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a/_build/html/_images/hammerdirt.png and /dev/null differ diff --git a/_build/html/_sources/a_report_class.ipynb b/_build/html/_sources/a_report_class.ipynb deleted file mode 100644 index 959095d..0000000 --- a/_build/html/_sources/a_report_class.ipynb +++ /dev/null @@ -1,837 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "64c16819-72da-4b47-a3aa-988d3f5a8203", - "metadata": { - "editable": true, - "pycharm": { - "name": "#%%\n" - }, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "%load_ext watermark\n", - "\n", - "import session_config\n", - "import reports\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "markdown", - "id": "64253a93-aae9-40d7-b29f-1aa216d30186", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "(surveyreporter)=\n", - "# Survey report class\n", - "\n", - "The `SurveyReport` class is used to generate descriptive statistics and identify objects of interest from a query defined by geographic, adminsitrative and/or temporal bounds.\n", - "\n", - "The `SurveyReport` class expects a dataframe as described in the [annex](the_survey_data). Therefore any subset of the survey data will produce a report.\n", - "\n", - "```{note}\n", - "\n", - "The best reports are focussed. Select a lake or municipality of interest. Selecting specific codes or groups of codes based on use case is another way to produce a report that is tailored to a specific situation.\n", - "\n", - "```\n", - "\n", - "## Make a report class\n", - "\n", - "Define the boundaries of your search (canton, lake, river, city) and filter the data. Simply call the `SurveyReport` class with the filtered data as the argument.\n", - "\n", - "```python\n", - "import session_config\n", - "import reports\n", - "\n", - "# available data\n", - "surveys = session_config.collect_survey_data()\n", - "\n", - "# boundaries / search parameters\n", - "feature_type = 'canton'\n", - "feature_name = 'Vaud'\n", - "\n", - "df = surveys[surveys[feature_type] == feature_name].copy()\n", - "vaud_report = reports.SurveyReport(dfc=df)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "556ba2a8-f939-4607-aa22-e32ced9f70d8", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "surveys = session_config.collect_survey_data()\n", - "feature_type = 'canton'\n", - "feature_name = 'Vaud'\n", - "\n", - "df = surveys[surveys[feature_type] == feature_name].copy()\n", - "vaud_report = reports.SurveyReport(dfc=df)" - ] - }, - { - "cell_type": "markdown", - "id": "e4a858c7-ea3e-4864-9f6e-b2e1c2babf5e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Report contents\n", - "\n", - "The report summarizes the survey results and provides the metadata necesary to describe the geographic and administrative extent of the survey results.\n", - "\n", - "1. Administrative boundaries: The number and name of municipalities and cantons included in the report\n", - "2. Feature inventory: The number and name of lakes, rivers and parks included in the report\n", - "3. The date range of the data\n", - "4. A complete inventory of the objects found, including summary statistics for each object type\n", - "5. Total quantity: the total number of objects found\n", - "6. Number of samples\n", - "7. Fail rate: the probability of finding at least one of the specified object at a sample\n", - "8. Sample results: the sample total for each sample\n", - "9. Sampling results summary: the distribution of the sample total for all samples\n", - "10. Object summary: the complete inventory and fail rate for each object in one table\n", - "11. Material report: the proportion of the objects by material type\n", - "\n", - "\n", - "### Administrative boundaries\n", - "\n", - "The administrative boundaries can be displayed using `SurveyReport.administrative_boundaries()`. The resulting dictionary contains the number and names of each type of boundary:\n", - "\n", - "__What cities are considered in this report ?__\n", - "\n", - "```python\n", - "# collect the boundaries report\n", - "admin_boundaries = vaud_report.administrative_boundaries()\n", - "admin_boundaries['city']\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6174d54f-f481-48b9-b831-7f80c4453f47", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['La Tour-de-Peilz' 'Vevey' 'Montreux' 'Tolochenaz' 'Morges' 'Gland'\n", - " 'Lausanne' 'Cudrefin' 'Saint-Sulpice (VD)' 'Préverenges' 'Rolle'\n", - " 'Bourg-en-Lavaux' 'Allaman' 'Yverdon-les-Bains' 'Lavey-Morcles'\n", - " 'Grandson']\n" - ] - } - ], - "source": [ - "admin_boundaries = vaud_report.administrative_boundaries()\n", - "print(admin_boundaries['city']['names'])" - ] - }, - { - "cell_type": "markdown", - "id": "e9d9e1b3-c838-40f9-8a36-31fc8cb1ec45", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "
\n", - "\n", - "__What river basins are considered in this report ?__\n", - "\n", - "```python\n", - "# collect the boundaries report\n", - "admin_boundaries['parent_boundary']\n", - "```\n", - "There are two river basins:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f8d1f858-887c-4889-9968-44386be41c34", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['rhone' 'aare']\n" - ] - } - ], - "source": [ - "print(admin_boundaries['parent_boundary']['names'])" - ] - }, - { - "cell_type": "markdown", - "id": "40aae8e3-d622-47f5-8cb0-1d7076c1da6b", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Feature inventory\n", - "\n", - "The number and name of lakes, rivers or parks in the data can be accessed usin `SurveyReport.feature_inventory()`.\n", - "\n", - "__Which lakes are included in this report ?__\n", - "\n", - "```python\n", - "# call the feature inventory\n", - "vaud_features = vaud_report.feature_inventory()\n", - "vaud_features['l']\n", - "```\n", - "There are two lakes in this report" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8c5f9347-d7dd-4ca3-b96e-a9426016563f", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['lac-leman', 'neuenburgersee']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vaud_features = vaud_report.feature_inventory()\n", - "vaud_features['l']['names']" - ] - }, - { - "cell_type": "markdown", - "id": "037cdb71-0c01-426d-b62a-ee4c5f44708b", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Date range, number of samples, total quantity\n", - "\n", - "These three attributes are avaialble directly:\n", - "\n", - "```python\n", - "dates = vaud_report.date_range\n", - "qty = vaud_report.total_quantity\n", - "nsamps = vaud_report.number_of_samples\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "0bbbac6b-c02d-4b05-b1d5-83476ae11fc4", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "date range : {'start': '2015-11-23', 'end': '2022-10-06'}\n", - "quantity : 64052\n", - "number of samples: 233\n" - ] - } - ], - "source": [ - "dates = vaud_report.date_range\n", - "qty = vaud_report.total_quantity\n", - "nsamps = vaud_report.number_of_samples\n", - "\n", - "\n", - "print(f'date range : {dates}\\nquantity : {qty}\\nnumber of samples: {nsamps}')" - ] - }, - { - "cell_type": "markdown", - "id": "d29325ec-aad9-4781-aba2-58d5626261c4", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Fail rate\n", - "\n", - "The fail rate is best understood as the chance of finding at lease one of a particular object at a survey:\n", - "\n", - "__What is the chance of finding at least one candy wrapper at an inventory ?__\n", - "\n", - "```python\n", - "fail_rate = vaud_report.fail_rate()\n", - "fail_rate.loc['G30']\n", - "```\n", - "\n", - "The fail rate (the chance of finding at leas one) for snack wrappers is 91%, there were 233 samples and there was at least one wrapper at 214 samples:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f205f754-a21b-48b7-a33b-723d9a637371", - "metadata": { - "editable": true, - "jupyter": { - "source_hidden": true - }, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "sample_id 233.000000\n", - "fails 214.000000\n", - "rate 0.918455\n", - "Name: G30, dtype: float64" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fail_rate = vaud_report.fail_rate()\n", - "fail_rate.loc['G30']" - ] - }, - { - "cell_type": "markdown", - "id": "89efab4a-124f-4218-8367-3c679d6ce306", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Sample results\n", - "\n", - "The sample results are the aggregated daily totals for all samples. All statistics will originate from here.\n", - "\n", - "```python\n", - "vaud_samples = vaud_report.sample_results\n", - "```\n", - "\n", - "The sample results are best viewed in a scatterplot" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "64b29bcb-8fe0-48c7-8c39-310d9489e8d2", - "metadata": { - "editable": true, - "jupyter": { - "source_hidden": true - }, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "image/png": 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ejTlz5mDo0KE4ffo02rRp0+T1mxIVFQUA+N///ue0z9CKiIiAn58frly5YjTv8uXLBuWJjIw0+xuyRht8mfscPz8/RERE2FV2cyQSCaZNm4ZXX31Vd7wBQFBQkNFvBKgPQLTb2HA9powfPx6ffvoptm3bhsTERBw8eBArVqzQzY+IiNBlyp588kmT60hKStL9feutt6JLly5YtmwZQkJCcPjwYXz00UcGyw8ePBhLly7FgQMHcMstt1jeASZs2rQJZWVl2LBhg8FvytT4R9aONwAYNmwYhg0bhqqqKhw4cABZWVkYPXo02rZti969e9tdPqLmxmcyPaZO6sBf1VXau2ftCbXhBf9f//qX08r28ccfG/y/b98+XLhwwWLvmKFDh0IIgUuXLqFnz55Gr65duzaqLI1Zr0QiQffu3bF48WKEh4fj8OHDAOqrbIKCgnD06FGD5T/77DObyqKf+TAnODgYd9xxB2bPng2NRoMTJ07YuKX2Gzx4MAICAnDu3DmT+6Znz54GZQeMM4O2Cg4ORq9evbBhwwaDddTV1eGjjz5C69atdWPkDBgwAN99950uEwUAtbW1No0/dd1116FVq1ZYs2aNQTfysrIyrF+/Xtejy17mjrfLly9DrVbrjjegvvdWw9/I6dOn7a4CGzRoEFq1aoWVK1di5cqVCAoKwqhRo3TzFQoFBgwYgOzsbHTr1s3k99cwA/fUU0/hiy++wKxZsxATE4P77rvPYP60adMQHByMSZMmQaVSGZVJCGGxy7qp840QAu+9957F95g63vTJZDL069cPCxYsAACD3oVEvsxnMj2DBw9G69atcdddd+H6669HXV0djhw5gjfeeAMhISF4+umnAdS3p4mIiMATTzyBOXPmIDAwEB9//DF+/vlnp5Xt0KFDmDhxIu677z5cvHgRs2fPRqtWrTBp0iSz70lPT8djjz2G8ePH49ChQ+jbty+Cg4Nx5coVfP/99+jatSv+/ve/210WW9e7ZcsWLF++HPfccw/atWsHIQQ2bNiA4uJiDBw4EAB0bYM++OADtG/fHt27d8ePP/6INWvW2FSWrl27YsOGDVixYgV69OgBPz8/9OzZE//3f/8HuVyO9PR0xMXFIS8vD1lZWVAqlbp2Dc7Qtm1bvPrqq5g9ezZ+++033H777YiIiMAff/yBH3/8EcHBwboBCbXB4YIFC3DHHXfA398f3bp101WZ2SIrKwsDBw7EgAED8Nxzz0EqlWL58uU4fvw41q5dq7tgvvjii9i8eTNuvfVWvPzyy1AoFHj77beNhlgwxc/PDwsXLsRDDz2EoUOH4vHHH0dVVRVef/11FBcX64ZfsNdjjz2G4uJijBw5El26dIG/vz9+/fVXLF68GH5+fpgxY4Zu2TFjxuDhhx/GpEmTMHLkSFy4cAELFy5Ey5Yt7fpMf39/PPLII1i0aBHCwsIwYsQIo27kb775JjIyMtCnTx/8/e9/R9u2bVFSUoKzZ8/i888/x/bt2w2Wf/jhhzFr1izs3r0bL774otH3l5SUhE8++QQPPPAAbrjhBkyePFnXlu/kyZP44IMPIITA8OHDTZZ54MCBkEqlGDVqFKZPn47KykqsWLHCqErRluPt5Zdfxu+//47bbrsNrVu3RnFxMd58802DNolEPs8drafdYd26dWL06NEiJSVFhISEiMDAQJGYmCjGjBmj6/KptW/fPtG7d2+hUChEy5YtxcSJE8Xhw4eNeiKNHTtWBAcHG31Wv379ROfOnY2mt2nTRgwZMkT3v7bH0tatW8WYMWNEeHi4kMvl4s477xRnzpwxeK+5Xh8ffPCB6NWrlwgODhZyuVy0b99ePPLIIwZd403R9t66evWqyfnW1vvrr7+KUaNGifbt2wu5XC6USqW4+eabxapVqwzWo1KpxMSJE0VMTIwIDg4Wd911lzh//rxNvbeuXbsm7r33XhEeHi4kEomuN9Tq1avFgAEDRExMjJBKpSI+Pl7cf//94ujRoxa3Wdt76/XXXzeYbq5XkrZMBw8eNJi+adMmMWDAABEWFiZkMplo06aNuPfee8W3336rW6aqqkpMnDhRtGzZUld27bYBEE8++aRR+Uz1YtqzZ4+49dZbdd/DLbfcIj7//HOj9+7du1fccsstQiaTidjYWPH888+Ld99916Yu69pt6tWrlwgKChLBwcHitttuE3v37rVpP5nyzTffiAkTJohOnToJpVIpAgICRFxcnBgxYoRu6AGturo6sXDhQtGuXTsRFBQkevbsKbZv326295alzz99+rSux9i2bdtMLpOTkyMmTJggWrVqJQIDA0XLli1FWlqaQW9JfePGjRMBAQHi999/N/u5586dE5MmTRLJyclCJpMJuVwuOnXqJJ555hmD/W/qOP78889F9+7dRVBQkGjVqpV4/vnnxVdffSUAiB07dgghbDvetmzZIu644w7RqlUrIZVKRXR0tLjzzjt1ww4QkRASIRw0NCrZbdWqVRg/fjwOHjxoUDVCRJ5Bo9Ggbdu2yMjIwH//+193F4eImshnqreIiGx19epVnDp1CitXrsQff/yhayhMRN6NQQ8RUQNffPEFxo8fj7i4OCxfvtygmzoReS9WbxEREZFP8Jku60REROTbGPQQERGRT2DQQ0RERD6h2Tdkrqurw+XLlxEaGmp2+HoiIiLyLEIIlJSUID4+3uCByE1dqdtUV1eL2bNni7Zt24qgoCCRlJQkXnnlFVFbW6tbpq6uTsyZM0fExcWJoKAg0a9fP3H8+HGbP+PixYu6wcr44osvvvjiiy/vel28eNFhcYdbMz0LFizAO++8g9WrV6Nz5844dOgQxo8fD6VSqXssxMKFC7Fo0SKsWrUKHTp0wLx58zBw4ECcOnUKoaGhVj9Du8zFixcRFhbm1O0hIiIix1Cr1UhISLDpWm8rt3ZZHzp0KGJiYvD+++/rpo0cORIKhQL/+c9/IIRAfHw8pk6dqntWT1VVFWJiYrBgwQI8/vjjVj9DrVZDqVRCpVIx6CEiIvISzrh+u7Uhc0ZGBr777jucPn0aAPDzzz/j+++/x5133gkAyMnJQV5eHgYNGqR7j/bpwfv27TO5zqqqKqjVaoMXERERkVurt2bMmAGVSoXrr78e/v7+qK2txT/+8Q+MGjUKAJCXlwcAiImJMXhfTEwMLly4YHKdWVlZuiddExEREWm5NdOzbt06fPTRR1izZg0OHz6M1atX45///CdWr15tsFzDXldCCLM9sWbNmgWVSqV7Xbx40WnlJyIiIu/h1kzP888/j5kzZ+LBBx8EAHTt2hUXLlxAVlYWxo4di9jYWAD1GZ+4uDjd+/Lz842yP1oymQwymcz5hSciIiKv4tZMT3l5uVHfe39/f9TV1QEAkpKSEBsbi23btunmazQa7Nq1C2lpaS4tKxEREXk3t2Z67rrrLvzjH/9AYmIiOnfujOzsbCxatAgTJkwAUF+tNXXqVGRmZiIlJQUpKSnIzMyEQqHA6NGj3Vl0IiIi8jJuDXqWLl2Kl156CZMmTUJ+fj7i4+Px+OOP4+WXX9YtM336dFRUVGDSpEkoKipCr169sHXrVof22yciIqLmz63j9LgCx+khIiLyPs1unB4iIiIiV2HQQ0RERD6h2T9lnYgcR1WuQUGpBurKaoTJAxEVLIVSIXV3sYiIbMKgh4hscrm4AjPWH8WeMwW6aX1TojB/ZDfEh8vdWDIiItuweouIrFKVa4wCHgDYfaYAM9cfhapc46aSERHZjkEPEVlVUKoxCni0dp8pQEEpgx4i8nwMeojIKnVltcX5JVbmExF5AgY9RGRVWFCgxfmhVuYTEXkCBj1EZFVUiBR9U6JMzuubEoWoEPbgIiLPx6CHiKxSKqSYP7KbUeDTNyUKC0Z2Y7d1IvIK7LJORDaJD5dj6ahUFJRqUFJZjdCgQESFcJweIvIeDHqIyGZKBYMcIvJerN4iIiIin8Cgh4iIiHwCgx4iIiLyCQx6iIiIyCcw6CEiIiKfwKCHiIiIfAKDHiIiIvIJDHqIiIjIJzDoISIiIp/AoIeIiIh8AoMeIiIi8gkMeoiIiMgnMOghIiIin8Cgh4iIiHwCgx4iIiLyCQx6iIiIyCcw6CEiIiKfwKCHiIiIfAKDHiIiIvIJDHqIiIjIJzDoISIiIp/g1qCnbdu2kEgkRq8nn3wSACCEwNy5cxEfHw+5XI7+/fvjxIkT7iwyEREReSm3Bj0HDx7ElStXdK9t27YBAO677z4AwMKFC7Fo0SIsW7YMBw8eRGxsLAYOHIiSkhJ3FpuIiIi8kFuDnpYtWyI2Nlb32rJlC9q3b49+/fpBCIElS5Zg9uzZGDFiBLp06YLVq1ejvLwca9ascWexiYiIyAt5TJsejUaDjz76CBMmTIBEIkFOTg7y8vIwaNAg3TIymQz9+vXDvn37zK6nqqoKarXa4EVERETkMUHPpk2bUFxcjHHjxgEA8vLyAAAxMTEGy8XExOjmmZKVlQWlUql7JSQkOK3MRERE5D08Juh5//33cccddyA+Pt5gukQiMfhfCGE0Td+sWbOgUql0r4sXLzqlvERERORdAtxdAAC4cOECvv32W2zYsEE3LTY2FkB9xicuLk43PT8/3yj7o08mk0EmkzmvsEREROSVPCLTs3LlSkRHR2PIkCG6aUlJSYiNjdX16ALq2/3s2rULaWlp7igmEREReTG3Z3rq6uqwcuVKjB07FgEBfxVHIpFg6tSpyMzMREpKClJSUpCZmQmFQoHRo0e7scRERETkjdwe9Hz77bfIzc3FhAkTjOZNnz4dFRUVmDRpEoqKitCrVy9s3boVoaGhbigpEREReTOJEEK4uxDOpFaroVQqoVKpEBYW5u7iEBERkQ2ccf32iDY9RERERM7GoIeIiIh8AoMeIiIi8gkMeoiIiMgnMOghIiIin8Cgh4iIiHyC28fpISIi11CVa1BQqoG6shph8kBEBUuhVEjdXSwil2HQQ0TkAy4XV2DG+qPYc6ZAN61vShTmj+yG+HC5G0tG5Dqs3iIiauZU5RqjgAcAdp8pwMz1R6Eq17ipZESuxaCHiKiZKyjVGAU8WrvPFKCglEEP+QYGPUREzZy6stri/BIr84maCwY9RETNXFhQoMX5oVbmEzUXDHqIiJq5qBAp+qZEmZzXNyUKUSHswUW+gUEPEVEzp1RIMX9kN6PAp29KFBaM7MZu6+Qz2GWdiMgHxIfLsXRUKgpKNSiprEZoUCCiQjhOD/kWBj1ERD5CqWCQQ76N1VtERETkExj0EBERkU9g0ENEREQ+gUEPERER+QQGPUREROQTGPQQERGRT2DQQ0RERD6BQQ8RERH5BAY9RERE5BMY9BAREZFPYNBDREREPoFBDxEREfkEBj1ERETkExj0EBERkU9g0ENEREQ+gUEPERER+QS3Bz2XLl3Cww8/jMjISCgUCtxwww346aefdPOFEJg7dy7i4+Mhl8vRv39/nDhxwo0lJiIiIm/k1qCnqKgI6enpCAwMxFdffYWTJ0/ijTfeQHh4uG6ZhQsXYtGiRVi2bBkOHjyI2NhYDBw4ECUlJe4rOBEREXkdiRBCuOvDZ86cib1792LPnj0m5wshEB8fj6lTp2LGjBkAgKqqKsTExGDBggV4/PHHrX6GWq2GUqmESqVCWFiYQ8tPREREzuGM67dbMz2bN29Gz549cd999yE6Ohqpqal47733dPNzcnKQl5eHQYMG6abJZDL069cP+/btc0eRiYiIyEu5Nej57bffsGLFCqSkpOCbb77BE088gaeeegoffvghACAvLw8AEBMTY/C+mJgY3byGqqqqoFarDV5EREREAe788Lq6OvTs2ROZmZkAgNTUVJw4cQIrVqzAI488oltOIpEYvE8IYTRNKysrC6+88orzCk1EREReya2Znri4OHTq1MlgWseOHZGbmwsAiI2NBQCjrE5+fr5R9kdr1qxZUKlUutfFixedUHIiIiLyNm4NetLT03Hq1CmDaadPn0abNm0AAElJSYiNjcW2bdt08zUaDXbt2oW0tDST65TJZAgLCzN4EREREbm1emvatGlIS0tDZmYm7r//fvz4449499138e677wKor9aaOnUqMjMzkZKSgpSUFGRmZkKhUGD06NHuLDoRERF5GbcGPTfddBM2btyIWbNm4dVXX0VSUhKWLFmChx56SLfM9OnTUVFRgUmTJqGoqAi9evXC1q1bERoa6saSExERkbdx6zg9rsBxeoiIiLxPsxunh4iIiMhVGPQQERGRT2DQQ0RERD6BQQ8RERH5BAY9RERE5BMY9BAREZFPYNBDREREPoFBDxEREfkEBj1ERETkExj0EBERkU9g0ENEREQ+gUEPERER+QQGPUREROQTGPQQERGRT2DQQ0RERD6BQQ8RERH5BAY9RERE5BMY9BAREZFPYNBDREREPoFBDxEREfkEBj1ERETkExj0EBERkU9g0ENEREQ+gUEPERER+QQGPUREROQTGPQQERGRT2DQQ0RERD6BQQ8RERH5BAY9RERE5BMY9BAREZFPYNBDREREPoFBDxEREfkEBj1ERETkE9wa9MydOxcSicTgFRsbq5svhMDcuXMRHx8PuVyO/v3748SJE24sceOoyjU4l1+K7NwinLtaClW5xt1FIiIi8jkB7i5A586d8e233+r+9/f31/29cOFCLFq0CKtWrUKHDh0wb948DBw4EKdOnUJoaKg7imu3y8UVmLH+KPacKdBN65sShfkjuyE+XO7GkhEREfkWt1dvBQQEIDY2Vvdq2bIlgPosz5IlSzB79myMGDECXbp0werVq1FeXo41a9a4udS2UZVrjAIeANh9pgAz1x9lxoeIiMiF3B70nDlzBvHx8UhKSsKDDz6I3377DQCQk5ODvLw8DBo0SLesTCZDv379sG/fPrPrq6qqglqtNni5S0Gpxijg0dp9pgAFpQx6iIiIXMWtQU+vXr3w4Ycf4ptvvsF7772HvLw8pKWlobCwEHl5eQCAmJgYg/fExMTo5pmSlZUFpVKpeyUkJDh1GyxRV1ZbnF9iZT4RERE5jluDnjvuuAMjR45E165d8be//Q1ffPEFAGD16tW6ZSQSicF7hBBG0/TNmjULKpVK97p48aJzCm+DsKBAi/NDrcwnIiIix3F79Za+4OBgdO3aFWfOnNH14mqY1cnPzzfK/uiTyWQICwszeLlLVIgUfVOiTM7rmxKFqBCpi0tERETkuzwq6KmqqsIvv/yCuLg4JCUlITY2Ftu2bdPN12g02LVrF9LS0txYStspFVLMH9nNKPDpmxKFBSO7Qalg0ENEzQ+H6SBP5dYu68899xzuuusuJCYmIj8/H/PmzYNarcbYsWMhkUgwdepUZGZmIiUlBSkpKcjMzIRCocDo0aPdWWy7xIfLsXRUKgpKNSiprEZoUCCiQqQMeIioWeIwHeTJ3Br0/P777xg1ahQKCgrQsmVL3HLLLThw4ADatGkDAJg+fToqKiowadIkFBUVoVevXti6davXjNGjpVQwyCGi5s/aMB1LR6XyXEhuJRFCCHcXwpnUajWUSiVUKpVb2/cQETV35/JLcduiXWbnf/dMP7SPDnFhicibOeP67VFteoiIyHtxmA7ydAx6iIjIIThMB3k6Bj1EROQQHKaDPB2DHiIicggO00Gezu1PWSciouaDw3SQJ2PQQ0REDsVhOshTsXqLiIiIfAKDHiIiIvIJDHqIiIjIJzDoISIiIp/AoIeIiIh8AoMeIiIi8gkMeoiIiMgnMOghIiIin8Cgh4iIiHwCgx4iIiLyCQx6iIiIyCcw6CEiIiKfwKCHiIiIfAKDHiIiIvIJDHqIiIjIJzDoISIiIp/AoIeIiIh8AoMeIiIi8gkMeoiIiMgnMOghIiIinxDQ2Df++OOP2LlzJ/Lz81FXV2cwb9GiRU0uGBEREZEjNSroyczMxIsvvojrrrsOMTExkEgkunn6fxMRERF5ikYFPW+++SY++OADjBs3zsHFISIiInKORrXp8fPzQ3p6uqPLQkREROQ0jQp6pk2bhrffftvRZSEiIiJymkZVbz333HMYMmQI2rdvj06dOiEwMNBg/oYNGxxSOCIiIiJHaVTQM2XKFOzYsQMDBgxAZGQkGy8TERGRx2tU0PPhhx9i/fr1GDJkiMMKkpWVhRdeeAFPP/00lixZAgAQQuCVV17Bu+++i6KiIvTq1Qtvv/02Onfu7LDPJSIiIt/QqDY9LVq0QPv27R1WiIMHD+Ldd99Ft27dDKYvXLgQixYtwrJly3Dw4EHExsZi4MCBKCkpcdhnExERkW9oVNAzd+5czJkzB+Xl5U0uQGlpKR566CG89957iIiI0E0XQmDJkiWYPXs2RowYgS5dumD16tUoLy/HmjVrmvy53kpVrsG5/FJk5xbh3NVSqMo17i6Sx+C+ISIiSxpVvfXWW2/h3LlziImJQdu2bY0aMh8+fNjmdT355JMYMmQI/va3v2HevHm66Tk5OcjLy8OgQYN002QyGfr164d9+/bh8ccfN7m+qqoqVFVV6f5Xq9U2l8XTXS6uwIz1R7HnTIFuWt+UKMwf2Q3x4XI3lsz9uG+IiMiaRgU999xzj0M+/JNPPsHhw4dx8OBBo3l5eXkAgJiYGIPpMTExuHDhgtl1ZmVl4ZVXXnFI+TyJqlxjdFEHgN1nCjBz/VEsHZUKpULqptK5F/cNERHZwq6g5/Tp0+jQoQPmzJnT5A++ePEinn76aWzduhVBQUFml2vYM0wIYbG32KxZs/DMM8/o/ler1UhISGhyed2toFRjdFHX2n2mAAWlGp+9sHPfEBGRLexq05OamoqOHTtixowZ2L9/f5M++KeffkJ+fj569OiBgIAABAQEYNeuXXjrrbcQEBCgy/BoMz5a+fn5RtkffTKZDGFhYQav5kBdWW1xfomV+c0Z9w0REdnCrqCnsLAQCxcuRGFhIYYPH46YmBg8+uij2Lx5MyorK+364Ntuuw3Hjh3DkSNHdK+ePXvioYcewpEjR9CuXTvExsZi27ZtuvdoNBrs2rULaWlpdn1WcxAWFGhxfqiV+c0Z9w0REdnCrqAnKCgId911F/7973/jypUr2LhxI1q2bImZM2ciMjISw4YNwwcffID8/Hyr6woNDUWXLl0MXsHBwYiMjESXLl0gkUgwdepUZGZmYuPGjTh+/DjGjRsHhUKB0aNHN3qDvVVUiBR9U6JMzuubEoWoEN+tvuG+ISIiWzSqyzpQ39YmLS0N8+fPx8mTJ3HkyBH07dsXq1atQkJCgkOezTV9+nRMnToVkyZNQs+ePXHp0iVs3boVoaGhTV63t1EqpJg/spvRxb1vShQWjOzm021WuG+IiMgWEiGEcPRKCwsLce3aNaSkpDh61XZTq9VQKpVQqVTNon2PqlyDglINSiqrERoUiKgQKS/qf+K+ISJqPpxx/W5Ul/XVq1cjKipK9xiK6dOn491330WnTp2wdu1atGnTBpGRkQ4pIBlSKnghN4f7hoiILGlU9VZmZibk8voB3/bv349ly5Zh4cKFiIqKwrRp0xxaQCIiIiJHaFSm5+LFi0hOTgYAbNq0Cffeey8ee+wxpKeno3///o4sHxEREZFDNCrTExISgsLCQgDA1q1b8be//Q1Afe+uiooKx5WOiIiIyEEalekZOHAgJk6ciNTUVJw+fVrXtufEiRNo27atI8tHRERE5BCNyvS8/fbb6N27N65evYr169frGi3/9NNPGDVqlEMLSEREROQITumy7kmaW5d1IiIiX+CM63ejMj0rV67Ep59+ajT9008/xerVq5tcKCIiIiJHa1TQM3/+fERFGQ/7Hx0djczMzCYXioiIiMjRGhX0XLhwAUlJSUbT27Rpg9zc3CYXioiIiMjRGhX0REdH4+jRo0bTf/75Z47ETERERB6pUV3WH3zwQTz11FMIDQ1F3759AQC7du3C008/jQcffNChBfQ12udHqSurESYPRFQwH61ARETkCI0KeubNm4cLFy7gtttuQ0BA/Spqa2sxduxYtulpgsvFFZix/ij2nCnQTeubEoX5I7shPlzuxpIRERF5vyZ1WT9z5gyys7Mhl8vRrVs3tGnTxpFlcwhv6bKuKtdg8tpsg4BHq29KFJaOSmXGh4iIfIbHPGUdAN5//30sXrwYZ86cAQCkpKRg6tSpmDhxokMK5msKSjUmAx4A2H2mAAWlGgY9RERETdCooOell17C4sWLMWXKFPTu3RtA/dPWp02bhvPnz2PevHkOLaQvUFdWW5xfYmU+ERERWdaooGfFihV47733DB45cffdd6Nbt26YMmUKg55GCAsKtDg/1Mp8IiIisqxRXdZra2vRs2dPo+k9evRATU1Nkwvli6JCpOibYjjgo0Lqj8m3JmPNxF5QVWhw7mopVOUaN5WQiIjIuzUq6Hn44YexYsUKo+nvvvsuHnrooSYXyhcpFVLMH9lNF/gopP54a1QqsnOLMPrfP2DEiv247Y1dmLI2G5eLK9xcWiIiIu/TqN5bU6ZMwYcffoiEhATccsstAIADBw7g4sWLeOSRRxAY+FdVzKJFixxX2kbwlt5bWtpxeuqEwKufn8Ces4VGy7A3FxERNXce03vr+PHjuPHGGwEA586dAwC0bNkSLVu2xPHjx3XLSSQSBxTRtygV9YMRnssvNRnwAOzNRURE1BiNCnp27Njh6HJQA+zNRURE5FiNatNDzsfeXERERI7FoMdDmerNpdU3JQpRIazaIiIisgeDHg/VsDeXVt+UKCwY2Y3teZo5VbkG5/JLkZ1bxKEKiIgcpNGPoSDniw+XY+moVBSUalBSWY3QoEBEhfCp680dHzxLROQczPR4OKVCivbRIbghMQLto0MY8DRzqnKNUcAD1PfYm7n+KDM+RERNwKCHyIPY8uBZIiJqHAY9RB6EQxUQETkPgx4iD8KhCoiInIdBD5EH4VAFRETOw6CHyINwqAIiIudxa9CzYsUKdOvWDWFhYQgLC0Pv3r3x1Vdf6eYLITB37lzEx8dDLpejf//+OHHihBtLTOR82qEKvnumHzZNSsN3z/TD0lGpiGN3dSKiJnFr0NO6dWvMnz8fhw4dwqFDh3Drrbdi2LBhusBm4cKFWLRoEZYtW4aDBw8iNjYWAwcORElJiTuLTeR0HKqAiMjxJEII4e5C6GvRogVef/11TJgwAfHx8Zg6dSpmzJgBAKiqqkJMTAwWLFiAxx9/3Kb1OePR9ERERORczrh+e0ybntraWnzyyScoKytD7969kZOTg7y8PAwaNEi3jEwmQ79+/bBv3z6z66mqqoJarTZ4EREREbk96Dl27BhCQkIgk8nwxBNPYOPGjejUqRPy8vIAADExMQbLx8TE6OaZkpWVBaVSqXslJCQ4tfxERETkHdwe9Fx33XU4cuQIDhw4gL///e8YO3YsTp48qZsvkUgMlhdCGE3TN2vWLKhUKt3r4sWLTis7EREReQ+3P3BUKpUiOTkZANCzZ08cPHgQb775pq4dT15eHuLi4nTL5+fnG2V/9MlkMshkMucWmoiIiLyO2zM9DQkhUFVVhaSkJMTGxmLbtm26eRqNBrt27UJaWpobS0hERETeyK2ZnhdeeAF33HEHEhISUFJSgk8++QQ7d+7E119/DYlEgqlTpyIzMxMpKSlISUlBZmYmFAoFRo8e7c5iExERkRdya9Dzxx9/YMyYMbhy5QqUSiW6deuGr7/+GgMHDgQATJ8+HRUVFZg0aRKKiorQq1cvbN26FaGhoe4stk9RlWtQUKqBurIaYfJARAVLOWYMERF5JY8bp8fROE5P410ursCM9Uex50yBblrflCjMH9kN8RwdmIiInKhZj9NDnkVVrjEKeABg95kCzFx/FKpyjZtKRkRE1Dhu771FnqmgVGMU8GjtPlOAglKN11VzsaqOiMi3Meghk9SV1Rbnl1iZ72lYVUdERKze8hCqcg3O5ZciO7cI566Wur36KCwo0OL8UCvzPQmr6oiICGCmxyN4YhYiKkSKvilR2G2iiqtvShSiQrynWqg5VtUREZH9mOlxM0/NQigVUswf2Q19U6IMpvdNicKCkd28KkhoblV1RETUOMz0uJknZyHiw+VYOioVBaUalFRWIzQoEFEh3tf4tzlV1RERUeMx6HEzT89CKBXeF+Q01Jyq6oiIqPFYveVmzEI4X3OqqiMiosZjpsfNmIVwjeZSVUdERI3HTI+bMQvhOkqFFO2jQ3BDYgTaR4dw3xIR+RhmejwAsxBERETOx6DHQzSHBsNERESejEEPuRSff0VERO7CoIdcxhNHniYiIt/BhszkEp468jRRc+Zpz/QjcjdmesglPHnkaaLmiJlVImMMesglPH3kaaLmxFxm9dCFIuw6fRU920SgtKqG7erI5zDoIZfgyNNErmMqs6qQ+uOtUalYuTcHszYc001n9od8Cdv0kEtoR542hSNPEzmWqczqhIwkrNybg71nCw2ms10d+RIGPeQSHHmayHVMZVZTE8KNAh4tbbs6ouaO1VvkMhx5msg1TD3Tr6qmzuJ72K6OfAEzPeRSfP4VkfOZyqzKAiyf7tmujnwBMz0ezNNHL/b08hH5soaZ1QiFcfZHi+3qyFcw6PFQnj7GhqeXj4iMn+k3f2Q3zFx/1CDwYbs68iUSIYRwdyGcSa1WQ6lUQqVSISwszN3FsYmqXIPJa7NNDubXNyUKS0eluvUE5enlIyLztBlatqsjT+eM6zczPR7I00cv9vTyEZF5DbM/RL6EDZk9kKePXuzp5SMiIjKFQY8H8vTRiz29fERERKYw6PFAnj56saeXj4iIyBQGPR7I00cv9vTyERERmcLeWx7M03tZeHr5iIjIe7H3lo/x9F4Wnl4+IiIifW6t3srKysJNN92E0NBQREdH45577sGpU6cMlhFCYO7cuYiPj4dcLkf//v1x4sQJN5WYiIiIvJVbg55du3bhySefxIEDB7Bt2zbU1NRg0KBBKCsr0y2zcOFCLFq0CMuWLcPBgwcRGxuLgQMHoqSkxI0lJyIiIm/jUW16rl69iujoaOzatQt9+/aFEALx8fGYOnUqZsyYAQCoqqpCTEwMFixYgMcff9zqOr25TQ8REZGvcsb126N6b6lUKgBAixYtAAA5OTnIy8vDoEGDdMvIZDL069cP+/btM7mOqqoqqNVqgxcRERGRxwQ9Qgg888wzyMjIQJcuXQAAeXl5AICYmBiDZWNiYnTzGsrKyoJSqdS9EhISnFtwIiIi8goeE/RMnjwZR48exdq1a43mSSQSg/+FEEbTtGbNmgWVSqV7Xbx40SnlJfJGqnINzuWXIju3COeulkJVrnF3kYiIXMYjuqxPmTIFmzdvxu7du9G6dWvd9NjYWAD1GZ+4uDjd9Pz8fKPsj5ZMJoNMJnNugcljaMcKUldWI0weiKhgdqM353JxBWasP2rwsNi+KVGYP7Ib4sPlbiwZuROPIfIlbs30CCEwefJkbNiwAdu3b0dSUpLB/KSkJMTGxmLbtm26aRqNBrt27UJaWpqri0se5kpxBb48nofzhWW4oqrEhcJyfHk8D1eKK9xdNI+jKtcYBTwAsPtMAWauP8qMj4+6XFyByWuzcduiXRi+fB9ue2MXpqzNxmUeQ9RMuTXT8+STT2LNmjX47LPPEBoaqmuno1QqIZfLIZFIMHXqVGRmZiIlJQUpKSnIzMyEQqHA6NGj3Vl0cjNVuQYXrpVjy9HL2Hu2UDc9PTkSSVHBUEj9ebeqp6BUYxTwaO0+U4CCUg33l4+xFggvHZXK3wQ1O24NelasWAEA6N+/v8H0lStXYty4cQCA6dOno6KiApMmTUJRURF69eqFrVu3IjQ01MWlJU9SXF6NpdvPGAQ8AHT/Z97TlSdsPerKaovzS6zMp+aHgTD5IrcGPbYMESSRSDB37lzMnTvX+QUir1GmqTEKeLT2ni1EmabGxSXybGFBgRbnh1qZT80PA2HyRR7Te4vIHmWaWovzy63M9zVRIVL0TYkyOa9vShSiQnhH72ucHQizpyB5Io/ovUVkr7Agyz9dpZyZC31KhRTzR3bDzPVHsbtB760FI7uxGsMHaQPh3SaquJoaCLOnIHkqj3oMhTPwMRTNj6pcg60n/8DmI5ex56zxCbtPShSWsRGmSdruySWV1QgNCkRUCLsn+7LLxRVmA+G4RgYnqnINJq/NNtleqG9KFBtIk82ccf1mpoe8TkGpBnM2n8Cy0akABPbote3pkxyJ14Z14UnVDKWCQQ79JT5cjqWjUh0aCLOBNHkyBj3kddSV1SjX1GLymmxMyEjCuPQkVNXUQRbgh+yLxVBXaAAEu7uYRF7B0YEwG0iTJ2PQQ15H2wCzXFOLZdvPGs0ffkMrVxeJiP7kKz0FOZK1d2LQQ17HmQ0wiahpfOH4ZENt78Uu6+R1tD2RGnbBZk8kIvdr7scnH+ni3Zjp8UBMm1rnjAaYROQYzfn4ZENt78agx8MwbWo79kQisp2rb6aa6/HJhtrejUGPB+EDAInIGXgz5Ti+0lC7uWKbHg9iS9qUiMgejmyDwkdL8JEu3o6ZHg/SmLQp2/8QkSWOaoPCbFE9PtLFuzHo8SD2pk1tOQkxKCLybY5og8Kqd0PNuaF2c8egx4PYM76FLSehMk1tk+/MGDQReTdHtEFhjyVjzbWhdnPHNj0exJ7xLaydhPJLqppcj3+5uAKT12bjtkW7MHz5Ptz2xi5MWZuNy8UVjdg6InIHR7RBYY8lai6Y6fEwtqZNrZ2EVBXVTboz85Z0tn4mSikPRLAsAKWVNcxMEf3JEW1Q2GOJmgsGPR7IlrSptZOQQupvcb61OzNvSGfrt2lSSP3x1qhUrNybg716T1339oaWrF4kR2hqGxRfeLQE+QYGPV7K2kkoWGr5q7V2Z+bp6eyGmagJGUlGAQ/geZkpe7C3DDlSU9qgsMcSNRcMeryUtZOQQurfpDszT09nN8xEpSaEm3ziOuA5mSl7eEv1IvkO9lii5oBBjxezdhKydmdmqerE09PZDTNRVTV1Fpd3d2bKXt5QvUi+SwCAxN2lILIfgx4vZyllbSkoslZ14unp7IaZKFmA5Y6I7s5M2cvTqxfJ97C6lZoDBj3NnKmgyNaqE3vT2a5sdNswE5V9sRjpyZFGbXoAz8hM2cvTqxfJ+9lzvLK6lZoLBj0eyNnBgz1VJ7Y2fnT1XWDDTNQH3+fgrVGpkAD4vkHvLWdlppz5PXl69SJ5N3uPV1a3UnPBoMfDuCJ4cHTVibvuAhtmosLkgXjj/htQWlnj9IaWzv6ePL16kbxXY45XVrdSc8Ggx4M05mTUmGyDo6tOXHkXaGp720eHGCwTE+aQj7JYBlcEeewtQ87QmOOV1a3UXDDo8SD2nowam21wdNWJq+4CPaUhpSuDPD7fhxytMccrq1upueCzt5xIVa7BufxSZOcW4dzVUqvPu7LnZGQt22Dps+x5xpctXHEX2JTtbcpnmvr+mOonb9aY49XR5wxb2XsOJbKGmR4naUxWwp6TUVOzDY6sOnHFXaCrG1Ja+v6Y6idv1tjj1dXVrZ6S2aXmhZkeJ2hsVsKepyE7ItugVNS3h7khMQLto0OaPES9M+8CXZldsfb9hQQFNPmp1UTu0pTj1VHnDGvckdkl+3ljJo6ZHidobFbCnh471rINQYH+yM4tctlDKp19F+jK7Iq176+sqoY9q8jjWerkECz1x0tDO6G4ohohUn8opAEIVwR6zG+XXeQ9n7dm4hj0OEFTshK2Bg+WUtQZyZHYcuyK7llUrvohOrPRrSsbUlr7/tQV1WjXMoQ9q8hjWbogSQBMNzNPqXBDYU1guznP5s2DVbq1emv37t246667EB8fD4lEgk2bNhnMF0Jg7ty5iI+Ph1wuR//+/XHixAn3FNYOTc1K2JJCNpeizkiOxLj0JHzwfY5uWnNICTuzCq1hijZEZtsT6l2V6ieyh7UL0s7TVxtdbeSq6gy2m/NstmTiPJVbMz1lZWXo3r07xo8fj5EjRxrNX7hwIRYtWoRVq1ahQ4cOmDdvHgYOHIhTp04hNDTUDSW2jauyEg2zQkGB/thy7AqeWpuNck2twbLNISXsjCo0U3fEWSO6ok9KlMmDmm12yNNZuyCNTWtrdp6lc4QrqzPYRd6zeXMmzq2ZnjvuuAPz5s3DiBEjjOYJIbBkyRLMnj0bI0aMQJcuXbB69WqUl5djzZo1biiteQ3vfgC4rHunfrahoroWy7afNQp4tDz5h2grR2ZXzN0Rv7blJJ4ckOzy7rnkXTy1Eae1C1JVTZ3ZeebOEa5uWOyuLvJkG2/OxHlsm56cnBzk5eVh0KBBumkymQz9+vXDvn378Pjjj7uxdH8xd/ezYGQ3l7f58OYfopYrH1pq7o64XFOLCasO4qun+qCmTrDNDhnx5Eac1s4DsgDz97rmzhHuaFjMEck9lzdn4jw26MnLywMAxMTEGEyPiYnBhQsXzL6vqqoKVVVVuv/VarVzCgjLdz8z/mzM1fARCc7kzT9EwPUXEkt3xOWaWhSVa3BDYoTDP9dWrgwAyXae3ojT0nmgT3Ik8tWVJt9n6RzhruoMjkjumbz52YAeG/RoSSQSg/+FEEbT9GVlZeGVV15xdrEAeF63Sm/+IbrjQmLtjjhMHui2wMOTMwm+ztOO+4aUCileHdYFszcdw96zhbrp6cmRGJ+RBAkkRm3WrJ0jHJ1F1j+uQmQBkPr7obhCg5AgBvfeIj5cjtfv646iMg3UlTUIkwcgQiFFTFiQu4tmkccGPbGxsQDqMz5xcXG66fn5+UbZH32zZs3CM888o/tfrVYjISHBKWX0xMZc7k4JNzZIcMeFxNId8cCO0ZD6+2Hy2myXBx6enknwdZ543DekqtAgNTECE9KTUFVTB1mAH7IvFmPymmwAwGdPpsNPIrH5HOHILLKpgD49ORLj05Mw6r0f0LNNBIN7L+CtN2YeG/QkJSUhNjYW27ZtQ2pqKgBAo9Fg165dWLBggdn3yWQyyGQyl5TRU9vQuCsl3JSDwNkXEnPBmLnM2Ny7O2PmhmNuCTw8PZPg6zz1uNcXIgvUjdNlip9EYlT1bumGxVFZZHMBvTYjNSEjCcu2n2Vw7+G8+cbMrUFPaWkpzp7968DMycnBkSNH0KJFCyQmJmLq1KnIzMxESkoKUlJSkJmZCYVCgdGjR7ux1H+JCpEia0RXRIfKUFVTh6BAfxzOLcIH3+egZ5sI3d2PL7TNaOpB4MwLibVgzFRmzJ2BhzdkEnyZN7Sds7eMttywOCKLbOm42nu2EBPSkwAwuPd03nxj5tag59ChQxgwYIDuf2211NixY7Fq1SpMnz4dFRUVmDRpEoqKitCrVy9s3brVY8boKdPU4sujV7DnrGGa9oNxN6FtCwWUCqnXpgDt1dSDwFkXEluDsYZl+62gzOJ6nRl4eEMmAfCNYN4Ub2g7Z08Z7blhaWoW2Z7u9AzuPZc335i5Nejp378/hBBm50skEsydOxdz5851XaFspDtRnDVO0/pLJFg6KtWrU4D2aupB4KwLSWODMXcGHt6QSfCVYN4cd7eds4WtZbTnGGlqoGtPd3pPCe7JmLfcmJnisW16PJ2tw3B7awrQXo44CJxxIWlsMObqwEP/YqKUByJzeFe8sPGYR2YSfCmYt8QbulPbUkZbjxFHBLqWjqv05EhkXyzWrdcTgnsyzRtuzMxh0NNItpwozOew/lqmuXDUQeDoC0ljgzFXVmGYupgM7BiNrBFdUVld53GZBG+uzydjthwjjgp0zR1X2t5bT63N9pjg3hpfrd4FvKOK1xwGPY3kiMyGuWW88WDy1IOgKcGYK6owzF1Mtv2Sj6qaOpcPcGkLb67PJ2O2HCOODHQbHlfBf47To6rQ4PPJGR4T3Fvi69W7gHdU8ZrCoKeRbL2Y2nvBbczBZC1IclUQ5eiDwBHlbmow5uwqDG/MmnhzfX5z46pjRL9hv0LqjwkZSUhNCNf1Wq2pM/88L3Of2fAcVVMn6gNqyV/LeCJW7/7FG6p4G2LQ00hKhRTz7umCFzYew/d6o55mJEdi3j1ddD8Eey64jTmYrAVJrr4j0T8ItCfk3wrK7D4hN7XcDdvIvH5fd5RW1njcHYk3Zk28uT6/OXHksW3thkUb6Cqk/nhrVCpW7s0xGAeoT3IUskZ0ResWCrduhyt4440K/UUiLHWfagbUajWUSiVUKhXCwsIctl5VuQbPfvozro8L093xaEc9PXVFjX/e193o4m/tgnsuvxS3Ldpl9jO/e6afQVWHqlxjNGKwVt+UKLx+X3c89+nPZuc74o7E3J1mU05k1rbLWrk9+STacH/V1QkMXLzb7PINv3NPcbm4wmwwH+eBF6rmpjHHiL1ZoYaPijh0oQj5JZX4MeeaweMttPqkRGGZneeUph7r7pCdW4Thy/eZnb9pUppbn9nXnDjj+s1MTyMVlGrw7S/5+PaXfJPzC8s0uuW0J5mkqGCLB7C9d/3W7jiKypx7R2IuuMga0bVJoxk35U7KXLbs0IUi7Dp9FT3bRKC0qsbuk74jqgVN7a+sEV2NnoOk5clZE2+tz28u7D1G7L0RuFxcgRn/MxySo09KFObc1Qn/2vWbyc/d04hzijdmTVi9690Y9DSSpQBFIfWHAOx+bpO9B5O1IEldWWNxflOqTvSDi4Z1/MXl1U06kTWlysfUSVQ/JT9rwzHddGsn/Zc/O67L5F1RVSJfEYjEFgq0irA/hW8uGHtty0l8MO4mSACPagBuC2+sz28u7DlG7K02V5VrjAIeoD6oeeXzk3isbzss+faM1c919HZ4ClbvejcGPY3UQiHF+2N7Gjx+4pMfc/HgzYkY1CkGcz87jj0NUsDWMh32HkxWnxIeZPnrbcodiTa4MFXHv/yhGy2+19qJrCl3UqZOohMykrByb45RSt7SSf/lz47jwZsTjdouZCRHYv6Ibna3XTB3R1uuqcWEVQfx1VN9UFMnmDUhm9hzjFjLplxRVwL4q+FwfkmVUcCjtedMAZ6+LcVs0GPvOcXadsil/gb/e0LPVk/tqUq2YdDTCJeLK/DipuMGJ4Zbr2+Jjyfegle3nEBqQrhRwKNlKdNh78FkLUiKCHbeHYk2uDAVUOiPqmqKtRNjU+6kTJ1EUxPCzT580dT3UVCqwfVxYSYDpe/PFmLWxmN2t12wdEdbrqlFUbmG7QDIZvYcI9ayKb9dLUPmF7/osp7FFZaXr637qxmofpYXAOqEgKrc9iopa4MVHs4tRmxYkMc90ofVu96LQY+dzD1+olO8Eq9uOYG9ZwvxUK82FtdhKdNhz8FkLkjqkxKFV4d1gQTAP+7pgotFFSiuqNZlpE5dUePVYV2adIBqgwtTAUX2xWKkJ0eabOxoS7Cl3a6GJ7j05EhMGpCMck0tlGYSLaZOovrP8zGl4fehqtBYDJRMtV2wdgfKdgDNgydkGgDTx75C6o+XhnbCjYnhf/aY1CAqWIpgmb/FdckC/AyynsFSy8uHBAWgT0oUfrpQZLInlz2BiFIhxavDumD2pmMG5wv9wQpvbtsCADBj/VH8dKEIk29NNuguv+v0VdzZJdYtGR8GOd6HQY+dGqaKtXc6gzvH6A78pmY67DmYtEFSnroSvxdVAKgPOu58aw96JEZg0oD2mPjhIZRragHUB0RZw7s2uYeNNrgwFVB88H0O3hqVCgAGJzJ70r/BUn/c2TUO49LaGvSMm7DqIHq2iTBbRWjqYmDv96GQBiBPXWXxPfqBki13oGwH4P08KdMAGN4glVVVI0wuxUubjhu1W5t5x/W49fqW2P7rVaN19NF79IM26xksDTB705KeHIkQaQCWjUpFcXk1XmwQrGjXY894NaoKDVITIzAhPcngWH9qbTbKNbW6Y61hkKU99/ZuF4kz+aWICJZ6xUCu5F4MeuyknyrWb8/SKe6v7nRNzXRYoyrXoLCsfjCvOiHgB2DelpNGVWp7zhagDgITMpJ0AdmeMwV4YeOxJncF1QYX5008jbxcU4un1mZjQkYSXhrSCZXVtXanfwtKNQYnb33WGkM3zJZFKOwLOPz8JFDKbcvM2PMUd0e3A/CUrIMv8NQB6bS/LV3X77PG5asVv2DG7dejqqbOKJvy0l2dcc/be3XTSiqrkRQVjCm3pgCA0fJTbk1BuCIQSsWfozQ3ohq/oRBZoNmsKlB/rKkrqw2q0s2NF+QpQ1OQ52LQYyf9agr9g3BCepJuur2ZjoYD6QXLAqCuqIa6shoRcik0dXUor6qBUiFFUIAfMr/8BcNvbK377PfH9jR78mlYNsBxXUHjw+Xw95OY7HJdrqnF0YvF+L+MpEZ9jiOe2q7/ufYEHAF+EoQGBSAjOdJg4EmtPnqBkj1dbh3ZDsDerAMDpKbx9K7Vlsq392whCkpNZ1NyCsp0WWCgPsBQKqRo00KBod3iDZbPL6lC2xYK3Xbaeoxa++3ZmgXVr3K21DlhxvqjeGloJ/j7Sfg7JyMMeuykf4DqH4T62R39TIc24EhsoUB0qMziiMr6dy/ZucV4a1QqFm07bXBgZyRH4rnB1+Ot7/6abq3Niqn5juoKGhMWhAVO6Mng6DYw9gQckcFSLNp6Cs8Nvh4SnDK4e85IjkTW8K52n/i17Km6NHexsDfr4GnVMt7I07tWWytfgL8EH3yfYxDgAMD7Y3vq/tYPMOLC5bizS6zB8dKzTYTN7dQUUn+0UEhxvqAML206ZnBT1jclCpnDu0JTWwdVRf1vO2tEV2R9+QvatgzRtdeJ+HOICO1nni/8K6tsrc3dxWvleHT1If7OyQiDHjspFVIsGNkNO09fRctQGZY/dCPkgf4ABIZ1j8ern5/Anj8Dn2Xbz1ocpbbhxUv/7mXyrclmew9JcArdE8N1dfTW2qyYmu/IhrPO6MngjDYwtgYcSoUULwzphDmfHUf3xHCMS69vVxQuD0SbSMNxepzVQNlSoFJZXWtz1sFTq2W8jac3RLdWPn+JBG+NStW1kwHqq6u07XlM3aRYO17MHaMKqT8+GHcT9p4rxJZjl023+dlwFDckRugCl4Edo/Hi0E54ceMxw8dbpERh7t2dIQHQOuKvc6itN3r8nVNDDHoaQQD48ugVgzYnGcmRmJCRhJ5JLTDuz+xOqwg54v7sbmlKw5S0/t2LxTuZswUYl95W97+lNkT6JzYtZzScdXRPBleOhWEqoxIfLsc/7+tuFMgB9Y8L0S4bEhSAgR2jsc3EyNyN3c/mBofTnsBfHNrJ4vv1sw6eXi3jLTy9Ibq1rt/7fytEdm6Rrn1fn5QozLunC0oqNBh+Q6tG3aSYO0ZfGtoJb28/i3HpbU2ek4D6m7fxetXu18WF4YWNxo2i95wpwMufHUdqYgRahct1Ven23Ojxd076GPTYSb/LuqmnDXeJV2LKn3dT2mfHmNMwJa1/92JPlZW5NkR9kqMwaUAyHl198K9pHjSAlrW6fleMhWGt6seWaqJ593QBAIPApynBWZ660uzgcLvPFKCuzvLj8vSzDp5eLeMtPH1AOkvDPGi7fpdrajHj9uuRmhCO7IvFmLflJF4Z1qVJVT+mjtE6ITBrwzGM6pVo8b365zBLN3nadolT1mbrRi+390aPv3PSYtBjJ0sjEQP1gYY2jWztDqNhSlr/7sTanYx+7yJtG6LH+rbD7Ds7QV1ZDYXUH0GB/vDzA9Y9dgvKNLUI+LNXkiMeCNnUhrG2tjNx5lgY9lT9WFr2xU3H8fp93THzDstPcbdln6nKNbqhB8zRBtS2ZB08vVrGm3j6gHTx4XK8NLQTLl4rN9n1GwB+L6rApI8P695TVWP8O2/YqaK0ssbib7bhMZqdWwTA+jksOlQGhdQf5Zpam27y9EcvrxUCw1NbYe7mE2aDPH38nZMWgx47WRqJGDDuJm7pDqNhSlr/7uXYJRX6JEea7JXVJzkK0WEyo7E3bmkXiawvDbuuN7zT++6Zfk3afqDpDWM9pZ2JPVU/1pYtrawxehq6/gUkWBqAn3KL8NqWk7oLkKl9VlCqsVpupTzQ5qyDp1fLeBtPHZBO+1urqa3Do6sPmV2uYSCi/zs316miYQ9Ua8e5NtC2lo05/UeJ7gaxYbkaZtETWygw+dZkfPB9jsHo5cv+DEJVFdWorK7Fvt8KDYI8bZn5OyctBj12sjQSsZZ+N3FLdxgNU+baaqqgAD90bx2OXkktUAfjKqvxGW2x4MtfMXtIJzw7qAaFZdVICJfj5c9OGAVJ2vdOyEjC0YvFDhkjqKkBi6e0MzFX9aM94VbV1CI7twhh8kDUCqG7KzWlYXBrKjBMT440aExqap+pK6stXiy03eWVCqlNWQdPr5ahptP/rU2+NRl9kqNMVo+aqvYB6n+7ljpV6LPlONcG2trzmR9g9kYsNTEcEzKSkH2xWDdEhLksuvb4CdPLcusHoZeLK/DOrnNGAQ9/56SPQY+dLI1ErKWQ+iMqRIqvns6ABMCJyyqUa2oRLg806rYuAXBHlziM/XPk4QCJBM8Nvg6ZX/yCn3KLdd3etenqP9SVOHZJhW9/zUdFTS1SEyNw9GIxXhrayWw7kL1nC/Fk/2Q8dHNikw9+RwQsTWln4sjxZkxV/ZittkyJMur9on83Wl0ncO5qKaKC68tiKjDUD0C16264z8KCAs220UpPjsRreo8PsTXr4OnVMtR4DYOVD77PwZr/uwUCwmCMKXPVPkD9jZmlThUN7T5TgEvFFSgo05it7tIG2k+tzcYnj92CcSVVJqvctDeIn/yYi8zhXfHipuPolhBuMuDae7YQEgBv3H+DyXLxd062YNBjJ0sjEQP1F8K3R9+Iqura+m7rO34xzNT8eecRHy6HqlyD6SYujvqDDZo68WjH1tAPZvL+fFKyOUGB/g5py2MtYCkq1+iyI+YCEnvamWiDnNKqaij/HGZfP7gzNeaHNvCwFhyZqvoxW215pgBC/FVtaWlE2NlDOlocKG5CepJBwFRYpgH+DJiiQqTo2SbCYJwn/cHhIhSNa5vgqdUy1DQNg5VyTS0mrj6ID8bdhL9X1kBVUY3oUBlO/1FiVO0D/FX181uD85m1NjbnC8sx6ePDBl3KI/XGkaqsrsXcuzojp7AMZVW1FqvclPJA/PO+7gDqe35pauvMBlzfny2EuqIaMWFBptfF3zlZwaCnEeLD5aiqqUWf5Cj89Gc3UG3dc0KEAscvFUMA+OLYFYOLp0Lqj+4J4ThfUIY8VQUUsgB0TwjHTxeKDE5G9vTc0gYz5qpdtKw9VsFW1gIWVUW17gRnrv7f1nYmDdP22blFVsf80I4R8vb2s0bBkalG0g2rfizd4R7OLcYLd3ZEakI4Av39EOAvQWpiBLJzi3X7f/eZAoy10hC5pk5YHEJ/wZ+9cBrOY5qeGtLehOgH0TV1AhJIECYPgNTfD8Eyf2QkRyG9faTZHoZhQYZtyWztEq7fpfzUFTVeGtoJszcdx54zBVj+0I2Y9PFhgwEQTYlQSFFSWYNZG45iz9lCLH/oRovL514rR7AsgAMOUqMw6GkkIeqzAk9Kk7F0+xndBer9sT0R/eddSMOAx1I9tf5dmD1jUGiDGVc1WLU2Hoh+mwFz9f+2tDNpmLa3FIzoj/kxISMJS7efMRkczfnsOOYN72rUG0U/JV5tpju49vvL+vIXo/YJ+t+fQuqPlqEyvD+2p24Yg8O5RQaj4cYpg7Dg618ttpdgmp5sERYUaHBu0VaNzv/aOMP8j+Fd8cKdHaGqMPxNqco1CGjwOBl7uoRrs5eB/hJ8f7YA49LaYtTNiUhoobC6rj7JUQj0l+gCHsD6+Q8ABxykRmPQ00iRwVL8dKEIm49cMjiYzWVpzFWbmGrnYesJRz+YcVWDVXOfY67NgLl2Ptbq3xum7W3NfpkLjhRSfzxwcyKe++8R3clVIfXHS0M74cbEcJRrahEmDzQ7Bo4t35/2grPw61+N2lNoA6PUxHBoGjz40dT+ah8dwhM6WRUVIsVLQztZHcl9z5kCzN5wDK8O64ykqGCDxr8z1h/VPcG8TgjsPVuo+y1LAJvaBtXUCdzROQ6vbDmh++zJtyYjIznSYhu1selt8dvVMoMbCWvnv2OXVOiWEI4rqkr8VlDGZ8mRXRj0NEHX1ko8/7+jBtPM3aXY2tsLqG+M+P7YnhZ7PfRJicKcuzvXtwdBfTDiqoZ8DT9HGuCHL4/nmWwzAJhvmGyp/r1h2yFbs1+2Bp36d8f6I2tnjehq8gGqtnx/1gKjl4Z2Qu92kcgvqbK4LRxIjWylVEhxY2K47jdsbST33wrKMGfzCcwf2Q3BUn+DbKq2HdmjGe0QLPWHEALj05MwIaMdFFJ/lFXVGI37oxWvlOM1vYAH0B80NUe37ml/6wBVRf3vW7subXse4/cZB0mPZiRBAgn+/f1vRtW/rw7rAlWFBiFBDILIPAY9jaC9OxptYsTR7IvFiFfWV2/p363Y006nR2IEKqvr0KNt/SMtNLV1aBkigzTAD1dUlfj08d746kQe7lr6vW6gOv0RhF1xsOt/zrn8UrMnWqBxA4M1bDtka/bL1qDTXIDy2paTulFfTVXhmVNVU2c1MHppaCe0jQpGrR0jKhNZY097wGBZALolhGPOZ8cx/fbrjRpBa3+/Cqk/PvlzUNOqmlq0baHAnB1nTTbQ75MciUB/idFwGfoPXn7hzk44X1gGWaAfHn3HsFFzw2O24QObg2UBuoDr2CUVfsy5ZrJqePamY0j9s20fHzRK5jDosZN+W5NxaW2N5n/wfQ7eHn0jFFI/TB6QDKD+gmctU5EUFaxr8NciWIqH/v2D2cbJyx+60eDi6u6H6jmjPVHDdZq7+8tIjsQ4vXS7ueCo4cXAXICiP+prTZ0wGFrfElmAn9ULTnlVrclt02fr/nJk133ybvo3CNbOM2VVNcjOLcL49CQUV5jPKJZravF7UQWe+/RnvDUqFZlfnsTYtLa66i8t7aNucgpN92bVBlL9OrTEc5/+bJTVAUwfs9r3ZSRHGjyY9P2xPbF42xmTn6WfMXf3OZE8F4MeO+k/hgKA0UBg5ZpaPLnmMF65qzN6to3AK3d3RlV1HfwbNBTUl54cCQmAnm0iUFCqQVVNrcXeWKZObO58qJ6tDZPtuUg3XKf27u+loZ3w8tBOqNDUIjQoEEGBfpi7+YRuf33wfQ4+GHcT/CQSg30d3qD3mqUApVxTazDqK1AfZJgLVPqkRKFVuByWw6K/Gp03tf1VU0fEpuZFP4i2JSOqq24dYvnBtbIAP4OM6L7frhkMoxCvlOPMH2r8kFOI2zpGW1xXdU0dXhzS0eS5y1J11st3dcb8r37RTbMnY84HjZIpDHrspH2u1VujUvHxDxcwNr0t6mB499OjTQR6tI3Aa1+c1D0mYtW4m0zeKWnb6Ugkf1UZWbrAmhtVFXBvWxBL7Ykae5G2tY2SqaehL2vwvpCgAIN9au2OWBrgZzDYYGGZBnPu6lz/rB+9IDcjORJj09pixIp9mJCRpBtVtqGGGRz9bVNV1P+m/PwkqKiuharc/InaUx7hQZ5DP4i2FEDoN0DWZp8t3YhlXyxG73aRuiyLfvUXUH9OiwiRYdPPl1FTJyyOBL3vt0IM7hyDY7+rTGZ1nlqbjReHdMS0v3VAfkkV2rRQ4KsTeRj93gE8eHMiHurVBpraOrSJDLa4Lxoe12wfRw0x6LFTWFCgwd3PgQZ3P0p5IKpq6uvB9Z+LdSi3CCcvq5CaGGEw4Fz2xWKs/eECMkd00y2rVEgx754ueGHjMYMLaJ/kKIxNb2tyVFXA/W1BTLUnaupF2pY2SuaW0X+IYmGZBnPu7qx7QKG1O+Ivj+fpskbaMX+0Y6E80b89Av39UF1bh/16z/rRv+Dof2/mMjhKhRRlmlrM/fyEzQGhpzzCgzyLfhBdVlWNecO6oFxTiwvXyk0+eBQALhSWm66ySonCK3d3Rk1dHa6WmH8WXHiwFK9/Uz/0QnZuMTZOSsOrW06aDbY6x4fhamkVXhvWBXM+O27QBqhHYjh6tm0BVbkGsgA/5KkrkZ1bhIJSjS7QmnxrMorKNHY9Xd3d50TyPAx67BQVIkV6e/N3PwCwZmIvo5OF9oLYcJyejORIZA7vajDCqKpcg1e3nMQNiREYrxcgAcCaHy6YrPry1Ifqufsi3fAhih9P7IVxaW1RUydwd7d4vLblhNkecg3H/NF+18u2n8XHE3vhoX//YPBZ5ZpazFx/FAtGdsPsIZ1QUlmDMHkAIhRSxIQFGVXxhcgC8PJnx+0KCJvyCA9q3hoG/2f+KDF4onpDtUIYjfwdLg9E++gQ3flIghKT740KkUIW4GdwbFwprjR5U6cNtqT+fjh0vv4J7N0TIzCuwXL3vL0X/7yvOyZ9fFiXTQf+ylilJoRjytpsmzJZgOeeE8m9vCLoWb58OV5//XVcuXIFnTt3xpIlS9CnTx+3lEWpkCLQ30r36UA/tJAZ7tqGPRLCggIRGhSAiGCp0ZDqBaUafPtLPr7VGz0V+KubtaZWGGUGPHW0XndepBtmmco1tbhWptGNGK3N3Ixr0ENEe5K21BtLZaIRqELqj/kju+GDBr3CBnaMNhipVqtPShQmpCehSyslurZSGg1maCogtOcRHuTbokNlVqvJTd20ffdMP8SEWV7HgpHdcK3M8MbuUG4RsnOLTB4z6cn1QzW8endnDPmz16kp2ps77fnysb7tMPOO65GvrkKwNMDoPKqprUPrCDmO/a4yyGR58jmR3Mvjg55169Zh6tSpWL58OdLT0/Gvf/0Ld9xxB06ePInEROMu464QIrO824KlAYgLCzI6WWhPMH1ToixW65gLFLQH/GdPpsNPIvGK0XrdeZE2lWXSr9bSP+Frh8zXZ6nRpKk2Qea6wV8XF4ZZG48ZTf/pQhEm9W+PQ+evYcm3f/VI0Q5mWFZl/Dtw1cjb5P3MNZjvkxKFsWnmq8n1b0TMrSM2LAh/NBhvylx7oj7JUZhzdyd8cewKNh+9jNTEcLOjM+tXT5VralFTJzD/q/oqNG3v1oaBmvbmZd3jtyBfXYXWEXLEhgV57DmR3Mvjg55Fixbh0UcfxcSJEwEAS5YswTfffIMVK1YgKyvLLWXy85NYrFf295M0qYeOpUChXFMLP4kE7aNDmrYRLuLOi7Sp4NHciblh7y7AcmPn7IvFRg03zWWGzE2fkJGEZTvOmh3MMPOerkbvcdXI29Q8mOoMEOAnwR1v7TGbbWl4I2JqHYVlVTicW2RwHtTPwjzZPxk1QkAR6I/Tf5Tg7mV7dY9peWtUKvwgMTh20pMj8dzg6/Dmd6cNPlv/2DHXDq9cU4ufLxbjvhtbI7GNgscAWeTRQY9Go8FPP/2EmTNnGkwfNGgQ9u3bZ/I9VVVVqKr66w5ErVY7vFwBfhLds55M1Sv7+0kA2N77qKHmdDfvzou0qeBR/8T80pBOqKyuNdm7C7A8IOLJyyo8O/g6CAhdo2VzmSFz060NZqipNf0+V428Tc1Dw7Y+qnINeraJsOv80nAd1VfqTN5AlGtqkZ1bhLu7x+Oet/di6ahUvLDxuO592uPv44m9MC69rUGbnkdXH8T8kd1QpfeYFv1jx9wNi/ZcEsfhGsgGHh30FBQUoLa2FjExMQbTY2JikJeXZ/I9WVlZeOWVV5xarshgKbK+/MVko711P+YaDMDVmBGSm9vdvLsu0uaCx3JNLY5eLMb/ZSQZlKHhPjc35k+flCi8OKQTJADeuP8GlFbWoKSyGkGB/ibLYS5jZG3MkbKqGrPzXDXyNjU/jji/RATXP/6iYUNoWYAf/lBX4qvjV1CuqTX5G2/Ytk5fwxsS/WOqYXueqpo6tIsKRpySVVlkO48OerQkEonB/0IIo2las2bNwjPPPKP7X61WIyEhwaHlUSqkeGVYF8xcf9To+S+OCkqa2928Oy7S9p7cze3zhmP+NPwetI0+zY2vlH2x2OT4PdbGCmKjZHKWpp5fYsKCkDm8K17YeMyoN+q49CTM+6J+QEFT1caA5aoq/RuShseUfnsea20jiUzx6KAnKioK/v7+Rlmd/Px8o+yPlkwmg0wmc3rZXBGU8G6+6ez9nqyN+WOJuSDr1BU1Mod3xYubjhtMzy+pMjs4nLdVY5L3aer5JTEyGG/cfwOKyjRQ/zk8Q1hQICqra7FmYi+z1cbAX1lUf4nE4g1Jc8t6k/tJhLDyUCE369WrF3r06IHly5frpnXq1AnDhg2zqSGzWq2GUqmESqVCWFiYM4tKBOCv52I1DLJMTS/7c2wfUyd0tlGg5uBycYXJ3/jCkd0gl/rbdENi7pii5s0Z12+PD3rWrVuHMWPG4J133kHv3r3x7rvv4r333sOJEyfQpk0bq+9n0EOejid0au74G6fGcMb126OrtwDggQceQGFhIV599VVcuXIFXbp0wZdffmlTwEPkDViNSc0df+PkKTw+09NUzPQQERF5H2dcvy13HyEiIiJqJhj0EBERkU9g0ENEREQ+gUEPERER+QQGPUREROQTGPQQERGRT2DQQ0RERD6BQQ8RERH5BAY9RERE5BM8/jEUTaUdcFqtVru5JERERGQr7XXbkQ+OaPZBT0lJCQAgISHBzSUhIiIie5WUlECpVDpkXc3+2Vt1dXW4fPkyQkNDIZFIbH7fTTfdhIMHDzb6c9VqNRISEnDx4sVGPzOkqWVo6vubug5H7IOmlsET3s/fQj3uBx4TWvwt1ON+sLwPhBAoKSlBfHw8/Pwc0xqn2Wd6/Pz80Lp1a7vf5+/v75AHnIWFhTV6PU0tgyO2wRHraMo+cEQZ3P1+Lf4W6nE/8JjQ4m+hHveD+X3gqAyPFhsym/Hkk0+6uwhNLoMjtoH7gfvAketwdxm4Hzzj/Y7gCdvA/eC4dbhKs6/eche1Wg2lUgmVSuWQOwFvxH1Qj/uhHvcD94EW90M97gfX7wNmepxEJpNhzpw5kMlk7i6K23Af1ON+qMf9wH2gxf1Qj/vB9fuAmR4iIiLyCcz0EBERkU9g0ENEREQ+gUEPERER+QQGPUREROQTGPSYkZWVhZtuugmhoaGIjo7GPffcg1OnThksI4TA3LlzER8fD7lcjv79++PEiRMGy7z77rvo378/wsLCIJFIUFxcbPLzvvjiC/Tq1QtyuRxRUVEYMWKEszbNLq7aDzt37oREIjH5aupooU3lyt/C6dOnMWzYMERFRSEsLAzp6enYsWOHMzfPZq7cD4cPH8bAgQMRHh6OyMhIPPbYYygtLXXm5tnEEfvg2rVrmDJlCq677jooFAokJibiqaeegkqlMlhPUVERxowZA6VSCaVSiTFjxpg9f7iaK/fDP/7xD6SlpUGhUCA8PNwVm2czV+2H8+fP49FHH0VSUhLkcjnat2+POXPmQKPRuGxbzXHlb+Huu+9GYmIigoKCEBcXhzFjxuDy5cv2FViQSYMHDxYrV64Ux48fF0eOHBFDhgwRiYmJorS0VLfM/PnzRWhoqFi/fr04duyYeOCBB0RcXJxQq9W6ZRYvXiyysrJEVlaWACCKioqMPut///ufiIiIECtWrBCnTp0Sv/76q/j0009dsZlWuWo/VFVViStXrhi8Jk6cKNq2bSvq6upctbkmufK3kJycLO68807x888/i9OnT4tJkyYJhUIhrly54opNtchV++HSpUsiIiJCPPHEE+LXX38VP/74o0hLSxMjR4501aaa5Yh9cOzYMTFixAixefNmcfbsWfHdd9+JlJQUo+27/fbbRZcuXcS+ffvEvn37RJcuXcTQoUNdur3muHI/vPzyy2LRokXimWeeEUql0pWbaZWr9sNXX30lxo0bJ7755htx7tw58dlnn4no6Gjx7LPPunybG3Llb2HRokVi//794vz582Lv3r2id+/eonfv3naVl0GPjfLz8wUAsWvXLiGEEHV1dSI2NlbMnz9ft0xlZaVQKpXinXfeMXr/jh07TJ7gq6urRatWrcS///1vp5bfUZy1HxrSaDQiOjpavPrqqw4tvyM4ax9cvXpVABC7d+/WTVOr1QKA+Pbbb52zMU3grP3wr3/9S0RHR4va2lrdtOzsbAFAnDlzxjkb00hN3Qda//3vf4VUKhXV1dVCCCFOnjwpAIgDBw7oltm/f78AIH799VcnbU3jOWs/6Fu5cqXHBT0NuWI/aC1cuFAkJSU5rvAO4sp98NlnnwmJRCI0Go3N5WP1lo20abYWLVoAAHJycpCXl4dBgwbplpHJZOjXrx/27dtn83oPHz6MS5cuwc/PD6mpqYiLi8Mdd9xhVCXgKZy1HxravHkzCgoKMG7cuCaV1xmctQ8iIyPRsWNHfPjhhygrK0NNTQ3+9a9/ISYmBj169HDsRjiAs/ZDVVUVpFKpwQMG5XI5AOD77793RNEdxlH7QDsabUBA/eMQ9+/fD6VSiV69eumWueWWW6BUKpt0XDmLs/aDt3HlflCpVLrP8SSu2gfXrl3Dxx9/jLS0NAQGBtpcPgY9NhBC4JlnnkFGRga6dOkCAMjLywMAxMTEGCwbExOjm2eL3377DQAwd+5cvPjii9iyZQsiIiLQr18/XLt2zUFb4BjO3A8Nvf/++xg8eDASEhIaX2AncOY+kEgk2LZtG7KzsxEaGoqgoCAsXrwYX3/9tce1ZXDmfrj11luRl5eH119/HRqNBkVFRXjhhRcAAFeuXHHQFjSdo/ZBYWEhXnvtNTz++OO6aXl5eYiOjjZaNjo6uknHlTM4cz94E1fuh3PnzmHp0qV44oknHFR6x3DFPpgxYwaCg4MRGRmJ3NxcfPbZZ3aVkUGPDSZPnoyjR49i7dq1RvMkEonB/0IIo2mW1NXVAQBmz56NkSNHokePHli5ciUkEgk+/fTTphXcwZy5H/T9/vvv+Oabb/Doo4826v3O5Mx9IITApEmTEB0djT179uDHH3/EsGHDMHToUI+62APO3Q+dO3fG6tWr8cYbb0ChUCA2Nhbt2rVDTEwM/P39m1x2R3HEPlCr1RgyZAg6deqEOXPmWFyHpfW4k7P3g7dw1X64fPkybr/9dtx3332YOHGiYwrvIK7YB88//zyys7OxdetW+Pv745FHHoGw48ESDHqsmDJlCjZv3owdO3agdevWuumxsbEAYBSp5ufnG0W0lsTFxQEAOnXqpJsmk8nQrl075ObmNqXoDuXs/aBv5cqViIyMxN133934AjuBs/fB9u3bsWXLFnzyySdIT0/HjTfeiOXLl0Mul2P16tWO2QgHcMVvYfTo0cjLy8OlS5dQWFiIuXPn4urVq0hKSmr6BjiAI/ZBSUkJbr/9doSEhGDjxo0GKfrY2Fj88ccfRp979erVRh9XzuDs/eAtXLUfLl++jAEDBqB379549913nbAljeeqfRAVFYUOHTpg4MCB+OSTT/Dll1/iwIEDNpeTQY8ZQghMnjwZGzZswPbt241OtklJSYiNjcW2bdt00zQaDXbt2oW0tDSbP6dHjx6QyWQGXfyqq6tx/vx5tGnTpukb0kSu2g/6n7dy5Uo88sgjHnPyc9U+KC8vBwCDtiza/7UZQXdy9W8BqE+Bh4SEYN26dQgKCsLAgQObtA1N5ah9oFarMWjQIEilUmzevBlBQUEG6+nduzdUKhV+/PFH3bQffvgBKpWq0fvSkVy1HzydK/fDpUuX0L9/f9x4441YuXKl0XnCXdz5W9BmeKqqquwqMJnw97//XSiVSrFz506DbtTl5eW6ZebPny+USqXYsGGDOHbsmBg1apRR99wrV66I7Oxs8d577+l65mRnZ4vCwkLdMk8//bRo1aqV+Oabb8Svv/4qHn30UREdHS2uXbvm0m02xZX7QQghvv32WwFAnDx50mXbaI2r9sHVq1dFZGSkGDFihDhy5Ig4deqUeO6550RgYKA4cuSIy7e7IVf+FpYuXSp++ukncerUKbFs2TIhl8vFm2++6dLtNcUR+0CtVotevXqJrl27irNnzxqsp6amRree22+/XXTr1k3s379f7N+/X3Tt2tVjuqy7cj9cuHBBZGdni1deeUWEhISI7OxskZ2dLUpKSly+3Q25aj9cunRJJCcni1tvvVX8/vvvBsu4m6v2wQ8//CCWLl0qsrOzxfnz58X27dtFRkaGaN++vaisrLS5vAx6zABg8rVy5UrdMnV1dWLOnDkiNjZWyGQy0bdvX3Hs2DGD9cyZM8fqejQajXj22WdFdHS0CA0NFX/729/E8ePHXbSllrlyPwghxKhRo0RaWpoLtsx2rtwHBw8eFIMGDRItWrQQoaGh4pZbbhFffvmli7bUMlfuhzFjxogWLVoIqVQqunXrJj788EMXbaVljtgH2q76pl45OTm65QoLC8VDDz0kQkNDRWhoqHjooYesDvXgKq7cD2PHjjW5zI4dO1y3wWa4aj+sXLnS7DLu5qp9cPToUTFgwADRokULIZPJRNu2bcUTTzwhfv/9d7vKK/mz0ERERETNmmdUChIRERE5GYMeIiIi8gkMeoiIiMgnMOghIiIin8Cgh4iIiHwCgx4iIiLyCQx6iIiIyCcw6CEij9W/f39MnTrV3cUgomaCQQ8RNQs7d+6ERCJBcXGxu4tCRB6KQQ8RERH5BAY9ROQRysrK8MgjjyAkJARxcXF44403DOZ/9NFH6NmzJ0JDQxEbG4vRo0cjPz8fAHD+/HkMGDAAABAREQGJRIJx48YBqH8S88KFC9GuXTvI5XJ0794d//vf/1y6bUTkGRj0EJFHeP7557Fjxw5s3LgRW7duxc6dO/HTTz/p5ms0Grz22mv4+eefsWnTJuTk5OgCm4SEBKxfvx4AcOrUKVy5cgVvvvkmAODFF1/EypUrsWLFCpw4cQLTpk3Dww8/jF27drl8G4nIvfjAUSJyu9LSUkRGRuLDDz/EAw88AAC4du0aWrdujcceewxLliwxes/Bgwdx8803o6SkBCEhIdi5cycGDBiAoqIihIeHA6jPHkVFRWH79u3o3bu37r0TJ05EeXk51qxZ44rNIyIPEeDuAhARnTt3DhqNxiAwadGiBa677jrd/9nZ2Zg7dy6OHDmCa9euoa6uDgCQm5uLTp06mVzvyZMnUVlZiYEDBxpM12g0SE1NdcKWEJEnY9BDRG5nLeFcVlaGQYMGYdCgQfjoo4/QsmVL5ObmYvDgwdBoNGbfpw2MvvjiC7Rq1cpgnkwma3rBicirMOghIrdLTk5GYGAgDhw4gMTERABAUVERTp8+jX79+uHXX39FQUEB5s+fj4SEBADAoUOHDNYhlUoBALW1tbppnTp1gkwmQ25uLvr16+eirSEiT8Wgh4jcLiQkBI8++iief/55REZGIiYmBrNnz4afX31fi8TEREilUixduhRPPPEEjh8/jtdee81gHW3atIFEIsGWLVtw5513Qi6XIzQ0FM899xymTZuGuro6ZGRkQK1WY9++fQgJCcHYsWPdsblE5CbsvUVEHuH1119H3759cffdd+Nvf/sbMjIy0KNHDwBAy5YtsWrVKnz66afo1KkT5s+fj3/+858G72/VqhVeeeUVzJw5EzExMZg8eTIA4LXXXsPLL7+MrKwsdOzYEYMHD8bnn3+OpKQkl28jEbkXe28RERGRT2Cmh4iIiHwCgx4iIiLyCQx6iIiIyCcw6CEiIiKfwKCHiIiIfAKDHiIiIvIJDHqIiIjIJzDoISIiIp/AoIeIiIh8AoMeIiIi8gkMeoiIiMgnMOghIiIin/D/kUdMtHW4c9EAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.dates as mdates\n", - "vaud_samples = vaud_report.sample_results\n", - "vaud_samples['date'] = pd.to_datetime(vaud_samples['date'])\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.xaxis.set_minor_locator(mdates.MonthLocator(interval=3))\n", - "ax.xaxis.set_major_locator(mdates.YearLocator())\n", - "\n", - "sns.scatterplot(data=vaud_samples,x='date', y='pcs/m')\n", - "ax.set_title(\"Sample results method of SurveyClass\", loc='left')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "70e41b0a-f3d2-442f-a0f9-fc26a38e34c3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Sample results summary\n", - "\n", - "The sample results summary returns the quintiles of the survey totals, the average, the date range and the number of samples in one dictionary.\n", - "\n", - "```python\n", - "sampling_summary = vaud_report.sampling_results_summary\n", - "```\n", - "The average in the Canton of Vaud was 7.69 pcs/m." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6c241f9f-0b3a-487a-9a41-651de17711f0", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'total': 64052,\n", - " 'nsamples': 233,\n", - " 'average': 7.687682403433477,\n", - " 'quantiles': array([ 0.728, 2.4 , 4.6 , 8.92 , 25.494]),\n", - " 'std': 9.99752169901663,\n", - " 'max': 77.1,\n", - " 'start': '2015-11-23',\n", - " 'end': '2022-10-06'}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sampling_summary = vaud_report.sampling_results_summary\n", - "sampling_summary" - ] - }, - { - "cell_type": "markdown", - "id": "91ec8e58-1694-4662-8766-be71d5498688", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Object summary\n", - "\n", - "The object summary is the detailed inventory with % of total, fail-rate and average pcs/m for each object.\n", - "\n", - "```python\n", - "object_summary = vaud_report.object_summary()\n", - "```\n", - "The five most abundant objects. G27, cigarette ends, are 19% of the total with an average density of 1.34 pcs/m. The fail rate for cigarette ends is less than the fail rate for candy wrappers (G30) and fragmented plastics (Gfrags)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6ddd4e7f-e57b-4149-803b-18606fc74d75", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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quantitypcs/m% of totalsample_idfailsrate
code
G27126731.3426180.197855233209.00.896996
Gfoams88110.9933480.137560233199.00.854077
Gfrags84391.2162660.131752233222.00.952790
G3043140.5284550.067352233214.00.918455
G9534540.4315450.053925233194.00.832618
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" - ], - "text/plain": [ - " quantity pcs/m % of total sample_id fails rate\n", - "code \n", - "G27 12673 1.342618 0.197855 233 209.0 0.896996\n", - "Gfoams 8811 0.993348 0.137560 233 199.0 0.854077\n", - "Gfrags 8439 1.216266 0.131752 233 222.0 0.952790\n", - "G30 4314 0.528455 0.067352 233 214.0 0.918455\n", - "G95 3454 0.431545 0.053925 233 194.0 0.832618" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "object_summary = vaud_report.object_summary()\n", - "object_summary.sort_values('quantity', ascending=False).head()" - ] - }, - { - "cell_type": "markdown", - "id": "3b7e95e9-0ef5-4260-98fb-2bc45dd20e8d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Material report\n", - "\n", - "The material gives the proportion of each material type for material totals greater than 1% of the total\n", - "\n", - "```python\n", - "object_summary = vaud_report.material_report\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "3cb6ebff-615d-4061-a2db-8956a48bc26e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "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", - "
% of total
material
glass6%
metal3%
paper2%
plastic86%
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" - ], - "text/plain": [ - " % of total\n", - "material \n", - "glass 6%\n", - "metal 3%\n", - "paper 2%\n", - "plastic 86%" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vaud_report.material_report" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "54fba949-faaf-45c5-a4b7-f0111a5c8872", - "metadata": { - "editable": true, - "pycharm": { - "name": "#%%\n" - }, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Author: hammerdirt-analyst\n", - "\n", - "conda environment: cantonal_report\n", - "\n", - "seaborn : 0.12.2\n", - "numpy : 1.25.2\n", - "pandas : 2.0.3\n", - "matplotlib: 3.7.1\n", - "\n" - ] - } - ], - "source": [ - "%watermark -a hammerdirt-analyst -co --iversions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1271b638-c1bc-4763-ab43-758fbdbfddb0", - "metadata": { - "editable": true, - "jupyter": { - "source_hidden": true - }, - "pycharm": { - "name": "#%%\n" - }, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_build/html/_sources/combined.ipynb b/_build/html/_sources/combined.ipynb deleted file mode 100644 index 73084b7..0000000 --- a/_build/html/_sources/combined.ipynb +++ /dev/null @@ -1,2732 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "5bf92247-ef77-4aae-b62f-9388cba6765e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "import session_config\n", - "import reports\n", - "import userdisplay\n", - "import geospatial\n", - "import gridforecast as gfcast\n", - "\n", - "import logging\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "import matplotlib.colors\n", - "from matplotlib.colors import LinearSegmentedColormap, ListedColormap\n", - "from matplotlib.lines import Line2D\n", - "import matplotlib.dates as mdates\n", - "import seaborn as sns\n", - "import datetime as dt\n", - "\n", - "import geopandas as gpd\n", - "import contextily as ctx\n", - "from shapely.geometry import box\n", - "from shapely.geometry import Point\n", - "\n", - "from myst_nb import glue\n", - "from IPython.display import display, Markdown\n", - "\n", - "def display_forecast(fcast_summary):\n", - " average = fcast_summary['average']\n", - " hdi_min, hdi_max = fcast_summary['hdi'][0], fcast_summary['hdi'][1]\n", - " \n", - " range_90_min, range_90_max= fcast_summary['range'][0], fcast_summary['range'][-1]\n", - " alist = f'\\n* Average: {round(average, 2)}\\n* HDI 95%: {round(hdi_min, 2)} - {round(hdi_max, 2)}\\n* 90% Range: {round(range_90_min, 2)} - {round(range_90_max,2)}'\n", - " return alist\n", - "\n", - "def display_forecast_summary(asummary, label):\n", - " forecast_summary = display_forecast(asummary)\n", - " forecast_summary = Markdown(f'{label}{forecast_summary}')\n", - " return forecast_summary\n", - "\n", - "def extract_dates_for_labels_from_summary(summary):\n", - " start = dt.datetime.strftime(summary.pop('start'), format=session_config.date_format)[:4]\n", - " end = dt.datetime.strftime(summary.pop('end'), format=session_config.date_format)[:4]\n", - " return f\"{start} - {end}\"\n", - "\n", - "def format_boundaries_feature_inv(boundaries, topop, displayfunc, session_language='en'):\n", - " for thingtoremove in topop:\n", - " boundaries.pop(thingtoremove)\n", - " display_boundaries = displayfunc(boundaries, session_language=session_language)\n", - " return Markdown(display_boundaries)\n", - "\n", - "def format_river_lake_summary(d):\n", - " d.drop('feature_type', axis=1, inplace=True)\n", - " d.rename(columns={'feature_name':'Name', 'sample_id': 'samples'}, inplace=True)\n", - " d['pcs/m'] = d['pcs/m'].round(2)\n", - " d['Name'] = d.Name.apply(lambda x: x.capitalize())\n", - " d.set_index('Name', inplace=True)\n", - " d.index.name = None\n", - " return d\n", - "\n", - "\n", - "highlight_props = 'background-color:#FAE8E8'\n", - "def highlight_max(s, arg, props: str = highlight_props):\n", - " return np.where((s > arg) & (s != 0), props, '')\n", - "\n", - "logging.basicConfig(\n", - " filename='app.log', \n", - " level=logging.DEBUG,\n", - " format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n", - ")\n", - "\n", - "logger = logging.getLogger(__name__)\n", - "color_style = {'prior':'color: #daa520', 'likelihood':'color: #1e90ff'}\n", - "palette = {'prior':'goldenrod', 'likelihood':'dodgerblue'}" - ] - }, - { - "cell_type": "markdown", - "id": "d2d5a3de-1688-4113-95ce-bf614dbf9695", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "fb4d8635-eb6f-402a-9cd6-8be9c76cce30", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "data = session_config.collect_survey_data()\n", - "\n", - "o_dates = {'start':'2020-01-01', 'end':'2021-12-31'}\n", - "prior_dates = {'start':'2015-11-15', 'end':'2019-12-31'}\n", - "\n", - "# all data\n", - "# canton = 'Bern'\n", - "cantons = ['Bern', 'Vaud', 'Genève', 'Zürich', 'Valais']\n", - "d = data[data.canton.isin(cantons)].copy()\n", - "d= d.reset_index(drop=True)\n", - "\n", - "# all surveys lakes, rivers combined\n", - "alldata_ofinterest, locations = gfcast.filter_data(d,{'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_surveys, call_land = gfcast.make_report_objects(alldata_ofinterest)\n", - "\n", - "# summary and labels\n", - "all_summary = call_surveys.sampling_results_summary.copy()\n", - "all_labels = extract_dates_for_labels_from_summary(all_summary)\n", - "\n", - "# material proportions all data\n", - "material_report = call_surveys.material_report\n", - "material_report = material_report.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior = d[(d['date'] <= prior_dates['end'])].copy()\n", - "o_report, o_land_use = gfcast.make_report_objects(o_prior)\n", - "results = gfcast.reports_and_forecast({'date_range':o_dates}, {'date_range':prior_dates}, ldata=d.copy(), logger=logger, other_data=o_land_use.df_cat)\n", - "\n", - "# prior summary and label\n", - "p_summary = results['prior_report'].sampling_results_summary.copy()\n", - "prior_labels = extract_dates_for_labels_from_summary(p_summary)\n", - "\n", - "# likelihood summary and label\n", - "l_summary = results['this_report'].sampling_results_summary.copy()\n", - "likelihood_labels = extract_dates_for_labels_from_summary(l_summary)\n", - "\n", - "# forecasts\n", - "xii = results['posterior_no_limit'].sample_posterior()\n", - "\n", - "# limit to the 99th percentile\n", - "sample_values, posterior, summary_simple = gfcast.dirichlet_posterior(results['posterior_99'])\n", - "\n", - "# forecast weighted prior all data\n", - "weighted_args = [results['this_land_use'], session_config.feature_variables, call_land.df_cat, results['this_report'].sample_results['pcs/m']]\n", - "weighted_forecast, weighted_posterior, weighted_summary, _ = gfcast.forecast_weighted_prior(*weighted_args)\n", - "\n", - "# forecast summaries\n", - "forecast_99 = display_forecast_summary(summary_simple, '__Given the 99th percentile__')\n", - "forecast_maxval = display_forecast_summary(results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - "forecast_weighted = display_forecast_summary(weighted_summary, '__Given the weighted prior__')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d504b355-6ec4-4d6d-b9d6-3459215fcb8b", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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", 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 % of total
material 
glass6%
metal4%
paper3%
plastic81%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "material-report" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 4.71\n* HDI 95%: 0.1 - 17.6\n* 90% Range: 0.5 - 17.6", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-weighted-prior" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 6.86\n* HDI 95%: 0.02 - 40.45\n* 90% Range: 0.17 - 49.31", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-max-val" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 3.46\n* HDI 95%: 0.1 - 11.1\n* 90% Range: 0.2 - 9.1", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-99-max" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__The most common objects account for 72% of all objects__", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "ratio-most-common" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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 \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
ObjectQuantitypcs/m% of totalFail rate
Fragmented plastics6'7960,830,150,88
Cigarette filters6'3060,600,140,87
Expanded polystyrene4'3190,550,100,67
Food wrappers; candy, snacks2'8900,310,060,88
Industrial sheeting2'0310,240,050,69
Industrial pellets (nurdles)1'7150,180,040,36
plastic caps, lid rings: G21, G22, G23, G241'7040,190,040,71
Glass drink bottles, pieces1'5020,190,030,65
Foam packaging/insulation/polyurethane1'4350,090,030,87
Cotton bud/swab sticks1'3850,160,030,56
Plastic construction waste8360,090,020,53
Metal bottle caps, lids & pull tabs from cans5510,050,010,56
Tobacco; plastic packaging, containers5290,060,010,50
Foil wrappers, aluminum foil3860,040,010,50
Straws and stirrers3820,040,010,50
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 263\n* Total objects: 45003\n* Average pcs/m: 5.01\n* Standard deviation: 8.24\n* Maximum pcs/m: 66.17\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2015 - 2019\n* Number of samples: 568\n* Total objects: 85733\n* Average pcs/m: 4.01\n* Standard deviation: 6.73\n* Maximum pcs/m: 77.1\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2015 - 2021\n* Number of samples: 831\n* Total objects: 130736\n* Average pcs/m: 4.32\n* Standard deviation: 7.25\n* Maximum pcs/m: 77.1\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Rivers: 19\n* Lakes: 8\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 131\n* Cities: 70\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# most common objects all data\n", - "os = results['this_report'].object_summary()\n", - "os.reset_index(drop=False, inplace=True)\n", - "most_common_objects, mc_codes, proportions = userdisplay.most_common(os)\n", - "most_common_objects = most_common_objects.set_caption(\"\")\n", - "\n", - "# display the inventory of features\n", - "feature_inv = call_surveys.feature_inventory()\n", - "feature_inventory = format_boundaries_feature_inv(feature_inv, ['p'], userdisplay.feature_inventory)\n", - "\n", - "# display the inventory of boundaries\n", - "aboundaries = call_surveys.administrative_boundaries().copy()\n", - "administrative_boundaries = format_boundaries_feature_inv(aboundaries, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - "# display the sampling summaries\n", - "all_info = userdisplay.sampling_result_summary(all_summary, session_language='en')[1]\n", - "all_samp_sum = Markdown(f'{all_labels}\\n{all_info}')\n", - "\n", - "p_header = f\"{prior_labels}\"\n", - "p_info = userdisplay.sampling_result_summary(p_summary, session_language='en')[1]\n", - "p_samp_sum = Markdown(f'{p_header}\\n{p_info}')\n", - "\n", - "l_header = f\"{likelihood_labels} \"\n", - "l_info = userdisplay.sampling_result_summary(l_summary, session_language='en')[1]\n", - "l_samp_sum = Markdown(f'{l_header}\\n{l_info}')\n", - "\n", - "ratio_most_common = Markdown(f'__The most common objects account for {int(proportions*100)}% of all objects__')\n", - "\n", - "caption_histo = Markdown(f'Survey total pcs/m {likelihood_labels} versus {prior_labels}. All locations considered.')\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.histplot(data=results['this_report'].sample_results, x='pcs/m', stat='probability', label=likelihood_labels, ax=ax, color=palette['likelihood'])\n", - "sns.histplot(data=results['prior_report'] .sample_results, x='pcs/m', stat='probability', label=prior_labels, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "title = f'All samples: {prior_dates[\"start\"]} - {o_dates[\"end\"]}'\n", - "\n", - "ax.xaxis.set_minor_locator(mdates.MonthLocator(interval=3))\n", - "ax.xaxis.set_minor_formatter(mdates.DateFormatter(\"%b\"))\n", - "\n", - "ax.xaxis.set_major_locator(mdates.YearLocator())\n", - "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"\\n%Y\"))\n", - "\n", - "sns.scatterplot(data=results['this_report'].sample_results, x='date', y='pcs/m', marker='x', label=likelihood_labels, ax=ax, color=palette['likelihood'])\n", - "sns.scatterplot(data=results['prior_report'] .sample_results, x='date', y='pcs/m', marker='x', label=prior_labels, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "ax.set_xlabel('')\n", - "ax.set_title(title)\n", - "plt.tight_layout()\n", - "glue('scatter-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.ecdfplot(results['prior_report'].sample_results['pcs/m'], label=prior_labels, ls='-', ax=ax, c=palette['prior'], zorder=1)\n", - "sns.ecdfplot(results['this_report'].sample_results['pcs/m'], label=likelihood_labels, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "sns.ecdfplot(sample_values, label='expected 99%', ls=':', zorder=2)\n", - "sns.ecdfplot(xii, label='expected max', ls='-.', zorder=2)\n", - "sns.ecdfplot(weighted_forecast, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, results['this_report'].sample_results['pcs/m'].quantile(.99))\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_list = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo', caption_histo, display=False)\n", - "glue('material-report', material_report, display=False)\n", - "glue('forecast-weighted-prior', forecast_weighted, display=False)\n", - "glue('forecast-max-val', forecast_maxval, display=False)\n", - "glue('forecast-99-max', forecast_99, display=False)\n", - "glue('ratio-most-common', ratio_most_common, display=False)\n", - "glue('most_common_objects', most_common_objects, display=False)\n", - "glue('l-sampling-summary', l_samp_sum, display=False)\n", - "glue('prior-sampling-summary', p_samp_sum, display=False)\n", - "glue('sampling-summary', all_samp_sum, display=False)\n", - "glue('feature-inventory', feature_inventory, display=False)\n", - "glue('administrative-boundaries', administrative_boundaries, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c016aff6-dc3f-49a1-be4d-48b6448c9d22", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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material 
glass5%
metal4%
paper3%
plastic83%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "material-report-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 5.04\n* HDI 95%: 0.2 - 15.9\n* 90% Range: 0.3 - 15.9", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-weighted-prior-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 8.57\n* HDI 95%: 0.22 - 52.72\n* 90% Range: 0.47 - 48.54", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-max-val-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 5.1\n* HDI 95%: 0.1 - 17.6\n* 90% Range: 0.4 - 16.65", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-99-max-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__The most common objects account for 74% of all objects__", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "ratio-most-common-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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 \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
ObjectQuantitypcs/m% of totalFail rate
Fragmented plastics6'7350,940,160,93
Cigarette filters6'0810,660,140,91
Expanded polystyrene4'3020,630,100,75
Food wrappers; candy, snacks2'7890,340,060,91
Industrial sheeting1'8820,260,040,74
Industrial pellets (nurdles)1'7050,210,040,40
plastic caps, lid rings: G21, G22, G23, G241'6770,210,040,78
Foam packaging/insulation/polyurethane1'4220,100,030,98
Glass drink bottles, pieces1'4000,200,030,69
Cotton bud/swab sticks1'3790,180,030,64
Plastic construction waste8040,100,020,59
Tobacco; plastic packaging, containers5220,070,010,56
Metal bottle caps, lids & pull tabs from cans4880,050,010,60
Straws and stirrers3740,050,010,54
Foil wrappers, aluminum foil3710,040,010,53
Lollypop sticks3540,040,010,52
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 230\n* Total objects: 43151\n* Average pcs/m: 5.5\n* Standard deviation: 8.63\n* Maximum pcs/m: 66.17\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2015 - 2019\n* Number of samples: 311\n* Total objects: 74895\n* Average pcs/m: 6.28\n* Standard deviation: 8.34\n* Maximum pcs/m: 77.1\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2015 - 2021\n* Number of samples: 541\n* Total objects: 118046\n* Average pcs/m: 5.95\n* Standard deviation: 8.47\n* Maximum pcs/m: 77.1\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Lakes: 8\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 78\n* Cities: 40\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries-l" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# lakes\n", - "d = data[data.canton.isin(['Bern', 'Vaud', 'Genève', 'Zürich', 'Valais'])].copy()\n", - "d = d.reset_index(drop=True)\n", - "# data = ()\n", - "lake_params = {'date_range':o_dates, 'feature_type': 'l'}\n", - "lake_params_p = {'date_range':prior_dates, 'feature_type':'l'}\n", - "\n", - "# all surveys lakes, rivers combined\n", - "c_all_l, _ = gfcast.filter_data(d,{'feature_type': 'l', 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_l_surveys, call_l_land = gfcast.make_report_objects(c_all_l)\n", - "\n", - "# summary and labels\n", - "all_summary_l = call_l_surveys.sampling_results_summary\n", - "all_labels_l = extract_dates_for_labels_from_summary(all_summary_l)\n", - "\n", - "# material proportions all data\n", - "material_report_l = call_l_surveys.material_report\n", - "material_report_l = material_report_l.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior_l = d[(d['date'] <= prior_dates['end'])&(d.feature_type == 'l')].copy()\n", - "o_report_l, o_land_use_l = gfcast.make_report_objects(o_prior_l)\n", - "lake_results = gfcast.reports_and_forecast(lake_params,lake_params_p , ldata=d.copy(), logger=logger, other_data=o_land_use_l.df_cat)\n", - "\n", - "# prior summary and label\n", - "p_summary_l = lake_results['prior_report'].sampling_results_summary.copy()\n", - "prior_labels_l = extract_dates_for_labels_from_summary(p_summary_l)\n", - "\n", - "# likelihood summary and label\n", - "l_summary_l = lake_results['this_report'].sampling_results_summary.copy()\n", - "likelihood_labels_l = extract_dates_for_labels_from_summary(l_summary_l)\n", - "\n", - "# forecasts\n", - "xii_l = lake_results['posterior_no_limit'].sample_posterior()\n", - "\n", - "# limit to the 99th percentile\n", - "sample_values_l, posterior_l, summary_simple_l = gfcast.dirichlet_posterior(lake_results['posterior_99'])\n", - "\n", - "# forecast weighted prior all data\n", - "weighted_args_l = [lake_results['this_land_use'], session_config.feature_variables, call_l_land.df_cat, lake_results['this_report'].sample_results['pcs/m']]\n", - "weighted_forecast_l, weighted_posterior_l, weighted_summary_l, _ = gfcast.forecast_weighted_prior(*weighted_args_l)\n", - "\n", - "# forecast summaries\n", - "forecast_99_l = display_forecast_summary(summary_simple_l, '__Given the 99th percentile__')\n", - "forecast_maxval_l = display_forecast_summary(lake_results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - "forecast_weighted_l = display_forecast_summary(weighted_summary_l, '__Given the weighted prior__')\n", - "\n", - "# most common objects all lake data\n", - "os_l = lake_results['this_report'].object_summary()\n", - "os_l.reset_index(drop=False, inplace=True)\n", - "most_common_objects_l, mc_codes_l, proportions_l = userdisplay.most_common(os_l)\n", - "most_common_objects_l = most_common_objects_l.set_caption(\"\")\n", - "\n", - "# display the inventory of features\n", - "feature_inv_l = call_l_surveys.feature_inventory()\n", - "feature_inventory_l = format_boundaries_feature_inv(feature_inv_l, ['p', 'r'], userdisplay.feature_inventory)\n", - "\n", - "# display the inventory of boundaries\n", - "aboundaries_l = call_l_surveys.administrative_boundaries().copy()\n", - "administrative_boundaries_l = format_boundaries_feature_inv(aboundaries_l, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - "# display the sampling summaries\n", - "all_info_l = userdisplay.sampling_result_summary(all_summary_l, session_language='en')[1]\n", - "all_samp_sum_l = Markdown(f'{all_labels_l}\\n{all_info_l}')\n", - "\n", - "p_header_l = f\"{prior_labels}\"\n", - "p_info_l = userdisplay.sampling_result_summary(p_summary_l, session_language='en')[1]\n", - "p_samp_sum_l = Markdown(f'{p_header_l}\\n{p_info_l}')\n", - "\n", - "l_header_l = f\"{likelihood_labels_l} \"\n", - "l_info_l = userdisplay.sampling_result_summary(l_summary_l, session_language='en')[1]\n", - "l_samp_sum_l = Markdown(f'{l_header_l}\\n{l_info_l}')\n", - "\n", - "ratio_most_common_l = Markdown(f'__The most common objects account for {int(proportions_l*100)}% of all objects__')\n", - "\n", - "caption_histo_l = Markdown(f'Survey total pcs/m {likelihood_labels_l} v/s {prior_labels_l}. All locations considered.')\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.histplot(data=lake_results['this_report'].sample_results, x='pcs/m', stat='probability', label=likelihood_labels_l, ax=ax, color=palette['likelihood'])\n", - "sns.histplot(data=lake_results['prior_report'].sample_results, x='pcs/m', stat='probability', label=prior_labels_l, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('lake-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.ecdfplot(lake_results['prior_report'].sample_results['pcs/m'], label=prior_labels_l, ls='-', ax=ax, c=palette['prior'], zorder=1)\n", - "sns.ecdfplot(lake_results['this_report'].sample_results['pcs/m'], label=likelihood_labels_l, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "sns.ecdfplot(sample_values_l, label='expected 99%', ls=':', zorder=2)\n", - "sns.ecdfplot(xii_l, label='expected max', ls='-.', zorder=2)\n", - "sns.ecdfplot(weighted_forecast_l, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, lake_results['this_report'].sample_results['pcs/m'].quantile(.99))\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('lake-cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_list = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo-l', caption_histo_l, display=False)\n", - "glue('material-report-l', material_report_l, display=False)\n", - "glue('forecast-weighted-prior-l', forecast_weighted_l, display=False)\n", - "glue('forecast-max-val-l', forecast_maxval_l, display=False)\n", - "glue('forecast-99-max-l', forecast_99_l, display=False)\n", - "glue('ratio-most-common-l', ratio_most_common_l, display=False)\n", - "glue('most_common_objects-l', most_common_objects_l, display=False)\n", - "glue('l-sampling-summary-l', l_samp_sum_l, display=False)\n", - "glue('prior-sampling-summary-l', p_samp_sum_l, display=False)\n", - "glue('sampling-summary-l', all_samp_sum_l, display=False)\n", - "glue('feature-inventory-l', feature_inventory_l, display=False)\n", - "glue('administrative-boundaries-l', administrative_boundaries_l, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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", 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 % of total
material 
cloth5%
glass7%
metal9%
paper7%
plastic65%
wood2%
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ObjectQuantitypcs/m% of totalFail rate
Diapers - wipes2990,210,160,30
Cigarette filters2250,210,120,64
Industrial sheeting1490,090,080,36
Glass drink bottles, pieces1020,110,060,36
Food wrappers; candy, snacks1010,110,050,67
Packaging films nonfood or unknown680,090,040,27
Metal bottle caps, lids & pull tabs from cans630,080,030,30
Fragmented plastics610,050,030,52
Bags; plastic shopping/carrier/grocery and pieces550,050,030,24
Construction material; bricks, pipes, cement420,020,020,12
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 33\n* Total objects: 1852\n* Average pcs/m: 1.58\n* Standard deviation: 2.82\n* Maximum pcs/m: 16.0\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2015 - 2019\n* Number of samples: 257\n* Total objects: 10838\n* Average pcs/m: 1.25\n* Standard deviation: 1.39\n* Maximum pcs/m: 11.1\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2017 - 2021\n* Number of samples: 290\n* Total objects: 12690\n* Average pcs/m: 1.29\n* Standard deviation: 1.62\n* Maximum pcs/m: 16.0\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Rivers: 19\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 53\n* Cities: 36\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries-r" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# rivers\n", - "d = data[data.canton.isin(['Bern', 'Vaud', 'Genève', 'Zürich', 'Valais'])].copy()\n", - "d = d.reset_index(drop=True)\n", - "river_params = {'date_range':o_dates, 'feature_type': 'r'}\n", - "river_params_p = {'date_range':prior_dates, 'feature_type':'r'}\n", - "\n", - "# all surveys lakes, rivers combined\n", - "c_all_r, _ = gfcast.filter_data(d,{'feature_type': 'r', 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_r_surveys, call_r_land = gfcast.make_report_objects(c_all_r)\n", - "\n", - "# summary and labels\n", - "all_summary_r = call_r_surveys.sampling_results_summary\n", - "all_labels_r = extract_dates_for_labels_from_summary(all_summary_r)\n", - "\n", - "# material proportions all data\n", - "material_report_r = call_r_surveys.material_report\n", - "material_report_r = material_report_r.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior_r = d[(d.feature_type == 'r')].copy()\n", - "o_report_r, o_land_use_r = gfcast.make_report_objects(o_prior_r)\n", - "river_results = gfcast.reports_and_forecast(river_params,river_params_p , ldata=d.copy(), logger=logger, other_data=o_land_use_r.df_cat)\n", - "\n", - "# prior summary and label\n", - "p_summary_r = river_results['prior_report'].sampling_results_summary.copy()\n", - "prior_labels_r = extract_dates_for_labels_from_summary(p_summary_r)\n", - "\n", - "# likelihood summary and label\n", - "l_summary_r = river_results['this_report'].sampling_results_summary.copy()\n", - "likelihood_labels_r = extract_dates_for_labels_from_summary(l_summary_r)\n", - "\n", - "# forecasts\n", - "xii_r = river_results['posterior_no_limit'].sample_posterior()\n", - "\n", - "# limit to the 99th percentile\n", - "sample_values_r, posterior_r, summary_simple_r = gfcast.dirichlet_posterior(river_results['posterior_99'])\n", - "\n", - "# forecast weighted prior all data\n", - "weighted_args_r = [river_results['this_land_use'], session_config.feature_variables, call_r_land.df_cat, river_results['this_report'].sample_results['pcs/m']]\n", - "weighted_forecast_r, weighted_posterior_r, weighted_summary_r, _ = gfcast.forecast_weighted_prior(*weighted_args_r)\n", - "\n", - "# forecast summaries\n", - "forecast_99_r = display_forecast_summary(summary_simple_r, '__Given the 99th percentile__')\n", - "forecast_maxval_r = display_forecast_summary(river_results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - "forecast_weighted_r = display_forecast_summary(weighted_summary_r, '__Given the weighted prior__')\n", - "\n", - "# most common objects all lake data\n", - "os_r = river_results['this_report'].object_summary()\n", - "os_r.reset_index(drop=False, inplace=True)\n", - "most_common_objects_r, mc_codes_r, proportions_r = userdisplay.most_common(os_r)\n", - "most_common_objects_r = most_common_objects_r.set_caption(\"\")\n", - "\n", - "# display the inventory of features\n", - "feature_inv_r = call_r_surveys.feature_inventory()\n", - "feature_inventory_r = format_boundaries_feature_inv(feature_inv_r, ['p', 'l'], userdisplay.feature_inventory)\n", - "\n", - "# display the inventory of boundaries\n", - "aboundaries_r = call_r_surveys.administrative_boundaries().copy()\n", - "administrative_boundaries_r = format_boundaries_feature_inv(aboundaries_r, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - "# display the sampling summaries\n", - "all_info_r = userdisplay.sampling_result_summary(all_summary_r, session_language='en')[1]\n", - "all_samp_sum_r = Markdown(f'{all_labels_r}\\n{all_info_r}')\n", - "\n", - "p_header_r = f\"{prior_labels}\"\n", - "p_info_r = userdisplay.sampling_result_summary(p_summary_r, session_language='en')[1]\n", - "p_samp_sum_r = Markdown(f'{p_header_r}\\n{p_info_r}')\n", - "\n", - "l_header_r = f\"{likelihood_labels_r} \"\n", - "l_info_r = userdisplay.sampling_result_summary(l_summary_r, session_language='en')[1]\n", - "l_samp_sum_r = Markdown(f'{l_header_r}\\n{l_info_r}')\n", - "\n", - "ratio_most_common_r = Markdown(f'__The most common objects account for {int(proportions_r*100)}% of all objects__')\n", - "\n", - "caption_histo_r = Markdown(f'Survey total pcs/m {likelihood_labels_r} v/s {prior_labels_r}. All locations considered.')\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.histplot(data=river_results['this_report'].sample_results, x='pcs/m', stat='probability', label=likelihood_labels_r, ax=ax, color=palette['likelihood'])\n", - "sns.histplot(data=river_results['prior_report'].sample_results, x='pcs/m', stat='probability', label=prior_labels_r, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('river-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.ecdfplot(river_results['prior_report'].sample_results['pcs/m'], label=prior_labels_r, ls='-', ax=ax, c=palette['prior'], zorder=1)\n", - "sns.ecdfplot(river_results['this_report'].sample_results['pcs/m'], label=likelihood_labels_r, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "sns.ecdfplot(sample_values_r, label='expected 99%', ls=':', zorder=2)\n", - "sns.ecdfplot(xii_r, label='expected max', ls='-.', zorder=2)\n", - "sns.ecdfplot(weighted_forecast_r, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, river_results['this_report'].sample_results['pcs/m'].quantile(.99))\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('river-cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_rist = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo-r', caption_histo_r, display=False)\n", - "glue('material-report-r', material_report_r, display=False)\n", - "glue('forecast-weighted-prior-r', forecast_weighted_r, display=False)\n", - "glue('forecast-max-val-r', forecast_maxval_r, display=False)\n", - "glue('forecast-99-max-r', forecast_99_r, display=False)\n", - "glue('ratio-most-common-r', ratio_most_common_r, display=False)\n", - "glue('most_common_objects-r', most_common_objects_r, display=False)\n", - "glue('l-sampling-summary-r', l_samp_sum_r, display=False)\n", - "glue('prior-sampling-summary-r', p_samp_sum_r, display=False)\n", - "glue('sampling-summary-r', all_samp_sum_r, display=False)\n", - "glue('feature-inventory-r', feature_inventory_r, display=False)\n", - "glue('administrative-boundaries-r', administrative_boundaries_r, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "12c52a87-8340-419f-bfd9-75ca85260a97", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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The structure and the format are based off of the federal report, ([IQAASL](https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/)). This version is intended for use as a decsion support tool. Thus, the user is expected to be familiar with the results in the federal report and the methods described in the _Guide for Monitoring Marine Litter on European Seas_ \n", - "([The guide](https://mcc.jrc.ec.europa.eu/main/dev.py?N=41&O=439&titre_chap=TG%20Litter&titre_page=Guidance%20for%20the%20Monitoring%20of%20Marine%20Litter)).\n", - "\n", - "\n", - ":::{dropdown} Initial assessment: stakeholder discussion and priorities.\n", - "\n", - "Stakeholders should consider the following questions while consulting the report:\n", - "\n", - "1. Are the major rivers and lakes included?\n", - "2. Was their more or less observed in 2021 vs the prior results?\n", - "3. __How do these results compare to assessments from other sources (EAWAG, EMPA, Internal reports)?__\n", - " * This includes reports from NGOS in the region\n", - " * Is the data comparable?\n", - "4. Are the objects identified as the _most common_ currently the focus of reduction or prevention campaigns?\n", - " * __How does the canton decide priorties in this regard?__\n", - " * __Did or does the object appear in any regional action plan or strategy?__\n", - "5. For objects that have been the focus of prevention or reduction campaigns in the past: are they on the _most common objects_ list now?\n", - " * If the objects are on the most common list, is this inline with expectations ?\n", - " * What excatly was the mechanism or process that was intended to reduce the presence of the object?\n", - " * __With respect to the amount of resources attributed to prevention and mitigation:__ Do the attributed amounts reflect the importance of the object in terms of total amount found and frequency of occurence?\n", - "6. __Does the sampling distribution reflect the topography and land-use of the canton?__\n", - "7. Do the municipalities with elevated pcs/m have common land-use attributes?\n", - "8. __Are the municipalities of strategic importance to the canton included?__\n", - "9. Are their locations in the canton that should have been surveyed, according to cantonal priorities?\n", - "10. Are their products of regional interest that should be included in the cantonal report?\n", - ":::\n", - "\n", - ":::::{dropdown} Map of survey locations\n", - "\n", - "\n", - "\n", - "::::{grid}\n", - ":padding: 2\n", - "\n", - ":::{grid-item-card}\n", - "\n", - "```{glue} map-of-survey-locations\n", - "```\n", - "\n", - ":::\n", - "::::\n", - ":::::\n", - "## Vital statistics\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory\n", - "```\n", - "```{glue} administrative-boundaries\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory-l\n", - "```\n", - "```{glue} administrative-boundaries-l\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} lake-prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo-l\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory-r\n", - "```\n", - "```{glue} administrative-boundaries-r\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} river-prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo-r\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::::{dropdown} How did we get this data ?\n", - "\n", - "\n", - "\n", - "::::{grid}\n", - ":padding: 2\n", - "\n", - ":::{grid-item-card}\n", - "\n", - "```{glue} scatter-prior-likelihood\n", - "```\n", - "+++\n", - "The data is a combination of observations from variety of groups in Switerland since 2015. The observations were recorded using an interpretation of the _Guide for Monitoring Marine Litter on European Seas_ [The guide](https://mcc.jrc.ec.europa.eu/main/dev.py?N=41&O=439&titre_chap=TG%20Litter&titre_page=Guidance%20for%20the%20Monitoring%20of%20Marine%20Litter). The guide and the monitoring of beach litter are part of decades of research, here is the brief history [A Brief History of Marine Litter Research](https://link.springer.com/chapter/10.1007/978-3-319-16510-3_1).\n", - ":::\n", - "::::\n", - "\n", - "__Common sense guidance:__\n", - "\n", - "1. The data should be considered as a reasonable estimate of the minimum amount of trash on the ground at the time of the survey.\n", - "2. There are many sources of variance. We have considered the following:\n", - " * litter density between sampling groups.\n", - " * litter density with respect to topographical features.\n", - "3. There are differences in detect-ability and appearance for items of the same classification that are due to the effects of decomposition.\n", - "4. Many surveyors are volunteers and have different levels of experience or physical constraints that limit what will actually be collected and counted.\n", - ":::::\n", - "\n", - ":::{dropdown} How to make a report\n", - "\n", - "__Survey and Land use__\n", - "\n", - "A report is the implementation of a `SurveyReport` and a `LandUseReport`. The `SurveyReport` is the basic \n", - "element and does the initial aggregating and descriptive statistics for a query.\n", - "\n", - "The land-use-report accepts `SurveyReport.sample_results` and assigns the land-use attributes to the record. The \n", - "land-use-report provides the baseline assessment of litter density with reference to the surrounding environment. \n", - "\n", - "The assessment accepts as variables the proportion of available space that a topographical feature occupies in a \n", - "circle of $\\pi r² \\text{ where r = 1 500 meters}$ and the center of that circle is the survey location. \n", - "These proportions are compared to the `average pieces per meter` for an object or group of objects.\n", - "\n", - "\n", - "__Create a report__\n", - "\n", - "A report can be intiated by providing the name of the canton. If your canton does not appear this is because we have no data. The prior dates will be calculated automatically, by taking all data prior to the start date of the querry.\n", - "\n", - "```{code} python\n", - "\n", - "import reports\n", - "import geospatial\n", - "import gridforecast\n", - "\n", - "# suppose you have defined your data into df\n", - "observed_dates = {'start':'2020-01-01', 'end':'2021-12-31'}\n", - "\n", - "# everything that was seen before\n", - "prior_dates = {'start':'2015-11-15', 'end':'2019-12-31'}\n", - "\n", - "# name the canton\n", - "canton = 'Bern'\n", - "\n", - "# define the data of interest\n", - "data_of_interest = {'canton':canton, 'date_range':observed_dates}\n", - "\n", - "# load the data\n", - "df = session_config.collect_survey_data()\n", - "\n", - "# filter the data. \n", - "filtered_data, locations = gridforeacast.filter_data(df, data_of_interest)\n", - "\n", - "# make a survey report\n", - "this_report = reports.SurveyReport(dfc=filtered_data)\n", - "\n", - "# generate the parameters for the landuse report\n", - "target_df = this_report.sample_results\n", - "features = geospatial.collect_topo_data(locations=target_df.location.unique())\n", - "\n", - "# make a landuse report\n", - "this_land_use = geospatial.LandUseReport(target_df, features)\n", - "```\n", - "\n", - "Each report and the inference method are documented: [SurveyReport](surveyreporter), [LandUseReport](landusereporter), [GridForecaster](gridforecaster)\n", - ":::\n" - ] - }, - { - "cell_type": "markdown", - "id": "160aae5f-e9ed-4754-86a8-a76af4616553", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "source": [ - "## Most common objects 2020 - 2021\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common-l\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects-l\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common-r\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects-r\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::{dropdown} Defining the most common objects\n", - "\n", - "The default method for defining _the most common objects_ is based on the number of items collected and the number of times that at least one of an object was found with respect to the number of surveys in the query, the _fail rate_.\n", - "\n", - "Adjusting the fail rate will increase or decrease the number of the most common objects. The fail rate is included with the object inventory. \n", - "\n", - "```{code} python\n", - "\n", - "# the most common objects are accesible in the survey report\n", - "# the report.object_summary method aggregates the data to code\n", - "# and attaches the fail rate and % of total\n", - "inventory = this_report.object_summary()\n", - "\n", - "# userdisplay.most_common, takes the 10 most abundant and filters\n", - "# the data for fail rate >= 0.5. The method returns a formatted table,\n", - "# a list of the codes and the ratio of the quantity of the most common to the whole \n", - "mostcommon, codes, ratio = userdisplay.most_common(inventory)\n", - "\n", - "```\n", - "\n", - "\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "id": "1153176b-fd0c-4e93-8928-6c89886b9525", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Land use\n", - "\n", - "\n", - "Land use refers to the measurable topographic features within a cirlce of r = 1 500 m and area = $\\pi r²$ with the survey location in the middle. The features, measured in meters squared, are given as a ratio $\\frac{\\text{area of feature}}{\\text{area of circle}}$. Thus a location with high percentage of buildings will have a rating or value between 60% and 100%. The pcs/m rating is the average of all locations with a land-use profile of the same rating." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards5.030.090.000.000.00
Vineyards5.040.565.010.000.00
Buildings2.627.463.802.926.27
Forest4.862.7313.130.000.00
Undefined5.456.222.951.840.00
Public Services4.2614.600.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards100%0%0%0%0%
Vineyards98%1%1%0%0%
Buildings14%18%10%22%36%
Forest68%24%8%0%0%
Undefined57%22%16%6%0%
Public Services93%7%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "g = results['this_land_use'].n_samples_per_feature().copy()\n", - "g = userdisplay.landuse_profile(g[session_config.feature_variables[:-1]], nsamples=results['this_report'].number_of_samples)\n", - "g = g.set_caption(\"\")\n", - "\n", - "gt = results['this_land_use'].rate_per_feature().copy()\n", - "gt = userdisplay.litter_rates_per_feature(gt.loc[session_config.feature_variables[:-1]])\n", - "gt = gt.set_caption(\"\")\n", - "\n", - "glue('rate-per-feature', gt, display=False)\n", - "glue('sampling-profile', g, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets31%46%21%2%1%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets5.264.335.664.1214.56
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets = results['this_land_use'].n_samples_per_feature().copy()\n", - "streets = streets[[session_config.feature_variables[-1]]].copy()\n", - "streets = userdisplay.street_profile(streets.T, nsamples=results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets = streets.set_caption(caption)\n", - "\n", - "streets_r = results['this_land_use'].rate_per_feature().copy()\n", - "streets_r = streets_r.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r = userdisplay.street_profile(streets_r, nsamples=results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r = streets_r.set_caption(caption)\n", - "\n", - "glue('street-profile', streets, display=False)\n", - "glue('street-rates-feature', streets_r, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards5.500.000.000.000.00
Vineyards5.510.005.010.000.00
Buildings2.947.835.123.336.39
Forest5.203.1314.920.000.00
Undefined5.887.423.001.910.00
Public Services4.6416.310.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards100%0%0%0%0%
Vineyards99%0%1%0%0%
Buildings12%20%8%21%39%
Forest70%22%8%0%0%
Undefined58%20%16%6%0%
Public Services93%7%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "gl = lake_results['this_land_use'].n_samples_per_feature().copy()\n", - "gl = userdisplay.landuse_profile(gl[session_config.feature_variables[:-1]], nsamples=lake_results['this_report'].number_of_samples)\n", - "gl = gl.set_caption(\"\")\n", - "\n", - "gtl = lake_results['this_land_use'].rate_per_feature().copy()\n", - "\n", - "gtl = userdisplay.litter_rates_per_feature(gtl.loc[session_config.feature_variables[:-1]])\n", - "gtl = gtl.set_caption(\"\")\n", - "\n", - "glue('lake-rate-per-feature', gtl, display=False)\n", - "glue('lake-sampling-profile', gl, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets35%48%16%0%1%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets5.334.607.68021.80
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets_p = lake_results['this_land_use'].n_samples_per_feature().copy()\n", - "streets_p = streets_p[[session_config.feature_variables[-1]]].copy()\n", - "streets_p = userdisplay.street_profile(streets_p.T, nsamples=lake_results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets_p = streets_p.set_caption(caption)\n", - "\n", - "streets_r_l = lake_results['this_land_use'].rate_per_feature().copy()\n", - "streets_r_l = streets_r_l.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r_l = userdisplay.street_profile(streets_r_l, nsamples=lake_results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r_l = streets_r_l.set_caption(caption)\n", - "\n", - "\n", - "glue('lake-street-profile', streets_p, display=False)\n", - "glue('lake-street-rates-feature', streets_r_l, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "82f55461-c497-483a-8c38-fbd509809afb", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards1.620.090.000.000.00
Vineyards1.640.560.000.000.00
Buildings1.501.931.170.434.06
Forest1.821.012.380.000.00
Undefined1.641.192.461.500.00
Public Services1.670.070.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards97%3%0%0%0%
Vineyards94%6%0%0%0%
Buildings24%9%27%24%15%
Forest55%36%9%0%0%
Undefined45%33%12%9%0%
Public Services94%6%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "gr = river_results['this_land_use'].n_samples_per_feature().copy()\n", - "gr = userdisplay.landuse_profile(gr[session_config.feature_variables[:-1]], nsamples=river_results['this_report'].number_of_samples)\n", - "gr = gr.set_caption(\"\")\n", - "\n", - "gtlr = river_results['this_land_use'].rate_per_feature().copy()\n", - "\n", - "gtlr = userdisplay.litter_rates_per_feature(gtlr.loc[session_config.feature_variables[:-1]])\n", - "gtlr = gtlr.set_caption(\"\")\n", - "\n", - "\n", - "glue('river-rate-per-feature', gtlr, display=False)\n", - "glue('river-sampling-profile', gr, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "9b396025-1fa6-4661-9116-593fa1ed741d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets3%30%52%12%3%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets0.221.361.274.120.08
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets_p_r = river_results['this_land_use'].n_samples_per_feature().copy()\n", - "streets_p_r = streets_p_r[[session_config.feature_variables[-1]]].copy()\n", - "streets_p_r = userdisplay.street_profile(streets_p_r.T, nsamples=river_results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets_p_r = streets_p_r.set_caption(caption)\n", - "\n", - "streets_r_r = river_results['this_land_use'].rate_per_feature().copy()\n", - "streets_r_r = streets_r_r.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r_r = userdisplay.street_profile(streets_r_r, nsamples=river_results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r_r = streets_r_r.set_caption(caption)\n", - "\n", - "\n", - "glue('river-street-profile', streets_p_r, display=False)\n", - "glue('river-street-rates-feature', streets_r_r, display=False)" - ] - }, - { - "cell_type": "markdown", - "id": "e45988a7-3442-434a-acab-c0b13cbfcd42", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "1. The rate per feature refers to the average pcs/m observed at a particular land use rate\n", - " * Under what conditions is the pcs/m elevated? Where is it the least?\n", - "2. The sampling profile refers to the ratio of samples that were taken at a particular land use rate\n", - " * Does the sampling profile reflect the topography of the region?\n", - "\n", - "\n", - "\n", - "### Rate per feature 2020 - 2021\n", - "\n", - "::::{tab-set}\n", - ":::{tab-item} All lakes and rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} street-rates-feature\n", - "``` \n", - ":::\n", - "\n", - ":::{tab-item} Lakes\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} lake-rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} lake-street-rates-feature\n", - "```\n", - ":::\n", - "\n", - ":::{tab-item} Rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} river-rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} river-street-rates-feature\n", - "``` \n", - ":::\n", - "\n", - "::::\n", - "\n", - "### Sampling profile 2020 - 2021\n", - "\n", - "::::{tab-set}\n", - ":::{tab-item} All lakes and rivers\n", - "\n", - "__Land use__\n", - "\n", - "\n", - "```{glue} sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lengths for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} street-profile\n", - "``` \n", - ":::\n", - "\n", - ":::{tab-item} Lakes\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} lake-sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} lake-street-profile\n", - ":::\n", - "\n", - ":::{tab-item} Rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} river-sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "\n", - "\n", - "```{glue} river-street-profile\n", - "``` \n", - ":::\n", - "\n", - "::::\n", - "\n", - ":::{dropdown} Defining land use\n", - "\n", - "__Land cover__\n", - "\n", - "These measured land-use attributes are the labeled polygons from the map layer Landcover defined here ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)), they are extracted using vector overlay techniques in QGIS ([QGIS](https://qgis.org/en/site/)).\n", - "\n", - "* Buildings: built up, urbanized\n", - "* Woods: not a park, harvesting of trees may be active\n", - "* Vineyards: does not include any other type of agriculture\n", - "* Orchards: not vineyards\n", - "* Undefined: areas of the map with no predefined label\n", - "\n", - "\n", - "```{code}\n", - "\n", - "# the land use is summarized using a LandUseReport object\n", - "# the average pieces per meter by land use category\n", - "rate_per_feature = this_land_use.n_pieces_per_feature()\n", - "\n", - "# the sampling distribution\n", - "samples_per_feature = this_land_use.n_samples_per_feature()\n", - "\n", - "# the variety of locations per feature\n", - "locations_per_feature = this_land_use.locations_per_feature()\n", - "\n", - "# format for display .html\n", - "styled_rate_per_feature = userdisplay.litter_rates_per_feature(rate_per_feature)\n", - "```\n", - "\n", - "__Public services__\n", - "\n", - "Public services are the labled polygons from the Freizeitareal and Nutzungsareal map layers, defined in ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)). Both layers represent areas used for specific activities. Freizeitareal identifies areas used for recreational purposes and Nutzungsareal represents areas such as hospitals, cemeteries, historical sites or incineration plants. As a ratio of the available dry-land in a hex, these features are relatively small (less than 10%) of the total dry-land. For identified features within a bounding hex the magnitude in meters² of these variables is scaled between 0 and 1, thus the scaled value represents the size of the feature in relation to all other measured values for that feature from all other hexagons.\n", - "\n", - "* Recreation: parks, sports fields, attractions\n", - "* Infrastructure: Schools, Hospitals, cemeteries, powerplants\n", - "\n", - "__Streets and roads__\n", - "\n", - "Streets and roads are the labled polylines from the TLM Strasse map layer defined in ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)). All polyines from the map layer within a bounding hex are merged (disolved in QGIS commands) and the combined length of the polylines, in meters, is the magnitude of the variable for the bounding hex.\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "id": "501575a0-10d5-4609-8550-8d80807fda4d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Forecast\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max\n", - "```\n", - "```{glue} forecast-weighted-prior\n", - "```\n", - "\n", - "```{glue} forecast-max-val\n", - "```\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max-l\n", - "```\n", - "\n", - "```{glue} forecast-weighted-prior-l\n", - "```\n", - "\n", - "```{glue} forecast-max-val-l\n", - "```\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} lake-cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max-r\n", - "```\n", - "\n", - "```{glue} forecast-weighted-prior-r\n", - "```\n", - "\n", - "```{glue} forecast-max-val-r\n", - "```\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} river-cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::{dropdown} Forecast methods\n", - "\n", - "The applied method would best be classified as Empirical Bayes, in the sense that the prior is derived from the data ([Bayesian Filtering and Smoothing](https://users.aalto.fi/~ssarkka/pub/cup_book_online_20131111.pdf) or [Empirical Bayes methods in classical and Bayesian inference](https://hannig.cloudapps.unc.edu/STOR757Bayes/handouts/PetroneEtAl2014.pdf)). However, we share the concerns of Davidson-Pillon [Bayesian methods for hackers](https://dataorigami.net/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/#contents) about double counting and eliminate it the possibility as part of the formulation of the prior. \n", - "\n", - "__Model assumptions__\n", - "\n", - "1. Locations with similar land use attributes will have similar litter density rates\n", - "2. The data is a best estimate of what was present on the day of the survey\n", - "3. There are regional differences with respect to the density of specific objects\n", - "4. The locations surveyed are maintained by a public administration\n", - "\n", - "The choice of land use features was a natural choice but was further explored in [Near or Far](https://hammerdirt-analyst.github.io/landuse/titlepage.html).\n", - "\n", - "Our parameter estimates are thus derived from the data and they remain testable and quantifiable according to [Prior Probabilities, E T Jaynes](https://bayes.wustl.edu/etj/articles/prior.pdf). This makes our calculation very repetetive but also very well understood. It can be defined in a few lines of code for any set of survey results.\n", - "\n", - "```{code} python\n", - "\n", - "# standared libaries\n", - "import numpy as np\n", - "from scipy.stats import dirichlet, multinomial\n", - "\n", - "# collect the data of interest\n", - "h = array of survey values\n", - "\n", - "# count the number of times that each survey values exceed a value on the gird\n", - "counts = np.array([np.sum((h > x) & (h <= x + .1)) for x in grid_range])\n", - "\n", - "# use the dirichlet dist to estimate p(Y >= x) for each x on the grid\n", - "# and sample from the estimation\n", - "adist = dirichlet(counts)\n", - "this_dist = adist.rvs(1-[0]\n", - "\n", - "# draw samples from the conjugate\n", - "posterior_samples = multinomial.rvs(nsamples, p=this_dist)\n", - "\n", - "```\n", - ":::" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b0bda4c0-1b4a-4011-9ad1-fb3a52aac885", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 quantitypcs/msamplesorchardsvineyardsbuildingsforestundefinedpublic servicesstreets
canton          
Bern98892.81981122312
Genève95595.88331151113
Valais890223.70131123211
Vaud624687.782261141112
Zürich272283.991711141212
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-municipal-results" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dxl = call_l_surveys.df\n", - "dxf = call_l_land.df_cont\n", - "\n", - "dxlc = dxl[['location', 'canton', 'feature_type']].drop_duplicates('location')\n", - "dxlc.set_index(['location'], inplace=True, drop=True)\n", - "dxf['canton'] = dxf.location.apply(lambda x : dxlc.loc[x, 'canton'])\n", - "sumlu = {x:'sum' for x in session_config.feature_variables}\n", - "dxf = dxf.groupby(['sample_id', 'canton', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n", - "\n", - "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n", - "\n", - "for alabel in session_config.feature_variables:\n", - " dxf[alabel] = dxf[alabel]/dxf.sample_id\n", - " \n", - "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n", - "dxfc = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n", - "dxfc.rename(columns={'sample_id':'samples'}, inplace=True)\n", - "dxfc.drop('check', axis=1, inplace=True)\n", - "dxfc = dxfc.style.set_table_styles(userdisplay.table_css_styles)\n", - "dxfc = dxfc.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n", - "dxfc = dxfc.format(userdisplay.format_kwargs, precision=2)\n", - "\n", - "glue('lake-municipal-results', dxfc , display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 quantitypcs/msamplesorchardsvineyardsbuildingsforestundefinedpublic servicesstreets
canton          
Bern37011.21971131213
Genève16164.3891141113
Valais780.4041122213
Vaud7382.6451132213
Zürich65571.151751141213
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-municipal-results" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dxl = call_r_surveys.df\n", - "dxf = call_r_land.df_cont\n", - "\n", - "dxlc = dxl[['location', 'canton', 'feature_type']].drop_duplicates('location')\n", - "dxlc.set_index(['location'], inplace=True, drop=True)\n", - "dxf['canton'] = dxf.location.apply(lambda x : dxlc.loc[x, 'canton'])\n", - "sumlu = {x:'sum' for x in session_config.feature_variables}\n", - "dxf = dxf.groupby(['sample_id', 'canton', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n", - "\n", - "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n", - "\n", - "for alabel in session_config.feature_variables:\n", - " dxf[alabel] = dxf[alabel]/dxf.sample_id\n", - " \n", - "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n", - "dxfcr = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n", - "dxfcr.rename(columns={'sample_id':'samples'}, inplace=True)\n", - "dxfcr.drop('check', axis=1, inplace=True)\n", - "dxfcr = dxfcr.style.set_table_styles(userdisplay.table_css_styles)\n", - "dxfcr = dxfcr.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n", - "dxfcr = dxfcr.format(userdisplay.format_kwargs, precision=2)\n", - "# glue('all-data-municipal-results', i , display=False)\n", - "glue('river-municipal-results', dxfcr, display=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "2d5b8904-044b-4aed-916c-5e36018f4087", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 samplespcs/m
Bielersee504.08
Brienzersee53.96
Greifensee344.71
Katzensee121.77
Lac-leman2498.91
Neuenburgersee231.78
Thunersee431.21
Zurichsee1254.01
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lakes-i-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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 \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
 samplespcs/m
Aare611.27
Aarenidau-buren-kanal31.26
Arve82.92
Chriesbach120.92
Dorfbach10.11
Emme90.60
Glatt71.67
Grandelbach41.33
Jona122.04
La-thiele10.46
Langeten111.94
Limmat611.13
Ognonnaz12.85
Rhein101.52
Rhone83.43
Schuss21.04
Sihl511.08
Toss170.59
Zulg110.63
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "rivers-i-summary" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "summardata = alldata_ofinterest.groupby(['sample_id', 'feature_type','feature_name'], as_index=False).agg({'pcs/m':'sum'})\n", - "# lakes\n", - "feature_individual_summary = summardata.groupby(['feature_type','feature_name'], as_index=False).agg({'sample_id':'nunique', 'pcs/m':'mean'})\n", - "\n", - "lakes_individual_summary = feature_individual_summary[feature_individual_summary.feature_type == 'l'].copy()\n", - "rivers_individual_summary = feature_individual_summary[feature_individual_summary.feature_type == 'r'].copy()\n", - "\n", - "\n", - " \n", - "lakes_i_sum = format_river_lake_summary(lakes_individual_summary).style.set_table_styles(userdisplay.table_css_styles).format(precision=2)\n", - "rivers_i_sum = format_river_lake_summary(rivers_individual_summary).style.set_table_styles(userdisplay.table_css_styles).format(precision=2)\n", - "\n", - "glue('lakes-i-summary', lakes_i_sum, display=False)\n", - "glue('rivers-i-summary', rivers_i_sum, display=False)" - ] - }, - { - "cell_type": "markdown", - "id": "b6d8a919-cdad-4915-99d0-8ffd21a40e98", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Lakes and rivers sampled - all data\n", - "\n", - "::::{grid} 2 2 2 2\n", - "\n", - ":::{grid-item}\n", - "**Lakes sampled**\n", - "\n", - "```{glue} lakes-i-summary\n", - "```\n", - "\n", - ":::\n", - "\n", - ":::{grid-item}\n", - "**Rivers sampled**\n", - "\n", - "```{glue} rivers-i-summary\n", - "```\n", - ":::\n", - "::::\n", - "\n", - "## Municipal Results - all data\n", - "\n", - "The average pieces per meter of the most common objects for each canton.\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "```{glue} lake-municipal-results\n", - "```\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "```{glue} river-municipal-results\n", - "``` \n", - ":::::::::\n", - "\n", - "::::::::::" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_build/html/_sources/geneve.ipynb b/_build/html/_sources/geneve.ipynb deleted file mode 100644 index 5469240..0000000 --- a/_build/html/_sources/geneve.ipynb +++ /dev/null @@ -1,2636 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "5bf92247-ef77-4aae-b62f-9388cba6765e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "import session_config\n", - "import reports\n", - "import userdisplay\n", - "import geospatial\n", - "import gridforecast as gfcast\n", - "\n", - "import logging\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "import matplotlib.colors\n", - "from matplotlib.colors import LinearSegmentedColormap, ListedColormap\n", - "import matplotlib.dates as mdates\n", - "import seaborn as sns\n", - "import datetime as dt\n", - "\n", - "import geopandas as gpd\n", - "import contextily as ctx\n", - "from shapely.geometry import box\n", - "from shapely.geometry import Point\n", - "\n", - "from myst_nb import glue\n", - "from IPython.display import display, Markdown\n", - "\n", - "def display_forecast(fcast_summary):\n", - " average = fcast_summary['average']\n", - " hdi_min, hdi_max = fcast_summary['hdi'][0], fcast_summary['hdi'][1]\n", - " \n", - " range_90_min, range_90_max= fcast_summary['range'][0], fcast_summary['range'][-1]\n", - " alist = f'\\n* Average: {round(average, 2)}\\n* HDI 95%: {round(hdi_min, 2)} - {round(hdi_max, 2)}\\n* 90% Range: {round(range_90_min, 2)} - {round(range_90_max,2)}'\n", - " return alist\n", - "\n", - "def display_forecast_summary(asummary, label):\n", - " forecast_summary = display_forecast(asummary)\n", - " forecast_summary = Markdown(f'{label}{forecast_summary}')\n", - " return forecast_summary\n", - "\n", - "def extract_dates_for_labels_from_summary(summary):\n", - " start = dt.datetime.strftime(summary.pop('start'), format=session_config.date_format)[:4]\n", - " end = dt.datetime.strftime(summary.pop('end'), format=session_config.date_format)[:4]\n", - " return f\"{start} - {end}\"\n", - "\n", - "def format_boundaries_feature_inv(boundaries, topop, displayfunc, session_language='en'):\n", - " for thingtoremove in topop:\n", - " boundaries.pop(thingtoremove)\n", - " display_boundaries = displayfunc(boundaries, session_language=session_language)\n", - " return Markdown(display_boundaries)\n", - "\n", - "def format_river_lake_summary(d):\n", - " d.drop('feature_type', axis=1, inplace=True)\n", - " d.rename(columns={'feature_name':'Name', 'sample_id': 'samples'}, inplace=True)\n", - " d['pcs/m'] = d['pcs/m'].round(2)\n", - " d['Name'] = d.Name.apply(lambda x: x.capitalize())\n", - " d.set_index('Name', inplace=True)\n", - " d.index.name = None\n", - " return d\n", - "\n", - "\n", - "highlight_props = 'background-color:#FAE8E8'\n", - "def highlight_max(s, arg, props: str = highlight_props):\n", - " return np.where((s > arg) & (s != 0), props, '')\n", - "\n", - "logging.basicConfig(\n", - " filename='app.log', \n", - " level=logging.DEBUG,\n", - " format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n", - ")\n", - "\n", - "logger = logging.getLogger(__name__)\n", - "color_style = {'prior':'color: #daa520', 'likelihood':'color: #1e90ff'}\n", - "palette = {'prior':'goldenrod', 'likelihood':'dodgerblue'}" - ] - }, - { - "cell_type": "markdown", - "id": "d2d5a3de-1688-4113-95ce-bf614dbf9695", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "fb4d8635-eb6f-402a-9cd6-8be9c76cce30", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "data = session_config.collect_survey_data()\n", - "\n", - "o_dates = {'start':'2020-01-01', 'end':'2021-12-31'}\n", - "prior_dates = {'start':'2015-11-15', 'end':'2019-12-31'}\n", - "\n", - "# all data\n", - "canton = 'Genève'\n", - "d= data.reset_index(drop=True)\n", - "\n", - "# all surveys lakes, rivers combined\n", - "alldata_ofinterest, locations = gfcast.filter_data(d,{'canton': canton, 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_surveys, call_land = gfcast.make_report_objects(alldata_ofinterest)\n", - "\n", - "# summary and labels\n", - "all_summary = call_surveys.sampling_results_summary.copy()\n", - "all_labels = extract_dates_for_labels_from_summary(all_summary)\n", - "\n", - "# material proportions all data\n", - "material_report = call_surveys.material_report\n", - "material_report = material_report.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior = d[(d.canton != canton)&(d['date'] <= prior_dates['end'])].copy()\n", - "o_report, o_land_use = gfcast.make_report_objects(o_prior)\n", - "results = gfcast.reports_and_forecast({'canton':canton, 'date_range':o_dates}, {'canton':canton, 'date_range':prior_dates}, ldata=d.copy(), logger=logger, other_data=o_land_use.df_cat)\n", - "\n", - "# prior summary and label\n", - "p_summary = results['prior_report'].sampling_results_summary.copy()\n", - "prior_labels = extract_dates_for_labels_from_summary(p_summary)\n", - "\n", - "# likelihood summary and label\n", - "l_summary = results['this_report'].sampling_results_summary.copy()\n", - "likelihood_labels = extract_dates_for_labels_from_summary(l_summary)\n", - "\n", - "# forecasts\n", - "xii = results['posterior_no_limit'].sample_posterior()\n", - "\n", - "# limit to the 99th percentile\n", - "sample_values, posterior, summary_simple = gfcast.dirichlet_posterior(results['posterior_99'])\n", - "\n", - "# forecast weighted prior all data\n", - "weighted_args = [results['this_land_use'], session_config.feature_variables, call_land.df_cat, results['this_report'].sample_results['pcs/m']]\n", - "weighted_forecast, weighted_posterior, weighted_summary, _ = gfcast.forecast_weighted_prior(*weighted_args)\n", - "\n", - "# forecast summaries\n", - "forecast_99 = display_forecast_summary(summary_simple, '__Given the 99th percentile__')\n", - "forecast_maxval = display_forecast_summary(results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - "forecast_weighted = display_forecast_summary(weighted_summary, '__Given the weighted prior__')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d504b355-6ec4-4d6d-b9d6-3459215fcb8b", - "metadata": { - "editable": true, - "jupyter": { - "source_hidden": true - }, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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 % of total
material 
glass2%
metal4%
plastic87%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "material-report" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 4.57\n* HDI 95%: 0.6 - 17.9\n* 90% Range: 0.7 - 17.9", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-weighted-prior" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 7.84\n* HDI 95%: 0.22 - 20.11\n* 90% Range: 0.58 - 18.9", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-max-val" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 5.14\n* HDI 95%: 0.6 - 17.7\n* 90% Range: 0.7 - 16.08", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-99-max" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__The most common objects account for 78% of all objects__", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "ratio-most-common" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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 \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
ObjectQuantitypcs/m% of totalFail rate
Fragmented plastics1'0300,660,190,90
Cigarette filters9430,580,171,00
Industrial pellets (nurdles)4940,250,090,62
Food wrappers; candy, snacks4050,380,070,95
Plastic fragments angular <5mm2220,110,040,33
plastic caps, lid rings: G21, G22, G23, G242090,200,040,81
Metal bottle caps, lids & pull tabs from cans2030,180,040,86
Glass drink bottles, pieces1760,180,030,62
Industrial sheeting1230,140,020,48
Cotton bud/swab sticks1140,090,020,57
Expanded polystyrene970,060,020,62
Foil wrappers, aluminum foil620,040,010,71
Foam packaging/insulation/polyurethane570,020,010,86
Other medical (swabs, bandaging, adhesive plaster)520,030,010,67
Cups, lids, single use foamed and hard plastic500,050,010,52
Straws and stirrers420,030,010,62
Toys and party favors350,030,010,52
Hair clip, hair ties, personal accessories plastic200,020,000,52
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 21\n* Total objects: 5499\n* Average pcs/m: 4.15\n* Standard deviation: 4.89\n* Maximum pcs/m: 17.88\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2016 - 2018\n* Number of samples: 21\n* Total objects: 5676\n* Average pcs/m: 6.97\n* Standard deviation: 5.63\n* Maximum pcs/m: 23.11\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2016 - 2021\n* Number of samples: 42\n* Total objects: 11175\n* Average pcs/m: 5.56\n* Standard deviation: 5.46\n* Maximum pcs/m: 23.11\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Rivers: 2\n* Lakes: 1\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 8\n* Cities: 3\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# most common objects all data\n", - "os = results['this_report'].object_summary()\n", - "os.reset_index(drop=False, inplace=True)\n", - "most_common_objects, mc_codes, proportions = userdisplay.most_common(os)\n", - "most_common_objects = most_common_objects.set_caption(\"\")\n", - "\n", - "# display the inventory of features\n", - "feature_inv = call_surveys.feature_inventory()\n", - "feature_inventory = format_boundaries_feature_inv(feature_inv, ['p'], userdisplay.feature_inventory)\n", - "\n", - "# display the inventory of boundaries\n", - "aboundaries = call_surveys.administrative_boundaries().copy()\n", - "administrative_boundaries = format_boundaries_feature_inv(aboundaries, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - "# display the sampling summaries\n", - "all_info = userdisplay.sampling_result_summary(all_summary, session_language='en')[1]\n", - "all_samp_sum = Markdown(f'{all_labels}\\n{all_info}')\n", - "\n", - "p_header = f\"{prior_labels}\"\n", - "p_info = userdisplay.sampling_result_summary(p_summary, session_language='en')[1]\n", - "p_samp_sum = Markdown(f'{p_header}\\n{p_info}')\n", - "\n", - "l_header = f\"{likelihood_labels} \"\n", - "l_info = userdisplay.sampling_result_summary(l_summary, session_language='en')[1]\n", - "l_samp_sum = Markdown(f'{l_header}\\n{l_info}')\n", - "\n", - "ratio_most_common = Markdown(f'__The most common objects account for {int(proportions*100)}% of all objects__')\n", - "\n", - "caption_histo = Markdown(f'Survey total pcs/m {likelihood_labels} versus {prior_labels}. All locations considered.')\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.histplot(data=results['this_report'].sample_results, x='pcs/m', stat='probability', label=likelihood_labels, ax=ax, color=palette['likelihood'])\n", - "sns.histplot(data=results['prior_report'] .sample_results, x='pcs/m', stat='probability', label=prior_labels, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "title = f'All samples {canton}: {prior_dates[\"start\"]} - {o_dates[\"end\"]}'\n", - "\n", - "ax.xaxis.set_minor_locator(mdates.MonthLocator(interval=3))\n", - "ax.xaxis.set_minor_formatter(mdates.DateFormatter(\"%b\"))\n", - "\n", - "ax.xaxis.set_major_locator(mdates.YearLocator())\n", - "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"\\n%Y\"))\n", - "\n", - "sns.scatterplot(data=results['this_report'].sample_results, x='date', y='pcs/m', marker='x', label=likelihood_labels, ax=ax, color=palette['likelihood'])\n", - "sns.scatterplot(data=results['prior_report'] .sample_results, x='date', y='pcs/m', marker='x', label=prior_labels, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "ax.set_xlabel('')\n", - "ax.set_title(title)\n", - "plt.tight_layout()\n", - "glue('scatter-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.ecdfplot(results['prior_report'].sample_results['pcs/m'], label=prior_labels, ls='-', ax=ax, c=palette['prior'], zorder=1)\n", - "sns.ecdfplot(results['this_report'].sample_results['pcs/m'], label=likelihood_labels, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "sns.ecdfplot(sample_values, label='expected 99%', ls=':', zorder=2)\n", - "sns.ecdfplot(xii, label='expected max', ls='-.', zorder=2)\n", - "sns.ecdfplot(weighted_forecast, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, results['this_report'].sample_results['pcs/m'].quantile(.99))\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_list = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo', caption_histo, display=False)\n", - "glue('material-report', material_report, display=False)\n", - "glue('forecast-weighted-prior', forecast_weighted, display=False)\n", - "glue('forecast-max-val', forecast_maxval, display=False)\n", - "glue('forecast-99-max', forecast_99, display=False)\n", - "glue('ratio-most-common', ratio_most_common, display=False)\n", - "glue('most_common_objects', most_common_objects, display=False)\n", - "glue('l-sampling-summary', l_samp_sum, display=False)\n", - "glue('prior-sampling-summary', p_samp_sum, display=False)\n", - "glue('sampling-summary', all_samp_sum, display=False)\n", - "glue('feature-inventory', feature_inventory, display=False)\n", - "glue('administrative-boundaries', administrative_boundaries, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c016aff6-dc3f-49a1-be4d-48b6448c9d22", - "metadata": { - "editable": true, - "jupyter": { - "source_hidden": true - }, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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 % of total
material 
glass2%
metal4%
plastic88%
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ObjectQuantitypcs/m% of totalFail rate
Fragmented plastics1'0300,690,200,95
Cigarette filters8930,480,171,00
Industrial pellets (nurdles)4940,260,100,65
Food wrappers; candy, snacks3640,290,070,95
Plastic fragments angular <5mm2220,120,040,35
plastic caps, lid rings: G21, G22, G23, G241960,180,040,80
Metal bottle caps, lids & pull tabs from cans1620,090,030,85
Glass drink bottles, pieces1320,080,030,60
Industrial sheeting1230,140,020,50
Cotton bud/swab sticks1140,100,020,60
Expanded polystyrene970,060,020,65
Foil wrappers, aluminum foil610,030,010,70
Foam packaging/insulation/polyurethane570,020,010,90
Other medical (swabs, bandaging, adhesive plaster)520,030,010,70
Cups, lids, single use foamed and hard plastic470,050,010,50
Straws and stirrers420,030,010,65
Toys and party favors350,030,010,55
Hair clip, hair ties, personal accessories plastic170,010,000,50
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 20\n* Total objects: 5179\n* Average pcs/m: 3.56\n* Standard deviation: 4.21\n* Maximum pcs/m: 17.88\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2016 - 2018\n* Number of samples: 13\n* Total objects: 4380\n* Average pcs/m: 9.46\n* Standard deviation: 5.75\n* Maximum pcs/m: 23.11\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2016 - 2021\n* Number of samples: 33\n* Total objects: 9559\n* Average pcs/m: 5.88\n* Standard deviation: 5.66\n* Maximum pcs/m: 23.11\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Lakes: 1\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 6\n* Cities: 2\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries-l" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# lakes\n", - "data = session_config.collect_survey_data()\n", - "lake_params = {'canton':canton, 'date_range':o_dates, 'feature_type': 'l'}\n", - "lake_params_p = {'canton':canton, 'date_range':prior_dates, 'feature_type':'l'}\n", - "\n", - "# all surveys lakes, rivers combined\n", - "c_all_l, _ = gfcast.filter_data(d,{'canton': canton, 'feature_type': 'l', 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_l_surveys, call_l_land = gfcast.make_report_objects(c_all_l)\n", - "\n", - "# summary and labels\n", - "all_summary_l = call_l_surveys.sampling_results_summary\n", - "all_labels_l = extract_dates_for_labels_from_summary(all_summary_l)\n", - "\n", - "# material proportions all data\n", - "material_report_l = call_l_surveys.material_report\n", - "material_report_l = material_report_l.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior_l = d[(d.canton != canton)&(d['date'] <= prior_dates['end'])&(d.feature_type == 'l')].copy()\n", - "o_report_l, o_land_use_l = gfcast.make_report_objects(o_prior_l)\n", - "lake_results = gfcast.reports_and_forecast(lake_params,lake_params_p , ldata=d.copy(), logger=logger, other_data=o_land_use_l.df_cat)\n", - "\n", - "# prior summary and label\n", - "p_summary_l = lake_results['prior_report'].sampling_results_summary.copy()\n", - "prior_labels_l = extract_dates_for_labels_from_summary(p_summary_l)\n", - "\n", - "# likelihood summary and label\n", - "l_summary_l = lake_results['this_report'].sampling_results_summary.copy()\n", - "likelihood_labels_l = extract_dates_for_labels_from_summary(l_summary_l)\n", - "\n", - "# forecasts\n", - "xii_l = lake_results['posterior_no_limit'].sample_posterior()\n", - "\n", - "# limit to the 99th percentile\n", - "sample_values_l, posterior_l, summary_simple_l = gfcast.dirichlet_posterior(lake_results['posterior_99'])\n", - "\n", - "# forecast weighted prior all data\n", - "weighted_args_l = [lake_results['this_land_use'], session_config.feature_variables, call_l_land.df_cat, lake_results['this_report'].sample_results['pcs/m']]\n", - "weighted_forecast_l, weighted_posterior_l, weighted_summary_l, _ = gfcast.forecast_weighted_prior(*weighted_args_l)\n", - "\n", - "# forecast summaries\n", - "forecast_99_l = display_forecast_summary(summary_simple_l, '__Given the 99th percentile__')\n", - "forecast_maxval_l = display_forecast_summary(lake_results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - "forecast_weighted_l = display_forecast_summary(weighted_summary_l, '__Given the weighted prior__')\n", - "\n", - "# most common objects all lake data\n", - "os_l = lake_results['this_report'].object_summary()\n", - "os_l.reset_index(drop=False, inplace=True)\n", - "most_common_objects_l, mc_codes_l, proportions_l = userdisplay.most_common(os_l)\n", - "most_common_objects_l = most_common_objects_l.set_caption(\"\")\n", - "\n", - "# display the inventory of features\n", - "feature_inv_l = call_l_surveys.feature_inventory()\n", - "feature_inventory_l = format_boundaries_feature_inv(feature_inv_l, ['p', 'r'], userdisplay.feature_inventory)\n", - "\n", - "# display the inventory of boundaries\n", - "aboundaries_l = call_l_surveys.administrative_boundaries().copy()\n", - "administrative_boundaries_l = format_boundaries_feature_inv(aboundaries_l, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - "# display the sampling summaries\n", - "all_info_l = userdisplay.sampling_result_summary(all_summary_l, session_language='en')[1]\n", - "all_samp_sum_l = Markdown(f'{all_labels_l}\\n{all_info_l}')\n", - "\n", - "p_header_l = f\"{prior_labels}\"\n", - "p_info_l = userdisplay.sampling_result_summary(p_summary_l, session_language='en')[1]\n", - "p_samp_sum_l = Markdown(f'{p_header_l}\\n{p_info_l}')\n", - "\n", - "l_header_l = f\"{likelihood_labels_l} \"\n", - "l_info_l = userdisplay.sampling_result_summary(l_summary_l, session_language='en')[1]\n", - "l_samp_sum_l = Markdown(f'{l_header_l}\\n{l_info_l}')\n", - "\n", - "ratio_most_common_l = Markdown(f'__The most common objects account for {int(proportions_l*100)}% of all objects__')\n", - "\n", - "caption_histo_l = Markdown(f'Survey total pcs/m {likelihood_labels_l} v/s {prior_labels_l}. All locations considered.')\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.histplot(data=lake_results['this_report'].sample_results, x='pcs/m', stat='probability', label=likelihood_labels_l, ax=ax, color=palette['likelihood'])\n", - "sns.histplot(data=lake_results['prior_report'].sample_results, x='pcs/m', stat='probability', label=prior_labels_l, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('lake-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.ecdfplot(lake_results['prior_report'].sample_results['pcs/m'], label=prior_labels_l, ls='-', ax=ax, c=palette['prior'], zorder=1)\n", - "sns.ecdfplot(lake_results['this_report'].sample_results['pcs/m'], label=likelihood_labels_l, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "sns.ecdfplot(sample_values_l, label='expected 99%', ls=':', zorder=2)\n", - "sns.ecdfplot(xii_l, label='expected max', ls='-.', zorder=2)\n", - "sns.ecdfplot(weighted_forecast_l, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, lake_results['this_report'].sample_results['pcs/m'].quantile(.99))\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('lake-cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_list = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo-l', caption_histo_l, display=False)\n", - "glue('material-report-l', material_report_l, display=False)\n", - "glue('forecast-weighted-prior-l', forecast_weighted_l, display=False)\n", - "glue('forecast-max-val-l', forecast_maxval_l, display=False)\n", - "glue('forecast-99-max-l', forecast_99_l, display=False)\n", - "glue('ratio-most-common-l', ratio_most_common_l, display=False)\n", - "glue('most_common_objects-l', most_common_objects_l, display=False)\n", - "glue('l-sampling-summary-l', l_samp_sum_l, display=False)\n", - "glue('prior-sampling-summary-l', p_samp_sum_l, display=False)\n", - "glue('sampling-summary-l', all_samp_sum_l, display=False)\n", - "glue('feature-inventory-l', feature_inventory_l, display=False)\n", - "glue('administrative-boundaries-l', administrative_boundaries_l, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "671460d2-c096-49b6-ab55-7cc9a003da78", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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", 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 % of total
material 
glass4%
metal4%
plastic85%
wood3%
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ObjectQuantitypcs/m% of totalFail rate
Cigarette filters502,500,161,00
Packaging films nonfood or unknown472,350,151,00
Glass drink bottles, pieces442,200,141,00
Metal bottle caps, lids & pull tabs from cans412,050,131,00
Food wrappers; candy, snacks412,050,131,00
Styrofoam < 5mm291,450,091,00
plastic caps, lid rings: G21, G22, G23, G24130,650,041,00
Paper packaging100,500,031,00
Cutlery, plates and trays70,350,021,00
Other metal pieces < 50cm70,350,021,00
coffee capsules aluminum50,250,021,00
Labels, bar codes40,200,011,00
Hair clip, hair ties, personal accessories plastic30,150,011,00
Cups, lids, single use foamed and hard plastic30,150,011,00
Pens, lids, mechanical pencils etc.20,100,011,00
Clothes, footware, headware, gloves20,100,011,00
Dog feces bag10,050,001,00
Bottles, containers, drums to transport, store material10,050,001,00
Foil wrappers, aluminum foil10,050,001,00
String < 1cm10,050,001,00
Mask medical, synthetic10,050,001,00
Other metal pieces > 50cm10,050,001,00
Batteries - household10,050,001,00
Safety pins, paper clips, small metal utility items10,050,001,00
Other textiles10,050,001,00
Balloons and balloon sticks10,050,001,00
Razor blades10,050,001,00
Medical; containers/tubes/ packaging10,050,001,00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2021 - 2021 \n* Number of samples: 1\n* Total objects: 320\n* Average pcs/m: 16.0\n* Standard deviation: 0.0\n* Maximum pcs/m: 16.0\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2016 - 2018\n* Number of samples: 8\n* Total objects: 1296\n* Average pcs/m: 2.92\n* Standard deviation: 1.77\n* Maximum pcs/m: 6.81\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2017 - 2021\n* Number of samples: 9\n* Total objects: 1616\n* Average pcs/m: 4.38\n* Standard deviation: 4.44\n* Maximum pcs/m: 16.0\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Rivers: 2\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 2\n* Cities: 2\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries-r" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# rivers\n", - "data = session_config.collect_survey_data()\n", - "river_params = {'canton':canton, 'date_range':o_dates, 'feature_type': 'r'}\n", - "river_params_p = {'canton':canton, 'date_range':prior_dates, 'feature_type':'r'}\n", - "\n", - "# all surveys lakes, rivers combined\n", - "c_all_r, _ = gfcast.filter_data(d,{'canton': canton, 'feature_type': 'r', 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_r_surveys, call_r_land = gfcast.make_report_objects(c_all_r)\n", - "\n", - "# summary and labels\n", - "all_summary_r = call_r_surveys.sampling_results_summary\n", - "all_labels_r = extract_dates_for_labels_from_summary(all_summary_r)\n", - "\n", - "# material proportions all data\n", - "material_report_r = call_r_surveys.material_report\n", - "material_report_r = material_report_r.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior_r = d[(d.canton != canton)&(d['date'] <= prior_dates['end'])&(d.feature_type == 'r')].copy()\n", - "o_report_r, o_land_use_r = gfcast.make_report_objects(o_prior_r)\n", - "newd = o_land_use_r.df_cat.copy()\n", - "newd['feature_type'] = 'r'\n", - "river_results = gfcast.reports_and_forecast(river_params,river_params_p , ldata=d.copy(), logger=logger, other_data=newd)\n", - "\n", - "\n", - "\n", - "if river_results['this_report'] == 'No likelihood':\n", - " likelihood_labels_r = likelihood_labels\n", - " l_header_r = f\"{likelihood_labels} \"\n", - " l_info_r = '* No data for the period requested\\n'\n", - " l_samp_sum_r = Markdown(f'{l_header_r}\\n{l_info_r}')\n", - " if river_results['prior_report'] == 'No prior':\n", - " prior_labels_r = prior_labels\n", - " p_header_r = f\"{prior_labels_r}\"\n", - " p_info_r = '* No data for the period requested\\n'\n", - " river_results.update({'prior_report':'No prior data'})\n", - " p_samp_sum_r = Markdown(f'{p_header_r}\\n{p_info_r}')\n", - " observedvals = [([0], 'No data', palette['prior']),([0], 'No data', palette['likelihood'])]\n", - " forecasts = [([0], 'No data', '-', 'black')]\n", - " else:\n", - " p_summary_r = river_results['prior_report'].sampling_results_summary.copy()\n", - " prior_labels_r = extract_dates_for_labels_from_summary(p_summary_r)\n", - " p_header_r = f\"{prior_labels}\"\n", - " p_info_r = userdisplay.sampling_result_summary(p_summary_r, session_language='en')[1]\n", - " p_samp_sum_r = Markdown(f'{p_header_r}\\n{p_info_r}')\n", - " observedvals = [(river_results['prior_report'].sample_results['pcs/m'], prior_labels_r, palette['prior']),([0], 'No data', palette['likelihood'])]\n", - " forecasts = [\n", - " (river_results['prior_report'].sample_results['pcs/m'], prior_labels_r, '-', palette['prior']),\n", - " ([0],f'{likelihood_labels_r} - no data', ':', palette['likelihood']),\n", - " ]\n", - " \n", - "else:\n", - " l_summary_r = river_results['this_report'].sampling_results_summary.copy()\n", - " likelihood_labels_r = extract_dates_for_labels_from_summary(l_summary_r)\n", - " l_header_r = f\"{likelihood_labels_r} \"\n", - " l_info_r = userdisplay.sampling_result_summary(l_summary_r, session_language='en')[1]\n", - " l_samp_sum_r = Markdown(f'{l_header_r}\\n{l_info_r}')\n", - "\n", - " # most common objects all lake data\n", - " os_r = river_results['this_report'].object_summary()\n", - " os_r.reset_index(drop=False, inplace=True)\n", - " most_common_objects_r, mc_codes_r, proportions_r = userdisplay.most_common(os_r)\n", - " most_common_objects_r = most_common_objects_r.set_caption(\"\")\n", - " ratio_most_common_r = Markdown(f'__The most common objects account for {int(proportions_r*100)}% of all objects__')\n", - "\n", - " # display the inventory of features\n", - " feature_inv_r = call_r_surveys.feature_inventory()\n", - " feature_inventory_r = format_boundaries_feature_inv(feature_inv_r, ['p', 'l'], userdisplay.feature_inventory)\n", - "\n", - " # display the inventory of boundaries\n", - " aboundaries_r = call_r_surveys.administrative_boundaries().copy()\n", - " administrative_boundaries_r = format_boundaries_feature_inv(aboundaries_r, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - " # display the sampling summaries\n", - " all_info_r = userdisplay.sampling_result_summary(all_summary_r, session_language='en')[1]\n", - " all_samp_sum_r = Markdown(f'{all_labels_r}\\n{all_info_r}')\n", - " \n", - " if river_results['prior_report'] == 'No prior':\n", - " prior_labels_r = prior_labels\n", - " p_header_r = f\"{prior_labels_r}\"\n", - " p_info_r = '* see weighted prior\\n'\n", - " river_results.update({'prior_report':'see weighted'})\n", - " p_samp_sum_r = Markdown(f'{p_header_r}\\n{p_info_r}')\n", - " forecast_maxval_r = '* No data to consider see weighted\\n'\n", - " forecast_99_r = '* No data to consider see weighted\\n'\n", - " observedvals = [\n", - " # (river_results['prior_report'].sample_results['pcs/m'], prior_labels_r , palette['prior']),\n", - " (river_results['this_report'].sample_results['pcs/m'], likelihood_labels_r, palette['likelihood'])\n", - " ]\n", - "\n", - " # forecast weighted prior all data\n", - " weighted_args_r = [river_results['this_land_use'], session_config.feature_variables, call_r_land.df_cat, river_results['this_report'].sample_results['pcs/m']]\n", - " weighted_forecast_r, weighted_posterior_r, weighted_summary_r, selectedr = gfcast.forecast_weighted_prior(*weighted_args_r)\n", - " forecast_weighted_r = display_forecast_summary(weighted_summary_r, '__Given the weighted prior__')\n", - " forecasts = [\n", - " \n", - " (weighted_forecast_r, 'weighted prior', '-.', 'black'),\n", - " ]\n", - " \n", - " else:\n", - " p_summary_r = river_results['prior_report'].sampling_results_summary.copy()\n", - " prior_labels_r = extract_dates_for_labels_from_summary(p_summary_r)\n", - " p_header_r = f\"{prior_labels}\"\n", - " p_info_r = userdisplay.sampling_result_summary(p_summary_r, session_language='en')[1]\n", - " p_samp_sum_r = Markdown(f'{p_header_r}\\n{p_info_r}')\n", - " xii_r = river_results['posterior_no_limit'].sample_posterior()\n", - " forecast_maxval_r = display_forecast_summary(river_results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - " observedvals = [\n", - " (river_results['prior_report'].sample_results['pcs/m'], prior_labels_r , palette['prior']),\n", - " (river_results['this_report'].sample_results['pcs/m'], likelihood_labels_r, palette['likelihood'])\n", - " ]\n", - " # limit to the 99th percentile\n", - " sample_values_r, posterior_r, summary_simple_r = gfcast.dirichlet_posterior(river_results['posterior_99'])\n", - " forecast_99_r = display_forecast_summary(summary_simple_r, '__Given the 99th percentile__')\n", - "\n", - " # forecast weighted prior all data\n", - " weighted_args_r = [river_results['this_land_use'], session_config.feature_variables, call_r_land.df_cat, river_results['this_report'].sample_results['pcs/m']]\n", - " weighted_forecast_r, weighted_posterior_r, weighted_summary_r, selectedr = gfcast.forecast_weighted_prior(*weighted_args_r)\n", - " forecast_weighted_r = display_forecast_summary(weighted_summary_r, '__Given the weighted prior__')\n", - "\n", - " forecasts = [\n", - " (river_results['prior_report'].sample_results['pcs/m'], prior_labels_r, '-',palette['prior']),\n", - " (river_results['this_report'].sample_results['pcs/m'], likelihood_labels_r, '-',palette['likelihood']),\n", - " (sample_values_r, 'expected 99th', '-', 'blue'),\n", - " (xii_r, 'observed max', ':', 'red'),\n", - " (weighted_forecast_r, 'weighted prior', '-.', 'black'),\n", - " ]\n", - " \n", - "\n", - " \n", - "\n", - " # forecast weighted prior all data\n", - " weighted_args_r = [river_results['this_land_use'], session_config.feature_variables, call_r_land.df_cat, river_results['this_report'].sample_results['pcs/m']]\n", - " weighted_forecast_r, weighted_posterior_r, weighted_summary_r, selectedr = gfcast.forecast_weighted_prior(*weighted_args_r)\n", - " forecast_weighted_r = display_forecast_summary(weighted_summary_r, '__Given the weighted prior__')\n", - "\n", - "caption_histo_r = Markdown(f'Survey total pcs/m {likelihood_labels_r} v/s {prior_labels_r}. All locations considered.')\n", - "\n", - "\n", - "\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "for vals in observedvals:\n", - " sns.histplot(data=vals[0], stat='probability', label=vals[1], ax=ax, color=vals[2])\n", - "# sns.histplot(data=river_results['prior_report'].sample_results, x='pcs/m', stat='probability', label=prior_labels_r, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('river-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "for vals in forecasts:\n", - " sns.ecdfplot(vals[0], label=vals[1], ls=vals[2], ax=ax, c=vals[3], zorder=1)\n", - "# sns.ecdfplot(river_results['this_report'].sample_results['pcs/m'], label=likelihood_labels_r, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "# sns.ecdfplot(sample_values_r, label='expected 99%', ls=':', zorder=2)\n", - "# sns.ecdfplot(xii_r, label='expected max', ls='-.', zorder=2)\n", - "# sns.ecdfplot(weighted_forecast_r, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, 10)\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('river-cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_rist = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo-r', caption_histo_r, display=False)\n", - "glue('material-report-r', material_report_r, display=False)\n", - "glue('forecast-weighted-prior-r', forecast_weighted_r, display=False)\n", - "glue('forecast-max-val-r', forecast_maxval_r, display=False)\n", - "glue('forecast-99-max-r', forecast_99_r, display=False)\n", - "glue('ratio-most-common-r', ratio_most_common_r, display=False)\n", - "glue('most_common_objects-r', most_common_objects_r, display=False)\n", - "glue('l-sampling-summary-r', l_samp_sum_r, display=False)\n", - "glue('prior-sampling-summary-r', p_samp_sum_r, display=False)\n", - "glue('sampling-summary-r', all_samp_sum_r, display=False)\n", - "glue('feature-inventory-r', feature_inventory_r, display=False)\n", - "glue('administrative-boundaries-r', administrative_boundaries_r, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "12c52a87-8340-419f-bfd9-75ca85260a97", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "map-of-survey-locations" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# Create a GeoDataFrame from the list of locations\n", - "dbc = gpd.read_file('data/shapes/kantons.shp')\n", - "dbc = dbc.to_crs(epsg=4326)\n", - "dbckey = dbc[['NAME', 'KANTONSNUM']].set_index('NAME')\n", - "dbckey = dbckey.drop_duplicates()\n", - "thiscanton = dbckey.loc[canton, 'KANTONSNUM']\n", - "db = gpd.read_file('data/shapes/municipalities.shp')\n", - "db = db.to_crs(epsg=4326)\n", - "thesecities = db[db.KANTONSNUM == thiscanton]\n", - "surveyedcities = alldata_ofinterest.city.unique()\n", - "\n", - "from matplotlib.lines import Line2D\n", - "# Define the plot\n", - "fig, ax = plt.subplots(figsize=(18, 10))\n", - "\n", - "citymap = thesecities.plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.1)\n", - "\n", - "surveyed = thesecities[thesecities.NAME.isin(surveyedcities)].plot(ax=ax, color='salmon', alpha=0.6)\n", - "\n", - "# Add a basemap using contextily\n", - "# ctx.add_basemap(ax, source=ctx.providers.SwissFederalGeoportal.NationalMapColor, crs=\"EPSG:4326\", aspect='equal')\n", - "dbc[dbc.NAME == canton].plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.2)\n", - "\n", - "sres = lake_results['this_report'].sample_results\n", - "pres = lake_results['prior_report'].sample_results\n", - "ares = call_surveys.sample_results\n", - "\n", - "sresr = river_results['this_report'].sample_results\n", - "# presr = river_results['prior_report'].sample_results\n", - "\n", - "marker_statsa, map_boundsa = userdisplay.map_markers(ares)\n", - "geometrya = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsa]\n", - "gdfa = gpd.GeoDataFrame(marker_statsa, geometry=geometrya, crs=\"EPSG:4326\")\n", - "\n", - "marker_stats, map_bounds = userdisplay.map_markers(sres)\n", - "geometry = [Point(loc['longitude'], loc['latitude']) for loc in marker_stats]\n", - "gdf = gpd.GeoDataFrame(marker_stats, geometry=geometry, crs=\"EPSG:4326\")\n", - "\n", - "marker_statsp, map_boundsp = userdisplay.map_markers(pres)\n", - "geometryp = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsp]\n", - "gdfp = gpd.GeoDataFrame(marker_statsp, geometry=geometryp, crs=\"EPSG:4326\")\n", - "\n", - "\n", - "marker_statsr, map_boundsr = userdisplay.map_markers(sresr)\n", - "geometryr = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsr]\n", - "gdfr = gpd.GeoDataFrame(marker_statsr, geometry=geometryr, crs=\"EPSG:4326\")\n", - "\n", - "gdfa.plot(ax=ax, color='grey', markersize=80)\n", - "\n", - "gdfp.plot(ax=ax, color=palette['prior'], markersize=40, edgecolor='w', lw=0.5)\n", - "\n", - "# gdfrp.plot(ax=ax, color=palette['prior'], markersize=40, edgecolor='w', lw=0.5)\n", - "gdfr.plot(ax=ax, color=palette['likelihood'], markersize=25, marker='X', edgecolor='w', lw=0.5)\n", - "gdf.plot(ax=ax, color=palette['likelihood'], markersize=25, marker='X', edgecolor='w', lw=0.5)\n", - "# Add title and labels\n", - "ax.set_title(f'Survey locations {canton}')\n", - "plt.xlabel('')\n", - "plt.ylabel('')\n", - "\n", - "plt.axis('off')\n", - "\n", - "# Create a custom legend\n", - "legend_elements = [\n", - " Line2D([0], [0], marker='X', color='w', label=likelihood_labels, markersize=10, markeredgecolor='w', markerfacecolor=palette['likelihood']),\n", - " Line2D([0], [0], marker='o', color='w', label=prior_labels, markerfacecolor=palette['prior'], markersize=10, markeredgecolor='w')\n", - "]\n", - "\n", - "plt.legend(handles=legend_elements, loc='upper right')\n", - "\n", - "glue('map-of-survey-locations', fig, display=False)\n", - "plt.close()" - ] - }, - { - "cell_type": "markdown", - "id": "720e6d85-e449-48cd-8412-3e243934e678", - "metadata": { - "editable": true, - "jp-MarkdownHeadingCollapsed": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Canton Genève\n", - "\n", - "__Density of trash along lakes and rivers__\n", - "\n", - "This is a sample cantonal report. The structure and the format are based off of the federal report, ([IQAASL](https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/)). This version is intended for use as a decsion support tool. Thus, the user is expected to be familiar with the results in the federal report and the methods described in the _Guide for Monitoring Marine Litter on European Seas_ \n", - "([The guide](https://mcc.jrc.ec.europa.eu/main/dev.py?N=41&O=439&titre_chap=TG%20Litter&titre_page=Guidance%20for%20the%20Monitoring%20of%20Marine%20Litter)).\n", - "\n", - "\n", - ":::{dropdown} Initial assessment: stakeholder discussion and priorities.\n", - "\n", - "Stakeholders should consider the following questions while consulting the report:\n", - "\n", - "1. Are the major rivers and lakes included?\n", - "2. Was their more or less observed in 2021 vs the prior results?\n", - "3. __How do these results compare to assessments from other sources (EAWAG, EMPA, Internal reports)?__\n", - " * This includes reports from NGOS in the region\n", - " * Is the data comparable?\n", - "4. Are the objects identified as the _most common_ currently the focus of reduction or prevention campaigns?\n", - " * __How does the canton decide priorties in this regard?__\n", - " * __Did or does the object appear in any regional action plan or strategy?__\n", - "5. For objects that have been the focus of prevention or reduction campaigns in the past: are they on the _most common objects_ list now?\n", - " * If the objects are on the most common list, is this inline with expectations ?\n", - " * What excatly was the mechanism or process that was intended to reduce the presence of the object?\n", - " * __With respect to the amount of resources attributed to prevention and mitigation:__ Do the attributed amounts reflect the importance of the object in terms of total amount found and frequency of occurence?\n", - "6. __Does the sampling distribution reflect the topography and land-use of the canton?__\n", - "7. Do the municipalities with elevated pcs/m have common land-use attributes?\n", - "8. __Are the municipalities of strategic importance to the canton included?__\n", - "9. Are their locations in the canton that should have been surveyed, according to cantonal priorities?\n", - "10. Are their products of regional interest that should be included in the cantonal report?\n", - ":::\n", - "\n", - ":::::{dropdown} Map of survey locations\n", - "\n", - "\n", - "\n", - "::::{grid}\n", - ":padding: 2\n", - "\n", - ":::{grid-item-card}\n", - "\n", - "```{glue} map-of-survey-locations\n", - "```\n", - "\n", - ":::\n", - "::::\n", - ":::::\n", - "## Vital statistics\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory\n", - "```\n", - "```{glue} administrative-boundaries\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory-l\n", - "```\n", - "```{glue} administrative-boundaries-l\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} lake-prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo-l\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory-r\n", - "```\n", - "```{glue} administrative-boundaries-r\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} river-prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo-r\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::::{dropdown} How did we get this data ?\n", - "\n", - "\n", - "\n", - "::::{grid}\n", - ":padding: 2\n", - "\n", - ":::{grid-item-card}\n", - "\n", - "```{glue} scatter-prior-likelihood\n", - "```\n", - "+++\n", - "The data is a combination of observations from variety of groups in Switerland since 2015. The observations were recorded using an interpretation of the _Guide for Monitoring Marine Litter on European Seas_ [The guide](https://mcc.jrc.ec.europa.eu/main/dev.py?N=41&O=439&titre_chap=TG%20Litter&titre_page=Guidance%20for%20the%20Monitoring%20of%20Marine%20Litter). The guide and the monitoring of beach litter are part of decades of research, here is the brief history [A Brief History of Marine Litter Research](https://link.springer.com/chapter/10.1007/978-3-319-16510-3_1).\n", - ":::\n", - "::::\n", - "\n", - "__Common sense guidance:__\n", - "\n", - "1. The data should be considered as a reasonable estimate of the minimum amount of trash on the ground at the time of the survey.\n", - "2. There are many sources of variance. We have considered the following:\n", - " * litter density between sampling groups.\n", - " * litter density with respect to topographical features.\n", - "3. There are differences in detect-ability and appearance for items of the same classification that are due to the effects of decomposition.\n", - "4. Many surveyors are volunteers and have different levels of experience or physical constraints that limit what will actually be collected and counted.\n", - ":::::\n", - "\n", - ":::{dropdown} How to make a report\n", - "\n", - "__Survey and Land use__\n", - "\n", - "A report is the implementation of a `SurveyReport` and a `LandUseReport`. The `SurveyReport` is the basic \n", - "element and does the initial aggregating and descriptive statistics for a query.\n", - "\n", - "The land-use-report accepts `SurveyReport.sample_results` and assigns the land-use attributes to the record. The \n", - "land-use-report provides the baseline assessment of litter density with reference to the surrounding environment. \n", - "\n", - "The assessment accepts as variables the proportion of available space that a topographical feature occupies in a \n", - "circle of $\\pi r² \\text{ where r = 1 500 meters}$ and the center of that circle is the survey location. \n", - "These proportions are compared to the `average pieces per meter` for an object or group of objects.\n", - "\n", - "\n", - "__Create a report__\n", - "\n", - "A report can be intiated by providing the name of the canton. If your canton does not appear this is because we have no data. The prior dates will be calculated automatically, by taking all data prior to the start date of the querry.\n", - "\n", - "```{code} python\n", - "\n", - "import reports\n", - "import geospatial\n", - "import gridforecast\n", - "\n", - "# suppose you have defined your data into df\n", - "observed_dates = {'start':'2020-01-01', 'end':'2021-12-31'}\n", - "\n", - "# everything that was seen before\n", - "prior_dates = {'start':'2015-11-15', 'end':'2019-12-31'}\n", - "\n", - "# name the canton\n", - "canton = 'Bern'\n", - "\n", - "# define the data of interest\n", - "data_of_interest = {'canton':canton, 'date_range':observed_dates}\n", - "\n", - "# load the data\n", - "df = session_config.collect_survey_data()\n", - "\n", - "# filter the data. \n", - "filtered_data, locations = gridforeacast.filter_data(df, data_of_interest)\n", - "\n", - "# make a survey report\n", - "this_report = reports.SurveyReport(dfc=filtered_data)\n", - "\n", - "# generate the parameters for the landuse report\n", - "target_df = this_report.sample_results\n", - "features = geospatial.collect_topo_data(locations=target_df.location.unique())\n", - "\n", - "# make a landuse report\n", - "this_land_use = geospatial.LandUseReport(target_df, features)\n", - "```\n", - "\n", - "Each report and the inference method are documented: [SurveyReport](surveyreporter), [LandUseReport](landusereporter), [GridForecaster](gridforecaster)\n", - ":::\n" - ] - }, - { - "cell_type": "markdown", - "id": "160aae5f-e9ed-4754-86a8-a76af4616553", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "source": [ - "## Most common objects 2020 - 2021\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common-l\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects-l\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common-r\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects-r\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::{dropdown} Defining the most common objects\n", - "\n", - "The default method for defining _the most common objects_ is based on the number of items collected and the number of times that at least one of an object was found with respect to the number of surveys in the query, the _fail rate_.\n", - "\n", - "Adjusting the fail rate will increase or decrease the number of the most common objects. The fail rate is included with the object inventory. \n", - "\n", - "```{code} python\n", - "\n", - "# the most common objects are accesible in the survey report\n", - "# the report.object_summary method aggregates the data to code\n", - "# and attaches the fail rate and % of total\n", - "inventory = this_report.object_summary()\n", - "\n", - "# userdisplay.most_common, takes the 10 most abundant and filters\n", - "# the data for fail rate >= 0.5. The method returns a formatted table,\n", - "# a list of the codes and the ratio of the quantity of the most common to the whole \n", - "mostcommon, codes, ratio = userdisplay.most_common(inventory)\n", - "\n", - "```\n", - "\n", - "\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "id": "1153176b-fd0c-4e93-8928-6c89886b9525", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Land use\n", - "\n", - "\n", - "Land use refers to the measurable topographic features within a cirlce of r = 1 500 m and area = $\\pi r²$ with the survey location in the middle. The features, measured in meters squared, are given as a ratio $\\frac{\\text{area of feature}}{\\text{area of circle}}$. Thus a location with high percentage of buildings will have a rating or value between 60% and 100%. The pcs/m rating is the average of all locations with a land-use profile of the same rating." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards4.150.000.000.000.00
Vineyards4.150.000.000.000.00
Buildings0.000.000.000.004.15
Forest4.150.000.000.000.00
Undefined4.150.000.000.000.00
Public Services3.438.510.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards100%0%0%0%0%
Vineyards100%0%0%0%0%
Buildings0%0%0%0%100%
Forest100%0%0%0%0%
Undefined100%0%0%0%0%
Public Services86%14%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "g = results['this_land_use'].n_samples_per_feature().copy()\n", - "g = userdisplay.landuse_profile(g[session_config.feature_variables[:-1]], nsamples=results['this_report'].number_of_samples)\n", - "g = g.set_caption(\"\")\n", - "\n", - "gt = results['this_land_use'].rate_per_feature().copy()\n", - "gt = userdisplay.litter_rates_per_feature(gt.loc[session_config.feature_variables[:-1]])\n", - "gt = gt.set_caption(\"\")\n", - "\n", - "glue('rate-per-feature', gt, display=False)\n", - "glue('sampling-profile', g, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets0%29%67%5%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets05.812.6016.000
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets = results['this_land_use'].n_samples_per_feature().copy()\n", - "streets = streets[[session_config.feature_variables[-1]]].copy()\n", - "streets = userdisplay.street_profile(streets.T, nsamples=results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets = streets.set_caption(caption)\n", - "\n", - "streets_r = results['this_land_use'].rate_per_feature().copy()\n", - "streets_r = streets_r.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r = userdisplay.street_profile(streets_r, nsamples=results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r = streets_r.set_caption(caption)\n", - "\n", - "glue('street-profile', streets, display=False)\n", - "glue('street-rates-feature', streets_r, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards3.560.000.000.000.00
Vineyards3.560.000.000.000.00
Buildings0.000.000.000.003.56
Forest3.560.000.000.000.00
Undefined3.560.000.000.000.00
Public Services2.698.510.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards100%0%0%0%0%
Vineyards100%0%0%0%0%
Buildings0%0%0%0%100%
Forest100%0%0%0%0%
Undefined100%0%0%0%0%
Public Services85%15%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "gl = lake_results['this_land_use'].n_samples_per_feature().copy()\n", - "gl = userdisplay.landuse_profile(gl[session_config.feature_variables[:-1]], nsamples=lake_results['this_report'].number_of_samples)\n", - "gl = gl.set_caption(\"\")\n", - "\n", - "gtl = lake_results['this_land_use'].rate_per_feature().copy()\n", - "\n", - "gtl = userdisplay.litter_rates_per_feature(gtl.loc[session_config.feature_variables[:-1]])\n", - "gtl = gtl.set_caption(\"\")\n", - "\n", - "glue('lake-rate-per-feature', gtl, display=False)\n", - "glue('lake-sampling-profile', gl, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets0%30%70%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets05.812.6000
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets_p = lake_results['this_land_use'].n_samples_per_feature().copy()\n", - "streets_p = streets_p[[session_config.feature_variables[-1]]].copy()\n", - "streets_p = userdisplay.street_profile(streets_p.T, nsamples=lake_results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets_p = streets_p.set_caption(caption)\n", - "\n", - "streets_r_l = lake_results['this_land_use'].rate_per_feature().copy()\n", - "streets_r_l = streets_r_l.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r_l = userdisplay.street_profile(streets_r_l, nsamples=lake_results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r_l = streets_r_l.set_caption(caption)\n", - "\n", - "\n", - "glue('lake-street-profile', streets_p, display=False)\n", - "glue('lake-street-rates-feature', streets_r_l, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "82f55461-c497-483a-8c38-fbd509809afb", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards16.000.000.000.000.00
Vineyards16.000.000.000.000.00
Buildings0.000.000.000.0016.00
Forest16.000.000.000.000.00
Undefined16.000.000.000.000.00
Public Services16.000.000.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards100%0%0%0%0%
Vineyards100%0%0%0%0%
Buildings0%0%0%0%100%
Forest100%0%0%0%0%
Undefined100%0%0%0%0%
Public Services100%0%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "gr = river_results['this_land_use'].n_samples_per_feature().copy()\n", - "gr = userdisplay.landuse_profile(gr[session_config.feature_variables[:-1]], nsamples=river_results['this_report'].number_of_samples)\n", - "gr = gr.set_caption(\"\")\n", - "\n", - "gtlr = river_results['this_land_use'].rate_per_feature().copy()\n", - "\n", - "gtlr = userdisplay.litter_rates_per_feature(gtlr.loc[session_config.feature_variables[:-1]])\n", - "gtlr = gtlr.set_caption(\"\")\n", - "\n", - "\n", - "glue('river-rate-per-feature', gtlr, display=False)\n", - "glue('river-sampling-profile', gr, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9b396025-1fa6-4661-9116-593fa1ed741d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets0%0%0%100%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets00016.000
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets_p_r = river_results['this_land_use'].n_samples_per_feature().copy()\n", - "streets_p_r = streets_p_r[[session_config.feature_variables[-1]]].copy()\n", - "streets_p_r = userdisplay.street_profile(streets_p_r.T, nsamples=river_results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets_p_r = streets_p_r.set_caption(caption)\n", - "\n", - "streets_r_r = river_results['this_land_use'].rate_per_feature().copy()\n", - "streets_r_r = streets_r_r.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r_r = userdisplay.street_profile(streets_r_r, nsamples=river_results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r_r = streets_r_r.set_caption(caption)\n", - "\n", - "\n", - "glue('river-street-profile', streets_p_r, display=False)\n", - "glue('river-street-rates-feature', streets_r_r, display=False)" - ] - }, - { - "cell_type": "markdown", - "id": "e45988a7-3442-434a-acab-c0b13cbfcd42", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "1. The rate per feature refers to the average pcs/m observed at a particular land use rate\n", - " * Under what conditions is the pcs/m elevated? Where is it the least?\n", - "2. The sampling profile refers to the ratio of samples that were taken at a particular land use rate\n", - " * Does the sampling profile reflect the topography of the region?\n", - "\n", - "\n", - "\n", - "### Rate per feature 2020 - 2021\n", - "\n", - "::::{tab-set}\n", - ":::{tab-item} All lakes and rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} street-rates-feature\n", - "``` \n", - ":::\n", - "\n", - ":::{tab-item} Lakes\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} lake-rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} lake-street-rates-feature\n", - "```\n", - ":::\n", - "\n", - ":::{tab-item} Rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} river-rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} river-street-rates-feature\n", - "``` \n", - ":::\n", - "\n", - "::::\n", - "\n", - "### Sampling profile 2020 - 2021\n", - "\n", - "::::{tab-set}\n", - ":::{tab-item} All lakes and rivers\n", - "\n", - "__Land use__\n", - "\n", - "\n", - "```{glue} sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lengths for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} street-profile\n", - "``` \n", - ":::\n", - "\n", - ":::{tab-item} Lakes\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} lake-sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} lake-street-profile\n", - ":::\n", - "\n", - ":::{tab-item} Rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} river-sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "\n", - "\n", - "```{glue} river-street-profile\n", - "``` \n", - ":::\n", - "\n", - "::::\n", - "\n", - ":::{dropdown} Defining land use\n", - "\n", - "__Land cover__\n", - "\n", - "These measured land-use attributes are the labeled polygons from the map layer Landcover defined here ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)), they are extracted using vector overlay techniques in QGIS ([QGIS](https://qgis.org/en/site/)).\n", - "\n", - "* Buildings: built up, urbanized\n", - "* Woods: not a park, harvesting of trees may be active\n", - "* Vineyards: does not include any other type of agriculture\n", - "* Orchards: not vineyards\n", - "* Undefined: areas of the map with no predefined label\n", - "\n", - "\n", - "```{code}\n", - "\n", - "# the land use is summarized using a LandUseReport object\n", - "# the average pieces per meter by land use category\n", - "rate_per_feature = this_land_use.n_pieces_per_feature()\n", - "\n", - "# the sampling distribution\n", - "samples_per_feature = this_land_use.n_samples_per_feature()\n", - "\n", - "# the variety of locations per feature\n", - "locations_per_feature = this_land_use.locations_per_feature()\n", - "\n", - "# format for display .html\n", - "styled_rate_per_feature = userdisplay.litter_rates_per_feature(rate_per_feature)\n", - "```\n", - "\n", - "__Public services__\n", - "\n", - "Public services are the labled polygons from the Freizeitareal and Nutzungsareal map layers, defined in ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)). Both layers represent areas used for specific activities. Freizeitareal identifies areas used for recreational purposes and Nutzungsareal represents areas such as hospitals, cemeteries, historical sites or incineration plants. As a ratio of the available dry-land in a hex, these features are relatively small (less than 10%) of the total dry-land. For identified features within a bounding hex the magnitude in meters² of these variables is scaled between 0 and 1, thus the scaled value represents the size of the feature in relation to all other measured values for that feature from all other hexagons.\n", - "\n", - "* Recreation: parks, sports fields, attractions\n", - "* Infrastructure: Schools, Hospitals, cemeteries, powerplants\n", - "\n", - "__Streets and roads__\n", - "\n", - "Streets and roads are the labled polylines from the TLM Strasse map layer defined in ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)). All polyines from the map layer within a bounding hex are merged (disolved in QGIS commands) and the combined length of the polylines, in meters, is the magnitude of the variable for the bounding hex.\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "id": "501575a0-10d5-4609-8550-8d80807fda4d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Forecast\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max\n", - "```\n", - "```{glue} forecast-weighted-prior\n", - "```\n", - "\n", - "```{glue} forecast-max-val\n", - "```\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max-l\n", - "```\n", - "\n", - "```{glue} forecast-weighted-prior-l\n", - "```\n", - "\n", - "```{glue} forecast-max-val-l\n", - "```\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} lake-cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max-r\n", - "```\n", - "\n", - "```{glue} forecast-weighted-prior-r\n", - "```\n", - "\n", - "```{glue} forecast-max-val-r\n", - "```\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} river-cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::{dropdown} Forecast methods\n", - "\n", - "The applied method would best be classified as Empirical Bayes, in the sense that the prior is derived from the data ([Bayesian Filtering and Smoothing](https://users.aalto.fi/~ssarkka/pub/cup_book_online_20131111.pdf) or [Empirical Bayes methods in classical and Bayesian inference](https://hannig.cloudapps.unc.edu/STOR757Bayes/handouts/PetroneEtAl2014.pdf)). However, we share the concerns of Davidson-Pillon [Bayesian methods for hackers](https://dataorigami.net/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/#contents) about double counting and eliminate it the possibility as part of the formulation of the prior. \n", - "\n", - "__Model assumptions__\n", - "\n", - "1. Locations with similar land use attributes will have similar litter density rates\n", - "2. The data is a best estimate of what was present on the day of the survey\n", - "3. There are regional differences with respect to the density of specific objects\n", - "4. The locations surveyed are maintained by a public administration\n", - "\n", - "The choice of land use features was a natural choice but was further explored in [Near or Far](https://hammerdirt-analyst.github.io/landuse/titlepage.html).\n", - "\n", - "Our parameter estimates are thus derived from the data and they remain testable and quantifiable according to [Prior Probabilities, E T Jaynes](https://bayes.wustl.edu/etj/articles/prior.pdf). This makes our calculation very repetetive but also very well understood. It can be defined in a few lines of code for any set of survey results.\n", - "\n", - "```{code} python\n", - "\n", - "# standared libaries\n", - "import numpy as np\n", - "from scipy.stats import dirichlet, multinomial\n", - "\n", - "# collect the data of interest\n", - "h = array of survey values\n", - "\n", - "# count the number of times that each survey values exceed a value on the gird\n", - "counts = np.array([np.sum((h > x) & (h <= x + .1)) for x in grid_range])\n", - "\n", - "# use the dirichlet dist to estimate p(Y >= x) for each x on the grid\n", - "# and sample from the estimation\n", - "adist = dirichlet(counts)\n", - "this_dist = adist.rvs(1-[0]\n", - "\n", - "# draw samples from the conjugate\n", - "posterior_samples = multinomial.rvs(nsamples, p=this_dist)\n", - "\n", - "```\n", - ":::" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b0bda4c0-1b4a-4011-9ad1-fb3a52aac885", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 quantitypcs/msamplesorchardsvineyardsbuildingsforestundefinedpublic servicesstreets
city          
Genève74866.06291151113
Versoix20734.6241151112
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-municipal-results" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dxl = call_l_surveys.df\n", - "dxf = call_l_land.df_cont\n", - "\n", - "dxlc = dxl[['location', 'city', 'feature_type']].drop_duplicates('location')\n", - "dxlc.set_index(['location'], inplace=True, drop=True)\n", - "dxf['city'] = dxf.location.apply(lambda x : dxlc.loc[x, 'city'])\n", - "sumlu = {x:'sum' for x in session_config.feature_variables}\n", - "dxf = dxf.groupby(['sample_id', 'city', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n", - "\n", - "dxf = dxf.groupby(['city']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n", - "\n", - "for alabel in session_config.feature_variables:\n", - " dxf[alabel] = dxf[alabel]/dxf.sample_id\n", - " \n", - "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n", - "dxfc = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n", - "dxfc.rename(columns={'sample_id':'samples'}, inplace=True)\n", - "dxfc.drop('check', axis=1, inplace=True)\n", - "dxfc = dxfc.style.set_table_styles(userdisplay.table_css_styles)\n", - "dxfc = dxfc.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n", - "dxfc = dxfc.format(userdisplay.format_kwargs, precision=2)\n", - "\n", - "glue('lake-municipal-results', dxfc , display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 quantitypcs/msamplesorchardsvineyardsbuildingsforestundefinedpublic servicesstreets
city          
Genève32016.0011151114
Veyrier12962.9281141113
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-municipal-results" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dxl = call_r_surveys.df\n", - "dxf = call_r_land.df_cont\n", - "\n", - "dxlc = dxl[['location', 'city', 'feature_type']].drop_duplicates('location')\n", - "dxlc.set_index(['location'], inplace=True, drop=True)\n", - "dxf['city'] = dxf.location.apply(lambda x : dxlc.loc[x, 'city'])\n", - "sumlu = {x:'sum' for x in session_config.feature_variables}\n", - "dxf = dxf.groupby(['sample_id', 'city', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n", - "\n", - "dxf = dxf.groupby(['city']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n", - "\n", - "for alabel in session_config.feature_variables:\n", - " dxf[alabel] = dxf[alabel]/dxf.sample_id\n", - " \n", - "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n", - "dxfcr = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n", - "dxfcr.rename(columns={'sample_id':'samples'}, inplace=True)\n", - "dxfcr.drop('check', axis=1, inplace=True)\n", - "dxfcr = dxfcr.style.set_table_styles(userdisplay.table_css_styles)\n", - "dxfcr = dxfcr.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n", - "dxfcr = dxfcr.format(userdisplay.format_kwargs, precision=2)\n", - "# glue('all-data-municipal-results', i , display=False)\n", - "glue('river-municipal-results', dxfcr, display=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "2d5b8904-044b-4aed-916c-5e36018f4087", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 samplespcs/m
Lac-leman335.88
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lakes-i-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 samplespcs/m
Arve82.92
Rhone116.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "rivers-i-summary" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "summardata = alldata_ofinterest.groupby(['sample_id', 'feature_type','feature_name'], as_index=False).agg({'pcs/m':'sum'})\n", - "# lakes\n", - "feature_individual_summary = summardata.groupby(['feature_type','feature_name'], as_index=False).agg({'sample_id':'nunique', 'pcs/m':'mean'})\n", - "\n", - "lakes_individual_summary = feature_individual_summary[feature_individual_summary.feature_type == 'l'].copy()\n", - "rivers_individual_summary = feature_individual_summary[feature_individual_summary.feature_type == 'r'].copy()\n", - "\n", - "\n", - " \n", - "lakes_i_sum = format_river_lake_summary(lakes_individual_summary).style.set_table_styles(userdisplay.table_css_styles).format(precision=2)\n", - "rivers_i_sum = format_river_lake_summary(rivers_individual_summary).style.set_table_styles(userdisplay.table_css_styles).format(precision=2)\n", - "\n", - "glue('lakes-i-summary', lakes_i_sum, display=False)\n", - "glue('rivers-i-summary', rivers_i_sum, display=False)" - ] - }, - { - "cell_type": "markdown", - "id": "b6d8a919-cdad-4915-99d0-8ffd21a40e98", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Lakes and rivers sampled - all data\n", - "\n", - "::::{grid} 2 2 2 2\n", - "\n", - ":::{grid-item}\n", - "**Lakes sampled**\n", - "\n", - "```{glue} lakes-i-summary\n", - "```\n", - "\n", - ":::\n", - "\n", - ":::{grid-item}\n", - "**Rivers sampled**\n", - "\n", - "```{glue} rivers-i-summary\n", - "```\n", - ":::\n", - "::::\n", - "\n", - "## Municipal Results - all data\n", - "\n", - "The average pieces per meter of the most common objects for each city.\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "```{glue} lake-municipal-results\n", - "```\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "```{glue} river-municipal-results\n", - "``` \n", - ":::::::::\n", - "\n", - "::::::::::" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_build/html/_sources/landuse_class.ipynb b/_build/html/_sources/landuse_class.ipynb deleted file mode 100644 index 869f7bf..0000000 --- a/_build/html/_sources/landuse_class.ipynb +++ /dev/null @@ -1,1158 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "4e39e538-f20c-4fa0-954e-b17552001446", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "%load_ext watermark\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "import matplotlib.colors\n", - "from matplotlib.colors import LinearSegmentedColormap, ListedColormap\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "markdown", - "id": "b2affb94-49ce-4cc8-b21a-e4dbddb47cd3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "(landusereporter)=\n", - "# Land use class\n", - "\n", - "The landuse class groups the survey results according to the rate of landuse at each survey location. Provides the correlation matrix of the feature variables and the number of samples and the average result for each landuse type and magnitude.\n", - "\n", - "## Why is this important?\n", - "\n", - "__Because it is another way to get proxies for usage and population.__\n", - "\n", - "We assume there is a relationship between how the land is used and what it is we find on the ground. Archeaologists and Anthropologists make this basic assumption every time they undertake an excavation and interpret the results in the context of other findings. This interpretation of beach litter data does exactly the same. As discussed in [Near or far](https://www.hammerdirt.ch) and the federal report [IQAASL](https://www.hammerdirt.ch) at the national level there is strong evidence to support a correlation between the density of objects found and specific topographic features that can be isolated on a standard topographical map.\n", - "\n", - "### What is important?\n", - "\n", - "__The relationship between the topograhpical features and the density of the objects found.__\n", - "\n", - "However, the measured features are not independent of each other. For example if their are buildings in an area we expect to also find a road that leads to those buildings. This multicolinearity can lead to unstable coefficient estimates and make it challenging to interpret the individual effects of the correlated variables on the target variable.\n", - "\n", - "The topographical data from the confederation provides continuity to what could be interpreted as unrelated observations. Furthermore, the labels provided for the various topographical features are indicators of use and have a real meaning to georaphers and engineers in planning and development. Local associations that are involved in preventing and reducing litter may also be interested.\n", - "\n", - "## Make a land use object\n", - "\n", - "After the topographical features are extracted the results are applied to the data. The land use clas is available by calling `geospatial.LandUseReport(df_target, features)`. The `df_target` and `features` variables are generated in the `SurveyReport`. \n", - "\n", - "__Instantiate a `LandUseReport`__\n", - "\n", - "```python\n", - "# start a survey report\n", - "import session_config\n", - "import reports\n", - "import geospatial\n", - "\n", - "# available data\n", - "surveys = session_config.collect_survey_data()\n", - "\n", - "# boundaries / search parameters\n", - "feature_type = 'canton'\n", - "feature_name = 'Vaud'\n", - "\n", - "df = surveys[surveys[feature_type] == feature_name].copy()\n", - "vaud_report = reports.SurveyReport(dfc=df)\n", - "\n", - "# the parameters for the land use report\n", - "target_df = vaud_report.sample_results\n", - "features = vaud_report.sampling_conditions()\n", - "land_use_report = geospatial.LandUseReport(target_df, features)\n", - "```\n", - "### Report contents\n", - "\n", - "1. Number of samples per feature and magnitude\n", - "2. The total number of objects collected per feature and magnitude\n", - "3. The number of locations per feature and magnitude\n", - "4. The average pieces per meter for each feature and magnitude\n", - "5. The correlation matrix of the feature variables\n", - "6. The correlated pairs\n", - "7. The landuse on a conintuous scale\n", - "8. The landuse on a categorical scale" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "141c08fc-f3ab-4115-8179-2fa101ca75fd", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "import session_config\n", - "import reports\n", - "import geospatial\n", - "\n", - "# available data\n", - "surveys = session_config.collect_survey_data()\n", - "\n", - "# boundaries / search parameters\n", - "feature_type = 'feature_name'\n", - "feature_name = 'lac-leman'\n", - "\n", - "df = surveys[surveys[feature_type] == feature_name].copy()\n", - "vaud_report = reports.SurveyReport(dfc=df)\n", - "\n", - "# the parameters for the landuse report\n", - "target_df = vaud_report.sample_results\n", - "features = geospatial.collect_topo_data(locations=target_df.location.unique())\n", - "land_use_report = geospatial.LandUseReport(target_df, features)\n", - "\n", - "# creates an array of tuples of the correlated pairs\n", - "correlated_pairs = land_use_report.correlated_pairs()\n", - "\n", - "# pass the correlated pairs to combine features method\n", - "# this will categorize the features and combine the correlated pairs\n", - "# into new columns\n", - "land_use_report.combine_features(correlated_pairs)" - ] - }, - { - "cell_type": "markdown", - "id": "8a13e8ed-6d26-4c0e-8fa0-cb4e1565574b", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Number of samples per feature" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "eed4a6d4-2a69-40dc-96ee-e337e4ead320", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " orchards vineyards buildings forest undefined public services streets\n", - "1 251 248 25 216 208 200 46\n", - "2 0 1 17 22 18 51 160\n", - "3 0 2 1 13 25 0 45\n", - "4 0 0 50 0 0 0 0\n", - "5 0 0 158 0 0 0 0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "samples_per_feature = land_use_report.n_samples_per_feature()\n", - "samples_per_feature[session_config.feature_variables]" - ] - }, - { - "cell_type": "markdown", - "id": "4c005802-f88e-47e7-b31a-4a2c6167bf8c", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Quantity per feature" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8f92736b-a6da-4be0-bed9-cf0f86ebd5f3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " orchards vineyards buildings forest undefined public services streets\n", - "1 80016 79749 2115 69630 68202 62279 14442\n", - "2 0 146 9513 1484 9699 17737 50897\n", - "3 0 121 186 8902 2115 0 14677\n", - "4 0 0 13315 0 0 0 0\n", - "5 0 0 54887 0 0 0 0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "q_pf = land_use_report.n_pieces_per_feature()\n", - "q_pf[session_config.feature_variables]" - ] - }, - { - "cell_type": "markdown", - "id": "9e4808a4-0ddf-4178-b46e-b2672b5256ef", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Locations per feature" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "fa0f9b5b-88e6-49e3-bb43-367e57b78cbf", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " orchards vineyards buildings forest undefined public services streets\n", - "1 38 36 3 35 31 29 7\n", - "2 0 1 3 2 4 9 22\n", - "3 0 1 1 1 3 0 9\n", - "4 0 0 5 0 0 0 0\n", - "5 0 0 26 0 0 0 0" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "l_pf = land_use_report.locations_per_feature()\n", - "l_pf[session_config.feature_variables]" - ] - }, - { - "cell_type": "markdown", - "id": "6c37684a-9cb7-49bb-b2c6-a8e62f26648e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### density per feature" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a0d18011-5e85-4829-a7ba-580c66d27663", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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orchardsvineyardsbuildingsforestundefinedpublic servicesstreets
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" - ], - "text/plain": [ - " orchards vineyards buildings forest undefined public services \\\n", - "1 8.921753 8.949919 4.6128 8.507593 8.632308 7.8458 \n", - "2 0 9.75 19.01 4.256364 18.251111 13.141176 \n", - "3 0 5.015 5.35 23.698462 4.6128 0 \n", - "4 0 0 5.5604 0 0 0 \n", - "5 0 0 9.60443 0 0 0 \n", - "\n", - " streets \n", - "1 11.502174 \n", - "2 7.273375 \n", - "3 12.144889 \n", - "4 0 \n", - "5 0 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r_pf = land_use_report.rate_per_feature().T\n", - "r_pf[session_config.feature_variables]" - ] - }, - { - "cell_type": "markdown", - "id": "0708d4a4-12e3-4cf2-bbaf-90687151c856", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Corelation matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "99309a88-abfc-4edb-a089-e94b5fad016f", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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orchardsvineyardsbuildingsforestundefinedpublic servicesstreets
orchards1.0000000.215632-0.232329-0.0443110.205992-0.195685-0.087427
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" - ], - "text/plain": [ - " orchards vineyards buildings forest undefined \\\n", - "orchards 1.000000 0.215632 -0.232329 -0.044311 0.205992 \n", - "vineyards 0.215632 1.000000 -0.100630 -0.190298 -0.057757 \n", - "buildings -0.232329 -0.100630 1.000000 -0.872981 -0.946109 \n", - "forest -0.044311 -0.190298 -0.872981 1.000000 0.767505 \n", - "undefined 0.205992 -0.057757 -0.946109 0.767505 1.000000 \n", - "public services -0.195685 -0.215319 0.480962 -0.367753 -0.425264 \n", - "streets -0.087427 -0.155203 0.532584 -0.538793 -0.412409 \n", - "\n", - " public services streets \n", - "orchards -0.195685 -0.087427 \n", - "vineyards -0.215319 -0.155203 \n", - "buildings 0.480962 0.532584 \n", - "forest -0.367753 -0.538793 \n", - "undefined -0.425264 -0.412409 \n", - "public services 1.000000 0.542128 \n", - "streets 0.542128 1.000000 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "land_use_report.correlation_matrix()" - ] - }, - { - "cell_type": "markdown", - "id": "eaa3bc5e-6e35-4f7d-a717-f14cddd63afd", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Corelated pairs\n", - "\n", - "The correlated pairs method identifies the land-use features that are correlated with each other. The method returns a tuple with the two features that are correlated and the method that could be used to combine them." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "34bb3925-8cf2-45b6-b084-86c22bae2b09", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Correlated pairs:\n", - "[('buildings', 'public services', 'rate'), ('forest', 'undefined', 'sum')]\n" - ] - } - ], - "source": [ - "print(f'Correlated pairs:\\n{correlated_pairs}')" - ] - }, - { - "cell_type": "markdown", - "id": "f4da46ed-0fee-4942-8260-a131df10ff0b", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Continuous land use" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c6e31ebb-24f8-4d6b-904b-5985f806e74a", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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orchardsvineyardsbuildingsforestundefinedpublic servicesstreets
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la-pecherie0.1710.1620.1060.0960.4640.0104490.166033
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" - ], - "text/plain": [ - " orchards vineyards buildings forest undefined \\\n", - "location \n", - "la-pecherie 0.171 0.162 0.106 0.096 0.464 \n", - "veveyse 0.000 0.027 0.958 0.000 0.015 \n", - "\n", - " public services streets \n", - "location \n", - "la-pecherie 0.010449 0.166033 \n", - "veveyse 0.047393 0.344649 " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "continuous = land_use_report.df_cont.copy()\n", - "examps = continuous[continuous.location.isin(['veveyse', 'la-pecherie'])].drop_duplicates('location')\n", - "examps[['location', *session_config.feature_variables]].fillna(0).set_index('location')" - ] - }, - { - "cell_type": "markdown", - "id": "4bf9ba59-652b-4f51-af36-55290b394a2a", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "### Categorical land use" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b9085da4-47c5-40e3-a8c3-3c037297004a", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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orchardsvineyardsbuildingsforestundefinedpublic servicesstreets
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" - ], - "text/plain": [ - " orchards vineyards buildings forest undefined public services \\\n", - "location \n", - "la-pecherie 1 1 1 1 3 1 \n", - "veveyse 1 1 5 1 1 1 \n", - "\n", - " streets \n", - "location \n", - "la-pecherie 1 \n", - "veveyse 2 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cat = land_use_report.df_cat.copy()\n", - "examps = cat[cat.location.isin(['veveyse', 'la-pecherie'])].drop_duplicates('location')\n", - "examps[['location', *session_config.feature_variables]].set_index('location')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "cf508f4a-8861-484b-88bb-a7febdb1b8c2", - "metadata": { - "editable": true, - "jupyter": { - "source_hidden": true - }, - "pycharm": { - "name": "#%%\n" - }, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Author: hammerdirt-analyst\n", - "\n", - "conda environment: cantonal_report\n", - "\n", - "pandas : 2.0.3\n", - "seaborn : 0.12.2\n", - "numpy : 1.25.2\n", - "matplotlib: 3.7.1\n", - "\n" - ] - } - ], - "source": [ - "%watermark -a hammerdirt-analyst -co --iversions" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_build/html/_sources/titlepage.md b/_build/html/_sources/titlepage.md deleted file mode 100644 index 7341939..0000000 --- a/_build/html/_sources/titlepage.md +++ /dev/null @@ -1,53 +0,0 @@ -# The litter assistant - -::::{grid} - - -:::{grid-item} - -This is the proposed format and layout for an up-dated survey of the trash along swiss rivers, lakes and hiking trails. We have created reports for five cantons and one that provides a _federal perspective_. - -There have been developments in the domain of beach litter reporting since the first federal project. The Guide for Monitoring Litter on European seas was updated in 2023, the code system has changed for classifying objects and in May 2024 a guide for monitoring and removing plastics from inland waters was released. - -Developments in the fields of machine learning (ML) and artificial intelligence (AI) give us new ways to assess the results of surveys and synthesise the current research. Topics of interest for those charged with reducing or preventing plastics in the environment. - -::: - -:::{grid-item-card} - -```{image} resources/hammerdirt.png -:alt: bob -:class: bg-primary mb-1 -:width: 400px -:align: center -``` -::: -:::: -::::{grid} - -:::{grid-item} - -The data in these reports is a combination of observations from variety of groups in Switerland since 2015. The observations were recorded using an interpretation of the _Guide for Monitoring Marine Litter on European Seas_ [The guide](https://mcc.jrc.ec.europa.eu/main/dev.py?N=41&O=439&titre_chap=TG%20Litter&titre_page=Guidance%20for%20the%20Monitoring%20of%20Marine%20Litter). The guide and the monitoring of beach litter are part of decades of research, here is the brief history [A Brief History of Marine Litter Research](https://link.springer.com/chapter/10.1007/978-3-319-16510-3_1). - -::: -:::{grid-item} -__Associated projects__ - -* https://github.com/hammerdirt-analyst/IQAASL-End-0f-Sampling-2021 -* https://github.com/hammerdirt-analyst/cantonal_reports -* https://github.com/hammerdirt-analyst/solid-waste-team -* https://github.com/hammerdirt-analyst/landuse -* [plastock project with ASL](https://asleman.org/) -* https://github.com/hammerdirt-analyst/finding-one-object - - -::: - -:::{grid-item} -:columns: 12 - -__THE TEAM__ - -Shannon Erismann, Montserrat Filella, Téo Gursoy, Christian Ludwig, Roger Erismann, Bettina Siegenthaler -::: -:::: diff --git a/_build/html/_sources/valais.ipynb b/_build/html/_sources/valais.ipynb deleted file mode 100644 index 22f00d0..0000000 --- a/_build/html/_sources/valais.ipynb +++ /dev/null @@ -1,2634 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "5bf92247-ef77-4aae-b62f-9388cba6765e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "import session_config\n", - "import reports\n", - "import userdisplay\n", - "import geospatial\n", - "import gridforecast as gfcast\n", - "\n", - "import logging\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "import matplotlib.colors\n", - "from matplotlib.colors import LinearSegmentedColormap, ListedColormap\n", - "import matplotlib.dates as mdates\n", - "import seaborn as sns\n", - "import datetime as dt\n", - "\n", - "import geopandas as gpd\n", - "import contextily as ctx\n", - "from shapely.geometry import box\n", - "from shapely.geometry import Point\n", - "\n", - "from myst_nb import glue\n", - "from IPython.display import display, Markdown\n", - "\n", - "def display_forecast(fcast_summary):\n", - " average = fcast_summary['average']\n", - " hdi_min, hdi_max = fcast_summary['hdi'][0], fcast_summary['hdi'][1]\n", - " \n", - " range_90_min, range_90_max= fcast_summary['range'][0], fcast_summary['range'][-1]\n", - " alist = f'\\n* Average: {round(average, 2)}\\n* HDI 95%: {round(hdi_min, 2)} - {round(hdi_max, 2)}\\n* 90% Range: {round(range_90_min, 2)} - {round(range_90_max,2)}'\n", - " return alist\n", - "\n", - "def display_forecast_summary(asummary, label):\n", - " forecast_summary = display_forecast(asummary)\n", - " forecast_summary = Markdown(f'{label}{forecast_summary}')\n", - " return forecast_summary\n", - "\n", - "def extract_dates_for_labels_from_summary(summary):\n", - " start = dt.datetime.strftime(summary.pop('start'), format=session_config.date_format)[:4]\n", - " end = dt.datetime.strftime(summary.pop('end'), format=session_config.date_format)[:4]\n", - " return f\"{start} - {end}\"\n", - "\n", - "def format_boundaries_feature_inv(boundaries, topop, displayfunc, session_language='en'):\n", - " for thingtoremove in topop:\n", - " boundaries.pop(thingtoremove)\n", - " display_boundaries = displayfunc(boundaries, session_language=session_language)\n", - " return Markdown(display_boundaries)\n", - "\n", - "def format_river_lake_summary(d):\n", - " d.drop('feature_type', axis=1, inplace=True)\n", - " d.rename(columns={'feature_name':'Name', 'sample_id': 'samples'}, inplace=True)\n", - " d['pcs/m'] = d['pcs/m'].round(2)\n", - " d['Name'] = d.Name.apply(lambda x: x.capitalize())\n", - " d.set_index('Name', inplace=True)\n", - " d.index.name = None\n", - " return d\n", - "\n", - "\n", - "highlight_props = 'background-color:#FAE8E8'\n", - "def highlight_max(s, arg, props: str = highlight_props):\n", - " return np.where((s > arg) & (s != 0), props, '')\n", - "\n", - "logging.basicConfig(\n", - " filename='app.log', \n", - " level=logging.DEBUG,\n", - " format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n", - ")\n", - "\n", - "logger = logging.getLogger(__name__)\n", - "color_style = {'prior':'color: #daa520', 'likelihood':'color: #1e90ff'}\n", - "palette = {'prior':'goldenrod', 'likelihood':'dodgerblue'}" - ] - }, - { - "cell_type": "markdown", - "id": "d2d5a3de-1688-4113-95ce-bf614dbf9695", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "fb4d8635-eb6f-402a-9cd6-8be9c76cce30", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "data = session_config.collect_survey_data()\n", - "\n", - "o_dates = {'start':'2020-01-01', 'end':'2021-12-31'}\n", - "prior_dates = {'start':'2015-11-15', 'end':'2019-12-31'}\n", - "\n", - "# all data\n", - "canton = 'Valais'\n", - "d= data.reset_index(drop=True)\n", - "\n", - "# all surveys lakes, rivers combined\n", - "alldata_ofinterest, locations = gfcast.filter_data(d,{'canton': canton, 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_surveys, call_land = gfcast.make_report_objects(alldata_ofinterest)\n", - "\n", - "# summary and labels\n", - "all_summary = call_surveys.sampling_results_summary.copy()\n", - "all_labels = extract_dates_for_labels_from_summary(all_summary)\n", - "\n", - "# material proportions all data\n", - "material_report = call_surveys.material_report\n", - "material_report = material_report.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior = d[(d.canton != canton)&(d['date'] <= prior_dates['end'])].copy()\n", - "o_report, o_land_use = gfcast.make_report_objects(o_prior)\n", - "results = gfcast.reports_and_forecast({'canton':canton, 'date_range':o_dates}, {'canton':canton, 'date_range':prior_dates}, ldata=d.copy(), logger=logger, other_data=o_land_use.df_cat)\n", - "\n", - "# prior summary and label\n", - "p_summary = results['prior_report'].sampling_results_summary.copy()\n", - "prior_labels = extract_dates_for_labels_from_summary(p_summary)\n", - "\n", - "# likelihood summary and label\n", - "l_summary = results['this_report'].sampling_results_summary.copy()\n", - "likelihood_labels = extract_dates_for_labels_from_summary(l_summary)\n", - "\n", - "# forecasts\n", - "xii = results['posterior_no_limit'].sample_posterior()\n", - "\n", - "# limit to the 99th percentile\n", - "sample_values, posterior, summary_simple = gfcast.dirichlet_posterior(results['posterior_99'])\n", - "\n", - "# forecast weighted prior all data\n", - "weighted_args = [results['this_land_use'], session_config.feature_variables, call_land.df_cat, results['this_report'].sample_results['pcs/m']]\n", - "weighted_forecast, weighted_posterior, weighted_summary, _ = gfcast.forecast_weighted_prior(*weighted_args)\n", - "\n", - "# forecast summaries\n", - "forecast_99 = display_forecast_summary(summary_simple, '__Given the 99th percentile__')\n", - "forecast_maxval = display_forecast_summary(results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - "forecast_weighted = display_forecast_summary(weighted_summary, '__Given the weighted prior__')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d504b355-6ec4-4d6d-b9d6-3459215fcb8b", - "metadata": { - "editable": true, - "jupyter": { - "source_hidden": true - }, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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wDKWmpmrOnDn6yU9+Ikn64x//qMDAQK1du1aPP/64JCkxMVHSxZBVmwsXLuipp57SkiVLNHXqf96d+/2RwYb685//7DT9hz/8Qe+8847+53/+R5MnT5YkLVu2TFOnTtW0adMkSampqfrggw+0YsUKLVq0SJI0adIkSZd/96yrq6uCgoKc2t59912NHz9eHTp0aPR+XAnBDgCAVuLGkA66oZu32rVvJ1cXV7PLuaLi4mJJkp+fnyQpNzdXBQUFiouLc/Sx2+2KjY3Vzp07HcHuavbs2aMTJ07IxcVFAwYMUEFBgfr376+lS5c6XdJtCmVlZTp//rxjHyorK5WVlaXZs2c79YuLi9POnTsbvJ2srCzt3btXy5cvb1S9dcHNEwAAoF4Mw1BSUpJuv/12RUZGSpIKCgokSYGBgU59AwMDHfPq4ssvv5QkzZ8/X3PnztWWLVvUqVMnxcbG6vTp0020BxfNnj1b1113nUaOHClJKioqUlVVVaP34Ydef/11RUREaMiQIY2qty4IdgAAoF5mzJihffv2ad26dTXm2Ww2p2nDMGq0XUl1dbUkac6cOfrpT3+qqKgorVq1SjabTW+//Xaty6SkpKhDhw6OT15e3lW3s3jxYq1bt04bN26Up6dnk+7D95WXl2vt2rVOl5WbE5diAQBAnc2cOVObN2/W9u3b1a1bN0f7pe+UFRQUqGvXro72wsLCGiNgV3Jp2T59+jja7Ha7rr/++ssGtvj4eD3wwAOO6eDg4CtuY+nSpUpJSdFHH32km2++2dHu7+8vV1fXGqNz9d2H73vnnXdUVlbm+A5fc2PEDgAAXJVhGJoxY4Y2btyojz/+uMbjPMLDwxUUFKSMjAxHW2VlpTIzM+t1CTIqKkp2u12HDh1ytJ0/f15Hjx5VaGhorcv4+fnpxhtvdHzc3C4/brVkyRL97ne/0/vvv6/o6GineR4eHoqKinLaB0nKyMho8GXU119/XWPHjlWXLl0atHx9MWIHAACuavr06Vq7dq02bdokb29vx6iWr6+vvLy8ZLPZlJiYqJSUFPXo0UM9evRQSkqK2rVrpwkTJjjWU1BQoIKCAh05ckSStH//fnl7e6t79+7y8/OTj4+P4uPjNW/ePIWEhCg0NFRLliyRpEY//23x4sV69tlntXbtWoWFhTn24dIlXElKSkrSpEmTFB0drZiYGKWnpysvL0/x8fGO9Zw+fVp5eXk6efKkJDlCaFBQkNPdsEeOHNH27du1devWRtVdHwQ7AABaiSPHv1NFRbU8vSqa/XEn9bVixQpJ0vDhw53aV61apYcffliSNGvWLJWXlyshIcHxgOIPP/zQ8Qw7SVq5cqUWLFjgmB42bFiN9SxZskRubm6aNGmSysvLNWjQIH388cfq1KlTvev+vrS0NFVWVur+++93ap83b57mz58vSRo/frxOnTqlhQsXKj8/X5GRkdq6davTaOHmzZv1i1/8wjH94IMP1liPJL3xxhu67rrrnO4Ubm42wzCMFttaK1BSUiJfX18VFxfLx8fH7HIsZ8+ePYqKitLWtDjd1MPP7HKAGt79n6N68vldemfp7RrUr9vVFwCa0AUXP5V2Gq/QkK7y9PjP40yuhQcUo3mdO3dOubm5Cg8Pr3EzR32yCyN2AACYrHtIsP6/zz9yvFIsPDy81b1SDNcGgh0AAK1A95BgdekSoPYdSxQREeF4TRdQH9wVCwAAYBEEOwAAAIsg2AEAAFgEwQ4AgBZjSMbFfwDf11QPKSHYAQDQQlyqz0rGBZVXXDC7FLQyZWVlkiR3d/dGrYe7YgEAaCEuqpR7+X59U2SX5Ccvu5v0vXfLV56vkiRVVFTI1dW19pXAUgzDUFlZmQoLC9WxY8dGn3eCHQAALaj9uV06K6mw6ibJ5hzszp+vVtHpc3J3d5eHh4dpNaLldezY0el1ZA1FsAMAoAXZJHU4t0vV57JU7dJB3092/zpWrPgFn2rDhg3q1auXaTWiZbm7uzfZCC3BDgAAE7jovFyqzzi1VZ07rWPHjslms9V4rRRQF9w8AQAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiTA92aWlpCg8Pl6enp6KiorRjx446Lff//t//k5ubm/r379+8BQIAAFwjTA1269evV2JioubMmaPs7GwNHTpUY8aMUV5e3hWXKy4u1uTJkzVixIgWqhQAAKD1MzXYLVu2TFOnTtW0adMUERGh1NRUhYSEaMWKFVdc7vHHH9eECRMUExPTQpUCAAC0fqYFu8rKSmVlZSkuLs6pPS4uTjt37rzscqtWrdK///1vzZs3r7lLBAAAuKa4mbXhoqIiVVVVKTAw0Kk9MDBQBQUFtS5z+PBhzZ49Wzt27JCbW91Kr6ioUEVFhWO6pKSk4UXXU15enoqKilpse61BTk6OJKmsrEyl37mbXE1N7u7u8rR7ml0GAADNwrRgd4nNZnOaNgyjRpskVVVVacKECVqwYIF69uxZ5/UvWrRICxYsaHSd9ZWXl6feEREqLytr8W23BgcPHlTZmdYX7FxdXDVw0EDCHQDAkkwLdv7+/nJ1da0xOldYWFhjFE+SSktLtXv3bmVnZ2vGjBmSpOrqahmGITc3N3344Ye68847ayyXnJyspKQkx3RJSYlCQkKaeG9qKioqUnlZmQY9+aZ8ukU0+/Zai5KvcvTZSxPlE9JHfqG+Zpfj5EJFmUq+Oqjz588T7AAAlmRasPPw8FBUVJQyMjJ03333OdozMjI0bty4Gv19fHy0f/9+p7a0tDR9/PHHeueddxQeHl7rdux2u+x2e9MWXw8+3SLU6fpbTNu+WVw92snN09vsMgAAaFNMvRSblJSkSZMmKTo6WjExMUpPT1deXp7i4+MlXRxtO3HihNasWSMXFxdFRkY6LR8QECBPT88a7QAAAG2RqcFu/PjxOnXqlBYuXKj8/HxFRkZq69atCg0NlSTl5+df9Zl2AAAAuMj0mycSEhKUkJBQ67zVq1dfcdn58+dr/vz5TV8UAADANcj0V4oBAACgaRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAItwM7sAoKWVnS0zuwT8gLu7uzztnmaXAQDXPIId2oyq85WSbDqYc9DsUvADri6uGjhoIOEOABqJYIc2w6i+IMlQh+De8vDqYHY5+D8XKspU8tVBnT9/nmAHAI1EsEOb4+rRTm6e3maXAQBAk+PmCQAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFhEg4Ld6tWrVVZW1tS1AAAAoBEaFOySk5MVFBSkqVOnaufOnY0qIC0tTeHh4fL09FRUVJR27Nhx2b6ffvqpbrvtNnXu3FleXl7q3bu3XnzxxUZtHwAAwCoaFOy++uorvfnmmzpz5ozuuOMO9e7dWy+88IIKCgrqtZ7169crMTFRc+bMUXZ2toYOHaoxY8YoLy+v1v7t27fXjBkztH37duXk5Gju3LmaO3eu0tPTG7IbAAAAltKgYOfq6qqxY8dq48aNOn78uB577DH9+c9/Vvfu3TV27Fht2rRJ1dXVV13PsmXLNHXqVE2bNk0RERFKTU1VSEiIVqxYUWv/AQMG6KGHHlLfvn0VFhamiRMn6q677rriKB8AAEBb0eibJwICAnTbbbcpJiZGLi4u2r9/vx5++GHdcMMN2rZt22WXq6ysVFZWluLi4pza4+Li6nx5Nzs7Wzt37lRsbOxl+1RUVKikpMTpAwAAYEUNDnZff/21li5dqr59+2r48OEqKSnRli1blJubq5MnT+onP/mJpkyZctnli4qKVFVVpcDAQKf2wMDAq17S7datm+x2u6KjozV9+nRNmzbtsn0XLVokX19fxyckJKR+OwoAAHCNaFCwu+eeexQSEqLVq1fr0Ucf1YkTJ7Ru3TqNHDlSkuTl5aVf/vKXOn78+FXXZbPZnKYNw6jR9kM7duzQ7t27tXLlSqWmpmrdunWX7ZucnKzi4mLHpy41AQAAXIvcGrJQQECAMjMzFRMTc9k+Xbt2VW5u7mXn+/v7y9XVtcboXGFhYY1RvB8KDw+XJN100036+uuvNX/+fD300EO19rXb7bLb7VdcHwAAgBU0aMQuNjZWt9xyS432yspKrVmzRtLFkbjQ0NDLrsPDw0NRUVHKyMhwas/IyNCQIUPqXIthGKqoqKhzfwAAAKtqULD7xS9+oeLi4hrtpaWl+sUvflHn9SQlJem1117TG2+8oZycHD399NPKy8tTfHy8pIuXUSdPnuzov3z5cr333ns6fPiwDh8+rFWrVmnp0qWaOHFiQ3YDAADAUhp0KfZy34P76quv5OvrW+f1jB8/XqdOndLChQuVn5+vyMhIbd261THSl5+f7/RMu+rqaiUnJys3N1dubm664YYb9Pzzz+vxxx9vyG4AAABYSr2C3YABA2Sz2WSz2TRixAi5uf1n8aqqKuXm5mr06NH1KiAhIUEJCQm1zlu9erXT9MyZMzVz5sx6rR8AAKCtqFewu/feeyVJe/fu1V133aUOHTo45nl4eCgsLEw//elPm7RAAAAA1E29gt28efMkSWFhYRo/frw8PT2bpSgAAADUX4O+Y3elBw8DAADAHHUOdn5+fvrXv/4lf39/derU6YoPET59+nSTFAcAAIC6q3Owe/HFF+Xt7e34+WpvhwAAAEDLqnOw+/7l14cffrg5agEAAEAj1DnYlZSU1HmlPj4+DSoGAAAADVfnYNexY8erXn699ODiqqqqRhcGAACA+qlzsPvkk0+asw4AAAA0Up2DXWxsbHPWAQAAgEaqc7Dbt2+fIiMj5eLion379l2x780339zowgAAAFA/dQ52/fv3V0FBgQICAtS/f3/ZbDYZhlGjH9+xAwAAMEedg11ubq66dOni+BkAAACtS52DXWhoaK0/AwAAoHVo0LtiJenQoUN6+eWXlZOTI5vNpt69e2vmzJnq1atXU9YHAACAOnJpyELvvPOOIiMjlZWVpX79+unmm2/Wnj17FBkZqbfffrupawQAAEAdNGjEbtasWUpOTtbChQud2ufNm6ff/OY3+tnPftYkxQEAAKDuGjRiV1BQoMmTJ9donzhxogoKChpdFAAAAOqvQcFu+PDh2rFjR432Tz/9VEOHDm10UQAAAKi/Ol+K3bx5s+PnsWPH6je/+Y2ysrI0ePBgSdKuXbv09ttva8GCBU1fJQAAAK6qzsHu3nvvrdGWlpamtLQ0p7bp06crPj6+0YUBAACgfuoc7Kqrq5uzDgAAADRSg75jBwAAgNanwQ8oPnv2rDIzM5WXl6fKykqneU8++WSjCwMAAED9NCjYZWdn60c/+pHKysp09uxZ+fn5qaioSO3atVNAQADBDgAAwAQNuhT79NNP65577tHp06fl5eWlXbt26dixY4qKitLSpUubukYAAADUQYOC3d69e/XLX/5Srq6ucnV1VUVFhUJCQrR48WI988wzTV0jAAAA6qBBwc7d3V02m02SFBgYqLy8PEmSr6+v42cAAAC0rAZ9x27AgAHavXu3evbsqTvuuEO//e1vVVRUpD/96U+66aabmrpGAAAA1EGDRuxSUlLUtWtXSdLvfvc7de7cWU888YQKCwuVnp7epAUCAACgbho0YhcdHe34uUuXLtq6dWuTFQQAAICGafBz7CSpsLBQhw4dks1mU69evdSlS5emqgsAAAD11KBLsSUlJZo0aZKuu+46xcbGatiwYQoODtbEiRNVXFzc1DUCAACgDhoU7KZNm6bPPvtMW7Zs0bfffqvi4mJt2bJFu3fv1qOPPtrUNQIAAKAOGnQp9m9/+5s++OAD3X777Y62u+66S3/4wx80evToJisOAAAAddegEbvOnTvL19e3Rruvr686derU6KIAAABQfw0KdnPnzlVSUpLy8/MdbQUFBfr1r3+tZ599tsmKAwAAQN3V+VLsgAEDHG+bkKTDhw8rNDRU3bt3lyTl5eXJbrfrm2++0eOPP970lQIAAOCK6hzs7r333mYsAwAAAI1V52A3b9685qwDAAAAjdSoBxRnZWUpJydHNptNffr00YABA5qqLgAAANRTg4JdYWGhHnzwQW3btk0dO3aUYRgqLi7WHXfcobfeeos3UAAAAJigQXfFzpw5UyUlJTpw4IBOnz6tM2fO6J///KdKSkr05JNPNnWNAAAAqIMGjdi9//77+uijjxQREeFo69Onj5YvX664uLgmKw4AAAB116ARu+rqarm7u9dod3d3V3V1daOLAgAAQP01KNjdeeedeuqpp3Ty5ElH24kTJ/T0009rxIgRTVYcAAAA6q5Bwe6VV15RaWmpwsLCdMMNN+jGG29UeHi4SktL9fLLLzd1jQAAAKiDBn3HLiQkRHv27FFGRoa++OILGYahPn36aOTIkU1dHwAAAOqo3sHuwoUL8vT01N69ezVq1CiNGjWqOeoCAABAPdX7Uqybm5tCQ0NVVVXVHPUAAACggRr0Hbu5c+cqOTlZp0+fbup6AAAA0EAN+o7dSy+9pCNHjig4OFihoaFq37690/w9e/Y0SXEAAACouwYFu3vvvVc2m02GYTR1PQAAAGigegW7srIy/frXv9Zf//pXnT9/XiNGjNDLL78sf3//5qoPAAAAdVSv79jNmzdPq1ev1t13362HHnpIH330kZ544onmqg0AAAD1UK8Ru40bN+r111/Xgw8+KEn6+c9/rttuu01VVVVydXVtlgIBAABQN/UasTt+/LiGDh3qmB44cKDc3NycXi0GAAAAc9Qr2FVVVcnDw8Opzc3NTRcuXGjSogAAAFB/9boUaxiGHn74YdntdkfbuXPnFB8f7/TIk40bNzZdhQAAAKiTegW7KVOm1GibOHFikxUDAACAhqtXsFu1alVz1QEAAIBGatArxQAAAND6EOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWITpwS4tLU3h4eHy9PRUVFSUduzYcdm+Gzdu1KhRo9SlSxf5+PgoJiZGH3zwQQtWCwAA0HqZGuzWr1+vxMREzZkzR9nZ2Ro6dKjGjBmjvLy8Wvtv375do0aN0tatW5WVlaU77rhD99xzj7Kzs1u4cgAAgNbH1GC3bNkyTZ06VdOmTVNERIRSU1MVEhKiFStW1No/NTVVs2bN0q233qoePXooJSVFPXr00HvvvdfClQMAALQ+pgW7yspKZWVlKS4uzqk9Li5OO3furNM6qqurVVpaKj8/v+YoEQAA4JpSrzdPNKWioiJVVVUpMDDQqT0wMFAFBQV1Wsd///d/6+zZs3rggQcu26eiokIVFRWO6ZKSkoYVDACAyfLy8lRUVGR2GfgBf39/de/e3ewyJJkY7C6x2WxO04Zh1Girzbp16zR//nxt2rRJAQEBl+23aNEiLViwoNF1AgBgpry8PPWOiFB5WZnZpeAHvNq10xc5Oa0i3JkW7Pz9/eXq6lpjdK6wsLDGKN4PrV+/XlOnTtXbb7+tkSNHXrFvcnKykpKSHNMlJSUKCQlpeOEAAJigqKhI5WVlGvTkm/LpFmF2Ofg/JV/l6LOXJqqoqKhtBzsPDw9FRUUpIyND9913n6M9IyND48aNu+xy69at0yOPPKJ169bp7rvvvup27Ha77HZ7k9QMAIDZfLpFqNP1t5hdBlopUy/FJiUladKkSYqOjlZMTIzS09OVl5en+Ph4SRdH206cOKE1a9ZIuhjqJk+erN///vcaPHiwY7TPy8tLvr6+pu0HAABAa2BqsBs/frxOnTqlhQsXKj8/X5GRkdq6datCQ0MlSfn5+U7PtHv11Vd14cIFTZ8+XdOnT3e0T5kyRatXr27p8gEAAFoV02+eSEhIUEJCQq3zfhjWtm3b1vwFAQAAXKNMf6UYAAAAmgbBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZherBLS0tTeHi4PD09FRUVpR07dly2b35+viZMmKBevXrJxcVFiYmJLVcoAABAK2dqsFu/fr0SExM1Z84cZWdna+jQoRozZozy8vJq7V9RUaEuXbpozpw56tevXwtXCwAA0LqZGuyWLVumqVOnatq0aYqIiFBqaqpCQkK0YsWKWvuHhYXp97//vSZPnixfX98WrhYAAKB1My3YVVZWKisrS3FxcU7tcXFx2rlzZ5Ntp6KiQiUlJU4fAAAAKzIt2BUVFamqqkqBgYFO7YGBgSooKGiy7SxatEi+vr6OT0hISJOtGwAAoDUx/eYJm83mNG0YRo22xkhOTlZxcbHjc/z48SZbNwAAQGviZtaG/f395erqWmN0rrCwsMYoXmPY7XbZ7fYmWx8AAEBrZdqInYeHh6KiopSRkeHUnpGRoSFDhphUFQAAwLXLtBE7SUpKStKkSZMUHR2tmJgYpaenKy8vT/Hx8ZIuXkY9ceKE1qxZ41hm7969kqTvvvtO33zzjfbu3SsPDw/16dPHjF0AAABoNUwNduPHj9epU6e0cOFC5efnKzIyUlu3blVoaKikiw8k/uEz7QYMGOD4OSsrS2vXrlVoaKiOHj3akqUDAAC0OqYGO0lKSEhQQkJCrfNWr15do80wjGauCAAA4Npk+l2xAAAAaBoEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAItwM7sAAJCksrNlLbKdc+fOXfxn+TmVflfaItu8Frm7u8vT7ml2GQDqiWAHwFRV5ysl2XQw52CLbO/LL8sv/jP3S1WfO94i27wWubq4auCggYQ74BpDsANgKqP6giRDHYJ7y8OrQ7Nvr/03JyTtVfvA6+V3Q2Czb+9adKGiTCVfHdT58+cJdsA1hmAHoFVw9WgnN0/v5t+Om+f/bc+zRbYHAC2JmycAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWITpwS4tLU3h4eHy9PRUVFSUduzYccX+mZmZioqKkqenp66//nqtXLmyhSoFAABo3UwNduvXr1diYqLmzJmj7OxsDR06VGPGjFFeXl6t/XNzc/WjH/1IQ4cOVXZ2tp555hk9+eST2rBhQwtXDgAA0PqYGuyWLVumqVOnatq0aYqIiFBqaqpCQkK0YsWKWvuvXLlS3bt3V2pqqiIiIjRt2jQ98sgjWrp0aQtXDgAA0PqYFuwqKyuVlZWluLg4p/a4uDjt3Lmz1mX+8Y9/1Oh/1113affu3Tp//nyz1QoAAHAtcDNrw0VFRaqqqlJgYKBTe2BgoAoKCmpdpqCgoNb+Fy5cUFFRkbp27VpjmYqKClVUVDimi4uLJUklJSWN3YUr+u677yRJp/+dpQvnvmvWbbUmJScOSZIO/StfZ0ub9xjX1/mzJSo/Uymvsyfl7nnG7HLwf1r6vBzN+0aS9K8jhTpXcaHZt3ctqqqs0NnCSp3TSXl62c0up03JPXFWkpSVleX4O3LJoUMX//va1v6utHaX/u599913zZYtLq3XMIyr9jUt2F1is9mcpg3DqNF2tf61tV+yaNEiLViwoEZ7SEhIfUttkKxXH2uR7bQ2r/xxv9klXAGhrnVq2fOS9ud/tej2rk38u2KWxx67/N+Otvp3pbWLjY1t9m2UlpbK19f3in1MC3b+/v5ydXWtMTpXWFhYY1TukqCgoFr7u7m5qXPnzrUuk5ycrKSkJMd0dXW1Tp8+rc6dO18xQDZGSUmJQkJCdPz4cfn4+DTLNnB5HH/zcQ7MxfE3H+fAXFY7/oZhqLS0VMHBwVfta1qw8/DwUFRUlDIyMnTfffc52jMyMjRu3Lhal4mJidF7773n1Pbhhx8qOjpa7u7utS5jt9tltztfSujYsWPjiq8jHx8fS/xCXas4/ubjHJiL428+zoG5rHT8rzZSd4mpd8UmJSXptdde0xtvvKGcnBw9/fTTysvLU3x8vKSLo22TJ0929I+Pj9exY8eUlJSknJwcvfHGG3r99df1q1/9yqxdAAAAaDVM/Y7d+PHjderUKS1cuFD5+fmKjIzU1q1bFRoaKknKz893eqZdeHi4tm7dqqefflrLly9XcHCwXnrpJf30pz81axcAAABaDdNvnkhISFBCQkKt81avXl2jLTY2Vnv27GnmqhrHbrdr3rx5NS4Bo2Vw/M3HOTAXx998nANzteXjbzPqcu8sAAAAWj3T3xULAACApkGwAwAAsAiCHQAAgEUQ7JpYWlqawsPD5enpqaioKO3YscPskixr+/btuueeexQcHCybzaa//vWvTvMNw9D8+fMVHBwsLy8vDR8+XAcOHDCnWAtatGiRbr31Vnl7eysgIED33nuv45VHl3AOmteKFSt08803O57VFRMTo7///e+O+Rz/lrVo0SLZbDYlJiY62jgHzWf+/Pmy2WxOn6CgIMf8tnrsCXZNaP369UpMTNScOXOUnZ2toUOHasyYMU6PbEHTOXv2rPr166dXXnml1vmLFy/WsmXL9Morr+jzzz9XUFCQRo0apdLS0hau1JoyMzM1ffp07dq1SxkZGbpw4YLi4uJ09uxZRx/OQfPq1q2bnn/+ee3evVu7d+/WnXfeqXHjxjn+eHH8W87nn3+u9PR03XzzzU7tnIPm1bdvX+Xn5zs++/f/53WWbfbYG2gyAwcONOLj453aevfubcyePdukitoOSca7777rmK6urjaCgoKM559/3tF27tw5w9fX11i5cqUJFVpfYWGhIcnIzMw0DINzYJZOnToZr732Gse/BZWWlho9evQwMjIyjNjYWOOpp54yDIN/B5rbvHnzjH79+tU6ry0fe0bsmkhlZaWysrIUFxfn1B4XF6edO3eaVFXblZubq4KCAqfzYbfbFRsby/loJsXFxZIkPz8/SZyDllZVVaW33npLZ8+eVUxMDMe/BU2fPl133323Ro4c6dTOOWh+hw8fVnBwsMLDw/Xggw/qyy+/lNS2j73pDyi2iqKiIlVVVSkwMNCpPTAwUAUFBSZV1XZdOua1nY9jx46ZUZKlGYahpKQk3X777YqMjJTEOWgp+/fvV0xMjM6dO6cOHTro3XffVZ8+fRx/vDj+zeutt97Snj179Pnnn9eYx78DzWvQoEFas2aNevbsqa+//lrPPfechgwZogMHDrTpY0+wa2I2m81p2jCMGm1oOZyPljFjxgzt27dPn376aY15nIPm1atXL+3du1fffvutNmzYoClTpigzM9Mxn+PffI4fP66nnnpKH374oTw9PS/bj3PQPMaMGeP4+aabblJMTIxuuOEG/fGPf9TgwYMltc1jz6XYJuLv7y9XV9cao3OFhYU1/o8Bze/SnVGcj+Y3c+ZMbd68WZ988om6devmaOcctAwPDw/deOONio6O1qJFi9SvXz/9/ve/5/i3gKysLBUWFioqKkpubm5yc3NTZmamXnrpJbm5uTmOM+egZbRv31433XSTDh8+3KZ//wl2TcTDw0NRUVHKyMhwas/IyNCQIUNMqqrtCg8PV1BQkNP5qKysVGZmJuejiRiGoRkzZmjjxo36+OOPFR4e7jSfc2AOwzBUUVHB8W8BI0aM0P79+7V3717HJzo6Wj//+c+1d+9eXX/99ZyDFlRRUaGcnBx17dq1bf/+m3bbhgW99dZbhru7u/H6668bBw8eNBITE4327dsbR48eNbs0SyotLTWys7ON7OxsQ5KxbNkyIzs72zh27JhhGIbx/PPPG76+vsbGjRuN/fv3Gw899JDRtWtXo6SkxOTKreGJJ54wfH19jW3bthn5+fmOT1lZmaMP56B5JScnG9u3bzdyc3ONffv2Gc8884zh4uJifPjhh4ZhcPzN8P27Yg2Dc9CcfvnLXxrbtm0zvvzyS2PXrl3Gj3/8Y8Pb29vxN7etHnuCXRNbvny5ERoaanh4eBi33HKL49EPaHqffPKJIanGZ8qUKYZhXLzdfd68eUZQUJBht9uNYcOGGfv37ze3aAup7dhLMlatWuXowzloXo888ojjvzddunQxRowY4Qh1hsHxN8MPgx3noPmMHz/e6Nq1q+Hu7m4EBwcbP/nJT4wDBw445rfVY28zDMMwZ6wQAAAATYnv2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AmlF5ebnatWunL774wuxSALQBBDsAaEYZGRkKCQlR7969zS4FQBtAsAPQpg0fPlwzZszQjBkz1LFjR3Xu3Flz587VpddoV1RUaNasWQoJCZHdblePHj30+uuvS5LOnDmjn//85+rSpYu8vLzUo0cPrVq1ymn9mzZt0tixYyVJ8+fPV//+/fXGG2+oe/fu6tChg5544glVVVVp8eLFCgoKUkBAgP7rv/6rZQ8CAMtwM7sAADDbH//4R02dOlWfffaZdu/erccee0yhoaF69NFHNXnyZP3jH//QSy+9pH79+ik3N1dFRUWSpGeffVYHDx7U3//+d/n7++vIkSMqLy93rLe6ulpbtmzRhg0bHG3//ve/9fe//13vv/++/v3vf+v+++9Xbm6uevbsqczMTO3cuVOPPPKIRowYocGDB7f4sQBwbSPYAWjzQkJC9OKLL8pms6lXr17av3+/XnzxRcXGxuovf/mLMjIyNHLkSEnS9ddf71guLy9PAwYMUHR0tCQpLCzMab27du1SdXW1hgwZ4mirrq7WG2+8IW9vb/Xp00d33HGHDh06pK1bt8rFxUW9evXSCy+8oG3bthHsANQbl2IBtHmDBw+WzWZzTMfExOjw4cPKzs6Wq6urYmNja13uiSee0FtvvaX+/ftr1qxZ2rlzp9P8TZs26cc//rFcXP7zn9qwsDB5e3s7pgMDA9WnTx+nPoGBgSosLGyq3QPQhhDsAOAyPD09rzh/zJgxOnbsmBITE3Xy5EmNGDFCv/rVrxzzN2/erHHjxjkt4+7u7jRts9lqbauurm5k9QDaIoIdgDZv165dNaZ79Oihfv36qbq6WpmZmZddtkuXLnr44Yf15ptvKjU1Venp6ZKkw4cP6+jRo4qLi2vW2gHg+wh2ANq848ePKykpSYcOHdK6dev08ssv66mnnlJYWJimTJmiRx55RH/961+Vm5urbdu26S9/+Ysk6be//a02bdqkI0eO6MCBA9qyZYsiIiIkXbwMO3LkSLVr187MXQPQxnDzBIA2b/LkySovL9fAgQPl6uqqmTNn6rHHHpMkrVixQs8884wSEhJ06tQpde/eXc8884wkycPDQ8nJyTp69Ki8vLw0dOhQvfXWW5IuBrspU6aYtk8A2iabcelhTQDQBg0fPlz9+/dXampqk62zqKhIXbt21fHjxxUUFNRk6wWAq+FSLAA0sdOnT2vZsmWEOgAtjkuxANDEevbsqZ49e5pdBoA2iEuxAAAAFsGlWAAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIv4/wFhBQ6N6kFG0QAAAABJRU5ErkJggg==", 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", 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 % of total
material 
plastic96%
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ObjectQuantitypcs/m% of totalFail rate
Expanded polystyrene1'4763,260,190,87
Fragmented plastics1'1832,640,150,87
plastic caps, lid rings: G21, G22, G23, G245761,350,080,73
Industrial sheeting5161,350,070,87
Food wrappers; candy, snacks4781,260,060,73
Cotton bud/swab sticks4530,990,060,73
Foam packaging/insulation/polyurethane4520,430,061,47
Plastic construction waste2950,650,040,80
Plastic shotgun wadding2190,470,030,80
Food containers single use foamed or plastic1750,390,020,73
Tobacco; plastic packaging, containers1330,350,020,60
Cups, lids, single use foamed and hard plastic870,200,010,73
Lollypop sticks850,190,010,73
Straws and stirrers750,180,010,73
Cigarette filters740,160,010,67
Toys and party favors720,160,010,80
Biomass holder460,110,010,67
Medical; containers/tubes/ packaging450,100,010,60
Fireworks; rocket caps, exploded parts & packaging350,070,000,60
Plastic flower pots340,090,000,53
Straps/bands; hard, plastic package fastener310,080,000,53
Drink bottles < = 0.5L290,060,000,67
Pens, lids, mechanical pencils etc.270,060,000,53
Sanitary pads /panty liners/tampons and applicators240,060,000,53
Metal bottle caps, lids & pull tabs from cans240,050,000,53
Corks230,050,000,53
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 15\n* Total objects: 7638\n* Average pcs/m: 17.44\n* Standard deviation: 17.21\n* Maximum pcs/m: 52.73\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2016 - 2017\n* Number of samples: 2\n* Total objects: 1342\n* Average pcs/m: 24.01\n* Standard deviation: 17.46\n* Maximum pcs/m: 41.47\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2016 - 2021\n* Number of samples: 17\n* Total objects: 8980\n* Average pcs/m: 18.22\n* Standard deviation: 17.37\n* Maximum pcs/m: 52.73\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Rivers: 1\n* Lakes: 1\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 5\n* Cities: 5\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# most common objects all data\n", - "os = results['this_report'].object_summary()\n", - "os.reset_index(drop=False, inplace=True)\n", - "most_common_objects, mc_codes, proportions = userdisplay.most_common(os)\n", - "most_common_objects = most_common_objects.set_caption(\"\")\n", - "\n", - "# display the inventory of features\n", - "feature_inv = call_surveys.feature_inventory()\n", - "feature_inventory = format_boundaries_feature_inv(feature_inv, ['p'], userdisplay.feature_inventory)\n", - "\n", - "# display the inventory of boundaries\n", - "aboundaries = call_surveys.administrative_boundaries().copy()\n", - "administrative_boundaries = format_boundaries_feature_inv(aboundaries, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - "# display the sampling summaries\n", - "all_info = userdisplay.sampling_result_summary(all_summary, session_language='en')[1]\n", - "all_samp_sum = Markdown(f'{all_labels}\\n{all_info}')\n", - "\n", - "p_header = f\"{prior_labels}\"\n", - "p_info = userdisplay.sampling_result_summary(p_summary, session_language='en')[1]\n", - "p_samp_sum = Markdown(f'{p_header}\\n{p_info}')\n", - "\n", - "l_header = f\"{likelihood_labels} \"\n", - "l_info = userdisplay.sampling_result_summary(l_summary, session_language='en')[1]\n", - "l_samp_sum = Markdown(f'{l_header}\\n{l_info}')\n", - "\n", - "ratio_most_common = Markdown(f'__The most common objects account for {int(proportions*100)}% of all objects__')\n", - "\n", - "caption_histo = Markdown(f'Survey total pcs/m {likelihood_labels} versus {prior_labels}. All locations considered.')\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.histplot(data=results['this_report'].sample_results, x='pcs/m', stat='probability', label=likelihood_labels, ax=ax, color=palette['likelihood'])\n", - "sns.histplot(data=results['prior_report'] .sample_results, x='pcs/m', stat='probability', label=prior_labels, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "title = f'All samples {canton}: {prior_dates[\"start\"]} - {o_dates[\"end\"]}'\n", - "\n", - "ax.xaxis.set_minor_locator(mdates.MonthLocator(interval=3))\n", - "ax.xaxis.set_minor_formatter(mdates.DateFormatter(\"%b\"))\n", - "\n", - "ax.xaxis.set_major_locator(mdates.YearLocator())\n", - "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"\\n%Y\"))\n", - "\n", - "sns.scatterplot(data=results['this_report'].sample_results, x='date', y='pcs/m', marker='x', label=likelihood_labels, ax=ax, color=palette['likelihood'])\n", - "sns.scatterplot(data=results['prior_report'] .sample_results, x='date', y='pcs/m', marker='x', label=prior_labels, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "ax.set_xlabel('')\n", - "ax.set_title(title)\n", - "plt.tight_layout()\n", - "glue('scatter-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.ecdfplot(results['prior_report'].sample_results['pcs/m'], label=prior_labels, ls='-', ax=ax, c=palette['prior'], zorder=1)\n", - "sns.ecdfplot(results['this_report'].sample_results['pcs/m'], label=likelihood_labels, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "sns.ecdfplot(sample_values, label='expected 99%', ls=':', zorder=2)\n", - "sns.ecdfplot(xii, label='expected max', ls='-.', zorder=2)\n", - "sns.ecdfplot(weighted_forecast, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, results['this_report'].sample_results['pcs/m'].quantile(.99))\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_list = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo', caption_histo, display=False)\n", - "glue('material-report', material_report, display=False)\n", - "glue('forecast-weighted-prior', forecast_weighted, display=False)\n", - "glue('forecast-max-val', forecast_maxval, display=False)\n", - "glue('forecast-99-max', forecast_99, display=False)\n", - "glue('ratio-most-common', ratio_most_common, display=False)\n", - "glue('most_common_objects', most_common_objects, display=False)\n", - "glue('l-sampling-summary', l_samp_sum, display=False)\n", - "glue('prior-sampling-summary', p_samp_sum, display=False)\n", - "glue('sampling-summary', all_samp_sum, display=False)\n", - "glue('feature-inventory', feature_inventory, display=False)\n", - "glue('administrative-boundaries', administrative_boundaries, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c016aff6-dc3f-49a1-be4d-48b6448c9d22", - "metadata": { - "editable": true, - "jupyter": { - "source_hidden": true - }, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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", 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 % of total
material 
plastic96%
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ObjectQuantitypcs/m% of totalFail rate
Expanded polystyrene1'4744,440,191,00
Fragmented plastics1'1793,600,161,00
plastic caps, lid rings: G21, G22, G23, G245761,840,081,00
Industrial sheeting4981,810,071,00
Food wrappers; candy, snacks4771,720,060,91
Cotton bud/swab sticks4531,350,061,00
Foam packaging/insulation/polyurethane4520,480,062,00
Plastic construction waste2910,870,041,00
Plastic shotgun wadding2180,640,031,00
Food containers single use foamed or plastic1750,540,021,00
Styrofoam < 5mm1660,490,020,64
Tobacco; plastic packaging, containers1330,470,020,82
Cups, lids, single use foamed and hard plastic870,270,011,00
Lollypop sticks850,260,011,00
Straws and stirrers750,240,011,00
Cigarette filters720,220,010,82
Toys and party favors710,220,011,00
Biomass holder460,140,010,91
Medical; containers/tubes/ packaging450,140,010,82
Industrial pellets (nurdles)430,140,010,64
Glass drink bottles, pieces370,110,000,64
Labels, bar codes350,130,000,55
Fireworks; rocket caps, exploded parts & packaging350,100,000,82
Plastic flower pots340,120,000,73
Foamed items & pieces (non packaging/insulation) foamed sponge material320,100,000,55
Straps/bands; hard, plastic package fastener310,110,000,73
Drink bottles < = 0.5L290,080,000,91
Bags; plastic shopping/carrier/grocery and pieces290,110,000,55
Pens, lids, mechanical pencils etc.270,080,000,73
Tape; electrical, insulating230,070,000,64
Metal bottle caps, lids & pull tabs from cans230,060,000,64
Corks230,070,000,73
Sanitary pads /panty liners/tampons and applicators210,080,000,64
Small plastic bags; freezer, zip-lock etc.210,080,000,55
Cigarette lighters170,060,000,55
Paraffin wax100,030,000,64
Drink bottles > 0.5L80,030,000,55
Rubber bands80,030,000,55
Syringes - needles80,030,000,55
Pheromone baits for vineyards70,020,000,55
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 11\n* Total objects: 7560\n* Average pcs/m: 23.64\n* Standard deviation: 16.12\n* Maximum pcs/m: 52.73\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2016 - 2017\n* Number of samples: 2\n* Total objects: 1342\n* Average pcs/m: 24.01\n* Standard deviation: 17.46\n* Maximum pcs/m: 41.47\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2016 - 2021\n* Number of samples: 13\n* Total objects: 8902\n* Average pcs/m: 23.7\n* Standard deviation: 16.34\n* Maximum pcs/m: 52.73\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Lakes: 1\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 1\n* Cities: 1\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries-l" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# lakes\n", - "data = session_config.collect_survey_data()\n", - "lake_params = {'canton':canton, 'date_range':o_dates, 'feature_type': 'l'}\n", - "lake_params_p = {'canton':canton, 'date_range':prior_dates, 'feature_type':'l'}\n", - "\n", - "# all surveys lakes, rivers combined\n", - "c_all_l, _ = gfcast.filter_data(d,{'canton': canton, 'feature_type': 'l', 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_l_surveys, call_l_land = gfcast.make_report_objects(c_all_l)\n", - "\n", - "# summary and labels\n", - "all_summary_l = call_l_surveys.sampling_results_summary\n", - "all_labels_l = extract_dates_for_labels_from_summary(all_summary_l)\n", - "\n", - "# material proportions all data\n", - "material_report_l = call_l_surveys.material_report\n", - "material_report_l = material_report_l.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior_l = d[(d.canton != canton)&(d['date'] <= prior_dates['end'])&(d.feature_type == 'l')].copy()\n", - "o_report_l, o_land_use_l = gfcast.make_report_objects(o_prior_l)\n", - "lake_results = gfcast.reports_and_forecast(lake_params,lake_params_p , ldata=d.copy(), logger=logger, other_data=o_land_use_l.df_cat)\n", - "\n", - "# prior summary and label\n", - "p_summary_l = lake_results['prior_report'].sampling_results_summary.copy()\n", - "prior_labels_l = extract_dates_for_labels_from_summary(p_summary_l)\n", - "\n", - "# likelihood summary and label\n", - "l_summary_l = lake_results['this_report'].sampling_results_summary.copy()\n", - "likelihood_labels_l = extract_dates_for_labels_from_summary(l_summary_l)\n", - "\n", - "# forecasts\n", - "xii_l = lake_results['posterior_no_limit'].sample_posterior()\n", - "\n", - "# limit to the 99th percentile\n", - "sample_values_l, posterior_l, summary_simple_l = gfcast.dirichlet_posterior(lake_results['posterior_99'])\n", - "\n", - "# forecast weighted prior all data\n", - "weighted_args_l = [lake_results['this_land_use'], session_config.feature_variables, call_l_land.df_cat, lake_results['this_report'].sample_results['pcs/m']]\n", - "weighted_forecast_l, weighted_posterior_l, weighted_summary_l, _ = gfcast.forecast_weighted_prior(*weighted_args_l)\n", - "\n", - "# forecast summaries\n", - "forecast_99_l = display_forecast_summary(summary_simple_l, '__Given the 99th percentile__')\n", - "forecast_maxval_l = display_forecast_summary(lake_results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - "forecast_weighted_l = display_forecast_summary(weighted_summary_l, '__Given the weighted prior__')\n", - "\n", - "# most common objects all lake data\n", - "os_l = lake_results['this_report'].object_summary()\n", - "os_l.reset_index(drop=False, inplace=True)\n", - "most_common_objects_l, mc_codes_l, proportions_l = userdisplay.most_common(os_l)\n", - "most_common_objects_l = most_common_objects_l.set_caption(\"\")\n", - "\n", - "# display the inventory of features\n", - "feature_inv_l = call_l_surveys.feature_inventory()\n", - "feature_inventory_l = format_boundaries_feature_inv(feature_inv_l, ['p', 'r'], userdisplay.feature_inventory)\n", - "\n", - "# display the inventory of boundaries\n", - "aboundaries_l = call_l_surveys.administrative_boundaries().copy()\n", - "administrative_boundaries_l = format_boundaries_feature_inv(aboundaries_l, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - "# display the sampling summaries\n", - "all_info_l = userdisplay.sampling_result_summary(all_summary_l, session_language='en')[1]\n", - "all_samp_sum_l = Markdown(f'{all_labels_l}\\n{all_info_l}')\n", - "\n", - "p_header_l = f\"{prior_labels}\"\n", - "p_info_l = userdisplay.sampling_result_summary(p_summary_l, session_language='en')[1]\n", - "p_samp_sum_l = Markdown(f'{p_header_l}\\n{p_info_l}')\n", - "\n", - "l_header_l = f\"{likelihood_labels_l} \"\n", - "l_info_l = userdisplay.sampling_result_summary(l_summary_l, session_language='en')[1]\n", - "l_samp_sum_l = Markdown(f'{l_header_l}\\n{l_info_l}')\n", - "\n", - "ratio_most_common_l = Markdown(f'__The most common objects account for {int(proportions_l*100)}% of all objects__')\n", - "\n", - "caption_histo_l = Markdown(f'Survey total pcs/m {likelihood_labels_l} v/s {prior_labels_l}. All locations considered.')\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.histplot(data=lake_results['this_report'].sample_results, x='pcs/m', stat='probability', label=likelihood_labels_l, ax=ax, color=palette['likelihood'])\n", - "sns.histplot(data=lake_results['prior_report'].sample_results, x='pcs/m', stat='probability', label=prior_labels_l, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('lake-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.ecdfplot(lake_results['prior_report'].sample_results['pcs/m'], label=prior_labels_l, ls='-', ax=ax, c=palette['prior'], zorder=1)\n", - "sns.ecdfplot(lake_results['this_report'].sample_results['pcs/m'], label=likelihood_labels_l, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "sns.ecdfplot(sample_values_l, label='expected 99%', ls=':', zorder=2)\n", - "sns.ecdfplot(xii_l, label='expected max', ls='-.', zorder=2)\n", - "sns.ecdfplot(weighted_forecast_l, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, lake_results['this_report'].sample_results['pcs/m'].quantile(.99))\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('lake-cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_list = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo-l', caption_histo_l, display=False)\n", - "glue('material-report-l', material_report_l, display=False)\n", - "glue('forecast-weighted-prior-l', forecast_weighted_l, display=False)\n", - "glue('forecast-max-val-l', forecast_maxval_l, display=False)\n", - "glue('forecast-99-max-l', forecast_99_l, display=False)\n", - "glue('ratio-most-common-l', ratio_most_common_l, display=False)\n", - "glue('most_common_objects-l', most_common_objects_l, display=False)\n", - "glue('l-sampling-summary-l', l_samp_sum_l, display=False)\n", - "glue('prior-sampling-summary-l', p_samp_sum_l, display=False)\n", - "glue('sampling-summary-l', all_samp_sum_l, display=False)\n", - "glue('feature-inventory-l', feature_inventory_l, display=False)\n", - "glue('administrative-boundaries-l', administrative_boundaries_l, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "671460d2-c096-49b6-ab55-7cc9a003da78", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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 % of total
material 
cloth5%
metal5%
plastic88%
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ObjectQuantitypcs/m% of totalFail rate
Industrial sheeting180,090,230,50
Diapers - wipes130,070,170,50
Bags; plastic shopping/carrier/grocery and pieces90,040,120,25
Plastic construction waste40,020,050,25
Tape-caution for barrier, police, construction etc.40,020,050,50
Sheeting ag. greenhouse film40,020,050,25
Fragmented plastics40,020,050,50
Sanitary pads /panty liners/tampons and applicators30,010,040,25
Rope , string or nets20,010,030,25
Cigarette filters20,010,030,25
Expanded polystyrene20,010,030,50
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 4\n* Total objects: 78\n* Average pcs/m: 0.4\n* Standard deviation: 0.37\n* Maximum pcs/m: 1.02\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2016 - 2017\n* see weighted prior\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021\n* Number of samples: 4\n* Total objects: 78\n* Average pcs/m: 0.4\n* Standard deviation: 0.37\n* Maximum pcs/m: 1.02\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Rivers: 1\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 4\n* Cities: 4\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries-r" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# rivers\n", - "data = session_config.collect_survey_data()\n", - "river_params = {'canton':canton, 'date_range':o_dates, 'feature_type': 'r'}\n", - "river_params_p = {'canton':canton, 'date_range':prior_dates, 'feature_type':'r'}\n", - "\n", - "# all surveys lakes, rivers combined\n", - "c_all_r, _ = gfcast.filter_data(d,{'canton': canton, 'feature_type': 'r', 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_r_surveys, call_r_land = gfcast.make_report_objects(c_all_r)\n", - "\n", - "# summary and labels\n", - "all_summary_r = call_r_surveys.sampling_results_summary\n", - "all_labels_r = extract_dates_for_labels_from_summary(all_summary_r)\n", - "\n", - "# material proportions all data\n", - "material_report_r = call_r_surveys.material_report\n", - "material_report_r = material_report_r.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior_r = d[(d.canton != canton)&(d['date'] <= prior_dates['end'])&(d.feature_type == 'r')].copy()\n", - "o_report_r, o_land_use_r = gfcast.make_report_objects(o_prior_r)\n", - "newd = o_land_use_r.df_cat.copy()\n", - "newd['feature_type'] = 'r'\n", - "river_results = gfcast.reports_and_forecast(river_params,river_params_p , ldata=d.copy(), logger=logger, other_data=newd)\n", - "\n", - "\n", - "\n", - "if river_results['this_report'] == 'No likelihood':\n", - " likelihood_labels_r = likelihood_labels\n", - " l_header_r = f\"{likelihood_labels} \"\n", - " l_info_r = '* No data for the period requested\\n'\n", - " l_samp_sum_r = Markdown(f'{l_header_r}\\n{l_info_r}')\n", - " if river_results['prior_report'] == 'No prior':\n", - " prior_labels_r = prior_labels\n", - " p_header_r = f\"{prior_labels_r}\"\n", - " p_info_r = '* No data for the period requested\\n'\n", - " river_results.update({'prior_report':'No prior data'})\n", - " p_samp_sum_r = Markdown(f'{p_header_r}\\n{p_info_r}')\n", - " observedvals = [([0], 'No data', palette['prior']),([0], 'No data', palette['likelihood'])]\n", - " forecasts = [([0], 'No data', '-', 'black')]\n", - " else:\n", - " p_summary_r = river_results['prior_report'].sampling_results_summary.copy()\n", - " prior_labels_r = extract_dates_for_labels_from_summary(p_summary_r)\n", - " p_header_r = f\"{prior_labels}\"\n", - " p_info_r = userdisplay.sampling_result_summary(p_summary_r, session_language='en')[1]\n", - " p_samp_sum_r = Markdown(f'{p_header_r}\\n{p_info_r}')\n", - " observedvals = [(river_results['prior_report'].sample_results['pcs/m'], prior_labels_r, palette['prior']),([0], 'No data', palette['likelihood'])]\n", - " forecasts = [\n", - " (river_results['prior_report'].sample_results['pcs/m'], prior_labels_r, '-', palette['prior']),\n", - " ([0],f'{likelihood_labels_r} - no data', ':', palette['likelihood']),\n", - " ]\n", - " \n", - "else:\n", - " l_summary_r = river_results['this_report'].sampling_results_summary.copy()\n", - " likelihood_labels_r = extract_dates_for_labels_from_summary(l_summary_r)\n", - " l_header_r = f\"{likelihood_labels_r} \"\n", - " l_info_r = userdisplay.sampling_result_summary(l_summary_r, session_language='en')[1]\n", - " l_samp_sum_r = Markdown(f'{l_header_r}\\n{l_info_r}')\n", - "\n", - " # most common objects all lake data\n", - " os_r = river_results['this_report'].object_summary()\n", - " os_r.reset_index(drop=False, inplace=True)\n", - " most_common_objects_r, mc_codes_r, proportions_r = userdisplay.most_common(os_r)\n", - " most_common_objects_r = most_common_objects_r.set_caption(\"\")\n", - " ratio_most_common_r = Markdown(f'__The most common objects account for {int(proportions_r*100)}% of all objects__')\n", - "\n", - " # display the inventory of features\n", - " feature_inv_r = call_r_surveys.feature_inventory()\n", - " feature_inventory_r = format_boundaries_feature_inv(feature_inv_r, ['p', 'l'], userdisplay.feature_inventory)\n", - "\n", - " # display the inventory of boundaries\n", - " aboundaries_r = call_r_surveys.administrative_boundaries().copy()\n", - " administrative_boundaries_r = format_boundaries_feature_inv(aboundaries_r, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - " # display the sampling summaries\n", - " all_info_r = userdisplay.sampling_result_summary(all_summary_r, session_language='en')[1]\n", - " all_samp_sum_r = Markdown(f'{all_labels_r}\\n{all_info_r}')\n", - " \n", - " if river_results['prior_report'] == 'No prior':\n", - " prior_labels_r = prior_labels\n", - " p_header_r = f\"{prior_labels_r}\"\n", - " p_info_r = '* see weighted prior\\n'\n", - " river_results.update({'prior_report':'see weighted'})\n", - " p_samp_sum_r = Markdown(f'{p_header_r}\\n{p_info_r}')\n", - " forecast_maxval_r = '* No data to consider see weighted\\n'\n", - " forecast_99_r = '* No data to consider see weighted\\n'\n", - " observedvals = [\n", - " # (river_results['prior_report'].sample_results['pcs/m'], prior_labels_r , palette['prior']),\n", - " (river_results['this_report'].sample_results['pcs/m'], likelihood_labels_r, palette['likelihood'])\n", - " ]\n", - "\n", - " # forecast weighted prior all data\n", - " weighted_args_r = [river_results['this_land_use'], session_config.feature_variables, call_r_land.df_cat, river_results['this_report'].sample_results['pcs/m']]\n", - " weighted_forecast_r, weighted_posterior_r, weighted_summary_r, selectedr = gfcast.forecast_weighted_prior(*weighted_args_r)\n", - " forecast_weighted_r = display_forecast_summary(weighted_summary_r, '__Given the weighted prior__')\n", - " forecasts = [\n", - " \n", - " (weighted_forecast_r, 'weighted prior', '-.', 'black'),\n", - " ]\n", - " \n", - " else:\n", - " p_summary_r = river_results['prior_report'].sampling_results_summary.copy()\n", - " prior_labels_r = extract_dates_for_labels_from_summary(p_summary_r)\n", - " p_header_r = f\"{prior_labels}\"\n", - " p_info_r = userdisplay.sampling_result_summary(p_summary_r, session_language='en')[1]\n", - " p_samp_sum_r = Markdown(f'{p_header_r}\\n{p_info_r}')\n", - " xii_r = river_results['posterior_no_limit'].sample_posterior()\n", - " forecast_maxval_r = display_forecast_summary(river_results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - " observedvals = [\n", - " (river_results['prior_report'].sample_results['pcs/m'], prior_labels_r , palette['prior']),\n", - " (river_results['this_report'].sample_results['pcs/m'], likelihood_labels_r, palette['likelihood'])\n", - " ]\n", - " # limit to the 99th percentile\n", - " sample_values_r, posterior_r, summary_simple_r = gfcast.dirichlet_posterior(river_results['posterior_99'])\n", - " forecast_99_r = display_forecast_summary(summary_simple_r, '__Given the 99th percentile__')\n", - "\n", - " # forecast weighted prior all data\n", - " weighted_args_r = [river_results['this_land_use'], session_config.feature_variables, call_r_land.df_cat, river_results['this_report'].sample_results['pcs/m']]\n", - " weighted_forecast_r, weighted_posterior_r, weighted_summary_r, selectedr = gfcast.forecast_weighted_prior(*weighted_args_r)\n", - " forecast_weighted_r = display_forecast_summary(weighted_summary_r, '__Given the weighted prior__')\n", - "\n", - " forecasts = [\n", - " (river_results['prior_report'].sample_results['pcs/m'], prior_labels_r, '-',palette['prior']),\n", - " (river_results['this_report'].sample_results['pcs/m'], likelihood_labels_r, '-',palette['likelihood']),\n", - " (sample_values_r, 'expected 99th', '-', 'blue'),\n", - " (xii_r, 'observed max', ':', 'red'),\n", - " (weighted_forecast_r, 'weighted prior', '-.', 'black'),\n", - " ]\n", - " \n", - "\n", - " \n", - "\n", - " # forecast weighted prior all data\n", - " weighted_args_r = [river_results['this_land_use'], session_config.feature_variables, call_r_land.df_cat, river_results['this_report'].sample_results['pcs/m']]\n", - " weighted_forecast_r, weighted_posterior_r, weighted_summary_r, selectedr = gfcast.forecast_weighted_prior(*weighted_args_r)\n", - " forecast_weighted_r = display_forecast_summary(weighted_summary_r, '__Given the weighted prior__')\n", - "\n", - "caption_histo_r = Markdown(f'Survey total pcs/m {likelihood_labels_r} v/s {prior_labels_r}. All locations considered.')\n", - "\n", - "\n", - "\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "for vals in observedvals:\n", - " sns.histplot(data=vals[0], stat='probability', label=vals[1], ax=ax, color=vals[2])\n", - "# sns.histplot(data=river_results['prior_report'].sample_results, x='pcs/m', stat='probability', label=prior_labels_r, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('river-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "for vals in forecasts:\n", - " sns.ecdfplot(vals[0], label=vals[1], ls=vals[2], ax=ax, c=vals[3], zorder=1)\n", - "# sns.ecdfplot(river_results['this_report'].sample_results['pcs/m'], label=likelihood_labels_r, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "# sns.ecdfplot(sample_values_r, label='expected 99%', ls=':', zorder=2)\n", - "# sns.ecdfplot(xii_r, label='expected max', ls='-.', zorder=2)\n", - "# sns.ecdfplot(weighted_forecast_r, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, 10)\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('river-cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_rist = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo-r', caption_histo_r, display=False)\n", - "glue('material-report-r', material_report_r, display=False)\n", - "glue('forecast-weighted-prior-r', forecast_weighted_r, display=False)\n", - "glue('forecast-max-val-r', forecast_maxval_r, display=False)\n", - "glue('forecast-99-max-r', forecast_99_r, display=False)\n", - "glue('ratio-most-common-r', ratio_most_common_r, display=False)\n", - "glue('most_common_objects-r', most_common_objects_r, display=False)\n", - "glue('l-sampling-summary-r', l_samp_sum_r, display=False)\n", - "glue('prior-sampling-summary-r', p_samp_sum_r, display=False)\n", - "glue('sampling-summary-r', all_samp_sum_r, display=False)\n", - "glue('feature-inventory-r', feature_inventory_r, display=False)\n", - "glue('administrative-boundaries-r', administrative_boundaries_r, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "12c52a87-8340-419f-bfd9-75ca85260a97", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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- "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "map-of-survey-locations" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# Create a GeoDataFrame from the list of locations\n", - "dbc = gpd.read_file('data/shapes/kantons.shp')\n", - "dbc = dbc.to_crs(epsg=4326)\n", - "dbckey = dbc[['NAME', 'KANTONSNUM']].set_index('NAME')\n", - "dbckey = dbckey.drop_duplicates()\n", - "thiscanton = dbckey.loc[canton, 'KANTONSNUM']\n", - "db = gpd.read_file('data/shapes/municipalities.shp')\n", - "db = db.to_crs(epsg=4326)\n", - "thesecities = db[db.KANTONSNUM == thiscanton]\n", - "surveyedcities = alldata_ofinterest.city.unique()\n", - "\n", - "from matplotlib.lines import Line2D\n", - "# Define the plot\n", - "fig, ax = plt.subplots(figsize=(18, 10))\n", - "\n", - "citymap = thesecities.plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.1)\n", - "\n", - "surveyed = thesecities[thesecities.NAME.isin(surveyedcities)].plot(ax=ax, color='salmon', alpha=0.6)\n", - "\n", - "# Add a basemap using contextily\n", - "# ctx.add_basemap(ax, source=ctx.providers.SwissFederalGeoportal.NationalMapColor, crs=\"EPSG:4326\", aspect='equal')\n", - "dbc[dbc.NAME == canton].plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.2)\n", - "\n", - "sres = lake_results['this_report'].sample_results\n", - "pres = lake_results['prior_report'].sample_results\n", - "ares = call_surveys.sample_results\n", - "\n", - "sresr = river_results['this_report'].sample_results\n", - "# presr = river_results['prior_report'].sample_results\n", - "\n", - "marker_statsa, map_boundsa = userdisplay.map_markers(ares)\n", - "geometrya = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsa]\n", - "gdfa = gpd.GeoDataFrame(marker_statsa, geometry=geometrya, crs=\"EPSG:4326\")\n", - "\n", - "marker_stats, map_bounds = userdisplay.map_markers(sres)\n", - "geometry = [Point(loc['longitude'], loc['latitude']) for loc in marker_stats]\n", - "gdf = gpd.GeoDataFrame(marker_stats, geometry=geometry, crs=\"EPSG:4326\")\n", - "\n", - "marker_statsp, map_boundsp = userdisplay.map_markers(pres)\n", - "geometryp = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsp]\n", - "gdfp = gpd.GeoDataFrame(marker_statsp, geometry=geometryp, crs=\"EPSG:4326\")\n", - "\n", - "\n", - "marker_statsr, map_boundsr = userdisplay.map_markers(sresr)\n", - "geometryr = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsr]\n", - "gdfr = gpd.GeoDataFrame(marker_statsr, geometry=geometryr, crs=\"EPSG:4326\")\n", - "\n", - "gdfa.plot(ax=ax, color='grey', markersize=80)\n", - "\n", - "gdfp.plot(ax=ax, color=palette['prior'], markersize=40, edgecolor='w', lw=0.5)\n", - "\n", - "# gdfrp.plot(ax=ax, color=palette['prior'], markersize=40, edgecolor='w', lw=0.5)\n", - "gdfr.plot(ax=ax, color=palette['likelihood'], markersize=25, marker='X', edgecolor='w', lw=0.5)\n", - "gdf.plot(ax=ax, color=palette['likelihood'], markersize=25, marker='X', edgecolor='w', lw=0.5)\n", - "# Add title and labels\n", - "ax.set_title(f'Survey locations {canton}')\n", - "plt.xlabel('')\n", - "plt.ylabel('')\n", - "\n", - "plt.axis('off')\n", - "\n", - "# Create a custom legend\n", - "legend_elements = [\n", - " Line2D([0], [0], marker='X', color='w', label=likelihood_labels, markersize=10, markeredgecolor='w', markerfacecolor=palette['likelihood']),\n", - " Line2D([0], [0], marker='o', color='w', label=prior_labels, markerfacecolor=palette['prior'], markersize=10, markeredgecolor='w')\n", - "]\n", - "\n", - "plt.legend(handles=legend_elements, loc='upper right')\n", - "\n", - "glue('map-of-survey-locations', fig, display=False)\n", - "plt.close()" - ] - }, - { - "cell_type": "markdown", - "id": "720e6d85-e449-48cd-8412-3e243934e678", - "metadata": { - "editable": true, - "jp-MarkdownHeadingCollapsed": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Canton Valais\n", - "\n", - "__Density of trash along lakes and rivers__\n", - "\n", - "This is a sample cantonal report. The structure and the format are based off of the federal report, ([IQAASL](https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/)). This version is intended for use as a decsion support tool. Thus, the user is expected to be familiar with the results in the federal report and the methods described in the _Guide for Monitoring Marine Litter on European Seas_ \n", - "([The guide](https://mcc.jrc.ec.europa.eu/main/dev.py?N=41&O=439&titre_chap=TG%20Litter&titre_page=Guidance%20for%20the%20Monitoring%20of%20Marine%20Litter)).\n", - "\n", - "\n", - ":::{dropdown} Initial assessment: stakeholder discussion and priorities.\n", - "\n", - "Stakeholders should consider the following questions while consulting the report:\n", - "\n", - "1. Are the major rivers and lakes included?\n", - "2. Was their more or less observed in 2021 vs the prior results?\n", - "3. __How do these results compare to assessments from other sources (EAWAG, EMPA, Internal reports)?__\n", - " * This includes reports from NGOS in the region\n", - " * Is the data comparable?\n", - "4. Are the objects identified as the _most common_ currently the focus of reduction or prevention campaigns?\n", - " * __How does the canton decide priorties in this regard?__\n", - " * __Did or does the object appear in any regional action plan or strategy?__\n", - "5. For objects that have been the focus of prevention or reduction campaigns in the past: are they on the _most common objects_ list now?\n", - " * If the objects are on the most common list, is this inline with expectations ?\n", - " * What excatly was the mechanism or process that was intended to reduce the presence of the object?\n", - " * __With respect to the amount of resources attributed to prevention and mitigation:__ Do the attributed amounts reflect the importance of the object in terms of total amount found and frequency of occurence?\n", - "6. __Does the sampling distribution reflect the topography and land-use of the canton?__\n", - "7. Do the municipalities with elevated pcs/m have common land-use attributes?\n", - "8. __Are the municipalities of strategic importance to the canton included?__\n", - "9. Are their locations in the canton that should have been surveyed, according to cantonal priorities?\n", - "10. Are their products of regional interest that should be included in the cantonal report?\n", - ":::\n", - "\n", - ":::::{dropdown} Map of survey locations\n", - "\n", - "\n", - "\n", - "::::{grid}\n", - ":padding: 2\n", - "\n", - ":::{grid-item-card}\n", - "\n", - "```{glue} map-of-survey-locations\n", - "```\n", - "\n", - ":::\n", - "::::\n", - ":::::\n", - "## Vital statistics\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory\n", - "```\n", - "```{glue} administrative-boundaries\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory-l\n", - "```\n", - "```{glue} administrative-boundaries-l\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} lake-prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo-l\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory-r\n", - "```\n", - "```{glue} administrative-boundaries-r\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} river-prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo-r\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::::{dropdown} How did we get this data ?\n", - "\n", - "\n", - "\n", - "::::{grid}\n", - ":padding: 2\n", - "\n", - ":::{grid-item-card}\n", - "\n", - "```{glue} scatter-prior-likelihood\n", - "```\n", - "+++\n", - "The data is a combination of observations from variety of groups in Switerland since 2015. The observations were recorded using an interpretation of the _Guide for Monitoring Marine Litter on European Seas_ [The guide](https://mcc.jrc.ec.europa.eu/main/dev.py?N=41&O=439&titre_chap=TG%20Litter&titre_page=Guidance%20for%20the%20Monitoring%20of%20Marine%20Litter). The guide and the monitoring of beach litter are part of decades of research, here is the brief history [A Brief History of Marine Litter Research](https://link.springer.com/chapter/10.1007/978-3-319-16510-3_1).\n", - ":::\n", - "::::\n", - "\n", - "__Common sense guidance:__\n", - "\n", - "1. The data should be considered as a reasonable estimate of the minimum amount of trash on the ground at the time of the survey.\n", - "2. There are many sources of variance. We have considered the following:\n", - " * litter density between sampling groups.\n", - " * litter density with respect to topographical features.\n", - "3. There are differences in detect-ability and appearance for items of the same classification that are due to the effects of decomposition.\n", - "4. Many surveyors are volunteers and have different levels of experience or physical constraints that limit what will actually be collected and counted.\n", - ":::::\n", - "\n", - ":::{dropdown} How to make a report\n", - "\n", - "__Survey and Land use__\n", - "\n", - "A report is the implementation of a `SurveyReport` and a `LandUseReport`. The `SurveyReport` is the basic \n", - "element and does the initial aggregating and descriptive statistics for a query.\n", - "\n", - "The land-use-report accepts `SurveyReport.sample_results` and assigns the land-use attributes to the record. The \n", - "land-use-report provides the baseline assessment of litter density with reference to the surrounding environment. \n", - "\n", - "The assessment accepts as variables the proportion of available space that a topographical feature occupies in a \n", - "circle of $\\pi r² \\text{ where r = 1 500 meters}$ and the center of that circle is the survey location. \n", - "These proportions are compared to the `average pieces per meter` for an object or group of objects.\n", - "\n", - "\n", - "__Create a report__\n", - "\n", - "A report can be intiated by providing the name of the canton. If your canton does not appear this is because we have no data. The prior dates will be calculated automatically, by taking all data prior to the start date of the querry.\n", - "\n", - "```{code} python\n", - "\n", - "import reports\n", - "import geospatial\n", - "import gridforecast\n", - "\n", - "# suppose you have defined your data into df\n", - "observed_dates = {'start':'2020-01-01', 'end':'2021-12-31'}\n", - "\n", - "# everything that was seen before\n", - "prior_dates = {'start':'2015-11-15', 'end':'2019-12-31'}\n", - "\n", - "# name the canton\n", - "canton = 'Bern'\n", - "\n", - "# define the data of interest\n", - "data_of_interest = {'canton':canton, 'date_range':observed_dates}\n", - "\n", - "# load the data\n", - "df = session_config.collect_survey_data()\n", - "\n", - "# filter the data. \n", - "filtered_data, locations = gridforeacast.filter_data(df, data_of_interest)\n", - "\n", - "# make a survey report\n", - "this_report = reports.SurveyReport(dfc=filtered_data)\n", - "\n", - "# generate the parameters for the landuse report\n", - "target_df = this_report.sample_results\n", - "features = geospatial.collect_topo_data(locations=target_df.location.unique())\n", - "\n", - "# make a landuse report\n", - "this_land_use = geospatial.LandUseReport(target_df, features)\n", - "```\n", - "\n", - "Each report and the inference method are documented: [SurveyReport](surveyreporter), [LandUseReport](landusereporter), [GridForecaster](gridforecaster)\n", - ":::\n" - ] - }, - { - "cell_type": "markdown", - "id": "160aae5f-e9ed-4754-86a8-a76af4616553", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "source": [ - "## Most common objects 2020 - 2021\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common-l\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects-l\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common-r\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects-r\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::{dropdown} Defining the most common objects\n", - "\n", - "The default method for defining _the most common objects_ is based on the number of items collected and the number of times that at least one of an object was found with respect to the number of surveys in the query, the _fail rate_.\n", - "\n", - "Adjusting the fail rate will increase or decrease the number of the most common objects. The fail rate is included with the object inventory. \n", - "\n", - "```{code} python\n", - "\n", - "# the most common objects are accesible in the survey report\n", - "# the report.object_summary method aggregates the data to code\n", - "# and attaches the fail rate and % of total\n", - "inventory = this_report.object_summary()\n", - "\n", - "# userdisplay.most_common, takes the 10 most abundant and filters\n", - "# the data for fail rate >= 0.5. The method returns a formatted table,\n", - "# a list of the codes and the ratio of the quantity of the most common to the whole \n", - "mostcommon, codes, ratio = userdisplay.most_common(inventory)\n", - "\n", - "```\n", - "\n", - "\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "id": "1153176b-fd0c-4e93-8928-6c89886b9525", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Land use\n", - "\n", - "\n", - "Land use refers to the measurable topographic features within a cirlce of r = 1 500 m and area = $\\pi r²$ with the survey location in the middle. The features, measured in meters squared, are given as a ratio $\\frac{\\text{area of feature}}{\\text{area of circle}}$. Thus a location with high percentage of buildings will have a rating or value between 60% and 100%. The pcs/m rating is the average of all locations with a land-use profile of the same rating." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards18.680.090.000.000.00
Vineyards20.040.560.000.000.00
Buildings0.4723.640.000.180.00
Forest0.140.3021.760.000.00
Undefined0.4321.700.000.000.00
Public Services17.440.000.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards93%7%0%0%0%
Vineyards87%13%0%0%0%
Buildings20%73%0%7%0%
Forest13%7%80%0%0%
Undefined20%80%0%0%0%
Public Services100%0%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "g = results['this_land_use'].n_samples_per_feature().copy()\n", - "g = userdisplay.landuse_profile(g[session_config.feature_variables[:-1]], nsamples=results['this_report'].number_of_samples)\n", - "g = g.set_caption(\"\")\n", - "\n", - "gt = results['this_land_use'].rate_per_feature().copy()\n", - "gt = userdisplay.litter_rates_per_feature(gt.loc[session_config.feature_variables[:-1]])\n", - "gt = gt.set_caption(\"\")\n", - "\n", - "glue('rate-per-feature', gt, display=False)\n", - "glue('sampling-profile', g, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets73%7%13%7%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets23.641.020.200.180
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets = results['this_land_use'].n_samples_per_feature().copy()\n", - "streets = streets[[session_config.feature_variables[-1]]].copy()\n", - "streets = userdisplay.street_profile(streets.T, nsamples=results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets = streets.set_caption(caption)\n", - "\n", - "streets_r = results['this_land_use'].rate_per_feature().copy()\n", - "streets_r = streets_r.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r = userdisplay.street_profile(streets_r, nsamples=results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r = streets_r.set_caption(caption)\n", - "\n", - "glue('street-profile', streets, display=False)\n", - "glue('street-rates-feature', streets_r, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards23.640.000.000.000.00
Vineyards23.640.000.000.000.00
Buildings0.0023.640.000.000.00
Forest0.000.0023.640.000.00
Undefined0.0023.640.000.000.00
Public Services23.640.000.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards100%0%0%0%0%
Vineyards100%0%0%0%0%
Buildings0%100%0%0%0%
Forest0%0%100%0%0%
Undefined0%100%0%0%0%
Public Services100%0%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "gl = lake_results['this_land_use'].n_samples_per_feature().copy()\n", - "gl = userdisplay.landuse_profile(gl[session_config.feature_variables[:-1]], nsamples=lake_results['this_report'].number_of_samples)\n", - "gl = gl.set_caption(\"\")\n", - "\n", - "gtl = lake_results['this_land_use'].rate_per_feature().copy()\n", - "\n", - "gtl = userdisplay.litter_rates_per_feature(gtl.loc[session_config.feature_variables[:-1]])\n", - "gtl = gtl.set_caption(\"\")\n", - "\n", - "glue('lake-rate-per-feature', gtl, display=False)\n", - "glue('lake-sampling-profile', gl, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets100%0%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets23.640000
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets_p = lake_results['this_land_use'].n_samples_per_feature().copy()\n", - "streets_p = streets_p[[session_config.feature_variables[-1]]].copy()\n", - "streets_p = userdisplay.street_profile(streets_p.T, nsamples=lake_results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets_p = streets_p.set_caption(caption)\n", - "\n", - "streets_r_l = lake_results['this_land_use'].rate_per_feature().copy()\n", - "streets_r_l = streets_r_l.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r_l = userdisplay.street_profile(streets_r_l, nsamples=lake_results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r_l = streets_r_l.set_caption(caption)\n", - "\n", - "\n", - "glue('lake-street-profile', streets_p, display=False)\n", - "glue('lake-street-rates-feature', streets_r_l, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "82f55461-c497-483a-8c38-fbd509809afb", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards0.500.090.000.000.00
Vineyards0.240.560.000.000.00
Buildings0.470.000.000.180.00
Forest0.140.301.020.000.00
Undefined0.430.300.000.000.00
Public Services0.400.000.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards75%25%0%0%0%
Vineyards50%50%0%0%0%
Buildings75%0%0%25%0%
Forest50%25%25%0%0%
Undefined75%25%0%0%0%
Public Services100%0%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "gr = river_results['this_land_use'].n_samples_per_feature().copy()\n", - "gr = userdisplay.landuse_profile(gr[session_config.feature_variables[:-1]], nsamples=river_results['this_report'].number_of_samples)\n", - "gr = gr.set_caption(\"\")\n", - "\n", - "gtlr = river_results['this_land_use'].rate_per_feature().copy()\n", - "\n", - "gtlr = userdisplay.litter_rates_per_feature(gtlr.loc[session_config.feature_variables[:-1]])\n", - "gtlr = gtlr.set_caption(\"\")\n", - "\n", - "\n", - "glue('river-rate-per-feature', gtlr, display=False)\n", - "glue('river-sampling-profile', gr, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9b396025-1fa6-4661-9116-593fa1ed741d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets0%25%50%25%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets01.020.200.180
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets_p_r = river_results['this_land_use'].n_samples_per_feature().copy()\n", - "streets_p_r = streets_p_r[[session_config.feature_variables[-1]]].copy()\n", - "streets_p_r = userdisplay.street_profile(streets_p_r.T, nsamples=river_results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets_p_r = streets_p_r.set_caption(caption)\n", - "\n", - "streets_r_r = river_results['this_land_use'].rate_per_feature().copy()\n", - "streets_r_r = streets_r_r.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r_r = userdisplay.street_profile(streets_r_r, nsamples=river_results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r_r = streets_r_r.set_caption(caption)\n", - "\n", - "\n", - "glue('river-street-profile', streets_p_r, display=False)\n", - "glue('river-street-rates-feature', streets_r_r, display=False)" - ] - }, - { - "cell_type": "markdown", - "id": "e45988a7-3442-434a-acab-c0b13cbfcd42", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "1. The rate per feature refers to the average pcs/m observed at a particular land use rate\n", - " * Under what conditions is the pcs/m elevated? Where is it the least?\n", - "2. The sampling profile refers to the ratio of samples that were taken at a particular land use rate\n", - " * Does the sampling profile reflect the topography of the region?\n", - "\n", - "\n", - "\n", - "### Rate per feature 2020 - 2021\n", - "\n", - "::::{tab-set}\n", - ":::{tab-item} All lakes and rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} street-rates-feature\n", - "``` \n", - ":::\n", - "\n", - ":::{tab-item} Lakes\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} lake-rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} lake-street-rates-feature\n", - "```\n", - ":::\n", - "\n", - ":::{tab-item} Rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} river-rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} river-street-rates-feature\n", - "``` \n", - ":::\n", - "\n", - "::::\n", - "\n", - "### Sampling profile 2020 - 2021\n", - "\n", - "::::{tab-set}\n", - ":::{tab-item} All lakes and rivers\n", - "\n", - "__Land use__\n", - "\n", - "\n", - "```{glue} sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lengths for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} street-profile\n", - "``` \n", - ":::\n", - "\n", - ":::{tab-item} Lakes\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} lake-sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} lake-street-profile\n", - ":::\n", - "\n", - ":::{tab-item} Rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} river-sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "\n", - "\n", - "```{glue} river-street-profile\n", - "``` \n", - ":::\n", - "\n", - "::::\n", - "\n", - ":::{dropdown} Defining land use\n", - "\n", - "__Land cover__\n", - "\n", - "These measured land-use attributes are the labeled polygons from the map layer Landcover defined here ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)), they are extracted using vector overlay techniques in QGIS ([QGIS](https://qgis.org/en/site/)).\n", - "\n", - "* Buildings: built up, urbanized\n", - "* Woods: not a park, harvesting of trees may be active\n", - "* Vineyards: does not include any other type of agriculture\n", - "* Orchards: not vineyards\n", - "* Undefined: areas of the map with no predefined label\n", - "\n", - "\n", - "```{code}\n", - "\n", - "# the land use is summarized using a LandUseReport object\n", - "# the average pieces per meter by land use category\n", - "rate_per_feature = this_land_use.n_pieces_per_feature()\n", - "\n", - "# the sampling distribution\n", - "samples_per_feature = this_land_use.n_samples_per_feature()\n", - "\n", - "# the variety of locations per feature\n", - "locations_per_feature = this_land_use.locations_per_feature()\n", - "\n", - "# format for display .html\n", - "styled_rate_per_feature = userdisplay.litter_rates_per_feature(rate_per_feature)\n", - "```\n", - "\n", - "__Public services__\n", - "\n", - "Public services are the labled polygons from the Freizeitareal and Nutzungsareal map layers, defined in ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)). Both layers represent areas used for specific activities. Freizeitareal identifies areas used for recreational purposes and Nutzungsareal represents areas such as hospitals, cemeteries, historical sites or incineration plants. As a ratio of the available dry-land in a hex, these features are relatively small (less than 10%) of the total dry-land. For identified features within a bounding hex the magnitude in meters² of these variables is scaled between 0 and 1, thus the scaled value represents the size of the feature in relation to all other measured values for that feature from all other hexagons.\n", - "\n", - "* Recreation: parks, sports fields, attractions\n", - "* Infrastructure: Schools, Hospitals, cemeteries, powerplants\n", - "\n", - "__Streets and roads__\n", - "\n", - "Streets and roads are the labled polylines from the TLM Strasse map layer defined in ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)). All polyines from the map layer within a bounding hex are merged (disolved in QGIS commands) and the combined length of the polylines, in meters, is the magnitude of the variable for the bounding hex.\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "id": "501575a0-10d5-4609-8550-8d80807fda4d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Forecast\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max\n", - "```\n", - "```{glue} forecast-weighted-prior\n", - "```\n", - "\n", - "```{glue} forecast-max-val\n", - "```\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max-l\n", - "```\n", - "\n", - "```{glue} forecast-weighted-prior-l\n", - "```\n", - "\n", - "```{glue} forecast-max-val-l\n", - "```\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} lake-cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max-r\n", - "```\n", - "\n", - "```{glue} forecast-weighted-prior-r\n", - "```\n", - "\n", - "```{glue} forecast-max-val-r\n", - "```\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} river-cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::{dropdown} Forecast methods\n", - "\n", - "The applied method would best be classified as Empirical Bayes, in the sense that the prior is derived from the data ([Bayesian Filtering and Smoothing](https://users.aalto.fi/~ssarkka/pub/cup_book_online_20131111.pdf) or [Empirical Bayes methods in classical and Bayesian inference](https://hannig.cloudapps.unc.edu/STOR757Bayes/handouts/PetroneEtAl2014.pdf)). However, we share the concerns of Davidson-Pillon [Bayesian methods for hackers](https://dataorigami.net/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/#contents) about double counting and eliminate it the possibility as part of the formulation of the prior. \n", - "\n", - "__Model assumptions__\n", - "\n", - "1. Locations with similar land use attributes will have similar litter density rates\n", - "2. The data is a best estimate of what was present on the day of the survey\n", - "3. There are regional differences with respect to the density of specific objects\n", - "4. The locations surveyed are maintained by a public administration\n", - "\n", - "The choice of land use features was a natural choice but was further explored in [Near or Far](https://hammerdirt-analyst.github.io/landuse/titlepage.html).\n", - "\n", - "Our parameter estimates are thus derived from the data and they remain testable and quantifiable according to [Prior Probabilities, E T Jaynes](https://bayes.wustl.edu/etj/articles/prior.pdf). This makes our calculation very repetetive but also very well understood. It can be defined in a few lines of code for any set of survey results.\n", - "\n", - "```{code} python\n", - "\n", - "# standared libaries\n", - "import numpy as np\n", - "from scipy.stats import dirichlet, multinomial\n", - "\n", - "# collect the data of interest\n", - "h = array of survey values\n", - "\n", - "# count the number of times that each survey values exceed a value on the gird\n", - "counts = np.array([np.sum((h > x) & (h <= x + .1)) for x in grid_range])\n", - "\n", - "# use the dirichlet dist to estimate p(Y >= x) for each x on the grid\n", - "# and sample from the estimation\n", - "adist = dirichlet(counts)\n", - "this_dist = adist.rvs(1-[0]\n", - "\n", - "# draw samples from the conjugate\n", - "posterior_samples = multinomial.rvs(nsamples, p=this_dist)\n", - "\n", - "```\n", - ":::" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b0bda4c0-1b4a-4011-9ad1-fb3a52aac885", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 quantitypcs/msamplesorchardsvineyardsbuildingsforestundefinedpublic servicesstreets
city          
Saint-Gingolph890223.70131123211
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-municipal-results" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dxl = call_l_surveys.df\n", - "dxf = call_l_land.df_cont\n", - "\n", - "dxlc = dxl[['location', 'city', 'feature_type']].drop_duplicates('location')\n", - "dxlc.set_index(['location'], inplace=True, drop=True)\n", - "dxf['city'] = dxf.location.apply(lambda x : dxlc.loc[x, 'city'])\n", - "sumlu = {x:'sum' for x in session_config.feature_variables}\n", - "dxf = dxf.groupby(['sample_id', 'city', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n", - "\n", - "dxf = dxf.groupby(['city']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n", - "\n", - "for alabel in session_config.feature_variables:\n", - " dxf[alabel] = dxf[alabel]/dxf.sample_id\n", - " \n", - "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n", - "dxfc = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n", - "dxfc.rename(columns={'sample_id':'samples'}, inplace=True)\n", - "dxfc.drop('check', axis=1, inplace=True)\n", - "dxfc = dxfc.style.set_table_styles(userdisplay.table_css_styles)\n", - "dxfc = dxfc.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n", - "dxfc = dxfc.format(userdisplay.format_kwargs, precision=2)\n", - "\n", - "glue('lake-municipal-results', dxfc , display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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 \n \n \n \n \n \n \n
 quantitypcs/msamplesorchardsvineyardsbuildingsforestundefinedpublic servicesstreets
city          
Leuk150.3011112213
Riddes30.0912211113
Salgesch511.0211213112
Sion90.1811141114
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-municipal-results" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dxl = call_r_surveys.df\n", - "dxf = call_r_land.df_cont\n", - "\n", - "dxlc = dxl[['location', 'city', 'feature_type']].drop_duplicates('location')\n", - "dxlc.set_index(['location'], inplace=True, drop=True)\n", - "dxf['city'] = dxf.location.apply(lambda x : dxlc.loc[x, 'city'])\n", - "sumlu = {x:'sum' for x in session_config.feature_variables}\n", - "dxf = dxf.groupby(['sample_id', 'city', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n", - "\n", - "dxf = dxf.groupby(['city']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n", - "\n", - "for alabel in session_config.feature_variables:\n", - " dxf[alabel] = dxf[alabel]/dxf.sample_id\n", - " \n", - "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n", - "dxfcr = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n", - "dxfcr.rename(columns={'sample_id':'samples'}, inplace=True)\n", - "dxfcr.drop('check', axis=1, inplace=True)\n", - "dxfcr = dxfcr.style.set_table_styles(userdisplay.table_css_styles)\n", - "dxfcr = dxfcr.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n", - "dxfcr = dxfcr.format(userdisplay.format_kwargs, precision=2)\n", - "# glue('all-data-municipal-results', i , display=False)\n", - "glue('river-municipal-results', dxfcr, display=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "2d5b8904-044b-4aed-916c-5e36018f4087", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 samplespcs/m
Lac-leman1323.70
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lakes-i-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 samplespcs/m
Rhone40.40
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "rivers-i-summary" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "summardata = alldata_ofinterest.groupby(['sample_id', 'feature_type','feature_name'], as_index=False).agg({'pcs/m':'sum'})\n", - "# lakes\n", - "feature_individual_summary = summardata.groupby(['feature_type','feature_name'], as_index=False).agg({'sample_id':'nunique', 'pcs/m':'mean'})\n", - "\n", - "lakes_individual_summary = feature_individual_summary[feature_individual_summary.feature_type == 'l'].copy()\n", - "rivers_individual_summary = feature_individual_summary[feature_individual_summary.feature_type == 'r'].copy()\n", - "\n", - "\n", - " \n", - "lakes_i_sum = format_river_lake_summary(lakes_individual_summary).style.set_table_styles(userdisplay.table_css_styles).format(precision=2)\n", - "rivers_i_sum = format_river_lake_summary(rivers_individual_summary).style.set_table_styles(userdisplay.table_css_styles).format(precision=2)\n", - "\n", - "glue('lakes-i-summary', lakes_i_sum, display=False)\n", - "glue('rivers-i-summary', rivers_i_sum, display=False)" - ] - }, - { - "cell_type": "markdown", - "id": "b6d8a919-cdad-4915-99d0-8ffd21a40e98", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Lakes and rivers sampled - all data\n", - "\n", - "::::{grid} 2 2 2 2\n", - "\n", - ":::{grid-item}\n", - "**Lakes sampled**\n", - "\n", - "```{glue} lakes-i-summary\n", - "```\n", - "\n", - ":::\n", - "\n", - ":::{grid-item}\n", - "**Rivers sampled**\n", - "\n", - "```{glue} rivers-i-summary\n", - "```\n", - ":::\n", - "::::\n", - "\n", - "## Municipal Results - all data\n", - "\n", - "The average pieces per meter of the most common objects for each city.\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "```{glue} lake-municipal-results\n", - "```\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "```{glue} river-municipal-results\n", - "``` \n", - ":::::::::\n", - "\n", - "::::::::::" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_build/html/_sources/vaud.ipynb b/_build/html/_sources/vaud.ipynb deleted file mode 100644 index 28623c8..0000000 --- a/_build/html/_sources/vaud.ipynb +++ /dev/null @@ -1,2553 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "5bf92247-ef77-4aae-b62f-9388cba6765e", - "metadata": { - "editable": true, - "jupyter": { - "source_hidden": true - }, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "import session_config\n", - "import reports\n", - "import userdisplay\n", - "import geospatial\n", - "import gridforecast as gfcast\n", - "\n", - "import logging\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "import matplotlib.colors\n", - "from matplotlib.colors import LinearSegmentedColormap, ListedColormap\n", - "import matplotlib.dates as mdates\n", - "import seaborn as sns\n", - "import datetime as dt\n", - "\n", - "import geopandas as gpd\n", - "import contextily as ctx\n", - "from shapely.geometry import box\n", - "from shapely.geometry import Point\n", - "\n", - "from myst_nb import glue\n", - "from IPython.display import display, Markdown\n", - "\n", - "def display_forecast(fcast_summary):\n", - " average = fcast_summary['average']\n", - " hdi_min, hdi_max = fcast_summary['hdi'][0], fcast_summary['hdi'][1]\n", - " \n", - " range_90_min, range_90_max= fcast_summary['range'][0], fcast_summary['range'][-1]\n", - " alist = f'\\n* Average: {round(average, 2)}\\n* HDI 95%: {round(hdi_min, 2)} - {round(hdi_max, 2)}\\n* 90% Range: {round(range_90_min, 2)} - {round(range_90_max,2)}'\n", - " return alist\n", - "\n", - "def display_forecast_summary(asummary, label):\n", - " forecast_summary = display_forecast(asummary)\n", - " forecast_summary = Markdown(f'{label}{forecast_summary}')\n", - " return forecast_summary\n", - "\n", - "def extract_dates_for_labels_from_summary(summary):\n", - " start = dt.datetime.strftime(summary.pop('start'), format=session_config.date_format)[:4]\n", - " end = dt.datetime.strftime(summary.pop('end'), format=session_config.date_format)[:4]\n", - " return f\"{start} - {end}\"\n", - "\n", - "def format_boundaries_feature_inv(boundaries, topop, displayfunc, session_language='en'):\n", - " for thingtoremove in topop:\n", - " boundaries.pop(thingtoremove)\n", - " display_boundaries = displayfunc(boundaries, session_language=session_language)\n", - " return Markdown(display_boundaries)\n", - "\n", - "def format_river_lake_summary(d):\n", - " d.drop('feature_type', axis=1, inplace=True)\n", - " d.rename(columns={'feature_name':'Name', 'sample_id': 'samples'}, inplace=True)\n", - " d['pcs/m'] = d['pcs/m'].round(2)\n", - " d['Name'] = d.Name.apply(lambda x: x.capitalize())\n", - " d.set_index('Name', inplace=True)\n", - " d.index.name = None\n", - " return d\n", - "\n", - "\n", - "highlight_props = 'background-color:#FAE8E8'\n", - "def highlight_max(s, arg, props: str = highlight_props):\n", - " return np.where((s > arg) & (s != 0), props, '')\n", - "\n", - "logging.basicConfig(\n", - " filename='app.log', \n", - " level=logging.DEBUG,\n", - " format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n", - ")\n", - "\n", - "logger = logging.getLogger(__name__)\n", - "color_style = {'prior':'color: #daa520', 'likelihood':'color: #1e90ff'}\n", - "palette = {'prior':'goldenrod', 'likelihood':'dodgerblue'}" - ] - }, - { - "cell_type": "markdown", - "id": "d2d5a3de-1688-4113-95ce-bf614dbf9695", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "fb4d8635-eb6f-402a-9cd6-8be9c76cce30", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "data = session_config.collect_survey_data()\n", - "\n", - "o_dates = {'start':'2020-01-01', 'end':'2021-12-31'}\n", - "prior_dates = {'start':'2015-11-15', 'end':'2019-12-31'}\n", - "\n", - "# all data\n", - "canton = 'Vaud'\n", - "d= data.reset_index(drop=True)\n", - "\n", - "# all surveys lakes, rivers combined\n", - "alldata_ofinterest, locations = gfcast.filter_data(d,{'canton': canton, 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_surveys, call_land = gfcast.make_report_objects(alldata_ofinterest)\n", - "\n", - "# summary and labels\n", - "all_summary = call_surveys.sampling_results_summary.copy()\n", - "all_labels = extract_dates_for_labels_from_summary(all_summary)\n", - "\n", - "# material proportions all data\n", - "material_report = call_surveys.material_report\n", - "material_report = material_report.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior = d[(d.canton != canton)&(d['date'] <= prior_dates['end'])].copy()\n", - "o_report, o_land_use = gfcast.make_report_objects(o_prior)\n", - "results = gfcast.reports_and_forecast({'canton':canton, 'date_range':o_dates}, {'canton':canton, 'date_range':prior_dates}, ldata=d.copy(), logger=logger, other_data=o_land_use.df_cat)\n", - "\n", - "# prior summary and label\n", - "p_summary = results['prior_report'].sampling_results_summary.copy()\n", - "prior_labels = extract_dates_for_labels_from_summary(p_summary)\n", - "\n", - "# likelihood summary and label\n", - "l_summary = results['this_report'].sampling_results_summary.copy()\n", - "likelihood_labels = extract_dates_for_labels_from_summary(l_summary)\n", - "\n", - "# forecasts\n", - "xii = results['posterior_no_limit'].sample_posterior()\n", - "\n", - "# limit to the 99th percentile\n", - "sample_values, posterior, summary_simple = gfcast.dirichlet_posterior(results['posterior_99'])\n", - "\n", - "# forecast weighted prior all data\n", - "weighted_args = [results['this_land_use'], session_config.feature_variables, call_land.df_cat, results['this_report'].sample_results['pcs/m']]\n", - "weighted_forecast, weighted_posterior, weighted_summary, _ = gfcast.forecast_weighted_prior(*weighted_args)\n", - "\n", - "# forecast summaries\n", - "forecast_99 = display_forecast_summary(summary_simple, '__Given the 99th percentile__')\n", - "forecast_maxval = display_forecast_summary(results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - "forecast_weighted = display_forecast_summary(weighted_summary, '__Given the weighted prior__')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d504b355-6ec4-4d6d-b9d6-3459215fcb8b", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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", 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", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "cumumlative-dist-forecast-prior" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "Survey total pcs/m 2020 - 2021 versus 2015 - 2019. All locations considered.", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "caption-histo" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 % of total
material 
glass5%
metal3%
paper2%
plastic86%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "material-report" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 5.97\n* HDI 95%: 0.1 - 23.8\n* 90% Range: 0.4 - 23.8", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-weighted-prior" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 13.99\n* HDI 95%: 0.1 - 62.03\n* 90% Range: 0.71 - 64.44", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-max-val" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 9.1\n* HDI 95%: 0.3 - 35.0\n* 90% Range: 0.69 - 33.19", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-99-max" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__The most common objects account for 79% of all objects__", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "ratio-most-common" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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 \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
ObjectQuantitypcs/m% of totalFail rate
Fragmented plastics2'9241,270,160,96
Cigarette filters2'4120,800,130,92
Expanded polystyrene2'1440,890,120,82
Food wrappers; candy, snacks1'1400,330,060,94
Industrial pellets (nurdles)9590,390,050,45
Cotton bud/swab sticks6790,230,040,80
Industrial sheeting6560,190,040,83
Foam packaging/insulation/polyurethane6300,120,031,12
plastic caps, lid rings: G21, G22, G23, G245680,170,030,84
Styrofoam < 5mm5470,190,030,29
Glass drink bottles, pieces4590,160,030,61
Plastic construction waste2790,080,020,65
Straws and stirrers1880,060,010,67
Foil wrappers, aluminum foil1610,050,010,57
Tobacco; plastic packaging, containers1560,050,010,53
Lollypop sticks1460,050,010,63
Metal bottle caps, lids & pull tabs from cans1370,050,010,61
Medical; containers/tubes/ packaging1320,060,010,57
Toys and party favors1110,040,010,58
Cups, lids, single use foamed and hard plastic860,020,000,54
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 89\n* Total objects: 18295\n* Average pcs/m: 6.54\n* Standard deviation: 9.26\n* Maximum pcs/m: 66.17\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2015 - 2019\n* Number of samples: 142\n* Total objects: 44911\n* Average pcs/m: 8.37\n* Standard deviation: 10.43\n* Maximum pcs/m: 77.1\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2015 - 2021\n* Number of samples: 231\n* Total objects: 63206\n* Average pcs/m: 7.67\n* Standard deviation: 10.04\n* Maximum pcs/m: 77.1\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Rivers: 3\n* Lakes: 2\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 41\n* Cities: 16\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# most common objects all data\n", - "os = results['this_report'].object_summary()\n", - "os.reset_index(drop=False, inplace=True)\n", - "most_common_objects, mc_codes, proportions = userdisplay.most_common(os)\n", - "most_common_objects = most_common_objects.set_caption(\"\")\n", - "\n", - "# display the inventory of features\n", - "feature_inv = call_surveys.feature_inventory()\n", - "feature_inventory = format_boundaries_feature_inv(feature_inv, ['p'], userdisplay.feature_inventory)\n", - "\n", - "# display the inventory of boundaries\n", - "aboundaries = call_surveys.administrative_boundaries().copy()\n", - "administrative_boundaries = format_boundaries_feature_inv(aboundaries, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - "# display the sampling summaries\n", - "all_info = userdisplay.sampling_result_summary(all_summary, session_language='en')[1]\n", - "all_samp_sum = Markdown(f'{all_labels}\\n{all_info}')\n", - "\n", - "p_header = f\"{prior_labels}\"\n", - "p_info = userdisplay.sampling_result_summary(p_summary, session_language='en')[1]\n", - "p_samp_sum = Markdown(f'{p_header}\\n{p_info}')\n", - "\n", - "l_header = f\"{likelihood_labels} \"\n", - "l_info = userdisplay.sampling_result_summary(l_summary, session_language='en')[1]\n", - "l_samp_sum = Markdown(f'{l_header}\\n{l_info}')\n", - "\n", - "ratio_most_common = Markdown(f'__The most common objects account for {int(proportions*100)}% of all objects__')\n", - "\n", - "caption_histo = Markdown(f'Survey total pcs/m {likelihood_labels} versus {prior_labels}. All locations considered.')\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.histplot(data=results['this_report'].sample_results, x='pcs/m', stat='probability', label=likelihood_labels, ax=ax, color=palette['likelihood'])\n", - "sns.histplot(data=results['prior_report'] .sample_results, x='pcs/m', stat='probability', label=prior_labels, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "title = f'All samples {canton}: {prior_dates[\"start\"]} - {o_dates[\"end\"]}'\n", - "\n", - "ax.xaxis.set_minor_locator(mdates.MonthLocator(interval=3))\n", - "ax.xaxis.set_minor_formatter(mdates.DateFormatter(\"%b\"))\n", - "\n", - "ax.xaxis.set_major_locator(mdates.YearLocator())\n", - "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"\\n%Y\"))\n", - "\n", - "sns.scatterplot(data=results['this_report'].sample_results, x='date', y='pcs/m', marker='x', label=likelihood_labels, ax=ax, color=palette['likelihood'])\n", - "sns.scatterplot(data=results['prior_report'] .sample_results, x='date', y='pcs/m', marker='x', label=prior_labels, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "ax.set_xlabel('')\n", - "ax.set_title(title)\n", - "plt.tight_layout()\n", - "glue('scatter-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.ecdfplot(results['prior_report'].sample_results['pcs/m'], label=prior_labels, ls='-', ax=ax, c=palette['prior'], zorder=1)\n", - "sns.ecdfplot(results['this_report'].sample_results['pcs/m'], label=likelihood_labels, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "sns.ecdfplot(sample_values, label='expected 99%', ls=':', zorder=2)\n", - "sns.ecdfplot(xii, label='expected max', ls='-.', zorder=2)\n", - "sns.ecdfplot(weighted_forecast, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, results['this_report'].sample_results['pcs/m'].quantile(.99))\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_list = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo', caption_histo, display=False)\n", - "glue('material-report', material_report, display=False)\n", - "glue('forecast-weighted-prior', forecast_weighted, display=False)\n", - "glue('forecast-max-val', forecast_maxval, display=False)\n", - "glue('forecast-99-max', forecast_99, display=False)\n", - "glue('ratio-most-common', ratio_most_common, display=False)\n", - "glue('most_common_objects', most_common_objects, display=False)\n", - "glue('l-sampling-summary', l_samp_sum, display=False)\n", - "glue('prior-sampling-summary', p_samp_sum, display=False)\n", - "glue('sampling-summary', all_samp_sum, display=False)\n", - "glue('feature-inventory', feature_inventory, display=False)\n", - "glue('administrative-boundaries', administrative_boundaries, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c016aff6-dc3f-49a1-be4d-48b6448c9d22", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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", 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material 
glass5%
metal3%
paper2%
plastic86%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "material-report-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 6.7\n* HDI 95%: 0.2 - 22.4\n* 90% Range: 0.5 - 19.17", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-weighted-prior-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 16.86\n* HDI 95%: 0.18 - 60.79\n* 90% Range: 0.75 - 63.46", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-max-val-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 6.69\n* HDI 95%: 0.2 - 18.7\n* 90% Range: 1.0 - 15.66", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-99-max-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__The most common objects account for 81% of all objects__", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "ratio-most-common-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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 \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
ObjectQuantitypcs/m% of totalFail rate
Fragmented plastics2'9201,320,170,98
Cigarette filters2'4100,840,140,94
Expanded polystyrene2'1410,930,120,84
Food wrappers; candy, snacks1'1230,350,060,95
Industrial pellets (nurdles)9590,410,050,47
Cotton bud/swab sticks6790,240,040,84
Foam packaging/insulation/polyurethane6300,130,041,18
plastic caps, lid rings: G21, G22, G23, G245670,180,030,87
Industrial sheeting5510,180,030,82
Styrofoam < 5mm5440,200,030,29
Glass drink bottles, pieces4590,170,030,64
Plastic construction waste2530,080,010,64
Straws and stirrers1870,060,010,69
Foil wrappers, aluminum foil1610,050,010,60
Tobacco; plastic packaging, containers1550,050,010,54
Lollypop sticks1450,060,010,65
Metal bottle caps, lids & pull tabs from cans1370,050,010,64
Medical; containers/tubes/ packaging1320,060,010,60
Toys and party favors1100,040,010,60
Cups, lids, single use foamed and hard plastic840,020,000,54
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 85\n* Total objects: 17692\n* Average pcs/m: 6.73\n* Standard deviation: 9.43\n* Maximum pcs/m: 66.17\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2015 - 2019\n* Number of samples: 141\n* Total objects: 44776\n* Average pcs/m: 8.41\n* Standard deviation: 10.46\n* Maximum pcs/m: 77.1\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2015 - 2021\n* Number of samples: 226\n* Total objects: 62468\n* Average pcs/m: 7.78\n* Standard deviation: 10.12\n* Maximum pcs/m: 77.1\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Lakes: 2\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 36\n* Cities: 15\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries-l" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# lakes\n", - "data = session_config.collect_survey_data()\n", - "lake_params = {'canton':canton, 'date_range':o_dates, 'feature_type': 'l'}\n", - "lake_params_p = {'canton':canton, 'date_range':prior_dates, 'feature_type':'l'}\n", - "\n", - "# all surveys lakes, rivers combined\n", - "c_all_l, _ = gfcast.filter_data(d,{'canton': canton, 'feature_type': 'l', 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_l_surveys, call_l_land = gfcast.make_report_objects(c_all_l)\n", - "\n", - "# summary and labels\n", - "all_summary_l = call_l_surveys.sampling_results_summary\n", - "all_labels_l = extract_dates_for_labels_from_summary(all_summary_l)\n", - "\n", - "# material proportions all data\n", - "material_report_l = call_l_surveys.material_report\n", - "material_report_l = material_report_l.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior_l = d[(d.canton != canton)&(d['date'] <= prior_dates['end'])&(d.feature_type == 'l')].copy()\n", - "o_report_l, o_land_use_l = gfcast.make_report_objects(o_prior_l)\n", - "lake_results = gfcast.reports_and_forecast(lake_params,lake_params_p , ldata=d.copy(), logger=logger, other_data=o_land_use_l.df_cat)\n", - "\n", - "# prior summary and label\n", - "p_summary_l = lake_results['prior_report'].sampling_results_summary.copy()\n", - "prior_labels_l = extract_dates_for_labels_from_summary(p_summary_l)\n", - "\n", - "# likelihood summary and label\n", - "l_summary_l = lake_results['this_report'].sampling_results_summary.copy()\n", - "likelihood_labels_l = extract_dates_for_labels_from_summary(l_summary_l)\n", - "\n", - "# forecasts\n", - "xii_l = lake_results['posterior_no_limit'].sample_posterior()\n", - "\n", - "# limit to the 99th percentile\n", - "sample_values_l, posterior_l, summary_simple_l = gfcast.dirichlet_posterior(lake_results['posterior_99'])\n", - "\n", - "# forecast weighted prior all data\n", - "weighted_args_l = [lake_results['this_land_use'], session_config.feature_variables, call_l_land.df_cat, lake_results['this_report'].sample_results['pcs/m']]\n", - "weighted_forecast_l, weighted_posterior_l, weighted_summary_l, _ = gfcast.forecast_weighted_prior(*weighted_args_l)\n", - "\n", - "# forecast summaries\n", - "forecast_99_l = display_forecast_summary(summary_simple_l, '__Given the 99th percentile__')\n", - "forecast_maxval_l = display_forecast_summary(lake_results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - "forecast_weighted_l = display_forecast_summary(weighted_summary_l, '__Given the weighted prior__')\n", - "\n", - "# most common objects all lake data\n", - "os_l = lake_results['this_report'].object_summary()\n", - "os_l.reset_index(drop=False, inplace=True)\n", - "most_common_objects_l, mc_codes_l, proportions_l = userdisplay.most_common(os_l)\n", - "most_common_objects_l = most_common_objects_l.set_caption(\"\")\n", - "\n", - "# display the inventory of features\n", - "feature_inv_l = call_l_surveys.feature_inventory()\n", - "feature_inventory_l = format_boundaries_feature_inv(feature_inv_l, ['p', 'r'], userdisplay.feature_inventory)\n", - "\n", - "# display the inventory of boundaries\n", - "aboundaries_l = call_l_surveys.administrative_boundaries().copy()\n", - "administrative_boundaries_l = format_boundaries_feature_inv(aboundaries_l, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - "# display the sampling summaries\n", - "all_info_l = userdisplay.sampling_result_summary(all_summary_l, session_language='en')[1]\n", - "all_samp_sum_l = Markdown(f'{all_labels_l}\\n{all_info_l}')\n", - "\n", - "p_header_l = f\"{prior_labels}\"\n", - "p_info_l = userdisplay.sampling_result_summary(p_summary_l, session_language='en')[1]\n", - "p_samp_sum_l = Markdown(f'{p_header_l}\\n{p_info_l}')\n", - "\n", - "l_header_l = f\"{likelihood_labels_l} \"\n", - "l_info_l = userdisplay.sampling_result_summary(l_summary_l, session_language='en')[1]\n", - "l_samp_sum_l = Markdown(f'{l_header_l}\\n{l_info_l}')\n", - "\n", - "ratio_most_common_l = Markdown(f'__The most common objects account for {int(proportions_l*100)}% of all objects__')\n", - "\n", - "caption_histo_l = Markdown(f'Survey total pcs/m {likelihood_labels_l} v/s {prior_labels_l}. All locations considered.')\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.histplot(data=lake_results['this_report'].sample_results, x='pcs/m', stat='probability', label=likelihood_labels_l, ax=ax, color=palette['likelihood'])\n", - "sns.histplot(data=lake_results['prior_report'].sample_results, x='pcs/m', stat='probability', label=prior_labels_l, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('lake-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.ecdfplot(lake_results['prior_report'].sample_results['pcs/m'], label=prior_labels_l, ls='-', ax=ax, c=palette['prior'], zorder=1)\n", - "sns.ecdfplot(lake_results['this_report'].sample_results['pcs/m'], label=likelihood_labels_l, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "sns.ecdfplot(sample_values_l, label='expected 99%', ls=':', zorder=2)\n", - "sns.ecdfplot(xii_l, label='expected max', ls='-.', zorder=2)\n", - "sns.ecdfplot(weighted_forecast_l, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, lake_results['this_report'].sample_results['pcs/m'].quantile(.99))\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('lake-cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_list = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo-l', caption_histo_l, display=False)\n", - "glue('material-report-l', material_report_l, display=False)\n", - "glue('forecast-weighted-prior-l', forecast_weighted_l, display=False)\n", - "glue('forecast-max-val-l', forecast_maxval_l, display=False)\n", - "glue('forecast-99-max-l', forecast_99_l, display=False)\n", - "glue('ratio-most-common-l', ratio_most_common_l, display=False)\n", - "glue('most_common_objects-l', most_common_objects_l, display=False)\n", - "glue('l-sampling-summary-l', l_samp_sum_l, display=False)\n", - "glue('prior-sampling-summary-l', p_samp_sum_l, display=False)\n", - "glue('sampling-summary-l', all_samp_sum_l, display=False)\n", - "glue('feature-inventory-l', feature_inventory_l, display=False)\n", - "glue('administrative-boundaries-l', administrative_boundaries_l, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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", 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 % of total
material 
cloth9%
glass7%
metal8%
plastic71%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "material-report-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 2.97\n* HDI 95%: 1.2 - 4.7\n* 90% Range: 1.2 - 4.7", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-weighted-prior-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 2.64\n* HDI 95%: 0.46 - 4.01\n* 90% Range: 0.46 - 4.17", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-max-val-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 3.95\n* HDI 95%: 0.5 - 4.7\n* 90% Range: 0.5 - 4.7", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-99-max-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__The most common objects account for 85% of all objects__", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "ratio-most-common-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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 \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 \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
ObjectQuantitypcs/m% of totalFail rate
Diapers - wipes1690,580,280,75
Industrial sheeting1050,420,171,00
Bags; plastic shopping/carrier/grocery and pieces410,360,070,75
Construction material; bricks, pipes, cement360,090,060,25
Plastic construction waste260,090,040,75
Clothing, towels & rags240,060,040,50
Food wrappers; candy, snacks170,060,030,75
Tampons140,070,020,75
Cans, beverage140,050,020,75
Other metal pieces > 50cm100,030,020,25
Clothes, footware, headware, gloves90,080,010,50
Drink bottles > 0.5L80,020,010,50
Packaging films nonfood or unknown70,070,010,50
Rope, synthetic50,040,010,75
Straps/bands; hard, plastic package fastener50,020,010,75
Foamed items & pieces (non packaging/insulation) foamed sponge material40,030,010,50
Fragmented plastics40,010,010,50
Drink bottles < = 0.5L30,020,000,50
Bags30,010,000,50
Cans, food30,020,000,50
Expanded polystyrene30,030,000,50
Cups, lids, single use foamed and hard plastic20,020,000,50
Cigarette filters20,020,000,50
Industrial scrap20,010,000,50
Dog feces bag20,010,000,50
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 4\n* Total objects: 603\n* Average pcs/m: 2.58\n* Standard deviation: 1.8\n* Maximum pcs/m: 4.55\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2015 - 2019\n* Number of samples: 1\n* Total objects: 135\n* Average pcs/m: 2.85\n* Standard deviation: 0.0\n* Maximum pcs/m: 2.85\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2017 - 2021\n* Number of samples: 5\n* Total objects: 738\n* Average pcs/m: 2.64\n* Standard deviation: 1.61\n* Maximum pcs/m: 4.55\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Rivers: 3\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 5\n* Cities: 3\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries-r" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# rivers\n", - "data = session_config.collect_survey_data()\n", - "river_params = {'canton':canton, 'date_range':o_dates, 'feature_type': 'r'}\n", - "river_params_p = {'canton':canton, 'date_range':prior_dates, 'feature_type':'r'}\n", - "\n", - "# all surveys lakes, rivers combined\n", - "c_all_r, _ = gfcast.filter_data(d,{'canton': canton, 'feature_type': 'r', 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_r_surveys, call_r_land = gfcast.make_report_objects(c_all_r)\n", - "\n", - "# summary and labels\n", - "all_summary_r = call_r_surveys.sampling_results_summary\n", - "all_labels_r = extract_dates_for_labels_from_summary(all_summary_r)\n", - "\n", - "# material proportions all data\n", - "material_report_r = call_r_surveys.material_report\n", - "material_report_r = material_report_r.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior_r = d[(d.canton != canton)&(d['date'] <= prior_dates['end'])&(d.feature_type == 'r')].copy()\n", - "o_report_r, o_land_use_r = gfcast.make_report_objects(o_prior_r)\n", - "river_results = gfcast.reports_and_forecast(river_params,river_params_p , ldata=d.copy(), logger=logger, other_data=o_land_use_r.df_cat)\n", - "\n", - "# prior summary and label\n", - "p_summary_r = river_results['prior_report'].sampling_results_summary.copy()\n", - "prior_labels_r = extract_dates_for_labels_from_summary(p_summary_r)\n", - "\n", - "# likelihood summary and label\n", - "l_summary_r = river_results['this_report'].sampling_results_summary.copy()\n", - "likelihood_labels_r = extract_dates_for_labels_from_summary(l_summary_r)\n", - "\n", - "# forecasts\n", - "xii_r = river_results['posterior_no_limit'].sample_posterior()\n", - "\n", - "# limit to the 99th percentile\n", - "sample_values_r, posterior_r, summary_simple_r = gfcast.dirichlet_posterior(river_results['posterior_99'])\n", - "\n", - "# forecast weighted prior all data\n", - "weighted_args_r = [river_results['this_land_use'], session_config.feature_variables, call_r_land.df_cat, river_results['this_report'].sample_results['pcs/m']]\n", - "weighted_forecast_r, weighted_posterior_r, weighted_summary_r, _ = gfcast.forecast_weighted_prior(*weighted_args_r)\n", - "\n", - "# forecast summaries\n", - "forecast_99_r = display_forecast_summary(summary_simple_r, '__Given the 99th percentile__')\n", - "forecast_maxval_r = display_forecast_summary(river_results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - "forecast_weighted_r = display_forecast_summary(weighted_summary_r, '__Given the weighted prior__')\n", - "\n", - "# most common objects all lake data\n", - "os_r = river_results['this_report'].object_summary()\n", - "os_r.reset_index(drop=False, inplace=True)\n", - "most_common_objects_r, mc_codes_r, proportions_r = userdisplay.most_common(os_r)\n", - "most_common_objects_r = most_common_objects_r.set_caption(\"\")\n", - "\n", - "# display the inventory of features\n", - "feature_inv_r = call_r_surveys.feature_inventory()\n", - "feature_inventory_r = format_boundaries_feature_inv(feature_inv_r, ['p', 'l'], userdisplay.feature_inventory)\n", - "\n", - "# display the inventory of boundaries\n", - "aboundaries_r = call_r_surveys.administrative_boundaries().copy()\n", - "administrative_boundaries_r = format_boundaries_feature_inv(aboundaries_r, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - "# display the sampling summaries\n", - "all_info_r = userdisplay.sampling_result_summary(all_summary_r, session_language='en')[1]\n", - "all_samp_sum_r = Markdown(f'{all_labels_r}\\n{all_info_r}')\n", - "\n", - "p_header_r = f\"{prior_labels}\"\n", - "p_info_r = userdisplay.sampling_result_summary(p_summary_r, session_language='en')[1]\n", - "p_samp_sum_r = Markdown(f'{p_header_r}\\n{p_info_r}')\n", - "\n", - "l_header_r = f\"{likelihood_labels_r} \"\n", - "l_info_r = userdisplay.sampling_result_summary(l_summary_r, session_language='en')[1]\n", - "l_samp_sum_r = Markdown(f'{l_header_r}\\n{l_info_r}')\n", - "\n", - "ratio_most_common_r = Markdown(f'__The most common objects account for {int(proportions_r*100)}% of all objects__')\n", - "\n", - "caption_histo_r = Markdown(f'Survey total pcs/m {likelihood_labels_r} v/s {prior_labels_r}. All locations considered.')\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.histplot(data=river_results['this_report'].sample_results, x='pcs/m', stat='probability', label=likelihood_labels_r, ax=ax, color=palette['likelihood'])\n", - "sns.histplot(data=river_results['prior_report'].sample_results, x='pcs/m', stat='probability', label=prior_labels_r, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('river-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.ecdfplot(river_results['prior_report'].sample_results['pcs/m'], label=prior_labels_r, ls='-', ax=ax, c=palette['prior'], zorder=1)\n", - "sns.ecdfplot(river_results['this_report'].sample_results['pcs/m'], label=likelihood_labels_r, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "sns.ecdfplot(sample_values_r, label='expected 99%', ls=':', zorder=2)\n", - "sns.ecdfplot(xii_r, label='expected max', ls='-.', zorder=2)\n", - "sns.ecdfplot(weighted_forecast_r, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, river_results['this_report'].sample_results['pcs/m'].quantile(.99))\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('river-cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_rist = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo-r', caption_histo_r, display=False)\n", - "glue('material-report-r', material_report_r, display=False)\n", - "glue('forecast-weighted-prior-r', forecast_weighted_r, display=False)\n", - "glue('forecast-max-val-r', forecast_maxval_r, display=False)\n", - "glue('forecast-99-max-r', forecast_99_r, display=False)\n", - "glue('ratio-most-common-r', ratio_most_common_r, display=False)\n", - "glue('most_common_objects-r', most_common_objects_r, display=False)\n", - "glue('l-sampling-summary-r', l_samp_sum_r, display=False)\n", - "glue('prior-sampling-summary-r', p_samp_sum_r, display=False)\n", - "glue('sampling-summary-r', all_samp_sum_r, display=False)\n", - "glue('feature-inventory-r', feature_inventory_r, display=False)\n", - "glue('administrative-boundaries-r', administrative_boundaries_r, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "12c52a87-8340-419f-bfd9-75ca85260a97", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "map-of-survey-locations" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# Create a GeoDataFrame from the list of locations\n", - "dbc = gpd.read_file('data/shapes/kantons.shp')\n", - "dbc = dbc.to_crs(epsg=4326)\n", - "dbckey = dbc[['NAME', 'KANTONSNUM']].set_index('NAME')\n", - "dbckey = dbckey.drop_duplicates()\n", - "thiscanton = dbckey.loc[canton, 'KANTONSNUM']\n", - "db = gpd.read_file('data/shapes/municipalities.shp')\n", - "db = db.to_crs(epsg=4326)\n", - "thesecities = db[db.KANTONSNUM == thiscanton]\n", - "surveyedcities = alldata_ofinterest.city.unique()\n", - "\n", - "from matplotlib.lines import Line2D\n", - "# Define the plot\n", - "fig, ax = plt.subplots(figsize=(18, 10))\n", - "\n", - "citymap = thesecities.plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.1)\n", - "\n", - "surveyed = thesecities[thesecities.NAME.isin(surveyedcities)].plot(ax=ax, color='salmon', alpha=0.6)\n", - "\n", - "# Add a basemap using contextily\n", - "# ctx.add_basemap(ax, source=ctx.providers.SwissFederalGeoportal.NationalMapColor, crs=\"EPSG:4326\", aspect='equal')\n", - "dbc[dbc.NAME == canton].plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.2)\n", - "# Set the extent to Switzerland\n", - "# ax.set_ylim([newmapbounds['min_lat'], newmapbounds['max_lat']])\n", - "# ax.set_xlim([newmapbounds['min_lon'], newmapbounds['max_lon']])\n", - "# Plot the GeoDataFrame\n", - "\n", - "sres = lake_results['this_report'].sample_results\n", - "pres = lake_results['prior_report'].sample_results\n", - "ares = call_surveys.sample_results\n", - "\n", - "sresr = river_results['this_report'].sample_results\n", - "presr = river_results['prior_report'].sample_results\n", - "\n", - "marker_statsa, map_boundsa = userdisplay.map_markers(ares)\n", - "geometrya = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsa]\n", - "gdfa = gpd.GeoDataFrame(marker_statsa, geometry=geometrya, crs=\"EPSG:4326\")\n", - "\n", - "marker_stats, map_bounds = userdisplay.map_markers(sres)\n", - "geometry = [Point(loc['longitude'], loc['latitude']) for loc in marker_stats]\n", - "gdf = gpd.GeoDataFrame(marker_stats, geometry=geometry, crs=\"EPSG:4326\")\n", - "\n", - "marker_statsp, map_boundsp = userdisplay.map_markers(pres)\n", - "geometryp = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsp]\n", - "gdfp = gpd.GeoDataFrame(marker_statsp, geometry=geometryp, crs=\"EPSG:4326\")\n", - "\n", - "\n", - "marker_statsr, map_boundsr = userdisplay.map_markers(sresr)\n", - "geometryr = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsr]\n", - "gdfr = gpd.GeoDataFrame(marker_statsr, geometry=geometryr, crs=\"EPSG:4326\")\n", - "\n", - "marker_statsrp, map_boundsrp = userdisplay.map_markers(presr)\n", - "geometryrp = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsrp]\n", - "gdfrp = gpd.GeoDataFrame(marker_statsrp, geometry=geometryrp, crs=\"EPSG:4326\")\n", - "\n", - "gdfa.plot(ax=ax, color='grey', markersize=80)\n", - "\n", - "gdfp.plot(ax=ax, color=palette['prior'], markersize=40, edgecolor='w', lw=0.5)\n", - "\n", - "gdfrp.plot(ax=ax, color=palette['prior'], markersize=40, edgecolor='w', lw=0.5)\n", - "gdfr.plot(ax=ax, color=palette['likelihood'], markersize=25, marker='X', edgecolor='w', lw=0.5)\n", - "gdf.plot(ax=ax, color=palette['likelihood'], markersize=25, marker='X', edgecolor='w', lw=0.5)\n", - "# Add title and labels\n", - "ax.set_title(f'Survey locations {canton}')\n", - "plt.xlabel('')\n", - "plt.ylabel('')\n", - "\n", - "plt.axis('off')\n", - "\n", - "# Create a custom legend\n", - "legend_elements = [\n", - " Line2D([0], [0], marker='X', color='w', label=likelihood_labels, markersize=10, markeredgecolor='w', markerfacecolor=palette['likelihood']),\n", - " Line2D([0], [0], marker='o', color='w', label=prior_labels, markerfacecolor=palette['prior'], markersize=10, markeredgecolor='w')\n", - "]\n", - "\n", - "plt.legend(handles=legend_elements, loc='upper right')\n", - "\n", - "glue('map-of-survey-locations', fig, display=False)\n", - "plt.close()\n" - ] - }, - { - "cell_type": "markdown", - "id": "720e6d85-e449-48cd-8412-3e243934e678", - "metadata": { - "editable": true, - "jp-MarkdownHeadingCollapsed": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Canton Vaud\n", - "\n", - "__Density of trash along lakes and rivers__\n", - "\n", - "This is a sample cantonal report. The structure and the format are based off of the federal report, ([IQAASL](https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/)). This version is intended for use as a decsion support tool. Thus, the user is expected to be familiar with the results in the federal report and the methods described in the _Guide for Monitoring Marine Litter on European Seas_ \n", - "([The guide](https://mcc.jrc.ec.europa.eu/main/dev.py?N=41&O=439&titre_chap=TG%20Litter&titre_page=Guidance%20for%20the%20Monitoring%20of%20Marine%20Litter)).\n", - "\n", - "\n", - ":::{dropdown} Initial assessment: stakeholder discussion and priorities.\n", - "\n", - "Stakeholders should consider the following questions while consulting the report:\n", - "\n", - "1. Are the major rivers and lakes included?\n", - "2. Was their more or less observed in 2021 vs the prior results?\n", - "3. __How do these results compare to assessments from other sources (EAWAG, EMPA, Internal reports)?__\n", - " * This includes reports from NGOS in the region\n", - " * Is the data comparable?\n", - "4. Are the objects identified as the _most common_ currently the focus of reduction or prevention campaigns?\n", - " * __How does the canton decide priorties in this regard?__\n", - " * __Did or does the object appear in any regional action plan or strategy?__\n", - "5. For objects that have been the focus of prevention or reduction campaigns in the past: are they on the _most common objects_ list now?\n", - " * If the objects are on the most common list, is this inline with expectations ?\n", - " * What excatly was the mechanism or process that was intended to reduce the presence of the object?\n", - " * __With respect to the amount of resources attributed to prevention and mitigation:__ Do the attributed amounts reflect the importance of the object in terms of total amount found and frequency of occurence?\n", - "6. __Does the sampling distribution reflect the topography and land-use of the canton?__\n", - "7. Do the municipalities with elevated pcs/m have common land-use attributes?\n", - "8. __Are the municipalities of strategic importance to the canton included?__\n", - "9. Are their locations in the canton that should have been surveyed, according to cantonal priorities?\n", - "10. Are their products of regional interest that should be included in the cantonal report?\n", - ":::\n", - "\n", - ":::::{dropdown} Map of survey locations\n", - "\n", - "\n", - "\n", - "::::{grid}\n", - ":padding: 2\n", - "\n", - ":::{grid-item-card}\n", - "\n", - "```{glue} map-of-survey-locations\n", - "```\n", - "\n", - ":::\n", - "::::\n", - ":::::\n", - "## Vital statistics\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory\n", - "```\n", - "```{glue} administrative-boundaries\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory-l\n", - "```\n", - "```{glue} administrative-boundaries-l\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} lake-prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo-l\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory-r\n", - "```\n", - "```{glue} administrative-boundaries-r\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} river-prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo-r\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::::{dropdown} How did we get this data ?\n", - "\n", - "\n", - "\n", - "::::{grid}\n", - ":padding: 2\n", - "\n", - ":::{grid-item-card}\n", - "\n", - "```{glue} scatter-prior-likelihood\n", - "```\n", - "+++\n", - "The data is a combination of observations from variety of groups in Switerland since 2015. The observations were recorded using an interpretation of the _Guide for Monitoring Marine Litter on European Seas_ [The guide](https://mcc.jrc.ec.europa.eu/main/dev.py?N=41&O=439&titre_chap=TG%20Litter&titre_page=Guidance%20for%20the%20Monitoring%20of%20Marine%20Litter). The guide and the monitoring of beach litter are part of decades of research, here is the brief history [A Brief History of Marine Litter Research](https://link.springer.com/chapter/10.1007/978-3-319-16510-3_1).\n", - ":::\n", - "::::\n", - "\n", - "__Common sense guidance:__\n", - "\n", - "1. The data should be considered as a reasonable estimate of the minimum amount of trash on the ground at the time of the survey.\n", - "2. There are many sources of variance. We have considered the following:\n", - " * litter density between sampling groups.\n", - " * litter density with respect to topographical features.\n", - "3. There are differences in detect-ability and appearance for items of the same classification that are due to the effects of decomposition.\n", - "4. Many surveyors are volunteers and have different levels of experience or physical constraints that limit what will actually be collected and counted.\n", - ":::::\n", - "\n", - ":::{dropdown} How to make a report\n", - "\n", - "__Survey and Land use__\n", - "\n", - "A report is the implementation of a `SurveyReport` and a `LandUseReport`. The `SurveyReport` is the basic \n", - "element and does the initial aggregating and descriptive statistics for a query.\n", - "\n", - "The land-use-report accepts `SurveyReport.sample_results` and assigns the land-use attributes to the record. The \n", - "land-use-report provides the baseline assessment of litter density with reference to the surrounding environment. \n", - "\n", - "The assessment accepts as variables the proportion of available space that a topographical feature occupies in a \n", - "circle of $\\pi r² \\text{ where r = 1 500 meters}$ and the center of that circle is the survey location. \n", - "These proportions are compared to the `average pieces per meter` for an object or group of objects.\n", - "\n", - "\n", - "__Create a report__\n", - "\n", - "A report can be intiated by providing the name of the canton. If your canton does not appear this is because we have no data. The prior dates will be calculated automatically, by taking all data prior to the start date of the querry.\n", - "\n", - "```{code} python\n", - "\n", - "import reports\n", - "import geospatial\n", - "import gridforecast\n", - "\n", - "# suppose you have defined your data into df\n", - "observed_dates = {'start':'2020-01-01', 'end':'2021-12-31'}\n", - "\n", - "# everything that was seen before\n", - "prior_dates = {'start':'2015-11-15', 'end':'2019-12-31'}\n", - "\n", - "# name the canton\n", - "canton = 'Bern'\n", - "\n", - "# define the data of interest\n", - "data_of_interest = {'canton':canton, 'date_range':observed_dates}\n", - "\n", - "# load the data\n", - "df = session_config.collect_survey_data()\n", - "\n", - "# filter the data. \n", - "filtered_data, locations = gridforeacast.filter_data(df, data_of_interest)\n", - "\n", - "# make a survey report\n", - "this_report = reports.SurveyReport(dfc=filtered_data)\n", - "\n", - "# generate the parameters for the landuse report\n", - "target_df = this_report.sample_results\n", - "features = geospatial.collect_topo_data(locations=target_df.location.unique())\n", - "\n", - "# make a landuse report\n", - "this_land_use = geospatial.LandUseReport(target_df, features)\n", - "```\n", - "\n", - "Each report and the inference method are documented: [SurveyReport](surveyreporter), [LandUseReport](landusereporter), [GridForecaster](gridforecaster)\n", - ":::\n" - ] - }, - { - "cell_type": "markdown", - "id": "160aae5f-e9ed-4754-86a8-a76af4616553", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "source": [ - "## Most common objects 2020 - 2021\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common-l\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects-l\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common-r\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects-r\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::{dropdown} Defining the most common objects\n", - "\n", - "The default method for defining _the most common objects_ is based on the number of items collected and the number of times that at least one of an object was found with respect to the number of surveys in the query, the _fail rate_.\n", - "\n", - "Adjusting the fail rate will increase or decrease the number of the most common objects. The fail rate is included with the object inventory. \n", - "\n", - "```{code} python\n", - "\n", - "# the most common objects are accesible in the survey report\n", - "# the report.object_summary method aggregates the data to code\n", - "# and attaches the fail rate and % of total\n", - "inventory = this_report.object_summary()\n", - "\n", - "# userdisplay.most_common, takes the 10 most abundant and filters\n", - "# the data for fail rate >= 0.5. The method returns a formatted table,\n", - "# a list of the codes and the ratio of the quantity of the most common to the whole \n", - "mostcommon, codes, ratio = userdisplay.most_common(inventory)\n", - "\n", - "```\n", - "\n", - "\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "id": "1153176b-fd0c-4e93-8928-6c89886b9525", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Land use\n", - "\n", - "\n", - "Land use refers to the measurable topographic features within a cirlce of r = 1 500 m and area = $\\pi r²$ with the survey location in the middle. The features, measured in meters squared, are given as a ratio $\\frac{\\text{area of feature}}{\\text{area of circle}}$. Thus a location with high percentage of buildings will have a rating or value between 60% and 100%. The pcs/m rating is the average of all locations with a land-use profile of the same rating." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards6.540.000.000.000.00
Vineyards6.580.005.010.000.00
Buildings4.852.972.904.099.43
Forest6.782.054.550.000.00
Undefined8.282.003.822.230.00
Public Services4.4117.980.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards100%0%0%0%0%
Vineyards98%0%2%0%0%
Buildings7%8%2%36%47%
Forest94%4%1%0%0%
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Public Services84%16%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "g = results['this_land_use'].n_samples_per_feature().copy()\n", - "g = userdisplay.landuse_profile(g[session_config.feature_variables[:-1]], nsamples=results['this_report'].number_of_samples)\n", - "g = g.set_caption(\"\")\n", - "\n", - "gt = results['this_land_use'].rate_per_feature().copy()\n", - "gt = userdisplay.litter_rates_per_feature(gt.loc[session_config.feature_variables[:-1]])\n", - "gt = gt.set_caption(\"\")\n", - "\n", - "glue('rate-per-feature', gt, display=False)\n", - "glue('sampling-profile', g, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets9%74%17%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets5.234.6015.7900
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets = results['this_land_use'].n_samples_per_feature().copy()\n", - "streets = streets[[session_config.feature_variables[-1]]].copy()\n", - "streets = userdisplay.street_profile(streets.T, nsamples=results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets = streets.set_caption(caption)\n", - "\n", - "streets_r = results['this_land_use'].rate_per_feature().copy()\n", - "streets_r = streets_r.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r = userdisplay.street_profile(streets_r, nsamples=results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r = streets_r.set_caption(caption)\n", - "\n", - "glue('street-profile', streets, display=False)\n", - "glue('street-rates-feature', streets_r, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards6.730.000.000.000.00
Vineyards6.770.005.010.000.00
Buildings4.913.095.354.099.43
Forest6.861.430.000.000.00
Undefined8.281.904.332.230.00
Public Services4.5117.980.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards100%0%0%0%0%
Vineyards98%0%2%0%0%
Buildings6%6%1%38%49%
Forest98%2%0%0%0%
Undefined73%19%7%1%0%
Public Services84%16%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "gl = lake_results['this_land_use'].n_samples_per_feature().copy()\n", - "gl = userdisplay.landuse_profile(gl[session_config.feature_variables[:-1]], nsamples=lake_results['this_report'].number_of_samples)\n", - "gl = gl.set_caption(\"\")\n", - "\n", - "gtl = lake_results['this_land_use'].rate_per_feature().copy()\n", - "\n", - "gtl = userdisplay.litter_rates_per_feature(gtl.loc[session_config.feature_variables[:-1]])\n", - "gtl = gtl.set_caption(\"\")\n", - "\n", - "glue('lake-rate-per-feature', gtl, display=False)\n", - "glue('lake-sampling-profile', gl, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets9%78%13%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets5.234.6020.5900
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets_p = lake_results['this_land_use'].n_samples_per_feature().copy()\n", - "streets_p = streets_p[[session_config.feature_variables[-1]]].copy()\n", - "streets_p = userdisplay.street_profile(streets_p.T, nsamples=lake_results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets_p = streets_p.set_caption(caption)\n", - "\n", - "streets_r_l = lake_results['this_land_use'].rate_per_feature().copy()\n", - "streets_r_l = streets_r_l.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r_l = userdisplay.street_profile(streets_r_l, nsamples=lake_results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r_l = streets_r_l.set_caption(caption)\n", - "\n", - "\n", - "glue('lake-street-profile', streets_p, display=False)\n", - "glue('lake-street-rates-feature', streets_r_l, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "82f55461-c497-483a-8c38-fbd509809afb", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards2.580.000.000.000.00
Vineyards2.580.000.000.000.00
Buildings4.552.670.460.000.00
Forest0.462.674.550.000.00
Undefined0.002.852.310.000.00
Public Services2.580.000.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards100%0%0%0%0%
Vineyards100%0%0%0%0%
Buildings25%50%25%0%0%
Forest25%50%25%0%0%
Undefined0%50%50%0%0%
Public Services100%0%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "gr = river_results['this_land_use'].n_samples_per_feature().copy()\n", - "gr = userdisplay.landuse_profile(gr[session_config.feature_variables[:-1]], nsamples=river_results['this_report'].number_of_samples)\n", - "gr = gr.set_caption(\"\")\n", - "\n", - "gtlr = river_results['this_land_use'].rate_per_feature().copy()\n", - "\n", - "gtlr = userdisplay.litter_rates_per_feature(gtlr.loc[session_config.feature_variables[:-1]])\n", - "gtlr = gtlr.set_caption(\"\")\n", - "\n", - "\n", - "glue('river-rate-per-feature', gtlr, display=False)\n", - "glue('river-sampling-profile', gr, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9b396025-1fa6-4661-9116-593fa1ed741d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets0%0%100%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets002.5800
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets_p_r = river_results['this_land_use'].n_samples_per_feature().copy()\n", - "streets_p_r = streets_p_r[[session_config.feature_variables[-1]]].copy()\n", - "streets_p_r = userdisplay.street_profile(streets_p_r.T, nsamples=river_results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets_p_r = streets_p_r.set_caption(caption)\n", - "\n", - "streets_r_r = river_results['this_land_use'].rate_per_feature().copy()\n", - "streets_r_r = streets_r_r.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r_r = userdisplay.street_profile(streets_r_r, nsamples=river_results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r_r = streets_r_r.set_caption(caption)\n", - "\n", - "\n", - "glue('river-street-profile', streets_p_r, display=False)\n", - "glue('river-street-rates-feature', streets_r_r, display=False)" - ] - }, - { - "cell_type": "markdown", - "id": "e45988a7-3442-434a-acab-c0b13cbfcd42", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "1. The rate per feature refers to the average pcs/m observed at a particular land use rate\n", - " * Under what conditions is the pcs/m elevated? Where is it the least?\n", - "2. The sampling profile refers to the ratio of samples that were taken at a particular land use rate\n", - " * Does the sampling profile reflect the topography of the region?\n", - "\n", - "\n", - "\n", - "### Rate per feature 2020 - 2021\n", - "\n", - "::::{tab-set}\n", - ":::{tab-item} All lakes and rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} street-rates-feature\n", - "``` \n", - ":::\n", - "\n", - ":::{tab-item} Lakes\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} lake-rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} lake-street-rates-feature\n", - "```\n", - ":::\n", - "\n", - ":::{tab-item} Rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} river-rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} river-street-rates-feature\n", - "``` \n", - ":::\n", - "\n", - "::::\n", - "\n", - "### Sampling profile 2020 - 2021\n", - "\n", - "::::{tab-set}\n", - ":::{tab-item} All lakes and rivers\n", - "\n", - "__Land use__\n", - "\n", - "\n", - "```{glue} sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lengths for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} street-profile\n", - "``` \n", - ":::\n", - "\n", - ":::{tab-item} Lakes\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} lake-sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} lake-street-profile\n", - ":::\n", - "\n", - ":::{tab-item} Rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} river-sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "\n", - "\n", - "```{glue} river-street-profile\n", - "``` \n", - ":::\n", - "\n", - "::::\n", - "\n", - ":::{dropdown} Defining land use\n", - "\n", - "__Land cover__\n", - "\n", - "These measured land-use attributes are the labeled polygons from the map layer Landcover defined here ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)), they are extracted using vector overlay techniques in QGIS ([QGIS](https://qgis.org/en/site/)).\n", - "\n", - "* Buildings: built up, urbanized\n", - "* Woods: not a park, harvesting of trees may be active\n", - "* Vineyards: does not include any other type of agriculture\n", - "* Orchards: not vineyards\n", - "* Undefined: areas of the map with no predefined label\n", - "\n", - "\n", - "```{code}\n", - "\n", - "# the land use is summarized using a LandUseReport object\n", - "# the average pieces per meter by land use category\n", - "rate_per_feature = this_land_use.n_pieces_per_feature()\n", - "\n", - "# the sampling distribution\n", - "samples_per_feature = this_land_use.n_samples_per_feature()\n", - "\n", - "# the variety of locations per feature\n", - "locations_per_feature = this_land_use.locations_per_feature()\n", - "\n", - "# format for display .html\n", - "styled_rate_per_feature = userdisplay.litter_rates_per_feature(rate_per_feature)\n", - "```\n", - "\n", - "__Public services__\n", - "\n", - "Public services are the labled polygons from the Freizeitareal and Nutzungsareal map layers, defined in ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)). Both layers represent areas used for specific activities. Freizeitareal identifies areas used for recreational purposes and Nutzungsareal represents areas such as hospitals, cemeteries, historical sites or incineration plants. As a ratio of the available dry-land in a hex, these features are relatively small (less than 10%) of the total dry-land. For identified features within a bounding hex the magnitude in meters² of these variables is scaled between 0 and 1, thus the scaled value represents the size of the feature in relation to all other measured values for that feature from all other hexagons.\n", - "\n", - "* Recreation: parks, sports fields, attractions\n", - "* Infrastructure: Schools, Hospitals, cemeteries, powerplants\n", - "\n", - "__Streets and roads__\n", - "\n", - "Streets and roads are the labled polylines from the TLM Strasse map layer defined in ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)). All polyines from the map layer within a bounding hex are merged (disolved in QGIS commands) and the combined length of the polylines, in meters, is the magnitude of the variable for the bounding hex.\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "id": "501575a0-10d5-4609-8550-8d80807fda4d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Forecast\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max\n", - "```\n", - "```{glue} forecast-weighted-prior\n", - "```\n", - "\n", - "```{glue} forecast-max-val\n", - "```\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max-l\n", - "```\n", - "\n", - "```{glue} forecast-weighted-prior-l\n", - "```\n", - "\n", - "```{glue} forecast-max-val-l\n", - "```\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} lake-cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max-r\n", - "```\n", - "\n", - "```{glue} forecast-weighted-prior-r\n", - "```\n", - "\n", - "```{glue} forecast-max-val-r\n", - "```\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} river-cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::{dropdown} Forecast methods\n", - "\n", - "The applied method would best be classified as Empirical Bayes, in the sense that the prior is derived from the data ([Bayesian Filtering and Smoothing](https://users.aalto.fi/~ssarkka/pub/cup_book_online_20131111.pdf) or [Empirical Bayes methods in classical and Bayesian inference](https://hannig.cloudapps.unc.edu/STOR757Bayes/handouts/PetroneEtAl2014.pdf)). However, we share the concerns of Davidson-Pillon [Bayesian methods for hackers](https://dataorigami.net/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/#contents) about double counting and eliminate it the possibility as part of the formulation of the prior. \n", - "\n", - "__Model assumptions__\n", - "\n", - "1. Locations with similar land use attributes will have similar litter density rates\n", - "2. The data is a best estimate of what was present on the day of the survey\n", - "3. There are regional differences with respect to the density of specific objects\n", - "4. The locations surveyed are maintained by a public administration\n", - "\n", - "The choice of land use features was a natural choice but was further explored in [Near or Far](https://hammerdirt-analyst.github.io/landuse/titlepage.html).\n", - "\n", - "Our parameter estimates are thus derived from the data and they remain testable and quantifiable according to [Prior Probabilities, E T Jaynes](https://bayes.wustl.edu/etj/articles/prior.pdf). This makes our calculation very repetetive but also very well understood. It can be defined in a few lines of code for any set of survey results.\n", - "\n", - "```{code} python\n", - "\n", - "# standared libaries\n", - "import numpy as np\n", - "from scipy.stats import dirichlet, multinomial\n", - "\n", - "# collect the data of interest\n", - "h = array of survey values\n", - "\n", - "# count the number of times that each survey values exceed a value on the gird\n", - "counts = np.array([np.sum((h > x) & (h <= x + .1)) for x in grid_range])\n", - "\n", - "# use the dirichlet dist to estimate p(Y >= x) for each x on the grid\n", - "# and sample from the estimation\n", - "adist = dirichlet(counts)\n", - "this_dist = adist.rvs(1-[0]\n", - "\n", - "# draw samples from the conjugate\n", - "posterior_samples = multinomial.rvs(nsamples, p=this_dist)\n", - "\n", - "```\n", - ":::" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b0bda4c0-1b4a-4011-9ad1-fb3a52aac885", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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 \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 \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
 quantitypcs/msamplesorchardsvineyardsbuildingsforestundefinedpublic servicesstreets
city          
Allaman6317.2331111311
Bourg-en-Lavaux1215.0121321212
Cudrefin2202.3991121412
Gland14844.26221112311
Grandson1041.4111121312
La Tour-de-Peilz874310.60241151112
Lausanne381912.27201151123
Montreux167146.27531141112
Morges1705.6311151112
Préverenges39476.13151141112
Rolle1469.7511241112
Saint-Sulpice (VD)919418.24151151122
Tolochenaz6763.4731131212
Vevey150648.37441151112
Yverdon-les-Bains14351.38131141112
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-municipal-results" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dxl = call_l_surveys.df\n", - "dxf = call_l_land.df_cont\n", - "\n", - "dxlc = dxl[['location', 'city', 'feature_type']].drop_duplicates('location')\n", - "dxlc.set_index(['location'], inplace=True, drop=True)\n", - "dxf['city'] = dxf.location.apply(lambda x : dxlc.loc[x, 'city'])\n", - "sumlu = {x:'sum' for x in session_config.feature_variables}\n", - "dxf = dxf.groupby(['sample_id', 'city', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n", - "\n", - "dxf = dxf.groupby(['city']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n", - "\n", - "for alabel in session_config.feature_variables:\n", - " dxf[alabel] = dxf[alabel]/dxf.sample_id\n", - " \n", - "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n", - "dxfc = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n", - "dxfc.rename(columns={'sample_id':'samples'}, inplace=True)\n", - "dxfc.drop('check', axis=1, inplace=True)\n", - "dxfc = dxfc.style.set_table_styles(userdisplay.table_css_styles)\n", - "dxfc = dxfc.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n", - "dxfc = dxfc.format(userdisplay.format_kwargs, precision=2)\n", - "\n", - "glue('lake-municipal-results', dxfc , display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 quantitypcs/msamplesorchardsvineyardsbuildingsforestundefinedpublic servicesstreets
city          
Lavey-Morcles5943.2931122213
Vevey1352.8511151113
Yverdon-les-Bains90.4611131313
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-municipal-results" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dxl = call_r_surveys.df\n", - "dxf = call_r_land.df_cont\n", - "\n", - "dxlc = dxl[['location', 'city', 'feature_type']].drop_duplicates('location')\n", - "dxlc.set_index(['location'], inplace=True, drop=True)\n", - "dxf['city'] = dxf.location.apply(lambda x : dxlc.loc[x, 'city'])\n", - "sumlu = {x:'sum' for x in session_config.feature_variables}\n", - "dxf = dxf.groupby(['sample_id', 'city', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n", - "\n", - "dxf = dxf.groupby(['city']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n", - "\n", - "for alabel in session_config.feature_variables:\n", - " dxf[alabel] = dxf[alabel]/dxf.sample_id\n", - " \n", - "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n", - "dxfcr = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n", - "dxfcr.rename(columns={'sample_id':'samples'}, inplace=True)\n", - "dxfcr.drop('check', axis=1, inplace=True)\n", - "dxfcr = dxfcr.style.set_table_styles(userdisplay.table_css_styles)\n", - "dxfcr = dxfcr.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n", - "dxfcr = dxfcr.format(userdisplay.format_kwargs, precision=2)\n", - "# glue('all-data-municipal-results', i , display=False)\n", - "glue('river-municipal-results', dxfcr, display=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "2d5b8904-044b-4aed-916c-5e36018f4087", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 samplespcs/m
Lac-leman2038.46
Neuenburgersee231.78
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lakes-i-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 samplespcs/m
La-thiele10.46
Ognonnaz12.85
Rhone33.29
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "rivers-i-summary" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "summardata = alldata_ofinterest.groupby(['sample_id', 'feature_type','feature_name'], as_index=False).agg({'pcs/m':'sum'})\n", - "# lakes\n", - "feature_individual_summary = summardata.groupby(['feature_type','feature_name'], as_index=False).agg({'sample_id':'nunique', 'pcs/m':'mean'})\n", - "\n", - "lakes_individual_summary = feature_individual_summary[feature_individual_summary.feature_type == 'l'].copy()\n", - "rivers_individual_summary = feature_individual_summary[feature_individual_summary.feature_type == 'r'].copy()\n", - "\n", - "\n", - " \n", - "lakes_i_sum = format_river_lake_summary(lakes_individual_summary).style.set_table_styles(userdisplay.table_css_styles).format(precision=2)\n", - "rivers_i_sum = format_river_lake_summary(rivers_individual_summary).style.set_table_styles(userdisplay.table_css_styles).format(precision=2)\n", - "\n", - "glue('lakes-i-summary', lakes_i_sum, display=False)\n", - "glue('rivers-i-summary', rivers_i_sum, display=False)" - ] - }, - { - "cell_type": "markdown", - "id": "b6d8a919-cdad-4915-99d0-8ffd21a40e98", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Lakes and rivers sampled - all data\n", - "\n", - "::::{grid} 2 2 2 2\n", - "\n", - ":::{grid-item}\n", - "**Lakes sampled**\n", - "\n", - "```{glue} lakes-i-summary\n", - "```\n", - "\n", - ":::\n", - "\n", - ":::{grid-item}\n", - "**Rivers sampled**\n", - "\n", - "```{glue} rivers-i-summary\n", - "```\n", - ":::\n", - "::::\n", - "\n", - "## Municipal Results - all data\n", - "\n", - "The average pieces per meter of the most common objects for each city.\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "```{glue} lake-municipal-results\n", - "```\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "```{glue} river-municipal-results\n", - "``` \n", - ":::::::::\n", - "\n", - "::::::::::" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_build/html/_sources/zurich.ipynb b/_build/html/_sources/zurich.ipynb deleted file mode 100644 index 75710ab..0000000 --- a/_build/html/_sources/zurich.ipynb +++ /dev/null @@ -1,2550 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "5bf92247-ef77-4aae-b62f-9388cba6765e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "import session_config\n", - "import reports\n", - "import userdisplay\n", - "import geospatial\n", - "import gridforecast as gfcast\n", - "\n", - "import logging\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "import matplotlib.colors\n", - "from matplotlib.colors import LinearSegmentedColormap, ListedColormap\n", - "import matplotlib.dates as mdates\n", - "import seaborn as sns\n", - "import datetime as dt\n", - "\n", - "import geopandas as gpd\n", - "import contextily as ctx\n", - "from shapely.geometry import box\n", - "from shapely.geometry import Point\n", - "\n", - "from myst_nb import glue\n", - "from IPython.display import display, Markdown\n", - "\n", - "def display_forecast(fcast_summary):\n", - " average = fcast_summary['average']\n", - " hdi_min, hdi_max = fcast_summary['hdi'][0], fcast_summary['hdi'][1]\n", - " \n", - " range_90_min, range_90_max= fcast_summary['range'][0], fcast_summary['range'][-1]\n", - " alist = f'\\n* Average: {round(average, 2)}\\n* HDI 95%: {round(hdi_min, 2)} - {round(hdi_max, 2)}\\n* 90% Range: {round(range_90_min, 2)} - {round(range_90_max,2)}'\n", - " return alist\n", - "\n", - "def display_forecast_summary(asummary, label):\n", - " forecast_summary = display_forecast(asummary)\n", - " forecast_summary = Markdown(f'{label}{forecast_summary}')\n", - " return forecast_summary\n", - "\n", - "def extract_dates_for_labels_from_summary(summary):\n", - " start = dt.datetime.strftime(summary.pop('start'), format=session_config.date_format)[:4]\n", - " end = dt.datetime.strftime(summary.pop('end'), format=session_config.date_format)[:4]\n", - " return f\"{start} - {end}\"\n", - "\n", - "def format_boundaries_feature_inv(boundaries, topop, displayfunc, session_language='en'):\n", - " for thingtoremove in topop:\n", - " boundaries.pop(thingtoremove)\n", - " display_boundaries = displayfunc(boundaries, session_language=session_language)\n", - " return Markdown(display_boundaries)\n", - "\n", - "def format_river_lake_summary(d):\n", - " d.drop('feature_type', axis=1, inplace=True)\n", - " d.rename(columns={'feature_name':'Name', 'sample_id': 'samples'}, inplace=True)\n", - " d['pcs/m'] = d['pcs/m'].round(2)\n", - " d['Name'] = d.Name.apply(lambda x: x.capitalize())\n", - " d.set_index('Name', inplace=True)\n", - " d.index.name = None\n", - " return d\n", - "\n", - "\n", - "highlight_props = 'background-color:#FAE8E8'\n", - "def highlight_max(s, arg, props: str = highlight_props):\n", - " return np.where((s > arg) & (s != 0), props, '')\n", - "\n", - "logging.basicConfig(\n", - " filename='app.log', \n", - " level=logging.DEBUG,\n", - " format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n", - ")\n", - "\n", - "logger = logging.getLogger(__name__)\n", - "color_style = {'prior':'color: #daa520', 'likelihood':'color: #1e90ff'}\n", - "palette = {'prior':'goldenrod', 'likelihood':'dodgerblue'}" - ] - }, - { - "cell_type": "markdown", - "id": "d2d5a3de-1688-4113-95ce-bf614dbf9695", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "fb4d8635-eb6f-402a-9cd6-8be9c76cce30", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [], - "source": [ - "data = session_config.collect_survey_data()\n", - "\n", - "o_dates = {'start':'2020-01-01', 'end':'2021-12-31'}\n", - "prior_dates = {'start':'2015-11-15', 'end':'2019-12-31'}\n", - "\n", - "# all data\n", - "canton = 'Zürich'\n", - "d= data.reset_index(drop=True)\n", - "\n", - "# all surveys lakes, rivers combined\n", - "alldata_ofinterest, locations = gfcast.filter_data(d,{'canton': canton, 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_surveys, call_land = gfcast.make_report_objects(alldata_ofinterest)\n", - "\n", - "# summary and labels\n", - "all_summary = call_surveys.sampling_results_summary.copy()\n", - "all_labels = extract_dates_for_labels_from_summary(all_summary)\n", - "\n", - "# material proportions all data\n", - "material_report = call_surveys.material_report\n", - "material_report = material_report.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior = d[(d.canton != canton)&(d['date'] <= prior_dates['end'])].copy()\n", - "o_report, o_land_use = gfcast.make_report_objects(o_prior)\n", - "results = gfcast.reports_and_forecast({'canton':canton, 'date_range':o_dates}, {'canton':canton, 'date_range':prior_dates}, ldata=d.copy(), logger=logger, other_data=o_land_use.df_cat)\n", - "\n", - "# prior summary and label\n", - "p_summary = results['prior_report'].sampling_results_summary.copy()\n", - "prior_labels = extract_dates_for_labels_from_summary(p_summary)\n", - "\n", - "# likelihood summary and label\n", - "l_summary = results['this_report'].sampling_results_summary.copy()\n", - "likelihood_labels = extract_dates_for_labels_from_summary(l_summary)\n", - "\n", - "# forecasts\n", - "xii = results['posterior_no_limit'].sample_posterior()\n", - "\n", - "# limit to the 99th percentile\n", - "sample_values, posterior, summary_simple = gfcast.dirichlet_posterior(results['posterior_99'])\n", - "\n", - "# forecast weighted prior all data\n", - "weighted_args = [results['this_land_use'], session_config.feature_variables, call_land.df_cat, results['this_report'].sample_results['pcs/m']]\n", - "weighted_forecast, weighted_posterior, weighted_summary, _ = gfcast.forecast_weighted_prior(*weighted_args)\n", - "\n", - "# forecast summaries\n", - "forecast_99 = display_forecast_summary(summary_simple, '__Given the 99th percentile__')\n", - "forecast_maxval = display_forecast_summary(results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - "forecast_weighted = display_forecast_summary(weighted_summary, '__Given the weighted prior__')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d504b355-6ec4-4d6d-b9d6-3459215fcb8b", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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", 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", - "application/papermill.record/text/plain": "
" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "cumumlative-dist-forecast-prior" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "Survey total pcs/m 2020 - 2021 versus 2017 - 2018. All locations considered.", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "caption-histo" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 % of total
material 
glass8%
metal8%
paper8%
plastic70%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "material-report" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 2.22\n* HDI 95%: 0.1 - 8.9\n* 90% Range: 0.2 - 8.04", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-weighted-prior" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 3.01\n* HDI 95%: 0.02 - 21.29\n* 90% Range: 0.16 - 21.63", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-max-val" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 2.18\n* HDI 95%: 0.1 - 6.1\n* 90% Range: 0.2 - 5.91", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-99-max" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__The most common objects account for 66% of all objects__", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "ratio-most-common" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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 \n \n \n \n \n \n
ObjectQuantitypcs/m% of totalFail rate
Cigarette filters1'2060,700,270,92
Glass drink bottles, pieces4820,400,110,80
Fragmented plastics3360,220,070,73
Food wrappers; candy, snacks2780,160,060,92
Packaging films nonfood or unknown1440,090,030,39
Metal bottle caps, lids & pull tabs from cans1360,080,030,73
Expanded polystyrene1280,090,030,47
plastic caps, lid rings: G21, G22, G23, G241240,070,030,65
Tobacco; plastic packaging, containers1050,070,020,57
Paper fragments1020,070,020,49
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 49\n* Total objects: 4543\n* Average pcs/m: 2.87\n* Standard deviation: 5.28\n* Maximum pcs/m: 37.0\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2017 - 2018\n* Number of samples: 297\n* Total objects: 29242\n* Average pcs/m: 2.51\n* Standard deviation: 3.5\n* Maximum pcs/m: 38.0\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2017 - 2021\n* Number of samples: 346\n* Total objects: 33785\n* Average pcs/m: 2.56\n* Standard deviation: 3.8\n* Maximum pcs/m: 38.0\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Rivers: 9\n* Lakes: 3\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 37\n* Cities: 20\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# most common objects all data\n", - "os = results['this_report'].object_summary()\n", - "os.reset_index(drop=False, inplace=True)\n", - "most_common_objects, mc_codes, proportions = userdisplay.most_common(os)\n", - "most_common_objects = most_common_objects.set_caption(\"\")\n", - "\n", - "# display the inventory of features\n", - "feature_inv = call_surveys.feature_inventory()\n", - "feature_inventory = format_boundaries_feature_inv(feature_inv, ['p'], userdisplay.feature_inventory)\n", - "\n", - "# display the inventory of boundaries\n", - "aboundaries = call_surveys.administrative_boundaries().copy()\n", - "administrative_boundaries = format_boundaries_feature_inv(aboundaries, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - "# display the sampling summaries\n", - "all_info = userdisplay.sampling_result_summary(all_summary, session_language='en')[1]\n", - "all_samp_sum = Markdown(f'{all_labels}\\n{all_info}')\n", - "\n", - "p_header = f\"{prior_labels}\"\n", - "p_info = userdisplay.sampling_result_summary(p_summary, session_language='en')[1]\n", - "p_samp_sum = Markdown(f'{p_header}\\n{p_info}')\n", - "\n", - "l_header = f\"{likelihood_labels} \"\n", - "l_info = userdisplay.sampling_result_summary(l_summary, session_language='en')[1]\n", - "l_samp_sum = Markdown(f'{l_header}\\n{l_info}')\n", - "\n", - "ratio_most_common = Markdown(f'__The most common objects account for {int(proportions*100)}% of all objects__')\n", - "\n", - "caption_histo = Markdown(f'Survey total pcs/m {likelihood_labels} versus {prior_labels}. All locations considered.')\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.histplot(data=results['this_report'].sample_results, x='pcs/m', stat='probability', label=likelihood_labels, ax=ax, color=palette['likelihood'])\n", - "sns.histplot(data=results['prior_report'] .sample_results, x='pcs/m', stat='probability', label=prior_labels, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "title = f'All samples {canton}: {prior_dates[\"start\"]} - {o_dates[\"end\"]}'\n", - "\n", - "ax.xaxis.set_minor_locator(mdates.MonthLocator(interval=3))\n", - "ax.xaxis.set_minor_formatter(mdates.DateFormatter(\"%b\"))\n", - "\n", - "ax.xaxis.set_major_locator(mdates.YearLocator())\n", - "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"\\n%Y\"))\n", - "\n", - "sns.scatterplot(data=results['this_report'].sample_results, x='date', y='pcs/m', marker='x', label=likelihood_labels, ax=ax, color=palette['likelihood'])\n", - "sns.scatterplot(data=results['prior_report'] .sample_results, x='date', y='pcs/m', marker='x', label=prior_labels, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "ax.set_xlabel('')\n", - "ax.set_title(title)\n", - "plt.tight_layout()\n", - "glue('scatter-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.ecdfplot(results['prior_report'].sample_results['pcs/m'], label=prior_labels, ls='-', ax=ax, c=palette['prior'], zorder=1)\n", - "sns.ecdfplot(results['this_report'].sample_results['pcs/m'], label=likelihood_labels, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "sns.ecdfplot(sample_values, label='expected 99%', ls=':', zorder=2)\n", - "sns.ecdfplot(xii, label='expected max', ls='-.', zorder=2)\n", - "sns.ecdfplot(weighted_forecast, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, results['this_report'].sample_results['pcs/m'].quantile(.99))\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_list = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo', caption_histo, display=False)\n", - "glue('material-report', material_report, display=False)\n", - "glue('forecast-weighted-prior', forecast_weighted, display=False)\n", - "glue('forecast-max-val', forecast_maxval, display=False)\n", - "glue('forecast-99-max', forecast_99, display=False)\n", - "glue('ratio-most-common', ratio_most_common, display=False)\n", - "glue('most_common_objects', most_common_objects, display=False)\n", - "glue('l-sampling-summary', l_samp_sum, display=False)\n", - "glue('prior-sampling-summary', p_samp_sum, display=False)\n", - "glue('sampling-summary', all_samp_sum, display=False)\n", - "glue('feature-inventory', feature_inventory, display=False)\n", - "glue('administrative-boundaries', administrative_boundaries, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c016aff6-dc3f-49a1-be4d-48b6448c9d22", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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", 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" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-cumumlative-dist-forecast-prior" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "Survey total pcs/m 2020 - 2021 v/s 2017 - 2018. All locations considered.", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "caption-histo-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 % of total
material 
glass8%
metal8%
paper7%
plastic71%
wood2%
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ObjectQuantitypcs/m% of totalFail rate
Cigarette filters1'1430,810,270,95
Glass drink bottles, pieces4530,470,110,82
Fragmented plastics3230,260,080,80
Food wrappers; candy, snacks2600,180,060,95
Packaging films nonfood or unknown1420,110,030,45
Expanded polystyrene1280,110,030,57
plastic caps, lid rings: G21, G22, G23, G241220,090,030,75
Metal bottle caps, lids & pull tabs from cans1200,080,030,78
Tobacco; plastic packaging, containers1040,080,020,68
Paper fragments950,070,020,55
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 40\n* Total objects: 4297\n* Average pcs/m: 3.33\n* Standard deviation: 5.73\n* Maximum pcs/m: 37.0\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2017 - 2018\n* Number of samples: 131\n* Total objects: 22931\n* Average pcs/m: 4.2\n* Standard deviation: 4.55\n* Maximum pcs/m: 38.0\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2017 - 2021\n* Number of samples: 171\n* Total objects: 27228\n* Average pcs/m: 3.99\n* Standard deviation: 4.87\n* Maximum pcs/m: 38.0\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Lakes: 3\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory-l" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 16\n* Cities: 9\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries-l" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# lakes\n", - "data = session_config.collect_survey_data()\n", - "lake_params = {'canton':canton, 'date_range':o_dates, 'feature_type': 'l'}\n", - "lake_params_p = {'canton':canton, 'date_range':prior_dates, 'feature_type':'l'}\n", - "\n", - "# all surveys lakes, rivers combined\n", - "c_all_l, _ = gfcast.filter_data(d,{'canton': canton, 'feature_type': 'l', 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_l_surveys, call_l_land = gfcast.make_report_objects(c_all_l)\n", - "\n", - "# summary and labels\n", - "all_summary_l = call_l_surveys.sampling_results_summary\n", - "all_labels_l = extract_dates_for_labels_from_summary(all_summary_l)\n", - "\n", - "# material proportions all data\n", - "material_report_l = call_l_surveys.material_report\n", - "material_report_l = material_report_l.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior_l = d[(d.canton != canton)&(d['date'] <= prior_dates['end'])&(d.feature_type == 'l')].copy()\n", - "o_report_l, o_land_use_l = gfcast.make_report_objects(o_prior_l)\n", - "lake_results = gfcast.reports_and_forecast(lake_params,lake_params_p , ldata=d.copy(), logger=logger, other_data=o_land_use_l.df_cat)\n", - "\n", - "# prior summary and label\n", - "p_summary_l = lake_results['prior_report'].sampling_results_summary.copy()\n", - "prior_labels_l = extract_dates_for_labels_from_summary(p_summary_l)\n", - "\n", - "# likelihood summary and label\n", - "l_summary_l = lake_results['this_report'].sampling_results_summary.copy()\n", - "likelihood_labels_l = extract_dates_for_labels_from_summary(l_summary_l)\n", - "\n", - "# forecasts\n", - "xii_l = lake_results['posterior_no_limit'].sample_posterior()\n", - "\n", - "# limit to the 99th percentile\n", - "sample_values_l, posterior_l, summary_simple_l = gfcast.dirichlet_posterior(lake_results['posterior_99'])\n", - "\n", - "# forecast weighted prior all data\n", - "weighted_args_l = [lake_results['this_land_use'], session_config.feature_variables, call_l_land.df_cat, lake_results['this_report'].sample_results['pcs/m']]\n", - "weighted_forecast_l, weighted_posterior_l, weighted_summary_l, _ = gfcast.forecast_weighted_prior(*weighted_args_l)\n", - "\n", - "# forecast summaries\n", - "forecast_99_l = display_forecast_summary(summary_simple_l, '__Given the 99th percentile__')\n", - "forecast_maxval_l = display_forecast_summary(lake_results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - "forecast_weighted_l = display_forecast_summary(weighted_summary_l, '__Given the weighted prior__')\n", - "\n", - "# most common objects all lake data\n", - "os_l = lake_results['this_report'].object_summary()\n", - "os_l.reset_index(drop=False, inplace=True)\n", - "most_common_objects_l, mc_codes_l, proportions_l = userdisplay.most_common(os_l)\n", - "most_common_objects_l = most_common_objects_l.set_caption(\"\")\n", - "\n", - "# display the inventory of features\n", - "feature_inv_l = call_l_surveys.feature_inventory()\n", - "feature_inventory_l = format_boundaries_feature_inv(feature_inv_l, ['p', 'r'], userdisplay.feature_inventory)\n", - "\n", - "# display the inventory of boundaries\n", - "aboundaries_l = call_l_surveys.administrative_boundaries().copy()\n", - "administrative_boundaries_l = format_boundaries_feature_inv(aboundaries_l, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - "# display the sampling summaries\n", - "all_info_l = userdisplay.sampling_result_summary(all_summary_l, session_language='en')[1]\n", - "all_samp_sum_l = Markdown(f'{all_labels_l}\\n{all_info_l}')\n", - "\n", - "p_header_l = f\"{prior_labels}\"\n", - "p_info_l = userdisplay.sampling_result_summary(p_summary_l, session_language='en')[1]\n", - "p_samp_sum_l = Markdown(f'{p_header_l}\\n{p_info_l}')\n", - "\n", - "l_header_l = f\"{likelihood_labels_l} \"\n", - "l_info_l = userdisplay.sampling_result_summary(l_summary_l, session_language='en')[1]\n", - "l_samp_sum_l = Markdown(f'{l_header_l}\\n{l_info_l}')\n", - "\n", - "ratio_most_common_l = Markdown(f'__The most common objects account for {int(proportions_l*100)}% of all objects__')\n", - "\n", - "caption_histo_l = Markdown(f'Survey total pcs/m {likelihood_labels_l} v/s {prior_labels_l}. All locations considered.')\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.histplot(data=lake_results['this_report'].sample_results, x='pcs/m', stat='probability', label=likelihood_labels_l, ax=ax, color=palette['likelihood'])\n", - "sns.histplot(data=lake_results['prior_report'].sample_results, x='pcs/m', stat='probability', label=prior_labels_l, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('lake-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.ecdfplot(lake_results['prior_report'].sample_results['pcs/m'], label=prior_labels_l, ls='-', ax=ax, c=palette['prior'], zorder=1)\n", - "sns.ecdfplot(lake_results['this_report'].sample_results['pcs/m'], label=likelihood_labels_l, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "sns.ecdfplot(sample_values_l, label='expected 99%', ls=':', zorder=2)\n", - "sns.ecdfplot(xii_l, label='expected max', ls='-.', zorder=2)\n", - "sns.ecdfplot(weighted_forecast_l, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, lake_results['this_report'].sample_results['pcs/m'].quantile(.99))\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('lake-cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_list = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo-l', caption_histo_l, display=False)\n", - "glue('material-report-l', material_report_l, display=False)\n", - "glue('forecast-weighted-prior-l', forecast_weighted_l, display=False)\n", - "glue('forecast-max-val-l', forecast_maxval_l, display=False)\n", - "glue('forecast-99-max-l', forecast_99_l, display=False)\n", - "glue('ratio-most-common-l', ratio_most_common_l, display=False)\n", - "glue('most_common_objects-l', most_common_objects_l, display=False)\n", - "glue('l-sampling-summary-l', l_samp_sum_l, display=False)\n", - "glue('prior-sampling-summary-l', p_samp_sum_l, display=False)\n", - "glue('sampling-summary-l', all_samp_sum_l, display=False)\n", - "glue('feature-inventory-l', feature_inventory_l, display=False)\n", - "glue('administrative-boundaries-l', administrative_boundaries_l, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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", 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 % of total
material 
glass7%
metal10%
paper11%
plastic66%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "material-report-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 1.14\n* HDI 95%: 0.1 - 3.0\n* 90% Range: 0.2 - 3.0", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-weighted-prior-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 1.23\n* HDI 95%: 0.02 - 4.67\n* 90% Range: 0.13 - 6.88", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-max-val-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 1.18\n* HDI 95%: 0.2 - 3.0\n* 90% Range: 0.2 - 2.52", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "forecast-99-max-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__The most common objects account for 72% of all objects__", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "ratio-most-common-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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 \n \n \n \n \n \n
ObjectQuantitypcs/m% of totalFail rate
Cigarette filters630,220,260,78
Glass drink bottles, pieces290,090,120,67
Food wrappers; candy, snacks180,060,070,78
Metal bottle caps, lids & pull tabs from cans160,060,070,56
Fragmented plastics130,050,050,44
Newspapers or magazines120,060,050,22
Labels, bar codes90,030,040,22
Paper fragments70,030,030,22
Foil wrappers, aluminum foil60,020,020,44
Straws and stirrers50,020,020,44
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "most_common_objects-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2020 - 2021 \n* Number of samples: 9\n* Total objects: 246\n* Average pcs/m: 0.8\n* Standard deviation: 0.71\n* Maximum pcs/m: 2.31\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "l-sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2017 - 2018\n* Number of samples: 166\n* Total objects: 6311\n* Average pcs/m: 1.17\n* Standard deviation: 1.24\n* Maximum pcs/m: 11.1\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "prior-sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "2017 - 2021\n* Number of samples: 175\n* Total objects: 6557\n* Average pcs/m: 1.15\n* Standard deviation: 1.23\n* Maximum pcs/m: 11.1\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-summary-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Features surveyed__\n* Rivers: 9\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "feature-inventory-r" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/markdown": "__Administrative boundaries__\n* Survey locations: 21\n* Cities: 13\n\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "administrative-boundaries-r" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# rivers\n", - "data = session_config.collect_survey_data()\n", - "river_params = {'canton':canton, 'date_range':o_dates, 'feature_type': 'r'}\n", - "river_params_p = {'canton':canton, 'date_range':prior_dates, 'feature_type':'r'}\n", - "\n", - "# all surveys lakes, rivers combined\n", - "c_all_r, _ = gfcast.filter_data(d,{'canton': canton, 'feature_type': 'r', 'date_range': {'start':prior_dates['start'], 'end':o_dates['end']}})\n", - "call_r_surveys, call_r_land = gfcast.make_report_objects(c_all_r)\n", - "\n", - "# summary and labels\n", - "all_summary_r = call_r_surveys.sampling_results_summary\n", - "all_labels_r = extract_dates_for_labels_from_summary(all_summary_r)\n", - "\n", - "# material proportions all data\n", - "material_report_r = call_r_surveys.material_report\n", - "material_report_r = material_report_r.style.set_table_styles(userdisplay.table_css_styles)\n", - "\n", - "# prior data does not include locations in canton\n", - "o_prior_r = d[(d.canton != canton)&(d['date'] <= prior_dates['end'])&(d.feature_type == 'r')].copy()\n", - "o_report_r, o_land_use_r = gfcast.make_report_objects(o_prior_r)\n", - "river_results = gfcast.reports_and_forecast(river_params,river_params_p , ldata=d.copy(), logger=logger, other_data=o_land_use_r.df_cat)\n", - "\n", - "# prior summary and label\n", - "p_summary_r = river_results['prior_report'].sampling_results_summary.copy()\n", - "prior_labels_r = extract_dates_for_labels_from_summary(p_summary_r)\n", - "\n", - "# likelihood summary and label\n", - "l_summary_r = river_results['this_report'].sampling_results_summary.copy()\n", - "likelihood_labels_r = extract_dates_for_labels_from_summary(l_summary_r)\n", - "\n", - "# forecasts\n", - "xii_r = river_results['posterior_no_limit'].sample_posterior()\n", - "\n", - "# limit to the 99th percentile\n", - "sample_values_r, posterior_r, summary_simple_r = gfcast.dirichlet_posterior(river_results['posterior_99'])\n", - "\n", - "# forecast weighted prior all data\n", - "weighted_args_r = [river_results['this_land_use'], session_config.feature_variables, call_r_land.df_cat, river_results['this_report'].sample_results['pcs/m']]\n", - "weighted_forecast_r, weighted_posterior_r, weighted_summary_r, _ = gfcast.forecast_weighted_prior(*weighted_args_r)\n", - "\n", - "# forecast summaries\n", - "forecast_99_r = display_forecast_summary(summary_simple_r, '__Given the 99th percentile__')\n", - "forecast_maxval_r = display_forecast_summary(river_results['posterior_no_limit'].get_descriptive_statistics(), '__Given the observed max__')\n", - "forecast_weighted_r = display_forecast_summary(weighted_summary_r, '__Given the weighted prior__')\n", - "\n", - "# most common objects all lake data\n", - "os_r = river_results['this_report'].object_summary()\n", - "os_r.reset_index(drop=False, inplace=True)\n", - "most_common_objects_r, mc_codes_r, proportions_r = userdisplay.most_common(os_r)\n", - "most_common_objects_r = most_common_objects_r.set_caption(\"\")\n", - "\n", - "# display the inventory of features\n", - "feature_inv_r = call_r_surveys.feature_inventory()\n", - "feature_inventory_r = format_boundaries_feature_inv(feature_inv_r, ['p', 'l'], userdisplay.feature_inventory)\n", - "\n", - "# display the inventory of boundaries\n", - "aboundaries_r = call_r_surveys.administrative_boundaries().copy()\n", - "administrative_boundaries_r = format_boundaries_feature_inv(aboundaries_r, ['canton', 'parent_boundary'], userdisplay.boundaries)\n", - "\n", - "# display the sampling summaries\n", - "all_info_r = userdisplay.sampling_result_summary(all_summary_r, session_language='en')[1]\n", - "all_samp_sum_r = Markdown(f'{all_labels_r}\\n{all_info_r}')\n", - "\n", - "p_header_r = f\"{prior_labels}\"\n", - "p_info_r = userdisplay.sampling_result_summary(p_summary_r, session_language='en')[1]\n", - "p_samp_sum_r = Markdown(f'{p_header_r}\\n{p_info_r}')\n", - "\n", - "l_header_r = f\"{likelihood_labels_r} \"\n", - "l_info_r = userdisplay.sampling_result_summary(l_summary_r, session_language='en')[1]\n", - "l_samp_sum_r = Markdown(f'{l_header_r}\\n{l_info_r}')\n", - "\n", - "ratio_most_common_r = Markdown(f'__The most common objects account for {int(proportions_r*100)}% of all objects__')\n", - "\n", - "caption_histo_r = Markdown(f'Survey total pcs/m {likelihood_labels_r} v/s {prior_labels_r}. All locations considered.')\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.histplot(data=river_results['this_report'].sample_results, x='pcs/m', stat='probability', label=likelihood_labels_r, ax=ax, color=palette['likelihood'])\n", - "sns.histplot(data=river_results['prior_report'].sample_results, x='pcs/m', stat='probability', label=prior_labels_r, ax=ax, color=palette['prior'])\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('river-prior-likelihood', fig, display=False)\n", - "plt.close()\n", - "\n", - "fig, ax = plt.subplots()\n", - "sns.ecdfplot(river_results['prior_report'].sample_results['pcs/m'], label=prior_labels_r, ls='-', ax=ax, c=palette['prior'], zorder=1)\n", - "sns.ecdfplot(river_results['this_report'].sample_results['pcs/m'], label=likelihood_labels_r, ls='-', ax=ax, c=palette['likelihood'], zorder=1)\n", - "sns.ecdfplot(sample_values_r, label='expected 99%', ls=':', zorder=2)\n", - "sns.ecdfplot(xii_r, label='expected max', ls='-.', zorder=2)\n", - "sns.ecdfplot(weighted_forecast_r, label='weighted prior', c='black', ls='--', lw=2, ax=ax, zorder=5)\n", - "ax.set_xlim(-.1, river_results['this_report'].sample_results['pcs/m'].quantile(.99))\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "glue('river-cumumlative-dist-forecast-prior', fig, display=False)\n", - "plt.close()\n", - "\n", - "# one_rist = Markdown(f'{feature_inventory}\\n{administrative_boundaries}')\n", - "glue('caption-histo-r', caption_histo_r, display=False)\n", - "glue('material-report-r', material_report_r, display=False)\n", - "glue('forecast-weighted-prior-r', forecast_weighted_r, display=False)\n", - "glue('forecast-max-val-r', forecast_maxval_r, display=False)\n", - "glue('forecast-99-max-r', forecast_99_r, display=False)\n", - "glue('ratio-most-common-r', ratio_most_common_r, display=False)\n", - "glue('most_common_objects-r', most_common_objects_r, display=False)\n", - "glue('l-sampling-summary-r', l_samp_sum_r, display=False)\n", - "glue('prior-sampling-summary-r', p_samp_sum_r, display=False)\n", - "glue('sampling-summary-r', all_samp_sum_r, display=False)\n", - "glue('feature-inventory-r', feature_inventory_r, display=False)\n", - "glue('administrative-boundaries-r', administrative_boundaries_r, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "12c52a87-8340-419f-bfd9-75ca85260a97", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", 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" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "map-of-survey-locations" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# Create a GeoDataFrame from the list of locations\n", - "dbc = gpd.read_file('data/shapes/kantons.shp')\n", - "dbc = dbc.to_crs(epsg=4326)\n", - "dbckey = dbc[['NAME', 'KANTONSNUM']].set_index('NAME')\n", - "dbckey = dbckey.drop_duplicates()\n", - "thiscanton = dbckey.loc[canton, 'KANTONSNUM']\n", - "db = gpd.read_file('data/shapes/municipalities.shp')\n", - "db = db.to_crs(epsg=4326)\n", - "thesecities = db[db.KANTONSNUM == thiscanton]\n", - "surveyedcities = alldata_ofinterest.city.unique()\n", - "\n", - "from matplotlib.lines import Line2D\n", - "# Define the plot\n", - "fig, ax = plt.subplots(figsize=(18, 10))\n", - "\n", - "citymap = thesecities.plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.1)\n", - "\n", - "surveyed = thesecities[thesecities.NAME.isin(surveyedcities)].plot(ax=ax, color='salmon', alpha=0.6)\n", - "\n", - "# Add a basemap using contextily\n", - "# ctx.add_basemap(ax, source=ctx.providers.SwissFederalGeoportal.NationalMapColor, crs=\"EPSG:4326\", aspect='equal')\n", - "dbc[dbc.NAME == canton].plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.2)\n", - "# Set the extent to Switzerland\n", - "# ax.set_ylim([newmapbounds['min_lat'], newmapbounds['max_lat']])\n", - "# ax.set_xlim([newmapbounds['min_lon'], newmapbounds['max_lon']])\n", - "# Plot the GeoDataFrame\n", - "\n", - "sres = lake_results['this_report'].sample_results\n", - "pres = lake_results['prior_report'].sample_results\n", - "ares = call_surveys.sample_results\n", - "\n", - "sresr = river_results['this_report'].sample_results\n", - "presr = river_results['prior_report'].sample_results\n", - "\n", - "marker_statsa, map_boundsa = userdisplay.map_markers(ares)\n", - "geometrya = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsa]\n", - "gdfa = gpd.GeoDataFrame(marker_statsa, geometry=geometrya, crs=\"EPSG:4326\")\n", - "\n", - "marker_stats, map_bounds = userdisplay.map_markers(sres)\n", - "geometry = [Point(loc['longitude'], loc['latitude']) for loc in marker_stats]\n", - "gdf = gpd.GeoDataFrame(marker_stats, geometry=geometry, crs=\"EPSG:4326\")\n", - "\n", - "marker_statsp, map_boundsp = userdisplay.map_markers(pres)\n", - "geometryp = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsp]\n", - "gdfp = gpd.GeoDataFrame(marker_statsp, geometry=geometryp, crs=\"EPSG:4326\")\n", - "\n", - "\n", - "marker_statsr, map_boundsr = userdisplay.map_markers(sresr)\n", - "geometryr = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsr]\n", - "gdfr = gpd.GeoDataFrame(marker_statsr, geometry=geometryr, crs=\"EPSG:4326\")\n", - "\n", - "marker_statsrp, map_boundsrp = userdisplay.map_markers(presr)\n", - "geometryrp = [Point(loc['longitude'], loc['latitude']) for loc in marker_statsrp]\n", - "gdfrp = gpd.GeoDataFrame(marker_statsrp, geometry=geometryrp, crs=\"EPSG:4326\")\n", - "\n", - "gdfa.plot(ax=ax, color='grey', markersize=80)\n", - "\n", - "gdfp.plot(ax=ax, color=palette['prior'], markersize=40, edgecolor='w', lw=0.5)\n", - "\n", - "gdfrp.plot(ax=ax, color=palette['prior'], markersize=40, edgecolor='w', lw=0.5)\n", - "gdfr.plot(ax=ax, color=palette['likelihood'], markersize=25, marker='X', edgecolor='w', lw=0.5)\n", - "gdf.plot(ax=ax, color=palette['likelihood'], markersize=25, marker='X', edgecolor='w', lw=0.5)\n", - "# Add title and labels\n", - "ax.set_title(f'Survey locations {canton}')\n", - "plt.xlabel('')\n", - "plt.ylabel('')\n", - "\n", - "plt.axis('off')\n", - "\n", - "# Create a custom legend\n", - "legend_elements = [\n", - " Line2D([0], [0], marker='X', color='w', label=likelihood_labels, markersize=10, markeredgecolor='w', markerfacecolor=palette['likelihood']),\n", - " Line2D([0], [0], marker='o', color='w', label=prior_labels, markerfacecolor=palette['prior'], markersize=10, markeredgecolor='w')\n", - "]\n", - "\n", - "plt.legend(handles=legend_elements, loc='upper right')\n", - "\n", - "glue('map-of-survey-locations', fig, display=False)\n", - "plt.close()\n" - ] - }, - { - "cell_type": "markdown", - "id": "720e6d85-e449-48cd-8412-3e243934e678", - "metadata": { - "editable": true, - "jp-MarkdownHeadingCollapsed": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Canton Zürich\n", - "\n", - "__Density of trash along lakes and rivers__\n", - "\n", - "This is a sample cantonal report. The structure and the format are based off of the federal report, ([IQAASL](https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/)). This version is intended for use as a decsion support tool. Thus, the user is expected to be familiar with the results in the federal report and the methods described in the _Guide for Monitoring Marine Litter on European Seas_ \n", - "([The guide](https://mcc.jrc.ec.europa.eu/main/dev.py?N=41&O=439&titre_chap=TG%20Litter&titre_page=Guidance%20for%20the%20Monitoring%20of%20Marine%20Litter)).\n", - "\n", - "\n", - ":::{dropdown} Initial assessment: stakeholder discussion and priorities.\n", - "\n", - "Stakeholders should consider the following questions while consulting the report:\n", - "\n", - "1. Are the major rivers and lakes included?\n", - "2. Was their more or less observed in 2021 vs the prior results?\n", - "3. __How do these results compare to assessments from other sources (EAWAG, EMPA, Internal reports)?__\n", - " * This includes reports from NGOS in the region\n", - " * Is the data comparable?\n", - "4. Are the objects identified as the _most common_ currently the focus of reduction or prevention campaigns?\n", - " * __How does the canton decide priorties in this regard?__\n", - " * __Did or does the object appear in any regional action plan or strategy?__\n", - "5. For objects that have been the focus of prevention or reduction campaigns in the past: are they on the _most common objects_ list now?\n", - " * If the objects are on the most common list, is this inline with expectations ?\n", - " * What excatly was the mechanism or process that was intended to reduce the presence of the object?\n", - " * __With respect to the amount of resources attributed to prevention and mitigation:__ Do the attributed amounts reflect the importance of the object in terms of total amount found and frequency of occurence?\n", - "6. __Does the sampling distribution reflect the topography and land-use of the canton?__\n", - "7. Do the municipalities with elevated pcs/m have common land-use attributes?\n", - "8. __Are the municipalities of strategic importance to the canton included?__\n", - "9. Are their locations in the canton that should have been surveyed, according to cantonal priorities?\n", - "10. Are their products of regional interest that should be included in the cantonal report?\n", - ":::\n", - "\n", - ":::::{dropdown} Map of survey locations\n", - "\n", - "\n", - "\n", - "::::{grid}\n", - ":padding: 2\n", - "\n", - ":::{grid-item-card}\n", - "\n", - "```{glue} map-of-survey-locations\n", - "```\n", - "\n", - ":::\n", - "::::\n", - ":::::\n", - "## Vital statistics\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory\n", - "```\n", - "```{glue} administrative-boundaries\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory-l\n", - "```\n", - "```{glue} administrative-boundaries-l\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} lake-prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo-l\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary-l\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "\n", - "::::::::{grid} 2 2 2 2\n", - ":gutter: 1\n", - "\n", - ":::::::{grid-item}\n", - ":columns: 12 4 4 4\n", - "\n", - "```{glue} feature-inventory-r\n", - "```\n", - "```{glue} administrative-boundaries-r\n", - "```\n", - "__Material composition__\n", - "\n", - "```{glue} material-report-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":columns: 12 8 8 8\n", - ":shadow: none\n", - "\n", - "```{glue} river-prior-likelihood\n", - "```\n", - "\n", - "+++\n", - "```{glue} caption-histo-r\n", - "```\n", - ":::::::\n", - "::::::::\n", - "\n", - "::::::::{grid} 3 3 3 3 \n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} l-sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - ":::::::{grid-item-card}\n", - ":shadow: none\n", - ":padding: 1\n", - "\n", - "```{glue} prior-sampling-summary-r\n", - "```\n", - ":::::::\n", - "\n", - "::::::::\n", - "\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::::{dropdown} How did we get this data ?\n", - "\n", - "\n", - "\n", - "::::{grid}\n", - ":padding: 2\n", - "\n", - ":::{grid-item-card}\n", - "\n", - "```{glue} scatter-prior-likelihood\n", - "```\n", - "+++\n", - "The data is a combination of observations from variety of groups in Switerland since 2015. The observations were recorded using an interpretation of the _Guide for Monitoring Marine Litter on European Seas_ [The guide](https://mcc.jrc.ec.europa.eu/main/dev.py?N=41&O=439&titre_chap=TG%20Litter&titre_page=Guidance%20for%20the%20Monitoring%20of%20Marine%20Litter). The guide and the monitoring of beach litter are part of decades of research, here is the brief history [A Brief History of Marine Litter Research](https://link.springer.com/chapter/10.1007/978-3-319-16510-3_1).\n", - ":::\n", - "::::\n", - "\n", - "__Common sense guidance:__\n", - "\n", - "1. The data should be considered as a reasonable estimate of the minimum amount of trash on the ground at the time of the survey.\n", - "2. There are many sources of variance. We have considered the following:\n", - " * litter density between sampling groups.\n", - " * litter density with respect to topographical features.\n", - "3. There are differences in detect-ability and appearance for items of the same classification that are due to the effects of decomposition.\n", - "4. Many surveyors are volunteers and have different levels of experience or physical constraints that limit what will actually be collected and counted.\n", - ":::::\n", - "\n", - ":::{dropdown} How to make a report\n", - "\n", - "__Survey and Land use__\n", - "\n", - "A report is the implementation of a `SurveyReport` and a `LandUseReport`. The `SurveyReport` is the basic \n", - "element and does the initial aggregating and descriptive statistics for a query.\n", - "\n", - "The land-use-report accepts `SurveyReport.sample_results` and assigns the land-use attributes to the record. The \n", - "land-use-report provides the baseline assessment of litter density with reference to the surrounding environment. \n", - "\n", - "The assessment accepts as variables the proportion of available space that a topographical feature occupies in a \n", - "circle of $\\pi r² \\text{ where r = 1 500 meters}$ and the center of that circle is the survey location. \n", - "These proportions are compared to the `average pieces per meter` for an object or group of objects.\n", - "\n", - "\n", - "__Create a report__\n", - "\n", - "A report can be intiated by providing the name of the canton. If your canton does not appear this is because we have no data. The prior dates will be calculated automatically, by taking all data prior to the start date of the querry.\n", - "\n", - "```{code} python\n", - "\n", - "import reports\n", - "import geospatial\n", - "import gridforecast\n", - "\n", - "# suppose you have defined your data into df\n", - "observed_dates = {'start':'2020-01-01', 'end':'2021-12-31'}\n", - "\n", - "# everything that was seen before\n", - "prior_dates = {'start':'2015-11-15', 'end':'2019-12-31'}\n", - "\n", - "# name the canton\n", - "canton = 'Bern'\n", - "\n", - "# define the data of interest\n", - "data_of_interest = {'canton':canton, 'date_range':observed_dates}\n", - "\n", - "# load the data\n", - "df = session_config.collect_survey_data()\n", - "\n", - "# filter the data. \n", - "filtered_data, locations = gridforeacast.filter_data(df, data_of_interest)\n", - "\n", - "# make a survey report\n", - "this_report = reports.SurveyReport(dfc=filtered_data)\n", - "\n", - "# generate the parameters for the landuse report\n", - "target_df = this_report.sample_results\n", - "features = geospatial.collect_topo_data(locations=target_df.location.unique())\n", - "\n", - "# make a landuse report\n", - "this_land_use = geospatial.LandUseReport(target_df, features)\n", - "```\n", - "\n", - "Each report and the inference method are documented: [SurveyReport](surveyreporter), [LandUseReport](landusereporter), [GridForecaster](gridforecaster)\n", - ":::\n" - ] - }, - { - "cell_type": "markdown", - "id": "160aae5f-e9ed-4754-86a8-a76af4616553", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "source": [ - "## Most common objects 2020 - 2021\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common-l\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects-l\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "::::{grid} 2 2 2 2 \n", - ":::{grid-item}\n", - ":columns: 4\n", - "\n", - "```{glue} ratio-most-common-r\n", - "```\n", - "\n", - "The most common objects from the selected data. The most common objects are a combination of the top ten most abundant objects and those objects that are found in more than 50% of the samples. Some objects are found frequently but at low quantities.Other objects are found in fewer samples but at higher quantities.\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 8\n", - ":shadow: none\n", - "\n", - "```{glue} most_common_objects-r\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::{dropdown} Defining the most common objects\n", - "\n", - "The default method for defining _the most common objects_ is based on the number of items collected and the number of times that at least one of an object was found with respect to the number of surveys in the query, the _fail rate_.\n", - "\n", - "Adjusting the fail rate will increase or decrease the number of the most common objects. The fail rate is included with the object inventory. \n", - "\n", - "```{code} python\n", - "\n", - "# the most common objects are accesible in the survey report\n", - "# the report.object_summary method aggregates the data to code\n", - "# and attaches the fail rate and % of total\n", - "inventory = this_report.object_summary()\n", - "\n", - "# userdisplay.most_common, takes the 10 most abundant and filters\n", - "# the data for fail rate >= 0.5. The method returns a formatted table,\n", - "# a list of the codes and the ratio of the quantity of the most common to the whole \n", - "mostcommon, codes, ratio = userdisplay.most_common(inventory)\n", - "\n", - "```\n", - "\n", - "\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "id": "1153176b-fd0c-4e93-8928-6c89886b9525", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Land use\n", - "\n", - "\n", - "Land use refers to the measurable topographic features within a cirlce of r = 1 500 m and area = $\\pi r²$ with the survey location in the middle. The features, measured in meters squared, are given as a ratio $\\frac{\\text{area of feature}}{\\text{area of circle}}$. Thus a location with high percentage of buildings will have a rating or value between 60% and 100%. The pcs/m rating is the average of all locations with a land-use profile of the same rating." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards2.870.000.000.000.00
Vineyards2.870.000.000.000.00
Buildings0.003.271.381.433.66
Forest2.920.460.000.000.00
Undefined3.451.516.080.000.00
Public Services2.870.000.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards100%0%0%0%0%
Vineyards100%0%0%0%0%
Buildings0%4%4%31%61%
Forest98%2%0%0%0%
Undefined65%33%2%0%0%
Public Services100%0%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "g = results['this_land_use'].n_samples_per_feature().copy()\n", - "g = userdisplay.landuse_profile(g[session_config.feature_variables[:-1]], nsamples=results['this_report'].number_of_samples)\n", - "g = g.set_caption(\"\")\n", - "\n", - "gt = results['this_land_use'].rate_per_feature().copy()\n", - "gt = userdisplay.litter_rates_per_feature(gt.loc[session_config.feature_variables[:-1]])\n", - "gt = gt.set_caption(\"\")\n", - "\n", - "glue('rate-per-feature', gt, display=False)\n", - "glue('sampling-profile', g, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets53%6%35%0%6%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets2.630.741.54014.56
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets = results['this_land_use'].n_samples_per_feature().copy()\n", - "streets = streets[[session_config.feature_variables[-1]]].copy()\n", - "streets = userdisplay.street_profile(streets.T, nsamples=results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets = streets.set_caption(caption)\n", - "\n", - "streets_r = results['this_land_use'].rate_per_feature().copy()\n", - "streets_r = streets_r.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r = userdisplay.street_profile(streets_r, nsamples=results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r = streets_r.set_caption(caption)\n", - "\n", - "glue('street-profile', streets, display=False)\n", - "glue('street-rates-feature', streets_r, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards3.330.000.000.000.00
Vineyards3.330.000.000.000.00
Buildings0.006.081.101.714.06
Forest3.330.000.000.000.00
Undefined4.061.676.080.000.00
Public Services3.330.000.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards100%0%0%0%0%
Vineyards100%0%0%0%0%
Buildings0%2%2%30%65%
Forest100%0%0%0%0%
Undefined65%32%2%0%0%
Public Services100%0%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "gl = lake_results['this_land_use'].n_samples_per_feature().copy()\n", - "gl = userdisplay.landuse_profile(gl[session_config.feature_variables[:-1]], nsamples=lake_results['this_report'].number_of_samples)\n", - "gl = gl.set_caption(\"\")\n", - "\n", - "gtl = lake_results['this_land_use'].rate_per_feature().copy()\n", - "\n", - "gtl = userdisplay.litter_rates_per_feature(gtl.loc[session_config.feature_variables[:-1]])\n", - "gtl = gtl.set_caption(\"\")\n", - "\n", - "glue('lake-rate-per-feature', gtl, display=False)\n", - "glue('lake-sampling-profile', gl, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets65%0%30%0%5%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets2.6301.77021.80
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets_p = lake_results['this_land_use'].n_samples_per_feature().copy()\n", - "streets_p = streets_p[[session_config.feature_variables[-1]]].copy()\n", - "streets_p = userdisplay.street_profile(streets_p.T, nsamples=lake_results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets_p = streets_p.set_caption(caption)\n", - "\n", - "streets_r_l = lake_results['this_land_use'].rate_per_feature().copy()\n", - "streets_r_l = streets_r_l.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r_l = userdisplay.street_profile(streets_r_l, nsamples=lake_results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r_l = streets_r_l.set_caption(caption)\n", - "\n", - "\n", - "glue('lake-street-profile', streets_p, display=False)\n", - "glue('lake-street-rates-feature', streets_r_l, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "82f55461-c497-483a-8c38-fbd509809afb", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards0.800.000.000.000.00
Vineyards0.800.000.000.000.00
Buildings0.000.461.650.281.07
Forest0.850.460.000.000.00
Undefined0.790.820.000.000.00
Public Services0.800.000.000.000.00
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-rate-per-feature" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
Orchards100%0%0%0%0%
Vineyards100%0%0%0%0%
Buildings0%11%11%33%44%
Forest89%11%0%0%0%
Undefined67%33%0%0%0%
Public Services100%0%0%0%0%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-sampling-profile" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "gr = river_results['this_land_use'].n_samples_per_feature().copy()\n", - "gr = userdisplay.landuse_profile(gr[session_config.feature_variables[:-1]], nsamples=river_results['this_report'].number_of_samples)\n", - "gr = gr.set_caption(\"\")\n", - "\n", - "gtlr = river_results['this_land_use'].rate_per_feature().copy()\n", - "\n", - "gtlr = userdisplay.litter_rates_per_feature(gtlr.loc[session_config.feature_variables[:-1]])\n", - "gtlr = gtlr.set_caption(\"\")\n", - "\n", - "\n", - "glue('river-rate-per-feature', gtlr, display=False)\n", - "glue('river-sampling-profile', gr, display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9b396025-1fa6-4661-9116-593fa1ed741d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets0%33%56%0%11%
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-street-profile" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/text/html": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 0 - 20%20 - 40%40 - 60%60 - 80%80 - 100%
streets00.740.9900.08
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-street-rates-feature" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "streets_p_r = river_results['this_land_use'].n_samples_per_feature().copy()\n", - "streets_p_r = streets_p_r[[session_config.feature_variables[-1]]].copy()\n", - "streets_p_r = userdisplay.street_profile(streets_p_r.T, nsamples=river_results['this_report'].number_of_samples)\n", - "caption = \"\"\n", - "streets_p_r = streets_p_r.set_caption(caption)\n", - "\n", - "streets_r_r = river_results['this_land_use'].rate_per_feature().copy()\n", - "streets_r_r = streets_r_r.loc[[session_config.feature_variables[-1]]].copy()\n", - "streets_r_r = userdisplay.street_profile(streets_r_r, nsamples=river_results['this_report'].number_of_samples, caption='rate')\n", - "caption = \"\"\n", - "streets_r_r = streets_r_r.set_caption(caption)\n", - "\n", - "\n", - "glue('river-street-profile', streets_p_r, display=False)\n", - "glue('river-street-rates-feature', streets_r_r, display=False)" - ] - }, - { - "cell_type": "markdown", - "id": "e45988a7-3442-434a-acab-c0b13cbfcd42", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "1. The rate per feature refers to the average pcs/m observed at a particular land use rate\n", - " * Under what conditions is the pcs/m elevated? Where is it the least?\n", - "2. The sampling profile refers to the ratio of samples that were taken at a particular land use rate\n", - " * Does the sampling profile reflect the topography of the region?\n", - "\n", - "\n", - "\n", - "### Rate per feature 2020 - 2021\n", - "\n", - "::::{tab-set}\n", - ":::{tab-item} All lakes and rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} street-rates-feature\n", - "``` \n", - ":::\n", - "\n", - ":::{tab-item} Lakes\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} lake-rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} lake-street-rates-feature\n", - "```\n", - ":::\n", - "\n", - ":::{tab-item} Rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} river-rate-per-feature\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} river-street-rates-feature\n", - "``` \n", - ":::\n", - "\n", - "::::\n", - "\n", - "### Sampling profile 2020 - 2021\n", - "\n", - "::::{tab-set}\n", - ":::{tab-item} All lakes and rivers\n", - "\n", - "__Land use__\n", - "\n", - "\n", - "```{glue} sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lengths for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} street-profile\n", - "``` \n", - ":::\n", - "\n", - ":::{tab-item} Lakes\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} lake-sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "

\n", - "\n", - "```{glue} lake-street-profile\n", - ":::\n", - "\n", - ":::{tab-item} Rivers\n", - "\n", - "__Land use__\n", - "\n", - "```{glue} river-sampling-profile\n", - "```\n", - "\n", - "__Streets__\n", - "\n", - "The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area $\\pi r²$ and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.\n", - "\n", - "\n", - "```{glue} river-street-profile\n", - "``` \n", - ":::\n", - "\n", - "::::\n", - "\n", - ":::{dropdown} Defining land use\n", - "\n", - "__Land cover__\n", - "\n", - "These measured land-use attributes are the labeled polygons from the map layer Landcover defined here ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)), they are extracted using vector overlay techniques in QGIS ([QGIS](https://qgis.org/en/site/)).\n", - "\n", - "* Buildings: built up, urbanized\n", - "* Woods: not a park, harvesting of trees may be active\n", - "* Vineyards: does not include any other type of agriculture\n", - "* Orchards: not vineyards\n", - "* Undefined: areas of the map with no predefined label\n", - "\n", - "\n", - "```{code}\n", - "\n", - "# the land use is summarized using a LandUseReport object\n", - "# the average pieces per meter by land use category\n", - "rate_per_feature = this_land_use.n_pieces_per_feature()\n", - "\n", - "# the sampling distribution\n", - "samples_per_feature = this_land_use.n_samples_per_feature()\n", - "\n", - "# the variety of locations per feature\n", - "locations_per_feature = this_land_use.locations_per_feature()\n", - "\n", - "# format for display .html\n", - "styled_rate_per_feature = userdisplay.litter_rates_per_feature(rate_per_feature)\n", - "```\n", - "\n", - "__Public services__\n", - "\n", - "Public services are the labled polygons from the Freizeitareal and Nutzungsareal map layers, defined in ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)). Both layers represent areas used for specific activities. Freizeitareal identifies areas used for recreational purposes and Nutzungsareal represents areas such as hospitals, cemeteries, historical sites or incineration plants. As a ratio of the available dry-land in a hex, these features are relatively small (less than 10%) of the total dry-land. For identified features within a bounding hex the magnitude in meters² of these variables is scaled between 0 and 1, thus the scaled value represents the size of the feature in relation to all other measured values for that feature from all other hexagons.\n", - "\n", - "* Recreation: parks, sports fields, attractions\n", - "* Infrastructure: Schools, Hospitals, cemeteries, powerplants\n", - "\n", - "__Streets and roads__\n", - "\n", - "Streets and roads are the labled polylines from the TLM Strasse map layer defined in ([swissTLMRegio product information](https://www.swisstopo.admin.ch/fr/modele-du-territoire-swisstlm3d#dokumente)). All polyines from the map layer within a bounding hex are merged (disolved in QGIS commands) and the combined length of the polylines, in meters, is the magnitude of the variable for the bounding hex.\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "id": "501575a0-10d5-4609-8550-8d80807fda4d", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Forecast\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} All data\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max\n", - "```\n", - "```{glue} forecast-weighted-prior\n", - "```\n", - "\n", - "```{glue} forecast-max-val\n", - "```\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max-l\n", - "```\n", - "\n", - "```{glue} forecast-weighted-prior-l\n", - "```\n", - "\n", - "```{glue} forecast-max-val-l\n", - "```\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} lake-cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "::::{grid} 1 1 2 2\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 5 5 5 \n", - "\n", - "Minimum expected survey results 2025\n", - "^^^\n", - "\n", - "\n", - "```{glue} forecast-99-max-r\n", - "```\n", - "\n", - "```{glue} forecast-weighted-prior-r\n", - "```\n", - "\n", - "```{glue} forecast-max-val-r\n", - "```\n", - "\n", - "\n", - ":::\n", - "\n", - ":::{grid-item-card}\n", - ":columns: 12 7 7 7 \n", - ":shadow: none\n", - "```{glue} river-cumumlative-dist-forecast-prior\n", - "```\n", - "+++\n", - "Cumulative distribution of observed, sampling history and forecasts using to different priors.\n", - ":::\n", - "::::\n", - ":::::::::\n", - "\n", - "::::::::::\n", - "\n", - ":::{dropdown} Forecast methods\n", - "\n", - "The applied method would best be classified as Empirical Bayes, in the sense that the prior is derived from the data ([Bayesian Filtering and Smoothing](https://users.aalto.fi/~ssarkka/pub/cup_book_online_20131111.pdf) or [Empirical Bayes methods in classical and Bayesian inference](https://hannig.cloudapps.unc.edu/STOR757Bayes/handouts/PetroneEtAl2014.pdf)). However, we share the concerns of Davidson-Pillon [Bayesian methods for hackers](https://dataorigami.net/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/#contents) about double counting and eliminate it the possibility as part of the formulation of the prior. \n", - "\n", - "__Model assumptions__\n", - "\n", - "1. Locations with similar land use attributes will have similar litter density rates\n", - "2. The data is a best estimate of what was present on the day of the survey\n", - "3. There are regional differences with respect to the density of specific objects\n", - "4. The locations surveyed are maintained by a public administration\n", - "\n", - "The choice of land use features was a natural choice but was further explored in [Near or Far](https://hammerdirt-analyst.github.io/landuse/titlepage.html).\n", - "\n", - "Our parameter estimates are thus derived from the data and they remain testable and quantifiable according to [Prior Probabilities, E T Jaynes](https://bayes.wustl.edu/etj/articles/prior.pdf). This makes our calculation very repetetive but also very well understood. It can be defined in a few lines of code for any set of survey results.\n", - "\n", - "```{code} python\n", - "\n", - "# standared libaries\n", - "import numpy as np\n", - "from scipy.stats import dirichlet, multinomial\n", - "\n", - "# collect the data of interest\n", - "h = array of survey values\n", - "\n", - "# count the number of times that each survey values exceed a value on the gird\n", - "counts = np.array([np.sum((h > x) & (h <= x + .1)) for x in grid_range])\n", - "\n", - "# use the dirichlet dist to estimate p(Y >= x) for each x on the grid\n", - "# and sample from the estimation\n", - "adist = dirichlet(counts)\n", - "this_dist = adist.rvs(1-[0]\n", - "\n", - "# draw samples from the conjugate\n", - "posterior_samples = multinomial.rvs(nsamples, p=this_dist)\n", - "\n", - "```\n", - ":::" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b0bda4c0-1b4a-4011-9ad1-fb3a52aac885", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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 \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
 quantitypcs/msamplesorchardsvineyardsbuildingsforestundefinedpublic servicesstreets
city          
Fällanden7911.9761122311
Greifensee561811.8191131311
Küsnacht (ZH)16853.70221151111
Maur3342.5731111411
Männedorf4923.16121151111
Richterswil18232.10341141211
Stäfa7978.0291121311
Uster10382.14161121311
Zürich146504.34601141213
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lake-municipal-results" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dxl = call_l_surveys.df\n", - "dxf = call_l_land.df_cont\n", - "\n", - "dxlc = dxl[['location', 'city', 'feature_type']].drop_duplicates('location')\n", - "dxlc.set_index(['location'], inplace=True, drop=True)\n", - "dxf['city'] = dxf.location.apply(lambda x : dxlc.loc[x, 'city'])\n", - "sumlu = {x:'sum' for x in session_config.feature_variables}\n", - "dxf = dxf.groupby(['sample_id', 'city', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n", - "\n", - "dxf = dxf.groupby(['city']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n", - "\n", - "for alabel in session_config.feature_variables:\n", - " dxf[alabel] = dxf[alabel]/dxf.sample_id\n", - " \n", - "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n", - "dxfc = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n", - "dxfc.rename(columns={'sample_id':'samples'}, inplace=True)\n", - "dxfc.drop('check', axis=1, inplace=True)\n", - "dxfc = dxfc.style.set_table_styles(userdisplay.table_css_styles)\n", - "dxfc = dxfc.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n", - "dxfc = dxfc.format(userdisplay.format_kwargs, precision=2)\n", - "\n", - "glue('lake-municipal-results', dxfc , display=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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 \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 \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
 quantitypcs/msamplesorchardsvineyardsbuildingsforestundefinedpublic servicesstreets
city          
Adliswil9321.47121141112
Bauma1540.5061122311
Dietikon7871.30101141213
Dübendorf5610.92121141112
Dürnten2962.04121131212
Eglisau911.52101122311
Illnau-Effretikon751.3341122212
Küsnacht (ZH)120.1111141112
Opfikon701.6771151113
Unterengstringen3010.89131131212
Winterthur1680.9351123113
Zell (ZH)2370.3961123211
Zürich28731.06771141113
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "river-municipal-results" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "dxl = call_r_surveys.df\n", - "dxf = call_r_land.df_cont\n", - "\n", - "dxlc = dxl[['location', 'city', 'feature_type']].drop_duplicates('location')\n", - "dxlc.set_index(['location'], inplace=True, drop=True)\n", - "dxf['city'] = dxf.location.apply(lambda x : dxlc.loc[x, 'city'])\n", - "sumlu = {x:'sum' for x in session_config.feature_variables}\n", - "dxf = dxf.groupby(['sample_id', 'city', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n", - "\n", - "dxf = dxf.groupby(['city']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n", - "\n", - "for alabel in session_config.feature_variables:\n", - " dxf[alabel] = dxf[alabel]/dxf.sample_id\n", - " \n", - "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n", - "dxfcr = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n", - "dxfcr.rename(columns={'sample_id':'samples'}, inplace=True)\n", - "dxfcr.drop('check', axis=1, inplace=True)\n", - "dxfcr = dxfcr.style.set_table_styles(userdisplay.table_css_styles)\n", - "dxfcr = dxfcr.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n", - "dxfcr = dxfcr.format(userdisplay.format_kwargs, precision=2)\n", - "# glue('all-data-municipal-results', i , display=False)\n", - "glue('river-municipal-results', dxfcr, display=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "2d5b8904-044b-4aed-916c-5e36018f4087", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-input" - ] - }, - "outputs": [ - { - "data": { - "application/papermill.record/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
 samplespcs/m
Greifensee344.71
Katzensee121.77
Zurichsee1254.01
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "lakes-i-summary" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/papermill.record/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
 samplespcs/m
Chriesbach120.92
Dorfbach10.11
Glatt71.67
Grandelbach41.33
Jona122.04
Limmat611.13
Rhein101.52
Sihl511.08
Toss170.59
\n", - "application/papermill.record/text/plain": "" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "rivers-i-summary" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "summardata = alldata_ofinterest.groupby(['sample_id', 'feature_type','feature_name'], as_index=False).agg({'pcs/m':'sum'})\n", - "# lakes\n", - "feature_individual_summary = summardata.groupby(['feature_type','feature_name'], as_index=False).agg({'sample_id':'nunique', 'pcs/m':'mean'})\n", - "\n", - "lakes_individual_summary = feature_individual_summary[feature_individual_summary.feature_type == 'l'].copy()\n", - "rivers_individual_summary = feature_individual_summary[feature_individual_summary.feature_type == 'r'].copy()\n", - "\n", - "\n", - " \n", - "lakes_i_sum = format_river_lake_summary(lakes_individual_summary).style.set_table_styles(userdisplay.table_css_styles).format(precision=2)\n", - "rivers_i_sum = format_river_lake_summary(rivers_individual_summary).style.set_table_styles(userdisplay.table_css_styles).format(precision=2)\n", - "\n", - "glue('lakes-i-summary', lakes_i_sum, display=False)\n", - "glue('rivers-i-summary', rivers_i_sum, display=False)" - ] - }, - { - "cell_type": "markdown", - "id": "b6d8a919-cdad-4915-99d0-8ffd21a40e98", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "## Lakes and rivers sampled - all data\n", - "\n", - "::::{grid} 2 2 2 2\n", - "\n", - ":::{grid-item}\n", - "**Lakes sampled**\n", - "\n", - "```{glue} lakes-i-summary\n", - "```\n", - "\n", - ":::\n", - "\n", - ":::{grid-item}\n", - "**Rivers sampled**\n", - "\n", - "```{glue} rivers-i-summary\n", - "```\n", - ":::\n", - "::::\n", - "\n", - "## Municipal Results - all data\n", - "\n", - "The average pieces per meter of the most common objects for each city.\n", - "\n", - "::::::::::{tab-set}\n", - "\n", - ":::::::::{tab-item} Lakes\n", - "```{glue} lake-municipal-results\n", - "```\n", - ":::::::::\n", - "\n", - ":::::::::{tab-item} Rivers\n", - "```{glue} river-municipal-results\n", - "``` \n", - ":::::::::\n", - "\n", - "::::::::::" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_build/html/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_build/html/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css deleted file mode 100644 index 3225661..0000000 --- a/_build/html/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css +++ /dev/null @@ -1 +0,0 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- }); - } - } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); - }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); - } - return result; -}; - -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} diff --git a/_build/html/_static/basic.css b/_build/html/_static/basic.css deleted file mode 100644 index 5685b52..0000000 --- a/_build/html/_static/basic.css +++ /dev/null @@ -1,928 +0,0 @@ -/* - * basic.css - * ~~~~~~~~~ - * - * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/* -- main layout ----------------------------------------------------------- */ - -div.clearer { - clear: both; -} - -div.section::after { - display: block; - content: ''; - clear: left; -} - -/* -- relbar ---------------------------------------------------------------- */ - -div.related { - width: 100%; - font-size: 90%; -} - -div.related h3 { - display: none; -} - -div.related ul { - margin: 0; - padding: 0 0 0 10px; - list-style: none; -} - -div.related li { - display: inline; -} - -div.related li.right { - float: right; - margin-right: 5px; -} - -/* -- sidebar --------------------------------------------------------------- */ - -div.sphinxsidebarwrapper { - padding: 10px 5px 0 10px; -} - -div.sphinxsidebar { - float: left; - width: 270px; - margin-left: -100%; - font-size: 90%; - word-wrap: break-word; - overflow-wrap : break-word; -} - -div.sphinxsidebar ul { - list-style: none; -} - -div.sphinxsidebar ul ul, -div.sphinxsidebar ul.want-points { - margin-left: 20px; - list-style: square; -} - -div.sphinxsidebar ul ul { - margin-top: 0; - margin-bottom: 0; -} - -div.sphinxsidebar form { - margin-top: 10px; -} - -div.sphinxsidebar input { - border: 1px solid #98dbcc; - font-family: sans-serif; - font-size: 1em; -} - -div.sphinxsidebar #searchbox form.search { - overflow: hidden; -} - -div.sphinxsidebar #searchbox input[type="text"] { - float: left; - width: 80%; - padding: 0.25em; - box-sizing: border-box; -} - -div.sphinxsidebar #searchbox input[type="submit"] { - float: left; - width: 20%; - border-left: none; - padding: 0.25em; - box-sizing: border-box; -} - - -img { - border: 0; - max-width: 100%; -} - -/* -- search page ----------------------------------------------------------- */ - -ul.search { - margin: 10px 0 0 20px; - padding: 0; -} - -ul.search li { - padding: 5px 0 5px 20px; - background-image: url(file.png); - background-repeat: no-repeat; - background-position: 0 7px; -} - -ul.search li a { - font-weight: bold; -} - -ul.search li p.context { - color: #888; - margin: 2px 0 0 30px; - text-align: left; -} - -ul.keywordmatches li.goodmatch a { - font-weight: bold; -} - -/* -- index page ------------------------------------------------------------ */ - -table.contentstable { - width: 90%; - margin-left: auto; - margin-right: auto; -} - -table.contentstable p.biglink { - line-height: 150%; -} - -a.biglink { - font-size: 1.3em; -} - -span.linkdescr { - font-style: italic; - padding-top: 5px; - font-size: 90%; -} - -/* -- general index --------------------------------------------------------- */ - -table.indextable { - width: 100%; -} - -table.indextable td { - text-align: left; - vertical-align: top; -} - -table.indextable ul { - margin-top: 0; - margin-bottom: 0; - list-style-type: none; -} - -table.indextable > tbody > tr > td > ul { - padding-left: 0em; -} - -table.indextable tr.pcap { - height: 10px; -} - -table.indextable tr.cap { - margin-top: 10px; - background-color: #f2f2f2; -} - -img.toggler { - margin-right: 3px; - margin-top: 3px; - cursor: pointer; -} - -div.modindex-jumpbox { - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; - margin: 1em 0 1em 0; - padding: 0.4em; -} - -div.genindex-jumpbox { - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; - margin: 1em 0 1em 0; - padding: 0.4em; -} - -/* -- domain module index --------------------------------------------------- */ - -table.modindextable td { - padding: 2px; - border-collapse: collapse; -} - -/* -- general body styles --------------------------------------------------- */ - -div.body { - min-width: 360px; - max-width: 800px; -} - -div.body p, div.body dd, div.body li, div.body blockquote { - -moz-hyphens: auto; - -ms-hyphens: auto; - -webkit-hyphens: auto; - hyphens: auto; -} - -a.headerlink { - visibility: hidden; -} -a.brackets:before, -span.brackets > a:before{ - content: "["; -} - -a.brackets:after, -span.brackets > a:after { - content: "]"; -} - - -h1:hover > a.headerlink, -h2:hover > a.headerlink, -h3:hover > a.headerlink, -h4:hover > a.headerlink, -h5:hover > a.headerlink, -h6:hover > a.headerlink, -dt:hover > a.headerlink, -caption:hover > a.headerlink, -p.caption:hover > a.headerlink, -div.code-block-caption:hover > a.headerlink { - visibility: visible; -} - -div.body p.caption { - text-align: inherit; -} - -div.body td { - text-align: left; -} - -.first { - margin-top: 0 !important; -} - -p.rubric { - margin-top: 30px; - font-weight: bold; -} - -img.align-left, figure.align-left, .figure.align-left, object.align-left { - clear: left; - float: left; - margin-right: 1em; -} - -img.align-right, figure.align-right, .figure.align-right, object.align-right { - clear: right; - float: right; - margin-left: 1em; -} - -img.align-center, figure.align-center, .figure.align-center, object.align-center { - display: block; - margin-left: auto; - margin-right: auto; -} - -img.align-default, figure.align-default, .figure.align-default { - display: block; - margin-left: auto; - margin-right: auto; -} - -.align-left { - text-align: left; -} - -.align-center { - text-align: center; -} - -.align-default { - text-align: center; -} - -.align-right { - text-align: right; -} - -/* -- sidebars -------------------------------------------------------------- */ - -div.sidebar, -aside.sidebar { - margin: 0 0 0.5em 1em; - border: 1px solid #ddb; - padding: 7px; - background-color: #ffe; - width: 40%; - float: right; - clear: right; - overflow-x: auto; -} - -p.sidebar-title { - font-weight: bold; -} -div.admonition, div.topic, blockquote { - clear: left; -} - -/* -- topics ---------------------------------------------------------------- */ -div.topic { - border: 1px solid #ccc; - padding: 7px; - margin: 10px 0 10px 0; -} - -p.topic-title { - font-size: 1.1em; - font-weight: bold; - margin-top: 10px; -} - -/* -- admonitions ----------------------------------------------------------- */ - -div.admonition { - margin-top: 10px; - margin-bottom: 10px; - padding: 7px; -} - -div.admonition dt { - font-weight: bold; -} - -p.admonition-title { - margin: 0px 10px 5px 0px; - font-weight: bold; -} - -div.body p.centered { - text-align: center; - margin-top: 25px; -} - -/* -- content of sidebars/topics/admonitions -------------------------------- */ - -div.sidebar > :last-child, -aside.sidebar > :last-child, -div.topic > :last-child, -div.admonition > :last-child { - margin-bottom: 0; -} - -div.sidebar::after, -aside.sidebar::after, -div.topic::after, -div.admonition::after, -blockquote::after { - display: block; - content: ''; - clear: both; -} - -/* -- tables ---------------------------------------------------------------- */ - -table.docutils { - margin-top: 10px; - margin-bottom: 10px; - border: 0; - border-collapse: collapse; -} - -table.align-center { - margin-left: auto; - margin-right: auto; -} - -table.align-default { - margin-left: auto; - margin-right: auto; -} - -table caption span.caption-number { - font-style: italic; -} - -table caption span.caption-text { -} - -table.docutils td, table.docutils th { - padding: 1px 8px 1px 5px; - border-top: 0; - border-left: 0; - border-right: 0; - border-bottom: 1px solid #aaa; -} - -th { - text-align: left; - padding-right: 5px; -} - -table.citation { - border-left: solid 1px gray; - margin-left: 1px; -} - -table.citation td { - border-bottom: none; -} - -th > :first-child, -td > :first-child { - margin-top: 0px; -} - -th > :last-child, -td > :last-child { - margin-bottom: 0px; -} - -/* -- figures --------------------------------------------------------------- */ - -div.figure, figure { - margin: 0.5em; - padding: 0.5em; -} - -div.figure p.caption, figcaption { - padding: 0.3em; -} - -div.figure p.caption span.caption-number, -figcaption span.caption-number { - font-style: italic; -} - -div.figure p.caption span.caption-text, -figcaption span.caption-text { -} - -/* -- field list styles ----------------------------------------------------- */ - -table.field-list td, table.field-list th { - border: 0 !important; -} - -.field-list ul { - margin: 0; - padding-left: 1em; -} - -.field-list p { - margin: 0; -} - -.field-name { - -moz-hyphens: manual; - -ms-hyphens: manual; - -webkit-hyphens: manual; - hyphens: manual; -} - -/* -- hlist styles ---------------------------------------------------------- */ - -table.hlist { - margin: 1em 0; -} - -table.hlist td { - vertical-align: top; -} - -/* -- object description styles --------------------------------------------- */ - -.sig { - font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; -} - -.sig-name, code.descname { - background-color: transparent; - font-weight: bold; -} - -.sig-name { - font-size: 1.1em; -} - -code.descname { - font-size: 1.2em; -} - -.sig-prename, code.descclassname { - background-color: transparent; -} - -.optional { - font-size: 1.3em; -} - -.sig-paren { - font-size: larger; -} - -.sig-param.n { - font-style: italic; -} - -/* C++ specific styling */ - -.sig-inline.c-texpr, -.sig-inline.cpp-texpr { - font-family: unset; -} - -.sig.c .k, .sig.c .kt, -.sig.cpp .k, .sig.cpp .kt { - color: #0033B3; -} - -.sig.c .m, -.sig.cpp .m { - color: #1750EB; -} - -.sig.c .s, .sig.c .sc, -.sig.cpp .s, .sig.cpp .sc { - color: #067D17; -} - - -/* -- other body styles ----------------------------------------------------- */ - -ol.arabic { - list-style: decimal; -} - -ol.loweralpha { - list-style: lower-alpha; -} - -ol.upperalpha { - list-style: upper-alpha; -} - -ol.lowerroman { - list-style: lower-roman; -} - -ol.upperroman { - list-style: upper-roman; -} - -:not(li) > ol > li:first-child > :first-child, -:not(li) > ul > li:first-child > :first-child { - margin-top: 0px; -} - -:not(li) > ol > li:last-child > :last-child, -:not(li) > ul > li:last-child > :last-child { - margin-bottom: 0px; -} - -ol.simple ol p, -ol.simple ul p, -ul.simple ol p, -ul.simple ul p { - margin-top: 0; -} - -ol.simple > li:not(:first-child) > p, -ul.simple > li:not(:first-child) > p { - margin-top: 0; -} - -ol.simple p, -ul.simple p { - margin-bottom: 0; -} - -/* Docutils 0.17 and older (footnotes & citations) */ -dl.footnote > dt, -dl.citation > dt { - float: left; - margin-right: 0.5em; -} - -dl.footnote > dd, -dl.citation > dd { - margin-bottom: 0em; -} - -dl.footnote > dd:after, -dl.citation > dd:after { - content: ""; - clear: both; -} - -/* Docutils 0.18+ (footnotes & citations) */ -aside.footnote > span, -div.citation > span { - float: left; -} -aside.footnote > span:last-of-type, -div.citation > span:last-of-type { - padding-right: 0.5em; -} -aside.footnote > p { - margin-left: 2em; -} -div.citation > p { - margin-left: 4em; -} -aside.footnote > p:last-of-type, -div.citation > p:last-of-type { - margin-bottom: 0em; 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-} - -div[class*="highlight-"] { - margin: 1em 0; -} - -td.linenos pre { - border: 0; - background-color: transparent; - color: #aaa; -} - -table.highlighttable { - display: block; -} - -table.highlighttable tbody { - display: block; -} - -table.highlighttable tr { - display: flex; -} - -table.highlighttable td { - margin: 0; - padding: 0; -} - -table.highlighttable td.linenos { - padding-right: 0.5em; -} - -table.highlighttable td.code { - flex: 1; - overflow: hidden; -} - -.highlight .hll { - display: block; -} - -div.highlight pre, -table.highlighttable pre { - margin: 0; -} - -div.code-block-caption + div { - margin-top: 0; -} - -div.code-block-caption { - margin-top: 1em; - padding: 2px 5px; - font-size: small; -} - -div.code-block-caption code { - background-color: transparent; -} - -table.highlighttable td.linenos, -span.linenos, -div.highlight span.gp { /* gp: Generic.Prompt */ - user-select: none; - -webkit-user-select: text; /* Safari fallback only */ - -webkit-user-select: none; /* Chrome/Safari */ - -moz-user-select: none; /* Firefox */ - -ms-user-select: none; /* IE10+ */ -} - -div.code-block-caption span.caption-number { - padding: 0.1em 0.3em; - font-style: italic; -} - -div.code-block-caption span.caption-text { -} - -div.literal-block-wrapper { - margin: 1em 0; -} - -code.xref, a code { - background-color: transparent; - font-weight: bold; -} - -h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { - background-color: transparent; -} - -.viewcode-link { - float: right; -} - -.viewcode-back { - float: right; - font-family: sans-serif; -} - -div.viewcode-block:target { - margin: -1px -10px; - padding: 0 10px; -} - -/* -- math display ---------------------------------------------------------- */ - -img.math { - vertical-align: middle; -} - -div.body div.math p { - text-align: center; -} - -span.eqno { - float: right; -} - -span.eqno a.headerlink { - position: absolute; - z-index: 1; -} - -div.math:hover a.headerlink { - visibility: visible; -} - -/* -- printout stylesheet --------------------------------------------------- */ - -@media print { - div.document, - div.documentwrapper, - div.bodywrapper { - margin: 0 !important; - width: 100%; - } - - div.sphinxsidebar, - div.related, - div.footer, - #top-link { - display: none; - } -} \ No newline at end of file diff --git a/_build/html/_static/check-solid.svg b/_build/html/_static/check-solid.svg deleted file mode 100644 index 92fad4b..0000000 --- a/_build/html/_static/check-solid.svg +++ /dev/null @@ -1,4 +0,0 @@ - - - - diff --git a/_build/html/_static/clipboard.min.js b/_build/html/_static/clipboard.min.js deleted file mode 100644 index 54b3c46..0000000 --- a/_build/html/_static/clipboard.min.js +++ /dev/null @@ -1,7 +0,0 @@ -/*! - * clipboard.js v2.0.8 - * https://clipboardjs.com/ - * - * Licensed MIT © Zeno Rocha - */ -!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1 - - - - diff --git a/_build/html/_static/copybutton.css b/_build/html/_static/copybutton.css deleted file mode 100644 index f1916ec..0000000 --- a/_build/html/_static/copybutton.css +++ /dev/null @@ -1,94 +0,0 @@ -/* Copy buttons */ -button.copybtn { - position: absolute; - display: flex; - top: .3em; - right: .3em; - width: 1.7em; - height: 1.7em; - opacity: 0; - transition: opacity 0.3s, border .3s, background-color .3s; - user-select: none; - padding: 0; - border: none; - outline: none; - border-radius: 0.4em; - /* The colors that GitHub uses */ - border: #1b1f2426 1px solid; - background-color: #f6f8fa; - color: #57606a; -} - -button.copybtn.success { - border-color: #22863a; - color: #22863a; -} - -button.copybtn svg { - stroke: currentColor; - width: 1.5em; - height: 1.5em; - padding: 0.1em; -} - -div.highlight { - position: relative; -} - -/* Show the copybutton */ -.highlight:hover button.copybtn, button.copybtn.success { - opacity: 1; -} - -.highlight button.copybtn:hover { - background-color: rgb(235, 235, 235); -} - -.highlight button.copybtn:active { - background-color: rgb(187, 187, 187); -} - -/** - * A minimal CSS-only tooltip copied from: - * https://codepen.io/mildrenben/pen/rVBrpK - * - * To use, write HTML like the following: - * - *

Short

- */ - .o-tooltip--left { - position: relative; - } - - .o-tooltip--left:after { - opacity: 0; - visibility: hidden; - position: absolute; - content: attr(data-tooltip); - padding: .2em; - font-size: .8em; - left: -.2em; - background: grey; - color: white; - white-space: nowrap; - z-index: 2; - border-radius: 2px; - transform: translateX(-102%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); -} - -.o-tooltip--left:hover:after { - display: block; - opacity: 1; - visibility: visible; - transform: translateX(-100%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); - transition-delay: .5s; -} - -/* By default the copy button shouldn't show up when printing a page */ -@media print { - button.copybtn { - display: none; - } -} diff --git a/_build/html/_static/copybutton.js b/_build/html/_static/copybutton.js deleted file mode 100644 index 2ea7ff3..0000000 --- a/_build/html/_static/copybutton.js +++ /dev/null @@ -1,248 +0,0 @@ -// Localization support -const messages = { - 'en': { - 'copy': 'Copy', - 'copy_to_clipboard': 'Copy to clipboard', - 'copy_success': 'Copied!', - 'copy_failure': 'Failed to copy', - }, - 'es' : { - 'copy': 'Copiar', - 'copy_to_clipboard': 'Copiar al portapapeles', - 'copy_success': '¡Copiado!', - 'copy_failure': 'Error al copiar', - }, - 'de' : { - 'copy': 'Kopieren', - 'copy_to_clipboard': 'In die Zwischenablage kopieren', - 'copy_success': 'Kopiert!', - 'copy_failure': 'Fehler beim Kopieren', - }, - 'fr' : { - 'copy': 'Copier', - 'copy_to_clipboard': 'Copier dans le presse-papier', - 'copy_success': 'Copié !', - 'copy_failure': 'Échec de la copie', - }, - 'ru': { - 'copy': 'Скопировать', - 'copy_to_clipboard': 'Скопировать в буфер', - 'copy_success': 'Скопировано!', - 'copy_failure': 'Не удалось скопировать', - }, - 'zh-CN': { - 'copy': '复制', - 'copy_to_clipboard': '复制到剪贴板', - 'copy_success': '复制成功!', - 'copy_failure': '复制失败', - }, - 'it' : { - 'copy': 'Copiare', - 'copy_to_clipboard': 'Copiato negli appunti', - 'copy_success': 'Copiato!', - 'copy_failure': 'Errore durante la copia', - } -} - -let locale = 'en' -if( document.documentElement.lang !== undefined - && messages[document.documentElement.lang] !== undefined ) { - locale = document.documentElement.lang -} - -let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; -if (doc_url_root == '#') { - doc_url_root = ''; -} - -/** - * SVG files for our copy buttons - */ -let iconCheck = ` - ${messages[locale]['copy_success']} - - -` - -// If the user specified their own SVG use that, otherwise use the default -let iconCopy = ``; -if (!iconCopy) { - iconCopy = ` - ${messages[locale]['copy_to_clipboard']} - - - -` -} - -/** - * Set up copy/paste for code blocks - */ - -const runWhenDOMLoaded = cb => { - if (document.readyState != 'loading') { - cb() - } else if (document.addEventListener) { - document.addEventListener('DOMContentLoaded', cb) - } else { - document.attachEvent('onreadystatechange', function() { - if (document.readyState == 'complete') cb() - }) - } -} - -const codeCellId = index => `codecell${index}` - -// Clears selected text since ClipboardJS will select the text when copying -const clearSelection = () => { - if (window.getSelection) { - window.getSelection().removeAllRanges() - } else if (document.selection) { - document.selection.empty() - } -} - -// Changes tooltip text for a moment, then changes it back -// We want the timeout of our `success` class to be a bit shorter than the -// tooltip and icon change, so that we can hide the icon before changing back. -var timeoutIcon = 2000; -var timeoutSuccessClass = 1500; - -const temporarilyChangeTooltip = (el, oldText, newText) => { - el.setAttribute('data-tooltip', newText) - el.classList.add('success') - // Remove success a little bit sooner than we change the tooltip - // So that we can use CSS to hide the copybutton first - setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) - setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) -} - -// Changes the copy button icon for two seconds, then changes it back -const temporarilyChangeIcon = (el) => { - el.innerHTML = iconCheck; - setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) -} - -const addCopyButtonToCodeCells = () => { - // If ClipboardJS hasn't loaded, wait a bit and try again. This - // happens because we load ClipboardJS asynchronously. - if (window.ClipboardJS === undefined) { - setTimeout(addCopyButtonToCodeCells, 250) - return - } - - // Add copybuttons to all of our code cells - const COPYBUTTON_SELECTOR = 'div.highlight pre'; - const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) - codeCells.forEach((codeCell, index) => { - const id = codeCellId(index) - codeCell.setAttribute('id', id) - - const clipboardButton = id => - `` - codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) - }) - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} - - -var copyTargetText = (trigger) => { - var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - - // get filtered text - let exclude = '.linenos'; - - let text = filterText(target, exclude); - return formatCopyText(text, '', false, true, true, true, '', '') -} - - // Initialize with a callback so we can modify the text before copy - const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) - - // Update UI with error/success messages - clipboard.on('success', event => { - clearSelection() - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) - temporarilyChangeIcon(event.trigger) - }) - - clipboard.on('error', event => { - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) - }) -} - -runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/_build/html/_static/copybutton_funcs.js b/_build/html/_static/copybutton_funcs.js deleted file mode 100644 index dbe1aaa..0000000 --- a/_build/html/_static/copybutton_funcs.js +++ /dev/null @@ -1,73 +0,0 @@ -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -export function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} diff --git a/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css deleted file mode 100644 index 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var(--sd-color-tabs-overline),0 .0625rem var(--sd-color-tabs-underline);display:none;order:99;padding-bottom:.75rem;padding-top:.75rem;width:100%}.sd-tab-content>:first-child{margin-top:0 !important}.sd-tab-content>:last-child{margin-bottom:0 !important}.sd-tab-content>.sd-tab-set{margin:0}.sd-sphinx-override,.sd-sphinx-override *{-moz-box-sizing:border-box;-webkit-box-sizing:border-box;box-sizing:border-box}.sd-sphinx-override p{margin-top:0}:root{--sd-color-primary: #007bff;--sd-color-secondary: #6c757d;--sd-color-success: #28a745;--sd-color-info: #17a2b8;--sd-color-warning: #f0b37e;--sd-color-danger: #dc3545;--sd-color-light: #f8f9fa;--sd-color-muted: #6c757d;--sd-color-dark: #212529;--sd-color-black: black;--sd-color-white: white;--sd-color-primary-highlight: #0069d9;--sd-color-secondary-highlight: #5c636a;--sd-color-success-highlight: #228e3b;--sd-color-info-highlight: #148a9c;--sd-color-warning-highlight: #cc986b;--sd-color-danger-highlight: #bb2d3b;--sd-color-light-highlight: #d3d4d5;--sd-color-muted-highlight: #5c636a;--sd-color-dark-highlight: #1c1f23;--sd-color-black-highlight: black;--sd-color-white-highlight: #d9d9d9;--sd-color-primary-text: #fff;--sd-color-secondary-text: #fff;--sd-color-success-text: #fff;--sd-color-info-text: #fff;--sd-color-warning-text: #212529;--sd-color-danger-text: #fff;--sd-color-light-text: #212529;--sd-color-muted-text: #fff;--sd-color-dark-text: #fff;--sd-color-black-text: #fff;--sd-color-white-text: #212529;--sd-color-shadow: rgba(0, 0, 0, 0.15);--sd-color-card-border: rgba(0, 0, 0, 0.125);--sd-color-card-border-hover: hsla(231, 99%, 66%, 1);--sd-color-card-background: transparent;--sd-color-card-text: inherit;--sd-color-card-header: transparent;--sd-color-card-footer: transparent;--sd-color-tabs-label-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-hover: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-inactive: hsl(0, 0%, 66%);--sd-color-tabs-underline-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-underline-hover: rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem} diff --git a/_build/html/_static/design-tabs.js b/_build/html/_static/design-tabs.js deleted file mode 100644 index 36b38cf..0000000 --- a/_build/html/_static/design-tabs.js +++ /dev/null @@ -1,27 +0,0 @@ -var sd_labels_by_text = {}; - -function ready() { - const li = document.getElementsByClassName("sd-tab-label"); - for (const label of li) { - syncId = label.getAttribute("data-sync-id"); - if (syncId) { - label.onclick = onLabelClick; - if (!sd_labels_by_text[syncId]) { - sd_labels_by_text[syncId] = []; - } - sd_labels_by_text[syncId].push(label); - } - } -} - -function onLabelClick() { - // Activate other inputs with the same sync id. - syncId = this.getAttribute("data-sync-id"); - for (label of sd_labels_by_text[syncId]) { - if (label === this) continue; - label.previousElementSibling.checked = true; - } - window.localStorage.setItem("sphinx-design-last-tab", syncId); -} - -document.addEventListener("DOMContentLoaded", ready, false); diff --git a/_build/html/_static/doctools.js b/_build/html/_static/doctools.js deleted file mode 100644 index c3db08d..0000000 --- a/_build/html/_static/doctools.js +++ /dev/null @@ -1,264 +0,0 @@ -/* - * doctools.js - * ~~~~~~~~~~~ - * - * Base JavaScript utilities for all Sphinx HTML documentation. - * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ -"use strict"; - -const _ready = (callback) => { - if (document.readyState !== "loading") { - callback(); - } else { - document.addEventListener("DOMContentLoaded", callback); - } -}; - -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - parent.insertBefore( - span, - parent.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute("class", className); - addItems.push({ parent: parent, target: rect }); - } - } - } else if (node.matches && !node.matches("button, select, textarea")) { - node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); - } -}; -const _highlightText = (thisNode, text, className) => { - let addItems = []; - _highlight(thisNode, addItems, text, className); - addItems.forEach((obj) => - obj.parent.insertAdjacentElement("beforebegin", obj.target) - ); -}; - -/** - * Small JavaScript module for the documentation. - */ -const Documentation = { - init: () => { - Documentation.highlightSearchWords(); - Documentation.initDomainIndexTable(); - Documentation.initOnKeyListeners(); - }, - - /** - * i18n support - */ - TRANSLATIONS: {}, - PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), - LOCALE: "unknown", - - // gettext and ngettext don't access this so that the functions - // can safely bound to a different name (_ = Documentation.gettext) - gettext: (string) => { - const translated = Documentation.TRANSLATIONS[string]; - switch (typeof translated) { - case "undefined": - return string; // no translation - case "string": - return translated; // translation exists - default: - return translated[0]; // (singular, plural) translation tuple exists - } - }, - - ngettext: (singular, plural, n) => { - const translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated !== "undefined") - return translated[Documentation.PLURAL_EXPR(n)]; - return n === 1 ? singular : plural; - }, - - addTranslations: (catalog) => { - Object.assign(Documentation.TRANSLATIONS, catalog.messages); - Documentation.PLURAL_EXPR = new Function( - "n", - `return (${catalog.plural_expr})` - ); - Documentation.LOCALE = catalog.locale; - }, - - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords: () => { - const highlight = - new URLSearchParams(window.location.search).get("highlight") || ""; - const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); - if (terms.length === 0) return; // nothing to do - - // There should never be more than one element matching "div.body" - const divBody = document.querySelectorAll("div.body"); - const body = divBody.length ? divBody[0] : document.querySelector("body"); - window.setTimeout(() => { - terms.forEach((term) => _highlightText(body, term, "highlighted")); - }, 10); - - const searchBox = document.getElementById("searchbox"); - if (searchBox === null) return; - searchBox.appendChild( - document - .createRange() - .createContextualFragment( - '

' + - '' + - Documentation.gettext("Hide Search Matches") + - "

" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - const url = new URL(window.location); - url.searchParams.delete("highlight"); - window.history.replaceState({}, "", url); - }, - - /** - * helper function to focus on search bar - */ - focusSearchBar: () => { - document.querySelectorAll("input[name=q]")[0]?.focus(); - }, - - /** - * Initialise the domain index toggle buttons - */ - initDomainIndexTable: () => { - const toggler = (el) => { - const idNumber = el.id.substr(7); - const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); - if (el.src.substr(-9) === "minus.png") { - el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; - toggledRows.forEach((el) => (el.style.display = "none")); - } else { - el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; - toggledRows.forEach((el) => (el.style.display = "")); - } - }; - - const togglerElements = document.querySelectorAll("img.toggler"); - togglerElements.forEach((el) => - el.addEventListener("click", (event) => toggler(event.currentTarget)) - ); - togglerElements.forEach((el) => (el.style.display = "")); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); - }, - - initOnKeyListeners: () => { - // only install a listener if it is really needed - if ( - !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && - !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS - ) - return; - - const blacklistedElements = new Set([ - "TEXTAREA", - "INPUT", - "SELECT", - "BUTTON", - ]); - document.addEventListener("keydown", (event) => { - if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements - if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys - - if (!event.shiftKey) { - switch (event.key) { - case "ArrowLeft": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const prevLink = document.querySelector('link[rel="prev"]'); - if (prevLink && prevLink.href) { - window.location.href = prevLink.href; - event.preventDefault(); - } - break; - case "ArrowRight": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const nextLink = document.querySelector('link[rel="next"]'); - if (nextLink && nextLink.href) { - window.location.href = nextLink.href; - event.preventDefault(); - } - break; - case "Escape": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.hideSearchWords(); - event.preventDefault(); - } - } - - // some keyboard layouts may need Shift to get / - switch (event.key) { - case "/": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.focusSearchBar(); - event.preventDefault(); - } - }); - }, -}; - -// quick alias for translations -const _ = Documentation.gettext; - -_ready(Documentation.init); diff --git a/_build/html/_static/documentation_options.js b/_build/html/_static/documentation_options.js deleted file mode 100644 index 3063782..0000000 --- a/_build/html/_static/documentation_options.js +++ /dev/null @@ -1,14 +0,0 @@ -var DOCUMENTATION_OPTIONS = { - URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '', - LANGUAGE: 'en', - COLLAPSE_INDEX: false, - BUILDER: 'html', - FILE_SUFFIX: '.html', - LINK_SUFFIX: '.html', - HAS_SOURCE: true, - SOURCELINK_SUFFIX: '', - NAVIGATION_WITH_KEYS: true, - SHOW_SEARCH_SUMMARY: true, - ENABLE_SEARCH_SHORTCUTS: false, -}; \ No newline at end of file diff --git a/_build/html/_static/file.png b/_build/html/_static/file.png deleted file mode 100644 index a858a41..0000000 Binary files a/_build/html/_static/file.png and /dev/null differ diff --git a/_build/html/_static/images/logo_binder.svg b/_build/html/_static/images/logo_binder.svg deleted file mode 100644 index 45fecf7..0000000 --- a/_build/html/_static/images/logo_binder.svg +++ /dev/null @@ -1,19 +0,0 @@ - - - - -logo - - - - - - - - diff --git a/_build/html/_static/images/logo_colab.png b/_build/html/_static/images/logo_colab.png deleted file mode 100644 index b7560ec..0000000 Binary files a/_build/html/_static/images/logo_colab.png and /dev/null differ diff --git a/_build/html/_static/images/logo_deepnote.svg b/_build/html/_static/images/logo_deepnote.svg deleted file mode 100644 index fa77ebf..0000000 --- a/_build/html/_static/images/logo_deepnote.svg +++ /dev/null @@ -1 +0,0 @@ - diff --git a/_build/html/_static/images/logo_jupyterhub.svg b/_build/html/_static/images/logo_jupyterhub.svg deleted file mode 100644 index 60cfe9f..0000000 --- a/_build/html/_static/images/logo_jupyterhub.svg +++ /dev/null @@ -1 +0,0 @@ -logo_jupyterhubHub diff --git a/_build/html/_static/jquery-3.6.0.js b/_build/html/_static/jquery-3.6.0.js deleted file mode 100644 index fc6c299..0000000 --- a/_build/html/_static/jquery-3.6.0.js +++ /dev/null @@ -1,10881 +0,0 @@ -/*! - * jQuery JavaScript Library v3.6.0 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright OpenJS Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2021-03-02T17:08Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var flat = arr.flat ? function( array ) { - return arr.flat.call( array ); -} : function( array ) { - return arr.concat.apply( [], array ); -}; - - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - -var isFunction = function isFunction( obj ) { - - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML elements - // (i.e., `typeof document.createElement( "object" ) === "function"`). - // We don't want to classify *any* DOM node as a function. - // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 - // Plus for old WebKit, typeof returns "function" for HTML collections - // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) - return typeof obj === "function" && typeof obj.nodeType !== "number" && - typeof obj.item !== "function"; - }; - - -var isWindow = function isWindow( obj ) { - return obj != null && obj === obj.window; - }; - - -var document = window.document; - - - - var preservedScriptAttributes = { - type: true, - src: true, - nonce: true, - noModule: true - }; - - function DOMEval( code, node, doc ) { - doc = doc || document; - - var i, val, - script = doc.createElement( "script" ); - - script.text = code; - if ( node ) { - for ( i in preservedScriptAttributes ) { - - // Support: Firefox 64+, Edge 18+ - // Some browsers don't support the "nonce" property on scripts. - // On the other hand, just using `getAttribute` is not enough as - // the `nonce` attribute is reset to an empty string whenever it - // becomes browsing-context connected. - // See https://github.com/whatwg/html/issues/2369 - // See https://html.spec.whatwg.org/#nonce-attributes - // The `node.getAttribute` check was added for the sake of - // `jQuery.globalEval` so that it can fake a nonce-containing node - // via an object. - val = node[ i ] || node.getAttribute && node.getAttribute( i ); - if ( val ) { - script.setAttribute( i, val ); - } - } - } - doc.head.appendChild( script ).parentNode.removeChild( script ); - } - - -function toType( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; -} -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.6.0", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - even: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return ( i + 1 ) % 2; - } ) ); - }, - - odd: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return i % 2; - } ) ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - copy = options[ name ]; - - // Prevent Object.prototype pollution - // Prevent never-ending loop - if ( name === "__proto__" || target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - src = target[ name ]; - - // Ensure proper type for the source value - if ( copyIsArray && !Array.isArray( src ) ) { - clone = []; - } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { - clone = {}; - } else { - clone = src; - } - copyIsArray = false; - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - // Evaluates a script in a provided context; falls back to the global one - // if not specified. - globalEval: function( code, options, doc ) { - DOMEval( code, { nonce: options && options.nonce }, doc ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return flat( ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), - function( _i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); - } ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = toType( obj ); - - if ( isFunction( obj ) || isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.6 - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://js.foundation/ - * - * Date: 2021-02-16 - */ -( function( window ) { -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - nonnativeSelectorCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ( {} ).hasOwnProperty, - arr = [], - pop = arr.pop, - pushNative = arr.push, - push = arr.push, - slice = arr.slice, - - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[ i ] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + - "ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram - identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + - "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - - // "Attribute values must be CSS identifiers [capture 5] - // or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + - whitespace + "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + - whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + - "*" ), - rdescend = new RegExp( whitespace + "|>" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + - whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + - whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + - "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + - "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rhtml = /HTML$/i, - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), - funescape = function( escape, nonHex ) { - var high = "0x" + escape.slice( 1 ) - 0x10000; - - return nonHex ? - - // Strip the backslash prefix from a non-hex escape sequence - nonHex : - - // Replace a hexadecimal escape sequence with the encoded Unicode code point - // Support: IE <=11+ - // For values outside the Basic Multilingual Plane (BMP), manually construct a - // surrogate pair - high < 0 ? - String.fromCharCode( high + 0x10000 ) : - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + - ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - inDisabledFieldset = addCombinator( - function( elem ) { - return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - ( arr = slice.call( preferredDoc.childNodes ) ), - preferredDoc.childNodes - ); - - // Support: Android<4.0 - // Detect silently failing push.apply - // eslint-disable-next-line no-unused-expressions - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - pushNative.apply( target, slice.call( els ) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - - // Can't trust NodeList.length - while ( ( target[ j++ ] = els[ i++ ] ) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - setDocument( context ); - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { - - // ID selector - if ( ( m = match[ 1 ] ) ) { - - // Document context - if ( nodeType === 9 ) { - if ( ( elem = context.getElementById( m ) ) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && ( elem = newContext.getElementById( m ) ) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[ 2 ] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !nonnativeSelectorCache[ selector + " " ] && - ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && - - // Support: IE 8 only - // Exclude object elements - ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { - - newSelector = selector; - newContext = context; - - // qSA considers elements outside a scoping root when evaluating child or - // descendant combinators, which is not what we want. - // In such cases, we work around the behavior by prefixing every selector in the - // list with an ID selector referencing the scope context. - // The technique has to be used as well when a leading combinator is used - // as such selectors are not recognized by querySelectorAll. - // Thanks to Andrew Dupont for this technique. - if ( nodeType === 1 && - ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - - // We can use :scope instead of the ID hack if the browser - // supports it & if we're not changing the context. - if ( newContext !== context || !support.scope ) { - - // Capture the context ID, setting it first if necessary - if ( ( nid = context.getAttribute( "id" ) ) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", ( nid = expando ) ); - } - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + - toSelector( groups[ i ] ); - } - newSelector = groups.join( "," ); - } - - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - nonnativeSelectorCache( selector, true ); - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return ( cache[ key + " " ] = value ); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement( "fieldset" ); - - try { - return !!fn( el ); - } catch ( e ) { - return false; - } finally { - - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split( "|" ), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[ i ] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( ( cur = cur.nextSibling ) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return ( name === "input" || name === "button" ) && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - inDisabledFieldset( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction( function( argument ) { - argument = +argument; - return markFunction( function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ ( j = matchIndexes[ i ] ) ] ) { - seed[ j ] = !( matches[ j ] = seed[ j ] ); - } - } - } ); - } ); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - var namespace = elem && elem.namespaceURI, - docElem = elem && ( elem.ownerDocument || elem ).documentElement; - - // Support: IE <=8 - // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes - // https://bugs.jquery.com/ticket/4833 - return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9 - 11+, Edge 12 - 18+ - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( preferredDoc != document && - ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, - // Safari 4 - 5 only, Opera <=11.6 - 12.x only - // IE/Edge & older browsers don't support the :scope pseudo-class. - // Support: Safari 6.0 only - // Safari 6.0 supports :scope but it's an alias of :root there. - support.scope = assert( function( el ) { - docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); - return typeof el.querySelectorAll !== "undefined" && - !el.querySelectorAll( ":scope fieldset div" ).length; - } ); - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert( function( el ) { - el.className = "i"; - return !el.getAttribute( "className" ); - } ); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert( function( el ) { - el.appendChild( document.createComment( "" ) ); - return !el.getElementsByTagName( "*" ).length; - } ); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert( function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - } ); - - // ID filter and find - if ( support.getById ) { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute( "id" ) === attrId; - }; - }; - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode( "id" ); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( ( elem = elems[ i++ ] ) ) { - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find[ "TAG" ] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { - - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert( function( el ) { - - var input; - - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll( "[selected]" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push( "~=" ); - } - - // Support: IE 11+, Edge 15 - 18+ - // IE 11/Edge don't find elements on a `[name='']` query in some cases. - // Adding a temporary attribute to the document before the selection works - // around the issue. - // Interestingly, IE 10 & older don't seem to have the issue. - input = document.createElement( "input" ); - input.setAttribute( "name", "" ); - el.appendChild( input ); - if ( !el.querySelectorAll( "[name='']" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + - whitespace + "*(?:''|\"\")" ); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll( ":checked" ).length ) { - rbuggyQSA.push( ":checked" ); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push( ".#.+[+~]" ); - } - - // Support: Firefox <=3.6 - 5 only - // Old Firefox doesn't throw on a badly-escaped identifier. - el.querySelectorAll( "\\\f" ); - rbuggyQSA.push( "[\\r\\n\\f]" ); - } ); - - assert( function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement( "input" ); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll( "[name=d]" ).length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: Opera 10 - 11 only - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll( "*,:x" ); - rbuggyQSA.push( ",.*:" ); - } ); - } - - if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector ) ) ) ) { - - assert( function( el ) { - - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - } ); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - ) ); - } : - function( a, b ) { - if ( b ) { - while ( ( b = b.parentNode ) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { - - // Choose the first element that is related to our preferred document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( a == document || a.ownerDocument == preferredDoc && - contains( preferredDoc, a ) ) { - return -1; - } - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( b == document || b.ownerDocument == preferredDoc && - contains( preferredDoc, b ) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - return a == document ? -1 : - b == document ? 1 : - /* eslint-enable eqeqeq */ - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( ( cur = cur.parentNode ) ) { - ap.unshift( cur ); - } - cur = b; - while ( ( cur = cur.parentNode ) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[ i ] === bp[ i ] ) { - i++; - } - - return i ? - - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[ i ], bp[ i ] ) : - - // Otherwise nodes in our document sort first - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - ap[ i ] == preferredDoc ? -1 : - bp[ i ] == preferredDoc ? 1 : - /* eslint-enable eqeqeq */ - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - setDocument( elem ); - - if ( support.matchesSelector && documentIsHTML && - !nonnativeSelectorCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch ( e ) { - nonnativeSelectorCache( expr, true ); - } - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( context.ownerDocument || context ) != document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( elem.ownerDocument || elem ) != document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return ( sel + "" ).replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - - // If no nodeType, this is expected to be an array - while ( ( node = elem[ i++ ] ) ) { - - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[ 1 ] = match[ 1 ].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[ 3 ] = ( match[ 3 ] || match[ 4 ] || - match[ 5 ] || "" ).replace( runescape, funescape ); - - if ( match[ 2 ] === "~=" ) { - match[ 3 ] = " " + match[ 3 ] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[ 1 ] = match[ 1 ].toLowerCase(); - - if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { - - // nth-* requires argument - if ( !match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[ 4 ] = +( match[ 4 ] ? - match[ 5 ] + ( match[ 6 ] || 1 ) : - 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); - match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); - - // other types prohibit arguments - } else if ( match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[ 6 ] && match[ 2 ]; - - if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[ 3 ] ) { - match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - - // Get excess from tokenize (recursively) - ( excess = tokenize( unquoted, true ) ) && - - // advance to the next closing parenthesis - ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { - - // excess is a negative index - match[ 0 ] = match[ 0 ].slice( 0, excess ); - match[ 2 ] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { - return true; - } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - ( pattern = new RegExp( "(^|" + whitespace + - ")" + className + "(" + whitespace + "|$)" ) ) && classCache( - className, function( elem ) { - return pattern.test( - typeof elem.className === "string" && elem.className || - typeof elem.getAttribute !== "undefined" && - elem.getAttribute( "class" ) || - "" - ); - } ); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - /* eslint-disable max-len */ - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - /* eslint-enable max-len */ - - }; - }, - - "CHILD": function( type, what, _argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, _context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( ( node = node[ dir ] ) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( ( node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - - // Use previously-cached element index if available - if ( useCache ) { - - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - - // Use the same loop as above to seek `elem` from the start - while ( ( node = ++nodeIndex && node && node[ dir ] || - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || - ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction( function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[ i ] ); - seed[ idx ] = !( matches[ idx ] = matched[ i ] ); - } - } ) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - - // Potentially complex pseudos - "not": markFunction( function( selector ) { - - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction( function( seed, matches, _context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( ( elem = unmatched[ i ] ) ) { - seed[ i ] = !( matches[ i ] = elem ); - } - } - } ) : - function( elem, _context, xml ) { - input[ 0 ] = elem; - matcher( input, null, xml, results ); - - // Don't keep the element (issue #299) - input[ 0 ] = null; - return !results.pop(); - }; - } ), - - "has": markFunction( function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - } ), - - "contains": markFunction( function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; - }; - } ), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - - // lang value must be a valid identifier - if ( !ridentifier.test( lang || "" ) ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( ( elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); - return false; - }; - } ), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && - ( !document.hasFocus || document.hasFocus() ) && - !!( elem.type || elem.href || ~elem.tabIndex ); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return ( nodeName === "input" && !!elem.checked ) || - ( nodeName === "option" && !!elem.selected ); - }, - - "selected": function( elem ) { - - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - // eslint-disable-next-line no-unused-expressions - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos[ "empty" ]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( ( attr = elem.getAttribute( "type" ) ) == null || - attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo( function() { - return [ 0 ]; - } ), - - "last": createPositionalPseudo( function( _matchIndexes, length ) { - return [ length - 1 ]; - } ), - - "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - } ), - - "even": createPositionalPseudo( function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "odd": createPositionalPseudo( function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? - argument + length : - argument > length ? - length : - argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ) - } -}; - -Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || ( match = rcomma.exec( soFar ) ) ) { - if ( match ) { - - // Don't consume trailing commas as valid - soFar = soFar.slice( match[ 0 ].length ) || soFar; - } - groups.push( ( tokens = [] ) ); - } - - matched = false; - - // Combinators - if ( ( match = rcombinators.exec( soFar ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - - // Cast descendant combinators to space - type: match[ 0 ].replace( rtrim, " " ) - } ); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || - ( match = preFilters[ type ]( match ) ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - type: type, - matches: match - } ); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[ i ].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || ( elem[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || - ( outerCache[ elem.uniqueID ] = {} ); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( ( oldCache = uniqueCache[ key ] ) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return ( newCache[ 2 ] = oldCache[ 2 ] ); - } else { - - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[ i ]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[ 0 ]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[ i ], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( ( elem = unmatched[ i ] ) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction( function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( - selector || "*", - context.nodeType ? [ context ] : context, - [] - ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( ( elem = temp[ i ] ) ) { - matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) ) { - - // Restore matcherIn since elem is not yet a final match - temp.push( ( matcherIn[ i ] = elem ) ); - } - } - postFinder( null, ( matcherOut = [] ), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) && - ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { - - seed[ temp ] = !( results[ temp ] = elem ); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - } ); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[ 0 ].type ], - implicitRelative = leadingRelative || Expr.relative[ " " ], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - ( checkContext = context ).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { - matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; - } else { - matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[ j ].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens - .slice( 0, i - 1 ) - .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), - - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), - len = elems.length; - - if ( outermost ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - outermostContext = context == document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( !context && elem.ownerDocument != document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( ( matcher = elementMatchers[ j++ ] ) ) { - if ( matcher( elem, context || document, xml ) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - - // They will have gone through all possible matchers - if ( ( elem = !matcher && elem ) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( ( matcher = setMatchers[ j++ ] ) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !( unmatched[ i ] || setMatched[ i ] ) ) { - setMatched[ i ] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[ i ] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( - selector, - matcherFromGroupMatchers( elementMatchers, setMatchers ) - ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( ( selector = compiled.selector || selector ) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[ 0 ] = match[ 0 ].slice( 0 ); - if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { - - context = ( Expr.find[ "ID" ]( token.matches[ 0 ] - .replace( runescape, funescape ), context ) || [] )[ 0 ]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[ i ]; - - // Abort if we hit a combinator - if ( Expr.relative[ ( type = token.type ) ] ) { - break; - } - if ( ( find = Expr.find[ type ] ) ) { - - // Search, expanding context for leading sibling combinators - if ( ( seed = find( - token.matches[ 0 ].replace( runescape, funescape ), - rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || - context - ) ) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert( function( el ) { - - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; -} ); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert( function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute( "href" ) === "#"; -} ) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - } ); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert( function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -} ) ) { - addHandle( "value", function( elem, _name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - } ); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert( function( el ) { - return el.getAttribute( "disabled" ) == null; -} ) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; - } - } ); -} - -return Sizzle; - -} )( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -} -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Filtered directly for both simple and complex selectors - return jQuery.filter( qualifier, elements, not ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, _i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, _i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, _i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( elem.contentDocument != null && - - // Support: IE 11+ - // elements with no `data` attribute has an object - // `contentDocument` with a `null` prototype. - getProto( elem.contentDocument ) ) { - - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && toType( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( _i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // rejected_handlers.disable - // fulfilled_handlers.disable - tuples[ 3 - i ][ 3 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock, - - // progress_handlers.lock - tuples[ 0 ][ 3 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the primary Deferred - primary = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - primary.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( primary.state() === "pending" || - isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return primary.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); - } - - return primary.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( toType( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, _key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; - - -// Matches dashed string for camelizing -var rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g; - -// Used by camelCase as callback to replace() -function fcamelCase( _all, letter ) { - return letter.toUpperCase(); -} - -// Convert dashed to camelCase; used by the css and data modules -// Support: IE <=9 - 11, Edge 12 - 15 -// Microsoft forgot to hump their vendor prefix (#9572) -function camelCase( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); -} -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( camelCase ); - } else { - key = camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var documentElement = document.documentElement; - - - - var isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ); - }, - composed = { composed: true }; - - // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only - // Check attachment across shadow DOM boundaries when possible (gh-3504) - // Support: iOS 10.0-10.2 only - // Early iOS 10 versions support `attachShadow` but not `getRootNode`, - // leading to errors. We need to check for `getRootNode`. - if ( documentElement.getRootNode ) { - isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ) || - elem.getRootNode( composed ) === elem.ownerDocument; - }; - } -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - isAttached( elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, scale, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = elem.nodeType && - ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Support: Firefox <=54 - // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) - initial = initial / 2; - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - while ( maxIterations-- ) { - - // Evaluate and update our best guess (doubling guesses that zero out). - // Finish if the scale equals or crosses 1 (making the old*new product non-positive). - jQuery.style( elem, prop, initialInUnit + unit ); - if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { - maxIterations = 0; - } - initialInUnit = initialInUnit / scale; - - } - - initialInUnit = initialInUnit * 2; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); - -var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); - - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; - - // Support: IE <=9 only - // IE <=9 replaces "; - support.option = !!div.lastChild; -} )(); - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
" ], - col: [ 2, "", "
" ], - tr: [ 2, "", "
" ], - td: [ 3, "", "
" ], - - _default: [ 0, "", "" ] -}; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - -// Support: IE <=9 only -if ( !support.option ) { - wrapMap.optgroup = wrapMap.option = [ 1, "" ]; -} - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, attached, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( toType( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - attached = isAttached( elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( attached ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 - 11+ -// focus() and blur() are asynchronous, except when they are no-op. -// So expect focus to be synchronous when the element is already active, -// and blur to be synchronous when the element is not already active. -// (focus and blur are always synchronous in other supported browsers, -// this just defines when we can count on it). -function expectSync( elem, type ) { - return ( elem === safeActiveElement() ) === ( type === "focus" ); -} - -// Support: IE <=9 only -// Accessing document.activeElement can throw unexpectedly -// https://bugs.jquery.com/ticket/13393 -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Only attach events to objects that accept data - if ( !acceptData( elem ) ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = Object.create( null ); - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - - // Make a writable jQuery.Event from the native event object - event = jQuery.event.fix( nativeEvent ), - - handlers = ( - dataPriv.get( this, "events" ) || Object.create( null ) - )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // If the event is namespaced, then each handler is only invoked if it is - // specially universal or its namespaces are a superset of the event's. - if ( !event.rnamespace || handleObj.namespace === false || - event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - click: { - - // Utilize native event to ensure correct state for checkable inputs - setup: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Claim the first handler - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - // dataPriv.set( el, "click", ... ) - leverageNative( el, "click", returnTrue ); - } - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Force setup before triggering a click - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - leverageNative( el, "click" ); - } - - // Return non-false to allow normal event-path propagation - return true; - }, - - // For cross-browser consistency, suppress native .click() on links - // Also prevent it if we're currently inside a leveraged native-event stack - _default: function( event ) { - var target = event.target; - return rcheckableType.test( target.type ) && - target.click && nodeName( target, "input" ) && - dataPriv.get( target, "click" ) || - nodeName( target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -// Ensure the presence of an event listener that handles manually-triggered -// synthetic events by interrupting progress until reinvoked in response to -// *native* events that it fires directly, ensuring that state changes have -// already occurred before other listeners are invoked. -function leverageNative( el, type, expectSync ) { - - // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add - if ( !expectSync ) { - if ( dataPriv.get( el, type ) === undefined ) { - jQuery.event.add( el, type, returnTrue ); - } - return; - } - - // Register the controller as a special universal handler for all event namespaces - dataPriv.set( el, type, false ); - jQuery.event.add( el, type, { - namespace: false, - handler: function( event ) { - var notAsync, result, - saved = dataPriv.get( this, type ); - - if ( ( event.isTrigger & 1 ) && this[ type ] ) { - - // Interrupt processing of the outer synthetic .trigger()ed event - // Saved data should be false in such cases, but might be a leftover capture object - // from an async native handler (gh-4350) - if ( !saved.length ) { - - // Store arguments for use when handling the inner native event - // There will always be at least one argument (an event object), so this array - // will not be confused with a leftover capture object. - saved = slice.call( arguments ); - dataPriv.set( this, type, saved ); - - // Trigger the native event and capture its result - // Support: IE <=9 - 11+ - // focus() and blur() are asynchronous - notAsync = expectSync( this, type ); - this[ type ](); - result = dataPriv.get( this, type ); - if ( saved !== result || notAsync ) { - dataPriv.set( this, type, false ); - } else { - result = {}; - } - if ( saved !== result ) { - - // Cancel the outer synthetic event - event.stopImmediatePropagation(); - event.preventDefault(); - - // Support: Chrome 86+ - // In Chrome, if an element having a focusout handler is blurred by - // clicking outside of it, it invokes the handler synchronously. If - // that handler calls `.remove()` on the element, the data is cleared, - // leaving `result` undefined. We need to guard against this. - return result && result.value; - } - - // If this is an inner synthetic event for an event with a bubbling surrogate - // (focus or blur), assume that the surrogate already propagated from triggering the - // native event and prevent that from happening again here. - // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the - // bubbling surrogate propagates *after* the non-bubbling base), but that seems - // less bad than duplication. - } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { - event.stopPropagation(); - } - - // If this is a native event triggered above, everything is now in order - // Fire an inner synthetic event with the original arguments - } else if ( saved.length ) { - - // ...and capture the result - dataPriv.set( this, type, { - value: jQuery.event.trigger( - - // Support: IE <=9 - 11+ - // Extend with the prototype to reset the above stopImmediatePropagation() - jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), - saved.slice( 1 ), - this - ) - } ); - - // Abort handling of the native event - event.stopImmediatePropagation(); - } - } - } ); -} - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || Date.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - code: true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - which: true -}, jQuery.event.addProp ); - -jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { - jQuery.event.special[ type ] = { - - // Utilize native event if possible so blur/focus sequence is correct - setup: function() { - - // Claim the first handler - // dataPriv.set( this, "focus", ... ) - // dataPriv.set( this, "blur", ... ) - leverageNative( this, type, expectSync ); - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function() { - - // Force setup before trigger - leverageNative( this, type ); - - // Return non-false to allow normal event-path propagation - return true; - }, - - // Suppress native focus or blur as it's already being fired - // in leverageNative. - _default: function() { - return true; - }, - - delegateType: delegateType - }; -} ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - // Support: IE <=10 - 11, Edge 12 - 13 only - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( elem ).children( "tbody" )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { - elem.type = elem.type.slice( 5 ); - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.get( src ); - events = pdataOld.events; - - if ( events ) { - dataPriv.remove( dest, "handle events" ); - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = flat( args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - valueIsFunction = isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( valueIsFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( valueIsFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl && !node.noModule ) { - jQuery._evalUrl( node.src, { - nonce: node.nonce || node.getAttribute( "nonce" ) - }, doc ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && isAttached( node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html; - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = isAttached( elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - -var swap = function( elem, options, callback ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.call( elem ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - -var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - container.style.cssText = "position:absolute;left:-11111px;width:60px;" + - "margin-top:1px;padding:0;border:0"; - div.style.cssText = - "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + - "margin:auto;border:1px;padding:1px;" + - "width:60%;top:1%"; - documentElement.appendChild( container ).appendChild( div ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; - - // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 - // Some styles come back with percentage values, even though they shouldn't - div.style.right = "60%"; - pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; - - // Support: IE 9 - 11 only - // Detect misreporting of content dimensions for box-sizing:border-box elements - boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; - - // Support: IE 9 only - // Detect overflow:scroll screwiness (gh-3699) - // Support: Chrome <=64 - // Don't get tricked when zoom affects offsetWidth (gh-4029) - div.style.position = "absolute"; - scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - function roundPixelMeasures( measure ) { - return Math.round( parseFloat( measure ) ); - } - - var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, - reliableTrDimensionsVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - jQuery.extend( support, { - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelBoxStyles: function() { - computeStyleTests(); - return pixelBoxStylesVal; - }, - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - }, - scrollboxSize: function() { - computeStyleTests(); - return scrollboxSizeVal; - }, - - // Support: IE 9 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Behavior in IE 9 is more subtle than in newer versions & it passes - // some versions of this test; make sure not to make it pass there! - // - // Support: Firefox 70+ - // Only Firefox includes border widths - // in computed dimensions. (gh-4529) - reliableTrDimensions: function() { - var table, tr, trChild, trStyle; - if ( reliableTrDimensionsVal == null ) { - table = document.createElement( "table" ); - tr = document.createElement( "tr" ); - trChild = document.createElement( "div" ); - - table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; - tr.style.cssText = "border:1px solid"; - - // Support: Chrome 86+ - // Height set through cssText does not get applied. - // Computed height then comes back as 0. - tr.style.height = "1px"; - trChild.style.height = "9px"; - - // Support: Android 8 Chrome 86+ - // In our bodyBackground.html iframe, - // display for all div elements is set to "inline", - // which causes a problem only in Android 8 Chrome 86. - // Ensuring the div is display: block - // gets around this issue. - trChild.style.display = "block"; - - documentElement - .appendChild( table ) - .appendChild( tr ) - .appendChild( trChild ); - - trStyle = window.getComputedStyle( tr ); - reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + - parseInt( trStyle.borderTopWidth, 10 ) + - parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; - - documentElement.removeChild( table ); - } - return reliableTrDimensionsVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !isAttached( elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style, - vendorProps = {}; - -// Return a vendor-prefixed property or undefined -function vendorPropName( name ) { - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a potentially-mapped jQuery.cssProps or vendor prefixed property -function finalPropName( name ) { - var final = jQuery.cssProps[ name ] || vendorProps[ name ]; - - if ( final ) { - return final; - } - if ( name in emptyStyle ) { - return name; - } - return vendorProps[ name ] = vendorPropName( name ) || name; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }; - -function setPositiveNumber( _elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { - var i = dimension === "width" ? 1 : 0, - extra = 0, - delta = 0; - - // Adjustment may not be necessary - if ( box === ( isBorderBox ? "border" : "content" ) ) { - return 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin - if ( box === "margin" ) { - delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); - } - - // If we get here with a content-box, we're seeking "padding" or "border" or "margin" - if ( !isBorderBox ) { - - // Add padding - delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // For "border" or "margin", add border - if ( box !== "padding" ) { - delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - - // But still keep track of it otherwise - } else { - extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - - // If we get here with a border-box (content + padding + border), we're seeking "content" or - // "padding" or "margin" - } else { - - // For "content", subtract padding - if ( box === "content" ) { - delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // For "content" or "padding", subtract border - if ( box !== "margin" ) { - delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - // Account for positive content-box scroll gutter when requested by providing computedVal - if ( !isBorderBox && computedVal >= 0 ) { - - // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border - // Assuming integer scroll gutter, subtract the rest and round down - delta += Math.max( 0, Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - computedVal - - delta - - extra - - 0.5 - - // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter - // Use an explicit zero to avoid NaN (gh-3964) - ) ) || 0; - } - - return delta; -} - -function getWidthOrHeight( elem, dimension, extra ) { - - // Start with computed style - var styles = getStyles( elem ), - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). - // Fake content-box until we know it's needed to know the true value. - boxSizingNeeded = !support.boxSizingReliable() || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - valueIsBorderBox = isBorderBox, - - val = curCSS( elem, dimension, styles ), - offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); - - // Support: Firefox <=54 - // Return a confounding non-pixel value or feign ignorance, as appropriate. - if ( rnumnonpx.test( val ) ) { - if ( !extra ) { - return val; - } - val = "auto"; - } - - - // Support: IE 9 - 11 only - // Use offsetWidth/offsetHeight for when box sizing is unreliable. - // In those cases, the computed value can be trusted to be border-box. - if ( ( !support.boxSizingReliable() && isBorderBox || - - // Support: IE 10 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Interestingly, in some cases IE 9 doesn't suffer from this issue. - !support.reliableTrDimensions() && nodeName( elem, "tr" ) || - - // Fall back to offsetWidth/offsetHeight when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - val === "auto" || - - // Support: Android <=4.1 - 4.3 only - // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) - !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && - - // Make sure the element is visible & connected - elem.getClientRects().length ) { - - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Where available, offsetWidth/offsetHeight approximate border box dimensions. - // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the - // retrieved value as a content box dimension. - valueIsBorderBox = offsetProp in elem; - if ( valueIsBorderBox ) { - val = elem[ offsetProp ]; - } - } - - // Normalize "" and auto - val = parseFloat( val ) || 0; - - // Adjust for the element's box model - return ( val + - boxModelAdjustment( - elem, - dimension, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles, - - // Provide the current computed size to request scroll gutter calculation (gh-3589) - val - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "gridArea": true, - "gridColumn": true, - "gridColumnEnd": true, - "gridColumnStart": true, - "gridRow": true, - "gridRowEnd": true, - "gridRowStart": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: {}, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append - // "px" to a few hardcoded values. - if ( type === "number" && !isCustomProp ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( _i, dimension ) { - jQuery.cssHooks[ dimension ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, dimension, extra ); - } ) : - getWidthOrHeight( elem, dimension, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = getStyles( elem ), - - // Only read styles.position if the test has a chance to fail - // to avoid forcing a reflow. - scrollboxSizeBuggy = !support.scrollboxSize() && - styles.position === "absolute", - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) - boxSizingNeeded = scrollboxSizeBuggy || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - subtract = extra ? - boxModelAdjustment( - elem, - dimension, - extra, - isBorderBox, - styles - ) : - 0; - - // Account for unreliable border-box dimensions by comparing offset* to computed and - // faking a content-box to get border and padding (gh-3699) - if ( isBorderBox && scrollboxSizeBuggy ) { - subtract -= Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - parseFloat( styles[ dimension ] ) - - boxModelAdjustment( elem, dimension, "border", false, styles ) - - 0.5 - ); - } - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ dimension ] = value; - value = jQuery.css( elem, dimension ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( prefix !== "margin" ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && ( - jQuery.cssHooks[ tween.prop ] || - tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = Date.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 15 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY and Edge just mirrors - // the overflowX value there. - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - result.stop.bind( result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = Date.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -function classesToArray( value ) { - if ( Array.isArray( value ) ) { - return value; - } - if ( typeof value === "string" ) { - return value.match( rnothtmlwhite ) || []; - } - return []; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value, - isValidValue = type === "string" || Array.isArray( value ); - - if ( typeof stateVal === "boolean" && isValidValue ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( isValidValue ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = classesToArray( value ); - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, valueIsFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - valueIsFunction = isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( valueIsFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -support.focusin = "onfocusin" in window; - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, - stopPropagationCallback = function( e ) { - e.stopPropagation(); - }; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = lastElement = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - lastElement = cur; - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - - if ( event.isPropagationStopped() ) { - lastElement.addEventListener( type, stopPropagationCallback ); - } - - elem[ type ](); - - if ( event.isPropagationStopped() ) { - lastElement.removeEventListener( type, stopPropagationCallback ); - } - - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - - // Handle: regular nodes (via `this.ownerDocument`), window - // (via `this.document`) & document (via `this`). - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = { guid: Date.now() }; - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml, parserErrorElem; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) {} - - parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; - if ( !xml || parserErrorElem ) { - jQuery.error( "Invalid XML: " + ( - parserErrorElem ? - jQuery.map( parserErrorElem.childNodes, function( el ) { - return el.textContent; - } ).join( "\n" ) : - data - ) ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && toType( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - if ( a == null ) { - return ""; - } - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ).filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ).map( function( _i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - -originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() + " " ] = - ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) - .concat( match[ 2 ] ); - } - } - match = responseHeaders[ key.toLowerCase() + " " ]; - } - return match == null ? null : match.join( ", " ); - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 15 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available and should be processed, append data to url - if ( s.data && ( s.processData || typeof s.data === "string" ) ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + - uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Use a noop converter for missing script but not if jsonp - if ( !isSuccess && - jQuery.inArray( "script", s.dataTypes ) > -1 && - jQuery.inArray( "json", s.dataTypes ) < 0 ) { - s.converters[ "text script" ] = function() {}; - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( _i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - -jQuery.ajaxPrefilter( function( s ) { - var i; - for ( i in s.headers ) { - if ( i.toLowerCase() === "content-type" ) { - s.contentType = s.headers[ i ] || ""; - } - } -} ); - - -jQuery._evalUrl = function( url, options, doc ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - - // Only evaluate the response if it is successful (gh-4126) - // dataFilter is not invoked for failure responses, so using it instead - // of the default converter is kludgy but it works. - converters: { - "text script": function() {} - }, - dataFilter: function( response ) { - jQuery.globalEval( response, options, doc ); - } - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var htmlIsFunction = isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( htmlIsFunction ? 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DefaultType(){return{}}static get NAME(){throw new Error('You have to implement the static method "NAME", for each component!')}_getConfig(t){return t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t}_mergeConfigObj(t,e){const i=Mt(e)?be.getDataAttribute(e,"config"):{};return{...this.constructor.Default,..."object"==typeof i?i:{},...Mt(e)?be.getDataAttributes(e):{},..."object"==typeof t?t:{}}}_typeCheckConfig(t,e=this.constructor.DefaultType){for(const n of Object.keys(e)){const s=e[n],o=t[n],r=Mt(o)?"element":null==(i=o)?`${i}`:Object.prototype.toString.call(i).match(/\s([a-z]+)/i)[1].toLowerCase();if(!new RegExp(s).test(r))throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option "${n}" provided type "${r}" but expected type "${s}".`)}var i}}class ye extends ve{constructor(t,e){super(),(t=Ht(t))&&(this._element=t,this._config=this._getConfig(e),ge.set(this._element,this.constructor.DATA_KEY,this))}dispose(){ge.remove(this._element,this.constructor.DATA_KEY),de.off(this._element,this.constructor.EVENT_KEY);for(const t of Object.getOwnPropertyNames(this))this[t]=null}_queueCallback(t,e,i=!0){Yt(t,e,i)}_getConfig(t){return t=this._mergeConfigObj(t,this._element),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}static getInstance(t){return ge.get(Ht(t),this.DATA_KEY)}static getOrCreateInstance(t,e={}){return this.getInstance(t)||new this(t,"object"==typeof e?e:null)}static get VERSION(){return"5.2.3"}static get DATA_KEY(){return`bs.${this.NAME}`}static get EVENT_KEY(){return`.${this.DATA_KEY}`}static eventName(t){return`${t}${this.EVENT_KEY}`}}const we=(t,e="hide")=>{const i=`click.dismiss${t.EVENT_KEY}`,n=t.NAME;de.on(document,i,`[data-bs-dismiss="${n}"]`,(function(i){if(["A","AREA"].includes(this.tagName)&&i.preventDefault(),Ft(this))return;const s=Pt(this)||this.closest(`.${n}`);t.getOrCreateInstance(s)[e]()}))},Ae=".bs.alert",Ee=`close${Ae}`,Ce=`closed${Ae}`;class Te extends ye{static get NAME(){return"alert"}close(){if(de.trigger(this._element,Ee).defaultPrevented)return;this._element.classList.remove("show");const t=this._element.classList.contains("fade");this._queueCallback((()=>this._destroyElement()),this._element,t)}_destroyElement(){this._element.remove(),de.trigger(this._element,Ce),this.dispose()}static jQueryInterface(t){return this.each((function(){const e=Te.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}we(Te,"close"),Qt(Te);const Oe='[data-bs-toggle="button"]';class xe extends ye{static get NAME(){return"button"}toggle(){this._element.setAttribute("aria-pressed",this._element.classList.toggle("active"))}static jQueryInterface(t){return this.each((function(){const e=xe.getOrCreateInstance(this);"toggle"===t&&e[t]()}))}}de.on(document,"click.bs.button.data-api",Oe,(t=>{t.preventDefault();const e=t.target.closest(Oe);xe.getOrCreateInstance(e).toggle()})),Qt(xe);const ke={find:(t,e=document.documentElement)=>[].concat(...Element.prototype.querySelectorAll.call(e,t)),findOne:(t,e=document.documentElement)=>Element.prototype.querySelector.call(e,t),children:(t,e)=>[].concat(...t.children).filter((t=>t.matches(e))),parents(t,e){const i=[];let n=t.parentNode.closest(e);for(;n;)i.push(n),n=n.parentNode.closest(e);return i},prev(t,e){let i=t.previousElementSibling;for(;i;){if(i.matches(e))return[i];i=i.previousElementSibling}return[]},next(t,e){let i=t.nextElementSibling;for(;i;){if(i.matches(e))return[i];i=i.nextElementSibling}return[]},focusableChildren(t){const e=["a","button","input","textarea","select","details","[tabindex]",'[contenteditable="true"]'].map((t=>`${t}:not([tabindex^="-"])`)).join(",");return this.find(e,t).filter((t=>!Ft(t)&&Wt(t)))}},Le=".bs.swipe",De=`touchstart${Le}`,$e=`touchmove${Le}`,Se=`touchend${Le}`,Ie=`pointerdown${Le}`,Ne=`pointerup${Le}`,Pe={endCallback:null,leftCallback:null,rightCallback:null},je={endCallback:"(function|null)",leftCallback:"(function|null)",rightCallback:"(function|null)"};class Me extends ve{constructor(t,e){super(),this._element=t,t&&Me.isSupported()&&(this._config=this._getConfig(e),this._deltaX=0,this._supportPointerEvents=Boolean(window.PointerEvent),this._initEvents())}static get Default(){return Pe}static get DefaultType(){return je}static get NAME(){return"swipe"}dispose(){de.off(this._element,Le)}_start(t){this._supportPointerEvents?this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX):this._deltaX=t.touches[0].clientX}_end(t){this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX-this._deltaX),this._handleSwipe(),Xt(this._config.endCallback)}_move(t){this._deltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this._deltaX}_handleSwipe(){const t=Math.abs(this._deltaX);if(t<=40)return;const e=t/this._deltaX;this._deltaX=0,e&&Xt(e>0?this._config.rightCallback:this._config.leftCallback)}_initEvents(){this._supportPointerEvents?(de.on(this._element,Ie,(t=>this._start(t))),de.on(this._element,Ne,(t=>this._end(t))),this._element.classList.add("pointer-event")):(de.on(this._element,De,(t=>this._start(t))),de.on(this._element,$e,(t=>this._move(t))),de.on(this._element,Se,(t=>this._end(t))))}_eventIsPointerPenTouch(t){return this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static isSupported(){return"ontouchstart"in document.documentElement||navigator.maxTouchPoints>0}}const He=".bs.carousel",We=".data-api",Fe="next",Be="prev",ze="left",qe="right",Re=`slide${He}`,Ve=`slid${He}`,Ke=`keydown${He}`,Qe=`mouseenter${He}`,Xe=`mouseleave${He}`,Ye=`dragstart${He}`,Ue=`load${He}${We}`,Ge=`click${He}${We}`,Je="carousel",Ze="active",ti=".active",ei=".carousel-item",ii=ti+ei,ni={ArrowLeft:qe,ArrowRight:ze},si={interval:5e3,keyboard:!0,pause:"hover",ride:!1,touch:!0,wrap:!0},oi={interval:"(number|boolean)",keyboard:"boolean",pause:"(string|boolean)",ride:"(boolean|string)",touch:"boolean",wrap:"boolean"};class ri extends ye{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=ke.findOne(".carousel-indicators",this._element),this._addEventListeners(),this._config.ride===Je&&this.cycle()}static get Default(){return si}static get DefaultType(){return oi}static get NAME(){return"carousel"}next(){this._slide(Fe)}nextWhenVisible(){!document.hidden&&Wt(this._element)&&this.next()}prev(){this._slide(Be)}pause(){this._isSliding&&jt(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?de.one(this._element,Ve,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void de.one(this._element,Ve,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?Fe:Be;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&de.on(this._element,Ke,(t=>this._keydown(t))),"hover"===this._config.pause&&(de.on(this._element,Qe,(()=>this.pause())),de.on(this._element,Xe,(()=>this._maybeEnableCycle()))),this._config.touch&&Me.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of ke.find(".carousel-item img",this._element))de.on(t,Ye,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ze)),rightCallback:()=>this._slide(this._directionToOrder(qe)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new Me(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=ni[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=ke.findOne(ti,this._indicatorsElement);e.classList.remove(Ze),e.removeAttribute("aria-current");const i=ke.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(Ze),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===Fe,s=e||Ut(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>de.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(Re).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Ze),i.classList.remove(Ze,c,l),this._isSliding=!1,r(Ve)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return ke.findOne(ii,this._element)}_getItems(){return ke.find(ei,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Kt()?t===ze?Be:Fe:t===ze?Fe:Be}_orderToDirection(t){return Kt()?t===Be?ze:qe:t===Be?qe:ze}static jQueryInterface(t){return this.each((function(){const e=ri.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}de.on(document,Ge,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=Pt(this);if(!e||!e.classList.contains(Je))return;t.preventDefault();const i=ri.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===be.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),de.on(window,Ue,(()=>{const t=ke.find('[data-bs-ride="carousel"]');for(const e of t)ri.getOrCreateInstance(e)})),Qt(ri);const ai=".bs.collapse",li=`show${ai}`,ci=`shown${ai}`,hi=`hide${ai}`,ui=`hidden${ai}`,di=`click${ai}.data-api`,fi="show",pi="collapse",gi="collapsing",mi=`:scope .${pi} .${pi}`,_i='[data-bs-toggle="collapse"]',bi={parent:null,toggle:!0},vi={parent:"(null|element)",toggle:"boolean"};class yi extends ye{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=ke.find(_i);for(const t of i){const e=Nt(t),i=ke.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return bi}static get DefaultType(){return vi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>yi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(de.trigger(this._element,li).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(pi),this._element.classList.add(gi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi,fi),this._element.style[e]="",de.trigger(this._element,ci)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(de.trigger(this._element,hi).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(gi),this._element.classList.remove(pi,fi);for(const t of this._triggerArray){const e=Pt(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi),de.trigger(this._element,ui)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(fi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(_i);for(const e of t){const t=Pt(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=ke.find(mi,this._config.parent);return ke.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=yi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}de.on(document,di,_i,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=Nt(this),i=ke.find(e);for(const t of i)yi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(yi);const wi="dropdown",Ai=".bs.dropdown",Ei=".data-api",Ci="ArrowUp",Ti="ArrowDown",Oi=`hide${Ai}`,xi=`hidden${Ai}`,ki=`show${Ai}`,Li=`shown${Ai}`,Di=`click${Ai}${Ei}`,$i=`keydown${Ai}${Ei}`,Si=`keyup${Ai}${Ei}`,Ii="show",Ni='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Pi=`${Ni}.${Ii}`,ji=".dropdown-menu",Mi=Kt()?"top-end":"top-start",Hi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",Fi=Kt()?"bottom-start":"bottom-end",Bi=Kt()?"left-start":"right-start",zi=Kt()?"right-start":"left-start",qi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Vi extends ye{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ke.next(this._element,ji)[0]||ke.prev(this._element,ji)[0]||ke.findOne(ji,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return qi}static get DefaultType(){return Ri}static get NAME(){return wi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Ft(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!de.trigger(this._element,ki,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Ii),this._element.classList.add(Ii),de.trigger(this._element,Li,t)}}hide(){if(Ft(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!de.trigger(this._element,Oi,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Ii),this._element.classList.remove(Ii),this._element.setAttribute("aria-expanded","false"),be.removeDataAttribute(this._menu,"popper"),de.trigger(this._element,xi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Mt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${wi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Mt(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Ii)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Bi;if(t.classList.contains("dropstart"))return zi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Hi:Mi:e?Fi:Wi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(be.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=ke.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Wt(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Vi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ke.find(Pi);for(const i of e){const e=Vi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ci,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ni)?this:ke.prev(this,Ni)[0]||ke.next(this,Ni)[0]||ke.findOne(Ni,t.delegateTarget.parentNode),o=Vi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}de.on(document,$i,Ni,Vi.dataApiKeydownHandler),de.on(document,$i,ji,Vi.dataApiKeydownHandler),de.on(document,Di,Vi.clearMenus),de.on(document,Si,Vi.clearMenus),de.on(document,Di,Ni,(function(t){t.preventDefault(),Vi.getOrCreateInstance(this).toggle()})),Qt(Vi);const Ki=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",Qi=".sticky-top",Xi="padding-right",Yi="margin-right";class Ui{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,Xi,(e=>e+t)),this._setElementAttributes(Ki,Xi,(e=>e+t)),this._setElementAttributes(Qi,Yi,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,Xi),this._resetElementAttributes(Ki,Xi),this._resetElementAttributes(Qi,Yi)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&be.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=be.getDataAttribute(t,e);null!==i?(be.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Mt(t))e(t);else for(const i of ke.find(t,this._element))e(i)}}const Gi="backdrop",Ji="show",Zi=`mousedown.bs.${Gi}`,tn={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},en={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class nn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return tn}static get DefaultType(){return en}static get NAME(){return Gi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Ji),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ji),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(de.off(this._element,Zi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),de.on(t,Zi,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Yt(t,this._getElement(),this._config.isAnimated)}}const sn=".bs.focustrap",on=`focusin${sn}`,rn=`keydown.tab${sn}`,an="backward",ln={autofocus:!0,trapElement:null},cn={autofocus:"boolean",trapElement:"element"};class hn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return ln}static get DefaultType(){return cn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),de.off(document,sn),de.on(document,on,(t=>this._handleFocusin(t))),de.on(document,rn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,de.off(document,sn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ke.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===an?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?an:"forward")}}const un=".bs.modal",dn=`hide${un}`,fn=`hidePrevented${un}`,pn=`hidden${un}`,gn=`show${un}`,mn=`shown${un}`,_n=`resize${un}`,bn=`click.dismiss${un}`,vn=`mousedown.dismiss${un}`,yn=`keydown.dismiss${un}`,wn=`click${un}.data-api`,An="modal-open",En="show",Cn="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},On={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class xn extends ye{constructor(t,e){super(t,e),this._dialog=ke.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Ui,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return On}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||de.trigger(this._element,gn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(An),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(de.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){for(const t of[window,this._dialog])de.off(t,un);this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new nn({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ke.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,de.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){de.on(this._element,yn,(t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()})),de.on(window,_n,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),de.on(this._element,vn,(t=>{de.one(this._element,bn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(An),this._resetAdjustments(),this._scrollBar.reset(),de.trigger(this._element,pn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(de.trigger(this._element,fn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Cn)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Cn),this._queueCallback((()=>{this._element.classList.remove(Cn),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=xn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}de.on(document,wn,'[data-bs-toggle="modal"]',(function(t){const e=Pt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),de.one(e,gn,(t=>{t.defaultPrevented||de.one(e,pn,(()=>{Wt(this)&&this.focus()}))}));const i=ke.findOne(".modal.show");i&&xn.getInstance(i).hide(),xn.getOrCreateInstance(e).toggle(this)})),we(xn),Qt(xn);const kn=".bs.offcanvas",Ln=".data-api",Dn=`load${kn}${Ln}`,$n="show",Sn="showing",In="hiding",Nn=".offcanvas.show",Pn=`show${kn}`,jn=`shown${kn}`,Mn=`hide${kn}`,Hn=`hidePrevented${kn}`,Wn=`hidden${kn}`,Fn=`resize${kn}`,Bn=`click${kn}${Ln}`,zn=`keydown.dismiss${kn}`,qn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Vn extends ye{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return qn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||de.trigger(this._element,Pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Ui).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Sn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add($n),this._element.classList.remove(Sn),de.trigger(this._element,jn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(de.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(In),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove($n,In),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Ui).reset(),de.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new nn({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():de.trigger(this._element,Hn)}:null})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_addEventListeners(){de.on(this._element,zn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():de.trigger(this._element,Hn))}))}static jQueryInterface(t){return this.each((function(){const e=Vn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}de.on(document,Bn,'[data-bs-toggle="offcanvas"]',(function(t){const e=Pt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Ft(this))return;de.one(e,Wn,(()=>{Wt(this)&&this.focus()}));const i=ke.findOne(Nn);i&&i!==e&&Vn.getInstance(i).hide(),Vn.getOrCreateInstance(e).toggle(this)})),de.on(window,Dn,(()=>{for(const t of ke.find(Nn))Vn.getOrCreateInstance(t).show()})),de.on(window,Fn,(()=>{for(const t of ke.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Vn.getOrCreateInstance(t).hide()})),we(Vn),Qt(Vn);const Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Xn=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Yn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)||Xn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Un={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Gn={allowList:Un,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Jn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Zn={entry:"(string|element|function|null)",selector:"(string|element)"};class ts extends ve{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Gn}static get DefaultType(){return Jn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Zn)}_setContent(t,e,i){const n=ke.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Mt(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Yn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const es=new Set(["sanitize","allowList","sanitizeFn"]),is="fade",ns="show",ss=".modal",os="hide.bs.modal",rs="hover",as="focus",ls={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},cs={allowList:Un,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},hs={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class us extends ye{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return cs}static get DefaultType(){return hs}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),de.off(this._element.closest(ss),os,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=de.trigger(this._element,this.constructor.eventName("show")),e=(Bt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),de.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._queueCallback((()=>{de.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!de.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[as]=!1,this._activeTrigger[rs]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),de.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(is,ns),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(is),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new ts({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(is)}_isShown(){return this.tip&&this.tip.classList.contains(ns)}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=ls[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)de.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===rs?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===rs?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");de.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?as:rs]=!0,e._enter()})),de.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?as:rs]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},de.on(this._element.closest(ss),os,this._hideModalHandler)}_fixTitle(){const 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t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(us);const ds={...us.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},fs={...us.DefaultType,content:"(null|string|element|function)"};class ps extends us{static get Default(){return ds}static get DefaultType(){return fs}static get 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= 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (process.env.NODE_ENV !== \"production\") {\n if (!isHTMLElement(arrowElement)) {\n console.error(['Popper: \"arrow\" element must be an HTMLElement (not an SVGElement).', 'To use an SVG arrow, wrap it in an HTMLElement that will be used as', 'the arrow.'].join(' '));\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: \"arrow\" modifier\\'s `element` must be a child of the popper', 'element.'].join(' '));\n }\n\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n\n if (process.env.NODE_ENV !== \"production\") {\n var transitionProperty = getComputedStyle(state.elements.popper).transitionProperty || '';\n\n if (adaptive && ['transform', 'top', 'right', 'bottom', 'left'].some(function (property) {\n return transitionProperty.indexOf(property) >= 0;\n })) {\n console.warn(['Popper: Detected CSS transitions on at least one of the following', 'CSS properties: \"transform\", \"top\", \"right\", \"bottom\", \"left\".', '\\n\\n', 'Disable the \"computeStyles\" modifier\\'s `adaptive` option to allow', 'for smooth transitions, or remove these properties from the CSS', 'transition declaration on the popper element if only transitioning', 'opacity or background-color for example.', '\\n\\n', 'We recommend using the popper element as a wrapper around an inner', 'element that can have any CSS property transitioned for animations.'].join(' '));\n }\n }\n\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: The `allowedAutoPlacements` option did not allow any', 'placements. Ensure the `placement` option matches the variation', 'of the allowed placements.', 'For example, \"auto\" cannot be used to allow \"bottom-start\".', 'Use \"auto-start\" instead.'].join(' '));\n }\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport getComputedStyle from \"./dom-utils/getComputedStyle.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport validateModifiers from \"./utils/validateModifiers.js\";\nimport uniqueBy from \"./utils/uniqueBy.js\";\nimport getBasePlacement from \"./utils/getBasePlacement.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nimport { auto } from \"./enums.js\";\nvar INVALID_ELEMENT_ERROR = 'Popper: Invalid reference or popper argument provided. They must be either a DOM element or virtual element.';\nvar INFINITE_LOOP_ERROR = 'Popper: An infinite loop in the modifiers cycle has been detected! The cycle has been interrupted to prevent a browser crash.';\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n }); // Validate the provided modifiers so that the consumer will get warned\n // if one of the modifiers is invalid for any reason\n\n if (process.env.NODE_ENV !== \"production\") {\n var modifiers = uniqueBy([].concat(orderedModifiers, state.options.modifiers), function (_ref) {\n var name = _ref.name;\n return name;\n });\n validateModifiers(modifiers);\n\n if (getBasePlacement(state.options.placement) === auto) {\n var flipModifier = state.orderedModifiers.find(function (_ref2) {\n var name = _ref2.name;\n return name === 'flip';\n });\n\n if (!flipModifier) {\n console.error(['Popper: \"auto\" placements require the \"flip\" modifier be', 'present and enabled to work.'].join(' '));\n }\n }\n\n var _getComputedStyle = getComputedStyle(popper),\n marginTop = _getComputedStyle.marginTop,\n marginRight = _getComputedStyle.marginRight,\n marginBottom = _getComputedStyle.marginBottom,\n marginLeft = _getComputedStyle.marginLeft; // We no longer take into account `margins` on the popper, and it can\n // cause bugs with positioning, so we'll warn the consumer\n\n\n if ([marginTop, marginRight, marginBottom, marginLeft].some(function (margin) {\n return parseFloat(margin);\n })) {\n console.warn(['Popper: CSS \"margin\" styles cannot be used to apply padding', 'between the popper and its reference element or boundary.', 'To replicate margin, use the `offset` modifier, as well as', 'the `padding` option in the `preventOverflow` and `flip`', 'modifiers.'].join(' '));\n }\n }\n\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n var __debug_loops__ = 0;\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (process.env.NODE_ENV !== \"production\") {\n __debug_loops__ += 1;\n\n if (__debug_loops__ > 100) {\n console.error(INFINITE_LOOP_ERROR);\n break;\n }\n }\n\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref3) {\n var name = _ref3.name,\n _ref3$options = _ref3.options,\n options = _ref3$options === void 0 ? {} : _ref3$options,\n effect = _ref3.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n * Bootstrap v5.2.3 (https://getbootstrap.com/)\n * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend'; // Shout-out Angus Croll (https://goo.gl/pxwQGp)\n\nconst toType = object => {\n if (object === null || object === undefined) {\n return `${object}`;\n }\n\n return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n/**\n * Public Util API\n */\n\n\nconst getUID = prefix => {\n do {\n prefix += Math.floor(Math.random() * MAX_UID);\n } while (document.getElementById(prefix));\n\n return prefix;\n};\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target');\n\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href'); // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n\n if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n return null;\n } // Just in case some CMS puts out a full URL with the anchor appended\n\n\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n }\n\n selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n }\n\n return selector;\n};\n\nconst getSelectorFromElement = element => {\n const selector = getSelector(element);\n\n if (selector) {\n return document.querySelector(selector) ? selector : null;\n }\n\n return null;\n};\n\nconst getElementFromSelector = element => {\n const selector = getSelector(element);\n return selector ? document.querySelector(selector) : null;\n};\n\nconst getTransitionDurationFromElement = element => {\n if (!element) {\n return 0;\n } // Get transition-duration of the element\n\n\n let {\n transitionDuration,\n transitionDelay\n } = window.getComputedStyle(element);\n const floatTransitionDuration = Number.parseFloat(transitionDuration);\n const floatTransitionDelay = Number.parseFloat(transitionDelay); // Return 0 if element or transition duration is not found\n\n if (!floatTransitionDuration && !floatTransitionDelay) {\n return 0;\n } // If multiple durations are defined, take the first\n\n\n transitionDuration = transitionDuration.split(',')[0];\n transitionDelay = transitionDelay.split(',')[0];\n return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\n\nconst triggerTransitionEnd = element => {\n element.dispatchEvent(new Event(TRANSITION_END));\n};\n\nconst isElement = object => {\n if (!object || typeof object !== 'object') {\n return false;\n }\n\n if (typeof object.jquery !== 'undefined') {\n object = object[0];\n }\n\n return typeof object.nodeType !== 'undefined';\n};\n\nconst getElement = object => {\n // it's a jQuery object or a node element\n if (isElement(object)) {\n return object.jquery ? object[0] : object;\n }\n\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(object);\n }\n\n return null;\n};\n\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false;\n }\n\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'; // Handle `details` element as its content may falsie appear visible when it is closed\n\n const closedDetails = element.closest('details:not([open])');\n\n if (!closedDetails) {\n return elementIsVisible;\n }\n\n if (closedDetails !== element) {\n const summary = element.closest('summary');\n\n if (summary && summary.parentNode !== closedDetails) {\n return false;\n }\n\n if (summary === null) {\n return false;\n }\n }\n\n return elementIsVisible;\n};\n\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true;\n }\n\n if (element.classList.contains('disabled')) {\n return true;\n }\n\n if (typeof element.disabled !== 'undefined') {\n return element.disabled;\n }\n\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\n\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null;\n } // Can find the shadow root otherwise it'll return the document\n\n\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode();\n return root instanceof ShadowRoot ? root : null;\n }\n\n if (element instanceof ShadowRoot) {\n return element;\n } // when we don't find a shadow root\n\n\n if (!element.parentNode) {\n return null;\n }\n\n return findShadowRoot(element.parentNode);\n};\n\nconst noop = () => {};\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\n\n\nconst reflow = element => {\n element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery;\n }\n\n return null;\n};\n\nconst DOMContentLoadedCallbacks = [];\n\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback();\n }\n });\n }\n\n DOMContentLoadedCallbacks.push(callback);\n } else {\n callback();\n }\n};\n\nconst isRTL = () => document.documentElement.dir === 'rtl';\n\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery();\n /* istanbul ignore if */\n\n if ($) {\n const name = plugin.NAME;\n const JQUERY_NO_CONFLICT = $.fn[name];\n $.fn[name] = plugin.jQueryInterface;\n $.fn[name].Constructor = plugin;\n\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT;\n return plugin.jQueryInterface;\n };\n }\n });\n};\n\nconst execute = callback => {\n if (typeof callback === 'function') {\n callback();\n }\n};\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback);\n return;\n }\n\n const durationPadding = 5;\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n let called = false;\n\n const handler = ({\n target\n }) => {\n if (target !== transitionElement) {\n return;\n }\n\n called = true;\n transitionElement.removeEventListener(TRANSITION_END, handler);\n execute(callback);\n };\n\n transitionElement.addEventListener(TRANSITION_END, handler);\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement);\n }\n }, emulatedDuration);\n};\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\n\n\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length;\n let index = list.indexOf(activeElement); // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n }\n\n index += shouldGetNext ? 1 : -1;\n\n if (isCycleAllowed) {\n index = (index + listLength) % listLength;\n }\n\n return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\n\nlet uidEvent = 1;\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\n\nfunction getElementEvents(element) {\n const uid = makeEventUid(element);\n element.uidEvent = uid;\n eventRegistry[uid] = eventRegistry[uid] || {};\n return eventRegistry[uid];\n}\n\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, {\n delegateTarget: element\n });\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn);\n }\n\n return fn.apply(element, [event]);\n };\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector);\n\n for (let {\n target\n } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue;\n }\n\n hydrateObj(event, {\n delegateTarget: target\n });\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn);\n }\n\n return fn.apply(target, [event]);\n }\n }\n };\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string'; // todo: tooltip passes `false` instead of selector, so we need to check\n\n const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n let typeEvent = getTypeEvent(originalTypeEvent);\n\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent;\n }\n\n return [isDelegated, callable, typeEvent];\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction); // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n return fn.call(this, event);\n }\n };\n };\n\n callable = wrapFunction(callable);\n }\n\n const events = getElementEvents(element);\n const handlers = events[typeEvent] || (events[typeEvent] = {});\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff;\n return;\n }\n\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n fn.delegationSelector = isDelegated ? handler : null;\n fn.callable = callable;\n fn.oneOff = oneOff;\n fn.uidEvent = uid;\n handlers[uid] = fn;\n element.addEventListener(typeEvent, fn, isDelegated);\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector);\n\n if (!fn) {\n return;\n }\n\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n delete events[typeEvent][fn.uidEvent];\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {};\n\n for (const handlerKey of Object.keys(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n const event = storeElementEvent[handlerKey];\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n}\n\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '');\n return customEvents[event] || event;\n}\n\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false);\n },\n\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true);\n },\n\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n const inNamespace = typeEvent !== originalTypeEvent;\n const events = getElementEvents(element);\n const storeElementEvent = events[typeEvent] || {};\n const isNamespace = originalTypeEvent.startsWith('.');\n\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return;\n }\n\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n return;\n }\n\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n }\n }\n\n for (const keyHandlers of Object.keys(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '');\n\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n const event = storeElementEvent[keyHandlers];\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n },\n\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null;\n }\n\n const $ = getjQuery();\n const typeEvent = getTypeEvent(event);\n const inNamespace = event !== typeEvent;\n let jQueryEvent = null;\n let bubbles = true;\n let nativeDispatch = true;\n let defaultPrevented = false;\n\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args);\n $(element).trigger(jQueryEvent);\n bubbles = !jQueryEvent.isPropagationStopped();\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n defaultPrevented = jQueryEvent.isDefaultPrevented();\n }\n\n let evt = new Event(event, {\n bubbles,\n cancelable: true\n });\n evt = hydrateObj(evt, args);\n\n if (defaultPrevented) {\n evt.preventDefault();\n }\n\n if (nativeDispatch) {\n element.dispatchEvent(evt);\n }\n\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault();\n }\n\n return evt;\n }\n\n};\n\nfunction hydrateObj(obj, meta) {\n for (const [key, value] of Object.entries(meta || {})) {\n try {\n obj[key] = value;\n } catch (_unused) {\n Object.defineProperty(obj, key, {\n configurable: true,\n\n get() {\n return value;\n }\n\n });\n }\n }\n\n return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\nconst elementMap = new Map();\nconst Data = {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map());\n }\n\n const instanceMap = elementMap.get(element); // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n return;\n }\n\n instanceMap.set(key, instance);\n },\n\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null;\n }\n\n return null;\n },\n\n remove(element, key) {\n if (!elementMap.has(element)) {\n return;\n }\n\n const instanceMap = elementMap.get(element);\n instanceMap.delete(key); // free up element references if there are no instances left for an element\n\n if (instanceMap.size === 0) {\n elementMap.delete(element);\n }\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nfunction normalizeData(value) {\n if (value === 'true') {\n return true;\n }\n\n if (value === 'false') {\n return false;\n }\n\n if (value === Number(value).toString()) {\n return Number(value);\n }\n\n if (value === '' || value === 'null') {\n return null;\n }\n\n if (typeof value !== 'string') {\n return value;\n }\n\n try {\n return JSON.parse(decodeURIComponent(value));\n } catch (_unused) {\n return value;\n }\n}\n\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\n\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n },\n\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n },\n\n getDataAttributes(element) {\n if (!element) {\n return {};\n }\n\n const attributes = {};\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '');\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n attributes[pureKey] = normalizeData(element.dataset[key]);\n }\n\n return attributes;\n },\n\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {};\n }\n\n static get DefaultType() {\n return {};\n }\n\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!');\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n }\n\n _configAfterMerge(config) {\n return config;\n }\n\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n return { ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n };\n }\n\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const property of Object.keys(configTypes)) {\n const expectedTypes = configTypes[property];\n const value = config[property];\n const valueType = isElement(value) ? 'element' : toType(value);\n\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n }\n }\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst VERSION = '5.2.3';\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super();\n element = getElement(element);\n\n if (!element) {\n return;\n }\n\n this._element = element;\n this._config = this._getConfig(config);\n Data.set(this._element, this.constructor.DATA_KEY, this);\n } // Public\n\n\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY);\n EventHandler.off(this._element, this.constructor.EVENT_KEY);\n\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null;\n }\n }\n\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated);\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n } // Static\n\n\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY);\n }\n\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n }\n\n static get VERSION() {\n return VERSION;\n }\n\n static get DATA_KEY() {\n return `bs.${this.NAME}`;\n }\n\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`;\n }\n\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n const name = component.NAME;\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n if (isDisabled(this)) {\n return;\n }\n\n const target = getElementFromSelector(this) || this.closest(`.${name}`);\n const instance = component.getOrCreateInstance(target); // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n\n instance[method]();\n });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$f;\n } // Public\n\n\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n\n if (closeEvent.defaultPrevented) {\n return;\n }\n\n this._element.classList.remove(CLASS_NAME_SHOW$8);\n\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n } // Private\n\n\n _destroyElement() {\n this._element.remove();\n\n EventHandler.trigger(this._element, EVENT_CLOSED);\n this.dispose();\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](this);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nenableDismissTrigger(Alert, 'close');\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$e;\n } // Public\n\n\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this);\n\n if (config === 'toggle') {\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n event.preventDefault();\n const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n const data = Button.getOrCreateInstance(button);\n data.toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n },\n\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector);\n },\n\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector));\n },\n\n parents(element, selector) {\n const parents = [];\n let ancestor = element.parentNode.closest(selector);\n\n while (ancestor) {\n parents.push(ancestor);\n ancestor = ancestor.parentNode.closest(selector);\n }\n\n return parents;\n },\n\n prev(element, selector) {\n let previous = element.previousElementSibling;\n\n while (previous) {\n if (previous.matches(selector)) {\n return [previous];\n }\n\n previous = previous.previousElementSibling;\n }\n\n return [];\n },\n\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling;\n\n while (next) {\n if (next.matches(selector)) {\n return [next];\n }\n\n next = next.nextElementSibling;\n }\n\n return [];\n },\n\n focusableChildren(element) {\n const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n};\nconst DefaultType$c = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n};\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super();\n this._element = element;\n\n if (!element || !Swipe.isSupported()) {\n return;\n }\n\n this._config = this._getConfig(config);\n this._deltaX = 0;\n this._supportPointerEvents = Boolean(window.PointerEvent);\n\n this._initEvents();\n } // Getters\n\n\n static get Default() {\n return Default$c;\n }\n\n static get DefaultType() {\n return DefaultType$c;\n }\n\n static get NAME() {\n return NAME$d;\n } // Public\n\n\n dispose() {\n EventHandler.off(this._element, EVENT_KEY$9);\n } // Private\n\n\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX;\n return;\n }\n\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX;\n }\n }\n\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX;\n }\n\n this._handleSwipe();\n\n execute(this._config.endCallback);\n }\n\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n }\n\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX);\n\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return;\n }\n\n const direction = absDeltaX / this._deltaX;\n this._deltaX = 0;\n\n if (!direction) {\n return;\n }\n\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n }\n\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n\n this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n }\n }\n\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n } // Static\n\n\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n};\nconst DefaultType$b = {\n interval: '(number|boolean)',\n // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._interval = null;\n this._activeElement = null;\n this._isSliding = false;\n this.touchTimeout = null;\n this._swipeHelper = null;\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n\n this._addEventListeners();\n\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$b;\n }\n\n static get DefaultType() {\n return DefaultType$b;\n }\n\n static get NAME() {\n return NAME$c;\n } // Public\n\n\n next() {\n this._slide(ORDER_NEXT);\n }\n\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next();\n }\n }\n\n prev() {\n this._slide(ORDER_PREV);\n }\n\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element);\n }\n\n this._clearInterval();\n }\n\n cycle() {\n this._clearInterval();\n\n this._updateInterval();\n\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n }\n\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return;\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n return;\n }\n\n this.cycle();\n }\n\n to(index) {\n const items = this._getItems();\n\n if (index > items.length - 1 || index < 0) {\n return;\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n return;\n }\n\n const activeIndex = this._getItemIndex(this._getActive());\n\n if (activeIndex === index) {\n return;\n }\n\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n\n this._slide(order, items[index]);\n }\n\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose();\n }\n\n super.dispose();\n } // Private\n\n\n _configAfterMerge(config) {\n config.defaultInterval = config.interval;\n return config;\n }\n\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n }\n\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n }\n\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners();\n }\n }\n\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n }\n\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return;\n } // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n\n this.pause();\n\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout);\n }\n\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n };\n\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n };\n this._swipeHelper = new Swipe(this._element, swipeConfig);\n }\n\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return;\n }\n\n const direction = KEY_TO_DIRECTION[event.key];\n\n if (direction) {\n event.preventDefault();\n\n this._slide(this._directionToOrder(direction));\n }\n }\n\n _getItemIndex(element) {\n return this._getItems().indexOf(element);\n }\n\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return;\n }\n\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n activeIndicator.removeAttribute('aria-current');\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n newActiveIndicator.setAttribute('aria-current', 'true');\n }\n }\n\n _updateInterval() {\n const element = this._activeElement || this._getActive();\n\n if (!element) {\n return;\n }\n\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n this._config.interval = elementInterval || this._config.defaultInterval;\n }\n\n _slide(order, element = null) {\n if (this._isSliding) {\n return;\n }\n\n const activeElement = this._getActive();\n\n const isNext = order === ORDER_NEXT;\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n\n if (nextElement === activeElement) {\n return;\n }\n\n const nextElementIndex = this._getItemIndex(nextElement);\n\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n });\n };\n\n const slideEvent = triggerEvent(EVENT_SLIDE);\n\n if (slideEvent.defaultPrevented) {\n return;\n }\n\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // todo: change tests that use empty divs to avoid this check\n return;\n }\n\n const isCycling = Boolean(this._interval);\n this.pause();\n this._isSliding = true;\n\n this._setActiveIndicatorElement(nextElementIndex);\n\n this._activeElement = nextElement;\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n nextElement.classList.add(orderClassName);\n reflow(nextElement);\n activeElement.classList.add(directionalClassName);\n nextElement.classList.add(directionalClassName);\n\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName);\n nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n this._isSliding = false;\n triggerEvent(EVENT_SLID);\n };\n\n this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n\n if (isCycling) {\n this.cycle();\n }\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE);\n }\n\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n }\n\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element);\n }\n\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval);\n this._interval = null;\n }\n }\n\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n }\n\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n }\n\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n }\n\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config);\n\n if (typeof config === 'number') {\n data.to(config);\n return;\n }\n\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n const target = getElementFromSelector(this);\n\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return;\n }\n\n event.preventDefault();\n const carousel = Carousel.getOrCreateInstance(target);\n const slideIndex = this.getAttribute('data-bs-slide-to');\n\n if (slideIndex) {\n carousel.to(slideIndex);\n\n carousel._maybeEnableCycle();\n\n return;\n }\n\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next();\n\n carousel._maybeEnableCycle();\n\n return;\n }\n\n carousel.prev();\n\n carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel);\n }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n parent: null,\n toggle: true\n};\nconst DefaultType$a = {\n parent: '(null|element)',\n toggle: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isTransitioning = false;\n this._triggerArray = [];\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n\n for (const elem of toggleList) {\n const selector = getSelectorFromElement(elem);\n const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem);\n }\n }\n\n this._initializeChildren();\n\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n }\n\n if (this._config.toggle) {\n this.toggle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$a;\n }\n\n static get DefaultType() {\n return DefaultType$a;\n }\n\n static get NAME() {\n return NAME$b;\n } // Public\n\n\n toggle() {\n if (this._isShown()) {\n this.hide();\n } else {\n this.show();\n }\n }\n\n show() {\n if (this._isTransitioning || this._isShown()) {\n return;\n }\n\n let activeChildren = []; // find active children\n\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n toggle: false\n }));\n }\n\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return;\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n\n if (startEvent.defaultPrevented) {\n return;\n }\n\n for (const activeInstance of activeChildren) {\n activeInstance.hide();\n }\n\n const dimension = this._getDimension();\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE);\n\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n this._element.style[dimension] = 0;\n\n this._addAriaAndCollapsedClass(this._triggerArray, true);\n\n this._isTransitioning = true;\n\n const complete = () => {\n this._isTransitioning = false;\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n this._element.style[dimension] = '';\n EventHandler.trigger(this._element, EVENT_SHOWN$6);\n };\n\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n const scrollSize = `scroll${capitalizedDimension}`;\n\n this._queueCallback(complete, this._element, true);\n\n this._element.style[dimension] = `${this._element[scrollSize]}px`;\n }\n\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return;\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n\n if (startEvent.defaultPrevented) {\n return;\n }\n\n const dimension = this._getDimension();\n\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n reflow(this._element);\n\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n for (const trigger of this._triggerArray) {\n const element = getElementFromSelector(trigger);\n\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false);\n }\n }\n\n this._isTransitioning = true;\n\n const complete = () => {\n this._isTransitioning = false;\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n this._element.classList.add(CLASS_NAME_COLLAPSE);\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n };\n\n this._element.style[dimension] = '';\n\n this._queueCallback(complete, this._element, true);\n }\n\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW$7);\n } // Private\n\n\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle); // Coerce string values\n\n config.parent = getElement(config.parent);\n return config;\n }\n\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n }\n\n _initializeChildren() {\n if (!this._config.parent) {\n return;\n }\n\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n\n for (const element of children) {\n const selected = getElementFromSelector(element);\n\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected));\n }\n }\n }\n\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent); // remove children if greater depth\n\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n }\n\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return;\n }\n\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n element.setAttribute('aria-expanded', isOpen);\n }\n } // Static\n\n\n static jQueryInterface(config) {\n const _config = {};\n\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false;\n }\n\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config);\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n event.preventDefault();\n }\n\n const selector = getSelectorFromElement(this);\n const selectorElements = SelectorEngine.find(selector);\n\n for (const element of selectorElements) {\n Collapse.getOrCreateInstance(element, {\n toggle: false\n }).toggle();\n }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n};\nconst DefaultType$9 = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n};\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._popper = null;\n this._parent = this._element.parentNode; // dropdown wrapper\n // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n this._inNavbar = this._detectNavbar();\n } // Getters\n\n\n static get Default() {\n return Default$9;\n }\n\n static get DefaultType() {\n return DefaultType$9;\n }\n\n static get NAME() {\n return NAME$a;\n } // Public\n\n\n toggle() {\n return this._isShown() ? this.hide() : this.show();\n }\n\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return;\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n };\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._createPopper(); // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n\n this._element.focus();\n\n this._element.setAttribute('aria-expanded', true);\n\n this._menu.classList.add(CLASS_NAME_SHOW$6);\n\n this._element.classList.add(CLASS_NAME_SHOW$6);\n\n EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n }\n\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return;\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n };\n\n this._completeHide(relatedTarget);\n }\n\n dispose() {\n if (this._popper) {\n this._popper.destroy();\n }\n\n super.dispose();\n }\n\n update() {\n this._inNavbar = this._detectNavbar();\n\n if (this._popper) {\n this._popper.update();\n }\n } // Private\n\n\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n\n if (hideEvent.defaultPrevented) {\n return;\n } // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n\n if (this._popper) {\n this._popper.destroy();\n }\n\n this._menu.classList.remove(CLASS_NAME_SHOW$6);\n\n this._element.classList.remove(CLASS_NAME_SHOW$6);\n\n this._element.setAttribute('aria-expanded', 'false');\n\n Manipulator.removeDataAttribute(this._menu, 'popper');\n EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n }\n\n _getConfig(config) {\n config = super._getConfig(config);\n\n if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n }\n\n return config;\n }\n\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n }\n\n let referenceElement = this._element;\n\n if (this._config.reference === 'parent') {\n referenceElement = this._parent;\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference);\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference;\n }\n\n const popperConfig = this._getPopperConfig();\n\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n }\n\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n }\n\n _getPlacement() {\n const parentDropdown = this._parent;\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER;\n } // We need to trim the value because custom properties can also include spaces\n\n\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n }\n\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n }\n\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null;\n }\n\n _getOffset() {\n const {\n offset\n } = this._config;\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n\n return offset;\n }\n\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n }; // Disable Popper if we have a static display or Dropdown is in Navbar\n\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // todo:v6 remove\n\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }];\n }\n\n return { ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n };\n }\n\n _selectMenuItem({\n key,\n target\n }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n\n if (!items.length) {\n return;\n } // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n\n\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n return;\n }\n\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle);\n\n if (!context || context._config.autoClose === false) {\n continue;\n }\n\n const composedPath = event.composedPath();\n const isMenuTarget = composedPath.includes(context._menu);\n\n if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n continue;\n } // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n\n\n if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue;\n }\n\n const relatedTarget = {\n relatedTarget: context._element\n };\n\n if (event.type === 'click') {\n relatedTarget.clickEvent = event;\n }\n\n context._completeHide(relatedTarget);\n }\n }\n\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n const isInput = /input|textarea/i.test(event.target.tagName);\n const isEscapeEvent = event.key === ESCAPE_KEY$2;\n const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return;\n }\n\n if (isInput && !isEscapeEvent) {\n return;\n }\n\n event.preventDefault(); // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n const instance = Dropdown.getOrCreateInstance(getToggleButton);\n\n if (isUpOrDownEvent) {\n event.stopPropagation();\n instance.show();\n\n instance._selectMenuItem(event);\n\n return;\n }\n\n if (instance._isShown()) {\n // else is escape and we check if it is shown\n event.stopPropagation();\n instance.hide();\n getToggleButton.focus();\n }\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n event.preventDefault();\n Dropdown.getOrCreateInstance(this).toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body;\n } // Public\n\n\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth;\n return Math.abs(window.innerWidth - documentWidth);\n }\n\n hide() {\n const width = this.getWidth();\n\n this._disableOverFlow(); // give padding to element to balance the hidden scrollbar width\n\n\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width); // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n\n\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n }\n\n reset() {\n this._resetElementAttributes(this._element, 'overflow');\n\n this._resetElementAttributes(this._element, PROPERTY_PADDING);\n\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n }\n\n isOverflowing() {\n return this.getWidth() > 0;\n } // Private\n\n\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow');\n\n this._element.style.overflow = 'hidden';\n }\n\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth();\n\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return;\n }\n\n this._saveInitialAttribute(element, styleProperty);\n\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n };\n\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty);\n\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue);\n }\n }\n\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty); // We only want to remove the property if the value is `null`; the value can also be zero\n\n if (value === null) {\n element.style.removeProperty(styleProperty);\n return;\n }\n\n Manipulator.removeDataAttribute(element, styleProperty);\n element.style.setProperty(styleProperty, value);\n };\n\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector);\n return;\n }\n\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel);\n }\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true,\n // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n\n};\nconst DefaultType$8 = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n};\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isAppended = false;\n this._element = null;\n } // Getters\n\n\n static get Default() {\n return Default$8;\n }\n\n static get DefaultType() {\n return DefaultType$8;\n }\n\n static get NAME() {\n return NAME$9;\n } // Public\n\n\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n\n this._append();\n\n const element = this._getElement();\n\n if (this._config.isAnimated) {\n reflow(element);\n }\n\n element.classList.add(CLASS_NAME_SHOW$5);\n\n this._emulateAnimation(() => {\n execute(callback);\n });\n }\n\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n\n this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n\n this._emulateAnimation(() => {\n this.dispose();\n execute(callback);\n });\n }\n\n dispose() {\n if (!this._isAppended) {\n return;\n }\n\n EventHandler.off(this._element, EVENT_MOUSEDOWN);\n\n this._element.remove();\n\n this._isAppended = false;\n } // Private\n\n\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div');\n backdrop.className = this._config.className;\n\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE$4);\n }\n\n this._element = backdrop;\n }\n\n return this._element;\n }\n\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement);\n return config;\n }\n\n _append() {\n if (this._isAppended) {\n return;\n }\n\n const element = this._getElement();\n\n this._config.rootElement.append(element);\n\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback);\n });\n this._isAppended = true;\n }\n\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n\n};\nconst DefaultType$7 = {\n autofocus: 'boolean',\n trapElement: 'element'\n};\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isActive = false;\n this._lastTabNavDirection = null;\n } // Getters\n\n\n static get Default() {\n return Default$7;\n }\n\n static get DefaultType() {\n return DefaultType$7;\n }\n\n static get NAME() {\n return NAME$8;\n } // Public\n\n\n activate() {\n if (this._isActive) {\n return;\n }\n\n if (this._config.autofocus) {\n this._config.trapElement.focus();\n }\n\n EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n\n EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n this._isActive = true;\n }\n\n deactivate() {\n if (!this._isActive) {\n return;\n }\n\n this._isActive = false;\n EventHandler.off(document, EVENT_KEY$5);\n } // Private\n\n\n _handleFocusin(event) {\n const {\n trapElement\n } = this._config;\n\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return;\n }\n\n const elements = SelectorEngine.focusableChildren(trapElement);\n\n if (elements.length === 0) {\n trapElement.focus();\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus();\n } else {\n elements[0].focus();\n }\n }\n\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return;\n }\n\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n backdrop: true,\n focus: true,\n keyboard: true\n};\nconst DefaultType$6 = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._isShown = false;\n this._isTransitioning = false;\n this._scrollBar = new ScrollBarHelper();\n\n this._addEventListeners();\n } // Getters\n\n\n static get Default() {\n return Default$6;\n }\n\n static get DefaultType() {\n return DefaultType$6;\n }\n\n static get NAME() {\n return NAME$7;\n } // Public\n\n\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n relatedTarget\n });\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = true;\n this._isTransitioning = true;\n\n this._scrollBar.hide();\n\n document.body.classList.add(CLASS_NAME_OPEN);\n\n this._adjustDialog();\n\n this._backdrop.show(() => this._showElement(relatedTarget));\n }\n\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = false;\n this._isTransitioning = true;\n\n this._focustrap.deactivate();\n\n this._element.classList.remove(CLASS_NAME_SHOW$4);\n\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n }\n\n dispose() {\n for (const htmlElement of [window, this._dialog]) {\n EventHandler.off(htmlElement, EVENT_KEY$4);\n }\n\n this._backdrop.dispose();\n\n this._focustrap.deactivate();\n\n super.dispose();\n }\n\n handleUpdate() {\n this._adjustDialog();\n } // Private\n\n\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop),\n // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n });\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element);\n }\n\n this._element.style.display = 'block';\n\n this._element.removeAttribute('aria-hidden');\n\n this._element.setAttribute('aria-modal', true);\n\n this._element.setAttribute('role', 'dialog');\n\n this._element.scrollTop = 0;\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n\n if (modalBody) {\n modalBody.scrollTop = 0;\n }\n\n reflow(this._element);\n\n this._element.classList.add(CLASS_NAME_SHOW$4);\n\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate();\n }\n\n this._isTransitioning = false;\n EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n relatedTarget\n });\n };\n\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n if (event.key !== ESCAPE_KEY$1) {\n return;\n }\n\n if (this._config.keyboard) {\n event.preventDefault();\n this.hide();\n return;\n }\n\n this._triggerBackdropTransition();\n });\n EventHandler.on(window, EVENT_RESIZE$1, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog();\n }\n });\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return;\n }\n\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition();\n\n return;\n }\n\n if (this._config.backdrop) {\n this.hide();\n }\n });\n });\n }\n\n _hideModal() {\n this._element.style.display = 'none';\n\n this._element.setAttribute('aria-hidden', true);\n\n this._element.removeAttribute('aria-modal');\n\n this._element.removeAttribute('role');\n\n this._isTransitioning = false;\n\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN);\n\n this._resetAdjustments();\n\n this._scrollBar.reset();\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n });\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE$3);\n }\n\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const initialOverflowY = this._element.style.overflowY; // return if the following background transition hasn't yet completed\n\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return;\n }\n\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden';\n }\n\n this._element.classList.add(CLASS_NAME_STATIC);\n\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC);\n\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY;\n }, this._dialog);\n }, this._dialog);\n\n this._element.focus();\n }\n /**\n * The following methods are used to handle overflowing modals\n */\n\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n\n const scrollbarWidth = this._scrollBar.getWidth();\n\n const isBodyOverflowing = scrollbarWidth > 0;\n\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n }\n\n _resetAdjustments() {\n this._element.style.paddingLeft = '';\n this._element.style.paddingRight = '';\n } // Static\n\n\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](relatedTarget);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n const target = getElementFromSelector(this);\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return;\n }\n\n EventHandler.one(target, EVENT_HIDDEN$4, () => {\n if (isVisible(this)) {\n this.focus();\n }\n });\n }); // avoid conflict when clicking modal toggler while another one is open\n\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide();\n }\n\n const data = Modal.getOrCreateInstance(target);\n data.toggle(this);\n});\nenableDismissTrigger(Modal);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n backdrop: true,\n keyboard: true,\n scroll: false\n};\nconst DefaultType$5 = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isShown = false;\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n\n this._addEventListeners();\n } // Getters\n\n\n static get Default() {\n return Default$5;\n }\n\n static get DefaultType() {\n return DefaultType$5;\n }\n\n static get NAME() {\n return NAME$6;\n } // Public\n\n\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n\n show(relatedTarget) {\n if (this._isShown) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n relatedTarget\n });\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = true;\n\n this._backdrop.show();\n\n if (!this._config.scroll) {\n new ScrollBarHelper().hide();\n }\n\n this._element.setAttribute('aria-modal', true);\n\n this._element.setAttribute('role', 'dialog');\n\n this._element.classList.add(CLASS_NAME_SHOWING$1);\n\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate();\n }\n\n this._element.classList.add(CLASS_NAME_SHOW$3);\n\n this._element.classList.remove(CLASS_NAME_SHOWING$1);\n\n EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n relatedTarget\n });\n };\n\n this._queueCallback(completeCallBack, this._element, true);\n }\n\n hide() {\n if (!this._isShown) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n this._focustrap.deactivate();\n\n this._element.blur();\n\n this._isShown = false;\n\n this._element.classList.add(CLASS_NAME_HIDING);\n\n this._backdrop.hide();\n\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n\n this._element.removeAttribute('aria-modal');\n\n this._element.removeAttribute('role');\n\n if (!this._config.scroll) {\n new ScrollBarHelper().reset();\n }\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n };\n\n this._queueCallback(completeCallback, this._element, true);\n }\n\n dispose() {\n this._backdrop.dispose();\n\n this._focustrap.deactivate();\n\n super.dispose();\n } // Private\n\n\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n\n this.hide();\n }; // 'static' option will be translated to true, and booleans will keep their value\n\n\n const isVisible = Boolean(this._config.backdrop);\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n });\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return;\n }\n\n if (!this._config.keyboard) {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n\n this.hide();\n });\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](this);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n const target = getElementFromSelector(this);\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n if (isDisabled(this)) {\n return;\n }\n\n EventHandler.one(target, EVENT_HIDDEN$3, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus();\n }\n }); // avoid conflict when clicking a toggler of an offcanvas, while another is open\n\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide();\n }\n\n const data = Offcanvas.getOrCreateInstance(target);\n data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show();\n }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide();\n }\n }\n});\nenableDismissTrigger(Offcanvas);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\n/**\n * A pattern that recognizes a commonly useful subset of URLs that are safe.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i;\n/**\n * A pattern that matches safe data URLs. Only matches image, video and audio types.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst DATA_URL_PATTERN = /^data:(?:image\\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\\/(?:mpeg|mp4|ogg|webm)|audio\\/(?:mp3|oga|ogg|opus));base64,[\\d+/a-z]+=*$/i;\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase();\n\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue) || DATA_URL_PATTERN.test(attribute.nodeValue));\n }\n\n return true;\n } // Check if a regular expression validates the attribute.\n\n\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\n\nconst DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml;\n }\n\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml);\n }\n\n const domParser = new window.DOMParser();\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase();\n\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove();\n continue;\n }\n\n const attributeList = [].concat(...element.attributes);\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName);\n }\n }\n }\n\n return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n allowList: DefaultAllowlist,\n content: {},\n // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n};\nconst DefaultType$4 = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n};\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n};\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n } // Getters\n\n\n static get Default() {\n return Default$4;\n }\n\n static get DefaultType() {\n return DefaultType$4;\n }\n\n static get NAME() {\n return NAME$5;\n } // Public\n\n\n getContent() {\n return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n }\n\n hasContent() {\n return this.getContent().length > 0;\n }\n\n changeContent(content) {\n this._checkContent(content);\n\n this._config.content = { ...this._config.content,\n ...content\n };\n return this;\n }\n\n toHtml() {\n const templateWrapper = document.createElement('div');\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector);\n }\n\n const template = templateWrapper.children[0];\n\n const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n\n if (extraClass) {\n template.classList.add(...extraClass.split(' '));\n }\n\n return template;\n } // Private\n\n\n _typeCheckConfig(config) {\n super._typeCheckConfig(config);\n\n this._checkContent(config.content);\n }\n\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({\n selector,\n entry: content\n }, DefaultContentType);\n }\n }\n\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template);\n\n if (!templateElement) {\n return;\n }\n\n content = this._resolvePossibleFunction(content);\n\n if (!content) {\n templateElement.remove();\n return;\n }\n\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement);\n\n return;\n }\n\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content);\n return;\n }\n\n templateElement.textContent = content;\n }\n\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n }\n\n _resolvePossibleFunction(arg) {\n return typeof arg === 'function' ? arg(this) : arg;\n }\n\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = '';\n templateElement.append(element);\n return;\n }\n\n templateElement.textContent = element.textContent;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 0],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' + '
' + '
' + '
',\n title: '',\n trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n};\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n }\n\n super(element, config); // Private\n\n this._isEnabled = true;\n this._timeout = 0;\n this._isHovered = null;\n this._activeTrigger = {};\n this._popper = null;\n this._templateFactory = null;\n this._newContent = null; // Protected\n\n this.tip = null;\n\n this._setListeners();\n\n if (!this._config.selector) {\n this._fixTitle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$3;\n }\n\n static get DefaultType() {\n return DefaultType$3;\n }\n\n static get NAME() {\n return NAME$4;\n } // Public\n\n\n enable() {\n this._isEnabled = true;\n }\n\n disable() {\n this._isEnabled = false;\n }\n\n toggleEnabled() {\n this._isEnabled = !this._isEnabled;\n }\n\n toggle() {\n if (!this._isEnabled) {\n return;\n }\n\n this._activeTrigger.click = !this._activeTrigger.click;\n\n if (this._isShown()) {\n this._leave();\n\n return;\n }\n\n this._enter();\n }\n\n dispose() {\n clearTimeout(this._timeout);\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n }\n\n this._disposePopper();\n\n super.dispose();\n }\n\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements');\n }\n\n if (!(this._isWithContent() && this._isEnabled)) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n const shadowRoot = findShadowRoot(this._element);\n\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n\n if (showEvent.defaultPrevented || !isInTheDom) {\n return;\n } // todo v6 remove this OR make it optional\n\n\n this._disposePopper();\n\n const tip = this._getTipElement();\n\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n\n const {\n container\n } = this._config;\n\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip);\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n }\n\n this._popper = this._createPopper(tip);\n tip.classList.add(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n\n if (this._isHovered === false) {\n this._leave();\n }\n\n this._isHovered = false;\n };\n\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n\n hide() {\n if (!this._isShown()) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n const tip = this._getTipElement();\n\n tip.classList.remove(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n\n this._activeTrigger[TRIGGER_CLICK] = false;\n this._activeTrigger[TRIGGER_FOCUS] = false;\n this._activeTrigger[TRIGGER_HOVER] = false;\n this._isHovered = null; // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return;\n }\n\n if (!this._isHovered) {\n this._disposePopper();\n }\n\n this._element.removeAttribute('aria-describedby');\n\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n };\n\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n\n update() {\n if (this._popper) {\n this._popper.update();\n }\n } // Protected\n\n\n _isWithContent() {\n return Boolean(this._getTitle());\n }\n\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n }\n\n return this.tip;\n }\n\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml(); // todo: remove this check on v6\n\n\n if (!tip) {\n return null;\n }\n\n tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2); // todo: on v6 the following can be achieved with CSS only\n\n tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n const tipId = getUID(this.constructor.NAME).toString();\n tip.setAttribute('id', tipId);\n\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE$2);\n }\n\n return tip;\n }\n\n setContent(content) {\n this._newContent = content;\n\n if (this._isShown()) {\n this._disposePopper();\n\n this.show();\n }\n }\n\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content);\n } else {\n this._templateFactory = new TemplateFactory({ ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n });\n }\n\n return this._templateFactory;\n }\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n };\n }\n\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n } // Private\n\n\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n }\n\n _isAnimated() {\n return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n }\n\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n }\n\n _createPopper(tip) {\n const placement = typeof this._config.placement === 'function' ? this._config.placement.call(this, tip, this._element) : this._config.placement;\n const attachment = AttachmentMap[placement.toUpperCase()];\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n }\n\n _getOffset() {\n const {\n offset\n } = this._config;\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n\n return offset;\n }\n\n _resolvePossibleFunction(arg) {\n return typeof arg === 'function' ? arg.call(this._element) : arg;\n }\n\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [{\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }, {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n }, {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n }\n }]\n };\n return { ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n };\n }\n\n _setListeners() {\n const triggers = this._config.trigger.split(' ');\n\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context.toggle();\n });\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n\n context._enter();\n });\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n\n context._leave();\n });\n }\n }\n\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide();\n }\n };\n\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n }\n\n _fixTitle() {\n const title = this._element.getAttribute('title');\n\n if (!title) {\n return;\n }\n\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title);\n }\n\n this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n\n\n this._element.removeAttribute('title');\n }\n\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true;\n return;\n }\n\n this._isHovered = true;\n\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show();\n }\n }, this._config.delay.show);\n }\n\n _leave() {\n if (this._isWithActiveTrigger()) {\n return;\n }\n\n this._isHovered = false;\n\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide();\n }\n }, this._config.delay.hide);\n }\n\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout);\n this._timeout = setTimeout(handler, timeout);\n }\n\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true);\n }\n\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element);\n\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute];\n }\n }\n\n config = { ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n };\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n }\n\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container);\n\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n };\n }\n\n if (typeof config.title === 'number') {\n config.title = config.title.toString();\n }\n\n if (typeof config.content === 'number') {\n config.content = config.content.toString();\n }\n\n return config;\n }\n\n _getDelegateConfig() {\n const config = {};\n\n for (const key in this._config) {\n if (this.constructor.Default[key] !== this._config[key]) {\n config[key] = this._config[key];\n }\n }\n\n config.selector = false;\n config.trigger = 'manual'; // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n\n return config;\n }\n\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy();\n\n this._popper = null;\n }\n\n if (this.tip) {\n this.tip.remove();\n this.tip = null;\n }\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = { ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
' + '
' + '

' + '
' + '
',\n trigger: 'click'\n};\nconst DefaultType$2 = { ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n};\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default$2;\n }\n\n static get DefaultType() {\n return DefaultType$2;\n }\n\n static get NAME() {\n return NAME$3;\n } // Overrides\n\n\n _isWithContent() {\n return this._getTitle() || this._getContent();\n } // Private\n\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n };\n }\n\n _getContent() {\n return this._resolvePossibleFunction(this._config.content);\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n offset: null,\n // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n offset: '(number|null)',\n // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n};\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config); // this._element is the observablesContainer and config.target the menu links wrapper\n\n this._targetLinks = new Map();\n this._observableSections = new Map();\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n this._activeTarget = null;\n this._observer = null;\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n };\n this.refresh(); // initialize\n } // Getters\n\n\n static get Default() {\n return Default$1;\n }\n\n static get DefaultType() {\n return DefaultType$1;\n }\n\n static get NAME() {\n return NAME$2;\n } // Public\n\n\n refresh() {\n this._initializeTargetsAndObservables();\n\n this._maybeEnableSmoothScroll();\n\n if (this._observer) {\n this._observer.disconnect();\n } else {\n this._observer = this._getNewObserver();\n }\n\n for (const section of this._observableSections.values()) {\n this._observer.observe(section);\n }\n }\n\n dispose() {\n this._observer.disconnect();\n\n super.dispose();\n } // Private\n\n\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body; // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n }\n\n return config;\n }\n\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return;\n } // unregister any previous listeners\n\n\n EventHandler.off(this._config.target, EVENT_CLICK);\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash);\n\n if (observableSection) {\n event.preventDefault();\n const root = this._rootElement || window;\n const height = observableSection.offsetTop - this._element.offsetTop;\n\n if (root.scrollTo) {\n root.scrollTo({\n top: height,\n behavior: 'smooth'\n });\n return;\n } // Chrome 60 doesn't support `scrollTo`\n\n\n root.scrollTop = height;\n }\n });\n }\n\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n };\n return new IntersectionObserver(entries => this._observerCallback(entries), options);\n } // The logic of selection\n\n\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n\n this._process(targetElement(entry));\n };\n\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n this._previousScrollData.parentScrollTop = parentScrollTop;\n\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null;\n\n this._clearActiveClass(targetElement(entry));\n\n continue;\n }\n\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop; // if we are scrolling down, pick the bigger offsetTop\n\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry); // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n\n if (!parentScrollTop) {\n return;\n }\n\n continue;\n } // if we are scrolling up, pick the smallest offsetTop\n\n\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry);\n }\n }\n }\n\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map();\n this._observableSections = new Map();\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue;\n }\n\n const observableSection = SelectorEngine.findOne(anchor.hash, this._element); // ensure that the observableSection exists & is visible\n\n if (isVisible(observableSection)) {\n this._targetLinks.set(anchor.hash, anchor);\n\n this._observableSections.set(anchor.hash, observableSection);\n }\n }\n }\n\n _process(target) {\n if (this._activeTarget === target) {\n return;\n }\n\n this._clearActiveClass(this._config.target);\n\n this._activeTarget = target;\n target.classList.add(CLASS_NAME_ACTIVE$1);\n\n this._activateParents(target);\n\n EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n relatedTarget: target\n });\n }\n\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n return;\n }\n\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both