks1mU1SOMssnQEIon1UwOXUhhKVApmAhPLiEhDCYdSOnrDAh/OGHH6SbRVsnt9uNmpoaaQsp/bCWM2Xq8rQnhA6vH6uLaiUZDJTGpcfgoSHpSLErk8Iqtx/ba7xYUOBpsbWys/auyDLjzBQzEloJYaXLh7vXl4EJW2cp3mrEP4YkgW0blZMOOYCPCgAf/PD4/Ciod8PNDg8CiDQbYTeb0OA1SJfXSH/qB1LswIAYYGNlSyFMt/lxbZYBMXJCk3I6R3naECAhFLMoSAj5cyYh5M+YtUBCyJ8zCSF/xqwFPQjhilIgS7AQFizJw1kUIeS6yIQJYfOnJZpGdMMNN+Dtt9/mOkCq/DCB9oTwQL0bly7dKRvRpN5JuK1/KuzNnncIVLjS7cfzO1zYWyfv7B+rL9VuwN29rciNbLtt9KVt1filo0N9RzozOsWOyb3ZdlF52yPZzaebqvz4usgr3WjKpK+ppMEApEVYUO1hMmhAk4temQl8W+oDe4qieRqTCIxJhvSv0ZT4ECAh5MO1da0khPw5kxDyZ0xCKIYxCaEYznoQQnYRX7ZgITywJA/jSAi5LjJNhbA9SeQ62i5ceWshZM853P3DfvxR3cFtKh2wemVUDkZ2i1JE8vNDbsw/EODGkyM1MtfsHmXEfX2siLW0FbqVxQ78e0fn20YfOSkBA+MD3y7aNIhDDW58V9SIUrcVv9X4jwqh0cCeszCgW4QVTGyZOLI0NtmAP+p82Ffvl7a1Wo3HBHJyjh8pVsBsVhZJVQS0i2cmIRSzAEgI+XMmIeTPmIRQDGMSQjGc9SCE3zEhPF/sLaNMCM9MpltGea4yEkKedHVUd2shLG5048rlu9HArtlUkGadnIFLcuIVlAAONPjxyG8OHNmF2WnZSDOLsJlweZYFke1E+AodXlQ7O+4zE8oEqxEpAW4XbepEaaMHc34pwbu7K/F//VOQEBGJn6sPR/eS7GaYjeytRPbuoBFGgx9nJBmwqtyLg41+mFn4EECMBYgyGZAVAVzWzYd4C9q8Q+jw+mBXeTGPIthdIDMJoZhJJiHkz5mEkD9jEkIxjEkIxXDWgxCuLAVOECyE+5fkYSwJIddFRkLIFa9+Km8thH9UOXDdt3sUd/DC7Dg8Pvzw+5FyE4uuPbfDhXwZ20ZT7Abc0t2GgbHytnvK7UN7+dhTGB/l10hXxszaeEjKwqKf47PjMSgxGvsbDNK5x24RBpwUa8COeuC7Eo/0JiHbjmpsds3M4YfqjTgjziVtF23+MD27rOf+tQfwyMkZyI6yIkLmVtZgxnY8lyUhFDO7JIT8OZMQ8mdMQiiGMQmhGM56EMLvy4AcwUK4b3GedBxn0qRJYkB3wVaECuHHH3/cAvEVV1yB1n/WlCE3N7cLTge/IecfKsbPv/yCi/40HmYj8Pm+Kjzx02EJUpIGJ0ZgzqnZSFZ4ucwnB934pKDzbaMsupcTZcT9fayIa2e7qJJ+Bsrr9Pqx8GA9Ptxbi2tyozBzYyEqnIf7NzYtGn86IQk764EoswHlTp90Y2lulBHshQnWT/YMx5EA4dGmWJSwb4QHVpNREkIWFfy5vBHT1x+Ufs/SXQNScUF2rHR7KSV1BEgI1XFTWoqEUCkx5flJCJUzU1OCLpVRQ01ZGRJCZbzU5taLEOZqIIRnkBCqXTayygkVQlk9OpJp06ZNSrJT3nYINHiBggagyAEU1jRiX3EZhvTMRrodiDC4cPU3uxRzuyI3ATOGpisut6/Bh5m/OTvdNsq2i45OMuPqEyywy7sgVHE/mgrsrnVjxk9l0v+OTLbj57JaLDtYK/0/k73Zp2Ti7X3Htqay7tjNBlgMBun5hvaEeGKuBRlGpySDNT4j5u0oxwd7Ktr0cXhyJB4blqH4xlbVgz3OCpIQiplQEkL+nEkI+TNmLZAQ8udMQsifMWtBD0K4qgzoLlgI8xfn4XQSQq6LTJgQfvjhh4oGcvXVVyvKT5lbEqjzACtKgILGw3/OfoiUlZUhOztb+v/BsR489ONusHcIFVwaiqdOycL4zFjFuMucfryZ70aps/MnI64/wYLB8ZxtEMD6Mgee21opjSM9wox+sWY83ixies/AVOxyWFHQ0LK/7OmJ7jFW2Fpt+4w0AdP62GB2N6LcZ8L0DYewq8bZISd2nvCpUzJxclJkm7oUw+1iBUgIxUw4CSF/ziSE/BmTEIphTEIohrMehHB1GdDjArGXyuxdnIfRSbRllOcqEyaEPAdBdbckUO8F3tsPuJvdvdJaCLMjfJi/4xA2lNaAyUyLV9c7AfrR2T3RM1bd474LizwfG4rZAAAgAElEQVT4oczbYe1mo0HaLhojYDflW7tqsORg/dG+XNcjBg+uO4CaI1eJnp4WhaGpSVjVqr/sXcKcaAtYX5unQXEmjEs1YGNRDf6xpRiG1vtJOxj11T0ScXWPBGRGWWgZyyRAQigTVJDZSAiDBCijOAmhDEghyEIRwhBADFAFCSF/xqwFPQjhGg2EcA8JIfcFRkLIHbHYBhpYZLAU2N/Qst3WQsi+OijGgymrd8Po90rnCgOlx0/OwIUKbxhtXmely4/OmjEdkaxoAUL4zG+V2Fh+7MmN0al2rC2qwXeFdVKXYyxG/G1YJt7Z3/JG0ySbCRntyNvEHCvS7X5M/n4v2PuOcoWQtfX5n3ohLULAoANNcJh8nYRQzESREPLnTELInzFrgYSQP2cSQv6M9SKEP5QBPQVHCHcvzsMoihByXWQkhFzxiq98Tz2wpKhtu+0JIYsMxhga8Pim/TDD1+aSlOa1TB3QDeOzY5EecXxEshYW1GPe7pqjQ8yKNCMn0oi5vxYf/bN7B3XDlhoLCh3HpJBtF41iV4o2S+y84719bIizGvDclkJ8uLdKthAOSIjA86dlId5KD9nL/W4hIZRLKrh8JITB8ZNTmoRQDqXg85AQBs8wUA0khIEIhebreogQ/lgO9BIshLsW5WEkCWFoFlEHtZAQcsUrvvL1FcDGw0fjWqT2hJBlYFJ4YrQbH+0sxrrSWjjYo3vNUje7GTOHZRw56yYjjChzyCUOD2IsJs2eYNhW7cJjm8tb9HZ8RgQyI8zSw/QssbcMdzUY8X3JkW2uBsBmNLQ58zcg1oQrs83Su4m/lNbhr2sOyBbCW09Mxl/7JsukRtkYARJCMeuAhJA/ZxJC/oxZCySE/DmTEPJnzFrQgxCuLQd6CxbCnYvycBoJIddFRkLIFa/YypnLLS0C9rbaLtr0Q6T5pTKte3ZJug8meMHezNte7UB6pEU6K9jo8UuXqIQyFdS7ce+6g5jQMwFj0mOQaA2daMrtZ4nDKwlhubPjM41s2+jlObH4qOBwnigTu13UhNbPCF6WacZJ8YcjfAdqGnH32oM41Nj5ExtN/Xx/XHf0UHkmU+5Yj7d8JIRiZpSEkD9nEkL+jEkIxTAmIRTDWS9C2OfPYi+VYUJ4aiJdKsNzlZEQ8qQruO56D/B18eFnJlqnjiKETfnOTQN6RvHrcJnDC6/fj41lDZjzS8nRhtgTDA8NTkWqwncNQ9HTnbVuPHrk6YmO6huVGon+cYeFeGCCDdbWV7IaDNL5y7gjRwCr6xvx8vZKfHng2HbUjuruG2fD06dkIcluQb3XjwTOby+Ggpke6iAhFDMLJIT8OZMQ8mdMQiiGMQmhGM56EMJ15UBfwUK4Y1EeTiEh5LrISAi54hVf+cpSYGs7LhJICG/IZREwPv2tdHqxvqwRn+VX4ddKR5sIG7u588kRGTgxzgZ76/Abny5JtTZ6fNhS6cLc39vZY9usXRYpnNY/EQPiA5+fZB+itzcAU344ELDnN/ZOwqQ+yfjyoAO/Vrlwz4nRSLFxmoSAvQmfDCSEYuaKhJA/ZxJC/oxJCMUwJiEUw1kPQri+HDhRsBBuX5SHESSEXBcZCSFXvOIr3113OErYOnUmhLFm4NJMINS3e9Z5fNhd48bP5Q149tdSVLm8YLtD2Y2a7D2/1unqHvFgv5JsfG7cPNjgQWZk27qLGj14YVsVihu9aPAcu0CGXfTSL86KCT2iZUcw6+rqUGOw4rY1B1DS6O50AbwxJhcLC13YVXts2+rNPSIxKNGKKIFiLH6VBtciCWFw/OSWJiGUS0p9PhJC9eyUlKQzhEpoqctLQqiOm9JSehDCDRXihfCPhSSESteK0vwkhEqJ6Tx/tRtYUAA4W76W0OZh+ubDGB4PnJwEhFLDKpxerCxuxJqiWnywp6oNtVS7CSwy2Np7+sTa8MTwdOmx+FAlJqbrSxqkSODU/okYlmxHQqtbPatcPnj8ftR7/Mivc6NPrBXsMlG7yaRIlJkQGq02vLajEmtLjr1z2HwsPj+kbaJnZSVgWZGrzTBHJVtxSXYkEq1tpTlUTMK5HhJCMbNHQsifMwkhf8asBRJC/pxJCPkzZi3oQQg3VgD9BEcIty3Mw3CKEHJdZCSEXPFqU/n+RuCrQy3b7ihCmGIDzk8LXXTQ6fOj1OHF37eU4pSkCHyxvwqbyhrbBRFtNoC969f6kfe3zsgO2UUrlS4f3t5ZiW8Kj920MzYtEjf0ikOK3Qy3H2Dy2s0emq2aTAhtNhvq/UYw8Wud2C2ue+o8YG8yrix1odrVytyPFEiwGnBX3xh0sxvbnlvUZlnpplUSQjFTQULInzMJIX/GJIRiGJMQiuGsByHcVAH0v1DspTK/L8zDsAS6VIbnKiMh5ElXo7qZ5Oyvb7l1lP0Q8dVVISol/WivutmAsSlAsi00Ha1x+7G2tAH/+uPwmTwWZYu1GPC/HRVweNsXHxYhZNFA25FQYa9YG/45PA1pQb536PT6UdDgwT82l6KsnZtEE2wmPDAwCcUuAxYWOHB7n0jkRJmlLa3BpCYhtFjanjc80ODFS9trUeVqxxQ7aPSqEyJwWooNMWaKFjYhIiEMZoXKL0tCKJ+V2pwkhGrJKStHEUJlvNTkJiFUQ015Gb0I4QANhPBkEkLlC0ZBCRJCBbDCLSvbPrq7xgOz0Su9kbe7yoGMGBvcfgOS7FYkWUMXGWRsXD4/XttehW8KD2+VZG41a3AKfihtwJvby8DO6rWXMiLNR98jvL5XAm7tmxQU6mqnF8sK6/H2rmr44YfbB+nMYnOlcvv9KHP4cFlOnPS8xqpSD85Js2Jcmg3xQdz22ZkQ7qjx4JlttYrGdmqSBTf0iJa2r1I6TICEUMxKICHkz5mEkD9j1gIJIX/OJIT8GbMW9CCEP1UAAwUL4daFeRhKQsh1kZEQcsWrbeUHG9yY/H0+Cupd2FntgM/nR3KkBb1jbLikewKu6pGAjMjQvjH4W6UTj/5cim4RZlyRE4ff6sz4o8aD0QkefHOwGisOtRQiFhjMjrTAdER4Xj8jG72DeJePvS84d2s5WD/YMxfsTGCFy4cEqxFsi6r0A9UL6c+a0oB4Ky7PjceaUg+6RZhwW+9I1U9AdCaEFU4fnvq9Fmwbq9x0b78Y9GO3/lA6SoCEUMxiICHkz5mEkD9jEkIxjEkIxXDWgxD+XAmcJFgIf/sqD0NICLkuMhJCrni1qbzC5cWqwhpM+GYPmjYnsh/Wnv/f3nlASVWkbfjtPDkxMwxxyCASxKyYkEUR16yAOee4a173N+e85rgqqy6uqBgwIoKYUUCCwCCZgWFyjh3+89XMHXpi9+2ee2mm3zqHsy5T996qp2qafu5X9ZXbDf+ljEf0SsITB/XF8JTYLmuo7B+cubECWXEx+KJAooQWWCxAUa0bkrzG6qnFUyt3nkOYYLciM9amoncDEpx4ZP/eyAwjoYxk9rx1cSG2VzegoNaL2qaNfCKeIoUigzWelks25bD560emY872nVlBzxkYg9GpDhVZ1VM6E0KJSv5nfTV+LGybSKa9ZyQ4LLhjdHJYEUs9bd9d6lIIzRkpCqHxnCmExjOmEJrDmEJoDudIEMKlu0AIl1MIDZ9gFELDEZv/gPnbyjHl87UtHtyeEEqFAzMT8O8jBioZ64pS7gbe39qAefmNRymIDErimM2VjRLUNxY4Mt2Hf63Ix5qyOrV/MK4pcjd9UCou3yO85aKSUfS5VSV4c315swyL0yXarSoqKH4oB8lbpWFNZWKveOyRGo+lJS2XtB6c4cRxfVxqH2SwpTMhlHtItPSxIJeN7pvmwPmD4uHUKaXBtnV3rUchNGfkKITGc6YQGs+YQmgOYwqhOZwjQQh/LwHGmBwhXPbJfzCWEUJDJxmF0FC85t9cloce9vFq5FW3PAOvIyGUFt4+rjcu2zMTcu5eOKWswYeZW73YUOXDtuqdcpXstEIOp/dPLDMx3Yc1JZVYVFAJLV/KS4f0xfDkmJCbIEdHzN9Ri6oGDx5cXth8H9G5NKcVO2p3LtWUYxBtlsbo5S1jMrCq3IeiVmd1jEmx46xBsbrOBAwkhCV1XtyzshwVkvknQLlsWAL2SW2bnCbQdd395xRCc0aYQmg8Zwqh8YwphOYwphCawzkShHB5sQd7/fVMczrc9JSlc97G6FQrzjnnHFOfG00PoxB2s9H+3/pinDd/Q5tedSaEQ5Jd+PjoYcgOI0r4Z6UPb21pFC63D9ha1QBtZWas3aKkr8rv0Hept1+qBanWOqwsrlaH0Z89LA0ZrtCkNL/Og6dWlUMyeZ7SLw5PrCxskV002WFFRYMX/tv3RAZluehtYzLxUW7LZZyZMVYc2ycG+6bp278XSAhl2ehrf1ZhUXHnh9bH2oA7Riep6CpLSwIUQnNmBIXQeM4UQuMZUwjNYUwhNIdzJAjhyuIG7HPsdHM63PSUxXNmYmSag0JoIHUKoYFwd8Wtr/txM15aVaBLCKVyztTR6BuGEObW+PDseq/KhikROUnuUinpPZtKvN3SYpmm/PVRPW04oXfXCI8kbHn0jzLk1niwXw8X1pbVYF5TtlN5VozNorKeytEYzcUC/KV3PMb1SMRvfoI2MsWuBHVSlgM9dB4OH0gI5dkryxrw5OrKTqfHuFQ7zhucoHsP466Yc2Y/k0IYBHGvG57irbAmZsDiig/igrZVKIQhYdN1EYVQF66QKzPLaMjogr6QQhg0qrAqRoIQriqqxX5TTgurH3ov/vXTdzGiRwyFUC84HfUphDpgRXpV2ad38bcbsTCv7dEG8mEtX6Tt9vYjXjMmDMSpA9NC6mK1BxAhfGOTFzXexqMs6j0+lPkJYXs3vmmYHQPiu+48hXc2VePT3GpkxNgwNsWOh/2WjcpTUpxW5LdaNnrT6AxsrBaBbZTXv2S5UNBgxe9lPuyfZsOJva1I7sI9hPIMkdL3tlRDzkrsqByS4cKoFC4XbY8PhTDAr2l9Feo3/Kr+2LOGwznsUFjjknX/blMIdSPTfQGFUDeykC6gEIaETddFFEJduEKuHAlCmFNYhQOmnBJyH0K58OfP3sewHnEUwlDgBXkNhTBIULtDtcJaD674biM+2Vzaprlen08dO2GzSd7PtuXb40Zg3wz9kQQ56/CzPJEtkUIvlpT6IPvzkh2yR68xWtheka1x5w2wofWJCrIP0GexhHTsw8ZKN+5Y1tj3qf3j8ODyAsj9tCLLRssbvOpcQilyOP2d4zLx4dZ69I61YmKvGHya58XWGkmEY4FENV1W4PR+NvSJsajoZ5XHhxUl9RiR7GxXFIOJEO4OcymS20gh7GR0aitQs3g2POU7M/lanHGIGXc8bEmZgC34lwwUQuN/CyiExjOWJ1AIjedMITSesTwhEoTwz4IKHHzMieZ0uOkpP34+G4PTEymEBlKnEBoId1fc+s7ftuHh37erR0v8SQ6Lz3A5cFx2Cqb0S8YLq/IxN7dcSZsc1i4l1m7FilNHoVdc8F8W633A+krg66bvnRIVHBQPvLfNA9kqKJk8ZR+cUzJ6tgNi7xQLjsmyNieUkXauKXfjhbVVcPt8uGp4AkYkOaAjOKfO93tgRRl21HpwYLoLbo8HG5qym6p+NmXrrJBNjgCGJbkQ53AAFiuSnFZ8mtfYUKHSO86qhFYrB/ewQtr83qYq/FBQq+4/bUC8SlbjXyiExs96CmFbxr6GWrjz16Fu+ecdDoBz6Hg4+oyCJSYhqEGiEAaFKaxKFMKw8AV9MYUwaFQhV6QQhoxO14WRIITrC0pxyOTjdLU73Mrff/4xBmakUAjDBdnJ9RRCA+Huilv/d10xLliwQR2vcPqQNJw/LB394l2o8/rwS0EV9suMhwM+rCuvx7Gf5yDBYcX9+/fDkOR4jE5zwg4ftlQ2IKesBsOSY9EvwQEJqGXF7pRFSRizqLAOFns8cmsbrUkikBMzfXhzswduWCH/ODR4fXBYrYizt5XC87Ot6B/XeK0sLf1sWx2+2VGLDJcVQxIsKrlMSW0DLBYf+sQ50C/egSSnHWVuC9KcjVlD2ytvbajCl9tr1I/GpDjh8XkR0xQVdVot2C89Bp9vr0OMTZa22tA/3oY1lRa1RFQr8rMerp1C6POJWAM2eHB0pgUf51ZhR40HEnG8engi+ifY4WqSayOEULY9bq/xIclpQYq+HDeodPvwR4UXIxKsuo7P2BVzN9hnUghbkvLVlKPuzx/gzl0ZEKEtpTdixh0HiyuwFFIIA+IMuwKFMGyEQd2AQhgUprAqUQjDwhf0xZEghJvyi3DY0ccG3eauqPjtF58iOzONQtgVMDu4B4XQQLi74ta5lfW4cOFGXDYyE1Yf8FNhNY7qk4Qrvt+CTZX1uGB4Oib3TcQzK3fghUOysbykFp/m1mJ7jRsD4m2QoxbeyClERUPjOYJSBic48ehB/ZHksmFLRQMuXLgFO2rcOG94Osb3SkF+vR2D4zz4tbASNYjBumoL4uTcvzovEuwWJDhsKlKoHS+R5AAuyLapZaX5dV48n1OFrdVujEuxo7qhDs+szEd5vUdJX7Xbq7KTxtqsOH1ICs4bloFZW904Z2AssuOtEMnzLznlDbhvRRncXh9yq92o83iR7LQhxWlHpQc4Y0A8Shu8qn1jU5yYk+dDgYQ7/UqM1YLYJvESsZYjIkrqd/I4ubcVX22rVMtPpZyWHY/De8Yg0W5BVwthpRtYUubF3HyvSjAzvY8VvWItimegIseAvLWpARurPdgrxYYpvRzd4pB7CuHOkfdWlaB20bvw1rbdN9zx/LAgbvyZsCb27HQKUQgD/YaF/3MKYfgMg7kDhTAYSuHVoRCGxy/YqyNBCLfsKMARR08OtsldUm/+l5+jX2YGhbBLaLZ/EwqhgXB3xa3XV9SipM6Hz7aUIDsxRh3/cO+SprWQTQ0alRqDfx8+AKtK6/DK2nJYLBbI+YWiVpkxNlw4JBFzc8uwtKi6uQserw/Xje6Jz7ZW4LfC2uZ9eJeMSMPe6Qm4b1kR+sbb8Nf+qfgwr/GMP0nuUuMBPD4fesWKFFrUM8akWHBUphXLSurx+voqFc0cHG/F5vIqvP1noYroyWHw+bVulZxGirZ8c0iSC8+Mz8Z3RT4MT7JjQs+We/nyajz459JirCzdeYyE3N9mtaBXrAM2ixX7pjtx1sB4JDms+Hi7B2sq20/u4vECBer8xJ0/Fyk7qQ/w5rqWX8CHJTpwxfBEuNy1cDgc6k+4pcINvLvVg001Ldt3ZLoVe6dakdhBtFCimX9WejBjU71iqxWR83OynegVa1V7I3fXQiHcOXK+umrULpsDT9HmoIfTGpOI2AOmwxKbRCEMmpoxFSmExnBtfVcKofGcKYTGM5YnRIIQ5ublYeJRk8zpcNNT5n31FXr3zKIQGkidQhgE3BkzZmD27NkqS+fEiRNx5ZVXwmbrmuMSgni8riqbK+qxsrQWaS4b7luah6+3tTzeQITMbrUoUXho/15IdLowc2OFih76x9pO7R8Ph9WHWeuLVLRtW1WDitY9e0h/vLS6GMX1Xlw2Ih1LS+qwIK8K2fFOSETqb6MyUOG1N5/3J4lbZK+iRNNkGWiiw4pRyRYlo+9ubhTOPrE2VNdV44U/8lUmUFmiWVjnVv/rX0QK5a9ECt8+chAWFXuRV+vBpUPjVeSrqNaDedsrUdoA9fdyfetEnhkxdvRPcMJpd8FukeWnXnyUtzPxjPY8Cf5tq/bAp564s4xMkrMTG/BdQW2bcZH7/d+e8ao/4QihbHFcX+XDO1tk+W37pW+MBdP6WZGkhV2bqpXW+zC/wI0fijq6Epic5cABPeyIj8wpHHC+UwhbInJvXoraP74OyE2rYO8zEjF7TgKsna8/ZoQwaKQhV6QQhoxO14UUQl24QqpMIQwJm+6LIkEI8/JyMWnSRN1tD+eCr76ah6ys3hTCcCAGuJZCGADQ119/jRdffBEPPfQQ4uPj8Y9//AMTJkzAmWeeaeCwhH7rNeV1WF5Ug6t+2KIOYvcvsrpSpEWStmiydfLAVFw6Ih0PrijGpsqWh6WPTXXhmN4xuOGnLShrWjLZJ8GBv43KQrzTjudXl6C4zoMUp00Jos9nUZk790h2tnjugEQHpvRJxBd5tRiSYMdf+8Yit9qLf+VUqsjf6ETgtkVbke6yorS+5fmF/jcSkRU5FUWb3C8ZN4zpiUqPFbO31uP0bBeWFFTiw02l2DMlRmUq9Vvl2aI9Q5JdOLxXEv6odGD/NODtLW61nFUOqteKRAclOY0ku/Evx2ZZsaSoCrly1karMiDBjksHxiIjxhqyEMoSUYl+/ljc9v7tzYrT+9qQHW9BrBUorPfh9Q11yK/r+DgL7R7ZcTacne3YLfcVUghbzgRfbTmq5r8c9IdG7H5TYevRL2B9CmFARGFXoBCGjTCoG1AIg8IUViUKYVj4gr44EoQwf/sWHP2XI4Juc1dU/GLufGT26kch7AqYHdyDQhgA7s0334xRo0bh7LPPVjXnzp0LiRjKn0grdW4vPsutwJnfbGzTNC0yKELVuoztEYsH9++HK39qubRUomsS0ZueHYvbftmqLhNp+uToobjqpx2o9njVAe6FdTvFU6RTlou2jsyJzN01LhPP5VRgWJID949Lw4N/lKt9gpV1NZi1rhCFNe42AubfVok0aoLWN96Ba0dnqb2BLocTL66txKn9XPhtRxmWFNfDr0ntDlN6jB137tMbsDqwuMSDHXU+lWjGv0i0TQRVKy4bMK2PBW+0Wi6q/fzEfvE4JsOiosehRAjlKMT3cj3I6WAJa0fzbWofG9KcPjyRU6drSsqcuGmECz2C2ZCo687GVqYQtuJbX4OaJR/CU5IbELzFFYe4g84OKtMohTAgzrArUAjDRhjUDSiEQWEKqxKFMCx8QV8cCUJYuG0jpvzl0KDb3BUVP/t6IXr0GkAh7AqYHdyDQhgA7vTp03HNNdfg4IMPVjU3bNiASy65BHPmzIHT2TISZuA4BXVryfT5Z3kd9v0wB7Lnr3WRPXz+0UHt52cPTcNhvVPwyPIi9XOtyH+OTXMh0erGjJxC9dcSn3t2fDbm5dXi2x3V6BljR0mDVx01IUVkUKSx9eP36hGDQ7IS8PHWGhzTOxZXDE/GmxuqUOf14vvcYvy4owL1Xi/K/c4N9G+/iKYITNOJEeq/Z0wYiMxYBxaV+PDlthpcNsSFf68uVIIqkbbOyqFZ8ThhYCaqPVbYrT78XOxRR2X4F3HBbTU7bzQ80YJMhxvf7mjMYtq63Ds2BWmoU/MiFCGU+31f5MWX+W2XsHbWl2sG21SE86X1dSgIIjqo3WtQvA1nZTtU4p/dqVAI245Ww8bfULd6fsBhtPcajpgxkwFL4HS1FMKAOMOuQCEMG2FQN6AQBoUprEoUwrDwBX1xJAhhce46/HXi+KDb3BUV58z7Aam9B1EIuwJmB/egEAaAe8IJJ+Duu+/G2LFjVc38/Hy1XHTWrFlITk6GCGOklDuefVklIHlqRQEW5LXcOyhtbEzMIvsHW8ri20cOwCe5tVhWWI2Smp1RJpvNjrMGJ+KdnDysLG7c72e1WnH6sAzsl5mEh5cXqQyeYoCVcjYCGs82bB0dlL8/d2gyNlU0qD8Xptdi+8JPMGDCcVhnS8PMdQXYXFmvjsDYUetu3CjYqtisgNcLeJt/aMHte2dhcu9EPLYkH3Hx8dgzthZP/piD9MwslHk6z5py4+gMVJTXYUdVPU4fk6WWWpYUNkqvVlJ69ECpzw4t4eqkTB++XbsV60p2JtvR6kpCneMdBXh/xqsqSU8oJTY2FmdcdjXeLOo82Yf/vXvFAOMqc7B5zUrY9z8WP5QG/+xJaW7kzp2FNWvWhNLcXXaNfPGQPzIXd3WRduzqIi8g/n75hbD/PitgUxqGTsR/5nyLbdu2Bayr9S3U+RzwAaygCAhnoxgbdd/dbeg4l80ZsXDmMudqcGMUzFyeOXNmcDcLoZasjivLzcHxRx4YwtWhX/LRvJ+R3GcohTB0hAGvpBAGQBQoQljYSiICEjewQkxyKv63oVQd2XDbr42H0/uX9paNytLLlw/Lxn3LSlCgsnrujE65bBbcMDIFFyxY3+I+N47Jwpj0BFz38w7E2CxKCovqvCoyKHsUWwuhPPfxA7Lw+B/lyIq14el9kmGpqYA3NhHv5dbjy82F+GZbBXrG2LCt2q2ykrYu/stF5Wdyzy+nDEO1x4L3NtfisJ4uLN1RjJ/zq8RaUeG2NGdCbX0vafNz4/tjUXGjPE3oacVPRR6sr2q959KCUrcP5fU+FT08qy/wSk5puyM4pU8sjsu0oqSkJKwIYYMjDrN2WJHXNmdNu889LN2CA+Nr4Xa7UWqLwzPrAoRG/e5y0zAHHHVtXxwYOEW75NYSVZE3/gkJgc/S65IHdnKTSPkSI8udPcs/gbes7e+91nyLPQaug85ClTu4lwbCuKsy5ho9Drvr/WUuy1E1SUnBvwTS09dIeGGhp71G1ZWoiizlj7RVPUb1d1fcV+ZaeXm5elEeSuFcDY5abW2teoHkcrk6vCA9PT24m4VQS4SwYutqnDRhvxCuDv2S2d8sQkLfERTC0BEGvJJCGACR7CEcM2ZMcxIZSTLzxhtvROQeQunKsyvzcWivRBz0UU67PWu9bHTa4FQc1DMRs7dUqyidf9k7zYWBcRY8/HvLL5lPHtwfTpsVz64uRX6NGz1j7UoIRdPaWy46KtWFib3lGTWY1CsG141MQawNqPb48EluDX7YXorX1xSqg95rPd52k+H4LxeVNsozPz56KOYXeLCqzI0bRsbjjkW5yK1uTIwjOV+qOnCjgzLjcNqQnlhf1RhhmtoXKHf7UKWtR/WDUOPxYW1FAxLtVmyorMeiwvZN7e6xKciOt3fJOYTz8r1YUBTcstErBtnQ09X4Bb+8wYfn19WjqINlt/5jmx1nxSYs2jQAACAASURBVHkDnIjfzZaLSh+4ZLT9Dy138VagoW30WqttccbBlpAOOGIC/sMgFbhkNChMYVXiktGw8AV9MZeMBo0q5IpcMhoyOl0XRsKS0aotK3HKhH10tTvcyu/PX4y4viMphOGC7OR6CmEAuJJE5pVXXsEjjzyCuLg43HrrrTj88MMjNsvohxtLlWi9nlOE73ZUtemdfP/3+S0bnXHEQHy0tVod4r6jpmWW0XMGJ2JRXhnmbitHbZMsxdgtuGPv3lhT3oBaj0Ud0C4RQrmneIjsHWwd3ztrcAqKGoD1lW78fY9k/EXWOTaVnwvr8NmWMjy5PA8SkZRz/lovG5UzBL1eH/wVaXzPeDx+UD/M2Nigjl64Ylgcnv2jAAu2N0a8JEpZ3NJvm5/599E9kRiTgKJ6IMkBnNAbiOvgCAY5SuO1dRVqX+LWKnd7q1nVGYs3jUxGmsvaJUIoCW6eWx84y2hWjAVn9LUh2e/Iw1lbG/BLceAooRxSf0RG4H1kBn72hHxrCmHI6HRdSCHUhSukyhTCkLDpvohCqBuZ7gsohLqRhXRBJAhh7eZlOO2IvUJqf6gXvbvgd8T0G00hDBVgENdRCIOAJBHBDz/8cLc4h7CgtgEL86qwtrwOdy9umTVUuuq/bFQSsrw9YQDuWVaCbdUNLZZqioTdOSYVdy/ORVGtGwVNyVX+MS4Ly4prUVLvwZR+qXh0ZRFibVZ19ITIU3vLRR/ZvyeezalUGUWfPyAdPfxORZfI4tZqN15fnY8PN5ciwW7F5qqGNklpGvyy1EgfFvx1GMo8Nry/pQ6Tslw4qZ8L3+ZV4c6mPkv1soadSWi0YZZsp68e2h+LShtlaEQiMD5dlrp2PBFyKtyQSGFHJdZqwbCkxvvJ8i9ZyhFqUhm5h3B8c4s34PERh6XbMDGjZcM3VHnx/LrA2UZvGhGDdGdwSweD+BUxtQqF0BzcFELjOVMIjWcsT6AQGs+ZQmg8Y3lCJAhh3aalmHr4GHM63PSU/327HK7+YymEBlKnEBoId1fdWs4ilL2AB37Y8bLRBp8PpwxIxXHZKXj1zwrkVrUMp0l20ZFJVsxaX4x6jw/ry+tw3rB0JDptmJtbruTxspE98dzqEnU0Q794p4qitTr6ECNSnJjSNwmzNldjYlYsrt0jGYmOliIi5yWuKK7GP3/NxcaKehXhLPNb9uivYnLlHfv0xqQ+Sfip2IsVpW7cPDIe2fE2tXz1goVbUN2U8rS9ZaP7ZcThzKE98WfTctHjewN+Acuwh6wrhFAaIZlGJeNoZ+XygTZIlNC/lNTLstE6lDYl+Wnv+r6xVlw0yNVhVDRsCAbfgEJoMOCm21MIjedMITSeMYXQHMYUQnM4R4QQblyMqYfuaU6HNSFcuBKuAXtTCA2kTiE0EO6uvPUfpXUorG27DFTaJPsInTYL0l02xDkcat+eJKJZV17XvCRSfv7B+iJsqapXyzVv2asXBiQ6ZZeg6paoitVqUQfcy947SfoixX8bnvxNnN2KGJsVdT4fku225khaazb5NQ3YVFmPV1cXYn5epbpv69I/wYm79u2tsqUmOBxw2GywWnxIdtiQ4bIoMd1R40FVU1pQkdPWCy9lyWvvuBgVyZT2Jdgbl412VekqIZTD5Ts7OkOO90i2+5DSKspX5wF21Hk7PYdRxj87bveMDso4UQi7arZ2fh8KofGcKYTGM6YQmsOYQmgO54gQwg2/Yuohe5jTYU0Iv1sF18B9KYQGUqcQGgh3V99alnWuK6tFcb0HK4trVBRwdI84DE1yoV+CU0X1/MuWynq1j3BNaR1WltYgO96JUWmx6BVnx6Ck4BJRhNtnacOf5dKOevxaWK1Edf/MeIxKjUV2ohO9YrvQ3sJtbDvXd5UQGtC0bnNLCqE5Q0khNJ4zhdB4xhRCcxhTCM3hHBFCuO4XTB0/3JwOa0L4wxq4Bu1PITSQOoXQQLiRdGtJVSz7KNLS0iKpWd2uLRRC44eUQmg8Y3kChdB4zhRC4xlTCM1hTCE0h3NECOGfP2HqwUPN6bAmhD+uhWvwgRRCA6lTCA2EG0m3XrFihcqW+uSTT0ZSs7pdWyiExg8phdB4xhRCcxhTCM3hzKQyxnOmEBrPWJ4QEUK49ntMPXCwOR3WhPCn9XANPZhCaCB1CqGBcCPp1hRCc0aDQmg8Zwqh8YwphOYwphCaw5lCaDxnCqHxjCNGCHMWYuoBA83psCaEP2+Ea9ghFEIDqVMIDYQbSbemEJozGhRC4zlTCI1nTCE0hzGF0BzOFELjOVMIjWccMUK4egGm7p9tToc1IVy0Ga7hh1EIDaROITQQbiTdmkJozmhQCI3nTCE0njGF0BzGFEJzOFMIjedMITSeccQI4apvMG2/vuZ0WBPCX7fCOWIChdBA6hRCA+FG0q0phOaMBoXQeM4UQuMZUwjNYUwhNIczhdB4zhRC4xlHjBD+8TWm7dPbnA5rQvjbNjhHTqQQGkidQmgg3Ei6NYXQnNGgEBrPmUJoPGMKoTmMKYTmcKYQGs+ZQmg844gRwpVfYdreWeZ0WBPCxXlw7jmJQmggdQqhgXAj6dYUQnNGg0JoPGcKofGMKYTmMKYQmsOZQmg8Zwqh8YwjRgiXf4Fp4zLN6bAmhEvz4Rx1NIXQQOoUQgPhRtKtKYTmjAaF0HjOFELjGVMIzWFMITSHM4XQeM4UQuMZR4wQLvsU0/ZKN6fDmhD+Xgjn6CkUQgOpUwgNhBtJt6YQmjMaFELjOVMIjWdMITSHMYXQHM4UQuM5UwiNZxwxQvj7J5g2Js2cDmtCuKwYzrF/pRAaSJ1CaCDcSLo1hdCc0aAQGs+ZQmg8YwqhOYwphOZwphAaz5lCaDzjiBHCpR9h2ugUczqsCeHyUjj3Op5CaCB1CqGBcCPp1hRCc0aDQmg8Zwqh8YwphOYwphCaw5lCaDxnCqHxjCNGCBfPxrRRieZ0WBPClRVwjjuRQmggdQqhgXAj6dYUQnNGg0JoPGcKofGMKYTmMKYQmsOZQmg8Zwqh8YwjRQiXfD4Te2Y6zelw01NW5tdj3OTpFEIDqVMIDYQbSbemEJozGhRC4zlTCI1nTCE0hzGF0BzOFELjOVMIjWccCUL4+++/Q/7sijJ27FjIHxZjCFAIjeEacXelEJozJBRC4zlTCI1nTCE0hzGF0BzOFELjOVMIjWccCUJoTi/5lF1BgEK4K6jvgmdSCM2BTiE0njOF0HjGFEJzGFMIzeFMITSeM4XQeMYUQnMYR+tTKIRRMvIUQnMGmkJoPGcKofGMKYTmMKYQmsOZQmg8Zwqh8YwphOYwjtanUAijZOQphOYMNIXQeM4UQuMZUwjNYUwhNIczhdB4zhRC4xlTCM1hHK1PoRBGychTCM0ZaAqh8ZwphMYzphCaw5hCaA5nCqHxnCmExjOmEJrDOFqfQiGMkpGnEJoz0BRC4zlTCI1nTCE0hzGF0BzOFELjOVMIjWdMITSHcbQ+hUIYJSNPITRnoCmExnOmEBrPmEJoDmMKoTmcKYTGc6YQGs+YQmgO42h9CoUwSkaeQmjOQFMIjedMITSeMYXQHMYUQnM4UwiN50whNJ4xhdAcxtH6FAphlIw8hdCcgaYQGs+ZQmg8YwqhOYwphOZwphAaz5lCaDxjCqE5jKP1KRTCaB159psESIAESIAESIAESIAESCDqCVAIo34KEAAJkAAJkAAJkAAJkAAJkEC0EqAQRuvIs98kQAIkQAIkQAIkQAIkQAJRT4BCGPVTgABIgARIgARIgARIgARIgASilQCFMApG/vvvv8cLL7yAoqIijBkzBjfeeCN69OgRBT3vmi7q4SfJCx5//HH89NNPSEhIwJlnnonjjjtONeTXX3/FzJkzsXbtWjgcDhxwwAG4/PLLVb1oL51xa49NMGOSl5eHCy+8UM35Bx54INoRq/4Hw00DFWhMiouL8dxzz+Hnn3+G1WrFEUccgb/97W9RzzkQt9aAOhuTTZs24emnn8aaNWsQGxuLiRMn4uKLL1a8WVoS0DO3H330USxbtgzbt2/HP/7xD0yYMIE42yGgZy4XFhbiySefVHO1tLQU77zzDtLS0prvOnv2bHz22WfIzc1FcnIyjj32WJxxxhnkrvNz+f3338eXX36JDRs24JhjjsF1113XLsP58+fjvvvuU/8GTp8+nZxJICABCmFARLt3Be1L8c0334y9995bfbkoKSnBww8/vHt3zKTW6+UnMrht2zb885//xJYtW9SXjfvvvx+jR4/Gp59+CpfLpf67trYWjzzyCLKzs3HDDTeY1JvIfUxn3Fq3OtgxkTGoqqpCTEwMhRBAsNw03p2NiWQUvOqqqzBw4ECcfvrpal7LfB83blzkTjKTWtaVc/myyy7D0KFDceWVV6oXejfddJN6yTRlyhSTerN7PEbv3BY5kbn7xBNP4Nxzz6UQdjDMeuayzM8ffvgBffr0gXzfaC2Er776Kvbaay8MGjQImzdvxl133aVeiE6aNGn3mGQGtVLv3F24cCHsdjtE+OQlUXtCKCIvn8/y4lledlAIDRq8bnZbCmE3G9DW3Xn77bexePFiyBtRKfn5+eoLhfx9RkZGN+99+N3Tw8/tduOkk05Sb+UkKiXlscceU/97/fXXt2nMvHnz8NZbb0H+oYzmopdbMGMi0YLPP/8ce+65J37//XcKIaB+54P9LAg0JvLF7/nnn8cbb7zBaJXfL28gbno/n0888UTcfffdzZ8n8gVdXnBcccUV0fyR0abveua2/8UXXXSR+veQEcK200nvXNbuUFZWhlNPPbWNELZ+gsi4zWbDNddcE9VzOdS5Ky/35Qim9oRQVm5kZmbit99+w9ixYymEUT3Dgu88hTB4VrtlTVkql5KSot7EaeXkk09Wkat99913t+yTmY3Ww0+Wwpx33nmQt8/x8fGqmfLfIn5PPfVUm2Y/88wzammNRLKiuejlFmhMJPoqkRWpt2DBAgph0+QKxM1/DgYaE3mJsXXrVtTX12P58uXo16+fYi7R72gugbi1ZhNoTP7zn/+goKBACaBEYG655Rb1BXq//faLZsxt+h6IY0ewKIQdTyO9c1mPEMoKg0svvVRtp9C2VETrhA517nYkhOvWrVMrwEQK5bsFhTBaZ5b+flMI9TPbra64/fbbMWTIEJxzzjnN7T777LPVPpTDDjtst+rLrmisHn5//vmnEm9Z32+xWFRzv/rqK/Wm9JVXXmnR/F9++QUPPvigWsIrS2yiuejhJpwCjcnLL7+soigyz2XPJiOEjbMrEDf/ORhoTB566CHMnTsXt956Kw455BAVjX3ttdcwY8YMJCYmRu10DsStNZhAY5KTk6NebIh8SznhhBPUUjCWlgQCcaQQ6p8xeueyHiGUF0qyp15elMqyxmguoc7d9oRQRPvaa69V+wZFBOXzmUIYzbNLX98phPp47Xa1Q337tNt11KAG6+EX7BvVpUuX4t5771V7KGRJY7SXYLlpnDobE1kGfccdd+Cll16C0+mkEPpNrq6cy7LcS5JHSLIqrcheQlm+JMmSorV05VweNWqUSrohy+/kj6wmkM8N4SvLHFl2EtAzt/25MULY8SzSO5eDFUJ5SScvTWX5s6xeivYS6txtTwglT4F8v5AVYFIohNE+u/T1n0Koj9duV1vWp8sHhJZERpYfyZcM7iEMbij18JM9F7LnRyJ/8mVOivyjJ2/ttD2EsrzuzjvvVNEaeXPHAgTDzZ9TZ2Mi2V1ffPFFtdleiiwflftLtjvZrxnNpSvn8ocffqgyBlIIW86orpzLshxXlqB/9NFHzfNZlqBLMgnJ5siyk4CeuU0hDG7m6J3LwQjhrFmz1HyWF0rMdN5ILNS5254QygsjibxqUdfKykqVgOaggw5qlsTgRp+1opEAhbCbj7qk1ZblobfddpvK8CX71kQKmWU0uIEPxE/eyElUStvTI0lkJHGPrN2XZV6y50c+pGVv1apVq9Q4SFZRbf+mLC2N9iUzMhKdcROeH3zwQXO6/c7GpK6uTmUW1YqIy8qVKxX31NTU4Aa9m9YKNJflxZEccyDLEgONiWQqluyM8qJj/Pjx+OKLL1RyJEkyE81LRgNxk58Lp8mTJ6ul4p2NiSSMkOyAp5xyiooQSrKOe+65R2VpjPZEHK1/RQPN7daf0w0NDepFnSzxF8aHH364+uLM4zxaku3sc7n1XJb/Ly8xysvLVebhN998U33mykoNKfIyQ6KDkuBOEp5IEd7CPZpLoLnb+nNZPhfkjyT1kv+VJeSSnEf+yL998m+gViTDuaxCks+PaP9cjuY5FmzfKYTBktqN63333XcqasJzCEMbxM74yZKMYcOG4fzzz1c39z+3SRLLnHXWWc2b5kXCZU+hf5E68g9ltJfOuIlIyxdg2acm/+hJCXZOcw9hy5nVGTdhJRFWLfrU2ZjIXZcsWaJeMEna9AEDBqjEJ1wC3flngHCTIyPkJZEcAxRoLsvclyisnDkmX6z32WcfXH311Ty7tJ0PTD2f07K0WV4U+Rf58sxkPS3BBvoM8J/LIifyoqN1mTNnjpq7ssxZXu75l4MPPlhtnYj2oudzWfZqS1TRv0ybNg2y/Ll14ZLRaJ9Z+vpPIdTHi7VJgARIgARIgARIgARIgARIoNsQoBB2m6FkR0iABEiABEiABEiABEiABEhAHwEKoT5erE0CJEACJEACJEACJEACJEAC3YYAhbDbDCU7QgIkQAIkQAIkQAIkQAIkQAL6CFAI9fFibRIgARIgARIgARIgARIgARLoNgQohN1mKNkREiABEiABEiABEiABEiABEtBHgEKojxdrkwAJkAAJkAAJkAAJkAAJkEC3IUAh7DZDyY6QAAmQAAmQAAmQAAmQAAmQgD4CFEJ9vFibBEiABEiABEiABEiABEiABLoNAQphtxlKdoQESIAESIAESIAESIAESIAE9BGgEOrjxdokQAIkQAIkQAIkQAIkQAIk0G0IUAi7zVCyIyRAAiRAAiRAAiRAAiRAAiSgjwCFUB8v1iYBEiABEiABEiABEiABEiCBbkOAQththpIdIQESIAESIAESIAESIAESIAF9BCiE+nixNgmQAAmQAAmQAAmQAAmQAAl0GwIUwm4zlOwICZAACZAACZAACZAACZAACegjQCHUx4u1SYAESIAESIAESIAESIAESKDbEKAQdpuhZEdIgARIgARIgARIgARIgARIQB8BCqE+XqxNAiRAAiRAAiRAAiRAAiRAAt2GAIWw2wwlO0ICJEACJEACJEACJEACJEAC+ghQCPXxYm0SIAESIAESIAESIAESIAES6DYEKITdZijZERIgARIgARIgARIgARIgARLQR4BCqI8Xa5MACZAACZAACZAACZAACZBAtyFAIew2Q8mOkAAJkAAJkAAJkAAJkAAJkIA+AhRCfbxYmwRIgARIgARIgARIgARIgAS6DQEKYbcZSnaEBEiABEiABEiABEiABEiABPQRoBDq48XaJEACJEACJEACJEACJEACJNBtCFAIu81QsiMkQAIkQAIkQAIkQAIkQAIkoI8AhVAfL9YmARIgARIgARIgARIgARIggW5DgELYbYaSHSEBEiABEiABEiABEiABEiABfQQohPp4sTYJkAAJkAAJkAAJkAAJkAAJdBsCFMJuM5TsCAmQAAmQAAmQAAmQAAmQAAnoI0Ah1MeLtUmABEiABEiABEiABEiABEig2xCgEHaboWRHSIAEzCbwzjvv4OGHH25+bExMDFJTUzFixAhMnjwZEydOhMViMbtZHT7vsssug8vlwr/+9a+AbXrllVfw2muv4fvvv2+u+/HHH+PVV1/F9u3bIX1dsGBBwPv4V9B4vffeexgwYICua1mZBEiABEiABEjAGAIUQmO48q4kQAJRQKC14NTV1SlZmj9/PkSoxo4di8cff1xJmJ7y7LPP4n//+59u4Qr0jHCEsKioCMcccwyuvfZaTJs2DXa7XT1OT1t3dyHU09dAY8GfkwAJkAAJkECkEKAQRspIsB0kQAK7HYHOBGfZsmW46KKLcNppp+HGG2/U1TejxEOPELZu8JIlS1R/3nrrLRUB1YqetlIIdU0DViYBEiABEiABUwhQCE3BzIeQAAl0RwKBBEdE8LvvvsM333yjllh+8MEHuPfeexUKWUras2dP7L333rjyyiuRlZWl/v7RRx/Ff//73xa4MjIy8Pnnnwd1vXbh3Llz8fzzz2Pbtm0YNmwYbr31Vjz55JMtlow+8cQT+PTTT9Xz7rvvPixatAjHHXccbr75ZhXh1JaM3nHHHfjkk09atOn0009X/7+jtrY33u3x0towc+ZM3H333fj111/Rq1cv3HbbbRg3bhwWLlyIp556Clu3bsWYMWNwzz33IDMzs/n22vVvv/027rrrLoi4pqSk4IwzzsCZZ57Zohm//PILXnjhBaxevVpFOIX9dddd12L5akdMbDZb2OOi3fvdd99V/fjpp5+QmJiIc889FxpPrcFr167Fiy++iMWLF6OhoQGjR4/G1VdfjT322KO5T1LnueeeU3Xq6+sxfPhwXHPNNapfLCRAAiRAAiQQLAEKYbCkWI8ESIAEWhEIJITyxf/BBx/Eyy+/3OZLutvtxqZNm5QAlpSU4M0339S1DLOz63/77TdccskluPDCC5UYyXJPkSr53x49ejTvIRRBmTNnjmqbCIlIh7YUtPUeQhG1Sy+9FCJeIh5aCTdCqLXhoIMOUktR+/fvj6effhpfffWV2p/52Wef4fLLL4csxxUhGjx4MB577LEWQih92GuvvZQADhkyRF0r3G+55RacfPLJqq7IoIj31KlTcf7556Ompgb333+/kkORWk3IO2MSTF87Gxft3gceeCBOOeUUDB06FLKfUsZG5FuEV8qqVatUNFaYSJtFgFesWIEvvvgCt99+u6qTk5Oj+nH44YfjiiuuUGIp81H2eL7++ustxJG/uCRAAiRAAiTQGQEKIecHCZAACYRIIJAQyl7C66+/Hg888ACOOuqodp8iUijSMmPGDOy5556qTjDiod2svesvvvhiiJiIZGhl8+bNOOmkk3DIIYe0EEIRURGsI444okX7zBRCaYNEug444ADVhtLSUpWQp2/fvpg1axYcDof6e9lXKZIoXBMSEtTfiWS11weJNkpCHJFFkdzzzjsPtbW1kEikVuQ5xx57LI4//ngVFe3sfl0xLlpbRQDHjx/f3A6JyookSlRUiiztzcvLU33XBL315BFRlDoyB/3riEgmJSWpvassJEACJEACJBAMAQphMJRYhwRIgATaIRBICGWp6A033NAshLKsT+RFlmnKl3mJUmnFXxo7EsJgrvf5fCqyJGIgf/yLCKFE4LQso5qg/Pjjj3A6nS3qmimEEnWU5ZOyLFMrRx55pBJE4aKVn3/+WUXDROokuuYvcHK9Jo7y9xr72bNnqwibCJhE1ESk/Ivcr6CgABLN9b9fe0zCGRft3tJXube/xMkyT4/Ho14EyBiLtJ911llq+Wd7RZaQSh2J/kqSH/8iy4SlL/PmzePvLAmQAAmQAAkERYBCGBQmViIBEiCBtgQCCaFEtB566CG1H0/2w8l/y7I/iV7JEsf4+HglI5K9U/aUTZkyRT2kI/EI5notuibRJm25pNZyWUIqkTV/Ifzoo4+UPLUuZgqhRPFkz6N/kcidRC39E/IsX75cRfo0nppkifS1PgJj6dKlasnsSy+9hOzsbBx99NEqWisS5V/+7//+DyKaX375ZbMQdsQknHHR2tpeX2V/p+z1fOONN9SyXokmt9dWrd1anc5+J2XZMAsJkAAJkAAJBEOAQhgMJdYhARIggXYIBBLC1kllZBmkZB2VJYFakWykErkKRgiDuV6LEMqyUREi/9JehFCilbLnblcKYXttECGcMGGCirBqpSMhlKhrqBFCiRjm5+e3iBB2xKQjIQxmXDQhbO/e/kIYTIRQ9lNKhFDGWPaKspAACZAACZBAOAQohOHQ47UkQAJRTaAzIRR5ESHTjp0QUZMv8fJ3F1xwQTM32RMn9/EXwn//+98qCvbDDz8019NzvSwVlfqSYEQrsodQIoaydNI/QhiuELbX1o4mRWdZRltLqV4hlD1zkmBFK8JTMrz67yEUkfLPilpWVqaisrKHTxLQdCZt8rNwx0XLMtq6r/5CKM8JZg+h1KmoqFB7T/2X2kb1LyQ7TwIkQAIkEBIBCmFI2HgRCZAACUCJnAidZIocMGCA2v+lHUwvmUVbH0wv0S7JICny0rt3b5VBU7J3fv311y2EUNv/Jkck7LPPPrBarQp3sNfL8REiDBJBmj59uspiKhJYWFjYJstouELYUVvbmx9GCaEciSGZUrUso7L8VPYe3nTTTSqbpxSJIEqWUuEhy05l/6bUkeydsidRjroIJIThjkuwQuifZfSqq66CHDuycuVKtdxYlrhKkSyj8mLh0EMPVVFCab/sS5WXCPK/f//73/krSgIkQAIkQAJBEaAQBoWJlUiABEigLQFNcLSfuFwupKamqpT/kydPVpky5bxBrYiYiUDKl3aRPNkrJhHD1nsIvV6vEkQREIkCaecQBnu9PE/2xIlQyt40OYpB9hSKFEobuzJC2FFbzRRCkVpZNip7M+UcwuTkZLVX8Oyzz27RDJFCOdtPO4dQZFsStwwaNKi5XkfSJhXCHZdghVATPsm8Kv2RIkeCyPJW/3MIN27cqPojR2pUV1erlwxaQhqZMywkQAIkQAIkEAwBCmEwlFiHBEiABEggIgl0JnAR2WA2igRIgARIgAQijACFMMIGhM0hARIgARIIngCFMHhWrEkCJEACJEAC7RGgEHJekAAJkAAJ7LYEKIS77dCx4SRAAiRAAhFCgEIYIQPBZpAACZAACZAACZAACZAACZCA2QQohGYT5/NIgARIgARIgARIgARIgARIIEIIUAgjZCDYDBIgFsBrpwAAAaxJREFUARIgARIgARIgARIgARIwmwCF0GzifB4JkAAJkAAJkAAJkAAJkAAJRAgBCmGEDASbQQIkQAIkQAIkQAIkQAIkQAJmE6AQmk2czyMBEiABEiABEiABEiABEiCBCCFAIYyQgWAzSIAESIAESIAESIAESIAESMBsAhRCs4nzeSRAAiRAAiRAAiRAAiRAAiQQIQQohBEyEGwGCZAACZAACZAACZAACZAACZhNgEJoNnE+jwRIgARIgARIgARIgARIgAQihACFMEIGgs0gARIgARIgARIgARIgARIgAbMJUAjNJs7nkQAJkAAJkAAJkAAJkAAJkECEEKAQRshAsBkkQAIkQAIkQAIkQAIkQAIkYDYBCqHZxPk8EiABEiABEiABEiABEiABEogQAhTCCBkINoMESIAESIAESIAESIAESIAEzCZAITSbOJ9HAiRAAiRAAiRAAiRAAiRAAhFCgEIYIQPBZpAACZAACZAACZAACZAACZCA2QQohGYT5/NIgARIgARIgARIgARIgARIIEIIUAgjZCDYDBIgARIgARIgARIgARIgARIwm8D/A/VGXR097PgoAAAAAElFTkSuQmCC" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.scatter_feature_importance() # works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "4ce4db18", - "metadata": {}, - "source": [ - "Putting importance of the drift into perspective according to the importance of the model to be deployed, can help the data scientist to validate that his model can be deployed.
\n" - ] - }, - { - "cell_type": "markdown", - "id": "fdbc35f4", - "metadata": {}, - "source": [ - "### Univariate analysis" - ] - }, - { - "cell_type": "markdown", - "id": "d3f6c8f1", - "metadata": {}, - "source": [ - "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b9aa98df", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('YearRemodAdd')" - ] - }, - { - "cell_type": "markdown", - "id": "6c914576", - "metadata": {}, - "source": [ - "### Distribution of predicted values" - ] - }, - { - "cell_type": "markdown", - "id": "30c84fc4", - "metadata": {}, - "source": [ - "This graph shows distributions of the production model outputs on both baseline and current datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "51c99e4c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\") # works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "9b50bcb0", - "metadata": {}, - "source": [ - "Jensen Shannon Divergence (JSD). The JSD measures the effect of a data drift on the deployed model performance. A value close to 0 indicates similar data distributions, while a value close to 1 tend to indicate distinct data distributions with a negative effect on the deployed model performance." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "ceffb5da", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_indicator(\n", - " fig_value=SD.js_divergence,\n", - " height=280,\n", - " width=500,\n", - " title=\"Jensen Shannon Datadrift\",\n", - " min_gauge=0,\n", - " max_gauge=0.2,\n", - " ) #works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "f7f93151", - "metadata": {}, - "source": [ - "## Compile Drift over years" - ] - }, - { - "cell_type": "markdown", - "id": "b2fcad1b", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2008" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3c658ba9", - "metadata": {}, - "outputs": [], - "source": [ - "house_df_2008 = house_df.loc[house_df['YrSold'] == 2008]\n", - "\n", - "y_df_2008=house_df_2008['SalePrice'].to_frame()\n", - "X_df_2008=house_df_2008[house_df_2008.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "5aa0896b", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2008,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "92ca7c35", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The variable Condition1 has mismatching unique values:\n", - "[\"Within 200' of East-West Railroad\"] | []\n", - "\n", - "The variable Condition2 has mismatching unique values:\n", - "['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to postive off-site feature', 'Near positive off-site feature--park, greenbelt, etc.'] | []\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "['Mixed'] | []\n", - "\n", - "The variable ExterCond has mismatching unique values:\n", - "['Excellent'] | []\n", - "\n", - "The variable ExterQual has mismatching unique values:\n", - "['Fair'] | []\n", - "\n", - "The variable Exterior1st has mismatching unique values:\n", - "[] | ['Imitation Stucco']\n", - "\n", - "The variable Exterior2nd has mismatching unique values:\n", - "[] | ['Other', 'Stone']\n", - "\n", - "The variable Foundation has mismatching unique values:\n", - "[] | ['Slab', 'Wood']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "['Major Deductions 2'] | []\n", - "\n", - "The variable GarageCond has mismatching unique values:\n", - "['Excellent'] | ['Poor']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Poor']\n", - "\n", - "The variable GarageType has mismatching unique values:\n", - "[] | ['More than one type of garage']\n", - "\n", - "The variable Heating has mismatching unique values:\n", - "['Hot water or steam heat other than gas', 'Floor Furnace'] | ['Wall furnace']\n", - "\n", - "The variable MSSubClass has mismatching unique values:\n", - "['1-Story w/Finished Attic All Ages'] | []\n", - "\n", - "The variable Neighborhood has mismatching unique values:\n", - "['Northpark Villa', 'Bluestem'] | []\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "['Membrane', 'Clay or Tile'] | ['Metal']\n", - "\n", - "The variable SaleCondition has mismatching unique values:\n", - "[] | ['Sale between family members']\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "['Contract 15% Down payment regular terms', 'Warranty Deed - Cash'] | ['Contract Low Interest', 'Other']\n", - "\n", - "The variable Street has mismatching unique values:\n", - "['Gravel'] | []\n", - "\n", - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6877714667557634\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2008', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "b87cbbee", - "metadata": {}, - "source": [ - "----" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "9400fdc8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" - ] - }, - { - "cell_type": "markdown", - "id": "04f0d6b9", - "metadata": {}, - "source": [ - "------" - ] - }, - { - "cell_type": "markdown", - "id": "03bca0b9", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2009" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "f28c44f9", - "metadata": {}, - "outputs": [], - "source": [ - "house_df_2009 = house_df.loc[house_df['YrSold'] == 2009]\n", - "\n", - "y_df_2009=house_df_2009['SalePrice'].to_frame()\n", - "X_df_2009=house_df_2009[house_df_2009.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "a6f46c7e", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2009,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "2618c106", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The variable BsmtCond has mismatching unique values:\n", - "['Poor -Severe cracking, settling, or wetness'] | []\n", - "\n", - "The variable Condition1 has mismatching unique values:\n", - "[] | ['Adjacent to East-West Railroad']\n", - "\n", - "The variable Condition2 has mismatching unique values:\n", - "['Adjacent to arterial street'] | []\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "[] | ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "\n", - "The variable ExterCond has mismatching unique values:\n", - "['Excellent'] | []\n", - "\n", - "The variable ExterQual has mismatching unique values:\n", - "['Fair'] | []\n", - "\n", - "The variable Exterior1st has mismatching unique values:\n", - "['Brick Common', 'Cinder Block'] | ['Stone', 'Imitation Stucco']\n", - "\n", - "The variable Exterior2nd has mismatching unique values:\n", - "['Brick Common', 'Cinder Block'] | ['Other']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "['Major Deductions 2'] | []\n", - "\n", - "The variable GarageCond has mismatching unique values:\n", - "['Excellent'] | ['Good']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Poor']\n", - "\n", - "The variable GarageType has mismatching unique values:\n", - "[] | ['More than one type of garage']\n", - "\n", - "The variable LotConfig has mismatching unique values:\n", - "[] | ['Frontage on 3 sides of property']\n", - "\n", - "The variable MSSubClass has mismatching unique values:\n", - "['1-Story w/Finished Attic All Ages'] | []\n", - "\n", - "The variable Neighborhood has mismatching unique values:\n", - "['Northpark Villa', 'Bluestem'] | ['Veenker']\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "[] | ['Metal', 'Wood Shakes']\n", - "\n", - "The variable RoofStyle has mismatching unique values:\n", - "['Mansard'] | []\n", - "\n", - "The variable SaleCondition has mismatching unique values:\n", - "[] | ['Adjoining Land Purchase']\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "[] | ['Other']\n", - "\n", - "The variable Utilities has mismatching unique values:\n", - "['Electricity and Gas Only'] | []\n", - "\n", - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.5405695039804042\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2009', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "6a045bbc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" - ] - }, - { - "cell_type": "markdown", - "id": "63c9c1d9", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2010" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "214b36a2", - "metadata": {}, - "outputs": [], - "source": [ - "house_df_2010 = house_df.loc[house_df['YrSold'] == 2010]\n", - "\n", - "y_df_2010=house_df_2010['SalePrice'].to_frame()\n", - "X_df_2010=house_df_2010[house_df_2010.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "e0d62327", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2010,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "860b4906", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The variable Condition1 has mismatching unique values:\n", - "[\"Within 200' of East-West Railroad\"] | []\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "[] | ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "\n", - "The variable ExterCond has mismatching unique values:\n", - "['Poor'] | []\n", - "\n", - "The variable ExterQual has mismatching unique values:\n", - "['Fair'] | []\n", - "\n", - "The variable Exterior1st has mismatching unique values:\n", - "['Asphalt Shingles'] | ['Stone', 'Imitation Stucco']\n", - "\n", - "The variable Exterior2nd has mismatching unique values:\n", - "['Asphalt Shingles', 'Brick Common'] | ['Other', 'Stone']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "[] | ['Major Deductions 1']\n", - "\n", - "The variable GarageCond has mismatching unique values:\n", - "[] | ['Poor', 'Good']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Good', 'Excellent', 'Poor']\n", - "\n", - "The variable GarageType has mismatching unique values:\n", - "[] | ['More than one type of garage']\n", - "\n", - "The variable Heating has mismatching unique values:\n", - "[] | ['Gas hot water or steam heat', 'Wall furnace']\n", - "\n", - "The variable HouseStyle has mismatching unique values:\n", - "[] | ['Two and one-half story: 2nd level finished', 'One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished']\n", - "\n", - "The variable LotConfig has mismatching unique values:\n", - "[] | ['Frontage on 3 sides of property']\n", - "\n", - "The variable LotShape has mismatching unique values:\n", - "[] | ['Irregular']\n", - "\n", - "The variable MSSubClass has mismatching unique values:\n", - "['1-Story w/Finished Attic All Ages'] | ['2-1/2 Story All Ages', '1-1/2 Story - Unfinished All Ages']\n", - "\n", - "The variable MSZoning has mismatching unique values:\n", - "[] | ['Residential High Density']\n", - "\n", - "The variable Neighborhood has mismatching unique values:\n", - "['Northpark Villa'] | ['Veenker']\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "[] | ['Wood Shingles', 'Metal', 'Gravel & Tar']\n", - "\n", - "The variable RoofStyle has mismatching unique values:\n", - "['Mansard', 'Shed'] | ['Flat']\n", - "\n", - "The variable SaleCondition has mismatching unique values:\n", - "[] | ['Adjoining Land Purchase']\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "['Contract 15% Down payment regular terms'] | ['Contract Low Down', 'Contract Low Down payment and low interest', 'Other']\n", - "\n", - "The variable Street has mismatching unique values:\n", - "['Gravel'] | []\n", - "\n", - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6978632478632478\n" - ] } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2010', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "1ee11cf8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" + ], + "metadata": { + "kernelspec": { + "display_name": "dev_eurybia", + "language": "python", + "name": "dev_eurybia" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "336px" + }, + "toc_section_display": true, + "toc_window_display": true } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dev_eurybia", - "language": "python", - "name": "dev_eurybia" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "336px" - }, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/tutorial/data_drift/tutorial02-datadrift-high-datadrift.ipynb b/tutorial/data_drift/tutorial02-datadrift-high-datadrift.ipynb index 81b0aba..389824f 100644 --- a/tutorial/data_drift/tutorial02-datadrift-high-datadrift.ipynb +++ b/tutorial/data_drift/tutorial02-datadrift-high-datadrift.ipynb @@ -1,1318 +1,1318 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "9d6173eb", - "metadata": {}, - "source": [ - "# Detect High Data Drift \n", - "With this tutorial you:
\n", - "Understand how to use Eurybia to detect datadrift\n", - "\n", - "Contents:\n", - "- Detect data drift \n", - "- Compile Drift over years\n", - "\n", - "This public dataset comes from :\n", - "\n", - "https://www.kaggle.com/sobhanmoosavi/us-accidents/version/10\n", - "\n", - "---\n", - "Acknowledgements\n", - "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. “A Countrywide Traffic Accident Dataset.”, 2019.\n", - "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. \"Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights.\" In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.\n", - "---\n", - "\n", - "In this tutorial, the data are not loaded raw, a data preparation to facilitate the use of the tutorial has been done. You can find it here : \n", - "https://github.com/MAIF/eurybia/blob/master/eurybia/data/dataprep_US_car_accidents.ipynb" - ] - }, - { - "cell_type": "markdown", - "id": "d83e6e7f", - "metadata": {}, - "source": [ - "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", - "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9aba586c", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from category_encoders import OrdinalEncoder\n", - "import catboost\n", - "from eurybia import SmartDrift\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn import metrics\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "id": "6bc05d7f", - "metadata": {}, - "source": [ - "## Import Dataset and split in training and production dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "391f650d", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f20754e8", - "metadata": {}, - "outputs": [], - "source": [ - "df_car_accident = data_loading(\"us_car_accident\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8ed3eb88", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_accNautical_Twilightseason_acctargettarget_multiyear_accDescription
033.0-117.10.040.093.02.03Daywinter022019At Carmel Mountain Rd - Accident.
129.5-98.50.083.065.010.04Daysummer132017At TX-345-SP/Woodlawn Ave/Exit 567B - Accident.
232.7-96.80.088.057.010.00Nightsummer022021Incident on RUGGED DR near BERKLEY AVE Expect ...
340.0-76.30.061.058.010.04Dayspring022020At PA-741/Rohrerstown Rd - Accident.
441.5-81.81.071.053.010.00Daysummer022020At 117th St/Exit 166 - Accident.
\n", - "
" + "cells": [ + { + "cell_type": "markdown", + "id": "9d6173eb", + "metadata": {}, + "source": [ + "# Detect High Data Drift \n", + "With this tutorial you:
\n", + "Understand how to use Eurybia to detect datadrift\n", + "\n", + "Contents:\n", + "- Detect data drift \n", + "- Compile Drift over years\n", + "\n", + "This public dataset comes from :\n", + "\n", + "https://www.kaggle.com/sobhanmoosavi/us-accidents/version/10\n", + "\n", + "---\n", + "Acknowledgements\n", + "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. \u201cA Countrywide Traffic Accident Dataset.\u201d, 2019.\n", + "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. \"Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights.\" In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.\n", + "---\n", + "\n", + "In this tutorial, the data are not loaded raw, a data preparation to facilitate the use of the tutorial has been done. You can find it here : \n", + "https://github.com/MAIF/eurybia/blob/master/eurybia/data/dataprep_US_car_accidents.ipynb" + ] + }, + { + "cell_type": "markdown", + "id": "d83e6e7f", + "metadata": {}, + "source": [ + "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", + "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9aba586c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from category_encoders import OrdinalEncoder\n", + "import catboost\n", + "from eurybia import SmartDrift\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import metrics\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "6bc05d7f", + "metadata": {}, + "source": [ + "## Import Dataset and split in training and production dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "391f650d", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f20754e8", + "metadata": {}, + "outputs": [], + "source": [ + "df_car_accident = data_loading(\"us_car_accident\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8ed3eb88", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_accNautical_Twilightseason_acctargettarget_multiyear_accDescription
033.0-117.10.040.093.02.03Daywinter022019At Carmel Mountain Rd - Accident.
129.5-98.50.083.065.010.04Daysummer132017At TX-345-SP/Woodlawn Ave/Exit 567B - Accident.
232.7-96.80.088.057.010.00Nightsummer022021Incident on RUGGED DR near BERKLEY AVE Expect ...
340.0-76.30.061.058.010.04Dayspring022020At PA-741/Rohrerstown Rd - Accident.
441.5-81.81.071.053.010.00Daysummer022020At 117th St/Exit 166 - Accident.
\n", + "
" + ], + "text/plain": [ + " Start_Lat Start_Lng Distance(mi) Temperature(F) Humidity(%) \\\n", + "0 33.0 -117.1 0.0 40.0 93.0 \n", + "1 29.5 -98.5 0.0 83.0 65.0 \n", + "2 32.7 -96.8 0.0 88.0 57.0 \n", + "3 40.0 -76.3 0.0 61.0 58.0 \n", + "4 41.5 -81.8 1.0 71.0 53.0 \n", + "\n", + " Visibility(mi) day_of_week_acc Nautical_Twilight season_acc target \\\n", + "0 2.0 3 Day winter 0 \n", + "1 10.0 4 Day summer 1 \n", + "2 10.0 0 Night summer 0 \n", + "3 10.0 4 Day spring 0 \n", + "4 10.0 0 Day summer 0 \n", + "\n", + " target_multi year_acc Description \n", + "0 2 2019 At Carmel Mountain Rd - Accident. \n", + "1 3 2017 At TX-345-SP/Woodlawn Ave/Exit 567B - Accident. \n", + "2 2 2021 Incident on RUGGED DR near BERKLEY AVE Expect ... \n", + "3 2 2020 At PA-741/Rohrerstown Rd - Accident. \n", + "4 2 2020 At 117th St/Exit 166 - Accident. " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " Start_Lat Start_Lng Distance(mi) Temperature(F) Humidity(%) \\\n", - "0 33.0 -117.1 0.0 40.0 93.0 \n", - "1 29.5 -98.5 0.0 83.0 65.0 \n", - "2 32.7 -96.8 0.0 88.0 57.0 \n", - "3 40.0 -76.3 0.0 61.0 58.0 \n", - "4 41.5 -81.8 1.0 71.0 53.0 \n", - "\n", - " Visibility(mi) day_of_week_acc Nautical_Twilight season_acc target \\\n", - "0 2.0 3 Day winter 0 \n", - "1 10.0 4 Day summer 1 \n", - "2 10.0 0 Night summer 0 \n", - "3 10.0 4 Day spring 0 \n", - "4 10.0 0 Day summer 0 \n", - "\n", - " target_multi year_acc Description \n", - "0 2 2019 At Carmel Mountain Rd - Accident. \n", - "1 3 2017 At TX-345-SP/Woodlawn Ave/Exit 567B - Accident. \n", - "2 2 2021 Incident on RUGGED DR near BERKLEY AVE Expect ... \n", - "3 2 2020 At PA-741/Rohrerstown Rd - Accident. \n", - "4 2 2020 At 117th St/Exit 166 - Accident. " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_car_accident.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e2c4a06a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(50000, 13)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_car_accident.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "7a696ca4", - "metadata": {}, - "outputs": [], - "source": [ - "# Let us consider that the column \"year_acc\" corresponds to the reference date. \n", - "#In 2016, a model was trained using data. And in next years, we want to detect data drift on new data in production to predict\n", - "df_accident_baseline = df_car_accident.loc[df_car_accident['year_acc'] == 2016]\n", - "df_accident_2017 = df_car_accident.loc[df_car_accident['year_acc'] == 2017]\n", - "df_accident_2018 = df_car_accident.loc[df_car_accident['year_acc'] == 2018]\n", - "df_accident_2019 = df_car_accident.loc[df_car_accident['year_acc'] == 2019]\n", - "df_accident_2020 = df_car_accident.loc[df_car_accident['year_acc'] == 2020]\n", - "df_accident_2021 = df_car_accident.loc[df_car_accident['year_acc'] == 2021]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "eb0e855a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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target01
year_acc
201671.40628728.593713
201767.25462032.745380
201866.63466233.365338
201979.55118220.448818
202089.94480410.055196
202198.2599301.740070
\n", - "
" + "source": [ + "df_car_accident.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e2c4a06a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(50000, 13)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "target 0 1\n", - "year_acc \n", - "2016 71.406287 28.593713\n", - "2017 67.254620 32.745380\n", - "2018 66.634662 33.365338\n", - "2019 79.551182 20.448818\n", - "2020 89.944804 10.055196\n", - "2021 98.259930 1.740070" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#We will train a classification model to predict the severity of an accident. 0 for a less severe accident and 1 for a severe accident.\n", - "#Let's check percentage in class 0 and 1\n", - "pd.crosstab(df_car_accident.year_acc, df_car_accident.target, normalize = 'index')*100" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a0c9e6b0", - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=df_accident_baseline['target'].to_frame()\n", - "X_df_learning=df_accident_baseline[df_accident_baseline.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2017=df_accident_2017['target'].to_frame()\n", - "X_df_2017=df_accident_2017[df_accident_2017.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2018=df_accident_2018['target'].to_frame()\n", - "X_df_2018=df_accident_2018[df_accident_2018.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2019=df_accident_2019['target'].to_frame()\n", - "X_df_2019=df_accident_2019[df_accident_2019.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2020=df_accident_2020['target'].to_frame()\n", - "X_df_2020=df_accident_2020[df_accident_2020.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2021=df_accident_2021['target'].to_frame()\n", - "X_df_2021=df_accident_2021[df_accident_2021.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]" - ] - }, - { - "cell_type": "markdown", - "id": "a8e0347a", - "metadata": {}, - "source": [ - "## Building Supervized Model" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "7ca4fe9e", - "metadata": {}, - "outputs": [], - "source": [ - "features = ['Start_Lat', 'Start_Lng', 'Distance(mi)', 'Temperature(F)',\n", - " 'Humidity(%)', 'Visibility(mi)', 'day_of_week_acc', 'Nautical_Twilight',\n", - " 'season_acc']" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "75deda6e", - "metadata": {}, - "outputs": [], - "source": [ - "features_to_encode = [col for col in X_df_learning[features].columns if X_df_learning[col].dtype not in ('float64','int64')]\n", - "\n", - "encoder = OrdinalEncoder(cols=features_to_encode)\n", - "encoder = encoder.fit(X_df_learning[features])\n", - "\n", - "X_df_learning_encoded=encoder.transform(X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "8de8fb2c", - "metadata": {}, - "outputs": [], - "source": [ - "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "c17eff5a", - "metadata": {}, - "outputs": [], - "source": [ - "train_pool_cat = catboost.Pool(data=Xtrain, label= ytrain, cat_features = features_to_encode)\n", - "test_pool_cat = catboost.Pool(data=Xtest, label= ytest, cat_features = features_to_encode)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b2928f7a", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a2ae76b7cf46416c80f3f172db408232", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))" + "source": [ + "df_car_accident.shape" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", - " learning_rate=0.143852,\n", - " iterations=300,\n", - " l2_leaf_reg=15,\n", - " max_depth = 4,\n", - " use_best_model=True,\n", - " custom_loss=['Accuracy', 'AUC', 'Logloss'])\n", - "\n", - "model = model.fit(train_pool_cat, plot=True,eval_set=test_pool_cat, verbose=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "3c3bf091", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7589233355711246\n" - ] - } - ], - "source": [ - "proba = model.predict_proba(Xtest)\n", - "print(metrics.roc_auc_score(ytest,proba[:,1]))" - ] - }, - { - "cell_type": "markdown", - "id": "339d1083", - "metadata": {}, - "source": [ - "## Use Eurybia for data validation" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "979f27b5", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "bcb4b433", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2017,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "f0a4fa1a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: total: 0 ns\n", - "Wall time: 0 ns\n", - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6585689489728102\n" - ] - } - ], - "source": [ - "%time \n", - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2017', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "0e097974", - "metadata": {}, - "source": [ - "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", - "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", - "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "f2d88c1b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Report saved to ./report_car_accident_datadrift_2017.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7a696ca4", + "metadata": {}, + "outputs": [], + "source": [ + "# Let us consider that the column \"year_acc\" corresponds to the reference date. \n", + "#In 2016, a model was trained using data. And in next years, we want to detect data drift on new data in production to predict\n", + "df_accident_baseline = df_car_accident.loc[df_car_accident['year_acc'] == 2016]\n", + "df_accident_2017 = df_car_accident.loc[df_car_accident['year_acc'] == 2017]\n", + "df_accident_2018 = df_car_accident.loc[df_car_accident['year_acc'] == 2018]\n", + "df_accident_2019 = df_car_accident.loc[df_car_accident['year_acc'] == 2019]\n", + "df_accident_2020 = df_car_accident.loc[df_car_accident['year_acc'] == 2020]\n", + "df_accident_2021 = df_car_accident.loc[df_car_accident['year_acc'] == 2021]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "eb0e855a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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target01
year_acc
201671.40628728.593713
201767.25462032.745380
201866.63466233.365338
201979.55118220.448818
202089.94480410.055196
202198.2599301.740070
\n", + "
" + ], + "text/plain": [ + "target 0 1\n", + "year_acc \n", + "2016 71.406287 28.593713\n", + "2017 67.254620 32.745380\n", + "2018 66.634662 33.365338\n", + "2019 79.551182 20.448818\n", + "2020 89.944804 10.055196\n", + "2021 98.259930 1.740070" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "#We will train a classification model to predict the severity of an accident. 0 for a less severe accident and 1 for a severe accident.\n", + "#Let's check percentage in class 0 and 1\n", + "pd.crosstab(df_car_accident.year_acc, df_car_accident.target, normalize = 'index')*100" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_car_accident_datadrift_2017.html', \n", - " title_story=\"Data drift\",\n", - " title_description=\"\"\"Car accident Data drift 2017\"\"\", # Optional: add a subtitle to describe report\n", - " project_info_file=\"../../eurybia/data/project_info_car_accident.yml\" # Optional: add information on report\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "afc893f8", - "metadata": {}, - "source": [ - "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", - "However, to illustrate functionalities, we will detail results with notebook mode analysis." - ] - }, - { - "cell_type": "markdown", - "id": "1762e71a", - "metadata": {}, - "source": [ - "## First Analysis of results of the data drift" - ] - }, - { - "cell_type": "markdown", - "id": "d9de8d0a", - "metadata": {}, - "source": [ - "Data driftn methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", - "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", - "Then a classification model (catboost) is learned to predict this target.\n", - "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" - ] - }, - { - "cell_type": "markdown", - "id": "1d4baec4", - "metadata": {}, - "source": [ - "### Detection data drift performance" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "2c3ecafd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Performance of datadrift classifier\n", - "SD.plot.generate_indicator(fig_value=SD.auc, height=300, width=500, title=\"Datadrift classifier AUC\")" - ] - }, - { - "cell_type": "markdown", - "id": "117022c4", - "metadata": {}, - "source": [ - "An Auc close to 0.5 means that there is little drift" - ] - }, - { - "cell_type": "markdown", - "id": "0c0dd1a7", - "metadata": {}, - "source": [ - "### Importance of features in data drift" - ] - }, - { - "cell_type": "markdown", - "id": "89ea4277", - "metadata": {}, - "source": [ - "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "61fc4a71", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "markdown", - "id": "723800cd", - "metadata": {}, - "source": [ - "We get the features with most gaps, those that are most important to analyse.\n", - "With date bias introduced, it is normal that date features are the most impacted. We will then decide to remove them.\n", - "Let's analyse other important variables" - ] - }, - { - "cell_type": "markdown", - "id": "5776d01b", - "metadata": {}, - "source": [ - "### Feature importance overview" - ] - }, - { - "cell_type": "markdown", - "id": "19ff31b6", - "metadata": {}, - "source": [ - "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "e0738fdb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.scatter_feature_importance() # works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "d98c9275", - "metadata": {}, - "source": [ - "Putting importance of the drift into perspective according to the importance of the model to be deployed, can help the data scientist to validate that his model can be deployed.
\n" - ] - }, - { - "cell_type": "markdown", - "id": "e52eefc9", - "metadata": {}, - "source": [ - "### Univariate analysis" - ] - }, - { - "cell_type": "markdown", - "id": "707c0073", - "metadata": {}, - "source": [ - "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "ee7e5803", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('season_acc')" - ] - }, - { - "cell_type": "markdown", - "id": "3fadad70", - "metadata": {}, - "source": [ - "### Distribution of predicted values" - ] - }, - { - "cell_type": "markdown", - "id": "fd14d06a", - "metadata": {}, - "source": [ - "This graph shows distributions of the production model outputs on both baseline and current datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "aa78bacc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\") # works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "b800780c", - "metadata": {}, - "source": [ - "Jensen Shannon Divergence (JSD). The JSD measures the effect of a data drift on the deployed model performance. A value close to 0 indicates similar data distributions, while a value close to 1 tend to indicate distinct data distributions with a negative effect on the deployed model performance." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "cbd93ddd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_indicator(\n", - " fig_value=SD.js_divergence,\n", - " height=280,\n", - " width=500,\n", - " title=\"Jensen Shannon Datadrift\",\n", - " min_gauge=0,\n", - " max_gauge=0.2,\n", - " ) #works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "192ce6b6", - "metadata": {}, - "source": [ - "## Compile Drift over years" - ] - }, - { - "cell_type": "markdown", - "id": "76890d30", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2018" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "cedd36b3", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2018,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "56954c52", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7036329129677259\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2018', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "17c9fb32", - "metadata": {}, - "source": [ - "----" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "ffd89494", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "b54f3c39", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\") # works if deployed_model is filled" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "8a34f826", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_indicator(\n", - " fig_value=SD.js_divergence,\n", - " height=280,\n", - " width=500,\n", - " title=\"Jensen Shannon Datadrift\",\n", - " min_gauge=0,\n", - " max_gauge=0.2,\n", - " ) # works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "9de0ccc4", - "metadata": {}, - "source": [ - "------" - ] - }, - { - "cell_type": "markdown", - "id": "2368d89a", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2019" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "5889552c", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2019,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "dfb1ac16", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7856527709300022\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2019', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "5a71d951", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" - ] - }, - { - "cell_type": "markdown", - "id": "a82b2857", - "metadata": {}, - "source": [ - "This result is interesting because we see that the auc is very high, but not the Jensen Shannon datadrift. This shows the interest of the latter." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "3753c08b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.scatter_feature_importance() # works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "abe585bd", - "metadata": {}, - "source": [ - "the first 3 variables that explain the data drift by the auc of datadrift classifier are not very important for the model" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "3ef3b98f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\") # works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "0805f1bb", - "metadata": {}, - "source": [ - "We can see that despite the data drift, the impact on predictions is quite small" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "908cb91d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_indicator(\n", - " fig_value=SD.js_divergence,\n", - " height=280,\n", - " width=500,\n", - " title=\"Jensen Shannon Datadrift\",\n", - " min_gauge=0,\n", - " max_gauge=0.2,\n", - " ) # works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "34db9bb1", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2020" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "142dec09", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2020,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "0a45a01e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7902450838961592\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2020', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "b67329ec", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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AAAEEEEAAAQQSKkAgTGjH02wEEEAAAQQQQAABBBBAgEDIGEAAAQQQQAABBBBAAAEEEipAIExox9NsBBBAAAEEEEAAAQQQQIBAyBhAAAEEEEAAAQQQQAABBBIqQCBMaMfTbAQQQAABBBBAAAEEEECAQMgYQAABBBBAAAEEEEAAAQQSKkAgTGjH02wEEEAAAQQQQAABBBBAgEDIGEAAAQQQQAABBBBAAAEEEipAIExox9NsBBBAAAEEEEAAAQQQQIBAyBhAAAEEEEAAAQQQQAABBBIqQCBMaMfTbAQQQAABBBBAAAEEEECAQMgYQAABBBBAAAEEEEAAAQQSKkAgTGjH02wEEEAAAQQQQAABBBBAgEDIGEAAAQQQQAABBBBAAAEEEipAIExox9NsBBBAAAEEEEAAAQQQQIBAyBhAAAEEEEAAAQQQQAABBBIqQCBMaMfTbAQQQAABBBBAAAEEEECAQMgYQAABBBBAAAEEEEAAAQQSKkAgTGjH02wEEEAAAQQQQAABBBBAgEDIGEAAAQQQQAABBBBAAAEEEipAIPR1fFdXl/T19Ulra2tChwPNRgABBBBAwG2Bnp4eaW5uTjdyYGAgZ4PnzJkz5Ov5zndbj9YhgICLAgRCEfH/n8OiRYsIhC6OdNqEAAIIIJB4gcHBQTn33HOlt7dX6urqJN8vgvv7++UPf/iDNDU1eXZ6fnd3t3R2dibeEgAEEHBHgEDo68v29nbZuHEjgdCd8U1LEEAAAQQQSAsEA2AwIOaj0oC4cOFCYZYwnxRfRwCBOAkQCAmEcRqv1BUBBBBAAIGiBfQXv3q0tLSky9BHQlesWCFz587NW65er08VMUOYl4oTEEAgRgIEwpCB8Pbbbx/WrVdccUWMupqqIoAAAggg4LaAzgDu2rVrSCP1cc/a2lrv35YuXSrTp08fFgg7OjqksbExK46Wu2zZMu/rzA66PYZoHQJJFCAQhgyEt91227Dxcf311/N/DEn8rqHNCCCAAAJVJ6DvBl511VVSU1MzLBCOGzfO+7eoM4RmzQFCYdV1PxVCAIEIAgTCkIEwk7E+ZsL/KUQYfVyKAAIIIICAJQENhHfddZfU19dnLTHqO4SFvnNoqWkUgwACCJRUgEBIICzpAKNwBBBAAAEEyiEQJhDmW2XULBpj3inUADl//vz0+4Xm0VF+GVyOHuUeCCBQLgECYWDbCQMf5gVzZgjLNUy5DwIIIIAAArkFwgRCLSHXPoTBQGj+239nwiAjEQEEXBMgEEboUQJhBDwuRQABBBBAwKJA2EBo8ZYUhQACCDghQCCM0I0Ewgh4XIoAAggggIBFAQKhRUyKQgCBRAkQCCN0N4EwAh6XIoAAAgggYFGAQGgRk6IQQCBRAgTCCN1NIIyAx6UIIIAAAghYFCAQWsSkKAQQSJQAgTBCdxMII+BxKQIIIIAAAhYFCIQWMSkKAQQSJUAgjNDdBMIIeFyKAAIIIICARQECoUXMChZltgbJtdq7fv7q6OiQxsZGr6bt7e3S1tbm/b23t1fq6uqKakEp95k0K9b667d48WLp6+uThoYGmTVrlkyfPl1aWlqKqjsXIRBFgEAYQY9AGAGPSxFAAAEEELAoQCC0g+kPV6bE5cuXS1NTU6gbZAo+oS48dFKhgdDcz2wHEuX+5QyEuqelhsHW1lav5UuXLi1JIDT9GQzYev/u7m7p7Owc0j362TZ4rgmu5sQlS5YQXAsZ1DE4l0AYoZMIhBHwuBQBBBBAAAGLAkkKhNt37pX/fWKTbNi6U04+ZpK8+eQjrUlqgNi4cWM6qGjBGgj0CIaHTDeNEsi0vDCB0H9f3VfyZz/7Wbq+Ue5fykAYtFLnI488MnTQLraD9bOqBvpNmzYNCXFhAqHxCP5CQMPrZz7zmaJnYottC9eVToBAGMGWQBgBj0sRQAABBBCwKJCUQLh+6055e9sq2b5rX1pv0Rkz5KtNp1nRzBQITSjUxxn1MU0TFMwN9ZFHExb1s5H/0NkmfYRT+yfT+fpvJsQFr5s7d2565kyDn86oaTjRmS2tywsvvCDLli1LX6YzV+bRUfOP2R49DbZBH0GdN2+eV0/zWKfes7m5eUj5/kc6/W31G2hg0jqaQ8vTw5T9ta99bcjX9d6PP/64d44pP1g/fztMyDNtz9ZGdb311lvlwx/+sCxcuFDMLKreJ0wgzDYWrAw0CqkqAQJhhO4gEEbA41IEEEAAAQQsCsQ1EN752EZZs3lHaInep7fII2tfGnb++846To6YODZ0OX/9xukyq+6wYednCwH+Rxw1aOihgU0PE1D0sdJMM3S5zjfXm3cCgzOEJlwF370z4TQYbMLOEGZ6D/Hyyy8fEgi1bPOorCnXhC+tl4ZA83V10zoFHwXVUFlfX58OxaYdwUdE9Xp/IPTXzwRTE+j0a/4Amq3TtczTTjvNC/E6y2vMwgbC4DWhBxcnxk6AQBihywiEEfC4FAEEEEAAAYsCcQ2Ezf/xG7mn/8XwEgcPiowYMez8gyIy/F+zF3vLh98kl847oqhAGLzIH2bCBDL/+Rp29L/NDGOmQBhcbMUfVIoJhMF7mvbke2TUf99s7/xlm3kLlp0rEGaqn//dvmCYzdbL/s+pWi//Y6NhZggJhOG/LeN+JoEwQg8SCCPgcSkCCCCAAAIWBeIaCOM4Q6jdFnws0iw0ki0QZjs/OKNWjkAYvGeuQBh8BDY4k2mu9S+04l+ExczkFRIItX7+R2HNPcy9wwRCDZX6GGrwEVQzy5gtFPuDJ4HQ4g+oKi+KQBihgwiEEfC4FAEEEEAAAYsCcQ2EhRLoO4Rva1slr/jeIfybhhnytfeW7x1CndHTQGFm9fLNEOY6v5pnCDUQ6eOWJlRlC0i5FsLRaxYtWiQXX3zxkMdRC50h9I+TMIEwGMCDoTJTIAyGVt4hLPS7M77nEwgj9B2BMAIelyKAAAIIIGBRICmBUMl0ldFf6iqjL73mrTL6V6ccZU0y2yqjuk+e2SIh+L6bPzhlCkeFnG/el/O/q1fII6NhVykN8w6hPwCacs0snQYu4+EPUnfccYfou4hmL0RTRnDBmkLeIdTO1VnD+fPne+9thgmEek5wT8bgzKj/3U+9RzC4s8qotW+rqi+IQBihiwiEEfC4FAEEEEAAAYsCSQqEFtmGFRVmH8LgCpg6A+YPbf4yMq0yGjzf/4ikfk1X6Cw2EJpgY1YbzbUCp6686Z85y7XKqD76qYdZmCXoZIJitpVJC3lkVO+TbyVXc79MYyHse4y57uEvN/jYbCH7UpZyrFK2PQECYQRLAmEEPC5FAAEEEEDAogCB0CImRSGAQKIECIQRuptAGAGPSxFAAAEEELAoQCC0iElRCCCQKAECYYTuJhBGwONSBBBAAAEELAoQCC1iUhQCCCRKgEAYobsJhBHwuBQBBBBAAAGLAgRCi5gUhQACiRIgEEbobgJhBDwuRQABBBBAwKIAgdAiJkUhgECiBAiEEbqbQBgBj0sRQAABBBCwKEAgtIhJUQggkCgBAmGE7iYQRsDjUgQQQAABBCwKEAgtYlIUAggkSoBAGKG7CYQR8LgUAQQQQAABiwIEQouYFIUAAokSIBBG6G4CYQQ8LkUAAQQQQMCiAIHQIiZFIYBAogQIhBG6m0AYAY9LEUAAAQQQsChAILSI6XhRS5culenTp0tLS4vjLS2+eYsXL5ZFixZJU1OTFOvV1dUl3d3d0tnZWXxFIlypn9M7OjqksbExQinJuJRAGKGfCYQR8LgUAQQQQAABiwIEQjuYPT090tzc7BW2ZMmS2IYmDTR9fX1plIaGhnQwKTbg2BG2U4q/n0yJK1askLlz51q5QTGBMBjAShkIg/2rjQ62n0AYfigQCMNbDTuTQBgBj0sRQAABBBCwKJCoQLhrm0j/z0S2PSty1HyRuW+3KJkqqr293fszjrNo/jBjYLQ9l112mReYXAqEAwMDXhNNQOzt7ZW6urrI4yGTYb5CyxnAgvUbHBwU/RkQ519i5PMt5dcJhBF0CYQR8LgUAQQQQAABiwKJCYTb1onc3Ciy6+XX9U5/v8i7brKoOTwQmg/c5ib+2Rj9PLR8+XJZtmyZ92X/bJz+t37dHP6vRSkzW2NNmbmCkQmEGqLMLKL/fA2PbW1t6Vv4HzvM1db+/n5ZuHDhEItgQNF762OUevi/ZmbT9PFGc+9cjzuaAGgCoZanIUkDfH19/bB66Hm5vIP9pP+tfaqPjAZ/ORAsR+v5s5/9LN0uc+1RRx3lXet/ZNQ/Fvzty9cnwf7OFFiDfW881FTv6x+zwXODM67GNVOfZrLU+vnL948hffS2tbXVa0KYfvbPfvrHSLYybXzjEwgjKBIII+BxKQIIIIAAAhYFYhsI//BjkcGnw0s80yPy7APDzz/joyITjgxfzqlNItNmZz0/GAL8sz/BMKJf8wc9/wdx/aCvX9NgoYeWa2Ydiy0zXyP1/rNmzUp/CA+eb0KZCYHBtuqHdlNf/bsGXRMQcrXVhAfzIT4YOoKPUPpDjbmPCUlqHAxT/naECYTBMJrLOzhr6q9brrHg79PgDGGwDf57GBsTovL1SZhAqOf4x5t/HGbq402bNnlj0fSbfzxs3LjRGz/BPjX18Lc12JYo/RzsB/P9kqvMfN8PYb5OIAyjlOUcAmEEPC5FAAEEEEDAokBsA+GP3ivy1C/DSxw8KDJiRPjzs535/k6Rk94SKhBmCif+GZdgEPB/KM/2eGaUMsM03j8Tpeeb2S4TGvyLyuiHbZ0pNLM4/vKDoS5XW4PBQsvxO/kDin7NbxD8wG/K8s8A5gqE/uCaqR65vPURU/3+8c+SZguEuYJqrkCYaebWH9KC4yRXn6hDtkdaswXCoKde/4UvfMF7hDgYFv3nhrEMBsJgP/vbkqufc81u5yozzPdDvnMIhPmEcnydQBgBj0sRQAABBBCwKBDbQBiDGUITNoLdZWazMoUkE7iCjxeaWasoZRY6bMyHelPfTOHDvxqmOd9/HxOWcrU1WyD0O/kfK/TP8mULCtkefc20qEzwMUf/tbm8zSOmwcdPzSqj/sCUK6jlCoSZAq6Wa2bi8vVJsM8LDYQmRJpHaj//+c8PWWTIPMYb7HMdv/oYcNDS/wuEYCAM/jJCyzQz6Ln62dwr0y8BcpVZ6PdDpvMJhBEUCYQR8LgUAQQQQAABiwKxDYSFGug7hDctENm9/fUrT3ufyLtvLrSknOf7Q0C+xxdzhST/TfwfnDdv3pzzkciwZYZtdK5ZS/+HdFNHE+LCzBCa8JsvEBYzQ5gvEGYKD2FmtTL1S7XNEObasqLQdwi1vSbMajjTw/8Ys/53pgWUwljmmyH0W+cKhHpecKbWXBscO2HHfdjzCIRhpTKcRyCMgMelCCCAAAIIWBRITCBUM2+V0f8R2fqsyNG6yug7LEqmigp+4A4GNP1gO3/+fO+Ru1zhTYOYeRQzX7gKW2auxuoH+FtvvXXI45/mQ73/fbXgI6MmfGRbbMTGDGG+d8syzVLaCoRqlqsP/f1tDLItKhMsx7znFhwzhb5DmK1PMvV3tlVG/Y8GB0OUf7ba75op9Jlxm+lrwQBoZmvN+Mq03YYpL99McNh3CNXE/70V9QcAgTCCIIEwAh6XIoAAAgggYFEgUYHQopu/KP9jhf6VEfWc4KOf/kVkcgXCXCt2Fltmvub7V/I05/oDQL7HE4OrOWpQsxEIzYf4XKuMmhU5MwURf7szLSpjvp7t2lze/sdkzQxapkdG9R7BR2r9C+GYPSw1mBWzyqiZpcu3h2GYfQgzzapp3+sRfF80+AiuebQ5m2Xwe8U/RrT8bOM+XyA0wd30ZbZVRvXruVahzfc9Evw6gbBQMd/5BMIIeFyKAAIIIICARQECoUVMikIAgdAC+R6pDl1QBU8kEEbAJxBGwONSBBBAAAEELAoQCC1iVnFR2RZHMVX2L9pSxc2gajEXCM5Q5tr3Mg5NJRBG6CUCYQQ8LkUAAQQQQMCiANnhOZEAACAASURBVIHQIiZFIYBAogQIhBG6m0AYAY9LEUAAAQQQsChAILSISVEIIJAoAQJhhO4mEEbA41IEEEAAAQQsChAILWJSFAIIJEqAQBihuwmEEfC4FAEEEEAAAYsCBEKLmBSFAAKJEiAQRuhuAmEEPC5FAAEEEEDAogCB0CImRSGAQKIECIQRuptAGAGPSxFAAAEEELAoQCC0iElRCCCQKAECYYTuJhBGwONSBBBAAAEELAoQCC1iUhQCCCRKgEAYobsJhBHwuBQBBBBAAAGLAgRCi5gUhQACiRIgEEbobgJhBDwuRQABBBBAwKIAgdAiJkUhgECiBAiEEbqbQBgBj0sRQAABBBCwKEAgtIhJUQggkCgBAmGE7iYQRsDjUgQQQAABBCwKEAgtYlIUAggkSoBAGKG7CYQR8LgUAQQQQAABiwIEQouYFIUAAokSIBBG6G4CYQQ8LkUAAQQQQMCiAIHQIiZFIYBAogQIhBG6m0AYAY9LEUAAAQQQsChAILSISVEIIJAoAQJhhO4mEEbA41IEEEAAAQQsChAILWJSFAIIJEqAQBihuwmEEfC4FAEEEEAAAYsCBEKLmBSFAAKJEiAQRuhuAmEEPC5FAAEEEEDAooCLgbC9vV3a2to8peXLl3t/Llu2TJYsWSItLS0W9SgKAQSSLEAgjND7BMIIeFyKAAIIIICARQHXAqE/DJpA2NTUJPrZo6GhQTo7Oy3qURQCCCRZgEAYofcJhBHwuBQBBBBAAAGLAq4FQv2MsWjRImltbfVCoM4QaiBcunSpdHd3y8DAgEU9ikIAgSQLEAgj9D6BMAIelyKAAAIIIGBRwMVAaB4N9QfCxYsXS19fH4HQ4tihKASSLkAgjDACCIQR8LgUAQQQQAABiwKuBUINfnroo6EmEM6fP18WLlyYnjm0yEdRCCCQYAECYYTOJxBGwONSBBBAAAEELAq4Fgh7enqkubk5o1BHR4c0NjZa1KMoBBBIsgCBMELvEwgj4HEpAggggAACFgVcC4RKMzg4KNou/7FixQqZO3euRTmKQgCBpAsQCCOMAAJhBDwuRQABBBBAwKKAi4HQIg9FIYAAAlkFCIQRBgeBMAIelyKAAAIIIGBRwLVAaLad8O85mOnfLBJSFAIIJFSAQBih4wmEEfC4FAEEEEAAAYsCYQNh8N28XNs3ZHpk039+V1eXt1G8/zBbRURtWrbVRNmHMKos1yOAQFCAQBhhTBAII+BxKQIIIIAAAhYFwgRCE/B6e3ulrq5ONNDpFg6611+mQ8OjHmYBF52h27hxY/r8fNdHaZ5/H0J/OexDGEWVaxFAIJMAgTDCuCAQRsDjUgQQQAABBCwKhAmEwQAXDIj5qqMBUUOhbgWhR6kDYUNDQ/pepm7sQ5ivl/g6AggUKkAgLFTMdz6BMAIelyKAAAIIIGBRIEwg1DCnR0tLS/rO+v/lYVfuDAbA4COjth4X1cqZmcDly5dLU1OTV1/zuKvN+1jsAopCAIGYChAII3QcgTACHpcigAACCCBgUUAD4VVXXSUjR44cUqqGqdraWu/fNGRNnz59WCAMs6+fmU3MFR7NBvImwEVpXn9/v7cJfaYjbICNcn+uRQCB5AgQCCP0NYEwAh6XIoAAAgggYFHABMKampphgXDcuHHevxU7Q2jCYL7gaPsR0kyL2pj3Hy3SURQCCCRcgEAYYQAQCCPgcSkCCCCAAAIWBcI8MlrMO4Rhw6A2xXYgtMhDUQgggEBWAQJhhMFBIIyAx6UIIIAAAghYFAgTCPOtMmoe0zSPZAb/O1hdfQTVrFBaSHC02GyKQgABBCILEAgjEBIII+BxKQIIIIAAAhYFwgRCvV2ufQiDAdBsBB+spnl01Cz8Yr7uXwDGRtPMiqKZysq1f6KNe1MGAggkR4BAGKGvCYQR8LgUAQQQQAABiwJhA6HFW5a0qGDYDN6MQFhSfgpHIFECBMII3U0gjIDHpQgggAACCFgUcC0Q6mcMPVhR1OIgoSgEEMgoQCCMMDAIhBHwuBQBBBBAAAGLAq4GQmYCLQ4SikIAAQKh7TFAILQtSnkIIIAAAggUJ+BaIDSPjBIIixsPXIUAAuEFmCEMbzXsTAJhBDwuRQABBBBAwKKAa4Ew3wqnFukoCgEEEi7gbCD0r8wVZtUv86y+jocw5+t5BMKEf/fQfAQQQACBqhFwLRD6P5dkQmbmsGqGHhVBIPYCTgZCXSZaj5aWFu9P/aGa66VsDY+LFi2SpqYmKWQfIQJh7Mc/DUAAAQQQcESAQOhIR9IMBBAou4CTgTAYAIMB0a9sHsnw/6Yt1/n+awmEZR+v3BABBBBAAIGMAq4FQroZAQQQKJeAc4HQzPD19vZKXV2d59jV1SV9fX3S2to6zDVTIMx1PoGwXEOT+yCAAAIIIBBegEAY3oozEUAAAb+Ac4HQBLxgIOzu7pbOzs6Mva8zfR0dHdLY2Jg1QN5+++3Drr3uuuuEZ/j5hkIAAQQQQKDyAi4GQn1iqa2tzcPV9Q30WLZsmSxZsiT9Wkzl5akBAgjEXcC5QFjoDKF2oAmR/s4M/rC97bbbhvX19ddfTyCM+3cA9UcAAQQQcELAtUDoD4MmEOpaB/pL7IaGhqy/5HaiM2kEAgiUVcC5QKh6hbxDmElb9/5529velp4xzNYjvENY1rHKzRBAAAEEEMgq4Fog1M8YuuCdvu6ifzcroLM/Id8ECCBgW8DJQJhvlVH/D9YgqL4/mOvxUv/5BELbw5HyEEAAAQQQKE7AxUBonlbyf24x22rxykpx44SrEEBguICTgVCbmWsfwmAg1BCoz+TrYX4bF2awEAjDKHEOAggggAACpRdwLRDq5xg9dP0D87ll/vz5snDhwoI+q5RenjsggEDcBZwNhOXoGAJhOZS5BwIIIIAAAvkFXAuEPT090tzcnLHh/oXw8stwBgIIIJBbgEAYYYQQCCPgcSkCCCCAAAIWBVwLhEpjFsrzM61YsULmzp1rUY6iEEAg6QIEwggjgEAYAY9LEUAAAQQQsCjgYiC0yENRCCCAQFYBAmGEwUEgjIDHpQgggAACCFgUIBBaxKQoBBBIlACBMEJ3Ewgj4HEpAggggAACFgVcC4T6GSPfwbuE+YT4OgIIhBEgEIZRynIOgTACHpcigAACCCBgUSCJgVD5CIUWBxFFIZBQAQJhhI4nEEbA41IEEEAAAQQsCrgWCHVPZV1pVLedMEd/f7+37YSGwPr6eu/vDQ0NQ86xSEpRCCCQEAECYYSOJhBGwONSBBBAAAEELAq4Fgj1M0amsKf/bvZMZpN6iwOIohBIsACBMELnEwgj4HEpAggggAACFgVcDITKMzAwMETJHxQJhBYHEEUhkGABAmGEzicQRsDjUgQQQAABBCwKuBYIly5dKt3d3bJ8+XJpamrypLq6umTZsmXpGcJss4gWWSkKAQQSIEAgjNDJBMIIeFyKAAIIIICARQHXAqF5XzATkb5DOG/ePNE2m8dHLVJSFAIIJEyAQBihwwmEEfC4FAEEEEAAAYsCrgVCpRkcHPRCn/9YsWKFzJ0716IcRSGAQNIFCIQRRgCBMAIelyKAAAIIIGBRwMVAaJGHohBAAIGsAgTCCIODQBgBj0sRQAABBBCwKOBaIDQb07PPoMVBQlEIIJBRgEAYYWAQCCPgcSkCCCCAAAIWBVwLhGYFUR4RtThIKAoBBAiEtscAgdC2KOUhgAACCCBQnIBrgVA3pm9raxMCYXHjgasQQCC8ADOE4a2GnUkgjIDHpQgggAACCFgUcC0QmlVGeWTU4iChKAQQYIbQ9hggENoWpTwEEEAAAQSKE3AtEJp3CLNpBDesL06NqxBAAAERZggjjAICYQQ8LkUAAQQQQMCiAIHQIiZFIYBAogQIhBG6m0AYAY9LEUAAAQQQsCjgWiC0SENRCCCAQE4BAmGEAUIgjIDHpQgggAACCFgUIBBaxKQoBBBIlACBMEJ3Ewgj4HEpAggggAACFgVcDIRmpVFlWr58uae1bNkyWbJkibS0tFjUoygEEEiyAIEwQu8TCCPgcSkCCCCAAAIWBVwLhP4waAJhU1OT6GePhoYG6ezstKhHUQggkGQBAmGE3icQRsDjUgQQQAABBCwKuBYI9TPGokWLpLW11QuBOkOogXDp0qXS3d0trDJqcfBQFAIJFyAQRhgABMIIeFyKAAIIIICARQEXA6F5NNQfCBcvXix9fX0EQotjh6IQSLoAgTDCCCAQRsDjUgQQQAABBCwKuBYINfjpoY+GmkA4f/58WbhwYXrm0CIfRSGAQIIFCIQROp9AGAGPSxFAAAEEELAo4Fog7Onpkebm5oxCHR0d0tjYaFGPohBAIMkCBMIIvU8gjIDHpQgggAACCFgUcC0QKs3g4KBou/zHihUrZO7cuRblKAoBBJIuQCCMMAIIhBHwuBQBBBBAAAGLAi4GQos8FIUAAghkFSAQRhgcBMIIeFyKAAIIIICARQHXAqF+xmA20OIAoSgEECAQlmIMEAhLoUqZCCCAAAIIFC7gYiA0Cuw7WPh44AoEEAgvwAxheKthZxIII+BxKQIIIIAAAhYFXA6EfiazH6FFOopCAIGEC1gLhP7VsPyPOPT393tLJOvh2qpYBMKEf/fQfAQQQACBqhFwLRD6Ydvb26WtrW2INRvTV83QoyIIxF7AWiBcunSpdHd3Zwx9JiwuWrRIWltbY49mGkAgdKYraQgCCCCAQMwFXA6E/q7p6uqSZcuWsTF9zMcr1UegmgSsBUINR3pk+o2Vf9lkl36jRSCspqFMXRBAAAEEkizgciDUTer7+vqYIUzyAKftCJRQoCyBUOufKzCWsH0lLZpAWFJeCkcAAQQQQCC0gGuB0HxuCgIsWbJEWlpaQrtwIgIIIJBPwFogNL+9yrREsnmP0LVVsgiE+YYXX0cAAQQQQKA8Aq4HQpeesCrPiOAuCCAQVsBaIDTPtOuNe3t7pa6uzquD/3FR11bGIhCGHWachwACCCCAQGkFXAyEri3GV9oRQOkIIFCsgLVAqBXI9Iy7qZhrs4PaLgJhscOO6xBAAAEEELAr4FogtKtDaQgggEB2AauBUG/jnyk0t3VtZtC0i0DItxYCCCCAAALVIUAgrI5+oBYIIBA/AeuBMH4ExdeYQFi8HVcigAACCCBgU8CFQKifK8wTVdkWlTFmvFNoc/RQFgLJFiAQRuh/AmEEPC5FAAEEEEDAogCB0CImRSGAQKIErAXCfL/JyrT6aNylCYRx70HqjwACCCDgioALgdCVvqAdCCAQL4GyBUJlcS0UEgjjNdipLQIIIICAuwIEQnf7lpYhgEBpBawFwlzVNAvNLFq0SFpbW0vbojKWTiAsIza3QgABBBBAIIeAa4HQ7OHsb7Jrv1hnQCOAQHUIlCUQalPNI6UuvQRNIKyOQUwtEEAAAQQQcCkQtre3S1tbW8ZOdXXldkYwAghUToBAGMGeQBgBj0sRQAABBBCwKOBKIPTPDPpnBHt6eqS5udkTY6bQ4sChKAQQEAJhhEFAIIyAx6UIIIAAAghYFAgbCP3BSm+f68mlwcFB0XL9R6mfdFq6dKl0d3dLpplAV1/BsTgMKAoBBIoQKEsgdPUHGIGwiBHHJQgggAACCJRAIEwgNAGvt7dX6urqRD+f9PX1ZV3fQMOjHo2Njd6f+ijnxo0bS7oewuLFi706mTr6qUz9zV6FJWCkSAQQSKCAtUCYb9sJtXXtEQcCYQK/Y2gyAggggEBVCoQJhMEAGAyI+RqmAVFDYWdnZ75Ti/66f3P6TIW4uCZD0VhciAACVgTKFghdC4OqTyC0MgYpBAEEEEAAgcgCYQKhhjk9Wlpa0vfT/y8P+xkl34xi5EYc+myRawaQQGhDmTIQQMAvYC0QJpGVQJjEXqfNCCCAAALVKKCB8KqrrpKRI0cOqV5TU5PU1tZ6/6bv502fPn1YIOzo6Eg/FpqtbWY2MWx4LNaIGcJi5bgOAQSKFSAQFivHDGEEOS5FAAEEEEDAroAJhDU1NcMC4bhx47x/K3aG0ITBMMExaqvCvIKj9yj14jZR28H1CCAQH4GyBEL/il4u/QBjhjA+A52aIoAAAgi4LRDmkdFi3iEsZxjUHiIQuj1OaR0C1ShQ0kBoVsryN5xAWI3DgDohgAACCCAQb4EwgTDfKqNmD0DzWGjwv+MtRO0RQACBzALWA2Fwfx//bcvxqEU5O5oZwnJqcy8EEEAAAQSyC4QJhHp1rn0IgwFQHzFta2sbdlPXPs8wrhBAINkC1gKh2Ug1G6dLM4OmjQTCZH/z0HoEEEAAgeoRCBsIq6fG1AQBBBCoDgFrgTD4zLsJgOaxUQJhdXQ4tUAAAQQQQMBFAQKhi71KmxBAoBwC1gIhM4Tl6C7ugQACCCCAAAKZBAiEjAsEEECgOAFrgdDcPtc7hKXeu6c4guKv4pHR4u24EgEEEEAAAZsCBEKbmpSFAAJJErAeCP14rDKapKFEWxFAAAEEEKicAIGwcvbcGQEE4i1Q0kCYadbQpXcJmSGM9+Cn9ggggAAC7gi4GAgz/WLd9JhLn6fcGYW0BIF4CpQlEMaTJn+tCYT5jTgDAQQQQACBcgi4FgiTuDZDOcYJ90AAgeECBMIIo4JAGAGPSxFAAAEEELAo4FogNKu3u7b+gsUupygEELAkQCCMAEkgjIDHpQgggAACCFgUcDUQ8mioxUFCUQggkFGAQBhhYBAII+BxKQIIIIAAAhYFXAuE5pFRAqHFQUJRCCBAILQ9BgiEtkUpDwEEEEAAgeIEXAuE/f39snDhQuGR0eLGA1chgEB4AWYIw1sNO5NAGAGPSxFAAAEEELAo4FogNO8QZiNi5tDi4KEoBBIuYD0Qmh9gHR0d0tjY6DQvgdDp7qVxCCCAAAIxEiAQxqizqCoCCFSVgPVAaPbMScIjDgTCqhrLVAYBBBBAIMECrgXCBHclTUcAgTILWA+E7e3t0tbWlohn3gmEZR6t3A4BBBBAAIEsAi4GQvMeob/JSfiFO4McAQTKK2A9EJofXjwyWt6O5G4IIIAAAggkWcC1QNjT0yPNzc0ZuzQJn7GSPJZpOwLlFrAeCJP0EjQzhOUertwPAQQQQACBzAKuBUKz7YR/RtD80n3RokXS2trKUEAAAQSsCBAIIzASCCPgcSkCCCCAAAIWBVwLhPoZI1PwY39Ci4OGohBAwBOwHgiT5EogTFJv01YEEEAAgWoWIBBWc+9QNwQQqGYBZwOhWe1U8ZcvXy5NTU05+8H/qGvYRzEIhNU8tKkbAggggECSBFwLhDwymqTRS1sRqKxASQKhWWnUhDH9c9myZbJkyRJpaWkpeYv1/nqYe2lwy7Uql4ZHDYEmNAb/O1uFCYQl70pugAACCCCAQCgB1wJhphVGDQSLyoQaEpyEAAIhBawHQn8YNIFQg5aGp4aGBuns7AxZteJPCwbAYEAMllzo+eZ6AmHxfcSVCCCAAAII2BRwLRCqDdtO2BwhlIUAAtkErAdC/0vQ+nfzuGa5XoIeHBwU/T+F3t5eqaur89rd1dUlfX19WVfk0q/rDKb5jZvW2399NjwCId9YCCCAAAIIVIeAi4GwOmSpBQIIuC5QkkBoHg31B0LzTt/AwEBJTc1v04KBsLu7O+vspLlGZzA1OGZ6h/D2228fVu/rrrtOSt2ekmJROAIIIIAAAo4IEAgd6UiagQACZRewHgg1+Omhj4aaQDh//nxZuHBhxqBlu8XFzBAGZwR1NlMP/x4/t91227CqXn/99QRC2x1IeQgggAACCBQhQCAsAo1LEEAAgVJsO9HT0yPNzc0Zccv1EnQh7wSa2UH/TJ8+QpprRtE0jkdG+R5CAAEEEECgOgRcCIT+9Rb8q59nEuYJpeoYd9QCARcErM8QKoqZpfMD5Vrl0zZkvlVG/Y+y6r2D/62znI2NjXlXRCUQ2u45ykMAAQQQQKA4AQJhcW5chQACCJQkEFYDa659CIMBMBhgw26PQSCshp6mDggggAACCIi3oNxdd90l9fX1cCCAAAIIFCDgbCAswKDoUwmERdNxIQIIIIAAAlYFXAuE/lXb/VD+tRqsAlIYAggkVoBAGKHrCYQR8LgUAQQQQMBJgVvuf0Z+9cQmr20nHz1JPnnZHJk0bnTJ25qUQFiubbxK3mHcAAEEqkbASiBM6kvQBMKqGcdUBAEEEECgCgRu+NVT0nb30O2lzpk1Te74+Dklr11SAmG5tvEqeYdxAwQQqBoBAmGEriAQRsDjUgQQQACBsgu8smuf7Nl/QPbuPyB79h36X+Dve/cdkN2H/m3IeXq+d+1B2b1vf7qM3YfK0XMfWD0oW1/bO6xda7/49pK31YVAaFY+z4eVab/kfNfwdQQQQCCbgJVAmFReAmFSe552I4AAAtkF/AFJA1Sm4GWCVsbgFQxoh/5by80Y5DKEtyHnHgpx5eizgwdFRowYfqfff/7NJX9sNEmBsLe3V+rq6srRpdwDAQQSIGAlEPpffM72ErSLlgRCF3uVNiGAgAroe2D/9egGeeL57aKP/C25bI6cO2taVeHs2puasfIHLp3d0n8rJDxpyDIhzpsB882Qadn+8GbKzxTydu7dX1U+mSozbvRIGTOqJvW/kYf+HFUjow/9feyhP81/63n6b6N954/1na9fT587skb+85Fn5aE1W4bcevrh4+SBf7yk5DYuBEI/UpI+T5V8cHADBBDIKUAgjDBACIQR8LgUAQSqVqB3zRZ533cfGlK/SbWj5Pa/PUfqJ4x9fZYqQ/BKh6tMjxyGfDRRZ838wWtIYDsU0PYdOFi1fqNHjvDClglRwQA1JIwFAtiQc01gyxDcMgW1jCHPBL1RNWXxWr91p3z8tt/In59/xbvfxNpR8t0PnVmWXya4FgjL0mHcBAEEEBARK4HQv+dfPtWBgaEvm+c7v5q/TiCs5t6hbgggUKxApoVBtKyDBw/KiEzPAxZ7o4jXmeBlZqk0TJnZL/+sVaaglD5Xg5svcPlnzUyoM7NmQ2bD/DNsvlCn5XKIbN+5V17etU+OnTKubByuBcL29nZpa2sT/97Imf6tbMDcCAEEnBWwEgiDG7vn0iIQOjuWaBgCCDgg0PfsNvlU5+9k3ZbXhrXmsDEjZULt6EOPG4449OdIGT0qNSOWClkjxQtq6ccS9RHF1Nf9X/POHfn6tcHQ5g9fmR5j1PI4EPALuBYIs60m6l/ZnRGAAAII2BCwEgj9FUnSM+/MENoYgpSBAALVIKCrQ37rntXe+1/ZZgJ7PndJWWd8qsGFOsRHwLVAmO3zFPsQxmdMUlME4iJgPRDGpeE26kkgtKFIGQggUEmBu//8orTfMyC/W7/Nq8aomhHynoYZorOB3X0bRLcp0EVB/mXhyfJXpxxVyapybwRyCrgYCBsaGqSzs3NIu9mHkG8EBBCwLUAgjCBKIIyAx6UIIFAxAV2P5ed/eF5uXLnaW0VUD32Ec/GbjpW/u2i2HD25tmJ148YIFCvgWiA0M4HLly+XpqYmj6Wnp0eam5uFfQiLHSVchwACmQSsBEL/8+z691wH7xAyEBFAAIHKCOw/cFDufOw5uXHlgDy9eYdXCd2G4IPnHC8fv3CWt4IoBwJxFXAtEObapH7FihUyd+7cuHYV9UYAgSoTIBBG6BBmCCPgcSkCCJRNYO/+g9L12/Vy072rRbcF0EO3A/jweTPlbxfMksPHjy5bXbgRAqUScC0QqlOmRfvYlL5UI4hyEUiugJVAmFQ+AmFSe552IxAPAd1s/YcPr5Pv3LdGNm3f5VV6yvjRcuWCWfKR82fKhLGj4tEQaolACAEXA2GIZnMKAgggEFmAQBiBkEAYAY9LEUCgZAKv7t4nt/Wuk1t61siWHXu8+9RPHCsfv2CWXHHOTKkdzZYNJcOn4IoJEAgrRs+NEUAg5gIEwggdSCCMgMelCCBgXUA3A//e/c/Ifzy41tsYXA9dIfTqi06U9555rLdwDAcCrgq4EAiTuiaDq2OSdiEQFwHrgbC9vV3a2tqko6ND5s2bJ/oDWo9MSyfHBSlbPQmEce9B6o+AGwI6C/id+56WHzy0Tl7bs99r1Mxp4+UTF8/2tpDQrSQ4EHBdgEDoeg/TPgQQKJWA9UCo++PoofvmmCWTTeWXLFkiLS0tpWpL2cslEJadnBsigIBP4PmXd8nN9z0tdzzyrOj7gnr8xVET5ZqLZ8s7Tj1GyIEMlyQJuBAIk9RftBUBBKpHwHog1JBkgp/ZgkKXR7711lulu7tb2HaiejqfmiCAQDwF1r/0mrSvXC0/6dsguoKoHvOnT5ZrL50jbz75yHg2ilojEFGAQBgRkMsRQCCxAtYDoc4Qzpo1Sz7zmc+kHxfVEGgeJSUQJnas0XAEEIgooHsHfvPuAVnx+EbRzeX1OHPmFLn2kjly4Un1EUvncgTiLeBCIMy3l7O/h1z6PBXvkUftEYi/gPVAaIKfoVm0aJG0traK/1HS+LOlWsAjo670JO1AoLoF+l94Rb7xq6fkF396IV3RBbPrvBnBs0+YWt2Vp3YIlEmAQFgmaG6DAALOCVgPhCqk4a+vr8/D0t9gdXV1ybJly9KPkrqiSCB0pSdpBwLVKdD37Db55t1Pyb1Pbk5X8LJ5R8qn/vIkOeWYSdVZaWqFQIUEXAiEfjpdh2HNmjXemgzmMBvVL1++XJqamiokzW0RQMA1gZIEQteQsrWHQJiUnqadCJRX4IHVg9J+z2rpXbPFu/GIESJvm3+0fPLSk2TOkRPKWxnuhkBMBFwLhP4tKPxd4OITVzEZYlQTAWcFCIQRupZAGAGPSxFAYJjA3X9+UdrvGZDfrd/mfW1kzQh51+nTvUdDdRsJDgQQyC7gYiDU1gbfFTRPYfEOId8NCCBgS8B6IGQfQltdQzkIIJAEgYMHRX7+x+e9GcEnnt/uNXnMyBppOnOGt4+gbizPgQAC+QVcC4Rm6y7/46E9PT3S3NzsQb+ZUwAAIABJREFU5N7O+XuYMxBAoFQC1gMh+xCWqqsoFwEEXBLYf+Cg3PnYc3LjygHR1UP1qB1dI+8/63i5+qIT5YiJY11qLm1BoOQCrgXC/v5+WbhwYUY33iEs+XDiBggkSsB6IGQfwkSNHxqLAAIFCui+gV2/XS833bta1m/d6V09Yewo+dC5x8vHLzhRDh8/usASOR0BBFTAtUCobTKLyPh7uKOjQxobG+l0BBBAwJqA9UDIPoTW+oaCEEDAIYHd+w7IDx9eJ9+5b41s2r7La9nkcaOlecEJ8tHzT5BJtaMcai1NQaD8Ai4GwvIrckcEEEiigPVAyD6ESRxGtBkBBLIJvLp7n9zWu05u6VkjW3bs8U6rmzBGPtY4S644d6aMHzMSPAQQsCBAILSASBEIIJBIAeuBUBXZhzCRY4lGI4CAT2D7zr1yy/3PyPcfXCv6dz2OnlwrV194olx+1nEydlQNXgggYFHAxUDo/yW7vjeoh4v7OlscBhSFAAJFCJQkEBZRj1hewrYTsew2Ko1ASQV0FvC7q9bI7b1r5bU9+717HTd1vHziotmy6MwZMqpmREnvT+EIJFXAtUAYfOLKLCSTbX/CpPY77UYAgegCBMIIhgTCCHhcioBjAi++sltuXLla7njkWdH3BfWYfcQEabl4trzz9OlCDnSsw2lO1Qm4Fgj1M8aiRYuktbVV9O8mEJrtKNiHsOqGIBVCILYCJQmE+oMr2+HSDzACYWzHPRVHwJrA+pdek/aVq+UnfRtEVxDV45RjJsm1l8yRvzrlKBnBhKA1awpCIJdA2EBo9vIzZYX9XKL/n79ixQqZO3duuhpdXV3eI5z+w4S4qL0VXLXdBEI2po8qy/UIIBAUsB4IzW+uCIQMNgQQcFlA9w785t0DsuLxjXIglQOl4fgpcu3Fs+XiuUe43HTahkBVCoQJhGYbh97eXqmrqxMNdH19fd4sXLbDvy5CpkCY7/pisfz7OpsZwvnz53t7E9oKncXWjesQQMAtAeuB0DzbfuONN3p7ApkfnvqDTffNaWlpcUaQGUJnupKGIBBaoP+FV+Qbv3pKfvGnF9LXnHfiNGm5ZI7onxwIIFAZgTCBMBgAgwExW83NeeUMhMGZTH/d2IuwMmOMuyLgqkBJAqH/mXfzQ8vFZ94JhK5+W9AuBIYL9D27Tb5191Oy8snN6S/qTOCnLjtJTp0xGTIEEKiwQJhAqAu16OH/5XSmR0GDTckVCP2PjNqeucu0MX0wlFaYndsjgIADAtYDodmYXh+/8M8KEggdGC00AYEECjywelDa71ktvWu2pFv/ljccJZ+87CSZe9TEBIrQZASqU0AD4VVXXSUjRw7d27OpqUlqa2u9SutnkenTpw8LhPlm3LIFwqCEf/GX6lSiVggggMBwAeuB0AQ/fT7/jjvukLa2tvRdGxoapLOz05l+YIbQma6kIQgME7in/0X51t0D8rv127yv6SqhulqoLhZzYv1hiCGAQJUJmEBYUzN0j08NhOPGjfNqa3uGMEgQ5p3EsGxmgb58YTVseZyHAAIIZBOwHgiDN/K/jG1e4nalOwiErvQk7UAgJXDwoMjP//i8NyP4xPPbvX8bPXKE/M0ZM+Sai2bLsVPHQ4UAAlUqEOaRUdvvEJYyEJrPTzwiWqUDjmoh4JBAyQOhQ1bDmkIgdLl3aVuSBPYfOCh3Pvac3LhyQHT1UD3GjqqRy886Tq65eLYcMXFskjhoKwKxFAgTCPOtMtrf3++t4hkMYdkeGdWnoswKpeYcWzN6ZmN6AmEshyOVRiBWAgTCCN1FIIyAx6UIVIGA7hvY9dv1ctN9T4vuJ6jH+DEj5YpzZ8rHL5gl0w4bUwW1pAoIIBBGIEwg1HJy7UOYKRD6n3TS6/0LxwS32jJ7BYapb75zTF1sBcx89+PrCCCQXAECYYS+JxBGwONSBCoosHvfAfnhw+vkO/etkU3bd3k1mTRutHz0vJly5YITvL9zIIBAvATCBsK4tMq8Q5itvgMDA3FpCvVEAIEqF7ASCPP90PIbuPQDjEBY5aOb6iEQEHh19z65vXedfK9njWzZscf7qs4CXtk4Sz507vEyYewozBBAIKYCBMKYdhzVRgCBigsQCCN0AYEwAh6XIlBGge0798ot9z8j339wrejf9ThyUq1cdeEs+cDZx3vvC3IggEC8BVwLhPHuDWqPAAJxErASCOPUYJt1JRDa1KQsBOwL6Czgd1etkdt718pre/Z7Nzh2yji5+qLZ8t4zj/VWEOVAAAE3BAiEbvQjrUAAgfILWA+EZlWsJUuWpDd+zfRv5W+q/TsSCO2bUiICNgRefGW33LhytdzxyLOi7wvqoXsHfuLi2fKu06fLSN1UkAMBBJwSIBA61Z00BgEEyihgPRCa1biC7wpqeGJj+jL2LLdCIIECulJo+8rV8pO+DaIriOox7+iJ0nLxHHnr/KO9zeU5EEDATQGXAqH5RbrpKZfWX3Bz9NEqBOItYD0QavDzL8lseMzSzC79UGOGMN6Dn9q7I6B7B37z7gFZ8fhGOZDKgXL6sYdLyyWz5bJ5R7rTUFqCAAJZBVwJhF1dXbJs2bJh7XTp8xPDGAEEqkugJIEw00xgtpnD6uIorDYEwsK8OBsB2wL9L7wi3/j1U/KLP76QLvrsE6bKtZfOkQWz62zfjvIQQKCKBVwJhObzktl/0MwW2tzjsIq7kaohgEAFBKwHQjMT6P/BZTaBzTRzWIE2W7slgdAaJQUhUJBA37Pb5Ft3PyUrn9ycvu7Ck+plyWUnScNxhxdUFicjgIAbAq4EQrOVl5kRNBvUu/YZyo1RRysQcEPAeiA0P7gy8axYsULmzp3rhpyIEAid6UoaEhOBB5/eIt+6e0B612xJ1/jNJx/pBcFTjpkUk1ZQTQQQKIWAS4HQ/6QVgbAUo4UyEUDAL2A9EGrhg4ODoj+Y/Udvb6/U1bn1CBeBkG8mBMojcE//i14Q/N36bd4NdXGYd5x6jFx7yRyZc+SE8lSCuyCAQFULEAirunuoHAIIVLFASQJhFbfXatUIhFY5KQyBIQIHD4r8/I/PS/s9q+WJ57d7XxtVM0Le0zBDrr1kthw7dTxiCCCAQFrApUAYpltZZCaMEucggEAYAQJhGKUs5xAII+BxKQJZBPYfOCh3PvacfHvlalm9+VXvrLGjaryN5HUfwaMn12KHAAIIDBMgEDIoEEAAgeIECITFuXlXEQgj4HEpAgEB3Tew67fr5ab7nhbdT1CPcaNHygfPOV4+fuEsqZ8wFjMEEIiDQP9PRfr/R2TXyyIzG0XO+buy1NqVQFgWLG6CAAII+AQIhBGGA4EwAh6XInBIYPe+A/Kjh5+V79z3tLywfZf3rxNrR8mHz5spf7tglhw+fjRWCCAQF4HHfijy/z4xtLZz3y5y+Y9K3gICYcmJuQECCDgqQCCM0LEEwgh4XJp4gVd375Pbe9fJ93rWyJYdezyPKeNHy5ULZslHzp8pE8aOSrwRAAjETuD7bxNZ98Dwav/fl0veFAJhyYm5AQIIOCpAIIzQsQTCCHhcmliB7Tv3yi33PyPff3Ct6N/1qJ84Vj5+wSy54pyZUju6JrE2NByBWAvs2y3y7XNEXlozvBn/uE6ktrR7hBIIYz16qDwCCFRQgEAYAZ9AGAGPSxMnoLOA3121Rn7Qu1Z27NnvtX/64ePkqgtPlMVvOtZbOIYDAQRiKLB3p8hvvifyQJvIqy+KjBgxtBFjJ4ksXV/yhhEIS07MDRBAwFEB64FQQ9KiRYuktbV1CNnixYu9/+7s7HSGkkDoTFfSkBIKvPjKbrlx5Wq545FnRd8X1GPmtPHeiqG6hYRuJcGBAAIxFNizQ+SR74g82C7y2pZUA6Y3iGxbL7Jjc+q/NQy+9Ysip3+g5A0kEJacmBsggICjAmULhEuXLpXu7m5xad8cAqGj3xU0y4qArhTavnK1/KRvg+gKonqcdOREablktrepPDnQCjOFIFB+AV099OGbRB66WWTn1tT9T7hQ5MJ/EJm5IPXfa3tSfx41v+SPihoAFwOh/jK9r68vYx+79Hmq/IOYOyKAgF+gbIHQ/FBz6QcYgZBvJgSGCzy9eYd8654BueuxjXIglQNl/vTJXhD8q1OOggwBBOIqsGubyAPfEnnkuyK7t6daMfsykUv+SeSYhoq3yrVAaH6Rng3Wpc9TFR88VACBhAtYCYT9/f2ycOHCvJSZHiXNe1EVn0AgrOLOoWplF+h/4RX5xq+fkl/88YX0vc+cOUWuvWSOXHhSfdnrww0RQMCSwI5BkQe/mXpPUB8T1eMv3ipy0VKRo0+zdJPoxbgWCPUzhh4rVqyQuXPnRgeiBAQQQCCLQFkDYW9vr9TV1TnTGQRCZ7qShkQQ+P2Gl+WGXz0pK5889M6QiCyYXSfXXjpHzj5haoSSuRQBBCoqoAvE3P91kd9+X2Rfao9QmfdOkYv+UeTIUypatUw3dzUQMhNYdUONCiHgnICVQOhfSEb/PnPmTPnVr37lHFawQQRC57uYBuYQePDpLfKtuwekd82hxSRE5LJ5R8rfXzpHTp0xGTsEEIirwPaNIj1fF+m7TWT/ntSqoae8JzUjWJeatarGw7VA6OLaC9U4bqgTAgiIlCQQuvZoaLaBQiDkWyiJAvf0v+gFwd+t3+Y1Xz8rvm3+0fLJS0+SOUdOSCIJbUbADYGX14vc92WRx34kcmCfyIgakVPfK3Lh50Smzqr6NroWCM3rODwyWvVDjwoiEHsBK4Ew1ypYQSGXHn0gEMZ+/NOAkAIHD4r8/I/PS/s9q+WJ51OLSYysGSHvOn2692iobiPBgQACMRXQjeTv+5LIH7pEDuwXqRklctr7RC76nMjkY2PTKNcCoXmHMFsHuPR5KjaDjIoi4KiAlUA4ODgo+oM4zOHSDzACYZge55w4C+w/cFDufOw5+fbK1bJ686teU8aMrJFFZ86Qay6e7W0sz4EAAjEVGBwQufeLIn/6LxH9rc/IMSJv/KDIBf9HZNL02DWKQBi7LqPCCCBQJQJWAqG/Ldk2pq+S9lqtBoHQKieFVZGA7hvY/eh6+fa9T4vuJ6hH7egaef9Zx8vVF50oR0wcW0W1pSoIIFCQwKY/pYLgn+9KXTZqrMgZHxFZ8GmRifHdGsa1QFhQn3IyAgggEEHAeiCMUJfYXUogjF2XUeE8Arv3HZAfPfysfOe+p+WF7alVBSeMHSUfOvd4+fgFJ8rh40djiAACcRV4/nGRlctFnvpFqgWjx4uc2Syy4FMih8V/BXACYVwHJvVGAIFKCxAII/QAgTACHpdWlcBre/bLrQ+ule/1rJEtO/Z4dZs8brQ0LzhBPnr+CTKpdlRV1ZfKIIBAAQIbfiOyslXk6btTF42ZIHLWx0TOXyIybkoBBVX3qS4Gwkz7PLPITHWPQ2qHQBwFCIQReo1AGAGPS6tCYPvOvXLL/c/I9x9cK/p3PeomjJGPNc6SK86dKePHjKyKelIJBBAoQmBtj8i9XxLRP/UYO0nk7KtFzrtGpPbwIgqs7ktcC4Q9PT3S3NycEb2jo0MaGxuru0OoHQIIxEbASiDUYNTQ0CCdnZ2SpFWxCISxGedUNCCgs4DfXbVGftC7Vnbs2e999ejJtXL1hSfK5WcdJ2NH1WCGAAJxFdCZQN0+4tmHUi3QWcBzPyFy9t+JjJ0Y11blrbdrgdDsQ+ifETQzhknZ3itvp3MCAghYESAQRmAkEEbA49KyCPzyTy/I9T99QjZs3end7/I3HSdjR9fIHY88K/q+oB7HTR0vn7hotrdy6KiaEWWpFzdBAIESCOi7gRoENz6aKnz8NJHzrhU56+MiYw4rwQ2rq0jXAmG2RfrYsL66xh21QcAFASuBsBoh/HsjLl++XJqamjJWM9eWGfm2yCAQVmPPUycjoI+ALvjSPbJ9174hKAd1M3kRmX3EBGm5eLa88/TpQg5k3CAQUwHdLqJ/hch9XxF54fepRkw4UuT8vxc580qR0cnZGoZAGNMxTLURQKDiAtYDoXlktJLPt7e3t3uwLS0t3p9ap0Jewu7q6pJNmzalr8/WSwTCio9fKpBD4NbetfL5O/807AxdLOZLf3OqvOUN8V1eno5HIPECBw+I/OknIqu+KvLin1Mck44RWfBJkYaPpLaSSNjhWiDkkdGEDWCai0AFBawHQjMzV0gAs93+YAAMBsR899Pre3t7pa4u9zLcBMJ8kny9nAIDm16V+1cPygOrB+WhNVvkld37vJnA4PHR82fK5xeeUs6qcS8EELAlcGC/yB+6RFZ9RWTL6lSphx+X2jrijVeIjEzu1jCuBcJMK4yaYVTJX7rbGsqUgwAC1SNgPRBq+GpraytoRs4mh3kE1B/odMavr69PWltb894q7OygFkQgzMvJCSUU2LR9l/QMDKZD4OZXdg+5m24V8dre/bJvvz4k+vrxlabTpOmMGSWsGUUjgIB1gQP7RB77kUjP10W2PpMqfuoskcZPi5z2PpEatoZxLRBqF7PthPXvJApEAIEMAtYDofnhVanfXpn7BwNhd3e3twpqviPb7ODtt98+7NLrrrtO8r1nmO9+fB2BsAL6LmDv06kZwPsHBmXN4I4hl+rKoG+aOVUWzKmTBbPr5ORjJsufn98u3/j1U96M4Ywp4+TNJx8ln/rLk8LekvMQQKDSAvt2i/zudpH7bxB5eUOqNnVzRC74rMj8JpERrAhsusjFQFjp4cf9EUAgGQLWA2Glt52IMkOY69HS2267bdiIuP766wmEyfg+qUgrdRXQR9dtTQfAP2zcJgd8k326EMypMw6X82fXef87c+YUGTOSD4cV6SxuioBtgX27RH77fZEHviHyygup0o+YlwqCp7ybIJjBm0BoexBSHgIIJEXAuUCoHVfMO4SZgmS+QcAjo/mE+HohAhr2nnjuZe8RUJ0B/M3al9JbQ5hyTqw/TM6fXS/nz57mhcAJY3lMrBBjzkWg6gX27hR55LsiD35LZMfmVHWPOlXkws+KzHtn1Ve/khV0MRD6V0wP2vKEUiVHG/dGwC0B64GwGnjyrTKqQS64FUWhC8+Y4MkP5Gro8fjWYe2W1w7NAG6WB5/eIi/v3DukMUdMHOsFP30M9II59VI/MXkrB8a3d6k5AgUI7H5F5JHviDzYLrJza+rC6WeIXPgPIie9pYCCknuqa4HQrDKarUf5/JHcsU7LEbAt4GQgVKRc+xAGA2FPT480NzeHWlnU3wHMENoeju6Xt2XHHm/270GdBVw9KBu3pTaMN8fE2lFy9gnT0u8B6l6BHAgg4LDArpdFer8t8vBNIvp3PY49OxUEZ1/mcMPtN821QGhewankqu32e4kSEUCgGgWsBMJ87w36G+7Sb7QIhNU4pKurTq/t2S8Pr9kiDzydegy0/4VXhlRw9MgRcsbxUw89Alovp82YLCPZJb66OpHaIFAKAZ0FfOCbqcdD97yausPMBakgeMKFpbij82W6Gghd+tzk/CCkgQjEVIBAGKHjCIQR8By9dN+Bg/LYs9vSW0H87tmtov/mP045ZlJ6IRidDawdzUIwjg4HmoXAcIEdg6kVQ3/bIbL3tdTXZ10scsk/icx4E2IRBFwLhOaRUQJhhEHBpQggEErASiD030l/gK1Zs2bIFg9mwZbge3uhaljFJxEIq7hzyli1J194JR0AdTZwx579Q+5+7NTx3jYQ+j99H/Dw8cndOLqM3cKtEKguAV0pVPcQ7PsPEd1KQo85bxa5eKnIMQ3VVdeY1sa1QGi20eKR0ZgOSKqNQIwErAdCDUkNDQ3D9vzTd/r0CLMXYFz8CIRx6Sm79Xz+Zd0QfrO3GMyDq7fI5leHbgg/9bAxct6J02TB7HppPKlOph8+zm4FKA0BBOIjsH2jyKqvpvYS3H9o0ai5bxe5+J9EjjwlPu2IQU1dC4T5Xsdh5jAGg5IqIhATgZIEQm178AeVWeTFpR9gBMKYjPKI1dy+c6+3AmhqO4jNoiuD+o9xo0fK2bOmyvkn1sn5c+pk3lGTZMSIiDflcgQQiLfA1rUiq74i8vgdIgf2ifdDYd5fp2YE6+fGu21VWnsCYZV2DNVCAIGqF7AeCM0z7/7HQ80qnplmDqteKEcFCYRx7r3sddcN4X+79qX0foB/fO5lOeh7DVAXfTnt2MPTj4A2HDdFdHEYDgQQQEBeWiNyb6vIH7pFDh5IbSD/hr8RufBzInVzACqhgGuBsIRUFI0AAggMEbAeCM0z75mceYeQ0VeNArrmyx83mg3hN8uj67YO2xD+pCMneiuB6mOg55w4TQ4bM7Iam0KdEECgUgKb+0Xu/aLIE/9PvN8g1YwUOXVxKghOmVmpWiXqvgTCRHU3jUUAAYsC1gOh1s0sIuOvZ0dHhzQ2NlqseuWLYoaw8n1QbA3WDO7w3gF8QPcEXLNF9LFQ/3H05Nr0DOCCOfVSN2FMsbfiOgQQcFlg059EVv6bSP//pFo5crTIae8XufCzIpOPdbnlVdc2FwOhf0/lILhLr+BU3WCiQggkTKAkgTAphgTC+PT04Ku6IXxqIRh9F1AXhvEfk8aNlvNmTfPeAdSVQGfVHRafxlFTBBAov8BzfSIrW0UG/vdQEBwj0vAhkcZPi0yaXv76cEdxLRCaV3CydS2BkEGPAAK2BAiEESQJhBHwSnypbv3w0NNb0gHwqU1DN4QfO6pGzjh+iujsn24H8Ybpk4X94EvcKRSPgAsCG34jcs+/iaxZmWrNqFqRMz8qsuDTIhOOcKGFsW2Da4HQrDLKthOxHZJUHIHYCJQkEOZaKtml32gRCKtnnOvm733rtqb3A3xs/TbZ79sQXhf4mz99sjf7pwHwzJlTRUMhBwIIIBBKYM29Ivd9WWTdA6nTxxwmcuaVIucvETmsLlQRnFRaAVcDoUufm0o7AigdAQSKFbAeCJP0iAOBsNhhZ+e6Pz+vG8JvlgdXD8pDa16SnXuHbgh/Qt1h6fcAz5tdJ5NqR9m5MaUggEByBAZ+JXLfl0R0ZlCPsRNFzvq4yPl/L1J7eHIcYtBS1wKh+TxFIIzB4KOKCMRcwHogNBvT33jjjd7z/OZRB30xWheVaWlpiTnZ69UnEJa3K/W9v1VPbfZmATUEbtmxZ0gF6ieMFQ1+C+bUyQVz6uTISbXlrSB3QwABdwR0kRjdR/C536XapOHvnL9L/a92sjvtdKglrgVCs2o7j4w6NEhpCgJVKlCSQLho0SJpbW0VDUxmdVEXf9NFICztqNaVP73N4HU10NWDsi6wIbxu/XD2rGleANTHQHVrCA4EEECgaAHdLuLPd4rc9xWRTX9MFTN+qsi514icdVVqdpCjagVcC4S5Xr/RTmDmsGqHIhVDIHYC1gOhzgTOmjXLC4T+WUECYezGRtkrrBvCP/KM2RB+szzx/PYhG8KPqhkhDcdPSb8HePqxh4tuEs+BAAIIRBLQDeT/2C2y6qsim59MFXVYvch516YeDx09LlLxXFweAQJheZy5CwIIuCdgPRCa4Nfb2yt33HGHtLW1pdUaGhqks7PTGUVmCKN1pa758vsN21IzgAOD3obwe/YfGFLovKMnepvB66Og58yaKuNGsyF8NHWuRgCBtMCB/SK/vyMVBF9ak/rniUeJnP/J1MqhuoIoR2wEXAuEsYGnogggEHsB64EwKOLfVFVDYl2dO6uxEQgLH/+rN7/qvf93/4AuBLNFtu/aN6SQGVPGpWcAdUuIKeNHF34TrkAAAQRyCezfK/LYD0R6bhDZti515uQZIgs+KfLGD4mMGotfDAUIhDHsNKqMAAJVIVDyQFgVrSxRJQiE+WF1Q/iegc1eANSZwE3bh24Ir4Hv3BNT7wDqu4DHTR2fv1DOQAABBIoR2LdbpO8/RO5vE9m+MVXC4cenNpN/4wdFaliJuBjWarnGxUCYlG28qmUMUQ8EkipAIIzQ8wTC4Xi6IbzOAOoiMBoAV7/46pCTakfXyFkzp8n5s3UxmHo5+ehJonsEciCAAAIlE9i7U+S3HSIPtIm8uil1m6mzRC74rMipi0VqeBS9ZPZlLNi1QJikbbzKOEy4FQIIZBCwHgg1JJlVRv3300dH9eAdQrfG4d79B713/8x+gI9veHnIhvC65stpMw6X8+fUeY+CnnH8FBkzkg3h3RoFtAaBKhXYs0Pkke+KPPgtkde2pCpZ/xciF/wfkTcsEhnBz6Iq7bmiqhU2EPb09Ehzc3P6HmFX69TPN+XcAiJJ23gV1eFchAAC1gTKFghZZdRan1W0IF2VXVf/1BlA/d/Dz2yRXXuHLgQzu36CFwD1MdBzT5wmE8byGFZFO42bI5A0gV0vizx8s8hDN4ns3Jpq/ZGnpGYET36X8FiCmwMiTCAcHBz09kg2axp0dXVJX1+ftzJ6tsO/FkK5A2FStvFyc0TSKgTiI1C2QGh+oIb9TVwcCJPyyOjGbTuHbAi/9bW9Q7pHN4D3HgGdXS+Nc+qkfiILMsRh/FJHBJwT2LVN5MF2kYe/I7J7e6p5R58ucuE/iMx9u3PNpUFDBcIEwmAADAbEbKbmvHIGwiRt48VYRgCBygpYCYT9/f2ycOHCvC3J9Chp3ouq+ARXA+G218yG8JvlgdVbZP1Lrw3phYm1o+TcWfoeYJ03E6gzghwIIIBAxQR2DIo8+E2R33xPRB8T1WPGmSIXfk5kzpsrVi1uXF6BMIGwvb3dq1RLS0u6cmEeBa1EIEzSNl7lHSncDQEEggJlDYRsO1GdA1Af+dRHP81+gPpIqP/Qd/703T/zHuCp0yezIXx1diW1QiBZAq++KHL/DSKPfl9EF47R47hzUzOCJ16SLAta6z0KetVVV8nIkUMXCWpqapLa2tSekhqypk+fPiwQdnR0SGNjY1bFSgTCYGVc3saL4YsAApUVsBII/U3ItqhMZZtZmrvHdYZw/4GDoou/PLA6tR1E37NbRReHMYeu+nnKMZPT+wGedcJUGTuY3em1AAAgAElEQVSKxRdKM4ooFQEEChbQLSN0D8Hf3SaiW0noccIFqSA4M/uH+oLvwwWxEjCBsKZm6P9faSAcN26c15Y4zRDGCp/KIoBArAWsB8JYaxRY+TgFwoFNr8oDT7++Ifyru4duCH/8tPHeIjD6GKj+OWkcG8IXOBw4HQEESi3w8nqR+74i8viPRHRzeT1OvFTk0n8WOaah1Hen/CoXCPPIaJzeIaxybqqHAAIOCZQkECZlI9VqDoSbX9ktqw5tCK+rgb74yqHfoh8avNMOG5N+B/CCOfVy9OTU4zQcCCCAQNUJbF0rcu8XRf7wY5ED+1PVO+ktIhcvEzn6tKqrLhWqjECYQJhvlVGzJkJw8ZhKPTLqf0w0qOrSIn2VGTHcFQEEjID1QJikjVTLHQjXb029I3PslNSjL/5DZ/xSW0Fs8R4FfXrzoYUVDp00fsxIOfuEqXL+7HpZMKdO5h41ke8CBBBAoLoFBgdSQfBPPxE5eGh7m3kLRS5amtpGggMBn0CYQKin59qHMFMgDIayci2Ql6TPUwxkBBCorID1QJikjVTLFQj/9Nx2uer238qGQ4FwxpRx0v7+Bnltz/70foC/37BNDrz+GqCMqhkhpx93uJx/Yp0XAN943BTv3zgQQACBqhfY3C+ycrnIE3emqqovNp/87tSMYN2cqq8+FayMQNhAWJnaFX5X87RVObe6KLyWXIEAAi4IlCQQJmUj1XIFwvO/eI/oXoD+Qz8f6Sbx/kNn/c47sc7bC/DsWdNEZwU5EEAAgdgIPP+4yL2tIk/+/FAQrBGZ3yRy0T+KTJ0Vm2ZQ0coIuBoIeTS0MuOJuyKQJAHrgTBJG6mWKxDO/Mf/GT4mDx6UY6aM8zaD9xaCmVMn+l4gBwIIIBA7gef6RO75N5HVv05VvWaUyGmXi1zwWZEpM2PXHCpcGQHXAqF5ZJRAWJnxxF0RSJKA9UCYpI1UKxkIT6w/TO7+zEVJGqu0FQEEXBNY2yNy35dFnlmVatnI0SKnf1Dkws+KTJruWmtpT4kFXAuE2Ra4KTEjxSOAQAIFrAfCoKHLG6mWKxB+5sePy3/1bRhCu+TSOfKpvzwpgUOWJiOAQOwFnr5H5L4viTz7UKopo8aKNHxEpPHTIhOPin3zaEBlBFwIhLlWaQ+qMnNYmXHGXRFwUaDkgdBFNNOmcgXC7Tv3yi33PyMPrdni3fqcWdMIgy4PLNqGgKsCT/0yFQQ3Pppq4ehxImc2i5z/SZEJR7jaatpVJgECYZmguQ0CCDgnQCCM0KXlCoQRqsilCCCAQOUF/rxCZNVXRHTRGD3GTBB509+KLPikyLgpla8fNXBCwIVA6ERH0AgEEIidgPVAaJ5590u4umQygTB2450KI4BAuQR038A//bfIqq+KvPhE6q5jJ4mcfbXIedeI1B5erppwn4QIEAgT0tE0EwEErAtYDYTt7e3S1taWsZLLly+XpqYm6w2oZIEEwkrqc+9QAv0/FfnFUpFtz6ZOP+fvRN7yxVCXchICRQloEPz9j0V6viqiG8vrobOAOvY0DNZOLqpYLkIgn4ArgbCrq0uWLVsmDQ0N0tnZ6TXb//nKxc9T+fqWryOAQGkFrAVC/8ygf0awp6dHmpubvVa4NlNIICzt4KT0iAK7tol841SRXS8PLehd3xY5/QMRC+dyBAICB/aJPP6fIqu+JrL1mdQXx08TOa9F5KyrRMYcBhkCJRVwJRCa1do7OjqksbFR/J+jDKD5WklBKRwBBBIjYC0Qmh9gmX5zZX7bZTasd0WXQOhKTzrYjlc3iTz6HyIrlw9v3KhakUnHpBb00L/rn97fD/2Z/vfxIqP164f+9H893/kjahxEpUkZBfbvFfndbSI9N4i8vD51ii4Qc/4SkTOvTI0tDgTKIOBKIDQrjZpVRP0Bcd68eaLtdO3zVBmGB7dAAIEcAtYCodleore3V+rq6obccnBw0PsB5n/8wYVeIRC60IsOtGH/HpEX/iCy/hGRDb9J/el9MD8oIiOGN/DgQZERGf7dJoXuJ5cxQB4KmF4QzRI0TQgNG1Z1E3OO8gvs25X6pcP93xB55fnU/SceLbLgUyJnfCS1lQQHAmUUcCkQ+j8vmYBoPl+Zz1tsO1HGwcWtEHBcwFog1B9YuQJf8DdeLrgSCF3oxRi24eUNIhs0/P329RAYbIbOyhz1BpHnHhfRwOg/dGXH2Ze9/i8aEPXD/d7XRPbuEtm3U2Sv+Z/5952H/v3Qf3vnHzrHf77++54d5UfVxxGDs5YaOL1QmSGEZgqj/pCavtbMntayGqbpVe33h28W6b1RZMfm14Pghf+Q2kKCA4EKCbgYCDP9Qp1AWKEBxm0RcFiAQBihcwmEEfC4NJzAvt0izz82dPbPzMb4S5g6S2TGm0SOPSv155FvEKkZKfLC70VWtoqsvV/k8ONE5r5D5OKl4e4d5Sx9jFADZtag6Q+Yh/6+J1PozBRSA+fpu2vlOnRmdVhYzPGorX+Wc8ijuSFmSvXaSh5re0Qeuin1DqouBHPJP4k8+fNUEHztpVTNppyQ2kz+9PeLMFNbyd7i3iLek0h33XWX1NfXx9rDPCKqM4IrV670FphZsmSJtLS0iKtPXMW6w6g8Ag4IEAgjdCKBMAIel2YW2Lbu9fCnM4C6b1sw8OgebtPPeD0AaghM8l5uB/b7wmeWWUsz+5kOqf5Z0AzhND376Zs51WuDs62lHseFvOcZKXyOG/oYsf4i4ebGDK079BjytNkiF3xW5NT3ivC+aKlHAeWHFHAlEGZaRMYsyufqmgwhu5jTEECgRAJWA2GYOrr0zDuBMEyPc05WAQ0dGx8VWf9w6s9nHxJ5bcvw0+tOEplxZup/x56dmv3jqJyAPhKrfRd8VNZ7XFZnNM3MqC+cFjT7eehxXC2v3IfOBGqw1JlpXaU2eBw+Q+TS/ysy360thMrNzP1KI+BKIFQdE/z072axPjM7qP/GKqOlGUOUikBSBQiEEXqeQBgBL4mXblmdWvRFZ/70T539Cx5jJ4pM1+B31uuPf7JvWxJHS6rNQ97TzPP+Zjqk7hbZq6E1w/n7d6fe8fTODcx++pWzrEckbFmS3LEYg5a7FAhjwE0VEUDAIQFrgdAhk9BNIRCGpkreiXtefT346QyghsCdW4c71M9Nzfp5M4BvEjliXvKsaHH1COisoIbF33eK/Orzw+t1dY/IUadWT32pCQI+gaQFQjNjyGwh3wYIIBBVgEAYQZBAGAHPpUt1lc4tA753/34j8uITIvrv/kPf8zPBT2cAdSZQZwQ5EKhGgf98n8iTP3u9Zm9pFTnnE9VYU+qEgCdAIGQgIIAAAsUJEAiLc/OuIhBGwIvzpbrq4kbd8kEf/zz0CGjwfStdaOOIk19/7FMDoC7EwYFA3AR0tdGZmRaYiVtDqK/rAgRC13uY9iGAQKkECIQRZAmEEfDicunBAyKb+4fO/m1+cnjtx08buu2DzgTq9gQcCCCAAAJlESAQloWZmyCAgIMCBMIInUogjIBXrZfqe34667f+/7d3tz92XPUdwE9iIHZ5CKEOIXFSaKjapShNcQNUav0PRF31jVfOO1S/IKis5BcIpKyQKInk7VtL5g0vLCURElb8opLVvOBNVUWV1YKWBES1lftAGwwJGAIFisJjda4967mzd+6d2XPu7r1nPleqQu1zzp3z+R3fme+duTPxwe9fvnH3z9d/PL618fl+9zxUC4CPhBCfA+hFgAABAgcmIBAeGL03JkBgyQUEwoQCCoQJeIvQNT6/7rvfGD/79/3/2L1lb7nnZvj7YAj3x9/+Hb9xa34vAgQIEFgYAYFwYUphQwgQWDIBgTChYAJhAt5BdP3p9RC+9S+1s39bN54ZV38deuONuyjGO34+cDMAvv13DmJrvScBAgQI9BAQCHtgaUqAAIGagECYsBwEwgS8eXf99S9DeOVrNy//vHnzl9e+uftd33bf+G//7v3jEN5wx7y3zvgECBAgkFlAIMwMajgCBAYjIBAmlFogTMDL3fUnr4YQn/dX/fbv2y/eePB2/RWD3r0P3wqA8fl/b70395YYjwABAgQOQEAgPAB0b0mAQBECAmFCGQXCBLyUrr98vXb27+bNX370rd0j3vnA+GMf4qWg8ZJQLwIECBAoTkAgLK6kJkSAwD4JCIQJ0AJhAl6frj96efzOn9/5Wgi/+vn4CG88EsJ9Hxg/+/fmu/u8i7YECBAgsMQCQwuES1wqm06AwIIJCIQJBREIE/DausbLPL/91fEA+ONXdre+63dv3vQl3vzlQzceAxEfB+FFgAABAoMUKDEQnjp1KmxtbU2s59WrVwdZZ5MmQCC/gECYYCoQJuBVXV/7r/Ebv7zy9RDiDWHqrze9OYT7jtcu//xwCL/1jgxvbggCBAgQKEWgtED4xBNPhEuXLrWWRyAsZeWaB4GDFxAIE2ogEPbEi494iA96rz/4PT4Kovn67d8bv/PnPe8P4bbbe76Z5gQIECAwJIHSAmE8xoivy5cvh5WVlSGV0lwJENhnAYEwAVwgnIH3/X8fD3+vfiOE3/x6vNMdbw3h2CO1APhICEfuSqiKrgQIECAwRIFSA6EzgUNczeZMYH8FBMIEb4Gwhvf6j0O49pVaAPxKCD97bbfu3X8Qwv0fuvXQ97tXQrjttoQq6EqAAAECBEIoLRBWl4wKhFY3AQLzFhAIE4QHGwh/85sQrv/b+Nm/722HEP+8/jr89hDuf+TWzV/u/2AId7wtQVxXAgQIECAwWaC0QLi9vR1WV1ddMmrBEyAwdwGBMIF4MIEwnumLZ/9ejmcA43P/vhLC6/87LhfP8r3zD8d/+3f09xN0dSVAgAABAt0FSguE1W8I2wScOey+NrQkQGC6gECYsEKKDITxN37f/dfxs3/XJ9zaOt7lM57xi/8XH/tw7E9CeNNbEjR1JUCAAAECexcQCPdupycBAsMWEAgT6l9EIPy/H9w46/fyl2/899pWCD//ybhKfL7fO99fe+zDh0J4x4MJcroSIECAAIG8AqUFwrw6RiNAgEC7gECYsDqWLhD++lchvPr18Ru//OA/dwu8+e7xSz/j2b83HkmQ0pUAAQIECMxXQCCcr6/RCRAoV0AgTKjtwgfCn34vhJf/+dbZv29/NYRf/Kxx9u8NIbzrj24GwJuXgN71ngQVXQkQIECAwP4LlBgIz58/H86dOzfCPHv27Oi/Gxsb4cyZM2F9fX3/kb0jAQJFCgiECWXd10D4zRdC+OY/3djalUdvhLj661e/COGVr43/9u+H/7N7dm991/hjH+77QAhvuCNBQVcCBAgQIHDwAqUFwnoYrALh2tpaiMcex48fDxcvXjx4dFtAgEARAgJhQhn3LRD+w2YI//i341v6l+dDiA91r377952XQvjl6+NtDr0phHvj2b/ac//uvD9hxroSIECAAIHFFCgtEMZjjJMnT4bNzc1RCIxnCGMg9HzCxVx/torAMgsIhAnV27dAuPnA7sc8xGf+NR/o/rZj47/9i3f/9CJAgAABAgMQKDEQVpeG1gPhqVOnwtbWVvDYiQEsalMksE8CAmEC9L4Fwr+5c/JW/s6f3gqAD3w4hLfckzAbXQkQIECAwPIKlBYIY/CLr3hpaBUIH3roodHD6qszh8tbLVtOgMAiCQiECdXYt0A46Qzhu/8shL96PmHrdSVAgAABAuUIlBYIX3jhhXD69OmJBbpw4UI4ceJEOcUzEwIEDlRAIEzg37dA+OIXQvi7vx7f0se+EMLKXyRsva4ECBAgQKAcgdICYazM9evXQ5xX/XX58uWwsrJSTuHMhACBAxcQCBNKsG+BMG7jD/87hO2/D+HwnSG8589DePu7E7ZcVwIECBAgUJZAiYGwrAqZDQECiyogECZUZl8DYcJ26kqAAAECBEoXEAhLr7D5ESAwL4FiA2F1F64IV92qeRpi87KMK1euhKNHj051FwjntSyNS4AAAQIE+gkIhP28tCZAgEAlUGQgjA9zja/19fXRf2Nwm3bN/fb29uiuXX2vyxcI/UMiQIAAAQKLIVBaIKweTB9vIPO+971v57eEHkq/GOvNVhAoSaDIQNgMgM2A2CxgfMjro48+2vuOXQJhSf8UzIUAAQIEllmgtEBYf+xE9TD6qj7V8wmXuV62nQCBxREoLhBWl37WL/l87rnnRg9x3dzcnCgfg138xi22ia+u374JhIuzkG0JAQIECAxboLRAGI8x6g+mj9WNVzI9/fTT4dKlSx5MP+zlbvYEsgoUFwiryz+bgTB+eMaHuzZfVYCsXy4av4mLr3qAfPbZZ3f1ffLJJ30gZ12OBiNAgAABAnsTKC0QxjOEDz74YPjEJz6xc7no1atXQ3UpafzfXgQIEMghUFwg7HuGcFL7+DDY+IFbD5DPPPPMLu+nnnpKIMyxCo1BgAABAgQSBUoLhFXwq1hOnjw5+qK6filpIpnuBAgQGAkUFwjjpPr+hrDZflIgnLReXDLqXxEBAgQIEFgMgdICYVSt3zE9nhGMP4HZ2NjYuZR0MeRtBQECyy5QZCCcdZfRGOTqj6KI7a9du7ZziWj8AI7fxK2trU2tr0C47Mvf9hMgQIBAKQIlBsJSamMeBAgstkCRgbD5rVrzOYTNQBjb1+/g1fXuXQLhYi9uW0eAAAECwxEQCIdTazMlQCCvQLGBMC/T5NEEwv1Q9h4ECBAgQGC2QImBsH7JaFPATWVmrwktCBDoJiAQdnOa2EogTMDTlQABAgQIZBQoLRA2nz0oEGZcLIYiQGBMQCBMWBACYQKergQIECBAIKNAaYEwHmPEV/2xWBm5DEWAAIEdAYEwYTEIhAl4uhIgQIAAgYwCpQZCl4ZmXCSGIkBgooBAmLAwBMIEPF0JECBAgEBGgdICYXXJqECYcZEYigABgTD3GhAIc4sajwABAgQI7E2gayCMzxo+ffr0zpvMClzT2lfPBaxvcfUA+b3N4lav7e3tsLq66pLRVEj9CRCYKeAM4Uyi9gYCYQKergQIECBAIKNAl0B4/fr1ENtduXIlHD16dPSg962trZ3nEDc3Z1b7Wf1Tplf9hrBtjFlBNuW99SVAYFgCAmFCvQXCBDxdCRAgQIBARoEugbAZ4JqBr7k5s9oLhBkLaCgCBA5MQCBMoBcIE/B0JUCAAAECGQW6BMLz58+P3nF9fX3nneO+vO1OnrPaNy8ZzXW5aEYWQxEgQGCmgEA4k6i9gUCYgKcrAQIECBDIKBAD4eOPPx4OHTo0Nura2lo4fPjw6M/ijVqOHTu2KxBeuHAhnDhxYtfW9G0fjwvOnj0b4nt6ESBAYFkEBMKESgmECXi6EiBAgACBjAJVILz99tt3BcIjR46M/mzWGb/m5vRtn/sS0vj+586dG21WDJrxtbGxEc6cOTMWajMyGooAgQEKCIQJRRcIE/B0JUCAAAECGQW6XDI66zeBzc1JbZ8yvXoYrAJhPPMYjz2OHz8eLl68mDK8vgQIENgREAgTFoNAmICnKwECBAgQyCjQJRDOumto81EPs9rHS0o3NzdHs6jatl1+2neq8Rij+k1i/VJUzyfsK6k9AQKzBATCWUJT/l4gTMDTlQABAgQIZBToEgjj2017ruCkZ/9Na1+Fs2oaOX8/GI8xqktD64Hw1KlTo0dleOxExsVjKAIDFxAIExaAQJiApysBAgQIEMgo0DUQZnzLuQ4Vg198xUtDq0D40EMPjR5W726mc6U3OIHBCQiECSUXCBPwdCVAgAABAhkFSguEzTOTdapcl6Vm5DcUAQJLLCAQJhRPIEzA05UAAQIECGQUKC0QRprqd4l1prZnJmakNBQBAgMTEAgTCi4QJuDpSoAAAQIEMgqUGAgz8hiKAAECrQICYcLiEAgT8HQlQIAAAQIZBQTCjJiGIkBgUAICYUK5BcIEPF0JECBAgEBGgVICYTy2mPbyUPqMi8ZQBAiMBATChIUgECbg6UqAAAECBDIKDCUQRjI3lcm4cAxFgIBAmLIGBMIUPX0JECBAgEA+gZIC4fHjx0ePm2i+qjuPeuxEvnVjJAIEnCFMWgMCYRKfzgQIECBAIJvAEAJhxKouKfVg+mxLx0AEBi/gktGEJSAQJuDpSoAAAQIEMgoIhBkxDUWAwKAEBMKEcguECXi6EiBAgACBjAKlBMJZJM4QzhLy9wQI9BUQCPuK1doLhAl4uhIgQIAAgYwCQwiE29vbYXV1NbT9xjAjp6EIEBiQgECYUGyBMAFPVwIECBAgkFFgCIEwI5ehCBAgsCMgECYsBoEwAU9XAgQIECCQUUAgzIhpKAIEBiUgECaUWyBMwNOVAAECBAhkFBAIM2IaigCBQQkIhAnlFggT8HQlQIAAAQIZBQTCjJiGIkBgUAICYUK5BcIEPF0JECBAgEBGAYEwI6ahCBAYlIBAmFBugTABT1cCBAgQIJBRQCDMiGkoAgQGJSAQJpRbIEzA05UAAQIECGQUEAgzYhqKAIFBCQiECeUWCBPwdCVAgAABAhkFBMKMmIYiQGBQAgJhQrkFwgQ8XQkQIECAQEYBgTAjpqEIEBiUgECYUG6BMAFPVwIECBAgkFFAIMyIaSgCBAYlIBAmlFsgTMDTlQABAgQIZBQQCDNiGooAgUEJCIQJ5RYIE/B0JUCAAAECGQUEwoyYhiJAYFACAmFCuQXCBDxdCRAgQIBARgGBMCOmoQgQGJSAQJhQboEwAU9XAgQIECCQUUAgzIhpKAIEBiUgECaUWyBMwNOVAAECBAhkFBAIM2IaigCBQQkIhAnlFggT8HQlQIAAAQIZBQTCjJiGIkBgUAICYUK5BcIEPF0JECBAgEBGAYEwI6ahCBAYlIBAmFBugTABT1cCBAgQIJBRQCDMiGkoAgQGJSAQJpRbIEzA05UAAQIECGQUEAgzYhqKAIFBCQiECeUWCBPwdCVAgAABAhkFBMKMmIYiQGBQAgJhQrkFwgQ8XQkQIECAQEYBgTAjpqEIEBiUgECYUG6BMAFPVwIECBAgkFFAIMyIaSgCBAYlIBAmlFsgTMDTlQABAgQIZBQQCDNiGooAgUEJCIQJ5RYIE/B0JUCAAAECGQUEwoyYhiJAYFACAmFCuQXCBDxdCRAgQIBARgGBMCOmoQgQGJSAQJhQboEwAU9XAgQIECCQUUAgzIhpKAIEBiUgECaUWyBMwNOVAAECBAhkFBAIM2IaigCBQQkIhAnlFggT8HQlQIAAAQIZBQTCjJiGIkBgUAICYUK5BcIEPF0JECBAgEBGAYEwI6ahCBAYlIBAmFBugTABT1cCBAgQIJBRQCDMiGkoAgQGJSAQJpRbIEzA05UAAQIECGQUEAgzYhqKAIFBCQiECeUWCBPwdCVAgAABAhkFBMKMmIYiQGBQAgJhQrkFwgQ8XQkQIECAQEYBgTAjpqEIEBiUgECYUG6BMAFPVwIECBAgkFFAIMyIaSgCBAYlIBAmlFsgTMDTlQABAgQIZBQQCDNiGooAgUEJCIQJ5RYIE/B0JUCAAAECGQUEwoyYhiJAYFACAmFCuQXCBDxdCRAgQIBARgGBMCOmoQgQGJSAQJhQboEwAU9XAgQIECCQUUAgzIhpKAIEBiUgECaUWyBMwNOVAAECBAhkFBAIM2IaigCBQQkIhAnlFggT8HQlQIAAAQIZBQTCjJiGIkBgUAICYUK5BcIEPF0JECBAgEBGAYEwI6ahCBAYlIBAmFBugTABT1cCBAgQIJBRQCDMiGkoAgQGJSAQJpRbIEzA05UAAQIECGQUEAgzYhqKAIFBCQiECeUWCBPwdCVAgAABAhkFBMKMmIYiQGBQAgJhQrkFwgQ8XQkQIECAQEYBgTAjpqEIEBiUgECYUG6BMAFPVwIECBAgkFFAIMyIaSgCBAYlIBAmlFsgTMDTlQABAgQIZBQQCDNiGooAgUEJCIQJ5RYIE/B0JUCAAAECGQUEwoyYhiJAYFACAmFCuQXCBDxdCRAgQIBARgGBMCOmoQgQGJRAsYHw1KlTYWtra1TMs2fPhrW1tdbCPvfcc2FjY2Ps70+ePBk2NzenLgaBcFD/VkyWAAECBBZYoGsgfOGFF8Lp06d3ZnL16tUFnpVNI0CAwPwFigyE58+fH8mtr6+P/huD2+XLl8PKyspE0RgIY3icFQCbnQXC+S9Q70CAAAECBLoIdAmE169fD7HdlStXwtGjR8Ne9/9dtkcbAgQILItAkYGwGQCbAbFZnL3uEATCZVnmtpMAAQIEShfoEgib+/tmQCzdyPwIECAwSaC4QDjpw31W4GteMtrlctHqzKNLTfzDIkCAAAECBy/QJRBO+oJ41lVEBz8zW0CAAIH5ChQXCLe3t8Pq6urO5SCRLwa+S5cuhYsXL3bSjDuH5u8On3322V19n3zyySAQdiLViAABAgQIzFUgBsLHH388HDp0aOx94j0EDh8+PPqzJ554Ihw7dmznJyXVl7sXLlwIJ06cmOv2GZwAAQKLKlBcINzLGcJmcSadUXzmmWd21fCpp55a1LraLgIECBAgMDiBT3/60+G2227bFQiPHDky+jNnCAe3JEyYAIEOAsUFwurbvvpNZGb9hrBLIOxgOfcmn//858Ndd9019Y6pc98Ib7CUAh/72MfCJz/5yfDe9753KbffRh+MwGuvvRY++tGPjq6y8CLQR+BLX/pSePHFF8OnPvWpPt3m3tZvCOdO7A0IEFhCgSID4ay7jDYvCY2XkFR3GK3OMC7i5SMC4RL+C1uQTRYIF6QQS7YZAuGSFWyBNndRA6G7jC7QIrEpBAgsjECRgTDqTnsO4aRAGH9jWL1mPbfwoKonEB6U/PK/r0C4/DU8iBkIhAehXsZ7LmogjLqeQ1jGGjMLAgTyCRQbCPMRLc5IAuHi1GLZtkQgXLaKLcb2CoSLUYdl3IpFDoTL6GmbCRAgME8BgXCeupnHFggzgw5oOIFwQMXOOFWBMCPmwIYSCAdWcNMlQGCpBSEj0SEAAAu4SURBVATCpS6fjSdAgAABAgQIECBAgMDeBQTCvdvpSYAAAQIECBAgQIAAgaUWEAiXunw2ngABAgQIECBAgAABAnsXEAj3bqcnAQIECBAgQIAAAQIEllpAINzH8sXHXVy+fDmsrKxkedft7e2wuroarl69mmU8gyymwDzq3Hw482LO3FalCsyjzvG5rcePHw9ra2upm6f/AgvMo86594ELzGfTCBAgsFQCAmHHck17rmE1xLSdXTyof/rpp8Pm5uaoefVw3KrvpKA4KwjEg734igdmzfHinzeDYpc5TDoIqPerc+UMtx3LsHTNctc5AnR5htasIBDr/JGPfGT05URzvJMnT+6s065rNbZrW//xz6tXc+ylK+g+bnDuOsdNP3/+fDh37txoFm21mBUEYj2rz5bmZ0PzMyFlDtXn37TPyH0sx1K9Ve46x8l32X903Qd22V9Nm0OOdbdUBbWxBAgQmLOAQNgBOO6Y4mt9fX303+ZOr75zagtJcYx77rln51v12CeOd+LEidAMfs2dZdsZwDjGZz/72Z2D+rhtcbzqwO/atWs7B/az5lDf+Z49e3bqt/9xez/zmc+EixcvdtAbdpPcda7WxpUrV8LRo0dDM/jVD8DbDvirMap1FWv/2GOPjcarDvxi3+oM0LQ5NA8Um+s/9m2OVf//h7062mc/jzrHtRFrXf27bQa/Lp8BcYznn39+9LkSt/GLX/zizuditfaqdZUyh6pvtZ6qsat1b920C+Suc7U/ybkPjNs4bX81bQ6p687aIUCAAIHdAgJhh1XRDIDNcBWHaB7ANIeNY1QHM5PO/NUPuqu+zQOs+pizQllzh9plDpPCwCSeeCD56KOP7oTPDoSDbDKPOjcDYPOgu4KOa7T+hUC9AHGMV199dedAvlmc+vruOoe29d913Q1ygUyZ9Dzq3AyAzc+IanOaIb6+mdP+7c8KgH3W6qQz3JM+I62b3QLzqHOXf8d99oHNrZ4VYtvWan3f2/YlWdu6s3YIECBA4JaAQDhjNUzamUw6WJm2M6x/qx7fbtLObdJlWtMCYfOMY3Ma9W3sOocugXBWEPWP65bAPOo86cuISZdpTQuE9TPLbYG/+o1Y1zm0rf+4Djc2NsKFCxdGXyDUvxixVtoF5lHnZqBquyS9LRA2zyw3t745XsocJn3GzrqU1Xq6IZC7zl33H332gdP2V33mENvudd1ZLwQIECAgEHZeA9XOpn6pUjxYuXTp0tglk9N2hvHA6OGHH945ozapfzzYOXbs2NhZm2mBMO70P/e5z+1c5lefUHNbus6hSyB0drDz0hldztlcJ6l1ntQ/hqwqcFVb1xYI49r4+Mc/3nq5b3PNdZ1D2/qv1l4MmFtbW62/W+uuOoyW86hzc51M+lyY9hkQ18ZLL73Uema5GSRT5jBp2wTCbms/d5277j/67AOn7a/i33Wdw6T12nXdddPUigABAsMQcIZwRp1zfDtavwlDfLuuZ13aAmHzBjWTdq71gNB1DrMC4bTLdobxz6XfLOdR55SzLnHr6zcias5m0u+0us5h2iWj9S9T4sFafFU3V+onOpzW86hz6pmj+o2ImpWIY8czwNXvrOPf55hDPLtcfzW/+BjOiug+09x17rr/mBYIm/vAajZVn2Zd+8xhr+uuu6iWBAgQKF9AIOxQ45TfTzQvF41v1/V3WW2BsO1y0bada3zPLnOYFQj9hqfDYqk1mUedU39b1na5aNtNO7rOYdLB4KS+k8449lMdRut51Dnlt2XTLhedFAarLx/iWeHmnZWbN4aZdnlzvdouN+629udR5y77j7ZAOGkfGGcybX/VZQ6p666bplYECBAYhoBA2KHOs+7QWd+5Ne+y2HaJ5aw7N8Yx2wLhpAOj6uB72l1O45htd0qtGNp+P+TsYIeFMqFJ7jrPunFHtQmTDrLbfv9Z/c5v2t1s2+6IW73ftDOE9bvWth3E7U233F7zqPOsG3dM+wxouxFR/CxquytxyhyalZ10GWC51U+b2TzqnHsfOGt/NWsOOdZdmrLeBAgQKEtAIOxYz2nPYGo+E6m63f+0b9Wrv6vevh7kmn8X21QHXW3fttZvGV+fUv1SnGlzmNS/eft4l2t1XCy1ZrnrXP+ioHqbepCrvkSob2m1ttrOLDfXb9W3OpMzbQ6xbdv6r39RUo155syZ1t+g9dctu0ezlql1jlrTnu027TNg0pnlSWstvke9xnudQxwnhsD4G9z651/ZFc83u5x1rn9REM/4TqpH331gl/1V2xxS110+ZSMRIECgHAGBcI61nHUThr28dfMGNXsZQ5/FF5hHnafdiGjxRWxhV4HcdZ51I6Ku26XdYgvMo87z2AcutqKtI0CAwHIKCIRzrNus2/v3fetZt33vO572iykwjzq3nVleTAFbtVeBedR51iNu9rqt+i2WwDzqnHsfuFhitoYAAQLlCAiE5dTSTAgQIECAAAECBAgQINBLQCDsxaUxAQIECBAgQIAAAQIEyhEQCMuppZkQIECAAAECBAgQIECgl4BA2ItLYwIECBAgQIAAAQIECJQjIBCWU0szIUCAAAECBAgQIECAQC8BgbAXl8YECBAgQIAAAQIECBAoR0AgLKeWZkKAAAECBAgQIECAAIFeAgJhLy6NCRAgQIAAAQIECBAgUI6AQFhOLc2EAAECBAgQIECAAAECvQQEwl5cGhMgQIAAAQIECBAgQKAcAYGwnFqaCQECBAgQIECAAAECBHoJCIS9uDQmQIAAAQIECBAgQIBAOQICYTm1NBMCBAgQIECAAAECBAj0EhAIe3FpTIAAAQIECBAgQIAAgXIEBMJyamkmBAgQIECAAAECBAgQ6CUgEPbi0pgAAQIECBAgQIAAAQLlCAiE5dTSTAgQIECAAAECBAgQINBLQCDsxaUxAQIECBAgQIAAAQIEyhEQCMuppZkQIECAAAECBAgQIECgl4BA2ItLYwIECBAgQIAAAQIECJQjIBCWU0szIUCAAAECBAgQIECAQC8BgbAXl8YECBAgQIAAAQIECBAoR0AgLKeWZkKAAAECBAgQIECAAIFeAgJhLy6NCRAgQIAAAQIECBAgUI6AQFhOLc2EAAECBAgQIECAAAECvQQEwl5cGhMgQIAAAQIECBAgQKAcAYGwnFqaCQECBAgQIECAAAECBHoJCIS9uDQmQIAAAQIECBAgQIBAOQICYTm1NBMCBAgQIECAAAECBAj0EhAIe3FpTIAAAQIECBAgQIAAgXIEBMJyamkmBAgQIECAAAECBAgQ6CUgEPbi0pgAAQIECBAgQIAAAQLlCAiE5dTSTAgQIECAAAECBAgQINBLQCDsxaUxAQIECBAgQIAAAQIEyhEQCMuppZkQIECAAAECBAgQIECgl4BA2ItLYwIECBAgQIAAAQIECJQjIBCWU0szIUCAAAECBAgQIECAQC8BgbAXl8YECBAgQIAAAQIECBAoR0AgLKeWZkKAAAECBAgQIECAAIFeAgJhLy6NCRAgQIAAAQIECBAgUI6AQFhOLc2EAAECBAgQIECAAAECvQQEwl5cGhMgQIAAAQIECBAgQKAcAYGwnFqaCQECBAgQIECAAAECBHoJCIS9uDQmQIAAAQIECBAgQIBAOQICYTm1NBMCBAgQIECAAAECBAj0EhAIe3FpTIAAAQIECBAgQIAAgXIEBMJyamkmBAgQIECAAAECBAgQ6CUgEPbi0pgAAQIECBAgQIAAAQLlCAiE5dTSTAgQIECAAAECBAgQINBLQCDsxaUxAQIECBAgQIAAAQIEyhEQCMuppZkQIECAAAECBAgQIECgl4BA2ItLYwIECBAgQIAAAQIECJQjIBCWU0szIUCAAAECBAgQIECAQC8BgbAXl8YECBAgQIAAAQIECBAoR0AgLKeWZkKAAAECBAgQIECAAIFeAgJhLy6NCRAgQIAAAQIECBAgUI6AQFhOLc2EAAECBAgQIECAAAECvQQEwl5cGhMgQIAAAQIECBAgQKAcgf8HvJyXmgZX1NEAAAAASUVORK5CYII=" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a011c100", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2021" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "43e75cb6", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2021, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "46ad16b6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7500011519622525\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True,\n", - " date_compile_auc = '01/01/2021', #optionnal, by default date of compile\n", - " datadrift_file = \"car_accident_auc.csv\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "467b9f08", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "9f91ee44", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Report saved to ./report_car_accident_datadrift_2021.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a0c9e6b0", + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=df_accident_baseline['target'].to_frame()\n", + "X_df_learning=df_accident_baseline[df_accident_baseline.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2017=df_accident_2017['target'].to_frame()\n", + "X_df_2017=df_accident_2017[df_accident_2017.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2018=df_accident_2018['target'].to_frame()\n", + "X_df_2018=df_accident_2018[df_accident_2018.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2019=df_accident_2019['target'].to_frame()\n", + "X_df_2019=df_accident_2019[df_accident_2019.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2020=df_accident_2020['target'].to_frame()\n", + "X_df_2020=df_accident_2020[df_accident_2020.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2021=df_accident_2021['target'].to_frame()\n", + "X_df_2021=df_accident_2021[df_accident_2021.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]" + ] + }, + { + "cell_type": "markdown", + "id": "a8e0347a", + "metadata": {}, + "source": [ + "## Building Supervized Model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7ca4fe9e", + "metadata": {}, + "outputs": [], + "source": [ + "features = ['Start_Lat', 'Start_Lng', 'Distance(mi)', 'Temperature(F)',\n", + " 'Humidity(%)', 'Visibility(mi)', 'day_of_week_acc', 'Nautical_Twilight',\n", + " 'season_acc']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "75deda6e", + "metadata": {}, + "outputs": [], + "source": [ + "features_to_encode = [col for col in X_df_learning[features].columns if X_df_learning[col].dtype not in ('float64','int64')]\n", + "\n", + "encoder = OrdinalEncoder(cols=features_to_encode)\n", + "encoder = encoder.fit(X_df_learning[features])\n", + "\n", + "X_df_learning_encoded=encoder.transform(X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8de8fb2c", + "metadata": {}, + "outputs": [], + "source": [ + "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c17eff5a", + "metadata": {}, + "outputs": [], + "source": [ + "train_pool_cat = catboost.Pool(data=Xtrain, label= ytrain, cat_features = features_to_encode)\n", + "test_pool_cat = catboost.Pool(data=Xtest, label= ytest, cat_features = features_to_encode)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b2928f7a", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a2ae76b7cf46416c80f3f172db408232", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", + " learning_rate=0.143852,\n", + " iterations=300,\n", + " l2_leaf_reg=15,\n", + " max_depth = 4,\n", + " use_best_model=True,\n", + " custom_loss=['Accuracy', 'AUC', 'Logloss'])\n", + "\n", + "model = model.fit(train_pool_cat, plot=True,eval_set=test_pool_cat, verbose=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3c3bf091", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7589233355711246\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "proba = model.predict_proba(Xtest)\n", + "print(metrics.roc_auc_score(ytest,proba[:,1]))" + ] + }, + { + "cell_type": "markdown", + "id": "339d1083", + "metadata": {}, + "source": [ + "## Use Eurybia for data validation" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "979f27b5", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "bcb4b433", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2017,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f0a4fa1a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: total: 0 ns\n", + "Wall time: 0 ns\n", + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6585689489728102\n" + ] + } + ], + "source": [ + "%time \n", + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2017', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "0e097974", + "metadata": {}, + "source": [ + "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", + "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", + "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "f2d88c1b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_car_accident_datadrift_2017.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_car_accident_datadrift_2017.html', \n", + " title_story=\"Data drift\",\n", + " title_description=\"\"\"Car accident Data drift 2017\"\"\", # Optional: add a subtitle to describe report\n", + " project_info_file=\"../../eurybia/data/project_info_car_accident.yml\" # Optional: add information on report\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "afc893f8", + "metadata": {}, + "source": [ + "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", + "However, to illustrate functionalities, we will detail results with notebook mode analysis." + ] + }, + { + "cell_type": "markdown", + "id": "1762e71a", + "metadata": {}, + "source": [ + "## First Analysis of results of the data drift" + ] + }, + { + "cell_type": "markdown", + "id": "d9de8d0a", + "metadata": {}, + "source": [ + "Data driftn methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", + "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", + "Then a classification model (catboost) is learned to predict this target.\n", + "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" + ] + }, + { + "cell_type": "markdown", + "id": "1d4baec4", + "metadata": {}, + "source": [ + "### Detection data drift performance" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2c3ecafd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Performance of datadrift classifier\n", + "SD.plot.generate_indicator(fig_value=SD.auc, height=300, width=500, title=\"Datadrift classifier AUC\")" + ] + }, + { + "cell_type": "markdown", + "id": "117022c4", + "metadata": {}, + "source": [ + "An Auc close to 0.5 means that there is little drift" + ] + }, + { + "cell_type": "markdown", + "id": "0c0dd1a7", + "metadata": {}, + "source": [ + "### Importance of features in data drift" + ] + }, + { + "cell_type": "markdown", + "id": "89ea4277", + "metadata": {}, + "source": [ + "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "61fc4a71", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.xpl.plot.features_importance()" + ] + }, + { + "cell_type": "markdown", + "id": "723800cd", + "metadata": {}, + "source": [ + "We get the features with most gaps, those that are most important to analyse.\n", + "With date bias introduced, it is normal that date features are the most impacted. We will then decide to remove them.\n", + "Let's analyse other important variables" + ] + }, + { + "cell_type": "markdown", + "id": "5776d01b", + "metadata": {}, + "source": [ + "### Feature importance overview" + ] + }, + { + "cell_type": "markdown", + "id": "19ff31b6", + "metadata": {}, + "source": [ + "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "e0738fdb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.scatter_feature_importance() # works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "d98c9275", + "metadata": {}, + "source": [ + "Putting importance of the drift into perspective according to the importance of the model to be deployed, can help the data scientist to validate that his model can be deployed.
\n" + ] + }, + { + "cell_type": "markdown", + "id": "e52eefc9", + "metadata": {}, + "source": [ + "### Univariate analysis" + ] + }, + { + "cell_type": "markdown", + "id": "707c0073", + "metadata": {}, + "source": [ + "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ee7e5803", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate('season_acc')" + ] + }, + { + "cell_type": "markdown", + "id": "3fadad70", + "metadata": {}, + "source": [ + "### Distribution of predicted values" + ] + }, + { + "cell_type": "markdown", + "id": "fd14d06a", + "metadata": {}, + "source": [ + "This graph shows distributions of the production model outputs on both baseline and current datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "aa78bacc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\") # works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "b800780c", + "metadata": {}, + "source": [ + "Jensen Shannon Divergence (JSD). The JSD measures the effect of a data drift on the deployed model performance. A value close to 0 indicates similar data distributions, while a value close to 1 tend to indicate distinct data distributions with a negative effect on the deployed model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "cbd93ddd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_indicator(\n", + " fig_value=SD.js_divergence,\n", + " height=280,\n", + " width=500,\n", + " title=\"Jensen Shannon Datadrift\",\n", + " min_gauge=0,\n", + " max_gauge=0.2,\n", + " ) #works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "192ce6b6", + "metadata": {}, + "source": [ + "## Compile Drift over years" + ] + }, + { + "cell_type": "markdown", + "id": "76890d30", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2018" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "cedd36b3", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2018,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "56954c52", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7036329129677259\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2018', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "17c9fb32", + "metadata": {}, + "source": [ + "----" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "ffd89494", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "b54f3c39", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\") # works if deployed_model is filled" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "8a34f826", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_indicator(\n", + " fig_value=SD.js_divergence,\n", + " height=280,\n", + " width=500,\n", + " title=\"Jensen Shannon Datadrift\",\n", + " min_gauge=0,\n", + " max_gauge=0.2,\n", + " ) # works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "9de0ccc4", + "metadata": {}, + "source": [ + "------" + ] + }, + { + "cell_type": "markdown", + "id": "2368d89a", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2019" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "5889552c", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2019,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "dfb1ac16", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7856527709300022\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2019', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5a71d951", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" + ] + }, + { + "cell_type": "markdown", + "id": "a82b2857", + "metadata": {}, + "source": [ + "This result is interesting because we see that the auc is very high, but not the Jensen Shannon datadrift. This shows the interest of the latter." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "3753c08b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.scatter_feature_importance() # works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "abe585bd", + "metadata": {}, + "source": [ + "the first 3 variables that explain the data drift by the auc of datadrift classifier are not very important for the model" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "3ef3b98f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\") # works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "0805f1bb", + "metadata": {}, + "source": [ + "We can see that despite the data drift, the impact on predictions is quite small" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "908cb91d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_indicator(\n", + " fig_value=SD.js_divergence,\n", + " height=280,\n", + " width=500,\n", + " title=\"Jensen Shannon Datadrift\",\n", + " min_gauge=0,\n", + " max_gauge=0.2,\n", + " ) # works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "34db9bb1", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2020" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "142dec09", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2020,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "0a45a01e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7902450838961592\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2020', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "b67329ec", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a011c100", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2021" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "43e75cb6", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2021, df_baseline=X_df_learning, deployed_model=model, encoding=encoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "46ad16b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7500011519622525\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True,\n", + " date_compile_auc = '01/01/2021', #optionnal, by default date of compile\n", + " datadrift_file = \"car_accident_auc.csv\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "467b9f08", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "9f91ee44", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_car_accident_datadrift_2021.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_car_accident_datadrift_2021.html', \n", + " title_story=\"Data drift\",\n", + " title_description=\"\"\"Car accident Data drift 2021\"\"\", # Optional: add a subtitle to describe report\n", + " project_info_file=\"../../eurybia/data/project_info_car_accident.yml\" # Optional: add information on report\n", + " )" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_car_accident_datadrift_2021.html', \n", - " title_story=\"Data drift\",\n", - " title_description=\"\"\"Car accident Data drift 2021\"\"\", # Optional: add a subtitle to describe report\n", - " project_info_file=\"../../eurybia/data/project_info_car_accident.yml\" # Optional: add information on report\n", - " )" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "eurybia_3_9", - "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" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "336px" - }, - "toc_section_display": true, - "toc_window_display": true + ], + "metadata": { + "kernelspec": { + "display_name": "eurybia_3_9", + "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" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "336px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "vscode": { + "interpreter": { + "hash": "36c4204cc0170e083c18487e195263df35fcafba9d65a5415ab6b0958d51e154" + } + } }, - "vscode": { - "interpreter": { - "hash": "36c4204cc0170e083c18487e195263df35fcafba9d65a5415ab6b0958d51e154" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/tutorial/data_validation/tutorial01-data-validation.ipynb b/tutorial/data_validation/tutorial01-data-validation.ipynb index ca9a7c4..50a9961 100644 --- a/tutorial/data_validation/tutorial01-data-validation.ipynb +++ b/tutorial/data_validation/tutorial01-data-validation.ipynb @@ -1,884 +1,884 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "86df95a5", - "metadata": {}, - "source": [ - "# Validate Data for model deployment\n" - ] - }, - { - "cell_type": "markdown", - "id": "1002fe1f", - "metadata": {}, - "source": [ - "With this tutorial you:
\n", - "Understand how use Eurybia to do data validation in a simple use case
\n", - "\n", - "Contents:\n", - "- Build a model to deploy\n", - "- Do data validation between learning dataset and production dataset\n", - "- Generate Report \n", - "- Analysis of results\n", - "\n", - "Data from Kaggle [Titanic](https://www.kaggle.com/c/titanic)
" - ] - }, - { - "cell_type": "markdown", - "id": "285f92bc", - "metadata": {}, - "source": [ - "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", - "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "33cd7e4f", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from category_encoders import OrdinalEncoder\n", - "import catboost\n", - "from eurybia.core.smartdrift import SmartDrift\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "markdown", - "id": "6fcf7d9c", - "metadata": {}, - "source": [ - "## Building Supervized Model\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a41f58d6", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ad036405", - "metadata": {}, - "outputs": [], - "source": [ - "titan_df = data_loading('titanic')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0e4deeff", - "metadata": {}, - "outputs": [], - "source": [ - "features = ['Pclass', 'Age', 'Embarked', 'Sex', 'SibSp', 'Parch', 'Fare']\n", - "features_to_encode = ['Pclass', 'Embarked', 'Sex']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0a5ece1d", - "metadata": {}, - "outputs": [], - "source": [ - "encoder = OrdinalEncoder(cols=features_to_encode)\n", - "encoder.fit(titan_df[features]) " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "2039bef4", - "metadata": {}, - "outputs": [], - "source": [ - "titan_df_encoded = encoder.transform(titan_df[features])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e8c52451", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(\n", - " titan_df_encoded,\n", - " titan_df['Survived'].to_frame(),\n", - " test_size=0.2,\n", - " random_state=11\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "2fc50a27", - "metadata": {}, - "outputs": [], - "source": [ - "i=0\n", - "indice_cat = []\n", - "for feature in titan_df_encoded:\n", - " if feature in features_to_encode:\n", - " indice_cat.append(i)\n", - " i=i+1" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e7367561", - "metadata": {}, - "outputs": [], - "source": [ - "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", - " learning_rate=0.143852,\n", - " iterations=500,\n", - " l2_leaf_reg=15,\n", - " max_depth = 4)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e0be1020", - "metadata": {}, - "outputs": [], - "source": [ - "train_pool_cat = catboost.Pool(data=X_train, label= y_train, cat_features = indice_cat)\n", - "test_pool_cat = catboost.Pool(data=X_test, label=y_test, cat_features = indice_cat) " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "596b695d", - "metadata": {}, - "outputs": [], - "source": [ - "model.fit(train_pool_cat, eval_set=test_pool_cat, silent=True)\n", - "y_pred = model.predict(X_test)" - ] - }, - { - "cell_type": "markdown", - "id": "df01806b", - "metadata": {}, - "source": [ - "## Creating a fake dataset as a production dataset\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "a1118f41", - "metadata": {}, - "outputs": [], - "source": [ - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "75875239", - "metadata": {}, - "outputs": [], - "source": [ - "df_production = titan_df.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "97659f0c", - "metadata": {}, - "outputs": [], - "source": [ - "df_production['Age'] = df_production['Age'].apply(lambda x: random.randrange(10, 76)).astype(float)\n", - "df_production['Fare'] = df_production['Fare'].apply(lambda x: random.randrange(1, 100)).astype(float)\n", - "list_sex= [\"male\", \"female\"]\n", - "df_production['Sex'] = df_production['Sex'].apply(lambda x: random.choice(list_sex))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "1764606d", - "metadata": {}, - "outputs": [], - "source": [ - "df_baseline = titan_df[features]\n", - "df_current = df_production[features]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c233105b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
" + "cells": [ + { + "cell_type": "markdown", + "id": "86df95a5", + "metadata": {}, + "source": [ + "# Validate Data for model deployment\n" + ] + }, + { + "cell_type": "markdown", + "id": "1002fe1f", + "metadata": {}, + "source": [ + "With this tutorial you:
\n", + "Understand how use Eurybia to do data validation in a simple use case
\n", + "\n", + "Contents:\n", + "- Build a model to deploy\n", + "- Do data validation between learning dataset and production dataset\n", + "- Generate Report \n", + "- Analysis of results\n", + "\n", + "Data from Kaggle [Titanic](https://www.kaggle.com/c/titanic)
" + ] + }, + { + "cell_type": "markdown", + "id": "285f92bc", + "metadata": {}, + "source": [ + "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", + "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "33cd7e4f", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from category_encoders import OrdinalEncoder\n", + "import catboost\n", + "from eurybia.core.smartdrift import SmartDrift\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "id": "6fcf7d9c", + "metadata": {}, + "source": [ + "## Building Supervized Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a41f58d6", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ad036405", + "metadata": {}, + "outputs": [], + "source": [ + "titan_df = data_loading('titanic')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0e4deeff", + "metadata": {}, + "outputs": [], + "source": [ + "features = ['Pclass', 'Age', 'Embarked', 'Sex', 'SibSp', 'Parch', 'Fare']\n", + "features_to_encode = ['Pclass', 'Embarked', 'Sex']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a5ece1d", + "metadata": {}, + "outputs": [], + "source": [ + "encoder = OrdinalEncoder(cols=features_to_encode)\n", + "encoder.fit(titan_df[features]) " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2039bef4", + "metadata": {}, + "outputs": [], + "source": [ + "titan_df_encoded = encoder.transform(titan_df[features])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e8c52451", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(\n", + " titan_df_encoded,\n", + " titan_df['Survived'].to_frame(),\n", + " test_size=0.2,\n", + " random_state=11\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2fc50a27", + "metadata": {}, + "outputs": [], + "source": [ + "i=0\n", + "indice_cat = []\n", + "for feature in titan_df_encoded:\n", + " if feature in features_to_encode:\n", + " indice_cat.append(i)\n", + " i=i+1" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e7367561", + "metadata": {}, + "outputs": [], + "source": [ + "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", + " learning_rate=0.143852,\n", + " iterations=500,\n", + " l2_leaf_reg=15,\n", + " max_depth = 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e0be1020", + "metadata": {}, + "outputs": [], + "source": [ + "train_pool_cat = catboost.Pool(data=X_train, label= y_train, cat_features = indice_cat)\n", + "test_pool_cat = catboost.Pool(data=X_test, label=y_test, cat_features = indice_cat) " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "596b695d", + "metadata": {}, + "outputs": [], + "source": [ + "model.fit(train_pool_cat, eval_set=test_pool_cat, silent=True)\n", + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "markdown", + "id": "df01806b", + "metadata": {}, + "source": [ + "## Creating a fake dataset as a production dataset\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a1118f41", + "metadata": {}, + "outputs": [], + "source": [ + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "75875239", + "metadata": {}, + "outputs": [], + "source": [ + "df_production = titan_df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "97659f0c", + "metadata": {}, + "outputs": [], + "source": [ + "df_production['Age'] = df_production['Age'].apply(lambda x: random.randrange(10, 76)).astype(float)\n", + "df_production['Fare'] = df_production['Fare'].apply(lambda x: random.randrange(1, 100)).astype(float)\n", + "list_sex= [\"male\", \"female\"]\n", + "df_production['Sex'] = df_production['Sex'].apply(lambda x: random.choice(list_sex))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1764606d", + "metadata": {}, + "outputs": [], + "source": [ + "df_baseline = titan_df[features]\n", + "df_current = df_production[features]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c233105b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Pclass Age Embarked Sex SibSp Parch Fare\n", + "PassengerId \n", + "1 Third class 22.0 Southampton male 1 0 7.25\n", + "2 First class 38.0 Cherbourg female 1 0 71.28\n", + "3 Third class 26.0 Southampton female 0 0 7.92\n", + "4 First class 35.0 Southampton female 1 0 53.10\n", + "5 Third class 35.0 Southampton male 0 0 8.05" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " Pclass Age Embarked Sex SibSp Parch Fare\n", - "PassengerId \n", - "1 Third class 22.0 Southampton male 1 0 7.25\n", - "2 First class 38.0 Cherbourg female 1 0 71.28\n", - "3 Third class 26.0 Southampton female 0 0 7.92\n", - "4 First class 35.0 Southampton female 1 0 53.10\n", - "5 Third class 35.0 Southampton male 0 0 8.05" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_baseline.head()" - ] - }, - { - "cell_type": "markdown", - "id": "38508afd", - "metadata": {}, - "source": [ - "## Use Eurybia for data validation" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "c0e83ee0", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "808b5a25", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=df_current,\n", - " df_baseline=df_baseline,\n", - " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "0cc98756", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 6 µs, sys: 2 µs, total: 8 µs\n", - "Wall time: 15.7 µs\n" - ] - } - ], - "source": [ - "%time \n", - "SD.compile(full_validation=True # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "5e290645", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Report saved to ./report_titanic.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "source": [ + "df_baseline.head()" + ] + }, + { + "cell_type": "markdown", + "id": "38508afd", + "metadata": {}, + "source": [ + "## Use Eurybia for data validation" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c0e83ee0", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "808b5a25", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=df_current,\n", + " df_baseline=df_baseline,\n", + " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0cc98756", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 6 \u00b5s, sys: 2 \u00b5s, total: 8 \u00b5s\n", + "Wall time: 15.7 \u00b5s\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "%time \n", + "SD.compile(full_validation=True # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "5e290645", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_titanic.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_titanic.html', \n", + " title_story=\"Data validation\",\n", + " title_description=\"\"\"Titanic Data validation\"\"\", # Optional: add a subtitle to describe report\n", + " project_info_file=\"../../eurybia/data/project_info_titanic.yml\" # Optional: add information on report \n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "20482b6f", + "metadata": {}, + "source": [ + "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", + "However, to illustrate functionalities, we will detail results with notebook mode analysis." + ] + }, + { + "cell_type": "markdown", + "id": "579c308d", + "metadata": {}, + "source": [ + "## Analysis of results of the data validation" + ] + }, + { + "cell_type": "markdown", + "id": "8981606c", + "metadata": {}, + "source": [ + "Data validation methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", + "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", + "Then a classification model (catboost) is learned to predict this target.\n", + "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" + ] + }, + { + "cell_type": "markdown", + "id": "1744d311", + "metadata": {}, + "source": [ + "### Detection data drift performance" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ab95a343", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Performance of data drift classifier\n", + "SD.plot.generate_indicator(fig_value=SD.auc, height=300, width=500, title=\"Datadrift classifier AUC\")" + ] + }, + { + "cell_type": "markdown", + "id": "32366d7b", + "metadata": {}, + "source": [ + "such a high auc means that datasets are not similar.The differences should be analysed before deploying model in production" + ] + }, + { + "cell_type": "markdown", + "id": "8deda6d0", + "metadata": {}, + "source": [ + "### Importance of features in data drift" + ] + }, + { + "cell_type": "markdown", + "id": "b686c77b", + "metadata": {}, + "source": [ + "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "dff579c5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.xpl.plot.features_importance()" + ] + }, + { + "cell_type": "markdown", + "id": "0f140e52", + "metadata": {}, + "source": [ + "Features that explain most differences are fare, age and sex. This makes sense because it is features that have been altered\n" + ] + }, + { + "cell_type": "markdown", + "id": "4376186f", + "metadata": {}, + "source": [ + "### Feature importance overview" + ] + }, + { + "cell_type": "markdown", + "id": "8e237594", + "metadata": {}, + "source": [ + "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "2bea57fb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.scatter_feature_importance() # works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "7795d380", + "metadata": {}, + "source": [ + "Features that have the most difference are quite important for the deployed model." + ] + }, + { + "cell_type": "markdown", + "id": "daf2cf3d", + "metadata": {}, + "source": [ + "### Univariate analysis" + ] + }, + { + "cell_type": "markdown", + "id": "2340efb1", + "metadata": {}, + "source": [ + "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "41999b2f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate('Sex')" + ] + }, + { + "cell_type": "markdown", + "id": "0f7343be", + "metadata": {}, + "source": [ + "### Distribution of predicted values" + ] + }, + { + "cell_type": "markdown", + "id": "1603ea1f", + "metadata": {}, + "source": [ + "This graph shows distributions of the production model outputs on both baseline and current datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "80822147", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\") # works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "1d95ec02", + "metadata": {}, + "source": [ + "Differences between 2 datasets generate a difference in the distribution of the predictions of the deployed model. These differences can have important impacts on the performance of the model in production" + ] + }, + { + "cell_type": "markdown", + "id": "233c3845", + "metadata": {}, + "source": [ + "Jensen Shannon Divergence (JSD). The JSD measures the effect of a data drift on the deployed model performance. A value close to 0 indicates similar data distributions, while a value close to 1 tend to indicate distinct data distributions with a negative effect on the deployed model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "d2c96e92", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_indicator(\n", + " fig_value=SD.js_divergence,\n", + " height=280,\n", + " width=500,\n", + " title=\"Jensen Shannon Datadrift\",\n", + " min_gauge=0,\n", + " max_gauge=0.2,\n", + " ) #works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "b149306f", + "metadata": {}, + "source": [ + "### Feature contribution on data drift's detection" + ] + }, + { + "cell_type": "markdown", + "id": "033b0175", + "metadata": {}, + "source": [ + "This graph represents the contribution of a variable to the data drift detection. This graph can help to understand the drift when the analysis of the dataset, either numerical or graphical, does not allow a clear understanding. In the drop-down menu, the variables are sorted by importance of the variables in the data drift detection." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d58a440c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.xpl.plot.contribution_plot('Sex')" + ] + }, + { + "cell_type": "markdown", + "id": "626b024b", + "metadata": {}, + "source": [ + "This graph is more complex and is usefull for few use case. It provides an understanding of interpretation of the datadrift classifier feature by feature" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_titanic.html', \n", - " title_story=\"Data validation\",\n", - " title_description=\"\"\"Titanic Data validation\"\"\", # Optional: add a subtitle to describe report\n", - " project_info_file=\"../../eurybia/data/project_info_titanic.yml\" # Optional: add information on report \n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "20482b6f", - "metadata": {}, - "source": [ - "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", - "However, to illustrate functionalities, we will detail results with notebook mode analysis." - ] - }, - { - "cell_type": "markdown", - "id": "579c308d", - "metadata": {}, - "source": [ - "## Analysis of results of the data validation" - ] - }, - { - "cell_type": "markdown", - "id": "8981606c", - "metadata": {}, - "source": [ - "Data validation methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", - "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", - "Then a classification model (catboost) is learned to predict this target.\n", - "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" - ] - }, - { - "cell_type": "markdown", - "id": "1744d311", - "metadata": {}, - "source": [ - "### Detection data drift performance" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "ab95a343", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Performance of data drift classifier\n", - "SD.plot.generate_indicator(fig_value=SD.auc, height=300, width=500, title=\"Datadrift classifier AUC\")" - ] - }, - { - "cell_type": "markdown", - "id": "32366d7b", - "metadata": {}, - "source": [ - "such a high auc means that datasets are not similar.The differences should be analysed before deploying model in production" - ] - }, - { - "cell_type": "markdown", - "id": "8deda6d0", - "metadata": {}, - "source": [ - "### Importance of features in data drift" - ] - }, - { - "cell_type": "markdown", - "id": "b686c77b", - "metadata": {}, - "source": [ - "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "dff579c5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "markdown", - "id": "0f140e52", - "metadata": {}, - "source": [ - "Features that explain most differences are fare, age and sex. This makes sense because it is features that have been altered\n" - ] - }, - { - "cell_type": "markdown", - "id": "4376186f", - "metadata": {}, - "source": [ - "### Feature importance overview" - ] - }, - { - "cell_type": "markdown", - "id": "8e237594", - "metadata": {}, - "source": [ - "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "2bea57fb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.scatter_feature_importance() # works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "7795d380", - "metadata": {}, - "source": [ - "Features that have the most difference are quite important for the deployed model." - ] - }, - { - "cell_type": "markdown", - "id": "daf2cf3d", - "metadata": {}, - "source": [ - "### Univariate analysis" - ] - }, - { - "cell_type": "markdown", - "id": "2340efb1", - "metadata": {}, - "source": [ - "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "41999b2f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('Sex')" - ] - }, - { - "cell_type": "markdown", - "id": "0f7343be", - "metadata": {}, - "source": [ - "### Distribution of predicted values" - ] - }, - { - "cell_type": "markdown", - "id": "1603ea1f", - "metadata": {}, - "source": [ - "This graph shows distributions of the production model outputs on both baseline and current datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "80822147", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\") # works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "1d95ec02", - "metadata": {}, - "source": [ - "Differences between 2 datasets generate a difference in the distribution of the predictions of the deployed model. These differences can have important impacts on the performance of the model in production" - ] - }, - { - "cell_type": "markdown", - "id": "233c3845", - "metadata": {}, - "source": [ - "Jensen Shannon Divergence (JSD). The JSD measures the effect of a data drift on the deployed model performance. A value close to 0 indicates similar data distributions, while a value close to 1 tend to indicate distinct data distributions with a negative effect on the deployed model performance." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "d2c96e92", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "SD.plot.generate_indicator(\n", - " fig_value=SD.js_divergence,\n", - " height=280,\n", - " width=500,\n", - " title=\"Jensen Shannon Datadrift\",\n", - " min_gauge=0,\n", - " max_gauge=0.2,\n", - " ) #works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "b149306f", - "metadata": {}, - "source": [ - "### Feature contribution on data drift's detection" - ] - }, - { - "cell_type": "markdown", - "id": "033b0175", - "metadata": {}, - "source": [ - "This graph represents the contribution of a variable to the data drift detection. This graph can help to understand the drift when the analysis of the dataset, either numerical or graphical, does not allow a clear understanding. In the drop-down menu, the variables are sorted by importance of the variables in the data drift detection." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "d58a440c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" + ], + "metadata": { + "kernelspec": { + "display_name": "dev_eurybia", + "language": "python", + "name": "dev_eurybia" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } - ], - "source": [ - "SD.xpl.plot.contribution_plot('Sex')" - ] - }, - { - "cell_type": "markdown", - "id": "626b024b", - "metadata": {}, - "source": [ - "This graph is more complex and is usefull for few use case. It provides an understanding of interpretation of the datadrift classifier feature by feature" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "dev_eurybia", - "language": "python", - "name": "dev_eurybia" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/tutorial/data_validation/tutorial02-data-validation-iteration.ipynb b/tutorial/data_validation/tutorial02-data-validation-iteration.ipynb index 3f253b4..f458ba9 100644 --- a/tutorial/data_validation/tutorial02-data-validation-iteration.ipynb +++ b/tutorial/data_validation/tutorial02-data-validation-iteration.ipynb @@ -1,1363 +1,1363 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "996903eb", - "metadata": {}, - "source": [ - "# Iterate on Data validation with display analysis\n" - ] - }, - { - "cell_type": "markdown", - "id": "463ecee0", - "metadata": {}, - "source": [ - "With this tutorial you:
\n", - "Understand how to use Eurybia to iterate on different phases of data validation
\n", - "We propose to go into more detail about the use of Eurybia
\n", - "\n", - "Contents:\n", - "- Validate your data \n", - "- Generate Report \n", - "- Iterate on analysis of results, data validation, data preparation\n", - "\n", - "Data from Kaggle [House Prices](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)" - ] - }, - { - "cell_type": "markdown", - "id": "b239e1e0", - "metadata": {}, - "source": [ - "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", - "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "cd5f25fb", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from category_encoders import OrdinalEncoder\n", - "from lightgbm import LGBMRegressor\n", - "from eurybia.core.smartdrift import SmartDrift\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "markdown", - "id": "6aed9f4b", - "metadata": {}, - "source": [ - "## Import Dataset and split in training and production dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1c6aca48", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7c55e4fa", - "metadata": {}, - "outputs": [], - "source": [ - "house_df, house_dict = data_loading('house_prices')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d4a2e665", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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MSSubClassMSZoningLotAreaStreetLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhood...EnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
Id
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14571-Story 1946 & Newer All StylesResidential Low Density13175PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeNorthwest Ames...0000022010Warranty Deed - ConventionalNormal Sale210000
14582-Story 1945 & OlderResidential Low Density9042PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeCrawford...0000250052010Warranty Deed - ConventionalNormal Sale266500
14591-Story 1946 & Newer All StylesResidential Low Density9717PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeNorth Ames...112000042010Warranty Deed - ConventionalNormal Sale142125
14601-Story 1946 & Newer All StylesResidential Low Density9937PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeEdwards...0000062008Warranty Deed - ConventionalNormal Sale147500
\n", - "

5 rows × 73 columns

\n", - "
" + "cells": [ + { + "cell_type": "markdown", + "id": "996903eb", + "metadata": {}, + "source": [ + "# Iterate on Data validation with display analysis\n" + ] + }, + { + "cell_type": "markdown", + "id": "463ecee0", + "metadata": {}, + "source": [ + "With this tutorial you:
\n", + "Understand how to use Eurybia to iterate on different phases of data validation
\n", + "We propose to go into more detail about the use of Eurybia
\n", + "\n", + "Contents:\n", + "- Validate your data \n", + "- Generate Report \n", + "- Iterate on analysis of results, data validation, data preparation\n", + "\n", + "Data from Kaggle [House Prices](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)" + ] + }, + { + "cell_type": "markdown", + "id": "b239e1e0", + "metadata": {}, + "source": [ + "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", + "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cd5f25fb", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from category_encoders import OrdinalEncoder\n", + "from lightgbm import LGBMRegressor\n", + "from eurybia.core.smartdrift import SmartDrift\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "id": "6aed9f4b", + "metadata": {}, + "source": [ + "## Import Dataset and split in training and production dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1c6aca48", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7c55e4fa", + "metadata": {}, + "outputs": [], + "source": [ + "house_df, house_dict = data_loading('house_prices')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d4a2e665", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MSSubClassMSZoningLotAreaStreetLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhood...EnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
Id
14562-Story 1946 & NewerResidential Low Density7917PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeGilbert...0000082007Warranty Deed - ConventionalNormal Sale175000
14571-Story 1946 & Newer All StylesResidential Low Density13175PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeNorthwest Ames...0000022010Warranty Deed - ConventionalNormal Sale210000
14582-Story 1945 & OlderResidential Low Density9042PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeCrawford...0000250052010Warranty Deed - ConventionalNormal Sale266500
14591-Story 1946 & Newer All StylesResidential Low Density9717PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeNorth Ames...112000042010Warranty Deed - ConventionalNormal Sale142125
14601-Story 1946 & Newer All StylesResidential Low Density9937PavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeEdwards...0000062008Warranty Deed - ConventionalNormal Sale147500
\n", + "

5 rows \u00d7 73 columns

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" + ], + "text/plain": [ + " MSSubClass MSZoning LotArea \\\n", + "Id \n", + "1456 2-Story 1946 & Newer Residential Low Density 7917 \n", + "1457 1-Story 1946 & Newer All Styles Residential Low Density 13175 \n", + "1458 2-Story 1945 & Older Residential Low Density 9042 \n", + "1459 1-Story 1946 & Newer All Styles Residential Low Density 9717 \n", + "1460 1-Story 1946 & Newer All Styles Residential Low Density 9937 \n", + "\n", + " Street LotShape LandContour Utilities \\\n", + "Id \n", + "1456 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", + "1457 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", + "1458 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", + "1459 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", + "1460 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", + "\n", + " LotConfig LandSlope Neighborhood ... EnclosedPorch 3SsnPorch \\\n", + "Id ... \n", + "1456 Inside lot Gentle slope Gilbert ... 0 0 \n", + "1457 Inside lot Gentle slope Northwest Ames ... 0 0 \n", + "1458 Inside lot Gentle slope Crawford ... 0 0 \n", + "1459 Inside lot Gentle slope North Ames ... 112 0 \n", + "1460 Inside lot Gentle slope Edwards ... 0 0 \n", + "\n", + " ScreenPorch PoolArea MiscVal MoSold YrSold \\\n", + "Id \n", + "1456 0 0 0 8 2007 \n", + "1457 0 0 0 2 2010 \n", + "1458 0 0 2500 5 2010 \n", + "1459 0 0 0 4 2010 \n", + "1460 0 0 0 6 2008 \n", + "\n", + " SaleType SaleCondition SalePrice \n", + "Id \n", + "1456 Warranty Deed - Conventional Normal Sale 175000 \n", + "1457 Warranty Deed - Conventional Normal Sale 210000 \n", + "1458 Warranty Deed - Conventional Normal Sale 266500 \n", + "1459 Warranty Deed - Conventional Normal Sale 142125 \n", + "1460 Warranty Deed - Conventional Normal Sale 147500 \n", + "\n", + "[5 rows x 73 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " MSSubClass MSZoning LotArea \\\n", - "Id \n", - "1456 2-Story 1946 & Newer Residential Low Density 7917 \n", - "1457 1-Story 1946 & Newer All Styles Residential Low Density 13175 \n", - "1458 2-Story 1945 & Older Residential Low Density 9042 \n", - "1459 1-Story 1946 & Newer All Styles Residential Low Density 9717 \n", - "1460 1-Story 1946 & Newer All Styles Residential Low Density 9937 \n", - "\n", - " Street LotShape LandContour Utilities \\\n", - "Id \n", - "1456 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", - "1457 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", - "1458 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", - "1459 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", - "1460 Paved Regular Near Flat/Level All public Utilities (E,G,W,& S) \n", - "\n", - " LotConfig LandSlope Neighborhood ... EnclosedPorch 3SsnPorch \\\n", - "Id ... \n", - "1456 Inside lot Gentle slope Gilbert ... 0 0 \n", - "1457 Inside lot Gentle slope Northwest Ames ... 0 0 \n", - "1458 Inside lot Gentle slope Crawford ... 0 0 \n", - "1459 Inside lot Gentle slope North Ames ... 112 0 \n", - "1460 Inside lot Gentle slope Edwards ... 0 0 \n", - "\n", - " ScreenPorch PoolArea MiscVal MoSold YrSold \\\n", - "Id \n", - "1456 0 0 0 8 2007 \n", - "1457 0 0 0 2 2010 \n", - "1458 0 0 2500 5 2010 \n", - "1459 0 0 0 4 2010 \n", - "1460 0 0 0 6 2008 \n", - "\n", - " SaleType SaleCondition SalePrice \n", - "Id \n", - "1456 Warranty Deed - Conventional Normal Sale 175000 \n", - "1457 Warranty Deed - Conventional Normal Sale 210000 \n", - "1458 Warranty Deed - Conventional Normal Sale 266500 \n", - "1459 Warranty Deed - Conventional Normal Sale 142125 \n", - "1460 Warranty Deed - Conventional Normal Sale 147500 \n", - "\n", - "[5 rows x 73 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "house_df.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "01c8631c", - "metadata": {}, - "outputs": [], - "source": [ - "# For the purpose of the tutorial split dataset in training and production dataset\n", - "# To see an interesting analysis, let's test for a bias between date of construction of training and production dataset\n", - "house_df_learning = house_df.loc[house_df['YearBuilt'] < 1980]\n", - "house_df_production = house_df.loc[house_df['YearBuilt'] >= 1980]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "61178753", - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", - "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YearBuilt'])]\n", - "\n", - "y_df_production=house_df_production['SalePrice'].to_frame()\n", - "X_df_production=house_df_production[house_df_production.columns.difference(['SalePrice','YearBuilt'])]" - ] - }, - { - "cell_type": "markdown", - "id": "50be1ce4", - "metadata": {}, - "source": [ - "## Use Eurybia for data validation" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c482bfae", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6373e289", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_production,\n", - " df_baseline=X_df_learning\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "1f33c08d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 3 µs, sys: 5 µs, total: 8 µs\n", - "Wall time: 14.8 µs\n", - "The variable BldgType has mismatching unique values:\n", - "[] | ['Two-family Conversion; originally built as one-family dwelling']\n", - "\n", - "The variable BsmtCond has mismatching unique values:\n", - "[] | ['Poor -Severe cracking, settling, or wetness']\n", - "\n", - "The variable CentralAir has mismatching unique values:\n", - "[] | ['No']\n", - "\n", - "The variable Condition1 has mismatching unique values:\n", - "[\"Within 200' of East-West Railroad\"] | ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", - "\n", - "The variable Condition2 has mismatching unique values:\n", - "['Near positive off-site feature--park, greenbelt, etc.'] | ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "[] | ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "\n", - "The variable ExterCond has mismatching unique values:\n", - "[] | ['Fair', 'Poor', 'Excellent']\n", - "\n", - "The variable ExterQual has mismatching unique values:\n", - "[] | ['Fair']\n", - "\n", - "The variable Exterior1st has mismatching unique values:\n", - "['Imitation Stucco'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", - "\n", - "The variable Exterior2nd has mismatching unique values:\n", - "['Other'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", - "\n", - "The variable Foundation has mismatching unique values:\n", - "['Wood'] | ['Brick & Tile', 'Stone']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "[] | ['Major Deductions 2', 'Severely Damaged']\n", - "\n", - "The variable GarageCond has mismatching unique values:\n", - "[] | ['Poor', 'Excellent']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Excellent', 'Poor']\n", - "\n", - "The variable GarageType has mismatching unique values:\n", - "[] | ['Car Port']\n", - "\n", - "The variable Heating has mismatching unique values:\n", - "[] | ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", - "\n", - "The variable HeatingQC has mismatching unique values:\n", - "[] | ['Fair', 'Poor']\n", - "\n", - "The variable HouseStyle has mismatching unique values:\n", - "[] | ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", - "\n", - "The variable KitchenQual has mismatching unique values:\n", - "[] | ['Fair']\n", - "\n", - "The variable LandSlope has mismatching unique values:\n", - "[] | ['Severe Slope']\n", - "\n", - "The variable MSSubClass has mismatching unique values:\n", - "[] | ['2-Story 1945 & Older', '2 Family Conversion - All Styles and Ages', '1-1/2 Story - Unfinished All Ages', '1-Story 1945 & Older', '2-1/2 Story All Ages', '1-Story w/Finished Attic All Ages']\n", - "\n", - "The variable MSZoning has mismatching unique values:\n", - "['Floating Village Residential'] | ['Commercial']\n", - "\n", - "The variable MasVnrType has mismatching unique values:\n", - "[] | ['Brick Common']\n", - "\n", - "The variable Neighborhood has mismatching unique values:\n", - "['Northridge', 'Somerset', 'Northridge Heights', 'Stone Brook', 'Bloomington Heights', 'Bluestem'] | ['Brookside', 'Iowa DOT and Rail Road', 'Meadow Village', 'Northpark Villa', 'Briardale', 'South & West of Iowa State University']\n", - "\n", - "The variable PavedDrive has mismatching unique values:\n", - "[] | ['Partial Pavement']\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "['Clay or Tile'] | ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", - "\n", - "The variable RoofStyle has mismatching unique values:\n", - "[] | ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", - "\n", - "The variable SaleCondition has mismatching unique values:\n", - "[] | ['Adjoining Land Purchase']\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "['Contract 15% Down payment regular terms'] | []\n", - "\n", - "The variable Utilities has mismatching unique values:\n", - "[] | ['Electricity and Gas Only']\n", - "\n" - ] - } - ], - "source": [ - "%time \n", - "SD.compile(full_validation=True # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "ab1ee858", - "metadata": {}, - "source": [ - "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", - "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", - "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "4ad7baff", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_v1.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "source": [ + "house_df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "01c8631c", + "metadata": {}, + "outputs": [], + "source": [ + "# For the purpose of the tutorial split dataset in training and production dataset\n", + "# To see an interesting analysis, let's test for a bias between date of construction of training and production dataset\n", + "house_df_learning = house_df.loc[house_df['YearBuilt'] < 1980]\n", + "house_df_production = house_df.loc[house_df['YearBuilt'] >= 1980]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "61178753", + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", + "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YearBuilt'])]\n", + "\n", + "y_df_production=house_df_production['SalePrice'].to_frame()\n", + "X_df_production=house_df_production[house_df_production.columns.difference(['SalePrice','YearBuilt'])]" + ] + }, + { + "cell_type": "markdown", + "id": "50be1ce4", + "metadata": {}, + "source": [ + "## Use Eurybia for data validation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c482bfae", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6373e289", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_production,\n", + " df_baseline=X_df_learning\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1f33c08d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 3 \u00b5s, sys: 5 \u00b5s, total: 8 \u00b5s\n", + "Wall time: 14.8 \u00b5s\n", + "The variable BldgType has mismatching unique values:\n", + "[] | ['Two-family Conversion; originally built as one-family dwelling']\n", + "\n", + "The variable BsmtCond has mismatching unique values:\n", + "[] | ['Poor -Severe cracking, settling, or wetness']\n", + "\n", + "The variable CentralAir has mismatching unique values:\n", + "[] | ['No']\n", + "\n", + "The variable Condition1 has mismatching unique values:\n", + "[\"Within 200' of East-West Railroad\"] | ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", + "\n", + "The variable Condition2 has mismatching unique values:\n", + "['Near positive off-site feature--park, greenbelt, etc.'] | ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "[] | ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "\n", + "The variable ExterCond has mismatching unique values:\n", + "[] | ['Fair', 'Poor', 'Excellent']\n", + "\n", + "The variable ExterQual has mismatching unique values:\n", + "[] | ['Fair']\n", + "\n", + "The variable Exterior1st has mismatching unique values:\n", + "['Imitation Stucco'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", + "\n", + "The variable Exterior2nd has mismatching unique values:\n", + "['Other'] | ['Asbestos Shingles', 'Brick Common', 'Asphalt Shingles', 'Stone', 'Cinder Block']\n", + "\n", + "The variable Foundation has mismatching unique values:\n", + "['Wood'] | ['Brick & Tile', 'Stone']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "[] | ['Major Deductions 2', 'Severely Damaged']\n", + "\n", + "The variable GarageCond has mismatching unique values:\n", + "[] | ['Poor', 'Excellent']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Excellent', 'Poor']\n", + "\n", + "The variable GarageType has mismatching unique values:\n", + "[] | ['Car Port']\n", + "\n", + "The variable Heating has mismatching unique values:\n", + "[] | ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", + "\n", + "The variable HeatingQC has mismatching unique values:\n", + "[] | ['Fair', 'Poor']\n", + "\n", + "The variable HouseStyle has mismatching unique values:\n", + "[] | ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", + "\n", + "The variable KitchenQual has mismatching unique values:\n", + "[] | ['Fair']\n", + "\n", + "The variable LandSlope has mismatching unique values:\n", + "[] | ['Severe Slope']\n", + "\n", + "The variable MSSubClass has mismatching unique values:\n", + "[] | ['2-Story 1945 & Older', '2 Family Conversion - All Styles and Ages', '1-1/2 Story - Unfinished All Ages', '1-Story 1945 & Older', '2-1/2 Story All Ages', '1-Story w/Finished Attic All Ages']\n", + "\n", + "The variable MSZoning has mismatching unique values:\n", + "['Floating Village Residential'] | ['Commercial']\n", + "\n", + "The variable MasVnrType has mismatching unique values:\n", + "[] | ['Brick Common']\n", + "\n", + "The variable Neighborhood has mismatching unique values:\n", + "['Northridge', 'Somerset', 'Northridge Heights', 'Stone Brook', 'Bloomington Heights', 'Bluestem'] | ['Brookside', 'Iowa DOT and Rail Road', 'Meadow Village', 'Northpark Villa', 'Briardale', 'South & West of Iowa State University']\n", + "\n", + "The variable PavedDrive has mismatching unique values:\n", + "[] | ['Partial Pavement']\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "['Clay or Tile'] | ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", + "\n", + "The variable RoofStyle has mismatching unique values:\n", + "[] | ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", + "\n", + "The variable SaleCondition has mismatching unique values:\n", + "[] | ['Adjoining Land Purchase']\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "['Contract 15% Down payment regular terms'] | []\n", + "\n", + "The variable Utilities has mismatching unique values:\n", + "[] | ['Electricity and Gas Only']\n", + "\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "%time \n", + "SD.compile(full_validation=True # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " )" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_v1.html', \n", - " title_story=\"Data validation V1\", \n", - " title_description=\"\"\"House price Data validation V1\"\"\" # Optional: add a subtitle to describe report\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "3ddfed51", - "metadata": {}, - "source": [ - "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", - "However, to illustrate functionalities, we will detail results with notebook mode analysis." - ] - }, - { - "cell_type": "markdown", - "id": "f98cd44a", - "metadata": {}, - "source": [ - "## First Analysis of results of the data validation" - ] - }, - { - "cell_type": "markdown", - "id": "be6daded", - "metadata": {}, - "source": [ - "Data validation methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", - "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", - "Then a classification model (catboost) is learned to predict this target.\n", - "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" - ] - }, - { - "cell_type": "markdown", - "id": "5baf3c29", - "metadata": {}, - "source": [ - "### Detection data drift performance" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "8953e093", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Performance of data drift classifier\n", - "SD.plot.generate_indicator(fig_value=SD.auc, height=300, width=500, title=\"Datadrift classifier AUC\")" - ] - }, - { - "cell_type": "markdown", - "id": "22b39a4f", - "metadata": {}, - "source": [ - "such a high auc means that datasets are not similar.The differences should be analysed before deploying model in production" - ] - }, - { - "cell_type": "markdown", - "id": "0e80bb96", - "metadata": {}, - "source": [ - "### Importance of features in data drift" - ] - }, - { - "cell_type": "markdown", - "id": "92895e23", - "metadata": {}, - "source": [ - "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "630e9efe", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.xpl.plot.features_importance()" - ] - }, - { - "cell_type": "markdown", - "id": "ff21ebcf", - "metadata": {}, - "source": [ - "We get the features with most gaps, those that are most important to analyse.\n", - "With date bias introduced, it is normal that date features are the most impacted. We will then decide to remove them.\n", - "Let's analyse other important variables" - ] - }, - { - "cell_type": "markdown", - "id": "6e232653", - "metadata": {}, - "source": [ - "### Univariate analysis" - ] - }, - { - "cell_type": "markdown", - "id": "b12a6268", - "metadata": {}, - "source": [ - "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "8de1e1c6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('BsmtQual')" - ] - }, - { - "cell_type": "markdown", - "id": "9b8d7382", - "metadata": {}, - "source": [ - "This feature on height of the basement seems to be correlated with the date of build.To avoid creating too much bias, the data scientist decides to remove it from his learning." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "56f2124d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('Neighborhood')" - ] - }, - { - "cell_type": "markdown", - "id": "c82afbd3", - "metadata": {}, - "source": [ - "This feature on neighborhood seems to be correlated with the date of build.To avoid creating too much bias, the data scientist decides to remove it from his learning." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "77e568d3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('Foundation')" - ] - }, - { - "cell_type": "markdown", - "id": "4bfb2876", - "metadata": {}, - "source": [ - "This feature on foundation seems to be correlated with the date of build.To avoid creating too much bias, the data scientist decides to remove it from his learning." - ] - }, - { - "cell_type": "markdown", - "id": "e7456527", - "metadata": {}, - "source": [ - "Data scientist thus discards all features that will not be similar to the production training" - ] - }, - { - "cell_type": "markdown", - "id": "6be6fc58", - "metadata": {}, - "source": [ - "## Second data validation after cleaning data preparation" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "4ab0aea1", - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", - "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YearBuilt','BsmtQual',\n", - " 'Neighborhood','Foundation','GarageYrBlt','YearRemodAdd',\n", - " 'GarageFinish','OverallCond','MSZoning','BsmtFinType1','MSSubClass',\n", - " 'ExterQual','KitchenQual','Exterior2nd','Exterior1st','OverallQual',\n", - " 'HeatingQC','FullBath','OpenPorchSF','GarageType','GrLivArea','GarageArea'])]\n", - "\n", - "y_df_production=house_df_production['SalePrice'].to_frame()\n", - "X_df_production=house_df_production[house_df_production.columns.difference(['SalePrice','YearBuilt','BsmtQual',\n", - " 'Neighborhood','Foundation','GarageYrBlt','YearRemodAdd',\n", - " 'GarageFinish','OverallCond','MSZoning','BsmtFinType1','MSSubClass',\n", - " 'ExterQual','KitchenQual','Exterior2nd','Exterior1st','OverallQual',\n", - " 'HeatingQC','FullBath','OpenPorchSF','GarageType','GrLivArea','GarageArea'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "eb6d2d30", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "aac6bf64", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The variable BldgType has mismatching unique values:\n", - "[] | ['Two-family Conversion; originally built as one-family dwelling']\n", - "\n", - "The variable BsmtCond has mismatching unique values:\n", - "[] | ['Poor -Severe cracking, settling, or wetness']\n", - "\n", - "The variable CentralAir has mismatching unique values:\n", - "[] | ['No']\n", - "\n", - "The variable Condition1 has mismatching unique values:\n", - "[\"Within 200' of East-West Railroad\"] | ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", - "\n", - "The variable Condition2 has mismatching unique values:\n", - "['Near positive off-site feature--park, greenbelt, etc.'] | ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "[] | ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "\n", - "The variable ExterCond has mismatching unique values:\n", - "[] | ['Fair', 'Poor', 'Excellent']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "[] | ['Major Deductions 2', 'Severely Damaged']\n", - "\n", - "The variable GarageCond has mismatching unique values:\n", - "[] | ['Poor', 'Excellent']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Excellent', 'Poor']\n", - "\n", - "The variable Heating has mismatching unique values:\n", - "[] | ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", - "\n", - "The variable HouseStyle has mismatching unique values:\n", - "[] | ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", - "\n", - "The variable LandSlope has mismatching unique values:\n", - "[] | ['Severe Slope']\n", - "\n", - "The variable MasVnrType has mismatching unique values:\n", - "[] | ['Brick Common']\n", - "\n", - "The variable PavedDrive has mismatching unique values:\n", - "[] | ['Partial Pavement']\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "['Clay or Tile'] | ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", - "\n", - "The variable RoofStyle has mismatching unique values:\n", - "[] | ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", - "\n", - "The variable SaleCondition has mismatching unique values:\n", - "[] | ['Adjoining Land Purchase']\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "['Contract 15% Down payment regular terms'] | []\n", - "\n", - "The variable Utilities has mismatching unique values:\n", - "[] | ['Electricity and Gas Only']\n", - "\n", - "CPU times: user 2min 4s, sys: 22.2 s, total: 2min 26s\n", - "Wall time: 7.31 s\n" - ] - } - ], - "source": [ - "%time SD.compile(full_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "57252bf7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_v2.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + }, + { + "cell_type": "markdown", + "id": "ab1ee858", + "metadata": {}, + "source": [ + "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", + "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", + "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4ad7baff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_v1.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_v1.html', \n", + " title_story=\"Data validation V1\", \n", + " title_description=\"\"\"House price Data validation V1\"\"\" # Optional: add a subtitle to describe report\n", + " )" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_v2.html', \n", - " title_story=\"Data validation V2\", \n", - " title_description=\"\"\"House price Data validation V2\"\"\" # Optional: add a subtitle to describe report\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "8358b37b", - "metadata": {}, - "source": [ - "## Second Analysis of results of the data validation" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "89fd6a9e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_indicator(fig_value=SD.auc, height=300, width=500, title=\"Datadrift classifier AUC\")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "0b5fceb0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('2ndFlrSF')" - ] - }, - { - "cell_type": "markdown", - "id": "b61737f6", - "metadata": {}, - "source": [ - "Let's assume that the datascientist is ok with these distribution gaps. \n" - ] - }, - { - "cell_type": "markdown", - "id": "bb54de9b", - "metadata": {}, - "source": [ - "Let's look at the impact on the deployed model. To do this, let's first build the model." - ] - }, - { - "cell_type": "markdown", - "id": "243bc506", - "metadata": {}, - "source": [ - "## Building Supervized Model\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d46d007c", - "metadata": {}, - "outputs": [], - "source": [ - "from category_encoders import OrdinalEncoder\n", - "\n", - "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", - "\n", - "encoder = OrdinalEncoder(\n", - " cols=categorical_features,\n", - " handle_unknown='ignore',\n", - " return_df=True).fit(X_df_learning)\n", - "\n", - "X_df_learning_encoded=encoder.transform(X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "7f997b95", - "metadata": {}, - "outputs": [], - "source": [ - "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "00a77748", - "metadata": {}, - "outputs": [], - "source": [ - "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" - ] - }, - { - "cell_type": "markdown", - "id": "d814b7c7", - "metadata": {}, - "source": [ - "## Third Analysis of results of the data validation" - ] - }, - { - "cell_type": "markdown", - "id": "2f311998", - "metadata": {}, - "source": [ - "Let's add model to be deployed to the SmartDrift to put into perspective differences in dataset distributions with importance of the features on model.
\n", - "To get the predicted probability distribution, we also need to add encoding used" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "816f8638", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_production,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "d898047a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The variable BldgType has mismatching unique values:\n", - "[] | ['Two-family Conversion; originally built as one-family dwelling']\n", - "\n", - "The variable BsmtCond has mismatching unique values:\n", - "[] | ['Poor -Severe cracking, settling, or wetness']\n", - "\n", - "The variable CentralAir has mismatching unique values:\n", - "[] | ['No']\n", - "\n", - "The variable Condition1 has mismatching unique values:\n", - "[\"Within 200' of East-West Railroad\"] | ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", - "\n", - "The variable Condition2 has mismatching unique values:\n", - "['Near positive off-site feature--park, greenbelt, etc.'] | ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "[] | ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "\n", - "The variable ExterCond has mismatching unique values:\n", - "[] | ['Fair', 'Poor', 'Excellent']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "[] | ['Major Deductions 2', 'Severely Damaged']\n", - "\n", - "The variable GarageCond has mismatching unique values:\n", - "[] | ['Poor', 'Excellent']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Excellent', 'Poor']\n", - "\n", - "The variable Heating has mismatching unique values:\n", - "[] | ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", - "\n", - "The variable HouseStyle has mismatching unique values:\n", - "[] | ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", - "\n", - "The variable LandSlope has mismatching unique values:\n", - "[] | ['Severe Slope']\n", - "\n", - "The variable MasVnrType has mismatching unique values:\n", - "[] | ['Brick Common']\n", - "\n", - "The variable PavedDrive has mismatching unique values:\n", - "[] | ['Partial Pavement']\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "['Clay or Tile'] | ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", - "\n", - "The variable RoofStyle has mismatching unique values:\n", - "[] | ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", - "\n", - "The variable SaleCondition has mismatching unique values:\n", - "[] | ['Adjoining Land Purchase']\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "['Contract 15% Down payment regular terms'] | []\n", - "\n", - "The variable Utilities has mismatching unique values:\n", - "[] | ['Electricity and Gas Only']\n", - "\n", - "CPU times: user 2min 8s, sys: 22.5 s, total: 2min 31s\n", - "Wall time: 7.43 s\n" - ] - } - ], - "source": [ - "%time SD.compile(full_validation=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "2112f374", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_v3.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + }, + { + "cell_type": "markdown", + "id": "3ddfed51", + "metadata": {}, + "source": [ + "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", + "However, to illustrate functionalities, we will detail results with notebook mode analysis." + ] + }, + { + "cell_type": "markdown", + "id": "f98cd44a", + "metadata": {}, + "source": [ + "## First Analysis of results of the data validation" + ] + }, + { + "cell_type": "markdown", + "id": "be6daded", + "metadata": {}, + "source": [ + "Data validation methodology is based on the ability of a model to discriminate whether an individual belongs to one of the two datasets.\n", + "For this purpose a target 0 is assigned to the baseline dataset and a target 1 to the current dataset.\n", + "Then a classification model (catboost) is learned to predict this target.\n", + "The level of capacity of the data drift classifier to detect if an individual belongs to one of the 2 datasets represents the level of difference between the 2 datasets" + ] + }, + { + "cell_type": "markdown", + "id": "5baf3c29", + "metadata": {}, + "source": [ + "### Detection data drift performance" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8953e093", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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YcN3DHEnPZvEX1gFwlkA5tb/Ar19OtHrYueWBl/ln4zY+e+dpep1e9WFQlW3xvIx5ZDhDrkjWqiMu99r0itxTGwIi6LWh1ETvqa3LXf2oj3j0dW2+3PLjaRGyO2++godGXGNF8J+N27nlgZfqJOgqqOmSmx7XApuU21W/kU15uZEzLroDby/PCkF31EL/35w3NIE6VYLuDAtdTYOoHevUfK+yuvWXZbMUW0G33KNc1SpAcfGKf/jm++Xag5Oy5m1jIpTXRFnsK9Zs1FziSujefvHBikBGZwn61bc/i5orr4v73BLlrqZc6nL9uuIfHnjmrRMmUeNajW/Lde+TE7VAS7Vbn3pYre5SQXYq2E4ffyBBcSdELTfUkoAIei1BNcXbaiPoymIZ9uAr2o+/WgqkXJTqUmtq1dra6jbiUBG96kfM1kJ/8uUP+fZ/K1kw7SU6tY+0Qm55aBg+5GIeu/d6q8/Ukq6hD76sBX/p18s7MofuLEGf9+NvPPPaxzz90C3cOKh/rYeRo3Poql9i+9+uLU378oOxVuWqYDnlvcjJza9ioVdXQcta7JeeGKFZ+jVdStTvGj1Bu0fdqy5nCfq4CdP54rulDBt8kbbaoDaXvYJ+zxMTtfiCpHNiCQ+tuhRSxW3M/2mF9sCnPDmWlRqWMT/8uot57B7rMarqqyz/8weP0qafFn76SsUURcWyNTcDX7w/Vpat1aZz5Z5qCYigy8CokcCJBF1FS780+TNtYxm1Vnnu+2MqIonVUi0VeKUCulRgl+VSwT/K1a1c8raCPn7KbGZ8+TPvvfawtqxNf1l27FICpSKzLdtvKgtULY9TVqKtoFssH9sod2XtKwv4eFHuzhJ0y5yrCrBTgXa1vbbtUlujPqtFuX825Zkqa6xrE+Wu1lBnH8tl4cxXKyLilRtdLf1SS8DUpbfQVT/G9+pWZRXBc69/wtffL9fiHVTcg1oDrmITbKPnlbv+8Zfe5/qrzufZUUOdKuiWrWINGHj/9Ue0aRf9pcaTso4vOb9Pxdv2CLriesF1jxDg58vSbyZrXp/qLuVhUp6mD994lL5nmTaBUfPgVw5/SkszbdKTVoF4irsKJlSb06gpDDXXr7/0G8u88tQdFXla7lEPAz8s/kNbtqlfmVDb8ST3NQ0CIuhNo5/tamVNW7/m5hWyY/d+bT25utSSHrXkRu+iVlagOvxE/UD2i++hWYoqaO7HX1dra4jVUjNbQf9t9UbufnyClo8SDrUGXe08d8NAk2WrgpGUQKqgOyX4qn7Kfanm0tV692N5BVYWerXr0HNy+X7RqhOuQ3eWoKvNT5TrW8UOqAA8S4DZLddeqLXveJeaA1fL4fTr0NXmMpu27tbSTp/0hJa8pjl0FUD4+rtztPXaSujUGmxlRR84dJRundrxxz//WQl63yvv08T8rNiutG4Zjrubm7aMTa1n7xgdyRfvj9Hm85XbWFmwfc44jTatm2nCvnvfIX5d+Q+eHh7MfvfZCg+Lsyx01U71kDh2wnTN0lU7qXXr3E7bilgJ6ep//qN1y2baJjKWyx5Btyx1vOnqATz14M01do+y0J9+9SMuTDqLieMqD+D57OtfeOXtWZrVrta7t4tsSXFJCWs37dDWwqv+V1NGKq7B9rLe+rWFdlaCr4+3FnOiPGBqLKn9Eyz7D9j1pZZELk1ABN2lu9exxh3vcJZWzcPp3iVa27dcRTlb3I76ElVA3IT35mq7nKk5WfUjdt2V55F4ThwDhlR/OpgSoS+/W8r+tCNakJ1+pzhljau9rpWIH07Ponl4CBcm9+beYQNRc6xq/tZ2i1rlVn7nk2/4eemfWuR7m1bNuH5g/xPuFOcsQVc81IPKuzPms33XPtRDhrpqu1OcsgKnzf2RtRu3a3t5q7lwtSzwmsuSKvaMr0nQlVWoWCqrbt/+w9pDgAoSfGjEtVoEtxJIvYWuNrJZ8ecmtu3cp7mF1YNE6xbhXHxeH24Y1J8g8/I2tTZbWYsqMO7Q0Uxthz+1+Y7K+9brLraKynemoCtuKvJfjZE/127RNrtRgqd2C1TR+CoyP75XV7sFXcViKOtc8axp+aUlc9WPak16YWExv35lvcxP9ZkS9nX/bic9I0fjGNW6GUlnxzJsyMXHXRKo1tvPmb9YO5wl7VA6KuBU7b2glt1d2v9s7TCkmg6HcezbLqldgYAIuiv0orRBCAgBISAEmjwBEfQmPwQEgBAQAkJACLgCARF0V+hFaYMQEAJCQAg0eQIi6E1+CAgAISAEhIAQcAUCIuiu0IvSBiEgBISAEGjyBETQm/wQEABCQAgIASHgCgRE0F2hF6UNQkAICAEh0OQJiKA3+SEgAISAEBACQsAVCIigu0IvShuEgBAQAkKgyRMQQW/yQ0AACAEhIASEgCsQEEF3hV6UNggBISAEhECTJyCC3uSHgAAQAkJACAgBVyAggu4KvShtcIjA4t/+Yfy7s7UDX87q2UU7y7t5REiVPC1nstt+8NYLD9L/3DMcqoMkhtr2g2KlTvp7cfJMNm1J0U6Ru3zAOTxy93W4uRkEpRBosgRE0Jts10vDFQF1ItyVw57ilafu5JyzuvPSpJnaSWMfvzm6CiB1GldJaWnF+/9s2MaDz77N8nlv4evjJUAdIFCXflDFqPPs1ZG86ohTdXTu7Y+M565brmDw5ckO1EKSCoHGTUAEvXH3n9TeQQIffPYdq/7+l2kTTWeLpx3O0I52rc3xps+89rGW5sXHb3ewFpK8rv1w9uX38s5LD2nng6vrudc/0Y5SffKBmwSmEGiyBETQm2zXS8MVgdEvvEd4WDCP33dDBZC+V97H+Gfupl98jxohqfOwEwc9yNRXR1WIihC1n0Bd++HdGQs4eDidJx+4mSPpmYx45HWeHTWMc/vU3Gf2105SCoHGQUAEvXH0k9Syngjc/9RkunVuy323Dqoo4aIbHuPhu4ZwUXLvGkv99n8rmTJtPj99Ph6DQeZtHe2euvbDpq0pPP7i++zed1Ar+sZB/Xn6oVscrYakFwKNmoAIeqPuPqm8owTqahlayrtt1GucFdeVe4dd5WgVJH0dPSXKO9J/yCiGD7mYYUMuJiPrGI+MnULi2bHcPfRK4SkEmiwBEfQm2/XScEVAzd2u/mdzRRDcwSMZ9B98/Dn0tEPpXHjDo/z0+etEtowQkE4gUJd+2Lv/EJfc9Dh//vieFuGurlnfLOLHX1fz2TtPO6E2koUQaJwERNAbZ79JrZ1EYN+Bwwy89RneGHMPfXp146XJn3HwcEaFwH+1cBktmoVZzc1O/XSB9hAwfZIpkE4uxwmcqB9Wr93Mzt0HNNd6aVkZ510zUrPO1b+s7GOMGjOFLh3b8OyooY5XRnIQAo2UgAh6I+04qbbzCCz67W/GT5nNkYzsKuvQ73zsDbp3ac9DI66pKPCSm0Zz1y1XMvDifs6rhOTE8frhw1kLWbZqfYUFvuG/nbw2ZTbbdqXi7eVJQu/TeXrkLQQF+AlJIdBkCYigN9mul4YLASEgBISAKxEQQXel3pS2CAEhIASEQJMlIILeZLteGi4EhIAQEAKuREAE3ZV6U9pyygjk5uayfv16EhIS6q8OpbuhdA+UZUHxdijPg5Ldpn+eHaFoZ+XfXrFQuL6yLpU71oJPLOSvB69o87+OULjT9No7Ggz+4NMZPELAq53pvXq6Vq5cSWxsLAEBAfVUgmQrBJoOARH0ptPX0tJ6JKAEfdCgQcybN88xcSrPgpL1ULwOjCWQ9z1oQr4bynUNUIJdoBNsnyTIW1Z5Q20E3XK3fxLk6NJ6mwXf8rkqV4m6+hd6GeAJ/nEQnOQQUacxc6gWklgIuA4BEXTX6UtpySkmMGbMGPr27ctFF11Uu5oYlaW9HoqWQokScCB/fmVazwQoXFn5t3sMlGwz/e0WDCXZlZ/5JELecvsE3TcRcnVpCYKyHFNe3jFQYC5T/R2YCFm6e8MGmuodEAchyXUS+Z9//pnff/+dcePG1Y6X3CUEhMBxCYigywARAk4icEKB0gR8BRQthuKlUJZiLcqesVCks7q9kqBAZzl7xJoeACxXma7ithZ7XSx0W4tcn69vLOTpygxMgixdnfxiIdfGta/EPTQZQvtDSD+T676aq84PQE7qJ8lGCLgqARF0V+1ZaddJJ6BcyDfffDPz5+us7LINUDgPiuZD6Tpwj4eSNZV1M+isbvWu3q2uBF4/D+6dCAU66/h4gu4dDwW6cvRz6H4JkKuz/I8n6Moiz9aVqQReL+D6+vrEQK7Omg9KgKyVJuu9+UBoPggCela0/YILLnB8iuKk97IUKAQaLgER9IbbN1KzRkhAC/LqlkqA109QPB/cYqBYJ6weCVCsd6MnQJHub3V/qcWtHgUlqZUUvKux2C0Wva2F7mszp24l6EmQW8Ocua1gB9jMr3tEQtF+U52UgOfbuOMzdeJvEXRLC5Q1X7AHWgwk1/8S1h+IrN8gwkY4fqTKQsARAiLojtCTtELAQqBkPpQsAPV/uT+Um0VPs7qDwGiekzZEQZlOpNU8eYFO0D3irR8ArKzweCjUPxzoXPSeXaBwa2V/1EXQPWOg0CzMtoLuFw+5ujL19QlMgGxd3QPMFrmlFp468fcIgiIzA/W5dyQU50HLgZrA00IOuZEvkxBwlIAIuqMEJX3TJVC+DoomQ3kalPxcycEtEUp0lqqbrZs9EsrMgu8WBcU6gVfz5oU661lZ7JZAuCpz7PFkpK0hNx/yCiD3GJWvS7uRl7VZ+1v9U678AF9QO6P6h3QjwLAZf/PfAd6YXvtCWKsEOKYTaf0curKwj+nmy4Ns5tPddQKuBLtA91ATFA+ZugeD4ETI0DGKuAi8WkGHhyAorumOKWm5EHCAgAi6A/AkaVMkoJaVzYDCiWDcYwYQBeU6UXbrAiU6a9ndxs1u63Yv01nwNqJtdO/Nnl1/smUnbN7lw+ZthWxWr3dCemb98A8Phm7RcFp76Nbem25ti+jWDtq0740h98/KQvUBcW5BUKyzwG3d7cqaV/Pplku56/N07nqvSCg0PwD4RkP0Q9B2OHhWH1BXPy2XXIVA4yYggt64+09qf7IIKGu8ZDKUTjeXGAtlOmtVBbeV6QTKGFnpdldWuH4u3FbQlQVfvIaDh2HVP378uzWfzdsxifhONwoK9JFnJ6vBVcvx9XGjW9tyTeyVwHdv78tZnQuIag74x0O2zgK3FXT93Luy3vN11rufTTBdQCxkm9kqUVdWe7BY7aeu56XkxkJABL2x9JTU89QQKJsOZTNMm7yU6oPXEqBE/3cilOrd7DZudyX4lmA38zy6EvBflsPSVS1ZuvIguywG/6lpqd2ldoyE5N4tSe52kORYiGqm9p7Rud99bQTb1t0emgjpOnbBCZChYxuSAAZP6DwSWslcu90dJQldnoAIust3sTTQLgLl06FkrM6tDijXODq3sv5vYzAYdRu92M6buydyYO9yFikB/x2W/e7Orj36CDO7atkgE3WMdCe5ZxnJPTEJfCeb+XL/WMjReTeUu77UzNXdxnVv+3fQ6dDpUYge1iDbLpUSAqeSgAj6qaQvZTc8AkrIy9TOZbvBmADlOkuRJCjTBawZbK3yeCjVuZ3Lg9i+I4fPv4avvvNi0+bihtfek1CjHh29GJxQzPVJ0LmdTbR7sE2wnLLWj+p3okuCIzrmoQlwdCX4RUP3sSLsJ6H/pIjGQ0AEvfH0ldS0PgkYp4NxLpT9VFmK0SbYTbnKS3XBb7Z/uyVobvlN/8GXC+Crb335b0tBfda60eV9egc/Bp+Tzw1K3CMB5U7Xu9eVq94SHKdap4Ll9NHytn83vwja3gDtxWJvdINBKux0AiLoTkcqGTYuArOg/BmTRa6u8lgw6tzBVaz0BCjTW+2m4LgNm+DL+fDVAje2bGsYQWwNvR9Oa+vG4IRyBidA93ZqP3hdMJyqvMUatzTE9u/gWMgw95V/NPSaCFEDG3qzpX5CoN4IiKDXG1sYD5EAACAASURBVFrJuGETWAqMA6MRjDqXrjEOVES75bK10omDMtPn6zYoEY/iq29S2bajYbe2odeuWxu49vw2DD5jHz2UuKvLVuBtrfPAWMjSPXw1SwKjAXqMgRbJDb3JUj8h4HQCIuhORyoZNmwCWcBIYEZlNcuV71e/s5utlR4L5SbhUPo//9tWvPJ6Gn/+3bBb2lhrF98Znrw5mqu678ZgMLciJB7S9RvT6KxzdYuPzVK4DsPhjIngJevYG+s4kHrXnYAIet2ZSYpGSqC0fBIl5ePw8eiOAZ3bXLnVjfq/bax04igtWsdns+G1CbBFt2dMI0XRKKrdLQqeGAQ3nqsObLOxxm2t8/AEOKLrwzA1N/8vxI2F0x5qFO2VSgoBRwmIoDtKUNI3eALlbKC4dBhGLK70YHw91CHeuiVoau4cnfvWPJdeUAAffAQTJnmyb19Jg2+rK1awbXNPHr2shBH9wdcL0M+dqwarvzN1faf2jS81QLF5GWFYHPSbAWGVJ725IidpkxAQQZcx4LIEjGRRVDqOorJJeHvEYtQFu3m5J+Ju0C2PUnPjurnzrMwevPXWRt5+F44edVlEjaphzYLgoUvh/mtPJ7hkU2XdbQU9PBHSdH2rrPuj6+GMsXD6Q+KGb1S9LpWtCwER9LrQknsbDYGS8hUUlt5CudEUve7h1gU3g7Wv3FdtR2ozd56Wtp43JsAHH0JubqNpbpOqaKAv3N0fHrkMWrSzsc4DYiBDtwWvIuMfA9nm9wKiIWkmtOrXpJhJY5sGARH0ptHPTaqVucVjySuehK9nAEadYHu5JwGVEe0ebnF4upnc8KWl8NprQbz4Yg6FhU0KV6NtrHK/j7kuiIcH5ODpYW6Gioy3LGVTbzVPggO6VQx+KnguF2JHQe8xjbbtUnEhUB0BEXQZFy5DoLR8HVmFt6L+V5eXexzuZsG2NNLbPdJK5H084lnx2xpGjIBtNoZdYwET6A/avwAvgvyLtddB6m8/0/uhasfacsjMgWP5cCwPcnJNr3PyPDiWV1rxfmNps76eXVvDRyMg4ex4OKSLhFeR77m61QsqUZDZ/a5eR8TBJfMgMLoxNlvqLASqEBBBl0HhEgRyi6dzrHgKbmzDqAt28/NKxGisnE/1dIvDYDAJflYWPPGYHzNn5GvL0Rrq1SwcOrTzo0NUPu2joEP7KDq0SqVDG4iKjMTdcra6aoB+e3j9/jalutbp39fdX0ZrUlMPsOsA7DoSxa6UVHbth10H/UhJzeeIWvHXQC+1vO2O/v68OjCPUH9zJZV46631ZomwXze37hkEgTHQ8z44bXgDbZlUSwjUnoAIeu1ZyZ0NkEC5MYv0glEoQVeXv1c/ystXVNTUQHAV17u3RyLTPl7Os09BenrDalT7dt4MSCgi+RzofloMnVpvw98PsD2CtQZRdkTQa0xrPvo0Nx92ZsSwfuM2lq6FJeu92H2gYe1P3ywQ3rwebh6YCAd04m1xtVsi31W3N0uAA+albkrQEyeCt6xbb1jfCKlNXQiIoNeFltzboAiUlO8mo+BJ8kvmWNUr0Due0rJK16ve9Z6yC+6+3Z3VqxrGSWcd2kFyvzCSz87ggkRo2TIKinX7xdck3CdT0Guy+t0j2bt3P8s2wNJNYSz9O4NdaQ1jiJzXzZ33byqjcwudta4i3S1Xs3g4oHPPq/c7XQfnvgpB4oJvGL0otagrARH0uhKT+xsEgdzi+aTl3oqy0AO9EynSnUXu7haMr3sAZcbK+VPlen/lheW8+uKprX6HaLggSc33tuSCfgdp2dxcHyuBDgKjeY28m+4cdY9YKDKLknpdbH7tHgPF5gAAYxCUm9OeyOWuPwjFNxZyzfmp13nm136xcMz82icG8s3lqG1Y8/W765nakXoElmwMZdk/mSzZwCkX+JcGwVPDbFztvuqs9lwo0h132yoJ9i0zWehXzYM2snXsqf2mSOn2EBBBt4eapDmlBDILP+Jw3h1WdQjwiqW4rNIC83avXKZ26CDcNRRWrzo11Q4IgJsGw30jAujRRbcWzhgJ5WZRdI+FEnP91VnqxWbr0cPmdZHlfZ2g64XeMxYKzfmcSNB9dMJdo6DHwzFzmYEJkG12UfvHQ7b5fXW+uUX0bfZb37jbnykL8vhsCeSdotUD/TrCVyOghQoOVJda2papi4AMj4VDOutd3TPgQ4gdcWoGjJQqBOwkIIJuJzhJdmoI7M0eTkbBDEJ94ynUnT3uZgjG081oFRAX6JXEol+Wcc+tkJV58uvbvRvcczsMHRJIYOAxUwWUWJeYhdA9EYrN87yeSVBkXl7lkQiF5ve9kqDQ/L56XWB+7a177RUPBeY89YLuHgkl5gcGS1Ccmg8vNr+nF3Q/nXAHJEGOuZzAJMgyvw7SvQ5MhExzHUOSIMN8T3AiZJjfD6o86/xYUSAzfj7G1B/hv70nvy8iAuCL2+G8RLMlbqmCvzqu1cZabx4PqWugx3C4fNrJr6yUKATsJCCCbic4SXZyCZQZs0jJHERusTolDdwNwQR4t6C4rNLSUla5EdPmMWpd+fhx8PEU04EqJ+vy9ISrr1RCHkFSgnmLOfM56aaKJ0Cx2cp111nfegtdvba41pVAV7yOh0KzcOsFXb3ONwuqXtC9dNa6RdCViOdbXOg6C91fJ+J6QdcLvXK/693yFqtc/35gPGSZ6xiUAJnmturOPV+2PYopX6cy7w8oPYmhDCoSfvT58OKl4OFuHhEqyl1vrQfHQM6hSnd822S4Zh74SLDcyfoOSTn2ExBBt5+dpDxJBJSYb02/AoyHrQTcyz0KT/ccyi3zzcqb6pXI7j3LuecW2Kg7BbW+q+rvD3fcGsEjDxwlSm1A55YIJWZL1dAFysy71BmCoVQ3d3vCZWbBUG6+370LFG8Fj3bgnQB4gkc0uEeBVydTE43u4NMD3E4gQGVZULARjOYKFOwwBeMV7QZjCeSshKI94BUDBeaHJkMQlJnn52uxDA51f6n5fr8YyDXnE5oIR5eTehQm/BjBh98fPanu+DPbwLzboM1pibBPFwmv9oBX/3J0QYkhMeDbHK7/TkS9vr9Ekr/DBETQHUYoGdQngfySdWw+ch5K1JWAuxmsBdzXowvotnRd9AM89YA7Odknx/QLC/PggXtKefBeCAuPh4ppgGAo0wk3uvlyYsDiWbBywevn0dXr3eAVB17Jpv+VeHvG1SfuqnnnrYPC3aD+z1wKuevAOxpyzFa+ssgtc+k6FztqXt1yj+3RpnqhD4onY+8aJn8P7/zsQUaO/kmh/poa7OfOrOvLuOw0XRm228Z6BYF7EBxLNQXLDV0CLU8y//pDIDm7IAERdBfsVFdpUm7xOnZkXElx2b6KJvl6dsFoTLOyyoN8+pGXv4JXnoHZJ2nKMzISHhkJd97eGn/fA5XI1dnqluh6QwKUWtzrSVBidovr5871r72uAkOIScC9zwO3dg2zKwv3QOYSk8CXZsHhBaZ66ufPQxIh3Wz9hiXBUXPbles93czE2zpSPq+8Fe9/l8aE7+DASYp5eKAfTLgCPFsnwD7d8atKzH1bQbpu///ANnDdt9BKRL1hDkyplQi6jIEGSUCJ+fpD5+Ht3oJyo/WhKv5ecZSUVfrTj+XA/dcHsGFt/Z+mEhMDox8LZeiNmaj5cpObOxbKLVHSiVBmFjK3OCix1DMKys2uXEMUlKaCcr97XQ0qCE6JuFsjXf+sLPiMpaYguYPfmKYU9EviVOR7oTkQT+21nm1mpRd3taub+QjUklKYsTKE8V9nsf1g/Q/PhJggFl6XQ4ivrqyIWDioi3xXAu/TCnIPwW1LRNTrv1ukBDsIiKDbAU2S1C8Bi5iXlpv2Gg337Ud+SeXub5ox6N2PgtIVHD0Md1wDu3fUb51CQw288Lw399xdiJubEvFEKLfMv8ZBxQOGcrXrzlq3stiVq/0QeA0Ej2vB6/L6rfSpyv3IQjjwFRyaD14tKufO9Ra5cmUbDVBinpYIjIUss4BGJMGhZZSXw5TFPjw3t5CsvPptTNdm8Ovt0EotbWtlY62rotV7e8wWvAqQE1Gv3w6R3O0iIIJuFzZJVF8EjhWvY2fG42QX/c+qiOpEPTO1N8Ou/pNDOo+3s+ulIqNvuw3Gj4ew0CQwWk7ushFuKys9AcrMP/6W4DiPq8DrTvC41NlVbNj5Hf4B9nwAhxaAORhOq3BYIhyxLG/THYGq9lcv0wl9sySObF/G43Ng+vL6XbHQJhh+fSaeTpYlgBayejG3vBd9AVwyHlqL+71hD8CmVTsR9KbV3w26tUrM/zpwHsoyD/PtR26xtVWuF/XN6+H+GyDXHERdHw3rGavORY+lT2+d61VZ3JYjWY1JUG4ReJ2VXuFSbwdeo8HjRqCJL3sqyYLUz2H7eChQ0fORUGB2w1djnWv9aRNMt+ZQT+6YuIENlSEVTu/2cD/4ZRj0amXOujoxjzRb68pSv2OJiLrTe0EytJeACLq95CSdUwnoxdyScU2ivvTXFTx6KxTV085j4eEejH2hlNtGgLLQfdVObJhF3ZgARkvwVA3udbckcB8N7k3MGq/tiDj0A2wdbwqUU6KdZxZ2W+s8PAGOmFkHx8KR9Zob/v2l8PQ8dzJz62clg78XfHM9XHiuzs1uaZtFzC1/i6jXttflvpNAQAT9JEBu6EUs/u0fxr87m8PpWZzVswsvPTGC5hE1W5QLfl7J+zO/5cDBo7RuGcFrz9xNj67t7W5mdtE61uy/Cj9PH/JLrA8ltxX1X+bDq49AWT38lqu58aG3wriXoXl4LEajScQ93OLw1J+rXh4DWPZO11nphsfB/W4wNNLgNrt70M6E2etg7xzY8popA/PcufZaCbg5SM5krasNX8zMQ2JJ372eJ76Bj1fUjxvew93AzCuNXN9d1zZbMVcfhcdAYSHctsBhS70u38OUvWm8MPFTNmzeSXhoMKPuHMzF58Xb2RGSzFUIiKC7Sk/a2Y7UtCNcOewpXnnqTs45qzsvTZrJ0YxsPn5zdLU5Lv19Hc+9/gnjHruVnt06knY4ndDgQCJbRthVg5LyLH7Z2V5zs6urRUACOUW65UNqutXsfv98Knw4vn5+wJs1h8/mdKRP351aPdwMUXi4KX++yafv45GAAUu94qDcEr0eDMaB4D4WECG3axDk7YZ/x8K+eVBinkPRC3pYAhw0s1cbv6iAujzTioEVh7tx7aubOWTeWdeu8mtIZADeugju7w1UJ+ZtEmCXLlDu6RTwtW9qpS7fw9KyMq4a/jT9+53BPcMGsmnLLu59ciKzpjxLTIcoZyKQvBoZARH0RtZhzq7uB599x6q//2XaxCe0rNMOZzBgyMMs/vJNWjYLq1LcNSOe4+ZrLmDQJec6XBUl5qtTLyejwFrAqxP1zyZE8um7utO9HC69MoPkAfDORxARHoyPZ2WEupd7ElAZBOfroY9ejwVDHBhEyJ3WFUrYN42FDLWJjeVUObXbnAEs55g3S4Q0czCdEvdSg/YAetN0+N9mp9XEKqNnL23D8z1tJu71Ym65u10CjFhol6jX5Xu4I2U/A297hr9+eh8fby+t9FFj3qFV83BG33dD/UCQXBsFARH0RtFN9VfJ0S+8R3hYMI/rfgj6Xnkf45+5m37xPawKLi4uodeFdzDyjmuZ9c0ijEYjFyX35pG7r8Pby7Iou/Z1/XlnHOXGQtwNuRSWWou1XtS/+AA+Mntla5/7ie/08IBHnwnioYeNYDCZeN4efXAzrK5I7O0eg9HsXvdyT8TdoMRECb0Scjli88SU7bjj0FLYOBYOL4NwnYDb7rseFg8HTfvGGz0CGf9jCc98W0ip/pQ5O4qvLsk7F8B9Z5o/qU7MgyLBLQCCWsGDS+pcal2+h9t2pXL17c/y988fVHzvlKDn5Obz8YTqPWt1rpAkaJQERNAbZbc5r9L3PzWZbp3bct+tgyoyveiGx3j4riGaWOuvfQcOc/GNozmjR2cmjrufktIy7n3iTfr3O5P7b6tMX5vard4/nN1ZM7RbPd2CCfZpQZ7lTG9zBkrUF3y1kldG1ibHut3TOsrA1JlGesSBn0cfjFSKuL9XPOVGk1B4uFUew2qgHT4eSsiH160wuds+Arumw99jIW+PKb1/DGSb59HVNrtp5kNg1Gfq74Nr+GM3DJlhYF+mc0/kUe73uQNh8IWJsFO3/7sqO0zV6xAUmNfU9xkON9Vty8K6fA/V9+6KoU9yyfl9uHfYVWzcksIdj75O105tmTXlGftYSyqXICCC7hLdaH8j6mIZHEnPIvmakbz94oOc3+8MrdCvv1/O3G9/5Yv3ldDV7tqZ+RF/HbA+z1yJephvNDmWk8WAtSvghbucHwCXfCG8+9HZ+AT+UVHhQK9ESs0bxRiwdr17eyjLvBeebqqN9s2R1o6M3FWFQHEWrBsLR9fBAcvxsjZu+BaJkFopstmBfbjpjdV872QXvIebgZ+vMXJ+W10tI9WDxNZKMbd8dN2HkFD789Tr8j1URSi3+8tvfcaWHXtp37YVHdq1Ji+/kDfH3iuDqAkTEEFvwp2vmq7m7lb/s7kiCO7gkQz6D655Dj3hqvt5YfTtnJ/Qyy5B35czn6W7B9Hcvw95xZspKbdeSN46IIHMwpVs3whP3QzFRc7rIC9vA6PHGbnZ/Dsb6J1IUalJCNzdgvH1aEFZuckCtLje3Qyx+HlOx13Nl8t16gikr4NlwyFjPehc7QTFQLpuZUSryhPUJv8Go783UFzqPGvd3xN+uw56tQDaJsAO6/gP1BaxLbpBymoYuQRiajctU9fvoW1H3DbqNRLPjmX4dRefuj6Skk85ARH0U94Fp7YCyo0+8NZneGPMPfTp1Y2XJn/GwcMZFQL/1cJltGgWxrl9TPPpb7w3l/X/7uStFx+gpKSMezSX+xncO3zgCRuSW7ybhdt6oYLh1BXsrU5Ky6WgxHr+vPxIPHdctYY8J24aExAE78yCpHMTKLIcmKK8uF4xlJhPPlPnqbsb0jCaI9tDfF7Hx+PRE7ZLbjiJBNa/ASsfMxWo1q17t4JM817/6ixzvbi3TGDVipVcNA2OOfHBMNwHVo+Op2OmzuWv6qPm0T0D4JC5Piri/am1EH7i1Q91/R6u/28nrVuEaxi++n4Zc+b/yo+zxuPn630SO0OKamgERNAbWo+cgvos+u1vxk+ZzZGM7Crr0O987A26d2nPQyOu0WqmAuNenDyTn5as0SJs1TzeI3cNwcscFDd+/HgMBgP33HMPAQEBFa0pLstiVerd7M2ea9VC5WoP8mnBsSKTlZVxGJ6+HjIPOw9Ey9YeTJ1bSrT5yPBgn5iKc9XdDMF4uhkrRNzfKxGDcQ+hvvO19edyNUACyv3+w0DwjYIDZgtZiXu5AYrM89hK3I+YxtS/h2DANHcO5jhv84K2gbBmCLTwM/OJsJlHt2DrdR3c9B74VU7V5ObmMnXqVC2odPToyiC2unwP3/7kGy0wtai4RItpeerBm+nYrnUD7Cyp0skkIIJ+Mmk3gbLUj9Wnn37KypUref755+nYsaPW6qW7h7MtfQZtQ5I5kre0ColWgQnsPbiSZ26EtN3OA9WuI7z/VSBtIltViLi7wTRHXm40uQB8PeIoM5rWlQd4DSPCbxIGmSt3XifUR05FWbBsJGw2BVYSatpJTrv81bGsuZXiHhJDamoaAz44xtajzqvM6eHw+7UQ2DkBdtq43lUx7RNh63LoOxxuMwXJ7dy5kzFjxtC3b1+GDh1q9dDrvJpJTk2VgAh6U+35em63EvTXX39ds9Sjz0pj2e5bK0ps5h9HUekuq/lzNVf++ohAtm5w3g4hPc90Z+LMMpS73cs9CjdDjk7Eu+DmVnmueqDXBQR530iAl0Sw1/PQcG72/02HzZ/D7l9M+aq16eqY0wyz21vNaauNaI6lkl0IAz51469U561r69c+iMUX5ODlrmuWKjOsA+ytPOKXW6fxc24rzTJ/7LHHSEhIcC4HyU0IACLoMgzqjcDBgwdJTf+HvW7PcTT/b6tyvNyDCfVtQY7Z1T5lNKz+2XlV6ZMMb37SGW/vQ5RZLHHPLhiNlSIe5NOPkrIVeLi1IypoPt7u4mJ3Xg+cxJwOr4MFAyFnD7TQHX2qhNW3FaRXinuhewuufm87P5o2BHTKdWtX+OQ8c1bK9Z6pW8JmKaHtmey8dCL+UZ1p2bKlU8qVTISALQERdBkT9UpgzqY4cop20yLgdA7lVnVLtglOYt7sZXz4nPOqcekQePRVtHPLQ3z6UFRaucY80Nv67+b+t9My4A3cDLIczXk9cApyUi74Xx+FdR9XFt48Hg7oAtfU36lrKDfC0AUwa5Pz6jl7AFyv1qgrF7vt1SEBUjdBRHt4fq3zCpWchIANARF0GRL1RmDN/rGs2T+uIv9WAX04VryZ4rLK8PXD++DFG90oKnCOG/TCa+Dlt62PXrU9Sz3Yux8FpSsI8x1G2+Dp9dZ+yfgUEFg4HDbOAHXs6T7dA6TtMaiRCYyYupKPNzinjn6ebvw7qJzoQF1+PuYlbDsrHyi5agwMqv2eDc6pneTSVAiIoDeVnj7J7dx/bAXfb7vMSrxVFQK8ovD18CO7aBslRfDSLZC2yzmV63M+jHvfZJnbntJmK+odQj8kwq/2G384p4aSy0khsO4j+E63cVE1Ys6elZqlPugb+Ha7c2oVFwarr1TxGkBzdQpbPmSYDpGpuLyD4OnfoG1P5xQquQgBHQERdBkOTidQVJbF9HW9MBpLCfYJJz3fHH2sK6ltcDITnlrK8m+cU3y3M9x594sOlBgqf51rEvUOodOI8JPgN+eQb6C5rJ8O395qstT36Cx1m1PTioM70//dXazY55wlbfefBm/fkgRbLIf66PhExkJ2Orh5wCtrwV+meRro6Gm01RJBb7Rd13Ar/mvKSP5Om1xRwejgJDIL11pZ6xuWwYdOOkeibWcYPwcCA4MJ9+1CTnHlvKmtqHdvNocIv+saLjwn1KywqFjbEvTfrSlkZedWm6M6KveMHupcdxe+Ns6BL3Snj9kegdo6XtsE5lhONn1nwyYnLWn7vj9cqj/FVFnlbXrBfzqRv3oMXCuudxcefaekaSLopwS76xZ6OG8DM9bHVmlgoFdUhbWefgBevcnkkXT0atbKg0nf+BAQWilctsevKlEvKNlIXMulBHi5ViR7SUkpW3ft498tKdohHUrEd+45QFnZ8WMS1Il5d9x0uaP4G376lKUwayBEnG5tqducmHaoNID46YXszS51uE0hXrDxSojyV+eom61yW9e7KuXl9RAtrneHgUsGFQRE0GUwOJXA+3/F4e5uAGM+mYW6PbbNpbTxT+TRwcvZ74R5y8AQeHE2dO6ktmy1PoJVL+oebsGc1Xo5gV6N/8dze0oqG/7bxaatKWzaksL2Xfu0U+/qejUZQVdgDm6A9xIrN5qxPf7UfPTpzu1b6fMFpBfWlWbV++MjYOV95+Kx7beqH6r5dU8/UFvMvyFR747TlhwsBETQZSw4jcAfqZP4385RFfl1Ck9mf471rnA/vgdLZjpepLePG2NnltO+mymv6o5gtYj62VFrCXQRy3zAdY+QdijdYYBNStAVrV1L4cPzwFbMbY4+/esQJM13I7/Y8VUXT3eHF22fITsnwb861/vwiXB5PZwP7PAIkQwaIwER9MbYaw2wzoWlWUxe3Z6iUtPBK5YryCeKEO9wjuSv5/AemDgUyhz3ajJyElx6RT8yClZUlKVEPcKvC1mFlXPovSNn0yrg+gZIzL4qiaDbx01LtX4uzNSNheqOPo1OYN7/VnK1EzY58nSDDZdA1yCgTSxkpUO6TdS72uN9aooEyDnQrZK0koAIuowGpxD4+r/hZBbuJD1/A0W6deaWzDuEJvHcLStI2VB397BtBS+4AW55wvSuOrGttDzNahtZyxGsvVpOo02wa0Wzi6A7OFz/nA5zb6169KnaVS6wVcVJafcsh/f+dbAsIKG5Oyvu7mdtlVuy9VXBcj2hZUd4UPZDcJy25CCCLmPAYQIpmUv5eK1p70tv92DaBMWxN8d62c7GX+Gr5x0uijYxbkyZ34esklUVmfl5RuHr6VdxYpv6oE/kh0SHuN4689oKerPwEDq1j2TVX9WrUpNzueuH3qqPYLZunXo1J6UVRp7DWW+u5t8Mx13vc/vAEH3Uu6pL10TYvR7yzKfDvbAETq/d2emOf4skB1clIILuqj17Etv10T/J7M6yFnDl+vb18OFQ3nptA5lJN0BuhmOV8vGHZ2dDRGtQW8Yeza8sU3O3+59Oev5KokOG0SfSNS2e6gQ9IiyY7l2i6R4TrR11e3rX9qj3jmZkk3T1Q9VCb9KCroh8NhzWzIB2CXBgExSYhVV9Zj4lbXs2xM2HfAeniFr5wI6LwM8DaBsLx9LhqI3rXYm5EnW5hIADBETQHYAnSeGftFn8lfYRe7OqHomq+LQJ6sOcif+y9PPq10PXheEDE305/dyCiiShvl0os3G3x4TfTp/Ij+qSbaO69/p7nickKMAk4F3aayLeollotW0QQT9B184aASt1e78rt3tAKzhoPswFmHMwlBu+z3R4jDzeM4BXL+4O23TbwOpz7ZoEV46EcwY6XJZk0HQJiKA33b53SstfXB5NZuEegr2jaOEfzb6cyiA1VUD6Pvjgdih3cOo8YRAMfTqYZv5dOJJXGfSmTm2L8FN1WI+/Zzsuj1mHl7vswKXYi6CfYIjnZ8HLcZCxB1rHwqHd1pZ6tDrcZSu3/5LNJzsc+7qoALkt/aGDWpuuv2IS4OAek8XePBqmpThWkKRu0gRE0Jt09zvW+D8PTGfOpspzzlVuUYFxeLgbOZRr2u51xoOQ6mBwUcv2MHoGeHqb6qvc7ZkF1jvPRYdeQO/W4wn1ca2NYxzpIRH0WtBLXQdfjYZN5vPUVRJ1qEpUr4rtWwvLoOe3sL3yTKFaZFz1lv7NYJHlTnl1DgAAHllJREFUGHTlei83wC7dmekqyahpMMC1AjntgiWJ7CIggm4XNkmkCLz/98VsS69+fY8S9r9+2cPnYxxzVyoRf/WrWAJapVhtHavc7R4Go3bIi7qSoqfRJVx+CPUjUwS9lt/TldNhmvnBVG36YjRAWqXbHe8g/itvzVnTtlDgoKfpq/PCuKZ726pCbqlq3EXw0k+1rLjcJgSsCYigy4iwi8AfqdOZtfFWooLicHczknbM+gAWFQj3yXDIczAQ7uK74Pyh6gSrYFoFdOGwjbu9ZUAcQd7RJEe7ZhCcXZ1jTiSCXgd6arAeSoF96yFfFyCn3O6pW7X3XtwEz26sQ57V3NrKG3Ykg586kU1/tVPbJRtg5zp4ZBpcKA+njpFumqlF0Jtmvzvc6ueWmObOLZetsK+eDcrwceRqHuXNqFlFuKvoYPMVGdiHXN2Z6oFe7bi+xzq8Zd68CmoR9DqMPjWf/mwcpJvHtDpQpUU32FEZxFZSDl1/9mZXVlEdMq5662tdYHRH8/t6Ibfc2iIaPpW5dIcgN9HEIuhNtOMdabbFOq8uDyXs5SVljLtiI0UOBrarXTETkuIoKTtCbvH+iuL01vr1p68lwk/mzavrCxH0Oo7yvevguV6gs8orcgiJBP9mLF69jgHVbM9el5KaeUHq8B54ububLPLqLrHS64JU7jUTEEGXoVBnAo//Ek2EXxuyi1LILqoUWktGG76D5VPrnK1Vgm7nwvUvmN7y9jBtVrP/mPVa9+R2r9M78lHHCnLh1CLodnTuwjfg88esE6r911PWVbjiB/4OC9LsyFuX5L1ucFebavIIi4Tm7eFwKswWK90xyk0vtQh60+tzh1q8ct90pq2tjGzvEt7PStjV8rTpwyDfgblzFQj3yrze5Pn9aVVXtVmNl7tRO8WtmV8sw+NqsG4caqHrJBZBt7Mvn4iDvetBBcipSPQDugA5YF/Ls+g89S+KHNhELtoXdvYDN4O5jhYh36Bb9vnCPOgn69Lt7MUmmUwEvUl2u/2NHr8ymW3p1payyq1dSByebkYWz1/Pogn2569SJt8GCTdB6wBTwJ3abU5/qX3hL+w4ieb+4mo/HmkRdDvH4Z518MlD8O9y6wzUUjMVuJayjue3wJgtduZvTja7B1x/jnn52o5qHk7jkmGi7B7nGOWmlVoEvWn1t0Ot3ZO9js83PsDODOvNYyyZGo2w4H5v0lPtDxoKbW3grmlGq0C46JB+Voe+9Il8iIs6TXKoLU0hsQi6A708bSR8P9mUgeUQlc2V476oDDovNrAvXx1qbt91eqgPG2OPc/j6aQkw8h3oLA+u9hFueqlE0Jten9vd4g/+Hs6KvTPw9QimQ1gvjuZtJ7Owcg59zxpY9JLd2WsJb5rgT2Jy7ypbyVoOfcku2s1dZ63Dx0N2gzsRaRH0ExE6zud5WfBIHES0sz5ExZKkaxI/rPiTy1bkO1AI/NADLgnXZREeCZGdYNs6yM2Gi4fB0w4uF3GohpK4MREQQW9MvXUK65pfksXdC6vuGd41oh/FZcdIzVnP/Ichfaf9lYzqCUNeN6WvaSvZq7tN44xWska3NpRF0GtD6Tj3/Dod3rbeCRH9Vq1Av1Ww0oG9kxKDYVkvoEMs+ASCfg7dUrUfMiFQHmAd7M0mkVwEvUl0s+ON/HnHJGZtHFVjRiW72jDn0X0OFXTz+HCax6Zb5aHfSjY6JIkRZ1R/CIxDBbtoYhF0J3TsM8mms8zV/HlhoWmTGd31XUEYVy5xIAIU+PPCdpyVX7mnQ5VaPzARhox0QmMkC1cnIILu6j3spPbd8300Qd6h+HsGsKOaOfTFL8CBtfYXFtYObpwKMeFJHMxdS2Gp9cbZStgHdp1I+1A5M7q2lEXQa0vqOPdtWgrvjYQU68BMbb/3dj0xblpBt5Ww1QHP+xVh8O1p1dRBzaHn58GxLJgvS9ic0Jsun4UIust3seMN3J21jsd+UX5B0+XnGUyn0DiO5u/Q5tDVErWv7wDsjw/iwtEQY9ZqH49gokN6ceDYPxXCflbrYdxwuswl1qU3RdDrQus49745HBbPMN2ghLxjL1BR6XmmLWJnHIDhm+wvS61c29Mb2qjDh9Tytda6OXRLtjPXQowEx9lPuWmkFEFvGv3sUCvfWTOcZXvMP2g2OXUIjePvuZks/ug4LsMTlB4U4cX9syPIKTlgdade2B85Zz1hvtEOtaOpJRZBd1KPq2NV742tIuSW3EtDWhP9w1H25xbbXeAr8e14IibUFAxX3XXpMBgjD7R2A24iCUXQm0hHO9LM55dfzP6cTWQUVN0VTuX74/2Qd9j+Es6+A7pfXrmWfb/NQS/xkcO4paf8mNWVsAh6XYkd5/43hsMvNg+10ZVryN/cB484EBDa3Rc29aih/IhIaN8D3vnRiQ2SrFyRgAi6K/aqE9u0KnU+Ly0fpOUYFdSFZv4t2Zu9lvwS0xx35i5Y/IT9BXoFwA2fgIf5rHOVk2WTGouwj01OIVys8zpDFkGvM7KaEygrfWh70+c6IbckUMeqRq6CzFL7y1x3OsT6mdOrte9q/fnRQ7DHHIg3YR6cJzvH2U/Y9VOKoLt+HzvUwomrhrM4paq7vVtEH7zcPfly4gq2f29/EYnDWnLhbe1Iyao81cqSW6hPFAltbmJgt1ftL6AJpxRBd3Lnf/gE/DILjqRWzTgmnmf+2MNLGw7ZXeijLeH1SxOgpBQ2Vf0+cPkweF48VXYDbgIJRdCbQCc70sQhX4aQX6I7H9oms0X3GijKti8azt0LrvkIvAMg1DeK1oEdSc1eS4Euwv3VASlE+MncuT19KIJuD7XjpFGBcHdUBodqO8h1ioPUXZrIHy2BNn9CoZ17vEd6GUjtfpzvUkAILHdg0buTcUh2DY+ACHrD65MGU6OV++YzZc0DRAV1JLswjf3HtlnV7cgGWPOa/dWNPhfOtVnarnahax8aR3r+Dpr5d2J0gqw7t5ewCLq95I6TbmQypO6AVp1Mke5qNzfddcMWmHPU/nIXd4LzA23St4mB8Fawbyc8+TacL253+wm7dkoRdNfuX4da9/rK4fyyq9LdHuEXpYl7QWk2KZnrWDcV9le/rXutyr14TDh9zz+9ilVuSfxI3++JbXFprfKSm6oSEEGvh1Gx6gcYfVnVjJW13jGO7//+l8v/tN4cqS61uDUMPmkHnBYPXr4mET+kc/FfOQxeFLd7XZg2pXtF0JtSb9exrQPn1Oxu9y4P4qc7cyktts+/6OkPV34MBjdTpbo360dp+TH2Zps28Aj3bcfEi3fXscZyu56ACHo9jYdro+GQeZmm2rLVOxDWm55sy4zQ7B/ILLOv7EBPd9LP9Mczz3pjpYrc1Bawjuw1a1+1JFUjISCC3kg66mRXc0fGBt74fRi7MqtfF5u2CjZNtb9WnS6BOJttslVuYb4mL8A5UUMY0OFe+wuQlIig19Mg+PpdWDTXNHd+uGqA3P27YYoDyzi/aAuDg2uou7Lcx34IXXvWU+Mk28ZMQAS9MfdePdb9k7VjmbZuHP6eak67K97unhzKSyE937QWfcMUOFRNIG5tq9T/ZX/Oiu9FfvFh0nKt5+ZVHtMHZuLvKQdS1JZndfeJoDtC7zhpD+yGQeYlbPrbomIguDmr/llL3/V5dhd+cwjMbGNO3iwSWkVDcSmkbIFj2XDfGHhgrN35S0LXJSCC7rp961DLhs+PY2emzf7V2hrxLkT4tmTqtb9RnGefu92vOZw3sbJ64X5RtA3uxOG87drmNb1bX8XohPkO1V8SIxZ6fQ6CxwbC8gWgNn1Rx53u3WllrXf+D3YU2VeBlt7upA3uB0cPQor1YTBajl3jYL4DByfYVy1J1QgIiKA3gk462VU8VpzFpbOqHpVqqUfeflj7rP216j7Em+irqv+1U5vX3HHmm5zZSoLh7CdsSikWuqMEj5N+xQ8w4eHKTV9sbn3uqBcv7LN/K9it7SHG6zjlr8mEIPFg1WMPN8qsRdAbZbfVb6V/27uQzza8hpsBDuamcNTsZreUemAR7Prc/jokvAFndu+Dl5sne7I3VOw6p3JUB798MTjL/swlZQUBEfR6HgyJIdbL1vxUpHsPKC1lx1+r6Vx1JqnWFZraAu621Wvlfo+M1vLnnmeg/+W1zk9ubBoERNCbRj/XqZVvrhrJ3H8nV6Rp7q82fYmuEPjfxu8n/Z86ZVlxc3AniH/OOm2boC5E+LUkLW8HvVoMYNQ5sizHPrrWqUTQnUHxOHk8Nxz+WARRHU1btO62do/33gF/FdhXhyGBMDfWLOBGA+zfDQd1AXjDHoJnJ9mXuaRyWQIi6C7btfY37OZv4tieUXX+XOVoNMK6UQZK8uzbHe7MWyIIu6DmnTeeP+87zo4Sy8P+3qtMKYLuDIrHyWPZQnjgihpveKEwnOe227cmPczDjfR2x4lR6RYH38k8ej33cKPLXgS90XVZ/VY4pyiLgXPakWc+fMW2tLy98O8L9teh59PKK6mC4DpSaixk29HKUHnlbp9/vbjb7acrFrqz2NU6n57qNHPdpZaVeZo2hPltVyqJ+2qdU5UbN0bB6bpDi6xuUO79DTVvyWx/qZKyMRMQQW/MvVcPdf/zwFJuX3Ae7UO60DygJUbKtHn0IyoSDkj7H+z70r6C3TzhnHfB4G6d/vTmffD18KF1YAce7fuJfZlLqioExEI/CYPi2dtMEe4FRbDBeh1nqRECt0Ohfc4sJofDg5Z5dMv8uZsHHD4Iu7bC3CVwTvJJaKQU0VgIiKA3lp46SfV898+xvPfXuCqlBXgF0zGsK8tf2cFeO7e2bB0XSIcHj9XYkif7TePSzsNPUktdvxgR9JPQx99Mh6eq2SHJXPT5RwNYkp5rV0UGRoYz74xOsGsL5FRjjY8cAw/LenS74LpoIhF0F+1Ye5v10I8DWbJ7QbXJ1fz5fw9DuZ3ra6MGQetLoUtEHEFeIeSVZLEjo3Inui8Gp9AqQE5Ws7fvbNOJoDuL5HHyUcFq/XWbzMTEgtqeVQnw5nWMy4Cxdh6QFmCA7Ei0YNRqrwuvgo9kv4aT0MuNpggR9EbTVSenovf/OIiDubvZdrTqlq/5KbDzdfvr0e1xCOxUNb0S+NYB7Xh1gPw42U+3akoRdGfSPE5e9wyE1D2agNteywog+YD99fi7BZxR3Xr0LrHQpj18Ms/+zCWlyxEQQXe5LrW/QSog7swPKjeU6RDShWCfEPw8fcktyeKfBVtImVVoVwHuXm5c+HFL0gur/3W7tPMwxiTJcrXawH1p8kzy8k/cD4VFJfy8dE21WXbr3I6YDlG1KY6hgy+ia6e2tbq3Sd40ejh8U3kqoZ5BaURrfNekUarcW3ZcU1v4cHePLqZNZAoKITsLduqWxx20L187qiJJGgEBEfRG0Eknq4qr9y/l5m/Oq7G4I19D9jL7ahPQFdo/CC38o2gZGKkFweWVZFe43J9NnMblMTJ/Xhu6fa+8j+wc+/cKr00Z+numvDyS5L5xdU3WdO7/ejo8bp5HVy73gGAoLKqY+04+DMvsnKYa5Qdv2p6Prif79RJIkMC4pjPYjt9SEXQZCRUEpq2bxMu/jaqRyIH3IP8/+4C1uAKaX1J92q7N4ph40XwiA9VB0HKdiIAI+okIneTP9+2BEVfB5ur3bhiTDc/XcBrqiWp6mRcsrHkXZnh+Itw58kTZyOdNhIAIehPp6No08/2/32DRrgV4unmg9nPPLDzCoVzTcjV17XkeSmreE+a4RfR9oj3H2qZUe4+KoP/9dll/Xps+UveIoNeW1Em8r3uI6SS0aq6FYe25Yn31Y/9ENezsDtsidHe1iISwCJMLvrQMLrkK7n/0RNnI502EgAh6E+no2jRzyJfJrN5f1afeIbQLQV7BfHfznxjL7Zuza/sMeDUHNS8f4dcMT3cPbV7+QM4uukT0YtpVS2tTRblHBL1hjoHBybBpLUS1rzLfva0Uuti3YRzuBigaEI+7iprfUc3Ja32T4Fv57jTMQXHyayWCfvKZN9gS+34czf5je6qtX8khSHvVzqq7Qcc3weBWffoH4sfwYB9ZT1tbumKh15bUSbzv9bEwoer+DaoGZUbwPgxldlZnWwgoS73a6/Q4WCpbwNqJ1uWSiaC7XJfa36B2k2pa8Ar5m+Dox/bl7d/Km7iXIqzc9/qc3r1sHhd0GGhf5pJKCDQEAj/Oh1sHVV+TFpHEbD/C9gL7jlNdGAhqLr3G66h9XrOGgE3q4FwCIujO5dloc9ubvZuzPmhPp/Au+Hr4EuITjJvBQGFpASVlRaz7ejOH5tsXquvbHZqNMKHp3jwOL3dv/Dx8tbyPFWcy+eK5nNZMoqgb7eCRisOmdTDiOpO73dcXCguhqMj0PnB5DnxfYh+oN0O8GdWjK3h5g48vqDNblAu+sAC2b4UNKdBONmSyj65rpRJBd63+tLs1K/ctZdCcmpesHfsKCqtf0nzCMgOTIfSqmm/bM1IsjBNClBsaPoGImj1cD+fBxBNvHVBtG+/2hKm+x2n+wiVwrixda/gDpP5rKIJe/4wbRQk/bJ/P8Pk1uAyBrKlQYl+gLqfdGkVuT91Zzjoiaqna7/9v7+6DrKrrOI5/tuvdB1h3kdoQQkFRmZH2wWIiStDRrHF10jGDIZ4sRZ6aSGCkIlwacBZCxoonQUYbWWBMzUI0ZFBQUKzIZKXSJV1cGHmS2AxJce9uc+7ddln23Lvew2/2e7i87z/OyPl9v7/zOnfvZ++ec37n9j1nhBGTRCClQFnfxIpxPq8Hun1OE99qvWMkHclrI9KmrilGrH5SupFTVumYZuq2BHqmHtk092vxn+/T439frbr6t3XsRPubZo/MkRqTP1clZbfCO6Xs5iVfi3sk/rTePS/xGKnzu/bSovLVac6WzREIocC4kdL+5tCub76F7fXEn9yfb5CuPR5szhdkSXV+i8vkF0h9LpKGj5KmcOtaMN3MGkWgZ9bxDLw3ldtma/62tlfpljSHr3fO+7mxbR8NmU6j7jOlSKH/iOEDxmpROUu+puPJtiEVmHCbtNZ/Cdh9jdIFwR66Ju/mkNjQQdKJ5mtYqk9ZM/4nFdJM7hIJ6buiU6dFoHcqd3ibTVx/m9bu8v8wavpIev80Pi+Kl/TSgQ/813Cf/pUK3f3V0ygeXlJmdrYJVM6W5vnfutbYo5ciu4M/peW/eVJuslP0I8dKK/il+Gx7u/ntL4HOuyAucMPqq/XSXv+F2hvfl/5TGRAqWyo86TPu0u79lRfNS1zpHs3TrQNGakxJ8yXwAVswDIFQCDy8UvpNleQ9OMf7Nu1dhV7TuhhM/nEp6Ar8h/OkpNfcXXmV9CyLy4TiPWA8CQLd+ACEpf2kZ+5Q7dG31NTUpGMnWpew/LDhuN6sqdGxhcFmmlUgFfw4+dinvrNZQy7kCt1guowKlcCLW6Trk98p0vO4dCDghGtzpb6XXJa4Je7/r3MLEw9Lv/gSafnKgJUZlkkCBHomHc3T2JevPXK1ttYl+Yb+rtSwLFjx3POzlTMl+YIaBHowV0aFUKCDQO/fEFXNiWA3o1dHpOJkf3IfepX0HN/QQ/iO6PQpEeidTh7Ohpcu6qu6f/vfctO4R2oIuEpcVm8pOv6kLxXZherX/aKW/7Hw67/SlRcOCScKs0IgHYGtW6WpP2gd8XZtYgGY5tfABukv6dQ7aduXI9LgZIFeWibtYPnXgLQZNYxAD/nh/OD4h7pnwUPa8vJrKji3iyaM/qaG33SN76x3vVmr4ePbXpQzY/IIjfn2Nzrcy5y5yRfFaKyRGlZ1WMJ3g6x+UjTFY84PTj+qbrmJW9h4IXBGC9TXS0XJn3V6TUzaHHANpY2fkq5L8iyEuNnHHRde9fhG/W7DNu2u3advlQ9VxbQUP5hn9IE4eydPoIf82HthvvfdQ1pYMVm1dfs1YcZCPTB/mr5Yclm7mXuB/sNZi/R01fyWfzsnElEkkuqTILHpFcvLlJXVGuo5kRx1zU6crzv4yiHtWhrsQehZl0vREcmRP/ppxx9EIT9ETA+BVoFo8l+Mb45Jvw/4dn/i85frlqKiRB/vojtvWdmTX3/t+Bv6xhd2KBqNaMPzf1KXvBwCPQPftwR6iA/qxw0xDb5xYjzAB5b2j8901s8fiv93zt3f8w/0exZr06PpX8GW9bPkH0R6VdK6gFDeOjI+i1j1Luitoq6f0at3dvxBFLAzwxDofIEryqT3jkj72q+MOFpSVcAZeTeUjkk1tumT/6Yw9xerFIvFCPSAxyLMwwj0EB+dd/YdVPmoGfrj08uU3zXxbXn1bzdp/abtWrt0lm+gj/r+vfrsp7spNzdHQ75UrMnfvVld8nI73MuUgf6KpA0dlvDfYJCk65OPbar45B9EAWfAMAQ6T+Ckv3Kd2nSypKUBZ7JE0iQCPaDe2TOMQA/xsf7H7nd067gK7dr8cMufw9dtfEkr1zyjdb++t93MDx+p1+tv1Kpfn146ePio5i9Zo4v79NSCWRNbtq2urtbOnTvbjC0tLVXpk6XJJV701q4MCDVUkv8p/3hBAj2gK8PCKZAi0H8kqfVkWHrTnydpRooh1Tt3+v5cl5SUtBvFN/T07M+krQn0EB+tdL+hn7orXriPmjxXO55doeg5kfg/e2HuG+ilKQI9xEZMDQEE0vu5JtAz9x1DoIf42Hrn0L98w0Q9eN90faE4cRGcd5Gcd7rM7xz6qbvyxj/rNGz8bO34w3JlZ0dDvKdMDQEEOkuAQO8s6c7vQ6B3vnlaHb2L4PYfOqKFFZO0Z+8BjZu+QMvmTW25yv3+FY/plvKh6tO7h7bv+Ju6Feard88iHTj8L825/5H4ufellXel1ZONEUAg8wQaYjHFYo2at3hN/KK4mVNGx++A8e6E4ZUZAgR6yI+jdx+6F+ovbH8tHs6Txt7U5j70suvu0LLKuzR44AA9tn6LHqxar0PvHVVhQb6GDCrRtAnDdF6h37MXQ77jTA8BBJwK/HLlE1pR9VSbmrePKNfU8cOc9qGYnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAnQCBbmdPZwQQQAABBJwJEOjOKCmEAAIIIICAncD/AP8rLrccwhvwAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "#Performance of data drift classifier\n", + "SD.plot.generate_indicator(fig_value=SD.auc, height=300, width=500, title=\"Datadrift classifier AUC\")" + ] + }, + { + "cell_type": "markdown", + "id": "22b39a4f", + "metadata": {}, + "source": [ + "such a high auc means that datasets are not similar.The differences should be analysed before deploying model in production" + ] + }, + { + "cell_type": "markdown", + "id": "0e80bb96", + "metadata": {}, + "source": [ + "### Importance of features in data drift" + ] + }, + { + "cell_type": "markdown", + "id": "92895e23", + "metadata": {}, + "source": [ + "This graph represents the variables in the data drift classification model that are most important to differentiate between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "630e9efe", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.xpl.plot.features_importance()" + ] + }, + { + "cell_type": "markdown", + "id": "ff21ebcf", + "metadata": {}, + "source": [ + "We get the features with most gaps, those that are most important to analyse.\n", + "With date bias introduced, it is normal that date features are the most impacted. We will then decide to remove them.\n", + "Let's analyse other important variables" + ] + }, + { + "cell_type": "markdown", + "id": "6e232653", + "metadata": {}, + "source": [ + "### Univariate analysis" + ] + }, + { + "cell_type": "markdown", + "id": "b12a6268", + "metadata": {}, + "source": [ + "This graphs shows a particular feature's distribution over its possible values. In the drop-down menu, the variables are sorted by importance of the variables in the data drift classification. For categorical features, the possible values are sorted by descending difference between the two datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8de1e1c6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate('BsmtQual')" + ] + }, + { + "cell_type": "markdown", + "id": "9b8d7382", + "metadata": {}, + "source": [ + "This feature on height of the basement seems to be correlated with the date of build.To avoid creating too much bias, the data scientist decides to remove it from his learning." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "56f2124d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate('Neighborhood')" + ] + }, + { + "cell_type": "markdown", + "id": "c82afbd3", + "metadata": {}, + "source": [ + "This feature on neighborhood seems to be correlated with the date of build.To avoid creating too much bias, the data scientist decides to remove it from his learning." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "77e568d3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate('Foundation')" + ] + }, + { + "cell_type": "markdown", + "id": "4bfb2876", + "metadata": {}, + "source": [ + "This feature on foundation seems to be correlated with the date of build.To avoid creating too much bias, the data scientist decides to remove it from his learning." + ] + }, + { + "cell_type": "markdown", + "id": "e7456527", + "metadata": {}, + "source": [ + "Data scientist thus discards all features that will not be similar to the production training" + ] + }, + { + "cell_type": "markdown", + "id": "6be6fc58", + "metadata": {}, + "source": [ + "## Second data validation after cleaning data preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4ab0aea1", + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", + "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YearBuilt','BsmtQual',\n", + " 'Neighborhood','Foundation','GarageYrBlt','YearRemodAdd',\n", + " 'GarageFinish','OverallCond','MSZoning','BsmtFinType1','MSSubClass',\n", + " 'ExterQual','KitchenQual','Exterior2nd','Exterior1st','OverallQual',\n", + " 'HeatingQC','FullBath','OpenPorchSF','GarageType','GrLivArea','GarageArea'])]\n", + "\n", + "y_df_production=house_df_production['SalePrice'].to_frame()\n", + "X_df_production=house_df_production[house_df_production.columns.difference(['SalePrice','YearBuilt','BsmtQual',\n", + " 'Neighborhood','Foundation','GarageYrBlt','YearRemodAdd',\n", + " 'GarageFinish','OverallCond','MSZoning','BsmtFinType1','MSSubClass',\n", + " 'ExterQual','KitchenQual','Exterior2nd','Exterior1st','OverallQual',\n", + " 'HeatingQC','FullBath','OpenPorchSF','GarageType','GrLivArea','GarageArea'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "eb6d2d30", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_production, df_baseline=X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "aac6bf64", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The variable BldgType has mismatching unique values:\n", + "[] | ['Two-family Conversion; originally built as one-family dwelling']\n", + "\n", + "The variable BsmtCond has mismatching unique values:\n", + "[] | ['Poor -Severe cracking, settling, or wetness']\n", + "\n", + "The variable CentralAir has mismatching unique values:\n", + "[] | ['No']\n", + "\n", + "The variable Condition1 has mismatching unique values:\n", + "[\"Within 200' of East-West Railroad\"] | ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", + "\n", + "The variable Condition2 has mismatching unique values:\n", + "['Near positive off-site feature--park, greenbelt, etc.'] | ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "[] | ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "\n", + "The variable ExterCond has mismatching unique values:\n", + "[] | ['Fair', 'Poor', 'Excellent']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "[] | ['Major Deductions 2', 'Severely Damaged']\n", + "\n", + "The variable GarageCond has mismatching unique values:\n", + "[] | ['Poor', 'Excellent']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Excellent', 'Poor']\n", + "\n", + "The variable Heating has mismatching unique values:\n", + "[] | ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", + "\n", + "The variable HouseStyle has mismatching unique values:\n", + "[] | ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", + "\n", + "The variable LandSlope has mismatching unique values:\n", + "[] | ['Severe Slope']\n", + "\n", + "The variable MasVnrType has mismatching unique values:\n", + "[] | ['Brick Common']\n", + "\n", + "The variable PavedDrive has mismatching unique values:\n", + "[] | ['Partial Pavement']\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "['Clay or Tile'] | ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", + "\n", + "The variable RoofStyle has mismatching unique values:\n", + "[] | ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", + "\n", + "The variable SaleCondition has mismatching unique values:\n", + "[] | ['Adjoining Land Purchase']\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "['Contract 15% Down payment regular terms'] | []\n", + "\n", + "The variable Utilities has mismatching unique values:\n", + "[] | ['Electricity and Gas Only']\n", + "\n", + "CPU times: user 2min 4s, sys: 22.2 s, total: 2min 26s\n", + "Wall time: 7.31 s\n" + ] + } + ], + "source": [ + "%time SD.compile(full_validation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "57252bf7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_v2.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_v2.html', \n", + " title_story=\"Data validation V2\", \n", + " title_description=\"\"\"House price Data validation V2\"\"\" # Optional: add a subtitle to describe report\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "8358b37b", + "metadata": {}, + "source": [ + "## Second Analysis of results of the data validation" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "89fd6a9e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_indicator(fig_value=SD.auc, height=300, width=500, title=\"Datadrift classifier AUC\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0b5fceb0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate('2ndFlrSF')" + ] + }, + { + "cell_type": "markdown", + "id": "b61737f6", + "metadata": {}, + "source": [ + "Let's assume that the datascientist is ok with these distribution gaps. \n" + ] + }, + { + "cell_type": "markdown", + "id": "bb54de9b", + "metadata": {}, + "source": [ + "Let's look at the impact on the deployed model. To do this, let's first build the model." + ] + }, + { + "cell_type": "markdown", + "id": "243bc506", + "metadata": {}, + "source": [ + "## Building Supervized Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d46d007c", + "metadata": {}, + "outputs": [], + "source": [ + "from category_encoders import OrdinalEncoder\n", + "\n", + "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", + "\n", + "encoder = OrdinalEncoder(\n", + " cols=categorical_features,\n", + " handle_unknown='ignore',\n", + " return_df=True).fit(X_df_learning)\n", + "\n", + "X_df_learning_encoded=encoder.transform(X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7f997b95", + "metadata": {}, + "outputs": [], + "source": [ + "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "00a77748", + "metadata": {}, + "outputs": [], + "source": [ + "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" + ] + }, + { + "cell_type": "markdown", + "id": "d814b7c7", + "metadata": {}, + "source": [ + "## Third Analysis of results of the data validation" + ] + }, + { + "cell_type": "markdown", + "id": "2f311998", + "metadata": {}, + "source": [ + "Let's add model to be deployed to the SmartDrift to put into perspective differences in dataset distributions with importance of the features on model.
\n", + "To get the predicted probability distribution, we also need to add encoding used" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "816f8638", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_production,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "d898047a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The variable BldgType has mismatching unique values:\n", + "[] | ['Two-family Conversion; originally built as one-family dwelling']\n", + "\n", + "The variable BsmtCond has mismatching unique values:\n", + "[] | ['Poor -Severe cracking, settling, or wetness']\n", + "\n", + "The variable CentralAir has mismatching unique values:\n", + "[] | ['No']\n", + "\n", + "The variable Condition1 has mismatching unique values:\n", + "[\"Within 200' of East-West Railroad\"] | ['Adjacent to arterial street', 'Adjacent to postive off-site feature']\n", + "\n", + "The variable Condition2 has mismatching unique values:\n", + "['Near positive off-site feature--park, greenbelt, etc.'] | ['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to feeder street', 'Adjacent to postive off-site feature', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad']\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "[] | ['60 AMP Fuse Box and mostly Romex wiring (Fair)', 'Fuse Box over 60 AMP and all Romex wiring (Average)', '60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "\n", + "The variable ExterCond has mismatching unique values:\n", + "[] | ['Fair', 'Poor', 'Excellent']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "[] | ['Major Deductions 2', 'Severely Damaged']\n", + "\n", + "The variable GarageCond has mismatching unique values:\n", + "[] | ['Poor', 'Excellent']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Excellent', 'Poor']\n", + "\n", + "The variable Heating has mismatching unique values:\n", + "[] | ['Gas hot water or steam heat', 'Gravity furnace', 'Wall furnace', 'Hot water or steam heat other than gas', 'Floor Furnace']\n", + "\n", + "The variable HouseStyle has mismatching unique values:\n", + "[] | ['One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level finished']\n", + "\n", + "The variable LandSlope has mismatching unique values:\n", + "[] | ['Severe Slope']\n", + "\n", + "The variable MasVnrType has mismatching unique values:\n", + "[] | ['Brick Common']\n", + "\n", + "The variable PavedDrive has mismatching unique values:\n", + "[] | ['Partial Pavement']\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "['Clay or Tile'] | ['Metal', 'Membrane', 'Gravel & Tar', 'Roll']\n", + "\n", + "The variable RoofStyle has mismatching unique values:\n", + "[] | ['Gabrel (Barn)', 'Mansard', 'Flat', 'Shed']\n", + "\n", + "The variable SaleCondition has mismatching unique values:\n", + "[] | ['Adjoining Land Purchase']\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "['Contract 15% Down payment regular terms'] | []\n", + "\n", + "The variable Utilities has mismatching unique values:\n", + "[] | ['Electricity and Gas Only']\n", + "\n", + "CPU times: user 2min 8s, sys: 22.5 s, total: 2min 31s\n", + "Wall time: 7.43 s\n" + ] + } + ], + "source": [ + "%time SD.compile(full_validation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "2112f374", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_v3.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_v3.html', \n", + " title_story=\"Data validation V3\", \n", + " title_description=\"\"\"House price Data validation V3\"\"\" # Optional: add a subtitle to describe report\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "94545fe0", + "metadata": {}, + "source": [ + "### Feature importance overview" + ] + }, + { + "cell_type": "markdown", + "id": "3a69968f", + "metadata": {}, + "source": [ + "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "77843e70", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.scatter_feature_importance()" + ] + }, + { + "cell_type": "markdown", + "id": "a70708c6", + "metadata": {}, + "source": [ + "Putting importance of the drift into perspective according to the importance of the model to be deployed, can help the data scientist to validate that his model can be deployed.
\n", + "Here we see that some features are necessary to analyse" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "94c20c9f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate('LotArea')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "ec9264a0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate('1stFlrSF')" + ] + }, + { + "cell_type": "markdown", + "id": "83c4f95a", + "metadata": {}, + "source": [ + "We see that for important features, the data in production will not be similar in distributions to that in training" + ] + }, + { + "cell_type": "markdown", + "id": "842637b6", + "metadata": {}, + "source": [ + "### Distribution of predicted values" + ] + }, + { + "cell_type": "markdown", + "id": "84f0009b", + "metadata": {}, + "source": [ + "This graph shows distributions of the production model outputs on both baseline and current datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "0f506b2f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\") # works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "c6ddb503", + "metadata": {}, + "source": [ + "Differences between 2 datasets generate a difference in the distribution of the predictions of the deployed model. These differences can have important impacts on the performance of the model in production.
\n", + "Such differences in predicted probabilities may call into question the decision to deploy the model as is." + ] + }, + { + "cell_type": "markdown", + "id": "1b65b795", + "metadata": {}, + "source": [ + "Jensen Shannon Divergence (JSD). The JSD measures the effect of a data drift on the deployed model performance. A value close to 0 indicates similar data distributions, while a value close to 1 tend to indicate distinct data distributions with a negative effect on the deployed model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "14df65a7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_indicator(\n", + " fig_value=SD.js_divergence,\n", + " height=280,\n", + " width=500,\n", + " title=\"Jensen Shannon Datadrift\",\n", + " min_gauge=0,\n", + " max_gauge=0.2,\n", + " ) #works if deployed_model is filled" + ] + }, + { + "cell_type": "markdown", + "id": "9d99cec3", + "metadata": {}, + "source": [ + "With this tutorial, we hope to have detailed how Eurybia can be used in a data validation phase before deploying a model." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_v3.html', \n", - " title_story=\"Data validation V3\", \n", - " title_description=\"\"\"House price Data validation V3\"\"\" # Optional: add a subtitle to describe report\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "94545fe0", - "metadata": {}, - "source": [ - "### Feature importance overview" - ] - }, - { - "cell_type": "markdown", - "id": "3a69968f", - "metadata": {}, - "source": [ - "This graph compares the importance of variables between the data drift classifier model and the deployed model. This allows us to put into perspective the importance of data drift in relation to the impacts to be expected on the deployed model. If the variable is at the top left, it means that the variable is very important for data drift classification, but that the variable has little influence on the deployed model. If the variable is at the bottom right, it means that the variable has little importance for data drift classification, and that the variable has a lot of influence on the deployed model." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "77843e70", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.scatter_feature_importance()" - ] - }, - { - "cell_type": "markdown", - "id": "a70708c6", - "metadata": {}, - "source": [ - "Putting importance of the drift into perspective according to the importance of the model to be deployed, can help the data scientist to validate that his model can be deployed.
\n", - "Here we see that some features are necessary to analyse" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "94c20c9f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('LotArea')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "ec9264a0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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hVFc6/N2bOrFjpQpz01WQs0cRFycq8urbFRxeBx4EEEAAAQQQQAABBBBwQ4BA6AYWH/WtQFHeIR2Yc6fy924sU5GQqARF9Rit8FbdfFtJ3o4AAggggAACCCDgcwGzdNPs9/v9c80118icVsrzmwCBkNHgNwJZ741R3qYvFBp3gSIu72/9NSi8jnLnT9DxHSutdkT3Hqfa7RP9pk1UFAEEEEAAAQQQQAABXwoQCH2pz7srLZC7MEVHvn9TQWGRih0+VzWiE0757uHv5urgosnWn9XrN4mZwkrL8kEEEEAAAQQQQAABJwsQCJ3c+37S9ryNXyhr3hirtrHD5ii0Yatya+4KhUFhdRQ/KpU9hX7Sv1QTAQQQQAABBBBAwHcCBELf2fPmSgrsndJHhbl7VLfHaEVePqDCbx2YfYe1fLRm045qMHBqJd/AxxBAAAEEEEAAAQQQcKYAgdCZ/e43rT66Nk058ycoJKqR4kelnbHe5uCZvVMSVXz8cKUC5BkL5AMIIIAAAggggAACCASwAIEwgDs3EJqW9W6y8jYvdSvcuZaYmqWjscPnlNlvGAgutAEBBBBAAAEEEEAAAU8IEAg9oUgZXhEwdwzue6mPVXbD0Uvc2hOYnTZex9bNZ+moV3qGQhFAAAEEEEAAAd8LHDt2TH369NFHH32kmjVrqrCwUE888YTWrFmjDh066NFHH/V9Jf2gBgRCP+gkp1bRdUhMeMuuqndLilsMpZeONkiaqprNOrr1fT6MAAIIIIAAAgggYG+B3wfC5cuXa+7cuXr++ecVHBxs78qfpna33nqr/v73v+uCCy6otvoTCKuNmhe5K5A5Pcm6hL6q10gc+upVmV+12vVWTOJ4d1/P5xFAAAEEEEAAAQRsLPD7QPjhhx9qw4YNevjhh6ul1mZGMiQkpORdxcXFMr/OJowSCKul63iJPwjkZ2xU5owk697BRslfVKnKpZecxo1MZS9hlRT5EgIIIIAAAgggYA8BE7beeOMNzZ8/X2FhYUpKStKzzz5rLRn97LPPNGPGDBUUFKhu3boaOnSoevbsedqKL1iwQG+//bb27dunBg0aaMyYMbrooot07bXX6s0331S9evWs77700kuqVauW/vKXv2jHjh267777dPPNN2vJkiVq0aKF8vPzFRMTo23btikjI0P/8z//o+joaL344ovW0lVTz759+6pfv35WeWlpafrPf/6jqKgobd261Vrmeu+996p9+/bWzKZpi/l+jRo1ztgGT/WKrWcIjx49qsmTJ+ubb75RZGSk1emJiYmnbfvXX3+tV155RQcOHLBQ77//ftWvX9/6/JnKmjRpktauXav09HSNHTtW3bt3L/Me02F33XWXdu3apY8//thTfUA55Qi4LqKP6NRfUT2Tq2zk2kt4tuVUuQJ8EQEEEEAAAQQQ8FOBlfuPKm17TrXX/rLYCPVuGlXmvQsXLtSsWbP0z3/+0wp9EydO1Lfffluyh/D999/Xxo0bzzhDaAKZyRgTJkxQmzZttHfvXhUVFSkhIeGMgXD48OEaMmSIBg4caM0GmjqYWUkT5kzuMOWY0GjCpQmR2dnZevDBB60MccUVV1iB0ITF5557znq3Web66quv6vXXX7faywzh77rddNSePXusDaE7d+60gtqTTz6pdu3alRkgJpGbDjLgZhPpCy+8YHXAM888Y332TGWZKebmzZtb/5fBdHJ5gfDdd9+1Os0MNAKhd//dkJ7SXcXHD1V4EX1lavDbTCOX1VfGi88ggAACCCCAAAIugakbMnXnsu3VDnJHm1i9cnWzMu81WeCyyy6zZtzMY/6b/O6773Y7ED7++OO68MILrfD1++dMM4S33XabNUNpDrExjznEplGjRlYOMY+Z9Rs9erQ++OCDkqWjJmds2rRJDzzwgBUIly5dKjMZZR4zw9irVy+lpqZaM5EEwlI9YqZ7b7rpJit1m9k+86SknDxYJDm57IyR2UC6atWqElwz/WtmFM2fm2ncypZlOtl87/eBMDMz05pxvOeee/TYY48RCL34rwbXtRGhcRco9ra5Z/0m12X10b3HqXb7088wn/WLKAABBBBAAAEEEAggAbvNEN5xxx3WxE3nzp0t5YMHD1pLMV2njFZ2hvDOO+/UgAEDdM0117gdCM3sn3mP6zGBsG3btrrxxhutPzKTR+bP4uPjSz5jcs3555+v8ePHW4HQZJZx48aV/Lx0CCUQluqS3bt3W+tmTaKOiIiwfmJ+v3jxYmtK9vfPU089Za23NdOxrsf83wPzfxJMaq9sWacLhGZKuUuXLlZZJpAyQ+i9f9tV5e7Bimrz2+X2CYofleq9ilMyAggggAACCCCAgNcEzH/XX3311daMmnm2b98u89/u7gbCimYIb7jhBmsJpyvQPf3009bvS+8hrCgQbtmyxVqy+s477ygoKKiMxZkCoQmqJjhyyqgkg2nCndnw6cI064bN5s9p06aVwTUdazZ2Dh48uORngwYN0u23326tB65sWeUFwu+++87qVDO1a9YIEwi99s+5zHURGZNP7t909+7Bimq1d0qiCnPTq3xiqfdaTMkIIIAAAggggAAClREwucAsrTSrBs1hLWarl5mkcTcQmj2E5rtmwqd169bWHkKzH9BM/Jjlnn/84x91/fXXW1vXRo4cKRMSKxsIXXsIzayhySKmnmbrmzkR1bzrTIHQvO+WW24pd/taZYyq8hnbHipjlxnCEydOyExPm2ndc889l0BYlVHmxndcs3lVuXuwote47jSs2bSjGgyc6kaN+CgCCCCAAAIIIICAHQRM2HrttdesUzrNKaBm6eiUKVPcDoSmLZ9++qk14WO2hcXGxlqnjJp9hWZSykwCmesk4uLirL2CDRs2rHQgNGWbc0zMQZdmaajZI3jOOedYS13N/sczBcJly5ZZbTIBcsSIESWzod70t20gNGttzVpcM01rErZ5zMEwJr2fbg/h6tWrSw6RMZ1rplxdewgrW9bvZwjN8bJmltGcZGQec9LooUOHrOWp5oCbli1berN/HFe261TQuj1GK/LyAR5rf+mL6rmCwmOsFIQAAggggAACCCDg5wK2DYTG1UwHm8NhzCmj5qqHhx56yNqk6TpldPr06bruuuvUuHFj67oIE9weeeQRXXLJJdZxriYUuk4ZPVNZJr2bsGmWlprNnF27drXu/zBPTs5vx+1u3rzZml6ePXu2FRJdn/HzcWCb6rtOF/VGaHOFTS6qt013UxEEEEAAAQQQQAABHwvYOhCWvjvQHCxj7vsofQ+h2VBqAqK5ZsI8Zop16tSpZ7yHsLyyzIlB69evP6U7zAxgp06dTvkz9hB6b8S6LpIPiWqk+FFpHn9R6YvqPbk/0eMVpUAEEEAAAQQQQACBsxIwmeDLL78sU4Y5WdRsB+P5TcDWgZCOcpaAt/YPllZ0nWDKFRTOGlu0FgEEEEAAAQQQQKB8AQIhI8M2ArkLU3Tk+zdVp8sI65c3nuoInd6oN2UigAACCCCAAAIIIOANAQKhN1Qps0oCrgvkGyRNVc1mHatUxpm+5K1rLc70Xn6OAAIIIIAAAggggIAdBQiEduwVh9Zpz5OXWS1PGLvCqwIsG/UqL4UjgAACCCCAAAII+JEAgdCPOiuQq3pi+0rtn3OHQuMuUOxtc73aVNedhJ6+69CrlaZwBBBAAAEEEEAAAQS8IEAg9AIqRbov4App1XElhOu00aCwOmqUvMT9yvINBBBAAAEEEEAAAQQCRIBAGCAd6e/NcN0RWF2nf2ZOG6D8fZtUr98khbfq5u981B8BBBBAAAEEEEAAgSoJEAirxMaXPC2wd0ofFebuUeywOQpt2MrTxZcp79BXr8r8qo4ZSa83hhcggAACCCCAAAIIIFBFAQJhFeH4mucESp/86e0DZVy1zs/YqMwZSQqJSlD8qFTPNYaSEEAAAQQQQAABBBDwIwECoR91VqBW1XWgTFjTjqo/cGq1NXPvlEQV5qZX26xktTWMFyGAAAIIIIAAAgggUEkBAmElofiY9wRcyze9eSF9ebXPXZiiI9+/qep+r/ckKRkBBBBAAAEEEEAAAfcECITuefFpLwi4LqSv7gNe8jZ+oax5YxQa30qxw+d4oWUUiQACCCCAAAIIIICAvQUIhPbuH0fULj2lu4qPH1LcyFTViE6o1janp3RT8fHDPnl3tTaUlyGAAAIIIIAAAgggUI4AgZBh4VOB3+4EjFSj5C+qvS5Z7yYrb/NS1e0xWpGXD6j29/NCBBBAAAEEEEAAAQR8KUAg9KU+79bRtWnKmT9B4S27qt4tKdUu4uv3V3uDeSECCCCAAAIIIIAAAqUECIQMB58K+Ppgl9JXXjQcvUTB4XV86sHLEUAAAQQQQAABBBCoTgECYXVq864yAq4DZRokTVXNZh19IuSqQ3TvcardPtEndeClCCCAAAIIIIAAAgj4QoBA6At13lkisOfJy6zf+3J27vB3c3Vw0WTVatdbMYnj6R0EEEAAAQQQQAABBBwjQCB0TFfbr6GuC+lD4y5Q7G1zfVbB/IyNypyRpJCoBMWPSvVZPXgxAggggAACCCCAAALVLUAgrG5x3lciYKeZub1TElWYm67YYXMU2rAVvYQAAggggAACCCCAgCMECISO6GZ7NjI7bbyOrZtviysf7FQXe/YWtUIAAQQQQAABBBAIRAECYSD2qp+0KXN6kvL3brTFrBzXT/jJoKGaCCCAAAIIIIAAAh4VIBB6lJPC3BFwHSiTMHaFO1/zymfZR+gVVgpFAAEEEEAAAQQQsLkAgdDmHRSo1bPLgTKlfdNTuqn4+GHFjUxVjeiEQKWnXQgggAACCCCAAAIIlAgQCBkMPhGw4xLNrHeTlbd5qbiP0CdDgpcigAACCCCAAAII+ECAQOgDdF4pHfrqVetXnS4jrF92eFx1iujUX1E9k+1QJeqAAAIIIIAAAggggIBXBQiEXuWl8NMJHJh9h47vWKkGSVNVs1lHW0CVLGONb6XY4XNsUScqgQACCCCAAAIIIICANwUIhN7UpezTCtjphNHSlXQddNNw9BIFh9ehBxFAAAEEEEAAAQQQCGgBAmFAd699G2enE0ZLK9lx5tK+vUjNEEAAAQQQQAABBPxdgEDo7z3oh/UvyNmjfS/1UUhUI8WPSrNVC3IXpujI92/aam+jrYCoDAIIIIAAAggggEBACRAIA6o7/aMxrr16YU07qv7AqbaqdN7GL5Q1b4zsWDdbQVEZBBBAAAEEEEAAgYAQIBAGRDf6VyPsfJpnUd4hZUzurqCwOmqUvMS/YKktAggggAACCCCAAAJuChAI3QTj42cvYPdlmXunJKowN12xw+YotGGrs28wJSCAAAIIIIAAAgggYFMBAqFNOyaQq2X3g1uy08br2Lr5qttjtCIvHxDIXUHbEEAAAQQQQAABBBwuQCB0+ADwRfP3Tumjwtw9tp2BO7o2TTnzJ6hWu96KSRzvCyLeiQACCCCAAAIIIIBAtQgQCKuFmZeUFrDrlROuOuZnbFTmjCSFRCUoflQqnYcAAggggAACCCCAQMAKEAgDtmvt2bDfwpb9rpwoLZae0k3Fxw8rbmSqakQn2BOTWiGAAAIIIIAAAgggcJYCBMKzBOTr7gnY+cqJ0i1x7XOs12+Swlt1c6+RfBoBBBBAAAEEEEAAAT8RIBD6SUcFSjXtfOVEaWN/qWegjAvagQACCCCAAAIIIOAbAQKhb9wd+1bXlRN2P8HTNZMZGt9KscPnOLa/aDgCCCCAAAIIIIBAYAsQCAO7f23XOrtfOVEazO6H39iuc6kQAggggAACCCCAgN8JEAj9rsv8u8KuKyf84bCWzGkDlL9vkxokTVXNZh39G57aI4AAAggggAACCCBQjgCBkGFRrQL+NOvmWt5ap8sImV88CCCAAAIIIIAAAggEmoCtA+HRo0c1efJkffPNN4qMjFRSUpISExNP2wdff/21XnnlFR04cEDt27fX/fffrxuwrNIAACAASURBVPr161ufP1NZkyZN0tq1a5Wenq6xY8eqe/fuJe/58MMP9cknn2j37t2KiorS9ddfrwEDBgTaWPB6e1xXToTGXaDY2+Z6/X1n+4K8jV8oa94YhTXtqPoDp55tcXwfAQQQQAABBBBAAAHbCdg6EJowuGfPHj366KPauXOnFdSefPJJtWvXrgxkRkaGhg8frgcffFAdOnTQCy+8oOzsbD3zzDPWZ89Ulgl9zZs317PPPqshQ4acEginT5+uSy65ROedd5527NihCRMm6K677lLPnj1t16F2rpC/XDnhMizI2aN9L/VRUFgdNUpeYmda6oYAAggggAACCCCAQJUEbBsICwoKdNNNN2nixInWbJ95UlJSrL8mJyeXaezcuXO1atUqmZk+8+zbt8+aUTR/HhMTU+mybrvtNut7pWcIf/8yExpDQkL017/+tUroTv2S6yoHf1qCuXdKogpz0xU7bI5CG7ZyatfRbgQQQAABBBBAAIEAFbBtIDTLM4cOHSozcxcREWHxm98vXrxYzz//fJnueOqppxQdHW3N3Lmevn37WrOKjRo1qnRZZwqExcXFuuOOO6ylqxUtXw3Q8XJWzcpOG69j6+bL7ldOlG5k1rvJytu8VNG9x6l2+9MvVz4rGL6MAAIIIIAAAggggICPBGwbCLds2WKFuwULFigoKMjiWbhwod5++21NmzatDNfjjz+uFi1aaPDgwSU/GzRokG6//XYlJCRUuqwzBUKzfHTFihVWKA0NDfVRt/nna/3pygmXMBfU++dYo9YIIIAAAggggAAClROwbSC04wzhW2+9ZQVUsx/RzEbyuCeQntJdxccPyR+unHC1zN/2PbrXI3waAQQQQAABBBBAwOkCtg2EZg/hjTfeqKefflpt27a1+skEMbNk83R7CFevXl1yiExmZqZ1EqhrD2FlyzrdDOF7772n1NRU69AZ18mlTh887rbfn66ccLWtKO+QMiafPHE2YewKd5vM5xFAAAEEEEAAAQQQsLWAbQOhUTOHyJjDYcwpo7t27dJDDz2kJ554ouSUUbN887rrrlPjxo2t6yLM8tBHHnnEOhH0xRdflAmFrlNGz1RWfn6+FTbNMtVbb71VXbt2VY0aNRQcHGztXTSzg+bAmri4OKtDzZ+bn/NUTsA10+YvV06UbhUHy1Suj/kUAggggAACCCCAgP8J2DoQlr470BwsM3DgwFMOcunVq5cVEM01E+ZZtmyZpk6desZ7CMsr67777tP69etP6UFzxUWnTp2sU0dNMC39dO7c2bp+gqdyAv58p5/rYJl6/SYpvFW3yjWYTyGAAAIIIIAAAggg4AcCtg6EfuBHFSsp4I9XTria5s91r2T38DEEEEAAAQQQQAABhwoQCB3a8dXdbNeVE/54fYM/z25Wdz/zPgQQQAABBBBAAAH/EiAQ+ld/+W1t/fHKCRd2Qc4e7Xupj4LC6qhR8hK/7QMqjgACCCCAAAIIIIDA7wUIhIyJahFwXTnRcPQSBYfXqZZ3evIl6SndVHz8sPy1/p60oCwEEEAAAQQQQACBwBEgEAZOX9q6Jf545URpUH+e4bT1wKByCCCAAAIIIIAAAj4VIBD6lN8ZLw+Ey91zF6boyPdvqk6XEdYvHgQQQAABBBBAAAEEAkGAQBgIvWjzNrgOZQlv2VX1bkmxeW3Lr97RtWnKmT9B/twGv4Sn0ggggAACCCCAAAJeFSAQepWXwo1AIFzbkJ+xUZkzkhQa30qxw+fQsQgggAACCCCAAAIIBIQAgTAgutHejfDnKydKy/r7Pkh7jxJqhwACCCCAAAIIIOALAQKhL9Qd9s5AOZAlc9oA5e/bpAZJU1WzWUeH9SLNRQABBBBAAAEEEAhEAQJhIPaqzdrkmlnz9ysbXDOddXuMVuTlA2ymTHUQQAABBBBAAAEEEHBfgEDovhnfcEOgKO+QMiZ3t76RMHaFG9+030cPfzdXBxdNVkSn/orqmWy/ClIjBBBAAAEEEEAAAQTcFCAQugnGx90TCIQrJ1wtDqS2uNeLfBoBBBBAAAEEEEAgUAUIhIHaszZpVyBd1xBIs502GR5UAwEEEEAAAQQQQMDHAgRCH3dAoL8+EK6cKN1He6ckqjA3XXEjU1UjOiHQu4/2IYAAAggggAACCAS4AIEwwDvY183LejdZeZuXKrr3ONVun+jr6pz1+10nptbrN0nhrbqddXkUgAACCCCAAAIIIICALwUIhL7Ud8C7A+XKCVdXBdqMpwOGIE1EAAEEEEAAAQQQqECAQMjw8KpAoF3mnrfxC2XNG6Owph1Vf+BUr9pROAIIIIAAAggggAAC3hYgEHpb2MHluw5hCQqLVKPkLwJCoiBnj/a91EchUQmKH5UaEG2iEQgggAACCCCAAALOFSAQOrfvvd7yQL2mwTXr2XD0EgWH1/G6Iy9AAAEEEEAAAQQQQMBbAgRCb8lSrlxXTtRq11sxieMDRiTQ9kUGTMfQEAQQQAABBBBAAAG3BQiEbpPxhcoKBOoBLLkLU3Tk+zdVp8sI6xcPAggggAACCCCAAAL+KkAg9Nee84N6u66cCLQrGgJ15tMPhhRVRAABBBBAAAEEEPCwAIHQw6AU95tAoC6tdO2NDI1vpdjhc+hyBBBAAAEEEEAAAQT8VoBA6LddZ/+KB9qVE6XFA7lt9h9Z1BABBBBAAAEEEEDAUwIEQk9JUs4pAq7rGQLpyonSDcycNkD5+zapQdJU1WzWkd5HAAEEEEAAAQQQQMAvBQiEftlt9q90oF454ZLPThuvY+vmK7r3ONVun2j/DqGGCCCAAAIIIIAAAgiUI0AgZFh4ReDwd3N1cNFkBdqVEy4s1wmqEZ36K6pnslcMKRQBBBBAAAEEEEAAAW8LEAi9LezQ8gP1yglXdwb6DKhDhy3NRgABBBBAAAEEHCdAIHRcl1dPg10njAbalRMuvaK8Q8qY3N3624SxK6oHlbcggAACCCCAAAIIIOBhAQKhh0Ep7qRA5vQk5e/dGNCHruydkqjC3HTFjUxVjegEuh4BBBBAAAEEEEAAAb8TIBD6XZf5R4WdcC1DoM+C+sdIo5YIIIAAAggggAACZyNAIDwbPb5broDryomQqEaKH5UWsEqBvk8yYDuOhiGAAAIIIIAAAgiUCBAIGQweF3DKgSt5G79Q1rwxCmvaUfUHTvW4IwUigAACCCCAAAIIIOBtAQKht4UdWL7ryolAv5Lht5nQBMWPSnVgT9NkBBBAAAEEEEAAAX8XIBD6ew/asP5OWkrp2ivZcPQSBYfXsWFvUCUEEEAAAQQQQAABBE4vQCBkdHhcwHXYSoOkqarZrKPHy7dTgU5qq53cqQsCCCCAAAIIIICAZwQIhJ5xpJRSAq4rJ2KHzVFow1YBbZO7MEVHvn9TdbqMsH7xIIAAAggggAACCCDgTwIEQn/qLT+pqxOunHB1xdG1acqZP0G12vVWTOJ4P+khqokAAggggAACCCCAwEkBAiEjwaMCTrlywoXmOlE1NL6VYofP8aglhSGAAAIIIIAAAggg4G0BAqG3hR1WvlOunCjdrU6aEXXYcKa5CCCAAAIIIIBAwAsQCAO+i6u3ga4TRgP9yonSqpnTBih/3yY54RCd6h1NvA0BBBBAAAEEEEDA2wIEQm8LO6x8Jx6ykp02XsfWzVd073Gq3T7RYT1OcxFAAAEEEEAAAQT8WcCvA+GOHTs0adIkbdmyRY0bN9a9996rtm3bltsfR48e1eTJk/XNN98oMjJSSUlJSkz87T/eKypr1apVmj17tjZv3qwGDRrotdde8+c+92rdnXgNgxNnRb06iCgcAQQQQAABBBBAoNoE/DYQFhUVafjw4ercubMGDBighQsXaubMmZo1a5YiIiLKAJowuGfPHj366KPauXOnxo4dqyeffFLt2rXTmcrasGGD0tPTlZWVpY8++ohAWMHw3Duljwpz98gJV064GJy4b7La/g3FixBAAAEEEEAAAQS8KuC3gXD9+vV68MEHNW/ePIWFhVlIgwcPtn716NHjFLSCggLddNNNmjhxotq3b2/9LCUlxfprcnKyKlvWl19+aYVBZghPPyadeMBKUd4hZUzubqEkjF3h1X9gKRwBBBBAAAEEEEAAAU8K+G0g/Pjjj5WamqpXXnmlxGPChAlq0qSJNXNY+tm9e7eGDh2qDz/8sGT20Px+8eLFev7551XZsgiEFQ+9/IyNypyRpJCoRooflebJcWr7stJTuqn4+GHFjUxVjegE29eXCiKAAAIIIIAAAgggYAT8NhCamcHly5eXzPSZxpj9hGa28J577jmld80ew7vuuksLFixQUFCQ9TOzxPTtt9/WtGnTrFnGypRFIKz4HxonL5107Z2s12+Swlt1498uCCCAAAIIIIAAAgj4hYDfBsLKzuqZXmCGsHrGoutwlTpdRsj8ctLj5LY7qZ9pKwIIIIAAAgggEGgCfhsIzb6/hx56SO+//75CQ0OtfjHLQgcOHFjuHsIbb7xRTz/9dMkppOaQmeLi4pI9hJUpixnCioe/68qJuj1GK/LyAYH2z0qF7cnb+IWy5o1ReMuuqnfLyf2pPAgggAACCCCAAAII2F3AbwOhORl02LBh6tq1q/r3769FixZpxowZJaeMrl69Wtu3b9cNN9xg9YE5RGbfvn3WKaO7du2ywuQTTzxRcspoRWWZd5mDaZYtW2adZPrqq69aS09dQdTunVxd9XPilRMu29/2TyYoflRqdZHzHgQQQAABBBBAAAEEzkrAbwOhabUJfGbf4NatW5WQkKD77ruvZAbwrbfesu4cfO655yyg0vcQmmspzExi6XsIKyrL3ENoTjQt/bRu3VovvPDCWeEH2pddV0449WAV1wmrDUcvUXB4nUDrXtqDAAIIIIAAAgggEIACfh0IA7A//LpJTrxyonSHOXmG1K8HLpVHAAEEEEAAAQQcLEAgdHDne7LpTr5ywuXo5D2UnhxLlIUAAggggAACCCBQfQIEwuqzDug3OfnKCVfHHv5urg4umqxa7XorJnF8QPc3jUMAAQQQQAABBBAIDAECYWD0o89b4bp2IaJTf0X1TPZ5fXxRAVcoDo1vpdjhc3xRBd6JAAIIIIAAAggggIBbAgRCt7j48OkEuIfvpIzT91HyTwgCCCCAAAIIIICAfwkQCP2rv2xbWw5UOdk1mdMGKH/fJsUOm6PQhq1s219UDAEEEEAAAQQQQAABI0AgZBx4RCBzepLy9250fBDKThuvY+vmK7r3ONVun+gRWwpBAAEEEEAAAQQQQMBbAgRCb8k6rFyWSp7scNfBMk7eS+mwoU9zEUAAAQQQQAABvxYgEPp199mj8gU5e7TvpT4KiWqk+FFp9qiUj2rBwTI+gue1CCCAAAIIIIAAAlUSIBBWiY0vlRbgyolTxwOzpfzzgQACCCCAAAIIIOAvAgRCf+kpG9eTZZKndo7rYJkGSVNVs1lHG/ccVUMAAQQQQAABBBBwugCB0OkjwAPt58qJUxFdB8vU7TFakZcP8IAwRSCAAAIIIIAAAggg4B0BAqF3XB1VKldOnNrdrhnTWu16KyZxvKPGAo1FAAEEEEAAAQQQ8C8BAqF/9Zcta8uVE6d2CwfL2HKYUikEEEAAAQQQQACBcgQIhAyLsxbgEJWyhJic9bCiAAQQQAABBBBAAIFqECAQVgNyIL+CKyfK710OlgnkUU/bEEAAAQQQQACBwBEgEAZOX/qkJVw5UT47B8v4ZDjyUgQQQAABBBBAAAE3BQiEboLx8VMFuHKi/BHBwTL8k4IAAggggAACCCDgDwIEQn/oJRvXkSsnyu8cDpax8aClaggggAACCCCAAAIlAgRCBsNZCXDlxOn5OFjmrIYWX0YAAQQQQAABBBCoBgECYTUgB/IruHLi9L3LwTKBPPJpGwIIIIAAAgggEBgCBMLA6EeftYJZsNPTc7CMz4YlL0YAAQQQQAABBBCopACBsJJQfKysAFdOVDwqjq5NU878CarVrrdiEsczhBBAAAEEEEAAAQQQsJ0AgdB2XeI/FeLKiYr7Kj9jozJnJCk0vpVih8/xn46lpggggAACCCCAAAKOESAQOqarPd/Q6rpy4mhBkdYcOKp1Wcdkft++fm39IaGO5xvkhRJdS2objl6i4HD/qLMXGCgSAQQQQAABBBBAwKYCBEKbdow/VMubV07sOnJC/1yTobQdOdp26EQZjjqhwbrx3Bj1ax6jG5pF25aLU1ht2zVUDAEEEEAAAQQQQEASgZBhUGUBb4Wd2ZsPaNAX206pV9uYWqofXsP6s12HT2jroeMlP68dEqxeTaN06/n1rIBopyd3YYqOfP+m6nQZYf3iQQABBBBAAAEEEEDATgIEQjv1hp/VxRtXTty7fIeeX7/PkritVQPd3iZW7evVVnhI0Ck6G3Pz9MmOXH2yM1cLdh8s+VlseA3d3jpWIy+MU+OIUJ+Lug6WCW/ZVfVuSfF5fagAAggggAACCCCAAAKlBQiEjIcqC3jyyom9xwrUd+EWLd97WBE1gjX3D+epTyWXgh4rLNJHO3L18k/7tHjPoZL2mNnCv14Up2sa+W7vnutgmZCoBMWPSq2yNV9EAAEEEEAAAQQQQMAbAgRCb6g6oExPXjmRmVegDu+v164j+WoSEaqPrrtA7evVqpLihpw8Pbdur2Zu3q+8wmKrjHYxtXRP2zgNatmgzExjlV7i5pc4WMZNMD6OAAIIIIAAAgggUG0CBMJqow6sF3nyyonEzzZr/o5cXVyvlhZe30pm2efZPgeOF+iVnzL10k/7tOdovlVcVGiIbmvdQPe0jVezyJpn+4pKf99bey0rXQE+iAACCCCAAAIIIIDAaQQIhAyNKgl46sqJNzYd0NCl26wQ+N2NF+rcOp4Pam9uzVLK2gyt3H+0pK2JTaOsYNizcd0qtd+dL3GwjDtafBYBBBBAAAEEEECgOgUIhNWpHUDv8sSVE4fyi9TirbXal1egf/9Xi0rvGawq49d7D+tf6/bq3W3ZJUW0iQ63DqD5S6sG1t5FbzwcLOMNVcpEAAEEEEAAAQQQ8IQAgdATig4swxPLIF0nil4dH6mv+rSuNkVzx+G/ftynaT9nKudEofXeuqHBVij8a9t4nVcnzKN14WAZj3JSGAIIIIAAAggggIAHBQiEHsR0UlFne+XEj9nH1O699RbZT7e0lZmpq+7naEGRXtu4X8/9uFdbDv52r+GQlvWtS+9vPNdzF95zsEx19y7vQwABBBBAAAEEEKiMAIGwMkp8pozA2V450SX1Zy3be1ij28Ur5cpzfC5sDrWZvDZDS9J/u7YiumaIbjkvRkMvaKDO8ZFnVUdPzKieVQX4MgIIIIAAAggggAAC5Qh4NRAmJSWpb9++uu666xQREUEHBIjA2V45YQ55GbD4FzUIq6Ff+rdXnVDv7N2rCvf67GOa9vN+vbn1gMzdiK6nZd0wDWxZX4Na1lfzKiwp5WCZqvQG30EAAQQQQAABBBDwtoBXA+HIkSP13XffKTw8XNdee61uuukmtW3b1tttonwvC5zNlRNmmWaLt9Yp/Vi+pl1zroa3auDl2lat+KJi6fM9BzV3ywF9sC1Hufkn9xoGSboqPtIKhn8+v56iaoZU6gUcLFMpJj6EAAIIIIAAAgggUM0CXg2Epi3p6en697//rbS0NGVkZKhly5bWrGGvXr0UGXl2y/Cq2YrX/Z/A2Vw58c81GXrgu13qUL+2Vva90G9M39+Wrbd/ydI7v/x2Qqmp/H+fF6MBLerrhmYV7zfkYBm/6WoqigACCCCAAAIIOErA64HQpVlUVKRvv/1WH3zwgZYuXaoaNWqoZ8+e6tevn9q1a+codH9vbFWvnCgoLta5c9dq99F8vdvjfN3cPMbvKA7nF+n9X7P15pYDWrT7oAqKTzahfliIbj3/5JLSK+LKXx6dntJNxccPq+HoJQoOr+N3bafCCCCAAAIIIIAAAoEnUG2B0EW3a9cuvf7661YwdD0dOnTQ+PHj1bhx48ATDsAWVfWAlFmbD2jwF9tk9uNt+rP//0+A/XkFemtrlvXL3HHoes6NrGldYWF+nRNRs+TPq+oWgEOIJiGAAAIIIIAAAgjYRKBaAuHx48f1+eefW0tHV6xYoQYNGuiGG26w9hSagDhlyhSLwwRFHvsLVPXKiW7zN2pp+iHNuOZcKywF0rPzyAnN3ZKlt7dm6YcDR0uadkvzGI3v2FgXxoSrqjOrgeREWxBAAAEEEEAAAQTsJeDVQPjzzz9bIfDjjz/WkSNHdOWVV1pLRLt06WItGXU9OTk51qEzZklp6efo0aOaPHmyvvnmG2u/oTm1NDEx8bSCX3/9tV555RUdOHBA7du31/3336/69etbnz9TWTt27NCkSZO0ZcsWa6by3nvvLTkAx3z3+eeftw7IMUtfL7nkEuvnMTH+t+TRE8OvKldOmMDUdO5a6/UHh3aw1cminjApXcZP2Xl6cf1ezdx8QEcKiqwfmeWxE2O2KvKzsQpr2lH1B0719GspDwEEEEAAAQQQQAABtwW8Ggg7duxozQb26dPHOkimUaNGp63gnXfeaYW50o8Jg3v27NGjjz6qnTt3auzYsXryySfL3XNoDqwZPny4HnzwQZklqC+88IKys7P1zDPPWEVWVJYJeea7nTt31oABA7Rw4ULNnDlTs2bNsq7LePnll7Vu3Tr9z//8j2rWrKl//OMf1p8//PDDboP7+xeqeuXEM2sy9OB3u9T33GjN69nC3xkqVf+s44X655p0vbh+nw4XFKnhiUy9t+sBNYyup/MfXFqpMvgQAggggAACCCCAAALeFPBqIFyyZEmZ2cDKNqagoMBaUjpx4kRrts88KSkp1l+Tk5PLFDN37lytWrXKmuUzz759+6wZRfPnZiavorLWr19vBcl58+YpLCzM+v7gwYOtXz169NC4cePUokULDRo0yPqZWf76zjvvaOpU583yVPXKiUvmrdearGN6r8f56ueHh8lUdtyW9zmz13DS2gwrGE5bf7siio7qkx6vafTVl6pF3ZPjjQcBBBBAAAEEEEAAAV8IeDUQmgvpP/3009O2q6Kf7969W0OHDtWHH35Ycqm9+f3ixYut5Zu/f5566ilFR0frrrvuKvmRmZU0s4pmZrKissyS1tTU1FNmKCdMmKAmTZpYM4dm3+Ps2bP12GOPlcwQnnfeeRo2bJgv+syn76zKlRNbDx5Xi7fXKbJGsLKGXKrQYHObn/MeEwy/e2mIinf9oCea3Kvv63ZUUov6eqxDAsHQecOBFiOAAAIIIIAAArYQ8GogNEtGV65cWW5DzTLNTp06nfbnZi+fCXcLFixQUNDJAGGWcr799tuaNm1amTIff/xxaxbPzOq5HjOjd/vttyshIaHCsszM4PLly0tmIM33zUyjmS285557ZPY4mmWiJhiap02bNtbf16pVyxadWJ2VqMrBKI+u2K2JP6RrSMv6er1b8+qsru3eZfyyl07V4qZ/1qiCHjpaWKSQIFnXVTzeIUHN6zBjaLtOo0IIIIAAAggggEAAC/gsEH7//fd64IEHZJaVlvfYaYbQ7BU0ewdHjx6t0NBQK5Ca01Fd+xMDeHyUaVpVrk44Z+4a7TqSr8/+dIH+q0ldJ3GVaWvexi+UNW+MdbBM8c1T9PTqdL38U6YVDGsESYMvaKBxHRLUNPK36yocDUbjEUAAAQQQQAABBLwq4JVA2LVrV6vShw8ftk4H/f2Tn58vcxWFuXrCzOyV95g9hDfeeKOefvrpktM+zcEwxcXFp91DuHr16pKQlpmZaR0Q49pDWFFZZg/hQw89pPfff98KfOYxS0wHDhxo7SG89dZbdd9991mnpJpn27ZtGjFihD755JNTTkv1ak/ZpHB3r5z4z97D6pz6s2Jqhihz8KXWbJiTH9ehPEFhddQo+eT/DNl3rED/WGOC4T4dKyxWaFCQhlxwcikpwdDJo4W2I4AAAggggAAC3hfwSiB03Ss4Y8aMcvfZmaWWzZs3lwmOwcHBp22lOUTGHA5jThk1M3ImtD3xxBMlp4xOnz5dZh+iuSYiPT3dWh76yCOPWNdCvPjiizKh0DWLV1FZZvmq2Q9o6tO/f38tWrRIpu6uU0bNfkLzmMNszHUZZobwhx9+kHm/0x53r5y4Z/kO6zCVuy+K0wudmzqNq9z27p2SqMLcdMUOm6PQhq1KPmOC4VOr0zV1w2/BcFjrBnrs0gQ1jjj5Pyp4EEAAAQQQQAABBBDwpIBXAqGrgmYf3pgxY6pc39J3B5prHsyMXel7CHv16mUFRHPNhHmWLVtmnfx5pnsIyytr+/bt1r7BrVu3WnsOzYxg27ZtrXKzsrKsgGlmIE14NHsVR40aZYVaJz3uXjlRWCzFzvxB2ScKtbxPa/2/+LKzxU7yc7U1691k5W1equje41S7fdl7NU0wfHL1Hk3dkKm8wmLVDA7S8FYN9AjB0InDhTYjgAACCCCAAAJeFfBqIPRqzSm82gXcvXLis10Hdd0nm9QkIlQ7B1xc7fW16wtdJ7XWatdbMYnjT1vN9KP51h7DV38+GQzDgoN0W+tYPXJpIzWqzYyhXfuXeiGAAAIIIIAAAv4k4PFAeNVVV1nt//rrr+X6fUUg5nM8/iHg7pUTf1m6Ta9vOqCxlzTSxE6N/aOR1VDL/IyNypyRpJCoBMWPSj3jG/cczbeWkpqlt67noYsb6oq4SMWEhVj7DDmd9IyMfAABBBBAAAEEEECgHAGPB0LXlRC33XZbuddD/L4O5nM8/iHgzpUTZkYrbtYPOpRfpB9vvkgXxTjvio6KejU9pZuKjx9W3MhU1YhOqNQAMMFw4g979NJPmWU+Hx4SpNbR4WoVVUsXxoRbv4+rFaqQoCDVrhGsiBrBJ/8aan4fIvN5HgQQQAABBBBAAAEEPB4IIQ1cAXeunHhvW7ZuWbRVF8WE68ebT+7F5PlNIDttvI6t4cCtPgAAIABJREFUm6+6PUYr8vIBbtFkHy/U+79my5zgujEnTxtyjunA8UK3yjAfjiwVEF2B8YLocPU/v57+dE6U2+XxBQQQQAABBBBAAAH/E/B6IDQHvNSvX79E5ssvv5S55sFcSn/ZZZf5n5iDa+zOlRN9F27RB7/m6KlOjfXQJY0crFZ+04+uTVPO/AkKb9lV9W5JOWufrOOFVjDckJ2nn3OO6eecPB0uKFJRcbEO5RfqSH6RjhQUWX/NzT9zeGwaUVN3XRir4a1jFRte46zrRwEIIIAAAggggAAC9hTwaiD87LPPZALgxIkTrdZ//PHHeuyxx6y7/sw9g+ZUz27dutlThlqVEajslRPHCotUe8Yq6/u/9m+vZlyyXsayvPsIq3vI5ZxwBcVCKywezi/SJztztXBXrlbsP1pSnUEt62vkhXG6Mi6iuqvI+xBAAAEEEEAAAQS8LODVQDho0CCNHTtWbdq0sZoxZMgQxcXF6R//+Ifee+8962L31157zctNpHhPCLhz5cT8HblK/GyzroqP1LI+rT3x+oAs43T3EdqhsT9l5+nZdRmaveWAdcKpeW5sFq1pXc9V/TBmDO3QR9QBAQQQQAABBBDwhIBXA6E5ZfTzzz9XeHi4Dh06pO7du1v3+V155ZU6fPiwrr/+ei1dutQT7aAMLwu4c+XEyGXb9fKGTJaLnqFPXPsI63QZIfPLjs/B/EJN+3m/nli1x7pPMqF2qD6+7gJdXJ9DguzYX9QJAQQQQAABBBBwV8CrgbBnz57WDGCTJk1klo8+/vjjVgA0ATEnJ0d9+/bV4sWL3a0zn/eBgDtXTjSZs0a7j+ZrTb+L1L4eweF03ZW38QtlzRujsKYdVX/gVB/0auVfeeB4gQYt2WYtKTUH0Mz5w3m6oVl05QvgkwgggAACCCCAAAK2FPBqIHz44YeVnZ2tXr16afr06WrevLmee+45C+Kbb77R7NmzrRlDHvsLVPbKiR+zj6nde+utmaTdSVxGX1HPFuUdUsbk7tZHEsausP8gkHTnsu2auuHktRf/uLyJHri4oV/Um0oigAACCCCAAAIIlC/g1UCYkZGhhx56SOvWrdO5556ryZMnq1mzZlZN7rvvPt188826+uqr6Rs/EKjslRPm8vR7lu+QOYhkZrfmftAy31Yxc9oA5e/bpAZJU1WzWUffVqaSb5+8bq/u/2aniiQltainGV2bq2Yw9xpWko+PIYAAAggggAACthLwaiB0tdScKFqjxqkHUaSnp6tRI64jsNVoqKAylb1y4r8XbdW727I1/ZpzNaxVA39pns/qmbswRUe+f1MRnforqmeyz+rh7otTt+fohgVbrK9xeJC7enweAQQQQAABBBCwj0C1BEL7NJeaVFWgsldONJy9WnuPFWjLn9vp/LphVX2dY77nOqwnNL6VYofP8at2r9p/VL0+3WT198AW9TWrOzPCftWBVBYBBBBAAAEEEJDk9UC4du1apaWlac+ePdbJor9/3njjDTrC5gKVvXLCXIbe5t0f2T/oZn+6wnbD0UsUHF7HzW/79uMrMo/qmrQNOlZYrJevbqY728T6tkK8HQEEEEAAAQQQQMAtAa8GwjfffNO6fL5x48bWHsKIiLIXWz/11FNuVZgPV79AZa+ceGVDpu5atl0Dzq9nnULJUzkB1/7Mev0mKbxVt8p9yUafmrPlgAYu2aZaIUH6od9FahUVbqPaURUEEEAAAQQQQACBigS8GgivvfZaDRw4UOaCeh7/FajslRP9F/+it7ZmaerVzTSCmaJKd7jLt1a73opJHF/p79npg30+26y0HblqF1NLq/pdqBpBHDJjp/6hLggggAACCCCAwOkEvBoIzcX05v7ByMhIesCPBSp75USj2auVcaxAP/93W2aJ3Ojv/IyNypyRpJCoBMWPSnXjm/b5aPbxQrV6Z50y8wr04MUN9fTlTexTOWqCAAIIIIAAAgggcFoBrwbC5ORka4bw0ksvpQv8WKAyV05sys1Tq3d+VL2wEB0YTH+7293pKd1UfPyw4kamqkZ0grtft8XnF+w6qGs/2SQzN7j8hja6Mq7sEnFbVJRKIIAAAggggAACCJQIeDUQ5uTkWHsI//SnP+mKK64oc/UE/eAfApW5cuJ/f87UiK+267/Pi9HbfzzfPxpmo1pmvZusvM1LFd17nGq3T7RRzdyrirmD0txFeU5ETf14y0WqGxriXgF8GgEEEEAAAQQQQKBaBbwaCHv27Kni4mJlZ2crODhYUVFRCvrd3qKFCxdWa4N5mfsClblyYuCSXzRnS5amXNVUIy+Mc/8lDv9GIOwjNF2YV1isS+at18bcPN3SPEbv9OB/Djh8aNN8BBBAAAEEELC5gFcD4bPPPnvG5v/tb38742f4gO8EKnvlxDlz12jXkXz9dEtbtYnmlEl3e8zlHBRWR42Sl7j7dVt93txP2PGDn6w6zf3Deep/fj1b1Y/KIIAAAggggAACCPwm4NVACLT/C1Tmyolth47rvLfWqX5YiPazf7DKnb53SqIKc9MVO2yOQhu2qnI5dvji06vT9fD3uxVfq4a29W+vWiHBdqgWdUAAAQQQQAABBBD4nQCBkCFRoYDrhNGITv0V1TO53M++tnG/hn35q/o1j9F7LBGs8ojKThuvY+vmq26P0Yq8fECVy7HDF/OLitXmnR+19dBxTeiYoMc7+OdBOXawpA4IIIAAAggggIA3BbweCNevX69p06ZpzZo1ys3N1cqVK632TJ48WYMHD1aDBg282T7KPkuB3IUpOvL9m6rTZYT1q7xn6Bfb9MbmA3qhc1PdfRH7B6tKfnRtmnLmT1B4y66qd0tKVYuxzfc+2ZmrXp9uVmSNYO1MuljRNTlgxjadQ0UQQAABBBBAAIH/E/BqIPz+++919913q127durYsaMVDF2BcM6cOdq/f7/uvfdeOsPGApW5cuK8t9Zq26ETWtvvIrWrV8vGrbF31YryDiljcnerkgljV9i7spWsXb+FW/T+rzka3zFB45glrKQaH0MAAQQQQAABBKpPwKuBcOjQoercubNGjDg5s2RCoSsQ/vrrr7rnnnuUlpZWfa3lTW4LpKd0V/HxQ6e9Hy/jWL4azV7D/YNuy5b/hcxpA5S/b5Pq9Zuk8FbdPFSq74r5MfuY2r+3XlE1Q7Rn4MXsJfRdV/BmBBBAAAEEEECgXAGvBsIrr7xSn332mXXdxO8DYV5enrp27apvv/2WrrGxwJmunJi5+YCGfLFNNzSL1of/1cLGLfGPqlVmz6Z/tOS3Wt6wYItSt+fon1c00Zj2Df2t+tQXAQQQQAABBBAIaAGvBkIT+ObOnavGjRuXCYS//PKLNXO4aNGigAb258ZV5oTR4V/+qhkb92vylefob+3i/bm5tqh7fsZGZc5IUkhUguJHpdqiTmdbiZX7j+qyD35Sg7Aa2j3wYtUMDjrbIvk+AggggAACCCCAgIcEvBoIzR2DkZGRGj9+vEJCQkqWjBYWFurRRx+1Lql/8sknPdQUivG0QGUuS2/x1jrrJMmVN12oDg1qe7oKjiwvkK6fcHXgHz/aqMV7Dumlq5rprgtjHdmvNBoBBBBAAAEEELCjgFcD4ebNm2X2ETZs2FDXXHONZs6cqTvuuENLly7Vzp07NWvWLDVr1syOLtRJ0plOGHXtHzSnSOYO7SAmfjwzbFzutdr1VkzieM8U6uNSPt99UD0+3qRmkTW15dZ2qhHELKGPu4TXI4AAAggggAACloBXA6F5waZNm/Svf/1LK1asUEFBgYKDg62ZwuTkZLVs2ZJusLHAmU4YnbslS0lLflHvplFKu5a+9FRXupaNBoXVUaPkJZ4q1uflmGWjZvnoG92aa3DL+j6vDxVAAAEEEEAAAQQQqIZA6ELOz8/XwYMHrSWkYWFh2PuBwJlOGL3jq+169edMDgvxQl+6lo0Gymmjhujf23N044Italk3TBv/3E7MEXph4FAkAggggAACCCDgpoBXZwjNSaI//fSTdd+geWJjY9WmTRuFh4e7WU0+Xt0ClbkTr/U7P2pjbp6+u7GNOsVGVHcVA/p9ldm/6W8AxZJavb1Omw8e13s9zle/5jH+1gTqiwACCCCAAAIIBJyAVwLh8ePH9fzzz+v999/XiRMnTkGrWbOm+vXrZ91ByEyhfcfTmU4YzTpeqPozfxD7B73ThwU5e7TvpT4KtGWjszcf0KAvtumimHD9eHNb7+BRKgIIIIAAAggggEClBTweCIuLizVq1Chrz6C5duLyyy+3ZgbNn2dmZur777+3DpXp1KmTXnzxReukUR77CZxphurtrVm6dfEvuq5JXX3ypwvs14AAqFGgXVJvuqSoWDrvrbXafviEPrmupa475+QdpTwIIIAAAggggAACvhHweCBcuHChJkyYoJdfflnt2rUrt1Vr167VyJEjrc/98Y9/9E3LeWuFAmc6YXTksu16eUOmnurUWA9d0ghNLwicKZR74ZXVUuQrGzJ117Lt6hwfqa/7tK6Wd/ISBBBAAAEEEEAAgfIFPB4Ix4wZYwXBIUOGVGj++uuv68cff9SkSZPoGxsKnOmE0Yve/VE/5eRpeZ/W+n/xkTZsgf9XqfSyUXNJfXB4Hf9vlKQTRcVqMmeNMvMK9GVia3VpyPgJiI6lEQgggAACCCDglwIeD4TXX3+9XnrppTPeL/jrr7/q7rvv1vz58/0SLtAr7TphtOHoJWWCiPkP+bhZqxVRI1iH/9Ih0Cl82j7XstHo3uNUu32iT+viyZenrM3QmG936U/nROnj67iyxJO2lIUAAggggAACCLgj4PFA2LlzZ33xxRcyh8dU9JiDZ/7whz/o66+/dqe+fLYaBFwnjAaFRapR8hdl3jhvW7ZuXrRVPRvX1YJe7B/0Zpe4lo2Gt+yqerekePNV1Vr2wfxCNZq1RscKi/TTLW3VOpqTh6u1A3gZAggggAACCCDwfwIeD4Tm0vmVK1dWCtidz1aqQD7kEYEznTD61+U79ML6fXrissZ65FL2D3oE/TSFlL7+o7zZWm++29tlu/ahjmgdq6ldmnn7dZSPAAIIIIAAAgggUI6AVwLhvHnzKoVtrp+obHisVIF8yCMCrlmpiE79FdUzuUyZF89br7VZx/RVYmtdzf4vj5hXVEjWu8nK27xUgbZs1Nxhae6yNM+hoR0UGRrsdUtegAACCCCAAAIIIHCqgFcCoTvIBEJ3tKrns9lp43Vs3XzV7TFakZcPOOWl2ccLVW/mD6oVEqSjwzpWT4Uc/paja9OUM3+CAm3ZqOnWq1J/1vK9hzWzW3MNalnf4T1N8xFAAAEEEEAAgeoX8HggfPvtt91qxZ///Ge3Ps+HvS9Q0Qmjn+zMVa9PN+uq+Egt48oA73eGubsv75AyJne33hVoy0Zf/ilTI7/erj8m1NGi61tViycvQQABBBBAAAEEEPhNwOOB0JO4R48e1eTJk/XNN98oMjJSSUlJSkw8/UmL5oCaV155RQcOHFD79u11//33q379k7MOZyprx44d1hUYW7ZsUePGjXXvvfeqbdu2Jc35+eefrbsVN2/ebNVl6NCh6tWrlyeba5uy9jx52WnDx7iVe/T3VXt0f/uGeuaKJrapc6BXJFCXjeacKFTczNUqKC7WzgEXq3FEaKB3Je1DAAEEEEAAAQRsJWDrQGjC4J49e/Too49q586dGjt2rJ588slyL7zPyMjQ8OHD9eCDD6pDhw564YUXlJ2drWeeecYCr6isoqIi67vmhNQBAwZo4cKFmjlzpmbNmqWIiAgrYN52220aPHiwunTpory8PCtgXnBB4J2w+dvdd+WfMHrtx5u0YPdBffhfLXRDs2hbDeZAroxr2WhofCvFDp8TUE3tu3CLPvg1R09f3kQPXtwwoNpGYxBAAAEEEEAAAbsL2DYQFhQU6KabbtLEiROt2T7zpKScPHY/ObnsQSdz587VqlWrSi6637dvnzWjaP48JiamwrLWr19vBUlzGE5YWJj1DhP+zK8ePXpYM4OHDh3SAw88YPf+POv6nemE0ajXV+lgfpH2D75E9cNqnPX7KKByAqWXjcaNTFWN6ITKfdEPPvXhrzm6aeEWtYkOt66g4EEAAQQQQAABBBCoPgHbBsLdu3dbyzI//PBDa5bOPOb3ixcv1vPPP19G6KmnnlJ0dLTuuuuukp/17dvXmlVs1KhRhWV9/PHHSk1NtZabup4JEyaoSZMm1szhX//6V1100UVasWKF9u/fb/3e/FlcXFz19VQ1venQV6/K/CrvhNENOXm68N0f1aJumDb/uV011YjXuAQqOuzHn5Xyi4oVN2u1zPLRFTddqI4Navtzc6g7AggggAACCCDgVwK2DYRmL58JdwsWLFBQUJCFapZymkNrpk2bVgb58ccfV4sWLaxZPdczaNAg3X777UpISKiwLDMzuHz58pIZSPN9s5/QzBbec8891jLS/Px8mdBp9hf+61//kpmBNMtQA+2pKHS8vmm//rL0Vw1pWV+vd2seaE23fXvyNn6hrHljFIjLRu/+eoem/LRP97aN03P/r6nt+4IKIoAAAggggAACgSJg20BopxlCM1N5xRVXlMw+mn2NQ4YMsWYVa9WqFShjwWpHRSeM3vHVdr36c6ZeuqqZ7rowNqDa7S+NSU/ppuLjhxVoy0a/3XdEV/57g6Jrhmj/4EsVcvL/AfEggAACCCCAAAIIeFnAtoHQ7CG88cYb9fTTT5ec9mlm5IqLi0+7h3D16tUlh8hkZmZaM3uuPYQVlWX2ED700EN6//33FRp68pRDEwIHDhxo7SH8+9//rtjYWEcEQtcJowljV5QZeq4L6Vf1vVCX1mdZn5f/2Sy3+EBdNmoae95ba7Xt0AmlXdtSvZtG+YKXdyKAAAIIIIAAAo4TsG0gND1hDpExSzPNKaO7du2yQtsTTzxRcsro9OnTdd1111nLONPT063loY888oguueQSvfjiizKh0HXKaEVlmVNGhw0bpq5du6p///5atGiRZsyYUXLK6LfffmstITVlmeWnriWj5s8C6XGdMBoS1Ujxo9JOadqxwiJFzlilsJAgHf5LRwUzg+OTrnctGw2JSlD8qFSf1MFbL33ih3Q9tmK3bmkeo3d6nO+t11AuAggggAACCCCAQCkBWwfC0ncHmoNlzIxd6XsIzT2AJiCaaybMs2zZMk2dOvWM9xCWV9b27dut0Ld161Yr9N13332n3ENoZg/N/sXjx49bp56aQ2UaNGgQUIOpohNGl+w5pD98tFHXNIzU0sTWAdVuf2uMa9lo7LA5Cm0YOJe57z6SryZz1yg0KEhZQy5VZGiwv3UN9UUAAQQQQAABBPxOwNaB0O80/bzCrhNG63QZIfOr9PPU6nSN/X43F9LboI9zF6boyPdvqla73opJHG+DGnmuCl3TftaXGYc19epmGtGGfaqek6UkBBBAAAEEEECgfAECISOjRCDr3WTlbV6q6N7jVLt94ikyNyzYotTtOZrX43z1bR6Dmg8FXEt7g8LqWMtGg8Pr+LA2nn319I37dduXv+rq+Eh91YeZaM/qUhoCCCCAAAIIIFBWgEDIqCgRyJyepPy9G9UgaapqNut4ikzszNXaf7xAmYMuUYNwLqT39bBxnQZbXnj3dd3O5v2H84sUO+sH5RUW65db26l5nbCzKY7vIoAAAggggAACCJxBgEDIECkRON0Jo9sOHdd5b63TORE1tWNAe8RsIHB0bZpy5k8IyDsJb/38F739S5bGd0zQuA4JNtCmCggggAACCCCAQOAKEAgDt2/dall+xkZlzkhSeSeMvrk1SwMW/6Kbm8foXU5/dMvVmx92HS5T3oyuN9/r7bI/2pGr3p9tVvM6NfXLrfwPCG97Uz4CCCCAAAIIOFuAQOjs/i9pves6g7CmHVV/4NRTVO5dvkPPr9+nSVc0UXL7hojZRMB1CFCgHS5TWCw1mr1amXkF+rpPa3WOj7SJONVAAAEEEEAAAQQCT4BAGHh9WqUWVXTC6BUfbtB3mUe0rE9rXcV/nFfJ1xtfch0uY8puOHpJQB0u87f/7NRzP+7VHW1i9crVzbzBR5kIIIAAAggggAACkgiEDANL4HQnjOYXFStixirrM0eGdVAoN9LbasS4+q28q0JsVVE3K7Nq/1F1/OAnRdcM0b5BlzDu3PTj4wgggAACCCCAQGUFCISVlQrwz7lOGP39Zef/2XtYnVN/1mUNauv7my4McAX/a55rqW9IVIJ1BUUgPRe++6M25ORZ+1bN/lUeBBBAAAEEEEAAAc8LEAg9b+qXJZ7uhNHn1u3V377ZqbsvitMLnZv6ZdsCvdJ7pySqMDdd9fpNUnirbgHT3GfWZOjB73apT7No/fu/WgRMu2gIAggggAACCCBgJwECoZ16w0d1cZ0wGhp3gWJvm3tKLVxXAMzu3lxJLer7qIa8tiKBw9/N1cFFkxXesqvq3ZISMFi7j+Srydw1VnuyBl+qmLCQgGkbDUEAAQQQQAABBOwiQCC0S0/4sB6uZYflBYpmc9dqx5ET2vzndmpRl0vCfdhNp311Ud4hZUzubv08bmSqakQHzt193eZv1NL0Q5p6dTONaBNrR37qhAACCCCAAAII+LUAgdCvu88zlT/dCaP78woUO2u16oYGK3doB8+8jFK8IpCdNl7H1s1XRKf+iuqZ7JV3+KLQVzdk6o5l29WtUR0t6d3KF1XgnQgggAACCCCAQEALEAgDunsr17gDs+/Q8R0ry+xB+/DXHN20cIuuPydK869rWbnC+JRPBFzLfoPC6qhR8hKf1MEbL80+Xqh6M3+wit4xoL3OiajpjddQJgIIIIAAAggg4FgBAqFju/63hu+d0keFuXv0+xNGzYEe5mCPv3dM0GMdAmcZYqB2eea0Acrft0nRvcepdvvEgGnmDQu2KHV7jv55RRONad8wYNpFQxBAAAEEEEAAATsIEAjt0As+rsPpThjtmvazvsw4rAW9LlDPxnV9XEtefyaBo2vTlDN/gmo27agGA6ee6eN+8/O3tmap/+Jf1KF+ba3sy9UnftNxVBQBBBBAAAEE/EKAQOgX3eS9Sp7YvlL759yh358wWlQsRb62UscKi5U79FLVDeWER+/1gmdKNofLmCsoio8fLjPb65k3+KaUvMJixbyxSuavW/7cTudzuJFvOoK3IoAAAggg8P/buw/wqMq0/+O/9IQESGihCYhAVIoI6iq7iggoKmBBXwXEgoVVV92/6Oq6roJrWwV91/aKi1hBLCgCVlCxoaIggiBVeiihJ0BCyvyv+4QZAoQkkMycM5Pvua5caWee8nnOlPs8DYGIFCAgjMhmrXil/L1KB64w+vPmXer07gIdl5qoBZe2q3iCnOmqwPapI7XzxzeU1L630voMc7UsVZm59RBaT+Hwzo11H8OXq5KWtBBAAAEEEECgmgsQEFbzC8AfQNQ8/QbZl//4vwVZuunblbqmTT2N6dqimiuFT/ULtmVq43N9ZYvLpN88SdGJNcOn8GWU1OYQ2lzCY2omaOnl7SOiTlQCAQQQQAABBBDwggABoRdawcUy+FcYrTdwlOKbdw6U5Krpy/Xqks3s/+Zi2xxp1v42jaTFZfKLfKrzys/KKSjSnIvb6oS6SUfKw+MQQAABBBBAAAEESggQEFbzy2HdyG7y5WWr4e1f7NeblPHWPC3enqdf+rVVhzp8+A6ny8Q/DDimdmOnlzBSjmu/WqExizbprhMa6tFTmkZKtagHAggggAACCCDgqgABoav87ma+b3hhihoNnR4ozI78QtV++WclxUQp55rOio5yt5zkfvgCtrhM4fZ1OrDn9/BT8s4jpq3doZ4fLnb2IrQ9CTkQQAABBBBAAAEEKi9AQFh5w7BNIXfRdG2ZcIcSmnVW3RLbFHy0ervO+3iJzmxUU1/0zgjb+lXngmd//YLsK5IWlyn0SU3GztGG3QWa0fdYnZaeUp2bmLojgAACCCCAAAJVIkBAWCWM4ZmIP2g4cEGZ+2dl6oHZmbr7hIZ6hKF5Ydm4/t5fK3yDmyYpNrVxWNbjwELfMmOVnpm/Ube0baCnujSLiDpRCQQQQAABBBBAwE0BAkI39V3O+1ALypzz4WJ9unaHJp7dShc0T3W5lGR/pAJbJw/T7nlTnNVjS64ge6TpeeFxMzbk6I+TFio9KVbrrugoRjN7oVUoAwIIIIAAAgiEswABYTi3XiXLnvnwSU4KBy4oU/vl2dqRX6SsQR1VLzG2krnwcLcE9qycpU1jhyjSFpdpNm6uVu/co2nntVH3JrXc4iVfBBBAAAEEEEAgIgQICCOiGQ+/EvnrFylrzEDF1G6k9JsnBxL4bVuujn/7VzVPideK/izccfiy3nqEf3GZOv1GKDHjTG8V7ghLc9fMNXrsl/W6NqOeRp/BHplHyMjDEEAAAQQQQAABR4CAsJpeCP6tCRJbd1WdS0cGFF5atEmDv1qhy1rW0fjuLaupTuRUO2fmOO2Y9oTim3VWvRILB4VzDeds3qUT312glNhobbnqRMWxDG44NydlRwABBBBAAAGXBQgIXW4At7LfPnWkdv74xkHzy4Z8vVIvLMzSk6cepb+2T3ereORbRQJFudmyXkJfXk5ELS7Tavw8LcvO06SzW6kP81yr6GohGQQQQAABBBCojgIEhNWx1SUdakGZEybM19wtu/XdBcfp1AbJ1VQnsqrtX1wm+eT+qt1zaERUbvjsTA2blakBx9TR2LPoyY6IRqUSCCCAAAIIIOCKAAGhK+zuZ1ragjK7C4uUMma2YqKitHNwJ4biud9MVVIC/3zRqISaajT0iypJ0+1Elu3IU6s35ykxJkpbr+rkfOdAAAEEEEAAAQQQOHwBAsLDNwv7R/hXn4xr0Eb1rxsXqM8Xmdk664NFOqV+sn648LiwrycV2CeQNXqA8jcuVmrv+1WjQ5+IoOn87gLN3rxL489qqcuOqRMRdaKTAAKjAAAgAElEQVQSCCCAAAIIIIBAqAUICEMt7oH8/AuNJLXvrbQ+wwIlemTOOt3z41rd2raB/sOm3x5oqaorgn8Robj0DNW/dmzVJexiSo//sl5/m7lGFzZP1Xtnt3KxJGSNAAIIIIAAAgiErwABYfi23RGX3D+nrFaP25VyyoBAOhd8ulSTVm7TuLNaqj89Lkfs69UHrht5prO4TP3BYxXXMMOrxaxwuWwvQtuTMC4qylltNCUuusKP5UQEEEAAAQQQQACBYgECwmp4JWS9OFD5Gxap3sBRim/eOSBQ/9U52pRXoGWXt1fLmgnVUCayq5z99QuyrwN7hsO51qdPWqhvNuTopa4tdHWbeuFcFcqOAAIIIIAAAgi4IkBA6Aq7u5n6F5RpfM9PgYIsz85Ty/HzVC8hVllXdnS3gOQeFIGCbZna+FxfJ+0GN01SbGrjoOQTykSfnb9Rf5mxSmc3qaVPzmsTyqzJCwEEEEAAAQQQiAgBAsKIaMaKV+JQC8q8sWyLBnz+u/o0q61J57SueIKcGVYC/uHCkdJLuDWvUPVf+9lpg6xBJyotISas2oPCIoAAAggggAACbgsQELrdAiHO37+gzIF70t02Y5Wemr9RD53URPec2CjEpSK7UAlEYi/hOR8u1qdrd+jZPzbTTcc3CBUl+SCAAAIIIIAAAhEhQEAYEc1Y8UpseXuocpd8edD2A3+Y+JtmZu3UtPPaqHuTWhVPkDPDTiDSeglfWrRJg79aoT+lp+jrvseGXXtQYAQQQAABBBBAwE0BAkI39V3Ie8OzfVW4PXO/lSbzi3xKHjNbhT6fcgZ3UlIMqzW60DQhyzLSeglz8otU55WfVeDzaeWADjoqOT5klmSEAAIIIIAAAgiEuwABYbi34GGUvyg3W+uf6OY8ouSCMt9tyFGXSQvVLi1J8y5pexgpcmq4CkRaL+FFny7VxJXb9PgfmuqODg3DtVkoNwIIIIAAAgggEHIBAsKQk7uXoX9BmYRmnVX3ilGBgjw5b4Nu/361rsuop/+e0cK9ApJzyAQirZfwzWVbdPnnv6tT3RqadfHxIXMkIwQQQAABBBBAINwFCAjDvQUPo/z+fegOXFDmss+W6a3ft2r0GS10bQZ7uR0GaVifGkm9hLmFPqW9Mlv2fell7XVMLfbRDOuLk8IjgAACCCCAQMgEPB0Q7tq1S0888YS+//57paSkaODAgerTp88hcb799ls9//zz2rx5szp06KA777xTdevWdc4vL61Vq1ZpxIgRWrp0qZo0aaLbbrtN7dq12y+vwsJC3XjjjVqzZo0+/PDDkDVSVWV0qAVlmo+bq1U79zjDRW3YKEf1EIi0XsKBn/+uccu2aHjnxrqvU/jvsVg9rkJqiQACCCCAAAJuC3g6ILRgMDMzU/fee69Wr16te+65Rw8//LDat29/kNv69et17bXX6q677lKnTp309NNPa+vWrXrsscecc8tKq6ioyHlsly5dNGDAAE2dOlWvvvqqXnvtNSUnJwfyevvttzVjxgwtWrQoLAPCdSO7yZeXvd+m5JtyC1T/tTlKiolSzjWdFR3l9iVJ/qEU8PcaJ7Y5U3UuGRHKrKs8r8krt6nvp0t1TM0ELb384NeIKs+QBBFAAAEEEEAAgQgQ8GxAWFBQoIsuukgPPfSQ09tnx8iRI53vQ4cOPYh+3Lhxmj17ttPLZ8fGjRudHkX7e1paWplpzZ8/3wkkJ0yYoISE4qFmV155pfPVo0cP5/esrCynx/GWW27RP//5z7ALCP29QVEJKWo0dHrA770V23Tx1KXq3rimpp2fEQGXNFU4HAFbaGjDs33ky8tRvYGjFN+88+E83FPn2mq56a/N0dY9hZpzcVudUJfebk81EIVBAAEEEEAAAU8KeDYgXLt2ra6++mpNnDgx0EtnP3/++ed66qmnDsJ85JFHlJqa6gzp9B8XX3yx06vYqFGjMtOy4Z+TJk1yhpv6j+HDh6tp06ZOz6Ed9vvpp5/upGUBabgNGc1dNF1bJtyhAxeUuWvmGj32y3r948RGevCkJp68SClUcAX8vYTxzTqrXonFhoKba3BSv/6rFRq9aJPuOqGhHj2laXAyIVUEEEAAAQQQQCCCBDwbENpcPgvuPv30U0VFFY9jtKGcb775pkaPHn1QE9x3331q1aqV06vnPwYNGqTrr79ejRs3LjMt6xm0oaD+Hkh7vPU0Wm+h9QjOnDlTb731lvO33377LSwDQv+H/pqn3yD78h9dJy/UV+tzNOnsVurTPDWCLm2qUlGBSOol/DwzW90/WOTsRbhqQPHIAg4EEEAAAQQQQACBQwt4NiD0Sg+hBZVDhgzR/fffrxYtWoRtQLj59SHKWzVLdfqNUGLGmc4VUeSTUl6apd2FPmUN6qh6ibE8V6qpQKT0EvokNXp9jjbsLtCMvsfqtPSUatqiVBsBBBBAAAEEEKiYgGcDQptDeOGFF+rRRx8NrPZpC8P4fL5DziGcM2dOYBEZm/NnC8T45xCWlZbNIbz77rv17rvvKi4uzpGz4apXXHGF2rRp4/Qy1qpVy/m7rTSanZ3tDE+1BW5at25dMWmXzyptQZmfN+9Sp3cXqGXNBC1jEQ6XW8jd7COpl/C2Gav01PyNurVtA/2nSzN3YckdAQQQQAABBBDwuIBnA0JzsyGctjiMrTJqWz1Y0Pbggw8GVhl98cUX1atXL2ebiHXr1jmB2z/+8Q917NhRzzzzjLMQjH+V0bLSslVGBw8erK5du6p///6aNm2axowZ46wympSUpG3btgWaccmSJc58wtdff90JEmNjvd+r5l9QJqZ2I6XfPDlQl/9bkKWbvl2pAcfU0dizWnr8UqV4wRaIlF7C7zfu1Gnv/6b0pFitu6KjWDg32FcO6SOAAAIIIIBAOAt4OiAsuXegbf9gPXYl9yE877zznADRtpmw45tvvtGoUaPK3YewtLRWrlzpzBFctmyZM+fwr3/960H7EFoe4TiHcNfcydo2ZbgSW3dVnUuLV2q146rpy/Xqks36z2nNdGu7BuF8HVP2KhCIpF7CZuPmavXOPZp2Xht1b1Lcu8+BAAIIIIAAAgggcLCApwNCGqxqBLZPHamdP77hLCZTckGZjLfmafH2PP1w4XE6pf6+/RarJldSCUcBfy9hTO3Gqn/tWEUn1gzHaujvM9fo0V/W67qMevrvGS3Csg4UGgEEEEAAAQQQCIUAAWEolF3Ow7+gTMl95nbkF6r2yz8rLipKOwd3Uhw70rvcSt7JPmv0AOVvXKzkk/urds+D9/z0TkkPXZK5W3brhAnzlRIbrS1Xncj1HQ6NRhkRQAABBBBAwBUBAkJX2EObaebDJzkZNrz9i0CPz0ert+u8j5fotAbJmnHBcaEtELl5WiB//SJljRnolLHkqrSeLnQphWs1fp6WZedp8jmt1btZ7XArPuVFAAEEEEAAAQRCIkBAGBJm9zLxf7g/cEGZYbMyNXx2pv5f+3Q9cepR7hWQnD0pkDNznHZMe0JRCTWVfvOksBw6+uDP6/TPn9ZqYKs6er0biyZ58kKjUAgggAACCCDgugABoetNENwC+D/YJ7XvrbQ+wwKZ9fhgkT7LzNbbPY7RJUenBbcQpB6WAv6hxvHNOqveFaPCrg7LduSp1ZvzlBgTpa1XdXK+cyCAAAIIIIAAAgjsL0BAGOFXxNbJw7R73hTV6nG7Uk4Z4NTWNqRPfmmWcgt92nzliaqTEBPhClTvSARKrjpa8vo5krTcesxJ7y3QrE279Gb3lvqflnXcKgb5IoAAAggggAACnhUgIPRs01RNwTY821eF2zNVf/BYxTXMcBKdmbVTf5j4m1rXStDiy9pXTUakEpECuYuma8uEO5yho/UGPh+4hsKlsiPnrtcdP6zRhc1T9d7ZrcKl2JQTAQQQQAABBBAImQABYcioQ5+Rf0P6qIQUNRo6PVAA/4fkwRn19CJL8oe+YcIsR/+2JXHpGc5WFOF0bNhdoEavz1FsVJSz2mhKXHQ4FZ+yIoAAAggggAACQRcgIAw6sXsZHGpD+gs/Xar3V27TS11b6Oo29dwrIDmHhYANHc16cYAKt68Ly60ozpi8UF+vz+F6D4urjUIigAACCCCAQKgFCAhDLR7C/EqbP2jZn/zeAv20aZd+v7y9jq6ZEMISkVW4CoTzVhTP/5alG79ZqV5Na+mjc9uEaxNQbgQQQAABBBBAICgCBIRBYfVGoutGdpMvL1sNbpqk2NTGTqHmbN6lE99doBYp8Vrev4M3CkopwkIg++sXZF/hNp9wU26B6r82Rw2TYrXosvaqFcciSmFxwVFIBBBAAAEEEAiJAAFhSJhDn8mh9h/0zx+8LqOe/sv8wdA3TJjn6O91tqCwds/bVaNDn7CokX+Y9L9Paaq/ndAwLMpMIRFAAAEEEEAAgVAIEBCGQtmFPA61/2CvjxbrkzU79MZZLXX5MSzD70LThH2W/qDQKpLa+/6wCAo/Wr1d5328RM339oyzI2HYX4ZUAAEEEEAAAQSqSICAsIogvZbMlreHKnfJlwd9YE8aU7z/4MZBHVU/MdZrxaY8YSLgX3k0nILCluPnav2ufM286Hi1S0sKE2mKiQACCCCAAAIIBFeAgDC4vq6lnvnwSU7eDW//QtGJNZ2fbaVFW3GxQ50k/dKvrWtlI+PIEPCvYhsuQeFjv6zXXTPX6PyjamtKr9aR0QjUAgEEEEAAAQQQqKQAAWElAb348D0rZ2nT2CGKa9BG9a8bFyji/bMy9cDsTP2/9ul64tSjvFh0yhRmAiWDwpRTBqhWj9s9W4Ps/CI1GTtH9n3eJW3pJfRsS1EwBBBAAAEEEAilAAFhKLVDlJd/Ncjkk/urds+hgVz/OGmhZmzI0ZRzWuv8ZrVDVBqyiXSBkkGhLTJj8wq9etz5wxqNmLteA1vV0evdWnq1mJQLAQQQQAABBBAImQABYcioQ5dR1osDlb9hker0G6HEjDOdjHcWFCn15dnOz9uu7qTk2OjQFYicIl7AgkKbV+jLy1FceobqDnw+MFTZS5XP3JWv5uN+UZFPWj3wBDWuEeel4lEWBBBAAAEEEEAg5AIEhCEnD26GRbnZWv9ENyeTxvf8FMjsg1Xb1fuTJfpjeoq+6XtscAtB6tVSwLY62TLhDhVuX+fpvQqv+XK5Xl68WX9p20BPd2lWLduKSiOAAAIIIIAAAn4BAsIIuxZyF013PpQnNOusuleMCtTu9u9X68l5G3Rfp8Ya3rl4k3oOBKpawG5IbH3nDuWtmuUk7cVtKRZvz9Wxb/2qxJgorR3YUWkJbFRf1dcB6SGAAAIIIIBA+AgQEIZPW1WopP494mxxD1vkw3+cMGG+5m7ZrS97Z+iMRsWrjnIgECyBknsVenGxmb6fLNHkVdt174mN9K+TmgSLgXQRQAABBBBAAAHPCxAQer6JDq+AG57tq8Ltmao/eKziGmY4D96aV6g6r/6shOgo5QzupNgotuU+PFXOPhKBkovNxDfrrDqXjPDMvMJv1ufo9MkLlRYfo7VXnKCkGObUHkkb8xgEEEAAAQQQCH8BAsLwb8NADQq2ZWrjc30VlZCiRkOnB/4+ftkW9f/8d/VqWksfndsmgmpMVbwuYPMKbQsU/2IzqeffF7hR4XbZT35vgX7atEsjTz1Kt7dPd7s45I8AAggggAACCLgiQEDoCntwMvX3yCS27qo6l44MZHL9Vys0etEmjfhDUw3t0DA4mZMqAocQsBsVNq8wf+NiZ7GZtN73B1a/dRNt4optumjqUmel0ZUDOtBz7mZjkDcCCCCAAAIIuCZAQOgafdVnvOXtocpd8uVBC3k0HzdXq3bu0c8XH6+OdWtUfcakiEA5ArbYjG1LsXveFOfMmqff4Hy5efgkHfvWPC3enqcxZ7TQNRn13CwOeSOAAAIIIIAAAq4IEBC6wh6cTNeN7CZfXrYa3DRJsanFK4kuz85Ty/HznLlSW646MTgZkyoCFRTImTlOO6Y94Zxtm9jb4kfRie4tcjR64SZd//UKtamdoIX/017Mrq1gQ3IaAggggAACCESMAAFhhDSlzdXKGjNQMbUbKf3myYFa/Xdhlm74eqX+p2Wa3ux+TITUlmqEs4BtjbJ1yrDAvMK0fo8HbmCEul57inxqOvYXZeUW6N2erXRRi9RQF4H8EEAAAQQQQAABVwUICF3lr7rM/T0vSe17K63PsEDCtpiMLSrzwunNdf2x9asuQ1JCoBICdgNj25ThgXmF9QY+79piM4/9sl53zVzDokuVaE8eigACCCCAAALhK0BAGL5tt1/JN78+xNkMvE6/Efst2JH+2hxtzC3Qssvbq2XNhAipLdWIBIGSm9jbYjPpN09yZfhodn6RmoydI/v+ybltdHbTWpHASx0QQAABBBBAAIEKCRAQVojJ+ydlPnySU8iGt38R+FBtG9HbhvTNkuOdVRQ5EPCigP9mRlx6hupfO9aVIv5rdqbum5WpDnWS9Eu/tq6UgUwRQAABBBBAAAE3BAgI3VCv4jxtTtaWCXcorkEb1b9uXCD1/523Qf/v+9W6LqOe/ntGiyrOleQQqBoB6ynMenGACrevcxaaSe19f9UkfBip7Coo0tFvzHV60+klPAw4TkUAAQQQQACBsBcgIAz7JpSznP/OH984aCn/Pp8s0ZRV2/XGWS11+TF1IqCmVCFSBUpuYG8BoQWGoT7GLNqka79aoUZJcfrtf9qpdnxMqItAfggggAACCCCAQMgFCAhDTl71GW54tq8Kt2eq/uCxgYU5Cnw+pb78s3YWFGnLlScqLYEPt1UvT4pVKbBr7mRnoRk7Sl7LVZlHeWmd9v5v+n7jTt1wbH2NOr15eafzfwQQQAABBBBAIOwFCAjDvAn9201EJaSo0dDpgdp8uyFHf5q0UO3TkjT3EuZEhXkzV5vi+3u73VpkZvH2XLV9+1cV+KTvLjhOpzZIrjb2VBQBBBBAAAEEqqcAAWGYt7v/A/SB2008t2Cj7vx+jYYcX19PnHpUmNeS4lcnAbcXmRk2K1PDZ2eqda0ELb6sfXWip64IIIAAAgggUA0FCAjDvNH9w0XrDRyl+OadA7Wx1UVtldFPz2ujnk1YRj/Mm7laFd/tRWbyCn067u15Wp69R/84sZEePKlJtfKnsggggAACCCBQvQQICMO4vf3DRWNqN1L6zZMDNVmRvUdHj5+rGjHR2nFNJ8VEhXElKXq1FCi5yEytHrcr5ZQBIXX4cl22zpyySLFR0px+bdU2LSmk+ZMZAggggAACCCAQKgECwlBJByGfrZOHafe8KUo+ub9q9xwayOHJeRt0+/er1e/oNL3T45gg5EySCARfwL/IjM0ntP0JY1MbBz/TEjlc8+Vyvbx4szrVraGfLj5e3FcJKT+ZIYAAAggggECIBAgIQwQdjGzWjewmX172QSsyXjJtmSYs36pXzjxaV7auG4ysSROBkAj4b3rEN+useleMCkme/ky25hUq4615ysot0FNdmumWtg1Cmj+ZIYAAAggggAACoRAgIAyFchDyONRm9Bt2F6jR63MUHSVlDWK7iSDQk2QIBUrOJ3Rj6OhrSzbryunLlRQT5Sww0zQ5PoS1JysEEEAAAQQQQCD4AgSEwTcOSg7+npMDPyQ/9etG3fbdKp13VG190Kt1UPImUQRCKbBn5SxtGjvEydKN/QnPmrJIX6zL1sBWdfR6t5ahrDp5IYAAAggggAACQRcgIAw6cdVnYL0m65/o5iTc4KZJ+82t6vL+b/pu40693u1oDWzFcNGq1ydFNwT826vEpWc48wlDefyenad2b/+q3YU+Pd2lmf7C0NFQ8pMXAggggAACCARZwNMB4a5du/TEE0/o+++/V0pKigYOHKg+ffockuTbb7/V888/r82bN6tDhw668847VbducVBUXlqrVq3SiBEjtHTpUjVp0kS33Xab2rVr5zx24sSJ+uijj7R27VrVrl1b559/vgYMCO2qhyUr7V9sI6FZZ9UtMa9qZc4etXhjrhJjopzhoilx0UG+fEgegdAIlBw6WvP0G2RfoTzGLt2sK75YrroJMZp2foY61q0RyuzJCwEEEEAAAQQQCJqApwNCCwYzMzN17733avXq1brnnnv08MMPq337gzeLXr9+va699lrddddd6tSpk55++mlt3bpVjz32mINXVlpFRUXOY7t06eIEelOnTtWrr76q1157TcnJyXrxxRfVsWNHtWzZUhY4Dh8+XDfeeKN69uwZtIYpK+Etbw9V7pIvldr7ftXosC9AfnTOOv39x7W6rGUdje/O0DZXGodMgybg9tDRW2es0tPzNzpB4c/92uoo5hMGra1JGAEEEEAAAQRCJ+DZgLCgoEAXXXSRHnroIae3z46RI0c634cO3bfFgp9q3Lhxmj17ttPLZ8fGjRudHkX7e1paWplpzZ8/3wkkJ0yYoISEBOfxV155pfPVo0ePg1rjySefVExMjG699dbQtdTenAq2ZWrjc32d3xre/oWiE2sGytBxwnz9smW3Jp7dShc0Tw152cgQgWALuDl0tMgn9fposaau3aHjUhP140XHKzmWXvhgtznpI4AAAggggEBwBTwbENrwzKuvvtoZrmm9dHbYz59//rmeeuqpg1QeeeQRpaamOj13/uPiiy92ehUbNWpUZloffvihJk2a5Aw39R/WC9i0aVOn57Dk4fP5NGTIEGfoalnDV4PVbDkzx2nHtCeU2Lqr6lxaHCDbsWxHnlq9OU8psdHactWJirNlRjkQiDABt4eO7iwo0snvLdBv23LVs0ktfXxuG2dFXw4EEEAAAQQQQCBcBTwbENpcPgvuPv30U0VFFX/isqGcb775pkaPHn2Q93333adWrVo5vXr+Y9CgQbr++uvVuHHjMtOynsEZM2YEeiDt8dbTaL2Ft9xyy3552fDRn376yQlK4+LiQt7uWS8OVP6GRarTb4QSM84M5H//rEw9MDtT12XU03/PaBHycpEhAqEScHvo6Jqde5ygcP3uAt3atoH+06VZqKpOPggggAACCCCAQJULeDYg9GIP4fjx450A1eYjWm9kqA//cNGohBQ1Gjp9v+xbjZ+nZdl5+vz8DHVrvG8YaajLSH4IhELAzaGjVr+5W3br1IkLnJVHx5zRQtdk1AtFtckDAQQQQAABBBCocgHPBoQ2h/DCCy/Uo48+Gljt0wIxG7J5qDmEc+bMCSwik5WV5SwQ459DWFZaNofw7rvv1rvvvhvo9bPhqldccUVgDuE777zjDCu1+YP+lUurvDXKSdD/ITipfW+l9RkWOHv2pl3q/N4CNUiM1fpBHcUItlC3DPm5IZA1eoDyNy52VhwN9aqjVt8pq7ar7ydLnCGjH53bxhlCyoEAAggggAACCISbgGcDQoO0RWRscRhbZXTNmjVO0Pbggw8GVhm14Zu9evVytolYt26dMzz0H//4h7Mi6DPPPCMLCv2rjJaVlq0yOnjwYHXt2lX9+/fXtGnTNGbMmMAqozZ30XoHbRhpgwYNnDaOjo5WbGxsSNt7w7N9Vbg9U/UGjlJ8886BvP/2wxo9Pne9bm+frpGnHhXSMpEZAm4J5K9fpKwxA53sD3xOhKpMI+au150/rHEWl7FFZmyxGQ4EEEAAAQQQQCCcBDwdEJbcO9AWlrEeu5ILuZx33nlOgGjbTNjxzTffaNSoUeXuQ1haWitXrnQCvmXLljlzDv/6178GeiZttVILTEsetkWFLTwTqsP/4TemdiOl3zx5v2ybjZur1Tv36McLj9dJ9dkfLVRtQj7uC2R//YLsK6Z2Y2fD+pKr7oaqdIO/XKGXFm9S0+Q4/XxxW9VLDO2NolDVk3wQQAABBBBAIDIFPB0QRib5kdVq6+Rh2j1vipJP7q/aPfdtuzFjQ47+OGmhWtdK0OLLDt6f8chy41EIhI+Af+jogc+NUNbgnA8X69O1O9SyZoK+7JOhpuxRGEp+8kIAAQQQQACBSggQEFYCL1QPtaX2bbioLy9b9QePVVzDjEDW/s2yh3VurPs7NQ5VkcgHAc8IeGHo6I78QnV5/zfN35qrdmlJmndJW8/4UBAEEEAAAQQQQKAsAQLCMLg+ds2drG1ThiuhWWfVvWLUfiVOf22ONuYWaOll7XVMrYQwqA1FRKDqBbwwdHRLXqF6frBIszfv0in1kzX1/DaqFRdT9ZUlRQQQQAABBBBAoAoFCAirEDNYSfkXk0ntfb9qdOgTyGba2h3q+eFida5XQz9ddHywsiddBMJCwAtDR3Pyi3TWBwv1Y9YudaybpM/PP1ZpCQSFYXEBUUgEEEAAAQSqqQABoccb3t87WNpiMtd+tUJjFm3SiD801dAODT1eE4qHQHAFvDB01GpoQeE5Hy2Wze9tm5ao6b2PZaGZ4DY9qSOAAAIIIIBAJQQICCuBF4qHZr04UPkbFunA3sH8Ip/qvPKzdhYUaf0VHdUgiZUNQ9Ee5OFtAS8MHTWh3YVF6vFBcVCYUTtR3/QlKPT2lUPpEEAAAQQQqL4CBIQebvs9K2dp09ghKq138KlfN+q271apW6Oa+rz3vkVmPFwdioZASAQ2vz5EeatmHbQib0gyL5HJroIinffxEn25LlvH1k7UF30y1DApLtTFID8EEEAAAQQQQKBMAQJCD18gm14foj2rZqnm6Tc4XyWP9u/M169bd2tst5Ya0KqOh2tB0RAIrUDBtkxlvThAvrwc1ek3QokZZ4a2ACVyyyv0qddHizV9XbazJcVXfY5Vk2SCQtcahIwRQAABBBBA4CABAkKPXhT+3sGohBRnI/qSG27/lLVLJ09coLoJMVo/qKNio6I8WguKhYA7Ajkzx2nHtCcUlVBT6TdPcmXDen/NSwaFzVPi9WWfY2XfORBAAAEEEEAAAS8IEBB6oRVKKUNZvYN/+2GNHp+7Xvd0bKSHTm7i0RpQLATcFfAPHU1sc6bqXDLC1cLkFvp00adL9NxeXckAACAASURBVPGaHWqaHOcEhdZjyIEAAggggAACCLgtQEDodguUkr8Nedv4XF+V1juYuStfR78xV0U+n1YNOEGNajD8zINNSJE8IOCloaPGYQtBnf/xEk1du0NNasTpq74EhR64TCgCAggggAAC1V6AgNCDl8DWycO0e96UUhfFuP371Xpy3gYNzqinF89o4cHSUyQEvCPgpaGj/qDwok+X6oPV25WeFOv0FNoqpBwIIIAAAggggIBbAgSEbskfIl9/76D9u8FNkxSb2jhw5rY9hWr8+hzZnKTf+3dgHpLH2o7ieFPAS0NHTajA59OFnxQHhfUSYjW9T4bapiV5E49SIYAAAggggEDECxAQeqyJ/b2DSe17K63PsP1K98+f1urBn9fpspZ1NL57S4+VnOIg4E0Brw0dPTAoTIuPcbaO6Vi3hjcBKRUCCCCAAAIIRLQAAaGHmres3sGc/CI1en2OcgqK9OslbelR8FC7URTvC3ht6Kg/KPyfacv03optqhUX7QwfJSj0/rVECRFAAAEEEIg0AQJCD7Vo9tcvyL5K6x389y/rdffMNerdrLYmn9PaQ6WmKAiEh8CWt4cqd8mX8sKqo36xQp906bSlgaBw2vkZOrl+cniAUkoEEEAAAQQQiAgBAkKPNGNRbrY2PNtXvrxs1R88VnENMwIl21PkU9Oxvygrt0CzLjpeneoxtMwjzUYxwkig+DnWx9mwPrX3/arRoY8nSm9B4RVf/K7xy7YoJTZan5zXRl3SUzxRNgqBAAIIIIAAApEvQEDokTb2zx1MbN1VdS4duV+pnp2/UX+ZsUrdGtV05hpxIIDAkQnkLpquLRPucDasr3/t2P0WbTqyFKvmUT5JAz4vDgqTYqL04bltdGajmlWTOKkggAACCCCAAAJlCBAQeuDyyF+/SFljBjolOXBlUVuRsPm4ubL9B6ed10bdm9TyQIkpAgLhK+AfOhrfrLPqXTHKMxUpGRQmREfp4/MICj3TOBQEAQQQQACBCBYgIPRA42a9OFD5Gxap5uk3OF8lj5cXb9I1X65Qp7o1NOvi4z1QWoqAQHgLlBw6WqvH7Uo5ZYBnKmRB4dXTl+vVJZtlQeHkXq3Vk5tAnmkfCoIAAggggEAkChAQutyq/tUPY2o3Uv1rxyk6cf9hYp+u2aHhszN11wkN1bd5qsulJXsEIkPAP3TUauOl+YR+3Ru+XqH/Ltzk/Pr+2a147kfGZUctEEAAAQQQ8KQAAaGLzVK8P9pAZyGZegNHKb55ZxdLQ9YIVC+B7VNHauePbziV9lpQaD2FQ/YGhbFR0ls9WumiFtwQql5XKLVFAAEEEEAgNAIEhKFxLjWXLe/codzF01XaQjIuFousEag2Al4OCq0RbpmxSs/M3+i0x+vdjtbAVnWrTdtQUQQQQAABBBAIjQABYWicD8pl32qHKUq/efJBQ0VdKhbZIlDtBHbNnaxtU4Y79a7VY6hSTunvKYP/991q/e+vG5wy3dGhoR7/Q1NPlY/CIIAAAggggEB4CxAQutB+tqiFDRUt3J4pry1q4QIHWSLgukDJoND2J7QhpF46bB7xsFmZTpE616uhyee0VqMacV4qImVBAAEEEEAAgTAVICB0oeH8w9QSmnVWXQ8te+8CBVki4BkBrweF7yzf6qxAurOgSPUTYzWhZyud3pAN7D1zAVEQBBBAAAEEwlSAgDDEDVdyz8H6g8cqriEbzYe4CcgOgUMK2FDurVOGyZeXo8Q2Zzo9hQeu/Osm35Lteeo3danmbd0tW2xmxKlH6bZ26W4WibwRQAABBBBAIMwFCAhD3IBl7TkY4qKQHQIIlCJgN202jR3iBIVRCTVVu+dQ1ejQ2zNWeYU+3fTtSo1ZVLwtxWUt6+ilM1soKSbaM2WkIAgggAACCCAQPgIEhCFsK/9Q0UPtORjCopAVAgiUIWBbwmyfMlx5q2Y5Z8WlZ6h2j9s9tTXMuKVbdN1Xy7W70Ke2aYn6oFcbNU+Jp10RQAABBBBAAIHDEiAgPCyuIz+55PwkhooeuSOPRCCUAva8zf76BRVuX+dkawvOpPzpesWmNg5lMQ6Z12/bcnXBJ0u0ZEeeUuNjNL77MTqnaS1PlI1CIIAAAggggEB4CBAQhqCdSs4b9NoG2CGoPlkgENYCtiqwbWCfM3OcM4zUHxjGN+us+GadXA8ObZEZ28R+7NItTtme7tJMf2nbIKzNKTwCCCCAAAIIhE6AgDDI1sXzkf4sX162ktr3VlqfYUHOkeQRQCAYAjaM1HoLd8+bsl/yMbUbK6G5BYfuBogv/JalId+sdMp2aoNk/e2ERrqoRWowKEgTAQQQQAABBCJIgIAwiI1Zcr9BgsEgQpM0AiEUsMAwd/F07Vk5y5lj6O81LK0ItihNXHqbcktnQ1BtbvGBhwWZ/iM6IaXcVYnnb92tyz5bpvlbc52HnVAnSf/s1Fj9jk4rtwycgAACCCCAAALVU4CAMEjtbsHg5rF/Vv6GRYpr0MbZb9BLy9cHqdoki0C1E7BRABYYViRArCyOBZj+3sjENl1LHa5a4PPp5UWb9cicdfo9O8/Jsn1aku7rXBwYRlW2EDweAQQQQAABBCJKgIAwSM255Z07nF4EgsEgAZMsAmEgYDeGCjYsLrekBdszAwvXlDzZgkz/Udo5JYerWoB44E2nV5ds1kM/Z2rx9uLA8PjURJ3ZuKb+3rGRmiazImm5DcMJCCCAAAIIVAMBAsIgNPK2KcNlqxNGJaSo3sBR5Q7zCkIRSBIBBCJQwIar7lk1S3krZzk3nA4crmrbYySf3P+gfRPHL9uif83O1IJtxUNJ/cfpDVN0aoMUnZaeoj+mp6hBUmwEqlElBBBAAAEEEChLgICwCq8P6w2wnkH7wEYwWIWwJIUAAqUKlByumrvky8A5NrQ05ZT+zkJWJbfI+HD1dr2+ZLOmrd2hrNyCg9JskRKvPzRI0dVt6qpWfIyOSonXUfQkcvUhgAACCCAQ0QIEhFXUvCVXEyUYrCJUkkEAgcMSsJEJO2e+ofyN+4ap2t6JySddftBIheXZefph4059tyFH323M0Y9Zuw6ZlwWKR9dMcAJE+94sJV7N7atmglrVSjisMnIyAggggAACCHhLgICwCtojZ+Yb2jFtpJNSQrPOSrtkBAvIVIErSSCAwJEJ2NzDnXMn77dFhq1YasFhjQ69D5nojA05mrlxp2Zt2qXF23O1bEeuNucVlluIxjXiAgFiy5oJzvzElrUSnN7F49MSy308JyCAAAIIIICAewIEhJW09y8eY8nY3J3aPYdWMkUejgACCFSNgM053PnjG86cZv98Q1uIxj/PsCIrH+cV+pzVSlfaV84erdj73X62v63bla+icopbLyFWzWsW9yq2qJmg5ikJgd/t57SEmKqpMKkggAACCCCAwGELEBAeNtn+D8h8+CRnvmBa72FKzDizkqnxcAQQQKDqBWx+sy1Ck/31C4HVTG2eYVLGmUr50/Wlbl9xOKVYuiNPq3P2yIah+gPFVXt/X5Gzp9ykasZFFweJzjBUCxwT1KJmvFrWTNRJ9WuU+3hOQAABBBBAAIEjFyAgPHI755H2AevAhRsqmSQPRwABBIImkLtoutNraHsn+g//cNLStq6oioKs3rlHToC4I8/5viKn+LsFkPY9t9B3yGxs2KkNP60dH6O2aUnKqJ2oY1MTdXxakiyQ5EAAAQQQQACBygkQEFbOj0cjgAACYSlgw0nthtaB21cktjlTxV8H72sYrIpu2F2glTk2LHWP833F3u8bdueXudiNzV20wPC41EQnULSfM1ITZX/nQAABBBBAAIGKCRAQVsxJr776qiZOnKjCwkJ1795dN998s2JimPdSQT5OQwABjwr4h5Naz2HJrSusuG4EhwcyZecXaeG23Vq4LVe/2dfW4p+X7shVwSE6FmvHxei4tOKexONSkwLfracxJsqjDUGxEEAAAQQQcEmAgLAC8J999plGjRqlf//730pOTtY999yjbt26aeDAgRV4NKcggAAC4SFQXnBoQ0vj0tsoOiHloG0s3KhhyQDRfl60fbcWbM3VzoJDL3NzvAWJe3sVLWDsd3QdJRIlltl8W/MKtbOgULsKihzbnflFJX7e93f7f36RT9FRUkpcjDOkt6bzfe/P8TFKiY1RzfjivyfHMuTXjecNeSKAAAIHChAQVuCauOuuu9SuXTsNGjTIOXvatGlOj6F9cSCAAAKRKFBWcFiyvhYk2pHQvPh7XHqGs9BWWYf/3GC52ZxF60VcsHW3Fm3LLf55227Z0NTSjja1E9SoRrzzL+tAtPmKdRNjlZ4Up9T4GNVLjHV+d74nFH+3r4oeeSv3zdf0PyZ/w2KlnNK/oklUKGCzYM0J2g4RsO37vwV4pQd1+x5bqB355a0fW/ni19obNPoDyFoWNJYSTBb/bV9gaT+nHBBw1iDArHyDkIJnBXYXHvz8tud78XN+/xszeYVFurtjI8/WhYJ5T4CAsAJtcvnll+vWW29Vly5dnLOXL1+uG264QR988IHi44s/RHAggAACkSrgDw4tsCnavk4F2zMDq5UGs84VCS4t/9jUxoqpXf6Hn92FPq3fla91u/Zo/e4C5/vanfnanFd6oFhW3Rrkb9JRBZuUGBOtpNhoJURHqZZ2Kz1nuaKjKjYudcpFH5XLV+jzOYFt5q78QLDn760LRcBmBbSgLTk2Rslx0bKgy3r2kuNi9v0cu/fvcTGKj45Skc+n7XsKZcN9c/Lte3EAWvy3QmXvKVJOQWGZiwmVC3OIE2y4cCBQ3NsT6e+l3BdA7u25jN+/F3O/ADM+Wkkx9GAeaTuE8nEWEBX4fLKBAfZ8KSjyOb8XFmnv3317/178e+CcIp+zZY71attjbG2rfY/dm4b/b/7/701/Xz7FefoOvS5WhSnmbtmlLXkW2BUGbuoEeuXLGPVwqAx8159U4bw5EQECwgpcAxdccIEeeOABnXDCCc7ZGzdudIaLvvPOO5o0aVIFUuAUBBBAIPIEYgpzVWP3eqdiyTnLne9Ju9YptiivzMr6z/WSSKGiVKQoFUp7v1tgIxX//YDvvigVVSzmc6r4a9JxB1X196TmGp1eNdMOklSoBPmUEFWkeBUpwb6ifPt+3vu7/c3+t+8cO097zy/xdxU/NjnKNIJ77PTFKFfRe79ilOeL0m4V/213UZTz3f+33T7/edHao2jt8v/ui1G2mNMf3JYidS8I1HCe63ufq1FFSrTnaonnuvPctr9H+fT+Nb28UGTKECYCBIQVaKiyegjHjx9fgRQ4BQEEEEDgcAUqElxamvH52xS/Z9vhJl+p8/fEp2prXD0nKHG+FKOt0TW0Kqm59vgOI1ospxQ2H6+B9qhedH6JAC90AVulkFx48E7FKtdXHEjmqjjALA4cY5S3N/B0/qYY7XbOK/67E2yWCDhzVPEhwS5UkyxLCMSpyLkdEC1f4Kv49+K/xUTZ36Von81v9ZU4V84iU9G+Iuf/zs/+NHz2OH8ae9N3zi2RnqVtj7PH7P2ffXd+d76K04zyFT+++Oe9j3fKYufuy9M/lTnObu6UCPosyEtybksd3nHllVce3gM4u1oLEBBWoPltDmGHDh0Ci8jYIjOvvPIKcwgrYMcpCCCAAAIIIIAAAggg4F0BAsIKtI0tIjN69Gg9/vjjqlGjhv7+97+ra9eurDJaATtOQQABBBBAAAEEEEAAAe8KEBBWsG2sR/D9999nH8IKenEaAggggAACCCCAAAIIeF+AgND7bUQJEUAAAQQQQAABBBBAAIGgCBAQBoWVRBFAAAEEEEAAAQQQQAAB7wsQEHq/jSghAggggAACCCCAAAIIIBAUAQLCoLCSKAIIIIAAAggggAACCCDgfQECQu+3ESVEAAEEEEAAAQQQQAABBIIiQEAYFFYSRQABBBBAAAEEEEAAAQS8L0BA6P02ooQIIIAAAggggAACCCCAQFAECAiDwkqiCCCAAAIIIIAAAggggID3BQgIvd9GlBABBBBAAAEEEEAAAQQQCIoAAWFQWEkUAQQQQAABBBBAAAEEEPC+AAGh99uIEiKAAAIIIIAAAggggAACQREgIAwKK4kigAACCCCAAAIIIIAAAt4XICD0fhtRQgQQQAABBBBAAAEEEEAgKAIEhEFhJVEEEEAAAQQQQAABBBBAwPsCBITebyNKiAACCCCAAAIIIIAAAggERYCAMCisJIoAAggggAACCCCAAAIIeF+AgND7bUQJEUAAAQQQQAABBBBAAIGgCBAQVpL1s1nLKpkCD0cAAQQQQAABBBBAoOoEunc+puoSI6WIFyAgjPgmpoIIIIAAAggggAACCCCAQOkCBIRcGQgggAACCCCAAAIIIIBANRUgIKymDU+1EUAAAQQQQAABBBBAAAECQq4BBBBAAAEEEEAAAQQQQKCaChAQVtOGp9oIIIAAAggggAACCCCAAAGhi9fA4sWLdfPNN+9XghtvvFEXX3xx4G+vvvqqJk6cqMLCQnXv3t05PyYmxvn/qlWrNGLECC1dulRNmjTRbbfdpnbt2rlYo+BlbfWcO3eu1q1bp3vuuUfdunXbL7Nvv/1Wzz//vDZv3qwOHTrozjvvVN26dZ1zdu3apSeeeELff/+9UlJSNHDgQPXp0yfw+Eh2fPfdd/Xpp59q+fLlOvfcc/XXv/51PzdzyM3NDfytS5cuGj58eIWuv/Jcg3c1BD9le8599NFHWrt2rWrXrq3zzz9fAwYMqPA1U5nrMfi1C14O5bnde++9+uGHHwIFSE5Odl7f/EdZbnZOWa+HwatV8FNeuHChnn76aa1Zs8bJ7Pjjj9df/vIX53Xdf1TmvaA81+DXMDg57N69W/fff7+WLVvmvI41a9ZM1157rU466SQnw7ffflsvvPDCfpnb+8QxxxSvvljea1ikupUEmT59uh566CHH7fLLL6+S52Ikv6f6gdavX++Y2eeNRx55JODGe2pwnuukGnwBAsLgGx8yBwsI7cP3Sy+9FDgnNjZW0dHRzu+fffaZRo0apX//+9+yD07+QMgCmqKiIufFyD7A2wfVqVOnOh+WXnvtNefcSDvsQ+PRRx+tJ598UlddddV+AaH/hfmuu+5Sp06dnA9WW7du1WOPPeYwWDCYmZkp+zC6evVqx/Hhhx9W+/btI97x66+/ll1T9qaflJRUakD43HPPKT093bGya8/OL+/6K8813K+/F198UR07dlTLli2dGy/2PLWbNT179iz3mqnM9RjJblY3ew7aa1aPHj2cqkZFRSkuLs75uTy3sl4Pw90tKyvLec2y52FBQYHeeust/fbbb3rqqafKfS6W915Qnms42+Xn5+vXX391AkG7jn766SeNHDlSb775pnPzzwJCCxZvv/32QDXtPLvuynsNi2Q3P4YFxHbjwUzsJqs/ICyv7nw2KX4t27lzpxITEw8KCHlPDedXlepbdgJCF9veHxCOHTu21FJYgGM9foMGDXL+P23aNCfos6/58+fL/j9hwgQlJCQ4/7/yyiudL/+HLRerFrSsr7vuOqeHr2QP4bhx4zR79mynt9SOjRs3OufY39PS0nTRRRc5d0DtTp4d9oHBjqFDh1YbRwuSrZe5tB7C//73v2rYsOFBbVbW9WcfWstyDdoF4FLCdiPCeuZvvfXWcq+ZylyPLlUvaNmWdLNM7EPU6aefrnPOOeegPMtyq1+/vvN6d6jXw6BVwIWEfT6f3nvvPef165133nFKUJn3gvJcXahi0LL0j7oZPXq0mjdv7gSENjrib3/720F5lvcaVh3cLHBp0KCBZs2apRNOOCEQEJZX98pcj0Fr/BAmbD3HH3/8sdq2batffvnloICQ99QQNgZZVZkAAWGVUR5+QvbmZR/QbWijBXUnn3yyE9BZT44ddrfOPoDaHXU77I3thhtu0AcffOAEh5MmTXKGSfoP68Vo2rSp03MYqUdpAaEN10hNTXV6cPyHDbu1nsBGjRrp6quvdoal+XtO7efPP//cufv+4YcfVgvHsgJCC5rtQ2hGRoZz7ZhZedef9WiU5RpJ15/ZDBkyxBlmbF/lXTOVuR4j2c0fEP7+++9ONe21ykY3WE+sHWW52RDAsl4P4+Pjw54uOztbgwcPdoY+5uXlOddcv379yn0ulvdeUJ5r2MPtvbln11VOTo7++Mc/atiwYU61LCAcP3680wNm77O9evUKTBew4eBlvYZFupv1nNooGgsK7UZNyYCwvLpX588m9vz885//7Lxeffnll6UGhLynRsKrSvWrAwGhi21u890WLVrk3MnctGmT/u///s8Z+mKBjB0XXHCBHnjgAeeF2g5/z5fdNbYPATNmzAj0dtn/rYfMAstbbrnFxVoFN+vSAsL77rtPrVq1coJp/2G9qtdff70aN27sBIo2j84/TMiG19qQIruLbD2s1cHxUAGhDf1p3bq1bOiVmdgwNXOx66is688CwrJcg3sVhDZ1Gz5qQ9HsBoJ9sCzvmqnM9RjamgU3twPdLDebP1inTh3n+rLnnY12ePbZZ53h4GW5nXHGGWVejzbPM9wPu/Fgw0a3bdumTz75RKeeeqpOPPHESr8XlOca7m5W/h07djjBoA2Rt2vrwgsvdKpl768WXFswuGTJEuc5bDe9bE6wzb0v6zUskt3sWrM1B8zCPl/8/e9/3y8gLK/u1fmzifX+2TBR+4xhNxsO7CHkPTUSXlGqZx0ICD3U7vbmZS/SU6ZMceZxVee7cIdqFnoIj+yCPVRAWDI1G1JqPRJ2E8KG15Z1/VWXHkJ7w7ebCTYP1Xqh7aCHsPxrsDS30h71j3/8Q8cee6zz4aoyvRKR0ENY0scCw2uuucb5wGkfPivzXlCea/mtGV5nWC+rzRksbYE1m5s5c+ZM5+Zpde4htNewOXPmBG4+HxgQlnfNVOZ6DK+raf/Srly50lnEyBYqstec0gJC3lPDuYWrd9kJCD3U/jaE46abbnICQuuJsHH69sHc5sPZYXeeXnnllcAcwrvvvlu2iqR/UQYb/nLFFVdUyzmE9ubmX0TGghUbiuafQ2h3ix999NHABwT7cG93SP1zCKuDY0UCQluc4pJLLtE///lPp2eirOvP5t+U5eqhp9URF8V64m1Yts2D869Ya4nZ/N2yrhm77o70ejziwnrogYdyK62I9uHKegfttassN/8cwkO9Hnqo+lVSlC1btuiyyy6TzS+3OV6VeS8oz7VKCuyhRCyQttd/WwDqwMPeL60X0Z7T5b2GRbLbgw8+6Ix68H92sN5Vuwl92mmnOUFieXWvzPXooUvlsIsyefJkZ6E//7QeGz5q15GNeihtLQjeUw+bmAe4KEBA6CK+LYRSq1YtZ86WDQe1D+01atSQvVjbYcNCbfje448/7vzd7uJ17drVCRDthcbuhNrv/fv3d84dM2ZMxK4yakMaLYizIT52d9Lq7V+R1baisOGh1ttg85GeeeYZWVDoDxBtERnztXkStqy7fZg3Y/8qo5HsaL1+9mXDke27rShni6PY14oVK5wharYEuw2reuONN5xhfHYd2fVW1vVn12dZri4+raoka5tnand/rSfBPpDb4V+BtbznXmWuxyopvIuJlOVmH57s+rIhavZB9JtvvnFe8+zDufUSludW3vXoYrUrnbUFKdYD3aJFC23fvl02LM16I15++eVKvxeU51rpwruYgM3Dt9d226bD3h9sfr09b+1906YLfPHFF2rTpo1ja+fait02CuLSSy91Sl3Wa1gku9nqmPaa7z9s1W1bIMVuCNasWbNSz8XyXh9dvFwqnbWZmZ3/eP/9950bhPbZw+YN8p5aaWIScFGAgNBFfBu2YR/Cbf6gBYa2qIwFNiXnw1iPoL3olLYPoX1gsA+s1rNob362QE2k7kNodbMX3pKHvYmZmR324dLu3JW3D6EtLGO9qCX3IYxkR9vSxO72ljys58GG3treZ9ZbakOnbPiLfSi368+2WvAfZV1/JffwKs3VxadWpbO2my72QbPkUXKPxvKumcpcj5UuvIsJlOVme8ZZ74MtjmV31W1RGXsu/ulPfwqUuCw3O6ms69HFalc6a5vXbM/TDRs2ODdj7HXcnosl9yGszHtBea6VroBLCdi8wP/85z9O8Gw3bCygtrnknTt3dkpk/7O6Ww+Y9TLb6rZ2A9W/tVN5r2GR6nZgcx04ZLS899TynovlvT66dLlUebYHDhnlPbXKiUkwhAIEhCHEJisEEEAAAQQQQAABBBBAwEsCBIReag3KggACCCCAAAIIIIAAAgiEUICAMITYZIUAAggggAACCCCAAAIIeEmAgNBLrUFZEEAAAQQQQAABBBBAAIEQChAQhhCbrBBAAAEEEEAAAQQQQAABLwkQEHqpNSgLAggggAACCCCAAAIIIBBCAQLCEGKTFQIIIIAAAggggAACCCDgJQECQi+1BmVBAAEEEEAAAQQQQAABBEIoQEAYQmyyQgABBBBAAAEEEEAAAQS8JEBA6KXWoCwIIIAAAggggAACCCCAQAgFCAhDiE1WCCCAAAIIIIAAAggggICXBAgIvdQalAUBBBBAAAEEEEAAAQQQCKEAAWEIsckKAQQQQAABBBBAAAEEEPCSAAGhl1qDsiCAAAIIIIAAAggggAACIRQgIAwhNlkhgAACCCCAAAIIIIAAAl4SICD0UmtQFgQQQAABBBBAAAEEEEAghAIEhCHEJisEEEAAAQQQQAABBBBAwEsCBIReag3KggACCCCAAAIIIIAAAgiEUICAMITYZIUAAggggAACCCCAAAIIeEmAgNBLrUFZEEAAAQQQQAABBBBAAIEQChAQhhCbrBBAAAEEEEAAAQQQQAABLwkQEHqpNSgLAggggAACCCCAAAIIIBBCAQLCEGKTFQIIIIAAAggggAACCCDgJQECQi+1BmVBAAEEEEAAAQQQQAABBEIoQEAYQmyyQgABBBBAAAEEEEAAIXNABgAAByBJREFUAQS8JEBA6KXWoCwIIIAAAggggAACCCCAQAgFCAhDiE1WCCCAAAIIIIAAAggggICXBAgIvdQalAUBBBBAAAEEEEAAAQQQCKEAAWEIsckKAQQQQAABBBBAAAEEEPCSAAGhl1qDsiCAAAIIIIAAAggggAACIRQgIAwhNlkhgAACCCCAAAIIIIAAAl4SICD0UmtQFgQQQMADAkVFRfrpp5/04Ycf6rPPPtOuXbs0efJkNW7cuEpK9+abb+qxxx4rNa2nn35aXbp00ejRo/XSSy/p22+/rVCedt4rr7yi33//XXv27FHz5s3Vs2dPXXjhhapVq5aTRkXyrVBmnIQAAggggEAECRAQRlBjUhUEEECgKgTmzp2r5557Tueee642bNigUaNGHVFA+Oyzz+qtt97Sl19+uV+x/IHZhAkT1KJFi1KLfDgB4fvvv68HHnhAgwYNUv/+/Z0AcMaMGXr88cfVo0cP3XHHHfsFhGXlWxV+pIEAAggggEA4CRAQhlNrUVYEEEAgxALjxo3TyJEjPR0QXnrppUpMTNRrr722n87mzZudYPTiiy8mIAzxdUN2CCCAAALhI0BAGD5tRUkRQACBkAuUFRCuX79eTz31lDO81IaVHn300U4v3dlnn60RI0bojTfe2K+89evX18cffxwYunk4PYRPPvmkM4TV0nzooYf0448/qk+fPrrrrrvUq1cvHXfccbJzyjoq0jMZcmAyRAABBBBAwGUBAkKXG4DsEUAAAS8LlBUQXn311U7P3L333qt69epp+fLlTi/d0KFDVbduXVXlkFEL9j744AN16tTJGRbavn17xcbGOnR33nmnM0TU5h/a/w91EBB6+UqjbAgggAACbgkQELolT74IIIBAGAgcKiAsKCjQH/7wB91///3q27dvqTUpLyA88EHt2rVzFoax48A5hBYQvv76687w1TPPPHO/h9o8x7vvvls299EWk7FgsXPnzjrjjDOUmpoaOPdQi8qUzDcMmoQiIoAAAgggUKUCBIRVykliCCCAQGQJlNVD2K9fPxUWFuqmm27Saaedppo1a+5X+fICwsMdMmoB4Xfffaf4+PhSkRcuXKiZM2dqwYIFzuqk0dHRzmIzXbt2dc6nhzCyrk1qgwACCCBQNQIEhFXjSCoIIIBARAqUFRCuWLHCmbf3/fffO4Gh9bTZcM5zzjnHsajqgHDSpEn64osvKuS8detW3XjjjVq3bp0z9zA5OZmAsEJynIQAAgggUN0ECAirW4tTXwQQQOAwBCqyymhubq5+/vlnvfvuu/r888/1v//7vzr99NOrPCC0wG7q1KkVLr0tQGOL27z88svOMFJ6CCtMx4kIIIAAAtVIgICwGjU2VUUAAQQOV6AiAaE/TdvQ3jaVv+qqq5zeuTFjxjhzAW3Bl5JHRQKz0uYQHiogfOaZZ/TnP/85sMiMPy9bAdXmJPqHplYk38P14XwEEEAAAQTCXYCAMNxbkPIjgAACQRQ4VEBoW048+uijzhDRjIwMxcXF6aOPPtIjjzyi559/XieffLIzvNM2hbffbZEXm9NnR0UCs8MJCG3bibS0NGcu40knnaS8vDx99dVXTvk6duyo5557rsL5BpGSpBFAAAEEEPCkAAGhJ5uFQiGAAALuCfhXEC2tBH/605/0n//8x/nX119/7QR3toiLPaZly5a68sorddZZZzn/tx7Df/3rX05gmJ2drarYh7C0IaM2l3HixInOXMbVq1fL5/OpadOm6t69u9NbaVtjVDQQdU+dnBFAAAEEEHBHgIDQHXdyRQABBBBAAAEEEEAAAQRcFyAgdL0JKAACCCCAAAIIIIAAAggg4I4AAaE77uSKAAIIIIAAAggggAACCLguQEDoehNQAAQQQAABBBBAAAEEEEDAHQECQnfcyRUBBBBAAAEEEEAAAQQQcF2AgND1JqAACCCAAAIIIIAAAggggIA7AgSE7riTKwIIIIAAAggggAACCCDgugABoetNQAEQQAABBBBAAAEEEEAAAXcECAjdcSdXBBBAAAEEEEAAAQQQQMB1AQJC15uAAiCAAAIIIIAAAggggAAC7ggQELrjTq4IIIAAAggggAACCCCAgOsCBISuNwEFQAABBBBAAAEEEEAAAQTcESAgdMedXBFAAAEEEEAAAQQQQAAB1wUICF1vAgqAAAIIIIAAAggggAACCLgjQEDojju5IoAAAggggAACCCCAAAKuCxAQut4EFAABBBBAAAEEEEAAAQQQcEeAgNAdd3JFAAEEEEAAAQQQQAABBFwXICB0vQkoAAIIIIAAAggggAACCCDgjgABoTvu5IoAAggggAACCCCAAAIIuC5AQOh6E1AABBBAAAEEEEAAAQQQQMAdAQJCd9zJFQEEEEAAAQQQQAABBBBwXYCA0PUmoAAIIIAAAggggAACCCCAgDsC/x+dxTtAz3BCswAAAABJRU5ErkJggg==" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_fig_univariate('1stFlrSF')" - ] - }, - { - "cell_type": "markdown", - "id": "83c4f95a", - "metadata": {}, - "source": [ - "We see that for important features, the data in production will not be similar in distributions to that in training" - ] - }, - { - "cell_type": "markdown", - "id": "842637b6", - "metadata": {}, - "source": [ - "### Distribution of predicted values" - ] - }, - { - "cell_type": "markdown", - "id": "84f0009b", - "metadata": {}, - "source": [ - "This graph shows distributions of the production model outputs on both baseline and current datasets." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "0f506b2f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "SD.plot.generate_fig_univariate(df_all=SD.df_predict,col='Score',hue=\"dataset\") # works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "c6ddb503", - "metadata": {}, - "source": [ - "Differences between 2 datasets generate a difference in the distribution of the predictions of the deployed model. These differences can have important impacts on the performance of the model in production.
\n", - "Such differences in predicted probabilities may call into question the decision to deploy the model as is." - ] - }, - { - "cell_type": "markdown", - "id": "1b65b795", - "metadata": {}, - "source": [ - "Jensen Shannon Divergence (JSD). The JSD measures the effect of a data drift on the deployed model performance. A value close to 0 indicates similar data distributions, while a value close to 1 tend to indicate distinct data distributions with a negative effect on the deployed model performance." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "14df65a7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" + ], + "metadata": { + "interpreter": { + "hash": "d08e6294e2d60f50397263035a337d71f3055486232bc02b45ce2785f62e7d8b" + }, + "kernelspec": { + "display_name": "dev_eurybia", + "language": "python", + "name": "dev_eurybia" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } - ], - "source": [ - "SD.plot.generate_indicator(\n", - " fig_value=SD.js_divergence,\n", - " height=280,\n", - " width=500,\n", - " title=\"Jensen Shannon Datadrift\",\n", - " min_gauge=0,\n", - " max_gauge=0.2,\n", - " ) #works if deployed_model is filled" - ] - }, - { - "cell_type": "markdown", - "id": "9d99cec3", - "metadata": {}, - "source": [ - "With this tutorial, we hope to have detailed how Eurybia can be used in a data validation phase before deploying a model." - ] - } - ], - "metadata": { - "interpreter": { - "hash": "d08e6294e2d60f50397263035a337d71f3055486232bc02b45ce2785f62e7d8b" - }, - "kernelspec": { - "display_name": "dev_eurybia", - "language": "python", - "name": "dev_eurybia" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/tutorial/model_drift/tutorial01-modeldrift.ipynb b/tutorial/model_drift/tutorial01-modeldrift.ipynb index 3bdaddc..3cb797a 100644 --- a/tutorial/model_drift/tutorial01-modeldrift.ipynb +++ b/tutorial/model_drift/tutorial01-modeldrift.ipynb @@ -1,1227 +1,1227 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "d3a55be4", - "metadata": {}, - "source": [ - "# Modeldrift with Eurybia\n", - "With this tutorial you:
\n", - "Understand how to use Eurybia to detect model drift\n", - "\n", - "Contents:\n", - "- Detect data drift \n", - "- Display model drift over years\n", - "\n", - "This tutorial contains only additional features of model drift.\n", - "For more detailed information on data drift, you can consult these tutorials :\n", - "(https://github.com/MAIF/eurybia/tree/master/tutorial/data_drift)" - ] - }, - { - "cell_type": "markdown", - "id": "7dab5e19", - "metadata": {}, - "source": [ - "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", - "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "ba3029c1", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from category_encoders import OrdinalEncoder\n", - "from lightgbm import LGBMRegressor\n", - "from eurybia import SmartDrift\n", - "from eurybia.data.data_loader import data_loading\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import mean_squared_log_error" - ] - }, - { - "cell_type": "markdown", - "id": "a37f9001", - "metadata": {}, - "source": [ - "## Import Dataset and split in training and production dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "5e301c02", - "metadata": {}, - "outputs": [], - "source": [ - "house_df, house_dict = data_loading('house_prices')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "fd3a5e27", - "metadata": {}, - "outputs": [], - "source": [ - "# Let us consider that the column \"YrSold\" corresponds to the reference date. \n", - "#In 2006, a model was trained using data. And in 2007, we want to detect data drift on new data in production to predict\n", - "#house price\n", - "house_df_learning = house_df.loc[house_df['YrSold'] == 2006]\n", - "house_df_2007 = house_df.loc[house_df['YrSold'] == 2007]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d747da67", - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", - "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YrSold'])]\n", - "\n", - "y_df_2007=house_df_2007['SalePrice'].to_frame()\n", - "X_df_2007=house_df_2007[house_df_2007.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "markdown", - "id": "f280f685", - "metadata": {}, - "source": [ - "## Building Supervized Model\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c9af09e", - "metadata": {}, - "outputs": [], - "source": [ - "from category_encoders import OrdinalEncoder\n", - "\n", - "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", - "\n", - "encoder = OrdinalEncoder(\n", - " cols=categorical_features,\n", - " handle_unknown='ignore',\n", - " return_df=True).fit(X_df_learning)\n", - "\n", - "X_df_learning_encoded=encoder.transform(X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ec4277c7", - "metadata": {}, - "outputs": [], - "source": [ - "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d3f7cc5d", - "metadata": {}, - "outputs": [], - "source": [ - "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" - ] - }, - { - "cell_type": "markdown", - "id": "086c7e3d", - "metadata": {}, - "source": [ - "## Use Eurybia for data drift" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "5bd64f9e", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2007,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "bead8a97", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 9 µs, sys: 0 ns, total: 9 µs\n", - "Wall time: 30 µs\n", - "The variable BsmtCond has mismatching unique values:\n", - "['Poor -Severe cracking, settling, or wetness'] | []\n", - "\n", - "The variable Condition2 has mismatching unique values:\n", - "['Near positive off-site feature--park, greenbelt, etc.', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad'] | ['Adjacent to feeder street']\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "['Mixed'] | ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "\n", - "The variable ExterQual has mismatching unique values:\n", - "['Fair'] | []\n", - "\n", - "The variable Exterior1st has mismatching unique values:\n", - "[] | ['Stone', 'Imitation Stucco']\n", - "\n", - "The variable Exterior2nd has mismatching unique values:\n", - "['Asphalt Shingles', 'Brick Common'] | ['Other']\n", - "\n", - "The variable Foundation has mismatching unique values:\n", - "[] | ['Stone', 'Wood']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "['Major Deductions 2', 'Severely Damaged'] | ['Moderate Deductions']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Excellent']\n", - "\n", - "The variable Heating has mismatching unique values:\n", - "[] | ['Wall furnace']\n", - "\n", - "The variable HeatingQC has mismatching unique values:\n", - "['Poor'] | []\n", - "\n", - "The variable LotConfig has mismatching unique values:\n", - "[] | ['Frontage on 3 sides of property']\n", - "\n", - "The variable MSSubClass has mismatching unique values:\n", - "['1-Story w/Finished Attic All Ages'] | []\n", - "\n", - "The variable Neighborhood has mismatching unique values:\n", - "['Northpark Villa'] | []\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "['Roll'] | ['Metal']\n", - "\n", - "The variable RoofStyle has mismatching unique values:\n", - "['Mansard', 'Shed'] | []\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "['Warranty Deed - Cash'] | ['Contract Low Interest', 'Contract Low Down', 'Contract Low Down payment and low interest']\n", - "\n", - "The variable Street has mismatching unique values:\n", - "['Gravel'] | []\n", - "\n", - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.626082251082251\n" - ] - } - ], - "source": [ - "%time \n", - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2007', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "625d0912", - "metadata": {}, - "source": [ - "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", - "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", - "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." - ] - }, - { - "cell_type": "markdown", - "id": "a8ad7820", - "metadata": {}, - "source": [ - "## Add model drift in report" - ] - }, - { - "cell_type": "markdown", - "id": "e39dc67c", - "metadata": {}, - "source": [ - "For the moment, the model drift part of eurybia only consists of displaying performance of deployed model. \n", - "(We hope to bring new features in the future on this part)" - ] - }, - { - "cell_type": "markdown", - "id": "82d0de33", - "metadata": {}, - "source": [ - "### Put model performance in DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "79ae3c07", - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = regressor.predict(Xtest)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "28635fd0", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "d3a55be4", + "metadata": {}, + "source": [ + "# Modeldrift with Eurybia\n", + "With this tutorial you:
\n", + "Understand how to use Eurybia to detect model drift\n", + "\n", + "Contents:\n", + "- Detect data drift \n", + "- Display model drift over years\n", + "\n", + "This tutorial contains only additional features of model drift.\n", + "For more detailed information on data drift, you can consult these tutorials :\n", + "(https://github.com/MAIF/eurybia/tree/master/tutorial/data_drift)" + ] + }, { - "data": { - "text/plain": [ - "0.031487" + "cell_type": "markdown", + "id": "7dab5e19", + "metadata": {}, + "source": [ + "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", + "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "performance_test = mean_squared_log_error(ytest, y_pred).round(6)\n", - "performance_test" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "c12a14e7", - "metadata": {}, - "outputs": [], - "source": [ - "#Create Dataframe to track performance over the years\n", - "df_performance = pd.DataFrame({'annee': [2006], 'mois':[1], 'performance': [performance_test]})" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "4f164198", - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "0.03309" + "cell_type": "code", + "execution_count": 2, + "id": "ba3029c1", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from category_encoders import OrdinalEncoder\n", + "from lightgbm import LGBMRegressor\n", + "from eurybia import SmartDrift\n", + "from eurybia.data.data_loader import data_loading\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_log_error" ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_2007_encode=encoder.transform(X_df_2007)\n", - "y_pred_2007 = regressor.predict(df_2007_encode)\n", - "performance_2007 = mean_squared_log_error(y_df_2007, y_pred_2007).round(6)\n", - "performance_2007" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd9fe858", - "metadata": {}, - "outputs": [], - "source": [ - "df_performance = df_performance.append({'annee': 2007, 'mois':1, 'performance': performance_2007}, ignore_index=True)" - ] - }, - { - "cell_type": "markdown", - "id": "52912cfe", - "metadata": {}, - "source": [ - "### Add performance Dataframe in Smartdrift" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "f0e96f82", - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "ef937e7f", - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "markdown", + "id": "a37f9001", + "metadata": {}, + "source": [ + "## Import Dataset and split in training and production dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5e301c02", + "metadata": {}, + "outputs": [], + "source": [ + "house_df, house_dict = data_loading('house_prices')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fd3a5e27", + "metadata": {}, + "outputs": [], + "source": [ + "# Let us consider that the column \"YrSold\" corresponds to the reference date. \n", + "#In 2006, a model was trained using data. And in 2007, we want to detect data drift on new data in production to predict\n", + "#house price\n", + "house_df_learning = house_df.loc[house_df['YrSold'] == 2006]\n", + "house_df_2007 = house_df.loc[house_df['YrSold'] == 2007]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d747da67", + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", + "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YrSold'])]\n", + "\n", + "y_df_2007=house_df_2007['SalePrice'].to_frame()\n", + "X_df_2007=house_df_2007[house_df_2007.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "markdown", + "id": "f280f685", + "metadata": {}, + "source": [ + "## Building Supervized Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c9af09e", + "metadata": {}, + "outputs": [], + "source": [ + "from category_encoders import OrdinalEncoder\n", + "\n", + "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", + "\n", + "encoder = OrdinalEncoder(\n", + " cols=categorical_features,\n", + " handle_unknown='ignore',\n", + " return_df=True).fit(X_df_learning)\n", + "\n", + "X_df_learning_encoded=encoder.transform(X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ec4277c7", + "metadata": {}, + "outputs": [], + "source": [ + "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d3f7cc5d", + "metadata": {}, + "outputs": [], + "source": [ + "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" + ] + }, + { + "cell_type": "markdown", + "id": "086c7e3d", + "metadata": {}, + "source": [ + "## Use Eurybia for data drift" + ] + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_modeldrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "code", + "execution_count": 9, + "id": "5bd64f9e", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2007,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bead8a97", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 9 \u00b5s, sys: 0 ns, total: 9 \u00b5s\n", + "Wall time: 30 \u00b5s\n", + "The variable BsmtCond has mismatching unique values:\n", + "['Poor -Severe cracking, settling, or wetness'] | []\n", + "\n", + "The variable Condition2 has mismatching unique values:\n", + "['Near positive off-site feature--park, greenbelt, etc.', 'Adjacent to North-South Railroad', 'Adjacent to East-West Railroad'] | ['Adjacent to feeder street']\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "['Mixed'] | ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "\n", + "The variable ExterQual has mismatching unique values:\n", + "['Fair'] | []\n", + "\n", + "The variable Exterior1st has mismatching unique values:\n", + "[] | ['Stone', 'Imitation Stucco']\n", + "\n", + "The variable Exterior2nd has mismatching unique values:\n", + "['Asphalt Shingles', 'Brick Common'] | ['Other']\n", + "\n", + "The variable Foundation has mismatching unique values:\n", + "[] | ['Stone', 'Wood']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "['Major Deductions 2', 'Severely Damaged'] | ['Moderate Deductions']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Excellent']\n", + "\n", + "The variable Heating has mismatching unique values:\n", + "[] | ['Wall furnace']\n", + "\n", + "The variable HeatingQC has mismatching unique values:\n", + "['Poor'] | []\n", + "\n", + "The variable LotConfig has mismatching unique values:\n", + "[] | ['Frontage on 3 sides of property']\n", + "\n", + "The variable MSSubClass has mismatching unique values:\n", + "['1-Story w/Finished Attic All Ages'] | []\n", + "\n", + "The variable Neighborhood has mismatching unique values:\n", + "['Northpark Villa'] | []\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "['Roll'] | ['Metal']\n", + "\n", + "The variable RoofStyle has mismatching unique values:\n", + "['Mansard', 'Shed'] | []\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "['Warranty Deed - Cash'] | ['Contract Low Interest', 'Contract Low Down', 'Contract Low Down payment and low interest']\n", + "\n", + "The variable Street has mismatching unique values:\n", + "['Gravel'] | []\n", + "\n", + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.626082251082251\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "%time \n", + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2007', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )\n", + " " ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_modeldrift_2007.html', \n", - " title_story=\"Data drift\",\n", - " title_description=\"\"\"House price model drift 2007\"\"\", # Optional: add a subtitle to describe report\n", - " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" # Optional: add information on report\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "84c8883b", - "metadata": {}, - "source": [ - "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", - "However, to illustrate functionalities, we will detail results with notebook mode analysis." - ] - }, - { - "cell_type": "markdown", - "id": "4add0130", - "metadata": {}, - "source": [ - "This tutorial contains only anlysis on additional features of model drift. For more detailed information on data drift, you can consult these tutorials : (https://github.com/MAIF/eurybia/tree/master/tutorial/data_drift)" - ] - }, - { - "cell_type": "markdown", - "id": "88cfeb49", - "metadata": {}, - "source": [ - "### Display model drift" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "6d33cabf", - "metadata": {}, - "outputs": [ + }, { - "data": { - "image/png": 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yv//7v2bZnS6F1qW4Vol0LDoxIIy0TeGOz1D97dR66Zc2Ol50ybv+rglcCWC1SYM1/aJBS2BAGOn7y7LWJek6GxisjB492szWtWnTxvzetEpBYy9SZ12OPnnyZBOcvvbaa5dUSf8eqJF+uaibhlklEodI/EONOX3drr8DkThH0uZIrNWDgLAwo4ZjvCRAQOil3qQtBQoUdYmdLke0ZtysDz76PIIW67+BN9NASgMqq1h/XHVpkz5jVFC5XECoS0n19ebNm5slkMGKzoLphgr637zBWGHrYB2ns6O6w2reopuhaMCmgbMGpsGKfkOu39Krm26kEFh0BlM/HG3fvt1sqqObIuQtuiGELokNLKHqpcdqoKWzAhoEWs8hWs8t6uyX9eE01Fsj0v4u6Po6W6k2urmO+ukHWt2ARAOuYBtghKqn9XpRx3PgdXV5n8426zJknb3QLxysohsq6ZJa/RLDmg3S1yIdi04MCCNtU7jjM1QfO7VeGtjocvrLBWW68ZbOymsJDAgjfX9Z1voM7IMPPhiU0AoeateubepplYLGXqTO+lycPkagzzbrlyf6ntai73Wdhdffcy+88MIlm35F4hCJf6gxp6+HGs/h/h2IxDmSNkdirR4EhIUZNRzjJQECQi/1Jm0pUKCoH6Ct5xYKS5p3KaX1x1Wfr7N23Qt2rcsFhFYdQm06oh/Q9Dk9/eOp30hbpbB1CHWc9UEgcOfGvG2xAsK87dUgVT+ohSrBZidD1UuvaQWEgUtVLbe8u8Verg6R9vflrq07w+qzlLqZiwavVtFnMnWJnbazqDOHRR3Peeuny+t0yXPgUmTdiU+XA+vzV7ocN/C5qEjHohMDwkjbFO74DPVecHq9dCZfvyAKVgJ3hA0MCCN9f4UKVrQuOjuos4S6M7KO31ABYaTOen1rtl2XqOsyWi3WCoVgM4eROFjnhuMfaswFBoQF/c0K9+9AJM6RtDkSawLCwowYjvGaAAGh13qU9gQVKOoHaOtbTQ3m9A9kUUthPizqNYtjhjBUUBqqruF+EAicLdBnDHX2UZe0aqBhBUBWv0QzIAxnhjDS/i7M+NDNiHR5sc6Ubt682fxPZ5t12avuiFqUUtTxnPfa1jIunVXVD7BadKdTXXobbJOkSL7lD/XhSjcF0ufPCko7kZ6envtMmJV2wmpPJJvKRNqmUO8brWOwLyxC9bNT6xXJbE2k7y8nzhBqP+qqCP0yTL9E0YC0XLlyuTNtGpDoM9KBJRKHSPxDjTk7A8JIxnMkbY7EOtTvrMJ4cgwCbhMgIHRbj1HfsASK+gFaZ5z0Q7puva/LJIs6g1OYD4uhAkJrCVRhnyHUZVK6XMoqha1DqOPCDQj/+Mc/mpQSuruq/jdYuf/++01KhmgGhOE8Qxhpf4czKDU41GdQtejzSHlTZ1zumkUdz3mvdezYMbPczcqlppvHWLkHdffXW2655ZJTIh2Ll5shtJ4/zZvz0KqABoGPPfaY+b95A0J9flafo1UPawOmvG0t6N6RtinU+ybcgNDp9dJndfV3TVGeIYz0/WXHM4SROlvjTJe765c7+niA7s6s72nNzajLsXUH1sASiYNV33D8C/P7KdR4DvfvQCTOkbQ5EmsCwsKMGI7xmgABodd6lPYEFSjqB2h99ktntDRNgT7bpn/0i1JC/XG1rnW5GcLAHdYK2mVUPyhr2gxdfqgpDgJLYesQ6rhwPwjoMkl9tiZYWgit59q1a3M3sYlmQBi4y6huDa8BQagSaX+Hun6w13X5qG6Aoc8iaT5EdSpsKep4Dnbd4cOHm+e8dAmafpDVreH1OUfdMdfKmWadF+lYvFxAqJsG6a6MumOvpuYILIE76QYLCK28isHeH9Z1Crp3pG0K9b4JNyB0ar0CdxPOu4mWtlWfmdOgXN9LWgKXjEb6/rKsdVzqBi55dzLWuulMnY6XvF8O2NX/1viyfj/qlyr6PtL8rjozqDOEeUskDpH4F+b3SqjxHO7fgUjGcyRtjsRavXiGsDCjhmO8JEBA6KXepC0FCoTzAdracVEvqrMp+kdekx7rVuK6OYpu0a/Pha1evTp32Z1VgVB/XK3jQuUh1C3UreTg+gdKn1XR3TP1D6XO5Oi9teizLJrrL7AUtg6hjgv3g4Dm2LNSLegzlhpga0oLfc5I26Tf/loJpqMZEKrB7373O7OhgxZ9BlOfmdMZOCsPobppHwbmIYykvwsaePpckX6I1cBPg/aUlBQzs6JbsesSSXXQ/69BWFE2mQlnPOetoxpoAFa+fHljozNtlwugIxmLl/twZeUK0/eVBnY6Y6g72eqzlzrGdfbFKnlnCHUWX2cCbrjhBrMrsM6e5J3Nv9y9I2lTqPdNuAGhnufUeumzg/r7QPsnWB7CL774Irev8uYhjOT9ZVnrxfV9orNxt956q7mXblqlO4/q72Ndkq7jIXAnZLv632qozrJrqhb9faLvZQ1KNWjNmxrDOj4Sh0j8Q308CDWew/07EOl4jqTNkVgTEIYaMbzuNQECQq/1KO0JKhDOB2h9vmvOnDny0ksvXVZVPwR8+umnYQVjoQJC3cFOP3jphh8FlYJmMEP9gbeuF+q4cD8I6AcjrZsmcQ5W9Bt1/QC3Zs2aqC4Z1XtduHDB7HiqvgWVvDOXkfR3QffQvtNUHZcrQ4YMEV06W5RSlLQTBW1KpB9k9XlBzddllbypSwLrFMlYvNyHK72uzhLqlxzBiu4qqek7tOQNCHVmUb9MyLvzb+AGSKHubef7K5xnCLWdkViHej/r9cOtl6Z6UU8N1oOV7t27y6JFi8xLeQPCSN5fVpv0WWR9zlW/2Mlb9Pk9/QJBvxwILHb1f+A9rOXx+jPdRdj6MiqYUSQOkfiH+v0SatyE+3cg0vEcSZsjsSYgDDVieN1rAgSEXutR2hNUIJyA0LrQl19+aVJQ6EyFLkPRPzL6bKEmm9dvqTW9gc7+BJZQf1ytY0MFhHqcfnDXD0E6m6Q7Q+oGHDpLqB88NKXFTTfdFLTNha1DqOMi+SBw7tw5s2xUn6fRmUHNCaizODoLpEGQBm06OxbtGUILRAN1XUqrG9xo4KMb2uj9NQefJq/Wfsxbwunvgt52+mFG8yzq/7755hszi6H9Wa1aNdN/usytcePGRX7XRiMg1JsG7gLbpEkT0W/UL1fCHYuhPlypkz5nqk4606L9pNv46/NYOquqM+Na8gaE+jNdeqyzrTt37hTduEffn4UNCO1+f4UbeDm5Xvr758UXXzSbqOh7unTp0iZfqwaD9evXz91VOW9AGK3fp7pcdP78+eb3se6urL8LNfWOLuvXJc95S6ixF+6YDrzPoUOHzCoSLXl3ey7o/RTu75lI/Quqj51/ByIZz3pupG0OxzrUuCnyL21OQMDhAgSEDu8gqocAAggggICfBUIFK7G22bBhgwwYMMAEp8uWLTNffFEQQAABNwkQELqpt6grAggggAACPhNwekA4aNAgM1OtS591CTIFAQQQcJsAAaHbeoz6IoAAAggg4CMBpwaEurOqLvufNm2aJCQkmHQcNWvW9FHP0FQEEPCKAAGhV3qSdiCAAAIIIOBBAacFhPqc65133nmJdM+ePc0uuRQEEEDAjQIEhG7sNeqMAAIIIICATwScGhBqShlNg6GbHj300EP5Up74pHtoJgIIeECAgNADnUgTEEAAAQQQQAABBBBAAIFwBAgIw1HjHAQQQAABBBBAAAEEEEDAAwIEhB7oRJqAAAIIIIAAAggggAACCIQjQEAYjhrnIIAAAggggAACCCCAAAIeECAg9EAn0gQEEEAAAQQQQAABBBBAIBwBAsJw1DgHAQQQQAABBBBAAAEEEPCAAAGhBzqRJiCAAAIIIIAAAggggAAC4QgQEIajxjkIIIAAAggggAACCCCAgAcECAg90Ik0AQEEEEAAAQQQQAABBBAIR4CAMBw1zkEAAQQQQAABBBBAAAEEPCBAQOiBTqQJCCCAAAIIIIAAAggggEA4AgSE4ahxDgIIIIAAAggggAACCCDgAQECQg90Ik1AAAEEEEAAAQQQQAABBMIRICAMR41zEEAAAQQQQAABBBBAAAEPCBAQeqATaQICCCCAAAIIIIAAAgggEI4AAWE4apyDAAIIIIAAAggggAACCHhAgIDQA51IExBAAAEEEEAAAQQQQACBcAQICMNR4xwEEEAAAQQQQAABBBBAwAMCBIQe6ESagAACCCCAAAIIIIAAAgiEI0BAGI4a5yCAAAIIIIAAAggggAACHhAgIPRAJ9IEBBBAAAEEEEAAAQQQQCAcAQLCcNQ4BwEEEEAAAQQQQAABBBDwgAABoQc6kSYggAACCCCAAAIIIIAAAuEIEBCGo8Y5CCCAAAIIIIAAAggggIAHBAgIPdCJNAEBBBBAAAEEEEAAAQQQCEeAgDAcNc5BAAEEEEAAAQQQQAABBDwgQEDogU6kCQgggAACCCCAAAIIIIBAOAIEhOGocQ4CCCCAAAIIIIAAAggg4AEBAkIPdCJNQAABBBBAAAEEEEAAAQTCESAgDEeNcxBAAAEEEEAAAQQQQAABDwgQEHqgE2kCAggggAACCCCAAAIIIBCOAAFhOGqcgwACCCCAAAIIIIAAAgh4QICA0AOdSBMQQAABBBBAAAEEEEAAgXAECAjDUeMcBBBAAAEEEEAAAQQQQMADAgSEHuhEmoAAAggggAACCCCAAAIIhCNAQBiOGucggAACCCCAAAIIIOBjgdOnT8uMGTNk/fr1kpycLD169JAOHToUKLJ27VqZN2+eHDlyRBo1aiSPP/64VKlSxRz/3HPPybp16+T48ePmZx07dpRu3bqZ186cOSPjx4+Xr7/+Ws6ePStXXnml9O3bV5o1a+Zj/eg2nYAwup5cDQEEEEAAAQQQQAABzwtoMLhv3z4ZM2aMpKeny+jRoyUtLU0aNmyYr+0HDhwwQdzIkSOlSZMmJgA8duyYTJ061Ry7bds2qVatmpQtW1YyMjJk3Lhx5lgN+s6fPy/bt283gWCJEiVk06ZNMn36dFm8eLEJRP/2t7/JL37xC89729lAAkI7dbk2AggggAACCCCAAAIeE8jOzpbOnTvLpEmTzGyfFg3StAwfPjxfaxctWiRbtmyRadOmmdcyMzPNjKL+XAPBwKKzhIMHD5auXbvKPffck+9aX331lQwcOFDmz58vV111lQkcp0yZ4jHh4m0OAWHxenM3BBBAAAEEEEAAAQRcLbB3717p3bu3LF261MzqadF/r1q1SubMmZOvbc8++6xUrFhR+vfvn/taly5dzKyitfRzwYIFsmzZMjl58qTUrFlTZs2aZc6xigaae/bskaysLGnVqpU89dRT5qV7771XXnvtNVd7xrryBISx7gHujwACCCCAAAIIIICAiwR2795tgrsVK1ZIXFycqfnKlSvNMk6ductbdAlo3bp1pVevXrkv3X///dKvXz9p3bq1+Zk+k6jB3o4dO2Tnzp3Sp08fKVmyZO7xGijq62vWrJGkpCTp1KmTeU2fN3z77bddpOe8qhIQOq9PqBECCCCAAAIIIIAAAo4VsGOGMLCxs2fPlqpVq5plpcGKBovDhg2TBg0aMEMYhVFCQBgFRC6BAAIIIIAAAggggIBfBPQZQp2hmzx5sgnKtOgmMzk5OQU+Q7h169bcTWQOHTok3bt3D/oMoV5Ll4vqPUaMGBGU9MEHHzTnt23blmcIozDoCAijgMglEEAAAQQQQAABBBDwk4BuIqObw+guo7oz6KhRo2TixIm5u4zqM4Ht2rWTWrVqyf79+83y0CeffFIaN24sc+fOFQ0KdZdRXSq6fPlyadGihXkeUQNH3SRGZwBvv/120U1k9D7169c3Aed7771nnhnUpan6rOGHH34od9xxh5/oo95WAsKok3JBBBBAAAEEEEAAAQS8LRCYh1ADuZ49e16Sh7B9+/YmQNQ0E1o+/vhjef755/PlIdTcghMmTDDPDeq/a9SoYa5jPQ8UXikAACAASURBVCO4a9cu0SWk3333ncTHx0udOnXMs4hNmzb1NnAxto6AsBixuRUCCCCAAAIIIIAAAggg4CQBAkIn9QZ1QQABBBBAAAEEEEAAAQSKUYCAsBixuRUCCCCAAAIIIIAAAggg4CQBAkIn9QZ1QQABBBBAAAEEEEAAAQSKUYCAsBixuRUCCCCAAAIIIIAAAm4R+PbUj/Jd1jm5sUoZqVgywS3Vpp5FFCAgLCIYhyOAAAIIIIAAAggg4GWB4z9ekM4rdstH+0+ZZmowOLNFben9k6pebrZv20ZA6Nuup+EIIIAAAggggAACCOQXGLLue5m9PfOSFzQoPPbATXB5UICA0IOdSpMQQAABBBBAAAEEEAhX4Od/3Smr/292MPAaf7/7evl5jXLhXpbzHCpAQOjQjqFaCCCAAAIIIIAAAggUt8DnR87IHe/vlMNns/Pd+pt7G0mdciWLu0rcz2YBAkKbgbk8AggggAACCCCAAAJOF8g8ky2jP82QP+08LBdzRCTu0hq3qVFOPrr7eqc3g/qFIUBAGAYapyCAAAIIIIAAAggg4AWB09kXZc72g5K2db+cOn9RrimXJFOap0piXJws/faYfJv1o1kmOqThFew06oUOD9IGAkKPdizNQgABBBBAAAEEEECgIAGdBHx51xEzK5jxw3mpnJQg45rUlEdvSJGkhDzTgzB6WoCA0NPdS+MQQAABBBBAAAEEELhUYENmlgz6JF02HvpBEuNEHq2fIhOa1jJBIcV/AgSE/utzWowAAggggAACCCDgQ4E9p87J2E/3yqtfHxWdIexcp6Kk3Zwq9SqW8qEGTbYECAgZCwgggAACCCCAAAIIeFhAnw18Zss+86zguYs50qhyaZnT8krRjWIoCBAQMgYQQAABBBBAAAEEEPCgQHZOjrzw5WEZu2mvZJ7NlpRSifJMs1ryUL1qEs9jgh7s8fCaREAYnhtnIYAAAggggAACCCDgWIEP0k/IkHXpsvPEWSmTEC8jbqwuIxpVl3Il4h1bZyoWGwECwti4c1cEEEAAAQQQQAABBKIu8PmR0zJyY4YszzhpUgned21lk0YitSwJ5aOO7ZELEhB6pCNpBgIIIIAAAggggIB/BS5JLC8irasny9TmqdI8Jdm/KLS8UAIEhIVi4iAEEEAAAQQQQAABBJwnUFBi+W5XV3JeZamRIwUICB3ZLVQKAQQQQAABBBBAAIGCBUgsz+iIlgABYbQkuQ4CCCCAAAIIIIAAAsUgQGL5YkD20S0ICH3U2TQVAQQQQAABBBBAwL0CJJZ3b985ueYEhE7uHeqGAAIIIIAAAggg4HsBEsv7fgjYCuDagPD777+XadOmye7du6VWrVoyePBgadCgQVCs06dPy4wZM2T9+vWSnJwsPXr0kA4dOphjz5w5I+PHj5evv/5azp49K1deeaX07dtXmjVrZl5ftmyZLFmyRA4ePChlypSRli1bSv/+/aVUqVK2dgwXRwABBBBAAAEEEPC3AInl/d3/xdV6VwaEFy9eNEGbBmfdu3eXlStXyksvvSQLFy6UsmXL5rPTYHDfvn0yZswYSU9Pl9GjR0taWpo0bNhQzp8/L9u3bzeBYIkSJWTTpk0yffp0Wbx4sQke9+zZI4mJiVKpUiU5ceKEzJo1S2644QZzfwoCCCCAAAIIIIAAAnYIkFjeDlWuGUzAlQHhjh07ZOTIkfLGG29IUlKSaVevXr3M/+64445L2pmdnS2dO3eWSZMmSaNGjcxrGvBpGT58eD6Tr776SgYOHCjz58+Xq6666pLXNXicPHmy+dnYsWMZUQgggAACCCCAAAIIRFWAxPJR5eRihRBwZUD4/vvvyzvvvCPz5s3LbeKECRMkNTU138zd3r17pXfv3rJ06dLc2UP996pVq2TOnDm552twqLOBWVlZ0qpVK3nqqadyX9OlphpE6ms6i6jBpc4uUhBAAAEEEEAAAQQQiIYAieWjocg1whFwZUCoM4OffPJJ7kyfNlyfJ9TZwscee+wSB33GUJ/5W7FihcTFxZnXdImpLgnVWUCrnDx50gR8a9asMdfp1KlT7ms6M6iv63LTjz76SO677z654oorzOv6cwoCCCCAAAIIIIAAAuEInLuQI7//6phM++cRycrOkTplS8jTjatJx9TkcC7ny3PKly/vy3ZHq9GuDAjtmCEMBO3Tp48MGzYs6CY1GhDq/adOnWpO0SWpFAQQQAABBBBAAAEEiiKgieXf+u6EjNq4V77J+lEqJyXImMbV5eHrq0pSwr8nMSiFE9D9PijhC7gyINRnCEeNGiVvvvmmWcKpRZeF9uzZM+gzhDrbp8/+WbuQ6iYzOTk5QZ8h1Gs9+OCDZrOatm3b5pP9+9//Li+88ILZwIaCAAIIIIAAAggggEBRBUgsX1QxjrdTwJUBoe4yqrN4bdq0Mcs3P/zww9wgTXcZ3bp1q3z33XfSsWNHY6fP/2VmZppdRjMyMkwwOXHiRPMcoG4io6/Vr1/fBInvvfeevPbaa2Y5ac2aNeXtt982m9FUq1ZNrFQXet7QoUPt7BeujQACCCCAAAIIIOAxgbyJ5e9MLS+zWtSWehVLe6ylNMdNAq4MCBVYAz59blDzB2rgNmTIkNwZQA3odCMYTRGhJTAPoQaMOpNo5SHctWuXzJ4921wvPj5e6tSpY3Yrbdq0qTlXN67RZaKacqJy5com1YUGo6VL88Z100CnrggggAACCCCAQKwEgiWWn3JLqrSrXSFWVeK+COQKuDYgpA8RQAABBBBAAAEEEHCyAInlndw71M0SICBkLCCAAAIIIIAAAgggEGUBEstHGZTL2SZAQGgbLRdGAAEEEEAAAQQQ8JsAieX91uPuby8Bofv7kBYggAACCCCAAAIIxFiAxPIx7gBuH7YAAWHYdJyIAAIIIIAAAggg4HcBTSw/c9sBSdu6X3TzmGvKJcmU5qnS7epKfqeh/S4RICB0SUdRTQQQQAABBBBAAAHnCGhi+Te+OSYjN2SIppPQxPLjmtSUR29IIbG8c7qJmhRCgICwEEgcggACCCCAAAIIIICAJUBiecaClwQICL3Um7QFAQQQQAABBBBAwDYBEsvbRsuFYyhAQBhDfG6NAAIIIIAAAggg4HwBEss7v4+oYfgCBITh23EmAggggAACCCCAgIcFSCzv4c6labkCBIQMBgQQQAABBBBAAAEE8giQWJ4h4RcBAkK/9DTtRAABBBBAAAEEEAgp8OXxMzJkXboszzgpcSJy37WVTRqJ1LIlQ57LAQi4UYCA0I29Rp0RQAABBBBAAAEEoiqgieWf+WyfzPtnpmTniLSunixTm6dK85TkqN6HiyHgNAECQqf1CPVBAAEEEEAAAQQQKDYBEssXGzU3cqgAAaFDO4ZqIYAAAggggAACCNgnkDexfLkS8TK6cQ0Z2rA6ieXtY+fKDhQgIHRgp1AlBBBAAAEEEEAAAfsEgiWWH3tTTUkpnWjfTbkyAg4VICB0aMdQLQQQQAABBBBAAIHoCpBYPrqeXM0bAgSE3uhHWoEAAggggAACCCBQgACJ5RkaCBQsQEDI6EAAAQQQQAABBBDwpACJ5T3ZrTQqygIEhFEG5XIIIIAAAggggAACsRcgsXzs+4AauEOAgNAd/UQtEUAAAQQQQAABBAohQGL5QiBxCAIBAgSEDAcEEEAAAQQQQAAB1wuQWN71XUgDYiRAQBgjeG6LAAIIIIAAAgggELkAieUjN+QK/hYgIPR3/9N6BBBAAAEEEEDAlQIklndlt1FpBwoQEDqwU6gSAggggAACCCCAQMECJJZndCAQPQECwuhZciUEEEAAAQQQQAABGwVILG8jLpf2rQABoW+7noYjgAACCCCAAALuECCxvDv6iVq6U4CA0J39Rq0RQAABBBBAAAHPC1zMEZn/5SEZu2mvZJ7NlpRSifJMs1ryUL1qEh/n+ebTQASKRYCAsFiYuQkCCCCAAAIIIIBAUQQ0sfzIjRnyxdEzUiYhXkbcWF1GNKou5UrEF+UyHIsAAiEECAgZIggggAACCCCAAAKOESCxvGO6gor4RICA0CcdTTMRQAABBBBAAAEnC+RNLH9LtbIyp2VtaZ6S7ORqUzcEXC9AQOj6LqQBCCCAAAIIIICAewUKSizf9epKwmOC7u1Xau4eAQJC9/QVNUUAAQQQQAABBDwjQGJ5z3QlDXG5AAGhyzuQ6iOAAAIIIIAAAm4TILG823qM+npZgIDQy71L2xBAAAEEEEAAAQcJkFjeQZ1BVRD4PwECQoYCAggggAACCCCAgK0CJJa3lZeLIxCRAAFhRHycjAACCCCAAAIIIFCQAInlGRsIOF+AgND5fUQNEUAAAQQQQAAB1wmQWN51XUaFfSrg2IDw9OnTMmPGDFm/fr0kJydLjx49pEOHDgV209q1a2XevHly5MgRadSokTz++ONSpUoVc/xzzz0n69atk+PHj5ufdezYUbp162Ze27dvnyxYsEC++OILOXfunNSrV08GDBggderUMa9rPebMmSMbN26UixcvSuPGjWXw4MFSqVIlnw4Zmo0AAggggAACCBQsQGJ5RgcC7hJwbECowaAGa2PGjJH09HQZPXq0pKWlScOGDfMJHzhwQPr27SsjR46UJk2amADw2LFjMnXqVHPstm3bpFq1alK2bFnJyMiQcePGmWObNWsm27dvlx07dkiLFi3M6y+++KJ89tlnsnDhQnPuH/7wB3P+M888IyVLlpQpU6aY437729+6q6epLQIIIIAAAgggYKMAieVtxOXSCNgo4MiAMDs7Wzp37iyTJk0ys31apk+fbv47fPjwfByLFi2SLVu2yLRp08xrmZmZZkZRf66BYGDRWUKd4evatavcc889+a6lM4z33nuvLFmyRCpWrCjjx4+XunXryv3332+O/dvf/iZ/+ctf5Pnnn7exW7g0AggggAACCCDgDgESy7ujn6glAgUJODIg3Lt3r/Tu3VuWLl1qZuO06L9XrVpllm/mLc8++6wJ3vr375/7UpcuXcysos4CatFlocuWLZOTJ09KzZo1ZdasWeacvGXNmjVmhnHx4sUSFxcnmzZtkpdfflnGjh2bO0N4zTXXSJ8+fcypFy5cYHQhgIBPBXJyNK0yBQEEEPCngP4GfOu7EzJq4175JutHKVciXkY1ukIG/zRFkhLiXIGin/Uo7hdISEhwfyNi2AJHBoS7d+82wd2KFStMUKZl5cqVJkibP39+Pi5dAqqzeL169cp9TWf0+vXrJ61btzY/02cBs7KyzPLQnTt3moBOl4AGloMHD8qgQYPMM4Rt2rQxL+mMoi4T1cBQyw033GD+f+nSpc3/P3HiRAy7j1sjgEAsBfggEUt97o0AArEU2HTkjDyx5ZBsPnpWEuNE+tStKE/UryLVSrnrgzlf7MVyFEXv3hUqVIjexXx4JUcGhHbMEAb27ezZs6Vq1apmWalVdKnosGHDpFOnTma5qlX0WUENHPW1EiVKmIBUn0O0nk/04ZihyQgggAACCCDgU4GMH36UkRsy5NWvj4rOEN6ZWl5mtagt9Sr++4tyCgIIuE/AkQGhPkOogdnkyZOlQYMGRlU3mdFvcQp6hnDr1q25QdqhQ4eke/fuQZ8h1GvpclG9x4gRI8y1jx49agK+du3amecHA4v+/yFDhsitt95qfvzNN9/Iww8/bJafJiYmuq/HqTECCCCAAAIIIFBEAU0sP+2LAzLt8wNy+sJFaVS5tEy5JVXa1WZmpoiUHI6A4wQcGRCqkm4io5vD6C6jOiM3atQomThxYu4uo/pMoAZwtWrVkv3795vloU8++aRJCzF37lzRoFBn8XSp6PLly3N3EdXAUZd8agB4++23myWhGmS2bNkyd+MYvb/OBupysAkTJphO02M0ANQZQt2FVO9PQQABBBBAAAEEvCxAYnkv9y5tQ+DfAo4NCAPzEOrGMj179rwkD2H79u1NgKhpJrR8/PHHZufPvHkIz549a4I6fW5Q/12jRg1zHZ2B1KLBorU7aeCg0JyG1157rZk91ABTA0nNQ6jPKg4cOFCuvvpqxhACCCCAAAIIIOBZgcDE8knxcTKowRUytklNs3kMBQEEvCPg2IDQO8S0BAEEEEAAAQQQcI9AsMTyz9xcS64pl+SeRlBTBBAotAABYaGpOBABBBBAAAEEEPCuAInlvdu3tAyBywkQEDI+EEAAAQQQQAABHwuQWN7HnU/TEXDyM4T0DgIIIIAAAggggIB9Apo24o1vjpk0EntOnTPPBo5uXEOGNqzumsTy9ulwZQT8I8AMoX/6mpYigAACCCCAAAJGYENmlgz6JF02HvrBJJZ/tH6KjL2ppqSUJqUWQwQBvwkQEPqtx2kvAggggAACCPhWgMTyvu16Go5AgQIEhAwOBBBAAAEEEEDA4wJ5E8tfX6GUzGpRm8TyHu93modAYQQICAujxDEIIIAAAggggIALBQpKLN+nXlVJjItzYYuoMgIIRFuAgDDaolwPAQQQQAABBBBwgACJ5R3QCVQBARcIEBC6oJOoIgIIIIAAAgggUFgBEssXVorjEEBABQgIGQcIIIAAAggggIAHBEgs74FOpAkIxECAgDAG6NwSAQQQQAABBBCIlgCJ5aMlyXUQ8KcAAaE/+51WI4AAAggggIAHBF4nsbwHepEmIBBbAQLC2PpzdwQQQAABBBBAoMgCmlj+iQ0Z8o8DWSSWL7IeJyCAQKAAASHjAQEEEEAAAQQQcIkAieVd0lFUEwEXCRAQuqizqCoCCCCAAAII+FOAxPL+7HdajUBxCBAQFocy90AAAQQQQAABBMIQILF8GGicggACRRIgICwSFwcjgAACCCCAAALFI0Bi+eJx5i4I+F2AgNDvI4D2I4AAAggggICjBEgs76juoDIIeF6AgNDzXUwDEUAAAQQQQMANAiSWd0MvUUcEvCdAQOi9PqVFCCCAAAIIIOAiARLLu6izqCoCHhQgIPRgp9IkBBBAAAEEEHCHAInl3dFP1BIBLwsQEHq5d2kbAggggAACCDhSIDCxfLyIPHh9VUm7OVVSSic6sr5UCgEEvCtAQOjdvqVlCCCAAAIIIOAwgWCJ5afckio3VinjsJpSHQQQ8IsAAaFfepp2IoAAAggggEDMBEgsHzN6bowAAiEECAgZIggggAACCCCAgE0CJJa3CZbLIoBA1AQICKNGyYUQQAABBBBAAIH/CJBYntGAAAJuECAgdEMvUUcEEEAAAQQQcI0AieVd01VUFAEERISAkGGAAAIIIIAAAghEQeDouQsyfvNemffPTMnOEbmlWlmZ07K2NE9JjsLVuQQCCCBgjwABoT2uXBUBBBBAAAEEfCKgieXn/StTnt6yTzQovKZckkxpnipdr64kcT4xoJkIIOBeAQJC9/YdNUcAAQQQQACBGAuQWD7GHcDtEUAgYgECwogJuQACCCCAAAII+E2AxPJ+63Hai4B3BQgIvdu3tAwBBBBAAAEEoixAYvkog3I5BBCIuQABYcy7gAoggAACCCCAgNMFSCzv9B6ifgggEK4AAWG4cpyHAAIIIIAAAp4XILG857uYBiLgewECQt8PAQAQQAABBBBAIJgAieUZFwgg4AcBAkI/9DJtRAABBBBAAIFCC5BYvtBUHIgAAh4QcGxAePr0aZkxY4asX79ekpOTpUePHtKhQ4cCydeuXSvz5s2TI0eOSKNGjeTxxx+XKlWqmOOfe+45WbdunRw/ftz8rGPHjtKtWzfz2r59+2TBggXyxRdfyLlz56RevXoyYMAAqVOnTu69vvzyS/nDH/4gu3btMnXp3bu3tG/f3gPdTxMQQAABBBBAwBIgsTxjAQEE/Cjg2IBQg0EN1saMGSPp6ekyevRoSUtLk4YNG+brpwMHDkjfvn1l5MiR0qRJExMAHjt2TKZOnWqO3bZtm1SrVk3Kli0rGRkZMm7cOHNss2bNZPv27bJjxw5p0aKFef3FF1+Uzz77TBYuXGjO1QDzoYcekl69esltt90mZ8+eFQ1Wf/KTn/hxvNBmBBBAAAEEPCeQN7F8atkSknZzqvS8rgqJ5T3X2zQIAQTyCjgyIMzOzpbOnTvLpEmTzGyflunTp5v/Dh8+PF8vLlq0SLZs2SLTpk0zr2VmZpoZRf25BoKBRWcJBw8eLF27dpV77rkn37U0ALz33ntlyZIlUrFiRTMzeOrUKXniiScYPQgggAACCCDgMYFgieUHNbhCyiTGe6ylNAcBBBAILuDIgHDv3r1mWebSpUvNrJ0W/feqVatkzpw5+Vry7LPPmuCtf//+ua916dLFzCrqLKAWXRa6bNkyOXnypNSsWVNmzZplzslb1qxZY2YYFy9eLHFxcTJo0CD56U9/Kps2bZLDhw+bf+vPUlJSGFMIIIAAAggg4FIBEsu7tOOoNgIIRF3AkQHh7t27TXC3YsUKE5RpWblypQnS5s+fnw9Bl4DWrVvXLOu0yv333y/9+vWT1q1bmx/pMs+srCyzPHTnzp3Sp08fKVmy5CXXOnjwoAn29BnCNm3amNe6d+8u58+fFw06a9WqJbNnzzYzkLqk1bpu1HuFCyKAgCsEcnJyXFFPKokAAv8R2Hv6vIz9LFOWfHdS9B18R42y8kzjFGlYqRRMPhSwPmf6sOmeanKZMmU81Z7ibowjA0I7ZggDYTWoq1q1qllWahVdKjps2DDp1KmTWa5qFZ2pbN68ee7soz7X+MADD8g777wjpUuXNs8UUhBAwJ8CfJDwZ7/TancKaGL5mTsOyawdh+X0hYvyk/JJMu3mGvLftcq5s0HUOioCfLEXFcaYX6RUKb7QiaQTHBkQ6jOEGphNnjxZGjRoYNqnM3L6pi3oGcKtW7fmbiJz6NAhM7MX7BlCvZYuF9V7jBgxwlz76NGjJhhs166deX4wsDz99NPmOURrOWregDASfM5FAAEEEEAAAXsFSCxvry9XRwAB9ws4MiBUVt1ERpdm6i6jujPoqFGjZOLEibm7jOozgRrA6TLO/fv3m+WhTz75pDRu3Fjmzp0rGhTqLqO6VHT58uW5u4hq4DhlyhQTAN5+++0mFYUGmS1bthRdZmqVEiVKmOWqGzZsMJvV6LX02UNryai1gY37hwAtQAABBBBAwJsCq/efkkGffC9fHD0jSfFxopvFjG1SU8qVYMMYb/Y4rUIAgXAEHBsQBuYh1I1levbseUkeQs0DqAGippnQ8vHHH8vzzz+fLw+hLumcMGGCeW5Q/12jRg1zHZ2B1KLBYrDgTnMaXnvtteaYN9980zy/qHkKdddTfc5Ql5xSEEAAAQQQQMB5Al8ePyujP82Qt749btJG3HdtZXnm5lpyTbkk51WWGiGAAAIxFnBsQBhjF26PAAIIIIAAAi4TILG8yzqM6iKAgCMECAgd0Q1UAgEEEEAAAQTCFSCxfLhynIcAAgiIEBAyChBAAAEEEEDAtQIklndt11FxBBBwiAABoUM6gmoggAACCCCAQOEFSCxfeCuORAABBC4nQEDI+EAAAQQQQAAB1whk/PCjjNyQIa9+fdQklr8ztbxMuSVVbqxCYmrXdCIVRQABRwkQEDqqO6gMAggggAACCAQT0MTy0744INM+P2ASy19foZTMalFb2tWuABgCCCCAQAQCBIQR4HEqAggggAACCNgrQGJ5e325OgIIIEBAyBhAAAEEEEAAAUcKkFjekd1CpRBAwGMCBIQe61CagwACCCCAgNsFAhPLa1s616ko026tTWJ5t3cs9UcAAUcKEBA6sluoFAIIIIAAAv4TCJZYfmrzVGlTo5z/MGgxAgggUEwCBITFBM1tEEAAAQQQQCC4AInlGRkIIIBA7AQICGNnz50RQAABBBDwvQCJ5X0/BABAAIEYCxAQxrgDuD0CCCCAAAJ+FCCxvB97nTYjgIATBQgIndgr1AkBBBBAAAGPCpBY3qMdS7MQQMC1AgSEru06Ko4AAggggIB7BE5nX5Qpn5NY3j09Rk0RQMAvAgSEfulp2okAAggggEAMBDSx/Cu7j8joTzMk44fzklIqUZ5pVkv61KsqiXFxMagRt0QAAQQQCBSwNSDcsWOHzJ8/Xz7//HM5ceKEbN682dx7xowZ0qtXL6latSq9gQACCCCAAAIeFSCxvEc7lmYhgICnBGwLCD/99FP5zW9+Iw0bNpSmTZuawNAKCF955RU5fPiwDB482FOYNAYBBBBAAAEEREgszyhAAAEE3CNgW0DYu3dvadmypTz88MNGQ4NCKyD89ttv5bHHHpN3333XPVLUFAEEEEAAAQQuK0BieQYIAggg4D4B2wLCW2+9VZYvXy4VKlTIFxCePXtW2rRpIxs2bHCfGDVGAAEEEEAAgUsESCzPgEAAAQTcK2BbQKgB36JFi6RWrVr5AsI9e/aYmcMPP/zQvXLUHAEEEEAAAQSExPIMAgQQQMDdArYFhEOHDpXk5GR56qmnJCEhIXfJ6IULF2TMmDESFxcnaWlp7taj9ggggAACCPhU4PMjp2XQJ9/LPw5kSbyIPHh9VUm7OVVSSif6VIRmI4AAAu4UsC0g3LVrl+hzhNWrV5fWrVvLSy+9JI888oisXr1a0tPTZeHChXLVVVe5U41aI4AAAggg4FMBTSz/1OZ98qedh+WiiNyZWl6m3JIqN1Yp41MRmo0AAgi4W8C2gFBZvvrqK5k9e7Zs2rRJsrOzJT4+3swUDh8+XK677jp3y1F7BBBAAAEEfCRAYnkfdTZNRQABXwnYGhBakufPn5eTJ0+aJaRJSUm+AqaxCCCAAAIIuFkgb2L5ykkJMq5JTRn40xQSy7u5Y6k7Aggg8H8CxRIQoo0AAggggAAC7hMIllh+VOMaokEhBQEEEEDAGwK2BYT/+te/5PXXX5exY8fmk3r66aflV7/6ldxwww3eUKQVCCCAAAIIeEiAxPIe6kyaggACCIQQsC0gHDBggNlU5pZbbslXBc0/qJvKzJ07lw5CCKmI2gAAIABJREFUAAEEEEAAAYcIkFjeIR1BNRBAAIFiFLAtILztttvkgw8+kLJly+Zrzg8//CDt27c3O45SEEAAAQQQQCC2AiSWj60/d0cAAQRiKWBbQPjzn/9c/vznP0udOnXyte+bb74xs4cEhLHseu6NAAIIIICAkFieQYAAAgj4XMC2gFBTS2jy+cmTJ0ti4n+S1Gr6iVGjRokmqJ85c6bP+Wk+AggggAACsREgsXxs3LkrAggg4DQB2wJCKzF9tWrV5Pbbb5eqVavK4cOHZdWqVXLo0CEze0guQqcNB+qDAAIIIOB1ARLLe72HaR8CCCBQNAHbAkKthu40qonpN2/eLBcvXpSEhARp0qSJDB48mB1Gi9ZPHI0AAggggEBEAiSWj4iPkxFAAAHPCtgaEFpqP/74o5w6dUrKlSsnJUuW9CwmDUMAAQQQQMBpAiSWd1qPUB8EEEDAWQLFEhA6q8nUBgEEEEAAAX8IkFjeH/1MKxFAAIFIBGwNCL/44gt59913Zd++fZKVlZWvni+++GIkdedcBBBAAAEEEAgiQGJ5hgUCCCCAQGEFbAsIX331VZk2bZrUqlXLpJ4Ilo/w2WefLWw9OQ4BBBBAAAEEQgiQWJ4hggACCCBQVAHbAsI777xTevbsKffff39R68TxCCCAAAIIIFAEgeycHPndjkx5ess+0aAwtWwJSbs5VXpeV0XiinAdDkUAAQQQ8J+AbQFhq1atZPny5ZKcnByW6unTp2XGjBmyfv16c40ePXpIhw4dCrzW2rVrZd68eXLkyBFp1KiRPP7441KlShVz/HPPPSfr1q2T48ePm5917NhRunXrZl7T5awLFiwQXd567tw5qVevngwYMMDMagYWzZvYv39/ycjIkPfffz+sNnESAggggAAC0RZ47/sTMmJ9unx54qyUKxEvoxvXkEENrpAyifHRvhXXQwABBBDwoIBtAaEmptcZwptuuiksNg0GNVgbM2aMpKeny+jRoyUtLU0aNmyY73oHDhyQvn37ysiRI01aCw0Ajx07JlOnTjXHbtu2TTQfoi5b1YBu3Lhx5thmzZrJ9u3bZceOHdKiRQvzuj7X+Nlnn8nChQsvuc+SJUvkk08+kZ07dxIQhtWjnIQAAgggEE0BEstHU5NrIYAAAv4VsC0g1Nk4fYbwl7/8pTRv3lwSExMLrZydnS2dO3eWSZMmmdk+LdOnTzf/1UAzb1m0aJFs2bLF3E9LZmammVHUn2sgGFi0XpoHsWvXrnLPPffku5bOMN57772iAWDFihXN64cOHTIzjo899piMHTuWgLDQPcmBCCCAAALRFsibWL519WSZ0/JKubFKmWjfiushgAACCPhAwLaAsG3btpKTk2Nm6uLj46VChQoSF3fpkwwrV64MSrx3717p3bu3LF26NHczGv33qlWrZM6cOfnO0c1pNHjTJZ1W6dKli5lV1FlALbosdNmyZXLy5EmpWbOmzJo1KzfgC7zgmjVrzAzj4sWLc+s7YcIEue2226RGjRomIGXJqA/eGTQRAQQQcJhA3sTy9SqUkmm31pa7rqzgsJpSHQQQQAABNwnYFhDOnDkzpMPQoUODHrN7924T3K1YsSI3KNPgUYO0+fPn5ztHl4DWrVtXevXqlfuabmbTr18/ad26tfmZPpOoqS90eagu++zTp4+ULFnykmsdPHhQBg0aZJ4hbNOmjXlt48aN8pe//MXMPv7rX//KFxBqwEtBAAEEEEDALgFNLP96epY8ve2w7DtzQSqVjJcn6leWvteWl8Q8X7TaVQeuiwACCDhZoFKlSk6unuPrZltAGEnL7ZghDKzP7NmzpWrVqmZZqVV0qeiwYcOkU6dOZrmqlh9//FEeeeQRGT9+vNlkJlhAqLOgFAQQQAAB9wrkXb3ipJaQWN7+3uDvuP3G3AEBuwWc/Hvc7rZH4/qODAj1GUINzCZPniwNGjQw7dRNZvSXdkHPEG7dujV3Exl95q979+5BnyHUa+lyUb3HiBEjzLWPHj1qgsF27dqZ5wet8v3335tZxvLly5sf6U6jp06dMktNdYOb6667Lhp9wDUQQAABBBC4RIDE8gwIBBBAAIHiErA1INSgS5dn6oyf/jtvad++fYHt1E1kdHMY3WVUdwYdNWqUTJw4MXeXUX0mUAM4TXy/f/9+E7g9+eST0rhxY5k7d67ZCEZ3GdWlopr+wtpFVAPHKVOmmADw9ttvN6koNMhs2bLlJTkTS5QoYQJQfd0qu3btEn2e8OWXXzZBYlE2yimuDuU+CCCAAALuFSCxvHv7jpojgAACbhWwLSDUVBBDhgwRDaIKKps3by7wtcA8hJoOQlNYBOYh1GBSA0RNM6Hl448/lueffz5fHsKzZ8+aIE4DU/23bgyj19EZSC0aLFq7kwZWRnMaXnvttZfUL9iSUbd2PPVGAAEEEHCOAInlndMX1AQBBBDwm4BtAaGmZ9ClmE888YTojp+6w6fO5GkAdvjwYbNcMyUlxW/etBcBBBBAAIFLBEgsz4BAAAEEEIilgG0Boc7g/e53v5Orr75amjZtKoGzge+8847885//NMtAKQgggAACCPhRgMTyfux12owAAgg4T8C2gFDz/61fv948Z9eqVSszM5icnGwEdOmmBoyaV5CCAAIIIICAnwRILO+n3qatCCCAgPMFbAsIA2cFu3XrJppzUANDLfosnuYZ/Oijj5wvRA0RQAABBBCIggCJ5aOAyCUQQAABBKIuUCwB4QsvvGB25vz1r39tksG//vrrZjdQTd1AQQABBBBAwMsCmlj+ld1HZPSnGZLxw3mpnJQg45rUlIE/TSGxvJc7nrYhgAACLhGwLSB86623LknwPnPmTFmxYoVZLqopHjRFhObzoyCAAAIIIOBVARLLe7VnaRcCCCDgHQHbAsKCiDS3X1xcnHcEaQkCCCCAAAJ5BPacOicj1qfLW9/+O5dt5zoVZdqtteWacklYIYAAAggg4CiBYg8IHdV6KoMAAggggEAUBTSx/OSt+2XO9oNy7mKO3FKtrExtniptapSL4l24FAIIIIAAAtETsD0gzMrKMvkH9b95y0033RS9lnAlBBBAAAEEYiRAYvkYwXNbBBBAAIGIBWwLCA8cOCBTpkyRNWvWiC4TDVYCcxNG3BIugAACCCCAQAwEAhPLl0mIlxE3VpeRN1aXMonxMagNt0QAAQQQQKBoArYFhA8//LBkZmZK9+7d5aqrrpIyZcrkq1nDhg2LVluORgABBBBAwCECwRLLP9W0pqSWLemQGlINBBBAAAEEQgvYFhDeeuutsmTJEqldu3boWnAEAggggAACLhEgsbxLOopqIoAAAggUSsC2gFCT0f/+97+XlJSUQlWEgxBAAAEEEHCyAInlndw71A0BBBBAIFwB2wJCfXZw1apVMnr0aClRokS49eM8BBBAAAEEYipAYvmY8nNzBBBAAAGbBWwLCLXeGzdulEmTJkn9+vWlcuXK+fIPjhgxwubmcXkEEEAAAQTCFyCxfPh2nIkAAggg4A4B2wLCTZs2iQZ8p06dkgoVKkjp0qXzibz33nvuUKKWCCCAAAK+EiCxvK+6m8YigAACvhawLSDs2rWrNG3aVAYMGCAVK1b0NTKNRwABBBBwhwCJ5d3RT9QSAQQQQCB6ArYFhK1atZIPPvhAypUrF73aciUEEEAAAQRsECCxvA2oXBIBBBBAwBUCtgWEgwcPln79+kmDBg1cAUElEUAAAQT8KUBieX/2O61GAAEEEPi3gG0B4eHDh2X27Nly9913S7NmzSQhIQFzBBBAAAEEHCNAYnnHdAUVQQABBBCIoYBtAWHbtm0lJydHjh07JvHx8WZjmbi4uEuaunLlyhg2nVsjgAACCPhRgMTyfux12owAAgggUJCAbQHhzJkzQ6oPHTo05DEcgAACCCCAQDQESCwfDUWugQACCCDgNQHbAkKvQdEeBBBAAAF3CuSIyMu7jsjoTzMk44fzUjkpQcY1qSkDf5oiiXlWrrizhdQaAQQQQACB8AVsCwinTJkiI0eODL9mnIkAAggggECEAppY/okNGbLx0A+SFB8ngxpcIaMa1zBBIQUBBBBAAAEEbNxURtNOrF69WhITE3FGAAEEEECgWAVILF+s3NwMAQQQQMDFArbNEA4ZMkQeeOABuemmm1zMQ9URQAABBNwkkDexfKPKpWVOyyulTQ1y4rqpH6krAggggEDxCdgWEGraialTp0r79u2lZcuWUrJkyeJrFXdCAAEEEPCVQEGJ5XvUrSLxl25w7SsXGosAAggggEAoAdsCQk07cfHiRTl+/LipQ/ny5fMtHyXtRKju4XUEEEAAgVACJJYPJcTrCCCAAAIIFCxgW0BI2gmGHQIIIICAnQIklrdTl2sjgAACCPhFwLaA0C+AtBMBBBBAoHgFSCxfvN7cDQEEEEDA2wIEhN7uX1qHAAIIeEaAxPKe6UoaggACCCDgIAFbA8L9+/fLiy++KJs3b5YTJ05IhQoVpFmzZmb30erVqzuIgaoggAACCDhVgMTyTu0Z6oUAAggg4AUB2wLCPXv2yIMPPijZ2dnSuHFjqVy5shw9elS2bt1qdhz905/+JHXq1PGCIW1AAAEEELBJIDCxfGKcyKP1U2RC01oklrfJm8sigAACCPhPwLaAUPMQnj9/XtLS0szMoFV0pnD06NFSokQJmTVrlv/EaTECCCCAQEiBYInl025OlXoVS4U8lwMQQAABBBBAoPACtgWEt912m7z66quSmpqarzYZGRnSvXt3+cc//lH4mnIkAggggIDnBUgs7/kupoEIIIAAAg4TsC0gbNGihfz1r3+VKlWq5GvykSNHpEOHDvLJJ584jIPqIIAAAgjEQoDE8rFQ554IIIAAAgiI2BYQ6vOD9evXl8cffzyf8/Tp02XHjh3ywgsv0AcIIIAAAj4XILG8zwcAzUcAAQQQiKmAbQHh+vXr5bHHHpNrr71WdPlopUqV5NixY/Lxxx/L7t275Xe/+53ccsstBTb+9OnTMmPGDNHrJCcnS48ePcysYkFl7dq1Mm/ePNHZx0aNGplA1JqdfO6552TdunVy/Phx87OOHTtKt27dzKX27dsnCxYskC+++ELOnTsn9erVkwEDBuRueLN06VJZtmyZ7N271zwLedddd5nlrhQEEEAAgcgESCwfmR9nI4AAAgggEA0B2wJCrZwGcxqkbd++XXJyciQuLk4aNGhgAq7LBYN6rgaDGqyNGTNG0tPTzUY0ukFNw4YN87X7wIED0rdvXxk5cqQ0adJENADU4HPq1Knm2G3btkm1atWkbNmyos8vjhs3zhyrKTC0bjpbqUtc9XVNk/HZZ5/JwoULzbkaLOouqddcc418//33MmHCBOnfv7+0bds2Gv5cAwEEEPCdQOaZbBn9aYb8aedhuSgirasny5yWV8qNVcr4zoIGI4AAAgggEGsBWwNCq3E683bq1CkpV66cJCUlhWyzpqro3LmzTJo0ycz2adFlplqGDx+e7/xFixbJli1bZNq0aea1zMxMM6OoP9dAMLDoLOHgwYOla9eucs899+S7ls4w3nvvvbJkyRKpWLFivtdnzpwpCQkJMmjQoJDt4AAEEEAAgf8IaGL5OdsPStrW/XLq/EWpV6GUTLu1ttx15X92osYLAQQQQAABBIpXIKoBYZs2bWT16tWmBRqcjRgxIqzW6PLM3r17iy7X1Fk7LfrvVatWyZw5c/Jd89lnnzXBm87cWaVLly5mVlFnAbXoTJ8u/Tx58qTUrFnTpLwIFvCtWbPGzDAuXrzYzGgGFp3lfOSRR8zS1cstXw2r0ZyEAAIIeFSAxPIe7ViahQACCCDgCYGoBoS6DFR3Dk1MTJSmTZvK5s2bw0LSZww1uFuxYkVuULZy5UoTpM2fPz/fNXUJaN26daVXr165r91///3Sr18/ad26tfmZPpOYlZVllofu3LlT+vTpIyVLlrzkWgcPHjQzf7qkVYPbvEWDyk2bNpmgVPMoavnxxx/DaiMnIYAAAn4Q+MeBLBm9eb98eviMaGL5ftdXkXGNq5NY3g+dTxsRQACBYhLI+5m+mG7rmdtENSDUzVp+/etfm2DQWrJ5Oanrr78+6Mt2zBAG3mj27NlStWpVU0er6FLRYcOGSadOncxy1bzltddeMwGqPtsYOLP4ww8/eGYw0BAEEEAgWgLf/nBefrv5gLybkWUu2SE1WSY0vkJ+Uv7SL+KidT+ugwACCCDgXwFrRaF/BSJreVQDwrffflsmTpwoFy/qNgGhS0EziPoMoQZmkydPNpvQaNFATJdsFvQM4datW3M3kTl06JDZCTTYM4R6LV0uqvewlrQePXrUBIPt2rUzzw/mLa+//rq88847os8PBsurGLqlHIEAAgj4Q4DE8v7oZ1qJAAIIIOAdgagGhMqiM226G+dDDz0kzz///GWlrOf7gh2km8jo5jC6y6juDDpq1CgTbFq7jOryTQ3gatWqJfv37zfLQ5988kmzI+jcuXNFg0LdZVSXii5fvjx3F1ENHKdMmWICwNtvv92kotAgs2XLlqLLTK2iS0L1GUJ9dlFnB/WZyJSUFPNyfHy8WRZLQQABBBD4twCJ5RkJCCCAAAIIuFMg6gGhxTB+/HiToiHcEpiHUKeBe/bseclGLu3btzcBoqaZ0KL5DTUAzZuH8OzZs6Ye+tyg/rtGjRrmOjoDqUWDRWt30sC6aroMzaGoy0o1MA0sGjxG0rZwTTgPAQQQcKIAieWd2CvUCQEEEEAAgcIJ2BYQ6iyc5vqjIIAAAgh4U4DE8t7sV1qFAAIIIOAvAdsCwlatWpkUFCyt9NeAorUIIOB9ARLLe7+PaSECCCCAgH8EbAsIhwwZIg888IDcdNNN/tGkpQgggICHBfImlr+mXJJMaZ4q3a6u5OFW0zQEEEAAAQS8LWBbQHj48GGzqYs+66fP3JEfxNsDidYhgIB3BQpKLP/oDSmSlBDn3YbTMgQQQAABBHwgYFtA2LZtW5N+Qnfx1FK+fPl8y0c12TwFAQQQQMC5Aqv3n5InNmTIxkM/mMTyj9ZPkQlNa5FY3rldRs0QQAABBBAokoBtAaHm7AtVhg4dGuoQXkcAAQQQiIHAnlPnZMT6dHnr239/qde5TkVJuzlV6lUsFYPacEsEEEAAAQQQsEvAtoDQrgpzXQQQQAAB+wRILG+fLVdGAAEEEEDAiQIEhE7sFeqEAAIIFLMAieWLGZzbIYAAAggg4BABWwPCHTt2yPz58+Xzzz+XEydOyObNm02zZ8yYIb169ZKqVas6hIFqIIAAAv4V+CD9hAxZly47T5yVMgnxMuLG6jLyxupSJjHevyi0HAEEEEAAAZ8I2BYQfvrpp/Kb3/xGGjZsKE2bNjWBoRUQvvLKK6K7kA4ePNgnzDQTAQQQcJ6AJpYfuTFDlmecFA39Hry+qjzVtKakli3pvMpSIwQQQAABBBCwRcC2gLB3794m3cTDDz9sKq5BoRUQfvvtt/LYY4/Ju+++a0ujuCgCCCCAQMECJJZndCCAAAIIIICAJWBbQHjrrbfK8uXLpUKFCvkCwrNnz0qbNm1kw4YN9AQCCCCAQDEJkFi+mKC5DQIIIIAAAi4SsC0g1IBv0aJFUqtWrXwB4Z49e8zM4YcffugiKqqKAAIIuFOAxPLu7DdqjQACCCCAQHEI2BYQao7B5ORkeeqppyQhISF3yeiFCxdkzJgxEhcXJ2lpacXRRu6BAAII+FaAxPK+7XoajgACCCCAQKEEbAsId+3aJfocYfXq1aV169by0ksvySOPPCKrV6+W9PR0WbhwoVx11VWFqiQHIYAAAggUTYDE8kXz4mgEEEAAAQT8KmBbQKigX331lcyePVs2bdok2dnZEh8fb2YKhw8fLtddd51fzWk3AgggYJsAieVto+XCCCCAAAIIeFLA1oDQEjt//rycPHnSLCFNSkryJCSNQgABBGIpQGL5WOpzbwQQQAABBNwrYHtAqDOD+/fvl8zMTElJSZEaNWpIYmKie8WoOQIIIOAwARLLO6xDqA4CCCCAAAIuErA1IHzjjTfkj3/8o0lCb5WqVauaZwm7dOniIiaqigACCDhPIDCxfJyI3HdtZZnSPJXE8s7rKmqEAAIIIICAYwVsCwh105i5c+fK3XffLT/72c+kUqVKcuzYMVmzZo389a9/lcGDB0uPHj0cC0PFEEAAAacKBEssP7V5qjRPSXZqlakXAggggAACCDhUwLaA8Je//KWZCezUqVO+pr/55puyYMECee+99xzKQrUQQAAB5wmQWN55fUKNEEAAAQQQcLuAbQFhy5YtZcWKFWYjmbzl1KlT0q5dO1m7dq3b/ag/AgggYLsAieVtJ+YGCCCAAAII+FbAtoBw4MCB0q9fP2ncuHE+3M8++8zMEOqSUgoCCCCAQMECJJZndCCAAAIIIICAnQK2BYQHDx6UadOmmWcIdbawRIkSoukndFZQnyEcOXKkVKtWzc62cW0EEEDAtQIklndt11FxBBBAAAEEXCVgW0DYtm1buXjxohw/ftyA6NLRrKws82/dYCYuTvfE+09ZuXKlq+CoLAIIIGCHwKnzF+WZLftkzvaDcu5ijjSqXFrmtLxS2tQoZ8ftuCYCCCCAAAII+FzAtoBw5syZRaIdOnRokY7nYAQQQMBLAppY/oUvD8vYTXsl82y2pJYtIWk3p0qPulUk/tLvz7zUbNqCAAIIIIAAAjEWsC0gjHG7uD0CCCDgGgESy7umq6goAggggAACnhMgIPRcl9IgBBBwiwCJ5d3SU9QTAQQQQAAB7woQEHq3b2kZAgg4VIDE8g7tGKqFAAIIIICADwUICH3Y6TQZAQRiI0Bi+di4c1cEEEAAAQQQKFiAgJDRgQACCNgsQGJ5m4G5PAIIIIAAAgiELUBAGDYdJyKAAAKhBUgsH9qIIxBAAAEEEEAgdgIEhLGz584IIOBhARLLe7hzaRoCCCCAAAIeEiAg9FBn0hQEEIi9AInlY98H1AABBBBAAAEECi9AQFh4K45EAAEEChTIm1g+pVSiPNOsljxUrxqJ5Rk3CCCAAAIIIOBYAQJCx3YNFUMAAbcIBEssP6JRdSlXIt4tTaCeCCCAAAIIIOBTAQJCn3Y8zUYAgcgFSCwfuSFXQAABBBBAAIHYChAQxtafuyOAgAsFSCzvwk6jyggggAACCCAQVMC1AeH3338v06ZNk927d0utWrVk8ODB0qBBg6CNPH36tMyYMUPWr18vycnJ0qNHD+nQoYM59syZMzJ+/Hj5+uuv5ezZs3LllVdK3759pVmzZub1pUuXyrJly2Tv3r1SoUIFueuuu6R79+4MJwQQ8KEAieV92Ok0GQEEEEAAAY8LuDIgvHjxognaWrZsaYKzlStXyksvvSQLFy6UsmXL5usyDQb37dsnY8aMkfT0dBk9erSkpaVJw4YN5fz587J9+3YTCJYoUUI2bdok06dPl8WLF5vgccGCBdK4cWO55pprRIPQCRMmSP/+/aVt27YeHxo0DwEELAESyzMWEEAAAQQQQMCrAq4MCHfs2CEjR46UN954Q5KSkkzf9OrVy/zvjjvuuKSvsrOzpXPnzjJp0iRp1KiReU0DPi3Dhw/P169fffWVDBw4UObPny9XXXVVvtdnzpwpCQkJMmjQIK+OCdqFAAIBAhsys2TQJ+my8dAPkhgn8mj9FJnQtJZUTkrACQEEEEAAAQQQcL2AKwPC999/X9555x2ZN29ebgfozF1qaqqZOQwsutSzd+/eZumnNXuo/161apXMmTMn91ANDvfs2SNZWVnSqlUreeqpp/J1bk5OjjzyyCNmuam15NT1I4AGIIBAUAFNLD/2073y6tdHRWcIO9epKGk3p0q9iqUQQwABBBBAAAEEPCPgyoBQZwY/+eST3Jk+7Q19nlBnCx977LFLOkefMdQlnitWrJC4uDjzmi4x1SWhOgtolZMnT5pgcM2aNeY6nTp1ytfJunxUl5RqIKnLS7XoeRQEEPCOQFZ2jkzZflie33Vczl3MkQYVSsqUJinys5Qy3mkkLUEAAQQQQMBDAuXLl/dQa4q/Ka4MCO2YIQyk79OnjwwbNuySTWpee+01E1Tq84gVK1bMPVyXpFIQQMD9AppY/s9fHZXxW/ZL5tls0cTyE5rUkD4/qUJiefd3Ly1AAAEEEPCwQGJioodbZ3/TXBkQ6jOEo0aNkjfffDN3pk6Xhfbs2TPoM4Q62zd58uTcAE+DOl3+GewZQiV/8MEHzWY11sYxr7/+ulmiqs8PVqlSxf5e4Q4IIFCsAiSWL1ZuboYAAggggAACDhJwZUCou4zqLF6bNm3kvvvukw8//FBeeOGF3F1Gt27dKt9995107NjRUOsmMpmZmWaX0YyMDBNMTpw40ewyqpvI6Gv169c3QeJ7770nOhuoy0lr1qxpnj3U/69LUlNSUsz14uPjhW8iHDSKqQoCYQqQWD5MOE5DAAEEEEAAAc8IuDIgVH0N+DRI0/yBGrgNGTIkdwZQAzjNOThr1izTUYF5CHVjGZ1JtDaF2bVrl8yePdtcTwO9OnXqmN1KmzZtas7VnIUaMAYWTXehm9hQEEDAnQIklndnv1FrBBBAAAEEEIi+gGsDwuhTcEUEEPC6wLkLOTJz2wFJ27pfTp2/KNeUS5IpzVOl29WVvN502ocAAggggAACCAQVICBkYCCAgOcFNG3EG98ck5EbMkTTSWgOwXFNasqjN6RIUsK/dx+mIIAAAggggAACfhQgIPRjr9NmBHwkQGJ5H3U2TUUAAQQQQACBIgsQEBaZjBMQQMANAnkTy9+ZWl5mtagt9SqWdkP1qSMCCCCAAAIIIFAsAgSExcLMTRBAoLgE9NnAZ7bskznbD5rE8o0ql5Ypt6RKu9oViqsK3AcBBBBAAAEEEHCNAAGha7qKiiKAwOUENLH8C18elrGb9uYmln+mWS15qF41EsszdBC+3ZiXAAAgAElEQVRAAAEEEEAAgQIECAgZGggg4HoBEsu7vgtpAAIIIIAAAgjESICAMEbw3BYBBCIXILF85IZcAQEEEEAAAQT8LUBA6O/+p/UIuFKAxPKu7DYqjQACCCCAAAIOFCAgdGCnUCUEEAguQGJ5RgYCCCCAAAIIIBBdAQLC6HpyNQQQsEGAxPI2oHJJBBBAAAEEEEBARAgIGQYIIOBoARLLO7p7qBwCCCCAAAIIuFyAgNDlHUj1EfCqAInlvdqztAsBBBBAAAEEnCRAQOik3qAuCCAgJJZnECCAAAIIIIAAAsUnQEBYfNbcCQEELiNAYnmGBwIIIIAAAgggUPwCBITFb84dEUAgjwCJ5RkSCCCAAAIIIIBAbAQICGPjzl0RQEBEvjx+RoasS5flGSclTkTuu7ayTGmeKqllS+KDAAIIIIAAAgggUAwCBITFgMwtEEDgUgFNLP/MZ/tk3j8zJTtHpHX1ZJnaPFWapyRDhQACCCCAAAIIIFCMAgSExYjNrRDwuwCJ5f0+Amg/AggggAACCDhNgIDQaT1CfRDwoEDexPLlSsTL6MY1ZGjD6pKUoItFKQgggAACCCCAAAKxECAgjIU690TARwLBEsuPvammpJRO9JECTUUAAQQQQAABBJwpQEDozH6hVgi4XoDE8q7vQhqAAAIIIIAAAj4QICD0QSfTRASKU4DE8sWpzb0QQAABBBBAAIHIBAgII/P7/+3dCXhV1b338X9CSIAEIYYwBOgFRMErQ6Ag4KPwgq030soQsFJkMgGsUoEyGATBMg9lBiUoIFqKIirU91VEBCcmuRi4oRQQVCwJRMIMMg/v81/37nNPBnJykpNk753vfh4f6Mk+e6/1WSd0/86aeDcCCPyPABvL81FAAAEEEEAAAQScJ0AgdF6bUWIEbCfAxvK2axIKhAACCCCAAAII5EuAQJgvJk5CAIHcBNhYns8FAggggAACCCDgbAECobPbj9IjUCICbCxfIuzcFAEEEEAAAQQQCLgAgTDgpFwQAfcKsLG8e9uWmiGAAAIIIIBA6RQgEJbOdqfWCPglwMbyfnFxMgIIIIAAAggg4BgBAqFjmoqCIlAyAmwsXzLu3BUBBBBAAAEEECgOAQJhcShzDwQcKMDG8g5sNIqMAAIIIIAAAgj4KUAg9BOM0xFwuwAby7u9hakfAggggAACCCDwvwIEQj4NCCBgBG7eElmyP1PG7kyX45evS9VyITKxRU3p3zBagoNAQgABBBBAAAEEEHCjAIHQja1KnRDwU0A3lk/akSappy5JhTLBMqJpdRnRpLpULBvs55U4HQEEEEAAAQQQQMBJAgRCJ7UWZUUgwAJsLB9gUC6HAAIIIIAAAgg4TIBA6LAGo7gIBEIg+8by90eHy/wHakurqhGBuDzXQAABBBBAAAEEEHCIAIHQIQ1FMREIhMDtNpbvVjdSmCYYCGGugQACCCCAAAIIOEuAQOis9qK0CBRIgI3lC8TGmxBAAAEEEEAAAdcL2DYQXrx4UWbPni3bt2+XiIgIefLJJ+Wxxx67bYNs2bJFkpOT5eTJk9KkSRMZOXKkREVFmfMXLFgg27ZtkzNnzpjXOnfuLN27d/dca+bMmZKamirHjh2T0aNHS/v27T0/03LMnz9fduzYITdv3pTY2FgZMmSIREZGuv7DQQXdIcDG8u5oR2qBAAIIIIAAAggUhYBtA6GGwaNHj8qLL74oR44cMUFtypQp0rhx4xwOGRkZkpiYKElJSdK8eXMTAE+fPi0zZsww5+7Zs0eio6MlPDxc0tLSZNy4cebcFi1amJ+vXbtW6tatK3PmzJG+fftmCYSLFi0y7584caKEhobK9OnTzXVeeOGFomgProlAwATYWD5glFwIAQQQQAABBBBwrYAtA+H169ela9euMnnyZNPbp8esWbPMn8OHD8/RGCtXrpSUlBTRnj49jh8/bnoU9XUNgt6H9hJqD1+3bt2kU6dOWX7Wv39/8z7vHsKXXnpJ6tevL7179zbnbty4Ud555x1ZvHixaz8UVMzZAmws7+z2o/QIIIAAAggggEBxCtgyEKanp0u/fv1Mz532xumhf9+0aZMZvpn9mDp1qlSuXFmeeeYZz4/i4+NNr6LVC7h06VJZt26dnDt3TmJiYmTu3LnmPd5HboFw586dsmLFChk7dqynh7BevXqSkJBg3nrjxo3ibC/uhcBtBXRj+WXfnpRx3xz1bCw/4ZcxknBPFBvLF9Hn5tYtnZ3JgQACCCDgVIGgIJZUc2rbeZe7TJkybqhGidXBloHw0KFDJtx98sknYv2ibtiwQVatWiVLlizJgaVDQLUXr0+fPp6faY/egAEDpG3btuY1nQt44cIF2bt3rxw4cMAEOh0C6isQao+iDhPVYKjHvffea/53+fLlzf8+e/ZsiTUeN0bAEvj02M/y59QT8o+zV6V8cJA81zBSBje8UyJC+D+6ovyU8CBRlLpcGwEEECh6Ab7YK3rj4rhDpUqViuM2rr2HLQNhUfQQerfgvHnzpEqVKmZ4qK9AqHMFNTgOGzZMypYtawKpzkO05ie69pNBxRwhwMbyjmgmCokAAggggAACCNhWwJaBUOcQdunSRaZNmyaNGjUyeLrIjH6Lc7s5hLt37/aEtMzMTOnZs2eucwj1WjpcVO8xYsQIn4GwR48eMnToUGndurU594cffpCBAwea4achISG2bVgK5m4BNpZ3d/tSOwQQQAABBBBAoLgEbBkItfK6iIwuDqOrjGqP3KhRo2TSpEmeVUZ1TmBcXJzUrFnTbBehw0PHjBljtoVYuHChaCjUXjwdKrp+/Xpp06aNmY+owVGHfGqPX4cOHYzztWvXTNjUYaoaANu1a2fCXnBwsIwfP96co0FUX9Mewl27donenwOB4hZgY/niFud+CCCAAAIIIICAuwVsGwi99yHUINerV68s+xB27NjRBETdZkKPzZs3m5U/s+9DePnyZRPqdN6g/r1GjRrmOtoDaR3aA6hzC70P3eKiZcuWcurUKRMwNUjqPoQ6V3HQoEFmmwoOBIpLgI3li0ua+yCAAAIIIIAAAqVLwLaBsHQ1A7VF4PYCbCzPpwMBBBBAAAEEEECgqAQIhEUly3URKKRA2s9XJenrNHnru1OiPYT/UesOmdumtjSs/N8r3HIggAACCCCAAAIIIFBYAQJhYQV5PwIBFtCN5WemZsjM/8qQizduSpM7y8v0+2tJXG2WVA4wNZdDAAEEEEAAAQRKvQCBsNR/BACwi4BuLL9kf6aM3Znu2Vh+Youa0r9hNBvL26WRKAcCCCCAAAIIIOAyAQKhyxqU6jhT4OMjZyVpR5qknrokYcFBMrhRNRnbPEYqlg12ZoUoNQIIIIAAAggggIAjBAiEjmgmCulWgdw2lp/YsqbUqxjm1ipTLwQQQAABBBBAAAEbCRAIbdQYFKX0CLCxfOlpa2qKAAIIIIAAAgjYWYBAaOfWoWyuE2Bjedc1KRVCAAEEEEAAAQQcLUAgdHTzUXinCLCxvFNainIigAACCCCAAAKlS4BAWLram9qWgAAby5cAOrdEAAEEEEAAAQQQyJcAgTBfTJyEgP8CbCzvvxnvQAABBBBAAAEEECheAQJh8Xpzt1IgwMbypaCRqSICCCCAAAIIIOASAQKhSxqSapS8ABvLl3wbUAIEEEAAAQQQQAAB/wQIhP55cTYCuQqwsTwfDAQQQAABBBBAAAEnChAIndhqlNk2Amwsb5umoCAIIIAAAggggAACBRAgEBYAjbcgwMbyfAYQQAABBBBAAAEE3CBAIHRDK1KHYhNgY/lio+ZGCCCAAAIIIIAAAsUgQCAsBmRu4Q6Bd384LUlfp8n3569IxbLBMjq2hvypcXUJKxPkjgpSCwQQQAABBBBAAIFSJ0AgLHVNToX9FdCN5Z//Ok2+zLggIUEif/j3qjK2WYxULR/i76U4HwEEEEAAAQQQQAABWwkQCG3VHBTGTgJsLG+n1qAsCCCAAAIIIIAAAkUhQCAsClWu6WiB7BvLN6hUTua2qS1xtSs5ul4UHgEEEEAAAQQQQACB7AIEQj4TCPyPwO02lk9oWEVCgpgnyAcFAQQQQAABBBBAwH0CBEL3tSk1KoAAG8sXAI23IIAAAggggAACCDhegEDo+CakAoURYGP5wujxXgQQQAABBBBAAAGnCxAInd6ClL9AAmwsXyA23oQAAggggAACCCDgMgECocsalOrkLcDG8nxCEEAAAQQQQAABBBD4XwECIZ+GUiPAxvKlpqmpKAIIIIAAAggggEA+BQiE+YTiNOcKeG8sHywiTzWoIlNa1mJjeec2KSVHAAEEEEAAAQQQCJAAgTBAkFzGfgK5bSw//f5a0jSqgv0KS4kQQAABBBBAAAEEECgBAQJhCaBzy6IVYGP5ovXl6ggggAACCCCAAALuESAQuqctS31N2Fi+1H8EAEAAAQQQQAABBBDwU4BA6CcYp9tTgI3l7dkulAoBBBBAAAEEEEDA3gIEQnu3D6XzIcDG8nxEEEAAAQQQQAABBBAouACBsOB2vLMEBU5duSEvfZMuyf88LtdvidwfHS7zH6gtrapGlGCpuDUCCCCAAAIIIIAAAs4SIBA6q71KfWl1Y/nkfcdlQspR0VBYr2KYTG9VS7rVjZSgUq8DAAIIIIAAAggggAAC/gkQCP3z4uwSFGBj+RLE59YIIIAAAggggAACrhQgELqyWd1VKTaWd1d7UhsEEEAAAQQQQAAB+wjYNhBevHhRZs+eLdu3b5eIiAh58skn5bHHHrut3JYtWyQ5OVlOnjwpTZo0kZEjR0pUVJQ5f8GCBbJt2zY5c+aMea1z587SvXt3z7VmzpwpqampcuzYMRk9erS0b98+y332798vixYtkoMHD5qy9OvXTzp27GifVnRpSdhY3qUNS7UQQAABBBBAAAEEbCNg20CoYfDo0aPy4osvypEjR0xQmzJlijRu3DgHXkZGhiQmJkpSUpI0b97cBMDTp0/LjBkzzLl79uyR6OhoCQ8Pl7S0NBk3bpw5t0WLFubna9eulbp168qcOXOkb9++WQKhBsz+/ftLnz595KGHHpLLly+LhtV77rnHNo3otoKwsbzbWpT6IIAAAggggAACCNhVwJaB8Pr169K1a1eZPHmy6e3TY9asWebP4cOH57BcuXKlpKSkiPb06XH8+HHTo6ivaxD0PrSXcMiQIdKtWzfp1KlTlp9p8NP3efcQas/g+fPn5fnnn7drG7qmXGws75qmpCIIIIAAAggggAACDhGwZSBMT083wzK150579fTQv2/atEnmz5+fg3bq1KlSuXJleeaZZzw/i4+PN72KVi/g0qVLZd26dXLu3DmJiYmRuXPnmvd4H7kFwsGDB8t9990nO3fulBMnTpi/62tVq1Z1SBM7o5hsLO+MdqKUCCCAAAIIIIAAAu4SsGUgPHTokAl3n3zyiQQF/fdmAhs2bJBVq1bJkiVLcrSADgGtX7++GdZpHb1795YBAwZI27ZtzUs6zPPChQuyd+9eOXDggCQkJEhoaKjPQNizZ0+5du2aaOisWbOmzJs3z/RA6pBW67ru+kgUb20OnL0iz3+TIZ8e+9lsG/H4v90h42KrSt2IrG1TvKXibggggAACCCCAAAJOEahQoYJTimrLctoyEBZFD6G3voa6KlWqmOGh3kduPYTaU9mqVStP76POa9R5hh988IGUL19erly5YsuGtXuhTl+9KRN2Z8hrB06ajeVbVikvs1rGyP3R/ELbve0oHwIIIIAAAgggYCeBsLAwOxXHcWWxZSDUOYRdunSRadOmSaNGjQyq9sjdunXrtnMId+/e7VlEJjMzU7RnL7c5hHotHS6q9xgxYoTPQDhhwgQzD9Eajpo9EDquxUu4wNk3lq8VXlamtKwlve6OYmP5Em4bbo8AAggggAACCCBQ+gRsGQi1GXQRGR2aqauM6sqgo0aNkkmTJnlWGdU5gXFxcWYYp24XocNDx4wZI7GxsbJw4ULRUKirjOpQ0fXr10ubNm3MfEQNjtOnT5dhw4ZJhw4dTIvrkFANmxr6evToIe3atZOQkBAJDg6Wr7/+2ixWo9fSuYfWkFFrAZvS95EpeI1z21h+cKNqUiEkuOAX5Z0IIIAAAggggAACCCBQYAHbBkLvfQg1yPXq1SvLPoS6D6AGRN1mQo/NmzfL4sWLc+xDqNtEjB8/3swb1L/XqFHDXEd7IK1j6NChZm6h96FbXLRs2dK89P7775v5izo8VFc91UVldMgpR/4E2Fg+f06chQACCCCAAAIIIIBAcQvYNhAWNwT3C7wAG8sH3pQrIoAAAggggAACCCAQSAECYSA1uZYRYGN5PggIIIAAAggggAACCDhDgEDojHZyRCnZWN4RzUQhEUAAAQQQQAABBBDwCBAIS/GHYcuWLZKcnJxj3mVuJN5zOiMiIsyWHToXU4/9+/fLlNnzJD09TW7euiVno/5NHuyRIJMfaSYVywZLSkqKrFixQg4ePGjmXr7++utZbqHX0fmd1vHAAw+YeZ8cCCCAAAIIIIAAAgggULQCBMKi9bXt1TMyMiQxMVGSkpLMwjwLFiyQ06dPe7buyF5w3fZDt9zQVV+PHDkio0ePFl14p2ztu2XUxj3y2aF0uVQhUuJ/UVFiD2+Vo999K/PnzzeX2bdvn1kJ9tSpU/Lhhx/mGghfeeUVqVatmjlfV3fVVV45EEAAAQQQQAABBBBAoGgFCIRF62vbq+sejdpzZ22foVt8aK9fbns36p6NXbt2lcmTJ5tVVvWYMmOmpJz4Wf5+z2/MxvL3R4fL/Adqmz/XrFljrvPuu+9mqf+XX35pwmBuPYSvvfaaVK9e3bZeFAwBBBBAAAEEEEAAATcKEAjd2Kr5qNPUqVOlcuXKZu9F64iPjzc9fy1atMhyhfT0dOnXr5+sXbtWQspVkOR9x+WVlaul0ve75FinwWZj+c7VQyUxIcEM/dTtOZ5++mnp1q1bvgNhZGSk2QuyQYMGpudStwfhQAABBBBAAAEEEEAAgaIVIBAWra9trz5u3DipX7++9OnTx1PG3r17y4ABA6Rt27ZZyn3o0CETHAcuXiWjdqTL9+evSJ0ju6Tp91tk5fJlZmN5DXM65PTMmTOyfv16ad26tTRr1ixfgXDjxo1y9913y7Vr18x+jzrEdMmSJRIWFmZbPwqGAAIIIIAAAggggIAbBAiEbmjFAtTBnx7Cj1K/lTnDB8lnXcbJrdDy8lSDKtLupxT5z68+98wT9C6CBsOnnnpK3n77bSlXrpznR7cbMur93hs3bpiexQkTJniGpxagerwFAQQQQAABBBBAAAEE8iFAIMwHkhtP0Tl+u3fv9iwik5mZKT179swyh9Czsfy3mfJ/1o6Xst2fkxld20nTqAqii8xor+Dw4cNz8OjiMU888YT87W9/k6pVq/oVCG/evCndu3eXsWPH5uhhdGM7UCcEEEAAAQQQQAABBEpSgEBYkvoleG9d9VOHh44ZM0ZiY2Nl4cKFoqFwxowZZmP5sWs2yQcp++WHu1pLg0rl5NH9/1dCfz5jVhlNS0uTUaNGyaRJk6Rx48by1VdfmfmIderUkbNnz4ouEPPjjz/K8uXLTQ015OnCNJs3b5Y333xTXn31VQkKCpKyZcvK4cOHzVDTu+66y8w9fOutt2Tr1q2ybNkyqVChQgkKcWsEEEAAAQQQQAABBNwvQCB0fxvftoYa0BYvXuzZh3D4iJGyJvOmjN2ZLhV2fSo1Mg5Iv7FTJKFhFbl66ZLpFdy+fbuEh4dLr169PPsQbtiwwfQs/vTTTybENWrUyITNmjVrmnvraqa6vYX30bBhQ7PVhe5hqNfVhWtCQ0NFX9f31qtXrxS3DFVHAAEEEEAAAQQQQKB4BAiExeNs+7t8cey8DN76L0k9dUnCgoNkcKNqMrZ5jNlYngMBBBBAAAEEEEAAAQTcKUAgdGe75rtW+89cltH/mSZrDp8x7+lap7LMbF1b6lVkhc98I3IiAggggAACCCCAAAIOFSAQOrThClvsU1duyEvfpEvyP497Npaf0aqWtKtRsbCX5v0IIIAAAggggAACCCDgEAECoUMaKlDFvHLjltlYfkLKUdFQWCu8rNlYvtfdURIUqJtwHQQQQAABBBBAAAEEEHCEAIHQEc3kfyHPXL0hb3x7QtYePiOVQ8vIkMbV5MTl65L0dZrZWF7nBo6OrWHmCurG8hwIIIAAAggggAACCCBQ+gQIhC5t89j39sp/nbqUo3Ya/XRjee0VrFo+xKW1p1oIIIAAAggggAACCCCQHwECYX6UHHbO7pMXpdn7/8xR6hrly8q6R++RplHlHVYjiosAAggggAACCCCAAAJFIUAgLArVEr7m58fOS/v/dyBHKXTBmM9/26CES8ftEUAAAQQQQAABBBBAwC4CBEK7tEQAy6HzByPf2JXjinNa15ahjasF8E5cCgEEEEAAAQQQQAABBJwsQCB0cuvlUfbl356Qp7447DlDewfXPlLfLDDDgQACCCCAAAIIIIAAAgioAIHQ5Z8DHT6qITA2qoLLa0r1EEAAAQQQQAABBBBAwF8BAqG/YpyPAAIIIIAAAggggAACCLhEgEDokoakGggggAACCCCAAAIIIICAvwIEQn/FOB8BBBBAAAEEEEAAAQQQcIkAgdAlDUk1EEAAAQQQQAABBBBAAAF/BQiE/opxPgIIIIAAAggggAACCCDgEgECoUsakmoggAACCCCAAAIIIIAAAv4KEAj9FeN8BBBAAAEEEEAAAQQQQMAlAgRClzQk1UAAAQQQQAABBBBAAAEE/BUgEPorxvkIIIAAAggggAACCCCAgEsECIQuaUiqgQACCCCAAAIIIIAAAgj4K0Ag9FeM8xFAAAEEEEAAAQQQQAABlwgQCF3SkFQDAQQQQAABBBBAAAEEEPBXgEDorxjnI4AAAggggAACCCCAAAIuESAQuqQhqQYCCCCAAAIIIIAAAggg4K8AgdBfMc5HAAEEEEAAAQQQQAABBFwiYNtAePHiRZk9e7Zs375dIiIi5Mknn5THHnvstuxbtmyR5ORkOXnypDRp0kRGjhwpUVFR5vwFCxbItm3b5MyZM+a1zp07S/fu3T3XmjlzpqSmpsqxY8dk9OjR0r59+xz3uXHjhjzzzDOSlpYmH330kUuan2oggAACCCCAAAIIIIBAaRawbSDUMHj06FF58cUX5ciRIyaoTZkyRRo3bpyjvTIyMiQxMVGSkpKkefPmJgCePn1aZsyYYc7ds2ePREdHS3h4uAl048aNM+e2aNHC/Hzt2rVSt25dmTNnjvTt2zfXQLh69WrZunWrHDhwgEBYmn9jqDsCCCCAAAIIIIAAAi4SsGUgvH79unTt2lUmT55sevv0mDVrlvlz+PDhOfhXrlwpKSkpoj19ehw/ftz0KOrrGgS9D+0lHDJkiHTr1k06deqU5Wf9+/c378veQ5iZmWl6HJ977jkZO3YsgdBFvwBUBQEEEEAAAQQQQACB0ixgy0CYnp4u/fr1Mz132qunh/5906ZNMn/+/BztNXXqVKlcubIZ0mkd8fHxplfR6gVcunSprFu3Ts6dOycxMTEyd+5c8x7v43aBcPz48fLQQw9JjRo1TCBlyGhp/pWh7ggggAACCCCAAAIIuEfAloHw0KFDJtx98sknEhQUZLQ3bNggq1atkiVLluTQ1yGg9evXlz59+nh+1rt3bxkwYIC0bdvWvKZzEi9cuCB79+41wz4TEhIkNDTUZyDcsWOHvPPOO6b3cd++fTkCoQ5N5UAAAQQQQAABBBBAAIGSEYiMjCyZG7vkrrYMhEXRQ+jdXvPmzZMqVaqY4aHeR/YewqtXr8rTTz8tL730ktSpUyfXQHjr1i2XfBSoBgIIIIAAAggggAACzhOwOpCcV3J7lNiWgVDnEHbp0kWmTZsmjRo1MlK6yIyGr9vNIdy9e7dnERmd89ezZ89c5xDqtXS4qN5jxIgReQbCf/3rX6aX8Y477jDn6Uqj58+fN0NNdYGbu+++2x6tSCkQQAABBBBAAAEEEEAAgQII2DIQaj10ERldHEZXGdWVQUeNGiWTJk3yrDKqcwLj4uKkZs2aZrsIDW5jxoyR2NhYWbhwoWgo1FVGdajo+vXrpU2bNmY+ogbH6dOny7Bhw6RDhw6G7Nq1ayZs6jDVHj16SLt27SQkJMT8TBehsY6DBw+KzidcsWKFCYnWOQVw5y0IIIAAAggggAACCCCAQIkL2DYQeu9DqEGuV69eWfYh7NixowmIus2EHps3b5bFixfn2Ifw8uXLJsTpvEH9uy4Mo/sZag+kdQwdOtTMLfQ+tAewZcuWWV7LbQ5hibcgBUAAAQQQQAABBBBAAAEECihg20BYwPrwNgQQQAABBBBAAAEEEEAAgXwKEAjzCVXQ0958802zZYbOP3z44Ydl0KBBUqZMGXM5Xbk0NTXVDHnVLTKy73+o58yZM0fuuece+c1vfiNbtmyR5OTkHL2get77779vVmX94Ycf5NFHHxXt9cx+6LYd27dvN1t66JBbvfeVK1ekYcOG8uyzz5qFc6wjr3t5995GRESYxXm019U6tK6vv/66Gaqr59atW9fUtVy5cgVl5H0IIIAAAggggECpE9D1LPQZSlfg12lSupe2tb6G7sGt05h0SpMulqjPXtmPkydPmmfPt99+2zyT6Zoc+iyY/fntxIkTZo0NHVGn06V0Zf8777wzx/X0GVJH6O3cudNs56YLQVaqVMk8p+r6Hdbh61kxr+fM3Ebu1atXz4wE5CgaAQJh0biaq27cuNF8eHXOog57tUKftbqpBkUNSxr6+vbtm2sg1F8uXRVVQ1ZiYqIkJSWZYbILFiwQ3fJC50nq8dVXX5k5jZ9//rmUL18+10Co+zXqMNjq1aubIbLWvMo33nhDdu3aJX/961/NtTIyMvK8l/5jcvToUTO/88iRI3qpIwAAAA+lSURBVKZeOsS2cePG5v1a5z179sjgwYOlatWq8t1330mTJk2kbNmyRajNpRFAAAEEEEAAAfcI3Lx50zyPPfDAAyZs6RZs2tGgz2v6XKlTmbRT4dSpU/Lhhx/mGgh172xrylNez28aHLdu3WpCpz5r5hYI9dlP1/TQMmjHgq7boUFNQ6tOz9K1OH7961+bBsjrXr6eM621PayWfOGFF6RZs2Zm+hhH0QgQCIvG1VxVf6H0WxzdE1GPTz/91PwS6X/eR/btLqyfff/992al1VdffdWsmKrfBOm3RHrogjsaLPX16Ohoz+U0KGp4zN5DqP+oPPHEE2YfR/0mx/vQfwR0MZ3Vq1ebFVTzupfu89K1a1eZPHmyCXl66AJAeugKsPqtkv7CapljYmKKUJdLI4AAAggggAAC7hXQL+/1WfK9996TsLAwU1Hdc1v/+9WvfuWp+JdffmnCYG49hH/+85/Nua1bt87z+c262NmzZ6V79+65BsI1a9aYDgHtccx+aOeGjoDTzgBdyT+vZ8X8PtNaz7v6HK3PztWqVXNvY5dwzQiERdgAGrL0F0O/2dFDh3MOHDjQfIsTGhrqufPtAqF27//888/m2yHt3dOwpt++WEd8fLzpnWvRooXPQKjfDi1atEjmz5+fo8bau6hBUr8N0n1c8rqXLsqjwwW0d1O/ndJD/67DUfXaOoRAezQffPBBM5RAw+fjjz8uv/3tb4tQmksjgAACCCCAAALuEtDevQ8++MBMF7IO7YmrVauWeTa0jtsFQg1mv/vd78ywUh1VltfzW34CofbUdevWLctzp75PV+rXfbt1+pD+52s/8fw+0+q1tey6Q4DVIeKuFrZPbQiERdgWnTt3lgkTJkjTpk3NXaxevXfffTdLL93tAuGf/vQnSUhIMEMxx40bJ/Xr1zffClmHfmOi2220bdvWZyBcvny5GVKavbv9p59+MqFV5xDqdht65HUv7fXTUKrzFa1NQHUIg4ZJ7X38+OOPTY+hruKq/1jpcFEdXjBx4kQztIADAQQQQAABBBBAwLeA9gzqME5rJJa+Q4OR9hY+99xzPgOhTgfS3ri//OUvZg5iXs9vvgKhrtSvw1a1s8K7U0Pfp8NHtUNAOwZ0epCve+X3mVavrVOq9NnVGorqW40zCiJAICyIWj7fU5gewgsXLphfAh3GGRwcXOgeQv1HQAOmLlBjHTpUVPdj1PCmXfvWUZgeQu0p1Pd79yDqsFedmKy9oxwIIIAAAggggAACvgUK20OoPYtRUVFmpJavXjtfgXDbtm1mhJsuKON9aEDUTgKdM6gj2fTwda/89hDqehQ6Ek6fhVmY0PfnpTBnEAgLo+fjvTruW+fZWYvI6CIzuoBLfuYQ6uIwureiLtyih37Do13m1iIymZmZ5pua/Mwh1MnGGgj1l9bq1dPXNAzGxcWZ+YPeR1730jmEGiA15FmrXOk/AjpcQOcQHj582PRaEgiL8IPFpRFAAAEEEEDA9QI6h1BHWelK8tbCfDrsU3vM8jOHUEeZ6RzCX/ziF2ZeX17Pb74CoU4H0gVkvFeV1xFvOqRV5w9q8LQOX/fK7zOt9obq8+XIkSNd39YlXUECYRG2gC4io8Motau+QoUKomOvdVimFRCtVZQ0rGko05/psE7tEdSVSXU1UauLXFeR0qA1ZswYM/Ry4cKFoqHQCoi6kIz+p/ME9c8//vGPZnKv/qfDOP/xj3/IiBEjTG114RcNbzq30VrwRl/Xf2w0MPq6lw5d0OGvGlbT0tLMP1b6jZG1yqje+7777hMdCqtDRp9//nkzZNQaOluE5FwaAQQQQAABBBBwhYAuCKihTp8Pf//735vFCZctW+ZZZVR/ruFLOxC0s0EX9NPnOH2e05U8NUhZK8griK/nt6tXr8q5c+fMvXTunnYCWMND9dlVg5+uHq+HfvGvHQ0a2qzX9PlVn2N93cvXc6a+X4eo6vxHfb60FjF0RaPatBIEwiJuGO0R/Pvf/57rPoS57bOi2zfoIjG6Iqj+Ylvd71pM/YXXLR10qKf+cugvuvWNjK4spd+4eB96DQ1lOgFZ9zi05hrq/oC5Tc7VoQV33XWXuURe9/LeW0YXltFvqry/MdJ5ifqPjn6zpUNF9R+Wjh07FrE0l0cAAQQQQAABBNwl8OOPP5pnNv2CXddx0GdH730IdTSa96F7S+tCgRrYdGsw77mGeT2/aWeCjhrLfugwUR0CqqvLayeHdWhA1M4B70M7GvSZUw9fz4p5PWfq+zX8WqPqrNFt7mpZe9WGQGiv9jCl2b9/v/llfvnllwtdOv0F17Hj1p41hb4gF0AAAQQQQAABBBCwtYDOvdPFDVu1alXocurCgbodBWtBFJrSthcgENqwaTQQapf9/fffX+jS6fBQ3VbCuwev0BflAggggAACCCCAAAK2FdAQp3MGrf0LC1PQL774QurWrWvmInK4U4BA6M52pVYIIIAAAggggAACCCCAgE8BAqFPIk5AAAEEEEAAAQQQQAABBNwpQCB0Z7tSKwQQQAABBBBAAAEEEEDApwCB0CcRJyCAAAIIIIAAAggggAAC7hQgELqzXakVAggggAACCCCAAAIIIOBTgEDok4gTEEAAAQQQQAABBBBAAAF3ChAI3dmu1AoBBBBAAAEEEEAAAQQQ8ClAIPRJxAkIIIAAAggggAACCCCAgDsFCITubFdqhQACCCCAAAIIIIAAAgj4FCAQ+iTiBAQQQAABBBBAAAEEEEDAnQIEQne2K7VCAAEEEEAAAQQQQAABBHwKEAh9EnECAggggAACCCCAAAIIIOBOAQKhO9uVWiGAAAIIIIAAAggggAACPgUIhD6JOAEBBBBAAAEEEEAAAQQQcKcAgdCd7UqtEEAAAQQQQAABBBBAAAGfAgRCn0ScgAACCCCAAAIIIIAAAgi4U4BA6M52pVYIIIAAAggggAACCCCAgE8BAqFPIk5AAAEEEEAAAQQQQAABBNwpQCB0Z7tSKwQQQAABBBBAAAEEEEDApwCB0CcRJyCAAAIIIIAAAggggAAC7hQgELqzXakVAggggAACCCCAAAIIIOBTgEDok4gTEEAAAQQQQAABBBBAAAF3ChAI3dmu1AoBBBBAAAEEEEAAAQQQ8ClAIPRJxAkIIIAAAggggAACCCCAgDsFCITubFdqhQACCCCAAAIIIIAAAgj4FCAQ+iTiBAQQQAABBBBAAAEEEEDAnQIEQne2K7VCAAEEEEAAAQQQQAABBHwKEAh9EnECAggggAACCCCAAAIIIOBOAQKhO9uVWiGAAAIIIIAAAggggAACPgUIhD6JOAEBBBBAAAEEEEAAAQQQcKcAgdCd7UqtEEAAAQQQQAABBBBAAAGfAgRCn0ScgAACCCBgR4FVq1bJjBkzPEUrV66cREZGSsOGDSUuLk4efvhhCQoKsmPRKRMCCCCAAAK2ESAQ2qYpKAgCCCCAgD8CViB87733pE6dOnLlyhU5duyYfP7557JkyRJp2rSpzJ49W8LCwvy5rLz88svyzjvvyBdffOHX+zgZAQQQQAABJwoQCJ3YapQZAQQQQECyB0JvktTUVOnfv788/vjjMnLkSL+0CIR+cXEyAggggIDDBQiEDm9Aio8AAgiUVoG8AqGaaBDcvHmzfPbZZ6LDSdesWSOTJk0yXDqUtFq1atK8eXMZNGiQVK9e3bw+c+ZMeeutt7KQRkdHy8cff2xeO3jwoLzyyiuSkpIiV69elQYNGsjgwYPNdTgQQAABBBBwogCB0ImtRpkRQAABBPLsIVSe1atXy7Rp0+S1117LEdiuX78uP/74owmAp0+flhUrVkhISIhRvV0P4bfffitPPfWUtGvXTp599lmpWLGiKcPSpUtl+fLlcu+999IqCCCAAAIIOE6AQOi4JqPACCCAAAIq4KuHUOcSDh8+XKZOnSqPPPJIrmgaCuPj4+XNN9+U++67L89AqD2JGRkZ5r5WeNQ36NDUO+64w8xX5EAAAQQQQMBpAgRCp7UY5UUAAQQQMAK+AqEOFR0xYoQnEOoQT+0J/Oijj0ywu3TpkkfSOzTm1kN47do1efDBB6Vnz54yZMiQLC2waNEi0xu5adMmWgYBBBBAAAHHCRAIHddkFBgBBBBAID+BUFcKnT59ullxtFmzZubv69evlwkTJkhsbKyEh4dLZmamPProozJx4kTp2LGjgc0tEJ48efK2vYxWa3zzzTc0DAIIIIAAAo4TIBA6rskoMAIIIIBAfgJh9kVldF9CXXX0D3/4gwdQVyPVeYG+AqFuaaE9hAMGDJCBAwfSAAgggAACCLhGgEDomqakIggggEDpEshryOiePXskMTHRs+3ErVu3TKDT1xISEjxQurG9Xsc7EC5btsz0Km7dujULqAbJ8+fPm/mGZcqUKV3Y1BYBBBBAwLUCBELXNi0VQwABBNwtkD0Q6hxBa2N6XVk0+8b0Op9w3759ZvGXmJgYWbdunezcuVM2btyYJRBacw+Tk5Pll7/8pQQHBxtIXWVUw+RDDz1keglr1Khh5iJqcNQ/hw0b5m5waocAAggg4EoBAqErm5VKIYAAAu4XsAKhVdOwsDCJjIw02z/ExcWJDhHV/QatQ7eX0B5BDXAa8nTlUe0xzD6H8ObNmyYgajDUHkHvfQgPHz4sixcvlh07dsjFixdNsNSex169epnzOBBAAAEEEHCaAIHQaS1GeRFAAAEEEEAAAQQQQACBAAkQCAMEyWUQQAABBBBAAAEEEEAAAacJEAid1mKUFwEEEEAAAQQQQAABBBAIkACBMECQXAYBBBBAAAEEEEAAAQQQcJoAgdBpLUZ5EUAAAQQQQAABBBBAAIEACRAIAwTJZRBAAAEEEEAAAQQQQAABpwkQCJ3WYpQXAQQQQAABBBBAAAEEEAiQAIEwQJBcBgEEEEAAAQQQQAABBBBwmgCB0GktRnkRQAABBBBAAAEEEEAAgQAJEAgDBMllEEAAAQQQQAABBBBAAAGnCRAIndZilBcBBBBAAAEEEEAAAQQQCJAAgTBAkFwGAQQQQAABBBBAAAEEEHCaAIHQaS1GeRFAAAEEEEAAAQQQQACBAAkQCAMEyWUQQAABBBBAAAEEEEAAAacJEAid1mKUFwEEEEAAAQQQQAABBBAIkACBMECQXAYBBBBAAAEEEEAAAQQQcJoAgdBpLUZ5EUAAAQQQQAABBBBAAIEACRAIAwTJZRBAAAEEEEAAAQQQQAABpwkQCJ3WYpQXAQQQQAABBBBAAAEEEAiQAIEwQJBcBgEEEEAAAQQQQAABBBBwmgCB0GktRnkRQAABBBBAAAEEEEAAgQAJEAgDBMllEEAAAQQQQAABBBBAAAGnCfx/7OPAQFNA9qoAAAAASUVORK5CYII=" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_modeldrift_data() # works if date_compile_auc and/or datadrift_file are filled" - ] - }, - { - "cell_type": "markdown", - "id": "5f1241e2", - "metadata": {}, - "source": [ - "### Display model drift with multiple indicators" - ] - }, - { - "cell_type": "markdown", - "id": "08d89b46", - "metadata": {}, - "source": [ - "If you have several metrics or indicators for performance monitoring, it is possible to have reference columns.\n", - "Let's create a dummy performance table to show the use." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "e5dff49d", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "625d0912", + "metadata": {}, + "source": [ + "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", + "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", + "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." + ] + }, { - "data": { - "text/html": [ - "
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indicatorlevel_1anneemoisperformance
0rmse02006.01.00.031487
1rmse12007.01.00.033090
2mse02006.01.01.031988
3mse12007.01.01.033644
\n", - "
" + "cell_type": "markdown", + "id": "a8ad7820", + "metadata": {}, + "source": [ + "## Add model drift in report" + ] + }, + { + "cell_type": "markdown", + "id": "e39dc67c", + "metadata": {}, + "source": [ + "For the moment, the model drift part of eurybia only consists of displaying performance of deployed model. \n", + "(We hope to bring new features in the future on this part)" + ] + }, + { + "cell_type": "markdown", + "id": "82d0de33", + "metadata": {}, + "source": [ + "### Put model performance in DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "79ae3c07", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = regressor.predict(Xtest)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "28635fd0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.031487" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " indicator level_1 annee mois performance\n", - "0 rmse 0 2006.0 1.0 0.031487\n", - "1 rmse 1 2007.0 1.0 0.033090\n", - "2 mse 0 2006.0 1.0 1.031988\n", - "3 mse 1 2007.0 1.0 1.033644" + "source": [ + "performance_test = mean_squared_log_error(ytest, y_pred).round(6)\n", + "performance_test" ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_performance_mse = df_performance.copy()\n", - "df_performance_mse['performance']= np.exp(df_performance_mse['performance'])\n", - "df_performance2 = pd.concat([df_performance, df_performance_mse], keys=[\"rmse\", \"mse\"]).reset_index().rename(columns={\"level_0\": \"indicator\"})\n", - "df_performance2" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "d38140b2", - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance2,metric='performance',reference_columns=['indicator']) " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "f55d65b4", - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_modeldrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "code", + "execution_count": 13, + "id": "c12a14e7", + "metadata": {}, + "outputs": [], + "source": [ + "#Create Dataframe to track performance over the years\n", + "df_performance = pd.DataFrame({'annee': [2006], 'mois':[1], 'performance': [performance_test]})" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4f164198", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.03309" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "df_2007_encode=encoder.transform(X_df_2007)\n", + "y_pred_2007 = regressor.predict(df_2007_encode)\n", + "performance_2007 = mean_squared_log_error(y_df_2007, y_pred_2007).round(6)\n", + "performance_2007" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_modeldrift_2007.html', \n", - " title_story=\"Data drift\",\n", - " title_description=\"\"\"House price model drift 2007\"\"\", # Optional: add a subtitle to describe report\n", - " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" # Optional: add information on report \n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "745f1602", - "metadata": {}, - "source": [ - "## Compile Drift over years" - ] - }, - { - "cell_type": "markdown", - "id": "836e07cc", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2008" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "4b495e2a", - "metadata": {}, - "outputs": [], - "source": [ - "house_df_2008 = house_df.loc[house_df['YrSold'] == 2008]\n", - "\n", - "y_df_2008=house_df_2008['SalePrice'].to_frame()\n", - "X_df_2008=house_df_2008[house_df_2008.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "a0afc6d0", - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "0.028883" + "cell_type": "code", + "execution_count": null, + "id": "bd9fe858", + "metadata": {}, + "outputs": [], + "source": [ + "df_performance = df_performance.append({'annee': 2007, 'mois':1, 'performance': performance_2007}, ignore_index=True)" ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_2008_encode=encoder.transform(X_df_2008)\n", - "y_pred_2008 = regressor.predict(df_2008_encode)\n", - "performance_2008 = mean_squared_log_error(y_df_2008, y_pred_2008).round(6)\n", - "performance_2008" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2eb05bf9", - "metadata": {}, - "outputs": [], - "source": [ - "df_performance = df_performance.append({'annee': 2008, 'mois':1, 'performance': performance_2008}, ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "25926a75", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2008,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "aba273ec", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The variable Condition1 has mismatching unique values:\n", - "[\"Within 200' of East-West Railroad\"] | []\n", - "\n", - "The variable Condition2 has mismatching unique values:\n", - "['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to postive off-site feature', 'Near positive off-site feature--park, greenbelt, etc.'] | []\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "['Mixed'] | []\n", - "\n", - "The variable ExterCond has mismatching unique values:\n", - "['Excellent'] | []\n", - "\n", - "The variable ExterQual has mismatching unique values:\n", - "['Fair'] | []\n", - "\n", - "The variable Exterior1st has mismatching unique values:\n", - "[] | ['Imitation Stucco']\n", - "\n", - "The variable Exterior2nd has mismatching unique values:\n", - "[] | ['Other', 'Stone']\n", - "\n", - "The variable Foundation has mismatching unique values:\n", - "[] | ['Slab', 'Wood']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "['Major Deductions 2'] | []\n", - "\n", - "The variable GarageCond has mismatching unique values:\n", - "['Excellent'] | ['Poor']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Poor']\n", - "\n", - "The variable GarageType has mismatching unique values:\n", - "[] | ['More than one type of garage']\n", - "\n", - "The variable Heating has mismatching unique values:\n", - "['Hot water or steam heat other than gas', 'Floor Furnace'] | ['Wall furnace']\n", - "\n", - "The variable MSSubClass has mismatching unique values:\n", - "['1-Story w/Finished Attic All Ages'] | []\n", - "\n", - "The variable Neighborhood has mismatching unique values:\n", - "['Northpark Villa', 'Bluestem'] | []\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "['Membrane', 'Clay or Tile'] | ['Metal']\n", - "\n", - "The variable SaleCondition has mismatching unique values:\n", - "[] | ['Sale between family members']\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "['Contract 15% Down payment regular terms', 'Warranty Deed - Cash'] | ['Contract Low Interest', 'Other']\n", - "\n", - "The variable Street has mismatching unique values:\n", - "['Gravel'] | []\n", - "\n", - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6877714667557634\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2008', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "6868d560", - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "46ad3795", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "52912cfe", + "metadata": {}, + "source": [ + "### Add performance Dataframe in Smartdrift" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f0e96f82", + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " + ] + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_modeldrift_2008.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "code", + "execution_count": 17, + "id": "ef937e7f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_modeldrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_modeldrift_2007.html', \n", + " title_story=\"Data drift\",\n", + " title_description=\"\"\"House price model drift 2007\"\"\", # Optional: add a subtitle to describe report\n", + " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" # Optional: add information on report\n", + " )" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_modeldrift_2008.html', \n", - " title_story=\"Model drift\",\n", - " title_description=\"\"\"House price model drift 2008\"\"\", # Optional: add a subtitle to describe report\n", - " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" # Optional: add information on report\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "78b3758e", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2009" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "c782c7de", - "metadata": {}, - "outputs": [], - "source": [ - "house_df_2009 = house_df.loc[house_df['YrSold'] == 2009]\n", - "\n", - "y_df_2009=house_df_2009['SalePrice'].to_frame()\n", - "X_df_2009=house_df_2009[house_df_2009.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "854430e6", - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "0.031778" + "cell_type": "markdown", + "id": "84c8883b", + "metadata": {}, + "source": [ + "Eurybia is designed to generate an HTML report for analysis, and less for use in notebook mode. \n", + "However, to illustrate functionalities, we will detail results with notebook mode analysis." ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_2009_encode=encoder.transform(X_df_2009)\n", - "y_pred_2009 = regressor.predict(df_2009_encode)\n", - "performance_2009 = mean_squared_log_error(y_df_2009, y_pred_2009).round(6)\n", - "performance_2009" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "f4d82f70", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2009,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "be02b63f", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The variable BsmtCond has mismatching unique values:\n", - "['Poor -Severe cracking, settling, or wetness'] | []\n", - "\n", - "The variable Condition1 has mismatching unique values:\n", - "[] | ['Adjacent to East-West Railroad']\n", - "\n", - "The variable Condition2 has mismatching unique values:\n", - "['Adjacent to arterial street'] | []\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "[] | ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "\n", - "The variable ExterCond has mismatching unique values:\n", - "['Excellent'] | []\n", - "\n", - "The variable ExterQual has mismatching unique values:\n", - "['Fair'] | []\n", - "\n", - "The variable Exterior1st has mismatching unique values:\n", - "['Brick Common', 'Cinder Block'] | ['Stone', 'Imitation Stucco']\n", - "\n", - "The variable Exterior2nd has mismatching unique values:\n", - "['Brick Common', 'Cinder Block'] | ['Other']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "['Major Deductions 2'] | []\n", - "\n", - "The variable GarageCond has mismatching unique values:\n", - "['Excellent'] | ['Good']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Poor']\n", - "\n", - "The variable GarageType has mismatching unique values:\n", - "[] | ['More than one type of garage']\n", - "\n", - "The variable LotConfig has mismatching unique values:\n", - "[] | ['Frontage on 3 sides of property']\n", - "\n", - "The variable MSSubClass has mismatching unique values:\n", - "['1-Story w/Finished Attic All Ages'] | []\n", - "\n", - "The variable Neighborhood has mismatching unique values:\n", - "['Northpark Villa', 'Bluestem'] | ['Veenker']\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "[] | ['Metal', 'Wood Shakes']\n", - "\n", - "The variable RoofStyle has mismatching unique values:\n", - "['Mansard'] | []\n", - "\n", - "The variable SaleCondition has mismatching unique values:\n", - "[] | ['Adjoining Land Purchase']\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "[] | ['Other']\n", - "\n", - "The variable Utilities has mismatching unique values:\n", - "['Electricity and Gas Only'] | []\n", - "\n", - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.5405695039804042\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2009', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f58ca3b1", - "metadata": {}, - "outputs": [], - "source": [ - "df_performance = df_performance.append({'annee': 2009, 'mois':1, 'performance': performance_2009}, ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "a14df209", - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "c54b73eb", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "4add0130", + "metadata": {}, + "source": [ + "This tutorial contains only anlysis on additional features of model drift. For more detailed information on data drift, you can consult these tutorials : (https://github.com/MAIF/eurybia/tree/master/tutorial/data_drift)" + ] + }, + { + "cell_type": "markdown", + "id": "88cfeb49", + "metadata": {}, + "source": [ + "### Display model drift" + ] + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_modeldrift_2009.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "code", + "execution_count": 18, + "id": "6d33cabf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "SD.plot.generate_modeldrift_data() # works if date_compile_auc and/or datadrift_file are filled" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_modeldrift_2009.html', \n", - " title_story=\"Model drift\",\n", - " title_description=\"\"\"House price model drift 2009\"\"\", # Optional: add a subtitle to describe report\n", - " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" # Optional: add information on report \n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "7701d3d9", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2010" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "32b79b14", - "metadata": {}, - "outputs": [], - "source": [ - "house_df_2010 = house_df.loc[house_df['YrSold'] == 2010]\n", - "\n", - "y_df_2010=house_df_2010['SalePrice'].to_frame()\n", - "X_df_2010=house_df_2010[house_df_2010.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "78d982b3", - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "0.023441" + "cell_type": "markdown", + "id": "5f1241e2", + "metadata": {}, + "source": [ + "### Display model drift with multiple indicators" ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_2010_encode=encoder.transform(X_df_2010)\n", - "y_pred_2010 = regressor.predict(df_2010_encode)\n", - "performance_2010 = mean_squared_log_error(y_df_2010, y_pred_2010).round(6)\n", - "performance_2010" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3edb53b5", - "metadata": {}, - "outputs": [], - "source": [ - "df_performance = df_performance.append({'annee': 2010, 'mois':1, 'performance': performance_2010}, ignore_index=True" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "13d0e1c8", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2010,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "1157cabb", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The variable Condition1 has mismatching unique values:\n", - "[\"Within 200' of East-West Railroad\"] | []\n", - "\n", - "The variable Electrical has mismatching unique values:\n", - "[] | ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", - "\n", - "The variable ExterCond has mismatching unique values:\n", - "['Poor'] | []\n", - "\n", - "The variable ExterQual has mismatching unique values:\n", - "['Fair'] | []\n", - "\n", - "The variable Exterior1st has mismatching unique values:\n", - "['Asphalt Shingles'] | ['Stone', 'Imitation Stucco']\n", - "\n", - "The variable Exterior2nd has mismatching unique values:\n", - "['Asphalt Shingles', 'Brick Common'] | ['Other', 'Stone']\n", - "\n", - "The variable Functional has mismatching unique values:\n", - "[] | ['Major Deductions 1']\n", - "\n", - "The variable GarageCond has mismatching unique values:\n", - "[] | ['Poor', 'Good']\n", - "\n", - "The variable GarageQual has mismatching unique values:\n", - "[] | ['Good', 'Excellent', 'Poor']\n", - "\n", - "The variable GarageType has mismatching unique values:\n", - "[] | ['More than one type of garage']\n", - "\n", - "The variable Heating has mismatching unique values:\n", - "[] | ['Gas hot water or steam heat', 'Wall furnace']\n", - "\n", - "The variable HouseStyle has mismatching unique values:\n", - "[] | ['Two and one-half story: 2nd level finished', 'One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished']\n", - "\n", - "The variable LotConfig has mismatching unique values:\n", - "[] | ['Frontage on 3 sides of property']\n", - "\n", - "The variable LotShape has mismatching unique values:\n", - "[] | ['Irregular']\n", - "\n", - "The variable MSSubClass has mismatching unique values:\n", - "['1-Story w/Finished Attic All Ages'] | ['2-1/2 Story All Ages', '1-1/2 Story - Unfinished All Ages']\n", - "\n", - "The variable MSZoning has mismatching unique values:\n", - "[] | ['Residential High Density']\n", - "\n", - "The variable Neighborhood has mismatching unique values:\n", - "['Northpark Villa'] | ['Veenker']\n", - "\n", - "The variable RoofMatl has mismatching unique values:\n", - "[] | ['Wood Shingles', 'Metal', 'Gravel & Tar']\n", - "\n", - "The variable RoofStyle has mismatching unique values:\n", - "['Mansard', 'Shed'] | ['Flat']\n", - "\n", - "The variable SaleCondition has mismatching unique values:\n", - "[] | ['Adjoining Land Purchase']\n", - "\n", - "The variable SaleType has mismatching unique values:\n", - "['Contract 15% Down payment regular terms'] | ['Contract Low Down', 'Contract Low Down payment and low interest', 'Other']\n", - "\n", - "The variable Street has mismatching unique values:\n", - "['Gravel'] | []\n", - "\n", - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6978632478632478\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2010', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "a2c985d0", - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "5651d11a", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "08d89b46", + "metadata": {}, + "source": [ + "If you have several metrics or indicators for performance monitoring, it is possible to have reference columns.\n", + "Let's create a dummy performance table to show the use." + ] + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_modeldrift_2010.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "code", + "execution_count": 19, + "id": "e5dff49d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indicatorlevel_1anneemoisperformance
0rmse02006.01.00.031487
1rmse12007.01.00.033090
2mse02006.01.01.031988
3mse12007.01.01.033644
\n", + "
" + ], + "text/plain": [ + " indicator level_1 annee mois performance\n", + "0 rmse 0 2006.0 1.0 0.031487\n", + "1 rmse 1 2007.0 1.0 0.033090\n", + "2 mse 0 2006.0 1.0 1.031988\n", + "3 mse 1 2007.0 1.0 1.033644" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "df_performance_mse = df_performance.copy()\n", + "df_performance_mse['performance']= np.exp(df_performance_mse['performance'])\n", + "df_performance2 = pd.concat([df_performance, df_performance_mse], keys=[\"rmse\", \"mse\"]).reset_index().rename(columns={\"level_0\": \"indicator\"})\n", + "df_performance2" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_modeldrift_2010.html', \n", - " title_story=\"Model drift\",\n", - " title_description=\"\"\"House price model drift 2010\"\"\",\n", - " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" \n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "c143a5a2", - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d38140b2", + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance2,metric='performance',reference_columns=['indicator']) " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "f55d65b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_modeldrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_modeldrift_2007.html', \n", + " title_story=\"Data drift\",\n", + " title_description=\"\"\"House price model drift 2007\"\"\", # Optional: add a subtitle to describe report\n", + " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" # Optional: add information on report \n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "745f1602", + "metadata": {}, + "source": [ + "## Compile Drift over years" + ] + }, + { + "cell_type": "markdown", + "id": "836e07cc", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2008" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "4b495e2a", + "metadata": {}, + "outputs": [], + "source": [ + "house_df_2008 = house_df.loc[house_df['YrSold'] == 2008]\n", + "\n", + "y_df_2008=house_df_2008['SalePrice'].to_frame()\n", + "X_df_2008=house_df_2008[house_df_2008.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a0afc6d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.028883" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2008_encode=encoder.transform(X_df_2008)\n", + "y_pred_2008 = regressor.predict(df_2008_encode)\n", + "performance_2008 = mean_squared_log_error(y_df_2008, y_pred_2008).round(6)\n", + "performance_2008" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2eb05bf9", + "metadata": {}, + "outputs": [], + "source": [ + "df_performance = df_performance.append({'annee': 2008, 'mois':1, 'performance': performance_2008}, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "25926a75", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2008,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "aba273ec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The variable Condition1 has mismatching unique values:\n", + "[\"Within 200' of East-West Railroad\"] | []\n", + "\n", + "The variable Condition2 has mismatching unique values:\n", + "['Adjacent to arterial street', \"Within 200' of North-South Railroad\", 'Adjacent to postive off-site feature', 'Near positive off-site feature--park, greenbelt, etc.'] | []\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "['Mixed'] | []\n", + "\n", + "The variable ExterCond has mismatching unique values:\n", + "['Excellent'] | []\n", + "\n", + "The variable ExterQual has mismatching unique values:\n", + "['Fair'] | []\n", + "\n", + "The variable Exterior1st has mismatching unique values:\n", + "[] | ['Imitation Stucco']\n", + "\n", + "The variable Exterior2nd has mismatching unique values:\n", + "[] | ['Other', 'Stone']\n", + "\n", + "The variable Foundation has mismatching unique values:\n", + "[] | ['Slab', 'Wood']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "['Major Deductions 2'] | []\n", + "\n", + "The variable GarageCond has mismatching unique values:\n", + "['Excellent'] | ['Poor']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Poor']\n", + "\n", + "The variable GarageType has mismatching unique values:\n", + "[] | ['More than one type of garage']\n", + "\n", + "The variable Heating has mismatching unique values:\n", + "['Hot water or steam heat other than gas', 'Floor Furnace'] | ['Wall furnace']\n", + "\n", + "The variable MSSubClass has mismatching unique values:\n", + "['1-Story w/Finished Attic All Ages'] | []\n", + "\n", + "The variable Neighborhood has mismatching unique values:\n", + "['Northpark Villa', 'Bluestem'] | []\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "['Membrane', 'Clay or Tile'] | ['Metal']\n", + "\n", + "The variable SaleCondition has mismatching unique values:\n", + "[] | ['Sale between family members']\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "['Contract 15% Down payment regular terms', 'Warranty Deed - Cash'] | ['Contract Low Interest', 'Other']\n", + "\n", + "The variable Street has mismatching unique values:\n", + "['Gravel'] | []\n", + "\n", + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6877714667557634\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2008', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "6868d560", + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "46ad3795", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_modeldrift_2008.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_modeldrift_2008.html', \n", + " title_story=\"Model drift\",\n", + " title_description=\"\"\"House price model drift 2008\"\"\", # Optional: add a subtitle to describe report\n", + " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" # Optional: add information on report\n", + " )" + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" + "cell_type": "markdown", + "id": "78b3758e", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2009" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c782c7de", + "metadata": {}, + "outputs": [], + "source": [ + "house_df_2009 = house_df.loc[house_df['YrSold'] == 2009]\n", + "\n", + "y_df_2009=house_df_2009['SalePrice'].to_frame()\n", + "X_df_2009=house_df_2009[house_df_2009.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "854430e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.031778" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2009_encode=encoder.transform(X_df_2009)\n", + "y_pred_2009 = regressor.predict(df_2009_encode)\n", + "performance_2009 = mean_squared_log_error(y_df_2009, y_pred_2009).round(6)\n", + "performance_2009" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f4d82f70", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2009,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "be02b63f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The variable BsmtCond has mismatching unique values:\n", + "['Poor -Severe cracking, settling, or wetness'] | []\n", + "\n", + "The variable Condition1 has mismatching unique values:\n", + "[] | ['Adjacent to East-West Railroad']\n", + "\n", + "The variable Condition2 has mismatching unique values:\n", + "['Adjacent to arterial street'] | []\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "[] | ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "\n", + "The variable ExterCond has mismatching unique values:\n", + "['Excellent'] | []\n", + "\n", + "The variable ExterQual has mismatching unique values:\n", + "['Fair'] | []\n", + "\n", + "The variable Exterior1st has mismatching unique values:\n", + "['Brick Common', 'Cinder Block'] | ['Stone', 'Imitation Stucco']\n", + "\n", + "The variable Exterior2nd has mismatching unique values:\n", + "['Brick Common', 'Cinder Block'] | ['Other']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "['Major Deductions 2'] | []\n", + "\n", + "The variable GarageCond has mismatching unique values:\n", + "['Excellent'] | ['Good']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Poor']\n", + "\n", + "The variable GarageType has mismatching unique values:\n", + "[] | ['More than one type of garage']\n", + "\n", + "The variable LotConfig has mismatching unique values:\n", + "[] | ['Frontage on 3 sides of property']\n", + "\n", + "The variable MSSubClass has mismatching unique values:\n", + "['1-Story w/Finished Attic All Ages'] | []\n", + "\n", + "The variable Neighborhood has mismatching unique values:\n", + "['Northpark Villa', 'Bluestem'] | ['Veenker']\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "[] | ['Metal', 'Wood Shakes']\n", + "\n", + "The variable RoofStyle has mismatching unique values:\n", + "['Mansard'] | []\n", + "\n", + "The variable SaleCondition has mismatching unique values:\n", + "[] | ['Adjoining Land Purchase']\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "[] | ['Other']\n", + "\n", + "The variable Utilities has mismatching unique values:\n", + "['Electricity and Gas Only'] | []\n", + "\n", + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.5405695039804042\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2009', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f58ca3b1", + "metadata": {}, + "outputs": [], + "source": [ + "df_performance = df_performance.append({'annee': 2009, 'mois':1, 'performance': performance_2009}, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a14df209", + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c54b73eb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_modeldrift_2009.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_modeldrift_2009.html', \n", + " title_story=\"Model drift\",\n", + " title_description=\"\"\"House price model drift 2009\"\"\", # Optional: add a subtitle to describe report\n", + " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" # Optional: add information on report \n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "7701d3d9", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2010" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "32b79b14", + "metadata": {}, + "outputs": [], + "source": [ + "house_df_2010 = house_df.loc[house_df['YrSold'] == 2010]\n", + "\n", + "y_df_2010=house_df_2010['SalePrice'].to_frame()\n", + "X_df_2010=house_df_2010[house_df_2010.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "78d982b3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.023441" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2010_encode=encoder.transform(X_df_2010)\n", + "y_pred_2010 = regressor.predict(df_2010_encode)\n", + "performance_2010 = mean_squared_log_error(y_df_2010, y_pred_2010).round(6)\n", + "performance_2010" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3edb53b5", + "metadata": {}, + "outputs": [], + "source": [ + "df_performance = df_performance.append({'annee': 2010, 'mois':1, 'performance': performance_2010}, ignore_index=True" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "13d0e1c8", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2010,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "1157cabb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The variable Condition1 has mismatching unique values:\n", + "[\"Within 200' of East-West Railroad\"] | []\n", + "\n", + "The variable Electrical has mismatching unique values:\n", + "[] | ['60 AMP Fuse Box and mostly knob & tube wiring (poor)']\n", + "\n", + "The variable ExterCond has mismatching unique values:\n", + "['Poor'] | []\n", + "\n", + "The variable ExterQual has mismatching unique values:\n", + "['Fair'] | []\n", + "\n", + "The variable Exterior1st has mismatching unique values:\n", + "['Asphalt Shingles'] | ['Stone', 'Imitation Stucco']\n", + "\n", + "The variable Exterior2nd has mismatching unique values:\n", + "['Asphalt Shingles', 'Brick Common'] | ['Other', 'Stone']\n", + "\n", + "The variable Functional has mismatching unique values:\n", + "[] | ['Major Deductions 1']\n", + "\n", + "The variable GarageCond has mismatching unique values:\n", + "[] | ['Poor', 'Good']\n", + "\n", + "The variable GarageQual has mismatching unique values:\n", + "[] | ['Good', 'Excellent', 'Poor']\n", + "\n", + "The variable GarageType has mismatching unique values:\n", + "[] | ['More than one type of garage']\n", + "\n", + "The variable Heating has mismatching unique values:\n", + "[] | ['Gas hot water or steam heat', 'Wall furnace']\n", + "\n", + "The variable HouseStyle has mismatching unique values:\n", + "[] | ['Two and one-half story: 2nd level finished', 'One and one-half story: 2nd level unfinished', 'Two and one-half story: 2nd level unfinished']\n", + "\n", + "The variable LotConfig has mismatching unique values:\n", + "[] | ['Frontage on 3 sides of property']\n", + "\n", + "The variable LotShape has mismatching unique values:\n", + "[] | ['Irregular']\n", + "\n", + "The variable MSSubClass has mismatching unique values:\n", + "['1-Story w/Finished Attic All Ages'] | ['2-1/2 Story All Ages', '1-1/2 Story - Unfinished All Ages']\n", + "\n", + "The variable MSZoning has mismatching unique values:\n", + "[] | ['Residential High Density']\n", + "\n", + "The variable Neighborhood has mismatching unique values:\n", + "['Northpark Villa'] | ['Veenker']\n", + "\n", + "The variable RoofMatl has mismatching unique values:\n", + "[] | ['Wood Shingles', 'Metal', 'Gravel & Tar']\n", + "\n", + "The variable RoofStyle has mismatching unique values:\n", + "['Mansard', 'Shed'] | ['Flat']\n", + "\n", + "The variable SaleCondition has mismatching unique values:\n", + "[] | ['Adjoining Land Purchase']\n", + "\n", + "The variable SaleType has mismatching unique values:\n", + "['Contract 15% Down payment regular terms'] | ['Contract Low Down', 'Contract Low Down payment and low interest', 'Other']\n", + "\n", + "The variable Street has mismatching unique values:\n", + "['Gravel'] | []\n", + "\n", + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6978632478632478\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2010', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"house_price_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "a2c985d0", + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "5651d11a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_modeldrift_2010.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_modeldrift_2010.html', \n", + " title_story=\"Model drift\",\n", + " title_description=\"\"\"House price model drift 2010\"\"\",\n", + " project_info_file=\"../../eurybia/data/project_info_house_price.yml\" \n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "c143a5a2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_modeldrift_data() # works if add_data_modeldrift used before " + ] + }, + { + "cell_type": "markdown", + "id": "7bb69515", + "metadata": {}, + "source": [ + "----" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "d08e6294e2d60f50397263035a337d71f3055486232bc02b45ce2785f62e7d8b" + }, + "kernelspec": { + "display_name": "dev_eurybia", + "language": "python", + "name": "dev_eurybia" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true } - ], - "source": [ - "SD.plot.generate_modeldrift_data() # works if add_data_modeldrift used before " - ] - }, - { - "cell_type": "markdown", - "id": "7bb69515", - "metadata": {}, - "source": [ - "----" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "d08e6294e2d60f50397263035a337d71f3055486232bc02b45ce2785f62e7d8b" - }, - "kernelspec": { - "display_name": "dev_eurybia", - "language": "python", - "name": "dev_eurybia" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": true - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/tutorial/model_drift/tutorial02-modeldrift-high-datadrift.ipynb b/tutorial/model_drift/tutorial02-modeldrift-high-datadrift.ipynb index e44b9ec..598de97 100644 --- a/tutorial/model_drift/tutorial02-modeldrift-high-datadrift.ipynb +++ b/tutorial/model_drift/tutorial02-modeldrift-high-datadrift.ipynb @@ -1,1193 +1,1193 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "9b6f3ff7", - "metadata": {}, - "source": [ - "# Detect High Model Drift \n", - "With this tutorial you:
\n", - "Understand how to use Eurybia to detect datadrift\n", - "\n", - "Contents:\n", - "- Detect data drift \n", - "- Compile Drift over years\n", - "\n", - "This public dataset comes from :\n", - "\n", - "https://www.kaggle.com/sobhanmoosavi/us-accidents/version/10\n", - "\n", - "---\n", - "Acknowledgements\n", - "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. “A Countrywide Traffic Accident Dataset.”, 2019.\n", - "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. \"Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights.\" In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.\n", - "---\n", - "\n", - "In this tutorial, the data are not loaded raw, a data preparation to facilitate the use of the tutorial has been done. You can find it here : \n", - "https://github.com/MAIF/eurybia/blob/master/eurybia/data/dataprep_US_car_accidents.ipynb" - ] - }, - { - "cell_type": "markdown", - "id": "6ee7dedd", - "metadata": {}, - "source": [ - "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", - "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8c767469", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "from category_encoders import OrdinalEncoder\n", - "import catboost\n", - "from eurybia import SmartDrift\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn import metrics\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "id": "939acff0", - "metadata": {}, - "source": [ - "## Import Dataset and split in training and production dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e0b10b1b", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6d3d1d90", - "metadata": {}, - "outputs": [], - "source": [ - "df_car_accident = data_loading(\"us_car_accident\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8a0a6ef4", - "metadata": {}, - "outputs": [ + "cells": [ + { + "cell_type": "markdown", + "id": "9b6f3ff7", + "metadata": {}, + "source": [ + "# Detect High Model Drift \n", + "With this tutorial you:
\n", + "Understand how to use Eurybia to detect datadrift\n", + "\n", + "Contents:\n", + "- Detect data drift \n", + "- Compile Drift over years\n", + "\n", + "This public dataset comes from :\n", + "\n", + "https://www.kaggle.com/sobhanmoosavi/us-accidents/version/10\n", + "\n", + "---\n", + "Acknowledgements\n", + "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, and Rajiv Ramnath. \u201cA Countrywide Traffic Accident Dataset.\u201d, 2019.\n", + "- Moosavi, Sobhan, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, and Rajiv Ramnath. \"Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights.\" In proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, 2019.\n", + "---\n", + "\n", + "In this tutorial, the data are not loaded raw, a data preparation to facilitate the use of the tutorial has been done. You can find it here : \n", + "https://github.com/MAIF/eurybia/blob/master/eurybia/data/dataprep_US_car_accidents.ipynb" + ] + }, + { + "cell_type": "markdown", + "id": "6ee7dedd", + "metadata": {}, + "source": [ + "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", + "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8c767469", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from category_encoders import OrdinalEncoder\n", + "import catboost\n", + "from eurybia import SmartDrift\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import metrics\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "939acff0", + "metadata": {}, + "source": [ + "## Import Dataset and split in training and production dataset" + ] + }, { - "data": { - "text/html": [ - "
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_accNautical_Twilightseason_acctargettarget_multiyear_accDescription
033.0-117.10.040.093.02.03Daywinter022019At Carmel Mountain Rd - Accident.
129.5-98.50.083.065.010.04Daysummer132017At TX-345-SP/Woodlawn Ave/Exit 567B - Accident.
232.7-96.80.088.057.010.00Nightsummer022021Incident on RUGGED DR near BERKLEY AVE Expect ...
340.0-76.30.061.058.010.04Dayspring022020At PA-741/Rohrerstown Rd - Accident.
441.5-81.81.071.053.010.00Daysummer022020At 117th St/Exit 166 - Accident.
\n", - "
" + "cell_type": "code", + "execution_count": 3, + "id": "e0b10b1b", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6d3d1d90", + "metadata": {}, + "outputs": [], + "source": [ + "df_car_accident = data_loading(\"us_car_accident\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8a0a6ef4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_accNautical_Twilightseason_acctargettarget_multiyear_accDescription
033.0-117.10.040.093.02.03Daywinter022019At Carmel Mountain Rd - Accident.
129.5-98.50.083.065.010.04Daysummer132017At TX-345-SP/Woodlawn Ave/Exit 567B - Accident.
232.7-96.80.088.057.010.00Nightsummer022021Incident on RUGGED DR near BERKLEY AVE Expect ...
340.0-76.30.061.058.010.04Dayspring022020At PA-741/Rohrerstown Rd - Accident.
441.5-81.81.071.053.010.00Daysummer022020At 117th St/Exit 166 - Accident.
\n", + "
" + ], + "text/plain": [ + " Start_Lat Start_Lng Distance(mi) Temperature(F) Humidity(%) \\\n", + "0 33.0 -117.1 0.0 40.0 93.0 \n", + "1 29.5 -98.5 0.0 83.0 65.0 \n", + "2 32.7 -96.8 0.0 88.0 57.0 \n", + "3 40.0 -76.3 0.0 61.0 58.0 \n", + "4 41.5 -81.8 1.0 71.0 53.0 \n", + "\n", + " Visibility(mi) day_of_week_acc Nautical_Twilight season_acc target \\\n", + "0 2.0 3 Day winter 0 \n", + "1 10.0 4 Day summer 1 \n", + "2 10.0 0 Night summer 0 \n", + "3 10.0 4 Day spring 0 \n", + "4 10.0 0 Day summer 0 \n", + "\n", + " target_multi year_acc Description \n", + "0 2 2019 At Carmel Mountain Rd - Accident. \n", + "1 3 2017 At TX-345-SP/Woodlawn Ave/Exit 567B - Accident. \n", + "2 2 2021 Incident on RUGGED DR near BERKLEY AVE Expect ... \n", + "3 2 2020 At PA-741/Rohrerstown Rd - Accident. \n", + "4 2 2020 At 117th St/Exit 166 - Accident. " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " Start_Lat Start_Lng Distance(mi) Temperature(F) Humidity(%) \\\n", - "0 33.0 -117.1 0.0 40.0 93.0 \n", - "1 29.5 -98.5 0.0 83.0 65.0 \n", - "2 32.7 -96.8 0.0 88.0 57.0 \n", - "3 40.0 -76.3 0.0 61.0 58.0 \n", - "4 41.5 -81.8 1.0 71.0 53.0 \n", - "\n", - " Visibility(mi) day_of_week_acc Nautical_Twilight season_acc target \\\n", - "0 2.0 3 Day winter 0 \n", - "1 10.0 4 Day summer 1 \n", - "2 10.0 0 Night summer 0 \n", - "3 10.0 4 Day spring 0 \n", - "4 10.0 0 Day summer 0 \n", - "\n", - " target_multi year_acc Description \n", - "0 2 2019 At Carmel Mountain Rd - Accident. \n", - "1 3 2017 At TX-345-SP/Woodlawn Ave/Exit 567B - Accident. \n", - "2 2 2021 Incident on RUGGED DR near BERKLEY AVE Expect ... \n", - "3 2 2020 At PA-741/Rohrerstown Rd - Accident. \n", - "4 2 2020 At 117th St/Exit 166 - Accident. " + "source": [ + "df_car_accident.head()" ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_car_accident.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "78f258f5", - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/html": [ - "
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_accNautical_Twilightseason_acctargettarget_multiyear_accDescription
033.0-117.10.040.093.02.03Daywinter022019At Carmel Mountain Rd - Accident.
129.5-98.50.083.065.010.04Daysummer132017At TX-345-SP/Woodlawn Ave/Exit 567B - Accident.
232.7-96.80.088.057.010.00Nightsummer022021Incident on RUGGED DR near BERKLEY AVE Expect ...
340.0-76.30.061.058.010.04Dayspring022020At PA-741/Rohrerstown Rd - Accident.
441.5-81.81.071.053.010.00Daysummer022020At 117th St/Exit 166 - Accident.
\n", - "
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Start_LatStart_LngDistance(mi)Temperature(F)Humidity(%)Visibility(mi)day_of_week_accNautical_Twilightseason_acctargettarget_multiyear_accDescription
033.0-117.10.040.093.02.03Daywinter022019At Carmel Mountain Rd - Accident.
129.5-98.50.083.065.010.04Daysummer132017At TX-345-SP/Woodlawn Ave/Exit 567B - Accident.
232.7-96.80.088.057.010.00Nightsummer022021Incident on RUGGED DR near BERKLEY AVE Expect ...
340.0-76.30.061.058.010.04Dayspring022020At PA-741/Rohrerstown Rd - Accident.
441.5-81.81.071.053.010.00Daysummer022020At 117th St/Exit 166 - Accident.
\n", + "
" + ], + "text/plain": [ + " Start_Lat Start_Lng Distance(mi) Temperature(F) Humidity(%) \\\n", + "0 33.0 -117.1 0.0 40.0 93.0 \n", + "1 29.5 -98.5 0.0 83.0 65.0 \n", + "2 32.7 -96.8 0.0 88.0 57.0 \n", + "3 40.0 -76.3 0.0 61.0 58.0 \n", + "4 41.5 -81.8 1.0 71.0 53.0 \n", + "\n", + " Visibility(mi) day_of_week_acc Nautical_Twilight season_acc target \\\n", + "0 2.0 3 Day winter 0 \n", + "1 10.0 4 Day summer 1 \n", + "2 10.0 0 Night summer 0 \n", + "3 10.0 4 Day spring 0 \n", + "4 10.0 0 Day summer 0 \n", + "\n", + " target_multi year_acc Description \n", + "0 2 2019 At Carmel Mountain Rd - Accident. \n", + "1 3 2017 At TX-345-SP/Woodlawn Ave/Exit 567B - Accident. \n", + "2 2 2021 Incident on RUGGED DR near BERKLEY AVE Expect ... \n", + "3 2 2020 At PA-741/Rohrerstown Rd - Accident. \n", + "4 2 2020 At 117th St/Exit 166 - Accident. " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " Start_Lat Start_Lng Distance(mi) Temperature(F) Humidity(%) \\\n", - "0 33.0 -117.1 0.0 40.0 93.0 \n", - "1 29.5 -98.5 0.0 83.0 65.0 \n", - "2 32.7 -96.8 0.0 88.0 57.0 \n", - "3 40.0 -76.3 0.0 61.0 58.0 \n", - "4 41.5 -81.8 1.0 71.0 53.0 \n", - "\n", - " Visibility(mi) day_of_week_acc Nautical_Twilight season_acc target \\\n", - "0 2.0 3 Day winter 0 \n", - "1 10.0 4 Day summer 1 \n", - "2 10.0 0 Night summer 0 \n", - "3 10.0 4 Day spring 0 \n", - "4 10.0 0 Day summer 0 \n", - "\n", - " target_multi year_acc Description \n", - "0 2 2019 At Carmel Mountain Rd - Accident. \n", - "1 3 2017 At TX-345-SP/Woodlawn Ave/Exit 567B - Accident. \n", - "2 2 2021 Incident on RUGGED DR near BERKLEY AVE Expect ... \n", - "3 2 2020 At PA-741/Rohrerstown Rd - Accident. \n", - "4 2 2020 At 117th St/Exit 166 - Accident. " + "source": [ + "df_car_accident.head()" ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_car_accident.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "05039303", - "metadata": {}, - "outputs": [ + }, { - "data": { - "text/plain": [ - "(50000, 13)" + "cell_type": "code", + "execution_count": 7, + "id": "05039303", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(50000, 13)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_car_accident.shape" ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_car_accident.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a1d226fa", - "metadata": {}, - "outputs": [], - "source": [ - "# Let us consider that the column \"year_acc\" corresponds to the reference date. \n", - "#In 2016, a model was trained using data. And in next years, we want to detect data drift on new data in production to predict\n", - "df_accident_baseline = df_car_accident.loc[df_car_accident['year_acc'] == 2016]\n", - "df_accident_2017 = df_car_accident.loc[df_car_accident['year_acc'] == 2017]\n", - "df_accident_2018 = df_car_accident.loc[df_car_accident['year_acc'] == 2018]\n", - "df_accident_2019 = df_car_accident.loc[df_car_accident['year_acc'] == 2019]\n", - "df_accident_2020 = df_car_accident.loc[df_car_accident['year_acc'] == 2020]\n", - "df_accident_2021 = df_car_accident.loc[df_car_accident['year_acc'] == 2021]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1e81bb4e", - "metadata": {}, - "outputs": [ + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a1d226fa", + "metadata": {}, + "outputs": [], + "source": [ + "# Let us consider that the column \"year_acc\" corresponds to the reference date. \n", + "#In 2016, a model was trained using data. And in next years, we want to detect data drift on new data in production to predict\n", + "df_accident_baseline = df_car_accident.loc[df_car_accident['year_acc'] == 2016]\n", + "df_accident_2017 = df_car_accident.loc[df_car_accident['year_acc'] == 2017]\n", + "df_accident_2018 = df_car_accident.loc[df_car_accident['year_acc'] == 2018]\n", + "df_accident_2019 = df_car_accident.loc[df_car_accident['year_acc'] == 2019]\n", + "df_accident_2020 = df_car_accident.loc[df_car_accident['year_acc'] == 2020]\n", + "df_accident_2021 = df_car_accident.loc[df_car_accident['year_acc'] == 2021]" + ] + }, { - "data": { - "text/html": [ - "
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target01
year_acc
201671.40628728.593713
201767.25462032.745380
201866.63466233.365338
201979.55118220.448818
202089.94480410.055196
202198.2599301.740070
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" + "cell_type": "code", + "execution_count": 9, + "id": "1e81bb4e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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target01
year_acc
201671.40628728.593713
201767.25462032.745380
201866.63466233.365338
201979.55118220.448818
202089.94480410.055196
202198.2599301.740070
\n", + "
" + ], + "text/plain": [ + "target 0 1\n", + "year_acc \n", + "2016 71.406287 28.593713\n", + "2017 67.254620 32.745380\n", + "2018 66.634662 33.365338\n", + "2019 79.551182 20.448818\n", + "2020 89.944804 10.055196\n", + "2021 98.259930 1.740070" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "target 0 1\n", - "year_acc \n", - "2016 71.406287 28.593713\n", - "2017 67.254620 32.745380\n", - "2018 66.634662 33.365338\n", - "2019 79.551182 20.448818\n", - "2020 89.944804 10.055196\n", - "2021 98.259930 1.740070" + "source": [ + "#We will train a classification model to predict the severity of an accident. 0 for a less severe accident and 1 for a severe accident.\n", + "#Let's check percentage in class 0 and 1\n", + "pd.crosstab(df_car_accident.year_acc, df_car_accident.target, normalize = 'index')*100" ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#We will train a classification model to predict the severity of an accident. 0 for a less severe accident and 1 for a severe accident.\n", - "#Let's check percentage in class 0 and 1\n", - "pd.crosstab(df_car_accident.year_acc, df_car_accident.target, normalize = 'index')*100" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c13ca2a5", - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=df_accident_baseline['target'].to_frame()\n", - "X_df_learning=df_accident_baseline[df_accident_baseline.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2017=df_accident_2017['target'].to_frame()\n", - "X_df_2017=df_accident_2017[df_accident_2017.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2018=df_accident_2018['target'].to_frame()\n", - "X_df_2018=df_accident_2018[df_accident_2018.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2019=df_accident_2019['target'].to_frame()\n", - "X_df_2019=df_accident_2019[df_accident_2019.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2020=df_accident_2020['target'].to_frame()\n", - "X_df_2020=df_accident_2020[df_accident_2020.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", - "\n", - "y_df_2021=df_accident_2021['target'].to_frame()\n", - "X_df_2021=df_accident_2021[df_accident_2021.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]" - ] - }, - { - "cell_type": "markdown", - "id": "676b7cd8", - "metadata": {}, - "source": [ - "## Building Supervized Model" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "daba7f7d", - "metadata": {}, - "outputs": [], - "source": [ - "features = ['Start_Lat', 'Start_Lng', 'Distance(mi)', 'Temperature(F)',\n", - " 'Humidity(%)', 'Visibility(mi)', 'day_of_week_acc', 'Nautical_Twilight',\n", - " 'season_acc']" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "8f971d9b", - "metadata": {}, - "outputs": [], - "source": [ - "features_to_encode = [col for col in X_df_learning[features].columns if X_df_learning[col].dtype not in ('float64','int64')]\n", - "\n", - "encoder = OrdinalEncoder(cols=features_to_encode)\n", - "encoder = encoder.fit(X_df_learning[features])\n", - "\n", - "X_df_learning_encoded=encoder.transform(X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "1e7fc14c", - "metadata": {}, - "outputs": [], - "source": [ - "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "d65eadcb", - "metadata": {}, - "outputs": [], - "source": [ - "train_pool_cat = catboost.Pool(data=Xtrain, label= ytrain, cat_features = features_to_encode)\n", - "test_pool_cat = catboost.Pool(data=Xtest, label= ytest, cat_features = features_to_encode)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "8bcecc82", - "metadata": {}, - "outputs": [ + }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "69e0032963b14e3d8792d75564cd1a25", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))" + "cell_type": "code", + "execution_count": 10, + "id": "c13ca2a5", + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=df_accident_baseline['target'].to_frame()\n", + "X_df_learning=df_accident_baseline[df_accident_baseline.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2017=df_accident_2017['target'].to_frame()\n", + "X_df_2017=df_accident_2017[df_accident_2017.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2018=df_accident_2018['target'].to_frame()\n", + "X_df_2018=df_accident_2018[df_accident_2018.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2019=df_accident_2019['target'].to_frame()\n", + "X_df_2019=df_accident_2019[df_accident_2019.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2020=df_accident_2020['target'].to_frame()\n", + "X_df_2020=df_accident_2020[df_accident_2020.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]\n", + "\n", + "y_df_2021=df_accident_2021['target'].to_frame()\n", + "X_df_2021=df_accident_2021[df_accident_2021.columns.difference([\"target\", \"target_multi\", \"year_acc\", \"Description\"])]" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", - " learning_rate=0.143852,\n", - " iterations=300,\n", - " l2_leaf_reg=15,\n", - " max_depth = 4,\n", - " use_best_model=True,\n", - " custom_loss=['Accuracy', 'AUC', 'Logloss'])\n", - "\n", - "model = model.fit(train_pool_cat, plot=True,eval_set=test_pool_cat, verbose=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "ae73b71a", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.7589233355711246\n" - ] - } - ], - "source": [ - "proba = model.predict_proba(Xtest)\n", - "print(metrics.roc_auc_score(ytest,proba[:,1]))" - ] - }, - { - "cell_type": "markdown", - "id": "f8010a48", - "metadata": {}, - "source": [ - "## Use Eurybia for data validation" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c7ae204e", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "f8456034", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2017,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3d998196", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "676b7cd8", + "metadata": {}, + "source": [ + "## Building Supervized Model" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: total: 0 ns\n", - "Wall time: 0 ns\n", - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6585689489728102\n", - "car_accident_auc.csv did not exist and was created. \n" - ] - } - ], - "source": [ - "%time\n", - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2017', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "01c2f690", - "metadata": {}, - "source": [ - "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", - "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", - "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." - ] - }, - { - "cell_type": "markdown", - "id": "c733b40f", - "metadata": {}, - "source": [ - "## Add model drift in report" - ] - }, - { - "cell_type": "markdown", - "id": "ba8578c8", - "metadata": {}, - "source": [ - "For the moment, the model drift part of eurybia only consists of displaying performance of deployed model. \n", - "(We hope to bring new features in the future on this part)" - ] - }, - { - "cell_type": "markdown", - "id": "65e4592d", - "metadata": {}, - "source": [ - "### Put model performance in DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "f53935dd", - "metadata": {}, - "outputs": [], - "source": [ - "proba = model.predict_proba(X_df_2017)\n", - "performance = metrics.roc_auc_score(y_df_2017,proba[:,1]).round(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "4be8debb", - "metadata": {}, - "outputs": [], - "source": [ - "#Create Dataframe to track performance over the years\n", - "df_performance = pd.DataFrame({'annee': [2017], 'mois':[1], 'performance': [performance]})" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "136261b6", - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "af9bf77a", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 11, + "id": "daba7f7d", + "metadata": {}, + "outputs": [], + "source": [ + "features = ['Start_Lat', 'Start_Lng', 'Distance(mi)', 'Temperature(F)',\n", + " 'Humidity(%)', 'Visibility(mi)', 'day_of_week_acc', 'Nautical_Twilight',\n", + " 'season_acc']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8f971d9b", + "metadata": {}, + "outputs": [], + "source": [ + "features_to_encode = [col for col in X_df_learning[features].columns if X_df_learning[col].dtype not in ('float64','int64')]\n", + "\n", + "encoder = OrdinalEncoder(cols=features_to_encode)\n", + "encoder = encoder.fit(X_df_learning[features])\n", + "\n", + "X_df_learning_encoded=encoder.transform(X_df_learning)" + ] + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_car_accident_modeldrift_2017.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "code", + "execution_count": 13, + "id": "1e7fc14c", + "metadata": {}, + "outputs": [], + "source": [ + "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d65eadcb", + "metadata": {}, + "outputs": [], + "source": [ + "train_pool_cat = catboost.Pool(data=Xtrain, label= ytrain, cat_features = features_to_encode)\n", + "test_pool_cat = catboost.Pool(data=Xtest, label= ytest, cat_features = features_to_encode)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8bcecc82", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "69e0032963b14e3d8792d75564cd1a25", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "model = catboost.CatBoostClassifier(loss_function= \"Logloss\", eval_metric=\"Logloss\",\n", + " learning_rate=0.143852,\n", + " iterations=300,\n", + " l2_leaf_reg=15,\n", + " max_depth = 4,\n", + " use_best_model=True,\n", + " custom_loss=['Accuracy', 'AUC', 'Logloss'])\n", + "\n", + "model = model.fit(train_pool_cat, plot=True,eval_set=test_pool_cat, verbose=False)" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_car_accident_modeldrift_2017.html', \n", - " title_story=\"Model drift Report\",\n", - " title_description=\"\"\"US Car accident model drift 2017\"\"\", # Optional: add a subtitle to describe report\n", - " project_info_file=\"../../eurybia/data/project_info_car_accident.yml\" # Optional: add information on report\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "0aca5ec4", - "metadata": {}, - "source": [ - "This tutorial contains only anlysis on additional features of model drift. For more detailed information on data drift, you can consult these tutorials : (https://github.com/MAIF/eurybia/tree/master/tutorial/model_drift/tutorial02-datadrift-high-datadrift.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "6710b459", - "metadata": {}, - "source": [ - "## Compile Drift over years" - ] - }, - { - "cell_type": "markdown", - "id": "4bd535e1", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2018" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "756c9de1", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2018,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "572b1f06", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7036329129677259\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2018', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "ecebfa0c", - "metadata": {}, - "outputs": [], - "source": [ - "proba = model.predict_proba(X_df_2018)\n", - "performance = metrics.roc_auc_score(y_df_2018,proba[:,1]).round(5)\n", - "df_performance = df_performance.append({'annee': 2018, 'mois':1, 'performance': performance}, ignore_index=True)" - ] - }, - { - "cell_type": "markdown", - "id": "810c6da6", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2019" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "0912c225", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2019,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "eacffb97", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 16, + "id": "ae73b71a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7589233355711246\n" + ] + } + ], + "source": [ + "proba = model.predict_proba(Xtest)\n", + "print(metrics.roc_auc_score(ytest,proba[:,1]))" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7856527709300022\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2019', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "985c1960", - "metadata": {}, - "outputs": [], - "source": [ - "proba = model.predict_proba(X_df_2019)\n", - "performance = metrics.roc_auc_score(y_df_2019,proba[:,1]).round(5)\n", - "df_performance = df_performance.append({'annee': 2019, 'mois':1, 'performance': performance}, ignore_index=True)" - ] - }, - { - "cell_type": "markdown", - "id": "1fbd247b", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2020" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "bf363bc6", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2020,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "f7b102bf", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "f8010a48", + "metadata": {}, + "source": [ + "## Use Eurybia for data validation" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7902450838961592\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2020', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "2636bcb7", - "metadata": {}, - "outputs": [], - "source": [ - "proba = model.predict_proba(X_df_2020)\n", - "performance = metrics.roc_auc_score(y_df_2020,proba[:,1]).round(5)\n", - "df_performance = df_performance.append({'annee': 2020, 'mois':1, 'performance': performance}, ignore_index=True)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "1846cdbe", - "metadata": {}, - "source": [ - "### Compile Drift et generate report for Year 2021" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "da3c7624", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2021,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "6b838b56", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 17, + "id": "c7ae204e", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7500011519622525\n" - ] - } - ], - "source": [ - "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", - " date_compile_auc = '01/01/2021', # Optional: useful when computing the drift for a time that is not now\n", - " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "ff3d4d8a", - "metadata": {}, - "outputs": [], - "source": [ - "proba = model.predict_proba(X_df_2021)\n", - "performance = metrics.roc_auc_score(y_df_2021,proba[:,1]).round(5)\n", - "df_performance = df_performance.append({'annee': 2021, 'mois':1, 'performance': performance}, ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "f9d09d5e", - "metadata": {}, - "outputs": [], - "source": [ - "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "a936527c", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 18, + "id": "f8456034", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2017,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2324467b", - "metadata": {}, - "source": [ - "In 2019 and 2020, data drift is very high. Is there any impact on the performance of the model?" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "64665647", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 19, + "id": "3d998196", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: total: 0 ns\n", + "Wall time: 0 ns\n", + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.6585689489728102\n", + "car_accident_auc.csv did not exist and was created. \n" + ] + } + ], + "source": [ + "%time\n", + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2017', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )\n", + " " + ] + }, { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "SD.plot.generate_modeldrift_data() # works if add_data_modeldrift used before " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "438706e2", - "metadata": {}, - "source": [ - "While data drift was high in 2019, the impact on model performance is low. In 2020, data drift leads to a decrease in model performance." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "c9089d96", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "01c2f690", + "metadata": {}, + "source": [ + "As soon as compile() method, Eurybia displays default consistency checks as warnings.
\n", + "If some modalities are not present during training and are in production dataset, the deployed model will consider them wrongly.
\n", + "Inversely, if some modalities are present during training and are not in production dataset, it means that some profiles are missing." + ] + }, + { + "cell_type": "markdown", + "id": "c733b40f", + "metadata": {}, + "source": [ + "## Add model drift in report" + ] + }, + { + "cell_type": "markdown", + "id": "ba8578c8", + "metadata": {}, + "source": [ + "For the moment, the model drift part of eurybia only consists of displaying performance of deployed model. \n", + "(We hope to bring new features in the future on this part)" + ] + }, + { + "cell_type": "markdown", + "id": "65e4592d", + "metadata": {}, + "source": [ + "### Put model performance in DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "f53935dd", + "metadata": {}, + "outputs": [], + "source": [ + "proba = model.predict_proba(X_df_2017)\n", + "performance = metrics.roc_auc_score(y_df_2017,proba[:,1]).round(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4be8debb", + "metadata": {}, + "outputs": [], + "source": [ + "#Create Dataframe to track performance over the years\n", + "df_performance = pd.DataFrame({'annee': [2017], 'mois':[1], 'performance': [performance]})" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "136261b6", + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "af9bf77a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_car_accident_modeldrift_2017.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_car_accident_modeldrift_2017.html', \n", + " title_story=\"Model drift Report\",\n", + " title_description=\"\"\"US Car accident model drift 2017\"\"\", # Optional: add a subtitle to describe report\n", + " project_info_file=\"../../eurybia/data/project_info_car_accident.yml\" # Optional: add information on report\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "0aca5ec4", + "metadata": {}, + "source": [ + "This tutorial contains only anlysis on additional features of model drift. For more detailed information on data drift, you can consult these tutorials : (https://github.com/MAIF/eurybia/tree/master/tutorial/model_drift/tutorial02-datadrift-high-datadrift.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "6710b459", + "metadata": {}, + "source": [ + "## Compile Drift over years" + ] + }, + { + "cell_type": "markdown", + "id": "4bd535e1", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2018" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "756c9de1", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2018,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "572b1f06", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7036329129677259\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2018', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "ecebfa0c", + "metadata": {}, + "outputs": [], + "source": [ + "proba = model.predict_proba(X_df_2018)\n", + "performance = metrics.roc_auc_score(y_df_2018,proba[:,1]).round(5)\n", + "df_performance = df_performance.append({'annee': 2018, 'mois':1, 'performance': performance}, ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "id": "810c6da6", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2019" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "0912c225", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2019,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "eacffb97", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7856527709300022\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2019', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "985c1960", + "metadata": {}, + "outputs": [], + "source": [ + "proba = model.predict_proba(X_df_2019)\n", + "performance = metrics.roc_auc_score(y_df_2019,proba[:,1]).round(5)\n", + "df_performance = df_performance.append({'annee': 2019, 'mois':1, 'performance': performance}, ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "id": "1fbd247b", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2020" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "bf363bc6", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2020,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, { - "data": { - "text/markdown": [ - "Report saved to ./report_car_accident_modeldrift_2021.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cell_type": "code", + "execution_count": 31, + "id": "f7b102bf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7902450838961592\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2020', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "2636bcb7", + "metadata": {}, + "outputs": [], + "source": [ + "proba = model.predict_proba(X_df_2020)\n", + "performance = metrics.roc_auc_score(y_df_2020,proba[:,1]).round(5)\n", + "df_performance = df_performance.append({'annee': 2020, 'mois':1, 'performance': performance}, ignore_index=True)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "1846cdbe", + "metadata": {}, + "source": [ + "### Compile Drift et generate report for Year 2021" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "da3c7624", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2021,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=model, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder # Optional: if deployed_model and encoder to use this model\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "6b838b56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed AUC on the X_test used to build datadrift_classifier is equal to: 0.7500011519622525\n" + ] + } + ], + "source": [ + "SD.compile(full_validation=True, # Optional: to save time, leave the default False value. If True, analyze consistency on modalities between columns.\n", + " date_compile_auc = '01/01/2021', # Optional: useful when computing the drift for a time that is not now\n", + " datadrift_file = \"car_accident_auc.csv\" # Optional: name of the csv file that contains the performance history of data drift\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "ff3d4d8a", + "metadata": {}, + "outputs": [], + "source": [ + "proba = model.predict_proba(X_df_2021)\n", + "performance = metrics.roc_auc_score(y_df_2021,proba[:,1]).round(5)\n", + "df_performance = df_performance.append({'annee': 2021, 'mois':1, 'performance': performance}, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "f9d09d5e", + "metadata": {}, + "outputs": [], + "source": [ + "SD.add_data_modeldrift(dataset=df_performance,metric='performance') " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "a936527c", + "metadata": {}, + "outputs": 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QQAABBBBAAIGYChAIY9rxNBsBBBBAAAEEEEAAAQQQIBAyBhBAAAEEEEAAAQQQQACBmAoQCGPa8TQbAQQQQAABBBBAAAEEECAQMgYQQAABBBBAAAEEEEAAgZgKEAhj2vE0GwEEEEAAAQQQQAABBBAgEDIGEEAAAQQQQAABBBBAAIGYChAIY9rxNBsBBBBAAAEEEEAAAQQQIBAyBhBAAAEEEEAAAQQQQACBmAoQCGPa8TQbAQQQQAABBBBAAAEEECAQMgYQQAABBBBAAAEEEEAAgZgKEAhj2vE0GwEEEEAAAQQQQAABBBAgEDIGEEAAAQQQQAABBBBAAIGYChAIY9rxNBsBBBBAAAEEEEAAAQQQIBAyBhBAAAEEEEAAAQQQQACBmAoQCGPa8TQbAQQQQAABBBBAAAEEECAQMgYQQAABBBBAAAEEEEAAgZgKEAhj2vE0GwEEEEAAAQQQQAABBBAgEDIGEEAAAQQQQAABBBBAAIGYChAIY9rxNBsBBBBAAAEEEEAAAQQQIBAyBhBAAAEEEEAAAQQQQACBmAoQCH0dv2rVKuns7JTGxsaYDgeajQACCCCAgNsC7e3tUl9f39PI9evXp23wzJkz+3w90/lu69E6BBBwUYBAKCL+/3Oora0lELo40mkTAggggEDsBbq7u+Wss86Sjo4OqayslEy/CO7q6pKnnnpK6urqPDs9v7W1VVpaWmJvCQACCLgjQCD09eXKlStl48aNBEJ3xjctQQABBBBAoEcgGACDATETlQbERYsWCbOEmaT4OgIIlJMAgZBAWE7jlboigAACCCCQs4D+4lePhoaGnjL0kdC2tjapqqrKWK5er08VMUOYkYoTEECgjAQIhCED4a233tqvWz/xiU+UUVdTVQQQQAABBNwW0BnAvXv39mmkPu45fPhw79+WLVsmU6dO7RcIm5ubpaamJiWOlrt8+XLv68wOuj2GaB0CcRQgEIYMhLfccku/8XHdddfxfwxx/K6hzQgggAACJSeg7wYuWbJEBg4c2C8QVlRUeP8WdYbQrDlAKCy57qdCCCAQQYBAGDIQJjPWx0z4P4UIo49LEUAAAQQQsCSggfCOO+6QCRMmpCwx6juE2b5zaKlpFIMAAgjkVYBASCDM6wCjcAQQQAABBAohECYQZlpl1CwaY94p1AA5d+7cnvcLzaOj/DK4ED3KPRBAoFACBMLAthMGPswL5swQFmqYch8EEEAAAQTSC4QJhFpCun0Ig4HQ/N1/Z8IgIxEBBFwTIBBG6FECYQQ8LkUAAQQQQMCiQNhAaPGWFIUAAgg4IUAgjNCNBMIIeFyKAAIIIICARQECoUVMikIAgVgJEAgjdDeBMAIelyKAAAIIIGBRgEBoEZOiEEAgVgIEwgjdTSCMgMelCCCAAAIIWBQgEFrEpCgEEIiVAIEwQncTCCPgcSkCCCCAAAIWBQiEFjEpCgEEYiVAIIzQ3QTCCHhcigACCCCAgEUBAqFFzCIWZbYGSbfau37+am5ulpqaGq+mK1eulKamJu/PHR0dUllZmVML8rnPpFmx1l+/xYsXS2dnp1RXV8v06dNl6tSp0tDQkFPduQiBKAIEwgh6BMIIeFyKAAIIIICARQECoR1Mf7gyJa5YsULq6upC3SBZ8Al14ZGTsg2E5n5mO5Ao9y9kINQ9LTUMNjY2ei1ftmxZXgKh6c9gwNb7t7a2SktLS5/u0c+2wXNNcDUnLl26lOCazaAug3MJhBE6iUAYAY9LEUAAAQQQsCgQp0C4a88BuXftFnllxx6ZPWWMLJg9yZqkBoiNGzf2BBUtWAOBHsHwkOymUQKZlhcmEPrvq/tK3nXXXT31jXL/fAbCoJU6T5o0KXTQzrWD9bOqBvotW7b0CXFhAqHxCP5CQMPrlVdemfNMbK5t4br8CRAII9gSCCPgcSkCCCCAAAIWBeISCF/esUcubnpEdu092KNX+45j5fq6061oJguEJhTq44z6mKYJCuaG+sijCYv62ch/6GyTPsKp/ZPsfP03E+KC11VVVfXMnGnw0xk1DSc6s6V12bx5syxfvrznMp25Mo+Omn9M9ehpsA36COqsWbO8eprHOvWe9fX1fcr3P9Lpb6vfQAOT1tEcWp4epuwbbrihz9f13qtXr/bOMeUH6+dvhwl5pu2p2qiuN998s3zqU5+SRYsWiZlF1fuECYSpxoKVgUYhJSVAIIzQHQTCCHhcigACCCCAgEWBcg2Etz+5UZ7f9lZoiY7ntssfN7zW7/yPvfs4mTh6WOhyLjtjqkyvHNnv/FQhwP+IowYNPTSw6WECij5WmmyGLt355nrzTmBwhtCEq+C7dyacBoNN2BnCZO8hfvSjH+0TCLVs86isKdeEL62XhkDzdXXTOgUfBdVQOWHChJ5QbNoRfERUr/cHQn/9TDA1gU6/5g+gqTpdyzz99NO9EK+zvMYsbCAMXhN6cHFi2QkQCCN0GYEwAh6XIoAAAgggYFGgXANh/f/3uDzYtTW8xOHDIgMG9Dv/sIj0/9fUxf7wU++SD8yamFMgDF7kDzNhApn/fA07+nczw5gsEAYXW/EHlVwCYfCepj2ZHhn13zfVO3+pZt6CZacLhMnq53+3LxhmU/Wy/3Oq1sv/2GiYGUICYfhvy3I/k0AYoQcJhBHwuBQBBBBAAAGLAuUaCMtxhlC7LfhYpFloJFUgTHV+cEatEIEweM90gTD4CGxwJtNc619oxb8Ii5nJyyYQav38j8Kae5h7hwmEGir1MdTgI6hmljFVKPYHTwKhxR9QJV4UgTBCBxEII+BxKQIIIIAAAhYFyjUQZkug7xBe1PSIvOF7h/CK6mPlho8U7h1CndHTQGFm9TLNEKY7v5RnCDUQ6eOWJlSlCkjpFsLRa2pra+X888/v8zhqtjOE/nESJhAGA3gwVCYLhMHQyjuE2X53lu/5BMIIfUcgjIDHpQgggAACCFgUiEsgVDJdZfQeXWX0td3eKqML50y2JplqlVHdJ89skRB8380fnJKFo2zON+/L+d/Vy+aR0bCrlIZ5h9AfAE25ZpZOA5fx8Aep2267TfRdRLMXoikjuGBNNu8QaufqrOHcuXO99zbDBEI9J7gnY3Bm1P/up94jGNxZZdTat1XJF0QgjNBFBMIIeFyKAAIIIICARYE4BUKLbP2KCrMPYXAFTJ0B84c2fxnJVhkNnu9/RFK/pit05hoITbAxq42mW4FTV970z5ylW2VUH/3UwyzMEnQyQTHVyqTZPDKq98m0kqu5X7KxEPY9xnT38JcbfGw2m30p8zlWKdueAIEwgiWBMAIelyKAAAIIIGBRgEBoEZOiEEAgVgIEwgjdTSCMgMelCCCAAAIIWBQgEFrEpCgEEIiVAIEwQncTCCPgcSkCCCCAAAIWBQiEFjEpCgEEYiVAIIzQ3QTCCHhcigACCCCAgEUBAqFFTIpCAIFYCRAII3Q3gTACHpcigAACCCBgUYBAaBGTohBAIFYCBMII3U0gjIDHpQgggAACCFgUIBBaxKQoBBCIlQCBMEJ3Ewgj4HEpAggggAACFgUIhBYxKQoBBGIlQCCM0N0Ewgh4XIoAAggggIBFAQKhRUyKQgCBWAkQCCN0N4EwAh6XIoAAAgggYFGAQGgRk6IQQCBWAgTCCN1NIIyAx6UIIIAAAghYFCAQWsR0vKhly5bJ1KlTpaGhwfGW5t68xYsXS21trdTV1UmuXqtWrZLW1lZpaWnJvSIRrtTP6c3NzVJTUxOhlHhcSiCM0M8Ewgh4XIoAAggggIBFAQKhHcz29napr6/3Clu6dGnZhiYNNJ2dnT0o1dXVPcEk14BjR9hOKf5+MiW2tbVJVVWVlRvkEgiDASyfgTDYv9roYPsJhOGHAoEwvFW/MwmEEfC4FAEEEEAAAYsCsQqEe18X6bpL5PWXRCbPFam62KJkoqiVK1d6/y3HWTR/mDEw2p758+d7gcmlQLh+/XqviSYgdnR0SGVlZeTxkMwwU6GFDGDB+nV3d4v+DCjnX2Jk8s3n1wmEEXQJhBHwuBQBBBBAAAGLArEJhK+/KHJTjcjenb168z4u8qHvWdTsHwjNB25zE/9sjH4eWrFihSxfvtz7sn82Tv+uXzeH/2tRykzVWFNmumBkAqGGKDOL6D9fw2NTU1PPLfyPHaZra1dXlyxatKiPRTCg6L31MUo9/F8zs2n6eKO5d7rHHU0ANIFQy9OQpAF+woQJ/eqh56XzDvaT/l37VB8ZDf5yIFiO1vOuu+7qaZe5dvLkyd61/kdG/WPB375MfRLs72SBNdj3xkNN9b7+MRs8NzjjalyT9WkyS62fv3z/GNJHbxsbG70mhOln/+ynf4ykKtPGNz6BMIIigTACHpcigAACCCBgUaBsA+FTPxPpfi68xAvtIi891v/8d/y/IqMmhS/ntDqR8SelPD8YAvyzP8Ewol/zBz3/B3H9oK9f02Chh5ZrZh1zLTNTI/X+06dP7/kQHjzfhDITAoNt1Q/tpr76Zw26JiCka6sJD+ZDfDB0BB+h9Icacx8TktQ4GKb87QgTCINhNJ13cNbUX7d0Y8Hfp8EZwmAb/PcwNiZEZeqTMIFQz/GPN/84TNbHW7Zs8cai6Tf/eNi4caM3foJ9aurhb2uwLVH6OdgP5vslXZmZvh/CfJ1AGEYpxTkEwgh4XIoAAggggIBFgbINhD/5iMi6e8JLHD4sMmBA+PNTnfnxFpGTPxgqECYLJ/4Zl2AQ8H8oT/V4ZpQywzTePxOl55vZLhMa/IvK6IdtnSk0szj+8oOhLl1bg8FCy/E7+QOKfs1vEPzAb8ryzwCmC4T+4JqsHum89RFT/f7xz5KmCoTpgmq6QJhs5tYf0oLjJF2fqEOqR1pTBcKgp15/zTXXeI8QB8Oi/9wwlsFAGOxnf1vS9XO62e10ZYb5fsh0DoEwk1CarxMII+BxKQIIIIAAAhYFyjYQlsEMoQkbwe4ys1nJQpIJXMHHC82sVZQysx025kO9qW+y8OFfDdOc77+PCUvp2poqEPqd/I8V+mf5UgWFVI++JltUJviYo//adN7mEdPg46dmlVF/YEoX1NIFwmQBV8s1M3GZ+iTY59kGQhMizSO1V199dZ9FhsxjvME+1/GrjwEHLf2/QAgGwuAvI7RMM4Oerp/NvZL9EiBdmdl+PyQ7n0AYQZFAGAGPSxFAAAEEELAoULaBMFsDfYfwe+eI7NvVe+XpHxO5/KZsS0p7vj8EZHp8MV1I8t/E/8F527ZtaR+JDFtm2Eanm7X0f0g3dTQhLswMoQm/mQJhLjOEmQJhsvAQZlYrWb+U2gxhui0rsn2HUNtrwqyGMz38jzHr35MtoBTGMtMMod86XSDU84Izteba4NgJO+7DnkcgDCuV5DwCYQQ8LkUAAQQQQMCiQGwCoZp5q4zeKbLjJZFjdJXRSyxKJooKfuAOBjT9YDt37lzvkbt04U2DmHkUM1O4CltmusbqB/ibb765z+Of5kO9/3214COjJnykWmzExgxhpnfLks1S2gqEapauD/39bQxSLSoTLMe85xYcM9m+Q5iqT5L1d6pVRv2PBgdDlH+22u+aLPSZcZvsa8EAaGZrzfhKtt2GKS/TTHDYdwjVxP+9FfUHAIEwgiCBMAIelyKAAAIIIGBRIFaB0KKbvyj/Y4X+lRH1nOCjn/5FZNIFwnQrduZaZqbm+1fyNOf6A0CmxxODqzlqULMRCM2H+HSrjJoVOZMFEX+7ky0qY76e6tp03v7HZM0MWrJHRvUewUdq/QvhmD0sNZjlssqomaXLtIdhmH0Ik82qad/rEXxfNPgIrnm0OZVl8HvFP0a0/FTjPlMgNMHd9GWqVUb16+lWoc30PRL8OoEwWzHf+QTCCHhcigACCCCAgEUBAqFFTIpCAIHQApkeqQ5dUBFPJBBGwCcQRsDjUgQQQAABBCwKEAgtYpZwUakWRzFV9i/aUsLNoGplLhCcoUy372U5NJVAGKGXCIQR8LgUAQQQQAABiwIEQtjV82QAACAASURBVIuYFIUAArESIBBG6G4CYQQ8LkUAAQQQQMCiAIHQIiZFIYBArAQIhBG6m0AYAY9LEUAAAQQQsChAILSISVEIIBArAQJhhO4mEEbA41IEEEAAAQQsChAILWJSFAIIxEqAQBihuwmEEfC4FAEEEEAAAYsCBEKLmBSFAAKxEiAQRuhuAmEEPC5FAAEEEEDAogCB0CImRSGAQKwECIQRuptAGAGPSxFAAAEEELAoQCC0iElRCCAQKwECYYTuJhBGwONSBBBAAAEELAoQCC1iUhQCCMRKgEAYobsJhBHwuBQBBBBAAAGLAgRCi5gUhQACsRIgEEbobgJhBDwuRQABBBBAwKIAgdAiJkUhgECsBAiEEbqbQBgBj0sRQAABBBCwKEAgtIhJUQggECsBAmGE7iYQRsDjUgQQQAABBCwKEAgtYlIUAgjESoBAGKG7CYQR8LgUAQQQQAABiwIEQouYFIUAArESIBBG6G4CYQQ8LkUAAQQQQMCiAIHQIiZFIYBArAQIhBG6m0AYAY9LEUAAAQQQsChAILSISVEIIBArAQJhhO4mEEbA41IEEEAAAQQsChAILWJSFAIIxEqAQBihuwmEEfC4FAEEEEAAAYsCLgbClStXSlNTk6e0YsUK77/Lly+XpUuXSkNDg0U9ikIAgTgLEAgj9D6BMAIelyKAAAIIIGBRwLVA6A+DJhDW1dWJfvaorq6WlpYWi3oUhQACcRYgEEbofQJhBDwuRQABBBBAwKKAa4FQP2PU1tZKY2OjFwJ1hlAD4bJly6S1tVXWr19vUY+iEEAgzgIEwgi9TyCMgMelCCCAAAIIWBRwMRCaR0P9gXDx4sXS2dlJILQ4digKgbgLEAgjjAACYQQ8LkUAAQQQQMCigGuBUIOfHvpoqAmEc+fOlUWLFvXMHFrkoygEEIixAIEwQucTCCPgcSkCCCCAAAIWBVwLhO3t7VJfX59UqLm5WWpqaizqURQCCMRZgEAYofcJhBHwuBQBBBBAAAGLAq4FQqXp7u4WbZf/aGtrk6qqKotyFIUAAnEXIBBGGAEEwgh4XIoAAggggIBFARcDoUUeikIAAQRSChAIIwwOAmEEPC5FAAEEEEDAooBrgdBsO+HfczDZv1kkpCgEEIipAIEwQscTCCPgcSkCCCCAAAIWBcIGwuC7eem2b0j2yKb//FWrVnkbxfsPs1VE1KalWk2UfQijynI9AggEBQiEEcYEgTACHpcigAACCCBgUSBMIDQBr6OjQyorK0UDnW7hoHv9JTs0POphFnDRGbqNGzf2nJ/p+ijN8+9D6C+HfQijqHItAggkEyAQRhgXBMIIeFyKAAIIIICARYEwgTAY4IIBMVN1NCBqKNStIPTIdyCsrq7uuZepG/sQZuolvo4AAtkKEAizFfOdTyCMgMelCCCAAAIIWBQIEwg1zOnR0NDQc2f9//KwK3cGA2DwkVFbj4tq5cxM4IoVK6Surs6rr3nc1eZ9LHYBRSGAQJkKEAgjdByBMAIelyKAAAIIIGBRQAPhkiVLZNCgQX1K1TA1fPhw7980ZE2dOrVfIAyzr5+ZTUwXHs0G8ibARWleV1eXtwl9siNsgI1yf65FAIH4CBAII/Q1gTACHpcigAACCCBgUcAEwoEDB/YLhBUVFd6/5TpDaMJgpuBo+xHSZIvamPcfLdJRFAIIxFyAQBhhABAII+BxKQIIIIAAAhYFwjwymss7hGHDoDbFdiC0yENRCCCAQEoBAmGEwUEgjIDHpQgggAACCFgUCBMIM60yah7TNI9kBv8erK4+gmpWKM0mOFpsNkUhgAACkQUIhBEICYQR8LgUAQQQQAABiwJhAqHeLt0+hMEAaDaCD1bTPDpqFn4xX/cvAGOjaWZF0WRlpds/0ca9KQMBBOIjQCCM0NcEwgh4XIoAAggggIBFgbCB0OIt81pUMGwGb0YgzCs/hSMQKwECYYTuJhBGwONSBBBAAAEELAq4Fgj1M4YerChqcZBQFAIIJBUgEEYYGATCCHhcigACCCCAgEUBVwMhM4EWBwlFIYAAgdD2GCAQ2halPAQQQAABBHITcC0QmkdGCYS5jQeuQgCB8ALMEIa36ncmgTACHpcigAACCCBgUcC1QJhphVOLdBSFAAIxF3A2EPpX5gqz6pd5Vl/HQ5jz9TwCYcy/e2g+AggggEDJCLgWCP2fS5IhM3NYMkOPiiBQ9gJOBkJdJlqPhoYG77/6QzXdS9kaHmtra6Wurk6y2UeIQFj2458GIIAAAgg4IkAgdKQjaQYCCBRcwMlAGAyAwYDoVzaPZPh/05bufP+1BMKCj1duiAACCCCAQFIB1wIh3YwAAggUSsC5QGhm+Do6OqSystJzXLVqlXR2dkpjY2M/12SBMN35BMJCDU3ugwACCCCAQHgBAmF4K85EAAEE/ALOBUIT8IKBsLW1VVpaWpL2vs70NTc3S01NTcoAeeutt/a79tprrxWe4ecbCgEEEEAAgeILuBgI9YmlpqYmD1fXN9Bj+fLlsnTp0p7XYoovTw0QQKDcBZwLhNnOEGoHmhDp78zgD9tbbrmlX19fd911BMJy/w6g/ggggAACTgi4Fgj9YdAEQl3rQH+JXV1dnfKX3E50Jo1AAIGCCjgXCFUvm3cIk2nr3j8XXXRRz4xhqh7hHcKCjlVuhgACCCCAQEoB1wKhfsbQBe/0dRf9s1kBnf0J+SZAAAHbAk4GwkyrjPp/sAZB9f3BdI+X+s8nENoejpSHAAIIIIBAbgIuBkLztJL/c4vZVotXVnIbJ1yFAAL9BZwMhNrMdPsQBgOhhkB9Jl8P89u4MIOFQBhGiXMQQAABBBDIv4BrgVA/x+ih6x+Yzy1z586VRYsWZfVZJf/y3AEBBMpdwNlAWIiOIRAWQpl7IIAAAgggkFnAtUDY3t4u9fX1SRvuXwgvswxnIIAAAukFCIQRRgiBMAIelyKAAAIIIGBRwLVAqDRmoTw/U1tbm1RVVVmUoygEEIi7AIEwwgggEEbA41IEEEAAAQQsCrgYCC3yUBQCCCCQUoBAGGFwEAgj4HEpAggggAACFgUIhBYxKQoBBGIlQCCM0N0Ewgh4XIoAAggggIBFAdcCoX7GyHTwLmEmIb6OAAJhBAiEYZRSnEMgjIDHpQgggAACCFgUiGMgVD5CocVBRFEIxFSAQBih4wmEEfC4FAEEEEAAAYsCrgVC3VNZVxrVbSfM0dXV5W07oSFwwoQJ3p+rq6v7nGORlKIQQCAmAgTCCB1NIIyAx6UIIIAAAghYFHAtEOpnjGRhT//d7JnMJvUWBxBFIRBjAQJhhM4nEEbA41IEEEAAAQQsCrgYCJVn/fr1fZT8QZFAaHEAURQCMRYgEEbofAJhBDwuRQABBBBAwKKAa4Fw2bJl0traKitWrJC6ujpPatWqVbJ8+fKeGcJUs4gWWSkKAQRiIEAgjNDJBMIIeFyKAAIIIICARQHXAqF5XzAZkb5DOGvWLNE2m8dHLVJSFAIIxEyAQBihwwmEEfC4FAEEEEAAAYsCrgVCpenu7vZCn/9oa2uTqqoqi3IUhQACcRcgEEYYAQTCCHhcigACCCCAgEUBFwOhRR6KQgABBFIKEAgjDA4CYQQ8LkUAAQQQQMCigGuB0GxMzz6DFgcJRSGAQFIBAmGEgUEgjIDHpQgggAACCFgUcC0QmhVEeUTU4iChKAQQIBDaHgMEQtuilIcAAggggEBuAq4FQt2YvqmpSQiEuY0HrkIAgfACzBCGt+p3JoEwAh6XIoAAAgggYFHAtUBoVhnlkVGLg4SiEECAGULbY4BAaFuU8hBAAAEEEMhNwLVAaN4hTKUR3LA+NzWuQgABBESYIYwwCgiEEfC4FAEEEEAAAYsCBEKLmBSFAAKxEiAQRuhuAmEEPC5FAAEEEEDAooBrgdAiDUUhgAACaQUIhBEGCIEwAh6XIoAAAgggYFGAQGgRk6IQQCBWAgTCCN1NIIyAx6UIIIAAAghYFHAxEJqVRpVpxYoVntby5ctl6dKl0tDQYFGPohBAIM4CBMIIvU8gjIDHpQgggAACCFgUcC0Q+sOgCYR1dXWinz2qq6ulpaXFoh5FIYBAnAUIhBF6n0AYAY9LEUAAAQQQsCjgWiDUzxi1tbXS2NjohUCdIdRAuGzZMmltbRVWGbU4eCgKgZgLEAgjDAACYQQ8LkUAAQQQQMCigIuB0Dwa6g+Eixcvls7OTgKhxbFDUQjEXYBAGGEEEAgj4HEpAggggAACFgVcC4Qa/PTQR0NNIJw7d64sWrSoZ+bQIh9FIYBAjAUIhBE6n0AYAY9LEUAAAQQQsCjgWiBsb2+X+vr6pELNzc1SU1NjUY+iEEAgzgIEwgi9TyCMgMelCCCAAAIIWBRwLRAqTXd3t2i7/EdbW5tUVVVZlKMoBBCIuwCBMMIIIBBGwONSBBBAAAEELAq4GAgt8lAUAgggkFKAQBhhcBAII+BxKQIIIIAAAhYFXAuE+hmD2UCLA4SiEECAQJiPMUAgzIcqZSKAAAIIIJC9gIuB0Ciw72D244ErEEAgvAAzhOGt+p1JIIyAx6UIIIAAAghYFHA5EPqZzH6EFukoCgEEYi5gLRD6V8PyP+LQ1dXlLZGsh2urYhEIY/7dQ/MRQAABBEpGwLVA6IdduXKlNDU19bFmY/qSGXpUBIGyF7AWCJctWyatra1JQ58Ji7W1tdLY2Fj2aKYBBEJnupKGIIAAAgiUuYDLgdDfNatWrZLly5ezMX2Zj1eqj0ApCVgLhBqO9Ej2Gyv/ssku/UaLQFhKQ5m6IIAAAgjEWcDlQKib1Hd2djJDGOcBTtsRyKNAQQKh1j9dYMxj+/JaNIEwr7wUjgACCCCAQGgB1wKh+dwUBFi6dKk0NDSEduFEBBBAIJOAtUBofnuVbIlk8x6ha6tkEQgzDS++jgACCCCAQGEEXA+ELj1hVZgRwV0QQCCsgLVAaJ5p1xt3dHRIZWWlVwf/46KurYxFIAw7zDgPAQQQQACB/Aq4GAhdW4wvvyOA0hFAIFcBa4FQK5DsGXdTMddmB7VdBMJchx3XIYAAAgggYFfAtUBoV4fSEEAAgdQCVgOh3sY/U2hu69rMoGkXgZBvLQQQQAABBEpDgEBYGv1ALRBAoPwErAfC8iPIvcYEwtztuBIBBBBAAAGbAi4EQv1cYZ6oSrWojDHjnUKbo4eyEIi3AIEwQv8TCCPgcSkCCCCAAAIWBQiEFjEpCgEEYiVgLRBm+k1WstVHy12aQFjuPUj9EUAAAQRcEXAhELrSF7QDAQTKS6BggVBZXAuFBMLyGuzUFgEEEEDAXQECobt9S8sQQCC/AtYCYbpqmoVmamtrpbGxMb8tKmDpBMICYnMrBBBAAAEE0gi4FgjNHs7+Jrv2i3UGNAIIlIZAQQKhNtU8UurSS9AEwtIYxNQCAQQQQAABlwLhypUrpampKWmnurpyOyMYAQSKJ0AgjGBPIIyAx6UIIIAAAghYFHAlEPpnBv0zgu3t7VJfX++JMVNoceBQFAIICIEwwiAgEEbA41IEEEAAAQQsCoQNhP5gpbdP9+RSd3e3aLn+I99POi1btkxaW1sl2Uygq6/gWBwGFIUAAjkIFCQQuvoDjECYw4jjEgQQQAABBPIgECYQmoDX0dEhlZWVop9POjs7U65voOFRj5qaGu+/+ijnxo0b87oewuLFi706mTr6qUz9zV6FeWCkSAQQiKGAtUCYadsJtXXtEQcCYQy/Y2gyAggggEBJCoQJhMEAGAyImRqmAVFDYUtLS6ZTc/66f3P6ZIW4uCZDzlhciAACVgQKFghdC4OqTyC0MgYpBAEEEEAAgcgCYQKhhjk9Ghoaeu6n/18e9jNKphnFyI048tki3QwggdCGMmUggIBfwFogjCMrgTCOvU6bEUAAAQRKUUAD4ZIlS2TQoEF9qldXVyfDhw/3/k3fz5s6dWq/QNjc3NzzWGiqtpnZxLDhMVcjZghzleM6BBDIVYBAmKscM4QR5LgUAQQQQAABuwImEA4cOLBfIKyoqPD+LdcZQhMGwwTHqK0K8wqO3iPfi9tEbQfXI4BA+QgUJBD6V/Ry6QcYM4TlM9CpKQIIIICA2wJhHhnN5R3CQoZB7SECodvjlNYhUIoCeQ2EZqUsf8MJhKU4DKgTAggggAAC5S0QJhBmWmXU7AFoHgsN/r28hag9AgggkFzAeiAM7u/jv20hHrUoZEczQ1hIbe6FAAIIIIBAaoEwgVCvTrcPYTAA6iOmTU1N/W7q2ucZxhUCCMRbwFogNBuppuJ0aWbQtJFAGO9vHlqPAAIIIFA6AmEDYenUmJoggAACpSFgLRAGn3k3AdA8NkogLI0OpxYIIIAAAgi4KEAgdLFXaRMCCBRCwFogZIawEN3FPRBAAAEEEEAgmQCBkHGBAAII5CZgLRCa26d7hzDfe/fkRpD7VTwymrsdVyKAAAIIIGBTgEBoU5OyEEAgTgLWA6Efj1VG4zSUaCsCCCCAAALFEyAQFs+eOyOAQHkL5DUQJps1dOldQmYIy3vwU3sEEEAAAXcEXAyEyX6xbnrMpc9T7oxCWoJAeQoUJBCWJ03mWhMIMxtxBgIIIIAAAoUQcC0QxnFthkKME+6BAAL9BQiEEUYFgTACHpcigAACCCBgUcC1QGhWb3dt/QWLXU5RCCBgSYBAGAGSQBgBj0sRQAABBBCwKOBqIOTRUIuDhKIQQCCpAIEwwsAgEEbA41IEEEAAAQQsCrgWCM0jowRCi4OEohBAgEBoewwQCG2LUh4CCCCAAAK5CbgWCLu6umTRokXCI6O5jQeuQgCB8ALMEIa36ncmgTACHpcigAACCCBgUcC1QGjeIUxFxMyhxcFDUQjEXMB6IDQ/wJqbm6WmpsZpXgKh091L4xBAAAEEykiAQFhGnUVVEUCgpASsB0KzZ04cHnEgEJbUWKYyCCCAAAIxFnAtEMa4K2k6AggUWMB6IFy5cqU0NTXF4pl3AmGBRyu3QwABBBBAIIWAi4HQvEfob3IcfuHOIEcAgcIKWA+E5ocXj4wWtiO5GwIIIIAAAnEWcC0Qtre3S319fdIujcNnrDiPZdqOQKEFrAfCOL0EzQxhoYcr90MAAQQQQCC5gGuB0Gw74Z8RNL90r62tlcbGRoYCAgggYEWAQBiBkUAYAY9LEUAAAQQQsCjgWiDUzxjJgh/7E1ocNBSFAAKegPVAGCdXAmGcepu2IoAAAgiUsgCBsJR7h7ohgEApCzgbCM1qp4q/YsUKqaurS9sP/kddwz6KQSAs5aFN3RBAAAEE4iTgWiDkkdE4jV7aikBxBfISCM1KoyaM6X+XL18uS5culYaGhry3WO+vh7mXBrd0q3JpeNQQaEJj8O+pKkwgzHtXcgMEEEAAAQRCCbgWCJOtMGogWFQm1JDgJAQQCClgPRD6w6AJhBq0NDxVV1dLS0tLyKrlflowAAYDYrDkbM831xMIc+8jrkQAAQQQQMCmgGuBUG3YdsLmCKEsBBBIJWA9EPpfgtY/m8c1C/USdHd3t+j/KXR0dEhlZaXX7lWrVklnZ2fKFbn06zqDaX7jpvX2X58Kj0DINxYCCCCAAAKlIeBiICwNWWqBAAKuC+QlEJpHQ/2B0LzTt379+ryamt+mBQNha2trytlJc43OYGpwTPYO4a233tqv3tdee63kuz15xaJwBBBAAAEEHBEgEDrSkTQDAQQKLmA9EGrw00MfDTWBcO7cubJo0aKkQct2i3OZIQzOCOpsph7+PX5uueWWflW97rrrCIS2O5DyEEAAAQQQyEGAQJgDGpcggAAC+dh2or29Xerr65PiFuol6GzeCTSzg/6ZPn2ENN2Momkcj4zyPYQAAggggEBpCLgQCP3rLfhXP08mzBNKpTHuqAUCLghYnyFUFDNL5wdKt8qnbchMq4z6H2XVewf/rrOcNTU1GVdEJRDa7jnKQwABBBBAIDcBAmFublyFAAII5CUQlgJrun0IgwEwGGDDbo9BICyFnqYOCCCAAAIIiLeg3B133CETJkyAAwEEEEAgCwFnA2EWBjmfSiDMmY4LEUAAAQQQsCrgWiD0r9ruh/Kv1WAVkMIQQCC2AgTCCF1PIIyAx6UIIIBAFgI33rdO/vDCa94Vs48ZI1+YP1PGVAzJogROdV0gLoGwUNt4uT5eaB8CCPQKWAmEcX0JmkDItxICCCCQfwENg00P9N2yaMHsSfL9T74z/zfnDmUjEJdAWKhtvMqm46koAghEFiAQRiAkEEbA41IEEIiFwK49B2Tvwbdl74FDR/535M8H9e99/33fgUOyT8/1vtb79Qe7tsprb+3v57XhmxfHwpBGhhNwIRCalc8ztTjZfsmZruHrCCCAQCoBK4EwrrwEwrj2PO1GoDwF3tx3sDeEHTwk+wKBzAthvqDmhTNfkNPAFvz6nv3m33oD3L4jZdhSOnxYZMCA/qWdc1KlLDx1snxwzmSZMHqYrdtRTpkKxCkQdnR0SGVlZZn2FNVGAIFSE7ASCP0vPqd6CbrUGm6jPgRCG4qUgUBxBHTm6tpfr5XfP7/dq8CZ08fLVy+ZXbD30vb4ZsB6ZsN8M2Ne+DLneOFNg1eSmbYj/97n/EBw273/UHGQRWTUsMEyfMhAGT5kkAwfPEiGmT/r333/bv5cMXSQDAqkv7a/bJLnt73Vpw3BkFh93Fi5YPZk+eCpk+XEypFFay83Lp6AC4HQrxenz1PFGzXcGQEEVIBAGGEcEAgj4HEpAkUWuPJnq+Xnna/0qcU7jj9a/n3xvMQsWuCxxcTsWW8gM7NnfWbZMjwaaR6J1GsLfQwcIDJssC+EmUDm/Vvi34d5/z1yTs+/Jw9uiXN7g57587DBR/5tyCDRe9o4Xt6xR/7hlsflr6++4RU3dWyFfOXiWdL95n65Z81mL9Qfevtwz61OmjjKmzVcMGeynHbsUTaqQBllIOBaICwDcqqIAAKOCFgJhP49/zK5rF/fd2GATOeX8tcJhKXcO9QNgfQCZ3/zQdn4+p6iMQ0dpMEpVQjrDVXDfQGrZ3YtWbA7MgPnlen7ugluer9yP3RWd+fegzLt6Io+Tdm196Dcv3aL3Ltmszy0bqsX6M1xzFHDvWC4cM5kec+J42SQrZRa7pgO1t+1QLhy5UppamoS/97Iyf7Nwa6kSQggUGABK4EwuLF7ujYQCAvcw9wOAQT6CWhgeOfX7xN9py54TBk73Hu0sWembIg+5qiPO/pCmv+xx8C/J871PRqZYlYu2TtxdFV0AZ19fXjdNrnn6c3yQNcWeX33gZ5Cj6oYIh+YNdELh+edMlF0NpPDHQHXAmGq1UT9K7u703u0BAEEiilgJRD6GxCnZ96ZISzm0OXeCOQm0PFct/zzqr/IKzv29FuoRGeQWpaclVvBXFVyAvoYqe5dqI+V6uzhqzv39tRRZ1LPnTnBW5Rm/qxJomGRo7wFXAuEqT5PsQ9heY9Tao9AKQpYD4Sl2Mh81YlAmC9ZykXAvsDOPQfkmrY18ovOjV7hR1cMkfGjh8mzW9/0/q5h8KuL5sicKWPs35wSS0LgqY07vZnDu9ds7ul3rZg+Rqr9rzOH+nipPmbKUX4CLgbC6upqaWlp6dMZ7ENYfmOTGiNQ6gIEwgg9RCCMgMelCBRQ4I7Vm+SaO9bI9iN72dW+41j510tmMytUwD4otVu9uH23/ObpV72A+MTLr/ep3typR3nhUP83c9KoUqs69Ukh4FogNDOBK1askLq6Oq/V7e3tUl9fL+xDyLcBAgjYFLASCP3Ps+uf0x28Q2iz+ygLAQTSCWzdtVeuXLVa2td3e6fp6pTX150mZ81g/y5GTq/Atjf2eY+V6v86ntsuB30rluoWFmZRmjOmjU26HyKWpSHgWiBMt0l9W1ubVFVVlQY8tUAAgbIXIBBG6EJmCCPgcSkCeRTQPepu/f2L8q3f/FXe2n/I2/7g0+dMlysXnOKt7MmBQCoBXbH0t11b5O6nN3uL0/j3cJwwepgsmD3Je+/wrOmVMmSQpX016A4rAq4FQkVJtmgfm9JbGS4UggACPgErgTCuogTCuPY87S5lgRe635IvtTzZ8xjgKZNGy42LT5fZU9iPrpT7rRTrpiuWtq/fJveu2SL3rd0sO3wrlo4ePljeXzVRFsyeLOdXTZQRQweVYhNiVScXA2GsOpDGIoBA0QQIhBHoCYQR8LgUAcsCBw4dlpseflb+84FnZf+ht70tBf7pAzPlM++bwd5zlq3jWJyuWPr4hh3eaqX6aKl/D0vd4/GcmZXeO4cXzJ4k40YOjSNR0dtMICx6F1ABBBAoUwECYYSOIxBGwONSBCwKrN20Uz7/0yfluW2JFUPfdcLRckPdPDlu/AiLd6EoBHoF1mza5T1Weu/azfLM5jd6vqD7S77z+MSKpfpo6bSjK2ArkIALgTCuazIUaIhwGwQQSCFgPRCuXLlSmpqapLm5WWbNmiX6A1qPZEsnl3uvEAjLvQepf7kL6Abz/3ZPlzQ/9oLoe4P6GN/yi2bJx959XLk3jfqXkYCuWKqzhrqdxRMv7fDGojlmHTNaFs45RhbMmSSzj2FLk3x2K4Ewn7qUjQACLgtYD4S6P44eum+OWTLZAC5dulQaGhqc8SQQOtOVNKQMBcwG8+bRvfNPmSD/Vne6VI4aVoatocquCHS/ub/nsdLfPdct+iizOXS20KxY+s4TxnmLHXHYE3AhENrToCQEEEAgvID1QKghyQQ/swWFLo988803S2trq7DtRPjO4UwEEOgvoBvMX9u2Vn7e+Yr3RV358brLTpUPnjoZLgRKSuANXbH0ma3e7OFDXVu9FW/Noe8ZXjArsWLp2SdVeu+8ckQTIBBG8+NqBBCIr4D1QKgzhNOnT5crr7yy53FRDYHmUVICYXwHGy1HIKpA2+pN8jXfBvOL3zVNvnLxbBkzfHDUorkegbwK6EJHj63v9h4rvX/tFtn+1v6e++kKpeede6Re9wAAIABJREFUMtF771BXLtVHnzmyF3AhEGbay9mv4tLnqex7mysQQMCmgPVAaIKfqWRtba00NjaK/1FSmw0oZlk8MlpMfe4dJwHdYP6qn/9FfvvMNq/Z08ZVeIvGvPvEcXFioK2OCOi+939+8TVv5vCepzfLyzv29LRs8MAB8t4Zld47hxoQdQacI5wAgTCcE2chgAACQQHrgVBvoOGvs7PTu5f+BmvVqlWyfPnynkdJXekGAqErPUk7SlVAF+f40R90g/kueXPfQW/7iH84d7p8Yf7JPGJXqp1GvbIWWPvqLm+vw3vWvCp/fbV3xVItqPq4sd57hxeeeowcz6q5aW1dCIT+Buo6DM8//7y3JoM5zEb1K1askLq6uqzHGhcggAACyQTyEgjjQk0gjEtP085iCAQ3mNcVGr+z+HSpmsxKjcXoD+5ZGAGdLbz76Ve9gPinF1/rs2LpSRNHyQePbGcxd+pRhalQGd3FtUDo34LC3w0uPnFVRsOMqiLgpACBMEK3Eggj4HEpAikEdAPw7z30nPzHA+t7Npi/csEp8nc101mVkVETK4HX3tIVS7fIPWs3e+8f6nuI5jjmqOE9K5a+58Rx3ux5yRyb/yLSdVeiOmOPE5n38YJUzcVAqHDBdwXNU1i8Q1iQYcVNEIiFgPVAyD6EsRg3NBKBvAjoBvNfbFktz2xJPDZ35vTxcn3daXLs0WwwnxdwCi0bAV2h9LddiRVL9b/6CLU5jqoYIvNnTfLeO9TFaYq6YmnXr0Vu+5u+ru/5jMiF38q7tWuB0Gzd5X88tL29Xerr653c2znvA4QbIIBASgHrgZB9CBltCCCQrYBuMH/9vV3S/OgLogtujKkYIv9y8Sz5yDunZVsU5yPgvIDubfjYs91eONQVS7e9ua+nzcOHDJRzZ07wtrPQkKhhsaDH/14k8uJj/W/5tZ15r4ZrgbCrq0sWLVqU1I13CPM+nLgBArESsB4I2YcwVuOHxiIQWSC4wfzFpx0j11x6qlSOGhq5bApAwHUBXXip86Ud3nYWumLpS6/t7mmyPkaqj5PqaqW6MI0+ZpqX4+1DIi//QWT9vSJ/+G+RA7116Lnfl18UGT42L7c3hboWCLVdZhEZP1xzc7PU1NTk1ZLCEUAgXgLWAyH7EMZrANFaBHIV2LX3oFzbtkZa/9y7wfy3rjjN24eNAwEEchN4ZvMbie0s1myWNZt29SlEF6LRmcOFsyfLzEmjcruBueqtbpF1d4usv0/kuQdF9h25lybUAYH3GYeNEVn2crT7hbjaxUAYotmcggACCEQWsB4I2Ycwcp9QAALOC9z5l1fl6juelu43E5tz/+2Zx8uXL6ySUcPYkNv5zqeBBRPY+Pqenr0OH9/wmvc4tjlOrBzZsyjNGdPG9stw/SqpQW9Tp8i6exIzgZue6HvKyAkiMy8QGXusSMdNvQFRz/pgo8iZn817uwmEeSfmBggg4KiA9UCoTuxD6OhooVkIRBQIbjB/wvgRcuPieXLGcUdHLJnLEUAgncDruw/IvWsTM4ePru+WfQd7VyydMHqYLJg9yZs9PGt6pQwZdGSGb88OkeceEFl3r8iz94vs3t57iwEDRY59ZyIEzlwgMvn03pnBva+LbHhUZO9OkclzRSafVpDOcTEQ+n/Jru8N6uHivs4FGSDcBAEEUgrkJRDGxZttJ+LS07QzqoBOLvzkjy9J411/9VZHHDxwgHzmvBnyTx+YKUMHDYxaPNcjgEAWArv3H5KHntEVS7fIg11b5I29vSuWvmv4K/L/TFgnZx36s4zbsVrkcG9wlBHjRU6anwiB+t+K0vpFjmuBMPjElVlIJtX+hFkMAU5FAAEE+ggQCCMMCAJhBDwujY3AS9t3y9LbnpAnXn7da7O+x3Tj4tPlpImjY2NAQxEoVYGDe9+Qdb+7XXY/dZccv+N3MkF29FRVo+CGoafInhPmy7R3XypjZryn//uBJdQw1wKhfsaora2VxsZG0T+bQGi2o2AfwhIafFQFgTIXyEsg1B9cqQ6XfoARCMt89FP9vAp4G8w//Jz85wPrvcfTKoYMkn9eeIrUn31i5veV8lozCkcg5gLbuhLvAeqjoC91iLzdO0N4cNhYeWbUe+RXb82RVTur5PXDicVndJ2Ydx6fWLFUHy2ddnRFySGGDYRmLz/TgLCfS/T/89va2qSqqqqn7atWrfIe4fQfJsRFBQqu2m4CIRvTR5XlegQQCApYD4TmN1cEQgYbAvEVSLbBvM4KHnNU6X2IjG8v0fLYCBzYI/LCw4kAqEFwZ2DFzylnJN4D1EdBp75DRN8PFJH1W9703jnULS2e3th3H8FZx4yWhXOOkQVzJsnsY8aUBGWYQGi2cejo6JDKykrRQNfZ2enNwqU6/OsiJAuEma7PFce/r7OZIZw7d663N6Gt0Jlr3bgOAQTcErAeCM2z7d/97ndFfzibH576g033zWloaHBGkBlCZ7qShlgS0A3mb7j3Gfnho897KxqOHTFErl40Ry4/Y6qlO1AMAgiEEtixIbEthIbAFx8VOdi7eb23H+CM94ucvEDkpAtERlZmLPLVnXt7Viz9wwvb+6xYqrOFOmu4YPZkOXnSaPn6nWvl3jWbRbeW0cVqls4/WeZMyX9oDBMIgwEwGBBTQZjzChkIgzOZ/rqxF2HGIcsJCCCQhUBeAqH/mXfzQ8vFZ94JhFmMNE51XuCPL7wmX2x5UnSpez0umzfFC4PjRrLBvPOdTwOLL3Bov8iG9t5ZwNee71snXenTzAJOe3fPLGAuFd+554Dct3aLFxAfWbetz4qlwwYP7PN3LV9nEO9amv+N1MMEQl2oRQ//L6eTPQoadEkXCP2PjNqeuUu2MX0wlObSh1yDAAII+AWsB0KzMb0+fuGfFSQQMvAQcFNAZwGua1sjq45sMD95zHD5du1pcu7JE9xsMK1CoFQEdm06sjn8vSLPPyxyYHdvzXQzeJ0FNNtCjJqYl1rvOXBIHl63Te55erM80LVVdu4+kPQd4Q3fvDgv9/cXqoFwyZIlMmjQoD73qqurk+HDh3v/pp9Fpk6d2i8QZppxSxUIg43yL/6S9wZzAwQQQMCSgPVAaIKfPp9/2223SVNTU09Vq6urpaWlxVLVi18MM4TF7wNqUFyBu556Vb56e2KDeV104pNnnSBXfbBKRgzt+4GsuLXk7gg4IqCLv7z0+8R7gPq/rX/t27BJc47MAi4QmfYekYGF/T7UhaQu/o926dr8Rj/wQgbCgQP7bmWjgbCiIvH+su0ZwmBDw7yTGHY0mgX6MoXVsOVxHgIIIJBKwHogDN7I/zK2eYnble4gELrSk7QjW4HgBvMzJozytpI47dix2RbF+QggkE7gza29AfC534rs29V79rDRItPPOzILuFBk9OSiW17Ttkb+97ENfeqhC9D8Zum5ea9bmEdGbb9DmM9AaD4/8Yho3ocON0Ag9gJ5D4QuCxMIXe5d2pZK4Cd/eEkaf/NXbzPrIYMGSMP7Z8pnzzvJ+zMHAghEFNCN4F/5U28IfHV13wInzkosBKMLwkw7U2TQkIg3tHv5rj0H5Jq2tXLv2s3ez4gLZk+SL5TQojKZVhnt6uryVvEMhrBUj4zqU1FmhVJzjq0ZPbMxPYHQ7hilNAQQ6C9AIIwwKgiEEfC4tOwEdIP5K1c9KY9vSGxcfca0sfKdxfPkxMqRZdcWKoxASQns2SGy/r5ECHz2fhH9uzmGjhQ58X2JADhzociYKSVV9VKqTJgZQq1vun0IkwVC/5NOer1/4ZjgVltmr0AbLqYutgKmjTpRBgIIuClAIIzQrwTCCHhcWjYC+l7Qfz/yvDTdv85bPXDk0EFy1YVV8okzT2CD+bLpRSpaUgKHD4u8+mTvLODGP4vov5mj8uTexWCOP7vkZgFLytJXmbCBsFTrH6yXeYcwVX3Xr19fLk2hngggUOICVgJhph9afgOXfoARCEt8dFO9yALBDeZrZlZ6K4iywXxkWgqIm4C++/fsA72zgPpuoDmGVIiceG5iQZiTPyhy1LFx07HSXgKhFUYKQQCBGAoQCCN0OoEwAh6XlrSAbjD/nfuekf9pT2wwP37kULn60jly6ek8rlbSHUflSktg61qRdfckQuDLfxB5+1Bv/cbP6N0X8IQakUHs1xm181wLhFE9uB4BBBAIK2AlEIa9mWvnEQhd61HaowLBDeavqD5WvrpothxVUVqLV9BbCJScwP63RF54uHdz+F0be6s4eLjIiTW9K4IefULJVb/cK0QgLPcepP4IIFAsAeuB0KyKtXTp0p6NX5P9W7EabPO+BEKbmpRVbAHdYP4bv14rLX962avK1LEVcn3daXLWjMpiV437I1C6AtufTcwArrtX5MXHRA7t763r0Sf2zgLqI6GDh5VuOxyoGYHQgU6kCQggUBQB64HQrMYVfFdQwxMb0xelj7kpAhkFfvP0Zvnqr56WbW/uk4EDROrPOVH+eUGVDB/Sd4PnjAVxAgKuCxzcJ7LhEZF1R1YF3fFCb4s18B1/Tu+CMPpYKEfBBFwKhOYX6QbPpfUXCjYguBECCIQWsB4INfj5l2Q2NTFLM7v0Q40ZwtDjjBNLVCC4wfwpk0Z7G8zPnnJUidaYaiFQBIHXXxJZf09iFvCFR0QO7u2txNjjewOgzgLqAjEcRRFwJRCuWrVKli9f3s/Qpc9PRRkg3BQBBFIK5CUQJpsJTDVzWM59QyAs596j7j/940uy4q7EBvPDBg+Uz39gpvzj+2bIIJ0i5EAgzgKHDoi89LvedwG71/Vq6EbwuhWErgg68wIR3SKCoyQEXAmE5vOS2X/QzBba3OOwJDqMSiCAQMkIWA+EZibQ/4PLbAKbbOawZCRyqAiBMAc0Lim6QLIN5ps+eoYcN35E0etGBRAomsAbm3tnAZ9/SGT/m71V0W0gTrogsTm8bhKvm8VzlJyAK4HQbOVlZgTNBvWufYYquQFEhRCIsYD1QGh+cCUzbWtrk6qqKme4CYTOdGUsGqIbzH//kefl349sMD96+GD58oWz5OPvPo4N5mMxAmhkH4HDbye2gtDHQHVRmC1P93554GCR485KBEANghNngVcGAi4FQv+TVgTCMhh8VBGBMhewHgjVo7u7W/QHs//o6OiQykq3ViskEJb56I9R9YMbzJ9/ygT51hWnycQxw2OkQFNjL7B7u8j6I4vB6Cbxe1/vJRkzJRH+9DHQGeeLDB0Ve65yAyAQlluPUV8EECgVgbwEwlJpXL7rQSDMtzDlRxXYd1A3mF8nP3jkOW+D+Qmjhsm1HzpVLjx1ctSiuR6B0hc4fFhkU2dvCNz4Z98s4CCRae858i7gApFJc0q/PdQwrYBLgTBMV7PITBglzkEAgTACBMIwSinOIRBGwOPSvAvoBvNXrnpSXn5tj3evxe+cJl+5ZLaMGT447/fmBggUTWDvTpFn7088Bqr/fau7tyqjJh1ZEVRnAd8vMmxM0arJje0LEAjtm1IiAgjEQ4BAGKGfCYQR8Lg0bwLeBvN3rpWWxxMbzE8bVyE31M2Td584Lm/3pGAE8i7Q9WuRrjtFNPCdUCNy5j/23nLzU4kAqP97+Y8i+n6gHgMGikx7d++2EJNPy3s1uUHxBFwJhMUT5M4IIBBXAQJhhJ4nEEbA49K8CNz99Gb519uflm1v7PO2j/i7munypQtO9raV4ECgbAV+/18idy/rW/0TzhEZNz3xOOgbr/Z+beQEkZPmJxaEmfEBkeHsqVm2/Z5lxQmEWYJxOgIIIHBEgEAYYSgQCCPgcalVge4398n/WbVafvvMNq/c2ceMke8sPl2qJvNInFVoCiuOwP9eJPLiY6nvfew7RU5emAiCU6qLU0fuWnQBAmHRu4AKIIBAmQoQCCN0HIEwAh6XWhO47Y8vy4q71oo+KqozgToj+PfnzhD2l7dGTEHFFvjOLJFdm/rX4or/SYTAiqOLXUPuXwICBMIS6ASqgAACZSlAIIzQbQTCCHhcGlkguMH8u0442ntXkA3mI9NSQCkJdHxX5J6viMjhvrXSBWGWJd6T5UBABQiEjAMEEEAgNwHrgVBDUm1trTQ2Nvap0eLFi72/t7S05FbTEryKQFiCnRKDKukG8z9of15uvG+d6LYSYyqGyFcunuWtIsqBgDMC258T+Xm9yKYnE2FwyEiRA7sTzdMwePn3RKoucaa5NCS6AIEwuiElIIBAPAUKFgiXLVsmra2t4tK+OQTCeH7TFLPVXZt3ydKfPinPbHnDq4buJ3jdh+ZK5aihxawW90bAnsDbh0Qe/Y7Iw98WObRfRLeKuPymxDYRG9oT95k8V2T4WHv3pCQnBFwMhPrL9M7OzqT949LnKScGII1AoIwFChYIzQ81l36AEQjLeOSXWdV1JlBnBHVmUGcIJ4weJt+64jR5f9XEMmsJ1UUgjcCWNSI//zuRrWsTJ1V/SmThN0SGjYYNgYwCrgVC84v0VA136fNUxs7lBAQQyKuAlUDY1dUlixYtyljRZI+SZryohE8gEJZw5zhUteAG83/znuNk2UWzZNQwNph3qJvj3ZSD+0QeahT53X+I6AzhUdNEPvx9kePfG28XWp+VgGuBUD9j6NHW1iZVVVVZWXAyAgggkI1AQQNhR0eHVFZWZlO/kj6XQFjS3VP2ldNVQ3X1UF1FVI8Txo+QGxfPkzOOY0XFsu9cGtAr8MrjiVnBHRtEBgwQec9nRD5wtciQCpQQyErA1UDITGBWw4CTEUAgBwErgdC/kIz++YQTTpD77rsvh+qU1yUEwvLqr3Kq7YNdW719Bbe/tV8GDxwgn3nfDPmn+TNl6CA2mC+nfqSuaQT2vyVy/9dE/vj9xEm6yXxts8iUM2BDICcB1wKhi2sv5NSxXIQAAnkXyEsgdO3R0FS9QCDM+/iM3Q2639wv//Krp+Tupzd7bdcN5v/jY/PkpIm8QxW7weByg194ROSXSxJ7Cw4cJHL2F0XOu0pkEIsjudzt+W6ba4HQvI7DI6P5HjmUjwACVgJhulWwgsQuPfpAIOQbyKZAy+MvyzfuTGwwXzFkkFy54BSpP+dENpi3iUxZxRXYu1Pk7i+LPPmTRD0mzhbRzeUnzSluvbi7EwKuBULzDmGqznHp85QTA5BGIFDGAlYCYXd3t7chbJjDpR9gBMIwPc45mQSCG8yfOX28XF93mhx79IhMl/J1BMpHYN09Ird/VuSt7sRM4HlfFjn7C4kZQg4ELAgQCC0gUgQCCMRSwEog9Mul2pjeRV0CoYu9Wrg2vX1Y5AePPCffObLB/NgRQ+RfL5ktV1QfW7hKcCcE8i3w1jaRX39J5K93JO40ZZ7IFc0i42fk+86UHzMB1wJhzLqP5iKAQBEFrAfCIral4LcmEBac3Jkb6gbzX2pZLWtf3eW16dLTp8jXLp0j40byDpUznUxDRFb/VOTuZSJ7dogMGSEy/2qRdy9JrCbKgYBlAQKhZVCKQwCB2AgQCCN0NYEwAl5ML9UN5v/9/nXy/UcSG8xPHjNcvl17mpx78oSYitBsJwV2bRT5xRKRDe2J5h13VmJfwbHHOdlcGlUaAi4GwmT7PLPITGmMN2qBgEsCBMIIvUkgjIAXw0v9G8zrBMknzjxBvnxhlYwYyjtUMRwObjb58GGRx3+Q2E5Ct5UYNkZk4TdEqj/pZntpVUkJuBYI29vbpb6+Pqlxc3Oz1NTUlJQ/lUEAgfIVsBIINRhVV1dLS0uLxGlVLAJh+Q78Qtb8zX0H5Rt3/lV++seXvNvOmDBKblx8upx27NhCVoN7IZBfgR0vJDaYf+VPifvMXCDyof8SGcnsd37hKd0IuBYIzT6E/hlBM2MYl+29GN0IIFAYAQJhBGcCYQQ8Ry99ecceb0P53z+/3WvhKZPHSPcbe70N5ocMGiCfO3+mfO78k7w/cyDghMDht0UeaxJ5qFHk4D6REeNFLvq2yKm1TjSPRpSPgGuBMNUifWxYXz5jkpoiUC4CVgJhKTbWvzfiihUrpK6uLmk1022ZkWmLDAJhKfZ8cev097f8Se5bu6VPJQ6LyGlTj/JmBdlgvrj9w90tC2xZk9hgfvNTiYI1BF58vUjF0ZZvRHEIZBYgEGY24gwEEEAgmYD1QGgeGS3m8+0rV6702trQ0OD9V+uUzUvYq1atki1btvRcn2roEAj5pgoKnPDlO5OibPjmxWAh4I7Aof0iD39b5NEbRd4+KDJqksjlN4nMeL87baQlZSfgWiDkkdGyG4JUGIGyFbAeCM3MXDYBzLZeMAAGA2Km++n1HR0dUllZmfZUAmEmyXh8XbeOuP3JTdK2eqNsfH2vBB8GHT18sDz1tYXxwKCV7gtsekKktV7ktecTbdUFYxauEBk22v2208KSFnAtECZbYdR0QDF/6V7Sg4DKIYBATgLWA6GGr6ampqxm5HKqeYqLzCOg/kCnM36dnZ3S2NiY8VZhZwe1IAJhRk5nT3hlxx65/cmNXhBct+WNnnaOHTFUXt+9v0+7daP5Gz5yurMWNCwmAgf2iDxwjcgfbhLR1USPmpbYSuL498YEgGaWuoBrgVC92Xai1Ecd9UPADQHrgdD88CrWb6/M/YOBsLW11VsFNdORanbw1ltv7XfptddeK5neM8x0P75ePgKv7z4gd6ze5AXBP7+4o6fiJ4wfIZfNmyqXVx8r40YMkWva1vYsKnPm9PFy9aLZMqZiSPk0lJoiEBR48Xciv/gHkZ0vJzaV183l539NZEgFVgiUjICLgbBkcKkIAgg4LWA9EBZ724koM4TpHi295ZZb+g2E6667jkDo9LeHyO79h+TeNZvlV09ulPb13d5m8npUjhoqi06f4gXBedPYPsLxYRDf5u17Q+TuZSJPHPmF2LjpIrXNIlPOiK8JLS9ZAQJhyXYNFUMAgRIXcC4Qqncu7xAmC5KZ+o5HRjMJlefXDxw6LI+s2+aFwPvWbpa9B972GjJy6CBZeOpk+dAZU+WckybIQHaOKM8OptbhBNbdI3LH50Xe3CIycJDI2V8UOe8qkUFDw13PWQgUWMDFQOhfMT3IyRNKBR5g3A4BhwWsB8JSsMq0yqgGueBWFNkuPGOCJz+QS6HHo9dBX4n644bXvMdB7/zLq7JzzwGvUN0v8H0nT/RC4AWzJ8mwwQOj34wSEChlgd3bRe78Z5E1v0jUcuJskSv+R2TSnFKuNXVDQFwLhGaV0VRdy+cPBj0CCNgScDIQKk66fQiDgbC9vV3q6+tDrSzqh2eG0NYwLF45ZoXQO57cKK/u3NtTkXefOM57HPSS046Ro3j/r3gdxJ0LK/CXn4n85v+K7NmRmAk878siZ38hMUPIgUCJC7gWCM0rOMVctb3Eu5zqIYCAJQErgTDTe4P+urr0Gy0CoaVRWOBidIVQfRz0V09slGe3vtlz91Mmj5YPzZvqBcEpY4cXuFbcDoEiCryxWeRXnxF57reJSkyZJ3JFs8j4GUWsFLdGIDsBVwOhS5+bsutRzkYAgUIJEAgjSBMII+AV+NLX3tovbUdWCO186fWeu2vw0wCoW0OcNHFUgWvF7RAoAYE/NYvc91URXUBmyAiR+VcnVhHV1UQ5ECgjAdcCoXlklEBYRoOQqiJQpgJWAqG/7foD7Pnnn++zxYNZsCX43l6ZmvVUm0BY2j341v5Dcs/Tm733Ah99tneFUH0EVB8F1SD4rhPG8bm3tLuR2uVLYMcLIr/8R5GXOhJ3OO6sxL6CY4/L1x0pF4G8CrgWCM02WjwymtdhQ+EIICAi1gOhhqTq6up+e/7pO316hNkLsFx6hkBYej2lK4Q+9MxWLwTet3aL7DuYWCFUF4PRRWF0cRhdJEYXi+FAIJYCh98W6fiuyINfFzm4V2TYGJGF3xCp/mQsOWi0OwKuBcJMr+Mwc+jO2KUlCBRbIC+BUBsV/EFlFnlx6QcYgbDYwzdxf10h9A8vHFkh9KlXZdeRFUJ1WwjdHkJDoG4XodtGcCAQa4Ftz4j8/NMim59KMMxcIHLpf4qMnhxrFhrvhgCB0I1+pBUIIFB4AeuB0Dzz7n881KzimWzmsPBNtndHAqE9y1xKWrNpl7c4TNuTm2Tzrt4VQnWjeH0c9LJ5U2TcSPZMy8WWaxwTOHRApP0GkfbrRfTPI8aLXPRtkVNrHWsozYmzgGuBMM59SdsRQKCwAtYDoXnmPVkzeIewsJ3r4t1efm23FwJvf2KTPLutd4XQ6ZUj5bIzpsqHz5gq08aNcLHptAmB3AQ2/0Wk9dMi3esS12sIvPh6kYqjcyuPqxAoUQECYYl2DNVCAIGSF7AeCLXFZhEZf+ubm5ulpqam5EGyqSAzhNlo5X6urhB6h64Q+sRGeeLl3hVCJ4weJpeePsV7JHTu1KNyvwFXIuCiwMF9Ig9cK/L7/xLR9wZHTRK5/CaRGe93sbW0CQHnNqbXLvXvqRzsYpdewWH4IoBAcQXyEgiL26TC3Z1AmD9rXSH07qdf9fYKfOzZbnn7cOJeo4YNlg+eOtkLge+dUSn6niAHAggEBF78nciv/lFkx4bEF3TBmIUrRIaNhgoBZwVcmyE0r+Ck6jACobNDmYYhUHABAmEEcgJhBLwkl+4/9LY89Mw2LwQ+8NfeFUJ1RdDzT5nohcAPzJrkrRjKgQACSQR0L8F7/1Xkz/+b+OJR0xJbSRz/XrgQcF7AtUBoVhll2wnnhy4NRKDoAnkJhOmWSnbpN1oEwujjV2f+fv/8dm+biN88vblnhVAt+czp4+VD86bKRacdI2OGD45+M0pAwGWB5x4U+eVnRN7ckthUXjeXn/81kSEVLreatiHQI+BqIHTpcxPDFQEESlPAeiCM0yMOBMLcB/XTG3fK7U9u8t4N3OJbIXT2MWO81UF1NnDSmOG534ArEYiLwJ4dInf9X5GnfpZo8bjpIrXNIlPOiIsA7UTAE3AtEJrPUwRCBjgCCORbwHogNBvTf/e73/V+OJtHHfTFaF1UpqFfL8x0AAAgAElEQVShId9tKlj5BMLsqHWF0F8+sdGbDXxu21s9Fx97dIW3TcTlZ0yVkyaOyq5QzkYgzgJP/1zkrv8jsnu7yMBBImd/QeS8L4sMYruVOA+LuLbdtUBoVm3nkdG4jmjajUDhBPISCGtra6WxsVE0MJnVRV38TReBMPNA7X5TVwhNbBOx+pXeFUKPHjFELjltihcE33kCy99nluQMBHwCb20T+dVnRdbfm/jHibNFrvgfkUlzYEIgtgKuBcJ0r99oJzNzGNuhTsMRsC5gPRDqTOD06dO9QOifFSQQWu+7ki3wzX0HvfcBdZuI3z3Xu0Lo8CEDZcHsxAqh5548QQazRGjJ9iEVK2GBzltE7vmKyL5diZnA864SOfuLiRlCDgRiLEAgjHHn03QEEIgkYD0QmuDX0dEht912mzQ1NfVUsLq6WlpaWiJVuJQuZoawtzd0hdAHu7Z6IVD/u+/g294XBw0cIDUzK73FYRbMmSwjhvKhtZTGMHUpI4HXXxL5xT+IvNSRqPSUeSJXNIuMn1FGjaCqCORPwLVAmD8pSkYAAQT6ClgPhEFg/6aqGhIrKyud6YO4B8KeFUKf0BVCX5Vdew/2hv/jxnozgfpY6LiRvM/kzKCnIYUXOHxY5A83JTaZP7BbZMgIkflXJ1YR1dVEORBAwBMgEDIQEEAAgdwE8h4Ic6tWeVwV10D41Mad3l6Bbas3ydY39vV01kkTRsllZ0yRy884VnShGA4EEIgosP05kZ/Xi2x6MlHQcWcl9hUce1zEgrkcAfcEXAyEcdnGy73RSIsQKC8BAmGE/opTINywfbcXAnWF0Be6e1cI1a0hLj09sU3EnCljImhyKQII9Ai8fUjk0RtEHv43kUP7RYaNEVn4DZHqT4KEAAIpBFwLhHHaxotBjQACxRWwHgg1JJlVRv1N00dH9eAdwuJ2eDZ31xVCNQDq//7yys6eS0cPHywXnXqMXHbGVG/zeNaGyUaVcxHIILD5LyK/WCKydW3ixBnni3zoJpHRk6FDAIE0AmEDYXt7u9TX1/eUFHa1Tv18U8gtIOK0jRcDGwEEiitQsEDIKqPF7eiwd9cVQu966lVv03hdIVRfX9Jj6KCB8v6qid5M4PlVE2XY4IFhi+Q8BBAII3Bwn8hvV4j87j9EDr8tMmK8yEXfFjm1NszVnINA7AXCBMLu7m7vXUOzpsGqVauks7PTWxk91eFfC6HQgTAu23jFfvACgECRBQoWCM0P1LC/iSuyS6jbu/LIqK4I+tuurd4jobpCqK4YqoeuV3HW9PHeTODFc4+RUcMGh3LhJAQQyFLglcdFfv53Ijs2JC7UEHjx9SIV7NGZpSSnx1ggTCAMBsBgQEzFZ84rZCCM0zZeMR62NB2BkhCwEgi7urpk0aJFGRuU7FHSjBeV8AnlHAh1hVCdAdRtIu5es1ne8K0Qqu8C6jYROhs4YfSwEu4BqoZAmQvsf0vkvqtFHv9BoiGjJolcfpPIjPeXecOoPgKFFwgTCFeuXOlVrKGhoaeCYR4FLUYgjNM2XoUfLdwRAQT8AgUNhGw7UfzBt/qV1+X2JzZJ2182yTbfCqHTxo2Qy+ZNkQ9XHyvTK0cWv6LUAAHXBV54ROSXS0R2bUq0VBeMWbhCZNho11tO+xDIi4AGwiVLlsigQX33u62rq5Phw4d799SQNXXq1H6BsLm5WWpqalLWqxiBMFgZl7fxysuAoFAEEAgtYCUQ+u+WalGZ0DUqoxPLZYZQVwj9Zecr3uIw+mdz6P6Auk+gzgRWHze2jOSpKgJlLLB3p8hvrhJZ/dNEI46althK4vj3lnGjqDoCxRcwgXDgwL7vuGsgrKhIbIVUTjOExRelBgggEBcB64EwLnDazlIOhDr79ytvhdBN8vTG3hVCK4YMkoWnTpbL5k2Vc2dWyiCWCI3TkKWtxRZYe7vInVeKvLUt8ZKubi4//2siQ9i3s9hdw/3LXyDMI6Pl9A5h+fcILUAAgXIRyEsgjMtGqqUWCHftPSi/8VYI3Sgdz2/vWSF08MABUnPyBO+9wIVzJsvwIawQWi7foNTTEQENgL/6rMj6exMNGjddpLZZZMoZjjSQZiBQfIEwgTDTKqNmTYTg4jHFemTU/5hoUNilRfqKP3qoAQLxFrAeCOO0kWqhA+HLO/Z4o3Xa0b2zCbpC6AN/3eLNBOpKoWaFUD3vHccf7T0Ouui0KTJ2xJB4j3Raj0CxBJ78icjdy0T2vi4ycJDI2V8QOe/LIoOGFqtG3BcBJwXCBEJteLp9CJMFwmAoK9QCeXH6POXkgKRRCJSRgPVAGKeNVAsVCNds2iVLbv2TvHIkEB57dIUsed8MWf3y63L305tF9w40x8xJo7zHQXU2UM/jQACBIgns2pjYYH5De6ICE2eLXPE/IpPmFKlC3BYBtwXCBsJyUTBPWxVyq4tysaGeCCBgVyAvgTAuG6kWKhCe/c0HZePridlBc+h+8QOO/GXymOGyaN4Uuez0KXLq1KPsjhBKQwCB7AQOHxZ5/Psi918jottK6EzgeVeJnP3FxAwhBwII5EXA1UDIo6F5GS4UigACPgHrgTBOG6kWKhCe8OU7kw7aj757mjcbeOaJ4731KTgQQKDIAjteSGww/8qfEhWZMk/kimaR8TOKXDFuj4D7Aq4FQvPIKIHQ/bFLCxEotoD1QBinjVSLGQhnHTNafrP03GKPH+6PAAIq8PYhkd81iTz0TZGD+0SGjBD5wFdF3vOZxGqiHAggkHcB1wJhqgVu8g7JDRBAIHYC1gNhUNDljVQLFQiv/Nlq+XnnK31ol35gpnzxgpNjN2BpMAIlJ7BlTWJWcOvaRNWOOyuxr+DY40quqlQIAZcFXAiE6VZpD/YdM4cuj2bahkBhBfIeCAvbnMLerVCBcNeeA/LDR1+Q3z+/3WvgmdPHEwYL29XcDYH+Aof2izz0LZHHbkzMEA4bI7Lw6yLVn0ILAQSKIEAgLAI6t0QAAScECIQRurFQgTBCFbkUAQTyIbDpCZHWepHXnk+UPuN8kQ/dJDJ6cj7uRpkIIBBCwIVAGKKZnIIAAghYF7AeCM0z7/6aurpkMoHQ+nikQARKW+DAHpH7vybyx/8W0dVER4wTuejfRE6tLe16UzsEYiBAIIxBJ9NEBBDIi4DVQLhy5UppampKWtEVK1ZIXV1dXhpRrEIJhMWSL+H7vv6iyK8+K7Lh0UQlTzhH5KM/Fhk+toQrTdVCCbzwSKJvd76cOF1D4MXXi1QcHepyTkIAgfwKuBIIV61aJcuXL5fq6mppaWnx0Pyfr1z8PJXfkUHpCCCQScBaIPTPDPpnBNvb26W+vt6rh2szhQTCTMMrhl//6cdEnrmrb8NPuUjkYz+NIYYjTd67U+Se5SJP/CjRoFGTRC6/SWTG+x1pIM1AwA0BVwKhWa29ublZampqxP85yvSU+ZobPUcrEECg2ALWAqH5AZbsN1fmt11mw/piN9rW/QmEtiQdKUcfJ/xGinfIJs4WGTpSZNjoxH+Hjur987BRib97/9Nzgn8/co1uZcBRWIF194jc/lmRt7oT963+pMjCFYm+40AAgZIScCUQmpVGzSqi/oA4a9Ys0Xa69nmqpAYSlUEghgLWAqHZXqKjo0MqKyv7UHZ3d3s/wPyPP7hgTSB0oRcjtmHnK4kZwXX3imx4ROTA3v77zum7Zjb2ohswMHNoNMHSC5WpwqX52mgRLZOjv8Du7SJ3Ximy5peJrx01LbGVxPHvRQsBBEpUwKVA+P+3d3+xclz3fcCPRNki+xA5BVEmYRRXqmPTSBQjBFKndRgHaBLJQvlikGDbBxvhi4L4FlRrwIgIA44lgNevBGj4jYAkJDEhPhSgIUfKHyVQa77UjBQU6FUou1ICRrbDykksJZHiRMHZvXPv3OH+mbnn7N7ZM58FBErUnLNzPr+ZnfnuzJ6pny9VAbE6v6rOtzx2oqcbotUisIIC2QJh/MCaFfia33itoNUtqywQllDFXYwh/j7w+rPjf77zf3d2cOddIbz11zv/Lt5aeOy/j/8uPp4g3oL49pshvP29zT//btzmrTc2/zv++UbtvzeXi1cgc7/uuHP76mQMkfHRCfFKZD1M7r8rhHcd2LyquRkmm8Hz3dVVzAO513D5/cVbQ5/9bAh/993xe//sr40fMh8NvAgQ6K1AiYFw0hfqAmFvN0ErRmBlBQTChNIJhAl4q9Q0BoMY/l76nRC+8fvjQFe9Yki45+dDeP/9Ibz/YyG8+1+E8NVf3zmpzMe+kGdSmXilMQbF0T9vhvBWFSgnBMi3YuBs/n0MmY02MaDmfN2+b2fAnHYr7J3xttnNEDntFtmqbY6rq9PG+Nx6CK9uTgD0wz8dwmt/vF27f3lvCCcuhvAjP51TSF8ECCxIoJRAWN0iGq8IPvfcc6MJZs6cORPW1tZCqXdcLWiT0C0BAi0FBMKWUJMWEwgT8Pre9Ft/EkL8/Vj858b/Hj9ioHrF2wdHAfD+EO75aAjxKtuqvr7/9/PD5Sh8TgiXy7qKGa9Ytv2d5Wi5GUEzXgGNoTW+Yhj8oy/srFys8747QvjIwyH8wq+HsO/dq1pZ601gcAKlBMJJk8hUk/KVOifD4DZWAybQM4GsgbDN2Eq6510gbFPxFVnmH/42hG88F8LLvxfCS18N4Xuv7Vzxuz+8GQIfCOHQT6zIoPZoNUe3xNauYkbbeGWyCpWj/xf/7m82w2jtqucoZNauYsZ/X8QrPgYkvtc/ff/W3n/1+RB+6KcW8a76JEBggQKlBMJIVAW/+O/VZH3V1cH4d2YZXeCGpGsCAxQQCBOKLhAm4PWh6Xdf2bwKGG8F/YOdaxR/Nze6CvhACO/7xRDif3vtjUAMlDNvkW1cvRwFys1AGtv+fQyem8v87evbY5g22c9vNH4Dujej9q4ECHQUKCkQdhy6xQkQIJAkkC0QJq3FijYWCFewcPHh4td/d/ybwL/c2DmA+GiIGADf/8sh/Ni/W8HBWeXWAvF3oZd/ZXxVuP6KtwP/t//TuhsLEiDQH4GhBcLqiqGrhf3ZBq0JgVUVEAgTKicQJuAtq2l8fEAMf/G3gC///vg2xep1x/4Q7v3o9oQwP/Ajy1or79MHgfg70d/+LyH89Z+P1yaGwf/8W24X7UNtrAOBXQgIhLtA04QAAQIhBIEwYTMQCBPwFtn0tRdDuF5NCPP1cieEWaThkPr+q1fHo33Pe4c0amMlUJyAQFhcSQ2IAIElCQiECdACYQJezqbx92Xf/MMQ/vR3xlcDv/et7d7jg9fv/rebt4LeH0K8LdSLAAECBIoTEAiLK6kBESCwJAGBMAFaIEzAS236V38WwktPj28FjQ+K/8e3t3s88IPjiWDipDDxz/jfXgQIECBQtIBAWHR5DY4AgQUKCIQJuAJhAl7XpvHxAK9+bRwA4+2gN6/v7GE0IczmrKDximC8MuhFgAABAoMREAgHU2oDJUAgs4BAmAAqECbgtWkaJ4QZPRx+87EQ9WfSxQlh7vn5cQj8wIMhmBCmjahlCBAgUKyAQFhsaQ2MAIEFCwiECcACYQLetKavvTAOgH/6bAg3vr5zqbt+NIQfjwHwgRDu+WgId9y5gBXQJQECBAisooBAuIpVs84ECPRBQCBMqIJAmIBXNY0TwsSHwo9uBX02hDe+vd3p1oQwm7eCmhAmA7guCBAgUKaAQFhmXY2KAIHFCwiECcYC4S7xvvtKCC99dRwCX40TwvzDdkdbE8L8cgjv+yUTwuySWDMCBAgMTUAgHFrFjZcAgVwCAmGCpEDYEi8Gvj/7WggvxcdCPBPC///GzoZbE8LcH8LdHzYhTEtWixEgQIDAtsDQAqHaEyBAIJeAQJggKRDOwHvz5uZvAZ8J4ZvPhWBCmIQtTVMCBAgQmCdQYiA8depUuHbt2sShX7/emG17HpD/T4AAgSkCAmHCpiEQ1vDeeSeE1/54PBlMvBX0LxoHsGpCmDgraJwd9F0HEuQ1JUCAAAECOwVKC4SPPPJIuHz58tQyC4T2AAIEcgkIhAmSgw+Eb7/RmBDmO9uacUKYH/2Z8YygcWbQQz+RIK0pAQIECBCYLVBaIIznGPF15cqVcOTIEeUnQIDAwgQEwgTaQQbC+Pu/0WMhnhn/LrA5Icy/+Q8hfOB+E8IkbFeaEiBAgEB3gVIDoSuB3bcFLQgQ6CYgEHbz2rH0IAJhDHxxJtDRA+KfCeH1b+4U+1cfDOH9D4wfEG9CmIStSVMCBAgQSBEoLRBWt4wKhClbhbYECLQREAjbKE1ZpthAOJoQZvOxEN94LoR4a2j1umN/CPcc2wyBD4QQfxvoRYAAAQIE9ligtEC4sbERjh8/7pbRPd6uvD2BIQgIhAlVLiYQxglh4iQw1VXA117YqfIDh8dXAOOVQBPCJGwxmhIgQIDAogRKC4TVbwineblyuKgtSb8EhicgECbUfKUDYbzq9/LvjUPg9d8N4c2/3JaoJoSpQqAJYRK2Ek0JECBAYBkCAuEylL0HAQIlCgiECVVduUB48/r4wfAxBL76tRD+6fvbo9//nhDe94vjK4E//kshHPjBBBlNCRAgQIDAcgVKC4TL1fNuBAgMWUAgTKh+7wNhnBDmlee3bwX97v/bOdo4IUx8JEQMgT/2syHEK4NeBAgQIEBgBQUEwhUsmlUmQKAXAgJhQhl6GQjf+M72hDDf/MMQ3n5ze4R33Dn+DeDoVtCPmRAmofaaEiBAgEC/BEoMhBcuXAjnz58fQZ87d27059mzZ8OZM2fC2tpavwpgbQgQWFkBgTChdEsNhPFK3yv/a7y2Rx4M4Yd+avzvcUKYG1/f/C3gMyG89uLOEW1NCHN/CPd8NIR3HUgYsaYECBAgQKCfAqUFwnoYrALhyZMnQzz3OHr0aLh06VI/C2GtCBBYOQGBMKFkSwuEz62H8Edf2Lmm//6/hhAfD/FynBDm5vb/2zEhzP0hHPrJhBFqSoAAAQIEVkOgtEAYzzFOnDgR1tfXRyEwXiGMgdDzCVdje7SWBFZJQCBMqNbSAuH63SG89Tc71zReGbzttvHf7b9rPCFM/OcDHzMhTEJNNSVAgACB1RQoMRBWt4bWA+GpU6fCtWvXgsdOrOZ2aq0J9FFAIEyoytIC4W/cNXktP/Lw+PeAd384hNv3JYxEUwIECBAgsNoCpQXCGPziK94aWgXC++67b/Sw+urK4WpXzNoTINAXAYEwoRJLC4STrhC+9yMh/MrTCWuvKQECBAgQKEegtED4/PPPh9OnT08s0MWLF8OxY8fKKZ6RECCwpwICYQL/0gLhC78Zwv/4tZ1r+p9+M4Qj/zFh7TUlQIAAAQLlCJQWCGNlbt68GeK46q8rV66EI0eOlFM4IyFAYM8FBMKEEiwtEMZ1/NafhPDK/xz/XvBf/1wI73lvwpprSoAAAQIEyhIoMRCWVSGjIUCgrwICYUJllhoIE9ZTUwIECBAgULqAQFh6hY2PAIFFCRQbCKtZuCJcNVXzLMTmbRlXr14NBw8enOkuEC5qs9QvAQIECBDoJiAQdvOyNAECBCqBIgNhfJhrfK2trY3+jMFt1j33Gxsbo1m7ut6XLxDakQgQIECAQD8ESguE1YPp4wQyH/zgB7d+S+ih9P3Y3qwFgZIEigyEzQDYDIjNAsaHvD744IOdZ+wSCEvaFYyFAAECBFZZoLRAWH/sRPUw+qo+1fMJV7le1p0Agf4IFBcIq1s/67d8PvXUU6OHuK6vr0+Uj8EufuMWl4mvtt++CYT92ZCtCQECBAgMW6C0QBjPMeoPpo/VjXcyPf744+Hy5cseTD/szd3oCWQVKC4QVrd/NgNh/PCMD3dtvqoAWb9dNH4TF1/1APnkk0/e0vbRRx/1gZx1c9QZAQIECBDYnUBpgTBeIbz33nvDpz/96a3bRa9fvx6qW0njv3sRIEAgh0BxgbDrFcJJy8eHwcYP3HqAfOKJJ27xfuyxxwTCHFuhPggQIECAQKJAaYGwCn4Vy4kTJ0ZfVNdvJU0k05wAAQIjgeICYRxU198QNpefFAgnbS9uGbUXESBAgACBfgiUFgijan3G9HhFMP4E5uzZs1u3kvZD3loQILDqAkUGwnmzjMYgV38URVz+xo0bW7eIxg/g+E3cyZMnZ9ZXIFz1zd/6EyBAgEApAiUGwlJqYxwECPRboMhA2PxWrfkcwmYgjMvXZ/BqO3uXQNjvjdvaESBAgMBwBATC4dTaSAkQyCtQbCDMyzS5N4FwGcregwABAgQIzBcoMRDWbxltCphUZv42YQkCBNoJCITtnCYuJRAm4GlKgAABAgQyCpQWCJvPHhQIM24suiJAYIeAQJiwQQiECXiaEiBAgACBjAKlBcJ4jhFf9cdiZeTSFQECBLYEBMKEjUEgTMDTlAABAgQIZBQoNRC6NTTjRqIrAgQmCgiECRuGQJiApykBAgQIEMgoUFogrG4ZFQgzbiS6IkBAIMy9DQiEuUX1R4AAAQIEdifQNhDGZw2fPn16603mBa5Zy1fPBayvcfUA+d2NYrvVxsZGOH78uFtGUyG1J0BgroArhHOJpi8gECbgaUqAAAECBDIKtAmEN2/eDHG5q1evhoMHD44e9H7t2rWt5xA3V2fe8vPapwyv+g3htD7mBdmU99aWAIFhCQiECfUWCBPwNCVAgAABAhkF2gTCZoBrBr7m6sxbXiDMWEBdESCwZwICYQK9QJiApykBAgQIEMgo0CYQXrhwYfSOa2trW+8cj+XTZvKct3zzltFct4tmZNEVAQIE5goIhHOJpi8gECbgaUqAAAECBDIKxED40EMPhX379u3o9eTJk2H//v2jv4sTtRw+fPiWQHjx4sVw7NixW9am6/LxvODcuXMhvqcXAQIEVkVAIEyolECYgKcpAQIECBDIKFAFwttvv/2WQHjgwIHR38274tdcna7L576FNL7/+fPnR6sVg2Z8nT17Npw5c2ZHqM3IqCsCBAYoIBAmFF0gTMDTlAABAgQIZBRoc8vovN8ENlcndfmU4dXDYBUI45XHeO5x9OjRcOnSpZTutSVAgMCWgECYsDEIhAl4mhIgQIAAgYwCbQLhvFlDm496mLd8vKV0fX19NIpq2Wm3n3YdajzHqH6TWL8V1fMJu0pangCBeQIC4TyhGf9fIEzA05QAAQIECGQUaBMI49vNeq7gpGf/zVq+CmfVMHL+fjCeY1S3htYD4alTp0aPyvDYiYwbj64IDFxAIEzYAATCBDxNCRAgQIBARoG2gTDjWy60qxj84iveGloFwvvuu2/0sHqzmS6UXucEBicgECaUXCBMwNOUAAECBAhkFCgtEDavTNapct2WmpFfVwQIrLCAQJhQPIEwAU9TAgQIECCQUaC0QBhpqt8l1pmmPTMxI6WuCBAYmIBAmFBwgTABT1MCBAgQIJBRoMRAmJFHVwQIEJgqIBAmbBwCYQKepgQIECBAIKOAQJgRU1cECAxKQCBMKLdAmICnKQECBAgQyChQSiCM5xazXh5Kn3Gj0RUBAiMBgTBhQxAIE/A0JUCAAAECGQWGEggjmUllMm44uiJAQCBM2QYEwhQ9bQkQIECAQD6BkgLh0aNHR4+baL6qmUc9diLfdqMnAgRcIUzaBgTCJD6NCRAgQIBANoEhBMKIVd1S6sH02TYdHREYvIBbRhM2AYEwAU9TAgQIECCQUUAgzIipKwIEBiUgECaUWyBMwNOUAAECBAhkFCglEM4jcYVwnpD/T4BAVwGBsKtYbXmBMAFPUwIECBAgkFFgCIFwY2MjHD9+PEz7jWFGTl0RIDAgAYEwodgCYQKepgQIECBAIKPAEAJhRi5dESBAYEtAIEzYGATCBDxNCRAgQIBARgGBMCOmrggQGJSAQJhQboEwAU9TAgQIECCQUUAgzIipKwIEBiUgECaUWyBMwNOUAAECBAhkFBAIM2LqigCBQQkIhAnlFggT8DQlQIAAAQIZBQTCjJi6IkBgUAICYUK5BcIEPE0JECBAgEBGAYEwI6auCBAYlIBAmFBugTABT1MCBAgQIJBRQCDMiKkrAgQGJSAQJpRbIEzA05QAAQIECGQUEAgzYuqKAIFBCQiECeUWCBPwNCVAgAABAhkFBMKMmLoiQGBQAgJhQrkFwgQ8TQkQIECAQEYBgTAjpq4IEBiUgECYUG6BMAFPUwIECBAgkFFAIMyIqSsCBAYlIBAmlFsgTMDTlAABAgQIZBQQCDNi6ooAgUEJCIQJ5RYIE/A0JUCAAAECGQUEwoyYuiJAYFACAmFCuQXCBDxNCRAgQIBARgGBMCOmrggQGJSAQJhQboEwAU9TAgQIECCQUUAgzIipKwIEBiUgECaUWyBMwNOUAAECBAhkFBAIM2LqigCBQQkIhAnlFggT8DQlQIAAAQIZBQTCjJi6IkBgUAICYUK5BcIEPE0JECBAgEBGAYEwI6auCBAYlIBAmFBugTABT1MCBAgQIJBRQCDMiKkrAgQGJSAQJpRbIEzA05QAAQIECGQUEAgzYuqKAIFBCQiECeUWCBPwNCVAgAABAhkFBMKMmLoiQGBQAgJhQrkFwgQ8TQkQIECAQEYBgTAjpq4IEBiUgECYUG6BMAFPUwIECBAgkFFAIMyIqSsCBAYlIBAmlFsgTMDTlAABAgQIZBQQCDNi6ooAgUEJCIQJ5RYIE/A0JUCAAAECGQUEwoyYuiJAYFACAmFCuQXCBDxNCRAgQIBARgGBMCOmrggQGJSAQJhQboEwAU9TAgQIECCQUUAgzIipKwIEBiUgECaUWyBMwNOUAAECBAhkFBAIM2LqigCBQQkIhAnlFggT8DQlQIAAAQIZBQTCjJi6IkBgUAICYUK5BcIEPE0JECBAgEBGAYEwI6auCBAYlIBAmFBugTABT1MCBAgQIJBRQCDMiKkrAgQGJSAQJpRbIEzA05QAAQIECGQUEAgzYuqKAIFBCQiECeUWCBPwNCVAgAABAhkFBBpFnAkAABCHSURBVMKMmLoiQGBQAgJhQrkFwgQ8TQkQIECAQEYBgTAjpq4IEBiUgECYUG6BMAFPUwIECBAgkFFAIMyIqSsCBAYlIBAmlFsgTMDTlAABAgQIZBQQCDNi6ooAgUEJCIQJ5RYIE/A0JUCAAAECGQUEwoyYuiJAYFACAmFCuQXCBDxNCRAgQIBARgGBMCOmrggQGJSAQJhQboEwAU9TAgQIECCQUUAgzIipKwIEBiUgECaUWyBMwNOUAAECBAhkFBAIM2LqigCBQQkIhAnlFggT8DQlQIAAAQIZBQTCjJi6IkBgUAICYUK5BcIEPE0JECBAgEBGAYEwI6auCBAYlIBAmFBugTABT1MCBAgQIJBRQCDMiKkrAgQGJSAQJpRbIEzA05QAAQIECGQUEAgzYuqKAIFBCQiECeUWCBPwNCVAgAABAhkFBMKMmLoiQGBQAgJhQrkFwgQ8TQkQIECAQEYBgTAjpq4IEBiUgECYUG6BMAFPUwIECBAgkFFAIMyIqSsCBAYlIBAmlFsgTMDTlAABAgQIZBQQCDNi6ooAgUEJCIQJ5RYIE/A0JUCAAAECGQUEwoyYuiJAYFACxQbCU6dOhWvXro2Kee7cuXDy5MmphX3qqafC2bNnd/z/EydOhPX19Zkbg0A4qH3FYAkQIECgxwJtA+Hzzz8fTp8+vTWS69ev93hUVo0AAQKLFygyEF64cGEkt7a2NvozBrcrV66EI0eOTBSNgTCGx3kBsNlYIFz8BuodCBAgQIBAG4E2gfDmzZshLnf16tVw8ODBsNvjf5v1sQwBAgRWRaDIQNgMgM2A2CzObg8IAuGqbObWkwABAgRKF2gTCJvH+2ZALN3I+AgQIDBJoLhAOOnDfV7ga94y2uZ20erKo1tN7FgECBAgQGDvBdoEwklfEM+7i2jvR2YNCBAgsFiB4gLhxsZGOH78+NbtIJEvBr7Lly+HS5cutdKMB4fm7w6ffPLJW9o++uijQSBsRWohAgQIECCwUIEYCB966KGwb9++He8T5xDYv3//6O8eeeSRcPjw4a2flFRf7l68eDEcO3ZsoeuncwIECPRVoLhAuJsrhM3iTLqi+MQTT9xSw8cee6yvdbVeBAgQIEBgcAKf/exnw2233XZLIDxw4MDo71whHNwmYcAECLQQKC4QVt/21SeRmfcbwjaBsIXlwhf50pe+FA4dOhQ+/vGPL/y9vEGaQLyi/M4774RPfOITaR1pvXCB+AXQ66+/Prqy4NVvga985Svh5ZdfDg8//HC/V9TahWeffTa88MIL4TOf+UyvNPyGsFflsDIECPREoMhAOG+W0eYtofEWkmqG0eoKYx9vHxEIe7LXtFgNgbAFUk8WEQh7UogWqyEQtkDqySJ9DYRmGe3JBmI1CBDolUCRgTAKz3oO4aRAGH9jWL3mPbdwryooEO6VfPf3FQi7m+1VC4Fwr+S7v69A2N1sr1r0NRBGD88h3KutwvsSINBXgWIDYV/BU9ZLIEzRW25bgXC53invJhCm6C23rUC4XO+Ud+tzIEwZl7YECBAoUUAgXKGqCoSrUyyBcHVqJRCuTq0EwtWplUC4OrWypgQIEBAIbQMECBAgQIAAAQIECBAYqIBAONDCGzYBAgQIECBAgAABAgQEQtsAAQIECBAgQIAAAQIEBiogEA608IZNgAABAgQIECBAgAABgXCJ20B83MWVK1fCkSNHsrzrxsZGOH78eLh+/XqW/nSyLaBWq7M1qNXq1Co+8/Xo0aPh5MmTWVbaZ2AWxomd5K5VfJPc++riRq9nAgQIDEtAIGxZ71nPNay6mHWwiycujz/+eFhfXx8tXj0ct2o7KSjOO9mJsyPGVzy5avYX/74ZFNuMYdJJQL1dnStnuG1ZhlaL7XacVeerXKs4hljD+nM1+/yFwV7Uat6Jadf96sKFC+H8+fOjzefEiRNb+3h9Y512ch3f6+zZs1uL9rlWKeOsf0ZWY2zzGbjMWjU/55qfb6v07LrctYp1aLOvtj0GtjlezRrDvFq1OSa3OphYiAABAgMREAhbFDoemOJrbW1t9GfzoFc/OE0LSbGPQ4cObX0zHtvE/o4dOxaawa95sJx2khj7+PznPz+64hhPVuIr9hdf8f1u3LixdXI6bwz1g++5c+dmfoMf1/dzn/tcuHTpUgu95S6SY5yrXKtJda9vB8utxux3W3atmie10/bVLvtV3O/iOKp9oRn8Zu1XVcCo9u+4bPy7Pu5XKeOstoLYx9NPP731mTTrM3DZtYqfuV/+8pe3PuObtak+k69evRoOHjwYYpC/du3axPC/1/vYImo1b1/tegycd7yaNYZ5tWq77ex1nbw/AQIE+iQgELaoRjMANg+OsYvqhGHaSWbsozqZmHTlr35yVD+BOn369MRbQueFsuYBtc0YqgNpvMox65aueNL74IMPboXPFoRLWyTHOFe5Vs1A0twOllaIFm+0V7Wata923a/aesf9u7lfNds2Q0cLwqUtkjLOaiXrnxttPwP3olb1z/NpAXBItZr0JWjqMbC54c4LsbM+x6bVYt4xeWk7jzciQIDACggIhHOKNOlgM+nb4VkHn+Y345MObpNuKWt+S11f1eZVrOYw6uvYdgxtAuG8E+a93OZzjHPVa1WdaJ85c2Z0taOv4X0vazVrX+2yX1X7S3WlP/73tNu82wTCSSfee7k/1d+7+YVVl3HWA1Z1NbTtZ+Be1GpSHScFoL7+Hi53rdruq12OgbOOV132q1n7nEDYl08P60GAwCoICIRzqlSd+FTfFMfFY9iKv9Gq39o178TlQx/60NYVtUnt44n74cOHt25Ziu8zKxDGg/4Xv/jF0e1LzVdzXdqOoU0g7GvAqJ8YzKvVrHHGE79Vr1U8UY0Td8Rb2uKrj79Ly7FN7rZWs/bVLvtVFeAuXry4tW9PGte07W3S50BfQ0Zcr92Os/ose/HFF7c+39p+Bu5FrSbVa9Lnc9OkLwf83LVqu692OQbWrSa1azuGWZ/lAmFftkjrQYDAKggIhHOqlOPb0Xhwq5+Ut/12fFogbE56MungWj95azuGeYGwz7cfxnXPMc5Vr1UzsFeTlvQtFO5lraadKHbdr6r9ZbdXCGP75gRA8e/qX2j05SCSetUpjvOTn/zk1gzLbT8D96pW8bfY1W/GYw2GdIWwWau2++qsANb8XK2266pN/XjVdb9q1qrZd18nP+vLvm09CBAgEAUEwhbbQZvfOk07GDZvQYxv1/b3M9MC4bTb2qYdXKsrGfUD46QTnHmBcNLvHFvwLXWRNrWaNs4SatUc/7QrVkstypQ326taTdtXd7Nf5fhtXcXT59uxU8ZZede/lGj7GbjsWsXPuEkBo/kzgUkhqQ/7VPUlQ/3RHtO+yJt0G/OkWrU9fnQ5Bta/wGuGwbZjmFYrgbAvW6L1IEBglQQEwhbVmjfDWv3g1vw2ctotlvNm2It9TguE9UlP6ieT8ZmEs2Y5jctOmym16mfSSUK1LvXZFFuw7ckibWo1LRCWUKs4hviqHm/S55kr96pW005cd7NfzZsMY95+Vd9J+noL4qT9f9pjNCZ9fsQw9e1vf3vHFbdqH5w20/K8k/pF1Cr2OW2G5WYAXOVZRmdtk9Nq1WZfnbZfTfpcrb4QmHa8mrdfzarVvG1nTw5M3pQAAQI9FxAIWxZo1jOYms9Eqp5FNu3b1nqArN6+fmCs2tVXrTpRmXQVKy5Xn96+3q7+7eusMUxq33xe2KRvclvyLXWx3YyzlFpVJ9rV7wfjlYI+PsagflJarWvzZHzaNrnbWjVt4n9X+2rKflVfz+ZzCGftV/H944ntpM+Ape4wLd9st+OsP8aj/lbNz7lmOJj2ubqIWlVfvjUpqsmZqlAcZ32uXn27Dbu+7rlr1dx3mvtq12Ngm+PVtDG0qdW09Wm5qVuMAAECgxMQCBdY8njgqk+kkOOtmhNp5OhTH+OrsWq1GluCWq1GneJaxtD3qU99KuuXEj4DF1P/RdRqEfvqYkavVwIECAxbQCBcYP2nfTO+27ecdWVkt31qNxZQq9XZEtRqdWo17zEeXUfiM7CrWPvlc9dqEZ+r7UdjSQIECBDoIiAQdtGyLAECBAgQIECAAAECBAoSEAgLKqahECBAgAABAgQIECBAoIuAQNhFy7IECBAgQIAAAQIECBAoSEAgLKiYhkKAAAECBAgQIECAAIEuAgJhFy3LEiBAgAABAgQIECBAoCABgbCgYhoKAQIECBAgQIAAAQIEuggIhF20LEuAAAECBAgQIECAAIGCBATCgoppKAQIECBAgAABAgQIEOgiIBB20bIsAQIECBAgQIAAAQIEChIQCAsqpqEQIECAAAECBAgQIECgi4BA2EXLsgQIECBAgAABAgQIEChIQCAsqJiGQoAAAQIECBAgQIAAgS4CAmEXLcsSIECAAAECBAgQIECgIAGBsKBiGgoBAgQIECBAgAABAgS6CAiEXbQsS4AAAQIECBAgQIAAgYIEBMKCimkoBAgQIECAAAECBAgQ6CIgEHbRsiwBAgQIECBAgAABAgQKEhAICyqmoRAgQIAAAQIECBAgQKCLgEDYRcuyBAgQIECAAAECBAgQKEhAICyomIZCgAABAgQIECBAgACBLgICYRctyxIgQIAAAQIECBAgQKAgAYGwoGIaCgECBAgQIECAAAECBLoICIRdtCxLgAABAgQIECBAgACBggQEwoKKaSgECBAgQIAAAQIECBDoIiAQdtGyLAECBAgQIECAAAECBAoSEAgLKqahECBAgAABAgQIECBAoIuAQNhFy7IECBAgQIAAAQIECBAoSEAgLKiYhkKAAAECBAgQIECAAIEuAgJhFy3LEiBAgAABAgQIECBAoCABgbCgYhoKAQIECBAgQIAAAQIEuggIhF20LEuAAAECBAgQIECAAIGCBATCgoppKAQIECBAgAABAgQIEOgiIBB20bIsAQIECBAgQIAAAQIEChIQCAsqpqEQIECAAAECBAgQIECgi4BA2EXLsgQIECBAgAABAgQIEChIQCAsqJiGQoAAAQIECBAgQIAAgS4CAmEXLcsSIECAAAECBAgQIECgIAGBsKBiGgoBAgQIECBAgAABAgS6CAiEXbQsS4AAAQIECBAgQIAAgYIEBMKCimkoBAgQIECAAAECBAgQ6CIgEHbRsiwBAgQIECBAgAABAgQKEhAICyqmoRAgQIAAAQIECBAgQKCLgEDYRcuyBAgQIECAAAECBAgQKEhAICyomIZCgAABAgQIECBAgACBLgICYRctyxIgQIAAAQIECBAgQKAgAYGwoGIaCgECBAgQIECAAAECBLoICIRdtCxLgAABAgQIECBAgACBggQEwoKKaSgECBAgQIAAAQIECBDoIiAQdtGyLAECBAgQIECAAAECBAoSEAgLKqahECBAgAABAgQIECBAoIuAQNhFy7IECBAgQIAAAQIECBAoSEAgLKiYhkKAAAECBAgQIECAAIEuAgJhFy3LEiBAgAABAgQIECBAoCABgbCgYhoKAQIECBAgQIAAAQIEuggIhF20LEuAAAECBAgQIECAAIGCBATCgoppKAQIECBAgAABAgQIEOgiIBB20bIsAQIECBAgQIAAAQIEChIQCAsqpqEQIECAAAECBAgQIECgi8A/Ax780MfGA45KAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_historical_datadrift_metric() # works if date_compile_auc and/or datadrift_file are filled" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "2324467b", + "metadata": {}, + "source": [ + "In 2019 and 2020, data drift is very high. Is there any impact on the performance of the model?" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "64665647", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.plot.generate_modeldrift_data() # works if add_data_modeldrift used before " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "438706e2", + "metadata": {}, + "source": [ + "While data drift was high in 2019, the impact on model performance is low. In 2020, data drift leads to a decrease in model performance." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "c9089d96", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_car_accident_modeldrift_2021.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_car_accident_modeldrift_2021.html', \n", + " title_story=\"Model drift Report\",\n", + " title_description=\"\"\"US Car accident model drift 2021\"\"\", # Optional: add a subtitle to describe report\n", + " project_info_file=\"../../eurybia/data/project_info_car_accident.yml\" # Optional: add information on report\n", + " ) " ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_car_accident_modeldrift_2021.html', \n", - " title_story=\"Model drift Report\",\n", - " title_description=\"\"\"US Car accident model drift 2021\"\"\", # Optional: add a subtitle to describe report\n", - " project_info_file=\"../../eurybia/data/project_info_car_accident.yml\" # Optional: add information on report\n", - " ) " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "eurybia_3_9", - "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" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "336px" - }, - "toc_section_display": true, - "toc_window_display": true + ], + "metadata": { + "kernelspec": { + "display_name": "eurybia_3_9", + "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" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "336px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "vscode": { + "interpreter": { + "hash": "36c4204cc0170e083c18487e195263df35fcafba9d65a5415ab6b0958d51e154" + } + } }, - "vscode": { - "interpreter": { - "hash": "36c4204cc0170e083c18487e195263df35fcafba9d65a5415ab6b0958d51e154" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/tutorial/tutorial01-Eurybia-overview.ipynb b/tutorial/tutorial01-Eurybia-overview.ipynb index 2671c47..b723d7e 100644 --- a/tutorial/tutorial01-Eurybia-overview.ipynb +++ b/tutorial/tutorial01-Eurybia-overview.ipynb @@ -1,255 +1,255 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "983686e9", - "metadata": {}, - "source": [ - "# Eurybia - Overview\n", - "This tutorial will help you understand how Eurybia works with a simple use case\n", - "\n", - "Contents:\n", - "- Compile Eurybia \n", - "- Generate report\n", - "\n", - "For a more detailed tutorial on :\n", - "- Data validation : (https://github.com/MAIF/eurybia/tree/master/tutorial/data_validation)\n", - "- Data drift : (https://github.com/MAIF/eurybia/tree/master/tutorial/data_drift)\n", - "- Model drift : (https://github.com/MAIF/eurybia/tree/master/tutorial/model_drift)" - ] - }, - { - "cell_type": "markdown", - "id": "9524ace9", - "metadata": {}, - "source": [ - "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", - "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f8489bfa", - "metadata": {}, - "outputs": [], - "source": [ - "from category_encoders import OrdinalEncoder\n", - "from lightgbm import LGBMRegressor\n", - "from eurybia import SmartDrift\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "markdown", - "id": "29ec936f", - "metadata": {}, - "source": [ - "## Import Dataset and split in training and production dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3cb3a493", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia.data.data_loader import data_loading\n", - "house_df, house_dict = data_loading('house_prices')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "019c6396", - "metadata": {}, - "outputs": [], - "source": [ - "# Let us consider that the column \"YrSold\" corresponds to the reference date. \n", - "#In 2006, a model was trained using data. And in 2007, we want to detect data drift on new data in production to predict\n", - "#house price\n", - "house_df_learning = house_df.loc[house_df['YrSold'] == 2006]\n", - "house_df_2007 = house_df.loc[house_df['YrSold'] == 2007]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4bda0775", - "metadata": {}, - "outputs": [], - "source": [ - "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", - "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YrSold'])]\n", - "\n", - "y_df_2007=house_df_2007['SalePrice'].to_frame()\n", - "X_df_2007=house_df_2007[house_df_2007.columns.difference(['SalePrice','YrSold'])]" - ] - }, - { - "cell_type": "markdown", - "id": "e294d0b5", - "metadata": {}, - "source": [ - "## Building Supervized Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ca7381d", - "metadata": {}, - "outputs": [], - "source": [ - "from category_encoders import OrdinalEncoder\n", - "\n", - "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", - "\n", - "encoder = OrdinalEncoder(\n", - " cols=categorical_features,\n", - " handle_unknown='ignore',\n", - " return_df=True).fit(X_df_learning)\n", - "\n", - "X_df_learning_encoded=encoder.transform(X_df_learning)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8ba398ad", - "metadata": {}, - "outputs": [], - "source": [ - "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2dc04f3e", - "metadata": {}, - "outputs": [], - "source": [ - "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" - ] - }, - { - "cell_type": "markdown", - "id": "12b12535", - "metadata": {}, - "source": [ - "## Use Eurybia for data drift" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9faf9a5e", - "metadata": {}, - "outputs": [], - "source": [ - "from eurybia import SmartDrift" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "493030c9", - "metadata": {}, - "outputs": [], - "source": [ - "SD = SmartDrift(df_current=X_df_2007,\n", - " df_baseline=X_df_learning,\n", - " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", - " encoding=encoder, # Optional: if deployed_model and encoder to use this model\n", - " dataset_names={\"df_current\": \"2007 dataset\", \"df_baseline\": \"Learning dataset\"} # Optional: Names for outputs\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5c51a243", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 2min 23s, sys: 32.1 s, total: 2min 55s\n", - "Wall time: 10.5 s\n" - ] - } - ], - "source": [ - "%time SD.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "ead7d949", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Report saved to ./report_house_price_datadrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + "cells": [ + { + "cell_type": "markdown", + "id": "983686e9", + "metadata": {}, + "source": [ + "# Eurybia - Overview\n", + "This tutorial will help you understand how Eurybia works with a simple use case\n", + "\n", + "Contents:\n", + "- Compile Eurybia \n", + "- Generate report\n", + "\n", + "For a more detailed tutorial on :\n", + "- Data validation : (https://github.com/MAIF/eurybia/tree/master/tutorial/data_validation)\n", + "- Data drift : (https://github.com/MAIF/eurybia/tree/master/tutorial/data_drift)\n", + "- Model drift : (https://github.com/MAIF/eurybia/tree/master/tutorial/model_drift)" + ] + }, + { + "cell_type": "markdown", + "id": "9524ace9", + "metadata": {}, + "source": [ + "**Requirements notice** : the following tutorial may use third party modules not included in Eurybia. \n", + "You can find them all in one file [on our Github repository](https://github.com/MAIF/eurybia/blob/master/requirements.dev.txt) or you can manually install those you are missing, if any." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f8489bfa", + "metadata": {}, + "outputs": [], + "source": [ + "from category_encoders import OrdinalEncoder\n", + "from lightgbm import LGBMRegressor\n", + "from eurybia import SmartDrift\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "id": "29ec936f", + "metadata": {}, + "source": [ + "## Import Dataset and split in training and production dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3cb3a493", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia.data.data_loader import data_loading\n", + "house_df, house_dict = data_loading('house_prices')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "019c6396", + "metadata": {}, + "outputs": [], + "source": [ + "# Let us consider that the column \"YrSold\" corresponds to the reference date. \n", + "#In 2006, a model was trained using data. And in 2007, we want to detect data drift on new data in production to predict\n", + "#house price\n", + "house_df_learning = house_df.loc[house_df['YrSold'] == 2006]\n", + "house_df_2007 = house_df.loc[house_df['YrSold'] == 2007]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4bda0775", + "metadata": {}, + "outputs": [], + "source": [ + "y_df_learning=house_df_learning['SalePrice'].to_frame()\n", + "X_df_learning=house_df_learning[house_df_learning.columns.difference(['SalePrice','YrSold'])]\n", + "\n", + "y_df_2007=house_df_2007['SalePrice'].to_frame()\n", + "X_df_2007=house_df_2007[house_df_2007.columns.difference(['SalePrice','YrSold'])]" + ] + }, + { + "cell_type": "markdown", + "id": "e294d0b5", + "metadata": {}, + "source": [ + "## Building Supervized Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ca7381d", + "metadata": {}, + "outputs": [], + "source": [ + "from category_encoders import OrdinalEncoder\n", + "\n", + "categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == 'object']\n", + "\n", + "encoder = OrdinalEncoder(\n", + " cols=categorical_features,\n", + " handle_unknown='ignore',\n", + " return_df=True).fit(X_df_learning)\n", + "\n", + "X_df_learning_encoded=encoder.transform(X_df_learning)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8ba398ad", + "metadata": {}, + "outputs": [], + "source": [ + "Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2dc04f3e", + "metadata": {}, + "outputs": [], + "source": [ + "regressor = LGBMRegressor(n_estimators=200).fit(Xtrain,ytrain)" + ] + }, + { + "cell_type": "markdown", + "id": "12b12535", + "metadata": {}, + "source": [ + "## Use Eurybia for data drift" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9faf9a5e", + "metadata": {}, + "outputs": [], + "source": [ + "from eurybia import SmartDrift" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "493030c9", + "metadata": {}, + "outputs": [], + "source": [ + "SD = SmartDrift(df_current=X_df_2007,\n", + " df_baseline=X_df_learning,\n", + " deployed_model=regressor, # Optional: put in perspective result with importance on deployed model\n", + " encoding=encoder, # Optional: if deployed_model and encoder to use this model\n", + " dataset_names={\"df_current\": \"2007 dataset\", \"df_baseline\": \"Learning dataset\"} # Optional: Names for outputs\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5c51a243", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2min 23s, sys: 32.1 s, total: 2min 55s\n", + "Wall time: 10.5 s\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "%time SD.compile()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ead7d949", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Report saved to ./report_house_price_datadrift_2007.html. To upload and share your report, create a free Datapane account by running `!datapane signup`." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SD.generate_report( \n", + " output_file='report_house_price_datadrift_2007.html', \n", + " title_story=\"Data drift\",\n", + " title_description=\"\"\"House price Data drift 2007\"\"\", # Optional: add a subtitle to describe report\n", + " project_info_file=\"../eurybia/data/project_info_house_price.yml\" # Optional: add information on report\n", + " )" ] - }, - "metadata": {}, - "output_type": "display_data" } - ], - "source": [ - "SD.generate_report( \n", - " output_file='report_house_price_datadrift_2007.html', \n", - " title_story=\"Data drift\",\n", - " title_description=\"\"\"House price Data drift 2007\"\"\", # Optional: add a subtitle to describe report\n", - " project_info_file=\"../eurybia/data/project_info_house_price.yml\" # Optional: add information on report\n", - " )" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "d08e6294e2d60f50397263035a337d71f3055486232bc02b45ce2785f62e7d8b" - }, - "kernelspec": { - "display_name": "dev_eurybia", - "language": "python", - "name": "dev_eurybia" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" + ], + "metadata": { + "interpreter": { + "hash": "d08e6294e2d60f50397263035a337d71f3055486232bc02b45ce2785f62e7d8b" + }, + "kernelspec": { + "display_name": "dev_eurybia", + "language": "python", + "name": "dev_eurybia" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 }