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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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", 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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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G27126731.3426180.197855233209.00.896996
Gfoams88110.9933480.137560233199.00.854077
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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": [ + "
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glass6%
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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/bern.ipynb b/_build/html/_sources/bern.ipynb new file mode 100644 index 0000000..f4bec9d --- /dev/null +++ b/_build/html/_sources/bern.ipynb @@ -0,0 +1,2606 @@ +{ + "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", + "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", + "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, o_land_use.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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", 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", 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 % of total
material 
cloth4%
glass6%
metal4%
paper3%
plastic76%
wood2%
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ObjectQuantitypcs/m% of totalFail rate
Cigarette filters1'6710,420,190,81
Fragmented plastics1'3230,460,150,89
Industrial sheeting6860,240,080,78
Food wrappers; candy, snacks5890,190,070,80
Expanded polystyrene4740,120,050,62
Glass drink bottles, pieces3480,120,040,65
Packaging films nonfood or unknown2610,090,030,49
Foam packaging/insulation/polyurethane2400,030,030,78
plastic caps, lid rings: G21, G22, G23, G242270,070,030,60
Industrial pellets (nurdles)1960,050,020,33
Plastic construction waste1600,060,020,52
\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: 89\n* Total objects: 9028\n* Average pcs/m: 2.76\n* Standard deviation: 2.74\n* Maximum pcs/m: 14.8\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 - 2019\n* Number of samples: 106\n* Total objects: 4562\n* Average pcs/m: 1.39\n* Standard deviation: 1.53\n* Maximum pcs/m: 7.92\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: 195\n* Total objects: 13590\n* Average pcs/m: 2.01\n* Standard deviation: 2.27\n* Maximum pcs/m: 14.8\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: 6\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: 40\n* Cities: 26\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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", + "application/papermill.record/text/plain": "
" + }, + "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 - 2019. 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
 % of total
material 
glass5%
metal3%
paper2%
plastic84%
\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: 7.64\n* HDI 95%: 0.2 - 33.6\n* 90% Range: 0.3 - 33.6", + "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: 3.06\n* HDI 95%: 0.07 - 12.26\n* 90% Range: 0.26 - 10.9", + "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: 2.22\n* HDI 95%: 0.3 - 6.8\n* 90% Range: 0.4 - 6.04", + "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
ObjectQuantitypcs/m% of totalFail rate
Cigarette filters1'5630,480,190,84
Fragmented plastics1'2830,540,150,95
Industrial sheeting6610,270,080,86
Food wrappers; candy, snacks5650,220,070,82
Expanded polystyrene4620,140,050,73
Glass drink bottles, pieces3190,140,040,72
Packaging films nonfood or unknown2490,100,030,53
Foam packaging/insulation/polyurethane2270,040,030,89
plastic caps, lid rings: G21, G22, G23, G242160,070,030,65
Industrial pellets (nurdles)1860,060,020,36
Plastic construction waste1590,070,020,61
Fireworks; rocket caps, exploded parts & packaging1130,050,010,53
Cotton bud/swab sticks1070,040,010,53
Tobacco; plastic packaging, containers1060,050,010,51
Foil wrappers, aluminum foil890,030,010,50
\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: 74\n* Total objects: 8423\n* Average pcs/m: 3.09\n* Standard deviation: 2.85\n* Maximum pcs/m: 14.8\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 - 2019\n* Number of samples: 24\n* Total objects: 1466\n* Average pcs/m: 1.96\n* Standard deviation: 1.49\n* Maximum pcs/m: 5.48\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: 98\n* Total objects: 9889\n* Average pcs/m: 2.81\n* Standard deviation: 2.63\n* Maximum pcs/m: 14.8\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: 19\n* Cities: 13\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, o_land_use_l.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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 % of total
material 
cloth15%
glass7%
metal10%
paper6%
plastic52%
wood5%
\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: 1.28\n* HDI 95%: 0.1 - 4.7\n* 90% Range: 0.2 - 3.56", + "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: 1.41\n* HDI 95%: 0.02 - 5.68\n* 90% Range: 0.02 - 4.93", + "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: 1.23\n* HDI 95%: 0.1 - 4.9\n* 90% Range: 0.1 - 4.42", + "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 65% 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
Diapers - wipes1170,290,190,33
Cigarette filters1080,170,180,67
Fragmented plastics400,060,070,60
Glass drink bottles, pieces290,050,050,33
Industrial sheeting250,070,040,33
Food wrappers; candy, snacks240,040,040,67
Tampons170,030,030,07
Foam packaging/insulation/polyurethane130,010,020,20
Packaging films nonfood or unknown120,020,020,33
Expanded polystyrene120,020,020,07
\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: 15\n* Total objects: 605\n* Average pcs/m: 1.12\n* Standard deviation: 1.11\n* Maximum pcs/m: 3.63\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 - 2019\n* Number of samples: 82\n* Total objects: 3096\n* Average pcs/m: 1.22\n* Standard deviation: 1.5\n* Maximum pcs/m: 7.92\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: 97\n* Total objects: 3701\n* Average pcs/m: 1.21\n* Standard deviation: 1.45\n* Maximum pcs/m: 7.92\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: 6\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: 14\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, o_land_use_r.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, + "jupyter": { + "source_hidden": 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", + "dbc = dbc[dbc.NAME == canton].copy()\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", + "bounds = dbc.total_bounds\n", + "minx, miny, maxx, maxy = bounds\n", + "\n", + "\n", + "rivers = gpd.read_file('data/shapes/rivers.shp')\n", + "rivers = rivers.to_crs(epsg=4326)\n", + "# Filter the background layer to cover the bounding box\n", + "rivers_within_bounds = rivers.cx[minx:maxx, miny:maxy]\n", + "\n", + "\n", + "lakes = gpd.read_file('data/shapes/lakes.shp')\n", + "lakes = lakes.to_crs(epsg=4326)\n", + "lakes_within_bounds = lakes.cx[minx:maxx, miny:maxy]\n", + "\n", + "\n", + "\n", + "# Define the plot\n", + "fig, ax = plt.subplots(figsize=(12,10))\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.plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.2)\n", + "rivers_within_bounds.plot(ax=ax, edgecolor='steelblue', alpha=.2)\n", + "lakes_within_bounds.plot(ax=ax, edgecolor='steelblue', color='steelblue', linewidth=.2, alpha=.2)\n", + "\n", + "# Set the extent to Switzerland\n", + "ax.set_ylim([miny, maxy])\n", + "ax.set_xlim([minx, maxx])\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 Bern\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, + "jupyter": { + "source_hidden": 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
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Buildings2.372.524.091.591.24
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Orchards100%0%0%0%0%
Vineyards100%0%0%0%0%
Buildings30%31%26%10%2%
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\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, + "jupyter": { + "source_hidden": 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
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\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
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\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, + "jupyter": { + "source_hidden": 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
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Vineyards3.090.000.000.000.00
Buildings2.522.525.352.381.24
Forest3.513.201.230.000.00
Undefined3.754.902.641.890.00
Public Services3.090.000.000.000.00
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Orchards100%0%0%0%0%
Vineyards100%0%0%0%0%
Buildings31%38%22%7%3%
Forest24%66%9%0%0%
Undefined35%8%41%16%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, + "jupyter": { + "source_hidden": 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%
streets49%51%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%
streets1.704.41000
\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, + "jupyter": { + "source_hidden": 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%
Orchards1.120.000.000.000.00
Vineyards1.120.000.000.000.00
Buildings1.510.001.200.600.00
Forest1.540.761.560.000.00
Undefined0.500.932.591.500.00
Public Services1.290.070.000.000.00
\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%
Orchards100%0%0%0%0%
Vineyards100%0%0%0%0%
Buildings27%0%47%27%0%
Forest40%53%7%0%0%
Undefined33%33%13%20%0%
Public Services87%13%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, + "jupyter": { + "source_hidden": 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%
streets7%40%40%13%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%
streets0.221.720.990.160
\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, + "jupyter": { + "source_hidden": 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          
Beatenberg1042.4321113211
Biel/Bienne32095.61151132112
Brienz (BE)6964.4931122312
Bönigen2773.1821132212
Erlach1011.8311121311
Gals481.2821122312
Ligerz1637.4031112211
Lüscherz2020.7551113111
Nidau632.5211141112
Spiez8380.79261122311
Thun2761.4031151112
Unterseen18791.89121112411
Vinelz20333.77231121312
\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, + "jupyter": { + "source_hidden": 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
 quantitypcs/msamplesorchardsvineyardsbuildingsforestundefinedpublic servicesstreets
city          
Belp600.11111121313
Bern18921.02321131213
Biel/Bienne981.0421141113
Brügg361.0211132213
Burgdorf410.8711132212
Kallnach821.3121113312
Köniz7572.86121141114
Langenthal3011.94111131212
Muri bei Bern901.6321131213
Port1181.3821132213
Rubigen573.2011111412
Steffisburg1140.63111141113
Utzenstorf410.5781122312
Walperswil140.2211112411
\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, + "jupyter": { + "source_hidden": 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
Bielersee504.08
Brienzersee53.96
Thunersee431.21
\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
 samplespcs/m
Aare611.27
Aarenidau-buren-kanal31.26
Emme90.60
Langeten111.94
Schuss21.04
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 and the combined land use classification 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/combined.ipynb b/_build/html/_sources/combined.ipynb new file mode 100644 index 0000000..73175fb --- /dev/null +++ b/_build/html/_sources/combined.ipynb @@ -0,0 +1,2589 @@ +{ + "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 = data[(~data.canton.isin(cantons))].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, + "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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+j7QcByER0bLciRDVhXI6yDm5iVPbX0c5bThyT2NLO1BsXc0QgKbpqdPiNsLaPVrMWLBKxlLCDNT8btDyJG9KKVeidY4NwvM3Ey+8QXjhujpN45q2rh0PdJqGTqcVm6QV3pMzX0l1hRDCPY5N04Ox0HJK1kzX/xr9C/7Rs+cWLDBdVpoOjJaizzVYcE+rt1vBedYss8Jj6kz+4HSU+S3lXzshLhClFNnJ67BnH/csd+SQvG5iifcFXzTMfWypdxnBFw0tx3tCdjn/HTp7Bmp+F6u/sfxJ3dD313BX9yYMahcNwObDKQx9fw2N6/qzfHwfd90HPt/Isj0neHloO27q1ACA3UlpDHpzFZHBZtY92Zd3b+lY7PsIIUSFFB7HFncDDPus4Fp+a9n4/RAQ7jpe8iRs+G/53qPRFXDnwoLzWW0h6xQ8uBbq5a2RufJVWPFixT/HWSSxE+ICsOec5NiyO8hN2XHOusHNhhMQ3RMAv/AOGCz1yvw+hRM5pYp2pZbXxntdrXStW7fi2LFjxMTEsHv37nPfCGTbHGw8lILN4aRPqwhpNRNCVC2VGMd2wTTo4mo1zEk/d908MsZOCC+wZ5/AlnGoSLly2kj8836cNs//U1oiuxepG9hgACHNhld4nJzXFvHN0yka956V9evXJyEhgdjYWI4ePVqm+3NsDm7/ZD0Op+Lzey7Hz6ivcFds4bpCCOEVhcexPREP/mGe18B3XbH58t5fthQT4gKyph3g0M9XlamuMagJsVd9gdE/2utxlLSIb0Vnq1Z0MkR+QqbTNObe39Xjmr6E8W6FNyg/V11Bwbgggx/o8n52Dhs4rOV/limg4NiW7dqdQ28Gfd7P3mEHR24ln5vj2n5ObwJ93uwbpwPsOeV/bnF/aHVGMJjynusEe3b5n1vcH1qdAQxmV1nhNcXK9dxivqOSxnOVR3HfUUlJRLmeW9x3pLnGebmfmwWUs02opO+ouN+/cj23hO+ouN+/sxUex5b/jHyF7/eoYy5aXh7FPtcEmCr33MKP89qThKgllHJiz0pynxdO6oxBjYu7AUvk5YS1feS8JHR5b8HQ+QXnhWevVna26rx580hISChz/dX7TzLy4/W0jg7ml7E9Kv7GoniFxwXd/jM0yfsZb5wDi8q5CLV/XXii0CSdz4fCoVVw0xy4eIirbPdPMO+O8sf5bKEt5hbcBzt/gGtegc55S/UcWg2fXlv+5xY35qnXROgzyVV2cg+826X8zy1uzNNl98CgV11lWafg5ablf25x31FJ47nKo7jvqKTxXOVR3HcU0QpGF+qy/KgPnCjbkAy34r6jkn7/yqOk76i4379aQhI7IcooK2kV1vRDnNjwdLHXg5rcSFTXVy9wVC7Z9oKxdHER5Z+9WprJkye7j4OCgrzzUFFxtmzIyfujVZEWLyGEp/xxbDWEjLET4hxyz+zl+JpxxU580HQmFApjQH0aXfs72vlayO0sZ892zbJBx49cxzsfgADvteq7x9eBq/Vu6NCis3LtDid//uvKLLs3C8fhVOg0rdguVuEl1kzpipWu2LznSlcsUP6u2HyFf6eqKBljJ0QF2bISsZ7Z41F2bPmdHucBDQbiFxpHWJuHL0hMZydx55rtWtF/n+wOJ1aHs0hClv/Pd2xsrDupO7uu1eHkrjl/A64FhGVsHK4vyp5TuYHTJdKKjtXRGwv+IFdU4VjdzzUUJBAVfq5f0TKdvvjxRuVR3Jgnnc4Lzy1mzJNWzM+8vEr6jir93BK+o8o+t6TvyFTJ1q2SvqPifv/Ko6TvqLjfvxpM/vUVIk9u6r8cXti/xOs6Ux0aXrPovI2TK055Z7p2inaNqauIn7cl8sjcLVzRLJzP77ncXX4qw9VaY7UX/Jf077uTuf9/G+nYKJRvH+iGTtNoVz8EcC0gXOvlj4PTGc/PGlZnj3kSQog8ktiJWksphS39AMppI2n1o1jPFAwGNoe28Wj60pvqENntNQx+4RcwPtc+rSUldcXNdj2f23qVxs+o58eHrrjwb1xVFV4fy5pZ+ZYTIYQoIxljJ2odpz2b9IMLSF7/VLHXQ1vfT/ilJe8EcSEU11J39j6t3k7iSuqK/XbBD2TnZBMUEMAN119Xal2Rp/D6WE8eK0jsvNoVW3MGewshSidj7ITI43R4LiiZlbSSxJUPFKmnN4dhDL6IyC6vYApq5NUYvLGtV0X2ac2yut7Uz6BHp3PdaHM4sZ2VkDmcig0HT2Mx6mkTG4Je5/km/zfkhiLPNuh17v1faxylXIPyK7NvpLWEQfYXYA0rIUTtJomdqDGc9myyElei8mbwpe6fS/bxv0q9J/Ti0YS1eRidvuKLTpaWuHlzW6+zk7p58+YxefJk0tOL32omOT0Xp1MRHmTmiccfY9y4cczfeJRJ322nV5NAfp92iytG4Hiqa3ZiZIgfZ+eOP/zwAx071pJ9WvPHxgVFnZ99I4UQ4jyTxE5Ua/bsE5z+5y2ctnTSD35f5vvCOzxDSPNbKpXQgfe38TpbaS11P/zwA3Xq1OH48eOkpKSU+IzjGa5mfE+q2EWHj2UUvd9qrcASGtWVt8fG1bD1sYQQVZ8kdqJasWUc5siSIai8NYmc1tRi61kiXVtZ6QwBhHd4CoMlyn1N0xkqtB9rcS1zJW3jdbbzsa3X559/zsGDB7nhhhvw9/dHAanZrrFaIZaCwXgauMdkDO1YnxsuiSEzI4Pfp8W666i8esUxmaSbkCePuf63cJI2YDr0m1r6fdVgfSwhRM0iiZ2oNk5ufYWUHe8Ue80Y2JiQ5iPQm0MJbHRdqS1xSrkSsvIoS5fq2ZMbCqvsRAer3Ynd6USv0zAbCiYr1Iupz5oNG/Ez6MmxO4ibvAQoeS05o16HUa/DP6wOR48erXhAvlCWBWIru1hpucbGeWHfSCGE8DJJ7ESVlZuyi+NrxwMKa1o8ylGwmnxQoxsIa+taIFjTWzAGlG2vxfPVdVqRyQ3l8cC0t/ns7Zfxw+pujevQoQP/tn+QbJuDlU/0ISrEj2eujQNcCVyNUnh/1NLIvpFCiFpOEjtRZR3+5ZpiyxsM/Bm/sIvPeX9luk5LUlKX6vleP27hnDewnz5KBpA/DK5BgwYedYx6HXdf0eT8BeFLhce+XQgyNk4IUU1JYieqpLQD37qPgxrdQNBFNwIalvBL0RkDz3m/U8GgryredVqSC7kAsFKK05muiQuGvJm+Op2O6GjXzhcRERH8/kxfwLWkSa3x+L6S13ArPHbSv27B2LjChnwIg98r/T1kbJwQopqSxE5UGcppJ+GPW8lO9myZieo+q3zPKUNS5+2uU6UU2TbXuK3CY9ty7Q4cToVBp8Nk0BWpazHq0fKCOHscXbbNQcfnf3Pdk/e86Ojoqj827nxtEJ7P5F+22aqyb6QQohaSxE5UGYcWDcSWtt+jrH7/78r9nCxbQVLXpA4sHO6ZwH03fx4vTJlMg3HFr/+Wb/jw4bz88sseZa1atSIjo+iaIKcyrdjy9lKNCvHj/fff59prr+XFX3Yz+6+D3FA/h29fHAuUvG5cRq6djBw7T/x3EdNv7ux+dtr6BaQmnqf1VM6Hj/rAid3nrldYr4nQZ5Lr+OQeeLeLq8XtiQPej08IIWowSeyEzyinA1v6QUBxfP0kj6Su8fV/orfUK9M6c4XH0uW31uVbOBwCzlqt44Wpk9m9+9yJR3Frwx07dqzEBYHzJWRAdna2R5nDZi3zunEqr7XL32Tg4IuDePbZDTy3zJU0BgUFnTPuGskUAI2ucO30IGPfhBCiRJLYiQvOYU0j/dCPnNjwTLHXm970Dzpj2RaGLW2Wa1xE8WPoevbsSWBgIH///TexsbFFK+QJDQ0tUhYTE1Nsix0UdD5qgMViAWDi1a0YP6Al2zZv5vvY2GLrnv2Mx/q39CgLDg4mNjaWoKAgpk2bVmK8PmPNcrXSAdy7zPWqSFdsvvCWxY+Nu3U+GPxk7JsQQpRCU0qV81/g6q08G+kK71JKoRw5HF16E7kpOzyu6Ux10BkDaHTN4mInRzicily7A5QGuoKJAqezHHT/RIGmc71cb0SrcAff3aQRYC6om2Nz4FTKvZYbgNOpyLEXHRtXnrr54+iKWzeuWsnfD7U89CZwWIvf8F4IIYRXlCd3qeZ/iURV53Tkkp30F05HNkl/jQHl9Lge1vZRwto8hKaVvu7a+vjTDP9oLX5+gWSH9iq4cOpvNOtJVJ1L2DgmFn8jzPrwU6aMfYaw0TlEBBV05aZk2bCbg5nzw+8M79wQgH0nMuj/+p+EBZjY9Ew/d90J327jhy3HeObaOPcSIglnsukxcxkWo55d0wa66076bjtLdxzn/l4XcV/Ppu5JEtXOrLautd/K45pXoNNdcPvPrnODTEwQQghfksROnFcpOz/g9PbXi73W+IZVGAOK7wq1O5z8vjsZgKta1XOXn70uXb4mdQpmuf531gzsp10zRxPO2iJVH1jCjgOVYNLrSM+1s3zPCUb3aeb151d5Oj006eHrKIQQQiBdsb4Op0ZSSmFN/Rdlz+TIrze6yy31LscU0pzwSyeh6Uyl7teaZbV7bI9lNuhJyXbQ8SMNNL17Dbr8LtMQsw5jXktZ/fr1SUhI8FjzDVyjviIjI1m3fsN56YotvHRJtVTRrlh9ORcDFEIIUS7SFSt86tTWV0jZ+a77XCkIvfxNIppd5y7LsTlw2u2YDXr0OlcyZHc4sTqc6DQNnabRsZFr8kKOXXM1xWkG92wDf2P+q+SFecuy5ptOpxU7Ns6vmOeWVNdcXRYHLrx3qt4MegPYsuGzwa6FfW+dD0aLT0MUQghROZLYCa8rnNTp/eszds99+GeEM7dQL+WQd1ezKzGN/93dmR7NIwD4898T3DXnb9rVD+HHh67grRua0LTtZdSbVPQ9mr/sOaP0999/p2XLlkUrCpez91q9aQ5cPMQ15tGRC0fWFhn/KIQQovqRxE54hS0zgaykVR7JQZ2WdxHQdhLb1y6BlNNkWe3lmjmamesg53TRtd8AjqV6ntvtBYPvZs6cSVZWFv7+st6ZW0l7rZoCoPtYWPu+rA8nhBA1gCR2olKsqfs5/c9bpB/6oci1kBYj0el1vDOiAzk2ByZ9wWzRBQ92w6mURzdmz+YR7Jw6AA2NLBvkOvXoQ1yTK+oFFCxfVtwoNoOh4Fd5xIgR3vlwNdXj+8BSaI2+VtdB3GBZH04IIWoASexEhdkyjnBoYV+PMlNIC4yBDbDUuxyHuQF2h5OBbaLc4+jyFTeGzaDXodfpCi04HE3sc64xcrseLH6xYVGM4iZBWLMKjk3+rvF1+fTyz4AQQtQU8i+6qJCcU1s5smSw+1zvF05Utzfwj+rmLuv98jIOnspi/qiudGocVqbnfvH1PBaMGEZgj4cxRbchsNt9dIoGi/ymll3+YsFCCCFqnWq6kqrwtcJJnX9UD6KvXsboXwzcOXs9ObaKrRWnFEx9bjIAGSvfou7W19n1IMy/SXoJvaZBFxlLJ4QQNZi0g4hyyzy2wn0c2HAQ0Ve8TZbVzrI9JwBw5i2N+MvYnihUmZYDyd/z9UBSurtsyrPTpPu1IorbZzWf0V+yZCGEqMEksRNlYnM4sTmc6FAkr3/SXR7c4SWyrHb0Oo2Xh7YDcC/oazGVLaHLtkOWLX9cnYtfWCwjbh7q3Q9RW8herUIIUWtJYifK5Kv1h5n8ww561tnGMw1dLUKhFz/IlbPWcDwtl4VjruCmTg2K3Ddv3jwmT55MerqrJa57j17M/uwLwJXUDZ0Py569EnvyXgAcaa7sLn97MFEOthxYcJ/reMiHYJR9W4UQoraRxE6Ui1Ku9eJ05lBCmt0C7Cy1/uTJk9m9e7f7/MfNJ2n9rmcdZ/pxHKme69UFBQV5Jd5aRTlgZ96yM4Pf820sQgghfEISO1Eip1Nx6LRrmYybL2tAl9QnsZ3cAMBFQ9ah6Ywsfzyy1HF0+S11Op0OLSgaXWB4kTpBYZH46V0rDmu4krpp06adh09Uw+lNcM0rBcdCCCFqHUnshJtSimybA71Ow2zQk2N30OeV5QBsfDgc7fQaTDowBjZE07lmNZRlHB2AFlSwJt3Gez3XpLOM+UO6XUtTeI/Xc+l0F+iqyd61QgghvE4SOwG4krqh769h46EU/q9DfV4d1h6AIBMoRy4Jy27DooNf1mXy9k97yHqwvsf9R48e9Th/7rnn+Oijj0hMTPQo7xQt4+fK5ew9Xs/l9p+hSY/zG5MQQogqSxI7AYDNoegfF0n8yUzsTtd+r346O9+1esij3ts/afwbf7zYZ+TPcAU4eTqVhISCcXOaOYiN90pSV24l7fEqhBBCFEMSOwGAyaDj/l5NGdm1kXv7r1PbXnVfD7/0SQIbXk3WE10A15i56Oho9/X8dejylyxJ3Rvi3udVMwfR8ZZpktRV1uP7XNuBlcYgM2GFEKI2k8ROAJBldTW1+ZtcvxJORy5ndv/XfT209b0e9aOjoz26XzOtnuvQhQycQsjAKcRFwPyhrjF1ktRVkslf1qgTQghRKknsBABtn/2VkV0aMapXU0J1yRz+qZf7Wv3+3xV7T37Xq1Iw6KuC8sKTIywGSegqxegP4/cXHAshhBClkMROAK5lRuasPsg/CWeYHnyrOxkzBjfFEn5psfcU7nrNFxch4+i8wp4LS/J2+BgwHQxm38YjhBCiWpDETgCw7dn+KKVImN/CnZT5x/QhpucHxdZ3quKTuoXDJanzCqcdNuR1hfebCkhiJ4QQ4twksROAa2xd4qrR7qTMENCA2N6fFKn3v/99ztNLc9mXZiZ/Kbr8rlfpdi2Hc61Np+mh10TXsc5Ycj0hhBCiEEnsarlcu4Pnf96FcloZnvUrJp2rvMkNf7rr5O/3+t5773H5Fb05ug38Il3XZF26CijL2nQProU+ky5cTEIIIWoESexqOYdT8b+1hwAY1saV1TUZ4plw5O/3umfPHi7r1ttdLuvSVZCsTSeEEOI8kcSultNrcOdF27BnHMWAAwCDpZ5Hnfz9XkeNGsVXfve7y2UJEy8oaW06g+XCxyKEEKLak8SuFlNKcebgLwwP+BAt0FVWr/MLJdY3h8YSf8Z1HBfhGlMn8BwvZ/QvyHbtua5JEDojGEyuMqcTrIXG1snadEIIIbxI5+sAhG8opRjy5mIu/1hHptPVOtTouuWENBvhrjNv3jxat27t3u/V7tppjCZ1ZParW/54uekxrlfWqYJrS550la0s2MGDk3vglWYXPk4hhBC1giR2tdTpxI1sSXRlajlOE/UuewFTUCOPOvlj65x5e8dq5iDAldTpJKlzqcx4uQZdZNFhIYQQXiWdabWQLTOBU8tu4pe2OnKcZuq3uZ2Q5iOK1MsfW6fT6bBEtcDSfxogLXUlenwf+NctOB8w3bUGXeHlSsJbwpPHXMeFu22FEEIIL5DErpZx2jJIPbKCHGXCgIPYVv9HePtxpd4TFR2NYfwuoBaPrXM6wJ5TtPzs8XKFEzWDmSILC+t0MqZOCCHEeePzrth3332XJk2a4OfnR8eOHVm5cmWp9b/44gvat2+Pv78/0dHR3HnnnZw6darUe4SLw5rG/nlteennDVz/z2t8emo49S6bhnaOBXBPFcpd5g+tpY1Mh1YXjKMr/JLxckIIIaoQnyZ2c+fO5ZFHHuGpp55i8+bN9OjRg6uvvprDhw8XW3/VqlXcdttt3H333ezYsYN58+axYcMG7rnnngscefV0YuNzHufmum1LrX/06FEyrYrIKUcBV2udv2yCUDwZLyeEEKIK0JRSyldvfvnll9OhQwfee+89d1nr1q0ZPHgwM2bMKFL/lVde4b333mP//v3usrfeeouZM2dy5MiRMr1nWloaISEhpKamEhwcXPkPUY38+2UTAGxOPY2H7cag02EylJ7bZ1ohLu/r2fkABJjOd5RVVEldsflkvJwQQojzpDy5i89a7KxWKxs3bqR///4e5f3792f16tXF3tOtWzeOHj3KokWLUEpx/Phx5s+fz6BBgy5EyNWeUpDhsFCn+QgsRn2pSZ1SrqRu0FcFZTU+b1EKrJmuVz6HDVa/BX9/AnqTa3xcca8a/8MRQghRHfgssTt58iQOh4PIyEiP8sjISJKSkoq9p1u3bnzxxRfcfPPNmEwmoqKiqFOnDm+99VaJ75Obm0taWprHqzayph/igX8nMGTHy3RbcDnZNkex9fLXrvMPr09ok/b8u/8gUAsmTZy9Hl0+hxV+fRoWPe46FkIIIaown0+e0M5q6VBKFSnLt3PnTsaMGcPkyZPZuHEjixcvJj4+nlGjRpX4/BkzZhASEuJ+NWjQwKvxVxfW1P280vRN/hOxhI6NQrEY9cXWy1+7Lud0AvrAeqT/+Sb+f79e8xckLmk9OlMAxN0gY+iEEEJUCz5rgwkPD0ev1xdpnUtOTi7SipdvxowZdO/enfHjxwPQrl07AgIC6NGjB88//zzR0dFF7pk0aRLjxhUs55GWllYrk7uUne8SqM/mzpaHaDGoa4nJc/7adWg6bMd30UA7ytTR02rXgsSP7/M8H/Kha+mSGp3ZCiGEqAl8ltiZTCY6duzI0qVLGTJkiLt86dKl3HDDDcXek5WVhcHgGbJe72p5KmkOiNlsxmw2F3uttnA6csk5uREAP52txKQOIP+nqA+Opt+7R1lUnpa6wnumlkrz3PjemuV6Z4Mf6PJaEh22inV9Fl4jzpYNygl6M+jzfm8cdnDkFr3v7PXoCjP6lT8OIYQQwgd8Ompq3LhxjBw5kk6dOtG1a1c+/PBDDh8+7O5anTRpEgkJCXz22WcAXHfdddx777289957DBgwgMTERB555BE6d+5MTExMaW9Vaznt2Rz7815sTj1fJQ+gTsidPGx3Fpk4MW/ePCZPnkxS3r6wUM416/LHqJVle62IVjC6UL2P+sCJ3XD7z9Ckh6ts4xzXuLby8K8LTxwoOP98KBxaBTfNgYvz/uNh908w747yPVcIIYSoJnya2N18882cOnWKqVOnkpiYSJs2bVi0aBGNGrn2LE1MTPRY0+6OO+4gPT2dt99+m8cee4w6depw5ZVX8tJLL/nqI1RpGUd/I/HPewGwY+J/yddA8nEe6OfEdNbwyvyxdfk0c1D5eh4rs2dqVSJj6YQQQlRjPl3Hzhdqyzp22Sc3c/TXG93nVqeBL8yfojcG8/S1rTEbPCdP3HLLLXy/7G+yEvdiqNeKNv+ZxqZZQ8ue3FkzC2aTPr6vaHemhyrYFZtP1qMTQghRxZQnd6nJC1jUWkopd1LnVBp+F40kpsPTTDeXvG3Eh7O/YFXeQsRN6sAft5Uhv8kfU2eweJab/Mu3H2pxSaDe6HpVhtFStExvKEjyhBBCiBrG58udCO+zntnlPnY0foBeP3Sm/XNL3WX5a9XVr1+f+vXrc+WVVzJ0fsH9C4dz7lmwhdd9O7kHdEboNdH1Osfes0IIIYQ4P6TpogZK3vCM+ziszcPw0+8e188eTxcUHELWCddxmfeDtWXBqX0FxwYT9JlU2dCFEEIIUQmS2NVAtgzXhBOdMYh6QWZ2Th3gcT1/rTqdTkd0dDTJWiSBedfKPBPWFOCagWrNLNoVK4QQQgifkK7YGsiRcxKbU88C3fN8tPIABp0Of1PRHD46Opq98UcJvO8PoBytdfl7qtqyXQmeTn6NhBBCiKpA/iLXMPack67/Rc/bG/TM+GU3dqfTfX3evHkkJCS4zwvPiS5Ta13hsXWfD/Vm6EIIIYSoJOmKrWHiv7sMAIvOyv91iOHQqWyPfWEnT57sPg4IDGLQVwX3lqkLtqasVyeEEELUQJLY1SC2jKPu46CLhjK9UztMep3HFmLuvWCB012nkXPGdRwXAZby/jbc/L9KRCuEEEIIb5PErgY5+KNrOy6n0gi6ZFqRRYgBJj41mWeWZKAzB+J/iasrNS7CtcRJudflNdTuPXiFEEKIqkYSuxrCWWg3hWMB1zDg2d8J8jOw7smrPCZO3Hn3fbyct6HDxntdkyUsBtlsQQghhKgJZPJEDZGdtNp93KCLaxxdy8ggj/F1SuGxELG/0fWSpE4IIYSoGaTFrgZw5J7h2Iq73OcX1Ytg59QBWIx6j/F1Bw4nsu1fB5qmp22z6PKPqRNCCCFElSZ/2qs5e/YJ4hdc7jpWOn41P0/oukMM79zQI6lTCi6+9DJyUxLQh8SyJ/loxVrqCq+PIoQQQogqRbpiqzF7VhLxCzoDrmTLoQvh9b+DmfzDDmwOp0fdL76eR26Ka/06g66MCxGfTSmYPbCSUQshhBDifJEWu2osYdlt7mNz6MVcdOXnXOPcT3JarsfYOoDnnytYv65RvaCKtdbZsiBpu+s4qi0Y/SsSthBCCCHOE0nsqiGlFJkJv2NN/RcAvV9dGl79MwCvDbsEs8Fz7TqA9IyC9esmPzut8kHcuVhmXQghhBBVjCR21VDOyc0k/nmv+zy0+ydcPv03AJY/3qdIUqcUnMpyHetDYhnyfxXcCkxngMvuyXtQRfpyhRBCCHE+yRi7asiRe9p9HNb2UcyhcRxPy+V4Wi4Kz8kN8+bNo1Xr1uSeSQRc4+sqPBvWYIZBr7pesjixEEIIUeVIi1015LRlAOBX9xLqth2Dw6lYOOYKgCK7TUyePJm9e3a7zys8vk4IIYQQVZ602FUzuWf2cHzNowDYc04BoNdpXBwTwsUxIeh1nlnbV/O+J+Len9GMfgTEtOL5aZUYX6cUZJ50vWTZEyGEEKLKkRa7asSRe4bDiwqWG/ELvxRwTabItjk8tg5zlUP9i1piubglDV7OZucDEGCqRAC2LHi5qev4yWNgCqjEw4QQQgjhbZLYVSMJf9zqPg66aCiRl89EKcWQd1eTkWvn/p4XMfjSWIx6HUrB/82DjYkF90sXrBBCCFGzSVdsNeJ0ZLuPIy+fiaZp5NqdhPob2ZecwWdrDmHI64rNtnsmdZ2iKzFpIp8pAJ5Ndb2ktU4IIYSocqTFrhqxpR0AIKb3HPeSJn5GPbPv7EyW1e6xN2z+ELjMjV/yVJcs6tn80bQRPolbCCGEEBeGJHbVhNOW6T7WmYKLXC88vs6pYNBXruMzPz7BuP8lEBsbyy23VCKxs+XA3FvB5A9DPgSjX8WfJYQQQojzQrpiq4ns5PXuY7+67Uusp/KSuh3L53FsemscaYkl1i0zpeDjvrBvKez8AZSj8s8UQgghhNdJi101kHN6B8dW3OU+17SCfDzb6uDqN/4E4JexPXGiZ+cJOLNoMvbkgvXrgoKCKh6A7BErhBBCVAuS2FUDRxZf6z621OvicU2hOJi3X5hTKYZ+m1ee69obVqfT0aJFC6ZVZv26wmSPWCGEEKLKksSuiss8tsJ9HNLsFiI6TXGfK6XQ6zTmj+oK4G6tA9fWYQ4gOjqaXbt2lf0NbTlFu1qtWQXHktQJIYQQVZYkdlVcevwC93G9zs+7j5VSDH1/DXd1b8KgdtEAZNkK7stNSajYGy64zzWOTgghhBDVjiR2VZjTkUv6IVeSFdjwWo9r2TYHGw+lYHM46dMqAovR4JHY5avU2LqzNegi4+uEEEKIKkwSuyrszJ7Z7uOgJoM9ruk0jcubhOFwKjS0IrtMXNqhI9lZmeceW2fNhOkxruMnj7mWMhn8XvF1jf7SFSuEEEJUYZLYVWGntrzkPg6MvQoAh1ORa3eg0zTm3u8aW5dlK7rLxPy//65YDibr0wkhhBDVliR2VVThBYmDmtzoPl69/yQjP15P6+hgfhnbw12etWUeZxZNpmU9E6/8+AOa1rhsb2T0h/H7C46FEEIIUW3JAsVVVPbJTQA4lI5/Ah/mj93HsTucJdbPX7fO6bDzxBPjGT9+fNneSNMgINz1km5WIYQQolqTFrsqKvv4agBs+HPPFzsA2Dl1AN2ahrNz6gB0hfaEzbIVrFu3Z89ulHJy8803+yZwIYQQQviMJHZVVNqB+QBoKpd29UMA14QJvU5z7wurFEUmTUSVd906ey4sedJ1PGA6GMxeiV8IIYQQF54kdlVQVtIqHDknAQgOb8WPt15RbL1suyupy9oyD0eqa926cnemOu2w4b+u435TAUnshBBCiOpKxthVMcppI+GPke7zyK6vnvOeM4smu4+9um6dEEIIIaoVSeyqmPTDi9zHdVrdjTmk+TnvyR9fB3hvT1ghhBBCVDuS2FUxZ3Z95D4Ov2QCOTYHN7zzF//33mpybI5S7oTY2FiGDh16vkMUQgghRBUlY+yqmNwU1wxYY2BjNJ0Rp92OXoONh1JwKuWulz8bFsC/w3BubJxCRN1QX4QshBBCiCpCErsqxJZx1H0c2vpeAPxNBu7v1ZSP/jyAxagHis6GDb3hZd59EPyNFzxkIYQQQlQhkthVIfasY+7jgCbDWLP/FAC9W0bQPy4SLW/tuiwb/PRwKxypx9CHxHD9W7uxyDcphBBC1HqSDlQhmQl/AGAMaoLVCcM/Wgu4FibWDJq7+3XQV6ByM1C56UQYMph/UyU2jSjUvSuEEEKI6k0SuyrkzN7PALClx6Oh0bxeIAALvv2W56c+y8HkdPLnTzjSXP2wOq2SSd3sgZWMWgghhBBVhSR2VYRSCuXIBiAg9iosJj1Lx/UCoHXr1uzevbvY+yq1bp0tC5K2u46j2oLRv+LPEkIIIYTPSWJXReSm7HQfh1/6lPtYKUhLL1inTh8SS70AV0tdUFCQ99atu3NxJZr+hBBCCFEVSGJXReSe2uo+NgU3we5wkmt3MvIHA7Zr3ifclo1mtNCx97UsGl7J7ldblutY00GjvO3KdPrKfQAhhBBC+JwkdlVE8oanPM6/3XSUCd9uR5nCscRdDZqeuAhYWNmk7pMBcGQd+NeFJw7AnQsrH7wQQgghqgTZeaIKcNoy3cehre8DwKTPa0FTDkDHxnth0XBXF2yFKQXXvQHth4NyVuJBQgghhKiKpMWuCsg4shgAh9I4Wvd+Eo6coU/rKFTkAFdLXT2NuhYvDIHT6aBeaxj0quslhBBCiBpFErsqIOfkZgCsysjgd9cAsOHpAaBzfT1PXbSRtWutmEwmOnbsWPk3NAVU/hlCCCGEqHIksasCUvd9AYAlrB2xdSwA3Pp9QfPczUNv4FhCArGxsRw9erTYZ5SJ3Qor81rqejwGBlPFnyWEEEKIKkcSu6pA04NyENH8Bv665kqybND6XdeluAjY7q33cdpgxYuu4+5jAEnshBBCiJpEJk/4WM7JLXkTJCAwtm+R6/OHXuCAhBBCCFFtSWLnQ0opDi25EQCr08A9Xx/k3s/+JsfqcNfx2prBSoE1y0sPE0IIIURVJF2xPqKUYuh7qxnlF0ljvyT8YvpzcGM2B09lcVin3PW+mz+PhISEyr5Zwfp1QgghhKixpMXOR7JtDjYePsM3J/qR7TTRqOuz3N+rKR0ahrL7pGsNu7gIeGHqZPc9Fd4X1pblmdQ16CL7wgohhBA1kCR2PqKcDkL06axNawPoMFgiGNqxPv+7p6u7/3X+UEgvtE+sV/aFfXwf3CX7wgohhBA1kXTF+ogx9wjzL54EQL3OL7jK9DpshTaEKJx7xcbGMnRoBWZSnD22zuQvSZ0QQghRQ0li5yMnNk93HwdfdLP7WKnialeQjK0TQgghahVJ7Hwk69gyAHSmOmg615g6pWDofM96u3btQimFVpFWNls25KQWnMvYOiGEEKJGk8TOB5RyYnUaePXoLZhDL+Z1mwM/o55sO+w84aoTFwEWA2gVnTABrm7X0evAmuk6N0o3rBBCCFGTSWLnA05bBg50/HHmMjgDrxbT/zp/aCVzMKcD7DmAJnvDCiGEELWEzIr1AeXIxajZeSB6Ps8MaolRX/RrWPDtPDRN44477uC1114r/5scWg3TY+CjPl6IWAghhBDVgSR2PpCV+CcGzcmNEcu564qmxSZ2zz/nWr/u008/5aOPPrrQIQohhBCiGpKuWB9I2fs5SdYwAJqq4rtc0zMquX5do27w5DFAxtQJIYQQtYUkdj6QfuYII3dPBWCn3YG/qeSvodzr1zlssHGO67jjHaA3VjxQIYQQQlQrPu+Kfffdd2nSpAl+fn507NiRlStXllo/NzeXp556ikaNGmE2m2natCmffPLJBYrWO5z2dPy0XPz0To/y7+bP49j01iRMqU9SYmLFHu6wwqLHXS+H1QvRCiGEEKK68GmL3dy5c3nkkUd499136d69Ox988AFXX301O3fupGHDhsXeM2zYMI4fP87HH39Ms2bNSE5Oxm63X+DIK86aFo9FZ+Wnto9Rv+/XWAq11k17bjL25N0e9Su8P6wQQgghah2fJnavvfYad999N/fccw8As2bNYsmSJbz33nvMmDGjSP3FixezYsUKDhw4QFiYa4xa48aNL2TIlZJrdzDuv1/htA/noZh5+EV0dl9TClICmqMLPoMzLYmY2FiCg4K8sz+sEEIIIWoFnyV2VquVjRs3MnHiRI/y/v37s3r16mLv+fHHH+nUqRMzZ87kf//7HwEBAVx//fVMmzYNi8VyIcKuEJW3Tp3DqViY3B6AMc3/9thNItsOltt+pD6uxYkXDZe1hIUQQghRPj5L7E6ePInD4SAyMtKjPDIykqSkpGLvOXDgAKtWrcLPz48FCxZw8uRJHnzwQU6fPl3iOLvc3Fxyc3Pd52lpad77EGWglGLo+2v4cGRHAgw27oj8CYDI9mNKvKfSixMLIYQQolby+azYs/dALW1fVKfTiaZpfPHFF4SEhACu7tyhQ4fyzjvvFNtqN2PGDJ577jnvB15G2TYHGw+l8MGfBxge+BG3RC4BwGy5pcR7JKkTQgghREX4bFZseHg4er2+SOtccnJykVa8fNHR0cTGxrqTOoDWrVujlOLo0aPF3jNp0iRSU1PdryNHjnjvQ5TDh38eIP3wQve5f2QXn8QhhBBCiJrLZ4mdyWSiY8eOLF261KN86dKldOvWrdh7unfvzrFjx8jIyHCX7d27F51OR/369Yu9x2w2Exwc7PG6kPxNBg6+OIiDLw7ConMtPxLe4Rk0XdH15U58dD1Jr3flpiHXl/+NrJl5r6zKhiyEEEKIasqnXbHjxo1j5MiRdOrUia5du/Lhhx9y+PBhRo0aBbha2xISEvjss88AGDFiBNOmTePOO+/kueee4+TJk4wfP5677rqrSk+eAMg5/Y/72BLRqdg61qObcKQmsMUeW/43mNUWsk5VNDwhhBBC1AA+TexuvvlmTp06xdSpU0lMTKRNmzYsWrSIRo0aAZCYmMjhw4fd9QMDA1m6dCkPP/wwnTp1om7dugwbNoznn3/eVx+hVLl2By/+4lqXbmjqEEx57aPmsLZF6uZNnPWeBl3A6O/lhwohhBCiKtOU8npKUaWlpaUREhJCamrqee+WzbLaiZvsmizxY5txWHRW/KN7E9tntkc9peCar2Dpg/VxpCYQExtLQgljBktkzfQ8N/rLLAwhhBCiBihP7uLzWbE1mUGnY3SfpjgdVgynHQBEX/FWkXrZdth5ouDcIx1TCmxnjZvTm0FvAFs2fDYYdAa4dT4Yq3Z3tBBCCCHOL0nsziOTQcf4Aa3IOr6WhN9diZ2m9ytSr8Q2U6XgkwFwZJ1n+U1z4OIhoJzgyIUja13HQgghhKjVKpTYZWZm8uKLL/L777+TnJyM0+mZVBw4cMArwdUUmUcLZv5qOs8fuVPBoK9KuNGWVTSpK8wUAN3Hwtr3ZTydEEIIISqW2N1zzz2sWLGCkSNHEh0dXeKCwrWdUorM7CwSd32OnwY6g+Ws666kLv6M69ygA0dJD3t8H5jykje9uaC81XUQN1jG0wkhhBCiYondL7/8wsKFC+nevbu346lRsm0O2kxdDrzGj23G0az3x57XC42ta1IHHP6QkFLCw0z+rha6s+mlN10IIYQQLhVaoDg0NJSwsDBvx1Lj+Ud29TgvPLZu4fALHIwQQgghapwKNfdMmzaNyZMn8+mnn+LvL2O7SmIx6tn8aCOOLP0//DSrxzWlYOj8gnNNcy3YnJaWdsF3xxBCCCFEzVChxO7VV19l//79REZG0rhxY4xGz+2xNm3a5JXgqjtN09Clb3NvJVZYlq2gGzYuAiwGV2InhBBCCFFRFUrsBg8e7OUwaialFKeOri2m3LO1bv5QmfsghBBCiMqrUGI3ZcoUb8dR4yilGPzOX/xztC/D6sH97dPd1wpPmoiLgIXfz2P69Bf47LPPaNKkCUFBQT6KWgghhBDVWaWmVG7cuJFdu3ahaRpxcXFceuml3oqr2rM6nHRoGMrWo6lsSm9JnUb1iq03fyh0aj+Z3bt30759e1q1asWuXbtcF/Nnwcq+r0IIIYQogwoldsnJyfznP/9h+fLl1KlTB6UUqamp9OnTh6+//pqIiAhvx1ntmA16JvbQMSRtHH6alaCGW4qtp2mQnl7Qmjdt2jTPCk8ek31fhRBCCFEmFVru5OGHHyYtLY0dO3Zw+vRpUlJS+Oeff0hLS2PMmDHejrFaUkpxYPlYLDormgZ6U0iha8XfExsby9ChQ8GWA9/c5nppeknqhBBCCFEmFUrsFi9ezHvvvUfr1q3dZXFxcbzzzjv88ssvXguuulJK8X/vruTqdWMBMAY1cV8rdQsx9wMcsPMH10uVuBeFEEIIIYSHCnXFOp3OIkucABiNxiL7xtZG2TYHm464uleznSaa9PsGKLqFWP4yJ0XoTXDNKwXHQgghhBBlUKHE7sorr2Ts2LF89dVXxMTEAJCQkMCjjz7KVVdd5dUAqyOLUc+8uInkOE34aVYMfuFA0S3EFg4voZdVb4TO916weIUQQghRM1SoK/btt98mPT2dxo0b07RpU5o1a0aTJk1IT0/nrbfe8naM1Y6maYT6OYkynSa09T3F1lk4HHTFJXVKgTXT9RJCCCGEKIcKtdg1aNCATZs2sXTpUnbv3o1Siri4OPr27evt+KolhzUNZc8CwD+6Z7F1SpwP8ckAOLIOmvWD7mOhUTfQ6c9TpEIIIYSoSSq1jl2/fv3o16+ft2KpMbLSj/HF8QEATKxzSTnuVK6kDmDfUshNh7sWez0+IYQQQtRMZU7s3nzzTe677z78/Px48803S61b25c8ST+2mjnHrwPgCb3lnPV/+OEHrFYrJs0Biwe5Ch/fBwHhstSJEEIIIcqszInd66+/zi233IKfnx+vv/56ifU0Tav1iZ3t1CauDgsGQK8beM76HTt2dB3kZkB+A51JFiUWQgghRPmUObGLj48v9lgUZUtayrj6VgIbXI3ZUDA+rqSFid0XZ587CRRCCCGEKEmFZsVOnTqVrKysIuXZ2dlMnTq10kFVV1lWO62fWcy1W2eQ7TSh96vrvqYUDJ1fys22LEja7jqOait7wwohhBCi3CqU2D333HNkZGQUKc/KyuK5556rdFDVWbbNQY4yAxDY4OqC8kJr2J29MPHPP//MvG+/4+e9NlfBnYulG1YIIYQQ5VahWbFKKbRiEo+tW7cSFhZW6aCqKz+DniUjDSRveBKzZsMS2bXYevOHeuZto0aNIiEhgdggjaPjjJLUCSGEEKJCypXYhYaGomkamqbRokULj+TO4XCQkZHBqFGjvB5kdaHTaYSc+QWd6TSAx8+n8Pi6/OJ58+YxefJkEhMTL2SYQgghhKihypXYzZo1C6UUd911F8899xwhISHuayaTicaNG9O1a/GtVDWdzeHks9XxnNyUyvXhOgLqtnFfK2l83eTJk9m9e7f7PMgsLXVCCCGEqLhyJXa33347drsdgL59+1K/fv3zElR1ZHM4mbZwNzCUq+uuJqr7G+5rJY2vS09PB0Cn09GieTOmtTtygaMWQgghRE1S7jF2BoOBBx98kF27dp2PeKotnabRL/owjuzj6HFiCmpcbL2zx9cBREdHs2vXbsg65SqQGbFCCCGEqIAKTZ64/PLL2bx5M40aNfJ2PNWWn1HPE/VeA2VH7xdeYr3C4+sSEhI8LwSUfJ8QQgghxLlUKLF78MEHeeyxxzh69CgdO3YkICDA43q7du28Elx1ozcF48g9TUBMn3PWnTlzpvs4KCgQFj7mOhkwHQzm8xWiEEIIIWqwCiV2N998M+C5J6ymae5lUBwOh3eiqyayrHa6z1iK0zqe/7WeQlR0z3Pes2HDBg4ePMjIkSMZO/p+2DDadaHfVEASOyGEEEKUX4USO9lSrKiUbCcQBIClXucy3dO4cWNWrlwJdiuszJs4oTOepwiFEEIIUdNVKLGTsXWe/Ax65l17hDN7PyUotAkGSz2P66XuEQtgMEGfSecvQCGEEELUChVK7AD279/PrFmz2LVrF5qm0bp1a8aOHUvTpk29GV+1oNNp1Gc7dfyS8A/v7XHtnHvECiGEEEJ4SYX2il2yZAlxcXGsX7+edu3a0aZNG9atW8fFF1/M0qVLvR1jtZBxeBEAyp7tUV7SGnbjx4/nnnvuYfz48eB0QvIu18vpvJBhCyGEEKIG0ZQ6Z0dhEZdeeikDBgzgxRdf9CifOHEiv/76K5s2bfJagN6WlpZGSEgIqampBAcHe+WZ6cc388k3rwBw6zXDqNv0Bve1LBu0ftd1vPMBCDC5juvXr+/aHzY2lqO7N8MrzVwXnjwGJs9ZxkIIIYSovcqTu1SoxW7Xrl3cfffdRcrvuusudu7cWZFHVmun43/k9YQRvJ4wAr/Y/h7Xitsj1kPWyYKkTgghhBCiEiqU2EVERLBly5Yi5Vu2bKFevXpFb6jhnFnH6Ba8lSvqxqPXm9zlZRpfZ7cWHDfoIrtOCCGEEKLCKjR54t577+W+++7jwIEDdOvWDU3TWLVqFS+99BKPPfaYt2Os8lTqFp5r/Cuhre/Dz6h3l5c0vq5Yj+9z7TxRbLOeEEIIIcS5VSixe+aZZwgKCuLVV19l0iTXMh0xMTE8++yzHosW1xaO7GQAdKaQEusUt0esB5O/JHVCCCGEqJQKdcVqmsajjz7K0aNHSU1NJTU1laNHjzJ27Fi02picaK782FynVclVCu0R27p1axITEy9EZEIIIYSoRSqU2OVLTk5my5YtbN26lRMnTngrpmonV5m5dddz9P/MTra19O3UJk+ezO7du3HmLWsSZK6FibAQQgghzosKJXZpaWmMHDmSmJgYevXqRc+ePYmJieHWW28lNTXV2zFWeU6njeO2uiSk2lCUvnpMz5496dSpEwCtWrZgWh/ZF1YIIYQQ3lGhxO6ee+5h3bp1LFy4kDNnzpCamsrPP//M33//zb333uvtGKs0pz0Hk2bj7WYz+e7u1pgN+lLrf/DBB2zYsAGlFLu2bmRonOwNK4QQQgjvqNDkiYULF7JkyRKuuOIKd9mAAQP46KOPGDhwoNeCqw6safvRa4qW/odp3vyist+oFMyuXT8rIYQQQpxfFWqxq1u3LiEhRWeAhoSEEBoaWumgqhVlr+B9Cm78CNoPh/qdZf06IYQQQlRahRK7p59+mnHjxnnM7ExKSmL8+PE888wzXguuOshOXodD6fgjrTvfb07A7ih5r9dOnTpRv3591xg7nQ7qtYZBr8Ldv8pSJ0IIIYSotAp1xb733nvs27ePRo0a0bBhQwAOHz6M2WzmxIkTfPDBB+66VXnfWG/ISlqNVRmYcXA4HNxC/4sjMeiLz5eTkpJISEhwndhzwWmXfWGFEEII4TUVSuwGDx7s5TCqL6ctHR2KjiEHsUR0QlfWlrdFj8O2edB9LPR4DAymc98jhBBCCFGKCiV2U6ZM8XYc1VbOyU2YdfB+/1NEdLy87DfqTWDPhgPLoffE8xafEEIIIWqPCiV2+TZu3MiuXbvQNI24uDguvfRSb8VVbRj8o7FnJWIMblrkmippSTvlhP4vQL+prkkTMr5OCCGEEF5QocQuOTmZ//znPyxfvpw6deqglCI1NZU+ffrw9ddfExER4e04qyynLQOlwBl4sUe5UjB0/tm18zK99CRQDhlfJ4QQQgivqtCs2Icffpi0tDR27NjB6dOnSUlJ4Z9//iEtLY0xY8Z4O8Yqy56djMOazuh9T9DhjQT6vbbCvaVYth125u2yFnZgHh3btSbx2DEfRiuEEEKImq5CLXaLFy/mt99+o3Xr1u6yuLg43nnnHfr37++14Kq6rMQ/caJxf/QCfjJMJS3HgZ+xaK6ctWQye/fsdp8HBfjJunVCCCGE8LoKJXZOpxOjsehWWEaj0b25fW2Qv+vEJSFHuGZIJyxGPVox4+UmPvkM+3dt4YUXX6ZVuI5pr30o4+qEEEII4XUVSuyuvPJKxo4dy1dffUVMTAwACQkJPProo1x11VVeDbAqyzj6KwCazoS/qeQf5c3DR+CvbuB5c976fv835EKEJ4QQQohapkJj7N5++23S09Np3LgxTZs2pVmzZjRp0oT09HTeeustb8dYZRks0TiUjrW5fViyI6nUXSeEEEIIIc63CrXYNWjQgE2bNrF06VJ2796NUoq4uDj69u3r7fiqNJ0xEKsy8NQ//eCfjeycOqDEXSeEEEIIIc63cid2drsdPz8/tmzZQr9+/ejXr9/5iKtayE5ehw7FJfXs6C0RHrtOFF7Dbu+ePRjtaRhOOmgZrvdBpEIIIYSoDcqd2BkMBho1aoTD4Tgf8VQrBv8ozNbdfDIwkbC4G9zlZ69hN2jgVRxLSCA2SOPouCAfRCqEEEKI2qBC/YZPP/00kyZN4vTp096Op1qxnnEtYWIOaeFRXngNu7hwhZZ18kKHJoQQQohaqEJj7N5880327dtHTEwMjRo1IiDAcweFTZs2eSW4qkw57e5jnbHkHSTm/5+Tlo8HA3mZXoMusoadEEIIIc6LCiV2gwcPRtM0VImbodYCyjUDNtdpZNjXOWi6VXxzf1fMBj1ZtoJqml4PepPrJDga7losa9gJIYQQ4rwoV2KXlZXF+PHj+f7777HZbFx11VW89dZbhIeHn6/4qixHrqsb2onG9mMZrjKn4v/mwcbEku7SJKkTQgghxHlTrsRuypQpzJkzh1tuuQWLxcKXX37JAw88wLx5885XfFWWLcuVvZk0O5/c0QkAh9J5JHWdosGi2cCa6YsQhRBCCFHLlCux++677/j444/5z3/+A8Att9xC9+7dcTgc6PW1axkPR/ZxAPSakytbRQJ4dMFuvBfqWkCzWSEnNa+0FnddCyGEEOK8K9es2CNHjtCjRw/3eefOnTEYDBw7dszrgVV1WcfX4FAamzLbsPLfEzicnkmbvzGv11XTg9HimyCFEEIIUauUK7FzOByYTCaPMoPBgN1uL+GOmsvgF45VGZmwfxQjP15Prr2Edf0MZrCE5p3I+DohhBBCnD/l6opVSnHHHXdgNpvdZTk5OYwaNcpjyZPvvvvOexFWUdknt6BD0TwkA4N/DDpNw3F2T6tS8MkAn8QnhBBCiNqnXC12t99+O/Xq1SMkJMT9uvXWW4mJifEoK493332XJk2a4OfnR8eOHVm5cmWZ7vvrr78wGAxccskl5Xo/b8k69gdmnY3ZnX/gl7E9MBv0HrtNAGDLgiPr2PD2vRzZv4cNGzb4JFYhhBBC1A7larGbPXu2V9987ty5PPLII7z77rt0796dDz74gKuvvpqdO3fSsGHDEu9LTU3ltttu46qrruL48eNejam8AmJ6A2ftNhEBFgOQN5kiestrcM3jYCp5IWMhhBBCiMqq0JZi3vLaa69x9913c88999C6dWtmzZpFgwYNeO+990q97/7772fEiBF07dr1AkXqyZFTsJVaUOMhRa7PH5o3caI2L+AshBBCiAvOZ4md1Wpl48aN9O/f36O8f//+rF69usT7Zs+ezf79+5kyZUqZ3ic3N5e0tDSPV2VlJrm6i3OdRm75bB83f7CGHFvB5In8pG7eQ53QnktjzC85fPjfTyr9vkIIIYQQpfFZYnfy5EkcDgeRkZEe5ZGRkSQlJRV7z7///svEiRP54osvMBjK1os8Y8YMj/F/DRo0qHTs+S12TjTWxZ9mXfxpnGe3ztmymPz9vwC8td7K62+9W+n3FUIIIYQojU+7YgG0s7bYUkoVKQPXUisjRozgueeeo0WLFmV+/qRJk0hNTXW/jhw5UumY7VkJAASGteKdER14Z0QHTPqiP8r03IJkb9q0aZV+XyGEEEKI0pRr8oQ3hYeHo9fri7TOJScnF2nFA0hPT+fvv/9m8+bNPPTQQwA4nU6UUhgMBn799VeuvPLKIveZzWaP5Vm84czujwEwGv0Y2CYKvU7z2HXibLGxMQwdOtSrMQghhBBCnM1nLXYmk4mOHTuydOlSj/KlS5fSrVu3IvWDg4PZvn07W7Zscb9GjRpFy5Yt2bJlC5dffvmFCh1jYEOUgoe23sTmwylAwTyJ4+9cSfMm9al/UQsSM2TyhBBCCCEuHJ+12AGMGzeOkSNH0qlTJ7p27cqHH37I4cOHGTVqFODqRk1ISOCzzz5Dp9PRpk0bj/vr1auHn59fkfLzzZZxmBxlYutJC++vOMAb0cEM/db1o3SmH+dYUoJH/aDAwAsanxBCCCFqJ58mdjfffDOnTp1i6tSpJCYm0qZNGxYtWkSjRo0ASExM5PDhw74MsUQaikZ19Ow5nkaOXXOvYRcUFolFnwooSEskyKwx7dlnfBqrEEIIIWoHTanatdhaWloaISEhpKamEhwcXO77bZlJ7FrQCz1OWt64EoN/FFk2aJ036XXnAxBgAqyZMD3GVfjkMVmcWAghhBAVUp7cxeezYqsTpRTDPvmH6/95jecO3YPeEsm8efMIMGkce6ElJ/93Cx4TemMu9VmsQgghhKh9fNoVW93YnYqb2pg5dfIgTp0FTdOYPHmy69qJvWiaDv2uBaAHWl0H3cfC2vfB6O/bwIUQQghRK0hiVw5GvY6ro3bTpvkrGAwWYAzp6enu6xFXP4X5uztcJ08ecyV3cYOhmHX5hBBCCCG8TRK7CrDorOjNdTzK9CGxBFzyfziCv0KvAZoO9PLjFUIIIcSFI5lHOTicir+3/Io9O5Y2Ma2LXM/VLOSOXIi/0QfBCSGEEKLWk8SuHHLtDu7degcAS6I+9m0wQgghhBBnkVmx5aChUddwhrqGM9RpfgtKwcms/GuKbWkXYXn9ItdSJ0IIIYQQF5i02JWDn1HH13FPAxBWfzXZdrA7XdcMOkWI4xRk+TBAIYQQQtRq0mJXDo6cE+5jvTkMpUAXUJfOF4VR157kw8iEEEIIISSxKxdr2n73saYzM3Q+NLj7K7rXyyDInLekSYMusm6dEEIIIXxCumLLITMtkamH7gLgzWwHO0/osYQ1okGwxrQ+Znh8HwSEy7p1QgghhPAJSezKwW7LYWVqBwAchbbYfbSr2XVg8pekTgghhBA+I4ldOdiOL+OhGCt6v3AWfp/B8TfeIvbWD6VDWwghhBBVgiR25aDHzg3hf6L3C+eaJxeRG7+bA9MupnW4jl2jA30dnhBCCCFqOWlrKiOnU7E/6ThHcyMIaHAt6RkFe8RO62P2YWRCCCGEEC7SYldG2TY7t20dDcCaLpmcyluvzhASw9C4DB9GJoQQQgjhIoldWSk7Abq8bC6kQ6GFiX0XkhBCCCFEYZLYlZEh5wjft3kCgKCw3e7yMIsG17ziOtGbfBGaEEIIIQQgiV2Z2bMS3cc6faExdRrQ+d4LH5AQQgghxFmkI7GMMg4vBFxbiQkhhBBCVEXSYldG2dnpzDxyKwAv2x2eF+NXuv63UTfQ6S9wZEIIIYQQLpLYlVFO+hGWplwLgN1ZsOsESsGnrnKePAamAB9EJ4QQQgghiV2Z+QVGcm/UAvyje2LQ6ah76+coey4fD1awf0peLdlOTAghhBC+I4ldGRl1imH1/qBepwEYDTr8mvcGoOdVwMCBPo1NCCGEEAJk8kSZZR1bxi/rMuk84FGaN6lPzr/LfR2SEEIIIYQHabErA3vWcZxK47V5aRxKzAUgwp6Ln8rC74M+rh7Ye5eByd+3gQohhBCiVpPErgzO/PsZucrI0fRAwJXYaQYzGgrdyfzFilWJ9wshhBBCXAjSFVsGKTvedR3kzY2IjonFr3lvNEnmhBBCCFGFSGJ3Dk57DgDLN6TgSD8FwOlsQCnmp8ukCSGEEEJUHZLYnYOyZwHwxvwz7jKHMQgLWVzs2O4qiGoLRhlfJ4QQQgjfksSujDKzne7jOtdM87x452LQZA07IYQQQviWJHbnYM9J9jgPDKuHf/shnuPrJKkTQgghRBUgs2LPwZZ+GACVN3Mi2+oAp5P5mTK+TgghhBBViyR253D6nzcBWPZmI36OWMQ7G8Ci5cj4OiGEEEJUOdIVew727CQA/ALqMrpPCwhqAVqhH5uMrxNCCCFEFSEtducw64v9pGc5iWjdh62Zdor8yCSpE0IIIUQVIYldKRy5Z/hmWTrHUxzoV/1I7BNPQGBTmkUYUO0nukbd6Yy+DlMIIYQQApDErlTpB39wHwf6GdAy96Osp/n6wa5o5kk+jEwIIYQvOZ1OrFarr8MQNYTRaESv13vlWZLYleLMnjnuY3+TARU5ADQ9Op10vwohRG1ltVqJj4/H6XSeu7IQZVSnTh2ioqLQKjnESxK7Uuj96rqPNQ3Q5f24lBOS97iOw1uCTuagCCFEbaCUIjExEb1eT4MGDdDJv/+ikpRSZGVlkZzsWjc3Ojq6Us+TxK4UuWf2csYeBJwhPcdOqHK6ZsTasuHdLq5KTx4DU4BP4xRCCHFh2O12srKyiImJwd9flroS3mGxWABITk6mXr16leqWlf/UKIUW2BSjzg5AttUJFGoe9a/regkhhKg1HA4HACaTyceRiJom/z8UbDZbpZ4jLXalMOqcBOiyyQCUzgSaRlwEWPwD4IkDvg5PCCGEj1R2HJQQZ/PW75S02JXT/KG49om1Zfs6FCGEEEIID5LYlSLn1LYiZRoKPhkAnw/1QURCCCFE+c2YMYPLLruMoKAg6tWrx+DBg9mzZ49HHaUUzz77LDExMVgsFnr37s2OHTs86nz44Yf07t2b4OBgNE3jzJkzRd6rcePGaJrm8Zo4cWKlP8NHH31Ejx49CA0NJTQ0lL59+7J+/foi9d59912aNGmCn58fHTt2ZOXKlR7Xv/vuOwYMGEB4eDiaprFly5Yiz9i/fz9DhgwhIiKC4OBghg0bxvHjxyv9GS4ESexKYTPFkB59BX6NL8XctEdeYRYcWQeHVoE107cBCiGEEGWwYsUKRo8ezdq1a1m6dCl2u53+/fuTmVnwd2zmzJm89tprvP3222zYsIGoqCj69etHenq6u05WVhYDBw7kySefLPX9pk6dSmJiovv19NNPV/ozLF++nOHDh7Ns2TLWrFlDw4YN6d+/PwkJCe46c+fO5ZFHHuGpp55i8+bN9OjRg6uvvprDhw+762RmZtK9e3defPHFYt8nMzOT/v37o2kaf/zxB3/99RdWq5Xrrruueixxo2qZ1NRUBajU1NRz1v3nm26q0YSfVaMJP6uGr9lUw1lKZWZkKDUl2PXKzbgAEQshhKgqsrOz1c6dO1V2dravQ6mU5ORkBagVK1YopZRyOp0qKipKvfjii+46OTk5KiQkRL3//vtF7l+2bJkCVEpKSpFrjRo1Uq+//vr5Ct3NbreroKAg9emnn7rLOnfurEaNGuVRr1WrVmrixIlF7o+Pj1eA2rx5s0f5kiVLlE6n88gTTp8+rQC1dOlS736IQkr73SpP7iItdqUwWo+x4OLx/PlgY9Dypx4rX4YkhBBCVFpqaioAYWFhAMTHx5OUlET//v3ddcxmM7169WL16tXlfv5LL71E3bp1ueSSS3jhhRfOyy4dWVlZ2Gw292ewWq1s3LjR4zMA9O/fv1yfITc3F03TMJvN7jI/Pz90Oh2rVq3yTvDnkcyKLYFyOli8PpMfVx1n/tUW1wrFSuH32UBfhyaEEKKKUEqhHL6ZTKfpLRWaSamUYty4cVxxxRW0adMGgKSkJAAiIyM96kZGRnLo0KFyPX/s2LF06NCB0NBQ1q9fz6RJk4iPj+e///1vuWMtzcSJE4mNjaVv374AnDx5EofDUexnyP98ZdGlSxcCAgKYMGEC06dPRynFhAkTcDqdJCYmevUznA+S2JXA6cjijfln0GlwNDkDAAtZ6I5vd1WIagtGWZxSCCFqM+XIZv83F/vkvZsO24FmKP/foYceeoht27YV2/p0dqKolCp38vjoo4+6j9u1a0doaChDhw51t+Kdbfr06UyfPt19vnPnTho2bFjqe8ycOZOvvvqK5cuX4+fn59XPEBERwbx583jggQd488030el0DB8+nA4dOnhtP9fzSRK7EuSe3kFmtpMTaYr+1w7DOGanZ4U7F+ftMyaEEEJUDw8//DA//vgjf/75J/Xr13eXR0VFAa6Wu8JbWiUnJxdpASuvLl1cOzXt27ev2MRu1KhRDBs2zH0eExNT6vNeeeUVpk+fzm+//Ua7du3c5eHh4ej1+iKtcxX5DP3792f//v2cPHkSg8Hg3se1SZMm5XqOL0hiV4Ls5PUoNJwOB8knThLrdOCvZRVUkKROCCFqPU1voemwHeeueJ7eu6yUUjz88MMsWLCA5cuXF0lQmjRpQlRUFEuXLuXSSy8FXGPWVqxYwUsvvVSpODdv3gyUvAdqWFiYe5zcubz88ss8//zzLFmyhE6dOnlcM5lMdOzYkaVLlzJkyBB3+dKlS7nhhhsqFHt4eDgAf/zxB8nJyVx//fUVes6FJIldCXJObnKtWQf4m/R8m3kNnRxF18sRQghRe2maVqHu0Att9OjRfPnll/zwww8EBQW5W7VCQkKwWFxj9R555BGmT59O8+bNad68OdOnT8ff358RI0a4n5OUlERSUhL79u0DYPv27QQFBdGwYUPCwsJYs2YNa9eupU+fPoSEhLBhwwYeffRRrr/++nN2r57LzJkzeeaZZ/jyyy9p3Lix+zMEBgYSGBgIwLhx4xg5ciSdOnWia9eufPjhhxw+fJhRo0a5n3P69GkOHz7MsWPHANzr+UVFRblbLmfPnk3r1q2JiIhgzZo1jB07lkcffZSWLVtW6jNcEN6drFv1lXXK8L557VVkqF4BKiYmpmCJkynBSv23v1JO5wWKWAghRFVRXZc7wbWkQ5HX7Nmz3XWcTqeaMmWKioqKUmazWfXs2VNt377d4zlTpkwp9TkbN25Ul19+uQoJCVF+fn6qZcuWasqUKSozM7PSn6FRo0bFvveUKVM86r3zzjuqUaNGymQyqQ4dOriXdMk3e/bscz5nwoQJKjIyUhmNRtW8eXP16quvKud5/rvvreVONKVUrVq/Iy0tjZCQEFJTUwkODi6x3r9fNqHHQ0c4nuLALzSWD8bfDMDQ0U/jH1RHumKFEKIWysnJIT4+3r2zgRDeUtrvVllzF5Cu2BJlO02ctNUBTmFzwjOWV4irpzEyCJCcTgghhBBVkCxQXAynPZtf16XjyDgFQF11kjB1ivlDpaFOCCGEEFWXJHbFyE5ez3vfnXSfh5lsjMqZhWaTvWGFEEIIUXVJYlcMW3o8d1wdzMj+QQBM62Pm/ty3fByVEEIIIUTpJLErRu6Z3fznyiCmjelOZlY2j3XPolFoKpgCfB2aEEIIIUSJJLErhsESic2hQ78lnoSPbwHl9HVIQgghhBDnJLNii5G6fy4OpaO+LRFOJmIJTidbH+LrsIQQQgghSiWJXTGctgxOn7FxNM2JXgNCZCqsEEIIIao+SeyKoeyZ3PzsUY6nOIgN0gicqvd1SEIIIYQQ5yRj7M4yb948Bo4/xokzDo/yuAiwSBoshBBCiCpMEruzTJ48mQPHrDjzNloLMru6YWVxYiGEENXVjBkzuOyyywgKCqJevXoMHjyYPXv2eNRRSvHss88SExODxWKhd+/e7Nixw3399OnTPPzww7Rs2RJ/f38aNmzImDFjSE1N9XhOSkoKI0eOJCQkhJCQEEaOHMmZM2cq/Rk++ugjevToQWhoKKGhofTt25f169cXqffuu++6t+Xq2LEjK1eudF+z2WxMmDCBtm3bEhAQQExMDLfddhvHjh3zeMaHH35I7969CQ4ORtM0r8R/oUhid5b09LxfUE1Hq3Ad0/qYXaeS1AkhhKimVqxYwejRo1m7di1Lly7FbrfTv39/MjMLFt6fOXMmr732Gm+//TYbNmwgKiqKfv36kZ6eDsCxY8c4duwYr7zyCtu3b2fOnDksXryYu+++2+O9RowYwZYtW1i8eDGLFy9my5YtjBw5stKfYfny5QwfPpxly5axZs0aGjZsSP/+/UlISHDXmTt3Lo888ghPPfUUmzdvpkePHlx99dUcPnwYgKysLDZt2sQzzzzDpk2b+O6779i7dy/XX3+9x3tlZWUxcOBAnnzyyUrHfcGpWiY1NVUBKjU1tdjrMdGRClDhoUalpgQrNSVYtXw9Q2VaL3CgQgghqpzs7Gy1c+dOlZ2d7etQKiU5OVkBasWKFUoppZxOp4qKilIvvviiu05OTo4KCQlR77//fonP+eabb5TJZFI2m00ppdTOnTsVoNauXeuus2bNGgWo3bt3e/Uz2O12FRQUpD799FN3WefOndWoUaM86rVq1UpNnDixxOesX79eAerQoUNFri1btkwBKiUlxWtxl6S0361z5S6FSYtdIUopTmfZAdAja9cJIYSomfK7T8PCwgCIj48nKSmJ/v37u+uYzWZ69erF6tWrS31OcHAwBoNrEPqaNWsICQnh8ssvd9fp0qULISEhpT6nIrKysrDZbO7PYLVa2bhxo8dnAOjfv/85P4OmadSpU8er8fmKzxO70vrCz/bdd9/Rr18/IiIiCA4OpmvXrixZssRrsWTbHNjy5kworz1VCCFETaUUZNl881IV/EOllGLcuHFcccUVtGnTBoCkpCQAIiMjPepGRka6r53t1KlTTJs2jfvvv99dlpSURL169YrUrVevXonPqaiJEycSGxtL3759ATh58iQOh6NcnyEnJ4eJEycyYsQIgoODvRqfr/h0nmd+X/i7775L9+7d+eCDD7j66qvZuXMnDRs2LFL/zz//pF+/fkyfPp06deowe/ZsrrvuOtatW8ell15a6Xh0mobZoMgCNJ3JXd4qXGbECiGEKCrbDq3f9c1773oQ/I3lv++hhx5i27ZtrFq1qsg17awB5UqpImUAaWlpDBo0iLi4OKZMmVLqM0p7DsD06dOZPn26+7ykHKCwmTNn8tVXX7F8+XL8/Pwq9BlsNhv/+c9/cDqdvPuuj77E88Cn6cprr73G3XffzT333APArFmzWLJkCe+99x4zZswoUn/WrFke59OnT+eHH37gp59+8kpi52fU8/un93N696c4tEDIdGX4XwyWyRNCCCGqv4cffpgff/yRP//8k/r167vLo6KiAFeLW3R0tLs8OTm5SAtYeno6AwcOJDAwkAULFmA0Gj2ec/z48SLve+LEiSLPyTdq1CiGDRvmPo+JiSn1M7zyyitMnz6d3377jXbt2rnLw8PD0ev1RVrnivsMNpuNYcOGER8fzx9//FFjWuvAh4ldfl/4xIkTPcrP1RdemNPpJD093d2/Xpzc3Fxyc3Pd52lpaaU+s57jD+rWN6GFXgbrfgIkqRNCCFE8i8HVcuar9y4rpRQPP/wwCxYsYPny5TRp0sTjepMmTYiKimLp0qXuhhKr1cqKFSt46aWX3PXS0tIYMGAAZrOZH3/8sUhrWdeuXUlNTWX9+vV07twZgHXr1pGamkq3bt2KjS0sLKzUv+OFvfzyyzz//PMsWbKETp06eVwzmUx07NiRpUuXMmTIEHf50qVLueGGG9zn+Undv//+y7Jly6hbt26Z3ru68FliV5G+8LO9+uqrZGZmemT6Z5sxYwbPPfdcmeNy2jIAUAYjC403UM95nIuN/mW+XwghRO2haRXrDr3QRo8ezZdffskPP/xAUFCQ++9sSEgIFosFTdN45JFHmD59Os2bN6d58+ZMnz4df39/RowYAbha6vr3709WVhaff/45aWlp7saSiIgI9Ho9rVu3ZuDAgdx777188MEHANx3331ce+21tGzZslKfYebMmTzzzDN8+eWXNG7c2P0ZAgMDCQwMBGDcuHGMHDmSTp060bVrVz788EMOHz7MqFGjALDb7QwdOpRNmzbx888/43A43M8JCwvDZHINw0pKSiIpKYl9+/YBsH37doKCgmjYsGGZk1Cf8e5k3bJLSEhQgFq9erVH+fPPP69atmx5zvu//PJL5e/vr5YuXVpqvZycHJWamup+HTlypMQpw1m5dtXhqc9Vh6c+Vwf3/KKav56tGr7ulKVOhBBCKKWq73InuOYEFnnNnj3bXcfpdKopU6aoqKgoZTabVc+ePdX27dvd1/OX/ijuFR8f76536tQpdcstt6igoCAVFBSkbrnlFq8sF9KoUaNi33vKlCke9d555x3VqFEjZTKZVIcOHdxLuiilVHx8fImfYdmyZe56U6ZMOefPy9u8tdyJplRF59VUjtVqxd/fn3nz5nk0mY4dO5YtW7awYsWKEu+dO3cud955J/PmzWPQoEHlet+0tDRCQkLcU7Q9rx2n6a2v4LTl8vyQi3kx2zXTp6IDVIUQQtQsOTk5xMfHu1dzEMJbSvvdKi13OZvPljsp3Bde2NKlS0vshwf46quvuOOOO/jyyy/LndSdy4/zv+DkT69wevFbvPD4AxxKCcGiMs99oxBCCCFEFeDTWbHn6gufNGkSCQkJfPbZZ4Arqbvtttt444036NKli7tf3GKxEBISUul4nn/xLfdx/h6xQgghhBDVhU8Tu5tvvplTp04xdepUEhMTadOmDYsWLaJRo0YAJCYmuvd3A/jggw+w2+2MHj2a0aNHu8tvv/125syZU+l40jOy3MdPv/IRl56+nmxk4oQQQgghqgefjbHzldL6qWOjIziWdJI6dQI4mJhGuw9cPdUyxk4IIQTIGDtx/lT7MXZVkdNhBSDd7oc1K93H0QghhBBClI8kdoUop2sh43AtlYg3GzIt6zFMKvccdwkhhBBCVA2S2BWiaXoADDgA6GhfR7MIk+wTK4QQQohqQVKWwpTD43Ro0GL+vkmTLcWEEEIIUS1Ii12ebKuDdFMU5qA6RAW6MjmFJHVCCCGEqD4kscujUITf9g5NHvwvf98X6OtwhBBCCFFBy5cvR9M0zpw54+tQLjhJ7PKYNAevN32Nl5q8de7KQgghhPCqC52M7d+/nyFDhhAREUFwcDDDhg3j+PHjHnU2bdpEv379qFOnDnXr1uW+++4jIyPDff306dNcd911BAYG0qFDB7Zu3epx/4MPPsirr756QT5PPkns8mQnLKFNwAHiAg76OhQhhBBCnEeZmZn0798fTdP4448/+Ouvv7BarVx33XU4nU4Ajh07Rt++fWnWrBnr1q1j8eLF7NixgzvuuMP9nBdeeIH09HQ2bdpEr169uOeee9zX1qxZw/r163nkkUcu6GeTxC6PPeeUr0MQQgghzgulFDNnzuSiiy7CYrHQvn175s+f777Wt29fBg4cSP6eBWfOnKFhw4Y89dRTQEFr2sKFC2nfvj1+fn5cfvnlbN++3eN9Vq9eTc+ePbFYLDRo0IAxY8aQmVmw53pubi5PPPEEDRo0wGw207x5cz7++GMOHjxInz59AAgNDUXTNHcCVVrs+RYtWkSLFi2wWCz06dOHgwcPlvrz+Ouvvzh48CBz5syhbdu2tG3bltmzZ7Nhwwb++OMPAH7++WeMRiPvvPMOLVu25LLLLuOdd97h22+/Zd++fQDs2rWL//znP7Ro0YL77ruPnTt3AmCz2XjggQd4//330ev15f26KkUSuzy5WSe4510YMUvH/T9l+zocIYQQ1UiW1U6W1U7hzZysdidZVju5dkexdZ3Ogro2h6tujq1sdcvr6aefZvbs2bz33nvs2LGDRx99lFtvvZUVK1agaRqffvop69ev58033wRg1KhRREZG8uyzz3o8Z/z48bzyyits2LCBevXqcf3112Oz2QDYvn07AwYM4MYbb2Tbtm3MnTuXVatW8dBDD7nvv+222/j6669588032bVrF++//z6BgYE0aNCAb7/9FoA9e/aQmJjIG2+8cc7YAY4cOcKNN97INddcw5YtW7jnnnuYOHFiqT+P3NxcNE3DbDa7y/z8/NDpdKxatcpdx2QyodMVpEoWiwXAXad9+/b88ccf2O12lixZQrt27QB46aWX6N27N506dSrHt+QlqpZJTU1VgEpNTfUoP7h6stIH1lWAig3SlJoSrG74X4ZyOn0UqBBCiConOztb7dy5U2VnZ3uUN5rws2o04Wd1Mj3HXfbW73tVowk/qwnzt3rUbfX0L6rRhJ/V4VOZ7rL/rjygGk34WY35apNH3Uun/qoaTfhZ7UlKc5d9ue5QuWLOyMhQfn5+avXq1R7ld999txo+fLj7/JtvvlFms1lNmjRJ+fv7qz179rivLVu2TAHq66+/dpedOnVKWSwWNXfuXKWUUiNHjlT33Xefx3usXLlS6XQ6lZ2drfbs2aMAtXTp0mLjzH+PlJSUcsU+adIk1bp1a+Us9Ad7woQJRZ5VWHJysgoODlZjx45VmZmZKiMjQ40ePVoB7s/wzz//KIPBoGbOnKlyc3PV6dOn1Y033qgANX36dKWUUmfOnFHDhw9XDRs2VD179lQ7duxQe/fuVc2bN1cnT55U999/v2rSpIm66aab1JkzZ4qNJV9Jv1tKlZy7FEda7PKk7/8Ko87uUfbFYGS5EyGEENXazp07ycnJoV+/fgQGBrpfn332Gfv373fXu+mmm7jxxhuZMWMGr776Ki1atCjyrK5du7qPw8LCaNmyJbt27QJg48aNzJkzx+M9BgwYgNPpJD4+ni1btqDX6+nVq5dXY9+1axddunRBK/QHu3CcxYmIiGDevHn89NNPBAYGuvdh7dChg7vr9OKLL+bTTz/l1Vdfxd/fn6ioKC666CIiIyPddUJCQvjyyy85dOgQK1asIC4ujvvvv5+XX36ZL774ggMHDrBnzx78/f2ZOnVqmT93ZcgCxXnMOhsh+gxyCpVJUieEEKIsdk4dAIDFWDCe6r6eTbnriibodZ5/TDY+0xcAP0NB3du6NmJ45wbozvrDs2pCnyJ1h3asX67Y8icDLFy4kNjYWI9rhbsis7Ky2LhxI3q9nn///bfMz89PqJxOJ/fffz9jxowpUqdhw4bucWnejl0V6v4uj/79+7N//35OnjyJwWCgTp06REVF0aRJE3edESNGMGLECI4fP05AQACapvHaa6951Cnsk08+oU6dOtxwww3ceOONDB48GKPRyE033cTkyZMrFGd5SWKX55d1mRxPcY1tsGpm1hiuoL0mDZpCCCHOzd9U9M+pyaDDVMxQ9uLqGvU6jPqy1y2PuLg4zGYzhw8fLrW17LHHHkOn0/HLL79wzTXXMGjQIK688kqPOmvXrqVhw4YApKSksHfvXlq1agVAhw4d2LFjB82aNSv2+W3btsXpdLJixQr69u1b5LrJZALA4SgYZ1iW2OPi4vj++++LxFlW4eHhAPzxxx8kJydz/fXXF6kTGRkJuBI3Pz8/+vXrV6TOiRMnmDZtmnv8ncPhcI8/tNlsHp/rvDpnZ20NU1w/tcOaoS6KMSpAAcpQr5VqOEupTKsPAxVCCFHllDYOqip76qmnVN26ddWcOXPUvn371KZNm9Tbb7+t5syZo5RS6ueff1Ymk0lt3LhRKaXU008/rerXr69Onz6tlCoY/3bxxRer3377TW3fvl1df/31qmHDhio3N1cppdTWrVuVxWJRDz74oNq8ebPau3ev+uGHH9RDDz3kjuOOO+5QDRo0UAsWLFAHDhxQy5Ytc4/RO3r0qNI0Tc2ZM0clJyer9PT0MsV+6NAhZTKZ1KOPPqp2796tvvjiCxUVFVXqGDullPrkk0/UmjVr1L59+9T//vc/FRYWpsaNG+dR56233lIbN25Ue/bsUW+//bayWCzqjTfeKPZ5w4cPV2+99Zb7/KWXXlIdO3ZUO3fuVFdffbV68MEHS/2OvDXGThI7pVTOmb2qXqjBndiF3z5XEjshhBBFVNfEzul0qjfeeEO1bNlSGY1GFRERoQYMGKBWrFihkpOTVWRkpHtCgFJK2Ww21blzZzVs2DClVEFi99NPP6mLL75YmUwmddlll6ktW7Z4vM/69etVv379VGBgoAoICFDt2rVTL7zwgvt6dna2evTRR1V0dLQymUyqWbNm6pNPPnFfnzp1qoqKilKapqnbb7/9nLHn++mnn1SzZs2U2WxWPXr0UJ988sk5E7sJEyaoyMhIZTQaVfPmzdWrr77qMQFDKdeEkLCwMGUymVS7du3UZ599VuyzFi9erDp37qwcDoe7LDMzU910000qKChIXXXVVer48eMlxpL/s/FGYqcpVcHO6WoqLS3NPUgyODgYgNT9c2lx2a0kp9jRB9al2XP7ydaHsOtB8Df6OGAhhBBVRk5ODvHx8TRp0gQ/Pz9fh3PBLF++nD59+pCSkkKdOnV8HU6NVNrvVnG5S0lkjB2A00FokI6gOqGkn0xnd1pDUvV1sTi3AwG+jk4IIYQQokxkdgCATs974yK4tGU4GZb6zDcNJzCqKZrJ39eRCSGEEEKUmbTYAZmndnLUdAlrVSP8r+7F0/6DuOZ2f/xlvRMhhBCC3r17V3hZEXFhSWIHOA2hPHPoAfSXgH/kAJrUM2CRsXVCCCGEqGakKxawnt5GM8sRDEZ/Hsl5iR/qzUBzWH0dlhBCCCFEuUiLHdDv9q84fvpTbMFNefSB47AK6DkGMPk6NCGEEEKIMpMWOyDLqiMzx4kzN9PXoQghhBBCVJgkdgDqAm3zIYQQQghxHtX6xE4pRYrVAkBd5wkfRyOEEEIIUXG1PrGzZqfgr88BwIAdAGdkWzDKGnZCCCFEdbR8+XI0TePMmTO+DuWCq/WJnSNtNybN5lGWc9tikDXshBBCiAumNidj3lTrE7vMY38UUypJnRBCCCGqn1qf2DkxYVOy6osQQoiaSynFzJkzueiii7BYLLRv35758+e7r/Xt25eBAwe6d5c4c+YMDRs25KmnngIKWtMWLlxI+/bt8fPz4/LLL2f79u0e77N69Wp69uyJxWKhQYMGjBkzhszMghUncnNzeeKJJ2jQoAFms5nmzZvz8ccfc/DgQfr06QNAaGgomqZxxx13nDP2fIsWLaJFixZYLBb69OnDwYMHz/kz0TSNDz74gGuvvRZ/f39at27NmjVr2LdvH7179yYgIICuXbuyf/9+9z379+/nhhtuIDIyksDAQC677DJ+++039/Xdu3fj7+/Pl19+6S777rvv8PPzK/KzOm9ULZOamqoAlZqaqpRS6vCGmUofWFcBKjZIU2pKsMrMyPBxlEIIIaqi7OxstXPnTpWdne15ITej/C+7reB+u81VZs0q23PL6cknn1StWrVSixcvVvv371ezZ89WZrNZLV++XCml1NGjR1VoaKiaNWuWUkqpm2++WXXq1ElZrVallFLLli1TgGrdurX69ddf1bZt29S1116rGjdu7K6zbds2FRgYqF5//XW1d+9e9ddff6lLL71U3XHHHe44hg0bpho0aKC+++47tX//fvXbb7+pr7/+WtntdvXtt98qQO3Zs0clJiaqM2fOlCn2w4cPK7PZrMaOHat2796tPv/8cxUZGakAlZKSUuLPBFCxsbFq7ty5as+ePWrw4MGqcePG6sorr1SLFy9WO3fuVF26dFEDBw5037Nlyxb1/vvvq23btqm9e/eqp556Svn5+alDhw6567zzzjsqJCREHTx4UCUkJKiwsDD1+uuvn/M7KvF3SxXNXUpT6xO7IxtmKlNQHUnshBBCnFOJf3ynBJf/9c93Bff/852r7JNrPJ/7UpPi7y2HjIwM5efnp1avXu1Rfvfdd6vhw4e7z7/55htlNpvVpEmTlL+/v9qzZ4/7Wn5i9/XXX7vLTp06pSwWi5o7d65SSqmRI0eq++67z+M9Vq5cqXQ6ncrOzlZ79uxRgFq6dGmxcea/R+FkrCyxT5o0SbVu3Vo5nU739QkTJpQpsXv66afd52vWrFGA+vjjj91lX331lfLz8yvxGUopFRcXp9566y2PskGDBqkePXqoq666SvXr188jtpJ4K7Gr9X2QWvpO3rzPxIfpb/Fc+hO+DkcIIYTwqp07d5KTk0O/fv08yq1WK5deeqn7/KabbmLBggXMmDGD9957jxYtWhR5VteuXd3HYWFhtGzZkl27dgGwceNG9u3bxxdffOGuo5TC6XQSHx/P9u3b0ev19OrVy6ux79q1iy5duqAVmvRYOM7StGvXzn0cGRkJQNu2bT3KcnJySEtLIzg4mMzMTJ577jl+/vlnjh07ht1uJzs7m8OHD3s895NPPqFFixbodDr++ecfj9jOt1qf2NmzE+l6aV1eO3EXLdL+xyEDNDTK5AkhhBDl8OSx8t+jNxcct7rO9QztrKHvj1R+XJbT6QRg4cKFxMbGelwzmwtiyMrKYuPGjej1ev79998yPz8/aXE6ndx///2MGTOmSJ2GDRuyb9++8xK7yhsXWBFGo9F9nP85iivLj2P8+PEsWbKEV155hWbNmmGxWBg6dChWq+f+8lu3biUzMxOdTkdSUhIxMTEVjrG8an1i58g5BUCO5k+/kHXsfAA02SJWCCFEeZgCKne/3uB6efu5QFxcHGazmcOHD5faWvbYY4+h0+n45ZdfuOaaaxg0aBBXXnmlR521a9fSsGFDAFJSUti7dy+tWrUCoEOHDuzYsYNmzZoV+/y2bdvidDpZsWIFffv2LXLdZHL98XU4CnaDKkvscXFxfP/990XiPB9WrlzJHXfcwZAhQwDIyMgoMlHj9OnT3HHHHTz11FMkJSVxyy23sGnTJiwWy3mJ6Wy1flZsVlYqz8TfC6fWgXLI8nVCCCFqlKCgIB5//HEeffRRPv30U/bv38/mzZt55513+PTTTwFXi9gnn3zCF198Qb9+/Zg4cSK33347KSkpHs+aOnUqv//+O//88w933HEH4eHhDB48GIAJEyawZs0aRo8ezZYtW/j333/58ccfefjhhwFo3Lgxt99+O3fddRfff/898fHxLF++nG+++QaARo0aoWkaP//8MydOnCAjI6NMsY8aNYr9+/czbtw49uzZw5dffsmcOXPOy8+yWbNmfPfdd2zZsoWtW7cyYsQId2tevlGjRtGgQQOefvppXnvtNZRSPP744+clnmKdcxReDXP2AMSt/2uhom6fpaJufVk1emS5yrT6OEAhhBBVVmkD3Ksyp9Op3njjDdWyZUtlNBpVRESEGjBggFqxYoVKTk5WkZGRavr06e76NptNde7cWQ0bNkwpVTCx4aefflIXX3yxMplM6rLLLlNbtmzxeJ/169erfv36qcDAQBUQEKDatWunXnjhBff17Oxs9eijj6ro6GhlMplUs2bN1CeffOK+PnXqVBUVFaU0TVO33377OWPP99NPP6lmzZops9msevTooT755JMyTZ5YsGCB+zw+Pl4BavPmze6ysyd0xMfHqz59+iiLxaIaNGig3n77bdWrVy81duxYpZRSn376qQoICFB79+51P+Pvv/9WJpNJLVy4sNTvyFuTJ7S8D1drpKWlERISQmpqKsHBwez+sgVdHjxKamo2sUEah6dfhu6+ZWCSLcWEEEJ4ysnJIT4+niZNmuDn5+frcC6Y5cuX06dPH1JSUqhTp46vw6mRSvvdOjt3KU2t7op12nPQY8Osc20plqv5ocxBYLww/eBCCCGEEN5UqxO73JQdHudntFByb18q+8QKIYQQolqq1bNibRmHcCgNu9IDdhSaJHVCCCHEWXr37l2pZUXEhVOrW+xs6Yf5eW02p8/kAuCvMsFh83FUQgghhBAVU6sTO50piHe/PeU+Dzflgq5WN2IKIYQQohqr1YldzqltZOUUrD+TO+hD6YoVQgghRLVVq5unDH7h7uPYII3AS4b4MBohhBBCiMqp1S12QgghhBA1Sa1usctK+ot505vT/lACGnBLmANLrf6JCCGEEKI6q9Utdqbgi7BYDASbNYLMGh9fq2SInRBCCHEOjRs3ZtasWWWuf/DgQTRNY8uWLectpsLmzJlz3nbIePbZZ7nkkkvOy7O9oVYndpnHlmHQHO5zk6FW/ziEEEKIMtmwYQP33XefV595PpMxb3r88cf5/ffffR1GiWp1x6Ny5KDXCvZjM+olsRNCCCHOJSIiwtchXHBKKRwOB4GBgQQGBlbqWTabDaPR6KXIPNXqTEYpGLZgIM8uz+G1Nbm+DkcIIYTwup9++ok6dergdLqW99qyZQuapjF+/Hh3nfvvv5/hw4e7z1evXk3Pnj2xWCw0aNCAMWPGkJmZ6b5+dlfs7t27ueKKK/Dz8yMuLo7ffvsNTdP4/vvvPWI5cOAAffr0wd/fn/bt27NmzRoAli9fzp133klqaiqapqFpGs8++ywAVquVJ554gtjYWAICArj88stZvny5x3PnzJlDw4YN8ff3Z8iQIZw6dYrS5HcNf/3113Tr1g0/Pz8uvvhij+cuX74cTdNYsmQJnTp1wmw2s3LlyiJdsU6nk6lTp1K/fn3MZjOXXHIJixcvLvJe33zzDb1798bPz4/PP/+81Pgqo9YmdkopcrCw749veG6FldfWWDHrZLsUIYQQNUvPnj1JT09n8+bNAKxYsYLw8HBWrFjhrrN8+XJ69eoFwPbt2xkwYAA33ngj27ZtY+7cuaxatYqHHnqo2Oc7nU4GDx6Mv78/69at48MPP+Spp54qtu5TTz3F448/zpYtW2jRogXDhw/HbrfTrVs3Zs2aRXBwMImJiSQmJvL4448DcOedd/LXX3/x9ddfs23bNm666SYGDhzIv//+C8C6deu46667ePDBB9myZQt9+vTh+eefL9PPZvz48Tz22GNs3ryZbt26cf311xdJCp944glmzJjBrl27aNeuXZFnvPHGG7z66qu88sorbNu2jQEDBnD99de748s3YcIExowZw65duxgwYECZ4qsQVcukpqYqQJ1JOa22ft5aGUJiFKBigzSVmX7G1+EJIYSowrKzs9XOnTtVdna2R/mrr76qYmNjz/m67rrrijzzuuuuK9O9r776aoXj7tChg3rllVeUUkoNHjxYvfDCC8pkMqm0tDSVmJioALVr1y6llFIjR45U9913n8f9K1euVDqdzv25GzVqpF5//XWllFK//PKLMhgMKjEx0V1/6dKlClALFixQSikVHx+vAPXf//7XXWfHjh0e7zt79mwVEhLi8b779u1TmqaphIQEj/KrrrpKTZo0SSml1PDhw9XAgQM9rt98881FnlVYfjwvvviiu8xms6n69eurl156SSml1LJlyxSgvv/+e497p0yZotq3b+8+j4mJUS+88IJHncsuu0w9+OCDHu81a9asEuNRquTfLaUKcpfU1NRSn6GUUrV6jF1hztrbeCmEEKKS0tLSSEhIOGe9Bg0aFCk7ceJEme5NS0urUGwAvXv3Zvny5YwbN46VK1fy/PPP8+2337Jq1SrOnDlDZGQkrVq1AmDjxo3s27ePL774wn2/Ugqn00l8fDytW7f2ePaePXto0KABUVFR7rLOnTsXG0fhFq/o6GgAkpOT3e99tk2bNqGUokWLFh7lubm51K1bF4Bdu3YxZIjnBgNdu3b16A4tSdeuXd3HBoOBTp06sWvXLo86nTp1KvH+tLQ0jh07Rvfu3T3Ku3fvztatW8v8HG+qtYmd0+b6P4jCtb6JFhyFf2CIL0MSQghRTQUHBxMbG3vOesVNOoiIiCjTvcHBwRWKDVyJ3ccff8zWrVvR6XTExcXRq1cvVqxYQUpKirsbFlxdq/fffz9jxowp8pyGDRsWKVNKoZVxrbDCEwby78kf+1ccp9OJXq9n48aN6PV6j2v5ExiU8u4wqrM/S0BAQLnvKe5nUpbneEMtTuyyfB2CEEKIGmLcuHGMGzeuQvf++OOPXo6mqPxxdrNmzaJXr15omkavXr2YMWMGKSkpjB071l23Q4cO7Nixg2bNmpXp2a1ateLw4cMcP36cyMhIwLUcSnmZTCYcDodH2aWXXorD4SA5OZk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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: 4.35\n* HDI 95%: 0.2 - 17.9\n* 90% Range: 0.2 - 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: 8.49\n* HDI 95%: 0.02 - 47.49\n* 90% Range: 0.15 - 36.61", + "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: 4.56\n* HDI 95%: 0.1 - 20.8\n* 90% Range: 0.3 - 19.18", + "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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5Ce+P7Ig725tX+u45mYuhH25HXOMAbJrWx9r2yS/SsPHQebw+tD2GdY4BABw8m48739mK8GA1djzf19p2yrd7sXb/Wcwd1A4PdYsDAJy4pEGfNzYhyE+B/S+ZR+cMJhMWpR4GAIzq1hwqcGSOiGoYIYCl/YFTO+zPF7u+bIyBHVE1yM/Pr7Dm3NmzZ3HkyBGP36P06FxlSpG0U+zC5Z+HI8/FWdTSdduKTSrc8c5WAMCq8d3hp/T9aFiASoF7OzbDiYuF8K8B/SEicmDUA9f0AbL3AQbPKgtwSzGialDRiB1QUktu6NChbj/fJIA7vy67LElZhX1LlyUxGTQ4/tMt8IO2wtQ422CudN02jc6AxOT1AMz5bAEqhc+nYgGg2GCESi5jjTkiqtlKTb3m5+cjJCzKpdiFgR1RLScEcEc5QV3nSCBlqABMWocby5tSrcxG8kaTwLZjFwAA3Vs0gVzGQIqoTnOWF1YemRJQXC2cbjKVjE6pSvJyodcCwuReP2QKQKF27JPdc4vMOSbukOSA0q/ktSXwUgaUJCEbigGTm/srSjJA6e/4XNv+gnvFElUrZ/u6lpaRkeG10iW2U65CCBQWaZF+3hyAxYWYsHp4kd1om79c4PR69zam9wvrDLm6sccjW3KZhJ6twjy6l4hqmbLywsrTawbQZ6b5+MIhYElXIKAx8Nx/JW2+GAqc2OpeX258HLhzkflYcxF4vYX5+KW8kjarxgDpP7r33MRBwPDPS17PjzL/d9oxoEET8/H654Fdn7r33OY3A4+sKXn9Vjug9QCgxySgSRvAg1X6DOyIKsnZvq6leWtg3HFBhASgZFTtXeV1OPuDG/9qhvN9TSuzJZbBaMIfR8zDh7e0CrNOuxJRHaXXuBfUUdkkGfDP18ClTODRdZ49glOxRJXTrFkzZGVl2e3rWpqnI3bOFkR0+sR523aKXVjccHiZuXFlbUzv7X1NneXXEVEdptMAn1xd1f7wWvspy7JwKvbqc8uYirV9LjgVS+QT5e3r6omKypWkNO4MP0kDdcO2iO7zf/BXtoMkHSjzeZ4EcEIIFBtMdqtatTojBATUCrk1d85gNEFnNEEmSZBJEto3CwEAyLhIgaj2sw2Q5CpAfnUllskIGIoASMCESozYyWQOOWUA7AMeT0hSGc91IfCsiLPnKtQA1N5/rpsY2FGt4iyfLSIiAn///bddu7Fjx2LNmjWlb3cwYsQIvP7663bn2rZtiytXrlR474cffoi77roLP/74I3Q6HVQqlYufomLOtvWy1U6xCw2li7jm3l2VyoUrvw8CQz/cDn+lHF88fpP1/N3vbcWRnCv4+omu6NaiMQDgt4M5GPt/aejUPBTfPdkdPz11s9f7Q0Q+UDp/7o43gC5PmI9PbAP+dxcQ1rZygR15FQM7qlVcyWcDgEuXLlVYNw4AcnNzHc6dOXOm3IUQFlqteeqgU6dOZbZxd+cHyz1DUwTSL5QEaz9dMxJS/l7raz9o4dcoscqCOgDQ6o1IO2H+/mh0Bk6pEtVHzJ+rdfg3NdUqloDLNp8tIiLCoV2jRo0QHR1d4fNCQ0MdzkVFRbk0YufvX/40QeV2figJ1topdsE/f7tdapw6NBExA36u0npsKrkMi++73lr7zeKnp262TsVa3Na2KdLn9ufUK1FdNvUo4N+w5HXz7sDzZ2D79xX5HgM7qpUqymf76KOPPH62KyOCtsoalavMzg8A0EJxAIsbDocfNJCk8gsCu0KjM3fSTyGH7GpunN5ogv5qbpyzPLqBHaIcatDZFge2UNgUHSYiHyqrnpxcDciv/so3GgBjcdmJ+7Z0Ns9SBZTk1wGATO6VnDDyLgZ2VGt89dVXLk2vVidXR+XK2vnB/AwBYdAga+MoFF8uCSr9oMU195YUCa7s6tWbX9uIS4U6/Dr5FrQON6/QXZl2GjO/349+ieH45KHO1rZ939yMrMta/DihBzrENPT4PYmoGpVXT27YcqDdEPPxwZ+BlIcda6gtvs5c+41qNQZ2VGtoNCX/cvRWsV9XVWZUrnMk0NgfAOy377I8+PRv5uLBMgD+NnFbZYsEE1E9U5X5cDFdzSU4qMZjYEc11qFDh2AwGKBQKNCmTRsEBAQgOjrauqdqdansqJy/AgBMOLVuYIW7P1R2urU8W6eb60z52eTGDe3UDIOuj3LIjdswpZdDHh0R1SJTj5qnTi3kNmU42g4058ZJpdInntlf9vNK1VWjmouBHdVYt912G7KyshAdHY3Tp09j5MiRGDlyZKWf6+5KVXdG5Zz9vSeEqDCoswR0kiKgUsFceXl0zla1KuUyKJ3kxjnLoyOiGsqSG1c6H66s/De5oiTfzhbz5eoEBnZU41hq1WVne77yoKzgTQhg6Eog/bxnzy1vVK6soM5YfNEa1CmD4hA7YHWV7f7Qad4GaPVGbHmuD2Iamf+1/umWTLy27iAGXR+F1+5tb7dIgojqAMu+pURgYEc1UOlade7m01WuzEjZyhqVE8KcO+ewN58QOL1huN1IXeyA1ZApq/dfxSqFeUQuK1cLtYIrV4lqNX2ReRN7ABjysfNdFJgPV68xsKMax7ZWXevWrd3Op9MaKg7qEsOAlUPdSxlxNionhMDp1GEoupBW4f1+YZ0hKbz7l22xwYjZP5q3EZszqB3SXuxrfi+b3LiHujXHiC4x8FfKuRCDqLYTRiD9R/Px4A/M/33+jH0b5sPVawzsqMaKjIxERkZGpZ7h7tSpu4RRW2FQ52n+nBACWr0Rcplkt4jBkkfnr5TDaBL4ZtcpAEDywES38uioDimrdlmZJPvEep0GgAAUfubaZABg1ANGnft9cbaJu7MaapV67tVN3J3uW+omZ5u4l7VBvTsU/uY9UAHAoANM+rI3qHeHJDdv6wWYPz/A3Diyw8COapSUlJRK16oTNnOiAcqy68d5hc2bxd9TUnPOlif5c5Z9WtNO5OKRHnGYPbCd9Vpi8noAQNoLfRHkp8TUpNYAAIWMwVu9VF7tsrKU3tvzkz7A+YPA6NVAfE/zubTlwNqp7vUloDHw3H8lr78YCpzY6ryGmrteyis5XjXGPGrlbN9Sd007BjRoYj5e/zyw61Og1wygz0zzuQuHgCVd3X/u+L+Apgnm4y2LgM2vAjc+Dty5yHxOcxF4vYX7zx29uuQzEznBwI5qlOTkZOuxJ7XqLIsjqooln87yZifXlfwikSkCnAZ2ntAbBbpe0wj/nLoMvdFUZjuVQoanbm3llfekWop7eRKRDUkI4ZDzXZfl5+cjJCQEeXl5CA4O9nV3qJTs7GwcO3YMDz/8MF599VUMHTq03PalV79q9ECnT8zHiWHA2hFemnK9ujtE6cUQFua9W1d7PYdNozOUOxXLnDmCQWceEQKAruNKpufKxalYq9o2FWv7M6J6w53YhYEd1Sq2gVxFpUvSnwQauPI7rsL3LH+BhDmo+xlS6WKflXg/rd4IAE5z5qiGcfYLWpLbr1a01BlzFkS4o6y9PW2DCCKqc9yJXfhbg3zKUrPOshI2OTkZY8aMcdrWnTImnSO9l1snDBq7oM52dwjAezXoLLR6IxKT12Piba0wrtc1HJmrycrKb0scBAz/vOS1pc6Ys3wud5S1t6dtPhcR1WsM7MinStesu3LlSpltyypj4qx0iddWvV6tRWcRf8+uatu/9Z3fjmDb0QtIGdetyt+LPMT8NiKqYRjYkU/Z1qyLjIxEYGCgS/fZljHxZhBnXRhxlcmgsebUqUMTqyWo81fKkT63v/WYo3W1hO3enFKpHChLnTHborH95wP95rr3HmXt7anwd2xLRPUSAzuqESIjI3H69Oly21RlGRNXCg036/ttuUGWySRQZHDMjSs2GGE0CShkMusuEM7y6HQGE9749RCUcgmTbmttbVvjlZcE7jS5vYzEfXeUldzuLHHfreeWkdzuLHHf2verytub09l5hRqA2vG8O1i/jIhKYWBHVcaSP9exY0d8+eWXdtduvfVWHD582OX9YKu8jEkFhYZd2TUi67IWPRduhL9Sjox5A6znZ/94AN/sOoWpSa2tpUkuFerQ6eUNAIDjr94JADCYTPj4D3MNsAl9WkKFWhDYVVRDzVmdsbJqqLnDWZ2xsmqouaOsOmPOaqgREdVADOyoyljy50wmx1GTc+fO2RUiLqtmnWUVrEZfsvo1Mcw8/eotQgiYDCUjL84KDXt7gYQzASoFHukRh/2n8+CvrCXlDJhjZsa9OYmohmC5E6oyzZo1swZvpf+YWUbsAHNQN2/ePIeadSYB3Pm1YzkTb5YxcVabrsXwAy4XGtYbTfh8+wkAwINdY2E0mT+nJ1OxlrYquaz25NXpCktWfNrmmFnU9alYC+7NSURViOVOqEaJjo52OPf777+Xe48oI6jzRhmT8ooN+4V1hiQvPxHdEpQFqBTQG02Yt9r8jBFdYpzWnbMtLmwhSZLLbaudEOYgrHRBWWc11FzNMQPMRVWdXS8dDLpLJnP+XGUlFxRIUhnP9XM8R0RUQzCwI68pXZPO1fy50oQALmpLgrr4hsCaqztIVHYFbFmLJCy16SRFQLmjZZY9XDMvFGL3i/0gkyQMut48YiWrCyM2lpy5rk867u1ZVg01IiKqMRjYkdeUrkln4c6er86KEK8Z4dnUa1nlS5wVG64ooLPQ6o1IO5ELwLy1V4BKgbfvv8H9ztVUlpw5ow5oleT6qkvmmBER1QgM7Mhr7r//fvzzzz9YtWqVdfrVkj/nKo3ePqhzZ+rVLpC7WljY2b6uFp4UG/ZTyPHr5Fug1RnhVxOmTavKmT0lx20HmuuwlVVDDWCOGRFRDcHAjrxm9uzZlbq/dEmTtCeAxv7lxwvWYM6FQM6WX1hnj4oNy2QSWoe7PgJZI1n2F3V6zUk9OrmiJN/OFmuoERHVOAzsqMYoXdLEWVDn7qhc6X1dLaqjfEmNZVnFSkREdQ4DO/IpS506yypYi9J7v5rbVrw7ROlAztsBnN5owso08w4ZQzs1g1JeC4oIe4I5c0REtRIDO/IZZwslAPNonbO8OlFq4YOFbTBX1SNxeqMJM78355YNuj6qdgR2+iLzbgkAMOTjkn1Ly8OcOSKiWomBHXmNpSBxdHS0031fLaNzFqUXSgDmoM5S2sT+XhNOrrvL+tp2d4jqnFaVSRL6JYZbj32uvH1aAUDhZy6ma9kCa/AHzI0jIqrDGNiRW0rXqrNVXt26skbnLNKeMI/S2daps10YcXLdXdAXHAdgHqHzZOGDN/gp5fjkoc7V/r5OVbRPKwCMXg3EdjXv2QqYd4IgIqI6i4EduaWsWnW2StetsxQcLiuo6xzpuFCirHw6ZVAcYgb8XH8XPthydZ9WuRLo8kTV94eIiHyOgR255YMPPsChQ4cwbtw4p1uFla5b52ykzjI6Z+FsNwlh1DrdHcIc1NWCvLbq5myfVsA8FUtERPUGAztyS+/evdG7d2+MHTu2wrbORuqcjc6VefNVlny6qsylM5oEig1GSJDgryopPFykN8IkBFRyGRRyGbQ6I25dtAkyScKGKb3s2lYrIdzbp5WIiOoFDn1QhVJSUpCQkIBmzZph06ZNZbYTwrwgQqMHCnXAHV8DnT4puZ72BLByWMVBnbhan85CpggwB3ZVOP26M/MSEpPX4+73ttqdf/x/fyMxeT1W7zNHpwICN8Q2RNZlLQSEs0dVPUtu3RstffP+RERUY3HEjipkm1dXXFzstE1FiyNcHakTQsBYfNFadFgdmghJ7u9x370tQKXA2FtaICe/GP5KH24pdv9XwJ+LgW3vsuYcERFZSUIIHw07+EZ+fj5CQkKQl5eH4OBgX3enVrCUMQGAjRs3onfv3g5tCnVA4geO9yaGmYsNByhdHKkrtWCixbB/IVNW/RSjq1OxlrYyCb5fwGHZGow154iI6jR3YheO2JHLoqOjnQZ1zvZ4tSyOcLYwoiylCxD7hXWGpLAfiRJCwCQAucz8UIPRBJ3RBJkkwU/pGJCpFfJy2xqMJqzZnw0/pRy3tW1qDd6sfXAyKmd5XpUqXZ9O4Q/IrvbNoANMeubUERGRA+bYkVO2eXVl1aez5NRd1Dru8RqgdG2UruRZjgWIm/X91m5UTAiBoR9ux56TudZz6w+cQ2LyeoxeutPueUOWbENi8npsO3bBeu6PI+eRmLwewz/abj2nM5ow6Zu9GPt/adAZTa51tqpZcujmR5V8XThUcn3TfPO5Nc8CBudT40REVD9xxI6cclavzrY+XVk5dc72eK2IEAKn1g2ssACxVm9E2olcfLj5P7wTFYwAVeX/+AaoFOjUPBQS4NucOVsV1aeTq83/PfsvCw4TEZEdBnbklO3OEtHR0Xb16coqONw50vkerxURRq11sUR5BYglSIhrHIBD5/IhwXy9f7twpM/t77C916rx3a1TsRa3tApz2vbLx2+CWiHzfc6cM5b6dAqbBSQ9nwV6TGRuHREROWBgR0452+sVAEwCuPPrkqlXwPl2YK6wbBlmMpTkksUOWF1mAWJ/lRybpvWxO6ewWdRgy1lunDttfcKSV1dRfTqFCgBH6oiIyJHPc+yWLFmC+Ph4+Pn5oVOnTtiyZUu57b/88kt06NABAQEBiIyMxCOPPIKLFy9WU2/rvpSUFLz55ptOrwknQZ2ljIl7+XQCJn0hTq27C8e+bYfM728suVhfR6Bs8+pYn46IiDzk08BuxYoVeOaZZzBr1izs2bMHPXv2xO23346TJ086bb9161Y89NBDeOyxx3DgwAGkpKRg165dePzxx6u553VXcnIyTp8+jePHjztc0xpKgrr4hkD6k64VHLZlKWlyLOVa6/SrhV9Y5xpVs65aOcurY306IiJyk0/r2N10003o2LEjPvigpABaQkICBg8ejAULFji0f+ONN/DBBx/g2LFj1nPvvvsuFi5ciFOnTrn0nqxjV75mzZohISEBOTk5+Oeff+yuafRAwhLzcfqTQAM3ZwMtxYdtR+jUoYlo1vdbQJIq3DKsSG/Ek1+Yy6F88GCnmjOF6g26QvNoHVCSV8ccOiIignuxi89G7HQ6HdLS0pCUlGR3PikpCdu2bXN6T/fu3XH69GmsXbsWQgicO3cOK1euxJ133lkdXa43NmzY4HR62/afAJ6sfD2dOswuqIu/ZxdiBqyGTNnApS3DTEJg46Hz2HjoPEy1pa62EOagzfJl229DsfmcQWd/jyWvjkEdERG5yWeLJy5cuACj0Yjw8HC78+Hh4Th79qzTe7p3744vv/wS9913H4qKimAwGHD33Xfj3XffLfN9iouL7bbBys/P984HqINSUlKsO0yUZlk04S7bBRKliw87K2lSHqVchteHtrce13iWvDnbKdZpx4AGTczH658Hdn0K9JoB9JoOjP/LfF5RT6ejiYio0ny+Krb0L3YhRJm/7NPT0zFx4kQkJyejf//+yM7OxrRp0zBu3Dh89tlnTu9ZsGAB5syZ4/V+10XJycnW49I16+78Gsi8bH6dGGZeAVsRZ1uEAeaROneDOsAczA3rHOPWPT5VUT06WzIZ0DShavtDRER1ns8CuyZNmkAulzuMzuXk5DiM4lksWLAAPXr0wLRp0wAA7du3R4MGDdCzZ0+8/PLLiIyMdLhn5syZmDJlivV1fn4+YmJqUXBQjWxr11lq1gGOiybWjCh/lrCsUTrAs5E6wLw/68Gz5tHWthHB1bOtlzfZ5s1Z9J8P9JsLyDwo/kdEROSEzwI7lUqFTp06ITU1FUOGDLGeT01NxaBBg5zeo9FooFDYd1kuNyfQl7UGRK1WQ61We6nX9UN0dDSGDh3q9NqaEUB5MVV5o3QyRUCFCyTKUmww4s53tgIA0uf298quE9XKaT06NQD+2SQiIu/x6W/HKVOmYNSoUejcuTO6deuGjz/+GCdPnsS4ceMAmEfbsrKy8PnnnwMABg4ciCeeeAIffPCBdSr2mWeeQZcuXRAVFeXLj1LrlZdf586iCWHUem2UzpYECeHBausxEREROfJpYHfffffh4sWLmDt3LrKzs3Httddi7dq1aN68OQAgOzvbrqbdww8/jIKCArz33nt49tln0bBhQ9x666147bXXfPUR6ozffvsNrVu3RlFREQICSqYLhQCGrvTsmZUdpSvSGzHl270AgDeHX48dz/f1rCNERET1hE/r2PkC69iV7fjx4xg1ahQmTZpknYq1rV2XGAasrSC/zmTQ4Ni37QAALYYfgEzheYFdjc6AxOT1AGrp9KttbbrnzzhOxRIREbnAndillv2mpKoUFxfnsKWbbdi/cmj1lVYTQkAukzB3kDlIrNHlTfRFgDACchUgv7oQwmS03/OViIioGtTg35ZUXVJSUpwuWCk9DVvRSliTQQOTofLBjBACQz/cjjOXi/BQtzg81C2uZgd2q8aYR+bSlpecO7GNe74SEVG144gdITk5GTKZDMePH0dcXBwAc1B3UVtS5qSs2nVCCAiDBqc3DHfY+9VTWr0RaSdy8dHmY0gemFj7pmBL456vRERUTZhjR2jWrBlycnKQkJCAf/75B0IA96YAadklbZztDVtWaRPAvBK2Wd9vPVo0odEZcNP83yAEsHPWbTUvsCudOyfJnU/FGorMx9zzlYiIKoE5duQ2vV5v3R9Wo7cP6jpHAgGlaugKIWAsvmgX1KlDE9Gs77eAJHm8EhYAAlQK7H+pv0f3+oTSz/GcTM7FEkREVO0Y2NVDKSkpSE5Otu40kZ1dEsWVzqtLewJo7G8/4ORspM7TbcKIiIjIexjY1UPJyck4ePCgw/mgoCC77cMSwxyDOsCxCLE3ChBbFBuMeP77fwEA8++5FmqFvNLPJCIiqi8Y2NVDtnvCRkdHAzAHdXPnzoNGX9LOlfIm3h6pM5oEvtt9GgAwb3A7rzyTiIiovmBgVw8lJyfjypUrCAwMxJgxYwDAumBi6icl7cqM1WzW28gUAW4HdUII6Iwmu9E4jc4AAJDLJMy8vS0AQCGr5hInei0gTIBcDciv/q9hNADGYvt2rE9HREQ1FAO7esgSzNnSGhwXTJRV3uT0huEev7elRl1iZDDmDb7Wer79S7/CYBL4a+ZtGNurhcfPr5QvhgIntgLDlgPthpjPHfwZSHnYN/0hIiJyEwO7eiQ7OxtGoxFyuRyRkZFltnO2YMJCGLXWenXq0ERIcn+3+mCpUZd2Ihcz72jru1ImukJg8XXm42f2e7aClfXpiIiohmFgV4/ceOONyMrKQnR0NE6fPm13zbaaYYDStbJrntSpU8hkmHRbK+iMJrup1n0vJQEA/KpzsYTmov3rB1eWTMVatB1orlXnDOvTERFRDcPAjhxKnFTY2MKDoEalkGFyv9YO560jd0KYR9PKJQEqm5EynQaAABR+5vpxAGDUA0Zd2Y9wliendDL6KFeU5NsRERHVcPyNRdDoK946DACEMOHkuruqriNCAEv7A6d2lN8urC0wwabNJ32A8weB0auB+J7mc2nLgbVTq6yrRERENREDu3rOJIA7vy55XVaJEyEETq0bCH3BcQCe5ddZnpNfZF4BG+ynsJ/K1WsqDuq8jXlyRERUhzCwq8fE1aAu87L5dWKY49Zh1rY2iyaUQXGIGfCzR7XrtHojOsz5FQCQPrd/2Ysnph61n261U+p9n9gI61SsRaeHgetHVtwh5skREVEdwsCuHrPdZSK+IbBmhGsxTuyA1ZCksmvMiat5eJbAT2cwwWAyOa9LJ4T5q/Q1VYDrK1WdBYBypfmLiIioHqnmCrBUU60ZAcjKC+pcXDRhqVNnMJW0f+PXQ0hMXo83fj0Ef6UcR165HUdeuR3+Cpk5p+7CoZIHKNyf3iUiIiIzBnb1lEYPu+3Dyhupc6cosaVO3Uebj1l3k7AlSRKUchmUchkkg9acU/fn2+aVsKoGQI9JzHsjIiLyEAO7euiiFkhYAnT6pOK2QggYiy+6XZT4jV8PW4+nJrVB+tz+mJrUxrFhQGPg8LqS1z2fBR5dx7w3IiIiDzDHrh7SG+1fl7t9WOowFF1Is56rqCixv1KOf2YnWY8Bc+06lbN/Q6gaAM/9Z39OoXLtQxAREZEDjtjVUSkpKUhISECzZs3w1VdfAQB+++037NzzL5qO/w2AeeuwjPHAymFllDgxaOyCOr+wzpAU5U+RSpKEEH8lQvyVHq2aJSIiIs9xxK6OSk5OxsGDBwEAGo15l4XWrdvgjq8BZbi5TYCynPImpfLq4u/ZBbm6MYM1IiKiGoyBXR1VUFBgPQ4IMI+yubrDBGBft04dmuhyUKczmPD+xqMAgAl9WkKlKGdQWK8FvhhqPn5wpfMtvYiIiMhlDOzquOjoaIwcOdJhP9iydphwpqK8OiEEtHojAlQKGEwmvP3bEQDA2F7XmHPrhDDvKlGaTgOc2Hr1ISZXPxIRERGVgYFdHZOSkoLk5GRkZ2fbnbctRlzeDhNWbtat++fUZRydfwfkMgmjujYHAMhlkuv7vxIREVGlMbCrY2xz6wAgKCjIoU1Fo3VCmHBy3V0uvZ+lbh0AaHQGBKgUmDf42pIGusKKgzrWrSMiIvIKBnZ1jCW3TiaToXXr1pg3bx4AlwfgIITAqXUDoS84DqDiunV+Cjn+mnkbNDoD/BTy8jtX1v6v3K+ViIjIKxjY1TERERHW//79998A4JBfVx7bRRPKoDjEDPi53Pw6mUxCRIifaw93Z/9XIiIichsDuzrGEszZKp1fV95qWNuhvdgBqyFJlSx1KFMANz5eckxERERVhr9p6wHbadjy8usccutcLG+y7M9MAMAjPeIdy5so1MCdi9ztMhEREXmAgV0dV3oatuygzr3cOguDyYQFv5gXa4zq1tx+6zAhAM1F83FAY+bRERERVTEGdnWcq0WJ3cmts9StA8wlTe7t2Mx6bEevAV5vYT5+/gzz64iIiKoYA7s6ZuzYsbh06RIaNWqEDz/8yPWixC7m1lnq1qWdyEWQnwL7X+qPRcM7ePETEBERkacY2NUxa9asQVZWFqKjox1G61zdF7b8gsTAG8M64KPNx7B6X3aZ7QCYR+heynPzExAREZGnKrnkkWoyV0frhEFjty9sebl1MpmE+CYNkDwwETtn3ebN7hIREVElccSujhLwbLTOnX1hy6UvAlaNMR8P+RhQuljrjoiIiDzGEbt6wK3ROkXZW3sJITB4yTb0e/MPfL79OPRGU9lvKoxA+o/mL2GsTPeJiIjIRQzs6qiLmpLj8kqcuDNaV2wwIbqhH7Iua7Ey7TQUpVfBEhERkU9xKraOMlwdTCu3xIkbo3UA4KeUY8kDnaDRGeCvlJcbBBIREVH1Y2BXhwgBXNTanytrGtbd0TpbFebXCQHoNOW3ISIiIq9jYFeHaA2A3iadrXNkOYsmbAoSuzJa5zIhgKX9gVM7vPM8IiIichlz7Oqopg2AlcNcK0js6midVmfETfM34Kb5G6DVlbEgQq+xD+piugJKLwWNREREVC6O2NUxAR1HwKTNxfDuoS4vmnB1D1cBgXP5xdbjCk09CjRowj1iiYiIqgkDuzpECCB00OsAgPnjy2ln1CL/4hGYoEJgo1bWgsRGk0CxwQgJEvxVcmv7Ir0RJiEgl0lYM/FmAIBaLgN0heYGtnvAGopLjlUBDOqIiIiqEQO7OkII+50mKvJ85njsK2yF9+5LxDVXg689J3Mx9MPtiGscgE3T+ljbPvlFGjYeOo/Xh7bHsM4x5pOFF4DXW5iPbbcNW/1MJT8JEREReYo5dnWAZTWs7U4TZZY4EQImgwtF7iqLuXVERETVThJCuJAsVXfk5+cjJCQEeXl5CA4O9nV3Kk0I4N4UIC275Fz6k0ADlbO2JpxaNxDFuekoNilhgoS2w9KgUgcCqHgqVimXQSmXlbyx/mqAaDsVqy8y7zSh5DQsERGRN7gTu3DErpbTGuyDuksL2yKySTDatm1r104IgaNrBuOhHYMx9vAMAEBoeHsobYIyuUxCgEphF9QB5sLEASqFOagzFAOrngTWTgVkCvugDjDvCatqwKCOiIjIB5hjV8tZxls1e1MQuj0ZmrNHYDKZcOXKFft2Ri20lw+hkaIXdl9pi7ghf6NBQJB7u0cIAXx6G3B2v/l1v7kA1N75IERERFRpDOxqMdsFE5fXJuNCzkHrtaCgIIf2/jIdhodtgNSoi/tBHWCeerUEdRHXMYeOiIiohmFgV4tpDSULJuT6AhgAyGQytG7dGvPmzbNvfHVo7/rAQxg2vGPl93l9ZB2nW4mIiGoYBnZ1ROMA4EwuEBkZiYyMDLtrQggcT70PO/LbAQCuMQnInT2kIrbrbBjUERER1TgM7OoAzd4UXMjKKvO6MGpx5dIRvHD8TQDAEKjhZNFs+YQAlg3wvJNERERU5RjY1QGX1yZbj53l1gGADAKt/U9A3ehayGUeLIZmfh0REVGNx3IndUDooNfx8oKFAOCYW3eVWqbH+61ex49Pdoaf0qOJ2BLMryMiIqqRGNjVUkIAGr352L/dXZj87DQIITB06NCqe0MLBnVEREQ1EgO7WiIlJQUJCQlo1qwZmjVrhoAmzRAR1QxZs5tBdyqt/Jsru7kI8+uIiIhqBebY1RLJyck4ePCg02ttGurK3Rv29IbhKDYp8dx/T8H/4zR8+UR396ZjDUWAOsR8HHk98+uIiIhqKAZ2tYAQwPGcAvMLSQZ5cKT1WtMGwBsDVGXOjgqjFsW56TBBhXRNC+BkPkzujuAp/YFH1gC6Qu4BS0REVIMxsKsFtAZAbzQfy4MjET3nNACgcySwcljZcZYQAiaDBgCgkgz4cEQ7SAo/qORuzMALYV4RCzjuC0tEREQ1CgO7WqZpAyBjvPnYX1F+UHc6dRiKLpjz7+SSCUntmkKmcGMaVQhgaX/g1A4goDHw3H+V7D0RERFVJS6eqOFsV78C5kAuQGn+Km9GVBg01qAOAPzCOkOS+7v/5gPfBjqMAITJzZ4TERFRdZOEqOySydolPz8fISEhyMvLQ3BwsK+7Uy6TAO782rwfrKmoAIDA309ICG/kvAixhRACp9bdheLcdABA/D27AGUj7DqeCwDoEt8IcpkbeXK6QvN/ORVLRERU7dyJXTgVW0MJm6AOAGR+QegcCTQNdeFeoxaaSwehEyr4N2wDuboxtHojRnzyFwAgfW5/BKjc+NEzoCMiIqoVGNjVUBp9SVAXeDgFT157GROGPeHygtRNlzvi1VMPo0eLUHwpSZAgoVXTQACABBcfYtABWxaZj3s+Cyjc3mGWiIiIqhEDuxpICGDoypLXht+ScSHsPpw4cRxxcXGuPaAUf5UcqVN6udcRkx7Y/Kr5uMdEAAzsiIiIajIGdjWQ1lAyWpcYBuy/UoB9+/Zh0KBB+Oeff8q8z2gS2H/6Ms5tn4KeIXvRPWQKWt77dzX1moiIiHyNq2JruJVXt35dtWoVLl68WG7bYoMRg5dsw9g9Q2GEDA0bt4S/X2A19JKIiIhqAp8HdkuWLEF8fDz8/PzQqVMnbNmypdz2xcXFmDVrFpo3bw61Wo0WLVpg6dKl1dTb6ufOJg8SJEQ39EO48iIkCDTr+y0k7hJBRERUb/h0KnbFihV45plnsGTJEvTo0QMfffQRbr/9dqSnpyM2NtbpPcOHD8e5c+fw2WefoWXLlsjJyYHBYKjmnlctTwvQ+Kvk2DK1G4592858gkEdERFRveLTwO7NN9/EY489hscffxwAsHjxYqxfvx4ffPABFixY4NB+3bp12Lx5M/777z80atQIAFxbTFCLlF444dEDiIiIqF7y2VSsTqdDWloakpKS7M4nJSVh27ZtTu/56aef0LlzZyxcuBDR0dFo3bo1pk6dCq1WWx1drhalF074lxF6a3QGaHQGmEzmQK5Ib8Qjy3biocVLoTNVMl4XwlyUWKep3HOIiIioWvlsxO7ChQswGo0IDw+3Ox8eHo6zZ886vee///7D1q1b4efnh1WrVuHChQsYP348Ll26VGaeXXFxMYqLi62v8/PzvfchqtjKocDKlSnIyspyuHbzaxtxqVCHXyffgtbhQTAJgf8uXMGJi9fAGCmDOjTR/S3EAPv9YYmIiKhW8fniidLJ/UKIMhP+TSYTJEnCl19+iS5duuCOO+7Am2++ieXLl5c5ardgwQKEhIRYv2JiYrz+GaqKJAHJycnW10FBZW8lFqBSYOzNMWgXcAx+ks7zhRN6jWNQF9MVUAa4/ywiIiKqVj4bsWvSpAnkcrnD6FxOTo7DKJ5FZGQkoqOjERISYj2XkJAAIQROnz6NVq1aOdwzc+ZMTJkyxfo6Pz+/Rgd3pVPkWrVqhcuXL+Ps2bOIH/AoJn2zB6/d2x5bp/cBAPgp5FfvE+h6aQo6t/jXvGbC44UTEhDW1nz48FpA6WcO6rgQg4iIqMbz2YidSqVCp06dkJqaanc+NTUV3bt3d3pPjx49cObMGVy5csV67vDhw5DJZGjWrJnTe9RqNYKDg+2+aipnCyd++uknZGdno7BYj3R1In7cewYmIRCgUiBApYBMZg64hFELY545qKvUNCwATNhh/mrQ2LxPLIM6IiKiWsGnU7FTpkzBp59+iqVLlyIjIwOTJ0/GyZMnMW7cOADm0baHHnrI2n7kyJFo3LgxHnnkEaSnp+OPP/7AtGnT8Oijj8Lf34NApoYpb+GEUi7Di3cl4sW7EqGUl/9j82ga1pJb90kfN3tNRERENYVPy53cd999uHjxIubOnYvs7Gxce+21WLt2LZo3bw4AyM7OxsmTJ63tAwMDkZqaiqeffhqdO3dG48aNMXz4cLz88su++ghVZuVQ+4EypVyGx26Od+3myubW6QrNI3VERERUq0hC1K/CZ/n5+QgJCUFeXl6Nm5bV6IGEJebjjPFAgNJ8bDIJZF02Lw6JbuhvnX61ZdIX4ljKtQCAFsMPQKZwc7GDyQic2AYYioAWtwIyucefg4iIiLzHndjFpyN2VLG7774b53JysP+iQNN7k5E+tz8CVPY/NiEETm8YXrk3ksmB+J6VewYRERH5FAO7Gur7lSl4ZW4yDh8+DJPJBEVQY/grnY+iCYMGxbnpACqxcIKIiIhqPY8Cu8LCQrz66qv47bffkJOTA5PJZHf9v//+80rn6rOX5yTj0KGD1tcto8OQMW+AQ7vSo3Ue168z6oG05ebjTg8DcqX7zyAiIiKf8iiwe/zxx7F582aMGjUKkZGRngUSVK6CKwUAAJlMhtatW2PevHlO2zmM1rmbW2dh1AFrp5qPrx/JwI6IiKgW8iiw++WXX7BmzRr06NHD2/0hwK5KcWRkJG6f8zX+lmQYaDBCrZDbNDPh5Lq7rK89Hq0r9Z5ERERUO3lUxy40NBSNGjXydl8IAIRA/ppJOJudbX4JIPXAOXyz6xSMJmHTTODUuoHQFxwHUMnROiGAZY7TvERERFS7eBTYzZs3D8nJydBoNN7uT70mBABhhNBesOYtBgcFYWyva9C5eajd4glh1FqnYJVBcYgZ8LPno3V6DXB2v/k44jruC0tERFRLeTQVu2jRIhw7dgzh4eGIi4uDUmmfj7V7926vdK4+sd1OTKYKgDywMVpGh2HevHm4+5YWmNCnpX3gZjN1GjtgNSTJS5uIPLKOW4gRERHVUh4FdoMHD/ZyN8i6nZhMgZse/xRrf/u0zPjKoW5dZfLq9BpAZzPyyqCOiIio1vIosJs9e7a3+1Hvfb8yBdkLX0HjBz/HsgfjIUlBZba1nYb1uG6dZW9YyzZiREREVOtVqkBxWloaMjIyIEkSEhMTccMNN3irX/XOy3OSoT9zEGcXdsC1X8UjK/OI3QrYsni8EtZ2b1iLmK7MryMiIqrFPArscnJycP/992PTpk1o2LAhhBDIy8tDnz598M033yAsLMzb/azzLHXrAEDqdJ/dClgHtqVJKjN1GtDY/N8JOwGlvzmo41QsERFRreVRxv3TTz+N/Px8HDhwAJcuXUJubi7+/fdf5OfnY+LEid7uY70iDw7H5DGjoZA5/9GUrl3nESEASQY895/5q0ETQNWAQR0REVEt59GI3bp167BhwwYkJCRYzyUmJuL9999HUlKS1zpXX6SkpOBMVpb5haTAM/3aQuXkJ+O0dp27+XWW3DqZEnhkTeU6TkRERDWKR4GdyWRyKHECAEql0mHfWKpYcnKy9VhSu7ZowuPadba5dbpC80gdERER1QkeTcXeeuutmDRpEs6cOWM9l5WVhcmTJ+O2227zWufqi4KCkvy6hre/BI3OAOFsiy9v1K6Tq4Fhy4HBH5qPiYiIqM7wKLB77733UFBQgLi4OLRo0QItW7ZEfHw8CgoK8O6773q7j/WGPCQaDSIC0Xneemj1RrtrXqtdJ1cA7YYA148wHxMREVGd4dFv9piYGOzevRupqak4ePAghBBITExE3759vd2/Ok8I4IILO7N5pXYdERER1WmVGrLp168f+vXr562+1EsaPRD66I8QBh2iGirxx8zrIZNgty9saR7XrgMAowE4+LP5uO1AjtoRERHVIS7/Vn/nnXcwZswY+Pn54Z133im3LUueuMayP6wqphMAYMuTQAOVCzdWpiyJsRhIedh8/PwZBnZERER1iMu/1d966y088MAD8PPzw1tvvVVmO0mSGNi56PsfV+PvVC0kpT869b4LcskIjU4gwFmtEyIiIqIKuBxBZGZmOj0m96WkpCA5ORkHDx4EYF40sfK903h5TTq+2XkKY3tdg0m3tYZK4dHaFiIiIqqnPIoc5s6dC43GMeNfq9Vi7ty5le5UXWcb1AHm2nWSBCjlMhhMAjv+uwSlvNR0q7PyJ0REREQ2PArs5syZgytXrjic12g0mDNnTqU7VddZ69ZJMiiatkXDO+YBAGbc3hbpc/sjZVw3u8URDqVOiIiIiJzwKJlLCOF0VeY///yDRo0aVbpT9YU8OBJRz2cgMQzwVwCS5HwlrDBovFPqRAhA50JtFSIiIqqV3ArsQkNDIUkSJElC69at7YI7o9GIK1euYNy4cV7vZF2SkpKCLMu+sADiGwIp9xoQP3M9ACB9bn+7xRNCmHBy3V3W1x6XOrHsEWvZToyIiIjqHLcCu8WLF0MIgUcffRRz5sxBSEiI9ZpKpUJcXBy6devm9U7WJaX3hV0zAigrTBNC4NS6gdAXHAdwdbROEeDZG9vuEQsAMV0BpYfPIiIiohrJrcBu9OjRMBgMAIC+ffuiWbNmVdKpusx2D9iGd8yDJAH+CjnSXjDv2mFbmNh2twllUBxiBvzseWFiW1OPAg2aVK4eHhEREdU4bufYKRQKjB8/HhkZGVXRnzovI+Mg7vgaSD9vfz7IT1lueZPYAashSV4qf6IKYFBHRERUB3kUKdx0003Ys2ePt/tSL2gNJUFdYhjgJxcY+uF2fPzHMcfGtiVOGIgRERFRBTxaFTt+/Hg8++yzOH36NDp16oQGDRrYXW/fvr1XOlcX2cZqK4cCRQYj0k7k4vDZAjx6c7x14YTXS5yoGgAv5XnveURERFTjeBTY3XfffQDs94SVJMlaBsVoNHqnd3XM1KnTsCItF3lSKEIHvQ5JAuSShPtvjIHOaIJcZlO7zia/rlIlTvRFwKox5uMhHwNKv8p+DCIiIqqhPArsuKWYZ77+5mucycqCPCQaPR5/3Vq77tV7yx/h9KjEiRCAMAHCCKT/aD43+AMPe05ERES1gUeBXfPmzb3dj3pn5VA30uY8CeqW9gdufRGI7Qrc8Yb5vFzl3nOIiIioVvEosAOAY8eOYfHixcjIyIAkSUhISMCkSZPQokULb/avzqrStRCWmnV/vg1EdwS6PFGFb0ZEREQ1hUerYtevX4/ExETs3LkT7du3x7XXXosdO3agXbt2SE1N9XYf6zSNzoCEF9ch4cV10OgM3n34Uf4siIiI6hOPRuxmzJiByZMn49VXX3U4P336dPTr188rnasvtPpKLjYRwjxKZ8H9YImIiOoljwK7jIwMfPvttw7nH330USxevLiyfapX/BRybHmuj/XYyrYuSnm4BywRERFd5dFUbFhYGPbu3etwfu/evWjatGll+1Qv6I0mfLY1E8u2HUdEiB9iGgVAdrXciVs17ErvAWuL+8ESERHVKx6N2D3xxBMYM2YM/vvvP3Tv3h2SJGHr1q147bXX8Oyzz3q7j3WS3mjCvNXmOnUjusRAKS+JsYVB41kNu6lHzduFWSi5dRgREVF94lFg9+KLLyIoKAiLFi3CzJkzAQBRUVF46aWX7IoWUwkhgIs2qW9yScKg66MAADKb4Kv0aJ1bNexUAeYdJoiIiKhe8iiwkyQJkydPxuTJk1FQUAAACAoK8mrH6hqtAVC2vRMyzSU0btwIDf3lePv+GxzaOew4oXBhKrX708C2d73dZSIiIqplPK5jBwA5OTk4dOgQJElCmzZtEBYW5q1+1UmN7vsIAJD+ZDkzpDaLJlwarVM1AHo8A5zaxXw6IiKies6jxRP5+fkYNWoUoqKi0KtXL9xyyy2IiorCgw8+iLw8bjRfkbJiNYdFE+UFdYZiYM2z5i9VIPDoOubTERER1XMeBXaPP/44duzYgTVr1uDy5cvIy8vD6tWr8ffff+OJJ7jLgTOlq5dodAZcP/dXdJyXai1M7DANK/MDdIWOXyYTYDIAuz41fwkjgzoiIiLybCp2zZo1WL9+PW6++Wbruf79++OTTz7BgAEDvNa5ukII4OZpKTj389to/OAXAOIAALe2bYrvd2c5vafZbSsgLRvgvJTJ+L+ARi2AXjPMr2XKquk4ERER1SoeBXaNGzdGSEiIw/mQkBCEhoZWulN1jdYA/PtNMgw5B3FmXjzu2toHv//+O8b1aoGTFzXwV8odbzIUlV90WKEC+sysuk4TERFRreNRYPfCCy9gypQp+PzzzxEZGQkAOHv2LKZNm4YXX3zRqx2sK0RxgfW4fb9h+HrnSdzTMRop47pVvECidH06hYt17YiIiKhe8Siw++CDD3D06FE0b94csbGxAICTJ09CrVbj/Pnz+Oijj6xtd+/e7Z2e1hGRUdH4IS8WP3y/H4Ouj4KksAnq7BLxbI5Zn46IiIhc4FFgN3jwYC93o277fmUKjHkluXT9EsMBlFOYWAhI/xtcnV0kIiKiOsCjwG727Nne7kedNm9OsvU4OCgInzzU2aGN7YpYv5C2kI5sN1+IuI716YiIiMgllSpQnJaWhoyMDEiShMTERNxwg+NOCvWdEMCJnJL8uhdfmlfhPdF9/g/4u6X5xSOsT0dERESu8Siwy8nJwf33349NmzahYcOGEEIgLy8Pffr0wTfffMMdKGxoDYDBZD5Wh0ZjxPB7odEZEKAq51tvG8gxqCMiIiIXeVSg+Omnn0Z+fj4OHDiAS5cuITc3F//++y/y8/MxceJEb/exzmgcAAx8bysSk9ejx6u/Q6szOm+o9DPXqhv/F1fAEhERkcs8GrFbt24dNmzYgISEBOu5xMREvP/++0hKSvJa5+qKhncvhNBpMPd2f3S+9Tp89McxnM0rgp+yjLhakgFNE5xfIyIiIiqDR4GdyWSCUum424FSqYTJZKp0p+qaBp1GAgBGjAQClMAbwzrAXym3r19nKXUiBKTNb5h3k+j5rLkQMREREZELPJqKvfXWWzFp0iScOXPGei4rKwuTJ0/Gbbfd5rXO1VUBKoVdUGdb6kQSgLRlEbD5VcCk91UXiYiIqBbyaMTuvffew6BBgxAXF4eYmBhIkoSTJ0/iuuuuwxdffOHtPtZqtjWHDUYTfvg3GwBwV/tIKOTmuNq21IkqNAGic6J5zYSsUouWiYiIqJ7xKHKIiYnB7t27kZqaioMHD0IIgcTERPTt29fb/avVhACGrgT05w5BmAxIzwCeWXEcAJDULtwa2NlqlrQSkpK7TBAREZH73A7sDAYD/Pz8sHfvXvTr1w/9+vWrin7VCVoDkH4eyFlyG4x5Wbj3i2j0eek7APa7Ttjm10FzEVBogYDGLHVCREREbnE7sFMoFGjevDmMxjJKdVCZJABfPH6T3bnS+XWyt64zX3j+DPeHJSIiIrd4tHjihRdewMyZM3Hp0iVv96fO0ey13ye2NGt+nRCIPaOrxp4RERFRXeNRjt0777yDo0ePIioqCs2bN0eDBvYjS7t37/ZK5+qCy2tL9onN1cvR783N+Ompm+Gvktu1kwSg0hSaX3B/WCIiIvKAR4Hd4MGDIUkShO2ST3JKFJfsExvQbSSO5FyBQAXfN+4PS0RERB5wK7DTaDSYNm0afvjhB+j1etx2221499130aRJk6rqX50RFR2NlYumAgDUCvvROggBmckm2GNQR0RERB5wK7CbPXs2li9fjgceeAD+/v746quv8OSTTyIlJaWq+lendGvR2PGkyYRmpwrgX2So/g4RERFRneJWYPf999/js88+w/333w8AeOCBB9CjRw8YjUbI5fIK7q7fivUmrD9wFre1bVpSmFgIZP06FDG2QV1MV+bXERERkUfcWhV76tQp9OzZ0/q6S5cuUCgUdluLkXOXNTqM/b806Iwle+kKoxbFlw+WvJ56BHiU+XVERETkGbdG7IxGI1Qq+03pFQoFDAZOI5YlYsouCJMO7dX/IKBhqH1h4lIkVQMGdUREROQxtwI7IQQefvhhqNVq67mioiKMGzfOruTJ999/770e1nLykEgAwHfjmyNA6bxNYYASDTT6auwVERER1UVuTcWOHj0aTZs2RUhIiPXrwQcfRFRUlN05dyxZsgTx8fHw8/NDp06dsGXLFpfu+/PPP6FQKHD99de79X7VqcJqMEJAyCTkNvKDaHYjc+uIiIioUiThw2J0K1aswKhRo7BkyRL06NEDH330ET799FOkp6cjNja2zPvy8vLQsWNHtGzZEufOncPevXtdfs/8/HyEhIQgLy8PwcHBXvgUzgkB3PE1kJ5jBC5ux3VNgZXjusFPKb963YQTq2+DvuA4IARaDD8AmZJbiBEREZE9d2IXj7YU85Y333wTjz32GB5//HEkJCRg8eLFiImJwQcffFDufWPHjsXIkSPRrVu3auqp+7QGIP08cGXbxyj4czm2r1kB09UYWgiBU+sGQp+fiZDLRWisC4UklTFPS0REROQinwV2Op0OaWlpSEpKsjuflJSEbdu2lXnfsmXLcOzYMcyePdul9ykuLkZ+fr7dV1VKSUlBQkICWsU3Q9bsZriUMh65Gz+DbO93UFnKnFzdH1YSQNMcDRqd+A+SiTl2REREVDkebSnmDRcuXIDRaER4eLjd+fDwcJw9e9bpPUeOHMGMGTOwZcsWKBSudX3BggWYM2dOpfvrquTkZBw8eNDhfJPQEGv9Olui7V2QZHJAYh1AIiIiqhyfBXYWUqnyHkIIh3OAudTKyJEjMWfOHLRu3drl58+cORNTpkyxvs7Pz0dMTIznHa5AQYF5b1iZTAYpyLwitmVEEObNm+fQVsgkiKGfQFJw0QQRERFVns8CuyZNmkAulzuMzuXk5DiM4gHmgOnvv//Gnj178NRTTwEATCYThBBQKBT49ddfceuttzrcp1ar7cqzVJeIyEgopp0GhMB7/S9ArQCMJgG5jHXqiIiIqGr4LMdOpVKhU6dOSE1NtTufmpqK7t27O7QPDg7G/v37sXfvXuvXuHHj0KZNG+zduxc33XRTdXW9XI0bN4ZSabMQQhjx+P92YtRnO1FsMPquY0RERFTn+XQqdsqUKRg1ahQ6d+6Mbt264eOPP8bJkycxbtw4AOZp1KysLHz++eeQyWS49tpr7e5v2rQp/Pz8HM770o8//og777wTRqMJWgCQJLSJCIZMgsOuE5JJQPayeboWz58BVCx3QkRERJ7zaWB333334eLFi5g7dy6ys7Nx7bXXYu3atWjevDkAIDs7GydPnvRlF90WFxeHli1bYtiIUZh1DoAkx6rxPe13nfBd6UAiIiKqw3xaoNgXqqNAsbU48Xnziz1jTGgUYClMLHBq3V3mcicmgZZHc803ccSOiIiInKg1BYrrKktxYgiBgPztePL/dlqvWWrYAYC6YVsf9ZCIiIjqIp+XO6lLHnjgAVy4cAENGzUBun4JCCO0mlzsyAQ0OgMCVPbf7ug+/wf83dJHvSUiIqK6hoGdF23evBlZWVmIio6GoisASYY37+sIYTJad52w46ReHxEREZGnGNhVAWvWoiTDgGsj7RdO2DbSa6qzW0RERFTHMceuCuQUVtBACDQ7VQDZW9dVS3+IiIiofuCInReVXl7cKULgwOlcSBJwQ2yoddcJSQD+RYaShjFdASW3FSMiIqLKYWBXRdKeAPzlRrSbvR0AkD63v3nxROnqMlOPAg2aMN+OiIiIKo2BXRUJUAKSkBDX2DwSJ0GCEAKnNwy3b6gKYFBHREREXsHArgr5q+TYNK2P9bXJoDHXsJOA8y3bosn1MyDJ1T7sIREREdUlDOx8QZLQePjvkJTcaYKIiIi8h6tifYXTr0RERORlHLHzEiGAgJueQNDFPMj8QwAARXojnvwiDQDwwYOdoLKsmxUCSP8ZkKuAtgMBOX8MREREVHmMKLxEawB0t8xGKIDEMMBfAWj1AhsPnQcAGE0mnP7dvHBCEoDs+zHmG58/w8COiIiIvIIRRRVYOdQ806qUy/D60PYAAAWKzQsnAKhD20I0l0GCBEicDSciIiLvYGBXBSzpc0q5DMM6xwAwr4i1iE76jgsniIiIyOs4XFSFhBDQ6AylTwJ6LaCraN8xIiIiIvcwsKuklJQUJCQkoFV8M5x8RsLJZyS0im8GIQTu/WAbZny3HwfO5MFoEiV7xC5sASzmPrFERETkXZyKraTk5GQcPHjQ7lxQYBCK9CZkXdZi98nLOHb+Cn56sqPjHrFEREREXsTArpIKCgrMB5IM8uBI+DUIwrx58+CvkmPH832h0Rngr5RDGLX2N07YWf2dJSIiojqNgZ2XyIMjET3nNNKfBFRyEzQ6A2SShACV+VsshLC/Qenvg14SERFRXcYcOy+TJODrnSeRmLweU77dC8Ac1J1OHYZmp/J92zkiIiKq0xjYVQNh0EB3KR1+xUbz64jrAGWAj3tFREREdQ2nYqvAiC6xGNqpGWSSZB6t2zDc7rr0yDruFUtERERex8Cukr744gvkFRZjzC9qQAhkZOejUYACzRsFQCaTYDJoUJybDrswjkEdERERVQEGdpXUu3dvaPSA31EAJgPuXbIVAJA+t7914YS5fh3z64iIiKhqMbDzJpkCQX4KtAkPgr9Sbj0tCVjz68D8OiIiIqoiDOwqYdOmTSguLoZJpgbQGwCwcdptaBwgh2Qz3SokIKdpAJpcOwWy7pM4FUtERERVgoFdJTz44IPIysqCOjQa4cknAWFCgErhNG7LC1GjSfcJgEJV/R0lIiKieoHlTrzAYAJw5Rikc+vx4qp/UGwwllw0mdDsVAH8tdxKjIiIiKoWAztvkczfyhMXC6GSm4+FEMj6dSj8iwwIvVQE6DS+7CERERHVcZyK9QoBNIjD3880t8uvE0Ytii8fRLFKDpVJAUnBbcSIiIio6nDErhKsu78aiwEhrubX2SfYCZmEk3EhkD97DJKqQbX3kYiIiOoPBnZuSklJQUJCApo1a4YzZ7Kt59uGAf6lxz+FKDnmSlgiIiKqYpyKdVNycjIOHjxod04dFIZfHrBfDetsKzEiIiKiqsQROzdlZGQgMzMT117XHvKQaCiatsVHb86DrNSAnLBsJWYSaH6qCNJHfbh4goiIiKoUR+w8EBcXhy07dqPD6xkAgLsGJdhdt47WCQGZSUCl1QDag7DJyiMiIiLyOgZ2HjKYBCTNiavHbe2uCaMWxZcOmOvXFbF+HREREVUPBnYekksyiMBWAACFrNSMthCQBOyDupiu3COWiKiOMJlM0Ol0vu4G1RFKpRJyubzihi5gYOemOXPm4PLlPKw+GQLcMhsAoLL5LjpdNDH1KNCgCVfGEhHVATqdDpmZmTCZTL7uCtUhDRs2REREhEPZNHcxsHPTJ598gqysLMhDohF98ywkhivsypwIo9a8aML2JlUAgzoiojpACIHs7GzI5XLExMRAVnrGhshNQghoNBrk5OQAACIjIyv1PAZ2njIWAVeOYumjLWD+NtrVOkGzU/m+6hkREVURg8EAjUaDqKgoBAQwvYa8w9/fvDNVTk4OmjZtWqlpWf5Tw1MyBaTCY5jw5S6HS5IA/IqN5hcR1zG3joiojjAazX+3q1QqH/eE6hrLPxT0en2lnsMRO09JCojw/vi/x+Xlz4c/so7TsEREdUxl86CISvPWnykGdpUhs9ltQghArwFQKpmW//MTERFRNeFUrKeEARBXgzghgKX9gflROPfzQAgJuNjID6Lns4BM6dt+EhFRvbdgwQLceOONCAoKQtOmTTF48GAcOnTIro0QAi+99BKioqLg7++P3r1748CBA3ZtPv74Y/Tu3RvBwcGQJAmXL192eK+4uDhIkmT3NWPGjEp/hk8++QQ9e/ZEaGgoQkND0bdvX+zcudOh3ZIlSxAfHw8/Pz906tQJW7Zssbv+/fffo3///mjSpAkkScLevXsdnnHs2DEMGTIEYWFhCA4OxvDhw3Hu3LlKf4bqwMDOTda9I0w2gZ1eA1w8aj7MOwZIEgpbdQZufRFQMA+DiIh8a/PmzZgwYQL++usvpKamwmAwICkpCYWFhdY2CxcuxJtvvon33nsPu3btQkREBPr164eCggJrG41GgwEDBuD5558v9/3mzp2L7Oxs69cLL7xQ6c+wadMmjBgxAhs3bsT27dsRGxuLpKQkZGVlWdusWLECzzzzDGbNmoU9e/agZ8+euP3223Hy5Elrm8LCQvTo0QOvvvqq0/cpLCxEUlISJEnC77//jj///BM6nQ4DBw6sHSVuRD2Tl5cnAIi8vDyP7o+OjhYAhDw4XMS+ZRSFOvN5k8kkTv7cXxz+ork4/GWcMOqueLHXRERUE2i1WpGeni60Wq2vu1IpOTk5AoDYvHmzEML8OywiIkK8+uqr1jZFRUUiJCREfPjhhw73b9y4UQAQubm5DteaN28u3nrrrarqupXBYBBBQUHif//7n/Vcly5dxLhx4+zatW3bVsyYMcPh/szMTAFA7Nmzx+78+vXrhUwms4sTLl26JACI1NRU734IG+X92XInduGInackBSCVfPuEUYui/EOAJEEdmghJwZWwRERUM+Xl5QEAGjVqBADIzMzE2bNnkZSUZG2jVqvRq1cvbNu2ze3nv/baa2jcuDGuv/56vPLKK1WyS4dGo4Fer7d+Bp1Oh7S0NLvPAABJSUlufYbi4mJIkgS1Wm095+fnB5lMhq1bt3qn81WIiyfcIISALCoRtyYk4q/csDLbNev7LVdMERHVA0IICKPWJ+8tyf09+l0jhMCUKVNw880349prrwUAnD17FgAQHh5u1zY8PBwnTpxw6/mTJk1Cx44dERoaip07d2LmzJnIzMzEp59+6nZfyzNjxgxER0ejb9++AIALFy7AaDQ6/QyWz+eKrl27okGDBpg+fTrmz58PIQSmT58Ok8mE7Oxsr36GqsDAzg1avRGyWyfhztta4+i/8SUX9FpI/3cPos/n40x0EFfCEhHVE8KoxbFv2/nkvVsMP+DR7NBTTz2Fffv2OR19Kh0oCiHcDh4nT55sPW7fvj1CQ0MxdOhQ6yheafPnz8f8+fOtr9PT0xEbG1vueyxcuBBff/01Nm3aBD8/P69+hrCwMKSkpODJJ5/EO++8A5lMhhEjRqBjx45e28+1KjGw88A7vx0Gwm0CO2GCdHI7OPlKREQ12dNPP42ffvoJf/zxB5o1a2Y9HxERAcA8cme7pVVOTo7DCJi7unbtCgA4evSo08Bu3LhxGD68ZI/1qKiocp/3xhtvYP78+diwYQPat29vPd+kSRPI5XKH0TlPPkNSUhKOHTuGCxcuQKFQWPdxjY+Pr/hmH2Ng5wZ/pRx/PZ+Emz4DIMmRGAb4KwChE+AYHRFR/SPJ/dFi+IGKG1bRe7tKCIGnn34aq1atwqZNmxwClPj4eERERCA1NRU33HADAHPO2ubNm/Haa69Vqp979uwBUPYeqI0aNbLmyVXk9ddfx8svv4z169ejc+fOdtdUKhU6deqE1NRUDBkyxHo+NTUVgwYN8qjvTZo0AQD8/vvvyMnJwd133+3Rc6oTAzs3rFy5Em8tfhuG276AonEcVg41z7qaDFprYKdu2Nat/9mIiKj2kiSpViyWmzBhAr766iv8+OOPCAoKso5qhYSEwN/fnKv3zDPPYP78+WjVqhVatWqF+fPnIyAgACNHjrQ+5+zZszh79iyOHjWX+Nq/fz+CgoIQGxuLRo0aYfv27fjrr7/Qp08fhISEYNeuXZg8eTLuvvvuCqdXK7Jw4UK8+OKL+OqrrxAXF2f9DIGBgQgMDAQATJkyBaNGjULnzp3RrVs3fPzxxzh58iTGjRtnfc6lS5dw8uRJnDlzBgCs9fwiIiKsI5fLli1DQkICwsLCsH37dkyaNAmTJ09GmzZtKvUZqoVX1+rWAp6WOynWG0XTmGuEXKEUkipA+F97tyjUlZQ5EbODhZgdLIxXcqqo50RE5Gu1tdwJzGVYHb6WLVtmbWMymcTs2bNFRESEUKvV4pZbbhH79++3e87s2bPLfU5aWpq46aabREhIiPDz8xNt2rQRs2fPFoWFhZX+DM2bN3f63rNnz7Zr9/7774vmzZsLlUolOnbsaC3pYrFs2bIKnzN9+nQRHh4ulEqlaNWqlVi0aJEwmUyV/gzl8Va5E0kIIVCP5OfnIyQkBHl5eQgODnb5Po3OgODGETBeuQgAaPJwCk58PBR+kgb/fZOIlkdzAQBiZhYkdWCV9J2IiHyrqKgImZmZ1p0NiLylvD9b7sQurGPnIrlMQoDavBpGHhKNgOuHOm3HMidERETkKwzsXKRWyBHsx31fiYiIqOZiYEdERERURzCwc4NtMqKl1AnqV4oiERER1WAM7Fyk0RlwLr/I+nrlUAAQOL1hOCQGd0RERFQDMLBzkRCAseCi9bUkmbeSKc5NR+MLvtknkIiIiMgWAzsXCcgRePOTAAC/BkHmadjSYroCyppfqJKIiIjqJu484SKZTIIq6noomrbFh4vmmUfsrl67EBaA4LHpkPk3Ng/lEREREfkAAzsX6QxGBHZ9FIHdx+Cee6+eFAJyg8l8rPRnUEdEREQ+xcDORbNW7QPOXwAaXAOdIR4BShmg1+Ka/y4DAEx6LaBs4NtOEhERUb3GHDsXFeddhPHyGRjPH4BSzpE5IiKqPRYsWIAbb7wRQUFBaNq0KQYPHoxDhw7ZtRFC4KWXXkJUVBT8/f3Ru3dvHDhwwHr90qVLePrpp9GmTRsEBAQgNjYWEydORF5ent1zcnNzMWrUKISEhCAkJASjRo3C5cuXK/0ZPvnkE/Ts2ROhoaEIDQ1F3759sXPnTod2S5YssW7L1alTJ2zZssV6Ta/XY/r06bjuuuvQoEEDREVF4aGHHsKZM2fsnvHxxx+jd+/eCA4OhiRJXul/dWFg5wIhgC9mPYysDx7G2eXjS7YNUwXgSOtGONK6EaDiogkiIqqZNm/ejAkTJuCvv/5CamoqDAYDkpKSUFhYaG2zcOFCvPnmm3jvvfewa9cuREREoF+/figoKAAAnDlzBmfOnMEbb7yB/fv3Y/ny5Vi3bh0ee+wxu/caOXIk9u7di3Xr1mHdunXYu3cvRo0aVenPsGnTJowYMQIbN27E9u3bERsbi6SkJGRlZVnbrFixAs888wxmzZqFPXv2oGfPnrj99ttx8uRJAIBGo8Hu3bvx4osvYvfu3fj+++9x+PBh3H333XbvpdFoMGDAADz//POV7ne1E/VMXl6eACDy8vJcvqdQJ4Q8JFoAEOrQaGEyCSFMJmEsPC+O/F9zcfjLOGHUF1Zdp4mIqEbQarUiPT1daLVaX3elUnJycgQAsXnzZiGEECaTSURERIhXX33V2qaoqEiEhISIDz/8sMznfPvtt0KlUgm9Xi+EECI9PV0AEH/99Ze1zfbt2wUAcfDgQa9+BoPBIIKCgsT//vc/67kuXbqIcePG2bVr27atmDFjRpnP2blzpwAgTpw44XBt48aNAoDIzc31Wr/LUt6fLXdiF47YVUCjM6DtyFkw5pn/RdA4AJAggKX9IVvYAuFnr/i4h0RERO6xTJ82atQIAJCZmYmzZ88iKSnJ2katVqNXr17Ytm1buc8JDg6GQmFO2d++fTtCQkJw0003Wdt07doVISEh5T7HExqNBnq93voZdDod0tLS7D4DACQlJVX4GSRJQsOGDb3aP1/xeWBX3lx4ad9//z369euHsLAwBAcHo1u3bli/fn2V9zH798+tx0GBQYBeA5zaUeXvS0RENZsQgEbvmy9PNz0SQmDKlCm4+eabce211wIAzp49CwAIDw+3axseHm69VtrFixcxb948jB071nru7NmzaNq0qUPbpk2blvkcT82YMQPR0dHo27cvAODChQswGo1ufYaioiLMmDEDI0eORHBwsFf75ys+XRVrmQtfsmQJevTogY8++gi333470tPTERsb69D+jz/+QL9+/TB//nw0bNgQy5Ytw8CBA7Fjxw7ccMMNVdJHP4UcjdUmnLv6+sWX5tldzwnnSlgiovpKawASlvjmvTPGAwFK9+976qmnsG/fPmzdutXhmlSqbJcQwuEcAOTn5+POO+9EYmIiZs+eXe4zynsOAMyfPx/z58+3vi4rBrC1cOFCfP3119i0aRP8/Pw8+gx6vR73338/TCYTlizx0Q+xCvg0sHvzzTfx2GOP4fHHHwcALF68GOvXr8cHH3yABQsWOLRfvHix3ev58+fjxx9/xM8//1xlgZ0kAfKrfyDkIdEYcu9QQJQkmwpJgjo0EZLcv0ren4iIyFuefvpp/PTTT/jjjz/QrFkz6/mIiAgA5hG3yMhI6/mcnByHEbCCggIMGDAAgYGBWLVqFZRKpd1zzp07h9LOnz/v8ByLcePGYfjw4dbXUVFR5X6GN954A/Pnz8eGDRvQvn176/kmTZpALpc7jM45+wx6vR7Dhw9HZmYmfv/99zozWgf4MLCzzIXPmDHD7nxFc+G2TCYTCgoKrPPrzhQXF6O4uNj6Oj8/3+U+CiEw9MPt0OiN5bZr1vfbMv8lQkREdZe/wjxy5qv3dpUQAk8//TRWrVqFTZs2IT4+3u56fHw8IiIikJqaah0o0el02Lx5M1577TVru/z8fPTv3x9qtRo//fSTw2hZt27dkJeXh507d6JLly4AgB07diAvLw/du3d32rdGjRqV+3vc1uuvv46XX34Z69evR+fOne2uqVQqdOrUCampqRgyZIj1fGpqKgYNGmR9bQnqjhw5go0bN6Jx48YuvXdt4bPAzpO58NIWLVqEwsJCu0i/tAULFmDOnDke9VGrNyLtRC40xYbyGzKoIyKqlyTJs+nQ6jZhwgR89dVX+PHHHxEUFGT9PRsSEgJ/f39IkoRnnnkG8+fPR6tWrdCqVSvMnz8fAQEBGDlyJADzSF1SUhI0Gg2++OIL5OfnWwdLwsLCIJfLkZCQgAEDBuCJJ57ARx99BAAYM2YM7rrrLrRp06ZSn2HhwoV48cUX8dVXXyEuLs76GQIDAxEYGAgAmDJlCkaNGoXOnTujW7du+Pjjj3Hy5EmMGzcOAGAwGDB06FDs3r0bq1evhtFotD6nUaNGUKlUAMwjl2fPnsXRo0cBAPv370dQUBBiY2NdDkJ9xruLdV2XlZUlAIht27bZnX/55ZdFmzZtKrz/q6++EgEBASI1NbXcdkVFRSIvL8/6derUKZeXDGt1BvHkF3+LgNCmAoCQh0SLQp0QoviKELODhZgdLI78X3OWOiEiqidqa7kTmLc3d/hatmyZtY3JZBKzZ88WERERQq1Wi1tuuUXs37/fet1S+sPZV2ZmprXdxYsXxQMPPCCCgoJEUFCQeOCBB7xSLqR58+ZO33v27Nl27d5//33RvHlzoVKpRMeOHa0lXYQQIjMzs8zPsHHjRmu72bNnV/j98jZvlTuRhPB0XU3l6HQ6BAQEICUlxW7IdNKkSdi7dy82b95c5r0rVqzAI488gpSUFNx5551uvW9+fj5CQkKsS7RdsWf/IdzxpQGSTIGjL7WGv+48pDdaAQCOtgzFNfenQ6ZggWIiorquqKgImZmZ1moORN5S3p8td2IXn5U7sZ0Lt5WamlrmPDwAfP3113j44Yfx1VdfuR3UeUII4Pn9baCKbAdl09ZQf97fGtQBgLphWy6cICIiohrBp6tiK5oLnzlzJrKysvD55+Y6cl9//TUeeughvP322+jatat1Xtzf3x8hISFV0ketAUg/bz6+oYkG8qMl9eu0fgpEJ63kwgkiIiKqEXwa2N133324ePEi5s6di+zsbFx77bVYu3YtmjdvDgDIzs627u8GAB999BEMBgMmTJiACRMmWM+PHj0ay5cv93r/tDojer++ESgE0LQPvrxHDqweBCGM+E+3FSaZhBYyn9d4JiIiIgLg48AOAMaPH4/x452vFS8drG3atKnqO2TDJARyCoqhSd8Ek/8xfNsoCI889DmEQQPTt+2qtS9EREREFfF5YFeTmSCHaHIzcv8YA2PeGbz4ZzQeuX8QIOPUKxEREdU8nEcsh1wmAcoQAFcDOc0FYH75FbGJiIiIfIWBnTsMOvN/dRrf9oOIiIjICQZ25dAbTYDmFCCubikWFAFM2gcoWLuIiIiIah7m2JVDbzRBytsHmPTmE5IMomEsTq+7y7cdIyIiInKCI3blkEFC4bF9MF65aD0njFoU56YDANShiSxOTEREVMNs2rQJkiTh8uXLvu5KtWNgVwYhgAd+lOPyH59bzwVJWki/zIRkMu/C1qzvtyxOTERE5AXVHYwdO3YMQ4YMQVhYGIKDgzF8+HCcO3fOrs3u3bvRr18/NGzYEI0bN8aYMWNw5coV6/VLly5h4MCBCAwMRMeOHfHPP//Y3T9+/HgsWrSoWj6PBQO7Mlh2nBDFBdZz87ppIaUtL2nEoI6IiKjWKSwsRFJSEiRJwu+//44///wTOp0OAwcOhMlkAgCcOXMGffv2RcuWLbFjxw6sW7cOBw4cwMMPP2x9ziuvvIKCggLs3r0bvXr1wuOPP269tn37duzcuRPPPPNMtX42BnZl0OqMQM5GyP0bIDIqGp063oChiUpfd4uIiMhtQggsXLgQ11xzDfz9/dGhQwesXLnSeq1v374YMGAAhDDPSF2+fBmxsbGYNWsWgJLRtDVr1qBDhw7w8/PDTTfdhP3799u9z7Zt23DLLbfA398fMTExmDhxIgoLC63Xi4uL8dxzzyEmJgZqtRqtWrXCZ599huPHj6NPnz4AgNDQUEiSZA2gyuu7xdq1a9G6dWv4+/ujT58+OH78eLnfjz///BPHjx/H8uXLcd111+G6667DsmXLsGvXLvz+++8AgNWrV0OpVOL9999HmzZtcOONN+L999/Hd999h6NHjwIAMjIycP/996N169YYM2YM0tPNqVp6vR5PPvkkPvzwQ8jlcnd/XJXCwK4MAgKSUYPI0W9i36FM/P1EgK+7RERENZRGZ4BGZ7AGRgCgM5ig0RlQbDA6bWsylbTVG81ti/SutXXXCy+8gGXLluGDDz7AgQMHMHnyZDz44IPYvHkzJEnC//73P+zcuRPvvPMOAGDcuHEIDw/HSy+9ZPecadOm4Y033sCuXbvQtGlT3H333dDrzQsM9+/fj/79++Oee+7Bvn37sGLFCmzduhVPPfWU9f6HHnoI33zzDd555x1kZGTgww8/RGBgIGJiYvDdd98BAA4dOoTs7Gy8/fbbFfYdAE6dOoV77rkHd9xxB/bu3YvHH38cM2bMKPf7UVxcDEmSoFarref8/Pwgk8mwdetWaxuVSgWZzdah/v7mvHpLmw4dOuD333+HwWDA+vXr0b59ewDAa6+9ht69e6Nz585u/JS8RNQzeXl5AoDIy8srt11+kUnELrwoYhdeFPn5BULMDhZidrAwLekmDn/RXBz+Mk4Y9YXV1GsiIqoJtFqtSE9PF1qt1u588+mrRfPpq8WFgiLruXd/OyyaT18tpq/8x65t2xd+Ec2nrxYnL5b8Dvl0y3+i+fTVYuLXu+3a3jD3V9F8+mpx6Gy+9dxXO0641ecrV64IPz8/sW3bNrvzjz32mBgxYoT19bfffivUarWYOXOmCAgIEIcOHbJe27hxowAgvvnmG+u5ixcvCn9/f7FixQohhBCjRo0SY8aMsXuPLVu2CJlMJrRarTh06JAAIFJTU5320/Ieubm5bvV95syZIiEhQZhMJuv16dOnOzzLVk5OjggODhaTJk0ShYWF4sqVK2LChAkCgPUz/Pvvv0KhUIiFCxeK4uJicenSJXHPPfcIAGL+/PlCCCEuX74sRowYIWJjY8Utt9wiDhw4IA4fPixatWolLly4IMaOHSvi4+PFsGHDxOXLl532xaKsP1tCuB67CCEER+zKIJdJgKoRoGpkPr7qdEwwc+uIiKjWSE9PR1FREfr164fAwEDr1+eff45jx45Z2w0bNgz33HMPFixYgEWLFqF169YOz+rWrZv1uFGjRmjTpg0yMjIAAGlpaVi+fLnde/Tv3x8mkwmZmZnYu3cv5HI5evXq5dW+Z2RkoGvXrnaLGW376UxYWBhSUlLw888/IzAwECEhIcjLy0PHjh2tU6ft2rXD//73PyxatAgBAQGIiIjANddcg/DwcGubkJAQfPXVVzhx4gQ2b96MxMREjB07Fq+//jq+/PJL/Pfffzh06BACAgIwd+5clz93ZbCOXRmK9EbAJACZ/beo+PJBQCax1AkREVmlz+0PAPBXluRTjbmlBR69Od5ucAAA0l7sCwDwU5S0fahbc4zoEgNZqYGDrdP7OLQd2qmZW32zLAZYs2YNoqOj7a7ZTkVqNBqkpaVBLpfjyJEjLj/fElCZTCaMHTsWEydOdGgTGxtrzUvzdt+FzfS3O5KSknDs2DFcuHABCoUCDRs2REREBOLj461tRo4ciZEjR+LcuXNo0KABJEnCm2++adfG1tKlS9GwYUMMGjQI99xzDwYPHgylUolhw4YhOTnZo366i4FdGR5fvgO4ZMKlXz/CyI1XEH5Ki48GlgRyLHVCREQWASrHX6cqhQwqJ6nsztoq5TIo5a63dUdiYiLUajVOnjxZ7mjZs88+C5lMhl9++QV33HEH7rzzTtx66612bf766y/ExsYCAHJzc3H48GG0bdsWANCxY0ccOHAALVu2dPr86667DiaTCZs3b0bfvn0drqtUKgCA0ViSZ+hK3xMTE/HDDz849NNVTZo0AQD8/vvvyMnJwd133+3QJjw8HIA5cPPz80O/fv0c2pw/fx7z5s2z5t8ZjUZr/qFer7f7XFWJgV0Z5DIJkj4P2oz1+HHneUQHlQriGNQREVEtEBQUhKlTp2Ly5MkwmUy4+eabkZ+fj23btiEwMBCjR4/GmjVrsHTpUmzfvh0dO3bEjBkzMHr0aOzbtw+hoaHWZ82dOxeNGzdGeHg4Zs2ahSZNmmDw4MEAgOnTp6Nr166YMGECnnjiCTRo0AAZGRlITU3Fu+++i7i4OIwePRqPPvoo3nnnHXTo0AEnTpxATk4Ohg8fjubNm0OSJKxevRp33HEH/P39Xer7uHHjsGjRIkyZMgVjx461TglXZNmyZUhISEBYWBi2b9+OSZMmYfLkyWjTpo21zXvvvYfu3bsjMDAQqampmDZtGl599VU0bNjQ4XmTJk3Cs88+ax1Z7NGjB/7v//4PSUlJ+Pjjj9GjR49K/RxdVmEWXh3jagLixUKDiH1TL+Qh0QKAiA6ShJgdLI78HxdOEBHVV+UluNdkJpNJvP3226JNmzZCqVSKsLAw0b9/f7F582aRk5MjwsPDrQsChBBCr9eLLl26iOHDhwshShY2/Pzzz6Jdu3ZCpVKJG2+8Uezdu9fufXbu3Cn69esnAgMDRYMGDUT79u3FK6+8Yr2u1WrF5MmTRWRkpFCpVKJly5Zi6dKl1utz584VERERQpIkMXr06Ar7bvHzzz+Lli1bCrVaLXr27CmWLl1a7uIJIcwLLMLDw4VSqRStWrUSixYtsluAIYR5QUijRo2ESqUS7du3F59//rnTZ61bt0506dJFGI1G67nCwkIxbNgwERQUJG677TZx7ty5Mvti+d54Y/GEJISHk9O1VH5+vjVJMjg4uMx2hTog8QMga3YzGPOyEB0k4fSUIBxtGQohk9Bi+AHIFCyBQkRUnxQVFSEzMxPx8fHw8/PzdXeqzaZNm9CnTx/k5uY6Ha2iyivvz5arsQvAqVgHRXojhizZhv9yAc3xMzDmZfm6S0REREQuYWBXikkIZGTnAwAu//KS9XxQYAC0fgoICVwRS0RERDUS69iVolbI8enoLhCNutjtEzvnjXdxOiYIkCSuiCUionqld+/eEEJwGrYWYGBXilwmoXvLMEAdZj0XHR2FocPvK1kJy6COiIiIaiAGdq7Iz4b0ZjtIpnq1zoSIiIhqGebYlWIwmrD50HmgCGhww30YEnkCTTRHoJNdhJD47SIiIqKai5FKKTqjCU9+8TckAA3vfg1LnlLAT38Bx1Z1BiRuJUZEREQ1FwO7UiRI8PMPgdZw9YSuEFAHWPPquHCCiIiIaioGdqUISY6Lx7NxeW0yYkwn4F8YDHQbDwhhDu4Y1BEREVENxcUTTlxemwxDzkFkXtBCMmiBw+t93SUiIiJy0aZNmyBJEi5fvuzrrlQ7BnZO2NavAwDx0CqO1BEREVWh+hyMeRMDu1KK9EbApAMARAeZg7msjQ/5sktERERELmFgV4pJCECY7M4VXz4IgFuJERFR7SSEwMKFC3HNNdfA398fHTp0wMqVK63X+vbtiwEDBkAIc73Wy5cvIzY2FrNmzQJQMpq2Zs0adOjQAX5+frjpppuwf/9+u/fZtm0bbrnlFvj7+yMmJgYTJ05EYWGh9XpxcTGee+45xMTEQK1Wo1WrVvjss89w/Phx9OnTBwAQGhoKSZLw8MMPV9h3i7Vr16J169bw9/dHnz59cPz48Qq/J5Ik4aOPPsJdd92FgIAAJCQkYPv27Th69Ch69+6NBg0aoFu3bjh27Jj1nmPHjmHQoEEIDw9HYGAgbrzxRmzYsMF6/eDBgwgICMBXX31lPff999/Dz8/P4XtVZUQ9k5eXJwCIvLw859e1RqEIjhAARHSQJExLuovDXzQXh7+ME0bdlWruLRER1SRarVakp6cLrVZrf6H4ivtfBn3J/Qa9+ZxO49pz3fT888+Ltm3binXr1oljx46JZcuWCbVaLTZt2iSEEOL06dMiNDRULF68WAghxH333Sc6d+4sdDqdEEKIjRs3CgAiISFB/Prrr2Lfvn3irrvuEnFxcdY2+/btE4GBgeKtt94Shw8fFn/++ae44YYbxMMPP2ztx/Dhw0VMTIz4/vvvxbFjx8SGDRvEN998IwwGg/juu+8EAHHo0CGRnZ0tLl++7FLfT548KdRqtZg0aZI4ePCg+OKLL0R4eLgAIHJzc8v8ngAQ0dHRYsWKFeLQoUNi8ODBIi4uTtx6661i3bp1Ij09XXTt2lUMGDDAes/evXvFhx9+KPbt2ycOHz4sZs2aJfz8/MSJEyesbd5//30REhIijh8/LrKyskSjRo3EW2+9VeHPqMw/W6Li2MXuc1XYoo6p6JtTqBNCERJlDeyMhTni8Jdx5sBOX1jNvSUiopqkzF++s4Pd//r3+5L7//3efG7pHfbPfS3e+b1uuHLlivDz8xPbtm2zO//YY4+JESNGWF9/++23Qq1Wi5kzZ4qAgABx6NAh6zVLYPfNN99Yz128eFH4+/uLFStWCCGEGDVqlBgzZozde2zZskXIZDKh1WrFoUOHBACRmprqtJ+W97ANxlzp+8yZM0VCQoIwmUzW69OnT3cpsHvhhResr7dv3y4AiM8++8x67uuvvxZ+fn5lPkMIIRITE8W7775rd+7OO+8UPXv2FLfddpvo16+fXd/K4q3AjuVOKsRFE0REVHulp6ejqKgI/fr1szuv0+lwww03WF8PGzYMq1atwoIFC/DBBx+gdevWDs/q1q2b9bhRo0Zo06YNMjIyAABpaWk4evQovvzyS2sbIQRMJhMyMzOxf/9+yOVy9OrVy6t9z8jIQNeuXe1qzNr2szzt27e3HoeHhwMArrvuOrtzRUVFyM/PR3BwMAoLCzFnzhysXr0aZ86cgcFggFarxcmTJ+2eu3TpUrRu3RoymQz//vtvtda/ZWBXisFgQmPTBZzzdUeIiKj2eP6M+/fI1SXHbQeanyGVSn1/pvJ5WSaTOW98zZo1iI6OtrumVpf0QaPRIC0tDXK5HEeOHHH5+ZagxWQyYezYsZg4caJDm9jYWBw9erRK+i6E5/u4K5VK67Hlczg7Z+nHtGnTsH79erzxxhto2bIl/P39MXToUOh0Orvn/vPPPygsLIRMJsPZs2cRFRXlcR/dxcDOhhDAgykFUEBvPiFXAgo/33aKiIhqPlWDyt0vV5i/vP1cAImJiVCr1Th58mS5o2XPPvssZDIZfvnlF9xxxx248847ceutt9q1+euvvxAbGwsAyM3NxeHDh9G2bVsAQMeOHXHgwAG0bNnS6fOvu+46mEwmbN68GX379nW4rlKpAABGo9GtvicmJuKHH35w6GdV2LJlCx5++GEMGTIEAHDlyhWHhRqXLl3Cww8/jFmzZuHs2bN44IEHsHv3bvj7V8/iSwZ2NrQG4NBFGV68MwbCoEOTYW/j9G/3+bpbREREHgsKCsLUqVMxefJkmEwm3HzzzcjPz8e2bdsQGBiI0aNHY82aNVi6dCm2b9+Ojh07YsaMGRg9ejT27duH0NBQ67Pmzp2Lxo0bIzw8HLNmzUKTJk0wePBgAMD06dPRtWtXTJgwAU888QQaNGiAjIwMpKam4t1330VcXBxGjx6NRx99FO+88w46dOiAEydOICcnB8OHD0fz5s0hSRJWr16NO+64A/7+/i71fdy4cVi0aBGmTJmCsWPHIi0tDcuXL6+S72XLli3x/fffY+DAgZAkCS+++KJ1NM9i3LhxiImJwQsvvACdToeOHTti6tSpeP/996ukTw4qzMKrY8pLQCzUCRG72Px1pVgIo+6KdeHEibV3uJT8SEREdVd5Ce41mclkEm+//bZo06aNUCqVIiwsTPTv319s3rxZ5OTkiPDwcDF//nxre71eL7p06SKGDx8uhChZ2PDzzz+Ldu3aCZVKJW688Uaxd+9eu/fZuXOn6NevnwgMDBQNGjQQ7du3F6+88or1ularFZMnTxaRkZFCpVKJli1biqVLl1qvz507V0RERAhJksTo0aMr7LvFzz//LFq2bCnUarXo2bOnWLp0qUuLJ1atWmV9nZmZKQCIPXv2WM+VXtCRmZkp+vTpI/z9/UVMTIx47733RK9evcSkSZOEEEL873//Ew0aNBCHDx+2PuPvv/8WKpVKrFmzptyfkbcWT0hXP1y9kZ+fj5CQEOTl5SE4ONju2v99nYJHn0lGk1Ff4Oj8jrj4210ozk0HALQY9i9kysoPiRMRUe1VVFSEzMxMxMfHw8+v/qTqbNq0CX369EFubi4aNmzo6+7USeX92SovdimNBYqvEgJ4cqp5j1j9f2uhLbpiDerUoYmQFAE+7iERERFR+RjYXaU1AEWF5j1iL65KRuj/boVkMg9mNuv7bbUuVSYiIiLyBBdPOBEdJEF+8TBgSRhlUEdERPVY7969K1VWhKoPR+ycEAFNYHrwOwjGc0RERFSLMLC7ymgSAK7+a0ShAuK6c6SOiIiIahUGdlcVG4yAsRiAeSEFERERUW3DwO4qSZQM0Em6K8DfyxjhERERUa3CwA4AhMCGqZ1gKLgIAJCK8yFb9zwkxnVERERUizCwAwC9Bi9996/1ZZBaQlFgMBdPEBERUa3CwO6qnY83wD/jGsA/qh1mLfoEpyLlgCSZixPLq2fjXiIiotogLi4Oixcvdrn98ePHIUkS9u7dW2V9srV8+fIq2yHjpZdewvXXX18lz/YGBnYwp9IFqSW0D5cj9vEPcPuQYdaEOxYnJiIisrdr1y6MGTPGq8+symDMm6ZOnYrffvvN190oEwsUw7zrhGXDMKk4ByrbcJdBHRERkZ2wsDBfd6HaCSFgNBoRGBiIwMDASj1Lr9dDqVR6qWf2OGJXSoeYUAQoGcwREVHd8PPPP6Nhw4YwmUwAgL1790KSJEybNs3aZuzYsRgxYoT19bZt23DLLbfA398fMTExmDhxIgoLC63XS0/FHjx4EDfffDP8/PyQmJiIDRs2QJIk/PDDD3Z9+e+//9CnTx8EBASgQ4cO2L59OwBg06ZNeOSRR5CXlwdJkiBJEl566SUAgE6nw3PPPYfo6Gg0aNAAN910EzZt2mT33OXLlyM2NhYBAQEYMmQILl68WO73xDI1/M0336B79+7w8/NDu3bt7J67adMmSJKE9evXo3PnzlCr1diyZYvDVKzJZMLcuXPRrFkzqNVqXH/99Vi3bp3De3377bfo3bs3/Pz88MUXX5Tbv8pgYHfVm9uL8dKmInTI34Gs3+7zdXeIiIi84pZbbkFBQQH27NkDANi8eTOaNGmCzZs3W9ts2rQJvXr1AgDs378f/fv3xz333IN9+/ZhxYoV2Lp1K5566imnzzeZTBg8eDACAgKwY8cOfPzxx5g1a5bTtrNmzcLUqVOxd+9etG7dGiNGjIDBYED37t2xePFiBAcHIzs7G9nZ2Zg6dSoA4JFHHsGff/6Jb775Bvv27cOwYcMwYMAAHDlyBACwY8cOPProoxg/fjz27t2LPn364OWXX3bpezNt2jQ8++yz2LNnD7p37467777bISh87rnnsGDBAmRkZKB9+/YOz3j77bexaNEivPHGG9i3bx/69++Pu+++29o/i+nTp2PixInIyMhA//79XeqfR0Q9k5eXJwCIvLw867mC/AIRHSQJACIqKlIc/jJOHP4yTpxYe4cwmUw+7C0REdUkWq1WpKenC61Wa3d+0aJFIjo6usKvgQMHOjxz4MCBLt27aNEij/vdsWNH8cYbbwghhBg8eLB45ZVXhEqlEvn5+SI7O1sAEBkZGUIIIUaNGiXGjBljd/+WLVuETCazfu7mzZuLt956SwghxC+//CIUCoXIzs62tk9NTRUAxKpVq4QQQmRmZgoA4tNPP7W2OXDggN37Llu2TISEhNi979GjR4UkSSIrK8vu/G233SZmzpwphBBixIgRYsCAAXbX77vvPodn2bL059VXX7We0+v1olmzZuK1114TQgixceNGAUD88MMPdvfOnj1bdOjQwfo6KipKvPLKK3ZtbrzxRjF+/Hi791q8eHGZ/RGi7D9bQjiPXcrCHDsAK1Z+h6wCc9E625rEXDhBRESuyM/PR1ZWVoXtYmJiHM6dP3/epXvz8/M96hsA9O7dG5s2bcKUKVOwZcsWvPzyy/juu++wdetWXL58GeHh4Wjbti0AIC0tDUePHsWXX35pvV8IAZPJhMzMTCQkJNg9+9ChQ4iJiUFERIT1XJcuXZz2w3bEKzIyEgCQk5Njfe/Sdu/eDSEEWrdubXe+uLgYjRs3BgBkZGRgyJAhdte7detmNx1alm7dulmPFQoFOnfujIyMDLs2nTt3LvP+/Px8nDlzBj169LA736NHD/zzzz8uP8ebGNgBeG3BfOtxYGADAAbzCwZ1RETkguDgYERHR1fYztmig7CwMJfuDQ4O9qhvgDmw++yzz/DPP/9AJpMhMTERvXr1wubNm5Gbm2udhgXMU6tjx47FxIkTHZ4TGxvrcE4I4fIgiO2CAcs9ltw/Z0wmE+RyOdLS0iCXy+2uWRYwCC/vElX6szRo0MDte5x9T1x5jjcwsANQqClJCJ1wbyMAOb7rDBER1TpTpkzBlClTPLr3p59+8nJvHFny7BYvXoxevXpBkiT06tULCxYsQG5uLiZNmmRt27FjRxw4cAAtW7Z06dlt27bFyZMnce7cOYSHhwMwl0Nxl0qlgtFotDt3ww03wGg0IicnBz179nR6X2JiIv766y+7c6Vfl+Wvv/7CLbfcAgAwGAxIS0srM5fQmeDgYERFRWHr1q3W5wDmxSdljVpWNS6esCEPiUafawsAgIWJiYiozggJCcH111+PL774Ar179wZgDvZ2796Nw4cPW88B5iT/7du3Y8KECdi7dy+OHDmCn376CU8//bTTZ/fr1w8tWrTA6NGjsW/fPvz555/WxRPupDPFxcXhypUr+O2333DhwgVoNBq0bt0aDzzwAB566CF8//33yMzMxK5du/Daa69h7dq1AICJEydi3bp1WLhwIQ4fPoz33nvPpWlYAHj//fexatUqHDx4EBMmTEBubi4effRRl/sMmBdgvPbaa1ixYgUOHTqEGTNmYO/evXbBcnViYFcG5tcREVFd0qdPHxiNRmsQFxoaisTERISFhdnlzbVv3x6bN2/GkSNH0LNnT9xwww148cUXrTlxpcnlcvzwww+4cuUKbrzxRjz++ON44YUXAAB+fn4u96979+4YN24c7rvvPoSFhWHhwoUAgGXLluGhhx7Cs88+izZt2uDuu+/Gjh07rPmKXbt2xaeffop3330X119/PX799Vfr+1fk1VdfxWuvvYYOHTpgy5Yt+PHHH9GkSROX+wyYA8tnn30Wzz77LK677jqsW7cOP/30E1q1auXWc7xFEt6enK7h8vPzERISgry8PGu+QlR0NLLPnEFYkAq/vNsKwcpCtBh+ADJFQAVPIyKi+qSoqAiZmZmIj493K2ipb/7880/cfPPNOHr0KFq0aOHr7jg4fvw44uPjsWfPnhqzPVh5f7acxS5lYY4dAFwNbVXQQ4CjdERERO5YtWoVAgMD0apVKxw9ehSTJk1Cjx49amRQV9cxsBMCHa6/HiaFGoqAIMhkBubXERERuaGgoADPPfccTp06hSZNmqBv375YtGiRr7tVL9XvwE4IiKX90eWuJ5HefwgEAIUskfl1REREbnjooYfw0EMP+bobLouLi/N6mZSaol4vnhA6DaRTO3DPxbfhLwrRQnEAIaHxkJhbR0RERLVQvR6x0xqAAAAdjHvQVBzB7IDHEX3bJo7WERERUa1UrwM7ALj7aw3OawT+9XsCo++dg3+NAoG+7hQREdVodXUaj3zHW3+m6n1gtzvbiKwCAWXQf/CTin3dHSIiqsEs21rpdDr4+3ORHXmPRqMBYL/tmifqdWBnGxyHKvLx83XPIkB1wHcdIiKiGk2hUCAgIADnz5+HUqmETFavU9XJC4QQ0Gg0yMnJQcOGDR32xHVXvQ3shAAe+MH+HMucEBFReSRJQmRkJDIzM3HixAlfd4fqkIYNGyIiIqLSz6m3gZ3WABy6UDJkV2j0Q1jvb7hwgoiIyqVSqdCqVSvodDpfd4XqCKVSWemROgufB3ZLlizB66+/juzsbLRr1w6LFy9Gz549y2y/efNmTJkyBQcOHEBUVBSee+45jBs3zv03FgIrCwag/9WXxSYVjMyFJSIiF8hkMm4pRjWST5MDVqxYgWeeeQazZs3Cnj170LNnT9x+++04efKk0/aZmZm444470LNnT+zZswfPP/88Jk6ciO+++879N9dr0M643/pSITMhwK+Bpx+FiIiIyOck4cM12zfddBM6duyIDz74wHouISEBgwcPxoIFCxzaT58+HT/99BMyMjKs58aNG4d//vkH27dvd+k9LRvpZp0+jahPEtHszQJkFQhER0fh9Omsyn8oIiIiIi+yxC55eXkIDg4ut63PRux0Oh3S0tKQlJRkdz4pKQnbtm1zes/27dsd2vfv3x9///039Hq9W++v1WlLnWFuHREREdVuPsuxu3DhAoxGI8LDw+3Oh4eH4+zZs07vOXv2rNP2BoMBFy5cQGRkpMM9xcXFKC4uqU+Xl5cHwLxhcX6xgGW4UgiB/Pz8SnwiIiIiIu+zxCeuTLL6fPFE6VWoQohyV6Y6a+/svMWCBQswZ84ch/M3dOho9/rMmTMICQlxqc9ERERE1a2goKDCWMVngV2TJk0gl8sdRudycnIcRuUsIiIinLZXKBRo3Lix03tmzpyJKVOmWF+bTCZcunQJSqUSsbGxOHXqVIXz1VQz5efnIyYmhj/DWo4/x7qBP8fajz/DmksIgYKCAkRFRVXY1meBnUqlQqdOnZCamoohQ4ZYz6empmLQoEFO7+nWrRt+/vlnu3O//vorOnfuXOYWHGq1Gmq12u5cw4YNrcOawcHB/ANcy/FnWDfw51g38OdY+/FnWDO5Oqvo03InU6ZMwaeffoqlS5ciIyMDkydPxsmTJ6116WbOnImHHnrI2n7cuHE4ceIEpkyZgoyMDCxduhSfffYZpk6d6quPQERERFRj+DTH7r777sPFixcxd+5cZGdn49prr8XatWvRvHlzAEB2drZdTbv4+HisXbsWkydPxvvvv4+oqCi88847uPfee331EYiIiIhqDJ8vnhg/fjzGjx/v9Nry5csdzvXq1Qu7d++u9Puq1WrMnj3bYZqWag/+DOsG/hzrBv4caz/+DOsGnxYoJiIiIiLv8WmOHRERERF5DwM7IiIiojri/9u725Cm2j8O4N+l+VTDsnQrmmk0LQlnadEimzXvgfRgL3qV1MIoKhWHhJQ9vinLF2ZmD0glQdQSephIioN0FWZoJUmRpMwSQsSeMFEjd90vovNvWPf9v8maZ/t+YC/OdZ1z+G1ftv3ctR3Z2BERERF5CZ9s7M6cOYPo6GgEBQUhMTER9+7d83RJ9A/u3r2LtWvXYubMmVAoFLh165bbvBAChw8fxsyZMxEcHIyUlBQ8e/bMM8XSDxUWFmLx4sVQKpWIiIjA+vXr0d7e7rYPcxz/zp49i/j4eOk6Z3q9HjU1NdI8M5SfwsJCKBQKWCwWaYw5ypvPNXbXrl2DxWLBvn378OTJEyQnJyMtLc3tsio0vgwMDECn06GsrOyH80VFRSguLkZZWRmam5uhVqvx119/ob+//w9XSj/jcDiQlZWFpqYm2O12fPnyBSaTCQMDA9I+zHH8mzVrFo4dO4aWlha0tLRg1apVSE9Pl970maG8NDc3o7y8HPHx8W7jzFHmhI9ZsmSJ2LFjh9vYvHnzxJ49ezxUEf0XAMTNmzelbZfLJdRqtTh27Jg0NjQ0JEJDQ8W5c+c8UCH9P3p7ewUA4XA4hBDMUc6mTp0qzp8/zwxlpr+/X2i1WmG324XBYBC5ublCCD4XvYFPfWL3+fNnPHr0CCaTyW3cZDKhsbHRQ1XRr3A6nejp6XHLNDAwEAaDgZmOYx8/fgQAhIWFAWCOcjQyMgKr1YqBgQHo9XpmKDNZWVlYvXo1UlNT3caZo/x5/ALFf1JfXx9GRkagUqncxlUqFXp6ejxUFf2Kb7n9KNNXr155oiT6F0II5OXlYfny5ViwYAEA5ignbW1t0Ov1GBoawuTJk3Hz5k3ExcVJb/rMcPyzWq14/PgxmpubR83xuSh/PtXYfaNQKNy2hRCjxkhemKl8ZGdn4+nTp7h///6oOeY4/sXGxqK1tRUfPnzA9evXYTab4XA4pHlmOL51d3cjNzcXdXV1CAoK+ul+zFG+fGopdvr06fDz8xv16Vxvb++ov05IHtRqNQAwU5nIyclBVVUV6uvrMWvWLGmcOcpHQEAA5s6di6SkJBQWFkKn0+HkyZPMUCYePXqE3t5eJCYmwt/fH/7+/nA4HCgtLYW/v7+UFXOUL59q7AICApCYmAi73e42brfbsWzZMg9VRb8iOjoaarXaLdPPnz/D4XAw03FECIHs7GzcuHEDd+7cQXR0tNs8c5QvIQSGh4eZoUwYjUa0tbWhtbVVuiUlJSEjIwOtra2YM2cOc5Q5n1uKzcvLw6ZNm5CUlAS9Xo/y8nK8fv0aO3bs8HRp9BOfPn1CR0eHtO10OtHa2oqwsDBERkbCYrHg6NGj0Gq10Gq1OHr0KEJCQrBx40YPVk3fy8rKwpUrV2Cz2aBUKqVPA0JDQxEcHCxdR4s5jm8FBQVIS0uDRqNBf38/rFYrGhoaUFtbywxlQqlUSt9t/WbSpEmYNm2aNM4cZc5zP8j1nNOnT4vZs2eLgIAAsWjRIumSCzQ+1dfXCwCjbmazWQjx9ef5hw4dEmq1WgQGBooVK1aItrY2zxZNbn6UHwBRUVEh7cMcx7/MzEzptTM8PFwYjUZRV1cnzTNDefr+cidCMEe5UwghhId6SiIiIiIaQz71HTsiIiIib8bGjoiIiMhLsLEjIiIi8hJs7IiIiIi8BBs7IiIiIi/Bxo6IiIjIS7CxIyIiIvISbOyIiIiIvAQbOyIiIiIvwcaOiOg3GhwcREhICF68eOHpUojIB7CxIyL6jex2OzQaDebNm+fpUojIB7CxIyKflpKSguzsbGRnZ2PKlCmYNm0a9u/fj2//Rnt4eBj5+fnQaDQIDAyEVqvFhQsXAADv379HRkYGwsPDERwcDK1Wi4qKCrfz22w2rFu3DgBw+PBhJCQk4OLFi4iMjMTkyZOxc+dOjIyMoKioCGq1GhEREThy5MiffRCIyGv4e7oAIiJPu3TpErZu3YqHDx+ipaUF27dvx+zZs7Ft2zZs3rwZDx48QGlpKXQ6HZxOJ/r6+gAABw4cwPPnz1FTU4Pp06ejo6MDg4OD0nldLheqq6tx/fp1aayzsxM1NTWora1FZ2cnNmzYAKfTiZiYGDgcDjQ2NiIzMxNGoxFLly79448FEckbGzsi8nkajQYnTpyAQqFAbGws2tracOLECRgMBlRWVsJutyM1NRUAMGfOHOm4169fY+HChUhKSgIAREVFuZ23qakJLpcLy5Ytk8ZcLhcuXrwIpVKJuLg4rFy5Eu3t7bh9+zYmTJiA2NhYHD9+HA0NDWzsiOg/41IsEfm8pUuXQqFQSNt6vR4vX77EkydP4OfnB4PB8MPjdu7cCavVioSEBOTn56OxsdFt3mazYc2aNZgw4X8vtVFRUVAqldK2SqVCXFyc2z4qlQq9vb1jdfeIyIewsSMi+omgoKB/nE9LS8OrV69gsVjw5s0bGI1G7N69W5qvqqpCenq62zETJ05021YoFD8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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: 4.64\n* HDI 95%: 0.2 - 14.8\n* 90% Range: 0.4 - 14.51", + "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: 11.63\n* HDI 95%: 0.16 - 46.51\n* 90% Range: 0.38 - 61.8", + "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: 6.08\n* HDI 95%: 0.1 - 23.2\n* 90% Range: 0.4 - 22.44", + "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 = data[(~data.canton.isin(cantons))&(data.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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wZWRk2NqqqqqUmZmpAQMGNKq/1atXa+LEiWrbtu1l9w0KCtK1115re7m7X3re6sUXX9Tvfvc7ffjhh3XusvXw8FBcXFytMUhSRkZGg8ZQXl4uSWrTpnbMcnNzU01Njd3naQxm7AAAwBVNnTpVa9eu1caNG+Xn52eb1QoICJC3t7csFouSk5OVmpqqrl27qmvXrkpNTZWPj4/Gjx9vO09BQYEKCgr0/fffS5L27dsnPz8/RUVF2W5ikKRPPvlEOTk5V7wM2xCLFy/WvHnztHbtWnXp0sU2houXcCUpJSVFiYmJio+PV//+/ZWenq7c3FwlJSXZznPq1Cnl5uYqLy9Pkmzr+YWHhys8PFw9evTQtddeq8mTJ2vJkiUKDg7W+++/r4yMDG3evLnZxlMfgh0AAC3EieKKFtvPxeU/fnpX5+rVq213rM6cOVMVFRWaMmWKiouLdfPNN+ujjz6yrWEnSStXrtTzzz9v27711lvrnEe6cNPEgAED1LNnzwbXeilpaWmqqqrSvffeW6t9/vz5eu655yRJ48aN08mTJ7VgwQLl5+crNjZWW7ZsUXR0tG3/TZs26Ze//KVt+4EHHqh1nrZt22rLli2aPXu27rrrLp09e1bXXnut/vznP+uOO+5otvHUh3XsHIR17AAAP3WptcquhgWK4VisYwcAgEkEBATo8L9/UFlZmdP69PX1JdSZEMEOAIAWICAggKCFJuOuWAAAAJMg2AEAAJgEwQ4AAMAkCHYAADhZK1uQAnZort8Jgh0AAE5y8QkKF59MAFx08XfiSk/ZuBLuigUAwEnc3NwUGBiowsJCSZKPj0+DHy4PczEMQ+Xl5SosLFRgYKDc3NyadD6CHQAAThQeHi5JtnAHSFJgYKDtd6MpCHYAADiRxWJRx44dFRoaqurqaleXgxagbdu2TZ6pu4hgBwCAC7i5uTXbf8yBi7h5AgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMwuXBLi0tTTExMfLy8lJcXJy2b99u13H/+Mc/5O7urj59+ji2QAAAgKuES4Pd+vXrlZycrLlz52rPnj0aPHiwRo0apdzc3MseV1JSookTJ2r48OFOqhQAAKDlc2mwW7p0qSZNmqTHHntMPXv21LJlyxQZGakVK1Zc9rjJkydr/Pjx6t+/v5MqBQAAaPlcFuyqqqqUnZ2thISEWu0JCQnKysq65HGrV6/W4cOHNX/+fEeXCAAAcFVx2ZMnioqKZLVaFRYWVqs9LCxMBQUF9R7z3Xffafbs2dq+fbvc3e0rvbKyUpWVlbbt0tLSxhcNAADQgrn85gmLxVJr2zCMOm2SZLVaNX78eD3//PPq1q2b3edfuHChAgICbK/IyMgm1wwAANASuSzYhYSEyM3Nrc7sXGFhYZ1ZPEk6c+aMdu3apWnTpsnd3V3u7u5asGCB/vWvf8nd3V2ffPJJvf3MmTNHJSUlttfRo0cdMh4AAABXc9mlWA8PD8XFxSkjI0N33323rT0jI0Njxoyps7+/v7/27dtXqy0tLU2ffPKJ3n33XcXExNTbj6enpzw9PZu3eAAAgBbIZcFOklJSUpSYmKj4+Hj1799f6enpys3NVVJSkqQLs23Hjx/Xm2++qTZt2ig2NrbW8aGhofLy8qrTDgAA0Bq5NNiNGzdOJ0+e1IIFC5Sfn6/Y2Fht2bJF0dHRkqT8/PwrrmkHAACACyyGYRiuLsKZSktLFRAQoJKSEvn7+zusn927dysuLk4jFmer/TX9HNJH8b93K2NmnLKzs9Wvn2P6AAAArtWQ7OLyu2IBAADQPAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJNxdXYDZnSs5oYpTeXbv7+bpKw/fAAdWBAAAzIpg5yBnzpyRWxtp++9HNug4L79AjVz+A+EOAAA0GMHOQc6dOydrjbRs/iB1CA2265hTpys0fe5HslaWSQQ7AADQQAQ7B2vv76ng9t6uLgMAALQC3DwBAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJly93kpaWphdffFH5+fm67rrrtGzZMg0ePLjefT///HPNmjVL33zzjcrLyxUdHa3JkydrxowZTq66ZTl48KDD+wgJCVFUVJTD+wEAAI3n0mC3fv16JScnKy0tTQMHDtSqVas0atQoHThwoN4Q4evrq2nTpumGG26Qr6+vPv/8c02ePFm+vr564oknXDAC16oozpcsbTRhwgSH9+Xt46NvDh4k3AEA0IK5NNgtXbpUkyZN0mOPPSZJWrZsmbZu3aoVK1Zo4cKFdfbv27ev+vbta9vu0qWLNmzYoO3bt7fKYFdddloyatT7iT8p9Np+Duun9NhB7XxlgoqKigh2AAC0YC4LdlVVVcrOztbs2bNrtSckJCgrK8uuc+zZs0dZWVl64YUXLrlPZWWlKisrbdulpaWNK7gFa9exu9pf47hgBwAArg4uu3miqKhIVqtVYWFhtdrDwsJUUFBw2WM7d+4sT09PxcfHa+rUqbYZv/osXLhQAQEBtldkZGSz1A8AANDSuPyuWIvFUmvbMIw6bT+1fft27dq1SytXrtSyZcu0bt26S+47Z84clZSU2F5Hjx5tlroBAABaGpddig0JCZGbm1ud2bnCwsI6s3g/FRMTI0m6/vrr9Z///EfPPfecHnzwwXr39fT0lKenZ/MUDQAA0IK5bMbOw8NDcXFxysjIqNWekZGhAQMG2H0ewzBqfYcOAACgtXLpXbEpKSlKTExUfHy8+vfvr/T0dOXm5iopKUnShcuox48f15tvvilJeu211xQVFaUePXpIurCu3ZIlSzR9+nSXjQEAAKClcGmwGzdunE6ePKkFCxYoPz9fsbGx2rJli6KjoyVJ+fn5ys3Nte1fU1OjOXPmKCcnR+7u7vrZz36mP/zhD5o8ebKrhgAAANBiuPzJE1OmTNGUKVPqfW/NmjW1tqdPn87sHAAAwCW4/K5YAAAANA+CHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyiUcFuzZo1Ki8vb+5aAAAA0ASNCnZz5sxReHi4Jk2apKysrOauCQAAAI3QqGB37NgxvfXWWyouLtawYcPUo0cPLVq0SAUFBc1dHwAAAOzUqGDn5uam0aNHa8OGDTp69KieeOIJ/eUvf1FUVJRGjx6tjRs3qqamprlrBQAAwGU0+eaJ0NBQDRw4UP3791ebNm20b98+PfLII/rZz36mbdu2NUOJAAAAsEejg91//vMfLVmyRNddd52GDh2q0tJSbd68WTk5OcrLy9M999yjhx9+uDlrBQAAwGW4N+agu+66S1u3blW3bt30+OOPa+LEiQoKCrK97+3trV/96ld66aWXmq1QAAAAXF6jgl1oaKgyMzPVv3//S+7TsWNH5eTkNLowAAAANEyjLsUOGTJE/fr1q9NeVVWlN998U5JksVgUHR3dtOoAAABgt0YFu1/+8pcqKSmp037mzBn98pe/bHJRAAAAaLhGBTvDMGSxWOq0Hzt2TAEBAU0uCgAAAA3XoO/Y9e3bVxaLRRaLRcOHD5e7+/8dbrValZOTo5EjRzZ7kQAAALiyBgW7sWPHSpL27t2r22+/Xe3atbO95+HhoS5duugXv/hFsxYIAAAA+zQo2M2fP1+S1KVLF40bN05eXl4OKQoAAAAN16jlTlh4GAAAoOWxO9gFBQXp22+/VUhIiNq3b1/vzRMXnTp1qlmKAwAAgP3sDnYvvfSS/Pz8bH++XLADAACA89kd7H58+fWRRx5xRC0AAABoAruDXWlpqd0n9ff3b1QxAAAAaDy7g11gYOAVL79eXLjYarU2uTAAAAA0jN3B7tNPP3VkHQAAAGgiu4PdkCFDHFkHAAAAmsjuYPfVV18pNjZWbdq00VdffXXZfW+44YYmFwYAAICGsTvY9enTRwUFBQoNDVWfPn1ksVhkGEad/fiOHQAAgGvYHexycnLUoUMH258BAADQstgd7KKjo+v9MwAAAFqGRj0rVpIOHTqkV199VQcPHpTFYlGPHj00ffp0de/evTnrAwAAgJ3aNOagd999V7GxscrOzlbv3r11ww03aPfu3YqNjdU777zT3DUCAADADo2asZs5c6bmzJmjBQsW1GqfP3++Zs2apfvuu69ZigMAAID9GjVjV1BQoIkTJ9ZpnzBhggoKCppcFAAAABquUcFu6NCh2r59e532zz//XIMHD25yUQAAAGg4uy/Fbtq0yfbn0aNHa9asWcrOztYtt9wiSdqxY4feeecdPf/8881fJQAAAK7I7mA3duzYOm1paWlKS0ur1TZ16lQlJSU1uTAAAAA0jN3BrqamxpF1AAAAoIka9R07AAAAtDyNXqC4rKxMmZmZys3NVVVVVa33nnrqqSYXBgAAgIZpVLDbs2eP7rjjDpWXl6usrExBQUEqKiqSj4+PQkNDCXYAAAAu0KhLsTNmzNBdd92lU6dOydvbWzt27NCRI0cUFxenJUuWNHeNAAAAsEOjgt3evXv1q1/9Sm5ubnJzc1NlZaUiIyO1ePFi/eY3v2nuGgEAAGCHRgW7tm3bymKxSJLCwsKUm5srSQoICLD9GQAAAM7VqO/Y9e3bV7t27VK3bt00bNgw/fa3v1VRUZH+53/+R9dff31z1wgAAAA7NGrGLjU1VR07dpQk/e53v1NwcLCefPJJFRYWKj09vVkLBAAAgH0aNWMXHx9v+3OHDh20ZcuWZisIAAAAjdPodewkqbCwUIcOHZLFYlH37t3VoUOH5qoLAAAADdSoS7GlpaVKTExUp06dNGTIEN16662KiIjQhAkTVFJS0tw1AgAAwA6NCnaPPfaYdu7cqc2bN+v06dMqKSnR5s2btWvXLj3++OPNXSMAAADs0KhLsX/729+0detWDRo0yNZ2++23609/+pNGjhzZbMUBAADAfo2asQsODlZAQECd9oCAALVv377JRQEAAKDhGhXsnn32WaWkpCg/P9/WVlBQoGeeeUbz5s1rtuIAAABgP7svxfbt29f2tAlJ+u677xQdHa2oqChJUm5urjw9PXXixAlNnjy5+SsFAADAZdkd7MaOHevAMgAAANBUdge7+fPnO7IOAAAANFGTFijOzs7WwYMHZbFY1KtXL/Xt27e56gIAAEADNSrYFRYW6oEHHtC2bdsUGBgowzBUUlKiYcOG6e233+YJFAAAAC7QqLtip0+frtLSUu3fv1+nTp1ScXGxvv76a5WWluqpp55q7hoBAABgh0bN2H344Yf6+OOP1bNnT1tbr1699NprrykhIaHZigMAAID9GjVjV1NTo7Zt29Zpb9u2rWpqappcFAAAABquUcHuv/7rv/T0008rLy/P1nb8+HHNmDFDw4cPb7biAAAAYL9GXYpdvny5xowZoy5duigyMlIWi0W5ubm6/vrr9dZbbzV3ja3OudMFdu1XdbZYklR5pkhVZSXy8K37mDcAANB6NCrYRUZGavfu3crIyNA333wjwzDUq1cv3Xbbbc1dX6tSXlEttzZSxsy4Bh23a+k9+tovUCOX/0C4AwCgFWtwsDt//ry8vLy0d+9ejRgxQiNGjHBEXa1SZeV5WWukVxYMU3Cw/5X3Lz2hM/nfyurfTSkvZMlaWSYR7AAAaLUaHOzc3d0VHR0tq9XqiHogqX2Ap4Lbe19xv3NtPOV+1k3nAzydUBUAAGjpGnXzxLPPPqs5c+bo1KlTzV0PAAAAGqlRwe6VV17R9u3bFRERoe7du6tfv361Xg2RlpammJgYeXl5KS4uTtu3b7/kvhs2bNCIESPUoUMH+fv7q3///tq6dWtjhgAAAGA6jbp5YuzYsbJYLDIMo0mdr1+/XsnJyUpLS9PAgQO1atUqjRo1SgcOHFBUVFSd/T/77DONGDFCqampCgwM1OrVq3XXXXdp586dPKcWAAC0eg0KduXl5XrmmWf0/vvvq7q6WsOHD9err76qkJCQRnW+dOlSTZo0SY899pgkadmyZdq6datWrFihhQsX1tl/2bJltbZTU1O1ceNGffDBBwQ7AADQ6jXoUuz8+fO1Zs0a3XnnnXrwwQf18ccf68knn2xUx1VVVcrOzq7zCLKEhARlZWXZdY6amhqdOXNGQUFBl9ynsrJSpaWltV4AAABm1KAZuw0bNuj111/XAw88IEl66KGHNHDgQFmtVrm5uTWo46KiIlmtVoWFhdVqDwsLU0GBfQv0/vGPf1RZWZnuv//+S+6zcOFCPf/88w2qDQAA4GrUoBm7o0ePavDgwbbtm266Se7u7rUeLdZQFoul1rZhGHXa6rNu3To999xzWr9+vUJDQy+535w5c1RSUmJ7HT16tNG1AgAAtGQNmrGzWq3y8PCofQJ3d50/f77BHYeEhMjNza3O7FxhYWGdWbyfWr9+vSZNmqR33nnnik+78PT0lKcn67wBAADza1CwMwxDjzzySK2gdO7cOSUlJcnX19fWtmHDhiuey8PDQ3FxccrIyNDdd99ta8/IyNCYMWMuedy6dev06KOPat26dbrzzjsbUj4AAICpNSjYPfzww3XaJkyY0OjOU1JSlJiYqPj4ePXv31/p6enKzc1VUlKSpAuXUY8fP64333xT0oVQN3HiRL388su65ZZbbLN93t7eCgjgUVoAAKB1a1CwW716dbN2Pm7cOJ08eVILFixQfn6+YmNjtWXLFkVHR0uS8vPzlZuba9t/1apVOn/+vKZOnaqpU6fa2h9++GGtWbOmWWsDAAC42jRqgeLmNGXKFE2ZMqXe934a1rZt2+b4ggAAAK5SjXqkGAAAAFoegh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMwt3VBcB1qspKZK0su+J+50pOSJJOnDihvLw8+fr6KiAgwNHlAQCABiLYtVJVZSX6cFoXnTtz2u5jRo4cKUkKDgrU4X//QLgDAKCFIdi1UtbKMp07c1qv/j5BQYHel933/LmzOn3kX+rdu7fKK910x5SPVFZWRrADAKCFIdi1ckGB3gpuf6Vgd16WU24KDfJS2Tl+ZQAAaKm4eQIAAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJNwd3UBaD7nThc4ZF8AAHB1INiZQHnFebm1kTJmxjX4WMN63gEVAQAAVyDYmUBllVXWGumVBcMUHOxv1zE5ucV6dtFnMgyrg6sDAADOQrAzkfYBngpu723XvsWnKxxcDQAAcDaX3zyRlpammJgYeXl5KS4uTtu3b7/kvvn5+Ro/fry6d++uNm3aKDk52XmFAgAAtHAuDXbr169XcnKy5s6dqz179mjw4MEaNWqUcnNz692/srJSHTp00Ny5c9W7d28nVwsAANCyuTTYLV26VJMmTdJjjz2mnj17atmyZYqMjNSKFSvq3b9Lly56+eWXNXHiRAUEBDi5WgAAgJbNZcGuqqpK2dnZSkhIqNWekJCgrKwsF1UFAABw9XLZzRNFRUWyWq0KCwur1R4WFqaCguZbY62yslKVlZW27dLS0mY7NwAAQEvi8psnLBZLrW3DMOq0NcXChQsVEBBge0VGRjbbuQEAAFoSlwW7kJAQubm51ZmdKywsrDOL1xRz5sxRSUmJ7XX06NFmOzcAAEBL4rJg5+Hhobi4OGVkZNRqz8jI0IABA5qtH09PT/n7+9d6AQAAmJFLFyhOSUlRYmKi4uPj1b9/f6Wnpys3N1dJSUmSLsy2HT9+XG+++abtmL1790qSzp49qxMnTmjv3r3y8PBQr169XDEEAACAFsOlwW7cuHE6efKkFixYoPz8fMXGxmrLli2Kjo6WdGFB4p+uade3b1/bn7Ozs7V27VpFR0frhx9+cGbpAAAALY7LHyk2ZcoUTZkypd731qxZU6fNMAwHVwQAAHB1cvldsQAAAGgeBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJuHu6gJwdSooKGjwMb6+vgoICHBANQAAQCLYoYHOllfLrY0UFxfX4GODgwJ1+N8/EO4AAHAQgh0apOLceVlrpPdfHqZOof52H3eiuEJ3TPlIZWVlBDsAAByEYIdG6dDeU+Eh3q4uAwAA/Ag3TwAAAJgEM3awW3lZucorqm1/PnPWze5jy8rPSZL27dt3yRsvKisr5enp2fRCLyMkJERRUVEO7UOScnNzVVRU5NA+nDUWAMDVg2CHK7JWV0my6MDBA/ohv0qSdODgAeUfsz/YFZ+xSpJGjhx56Z0sbSSjpimlXpG3j4++OXjQoYEoNzdXPXr2VEV5ucP6kJwzFgDA1YVghysyas5LMtQuoof83aySPpd/514KCvGz/xzF5yT9r2566i/y9Auu837h1/+rQxtfVK+HXlRwl+tt7W4eXmrrbX8/l1N67KB2vjJBRUVFDg1DRUVFqigv181PvSX/zj0d0oezxgIAuLoQ7GA3Nw8fuXtemHlz9/SRu5f9gavKMOTWRvrilYcuu9+BvzxTa9vLL1Ajl/8gD9+r705a/8491f6afq4uAwDQihDs4BSVlReWSXllwTAFB9ddJqWy9ITO5H8rv8jr5elz4f1Tpys0fe5HslaWSVdhsAMAwNkIdnCq9gGeCm5fd5mUc2085X7WTQGBXvL0ZRkVAAAag+VOAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJiEu6sLAJzt4MGDV/X5AQD2yc3NVVFRkcP7CQkJUVRUlMP7sQfBDq1GRXG+ZGmjCRMmOKW/qspKp/QDAKgrNzdXPXr2VEV5ucP78vbx0TcHD7aIcEewQ6tRXXZaMmrU+4k/KfTafg7rJ3/3Fn399jydP3/eYX0AAC6vqKhIFeXluvmpt+TfuafD+ik9dlA7X5mgoqIigh3gCu06dlf7axwX7EqPcSkWAFoK/849HfqZ39Jw8wQAAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmAR3xcKUqspKZK0sq912tliSVHmmSBWn8uocU2M9rzZuDf8n4ebpKw/fgMYVCgBAMyLYwXSqykr04bQuOnfmdL3v71p6T73tbm4WWa1Gg/vz8gvUyOU/EO4AAC5HsIPpWCvLdO7Mab36+wQFBXrb2itLT+hM/rfyi7xenj7+tY7JyS3Ws4s+0ysLhik42P+np7ykU6crNH3uRxdmBwl2AAAXI9ihxTt3uqBR+wcFeiu4/f8Fu3NtPOV+1k0BgV7y9PWudUzx6QpJUvsAz1rHAABwNSHYocUqr6iWWxspY2Zco443rDzSCwDQuhDs0GJVVp6XtUYNvjx68bKqYVgdWB0AAC0PwQ4tXkMvj168rAoAQGvDOnYAAAAmQbADAAAwCYIdAACASfAdO6AZ/HhJlis94UJq/FMuLh53ruSEJOnEiRPKy6u/j4vOnz8vd/eG9+Xr66uAANbmA4CrCcEOaILLLclyqSdcSI1/ysVPjxs5cuQVj3F3s+h8I/oKDgrU4X//QLgDgKsIwQ5ogvqWZLncEy6kxj/l4sfHBfi20ekj/1Lv3r3l69Puksd8k1OsxN98pvdfHqZOofb3daK4QndM+UhlZWUEOwC4irg82KWlpenFF19Ufn6+rrvuOi1btkyDBw++5P6ZmZlKSUnR/v37FRERoZkzZyopKcmJFQN1/XhJlss94UJq/FMufnxcez83WU65KTTIS37tLn2OE6cuHNOhvafCQxz/RI2SkhKVlZU1+LjGXC529qXixoyNy9lN19jfKX72aK1cGuzWr1+v5ORkpaWlaeDAgVq1apVGjRqlAwcOKCoqqs7+OTk5uuOOO/T444/rrbfe0j/+8Q9NmTJFHTp00C9+8QsXjADARSUlJfrZNV108tTpBh/bmMvFzrxU3NixcTm7aZryO8XPHq2VS4Pd0qVLNWnSJD322GOSpGXLlmnr1q1asWKFFi5cWGf/lStXKioqSsuWLZMk9ezZU7t27dKSJUsIdoCLlZWV6eSp09qSlqAODZiJbMzlYmdfKm7M2Lic3XSN/Z3iZ4/WzGXBrqqqStnZ2Zo9e3at9oSEBGVlZdV7zD//+U8lJCTUarv99tv1+uuvq7q6Wm3btnVYvQDs06G9d4Mu+zr7cnFTNHRsaB783AH7uSzYFRUVyWq1KiwsrFZ7WFiYCgoK6j2moKCg3v3Pnz+voqIidezYsc4xlZWVqqystG2XlJRIkkpLS5s6hMu6+J2QY8dPqeKcfQ+jP5535sIxR4tUdvbKj8WqKitRxclqFVmLG3RcQ/u62E9JmxPKO1XT4L7s6e/HfbT1Km9wjfb0VV8f9tbXkL4u109z9VXq20Zni6p14LsT8vY5e8ljvs+9cMyhw0U6XWp/XydPX/g3s2XLFgUHB8swrnyZ9NSpU5Kkg4f/o8KTnnb31Zgaf1xfUFCQXcdYLBa7xlGfhozNIosMGY2q0V5NGUtL6uNK/TT2d+riz37Hjh0KCQlRmzZtVFNT0/Rir8AZ/ZilD2f0c+jQIUnSqcPZOn/u0p+TTVV6/EI/Z8+edVi2uHheu/5NGi5y/PhxQ5KRlZVVq/2FF14wunfvXu8xXbt2NVJTU2u1ff7554YkIz8/v95j5s+fb0jixYsXL168ePG6ql9Hjx69Yr5y2YxdSEiI3Nzc6szOFRYW1pmVuyg8PLze/d3d3RUcHFzvMXPmzFFKSoptu6amRqdOnVJwcLAsFksTR1G/0tJSRUZG6ujRo/L3t3+JCbNozeNvzWOXWvf4W/PYpdY9/tY8donxO2P8hmHozJkzioiIuOK+Lgt2Hh4eiouLU0ZGhu6++25be0ZGhsaMGVPvMf3799cHH3xQq+2jjz5SfHz8Jb9f5+npKU/P2lP4gYGBTSveTv7+/q3yl/yi1jz+1jx2qXWPvzWPXWrd42/NY5cYv6PHb++NQC59VmxKSor++7//W2+88YYOHjyoGTNmKDc317Yu3Zw5czRx4kTb/klJSTpy5IhSUlJ08OBBvfHGG3r99df161//2lVDAAAAaDFcutzJuHHjdPLkSS1YsED5+fmKjY3Vli1bFB0dLUnKz89Xbm6ubf+YmBht2bJFM2bM0GuvvaaIiAi98sorLHUCAACgFvDkiSlTpmjKlCn1vrdmzZo6bUOGDNHu3bsdXFXTeHp6av78+XUuAbcWrXn8rXnsUusef2seu9S6x9+axy4x/pY2fothOOF+dgAAADicS79jBwAAgOZDsAMAADAJgh0AAIBJEOwcIC0tTTExMfLy8lJcXJy2b9/u6pIcbuHChbrxxhvl5+en0NBQjR071vY4l9Zo4cKFslgsSk5OdnUpTnH8+HFNmDBBwcHB8vHxUZ8+fZSdne3qspzi/PnzevbZZxUTEyNvb29dc801WrBggVMeyeQKn332me666y5FRETIYrHo/fffr/W+YRh67rnnFBERIW9vbw0dOlT79+93TbHN7HJjr66u1qxZs3T99dfL19dXERERmjhxovLy8lxXcDO70t/9j02ePFkWi0XLli1zWn2OZM/YDx48qNGjRysgIEB+fn665ZZbaq3s4SwEu2a2fv16JScna+7cudqzZ48GDx6sUaNGueQv15kyMzM1depU7dixQxkZGTp//rwSEhJsz8xtTb788kulp6frhhtucHUpTlFcXKyBAweqbdu2+vvf/64DBw7oj3/8o9MWAne1RYsWaeXKlVq+fLkOHjyoxYsX68UXX9Srr77q6tIcoqysTL1799by5cvrfX/x4sVaunSpli9fri+//FLh4eEaMWKEzpw54+RKm9/lxl5eXq7du3dr3rx52r17tzZs2KBvv/1Wo0ePdkGljnGlv/uL3n//fe3cudOupyRcLa409sOHD2vQoEHq0aOHtm3bpn/961+aN2+evLy8nFyp5LJnxZrVTTfdZCQlJdVq69GjhzF79mwXVeQahYWFhiQjMzPT1aU41ZkzZ4yuXbsaGRkZxpAhQ4ynn37a1SU53KxZs4xBgwa5ugyXufPOO41HH320Vts999xjTJgwwUUVOY8k47333rNt19TUGOHh4cYf/vAHW9u5c+eMgIAAY+XKlS6o0HF+Ovb6fPHFF4Yk48iRI84pyokuNf5jx44ZnTp1Mr7++msjOjraeOmll5xem6PVN/Zx48a1mH/zzNg1o6qqKmVnZyshIaFWe0JCgrKyslxUlWuUlJRIkoKCglxciXNNnTpVd955p2677TZXl+I0mzZtUnx8vO677z6Fhoaqb9+++tOf/uTqspxm0KBB+t///V99++23kqR//etf+vzzz3XHHXe4uDLny8nJUUFBQa3PQE9PTw0ZMqTVfQZKFz4HLRZLq5m9rqmpUWJiop555hldd911ri7HaWpqavS3v/1N3bp10+23367Q0FDdfPPNl71U7UgEu2ZUVFQkq9WqsLCwWu1hYWEqKChwUVXOZxiGUlJSNGjQIMXGxrq6HKd5++23tXv3bi1cuNDVpTjVv//9b61YsUJdu3bV1q1blZSUpKeeekpvvvmmq0tzilmzZunBBx9Ujx491LZtW/Xt21fJycl68MEHXV2a0138nGvtn4GSdO7cOc2ePVvjx49vNc9PXbRokdzd3fXUU0+5uhSnKiws1NmzZ/WHP/xBI0eO1EcffaS7775b99xzjzIzM51ej8ufPGFGFoul1rZhGHXazGzatGn66quv9Pnnn7u6FKc5evSonn76aX300Ueu+U6FC9XU1Cg+Pl6pqamSpL59+2r//v1asWJFrWc9m9X69ev11ltvae3atbruuuu0d+9eJScnKyIiQg8//LCry3OJ1v4ZWF1drQceeEA1NTVKS0tzdTlOkZ2drZdfflm7d+9uVX/Xkmw3So0ZM0YzZsyQJPXp00dZWVlauXKlhgwZ4tR6mLFrRiEhIXJzc6vzf6aFhYV1/g/WrKZPn65Nmzbp008/VefOnV1djtNkZ2ersLBQcXFxcnd3l7u7uzIzM/XKK6/I3d1dVqvV1SU6TMeOHdWrV69abT179jT9DUMXPfPMM5o9e7YeeOABXX/99UpMTNSMGTNa3cytJIWHh0tSq/4MrK6u1v3336+cnBxlZGS0mtm67du3q7CwUFFRUbbPwCNHjuhXv/qVunTp4uryHCokJETu7u4t5nOQYNeMPDw8FBcXp4yMjFrtGRkZGjBggIuqcg7DMDRt2jRt2LBBn3zyiWJiYlxdklMNHz5c+/bt0969e22v+Ph4PfTQQ9q7d6/c3NxcXaLDDBw4sM7SNt9++62io6NdVJFzlZeXq02b2h+lbm5upl3u5HJiYmIUHh5e6zOwqqpKmZmZpv8MlP4v1H333Xf6+OOPFRwc7OqSnCYxMVFfffVVrc/AiIgIPfPMM9q6daury3MoDw8P3XjjjS3mc5BLsc0sJSVFiYmJio+PV//+/ZWenq7c3FwlJSW5ujSHmjp1qtauXauNGzfKz8/P9n/sAQEB8vb2dnF1jufn51fn+4S+vr4KDg42/fcMZ8yYoQEDBig1NVX333+/vvjiC6Wnpys9Pd3VpTnFXXfdpd///veKiorSddddpz179mjp0qV69NFHXV2aQ5w9e1bff/+9bTsnJ0d79+5VUFCQoqKilJycrNTUVHXt2lVdu3ZVamqqfHx8NH78eBdW3TwuN/aIiAjde++92r17tzZv3iyr1Wr7HAwKCpKHh4erym42V/q7/2mQbdu2rcLDw9W9e3dnl9rsrjT2Z555RuPGjdOtt96qYcOG6cMPP9QHH3ygbdu2Ob9Y196Ua06vvfaaER0dbXh4eBj9+vVrFUt+SKr3tXr1aleX5jKtZbkTwzCMDz74wIiNjTU8PT2NHj16GOnp6a4uyWlKS0uNp59+2oiKijK8vLyMa665xpg7d65RWVnp6tIc4tNPP6333/rDDz9sGMaFJU/mz59vhIeHG56ensatt95q7Nu3z7VFN5PLjT0nJ+eSn4Offvqpq0tvFlf6u/8pMy13Ys/YX3/9dePaa681vLy8jN69exvvv/++S2q1GIZhOD4+AgAAwNH4jh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAwIEqKirk4+Ojb775xtWlAGgFCHYA4EAZGRmKjIxUjx49XF0KgFaAYAegVRs6dKimTZumadOmKTAwUMHBwXr22Wd18THalZWVmjlzpiIjI+Xp6amuXbvq9ddflyQVFxfroYceUocOHeTt7a2uXbtq9erVtc6/ceNGjR49WpL03HPPqU+fPnrjjTcUFRWldu3a6cknn5TVatXixYsVHh6u0NBQ/f73v3fuDwGAabi7ugAAcLU///nPmjRpknbu3Kldu3bpiSeeUHR0tB5//HFNnDhR//znP/XKK6+od+/eysnJUVFRkSRp3rx5OnDggP7+978rJCRE33//vSoqKmznramp0ebNm/X//t//s7UdPnxYf//73/Xhhx/q8OHDuvfee5WTk6Nu3bopMzNTWVlZevTRRzV8+HDdcsstTv9ZALi6EewAtHqRkZF66aWXZLFY1L17d+3bt08vvfSShgwZor/+9a/KyMjQbbfdJkm65pprbMfl5uaqb9++io+PlyR16dKl1nl37NihmpoaDRgwwNZWU1OjN954Q35+furVq5eGDRumQ4cOacuWLWrTpo26d++uRYsWadu2bQQ7AA3GpVgArd4tt9wii8Vi2+7fv7++++477dmzR25ubhoyZEi9xz355JN6++231adPH82cOVNZWVm13t+4caN+/vOfq02b//uo7dKli/z8/GzbYWFh6tWrV619wsLCVFhY2FzDA9CKEOwA4BK8vLwu+/6oUaN05MgRJScnKy8vT8OHD9evf/1r2/ubNm3SmDFjah3Ttm3bWtsWi6XetpqamiZWD6A1ItgBaPV27NhRZ7tr167q3bu3ampqlJmZecljO3TooEceeURvvfWWli1bpvT0dEnSd999px9++EEJCQkOrR0AfoxgB6DVO3r0qFJSUnTo0CGtW7dOr776qp5++ml16dJFDz/8sB599FG9//77ysnJ0bZt2/TXv/5VkvTb3/5WGzdu1Pfff6/9+/dr8+bN6tmzp6QLl2Fvu+02+fj4uHJoAFoZbp4A0OpNnDhRFRUVuummm+Tm5qbp06friSeekCStWLFCv/nNbzRlyhSdPHlSUVFR+s1vfiNJ8vDw0Jw5c/TDDz/I29tbgwcP1ttvvy3pQrB7+OGHXTYmAK2Txbi4WBMAtEJDhw5Vnz59tGzZsmY7Z1FRkTp27KijR48qPDy82c4LAFfCpVgAaGanTp3S0qVLCXUAnI5LsQDQzLp166Zu3bq5ugwArRCXYgEAAEyCS7EAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAm8f8BqlNiiKWQCCUAAAAASUVORK5CYII=", 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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 = data[(~data.canton.isin(cantons))&(data.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, o_land_use_r.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, + "jupyter": { + "source_hidden": 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", + "\n", + "dbc = dbc[dbc.NAME.isin(cantons)].copy()\n", + "dbckey = dbc[['NAME', 'KANTONSNUM']].set_index('NAME')\n", + "dbckey = dbckey.drop_duplicates()\n", + "thiscanton = dbckey.loc[cantons, 'KANTONSNUM']\n", + "# Get the total bounds of the selected polygons\n", + "bounds = dbc.total_bounds\n", + "minx, miny, maxx, maxy = bounds\n", + "\n", + "\n", + "rivers = gpd.read_file('data/shapes/rivers.shp')\n", + "rivers = rivers.to_crs(epsg=4326)\n", + "# Filter the background layer to cover the bounding box\n", + "rivers_within_bounds = rivers.cx[minx:maxx, miny:maxy]\n", + "\n", + "\n", + "lakes = gpd.read_file('data/shapes/lakes.shp')\n", + "lakes = lakes.to_crs(epsg=4326)\n", + "lakes_within_bounds = lakes.cx[minx:maxx, miny:maxy]\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(12,10))\n", + "\n", + "ax.set_ylim([miny, maxy])\n", + "ax.set_xlim([minx, maxx])\n", + "\n", + "\n", + "dbc.plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.8)\n", + "\n", + "\n", + "rivers_within_bounds.plot(ax=ax, edgecolor='steelblue', alpha=.2)\n", + "lakes_within_bounds.plot(ax=ax, edgecolor='steelblue', color='steelblue', linewidth=.2, alpha=.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", + "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='black', markersize=80, alpha=1)\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'All survey locations')\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 left')\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 - combined\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
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Orchards5.030.090.000.000.00
Vineyards5.040.565.010.000.00
Buildings2.627.463.802.926.27
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Public Services4.2614.600.000.000.00
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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": 8, + "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9", + "metadata": { + "editable": true, + "jupyter": { + "source_hidden": 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
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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
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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": 9, + "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86", + "metadata": { + "editable": true, + "jupyter": { + "source_hidden": 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
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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
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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": 10, + "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3", + "metadata": { + "editable": true, + "jupyter": { + "source_hidden": 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": 11, + "id": "82f55461-c497-483a-8c38-fbd509809afb", + "metadata": { + "editable": true, + "jupyter": { + "source_hidden": 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": 12, + "id": "9b396025-1fa6-4661-9116-593fa1ed741d", + "metadata": { + "editable": true, + "jupyter": { + "source_hidden": 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": 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
 quantitypcs/msamples
canton   
Bern98892.8198
Genève95595.8833
Valais890223.7013
Vaud624687.78226
Zürich272283.99171
\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'], as_index=False).agg(session_config.unit_agg)\n", + "\n", + "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique'})\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", + "dxf.rename(columns={'sample_id':'samples'}, inplace=True)\n", + "dxfc = dxf.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
 quantitypcs/msamples
canton   
Bern37011.2197
Genève16164.389
Valais780.404
Vaud7382.645
Zürich65571.15175
\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'], as_index=False).agg(session_config.unit_agg)\n", + "\n", + "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique'})\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", + "dxf.rename(columns={'sample_id':'samples'}, inplace=True)\n", + "# dxfcr.drop('check', axis=1, inplace=True)\n", + "dxfcr = dxf.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 \n \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", + "## Cantonal Results - all data\n", + "\n", + "The average pieces per meter for each canton.\n", + "\n", + "::::::::::{grid}\n", + "\n", + ":::::::::{grid-item-card}\n", + "Lakes\n", + "^^^\n", + "```{glue} lake-municipal-results\n", + "```\n", + ":::::::::\n", + "\n", + ":::::::::{grid-item-card}\n", + "Rivers\n", + "^^^\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/extracting_topo_data.ipynb b/_build/html/_sources/extracting_topo_data.ipynb new file mode 100644 index 0000000..d7f4c95 --- /dev/null +++ b/_build/html/_sources/extracting_topo_data.ipynb @@ -0,0 +1,1170 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "64c16819-72da-4b47-a3aa-988d3f5a8203", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "%load_ext watermark\n", + "import pandas as pd\n", + "# import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "7bca0862-a1e5-4fd3-ae17-65ac2fd7cd0a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Extracting land use values\n", + "\n", + "This is a summary of the methods to extract topographical features from vector and points layers using QGIS. Atribute names are translated to the target languages and Null values are either corrected or eliminated.\n", + "\n", + "QGIS is not the optimal tool for this operation if the intention is to automate the aquisition of the data. However for a limited number of users and relatively few sample locations added each year this method is adequate to the task.\n", + "\n", + "__Note: 1'500 meter buffer (circular)__\n", + "\n", + "The land-use chapter in the federal report generated alot of interest. It inspired an academic article and collaboration with Wagenigen Research and University. The principal was applied using an empirical Bayes method with the Solid-Waste-Team at the EPFL.\n", + "\n", + "1. the area of the buffer is $\\pi * 1500²$ or 7065000 m²\n", + "\n", + "## Land-use, land-cover and streets\n", + "\n", + "__Land-cover:__ Are elements such as buildings, forest, orchard or undefined. There is at least one land-cover element for each survey location. If so then it takes 100% of the available dry land. \n", + "\n", + "__Land-use:__ Are elements that may or may not be in the buffer. These are items like schools, hospitals, water-treatment plants. Individually they are only a small portion of the available dry land. \n", + "\n", + "### Extracting land-cover and land-use:\n", + "\n", + "For this method we are using the land-cover layer from swissTLM regio\n", + "\n", + "finished columns = slug, attribute , attribute_type, area, dry,\tscale\n", + "\n", + "In QGIS:\n", + "\n", + "1. create a buffer around each survey point\n", + " * make sure that the survey location and feature_type is in the attributes of the new buffer layer\n", + " * the survey locations are loaded as points from .csv file\n", + " * reproject the points layer to the project CRS \n", + "\n", + "2. use the new buffer layer as an overlay to the land-cover layer\n", + " * use the overlay intersection tool\n", + " * select the fields to keep from the buffer (slug and feature type)\n", + " * select the fields to keep from the land-cover layer\n", + " * run the function\n", + " * this creates a temporary layer called _intersection_\n", + "\n", + "3. get the surface area of all the land-cover and land-use features in each buffer of the temporary layer\n", + " * use the field calculator for the attribute table of the layer\n", + " * in the field calculator, make a new field and enter the formula `\\$area`\n", + " * for this example the method is elipsoid _bessel 1841 (epsg 7001)_\n", + " * this is set in the properties of the QGIS project\n", + " * Export the layer as .csv\n", + "\n", + "4. verify the land-use features per location\n", + " * drop duplicate values: use location, feature and area to define duplicates\n", + " * attention! different names for lake and reservoir\n", + " * change Stausee to See\n", + "\n", + "5. make a dry land feature\n", + " * this is the surface area of the buffer that is not covered by water\n", + " * substract the area of See from the area of the buffer\n", + " * identify survey locations that have siginifcant water features but are not listed as lakes\n", + " \n", + "6. Scale the land-use attributes of interest to the available dry-land\n", + " \n", + "__Example making dry land columns and scaling the land-use__\n", + "\n", + " \n", + "```python\n", + "# separate locations that are lakes\n", + "# recall that feature type has a designator for lakes\n", + "lakes = lg[(lg.feature_ty == 'l') | lg.slug.isin(snl)].copy()\n", + "\n", + "# from this subset of data separate the surface area covered by water\n", + "# set the slug to the index and substract the surface area of the water\n", + "# from the surface area of the buffer\n", + "lake_only = lakes[lakes.feature == \"See\"]\n", + "lo = lake_only[[\"slug\", \"area\"]].set_index(\"slug\")\n", + "\n", + "# substract the lake value from the area of the buffer\n", + "lo[\"dry\"] = 7065000 - lo.area\n", + "lodry = lo[\"dry\"]\n", + "\n", + "# merge the original land-use data from lakes with the\n", + "# the dry land results\n", + "lgi = lakes.merge(lo[\"dry\"], left_on=\"slug\", right_index=True)\n", + "# remove the lake feature from the features columns\n", + "lgi = lgi[lgi.feature != \"See\"].copy()\n", + "\n", + "# scale the landuse feature to the available dry land\n", + "lgi[\"scale\"] = (lgi.area/lgi.dry).round(3)\n", + "\n", + "# repeat the process for locations that do not have a lake feature\n", + "# these locations are accounted for above\n", + "eliminate = [*snl, *lo.index]\n", + "# recuperate all other locations\n", + "rivers_parcs = lg[~lg.slug.isin(eliminate)].copy()\n", + "# define the dry land as the area of the buffer\n", + "rivers_parcs[\"dry\"] = 7065000\n", + "# scale the features with the dry land value\n", + "rivers_parcs[\"scale\"] = rivers_parcs.area/rivers_parcs.dry\n", + "\n", + "# combine the two results\n", + "land_cover = pd.concat([rivers_parcs, lgi])\n", + "```\n", + "Once the dry land value is calculated for each buffer of the land-cover buffer use the dry-land value to scale the components of the land-use buffer\n", + "\n", + "### Extracting street lengths\n", + "\n", + "The tlmREgio streets or strasse layer is applicable.\n", + "\n", + "__Note:__ the street lenghts are not scaled\n", + "\n", + "finished columns = slug, attribute, attribute_type, length\n", + "\n", + "__Attention:__\n", + "\n", + "1. The streets layer can have many copies of the same street in a buffer\n", + "2. The cumulative sum of a street in a layer can exceed the radius of the buffer (windy roads)\n", + "3. Dissolving the polylines of each buffer in the layer is essential\n", + "4. Drop duplicate values on slug, attribute, length\n", + "\n", + "1. Using the same 1'500 m buffer that was used for land-cover and land-use. Intersect the buffer with the street layer\n", + "2. Dissolve the polylines in each buffer\n", + " * select the fields from the streets layer to keep (OBJVAL)\n", + " * select the fields from the buffer layer to kepp (slug, feature_ty)\n", + " * check the box `keep disjoint features separate`\n", + " * run the function\n", + " * export to .csv" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "54148c36-ff96-4891-b230-479c5598f430", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "land_cover_data = \"data/end_process/land_cover.csv\"\n", + "land_use_data = \"data/end_process/land_use.csv\"\n", + "street_data = \"data/end_process/streets.csv\"\n", + "intersection_attributes = \"data/end_process/river_intersect_lakes.csv\"\n", + "land_cover = pd.read_csv(land_cover_data)\n", + "land_use = pd.read_csv(land_use_data)\n", + "streets = pd.read_csv(street_data)\n", + "river_intersect_lakes = pd.read_csv(intersection_attributes)" + ] + }, + { + "cell_type": "markdown", + "id": "b6c9d25e-bf0d-42b4-abd2-4db7d19aa951", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Land-cover\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "dbae7be9-28a9-48dc-b337-d9085123f9cb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Land-cover attributes" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bf670e98-1084-4385-85b3-e37ae4662877", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['undefined', 'Siedl', 'Wald', 'Reben', 'Obstanlage'], dtype=object)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "land_cover.attribute.unique()" + ] + }, + { + "cell_type": "markdown", + "id": "b1f73253-461e-47f6-b05c-bca3b5d34078", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "land-cover attribute translations\n", + "\n", + "```python\n", + "land_cover_fr = {\n", + " 'undefined': 'Non défini',\n", + " 'Siedl': 'Siedl',\n", + " 'Wald': 'Forêt',\n", + " 'Reben': 'Vignes',\n", + " 'Obstanlage': 'Verger'\n", + "}\n", + "\n", + "land_cover_en = {\n", + " 'undefined': 'Undefined',\n", + " 'Siedl': 'Settlement',\n", + " 'Wald': 'Forest',\n", + " 'Reben': 'Vines',\n", + " 'Obstanlage': 'Orchard'\n", + "}\n", + "```\n", + "\n", + "Land-cover results for one location" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d1977967-21ae-4f66-a22f-490e2ff1a173", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "land_cover_fr = {\n", + " 'undefined': 'Non défini',\n", + " 'Siedl': 'Siedl',\n", + " 'Wald': 'Forêt',\n", + " 'Reben': 'Vignes',\n", + " 'Obstanlage': 'Verger'\n", + "}\n", + "\n", + "land_cover_en = {\n", + " 'undefined': 'Undefined',\n", + " 'Siedl': 'Settlement',\n", + " 'Wald': 'Forest',\n", + " 'Reben': 'Vines',\n", + " 'Obstanlage': 'Orchard'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b9d04e40-b101-48fa-8e18-238bcc0b9323", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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slugattributeattribute_typeareadryscale
141parc-des-pierrettesundefinedland-cover6828538325590.018
623parc-des-pierrettesSiedlland-cover360688538325590.941
624parc-des-pierrettesWaldland-cover15738938325590.041
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" + ], + "text/plain": [ + " slug attribute attribute_type area dry scale\n", + "141 parc-des-pierrettes undefined land-cover 68285 3832559 0.018\n", + "623 parc-des-pierrettes Siedl land-cover 3606885 3832559 0.941\n", + "624 parc-des-pierrettes Wald land-cover 157389 3832559 0.041" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "land_cover[land_cover.slug == \"parc-des-pierrettes\"]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "36d5a536-7f46-4b15-810b-05e8b0b009a9", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "#### Land-use\n", + "\n", + "Land-use attributes:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "41f9f450-1230-470a-880f-68aecb1d1ec6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Baumschule', 'Friedhof', 'Schul- und Hochschulareal',\n", + " 'Wald nicht bestockt', 'Abwasserreinigungsareal',\n", + " 'Historisches Areal', 'Kraftwerkareal', 'Schrebergartenareal',\n", + " 'Truppenuebungsplatz', 'Unterwerkareal',\n", + " 'Kehrichtverbrennungsareal', 'Spitalareal',\n", + " 'Oeffentliches Parkareal', 'Messeareal',\n", + " 'Massnahmenvollzugsanstaltsareal', 'Kiesabbauareal',\n", + " 'Steinbruchareal', 'Klosterareal', 'Deponieareal', 'Antennenareal',\n", + " 'Lehmabbauareal'], dtype=object)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "land_use.attribute.unique()" + ] + }, + { + "cell_type": "markdown", + "id": "5db7039a-c66d-4e36-ab0a-d051ac04536f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Land-use translations and groups\n", + "\n", + "```python\n", + "\n", + "land_use_fr = {\n", + " 'Baumschule': 'Pépinière',\n", + " 'Friedhof': 'Cimetière',\n", + " 'Schul- und Hochschulareal': 'Zone scolaire et universitaire',\n", + " 'Wald nicht bestockt': 'Forêt non peuplée',\n", + " 'Abwasserreinigungsareal': 'Zone de traitement des eaux usées',\n", + " 'Historisches Areal': 'Zone historique',\n", + " 'Kraftwerkareal': 'Zone de centrale électrique',\n", + " 'Schrebergartenareal': 'Zone de jardins familiaux',\n", + " 'Truppenübungsplatz': 'Terrain d\\'entraînement militaire',\n", + " 'Unterwerkareal': 'Zone de sous-station',\n", + " 'Kehrichtverbrennungsareal': 'Zone d\\'incinération des déchets',\n", + " 'Spitalareal': 'Zone d\\'hôpital',\n", + " 'Öffentliches Parkareal': 'Zone de parc public',\n", + " 'Messeareal': 'Zone d\\'exposition',\n", + " 'Massnahmenvollzugsanstaltsareal': 'Zone d\\'établissement de traitement',\n", + " 'Kiesabbauareal': 'Zone d\\'extraction de gravier',\n", + " 'Steinbruchareal': 'Zone de carrière',\n", + " 'Klosterareal': 'Zone de monastère',\n", + " 'Deponieareal': 'Zone de décharge',\n", + " 'Antennenareal': 'Zone d\\'antennes',\n", + " 'Lehmabbauareal': 'Zone d\\'extraction d\\'argile'\n", + "}\n", + "\n", + "land_use_en = {\n", + " 'Baumschule': 'Nursery',\n", + " 'Friedhof': 'Cemetery',\n", + " 'Schul- und Hochschulareal': 'School and University Area',\n", + " 'Wald nicht bestockt': 'Non-stocked Forest',\n", + " 'Abwasserreinigungsareal': 'Wastewater Treatment Area',\n", + " 'Historisches Areal': 'Historical Area',\n", + " 'Kraftwerkareal': 'Power Plant Area',\n", + " 'Schrebergartenareal': 'Allotment Garden Area',\n", + " 'Truppenübungsplatz': 'Military Training Ground',\n", + " 'Unterwerkareal': 'Substation Area',\n", + " 'Kehrichtverbrennungsareal': 'Waste Incineration Area',\n", + " 'Spitalareal': 'Hospital Area',\n", + " 'Öffentliches Parkareal': 'Public Park Area',\n", + " 'Messeareal': 'Exhibition Area',\n", + " 'Massnahmenvollzugsanstaltsareal': 'Correctional Facility Area',\n", + " 'Kiesabbauareal': 'Gravel Extraction Area',\n", + " 'Steinbruchareal': 'Quarry Area',\n", + " 'Klosterareal': 'Monastery Area',\n", + " 'Deponieareal': 'Landfill Area',\n", + " 'Antennenareal': 'Antenna Area',\n", + " 'Lehmabbauareal': 'Clay Extraction Area'\n", + "}\n", + "\n", + "# land_uses_grouped:\n", + "# outdoor non technical use:\n", + "lu_non_tech = ['Friedhof', 'Hitorisches Areal', 'Schrebergartenareal', 'Öffentliches Parkareal', 'Messeareal', 'Klosterareal', 'Wald nicht bestockt']\n", + "\n", + "# technical-extraction-incineration\n", + "lu_extraction = ['Kiesabbauareal', 'Steinbruchareal', 'Lehmabbauareal',]\n", + "\n", + "# waste-water-treatment-powere\n", + "lu_technical = ['Kehrichtverbrennungsareal', 'Deponieareal', 'Deponieareal', 'Abwasserreinigungsareal','Unterwerkareal', 'Antennenareal']\n", + "\n", + "# services:\n", + "lu_serives = ['Massnahmenvollzugsanstaltsareal', 'Schul- und Hochschulareal', 'Spitalareal']\n", + "```\n", + "\n", + "Land-use for one location:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f7be463f-7ffd-4809-8bf5-ace2f0d686cd", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "land_use_fr = {\n", + " 'Baumschule': 'Pépinière',\n", + " 'Friedhof': 'Cimetière',\n", + " 'Schul- und Hochschulareal': 'Zone scolaire et universitaire',\n", + " 'Wald nicht bestockt': 'Forêt non peuplée',\n", + " 'Abwasserreinigungsareal': 'Zone de traitement des eaux usées',\n", + " 'Historisches Areal': 'Zone historique',\n", + " 'Kraftwerkareal': 'Zone de centrale électrique',\n", + " 'Schrebergartenareal': 'Zone de jardins familiaux',\n", + " 'Truppenübungsplatz': 'Terrain d\\'entraînement militaire',\n", + " 'Unterwerkareal': 'Zone de sous-station',\n", + " 'Kehrichtverbrennungsareal': 'Zone d\\'incinération des déchets',\n", + " 'Spitalareal': 'Zone d\\'hôpital',\n", + " 'Öffentliches Parkareal': 'Zone de parc public',\n", + " 'Messeareal': 'Zone d\\'exposition',\n", + " 'Massnahmenvollzugsanstaltsareal': 'Zone d\\'établissement de traitement',\n", + " 'Kiesabbauareal': 'Zone d\\'extraction de gravier',\n", + " 'Steinbruchareal': 'Zone de carrière',\n", + " 'Klosterareal': 'Zone de monastère',\n", + " 'Deponieareal': 'Zone de décharge',\n", + " 'Antennenareal': 'Zone d\\'antennes',\n", + " 'Lehmabbauareal': 'Zone d\\'extraction d\\'argile'\n", + "}\n", + "\n", + "land_use_en = {\n", + " 'Baumschule': 'Nursery',\n", + " 'Friedhof': 'Cemetery',\n", + " 'Schul- und Hochschulareal': 'School and University Area',\n", + " 'Wald nicht bestockt': 'Non-stocked Forest',\n", + " 'Abwasserreinigungsareal': 'Wastewater Treatment Area',\n", + " 'Historisches Areal': 'Historical Area',\n", + " 'Kraftwerkareal': 'Power Plant Area',\n", + " 'Schrebergartenareal': 'Allotment Garden Area',\n", + " 'Truppenübungsplatz': 'Military Training Ground',\n", + " 'Unterwerkareal': 'Substation Area',\n", + " 'Kehrichtverbrennungsareal': 'Waste Incineration Area',\n", + " 'Spitalareal': 'Hospital Area',\n", + " 'Öffentliches Parkareal': 'Public Park Area',\n", + " 'Messeareal': 'Exhibition Area',\n", + " 'Massnahmenvollzugsanstaltsareal': 'Correctional Facility Area',\n", + " 'Kiesabbauareal': 'Gravel Extraction Area',\n", + " 'Steinbruchareal': 'Quarry Area',\n", + " 'Klosterareal': 'Monastery Area',\n", + " 'Deponieareal': 'Landfill Area',\n", + " 'Antennenareal': 'Antenna Area',\n", + " 'Lehmabbauareal': 'Clay Extraction Area'\n", + "}\n", + "\n", + "# land_uses_grouped:\n", + "# outdoor non technical use:\n", + "lu_non_tech = ['Friedhof', 'Hitorisches Areal', 'Schrebergartenareal', 'Öffentliches Parkareal', 'Messeareal', 'Klosterareal', 'Wald nicht bestockt']\n", + "\n", + "# technical-extraction-incineration\n", + "lu_extraction = ['Kiesabbauareal', 'Steinbruchareal', 'Lehmabbauareal',]\n", + "\n", + "# waste-water-treatment-powere\n", + "lu_technical = ['Kehrichtverbrennungsareal', 'Deponieareal', 'Deponieareal', 'Abwasserreinigungsareal','Unterwerkareal', 'Antennenareal']\n", + "\n", + "# services:\n", + "lu_serives = ['Massnahmenvollzugsanstaltsareal', 'Schul- und Hochschulareal', 'Spitalareal']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "78e2c3ef-3f33-4ba2-af29-cad7c2358e25", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " slug attribute_type attribute length\n", + "638 parc-des-pierrettes streets Autobahn 1685\n", + "639 parc-des-pierrettes streets Fahrstraes 1046\n", + "640 parc-des-pierrettes streets Fussweg 3257\n", + "641 parc-des-pierrettes streets HauptStrAB6 2918\n", + "642 parc-des-pierrettes streets NebenStr3 2850\n", + "643 parc-des-pierrettes streets VerbindStr4 67\n", + "644 parc-des-pierrettes streets VerbindStr6 2864" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "streets[streets.slug == \"parc-des-pierrettes\"]" + ] + }, + { + "cell_type": "markdown", + "id": "8023149a-8443-46f6-86a8-8ceb9be3957d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Rivers: intersection, size and class\n", + "\n", + "This requires the 1'500 m buffer created at the begininng and the rivers layer and the lakes layer.\n", + "\n", + "### intersection, size and class\n", + "1. Create a points layer of the intersection of rivers to lakes\n", + " * Keep only points that are labeled as river (fluss, fluss_u)\n", + " * Use the attribute table to select the points\n", + "2. Overlay the 1 500 m buffer on the new layer and collect all the intersections within each buffer\n", + " * export to .csv\n", + "3. Check for intersections that do not have lake name\n", + " * not all lakes and rivers have names in the layer\n", + " * find the correct name for the lake and add it to the record\n", + " * replace river names with nan value with \"no name\"\n", + "\n", + "### distance to intersection\n", + "\n", + "__Note:__ the intersection points layer needs to be available\n", + "\n", + "1. Using the `hub lines` tool in QGIS draw lines from survey location to intersect location\n", + " * Limit the distance to the radius of the buffer\n", + " * join on the slug field in both layers\n", + " * eliminate duplicates\n", + " * calculate the length of the line with `$length` in field calculator of the attribute table\n", + " * export to .csv\n", + " \n", + "2. Identify locations (slugs) that are missing either the river_name or lake\n", + " * use the previous results (intersection, size and class) to fill in the missing values\n", + " * check that slugs not in the distance matrix are truly without intersections \n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cbf3d05b-4fb5-402e-838b-0de866c56167", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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72parc-des-pierrettesLe LémanLa Mèbre88333river intersection
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" + ], + "text/plain": [ + " slug lake river_name size class distance \\\n", + "72 parc-des-pierrettes Le Léman La Mèbre 8 8 333 \n", + "\n", + " attribute_type \n", + "72 river intersection " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "river_intersect_lakes[river_intersect_lakes.slug == \"parc-des-pierrettes\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "54fba949-faaf-45c5-a4b7-f0111a5c8872", + "metadata": { + "editable": true, + "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", + "\n" + ] + } + ], + "source": [ + "%watermark -a hammerdirt-analyst -co --iversions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f444bc4-d773-4d87-8b8e-540b98e4e131", + "metadata": { + "editable": true, + "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/forecasts.ipynb b/_build/html/_sources/forecasts.ipynb new file mode 100644 index 0000000..af3218a --- /dev/null +++ b/_build/html/_sources/forecasts.ipynb @@ -0,0 +1,1039 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "64c16819-72da-4b47-a3aa-988d3f5a8203", + "metadata": { + "editable": true, + "jupyter": { + "source_hidden": true + }, + "pycharm": { + "name": "#%%\n" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "%load_ext watermark\n", + "import pandas as pd\n", + "import numpy as np\n", + "from scipy.stats import dirichlet\n", + "import logging\n", + "\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\n", + "\n", + "\n", + "from myst_nb import glue\n", + "from IPython.display import display, Markdown\n", + "\n", + "from scipy.stats import halfnorm, multinomial\n", + "import gridforecast as gfcast\n", + "\n", + "# available data\n", + "\n", + "columns = [\n", + " 'sample_id',\n", + " 'code',\n", + " 'quantity',\n", + " 'pcs/m',\n", + " 'feature_name',\n", + " 'location',\n", + " 'parent_boundary',\n", + " 'city', \n", + " 'canton',\n", + " 'feature_type',\n", + " 'date'\n", + "]\n", + "\n", + "\n", + "import logging\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", + "\n", + "def create_jeffreys_prior_matrix(index_range, categories, epsilon=0.01):\n", + " # Initialize the matrix\n", + " jeffreys_prior_matrix = np.zeros((len(index_range), len(categories)))\n", + " \n", + " # Calculate Jeffreys prior values using the modified formula\n", + " for i, x in enumerate(index_range):\n", + " prior = 1 / (x + epsilon) # Adding epsilon to avoid division by zero\n", + " # Assign this value to all categories for this index\n", + " jeffreys_prior_matrix[i, :] = prior\n", + " \n", + " return jeffreys_prior_matrix" + ] + }, + { + "cell_type": "markdown", + "id": "0244e5d0-e474-4fdf-9920-e66361fb59a4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Grid forecast class\n", + "\n", + "The _grid forecaster_ refers to the methods defined in `gridforecast.py`. The main purpose of the _grid forecaster_ is to implement estimate the probability that a survey result _y_ from a collection of survey results _Y_ will exceed a value _x_ on the grid _X_ from 0 - max(_X_) for every _x_ spaced 0.1, where max(_X_) is defined by _Y_. This is called a grid approximation, in this case we use a Bayesien framework and implement _multinomila-Dirichlet_ conjugate to estimate the probabilities on each point of the grid. The complete method is a defined in [grid approximation](#).\n", + "\n", + "The grid forecast for any two arrays can be initiated by calling `gridforecast.MulitnomialDirichlet` and providing two pd.series of float values. However, for reporting we use the grid forecast to supplement the [SurveyReport](surveyreporter) and the [LandUseReport](landusereporter).\n", + "\n", + "```{note}\n", + "The grid forecast allows us to estimate the probability of a set of survey results given another set of survey results. Therefore, to interpret the results of a grid forecast the relationship between the two arrays must be well understood. Our focus has been on the structural and geographic similarities of the survey locations.\n", + "```\n", + "\n", + "__Example creating reports and forecasts__\n", + "\n", + "```python\n", + "# collecting the default data\n", + "data = session_config.collect_survey_data()\n", + "\n", + "# the likelihood: the dates of the most recent samples\n", + "recent_dates = {'start':'2020-01-01', 'end':'2021-12-31'}\n", + "# the prior: the dates prior to the most recent samples\n", + "prior_dates = {'start':'2015-11-15', 'end':'2019-12-31'}\n", + "# the region of interest\n", + "canton = 'Vaud'\n", + "\n", + "# the search parameters for the prior and likelihood\n", + "likelihood_params = {'canton':canton, 'date_range':recent_dates}\n", + "prior_params = {'canton':canton, 'date_range':prior_dates}\n", + "\n", + "# verify the parameters exist in the data\n", + "# checking the parameters will verify that the requested data\n", + "# exists. If the query is possible it is executed and the value of\n", + "# comments='ok', if not empty arrays are returned with the message\n", + "# 'no survey results found'. The method returns the query data, a list\n", + "# of the sample locations and the comment.\n", + "likelihood_data, likelihood_locations, likelihood_comments = check_params(likelihood_params, data, logger)\n", + "prior_data, prior_locations, prior_comments = check_params(prior_params, data, logger)\n", + "\n", + "# if there is data for both the likelihood and the prior\n", + "# make a survey report and a land use report for both sets of data\n", + "likelihood_report, likelihood_land_use = make_report_objects(likelihood_data)\n", + "prior_report, prior_land_use = make_report_objects(likelihood_data)\n", + "\n", + "# make forecast from all the available liklihood data\n", + "forecast_object = MulitnomialDirichlet('comb', prior_report.sample_results['pcs/m'], likelihood_report.sample_results['pcs/m'], logger)\n", + "\n", + "# make forecast limiting the likelihood to the 99the percentile\n", + "posterior_counts, comments = posterior_dirichlet_counts(lkl, prr, max_range=0.99)\n", + "\n", + "# forecasts from all the data\n", + "forecasted_samples = forecast_object.sample_posterior()\n", + "forecasted_summary = forecast_object.get_descriptive_statistics()\n", + "\n", + "# forecasts limited to the 99th percentile\n", + "sample_values_99, posterior_99, summary_99 = gfcast.dirichlet_posterior(posterior_counts)\n", + "```\n", + "\n", + "__Using a weighted prior__\n", + "\n", + "To predict density given similar locations use the land-use report from a set survey results that does not contain any of the survey locations from the likelihood. The default method is to also only select values that have the same use case ie. parks, lakes or rivers. \n", + "\n", + "```python\n", + "# determine the proportion of each land-use feature in the likelihood\n", + "weights = land_use_weights(likelihood_land_use, session_config.feature_variables)\n", + "\n", + "# from the pool of available data select records that are not included in the likelihood\n", + "# in this case we eliminate the canton of interest, limit the date to the end date of the prior\n", + "# and create a survey report and land use report for *the other prior data*\n", + "other_data = data[(data.canton != canton)&(data['date'] <= prior_dates['end'])].copy()\n", + "other_prior_report, other_prior_land_use = gfcast.make_report_objects(other_prior_data)\n", + "\n", + "# using the weights from the likelihood and the other_prior_land_use\n", + "other_prior_data, prior_weights = select_prior_data_by_feature_weight(other_prior_land_use, weights, session_config.feature_variables)\n", + "posterior_by_weight, weighted_comments = posterior_dirichlet_counts(likelihood_data, g['pcs/m'].values)\n", + "posterior_sample_values, weighted_dist, weighted_summary = dirichlet_posterior(posterior_by_weight)\n", + "\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "08211a6b-e1e9-4f98-bc27-1a377efefe43", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "import session_config\n", + "import reports\n", + "import geospatial\n", + "import userdisplay as disp\n", + "import gridforecast as gfcast\n", + "\n", + "# collecting the default data\n", + "data = session_config.collect_survey_data()\n", + "data = data.reset_index()\n", + "\n", + "# the likelihood: the dates of the most recent samples\n", + "recent_dates = {'start':'2020-01-01', 'end':'2021-12-31'}\n", + "# the prior: the dates prior to the most recent samples\n", + "prior_dates = {'start':'2015-11-15', 'end':'2019-12-31'}\n", + "# the region of interest\n", + "canton = 'Vaud'\n", + "\n", + "# the search parameters for the prior and likelihood\n", + "likelihood_params = {'canton':canton, 'date_range':recent_dates}\n", + "prior_params = {'canton':canton, 'date_range':prior_dates}\n", + "\n", + "# verify the parameters exist in the data\n", + "# checking the parameters will verify that the requested data\n", + "# exists. If the query is possible it is executed and the value of\n", + "# comments='ok', if not empty arrays are returned with the message\n", + "# 'no survey results found'. The method returns the query data, a list\n", + "# of the sample locations and the comment.\n", + "likelihood_data, likelihood_locations, likelihood_comments = gfcast.check_params(likelihood_params, data, logger)\n", + "prior_data, prior_locations, prior_comments = gfcast.check_params(prior_params, data, logger)\n", + "\n", + "# if there is data for both the likelihood and the prior\n", + "# make a survey report and a land use report for both sets of data\n", + "likelihood_report, likelihood_land_use = gfcast.make_report_objects(likelihood_data)\n", + "prior_report, prior_land_use = gfcast.make_report_objects(prior_data)\n", + "\n", + "# make forecast from all the available liklihood data\n", + "forecast_object = gfcast.MulitnomialDirichlet('comb', prior_report.sample_results['pcs/m'], likelihood_report.sample_results['pcs/m'], logger)\n", + "\n", + "# make forecast limiting the likelihood to the 99the percentile\n", + "posterior_counts, comments = gfcast.posterior_dirichlet_counts(likelihood_report.sample_results['pcs/m'], prior_report.sample_results['pcs/m'], max_range=0.99)\n", + "\n", + "# forecasts from all the data\n", + "forecasted_samples = forecast_object.sample_posterior()\n", + "forecasted_summary = forecast_object.get_descriptive_statistics()\n", + "\n", + "# forecasts limited to the 99th percentile\n", + "sample_values_99, posterior_99, summary_99 = gfcast.dirichlet_posterior(posterior_counts)" + ] + }, + { + "cell_type": "markdown", + "id": "8f748fa9-f06b-423e-b92f-625c88ab6a38", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Grid forecaster methods\n", + "\n", + "The `gridforecast.MulitnomialDirichlet` is a class in `gridforecast.py` the built in methods are designed to generate forecasts under a variety of scenarios and provide the basic elements to evaluate those forecasts. In the examples below consider the forecast_object created in the previous example.\n", + "\n", + "\n", + "__list of methods__\n", + "\n", + "1. MultinomialDirichlet\n", + " * compute_grid\n", + " * compute_counts\n", + " * compute_posterior_params\n", + " * sample_posterior\n", + " * compute_percentiles\n", + " * compute_hdi\n", + " * compute_expected_average\n", + " * probability_of_x\n", + " * get_descriptive_statistics\n", + "2. select_prior_data_by_feature_weight\n", + "3. posterior_dirichlet_counts\n", + "4. dirichlet_posterior\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "### The grid size\n", + "\n", + "The grid size for each combination is based on the maximum value of either the likelihood or the prior. \n", + "\n", + "```python\n", + "forecast_object.compute_grid()\n", + "``` " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "206d8a29-58a9-48c8-b0bb-1cd1c1b6536d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.000e+00, 1.000e-02, 2.000e-02, ..., 7.707e+01, 7.708e+01,\n", + " 7.709e+01])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecast_object.compute_grid()" + ] + }, + { + "cell_type": "markdown", + "id": "e6a9d5e8-f262-4dfb-afdf-e9b9cd379d9d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### The counts\n", + "\n", + "The number of times that a survey result was either equal to zero or any other place on the grid can be accessed with `forecastobject.prior` or `forecastobject.compute_counts(forecast_object.prior_data)`\n", + "\n", + "```python\n", + "forecastobject.compute_counts(forecast_object.prior_data)\n", + "``` " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1034c511-5426-439e-a126-69dc42c7f8ea", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, ..., 0, 0, 1])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecast_object.compute_counts(forecast_object.prior_data)" + ] + }, + { + "cell_type": "markdown", + "id": "f7d88436-fae1-42f9-82be-67a3d1f138c2", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### The posterior parameters\n", + "\n", + "The parameters for the Dirichlet posterior\n", + "\n", + "```python\n", + "forecastobject.compute_posterior_params()\n", + "``` " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "031d0089-a494-4944-8b03-02658f8655f4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.01, 0.01, 0.01, ..., 0.01, 0.01, 1. ])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecast_object.compute_posterior_params()" + ] + }, + { + "cell_type": "markdown", + "id": "010dc2f7-db6b-433c-9e31-0fdd3f1e2607", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Sample the posterior distribution\n", + "\n", + "Sample the posterior distribution\n", + "\n", + "```python\n", + "forecast_object.sample_posterior()\n", + "``` " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "82a61b6a-b615-4068-8267-93ea238e0b37", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.44, 0.55, 0.71, 0.81, 0.85, 1.26, 1.26, 1.41, 1.43,\n", + " 1.5 , 1.59, 1.59, 1.65, 1.75, 1.76, 1.76, 1.86, 1.89,\n", + " 1.89, 2.03, 2.1 , 2.15, 2.21, 2.4 , 2.45, 2.56, 2.57,\n", + " 2.57, 2.57, 2.57, 2.64, 2.69, 2.74, 3.41, 3.46, 3.73,\n", + " 4.01, 4.76, 4.76, 5.39, 5.43, 5.59, 6.29, 6.29, 6.43,\n", + " 6.55, 6.85, 6.94, 6.94, 6.95, 6.97, 7.26, 7.4 , 7.68,\n", + " 8.4 , 8.92, 9.57, 9.69, 10.07, 10.56, 10.64, 10.67, 10.67,\n", + " 10.67, 11.62, 12.37, 12.37, 12.61, 13.6 , 15.74, 18.36, 18.61,\n", + " 18.9 , 19.26, 22.38, 22.38, 23.73, 27.33, 28.13, 31.53, 31.98,\n", + " 33.55, 34.73, 35.57, 37.77, 39.36, 39.54, 40.69, 41.74, 44.1 ,\n", + " 46.96, 47.76, 50.51, 51.84, 51.92, 53.96, 59.09, 70.24, 70.24,\n", + " 75.28])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecast_object.sample_posterior()" + ] + }, + { + "cell_type": "markdown", + "id": "15028961-3710-4bab-8f72-f8bb31b77689", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### The 90% interval of the predictions\n", + "\n", + "The 90% interval of the predictions\n", + "\n", + "```python\n", + "forecast_object.compute_percentiles()\n", + "``` " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "28c992bb-936d-4dd0-818e-67370310d8f9", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.74 , 2.8 , 7.065 , 22.18 , 58.5515])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecast_object.compute_percentiles()" + ] + }, + { + "cell_type": "markdown", + "id": "abbddc94-6891-43b1-93b7-0ac635a81ba8", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### The 90% HDI\n", + "\n", + "The 90% highest density interval\n", + "\n", + "```python\n", + "forecast_object.compute_hdi()\n", + "``` " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bc590e3c-5db0-41ec-8102-9e29ff6228dc", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.18, 58.83)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecast_object.compute_hdi()" + ] + }, + { + "cell_type": "markdown", + "id": "3cd8cf38-afad-4478-bc3a-78d3b13696ac", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### The expected mean\n", + "\n", + "The 90% highest density interval\n", + "\n", + "```python\n", + "forecast_object.compute_hdi()\n", + "``` " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d3d7ed3a-db17-4b5b-922c-22fd67180839", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3.26850793e-05, 3.26850793e-05, 3.26850793e-05, ...,\n", + " 3.26850793e-05, 3.26850793e-05, 3.26850793e-03])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecast_object.compute_expected_average()" + ] + }, + { + "cell_type": "markdown", + "id": "42c307cb-55cf-479b-aa52-81f003d1805f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### The probability of x\n", + "\n", + "The chance that a result will exceed a given value\n", + "\n", + "```python\n", + "# in this case we are asking what is the chance of finding\n", + "# at least one piece per meter\n", + "a, b, c = forecast_object.probability_of_x(1)\n", + "sum(a[b[0]:])\n", + "``` " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2f30a4ec-095b-47f0-bff5-cdec32353bcb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9288737193351585" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a, b, c = forecast_object.probability_of_x(1)\n", + "sum(a[b[0]:])" + ] + }, + { + "cell_type": "markdown", + "id": "fff79642-0521-4b49-b91a-2481df990a9f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### The descriptive statistices\n", + "\n", + "The average, hdi and the 90% range of the expected distribution\n", + "\n", + "```python\n", + "forecast_object.get_descriptive_statistics()\n", + "``` " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "12c96e73-5c75-47cc-9f12-17e732394e87", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'code': 'comb',\n", + " 'average': 17.717200000000002,\n", + " 'hdi': (0.18, 65.66),\n", + " 'range': array([ 0.64 , 2.86 , 6.9 , 19.17 , 59.2875]),\n", + " 'max_observed': 77.1}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecast_object.get_descriptive_statistics()" + ] + }, + { + "cell_type": "markdown", + "id": "109016ba-7ff3-4fb9-a425-5acb8b107caf", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Select prior data by feature weight\n", + "\n", + "\n", + "The average, hdi and the 90% range of the expected distribution\n", + "\n", + "```python\n", + "# get the land use weights from the observations of interest\n", + "weights = land_use_weights(likelihood_land_use, feature_variables)\n", + "\n", + "# prior data does not include locations in canton\n", + "# the surveys are limited to the prior date as defined\n", + "other_data = data[(data.canton != canton)&(data['date'] <= prior_dates['end'])].copy()\n", + "other_report, landuse_from_other = gfcast.make_report_objects(other_data)\n", + "\n", + "# use the land use object from the other data\n", + "# and the weights from the likelihood to draw random\n", + "# samples from the other data\n", + "the_random_samples, w = select_prior_data_by_feature_weight(landuse_from_other, weights, feature_variables)\n", + "``` " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "768b10bc-a72e-4e38-b41e-bbfde0865818", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "# get the land use weights from the observations of interest\n", + "weights = gfcast.land_use_weights(likelihood_land_use, session_config.feature_variables)\n", + "\n", + "# prior data does not include locations in canton\n", + "# the surveys are limited to the prior date as defined\n", + "other_data = data[(data.canton != canton)&(data['date'] <= prior_dates['end'])].copy()\n", + "other_report, landuse_from_other = gfcast.make_report_objects(other_data)\n", + "\n", + "# use the land use object from the other data\n", + "# and the weights from the likelihood to draw random\n", + "# samples from the other data\n", + "the_random_samples, new_weights = gfcast.select_prior_data_by_feature_weight(landuse_from_other.df_cat, weights, session_config.feature_variables)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "bb0e9b77-104e-4e3b-9a60-dd644df6d408", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sample_idlocationdatequantitypcs/mpublic servicesstreetsorchardsvineyardsbuildingsforestundefinedbuildings_public services
0('aare_kehrsatz_stolten', '2017-07-03')aare_kehrsatz_stolten2017-07-0350.1013112131
1('birs_basel_laderachs', '2018-03-24')birs_basel_laderachs2018-03-24311.1213114111
2('limmat_zurich_mortensena_meiera', '2017-08-07')limmat_zurich_mortensena_meiera2017-08-07391.3213115111
3('birs_reinach_dinuccin', '2017-05-29')birs_reinach_dinuccin2017-05-291383.8613114211
4('vierwaldstattersee_weggis_schoberls_1', '201...vierwaldstattersee_weggis_schoberls_12018-01-2750.2111112141
\n", + "
" + ], + "text/plain": [ + " sample_id \\\n", + "0 ('aare_kehrsatz_stolten', '2017-07-03') \n", + "1 ('birs_basel_laderachs', '2018-03-24') \n", + "2 ('limmat_zurich_mortensena_meiera', '2017-08-07') \n", + "3 ('birs_reinach_dinuccin', '2017-05-29') \n", + "4 ('vierwaldstattersee_weggis_schoberls_1', '201... \n", + "\n", + " location date quantity pcs/m \\\n", + "0 aare_kehrsatz_stolten 2017-07-03 5 0.10 \n", + "1 birs_basel_laderachs 2018-03-24 31 1.12 \n", + "2 limmat_zurich_mortensena_meiera 2017-08-07 39 1.32 \n", + "3 birs_reinach_dinuccin 2017-05-29 138 3.86 \n", + "4 vierwaldstattersee_weggis_schoberls_1 2018-01-27 5 0.21 \n", + "\n", + " public services streets orchards vineyards buildings forest undefined \\\n", + "0 1 3 1 1 2 1 3 \n", + "1 1 3 1 1 4 1 1 \n", + "2 1 3 1 1 5 1 1 \n", + "3 1 3 1 1 4 2 1 \n", + "4 1 1 1 1 2 1 4 \n", + "\n", + " buildings_public services \n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "4 1 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "the_random_samples.head()" + ] + }, + { + "cell_type": "markdown", + "id": "dad9da42-da80-43ab-ae12-add35fdedb3b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Posterior Dirichlet counts\n", + "\n", + "The posterior distribution from the likelihood and the weighted prior.\n", + "\n", + "```python\n", + "# get the land use weights from the observations of interest\n", + "likelihood = likelihood_report.sample_results['pcs/m'].values\n", + "prior = the_random_samples['pcs/m'].values\n", + "posterior_by_weight, comments = posterior_dirichlet_counts(likelihood, prior)\n", + "sample_values, adist, summary = dirichlet_posterior(posterior_by_weight)\n", + "``` " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2e065188-22ed-4494-98b5-7e16de471634", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'range': array([0.3 , 0.675, 2.3 , 4.7 , 9.97 ]),\n", + " 'nsamples': 100,\n", + " 'average': 3.6220000000000003,\n", + " 'hdi': (0.1, 15.100000000000001)}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "likelihood = likelihood_report.sample_results['pcs/m'].values\n", + "prior = the_random_samples['pcs/m'].values\n", + "posterior_by_weight, comments = gfcast.posterior_dirichlet_counts(likelihood, prior)\n", + "sample_values, adist, summary = gfcast.dirichlet_posterior(posterior_by_weight)\n", + "summary" + ] + } + ], + "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 new file mode 100644 index 0000000..4da9bca --- /dev/null +++ b/_build/html/_sources/geneve.ipynb @@ -0,0 +1,2606 @@ +{ + "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", + "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 = '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, o_land_use.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: 4.17\n* HDI 95%: 0.1 - 18.4\n* 90% Range: 0.1 - 18.4", + "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: 8.6\n* HDI 95%: 0.68 - 21.34\n* 90% Range: 1.29 - 21.04", + "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: 6.28\n* HDI 95%: 0.5 - 17.2\n* 90% Range: 0.5 - 17.2", + "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, + "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 
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, o_land_use_l.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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material 
glass4%
metal4%
plastic85%
wood3%
\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: 1.89\n* HDI 95%: 0.1 - 6.1\n* 90% Range: 0.1 - 6.0", + "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: 5.72\n* HDI 95%: 1.53 - 15.99\n* 90% Range: 1.55 - 14.86", + "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: 3.96\n* HDI 95%: 0.8 - 16.0\n* 90% Range: 0.8 - 16.0", + "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 100% 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 \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 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", + "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, o_land_use_r.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, + "jupyter": { + "source_hidden": 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", + "dbc = dbc[dbc.NAME == canton].copy()\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", + "bounds = dbc.total_bounds\n", + "minx, miny, maxx, maxy = bounds\n", + "\n", + "\n", + "rivers = gpd.read_file('data/shapes/rivers.shp')\n", + "rivers = rivers.to_crs(epsg=4326)\n", + "# Filter the background layer to cover the bounding box\n", + "rivers_within_bounds = rivers.cx[minx:maxx, miny:maxy]\n", + "\n", + "\n", + "lakes = gpd.read_file('data/shapes/lakes.shp')\n", + "lakes = lakes.to_crs(epsg=4326)\n", + "lakes_within_bounds = lakes.cx[minx:maxx, miny:maxy]\n", + "\n", + "\n", + "\n", + "# Define the plot\n", + "fig, ax = plt.subplots(figsize=(12,10))\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.plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.2)\n", + "rivers_within_bounds.plot(ax=ax, edgecolor='steelblue', alpha=.2)\n", + "lakes_within_bounds.plot(ax=ax, edgecolor='steelblue', color='steelblue', linewidth=.2, alpha=.2)\n", + "\n", + "# Set the extent to Switzerland\n", + "ax.set_ylim([miny, maxy])\n", + "ax.set_xlim([minx, maxx])\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 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, + "jupyter": { + "source_hidden": 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
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Vineyards100%0%0%0%0%
Buildings0%0%0%0%100%
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\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, + "jupyter": { + "source_hidden": 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
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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, + "jupyter": { + "source_hidden": 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
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Vineyards3.560.000.000.000.00
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Undefined3.560.000.000.000.00
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\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
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Vineyards100%0%0%0%0%
Buildings0%0%0%0%100%
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\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, + "jupyter": { + "source_hidden": 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
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\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
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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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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 and the combined land use classification 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 new file mode 100644 index 0000000..869f7bf --- /dev/null +++ b/_build/html/_sources/landuse_class.ipynb @@ -0,0 +1,1158 @@ +{ + "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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orchardsvineyardsbuildingsforestundefinedpublic servicesstreets
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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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orchardsvineyardsbuildingsforestundefinedpublic servicesstreets
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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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" + ], + "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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" + ], + "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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" + ], + "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/multinomial_conjugate.md b/_build/html/_sources/multinomial_conjugate.md new file mode 100644 index 0000000..840df76 --- /dev/null +++ b/_build/html/_sources/multinomial_conjugate.md @@ -0,0 +1,239 @@ +(gridforecaster)= +# Empirical Bayes + +We consider that forecasting or predicting is the process of making statements about events that have yet to occurr. In this case we are using historical results to form our opinion about the probability of an event in the future. The event we are considering is rather pedestrian: + +> What will I find at the beach today, given what has been found at __other similar__ beaches or what was found at the beach in the past ? + +Which means that the estimation of the most common objects we are likely to find is based on our own experience as well as the experience of others under similar conditions. + +Our first consideration, however, is wether or not our research question is comensurate with our assumptions of the model. Otherwise no amount of mathematical manipulation will persuade a reasonable individual that an outstanding or extreme result is probable when there is no evidence that rises to the same level. + +## Assumptions of the model about the sample data + +The data is assumed to be subject to the experience of the surveyor and each survey is independent and identically distributed. We add to these basic assumptions the particularities of the domain: + +1. Locations that have similar environmental conditions will yield similar survey results +2. There is an exchange of material (trash) between the beach and body of water +3. Following from two, the material recovered at the beach is a result of the assumed exchange +4. The type of activities adjacent to the survey location are an indicator of the trash that will be found there +5. Following from four and three, the local environmental conditions are an indicator of the local contribution to the mix of objects at the beach +6. Surveys are not accurate + * Some objects will be misidentified + * Not all objects will be found + * There will be inaccuracies in object counts or data entry + +**Following 1 through 6:** the survey results are a reasonable estimate of the minimum number of objects that were present at the time the survey was completed. + + +## Conditional probability + +[Conditional probability](https://en.wikipedia.org/wiki/Conditional_probability) is a fundamental concept in probability theory that describes the probability of an event occurring given that another event has already occurred. It is denoted as $P(A∣B)$, which reads as "the probability of A given B". + +The conditional probability of event \(A\) given event \(B\) is defined as: + +$$P(A|B) = \frac{P(A \cap B)}{P(B)}$$ + +where: + +- $P(A|B)$ is the conditional probability of $A$ given $B$. +- $P(A \cap B)$ is the joint probability of both $A$ and $B$ occurring. +- $P(B)$ is the probability of event $B$ occurring, provided that $P(B) > 0$. + + +### Bayes' theorem + +[Bayes' Theorem](https://en.wikipedia.org/wiki/Bayes%27_theorem) is a fundamental principle in probability theory and statistics that describes the probability of an event based on __prior__ knowledge of conditions that might be related to the event. It allows for the updating of probabilities as new evidence or information becomes available. It is derived from the definition of conditional probability. + +__Deriving Bayes theorem__ + +::::{grid} 2 2 2 2 +:gutter: 1 + +:::{grid-item-card} Define conditional probability + +For events \(A\) and \(B\): + +$$P(A|B) = \frac{P(A \cap B)}{P(B)}$$ + +$$P(B|A) = \frac{P(A \cap B)}{P(A)}$$ + +::: + +:::{grid-item-card} The joint probability $P(A \cap B)$ + +From the first equation: + +$$P(A \cap B) = P(A|B) \cdot P(B)$$ + +From the second equation: + +$$P(A \cap B) = P(B|A) \cdot P(A)$$ + +::: + +:::{grid-item-card} Equate the two expressions + +$$P(A|B) \cdot P(B) = P(B|A) \cdot P(A)$$ + +Solve for $P(A|B)$ + +$$P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}$$ + +::: + +:::{grid-item-card} This is Bayes' Theorem + +$$P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}$$ + +::: +:::: + +#### Prior knowledge + +In the context of Bayes' theorem, the term "prior" refers to the prior probability, which is the probability of an event or hypothesis before any new evidence or data is taken into account. It represents the initial degree of belief in a particular outcome based on existing knowledge or assumptions. + +> In this use case the __prior__ is what we __beleive__ we will find at the beach, before we get to the beach, given everything we know about beaches and litter in Switzerland. Our beliefs are based on the cumulative experience from all previous visits to the beach, or beaches that are similar. Our beliefs come from what we have actually experienced. + +Mathematically, if we are trying to determine the probability of a hypothesis A given new evidence B, the prior probability is denoted as P(A). It is the baseline probability of A before considering the new evidence provided by B. + +Bayes' Theorem uses the prior probability along with the likelihood of the evidence given the hypothesis and the marginal probability of the evidence to update the probability of the hypothesis. This updated probability is called the posterior probability. + +### Empirical Bayes + +Empirical Bayes methods are statistical techniques that combine the principles of Bayesian inference with empirical data. These methods use data to estimate the prior distribution, which is then used in the Bayesian framework to update probabilities and make inferences. Using this method means that our prior distribution is testable in the sense of Jaynes + +> A piece of information _I_ concerning a parameter $\theta$ will be called __testable__ if, given any proposed prior probability assignment $f( \theta )$ $d\theta$, there is a procedure which will determine unambiguously whether $f( \theta )$ does or does not agree with the information _I_. ([Jaynes,1968](https://bayes.wustl.edu/etj/articles/prior.pdf)) + +In traditional Bayesian analysis, the prior distribution is chosen based on subjective beliefs or historical data. In contrast, empirical Bayes methods __estimate the prior distribution directly from the observed data, making the process more objective and often more practical in large-scale problems__. ([Petrone, S. et al, 2014](https://link.springer.com/article/10.1007/s40300-014-0044-1)) + +#### Conjugate prior + +In Bayesian statistics, a [conjugate prior](https://en.wikipedia.org/wiki/Conjugate_prior) is a prior distribution that, when combined with a given likelihood through Bayes' theorem, results in a [posterior distribution](https://en.wikipedia.org/wiki/Posterior_probability) of the same family as the prior. This property simplifies the computation of the posterior distribution. + +1. Jaynes, E.T.: ["Probability Theory: The Logic of Science"](https://bayes.wustl.edu/etj/prob/book.pdf): Emphasized the logical consistency and practical advantages of conjugate priors. +2. Gelman, A. et al.: ["Bayesian Data Analysis"](http://www.stat.columbia.edu/~gelman/book/) : discusses conjugate priors in the context of hierarchical models and practical Bayesian analysis. + +#### Deriving conjugate relationship + +The [binomial](https://en.wikipedia.org/wiki/Binomial_distribution), [multinomial](https://en.wikipedia.org/wiki/Multinomial_distribution), and [Dirichlet](https://en.wikipedia.org/wiki/Dirichlet_distribution) distributions are intrinsically linked through the concept of conjugate priors in Bayesian statistics. The binomial distribution describes the probability of a fixed number of successes in a series of independent trials, with a success probability _p_. When modeling this in a Bayesian framework, the Beta distribution is used as a conjugate prior for _p_. This means that the posterior distribution, after observing data, remains a Beta distribution, simplifying the update process. + +Extending this to multiple categories, the multinomial distribution generalizes the binomial by modeling the counts of outcomes across multiple categories. The Dirichlet distribution serves as the conjugate prior for the multinomial distribution, just as the Beta distribution does for the binomial. When using a Dirichlet prior, the posterior distribution after observing data also remains a Dirichlet distribution. + +::::{grid} 2 2 2 2 +:gutter: 1 + +:::{grid-item-card} Binomial Likelihood: + +The binomial distribution models the number of successes in _n_ trials, given a success probability _p_: + +$$P(X = k | p) = \binom{n}{k} p^k (1 - p)^{n - k}$$ + +The Beta distribution is a conjugate prior for the binomial likelihood, parameterized by $\alpha$ and $\beta$: + +$$P(p | \alpha, \beta) = \frac{p^{\alpha - 1} (1 - p)^{\beta - 1}}{B(\alpha, \beta)}$$ +::: + +:::{grid-item-card} Posterior Distribution: + +Combining the likelihood and prior using Bayes' theorem gives the posterior distribution: + +$$P(p | k, n) \propto p^k (1 - p)^{n - k} \cdot p^{\alpha - 1} (1 - p)^{\beta - 1}$$ + +$$P(p | k, n) \propto p^{k + \alpha - 1} (1 - p)^{n - k + \beta - 1}$$ + +Which is a Beta distribution: + +$$P(p | k, n) = \text{Beta}(p | k + \alpha, n - k + \beta)$$ +::: + +:::{grid-item-card} Generalize binomial to multinomial + +The multinomial distribution generalizes the binomial to more than two categories. For counts +$\mathbf{x} = (x_1, x_2, \ldots, x_K) \quad \text{in} \quad K \quad \text{categories, given probabilities} \quad \mathbf{p} = (p_1, p_2, \ldots, p_K)$: + +$$P(\mathbf{x} | \mathbf{p}) = \frac{n!}{x_1! x_2! \cdots x_K!} p_1^{x_1} p_2^{x_2} \cdots p_K^{x_K}$$ + +where: $n = \sum_{i=1}^K x_i$ +::: + +:::{grid-item-card} The conjugate prior to the multinomial + +The Dirichlet distribution is a conjugate prior for the multinomial distribution, parameterized by α=(α1​,α2​,…,αK​): + +$P(\mathbf{p} | \boldsymbol{\alpha}) = \frac{1}{B(\boldsymbol{\alpha})} \prod_{i=1}^K p_i^{\alpha_i - 1}$ + +where B(α) is the multivariate Beta function. +::: + +:::{grid-item-card} Posterior Distribution +The posterior distribution is a combination of the likelihood and prior: + +$$P(\mathbf{p} | \mathbf{x}) \propto \left( \prod_{i=1}^K p_i^{x_i} \right) \left( \prod_{i=1}^K p_i^{\alpha_i - 1} \right)$$ + +Which is a Dirichlet distribution: + +$P(\mathbf{p} | \mathbf{x}) \propto \prod_{i=1}^K p_i^{x_i + \alpha_i - 1}$ + +::: +:::{grid-item-card} Posterior with updated parameters + +$$P(\mathbf{p}|\mathbf{x}) = \text{D}(\mathbf{p}|x_1 + \alpha_1, \ldots, x_K + \alpha_K)$$ + +Where D is a _Dirichlet_ distribution +::: +:::: + +#### Grid Approximation + +Grid approximation is a technique used in numerical analysis and statistical inference to approximate the values of a continuous function or parameter by evaluating it at a discrete set of points. This involves creating a grid of possible values within a defined range and calculating the function or parameter at each grid point. We are using $P(\mathbf{p}|\mathbf{x}) = \text{D}(\mathbf{p}|x_1 + \alpha_1, \ldots, x_K + \alpha_K)$ to approximate the grid. + +### Empirical Bayes grid approximation using a conjugate prior + +Empirical Bayes grid approximations involves estimating the prior and posterior distributions of parameters using a discretized set of values. In the context of the multinomial-Dirichlet conjugate relationship, this method is particularly effective. The Dirichlet distribution serves as the prior for the multinomial likelihood, and empirical Bayes methods estimate this prior directly from the data. By defining a grid of possible parameter values, in this case spaced every 0.1 units from 0 to the maximum observed value or 100, the posterior distribution is approximated by evaluating the likelihood and updating the Dirichlet prior at each grid point. + +This approach simplifies the computational complexity of Bayesian inference. Instead of integrating over a continuous parameter space, which can be analytically challenging, grid approximation transforms the problem into a finite summation. The [multinomial-Dirichlet conjugate pair](https://en.wikipedia.org/wiki/Dirichlet-multinomial_distribution) ensures that the posterior remains in the Dirichlet family, making the updates straightforward. + +__Adding probability__ + +We have noticed that this method is sensitive to the max value of the likelihood. The further out the maximum is the more likely elevated values appear. This is because we are adding uncertainty to the points on the grid between the observed or designated max value and other observed values. + +:::{dropdown} Default parameters and methods + +__Default parameters__ + +1. range of the default index $X = \{ x \in \mathbb{R} \mid x = 0.1k, \; k \in \mathbb{Z}, \; 0 \leq x < 100 \}$ + * or `np.arange(0, 100, 0.1)` +2. Max range of forecast grid = $\max_{i} \{ x_i \} \text{ or } P_{99} = \text{percentile}_{99} \{ x_i \}$ +3. The magnitude of the land use for each survey location is categorized in the following manner: + +$$ +\text{binning}(x) = +\begin{cases} +1 & \text{if } -1 \leq x < 0.2 \\ +2 & \text{if } 0.2 \leq x < 0.4 \\ +3 & \text{if } 0.4 \leq x < 0.6 \\ +4 & \text{if } 0.6 \leq x < 0.8 \\ +5 & \text{if } 0.8 \leq x \leq 1 +\end{cases}, +\text{ where x is the \% of land occupied by a land-use feature } +$$ + +__Distributions__ + +The posterior distribution is $P(\text{Likelihood} \mid \text{Prior}) \approx \text{Dirichlet}(\alpha)$ or more commonly: $P(\theta \mid \mathbf{X}) \approx text{Dirichlet}(\alpha + \mathbf{n})$ + +1. $\theta$ is the parameters of the Dirichlet distribution +2. $\mathbf{X}$ is the observed data +3. $\alpha$ is the parameters of the prior Dirichlet distribution +4. $\mathbf{n}$ is the count data from the likelihood + +__Forecasted samples__ + +$$ +\begin{align*} +\theta &\sim \text{Dirichlet}(\alpha) \\ +\mathbf{X} \mid \theta &\sim \text{Multinomial}(N, \theta) +\end{align*} +$$ +::: \ No newline at end of file diff --git a/_build/html/_sources/project.md b/_build/html/_sources/project.md new file mode 100644 index 0000000..93d0064 --- /dev/null +++ b/_build/html/_sources/project.md @@ -0,0 +1,98 @@ +# Proposal + +The challenge in front of us is to transform the observations into accurate and actionable information for two different purposes. There is the administration of the territory and identification of priorities with regard to plastics in the environment. Then, once the priorities have been established these new indicators must be reported on. This project concerns the infrastructure and processes necessary to identify priorities and develop indicators to reduce plastics in the environment from litter density counts. + +Therefore, we propose a haromnised reporting system that is administered by regional or national NGOS in collaboration with an academic partner with the stated goal of producing reliable reports for the public administration and others who communicate on such matters. This allows administrations to identify priorities and set baseline values according to agreed upon and observed indicators. + +The first report proved that a relatively small team could cover a large territory with the correct technology. This remains the case. That is why we see this project, in its current itteration, specifically attractive to stakeholders with an internal data-science team. Stakeholders with experience in python or R will feel right at home in the development environment and will be the source of many improvements. For those interested in learning the basics, we are here to coach them and help them in their projects. + +The proposal is thus to establish a hub that is dedicated to the collection, cleaning, aggregating and distribution of litter density data. The distribution of the data has the primary goal of producing decision support products to local administrations and other organizations charged with reducing plastics in the environment. The _hub_ is staffed by three people and who conduct surveys, produce value added research, teach other NGOs to collect samples, and respond to requests from local administrations. Activites or changes in protocol are considered in consultation with academic advising team. + +## Goals + +> * Develop a reporting and research tool for stakeholders in the waste management, life cycle assessment and environmental monitoring sectors. +> * Increase capacity of stakeholders to monitor and report observations according to the current standard in the federal report using open source tools. +> * Assisst in the reduction and prevention of plastics in the environment + +:::{dropdown} Deliverables: three year contract +1. 700 samples +2. Revised updated federal report +3. New cantonal and municipal reports +4. Cloud service for cantonal and municipal reporting +5. Article for publication +::: + +:::{dropdown} Objectives +1. Collect 400 - 500 samples in a 12 month period + * reduce the number of items reported as _fragmented_ or _Gfrags_ + 1. This value can range from 20% - 30% of the total items collected + * include polymer identification for a subset of samples + 1. Subset should support an academic publication + 2. Subset should support a regional assessment of polymer types + * Increase knowledge sharing between surveyors + * Capacity building for surveyors + +2. Increase the number of associations competent to collect and report data + * Develop one association on Lac de Constance or Zurichsee + * Develop training materials and protocols with ASL + +3. Strucutre report specifically for cantonal and municipal reporting + * Revise and improve the current report + * Consider the format of the report given cantonal requirements (attached) + +4. Build upon previous research and reports + * Continue to develop modeling and parameter estimation for litter density + * Focus on topographical features and densities + * Consider polymer types and densities + +5. Define groups of objects of interest with stakeholders + * single use plastics + * agricultural plastics + * construction plastics + * industrial plastics + +6. Develop a cloud first infrastructure and chat agent + * Move all data and software assests to Infrastructure as a Service (IaaS) + * Develop machine learning and artificial intelligence pipelines for data analysis + * Develop Automated programming interfaces (APIs) for data collection and reporting + * Develop a user interface for data collection and reporting + * Deploy chat agent +::: + +:::{dropdown} Benchmarks +1. Year one: Proposed updates and revisions to the fedral report. Prposed model for cantonal and municipal reporting. + * Formal research proposal for polymer identification + * Formal research proposal for topographical features + * Functioning prototype of cloud service + * 100 samples collected + * stakeholder meeting +2. Year two: + * Assesment of software and data collection + * Revised report with current data + * Update on capacity building + * End of year two: + 1. Presentation of revised report with current data + 2. Summary of capacity building + 3. 400 samples + 3. stakeholder meeting +3. Year three: + * Presentation of feedback + * stakeholder meeting + * Proposed changes to app and report + * Final report: + 1. implementation of feeedback + 2. 200 samples + 3. cloud service for cantonal and municipal reporting + +::: + +:::{dropdown} Cost + +> Less than 1% of the estimated cost to prevent and remove litter annualy in Switzerland. [BAFU/OFEV - litter costs](https://www.bafu.admin.ch/bafu/en/home/topics/waste/publications-studies/publications/litter-dropping-costs-money.html) + +::: + + +## Interested ? + +Interested administrations or other potential financial partners should contact roger at hammerdirt. \ No newline at end of file diff --git a/_build/html/_sources/the_survey_data.ipynb b/_build/html/_sources/the_survey_data.ipynb new file mode 100644 index 0000000..8d06331 --- /dev/null +++ b/_build/html/_sources/the_survey_data.ipynb @@ -0,0 +1,302 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "64c16819-72da-4b47-a3aa-988d3f5a8203", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "%load_ext watermark\n", + "import pandas as pd\n", + "import numpy as np\n", + "# from typing import Type, Optional, Callable\n", + "# from typing import List, Dict, Union\n", + "\n", + "# # from review_methods_tests import collect_vitals, find_missing, find_missing_loc_dates\n", + "# # from review_methods_tests import make_a_summary,combine_survey_files\n", + "\n", + "# import matplotlib.pyplot as plt\n", + "# import matplotlib as mpl\n", + "# import matplotlib.colors\n", + "# from matplotlib.colors import LinearSegmentedColormap, ListedColormap\n", + "\n", + "# from setvariables import *\n", + "\n", + "# import reportclass\n", + "\n", + "import session_config\n", + "\n", + "# # surveys = combine_survey_files(survey_files)\n", + "# codes = pd.read_csv(code_data).set_index(\"code\")\n", + "# beaches = pd.read_csv(beach_data).set_index(\"slug\")\n", + "# land_cover = pd.read_csv(land_cover_data)\n", + "# land_use = pd.read_csv(land_use_data)\n", + "# streets = pd.read_csv(street_data)\n", + "# river_intersect_lakes = pd.read_csv(intersection_attributes)" + ] + }, + { + "cell_type": "markdown", + "id": "7bca0862-a1e5-4fd3-ae17-65ac2fd7cd0a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "(the_survey_data)=\n", + "# The survey data\n", + "\n", + "Once the surveyor has entered the data for the observations, there are labels attached to each record that identify attributes of the survey location. These labels are used for grouping records or collecting additional information about the survey location or the object found. Once the labels are complete the data is ready for analysis.\n", + "\n", + "## Observation data\n", + "\n", + "This data is received from the client in two packets:\n", + "\n", + "::::{grid} 1 2 2 2\n", + "\n", + ":::{grid-item}\n", + "\n", + "__Meta data :__ .JSON object or python dictionary\n", + "\n", + "* user\n", + "* location name, lat and lon\n", + "* date\n", + "* length of survey\\*\\*\n", + "* area of survey\\*\\*\n", + "* weights (plastic, all)\\*\n", + "* number of participants\\*\n", + "* number of staff\\*\n", + "* time in minutes to complete all tasks\\*\n", + "* total number of objects found\n", + ":::\n", + "\n", + ":::{grid-item}\n", + "\n", + "__Object inventory :__ an array, four columns:\n", + "\n", + "* location name\n", + "* date\n", + "* code\n", + "* quantity\n", + "\n", + ":::\n", + "\n", + "::::\n", + "\n", + "\\* Not all projects provides all the data. For detailed results contact the maintainer\n", + "\n", + "\\*\\* The data is all formatted in pcs/m, this is set to the units used for reporting baseline values\n", + "\n", + "### Adding features\n", + "\n", + "Survey locations are predefined, or must be configured before entering data for a survey. The configuration process has several steps.\n", + "\n", + "1. Identify the correct city and canton name\n", + "2. Identify the type of location: _river, lake, or park_\n", + "3. Identify the riverbassin or geographic area of the location\n", + "4. Calculate the land use ratios for the location\n", + "5. For lakes, determine the location and distance to river intersections\n", + "6. The plain english (french, german) description of the code/object\n", + "\n", + "__Aggregated data__\n", + "\n", + "The following codes are aggregated together unless otherwise specified:\n", + "\n", + "* 'Gfoams': 'G81', 'G82', 'G83'\n", + "* 'Gfrags': 'G78', 'G79', 'G80', 'G75', 'G76', 'G77'\n", + "* 'Gcaps': 'G21', 'G22', 'G23', 'G24'\n", + "\n", + "_The unaggregated data is available on the repo using the following file files from the `data/end_process/` directory:_\n", + "\n", + "* slr.csv\n", + "* mcbp.csv\n", + "* iqaasl.csv\n", + "* after_may_2021.csv\n", + "\n", + "## Important dates\n", + "\n", + "The dates of the sampling campaigns are expanded to include the surveys that happened between large organized campaigns. The start and end dates are defined below.\n", + "\n", + "__Attention!!__ The codes used for each survey campaign are different. Different groups organized and conducted surveys using the MLW protocol. The data was then sent to us.\n", + "\n", + "__MCBP:__ November 2015 - November 2016. The initial sampling campaign. Fragmented plastics (Gfrags/G79/G78/G76) were not sorted by size. All unidentified hard plastic items were classified in this manner.\n", + "\n", + "* start_date = 2015-11-15\n", + "* end_date = 2017-03-31\n", + "\n", + "__SLR:__ April 2017 - May 2018. Sampling campaign by the WWF. Objects less than 2.5 cm were not counted.\n", + "\n", + "* start_date = 2017-04-01\n", + "* end_date = 2020-03-31\n", + "\n", + "__IQAASL:__ April 2020 - May 2021. Sampling campaign mandated by the Swiss confederation. Additional codes were added for regional objects.\n", + "\n", + "* start_date = 2020-04-01\n", + "* end_date = 2021-05-31\n", + "\n", + "__Plastock (not added yet):__ January 2022 - December 2022. Sampling campaign from the Association pour la Sauvegarde du Léman. Not all objects were counted, They only identified a limited number of objects." + ] + }, + { + "cell_type": "markdown", + "id": "11132a65-b698-4c69-9589-94327977eb7c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "source": [ + "## The current survey data:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "44b00952-0a05-4f54-a22a-50d5ba8e1282", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "surveys = session_config.collect_survey_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8b02e62a-ed8d-4677-ad9f-70058333188b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 298848 entries, 0 to 88214\n", + "Data columns (total 16 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 sample_id 298848 non-null object \n", + " 1 code 298848 non-null object \n", + " 2 quantity 298848 non-null int64 \n", + " 3 pcs/m 298848 non-null float64\n", + " 4 feature_name 298848 non-null object \n", + " 5 location 298848 non-null object \n", + " 6 parent_boundary 298848 non-null object \n", + " 7 length 298848 non-null float64\n", + " 8 groupname 298848 non-null object \n", + " 9 city 298848 non-null object \n", + " 10 canton 298848 non-null object \n", + " 11 feature_type 298848 non-null object \n", + " 12 en 298848 non-null object \n", + " 13 fr 298848 non-null object \n", + " 14 de 298848 non-null object \n", + " 15 date 298848 non-null object \n", + "dtypes: float64(2), int64(1), object(13)\n", + "memory usage: 38.8+ MB\n" + ] + } + ], + "source": [ + "surveys.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "54fba949-faaf-45c5-a4b7-f0111a5c8872", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Author: hammerdirt-analyst\n", + "\n", + "conda environment: cantonal_report\n", + "\n", + "numpy : 1.25.2\n", + "pandas: 2.0.3\n", + "\n" + ] + } + ], + "source": [ + "%watermark -a hammerdirt-analyst -co --iversions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba1596c3-a6f5-4673-b9f6-c73099199d4a", + "metadata": { + "editable": true, + "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/titlepage.md b/_build/html/_sources/titlepage.md new file mode 100644 index 0000000..7341939 --- /dev/null +++ b/_build/html/_sources/titlepage.md @@ -0,0 +1,53 @@ +# 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/use_cases.ipynb b/_build/html/_sources/use_cases.ipynb new file mode 100644 index 0000000..09be68f --- /dev/null +++ b/_build/html/_sources/use_cases.ipynb @@ -0,0 +1,189 @@ +{ + "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", + "import pandas as pd\n", + "import numpy as np\n", + "\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\n", + "\n", + "import session_config\n", + "import reports\n", + "import geospatial\n", + "import userdisplay as disp\n", + "from myst_nb import glue\n", + "from IPython.display import display, Markdown\n", + "\n", + "# available data\n", + "surveys = session_config.collect_survey_data()" + ] + }, + { + "cell_type": "markdown", + "id": "8e3e8dd7-00b7-4cd7-9536-6739b92c5812", + "metadata": { + "editable": true, + "pycharm": { + "name": "#%% md\n" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Use cases\n", + "\n", + "How each stake holder interprets the data is dependent on the use case. Here we present the answers to some of the most common questions that can be answered using the report or the methods desccribed in this document." + ] + }, + { + "cell_type": "markdown", + "id": "af5e858a-59c6-477c-894e-c99c0e4ee78b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Is their more now than there was before ?\n", + "\n", + "__How do you know ?__" + ] + }, + { + "cell_type": "markdown", + "id": "64d54267-17b4-42f4-88fe-d77c4c755bd4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Where is the cleanest beach ?\n", + "\n", + "__How do you find the cleanest locations ?__" + ] + }, + { + "cell_type": "markdown", + "id": "1424bbb1-8db1-408f-b6d8-9bbde53ff368", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## How much of the items are single use plastics ?\n", + "\n", + "__Is their more now than there was before ?__" + ] + }, + { + "cell_type": "markdown", + "id": "5bca0d5f-c0c1-49bf-b1b5-4eadc3a0551a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## How much of the items can you identify ?\n", + "\n", + "__Is their more now than there was before ?__" + ] + }, + { + "cell_type": "markdown", + "id": "aff52a36-ef5b-4493-b07c-92a2ae954d37", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## What objects are common to all cantons ?" + ] + }, + { + "cell_type": "markdown", + "id": "919abbdc-91ac-4bcf-8d00-9cb4ab455e37", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## What is the chance of finding X ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3de07265-3ade-40dc-a517-b6fdedb45abe", + "metadata": { + "editable": true, + "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/valais.ipynb b/_build/html/_sources/valais.ipynb new file mode 100644 index 0000000..db63130 --- /dev/null +++ b/_build/html/_sources/valais.ipynb @@ -0,0 +1,2696 @@ +{ + "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, + "jupyter": { + "source_hidden": 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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", 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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 polystyrene1'4763,260,190,87
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, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "application/papermill.record/image/png": 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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 = Markdown('__Given the observed max__\\n* No data to consider see weighted\\n')\n", + " forecast_99_r = Markdown('__Given the observed 99__\\n* No data to consider see weighted prior\\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, o_land_use_r.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", + " (selectedr['pcs/m'], 'random weighted samples', '--', palette['prior']),\n", + " (river_results['this_report'].sample_results['pcs/m'], likelihood_labels_r, '-',palette['likelihood']),\n", + " \n", + " (weighted_forecast_r, 'forecast 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, o_land_use_r.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, + "jupyter": { + "source_hidden": 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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" + }, + "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", + "dbc = dbc[dbc.NAME == canton].copy()\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", + "\n", + "\n", + "bounds = dbc.total_bounds\n", + "minx, miny, maxx, maxy = bounds\n", + "\n", + "\n", + "rivers = gpd.read_file('data/shapes/rivers.shp')\n", + "rivers = rivers.to_crs(epsg=4326)\n", + "# Filter the background layer to cover the bounding box\n", + "rivers_within_bounds = rivers.cx[minx:maxx, miny:maxy]\n", + "\n", + "\n", + "lakes = gpd.read_file('data/shapes/lakes.shp')\n", + "lakes = lakes.to_crs(epsg=4326)\n", + "lakes_within_bounds = lakes.cx[minx:maxx, miny:maxy]\n", + "\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.plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.2)\n", + "\n", + "rivers_within_bounds.plot(ax=ax, edgecolor='steelblue', alpha=.2)\n", + "lakes_within_bounds.plot(ax=ax, edgecolor='steelblue', color='steelblue', linewidth=.2, alpha=.2)\n", + "ax.set_ylim([miny, maxy])\n", + "ax.set_xlim([minx, maxx])\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, + "jupyter": { + "source_hidden": 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
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\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, + "jupyter": { + "source_hidden": 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
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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
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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, + "jupyter": { + "source_hidden": 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
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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
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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, + "jupyter": { + "source_hidden": 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
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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
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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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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 and the combined land use classification 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 new file mode 100644 index 0000000..762d9bd --- /dev/null +++ b/_build/html/_sources/vaud.ipynb @@ -0,0 +1,2603 @@ +{ + "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", + "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 = '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, o_land_use.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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", 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 % of total
material 
glass5%
metal3%
paper2%
plastic86%
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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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", + "application/papermill.record/text/plain": "
" + }, + "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 2015 - 2019. 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
 % of total
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: 6.09\n* HDI 95%: 0.2 - 17.6\n* 90% Range: 0.5 - 16.46", + "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: 15.74\n* HDI 95%: 0.1 - 62.89\n* 90% Range: 0.81 - 63.41", + "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: 7.32\n* HDI 95%: 0.3 - 26.9\n* 90% Range: 0.5 - 22.62", + "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, o_land_use_l.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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" + }, + "metadata": { + "scrapbook": { + "mime_prefix": "application/papermill.record/", + "name": "river-cumumlative-dist-forecast-prior" + } + }, + "output_type": "display_data" + }, + { + "data": { + "application/papermill.record/text/markdown": "Survey total pcs/m 2020 - 2021 v/s 2017 - 2017. All locations considered.", + "application/papermill.record/text/plain": "" + }, + "metadata": { + "scrapbook": { + "mime_prefix": "application/papermill.record/", + "name": "caption-histo-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
 % 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: 1.81\n* HDI 95%: 0.1 - 6.7\n* 90% Range: 0.1 - 5.94", + "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: 2.91\n* HDI 95%: 0.32 - 4.55\n* 90% Range: 0.46 - 4.55", + "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: 3.8\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, o_land_use_r.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, + "jupyter": { + "source_hidden": 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", + "dbc = dbc[dbc.NAME == canton].copy()\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", + "bounds = dbc.total_bounds\n", + "minx, miny, maxx, maxy = bounds\n", + "\n", + "\n", + "rivers = gpd.read_file('data/shapes/rivers.shp')\n", + "rivers = rivers.to_crs(epsg=4326)\n", + "# Filter the background layer to cover the bounding box\n", + "rivers_within_bounds = rivers.cx[minx:maxx, miny:maxy]\n", + "\n", + "\n", + "lakes = gpd.read_file('data/shapes/lakes.shp')\n", + "lakes = lakes.to_crs(epsg=4326)\n", + "lakes_within_bounds = lakes.cx[minx:maxx, miny:maxy]\n", + "\n", + "\n", + "\n", + "# Define the plot\n", + "fig, ax = plt.subplots(figsize=(12,10))\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.plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.2)\n", + "rivers_within_bounds.plot(ax=ax, edgecolor='steelblue', alpha=.2)\n", + "lakes_within_bounds.plot(ax=ax, edgecolor='steelblue', color='steelblue', linewidth=.2, alpha=.2)\n", + "\n", + "# Set the extent to Switzerland\n", + "ax.set_ylim([miny, maxy])\n", + "ax.set_xlim([minx, maxx])\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, + "jupyter": { + "source_hidden": 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
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Vineyards6.580.005.010.000.00
Buildings4.852.972.904.099.43
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Public Services4.4117.980.000.000.00
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Orchards100%0%0%0%0%
Vineyards98%0%2%0%0%
Buildings7%8%2%36%47%
Forest94%4%1%0%0%
Undefined70%20%9%1%0%
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, + "jupyter": { + "source_hidden": 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
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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
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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, + "jupyter": { + "source_hidden": 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
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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
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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, + "jupyter": { + "source_hidden": 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
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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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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 and the combined land use classification 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 new file mode 100644 index 0000000..ccec5f5 --- /dev/null +++ b/_build/html/_sources/zurich.ipynb @@ -0,0 +1,2603 @@ +{ + "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", + "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 = '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, o_land_use.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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", 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", 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 % of total
material 
glass8%
metal8%
paper8%
plastic70%
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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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 % of total
material 
glass8%
metal8%
paper7%
plastic71%
wood2%
\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: 7.42\n* HDI 95%: 0.2 - 39.6\n* 90% Range: 0.4 - 25.44", + "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: 5.47\n* HDI 95%: 0.14 - 28.25\n* 90% Range: 0.7 - 30.71", + "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: 3.57\n* HDI 95%: 0.4 - 13.0\n* 90% Range: 0.7 - 13.0", + "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 67% 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
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, o_land_use_l.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%
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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, o_land_use_r.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, + "jupyter": { + "source_hidden": true + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "application/papermill.record/image/png": 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IRRddRD788MMWq2j/9re/NTlmS3+zn376idx0000kIyODGAwGUlRURO666y4SDAYbnfO512RLn6+Ojk58oAhpEHvQ0dHRaYOqqir07NkTjz32WEw7cOjo6OjoxA49RKujoxMRJ0+exNGjR/G3v/0NNE03Cinr6Ojo6CQXepGFjo5ORLz55psYM2YM9u3bh/fffx8FBQWdPSUdHR0dnRbQQ7Q6Ojo6Ojo6Ot0M3YOno6Ojo6Ojo9PN0A08HR0dHR0dHZ1uhm7g6ejo6Ojo6Oh0M3QDT0dHR0dHR0enm3Fey6QohOBkjRe8pMDEMSjMsEbUkzFZIYTgWJUHkkKQlWJCqtUY92OeqPEiKMoJ+fwESUadh4cnKGqv2U0c0u1GGFgmbsfViYzT9X54g2Kz154oK3D6ePx0qAzff78JNE3DZDKhR48eOHLkCARBwA0zZ6AkNw1Grmv8LUVZgdsvwO0XIClna9UsBhapVgOsJq4TZ6fTXg6dVnv49syydev7yslaLwKCrP2en26B1ahfs92J876KVpBknKjxQiHqjTkzxdRlHjDnUu0OwOkTQFMUSnLsoONsrPp4ERV1flAAembbwTHxcQiLsoI6TxDuwFnDzmbikG4zdtm/VXfkVK0PTj+PVIsRVhMLhRDwogI/L2Lrtu04evQoMjMzMXH8ONjNBliNLDiWho+XUFHjxrvvvY8J48djYP9eSLMak2qxJUjqg7C5Bz4hBH5egtMvwM9L2usWA4MshzluRoIoK5BlBQpR50BAQEH9zIwcA5qm4PYLMLAMLMbzei0fFeU1XvCiDIfFgGyHubOnEzfOOP1wB0TYTBwcFoN+jXRDznsDD1ANldN1foQ/iBQzhzRb1/AKKYTAGxDh9PHgJQUAkOMwI8Vi6PC4gqQ+QGRCoCgECiGgKQosQ8PIMahxB+ENiu2+EUqygqAog6UpmAxNby6CJKPOy8PTwLCzGFlk2ruuEd4dkRUFTp+AzT8ewfaduyH4XUh32KEoCgACiqIxYMAADLyoH9Jsxma9BLJCcLreh6+WrYDH48WNM69HlsMKm4ntdENPIQQBQQJDUeBYBgzd8nzCnkqnTwBRFNA0jcwYetMlWYE3KMIdEMGLcov75aSaYTVyOHrGDQNLIyvFjBp3AGk2E4wc3SXubZ2FLyiiot4PAMhLs8DWTT2xkqyAogCG1jO1uiu6gReCF2XUeoLwNVqBs8hIMcGUhMaEKCtw+QS4/AKUBn/CbIcZjg4YdwFBQr2Xb/Q5tEWPTFtEn5EkK/ALEoKCjKAoN3pAmQ0MMuwmmA0sgqIMp7dxKLbhdp3koLa2Fl+tWI0atx+SrCAjIwNXXXUVqk6fgtHAoqSkBAaWholjYeRaN4zCuP0Cjp6qwtfLlsNsNuO6a6egMCul0//uhBCIshKxYSRIMqpcAQQEGVYji5xUCwBE9Bk0h5+XUO/jG3kIAWhec5oCKAogoABCkG43wWpkUVHvh5FlQFFAnZdHisUAt1+AmWNgMrAwGxiYDIz+kD+HcDSEoSkUZ8c/GqKjEw90A+8cAoKEOg8Pv3D2RpqofLZI8PMSnL7GBhhLU3BYDXBYDO2+Uft5CbWeIIINjC6aosAxFGiaAk2p/ydEfXgJIW9hus2IDLupzfFlRc0PVM653DiGhigrLb7PYmSRYTM26+HT6TzeX7QYXonG5VdcBbvdDgNLI81mhN3EgaIoLF26FFOnTm3X2GGP4KmqOnz62ecYPnw4Rg27JOGelPB12ZHUA5dfgNnAoMoVgKIQ5KVboxpPVggq6nyNvpdGjkGKmYPNxIGNcCxZUeDjJQiignofD5amGuUNMjQFjqFBAbBbDCCEwBsUYWAZmDjVCDyfvH4N85lzUs1IMXcsIqKj0xnoBl4LiLKCWndQ8yLlplpgN3eeq97Hi6h1B7UwLKB6tVKtRliN7QtjEULgCYpwetXwrkIIArwEhqZhYCnQ5xiLYYOPYWgwNAWGpjTvAE1TWv4PRQHabCj1VU9ARJ03CIamYTdzMBtYmAyMZuDVe3m4/IL6FgBWkxomT0bv6fmOrBC8+No7mHb9TNAUkGE3wWExNLoGa2pq8NNPP2HUqFEdOs4Zpx+LP/4MFw8YgJGXD0x4ErjbL8BiZCM2pJpDzfP1ASBIsxnBiwpyU81tfmdlRQFD041ywlKthg4bWmFjz8+r3vSwIUsIgcsvgKIoFKZb4Ak29hYaWRoFGTbUe3mkWLhub/CFvXgpZk7zwOrodCV0t0gLcAyN3DQLaFcALr+AWk+wUww8hRBUuQJaHhoFIKWDN3pZIXD6VINKVghkhcAXFKAQwG7mWvQCKoSAlwggNfa4EYWAaiP05A2KsBhYpFg4ZKU0ztfjGBrZDjMy7CYohIClqU7Pu9JpGaePB2gWDE2hMMPa7HWYmZmJQCAAv98Pi6V9D0eGppCXZsFtN83Ewvc/gMlkwrBBfRMaru1oLiugFmYUZdkQFNQ0EI6lG13foqzgjNOPbIcZoqTgjCsQypszQSFqTi1DUx0yMhvC0DRSzAbNKyUrBKIkQ5TVPFtZUfNvcxxmLZWCF2VwLKOmcPh4cAyFijo/HBYDUiyGdoeek5nwdS3J8feBKIoCQRDifhyd5IfjODBMbBZPuoHXBhl2E1x+AaKsQJKVmN1kI0FWFJyq82u5ag6LARl2Y4fyZXy8iCpnAFLIsAvwIggoWE0GLUwT9rBxrBqyodS0HsgKgSQrkEL/V6v4CBSoRRiAul8YtbJPJcVsQLbD1GqlFkNTYND9HhTdDR8vgRCCTLup1UXG+PHjsXTpUkybNq3dx6Io1cibM+tWvLXwXRgMHC69sFeX8+xyDA3OTMPANv3uEkIQENS0ByX0vSSEwMdLcFiM4Lj43nMYmgJjYGECUJydghNh6SgDoxm4hKjGnygpsJs51SiUFdR4gqjzBpFmMyLVauxWuWpSyLPJMPE9J0EQUFZWFipK0tEBUlNTkZub22FHh27gtUHY6BFDBk2iDDxJVnCy1gdRVkCHHnIdLWOvcQdQ7Q5CJgSSpIChKVhDOVMcQyPdZoTdzLV4UbEM9OpVHciyAkJIs8ZKQ2iaRnFxMZYuXYo+ffqgR48e7fLmURSFvHQL7ppzO958eyGMxmm4qFdBlzPygOa/PwxNIyfVrEqbUBR6cAxoqnlJlnhj4hhYjSx8vFpsFQ5NUhQFhqLAGGjkGlQJHIahUe/l1XQWDw+XT0BmirlTU1liSbigJZ7XGSEEp0+fBsMw6NGjR5O0GJ3zC0II/H4/qqqqAAB5eXkdGk838CKAoSmIshpKSYQqUkPjrrUwWDRUuwI4eNqFoCjDZmK11TbH0Ei3n02O19Fpi/B1Iitth64GDRqE/v3749SpU9i5cyd4nsfWrVvx+9//Pqpj0hSFgnQb7rn7Trzzn/dQPXw4hl5yEdJsXd9rxNBUoyR+hu48wzUoypoXvrXQJE1RanjWzMETEFHrCUJSCCqdftT7GKTb2p8bnAwIkqwVtsSzuEeSJPj9fuTn57c7lUGne2E2q1ZGVVUVsrOzOxSu1ZcLEZCTakZJtj0hlVRiA+OOjZFxV+UKwOkXQFNAmtWITLsZNiOH3FQLikPn1VVvxDqJx2RgAJBGleatYTAYUFJSgpEjR8Lr9eKRRx5p13HDi5377p6D4+XleP2d97D/eBWcPh56rVj7CYoyatxBHKvy4ESNV/u72i1tGzYURSHFYkDPbDvSbUZQUCWnTtf74eMlBCO8RpKNcM6zxdCxApu2kOWQgLZBr9LVOUvY2BdFsY09W0f34EVAokIlkqzgVAPjriBGxl24OnVAjzSkWJJD7kWn6xKuZPUGhCYFM62xdOlSXH311UhJSWn3sRmaRn66FbfOmILKOg+++PIrAMC4sWORk5WOFIsBViMHhqZACGlx4UIIwRlXAKnW87dSOywu3bBdVbiCPcXMRdVqjaYorZra6RPgFySwNIUTtT4YOQY5DnOXSu9wh+6ZkRi5sUBfYOs0JFbXg27gJQkKIaio90OUFXAMjYKM6PSymqPafda4i0V3Cx0dQNUmHDr0cqzf+D0Kpk+IeBFis9lQW1uL9PT0Ds8hxWyAPT8d9995GypqXFi3fj08Hi9EUcSEsaNQVX4YeRcMRM/87FA6ArTvQqrVqHVI8QREpIcKBLpjJWhr1LgDmnFnNbJq+zgT26GQN8uonTsA1QsW9uiV13iRaTchzZb8C0xelCEpBBTiG56NFQpRha4jfV3n/EE38JKEGncQvCiDptSmzx017uq8QTh96gMtWzfudGIIQ1Po26sYW7duhS8owWCLzMAbM2YMtmzZgpUrV2L8+PEdTiinKAqpViNSrdm44LYZ8AREuP08dm7eiP79+uKbjd9hmceDO2+dAZvFghPVblgsauupVKsBQVGGn5c0Y69HpvW86ehACNHCkAXp1rj0IVWr8e2ocgXg4yXUeIIQZSXp+7sGQmFlk4FJSH4nIapMDRW6/zM0DToKy+xwHdA7DWj4yJAV4Eg90DcjDhPW6TKcH3ezJMfHi5p3ITfNEhMh01oPD0DtwtGR1mU6Os1hNakGgY+PLkdk+PDhGD16NJYuXYrdu3fHbD4GVm1lV5LjwMxpUzCgfx/MnXUj7r7jNqxcWYr/+9+/4MzJMmQ5zGAZGgxNoyDdipxUc6iIStEWROcDfkECgerhiWeTeZZRQ+pZIa+eyy+oOopJhigrmhh7jTsIAHET1ZYVBZVOPw6fdmndMpSQNI6qQShDlJSI8kr/vgmY9gFwzHVWnlRSgGNO4LoP1O3x4LnnnsPll18Ou92O7OxsXH/99Thw4ECjfQgheOaZZ5Cfnw+z2YwxY8Zg37592va6ujo89thj6NevHywWC4qKivD444/D5XI1Gqe+vh6zZ8+Gw+GAw+HA7Nmz4XQ6O3wOb7zxBq6++mqkpaUhLS0Nv/jFL7B169Ym+7388ssoKSmByWTCZZddhg0bNmjbRFHE7373OwwcOBBWqxX5+fmYM2cOKioqGo3x+uuvY8yYMUhJSQFFUTGZfyToBl4nQwhBtUu9oThC+UMdhRfPtlhKlhZrOt0Li5FFfn4+fj50JKJq2oaYTCZMmzYNdrsdS5Ys0SQBYkU4f8Vu5lCQaccdt92M/v364hejRjRZ7KSYDbCEhJOjPY+uCiFEM2LsCWrBlWo1IjPU0rDa3bglYmfj9gs4VuVBpdOPWg8PArW4ItUau89GkhW4AwLqvTyOV3vhCYhoeLVRNAWOpbU0AVlRWm3hCKjG2//bCgQk4IGlqmFHQjr0D3wNBCV1ezyMvHXr1uGRRx7B5s2bUVpaCkmSMGHCBPh8Pm2fefPm4R//+Afmz5+Pbdu2ITc3F+PHj4fH4wEAVFRUoKKiAi+88AJ+/PFHvPPOO1ixYgXuueeeRseaNWsWdu/ejRUrVmDFihXYvXs3Zs+e3eFzWLt2LW677TasWbMGmzZtQlFRESZMmIBTp05p+3z44Yd44okn8D//8z/YtWsXrr76akyePBnl5eUAAL/fj507d+KPf/wjdu7cic8++wwHDx5sov3p9/sxadIk/Pd//3eH5x0VRKdTcfp4crDCSQ6fdhFJVmIypis0Znm1Jybj6eg0x9EzLvLXfy8gbr/QoXE2bNhAVqxYQWRZjtHMmvLmm2+2uO14lZscrHAST6Bj59FVqHYFGtxz4veZN8epWi85WOEkZWfcRFZic7/rCJIsk4MVTnKwwkmOVbnJqVovqfcGiRLDufGiRA6fdmrHCZ+/nxeJ1+cj+376iQQCgUZzCvAiCfAikZt5JsgKIQdqCOk3n5CiF8/+/GolIc4AIU+saPx6v/nq/lIc/9RVVVUEAFm3bh0hhBBFUUhubi55/vnntX2CwSBxOBzk1VdfbXGcjz76iBgMBiKKIiGEkJ9++okAIJs3b9b22bRpEwFAfv7555iegyRJxG63k4ULF2qvDRs2jDz44ION9uvfvz956qmnWhxn69atBAA5fvx4k21r1qwhAEh9fX2rcwkEAuSnc66L9qB78DoRhRDUetSVdLotNknenoCIancAABLa0knn/COcgB5tmPZcRo4cidGjR2PZsmXYs2dPLKYWFWHH3flQyBhuNQYgFJ5O7COgYUi82hVI6LGbI5waY2Rp9MyyIz/dilSrMaZVrbUeHgoBWJqCxcAiK8WEoiwbzAYWDE036d3D0Gdb2clEDd2SBuFamlJz7r6eBfRpUK/06X5g0GvAZz+ffa1PurrfuTl6sSYcVg0XUJWVlaGyshITJkzQ9jEajRg9ejS+//77VsdJSUkBy6rPrk2bNsHhcGD48OHaPldccQUcDker47QHv98PURS1cxAEATt27Gh0DgAwYcKENs+BoiikpqbGdH7tQTfwOpF6Lw9ZUXuvOjoYDpAVBZX1flQ6/VCIqpjfFSrWdLouFiMLmqbhDXQ8d81kMmHq1KmwWq0xD9sGAoFWdcbCHTk8/pYNVW9Q1FpXdWXOhma5TqkQDXftAAB3QMTpej8EqfPCteEq4ngVoQUFCd6gel3lp1tRkGGNqKWbkWNgMrDgGNXYO9fgZGigpwNYehtww4XNj3HDher2no74GneEEDz55JMYOXIkLr74YgBAZWUlACAnJ6fRvjk5Odq2c6mtrcVf/vIXPPDAA9prlZWVyM7ObrJvdnZ2i+O0l6eeegoFBQX4xS9+AQCoqamBLMtRnUMwGMRTTz2FWbNmdUgOKlboBl4nIcoK6r3qSjojxdShai1RVtS8jtCNJN1mRI8M63kn+6CTWEwcg9zcXJSfOBkzoeHevXtj2rRpOHjwIJYvXx6T/pyrVq3CuHHjWtwezlP1BFWD49xcPIUQnKjxotYb7PBcOptwXpe9E+U/rEZOK7rwBkUcr/aiyhWAnOBerIQQTYg5HlqIsqLgdL0fgGpQx1oHkKUBIwM8Par57U+PUre30VGwwzz66KPYs2cPFi9e3GTbuYYpaUGb0u1249prr8VFF12Ep59+utUxWhsHAJ599lnYbDbtJ5wv1xrz5s3D4sWL8dlnn8FkMrXrHERRxK233gpFUfDyyy+3ecxEoBt4nQAhBGecfhAAZgPT4Q4ZvqCoPZR6ZFiRYTfpwpk6cYehafTt2xeHDh2CFOMChZEjR+Kaa67BsmXLOlxtW11djfz8/Ba3W4wsckLSHd6giBM13kZeJZqiYDNxMMdZqDcRRR7hgpIzrkCneiRTrUb0yLRpFbzeoIiyMx7UeoJQEtSVxMdLIa04KubGV1CUcaLGB0kh4Bg6KkHwcyGEQJIViKEyWVFSwIsyRJmAlwmeWdf8+55ZB/Dy2eraePDYY49hyZIlWLNmDQoLC7XXc3NzAaCJp6uqqqqJR8zj8WDSpEmw2Wz4/PPPwXFco3HOnDnT5LjV1dVNxgnz4IMPYvfu3dpPa999AHjhhRfw7LPPYtWqVRg0aJD2emZmJhiGiegcRFHEzTffjLKyMpSWliaF9w7QDbxOocqlCoxSQEw0oUwNcu10w04nkRiNRoiiGJdWYeGwrcPhwNKlS2NebduQFItB83qLsoLyai/qvEHtvHpk2uLaBabWE8TBCqfm1Y8XWQ4zOIaGrBBU1PkSZkw1h4ljUJBuRWGGFUaOBgFQ5+VRXu2Ne4szPy/hjFPNAXRYYtuqkRdlnKjxah2J8tIsHYqmUBQFlqHBhVxxHEuDZWiUu4Gpi6lGOXcN+exnYOpi4LhL1cWLJYQQPProo/jss8/w7bffoqSkpNH2kpIS5ObmorS0VHtNEASsW7cOV155pfaa2+3GhAkTYDAYsGTJkibesxEjRsDlcjWSL9myZQtcLlejcRqSnp6OCy64QPsJ5/M1x9/+9jf85S9/wYoVKzB06NBG2wwGAy677LJG5wAApaWljY4dNu4OHTqEb775BhkZySM+qGfhJ5g6bxD1Ph40RSE/veOtyAA1h4im1GRxSVFgRNdpCaTTdeFFGTt37sTFFw+Ia7/OkpISlJSU4LvvvsOuXbtaFEk+cOAAysvLwXEcRo0aFbWQssnAomeWDZX1AfgFCbUeHgFeRm4HH9CtoRCCY1UeiJICmqbg56W45s4yNIX8dAtO1PjASwqqnAHkpnVuk3uzgUVBug2egIgadwCirOBErQ8OiwEZdlPMPntPQERAkMCLMpw+Xs1z4xik22P7eYdz7owsjYIMW8yvHYUAR+opTPtAlUgJc8OFalj2mXVnCy0O1QHXLgKW3BrbQotHHnkEixYtwpdffgm73a55uRwOB8xmMyiKwhNPPIFnn30Wffr0QZ8+ffDss8/CYrFg1qxZAFTP3YQJE+D3+/Hee+/B7XbD7XYDALKyssAwDC688EJMmjQJ9913H1577TUAwP3334+pU6eiX79+HTqHefPm4Y9//CMWLVqE4uJi7RzCoV0AePLJJzF79mwMHToUI0aMwOuvv47y8nI8+OCDAABJknDjjTdi586dWLp0KWRZ1sZJT0/Xcn8rKytRWVmJw4cPAwB+/PFH2O12FBUVxaSzT0tQJB5Lb51m8fEijp5xo8oVRJrVgIJ0K1Ishg5Vu4qygtN16s2aAlCSk6Ln3ulEjSQrcPkFBAQJVqNa3Wc3c616Nk7V+fD2wncx987ZKEi3tjp+XV1dTG5kwWAQ3333HRRFwalTp5CTkwOWZUFRFHr37o2SkhL4/X6sX78ehBCUlJTg8OHDmDp1alTHcfsFVLkCIFD1JHNTzY085bFCVgh+PlkPmgLS7SbkpCbG2AoIEk7WqpplWSmmpNHLlBWCaldAyydmaArZDnOHCkIUQnDGGdAML0VRcNrph8XAoW+BA6kNPLOyogpe+4IiBEnRxKCNHAO72QCbiW218lhWCE7WeiFICtJtRmTYTS3uC6jXc1lZmSakGw1hHTwgVC17G2Bg1LDs1MWqcQcAjw8Dfj0iqqHbpKX7woIFC3DXXXcBUL18f/7zn/Haa6+hvr4ew4cPx7///W+tEGPt2rW45pprmh2nrKwMxcXFANR7x+OPP44lS5YAAKZNm4b58+d3uEq1uLgYx48fb/L6008/jWeeeUb7/eWXX8a8efNw+vRpXHzxxfjnP/+JUaPUxMdjx4418V6GWbNmDcaMGQMAeOaZZ/DnP/+5yT4NP6+GdOS6aIhu4CUIQVLd9r6gBB8vIrNBToaBVQWJ7WYuqmILHy+isj4AhRDQlBoKiKcqvU73JCBI2PrjQXz//fegaRaCJCHNkYKbZ1zXYgpBjTuIj75Yil69SnDN8MGt5jAtW7YMLpcL48ePR2ZmZrvnKUkSNm7cCFFUH9S9evVC7969W9xfURT83//9H375y1+2KycmKMqoqPNBDvUlzY9TSy9ZIaAoJKQtVkOcPh7VoaraHhnWuBiw7cXHi6h2BbWiEIfFAIfFEHWunCDJoUrds+PIioI6Lw+WodX8O5ZGisUAXpThDrQt+WM1skizGZsszAkhOFHr01pOFmXZ22w52dEH+d83AW/sVKVQejrUggpJUcOy1y4C7hsSe+NOJ/7EysBLnm90N0ZWFFTUqfIlaTYjLi5KAy+pHhNvQF0pVrkCqHEH4bAYkGo1gGVoEEIQEGQwdOMkYFlRUO0Oar0kjSyNvHRrh/vX6px/CJKMHw4cw4YNGzHn9ttgM3MQJQVffL0SB4+WI+OSvppHWCEE3oCIeh+PZStWIS0tDVdcenGja7OsrAw//vgjCCEYPXo0vvnmG0yYMAEpKSlYunSp5knbt28fTp48CZqmkZeXh4suuqjVkGo4FHTllVdGfMOjaRoDBgyAILRPxsXEMSjKtOGMUw3ZVtT5kJdmgTXGFaid5XFPtRoREGR4gyIqnQH0zLIlTQ6v1cjBnMWizsPDHRDgD6rtHO1mDuk2Y5upLaKswBcUQxp0BEwoFy5slOWkWuD08aj38uAlRTN0AfV+mmo1wmRgQFEUFIXAz0vwBATwkgIfL8HHSzCFpKjC3sWAIGvGXUGGLSH341+PAK7rCxQ3kEJhafX3JbfqvWjPd3QDL84QQlBR59cSbvPTLaBpGmYDDbOBhZxC4A71Z5QUgnofj3ofD7uJgyCr1VKA+rCxmzkQoiYih5OjHRYDMjsos6Jz/lLtDmL5ihW45+47UZhh066jiePH4Z3/vIeSnoWwmznwoozT1fVYsXIVnE4ncnNzMXnsSC2053Q6sX79ehQWFmLatGlQFAVr1qzBzJkzNcNtwIABWLJkCcxmM/r27YuJEycCAE6ePIk1a9Y0mVtOTo5m+NlsNowdOzbq8yssLMTBgwfb7TlkGRp56RacrvOrRl69HzRFwcDSMHIMbCauS3vNsx1m+HlJ68UaLz249kBTFDJTTHBYDah2BSDyEjwBEZ6ACKuRhcOqtpkLG6VBUYI3IKnh1QZVBUaOQV6apZHBRVMU0m1qn241NUFdSDuaS5lhzuqKhvP33AERQVH1DhpZGul2kyZYzDF0XGRXWqK53DqGVl/XOb/RQ7RxptLphycggqaAwgxbiyEGQgh8vIR6Lx9Rn0aOUQVD9W4VOu1FVhQsX78dPp8PMyaOauQVCQgS1m39AZs3b4HVagUhBBzH4topk5HpsKLuTAV+2rcXU6ZMwapVq2CxWLR8k1hx8uRJrYF5VlZWIwmDaHjhhRfwq1/9CgzT/ocuIQTV7qDW+aAhFiOLvDRLl11k1XmDqPXw4Bg6qbx45xIUJNR6efj5s5UFWXajJgPi5QWIEgFAAFAhA5yNSFg4WiRZzdVz+nitp6ykKHB6eZg4Fr1zU2A2snEP0ep0T1q6LnhJxpk6P4qy7RGNoxt4caRhjkt+FKGdYGiVqChE886FE+BpioI51Ag7WW/EOl0Db1DEKwvex003zERxdtMcNT8vabpkYW+V1XjWY+L3+/HNN99g0qRJrXaK6GycTidefvllPPzwwx1OzFYIgSApEEQZAUHSmsYbOQYF6ZaEt/6KBQohKDvjgUJIRIUBnY0gyXD6BHiDItKsBgiSAiPHwB0QwTE0LEa2zWKIWCErCuq9Alx+HoKkICBIYGha8+qm24xIs7VsYOoGnk5znHtdeAICjlV5UO0JgBAK4wYWRDSObuDFCUGSUV7tBQGQaTfpbcN0ko56L4+33vsQt99yI/I6WSoj3kiShIULF8JiseDWW2+N2eIoKEg4VeeHQlRB28IMa1wlY+KFOyBounBFmS1HGpINSVYrXRmaAoXO0wFVCFElWAQFkqIgKMpaeg1LU8hJbb4ATjfwdJojfF1k5RXglFNAlSsIukGubqQGXte7E3UR6jy81qlCN+50kpFwhSLLUJoEQXeFZVncc889GDlyJF566SVs2bIlJuOaDCx6ZFrBhgSST9b6tM+1K5FiNsAaMkDOOP1xEa6OByxDgwtVw3ZmRIOmKFiNHNLtRmQ7zCjKtCE31QKWpiApBKfqfKhyBTpVWFqn6xDuXrL/pBNVriAYmoLFwCLTbmpTkqohuoEXBwghmuZSZpKHO3TOX/y8CFmW4XU7kZKSgq+//hpLlizBwoULm9WH6g706NEDjz/+OAKBAP71r3+htra2w2MaWAaFmWrVpCgrTVqddRWyHWbQFMBLCmo98e2ocT5gN3PomW2HI1S44vILOFXrS0hLOp2ujSgpkGQFskKQZjOiIN2KzBQTLEY2qqp7PUQbByRZQVmVBwDQJ8/RybPR0WnMkSNHsPrbtSiv8eDq0WPA8fW4csSIRiGi0tJSjB8/HgCwf/9+VFRUIDc3FwMGDOisacccRVHwn//8B3l5eVpFb0eQGnjwumq41hMQUen0AwDy0y2wGmMrCXO+4guqn6tCVBmW/HT12tBDtDrnIkgy3B4fTpQfB2XLhNXSNH0mUruia919ugi6xayTzKxduxa33TEbd951Ny7s0xuyJLX6cKmoqMC4ceO6nVePpmncddddqKioiMl4bNioC4VrwyLJXQm7mdM8TpX1/kYVqzrtx2riUBCSIeKl2IfyiSK1+rtO10CUzuZuUhQFE9cxlQxdYyOO6DWuOslKWNnf0EIyfVFREb799lsAQCAQgKIoyM3NxcmTJ1FYWJiweXY1WIZGQYZV6/V6ut6HgnRrl6p4z0wxQZBkBAQZp0Lizh1pFaajYuIY9Mi04lTIuKuo8yHb1rFHMFFUY8B7YgW85cshCy4wBgdsRZNh6zEZAEDRXaNg5nxHkGS1aCi0JoyFtI/uwYsDetRbJ9mRQt6DY0ePwGhsWgTUr18/jB07FmPHjsXUqVNB0zSGDBmCH3/8MdFTjTuEkJh+Zw0sg4IMK2hK7W7g62JeMJqikJ9u1YouTtf7Ue/ltc9IktUq0aAo60UDURLO16QpCoKkwNmMrmKkEKLAf3o9yr64ApXfPQbviWUInPkO3hPLUPndYyj74gr4T68HIbEv+nnuuedw+eWXw263Izs7G9dff72mWXl2fgTPPPMM8vPzYTabMWbMGOzbt6/RPq+//jrGjBmDlJQUUBQFp9PZaPvatWtBhQpozv3Ztm1bh87hjTfewNVXX420tDSkpaXhF7/4BbZu3dpkv5dfflkLoV922WXYsGFDo+2fffYZJk6ciMzMTFAUhd27dzcZo7KyErNnz0Zubi6sViuGDBmCTz75RPuceFGGrBBIMoFCCCiaauIhkhUCELUoLlJ0Ay8OhFfrBLqxp5NcrFu3Dh6PB5JC8PPPPyMY8GuNsyNFUbpelWhr5OXl4dixYzEd08QxcISa2DcnjpzshHtbh8O1NZ4gjlS6cfi0C2VVHpyo8eJEjRdHKt2hXq9dr6iks+AYGlkONSXCF0Hv2+Ygigx/xTpUrL8PcrCm2X3kYA0q1t8Hf8U6zdMXK9atW4dHHnkEmzdvRmlpKSRJwoQJE+Dz+bR95s2bh3/84x+YP38+tm3bhtzcXIwfPx4ej0fbx+/3Y9KkSfjv//7vZo9z5ZVX4vTp041+7r33XhQXF2Po0KEdOoe1a9fitttuw5o1a7Bp0yYUFRVhwoQJOHXqlLbPhx9+iCeeeAL/8z//g127duHqq6/G5MmTUV5eru3j8/lw1VVX4fnnn2/xWLNnz8aBAwewZMkS/Pjjj5g5cyZuueUWbN+xAwFBgiDJECUFMlENPJxjNygKQbbDjD75DpQ0o1naEnqRRRyo9/Ko8QTBMTSKI1Sc1unauP0C3AEBZgOLdJsxaUNyb731FubOnYsTNV6sXrseN183IareqsFgEN9//3272oYlK16vF4sWLcL9998f03EFScbxai8AoDi77cbzyYrTx4cEr8++xtAUCEEjD16KmUOG3dTlCks6A4UQHK10QxIFKN5q9OrVK6oiC6LIKPviihaNu4YwpiyUXL8prqHa6upqZGdnY926dRg1ahQIIcjPz8cTTzyB3/3udwAAnueRk5ODv/71r3jggQcavX/t2rW45pprUF9f36oYuSiKKCwsxKOPPoo//vGPMT0HWZaRlpaG+fPnY86cOQCA4cOHY8iQIXjllVe0/S688EJcf/31eO655xq9/9ixYygpKcGuXbswePDgRttsNhteeeUVzJ49G4BqsGVlZeLP//ss5tx599kdKQAEEPggTp0ohzE1B4Rm0TPLBks7Cp70b2KMkRWCOq8qMZCu69+dN0iKgoAgo87Lo7zGC6ePR1CQks6Dy7IsRFEEKKpd3SdMJpP6/m6EzWZDfn4+Nm/eHNNxDSwDs0F9qLq7oBcvTKrViF45KeiZZUPPLBsuyE1Br5wU9M5NQY9MmxbKdQdEHKvydOlzTRQ0RWmGcLR3CKJI8J5YHpFxBwBysBreEyviWnjhcrkAAOnp6QCAsrIyVFZWYsKECdo+RqMRo0ePxvfff9/u4yxZsgQ1NTW46667OjTf5vD7/RBFUTsHQRCwY8eORucAABMmTIj6HEaOHIkPP/wQdXV1UBQFiz9YDJ7nMfLqUaApCgxDgWNpGBhaC8GqXXNkWI1su4w7QDfwYk5YzJJjaNjNemLy+YLVyCErxQSaUgsYqt1BnKj14XClG+U13oj6CyeCq6++GqtXrwYFNdTaniLPwsJClJWVxXxukXLo0CGUlZWhpqYGghAbY+Laa6/tcE5Pc6SEQpxuv5B0xn40UBQFA8vAwDKNvNMmjkF+uhWFGVYYOQYEwBlXAO6AbuS1hZZLFeVlQdEsvOXLo3qP98RyUHR8aioJIXjyyScxcuRIXHzxxQDUnDMAyMnJabRvTk6Otq09vPXWW5g4cSJ69OjR/gm3wFNPPYWCggL84he/AADU1NRAluWYnMOHH34ISZKQkZEBo9GIhx96CIs//AT9+/YBy1BgKEpLuaMpSs3Bg2pPmDhay5mOFr2KNoYERVkTOM5NNSdtmE4n9hg5BkaOgd3MqX2D+bNJ6Lwoo7LeH3W4XpBkVDoDyEuzxCy816tXL6xevRqXjqBB03Qodyq6hciAAQPw1ltvwWg04o477ojJvNqioqJCK/Do1asXBEHAsWPH4PV6IcttG8+KouDIkSPo06eP9lpaWhoGDx4Mmqbx8ccfY+bMmTGft83EoZoKQFII/LwUVTi8K2E2sCjKtKHKFYDLL6DaFYDZwHbZsHQiMLCqd5e0Q1hLFlxR7a9EuX80PProo9izZw82btzYZNu5z0BCSLufiydPnsTKlSvx0Ucftbrfs88+i2effVb7/aeffkJRUVGr75k3bx4WL16MtWvXNgmVx+Ic/vCHP6C+vh7LVqxEekYGli5Zgtm334pvvl2Diy8e2GR/lqIAigLLUKj3iXAFJFAAzEYWGTYjTIbITDfdwIshDVfoHKuXpp+PMDSNdJsJsKm/BwUJJ0KyCAohEZe+NxRGdfp4ZKWYYzZHQgiMLANZlhEU2udZvOWWW7B161YIgtCuUG+0/Pzzzx0WI5ZlGePGjdN+r6qqwtq1a0EIwZEjR3DzzTd3dJpNoCkKdrMBLr8Al1/otgZemKwUk9aH9XS9HwXp1qiU9yPF5XLB4ejaIvI2E4c6F6Cck8sYCYwhunOnz9lfkpVGXiGWoduVO/nYY49hyZIlWL9+fSP5pNzcXACqJy8vL097vaqqqolHLFIWLFiAjIwMTJs2rdX9HnzwwUbf5fz8/Fb3f+GFF/Dss8/im2++waBBg7TXMzMzwTBME29dtOdw5MgRzJ8/H1t37EL/Cy8Cx9C4ZNAl+G7jRrz2yit46d8vN/s+hqLQOzsFMsVADPVc9vMS/LwUsdCxbuDFELOBhYGl1fJ3H48MvU3ZeY8p5MUQZQXeoIgUc9vGkMsvoMqlNn43cQzSrLHN5ezRowcqTx1HZmYmjhw/gezUvlF7Wmw2G0aNGoV169Y1MpriRSzCm9nZ2aioqNBu+NnZ2VqxSMOquFjjsKgGno+XICsKGLr7erUoikJuqhknarzgRVVHLz/NEtPCC0VR8L//+7/o16+fdl307NkTsiyjqqqqkUe34XVDUZTmfSkqKtJCcZ2FxcjCyNEQAEiSErFniCgSbEWT4T2xLOJj2XpMBlEkUDQLRSGaccfQFJhQL99oIITgsccew+eff461a9eipKSk0faSkhLk5uaitLQUl156KQA1p23dunX461//GtWxwsdbsGAB5syZA45rfZGUnp6u5dG1xd/+9jf87//+L1auXNmkKtdgMOCyyy5DaWkpZsyYob1eWlqK6dOnRzxvl1Y1rBYmyQoJfe4MFEU13Bia0q5PRSHaa1kOC0wmEwghECQF9T4enigqr3UDL8ak2Yw44wzA7Rd0A6+TCAgSCFFvoMmA3cwhKMgwRujVNXIMqND7sh2xD/VPmjQJr776KqbdPAervl2Lvr2KkJvatB1OW7AsC0nqOhpvNK2GpZsjnvlxRo4BHa6OkxSYDd3XwAPCOoA2nKpVjbzyGi8y7SZYTVxMvHmlpaV44IEHcMEFF2ivHT58GBzH4Re/+EWbBkBdXR0WL17c6QYeAGTYTfDWQkvlYBkaNE21anBRNAtbj8lgTJkRV9HaekzSqmilkMwRTVPtjjQ98sgjWLRoEb788kvY7XbNy+VwOGA2q/esJ554As8++yz69OmDPn364Nlnn4XFYsGsWbO0cSorK1FZWYnDhw8DAH788UfY7XYUFRU1MtK+/fZblJWV4Z577mnXfJtj3rx5+OMf/4hFixahuLhYOwebzQabTQ3BPPnkk5g9ezaGDh2KESNG4PXXX0d5eTkefPBBbZy6ujqUl5drHXHCeoCZWdnIzMpGnz790Lv3BfjV44/iL88+j7T0dCz/+iusXv0NPvv8S9Ch/NaGEJkB3eC7QlEUjByD3FQL0qxRRF2ITkyp9wbJwQonOVrp6uypnJdIskIOVTjJkUoXkWSls6dDCCFEUZQmv5/7mijJjV7jRSmuc/r888/J9l0/kFXf7yZffPM9qfUE2jXOqlWriCiKMZ5dYzZs2EC+//77Do/zzTfftLjttdde6/D4rXG82k0OVjiJy8/H9TjJBC9K5FiVet7hn2NVbuL08U2u/2h4/fXXOzSv119/nciy3KExYkUgECD79u0jtU438QQE4udFEgj9BAWJCKJM5GbuY4osEe/Jb8nBRb3JwfeLW/5Z1Jt4T60hiqzeT2RZ0cZvbtxIQUjm9dyfBQsWnJ2jopCnn36a5ObmEqPRSEaNGkV+/PHHRuM8/fTTbY5DCCG33XYbufLKK9s93+bo2bNns8d++umnG+3373//m/Ts2ZMYDAYyZMgQsm7dukbbFyxY0Ow4//2HP2qf9Y97fyLTr59BsrKzicViIRdfPJC8+fYCEuBFIkpNr8VAIEB++uknEgi0774cRtfBiyGirOB4lQcEai5KaoxDazoqkqzAL0igQDVbqez08eBFGZkp5rjk/3QUQZLB0LQ2N1FW4AuKECQF2Y7Y5dq1xSuvvIIrR/8C5WfqwLIsRgy5OOprVhAEbNiwISZh2i1btsDr9TZ6zefz4YorrkB2dnaHx1+9enWL83zttdeaaHPFkvJQyDI/zdLt8/AaIisELr8aVgq3xwNUsV+riQUFCiYDE1UrtDfeeAP33Xdfu+f01ltvxdQT1BG8Pj/KyspQVNQTnNEIhqab9SYztCqf0dCbT4gCf8U6nNnyO8jB6qbvMWUhZ/hfYckfDQI1/CfJatcWpgPeO53mURQFYrgTRSv7ad0qKDX30cQxTaI0wWAQZWVlWgeN9pIcMaxuACEEp+v9IFDzpnTjLn7wkowzzgAMbPNSNEaOgSApSELbDoD6cAt/oZ0+Hk6fgKJMK6rcQS0/IxE89NBDeOWVV3DZiNE4VV2PtZt2YPhlg5GXFnm41mAwQBAE7N27V5NIaAmv16uFPhpSWVmJrVu3xsyQaw+FhYU4cuQIevfuHZfxlZAezflWWc/QFNJtJqTbTJBkBZ6AiDovD1FW4PSFpFR8QI7DrEnKtEZFRQUyMjLiPOvEQAhBlSug1tBS6n0rbOCFuyDJipqTJSsKZKIm3jO0GsalKBqWvFEouX6T2ov2xHIoggu0wQFbj8mw9ZgUOg7VqNMI1UCDT6fjKArRiugotN2DnmEoEJlo+XiyQqAQRdXDo6mY3iN0Ay9GhKugaArISU2cF+Z8JFwQIEpNtYEIIThV6wOBmtxu5JJvldrwC2w3cxAkBYKktCsPrqOEjbzhV4/Fyap6zPvrc/j1ow80qohri8mTJ6OsrAzLly9Heno6hg8f3mj7/v37ceTIEdhsNgQCAeTl5WlK72vXrgXLsm1WxnWUnTt34pJLLmlx+5QpU/D666/HzcALe2XoZF11JACWoZFmMyLFYoA3KIIXZQiSjICgyktFYuAtX75c6zLQXpLFyA5/7wFVdSFcfEOFNdEoCgx91oAghEAmBLIin/XohfLqbD0mwt7zWm1staAilHPXwLhrzhOo0z6iNezCUFC/C6KkVjILFAWaAhQQKIoqeBwrdAMvRjA0jR4ZVvCS0iRhUie2cA0U4M+tSqQoCr1zU8BLSky/KPGCoWlkpZg69Yb70EMP4dVXXwXNGfDCs3/G4kWLQAjBHXfcEfG8SkpKUFJSgqqqKixfvhwcx8HhcMDpdKK+vr6RbEF5eTmWLVsGSZJw5ZVXIjMzM16nplFbW4shQ4a0uD3cwDxeNFwAnu8wNKX1uK3zBhGIQqpHUZQ2iyhaQ5IkHD9+HLIsg2GS5z7dWlEFTVMw0gxkRQl581SPHiGqMUBRVBMR4/DvCiGQQxFfjqW7dQV3oiBENexkJTrDriGqkUeBQrioBiFPHoHUHvX5FtANvBhCURRMSegx6m5QFAWWpiApBLyowGKkm2zvSn+HZFhNN6wKu+OOO1BRUYG///3vuPnmm9sUCW1IdnY2Jk+ejNraWnz//fe47rrrmuxTVFQU1ZiJYvjw4Vi3bh1Gjx4d87HD9+xo5Si6M35eQn2orWOkeYkdNcpYlsWtt96Kl156CU888USHxuoofMizFukVoebtArSien6UkHSGgaWbvYeEW12BqPcY/drrGCRkfMkhiZmOfJoEqhcv7KygKApUSJtQDv1tY4Fuzut0ScwhJe+A0HVkOroS+fn5+PWvf41vvvkGGzZsiPr9GRkZsFgsKC0txerVq5sUTyQSRVFalEdpyCWXXKJJHMT0+OdosekAtZ4gTtX5oBA1ZzklgraOsizH5PO78MILk0Ik2eMXAeqsPl+kMDQNLtQyjhACMaShdy6SrGjGHdeCEagTGbKsgJdkzbjrEBRgZGmt7V/478I20CMURBmyooBv0OJSVhTUeoI4VuVpdtjm0D14Ol0Ss5GFJyjqBl4coSgKc+fOxYoVK/DFF1/g+uuvj+r94YpVSZKwefNmBAIB1NXV4aabborI4IoVO3bs0MRWI4F0oJ1SS5g4Gi6tH+35/aAVJBl1Ic9diplDVoRajzt37sTAgU3bOkVLMghHKISoSgAUrfavFgSYzZHnbtMUBY6hIchqO8SgKIMN6edRFAWaprTCHq4dQsY6KpFWxkYKQ6sFLi1d71yoUUIw4IesAGfcAlxBBQxDIyhIUfcO1w08nS6J2aCGagKCjBM1XuTFWC1f5yyTJk3Cli1b8O6772L27NlRv59lWYwcORIAsHTp0oQadwDgdDojVrYfO3YsVq5ciUmTJsXs+DRFQSGAkWPhF6SIupl0Z8SQF4RjaOREUVi0d+/eDhdYAIDH42m2mjuRyOGqapqB1WpFdXU1OI6L+rsR7kohEwWSrBohBOp/ZELAMRSEkKeIjXGFZncm/LnGwrAjUO8BLEMDNAW5FZ8EIQQ+nw91tTWw2OwATYOXFCBUjGNkaaTaIlfo0A28bgoJ5WfwkgxJVkCBgoGlYeSYbmEIGVhGawEWFGVU1PtRlNm5N+3uzPDhw2E2m/H2229j7ty57Rpj+/btjXo9JiMXXHABVq9eHfNxjawq3eMP6gYeEzIyos0zIoTEpDCirKwsqirxeBCWQqIoCtnZOSgvP47jx4+3e7ywxp1MCBD6XMPeHkbz7AEMQ5/n/uOWIUT11GkfYRQfFIXwQo6cfS+B+pm3w7BOS0tDbm4uJIXAH2pxaGCj04sEdAOv2+HjRdS6g6rV3wIMTcFsYGE3c7Aa2S67qivKsuFkrQ+8KDdqnK0THwYNGgSO4/Dmm2/i3nvvjfr9aWlp2LVrF7Kzszsk3hkNihL9ddGvX792eytbItVqgCcowhMUYfELEUmCdFfCArtyyEsS6YIzVvep48ePN5HySTTqolv17lAMiz59+kAQhA6PGxREnKrzQ1YIAkEBvBSq3iaA1cjCamKR7bDAajx/xLYbohpxAKBWI4uSAkFW4A+KqHEH4RUkWIxsxP2/CYjWQiwcBg8KEtwBAQ6zAUZD9CYWx3HaQoZjzlactwfdwOtmUKA0446CKp7JsTQIUXNfBEkt7/YGRXiDIliaQqpV1aZKxq4PrUFTFMRQJVpmit73NxFceOGFoCiqXZ683r17o6SkBJ9//jluuOGGOM2wMU6nE6mpqVG9Z8yYMThy5Aj+9re/4YEHHkBKSkqH58Gxaq6VQgBPhJpv3RWGVvtq8qKqfxepKHyscufq6uoSIs3TEm6/oAkcc4waVaEoKiaLHpPJBJo14FSdH1lmM3hRRp03CF6UIQYJfKIMvxhEilmByaBGQdQQIqIKl3clKup8qPUEwEshwWiZQApJzigEIfkZgGVpGE0mpNqMYCJYdMiyAofVgPw0a6PXTSYgteO3jJigG3jdDCPHIC/NAkOoSudcFEIQFGT4eBFuvwhJIajxBFHrCWpfeCPHIMViSPrE3LOrMcDSjpWSTvvo378/eJ7H+++/j9tvvz2q9+7fv18TOU4E6enp2LFjR9Tv6927N371q1/htddew+WXX45hw4a16/iCJKOy3t/Io36+h2gBtbiiWpTh9AkR32t8Pl9Mjh2rUG+0yIraucIbFAGoecQ5qZaYR1BSLEb4eRkuvwAjx6BffiqMHIM9x2ohKgROv4CAICHNZgTH0AgIMjiG0gw8OeT1FkQZLr8Im5mDxcgm/fOgIZIso94nwBsQcSIU5TlXZJyCuvCyMiy40POyOWF8oiigaFrrMqQQAhoU8tMtSLEkd8cq/anYzWBoqtU4PU1RsBhZWIwsMuwmeAIinD4egqQgIMgIQAYCIuq9PPLSrUmtJ0dRFGwmFpKs4GStDz0yrbqQZ4K45JJL4PV68eWXX2L69OkRv6+8vByTJ0+O48ya0p4wLaAWhzzyyCOYP38+Bg0a1C4Pi6IQzbgzsjTS7aao82i6IykWg9ayrM7Dt+mB/+6779CjR4+YHJsQEpdK6bZoaNxl2I1IsxrjNofcULtBp19AvY8Hy9DIcpjhDYrwBETwkoJKZwAMTamhcklBvU+AiaVhNrKgKMDpE+AXJIBQSLFy6JPrgMNiSNqUHkIIXH4Bbr8IHy9qof9shwkklA9HQc1FjPQcKAooyXWAZWi4/Dy8AQlmI4s0a/J+Dg3Rn4bnMTSlxvd7ZtlRlGlDjsOM9NCqTlIIKup8jXoYJiMZdhMkWVUWr/XwnT2d84qrrroKJpMJ69ati2j/9hpaHWX48OEoLS3Fc889hy+//DLq98+ePRuffPJJu47NsTRyUy0ozrajKMuuG3chaIpCtkOVBXH5W//e7ty5E+Xl5VHL9LRETk4OKioqYjJWpARD4WgAKMywIt0W/+41uWkWZNiNZ4sGCGAzcchJNcNiVH07skJAU2p40hPgUe0JQJDUVmgpFgPsZgMYhoLTJ+DH8lp4Ah3PE4wV7oDqifTxIirqfDh42oUzrgB4SW6U16m2Z6Mb9PCN7HMnioIemTZtLIfFiIIMK9Jt8TPMY41u4OkAgBaWzbCb0CPTBgOruqRP1HjhC92YkhEDyyA7NfygEPRii1ZQm5crCAiSJlXRUSZOnIi9e/ciEAi0ue/69evj0iWiLVJTUzF+/HgMHTo0Km9jGIfDAb/f365jMzQNu5nTFOt1zmI1httpocXrcf/+/fjhhx9w2223xey4w4cPx+bNm2M2XiSE76E2E6eJtCeCrBQz+uSlINNuQkmuHRcWpoFlaGTaTchNVRf0+WlWFKRZ0K8gFb1zU2EzGzQPo8NiQG6qGRk2EwDV0EsGgqKEw5UubD1UhePVHvh4CXQMunUoCoEoyaBpoCjLDmMXbzuqh2h1msDQFArSraio94MPSZCYOLVEOxmLMaxGrl1J2+cbP/zwA77bsh0yKOTk98TokVciy2Hu8N/znnvuwXvvvddmZS3P87BYOieR2+v1dujYRqMRXq+30/XTuhNUSKxXlBUIktzECD527BjWrVvXqI1eLMjMzMS+ffswZcqUqMSFOwJpIFmSaFiGQZrtrKFiCEVoDCwDA8tAlhXkpllgN3M4UeODKKvpOgXpVuRBDdVWufywmzkERBkVdT7kp1tbPmCcECQZ9d4gfLwMXpBBFMBiZMFQrS+eFEWVj6HQVLJElhXYTBxMBhYEBAaWhtXIdQspMUA38HRagGVoFGZYUesJwukTEBRlBEMVWWk2I1KtxqRKurUaWfCijGp3EIQAaVGIQZ4PlJWVYdeuXZh7913wBER8t3UnXnlzAW695WaU5KbB1AGvQiS5aU6ns1ONoxMnTnQoh+uGG27AJ598grvuuit2k9LRDDxZPlshK4oi3n33XdA0HXPjLsycOXPw6aefgufV8DBFUejVqxfGjBkTl+OZDQzqfWoFbarV0GwBXKLokWlFjYcHL8pQFIIMm1lbFOelWXCixouAIMPHS7CZOKTZjAgKErwhL5k7IIJx+uNWdVvrCSIgSJAUBSkmA4KijCpnANWeABwWI9JsRrAsjRS27WIlRSFItRmQZjVCVtQCw6AoaULTaVZLxH2QuyK6gafTIjRFISvFjDSrEd6gCJdfgCCpuW5On4AMuwkpZi4p8hHSbEYIkgJvUESNJwizgemQ0dKd8Pv9+OSTT/Bf//VfANQ+vpNHD8eAi/rj/UUfYOzYazB8UL8OFdS0pcB/+vTphIvLKoqCnTt3or6+Hm63u13h2TA2mw1+v79TkvO7KwohZ0OzlNrS7r333kMwGMSsWbNiIk/TEsXFxSguLm702tatW/HPf/4Td9xxB7KysmJ6PKuJg4ljEBTlUEGYrdPC9iyj6rY1h5FjYDdzcAdE8KKs5Yym2oxw+oVQLpsaqhUkBQUZ1pgu9F1+HodPu+AOiFAIQXGWXW27BvUeE20KDstSyHGcPVc1PH7+LP4pkgyN+XS6BIQQeAIiaj1BSA36HKZaQ8m4SRC6rajzwcdLyLAbkW7TtfEAYMGCBbj11lubhKNkheB0vQ9vvP0ObrnpJvQrym7337AtXTyn04kDBw4kVGD2zTffxLRp05CdnR2T8Y4ePYq9e/di2rRpMRnvfGL//v3YuXMnMjMzUVRUhMLCQgQUFi6/AJqi0DPLhtdfexW33XYb0tLSOm2eiqJg4cKFsNlsuOmmm2I6tqwoWgg0xcwlre5clSsAl19AmtXYqLr5YIWz0eKGEAKapmAzsjAZ2HanxhBCUOflUe/lIStqj15A7f7CsaoRHBAkVLuD4BgKeWmRhYdlRUGPDFu39tC1he7i0IkYiqJClVUcXH4BtR5V5qDaHUS1OwiLgVW19Nizza0VoqrV86ICXpS1m4KRZWA1cbCaYquvZGAZ+HgJkqyvW8IoitJsrhFDqzfLO26fhUWLP8RD996lVTZGiyy3Xm2dkpICr9fbrrHbS69evWJm3IXHW7p0KQRBgMGga9lFgizLePvtt5GTk4OZM2fi9OnTOHHiBLZu3Yr6gIJrp8+Excji8KGDKCws7FTjDlC9RHfffTeOHj2KF198EWlpaRgxYgT69OnTYc8tQ9PITDHhdL0fQTF51QnC4UuWaXy+6XYj6jy89jlQFAU/L6HsjAdpVgPy0q3ITTW3KVUV1pOTFYJaTwD1XgGg1IgR3YLMVzgnLtL7uqIQ5Kd17/BrJOgGnk7UUJTa/cJuNsDtF+AOqO56vyBpq69WUdQ+uZ6gCIamkG4zxkxfiQKBxcDAYuza1U+xpDUnPUNTKMhIQU5ODo4cP4XMgb2jNrjr6urazMOLtol6LHC5XFAUJabHnjNnDt59913Isoy8vDxce+21nXJuXYHt27dj48aNmDNnDtLT0wGoRnKvXr0wevRo7PrxJyxcuBDpqQ6I3lr87jdPdvKMz9KrVy888cQTEAQBGzZswIYNG7RthBD07dsXo0aNinpcJWQ8JVP+ckMUQuDn1Yrfc/ME020muHyCJi4PqCHPDLsJtZ6g2orPz6NHlr1JKzRZIaj38vDzIup9PBwWIwKCBJahmwgQNwdLU1prN1lRWjQiww6Enlk2PUUHeohWJ0YIkgw/LyEY6gsbvgnQFMDSNDiWhsnAgKFpyLJqDHoDohbqZWnVO2g1cTA08ABGy6k6H/y8hKwUk15NC0AQBCxevBh33nlnq/sdPFWHJV8txUN33Rb1qvf1119HVlYWLrroIvTr16/ZfWpqanDgwAFcddVVUY3dEQRBwKpVq1BcXIyLL7445uMfP34cq1atAiEEJSUl+MUvfqHn54VYv3496urq2tSuO17lAi8R2Excp1RmtpcdO3Zgw4YNuPbaa9GnT5+I31frCaLOy8Nu5lrMg+tMvEERp+v9YGkKJTlNcyAlWUF5jVftpdvgWhcktSuJNyBgYHE6clPV3DxCCPyCDE+Ax6EKN4KiBFAUMmwm2M2R3WdUeSc1jOv288hPt8LSTC9dQtTK4KIsW9Ia0IlGN3F1YkK45D4iODU8m2k3weUXUOflIYW+wHVePrSLmtsXrWcvK8UEb1BMqNZUMrN582YMGTKkzf1SbWbIsiozE42BRwiBoiiYMWMG9uzZg+XLl2PixImNvFqKomDt2rW48cYb23UO7eXgwYMAELfijp49e+K+++4DAPz888948803Aai6e9OnTz9vw7herxc//PADHnvssTb3zUyxoKLe3+X0Ky+77DJcdtll+Pzzz/HNN99g7ty5MBrbXlCGW2H5eSkpC3bCwvYteb9YhkZJth2natVc57D3zcAyyEoxwWExwBuQcNDnBMNQkGQCilLD0waOhs1sholjIpIhkWUFRo6BzWxAus0UMo6DCPByk8+OEAIjx6Ao05Z0n2lnoj8FdTqNcKg3xWKANyDCExQRFGStuq7aHYQ7ICI31dym8egLijhZ64PVxCLb0fb+5wOyouCHH37Ao48+2ua+FiMLmqbBR5kbtG7dOlx55ZUAgEGDBuGCCy7A6tWrAah/X0VRUFNTk1DjrqKiArt370a/fv0wderUhByzf//+6N+/PwCguroa77//Pvx+PyZPnoxevXolZA7Jwuuvvx6RcQeczfdCF30oz5gxA4FAAG+++SYuueQSjBw5stX9LUZWyz/zBESkWJJrERC+b7Z2H6AoCoWZNpyq9cHLi5q3jKIozYBlQvl7DfP4Ii16kxUFViOLzAxro4U6F+pGkZNqgKIQeHkp9FmqWnYF6VbduDsH3cDT6XToUPFG+GYnK4pWrcuLMsqrvUixqN48I8dohRuSTCBIstoUOyCqibq0umrUHXiq7EPvvv0j2pdjaBgMBnj9AQD2iI9x4MABPPDAA9rvFosF48eP135XFAWlpaUJ8WaFQ7JZWVmYMmVK3I/XEllZWbj77rsBAIsXL8bWrVtx6623dtp8EsWRI0fw1VdfYezYseC4tr3AhBDU+1SPfbirRVfEbDbjkUcewdtvv92mgRduD1nn5eH0C0ln4IUNKlFWIMlKq562ggxrSDNPip1hRYDiLHuzHsSwd9HAMshMMWmtyqxGTm8B2AJ6drBO0sHQNFKtRvTMssNsYECgtiErr/Gi1hOELyjhVK0PCiHw8WrzZ4am1LJ+u0nrs3g+U1ZWhu3btmHKxPER3XxZhkJeVhqCXlfEx4gkxETTNEQxMa3uPvjgA0yZMiWhUixtcdttt2HYsGF44YUXcOrUqc6eTlwoLy/Hiy++iJ9//hm//OUvMXjw4Ije5wmKECQFNIVukS8bqZHjCBl1fEg8PplgaErT5xOktsPmhRlWGDmm1UKuSFEIQUlOy8URYa9i2EuYYjYgx2HRjbtW0A08naRF7aZhQ3762S+xxcjCyNFQCGBgadhDvR2Ls+16aBZAZWUl5s+fj507d0YUmg3D0DQcRgrp1shvlqtWrYpI+b9fv37YvXt3xOO2l0GDBqGsrCzux4mWXr164de//jW++uor7Nmzp7OnE3O+/PJL/PKXv8S1114beSN3QlDnUb13aTZjUmhoJgqWobX7mdufHL1dGxIOq0aSF0lRFHpk2mA3cZAVAkmWIUpy1L2uFUVBXpoFLNPy/VvWtFfPn2ulo+iuDp2kx2rkYDVyjcrje+eqFV7nu0EXhud5vP3220hNTcUjjzwSdcgkIEg4UlGLiwZH3j3g0KFDmDhxYpv79enTB6WlpXC73S12J/jyyy9RUlKCQYMGRXx8RVHw5ZdfIj8/H8OHD8fgwYOxZMkS9O7dO+IxEgVFUXjwwQfxwQcfoL6+HqNHj2603e/3Y9myZXC5XOjdu3fcWmbFmrKyMuTn50d9vfGiagR0F+8doF6PkUAIgRLyeCWjiIWBZRAQZPBSZN5FmqKQF+pbKysKCAEkRUFQkCErBIIsQ5FVDx0h6jkTECiKmlLDMlRELcO0T0rPs4sY3cDT6TK0JaB5PvPSSy/h0UcfbVWPLijKcPp4BHi1F6N2wyQEvCihus4FgzGyROitW7di4MCBEc8vPz8fCxcuxAMPPNBsPp7NZkN5eTn69+8PURSxbt26JlWJOTk5mtyJ1+vFihUrMHXqVKxZs0bbJz09Hfv27cOAAQMinlsiufXWW7F8+XJ88cUXmoRIXV0dXnvtNdx7773IysrC/PnzMXr06IiMpnAVM9OK5yOefP3113jkkUeifl9AUI0HsyG2QuediSjJOFXra6IFGtZvo6BGHRQCzcPlSELjNuxNVZTojc/wPZplaJi42JoXNEVBBoGsKAD0hX0k6Aaejk4XZ+XKlZg0aVKLxp0Uqkj2BpvmwimE4FSNB8u/+hyP3HUrMuyRGXhbtmyJuFISUD2MI0eOxFdffYVx48YhNTW1yT5TpkzBV199BYqiMHXq1CYCwhs3bkRVVRUkScLOnTu1ytxLLrkE3377LbxeL/r06YMLL7ww4nl1BpMnT8bmzZuxcOFCzJo1C6+99hqeeuopzaC79tpr8dxzzyE7OxsjRoxo0Vjdt28fVq5cCYfDAUVRQAjBJZdcgmHDhkXsUfN4PPjiiy9w4403Ntvt5FwIIfj++++xb98+AMCoUaPalWAf9mB1l9CsQgjcEgsfLzb5PEiD//OhvDaaAnJSLR3q/xwPFEK0sHGyRUdMBgZiQIEvKDURUtZpHl3oWEeni/PWW2/hnnvuaXH74dMuVLsD4EUJffJSYTMZ1AIVdx2Wff01qr0SJl17HS7pnReRPtX27dvh8/mahBkjeZ/dbsehQ4cwZMgQ5Ofna9tWr16NcePG4dChQxBFERdddFGzYyxZsgSjRo3C7t27G4Ux9+7dGxcx43hy8OBBLFq0CL/97W9hsTQvejt//vwmuZSEELzzzjtIS0trJCRMCMGOHTuwa9cuAGpYuGfPnhg9ejQURUFlZSWOHz+O8vJyBINBAGrV8+WXX46ffvqpRVFiSZLw9ddf48yZMwDOasB1hDpvELUePql7sraF2y/A5RcgK6pXad/+n0HJIsaPukL7HjV8uBJC1KIKAlhNbFJGJMJCzCxNoWe2Pam8qz5eREWdHxSA4mx7RPeq8x3dg6ej08VpbY3mCQj46egprF2/AXlpZhw1yJAJgajQyM1Iwd133QnQDGgq8hD4999/j8cffzzqeQ4dOhTLly/H1KlTsXr1avh8viZdANrqCjB06FDs378fsiw3yunrasYdAPTt2xfPPPNMq/s4HA688sorYFlW82jyPI+ZM2ciNze30b4URWHo0KEYOnQoAPW6OHz4MBYvXgyWZZGRkYGePXti2LBhTbx1DVtxhfn6669x6tQp0DSNiRMnokePHh042+bpSt6FKldA+3dAkJpUmQ7o3w+rl30Bk6FlqZRk84qdSyAUXk6zGZPKuAPUXGwjS4OXFPh4SatG1mkZ3cDT0enitBYic/tFbNv4LR648w70yFBb/IR1BNvzsFm0aBHGjh3b7rlecMEF2L9/P8aNG4c9e/agtLQUfr+/RY/dueTn52P//v0QRREHDhzA5Zdf3u65dAVuu+02rFmzBuPHj4+6ry5FUejTp09UrbTCeDwe1NbW4v7774/6vZEQNh66UvzIdU7FK02phpCJY0HTFIwsjbVILtmTaFAIQTCUG5lsoeMwBo4BLymhPDydttANPB2dLk5LBp5CCA4ePYY+fS5ASbZdM+hoimqXcffee+/hggsu6JC3rE+fPli+fDkuvPDCqCpmGzJu3Di8//77mDRpUrvn0VVgWVbz0EZj3HWUjz76CHfccUfcxg+3uOoqD2pCCLJSTGpxEgE4VpU6aZhDGAgEWhkh+ZFkBQSq4dqSFl1nE9bC4zqpqKiroQexdXS6OPn5+Th69GiT1ykAGelpOHOmCn5eavrGCHF7fXjxxRcxePBgXHHFFR2YqUpBQQGOHz/eoTGmT5+OpUuXdnguXQGHw4Gampq4HmPYsGF4/fXX8fbbb8PlckEQhIh6q7YXhmp/pWZnEG6rmGE3ITPUc/XcApFFixZh1qxZnTTDjiPJ4cKX5DQLBEnWwuIWo27gRUJymuk6OjoRM27cOLz77rtNep5SFIWi3AxQFHD8dA1sJZEVUYQpKyvDihUrQDMs7rn3XthttpjMd9CgQVi6dCl69uzZ7jFsNhsGDhyI77//XuuF29147733kJeXB4qiWizCiBUDBw7EwIEDEQgE8PHHHyM7Ozuuxwt78LqIfdcmhBAIghBRJXKyEvaOJWtlc51XFcZW+/kmpxGabOgGno5OF4fjOMhy87k/DosBM6ZPw5tvL8C9c+9GQaYdIAQyIbAYWC28W+UKwMzI+Ka0VPMW5ebm4sEHHwRFUTEVZN22bRuKioo6PE7Pnj1RU1ODAwcOoF+/fjGYWXKRk5ODcePGJfSYZrMZc+bMwerVq+N6nHAOntxNLLwlS5Z06ZQBQZJR6wlVVidhq0dRVvuTA4hYyklHN/B0dLoFLeXhURSFHll23HH7LCz64ENk2DjwMg0ZNFINMmgQEELgFWk4rAZMnvAL5OTkRDx+tGzZsgVWqzVmVa+XXXYZSktLkZmZiYyMjJiMmSxcdtllWLlyZUTdQmKJ3++Pu3CyJqbblaosWqGiogLTp0/v7Gm0m2pXEARqcUW6LfnElz0BtcDFbGCStgAkFvh4Eb6gBIpSF+cdrbrWDTwdnW5AamoqampqkJmZ2WSbgWXQr0cWHrxnDlKtRtS4AxAktfdj+AYiSDIYmopr6GPTpk1ITU2NuRDx+PHj8emnn2Ly5MlxD2UmitWrV0NRFJw4cQLLli3DlClTEnJcRVGwdOlSTUQ6XjQMAzZsQdgV2b17d9KLa7eGIMla942cVHPMFnOxJJx7Z07S4o9Y4PILjaR4nD4BKWYOGXbTWV1FQiArJOJUm+77aenonEcUFhbi5MmTzRp4gNo6KCtFzQ/KS7M22R5vfa7vvvsOmZmZcQulzpgxAx9//DGmT5/earu2roKiKBg/fjwA1aOWKJYuXdpsF5FYQ1EUaErNwZMVgq6sWbt582Y8+OCDnT2NdhMudGHp9lXXJwIudIGIUteouo4WSVZQHTLu7CYOMiHw8xLcARHugAiWpqAQouWs9slzRDSubuDp6HQDbDYb6uvrO+XYgiDgo48+Ql5eHhRFQUZGBoYMGaJt37hxI/Ly8tC7d++4zYGmadx00034+OOPMWPGjGb73XYlGhpYifJKrl+/HkOGDEnY8RiahiIrXToPz+VywWptumDSiS3aNZJ8zsWY4OclEKi9inPT1O9fQJBQ4w4iKMqQ2vkd0Q08naTi8OHDWLNmDVj27KVZXFyMa665phNnlfzYbDacOnWqU469atUqzJo1SzNKKisrUVpaCpqm4fP5MHDgQJSUlMR9Hg2NvBtuuKHRNdTZuN1ubNy4EQzDgOM4jBw5Ejt27IDf728UEtu3bx8eeeQRcByHYDCYUG9kIBBI6GdG0xQgd+1CixUrVjRazHRFwsZDMoZmATWEHxaZTjF37YVbS/CSWiTXMARtNrDokWmDKCuQZCWUQkNF1WEkee6AOuc969evx6lTp3Dvvfc2utn84x//wMiRI8FxeoPplrDZbPD5fAk/blVVFTIyMhp5nHJzc5u00UoUDY28m266KaHiwM0hSRJWrVoFk8mk5dH5/X58//33GDJkiNZqDQBOnjwJk8mEb775Bm63GzU1NSgsLEzYXCdOnIhly5Zh2LBhLYb6YwlDd/1K2hkzZuD555/HgAEDOnsq7UJWFNS4k7d6FgBE+ez1YTIkZwi5o4RD476gCJJiavT84xhaC1FHS3L+RXXOS/Ly8iBJUpOV5P33349XX30Vffv2TXhFYVfBYDBAktovZtxetm/fnrACgEgJ905dvHgxbr/99k6dy1dffYXrrruukWfMYrFgzJgxTfYNt3DrTKN0ypQpWLp0Ka688kqkp6fH9Vjhh5Ykd828KkII/v73v+O3v/1tZ0+l3VS7gxBlBSxNIcOefNWzAGBkabA0BUkhCAgSrMbut9C3mznUuIOQFAIfL8Fmis05duHUVp3uRnFxMXbt2tXkdZvNhsceewzZ2dn45z//ibKysib7KIqCAwcOJGKaSQnLsgk38LZv356Q0Gs0KIqC0tJS7Nixo9ONO0DtQhFN2LOzPY4AkJWVherq6rgfx8Cq5+rrQJeVzuTFF1/E/fff32WLegghmrZcbpolaSuZKYrShLHRdZ29rUJTlCb/EssFj+7B00kafD4ffD4fVq5cCVAUhl81GqnWs6vKSy+9FIMHD8Ynn3yCtWvXaq8TQkBRFAoKClBaWors7GzMmDHjvArpms1mBIPBhByrsrIS27dvR79+/drVyD5e7NmzB+Xl5RgzZgxsMeq6kUiSwbjbtm0brFZrQoSjwyFBXpShEBJVblFns3DhQlx//fXdRnsxmg43iUYhRJNJMXRTDTyFqN5JILZ9gHUDTydpeP311/GHP/wBoChs/6kMazftwISrhzXKDaEoCjfddFOLY0yYMAEVFRV49913QQgBTdNaF4a5c+fG/Rw6i1h3m2iOcD5ZWloapk6dGtdjtQcl1Li+u2jhJRq32w23243LL788IcczsIwWevMFJdjNXWNBJooieJ5POu91tFAUBSPHgBdluHwCMlOS0xPpC6qGD0tT7c5FS3YCoSpalqZiKuSsG3g6SUNmZqbmxSguzMNP+3+Gyy9Enfybn5/fxJh76623YjbPZCWeVXAbN26E2+3GhAkTkqo6tSGDBw/GRRddhKVLl6Jfv34teqEURcGpU6dQVlYGURS11202G4YPH67pziXSUBQEIe7dI9piz549GDFiREKPaTcbUO/j4fYLXcbA+/jjjzFz5szOnkZMSLMaUen0o97Hg6LUXDCOoZOmotYXFFHjDunDddMKWuCskHMsvXeAbuDpJBEN+6maDSwMBgNkJTb5CDRNQ1GUpAiDxQslRp9Vc1RXV+Oyyy5LWuMujMFgwLRp07B37168++67yM/Pb3a/3NxcDBs2rFH+1JEjR1BaWoq6ujoYDAaMHj067oUGYYLBIPx+P0pLSzUZlUR/1sFgMOHezxQLh3ofD78gISjKSd2G6k9/+hMKCwtRV1eXkCrjRGA3c/DzHNwBEXVeHnVeHgBAU4CRY5BpN8Xc6IgGHy9BUgg4hkZaErZQixV0nFr3JffdWue8wmQyae22wgvIWCkoFBcX4/Dhw+jbt29sBkxCDAYDAoEAzGZzzMeeMWMGvvvuO5w5cyZhIbyOcPHFF+PMmTMYN25cxO/p3bs3TCYTcnJywLIsli1bhkGDBrUoVSKHqvqkkFgvx9KwGrlGbbgiJSUlpZGMyvr167UFT15eXsx69yYbBpaB3cTBExTh9PKayGuyUFNTg59//hlGoxFTpkzBFVdcEdeFVGeQ7TDDbGTh9gsICOo1pxAgIMg4UeuD3cwhzWqEsROMb4uRBU1RSLMZ2vW96mrEuqJcN/B0koaamhptdUxTFGRZhijJbb8xAoqLi3HgwIFubeBdd911WLJkCW655Za4jH/VVVfh0KFDWLJkSULaWXUGBQUF2r+nTJmC0m9Wo7Lei549e8LEsbAYWYiyArdfwInKGuzbty9U5UdDlmUMuXQwinLSkWJpfzjJYrFg7Nix2u+rV6+Oq4G3bNkyZGdnaxXIoihiwIAB6NGjBz788ENkZ2ejtrYWN954Y1z+5qk2IzxBEZ6giAxZSao8qy+++AKDBg3Chg0b8Ktf/QpAchTDxBKKopBiNmgiwrKiQJIJ6n08PAFR+6EpClYjC7ORhaMD13c02ExczCRDkhUffzYMHeteu7qBp5M0pKamagaYkWMwaNBAbN22HbnjR8HawS/5xo0bWy3O6A6kpaXB7XbH9Rh9+vRBQUEBPvvsM4wdOzZhIcxEIysKaj08igcMxd69e7H/8HEAQP/+/XHy5Ek4nU7YbDaMGnkVDCwNmqbgD4rYvHUb9v/M4dqxV8FiZDFs2DCUlpZq44b7yyYTRqMRhYWFKC8vx9SpU2EwGLBnzx5s2LABU6ZMQXp6OgRBwIoVK+KieWjiGJgNDAJC8iT7r1ixAidPnkROTg6GDRuGYcOGdfaUEgZD02BoIDfVglSLhFovjwAvQSEEnqCIoCgnzMDr7gQFCRV1as6v2cAgwx7ba1838HQ6nfXr14Nl2UY5RwxNIT8rDbt4HvU+vsMGniiK4DgOS5cuxenTp1FRUYGHHnoI2dnZHZ3+eYfFYsGNN96IlStXorCwsMuq+DcHIQROn4A6b1BLD7h8yCUwsAz8vISPPvkUY6+5BlcMvRQOi6FRARAhBOarr8TPh8vw3ZbtGD/qCthsNs2oW7lyZWecUquEiztyc3MbFQ4MGjQIgwYN0n43GAzIz8/H8ePH0bNnz5jPI81qREDww+Xn4bAaOt2Ld/r0adx7772dOodkwGRgUZDOghCCoCjDz0vnRag0UdT71BZsFiOL/DRLzItbupevWadL8uOPP+Lw4cOYPXt2o9dTrUb06tULm7buQHm1B+5QP8L24PP5sGDBAlx88cW477778D//8z/4/PPPm+zHi2pIWJSVmCe8JoLs7OyE9aSdOHEiRFHEt99+G5PxCCFxl3ppjYAg4USNFzUe1bgzsjTy0y0ozLAh22HGkR+34qpL+yNQU468NEuT6m6KomA2MCgsLIQnIGDVt+sgdlB8+siRIzFtVyZJEt566y2sXLkSpaWl2LZtGwYPHhzRewcPHoyff/45ZnNpiNXEwcgxUAhQ5QzE5RiJorOv43igXtssMuymRtqkOh0jfJ2YDUxcKpd1D55OpxIMBmG1WjFnzpwm2ziGxoA+xRBFEavXrkdubi5GDh3ULjmFxx57rNHvLKtW6S5YsACAWoGqKAQ+YoCDk+HigeFXXo3+vXrAyDEIihIYWu0JmKw9GwFg8uTJeP/993H33Xcn5HiDBw9GZWUlPvnkE0ybNg0GQ/Shm6Aoo9oVQFAMN9xm4LAYYTWxCRG/9fEi6jy8dnyaopCZYmo2DFXSs6jVscI5NAMHXQKv14vFS76BlQMGX9SnXblbPXv2xMaNG3Hy5EkEAoEO6w+Wlpbizjvv1DqflJaWIjU1tUNjxorcVDPKq73wCxI8AbHLyKaECQgSnD4B3qAqvcPQFOxmDg6LQes1qqPTEFZr1xefBUHyPql0zgtMJrWx8htvvIGioqImvWYz7CaMuXwAavtfgG/WfYe9h45j2MW9YxImONcI4kUZ5VUuFGXZcbLWh2++XYOdm9aC0CyCsvpFpBQJj983O2m7ZBgMBghC+z2d7SEc3vv8889xww03RPVeQZJxqtbbqFo6IMgICH5QUEMXNhMHs5FtMWxHCAEvyuBYOqp2Sz5eRK2H17y2AOCwGJBhNzYZx+12w2KxtCi7EsbIMchPt6Dey4NzpGi6clWnyzFw4MCI5xaGZVmtb+3q1aujfj8ALF++HJdddhlMJhOMRqOWCsGyLCZPnhzVWAUFBXEL0xpYBpkpJhACWE2d+2iKtu2f08ej2t24k4ysqOF+p09AmtWINJtRD2/qaBBC4AstBkyG+CwAdANPJ2EcP34cO3bswLXXXguj8ayb/8477wQAzZt2LgxNIzPFhP79+6OsrAzyRSVg6Nh/IYwcgz4FatFA77xUFNw8DRxDw89L8PMiZIVAEoKYP3++VlGXjOTl5aGsrCyhSvs0TbfLE1TlCqjhUI5Bbqoq7xKu2hNlBT5e0nqVGlhak2wIhzNUA9EHSSGgAGSnmrVqwHMJijKcPh5BQW2NJYesSgpAisWAdJuxxZZNe/bswZAhQyI6J6uR0xqihx/8mXlFsDoSLwGyf/9+9O3bF8ePH0dZWRluvvnmDo138cUXY+XKlXEx8AAkRfgvEAhEtUjy85Jm3NnNHFKtRnAMhaAgw+kT4BckVcw5ICArxdzlPJM68cEbVDX+aIqCPU6VwrqBp5MwHA4HTp48iQ8++ACiKIKiKFAUpQkQtyYeGhRk7Nu3D1deMTxh4Y6w6KrdzDW4KVvhcDiSWjT5uuuuw6uvvoqHHnoooceNNofEGxQREGRQAPLSLJqHLsOuVpPxogxvUISPl8CLMgRJrWz1BkTkpllgYBnUe3lIIUONQM3fokCBoSnUu33w8xIESYYnIGohWG2+ABxWA9KsLRt2YXJycvDdd98BAC6//PKIjdlUqxGyQlDn7Xi3Bq/Xq3nxTp8+jTvuuKPZ/Q4cOICTJ08CUP8mY8eORe/evaPSL3S73bDb7Y3+pm63GwsWLMCkSZPafQ5dgTfffBMPPPBARPsSQlDlUnMGU8wcclLPGvFWEw2riYMvKKLaHYQoK6h0+uEJqLlsnaErp5McBAVJu25SrYa4dQ7RDTydhJGamgqTyaR57KLB7RcgSRKsSdCuZsiQIdi0aROuuuqqzp5Ks1AUBY7j4iZ63BLRJpbXhLweaTZjs+FXI8fAyDHIsKuyJZ5AKKQqKThR40VmilkTZs1Ls6DOq4ZbK51+BINBVHkEvPLOYkybNk0b027iYLdwoCkKhihCun369EGfPn2gKAq2bduGY8eORaw3mGYzosYdgDsgIHBaAsvQsJlZWI1qYUGkeYbTp0/X/n3y5Ens2bOnUaVrmCNHjnRIzuTEiRN45513UFJSohmRkiRh06ZNGDZsWIst4LoDgUAAVVVVEadgeIKqp1nN22z+u2Y1cbAYWdR6VEUA1Svthd3MIcNu6vSKYZ3EIisKTtX5tEKueHbo0A08nYTSHq+XJCs4dOwEsrKykiKEc8kll+Cll+YnrYEHALfffjveffdd3H///Qk5XmVlZUT7+XgRHr8IvyBBVghoChHd4BiaRqrVCKuJQ2W9H0FR1lbAgOptzU+zoNYTBC/K2Ll3DyZPHA+KUgtjTAYGKZaOy2/QNI3hw4fD6/VG/B4pVJHNMgwI1ArtOg+PalewQYNxFml2Y8ShmsLCQuzcuRM1NTXaa4QQ1NfXY8KECVGeVWMWLVqEP/zhD3jrrbdQUVGB3bt3g2VZjBs3Lulb1XUUs9mMe+65B//4xz9w/fXXo3fv3q3uXx9q7dVWpwUqVLiTYuFQ4w7Cx0taKoLDYoDVxGphfZ3ujdMnQCFqyklBhi2uhWTd+9uqk3S0x8Bz+QUcOXIE48aMSopelUFRRp/BI/D83/8f7r7jFuTk5HT2lJpgNpuhKAoEQWhXZWs07Nu3D5WVlU3agoWLH3hJrVD2BMVGBQ2AWkQTzQ2OY2gUZlhR5+XhDYqgAKTZTFqINRwiky++AEf3bofJZOp0Q5wCQFE0FEJACEAUBaAAljl7LfOSjJM1XvTItEWs3N/QMxlLsrKyQFEU7rrrLvzhD3/As88+m7TpCPGguLgYv/71r/HUU0/h+eefb3G/oCBBkBQ11G+JbOFpYBnkp1sRFCTUeIKquLNfgMsvoGeWTa+27eb4eQlOn7ooyLCb4l50oxt4Ogkl2lwDQgic3qCaxJ8E3rtwzs0FffogK82OnTt3Rl2JmChmz56NhQsX4r777ovbMb777juYzeZGxp03KGrFDOcGbcMFDTYTB5Mh8vBkozEoChl2U6uq7yUlJSgpKWl35Wkkc4gUjmXQJ88BPy+CpgCzkYPbL+CM0w+FqNIqBlY1oDp7AdMwzM6yLC666CI4nc5u27GkJRYtWtRijmOYcPGPxchG/aA2GVgUZtjgC4pwB9SFjygruoHXTfEERNR7g+AltdeskaUT0oLt/FmW6XRJXH4BW7Ztx9DLLoOtk6UTAOCMMwBBUnNuBvYrwe7du5O2+bjVaoXRaER1dXVcxl+2bBny8/O16lJJVlBe48Xpej8CIeOOpihYDCzsJg7pNiNKcuzIdpi1JuLnC6ommgFWkwE0RSHVakS/gjT0L0hFcbYd+elW5Kdb2yz2iDfBYLBRhfusWbPw73//G7Icm57QXYGvv/4ahYWFbfb/9Tcw8NqL1cQhL82C4my7HqKNIbWeII5Xe+Djxc6eCmo9QVQ6/ZpxZzdzKMiwJuTYuoGnk5QohKDWE0S1OwhZlpGZaotbpVEkyArB6Xo/PCHdotw0M4wGAx588EG8//77nTavtrjjjjvwwQcfxHRMQRDw4Ycf4sorr9SkWGRFLXzgRRk0BaTbjOiZZUPv3BQUZFiRm2YJhSS6xy0nVp0KOvOabg632w2r9ezDh2VZPPbYY/j73/+OsrKyTpxZYti9ezd8Ph9GjRrV6n5KqHUX0DEDTyf2KITA5RcgSMmx8HaG2pE5LAb0yrEjN9WSsPugfmXqJIwdO3agrKwMvlDDaoWoGkA0RYGiVCNKVghEWUFQkDTxW5uR6dQG5N6gqIXTALUJd3i1nZaWhmAw2Mq7OxeaptGjRw8cOXKkzYTxSKisrMR3332Hm266qVFeVq1HlSvhGLW9V3cPNSWbYRYrampqmoRjU1NT8dvf/hb/+c9/8PPPPzdKSdi9ezd27doFiqKQn5+PoUOHdulw7oABA/D1119ruauSrAAgjfIlAYReB2gK3f5a72rUeXjICgFDU1pnmc7CHRCgEFWjMyvFlPD7RvdYTuskLWENsjpvEOn5xTjjEVFR70edlw81dedRE/LU1Xl5uPwC/Lxq3LE0hRyHGSlGqlPDea5Q1VM4wf9cLbNkDdGGmT59ekwa3e/fvx/79u3DDTfc0Mi4EyQ1URwAclLN58UDTxQ7P/QTD2pqapCRkdHstjlz5iA9PR3z58+HLMuoqKjA5s2bcffdd2P27Nm44IILsGLFCsyfPx/Hjh1L7MRjBMdxePLJJ/HCCy9AFEUcOePG4dNureNAmLBIdnfxSHcXgqKM+lARQ7bD3KnPDXdAwJlQX2WbmeuURaHuwdOJCyfP1OLN/3wAW0gslRAClmVx66w7QEG94BmaAiGAohAQqHlKNEWBZSiYQhpoyeApkUMGXGaKqdkVYWZmJhYtWqRVrWrvk2W4XC785je/SdhcmyOsiyeKYlQt1srLy3Ho0CHNgM3Ly2tSKQuoCcQAYDGwnb5iTgRHjhxBQUFBZ08jLtTX16N///4tbh8+fDj69++Pf/zjHxBFEb///e8BAAzDoFevXujVqxcIIfjss8+wdOlS3HzzzcjOzk7U9GOCyWRCeno6vtm4Fb37DQDN0Dhd70OvXIdmMITDf3rrseSiyukHANhMXEKKGFrDGZLQSTFzyHYkTo+0Id3/bqyTMHxBEdW19Shd9iV8goJbbr0VLMvCyDEwsKoeGcfQsBjZTk8mjwRCCOp9vJYcy7ZwM585cyZqamrgcDiaSJIsXboUhw8fxgUXXBD3+bbG1KlT8dVXX2HmzJlt7rt8+XKwLNuiQXcu4WRzWxK2YIqHd/XAgQMdEhJOZnw+H1JSUlrdx+Fw4L/+679ACGl2AUZRFG644QYoioJFixbB7XZj9uzZsNvt8Zp2zCgvL8d7iz/CNZOmIS0zSzs/hQBHKl3IT7PCauKghDx4ukhx8iBIsnavzurElB5AXfSG55LZCaHZMLqBpxM14Rt7+P81Lj9WLV8Kl8sJkbXjgTlzwHIcRElJGi9ctAQFCWdcAW2lbjWyMLXgnaIoCllZWc1uu/baa/H66693uoGXl5cHp9MZ0b4DBgxATU1Nm1WEgBqqSuZk83jot3k8npiPmSzwPA+TKbKHY1vfa5qmcccdd0AURbz77ruQJAl33nlnoyrdZOKLL75AZZ0HM26fq+XcKQoBw1BQBX6AEzVe9My2IShKqHL54Q2ySLUaYIiiI4lOfAjnSDM01WkOBFlR4PILqPWo3rtUq6FTw/jJd0fWSRjBYBDPPfcc0tPTceedd2r9NcvLy2G1WpGRkYEzZ87ggw8+gN1uh6IoMFpsuOKq0UhPT8Ph46ewa+M3UAjBNROuxYW9e8DHizAYWFAUBcYQmwu7Yb/aeKMQghp3UMspoykKWSkmpFjaJxZMUVTchYYjJVJDu6ioCHv37o1o34Cgeu/C3tlkorKyssV8so4wevRofPnll7juuuu6pQBwrBdkHMdh7ty58Pl8+M9//gOmQcGCoii45557On0R+OKLL2Ls2LEYclUv8NJZSRiaBnIcZpyo9YFjaDAMjRo3D6efR1BU4A0GYDawYOiz0Ql1MXh2YasKXevGX7w5W/iS+M86KMqoD4mvh7GZOGS2otWZCHQD7zzmP//5D37729/CZDLhn//8J6655hoMGTIEn376KfLz8zVPxeOPP67doE5V1eHb1atR7/bBaHNg1py7kGIxarIR8dBy6t27N/bu3dts381Y4guKqHIFtOb1dhOHLEfHpT1++uknbNu2Lapm7/EgmnDlsGHD8N1337XZBSIWWmDxYt++fXHpYpGbm4vx48fjk08+wbRp0zSP1549e1BeXg4AuOKKK5CZmRnzY3dlrFZrE9Ht8vJyLFq0CLfffnvC5yPLMnbt2oVNmzZh6NChGDRoEFx+HqfrJS23TpQJLEYOPTJtqHb5IUgKfDyBkWWQZuVAUwawNK21oAt3pWiINyhAUYCCDCuyUrqPVFAyISsKqkOtCxN5LyKEoM7Loy6UbweoIsYOqxEpnVRY0ZDkuyvrJIyKigpN8+o3v/kN1q1bh5dffhnTp09Hr169mn1PQXY6Zt1ygyb2awwp78fzQu7ZsydWrlyJ4uLiNvOD2oMoqzeHsDI9S1PITjXHzFh99NFHsXr16k418A4fPowePXpEvH9mZiY2b94MRVHg9/uxadMmAGrT+aysLAwdOhTAWQ9eshl4Xq8XiqJEHG6MFovFghtvvBFffvklsrOzUV9fjwEDBmDq1KkAgI0bN8LtdqNPnz7o06dPXOYQDxL9QCoqKoIoiqiqqkpoMcbzzz+PnJwcDBgwAA8//LDmVXRYjHD5BfCiDFkhUBSCgCDCbjLAbnLAGxDgFyRQFIWSbDu4UMW4rCgICDL8vAQ/L0EMeZMIIaj38CAUhTpvMNSFxYjcVCtyUju3yrM7UekMaDJNrXW4iTUV9f6zOcgmDmk2Y6d3o2kIRWKl2KnT5Xj77bcxd+7czp5GxHz66ae44YYbYjZeUJTh9PFaFSig5kxE2x+1Lfbv34+qqiqMHj06ZmNGyxtvvIF77rknqpCi3+9HaWkpMjMzMWLECO29K1euxMSJEwEAx6o8EGUFhRnWpKqg/fLLLzF9+vSEHKuiogL5+fnNbjtw4ACOHTuG1NRUDB8+PCHziRZFUfDZZ5/B6XSipqYGTz31VEKPTwjBiy++CIfDAYqikJaWhunTp8fV2FywYAHuvvvuNuclSDIMbPR5xOG+wwAgiBL2naiDl5c1eRWFEHCMKgOVZTcjLQF9SbsrQUHCiVofAKAo06Y5HeKNIMk4Xu0FoIbx25vGE0+S546sk3CSXb/tXMaPH4/S0lKMHz++3WNIoTCKJyBqq2xA9UBl2E1xWX317dsXW7ZswdGjR0EIgcPhiKmhGgmiKEX9kLJYLM0aSc0Zicm0TNy4cWNCvaUtGXcA0K9fP/Tr1w+7d+/GgQMH0K9fv4TNqy1eeuklmEwmSJKEadOmdZr0C0VR+NWvfqX9fvz4ccyfPx+9e/eOS7VypD4NiqJg5Nr3iKQpKlyXAbORw+CSTFS5AqhyBeEJChBEAlkBKuoDOF3vR4rFgByHGZkp5qRaKHUFpAYVzYky7oDGuX7JFsEIk5yz0kkIXc1529HwbECQUFHn06qtKKi9INOshhYrZGMBwzC46667AAArVqxIuEju0qVLccUVw2PiEamsrITNZtN+T6YIU1VVFXbs2IGsrKxWja7OYPDgwVi2bFnSGHiKosBms7XpxeoMevbsicceewz79u3DSy+9hEsuuaTN1mHREK/im9ZgGQb56Tbkp9sgyQrqfTzOOAOodgWggILLL0JWCDwBEVYTB4uRRZrV2CXkpDobs4EBBTXVptYTTFiItqHHVUnSZ6lu4J2nVFRUJHWLrZbo1atX1J4QSVbgDpwtXTeyNFKtRlhNXFzDImVlZVi5ciWMRrUIRRRFDBs2DJdeemncjnkutbW12LFjh9YztiMcOXIER48ebdaDqkpVdw47d+7EmTNnkJ6e3qiNVrKRTPIghw8fRnFxcWdPo1UGDBiAAQMGYMuWLXjppZcwcuTImHx39u7diwEDBsRghu2DZWhkpZiRlWLGqVovKur9cAdEeIMSBFEBKAqCpECSFeSlJaYpfVeGoWlkO8w44wqgzssjxWJISEV/ONwOdE7lbiToBt55yocffohBgwYlPLm5o/Tu3RulpaVNDLyw/pAoKWBoGhSlruhESdF02gBVzy43zRLXL+TGjRvx448/oqCgAA888ECnVVJJkoTXX38df/rTn7BixYoOPdT27NkDj8fTxLjTzizB9l0wGMSaNWsAAJdccgmGDBmS2Am0gwsuuAD79+/HhRde2NlTwfbt2xOWo9hRhg8fjuHDh2P16tV46aWXMGnSpA4VrlRUVOCaa66J4QzbT5rNBHdAhJFjUOsJQpAVnK73IdNugsXQdrhRVhT4eRkUpYYJk9XQiDcpFgPcAQEBQZUrSUTnCJahYeIYBEUZFfV+FKRbky6PUjfwzkOWL1+Oa665BoMHD8Znn32G66+/vsvqeQmSDKdPgNsvtGpjmDgGKRZDQkrXDx06hIceeiiux4iEf/3rX3j00UdBURSKi4tx6NChdj0YN23aBLPZ3KzkCB26ockJClEcOXIEBw8ehMFgwMSJE7vUdZuWloaysrKkMPCCwaBWQd9VGDduHMaNG4dPP/0UmzZtwuzZs9v1XZZlGSwb+0efrBBQVHTenHDultnAIsdhxvFqL1x+HkQh8AdF5KRaWgzT1nmDWlQCAIwcg8IM63lr5JkNLAKC3GhBH2+yHWacrPWBF2VUuwLITbMk7NiRoBt43ZijR49i69at4DgOZrMZw4cPh9lsxtGjR7VQ1rRp07BkyRJcf/31nTvZKDCbzaiqdUKkOK1EHVBDr1YTp7nOWYbSVlkGNnlK1xPByy+/jJkzZ2rtoS688EIsX748agNv9erVKCoqavF94YeJosTPwFMUBevXr0cgEEBxcXFSh2FbY/Xq1ZgxY0ZnT6PLc8MNN+D48eN44YUX8MADD8RFOilaCCH4+WQ9GJpCmt2IrJTIJVBYhoKsKKjzCeBYtVAgIMigGRqV9T7kpVubaOdVuQKa3p6BpSHJCnhRRpUz+YyMeEMIwRlnAJ6QyHAiZUqMHIOCdAtO1PrgCYrIUkhSefF0A68b43K5YLVacdVVV8Hv9+Ojjz5CeXk5/vznP2v7sCyLYcOGYf369TFNZI4XnoCAgj4DsWLdJowYMQKAGnZNtRqTtpIpkUiShH/961+YMWNGk7y7goICHD9+HD179gQAOJ1OrXtJc6xcuRIDBw5stWAhfDOLR5JxuGgCAK6++upGxR3NERQkuAMixFB7OY5VOwskQ+hq5cqVSRMWBNTuEu+99x5uueUWcFzy9RBui549e8JsNrfLExcP9QCFqN5shqFxpt6Psko3zEYWWQ4TsuxmTS+vIbKiQJIJJElBpSugjkFRKM5OAQiBxcgiICooO+NBms2IVKsBNEWhyhWAOyTtlJViQqrVCD8v4VSdamQYvEGk2zq3g0KiECQZ1a4g/CE9zrDMVSJpWKCnEAIGuoGnkwAuvfRSbN68Genp6UhPT8dDDz0En8/XpHVWfn4+ampqsG/fvk5NPm4NXpRR5QogKMpqD1wADotB7QN5nnnnWsLpdOLll1/Go48+2qxXY9CgQfjoo49w8OBBAKqH94EHHmhxvL59++LEiROtGnhEq0iO3U0t2qIJQgiqXAFs3LIDfr9fC9uGH+QjrhiOzBQzHBZDp+RD7tmzB0VFRa0a04lm9uzZqKurw1//+lfce++9yM3N7ewpRcX333+PAQMGwGJJDm8VQ1MoyLCi1hOEkWNwut4PnyCjxsPjAOVEmtWEbIcJCgEESWmUoC+DQJQVmDlVqollKKRaDUixGFHl9IOXFK1bAkNT2nsbaq9ZjCyyUkyodqthW09AhNmgtkwzG9ikayMYC4KijFO1Xk0VIT/NAqspcYuVoCBBUoi2oASST5lCN/C6OTzPN/q9pbybQYMG4dtvv0VOTk5StViSFYI6bxBOnxqOoKAmJmdagEy7MSlzsDpDX3D79u3Ytm0bfve73zXq9XkuN998s/bv1atXtzpmSUkJKioqUF5ejqKioibbFUK0fJeOyjl0pGjiZI0HXy1fhcsuuww9C3JgMbAgUBcFdW4fNn73PaxWK64cNhS5aZaEhlCqqqpQV1eHMWPGJOyYkZKeno7f/va3eP/995NSLqU19u7di/vvvz/q9+3atQsXXXRRHGYEpJgNSDEbQAgBxzKoqPWBl2Qo/z97/x0c133meaOfEzoH5EAQBHPOlJgpJohiMEUq2fLYlkcapxnPzs7MvjNTs3Vv7W699dbeulW7tbN7dzz22Gs5jy2LohnEIIhBFEGKBJMYRDGTYEJGo3OfeP843U2CRGgA3QAo81PFItnoPud04/Q5z+8J3y8CLaE40YRGySPN/6Ig4HPamTPGhSSK6KaBLEoUeKzFSFWJj1BMpT0cJ5EMDAWgLN+Nz9U5mMn3ODBMk7ZQAkUzUDSFjqj1M5sk4nPZBm3CdDBoDcUxTKtMWpbnGjQNvLiqc78tktbfS2GXh58f99MA7wtMTU1Nn7w4V69ezZYtW3j55Ze7DZwMw+DevXvcvHkTRVGYM2cOhYWF2TrkToRiKs3BWHrF6nXaKPY7sUkiCxcsoK6ubli6Awx2lsg0TY4cOcK///f/vk+va29vR1GUxzK6D7N06VK2bt1KRUVFp3KYphs0dcRQdQNJFPpdHr927RpXr17FZrP1a2giEEmwd99BVq9ezagS32P2ciV+J+WF1Vy+dY+9+w7w/OqVg9aIbhgGtbW1A+670w0DEHISmNrtdnR98JrSs0V/y8onT57kW9/6VpaPpjOCIDCuzM+YUh/NSekOTTcozXfhkCWcNglJEpFFIaNrhc9lw+eype3THDaxWz/bQq+TPLedSEIjnhw4SKg6qv4gC+i2y/jcNrxO25C3LvQX0zSJJfuvBzO4UzSdu60RDNNEFMAuS0iigMsu43cPvffsozwN8L6A/OxnP8M0Tfx+f59dH1588UV27NjBwoULHyvb1NTUYBgGlZWVzJs3D7vdTk1NTdab3h/tq7BJIiV5zk4378LCQoLBYFb3mw2uX7+es4C3KwzTZM/HJ6icPDt50en9AtPS0kJtbS0LFizoMbhLUV5ezpkzZ3jmmWcIxzWCMaXTcEtFP7Nihw4doqSkJG171ld0w+Dz67cpLi6mvMDTpXewIAjkexzMmliFqijUHj3G6ucWU5af+9Lezp07+dKXvtTv18cVjfvtEc5/9jlFRUWMHjmCkjxnTloSTNMcdjenXDFY71MUBMry3Vk71zINYiRRTGYTrf8bpkk4rhKMWjIiUUUjqmg0C3GKfI4ha10YCKpuYAKikPnnMlAicZWmjhiGaeKQRUYWeYfVQEVXDK984lOygmEYvPXWW/2yw7Lb7YTDYW7dusW2bduIRqPpbYqiyNq1a9O9L1u2bOn3zbnL4zZNWoIxbjWH08FdoddBVYm3y5u3qqrDzm5tsCeSNd3g2rXrjJ8wKePVeE1NDZs3b+6y7PowgUCAbdu2MWrUKOY98wz326M0BB6YaztsEhUF7n67gCQSiQFJhnREFS5+fom5s2eS7+lZRNhpk5g7dTwFBQWcOneRuKL1+PyBcuzYMWbPnp1RAN0Vhmlyq6mDnbv2UFpaSltbG+/vreHzW40oWnYzbs888wxHjx7N6jaHI7quD8uWjlwjCgJ+l53KIi+jS7wUeh3IooBhmjQH49xqDhOMKUN9mH0iVdXpLpOZ7X3dSQpSa4aJTRKpGIaad13xNIP3BcM0TVpbW/v9+n379rF27VqKi4vRNI1PPvmESCSCrus8//zznZ5bXFw84AumYZokVJ24otMeSaS/uG673Gu2YvXq1bz77ru89tprw+LCvWPHjgH55PaHmKITDocpyPNl/JpMeizr6uoIBoNs3rwZ3TDSWk8ABR4HfrdtyIdbQjEVWZZ7De5SuB0yc2dM4f0P9tMamsLIotxd/tra2gbUPhBTNI7XnWDDurWMKfOjT66ioX0qO3fvxe2uZmxZXtZubnPnzuUnP/kJS5Ysycr2hit79uxh9erVQ30YQ4pdlijySRR6HXREFdrCCVTdoDEQIxBOUOhz4h3EQYX+kpJlGowYqy0cJ6boCFh9jgVexxMR3MHTAO8Lg6qqvPfeezQ3N/Paa6/1axunTp2ioqIiHQDIssyyZcuyeZidiCsaoZhKIPpg9WiTRIr9DrzO3jMfTqeTjRs38t577/HKK68MaZAXiUS4c+cOL7744qDuNxxTEUURr3NgX2XDMLhx4wY3b94kEokwZ84c5s+fD0BDe4yEqiMKUFHoyYoZeltb24Ber2g6l69ep7Kysk83pEKfkylTpnD81BnWrViYM2P3/mbuUsQVK9vkcdmRRBFJhJFFHl5YU83+Ax+xcd0aKgqfLKHibPPTn/4UWZZZuHAhkyZN6rXMeOfOHTZs2DBIRze8SbUu+N122sMJApEECc3gfnsUuyxS6HU+NsQxnFCSk6uDkcGLK9bCNt/joNj/ZMnPPA3wnhB++9vfEovFGDduHPPnzycUCnHixAnu37+PYRhIksTGjRspKyvr1/Zv3bpFNBrt0/Si3++nra2tXz1nhmnSEIgRjquIAuR5HHgccp/7QdxuNxs2bOC9997rd2A7UGKxGP/0T//EP/zDPwzqfk3T5Nbd+xQUFPQpyBkzZgw1NTWPBcRVVVWsWrWq0+OqbqTL5ZVF3qz0u2zZsoWKigqqq6v7vY2OqEJDQwOrVyzr02raJolMGD2SS5cu0RKMM6q4Z229/iJJUnpKWVEUqqqq+iRBFEloGIbRSbRVEkUqi/1MnTqVo3WneP65heS5BxZIpqisrOTs2bPMmjUrK9vLNamp33g8ztGjR6mtrU1LVKSuH7quU11dzbhx4wDYuHEjW7ZsGbLrxHBEFASKfE7yPXYCEYVAxJrAtVoxbIPSq9pXdMNMizx7BriwzQSPUyau6nREFfI99gErBgwmTwO8J4RYLMY3v/lNLl26xNatW/F4PMybN48NGzYMuEE2Go3y6aefsmnTpj697plnnmHPnj19XhXrhrVSVHUDj0OmqsQ3oJS32+1m3bp1bNmypV99hwOhqamJt99+m3/4h38YdMFYwzS5fv0648eP79N4/sSJEzN2tAgle3Ncdilrzcz5+flpker+oBsGHREFTdPI70eAU+B1MH36ND6pO0neisVpLbFs8qgsyr59+zIO8MJxlbPnP2P06NGPBe5Om8T0iaOpr6/n5v0WZowdkZVy0bp16/inf/onxo8f/0RZmDmdTlatWtWliLRpmrz//vt88MEH5Ofn89prrw3LwazhgCSKyUDPQSBiTdsGYyomUcryXMNmCOPhe4csCvhc2f/uPkqBx0E4ppLQDBo7Yox8gjLnT04o+hQkSWLatGl8/etf56WXXqKqqiorX7yDBw+ycePGPr9OFEUcDgeBQCDj1+iGye2WSLqnoSw/O7pkXq+XtWvXsnXr1gFvqy+cOXOG9evXD4kbgG6YBAIBiouLc3YBjiXLE9nqy1EUpUedvkxoCyf4/NIlpk6a0C9hU5skMqFqBOFwmDvNHah67gd1FCWzJvZwXOXzG/doaWlh6oTRXWYLCjwOli1ZxOnTZwhEEl1spX/85V/+Jf/yL/+Ste0NNYIgsHHjRv78z/+c6upq3n77bSKRyFAf1rBGEq2MXnkycxeKqdxri2Z9sKc/mGbne8dgaVoKgkBp8vOIJjTaw9n7zuWapwHeE0IuFbJtNlu/+9eqq6v56KOPMn5+WzieXn2NKvZm1V7M6/WyZs2aQQ3yXnjhBT7++ONB29+jGIaR0z4UJTlYka3s3WeffcaUKVP6/fqEqhOIWOXZmVPG9Xs7BR4Hq1c8R+2RI9xvj+bEai3FsWPHeOaZZ3p8TjShca8twr7aE3x28SJrVq2guBvLJUEQKPA6sNlsNLQGs3ZtsNlsbN68mf/xP/4HP/3pT/n888+zst3hQElJCd/5znf4q7/6q6E+lCcCn8vGiKSnbVTRqG8O0xqKD2mgF01oqLqBKAhUlXhz1j/bFU6bhD/ZkxjL8QR+Nnlaov0j59atW4wYMWJA25g1axYnTpzg2Wef7fF5qZszYIl+5kC/yOv1Ul1dzdatWwfN2D0/P5+bN28yZsyYQdlfCpskMm/eXI7X1TG5akPWVdR1w0yrtWcyMRsOhzl69CiiKCIIQpeBx40bN/j2t7/dr+OxTMWjfPzxxyxfsrBL6ZxMEQSBEYUepk6ZzEcf17J6xTIqCtxZy4QGAgF+9atfMXXqVBwOB6WlpY89xzBN2sMJWjoinDh5CkVRmDhxIkvmz6HY5+zxWHxOGwvmz+d4XR3jR67OmsH6xIkT+du//Vvu3LnDhx9+OKBg/ClPNl6njdElXpo6YsQUPS2UbJNECrwOfK7BFUpOueZ4nPKQTPCLyWxhJKFR3xLG77JlPME/VDwN8P7I+fzzzwesZTd27FguXLjQqytCczAGgMchD+jm3Bt+v5/q6mq2bdvG5s2bc7afYDDIr3/9a1wuF5WVlTnbT3cIgkDVyAqOHj1KQtWzHuCpydW6JGbmonD06FGqq6tzNs3cEorz8ZFPmDZ1CuNGlgx4e3ZZ4pnpE7DZ7OzYtZcNa9cwssiTlYxofn4+EydO7HaQJJrQOH+1nvMXPsPpdPLMvHkU+FwUeB0Z3bw0wyQSjWK323Nibf7ee+89zXY9BbssUVnkJRhV6IgqxJOuGE0dMZo7YrgcMgKWJ7VhmmnBbLdDxufKnpSSYZqEYioAjiGSZ8pz2wnFVHTDkvZqVnUEQcjaoFMueBrgPSUrbNiwgd///vdpiZVHMzWhmJrunXjUjzEX+P1+VqxYkZMgLxwO88tf/hKn08m3v/3tIem/S2G3SYBAQtXxOm0EAgEOHz7MsmXL+m1ubxgGp06f5nZjOxHFoL25EVkLM3ny5PRzusrOud3unAV3wZhC7fHTlJeXM2Ni171p/cFll5k5sQqfz8fO3XtZuWI5o8sLel2AmEkf3rhi3fBSHqGyJOJ2yLgdclok/LH3ElU4cuocHR1BVq9cQaHPgc/Zu82RqhskVJ2YotHQ0kFtbS0bN6zLeib8yJEjzJ07d9g01j9l6PG77fjddgzTpCNiBXuqbnRytHmYuGpl/Bw2Ca9TRhQsWzaXXepT0KcbJnFVozWUSLf25HmGJqCyyxJjS32oujXooWjW+38a4D1lwBiGkRM7obKyMu7du0dFRcWAtiOKIq+//nr6/7du3UrLRJimSUvUxEDAbYP4qHJmzJgxoP1lQn5+ftaDvJ///Ofous63vvWtAWudZQOBB7IQmqbx4Ycf8tprr3H06FGCwSAjR47s8bMOh8McP34cM7n61gyTmCZQOWYCM+ZOACw3kUh7E+fOnaOyspLZs2cPxltLk1B1zl26iaqqzJo6IetCrG6HzPiKQtzr1lKzbz9tU6cytmok+W47LoecLkPphkkkoRKKqUTiCqdPn0FRlE5Bra7rGIaBjMGzMx+fmg3FVA6fOIuqqqxYspDSfFevZa5wXKW5I8r5zz4nEAhYZvY2G19av7aTFp6i6SRU6zphYvVN9rV0a5omx48f52/+5m/69Lqn/HEgJvs/C7yO5AJHQxCE9HVIFKzvSSiuEk1oJJJeuA/jd9ko8jl7XaTFVZ27rWGSXSKIAhl9X3KJIAjYZSktyD8YMi0DQTBz2b3/lKzR2NjIL37xC/7u7/4u60FeTU1NTh0YWkNx2sIJJFFgTKmP2/X1fP7558iyzIoVKzqZ2OeCtrY2Dh8+3GcZmEeJxWJs2bKFb3zjG1k6soGRUHVOXbzOiZMnefOrr7J7x1ZeeeWVTp9nfX09Fy9eRBRFVq1alf7ZwYMH0XUdp9PJwoULkWWZYEyhMRBLvzbVa+N3Pcgu3bx5k4sXL2bdf7g7TNPkZlOQmn0H+NK6NVntk3sUyyovzsefnEhn3yZNmkRxUREA7YEAn332WXr/c+fMpjDPh8NmGY6bJiQ0nVt3Gjh77hyzZ81k2vhR6YBU0XTOXK7n2rXrVC9fQmkGmexgTKH2xDkaGhqYMmUKFeWlOG0SHqcNj0PGMC0pm46owoWLl2htbUUURQzDwOv1suCZOYwocGd8U/ztb3/L0qVLGTVqVH8+wqc8JY2mG4TjKnFFx8SSOElN5QtYPX5Ou4Tb8XhPnWGa3GwKoRsmsijgcsgU+ZxZb0PpD7phcr3RktsZXeIdckefnnga4D1BtLa28uMf/5i///u/H7DUxMPkMsCLJKwxe4DyfHcndXRFUfjoo49QVZXnn38+pxmxtrY2jhw50i85mBQ7d+5k9uzZw+LmF46rXLh2m127dvGtt/6U07UHWLVqJX6/v8vna5rGRx99hKZpRKNRZs2axfjx49M/jysat1stCQmXXaLA6+i2TJnrBcHDBKMK7+78gCWLFzNxZMGgKNcrmjUMFIopfHbx87R2mtvtZs6sGfjdDrxOW7elUUXTudcW5Ve/+TemT5/OvDmzcNllOsIxPtx/gBc3rGVkoafXQNU0TS7fbePoJ8fYsGYVRT5np17IcFzlTlOAAx99hMvlZuKECYyqrEBKbvfW3fvcuHGDdaufo6ibidxHOXz4MGfOnOF73/vekLYePOWLSVzRaArGH8vq+Zw2CryO9HeqI6rQ1BFDFoUB66TmgvrmEAnNwGGTqCzyDGlWsSeeBnhPGM3NzXz44Yf8yZ/8Sda2mc0bdlzRiKs6oiig6QatIUszyOe0UV7QtSq6pmlpD9xcMtAg7yc/+Um/J0AHimmaxBWNcFwlktDYuWsPV65c4S+++23uXf+MSRMnZjzoEY/HcTof3PAtY/sQmmHicci9WmDV1tYyc+bMboPJbHLxViNnzp5n7aqlFHoH3yZINww03bpEypKY8Y3m9JkzYPcQjKlcu3Ydh8NBJBKhetVKqkr9GWUiVN3g3V0HmD17NpMri9L7jisabeEEp85eoKWlBbsssWHNSnwuW6cAOBhT+MPu/SxZvIgJI/Izfs/hcJh3332XN998M+PXPOUpfSGSsDJ7cUVPO+WA1VZQ4nfSHk4QSWgUeh0ZL04GE6t8HMEwTeyyiEOW0E0TVTPQkrqasiRS6HPg70KM2TBNwskytm6YYALCg5YbgUf+L4DPZe9zy8XwLiA/5TFKSkqyrsZeVVXFtWvXOmV0MkU3DBTNugl2RBPpFPzD+Jw2yvK7L0fJsoxh5F5strCwkEWLFrFz585+BXnZzJpmiqLptIcTNLZ2sP/AAVqiBqKps2zxAjasX8exjw8wceJ48orKCMdVBAEErAvCg94YOvXJ2B2OTv2c7eEEWrIUkok10eLFi3n33XcpKirCMAxKSkqYM2dOTt77sbqTLFm8eMgamVM+sH2l4f591q9fj24YzJk6AUWzppwfDcJ6QhYF5s2dw6GPD+NbU43PZSMS1/j0wkWampoYPXo0i+bN4tQnh7qUa/A4bJSWlnK/oZFx5XkZZxm8Xu+gfB+f8seLx2FLVwjiqk5rME5MsXr2bjaHUFUDl0PGk0Wd1GzitEmUF7iSItBG2hv3YVTdoDEQQxKEtCC7qhu0hxMEowqZZtZM07Jmc8gi+R4H5QXujMvCw/PTe0qPDMQDtismT57M3r17+xTgKZpOSzBOpIspKrdDxjRNDBO8TjmjzMvixYvT05/RSISTHx9C0hRcZRXMfuaZrE1nFhcXPxbkHf3wA6bPX4g/L6/b133++ec0NjZm5RgyIZWlqT12gsuXL+P1elm4ZBmmZMdps6Y0dcPk2aUrAWgIdD2x2SumSUNHFF03Kct3cb89gihYsihiUh5FEsX0Y7IkIEsiX/nKV9KbOHv2LMeOHWPhwoXpxwzD4OzZs7S2tgLWOTt//vw+HVo0oSFJEh6nfVBKs9mkpKSEhoYGysvL+x2cCoJAVWkes2bOZN/BQ0iShKZpjB8/nnmzplGYlFTprtRrmiahUIiRI0fmrISUi8Gvp/xx4bRJjCzyoKXkV4JREqqB19V9G8RwwOOwdAJTk8SCYF0fU9n55o44UUUjqmjYZNEK7JJSL2D1OHudNmyyiCAAyUReqqhqmmBi0hZOoOkmuqHhcti40xphXFlm1ZOnAd4TyGuvvcbbb7/Nd7/73axtM9MAykzqETV1xNIrEDkZCLgcMvkeR78aYfPz82m+/DkXbl7GqyZYVFSIKIjEbn7O9XMnOj9ZELhvwHPf+vM+7wesIG/BggW8//77eEIB5og6n279Pc+9+Xj59eTJkxw7doyxY8fyj//4j/3aXyZYAbFJLKHREVXYf6iW+vp6pk6dyltvfI0CrwOnTUIzTEIxBUU1MLFS/aQ0qKwNpafOzPS/Hzz2GIJAqc9FRNGRJanLDGxXiEIquyVQMmoCN65f43dbd+J2OREFK2s4Y9o0Zs+ejSAI6YnqvhBPHovLMXwv8t3x7LPPsnfvXsrLywe0HZddZu6U0YwdNQJVN7AnV/GZfMcM00RRFFzO/pW4VN0grugomiUFo+lG8kZjPub8IYsCNlnELluexQ5ZxG6Thm1v0lOGH7IkUprnssSUZYHyHA5UZQu73L3siygKKJpOYyCWFviH3nucH0bTDdpCCYr9Tgo9DiIJyxM3U54GeE8gNpsNXdfRdT1rZcOUp2x32mnRhNX/FY6r6RFxp02iNC97jhQTbSJT8nyAL/2Yy+lkbBc3qBFKgqN7drF43YY+78cwDC6fPEHJvZvMKi7AJruIXrnO//e//lcqRo1iw4YNXLhwgXPnzjFjxgy+//3vD+Rt9YpumFxr7CASU2kOhDhUs4slixexZtVz6SxNCpskDKgXLSWhYcmikJxuM8E00c3kzTt5A0/9W3/oj6anAkswdANVB1SdkooqSiqqOu0rAVxtCCKLAoG4QSCSwOu0Zaxhp2g6oigO6ym1nnC5XP1ufXgYuyz1OHHb3eJMSGqP9aXJ2jRNgjGVuODk6Pmb3L51g9t37oAgYJpC8twBBBHDNK1gHkip3ba2NFNWWgKGQWFBPtWrVuBzO3E5JFx2+WnA95Qe0XRrcMFll3EPohVZNokkVFqCcRo7YoRjKiV51vXa7ZAp8jpwZvi+TNNMJ1KcNokiv5N8w8Hdtsz9lJ/MT/ApOByOrIrKLl++nO3bt3eSEtGT2aJAROlkyC4KkO9xUOh1ZG2F1XD/PkVm5j6HTruD4obbtLW2UpiUsciUQ7/9NYtcErbyUq41NrH/biPPvfJl1s6cSTQaZefOnYwZM4a//Mu/7Ovb6DMJTef09RbaQgmisTiHD9bwl299jarSvJyUJx408D74vfV1N1aw9yCb8+DfRpfBoGaYRBI6+w4fZ9asWbgdMoVeR69ekrphlf/kYTZBlynLly/n2LFjtLS0dCpfZ4Nr164xduzYHq8BsiiQ5/fT2NiYkZyDphvcaurg49qj6LKHA7XHmTBuLCuq12KTRSRRRE6W7sVk4zdYAZ9uGKi6yYkTp5g2YxaaYRBob+M37+0EXcNpl1m/fi35Xjdepw2PU37iyu5PyT0PL0asBcTw/u7rhomqGxiGSVTRaApEUXWD5o4YMdUgz21DFkV8yVJsOK4RiqvW4vqhRfaDfyerLaaJqj/IlKfMASRRYGQvQ3AP8zTAe0IxDCPr6euJEydy4cIFJk6ekpaJSJX2BCwDaq/ThtshZ33fV+uOsbggv0+vGVdUyCd7drL063+a8WtOH/6YWZKBTXZy+PJVGgvL+PP//H+nf+52uzv1l+WSuKpx4mozMUXH5ZCo/fAD/vKtrzG2PD9rTg25wOrLk8hkIaobBgnV4EtrVnKvqZWjR4/S3t7O0qVLGVFSRJHf2e1kWPrcG+YX+Z5YuHAhN27cYOfOnWzYsCFri7KLFy9y/fp1EolEtxlCQRCYM3M6H+w/yORxoyjxd58F1HSDE59d49z5z1i+/Dn8bifhuAII6XK8KArpdgxJtPqGDDOV8bX+necUKC9wYZhQ4ndSNbKcuKoTDEXY+n4NhhrH43Swbu0L5Hkt2SSP0zbsZDCeMjTYZausb5gmd1sjjBwGEiSabqDqBqr24G8l+ffDrQqqptPYEbOuW4KA0y6R73YgSyKhuNppmw+Ll5jpxzo9A7CutSV+V6eWjL58V54GeE8oubCEqho3gS3bd2MvfCC3YZNE8j12fC57Ti/CnmgY+mHcPN0mcOjd37H8tdd7fW7DvXv4r3+OPz+P3504TdXK5/nKqtX9OdwBoxsmZ663ElMsq5tPDn/EC9WrGV2WN6yDu74iiSJuh2XfVeQbyeTR5bz/wQFu3rzBuXPnmDx5MmMqR1DYRelCTGeIhl7JadeuXdhsNkRR5LnnnuuTZuPYsWMpKytj69atrFmzJivyMg6HIyNpo3yPnbKyMk6dvciKBbO6LA+Zpsm9tjBnz13gheerqSi0pvRM0woIMw2wNU2j2CMxvjwPRdOJKZbTQSShUexzMnLjOqvHNBRm6/sfgKHidthYu3YteV5XegE51Df0pwwdkihQUejmXluEuGopCAy2TIpuGJZbTUIjltC6bXHQkn2pKTkTE2uo0BqWsColqq6jRHSMZHYupXAAj6scQGdZlJS9W3MwTnMwjgDYZBFZEjPO4j0N8J5CQtVpDERJaAaz583n+PHj5HldRDta+cor2fVx7Q4jHOpXgOdzuZivKnz89o+Z+9pX8fp8XT7vzq1btB78gAk+Nz84fIzNf/HvGD127EAPu18Ypsnnd9tpC8cZUeCmvaUJSRSYPXX8E9tvlil2WcLvEHhuxTLaQgk+OXmGS5cuMWHCBEZXVuBz2XHZrc8gFImk+8iGkr179/Lss89SWlqKpmkcOnSIeDzOnDlzMrb4c7vdvPrqq+zduxegk9uIw+GgrKyMiRMnZrSttra2jH2GPU4bc2dO48DHRzh39Q4zJ1Q+FuQFIgoHPz7CsqVL08Ed9C9z2tHRATxoPs9z2zFNk5iiE4ophG0a+R4HFS+uJ5LQCHSEeW/HHkRTxe/18MKaNfiSQtIe59OevT9GXHaZEr+Lxo4YwagyaAFeysmmI6p0ftwwHgyuAYZh2TqKovDYIJHTlruQyoRuJVm642mA90eOouncSfr9iQKMKM5HGF9BZcUITp8+PWjHkbd0BbdOHGF0fvdSJd1ht9lZUmzn/O9/g2/pSsZNntzp558ePYL/6mfkGTr/59PP+fP/5/8zpD6yiqYTjquU+J0U+pzs3HaYN9/4epdaZl9EUn6O5QVu1q9YSHskQd3pc1y9epX8/HxmzJiBpmkcPlzLhnVrh1Qq4dChQ0yZMoXS0lLACsxWr7ayvnV1dZw5c4Z169ZlnFHvSsw7Ho9z48YNtm3bxooVK7oN3i5evEh9fT3RaLRP3spl+S6eW7KI9/d8gN/vZ0ShF39SukXTDRrbwxiGwciSvAEtMGRZZv78+Wm5oxSCIOB2WNI+paZJNGH1ITlsqhXsbdpANKHR1NrG77buQDQ18nxeVqxYTkm+H58rN20hTxm+2GTr+5Qa6Ms1umFwsylEKKahGzqCIOBIBm+S+Egf8ENfEZskIktWT2pKVurh/tRHz1jDMNPqB6YJeroXz0y3O5imNYFb7HOm9fMM0+xUKs6Up04WTyCmafJ3f/d3fPvb32b06NG43b2L0z6Kphvcb48ST1rG2GUr7ftweXD//v0sX748516xKT47cwbv+ZOMzOt/CetuR5DmqvE8s3wlAB9veYdpapRLDU1cd/r4xp//RZaOtv/caOrg89sBCnwOOpru0dDYyFdeXNPr0MEXhZQPbjgcTgcqqbLInYZmzp67gCCKLF44n4oi35BlNc+cOYPD4WDq1KndPkdRFD744AMqKiqYN28etbW1RKNRqqqqmPzIQiMT9u3bh9vtZvHixenHNE1jy5YtPPvss/2eyDVMkzvNQXbuqWH58uUU+D0UeBy0heO8t2s/SxYvoLLYn27JGMgARKbOOEYq2IupROIqJtbQUSyuEQiGOH2yDkON47DLrF+3ngKfC6/Lhtv+NNj7onOvLUIkoeF12hjRjQNSf9B0I5kF09N/J1Sr1NoWjhNTdPI8lqC4LIk4bTIOm5gM5ERkUUSSHgRyKbWBhxUHHpUQ6i8CMLbMP6DWqKcB3hNKQ0MDly5d4s6dOyQSCYLBIH/zN3+T8esDkQTNwThgBXcjulDHvnXrFhcvXuTChQv89V//9aAEeudP1JH/+VlGDCDI64hEOG1IuJQExUqM2tv3GLN0OavWrc/ikfafo5caiCo6pX4Hu9/fyVe//CpVJV2Xlr+o6LrOjh07eOmll/r8WsMwOHnyJKNGjRqwzlxPnDhxgqKiIsZmUMq/ccPqKXQ4HKxdu5bdu3dTXV3dr0zxli1bePXVV9P/r6mpYcWKFQPOOpumSWsozvsfHECUJEukWBTBmcfcaRNwJMtLNkmkqsTb7/Lovn37qK6u7tNrdMMkklAJxdS0cGxCtfr3wqEon3xyGNHQcLucrH1hDYV+Dz6X7Y9mUfTHxrWGDgwTKos86d+xbhjEFJ2EqqPpVtnUMCx5p4f1P1PZMYuHhhlMepQMkgQwBQElaQkpyxI2ScAm91/PMT2gJJCePk9l+1KZPkm02lCsx63eu8ZADFU3KPE7O1V2UqoFT50svuCUl5d3urn97ne/o7m5mZKSkoxen1oBi4JAVbEXQRAIBoO8++676eeYponL5eK73/0u//qv/5pzPTiAGc/O55NgkLyWu7hd3U/99USex8Ny0+DHR84yZtFS3vzOvxs2K/6OqGVTY5NE8twOBAHcGQheflFoa2vj2LFjqKpKXg/OIQ8TCASoq6sDHmi7zZ49m1OnTnX6Dty5c4dLly4Blt3WQKRJdu7cSUlJSUbBHViDFA8/d+3atezatavPlnj79+9nxYoVnR6TJInbt28PWE9PEASK/S7+5KW1tIUTRBMaAiBJQlp0NRBJoOoG0WT2ZLCQRAG/y47fZUc3LJ/OcEzFYdMo8Dgo/dIGoopGMBRm6849mJpCaUkxq1Ysx+d24HfZMtYXe8rwRxJFDN0glrDsy8JxNWMR9t6wSdYEuGmCxyHhdtoe02iMqzqRuOWXm9B0TJN0MGaVbJNT5ZKQFnxPZfVSwdtAMm/5HjuNHTEu3+ugLN+FYZhoD5WrJ47I7Nr59BvxBeHVV1/l7bff5jvf+U5Gz095/BlJZwqPQ+J//s//yT/+4z9isz1+YZ8+fTofffTRYzefXLBg5Squ/+xHjO1ngAcgCiLevDw29CNDlEvut0cBgfEj8pKrSSE9VPCkcurUKe7cucOGDRs4efIk4XA4/bOUp6koimiahsfjYf16K5O6fft2ampqutxmOBzG6/UCls1ZdXX1Y31ubW1tXLhwgenTp9PQ0MCFCxfSPW737t1j+/btLFq0KN0/1xccDseAAkRRFKmsrOyT0PGVK1fIz8+nuLi40+OrV6+mrq6OhoYGli5d2u9jSmGXJcq78RxWNYNQXEXRdGBoFh6SKJDntpPntqdL96GYitMuU+BxULZhPdGERmNjI//27h+QBZPx48fx7Ly5+Fx2vC5bn03ZnzJ8SGWpmjqiBCKJThksu2wFUymNTCnp8SWk+95S86wPYSZLqclet1T2D6AjZhCMaZQXuDo5Szht0pCeQz6XnXttERRVI67oiA8Fi30JG58GeF8QZFnOWE4iHFdJqDouu8Tte43keyp4f9sWvve973UZ3AGsWLGC//2//zfPPPNM+sabK0RRRLEPfHLKFY+SSCRwOIbP8EIwqiAmtY2agzFEURw22cX+0tzczIYNG9i1axcejyfj8tyECROYNGlSl6X/3vq4zp49y5QpU5Blme3bt2Oz2dKBI0BFRQWbNm3i0KFDtLa29thH9yj19fUU9VE8uyvmzJlDTU1NRgGeoihcvHixk9D4w8yfP5/6+nq2bt3K5s2bcyKTBFY2D0BRM2/k7grDMLJyjJJoWbPlexyoukE4phKKq7jsMoXeKsaOGkk0oXH16lV+/c57yILJpEkTmTdnNj6XDZ/L9oWfTP+iEFcsm8ZQTCWh6XiddmTJGnbwJaeqNd3sk5NDT9hlkXBcxS5L3G+LUpWBGPhgIYlWadjvdlBW4MIpS0iSmM4SZsrTAO8LxMyZM6mrq2P+/PkEowqBcIyq0jyCUQW7TULVDG5du8Tej09gIOCXVCSXl7qDLZSXlfaa6fje977H//7f/5u//du/zfl7UbsJNPtC9dRJbHvnd3zljW9m4YgGjmmaRBU9+eV9cPMz+2QmNbzQNA1RFJFludvgpDumTZvW7/02NzenA8mehhmWL1/Orl27+hTgXbhwoVOwOBAyDXLef//9Xidjq6qqKC8vZ+vWraxYseKxTF82cNtlS+Q8riJ2xCjxO/u8AEmVz5999tmsHptNEinwOijwOtKT6KGYisshUzB3OjOmTSGualy5coVfv/MeEgajRlWydPFC/G47XufTYG+4kXJL6ogqafkPVbN67ASsSoyUnMK2SSL32qzqgE0SscliOpv1wKEn+X86y/yIglVOFUUBhyxhl62FtWGa3GuLEFMszb2ybjLbQ4XLIeN1ZG7v+ChPA7wvEIsXL+a//Jf/QkFBAU0hhT273qeqrJCYIeKUTCKqwNyp4/jTr7+OYUKh19GnE8dms7Fq1Sp27NjBiy++mMN3YjW7DpQ8j4fY59eycDTZQRAEMB8IZFr2nb10/g5zDh06xHPPPTfg7Vy8eJHLly+nJ8KvXLnC1KlTqays7OWVvTNv3jyOHTtGIpFg+fLlgDUIsHTpUpyP+BynSsqDyeHDh5k/f35GwaDdbmfp0qXU19fnJMDzOG0U+Ry0hhJ0RBUSqs6IAnefrhPFxcV8+umnWT+2h7HLEoVeiUKvM92jFZJVVF0mf84MK9hTNG7X1/Pr372HiMG4cWNZvHA+PqcNr8vWyR3gKYNLQtUJRBKEYmpaNiSuaJgmyJLYqS85qmjUt4SRRQHNMJFEgVHF3qwI74uCQJHPyZ3WCKGYSrHfHDauKjZJRNWtwRKf62mA9xTgP/2n/0RNTQ3324L8xff/khEFbuKKhsMmpcfOB8KcOXM4ffo09+/fZ8SIEVk66sfJRoAHIAY7rGnBYVIGLfI5aAjEuHqvg0K/E123mng9Q9TvNFBUVX0sSOoLmqaxa9cuxo0b1ymD1dcpzJ4oLy/n3Llz2Gw2Ll68yGeffcZzzz3XyXv58uXLOBwOEokEs2fPztq+E4kEbW1tFBYWdvnz+vp6JEnqUyB748aNnAR3KQq9ThyyREPAklGqbwlTUeAetkMMDpuEwyZR5HMSV5OCyjERzWmjYNokJk4YR1zRuX79Gr/4t98jYTJ58iTmPzPHCvac/c+QPCVzUv3eqYUDWLI4qmogCJbAcarXzG2X8bmtjGt7OEE4rqIZJgJQlufKahDmssvYZRFFMwjFlGGjR+p12miPJIjEVXyu/t0fnp7VXzBEUWTt2rW88fqrlOdbQwrOpG5Utqbi3nzzTX7zm99kZVtdEQ6FsIVD/X69rhv87JMT/PLSDRyTMi/NDQbjyy1do4aOKO2hOKYJoZja+wuHIeFwGNcABmEATp8+zZw5c5gxY0anx0VRzGqf2Zo1a1i5ciWff/45L7/8MqWlpbz22mv88z//MydOnKC0tJT6+nqam5szdqfIhA0bNnDkyBHu3bv32M/C4TAnT57spHmXCYsXLyYWi7F9+3aCwWC2DrUTHqeNUcVebJKIbpjcbo08pvDfE0ORCQWrOb7E72JsmZ/KIg+FXgd+l51iv5PKsiKWr1jJihc2EFFMfvzTX9IcjHOjKcSd1jCBSAJ9iI77i0xc1WnqiHGjMUhTRyypfagQjCbABG/Sj9guixR6HYwp9TGyyIPfZcdpkxhR4Kaq2MuIAjdjy3xp8d9skpcU/g5EMj/Hc00qiB2Irt7wXJI9ZcAMRKi0NwRBYN26dXz44Yesrq5+rN9hoJx+97csKe4645EJoiggV47mT/8i97IufcXtsDGm1Mu1hiD326OUjpvG/o+P8PVNzz8R/UG7d+/GbrcjCAKqqmYkaNsT8+fPZ/fu3VRVVWXpCHvm5ZdfTv9bFEVWrFiRlhzavXt3v0TDe2Pjxo3s27ePRCLB6NGjqauro62tDY/H0ydHioeZNWsWs2bNYt++fVRWVvZLVLk37LLEqGIvjYEokYRGU0csneHwPAHOEi67nLS9chJTdLa+cwBvfhGRaARVM6isGk1zMIZNElE0mZii0xyM47bLeF02vE45p9fRLyqmaSYzqZaAdUreI6ZoKKrlEuF56LP1Om343T0LWKeytLnC57LTEownS6LasNBXDMethb9zACoLQ/8unvJEMn36dD78+Dijp1sZBEkUcNok2pvuEzqwC1thETGXhxFTpzF+4qQetxWNRDhx6CNskRCOSIj5+ZkZKXeHIAigJAa0jVwyttRPXNG50xrBl1fMp5cuEowqFPsHlg0bDBwOR9qqK1v4fL4ey5i55Nlnn2X37t1UVlby1ltv5Ww/1dXVHDt2jKtXrzJ37twBSbA8ut3du3fnJMCDlPm7h7ZwnLZQgpiiE1OiiIKAz2XD77Z3KSchSRKKogypJWAKQRCwiSbFPjtvffO1tHtGOOmeoekGkYRKe8hyzVA0naii0dQBboecnOC0DZverOGIqhvEFZ1oQiUc19JZp3hS5gPTClTykuVPuyyS57bjc9mGRRAtidb5HIypBKPKkAd4qSAZGFDl7WmA95SMUTSdYFTh4Afv09bWhtvl5ZNPPkEUxXRJpvnmDZbnF1AmOcgzVJS6w3xee5C420vE4aJg7Hg8Ph93PjuPIxrGFY/i1RQWFxUiOkRw9N2LtitKYmH+z3//b3zr//q7rGwvmwiCwLRRheR5HHx2uw1JFGgNRiny9X1icbDJhfFNMBjsVx9ftqQ4UudvrqRHUnQV1P3Lv/wL5eXlzJs3j9GjR/druz6fjxMnTmR9avVhCr1OfC47HRGFYExBN0w6otb0o10W8SbFYl12CUEQWLBgAcePH+/kSTuUvPfee7z44ovJ7JEVsBmmSSRulQtbgjEU3cQnicSSch1WZk+3nDU6YngcVmbP4/jjDvZU3bL4iis6cdX6++Eyom5YAxO6YSJLIn63PengYGXKulsUDDXeZIAXjmuUDnHf9sNX2YHY8T61KntKRnREFY6cOMsnxz7hay9vZPKEMUmxSeumr2hWajsc17h08RItF84yK8+DVxYpspm4kt/nUCSCqusU+vtvRZYpv687zVf/7/+a8/0MhEAkzonL9zh34ijfeeMrg+oe0Bfu3LnDxYsXaWlp4fXXX+9XMHT+/HkaGxsfe3zq1Kl97nuLRqPs3buX+fPnD3jSNhgMcvbs2SEJRlJ6f8eOHSMWi7Fy5cp+baepqYnTp09js9mynmF9FEvuRyMUfZAFSyGQLI06JD6p/ZgNawdWws8Wly5d4qOPPkIQBAoKCti0aVOn7GJ9S4jWUAJNN8hz2RFEAVXTiSQ0FM3ALou4HTJ2WUIgmdlz2fE45X7bWA1HdMNA1U30pLG9bpjJqX8TzTBIKDpxTcM0LemSFKZp/VzXLdEnp01KS5ekguLh/lmZpsmNphC6YT5mETYUNLRHCcVV3HaZkUX9q2o9zeA9pVfiisYP3/41xcXFfOfP3qQ0zyolplexgoDLLibFR6Fs0Rxq2hqQ4zFiBtxJCLhEKLKZ+DwDK7/2hTynjR1b3mXjK68O28xYvsfJ7HEjqDscoyOiDNsA79SpU33WuXuUxsbGrE3Hut1uXn75ZWpra7l69Wq/AyOwnDIikc7iqSdOnGDevHk5z+qlWLhwIc3Nzf3ycQUoLS1l7dq17N27NwdH1xlBsKzNPA6b5SEbt/xjo8mszYVLV2hsbGT8hAlcbwzitEnpSUWbbJm2P2oL1RaKE1cfWEIJQFWJN2vlu8mTJ6fL2E1NTfz6178mHA7zV3/1VwCMKPCg6ZZZvNMu4XdZhvN2WcKEdCYvELEylolk8CdgDaR4nTacdgk56SuaQjeMtBm9bjzskdrZ+sqyz8rdNcpIOjjohmkFbsmATdPNtGyT1kuqyHKXiNHQblUbSvNcViZOEDABmySRSszZZdGynnPbn5hspyAIFHodNAfjNAfj2CQxJwMdmVLocxCKq0QVDVU3+iXr8zTA+yPFME1UzbB89Xo5cf7ln/9/vPrq6xQWFlLo7b2U5rBJlJpxRjtN2jWBoEanQK/AZuIZhAz9CzNncKe1hV/+5/8XSl4h1a+8wtix43K/4z6S57EzZcpUjhw/weY1y4bdsMXhw4dZtGjRgLeTixvY0qVLaWpqYuvWrTz33HP9lg9xOBzs3bs3HdDZbDYaGhqyOlHbFQ/3qpWUlKBpGvF4vN/SM3PnzqW2tjYrlmaZIIkCfrd1IwdL3+zimRZeWLWMYMwK+CIJjUhC6/Q6UbD0zgRBSEtmpMlxTam0tJS33nqLH//4x+nHbJJIRaGHOy1h4oqe/r91/Elf3GSwl9B0onGNgP4g2Es1xA+ElBivLInIyeuy9cfyPpWTTgYpTNPEMK2/05k2o3PAlsq+9TSJmbIGSwWhAAImJlZZ1TStgHtUiRe7bC3kownNum885Gluk0R8LivYzeVARC7J9ziIKzqhuMq99igVBe4hC/LssoRDFkloBh0RhWJ/368JT3yAZ53kJqIgDNsszXBCNwxu3m/j/b0foqoqsiwze+YMJk8cj8supbWIdMPEIYv88z//M5s2bWJ8Vd9udI5EDJvbQandpECmU6AXSwg4RMiTTXySdbHPJmFV41YogmIY2EUZpyQyIhHml//3f+Y//utPu7VjGyrsssSzc2fz299vIa4uzkqAZ5gm0YTV7OyQBzaBNmLECA4dOsRrr702oGPKVTdIaWkpL7/8Mlu2bOHVV1/t1zYezQDG43FOnDiR8wBv2bJlHD58OF1WXbNmDXv27GHDhg2AJSSdEmfOhNLSUj777DNu3brV756+geCwSXjsAmX5Hor9BgnVat1QNKtvS9WMpLAtaecCAJ/LRr7bjigKpLS/c1nO6+pcTEly3GuPEoqp2KQ4RT6nlYly2TsFe9FHgz1NQZYseynR0jPHNFMZu5RXqpDe78P3Kz0p3iuJltNCT3ZUlmJB731Zqfviw9lDI6kjZ5KOy5LHIiJJVpDrtHV/H3XYJEYV+xAFkY6oQiyhMarYg8Mu47RJw25h2l/K8l2YAWuKtSEQZUypf8iykIU+J/fbLU/ePI+9z1m8JzbAiySs2nRKPLElGKcs340t6V33NNjrTOrCUnv4MB+f+owXN72Ex+0iGovz6aefcqzuhLWajkbxyjKYOs6224ydMbNHL81wOMxnZ8+SCHYgayqSpiJrKqWaAlg9DDYRSu0mhTYIqAIdGiQMaFIEWgCfDF7JxCGCNIBfW1MszuGGFi62Bzv3BQl2xo4ay9Sxk4ddcAcPVuBgDvimpmi6ZTUVUzrdBHxOG+UF/ZMAGT9+PNevXx/QcRmGkXNttFWrVmUte+V0Omlra6OmpgZN05g9e3ZOgj273d4p2BBFEbfbTTAYxO/3EwgEePfdd/sUXK9cuZI9e/bgcrl6tR/MJZIo4naInXq14EHGKNXjNdAFSH84ceJEl4LWHqeNEr+T5mCctnACW3JIAJLZyi6CvUgysEll1OBBKVYSM0s8pDJommaQMJPBWPJlqW0JQucseCpYM4zkfk2dSELH5ZCT2UAxHTjaZbHHcrcoWL+vVLZQeuTvVGZRFARGFnmSPXeW9WKhzzksJmGzhSAIlOe7uNWsW/7HcTWtkzfYeJ02nDaJuKrTEowzoo/X8Cc2wIsrOk6blE5Hm1h1/9stYWTJGsHO89iHdVPnYKEbBnfbIrz3+98xZ+oE/sP3v5380kvohp/xFauJKRpHavZR2N5CsdeDJMDIccVcbW/nyPs7kFQFWdesP5qWDuYcmMzMz8NuS34BJECSwOF77DhkAYrtJgU2CGrQoQmoJnQk/w3gEMElgku0BjMyDfiuBcO8c+02hmk1+ebl5eHz+QiFQnR0dHDz5k1EUeTq1atMmDAhOx9sP0mNwKem0ILRBFu27WLq9Nmomk5cFfs0ZZZqeg9EFGviL4kteYGPq1bJwR1T8Lv6fqFqaGigqKioz697mBs3bjBuXG7L44WFhXR0dGRtew/3HO7duzcnAV48Hn9s0bFy5Up27tyJw+Fgzpw5FBYW9jnIW7duHdu2bWPVqlX4B2GgqS9YQcfQZnzOnj3Ln/3Zn3X5s3yPA003aY8kaOyIJa2zOt8qHw72DNMklixDa7phBWQC6clRQRCSGbsHWclUds1IBnOpLFvqXtYVhmkNPwDpjKD4UNAXjqnopk4soVPgdeB2yFYLjiggPVT2TbXlpB7vq4G9TRIZWeThTmsERTO42xZlVJHnC5VUEQQBp11CjRkYAxljzQIleS5ut4QJx1VLOzUv84D6iQrw2sOWtlmB10Ge245hgsMm47DJ1pfSMBEFK4vRHkkgywK6bpLntn+hTr6+oBsG1+618/YvfsVLm1/kmSljOvXcSaJAPNzB5fe3scptx1Xq5uEmmGlFBWDEk4GbCNiTf/qPJECBDfJlk5gBIU0gaoBmWpm9hAEBrAkspwhuySrl2ro5p5ticd65dhvdNBkxYgTrq+dj025jhK8jesehyqPYva+O+/fv87vf/Y5vf/vblJWVDeg99AfDNAlEEhw7dY5z5y8giiKmad0Qpk6bxbjRVbSEEhBKIAoCLruV2bAnyz4pGx/DMNGTPZSqbhBNaOneGQCPQybPY8eT9HNsDVnZiGC0fwHeuXPnBixofPPmTVatWjWgbWTCjBkzOHXqFPPmzcv5vrLByZMnuzzWjRs3dvr/hg0bePfdd3nllVcyHvzYvHkzv//979m8efOw0KMbTvRmX1jsd6azN/fbI1QWebvNMooPSa9kg04ZzoeGIawhDWsBi0myS84q69okMXm9EDEASRByOglqlyUqizzcbgmTUC2B6NTw3ReGhzKnQ4nTJlGa56KpI0Y4rhJXNMaWZbZoe2ICvISqI4jQ3BHHZZce80W0mlFhTKmPUFwD06QtlEDRDIJRhbJ8Fw7bE/N2s0ZjQwNv/+JdVq/fzKTRI7ocqLhw7ChLCqyMW+qikSkmJoGOIB2KimKz48SkKj8zLTtBALdkBXBgBXgxHWKGQEwHxUz27BkCraoV7HklE59sZQNTHG5owUgGd6+sm03H8beIKw8yOaI9j1fW/Yj39liTnIcPH+53r1Z/0XSDs5dvsf393UyZMoVXX3kZl13GaZdw2mR00yCW0NP6UobZdXN6d6SEZ/M99scyIz6XjbawJVI7VL68giAMykRqVVUVZ8+ezXqA5/V6CQQC5Ofn9/i8s2fPcuPGDbxeLwCtra185Stf6fb58Xg8/dyecLvdbNiwgffee69PQd6CBQv42c9+xrRp0wZFBqatrW3YZQy7IhaL9fqcsnwXWqtBXNW51xZhVLF3UDxrB5LhNAtNmoNxOqIKzcE4juQEcy6wyxLl+VbPYkdUwe2Qh60KQH8YTvpxeW47DlmkIRBD1TNvdXliIh67LBJToLLI06PptSha5dnUSudOa5hg1CDfY/+jCfBM0xIhPfnJYa5du873v/sWkbhGSyhOTNEo9js7XTzKRo+l7vJFEnYHZiTCmHiIUQX5D7aHSUtbOyHDRLXZ038UmwPVZmfscwsZn2zoPv+bn/f7uGXB6sfzJb9aqgFRA8K6QFSHuAFxQ6DloWAPQ0v33K2vnk/H8bcwlM5lOkPpoOP4n7O++qf89FfbOX/+PMGb17GZBoaiMHLOM1Qnm9pzgWma3G4JseUP2/izt96ixO/C57I9Fmj5nA+en1B1YopOQrX6QB7095DO5smSiF0WccgS7h6sox5uzLXERzMP8KLRaFb6FgdLbrOtrS0ndmOLFy+mpqaGtWvXdvuc2tpafD5f2n7s/PnzBAKBx553584dPvnkEwoKCmhpacn4GPoa5CmKwqlTp/jud79LW1sbu3fvZtSoUY/5/qaIx+OIotgp21dbW0tpaSkTJ07M6BhlWU73Lubn5zN//vyM399gMnPmzPQU7YwZM7r0AxYFgYpCN7dbIqi6wb22CCOLvMNa9kMQBErzXOne9Ib2KFUlvpwds8dpLSoDEYW2cOILFeANN5x2mVHFHu62RTN+zbCPeFIZB0EQ+pRuTj0/rug0BWPEVYPhv67MDqqq8sMf/5R5s2fw3e9+B8M0kz6SKpGERqwlzMgib7rPa9L06TB9evr1TQ0NfPLRfuw2G5rdgWpzMGXdCiZmUNYU9cwyTplgEyEvOW2rmRDRrXJuzHgQ7F0PJojgJN/nIRG5jZKIYOviWmYoAWzabfLy8ujo6ODZkgKmFViZxg8+PcHZESOYNXdu1o79YTqiCr/fspXx48Zx9VwdNzO42Pr9fsaPH095Fuy7HpahEPt4ob9169YTkZFJcfjw4QHr9XVFb8HU3r17mTRpEmPHjk0/dv/+/S4zxZcuXer3RHIqyNu6dSvTpk1j6tSp3T53x44dae/dwsJC1q9fT21t7WMTtseOHSMSiRCPxwmFQnz5y19GFEUaGhoA6Ojo4OjRo10GQY/i9/vTQfDu3bv79R4Hg5UrV6Ynp3/84x93+94kUUwHeQnN6mUeWejJKGB6//33WbFiRUYZ2mxT4ncRV6zFYVs4TkkObRALPA4CEYWEqqPpxqBkOQcDm2SNsTwm4zOESKLIyMLMtWSHfYAHD8bI+4vLLvdLJHC4oOkGCU1HEh5oI3XHoUMfc+bMaf7y299CTA4+iIJAeb6bQq9OQyBGQtW52xqhssjTZV9JaXk5pa9/rV/HKmcxwOu0XQHy5M7BXlgXUA0TAxHZ5aOp5S4xZQQyGi4phkeMYBcf6FMZkRv4fD46OjpIPJTmnlVRzun6+pwFeO2RBIZh8MrGtRkvUtrb27l69SqnT5/u8ueapj0WUHRHUzAOWKXavg4dTZ06lfPnzz8RfW1XrlyhqqoqZ9ufMGEC+/btA+gkedHe3s7KlSsf0+CTJInDhw/jcDiymslyu928+uqrXLt2LS1sLMsymqaxcOFC8vPzOXToEIsXL34sMF26dCk7d+6kqKgoHXgEAgGee+453G43iqLw3nvv8dprr3H8+PF0sFxTU9Pn43xS+v4kqedSaKrf7E5rhISqc7/dCvK2bt3KihUruhxAMk2T+vp6fve732EYBm+88Ua/tQ37gyQKlOQ5udcWJRCxem9zNaksSyIOm0RCtcSfh2riNNtYFT+FhJbb6f++0pdYaNgHeIIgDEg6I1WvflJXFaZpsmPXXq7eaUbAxO3xMH36NEaPqsTnsqNqBo2t7Vw8cZjW1hamTJ/Fv//3/77LbdlliZGFHu62hkloVskh2+l7Sc/9aufhYK/NoXOCBFoshNNXRRwTDZmQ7iOk+7ALCn4piFuKIXrGEgpZch+Oh86HxkCAUXOyY/7+KHFF48DBQyxetAhfHwYcCgoKeg0Kzp8/z969e5FlmRUrViDLnb/O0WgURVVJJGPcQm9mwWVDQwPnzp1j0qRJjB49Gr/fTzAYzPjYu2Iw+v4mTpzI1q1bmTJlSk5upuPHj+9RMuhRVq5cSTgc5tixY50ez5ZcTFfHs3fvXgzDoKysrNup340bN/LOO+/w2muvIYpiWpoFrKBs3bp1/Ou//ivr1q1Lv8btdhMOh4ckG5VLIpEIDkfv3wuHTbImR1vCHKo9xtLF82lobGLv3r0Eg0FGjBjBpk2b0uf5xx9/zNKlS5k1axaRSISf//zn2O123njjjce+pwOlsSNKOKbhdkhJ5w2TYp8Lj8MSHQ7HVRo7YlQV5+5353HIJFR9SCVFso1dtu4RyjDK4PWVYR/gDZQHwpJDfCB95PLly+zbtw9BEFi+qpp5i5ah6QaBjiDnz5+nru4ENtEasW9p7+CltSv56rzHdZ0eRRIFRhZ5ud0SRtUN2sOJfilkd0UwGMTx0Ad9pz3ANZsbjxpnhseB0579ia6xfg82dJRQKyWFS/F5w0TjCaKGm5jhQjHttGjFOEQnmCPp6DiNAIz2PkhzN4fCzB81KuvHBhBJaDQ1NbF+zeqs98HMmDGDGTNmoCgKBw4cwDCMTjcPh8NhWXB5ihg3fiLNwTgVBe5eg60LFy5QXV3NpUuX2LdvH4lEIi2821cMw+DkyZMZ3USzwebNm9OSIoNlM9Ydoiji9/upqqriJz/5STrbOmnSpJztc+3atTQ0NFBeXt7j8zZu3MjOnTvZtGkTmtY56+71evnud7/b6bH58+fzySef9El0+UlQLjh06BALF/a+uNu7dy8NDQ1MnT6Tu7fr+W9Hj7Bu7QusWDQPr9PGjRs3+NGPfgRYgf3ly5f59re/DYDH4+F73/se7e3t/Ou//isFBQV89atfzdrnE4yoIEB7WAGswKTI60RITtJG4ioJVacjquQs+PI6rUGuaFIq5klNqDxMSoi7r20tw4mMA7yhmr4bTGKKlrOJo54wTRNFM3DYJD766CMuXLjA6NGj+fM///PHPnOzzM/M8RWompH+mSgK7K/ZC/Qe4IEV5BX5nDQEooTjatYCvPa2Nlo1k1hDIy1OD6NWvsDqKqvX5+ieXZQ33+s0YWtiBagDEcn02mSmFvi52B5k9746Xln3Q8Tj38OttKKbIiHdR0QcgWPa/5v39p1Fwc6cfCee5MDN8avXuRCKsqaX6cj+Eld0RNGSMMgVdru9RxmTE6fPUnf8OPMXLKAjqmRUJhZFkalTp/bY49UbFy5c4PTp06xbt67fFmJ9RRRFNm3a1MkNoju2bdvGiy++2G0gaBhG1oLEBQsWMGvWrKxsqzd6C+7AysjNmTOHjz/+GD2DrLvdbqe1tbXTYymLtSeZpUuX8s4777Bv3z5EUeRLX/pSl5nP27dv89WvfpVt27bx19/7Jvfboyiawf32KD6njark9do0TT744IPHgmawsvLf//73uX//Pj/4wQ949tlnMwoue0MUBWKKxrgyf1Lk/0FwLUsixUnh5pZgDK9TzokoscMmpQV5QzGVggyrBcOZSLL00R9pqeGCYGY43nblvjWZ6HXKuOwyPteTYSJ8tzVCVNEoy3OlFcm7IprQsMs997dlG90wCUYV7rW0s3v3HjzEWbRoIXP70Qv24YcfsmzZsoxLU7phcr3RKruNKvb2SVi3N7q7MTbcu8fNfXuY4bThcjrpCAY5EFGZ7RAZW1jQ7/01xeL8n89vPK6DF7mB6BlLXBzJ1g/PcK+pDVEQeLWqjNv1V2i3OXj2hXXMeeaZgbzdHrl0p5Vdu/fwF3/6eo/T37nmWv09dh88ynPLljK5srjHY+mv4X1X7Ny58zFNt1xz/vx5fD5fj1ZdtbW1jB8/nlOnTuH3+5k3bx5Hjx7tNO2rqirr168HrGGTCxcu4HA4uv1sugp4DMNgx44d6cna4cZHH32Ew+HIyGu4oaGBEydO4HJZDfvnz5/nr//6r7t9fjAY5ODBg8yePXtIbNP6QltbGydOnCAej9PU1JR+3OfzUV1dzfbt2zsJI5umSWsoQXvE0mYVk9mynu4xD3P9+nUuX77cqQzeX1IT9t3dj03TpL4ljKIZ+Fw2yvOzP2UOEIgkaA7GcdokRuWwHDxY1Cc1/sryXU9skNfnAE8AwnENn0vC47Tjc9rS1ijDkcZAlGBMpdDroMjXffCT8rMdDFJ2Uic/vcCZT8/gdDpZt3YtY0cU9jvQMgyDDz74oE8XjPP1bSiaZTUzutg7aBnaTz7YS/H9WzgNnfwvv4Gmqpzf+z5loQBNTg95msLU4r5Nj/bmZCEAhiCxfGQVH5w8w/Ivf5nnn1uQUzV90zQ5cvYqly9f5o1X1g9p2SKWUDlzs4VPjp9k5sQqFsye0u1Fq6amZsDCxilOnTpFcXFxTocfHmXv3r09ypmANeGZCt7a2to4f/48S5Ys6VTi3rt3LyNGjODu3buMHDmSWbNmcejQIVRVRRAE7ty5wze/+U1OnDhBc3MzoVCIJUuWUFlZ2Wk/K1asyIl0S7aoq6vD4/Ewbdq0Pr0u04XAiRMn6OjoyNqiIRds3769y+nr9vZ2du/ezbp16yjsYqI9rmg0dsTS5TyX3RKl7e268n/+z//hrbfeGrQ2griicbs1AlhSY7moVKm6wc2mEADjy/3DNibIlIaA5Ulsl0VGlzzuzPQkkPFveVyZD90wudcaoSOWIBiDEQUCoZiVxrRJIrbkNE0qEyZLApIoDmmmz2GTIKYS76VRsreTMa5ovU6wZkJDIMpHtce4evUqVVVVfO2rr5PvcfRrwvFhLFeEzLXG2sJxZFGgJargtEk0dsRytrJ7lEUvrKWlqYkDv3ybLyebtpd99RtEIxEmejycPPQRkYZbeFyZj/aP93v51pSx1Da08Fl7kI6OjrRtlQBMK/CztLyYfIeTMSuX8If9+/B6fSyaOzVnQZ5umDQ1NVFaWjrk2W5FNyn0uli+dCH3bl1nxwcfUb1iKcU+J7L04NwxTTNrQwDxeJxgMMj169dzHuBdunSJO3fuAL33uBmGQX19PZqmIcsyhYWFXfaWzZw5E1VVO5VWH37elStX2LZtG/Pnz+fZZ58FYMuWLVRUVKRv3LNnz2bHjh28/vrrA36PuWL+/Pns37+f0tLSPpXSH54qBigqKmLOnDmPPe/ZZ5/t9LzhxpkzZ7oNbgsKCvja17pXFHDaZaqKvQQiCq2hODFF51ZzuPeEQhbL/5ngtMv4XTaCMZWm5MBFthf0KWtE3TBRVH1IKxbZoNDrQNEsPdLWULzH3+dwJeMMHjxIwQJ4HTKSJBKJq2i9eLWV5bsIRhVcdhm3Q8ZpkwYtWxRXdW63hBGAcQNYVdxsCqHqBgUeR7971j744AM+/fwaE6bOYsG8OeR7H9hJZYO2tjYuXryYkdn69cYgumF26jvMc9uHjd3MxZ/9K5OK+le2jagat8IRErqBQxIZ7fWke+4AdBPuJgTe/fQiy179MvNnTcpJdi2a0PjVlp0sePYZ5kzKzRBHX47lblsE0zTxuWzcbWzl7NmzlJSUMH3aVHTDpLGxkUsXL/Di2mpKCweufXfx4kUuXLjApk2bct6rdf78eVwuV8ZTrpqm8cMf/pBvfvObWdX5UxSFPXv2sGnTJt59912Ki4sZN27coGYw+8tAy+mHDx9mwoQJXfYAZrPsn00Mw0gPmwwURdNp7ogTVaz+u57agn79619TW1vLD37wgwHvN1N0w8qwGSaU5rlyMnBxty1CNKHl1CZtMOmIKjR1WK4nVcXd29UNJn2Zh8g4xI4k1HRw1ykQyHOh6oal1abqKJqBoulJ7zwDwwRVM4gpViQcV3SKfA4CUYVCryPnhtNOm4RNElF1g0hcw+fqe0ClGwaSKKDqcPHCOfI8jm4bpnXDIKEamJh4HDYUReH3v/89bW1trFixglXVz2MYZk5OlMLCwozlLAo8DlpCcVx2mTy3nY6oQkdUQTdMSvNcQ5pxMgwD+wD09Dw2OS1i3BWSABUOk1dmTeWdd36LP+87TB1bkfWSgqLphMNhino4lsHC7ZBx22WiikZCNZg0upzS4kLq79yj9shRBEEgFArhdDq5cO02GmMeczzpC2fOnKG1tZW8vDw+++yzLjM72WTGjBns2rUr4wDv0KFDrFq1Kusizna7nenTp1NbW0txcXFaTPdJYOzYsVy8eLHfgzXLli3j3XffZeHChYwcObJfGapwOMz+/fvxeDxUVlYyefLkfh1LpuzZsycrfXCQlKEq8qSDnLiqdyuu//Wvf51EIpGV/WaKJIoU+ayBi9ZQHH8XbjoDxWWXiCY0YopOfuZ6vMOWPLedaEIjHFdpCycYUTD0rRaqbmR8Xc44wGvpKrhLkirPdlXXN0wzLVQcU3TsskhCsyZt7JKIKGqdIv1cTOt6nTbaIwkiCbVfAZ4kijiMKFt+/w4zpk3hflCntrYWu93O3LlzmT17NpIkcam+kUO1n9DR0YGIiV9WkSSJzZs3d+7fyGFMO27cOC5dutTrhbHA6yAUs0QcnXYJp91FY8AyM041lg7FRDHAZ+fPM8qb2y+SLMBIh8krs6fzzs9+znf+w99kvUSdUHV0XcPlGB4NumX5Lu60WrZLbaEElUUeRhZO4Nnp45FEy4E4FFM5duY8ez48QFVVFT6vBxEDRVGJKRqjq0bhczvIc9u7PD+uXbvGzZs3aWho4Otf//qgvr+CgoJ0Sbw32tvb8fly01czfvx4jh8/PiwzVj0xffp0ampqBjQ5/dJLL3Hp0iUuX77cqWXk7t27fPjhh5imyTPPPNNlPxvA0aNH2bhxI6IocuPGDX7wgx/w/e9/v9Nzbty4wfXr13G73Rm5a3TH0aNHqaioyGp2OaHqRJP+0dkcXMsWeW47raE4umFa1/4sH6PTJgMJ4kpuBO+HggKvg3BcJRxXqW8JU+xz4nYMXfm5l4JpJzI+SkUzEAUo8vUt7SoKAqJk2YalInpVNyj2mQRjKor2ILOWUHUU3chIq6svuB0y7ZFE+ovXFzRN4+2338bhcPDXf/WXnY5L13Xq6ur4xS9+gWma6DYP02c/w4iyUpx2mbJ816A3mk6ePJndu3dntPJVdetMsUsiTruMXRK53x5F1Q3utEbwu2wU+505GavvCV1J0BCNM97txgo7coNNhAonLB1RwtZ3t/L1r3054ym4TEj1fTpzKJHSF2RJTIu1qrrBreYwPpcNl13GmVz4FHgdrFwwm6u3yzhU+wnjxo1DkiRkWUaWZepOnCQSibBy5UpkUcBll9PlbVU3OHXxOiueW8bSIRA7Xbx4Mbt376a6uhpRFDsNTNy5c4dIJMLkyZOpr69HFMV+Tatnyp/8yZ+wbdu2YTs92x29uTr0hizLTJ8+nekPWR8+TEoX8eTJk+lrqSRJLFy4MD2Iksr8jR07ltLSUvbt24dpmsycOZOysjKuXr3KmjVraGtrY/v27cycOTMjR5cUmqaxY8cOnnnmmayWzg3TpCFg+YS6HXK/riWmaWKaZs7684SkG5KiGeiGQbazDalrnWaYXxg9PKdNosTvpCUYt1ygknZ1QxXk9aW41qcjLPJl52Zvk0QKkkKMzUGrZyGqaDhtYlLTR8/qh5c66fQ+nnTvv/8+169f54033iC/C500SZJYtGhRWmJANyxtuqGeHsrPz6elpaXHhum4qmOYJgKky8VOu0xViY+WYIxgTCUYUwnHNUrynIM6Jj772fkcV1Tid6/hyrG9j1uCmWX53Lh4gxOffsZzC2ZlxdZO041Ok3XDBVsyyEvpeKVK8yYPpACcdpnbl8/xvW+8iqLpac1Fh01EnzWJe01t7N67h4KCx7MwjY2N6W2W57v7lTEfCFVVVRw5coRgMNipr+rSpUtUVFSwc+dOCgoK0h6tuWThwoUcPnyYZcuW5XxfTwqiKD7m0KIoCsePHycWizFixIhOPysoKKC6uhrDMDhz5gznz59Pi2YXFhayadMmTp06xYULF1i3bl2vLhH19fWcPn2aF198MeuOEq2hOIpmtfOU5/fey2yaJj/96U87PRYKhRg5cmS/vYozQTcs/dFc2HeKgoBdtgLIuKrj/QIEeEB6ELIxECOS0AhEEkMY4OXAqkwShaw3ZeZ7HLgdMsGoSjQ5pVrsc2DLcl+eKAjpPryEpvca4J09e5YDBw5QXV3Nl770pYz3M9iZru4YOXIkV65c6THAC8Us1XOXQ+6UlZREgbJ8N363RlNy/L8xECMS1wY1I6m2t+Y8uEtRIEP1pHH87sMapk2ZRHkW+iyiCY14PI7Tbhs250UKuyxRVewlFFfTJuHRuJYO8Nra2vD5LAs7l13m0dh+fGUpf/7GayRU6/ukJTPBNknkxvVr1H1yGH9BCUyejGHmppm7O1KZo/r6eo4ePdqphDdQ4ea+EI1GuXDhAnfu3Hka4PWC3W7v9Bldu3aNsrIyvF5veqJbFMVuvZDnzZvHrFmz2LNnDxUVFennXblyhbFjx6YDuUAgwMWLF3OSVVV1g0DEuqaW5bky+s5/61vfeuyxgwcPZiRU3V9U3UA3TERRyJk/u8suo2gKwaiC1zm4C7xcIokiBV4HkWSPYU/tZJpuEElomKaJx2nL6mfdl+pmn0LQXEy+2mWJYn/uMxx22QrwVM2AHqrMt27d4tixYz0KeA5n4vE4J0+e7DFDoekGHcmLUb6n65uvKzn+3xZO0BZOEI6rKC06FYWenF0YHsaeiMMgXRsEAUrsJqMdMqfPfUb1krkDHv4JxVU+/PBDXn0xOw3c2UYQBPwuO6IgcL892klG6JNPPunVBUKWJGRJwvPIL6lg1jTmzZrGyfNXOHDgAKtWrSKmaIMmwZOiqqqKu3fvUl9fPyQTrLW1taxatSrrWaI/Bq5fv879+/eJxWJdVk66QpZlNm7cyI0bN9i+fTtz5szh+vXr1NfXp4PEu3fv8uabb2b9eDXd4G5SY85ll/AMIKi5efNmThcEimZ9z22SmDMli3yPNbQXSWhEE9qQ9qtlG6dNQhQEDNMkEFE6OXaYpklc1emIKITiavrxlmCcsixWM3JSotUNc1DFgLONkWz47e2k3rdvH2+99dZgHFJO2L59e6/pfaskZ5Vme5JpEQTL0szjkLmXLOndaQkzqtib894KhxIH2+DpDjlFWDJuFL+rPczCeTMo9PY/wNN0g0hMIRQKUV6cn72DzAGpQQlVN4gkVNRYJCuG8vOmT+DG9SuANbjhdiiDrga/ePFiampqch7gtbW1cfLkScDK3C1cuJBx48bx+eefM2PGjJzu+4tKf4OcsWPHMnbsWGpqah4Tu37//fdzYq/WEoqhJitDA5WZCofDvPfee4TD4S7vVQO9N+kPZdtzhV2WOmvulXif2LjhUQRBoNjvpKkjRkvI6smzJUvS0YSWjjMAHLL1GSc0g4ZAlOagVb72u+z4BjDBnLMM3pP6S9INS6YFeu+H0nX9iV117969mxdeeKHHBl3TNOmIWtm7ggx1ipx2mVHFXu4mJzAbAlEqi3JrRWNPxMEzuMKSHtHEkYgRS+gwgLfXEVX4cN8+qlcuz7kM0ECRRAGf00YornLldjMXTh7h668PvP9HEARGlhRw/tQxNNHBkoXPDondz2effYYoigMeHuiJrVu3dnIl2LFjB6FQ6DGB3La2Nurq6hBFkUuXLvHv/t2/y9kx9ZVgMEhtbe2giu8+ytatW8nLy+tVqDoTunJiWb9+fdY071KE4yqhmIZumowqdA/4+/7GG28gimKXE95vv/32gLYNoCWzmZKU23t5sd9FNKGh6gbt4cQTKRLcHXluO3FFIxhTO2XqwMqueZw28j0OnDYJ0zS53x4lktCSurM6MSVGIJKgLN+dc129PkUykYSaVWHewSISt6ZnHbLY4xewL04Qw426ujomTJjQa0kjdaKJgoDXmfmv3yaJVBS6qW8OE1N0grHcZWSikQhuXe31eSZmVqdsXRJMLMzjeN0JXlq7vF8rLN0wCUQStLa2EGxtoKbmrrVtl4slS5YM6Q20O0rzXVw9dZ2rN+pZunwNkbg6oDJTisWLFxNNaGzdcwBFzY1swr179zh16hRer5fly5cjiiI1NTVpl4rRo0fnXK6kqqqq0+/1xRdfZP/+/dTV1SFJEs3NzYA1/LRmzRpEURx250FtbS3z58/vk5NFthk9ejSVlZUZydz0B1EUGTNmDFeuXGHixIkD2pZhmrSHEgSilpZdid+VlAgZGHl53WtmZuP+pCc1NnKtcyqJAsV+Fw2BKE0dMQq9jkEzNxgMyvLd5Hl0wjEV3TCwyZZM3KMmDoIgUFHoIZEcaowpGu3hBAnN4E5rmKoSX06zqX06I++1RYdkMm6gpKYZe7NOOXbsWE6lE3LFjRs30DQto4tWMJm9y3Pb+/yFs8sShT4HraEELcE4HoctJxeKz86dY0ovGmV3O4J81tTKc6MruBGKookSYxwyvgF4fjpEGF9Wys7PLpCoXtYvjahAJMHZc+eZOH4c69esTj8eDAbTdk2aprF06dKsi+z2l0MffYTP52Pd8ysJxSxBz2wEeGBd6CdPnsy58xcYW7YkK9t8mLq6OjZv3kw8HufAgQPEYjGWL1+e/mx37dplDbsM0sBOitWrV1NfX4/dbu9yOMDtdhMOh7NSDs8G69evZ/fu3UybNo3Ro0cP+v51w2TO3Lkc2L8/pwH5rFmz2L59+4ACvHBcTQ6g6ZZGqk3q1Is1nEkFePIgLDBcdpFYwlJiuNkUojTPlbXrynDAaZMyvkekMnUpY4G7bVESqk5zR4yKwtwpQmcc4PlcNkIxlaaOKG6Hf8i9NftCqi7e2zGLokhdXV3aV3K4EwwGOXz4MCUlJRkLfqZK1X3J3j1MgcdBMKqmU+/9tW3riWcXLeLE5+eY28VNWTcMPm3roGzl80Q+OsiF4koWvLoSgDPHPkG7donR6BT2U8TWKYGoKMQVrc8Bnm4YtIXifPrpp/xff/mdTj/z+/2dykbbt2/n2WefpaKiol/HmS127drFvHnzKC8vJ6FaAuSpRuxsYJNFCvLzOHPmDPfaImldxatXLnPp0iVcLhdVVVX9dixIBUhOp7PLwGDdunXs2LEjp3p03WXjeur9Ky0tpbGxcdgEeGAFeYcOHSIcDnerY5crOqIJWkMJ2mKG5bLgtucss7Fq1So+/PBDnn/++T6/NhhTaAxY1lUOWaLY78TrzJ4jxJ07d9i/fz+6/vh3MBu9g6kATxyE+3c4rlke6Zhohsm99ihuh0zJABxyvghIokhZnov6lnBaciVXtm4Z3+XL8lzEFR1VNwjFlCfKZ+7BgEXPz1uwYAHnz58fhCMaGIqi8OGHH+L1enucdoxGoxw/fjxtl5QalAGw97P2LwgCJX4n99qjBCIJ8jy5uRD75y+m9dPjFPl8XGxsQskvQlYSBCQby/7024iiyNUbN1mwYmX6NXMWLoKFi7j6+UWuHD/KPJ8Tm9y3FaNTBCkaJppQ+3yOt4UT/GHbNl7e/GKvWe5Nmzaxb98+IpHIgMtFA2H16tV88MEHbNq0Kf39yGangigIFHqdTJgwgT0fHsDhcGATwWnG0r1Q165do6amBlVVWbduXa/ly/r6etxud0blRFEUmTRpErt27cJms34niUSik5zGUFBWVsavf/1rbt26xYgRIxg7duygZxm7Yvny5Zw6dYpjx46xcOHCQdtvQrWqLL78Ivbs/xhJACVwn29+85tZ35fP58PpdNLQ0NAnSZJgVKEx6Uvqd9kozXNlLbD70Y9+hCiKlJeX8/rrr6e1/vqKqunopolD7tnvPdfhXVzVaQ3Fcdgkpo4sQE9OnUYTmiWw7rSR5+naDeePAYdNotDroC2coDlo6ScW+51Zn3PI+NMVBIECr4OmjhitoThuh/zEROGpj+wJbrEDLBX4mpoagB5vhMeOHaOtrQ2Xy8WoUaM4c+YMc+bMSSqXW5/HQE4kj9OG0yalv8S5kMCYNH0Ghz49zRJM8mwyvuq15BcUdHpOdz0pE6ZMZcKUqRz4xdss8prYbZmvfO2CyZj8PC5cuEjFc/N7f0ESRdO529SOKIpMqMrsplFdXc3Ro0dJJBJDNm3pdDqZOHEix44d49n5CxAAE7K6qizwOlg0ezITx45OCppLjCp+kLkaP34848ePR9M03n33XV577bVuz+0zZ86gqiqBQICCgoK0BEZPdKV/t3PnTmbNmpW1gSrDMPrUV+f1evne976HpmncvXuXEydO0NzczLRp05g8eTLbt29n+fLlGcuEZJN58+bxq1/9itmzZw9a0DmiwI2i6fhdUwjGVEQBrp87nrP9LVu2jO3bt2c8cPGw6XxXdp0D4Wc/+xkvvPBCn9w4usI0TS7fD6ZlNOyyyNgyf5fX+lzeCiNxlYZAFMO0ypgFyf67PLed5g7L2CAUtwYUbJJIvsf+RMUT2SI1eNIWTtARVQjFFGySiJYUohYEgXyPfUADKn1KvfhdNhw2CcMkPVH5JJA6cRJq76WnTG4YQ8GhQ4fYvXs3K1asYO3atd3eTGpraykvL2f9+vWsXLmS8ePHp7WgjCw22JYkS7OhmNovC7hMmPfiS1xtaWdEYQHnDh/q8+tXffMtjkVUFFXJ+DU2AcoLC7h27Vqf9tUcjLN79y5efenFPg0iLVy4kLt37/ZpX9nm2rVrNDc3I4kChUkrwuZgnEii90GXTHHaLCP2iSPyOgV3D5PSMtu2bVu322lra2P+/PmoqkpRUVGX05KZsGHDBvbs2dOv1z7KxIkTuXjxYr9emxoEWbZsGS+//DKXLl1iy5YtLFq0iGPHjmXl+PqCYRhs376dlStXDnpG0S5LuJKaaXZZwu/3EwgEcra/efPm9foZG6ZJU0csZ8HdwYMHGTVq1ICDO7CSMOX5LgTBWsT7nLacZ+oepTUU5167Fdy57BIVhZ50JtEuW9eAUUUe/C7r2FTdoDkY51pDkGsNHTQHY+lExB8DRT4nIwrcyKKAYVqSKrphYmKdewNN6PVZ6HhkoZvbLVZwd7c1wsiiwRG+HQipBsfeAryGhgY8ntw1PPaH+vp6jh07hmEYzJgxo8eL7sWLF3G5XI81SVdXV7Nv3z6WPLcSyE6A50w2i6ZWtqOKvVnvy/T6fJhzF3Di1g2ELlL5RUVFvZrLr3zjTQ788mcs8gjYbb0HXjYx+cWSpIx1HyMJlbaOMLIsU1HUt96/Y8eOPWbdNNg4HI50oFTodaJoBqGYyv22KFUl3kFdWbvdblatWsX/+l//K90HlsrUiqJIS0sLQK9CzL0hiiJVVVXU1dVx+fJl1qxZ0+fpzX/7t39LvyZbw1mbNm1KZwMzzQjqhklCHbi9YzQaZdeuXWzYsCHtCzvYaMmkgU22vII/+eQTli9fnpN9VVZWcu7cuW6HcBKqTkMgmh7SK/A4stpzfOfOHa5evcq3v/3trG2zyOfEJou4H/KIfhi9L071faSpI5aW4Mpz2ynxO7ssEzvtMk67TLHfJBSzyraRhIZhQiCiEIyqjChwf6EEknvC67ThcchWcKcbSJKIJAroWfDy7fMnKImWl2Uqg3e3NUJFFvR/comc1Pzp7eR+7733+Iu/+IvBOKSMOX36NDNmzGDq1KmcOHGCU6dOddk71NTUxL1797psNPd4PCiKkh6wyNbvqsjnJBK3Bi6aOmKMyILF16NMnTMX5nR985w2bRpHjx7t9ca8Kh3k0WuQJwmQ73QSaQ6gqHqvk9emadLcEef999/na19+uc+fbTAYpLDwcU/XweTKlSssXbo0fVMvy3MRiasYptVLM9jf7fz8fCZPnvzYuaxpWp8zqz0xa9Yszp49y9e//nVqamr6lA00DIPS0tKcTHymAjtRFNNyL3fu3KGysrLL57eG4nRElQEFIHfu3OHUqVO88sorQyrh8rCMh91uR9NyUx1IsXbt2seGcFTdGpYKxqwMtpjMjPVlAvTv//7vu7XFSwU9iUSC733vewM4+q7pSr5KN0xagjEiCRVZFNP3xGwRjqvp4K4sz4U/A3tCSRTI9zjI9zgwTZNoQqMl6ed7ty1Cid/5RPX6DwRBEKyhvod647OROOtXiJwyLE8Febeaw3gcMn63Hc8j3qZPCg0NDRQUFAy7Y9+8eTPnz5/n4MGDrFy5kjNnznDixAmCwWCn59nt9h5vNsFQmI6kZlM2JTBGFLi53RohHFdpDcUHVdCyLzeAvgR5HpuA3tFBJKH1GuAFIgr3GhrxuF2MLMnP9NCHFZs2beKzzz5LT48LgoDPZaMjqhJXdPzZq0hlTFFR0WNN8LIs93vatjtmzZoF0OdA4uTJk8yePTurx/IoS5cu5Q9/+AMzZ87k2LFjPP/8849NXccVLX1jdTn6F4hfu3aN27dvZ1UAuL+k2moHS1RfFEXGjx/PxYsXmTR5Mm3hRNpTFsDjkCnLz8xbNoVpmowbN44/+7M/y8Uh94mU2kEw6V5kT/qpZlvPNpIU/HXYpH7JqAmCgMdpw+WQaQrECMVVmoOWU0SRz9nvTFZPfrF/DPQ7RLRJIpVFnrQzRCShcb89yo2mEK2hONGk0e5wINUr2NNJ8t577/HVr351sA6pT0ybNo1EwgrO5syZg91uxzRNVq9enf7Tm7XP1JlzqT1ylLpjRzlW+xGKknlfWk847XK6JyXlWTtcWfXGm3wSUUj08t49ksm0PDcf1uzr8RzWDYO2cJyamhreeP2VPpeoNU0bFoK3Fy9efCwrnCpL2eWhOb5nn32W06dPD8q+Lly4wKhRo/r0mkAgkHNRYKfTycaNG6mvr+eb3/wmdXV1nX6uaDp326JAqszTv5v2rVu30pP2Q03qXpwq1aaue7lkxowZ3G1s4bfba2gLxQGrZ7SyyENFoafPwd1f//Vf51xcuzfiqs799ig3m0Jpa0q7LFJe6KYwB4vw1DRsQtW52RQiGOvf/UUUBMoL3BQle4GDMZUbTSFuNoVoD/ftXIjEVW41h9Pn0h8jAypyy5JIZZGXhGo5GwSjKrph0hZOAAlkUcDvtnzXhrKEG0qm2p3d2JQN1+xdiiNHjrB06dL0/1NZh76QXzKCxYVllOW5cMpw+PBhVFWlpKRkwHIReW47CVWnI6rQ0B6lssjTa+ZrqFj1xpsc/PUvWICCoxtdKa8Ek8pL+cOpU7Q/v4pCf9el55ZgnE+OHWfe7JnkZVCSeBRFUWhvb6ehoYFr164Rj8e7fJ5hGPj9/pzJViQSiU6BpmGaxJPl/KHqgzl79mzaASLXnD9/ntdff71PrxmswNzpdKZLx4sXL6a2tpalS5dawV1rBMM0ccgD90AdLngcNqsPK6bid2vMnj0755It0YTG6MmzKE8k+GD3Lt782mv43P0rDQqCJew9fvz4LB9l7ximSSSuEUz2taVw22XyvfaculClSrItoTiaYdIYiBGKqowodPcrG1vodeK0ybQEYyQ0A1U3aAnF0Q0zozYERdNpCMQwktacXySrtL6QlauUwyZR4ncxrsxHWb4Lr9OGKAhoyWDvVnOYu62RjKZYc0HqZO/OWms4Z+8AYrHYgAVRU5NJDpuE3W5n9erVrF27Fo/Hw86dOwc8PVzid+J2yJjA3bZoVsVys83Kr3+T43G920yeKEC+bLJm8gR+/M8/6nJaPK7q3G8JcOPGDZ5fvqhfiwO3281LL71EQ0MDM2fOpLq6uss/a9asIT8/P+2EkW0WLFjA9u3buXXrFmBJpJhYWfrBXJidP3+e3bt3U1NTQ2FhYU400Lpi3rx5HDrU9yntwaa0tJRgMIii6dxpjaAZZtJC0PNECc/3hNsh40u2kNxri1JWPoLGxsasqxsYpkkwpnC3NcLdtggAhX4PX3t5HZc+OzegbX/lK1/ht7/9bTYOMyOiCY2G9ijXG4I0BKLp+53PaaOq2MvIIs+gWIz63XbGlPoo8jkQgKii0RHpf6XI7ZCpKvExrsxPcTJAa48keszk6YYV0N1uCWOYJnZZpPAJcRnJBVldhgqCgN9lZ0SBm7FlPsrz3biTmZyoonG7Jdzv1G1/0Q0z3bjbXQbPZsueEvlwJXWjjj8SZE+ePJnVq1ezdetWmpqa+r19a0TfjUMWMUyTe23RYZ0aX/n1b3I8YXQb5BXIkOe0Mdlp470/7O4U5JlJ6YT33nuPN7/x1QGJddrtdubMmdOrbdnkyZOZOnUq27dv7/e+uqO0tJTZs2fT1NREXNFoDVkX0MG+ME6bNg1VVVmzZk23AwW5YOLEiYwcOZL9+/dn/JpEIsGNGzdyeFRdU1FZxdEzn6Mb1s2rssgz4Em74UZpvispG2ESSaisXr2agwcPDmibiqZb05pJjbbrDUEaAzGiyoNgqLzATVFR0WP9zXfu3GHfvn3s27ePAwcOEA6He9xXSUkJgUAgZy1KimZVzJo6YtxsCnG3LUIorqYXZQUeB6NLvJQX5N7M/lFEQei0TyMLn4EkWhq8qbJtayjeSUolFdTdbYtwvTFIU0cMw7SSGZVFni/8vb0nclZ/EZON2j6XDUXTaQ5afXmNgRhxRe92hDrbPHyCdZcqlqThOwEM1s39/PnzAxLDdTtk4qpOJK4+Vk50u928+uqr7N+/n/z8/H6XbCXRMla+kxy+uTMIE9YDWdmv/NobfPRvv2KemcD9iHK8IMAIu4k+spwdZ89SO3osUyeMwS5LBKMJfv6r3/DCC2soKxg8q6mKigpWr17NO++8wyuvvJI1kV6ATz/9lC9t3Eh9s3XzSg1NDSaiKDJ16tS0MPdgMn78eGRZ7jSlHggE+Oijj6isrOSZZ55JP/fSpUsIgsC5c+eyol+WKeG4StR0UH/7GmNHVzGyqG/9Yd0xZ84c3nnnHUpLS4dFL54oCHhdyVJtVGFkoYdEIoGiKH2264omNFpD8ccWtmAFQ6l7VHfXqPr6ei5dukR1dXV6qrmuro5oNNrjfktLS/m3f/s3vva1r/XpeLtC0SwLwbiqE1f0x4ImUQCfy47fZRvy1phIQuVesi/UJon9al3pjkKvk3BMJaEZ3G+P4nFYsUU4OfGfIrXfPI990IZ1hiuDcjbYZYmKAjdt4URatRkY1L6Rnn7Nwz3Cr6qqYu/evQMK8LxOG23hRHr4pav3vHr1aq5cucK2bdvwer3k5eX12ZdXfmTC+nZLhNI8F15nbqarB9oLteJPvsHxD2souneTMQX5nX5mE6HMZjK+fAR7Dx2jrq4OURRRlAQb1q9j2rjKQdeA9Hq9vPbaa7z33ns8//zzWXE6qK2tZcGCBbSHlXTZrywH7iSZMHHiRHbt2sWkSZMGXYtt9OjRnWRYjh49yubNm2loaKCmpoampiaKi4sZN24c69evH7TjMkyTQCTBvZYgp06f5qWNGygvcGetLFtYWIjD4RgWwV2KfI+DQMSSdqo9/TlRXWL7jvd57dWXM3q9quu0hRJpqRN4IDvhdsj4XLYeM++///3vWbVqFRcvXmTt2rXpx2VZzsj3Ox6P89/+23/L6Fi7ItVPF4opRLoQknckje5ddhmPUx42gUww+uDzHlWcnQXIw5TkubjTGiGm6GnZL3gQrHudtkHPWg5nBi3cFwQhLcLYGIglGx8dWT8BHiXTNPndu3cZOXJkTo+lv5w/f55x48YNaBsOm4QoWGWPhGZYmjtdMHHixLQ36r1799i5cyczZ858TDy5J1IT1vfaIiQ0g4ZAFEkUcNllBMGSQjAME91MWbJYU2v5HseQfDkXPL+GpoYG6mp2McNpw/WQ6KkB2OwSb23aSIHPiaZbpbE8t33ISmOiKPLaa6+xa9cuZsyY0aOpfW/E43GCwSAlpaXcaAwBVj/lUPZ0rVu3jj179gxYzLg/pK4XD2eGy8vLKS8v77dBfX+JKxrNwQfZJ4fD0gyrKHR3uVgKBAL89Kc/5T/8h//Q532lMmMXL17kwoULQ66HV/vxISIJnWDCpLSsjBlz5yOJIpG4mpZ5UnXLnccui+nPQ9F02sIJQjEV0zARRMsiq9DryPj7mvIFvnLlSr8XUD/84Q/5u7/7uz6/LqHqBCLJ43/ocXcykHPaJBy2nn1mhxKPQ04rKTR1xLOujeqyy4wq8hCMqRiGiSyJuOxS1qS/vmgM+jfY77LjSEovdLUyGQrefPNNfvOb3xCJRIb6UB7DMAxu3ryZFUN6h8363DMddqmoqGDjxo20trayc+fOPkmryJJIZbGXQq8DUbD6JMJxlVBMtUpNikZC1VF1A0UzCMZU6lvCjzXQplLwMaXrc0UQhKyIoZaWl7PojT/js4Iy6ts70o+bAKKAy2GjxO9iRIF7QLpM2WTDhg1cunQp7e7QHz744APWrl1Le9iSUnDI4pBfLEVR7LfZerY4fPgwzz33XKfHotFoTq2zNN0gplh9Yi3BOLdbI+ngziZZk7Jrli+itrb2sdc2NDRw6NChfmvzTZ8+nZ///OeoqsrGjRt57733BvReBkpraytOGUrcYNOjuB02TOBee5QbjUFuNAa52RSiviXM9cYg1xs7uNYQ5FZzOK2a4HbKjCz0UJrnyvj7GggE+Pzzz1myZAmLFy/u9/Tu5s2b2bt3b8bPtwZnwtS3hAkmgztZFCjwONKDEvkeB0778NaZ9bvtVBRaQV04rubEdiwlzVVe4KbY7xzy69VwZkgK9k67TEJTBkVENZW67imPJwgCf/u3f8s//dM/9WvVlUv27NnDCy+8kJVtOWwSMUXv8zTzvHnzmDNnDtu2beO5557LWP9LTGZtC7wO4oqOoulp+y9REBBFIZ1VDEatUkRLyDKjtkki0YTWabghz23HbiY4cuQI8qscpQABAABJREFUixYtorCwkFGjRnHr1q2syRIseP4FGu7dY8fWdymvHEXMZuN8uIPR0TiK5hp2ji1r1qxJ20v1lStXrjBmzBgEQUiLYOdCI6s/DNVNLBwOc+vWLSKRyGOT65s2beLQoUOEw2GWLVvW62BMpoRiKm3heFp78GG8ThtFPseD885dmm76f5RNmzb1aVDkYaqqqvjTP/3T9P+ff/559u3bN2R6bq+++mr63/v27aOyyENz0HLs0B5quBIAw7QcaFK9aR6HTKHX0ed+NMMwqKmp4ctf/vKAj3/s2LEZex2nhtJS1zqv00a+xz6g4a2hxP3QcRsmDK8r5h8XQxPg2SQ6yDyTNBDEh0pNumF2W3q6cOHCgHrccoXT6SQcDmfFzsqRvEmoXdxIekMURV5++WW2bdvWydYno9cKAm6H3KOmmtdpoz2csAK8RzK7AlaA/tGR44zwSWzYsIGDBw8iyzILFizgyJEjWdWdKq+owDdlOouSN7fxzSF27t6LfU01VSX+YRfkVVZWcuvWrT6V0QGuX7/O2rVr0Q0j3aTs+SPxf+yOzZs3U1dXx5QpU7r8ecoXNbXYGcj30jDNtGp/CpskIgogSSJ+l71LV4CehqCyNbmZjd7ObDFmzBiuXr3KxIkTKfI5UDUjKdwrIQqWQXtq4WhL+nj2h61bt/Lyy5n1+GXCvHnzMrpeBqMKqm4giQKVRZ5hd33pK6lAVSA7dltP6T9D8unbkiXaXBofpxAFIf2FV3vQZjtw4ADr1q3L+fFkyuHDhzEMg+XLl3PkyJGsbDNVpuhK1y1TFi9enDPNsAKvNd5f5HNQ4HFQ4nfiSWrrAZiaknbsWLlyJZMmTWLPnj3EYrGcHE+KEYUe1r6whg9q9nHlbmtyTH94uLSAJXx94cKFPr/uQY/VgxtiNmQNnnTmz5/f63Ts5s2b+50tA+tzvtMSTgd3hV4H48p8jCn1UVXiY2Shp1+WT9kklUU1DINt27bl3Be2O8aPH8/NmzcBywvdaZdx2WUkUUh7eLrtVn9af4O7vXv3smLFiqxOpi9cuJBwOMzt27e7fY5pmum2lEKv44kP7uCBFNdQueA85QFDsly3PRRoqLqR8yjfJonoho6iG3RVgDpy5MigyzL0xJUrV/B6vWzbto1gMMjXv/71rGw3dfEbSHBSWlrKtWvXejQ/Hwh2WaLQK2GYJvXN4XQw6rKJ+O2dj7u0tHRQ/DNtksjoEh8vbljLhwcOkZ+fz4wZM3AkbyjWgIg95wNDPdGVlI5hGJw9e5bW1lY0TWP69Onp39nDQwSSaGU+VN0gFFOHhcH3cLE57IlnnnmGixcvdmsq3xMdEYWEZiAKAhWF7qyV45xOJ9FoNKsTyIqi0NHRwYEDBzAMA1mSqB7EYZNcc/z4ccaNG5cz67mCgoJufxZNaGjJytJgyxLlikjcWgi4/sirAcOBIfkNyJKI0yYRV3XutUWoLPLmdGrPJonEVR1d7/qmsXjxYv77f//vw0YmwOfz8emnn7Jx40ZkWc7aqjIV2A30s168eDEHDx7k/v37zJ8/PxuH9hhxRU8HdxWFbi6e+5Q5s/tu0ZYtZElkVLGP1za+wOfX6/nkk0/S+ont7e08v3olkyqLh6x3bPz48fzmN7+hoaEBQRDSAdK0adPSi5cTJ05w9uxZ5s2bx+3bt5k7d2769fkeO83BOG3hBD6XbUiDVSBrXsm5ZOzYsdTU1PQrwEu1IRR6HVkJ7rZs2UJ+fj7BYJCDBw9it9spLi7u98L1/PnztLS0pHv9vvGNb6BpGjeuXqXleC0nfvZjChcvZ9zkyYC1YDj+0QGKy0cwYeq0Ab+fwSKlaZiNIbauWLp0KQcOHODFF1/s8ueJZFXJbR8+UicDIRhV0lO0vm6co54yeAxZiF1e4OZ2SxhFM2gJxnKqu9VbaVIQBL70pS+xZcuWTs29Q0V5eTl2ux273Z5VqQItmbWRpYFfSFauXMmtW7fYtm0b69ev77MAaW+ksmO6YdIRUWhqauqzJl8u8LlszJ8+ntmTx5BQrd6fxvYwdSdOMqp01ZB5twKsWLGC+/fvd/s5pR4/duwYn332Wafg3O+2E4goiMLQNUb/6Ec/YsyYMVRWVjJixIh+9RU+CQSjStpBIVvnS35+/mMDEffv3+fDDz9MLzry8vKYN29er9eUa9euEQgE+MpXvtLpcVmWady9jemVFeS73TSdOMyh0ydwCAJ50SCz83y03LvJyeYmnlm+Mv26uoMHUG5cYXT1Wiqr+vf71DSNhoYGysvL+/X6rmhqaqK+vj7t9ZsLxowZwwcffNDtz1OJ6sRDA2hPMs1By087z23vVorrKYPHkN2NLDFVF/faojmXS0kFNCnrrGvXrrFr1y48Hg9gOVlMmTKFlpYW7ty5gyiKXLhwAUEQiEajbNy4cVA1oS5evMjEiROzvs/U+5eztN3Ro0czatQo9uzZ00k/LxsIAhT7nDR2xGhsDWBzerK27Wxgl6V0v0xHxOqVGuqy4siRI/n00097fd7ChQsfk38QBYGRRR7kZF/TUDBhwgSqq6u5ceMGly9f5tNPP+002Tkc6c226lF0w0zfBAu9udV9HDFiBCNGjEj/P5WR6+jo4LXXXuvyNbdu3eLmzZtdTs+Kooh75Cjy3VYJv9TvpzT1Q3cRABU2O557Nzm8dQsLN26i9ne/YbYN/MUF3PqohpOjJ/LM8hUZv4f9+/ejqipjxozJanCnKAoff/zxkC/o8z3WwkrRDFpDcUpyLSuRY1LXwKdiw8ODIS2SO23W7lN+sbkq06YyeJph0tzczI4dO/ibv/mb9M8TiQQXLlzA4/Fw4MABZs+enbamURSF3//+93z5y18etCDvypUrOektS5dos5DBSyGKIhs2bGDXrl2MHj16wJm81GRhU1sHwVCIkpIS6urq+JOXv5SlI84+mmFiGAbSEE+MaZpGc3NzvzNfw2HiLdVHOJgWYP1l3759rFiRebACEIgkMEzLLWSwvX6Li4tZs2YN9+7d69IOrqampstM4MMomgb0fNx5Hg9zlRgXf/YjlpWXIiSHeEbn55F/7wYH/u02K17/WkbX02XLlrFv375+lcF74qOPPmLp0qVZ3WZ3TJo0qVurSUkUKc93ca89SiCi4HHYhrQKMFDyPQ7aIwmaOmLYZfGJlXoZKKpuoCSHTSRRQJLEIbm+DumnL4lCugynaHrOToZUQiIej/OrX/yY//gf/2OnnzscDubNm9el/IDdbmfz5s28++67j5UssoFhGPzyl79kyZIlFBQUsH///pwp+BvJAC8XZYB169axa9cuNm7cOKDtBKMKl2/Uc/XqVSoqKrhdf4tFz87F4xyeFwrDNGlrD+D1erElA+dr166lp/7AEqH9kz/5k5wvEERRpKio6Ikta1ZXV3P27Fn279/P6tWrh/pwesUwjD7JpKi6QSBiTUwW+bLjxW0YBvv27SORSPT+5CQVFRWdpq7r6+s5ffo0q1at6lHbr+7gAabZMjtmp93B9PKyxx7P83hYousce/vHjF3/IuUVFen3cejQIRKJBIlEgnnz5lFZWYndbkeWZeLxOE5n9jQa16xZw7Zt23p9z9lgxYoV/OQnP+lWhsvjtOF32QjGVBoCUUYVe4fFYqs/FPudaLpBKK4SiCh/dAGephs0dcS6rEqKAthkCbskIooCgmAtql12OWcZzyH/9B02iWhCI5rQcnYySIKAruv88he/5D//3d/1+cLqdDrZsGFDTnr0RFFkyZIl7Nmzh5kzZ+YkiEwhidYqIhcuDKIo9rtvqqGhgXPnziEIAqGEwdX6+7z+5Vcp9DoQhJTe1eCWDW02W0Y3FQHweT00NjZy5vMb3L12kXFjx3TKgsTjcQ4ePJjzoEUURUpKSrh37x4VyRvnk8asWbNoampiy5YtvPjii1nv7cwm06dP7zIT9jCKZhnER5MOFYZpuYUMVAIlGo1y8OBBwPKQ7mvwU1JSwv79+zFNk6Kiol612poaGii4dRVfYX4/j/gBsiSxoLSQ6x++z/0J02itv8m9uMprX/lKevr36NGjXLhwAb/fj6qqtLS0ZH1qf/Pmzbzzzjts2rQpq8Hjo2RyvynJcxFLDpbda4swusSXs+PJNX63nVBcHRSd2+GCoumEYirBh4S47bKIgFU50wwTw7S0f7v6XGySZX/pd2d3wE0wh7hxKBhTaAzEEAUYU+rPSZk2oen880//jerVq5g9of9+s4FAgI8++qjPQr+ZYBgGP/7xj/ne976X9W0PJjt27Oh2YuxhDh48mNbVKioqSk90xlWdLbv2M3bsWEpKStLPd9klyvPdg2YRljKdz0RAORxXOXv5Fk1NTTwzZxaVRZ7HLup79+7tZFqeS3bu3DngTOpQYxgGO3bsYO7cuQPy2s01qd9rTLEWqSkRcc2wLPgelSSyyyIjCtz91jtraGjg1KlTOBwOVq1aNWhtI7U//wmLCvOyvt2OcBiv281x08ayl17p9LNwOEw8Hs+ZfAlY59kPf/hDvv/97+dsHwC//vWvURSFioqKbq8Dmm5wo8nygx5X5hvySfb+ouoGN5PvY+KI7J8zw4GEqhOMKRiGiWnSSaxcEgVGFno6ZeVM00TVDRKqgaZbw3mGaaJoBrGE1slpK+WtrOnW9UPAMmyQks5PkihQUZhZT/qQZ/B8ThttUgJVt5pMS/Oy32QqiwKqquL2eNENo99fnPz8fJYuXZpxENMXzp49mzPJkcHixo0bdHR0UFdX1+N72bJlC2vWrOmyNOK0Sby0biW7aw4iGBoVI0daXwJF505rhMoiz6AEeSNHjuT48eMZBXhep415U8dyt7SEuKoTjmuPZWhyUWbqjq408YYCw7Qufv1ZtImiyObNmzl8+DCNjY3D9ruR0EzqW8JcunKNy5cvs3r16sdkjZw2CZfdcnJx2ftnFN/W1sYnn3xCUVFRzlo4esIl5qaElJe0g/N1Mazi9Xofs4vrD2fOnCEQCKRlsOrr67ly5Ur656+88ko3r8wer7/+OqFQiD/84Q80NTVRWlr62HNkSUQWBTTDRNUMJPuTGeDJD33fNd0YFr7d2SSSULnXFn3scbddxuOUu5SZEgSh02DewximSTimEogkSGjGY5aFJg/mFPrKkH/ygiCkg7qOqEIwml39qx/84AdsefddRo20pslSRtT9pbi4mAULFrB79+5sHB6GYbB3715UVe3Rgmg4E41G2bFjB4FAgG984xu0tLR0+TzDMHj33XdZv359j30vHoeN1zauQQ+3EG25w+gSrxWk60Z6AjHXOJ1OVDXzc8Vpk9JCpTHl8f6LVatWDcj5oC9MnDiR69evD8q+eqIxEON2S5h4F59HpixbtoxLly5l8aiyQ0zRqG8JYzrz2H/wEJIk8uL6NVw5f5pin5OyPBejijyML/czqthLsd+J29F/o/gDBw6wYcOGx6afB4twfhGGmX3j+BS+WIR4Dhxp7ty5QyAQwOFwUFNTQ01NDcFgkOrq6vSfbE7ndocsyxQUFPDmm2/y61//utvnpVyeuvIlflIQHnKP0oaR44+VQdMH7NYTCFsxikO2BqUKvQ5GFXkYWeQh3+PocwJJFCyR66oSy8mmosDNiAI3o4q9jCn1MbrEy6hiLxWFbsrz3ZT4M08SDHkGDywtqILk9E1jRwwE8GdRJHH69OlUjB5PSyhBWziB12kb0KqirKyM2bNnD7jsdvToUYLBIKtWrRrWvUY9cfDgQRKJBF/60pfS5aLZs2c/lsVLBXd96XdZtmwZdXV1nD/7KdNmzOJ2a4RwXMU0zSGT8uiJ1Oos3kWPhSiKeL1eWlpaclpySrFkyRIOHTqU9k4dbHTDupiqusHt1gguu4TfZcdhk9IliEzYuXPnkGSsuiOu6rQ+5Jc8fdo08j0OCryWk8lFUacgy9Oxu3btGrTyfneUTZhI+MxR/N7c9IaNKi7k6P59LP9S9loLFEWhrq4uq/6yA0UQBJYsWcK+ffu6nFa2yxIxRScQSeC0d53xeRIQBQEd8zHpKD3ZuqDpJnpal1XEZZdyWpJuDEQJPpTccScHG2RJwJHMsCuaTls4gSgIFHgdXQ66NAaiaQ3LPI+DvCy7j9h6nLbt+7kwLAI86Dx90xiIoWoGRb6Bl7NKS0vJy8sjz+MgGFNRNKuJdWSRZ0AnVEVFBaqqdvtFzYRoNDrkF+6BoCgKiUTisfdQUVHxmB7bu+++y0svvdTnQHb+/Pl88MEHtCX9Ggdz2CIQCHDjxo2MJTtS8gYJVachEKXE7+pUnly+fDnbtm2juro6K6WnnmhoaOjThGe2kUSRUcVemoMxQjGVmKITU6wMjSgIjCx04+xlqGrnzp0sWrRoSN9HCkXTaQslOvXa5LntFHodnRaLHo+HYDA44MnMlpYWzp49SzQaZeLEiTk/X3qj6eZ1JuQouAMQBRF3uCOr29y+ffuglF/7ysKFC/lf/+t/sXz5cmy2zq0c+R47oZhKQjO41RzG67RR7Hc+cVO1qeBN000CkUTy+691W2YUsGKAXNgkBmNKOrhLCblHFS0dqIHVRmKa1iAEWNVEhyyiGSayaAWBimakF+9ep23IvaIzYVidNWX5LvI9VgDQFk7QHOx7yv7v//7vO5nPt7W1UVRUhCgIjChwIwoCCc3gTmskLfzbX0aPHs24cePS02z94WFP0CeNnpw2Zs+ezYkTJwAry7du3bp+ZSl1wySQENJj52X5rkHL3r366qu0t7ezc+dOgsFgr8+3SWK63SAUU7nZFHqsXLt582b27t1LNPp4D0e20DSNHTt2kJ+fn7N9ZIIkCpTnuxlT6kuL+gpYPSf326M99pSkgrvByHb2REr24FZzOB3c+Zw2Rpd4Kc1zPVYJWLJkCUePHu3zfmpra9m3b1/6z82bN1m+fDkbN25kctIObCixK5nLsPQXb7RvotE9sW/fPlauXDmoAvV94c/+7M/4yU9+8tjjdlliVLEnvVgMx1Xqm0MEIgniqhUkKZo+5KLqvZE6vIZAlOZgnHBc/f+z96fRUd1pmi/620PMg0IzAoHAgDEIg5gMGGywMcYmsZ2203ZmVmU5s7qqMrO6q0/frj63T9/7qde6X3o43avP6sqq7q6s7JzTmXY5jUlIjAUYgwGDAWOwLECMQmhWKMYde7wfdkQgoSkiFCEJZz1reRlJEbF3ROzh+b/v8z5P9nyX0/ndPpeM3+3AIYlY2CkYql68yVvDtOgKJ+gK23yg3Odi4awyGqr9VAfdlHmd+FxydtLVtO7uG0AqPSSV0k0iSS1L7mrLPFkuMdMxYyp4YJevq4MeHJJIT0QhHFcp8zrzKlM3NDTwN3/zN7zwwgtUVlbidDrxeOybbubkae+Lo+omt3pjlPnstpHXWZg+ZsGCBWiaxtGjR9m8efO4jz137hxA1lZh0aJFtLa2Ft3EcyYgU8VLJBKkUqmCKxp9UQXNpOih7Lki44948OBBnE7nhN9xmdeZPn6T2Wrx3Cr/sGP45ZdfHnfQZLKQZZl/+2//La2trdks0QxUVeWxxx6b0oqQQxKpDLipDNgX0ps9UXTTIpJQR21nzgRyZ5gmAzGVvqiCINjXJq9LpjLgHjeCSRTFgkhFKpUquBNQSpxsfh853Ed1MgZlpfWLq/d7OXPiOKs3bJzU65w7d47Zs2dP++JgPPj9/qwfYWNj47C/OWWJORU+UppB92ASRTNGaI8FbDuSykD+mq+pQFXQTW96nz1OGY9Lyvq9jUaM2vtiJFW7RVpbVpxFfKZ7APZ1uTJgX2vuHXYwLStbXfS5HEiikJWX2Npv26dXEOx83fupmjqjCF4GoXQ7NaUZKJqRF8F78cUXOXHiBCdOnODcuXP8x//4H4f93SlL1Ff66OhPpCd37ZWpJNrkspCy64MPPohhGBw/fpyNG+9enI4fP053d3e20lVRUUFNTQ179+6lvLyccDjMgw8+mPf2ZhJM08Q0zVFvaitXruRv/uZv+Ou//uuCXz+jdaop80yraeaTTz7J/v37x3yvQ+F1ycyt8tPeF89epOsrhxOql19+mbfffpsnnniiJJU2URRZunTpiMWDaZocPXqUcDhckrSUiSCJAiGfi96oQkLVKb8nFcE0TTRNm7abs2HaLaVwPEUipdMXVQh5XTw4pwyvq7gtmYz/I9hm6zMRlppioa4QKDG5A/C43Wgd7ZN6jY6ODiKRyLRpT/PBCy+8wH/9r/91BMHLwOWw71XhuEokqWKYdlatbphY2G3EaFIj4HHgdkr43Y4ZU1UK+Vx5tVvL/S6S/QmiSY142oVgLB1crkgO6fqMp+kXBQHfPee2yyFlbU5sodjMb8eOhmn3wRsLdwYSxBSN6gL68n/7t3/L97///XFvxka6gpDSbBPSTPl4bpW/4JDkCxcukEwmWbduHXv27KGxsTGr37rXm6y3txev15s19rxf0dvby/Xr18cMuFdVdVIDJB39cd47+AE7tm2lrnx6P6tYLMaZM2dyvnlohsmN7igWdlk/OIog95133mHTpk1TTmguXLhAIBCYltSLlGZws9dux9WUedASEfbu3cucOXOyK/epTrIwLZvYDcRSWR2OrhuouonLKeGS7SnpMq9zwgGtAwcOjAiw13Wdjz76iFQqhSiKmKZJdXX1uCbJ0w01leKjt99kqalSFZiaim9LOMryb/1pQc9VVZV9+/aVxKe0VGhra+PMmTO88sorOT/HsiySqpHtEmQgiQK1ZR587vuTjESTGj2RZPZeLApQV+4rKLpt6DVmQU3gS2fVkitmZAUP7saLFeL9snTpUj799FNWrlw55mMkUci2h6y0Jiie0unoj+N3O/A4Zfzu/Nq2y5cv59y5c/z6179m586dw9pg9xK5mdw+yAdVVVWcPXt2zL9Pdjq4MuCmpqaGy9duUu5bNKEwv5Tw+/15RUI5JJGKgIu+aIqeiILHJY9Ykb7wwgvs2bOHRx55ZFRvrFJh+fLl7N+/PyeCd/HiRVpaWqioqCgK8XI5JMq8TgYTKr2RJG3nz/Anf/Ink37dQhFJqvRFlGEO9BV+NwGPg5ii0TOYRDct+mP2FL7PJVPhd415LI62qPzd737H9u3b75sF3SdHPsDV9gWbq8oRmLoJ/2rBpP3mTTout+LouEUiWM7iDY9SUzsy9uxevPvuuzNqYjYXLFy4kAMHDuT1nIxcYF6Vn3hKJ6nqxJIaumnRMZCgKuAu+iT3VCDgceB3y3blPJYipRncGYgzvyZ/0+dMJOBkHTPud8zYd+5M+wEpav6iy61bt/LRRx/l/PiMF59DEjFMi8GESmc4wfXuKDElP9+8pqYmXn311REap2vXrqEoU+Ph9mWCyyGxesUyrly5Ql+s9ELvYqPc58Ili/ZgQX98hAeTYVo8teNZPjjy4ZQP3ASDQY4ePTrm303TZPfu3ViWxde+9jVqa2s5evQozc3Nkz6Wq4Nuujo72PO7vfjKpmdK1rQsOgdsEXZmWq62zMO8Kn9WquF3O2ioCVAb8mQr+4mUzq2+OB2jfJ9jwe/3Twm50w2TpKozmFAJx1PEh4jbc8XRn/wDD3bdZGlVBQJT2/KrKivj2G9+SUNXOytCATaIOq0/+wcu3jOVfy+am5vZsmXLjB2qGA+FLoIFQcDvdlAd9NBQE8hadvRGFeJ53rdmCgRBwOd2MLfSh1MWMS27FZ0vMgMR98OkaykxYyt4freDvmiKhKoTSap5++J5PB6i0SiBQG6j/bJk2zrEFS2dRmCviO4MJPC7HdSUuSclZv3Od76T9Yyrqqqasc78MxEVfheLFy/m9NnzVG95ZFq9oUbz+BsPgiBQV+HjZk+MlG5yM219YGGRTOmk0i2WZese47fv7uWlF6YuYmzjxo10dnaOqgVsaWnh8uXL7Ny5M5vM0NjYyM2bN5k1axa7d+9m586dBZMWQRCoLAsSCASYu/Ah4imt4EGnQnFnIJHVeFb4XZT7XaNqmERBIOhxEvQ4SWm2EDymaMRTOgOxVE52TsV6X6ZloaQzS3XDzP5fN6ysNmvEtrFvdCGfa8JQ8+6uLhai43VPXxbqCw/MxSHbN2bTMlEWLKZxlG5MLBbj2LFjgD2wdr92RYpxbIj3BAZ0DyaZ55RLEv05FRDS51xvVCGl5b/wlSURVTez0YF/qJixBM8pS1T4XbZdymAybwHpN7/5TX7yk5/wZ3/2Zzk/RxJtR+kg9hRQfzTFQNy+mCdVndkVvoL1eaIoZttb3d3d7Nmzh9ra2vue6JmmycDAQEm34ZQl5s+dw/Xr10mq+Q3dFBs1NTWcOHGCRCKRM7lxSCJzKrzcTg/2DMRHViKdThc1cxpGnaorJWbNmsWLL77ID37wg6wdh2ma1NbWjjqEkcmFfemll3jzzTfZtWtXQSTv2LFjpHSLxx57jJRm0NGfyE7bTsWqO5rUsuRuTkXuOh+XQ6Ku3MtALEVvVMm5w5CROluWZftvpSPcRFHAIY1v/KzqBomUnr4OTby9jFmqINiJCJph2zxEkjaJDvmcY+q0Wj85zcbyUE7vqVTIkDuw/fEqlXj2556eHs6cOQPYnoPbt2+/L6t2Q1FMGXxV0G1nIhsm/TGF6mDxoz+nCo5sFy//JByPUyKRbl/fO8j1h4QZS/DAXlVHkxqaYRJNanm5Rn/xxReUlRUedCwKAlVBN36Pg84B+8Z8uy9GfaV/wlXwRKiqqiIej7NmzZpJvc5MwNtvvz0l05iCMLq2aTqwa9cu3n77bV5++eWcn+N2ysyvCRBNqllhtNsh4XXJ6Om0h9lzG7h24dSUErwMlixZkpdNhyiKfO1rX+Mf//EfC6rkJRIJtm/fjqIZRBIq0aSKZph0hhOAt+QkL0OyK/yugkTcHpcMUTuybLy8zWzIuG7RE0kSSWijtnWdsng35SPdFk3pBinNGBFbJYkCLlnCIYvIkpDOMLX/PRpZTKo64bhKTNGyBq8ep8SskHfEfruVBIJjZlV9Zhkab//d32BW1TKvoeG+NocvNURBoDLgpjOcIJJQ72uC50171OmmhaIZeRVXMguhmTJVPF2Y0QRPEATKvHaZtj+qEPQ4cipnX716lWPHjvH9739/0vvgdkjMrfJzu9+2vLjdH2delX9Sws0MOZgphKVQ7N27lyeeeCLn6LFCYZgW4Ugc0zQLrqAWE6Iosm3btryj6jIWIfcic8M3Z1BuYy7IkLw333yTZ555Ji9vvcrKSjo7O5k1axbuMg+VAXfWtyqSUEtK8OIp24IJKDhqyCWLiIKAaVkcP/sFdzraWbhwIbNn1xFWTO4MJFBUlYuftzIYiWAYBuG4rSWSRAFZFDAte9Ia7ErbePmjHqeEz+XA55bzrmB7nDIep4xmmITjKQbjKknVoKM/Tn2Vf9hN0K0kwDGzBkFUQWD+wytZtWl8D8p/gr2gyOjvZqI/Xj4Q03q8mKIRS2o5X/t7Ism70ovAH271DmY4wQMo8zkZiKfQ08MPo90g+/r62Lt3b1b4fevWLf79v//3RdsHSRSYU+HjVm8MzTDpGEgwt9JXsHbC5XLd9+Tu4MGDrFixYkpipMLxFB+fOsUTWx6bdPW0WAiFQsyfP5/Tp0+PaRGTKzLEThKFKc/YPXbsGIqijIhMygeFkLzKykouXbqUDXqXRCFbsbdti8yS3KAM06Q77Wwf8k1seTIWhHQyTvdgktvX2lihK0Q/OsIXuo7HMPi8/SaSaZLUTdZ/7es4ZRGnLBHwOOzKRPp7tiwL3bRQ056fmmGSEdI50lW9YuV0OiSR6qCHMq+TW71xUrr9WcxK2w+1XWqlluIlCUwWFhYXegYo3/oUqx5YON27UzJ0d3dz/PhxNmzYMKnzX9EMusKJ7EIho8m7n+HPEDxFoyo4cSEhpd1dSNWUee7bLN9iYcYTvEzJuXswSV80RdDrRBQEvvjiCz788EMAysrKePnll7NtogMHDhT9RimJdpLCrd4YqbSzeKEn0FNPPcWBAwfu21bD8ePHaWhooL6+vuTb0g2Ttlt3CAQCVM6wdsOSJUs4evQoN2/ezGrTCkHmguyQxSmNIGpvb8fn87Fp06ZJv1aG5I3Vrj1w4ACiKBKPx3E4HMyZM2eEn6BTlnDJIindlmQUO5cyY4ekm5ZtYeOfXOXZ65KZHXIh6zGqTIU5VX70IV+fJICmJLjT3sqidY+M+hqCIOBIt1anyr/MKds6wtv9caKKRrxzEIcsceX4KR4tm77hiqFQNY3TkQRrXvtjvD7fdO9OSfFv/+2/5ezZs/zwhz8E7ON05cqVrFu3Luf7WDieyqZdiIJAbchTkPRgpsHnttu0mmHm1KbNREN6nXLB1fkvE+6LIyDocdAftX2qMi7XBw8e5C//8i9HPLa9vZ3aHDyTCoFTtnUrHQMJBhMqPrc8wgE7F7jdbkKhEB0dHcyePbsEe1o6nDlzhkAgwMKFU7Oi7osqfPrpeXZs35b3JPVUYPPmzbzzzjvU1NQU3Kq+m9E4vti+2Lh48WJRFxler3dUy4eMqXcuRDLodQ6LKSzW55Ehd0nVQBRgdoU37wnD3p4evvjgIF7LxKmpOLQULl3jK+UhnI4xKpd+Lzcut8AYBG+64HXJ1JZ5uNUd5ot33qTC6WSW38vtlECN06JsGu8M4USCi5KHx7+d+4Dc/QxBELKRiGAfq6dOncoSPkEQEMW7iz9BEFAUhT//8z9HlmX6Y0o2kcnnkqkNee779mwGQ9u0kYSKe4KiSmZ9rBkmqj69A3kzAfcFwRMEgYDHbtX2RRV8bjmbL3svWlpaRrjIFxM+tyNr0tozqOCplgsScq5fv549e/bcVwSvtbUVwzBYvnz5lGxP1Q0+OnWWpUuXUp1DeX66sG3bNg4ePMjOnTsLer5TFtEMky/arqOq+Xs+zQSYpkkqlcpaqgzFxx9/nPNnE/A47fxhw6Q/RwuSiWBZFp3hJPG0Lqeu3FfQhT9UXk5VpJ/F1VXgloHctGpqX29OEXdTjaDXSffJZnbMr8IwBeKGQMSAXlXAJ1nI06BPvz0YoW/+gzy++bGp3/gMgSAIPPLIIzzyyNiLgmg0yg9+8APc3gAxQ8JCwCOZeCQz66eZWRz5/X4efvhhFi9ePOr5OdMRTBuOR5N2stV4i76Ax0F/LIVm2JZUFQHXpCv19zPum2+73G8TPM0wxzUfnooWV2XAnZ3uvdEdpTbkLagc3tTUlJen2nTj9u3bUxoh1RtJMjAwwPo1TdOaQzsR9u/fPykHfcM0GYgqHPv4DH/2zZfGfVxcsUmKz+2YlMdVW1sb8+fPL/j590IURerr60doEsPhMMFg7jmmkihQFfTQPZikP5bCtKyCJwEzcYSDCTU7zDC7vLBzFUCWZQZ8uU/mDyYSXNBh9bf/YsaRO4BrVy7zQCqGxxsCEfyyhaoIKCZEdSifYo/YS/0DuDZsYVXaruefMDYCgQD/8l/+S270RFFUg6DXQV356K3scDjMuXPnOHnyJIZxV2NZUVHBc889N+NJn9dl+/kZpkUs3cEbC7afrY/uwSRJ1aAvmkIURh9u+0PAzP5msas4SdXITsUAY66+TdOktbWVDRs25HVTyRcZPV5nWs9zuz9OXbkXf54amvr6ej777LO8PNX+UKAbJh8eP8WaNWuKUsUpJfx+/6Ru4PGUQcDrZOnSpRw5forHNqyjJuTJVoZNy2IgluKLqze5fPkKkiSx6dGNzKnwFTx0cuXKlaJrQNetW8cbb7yRJXimafLee+/x6quv5vU6ZV4nerqCF47bg1X5ho7HFY2uwbu5lpJo65IKkVQMhUtVwJvba9w2BR7/o+mLYLsXpz44jNjVQcwXQNA0HogNUHeP511QtlBUgaghUO6YOj3opz39zH7mOWbdRx2N6UYkYVsuyZJATdnY949QKMTWrVtH/L69vZ0f/ehHdHR08M1vfpPFixeP+RqHDh3C6/Wyfv36Yux6Xsi4adjXg9SEE/ZOWaK+0p9tXfdFFSRR/INMtZhxBO9K21X2vfc+miXywotfw7wnKqfC78LtkEat1ImiyD//5/+cU6dOEYvFSKVSNDU1laQN6nHKNNQE6A4niSoadwYSzC735i2U3rFjB2+++SZf/epXJ53bWmpMZfswpmioqkp5WWDGTM6OhWAwSG9vb8FO+rph4nHKrF3+INdu3OLd3x/giS2PUxH0YFkQjiU59MGHVFVV8eSWx4jFE+x/7wAbN2xg2YJZM0Zncvr0aZqamrI/9/f3A6AoSt76xMqAm8GEimFaGIaZM8GzLCst5bA1SQ5JpNzvIuDJzyh9NHz+6ac05NG31IWZVbUzBgdY7XFgGgkMy8RRNnIR7JegB0iZENYgVOR74q2+fjo0g0dmVSEg0B+NckmzePjlrxOchG/plxmaYaZTbwwMw8LCXvRlih4hn6ugan59fT3r1q2jrKyM8+fPs3//fl577TWqq6uzj9F1nb/7u7+jqamJrq4udu/ePSW+p/ciQ/AyvpnBHAYoyn0uIgkt66+paM772hewEMwYgmdZFj/4+x/jCVay44VXiUQi/OjHP+Hp7dtZ0DAXj1PG75azAd8ul4t4PI7vngkrURSHrTJ2797Nk08+idPppLm5GcuycLvdRWk1ZqaVzAGLeEqnYyCB1ylTEXDl3FLMeIn95je/4ZVXXpmRrZwM2tvbaWtrm5IBi4xeyu+eMYfomNi4cSP79u3j2WefLej5TllE0QwsYN3DDzJndh0fHvsoS/ivXr3KH33jNWpCPvxuB0alH/+zO9h/oBlRXEvjgtl5XeAvXrzIQw89VNC+jofW1la+8Y1vAHDhwgVmz57NnDlzCIfDWTuUfJCP2sKyrHT+6t12bNDjoKbMU7RBjUjLZyzJw+vPmYjNKO2drGvgEBAFEVEefZ8kwW7N9muQNAVCo4afFY6oy8Pab7zGR7/6GU6Xm6rVG9n00NKibuPLgqGTsWPB65Ip9xfefmxvb6epqYkFCxZgmiY///nPMQyD119/nfb2dt544w2++93vZiM/jx8/zn/+z/8Zn8+HLMv8+Z//ecHbzgeyJGaTrboGk8iSOKHUQhAE5lb5CcdT2W5AUjWoDrpntOSnmJgx7/LT858RqKhh46ObcMoi82fX8P/5f/0lv/7lz3GoYTZu3Djs8bdv386p4vX888+ze/dufD4fO3bsQBRFIpEIu3fvZvHixSxdOrmLS8YPqzdqT/4lVJ1En45LFgl6nWl3emncG7Aoirz88ssznuQ1NDRw6dKlKSF4yZSts/TeJyfiZLSfPreDSFJD1U28LpkH6kJUPvtU1oxXNpJ4hRR+dwjI+DJ6eerJJ3j/4CFmVT2dl2WP2+2mra2NtrY2YrEYK1eupKGhoeD9z+CVV17hN7/5DT6fj6VLl3LhwgXu3LlTsA2LxykRT+nEFD27sBsNcUWjJ6JkiV0mhaaYNgkfNx9ggaVBHrFHD4QCnDjYzKNPlW7oKx/YBG/iz6TSYeEU7GpesRFz+3C6XDz++j8r/ot/yWAMMT53OSTcDikbQQe2Cf9458VEi4vjx48PG+QQRZFvfetbdHR08F/+y38hmUzyf/1f/9cwjd7GjRuzfn3Xrl3j//l//h/+6q/+akqm/ysDblTd1uDfGYjnlColibbNmigI9EUVUppBe1+cCr9rxkt/ioEZc/c88sFh/uy7f4lumsPsMNxu14ibz49+9CNeeeWVnM1Z7y0pB4NBnn/+eS5evMju3bvZunXrpDR7giBQHfQQ8rnojypEkxop3Ry2+gp4HFQH3WOOr8uyzAsvvMA//uM/8rWvfa3gfSkVYrEYHo+HeDw+8YMnCcM06ezqpqqqCucMb8+CfSHNTK7l/VzLojd9nATS7X1RELKr8p///Ods2LCBWfdY/0iiSE3Iw/z58znfcoknHlmRcxVv4cKFWZLe1tZGR0dHUQie0+nktddeG7adAwcOFPx6LodN8IwxPlvDtM+xaNJeDNiema6sV2ax8MGvf8kKSyWYR/UOwOlw4uzqKNp+TBayrgG5kd5ACe4MsUQC/4MPF/+Fv6Qo97so8zrzMkB/6623CIVCCIKAYRhomsbChQuzOdNDMTAwQE1NzYjfz549m7/+67/mpz/96agDGJl9WbBgAa+++ip/8zd/g8tl36e3bduGJJXuml0b8qD32Z54t/vj1FfmNhGfkWn0pu/P/bEUfrdjxst/JosZQfDeffddnnrqqVFLrn6/n3379vHtb38bSZL47W9/S0NDAwsWLJj0dhsbG2lsbKS5uRlBECbdtnVIIrUhL1VBk8GESjJloOoGumnZpE8zmFflH/NkdbvdPP3007z99tuTmsosBU6cOMGTTz5JR0cH58+fZ8WKFSXblmFa3Lx5kzWrV90XWYKHDh0q+NiJpCc8JVGguszNtWvXuHr1KmvWrCEUClFTUzNmxdTncrB44QN8dOIkiqrnpf9saWnh+vXrzJ07d0R1fKYgozEaehE2TNsEOa7YmaoZhHxOKvzuSU0Wj9h+PM6pX/2M9RUBHHL+Q1B9g4MMDkSKtj+ThU3wpgeqpnK+d4DHXls9bftwv0EUBERp7OPZNE06Ozu5ceMGsVgMURQpLy8fcS06efIkLS0tw7pVp0+fnjDz+lvf+taE+zhr1iz+xb/4FwBcvnyZH//4xxiGgSAIzJkzp2DZylgQBYHZFT7a+2Koukl7XzznYTNZEpkV8mKa8XRnQPsngldqaJrG9evXee6550b9+65duxgYGOAHP/gB8XicZcuWAdDc3IwkSaNOB+WLbdu2EQ6Heeedd1i/fn1BeqGhkMS0S356wa+oOrf746i6OWbcWgbBYJDKykq6u7tHXV1NFzLl/vr6elpaWkq8LQvLsnBOIj5rqqCqKqqqFjwFnUmxsK0ARK5evcqWLVv45JNPiMVixGKxcZ8viQKWZZFPjO3evXtZvHhx0S++o6G6ujqbOZsvMi3XTBvKMG1vK31o60oWqS7zFF1T093ZSfved9hUU45A/qTxw85eFjy1g20LHijqfk0GUabvZvZhROGp7//Ladv+/YozZ84wMDAw4veZ63F1dTWNjY3jdqDWr1/Pvn37mDNnDsFgkO7ubiKRyKQjFu/F4sWLh03ifvbZZ/zoRz/iO9/5TlG3I4kC9ZU+2vviaZIXo67cl7P9UaYzoGgzJ5avVJh2gnfs2DGeeuqpcR9TXl7OX/3VX3Hw4MFhq5MbN25w5MiREZFHhSAUCvHCCy+wd+/egg1rx4LbKdvt25gtmlU0g3Kfa8zVQyQSmVHkbsqRyemc5t3IBbdv3yYUChX8fI9TZjChElc0DNNk0aJFtLW15WxH4JBENE1jMJFiMJHis4tf4Pf7WP7gA6OKr2OxGH6/f1xLhGKiqamp4HNqqMUJQFK1q+GiYLdcfK7StViutH7B2spQQeQOIFBRQcME5K61pYVAMMjsOXMK2ka+GPD6MS0TcRqme921dTNWWzyTMTAwwLZt2yb9Os8++yy//vWv8fv9eL3eKfEzffjhh9F1nV/96ld8/etfL+prS6JIfaWPjv5Etl1bW+bJabrW65Lpj6VIpHTiijZlEYHTgWk/4zo6OgrO8WxoaMDr9XLjxo0JHxuJRDh8+PCYVh9vvPEGp06dwuFwlMQOpMLvIuSzD75oUuNmb4z4KIbNbW1tRWk/5wrTsmjvjdHeF6M3miSl2W2vy5cv09nZCdhGmZkpKrAjqSKR0rWeHJJAWVkZRz48mt2fmYoFCxbg8Xg4f/58Qc8PeBw4ZRHTgoGYSkNDA+3t7bk/32vnur7XfJj3Dx3BHwgwGIly+PhpwvFU9nGmZcsE3v/gGGvXD2/JaobJ7d4It3ujxFPFb+PV1NRw4cKFvJ6jpNuvAiCnCZ6ZJnxuh0yF313S9soDDy5hYHCw4OeP1Q7t7uzkxE/+gS9+9D+oOXcC6eBeWn/8Pzn1i59w7uTJgrc3ETo7OnggHp4WcheNx6isnzvl2/0nDMfzzz/P008/XZSuV65YtWoVPT09JQkgkESROZW+rP9s12CS3ogy4bY8Tplg2hNvqMzjy4hpr+ClUqkRVif5YPXq1TQ3N08oEs+UtE+cOEEqlSKVSvHUU0/hdrtRFIXa2loaGhro6enhzTff5Jvf/GbB+zQaMoMYfreD/miKhGrbqtRX+oa1lxYuXMj+/fsn1EdMFqqqcuLECZwuN0KgloryEEnVoCeskFIS3Ll2ndqqSj777DNu3rw5rMy+ceNGDhw4UHSj3AwkUaTp4WVE4gq/+Mff8fCSB1izcvmU5rTmg6amJo4fP87ly5ezlbFTp07R39/Ppk2b8E8gzq8KuOkYSNAzGOPEh80sXrQo5237XA42NC3j4YcW45BtM8+YonHkxBkuXLrOIw8vQpZEbvZEOXzkKD09PUT3HmTzo+upDfmRRIFbvVHeO/A+breb6upqHln1MNVlnqLpH9euXcuRI0e4du3aqIsXVTfQdBPNMNENK21unrHJcWS/98z/xxq6KAauXblC+OhBPKJI/RhxiLkgpKtcbW3lgSHi9sjgIO1732FddcWwx2bcE2PXv+DTLz4jFihj9spVLFh49zjo7elhYGCAxQ8+WND+yLI8wlN0qtAXT7K0xNezLyOuXbtWVA/XQrOyJwuns3iZ0vdCTLtY9EUV+mMpBuIpUrrBrAnyeOW0r6aZj7blPsS0E7zJQhTFnEr/oVAITdOy5W7TNDlw4AAOhwNRFNmwYQNut5udO3dy+PBhwuHwpFpvY8HjlJldIXFnIEE8pdMVTjKv2j/sZlqKVsaNGze4dOlS9meHw8GGDRtQFIUvWi9x8fplVNM23TUMjdeef2bM6aSpaLXUlHlIaSabH99Ke3s7v9z9Hl5ZwO+WeKoILYtiY+PGjRw+fJhUKsXVq1dpampizZo1fPTRR8TjcRYtWjT2sITbgUMS2bPn9/zxqy9RPY4r/WgIeBzDXNor/BLrVq1k73vvM7d+Nh6nzJGjH7H+kXUEfV7iisrhI8cQBAFJklAUhSe2PEZlKMjnl6+xe98BHlm7hkXzaied/JDB448/zr59+7h48SKKovDiSy8TTap0DsQxLQHHKL5sDkmkakgGscthPyajWywFOs98zCOV5ZN+nfpggCsnjtDudlPf0ICSTNLy5i9ZW1M55nP8Xi/L0199/4kPaPvgAIYoIhk6Xkmi0ung8ofvo7g8xJwuyh5qZNmKlWO/lzt3uPJFC6aiIBsaPeEIcwNenDlYpRQTmsP5T+3ZAhCNRunp6eHmzZslW0yXGmfPns3q5kuJyoAbhyzS3henL6IQS2osqA2MeQ/L3G6/3PTuS0Dw8kEymcz+WxRFduzYQSQS4b333hu2utm6dSt79uxh165dJdkPQRCoDXm52RNFM0x6I8owHzOHw1GQ+38Guq5z4sSJYe+3vr6e7dtH+nG53W42rB871HosLFq0iNbW1lHH74sBpyzRUO1nIJ5CnDuX+vp62traSOk6fVFlRnoYbd26lfPnzw+z5dm8eTMABw4coLq6ekwxdGXAzaxZddzpG6QqOHlz3pqQh/WPrGP/gWY8Hg+yLDO7qiwdA+Yh9PSTRBIqFrbX4Kxye8Xrf/hBGubWc+LjU5w9f4F1a9fi99pRYU5ZwiGLOCQRhywi52HfALYOSNd13ms+zJnWG3zxRStJXcATCLF00XwqA258brtlnfH9Gvr6GS1eqS7KSjJJVTwCnrFJWD5YVBGi9YP3OFlWCX29rKmpmPhJaVQEg4z26LK7Sgkin5/l/KenGQyEeHjLk4TK7xLTQ7/6BY1qjHXBIHLGtuKBwqQwk4VWIkJpWRaaYaLqZrYKbGEbhztl2zPufp6SzDgVNDc3T/OeFI6TJ0/yve99L+fHJ1UdSRQKSucJepyUeVQudQziccnIvbbR8bivNcHFJOOxKQr2QjwzyKHpJoZp4ZBtw+WpdHtQdSPnz+cPiuA1NTVx+PDhYRqEYDA4qu/cwoULR4yWFxN2NqaX2/1xBhMqZWlTZLBJwQcffFCQuHbfvn04nU7Wr18/YWtwMgiHw3zyySd8/vnnrFmzpmAd5XiQJZHqoIfKgJtYUsP10IOc/fQz/nHPfnZtf5y6qslXWoqNsexjtm/fPq79jd8ts2TJEtrarrKsoRZ5HHuEXOCQRB5qqKWyfDtKSsPncWZ1J7IkUlPmoTroxoJhFyevS2ZedQDnoxs4/cV1fvC/f0F9bQ2zaipwDtknp9PJQw89RMDnzZI+5xDyZxuyjnwPCc3kZk+EQbWNxzY9iiBAy6WrfHb2FLW1taxsXErA45hwKtayrKK3fU78fi+bq3InYblgSXkIMKC6+Mdq0OfDbnya3N79G9qcHlLVs9jw5DYqdIXK8plxfqTkyVWB9SEkTtUNNMPKtvTHh5XWWzlzEt//E4qLZDI5oVetohn0DCbta5Bw1xqpOuge121iNPREkiRUg1nlXgzTxLSgezBJfeXw+6BpWUQStkZ2rAVAxmS+ezCZndpPxexEjHuRMVb3u+Vx28KTgWlZJFWdwbhKPKWzuC63WL8/KIJXX19PMpnk5MmTE04pLl26lHfffbdkBA/sm6nf7cAwzWHCUFmWCzbOdTqdRZm6Gg+maXL9+nX+4i/+AoB33nmHmpqakmk8REEg6LUv0u61TQwmVI4eP8krzz1dku2VCo899tiISfAMBEGgLBgkkbCnwvw5Zq+OB6csMadibH2rIIw+IyqJAnMqfUjL5rN43re42NJCZ/cACx5aRoXfg9MhoWspvvjii1EHkgRBYNGiRZSHynE6RGRRRBQFTNPiiytXqa2t5dF1q7IxYnXly+mPLab1ynXeO3iYqqoqVq1ozE7KpjQjO/WW2b9SaHrKIwMIFfdnHuqcinLmAFq0h9Yf/09muwuPryo26tQER974BY88/yLucXSNlmWRUHVSmpEmdCaabgyzABIYXnQRGFKxS7f5tTQZjKd0kqpBUk2SSOnMKi/Mymg6cfTo0ZLeg0oFy7L4b//tv/Gv/tW/Gvdx3YPJLJkaip6IQk9EweWQqAm6s1ZJ8ZSGYVq40wlRGUSSdkwh2JKVqqCb610RrnbF8TglKgP2cWeYFp3hRNZ7NEP8M4sIw7QIx1MjLFSqg27iKR1Vsz3+HJJ9TVNUHd206B5M0j0IomC7ZnidMk7ZXuQKgr2ozjVPO7OfiqajqAaJSVi6/EERPLC9es6fP5+TWe94N+RiYVZo9HacJEn88pe/ZNOmTSWpjk0Ge/bsGeZb+Nxzz/H222/z8ssvD3tce3s79fX1Rd22aVn2wMzMnLcYF1VVVVRUVIxobff393Pq1CnCisWiRUvoHEhQV+6d9vH9WeU+Al4nVcFV9A7G+fDoRwxU1LJwfgNel4sXtm9GNzJtsruDEt3hOOc/v0wq+QUBl/1FiaKIaZoEg0E2rltFbejuzdYpS8wKealoWsJDi+Zz5Xo77x86QllZGY2NjSiqjietBRRgmC6vWDjz0TEWfwmqPA7ZwYNVxWkxFwuzgkFqsbjyxk/oq5nDpp3DpS+GaRFNDs8RvheZyrBTEnGmI7ucspgVy48G07Jv1n3RFFFFI6TquBwSmmGS0kwUTUfV7GM2U/ERBft49LllPE4ZAeyKoWEQ8DjzuklPFm1tbbjd7qIOWkwV/vZv/5Zvf/vb4y76LcsaQe4q/K7092aTtZRmcLs/wYLaAP1Re4hiKCRRQBKFrC435HNSHbTJnP29WbT3JTBMuzDRG01hmBayKLCgNoBumHQPpoiN4miRgccpEfK5Rq0oWpbFQDxFJKGhGfYxlEjp2cXovXBIIrIkZAnf0MWqbtjHoqIao54HkijgyzN7+L4ieGONPwuCgK7ro8aqjIYVK1Zw8uTJCTVkoVAIy7KIRCKTijIbD2NVIjKkcu/evTkRvNbWVm7evJlzfFuhOHfuHEuWLBmWAyyKIlu2bKG5uZlt27Zx7do1zp8/j67r1NTU5JQZnAssyyKuaFy6dIl1q+7PyKOmpiZ+97vfUV9fz6lTp0ilUgSDQXbs2IFhWtwZiJNUDToGEjRUT6AfmQL4XA58Lgchn5PQM9u42HqFT06dpHHFStp7nXhdDmpCHkTh7ndcG/IS8LowTIvqoBu3Q8ZMt1RdsjhmfqZTlqgNeal4eBGLF8zl+u1OPvjwGIbsYfWKRuZVB6gKuO5qyiYJJZmk+a3f8Ow3/xjjehveQOFTs/+E8SEgsLiyAjpuEItG8QcCqLpBOK4STarZKp0oCPjccrYq5xyn3T8RREGgwu8mpdn5pbf6Jo5ZNC27bWhXTIaTiYyu7178/Oc/5+WXXy56B+Ps2bMTesTOVDgcDgwj96pTQ7XdRs1c76oCbtu+qS+Oblrc7IllSY9TFrOEzjCtrF+mAMPyp6vLPCQ1A80wbK/RlEpfVEUW7UjD2/2J4fucrsq5HRLlfpddBVb0rL3ZaBDSx1iF341pWai6SSKlkdKGV58z+64ZJjannfizkUUBj1PG45LxOKWC7gWCVQqDmjyQq9N1a2srTqdzVJuFzs5OOjs7aWpqymvb+/fvZ+PGjROStzfffHPa8mHPnTvHzZs3s1YyLpcrK9wfilIOhQzFgQMHqKysZPXqkZFDZ86c4erVqzzwwAOsXr0a0zTZu3cvW7duZe/evXz1q1+dFNmLpzQ6+hOcOnmCb7zw9Iy1TRkPly9f5sqVK/T39/PCCy+M0ElalkVbZwQLmBXyDpuOnQlQNIO+SJKf/eo3vPDVrwICNUE3FfcMvfRGFAbiKZyyyNwqf0EiZN0wGYinuHa7l4sXziPLMps2bqAy6CHocRT0/ccVjZhir7ZPNx9gg6DSEQ1T55KpKdEi7p9gQ9M1Tsk+1j+7i96IwmefXSTS00PTps04JJGQz1n0HGHIVIHiw4iAM73QcKWrgbJkV1JM09Y6JVI6qm5iWpZdPZREynxOPE6Zjo4OTp48STAYJBgMUltbywcffJBNa9F1vWgpMZnhrHzvbdMNy7L4T//pP/Gv//W/HrPwohsm17qjADxQGxhVvxZNanSG7xKxCr+LyoAby7LQ0+TOME1M056yH0qCOsMJoknN/r0koadjDr0uGT1dbQPwuWQqA6X11czsb6ZKp+lmdh/MNAWTRbuy53ZIuJ1SUfR89w3BO3DgwKhToBlkqkf5wDRNDh06NO7zTp8+zc2bN1m5cuWYNhelhqqqWWJ04cIFHA7HsMrj4cOHaWpqKomty2g4fvw4Ho8np4vOuXPn6Ovr44knnuDdd9/lhRdeKHi7nQMJoorGJyeP8vWvfqXg15lOvPPOOxN+Bte6Iuimhc8lU+F3jVnxmi5cvHiRWDzBunVrSaY0PC7HiJuyYZpc745hWtawtkkhyOhiugeinPz4FACbHt1IyO8m4Hbk9PnEUxq9EYVPPz4FA30IloVXVZjj9+IWYY7Lui/b/vcTTMvks55BLrkr8ShJZosWgw6Zja98rWh2PGMhM3ErCsK4bd2JcOrUKVRVZdOmTbS3t3Pp0iWefPLJrM8q2Nfo1tbWEZKVQtHa2kprayu7du26r+xmIpEIv/71r/mzP/uzEX8bSrodksj8msAor2BD0QwUVcftlHHnQcIy3nhOWRyRAW9ZNjkUReG+yDsvFNN659B1fVoP2IwuaDwMDg7y0ksvsWfPnmkjeEOrXsuXL2f37t1ZgqcoCqlUasrIXSwWo6+vL+dq4VASWGjFLZHS6YkkiSdTfPDBB+x84tGCXmcmIJc2js/tSLcUdOIpnTkVuecslho3btxgYGAgW0X2uUevyEqiyKyQh46BBINxlXKfq+AbqyQKVAbclPtdzKrYRm8kwfETdupDeXk5jcuW4nXKuNNtjKHaLEUzCMdSfHr+AsqlFha4ZWq8LtwiGJaHfh0UE3pUgVrXl90Va3ohIBIKVNBkWXiCbmqcFlcSyZKTO7CvPZORO+i6zu9+9zuampqypvr19fVZjXHmPtba2kp3d3fRyB3AkiVLWLBgAe+88w6rV6+e0NR/piAYDI55ze+LKtmKavUEmlp32jIpX4R8TsLxFKpu0nOPFZkgCJN2KrgfMG13Dcuy+Pzzzykv0ih/oVOnExHMjL5v/fr1vPXWWzz33HNF05QVisceeyxr9/L+++8XPTt3LGRarq+++mrez21tbeWBB/IPXu+NKPQMxnnvvfeoqa7m5V07qCkvnf1LqZGJwhvvGKoOuvE4ZfqiCpphklT1GUHwuru7+eKLL3I2XfW5HbgdEkp6CnboBbYQiIJAeTryb9az24grGjc6Onnr179madCH6nShuTwsa1qFx+PBMAw6brfT//lnzMNkboWPCoeFJNwlck7R4nZKIGKAqApUOSy+xAv6aUXEANUCWYA6l4UkgGTM7Kgo0zQ5evQokUiEr3zlKxPqvL/44otJdSnGgtPp5MUXX+TkyZPcunVrVJnOTEMikRhzQSsOKZf3RJSSDJRJokhNmZfOcILBhD20Mdlr0P2GabtrtLS0sGfPHv7Nv/k3Ez62vb2durq6cR+jadqwMnmx4PF4SCQSVFdX8+KLL7Jnzx6ampqmdbK1vLwcwzB48803WbVq1ZRVQd955x2++tWvFvTc1tbWYQbAuaB7MMlgQkWWZYJeJ6/u2pY1u71fsXnzZo4ePTruZLYgCAQ8Dtv3KFH8XORCEIvFOH78eN43r4qAi47+BJGESmXAXZTvL/P5BDwOakMLiFyro0lNkDJNklqMzvf20GuZSEDA5WJx0Ee5w8ItjqzQeSWocVp0qwJhHRKGwCyXhev+6YTdFzAs6Nfs794m2fbvpTyE+FOJWCzGkSNHEASBTZs25TxkV+o4sP7+/inr1kwWFy9eHHOIMUO0okmtJDm1GQQ8Diw8dIWTf5Akb9oIXigU4oEHHsipGtbS0jKu/g7sKKTDhw/nbWliC2vHJoYulwtFUQgGg4iiyNKlS4lGo3ltI4Of/exn1NXV0djYyGeffQbY5PXll1/Oe0o3lUqxY8cOAoGxtQvFRHNzM5s2bSqoemmaZs4TzmBPGnUPJrOj5ic/OMALO56cdnJ37do1rly5gsvl4vHHHy/oNZxOZ87TZZmJvcGEilOW8LvlaRksybSnXnvttbyf63PZMWyaYdIzmKR2DFugQnDmg8NogwMYHbepmFNLxiFtvqcMw7J/cgggCOPfQMpkELHo0QRUC26nBOa6LBz/RPImBd2yp1IFoFcT0C1wChAc0m0LOSTO/+qnYFkIgGBZ6X9bCJaFZVrE6+p5ZPvURnX96le/4k//9E/zXjyLopiXo0M+iEQihEIhNm7cWPTXLgXG0yGKgj2tag9BlNYpIOhxggVdg394JG/aLmG//OUvC7phjAW/308qNdJleiLMnTuXGzdujPn39vZ2ampqsj+3tLTkrIHQ9bvth/7+fhYvXszGjRvp6Ohg+/btbN++nddff53jx4/ntc8ff/wxS5cunTJyBzZJG/o55ANRFCkrK6O7uzv7u5aWlmGfD9gRLF3hBCfOX2bfe3Y8T+unp/jqrmeoqChuwkAhuHTpEtu3b0fTxvZMKiaCXtt3K2POeWcgUdLV7lj4xS9+MSlNUXa1rmjcGUjkkECQG1w321gr6GyfUzvs96IADhGcIjm3WwMyNLjtyp1hQZd6f1eKpxOmBXdSAteSAjcUgeuKQCy9pql1Dm+BVwUCNPrcNPo9LPN7WBrwsjTo46GgnyVlAR4qDzKv5zafn/lkUvukKArNzc1cvnw5p8f7fL6COiOzZ8+mra0t7+flgtOnT7Nu3bqSvHYpoKoqnnGMreOKff2fiji5oNdJbfo6NJhQiSan5ho+3ZiWCt4vf/lLXnrppaJXI5YtW5aTgfFQLFiwgCNHjoxqv3L69GlWrhwe5v3888+ze/duHnnkkexY/JkzZxgYGGD+/PnZQQxVVfn5z39OKBTiueee45NPPmHbtm2IojjMYiTfi8i1a9cwTXPU/S0lPB4PsVis4PizTZs28dZbb2UrlQ8++CBHjhzBMAw00yKpCwTKq+no6KCqqoq1TSvouHIBydJKGrk2Ea5du0ZLSwsul6tobflcV/mSKDC3ysdATGUgniKe0okp+pRbpzzzzDPs3bu3YGmC1yUzK2RrYeIpnWRPjOqgG69LLnjwIjI4SNAo7kVaEqDOaXFDEUiaENPBP/3Sx/sOd1SBRJrQZb5dUbBbs+4C7uXVgQDxC2e5WV7BvDyue1evXqWtrQ3LsnC73WzZsoVDhw6xePHi7GNM0+STTz4hEokM03GvXbs27/1UVZVr166VzK7KsqySVAZLhbF08ZZl0RlOklBtgjdV17Og14lmmPTHUvREknhd8rR3hUqNKT9aLl26hMPhyJmghMPhnNuXDQ0N7Nu3Ly+CJ8vyqC0zRVHo6ekZ9UR//vnnaW5uJh6Po+s6pmmybds2Ll++nA2G7uvr4/XXX8c0Td59910SicSYZG7OnDncvHlzzJunaZqcPHmSlStX8tlnn+WtZSsGHn30UZqbmydslY+He6tAtbPr6Y8qxFM6mqbR1dXF9ie3UOF34XHK/I8P9vP6669PdtcLQmagZPbs2UUfYtm0aRNHjx4dlok8FiRRpCroRhCgP2Y7uU81waupqeH555/n1KlTXLhwgWeeeSbvhUnA48Ah++kOJ0jpJl2DSQRgdoETwp9+dJSNlcWv6jpECMkwoEO/LuCX/2myNh8MaJAwbGI322XhKVJxZn55GZ8e3E/V17+F1zd6/F53VxefnzyOV0lw5+YN5j/17IjrVSwWIxaL8fHHH2er4atWrSpKh2Dfvn3DEn6G4ujRozgcjmxEpmmaHD58GEVRSCQSOfmsjhYLOJNRXl7O7du3mTNnzrDfh+NqNjmipswzpWbu5X4X0aRWEsnITERRffD+y3/5L7YAOhDgj//4j+nt7aW+vh7Lsvjv//2/4/F4KCsr45VXXsn5NQ8cOJCtfOWC06dPM3v27JzjXW7cuEE0GmX58uXDft/f309bW9u4JfHjx48Ti8VyIj2KoowrwP31r39NVVXVsN9ZlsXs2bNpbW1lzpw5XLhwgddff33arGX279+f8wTleEiqOv3RVHYFB+B3Oyj3u4aNw/f399PS0sKmTZsmvc18sWfPHp566qlRv7OWlhZu3bqVzhkUWL16NRUVFSiKwgcffDDhZ3Tx4kW8Xm9eVdihpqDTmXChqiq///3veeCBB0acM7nAMC0GYiniKQ1VNyf0wBoLH7/5BmtKNAlhWHA9KWBi++N5pzdM5L5B3ICOlH2zrHFalBW5fGBh8VH3AJu/8+eIojiM0HmTcSowqakoR0CwH9sX4fFvD/dgM02T48ePEwgE8ioETIQLFy6g6/owW6hEIsEHH3yAKIps3LgRVVU5e/YspmliGAaPP/44fr+f3t5ePvnkk3GvG83NzTzwwANT3rmZDEzT5H/9r//Fd7/7XYDseZ9U7YJKVdr6aKqRSOnc7rdTTfxuB7Uhz5fWC68ggqcbJuG4SkLVEbCDdH/1k78H0yAQCPCNb3yDn/70p3g8HgYHB1FVlZdeeqkgm4xTp07R0dGR8/ReLBbj9OnTOVVHYHzSUoh5crHxn/7Tf+JP/uRPqK2tnfjBJURvby9tbW3ZFWghSKo6fVEle4IDBNwOKgKuMQnLvn37iuYKnysUReHYsWM5f/eZdI+uri5WrVpFc3MzVVVV9Pf38+yzz46Yeiv0Pd3qjaFoBuU+V0nyWPNBa2srX3zxBdu2bSuohW6mEzugMML62c9+xLKy0rXuu1WBQR0CEsz6J3+8CWFacEOxBymCEiXzFNR0nbMDEQKSOIzQjQZFTXFO9rHphRdLsi/34uDBg8iyTENDQ1bWsWXLlpwW5FeuXKGvr2/E9fXatWvZ68X9RO4y+Lu/+zu+973vEUmqdIWTw/42nQvVoQkZsmjbLwU8zi9dyzavNZZlWYQTKT4++zlnzp5BkmREUcSwYMHCRnY8sZmg14kgCHzve9/LPi8TX1UI1q1bl2175oJjx45Nqo04FLqu09zcTGVl5bRFxaxatWrayR3YRLtQomVaFt3hJNEhgc5Bj12xm+gEnz9/PpcvXx6mmyk1Dh06lFelsqOjIxvPBrYZtdfrZceOHfz2t7/NWstk2jLV1dUF7ZfPLaNoowdRTzWWLFnCkiVLOHDgAPX19SxdujSv52em6BTNIKkaeV3oVVXFrypA6QheQLIY1O8OB4wF3QLVtP9vWLaOzymCS8h9wOPLgIhufwayANXO0hFihyzzSHVu7VSnw4HedhklmcQ9jti/WHjyyScJh8O8++67fOtb38rruYsWLSIcDtPa2oplWdy8eROwzZS/973vcejQIa5cuZJXN2smwOWyK3SR9PRqwOOw81ULzFYtFgIeB5LooyucQDcteiIKfdEUdeXeGeE5Wizk/E5My6JzwBZJHzr8Af/8e39Omc+JgEBvJIlqmHRHFKKKli13Znrb7lAtt/viINglUb/bkRdTzqdHno8X3rVr17JO5KMhc5Pv7OzMau6mOi5mJugD7p12zReZ4wZsYlcRcI8a2j0ali5dyr59+6aE4Jmmyf79+6murs7rO75XJ7hy5Up2797NwoULWbt2LUePHiUajWJZFlu2bMnmCueLzLRZJks118+wlHjssceyU935wuuyCWtc0YaFhE+Ejz84zCMl0N8NhVuEVHrQIiAKVLksLOzfpUxQTIFUmtiNBiltB1Im/2HYrcQM+zoVkqcn8q29p4f69MJJNwwuDQwyUF7Npr8aOwu1FAiFQjnLg+7F2rVr+fjjj6moqODpp58e9rdt27ahKAp79+6lsrJyQquU48eP09PTk51iDYVCY8qN9u/fjyzLqKpasm5JSrMXpWVeO893JsDrkmmoCRBJqITjKpph0tEfZ8EYubj3I3L+pDv649nW2ryaMuZW3V09u50SN3vs3Mmh7bfRkEjp9Awm8XscBD3OorLlc+fO0djYmPPjL126lFOlZtasWcyaNQtFUXj77beLGkMzHvr7+wmHw3R0dOD1evF6vVOaoqGqKu+99x6SJBVcFY0k1Sy5KzRyy+fzTWqCNxccO3aMgYEBnn766bw/44MHD6JpGqIosmXLFpxOJzt37mTPnj08//zzvP/++/zRH/0RDsfkhiN8rrvJELGkNi36lXtx+vTpgiYOwU666I/Z08GKZoyIIzItC9O0RkzaytFBpBKzJhObvCVMgbBhEUmOzloE7KqVQwQp/ZyUaVfzBnQI6wIBGaqGmPt+GaGlia5nGu6LkViMM6IHXzRCh2oQqa7j0ddfva8qXRk88sgjY/7N7Xaza9cuuru72bt3Lw0NDaPe744cOUJtbe0wErhv375RX/PgwYOsWrWKmpoaLl++TEtLS0GLtYngkARSuq2/9VTMDIIHdich5HMR9Dq50R1FNy0UzcD3JXE6z/mTTqoGogB15T7c9zzLIYk8UBsgpZuomoEFw/y6BMFWSeimSSRhVx+iSc02OZRFaso8RQlUv3PnzpitVNM0+cUvfkFtbS3V1dUFCWzdbjfbt2/PKTC+GAiFQjQ1NdHf38/t27dJJpNomkZ3dzff+MY3SrZdRVF4//33cblc7Ny5s+ALpWVZ9EUUACr8rrzInWmaHDlypGQJJRmcO3eO9vZ2NmzYMGLIJReYpsng4CAvvvgiuq5nrV90Xc+2J77+9a9z+PDhokgHXGmCl8lxnG4kk0m8Xu+Yfz937hyiKI56vrkdEj6XbNu/JDXcDgnDtAinLWFSmr1YdEgisyu82ZaO5fGSiPXjLWFqgG6BT7INkJ3i3UqdUwCXCG7R9sxziYyoWFmWPXAQ1m27lUg6IaPGaeH7Eg5sJNLtWQFKXq20sLjaN4AhyUiGjmjo3AlHePGv/x2njh1jzcaN9yWxywc1NTXs3LmTy5cvs2fPHlasWJF1YBhrGGP9+vUcO3Zs2MBaa2sroVAo62+6ePFi9u3bVxKCV1Pm4VZfnHhKxzCtGad1EwUBWRLRTYMCU09nJHK+4zokkVnlXtwOiUQiMeLvQlpTM1EocIXfjZKOYIomNVK6ye3+OPNrgmN+6bdu3eLcuXPj6uASicS4poqiKFJbW8v27duzLdeHH3543H0dDcFgkE2bNrF3796SZ8CKopj11cvg2LFjJSujx2IxDh8+jMfjmRSxyyCh6uimlc0QzQXXrl3j0qVLiKLIY489VpLonwx5TCaTNDY2TkpfKYois2bNor29nfr6+lGTVNxu95SZI081li5dysGDB4e979bW1qyGaOnSpVy/fp2Ojo5RW1d+t4N4Sieh6qi6QUf/SCNkzTC51RtnTqUPt0Ni47Nf4dBP/oHNLieiUJqbuZLehQoH1LstMnw6l/uSINj+eX7ZImnYAxuqZU+YVjosAlLpidBUwbCgUxUQsc2iS1ml7BwcpM3h45HX/mSYpu6B9B153TRM208nFi9ezOLFizlz5gznz5/HMAzWrFkzquyooqKCSCSS/VlRlDHjI0uxoB5ahZ9h3C6LTD6uOQ1m8qVCzgRvbpUfSRR4//33x8yXyxVup4zbKVMVNLnWFcW0QNMNpHQV78aNG7hcrqyR8ObNm/nZz37GihUrxjzwcrGnyCDTci0UVVVVzJ49O3tTn0rEYrGSZBHu3bsXt9tdFGKXgZJu1/vccs5j6KX2+Tty5AixWCxrUVAMbNy4kXfeeWfcY2Hz5s0cOXKk4IizDOT0HTSlz4wMz/r6euLxOCdPnsxOAN6+fXtYtbK+vn7YsMlQuJ32gjClGdzqjWFa9lRbRcA2QhYFuN0XJ6Wb3OqN4Xc78LllHv36H3PmJz9kbW3+VddcoJpp/XD6VCj0puSRYK7bjkGL6NCnCfRp9nTuvakO9xusNGnVLHuwpMpRmhtjXEnSkjKp27SNx0eZJJ1JFbvjx4+TTCZH/F7VDTTdBAFcslSwwfdoWL16NatXr57QimvNmjXs378fURQZHBzkpZdeGvGYTZs28dFHH7F58+ai7R/YzhtgV8pmgq58NEjp/TJnSHekGMiZ4EmiwMcff0wkEhn1wCgEmm4ymFCRRIFIUsu2aX0+H2+99Rbz588HbC3ad7/7XRRFGbcdNNGJrmkaBw8exLIsJEnK2UplNDQ1NWUnCKcSiqKU5HV1XWfz5s1Fu1ialkU8PTU7UVU3g6NHj7Jhw4aibP9etLW1cfHiRR599NGCWrETYf369ePq0YLBYEFRevfC53LQF02RTOmYljUj/JuWLFnCuXPnOHz4MFu3bh01Tm2sWD2nLGWzak0LnLLInArfsBvgnEo/neEEiZROTNGIKRqSKFDz1C7aPtjHworyor+nTA1RnCDHNheIgk3m3CIM6vZwRtQAtw6hqfWsLiq6VIEBFfp0gQc8FtG0wXHKFLKaPIcA5UP0hwnDzqU1LCiXba+8zCFsWXarVxTuVgIN0+SMLrL1j/9kyt9fIejp6ckuUE3LIprUCMdTqPrwqnSZ10mF31VUojdRt6OmpoYdO3agquqYxvvBYLDgrPXxkHmfpmUxmFDzGqqaKjgdIigQTaqEfM4ZS0TzQc4E79q1a1y8eJHvfOc7k9qgZdkixpiiEY6rOCSReMpu2eqGSZnXSWVlJUuWLGHFihVUVFRMWDI2TZN4PM6BAweYN2/emBXGoS3V7u5u3n77bbZs2TIjck5zxUMPPcS5c+dYvnx5NlmiGKRs165dvPPOO7z44uQ9o1Kakc0cFbDF9LnA5XLR09NTcObtaMgY886bN6+klcFZs2bx6aefjvq3/v5+Tp48WRS7G5dDQhIFDNMipRkzZiKtqamJX/ziF+zdu5eurq68nju3yk9c0TAta1QvKkkUmFPhQ1HtqLZoUkU3LXAFueQuY4FlFr1V6xAsQEAxBaA4K/oy2Z6qHdTttm2/LhCcpqnTyaJPtUlq0hSolC1UC24qQrbiORRJUyAgWcQMW5OYQU+6mikJNqE20h+zAARlqHRYnOkd4PHv/MVUvKWiwOfz0dMfxpLsxISh7T6nLGJZtuRgMKFmiU6xid5EcDqd4w6STTaWcjRk9LQAiqrPSIIX9DgZiKVI6SYD8RQV/un1Gi0Gcj6qenp6CtKsDYWqG9zoidHeFycct31xqss8LEg72cdTOh0DCa52RVm2aj0HP/jQ3skJCIwoinzta19j+/bttLe357QvNTU1vPjii5w4caLg95PJFJ1KLFmyhK6uLn73u9+xadMmDh06xP79+3MO0R4Loiiybt06jh07NqnXiac0bvXG0AzTvjFX+nK281i3bh3hcHjS7yWDkydP0tzczK5du6bNxxDgk08+4dlnnx2WQTwZOGX785wJfnhDUVtby86dO/OOl5NEgaDXScjnGld8bcs63MyvCeBzyfYgy2C0JDo8f7roHDfgdkogWcSOeFCyK1uGBeGpvXwUBdF0jJsALPGZPOi3CMkwy2FXKUPpqeFKh4WArWfs0e6Su7I0ecsQO80aTu4sYFCHk90xarc9O6NasBlY6Q5FX1ShJ5KkK5ygvS9Gw7I17Gk+xmBCxbQsZFGgKuDmgdoADdUB5tcEmFPhy3Y1BhMq17qjdA8ms23M6cbjjz/Ohx9+WJTX0nWdpGrf18E+10O+6Z/+Hw2yJFIVtLWdfdFUtgN1PyPnM2flypVcuHBhUhuLJLU0k7fwuWTqyr3UlXuZVe5lXpWfMq8TURAwLYtPP7+EHKjmeneU/piS081sIl+7e9Ha2jopPeGmTZs4cuRIwc8vFDt27MDv9+P3+9m2bRs7duzIitong/r6+mFC3HyR0gzu9CewAI9ToqHan3eFadOmTdy5c4cbN24UvB/t7e3s2bOHuXPn8uyzU3eDePDBB7l48WLJt5NZ7Q9dFc8kjPZ5FzEREUEQqAy4Odd8gLnBMkqhiXaKdtyWgN1WbE8J3E4J2eGLyUAQbIIDMKAJaDPjvp4TNNOuPoJN5EIOW6dY7bSodtl6w2qnRbkjPaDisodKfJL983y3RY3TosIBC9wW89wWc10Wc90WD3gsFnkt5rgsFCVJuP4BDFcoa5I7E2CYJl3hBFe7InQMJOiPpQjHVSJJO4LLQsDpkGm9+CkXPzlGf/tlyv2uYb5qXpfM3Cr/CKJ3vTtKTySJMc1jnKIojprPni9+/stf0bT+Mdr77Fgwn0tmfk0g6+c5E1HmdRJMZ33fGUjM2Gtsrsj5zrdnz55RJwTzQULRQIDqoIfZFT78Q1p3LodETZmHB2oDSFqM6EAPjY2NaIZJXzTF9e4oN3uidIYTdIUThOOpEdMuV69ezYuw3bx5c8SUaj6YzunIUkWobdy4saAqnp42ibQAr1NmToWvYLPIxx9/nCtXruRcjc3ANE327NnDnTt32LVrV8GGo4ViwYIFnDt3blgouKqqSFJxL2iZ8yaSUGeMXUq+39VkIQkWtfFBEIa3/YqJMhka3BZBmSzRu6UIdKlCtuJUKAKy7RlnYmvZlLSB8kxHt2Zn9LrFuyR1PLjTUW+zXXZFb+j0sJCxnJHs18vo7hyWzoDbwepH1mIBXYNJ+qKl0R7ng5iicaMnRiSpYVp2NSrgcVDuc1Hhd1Fb5mFulZ+vP/cUj65ezpza6nHvL/cSPQsIx1WuddlEb7oqekeOHCk4bSeD69evM5gSCFXXAfY1a1a5d0ZohidCTZkHj9P+PrrCiaIuTqcaOZdXOjs7s147hUAzTFK6iSgI4zJ4QRD49JOPeenFFzFMi5iiEU2qJFWDlG6/BgBJjYFYipoyT84ary8rVFWlr6+vKK8VCoXyruJlNHe6aaXtdDyTFqg+9thjHD16NOeK7Pnz57l582ZBRsXFxGuvvcZvf/tbHn30UWbPno0sy0UZrhgKv9uBUxZRdTMtCJ7+lsdvf/vbcae7i32RlGUZV3ql3asJzBVLM5HqEO0BiQrZnoCNGravXdwQmOW08E6Cu9c4LW4pNkG9pdg77xRgtmtmpl8M6jbJFSjtBPCZgShbvv1niKJIb0RhIJ6iP2afQ5WBqddF3Ru16JBs79bxfD0rKio4e/ZsTo4HXpeM12XrUPtiKVKaQTiuMhhXCU6DRk/TtAmn/RVF4ZNPPhkx9BeNJ1m94TF+8pt3+fof2XFtcyt9RfG5nSoIgsCskJcbPdH7Xo+X86c+3vRqLsiswDxOady23fnz57ORKpIoUOZ1UuZ1ohkmSVVHN0xMk6zQumMggcshUZGnq79pmpiTLIU3NzfT39+PrutTGodzL95//32eeuqpor2ex+MhkUjk9J0PxBT6Y6msvcWcysIrd0Nx9OjRnEb1FUVh//79LFu2jF27dk16u4Xi6NGjxONxRFHE5/NlbXgy7cpie0sFPU56owqJlD4jCN6yZcvGrfAXm+QCOHxuJMGufN1OCdS5SpcW4RDtSlTZPb52s5wW/gJPfacIc1y2hYpq2toz1bIrevXumVU1SBjQk27NVjps8+eSbCeZJInAuY9PsnrDRqqCbiRRoDdqX2cEgSm92WqGyZ3+eLawUO5zURFwlaQS5XM78LkdxFMafVGb6A0dxij3u2ZEPOHu3bsJBoOsXbt22CCGZpi03Qnz3/7HP7D50Q0IgkCF33VfkbsMZEmkOuihazBJfzRF0OOcUpJdLOS8x9FotGBClEmtAKiaYAXW1dU1atXGIYkEPU4q/G6qgm4aagKEfHalJqUZ3OqJ0Je0UHPwBzt8+DB79+5ly5YtBbybuzBNk2984xvTSu7Ang4+dOhQ0V4vF5Gtqtu+ZW++d5xf/vb3SII9DVmsC5BhGBNW4k6fPs2hQ4d47rnnpiSrdjyoqsqOHTt44oknCAQCw8jcE088UdTvB9Ij/cycQYuJKnTBYJDOzs6ibjPl9THLaSECSdOe4ix1mzPjaxeQbEKWaa8WCnf69RZ6LRrctuYvaVLUoY5CoRjQqwrcUmz9oYU9fBIq4eXO6/GwtTrE4puXOfRf/wO6rlPud1EZsBcxfdEUdwZsy5yhEh1VN4p+LkST9sBYpvNUX+mjKujOidwlEgni8XhB2/W5HMwbQ6PXFR5pBD7V2LlzJ6lUasSUbSKlI0kSVUEfzzy2jvk1gWmpuBYLQa8z2zqfCRKBQpDzqfqNb3yD//Af/kO2MhGJRGhoaBjVvPReDMTt1bsARWPzoiBQHfRQ7nMRjqv0RQ36wxEutHWwfOHsbKzRaHj88cd54403SpKSMF144oknaG5uLoo2L5dKU/dgknf3vYfgCbGy8SHmVQf45PQp1q1bN+HzT506RTgcpry8vKAc00gkwqFDh1ixYkXBOajFRobgyLI8ovJ49uzZouvwMve2+0HTAvY5t2/fPmRZHnZ86LqOYRg8/fTTYy6Ubty4QXd394iw9IaHV5L88AD1wRCdKbuqdjslMLfELc6Mr52pCsQN6EwJzHNP3u7EIdr2IIO63XauFezXlKfhK+5WBQaHTPgK2ObMNVNkzuz1ePBWVmWPiQq/G8uC/lgq64UI9sLfMK0s2XPKIrNC3nFlQJnKWMZ4WBZt2ZBDFhEQ0E2TcFzNCuydssjsitzcAM6cOUNXVxcul2vSHYVM6zaR0umPKSRVg0hSI5LUbDuxgKso3ZJ7UV5eTnNzc/ZnQRBGRI+OlhplWRaffvopKx9unDH2TZNFVdBNe1+cSFKj3G+MyytmInL+Fqqrq/l3/+7fZX/+3//7f/PMM8/k9FynLJLSjAlLnIlEIpvfmSvs0WY3IZ8T947tHPrgQxbMqcHpH/uLEEWxKGa3s2fP5saNGzQ0NKCqKpFIpCQmurkg4xdYLNTX13Pt2rURmYa6YRJTND459xkPLllKx+1bJMPdHDrYjGmawy4MdXV1LF++nEgkwvHjx7O/X7VqFevWrePgwYNEIhGCweCI7YuiyL59+9i2bduwSt7JkyeJRCJTkgWcK8Zbrb/99tts3LhxUskpoyEzXCFOg4nakSNHSKVSbNq0KS/pxlgRe7qu895771FZWZlNwzBNk48++oh4PM6cOXOYO3cue/fuZd68eSxfvhyAefPn0/IBVIp2nNjtlF3Bu6MK1Dgs3CW8FgtpkndTsU19ezU7a3ayKJctorpdFbyR1uV5JZg9hakXUZ0suQtI4JHsDN2pJprzZYEjb/2Gx19+BbD1d363g4G4bfStm9aIapaqm7T3xZlXPbybEFM0EikdTTdJqKN40yRHDssJQLnfRbl//JZsd3c3Z8+eBeDhhx8umh1SBhmip6g6vVGb6NlRnyqVAXfRJRqF7r8sCVy/do1XvlacIISZAI9TzuZl90QU5lT4pnuX8kJBNFvXdfr7+3OqgNmJBvYJlfHvGgvHjx/niSeeKGSXkCURWRKRJGnKLoSNjY3s27ePK1euIEkS3d3dvPrqq1Oz8RKjsbGR/fv3s2DBAgzTpD+WQlENWi5dyRrZLm9cRsPc2XicEvWVI00xb968yf79+/H7/aMaMj/55JPs3r17VAPibdu2oes6Bw8eRNd1Ghsb+fTTT1m9enWWBMwEXLhwgZs3b466Wm9tbWXZsmVFJ3dw1/1/qmN1Ll++TCgUYvny5VmiZ5rmpIYoZFlm586ddHR0sGfPHiRJQhRFNm7cOIz879y5k7fffjtL8AASbptgSgLUOS1upUnerZSAJNiTql7Jwi8VPydVSpO82ym72lUm21Ohk4EjrcvrUgV0C0wLIpodneYUwSWAU7RwpidQi32tixt3bVAqHbadyXShKhDAnUiw+8c/4vnXbYN9l0NiVsj+zjXDRE+bqTsdEpZl0d4XR9VNbvbECPmcuBwSg3F1BKnzux34XDIW9qJVUQ1008Sy7EWT1ykT8k2su7px4waXLl3KOSZzMnA7Zeor/bZGL6KQ0k16IgqWRc5Z36WEU5YQRHv463ZfnJqQZ0ZoBieLqqCbeE+MREonntLwue6foc68Cd6VK1f47W9/y1/8RW7u4qpupiOVoDY0sqz7xhtvZJMkMhf2QqDqBomUjiiKfHi4mYceXDyqLqujo4Pu7m4eeOCBgrZzLzJVCdM0OXDgQFFes1BIklT0gY+YovHFzW5OnD6Hz2FbgTy26VG8LhmXQ6J7MDmmD9m8efMmnLx++OGHOXfu3KhGxLIs8/TTTwPwwx/+kH/2z/7ZZN9O0ZCxZFm8ePGwhJShaGtrG/Nvk0WmVZDS7OPe45SKGq1z+vRpBgcHAXvAqrGxkWAwSEtLS5aQZ6L+TNMsiuH37NmzJ7S2ubfaG3d5ALuV5hBt37V+zW6dGhbEDIgZAr3YhKXY0WBeydalxQxbjzenCIMebgkaPPZJldShI032dANsu1h7A7JgJ2OUycUhrwkD7qS1di6xtFq7iWBh0dYfpr+2nudfG/0cckjicAIh2Iknt/ttkpeZvM3A45RwyhIhn7NorbaMPVCmkzMV8Lkc+Kod9KUHT3qjCrIkEvBML/FwyhIeya6oJlSd231xGqr9933kl1OWKPM6GUyo9AwqeKvl++Y95XUK7969m3g8zr/5N/8m5+dkvHwkURxVL7Bhw4ZR9TX5YiB9Mm997FHmVPh48803RyV4LS0tCIJQtGSD48ePk0gkME2TTZs2FeU1C0VTUxNnz56d9GeZwQNLV/C/fvprZH8Fq1avoS7koaE6kF3VRpK239tkjvUFCxawd+9eli1bNu5QxYIFC1BVtSQWKLqu89Of/pSHH344q+nbv3//uERZEASeeeaZMfenu7ubysrKou9rBm6HlLVKud0fRxQEQj7nhO2kXHD69Gl8Pl/2s4hEIly8eJFYLDaqfYIoitNiTdPb3U3FQA9U3Y0adKanXS3Lnq5NmBDVbX1ejyZgUvyqVLXTIpke8Lil2PFjAYmi6AA9MsyXLFTTnrBVTXs7KdPObe3TBAa09PCDwyq4ghgfQu78EsyawpbwWOjwlfHEM/ktkGRJZG6Vn1hSI57SUXUDt0Oi3O8qiX4qc80/f/78mN2IUqEy4MYw7WzXznACCw9+t2NadbkeyaSh2p9NM0qqxrh2MvcLKgNuokktGzM3E5wLckHOn/z/+B//g3Xr1uXVn09pBr0Re/pkrC+5oaGB9vZ2Ojo6CjamNS2LSFpDkbFLGSvcHJi0YfNQhMPhMbVFU42M99Jkce7cOdrau0loFvMWPcS8uXMp9zmZXxMYRtIzlbvJrmaeeeYZ9u7dO64oWRRFfvGLX7Br166i6xwPHDjAt771LTo6Oti9ezfRaJQXXnhhUlmMssPBnTuddHd3FzVbdyjqyr30RVMkUnbmZX8sRTSpMavcm52+yxetra0ALF26NPu7YDDIxo0bi7LPk0UwGOTgwYMAOFov8Gh93aiPE4S0ga4EFQ6LAc3WyfVpAi7R1pQVC3Lau+5Oytbj9aUzVoMSVDknX9GTBHt61+5/2Cedma5OhnWb8EUMOxLMJwl2RFiO2zQsO00jrDOjyJ2AQE2kMAsqUbCj74JTmHe6YsWKvDOYi4HqoBvdMImndLrCSbpI4nFK+FwOgl5HSYYwJoJTtrcfVTSSqv6lIHiSKFAZcNETUeiLpkbNzJ6JyPmTf/HFF/n5z3+eM8HTDZP2vljWHy0z5j4aNm3axDvvvMNzzz1XUIt2aIvQIYkcPXqUlStXjvpYQRA4cOBAVuMzGX+/ixcv8tBDDxX8/GLi8uXLtLa2FlyZ7O/v5+TJk0iSxKyGB1mxZgGKaguZMy7k914sMrqryZrYiqLIggULaGlpGUYswK4eHTx4kLVr17J161aOHTvGqVOnWLNmzaSJ0+nTp7lx4waVlZXIspxtKRfqWWeYFn1RJR0yLrJ8/VaOnTmL29HKM09unhQRHm0B5JQl6sq9WJZFTNHpiSTRDJNbvTHmVPjyvrC2t7dz586dbOt1JmJodfrK9dacn1fusDNPB/W7U6/FnLR1izDPbREz7IphMk26tJTdti02YRIFe+I2KFu2nYkmEDfsSqVqCdSOQSwty67WJdOVQMXMUEabkE7VlGwueLCqnCs/+V+k3F5SHh+K20vTo5sIlpVN966NiunIzBUEgVnlXvqiCnFFz1bNkqpBX1ShfppMht1OiaiioagzwO+nSCjzOgnHVTTDZCCWoio48104cv7ma2pqePnll/nBD37AX/7lX074+IG4bX7rlEXqJzC/PXLkCA5H4X0TSRRwOyR+9JOfsfzB+Tw4v35MYXumeqfrOidOnKCtrS3vcPQM2tvbp0RcOx46Ozs5ffo08+fPn9RY/okTJ9i89Sn6Y6nsZJosibidImVe57BYuQwyBCKpGqj65EbIGxsb2b17N0uWLMleKD/55BP6+vqGWfFkWiInT57kk08+YdWqVQUNMezbt49Fixbx8ssvj/hbIRdqzbCFxUOn+ixg+cpV9PZ089vf/pZt27aNOjGcCw4ePMiCBQtGlQEIgh2Z5HVJdA4kSag63YPJnPQviqLw8ccfZ42It2/fXtD+TTVUVcVh5Tc1Xu2wSJn2hGqfJjDLVdwBFUmwBy3KZIukYRshJ02bVBZb+zcUbgnqJYuobg9IxA3bLmaoHtC0IKzbFb97Y9bcoj29W6hhc6kgCiKLqzPVegtLj3HlH39Fx5oNPLRi9AX8dCKZTE7LdjOWYdXBu1r0vqgd5RmOq8yaYoJnWRbJNLGbKYuFYkAQBKqDbjoGEgzEU5T5nDN+iCSvb37evHk8+uijOfmtZSZnKwPuMcndyZMn6evr49FHH80p0mU8BDwO5s+fz9r1m5hXPXZ7NoOMX1mhJ2UikZgUKS0WTp8+zYYNGybVthyIJhjUJLoG7c9CFARqyjx0hm1Jd8ZQ+l44ZQmvUyah6nT0J6iv9E3K7fvpp5/mwIEDrFu3jqNHj9LX18cf/dEfjfrYzCTtnj17WLduHbW1tTltIxKJcODAAXbs2DGpFuxQmJaVJXeyaH92bqeMYdrTedXBeWxcsZi33nprVEI5EY4dO8bTTz/N6dOnx32cJIrUVXi51hVFS7dtRiPm586do6urC1EUcblcrF27dtJJNVON7q4uBlIqtbqOI8cWniDYerlbih05VmkWRyc3Gjzp9my3KtCj2a3bCkfpkjbAzrd1iBYd6Unim4pAlcMmm32aQMbEQBbAJ4FLtPCKpfsMig0BgcVVFXR+dpqPe7p5ZNvMWYwcPnyYhx9+eLp3A6csZf+73R8f3RKmhNBMgevdUfT0dH/ZGPeO+xU+twOPUyKp2vKzuvKZfd3M+9Ruamri2rVr4z5mqKv4WIaHv/zlL5k7dy47d+6cNLmDuwTv89bLOaVZ7NmzJ6vlyQcZr7kPPvhg3FaWbph0hRPc7o+XNKx4165dfPjhh1y+fDlvHzxVN2jvi/Gbd9+jafVaRMFupS+oDeB33/3extMa1IY8yKKAZphpQ0i14Pfidrupra3l/PnzPP/887z++uu899574z7H4/FMGIz9ox/9CNM0uXz5MseOHePll18uGrkzTJOusN0alUSB+io/PrcDSRRwyhJBrzMryC2kemeaJgMDA9TU1LB582b27dvH4cOHx/yuRcGO9wOIJIZ/F319fbz77ruUlZWxY8cOtm/fzuOPP37fkTuA+rlzWfG9/4PfdeaXwewWyebHxkrcPbKrefa/wzpcTwokSrxNd9pmxSnYQxidqkC3KnBHFdBN29ZlvtuixmlP394v5G4oZgWDLOrt4Mibb/AP/79/P927w5EjR2hoaJiyKdpc4HbaB7lhWtlBx1JD0w2imoBuWkiiQG2Z576yFMkVVUFbDRtTNG73xQnHbe1zUrWHesx77veZ1Ke2zsH0fxGud0dp74vRPZgkklBRNKMkPKGg2u1EbZ9w3L6xeF3yqORA13Xq6uoKHqoYDZIo8kBDPYeOHCOu6OMaHYNNDHLJOr0X//E//kfWrFkzwpF/5P4IxFM6hmkRTWolFfy+8MILXLt2bdw4LEEQaGxszFa6wvEUrdc7OH/+PE1NK6kJ+Qj5XNnvS0mv/ATGT0uQJZE5lT7a0xWsrrCd3RfwOHDIIi5ZGtdV/l4M1RCKokhlZSWdnZ3jtmHH+x6OHz/O1q1b+fu//3tWr1496YEYVTdIaSYpzcjmIWdQW1Z836f9+/dnrWKCwSDPPvsskUiE3//+90iSlBWgZ9zm+/r62PDoZpoPn2DevHnUbl6DJAo0NzfjdDp57rnnirp/0wlRFJlfWTHxA++BU7DtRgxL4K4CrTSocdoefL1a2oQ5JTDfU9pKnku048/6NIGIbpO4WqdFSLZ1e18GlHm9rFVVlsyro+XiRZY2Nk7Lfhw5coS6uroRhvDTDVEQkEQBw7QwTIupCGDojihY2Jr7hprAfZOyky/cDonaMjunNqHqo1ZJZVHA53YgYMfMDb/KWJiGhWbY8qbB9G8FwOOyjZXdThmHJEx6SKbg092yrFGJnmlZ2cpB+RijxJFIJO/EilyQaUfFFG1c40dd1xEEoSBrh5UrV+akUxLSthV90RQD8dSEBE83bL9AhyTmLcYXRZGFCxeycOHCsV9f17lw4QIXLlwgplp8fuU6DQ3zefbpbdSUeUbo5wbSJN3ndky4P05ZoqE6QDieIhy3NXxDPahcDonqoDvn+Jr+/n5UVWXWrFkEg0GuXLlSsFlwJBJh48aNE/o22kRcJakayJIdki2JIqaVzgdVDXojyWzo+FA4JJGQz4lvlHboUGjaSLf88RAOh/F4PCOO02AwOKa/XktLC7duXOOZp7dz+MiHnPnsc+7caGPr1q0F6/9mMjqvtvFFTRUPVecuUchwclGYGpNorwRzRYsb6dSLpEHJ9W5iuh1d7YSYbqd7pKbWE7vkcDmduJxOLl+/BlNA8A4fPozH48HlcmUnZhsbG0fNTp8JEAUBAwvdNHFReoZnGCYg4HbKX1pyl0HQa5toR5IqumFXSXXDxDAt2zzbtF0NBtP3/qDXSVXAjSDYg06mZaHpJindIKXZ/5mWneebSA0njBmyLgg2rxAFRg0WGA0FXWaampr4+OOPR00USGkGFnYFa6wpvoqKCjo6Oorua+Z22lU1ZQLdwe3bt4lGo3lPSyYSiVEz+MZCmddFfzSFqtvt2kx4sSAI6OlpJ0XTSab0LHEQsAmRyyHhdkr4XHJRRt1lWaapqYlIQuXIx+dYvvxhHl27ckQYtGVZ9EXtvMe2tjaeWJ+bmNkeI7djc2KKRjJlT3Qp6YPXnuia+KA8efIkyWSSWCwG2P53hVRaM1i0aBGtra0sWbJkzMf0RhTC8dSwVVZSNfC55BFmqQAuWcTlkNKrLUfO4/JlZWUTViOH4ujRo3kPzmSmkAcTKtXV1Zz5rJXXX31+WibppgLP/r//v5z83W4gd/KspDm6Y4rvQZnja6rvfV7J1uJolh1DFviSHQqJO+2cPnkCp8uNy+3GnSZhbrcbr9dbtHuMYRjU1NTgcrmK5qNaKpjW3Ri3qRoECHqdCAgT3n+/LHA5JKodI/mAYVooqs617ihO2U7YGi3izHPPYaloBomURjJlkNKNbByladkVv0JQ0Km+evVqfvjDH45J8IAJfbhefPFF3n777YJE52PBJYs8/PByzn36KfOqN4/ZGmxoaKCyspLm5mYkScrZF++DDz7Ia2o2Q3p6o0o2JBpsEjfa15X5vaIZKJrBYML+XdDrpCroLsqqKKZoDAwMsOPJx0eQO9OyuNOfIKHqnD9/np7b1zBWPwSO3DVakmhrwLI6sKRKV3jstIvsfsViHDhwgDVr1jBv3jw6OzupqKiY9MV54cKFvPfee2MSvMGEykDcJnFOWcTnchCOp7KrqqGYbMD3pk2b+MlPfkJjYyOrVq0asbg4cuTIsCpf4ySqEkGPg5XLlxKLxTj+aSsrHlpYFBPkmQZRFNElBxgTEzwrbXasWjbh8U5B2yqDuGFr4jLxaVMJUbCnePs1e8pWFiw891dm+rjYWl2O3t6GYegYhmn/3zRJmBafJVU2/vlf8uknn5Ds7cGhpXCqKZxaimj9AjZs2ZrXtmZaK3YsZK5dGS3wVCDT8UjloIH/MkMSBbsA4LTTnmrLcisKuR2SzZvSdRDTsjDTLXbTsv/LVP9yRcEavLHadpltT7QToiiyZcsWDhw4UDRrBkEQCPr9KIoy4fYz+ajNzc15bSNfCw3bQV0knFBRVFuAmdkzlyzidsq4HRI+t13WVnVb36VoRlq0aTtnK5rBnArfpM0VdcPk6tWrxNYsp6ZszrC/9UUVBuNJLl++xNzqIC9u/2Oam5sn9f1oeibJZOz9zkx1vvDCC9nPN9cqVy6DJeO1mAfT5K7C78oSXlkS6IkoiMLdgHNRFIpCjmpra2loaBihlxQEIWuwXAwIgkBduY/HNj7CqbPneXf/QR7fvImakHfUydr7GYYkYxkWAmN/P5ZlR35lhhyqi2BAnA8U096YT7QJ11SjQrZQTPv9d6QE6lzWlBLcUkISRSSnExi5GPQkk9z88f9kSTCAx+3GEkHwOMDj4O9PnULVDebMm8fCUVKP7keoupHNDQdwTRG5A/u6CfaAhR1P+uVaTOaDmKJlJVeFRsiJgoAoCZPSTxa9WO9zy/RG7RZX92CSmnHYazHyK4dC1Q2+uHSZuXPnTliW1nWdQ4cO5Wx1cu3aNebOnVvQfvncjqw+SzPsYHZZEkc9ATLt2YxaKp7S6BxIkNIMbvfFmFXundSKzMJuW37xRQuSpWcnv8LhMIffb+Z2X5SNa1bwyGq7NZvvZO69SGaGNcY42ffs2cOSJUsK9hOUJGlUg+ShWLRo0aiPSWlGtjU+1Aom5HPhT0/CFjNzsKWlhUWLFlFVVTXCZsg0Tbq7u4u2LbBJ9ewKH088uoaOnkEOHv6AefPmsWLZg1QHPfeFE3suEA1tXHIHthddwrArd7NcxU2yyAUu0QJse5aQScGRYoVCEKDOaWW9+W6nBIKyTfzux0naXBH0eAi4PYR16Ezamb4i9uf/jUXzccd6+ez0bfgSELyUZmTDBTIIeKduMeeUpfQ1ReBWb4y6Sd6r7mdkCkwZSdZ0oeBTe6xKllOWsqRuMKHSF1VGfVx/fz+nT58uqrFqTNHo7u5m0YJ5E/qxiaLIwMAAGzZsmPB1VVXlo48+Yvny5ZPeR4ck4pSlnFc3PpeD+ko/oiCQ0k1u9MS42Rvjdl+cjv54dsxaTffsEymd/pjC7f44V7sitHUOcrUrws3eGJ0DCboHk1iWxdYtW/niiy+y72///v288rWX+Vff/Tbr195NK8m0svft28fly5fzfr+ZdmZM0bgzkBhWWd23bx+bN28eNTM4Vzz55JPE43FOnjw55mMWLFjAzZs3R/w+pthtvdF0jnIBwy4T4fr162MOwoiiWPAgyUQIepw8OLeKr+58GtM02fP797l6Z+BLo5W5faeTzsHBcR8TNezvstIx9eQObN2bX7IXWP3a9FzwxXSkWmaSNqLDDUWgR7UnfL+MSJlwKyXQq9nkDsAEkun3K4kiYp5RaDMRlmXRGU5gWnZnqDropr7SR/BeoVcJIYkCfklHEu1O1K3eGP0xJasl+0NCpnKqaNPbrp7UFO1YyOivugeT9MdSeJzysIGLRCLBoUOHiqq/A4gk7Bt2IIeDWhRFysrKxs057Ozs5OzZs8iyzGuvvVa0/cwXhpYi2nWDqvoFJFWDWELh/fffp7y8PPsYr9dLIBAYlocoimLWPiODvriGZKjDhN5Op5OKitHtJjKB82ATsnzJWE2ZG1kSCMdVYoqGFLGNgI8fP85DDz1UFA/EtWvX0tbWxt69e3nmmWdGXXyMRtbi6WmliaZfiwFVVfPO1CwmREGgKuhmyyMrOB8q49QnZ2DdOir8Lir8rrzIrG6Y9qSXYC/oMoNB04H33nuPLc/uRFdTnDp2hMWiSegej0PdujtYMR3kLoNyh0XMsNukljU9Lv+iYFumBCXb/Dhp3k24cAg2EQ3JU9u+LhVUE9oVARNb+1jpsC1rDMsmfu70ZUKKDHLov/1nNn73X+B2jx0/VUo/08kikZbziIJdtZ+M4fxk4JAE5lX5uTOQQNEM20UiliLky/86cz8j40Oopd0xpqtdXbI7TpnXiaLqRJIaXeEE86oDNrNXVd59911eeeWVom4vqeqc+uQMDz+8PC+N0Vg33YsXL9LX1zdp37TJorW1lba2NpYuXcrF08dwub0MDEb4k1e/CoKQzR7s7Q8Ti8V4dMN6vG57WtfttEvmpmmhGRaabnCpo58rX3yOJArMmzePtra2ce1VhsLn8xGLxbImwTdu3ODWrVvjTrlKokh10IPXKdMxkGAwoXLn5lVcLldRBcsLFy6krq6OvXv3snDhwhHt2CVLlnDhwoVsFTap6lkhsm8KwrBlWWZwgirTVMDjlOm4coEnt2wnqmj0x1LEFY2akBe3Q8Ky7CqwCThEAYcsIolitjocSaojxvgBvE6ZugrvlFzIOjo6OH78OOXl5Sxbtizrpzlv/gI+P3eOa+c/YanPjTs9oBNL765nmlMb3KJNNIw04ZzOQQdPOt4sYdjkLmHYU7b9GoQ1gQqHRfl9LNU00ybPJvbnPntIbJskgHPIcdBYU4VWEeLsyZNs3LJl+OukJSqiKNLV1cXevXtZunTpjBu2yCRH+dzytJE7gM8++4yTJ46zYcMG4ikjG33ZH0uhaAa1ZZ5p3b+pgiSKiIJ9HOqGOW2t6pLe2arLPCRSdmB9TNEIuGXeeustXnvttaIHMw/EUsTjcWZXV0yoLert7eXTTz/lxo0bo/5d13Xa2tp4/vnni7qP+aK5uZnKysqs59mCBQuIRCLcunWLkH/4SnNupQ+LsU2J3YCqi1y//AWrm1bikESWLl3K7t27uXHjRtaWZDxs3ryZt99+O1t1q6+vz2aYTgSf20HA7SCqaHx++RrfeKnw3Nyx4PV62bVrFy0tLezZs4etW7fi9/tpa2vj+vXr3L59O0vwMtKBMq9zSi44oijy+OOPc/DgwZyntkuBAwcO8MTWLYRCXnxJje5B29vvVm8MwzRtY2tRRMRuZQHp6S1rWBvblc69sleodgUhklCzqR2lxOnTp8es/i9raoKmJj796T+wPE3wUukBB680/RUYj2gnaCRMAc8M2B+vZH8uhmVrFAd0u13bqwkkTJg1xcMoxUKm7SwLUOea+D04ZBk9PvwaePToUaLRKC6Xi2g0yle+8hVCoRDnzp1j7969rFixYkZ44GmGPYgHFCzoLxb+7//7/+azzz7jJz/5CV1dXfzpn/4p7lCI7nCSRMq2DnFIIn63g4rAl2+qfygcspR2Y/iSEjxREPA4ZaKKhmla/P73v+fll18uOrlTdYOWy1eZPXv2uAbHGTidTvr7+/mzP/uzUf++d+/evP3HiolIJEJzczNbtmwZ0ToNBoOj2mcIwkQyc9tEUVVVAj5PtlSeD4kVRXHEjbW9vT3n57udEucufsGiBx/M+TmFYOnSpSxdupSDBw9iWRYNDQ1s27aNc+fOce3aNermzM2GYedyvBQLNTU1VFRUTDgUUko88cQTvPPOO7z88ssEPHauYm9EIapoJFIG4XiK6jI3QY8z64IvCgJSeigo4HEQ8DiGXbD6okp2hT4V8PlGekrdi0iwHIvhwxcz4Vbik+w2bUyHyhlUIZPS7dmAbDGoQ2964viWIjDLaeG+j7TyYQ0i6UOx1mkh5/DFa7qOXHa3tX/hwgVqa2vZvHkzb731Fk888UR2YdvU1ERTU1PWgWG6SV5/erHqcUrTHg0mCAIrVqxgxYoVRKNR3n33Xb75zW/ikER6IgopzY4xHYjbXquzQp4vrUenUxbT79cApud7KeiTNU0z5166mK6maYaJy+UqqrFxBuG4yq1bt9j+5BZUJcmhY8fYvn37mEQyGAzywgsv8Jvf/IYXX3wRURQ5ceIEyWQSURRpbGwsOgnNFRcuXOD27du8+OKLRX/twYSKaZrTtspTdZOenh5WPzw15ObeSllTUxO7d+/GV2EPMwTcjikzAR26D/v27Zs2gifLMo899hjNzc1s27YNWRKZVe6lQjfo6IvbHk6y7eFUU+bBstIkbxybmIz+Lq7Y/mPFMOYeigMHDgw7H3NJwVm9bTu33vw58yorslo3LYdossF4nKu6hc/QebAiNIm9Hh2+tOmwatmt41InWhSCMhk8osXtlJ280Z4SqHVaM94g2bRsn79omtxVOia2gumOxuhGJCo7Wb3mrt74+vXr2UX+WNXibdu2sXfv3mkleHHlrr/qvb6m041AIEAymQRsaci8Kj+GaRJP6fRGFDTD5FZfnLmVvi8lyZPT1yy9QJPiouxDIU9SFCVnexGfS2YwoRJNaiUTqQY9DpTYIJ+ePk6Zz57gnYigOZ1OXnnlFf77f//vNDY2sn79+qIF0OcC0zRpbW0ddqP//e9/T319fcGWIeNB0QzOfvoZDz300JQTPEVReOed3eCrxDCMYZq3SFJlMK6iGyZup0y5z1nSk33l6nV88NHHNDU1lTQfeDyUYpGTD2pqapg9ezanT5/ODtE4ZYmGmgD9sZQdsZOwB3Gqg560v9XY8LlknLKIqpt0hpPMLvcWTUwdi8VwuVw8/vjjeT3P5/fT6/YxD/CKFoPYuaxB+a64fig0XefMYJyypjWsbVrFkXfeZpGlIgrFJauSYBOoAR26VAFBmJ6p3ongFGGe26JbFYgZtp5NtaBctqbFx28iWBZZCxgBqHBYVORwmeu3BFb+0evous6JEydQFIVEIpFzek5DQwNnzpxh9erVEz+4yLAsi+5Bm0AFPY6coyCnE5IoEvQ48bkcdIYTJFI6vTmmHN1vyDhGTKcdVUFHhNfrRVFGtz8Z8ViXTMDjwO92cA1b/xaNRosqUnU7Zb737W9kf87VvPjo0aP88R//8ZgTpMXC0aNHs1q1oaLd+vp6Dhw4wKpVqzhy5AhPPfVUyfJCw7EUAwMDrF/TVNQKS2VlJTdv3mTevHljPubDDz9k27PP0dkfxTS07ER1OJ6iJ3L3OIopGjFFozbkKdl4v+DyE4vFCHgcY0bp/SFg6dKlHDt2bNiQjSDYySuyJNI9mCQcV9M5u+NXzARBoDbk5VZvjERKpzNs+18W48J2LF2NLwTSvAUkb1/F73bjN2zt252UwFz38LadhcXH/RE2f+fPswvDR7/yHJd/+vc8WFU56fdwLyocFqolEDegMyUwx22NSjqnG5Jga/B6NYGwbg9gRHQBrwgO0c5oFgW7IikJtrfcdOn1wrptfSJiD1TkOsAixCK8//77iKLII488kvciv7GxkfPnz3P8+HE2btyY/45PAopmoKclFNU5piVMJT788EP6+/tH/Zsk2m4K17ujJFUDVTe+VJ55qm4QTVdWp3OopCQ2KUMhCAKzQnbUVVNTEzdv3qSlpaWkU0izZs3Kkrze3l527Ngxwo5D13Wi0WhJyV1bWxsXLlxg27ZtY144bt26xalTp3jppZdKth+GaXKh9SreUDUOyZ6ILNaqoqmpib17944geENzfs10Wd7lclFbFkIQBAzTzrwFe9DB73YwmLCtVLrCSdwOqegnfFLVOXXmHMuXL6c6OH3tDNM00XV9Wm1TwI5O27t3L5WVlcPOjzKvE9O06I0q9EQUfDm0st0OidnlXjoGEsQUjURKp8LvIuRzTqqaFwqF6O7uLsgjcPWjm7j44895yO2mxmmhKnYVqjMlUO++e/36rKeftV//1rCqvyzL9ATKWcz4CRmFQEybDt9RbZLXqw7fn5kEQbBTP9wiWS+5iAEYgt3tHvLRiNy1WXFO4T1Nt+56C1Y584thm10WRKirY+kkYgFXrFjBhQsXJsy8LjYyJvIeZ+6+qqWGoij86le/IplM8sgjj/B//p//55iPtT1h7cq/pk/fIEKxMbRw4XJIBKdx8GVKqWVVVRWrV6+mrq6upNtpbGxk27ZtbNu2jRUrVoxqT/Hee++VpBUK9g1879692eip8VaFiqKU3IpFEkX6Om+weOECBuIprnZFuN0XJxxPoRYhN3DevHnDTJBVVeXHP/4xzc3NNDc3U1dXh54Ovnam9VrheCob5VIddON1ydSVe/Gm2wwDsdymc/OBQxKZM2cOt2/fzkb5TAeeeOIJ3nrrLRKJxLTtQwY7d+4cEZsG9vBJJk86mlRzei2f28HsCi8Oyc6k7I0qdAwkJiXNWL9+PWfPnuWHP/xhQc8PB0IARKIRZrssROxKTyRtnXJ9IEzlE0/jHWVwY/WOndzoGyhwz8eHINiVPOC+MBkOyDDfbVHntFufQdnWD/okezJYFuyp68G0efKdFCSn6BTrHWKHUpbnming83H12BHCA5P7npcvX05bW9ukXiNfZAyEZwIxOn/+PH/7t3/Lz3/+c772ta/x/e9/nzVr1oz5+JRm0BlO0BVOEEuqJL4kputJVc+SO69TZk5F8eQqhaDgEkJdXR03btzIRl3lg6k0jHS5XKjq8BtUf38/Pp9v0hUU0zRpaWmhs7Nz2O9TqRRPPfXUhFqrtra2cVubxUJ/fz+zqsqZW+UnruhohklC1bMnVWZsPeQrzDJk+fLl7Nmzh8WLF6MoCm+88Qavv/76sIpIe1+MpGrQFU7gckjZ8nVlwD3sBKgIuEj06cQUjRrLKurJIUsiC+fV0dLSQm9UmRKD41H3Q5Z55ZVXeOedd0adlJ5qjLUA8bpkuw2Uh0jY53LgrZaJJDV6Bm1rhJ6IMm5k4UR49tln886MzmDhhk0cPfUxs9dupvXEUea53MQFF1FDQE1GSS5rYuGCB0Z9rj8Q4FNfGfML3vOxEdN0rgzGaVclXKJArcOF31G8im7mKytmy1QQbFLnH2NQJWHAoG5PCMd0W7vnEW3i5RJtk+Fi3+sSBtmhimpnfveV7kiUdtlFZe0sPv/0HI9ufWJS+9LQ0MDly5cnlcyTDzIEb7r4QyqV4le/+hWKorBo0SK+//3v5/S8RErndn8csK1ENN3IepLe7+hNk7uAx5HtXE4nCr6ibN++nV/+8pd8+9vfLuLu5I5YLMadO3fo6uoa04vt8uXL9PX1sWzZsmxUlSAIxGIxfD5fwTeNDARB4MEHHxzVtiQXtLS0TIkdy8mTJ7NVwuqgrQ+IKzrxlEZSHT623lDtL4hUNTY28v777yPLMhUVFSOGXKqCHtp7Y6i6iZrOf3U5pBEDH26HnYxgWvbkdbFXpxV+F6tXr+LEx6cpf3LTlEb5DIUoirz44ovs2bOH1atXZ816pxqqqiJJo3/GmcPAzHNBJggCZV4noiDQGbbNrVOagdspZdudFhaWZS8ugl7HhLrQYDBYkM5p1uzZzHrhqwAsXrKED959l0RnH4IoEppTy4a168Z9/uLHttLx3rvMLg/ltd2x0J1UONrZS8tABAOBBB4ELD7uSLK0PMjmWVXUeCYnHwhrdjsVoMZ5N5qs1Mh46qkO208vmtbF2bFgAh4R5risohESy7r7PsvGGJ4Z9XlYHO/sY962p1n3QG4m77mgsbGRt99+m7lz5w5LxCiVHCNDihzy1Gq8jh49ysWLF3E4HLzyyisEAoGcn2uYFncG7M6FxylRGXDTM5j8UhA8y7KyVlEVU2i/NR4KPupcLhe6PrKsahgGd+7c4dKlS3R0dKAoCn6/n+rq6uxj2tvbRyVX98ZqjYZz587R1NSEx+Ohrq6ONWvW4PGMXh3Ytm0bkUiEkydPYpomkmQ79ft8PkzTpK6ujoceemhatFCJRAKvt/QMPzPUMRROWcLplyj3uzBMi3jK1r1phomiGQVNYy1YsIAFCxZw8ODBUUXxboc9pRlLalhYyKKIfxRtgiAIuBwSimaQVIsvvJVEkbm1FZw5k6B3MEnA7ZjWEvquXbs4cOAAmqYVVA2fLPbs2TOmF2JG11NowT3gcaAbbnqjCopmjOmT1xdVKPe7xrV5WLduHW+99VbBXYMMHt+1i7bOCBbQUD2xoL5m1ixOuLwUg363RWL8uu2WbRwNBINl+N0hEokEVixJy0CE1nCUVxfOZWGwsKlC1YSeIVm3nmnQdztF23+uQoaECYppTzAnzeImePRptplxJoYsVwgIOJxOGopI7jIYar+l6zr79u3Dsiy+9rWvFXU7hmllZS/yFFp6/fSnP2Xp0qV897vfLej5fVElK82ZXeFDyP6O+37QIkNSBWZG2xwmaXTscrn4h3/4h2G/E0WR6upqFi9ezKOPPoogCLz55pts27ZtUjsKdkRRKBQat7d/L4LB4JhTeO3t7Rw5cgTDMDBNs2SavNHg9XoxjNKvWg4dOsQTT4zdepBEgaDHSSypEU/Z8V2TGbdXVXXMPEeHJOZkLJxpDQ7GUwQ9xSdgIZ+LjRs2cOLjU8x+9oms1my6sH37dt566y0qKyun1KoH7AXTWAM+mc893wreUJT7XfjcMkn1bhsm83UKCMRTGqqejjJSDZyOu4bKmYukoij87ne/Y/369ZP2HBMEO35N1c2cK8TzNj3OxQN7aawqvJXenVT4ddstDMuirq6OZ7etQ1RvcePObQTPHOZUr+f3B09z584dft12i3/20IKCKnmZYQO/BFUOa1qj2RwilIlQhoVqCiimPRBRDNipG/a/qx35p20s97n44H//PYYk8+S3vl2cncK+/73yyiu8+eab+P1+Xn75ZY4cOUJvby9VVVVF2YZlWdwZiGOmK+Ae59Rdv3RdH5ZPng/C8VQ2caO6zJ1dQLpkkZRuTmviw2ShG2Z2aDCfqNRSY1IE71vf+taEj7Esa4QGrlB8+umnRR1IqK+vz94w9u/fP2zysxRoaWnhzp072Z97e3tLtq0MxiNcQ5HR3mXap4Xg8uXLRZmOLvM6GYilSOkmt/vj1Ia8RTUkFgUBr8eFZaVXwdNI8EzT5KOPPuLmzZtTUtG9d9sej2fM415OT1pnKgWFwimPPRFdhTs7dWbrQu3f98dS1JV78bsdvP/++1lD8mLAzGiXcpyOnV0/F/9LX+ejN3/JI5Uh5DFa2uPhaGcvZprcvfTMSgY//g5GapCkOhcApT3GS8/8Lf/4e+i8c4djnb28uCA/MmtYthUMQPk0k7t74RHt6l3CEAjIk2N5ejpnFuzWbCEGzB6Xi80uF5cGo5Pal9EgiiKvvvpq9ufHH3+cY8eOcerUKdatWzdpohdN2tIaAZhVRM/JUiKTdgP29X1o4obTIZHSzfTA38whR7liIJaiN3rX7qvMN70+p0NR8t5ksQ6+UpOvjRs3cvz4cTZt2lSybXR0dBSlkpkrLly4wLJly3J6rMdpG1InJzHNdPXq1aJUQWVJpK7Cy53+BEnV4GZPlKqgh7IiGRNbloWmG/TFda52RXhoTmjKV46qqvLuu+8SDAbZuHEjq1evZt++fZSVleVssjpZ3Lhxg61bt/L+++8jCAKmaRIMBlm/fj2iKGa1PapuYhV54GUoQj4XXpecNkMHRdNRVCO7wn/qqac4cuQIW7dunfS2MtnYAncTOHJBsKyMTd/5C4786uesklX8Y8hCRkNM02kZsNvCz25bx+DH38FUBxEEEDExEdHVKIMff49nt/0D//Cz3Xw+EOHpeh1fHoMXMcN2LnGJuevRpgou0fZUKfTqopn2tLFm2YMchmW/z6o8WrOjQTSnRvuVua8cP36cU6dOsWbNGmpqagp6rYzUIeh1Tnv3YSIomkE4liKq2EN1FaNIMZzy5IsL0wVVN7LkzuWQqAq4Z5Th9MzZkwmQaaMeOHAA0zRZs2ZN0UreYLdyY7HYxA+8j9DV1cXy5ctzeqzXZV8oVN1EUfWC0iS6urryfs5Y8LkczKv20xVOomgG3YNJBhMq1cHJnUCWZdEVTtpegILFweb3CT23k3nVuQuFi4FDhw7xwgsvDNN/fuUrX6G/v589e/ZQW1vLunXjDwBMBufOnSMSiYxIiOjv76e5uZlEImG788u28bZhWhMmWtyL48ePc/v27Zz0R05ZojJgH4OWZQ1rn7rd7jEHqfKBYQ5x/vc68/aCFEWRrd/8Fif3/56Gvg6qcxSX34jGsYCysjIc+i0U9a5tk5CeSDUtEVMN49BvUVZWxuDgIDdicZaVl+W8f1E9056dmZ56YGsEDSv36d5B3W4739valdNegpO18xR0veTFg6HIDAkdP36c06dPs3r16rw9HjMLk5iiURlwT2tSwr1IabYUw9ZQ68NIW3XQPapputcp04c95BdPadOep5sPMlVJr0tmTsXEGdlTjRm2zhsbDoeDZ599lu3bt7N9+3befffdUYc8JgOPx1NSkjd//vxhfnGlxmgDFmNBEsXsROuFmwO03h7gZk80L0ubnTt3sm/fPg4fPpzXtseCU5aor/RRFXAjYF882vvi9MdyS1HJwLIsVN3AME3uDCSIKhqCIPDKzidYv24tZ89fmFTlshCYpjnqcE9FRQW7du2irq6OPXv2lMRb68CBAzgcjlHjvyoqKti+fTt+v5+6urpsEzMfHd7ly5fZvXs3DQ0NPPzww1y6dCmv/RMEYURF1efzEYlE8nqdocjoljTDRBIFKgOFT7mt3/EMnQuXEc7xWqGmz4VAIIAZuzrsb6Jg/81MX4rN+LXsVGIqj9Z43CAb0zVVU7P5wCvZpE634KYiMKDdtXIZDQkDbqcEulWb3AnYFTuvZGvu5rmL04KeV17Giff2T/6F8sTGjRvZuXMnbW1tnDx5Mq/nBjyOrGF950BiUhrZyULVDWKKRl9U4VpXhJu9MbrSi/EMufO7Hcyp8I2ZiON2ytl7j921uT888RRVv2v3NUOmZu/FpE+RREovilluPhBFkXnz5k04/RpJqLT3xXLWEG3evJljx45Nev8URWH//v386Ec/IhwOA3Dx4kVaWlpKbvI8GVQH7dWgYZj0pTVw/XkYDldVVfHss8+yevVq3nnnHTo6Oia9T4IgUO53Mb8mkHUE74umiKe0CZ9rWRaDCZXr3VFu9MS42hUlnrIvHrPLvdSUeVgwt46+vj46+uMoU3RhOXny5ISDQvX19ezatYuLFy8WhSyDfVy++eabrFmzJjdrH0HMOp7looEMh8Ps3r2bZDLJ888/z+zZs1myZElRSOqjjz7K8ePHC36+ZtgiblGAORW+Scf1Na3fwHUzt8qJM72taDSK6B/uuSdif7eWZb+W6FtANGrrwlw5fOa6ZU+T3knd1aTJM6egk4UkQL3LjojT0/YmV5MCNxWBXtX2zrN99GwCeDslkDBsYlfpsHjAY5O6OS6LkKN4/n4O2YGvs704L1YANm3ahM/n49SpUzk/RxQEZoU8CEBC1bnWFSUcT02pv2wkefe6emcgQX8slZU+eJwSZV4ntWUeHqgN2Ab2E8RC1pZ58DglLOB2X/y+IHkZQ2O/21HS/PTJYNJ71T1o22vMLvdOqXFsLi2bcNp/K6ZoE+ZpApMu0yuKwvvvv4/f789O7h46dIhgMEgwGJwSz7vJQBJF6sq9KJpOz6BCUtXpj9mtgPrK3G+KwWCQF198kf379xfN302WRGpDXgTBXh32RhR81aMfb5ZlEUlq9EcVdHP4RU8UBGaVe7JtgEq/i2QsgmlBe1+c2RW+kmfU9vf3s379+pwe++STT9Lc3FxwHmsGN27c4NNPP+Wll17K+Tgf+smZY7TV9u/fjyiKmKaJy+Ua1XJl2bJl7N27l8WLFxdsAjvZc9MpS8yt8qEbVl7au3H36YEHid+6gm8CPV5DwLaDGBwcRJPnIjrLMNNtWlEwwbIreKIzhCbPZXDwLALQ4B/e8jEsmwBploBDsFM5+jSBDP33S/nZhUw1nCI0uC2iaUPkVFpXl0r75A2FiF2JDMmlHxZZHPBy/MB7bNz+dGk3NAaWL1/OG2+8QWNjY86DVm6nzJxKX9beqieiMJAeTCoF2bAsi7/7u7/j8ccfxzBNusLJ7N9csojTIeF1yfjdjoJi0wRBYHaFjzv9CRKqzu2+OHMqfTNKzzYUqn7X9qlqGmMvJ8KkPj3dsK0GgHEPqmJVIDI4f/58Tpl/Ppec1QPkivnz5w8LYM8HBw8eZOfOncNuRqZpEolESqqnGgvz5s3L+714nDJVAQ9YoKXJkaqbdIaTzAp5p13vURlwE0mX/6NJbZhRsmlZRBIqA/FUNn1BEgUq/C6C6QENgbuDP7qu8+ZvfsN3vvEiAwk73eN2f5y5lb6Srsg0beLqYwZ+vx9d11EUJadp6LFw5swZvvKVr+RFlBySiEMS0QyTeEobYQrd2dlJKBSakKw2NDTQ0NDAuXPnuHDhQs660HsxZ86cSfng2dO8BT11VKza+CjH7nTw/2fvP6Pcus8sb/R3IjIKhcrFYk4iKVGURIpilkgFSlaWZbdjt0NP2x1mevq+9641911rvs1ds95ePeOZng5q23K3226PZUls0TQpmiZNSkwiKVKiSDGJIouhcqEKGTjxfjgAWLkAFCrIPXstLUlVwDkHBeD89/95nr13VTRGrW1SNyTvOg+/IrOsOsiFvhh79p/kxe2vED3xR1hatFDBQwlR9eBf8ebbJxGA5dXBQQIL04abGQG9wN/ufA9dIlTLdllq0qmGKDhVxirZxrCdOLOUKZDNvS4BZ4YwKFc2hWMkWLZFd6SPpA2xvumbv96/fz+rVq0qWUXvUWXm1vmJpZ0WqWHZdPSnmVPnr2g2rWEYfO973+OLX/wis2fPHmRKvKAhWLE1QRSEgrgupRnc6k3SUOUp3LtnEizbEYeIglBRh4dKY0JXpheMFoUpW/gty+LGjRtFkZb8NZVSuW5qauLmzZslX1cqlcLtdo+4gFaa4BaLpUuX8umnn47/wCGo8qooslTI0gOnFX+zJ0F3LE17LmZmPNTV1ZXUeigGkigQ9KpkdYNzrT10RdO5SmOG611xumMZjNycVV3Qzbz6ACGfC1EQEAWhQO5SqRRvvPEGL7/8Ml6Ph6bwnTZCW19q3Lb+nj17+NnPfkYkEin5NTQ3N9Pa2lr045944gl+/etfl3yegXjuuef41a9+VdRjL126xLx584A7nk6p7PCWyenTp4uuRAKsWrVqQkKcu+++m4sXL5b9/MnAhhc/z91f/Sbp9Y9wq3945nUeGxtrEQWB9vZ23nz7Q9T7fkTggf+Bb9E38d71fyGv+O+8+faHtLW3YwoyK2vryOQ+grYNXZpD7uScqW9QcpSy1TLMdn02yN1QyIJjcdLgctqvc9w2s9021RVswQ5EPJXk474opzSb44KLo+4Q7mdeZvE3vsMT3y7PuHeiuHDhAoZhFFWwGAn51Ji5dQFkUUA3LfqTlcvxTiaT/OVf/iV/9Ed/xOzZjqXPwOSMSs//5Ule/l7cGU3TFU1Pafu5GLgVibl1AWbXTq1vaamY0G2h2O9gJQjOnj17kGWZSCTCM888M+HjjYbDhw/z1FNPlfy8gwcPsn379mE/lyRpSgyNK4mBebRuVaalxlcQJ8RSWSRJpLbKM+7O5f7776etrY3XXnuNz3/+8xVTqgU8Cte7DNK6E7GWN88EZ7NR7XdT5VXHtPVIJBIEg8HCHKcoCDRVe7mZi1Nr60sxu8Y36jFM0+RLX/oSr7/+Os8//3xJaSirV6/mrbfeKroSJYoiDQ0N7Nu3j02bNpVVyRNFkebmZvr7+wmNUmnKo7q6mtbWVhYuXIhblSAJWm7XbhgGkUiETz/9tGT1XyWQyZQmsJkqzF+4iKMXL1CXieEaIYO63uPmCwtn89rVm3S0t/PqT3ZSVVWF5K2iJ3abdPICCiam4OXRlkYsyc3NjEOCLJtCG7ZRtXNJEDNrwZvJsGyL0z1RvKse4J77izfJnwosW7YMVVXZsWMHn/vc58bNLx8NkijgcclE4hm6+tNU+1wVsTX62c9+xuzZs/H5pk4hKgoCs8K+gneeI9gwK+6H+m8BE9v3FZzux36YKIrouo6ilDejlyeIpc4h2QPK/sWgtbW1rEUrFovh9XpHJDAbN26suNp3POzatasQ39bV1TWhYzlDsw7J641n6E9qIFB0xba5uZmnnnqKgwcPsnXr1gldSx5uRaK+yotH1bBtG1kSkSSRkFclUGTyRX19PStWrODAgQOF6xpI8rK6SWc0PWpg9NKlSzl37hwvvvgir7/++iBj0zyGxvEJgsCiRYv49NNPS6p8AaxduxbDMHjnnXcKLd5NmzaV1Na59957OXHixIjq2YGor6/nzJkzALTdbuPge2cIBf1c8QjIskwwGMSyrLIXo3Kxd+/eMVNZphsPPfk5Pnn171jYMLK/2cKgn68umc+7HX1cjDpqQy2qoyMjoDA/GGBVTYhGj4osOO3LvD2ILDieb5WK+fq3gqymcSKeYePXvzktkZTFYOHChUSjUSKRSMnrTzSlOQRIN7Fx0iIUWaK1O0E44MIli2R0C5ci4i7BUzGPb3/723R1dfHKK68gyzLf+MY3SOfmBGRxctuTNQE3LkWis9/xQ23tiuP3KOg5dW444PpMWapMByb0iY/lKifjmS0++eST7Nq1ixdeeKGs85QTMg6OwggGV6TGwkcffVSWEGKsqp+qqlO6EPb391NXV1cygRiMO4w9T5ZUWaKp2keV14UqC0XPeCQSCSzLIpPJVMxvShAEQj4V3bRQJJF59eV52M2ZMwdd1zl8+HDBXFiVJRqrvbRFUjnj3RQNIc+w17t48WJ27drF3XffzVNPPcXOnTtxuVwFf8Zjx46xdOnSQdFahmFw6dIlZs2aVdZGQpblAhm1LIsdO3bw0ksvFf18t9td0vzflStX+PhKK4888ghBj0LDALJ78uTJstrT5VbzL126RENDA8FgsKznTwXeP3aMFdWhEX+XMCBiCGQFD2uaPNxdZ9KRStNvgGmLzPO5mOeTCMo2PinvjecIEEQBVOFOxNv/QXFIZzKcMkUe/oNvTfeljIve3l7Hd7IEDE1QEICGkA9wfCTbIkk03cKlStiWzcLGIEoZhu719fV897vf5ec//zmJRAJddASLUyGq9LsV1No7fqh5WxKAtkiKoEehNuiZ9tnwmYqyV1s7N9AOEPKPTWCam5vp7u4u91SkUqlx20pD0Z/MFuaGgt7xP4hnz54tzjpiCCKRyJTnh46F06dPl5TVOzLufFmGzqJ5XXJJUU2yLPO9730PoKIegIHcwL9jf1F+C3zhwoVUV1cPmhX0uRQaQk4FNJHRudWbxByBmKxevZpjx47h9/t59tlneeKJJ7h27Rr79u1DluVhuamyLLNixYqylaQDIYpiyd+JUrBlyxY+/fRTVq12NgreATvlAwcO0NTUNG4lcCRIklSwDgKHuL311lujtl4zmQyapnHp0iVWrVpV8vmmEn6fl8sm3Bgyi5exoF1zVKMi4JOg2S2xusbH2hof91R7meeTaHLZ+AZ8tUQBPJIjovg/5K40pDIZzoguHv69r0z3pRSFUjc+Wf1OgkLIpzK3zs/CxiALG4PMqw8S9rsIuFWawl4kQcC0bMwJzrElk0l8gWDRhZ1KwVHA+3Neeip1QXch1SiW1mntjheu6f9gMMqu4Fn2nTrPZEqZdcMoaobNtm1SmkE6a5LK6mTzZVy/q6gYqmw2S09PzyBRQk1NzZiLypUrV/j444957rnnxn8hUwTbtifcipBEAbcikdFNeuOZQZWbUuF2u/nOd77DmTNnyh4kHu0afS6ZZNYgkdEnZH2xYsUKPvjgAz744IPC+x30qMiiSHtfkqxucrMnyezawVYxjY2NnDp1alBlcirV0pZlsXfvXubNm1fRvy0479sj2x6ltdtRFw60jlmyZAn9/f3DCGwx2Lp1KwcOHCCTyaDrOgsXLuSZZ55h9+7dLFu2bJB4av/+/SQSCa5du8a///f/fuIvapKxbNV9sOo+rn1yhdvHDzGrykmh0HNrtyzAHLc9SEDgEUHLUhBU/B9MHLphcNKS2fry8LGJmYqamho6OjqKquyblk1bTujmdcnUBQfb9DhG3nfmdOuCHkzLmrD3o2VDeySJZTtpGgMdDKYCXpc86D7kdysFm7bOaJr+ZJbaoGfSba4+Syj7LzGwImo7MYOjIpFI0NraSk9PT9HxYpFEhkg8y8lTp2humkNrdxyXLOFWJfxuZVDbtT+ZpTeeHaboGSn3bjSMtDAPnaHKI29kPH/+/BlF7iqJ2qCbW71JYmkdUUw7aRJllhHq6+tZtGgRR44cKTvr9/Dhw6xfv35Qi9fvUUhmHTfxYt/n0bBq1Sree+89Lly4wLJlywDnhjK71s+t3mSu5ZGiZYjwYuvWrfzyl7+cls9Bfib1/PnzvPfee6O25Y8dO0Y67fhWJZPFKaDhjnLWo0qDWiAtLS1cunSpbLuTrVu3omkasiwX3s+nn36akydP0t7eTlNTE+fPn2fz5s2EQiH27NkzZVFSlcD8RYs5cf0aob4OfG5PwXhYYLg61CU6VT3DdkjeTMuQ/SziRl8/D//B9Khiy8Xq1avZu3dvUQQvmnJMhRVJpDFUXCbyRMmdaVkkDImsYd0xWp7msrLXJTOnzk80qRFJZMgaFrcjSXwumfoqT9GjWb/LKJrgtXbHcSkSHlVGEoVBTtN2juG9/fbb3L59G0VRWLx4MR6Ph2PHjuHxePjP//k/43IVF+cRS2n0xh2p9/3338/Fixc5fPR2oVLS1NjIXYsXIAgCumEWqnWSKDgsX5XxueUJf6jb29uH/ezUqVN0d3fzzDPPfKYWnVLhUWVqAi5641n6kxoZzaSp2lv2l2bhwoUlR1aBU6XatWsX4XCYc+fOsXLlysLvfC4FAWcHl9HNCbcM1q5dy+HDh7l27Rrz588HnPbArLCPW70JMiMIL7xeL2vXrmXXrl3TZmS9YsUKdu/ePervU6kU27ZtK/m4Gc2pnE9GhX6kudQ1a9Zw69Ytenp6Bhkmr1mzpuw53OnCg48+zsEfv8pGt42cWwhHiuYSBadlGzchqgu4Xf9HHTtRaO6RBW+fJXR0dPDee++hqipLliwZVNlOZpy1t9rvmvAaVwxs26Y7luHT1ptk0imWzK4vqis2FRBzSUdBr0Jv3FHcJrMGN3oSNE+S6fNnCUW/es2wCuayA+GSRSRR4K/+6q9Yt24d27dvJ5PJcOnSJWKxGN/97ndLuqCMZtCZCwT3uWSaqr0saFyLljMsTmYN3j1yjNlz5gx6Xk3AVTFpeB6mabJ37148Hk9h3qinp4cnn3yyYueYyQj73aiyo2LK6CY3exLMqvGV/eUuVWzS2trKBx98wJNPPomqquzbt2/Q7yVRwOdWSGR04mkNt1LcbnYsbNy4kf3796MoSqEF6VIGCy9ccpbqAdmDjY2NrF+/nrfeemvaKrqLFy8eVH1MpVIcO3aMOXPmFDztSoWWm7+sVPJDMWhpaRnW+q2treX999+fsmuYCK5d/YT5CxcB8NDLX+L2T39IU30DANoobdiQbBM3BWImKLpTxZMFp9onkDfnnprrHw/50ZzJNiGeCHRxZpCPUvHAAw/w3nvvYRgGtm0X7iXnz59nz549hEIh1q1bV5iLnqrvZTytE0/r/N6XvsSHxw9x8mAKcIifKIqsWbOGFStWTGtFTxJF6qs8VHlVOvpTaIbFrd4kLZNsWj/TUfQrb672ktIMNN3CtG1UyQmn97pkBEHg93//9/l//p//h9WrV+N2u7n33nvLuqD8oiIATdVeBEFAkRw5ts+tUBOAsMcxsQUQRQGvKk9KObalpYVt27Zx8OBBOjo6aG1tJZlM8uMf/5ivfvWrM26XGIlEyraiGQ15FVNbJIVuWtzsSTIrXN7OqJRB4sOHD6MoyriEKeBxCF4irQ+bRRl63mLfr0ceeYT/9t/+G3/wB39QGCnwuRTqgm66Yxl64hkUWSyYAAMcPXp0gsrliWHx4sWFOTbLsvjlL39JOBzmxo0bZVXv4M7UhTWCD9LixYs5f/58WcKkctDS0jKhFv9U4PCO11kQj3DsyDsI/gAuLcscn8+JIrOc/NXWFNS7BtuduCUIydBvONFjo0EEFBG8opMEMdkRXkMR0eFGfwpRlKj3u6lXbWaieDEjfzatM2prazl+/Dj333//oIjHFStWsGLFCnp6eti7dy8xQ2HV/Q8MMv+1bXtSCJZt2/TmxBx3zZ/F+nsGBwzous4bb7xBNBqdEd9Nl+IIMtoiSdKaSV9So+n/ELzx4XMrY8qia2pqCAQCEyYYPpeCKKSxbGcGaKRzqpJQVLbsRDCQjGzevJmTJ0+ybNky1q5dSyKR4De/+Q3gWHYUaz5rGAaGYUwocmokpFIpDhw4QDAYLEvZOB7yOZ63Iymyull2ZqvX6y1qDjMSiWAYRsG6JI+RbmBel4wAGJY9rE3b09PD8ePHcblcWJbFE088MeZ5b9++zUcffQTAd77zHfbv38+2bdsKKumQz4VmWERTGh19zjyeKou8+eabPProo5Oqah0Llm1z7VYnpuKnN55hz66dfPHzL/LOO++gaeWryzyqXBDaOOrpO4xizpw57N+/f8oI3ooVK+jo6GDnzp3cf//9ZQk8Jgu6rnP0X37MGr8bV3U1Dbmf27KLrO2izxDQcObsNOBWVsAnQZ1yh6TVKo74ImUKmLbz2KHbIYs72a39hkCdalM1RWtXzICPOvoIqAqaaXG5O41YX029OrNaytF4HO+SleM/cIZirDGP2tpannjiCX6xy+lktEVSKJKAZljkx+A9qkxN0F0xhWs0pWFYNpI48pqrKAovvfQSP/vZz2YEwYM7bdt0JEUyow+LtPy3hIrcHk6ePMnJkyf58pe/POFdhCQKVHld9CWzRBLZEQmeoiiD2lGTgXz0GDiVn4HVGb/fz+OPO8HUhmFw+PBhsllnZvCee+7h2rVrBd+3/PPBISiZTIaVK1dy4cIF6urqCqrNTCbDkSNHsCyL1tZWnn/++aIEKVevXuXy5cvDMnArDUkUmRX20d7n7IxuR5I0VXsHVbHGw4YNG9i9e/e4SSHhcHhEv7aRPlui4MxdJrMGyYw+6MZ29OjRwizXnj17RvXhO3z4MPF4nIaGhkFpJM899xyvvfYaL774YkGZXBd0o5sWqaxBa3eM99/9DS8+/2zFSXsxyGhGLl1E59TZC8yaNYs3d+1l9eoN3IqkqW2eQ9UEbmzVfheJjO4ITPpSzBkSyzPVEXyNjY08++yzvPfee5z58Czbn3gcZQrMaw8ePDiikt+yLJKJBKG2G2yY3QiIJAzIWAKZHBEzgUgsQdw0WFEbQrAtkpZI0gTDFpjjdgiSIEBYgbAysCrjtEPz/+R98WKmQMp04stEJj+mzLKhM2MhAHPDATyizbnuBG2xNKGwG3WGNDKiyQRd0RjKR+/zTus1Vm9/Cu8UJTBkdZO0ZiAIAh5VmtQZtdmzmjl58iSJRIItW7YU7mk2jvdruidBS61/wiRPN61C9S7sd43qfaooyoxLmPGqMqos5nLUU/QlRBpC3ikdN5kJqMit4dy5c/zxH/9xJQ4FOL4+fcksGd0koxnD2oGbN2/m9OnTk9qySaVShTSIsSDLMg8//HDh/z/44APuvffeUb3xjh07RiQS4bHHHmPnzp309vZiWRYul4stW7YgyzIHDhwoWm08f/58Pv300ylpF0uiEyHT0Z8mkdFp73NMgIeG0I+FOXPmsGPHjkF/n2ITSkbLI/S7lYJdSl5Ne/r06UFjAlu2bBmUptHR0cGHH34IOOKK0apvn//853nttdf4whe+gCiKCLm0i09u9bD/t4d47NHHEKewJWTbNn3JLLGUXsiCBnhw9QOcPX2CtfevxBcIYFg23lA9SCLJrF6W47skCjSH7yR7xFLajAj+Xr3mQT5u7eIff/ErHrpnIfeUqeYtBvv37+eee+6hvn7kdAqAd375S8719OJy++iORkmrLjKKC93lxpZllm56gHnVAa7u+BlGbQNyvJeGQJis5RA17yhrjiAMMScQQBUhINv0aAJ9BnRqArIwuQkXCRNu9/Yxt76GsGwjCNBSHeBiZy/xKg814vRX8Tr7+jBNk8UtswBYgs0HP/p7Zn/+KzRMYqSeadl0x9LDZtN9LhmfW8GjSoXKt6abmJbTSnWrUtFm8UOx5t7lLFm8EF3TOXrkXT735BNIooBhOteS1kxu9SSo9rsI+dSyhRiprIFlO4kVVeN8703TnLQ2cTkQBIGWGj/9ySz9ySxZw+JmT4LaoHvSu38zCRUheKFQiB/96Ee8+OKLVOW8nyYCOTffF0/r9Kc0Gkfood9///3cunWLAwcODPr5xYsXef755wfNMJSDdDpdFMEbivHMWAcqAQcqBQdiNCJz48YNPvjgg0EtqqmeAxRyEvmuKPSnNLr608iiWHS79u677x5kr3HgwAESicQwQuz3+4lEIoTD4XGP6XPLEM0LgUxUWaKvr2+QM7zX6yWbzXLkyBFisRh1dXXjtmzB+fsqisJbb71VSGLp7+vjo5OHefKJxzAsCvYpky3LNy1ncFgz7syp+twKAY+CzyUz/8lHAadlG0/r9MYzBXsXtyIh5qKFPKqM1yUVdeNXZYmw301PLhdyIMFbtmzZoBSQyYZpWfQnNWIpDbfbzdZHHiHTe5O33nqLxx57rKTYtmJw7Ngx5s2bNyK5My2LWEonkdFpWr2Z9lu3uHzzBqueepjqKj8uRcKtSIMqBrO+82eAU/k7/MNXqQ+GyFqjE7yxUKva6LZAwnRMlGe7Jm8mr89wqkNV8h2xh1+ykQVIGBY1yvQt6qZl8mlbBzVBPw3V1YWfCwhkQjWTSu40wyzMJoNTNbKxSWuOGDCZHT2iUhQcs/agR0FVpJyQpri/oyQKhP1uwE2Vz00mlSQYDCKJ0FR9p8sSSTgOCOXOTOfXIZcijXtt27dv5yc/+Qlf+9rXSj7PZCHvBxjyqXT0pUlpBt2xDPG0TpVXxT3JldaZgIoQvBdeeIF0Os0bb7xBKpViyZIlg6pa5SDoUYmnddJjfEmGKu5u3bqFLMsTJnfgVPAqvWAUC6/Xy40bN5gzQCl84MABfD4fzz77LLt27Sq8bsMwkEpIlqgEBEGgIeTFtCGZq+TNri1PXfvwww/z9ttvF9q2hmEU1LJDY6nmz5/P1atXB1kGgNM+9qoyKS3viTfydWzYsAHDMIoijXm0trYyb9485s6dy969e7nnnnv44IMPeOnFFzBMq+CRdzviKLYm07agN55Fy/lQ1QXd+D3KiFUAUXB23H63Qm88QzSlkRmQ9hHNub7XFbmbDXrVAlnM6maBtLS0tBCPx6eE5BmmswM3coIPAairchNsXMGyZcvYu3dvQWVYCZw7dw6PxzPss5avoEbiWQZuwxYumMeD9y4taiEVRRExZxml2wJQXgWsQbXRs05CRltWYLa78qKHjAW3e6M0hqoGzfu5RFBFKWdRNfWLZCab5XakD0mAhbOaEIXB3zvd0NHcXjra2nh/36959Mtfc9S/FcpPzd/38sdsqvYW7ISyukkio5PKGmRzGbHkHieLAoZlY1p2IUd2IAScjFWHwI2PrVu3DrJoyndZ4hmdSDyLblr0xDO01JSWtmRad5Kqitm4zp8/n1u3bvHqq6/yzW9+s6RzTTYkUWRWjY/+ZJaeWMbpDOacOoIehfqq6ff0myxUbHrD4/Hw1a9+FYBXXnllwgRPzunwzREUfKPh6NGjhdD3VCrFxYsXS873yyMWi9HU1FTWcyeKdevWsXPnTsLhMKlUiuPHj7Nx48YCMRlYWTx79mzZhrMTRWPIw60es1D+bqz2ltwKFEWxUK07efIklmWxbdu2ES1VFi5cyP79+wuLbk9PD2fOnAEgrdu0RRIsv2sJm1ffjSAIGIYxKNWjnBzTs2fP8swzzwBOxer8+fMFMipLIs1hb6GqdjtXySu19aIZJmnNRJVFXIrTuskvEvG0XnChz1cKGqs9Rf2dJVGgvspDyKeS0U1s+86skGZYRc/o5P0lR0oNWbZsGVeuXOGdd96ZFIFPHt2xDIZlI4tCwa0+b74siiJPPvlkoaKfb8OPhVhKw7Jtgl512Pt19erVEVWBmmEWMjEBVFmkyqvicyulk4YcGRjNOqUYiAI0u2xuZgQ0G7p1gYYKix5ihkDGtKh2K0jC4GNbpoE4xZtLgP54nP5kkoVNo1fnFFlhUTrFjT1v06K6+ed/fo31jz6GojjvVb7qndHNQtShKDqRXlndRNMNFFkuPDbgUQokoD+ZpTvmzJx5VImmau+gjZ0rV7mtyUVkOzGHwiCz8GRWJ57SSWZ1Bi5xNs5Gzu9Wit4wD+00CYJA0KOiyhJd/SlKWEIBZwMYiTvfN1FgkCXUaLAsi/fee4/169eXdrIpRMjnwudW6E9myWiO7VosrSNL4oSN8mcqJmU8d+HChRWzUChlrWxsbGT//v2Fmba8e385SCQS06aKBKd9+/rrrxcGy0dDb29v2T5nE4UoCDSHfbT1Oeratkiq6KrQQGzevJmdO3eyffv2Mb3yPvjgAy5fvlxoHYTDYbZt24YoipiWzaedMY4dO8bae5exfPlyzp07N6H80vPnzw/KjZ0zZ86gqioMNkLO6ibtkRTNYW/RO8KMZnCrNzmohiPlFpqBsHLkzqNKJZNoVR7eijBMq6SA7vycYzSlEfK5Bj138eLFuFyuQvLLpUuX+OY3v1kx4YlhWiQyzoxTc9g36qB0S0sL169fp6ura8yZOdu26YlnMC07pxBWkESBEydOkkgmqKurZ/ny++joS2HZNoZpoZtWYaEUBSf+aULziIoCBugT5GOy4FTybmcFYgZUy4woekiakDQFNOuOOlcSwCU47Vb3CH9S24ZIRsctigTlIZ9H2/nHMXGemhm8VDpNR18fblVl3jit1z4dUpKPYK5wtVG26N3zryQlBU11kZVVdEVB1bK4tAwiApriAtvElcngMrKYkkxWcZHyV7F68yZ8brWwOYLiqz8jVfV9LqXwPXa+687fsL0vRVoz6U9q1FdNzNfTrUjMqQsU/XjbtumM3pknzFcDi9m8JBIJTNOc8m5SqVAkkbqgB8O0uNYVB5xZw5ri/0yfKUwKwdu2bRvf//73p8xCIY+hFYS2tjbOnj3L8uXLOXToEIZh0N3dzRe+8IWSTXenA5///OfH/P2RI0eYN29eSS3HSkOWRGbX3BFe9MYzZQ2xjkZiT506RSQSARyrjNGMs/PZtIIg0J9Io2laSbFcI+HatWtFpVO4lDzJS5LSDDr600VF+ZiWVWjziIKz8zZz7RtwLGACHgWXLOWsEOySVMtjodR5wYBHIZJwWj5d0TRN1YPHF/Lk94MPPuChhx6qqKo4nUvUcOUqnGNh48aN7Ny5c8xNETiqwP6kRmd3DxcuXABg5cqVBALOnT5PKIfCo0rUV3kmPLtjKwoY+ojpFqXCKzlpGEnTEUSEh7y13ZrA1UiMjGEi4Cg3BBssbGzbJuBSmVXlp061C7Fq4Byrsz/G7LowniHHFAWwheFWLpVEOpOhs78fSxARsPHJMgvGGb8xbcdLMJqb7AnKEJZtFFFgsb8Ky3ZeV9y00KwssgpejxsBG8sGQZBwB7xIghfbhrQF3ZkkF956E83lwZ1NE0fgwac/R0No4vPmQG6z5Pzhq30u0lqKaErDrUhTKmqK5QyNwQkOCPlGV84OhWVZrFy5kjNnzvDRRx+RyWT47ne/O2MJX/777WzWfjerdzBJBK8S/ex8hcDKtZXKkTc3Nzfz7rvv0tnZyZYtW1BVtWAC+1nNkO3p6SkIWdLp9KAK03RBEISCGe5YXonF4sSJE/T39wOOaGX16tVFPc/vUXjggQf40T//jC1r7p6QwvrkyZMlVf/cqkxT2Em7SGR0uqLQEBp7hrMrmilkSs6u9SOJArppoRsWLmVw/ut0y/vz4pqbvUkSGZ2eWIbaITfGc+fOYRhG0e9XsUhmnZtxsX+De+65h9OnT7NkyRLeeecd+vr62LRpU6H6KgiOp9e+3b+kvnkWz23fhmU76tCRFjRZElEkEUUWy1Y+DkQmnUbvbIdgLTYOKZloMoRfskmaAglTGGS1YtlwM5pEEkWW1QfxiHbhXIYNaVPgVn+CK90RUuEwtapNUHI6J3FTwBIEgpKAMKQ9qwgQ8HrojSWY5fLhquDoqY3N1dvtuFVl3EodOG3upAnpnCo5f6XVsiNGGQhRcEjf0Ipk/sxD4ZGgShapc/uxbAE16McnweW9b3LhvgdZtuq+Ml7h6PC5FUI+lf6kRncsjc+tjFtp1zRtVAuoUpDIkbuwv/gZQHBspizLGpTwdPXqVfbt2zfIdmomwqVIv9NJF5P2ypYuXcqHH35YdqKFJIr4Rpn7KQVf/OIXB/2/KIqFYPnpTB4oF+fOnWP9+vXEYjEuX77M1q1bpz1Rw7RsUrls4nAR8xpj4Yc//CEvvPBCWVVJn0tBVRReevkLzK4tbah4KDo7O1mzZk3J528MeenoTxFL60jicBKUx8C2Y2O1t3ATV3JkYibCrToh3l3RNH3JLKJIYSE4f/48yWSy4t+p/mS2UFUotpqRtw46deoU27dvRxRFdu3aNay9Hg5Xs2XD1Obbtl69Su+h37C5McynKacCZlWA4OWVuFlrMGHUbUim08yqqx1xPq9KtgnV+biVUGnt6aFTkmmpCeEV4VZfnCq3a0QyJArQEPDxcUcPXZqfWS5nXks3DGRZciqFZaCtp4dkVmNBY8O41R/DdrwAk0MsCt0i1Ch2WerkkSDlPAoHEsBltWF6Pv6Ad69fY9PzL1bmRDnUBT0kMwZ67h4xnkVJb2/vhNeAjG4W7uG+Eg3ss9nssLSchQsXDnO4mEnIuz6ktZll71JpTBrB27x5M9///vfLJnhwp400imtI2Zg7dy43b96ko6ODxhF2iJFIpKyB/KnAokWLePfdd/F6vbz88su8/vrrPP3006Mqfg3DKLSn84bNlX5tmuHcYSVRmHDraiIt50pl0x48eLBsVWjAo2DZd0iQJAojDilnjTuD+pVynZ8KVHlVLMuZYeuNZxEFgc5b14nFYhVRsOYFJ2nNIJU1Cu3qkE8tqBSLwdAFZ+XKlezcuZP169cX7TFZabx/6CC1Nz7h3jrn8y0JDrkzbBir7t3Z00NdTXiYUhScald7Vzdpy6ZTV5BEiVmuqgK5sW3HMmSs5cstwsKAQq2nhvaUzpWObkRJwqMozAr6UEfxuatVbJpDVVzp7EFoqKXKStHeF0ERJSxRoNbrpSpQ3HBTNBGnIxqnpTpEcxHvj245iSCGnUtwkMArOqSuktXEsVAb8BPQ0hz+0fe5+/nPExpg0zIUH51+n76bN9j83AtFHdujSuhpq5A7Oxa2bt3K3r17AairqytLWOhWpIKJe6kVrWQyiaZpn4mxpzwGbqIrscGaqZg0glcJRpwfpp8Mcr1x40beeOMNXnjhhWG7nzNnzvDII49U/qQVwFBrmBdfdCKphioH29ra+OijjxBFcVB7+tixY6RSqcLjli5dOqyyUSpU2fn7mZYzkD7ZfnBjwe8eP5t2LBiGQTKZnJDApsqrYloWvfEsPfEM4ghGoemsQ/DkGZZnXAyq/S4s2yaSyHKrO8rJU2f5xldeLvt4lm3Tl8gSy8UiDYQA1ATcRSn5xkJ+RvDYsWP86le/oqWlpSKencXiwrlzzL51lZoBc1tiTp8w3hIeNwzSHV2YgoBXlgj5fXT2x7BEAcE0aQ6HcblceLMCV7ojg7z1nMKwPa6SUhAgpEAgqNDgrUW3BNySTVAa/YmKCPP8MrYd5GzrLYJeD6uaZuHPrSq9/f180tGBDMxpqB+RoGqaRmtPD9VeD0tnFW9v1aU75M4lQqNqT1uahktVWVer8ukvX+eq20dWdWMHq1izcRPXP7lC78fnCCRjzHGrzAbOHD3CfevHHx1xKRKkdZIDzNtHQ3Nzc8EarKOjg7feequsEaRyDYCffvppdu/ezcqVKwtryfXr12d0VcwaUDWaiXnKlcKkNp/vvvtuTp06VfZMTv49KEXtVwqefPLJQR5sAzHdbc9iIcvyMGPk/v5+Pvzww0EzEeC8pqFzaefOnWPfvn1kMpmCHUipkEQRlyySNSwiieyE1F8TvSn43DKi4GTTpjWjpKoPwL59+4pO1hgLYb8by4K+ZJauaBpBoJD44XhgOdF2/hmckXjlyhWuX7+OKIrU1NQMmkmsCbgRBIgAc1sauXTpEkuXLi3rPO19KVID/C7dioTXJeNR5Qk5/o+E+vp6Tpw4QWNjY8VnBcdC3/WrLBmlcj7eq5NEiXn1dQBkshmi8QTzGhuGPc4l2lgIZKw7ytb8rbNYMcRI7cix4JFgYVBBFRto7YtyvidGS3UVdYpNTShEDU7KQWtHJyYCtT4voWAQG5vWji5EQWBxib6laRNSpvN3a1Inz+C5FCyoDuX+y0SPdnH7n39ArdfDQr8fXKHC44RL57AeWjfu+uJ3K/TEMmQNq6Qs1cbGxlFTlCYLoijy9NNP88YbbxAOh/H7/Vy5cmVGW6bkOwN5cdtnCUMz18fCpBK89evX8w//8A+FG6lt2yQSCTo7O9F1fdwsWbNQwZucN8Dr9TJ37lzOnTs3yEtutCSJmYpPPvlk0P8nk8lxVYR55JMl8hYX5aIm6KYt4qi/XIo07tzIaJjo314UnDZtPKcIK4XgJRIJVFWtWKuhNuh20hdSGh/fiNAY9jkquVwEkCqLBGcowWttbeX27dsFstvW1sbevXvx+/2FTULY7yboUbETjcTj8bLOk8wZwgI0hDz43SObN1cKDQ0N/Mmf/AmpVKqoXORKwZ1OgXvwZzFfVSvl1bpdbtyukSs6XsmxHO7PGtQoEqrotH8ty2YyOZBLhAUBmZC7ltuJDJc6eujyemip8hFWbCRJYn7OUzTS388n7R3YtsW8ujoUZfDnP6EbtMaTaJaFKorMDfjwK4P/bjHT+YsFJGYEuRsKRZaZkyPkQ7E8HOLIzn8dd25PlkSq/S4iiSxd0RSKXFy27OXLlwuuA1OJ/AhQnlw++uij/NVf/RXLly8HnPu6DSUldkwm8vcYx+7HntR7TqXRl8gOczEYDZMuH2lpaeHVV18t/L/X66Wuro5Tp05x1113jflmS/k3oVSnxhKwYsUKjh07xp49e1i3bt20et+Vgz179vDkk09OuM3a0tLCtWvXmD9/flnP97kUagIueuNOxcoli2POcuQHcO0BJN627YqogvMxd/G0Tl3QXfQN5eDBgxVf8OurPESTWZKaSVtvkqxuFm4mtSVc21Tjww8/HLRJyLeB8ikj4NjIXLx4kfr6eh544IGyzhPPCU2CHqWkTONykV+AgsEgy5Yt4+zZs6xcuXLSz9sfjXIlLSOZJqJl4BJF0koIWVHGHUERitz0uEVYUFfN+dudCNRQ5ZKJZ3WyNpOaVQtOJaRWtakKuejxubnVn+Bsew/NwQDNAbWgzA2HQoRDw5/flc5wuKOHC32xQbVDAVhWHWRjYy31HjdxA2IFC5TP1kYcnG7HskSUKz/6e2xRxELI/RssQSTqDbDxRcceK+x3kcoaZHST272JgveiadnE0xpVXhXbttm1axc+nw9wBEZTbZvV1tbG0aNHef755ws/EwSBF154gVdeeYUvfe0PuNmbxDBtZBGqvC6aw95p7ZLJklhIFcloZtFRm9MN27ZJZUe2cBoJk/6qRlswm5ub+e1vfzum63x+lksvYtC0XOzbtw9RFFFVlR//+MfMmTMHl+uzEUb85ptvsmnTJurqRt4tloKlS5dy4MCBsgkeOBWdrO4ov9r7UsyrD4xKYGzbHjYIXyl4VRlRELBsR91bjDFwR0cHtbW1Fb/pCILAvIYghm2TzBh0R9NUeVXm1gVKNiyeSti2PeLg9ECSd+vWLb7xjW+UfY5YWisoZANTQO6GYv78+VOm9Nv67e8M+v9oNMrR/72DlSGF8aJcRbv4+1+jamM213OrN0p71EIRYEljHXXK1JAhRYQml01VrY9uzc/tvijtsThzw0GavcqIRPNqLMFrV29i5ao8VVVVBAIB4vE40WiUC30xLvbH2T53LgG3Q2RC8uST1slCtd9P9Sht1HQmzcH//VMe/r2vIOSN5CNJMrpJZzRNbzxTqDqlsganj/52kEF8JpOhra1tSl6HZVmFiMCRPFsXLlxIIp3hlZ/t5IEH1xeIe3t/mp5Ymqawj2q/a9ryYD2qTDzjpIl8VgheVjdLSiaZtle1bNkyjhw5Mg7Bc+58xSiJykFbWxuRSITa2lq8Xi/f/va3OXPmzIzwlhsNly5d4saNGyQSCR5//PGKzlskEokJH6Mh5CHdZWBYNomMUfTsSCUhCAIBj0I05RCIYojU+++/z+c+97lJuR4n7cJfiP9xfjZzekuJRIJ//Md/5OGHH+buu+9m7969zJ07d8RW9YoVKwoG5r/5zW/KPqdp2XTn8iCrfa5pucFa1mTa9I4Nrz+A36UiML6CTxVFNC2Lqo6/8ZQEmO2C2sYqNMtJuvBKlc+oHQ9eCea4barqgvToAm2RCK0RiyW1VTR45EJrtSud4bWrNzFtm6amJp7ctgbFuImV+BTRvwBdns3u35zk044+Xm/t5vl5CnN9KnUVjmSbKfC43Tyoafz2J//EI1/9fSRRoKXGRySRpS+RLdw/REGg/YaTy62qKm1tbbz77rtEo1G+/OUvT8q15eMkZVnGMAyy2SyPP/74qKbmummRkMP0RpNYto0kChiGhdslY2ETS+vE0jo+l4w/Fx03mVneQ+FzKw7ByxjUzUzTjGEYKkIbD9NKWzVN4/3330dRFOrq6oZlv+alzJNF8Jqbmws+efv27cPr9U7IHHeyYFkWP/nJT2hqamLBggUVEQGMhIceeoiDBw9OKEdYzOUg9iWzxNLaqARv8eLFvPXWW2zYsGFSbCvyBC+R1jGD9phCnatXrzJ37tyKX8NAVHlVqrwqHX0p4hmd3kSWWeGZsWs8cuQIf/zHf8y5c+fYsWMHjz32WFEbh1AoxP79+0kkEiWr9uJpDct2vuM1gempmKdSqWmr1pumhWhZRdkz+LxeEqkU4SIIHjitUK9ExXzgykVBmSvbBOrDRA34tDfCdVFgeX2IkAyHO3qwcuTuxe33Ej3xDTJatHAMUa3iqUf/jn95+yK9Pd1cjXTzYHVpoozPGlyqyjpRYP+Pvs/DX/8mkiRRE3AiIPO5uS5F4jcftvPEE08A8NFHHw3zfK0kjh07hq7rhfONB80wudmTRADuueduLn90huef3Ep9lRtJFMloBpGkRiprkMz9A3eiGH1uedIrez63jIBDREsRLkwnSh1Xm9Yywpe//GUsyyIej/ODH/xg2I76Tot28ndrM1k1e+XKFbZs2cJjjz3GwoULJ+089fX1eDwebty4Mejnp0+fZv/+/bz++utFHSfodUhdKmuM2l6fM2cOtbW1FY20GgiP6gSF20AkkRnzsZcuXRoksplMFCpVM6wAIYoiK1eu5IUXXii6Krx69Wq2bdvGli1b2L17d9HnSmsGPbmw9pBPnbY5RL/fT29v77ScWzdtJNMYFA02GjpjccKh0T3WZjqkXF7uHLfNgrowAa+Po9e7+DhucrbPyWF+ctsaoif+CGsAudNtmf6UxcUj/z+WrViCjsTFaIKEZox+st8RKLKCS9f41a9+xdmzZwHHTcLrkvG6ZCRRQJanboOYSCSGRYGOhc7+NJZt0xz284Wt92H336Q57EOWJARBwONSmBX2MafWT9jvKnQ00ppJTzxDa3eC25FkgdBOBkRBKNyPk6NEE840DM0oHw/TympCoRBr1qxhw4YN/P7v/z5vvfXWoN/nCZ5l24N8a/6t4datW8yePXtKzrV27VrOnDkziGz39vaybdu2on3DVFkq7Ib6EtlR37tMJjOpkv58xmB/Uhv1RnH16lWy2eykXcNQGLm/qzRDnDXPnTvHXXfdNaFjhEIhVqxYweHDh8d8nG3b9Cez3O51FnWvKpettq4U8kKLqUTeJkeyrHEJnmEaKDN481kK3CK0uG3qPAox1c++Po0MLkR/HenETbKZJKYtkjI9dGr1tGtN9JshMGIEpF4CPh9ZVM7mWvu/y/iou5eVX/wqzz77LB6Ph507d9LT0zPq42dSgSKZ0cnoJgIwq8aHIkksX76MDz/8cNhjXYpTnZxbF2BunZ+6oBuP6qwdqazBjZ4EHX2pgpl+pXEn0eKzsWko9e8wYz4Vc+bMobOzc9DPJFEoKA41Y3LatEeOHGH//v3THop869YtTp06Nervp/IL/MQTTxSc0Q3jzgd/xYoV7N+/n3379nHy5Mkxj1HlcxbuaErjWmeMrmgacwBpPHv2bEVm/saCz60UYnfa+1Ij7n4WLlzIunXr2Ldv34StYoqBkatGz5Q4stu3b1ekPT137lyCweCoZCmSyPBpZ5zuWMYhdy4nu3e6VcSLFy/mxo0bgz7nkwnbtrnZkyCZ0REtg9A4StDWrm7mNNZPybVNFdwShHwyi5YtQcDG7fXR03ubdr2J29oseoxasrbL+Z2QISz3MUtppT7otKgjupM5+7uKq5F+6h57imBuQ7148WKeffZZLl68yN69e0mlUuzYsaMwCwuTuz5omkYkEil6ZnVgtGD+PvfII4+wa9euMQUgqiwR8rloqfEzt85PIJdrHs/otHZPDtHLOz2kNbPk6th0IFNiRXNmDAHlUF1dTVdXF/X1d25oLkUkrZloxuT0yOfPn8/FixcxTZP9+/dTVVU1pQaoeVy6dIl77rmnoOodiPb29im9FrfbzZw5c9i9ezeKorBlyxZgsGP6eGQo6HEirfqTGrppEU1pZDSD2bV+BEHg1q1bw4yYJwMNIQ83uhPopkVnf4rmsG/YYxobG2lsbOTs2bO0trZO6jxevmU9WebdxSKvgJs3b17Fjrly5UqOHDlCa2srTU1NBZFGNKXRG3eqpJIoEPa7ynbNnwxs376dt99+m6effnrSz6UZFrppceP6Ne4JqmMqQW919+B1ucrOdZ2pkAVQDB2v14WXNFY6hjc4lww2NgISJl4pRUCKIwvOghaonouR+hQZA1UUaMsKhBWbKvl3K2bqdjQGq9cxa/Zw26uNGzeSSqU4dOgQzz333KB1ohTBUEY3iaW0AllwySJVPheJaB9Hjhyhqqpq0Bz2xx9/zIMPPlg0iUxm88r4wfPX/+k//Sd27NhBd3c38+bNY/v27aMeQ5UlGqu9hHST3niGVNYgntGJZ3QCboVwoDLq28EzbXmnvpkJzTBLLnTNKIL30ksv8eqrr/Lv/t2/K/xMlSWH4OkWlB+QMCoGkhZwQtNPnjxZctD8RCG7PGQtiXsf3IggOBUetyrjViT6+/un9FrAqdYtXryYs2fPlm38G/I5C3kyo9PRnyokXdQE3Gzfvp1du3YVbchcLiRRpKnay83eJMmsQSSRIewfee5vyZIlHD16dNIInmlZgxIbYikNQXA+464pHPC9ceMGZ86cYdu2bRVvkW/YsIGDBw9y8uRJ7rvvPuYvWEBnXwJBlAj7XYT9rmmv2g2FLMsoikJ/f/+k+2DmM4iTHW3UBYdvNvL46No1FjU14ZmkGdXphFeCelWmtT8KokwmHqGmeiOBQHzQDF4eohpCl2cTjZ7BDSwMuLGAXl2gVwdVcIQc1fLkxFpOFXriCfoWLefeFaPPA3u93hE3xnPmzBnXx9S2bbpjGT44f4mOjg4EQUAURXRdx7IsGsN+nnvuuWGb91WrVrFnz56iLLQ0446Nx9B7miiKvPTSS4DjOfruu++yadOmMY/nViRmhX1kdJNIPENyANHLJ94osujkLefWzWLvpc7fw2n3V3nVKVXwloNoSgMotLCLwYwieLIsD9uJqLLoJGBkNDyqVJhNL3yPBQpvriQKfPrJFW7fvg04lZlly5aVVL5esWIFp06dGpZuMRS2baObFnpuR64ZFprhlHlNy8a2wa1K+HKRSyN96CzbJpHR6YtnuHC9AyVYT13d4LdEkURCPg+2bU/5wqiqakWqmT63Ql2Vh87+NJFElmPvHsSlSHg8k8DYR4Bblamv8tAVTdMbz+JW5BFtOd59991J8+bLQ8wFzHfH8sIPZ9eoSGIhnsujSpOW5/vOO+/gcrnKyqosFvnd//79+7GQ2H/0fbxeD19/8ckZR+7A2dT5fL4pMTnPz4L6jAyMsW9yu9y/k+QOnApeo8/NrY425i66i5tXzrNn/0le3P7KMKGFqIaoevDvefPtkwjAooCXhT6JuGHTbwhkLNBsh+xZtmO2/FlENJXiWm0TDz20rqznL126lP37949KwjTDpKMvxYFD79LY2Mhjj2zGn2uBJjI6iYxeUJSOhKamJm7cuDGuoX6+PZv3Ih0NDz/8MP/jf/wP1qxZU5TQzq1INA8hehndHLFl6Xcr1Abd447BRFMammEhCsK0KfmLhW3bxFLO37a6hO7HjCJ44Az5v/POOwXFjmFatN7u4ur1awRUcdACMTDWyrIsZFkmGo3yxGOPosoS3V3t/OrXB5BEp4wv5XLnqqurWbVq1YjEL5PJcPXqVV5++eVBBO7Ov010szjRRyprFCo2kijgkiUEwVHvGJbFmbPn6e3tRRRF1q9bR124Kqf8tNF0i7TmqFC7Yxn6kxq1QXfhS/lZQ9CjkswYJDI6cV3gqScfndJ4mCqvSkYziKUdE+bZtb5hJX7LsiZ1lkUSRRqrvbRFUgC5kQObjG4V2tj5XZoiiXhUySF8LrliM3ulquEmgq1bt/Kzf91DU1MT8b6eGUnujh07hsfjYePGjVNyvjzB8xsZ4M6N+kLrDepDVdTk5q5awiHaenpongQLoZkAn2Tj0bPcu249bZ9epL29nTffhie3/cjxwUteQ/TNR5dbePPtU7S3tyPLMvMeeJDTt64zTzCZHQhg2tCnC/QZkDThs/rXOmcIbH589JZlMcgXRxIZnURad9YaUcC24ZPrt/jw7Fk2rF/Hwll1+AasIwGPQm88g1uVRr3PrFq1it27d49J8GzbHmBcPv469Z3vfIe/+7u/48///M+Lfo15oqcZJmnNJK0ZGKaFbTtb5axuFgir1yXjczlCrqH3nqxuFpT8tUH3jK/eZXQzF6lGSZ6hM47g3Xfffbzyyits3ryZZFbnyvXb/PrtXTyydRvLF89HEsU7DhM55/Pcf5LKZLl16xaGZWNoBr5QHXeFBqc8SKJArL+PHXv2IwkCouAcwQYEUaK7t4+t27ZztbO4bE1VFlEkEVWWUGURSRIKH5Z0juClNQPTclIV8ti5cyfr1q1jxfJlVHlVQr7hJWLLtonl5pd006K9L4VbkagLuseMAZup6L71Keeu3iaWSNIbz1AXnJoKXh51VR6yuknWsHIkz18gmYlEohD3M5nwuRTm1jlziMoAlXgy43xO0ppRmNPS0xax3A1TFgU8qoxbddoS5c6fLF68mAsXLoybA10JdPanWfPQBg7s38/XX35m0s9XKvbt28eiRYsmlN5SCizbJq05BC8Ri6J7q1FkhUg0SlN1CNOyudrezsKmJlwuF+lI/5Rc13RAFADbpqamhi9+8Yv8/Oc/p6Ojg1d/snNAksWnRKNnEEURWZb54he/yKJFi4AtXD5/ntaTR1kVDpHJFZ1cM3uNHhM+ZeL3c8u2aYskOXH6Q+LxeGGzqus61dXVPP3kEzRWe0ckcTWBO1U0r9dLLBYjGCzN/deybVyKhGnZ+IsgeC6XiwcffJD9+/eX3Dlx1tvhmedZ3aQ7liatmYUCSyyl0VjtLdwzDdPidsRR8nvU8nPTpxL5jb/PrZS0URbsiaa7TwJe+f4Peeq5l7h+u5Pr16/zyKb1aLFuzpw5U+jhjwbDHNwy1Qyn+maY1pjWY5Zl8cMf/pB7770XgP7+fh5//HEUySFwinzn37IkogwgcuPBsp28O8O0nDgeGw4ffpcnHn2kqIXatBx7ib5EtvAa/G4n+3W6Yl6K+VJ2dHTw0UcfFf5/yZIl1DY009bnVLDm1QemXE2qmxY3exKYlo3PJRdEF3v37uWxxx6bEXYDpmWT0QxSmkFaM0e0eFEkkYBHocqrltzO3bt3b9GGpeXCsm2udsQA6G9vRREtVq1aNannLBaWZbFjxw62bNkyKSbboyGrm9zoSSAKAvPr/Rx9ezeBjtvI2TR3tcwCQNc1PunoxNJ1ls2biyjOfPPVcpAy4XhXgqUvfJ45dQE6Ozs5fPgwH3/88aAxHVEUWb58ORs3bqShoWHQMQ7+ahcrsil6dRERJzlDmf6vb1k429XDfd/6btnPt22bUx9/ysF33uWhh9axdMEcpFz1Dpx5OL9bLoocGIbBoUOHht3f87nn48G0xjaWH4q/+Zu/4etf/zqBQKDo54yHjOaYJ/cntUK3TRJBkaRCW1eRRGbX+qdd8DYeMprBzd4kALNrfCUVd2ZEGShPgNKaQTytIQcb+N7f/gObNm7k0Yc30RjyYId9XLt2bdxjyZJDwEaKtjStfKvVLhDBPPETgP/3n/9JgcT98z/9I63nTw76QgiCUGgL529CoigW/tvj8XD//ffj9XoHnXegoWIeXkUompxJokBNwE2VV6UnniGe1gtl6Gqfi5rA1A+uF6Pa+vDDD0ckEh7VEc4k0jrV/qmdfVAkkeYBoov+ZLag6JwJ5A6c99vnVgptlIHfj7RmkNFMdNMRrEQSWaq8KmG/qyiid+HCBZYsWTLZLwFREAph3vMXLeHK+TNcvXp1Uo26i0EikeDtt9/m2WefLVs8VC7yCjhFFhFFkY1PPY1lWfz2Fz+nNp6gNuBHUVSWzZ5NIpn8nSV3AKYNliAU7lsNDQ289NJLbN++nevXr5PNZnG5XMybN2/UyvqiVffzya/2UF1VRa362SV3VyP9eNasL/v5lm3TFU0TCtfy4gsv0lLjm5Boa6RZ+LNnz7J8+fKinl8qYfqjP/oj/uf//J/8xV/8RUnPGwtuVcatygRz6UFpzeBGdxKXIlHtdyOJAg0hz4wndwBduVZywKOU3LmbVIJn2zZZw8LO5dDlCY3TqnAWqlTW4GZbO8ePv1fworp35UqeenwrQa9SyBE9depUobpWLiRRRFJFihldXrxwwbAdjGEYY7qHJxIJTp8+TSYzODlBEAQCgQAPPvhgOZddgCyJNIa8VPuc+YGUZtCXzGJYFo0h7/gHqCBCoRA9PT2jVkAMwxjVW9DvVkhrJvG0NuUED5wvf13QTXcsQ3csQyY5ejvCsp3NwHRVSuHOBiG/SciLc6JJx+ogmtKIpTTCARfVvrHJ/q1btyYt6m4o6qo8tPel6EtmWf3gWg4e2E8gEBhkgzTZOHbsGKlUqvA3yWQyIwajTwXyHl4Dc4hFUWTbF7/E2Z/9eND8mH8KxgWmEylLIONy4xmyYPl8vkH+bqPBsiw+3PUrFoaqcYtQNSNKFaXjw+4IjY8/RXNL6Ub2tm0TTWmDMmobQp6KKPKHbnZv377NypUrJ3zckSDLMlu2bCm6QlgK8lW6vmTW2WDZNrPCPmRJGPbZm4mIpRyTflGA2kDpoquKv8L8oGV0gM9OHqLgkKyhSp1f7foVX//616jyeZzBSPfw0OFQKERfX9+UtVRG6lyPFw3j9/tHHdY+evQov/jFLwiHwyxevHhC1+ZSJGbV+IilNTr708TTOgIpGqaQ5K1Zs2bMVt+hQ4dGzbQNeBS6YxmyuTb6dJCnkM9FMjej8dtjp/nis48P+r1pWfQnNfqTGookMKeucu2DiSKf9xv0qCSzOr3xLFndpDeeJZExaAx5Rv2b1tXVTUmLFhwi71aclkgyo/PYY4+xY8cOPve5z01K9Szv7efxeHj44YfZv38/8+bNm/aqYR56roI3kODlIZbgY/a7AMsGS1FRRvhbFINDP/sJC4OOIKV6HLPomYT+RILOnNl5vy2w6otfxVsGmbdtm7a+VEHEJ4sCdVWeionwgsFgoeI+2eIzgAceeID/8l/+y6R5o2Y0E5ciEfKpRQlAZgJMy6Yn7hSLqovs0AxFRd81y7a51ZukM5oukDtRYMAw+R0ZtiwKBDwK9VUe5tYHWdpSQ2O1l+AofjSLFy/m+vXrlbzcMVHp0cT169ejKAqPPPIIyWRyXLl5MQh6VBpCjlAhltbpiqYrft1jwePxcOHChRF/N1a1UxJFvLndU151NR1ozJXok+ks7ZFUwfqmK5rmWmecSC5mzbScKt5MhM+lMKfWT0OVB1HIzXl1J4gkMiMqvefMmTOp8XBDkSczeZf4Z555hnfffXdSznXu3DmWL19OKpXirbfeYtmyZTOG3AFoZp7gDSffovU7HM0wAgRAtO2y7lfH3t7NfTKQWyfGMoueaRCwMVfcx/KvfpP1X/tG2eSuoz9NKmsg4EQyzq0PVNRhYc2aNXz66ad0dHRw9OhRNmzYULFjj4Tvf//7PPPM5Amx9Fz13PsZqNrl0Z/MYlo2iiSWZI0yEBV7tbZt0x5JkcmVE6v9LvxupXAzy7drTctpd+VJ38mTJ7lyaWSS8LuGzZs3884774xa2SoHQY8KNnRG00RTGomMTpVXLWv4vlRs3ryZU6dOsW/fvkI7Vtd1FixYMG5CQsCjkNIM4ml9kIJrKpE3Qb7n7rv5zcF3uO+BNXhcd26SqixS7XcRKFG5NB0IelW8LpnO/jQpzaA3niWa1PB7lMJNzQbePXqcZz83+QkimuFU7SKJbGEuFpwq+GTFgq1cuZL9+/fz+OOPT3rOcTnIbxJG+l7+W6rgmbbjXWeWQe7OnTrF/EgHgUCAnjQYtiPYCMzwddvG5kpPhN6aRtZPYNQoP2+XyDgb46awtzDGVGk89thj7Ny5E9M0J+27ZNs2f/3Xf83TTz/NggULJuUcQOH+PZrP30yDM2OdAZz5+3LXn4p8LWzbpr0vRUpzdhSzwsOVHoIg5Hy/Bm+3PvjgA/7rf/2vlbiMCWHHjh00Nzezdu1aEokEilL5L004HC4pUqZYBHMy7554BtOyiSSy9Cc1GkOeQX5Hk4GhRsjvv/8+J06c4Ctf+cqYz/N7FLqi6cKM22ST0dHgUWXuWTwHwzQ4cvI09917Ly01Pqr9rkm7cU4WZEl0WvcpjZ54BiMXFdefdCT2lmXRm7Jo7U4Q9rsIeCaHuCazesHrT82pzgdWF1pbW3njjTfGVcSXC1mWZxy5y1eCAZQRsrWkfwMEz7IhZkCvDh9HoszZsrWk+1PbrZu4z5+hLhwCIChDjwYdmoCFPaPn8I509bHuq99gmau8SkxeWJfRjMK8XVP15JG7PJ599llaW1sn5dimafLf/tt/42tf+xqNjY2Tco48Ah6FjG7Sn9RmVEziSLBtm46+JNGcAnhhY2l2NQMx4a9ERjfpyfnOgLOjKEXpIUnSqMP4QxEMBjlw4ICTbJFIsHLlyop4WBmGQSQSYdOmTezdu5dMJjOpTv+TgaDXmS1IZJw4Li3n9TZ3iq1IHnjgAS5fvjzu40RBYE6dH0USp7065nXJrFwyj3gyzfVPLrGk+cHPHLkbiKBXxe9RSGUNEmmdrGE6yS+ChNfj5uT7p1m1ahWRRJaARxkUCj4Q5aan9CWc3FlFEgsejwOP89BDD3Hr1q1yX96IuHTpEjdu3JiydJRSka/eCTDiCIpo/263aDMm3M7A1WicqL+KB559gdn1VSXdm66fOsnaHLkDZ/buWlokaYJmQVyGOtWecX54hmkizZmHWia568p1Z/KQRIH6Cs7bjYfJiG7MZrP81V/9FX/6p39ast9eOQh4VHrjGXTTIpnRJ73wMRFEEllSOQFqTdBNXy7esxxMiOD1xDL0JbOF/y9nR5G3HilmIVm7di03btzg0qVL+P1+bt68WRGCJ8sya9eu5eDBg9OmsKsEBMGZa/S7ZW71JsnoJt3RdMHrbapQrEpyOpWpQxHwqDx0710cOn6a9z+6xPr7l3+mSZ4oCPjdyrBFIL10Dn3xDKIgDLJa8agSQY/T1o+nnVa/ZeczkSW8OZNlVZbQDDPnFK/kqvIObNumM3pns9cc9o74Hq9cuZIbN26QSCQqVmm7desWW7ZsmXLrk2KRX6BHqlRbloXwO1zBixpwPa5x2RRZ89xLBcunUjcP7mwahpjSBiUbEVAESFtwIyMQkKBGmTm2KTcjfTz0dHnrSmxAuk3Ip+JWHBHiVKYAVRqxWIz/9b/+F3/xF39RVExZJSCJAkGvSn9SozOaZrYyemrHdCKvjBYFgUXNVSQzBn05K6xyulxlE7xI4g65C7gVaorIfhsJ9fX13L59m5aWlqIef/78eZ544omKqnq6urq4ffv2Z5rcDYQgODu8Gz0JklmnolfldX0mPH+mEyGfi4ceuJc9vznIldoals9vmlEktBK4+sknPPXUU47VSlonltZykT8maS097PH5RI28GCafpQvQG8+i5NqvguCQmHwbsr5qdCUvwNNPP83rr79ese/cpk2bpiRLuByYlk001yYfaSceiUTwViDJYKrRF4+RzGg014YRhZHvx1EDLvdn6KxtYsvG9dRXle89NpTgWYAgOPN389w2PbpAwoS4CQlTIKzYhIvco+kW9OgCacvJyq2SbYKSc/yJIu7yjOvAMBKSGUc4BxD2u6ZtVrnS2L9/Py+//PKUkbs8wn4XyYwT/3m7N8nsWt+MiyhLa06evShAU8jL9a44hmWTNcypU9FqhmPJAI6CZ7T4k2Jw33338cEHHxT9eFVVKy7ZDoVCuMosn5cCwzC4dOkSqVRq0s/lUiTCOY+53niWTztjfNoZ43pXnNbuODd6EtyOJOmKpklmp0/JOtNQV+Vh68ObOXb8PW73JorKHP6s4NatW4Xqqig4O9qWGj/z6gOE/S5UWUQUHHuTWWEf8+sDNIe9VPtcBW8ta8ifQzct+pJOFTDvYN9c7S0q/mf79u3s2rWrIq9NVdVJmW+tBLK6ic2d9JGhOPfrPYRD1VN/YSXCME0+7ermo0SG45ZMdM0m6r74dY4pAU5nTS5F43T29RUenzbhWixLe7ieDVs20lTtLZvcpZJJ/Obg+5SI0/LOo8llM9tt45UcQVGvLtCRvZPmMBp0C25mHXJo2pC1oEsT6NDGf+5YMEyTD7oj+FatHv/BQ9AWSdLWl8LGGSEJT4Nf6GThhRde4Ne//vWUn1cSRVpqfMiiUCB55tAb2jRjaCRZ/vtS7uewrG1jvgXjVqQJDyw2NTXR3d09oWNMFKqqYpqTPwMjyzLf+ta3OHHiBOl0mkQiwaZNmybN289R30AspaObFqZlY44Q2BZNaaiySNjvnrBHkGVZ9Pf3s2PHDl544YUJHWs6IAoCTdVeNm/axG8PvctTj22lsXri/oKt164xd4oyT0fD2bNneeqpp4b9XJFEagLuESsEsiQWWtUFJbxp4VYlBEEgkdELXlweVSZYgnDD7/dzzz33VMzg1OVykclkpqQyYBgG/X191NbVjfvY/CZBHIHcnPrtAVbJ9oyrJOTRG4vRbULS68eoruXBpz8/rBq1+ck7n6muzk6OnHgPNZkgmdDoUdxs2byJ2uDE3pNTh9/loXB40M9EAdyi05rtMwTqVRu3CLNcNlEDujWBuAm2JtCgOlWRoTBsuJ0VMG0ny7ZOsclaDjlMmNBnUHQVcCDiqSQfyl42/cEfllyQSGR0krnvVNCjUFflmfY55Upj0aJFfPTRR9xzzz1Tet68EO1mT5KsYdEWSdIc9s2I7lYyqxfU0XlbFLcqkzU0euMZPKpc8nWWTPDsXGsHqIhj9u/aB3c8qKpaMEM+ePAgoVBoUs8X9rsJ+90YOYJn2zZmLg/XtGyyupMooRkWHf0pIglnsS93gFcURV566SX27dtX4VcydVAkkflNYXoWLODYqTM8sn41IZ8Ly7LY//prrHnsCULVxVdcDr35OslPLjH3//N/T+JVj42+vr4JDzMXlPADvvd5w+VyMXfuXKqrq9mxYwcPPPBAWf6QbW1tnD9/Hsuy6OrqqojH5FjoaGvjxtu/pEW0uWgLZF1udLeXrMuNv7GZe+67b9Cint995+90N65dI5NKIcsyDW3XCEzBkHm50E0TS1SICRKPbnt03MfXNzRQtf1z3OpNUGPDIlmkrmriwhcllUQawRQ5rNjczgrEDAhK4M59NKtkkAWb9lxlLpsRaFJtXAOWrIwF7VkBw3bass0uG1lwfPVEwaZTE4joAgGp9Hk+RZLxzZpTErlLZnWyOaUnOIv8RInxTMUTTzzB3/zN30w5wQNn9ntWjY/bvQkyusnt3gTNYd+0OTmAswnsjjqmxiGfWuBWYb+LREYvrM+zSpynL/kVRVNawQ6lmDZMMSjF7HLp0qXs2bNn0qTbUwnLssqazSgHsiTiUiTcqozP5QzfV3lV6qs8hRadKFBQ37ZFkhNqT7pcrilpRU8WvC6Ze5c5Jrnvn7tMJJrg8I++z2YV2t/6BedOnRz3GK2fXuXEP36ftYJG09x5k3zFY+PYsWOjpqxMN4LBIC+88EJJoxoDceHCBR577DGeeOKJSSd3506dJP6bX/FAXZiGmhoW14a5O+DlPgUesjIsuHqed//1zUHPyd/f8nvZm2c/oPrD97COH6J5BpM7gJqAn4RhsGrT5qKf0xvPYNlOAaClpjJh7u7s8PlQAK8E/lxL9nZWIGrcIdQ+ySFtuu1Ys3yQEGnLCPTr0KkJ3Mo45E4RoCVH7vIIyhRave2aMGw0YdzrdbnI9nQBzvtvmE5qj2aYuc22Vfhc2LZNdyxNWyRFb3yAue3vUFt2JKxcuZITJ05My7ndisSsGj+iIJA1LG72JOiKpokkMpjTMOoRS2nopoUkCoT9d0i9LIkFkWQqaxAboKYuBiWzi2wuoaLa76pIBQ8cyfR7773H2rVrx31sS0sLLS0t7N+/n7lz5xKJRPj4448nvHhZljUlkSwD0dTUxM6dO9m+ffu0qv8k0anahXwqfQmNvmSWZNagPZKiKewtS7G1fv16Dh06NCOH3otFyOdi/Zr7OPDuMfYeP8zTc2uQJYElNdX0XTnHyb4Iax67E/n12x//iGYzS8blISNKNOsZHqgJAZARZS5+dJb+s2fQqqp56LEnyrZNKBWZTGZSfB0rja1bt7Jv376Ss3KnqgtwdNdbLIxFqK0OjfoYr8fDgliMD37+UzSXm6zqwq5poWnO/ML90p1NEw4ECI96lJmBSCLBJXeAjX/w5ZKelzeTrfFXUNg1Zz5nPr3MXNEmHBgcG1iv2phZRyDRpQlEBEdFmydpDarN5aRI2oJPMwL1yh2y7Zec549gTUi9YnPTEshaDiFsVO2iRRdZC5KpLNc6o3x84RKxeHzQjKjP56OhoYH62hrSuuMDKolOhdytOvPTM6FtWA4SGZ1YSsOwbDyqRJVXHVFwtWnTJv7mb/5mwhnt5cKtSMyu9dEWSaGbVmH+LZbSnVm9KazoabkYw4BHGfa+uxWJmoCL3niW3nimJP/Skgle3mSxki/+u9/9LocPH+ZHP/oR3/jGN4p6TiwWY//+/VRVVZFOj7y7KwVTTe4Ali1bxtKlS9m7dy/BYHDS42DGgySK1AbdeF0ybZEkKc3gZk+iMGSvysV71smyPGOH3ktBfZWHLRse4lRXOx2ayGyX066p9vu52d9beNzBf/ln1vpUXOpA2w+HwGm6xsULF3gy0cfiYADLStP601fp8wUxG5p56OFHJvU1/Pa3v52S7NmJwu/3oygKkUiEcHjm0B8tm+XY//4JD/hceALj27o0BwM0A2ARz6a5+vFHXD77Af6WBjY89hiBRAw8NZN92RPCJ3396MtXsXH1mvIPUkF+ct/6DbB+A5c/Ps/Vsx9QnYxhqSqa6kJT3aRdCv7uLgKBagzbIWQiNn4ZahS4229xKyPQb0C3LjDbZVOrOoKM0aCI0KjatOXavG05kjcSGRyIhAHXEzqt/Sm6j59gwYIFzF+wADW3ybIsi1g8Tnd3N9euXaOjp491GzaxYk54QuMO0w3btp1s9MwdMUy+5Rz0KNQE3MN4w4MPPsihQ4fYsmXLVF8u4LRr59T5HUJq2sTTTiXtdiRZsepzMchv/mIpJ9lpaFEl5HPRl3BIczRVvFmzYBfZHzUtZ/dyozuBblo0V3srbhb4gx/8gG9/+9slP+/KlStcv3695J1/Hv39/Zw/f35aCdaRI0eYPXv2pLeYikVGM2jrSw1SGQlAbdBd9Idr//79n+kKXh6WbXPg0HtUX/+EsM9Ni8sZ2G6LxUnYIFgmLV4PnlEqcnoumksZoR2f1TSuxxLEAlWElq7grnvu4d2f/5SAZaK7PGiqC7mqmjUbNpS1Acnnsn7pS18q+bnThZ07d/Lss88W/fjJ/JzduHaNrgNvs6q+BqFExmLacD0tYOFUlPxonOvo5t5ZTTNWVAFwsauHpue/QLimPBJ6oztO1rBonsQYrZFw6PXXWK9ClyYSN53qXJPrzv1LtyBhOlU+n2TTUuR4W9KEjqzzPsqCU/HzjUIMUyZc7Mtw3V/N+q2PEPKpKLI4zNDdtm0s2yaVNbl4o5Orn3zCM9s2zGgD3rFg5aJKU5pzr6v2Oar8+AARlihAc9iHZ0gQwl//9V/zZ3/2Z1N+zSNBM0xu5dS1ThvXNyWegxnN4GZvEoC5df4RK579ySzdMWdOb3FTVVHHLfou82lnjKsdsUL5XRlh4HWiCAaD9Pb2jv/AIVi8eDEPPPAAb7zxBv39/SU//9KlS+i6Pq0Vp66urhkVr+RWZebU+gn7XQUzWxvH3PrfGkRBYMumB2kzHYVd3j6hORhgSVWAxdWhUckdOMRuJHIH4FJVltaGWeOSaPz4fc7986ssMTXu8Xu4X4GH7Cwru2/w4Y9/wNkT7415nW+++Sa7du0qzD6mUil2797N3Llzy55vmw4sW7aMs2fPTvdlcPHsh+jv/ob76mtLJnfgDPHnSUG1DKqicv/sWTOa3AE0eN30dHRM/EBT7ECx7pnnuNrbhyrmVMtDfq+IUK1Ai9umdhQe1ROP805PP32xWOFnPglmuW0UwVHdtmUF2rMC2pDlImvB9YTBNbePjY9udQhuLo99aOfDscBwbHPmNtaga9mC1+RnDXlSlJ/Nbw57qQ26CXpVZoV9tNT4cMkilg1tkRSaMdix4vnnn+cv//IvSSaT0/MCBkCVJWaFfYiCk9LV0Tf5c+SWbdOWO48rtxkYCSGfq2TdQ8l3GgGoCbgmxQD26aefZvfu3WU9NxwO89JLL3H48GHOnTtX0nPXrl3L/fffz9tvv82pU6fKOv9E8cILL3Dp0iX27t3LkSNHpuUahkLO2WfMrvWzKJeHZ8OwL+hI+KzMfRULRRJpWbWCZDrt7Ogn6JE1EgJeH8tCAWqHtAFVRWVlTTWzr13k+E//iVs3RhYYBYNBnnrqKY4fP87u3bvZtWsXL774IuvXr0cURY4ePVrZC54kLF68GE3T2Lt3LwcPHpyWjdflc+fwnj3FvDHm7YqFJFTGMHeqUO330/fRmfIPME0vVnW56KyqGVe05xugth2KTz1BHvnGH9KTq0Tl4RZhjtumWnbWwIQJrRmH6CVNiBsO8UvqOoplEPbJxdsF5ap2qSHnnOkwLZveeIYb3QmyuokoCLTU+IZVbT2qTEutH5ciYdn2sCLB7Nmz+Y//8T/ygx/8YEZs7FyKVBA2JLMG6Ul+XzI5c2NJFJhV4x/zc1Nf5SFYgpVZ0TN4+cBbgckbavZ6vWSz2fEfOApOnTpFMpnk/PnznD9/nueee65oT6xgMMj69es5f/582eefKNatWwdQMfPXSkIQBFRZRDMsUlljXIJ//Phx1q9fP0VXNzW474H7effjc/hxbvDtuZmcqZqFDnq9rAHa39nHUX+Y9c+/WPhdd3c3oVAIURTZunXrsOeuXLmSq1evliVimA6sXu2Yw6ZSKfbt24dpmqxfv37SbYXAmZFKnDzCwtqJzQF+hjjdMMzSM3S0tdHY3Fzyc6Xc+tCf0vC6iic6lcCGZ5/nwj/+ACVQS6bEfYFpWRh+x/4o6hpu7SIKUKvaBGSI5HzyErnUjDwa/F5WqSaXf/ojPGs3sWj58nHP61YlRFF0fEota8ZXeE3Loi+hEU1lC+pijyqNmV4jCgKNIQ+t3U66kmFag+bxZFnmP/yH/8AvfvELrl+/XtKIxmTAo8oE3EqhxZxvK2uGSSylk8w61iVOtrSAKAqosiMo8bqKo1W2bdMbzxbEHW5FKmrmryFUvC9r0QRvqrLvNK00GXAeqVSKrq4uvvjFL3Lp0iU6Ozs5fvw42WwWWZapq6vD5XLhdrvxeDwj5qW+99570774nT59elq8gYqBS5bQDIvuWAbNsAj7XaOKbXRdn7G5oBNBw/0P4D5zkoziJ2k61gzNrvEHryuJpqoq1Fgf7x48SOOsWUiSxMmTJ3n55ZfHfN7ChQvxeDwlz7hNJ7xeb0EgcuDAAWRZZvPmO5YdH3zwAZJU2W7C0bd28OCAUPtykb9X659BrVFTqIrjR96h8eXfK/m54YCLVK9BKmvQG89OqZebLMuouVuSZkPGHFyts7FJJlP4fcP9xK50dLLm6873Yu7Dj3Jj/27mhIbPOrlEZ7YvY0HccCp4AuCXoVq2EQWR2V4Xpz54vyiCJwoCC+bP4+zHl6ny3U1dwD0j/WGNXGpNNKkVuu+lGOSrspP/qpsWmmGNuHa8/PLLnD17lh/96EeFn9m2XcirN02z0K2bbHhcMgzmKLwAAQAASURBVPGMTiThuErohjnMLscmJzy1bDTDIpHRqStyTj2lGYW4V1kUJsUWp2iC1xvP4FakondkmmFi2TaqLBVNDo8cOcL999/P4cOH8Xq93H///cMec+vWLS5cuMCCBQtYuHBh4ef79u3jmWeeARyvvKVLlxZ+ZxgGkUiETCZDNpulr6+PM2fOYFkWGzZsGGQAO9VK2qFoa2sb8XXPBNRVuYknk3xy7QaGYaBbAqFwLcGgn5BPpTbgKXywZ+INqhJYsmIFJ86eZrnLUddlcgHnIdmxZZgqolcTDBK7cpE58+djGAZ33XUX+/fvL/ze4/EUWrMD0dzczMMPP8xrr73Giy++WJIPo2EYU+bbOBK2bt1KJBJh165drFixgvfee497772Xhx9+uKLnCSWjSFWB8R84DtTcZ8HCEVxM5SagEqiL95NJp3F7SjMq9qgyXlUmpRnT4inWGYngyYLqD3IblTnuwUbFuzt6mbW0HkXXEHu6WBnycTaWpunxZwqvtbG5meMNLdTFekadr3WL4FZt8lkmuqFzpTdOzF9FYOkqHl21quhrbmio58OL1+jsT5HRTJqrvdNqvDsQjhI2S2zAjKBLFgmXYYgvigKYY3vfrly5kpUrV476+8uXL/Pf//t/57nnnmPBggUj/v748eOD0qksy2Lz5s0sXry46Gv1uxX6Ell00yrYwwF4VZmAV8Gjyo5YxnLCA+IpnXhGpzuWweuSx+1yDZy1qw16holPKoGijxhJOExTEgWqvCoh38g+PZph0hVNk9ZMZFHAsm1CPhdhv2vcRX/fvn1s2LCBdevWEYlE2L17N4qiFBYpy7Joampi27ZtXLlypZCW0NfXx31D3OMHvUhZHrFiB3D48GHi8Ti6rrNs2bJi/xyTgmPHjk2bJ9B46Ozs5N133+XChQvoFmRRsRFAEGmYNZu7lt+DPruJ3ngGs6+dWxc+xtq6ddoJ82Sg5oEHSZ48wmx/wBm2tp2g8ojuOOtXT9Ho4ULJpqmxcUQ/vVgsxiuvvMJ3v/vdYb8LBoO8+OKLvPnmmzz11FNFiXveeecdbty4wVNPPVWwMLEsi1gsNiVt0zzC4TBPP/00p06dorq6mkBg4kRsKLQR2nPl4DPG54ZhQV2YI3t+xZYXP1/S8yzbLswt+ScYfVgONv9f/18yWY0DP/sFC4CYKVCTE14ICKwO+Zm1eQten4/+vj6OHvgN2/7gK8OO89Dj23nnB3/PAyHHuHggItEofZpBg9+LP0cKz0STrPvGH5V51QKi4LRns7rJzZ4E4YAbX67dZ5hWYUxmvHU0o5touokoOlmmkigMU/GOB9u2SWYN+pPZQjQpOPNpNX5X2WrffPvemEAG7JIlS1iyZAm/+MUv+M1vfoOiKAXCmM1mmT9/Pl/+8pcHbUZt2+bVV18tieBJosCcOj/prIFp27jksa3CfC4FMyc26Y5lxk2dyLd0oymNjv4Uolh51XnRBC/oUUhmDUzLJpLI0p/UqAm4CHrVQoUultLojN7xpMsYJrIoEklkMUxrzN5xJBLhrrvuKrRI/X7/mJYhA6t0E/GwyxskX7lyhdbWVo4fP87s2bPZvHnzlJOTSCRSmMObSfjkk0/4+c9/jmma2LZNuKqKQCBAfyxBJJak8/ZNutpuEb33Xly9ndwjZfnS7Hou/uM/0BesZvHGLdQ3NhbK7J919Hd0UKs4raAWt03ccBaRrOUQvbTFlMzm+USRRCJBeASCFwwGWbJkyajPlWWZL3zhC+zatQuXy0VzczMrVqwY8bHnzp0jHA6zefNm3nrrLbxeb0H4kM1mp6Xdm5/R2717Ny0tLRU9dtblASqraJxZkebFQUAgHC3d1SCrO4nXkihMqU3KQLhdKlQFsDUNY0gRcX5NNcd2/5JNL/8eoepqtr00+mjDxm/+O94/dgy9uxOXlkGXFbIuN/M2rGbJ/Pl88urf4fd4uB2NMmvL+FFuo8EwTDyywNw6P93RDLpp0RUd7u+qSCJhv2uY2a1t28QzOn2JbME0d6TnuhSpYKasysNnvnTTIpHWieaSFfLwuxVCPnXCVSaXIpHSDBIZfVRF6KFDh1izZg1e79izZuONpAyEIAjoeunfaVEQSiKzdVVuWrsTpLIGyaw+7ue/JuBEkemmRXskRUutv+BaUQkU/W41hLyFD1Ek7pQtu2MZeuPZQv89PyzoVWXqQx4USSyQvlhaxy0l+fu//V94vV62b9/OoUOHePzxx5kzZw7/+q//yle/+tWyXkQliNjixYtZvHgxu3fvZvXq1ezduxeANWvWUFtbO+Hjj4cDBw6wadOmST9Pqejs7OTnP/85hmHQ1NTEk9vWoBg3sRKfYnsX0KM3seedc/T29nDxzHG2z5tHYzCALMHSOsdHq+3XOzmheNBmL2XuXcsJB1wjfvDbbt1yhnFnzZr017X/V7vYsHVbye0nXddRPrmAP/faJAFCCoQUJ+C8R3Nmcnp0J/x8MtGruJhdpldZHk8//TQAN27cGJQfLIoi8+bNw+VyEYlECnNvzz333KAN1cC28FTBsiz6+/u5ceMGN2/e5MqVKyXtzMc9vi+AmeiZ8LC7IDgxWLrt+Kg1u6ZOkFMxlLEhy7ezKpV0VC7uWruB1l/uoKo2NOjnAgKzUjH6+/rGzZQWRZE1Y/ijGrJMIp2mo7aZB+fPL+s6DdPiwsWL3HXXXXhVmdm1jvHuQJIliQKWZaObFp3RNJFElmq/q7DGJrN6YT5MwBFuWDZYlhOTZuOQN920CoH24Mx+qbn3STesQaROFKDK66LKp45q3VEqgl6FvmSWVNYgq5vDPiPHjx8nGo3y13/913zpS18aVOSZSIHg17/+Nffdd9+Err0YDKzK9cQyeGvHHmlri6QwTIvO/jTVPpVEWp8eggcOCw56VAJuhWhKoy+RLTgr5zE0IDnoVZ3Yq+4IP/3Rz/hP/68/R1XVwgzQsWPH+PWvf43L5ZoRQ/mSJHH79m2efPJJAI4ePcrJkydpbGyctA+IZVlkMpkJh8FXGpZts/+3hzBNk6amJl7cfi/RE98go0UBMG2RPnsJ2zb837xzzCbRfZOLvd2sqR5cUWmurqYZuHb9Ih9evEiqqpoHt2yhLujBzKb44Lf7qUrFqTc1bppQ//VvjUnaTxzYj7+tlaysEHV5uX/rowSrijN+zKMq2kvb//4n+tw+EoEQtfPmYxoGmqZhmQaGbvDQxo3DruPojjdYXzvyolAlgyLYhUxMEYGQMjjjspJI+kt7zWNhzpw5wyrm+ar2QFEDDN5QybJMJpMpWq1eCezYsYO5c+cyZ84c/vAP/7Dilfb7HnqIjtd+zKwKbOwaXTa3M05Vt9S4q+mGjU0kWLqSWDcddaE6zTNkoqzSr7qxbCefduDfvcHv5dTRw2z53DMTOsfNvih9S5azbvtTZR9DMywSiQR1NWHHH09wokCr/a6cIbJD8EzLJprK0pfQRqzwiYJAyDfy+JRhWmQNk6xuktGcfxuW7fyTHWIJo0gEvSoBj1JxcaUqS4XjDr3GTCbD8ePH+fM//3OeeeYZ/vZv/5Y/+ZM/AZwCyOnTp6mvr6ehoYHbt287r8sw8Hg8fOUrXxn1PpBIJLh8+TJ/+qd/WtHXMhpqAm4yuknNKGNp8bROMqM7kW5pDb9bQZFEDMtCrbC/cNFJFiPBtm1SmqOWsiwbn1sZcegyksjwP//+Vf7wG19jVk3lZ2YqCcMw2LdvX4Hg5XHjxg3Onz+PKIo88sgjFSWje/furfgxJwrbtjl7tY23/uWH2LbNN7/6LNqZb2DlyF0ecdOPqvqoWv13vPqTnQjAn9+zBJ8yfO+gWxA1BHo1i5vROJrLxWwrw931wYKRrG4YvBNNUbVoCQ88tG7Eof7T//JP3Btwyvc2Nrd6++hze9E9PjRFJdjcwt0rV4658B/es5sHtTiSKGLZFqlUGlEUEAQRKbcwneuLU7tlG/MXOdWhG9euIR7ZT9M4ZDKiQ6/uvB4BZy4vXOFOVWdfH/ITz1Hf0DDqY6YiSUTTNI4cOcIjj0xu3NpAHDx4kPXr10/q9+Xij3/A4urKEOiU6Xik2Tgh9g2TXNmtFFp7eqn//Ffwlzjn2NGXIp7RqQm4BgWnTzViaY1zl64Rfe8Iy8NBahUbtwSWbXGwL8ljv//NCZ+jq7NzzO/guM/v6uLoidNcvtHBV37v5XHntsDZeEeTToXPsm28LpmQV8WlDDdUHgum5ahZ8y1dWRRwq9K02bR873vf47vf/S6u3MjJT3/6U9avX8/OnTt58MEHWbduHWfPnkVVVe66667C87q6unjttdeoq6vjC1/4wrC/wfe+9z3+5E/+ZEb4siazOm2RnBF9Vieay71NZw1URWJOzi+wUphQQ10QnBmL8frMqizh8/nQx/fHnVbs2LGDVCrFF7/4xWG/y1c4DMPg0KFDhRmke++9d1QBRzHIZDKIojijyB047+3HZ05h2zZVVVUoxs1C5W4gAlICzASKcZOqqiqi0SitiSTLR1gcFdHxkapWBMJqFf0GgJu44ShQwUl92FYTJNt5gxv/dJas6i7kTWZVN1lJYoV1Z8cpIDC7JsxsAEww0yQvfci194/mnuciIzuZlUp1DarbTbS7C0HXudndzbyGBkRBHNE24f76GtqOv8PRS5dY/7mnaXv3t6wpYtEPK04lr99wVLa9uoAqOLmYlUK3qHDvBBaWSkFV1YJaLaMZSNLoTuyVwurVqzl58uSkRgtmXZUjJl7Jmcls1wRiBkgI1H4GSF6XJ8CCMkQsRu7eON0q0KBH5e6l87koK5w4cZQW22R2lY8GVRwzeaYUTITcgePd+vAj20j+5p2iRTmiIBQqfBOBJIp4VJGZEH978OBBNmzYUCB3AF/60pf43ve+x5/92Z8VyNlI6tr6+nr+9E//lNu3b/O3f/u3uFwuRNERQ9i2zVNPPTUjyB1QSCtxySI1gQBSJBeL5pKxLJtYWqNOqYzICyZI8IqFR5VRFIXO7h7qqzxFGwFONYLBIC+88MKYj5FleVBV5NSpU3z44YfYtk1tbW3JFicHDhxg+/btZV3vZOLQP/8jsxJxLgOBQAAr8emYj7eS1wgEAkSjUbLm2NYIkgB1udDuXl2gVxcISINbVy5VZe4g4myDlSaTzuKuGruV7fN68Q0b0NXIdrZiWSZud85nqoibc3NVkOp0P7v/+1+yYVbxN/OADAHZsVJJmo4nVyUR943fzp+qBAjbtumKpommNCQjxXuHD05q9q3f7yeTmdzIvL4K70b9MtRj06UJ9BmOl1pght0GLRvSlpP00J9IELp7dVnHMUznwz7ZRL8YBD0q9y+bx5yWRi5/co3DR96hudqP6Z7+P34sFsPv9yNLIpZlYYxz3/xdxqeffso3vzm4oiqKIn/xF39R9DFmzZpVaOlOF6ycZ99IVVDTcgQsAHVVji1KPK2hGRYBt0w0pRNLadRW0AdxSj7lkijwpc8/z/965Yfwuc+xZN4sqnzqlJknD0Slvbzyaj6Ajo6OQYPqGzZsGFMJ1NfXRzAYnJFWIjUiWF5nJxGPxxH9w/2GBkL0zSced0igq8gbe7UM/YaT75i1Ro8PGoihdgWlwFVmldTjcrEy6EUtw1DXJTph5botUCktZX88hmfh3eM+TpIkUqnUuGq0iSCjGfSmbU5/dIGOjg6aa6t48MEHKy58GArDmLz4oIM/fIVVY7QWE7pBazyJZlmoosjcgA//CCMJQ1ElO2MKfYYjwvFKU2uQPRasXMZqxoK5bpsbhs19ZRqu5wf1p7uCl4csiTSGvLiXLaL15i3mbFjP7Nrpyf3etWsXnpyw69q1a3zzm9/EsGxCoRBtnd201PqnZV2cDli2IwARBYGlS5fy9ttvT3mxQzNMsrqFjY1XlUv+zNq2I3xp73PEEnmhiyKJBL0KXlUutM5jKR0bp3rnUWUymlFojwc9KvG0I5TJGlbFhBZTto2pq/LwnW9/gx//y8854ffx+OOP43e7EHMePT63PClGfwPR2trKu+++S0OucuNyuUgkEoWycF9f34SO39jYSGNjI+BUT44ePUo67QzCzpkzZ5D5MjiKoaGzfjMFLi1LfVUQAYhGo+jybES1atgMHoCohtDl2USjZxCAuf7x50jAGXr2iE7cT7pIgjddaKyv42ZHJ3Ny72+xcAk2IJAyK2d2GwoEqbvyEYc729n47POjPu7hhx9m7969Ff+M6aZFMuPsNj+51kqkP0ZT8yxefvqxgqXA/v37J43gnT17lkWLFk3KsQH8Pi8B73CC15XOcLijhwt9sUFUXQCWVQfZ2FhLvWfs1m6NYpM0He/EPn3mtGq7NUcIIgIZwyBRU17rMa05MVQ+l4I8wyTDfrfCooUL2bf/AA+tfZBlcxumlITu3LmTRx99dNiGSxXh7uXLOPTuYe5eNLtk8+DPKtKaQVskhUsW2bBhA7t27eIf/uEf+NrXvlYgwZMF3bTo6EuRGVKpdysStUF3UVwkoxm09aUwR/D0002L3niWXrI5uyC5YBQdyPXEExlnk6rKIm5VLpDDSn5tpozgiYITpPvH3/oaV292sGvXrwq/M00Tn8/HU9sfx6XI2DhBxh7ViTYJeJSKDH5evHixYMViGAbxeJzqATL5PXv2TMhTbyBEUSx47AGFHFBBEFAUhQULFkyJ/Uo5uHXzJlWyiF+RWVYd5EJfjD37T/Li9leInvijQSRPVENUPfj3vPn2SQRgeXVwRIHFaHCJNglTQLMqV+GaDAgImELpnwuv5JA6w4braQGvBH7Jxi+Vl8me1TTeT2ZRqmrQ0mmuXb3K/AGJLgNR6cpwKmvQHUujGRYnTpzAsizq6+p48ZmnqA26p2yxvHXrFk89Vb5qcTykQzUYmRjygIrt1ViC167edFowQFXOCzIejxONRrnQF+NSf5wvLJzNwuDo1SEhN55wOyvQb+SU19Nc6HJ8HJ3/bnLZ3IhE2fDC8DnkYhBLaciSWJQh71RDlkTuWzafxoZ6Dr7zLnpqMfffPbpX5ERgmBa98UyBQEiigCl70WwJLZlFzC36+XUtbwjdn8wOInhD16NEIsE777xDdXX1jPRMLQn5233uc/L000+TyWT46U9/imEYfOUrXynKhL1UaIbJ7d5kwWzZrUjYOPY+Gd3kVm+S2oB7xBnHvFWcblj0J7ODrGnqqjy4FQlZEklkHKVsWjMxLXtQCkh+RC0/q5p/v2VRwLDsEQljuZjyQYQqr8p9S2azfP6XyBoWtm2T0Uyu32rnF2/sQBAERFEkrovEblxn7cqlrFi/gUUtdbhLrPC1trYyd+7cEX8ny/IgcgewZcsWDh48OGJY+0SxcOHCQrRaJpNhz549qKpKLBabcfYo2XQaK0dmNjbWcqk/Tnt7O2++DU9u+5Hjg5e8huibjy638Obbp2hvb0cSBDY0lkZa83FO2ZnL7Qqwy1iwRAGaXTYdWQHdvhNO7hEp2RfNME2OJ7JsLUH9t3btWo4cOTJhQUJWN2mLJAv3ZEWExx99uGKbr2Jx7ty5YZXwSmPNlke4/S+vFmZAu9IZXrt6E9O2h3lBiv4F6PJs9uw/SXt7O69dvcm37po/ZiXPKzmV67TlhNY3uKbvw2/bdxTfYcW5tr5AdVmbA80wC0PkwVFMbKcbbkWi2u9i48aNnDn13qQQvKEEAqC/vx/R5aM7Nnh2tDHkJeBRCPlc3Hvvvbx77ATPPrqpUAn/yU9+QigUYtmyZXzyySd4PB62b99OT08Pe/bswePxVDyqb6pQ4HcDfuZ2u/nWt76Fpmn89Kc/RdM01qxZU7H4zozuvDeWbaNIIs1hbyFSTDctIvEMsbROTzyDxyU75C9nNGJYNj2xzCAfQbci0RDyIArCoA1ulVelyqti2TaxlDNn580dT5ZETMsmmTtOXjXrUiSMrEFGMyvWzZyWSVNBEHCrMu78PcAHdVULuHvxnMKg6bWuOHv+5aeskuHyr3cTX7eJFYvnDXPwHg1tbW18/PHHXLx4EXAGMMdT0miaNkjFM1lwu90FMcfBgwdJp9M8+uijM0bps3DJEi5m0tz48CRzQlV8YeFsXrt6k472dl79yc4B1YtPC21ZSRD4wsLZ47aohiLfls1azj+umTG2MyLEMkULbtGZa8pakLQE+nRncU+YFNTD48GyLY71Rnn4G39Y0rnD4fCERw/AyaK2AY8q0VTt44ZXHDNQ27KsggmyZVksWbJk1M1WKWhvb59065eTv/k1D9Xd2agc7ujBypG7oV6QAKJaxYvbX+HNt6GjvZ0jHT28MH/sdI1axeZmViBmQmgaP/dRwzFilgQIyTbt/f0s2va5ko9jWjYdfSlsKGSWz1SkNZNIJIKvxHtVMbBs2zGvtRwCURNwI4kCtz75mCqvii+X5a4ZJpph0dGfoj/pVJAMQaGtN05PLIXXFUQQBGbNmsUjjzzChQsXBo1a1NfX8+STT7Jjx46Kv4apgpUjTiMt56qq8o1vfAOAV155pSIEz7Rs2iMOuXMpErPC3kGbU0USc4EOjs1PW+6xIxnJBdwKiiyOGtmah+NNOPg+ads2vfEMlu2cMx9HJ+aOY5XvXDcMM+ZbKInCoNJ0TUBn2f2ruHb+FKvmzeXKyWPsvXyZe9evJ+B1Qo4DY+Qcer1eVFUtLAZXrlwZ1DIdCceOHZvymbiHH34YwzAKmXqPPPLIjBBd3LXyXj5RFK6eOMzCcDXfums+Rzp6+LgvRjQaJRp1Frh8W3ZDEfNHI0EWwC85ZKcjK9DivjN4btmO+tS2c7s8ASScG4KQO7eVe5xl3wl1z/8s/zURc88RcRYyVaQs82G3JJLJZnCXYaEhCA6ZdUs2pu0YIWdLaEu/1xVhw+9/u6zPxsaNG9m3bx+xWIxnnnmmZEseK5dLCdDXcZMLp2+NuxnJRw7mcf78+UECpLVr15ZVuV67di3Hjh2bVIuUYF83Ys4OJ6EbhZm7J7etIXpiuBekpUWJnvgOT257lVd/spOP+2I83mKMOarglu587nt1J+ViqqFbd6p3NYrzvUtZNn6reAWxbTvtp55YBsu2nSSa6skT9UwUyYzORxcu09PTwwvbH6748fMpT5Io0FLjK1R1Htm0jtbWVk4f/S2LFy9myZIltPelSGaNQhs36FVZufJeDh0+xnPbt9Jx81pBhDc0RnDHjh0oijIj04+KRSaXcZuvoI2E48ePV8wNoDuaLhDvWWHfqMSsrsoxKk5kNXrjWZpC3kJRySWLhAPusuckTcviZk+yIESqDTqKWcu2SeZm8maMD95kIuRTuXvVKt44fYoNgs49YS+92RS3f/kmSdVNtrqW1Rs30FDlGbF1GwqFMAyjoCIcb+Bb0zQm4Pk8IciyzPbt20mlUuzatWtasj1HwqJly+moCnHiN3u4x+vihfktPN5i0JpIkjUtXJLIXL+vpJm7kVCn2mQyzuD5jYyAX3KqeRlr8qbyvJKzqLlL4EsNdbXcaO9gblNTWec0cy3aZG79VMXiX50Zriu7whsKhdi2bRtvvvlmWX6L+aq6AIhYLFq0iPklxjKtWLGisEhZlsWxY8eIRqMsW7aspGMFg0FSqVRJ5y4Vunzn79waTxZm7kbzggSwtP6ivCAHIi+4SJrOHNxU26Z06QIWTru4KnfuheFqLv5mD8aWR5kzf2TlfFozSGYMLNsmlTUKi5UiiTRVe2eEPcpoiKY0Ojo62L5tS8VFfX2JLH3JLMCIM6mzZ8/mvffeY/78+QiCQHPYR0Y30XNKSlUWqQ+62XHlHG/u+jX33zWPNWvWjHiu5cuX097ePmPnuMeDbdsFYjsSobFtm1dffZWWlha++93vTvh8umkRz7VEG0OeMatukigyu9ZPa1eMrMtprTZVexEFYcJzpcnMne9LTcBVIIqprPN9EqBQ0asEZizBC3pUemMZnvy9L/OzV/8n33rofqplgdmeKhKmQE+qnw/efJ3q+x/k7mULR3RMtyyraIsIWZbx+/3s2bMHn8/HxhFiqiYbXq8Xn8+Hpmkzxvi4sbmZxq9/i2Nv72ZxXyfVfv+4C1epkAWY5XI843SbnAGyAylXeQOH7OUrcwOpkYgz65Z/rCjcqdqBUwG0cQiWmasKpkxImwIh2UmaKGYWTkDAFMvbXekW3MwK5CzCkAXHb6xYVCdHJhbFYvfu3WULE/I3NRtYsnQpe99+u2SCNxCiKBYqcHv27Cn5WM3NzYWs6CVLlkzoWkaCVt+MHu9GkRW0XPWg0l6Q4FSSqxUn+aR7im1TMqbzHYDhyRp31VTzwa9/xZw/+rPCz6KpLPG0TkYzh226RAHCfjchnzrjhBUjIZutvIdifzJLT9w5btjvIjiCe/COHTt4+umnB93b3Yo0yBJDlUW2PbwZzbSRRQHdtEYkzLquo+v6sJ/PdJiWTTyt0Z+8k7M7UjzXkSNHWLVqFQ888EBFzpvK3qmOFTPLb+Wy7YJeFZ+rcnPG+dfsdysjchYb6OxP0xDyVOS7NGMJHjhtW6/Xy+yNj/D+lQ95YP68QpurWpGocgW49tEpfnvjupNtmlOxAKRSqZLm6URRLGRuxmKxQjtpwYIFk+rnNRRbtmzht7/97bAW13Rj3fanuPrq31E9CaomcBa7OW6buAm6JSALNj5pdIVhnrSVIynXLceHLGE6nmRxU6BWsYusoJT3pevVHXKnCFAlO8kdpSzmc1SZsydPsnKUHf1YOHXqFEuXLi3bD0/JqSI1w+Knv3iLLzxXuTGGBQsWFL5rkiSxcePGcTc3A6uBP/vZzypO8B7auo1L//QPLKmtQc3d2CfLCzIs2yQMp3rdrQk0TlWrVrjzr5HGFQKuO+9Ba3ectGbm8lAt/C5n/kgSRVRFzC2AM5/YAVw+/yHpdIYr125Se+/iinjOxdN6QTxR7XNRExi+cO/YsYPHHnts3O+gkHObuNXrtPFu9yaZXesbRDB2797N0qVLZ9waMR6SGZ2O/nRhxkzMZe6OVEm9ePEi3/rWtypyXtu26c9VVoutjrXlEiYcP7vKFVvyIiSPOnh373PJBDwK8bROPKNj9dk05qqGA2FaNhnNKIhwxsPMraVD4UUse2AdJ+PZQb14SXAk/XdXe1mcinJqx+ucvdRaYOqHDh0qW10UDAZ54okneOKJJwB47bXXJvZCSoAsy1OWQFAqylGRlgJRcFpFtapNSBnbPkIQyvcLUkTns9PsspFzFiYdmsCtjIA2zp/eNks32NUtiOeqJY0um2rlDrlLZ7Nc7umlMxLBHqUh3RWNctUS8I4Qp1YMWltbJ0yC6oIeMpkMqq8KWa2cECm/UD322GNs3LiRw4cPl/R9m4wWVUfbbcwcA5ob8A3zghwJd7wgoyV7QeYraHET+qaoKOPOzaHa3BkZGAhDvrOoZTQD27ZQZIFZYR8ttX4aQl5qg26CHvUzQ+5OnjxJVdBPXV0dXp+3zK3aYGQ0g45+Z2SgyqtSGxxO7nbu3MmWLVuKnjmVJZFZNb5CBa+jLz1sfGjhKNZIMxWxtEZbX6qgXq0LuplXHxwzq7gSFSzbtumMOtZOosCYwrCByKex5EUylYBhWoUK3lDSKAgCjSEvzbn51WTW4EZ3gkRGx7YdxW17X4pPO2O09RU/ojKjCV6131Go6KbFumde4NjlT4Y9JijDkqDC/SE/vUcOcub8J2R1E1VVuXbt2oSvob+/n7Vr1074OKVg9uzZFbn2UtDT1cXVy5fGfIxdkVvizIFPctStYcWpZKQtZwawVxcYyYpI07Jller7Defv5pWchbUjFuNoLMWF9k7O2ArLvvEd5O3Pc0T0cEqH88kMn0T6uNTVw/GMibn5MR740tdZtHx5Wa/zhRde4M0335zQxsHrkgkH/ZimOcgmoJJQVZWtW7fy8MMPc+rUqaKeU11dzd69e7l161ZFruHcqZMkD7zNstowQMELUgD27D9J1YOvDCN5eS/IPfvL84J0S1CnOB+4Hl0YkXBNBgK5IkLMHP691gfMe86tCzC/Ici8umDBpPWzhlOnTuHxeLj77rupqamho6OzkCIwEfQlNcD5ftSNQO52797NQw89RDgcLum4joWHs7lIaUZhtg8cQ/19+/ZN+RpRLsycvQhA0KMwp84/rvq0EvPwlm3T3pcqVM3qqsaevRv4vPz5lQrOTAzkq6PF0vncSkEAkk/I+KTDIXX5+24phHNGt2hFQaDKqxJJZGmeM5/fpDOMpJ1Tcp5iQk2Q0x+comfebLZt28brr78+oZ2Opml0d3ePOug6WVi+fDl79+6teOtpKAbGtl06+R6Lot0cO32SuRsfprnFsXk49vZufJ230VweQvbMrCxOBKLgDLsHpTtt24gOMUMgrNgEpDuVwmtdPSxtmVXS8bOWY0UBEBAMTvbEadj0CJsWLULLZlFzYwT1DQ3Uf+6ZSr60AkRR5Nlnn+XNN9/k85//fNnHcSkSkiQVdreThfr6es6cOVPUY1evXs3hw4crMrN69Fe/ZGG0h9rq0KCfT7YXJEBIcWZDo8YdNflkW6d4JZs+Q0Af4WttDBCalOo/OlPQ0dHBRx99BEBTUxN33+3E+92zbAm/OfgufcksjaGJKX6t3E4wb38yEPv27WPVqlXUD8rULh4uRaK+ykNnNE1v3DFAVmWpYBly4cIF9uzZM2PTkPLoS2QL7c76quJmy9Jpp2pZbhXPtBxylM4pdZuqvUUrX3tijiWULAoV/exLoohXlUlpBj2xDM3hkav8XpfMvPoAkfgd0Y4oCAQ8CkGvWlKM2Yz/5ubZqm2D7Rl9/ksUnF1wiwg/++n/Zk5zmCUTjDN6++23efrppyd0jImgUqkaI+Hqlcuk9+5ECQTRFRdVukZtOEQt0H7wbQ7LbryZNKuCPtRwyHlSkeXtzyLybduE4Qy8GzZ0aQI9OG3jVLyX5jLEJd2agI1jiSHqGQJrHmJ+7nOpToHnYh5ut5tHH32UnTt3TkilrSgKO3a8yR/9wVcqeHXlI5lMkkwmy15E8/jw/VMsj/cSCAy/x9R73JPqBZlHnWKjWU5kWFtWYLbbLsvOpxgkDIjkKssjraFyPMbpI4e5f8PY1lIzCQMJHUBNTc2Ic2pBj4Jt22j6xEulblUipRlkRziWruucP3+emzdvlt0FCnpV4hmdVHY4KVi2bBmWZQ3KtwWn3Te0Apb/maIohVnzqYBt20RTd5TFxRK2Z555hjfeeKPkDWkqaxBLayQzTq6rKEBTta9oX8ZERieacqqy9VWVj0urCbpJ9SRIZg1iKW3U+T5REKgNugkHXJiWI7gph+zOeIKX/5wKgDhOBUkVodHvZpEt8OAjj9EcLl8QcOnSJRYsWDDlStob169z44PTVEcjHHrzFzzyeScyyLIsTp84warVqwtVt1Jx+vRpstks69ato+PcRzw0yHj2zk62qaqKJvidJnSjwS87lY2o4bRWDRsiBrT26dw/tybn9TX68wcG0eu2hNvlxydL1Co2SC76eyNT92KGIBQK8eCDD7J3797CfGkpsLFZtWoVH53Ojv/gCWLZsmUcPHhw3Dna/fv3V2QTlrx5g8AYM44Lg/5J84LMQ8jNFd/KWQa1ZwVmlZh2Ugx6NIG+XFVZxKlgD8XdNSFSbZ/ywT9dRL17FcsfWF3Zi6gAurq6+PDDDwv/PxqhG4qsYSIIQkWUkfkh+JFGOvKq9UgkUlB9NzU1sXLlypLOURd009rtkALNMAf5xg0UHI2Hc+fOTWlqkmFaOX9E5+9Uiv3H/Pnz2bVr16Au01iwbJvuaHpQJJhLFqkPeYuueGmGSWdunrLa5ypayFAK3IpE2O8iknBU136PMqbQRxQExAm0iWc8wUtpzhsWj8ewEvFxH1+t2DRYBlc+uU7t/cvGNFEcC9evXy9rESwXn1y4QOrEYRokgXVVVRCuoi+R4PS//Bi3oeHSs9wVDPLRxx/guu9Blt9XmrP3wYMHqaur4+LFi6TTadzdHdA0sarH7ypEwbGvCMk2KcsJhQ/4PXzY1sP8+hrq1eEWJ0OD6E0E0rgRiLCiykWguZp6jxs9k56W15RHY2Mj2WyWd955p+SdfL4dNRXRZHPmzME0zUHmyJIkcffddxeqdRcuXGDhwoUlbcKuXr5E97mPyLpcLFy5ipbZswHwpOIQGLtdV+9xT5oXZB6S4Iyb3MgIZCynilxJZW3MoEDuqmUIKaNXCb1uD/e4PZy+cQ1mGMHbuXMnLS0tZSlJYylnTfG5J/6e5dMHdGP0amA4HC6sJfl0l1Kgyk4ySCprEE/r1ATKW9Nu375daFNPNlJZg/a+ZIH4+tzDW9jj4atf/Sr//M//XEi0GAjbtulLZklnTTTDHBQLF/Qo+N0K3hHa5qMh39K1bKc1XhOYvOJG2O8iltIwLJtERh/RUqdSmDaCZ1oWfQkNSYKsblEbGGwMqZsWXTlG7pJEfvG3/5P/uO7ecY/rFqGlysfJ82fpW7KAhjJnLMqtkpWL3vffY3W+FZpDtd9PNQB3SsUr3W4iFz7k0IXzrHvpC+O2+SzL4q233mLt2rU0Nzc7P8tmECq0IP0uQ8h51fkkm1BtkK6szbWuXrpcKrNCQWpVxyj5aizB/756C80WMXDh9Qdxu32YqQx6qp/r0T5+GOvn+dkNpALTH7o7d+5cMpkMZ8+eLamakA/BnirR5Pz58wfNoRqGwblz5wptuGvXrpXcwrn02/082liDZGWIHHqby4aFprppsItv1/n+/+z9d3gcZ5rejf6qujrnRg4kmClGURKzGCRRVKSkkSiNdsLO7uzs7GxwWK/tz/b5zrHX9vmcfbzZ652gyTOKVKREUSTFJJJilJgDCBIEkYHOqeL5o7qbAInYaICgRvd14ZIIdFcXGl1vPe/z3MEqldwLsi/ydIHWrEBcg4B2I9JvLOibO1tmNcVFI3qeMLm0eBcvXmTmzJkj7lzdjHxRVopQd49DoisKWVUnlVWHHAW2trYWLXRy5wq8YoUhp0+fpqKioqjnjhZpWeV6bxIwu2g+l60ocU4wGESW5UJYQR4Zxey03fxeSKJAhd856pSJvukSFlGgJugaVz9HQRDwOm2Ek1lSWfXOKvCSWYV4SjF5RwPEiek5T5pwIotumAaRfpeNRFqhzOvA57ISTshEktkc0VFk60+/z+8tnj3izkFIMqgxFM5faiJ0z7xRO6ur6uitMMaC08ePM3sUf4mQ18P9GJz5xUucdwcIld964YbDYZ544gneffddNm3ahMvlovHCeeInDjNLNAiWj07V9ZsOc3Qr4KsppzWR4UJHNx0uBxarldeaulANB+Xl5axePh9J7aCzpwOnp4aasns5cOA4bW1tbGluZ+Xs8fERHC3mzp076m5Cng+bVo0xEaCLhSRJLFmypN/33n33XZYuXUp1dfWIjvHQt77NoX178V25yMLqSibrVeCymMkWMdUU/9Rbxl6MaJiWQDDyDGQAVZsgWe8IcfHixaJNu3XDIByNlYx6YxFFPDn/ssEKvFgsxr59+6ioqCjau64wCi6iKD179iyJRGJC3CA03VSuAv0SIIrF7/7u7/KjH/2IP/qjP0LTzdqhN3FDeFDmtWO3WrDlfBmLQXskjaLpSKJAXZl7QpJYsrmObyl8GIfCqAs8VTN3KilZRdfNIF6LRUAUBJIZpV+rNJFRsFrcOGwSsqoRT5sExvzOySaJzKzykciaJNX2cILu+I1TclgtHP7wHdZ7rYQGID8PBocF6nxujpz6nPDsGaMiS6qqyuuvv86TT44+cLtYpE4exx/0juo5AgIeAR5/4kk83lufm0gkeOutt3jxxRdpbbnGiX27mWEoTJtADsYXDaJgevT5A3Z63A5aowl2t3ShGQLl5eU8vm4O2uk/AzmCWy1D7DJwdCV47rG/LwTRN7e33e5fo4BEIsHWrVvRdX1EPLagx15IM+iJZwf0/JpobNq0iQ8//JBsNsuxY8dYsmTJkOpzh9PJoqXLiF0fOpViMiAkGcRVU3SR0syibyyQBLCLprI7rY08Gq0hk+Dkp4dYtHxi7aIGwvnz58fkLqDrBr29YbLZLMmMPKAp8WjhsFpyBZ4C9D/e9u3bsVqtRRekBRRZB1y9epVYLDZhVl+JjFJQzI61uAOTlqHphjnNS8kFp1CPw0ql3zFmukgqqxa8c2tD7qIpXSOFYZi/S/41i820HSlG/O5Eklkud8Ro6ozTEU0TTyskc4VevnBTddMpzd9HytvUGeNyR4yrXQl6+8ilqwJOGiq8hLwOppZ7OLrjfV77i//J8cOfYpPMD0fHpVO4rzUyv7521L9YSDKo1rJcvHy1YC44HK5evco777zD5s2b8YxTYsPNOP7JfuYUyQXpcboHLO4APB4PGx58kIOv/gph93ZWeJ1UfFnclQRW0TQsnu63E5ET2Mny4iN34z7/J3j0VnQsCAI4xAwokVwQ/TIM4HrcVH1OBthsNh577LERJ77YJAuVuQid9t5YYZG63XjkkUdobW3lqaee4uzZs0M+Vs5m2fvqr6ktm6y9uxuwijc6bd2DeDMahmlUHFbMPNu4ahaDgznZFHO7rfZ5CV08zZnjx4p4dmlw7Ngx3n//fbq7u5k3b17Rx5EsIkvmzaK2tpbGK9dKMqZ1O6wImGPaZB+PyD179nD33XcXbbg/EAYzQx8Mly5dmlAf10RO5OBzDS0eGCl+/JOfsvbRrxDNFXdWi0h1wEVN0FUSLnBvwvTn87tsA2bilhpdsUxBCFLld45Y3VssRnz0fBQLmJ03j8OMpxEF05BP0w1cdgmXXUIUBFRN52RzL4mMTLnPiSSKOG0WfE4bXqe133jn2KefErx+iX+1/h52XzjOga6rPLRpE6fefZPfXlFcFp1DBKfTw9ljJ5g9o2HYLt7+/fsRRZFnn322qNcrGpfO4QmOvvDKyjJadf2gPz+yby8Vl8+xIhiAEkatfIkbaE0msaDh9/tx6C3Ec0H0Gd3cxdsFc5RwcxD9lStXiuYPlRLF+Gf5nDYefmAN72/fhf+hB3DZJ8fIedWqVYApzjh9+vSA7+/B7dsItjTxeE0Zwh1i2h2yGiQ0gewAggslZ6ciD3DPFzHFGk7Ljcd2K6ZwA8z1cTRwWUSiReYwjwWnT5+mubmZRYsWFfzfxgJZ1Ygks7iDlZw4cYK1986j6PZYDlaLSLnPYSpFcx2Z3t5edF0fs31PHhahP29QVVVkWS46fnA8oGo6Kbl0nSlZltFUhcqyAKqmU+Z1lLwgyvvkBdzjf480LWNMC5aqgHNcuXd5jOrdqvA58LlsI6rMdcPAaZOwSSJ+p41Kv7OfiKIvPnz1Zf7p6qVYBHho7nSaOjt59b/9Z763fiBb4+FhGObC1ysLZFM65y42EbznrgFn67Is884777Bs2TKmTp1a1OsVi0M7d7DAVZxa50o8ycrNDw7681kLFhJpHLqb8SXGhsGC6PVcY1wSbnS4+gXRZ8ffZmQ8Ue5zEgwGaWnrGJdFdyxYuHAhJ06c6Ke+DQQCLFu2jOoZsxDar90xxR2YY9Vq2w3BhVs1R6uaAW2yWdyJmOPbfNdOMUyuXZssEJIMLIJZ3OX5d0Fp6BjAgXBN0Vh89/Ait1Qqhc1mK5lIrbW1tWRGvhlZpaUniYE5+ptWX8OewydZv3zxmOOobo7A2rdv35j8Jm+GJXfvyhd4uq7zN3/zN9xzzz2AGa9ZXV1NU1MTWo4zKQgCkUikZOcwHGJps3gxOXFj3wz88pe/5Bvf+AYut3PcuWoTzSd228d3NJvHiK/Ccq9jxDluYM62LaKA224b1LE5j3/6H/6//P1//Pd8fcFsyn0+pldW8odF7nxUw3SCj6ugC1DvtdN16jPCs6cP2MV77733ePbZZyfc7w7Ace0yzpuUsyNF1DO0ii8QDHLJEJhS1NG/xEgwWBC9JKjIhg3VuHF59Quin0CD45Fg6tSpNDY2jjj1xWoRuX/5vbz34U6m1FZNmi5eHjcLMbZt24Ysy0ybOZOfvL+Vp21WfJOo8zEcXBazk9ejCIRVAYdo0C6bXT2LAFPsRr+CTTegJWv+vEu5ceOy5nz2RpuQYWAQdg+83qiqyq5duwAzMeXo0aP86Z/+6Wh/xUFRynW5J2EK9xxWC0uml/O5nKA3EqUrmqY6WLrPw9mzZ5k9e3bJjgemQhTMAk/TDWw2GwsXLmTDhg2Iokhvby9dXV2sXr26JKkuo0XeFQMgUIKJkWEYpNPpcaNKGYZBdzwz/ANLiP5sAIOxdo5HghFfPYM5Lg8EWdXojKZJyyo2afiXcDqd/On/8595u7WbC23tI36dW15Xh2sZk5QsiaYzfKXdoFLNcu7i5QG5eD6fb8KKu0/efIOjv/oZn+7+mE8+2Mpc3+jC4z9saedUPMXJq81ULr5nyMce3bubWXdobuSdgsGC6POj2bRubihuDqKfNm3a7TnhQTB79mxOnz49qucEPXbq6uo4c/4SaXlycPEGw8aNG/nBD37Au+++y7Pf+h1OWhzIiny7T2tU8FrMyURHVuBK2hy15j3zbu7GiQLU2Q3KrAZuCzhzXL5i48903UB0mWtVKpkk1YdDumfPHtavX8/GjRt58MEHWbRoUckKjLzZfKmg5dZ/0yPNSk9rE7NnzyaeUWiPpEbM1R4O8+bNK3lOrGQRCxOoSC6+6oEHHuDjjz8GTK+9uXPn3pbizjAM2iNpdMPk19/snFEMurq6qKqqKsHZDYy2cIpILkc44LZNiHI2mTW5d1ZL8Yrf0WLErzKSFramG3REUlztSpDIKCTScj9V7VAQBIGv/M7vcuJa60hPqf9r58ayqgE2wQyRD1hN1Zjf7aX75Of0TnDF3hfH9+1lbjbOEo+DBd0tTOtqJWsInO6Ncrw7zOneKAll8BtlTzTKoief4e6v/w6L//ifMXPOnEEfqygK1otn8d5BXYo7EYMF0Tss5udMNmwI1v5B9DXVVbiHSEy4HRBFkYqKClpbR37tWS0ii+fPoaWlpWBbMFkhiiJTp07liSeewOfzse75FzkcTY2asH47IQnmYp3WIaWbRVu93RiUS2cRIGQ1C8B6h0GVrfjYM4soUt7Rwrmf/oArL/1vGi9cKPzMMIxCUbFjxw42bNhQ3IsMgJaWlpLmcee92MJJU+xnswhU5JTg8bTC1c54P5HEWOByuYjFYiU5Vh4hr51EIsE723bw3raPOHDgwJiy1kuBZEbhWk+yILgqlYdcZWUl4XB4zMcZCImMKRAFqA64qPCVPpJsIERzBWUpCuCRomRlpKxqXO2KFxQifpcNt8OGMgpjxlf+7m94YdnQnanB0CELKIY5hqh3GNhECErmAi5gUKFludTUfMsuraKigu3bt3Po0KGiXnckuNrYSNmVCwRyBVdMM9gfjvOXJy+w5cp13mtuY8uV6/zlyQu80dRCZ/rWQrRXh/ocR3C4juOut7Ywt8jR75cYHdZUlyMKQi6I/jNs97xEaOn/xD3tazjv+pcIi3/AGx98RltbGwLw9FcmWMQzQqxatYojR46M6jl+l4277rqLw8c+Qy1R92O88MADD7B3797Cv5f/1jf5vHt8biDjAUEAv9UgaDVwiTfWuInCnFCA2UE/ksfLonsGXqOz2WxJO0il5qrmOzWabpg+rLpOwG2nvsyNw2rBANojKbQizYj74oEHHmDfvn1jP+k+8DltVJf5SaXS1M6Yxz0r7p9w3nhfdMXStIZTZBUNAagNuiZEiTpW5Bs9Qbd9woqtWFomk3ufRjMNHStKwoTVdIPW3lQ/CxSbJHK5I46i6WQUbdg8uK1b3uDBmoqiqv+IYloFCJgck3x0m0cCR64pZlhdnPpkP9lUnEq/k2nTpuHxeApO/o2Njbz//vslI/TmkUomiez5iIU5Y+HGWIJXGq+hG2b/4EZoeZxoNMrZcIzzkThfnTmFmb4b/IO0Y+TduIefe559v/o599oEnI7b71X2RcZgQfSGM0AscZ1M4jR2FCRBYMH0aeM6dhgrFi1axLFjx0asVnTaJKbUVnPp0iV6E9lxCecuFSRJQpZvjGUdTifVG5/g8s4PmBEMAKYy/XIsgR0Du6FTGQxglSZutz0cglaIqiZ7pxS+eMWgOZnmWs4g2+Px9CvCFKV/96upqYlUKsXcuXNHJLpoampi+vTptLa2cujQoaJNgQeDkAtwN8dzWeqnNHDx4kVmz55NfZmb5u4EsqrTEy/NZ7m6upodO3YU7mmpVGrMucnVQTebn36CHXs+4cwZgzWrV1Lpd+K0TZzQSdV0Ovp4ufldNkIe+6AiymKRTpc+1rEnniGbazoFPRNXaPXGs7nXtE/IODgPwTCMoucUaVklmpJJZ1VU3cAiCjRUeArz5bZwikRGIeSxD2koGY1Geet//Be+sXz0MnjNgCtpAZ2B43euJTO83xbnUiyNhogVBRcZJFFg3rx5rF27tnDTjUQiHDp0qKQZtHtf+j6ryv0ICHSmM/zwXBOaYVBTU8NTj61ByFxGj19C9M7CcMzgnQ/20dbWhkUQ+M5d0wvh5Qd1ifufGV3355N33mJOMkzQ7UZRVfb2xgkGAlhVGV82zdTcje1LjB2d6UwhiN4AslhRMP2xpvvc3BNw0SPAw1//OvbbwJMZKd59913KysqorKwc0fgnmVX44OODTJs2jXvm1I+72q1YbN26lccee+yW7vepo0eoOf85Aa+X8z1h5v/uHwDmzfjsL3/MkqqJiXcaKbpkgYhqUk+m2A0m8u2Op1JcaZjDkpWmJU0sZiZC5Inwvb29HD9+nA0bNrBjxw7KysoIhUJcunQJwzDQdZ36+voBfewOHz6MruvEYjFcLhf331+cg8JIcK07QUbRSCXiRDuaWbdmNWCOG1tzKQxOm4UKn7OkHant27eXrGiNpWSaO3o5eOhTamtrWbZkAeVex7ioQTXd/JyJgoBhGLT0JMkoplK30u/EPw4dqddee40FCxaMyfPwZrRHUsRzE8Zyr4OgZ+LEbpfaohhAbcg1YQpaKLKDl1E0emKZgucNmH/82pC7H3nQabOQyCjD5uf94q/+gu/ct6SYUyGtgY7JUbm5uMt3y7KGiIgdyeWjzGNDS8eR4z2cPXuW8+fP8+KLLzJr1izC4TD19YN7y40EsixzaPfH2JJxrPEoS/3ugi3DvvZu9Fxxt/mptXRThUtPIDf+G2zLf0jKWs3mp9by+jt7aW9rY397N89OryeRSuKbM7xFwc1Y/dQzHN+3h8yVi3QrKhu+/V1EUaS1pQV15/tj+j2/RH/cHESfUXU0wULA4YGcf5jfMPjwRz+j7N77uPvuBbhsow/gHm88/PDD9Pb2cvLkyREVeG67lfvuWcKeffuZM7V6QvklI0W+aBiI2pBIpXDnNlF9lekul4tkVkbT9QkjRI8EIatBPOeLF1UhMIFv9wXFYHmuuANToNbv3EIh/H4/r7/+Ohs3biz8vO8YcceOHbfctE+fPo3FYmHZsmXjePY3UBVwcr0nSdPV5pxAIEWFz4HbYaXc66AnniEtazR3Jwi67ZR57ZPuOvW5bMxrqKQi+DBnLjbx5nsf8uSjG6gNlU51qhsG17vj7Ni9j4qKctYsW4Kam8gJQH25Z9jJXDE4d+4cuq6XtLjL5AIZwLR7G40jSCmQj7PriWcmtMAb9cqlGwbXexKF4s7ntFIbdDGt0lvUH3v3jo9YGfIW3d7NX3cG/WXInekMrzReQzMMptRU8uJXNvLYyhqWzkzz0P3T+O7vPk9VVRWqqvLrX/+a1tZWPvnkk6IMaCPhMAdf/gWnf/Fjrv/8hyxP9rLUonN3wIvNav4xE4rK2Vx354lH7ue6Us4zr/v5d8cWEXxsL39+YgnPvBGimyqeemwNBnAmHCOpqLSnMoPyXobDPWvWkVqyAmHe4sIN7sqendR92b0bF+SD6O+tCLKs3Mdsj8g0h9lZtokCs4N+LKc+Y+fPfsXLv36Tax1hMpNIhepwOKitrUUUxREHo7sdZqFaKoJ6qTEUZ9Wi67REYvTGYpQv7L+JWvNH/4QDmoWm3snD1bMI5qQCIKwKFD9/GR26YnEqVq4Z9nFLly5l8+bNtxR/edz8mbp69So9PT0lMTEeKWyShfpyD8vvvZuqqire/eAjPj15CVXTCXrsNFR6C0a94WSWlp5kSTimdrudTCZDc3Mz27Zt4xe/+MWYjmcRRaoCLpYunMPq1at494OPCI9C8GQYBllFGzDNwzAMWnsSvL11GytWrMBqtfHh3sM0dycAqAw4x6W4U1WVd999l69+9aslPW7e0Nhtlya8uANw2Sy0hZP0xrJc606gT9CFO+oOnqYbhUKqocIzpKFhflwzlPy8o62dxeVloz2NAlyi2b1TDXNHG8wVxzd3y87G/FiUBKlL/wdr2b+mk4pCt6ytrY0tW7bwve99b9Svr6oqZ994meWVZYANGFgheTWeRAfs3jKutDbRq/WwxDONfU0+1v1IJKPLrJ2SwZa6iKB1FVIPmmIJjFHw7wbCnIULC/9/+rMTzDQUYPLypb5osIpQJhqUWSGjgV9yE9JANTQubnmdIw439rp6VqxchtdpHfc8xJFg7dq17Nq1a0SqSIfVXEZKZTNRang8nkH5PMvWP0Bm+QoO7f6Y9Xfd1e9noiiy/rnniYTD7H79ZdZVhSaFSbLXAt2Ya15SM7nG440Wi42ls2aN+Tgej4edO3cCZhHh9/tZt27dmI87WlgtIrUhNyHPNGqrq9j58W6m1tcVwuZrgi7iaYXOaIqMonGlM47PZcPjsOK0WYrq6C1fvpzXXnuNxYsX8+ijj3Lq1CmuXr1KQ0PDmH4Xr9NKXbmf6dOn8+nn51i++K6CQtwiigiAoqp8+NFOvP4gixcvRtd1Dhw6jMPpZOHChTislpx9jIRkEWgPp3h76zYe3rCBKRU+yv0u3v/4IB2d3VTPmTJuKQzf//73+YM/+IOSHzermgXeRItANN3kdEZTMuVeh5nlrZg2ctWB8Xe5KIqD19geQzcM6svcQ5I7VU2nqTMODF4Mnj1zhsS2t7l3VvFy76hqxviIwFSHQVZT+cuTFzCAP/j2C8Ss1Tz3qoP7KpL8o/sy/PVRJ0e7PWx5PoVPaecfXnoVURT5sz/7s1FbWHz84x9yf8iDKAzdgTzaFeaNa734y2uZM0VG7dyJa/G/59/sL0c2RGwi/PjJOPED36F2zib2nXbR0tLCnKoKqgIBHvqtrxf9/vSFqqoceP89qiLdzAx8mU17u2AYpt1FXBVIapDMZmnLKCRcHmbdey/Tp9bhdVpLTlweDd566y0WLlw47Kg2mpJ5e9suKh1Wqnw2ltw/fKdnonHx4kVkWS46Ii4WjdK15ddMmyQZtt2yQDjHxZvqGP9uwOcZlXte+Nq4v87tgKxqHDp5iUgkwgMr7+1HM5BVjfZwqkDMB1PM53ZYKfPax7wZ27ZtW4HzfejQISKRSKHjvGDBAmprR57Dfqktymtvb6WqIoQtn3yhaQiCQDab5f777ycWi3H+/HkEQeC+++4jnU5z5syZwmMXLFhAa2srPT09rFy5koZKfyF+7eSVTo6d+JyvbXpwXDahO3fuxGazsWZNadcPwzBo6oyj6Qa1QVfh9xlv5DcI+YaY32XDZZdoy/E8x4u/2BdF7f3cdol4RiGRUYYs8CSLiE0SkVUdRdMH/FDMnjOHX/2se0wFnl+CmAoZHXoUgZ5ksqBQFTKXcekJllbfxYEWG8ffs5A1LNw/VcGVvYQgdxadEbrntZdZ7nUMW9xldIjqNhQkelMyZaFq3NP+EX93qZ64YMchGaRUjb8+pvH7C/4tvUqYnpiZerB47fqS5pZKksTap57h4FtvMBOtZMf9EqODIIDbAm6L2RFP2myUu+ykNOjev4c9ByXkYBkr1qzB77YXsp8nEs888wznz5/n3XffHVT9J6savfEMvZcusrLai9EpE5m/gEAwOKHnOhxmz57Nu+++W/S15PP7Oen0Mq20p1U0glaDqGpy8eK5+LLxhKh9cdcKm2Rh/qwG3nr/Im09UTx1ZYUOnU2yMLXCSzKrEE8rpLIqmm6QyChkZJWpFd4xXZfxeJxt27YBsGzZMkKhGxuIEydOcPr0aRRFYeXKlf1+NhCcNgsbH34Yl10qCAg03SiEJtgkkSkVPhbMrEfXDexWC3ZrOYtn1ZHMKCQzKkdPfEZZWTlLFi+kwufsF0PodjrQlSyprFryAq+7u5tLly6NS/cu/zcTBWHCYhVjKZmOqDk1sEsiZT5HgXvnd9mIpmQ6o2kMwxjXkXFRv63HaTULvLQycpPAQTaZkiThv/s+TlxtZklD8Z4+FTaDaxkzrzGp9ckIjV9Cbvw3/N+rP+K5V+2ouULz36/LIn/8HSyzfr+QEXrq4AGUowdJBMpY8/RXhny9T7d/yGI9i9058O+v6BDTzO5MVoegw4mCFWsqgj+wDGeongN7nWycluW/PZTmX+50sf96iD+6bwZGvJ2u2BlcgjguqQeJRILrsTiSmsUmijR43XiskydP9DcNomDeoL2SgWZAhc1NXBWIp2Oce+MVEi4v/hmzmDdvLl6nDbdDGhe1andnJ/t++VPmPPgwkXOn8c6+i1BV9YBeW7KqEU5k6eyNcfajD3i0towGr4RFcHHo3TdZ/dvfJhGP4/F6S36eo0VnZydHjhwZs2dYw6o1tO/eRnUgAEAylcLpHH6DNx6wCGaR16MIRFQBrzS+XTyLNnl4ouOBgNvOurVr2PbhdsqffYLQTSlDbru1cIPOyCpt4RSqbhBLyWNSYz733HODckTzkXu6rrNjx45hFbh2q4TPZcPrtI5q/Ge1iATcdgJuO3UbVqMbxoDri99lw+PxcPzMJZYvmlPSTtiPf/xj/vk//+clO15f5I3YfS7rhIhlYukbxZ3PaaXS7+z3uua/IZKU6YplsEmWcSs8ixrR6oZBY7vp0j2t0jukr0temmyXROrKPIPudn71/e9zj5Fhdk31aE+ngKa0mWQRTkb58FoLfr+f33piFoqtkv/3p3P5tDmL1QIZw8Ha6Tb+/J7Pscqd/HrrJaLRKM9Or2NB0E9WljkRT3PXU88SKruVH3jy00NUXjpDufdWxZJqmOOTeJ8Nr25AQhPY0XyNqCIzvSrA80/nVLTZS8iffsdU0dpnUU4HP9nyCR09YYJ+P5seXsPC+aVRE3V0dLB3717Onj3bj+wsAPOCPtZUlxdsWb7E7YeiQ1wzPzvdiTRdhkDc6Wbe8pVUhPy4HRJu+/CdvUQiwZUrV5BlGZvNVvCAjEWj+PymcvT4/n24L51hWsDPpfYOZtfUEE4m2XO1jcf/9F+gGQa6biCrOmlZ5erVZrrOnsGXSTAn4KHKdiPAPpXJ8HkkjktTqdr0LLX1ty8R+ciRIyiKwqpVq4Z/8Aiw66XvUyFC3O3DMXU68eYmZmTi1PiHzoYeD8g6XM2Y1JSZrvEt8C5EYsz77e+M62vcbnTF0vTGMxw5uJ9vbN7EgQMH6OrqYtOmTbcUYfkOjShApd+FxzG+ivh8SkgsFmPHjh3Mnz+fuXPn9ntMMqvQ2ptCEgWmV40P/aYjkuL9jz5myd2LWTSzriRThV/84hesXr26pKklecTTZgydADQMU6uUArph0NQRRzcM/C7bkH6K+drIZZeoC41PulFRZaMomFRjA3O+PRRCHjvJjEpW1Ykks4P64X3tu9/lh//rf+INR6guQuGZ0sziSgBm+1xsx/TXMxwzSFmrOdYmsqQsxfeWGvzkgpv9rQ5SK2fhEz1Eo0fNjFCP+SbbbTZWlNm49O7rXJ93N4uWryi8zuXz5/FeOEW5/9YLKKNBqyyg5d4Spwgui0FMFbgejXLP6vs5vHc77e1tvP7OXtMHT+7EMuv3EeVOfKKb1z/YT6yrDYvFwwMPbeByaxvJ5BFWLFs66vekLy5dusTLL7+MpmkFcvNIDJa/xO2DVYSQaFpjVNkcZldPU2nd8T4tNgdJh4spCxYxpbYat0PCabNglSyF3fegBb0gUOW0s9jrxLH4PmxOF9VN55G8QVplAWuwlsY0IHpw+yvY+s4HCLqORTcQDRVLJkM5Gst9bkJeD46bpjUuh4OV1eZ1fnzbu4S+/rs4Bul0jzei0WhJ47PW/853+t/sly/n6uVGjuz7mAVO24Qai+fvrTrmJnI8J/hf9A4egNdhJZKU8YcqePe991i+bBl33303r732Gs8//3y/v7vXaSWSzJJVddojKSyigN9lI+ixj0t3PV887tq1i2effZY9e/bgdDr7daXzdClVN8bN3qfS7+TB9Wv58KOdVJWHqA44x1TYfv7553i93nEp7rKKRmfU5LsF3BNjMCwrWqEDmo/BGwwhj70w9k9mlHHhBhbVwcvPjwFmVvuG/UCHE1m64xnsksjUisFHNn//X/4TX5tZj8cx+ptBWIFuRcBjMdMs3mhq4Ww4RnVORXsy7CUTacJ96c+Ztu4/F7plec+5+UEfz06/1QMvnEhw2uJg7fMv0tvdTcfWN7mr7FZ+kaJDSy4L1y5Cpc3kVrVm4HQ4Rt2a9dw9dxotzVeGLLREUUQURZ5/4avYA9Uomk7T5UbSvW0su++eUZFu8+jo6OAHP/gBqqqO2mD5S0w+ZHRI5MQZHbEEHUh0Gha8QR9TZs6itraG3o7rfPD2G+j6wJ8zAXOj9kRdBSICHm856k0rgYg5ChSF3H8xowDtooHTwoiyTQ0M9nVFeOD3Ss+tGQq6rqOqKvv27WPdunUjSlIYKz565desseoTmn6Rn1rU2g3co6BF6YZOV2+YhG6g2hzIVhuKzYZsQCDaw5yyYL/R85XOLmZ+54/H4TeYXLjaFUdW9X7m/N3d3ezdu5dnn+1vNJ+PPIum5ILthddhpTpYenXkzp07sdvtTJ8+ndraWnRdH1DlfqXTTI+qC7nHbeyXyqocOX2JlpYWHlq7mqoiizxZlvnbv/1b/tk/+2clP0dV07nWnUDVDZw2C3Uh94SMZzOKxrXuBKIAM6p8w75mWzhFTzxDyGOndhy6eKMq8HriGWIpGTUnCxkuoSIPRdO50hmnq7ODGQ11VPlvDSTe9tZbVLZcZvHU4oyG86qyoATlNuOW1IjFy5ZhZK9Ta7mM3T8DwzGddz7YP6KiRtU0TvRGEQ2Deyv7j2x1AyIqhBWhYLg81WEQV6EpnuWiIbHy8cepDXkKEu2Ojg727dvHmTNn+nVWRFFk/vz5rFmzxvTo03RaepIF+4nmxvMkor04rCKrVq4c1GvqZrz22mucOXOG6urqGwbLA4yGhyt2v8TkQ0aD8ym41BlmekUII53gejrDwXACwVCZXh3iiYeXYVWvoScuI3pmoEhTeH/HYa63tSMIIk9Mn4HPZsMqmN1Cm2gWcpYSrYdZWeaIIbHu+RdLc8ARQJZl/uIv/oL58+fzyCOPlDQjdTDous7Jn/2QhROYA90hC8T6rHsjweVwhJaKOhYtXUZ5xa1JHaqqcuC9dwj1dDDd76U3GuVKdw/Ju5fT1dXFN7/5zVL/GpMG+dGr1SIyrdJLS0sLJ06cGDJizDAM4mmlwLuaMg4GwK+88gq1tbX9FKb5sW1ftPYmSWbVcTfzTWQUjp66yIWLF3lg3RoaqgKjFl786le/4sknnxzxfWykMAyDa90JsqqO1SIypXxwalipYeToawYwtdwzrC2LpuucuNKDx2FleqW35OKVEZf4mq4XyIp5jCT/7vTJkxz8cBtaPIFgSBwKlvP13/0WVX1IoM1XrhD77AgP37N4FKd+A3nLCQCLYC5yN2eEXnz7IzweD7V+N8nEpcJY1iIIfHXmlCE7VpLFwtKK/gqmPD8qovYfyVbZDCKqwPlwkkhtAw+uWk6l39mvy1lVVcXmzZt57LHHuHLlCtlsFrvdzrRp0/rZtEi5D2dXNE08ozB1psm50HWdXQdPoMkZRENlw4Pr8HoGHqsmEgnOnD2LZsADD6ziQiLAd9+3c1/VLP6vlR/yHw45Odzh4JVnK3jqsTX8w0uvcjocQ6AFqyhS7XIyN+D9UoQxSSEbEImEWVzlZ4bbAK+b85ejGIZOqLyKFavmkD72HTKKadarGhZSYg3LV/wXtu2z0tPdzdHuCI/UVTLFYZSsqOsLu83GwmSSI7t2svTBh0r/AgPAZrOxaNGikmdLDwVRFNFmzSPZehl3EVOIYuASDWKY3dzyETy+O54gfddiHuxDO7kZkiSx9plnUVWVIwcPMv/hRazJcQx//vOf8/LLL7N27dqipgmTHTc3HhRFGXZ8KAgCPpeNZFYlkVGIpWQcJc5ldrlcI7IPsUkWkll12PSoscLjsLJ00Wxqa2vZvWcPs2fP4u67Zo6qqEylUiUv7gCSWZMSZqZruSbUfSAvWgYQR/C6imYUPAUHcxoZC0Z81+77gREFAbfd5PwMhJZrzXz4xutI0QgzPQ5+Z/ZM0+leg51N7ezdd5BNj67DYZU4+ukhTr69hW8WkUMLZhZtW9a0CxDpbxcw0+fhO3dNZ397N5+GVRKJBC2JNCIGAjA/6OP+EQoLNMMcASc1s6Dse/lYc+7yXskcFZ/pTaLPX8z9i+cR8gx+bLfbPaxtg0UUqA668OeiVlJZFQWYv8h03dd1nbd3HEBTZe5fvRqb1YogmOdoAOcvXCCu2/F4vPT0XkW1xrinYi4Hrlt5cYsLWYeVNXHCHRcwhDBen594LMqpsCmioSfC+9famOlzs6Gu6svR7SSDrAtoGLisFiyCQUJRaYpGcCCydsUCwp/9OXHNRplkIa27iGp+DATEz/8Tq5f/W97Z+jHXYlECU4JYhPEr4v1uN1XXm7hw+hRzFiwc/glF4he/+AXV1aZQayLGsjfj3vvXcOTnl7hngi4Tl8Vc92TD5CG7hrg/xJJJLnoCrBmiuOsLSZJY2aeo0HUdn8/H008/zVtvvcUzzzwzxrOffIgkzSZGPsli+vTpbNu2bUT2Oh6HlURGIT0OyTRDdRBvF9x2K9OqfLg2buDgkWN8fCDCsnvupsxrxzFE80c3DDTdGJa/Xyw8Diu1IRcYTLhpfCwlA6YlzUg4f3mD6URGoSeWwV1RWnrHiFdAJVfgOW0W6ssGJ+G/+tJL+Nqb+e35dyGK/cd8bgvcU1/NW0cOccDnItnZgtR4lt9ecV9RJ6/o0JoVkA1zkauxG7fwgvIZoQsqdI60dlHlC1FjN2jwuHGPoivVlhVI37Qpcorglww8FtPXLKHClViWRN00Vg9T3I0WTptU6Jgqmk46q5KWVdKyxvLly9F1nUOHj6JpGrQ0U+5xIqkqrYkEIOB2u7Blr2Jr/M/8x4ff5LGXXSR1A4ek869Wp+jc/R+JT/kKmiOIwxBpqK1CEKCtrY1oNEpjLElT/DIvzpz6pQhjEqE7Eqbc7SalmZ/RzoTpARnye6l3ttOoxkjobq7LtYSkXiyCjl3I4qGHSk8HtX470WiU1lSSgH18laC1fh8XjnxCd0Ul5ZWV4/Iac+fOxev13qIwnEj47llG7+ef4ne56IhEqB3Gv2wssAjgk0yaSK8i4LLcetNsi0RolRy4593NmiXFRR6C2aF8+umnAVi1ahX79u0ruSnt7UQkmS3krPrdox/pT7BN5YBQc5SfiepaWS0idWVuNqxZzvEzl/hw58fU1NRw15zZBNy2Wzp6aVnllbe3kUimsIjjtwuayLzXPAzDKGwQRtPJrPQ7SGYUsqpecrHFiCscUTQLi6pcB+fv//t/xa/KoGvEE0mWPfdVlixdSqLpIs8tG3gRiauQ0QUemTebs598zDSvncXz7xrwscNBM26IGiTBLO4cQxTMU5wiqTI/qUyWMncAxwh9ozLZDJd7YnTrdgTBoFxSkQSwGAaKYEYGdWP+Xi1pnXaXnydWLy9pcXczrBYRq8uGL+eCrekGsqpR98h6Du3ayeJqD1ZJQhTsnJc0Lsd6saTD1NXMQmn4X/z5AT9pw4pdMnf9/+t4iH+96t/T09vNvEU1+N0WArTg1a8SXDkf2TqNLe9+TFtbG680XvtShDGJkAh3UT1lBmkgocG1rIUMNuo8XqTMZSqsXfQoZfSqIaKqnxpbGxXWbkTBgHRTwQMyO0ExY3NCQT595w0Cv/P749JhkyRp0FiyicKcBQs4ePI41p4I0vzF2JrOUT4IhaIUCEim6XFaN61TbKIporjcE6bbE2DmhidZXuJxamVlJefOnaO7u5vy8pEMhyc3ZFWjO5YBoNzn6Nd9sVqtZDIZHEMopHXDKFCYWq9dpaFi/LrUg0HTdRJpMw96JPSpUiLgtnP/PXcxb/Z0zpxvZOfHu5k3bx41VZWIooBNEnHbrfTEM0hWG9W1AVquNtMZTRP0mArXtKySVUx/MZddmhSRjaNBVtVRdQNRMP3vRgqLKOJ1WomlFdKydnsKPE03R7P52I2KunpWqSmqcpYm//DWG7S3XmdtQ93AzzfMODEDs5O3af7MMe14wopZ3FkFqLMbBQ+uweC0wAy/g0uGwanWbupCfoIOCW9ODZhXCfalYPREosQyGXyhaqz64ETmpAbXMxAWsmx87tlh5dGlhkUUChd0INZNqE/8yRy/CwsGsdgNy5j9121sbLhhsLzvuh1txWxmB6vJXmnjWjKEbA3Qfe4v6bj7z7EFArzwzDpefWsPbW1t7G/v/lKEMUlw39xZNLd3ohvgDVVjFQVUJHpiKUTPDOyiTIWtCwPQkMgaDq7LdTjFFKJ1OrFcYop9AiPRllYE2fvLn/Lgt36vpMc9ceIEuq5PaHD9YJi27iFS8Tgz5s7l03gcW28bPtf4ZE9aRXN9S2nml02EPS0drP3O95hrHz+i/bp169i5c2dh1Nbc3Mw3vvGNCRG0lBqd0TQG5oTq5u7LcMWdoum0h1OF7t/pE4dREr2FjN3e3l5UVaWyxF3rRCJR+H9V02kLpzAwkxMmIrEhEg73S6yxSRaqAy6kGTVEzp2gtbWVS5cuARQsZnRdZ9q0acydMYUPk0l++NNfUldTw7Lly/l4+4ek264jGTqq28vm3/5tyvskQEx2pLJmcW23jj6r2G61QFoho5R2vD9iFa2sajS2xfA4rUwp92AYBn/9r/4Ff7xmBYIg0B6O8P3d+/n/fOXJAZ8fU03Fl02ABufYZ+95e4AamzGqsO2UZnLpznVGUDHHrOY+wTwnUTAQAV1TCbmc2F1eemSz/T7F0b+QNDDPIaoYfB6Ocf8LX6WuzDMhcuyBIGezNP/yJaaX91f63mwZM5CKts7ei0WLczxcxR9udbGoPM2frdD4iyMOPuu08lcb49RIYX71qzcQgT9dNGdUI+4vMX7QDZ3mjk6mVVdzIaHz0oVmJBT+6JuPIx//NrocRTcEEpqHhOYhpnnB4sE77//ivW27saLxj+dPo9wuTdiYKZ3J8LnTx+pNpeFxTabibiDsff0V7kPBPk7FT69ixjTmbaK6YnHSK9YxfdascXm9gZDJZPj0008Lhc2dgkgyS1csgwBMvSkzPRaLsX///kHFOtGUTFeuOBQFqA25+WTvburr67lw4QJOpxO/308sFiupH+ORI0coKytj2rRpRFMyPfEsumFyy+vHQcU7EH7y3/8rz/7BH+Lz+9n7+iuEUnESbh9CpJd5PjcngzUsvn8dhkGOTqQiCgIBtx2v00o8rfDhy6/giUe53NXLvdPqqfN50IHWaIJtV1p46NnNNEypx2GTCrw2m9UyIZ52o4GsajR3JTAoLmNWVjWudpkFe23IVbKidsR3aJtkQRQF1NwoRxAENv+TP+XHf/c3fHvVUqqDgUGLO4C4Zt45vJLB5fZ2Isk0984cu7nhaG9ILgtMEQ3ioQBJFfxWs4M32IBKNUBDwCIYxDSBgeJb23vDrHh2820t7gD2bf+Q+1y3qrfWVJdzPhKnrW1gg+Wg1YecimDJXKNOkllSPZuDrS6+9Z5ARpe4vyaFJX2ViBpB9FSQSUS4FE9yd2ji3fu/xK241tnF1KpKFB1E0cJ0n5v2WDfv7zjMc4/9H6Kffg/kKD4pjk+Ko2ohmPlP2X/kLAICU3x+orqVWNrs/kjCja62JbfxcYjcYmg8FjgdDmbHezn+yX7uWX3/mI517NgxgElb3AGs3fxVdv3kh6wJSuMSbeYSoQdzA2sYUOHzcuTgvgkt8BwOB4qiTNjrlQKKpvcbzd48FvT5fITDYXbu3ImmaSxbtoxAIIBhGLT2JNixZz+rVq3CYbVQFXAWnj937tx+PNAdO3aU7Jx7e3uJxWIsvPsernYlCjZaNkmkOuAa1pqjVFhSU8mJT/bjsFlZoKbxB3KK2IoQTb1h5m+4tyBW8Q4wsvz8w3d51K1jD1WwZmpFzprJXG+qK9xM88/lww/e5ahmYDhcYJUQHU7uW7GShil1BD32wvFvJzTdoLU31z21WkZd3IFZX+UzaqNJuWQF3qh88LpiabKK1k9k0XS5kT0v/WBYFezJti4+aenG5nEw/e7F+ANBDmx5jT++f3lRJ96eNePAfJJpTTJa5DuAUx0GdtFcFDXMUbJmmHJnjdx/DQDT587IBTfnyzgRcIgaJ2wu1j/5VFG/Symx9fXXmNpxjXlT+o9QG2MJXmm8hm4YGNDP+Nbr9fL0/R6EzFWiF36G++GPefinVmTdwCoKbHk+RcfOb6DXvsie8066u3u4tzzEijIXTos5HnKJJgfyNta3v7FobO9gZnUVPYpArwJpJct7lxsLHpCPb8j54CWb0BzTuZKs4pNPz5DubkYQLGyeOR2n1Y4yzGVkF82O+XB0iNHgWjSGsXId02fNLur5d0Jxl4eu6+z/5c+YYShUB0q7OTIMuJw216gpdgOHBeKpFE1TZ3PPqtUlfa2hMJA322RGWzhFIqNgt1qYUnarGa6u62zdurWgYn3rrbdYv349uuTg9Xe24XK58NpFnBa98PgFCxZQX99//S3l+/Lmm2+y8fEnacsVFaIgUOa143fZJqzBoOs6jS/9PReSGe4pD1B1U+Z0cyRKZ00DKx4283OvXm7k3I7tPPitb2Oz2/n0ow+Z1dOGfxjagqKT45YKKAYkZIUjV1pozygEZs/h8Scep8J/a2E+kch7D0qiwJRyD1KR3cW8B6PdamFqeWk4u0UlWdyMi+fOsfetLdhsNgwBEEQQzBGmIIroBtTOmsXDjz3e7wN4/PBh5P27WFpEJy+lwfVs8TmM+QKv3m668o8Vx1Iyy178xtgPVCQURWHHT37EYoeFqkFyMTvTGfa3d3MmHKPvO+b3+/nGUwuwGRFiUgN/fmIJO65YcAspFE1nZY3Md+dcwKZGeHfvNaLJDEsrylgacvbroEqCWWwPZdVwMxKKytV4ElnXsYkiDV73l557o0RrdzdJVSNiOAn4/VTbDDpTAxf03dEU3fEMEhpuQe4XTSfroBhm11o1hMJmxwDSmtnltuaoCqX0yzvd3cu0536rkIs7Uhw7dgxRFAuh7HcK2ltbady/h/JEhBk3JUaMBW1ZgcRNm97mSJTE3EX94hbHE3dSgZeWVVp6ksDg5sTvv/8+GzZs6McrfOutt5i5aBnHPzvJkw+vw+e0DisY+tWvfsVTTz2FZ4ximx07djBvwSKShskTdNkkqoMT6/UGcGD3xyzpuY5hMCjtIJVJcyqtMv3hx7i6cxv3+d0c7eghW9fA3HDHgFnuI4FqQEQRON3Zy8G2LtZ95TnuWTDrtnD1+qZ6TSlzD2kPMxQMwyh0YwNuGxW+0vgoluROOvuuu5h9178Z9fPuWbaMH73/LsWkrOZvMMVUp0ahKzeyuKXh0BqJkHWOT7jzSGG1WqlcsIjrVy8j9/ZSHwoi0P+Xy1vGPFKvcjWRJCarfHS9g2g0SoZy3F4fKX0W+6/beHhalv/6oMo/327lcKubP7xvOg6lCy35OT50Hiqvwms10ICUZir4VMMsukfiqt+ZzrCvvZuzNxWbAjAv6GPNCP0JvwTUlpcTUaC9rQc1m8HjtOPt4wF5JhwjGo3m/s52BCzM87vYWFvX7z22iXBjqe7/98tH8SkGdMkC1fbSeVgtKA/xyWu/YuW3vsMnW16nPB4m7fYiNszg3j7dp97eXs6cOUM2myWbzVJbW3vHFXcA1bW1VL/wW8jZLAc+2Eog3M0MtxPnGAURQckgoQnEVQhJpvhiasDPoSMHYIIKPDC7O+I45KCWGtGk6VnmdVoH5ayJoniLaOSZZ57hp6++zdTps9CMkfktvvjii3zwwQc88cQTYzpn5dwpzp44TujprxEK+KgN3ZoKNSHo7cFmHXoU6XI4We6A5o/eY67dhoDA0qpy4tFOvEUWd2Des8ttBqtrg8wsD7HlzdeJRTeyYundeBzWCRtRG4YZVQcM6/03HGJpBUXTsYhCSR04StLBGwte+/nPWCNkqfCNfPeu5QqJrG6KJOodo/sVVMPs4AHMchpFjxWvhyO0ODzMWLOe6knk6h6LRvls10f4o700OGx4hmiDv9HUwplwjJqaGl54aiWSBVq1+ltEGJViZ0FFu2CAKDPdMEnekZwIaKjR+VDj4r45qX27S19iaDRnBC51dnNfbRn+m9aZpGIW9GlVp1e3UeV0MscjDmkrNBAymlnkGZSu852Hqmlc7OxibnVloaOVyqQ5Fs+w/vf+AFmWefPNN9m0aROucVKj3k7sfvcdFid7+yltNV2nraeH+gHixMC85uI5HnEeLRlzs5UXWzT2RrCvfYiGGTPH+1cAzCK8sbGRZcuWTcjrFQtN17ncEQeGjhbbvn07GzduvOX7yYxCa9gMsh9p5+bm7mZjYyOSJNHQ0DDi8z7yxitM1xR2O6pZsnwZ0yu9E17gqarK8Z+/xL1lgQl93YFgGKZ4c/vFK/SIVqYuWMgD61aXrAM2FNrDKeIZBVGA6VW+fmlVo0U+Q7jUEXO3fRb23Ne/wc/+3f89qiSLDtks7vIjwdHCgtkpMjAtTkaiwjVyHQ0BgZZwhOsOD7MffZrVVVWjfv3xhs/vZ+1XNgPw+eHDpBvPU5FOMLU8dEtXb011OefCMdra2nj1nYM8/fhaGrwR5GwXyvzvIQkR7EZ3obizCAL3V9/qeyUKUGEz+Yz5fEwMgUpb/wK6M53hlcZrBX7YU4+tQchcRo9fQvTOwnDM4J0P9n3puTcIIvE4XfEEkgCGxbwpKZrB1bhGTVkZngHuU26rxPygv6Bkt4vgEEd/3TgsZlJMTDUj+pwDmOoWC8liYV5Ndb/vuRxOQrkNw0cffcTzzz9/R3SGisH6TU/x8c9/zBqT2AKYUUefuYI0axZ8iQg1gkFZn2in1izEVQGHaGDP/d3LbQYtGXNUe6onjnfV2gkr7gBCoRBHjx6dsNcrFvGcX5xdEodUnA7mged2WPE6rMQzCu2RNFMrPMPe4FVVZefOnSZVorubqVOncuXKFcrKym4Z3WYyGY58sh8hmcCuyKCpZOxO2mNJ5nlsCL1daLpBWtYmxBIlj7PHj6GdOMySSVDcgcn5rrYbPHNXAwlN4NC5U7zZ1cVzz3+FMu/43Tc6c/GhANUB15iKu6yiFYQyXmdpVfa3vcATRRE5EELV9CHJiYZhcL2nl4pQGcmckrXCOjrCd3c8wXlDpCybIuArI66JtMkCHg3KBzmWoqpciMbpDVWB08npkyd5/uvfYHV19a0PnoRYvGwZLFvGjl/+jFpVw3rTOKHS6eDFWVN5+VIzbW1t/J8fvYLf76empgaooK3tDNHoAcAs4obL7fVJIGCYRZ4GStYs8my593Zfezd6rrgrWLboCeTGf2N2C63VbH5qLa+/s5f2Lz33CpAVhStd3YRcTmbX1vT7WbcsIPjBZ7mhQhsIWd1chFxjqJGCkkFMNQuIjM6ou4CjhS6IJBIJHA7HF7a4A3OsSTYL3OjgCQiUedzcv8lMj7jadJmDx4/iS0SZYpdIGB4cIoVrC8y/R9AK53oTZOcsoqa6AcMwJrTLU1ZWNmjnazLATBwwx7O+YRSPVqu18Pm7GRV+BylZLShxK4fJn3300UfJZDLEYrFCh3PBggX89KUfMassiE3Oml9KFqemsjQYNNdrK2CVAIWo3cDAYA4ZrjVdxuecO6EFXuRaM8vLxy+ZpVj4JPBJBg/NrOXNU42cuXiZ1UvuwjIOa0ZW0YjmIsmqAs4xGxMncoWi2y6VnEtZkk+GputjeiOf+ea3+OAf/pbmlIzkr0AXRByihg2VKkPlvqn1/PzEaewVVayRFaSAeYMzuxEjK/Iu9EYQ7lnOusV3oygK+996E3ssTMAXJKGBYghM7TPq1XSd870RYlV1rPrWi4Wbi4JA1R1S3OXx6Y7t3CsZtxR3ecz0efj9eTPYcb2DxliywNfq/5iRZ9F6+xR5aR2uZkz/Q0VTOR7OYGBj1ZoVXEgE+M4HblbWLeR/PLaXf7nTxf7rNt56Dp56bA3/8NKrnAnHeKRe/dJzD9A0Db/71vFkRjc70gPFVPWFnPuxtYjuXR42EbwWiGum2XhNCbl4A6FWl/no7/6Kp//Fvx7X17nd2P3Ln7Eq5Lulw25V5ML/N0yfQcP0GQCcPnGCyMVzBJMxMhZXP/5eyKrTEwiwcPYcoikZWdWoCbrG5WY3EO69914ikQivv/46a9euLbnB71iRyXVMRGHojomu64TD4UGTOiyiSJXfSWs4hT5CppPD4ehXLJ49cZz1VoN6QQW7Bewu+hb5N8PvMdWqDZKdS6c/Jz59BuU+Y8JEFsYkt0nwS/DA3Jm8/tF25s+eMS5dPE3Pe+aCtwQ2LflusmcU6RcjxZjvmnpO/WGTRIIeOy6bNOrdos0TYFdnmvVPPcPCRYsK3/c5rZw5epDXjx/lH/+n/4ooivzv//z/8JTHT0pyoRlmPFNwBOuWT7IQVc15j9Vq5YHnXyCVTPLJO29jiaWp8ftQdLAIOhe6w4TLq1n9O9+9Y7oGiXict//6f9Fw13zq77m3cCO4eO4c9e3X8Pm8Qz6/0unga7MaSCoq56Mx2pIZEKDG6WRuwDvqAssjgU006FYEkppZXFxOZFCQ8Hg8JGLXUK0J7iu3su+KlXU/EkkbGuunJnFlLyHInfj9fqLRKFcTSeYHf7M992xWKzOqKjnT3EJl/Uyy+g2rnnzhNlw3Lf/jjCbgH2FU30AIWA3imvl31QxKqqi9GUGvh9DsOXfMdVgs7IEA0VjvLcpCZZDCYcGSJZATmBzZtxe99Rq+RJTpoSCGoRN0uakJumgPp0jLpglrbcg9YQR0j8eDy+Xi4sWLk67Ay9+gbZJlyMLogw8+4LHHHhvyWG6HdUgO31A4deQwgfMnqfGPXqDntECt3caxT/bj3/jghHDOIFfg3VbW/vAIWMGeiNPaFSboqR7T+HQg2K2WXKqXQdcIOrdDoe94djxUwGMu8DKyVuACpHtTiAIEPXaCbvuQhV7+F5NVjZ54lt/6gz+krLwcn9OKIAhEUzKxtEL9/PuYs2QF4aSMYcBzf/Sn/OC//GceXXg3B5qu89TsKoIjKD6qfV6UMye45HQya958AFxuNw//1tc4c7mVzw4dolXWsFqtrPzt72C1Dvxmq2ppo0RKBY/Xy/z1DzG1pRH1k10c3vcx3iVLySgqVaPwCHJbJe4tD0EJ4iVtItTaDTQDsjp0CCo2ZMq8DvxaE+nL/4l/s+Z1vvqG6bnnFGX+eHGcpr3/L8pmfgWXZ2JzUiczemMxIskUVfUziQ9gtu2z9B/VDYRAXmWpQYVRfDi6QwSbYBaWyZwtx3ihuSeMOHP++L3AJMHqTc9w6shhrrQ0IwCCYWDoOlPuG16ssHTNWsDsOH2y/UMc4S56m8/jeeJJppR7aAubdkfXuhNUBVwDms6WGrt27WLjxo3jkjc8VoxGVjiS8y82NUJTVaxFFh9RFSTJCu3dXGpqIbBw5oSkO9wJBZ7PAo/On8MrP/8ZZf/oT6gJllZpbBEFqgJO2sIpoikZt10qekybypr1hMtW+vEslKDAc9klplV6CSeyxNOyqaaMZ4kkZTwOKyGP/RZuXW8iQ0882+97MxtqKfc6Cn8Ih81CIq2QzKqkZPMrj8e//T22vvk29fcu46PDu/j2fXMZCab4fVz6dB/NDidTp9/w3quuDGF94CFcdom6kHvIY7jdbmKxGD7f7bVFGQhL7l/Dx79sYq3PRxkQPnWUC5pAVyqG+zYqDy2CmSAStIINFTUVJVQxC6XuL/jzY2XEseKwqmRUhb864uC78/89MSVMW/QaGWwYggXD+M00UdYMaGzvQrLZsQZrCsVdpc3AlltrRWFkXDhnLndZNUzPu7EkU7gsIKumAel4rvhHe2OE5bOsWbt23F5jsmDh0mWwtHj1qSiKrHnU7Dhl9+/n9OnTLFiwgCnlbtrDaVKySnskRVaxU+YdegM+VoiiOCmLO6BwI8138gbDYJv8UuHulavY392FKx3FNUTW7c1IqGauu9MCc0MuLh47zIyGOqoCN9b4lmvXqJ8yZcDnf374U5JtrYiGjqhreKbNZN49psjx/JkzGJrGXblJ2vXrrZw+ehRUDWsgQDocAf/kFr0JAtQ5BO6vCPHKz3/J1771jX7vTSngcVgLyRPd8UzRBV46V9eMF4+yJEe1WkQq/U4q/U5iaZnuWAZNN8wuXEouRIrIqk4qqxDLzZzzOw6Pw3rLguNz2vA5bciqRjKjFtqYpmt3Bd/93rdpbuvi/LHR7Vrq3C5OXW7sV+C57FZ64lnSWXVYQvKaNWvYtm0bDz74ILt370YUxclFJi6rANmU/wc9HlYA+IcuWicKDV43AhCNRjEcM0hZq9l/3cbGaVn+20Npk4PXUsY/K5sN8TYSibMISEhWD5fTAh7JJPkP16n6oiAhqxxoCVMTCiDYbGi6OWYttxm3WKGMFA7RpDV0KQK1YvGGxTbRjHSRx3E3f64nzGO//z0+OXCg3/e3b98OQFVVFYsXLx6/E7iDcf/997N9+3aOHz/ON7/5TWpDLrrjGSJJmXAyS1bVqA6Mn0HuZJ10ANhzpG1F09ENY9ARniiKqKo6roXq/ZueZudPfsT6ERZ4aQ3aZfN8/RL4LAbptMrpMxcILFtE+7UrtO3bTYOhsCOWZMM//rPCcy+fP0/3p58wxybidblMjocI508e5dDlS7gTUWptErIOew8fQVENnILAFJ8XAVDa2tF1kYhijkEnM5wWuK/aT6almy2vbeH5r26m3FfawrTM6yCelpFVnVhaxleEArYQM1fKeKA+GBcfPN0wSGdVehNZMsoA8yQg5LEXRYA0cnPvy82tvPurX/LthTOY5h35G/t5OMY93/rOLd9vbI+hGwb1ZW6cw3ga7d+/n2w2y7p169i1axcbNmxAFMVJYfC568NtrMvGbus5DIU3mlo4G45R3VdFe5PnXjkdvP7OXq63dTDNH2BNbU3BmBpMkn+FrbRpCpMNiqpysLmTquo6BMzCzG0x8EljM+eWdbiWMSOtXBaoK1Ikkc554lkFmOYsfZUXTiRon70Ib0UFZ86cwWazkc1mWbBgAeFwmCVLltDS0sIHH3zA7//+75f89e9knDhxgra2Nurq6m4pgGNpmc5IGgNzg10bco1LzFMikWDv3r08/vjjJT92KZBf74fiz6mqyu7du8c9mWPXh9tYk4kMm2iS0eF67tr1WKA6Z0HVmhVoCsdwo1DntFCRS4Q5Fomz7Ld/j872di7u2s4sQ6HcOzAX2zBM4VRUFcjcxIixCqZptpJLugFwW8wpQimCAsYTURV2X+mg3Rvgqy8+X3LRRU88Q28ii00SaagYmuc+EC61RTGAhgrPuFyH47I1EQUBt8OK22ElnlboTWRQVB2b1YLDasHjsBbdkkxlVT47c4HPtrzKHyxdTLVj5J+wZDqNMXPgca7bLhHPKCQyyrAF3v333whHX7t2LS+99BK1tbV0dnbyO7/zOyM+n1Ji76u/RtI0umQVI+i8RY03WbCmupzzkThtbW28/s5e0wdP7sQy6/cR5U58opvXP9hPW1sbVkHgsdoAlU6DVG7xSWjmQpTJmGkK423TcbtwvbubqbW1yLqZ/zoSr8aRwCaambJpHdQxUBvzXVQll9tcykaQbuicEmw8sNTMuMkbwTY1NfHxxx8XrrH6+vpRmcR+0ZFIJHj99dd58MEHB0348Dlt2CQLbb3JAi+vOuAas9XDzfB4PEyZMqUwJp5ssFtF0rKGrGqDFniSJE1IJ9Lr96PEuweN/AKTw9yaNYs7p2j6v+Ybj26LQaXPh9sCFX02bLNtFna+9ANmWAxWBgPAwMVNXDVN6vPFm4BZwPkkc33tu5GOqmaSTVKDq2kBnwQh6+TdbPslWNdQxeHWHv7u//eXfPXb36Y86MPnspWEs+hxWOlNZFGL4InrumkNl5ZVxHHqpI87ScLrtJaU1CsIAsFAgKDHQ7VDGNUH62JW5b771wz4M7vVQjyjkJEH7jgOBofDwebNmwkEArS2tnLs2LEJDz7/5N23WIKCy+2gPh0DJkZRVQwqnQ6+OnMKrzReo72tjX946dU+SRaXiEaPIgCWXJJF3pbFZTFtQDIatMnmYnQ9I1BhM8aV5H+7oAgWsmmZcDxBRdAKg+y8R4uoahZ3MHyc3FCwCOaXZphFnr2E69PJnghrfvvWLvv06dOZ3odaAdz2jvlkgsfjIRgMDmrrkYfDaimILzKKRms4VfREZSgsXLiQrVu3Mn369EmXPiKJIqChaUNfA4FAgN7eXkKh8fN+8/oDyJflQQs8OVfcaYa5OauxG/02VDbhxuP6HdflYv0Qb7uim+PefMdOEsAvDT0l8EumSXo+bCCiQlwTKLMa+CyTkycdsMLyujKmhIJ8+MMfkHW4mLJgIY89vA7/MD6IwyGbm1AO5eE7GERRRMsVebpu3LA5KCHuuNUxmpLxBwKk9dFVzJlsFrl+2qA/dzvMKiGjaPTEM6M6diAQAKC2tpaurq5RPXesOLb7Y+YmwgWSbm15+aTt3uUxM5eTOj/oK3DyWlpaCjFl84M+vnPX9AFjyhwWmOowcFtAx/RC7JaFUSnjJjt0QyejGvQkkkytDNERiQ7/pBEgo5m7b4Ayq/kejgWD3VjGgo5YHN/KtZOWoD/ZsWnTJrZu3WoaJw8BySJSX+Yu3OB6E1mu9ySL6kQMhccee4yPPvqopMcsBRw2c5JklYa+Bc6dO5dDhw6N67n4AwFkRRnwZ4puxnKqueKuzl6ablkmly2dyfF6y6wGDQ6DkHV4CohdNNfgOruZXKQZpujjelYo6VpQSvglmOUR+caS2bwwqx7rpTO88vIbBcPiYpHMqWA9RXbA84WhMk5OEXfcKiqrGoZh4NHSo/qgO+x29Pbrg/7cJlmo8jvpiKbpTWTxOq1FzcTr6+tpamq6pdMwHjhz7CjVLZcJDONxNxlR6XTw7PR6Hqk3c1Kzmo7dItLgcQ/ruWcRTPuVXsUcLYRV06i6ymaUdFR4u9DV04vkcENGxW8BfYwh9GAqZ9tkM0fWY4FQCZrqjtyoN6UJeMfgq5eHrCg0ugOsnffFt0UZL4iiyBNPPMG7777L008/PeRjBUGg0u/EYRXpjGZIySpXuxJU+BzDJjyM5nyczsk3UQi47QSG0Z41NjZy6dKlcecRBgIBupRbR8H5saxqmJupwYo7HXOTFdfNiUZ+46VjFl+6Yf6/aoBmmMKotGZq3+2iSQEphuPvssAU0SCaG/Gmc/zekNUgIE2+bp7ZoTS7lI6GWvZdbeX119/h6WeeJOS1j9ovT1a1fikURZ2TRURWdRRVh9JF0BZwx3XwbJKFaDSKzzb6d2O2oXDh9KlBf+5z2QrcwHyUzWixYMECLly4UNRzRwM5m8U48SlVd2Bx1xf5nNR7yoPMD/pHZagcsuaIxpjK0JbcYnino6q8nFhWwW4RsYljXyg1w1x48zeKyjGMZvsin5yRLNHm82RvhLVfea40B/sNws6dO9myZQsHcmpjl8uF2z1y5bzPZWdqhQe7JKIbBh3RNNd7kiNOZxgKqqpOijF6KpUikxndZObKlSs8+uijo36toXh7TU1N/f7d0tzM0a3v4HT2v59lNGjpc83WOQbv3Ck66DrIukFrVuBKxvxqzphdtTZZoEMW6FEEIiqkcsWdJyeyGouAUxDMEehUh4ErN1XpVszXVSZpNw9Mesr9DbWEutv4/l/+DacbW8nII+dbqppOa28KMD3sHMPw9geD9csOXn+4HRJXLl+mLDh6TkSZ18uVIwdhwcJBHxNw20hlVWIpmTKvvah4H7fbTSQSKYxuxwMH33mT1RVl43b8OwVeCayCQWuOE3ItY8Zn3enii5pQkKwOYOCx27l0vZXp1VVYLKPvKkdVc/duzXU+S0WIdormDlEzSpNLO9vn4dOPPmT5w4+U5Py+6NB1nZdeeokXXngBn89HY2Mjb7/9Nk888cSoj2WTTF5eJCnTEze7eW3hFDXBsQWpx2IxwuEwmUxmwDzXicLWrVsJBoPIssx99903onSN6upqmpubmTp16pCP03WdPXv2oOTGrL29vSxZsoSLFy9y7733Ul1dza5du5BlmalTp7Jt2zZiPd3MNBSqdYWVgQBYb/iqZjRzLJsXVNQMc80qhoAkQsAiIAkGeXs/QQALpgDKkvt/i2BGFTrF4Y3RRwNrbnwcVc1s7LQOzbm12DUx4SmjRoXN4MFpVdyVge0//TF76qby4teep3yYVBDdMLieEylZRIGKMfgC2nIUAXksirchcOcVeHYrDpeLpKKNOiZJ03VS9qH/eG67FbskklV1ehPZoiJg1qxZwyuvvMJXvvIVbEMoo4pFe2sr0zJxBPtvdnxXHg4LTLGbu1c5J76oto+dYzYZYADlPh9lPi+XW9uRLCINVVWjOoaimxeJTxrbbv1mCDkD64RmdgXGWuD5nE7qO1o4d/JkwWj1SwwOURSpr68nk8ng8/mYOXMmDQ0N7Nq1q6jNpSAIBD127FYL13uTpLIqLT1JaoOuokjkAKFQiOeee45t27ZRUVHB0pwyeiKxdetWHnnkkYI5/Wuvvcbzzz9f+Pm+ffvIZrMkEgmeeeaZwvenTJnCiRMnhi3wVFXl+vXrbN68GYfDQXt7O93d3WzatInDhw9z8uRJ1q9fXyhwFyxYQCIep+X1X1FVFux3rIzev7irtQ9PO8nqIIlQZdNvu+DML4FLvJFD3po1x8bF+naONzwSzHDD5sVzaeyN8YO//T/84T/5IwLuwSeEPfEMsqojCgL1Ze4x2ZvkowPlQezkxoo7rs9hEQUCgQDpbHbU/vkneiKs++rXhn1cKKcmS6QHJr6OBM8//zxbtmwZF5n95Y8/os7/ZXHXF1YR6vuMCVqzAmEFEorK6d4ox7vDnO6NkhiA6zIZkV/T81MyAYGZtTXUBINcbG2lo7dnRMdRDUjlNofWceDE5Me0MbU0QpeEqlI7zA31S9zAo48+yv79+wvjR0mS2LhxI8uWFZ+I4bJL1Je5EQWBrKJxrTuBrBZ/AxJFkccff5yysjLeeustEolE0ccaDU6dOsX27duZPXt2v+QhTdPYvn07ra2tpFLmmG3Dhg3MnDmT5uZmwOzKffDBB6xbt+6W48pyf/pOZ6eZm93R0QGYnb+FC80p0bJly3j00Udv6V56vF6SM+aQzKQL31NuskIZSXEH5Dr9pe3IjQX5bp7PYm5QO3Pj4ckqhLMIUGU3uKvcy9KAm1//6tVBP++GYRDLCTOqAs4xe9flO3iqbqCNUjg6EkzSunpoKIqCaBFHpRVti8aoeuDhEfFBXHYJAfNNzypaUQHdoijywgsv8Oqrr/LCCy+UjIdy7uTnzBHGp9q/02ERoNZm0KkIXE3K7GqN0BKLYkUufFYEYF7Qx5rq8oIFy2REnkt48wJvs9mYXVtLPJnkwvVW5tTVDnqMvgo8SWBcOppeC/QIplVKQjNH5sVCURW6ymuYM4rNiyAInD9/nrlzRxZX+EXEM888U+hKlWqdcdokppS7ae3N59gmqStzF527CjdsbrZv304gEBhTEToc9u/fj9frHTBl6MUXX0TXdU6dOsWxY8d47DEz3m3hwoVs376dU6dOYbVabxGpxGIxduzYgSzLbNiwAZvNxp49e6iurmbTpk2jPkd3MIRy3eTkqYZ5rQ5mhTIYtJyAAkprVTRWCLmiSVIEehXoVSCuCgSHsVOJqzeiLScaISvMLg/y+ckLRJJZKv23esyY6SfmfaRY5WxfWESxMDHsjmVKHqk2SWr+kcMwDBRFQRrG9ftmxAxomDFzRI8VBeFGZT0G8qMoimzevJlXX311WNuCkSJx/DBBz632IV/ChCBAIhPn/StXaIolkZGwecuoq6vH7/djAGfDMX54ronG2MR0EkaLTtksygTMxf6Wn/f24HW7sdoGX2BUwxSdKLnirn6EN4zRQhQgkFPQdiljI1afjiRY9fiTo3rOQw89RDabZdu2bSW7xu40iKLIV77yFbZs2VLS45q8PHdBfHG9J0EyW/xUI4+NGzdSWVnJ22+/TSxW+tSdbdu2UVtbO2SMnSiKLF68mE2bNvWz5CkrK+PcuXNs3LixX9ft1KlTHDhwgGeffZYXX3yRgwcPcuzYMTZt2lT02Ln7+BECHg+GAW25a3W0PNkC347Jp1oF036lKpc6pOTsVK4NIsBIaabtVWtWIHObehh+yaDcKtLWOfCERLKI1IXc1IRKV4jlOX+xtEJbOFUScVMe4xJVNp7QDYP39xxBP3qAR2fVjPhCOBdLsOAb3x7x61zrTpBRzLzGsRo1ZzIZ3n77bb761a+O6Tif7tzB/J5WnLeRrDzZ0ZnO8MNzTWiGQVlVLfetWArZ6/i0JsoqpmM4ZvDOB/toa2vDIgh8567pk6qT1ykLRHNT5MoBuCtXOzrxOe2EEymsVokpFRVDHseWu2GMU9QhYI6Rr2VNkYs9X0yO4vVOdnYjKTL6fatYvGx5UeeQSCTYsWMHq1atGhGB/ouI3t5eTp06NeBYcSzQdIPW3mQhdtIiCnidVoJue9HcvDx27NiB2+1m5cqVYz5PXdfZsmUL69evH9bseSj0NTY+duwYPT091NbWljyRo6X5Ktd3bafG4SaFFRFTjTqaa1U1oClt3gRnOCdvooRmmN25XtXsUko525f8WDmummtWPoatpsgIxbEircFPjp7j8X/0T5hRNXE0qGhKpjNqjut9TmvJOnl3XAdP1w0ivb24HfZRjWiNUXb88sqxeFpmrDWww+Fg06ZNvP7662M6jtDW8mVxNwz2tXejGwY1NTV87Zk1OIJ1qNYgVy9spzUbImatZvNTa6mpqUE3DPa3d9/uUy4gozFkcWfC4FpXDw1VlYMWd3CDlxOyjm9xB2bnoNJq0CnDxc4wXcrIr8x0JoM6ZwHTvvF7RRd3YKY4PPPMMxw/frzoY5QSl86e4egvfsyZn7/E+Z/8Axd+8Lfs+vlPxrXLGAqFCkrOUsIiCtT1MUXWdINIUqalJzlm3tCGDRuorKzkr//6r+nt7S36OIlEgtdee42nnnpqTMUdUCju9u/fj9PpZOPGjeMSt1Y/tYEFL/4O27uTGEB1ERsxSbghbgqP4rqbaFj62KlYBbMwvZoRuJIWuJwWaM8Vd26LGcN2uxBRBRI2J/YJNlr3u2zUBs2iLj4G7v/NuOM4eBlFo/1KE6vqykY1ctJHyU3xOK2kZJVk1rQLqB6jXYDL5WLjxo1s2bKFZ599tqhjZEMVqNkYUhFWGb8JSCgqZ8MxDOCpx9bQTRVfey/E8jILm+f/D/7HcRcnIm62vFDFU4+t4R9eepUz4RiP1Kuj8t8bL2Rz65rLwqCqs4aqKowqg88uNbJk1qwBH6MaN9IlJoJ4ncmNViLdHWD30tgTxVLup8xqDDs2akxlWbq2NB2nVFYlLhskM0rJs1VvRiadRtd1XAP4zR3/ZD9ll8+xxO/r9/3phs6RH3+f+keeoLZ+yrid29WmJtKJREmVyGLOFLnc5yCdVemMplE0nes9SerKPFiKmP+//vrrLFy4kFOnTvEnf/In7Nq1C4/Hw4oVKwZ9zqlTp2hra+vHNRRyH7KxTkj64tChQwQCAebNm1eyY94MRdNp6YxSbREIScVzZIOSQVuuY++TJo/YYiBIgimG68zl2ebzb0XMAjAkDb9mjBeiKuy4cIUlDzxImW9kjRTDMDc7qawKgml47HfZCp/JUaHPUwzDKO4YN+H239VGiYysYclmcFgEGIWO1hjlm+V32dB1g+54hmQJ7AIAfD4f69ev5+233x7WZf5mfPz9v6PaaiHlsOMbhYnpbxKuxs2dsN/vR8hcxqUnuL9uCTuu+DnRqqNrGe6ri2FJXkbQu/D7/USjUa4mkswPTh5V8nANYwEBq9WKgTFgLF17TolnFwfm8JUKumHy7q5FEsTSGWZXhgg6JC73apzv7KW+LHTL6+d/NYsANjSiwYG7kIZhoOYIRgJmF2moBa8zmub85WYkh4/WcAq/y0a5zzGmTdlgaGm+SudHH1CGTtxqJ+10kXa4mL9iFRc+PcjMaBflNxV3AKIgcl9FiCs7P+D4rPncs/r+kpyPqqoc2v0x1lgEW0szzrYmLAgcj8dK9hp5iIKA22GlThK51p0kq+q09poCjNG81/v372fDhg20tLSwefNmgMK/d+zYQTabHdDP79q1a+OeLHHq1Ck8Hs+4dO36ojOS5vDe3Szz2CmzFt+18kjgzGVMt8vCuPFtSwUpRxvJb0RFzHXqdnIIUxocaukhU13L8nsWjkhMpBsGHZF0Ic0CzE1mJClTG3KNWmGbD1fwFVsgDoBJXOsPDFEEWddIawbD5ET3gz7KES1A0GPvZxdwvTc56mPcjFAoxMqVK9m6deuIn5NJp5kqCcwuC31Z3A0BOTcu8nq96PFLyJ9+h//+UAqbxUIGGxaLjX+8NEPnwX+NnmjE6zVTQLLj5CI+WrhEs5gx47+GfuxdU+o5c6UZ46ZNTlIzny9ipnyMF/Rc9NmZ9m4EBBZUlzPTK1Fhg3nlHkJeD1e7e7nQFen3dbErzPmuMGe7Inx4sYXKJasKxH1NN+hNZLjaFedSe4wrnXGudMZp6oxzuSNOZzRNOuc2rxtmp64nnqG5K040JVNdXU08FqapqYloSqa5K1F4fKlw5thR5D0fcXdFiPqKcuYFvNxrt7DayCBvfYOFqTDl3qHTZaYFA0xpvsie114eM/0DIBGPY1w4zX2SweppUyjz+6nx+6i5cp4jH+8c8/EHgk2y5Iq63FRllOTwRCJBIBAo2InkUV9fz4YNG1iwYAGHDx/u97PDhw9zzz33lOT8h0JjY+O4F3fJjEJKVln50MP0VFZyaQzjaTDHuxbhRryZfgcw66WcWtYxhKp2IqAb0BSXOZHI8MxzXyHoGTolS9V0YmmZa90JkhkFw4Ayr51yrwOLKJid2Z7kqKyFZFUzu4CYYQtD4Vr3yMWBd5zIQlY1jp5u5OOXf8XXlsynznGrlcRA+Cyrce/zv1X0a17rTqAbUBN0lUQe3d3dXVjA1q5diyenjNV1nWvNzTSdP4ekqkiaQqqzg7WVQcQiitTfJJzujbLlynX8fj+/9cQsFFslf35iCTuuWPCQQNZ0VtSo/MHcC9Taw7zz0SWi0SjPTa+bNB28LtmME8oHeg8FVVO50NLKvIYphU5eW1YgoUFAMp3axwNZHdqzBudbO5lSXkaDR7rF1kAzzGIzvwm7+RJVDYGYZnCqJ4Fj3gLmzrsLwzBuuTEN1KcXBQZ8XGXAic9p4+DhY1y+3sWyFSZx32WXsFpEJIuARRSxSSIOq2XUu+RPt3/I1O7rVJRIxa6oKieiSeY89SyhsuJTaVqar6Lt/pD6AcyNexMJmsrrWL5xfNJB0rLK9R6zc2529yRskohNsmCTRCyieMv4trGxEVmWhx1/Hjt2jHA4DJjTj66urqJSOkaK7u5uPvnkE2bPnj2uo1mgYCQdcNuo8DlJxOMce+dN5gkqoSI/X30TMGyCGcf1RTB7Hyl0w1yb8g4EDotZRA6HlAY/PXaejd/7Y2bXBgddF2IpmUhKJtvHlFgUoDrgKlBCVE3nem+yYIQc8thx2G5cC4OhI5IillZw2yVqQ4M3cTRd53JHnNk1I7tf3XEFHpg7xhPnLrP/5Zf52pK7qBqB4uaEYnDfc8VzNLpjGcLJLKIA0yq9RUWYDQRd1/m7v/s7qpNRFldVYNEU/HY7fp9vXMZLX2QkFJW/PHkBA/iDb79AzFrNM2+EWFUZ40+WxPirY26Odjr5iwc6KaOXd954CwH400VzJgUHD8yC6EraXKTrRhDzo6gql1rbmDd1CqncAg9mcViq8Ww61xUUMMPKO9MKLT29zKiupM4OziJvIpoOx+MC1xNZut1e7l65giq/i6DHjtsuFegQhmGQklXiaYVkRikUd5Io4LRLOKwWPA5rP/pEV3cP723/mLuXrixsnvpCAFMJ6rEPO0rRdZ09r/yKJYKK11VanyqAS70R9LuXMm9Jcd2pgz/5IctCt46E84gmk5zzlrH6yaeKPcUhkcwqdEUzg+ZpWkQBq0VEEEx6wd69u3nk4Yfwu2wjXkd7e3txOBy4xuH9379/P6lUimAwyL333jvu2bn5mzRAQ4Wn3+fv3OefkTx6iAUBL9YiiP4Zzeys53007aIpWhhPqsbthqKb6ty42n8zKMCIUjSiKvz8VBOb/+RPqBuiuMrXAGBmyHqdVgLuWz/Dmm5yU7M3xY+JgoDDZsFtl/C5bIX7u6rpNHWan4f6MjfOIXJtDcMgo2hDPqYv7sgCD8xZ9xvvbqeq9SrrG8qGlYcfVwWWPvv80A8aArph5Bzddcq9jmHbuCPFgQMHCAQCiKqC87PD1A3A3fkSI8cbTS2cDceorqlh81Nr6TIqiXSeJ/35f6Bq+Z+je2aRDrewc89xMt3NLAj6eHZ6/e0+7X7Id+HKrQbBETSLFUXhUms7rsqpyIYp0KgsUfdO1uFsOEsknUHAdFuXRJEZFUGqbMaIdsiDIaaa4oyECnI2y2Wrk0eefpxK/+DxgIZhIKs6gsCIOC4//Ku/ZtHK1cy4awGarqPqBhlZ6zdO9DqsVPidAwoFUskkR379c5aHfEXdcEeK7kSCRm+I1ZueGf7BffDpR9uZF27D5Rg6UjGeSnHK5mHNs5vHcpqDwjAMUlmVjKKhqDqyqiGrOgZw8ODBfhnKgUCA2bNnIwrgd9kLhWHAbRvxjauU2LFjBxs2bJiw10vLJqcbzO5yhc/R77Os6zqfvPs2c1MRQkVQcjTDVNRGVdME2SJAg2PyWqgUC9WAXkUg1qewkwTTS1AzQM59s3aY2Mq4Cj87foFHv/eHzK4NDdrBk1WNtKz123wOBsMwiKZkkhkVWdUKfOI8JFGg3OfEZZfojqWJpRUcVjMPupS4Yws8gIOnm7j4xiu8sHjGsPLyo7qF5c88N6bXCyeydMfNSCDfCHf/w2H79u0Ft/WThz+l7MIpqnxDc3i+xODo64NXU1PDAw+soqf3KkLmGlOrpyC4pvPzdw7R2d2DW8jyvbsaJpUPHphh3WF1dH5QPWmVzzqjTKssL+li3pEVON3Zw7TyMpy5a8xjMfCMkTej6KYRs2qYhaxFgCsJmUsWB+ueMIu8sRKNj+3ZzZTrl1F0g6tl1ax67MZ4Ly2rhBNZkjnei00SqQ25sfZZuPNiisUVwQHFLKVGRs5yPClz7+YXB1Tn3oxIOEznW68wsyw0ouOnMhmOGRLrRxDXWCrohsEH23ewdu16ky9qmFzLeFq+pcMBZjpAKJeHO1HouwZPBPo2C8Ac89WVefoR+3Vd58pL/5uGMXg6aga0ZMx87pCVMQk5JhsyOa5hngLiskCZZODo87Hp6wU61TG4Olc34FxM5YOOMN/9w+8MmUNbLHTDQFY0UrJKNCnfUvAB1IZcuO2lVf9PjrnUKKHpOtd6khw5c5k5Ps+Ibmaj9cEbCD6XjVhaRlZ1YmmFWFqhwucY0wei78520bLlnHd7uHT1CjZVwaLISKqCVVWQcl9eyUJ5MDjEEX8zkVBUrsaTyLrOisoQBzt6aG1r5we/eh+3x0u518HBE5eIRo+SxYYoWNnUUD/pijsw813DqkBmhNoPzYAYViyiSEAqTXGnGqYa93o4it1iodo2/Lh4pNCNGxFqdtHsOIoCTPfYEBJZdr+7lbVPPkF1oPgi79iBA1RfaySU2yx54t3s+tmPWf+NbyGKIk6bhDMkkZFVWsMpZFWnuSuBw2YhGoly8eA+ZslxFpeHJqC0M+Gw2Vlls3Pm1V/gXrWOGXPvuuUxe197hYBibjLJpJlfPrLiDsDlcLBUzrLr5z/BXlNHqrWFh3Lvx3hBFATsFuEWs/iA20YsrZBIK9isYq7oU0hkzC+71YLfZcPjsBZlwTKZIQoCDRVesopGRzRtCvh6Ekyt8BY2GGdPn6beMzZBnUWAoNWgQxZIaVA2vs5BEwbDMDee+Vi3cuvAa1OZ1SChmQXu9axAubV/AZiHKECdWyIQ7yUe6SXgrin5OZvjWQmHTSLgthNOZImmZLRcoVfudZS8uIM7sIMnqxrXe5LE0gqvv/oGv9cQYMpN0wlN12nu6SXpcJERLdTIaa6HKlk1yvHHQMiPIqIpubD7rwu5cdmLq5V37tzJQw89NOLHtzQ3c+3jj1jstuGwlX6ncaehM51hX3t3wf8uDwEQLS6iGojoOMmYcT7AVH+IuaEyZnmshCbhohdRTPuRkQgt4MZOtaenm3trg9jGmC4A0JwRONfaQW3QzxSvfVgey2iQ//0kAaY4+o95EypcSSicw8qaJ5+gpkj/yUw6zaF33qQhE2dKbkOkGzpHu8JMe/xpqmtvZPgqmmn10Xy1mc5Tn1OuZApddEkwfcb80sQq/dpjca5X1hfEEbqus/tnP2aFx47dNrTKbjgoqgoYiKKFA5qF9c8VT10ZCbZt28bGjRuHLSQzikY4ke1nOyEAFX5nwWS51Dh79iwul4uGhoZxOf5w0HQzAi6r6oQ8dsq85oZzz/tbuV8du2tDRodrGQGLYCZdfBEQzaVejGT0nNJMykt+r+y2QI1t4G6eYRj8w4nT/PG//ffjct4DQdPNcxkvvv0dVeBlFY2WnmSBP3P8xGniRz7mt++ejUXIKcasLmR/gJUPbihkDHa2t5NMJJg+iDFsscgrX1x2aUhy5s1QVZXdu3ejqiozZsxg9uzZo37tT957h6nhTmp+gzl7jbEErzReQzeMgv+d1+slHo/TFU0Sx41N0Hio3EGl04rdItLgcZM2rIRVCEqm0myyoUM2eSWenKv7cA2M9qxAXAOvIaMneqmtqhrT66c1ON4eJeTxMMNrKblxav58B3v/Uxo0xRXOGBbWPPkktaHReaz1RWd7O5d2bWemoRTsSxrDEZQF97DgvhsZoppusP9nP2G+140kQFYXiPVRATtzAfATyWNKZtIcz+iseP5FDv3qZ6wM+Upuct4SiWLf8ES/greUSKVSbN++nWeeGfnmWtV04mmFaEou8PMqfA68TlvJu3kTPZ4dCHnyvt9lK/BPP3nnLVYgj/nYWd3crEkCTP+CFHjNGTMWccQc5T4iDNswm+bPrjYTnbOQBx95tIRnfPtwx2hr+hZ3dkmkJuhkwcK7KF+xjjdOnASgybCw/PkXWbPx0X4B0pXV1SUv7sBMuwBzQRoN3nnnHdavX8+jjz5aVHEHsPrJp1BWruN4d++Y44LuRHSmM7zSeA3NMKiuqeH3vvk0v/X4DB5fEucrD89gwyMb8AbK0AyBY9091LmdzA/6J41adig4RHMBSmjQmIvyac2aXbqBvB8lwbyQXXYrcgn2awag6joOmzQurvj23O+XHMQmymWB6V4r8wWN/e++x/WeZGGUMVpUVlez+mu/zUX9xi8yMxig/PxJ9r/7duF7509+zgKbecPw5grP6Q6DSpuBiKki7pYndlTodji5P+Ci8Wc/4P6KwLgk2NQH/Fze+WHJj5vHp59+yqxRrr2SRSTosTOt0lvo3HXFMlzuiNEVS4/pfCbjWplvWPQtXq3K2Is7GE0UwJ2BtGYWrQJmasdIYM0piac6DMqH4SHe3TCVw1vf4/r164C58RuNv+Nkwx3RwVM0nWvdCTTdwG61UBdyE0/LdMUyWC0i7ZfPcej1V7j3ua8xb+F8vA4rdqsFwzDQdANRFMalBZrMKLSGU9glkakVIxdGdHZ20tzczNKlS4d/8DDQdZ29r73MfC1TtH/SnYi+atnnHrub6KffQ5ejaIZIu1xNL1Nwz/ljznx+kXhnC/P7qGXzHaSR7gAnGoZh7jhjKgW7gzxETF5N8KaRYf4qvtLVyfQxELPBLCKPdSaxSRbmBx0lz7LVcgHpBuaIZbAiMqPB5YTKKVXg/iefpK7MXZQ9USqZpPWVnzG9vL/XXDqT4WhKYdVvfYPjb7zCUs/AfMy0ZgpCBKDePjCP505GNJWiZca8MWUBD4XTp0/T09PDunWjj6QzDINwMksspfTr5jlsEtacr+FIjhFNyUSSMidPn+HalctMr6tAFEDJZti0adOoz6uU6I5liKVlgm57wZ3h1JHDZC5fxJNKUOty4HYOrZIeDHHVTLcYKd1jskHRb8SZZXUIqyb3zieVPrM2mkxyVheZ/9hT/OTlLVRWV2N1uKmurmb61Dpqgu47jg866Qs8Tde51p1E0XRskkh9LvdQ0XSudsYLO5SzFxr5eOeHfPf3v4skmcamqqYXfu6ySwTctpISGWVV42qX6So9nH9NX1y8eJHz58+XdGE5c+wo+mdHuKt8YhR/txN9/e5+75tPIx//NrocBaBTriCsBkjqHiocWRpW/Ed+/ut3+vndtWQE0rqZ9OCd5A09zTAXuZQOcVUoSP8doin/v3lkeOF6K7Prasb8GWjJCFzo7OHemhCBEhfBig7nkwIBq0GNnSHHnlndLPJOygarnnyKKeWjL/J2v/Ea91v1Ad8TA4OjbZ0E0ZlZMzi5Om9dI2CStyfjxqBYtMdiqKseoGHGzHE5fiqV4uDBg6PiGg+Evi4GeZR57YQGKcxhYE+y3bt3s379esB0Q6gKlN5br5TY+aN/YH1FccK6PD/Xikoyk0DWdWyiSIPXjWcSTzN0A3oU0/T9ZthF0yO0VHQJA4Nz3WH0BUtYvHwFzd0JsorGufMXsUoiTU1NSJKFZ554dEgLp8mIyfsXJndx9qZQNB1JFKgN3aigrRaRSr+TjqjZsl80bxYt5z/D67KTlrVbTDdTWZVUVkUSBWySpVDwjcWKIX+cVFalK5oesovX29vL0aNHAZg2bVrJd43z772P1Ny7OLjlVSqUDLLDRTQSYVV96RVBtxt9M2et6jUyueIupnqJax7imo+gtRcPYdy03JI5mx9z3gm+UBYBLLk4n5DVIJ4jGGd0k6tXZeu/0M2oruT8tevcNWVs3n5ui4HPYedqJIWv3FXSbMtORcBuMUPiLcLQ+0u7CDM9EkJC5eB77yBueuqWcPsTx0/Qfu06omRh3YYHcdj7i48C8QhCaGDndwGBpTVVXO3spDcWI+QbmNNaZTMwcgHp3YqAVTDwTOrVc2TQdJ1Ldi/rx1jcXb16lXPnzqEoCg888EA/c+ndu3fz6KNj5zQF3DYyitZPhNETzyKrOtWDFGlt4RTZXKpAmde0X7H32YjH0gp2a3ZcrDFKhephrpGh0JnO8klXlNZYGIkb1ZIAzAv6WFNdPqmcBLSct128D//VJlDI1nZbDLyWkaVXjQTRVIpTmsjKr/8utty6EfLY6YymeWjV3ThtEumli/ir7/+UaCo7bvnW44VJuUQZhkEkKdObyKAbpsKkrqy/RxWYtiX5bp7XacNphfoyD5quk1E0JFHEbrUgqxqRpEwsZfrPqLJKKvc1GnHEQKjwObjaZaqgDMPoVzCqqsqePXvQNA2fzzfuZF6X282ab/5u4d/HXv75uL7e7UK/zNnEZQDSuoOIFkAxbHgtMdxiiqAUQU824fV6iUajhczZPPXrTijwboZXAkkwuJ41i42URr8upGSRmFFdxYWWVubUF0+c90tQ5vXQ1NlNWHWXzEMrrprnLACVIzymTYQZHgkSKp+88xb3P/0MaqKXU/sPYI0nCdpsTHM7UTWD3T/6GUooyOPPfwWLxcKBXTtZ4BxegdlQWUlHuJe2nl5qBvCVE3MB6fkouQ5ZQBINHHcMi3lgtHR3c99XvzWmY5w/f56Ojg4effRRdF3nnXfe4ZlnnuH06dNcvXrVNDUugRWLIAjUBF1oulmwxdIKndE08bSC1ZIpKFDzSMsqaVlDAKaUu7FJFvbs2cMzD6/B5/MRSWbpimXoimWwWy23xWR5OBw5eIC57uK6Ro2xBC9f7kQ1ROwY/URo0WiUs+EY5yNxvjpzCjN9t5/eE1dNdX2+sJMEk0YznlOWK0is+/o3+n3P47DiskuFQs7tsLJ2zWree28rX3/h2TuqizfpPtH54Op8B84uiVQGXIMaCrv75MIaBbKqiLtPNotNslDpd1Luc5CRNWRVozuWIZVViaVkfGOQ4FstIqIgoBumj1PfY7388su88MIL2MZoa1AsnJk0uCbvzrRY2HI3i3g8juiZgaJL9Cgmv8olJkEQkARztyq6pxOPm0WgvRB9ZR7nDqzvADMarM5uEFOFARc/m9XKlPIgja1tzKwtroMr5hbXsNvJ1XCCQIV7zAWxZpjdLzC7kaPh9tlEaHBZuNAZ4b2f/Io6t53ZQTf0yRCWLAINoQCKpvH63/2A6oCdeZKBe4Tc1KpgiEg8TlN7B9OrB1Yil1sNZEMo2C9U3uF5n1MrK/h467s8/GLx5scnTpzgxRdfBEAURebPn8+WLVtYuHDhuGTH5kf0eQFGZzRNb8Ls5OVNkvNrPJhiOJtkQdd1EokEvlyXNuC2k5E14hmFnniG+rLbX+TcjHuXr+DImc+4b5QcvLwIzTCslJVXsnHNQmodbeiJy+aaKU3h/R2HaWtr45XGa3znrum3tZOXN3eHGzm6LnH8rYnUQazGbu7SLZk3i86uHr7/45/ze9/6OjXBsTWGJgqTZv+paDqRZJaW7gSKpmMRBSr9TqaU93f4HgtEQciNZu2EvOYfNpIam1pJEASCnhsLTbLP+KC6uvq2FXe9PT34mXyKsVKgwetGAKLRKCmhni5moCNiE2ScojmyF9ERbQEUaQrRaBQBaMgZh95BHfZB4bQwZAaz0+GkKuDnSmdn0a/hsYAiK9htNooUsfZDr2KaG9sE0yJlNNB0nf1X22koL2NmeRkOp4fUICpcq0VkYUUZmZ4YgVFGPQW8Xqr9Ps41XzOTF26CIJjcTZtgCmBaswLNGYH0IOcy2SEgcC8yH//yZ+hFKkyXLl3KoUOHCv+ePXs2zz77bNEOAaOB32WjLLeWJzIKzd0JGtujXO1KkMmFwgdz49ddu3bdIvQo85lFTVrWyMgDEL5uM0RRpPrBR2iJREf1vH3t3eiGwZTqch5aPYfo5/+OpoP/gci5v6X38D+n+/A/YuWqBbgrppAxLOxt6x6n32BoqIa5UcoXd0HJFIO4x5iUMxR6kynOxBIcy2gEZs8d0XM8Disb1ixn9erVbP1wJ7J6Z1zwk6LAS8sqVzvjdMbSGIDTZqGhwpTIjzWuaDDkd39ZRRvzHyvoNsPRDaA1nCosLLcLmXSaz954hcrQFzPxwmOVmBf0YSDw+vYTuBb8W+w2OxXWLnTMzUBKrMZ+z//m/R2HEYD5Qd8tFimTWl1UAnhcLoIuFy1dxS3eaR2yuo7TbhtT5iyYhqt5wnTFIEajQ+FcWzf1ldW4bCJei/mXyyvqBoIoQH1lNXuaOkZ9rk6nk9l1tZy6fHXAn1sEqHeYSmYRUwhyPWdjcyfC73Kx2m3jyI//gZarA//OQ2HmzJmoqsrVIp5bCoQ8DqaUe/Dkpjn5zYjTZmZ75mPPFi1axI4dO/o912oR8eaed703SXSMG/7xwNTpM+iobSCZGZlFTEJRC8bvTz28FNv5f4muJkjpLtrkGtqUGnrTFnpO/CdWLFtAFhtHIgpXUxryBN26DMPs2l1N3xAvVdoMyotYG0YDVdM4Y3Oz6BvfZtkLv8WchYtG/Fy/y8as6Q10dXXd9nv8SHHbR7SyqtHaa5LmLaJIpc+Bx2EtqrCrqanhRz/6EcAtz3/wwQeZNm1a4d8WUSwIJOJphTJv8V3CPDekNZwilVXpiWWoK3OTSCSKPmaxaLl6lY4d77O2uuwLraZdU13OZ5Esnd097D5wnuc3/lccegu26BW6jWlkxSp++cEZUt1tSILA/dXlheeKmDy8UnSlJjv8HpOT2tbTQ01Z2fBP6IOEJmAgjnk3bRimMATAa6GoyDMNoZDQ4ZfMglHJKe0qB7FLEC0i5cFyjrd0cU99xahez2KxYB2Ck2XJjZGCVuiSTVJ4Zy4SKmQ1sA+wdZZ1s4tpFyefCtciitxXUUbznu0cnTqL+9Y/MOLnqqpKc3MzK1asGL8THAYOqyXHzzPQdB2LKN5iaVFZWcmDDz5YoM7keYEVfieKZvK2O6NpYikZj8OKopm8arfDitNmfmh1g1u44BOB5Rs28tmP/p6FjuFHtTeL0Fx6G1VWGxE1QNawI2BgE2RcRhiPrQOfx00skeR0LEPK48FhMQUNdhF8ltFRKUaKTsW0gQLTRHywGLFS43RvlLW/+92in2+1iEiShDbYznKS4bYXeF0xU0hht1qoLyvesR7g8ccfH/RnP/zhD/nOd77T73seh7Wgri0buY3dgBAEoSC4SGRkfvDzrXz16Yl1w/7800O4z33OkorR3cjvRATsDh6or2N7Swfxruv85JfXCiTiWOwy7bGzGIhYBTsvzqjqxy8RBcDgCzrAvhUhn4+uSITOSJjKwMi7unLuDXKJY1vMUpppjSJQfHJIX7NRQTCtSjpk09E+rjIoEdtutxJJ2Ymn0nhdI+cxXW5vZ+YIEkEsAlTbDayKQK9imlMnNDNGySGa42gwu6H5bOGULuCXhk8ouR2YGvATbL/K0V/8GLsgYFWyXJE1Hv3uHw34eFmW2bJlCy+++OK4ZtqOFBZRwCIOXin4fD42b97Mq6++yjPPPIPD4cAiCtSXuYkkZXriGTKK1q9DE0sr/Y4hiQI1IXfJqEMjwZG9e7jLOzK6wUAiNLsoU2XrxDD6b9bcWhO1AQ9yooeYItAhC5TZDFTDNCIPKwJuy+CblmIQVSkUdxNpVRVJJHDcs2xMn1MpV9xP1IhW0XTSWRWb1YJFFEhnVSIpmanlI+OL3vYCL5PrCVfeBvlxfoc32iSKwWCTLFw6dYz2SAokOw7XxBExP3nvHWZGuykPBibsNW8nwqpAvdvJ12fUcLG3kzPhGNFolGjU5KrYsVDpCVDlctCYMkgpUWb5Te+n/OX9m9DBy6MiEKAj3EtXOExFcGRFngEIhj7mPnBEM7Mg/Raj6FFvhcNKRyqD12UW6lYR/JJBRBWIqgIOcfBOQ1XQz8meLlaPsMDrjUXxORxYrSNvs5VZTd5Qr2Ly8bTcDfLmNFGPxTSqnozFXR5el4slhX+5iOoDFzKyLPPmm2/264bdCZAkiRdeeIEtW7bw4IMPEgqFclxqOx6nlVhKJqtoWEQBQRBIZhTUPouFqhu09iYJeewkMyoGBmVex7iqcMUrl5A8Tradb2RVXSU+z+AdiZtFaH1x8y02L0KT0CmzGdTYDaptBgim72Zav7Fp8VrAJxlFdeDzyOfIgnnNTKQP6VldZM09947pGDZJxOv10tnVM+7+iWlZ5XpPckxUotte4OWVr+JtWPFiOb5FXyXuQNB1nQMHDhCNRgdVhe3du5d4PM6aNWvoTsOBg4cmrIA4uHMHi1IRXN7JpwIbD+S7NgAzPTYW+ut5pF7laiJJVtPJaBrXEinORsM0J+wYCAgYuGhlQdDLnFAldqv9lpSILzqqgiGiiTiX2ttxSxI15eW3PMbAoLmj0xQ6uUM4rBayhoCnyGVGMygIEIodS+qGQWdGQbX2J7l5pTxP0BzVVg3B37HYHHTG4lT6zBtjQlG5Gk8OaPxqtVhoT8ZNU+NAYMTnmTefNgzznLIGyHrOtzPnmzdWLuPtQG90YIL/8ePHeeSRR+6o4i4PURTZvHkz7777LkuWLKG+3vSNtFrEW+xW8DsL9yndMLjSaaYqdcVumC63hVNMq/SOS5Mik06T6O3hU+dU7LPm4BaUIR/fV4SmSFMQbf6CEXxf3BChHQegyulEFMzrShTMDVRGN7t4CQ3iGsQ1M9c2X+yNJsow1qe4C0oQmkCawuWeMAufeWHMx7FJIosXL+bg4SMsmj2lBGc2MPoWdxZRwDAMdCNXYDpH/sbd9gJP03VEURz37p3VaiWbzWLvY4Kaz7eUBvB/kGWZvXv3FpRlK1asQBRFtm7d2q/I279/P7FYjJUrVxLMdUbC2Ti6rpudwZxkvyeeJZFREAUo8zpKaqypyFlcjsljVjme0HKqKwOTy+XM7SbdVon5QT+NsQQfNLajGwYWwEWaNA50RLJYORuOcTKSZl39FLw+J198qUV/+D1e/B4vmWyWxrZ2BAymVVUiiCJX2jrQMWiorCCRSnG65RoVDbPNAm2Ui7FmQCQXt2Zg8nmK5fIcu9aJ018+oPN+mdWgXRZQDLM7MFjqRsjr5UJ3N1gz7GvvLpDQ8+hn/Or24HV7iMbjXGxrp8ztJOQb2Ch5IAiCaUxtXpF3/udrjcvKiZ/+EG36LO5bu77w/fvuu499+/bxwAMP3L6TGyM2bdrEli1bqKysHNLxIM/ptggC1QEnXbEMgmDSfKIpGU03aO5KUOZ1jOoGPBI4nE7W/dN/CZjF3qH9+8ic/oyHZjQM+Pi8CO1sOMb7Ow7z3GP/pxDlmIdoC+Bf/ve88YEpQpsV8OOULFiE/ibCDhFq7GahF1UFErn4xLBqTlEcIgQkA88gPF3NMCkaWd0s7iTB3OgVS9UoBllZprOyltkjnFwMBbvVQkV5Ob3hCFlFKwh4Som+xZ3LJlETchWmKKPVJtzWAk/R8sUd457xNm/ePF5++WW+9a0bpp5ep5WMYhZfLruVVDzK4cOHEUURi8XC+vXrkaT+b9HcuXM5dOgQuq4TDodZuXIloVB/Y1SnTWLatGm899Ee1qxaSTIjc/jIERRFQVVVysrK2HD/Mpx2CSk3BhgLbG4PibZuPEXmFd4pMHLFnWKYnZDqmxaJvPeTltttl5WVEYlEsGsyWdGF21tGMtqDxdDY3tJBYHot1SWMrruT4LDbmVlTjW7oXOvsQtV1plVVFvhLAa8PwWhDwOSO3czdGQ7XsyY/DkxRS8UYjJIlkVuuwzwsAgQlgx5FIK4JOC2Dc4XSFpEfnL2MAQUS+lDGr36vF7/XS280yoWW68yur/1CC5cGg9NuZ5HdTqr9Kgd3fMTKDQ8D5Mjmd4aacCg89dRT7NixY8RpG26Htd/Ux2GzFLxb2yMpklkrZV7HuIgxHE4nZaEQFZVD86zXVJdzPhKnra2NNz6Axze8hFW9hp5sQnRPR5HqeeODI7S1tSEKArNCZn61a5BTdojgsBlUWE3qQVwzBUUZ3cy6zXf1PBYDSQQMs7venbNGknWzgycJAtOdE8t+Ph1Pc//mbwz/wBFAEAScdgmbzcq1tk5mTS1tUlQyo9AWTvUr7sbS/LptBZ5hGHTmYsZskmXc7FAANE1j586dfP3rX2fr1q1UVFSwbNkyAm475xuvcKHxCp8K0FDpH/YinzlzJslkkurqaioHCXUv89qZUldLMpnk432foOs6a1Yuo6bcTySR5eKVa/z6za2sWLECl8uFw2rB67TidVqLClNfsXYdR/cYuC+fZ3bZF88aRc8JIiKKuaCIDJzDmvd+ApjSMI37NjyPmujmwzd/yYbHvkICN2cPf0Rv21UshsKO6+3MslfidX7xzKBHClEQaRhATHC9u5tZU+oZnfuWCS03ogTT+sBjGVtqyKxyP5/1hKkcZAfuspg3k5Rmjmqrbbdy3CKZDIc729Ex1faPb1hm3vBGYPwa8vvxezycu9ZCmctF5SjVyF8UuBxO7G3XbvdplBySJBXtAQjgtluZXuWjJ54hkpSJpxUSaYWpFZ5BDfrHAiWbxeiz0UhnMrTF4kwJBbBKZuFZ6XTw1ZlTeKXxGu1tbfzo52/32dBcJho9joDpDfvg1Gn4bDas/3/2/jtKjjrN84U/YTLSm/K+SiUvhJGQASEJBEKAaLxtOz20GbM7O3d2due+7577nnfPvefe3Xvu3Xffne057073TtNND9093Y1phBVCWAkhhEAIBEhIKpVR+cpKb8K+f0RmVpXKZWZlSaJnv+fUAVVFRkZmRkY8v+f5GsG2MJoL+RGuX7Ywcl3zqC5M6eoBqAbkhMfIAngcIAGCYNGXFWh1Lo4690KMxhNUXXdDRffpdzvYvftOnnrmaf4f/+oHFaldDNMinLDPH6hMcQcgWJZ1SWYII7E0kaSKALRW0Mx4JvzX//pf+e53v0swaI9Z+vv7+fjjj5FlmeaWFvz17WQ1A4ck0lHnq8gHZlkWKVXHMCwUWcSVI+CalsVYPEMyo3Pg3XcJBkOsXr268Di/y0HQq5RF2E3E4xzb8yxXyBZBz6UN0E4Z9ogOpiZGyAJ4JAu3WFyeYNqwzWRtHzWBOodFk2Lhv6DxltB0/vaTU1jYnbvt9z7Go3u8bGnR+Y87NP7dmw7e67X4u1sifPb6bwiHxxAEgRsaGri5eXo01cWAZtqdJIdweZkvj0WjGKaJ6akmbtgr88Y5TJVnQlfavui3Oq3CGL1cnB4c4YusiOyQcIgi9R4vrgs6eqZldxIMyy74LoxWe/d8L33xGI1NTTxwxzUzjKyCBDf/mGde+ZjBgQGuqApwf+f0PN9UKsVQNIopiIiWSWttbUlijK86IokEI1duZPVVtn/Y/v372blz5yU+qoWjp6eHcDjMunXrFrSfjKozHE2T1U08TnnBUZiz4f19r7I8PEDA4+ZgOM727zzGe2+9SdWpT1k5Kb1mOJ3h4OAon81ASbiiKsBVtXUIkgsLaHOVp5S1LFuIkTTs+ETVgsGsgEOE1R6L6pyoSLegLzMxgal22PeBxbz+fTE2zto//pOK7tPmYcZ54cWX2H3LdlYsaVnQ/gzTpG8siarn1M9uBw1Bd0XqkEvSwctoRqFSrQ+5F7W4e/rpp9mxY0ehuANobm6muXkip9MwTc4NJ3JpGipVvoV3dARBwDvD+E8UBOoCbuoCsOS+2/ni1Jd8/P4BTFGhbclSqK0lntFQZJEqnxN/CZ6APr+fbd/6Iz5+7xDnP/uYNXXVl2SklM7FOM22Jo7o9lE5RdsDySnaRcCFBHTTsjM/TexVomXZvkwXFncw4f0EEIlE0BOjbGlx8nq3zI1PymQNuKVDJySmiEYjgF2Ed1kyN1nWRVdwD2Zt7zSwO5K1ikXwkjNiIZVOk8xmaa+vJ2VYGAiEyhivOnIX9IUIWYbTs/PlWv0BrqipI5Tjnoo565S8F51bnPDby+g6fXF7H7t3biL6/mPTSOemGiX6/p+xe+fjPP7kHj4bj3Fbqz7NHNvj8dCZWzxZWHxxroc1S2bmQv0hIuTz8eWJ43BV8QaxXwW0t7dz4sSJBRd4LkWmscpD90iCVFYnmlILpvqVxOZdt3Fk314Gzp+n/ZprEUURIxph2QURe/VuF/d3ThWhOSWRDp8XXZAZyV1faxdggyJM6uqBvXCVmPCKzEMW7IjF8zmaTV5wIWBHEYrY3paGLeLFK9mPL0eYlFVVBqJRIqksiXgcn3+BPmiTIAoCNX4Xt99+O08/8wx//ed/XHan1rIszodTqLqd3tUQdM8r+iwFl+SWksraKiCvUyZQRBB4uchmswwMDPDggw/OuZ0kitT4nYzEMozGM+imSa3ftahj4zxWr1zB6pUrME2TDz48xicfdpHR7ZAkwzDYsX0r9UF3oQNYDK65fguZa9Zx9tc/Z1nddKXkYiKq21YRRo6zVeeYUDZaFqiWvcrTLXvcanuD5ZWGto2ET7ZD3MPaxGqv2WmhWeCb5W1QJ41YDMPg1d//iv/4539VKO6cEvzHHRo/+2+/msIbqm1fwkd9p3A5HTMqKhcD6ZwiLQ8T29U9KF96Qv5oNEp7YyNgF0geqbxjyhd25Y5mz8QS/PZML6ZlzciX643HOJ+Is62lnaZc1qxTtJV9MV0oGApLAgynphq/ZmZQFAKYagSHbvspRqNRuhNJrpiUdXsh+oZHy876/SojGB3jlZdfRnY4KmbmHk1lSWZ0dGNC+FYTcOFchBHnbAiFQoTD4Wmc6lKhyBJVXifjyWyBhrQYRd6mXbczODjI8PAwpmmi9JxF6px5sZEXoYF9zR1VbQsUgIBUvsJ9NgiCPZ3QzKniKodoR5FFddt6RTXt61/2gm6AhX2N1LMCra75r0EjiQQjlkjW6SbrdOFsaeXa+69j2SIpvIMehURaQ5JlYimN2kB552kio5PVDEQBWqq9FRdtXKICz7Y78DgX9+mdTieSVNwbFvI6Cx28SFIlndVprPIsCodiJoiiyOaN17IZex4fSWZ59Y0DZHWT3rEkNX4nIa+z6E6Ty+0m6g0s7kFPgpUbkSVyhYtftmhQZhrDTqzybPNXe/yazSUT5HkckkAhhqpesQrF3mxQJn2RJUnitvu+yb9701Eo7rIG/Ls3HfzFfd/k5af/sVDk9fec4fjQ1LzWKYrKRQjgThgTqQ4B2V7RSpOK4Kxldy/zL9fK/b/JhNjBswBV6mLDsCh8VuV0BSaLZebjy73T181tS5YVOnkByS6gJ6dcaDMYv84GM9mF3+8nGo2SnccfM2sYlyxr+lKhLxIltnQVd+yqjIl7RtXpCycxDGuKVVZWh+RInOWNwYuy0AbYsmVLxUbOtQEXpmURTakMR9O4FWlR7iV5/qAoijTe/QCHP/yAYDxCh8+D2zl9EmVZcD5jd+1E7DFppYs7R24yk84pby9UzIo5JW2VY+JeoOauebJgP14zbWpO3gZpruuIbhic9lax7Z77KvtC5kHAoyCKIuOJFLWB8u4TkWQWsOuPxVDkXvQCz7Qs0jlz48Uu8AB8Ph/j4+MFC5O5UBdw41ZkhiI2h6JnJEFtoLKWJsVAEu0WcI3b5uTFMxpj8SyiIHD25AkikQiWZaHrOjt37pxVYWjUN6KOD6I4Fv8mNKZNZApWO6x5PY4cuQIlkCtj8nL6RE6dlS/uApLdqh/PWT/NdsvNez9Z2Ctx2VfLofMyt3RMcPAOnZf5qw21hEIhxsbG7OMeHgTmV1RWEvmVs1eaEANoFvRmJla080HAJkRfDmPdC5HKFfmKUF4HLy+WaSrw5R4jo0aRvS3I7gZMPc0Dd/x7nnkFBgYG+HBogFs6OoGZUy4ccxi/Xoi88SuAcw4V5NBYmKbgxVtAXWpous7H43Fadt7OdR0LH0mrukE4niWaUhFFYUYfVNOC8aRKdQUoM8UgFovhqSB3uT7oJptLxUhl9UUp8D777LMCh7ujcykdnfb5/cGBd7D6uqlJJ2ivnaDq5BeKYHfSFmuRGJQt0qpAqoiLmUOc7sIkS/bvs6Y9DZjrDDgZiXHDHz2ykMMtCx6nzJYtW3ht/+t0fOP+kj9fw7QKiSmL0eGFS1Dg5YmEoiBclO7Yo48+yhNPPMEPf1hc/pzP5cBVJzEUSZNSdUZitiCiIeQuxJRcLEiiQGOVB3dKZTyRxSnB0NBQIZJN13VeeeUVmpubufba6Q7dm2+6mZM//wkrFzm6LK+mAmgoM3pGukCdlTbsi5E3d4o4cytC2zh2eitvsvfT2NgYR/c/xW/usVW0P/tvv+Iv7vsmf7WhlqP7nyoUdwCNjY0lKSoXCsuaiABz5Ypcj0TBciD/XkzmnQi5H1Gw/5sfb4+o9nuRMuzx7kIc5gvHV2K3pH9kmIaaminxUNGcuGa2cfpcmByUnufLOXztVF/1P2GkR9ES3Th8HchSmnt2b+fHj/+WkXSK4WSSeq9NaHfkvLnGdYGILlDlKt34VQA6fLMT5MficWpC/3wKvL6xMHFBprltYeauqazGQCRFRjVQZGlOg3tREBhPZi5agXfixAnWrl1b0X06HRIZzSjc9yqJY8eOEQqFZnRz2LhtO7CdTDrNoddexTc+yhq/h+ykW/5iOpO5crfKbBEduNmQXxzOFfuayqTJLllxScy2JVGgrbmRN9+MlFXAG6aJW5EwTGvRaouL38HLmQsvtu9dHoqilOzVJEsiLTVeIskso7EMKVWneyRBfdBdcRPLuaDrOocOHSKVShEMBjl85swUPqEsy9x1112cOXOGAwcOsG3btimPF0WR7Io1fNLfhzeVoCXgw7kII6VRzW75O0WbQ7dQSML04kARLUAoFEEzIe/9ZFgWvd3nSD33MyKRCIZh8PLT/zilcwdM6xDlISpBHrjjxzzzCgwODHBwcHRGRWU50HPjVpGJEWuzYpE27QuZQ5y4OM6FgazdMc0TlTOmQIdr4RFYQY+HruGRiV9YFm6HRGO1vUgYGAuTVFXqfF6cioJuwZf9A6zOJQHkR+9gC2JKxYVB6ZavnZqr/5rBd/9qmuq16abHWbFiBV9++SXHR4a41TvRnfPJE/mvSctBiy/A+URxxq8Ay/3eaQKLyVizpJ2z/YO4FActMySC/KGhs6GecDy1oBupbhj0jiYQRbHom6GmmyTSKr5F5GoDfPjhhwwPD7Nly5aK7tetyERTtnVK0KNUbAyXSCTo6enhnnvumXM7l9vN9rvvJaPq7P3dM7ToWXwuF25xYdZF88GRuxfkr1EtztKvTfl3aq4C72RaY9OOm8s+zoXC73awYsUKDhw+yl23lGbHosgSrTWLmz510cvevE/ZxYwmu/HGG3n11VdLflzI66S9zodTFjEti8FIiuFomovlLHPVVVexZs0arrvuOk6ePMnu3btnHMcuW7YMXdcZHh6e9rd1229i3aPfYtl3f8iJxg6OGiKfx5J8ORrmWFrjw7EImj539M1csKyJ2LDJgopKwzlpRTgb8t5PkmAPJMbGxgrFvWEYU4o7yHeI/nQOReUmLOCz8RhJbWpMVrnIX6wmn/6CYHfx/HJxxR3YndKAbH+B3SI0zeD9Vg5Cfj+d9XUTPw31eJ0uTvadJ5VOoxsGy5saMS2L3pER2uvraa+poTdXFOY5mO4yOYIXBqVXX/U/TSvuwP6MBt76PrdsvxqAsUyajD71M8rbM2gWtIbqEQQhZ/z6Mcr6n+Hf8Ld4V/8V/g1/i7L+pzzzyscF41cPUuFaNRMEBJY1N+FVFEYjkdJf6FcM4XiC6vWbF7SPkVim5AJREkUGIqk5P4tKYHx8nHvvvbfi+/W5ZJTc/aNnNMFoLF2R1+Lz+bAsqyiRSzSl0juW5Ipbbud8TSOZdIzmEm2PykGNw0LEXmT1Z4WSFfX560fSmPnCNp5I4Ll6w8IOcoHwux2sX7eOD48dJ5kt/z66WLjoHby8SEDTTcyLZE9xxRVXcODAgbIeq8gSbbU+woks4YTNGVF1g6YqT1mmxKVgspXLt741txP3jh072LNnz6wrOlEU2bR124x/e+f3z9AZHS+LU5RPBYDFzdl05saTJtOVWZOxLODj+6s7Z/Z+EgRq6hsZHRqoiKIyP5qO6XYH0yHYcn+PaAfPz1RwLVRdmoco2EVew0Xg+Pu9Xpa7XXxw8kuuW2PzfaoCAaoC9vnicbtJZDKMx2JknPZ75C2ye2eYxpTx7uSgdEdoLUZ6dMZxKtifkZjuKnxGw6kk7ZMixSanXIiyi01NHRwZ6J7X+HVbSzt1bg9vnx3kpmWNc1oNhQIBTp3vp7aEzNqvIroEmc1r1ixoH8lMeYsky7KzXhfLU24xIQgCrTVeBiNpUhmN8aRKIqPTVutd8P1DkiR8vvk7QHkiv1uR2H3HzfSd+ZKu9w+ybJEpBoo4YYuSNqE7LdDkLJ5KEpAsvhxPkTJMnDV+ai9oIHxpimxZoK3NQiGJIkGvwvXXX8+vn36Bx75+30WbThaDi97BcysSsihgWhbjiexFe97ZhAjFQMj53jRV2ZlwadWgdzSJql9eMT1eb3kXwO33PYBxww6OjoSxSszOFIWJrlO68jSTAoRc8QRzd/Fgwvvpr65ayQOdLayqqWXrzju5/9s/ZOkqm2NTiqISmKKoNCwIa3AuLTCWU/caOV5cTLfVxOcyAoNZoSA4MHOdziFVwLTsgvWrBEmU5kxwqK+qIpxIklLtm/hMnJtYIl74/0gu5/VEV/eUbTr89jkcjUYxnR1oiZ45j0tPnC18RtoMaQQeaYLHqTh97GxfSps/UODk9fX1EY1GEYA2f4BdHUtp8vmQJZG62nre7R6Zts8L0VZTTd8M3fM/JKjawroTqayONo8qeTYIgkA8pRJLLd79wjTNBaVZzAVJFGmp9tJY5UESBTTDZCiSXtA+e3p6aMzZGc0HOVdIKrKt5F26ajWj/vlFh5WAS4JWl217ZWJTS9Qi3+beaBTXNVeRaW7nk5EIZ1N2BGJfRuCj4RihTdsWvbNbDKp9LlYsW0oqk+Xo8c8v9eFMwUUv8PLFEkA4kb3siqS54HM5aKv1Iee+pL2jSbLa5XP8pmmSyWTKemzH0mWsfvDr9IQjJT3OmCQaWExOB9iqTGBOu5TJyHs/NQYCdCxbicvtKaQOFK+otIuSvKIyoU8UdmbumBoUizaXRaNiUSXbnUzDsn2czmcFutICZ9MCA1mBYdX+UYRLf2EqHXMf87LmJs6PjQLTP6N0JsNILM5n3T0YpsFYMsWKpkZq/D60ScWDzyFTl+O+7X/7A5zVV8/5nJM/I8csHZGQbBVsdyzZzZaWNu5Zvootza1sbGxmS3Or/e+WtoLdCoDikLFkJ8l5vlNul4uMpmNaFzdj82JindvBoVdeKvvxGc1YEJFckkSGIotDj3nqqadIpVLoemVoGLPB71YKXchkVieeLr9oPn78OBs3bixq25DPbvNHU2rhfhXsXEo8mSz7+UuBM9fJc+eKvDFt/hvFaDyOsOkGNmy8lpt2bqfj7gf4WPbwQRbex0V45Tpkfy1dQzFGYumCGvVSQBIF6oNudu/ezYv7Xiccq4w3pGVZZFR9Qa/tkjhpBTwK3pxFykg0c9E4bZWA02GPbPO8vPPhJMYirfxKxc6dO3nhhRfKfnwgGGTIXRrpM5Oz9XAIE52SxUK+822WmM6hTdq+vqkVQRCmKCpnfK6CotIeD3plGWNSsoYiQKNi0e6yuXAu0ebQ1SoWS1wWLc4JC5O8sEIWwCda1DosRnXb8PkrhSK+pstrQwyExxlT4WRfPyf7+tANne7RMZY1N7Omo40v+/rx5kj2zfV1nDh3ju6hIXoGBznV08sN9SEAvvzyS5I0F/UZCUC9Z+YOdj7lAmy1ctIAlyzTHgiyLFRFeyA4Lfosj9pgkPPhmUfEk7G0qYGugcF5t/uqwu1y0TY6QNeXX5b1eEUWMcrs4OVhQiEBqZKoqqri3nvvvSiehk6HbYIMMBhJkVFLLypPnDjBqlWrit7e63TgyRnl9+fuV2uvWcdgpvLv5WwQhYmc26Qxt3AC4LSgsHzNFQAE3ArLmqq4867buPX+e9h99+1sXL8WhyRiWvY50Tua4MxglN7RBENlvq8LgdflwO9y8OCDD/HzXz1dkZomnMjSO5ZkJFp+t/eSWaXmjQFTqp4rkr46RZ6tsvXhkMRcSPDE6MC0LJIZjVhKJZZW0Rd4USsFoiiyfv16vizzIgzQsnkLw9FY0dtruZdXbsxNKcg/RanfHU2cuHm7PV7alq5AEARe3n+E4OYfTysgRCVE9Zb/zjuHJ9rtvzzdw4fhVEEt3O6y7WBmopDmRRP1kzp7S1wWSz0WV/onPAIHs0Lh/fsqoBgxTqPXidfh4MzQMC2NjaxsbeFs/wCduUQVAYHV7W001k78e92KFXQ0NNDe2MjK9jauaqin3Wf7ke155dCsn1Fw89/z8v4jhfHqbEUaTKRcAIxrxRO+JVEgYsy/chFFCUWSyaoX76Z5sdEUDDD21mtljTJ9Lgf1QTeWZZU9VhMFgXCyvAnFXKhUGkexqPE7Cx6wY/HSx87d3d2sWLGipMc0VrlxSCK6aRXGw0n3xeU0OkX7x8K2wZoNpmWi19RN+Z0kCnhdDgIeBZ/LQbXPRUedj+YqD16nbPOzLbtTHEtr9I4lL3pXrz7kxuvxkNU04umFXwcCOW+8TM5LsRxcsgJPkaUpnLbB8dRXqpMniQJ1QbtIjSSyxFJZwokMXUNx+sdTDEXTDEXSdA3HGY6mZ+zyLcbrXbZsGT09c/OW5kL7kk56peJWsrbIwK5wlItR4OXGmqWe6ml5qofdles3I4jiNEWlb81fU7/172m+5ddo0VPcssHH9759D42NTSQthWd7RhjPqlTJxauF8529vChEEOzCL89JCetfHTJeQzDIwFh4zm1EATqqfFzd3IAsiggIrGxrwzmDq/5cuKOtEalI1asgCKy54IYwEwKSfZ5a2DF4xSJtSWSLUFGbJfJXv2oYjMVIVZVvCVPtd7GiKYhbKb/Vr2omiUxl1Yrbt2/n9ddfr+g+58JkmlK2RIrSkSNHZvQ8nQ+SKNIQcgP2eBjAtXQFAyUs5iuBPPc4Y87lfyiiZObvWgmCXfQ1V3tZ1higrdZHQ8hdsKKJpS7uYksUBEJeheuv38Lzr7y24P0lJo3wy9VtXFIffJ/LQWuNl76xJBlNR9XNRYnrANvxXtf1BYktLoTX6cDpkIilsnzWFwGsQg6h4pAwDJOsbhZ8kELeiYo8qxmFrqXLIVHjd1Us2WOhZGHvmiuJffExgTlEG6Zl88vUXLzMxchRLaeDl8qqmN6pN6VQdS033nY3b7/6PAM5ReWKFSu485ZbiHz4N6iRSURZR4gtN/wdkbdPMTY6wpnwCJurmlkI8hm9vVmBmA7BEuxRLiWCPh9pNcx4PE7VHOHdF0YTlYO85c1vz/TOqXoVgG0t7VO4c7NBEKBGthjMpVzEdAgU8ZVrrKnmSP8Q2zqmG8rmkU6nkQRxUXwmLwfEk0nONS3hxlsWFuMlCAJuh0xWK+/mK0siqayOr4KB7NXV1Xi9Xl5//XWy2Sw333wzriLOp4Ugr7QsdXJVX1/P2NhY0QKLycjz7+Tcc6/dtJlTHg8nj7zLqpqLI7rwSBYxQyCqQ8gxu/OCM1vaWFIQBFwOCZdDQkBgMJIikdGo8jlxXMSAAp/LQWtrKx8ePTrvtpZloZuWTV0QBJyyiCAI6IbJeDJboCNU+5xlh0Jc8qAjlyLTVO1BFsVFK+4AHnvsMf7+7/+eJUuWcNddd1Vsvw1BN+PxDPG0hiLZ6Rx+t4PaQH61pDEay6Dq5pRR7mRkNIPz4SSB3OMWKrP2+Xzs27ev8O90Os1dd901qweVqqpT+Cdr163nw0+Pcc0czxHRbTWrJNgE2sW0SLkQpZSvXWNRrvza7mm/b25bwo477uP1F58GYPt1axg78K1plhzhlEDq2H9g23X/nr0v7uVsNE1Sq5/TBLcYuCQ7izY+yQh0sUUqlUBjdTVnBwdxORy4F/kmeKHlTTQaLXAiBeCKqgBXBrwMpFQokjoqixByWIxrAlFdwCVa83afRQFc3gBnRsZZVjfzjbBnbJxVrQsr/C9n+L1epKH+iuxroeX/YgjzrrvuOsLhMKFQiJdeeqmi94iZkJ/elPKV37dvH3V1dYyMjJSVuBHLdYSqJiWDrFx7JeHGJt57bS8OpxNdkjEkmcR4mOtlk8ACotssrGkWQz7JHtNmTTsTt9U183XPqZY/ivc4ZRySiGaY9I0maK31XbQiT5YENE1DECXiaRXNMJncbzEtC8O0yGrGNFW5KNiNk8mo8Tup9pV/nb3kBR7YnbDFRnNzsx1Cf9ttFd2v0yHRUuPD43KQVjVcDpnxpIrbKeN1OmyCa61MLK2RyGhIgoBLsVcasiQW7GJiaY1YWiOZ1an2Oe0g4zI9Ai90Y1dVlWeeeYZbbrmF6urqwu+PHz9OT08PbrcbVVVZuXIly5YtA0BraScTHsClTB+t6ZbNYwK7E3UxxrOmBZH8OLiEt8UURWRp5oVDNpMCmNMTL2l6scw4Tc5BqoP+WT3xykGtYpHK2N2knowdNybn+HsXs2DOI55K0p/Vibl9yIaON5OiyaXgv6CTWxcIcKirm1vWFE/0Lhd5y5vbWnW6E0myholTEunwTSRN1DiTfDY6goFEtd+Pa57riU+CjGHb+oQ1gfoiTKKDHjfnR9MEkmmGsyqqaaKIIq1uJ0MjIzTXVM+9gz8AdJoaZ06dZNnKhX3uiUQcxPLjx9JZA8uyECrsoZq/NtbV1dHf3z/Fh3SxYEFRr2X//v2sX7+eTCZDskz1az5F6sJGSnVNDVsf/ea07d9+6jds0jQUR2n3577xCH0uH6phcI2gE5x0/RAE25S9Lzf9GdeEGTv+rmwa0zTLSk6RRKEwGdQMk9FYhqaqymUMz/3cIk7FQTiZ5b/94ncIpoYgiIXPN5GI88ADD07pEOdt4yYXdw5JpMbvWnBy1mVR4F0MjI6O4vV6F0UpVeVzEk1lcSsODNNekUSSaqFwFQSBoEeZNVC4IeQh4NEZiqTRDJORWIaxeJYqn4JHkVEc0oIMoRVF4aGHHuKJJ54oFLqpVIo1a9ZMWameOHGCl19+mUAgwNabd3L8if/O2uqpF2LTssUBJnZaQTm5s+VgIGtbb8gCVDmK7wEYwuwXiLw9x2yeeIYl5tagFk7V9sSLRqNTPPEWAlmAVqdFf9b20svbBwhArcM2SzawV7tG7rXrFqQMAY80IdYoBpqucyoSQ5ZlJNNENA37x7JIOd3EvQECa9ZzxdVT+7aff/opn50+hSedxJOKk5QVhFVr6bxmM+EPD1E9x6i2kshb3syEap+Xbbnc2A+6ByBYh0uZ+82pclhkVft9j+rCvOdUJJOhK5vknZMDU82zgTVVAUKixMV5Jy4dagN+uj84XHaBNzw8zDvvvIMpOgk2tNLZ2VnWfkwsescStNX4Kl7kgd3N279//6IWeIpsX9NNyw6cdytTL6SxWIw333wTr9eLqqq0t7dTmxMmtbaWHptoWVZB8FfshOjGhx7lzcd/wra6UKETl85k+HR0nA0tDYgzXFtPjoapv+sBttbZnNi9v/sNN6TTeNzuwjYO0b6+DaoCcQNmYnU2BgJ8dOR9Nlx3fYmv1IYsiTRVeegZTZDIaKRVfdp7vFjQTYuO1iY2rLua5tqqKaldlmHw4x//PX/9r/8Sp0MudBZNy0LTTURRQBaFip3X/2wKvN/85jf8+Z//+aLsWxIFagNuhqP2qiOZsUcI0ZQ6a1F3IdyKTHudj1hKZTyRRTctxuJZxrDHug5JRBIFREFAEOwT2Ody4Fakok+G+vp6du3aRSaTmZFjsnbtWtauXUs4HObll1/mTE8/q0P+KUkDmgWqZfPh6ivAtZoPqmmPMJOGXVDWKqWNMs05Crz5PPF0y/56SBhIvk7icbsIdFaw3a/kFLlxAzKGQNayC7oRTWBEs1f4veNRhHwXUhAwRAlDgFUOi/aAa973I6NmOZK1uPG7P5y2Ir5wPH8h1lx5JVx5JcC0FfXRDw5zufWtrm1v5J3ecZprZzdlBptaUC1bjGp2pq9btMfmM2EgkeDA+R4syyrk5NpcwDjRaJTPx2OcjMR5ZFkbywKLmy15qbFUMDj56SesuvKqkh53+PBh0uk0Dz74IKZl8dTedzh58mRJdh95iIJAWjXo/4qmW+ThdcrEMxqp7NTi48SJE/T19c2bM1sKTGvCwr6UceUN3/ouHz35M66tr2E0Hud0oJbNP/g6H/3j42yom/j2R5NJ4qk0Saeb2lxxd/rzz1iajuGpCk3br1cCAcvOrjbAfcF3z+V0kh0ZKvVlToHTIRH0KERTKkORNC013kUf1SYzGqIgcNPWLbTX+XDNQOX58x98l5//w0/4i7/4i8LvREFYFIraP4sCr6uri6ampgUFZc+HoEfBNC1G4xlSqk5WMxiNZfC7HUV332wVjpOgRyGe1oinNTKaYVf3hsmFSuloSkUS7e5gyKvMGX3T399PXe6LNx+BuLq6mt27d2Pcdhtv//wf2FobKuzbKdpdJ9NaXOWsZcG4bo/Q8h5yVY7iY27yMOd472fyxJvMwXOKKq1KH5ajOue3ZhP7O3yVvamIgi20yAtVwpodfxbLZPlSh833PoDL7UQ3TLuYt2x7oQ/2vYKWMPErEqppj3Zdon0hdwgTF83z8SQ7/vhPZ3zuUjraF35/9JY2suHBy0pYIAoCriJj0twSeE3bl2tMF2gUpy8eIpkMB873YFoWjY1N3LFzIw6tD1JnEX1L0eQ2Xt5/hIGBAX57ppfvr+6k3r243MRLiSqfj7PHjkKRBZ6qqrz44otce+21dHR0APZntPna9Xz2xUmOHz/O1VfPbWY9E0RBIJZbQFdScHExke/sTOZi7d+/n4aGBm6//faKPtdkMUcp3SHF6aTja/dx8Pe/o3rjFrbmzJXb77ibM/teZFl1iPPRGOHOVay+dgMMTnhBVtc3cMIbRB0bRxYENIeC5nCgyQqaQyHsc6H3nccn+3FP+s5aWHwxOk7omuKMnOdCjd9JMqOhGSbnhuM4ZRFFlnA6JAIepaKxYhnNYGA8R/vxKDMWd2BTAHbu3Mnjjz+OIAhkMplFaz4J1lfJm6RM/P3f/z1/+qd/uijt/MkwLYszgzEsyyKd1fG4HNQFXIS85fNNAHTDRM1l95qm7SOV1QwSGb3gKSVgK3hcioRLkXE5JN5+++3CGFJVVXbvni42mPc1mSZv/vwf2FodmJXLVmlkcl27fCSZR4J6h1VWeP2RkTg1ux+c9e8H9r9E79kvaWxs5IE7riH6/p9OKfLyfmvPvPIxg4ODBBUn/+KKzkXPUO6LRBlpXcrGm2+Z8e/RlEo4nubIKy/jEUA0TQTLRLAsBMNA0XWaQ358DomTaZVrH3iQoEep+Hfgk5//hCsukgKvWJweHCXtCs07pgWbcjCUO9cyhq3sy99uLeDY0BBDqQQ1tXXccsNKEp/+H6BFEQQIyDGCbong5h/b58fAAFdUBbi/s/QR2lcJ0WSS/hVXceWGqUHvpmnyxhtvYJomu3bt4uTJk5w8eXJGgVc4nmEknqGvt5ehoSE2bdpU1rEoskhHXWWH44lEgo8//pitW7dWdL+TkVF1esdsLl1DyI1oqOzbt4+bbrppCk+6UoilVIaiaZwOifbaynSZP/voQ8bfO0DDjlsLpsSlIKsZfPR5F8nDB7iqxlcwI/+ot4+rv/8vUEq0VpoNmmEyEE6S1e1vdkbV0U0LjyKxvClYkUx5VbfjS03LwqPINFd7ir7W/uQnP+GHP/zhotQnf/AFnmma/MM//AN/8id/sqjPY5gWg+MpUqqOKAgEPDKRpJ2h0FLjXZT5v2VZxDMakUS2cPLmMXi+F9HIsPW6ha+CTNPkrSd+ypaQD0cFbWZmQsqw8wpN7DFancNaEM/v/dEEtXc8MOvfI+FRXnn215iGQVNTE7t3bsKh92ImuxC9nWhyKy/v/4CBgQFESWLn1x5k+P2D3N28cJHFTDBMk2NjERpvvo32eThKpmURT2sYpokkiqSzOrppIiCQyGQ5/sFRpGQMyR/gio2bCbgdNIQqSzb++FdPcKX/4hCYi4VpWbzdE6alrjjPNtW0OZ7DqkjIYRUsa7KGwdu93VjAPXfeiP7F/4qpJ9BMGYeY8xITMtR6TTzX/ncef3IPAvBXV61csMr6csdH0QQbv/1Y4d9vv/02iUSCHTt2MDQ0xLvvvsuaNWtm9WyzLIue0QRZzWB4eJgvv/ySbdu2lXwchmHSVuerqFCvq6sLXddLNhMuBb2jCTKagd/lIDrcy7lz5yretZuM4WiaaEol5FWoC7jnf8BFQiylcviDj/Ge+Zxrqty4JNANg8OGxI0PPlzR5wrHMwxEUqRy10mf00HQo1AXdKMZJrph4pQlvCV2hFXdoG/MDmtwynYIQimdwVOnTtHV1bUon/8f9lUI2LNnT1mdq1Kg6nZrVtXtEK3mag8uh4SqWaRUnb6xJI0hz4IVMWAXWydPnmRgYADLsrjpppsI1PnJqDrJrJ1bl87qnDnXw/LlyxmLZwqmmuVCFEVu+u73eesff8aWgGdRi7zhXBSYR7KjwMq1DkloOt3xJH2JNMkzJ6lvasU9Q5TVZE+8wcHBmf3WBAFRkrjxtrupa2zGueMODr/zMtc1hBb0Wi9ENJnkOA5u/OPpXLmZIObEO3lM/n/T8tC06yZ008Sy7NibWFqj2m9WjIei6zredBIuswJPFAQ8YvFCGEW0/fBUy1Yx1zpsVW1fPI6bNIFAkE7fEAnxJIJigWWRNH1EjBAZy0V/wqQt2UswGKyoyvpyRSQWZbnDwbHD75HUDaLRKNu2bSMQCADQ2dk5r4BCEATaa32cHozS0NCAy+Xitdde45ZbbimNSpPzDaskRkdHC24Ci4G8sALgxEeHqautWdTiDmz7DoB09uJGeM2HgEdhzRVrONzdxSrT5sHKksSqVILPjh3jinXrKvI8kWSWsUQWRbYdLBySSFrVyeomfWNTVcluxXa4EAUBSbRdL2ZbQMRSKiOxtE1ZkkWaq70lj31XrlzJm2++We5LmxN/8AXe8PAwbW1ti7Jvy7KIplRGYxksbLFFc5UHV65b11TtYXA8RTKrMxhJkco6qAu6yx7vvfTSSzidTpYvX84tt9yCqqq89dZb6LqO2+1m27ZtiKKIYZoEb7uZF/ba7uzrV3eWvCq5EKIocvN3v88bv3icLX4XDrnyvBfNtEUcAuUXd8PpDAcGR/l8PDahdtz/MoIg0LZ0BVeu30yoempnp7ltCXfc/w1OfHSEnrOnpvqtCQLtS1eydv2mwuMCAT9ftK9hePQM9RUqbs6OR0gsv4IdN1RmLGR3kScKvrSqk1YNokm1EBO4ULzxxhtIviqyI2OsrK2aUVV3qXBFrZ+PxiI0VoeK2j4o27YphmW77Fc5LLBMRCxCAR9S5hySkCskBPCLCVxihjG9BtVSGBkbQHSHsCqosr5c8YklIwbqGDp5kpvv/FpB3VkqMpqBYVhIkkAwGGT79u3s27ePW265pSCAmgumaVHlUwh6KjPKy2P9+vXs379/0YqufEH64dEP2Hn9NWW/f6Ug6FEIx+1Jz8VUlBYDj0tGD1Xz4VAvS8QM9V43tX4fQx8dJrNqFS73wjqO8bTGSMz21Qt5FWr8LkRBIKMZDEdSaIaFQxKQcibaadXgwrwkv9tByOssmERndYNIUiWVK5idDomWak9Fxr2VxOXzKS8CwuEw/kWwcbDzZnXGE5nCaNStSDSEPFO6I6Ig0FTlYTSeIZJUiaU1dNOiuar4+fxkOJ1Odu6ccJJXFKXw71Qqxf79+7Esi0AgwObNmxH0DA0NDYzEMhiWhc9VvOBjNtz8R9/jjV/8jOt9lOyPNB/yt0VRoKzi7kwswW/P9BYUYxeqHXvPfknfuTPceNvdNLctmfLYUHUtW3fuZsMNNzHU34emqTgcCg3Nrbjc04u41VddxXuvdHPPAk+vkUiEc5bE0tvuZkUZ7vTFosrrJK2mGE9mcTtnX5GWAl3X2fXoN9B1nXdfeoHGyBjLQoEKHO3C4XO7UMw4mmHgKII7KuZUtf1Zgahlq/zykSnxeBxP9RouTCx1iDoNjiHihh8x2MF4oosMTsqp70zLFnukTIGMYZ//QXlh9ITFQpVD5prddy54P8mMjjTpeul0Otm1axc///nPue++++blogkCqJpB31gCp0Oq2OhRlmUMwyjbh60YuB0ihpa5KMUd2P5sfreDWFojnMjSUn35nFi6YZHVdK55+OvUB918euwYkQ8OcV19NYdefYUb772/7H1rhslQZEL4MPkccTkk2i/gb2Y0g2RGQxDANO1iPJ7RCqLHmVDjd1LldS6IQ7dhwwYOHjxYcd7n5VVuVhjPPPMMDz30UMX2l1Z1hiIpuoZiDEZSZHUTUYC6gIvWmpndsgVBoC7gtkmXQCqrMxgpLYYF4OWXX57T/8jj8bBr1y5uu+02Vq5cyS9/+UvWrliCQP4kT9MzkqjIOOPmP3qMwymt4sHq+XfPtKY7es+H4XSG357pxbAsGpua+JPHHubrdy5n97o4X79zOX/y2MM0NjZiGgZvv/o8kfDojPtxuT10LFvJ8tVX0rFs5YzFXR7Nm7dyfKT0LMd4KsnnkRiHDRH9ptu5/o++R/0iFncAXpejEIXXH04xOJ5aUCLA4cOH2ZAj2cuyzI333MdgaP482IuJTW21DA4PF3Uu6RYkTYGkYdumRHLq7TwUfweiMn3sKggQ8ki0NdlTAgOJUUPCmOc5LcvuFuqm7X94LiMwqNrRdapldxMHVYG+jIB+mbGkM5HxiuzHpdhxjpMhiiJXXnklp06doru7e87HC4JARjNJqwYjs+R9l4tt27bx9ttvz7udaZp8/vnn8243GYosMdD1BTuuW1/u4ZWFfIJFKqtzbjhOfzhJIqNd0gx4y7IK2cL5ruKV69Zx/R//kAOjMfQFfqbjiSwWdjFXV8TkIh8bWu1zURtw0VjlobXGi9cpTzFDd0gifpeD9lof1T7XggUSGzZs4MSJEwvax0z4gy7wTNMsqtU/F1TdYCyeoWsoRt9Yklhaw7Rs9+lqn5Ml9f6iVLJepx2KDJDIaESSM8eWXYhIJMLTTz/N9u3bi/aMqq6u5jvf+Q4CFuHzZwjm5OCaYdI/nqrIF3rHt7/LkaxZ0SLPIdp2KCnd4MOxGB+NjnMiHCVRRND7gcFRTMuiqamJB+/eTszRiKbUkz7zUzSlnpijkQfv3k5TUxOWaXLioyMLPt7q6ipOKcGCkjkPTddIXxCWnVVVvhgZ40jWoH/1eq78zve54b4HaW1vX/BxFIvGkIdAjgcaz2h0jyToDyftJJWUPW648LXMhnA4TH391GzWlRs3MTxemZt/JSAKAts76hgeGSIcj8+4jWlBRLPNu1MG+GULsP0IVcu+PPr9fuKDH9J4w3+ZVuSJSojGG/6/mJGPaQwoCFjolsj5rDBjkTemwqmkwBcpgc+TAgcjAsPZCSPrKhmanbaJtYhd6J3PzLyvi42sqvLCSJRlD3y9IvvzuRwzen8JgsD1119PIpHg008/LWpftj9o5VSIgUCAVCo173bPPPMMmqZx+PDhkva/fv163nrrrXIPrywoskRD0F1Y9CezOgPjKbpHEosS/1YMklmdwaFhamtq8LkmuoqyLHPz937I9bfdsaD95y1onI7i/WIvhFuRaa72sqwxyIom+2dJvZ/GKk9FvesWQ0V7+fRpK4wTJ06wZMmSBe1jLJ6Zkh8rCvZFye9WCt2QC3HgwAGy2SzLly8v+D7l4XHK1PpdjMYzjMQy9igxd+GeycdO1Q1b/ZQVGE2o6EK2sJ9iwoc3btzI8ePH6T71Kdesv5benGotnMguWHgBcNM3v8Nbv36SjWRnjDQrFcPpDG/3R/gimkFGw4m9ssunBWxrrJ3RYyyh6QXO3d13bGOUBu59ppptLdfwf93xDn/zuoeD5xWee8D++09+9jt6zp5iww03zdmhKwZrbtjG8df3sK6hClVTORVLEm9owVtbT3JkCIeaRdQ1rPpWNj9404Kea6GQRIGGkIeg1160pLK2MCc5iXgtClAXdBNwz+5t9/nnn7N8+fJpv6+tq+NEVqN+hsdcKjhkie1L6hmLJ+iOjJAyRQxTwOd2Ibo8Uzp1iggh2SJtWMQNgWzu8hiPxzGlOsaO/2catvx/MNKjaIkeHL52JFcNY8f/M0rnD0gnh3GRwSXZtisDWTtjOH/dNnNdO820GT6qBYooIGLRqFj4JArbeiULvwTnc5FO3RmBKtkiKDNvrNpiQVEcBFtqCka2lYDXJRNNTYy+EokE7hznau3atfT09PDee+9x/fVzJxp4nHJFrYuGh4epqZnbLBugqqqKq6++mkOHDtHV1UVnZycnT56kp6eHhoYGrr76ao4fPw7YCRShUIh9+/ZhmiYPP1xZlWgxCHgUvC45J8YziKbsvNT+cIq2Wu9F55AlMxqnT5/mjp03zVjgeLwL8xwNehSSGY3xRJagV8FZxH3zUkFaBBuyP9gCb82aNbz00kvs2rWrrMo4kswWijuPIhe+GHNdRA4ePEh7ezvt7e18/vnn7N+/n0gkws0334woirz11lu2lcDu+0hldcbiE8XjeCJLlc9JyOtEEgVU3aBnJIGgeNiy/SbeOXRkykWuyuuk2u+c96J29dVXc+LECY4cfo+rr93MYCRFOJEl6FGQK6CmvOkb3+bt3/yKa60s7gX4FuX5c1lLwkLB6w/SGHQVlRbQHU8WOHdC5iweM8HWlnXs63Zy/S+cZA3Y1ZHFkz2NoA4X1I5D/X10LFu5oNfvdrs5bSnI4Qip5na2PPCtBe3vYsDlkGip9qLqBvG0ZnssmhZZ3cAwLYYiaZw5M9CZ0NPTM42A/tnx4+iH32ZN/eU1ps2jxu+jxj9x3hwfivH54Di11dU4coVd3hhaFiBhgN/pwUAqGGFriR4G3voBsrcF2V1PvPs59OR5RCWEkDPCloFrQ07Gc1m3vVkBWbCtWLRcJanknscNKIJFs3Nmj0dFhCanxVCuyBvVBCI61Djs4m/yVz/f4ROZ+vsLoeWMnfM2RB6Rov0lBQRkfWYeUrlwKzLjCbVg+jsyMjKFl9be3o7f759TYasbJq01lU+06O/vn5GH9+abbzI2NsayZcsYGxsD7Pzv119/ndOnT7N06VJ27drFnj17OHPmDOvWrcPtdnP27Fmi0Sg333zzokRmFgtJFPE6RbxOByGvQu9oAs0wOTsURxYFXIpMfdB1UYo9W9Bgp3osBhySSCSZxSEJ9I0mWFIfqKi58eWOP2gfvN7eXl577TUee+yx+TfOwbIswomJ4q7a55yx22WaJvv37y98+ZPJJO3t7aybQda9d+9eNm3axJkzZ+jo6OCzzz9nxZUbUXU7g1DVjYJYQxQg6HGSzNo3Xqcs0vXl59Q3tRAIhjDNCYm9LAq01HiL6uZ9+eWXnD9/nqVXbiSrGQQ9CvXByvkhvfWPP2dbqLyL7HA6w0+/6MKwLGoamrlmw9X4GKJBnJ4WIAnCtLSAj0bHebFngNbWVnavi5M+81Oq7niH638RImuAU4L3/ijC+CvbcS//AS9/5KOvr4/NN97K8tVXLvi1nzp5Creoc8uNWxa8r0sJy7LoH7d9oubyy9q3bx+7du2a8jvTNDn6xD9wbW0V0VSKw7pIrSTQpGVoCl1eliEJHQZUgayq0zUeZ1V91bSiKKrbaSKfjgwST47S2NQ0vxH2JKPjyX6OkyFiF5AuETzS1K7dbLAsiBs2Vy/Px5MFuwC0ctGBkws8dy7RJC9U0kw7c1e3bBPxyRCwLWKqHbZFzHz4cHScNQ99A1+FxGuaYXKqP1K4hn3wwQesW7cO+QIrJl3XefPNN7nuuuumCOd0w6SpyrNgM/mZoKoqe/bs4dZbbyUUCk05lrfeeovly5dTV1eHxzPzFCCVSqEoyrTXcrlhso9bHoos0lLtrUgTYDYYpsnhT88yNjbGnTdtqnhBaVlWYfw8Fs9QG3DjVuzF7WKHHpSDxx9/nO9973sV3eflfeYtEG1tbbS0tHDgwIGiTDQty2IgZ2sCdnt3puIuEonw2muvcdddd80b+wX2zS8UChGLxaivr+ezzz7DLWRpaZhQicXTGmPxDJphMp7j54kCNFV7+TIdZ01nS2HbREZjJJpGNy1GY5kCt28urFixAlmW+eL4UTrXrCOaUlF1g8aQpyJf4vYbtjH23lvUBEpXUU7mz91+85Wcfe9/JWmMkVSGARCVIA/c8WOeeQUGBwY4ODg6JS1AyV0Y4vE4on85yuaf8jevewrFXdaA//l1N/9+808R1WHi8dMAOByVWUWvXLWSU1+e5s2Dh9mx9bqK7PNSQBAEfC4HqayOqs9ObhZFcVpnQxRF6m/aSf+7b9IlONj9re9w9NC7WCePX4xDLxqqaSdXADR6ZKSMQTqbxeOaWiD4JYjr0BGs5lgqwsDAAM+8Art3/myaEfYzr3xQWHxsbbS7Tx4J2lwWScP+HjtyBVkxRdSFsFMzwCdZRHPxfboFM9GmTOwOXdKY/Yncua6dZtpdxqgOcV2gXplftXttbRXHfvdL2u68ryLCIDmXr52HbpiMpzR0Q81RWGwaiwWsXn897xw5RntrC53tLXhdDmr8zor4i84ERVF46KGHePnll1m+fDnJZLLQsTtz5swUR4OZMFvhd7lBkSWW1PvRdBPdtAV5qm7SPRKnMeRZsMXWbIimVE6fPs2O7VsXpVuY1Qw0w0QSBa5eUsPgeIq0ahezIa+TjKZjmhYuRS4pUvSrhD9okQXAbbfdxkcffVTUtuPJLMmsjgA0BN0zdrjOnDnDoUOHeOihh4oq7gC2b9/OE088QTptE+937NjB0aNH2bt3b0GB5Xc7bOJmyGPz/FyOgjI3m81iTlIT+VwOmqrsi0daLd64srOzk+qAFzU+hoDdHs9n5y0Ug6dOFRUNdSEm8+d279xE5Oi/wdQTiMLE6zXVKNH3/4zdOzdhAZ+Nx0hOEl50+L0IQDQaxXItJeVczsHzCrs6srz3RxFu7VA5cN5Jyrkcy7WUaDSKIAg0NFcuUmrliuV0D4bJZIsTz1yuKFziZunrd3V1MTo6SldX17S/dSxdxrnaZjY/YHOLNmy5gYG2ZfTHSlcaLwYMa6Kr5hbtUeeaxhoiiekCDFGwBRd+ReHK+nYEQWBwYIDHn9zDP718lpc/8vFPL5/l8SefZzBX3D2yrG1KZ1kRocphe+x5pPKKuwuPqcoBnW6LFqfN22tULNpcFkvdFsvcFu0ui1qHRUCyrV48kv38tQ6LBsWi023R6rL/v9Vl78cp2oXhoCowMIs4ZDLW1dUw8soeus+eWdgLwl5U5E14NcMklrVIq/aN2TDtzOv84ZgWXHn1OgZHxzn+2UlAIJrSODccZziaJpLMVlRJm8fu3bvJZrM0Njayc+dOdu7cyde//nWeeuqpKdflrzLyYfdep4PWGi9OWcS0YDCyMLX9XIgkbYHeYnRfAfRcR9Ih2ePopir7PpHRDAYjqYJ12XA0TddQnFi6OMGgZpjE07ZQ0qa4LPz9UVV1UTq9f9Aj2jx++9vfsmvXLqqqZs/M1AyT7uE4FnZxFxkd5Pjx44Uuxa233srHH3+MaZpcd13pXZrZPJXOnDnD6dN2R2n58uUzOqinUin27t3L/fdP+AFZ+dxboL3WV5KaZ8+ePdxx59foHU1gWlQkZePzn/yIlU2lr+hPhKM8e+48wWCQr+9eSv/h/4VxowqPmKLWMTZlW/+Gv+WfXrZ5LA90tkxJC3imq4/Px2M05lS0ozTgyZ5Gff/7KJt/Ssq5nFqGePr5dxgcHKR96Uq27qxswolhGHz8wSEeve9rFd3vxUQkmWUklsHrlPGI2pTRVH9/P1988QW33DJzPu5s+Oi3v+LqReqylIIR1eawyQK0uyZGmF8MjaIqIdwXeAOalj3KNS2QjAzdkWF647Epta8AXFEVYOssAqCvAiwLxnOdQQubm9egWLYX4Bw4Ox7BWLeZ1Vdfs6Dn7xqOkVYNhiNpPvn0U66+6kqqfM6cMtYuAsW8L5lpR0qd7TpHNBLhpq2bmXwHE4Aav6tgCbKYyGQyPPfcczz88MOL5pd3qWBZFn1jSTKagUMSaastLX5rPuiGydEvuhkYGODenVsWZWSa1Qx6Rm33yrZan50upRuMxbNkNQOXw06siKfVQjEY8irU+qfbnswVCwo52xS3o2xu++eff87Q0BA7duwo/YXOgT+ss3IW3Hbbbbz66qtzbhNLqfaF29B487VXGB4e5q677uLOO+/kjjvu4K233qK6urqs4g6Y9QKwbNkybr/9dm6//XZUVWXv3r0cPHhwyjYej4ft27fz0ksvFX4nCHaECpTWxQPb4+ndA+8UHOCLXbnMBdNVHp/vfNLuavr9fszEWczcKSlOYy+Bmewq8G8uTAvY1liLKAgMDAzw9PPvENAGcajDuJf/AIc6TECzfz8wMIAgiqxdX164+VyQJAnV/Gp/pT786CPee+89Dhx4h+eee27q3z78sOTiDkDJVKZLvBCoJkRyX5OGC1JSVjfUMh6LTnuMKIBfypPeXFzf3MY9y1expbmVFb4gd7U18VdXreT+ztavbHEH9gi42gGtLrubZ1jQnxUIazDX8n9pVQjP8Q/45P3SLEIuhCzaCQKGZSGK0BBy41ZknA4JRbZjpSRRxCGLKLLdZbrmipWsWdHJ6/tfpz7gosrrxCmLWMBoPMP5cHJRunmT4XK5ePDBB/nd736HWmFP0EsNIWfSL+fstYq19SoWoijg9/tJxMZnGxYsGE6HhFO2r8eprC0OUmSJpipPweakNuBiSb2f6tyCIJJUC6kXYPMEYymVntEEQ5F0obhzyuIUmx/NMAknsnQNxwknMpSKL774grVr1y7o9c6EP2gOXh6hUIje3t45t0lmNM6dO0c6MsRdd901jV90Ial8MbBmzRqAGdv+tbW1XH311ezfv7/A/XA5ZNKqQVYrrUVcXV2NLMuoqRjgJJXLsHUtwNMn6iy9wBtOZzgyEgZsNaq/cRXWZ78GQJJkHIGlaLGzhe1FbyfxuP1v5wWrpHq3i0eWtfHbM70MDgzwk5/9blKSxWmi0aP2PnKZshfGlVUMooRlWZcliXc+PPXUU2y4fitty+zx/9DgIK8deJ9btm7izOnT8+aLzgRVVXGrl35sHc9x0vJjywvhE2f+DvkkW+Bg5JImfLJMeyBIqz9ILDyI1/GHcwl1idDmtApq3TFNIK5D7RzdvJZgAMfpz/ggmWTjzaUX/2DnpOa/LZJg/799DRQK6lrDMKn2O/G6FEzTHpHJDfUsa93J3hef484776S2zk80pTISTZPK6nSPJKj2OYu2lSrr2GWZhx9+mKeeeoq77rrrK8O7KwayJFIbcBfGmdW+haU1TIYoCKxuqyE13EBvT880S7FKIJXVJwqyOe5tgiBQ43fhkEWGImlGY7aFlCgwpVsnCvY4+UJLM8O0SGV1IsksGc3uEKZVg6YqT9G8vkgksiipJl/tdkMJeOihh/hP/+k/0dfXN+PfTQsGBwe5tdSw6wpjrkq+tbWVJUuWFDp8+Q5eqQUe2F28D95/D1+OQDs4nlrQirdl8xZGSuRaHRgcxYKcuGI9g87NSOt/hCh5cG34r2Sv/Q1K3Q2ArVbU5DabPwd0+KYLS5YFfHx/dSdXVAUKnLy+vr7CY9rcLm7Yede0mLJKorGpmc++OLlo+18sHDli2/B0tDQWCv2GxkbSmsXTL+zj85OnppyXVi4wPZnRiKVVoimVWEolmdGmpKXIskxfbRNnxsJYi7ZWnx/p3FfEK818DM1eJ5FEctrvRQECucdEDaGQiiEKEDfkoo2hvyoQBKhTrEKXU8118/oythH0TC+33u8jc+IYul5ekH1dwI3HaS+MsrpJfdDNyuYQDSE3LocdDt9R76M+6MHrlPG7FZqrvXQ2BGioDvDII4/wxhtv0NPTQ9Cj0FZrc5cN02IkluF0/3jBCmQxIIoiDz30EK+88gqRSGRRnuNSweeSkUQB07KIzRLVVS5EQWDjxo18/PHHFd2vYdoZ8fmIsoDbUVQ0Y8CtEHQrZDWDoWhqSrfODjUIUOOfbh8jiQJ+t4O2Wh/1OSPpVFanezhOJJkt+hqxGE2BfxYcvDwsy+LJJ5+kqamJW2+9dcrf+sNJznT3gZpk+3XXXqIjhP3799Pc3Fzo5s2ETz/9lEwmwzXrr+XcsE0QX9pQur9PX18fPb19NC69AsO0cMoiLTXlcy3e/9Uv2OAvrpOX0HT+9pNTWMCfPPYwMUcj9z5TzfV1Uf7VtQn+7liIQ0Nenrt/FPcnP8Sz5v85zYpiLiQ1ne5Ekqxh4pREOnxevA6Z30ZFNu68dc7HLhSfHH2PB+5anKDyxYBpmrz00kvcddddhd+pukFGMxiaFKvnViRk0R6DzZd6UeV1UjspGigRj9P39K9ZUTM7D3axkDWhJ2Of0x0uC2WW9dubZwdpamiaZiRs5bh4hmX75eWVpqqmYyXCXNlyeXr/LRSGBeO5bl7+k5YEW6AiCjbfTcLkxHiEq+59gNq68jsQpmVxeiDKW+8cZPsN17O0KVRy1+3tt9+mqqqKq666Ck3TeGnfGyQyOkgOotEod95xGx11/kXtru/Zs4cbbrjhomXMXgyEExnG4lkEoKnKVtUapoWqG4iCgCKLC3pPw+Ewx44dK4v+kYcdeaZjYRGOZwvFvCQKdNT5ilbpplSdnpE48bTGqpYQHkUumVOXUXX6x1MF2xlJFGgMeWYNRwD42c9+VpKdW7H4Z9PBA7tC/s53vkNvby/Dw8NT/uZ3KzQ2NtI3OHNG6cXCzp07SafT03h4k3HllbZ329nTXxbyb5PZ0ldXra2tJBNxQk57NZXVTQbGk2V3JTyr1hIvIt4HZjAnzp7mhuYM7/bBd1/w8Xqfm21tOkGnRd2m/5NXD5yx+XMCBSuKueB1yFxRFWR9bRVXVAULo7T67OIrOs2v2Ndq79693HbbbVN+p8gSAbdCW423cGFKqwbxjEYio2FaFqJgr27dioTHKeNRZJQc52U8mWU4OlEc+vx+wo2tFc8vLgbjmn3z8UnMWtwBbG2vY3RkkHhqKofGtimxvxOxSV08xSETsRTCiUvPMVwMSII9nu1w2QkakmAXfQkDYrptr/LmcJzm3Q8TNRyMxTNTvNRKgSgIrGgKctvN2zlw8CDheOlj/RtvvBHLsnj++ed5/fXX2X3rDr71wJ08/LVbuHH7Ng4fOcrho8d4+eWXee655zhyZOFxhXmoqsrbb7+Nx+Ph5z//ecX2ezmgymuPuS2gf9zOYj+bi+7sGU3QPZJY0PQnTxkaHS3/3jscTTMYSTEUSResUQJuWxFcigWLbphIokhdwE7zKUcw4VJkltT7qQu4kEUBw7QYGE/Oqba1LItDhw6V/Fzz4at1J6oQ/viP/5if//znUzJZnTk7d+sy4E5de+21tLa2zplvuHHjRvr6+goj1lSmvPHIrl27OPD2m7TUeAvWKZO7NqXgyg0b+KLI41BzFwS/348Zt9Wu//v2JIoIqinglEX+71syZN77FuPnXilYzGysrV4QoX2VYtF1ZrrFRyUxl4fc5Ybu7m4CgcCszvouRaal2ktHnT1+qPW7qPW7aKn2srQhQHudn9YaHy3VXlpqvHTU+WnI2QtFc/m2edxwx50cH49d1FFtyrA5dABVjrmf1yFLbF3SgJmOkr7A7sYr2gWOmStw8mioDnF8LE00Wd53phxkDNujL6rb/7/YMxiHCPWKRWfOVqXWYVHjsKiSoc4p43TImBaEE1l6RuJTPvNikVZ1TvVHcEgi9Y1NHP30JL1jiZIFZFdffTV33303t99+e+GcVmSJptoQoihiOTzs3r2be++9l+rq6oJwzTRNnn32WQ4ePDiv9cnw8DD79u1j//79vPbaa7z22mu8++67bNy4kVtvvZVrrlmYqvhygyAINFdNOC3kFaeyKBRybc+PJRmLZxhPZMmU+JmBXZzP1dSYC1nNKIyP3YqEz+WgpdpLQ8hTchc4v7DwLdD7TxQEQl47q97lkDAtpiRXXYjvfe97mKbJj370o6Jt3YrBHw5DuAQIgsB3v/tdfvSjH3H11VezY8cOzNxJa1yi0OUL0dHRUbBPmQs+l5zz79PKJvdfddVVfHD4ENduup7z4SSJXHZfOVYDS2//Gl17n6ezOjTndjOZE/+/3vGimlkU0SKlqfzNaw7+/br/fYo5catvYekbTSE/R7pOwbLSBQPFQjMtVE1DcVx6a5C5cPDgQSRJYuvWrfNuq8hS0RfLgEcho9k5l5Fkdspo4spHv827Lz1PezZB2xy2RZWAlsuDBdso2FXkcnZjWz1vnxvEXd9U+J0gQFC2CGsCMV3AJ1mFUW5LXS0fDo9wLRD0Vi4d5kJYlm3SHL/gEiVgvzb7xxZELMY6VRBsgcqESMViiRknIKk4/AFGY7b5+vlwktoSrUqEXHcwmtaoa2rnyNGjLFnSQUY1cCsSzdXeBRnRhrxOrl2/zi7QMxo+l4Nly5bR0NDAU089RTab5dFHHyWRSLBv3z5EUSxkyebR3d3Nhx9+SEdHBzt37pyRq/2H4ot3IQTBHjNW++w4Q1kScUgiGc2wM851k+yk3PZSP3+A9evXc+TIETZtKs3hID6puGut8c2z9ezIqDqaYdp+kxWy2REEgfqgm57RBImMhmaYhanbhdi6dStbt27ltdde40c/+hG7du1i9erVC3r+f5YdPICGhgb+8i//kp6eHgYHB4mlNY4dO8bG9VfP/+DLCC7Fzsc1LZs/UA46Ozupq6vjow8OU5fjTY3FM1PI8sWivrGR+LLVRJPTCeuTMZM58bv9Lra2Cfz8a3G2NsY42CtONSdmZnFFqWjNxojFppvbVgpXX3U1bx2ofLu9UtB1nWeffZa2trZ5Q9zLRdBjd0+SWZ3zY0mGo2kymoHP7+fGR7+J7477OJxSGYouzsjcyHvYYRc+9fN07y7E+voQw5Gpx+bJJVFYMK3Iaq2v4+NwhqPnxzg9NNW/sVIY1+3nFbB5cB7J7ipa2IkU47kItu6cIGKx0TMeYah9OdU1NfjdDtrr/IUuz2g8w1AkVTTdw+WQc2R4idqAkzqvjFuZoAaMJxamxJZEoWALNRbPFI7L5/Px0EMP8eCDDyLLMqFQiNtvv51du3ZRXV3N3r172bt3L2fOnOHDDz/k/vvv59prr51ViHf06FHWr1+/oGO9nKHIEm5FLhQpLodEW62PkFch6FHw5D6zcBmfV3t7OwMDAyU/Lm+UnVYNYqnyKSCGlefMiRX1/HPmhEJgu3XMh1tvvZV/9a/+FadPn+bXv/71gp77n22Bl8d3vvMdfvKTn/CzXzzJ6S+/pLXxq0eY9rnsL1WyzDEtwKpVq6iurubEsQ9wOiT7Jlamamr9tu18OY8fnM8hsyandn3+lQPUMsRzD4T53zZ9gfn+N/mTlSf50Y4BPOoAz79yoGAoWwlbimsbgvS9/hKaVllVWB4+n5fR2NwF7qVCd3c3L774Ivfeey/t7e2L9jxOh0TIaxd5KVUnmlLpza1iAWrr67nh0W+hb7uF9+NpxuKVLbhHVIGsaRdkTU6r5I6W3+NCMFQyuk5PLMqZyDi98ShO7OOP69MTH5pqqglU1ZJSAuw/M0QiXVl7mLhuv4i6SSkUS90WS3LJFIEcT07LJXaoi9hM6hkL47zlDq6/7Y7C7/Jk8tpcvGMsrdEzUvyYtbXGR3tdgBVNIep9EksbAjSGbNuRcCJb1uh3Mqp8CqJgUyjODcenLGJnSiVqbW0teJRmMhm+9rX5Dcw3bNjA0aNHF3ScF0IzTDKacdkqtl0OibqAnfzUVG1/XqZllcXHLCfNIeR1FhaUI7EM5epGXZM87crlks4Gbxn36F27dpFdYDLSP/sCTxAE/ud/978QCFVz665bC8XS5QBJksgm24IaAAEAAElEQVRkppsmZjIZ9u3bh89nt6PzWYELuQDqus6JEydYtWpV4T0ox34lD0uaf5w3mzlxy+oHCQjjePVRfvXC+3QPjCBOyvmsBO5sCXH8hd9XbH8XwhQU4jPYblxKHDx40HaOv/fei2IFVBdw01bjpT7oLqzsB8ZT9I4mCoTj9iWdbPnmHxG+ahO9CyBZT0ZmEu+uSbHKiggbTmc4nYiy5/RJDvX38cFgP4f6+3it6ySfjgwSU1Xis3zd3E6F9qZGjo6k6B6bbqBcDnTLtiwBWywyGQ7RHkE3KHax554UPVbh+xRZVaV3LExKkmlubZtxmyqf0w6qz5nk9o0lGYmliy5QJo85/W4H3tyIvz+cLJmTNxmSKNJS7UXKEd/DiSznhouLqFq7du2sPNXJyOc0lwPDtBgcT00pPoci9r97RxOcHYwV1QG6lMgXzAKU3AU7dOgQGzduLOt58yNV07LKPuclUUTOHXOl49kKXHlVL/q++uKLL3LHHXfMv+Ec+Gdf4IF9UkajUWqqqi4rg9obb7yxkMCRSCTYu3cvr776Ku+99x4333xzIVXDrVRm5REMBqmtrUXO3fyNMldC4bExGjPzFzd5c2Ipl/P5k5/9jn966TSvHPPx+lun2bNnL2Ojo+iCk/s62yqaFjAYi1PTUDWnkGUh2HDteva98fai7Hs+9PV08+XJLwr/vhgj2dngUmSCHoXmak+ho5fRjGkX/+Gzp2mpqV7w86UNe0wJEJDAVYa/7ZlYgp9+0UVvOlNQere2thIMBrGA8eQ4hwfO05XIzJnb2lxbzZAhc7B3lLH4wor9SE4J7MyJPWaDKECj0/awy5rQlxXIVLCTdzaWwH37PRybJ9nA45SnjGwjSbuDW8zNzTRNxsYmxtyNVR48Sk7FGbbjs8qFS5HpzGV+5ycVQ5E0Q5FUxbo2GzZsKEmhm8rqxNMa58NJ4jmeVj4VYbL3nIWdDZvnW1+OyKj2Z6OUaJp/4sQJ0uk09fX1ZT1vfjTrckgLGq/mj3shzY0Z9ytLhSKvWLpBOBymsbH0+M/JuHzaVZcQbkXmxhu3s3ffa3z3kXvn9Ku5mIhGo/T29vKjH/2IdevWsWvXrhk7L5Nl4KZlIVH6CS7L8vSV5wKuIcUeQd6c+ODgKJ+Nx4hGo0SjdtfDDTQH/KytqabZVznBwpfjUdzbbmbX0mW8+sY7DI+MUr8AD6+ZIEkSicWckc2Czz48iuvER9TIIqcOvUVacXE8pfKt733/khp4C4JAXcBNyOskrepTz1nTJBQeRqxdWIGXMeF81s5TVXIWH6ViOJ3ht2d6MSyLpqYmdu/chEPvxUycRfQtRZPbeHn/Ec4MjHNseJAqRxNL5iBkV/l84PNxKhrDioyyKuSmxl8ajzRl2Pw6gGp5/tckC9CsWAzkxtS9GQGfZHv4uRcQ6GBhEQ5Wc2VTE40rVs27fX5k63NpDEXSqLrdzWut8c6ZLCDLMrfeeitPPPEE3/3udxEFgaZqD+dz2ajnx5J01PnKsrAA+1z0ux34XDLhRJZwIkssbdv/hLxO/G7HgpIv6uvrOXXqFHv37iUUCs0Yb6kbJrG0rTJPqxPFhCjYIqVI0i5YnLJIXdA2fM5nw/aHU4W/uXLWRPkYt9kI/BcLydwUKd+xnw/d3d188sknrFq1qmwfvKw2wdEMeRcmjnA5JDsBo8IFHtg5t4mMVrSlWSWK+MujkrnEkESB5R2tvPHGG4zFM3ic5StxKol//Md/xDRNduzYgWEYc96gxZyFw0JOCq/XSywWQ1RsJWC53kbVNTWcdTiZ24p4AvVuF/d3tnJb63Rz4nHDQdqEBVWbkzAQjSFvuoGOpcsAuO3m7fzsN89RX7ejIvufjJa2JXzw0cdsXH/xbBOGTp1ka8CPJIoEc6dxpLr+sglDd0giDvfUUde7e19hY8i/oP1mc4pZC1t80HhB3myxODA4ipkr7h644xqi7z9GRp0Ys4pKkAfu+DG/eek43UMRToSj1LvrZ4w/m4zaYACAU7EYxvgoa6rmL/RUE8Z1oTAK9kvgK/KK7ZKgzWUxpgrEDNvaJWEI+CXb8qScJkf32DjXPvjNkh/nczlw10ucD6fIagbnw3aRN1sR9fLLL+N0OvnOd75T+J0oCDRXezk/Zis2R+OZAj+vXOQjqtyKzEjMLkDzBZ8k2ga+LoeMzyXjKrJgyWPbtm2AbSZ/6NAhtmzZMuXv58PJKXZKToeEJAjUBFy4HBJBj4JpWjgdUmGq1FLjZTiaJpHWsLBjtLL61PGyLAoIgoBlWbgUmWqfc85iupIwTKvAsc13bufD6dOnufXWW2fkQBYD07IYGE9hYU+yin3e2ZA/JxejwMt/DqZlj4DnWkSoqoqjAi4Ml8dV/zJA18kTLFu2jIxmLJjMuxBYlsV/+A//gf/yX/4LK1asQJIkrrnmGiRJmifSRcg9vvznvuGGGzh06FDBrDarm2UXeeXkj15oTqxIcmG85KnAmRpNpRhs6WT5mium/P7WbZv4sILeQ3m0t7VysmvmaLxD+/Zy5Jc/58ibbxR+98Gbr/PGEz9FXQCx9qZHvs4nY+NTflfT382bzz5ddpTUYmPpVVdxLJJE08vjF2VN6MsI6JY9wiy3uEtoOp+Px7CA3Ts3EX3/TzHVqRw6U40Sff/PuGfXRmQ0hlJJBjMmepHfu9pAgIbaOo6Nzs5JsywYzqlhY7kUCV+uMCsFsgANTtuoOCDbV4i4YXc55xotz4ZRxY3PX1ohPjg4yP79+3nzjTc49fFhPjp6hKMffkTfWHJWlb6iKNwyQ2SkJNqWE2ALwCo1UvU4ZdprfTSE3AW6i2Fatno3maV3LMn5sbmNamdDa2sriUSi8G/LsmO08sVdtc9JR52P9lofLTXeAtFfkSVcijyFMiTmrEqWNQZYUu+nIeQm5FXwOidUrbppoRkmeq7Y6hlNlC2WKxV5/zuHJBZdVN5888288cYb8284C2IpFc0wkUWBpqqF5wDn/XCzulnxMbgoCIXzK5aa+zN5+eWX2bVr17Tf53mZxeJ/dPByOPL+e3zjuz9AEoVLOqLdu3cvDz/8MMuWLcOyLM6dO4cgCKxbt479+/fP+rhCB28Bz50nCCuyhFMWyeom/eEUzTlicrH4+IMjdHoWzpcb1iY6Mo4FFniqpvGp5OLGGQLR21qacX58gmg0RjDXaakUgjUNfHnmLCuWLcU0TQ7tfQXfUB/X+L04A17SI30cfeIfSIoSVyki/mo/H/zj4yy/50Fqy+Cj6JrGhe/8mroaTEvn1C/+O0P+arbcdQ8u9+L5tZWK5tY2mh/7Iacf/290ljgqN3OK0bwdSrOzvOIOpqarOPTeKZ27Kc+pRnDovdQFPAzGsoxl0ngdPuodxat160MhTg+NsXIG4dCoJhDN1eI+CapkqywuYR6KaAswApLNT8zkCuIGp1W0NyBAtZomEY/j8/tJzmODBPbo/eDBgzz44IOF3xmmyT8996otKIikaAg42bt3L0NDQ3z/+9+nv7+fmpqaWffpUmRkUUDPRWW5S+yszQZBEAi4FQJuBcO00HQDVTdtflxGI6XqdI8kyvJ300yL9z78hJ7+IXRzYsricQgYqQh33nknilx8cSIIAg5JsDvhk77GpmWRzjUnBEFgPGkrj4ejKdyKv+yRdrHIx4PlGwTFYKGThXzxGvI6S0qsmA2KLOVsxyyymlFy53Y+2BSVFNFUlhq/c1bO/8jICC0tLVN+Z1oWvaMJdNOiIVTc8/2PAg/44IMPuOqqq6jxV47EXy56enoKyplf/vKXPPzww0U97sSJEwwOj/CtB+5c0PPnx7T1IS99o4lcHqld5BWLdM85vAssIOK6zT0SgLoSPcwuhIXFB9EkNz72w1m3ue/OXfz0n57jxpt2LOi5LsSqlSs48t4Bhr74jOBIPxuCPhyT8ljdLhfrLhhPbGqo5fMXnyV2ww6Wrpqf6zQZh55/jq1107lsoiCyqq6WlVic+acnGPaFuOa23QSCwfJe2CJAF4uvYjTTLvqjum0LIgsLK+7ggnSVxNk5tzWTXQQCfiKxGIZpopr2sYSKnKq4nA5GE9MPNqxBJFfcNSoTubeVgFuCVqfF+ayAatncPL9kGzgXw83rrKnivZdfZNsjX6e9vZ0zZ86wbNmyWbd/5ZVX2L1795TfSaLI2mUtDA4MMBYOk4yGeeSe2+np6eH1118nHo9z9913z3kciiyhqzqqbuKeX9haMiRRQFJkXIrNh6vWDUZiGVJZndF4Bs0wqQu4Zr05Z1SdsRwnTDdMOq/YwMjICNes34AoirmUA4VqnxPLsti7d++096kciIJQcFQAe2SZNyEeiqRprvYsqohQFgVEYaLQKxabNm1i//797Ny5s+znjme0ipkTuxSbh5dZhALP53IUVNxp1Zi1mTTT55TIaOimVZLhd0lHb5gmkaRKWtUwTFv9Y698HDRVLe7Js5g4fPgw//Jf/stLfRhTWsKWZRGNRqeEVtfV1RW6eOl0uhAOf+TIEVwuJ263uzC2GAgnESWBhmBpbetkMonP50MURVprvPSOJUlmdXTDLHoF6FQz4Ci/WLYsu4sBUOWYOz+0GHw6Os6GR74973ZbN6zl8y9OsmZ1aUXVfNCyJmsyY4QmFXbzYU1tNT3vv8PH42GuuX7LrNud+vQT0u+9g08EwbS4yutBmINDKiCwvLaG5cDhvS9xwyPfKOWlLCqMIj2wErptAeISKYzwqx0LK+7ggnQV39I5txW9ncTjZxGxqM4tQOKGgICFU7LPYYuJjrpl2dYl5H5nWZAVJNKG/e+oLpA0YEwDwxJY4jZnLO5U0+bTOQTwSpTMpVNEm5s3mkvEsH8EROzxtleyi8qZrGUEBDqyCQb7+1m3bh179uyZtcA7ceIE7e3teDzTrz/rrr6KJ375a5ZecS1r165lMJqlpa1jzmJxMhyyCOri8KRmgiJLtFR7GU9kGY1niKZU0qpObcDm72m6iZqjs+iGRSSZnTJJcThkli9pw+dy4FKmJsIs5j1TEAQaQh56RxOkVJ3+cJKmGRJBrJxnnSAIC1KgOmQpxy8zSWY1vM7iVju1tbW0t7fz+uuvlyy0qA246BtLktWMspOcLkReaLEQtfZ8+09mdVR99gJvps5mPo60FJ5h0QXegc8HSWs6lmkhSSJYFssbA7gUW4kkCNBUtfCUgYuN999/n6uuuupSHwaWZfGLX/yCG2+8EYCnnnqKe+65Z8o2k2NzXnrppUKx197eTkOokb7BEb7oG+fK9moyuoGlASU2aAYHB1FVFZfLhUuRC6PajGbgK7LAs4SFVWS6RYHTVFWEanAu9ESiVO/Yhcc7/7m5esVyjn7yIlDZAu+Gm27k1Iu/Y3OJ2p32UJDRs1/wXiw6xVA2j2MHD1B77hTLGsoz5w6oFy8/tRgYcnEXrowpFNIbwB5jBirAI5+crqLJbYhKcBoHD0BUQmhyG9HoRwhAu89D2oKYBudUgZCjuMIrpkv0ZaduKABuySJpCPRn7fPfLdkdy3BOcJH/RjgEu8tX6vhWzlmphEy7sEzodvGZNiFtCoxqtgrZIdrP4RDsUa5LguZQkCNvv07j17/Ntm3beO655wp+nHlomoamaXN24r77rW+QzGoMhFN23NVYkoDbQV3QPW+HwuOUiaZU2xMuePGoBlU+Jw5ZZCiSQs3RV2aDy2ET/kVRwOt0zFk4XXPNNRw9epQNGzZU/JidDomGkIeu4RhDkRSJjI7PZR+XaVloujml4+aQxBwXsfTOldNhW4EkMlrJat4VK1bg9Xp56aWXuPPO4qZQeT4j2N3KShXL+QJcXaQCr5zjjCSzxEsUsEAJBZ5pmbgcErIooBomqmZyaiBGtV/B6ZCIJlV8bgX/AkN6LzaOHDlyybp36XSa3//+9yQSCSzLYtOmTaxevRrLshgeHqa1dXYd6oVfgp5Rm8grigJp1UAUBNKqjmmV1tK95ZZbeP/99zlz5gyPPfaYXczrpfnrpdweoHxCf1rX6YplUU2LSNJkTciDr8gEC9My6eofQHQomKZBV00zt3fO3Y2ZjMaaEOHxCNVVoTKPfmZEneURgGv9PpzjQ7zx61/i71xGIhpFsEyMaIS1RpraQHnq0+FojLB1+Wis1GyW84ODrF22ZN5ta3Ojy6xpFyALsf6YjHy6yufjMV7ef4QH7vjxNKGFqIQIbv57nnnlCALQ5g/gkmWclkXCEDAQMLBwCna+qsCkH2HCPkjEQhN0pFwGa35U2uaEqGEXckkDksb0765LtBdAmmX73DU7rXlVvDPBJYJLsah32CbKGdNOy0ib9r8n3DvsY3AIEJAt2g2V059/zvI1a7j33nun7benp4d0ev7Fg9fpoKPez1g8QzytEUtrpFWDpirPnCR9j1NGwBYUZFS94mO0uWArgv2EE1miSRULu5h35CxK8urbgEcp+rrb3Nw8j4BuYfC7HbgVifGELbwQ5yg2NcPk/FiSxipPwbetFDSG3KTU8mxmmpub8Xg8PPXUUzzwwANz8vPiaY2x3LgcKpcdCxNCCzUntKh0lzVf7MfTWlG2LqZlMRa3R/61OdV3sSh6y+tWNOCQxQIBsXc0zrFzY2TCBj6XTMjr5PxYgpDHSWMF1CwXA4cPH+aaay6ehUUer776Kt3d3SiKwr333ksoFJry9xdeeIHt27eXtlPLwsJeHZiWhSKLxNI5JVAJJ2hHRwf9/f2FWB4zV9iVco7XrVhN9Nh7BH2ltayG0xkODI7y+XiMLDIqCgIm3t4MV1QF2NZYO6/Z8ehYmObqKtxuD32jo9y0uzRO4s6btvKLZ15my5YbSnrcfEg6vEB5OYl+j4cbDAPj/BkUxYGAAB4ZKK+4e6NvgJW77+bGJZ30nOvio9de5d4f/GlZ+6oEPjl8COnz42xtqqd7aJiOhvnFJU7R/qk0tjXWcjISZ2BggGdegd07f2b74CW7EL2daHIrz7zyAQMDA4iCwJoau3sqCHaBBhOihrkQS6a42i/S5Lad9yffc12SRZUM45pAwpgY7bpFqHXYHTsjp7RNGLbIZIm7/BG1IIBTsN/PoGzZqRmmXUBqpoBm2XxYzYIxTUCQA/QcPsLS1atnLGJCoRBDQ0NFPbdDEmkMeQi4dQYjqULyRVvt7DYqthpRJqUuDk9qPkiiSF3ATa3fhWmVntgwE0KhEKOjo1MoOZVEXcCNlRufehQZr8vOMJckAacsIUsihmkyOJ4mpeoMjNvdyfwrc8gibsXOB/Y45VlfsyAIRY9mZ0IoFOK+++6bk5eY1QwGc0pSURCoC7oW9JwXwiGJCNidcs0wF+SJOBNCXoVYSi04dswn6kykNUzLQhaFgll8sSj6mzF5RSUKAg0hLx21WU71RwsKTqdDQjctDMuipQRS/qXCsWPH+NM/vbg3trGxMc6fP88Pfzg74b+zs5PDhw9PGcnOh6yqgWDL5Q3TojHkoS7gLktZND4+Tn19PapuFHgIrhK8lFatWcMXh98p+LAVgzOxBL8904uZK1RrA15MdxWJZAo1YdtXnIzEeWRZG8sCs+84bRjUu+0FRlJWylKLLoaIOis7MK1sSd3UyZAlCbmI+Ldi0OF18+Xxjxk5dIAWQ2W5cnF8smaDee4MV+RSLNKqxkgkQt0Fi56LhXy6ym/P9DI4MMDjT+4hGAzi9/uJx88Sjdp2OqIgsK2lndAkgYxXsoUWqmn/zMUdjaczNLVX5fY1/e+KaFuc1FtgYPtZTd5OEuwiUsvaZsYx3earVgKyABP3NLtQNS27oxjNdfjqRZFjhw9z7QzJKD6fj3iJ2cIep0xHnY/zY0myer7I88046tNz2azAJTX2FQRhwbzPPLZs2cLLL79cEbHFTAh5naSyOsmsTlrVaQi5pl1PJFGkudrDSCxDLKVO4ZCquomqq4WRqNcp43M78LscFe9wybI8ZyZtPK2SymqIgsAVbVUVUc9OhiAIKA6JrGaQ1Spf4CmyVFigaLox7w0nbx4d8Cglv9dl3cosy2IoksLrUrhhdSNj8SyjsTRZzWA4kiaV1vAoElW+S69KnQvFZAtWGr/61a/4F//iX8z697Nnz3LgwAH+7M/+rKT9arpOMh7hk48+IOgScE6SqguCQFtbG52dnfOGOR88eJAbbrC7V3kJukeRSz7JU+7iu7izpQdExroZFzqwHBv49MNjDAwM8NszvXx/deeMnTwLi2RmwkMu6yqPn7O8vZnz/QO0NDeV9fiZsGTlKs598DpL60IV22e5WFoVot3IIPtcgIuBeWKnFhvudBI89qiiNhSkd3iEeDKJvwje5GJgrnQVAah3OlnX1DqluAO76LKwR8cjqkCTc3ZDYVkoTmkoCLNfpEUBApLFiCmQNASqFqg2nwuiAH4Z/LJF0gDB6eTLU5/CDAWeKIpleYhJokhLjZe+MdsEeGA8RWvNVFGAaVn0h5OYloVDEnFfJqlDXwU0VXk4enqEoWgGhyzSUTd9AiAIttdgXcCFnpveWJZVsIxJZXU0wySZKxZHxQwhr0LQ46xIJ7Orq4uzZ8/y2WefsX79+hk7mmnVIJzIEvQ4OR9O0VTlqXih75RFOyEjmcXpECte5DkdIsmsRUYz5qXJy5Jgn+tldKrL+naMxbOoumlHyFR5aKn20jUct80gDZN4Vufj7jCbV9ThUS5fTl65Roa6rvP8888zPDxMKBTigQcemNF1WtM0Dh06xJdffln43caNG5Fm6cT09vbywgsv8Jd/+ZclH5PT6eT2XbfSUeebdjLquk5vby8HDhzAMOyV7/DwMN/4xnQFZSaTKYyM87y7cpzQxRLi0mZLDxAsgZTaiij7uO/2/ze/3wuDAwMcHBzl/s6p/MRwNMZoIsGKSUVZWimvwNuw7hqefHZvRQu8urpaTokuimcDLi7yq/eMAUO4GI6mkUTbZqGUbu1c+OKT4yS+OEHG6SbY1sFV69dP2+az48dpdk3lobTV13Gq7zxulxNZujQ38LnSVbqGwzic0xcYUd0enaZ0AdlhMawK1DisaR6O6UyW2goVY14JRjRbIGFY03NqLSx6hkdor6+zR/sVes5oKkVUN3n11VenXc+ampqmxx4WCbuL5KVnJE5WMxgYt2/g+SIvllLJ5u49zdWesjvilyO2bNnCgQMHCikYlYYgCHQ0+AknswyOJ2cs8CZv65h0Mk3OUs1qBomMRjSlYpg2P2w8odJU5Vmwh+zZs2fZuXMnN99885zbVftcCIJ9LOdzI/1KdvKCHoVYWiOrGfSOJllS76vo/p0OGUFQp8TUTUb+Pg32eL2uTHvWkj+NjKoznlvxN4QmRoBLGwL4nDK9YwliOZfxo2dG2bi8DneRBPmLjaVLl/LjH/8YURT5xje+MU0NNhnpdJpnn33WtlAQRe644w7a2toYGRnhiSeewDRNNm3axPrcTezxxx9HFEU2bdrE9u3b522tDg8P87vf/Y5//a//dVmvJa+BmOmCJ8synZ2ddHZ2Fn43l2myaZqIolhYFZXqawTgyGbAM3+HdHp6wGMFUruQHw/pCSLv/0t27/wHHn9yD5+Nx7itVcebO6+GxsaQZYmVLc2F/Wq6BlXl81kW45RVll9J39mPaK1aWCxXJWBYdqepPyuQDkd4//fPYVkmRlUNN++6mWARn918iH/2Cdd6FEAjeeo4pz56j4zTTcblJuVwsXLDRsa7zrBqhk7ditZmzvQPIssyliBgaDrLmxcWvF0KsjnBgYmDpYHgFKVqR5WfTyJR6kL22tuy7EixpJFXqJro2Ny1jwbGaFJAFC0ES8TndpNNRrlhSUNFjtOR4yJmTXuEGsidt4PjEYYlB4lAFdc89G2OPfUr1tcvnN+l6TqfReLIV67j5k2bZ9ymu7ubSCRS+Leu6/NODibDIYk0VXnpDydJZfUpRZ6ei+BYaF7s5YjBwUG++OILqqqqWLt27aI8R7XPhdMhYSFgmGZZRYvTIeF0SFT7nMTTGuFE1hZnhJM0BN0EKnDtmEtgYVkWHqdMnd/FeNJ+7vGESm2gchNDlzJBGdBNi2RWJ1BB08V8IawZ5oxCoc2bN/PCCy8UrNDKRcm3seGorY7yuxzTVDb1IQ8uRaJ7JMlo3M72O3JqmI3La/E4L/44dD7s2LGDHTt2oKoqTzzxxDRe3Pj4OL///e/RNA1FUbjvvvumCSLq6ur4wQ9+AMDzzz/PqVOnMAyDjRs3lsShe/fdd1EUBV3XS86gmxx5NJdCajIaGhro6+ubptT1eDykUil8Pp/tN0XpBZ5pmihacWO/udID9NzpKWAhaGEcei/BYJBoNMoHff10eJwICJimQWfN1G7b+XCEzXc/UtJxT0a1z00ikcTnq9yYcMmypXz05YmiM3oXCyljIv0BYG1NiJDDVlGOREZ48Z9+x4Zbd1FXHcDvVsoaf0TGx6nLJMBjc+u8Hg/LpniiqYzsf5E2gKrp/oACAssndVDPDQ2XfAzlYlyb8GEEiOn2uNWbqyf8HjfaaAawF1ZjmlDw46uSLXwyGJZFf0IjIstUuRy0OERqXDJWKkZbhYq7PLxSrliPpegVDWK+AMtv/Rrrm5sLPFr3Dbfy2eG3WVMdKFowpekajknWNWfD4wzXNLLlu4/OeQPu6OjANE2efvppHA4Hqqry0EMPlfSaPE6Z5uqpRV6Nz0kqF4dV6RipSwld13nxxRdZvnw5P/jBDzh58iRvv/12wTKrkpBEgdYaL6YF58Mp2mq8ZXPoBEEg4FHwuR0MR9LEMxqRlIrfXT4vL5FIkMlk6Orq4osvvuDmm2+eds/NNzOcikSd7GZgPMV4MossCUWpUouFItuFrJ7VMcrJ9psDkigULGX6xpL43A68TgdOh63Evvrqqzly5AjDw8PUl5FolEdJBV5G1cnqpp0uEJy5Wg54nKxokhCA4VgazbT4+FyY61c2XLZGyIqiFFqivb29vPLKK1iWhd/v55vf/CZOZ3Enzd13383nn39OPB4vqbgDuO+++3j88cdnHd/OBXOShUmxI4srrriCN954g1AoxOHDhxkfH+eBBx6gtraWkZERfD5fYb+lUis+PX6cJUUWRsWkB1gIxA0fSqwLv99PNBol4PezpHZ24+C04iypa3AhbrlxK0/+/tUCH7FSqLlmI8ePHeTqcnvuFcCIOlHc5cn6+eLFJbrwadD/8vN8prixahu47obrqQ+5Syr0jr/+GlurpydqTEbdDIXdbFCw0A190Ue2o6rAeM7hx5MzLE6bdkFc47AmCRksjJwht2ra3Lxqx4RdiShAXE3w/T/5YyJJlY8/Oka460uWBKtJ6HYRWCn4JIuTw1G0azaxbdOVKLKEZtgctnz4uxKq54uG5ZgjPawJueYUB3SHx+l3+1EdTtbGY4DFKUHhqnseZkWRn1lnZydVVVWEQiFOnjzJyZMnWVViKsvkIm8snqF7JE6t306Q8C9GhMUlwrvvvsvtt9+OK8fpXLVqFX19M2dYVwL1QQ+DkRRZzSCaUhdcFImCQGOVB29aw+eSF3Sfv/vuu3n++edZuXIl999/Py+88ALXXnstzc0T05l8Q0MQBHwumaBHIZpSGYlliCTVQlKEQxZxyhIBT/ndXo9TJpnVc+9T6SKHuVAfdKEZJlnNIJ7WCpz3Gr+Tap+L733ve/zn//yf+Tf/5t8AthdeKquXlCpVWoGXUy7ZMunZL/YuRWZ1axDrvMVINEMiq3N+LElrbYlOrxcRW7Zs4e/+7u9ob2/ne9/7XlmFFsCaNWvKPob8aLTkx1mlF2KiKJJOp/n444+5+eabicViHDlyhJUrV3L69Gk6OzsLnbtSMwxj/X14Z3CwnwlzpQfI6Mjo6MiM61Vk9U4Go72YCDjnOaasc2EGqJIksRjuC41NTXw+sJTGaDf1/osvIkgZtseZCDNaa9Q4LEIy1CkB4rpANDrMW0/9juvuvZ+gx4nisC+aiizOebGrio9DCckd86G+tpah0VFaGirb/ZoM3ZqICatxWFQ77AJvKJf6MJrr1AVki6xpMZC1SQQCUKdYBdsW04IvxiPc9kffwOty4HU5CG3fzPGaKj57/zBLvC6aPDLVJTTqLSw+GQmTralHNgxEQ0c2dCRDRzIMRqLjXLVkCX1jSTxOmURaKyggXQ4JSRS4Yv06PnhtBE9So8MrT/vs46kUH+kC6+95mKW5Qu7TDz7A5fWwfc0VJb+f+c7LqlWreOmll0ou8GCiyDsfTjIezyAg0F7rK4sXfLlC07RCcZdHuRzGYuB3O0irdlFUyTSQUgx4Z4MoilO8Fe+66y727dtHNpstUIysC+539UF78Zn3xcu/JFU3SAk6kWSW+jJHx363wljcHgOnVL2idiySKNJe68upmzVSucSofHNaEAS+9rWv8fTTT3P3vfcxErOnBqOxTNHj6BKjyqzcgc1fSciSxJqWEMn0CEnVYDSeuawLvGuuueaSeOJVAhOdttJWF5Pn+6FQiM8++4yxsbHCxSW/6kllSzNMVtQsFHn9nSs9QBCgQRkibvjRpFp0uYFI/CQyCh3zdAgVo3yj5TxcsrgoRpdrrr2Wo3v7WRxDhNmh5tSdYCsiZ+viSAIEZdsTrV6Rcckyn+x5FrFjKcvXXIHD4UAAfG4HdQHXtMXe4Tde50p/Zb0wZUkirS9uNFU+NswpUii+hFzqg0uDkZwvXcIQqPb56IonqQ94qbtASBFJJLj6tlumcHq9TgfXrVvD6NJO3n7tDcJjw6yq9lE/j18eQEbN8mFSY9M3vjur7c/yHAdK1c1CJ8CtSNQF3IViKKsZcOttfPjcM7gdDpqcE8+dSKf51Onn5kcfmLLfKzduLOatmxdr167l2LFjrFu3ruTHepxyznPOQpKEkheclztUdbo/pq7rdHd309HRsSjPmS/svgqF8q5du9i/f/+kAm/6NlU+JwGPg4xqFLwJVd0kkbGFDEPRNJphlpw3b49SZWJpuwCrZIGXh8cpzypOWb16NUeOHGFooJ/6mgaGo2nGk9miC7ySvilyiaR7WZIKpsf5i87/wMzYs2cPW7bMnjk6F4z8imaBMvVvfOMbhYQM0zTxuWTkXLs7UoKVhqJmit7W55BZ4fcgAC/vP0Jw848RlQnhuCSYVHsEVm7/P/jkw+N2NFQggHOe8asnnUTXF1bkbdm4nk8+PbGgfcwGeckqBqKJRdn3hTAte8TYnbFD5mWBQn7qfFBE6PDA1lovV431EHvpafqe+x1dLzzLR7/7Hfv2vYVxQbfB6fGiL0IxJi7yjV3Lc3tmeJqQA1qcdsfTIUDQIRISVBqU6SpZQRDIzJDkIOYsKHbtvhVl0xYOj8aJF3GKmoaJd+XqOT0dZUmktcZHXcBF0KPQVOWhtWZqp8vpkGip9rDill18MhYjnLskpzIZPpbcbLvvgVn2vnB0dHSQTCZ5/fXXy3p80KvglCUM0yrwwP9QsHbtWg4ePDjld7t37+bw4cPs27eP/fv388tf/pJIJML+/ft58803F/R8hjnhI+j9CiRPmaY5ZZGtyBNJE5MhiSJelwO/24HHKRPyKrTW+KjOpVyEE9mCQXIp8OSKunhauyTcz29/+9v89re/JehRqC2xQC3piunOGaKmVWPaRX02eJwOLMuiwl6Ef1AYGhpidHS0bOVU/qNYqGVAfkxwyy23sH//fgRBKKx4IsniUxhiloBpzXx+ZLIZugcGODs0zNnhYc4NDbEuYCvk7PSAj1HW/wz/hr/Fu/qv8G/4W5T1P+WZVz5mZLAfUYB1NSHm47y2Vldx5J23iz7mmdDYUE8mtThFWHNbM29F0nw0Os6JcJSENvudPqHpnAhHi9p2JoRznSewc1tbndaMYfKzwS4Iod3vZFNLNTe2BNlS52VF0EvN4Hme/fUzNtE5keW9t95E7/qysOioJERxcbsNWdN+UxRh5mP3SLDUbbHEbVErqjjFmdMjgl4v3WfOzfo8QY/CqmUddNxyG4dHEqjzXErHMhn0VHLe45dEm2ReH3TPGjPlUmQ6W+pwrL2GL8ZTjCazfCQo3Pjgw/Puf6HYunUrmzdv5sknnyz5saIg0BiyC9xERmM0liGaUhmMpBiNZVAXubu7mGhvbyeRmH6deeSRR9i1axc7d+7k0Ucf5cCBAwiCwKefflqx5/4qiFWOHz/OlVdeWfi3MrkjXQRq/C4acpnF+YizUuBzyQVeXz4y7GJCEAQeeOABfvWrX1Hlc9Jas0gcPEWWCuHzxeaoqbqBBfNKjDOaQSSRRTVy+W9AyOesqDT5csWTTz7JX//1X5f9eLNCHbw88kpaoGAkaphW0WPaGx58hOO/+CnX1E2Q7DPZLL1jYbwOifamxmmeXI+4PXOmBwiAJAjc1tpIlVMpWKjMBlmSEMfHSnzl0yEVaUhbLCLhUT796H16z36JZVmczh2iAKy5II5tcnTb5Fc707azIWNM8MqalMqR+92S3dECFy5V59jvfoMALPE66fQ7kYXKqdnyEE0D0zIRhcqvFtWczQgUl21b5fdyBUm+GB3BQMLnduNyKmSyKj3RGHf9+ffnfHy1z0mmOsTYmqv55PSnrK/xIAowGo9zPJZiW30Vo8kUPYqXVbvvZ0ldXQVepY2gR+GqK9fw7ugopwfO880fTPfDXCz4fD4CgfIERi7FHtWOxjMFq648IsksAY9Ctc/5lRzhzicIk2W5QKlZvnw5zz77LLt27ZrT2qunpwePxzPNLFgSxULBoumVT2qoNMbGxqaM9uVct6iUfPSAR8HCdgEJJ7I4HVLRWbuCIFAXcDMYsdW6DlmsiI1UKejs7OTIkSOcPn2a5cuXF/24kr8JeaJirMiRa1o1MA1rzjDgcCJD72iCeMY2FlR1k6xuMhRJk1YXzqW6nPHUU09x1113LYjnJefaCJUizHZ1ddHYaHuOjeWInU6HVHSHUJZlfNdtZSQWxzRNzvQPMBqLsaK5iea6+hkNV/PpAVdUBQqcvL6+PqLRKAJwRVWA76/upM1nr8SK+Wp70gvvvnU01zMwMLjg/QD0957jlWd/XSjugsEgra2tBINBLODz8Rg//aKLM7EEZ2IJfvpF17TiDpi27WzImnA+JwTwSVRUuQn2+LbdZdHmldhY62NZyA+yQl9GQFsEjnhLXR09FbZLsSzboLgv9z55JHAVeVWs9XvZ1l7LliY/zWIKOTFKs5impblqGmn+QgiCQGOVm6UrV9AbqOVcPMvHI2OMX7mBXX/2FxzIgmPn19j66DeprWBxl0d90M2m7VuJOz2cz6lU8z+RZHZRO2Lbtm3jueeeK+uxVT5ngVTvlEWqvE5cDgkLiKZUukcSjCey6GV4d15KlHL9b29v59577+XgwYN8+OGHqKrK8PD078Xx48c5d+4ce/bs4eTJk4XfW5ZVKI4q0RTQdZ1MZv6umGHaaRiZEu9Ts703pd4ygx6lUJiNRNMldS/9bgdVuYbWcDR9SShnjzzyCL///e9LOu6SL/l+t4ORWCZXiBlzVv+WZTGeyCJgzRqSOxbPEE7YqzG/y4HP7UAQ7LSMvEu1z+1AFAT7R7Tb9S6HdNFDpiuNrq4u0ul0WeqyyfAoMgL2iiat6mVFmuShqirHjh3j/vvvR9UN4jmbhfoSTSRXrr2Slz84wvKhITqbGorqusyVHpA3NVYzkLZsE1rnPAR1XzZDJp0uK482jy2bNvCLp1+kqWlhJruR8Chvv/o8pmHQ1NTE3XdsQ8icxYyfRvQvx3It5flXDhTi2CzLKliZ1NTUEIlEMAwDSZIIhUKMjY1hWBa/PdPLd1d14lNcCNgFiiRM8O5M7JD6Ysj85UDKRVgB+CWLvqxt8Hs+K9Dumj2qq6znkqR5R/Ol4nzWzlYFm3vXWMb7pDhkmmqqybv2negd4M0332THjh2FbY4fPz7NOkkSRZqqPBg3bOPouwe5/d6HCvGOu77xrXJeTtEQBIEar0zQ78MwrcI1eDLcikS1z7XgdIILUV1dzU033cSzzz7L/fffX/LjJ9+o80hmtdyo1mQ0nmE0ninYTXwVUOqoVBRFbr/9drq6ujhw4AAAqVSqkHP+0ksvsXbt2oIwYc+ePXR2dqIottWHN2f/MRLLlOWFN/ne/9ZbbyFJEppm3ysmR4zlFwrJjM5YPMPA4CCSJNHUUE/Ao+B1yijzNA8ufG/y9LByKEm1ARfxtIZuWiQyeknK39qAC8M0iaU1BiMpRNGzKKKLufDNb36Tn//85zz22GNFbS9YZQzhz+fMJ6t9zjlVKaZp8lHXKJGUxs1XNk/7QDKqTt+YbXRbF3BNGfnqhkl/2A6eng0eRaaxylORDLw8NMMkmStqKmmaOBmWZfHkk09iWRbf+c53KqLSHIqkiKU1nA5pWn5jKdizZw933XUXomhn8fWM2h2iZY2Bkvc5PDSEvvc5GkrwO5sPScNOXxCBVpc1IyE+D9MyOeTwc+PuOxf0nHtff4dgQxs11eW/jgP7X6L37Jc0Njby4N3bGaUBT/Y06vvfR9n8U1LO5dQyxNPPv8PAwEDhcW0dS9iw8yH0xCiv/v5X7LznG1iuKo6//Xv6e85hINIcqGFHs22GKWIb3xrYliiyYHfZKhWKPh80E3qzAoZlF0v+Cq/BkqkUiXSahpqaBe8rZdgFnogtOgnMoSyeD5NTAY6YMu2bruPIkSPcfffdHDt2DMMw6Ovrm7GgCScyBW5PW62vYlFxxWDvq6+y+Yab0PLUGEFA082CoTDYhV590F3xUV4sFmPfvn08+OCDFdmfZVlEU2ohYgqY9x51ueDJJ5/k3LlzyLLMv/23/7YsD09VVenq6qKnp4ebbrppSta6aZo8//zzBQsSzTDpGYljWhDyKtQF5l8EW5ZFLK0xnshimBZLG/wIgsC+ffvYtWtXYbsjR44QiUQYTxtkjalfqMaGenTdIDw+PuX3Po+LbddvIuRVpqny9+/fz86dOwvHcG44jm5aNFV5ih6zTsZILE0kqRJwO2gIlab2tyyLwUiaREZDAFprvBe90fTMM8/wwAPFCaLKOrKAWyGV1YmlVKp9zjlaqAIBj5NYWqN7OE6134Wmm5iWzeeaHGZ/YTElSyLtdX6SWfvLapo218yyLHTTsoOPVZ2hSKok47+ZoBumfWFIqYWAZa9z+jFVAkePHuXtt9/mkUceoaWlpWL7rfG7SORG3H2jCRpCnrIk8F6vtxAWnlccyaJQVsFY39DAp6JMJZ3LvJLdkUrnYrZanBbKLEWeKIi4UvEFP+ftt2zn8d/sYfuNN5X1+HQqWRjL3n3HNkZp4N5nqtnWcg3/1x3v8Devezh4XuG5B+DuO7bxk5/9DrA7dxt2PsSje7xc36zwN9/+M/7DAYtPhuE/3ng7I9HnSERjnI0l2d5g4JYlVAviuQmIgF1kXaziDuzoLJ9kjz2zpoC/qGF68fB6PAxE4xU5p1K5m49PZpKB8fxIptNomkowEEBAIJxIcExy43YIBBIxaOmgubmZ22+/naeeeoolS5awefNmVq1axdNPPz2toKn2uUirBqmszuB4ivY630XLWBUFYUb6jB3/lCWasm0mekbsa0olvM7yCAQC3H777WV38i6EINgik5DXSSSZZSSWyYXSK5c1L+/pp59m/fr1fPvb317QfhRFYdWqVTNOhERRLKQjbNq0CYckUhdwMxS1ix3TtGlUsiROO/d0w+bcR5LZwv1RFCCrmzMuRjZt2kQqq3M+PCEMUmSRkNdJ0KNgWhaJjG3sm9UMDNPifP8gL732Fjds2YLf7SDkVaYsKCzLFjhEUypmjqOfF32WCo9TJpJUSWR06ku0wRJyYp/+sEVKtV9jjd9Wrl+sIIdoNDr/RjmUVeD5XLKdC5grtGaTWudbwaZpMRrLFE6OyfA4ZZqqZq+ivU7HjG3QjKrTO5YkmZvpl7vqDScyhOPZKbehUgiYxSKZTPL444+zatWqsvNm54IsiTnX9xRZ3WQsnllQ4avqZkGG3rSA/SQ9lc9dbXJanM8KBY5Zq3O6VUUenvT86sNisOO6a/jw44/L8kocHugrcO6EzFk8ZoKtLevY1+1k2890dCPN9U0xYsOnMKwwbn8VyXiE8HiUZGSITXV1vNtl8Wi3iGrC5mYTMx0hGre7qyIGhhanwx8kY0DcEDAt28fOdQn4007Rtv/NLBINKuhSSKSS+DyVMYou5bIcTSY5FayjYdUaPj11EjmbwbdkFbu2TE88cblcPPLIRFxevqCZqchrDLnpHkmgGSajsQz1wYUZdS8UDkmkPuimyudkKJIirRoMRlJYuCsqfOvu7qatra1i+8sj5LVzUjO5tIZKdPEikci02KxKQBTFRcuenYzOzk6Gh4c5dOgQW7ZsmSI8iKW1abz6/Pdi8r1REgWqvE4CHgVJtBW9q1evBmyKUFYzyGpGQQQT9CjTzmVREAi4lcJ5ZJgWjSE3PrfCu4cOYVkWkihQ45GQBIuVK1cSSaqFfYoC1AXdZeXogt1QEgW7YZRWjZIpCIIg0FTtoT+cJK0ajOQU3fVB94LoUcUgmbSnp8WirKOxo2IchXb4hQVeRtNx5ThTPpfdBo1nNJIZjbqgG2euMnc7pbJn2C5Fxu92FGTPLTMUIflKf6bK2jDtDlX+zXI6JKq8Try54rXS+MUvfsEPf/jDecnXC4FbkWmv8zESTS+YN5NX5jokcUEjI7mphfT5s7iLjHsrBpJgKzj7cr5uY5pAo3PmTlFAU4lFowSCwRn/XiyWLung81NniEZjBIOlqQDz3BS/348ZP4165t/xf9/xDlueENE1HUWEv9yYIX7of0Nruw/BFSQVz4IJLzy/h3/50Df5qN9J1hQRHS7+0x06j///nsVp2ueuiFmIfHNJ4JIurfWBJ3fdTZv2yHa24rtc1FVV0T04tOACL1+IpovkfIcTCbrqWrj+1tsA6Fy6dJ5HTMfY2BgNM6RxSKJIQ8hNfzhFNDcZWeyu0+DgYOHcnA0OSaSl2stQjlg+FEmTyurUB90VuU6mUqmCqtY0Tfbu3UssFuPRRx9d8L4DHoVM1D7e/z93fx4d1Zmm+aK/PcQ8aB6QBELMICYzGzBgBmM84HTa6awcKrOzhq6uPtlV3X2rb6+zzu3b96y77ul1+p48t6rO6XNOVVdmVmZllTOdtkkwBmMswBgMmNEMxhiDEEhCaAhJMe/Y0/1jR2wkNEWEImTcz1osQNpT7Nj7+97vfZ/3eSomuc48fvw4XV1drF+/foR/92Qxd+5cjhw5wtatWwt63NGwdu1aOjs72bdvHytWrKChoQFJFAhFFVKqPiyYG5b0kEWCXidBr3PY937//n1mzJ5HW09kFF06wdagGw+SaFnPrVw4k3lN9QzGUiRSGm6HxIwq64vLUIVKfU7bri5fWBZnDsLp2MHtzJ3SJAoC9eU+BuMp+iIKSVWnZzBBRdBdVF7eL3/5S1761nez3j7vKCDj/2Y8QuELRZJ09sdY2FCOJFpmxLWlXlzp6Lsi4C4Yx6Tcb63S4opGTzhBRcCNKAgkUxrdgwkUzUAULC0+QcCyWHKIJBSdwbhimxbX5Gljkgu2b9/O8ePHeeaZZ4p6Hkc6kzdZZHiNmm7k5GLxKFY+uZ4bP7/MnAIFeFFVoy0SI2UYGEg4nH5kYeznqa6ynJMnjrP5+Rcnfe7nn9nKL986yJM5+tM6HGmhzEgEMTAH55qf8u+OeEnqEm6Hn6gO/8cVB/9u7f8LPd5LKnEHARNRlFj/9LP8l0seYrhxOGSShsB/f0zgxy9/l4Nv/YPtoTyRddtUwiFanahJw/pT6AAPoBDJwYwWYDbH6olE6KifxZrNW/I+38DAANeuXbPFxB+Fz+XAKYukNEuI1l/E7/TmzZvcu3dvzGsZCqss5UUUEra9VaEWwatXr+bw4cM8ePCA/v5+du7cyf3797l69eow7bN8kFnkJlV9GEcyV7S1teFyuXjttdc4cOAAXq+X8gl8lnPBrFmzuHnzZsGONxHq6urYvXs3Z86c4cqVK8O4epqu204RgWCQlStX4nSMn/SIK5od3MmigMsh4XZKlHhdOfHjhXRmz+eSuf0ggqIZ6IaBqps2p7JQpdAy/8MMb2coRl157kFehhIQ8Dho77X6BfrCSXxVxQnw7t+/T0lJCZUl2TuC5R3guRwSTdWBYatMRbPS+HVlvmFfbGXQMtWNJtWCckycskRV0G2bDIfjKk5ZHNaGbZjYhtsR1Ef2F6kp8UwJSbKiooLf/e53RQ/wJgtBEOxShCxaZfhQJHtrlNFQiDLtaJpwCg40QswNevDVl4yqCScgIGuFaWlPJpP0POjMeb/qaQ0IgsDg4CCmexZxRy0nO5zsmKnwn7cm0hw8L+KT8yj1BNGi5/EBFWUVTG+YztkLPrbM1PhPW5L898ccnOqQ+dcrK+1uWgEmtG6bahSbjTJZIWXNtKzHwNLX+7S9i6UN00aV8OkKR+ieOY+VGzbmfz5N4/333x9Wsh0NsmQFeHFFKzhNJIMLFy6gqmrOGaPqEg9Bj6PgItY7duygvb3dzow1NTVx8OBBW/qjvLycpUuX5uzT7ZBEO2C2sqK5j2GGYXDhwgWbI/jcc8/x5ptv8txzz+HN0m97ouO/8847fOtbxReafhRr164d83eGYRAKhdi7521efvnlYUFgBj09PWiaRkXAjTOtDZdvED0UQ3X6QlGFaLp07HPJBWv0ccoS9RU+OvqsMmtnKEZ9ee7dxJnrbaj02UHpZChj4+HNN9/kxz/+cW6cwXy6aMfC/f4YkYTK3GklxFM6HufD9mfdMGjriaIbZtZdO9kiklDpDSeGcfwCHgcV6aYORdUxTJOkqqNqBg5JpMTnLNoACnDo0CE6OjoeXk8ggCiKvPLKK4TDYc6ePcvcuXOZMWNG0a4hX2Q4QuF4igdpWyCvS7b1p3LFR+/s5UmUUSfPbHArHOWNW/esJhugpKQEnz9A52CKaDSKhyROweS12dOZHRy5ujktutmQRwYvlUrx0Ucf2d68DoeDgViSuUtW4XTk9uwUqov2mW98F9lfyfmWN7nXdsfWCHy5qbBlo8lAN6E1YenKNbrHboKZDLpCIbwuF0FfboGtaUJYh7ZwknbVBJcLn6yz/hsvc+kf/57VNcNFYTsGw4QXLGXxqtWTut6/+qu/4plnnmHhwoXjbpdpDgBLNqq6tDCl0AyOHTtGVVXVlHC+CoWuri6uXLmC1+tlw4YNOe2bGcMMw6C2zJezQO3+/ft55plnRnSk/uY3v+Fb3/pWXt2uQ/Hmm2+ye/fuUQOoxwGGYbBnzx62bduGpml0dnbS29tLMpnE5XIRDAYJh8MMDAwUrBsahsungRWsT6/0FSSAHIpkulHCMK33rXacfoCJ0BmyegKK0bn92Wefcfv27WH+8dmgoAFeZyhGOKFS5nfSF1GoKXFTEXgYyMWSKp39cQSgqSZYUHkT07S6WjTdxO2QvlITZVVV+cUvfsEf/dEfDft5e3s7169fx+/3s3r1as6ePUs8HrdbwB8XhEIhLl26xNatW4dNOAJWiX080epRj9fXR3z/m0zLo6zRnUjy089b0U2TadOmsWvbahzaPcKhO/QxE9FVy6fnLnH//n0kQeAPFzSNyOSdGIix+ff/WVbnu3nzJnfu3AGsgG79+vXDBt9YPMHvPviYNatzM2EfCPXy3p7XR+rgRW8h+mdjupt4572T9ufIRgcPGPMzf5UY1KA7JeBKCyEXC192dTGnNnt9QsOE1kiKaynwNM1h0eJmaksfyn8M9PfTuuc3LK2yJFjuDgyiLFvNgqW5N9aMhsuXLxMOh9m4cfxMYH9UoTdtp+SSRaaV+/JaWGVw7NgxFEUhGo2ydu3agnPIpgrnzp2jpKSEuXPnZr2PaZpcvRsimlTxux2U+lxUBd1ZVW0uXbqEx+MZtStV0zTefvvtCTOy4+FR6ZLHGSdPnsTj8VBXV0d1dfWIbGooFOKzzz6b8NnOFoZp0tUfJ6ZoeF0ytaX5N1VcvXqVGzduDAtANU0jGo1SWlpKTFHpDFmKEZMJzgbjKboHE3icEg0V2ZdRs8Ff//Vf82d/9mc571fQAC8cV6wALj1BNZT78D/ScXWnO4KqG1SXeKbc7mOq8M4777B48WJbZHI8tLe309HRMW66/KvAhQsXCAQCzJ07F0XV6QknSKTy15a68qufsygH7kAGb7e2c70/TO20aXzz2WUMfvInGKlBBrQSwnqQgFtk1ob/mbff+5Su+/dHzWbFk0kue0rY8MLuMc9jGAbvvfcedXV1w2xxRsM//u4Qa9auy/mzdN67w/H338E0DLur1rJji9iOHaIg8Npsq6twaNbyUQzddrSsZTGRUlWuhWNoJWW41BSuVJJpTplgWlC6OyUwqEGZDJVFElhOJpNcbb3DqoULstreNOF+SuB8f5TFL7xMZXB0aYOuzk763t+PSxIR12xk1vzsjp8tWltb+eKLL9i5c+e428UVjfv9cQzTRBYF6sp9eS9aU6kUBw8e5MUXX8y5zPm44eDBg2zYsCFruzPDNGl9ECEUTeJ2SnbzX8DtoDzgGlbyu3XrFqFQiNWrV9tVlvEW39FolMOHD09K4uXcuXNUVFRkNVc8DjBMkwcDCcr9rhHP44EDB7LidOYC3TDzSgS9+eablJSUoCgKFRUVNDc3c/ToUTuY3r9/Px6Ph5KSElatWjUskfGoJm+2yOjGigLMrp1cU99QnDx5EkEQWJ8j9xsKHOCBJSKYTLcejxYEZEQGC12mfVxw+fJlzp07xx/8wR9kvc/+/ft59tlnJ53uLzRaWlowDIMlS5ZQW1s77CV4lH85EU6//SarHbk9alFV46+ufIEJ/MH3d5O6+COMlKUBFNYCDOil+MQYVT4D5xM/42e/2ocA/Osl82zniwxC0SidM+ezbBQ5C3hoGZdNl/Mb7xziiVW5B3hgZfKuXTzL3dtfjFBoby4LsuERL9qTXb18NooX7aJHtp0q3Okf4EHFNNY/P7xUcPPn/xezKq3M1wNFIKxDQIJS2QQBHEL+IsKP4lpbGxU+LzWVlVmX/UMqtA4mCc9ZyPo1y8ftMr/behvTMGmcPbswF/zotYRCnD17dsIgT9UNOvpiqLrVLDa90p83B6mtrY2urq7HbiGZDw4ePMiuXbsm3C6l6XQNJFBUHVkUaKjw0RdVhtlMlXidVARcSKLIgQMHaGpq4v79+0Qikawya729vZw9ezar6xkNra2tRCKRES4njyMM07SlQRySSGOVf9gCKRQKcfXqVTZt2jRl1xQOhzl58uSw+5+xZXs08zowMMCJEydYtWoVbW1trF27ltbWVi5fvsyuXbvoiyR5c99BSktLKXULuNNdWKMtimbOnMnsR8YH0zT5sisMwIxKf8GqiP/b//a/8a/+1b/Ka9+CRxQTBW2ZAbnAPN3HAp988gk3btzIKbgDePbZZzlw4AC7d1sZpoks4KYKmdXruXPnuHLlCgBhTWbp8hXWoJlDgOdqmEH8zud4c7ANa4vEbM6dQ7tHMjW6wKORGsCh3aOkpITBwUHaojEWlQ1fQZX7/dz/4hqMEeCVlU3sH5qBxyGSUtWceXgApeWVbNi2i5XrN/Ogsx1VTeFwOOnv6WWz0kP5kIAtG+u2YiOeTNAVTaC4XIQlJwue/yazh7hIhAcHuXzoAIu9D7/XTCAXSWvyZeASLQkVj2TiEcnLxiw0OEhDeTklgewbdzQT+lWBNlNg58qlE0oIzWjKXf4kF1y9epXVqyfm9GV4Rx2hOIqq0xvOX9uysbGRS5cuEY1GxzWof9zR3d09qszMqNsOWsGdANSUenHIErWlXkp9On3hJPGUxmA8RSSRIhLqpq6+noULFxIIBLIeCyorK2lubs5b5uT69esFz3oVA4PxFP1RBVU30vfTMyL7XV5eTjQaRdMtJxS3Q0pn34Scs3C6YaLpBg55pPByBsePH0fTtBH8xTt37oy6eCotLWXdunUcOnSI733PsgJsamqisbGRAwcO0NfXx6vfeJVoUptwQXXs2DG7ZJ2BIAh4nTLxlMb1jn4ENcmVq1cJ+r08+/SGvOb0/fv3s3379pz3y2DKU0Zq2gRankp5/SnAyZMn6ejo4Pd///dz3leWZaZNm8atW7eoqpvOibOXGeju5Luvjl1SnEqsWrWKc+fOcb+7F9Wwgrpcu42eWLOGa9cusCCHAC+j7RYIBDCit4f9Tsd6WUTB2saItRIIBBgcHEQZw2i8TNfo7ekZYd6eSqWQpOxfvi0b1/PO0TOsXrUy630ehdvjpXH2PPv/jbPn8dHBfbw0yvztc8gjAtapQnckSuP3/hDnGDI3F37xdzzVNIOhQ0mpw0Q1LSFqsGRIdBMUw/rTr1n2YH4Z3KKJQ7DcSbJ5pPrjCWZn6QusGpAyYUAT+LJ/kLlbthVdDmkiJJNJEolE1jIbkmh1+t/tjRJTtLzlPs6dO0dlZeXXOrgDuHLlCk8//XRW22pp4+KKwHA/XbfD6qAc6l978doXbNn0FOF4KmeO4owZM1AUxRYQzha3bt2isbExp3N9FegetORxwFqU1ZX7xhT0Xb9+Pb/4x1/jDAy3EhQEWL6kmXmN0+xAJ6VZLha6YWKkS7Fup0woaqliWOcT+OT4YWoqyxgcHOTpp5+mtLSUGzdu2KXXlpYW+zzhcBjPOHNMZWWlHdxlIIqi3bxgmiYd6SxlX0QZ04Rhy5Yt7N27F6/Xa2f4DMMgpRvcHUwLNZf4WL1mLaFQiDcPHOGbzz6dk2KHaZq0trbm3FgxFFMe4GWEhYut+DzVuHr1Kn/yJ3+S9/6rV6/mo1NnOHLmCqVlZTQ1NXL9+vUJu+4Kjbfeeova2lo2bNhgd68lVZ3psxewoN4iN7sdUl5Cyn2BMkzMrMtqzvSLE4lEEP3DsyqGaf1OwuIFir4mIhErCBxNEy4Si3HDV8rWUTxM33vvvZzKCgG/D0Mbac4+WTRueJrzJ95jZfVXE8yNhhmVFZw60jKmn29g2UoiXW0EhkhGyILlNjIUkUSSG7EUmiEg6ybBkhLCGoTTz4JHtMSrxwvyTEyyCW10E3pSAt1xhV5FJSLJNG7ZwYJZdRPvXGDcunWLGzdu4EoHyPfv3+e7381eqBQsSaqMZFFKM/Dk2JZ88OBB5syZk1NzwuOKiooKbty4kdW46Hc76I8pxBR11MYwn8vBhc9Pc7ezm6rqOjTDTFt3KVQGPTmNcXPnziWRSHDp0qVxObyXL1/mzp07eDweEomEXbV53GCYJuF4isF4yta4qwi4JtS2Ky0t5bVvfYv+mIKq6Qw1r/rkk0/o7R9k7qyZKKpO14NuWltbrfMZBtXV1cyaNYtr164RiUQwTZNUKkUqZfLKps04ZYnDhw9TUVFBV1eXnfl0OBx2kBeLxSYVEGU0H0PRiaXBXnzRUmYYWsLNdG2rukFTdQABS4c3mUzy632H2LZxLdNrK8c44nD80z/906Tlc6Y0ykqm5UpEgSk11J4K5NvmrhsGyZRlp1M7cwG1MxfgcUrUl/vYt28f8+fPnzJi9NmzZ3nyySeRZZkDBw4QKKtk/vJ1KEMUyoMeB5V5cieX73iWe3t+zYyK7LIXjQEfApb3nipPR3SW2Bw8Iz3Vi4KB6CxFlaczOHhxTE24gM+HO5JEFEXi8TiffPIJuq6TSCTYuHFjzhZExRDvDQYDfD5tDn2DbVT4J6+xNR4SySShRIL6srJhPx+IRnE7Hbid1oQoCiLe6Njehys3b+H4P/yc9d6xA/feSJQvgxVseM2azEJ9fZw/dRo9msCZUjCSCkFJxFHiJyCbGCYkDetYMirRgX5Sokh7X4gnZ49PRjdMuK8I3B6MEq5pYPm6tbhkiVK/a8rHHMMwOHr06Ihu+nwgSSKaoWOMYvc4FsLhMIcPH2bnzp1f+8xdBsuXL+f48eN4vd4Js18lPif9MYVESh9Be+nq6uKTTz5h3bp1PPXUUximyUBMoT+qoGgGHaEYPpdMZdCddWlt6dKlnDlzhuvXr1NVVcXJkydtrbyqqipisRiBQOCxDeoU1fJDjimq3VAHFue3ujR7i7qAxzHMs9gwTVTNwL3hSS58eoVjH30MWLSYJ9euQZYlJEHgzr12fvWrX7H7hedZ0FSPxyXTGYoxEIkTiijUlnnZsWMHN2/epLq62j5+oTl/ctqmbyI8Oi9ndPvAsh7MJLHqK3yI4kwaGho4cfYcpqbwygvP4BqHZqMoCuFwmNocVAJGQ8GbLMZDRtvG55IL4rbwOOHnP/85P/rRj7LePppUCUWSw4InsAKo6hKL35BMJvnwww8nJGMXCkPJy0PT8gKW7c+jBtD54NT+fSyI9RPMUiR0rC7arlQNKdNJtVdl2pN/OW4XbQadPb2cCVRSO20aa9asmZT21PGPz+AsqaGivGzijXPEpwff4eWqwlm7DYVhGsRicT7vesCs7/wzbn54hBmpGHWlpYQTCW6W12JKMuL9dmrUJHXlpZwMRdn8w7F5pclEgi9e/wXNVSOzo22DYZLNT7DoiRVj7j8QU7h8/RYPWm/jUJJIpoEmO9BkB4LHw9wFC/H5fJiaAh/sYVbl8AVCJBqlLx5Hkx10mW5Cpsy0FatYvnDWlIiYj4fOzk7Onj07qQ5WwzS5fq+fcEKlxOvE55aRhx5LsJxnfC4HfreMIAjcuHGDW7dufS34Xfng4MGDrF69msrK8bMhHSHLu3NoU9/169cJhUKjaurphmGb2mcQ8Dgo97uyHvtOnDiBpmls2bLF/llnZyeqqhakJKsbJklVwyVLY/Kg44pGKJpE1QxMrHKfYT70kpUlEUXV0QwDXTdR0uXSoXBIIqU+JwGPsyCSZqZpElM0NN2w3C4c0jCqz4kTJ5gzdx411VX2z5Oqzr20TVljVf5NRsWGaZrcT8u6yKJAY3VgBHdwIKZYlABV5dSpj3li4RxWLF006vH+5m/+hh/84AfjlpuzwZQGeHd7oyiqnncb8uOMbAM8TTfoSpt3Z+CQRLwuedQA6tKlS4TD4SnpTGppaWHbtm12uzdAmc9Fmb8wCuUZnDr4LnMGeyjPQqR2LB28tvsdGK56qsrrOfLh+XF18DLQDYPT/go2bcuftGofS9f5p30trFtX+K7EaDTK4NF3eWraw+Cx/cEDGiYgl7eG+ok4XHiVBLVeD/5RguhTXT1UbNhC48yZdnahq7OTWx8dA7ebDS9909421NfHuZbDbHnp5TE5eBl8dukSqTPHGUBkfW0VkihiYvLRrTs8/a//7xN+5ljSMjvPWBJ5nNbgn0hpwzwuPzt8gJ0lMr2RMK2eAKY/SM2MRubOn4+iGbT3xQBoqBibJzTViMfj7N27l+985zs57WeYJoNpk3VF1UmkNHxuB+G45dVZPYrhuksWufXZJQI+D6tW5abV+HXDnj172LVr17gNEdGkyv3+OKIgMKsmgCAIHD58mB07dox77JRmNbXEhhi7l3idU+IRPB5M0+ROd8QW9be6gN1IokBS1YkmVDTDGNYpnC0EwOOS8blkvAV0jcgGra2ttLW1DQuKM+joixFPaZR4nVll1qYaumHSPZiwHbPGG3uGdnZfu3YNQU/xwo5Nw97jvr4+9u/fzw9/+MNRj6GoetYdulM2AuqGYQ/exXSQ+CqQSqXo6emZcDvdMGlPyx4IQGkWwdPy5csJhULs27ePhQsXTgmPJvOgZkoUhcbanbu49Pd/m1WAV+1x89rs6bxx6x5d9+/zs1/tIxgsQXOXEY93YEYvImIJ/r42e/q4siGSKCIqyYJ8BkmSEIXirI38fj+Dy9Zz5bMzLKkMYpgGH3R088OaarsMGo7FbBeHe/39tHuCLP3Gt5lTYvH3rn16ifDdO3gScdzxKOWSQL+mI1bVsnDR8FVjbV0dtd8eyQsrr6jgmdd+L6trXrR8OSxfjqZpfPDXP6GiYTpJfwlr//i/y2p/n9uBb4xxwTAt7llXfxxv0zwOnz/Bwp07eGrh8M/RG7bESku8zscmuAPwer0Ecuj6BWshmBkrwKK01JZ6cDlk7poR5Myi0OvCBNS0Hdfla9eJxTSWrVyMaZoF8e18XPHSSy/x29/+lqqqKpLJ5KjZSp9Ltm2vokltWOlwPDhlibpyH8mURl9EsTtuB+MpAm6rBOl1yVN+f1XdGObYZHUBj7ToBGueLfM5QRAQBYtfFo6nCEUVO6kgSwKyKCJLIu4hzlP5YmBggP7+fjRNwzAMNE3DNE37/7qu2z/P/DsejxMMBkcN7gBK/U7iIY1IQi14gKelLVSDXmfOnz2zAAtFFYx0nmxamXfcsccpS0yv8BGKKjQ3NzMwMMDfv/EOW55cyexGq/L0+uuv86d/+qdjHmMgplBTml0FbMpGQUVN2z1J4le6AiokTNPk9ddfZ3BwkB//+McTbt81EEfVDWRRoL7Cl/UKqby8nN27d3P16lX27dvH9u3bC+KDOBTRaNReCWcGinwaKbLBx/v3sSqHrtDZQT9/uKDJ1oTrC0dRwioCJn5y04QrlC8tgFzEx7h+xnS+jEbwd36OpCu8/OM/596+3+JxOrgtu4lqAosGBrnjcLNg18tsfKQzuHnZcli23P7/3bY25k6fzvwi8zllWUZasoLVE2RIcoEoCLgdElUlbtQ5czjd04MrMPL5yQRDviI9t/nixo0bOQnZZjr5VN2wuvECboIehx1M1JZ66QknUTUTn1u2F4ilPiceZzPnP73K4Q9PsXHdGmrL8ncAeNwhiiIvv/wy9+5ZEkn79u0bwW8TBIESr5NQVKE/pmQd4GXgdsrUV8jEFY2+SJKkqhNJqkSSKqJgLUwC7qkL9pyyhMcp2Vp0YD33ySHJE4ck4nJIo37WioC74DZaQ/HBBx+wbNkyRFHE6bSExGVZRhTFUf/O/Hs8+kImYDJMM6su8kzgNRBT0AwTl/zwPoGAx5np4jXsMUMSxZyejUftUR1p3l42c6YgWO+0L/1dbdq8hU/OnaOjO0RV0E1DQ8O4qg7RpEZ2QkFTGOClNOsBdBRzVpxidHd3E4vFxo22MwgnUnYH8bTy7IO7oVi8eDGLFi2ipaUFj8dTMFsYsLSDMpNQZph6lJNRCPR2d1M/0IOjrDSn/YZqwp3vTxLWoMJhsrTUk7UmXNvAIJWrclcDHwvFaLQYijmLFnE5Ekbsvcs3Kiq4FCinceky1s+bT293N6lUio1ZSjrMmCI5hlAoRDweL8qxvU4ZhySy7sknOX76FBsNk8YZDz+/S5ZQ09pZjxOuXLnCq6++mvX2mc5FUbDEeR8dK0q8TgZiKVTd4otlshqyJFJX7sO/9gmu327nnYOHeHrLZmZUlzxWGc1Cwul02oKzmzZtYs+ePSOcJUq8Tqt5It1EEAqFSCaTWWvdgbXY9br8JFMa4YRKNKmiGyaRhEokkQ72XFYW2pvOGmb04AzDss8sFB+03O+2FwAzqwMkUhqmadEavkqOWltbW1GqTKIgIAoChmmi6SaP5odM07SD3GRKt7+bDIbz3M1hZXewsuO5rIGiSZWuAWuMk0WB8kcWYNnC7ZBorPITiirMnjWLf/jHX1FTVcWf/tE/Szejjjyekm5UzRZTGOBZN9n1mJIk80FNTQ26rtPf309Z2fhk+1jSeqjKJ9nNJ4oiO3bsoLe3l3379tHc3DxCUTsfPHjwgEWLFqEbD1c1uTxI2eLG4YOsyzG4GwqfQ6YhEEA3YbrLxJ3lrewMh1GWrGThwtFJrfmgqqKUUP8A5ZP4PBNhydp1nDpl0PWgm62vPvS9rBzSRVYItLW18cUXX9j/D4fD7Nq1a9RMsWEY7N+/35b+ePLJJwkGg1y4cIH+/v6ieWsKgkBduZeOvhhr1j3JiTOnUXWdOU1W8Fpd6kEUeOwyVitXrmTfvn1s2bJlQostK2izaAQVgdGJ/ZmsVG8kadNehiLodbJ8fiPVVRW0HP2QhoZ6VixZ9JXzx4qNEydOjNo4IUsiQa/TFuv91re+xW9/+1teeumlnII8sDJ6bqdMVdBtZfOGBnvpzB5gy9oMhUMSqSpx43PlT1EyTdNOloBVms+2u7XY+OKLLybkNuYLQQBMa/ETVzQM09KaMwxzxH0G6/5XBNy4nZJdPXQ5RAzDJJHSEQQr2WSJMWf/TuiGZdUGVkNkVYlnwtLu2bNncblcLFmyxA4Cb9++zbFjxzAytpWl5fybH/9LdEEmFFXsMrpDEnHIIrIkICAQTqTGPdejmLImi/a+KImUTk2J5ysXGy0kDMPgf/1f/1f+4i/+YtztWh+E0QyT+nJfQUufly5doq2tjZ07d+Y8WA1FS0sLm7c8bfN+JmuNNBquXbpI7fVLlE5CssE04cuE9ZLM8phZ2V89CEfom9vMkjWFbYjQdZ1/3NfCk0VotHgUp09+xO+/mr++03i4ceMGPT09wzLChmHw5ptv8uqrrw4rn7S3t3P+/Hmef/55ZFnGMAxOnTrF4OAgs2fPHtWYvdDQDcN2d/jwaAt/9N1vTrzTY4CWlhbcbveoQQhYk0dHXxRFM3A7JBoqfKNmBXTD5F5vFFU3CHocY/JxDNMif1+/2UprayuzmppYsmA2pb7xtcy+jujs7KS1tXXMe5vSdNp6rMaxGZV+HJLAm2++ye7duyc1bmaQSGlEkyqxpGYvkMFqepEkkYSi2XaDQY/DJsm7nXLWC37dsKzrMhkphyQyo8o/ad5coZBN80o+0A2D2w8iJFIaTlkcNSATAKcs4nY+bBIpRsk8o3M3mlXbUFy5coWPP7bkYJYtW4amabaFmmmaNDQ0sG3bNhyPuCGFEyn6wslRg9YMREFgdm12XsxTlsHLqIo/buWTQmAijSnDfLjKcBW4rrd8+XKWLl3KoUOHqKqqmlTn3GA8lRdHMFuUlFeQ0kdmHHLB0PdJNcf3N00oCvfCUZLzFvFEgYM7sBotTKNwnL7x4PQG6Onto6pypBTJo0imNHojSZLplarLIVHidY3KMbl58+aI4A6sTPHu3bvZu3evXfI6c+YMmqYNy9CJojjmpFosSKJIQ4WP3nCS2pKp9eKdDLZt20ZXV9eIiTCl6YQiCtGkiok1gI9mBZVBXyRpv6fjNUGJadHW4NL5zG5q5Opnn7Pn3fd5ct066qtKKPE6/5tpwqirq+PChQtj/t4pS/jdDqJJlf6YQm2pl1dffbVgQZ7HKeNxylQFre/TMEzkIXxz3TDpDScIJ6xucYZ0ufrdjjGztRkYptWglyndVwRceTUGFAs3btxg1qziWPyF49a9stwuRDxOicqgBz3NUZWnkNef4Tr63CMDyNu3b/PBBx8AMG/ePP75P//nw7bJhlIV9DgJuB1oaas2VTNIaQaaYWCaVvyQS8a26AFepuRwtzdCban3v7mV4xtvvDGC8/EotPSKrljlI1EU2bVrF9evX+f48eN5SaoYxnA9vmIMHA0zZnABickWF/0SRHXoTgk0uMwRnqa90Sh3XH7k+lmsfLVwnLvR0DyrgVu3bjN7dnH9S+fPn8/lq9fYsmmjzSEZ7UVPqjrtfTE7W2CakEjpJFJx+iIiPreM12mtcG/fvs39+/fHfF4y2aaWlhYMw2DevHmPjbWSKAjc+uwS69au+aovJSfU1tZy5coVesNJ+mMj3VBkURiXo6vqhq3RVlPqzWo88bpkZrj8lK1eRs/gXE6eOs1nbjerV66gIuDOufHgccXs2bNpbW0ds6GlzO8imrQ4cxUBA4ckFjTIy2C0704SBWpKvQS9GuF4CsO0ApZ4OvMXTapUBtyjOm6Ypmlbqkni6LzMrxqJRGJCmlK+UHUD3TBRNAMf1rjndkhQIOHylKYzEEvZcjMTXQtgN7hk8Jvf/Aafz8cf//EfY2JRsrrTjhYZj9qg15HV+yoIAg5JwCGJTLb6XtQAzzRNOvpiJFUdVbfUrB+9MV939Pf3T2h+rWqjPxSFxsKFC3G73Xmnyku8TsLxFJph0tkfZ/oYJaLJIFpagWEkEIX870WV0ySRtLxOH6SEEbZYAymVtd+ZnMVLtlj1xDJ++85BBgcrKSnJLm2eD7weN1cG4rSlNbAEwSJxS6JIMqWRVHVM8+EA5HFKtrBrNKkyELOMwjt7Bjh79iwV5WW4BY1dO8b39ayurmbbtm1F+1yTQV1dHWfPnrXFub8uSKR0ukJhm8MIVhCW4eeO985lOHdOWcyZ6hHwOPC7g5Q/s4Vb97rYs3cvL7zwIuGETE2J52vPz1u4cCGHDx8eM8BzOx52oA7EFKqCHkSxOEHeWMhk+jJQVJ3eSJK4otl/VwbdiKKlARlPasTSnDOAmlLPYxfcgVVJ2rNnT1EWgAGPg9YHYVK6gSwJBV+QZPQODcOkdgzv2QwyyZqhlchkMkkoFOLb3/42ceVhYDcUGeHp2jLvpDiYuaKob7RmmOkIFqqCbhyySBEaM0dFUtWHkVGLgbNnz7J06dIJt7Oj/il4MZuammhubmbPnj1omjbxDkMgSyL1FZY9mKLqI3SVCoF1O3Zyp69/UseQBZjmNBF4mMmbqufqURiGwbSyAIcO7i/qeQRBIKno6ObDQaYvotA9mCCSUIe1/HucEtPKfLgcEi6HREXATVNNkNpSL59fucjTmzdRUzuNOYtXEEtOTYm5GGhsbGTDhg28+eabX/WlZI24orHwiXWcPn2acN8DZlYHmF0bpD5t4D7Rgkp45O9cIQiCxcFTBli6YI59TW09EfoiyaJ0zk8lHq1EPIqytMB+OJ6yP2smyNu3bx/JZGF0MrOFy2HZUlalS+3xlMbd3ih3uiM8GEgQSaq2vWdVcHINGsXE8ePHs5oL84FTFnHIElVBNzUlY1MX8oFuGMQVDUEwKQ9MbL6QoZoNdZL5+7//e370ox8RjqeGyRuV+pzUlHqoCrpxpmOfzlDcbqKaChQ1g5d5gRySaJf8DNNEynt4GhvRpGp3nggwrJtJEAQEASoL/IKcO3cuK4mU1BRl8DKoq6vjxRdf5MMPP0RRFDZu3Dhu997ly5dt827LgYD0vwv/PRmGgV6Avh6PBNVOkwcpgUEN4rpAucPg/kA/4frilxFv3rzJ7du3AauLtLl5MfuPnWbdunVFO6fTIVFb4iLgddtNS650JsfjkhEQkERhVJVzUbBWvqUeiVm1JfjcTiJJlc7+OH63g+oS92PXfZoNgsEgW7du5eTJk1POBcwVtv2fILBz+1bav7zKuU/O8OSTTwKWFuWpU6cAq4t51apVIzIimcltMm/QpUuXMAyDbU+tG6asH4paFl21pd6iaWAWGzNnzuT69ev2ePYofG4HTlkkpRkMxBRbEy4T5L399tu88MILRc/kPYpSnwuvS6YnnLTltFyymHaWcNjOLo8jjhw5wuzZs4tG39AN087aFdp6MJZufnFJ2UrMWG9e5qvo7OwkGAwiO5y0d0cAK+NYFfQMmz9LvE66By0OZihqVVNqsxQrngyKXqIFa3IRRCujp+uFL9OapklXfxwTRsgGWM0N1nV0huJ40w9IVUn2JtKjob+/P2t1+kygO5X8Q1mW7dLanj172LZt25hB3v3794esvh5OHcVYzZ85sJ8No3iW5oOgDCImnYrAF/1xvozH2Pmtb7GkKnsR5VwwMDDAqVOnEEWRWbNmjfAIbqqroO3uPRpnTC/K+VetWsX+9z7gO998gfpyH3qayJ0tTpw4wdq1axEEgepSDwxiyzwoqs70St9jGeSZpmXiXeob3fXl5s2b9PdPLitcbMQU1ebOZeQVpleuo62tjQMHDuBwOPB6vWzbts3uWj506NCISdM0jfTf+V3HpUuXAFixwvIGdsoSMyr9RBKq3bzREYpR7ncVVRC3WJg/fz4HDhwYM8ADS0euayBOf1ShxOu03yFRFPnmN7/5lQV5TtnK5gFfGxeSw4cPM3/+fGbMmFG0c2TkQlTdCsrL/YX5XnTDpD9q8WC9WSZ+Hn3v3nrrLX784x/TPZiwAkWHNGrgJggWB9PtTNlVF48zRUmRFUWmbDTPBFPDRQcLA0EQ7LDEJVsq3jUlHmbVWKWPunKvnTOMpzTiKY2ewcmlSSVJ4tq1a1lt60zX62PKV1MOe/nll2lpaSEajY76+5qaGq5fvw5YGbyMlVxnKGaXAwsBTdOoHOyz7bYKAZ9kNV2EJImqJSs5ceo0F67fpmsgTiEUgAzD4NixYxw8eJBr166xa9cudu7cOaqY58a1q+hsu4WqFud7djocVNbN4MDhY5ZCfI4LpaqqKr788kvgYYfl9Eo/siig6oat7/S4QDdMYopKRyhGKKpwrzc24jvN8H5isRj79+9ncHDwK7raCZC+bDlNts9UNBobG3nuuefYsWMHGzZsGCZJ4/f7OXToEHfv3uXatWvs27ePd/btA/JffMViMerq6kb8POBxMKPKTzCdKQlFLWP0ryOWL1/Ob37zG1paWjhy5AhHjhyhpaWFVMoKsANpmRIT6HnkM2aCvD179nwFV/4QxQzuVN0oyLh+6NAhmpubixrcgXUvMuXT/qgy6cSDaZqEEynu9UbtruQSX3aB1tAzX7p0iQULFqCoutUZDXapfSyUeJ1UphdOoUiyIHPUeCiqDl5c0egIxXBIVvfeQCxVFMPgjE4OwKya4KiZsrii0RtOpMmrVpYvX0268+fPc+LECZYtW0Z9ff2Eyt2qbnAnnb5trCqstlwueOutt9i5c+eosi6HDh1i7dq1lJaWWi35vZYeVyFX8sf37mEdqYJliRI69KgCrQMR9AVLeHL1MpIpnQ9PnUWWZVYuX0J9eX6NIvF4nA8++ACHw8HmzZuztoZTVZW/f2MfW7Zuz/mc2eLuvXY6277k977x3AgdpYlw6tQpKisrhz2ziqpzt9cK/gut05grTNNME5KVYRxQAcvncahf7aVLl6isrKRhiJvHsWPHiMVi7NixA6fz8dHbTKQ02vtiOWlYZXD58mUURWH16tW8++67NDavxuVyjTnWTYQDBw6M6tuawUBMsQOf6hJP0bMMUwHDMDh+/DiKolBeXs6S5Su4l37mKwKuYVkhwzDYs2cPr7zyyld1uUWBourc74/bXF2vS6bC77IbBkzTCqayfaYOHTo0oopRLJimSVtP1C5t5tNooaU70AeH8C8zXevZahHe7YmgaAYlXie//sV/5V/+dz+2tWMDbseETRpg0dRudYUBmF7hK3jZeSiKGuAlUxr3+mJIokBlwM2DwQQuWWRGVW7G22PBNE27tKAZJg5JZGb1xMfuGogTSTzk6NVkyTmJx+P89Kc/Zf78+TzzzDPAxINlBm09EVKaQV351HbRPIpHg7yuri5qa2vt32UGtcwg73c7mJbFQ5sNPv3F37G4vDDl06gG1wcS3MPB3A1PMWdGjZ15jCsaH35yCUmSWbt8UV4T1IEDB3j22WfH9UgcC533uzjyyWXWFkF7LwNVVTn18ccsm9/IqieW5bTvoUOHWLlyJZWVlfbPMu+EAFRN4aRumCbheIpYUkPR9BGrc0kU8LlkyvwjdcLGmmAMw+DQoUN4PJ4xDcynEqpuCdTmMgmMhWgsxn/5+zf45je/mddiMZlMsn///gmt0/oiSUJRBQGY8RUuSouB7u5uLl68iC65mNv8BGBlXkrTDRj79u3j2WefnfIFgm5YWfS4ouFOd8Fn+LTJlEZfVLEFk51pFwYgK+N53TBp64lklf1ySCJOWaTE5xx3riqWsPFYyIxRAY8jJ/5aSrP4pZEh2oOSaLnB5Cr6HU2q3O+Pc+Lkx9TVVjFnzjzbVaOxyj9h8iKl6fSlNS8BZlYHisrNL+pS3Zl+AHXjoVaZohnEFHXSQc5QcjBYX1htaXaZwcqAG0XV0wKCJp2hGE01AfvLSaQ0QhEFw7TMvAUEDr3/Pr1dHfzJn/yJ/eIbhkFfX19O1z1aeVLTDURRmBLRyldeeYU333yTmTNn0tXVRSQSYdasWfT09LBu3ToOHjzIrl277PLfo+3ek0HSVThOy4Okzp1gJZu3bqEq6B5WrvS6ZJ5csYQ33znEnNmzCHgcOd1bTdMQBCGv4A6gblotC2Y84IubXzJv7py8jjERHA4HmzZv5vbtO/zuwAd847nsM4Y7d+7krbfe4qWXXkKWrSGgusSDbph2m38sqVJdZOkMTTfs1e9QSKKA3+0Y1VrrXm8UQYCKUfTCMsjoQt68eZOrV6+yePHiolx/NtANg/beKJphTQIVE5RwJoIpOqmqqqL7QRdzp+XuGnL+/Plx+WkZlPtdxBVLfqc3nKQuzQ37bwHV1dXs3LmTy5cvE+69T7ByGj3hJKYJd25+xvz586c0uFN1g1hSpT+q2IL4iZTOvd4oJT4num7aTYMZpNICuADVJRPz9XrDCfR0EmR6pcXfDUUUYoo6QoFA1a2OfP8EWbJEYmopHQG3w/b+9btVe0E/FnTDpC+StLmvYHHkSn2WmHA+lR2/20FlwMX9e61s3LAew7TuaX3FxPzlmKLSGXro010ZcBe98bLoVmUPBuKEE6ol2SCLhBMqsihY3J9RPpxuWF57hmniccpjGu6298Xs9vFyv5sSX+6q3kOtgQJuh13nv9drHds0TX7xi7/H4XCxdu0a5syZM2ylB9Zqb+vWrRO6WWSsympK3Ki6STKl276vmRcss3JyOSQCHkdRV80ZQdATJ06wYMECKisrOXjwIKqq8txzz6Ea0N4Xyzormg2uXbpI/Y1PCXgnN1noJhzvDDP9xW8yZ1rpqCsw0zS5cKON261tbN24Nqcy86PBT77Yc+Aw02ctKKo+HsCdO204jQSbN2SfMRyrDNUfVehNt/GLgtXdV2hrq3AixWAshaLqmFgBXUYDTkoTqociGo1y584duru76YqmfZKjPXzzGy9NWDrPNsNeLGQy4bIo0FDpn9SAnkhpdIZiGCbc/vwqPoeZlTr+ozh79iwOh4Ply5ePu93Q0v1XSS0pJvbv38+6TdvpjylEIhFuf/EZr77wzKSPqxsmiqojSxa/1TDA7ZRwSCKGaZJSdRIpnUi6wSkDhyRSEXATjqeIp4bLXPlcMhWBtEaeYlmiyZI44QJ2aMm9ocI3TIcPhjd06IZJUtVQNQOf2zHm83rgwAHWr19PaWnpiN+F4ylMyHlhnQ0ynegTZcIfXTz6MjqTBSiHvv7662zctJlgWSWyKGRti3a3N4qi6rZG6WhqB4VG0ck2FQG33aXndcp2N0xnKEZ9hY+UZtgrxZSqD/Ngk9KGwZkHRTesck5fJGl1rMgideW+vLMMkihQVeKhvS82zCg6g48Ov8trL+8mWFZpX1csqQ0L8Hbv3m13wQEMDg6OKH9YpeQUgwkV3TDGjPQzK6dYmoNU7rfsaIoR5WfEQIdOELFYjGnTpiHLMski6KM1L3+Cy1cu0lyQiq+JKIpjBh6CIDCvsZ7LV67ROxjPKmDWNI233nqLbdu2TTq4A3j5uR38/Dd72bhpy6SPNR5mzmzk1OnTDIYjlASzC8ZFURyVv1fmt+QaHgw+lM7ojyoEPA4qH2n9zxamaSn267qVIRz6no1li5dMJnnzzTepq6vD4/HQ1NTEgoULae22Ao5s+WezZ88eVzZjquBOj335QkuPmYZpCfZu37ye+50ddsdnLhmn1atXs2/fPmpra216xmhwOSR8Ltkej6ZC1mGq0dzcTOedL5gxez5Hjx7lOy8/P+ljJlMW93y0aqhDEtENY8Tv3A4Jn1umxOtKZ7BlBuMpkqqedjSQh9GIHI9QKGJD5q8SrxOPU7YcMCJJBmJWBqvc7xoR3MHwhg6LEuGAcSThzpw5w9y5c0cN7jLnzGTPKtPzd6GaRmxFiglMyLsG4radX7YUrGyRSCSYXj+yUWkiqGld3uqSqROrLnqAJ0silUEP3YMJ+mMK08o8PBhIomgG52/14HU7cD3yYWXR6orVDcssu3swgcDwDha3Q6Ku3DfpzILHaam4h6IKetrvTRAEtPgATtFg2YJZJFV9GCH3UQzNEPz0pz/l7NmzrF69GrA4Rh19MVTDRBYFJFHE65Txua0BX5JEXLKYtmLRUTWDaFIlkbIm11BUSYthZmdLlC/a2tooKyuztcQyfAWfu7CPSH+wDBN1Up20AlDudnHryy+ZVfPEmPcl4HGwZdMG9h84xI5tW2maVj7uqikcDjN79uxh3LTJYvf2p3jvxBnWFJGPB9ak/eGJj9n9XHak5wsXLrBs2ejcPZfDks4IJ1KEIpZmUzhhPZNel4wsCTgkyc5IjIdYUqV7MDHCPLvc78Kf1iQbbfD/8MMP+e53vzusTD4005HtfJGNbMbXAZabAXY5SBQEGhoaqKurY//+/TQ3NzN79mx7+2vXrtHV1QVY2nBNTU32vbx+/TqlpaXjBncZlAfcxJQokYRKuV//by6L19TUxIEDB+jt7eXFbesn7ZKQkZkxTOw5yzGE7pLJKEmigEu2gjq/2zEiSZERpM4GKU2ns/9h6S+SUHE7JCtTmC7jFrJZrq+vj7VrRx/PTCwx6YGYVW5+MJigJ5zE55Lxui1Nv3znbNM0bWH2wDgeXv1R5WEjZRFs3VKpFLquI0m5HTczBE6l/M2UtMuVeJ1EEyrxlEYomqKhwktnKI5DFukZTFBX5qOm1EpZOmUJSRQwTZOBWIr+mNUWnZkeHJJoZ7YKhaDXOeJ4f/mXP+PP//zPAcszMIN7fTEErHR7ZdAzovvmRz/6ES0tLfb/+9PdgCUeJ4EKBxV+96gZR1lKy164rLJYOJFiIKqgaIadSclYTxUabW1tfPnll7ZuXsYbEcZ/kfLBk88+R+s//ZxZlflr4SUM0HUdl8s1YQlgemWQF557lpajH/JgzmxWLZ43rBMT4O7du9y8eZNoNFpwNfaKinLmTa8pul+tLEmQpf2bYRh0dnbaWmhjIehxEvQ4iSkq90PxYT6oGQTcDqpLPaN+D8PLvQJuh4QsWeTmiUolhmEMC+7C8RQ9YYvz43ZIOZV+mpqauHHjBvPn585ZmyySQ6zFJoPMpx0qGg9WJnb37t2cPXuWjo4OVq5cSUtLC/Pnz2fbtm0YhkFrayt/93d/x/e//32++OILFEXJ2q/a7ZDwumSrszmiTKpB5HHFmjVreP/999m6deukj9UfVTBMq7rUUOlH4OGEPhBTiCkaJV7nhPyxoUhpOppuYmIiCQKO9ByZQSaYEbAWteGEaj93omA1YeRyvonQ3NzM5cuXRx0rRUGgzO+i1OekP6YwELM6Vh9WyBL43Q4qAiObpiaCqhuYWJ9z6LybiRVSmk5M0ewsXz7nmAitra3IspxzcAdWUK8XSQt4LEyZHkJ1qcdqMVatL2FGVQDXgITH6UAUBeKKRsDjtB9cIf2glPldaLqBYVqirlPRiHDo0CE2bdpkv5guh3XejB+gifVSdYZizKwOjBhwm5ubbYP2voRJ/2CE773yYk5BaWZyjSRUugbihONWY0qhJSza2tq4devWML/RwXRKv9TnzLp9PFs4XS56vUHyDXUSOtyL6dw0RJ6eM3PC1ZAsWV3bz2zfyvmLn3Lg6Mc8tW41VWnnhitXrjA4OFhUv9XVK5bxmz3vEq+bhtdTnCAdQJvApgmsLOXBgwd5+eWXsz6uz+WgsTpALKmip+0HU6qOohlEkiqpPoOGdFYprmgMxBSUIXSLEq/T8tfM8t09efLksAxBpqMT0kKiOQYZCxcu5MCBA19JgJfBZJnOopjhSI3+Ha9evZpTp06xb98+vv3tb9vBsSiKzJ49m2nTpvGzn/2MTZs2jZl9GQsVATdxJUokqVKm6lPCHZpKVFZWUl9fT09PD1VVVZM6VsaFotQ/cvGZ4bNmi2hSpTecHLXRLSPtkkxp9AxaC59gWoKsPGAQjqeQRMu9ptCVn8bGRg4cODDuYlgQBMr9bsp8LhIpnbiiEU2qqLpVoYol1VG7s1XdsL3bJVHAIT+c81XdwDRNu3lT0x8mP5Kq1YEviYLNyy8bpxErH8RiMd566y3+4i/+Iq/9ZUlEN3RU3WSqJLSL3mQxFOF4igfph3FGpR+XQxpGAHVIInXl3q+sDGCaJm+99RbxeJwf/OAHw36npdPrTllE1a2yq2Ga1JR6CI6T5brbE+HEqTO89MzmEZmjbGCYJnfT+j9g3aPqEk9BAr1McPfoyvX2gzC6YRZNo+eLz65RfukMZVk6gWRgmHA7DmfDcZ5+9VXqyrxZp7sN0+KE3Lh9j6tXr7Jt69N03vqMspJA0TwUh53fMPjZb/ayecvkswRj4eK507z2olWi1TSNM2fOsHbtWmRZxjAMDh8+jMPhKEimAoaT/h2SiEMW7QkOrJV2RSD3gTbTyQ0PpZbAKjOV+115lTiuXr2Kx+MZVsacCoQTKR4MJBAFgaaaQN4L1IxExFiyRbdu3aKjo2PczNytW7fy/vz3++NEkyouWWR6pf9r4bKQK/bu3ctLL72U9/6haJK+iLUQmaz8haYbtKa1U8F6vwTBoi1lMlSlPisBoBsmXqdMbZl3ytySbty4gWEYOVMfFFW3+b1OWaShwo8kCsSSKn1RZYQTFVje2k5ZIpZUaX0QJuhzUlPiHaGTWeZ34nU57CpgIWEYBv/5P/9n/uIv/iJvfnZnKEYipVNd4pk0FSBbTGmAB9ARihFXNJvnA1YZ434ohmaYiIJFui505mg8hMNhXn/9dQzD4MUXXxwmnDoaTNOktdvSFKop8YybmesIxTj20cfMa5xG6EFHXsKQumHQF1Hs7iSA3/32n9i0biVr16whGo3yD//wD/a1p1KpCUnXYwV3ALe6BjHM4nTOnTjSQmX7bSpkmfLA+J3HjyKkwvnuMHO/8SpNNcG8JstkSqOjL8qbb+9hyeJmdmxcNWXp8tt32vj0yw4WNzcX5fgfnThBXUCyJV7WrFnDJ598gqZp6LrO9u3bC26/ZAV5cTu7DZYVV8DjxOXIfaC9fv06sizbQsy94ST9MQWfS560VMdX0VFrmia3H0QwTJO6Mi8pzUA3TJwOMWuphoz2FowujNra2kpbW1tRNf803aCtJ4phmgQ9jqy0175u6O7u5vr162zevDnnfYeK2RdCHLonnGAglsIli9Sng6AMhuq4glX+n17pn5Lq1lAMXYjlAlU3uNcbtTNuMNyZJTMe64Y5bFxRdZ2bnYP4PE6mpysGGdWJ8lF0MguJn//857z00kuUl5cX7RzFwJRL1teUeGhLl2oHYgqlPkseYXqln85QDEUz6OiL0lDhL1op4Pbt2xw/fhxd1y0f264uvvvd7zJrllU4NAyDeDw+pvRJZpAGiCRVEimN8jE0bZyySElJCaLsZOHChVy4cGFC7tOjkEQra1cRcNMTtnzsaqfPwnCX8dvf7UeJDvKnf/qn7Nmzh+rqat544w2CwSAzZsygr68PY0hZRxRFNE0jGo2OqtSekW2JRCIY5W4o8EszZ+EiuPdlzsGdbkK/JtDvDRD05i6Jk4HbKTOzJsh3XnuVQx8coT+qFNxZZSzMmtnIqfNX8iLoZoOVK1fyxdULfGv3wyCm2EKkHqfMzOqApadlmLgc0qidetnizp07wyaNzNcsFmBF3tDQQFtbW9FM0ceGNVY8mqGIuNQJs9BxRbODu1Lf6NzFy5cvTyrzlA1kSaSm1MP9fkv2yiROTYlnxLVnOH9TnSktBKqrq/niiy+4f/8+06ZNy2nfZJoH55DESQd3hmnaNJmKoHvEIqm6xINDEtMyLCIVgYm5yMXA9OnTbbmtXGBV6nx09EXteVQUoMRrcfeGctRTmlXe1XSTREpj9rQSBKA23Rk7VYtzgNKysq+NR3AGUx7gyWmdn55wkr5I0u4gkiVrpdIRitmWKtMr/ZNOtSaTSQ4dOkRPT4/9xdTX1/Pd7353WJZr79691NbW0tPTw6VLl3A4HGOu9p2ypVeXkXgBi5M3o2rkKsrlkJg/f74dxF69ejXvz2KJOXsJeFRkESqrqqmsqkYSBfoiSV54cTe//MXf873vfQ+3201bWxtLly4dIdhrGAZHjhwZ9RzR9Mrw88+u0n/PgaqqzJ49e5i1VW9vL4ODg3kN4rXTpvGFYZIL0yWpQ68q0N4/yLKdz9tefvlCEq3Obr/fT89AhIrAyEG0WHj5uW389sAx1q9fX/Bjez0eXIFKLl6+yhNLp07cVxKFcWkK2aK3t3eE9EKm2WeyTQoAS5cu5eDBg1Ma4EWTVverKAik0sGd2yGhqNbE1RtJjtk8peqGHdz5XPKYz319fT3nzp1j1apVWV/XqVOnWLt27bhi3slkkt/85jfU1dURDAZZu3YtNaUeHgwkGIwp/OpXv2L5glnD/DRDoRALFy5k//79LF26tOg+pZOBYRg2VzpTdsvQdH784x/ndKxC0txSmtVMIArCqIYAoiAUrCN2Mli8eDEHDx7MOcAD6x2YWR0gkdIRBQGPUxo1cHLKkp2Z6w0nSao6QY9jypx2NN2wmj0TBl/eH0QQBDuIz0d7d6rxlZhOlnidhBOWNl5POGlzSiRRoL7ca3POegYTeXdtmabJf/pP/4n6+nq2bds2Ydn1xRdf5M0336SpqYmXXnppWCfsoxAEgYYKP9GklbUIRS0pid5wckQ2KBM4ZFLNixcvziuLl0ql+OCDD9iyZQvt9+6xunk2FQF3Wt7FuoaBmMIrv/d93G6L8zTWRCaKo0tTaLpBKGrxIX1O0W48uHHjBocOHUKWZZ5++mk+/fRTEokE06ZNy9qjddhncWbPyepJCfSrJrf6B5DnNTNzWmVBVlBel8zqVSs488k5Gmu2FLTLbNzzejyUeGTiiURRGi4WLpjP6dOnmVFfR0XF16uccPbs2WHZu4zbDFCwAX3GjBlcu3aN5iKVyR/FQMziZJV4HfSnszK1ZV57ETsQSxHwjGxm0g2Drn6r9O2SRWrHyfStWrWK9vZ2Dh48iCzLbNu2bUIXlnA4zDvvvMOiRYvG9NI+cuQIv//7v48oioRCIQ4dOgRAfeNsrt26x87nX6K6LDCsXJvpfl66dCnnzp3j6tWrYwrifpW4fv26TVF5dAyrqKgYJnWVDazObisoz9hp5YtMk0G2a05NN2wJo2wXqhltyrhi6VO6nVJelZGamhra29snnF9HgySK+N3ZR8aZObRYcmEpTedOxwPa7txh7aoVqAa8ufcAvX19lJWVo+kGDlmy5vpIkt5IElGwGqge5bllyseyJGKaJtNy4IsXElOX3xwCQRCoSQdCQyU5wPryMgFfJKnaXINQKMTf/M3fkEqlRh5wFBw9epTdu3fzwx/+MKuHTxRFXnvtNfulnujLyHjZZVrCAQbjqWGcAcB+YTI/njFjhq1PlQ1aW1s5dOgQH330Edu3b2f//v3cuXOHOXPmUOZ3MasmQG2pF6csYpjQE04STmR3jx5FbzhpZRswhk2o8+fPZ+fOnWzYsIEPPviAzs5OnnvuOQ4cOJDXeVKOiQM83YQHisCdcJxLSZ3lr77G1s1rCtpFHPS6SaVShOP53a988eLOrVw4d65ox1+3bh1vHTwyrDT/uEPTtBE/y5CoHZJYsEG9ubmZu3fvEo1GC3K88ZDSdPszlPpcuNJZyFjSapYIpBcVDwbiw7JgiqrT+iBCUtWtclSZd8KJt6GhgV27drF582b27Nkz4bXJssxLL71EJBLh4MGDI34fCoXw+/12oFheXs7OnTvZuXMnkqniNBK43W7CCZWU9rDsPDSwXLVqFc899xyXL1/mwIEDo37HU414PM6+ffsAeOGFF0ZdoD7xxBP09vbmdFxJFO0O2d5wAi1Pi0dVN+geTGdts1h0DsQUWrsjdA3E07y2ic+bVHXaeqJ0hqwFRiSp0hNO0vogTNdAnIGYkrVF5YoVK7h8+XJW204WmXfg0Tm2ENANgzf2vc+Bw8dI6CL/5Wf/yN/98nXmL1jA73/vO/zgtd3Mry+lqTpgWTjaiZuRwV3mGpOqbnUMp40cvgp8JRk8sEqXZT4X/TGF7sEEHufD1YfbadmKhKIK77x/hP6O25SVlvD973+fn/zkJ/zZn/0ZPt/YhOvz589z5coVW8eumEimNFvC4VGNKngYKA4dwBcvXszhw4cRRZGOjg5+8IMfEA6HOXfu3LCmh0uXLhEOh4c1ZuzevZujR48OO37A48Dvlm3V8oFYKueSWUarCODmlfNs3TKyG8/tdvPMMw9tfJ555pm8iLZWBm/sAWRQgx4FbgyE8TQvZdeKJTnJC2SLUp+TuXPncub8JZ7ZtGbS/sjZQpIk6iuD9IX6qSgvK8o51q7fyK/3HOC7r7xQlOMXGkePHh0mVRNTLIFkoODSQLt27WLPnj05ScXkg4yDgM8lI0sibqeMoqXQdGssqCpxE1M0UppVBirzu+geTNAZimGaJgGvk4by3IRanU7nuNmy9vZ2rl27hiedPV6xYgXhcJg9e/ZQW1tLPG4FF7FYjN27d496jIULF7Jw4UI6QzFiikZ/VBm36WLTpk1omsbhw4dxuVxs2bIlb5/nyeDUqVNEo1FeeOGFCc+fjxdtmd9FJGFJgbT3xZheObE/6aOwdfQcEpUT+BYnU5qtQAEZkeU4QY9jTC6souq090bTJWBsilTmujNer45RLAPHQlNTE9c//5z58+cXtWRZjEMnVZ2OByEOfnAESZLZ/eILuB0SS5oXIUkipaNo5JZ4nZR4ncMXNoJgX59pgpaxXE1zDIvp6T0evrIAD6A84LK1cXrCiWFWOOV+F7GkyuUrn/Fn//Kf2x10/+7f/Tv+l//lf+GP//iPqagYLpbb19fHr371KxYuXDjp4E5RlOy20x4GKvUVI4POzDM5VMi/vmE6/vJqUqrBguYYhw8fxjAMampquHXrFmVlZXz00UcsXrx4hF+k2+0eNaCyAj0nA7GUneLPBapuEIlEKC0JYupqVgNcMBhkzpw5OZczUk4X6CONqjUTHqQE7oVj3HP4WPvyK9SU+go+wWfglCXmNU3nyy+/pGcwia96agI8gGe2buLvf7ufDRufKsrxvR4PFbXTOXPuImtXPVGUcxQSqvrwmdMNk55Ba+Lyux1UTTDR5YMNGzZw7NixonWearphZ4ZLfE5M0ySZ9haV0zZLFhfUTfdgglBUweOy7KkSqo4kWGXdfGSKampqaGlpQRAswXhN0ygpKWHdunWcPn16hJViMBjk5Zdfpru7m+rq6qzPU+Z3EVM0wgmVEp8+rvKBLMvs2rWLaDTKwYMHqaqqYs2aNTl/tnyxb98+1qxZk5V7B+TnNpBRgGjvtShGDwYSwzq/47EY3nRiIplI0HHvHrPnzRt2jET6GQl6HGi6gSgIYwYH4YzbUNqjtr3P8jrtSWeLHnaXijhly9niwUAck4wT1EN3pHK/i3hKI5myss65NDgGa2bQcuwjpk1vKspCPINCJO5U3SAUSfJ+y1H6+vqIxKxmynXrnqS2uoq6cl/Wn328hZcsMaVKIGNex1d5clEQqClNe8EmVAJu1U5LC4LlIbdp01O898FRvvXiTgIeB7Is8+///b/nJz/5Cd/5zneor69H13V++ctfIggC/+pf/auCrA6rqqqy4hYMDT5CkSRlj7RrZ8YJwzTRDYOkqtPVnxiSZnayftMWO3u0f/9+/H5/Xl1xDulhCtswzXFXU3fv3uXw4cMsWrSI+vp6Otrb+dWvXmdJ8yJ2bMieHzh37lxOnjzJzZs3x+TyPIqGeQsYOPMhpcGg/bOEDnfjGjeiCmXLVvDs4vmUeJ1F5y1UBNw4HA4U1TLvnsqurFn11XR03qe+LreOvayPP2smpz7+mDlNoceaj3fq1Cl7ITMQU+iLKPbzWz1Kp2YhUF1dTXd3t60VWGj0htN+2Q4Jt0OivS9mLwaHlt6CHgf9UYWUpnP7wSAOScIhCpT4XLgc+Q3PixcvZvHi4U02vb29HDp0aFT/4QxyCe7A6qAOuB1E0nZ0GbHr8eD3+3n++edpa2vjxIkTw7ywiwm/3591cAfDKy65INMherc3mg5+rWpKZ/s92va8QWD9ZhavXMUXN24QOHeCc+dO4ZizgGVpK0NNN2jvjZLSHtrCeZyWLefQe6sbhk1fKvFakkTTK/0Mxq0FflzRbJ4nWPaf1rxgZe6mlQ23vhTSDR35VDEyDRLheKqoAV6m/JwLzzCa1Ehplp+voum0tj9g797fsWPHM6xZtx5BsBrEStKi/l+nDtlsMOU6eKMho/kjiQKNVYFhX2BfJMn/+dNf8p1vv8as2pJhJc///X//3wkGg4TDYb773e+OyOhNFvv37+eFFyYucQ21ZALrgS/3u+3gr60nQkozSKTUtAijmOYVCSRVHUkURjhi5IuMSHFduXfMl/XkyZPU1dXR2NjI1atXrQ5jyUnF9DmUBPzMrM5NgBisSVoUxawnyy9/9n/SVGV5vqoG3I6bfBJV2PqNb1BbNrVi16ev3KR/IMyWtcsmJfGRD37+m71s3LSlqOc40nKYP/rOy0WRZpksWltb6ezsZMOGDcSSqu2pKYsC08qLr4d57do1bt26lVN2ZyIYpsntrjAmluPAYCyV1vi05B0e5VaF4ym6BhJEk1bDharpOGSpKELj+WqXjQVVN2jrjthZoYYKX9aT5OXLlxEEgSVLlhTsesbC3r172bx5c9bNHidPnmTZsmVjSmVNhIz7iiiAT0jRfeBtmivLaR0Io5jgSiWZWVOFgEA0kaBV0YgHSrmfFOlTBEqdVqbXBASHk7Wbn6IinWWLK5ptTyYKjKoJqqi6vXAYWmXKKDEUsiqi6Qa/PXCUJUuWsHBGZdGaIDIautnoDKY0nb6IMozfPzAwwH/5P/4L/+//+B8o9VlVAZdDnLK5xpK1EYrqKf8oHosAb6hbw6MimoZpcumLdo5/9BF/+L1vjehO6uzspK6urijX1d3dzc2bN9mwYcOE28YVjf6YMkzJvzKt4p/SdK60hUikNCqDHsp8TqrSTSaZz52xnpn0NQ8mGIynRtzHVCrFp59+yv3795k1a9aIFX4ipdHeF0MSBZqqA3mtZFpbW7l27RrPPffchFnUI3/5/6Ux4KPU6yHhCHClL0zzN1+lobIwgW4uOPXpF0TjCTaubJ7yAO/OvXbOXrvN8mXLinYOJZXi7KkT/Oj3iss5yxWhUIjTp0/bckSZATzgcYyqsVZMnDt3ztaM3LFjB21tbfT29uac3dMNk85QjKSq22r9GWmFsVx6TNMkqqj0DCbtUltc0WieXoZ3jEXam2++iWEYfOMb38iJL3by5Emam5sL2tU61NGkKujOKYvz4YcfMmvWLKZPn16w6xkLBw8eZNasWVlZ1nV3d9PZ2TmCIpMtTNOkIxSjfzDKnfff5Zm6IPIEj7NpwpcJAd2EOR4TWbQkoj7pGqThpW+N2N4pW/qoE41ZepoPJqQ7O4sxvt5o7+PCxUt889ktRdOvvdUVxjDHd1jSDdOWWsvA65LRDRMB+PL6Ze61fjnCqarYME2TL7vCwORdTnLBV8P8ewSZUi1YvIKYog77XWNdFfF43JbwGIpiBXdglSySyWRWHXdel0x9uY+Z1QE7CO2NJGnvi1pNJC6ZyqCbqqCbmlKrKy5jzAwQjqvjHT5r+NzWgx9NasPKDG+88QazZ89m9+7dI4I7sEpJYtoKZ2iQmguamprYunUrb7/9NgMDA6Nuk0gkePvtt5n98mvM/sN/ya3qBrojcSIl5VSXTNwtWGiYpsmD7h4qKyunTAtvKGZOb8BQYkU9h8vpZN6ipew9+EFRz5MLMtZpmeAuI2gK5G1HNhmsWrWKnTt3smPHDlpaWkgkEnl12kqiRbYWBfCmS1del8z0yrFdYQRBIOB2MqsmiGGYKKqOqhl0DSSGEbkz2LNnD8888wyvvvoq77zzDqFQCLDe8aEdsd3d3SP23bBhAx9//HHOn2s8eJyyvTiNJXMbOzZv3szFixft5o5iYteuXcRiMQ4ePDhhh7koisRi+b+XgiBQ6Xdw89B+5pSV0KVM/DxnrkgSrD8Abgnm+iXu32uzsoEumaqgm8YqP41VgXGDu3gsxkcfHObkvj0osUhaykXgy+uf8ckb/8SFN3/N+bd+w7Ff/yOqOrn5R5JkTNMsiCD5aFBUHcO0grSxAsjM4ioT3EmiQKnPSX25jxmVfqZX+nn6qfWsWbOGv/7rv867DJ8Phnb+ylM4zzwWGbwMMtknWRSYMaRUqxsGJy/e4OaXX/L7rzw35V61+/btG7OjbCwM9djNYLTVrW6YtD6wyjmFKMlY1khhDBMaKnx4nDJnzpyhqalpQo5NplReiPLYaKvlq1evcu/ePXbu3Dksw3fknX1s2vVc3h5/k0E4keLNd95n86ZNzKrJL3M5WVy9/jntIYXZs2YW9Txf3PySulLXlIogZ5BKpfjkk09IJKzmGkVRePbZZy2fXNOkvTeKohl4nBINFfmVxQqNlpaWYZ292ULVDfoiCSIJK9jJ5b2+1RVG1S3jdKdsLboqAm5cDgmHJPLewQOsW7eOyspKe58DBw7Q1NREb28vzc3NHD16lOXLl3Po0CEWLVpEd3c306dP58knn+To0aMsW7as4JZLmbEjI+ieCwzD4M033+TVV1+dku7aVCpFS0sLuq6PWW3IZ8wfCsMw+Ojv/441FaW0Ja3jN7pNnON8PNWAO0kBAZjjHT4tfyx5eeq557M+//G3fkvjYA/TKiqQRJHWUD/3vCWImsoCM0XlEB9wE5PTXX2s+O4P7CaQXBwbMtmps2fP8u0XthVloZzxsR9rfMjwDVXdQBSY0AnrwYMH/PznP+ff/tt/m1fHdK7QDWtehqnN4H2lTRaPojLoJq5oI7pqJVFk9swZnD5zhlhSw+mf2gBv9uzZORt1l/pceJwycUXDME3cDmlUXSNJFPCnicrhhDrpAE8QBHxuB5GESjiewuOUGRgYyIpAXeZzEUta97+9N0p9OkDMB7t27eLChQvDBKO7urrYvHnziAF164v5D6STgWmaXL3RSlVVFWX+4Q0dloWOOSV8icULF/DpnoNFD/DmzZ3D8Q8/ZPHC+eOS7SeLzs5Orl27Nux7liSJNWvWjKo7NhBTUDTDbqp4XJCviOtgLGUHd2PZi42GlGZlKWRRZE6tn66BBLGkytWbbdy7d4+BgQE2rltFWflwrvFzzz3H+++/z/bt2xFFkeeff579+/fzL/7Fv7A9am/dusXrr7/OzJkzCx7cqWm1f8CuSOQCURR54YUX2LdvH9/4xjcKem2jwel08vTTT/Puu++OGVA2NDTkRf9JpVKc+v/9z0yvKGNtRSX92sPjTxT3ZLJ2JpaiwNCSbrS9jfZ792gYp5T9+eVP6Wlvp7JxJn5Tp6HqoV9QU3kZjaaB4HYiMPw7EhBYV1vB+dd/ydxvfpuU4EYUs38XzRH/K3yAN57I8dBkSiY5MVGZuKamhj//8z/nJz/5CX/yJ39SdI9ZSRRwOyRLlqUvNiZdo9B4rAK8R7tqfa6HiuCZ0mNMUfMaRCaD5uZmDh06lLM1l8shZcVHCHgdaVHnFFVB96SzSEGP09YzUqMdVFVlZwwmS5Zp9f3+GImU9SBOJsgbza3jyJEjKIryWHhV9kcVrn5+k9Vr1uJ2yETTvsIJRbOJyZUBF2UF4EZOBI9TRFXVogZeAE+uX8/rew7wg9dGdmm3t7dz5cqVrHlKALdu3eL27duANVFn5H6ycVPIQFGte13md05pdj6SsL5vn1setSFp8eLFfPDBB7kHeHFLYinDwc0WGTFUp0NCliTqy3281/IhDk+AVSueANG6Nx2h2IiO1aH6lG6325ZCydhIzZ49m0AgwJkzZ3L6LNlgqKhvvhQLr9fLqlWrOHr0KE8//XShLm1UaJrG3r17+da3RvLaMlixYgWHDh3KOcBzOp2UNUynMejjgSIQTlfYqxzmhBw8AXAIoJqQMobbgO+YVkno2HscdQd5+lvfHrHvxRMfUd12k7nBAJHLn5AyDHAFh20jCmO/jwICK6orOfLbtyld/zTllZWU+V1ZZZoeivlbXbpTmX4ZGtx5XTK1pZ6sF+Uej4d//+//PX/1V3/FSy+9ZHvRFwuZ2EbVDe72RKnMka+aDx4LDt5QeNIixwDdg3FbUdvrkpk2bRo3bt62DYqnEoZhFM0ZwOuU023s8GAgkbWK+JjHS5swm0BX3wA9PT0cPnyY1tbWCfeVRIG6ch9ep4wJdPTFhnEiJ4utW7dy//59rl27VrBj5gNNN/jk088IVE4DwZo0M9ZRmeCuZzDOuS97aO0OF/16ntu+hY9Pniz6eRwOB1V1M/jk/KVhP29paaGjo4Ndu3YhiiKHDh3i0KFDJJMjea8ZXLp0if7+fnbs2MGOHTvYtm0bO3bsGNX/eDwU24JoNFidq3EG4yn6o6NrXhqGYQevWR83kbI1LzMON9lCUXU0TePs6Y85ePAgb731FslIP9s3rGRufTnT00Gdouq2GX0uqK6uZv369ePaMOYDj1PGl+7K7AzFbL2/XNHQ0EBVVRWXLl0q4NWNhCiKBIPBCR0YkskkLS0t3L17N+tjt99tQ1BT6CZ2cDfNaVI6zrqtX4VbcYEvEwLqGFObgEBFaSklwsgNLp38iLq7X1IbtMquAb+fimBwxHYToUcVaCwtZeDjDxFifVmXERVVp6enh/KysqLxmDNz/qNHz2gBZrh2uY4hoijyb/7Nv+Gtt97KymllMi4aTlliRqUft0PCxHKd6uiLFcWZI4PHLsADi2TtckjpgMci3zpliQ1PruPCxQsk1am3vJk+fTq3bt0qyrEFQaA8bR4dSarc6Y7QFxl7Ys0GmcmlYUaTTR5PJpMcPnx4wgFUFASmlXvtIK8zFE+r1qsFIaZu3LiRaDRa9IF8PESTKt3d3SyaM5NSrwtxiIl0TamHpuoAjdVBFFWnqz9edEKu2+Vi3bL5fHHzy6KeB2DO7FlcvnkXVVVpa2vjnXfeYfXq1XbH6Ny5c+1n5vTp0xw6dIgLFy4MO8aFCxcwDCMng/vRMDCk83wqNQgz7jMuh8S0stFdcY4ePZpTBiecSPFgwOIYlvpy03A0TZPQYJQjR46waeN6du3axauvvjqspO12ynZFI99FYFtbGwsXLsxr3/FQW+bF47Qmro5QfNTmkGywePFiotEohw4doqWlhZaWFq5evVrQaxVFkV27dhEIBNi7d++YDR4vvfQS27Zt4+bNm4Dl3zseLn58ktSHh1lUUYaWHi4kAfwTFEAGNWGYr49TgLEsWp3K8HkhHovh+uIalYHJ8VYjGoTT0+r6mgD6yRZu37iR1b66YXLnzh3mz5tTtCa5jAC02/kwP6ikO9UBAjk6Nz2KP/zDP+TXv/71uNsoqk5bd2RS1payJNJQ4bMqdUA8pXE/FM8pyBtrQTrq+fK4xqJDEARqSz3c7YmSSOkMxBRKfS68aQ5bMqVPma1UBrdu3cpLfDhblHidOGWR3nCSpKoTiio4ZDFny7EMgmnxy6Hl1YzF0OHDhyfcPxPk9Qwm0p3NGjFFs7uYvC6ZUp8r7xXb2rVr2bt3b94yBJOFU5bQNA23Q6Qi6B71PteX+xiIJpEkkXhKK+ozd+7cOUKhEF+2dTNv7pyinSeD9evX85f/19/x9JOrefHFF0fdRhRF2+mhs7OTQ4cOYRgGsixTVVU16e8uqep2ecXnkovmWPIoMrZMokB61T/yGTYMA0VRbEuviRBXNDu4C3gcVAayK+vrhpm2O0xy5NhxduzYTlngYVA31KYQHpZx822AevDgwajUiclCFASmlflsiYr2PquM/GjJPZJQCSdSJBQNQbCytpJoLa4csojLIfHk+vXDMjHt7e3DxqyFCxfmZW7/KGbPns3g4CB/+7d/y7/+1/96zO0SiQSHDx+mvb2dH/3oR6Nuc/GTT5je9gVlpSXAw0xTNvN2Jrirc1kbu8Wx+XpOdfjkfm7v26yvnDx/rFe1TljuAK8Ec8pLuXPmOJ8rCguWLh13X3NUN9bCIanqtu5fZozQDYPOkNXl7HXKk9bLLC8vJxKJjPl7y34uimFCf0wh4HHkTaUSBMHypnZIdPTFiKc0m5c3UQYynEjRmzZUyAaPZYAH1gRcGXTTE07SG07idT38EpOpqTfunQqRWI/TklPIdKSF47l7ymYgCsKw4O7EiRMoioLL5crahk1Mu4mU+S3RzJiiWROSatnZhOOpSXXbjka4nyp4XTLPPL2RfQc/YN26dcyZXj3qvXY7ZQyz8NmlVCrFiRMn0HXrWV62bBmrVq3CdfIMvb19VFYWVrT7UTgcDqY3zadh+oystq+rq7OzWaFQqCCk5FTGUkkWh1k6FROGadIbzmTZxl6gtLS0sHXrVj7//HOOHDliZ3C9Xi9PPvnkiO0jiYe+s7Xj+LIOxUBMsR0vLl++zKpVq6gt843rW5m5Z0MzGRPBMAzOnz/PwMDAsO7bQkMSBerLvbT3xUhpBp2hOA0V1ufRDZOugfgwCSbTBEM3UHVGmLG7ZMu71+OUqZ1WNyygO3jw4KQCvMuXL3P//n3Aeu/GC+4AW+z+1q1bYzr2xO93UJanKHImCHQIjNtlC1DpcnLu2BFWbdnKZ59eYqGgITA5jrBiWE0dIlAmPwzWZpaV0v7pJ1xNpVg8TqbeIYk0Nzdz/uKnNO58quBUi4GYNV/53Q57wdAzmEQzTBySSG1ZYeaRRYsWcenSJXvhum/fPnp6emhqamLZ6vWWR7AsUl/hL4jagscpU1/hs3UzO9Lvy1hZUN0w6BkcafE5Hh7bAA+sAdgivus8GEhQVeLB7XbTNxAe1fe1mFiyZAkXLlwoyur3Ufjdjrw9ZceCoihs27aNaDSaM//NKUu2aHJGwLU/qqDqBvd6o/jdDioCrpwI8uFw+CsN8ADqK0v45ovP8V7LMQYGZrB0wWwqg8MzNoIggGnSNZDAJYuUB9yTDvb27t1LIBBg48aNI1r0N29Yyy/eepfKyuJbOK1cuZJ3Wo7wnZeewe/L/n0qRHCnGybxDFdrCqVp+qMKmmEii8KYq+BUKmVld93uEVnKsbLfmUl6oiykourEFJVoUrPLSw5JZP2aFVz65CSrFzaOua9uGHauJJtn8MiRI/YC4oknnihqcJeBJIrUl/seksl7owQ9TuKKanNby3wu/B6Hrbup6SaabqBoOsmUjqobKJqBoqUYTJfDxLRIryyJpHAOs/LKBZqmcf/+/RGZ0Wwwe/ZsDh8+PLolo64NIzxluHSOLIYKh2gFWYoxcYBX4vczq+sux9/6Ld5wiPICZO9S6WlGFkZmDhtKS7j/+adcNg2Wrh7dO9gpS5QG/SSTSWJJjeAELhO5IqOtmKEdpTSdSNqhwmqqKMz4cffuXV555RXAmp/6+vr4wz/8Q/72b/+WWYstr/VSf/5Vq9Hgcco0VPhtH+HuwcSYC8T+aMoOMrPFY8nBG4rqEg8C6TStorJ+/QZOnDw5TKl6KtDY2EhnZ+eUnCuVHgilInCS/H7/pLw33Q6JEq+T6ZV+m1gdTar2gJ4tTp06NWomZCohCAK1ZV7WPbmeO/f7OHziHB19sWFdgZmGn4SicfP+IKFIbiuoR9HZ2UljYyNbt24dU3/JP4VuGps2b+FXbx2wA4GpgGma3OuN2l6avikqzRqmaWcDKoOeUVfKhmGwZ8+eMQOAOXPmcP369RE/17OoxQ3EFO72RumLKPb4VRV0M7M6QE2JB3GCUleGaC4KZJVBWLx4MQ6Hgx07dkxJcJeBnHbtcKQzd/22DA5Mr/RTGXTjdkg4ZYtCEvA4KPO7qC31MrM6QFN1gJpSy44qM5kZplU5iCZVXIEy/ul37+XERcpgsl26kiRx+PBhzpw5M6zpruGJlfQMEXdPN4bjyMK9IteewRKfj1WkWFyaeyPFUOgm9KQEHqSsi/SOES+nVJXq+vEzphn6SjzPBpuxEFNUm5+WUaTIjBtep1xQKz9FUeykw+uvv873vvc9AEzRgaobCFjJl0LD5ZDsLGQkodpj1FDohmF355dnSf+Ar0GAlynVAvRFFCrLSolGozbpciqxfv16Tpw4Yf//+PHjNkm3vb2dn/70pzYxeDINGZlykGGYBSH3h0IhOjo6CtoFnOm2nVHpxylbA3kujSEzZszgRpYk3mLD75ZZvmQRgWCAw0c/5G7vw+er1OdiRqWfgEeiL5LkevsASg5NPoZhcO7cOdra2gDL+3Qi7tqyRXO58UXxmy3A4tmtf2oTb+5/b0rOBxafJbMYqC7x2EF0sZHSDNtsfajlYTKZ5Cc/+QmHDx/mnXfe4ZVXXhmzC7ipqYn29vZhP0uk/UFhbJV9TTfozcg5OC03gqbqgC2TcOzYMTZv3jzu9WdKt4ZJVk0M1dXV+Hy+rLrnCw2nLDGjyk9V0E3Q46DUZy0Ks6FzyJLFPa4u8TCjKsDs2iDTK/3UlHqoCrpZvHAey5Yt5+CRj3Jy3dE0DVVVcTqtDKCex3i4detWduzYwdy5c2lpaeHw4cO8//773D74DhUlDwOuTJInYVh2Y2MholvZPpGxA6zR4HI6cUxCGD6pw92kwIBmKdf5JahwjD7XTCsJ8sWp4R3+d++0curXv+LYL35mLZyicVRVRS5gebY/qtAZshpggh4HoiAwGE/ZDVIBb2GDrY6ODn7729/S1dWFYRj2AjxlWp8p6HUWrYnE57KqYGB11z6awBqMW9k7pyzmFGQ+1iXaDEp9LiIJ1VrBpSU7YopWdA2ZR1FeXs7AwACGYSCKIoqi8OKLL9oE3JdeesleKb/zzjs0NTXlpcwe8DjpCSftiXCy2mBnzpwpmveeyyFRXWLp+0QTKmZJdgrobre7aB19uUJRDQIeJ5tWLuLu/WoOvX+YbVu3Ul/hx+d2pDst/VSVxIgpGmduPmBBXRnV43Ctjh07Ztv/LFu2zBb/zcaSac7sWZy5+j5Q/GYLAI/bjdNXxs1bt5k7u7haUDBcKy04CbJyrhhrseR2u5kzZw47duzI67ihyEOO0Fiakb0Ri2vndkij0ksURcHtHn9lLgoCXqdMPKURSahUBCYeF1avXs2ePXuYPn36lDvFiGkyeSGO43ZIdnBY6nORTOnoacePsXDhwgV6enrszy0IAps2bQKgayCBoupMK/PmlZUpLy8f9ryc6WofpjPnk6xu2JQJ7YpAhcOkbJTTDGrWs1/mMG2h42JDM6FDsTp3HQJUO81xg0unw0FzPM6pX/8Ksb4Ro6ONRi3JmpISFIfAgd++RVvCZOczOyjJURpoLGRcrcDK8FcGPaQ0q4QJ1riRLz99LPzH//gf6evr4+jRo3z/+98HrDFDw2rIC3qKSwsr97tJpPR0w1ac6ZUPuX6Zql6ufPevRYAHlkhgW4/lDekvKefWnXvUls6fcv/Q7du3c/jwYaqqqnA4HIiiyM6dO2lraxvGTdq5cyeHDh1i165deZzl4aBVCHK/y1XcQNjjlBEFK7OgqPqEafMLFy6gKIrdoflVI1MC8DhlFjVNoySwg3cOvs+2rVuZPa0UWbIM41fNruLSnT4GowqX7vQxe5rG7JqRZZLr168zbdq0YWLB1dXVOXWdSlNsmba4uZkPjx4peoAXTaq2hpsjfV+nCpnzeod0Q2dKshNlz4YiGAzS29trL+amlXsJRRTKA8PfM9M0iSRV+qOKPUBXjFJeaW1tZcaM7JpdfG4rwEvlwM998cUXeffdd4uqAjCVSKQ0TECWJFsA/1F88cUXaJo2aqld1Q07Q5KNEH02qN+yjY7jh6kvsbpoJQEa3CY9KYGIbnWpioJJyZDL1U2LdweQRaxeMGRkWVwi1LuyCyxLvV7WAImuO3h8bki7YcgOJ46ObjY+/zLTynwFma/iimYHdxl7z5RmyVUBeJwPOeGFRkVFhS0SDmkPXMPE6XAUtBw8FmpKrDhH0QxCUcUeL3wuB+G4xUmtDLqzbmR57Eu0GThlySbyL3liJUePHpu0Vlw+cLvdhEIhKisrhwUojY2Nw7J1TqeTqqoquzSXC4YmGiYrejxVyLzYE4lQX7p0CUVRvnL+3WgQsLIFDZUBdu3cwanTp4dxSpyyxOrZVSxoKMUpi/RHHq4yh+LWrVtZO0GMhSmUhLOxet2T/O0/vFE0Pt5ATOF+f9y+p48GRMWEaZq2YHfZkCzD3r17h2Xes8HatWv5+OOP7f+LgkBl0D0sMxlNqrT1RDlx9jJHP/yIEx99RCrSN6IJIxqNcuHCBZqbm7M6dyabH1fUrLXmZFlm6dKlHDx4sOCacl8FMsGtQzRHLZl1dnZy79491qwZvSkgw91zp/19C4GGGY3cDVaiag9F4SUBal0m5en1RG9KQE8Pj6YJD1ICJlYWLZtmjMkgpsMDRaA3JTCYHtLK5Nyzhp5Rssx1FeXc/uIGngJxafvTHLSAx0GJ10komuRuOuiZajtDVbdoUs4cGhsmA1kSqSqx7nGmkVE3TAbjll5oR1/Mpnpkg69NgAdW95WmGzgkiZrG2bQc/5hwIn/RwXyxc+fOrBouVq1axaeffprz8WVJtAeer0uAl8nETEQYXr58Of39/XkFvsXA0IA0M1cIgoBDlizbsEc+jyAITK8M0Dy9nIDXNWKRcfny5awn6/HwVQR4Xo+H9Zue5r/+41t2ebmQyMgbZQzpC11iGQ/xlJbm3wnDsjbBYDCv0uWsWbPGdIRIaTotJ85y7PhHBAN+Xty5lR+8+jyJcIj9+/fb7iC9vb0cPnzY7tzLBh6nZIvA3+uNEk1m9z01NTXZ4r4Zl5JUaurHzkLALrWPEpyEw2HOnz/Ptm3bRt13aHZotGzqZLBx9zc439U74uflsolLtPTuMsFVSBOI6dYEXOMsro5cQofOtG1av2ZlDmXBKiNPFpIALllGGxwomOpDMqXTG0kSiVuLpL6Igon17E+vHKmtWEwYpkU5mspKYdDjtEXDe8NJegYTJFIWZ7Qy6LbdO7LB16ZEC9bkKksCsZTOggWLuPjJSVpOnGXbhtUFb80eD+Xl5ROqmoNV/sk3GyKKAuhWWcnrlKe0lJUPhvoRToTt27fz0Ucf0dg4tiREsZERXE0NkarIpL0N0+Tq9S+YPn06zjFKONaLlrKFat1OmTNnzhCLxVg6gTBoNhiubT91cDmdbNm6nb97/Xe8uutpqqoK132ZaRJwSOKkhUlzgaYb9AxaQdVkBEqHYvHixVRXV/PWW2/x0ksv2UHiiRMnCIfDzF+whMrKFZQHXPa7sWrVKgzD4PDhwyiKgizLvPzyyzmdVxAsrbnOUJykqnO/P06Zz0VFwJXV52psbKSxsRFN03j33XdZuXJlQUSDpxKZxW9JabkdZNfW1jJv3jzee+89XnvttVH3M0zTdkYq8ToLJqx96ZMzKG23CcYiLKsaKVsiCFAim3SnBGK6QIlsMpCeo6udJp4ivwqhNM/PI1qBnYnVUFGImEU3IaVpiMHSgqodqZqOomsgyIiCYDXrTOEcn0FGUUGcYipYZdDDvd4ovZEE0YRGZdDNounlGIbJQA5OGl+rAM9aeQkE3A4CHgdPbdrCmZPHeefwhzy79amCr8jAEjvctGkTpaWlw34uCILdbDEW9u/fz/PPP5/Xecv9Lu73x4kpGl0DCWpKR5d1eFzgkEVIWXI2EzXvHz16NG9C+2RgmCbRhEokoQ4rvYqCxfHMIJqwbMw2bdwwZiAiCAJuh8WHOnPuAvHBPlauXEl1dXVBrnXD6pUcO/cpTyxfVpDj5QJJkti+4xkOHv+Y2Q1VbFg7OTuyDPxumf6YQiSpUp6njlkuSGm6LTtgmFbn91DtuzNnzlBRkb+gdHV1NS+99BJ79+7F6XQiSRLr168fMVYMRYazO9HYMR4k0bI76gknLS/dmCW7UlvmzTrTIMuyfe1ftwAvs1CYNXsus2ut0aa1tZX/6X/6n/gf/of/YdR9rOAuYWsgZpQZJotTHx5jzr0vKS8pAXfZmNu50l+LZlrl0gwHLlDAGTiqQUwXcIhW04SE1aUbT+cYapxmwUvBcR26IlFWbN816fdZ1Q1iSUsWpdTnoq7Mh0uWcDulKfWpHgpF1UmllCkr0WbgdkjUlHi4crcPlywR8Dxs4PLl0BT0tSnRqrphK9DPqglaemw+F9u3b0fXdd743X66BgrrGXrq1Cmam5s5e/bsiN9JkjRuiePy5cvMnTt3TK2zieB3O6hLa+NEkyqtDyKj8r0eF2QevmyMxiczueWLlKZztyfKg8GEHdyV+R5qbw3tfowrGoIgDJPSGA1f3vyckydPUlpeya5duwoW3AFUV1WSSoxtnTMVeHL9elTJxy9+u5/zl8Y3Zs8G7iGWQo/qDRYSmm5wvz9OW0+UUFSxxUEbKiwSuKZp7Nmzh9ra2knbrcmyzCuvvIKqqjz33HPjBndDMdnnX0hzkWpKPban5b3eaM4esPmOT18lHsrFmFYGPZnkk08+4T/8h/8wark9qerDytk1pd6CLZaf3LyFrixKk8khYsJR3Tq3XyrcXBXT4X7KKsP2qQL3kgJ3kgJ9aQuyoFwcnp8BmJKM05FfpGqaZlpHNcqd7ohtXVgRcFPud+NzO76S4M5Ma2b+5q09rFq1Gu8UapNmoOoGFQE3fo8z7+TV1ybA6x5IYJhWZFsRcNlES1EUeHbbZurq6vnlP71BZyg+IdE/G1y6dAmfz8fs2bMJh8MjgjlVVceVNSgE0d7ndlBb6kUWBQzTpHswMaoI4uMAj1NCT8u6jGWcnEql2L9/P3PmTI38RwYxReVeryXELIkC5X4XjVWW4GrAM3IASWk6kiSN2WF39epVDh48SHVFGRs2bKC0oqoo111TFiTUP1CUY2eL+vo61m98it6YyS/f2Ju1zd1YqC2znmfNMIuyYNENyz0hM5l7nTI1pR6mV/pxyhKdnZ0cOHCAl156qaAUgWeeeYb33ps6LcEMgh4nDZV+ZFFIO8vEsjYuj8fjRe+wLwYy3rUArZ29vPm7d3hu9zdQNIOkqpPSdDTdsLUHrcDXIujXlXsL7nncHygb1Y9VNy3OXUiFe4qAZlhBXSajVggOHFgNGyH1oVixX3o4sbtFqwxcPYbG3WQhC2Aauk11yRYpTacvkqStJ8r9/rjtNet2SFQF3XZy46uAbpjsbznJf/m7X7Jw4SKWLJw7JR20GRimZesXiipIokhtqSfvZiDBLGTKq0gYjKfoHkwgADOq/HYq+H5/3FI2l0XK/S7OX/uSYx9+yB/9sx9Mysrsxo0bDAwM2I4PGd6Mruts3bqVVCrF5cuX2bhxbDupZDLJBx98YPsYTgamadIXUezuouoSS+U9W7S2tvL5559TWlpatO7VRErl5PUuTBNWz6um1Ptw4ujo6ODy5cvIsszTTz89ZXpcqm5598UyIrSySH2Fb8IV4b3eCC3HPmL705sIeCw1fZdD4osvvuD27dvMnTuXOXPmoOkGrd1Wlm12bbAoJfRfvvUuT64vvm1ZNtB1nZMnTrB26TwWL1qQ93EyXsulPidVwcJ1xEWTKr1p/UiHJDKtzDsiSG9paRmTgD9ZXLhwgWAwOOULGLA67nrTDT/ZPosHDhzg2WefnfJseiEQTap8fuc+Fy5cyMqZwu92UBV0j+vzmy/Cg4P07fk1Myoe8u/iOnSlu2YNE8KaQNKACtkkZkJQgnlec9K8tZRhdeMmDSuom+G2yrB2H0qRWT2GCZ8NprhbPYMdTz85IhAKJ1JE4tZiS5IETNOq8mhDkjCiACVeFyU+Z8E9v3OFaZpc+fIu7x0+yivf/CYVAdeU6u2apsm9vpgt41MZcI9pqZgNHnsO3tDSbEXAPazOXxV02x6Hqm6ydtl8VE3lwPsf8N2Xn8upVp1Be3s7HR0dbN261f7ZUN5MS0sLbW1t/MEf/MG4x3G73TQ1NXH16lUWL16c83UMhZCWYTAxGYhZwa5DErNeiTY1NdHU1MTBgwcndR1jIZZOscuShG6aDEQVThw5jCRJ3Lt3j1WrVk2oB2gYBn/5l3/Jv/23/7Yg1zTUyB0sH8NyvzsrjlK5301FRQVv/e4dqqqs7Nz9jna2bFg97HPIkogoWNnVlGYUpXGgpixI14NuamsKV/7NF5IksWnzZq5eu0b7/eM8u21TXsfJTD6FDIijSZX7aZ0sSbQs6AqlcZYtVqxYwS/+9m/oP3caKTxA2ZZnaJo3r+jn1XSDUNQK7sr9rqzua8bV5usY3AHEw/3cuX6J3c89g6oZmKaJYVrZj8y/wWrIqAq685oLskWwpITrbh8ZJcN4umvVxBI7dkvgkUxSab9ZU4cocDsh4JHAk+bMuXL8KpKGdR497YQxzfWQYxfTNNoiMVKGgVMUaQz48OdZRh0PogCVHhe3H3QSU7RhAV4ipfFgYGxrR69Txu+x+PSPA788pelc/OxL3n3vMN///vepr/BNaTMYWFQLRdURBZhW5pt0tvmxD/AypVmXQ7LNhjOQJZHKoIfuwQR9kSSNngBLF87j/PkLRBJqzi91KBTi8uXLPPfcc6P+XhTFnJoDmpub7ZLkRCr12aAq6EE3TCIJazJrrPLntCKdPn06ra2tNDU1TfpahqI7nEAURbtM0Xa3nZkzZ7J48WJ6e3u5ceMGqVSKlpYW1q5dO6pZ/dtvv10wV4uBmGJzOdxpp41cJnuf28H2DSs5lIiweu2TJFWNW7crufJFG7NmzRm2onLKol0WKsZgsHPrU/x230FEUaS6gB2tk8Hi5mY6Ojr59Z53+b2Xc28iygTZhaBSWMcx7Ikk4HFQFSycAXku+OTIB+wOugj6XOCr4dLJYzTOmYMoipz/+CTa3VYUp4tN3/xWQc/bG0naPMNsbd9u3rxpL16+bujs7OTTTz/l1Zd3j7udYY6uk1cMxPwlGEYczRS5nw7u/BLUOh9m6fR0g0VME4gbFn8tpluNEaiW5IhHBK9k4hHBOc7QnpE+MbDKsLXpBoruRJITXb1c7w8PKxoLwMKyIBtrK6n2FLYZURbSpdpHXmdF1Tl16hRup4zPIaCkVJ5++mmcDjEtjv/VB3VgJZE+OnWWTy5do7S0lN///d+nttQ75cEdQEKxMnc+t6MgVILHdvlmmia94STxlIYA1JZ6RpUBKPE6cTsszZi+SBKPU0aSpJz145LJJEePHh0zuMsXzz33XEEzZ9UlHpyyiGGadkkmWyxevJjPP/+8YNcC1iQdV4bzL8qqqrl9twOAyspKFEXho48+YseOHZw/f36E2Or+/fvZvn17QQjfuvHQ87Pc72J6pT+vTI7HKRNwCdRX+GiqCbJi8QLmzZtHy4nTw3yQM8cORRT6o0pe/pYT4Vu7d3Hv1uf09vYV/Nj5or6+jjkLl/K3//AGg+HcmkEywVehmiwGYikM08QhidSUfDXBHYDc84Cg7yE1ZEl5CR/99G+4+ov/yvyO26zyuVgjqHz8s7+lu6urYOeNpfmGHlf2ckrz589HEAQuXbpUsOuYCrS1tfHZZ59l5RA0lQGEL9yPgEhXygq6POLw4A6sAC4owzS3ySyPyXS3SaXDxJfmzOkmRHXoTgm0JQVaEwJdisCAagV0cd3i9N1XBNqVh+epS2fuboWj/PTzVju4KykpoaGhgZKSEkzgen+Yn37eyq1wtKCf3YrrBATBmreTqk44niKcUFEUhac2rOf5ndso8Too87vwuR6PjB1YdJE73RGc/jIEAV7ctTPtPV68jO9Y0A2DwbhFwyoUT/SxzOAlU5Y0SCZIqwy6x23Brgy6bS9USRQwTWNYLV9RdRRNR9etF04UBCRJQBLg4vlzRKNRVFXNWZMqG4iiyIoVKzh79iyrV6+e/PEEgZoSD/f6YkQSKqVeLScCqMvlIplMFiSjCNZknVFDl0WRlG5gmgLhlEA4rhD0uoaVu3fs2MG1a9c4dOgQO3fu5NixYyxfvpzS0lLcbjfRaBS/35/39QzEUphYgVehZHMyTgXa9Do6Ojq4+uU9ViyYactuRJOqRSWIJOmLJCkPuCj3F3aV/HsvP89f/ddf8dwLLxb0uJNBSUmQTU9v552jp5HNFN/YtR33FJL2kymNeEpjIG1BVupzThjgFENP0jRNUqqGLxYB70PZFUkU2Vg7POvqkB2srSqj9dA7dMxewBMbn5r0+X1uR1oOJoVTlrLm565atYrz589z6dKlSXcTTwXa2tpobW1l+/btX/WljEBClOhLqiimExHLwWK8R00QwC1Y2bcyTIubZkDCgLhuceo005I5ieijH8gvWdInomBl7t64dQ/dNJk2bRq7tq3God3DiN5G9M9CladzsOUs9+/f541b9/jDBU3DMnmmaZ1bM63mj/EcLoz0tophZe6ShhWAPhiIc+7Tz3jQ3Y0sy6RSKdasWVPwppZCYSCm2GNH0/R6WNHMJ8ffL0ockA0yHf9OWSRQIEpBUe+8bpi0PghDOqiaWR2YMHJPpDTa+2IAWQscDvVCjSZUQMDtlGzT3qGEzrt379ouFKIo0rxoEU0LyyzuSpH4KI2NjbS2tg7zr5wM3E6ZgNtBJKnyYDDBjCGmxBNhy5Ytk/DIHft6VN3AKVsBXn9ad6wjFCeuWPpcQ9Hc3Ex9fT1vvPEGy5Yts3W4Vq9ezYcffpi3Rp6i6naXcdkkibF3794lkRjOH6kq8bB65QoOtxyhYVoN08q8OCSRxio/4bhKJJFC0Qz60ubzhQryNE1j//79LGiaxpe3bjOnyH6xuUCSJNauWYOu6/z24IdIWLY+kmiVxzc+uZaA/2FWy0jLIkD+PqApzRL4HerH6pBEAlk4YxTDoSOmaBx49zCrfCOpB2OhqbyUwfZbnDumsmrL1ol3GAc1JZZGZqYZzUzriGWDlStXcuHChYIHeTdu3KC9vR2wnt9t27ZNqrnKMAwuXbr02Prpbvm97/G7//oz5vkdlDkF5BzXEYKAxceToNxhcQiThvVHMayAT8CSOnGLVjfuUM7eia5ejHRw981nlzH4yY9IpgYBMEwBRa5h08af8O4xiZ6eHg50DPLCDA+iYBJVVG6F4+iyE9EwmFfqp8FtzZl6+jpSBuimYAeh3f0DxCQZh64hig7uDCQYOHWaBXNmsWH9k7gcEk7Z8gr2uYYHK4ZhcObMGXp6eti9e/wye7GQVHXbhSjTyFD/5Fr279/PqVOnptxKM6XpdrBZGXQXbCFa1ADPME0SKY2g14nPPXHNPaXpdIYsorTXJVNbmr1wpygI6IbB/e4Qfr8fhyRy+tPrJJMKc+bM5szHJ3G7nDQ01LN100Y0w7DIuViuBrGkSn25r2jt0Fu2bOHAgQM0NzcXRJ6hqsRNTLFMxyMJNWuV72IEsUGvk57BBCU+J0lVRzNMBpIGmq4zEE8hScKIbsnS0tIRivNOpxNVVfPWybvfH7f5mv4xTMgnwt27d7l69Sq1tbUjOqAz5P0VK57g6Ecf89Izm22dpjK/izK/y+5m7I8qlPmycxd4FLdu3eLq1at4vV4MwyCZTPLCCy8gyzK/evvdxyrAy0CSJNalu84zUJQU+46cpswjsXHjehKKRiKl25l5X54r+57BJCnNQMAaJzxOmaDXOeFY0dvbazcXFBKGaeKMDCL5HIzwthsHJT4fFXdv09k+l7qG6XmfP6OJJwoC/Wn+qUMSs+Ygr1ixgosXL3Lx4kWeeOKJnM4dDodH1QmdNWuW3a2saRotLS0YhsHTTz+dV/Xg8OHDX4k4ei7Y8Nrv8eHrv2Zd5URS7xNDFCzJE68EEz1TUVWzy7K7tq1m8JMfYaSDO9OEbrWaVMqBeP7/w5rV/0/2HTjOjXCC0sEkkqpjVNewePNziIJA94Mubp89hVzmRzVhMKnSHY2huL0YTie6IKA4nCzZ+QLzSoMkVY3Ozvt8p6Eer0vG53aMyV27ceMGYAV4q1evJh6PT/o+5QJL2y7FYDxlj0FOWRzG7X/hhRf46U9/Sm1tbcG56uMhQyvyOkcGxJNB0WVS4oqWVYrWME3aey1DYZdDoqHCl1Od/lbXIHFF45133mHpkmZS4R585dPobL+LF4WXX/7GCI6XaZok0r53iqrjcUo0VORfHswGJ0+epLy8vCANBaFokr6IYmeRsg0mTp48SVNTE3V1dZO+hgxuPwijG6ZNeB8IR3jwoIsnFi/E75IJ+lzUlXknvMZ4PM6pU6fykrIYiClEkyrTynx58bAOHjxITU0NK1asGHe7nnCCw8dOsmxJM81N00b8/sv7g5hA4xBJn1ywf//+MeV1Pvv8C1q7I8ybO/VSHPniXns73d09rFxhBQ9yOlD25LGYCsdTPBi0Mqu53N94PM6hQ4eKUn7p6Ivy2W9/zZKqUqry8BU92xNizT/744Isvh4MxAknVASgodKfE1H84sWLAKMGeYZhcPHiRfr7+4GHpW6fz8eqVauyys5lVAhSqRRbtmzB58tOyur27dsMDAxM+F5+1VBUnQvXW1HOnGBllS8dnBUf10KD7LnTQUlJCb+3axaR838OwKAWZFAvAUDAxC9FKV/+P7Kn5Q6D4QjP7HqeRQvmY5iWjIzHKdETTnL+kwtod2+T8nhxVFSzeNlSvC4HkiTY6g2FcKG5evUqpaWlU+KkkhE/T6blRwSsyl9VyUj61+nTpzFNM+csnmGaJBQNp0PKSe4lmdK4l65a5jtnjIWiF8ezrb/3DCZQ0mKU08pyUxq3xHWxgrX+AVK6xLLVGzBMqCkLsHzR6JOhIAhWplDy0NYTJZHSMdPmwsXC2rVrOX78+LAAT9M0Pv74YzZt2sSRI0cAK/hUFIXq6mpWrRrdKqrU52IgZq1GBuOprMoy7e3t6Lpe0OAOoLEqwN3eCLohUFPqxSlLtLa2Ek6oJNK6R8mURkOlH9c4D7DX681bTLfE68xbs+jEiRM88cQT1NbWTrhtud/N6lWrOPnxx8yqrxoRqMiSiKobaLpJLjFMPB7nwIED4+orLlowjwufvQtfowBvekMDX7a24XUIBHxufC5HXgF4TFHt4K7c78ppIDx9+nRBaQlgTejhRIr3973D6qCfQJ7OBMvLgpzc8yZPvTK6h2ouqC7xoOkm8ZRGZyhGY5U/ayeAJ554gosXL9LS0mKPg0PHwyVLlrBy5cq8ry2jQmAYBseOHUNRFJ588slx3T8Mw+DKlSuPbWl2KFwOidmN9Xx0s4q2aJj5Qbkgfq8TIZXOSgcCAYzobQAShtsO7gDcYpIyeQCv1kp50E883I9fNqh8pLJSEXCzcs0K1JXL8blk/G5HUbQDARYtWsSxY8eKHuAlUhr3+y0DBFGwPmPQ6xwzxrh48SJ/+qd/mvN5utLWohU5crD7otZ8F/Q4Cm7f+FiwH62B0uLG1JblrtqcTKtgJ5IKFZXVzJ7VaHtPLp4/cTlrKq1QOjo6Rgxox48fp7S0lN/85jfMmDFj2Mrh7t277N+/n/nz5zN37txh+4mC5crQE7bI/dmsrC5dulQQ8eVHIYkCTdVBBuNWiajc78LvtEoNqm7SHU4S8DhIaQZVQc+4XpDr16/n+PHjbNo0XGfNMAz+6Z/+yZZ3mDZtGkuXLrV/n29g3tnZiSAIWQV3YH1Wv8fyHr1y7TqRUDdgGZ43NzfjSAd42XZynzp1img0isvl4pvf/OaEmZzVSxbwxc0vv1ZZvE3r1/Hue4fZuXktwem5D+ixITp3PpeccwPNxo0bOXHixLCGn3yRTGkMxlOc/vgMUkcbqzxuqt0y7jwDPIcssyAa5bML51m0Iv8ACqx3oLbMy73eKKpu0NWfyEn0vbm5mX379mX1HOYLURTt7+HEiRNEIhFWr149Kj8504z1dUFl0M3KpzZxad8earwy5UVsxjRMa3x1iqKlIhGOEZFnEdZKiOgBAGQ03GKSgGR1uou+JiIRKwgczcVEEgXbJarYEEWxoNaio2GoPqZTtsTPR5sjP/30Uy5cuIBpmtTX1+d1Lp/bQUzRiCbUrAM8RdWJp4X4JyNoPBYeiwAvQ5Z2OaS86s+JlIaq6Zw/f5ZntqxnVm0AXTdHtaEaDUOtfYYGCbphoqg6LodYsCCwsbGRW7duDSM1L1iwgFAoxLe//e0R28+YMYMZM2Zw5MgRysrKRgyCJV6n1Y6uWvzF+rTf5ljweotrAVPidVHidXGvN4pLEphW5qM/phBXNCIJlYSikdJ0nLJA0Dv6A11aWko4HB7Bxdu/fz+vvfaaXWq/e/cuhw4dAqCqqiqvEk6G8Jtr6c4pi9TW1nLp6ud8Y/cL1v3vuMfbe/fTNGce/vIaBmLKhCKe586do7KyMqdywPy5szl39eDXKosnSRJPb9vOJ59+ysfnr+CSH75Thgmq8VCktq6yjM0b1tj79oaTtouL1ykzLQ8bo2QymbcMj6obpDQdAYFQNMnli5+i37lFo0uioszK3AXkyU1U5X4/PZfPE549h2BJycQ7jANJtKog93qjxFPahDQZwzDo6uqira2NW7du8d3vfnfKBJAz2epTp07xySefsHz5cru6cPPmTaZNm1awjv+pQGbRjc9PVDcoL5JF2H1FoD0SJ6WmmFHqI4mbWFihO1WDJtRhEsUjJqiQ+xAF6xpEZymqPJ3BwYuIosjMmTOLcm2PEzLcNr/bQU2pZ8yx+OTJk/yLf/EvJvXcZzjFimYQS2anw5tpCvS7C5+9g8ckwMsI5Oabzra8B02S8Rg1lRUE3LkN5EPLRTFFRRQEEimN/nTbsoDloekvUOvyggULOHz4MBcvXuSHP/whdXV1E+rTbd26lTfeeINnn32WYPAhiVdIl7Tb0yv29t4o9RW+ojwsucDrkpFlGU1NURlwk3BphCIKmmHSG1FI6QYrZ1WN+cJt376d/fv3M3fuXBYuXMjZs2dZsmTJsEk6E/wCdHV1cejQoRFZvbEQjUb56KOPEEWR55/PXazXKUtU1NThLq22PVXLq+sor67jwd0vuXTtCxYvWYIATCsfPei+e/cuyWRyzBL8eHDLj62E5bhYvmzZhNt8cfNLTpw5x8a1q1BU3Q7uSn2W6XY+mdrTp0/nLK9hpLU4M9/v4MAAd08co8kpUVXqpUQ28RbQMH5+RRmn9r7Fxh+M75KTDVxpgW9JFMYM7m7cuMHnn39OIBCgpqaGJUuWTHn3YAaZ8549e5ZLly7R3NzMZ5999rUozT4Kt1PCQEh3no4vOZIPkjrcG4gSnddMbcN0Tp86SUVFFb2hPs6du8rOTf8P1Cv/N7xGr72P6CylZM3/xdvvnUUURRYtWpQ1B/LrjEwVZSKHF5dr8ioasiRS5nPRH7NsRbMJ8DI2mrlYj+Z0TUU5ao6Q0jd+MsL2ummCaeaVaRMFAZdDsrNgj8LEIi97qoOTElE9e/Ysvb291NTU8MMf/pB4UuHtPXt59ZXsskevvvoqx44dIx6Ps2XLFlsvziGJNFT66eiLWUFeX4za0sKbaucCSRRoXryEK1eusHnDOsr9LnxOmf8/e38aZVd1Zomic/f79BEn+lAoQn2DaCSBJISEEAiQwAghGhs77XS6SWe6qrKpdtSrN8Z1vTHuj6q8VfmyXt26voWvjSvtTEzSikZCCMkC1KIGBGpR30Yfcdrd77Xej3X2VvRx2ogQZg7LSBHn7H6v9a3vm9+c3SkdWctFImPi84u9uH1GHOII90xVVTz55JM4deoUtmzZgtra2jG7mhobG9HY2IitW7eOG+Dt3LkTkiRh/fr1Rb/UgsAhIAvIGDbjqgQkpDQLuuWioXUO2mbPw/5DR/D5519g1coVmDOtZhCXxbIsHDlypGjS/8J5M6ecZEq5MG/uHBw8eBDzu3ughlk2S5GEvHxrmfC2A8clEHj2XksiKwUNvdd2zpBelYRhQaO3WPIkliSBx+XDB7GiOoQaiULmK5OZWRyQsOedt7D6idLlI8bqrN+zZw9UVZ1yAZSnFbpr166y84QnCrIoYMZdd+HE3j1wbRHTw1JZGy50AuiOg4WLFiIgi6jesB6pRD9ef+nvkeq6hj17XTy27v9iOnjZi+BDM2GLLXj9vcNob2+HKIpj8nwnCmfPnkUikajoPmSRh+Ww93w0Wab9+/fDsqyy7C8ckNCfNWHa49NzCKW+m0+lbBWnRIDHe9ZFRSrbcxwHkecAjkPWMAEUvjJprGKWZ6btQuB5SAKPcEBCNCDhSk8GlkPQmzZK4id0d3f7ThmenZYphtGd0vPiInjcFUIIXnrpJXz729/Gnj17sGbNGkgCj+m1IVzvzaKzpw+ff3Ecy5feNUwk2jAKc78oFlUhBXaYQzquojnO7kc8rCCoiOhMaOhOG8hYNq71ZDG9Njxq4Lxw4UK/IcUlrAlnrAzOokWLxtT02r17NxYtWpQ33240CBwHURDQUBX0zy+kiOjo16FZDgyb4I477kJXUsPu/YfxeVDAs9942A8y3nvvvZIm19vmz8O+I6+hdXpLWRxAphpWrFiBLTt3475ld0MKRiCPw8ullCJrOuhK6sMs0K5cuQIx1oSLnSn/2aGUDgre4mEFAs/BIRQZ3YZmOXAJQUBmfD+ROrjD7kNTVc2wfZcTsixBPv0F9vM8Vj5efq6s4zh49913sWTJEj/7PRXx0EMP4Z133oFlWbfk8337vDZU1dTi2P796Oxux/zqMOpyosSlIuty4ADwxEFNJKf6UBOG/Py38PLLL6OjowO/+u1biMViiEQiSKcv+GVZURTxrW99Cw0NDaUfSBG4ePEizp07BwCYMWMGnnnmmYruj3hjwQjXnVKKX/7yl2hrayuqqWIs5FNkGDhOVcp9p+IyKfnAdgkudTES6JzGaMElmM6EhoRm4ezFazh/+gt895uby+ZiADCpl+t9rI25LqoW3anpEYZtl+ByVxq6YeD48eMIBALo6mjHM5u+gbAq5RXNZzIZvPLKK1i3bh2OHz/uB44uoXhz6w7UT2vDuXPnMGPGDCyaNwuZ3nYcP34cq1evHtELthIYTe7DJRSGbaMzYcAlFNGAhIaq8XlVHf0adMtBwzjZya1bt45oOedZpN1+++0FnMXIGPjMzm0azJnqTulIaRbjljkuOhI6eLjoOvs5pjfVYOXKldi5cyfuvvvuMTsIx4PruvjlP76GZfeuRjj81Sy3HPn0U2Q1A0ERUGUO1dEoFt+xCIoiY/8nR0BcF/csW4bejOVXAESeQ0AW4RAC03axd9/owqUcRlcZC8gsa6hIAj587RWskim4kWaKMuL8jRuY1dyErG7gUyJg7fN/hI9eewWx5mm4a+V9RW/3rbfeQiAQgCAIWL16dd5BU1q30Z8xBolKcxz8BbAo8LBsliHxvF9FgUdIFYuSwRkIQgjeeustPPXUUyVtZ7LgEoretIHL1zpwbd+HqOI5xNws7moq3gM4YQPdNutu7owEsXYIvaSzsxN79uzByZMnB2k+emXZ1atXT3hwd+XKFZw6dQoAo9WUy3N8PFiOi8vdGXAAZjYMr769++67mDt3LubNm1e2fWYNGzf6Ncgij7a6yJif9UwdRJ7DzIbStRNHwpQI8Dw9N54DZjUUHuAZtourPRmkdQvHjh6GoWex+YnH0TCKf20xGEj0DqsssyeJPIyccKvAc4gExhZbfWXLu7hv9VoYtgtCKRxTx6Wzp9DYNgfhyM0b7PFmIgEpr6aTM2fO4MqVK352SFVVLFtxL7pTBj4/cRonTpzA0jsW4qFVpVul5Yuuri5cvHgRK4aI3w7EQNeS5urgmJwF72UFgOnj6HtdvHgRyWRyWBavnGKplFKc70iNqXdHKUVat/FlewJZ00F1SEZMYM9oNBrF2rVry3Isb7/3AZK6jdr6ZsyeNaMs25yq6OtP4OrVq7BsC3PnzIUgCDjwySEsmDcXbdOnIaRKqIve9KOllGLbe+/h4Uce9VfzFDlXAFEApRR9GROm7YKC8YADMnv3vHtKCMHJX7+AhXWVzd45roNr3T2Ykcsu246N4919WFQbR7emI337UvR2dkLSMiCSjFXjlHF37NgBnufBcRxkWR6zLOdlQE3bBccBiiggazo+/7AYhFVpVA/xfHHq1Cm4rluWRdlkIaVbOHm1D1cvX4HW1401vI6oQBERC+PnuRQ4lbTxpUXQpHLQeYpHvv3dET+bzWZx6dIlmKYJRVEwY8aMSePcvfXWW5PiWDFozqgZbmLwwgsv4Cc/+UlZ9+lp2vEch9mNYwdtnih+SBH9KlC5MakBnmG7flkUYPZSY8lnjIXrvVlolgOA4uzZczh16hS+9/yzZW357k0b6MuMrtEm8hycVBc+//wYGhoacO+99+Kjjz5i9kgchwwN+PwwSeDRHGct24RSZA0HKd2CbjqDMgrxsFJSNjKlW/js1AX09/fj8bUrCpagKRYfffQR7r333nEzBV1JHUnNgsBzaKuLjBogezZM+YpRv/vuu8OaJ3bt2lUWmQwP1/uy0Exn3HukmQ46kxocl2U4ptdWpgnm5JkvcfT4l4jG67Fwwfyyb3+qglKKDz/eiyceuAdNjYOzE/v27cNtt91WUqZ0z7vvYJmdgShUtnHp3I12zG5uHDVL2JdMoioaAc/xOK6ZuOtbI0/uAAvuFi5cmJfGmOcgNJqsT3VIQTQo+bp4ANMczeg2c/EQeb/qQClgOi7SOdmrWFAueQyerAChWDjuTc5XxnDw5radsFwXzS1t6Lh2GTE9g7CWRks0gogsQOIAhacICzcbDQkFCAABN8t9WRf4uNfAoo1PYUb92NmhqYJDhw6hra0N9fX1w35nOS4IRUFi3IWAUorL3ZlBrhXT4iGIAg/HcfDrX/8aP/7xj8u6T0KZPSuhQEtNaMwstidKXuocPxYmhYPnpa691aEnPlhs6RMA4hEFWq8DgMPKu+9AMpnEnoNH8PiDK8v2ANVEVIRURqb3iNyyyEOVRaQ0Cw6haJjWCunUSUSjUXz00Ue47777oKrqoIyP56/rdfXwHIdIQEIkIIFQCsNykdQsZAwbSc0q6eZHAzJmtrbgzJkz6ElqaIpX1qnDg23beZWBaqMqNNOB7RJ0J/VhvrUevOxLvgLYCxcuxBdffIE77rgj/4MuEBFVgmYyGYqaMcbboCKitTaM630aTNtFT8qoyIrttvnzcNv8eTj95Tns/fhDzJg9D9OahzttfNXAcRzWrlmNXQcPQuUpZrY2YeldbCGVyWRKCu4AINrTDjFe2jbygSTwMAwTgVFkQeID5FOolsXH722Dy3GI1dZhelsb4vE4eJ4vKLhzCcH13iwcwhYfYVUEoWzy5XKSHyOpB8iiMGbnX0RlpaqkZiEeVkYVy03rFjKmjeqgDFUeOYPf0NCAVCo1SD1gqmCoML6Rq0pQsErMkaOfYuFtt6G+Ns48g5ffhqzByt6H9+2HaGgQHAeiY6GGOJgeC8MBkNAtdGc0BCURjbEwaiUKhwJUkiZskV4OXLx4EXv37sVf/uVfDmpy8pIlksBXLFjlOA7TakLoSRnIGDYsh6AnbaCxKogtW7Zg48aNZd8nz3EIqRLSuo20bo8Z4HnvhFNk70E+mJAAz+sWcV0C3XaRzN70gouoEmqjaslq2QFZREAWmNelQ7B29Ur8+rcvYfWKu6GWMYunSsKI2/N8INt7k0hlssM6OTmOgyzyvlvHaIEKn3PXUGUBIV30rVVKQU1ExfLly/HBh3vx+MMPIB4uziN1LFy5csX3Gjxy5AhqamowZ86ccX13eY5DY1UAV3uzSBs2grqF6Aim8d51yDfAmzVrFn7xi1+go6MDTU1NFSnxSGL+L6jA86iPqrjam/VLX0FFrMhgvWDeHCyYNwcf7j2ID3efwbIVKxAMTIx46WRi+XJGB7h2/QZe/Kd30BQPIViihtre97fjrtDEXLu2hgacunIVC1pbxuX63VEbB+wMKCiMC93IHD+ES7YDwguocVx0JnpwLRbHvY+OLBJMKYVhu+hM6HAIZZ34NaExx2HLcaFbrm9XNRY8T1JvH43Vg33FLcdFe45TK/A80pqNuU2xEcelJUuW4MCBA8OEzycTjktwoy8L0yGIBCRUhRRkDRuJjOlXYFxCkc1m0VgXx7R4yD+3qpCCqpCCxm88DMthnugZw8a16zew9+QX4HgBUsN0LHjodvT29ODQ6ZOQEjqCpo4UBWTp1gnwamtrh3mOA0yAGBisQVsJSAITN/boQGndRk2EoLe3t2JcRFUSkNbtcedu730Y2hRWTlQswEvrNhJZEy4lsJ2bJ+C6BILAQ+S5ccnyhaI6rEDv05DWLcbDEQQYllO27Y+FWEjG8S/P48KFi1jz4GPIGDbkHEfPsF3oluMTlfN5qHmOQzQooxxr1qAioq0xjkuXonh7+y7MnjULM9taUB1WxuX4/df/+l/xgx/8wNehGypd4DgOtm7dipaWFjzyyCO4ePEi6uvrcdddd2Hbtm0IhUIjKtQPhCqLiIcV9GVMdCd1cGCZBG9A7Erq/oBQVYDa9/Lly1FVVQWO47Bjxw5WKi8jbJeAUgohz9KdIgngOQ6EUnQldeYXOgI3pFx4YNUKrL7XxZtb30fGYp2p+R7rrYyWac1omdaMS5ev4MKVS4ifPI3bb1tQ0DYIIdjz+quYb2URiExM1hsA5rU048ur1zE/T7cPDhwCqjpi1u94T8ewnxmWg96MCcNy/KYUgefQHA+OGNw5LoFusfFrIB9PEniEVQlBhS2sRwrM6mIBXMuJLV/pTqM2qkLgOfSkDGRNB6JwU+zaJdTnRg6FLMtlf3dLRUdCg5kbz71sjQdVEtBQFcCJU2eweMHMQcHdQLCkBPt7TURBbXQWFs5pg8BzUCUBiiTAbYxh+rQmXOvNQBR4TBPIiAvgqQCXsGfl2Bcn0d7ZBZcCmm7ifEcSosDn7jcHl1B/LmyYINeMgCxCEXnoloNf/fq3mJmHyDOlFGnDhmExzrwiCgipo7tF6ZaDlGb5rlzjpSJ4nydcyJkUhopw8FK6hc4E84wcmMJm6UsRiiggGhy7IaEYUEpxriMFgJVBf/nbf8KzTz81bjdLOfD5558jndUwfc4i/8UfCUFZRFO8MK/dciGRNdGbNnH+wgVcv34d0WgU9y5birrocMPlXbt2wbZtLFiwADdu3MCKFStw5MgR9PT0+BY3rutC13U8/vjjvtn40A7W1157Dd/4xjfGVaOnlOJqb9bnY3qZTI/8DTCfzXwEIV1C/Wdr27ZtZfcgdQlBb9rEjf4sCKGojwXy4gUCLBOZyJp+c05QETGtQgTbQfs1Tbz7/i6kdBeLlyz9ynbdjoQvz55Hf3c7HrrvbjQ3jS6PY1kWPvlwN+RsGkqyD/OjYUjixLNYbnR1oSocLsp1xhvNOQ44YHNY9fSz/u9SmuV7+QJsAgqrEupi6iD9UEIpklkLacP230cPSk5XbOCkwXNsO1UhZZgCwI2+DC51Z+ASF/1pC4okoCYaQEgRcw0tElSFRyyojOlRvXPnTqxbt67g61EJmLaLKz2MvB8PK9ByzSmyJKAqJPsBWLnGnq6khq6kAQ7A3ObYpIvYe/CywFnDQda08cnho8hms2hra8uLHiCLPKbXhis2F/73//7fIYoiNmzYgJkzZ6KzP43/7//5Ap7/1nO4a+70EYNuy3GR0myYjgvTdkfMrlWFZJ+2YDmsUz9r2L7sEsAWQI3VwTHpYV6cpIg8WisUo5R99KKUon+AeW4spEAW+QkJaAZeYN10YBjGmINGORGPx0EIwfTaMHrSBhJZttpVJAEBWfBLyBPpezsUXmlgeu0dSGTn49K1Dry/czei0Sjuv/ce1EQUCDwPQggMw/ADNa/M6omQjgVJGpwR3Lx5M15++WVs3LjRF2YeCRzHYVo8hETWRCJrgVDqZ+0AoKEqMObK1XJc9KZNZA071w3JYUZ9BKIowjCMstgdUUqR1Cz0ps2crRazpSkkw65KAhqrgiCUoi9tIh4pv//giPtVFDyz8TEQQvDO9l1IGi6WL18+YZZUk4l5c2cDc2djz6efwdl3GE8/8QjUEXw4D776MlaEZAgCDwzh3GVsB5fTWViEQOZ5tEVCCEuVCf40l6A5z+Au6wK6y8GmgEUAm7IuYdMwcIaToH5+igWKogxeUsHzLPNWHVagiPywSS5j2OhMaIOeaSXHMw7mzOddQpE1bN8GzSUUKd1GSrch8BwIYYt6r1JRE1Fx7kYCmVwDWVKzkNQsRFUR8YjK5oZJ13LIH15OROQ51ETUEfm3fX19ZeQMMsHueFgpe3Bn2G4um0uZvI0i+Vm2tG4hazh+pUIUbjbT2C7xs8CfffYZstksbr/9dtTGq3Oe6DwkkenJUpprPCHU1zL1MpTlpgp5uHjxIlpbW/Hkk09iy5YteO+999De0YHv/eCfQ5QkdCY0KJLoJwMoGPfdSyZ4YOoYku9upVsuElnLn98HgucYNSEakPOqTHrBn+kQ9KSMvBpMvfuSb79CWTN4fRkD/RnLf7FnNUQmNKDpTulIZC0IPHDs8+OwLAubHl1TsS6doRhojO1d1ko9wOWA5TDC/9X2Lpw8eRLVVVVYtWIpvvjsKObNm4t4dbxgH94dO3Zg3bp1gwIHQgg++ugj6LqOOXPmYO7cuWNuw1sZaqYDw3ZRFZTHlFAxczI5Ax/ksCqhqToIQgi2b99e0kracthLnTFsf0XHxHFldCR0n4xeCW5jpZDJZvHme79HdV0zC4D+QOC6Lg4fPgwBLhQB2PDwWgQDAfR0d0Pb9iamVVcN+nyXbmBPRw9O9acGPV8cgIXVUaxurEV9oHwdcOlMBrpto766Oq/Pd5gc0iNQfSilMAwTLnHgugQOIbjmUKx67lm01I++7UtdaV/2yWuwGIuXRynNTXrmsMlxIKJBkVV1KFuI266LsCoPClgaq4KIBEZ+zyc7g2c6LvozJngecF0WCI/VIDCaFmcx6EhoSJep25JS6j/H7f2ab3Q/EGIuwMsnMOA5Dp/s/xgbHn4Q0ZA6qQkMx3GwZcsW9PT0QJZl/Mmf/Ik/HlsOy7J1JHR09GsIqiLi4ZGvZUgRc0GqMIx+kDVsJAb0EIgCl/uciJAqFpzI8tQhAGBmfWTUd81xWRCYziU9hmqvjoayLUFTOstsAGzwq68KTOjNdlyCpB9Vczh27Bh+8sPvT1hwB7DslZctuhUmelkU0BwPoSo0HY31tbjR0YV3tu8CAPDBHkyjCjiwFXgsJOf18Oq6PuxnPM/7mm/btm1DW1vbmB22HMflMp75PZ7dKR0UbEVUFwtAFnm/VMXzPAKBAG7cuFGU9ZEnXOnBm/hiQRkcx8ElQE+uIyylWYhHVEQD0pS//+FQCN995gl8+vlxfPjhbixb/ofRiCEIgq/N6LouXn9/D3hiQW2/hE3TB0s5nE9l8E/nr7JsLTDAGSCNZDKJU/0pnEmk8c3Z0zE7Wh6eXigURH9nV/6fF1j3q8xRyDwgcSyTYFPAVBSYRAWhgMxTLBUoPt3yEqqe/x7CkZEDE820wfEc2uqieWWLuByVIqiIvuwFz8HP4jmEQJVECDyHhthNWoDtMkN2x6UwbJYZ6UxokMSRNS4dxwEhZMIzzo7LJGQ8zqBlOxBy3EFpFD9oQkhZ3385t59SG+5cQnC1JztMDieosPvj0UYGOrxUhVgQznOsHOlx5ySRh5KTx2nbuB47duwoOxUmH1BKsXXrVly7ds33FffGea/cmjFs/5wFnkNdLOBX1USB9xNSksAP0r8cCSFVystjNl/EgjKSmsXKvKYzjILEMqUuetOGfw6FNOaVJYM3UG+mOqQgHhnb2LcS8EQDeR7Yt+8Aamtr8dC9iyvm8TYSdu/ejeXLlxfFnZlsUEqRMRxopg3dYmKnhNy0c+I5DlUhGdWjmDZfvHgRX3755bhK5YQQbN26dUSHi2KOuXuAGfyM+sioD/8777yD1atXFySZkdZtdCRYcBeURVSFZQRlcdjgndIt9KSMm76CIo+GquCEPnulwHVdvLH1faRNYOW9fxiNGENx7swZWNcuI0pMRMwsGsIq/tfZy3ApRVNTEx5bt4x5e2YugA/Pgi1Ox7adh9De3g6B4/CjBTPLlsk739mJ2RV0GzjS2YN5zzyPqiFZQkopTl9LADywoLlqwhYplFLc6NOgWc6oepiWZeHdd9/F4sWLx/SkLjd60zp60iZ4joNLCGrCChzCJtnqnL3dUOzcuRP3339/2SzWBgr2ttaGRxxXCKUwbRc8x0HKUaJcQqFbTs5+k4NmOsOyrNPioUHlRMtxQQgFz3MFlYN37dqF5cuXj0nDGQu2bUPX9bzK2pRS7N69G19++SU4jsO6deswe/Zsv8vbcYlfARqIgCwgpEjDGiUM22WyQOByGbnCqlalojuloy9tIpTLKloOC/YGNkIBLHE2bRxtvaEoS4DnWXnxHLMEmYwGgmu9GeiWi4Ak4Hevvo5vPvs0WmsnrvsNGFyi/aogqVnoz5iDZG0GatVdvnwZJ0+eRGtrKxYtWpTXNi9evIhDhw7hySefLIkb55XkgfwaMF599VU8+eST4w68hFJ0Jm527oYUEU3VwTEnPEIpUpqF3rQBQtnL2DSOO8dUg2Ga2PLeB3AhgnIi7l665A8y2HNdF1v/6TdIpxNoamrC0xvuQvKTPwOxkv5neDmG2PL/idffO4aO9nbcVh3F5pn5db6Oh4vt7ZjRNLrgcTlwrLsXrU88jdohArRJzQQHIBqcGG6oB5dQXO1hSYLaiIrqUbrlDx06hGw2WzYXmHzQlzFBKUE4IOfF6S5nedZDe7+GjGH71JOBGHjt8oGYC0promrZOnK3bt2K++67ryjNyffffx+nT59GKBTCj370o1E/d/DgQRw7dgyUUqxcudKXIrMcF91JI2d0MBgDHaGGBuOUUrT3a8OC3vH43uVGV1LD1Z4MHEJHLBt7HNhYUC44aVBSidYjnXvuDiFFmpTgDrgphCuIXsfupBzGVw6xoIxogAk3diZ1pA0b4dxA09PTg9OnTxecmp85cyba2trw9ttvY9OmTUUfW0pjAVhDLIBoHt21Tz/9NF5++WV861vfGrPU4wljAsibW8cynIyz1JnQoVkObvRro9qYTUWoioJvbWLuH+lMFq+9txsWAdbct/IPKtCzTAOZDAvmHlu3DMlPfjAouAMAYiWR/OTP8di6X+FXv30LJ/tTeLTFQagMjRfNNXG0d3ejuW64+n+5cFddDY6/8wacDRvROIC6EJvgwM6DwHOIBiX0ps1hnbsDsWzZMnR1deGNN97Agw8+WLKIdT6IFyDNdPjw4WEaqOVAdVhBxmDlxqHiyonszQW4J8PkQRJ4qLIANydkHQ3mZ39ZCD766CMsWrQo73tBCEFHRweOHz+OEydO4P7778df/uVf4le/+tWwz37xxRfYt28fAGDx4sX4yU9+AkIpLJtxIjXTGRTYBWQBsihAEngEFHFUihbNSVV5wV1AFkBpToFhgmMYlzDbRJEyPUpR8GgPUskNqiWNRl1J3dd8kQS+aJuxsiB3EQgBKCXI6iYoHVl/qBL4/PPPJ8xEeaLB5TT5LIegP2uiJ2VA4gg+/vhjbN68uaht8jyPO++8Ex999FFRAqYuof5AFlLze4x5nsczzzyD119/Hc8+++yInzFt1y/5juePOxLEnAWdV3JKZK2y2uVNFBRVxapVq2FaFvbt+RjzFtyGhobKBRxTCV3t10ApRSwWg+RchTEkuPNArAQk5ypisRiSySQuZ7K4rTo/8vNYUGQFegXV7T3cXhfH9R1v4ywFCMfhmulg3U/+WcX3Oxo8gvl4wq/19fXYvHkzduzYgXnz5o0rpj6R6Orqwj333FP27aoS48ERCmiW6zfx2Q7xEyxe5skl1A8Cyy1FNhCff/45rly54i/YR8PBgwdx9OhRX16L4zjU1dVh4cKFeOSRRwbN0aZpYvv27ejs7AQhBPPmzcOPfvynfiB3uTvt8wAHIqSIqB1B7msovC7UgY0SxYzz5YIXtAdkcdTyeykoOsBzXOIHd7UFkPArhWhAQrftImvaeGjdo9jy1tv40feer5jH21C0t7dXZOVWCZw9exbnzp1DbW3tIOkTx3Fw8OBB9Pf3Y8OGDb62nYfqsIKUbsG0HfzutW343vMjB0n5YubMmdA0DYcOHcpLgmUg+jIGAFZuKIQvIcsyHn74YbzzzjvDeICeADHABoxiX3ouZz2nWQ7sMTQRpyporkQNAFXhAH74/Ca8sXUHHMfBtGmFN6rcavBEdSORCEjmwpifJdmLiEQiSCaTMMsUlPUmk4iVQdYnH0yLx/2/G/pw6YeJhJc5cUl+1/GRRx7Brl27pkyARwipaKZblUQkdQtn2xPDsnAhRfTLiiyoq9xcvHv3bti2jYULF4465xFCcObMGezevRutra346U9/Ou52OY7Dyy+/jIceeggtLS3Qco4/F7vSwz7LcxxUmXW5htWxGyMIpdBMBxnDRtawfV4bzwF10cCk0mh6UmweiwSkinC2iw7wvI4eReRH5UtMJGJBGdmcLygvyaiqqcO7O3bjiUfXjtoOXU7Ytg3LsspGrK0Erly5guPHj6OtrQ2PPfYYenp6sH37dvA8D55nmlhek8iWLVuGZeeEnO7TP23Zhvvue6As0lWLFi3CoUOHcOLEibw5fFnT9rl3dUVkx6qqqrB06VLs3bsXq1atAsCCmo5+DYbtggNKzkbftKG59QK8vowJw3bBc/Czj5sffwS7Pt6PPXsuYOndd3+lO249Lcd0Og0+PGvMz/KhmUinWRColMF2znEd9GWymDsJgbQtTi5f1HTYnCLkeR3JFHu3vHG0UlBlAdd6LRDKuNCiwPs+5vkIwJcDhmHAtm088sgjrLny8mUcOHAA2Wx20Oc4jsOsWbPwJ3/yJwjkOVb84Ac/AMAcIa72ZAZ1Dcsi0+gLyEw/bzxrU08gP63bvjaqB687eKAf/GTBy8TKo3Rkl4qiAzy/zD9F5CA4jkNzdRAduczDkruX4eiRw3jn/YkJ8h5//HG88cYbeOaZZyq6n2LQ09ODffv2oaWlZRD5t7a2dtSmkBUrVoxYPo0GJIQCCmRFRW/aLEv5cdmyZXjxxRcxc+bMvDqQPSHtWFAe0Qg9HzQ3N+OLL764uc0BOl7N8VDJvDnPhqaCNoMVgeW4fsmnLhoYdB0eun8lXNfF+7//GImMDpvyCIUjuPP2/ALzWwX1TS3gOA7JZBK2OB28HBvGwQMAXq6CLU5HMvkpOABtZXAHOd/RhXnTmkreTjGwxclbnGqm47/X+b7TU31BXW6oOU1SjlI0VAWLHvsKha7r2Lt3L65cuYIrV66gpaXF58s1NTVhw4YNiMVKpyaYtov+rOnbvrFmHxaI5St35lFs0ro9iIso8hxCqoRIQCqoC7WSMG3XV6molB9t0Weq5AyPLdsdRvqcLHA54/qu3Fi89O57cGSCgjye5/HAAw9MuiDnSAgGg1AUBd3d3bh48WJeMgNeADRUe4rjOEQV9u+0bqMuWh7Nv82bN/vyKeN11hoWW9mVumqtqalBR0cHGhsbEZRFpAQmJloOf2TvmlTaTLvc8EoGQUUcsXFFEAQ89vBa/9/X29vx0qsvob55OmbMnIOmMazAPHx27Bgcy/BLcqZDMG/+AsSHiAxPFgLBEKbPmourF85i285DeHrD/xyhi7YKseX/N15/7xA4ALdVR0tusOjo60NTLFrR7tmxYAkTP/FRStExoGNdlQRERxE7HgpRFNHR0YHt27dj1apVRUt0lBuV0utzCPM855F/EFwIUqkUPvroI3R1DdZhlGUZK1aswLp168o+zzMBYmeYLV40IKEmoo6bqQNYlSSt20hp1iCbUM+FIhLIP0CsNDzRfM10BnU9l2POGQklyaRc6EzBJRQtBWqzTAR6Ugb6syYSWRNHjhxBUOKw8dEHK15OPn78OAghU5aPd+LECVy8eDEvHTrDMHDgwIFhkgT79u2DWjMdkWi0rPeeEIJXX30VTz/99DD+nwfdcnCtl5UDyuGUMtR9pFwDmOdXyXMcZjeWy7KosjByjiAACur+9a7hngOHcOF6F1rbZo3K1du/bx8eWH4H2qYPlhTZ8fuP0d6Xwuy5C1BfV1vaiZQBib4evPfGSyCuO1gHL3sRfGgmbLEF23YeLpsOnm4Y6Eom0VZB/buxYDs29shRPPyN0vUpC0HGsNGeExKPBiTURgt3QyCEYN++fbhx4waeffbZCRdD7uvrw1tvvYVQKIR169bh/PnzBXOK80FvTlB9qFRVqfhP/+k/ob6+HqFQCKtWrcrLR7YUuIQikcvUDZV2CSki4mEFah5zSta0fSHjgQirEqIBCUFluGZpJUEpBaEYtbFl4PjqIaJKiEfKb0HnoaSZWZUEZHNGy1MtwGMDBbvQ99xzN/bu24e9hz/Dw6vuqWg0f/vtt2PXrl3o6upCff3U6zpctGgRampqsGvXLjz00ENjflZVVRBCsG3bNqxbtw4dHR04ceIERFFE8+wIbIKy3XvHcfD+++8jFouNOkAPdJVQpPL4+oZCIfT09KC2trYig8EUSGznjd50jvA7Dml5KLxgfPW9y7AawP5DR7B/3x7Ea+oxf/48/3Pnzl/Aojktw4I7AHjkwfsBAB/t+wQHz51G47Q2tLVOL+FsSkNVvBZrHt2Ij95/Gx0dHfjVb98a4GRxAcnkpwBYpvah+pqSRY6v9PZh/iQ2sEiihDm97Th2YD/uunflhO3XyU3wiiSgoaq4oIXneaxevRqZTAZ79uwpqiu/UHz++ec4cOAAAOZw8u1vfxsffPABrl+/jr6+vrLv7/Dhw3jzna1Yu+EpzJwxo6yL0YaGBp//VmlYDnMGGRjYeQLEkcDYtnje971s3UDveUXkEQ3KiASkCbdL00wHKd1CRmdcv1hQHpG6lM0FogLPoT4WQEAWK9rlDJSYwetK6khqVlk88iqFlGahM6mjJ2Xg3S2v4Ud//EeY0RCteGT/2muvYfPmzVPWzP3o0aMQRTGvTKPjOPj973+Puro6LF68GADQ0a8hbdhjipIWgpdeegnPPffcqJk7AD7xNiiLaKgK5JW+zwdvvfUWnnzyybJsy4O3WhN5DjMbpn4Gz3GJ361WqHbfrl27sGbNmmH37vSX53Dw2Gm0zpyDy1cuQZFUrFyxFG11kXHfv4OHP8Xx81dx+x13TWrpNtHXgxOfHsKVC19i4FDJcRxaZ83DoiXLkOhPwTr9GR6olhEoghN2pasbddEIAhPUOTsWetIZXK6bhuWPPDoh+xvo0lAOF6Rt27ZVxDLLW+jeuHEDADBv3jysWbNm2HP8d3/3d74Qc0dHB3p6emBZFmbMmIGNGzcWvN/Dhw/jk08+waJFizBv3jxs/egQ1qx5oKySGi+++GJFAzxKKTSLNTx4/DqR51ATVUcUIB4KL6jLGPYgiRSeAyKBwjh65YBpM+swJ2frNhJ/zpsXPV/1vpxmHwBUhWTURSemSa2kAM9LGY8WsU4VdPRrSGgmOroT2P/xLvzp9/+o4pp9lmVh27ZtJQn5Vgpnz57FmTNn8PjjjxcdgF7vzUKzHNRElLJwG/PhLl7sTMEhtOxK49euXcP169d9j9JywLAcXO3NjmlIPpXgHS/AHDgK4fgYhoFPPvlk1MzJoaOfYcH8uejXKfrSOkKKjHCAcfzGE119+Y130TxzHhonWYPP0DV03rgG27YgSTIamlugBgZnnI7+fheW2n2YFc8/oE9ls8gaBppqasp9yEUjrev4nFew5tlvTcj+ElkT3ambskdN8VDRE/aJEyegqipmz55d8nElk0m8+eabME3Tt8SaNWvsrmoP77//Pu644w40NbGGmePHj2PHjh3YuHEj5syZM+73BwZ2DzzwAADmsPLP/s3/hj/63nex4o65UMogqg1UJsAjlELPSZNkDGcQFzkgC2isCo65QDdsF1nDHrGMG5AFRHMNdhPdBWvmFu4Dgyaey5WFgzJ0y0Fv2hz1+xFVQl2scBpCsSjpCfEuLpnirYJBRUTasNFUV43GxkbsPfQp1q68u6Kt5R9++CHuv//+im2/WHz00UcIh8MlecEmsqavHh4ssyr6WIgEZF9oOaSIZXtJWlpacObMGaRSqby8EPNC7t0ogxPghECRmJ5UxrDRmdAQLMByUFVVXztuJCxbuhgAEAkBksAhqVlI6TaSmgWJFxBURYQUEbIkQBH5QVmRb23+Bn7z6juTHuCpgSDaZs8b8zNLH3wIl86fx+WTR7CmIZrX89mRSGLeFNMWjAQCuEvTcPDjj7FiAsawqhDL2vWkDTiE4npvBq11o/tKj4VFixbhjTfewKVLlwb9fKikCs/zWLZs2bD3/dSpU/j4448BAOFwGM8991xR3uKPPjo4A3r77bfj9ttvxy9+8QtwHDdmAPq3f/u3uPvuu/HP/tlg0WlBEPBXf/3X2PHBB7j3zrGfxcmA7RJkDRtZ04FuOkOCIA5hldltjcavMyzWbJE1nGFBXVAREVYlhNXyjfvFoD9rgoKNl9UhBaLAQZEEf6wMyCIoha9EANwMAKvDlePajYaCAzyXUNgugSzyN6PyKc4zknOrQUnk8fCDD+DXL72Kqx09eO4b61AfC5S9XPvZZ59h2rRpiA8QEZ0qiEajSCQSRX/fJcTvtKyNqGVLjedzD+IRZtdju+wYiuXsjIR169Zhy5YtWLFiBRobx+8EHQ9e1aFcXbSUUvRlTOiWg4Asoiokl3Wg8zrQL3U5cAhFf8asCO2iqTqEhqogUpoFw2bm4J6auwfPezGsSpBEHqHA5Ots5osZs2fDbm3Fm7t24C5kMKdm9AVDOpNBTXBqVj7Shgkz2Q/LNCErlb/+0aCMkCrhWm8GlkOgmU7RC/B83HUcx8Hhw4fR39+PPXv2IBAIoL6+HrNmzcKf/umfVozC86d/+qf4b//tv+E73/kO6urqRvxMVVWVn7UbCk3XoSillbHLBZdQGLYD3WQGA0MdJjxpkrDK9OuGXlNK6aD3f2Cpk0MuqBvFR3YyQCn1eXR1UXVU7nlNrjzrEgoOjHM3WSojBQV4/RkTvWljmMBtpUT6ygVpgAVOPCxh/WOP4/zFK/jVb/4RP/jud9BYHQQhBAcPHsTKleMTjPv6+nDp0iUsXbrU/1kmk8HevXsBABcvXsRPfvKTypxMiVi8eDE6Ojrw6quv4qmnnhqT8zYSkpoFCnbPy9mRnE+mi+c4NFQFcK03i5RuozZKyhrkbNq0Cbt370ZXV1fJXdDCAB08QmnJA3J3yvAt1HSLaT1Nrw0XleUYDRzHoTqsoDvFqBdeVi8fBIPBvDOgnm8vwPg1Sc2CabswbReEMtkU07H8800aFKmsjmhoagZDQyFJEpasfxwd7R04+cVR1Lk6ZkhAU9XgUn1PJoOZZVhMVAJN8Wo0EB2X/vFFJIIRVN+xBHNuu62i+xR4DgFZhOVYsJzR/WhLgeu62LFjB65duwaANQj91V/91YQ2xP3lX/4l/uZv/gbf+973wPM8CCFwXdf/M5qAM6UUFy9exIwZM8qycPz888+xZ88ezJ07d8zPed6vhu36CR7Dcodl2QDWeBlSRYSUkZ0ZBrtKDC7dcmCZrpAqIaSKUyKIHQiHUF/XdLzEBs9x4IXJP/68OXgDeRKeJx7ATnRaTWjK3Yyh8CRd6qMqulIGTNtBV/sNfHn2LOojMgKygEgkgkQigUAgAEIIJEnCfffdh2PHjiGVSvnbEgQBruvi7rvvxqFDh0AIQTAYxKpVq8DzPDRNw4EDB8btUp0MaJqGjz/+GDzP48aNG/j+979f0Pe9xpqQIqI5Xrqwq4dC9AM9P8KGWGBErbZSceLECXR0dJSsZ+hxBqtCMmoiatHvCKEU5zvY8xcLyr6GkiTwmF4bKnvJoiOhIa3bBXFrCSH4/e9/X/I18yaPrGlDMx24hMJ1HezYuRsbHn24pG1PJi5euIjshS9Rb6YwKyCgPhrG+c4uzL5F/H0/oSJWPlmc73QhSGoWupI6BJ5DW12krJmb69ev45e//CW++93v5s2lqxRs28a7777ru18IguD/fcmSJaitHVkq6PcHPsPH+w7g6ac3Y15LbVElv3Q6jZ///OdYunQpHn547HcqkWVJndFYWJLAQ5Vu0ixGGotcQpAxWFA3vHQLJkCsSggolQvqKKW5ErIDVeYRkAunFrmE4kInG4eZpE9gSmQWx0Le6RsvuPM6Zr3ovZwZhEpC5LmcQT37tyKJWDB3Fo4cPYqG6iY89tjwrrGDBw9i3759uPPOO4eVWwkh2LFjB9atWzcsCxYMBmGaoxMtJxMe0bfY5opIQEJSs5A1nbJkpjwIgpC3Mr23yjdsF5XoT120aBHq6urwxhtv5FXuGQ3xiIqupI5E1kJKsxCQRSiSkPNQvDmYUUpZBsshcAmBKPAQeUaBsB3ily/FXHu97RJc68mw//Zm0RwPlfU99AatQm4tz/PQdb3kfUsCDynAI5ITu/XkIBo2PYy33t+NaW2zJ1U+pVjMnDUTmMUExr+4cBHp82cQTWiY1UAnTdi4ENgSy7gePbAfXefPYsMf/XFF9hMJSOjPmLBdght9WQRkEZrlQOA41FcFSnrO9+3bh3/xL/7FlKDOSJKEp556quDvrbrnDrRMb8U/vPQ7/PmP/wQNuQVYISXA7u5uLFmyZMzgjlKKG/2a3/nJcxxUSYAk8hB49ndVHlmqilIK0yHQTJalG2g5Boxfui0XPFFhw2ILYtNyIEkiRIHDzPrCAzyB5xAPK+jLmEjprCTdUhOa0PKrd075LrwLqs956tLA1AzsXMJW/y6lAPsfKGVtzJ7CtWGzB1YWed9k2BqlGjBWVyXP86PafAHAkiVLcPDgwbJ2ZpYKQggSiQQ+++yzQeXlQuBp97iE+lmecmDBggU4ffp0XqVRb8Co5DNYX1+PBx98sCTZhVhQBscBvSlGHs+ajm+Hxlr8JdgOhW45efn6Dnz3ptWEcK03C8shuNqTQWttuGyyMV5Ov9Dgffny5YP8fcsBb/Csrorh+9/chD0HD+Pjjz/GfffdV1Fj90rCC/Yy6Qz+aed7eLY5PKnE8fGgGwZSXTdw8tcvYF4oAImrXOPQQBqGkSsLekhmrZLUD9Lp9JQI7kqBLAporo1AlmUkMgZudPVBEHg01lRBFJgW3EjjoueuYds23nnnnXEz7f1ZJuvh+XKzsWzk8cC0XWimA8thZVuPajEQSm6+Dasjl25Lge0StPdrsB3XX0R7+rwDUR1WIQgcYqHiqUU1ERVBRcT1Ac/nRGkAWw7r4CUU5Q/w6nI3eaoim1NEH2vokUQeGYNNsPGwClnkIUkSdMsuu91aY2Mjjh49CoBx9jwvWE9HbjLA8zy+//3v+/Y+VVVVRQWgsaCMvoyJ7qTOMlJleGEbGxtx4sSJcT+XzVnacICf5akUqqqqMG/ePBw6dKhoZfpoQEZElWDmBgPDcqFbDiyX4Mz1BGJBGQFF8lfIAs/BdglcQsHzHGSBhyTywwZGWRQwvTaMaz0ZOISiO2UgHmZdXaUGCx5rY+jKezzU19fj8OHDcBynYG5nvli94h7cs9jEiy+/hYfWPTxldSbzQTgSxp2PbcTR99/EsqbqyT6cURFQVTzWerPT1+Yqw4/z9yeLaKsLI6lZcF0KN8fbSusWaiJKUeN0X19fRQSIJwNBWcRzTz2BX/761wiHw6CUQFEDqKqqxqPrHvRlmTRNw+9+9zuYpgnTNNHY2Ij+/n788Ic/HGTrRin1/VspmAxJMsv4r/Uj0GBcQpA1HWiGA81yRtSB4zlAlVnZNqRKFVmMe00aXUndrygOXEQDzBkjEpChSHxZOlgppYOaSRx34lQSUpoNQgtLbJSkgzdV4BKKy91puIRC5DlIIg8OHDiOZQA4DpB4Dv1Z1iAQViU0VQdBKcWhU5fwT//0Cv73//e/LrtY4pkzZ3DgwAHMnj0bq1evHmSLNRWwe/duLF68GFVVVQV/93pfFprpQOQ5tNaVJwMxHg+PUIor3aw0OZpYZCo3UBk5j+SAIqKxKlgSV+Lzzz/H9evXsX79+rIEFJRStPdr6E5psByK21qq/WxyoejPmOjJOVD0pDQQCtw1owZhtfjF2EBNvEJFzD1Hkscff7zo/ecDTdfx0pb3seaBtRXdz0Tg1Afb8I2qWycb2Z5M4UZTK5ave2RC9kcoxcXONAilRYnqnzp1Cu+//z7+4i/+4pZeEAwFoRSOS3CuI4XetIFzp0/A7O/AiiWLcODAAaiqiueffx6BQACvvPIK7r33XkyfPpze4Nl6DkVQFtFYHYBhu3BcRhfRrOGZMQ5AQBFZCVfgR5Q7Kjd0y0Fn4mZg59FXsqYDQti4H1TEsgaWLiG43pv1q4GMAx2eEB5eX8bw9fUKMReYWv5iRaI3bcAlFJLAo60uPOKDdb0363d/NlTd5C1Mb2pAbW0d2vuzaKuLlJXkOX/+fFy5cgWrV6/2f1YpI+pisHbtWrzzzjtFaeI1VgVxNccDa+/XMC1eOhdhtO4x4GZQZLskx4VQQShF1nByWSsOHf3aILNpgNnIJLKlSX7ceeedmDNnDt5++20sWLAA8+fPL3pbAHvumqqDcFwCM8exKzbAqwrJrFxuWOhJmQiqEj672IP7FjQV/Syrsoi6qOp30zouydv4WxRFVFdXo6OjoyxyM6MhGAhg3aq7cfjYMdx1110V289EwIzVQjM7EVSmboVkIJpiUYR7bmD3P/4Ga57/o4qPZzzHJu+OhMa8WAP5W+kdOHAAZ8+exV/91V9V9BhLhUsIAK6gYIHnOD+j01gVxPRVKwAzjUvnv8RPf/rTQZ997rnnRt2Op2kaCbBMm+0QmI4LhxBc6EyP+B1Z5BFSmN9rJXl0o8EL7niOabHW5Pxcix1H892n6bB9xsMqYiF5QppLNfOmeHJElVAVyn+cmBqRRgmwXeLLKYymaZfULGgW4xM0VQcH3RSOA+qnteG3v3sVO/ceruixrlq1Cnv27MGZM2ewc+dO7N69u6L7ywetra04e/Zswd8TeBakcGCyHSOtAMsJ3XJ9wm9TdRAuIbjclUZHQsO13iwud2f8l68momB6bRjVOa5F1rBLFhwOBoPYtGkTEokEzp8/X/L5cByHuhyPIq3bI5Y58t1ObVTFzPools6uAyEUjssWNKWgKqSgLsd3Suk2LnenkTVHFzMeiJUrV+KTTz4paf/5YMb0FjhGekyR5VsBdyxdisN92mQfRkGIBINYGZRw8Ne/QE9XV+X3F5AQzHGdUtrY95tSiv/8n/8zfv3rX2Pnzp347ne/W/HjKxaG7eJKTwYXOtO40JlCZ0LLeyxwCUF3kjU2RVQJM+ojmDG9GWvXri3oGMRcUCmLPESB9zXtrAGZqqAi+l31M+sjaKuLoDbK+GiTofHmXaP6WBBN1cEJERD2rkddLIDq8MRpEaZ1Ft9EAhIaq4MFXe9bPsBL5YK7gCwgqIyckOzPqUrXRNRBD0LWZFYoy+6+C994YiOOfPZFwbyj8TBwdRsOh6FpGi5evIh169ZNiYnpzjvvxJkzZ4r6riIJPtmzN22WdO1OnDgxZmbMe6G9MsD13iwcwrp4vcc9KItoq4sgHmYCzLGQDA5MV+16Xza3Si4NK1aswKlTp0reDsC4RpLAgwJ5B09joT4WQG1UYR6JI2hUFYqqkILmeBCKyINQoKNfz0t/6/jx46iZIOutZ77xKA4dPjIh+6oUBEFAf+NM9GVL70KeSIiCgOV1cfRu24JTn32KY0cOY9+br+PT3/0WIIs4ewAAq9lJREFUn3zwftn3Fwmy7Mx478rp06exYsUKfOc738GSJUumrJuMZjq42pMZVPJM6Tau9mTGDPIcl6A3beBSVxpGjo8cj4xdsqOUImva6E0b6M+Yg3QGVVmAYTm40pNBV1IHoSzY84K5GfURTIuHUB8LIBaUy9bMVQo8/nVfxih6cVwoAjKLHXpSBrKG7Y+FhNKyzC0jQbecXOXKLUpv+JYu0ZIcORTAqHpoLiF+nT42ILVpOS46+tmquS4agGG54DgOrkuAMnHxOjo6hkXbkiT5wpLz58/H8ePHcfvtt4+5HUopOpM6XEKhiAKiwfxLFPlg6dKlo3b8dqd0ZA0HVSHZF6cdiGhQRjYnXNmZ0NBaO3KJfDx0dHRg0aJFo/5et252n3YkNDi5knxLTQg8z4EQOmzgkQQeTdVBtPdr0C0XV7ozaKoOjmqVky/uuOMOfPbZZ2VpmIkEJNZ2r1kl++tKAo/qkIqwKoPnObikdCHokCIhUCviUhfjuOqWM6Z/7NGjR0EIKWsn7ViQJAkSX5nBdSJxx7Jl+P2unVinZVEVrKxPdrkxJ16F3hNHoYoiQsEgEFKQ7e/AR795ESuf/y4kqTxlMzuXQRlv8XL48GE899xzkGW54lzQYpHSLXQlWEAflEU0VDH5o44cDeV6XxaNVQF/nHcJ9RMS2oAmAkY5Gp7BcnNuNBnDhuOSYc2HPWnGr1UlAd0pAyndYttQgOqQUnGv9lIRDytI56RKbvRlJ0SLtzYagJ4TeL6Rix04wL+2QVlEc7ywDNtIIJQipVlIZC0Wu3CAZjjoz7DOZselefubT34oXgJSmuU3VkRGqb0PXLx5D4BLKG70MUK6wAO24+Lk6TOIRiNlbeHesmXLsHT5unXr0NraCoCVR9vb28fdTk/a8F/s/qyJy90Z9OaI9eVAc3MzbNvG9u3b4Tg3B4+safsP2VirtvoYE/G1HDLIg288sG4sG1nThulQ/++eyK1mOtAtxxfbBJjAtm6xVWtznBlW8xw36vGFVAmtdczxwSEUV3uzfka3WLS1tflK+KXCc4owRtPqKRDNcdZQwga+8pT9eI5DKJcdzxrOqJ87ePAgRFHEPffcU5b95gu1TKbrk40lD63DB5aCtD41NTTHQk00yoK7HEJqALMdA9lMpizbzxq2P7ZExlkIWZYFVZ16AQrNTdxXutPoTOigYEFBU24cC8gimuIh8ByTHrncncHl7jQud3vlW90P7hRJQENVAK214WHNgZRSXO/Loj/L9AS9KZDn2DzpZaL6MiZu9Gs+x7G1NoK2uvCUD+4AQMxJRfEcK3Pf6MtWPFMr8BwEHsgaFhzCxuuBe9QsZ1DwXSgIpUhkTVzsTKM7ZcB2CTgANWEFIVXy576RHERGwy07MlJKkci1cleHR2+d924AN+B73ipJzGV+zl6+jNOnTuHH3/tWWdPPq1atwpkzZ7Bw4cJRP2Oapi8rcf78eVy4cAE8z6OmpgaLFy/O3fTceYYUpjlkOT75vVx+rKtXr4ZlWdi5cycIIbhj8d3QwTJ2oZzR82gQeB51MRWdCR39GTMvn9SBzigA0G/QUQMSzXRwo6sH8eoqxMNs8KkfsLodD56kSHdSR9qw0ZM2oFnOMD5mIbjvvvuwZ8+eQQ00xcAjVZdraJJFAdPiIVztycCwXaR1uyxyMqos5sQ9Rw9EM5nMpOg+ChzTagtMwUm9UCx9eD3e3/k+HnA11IbL57U8GdDAYVp1eeRfPPqHkisdjgfTZlUFwyKQJR61EXXS/EABNnl72mkAm4+qwwriQ+YuVcqNVSkjpy13czKXBCYAPrTJJGvaSGu5d5Njc5rXgFAfC0KRhi+CU7qFvrQJl1BEAhKqw8qU1LYdC6rExrprvVnolovOhI7G6sq+M/Gwyhr7eB4zG6J+leR6L+OA51MtppRCsxxkdOarnpPthe3c1A+UBB5VIRmRgOxLZ2mmA44DxAKqMrdsgJfOmc7zHDemXdXQqN6b3DkADdVBXO/N4uDBA/izP/lu2W2vzp8/j02bNo35mQ0bNuDdd9/FypUrcenSJaxbtw48z2P79u0wDAO8yCZnT3ASuGnnk9JtiIJRNlN4WZaxfv16HPnsC5y91o2WlhaEVQmNVeMPqNGAjETGhOkQdCT0MYOn3rThr8YlgQfPARLPBu+Bd8u7dbGgjD2nT+CaJGDt2rWIBqSCy5kCz6GxOohA7tpppoNk1iraTzcejyOVSpXcFV0J0WZFElCdU1zvTRsIq6UTob3j0y0X2RK6fiuBJ9Y/hF/+7i08OAWtAYvBknWPYv+hg2jouIR7GqqmvA3kaPDcLwAW/F+6dMl3q5kxY8YgLbbx4GWpxppAXdfF29t34uyNBC50pCHmOEsZk+YmzPJ5ZxeKRJZxlHmOBXax4OiLYG+R5uREgwFAznGPB8IlFN0pHWl9OCeRA1M6GO09jQbkkikhUwFqLgN6o09D2rAR0KyK6vWGVAlzmqKg1MvosefSy6op0sj3lHpajoad4++NvH2R5/znY+CYLQl8Uec14QEeoYwbkM5Fr4rIo74qWLAGXSIXIFSN06rsETAFnvPr2gDQUBVEUBYRDUhYvnwF/uevf4t//qM/LmuQN3v27HG13URRxIIFC/Dyyy/jL/7iL/yfP/LII3jvvffwyPoNAFiE71mDeTe6K6mjL2NC4LmyDV4Zw8bxc1dx36pViKgSGqpG7kweCXUxpkCvmQ6u9WQQz6l+D7w/iazpB3c1EcXPyJ0LCWitG51XMK85inmLFuOjD3fgO88Vbx8WC8oghKInbSBr2kUHeADw8MMPY/v27UU7XQBAX670HFLL+ypWhRQkciWatG6X/FwHZAHBnG1USh8e4I0lcVNpCIKAh1Yuxr59+7Dyvvsm7TjKiUXLVsA0F+PVPR+j0UpjtuhiWnUljPkqB5Pj0dnZiY8//hinTp0a9IzwPI+FCxfi/vvvR0NDw7jb8nizdq6BaGil5dCnx3D83DXcfc896E7q6Oxsx7Rp09i+OA69GeanygKriQ+YTZudezycn4YZyXmnKpIwYlUpY9joTupwcvNbLCj7NApC2fs6FZohJgIhRUJtREVP2kBPSocqCWV3yxgIcQQHHS8Z0Z8xEY+orHmOUvRnTWQNph04MKYTeA5hVYIqC/78KPIcFKm8kjMTKnQ8NE09EJ5AcTQgIxKQwHGcL+QIsAjWO3HHJbjYxfR5ZjVExiwHetkikef8l2GgWCalFJ0JHWcvXcXevXvxL//s+2V9OF5++WVs3rx5XI/VkdT/d+zYgfvvvx/tSQsOoZgWDw3qFB6YCauLqiUHeabt4sTFdmzbtg3zZrVh9T13oKmpyf99Pl6xmsm6frwOIw5s1RNSWaDXniOnDgzuANZ52dnZOep2+/v7EQqF8MADD+Dtt9/Gc889V3TmzMxJEwCFiUaOhF27dmH58uUFZSM8pHQLnQkdPAe01UXKPiB7z0dQFjGtJlTy9ryy+tDtOY6D1157DZs2bZpU7tON9g68vXMv7l/zQNmI/VMFV69cQd/5c4i4BsK2gYBjQALF3PqaKVtaO9bZjXfbe0EpBaUUsVgMkUgE6XQayWQSPM+D53l861vfwpw5c8bd3pXuNEyHoKEqMCj7dPb8BRw7e83XQ6SU4rXXXsWzzw7WfqOUeZGHFBGSyIMQ5oRAKUU0yMzjK5Ut7UxoSOk2ZJFHdUgZccHl5MpwLmHNgx4Hq76KHZftEFiOO4iHJQlM13Wi7LKmKihl/GrTdsFzHKbXhiZEOsVDR0LzM6mKJKC1NuybAXjwgrpKe/AOxIQGeN0pZrzOc6xzVZUF9KZN30zdAwvmMIh/wHMcIgFm16SZDnrSOiKqPGY3iWm7uNabgeWwVZ9nndIcHzzZUcr4X//w8iv44Xefz4tkSnMrrIGB51Bcu3YN586dK1iXyAMhBO+99x6WrlyLtGGPqOLuDRwA68isj409SLmEwrBuEnUHBhU3+rLY+v5OPPboOjRXB/Hhhx+itrYWnZ2dqK6uxtmzZzF37txxfWwdl6A/ayKj235QPRARVSqKK+FlQy3LwmuvvYbnnnuuaEusgcrg8bCCsCpBLkJ9nRBSdBbvUlcatkuGBbvlgm45uNabhchzmNlQevYna9h+95gnmaBpGt555x08++yzU0LA27Zt/Pa1d7Bg0V2orZ0YqZbJgmXb+PzwIcSSPViiuKiPlB7ElwtduoFfnr4Il1I0NTVh44bV4IwLIOlz4CNzQNVZePu9PWhvb4coivjxj388biavK6kjqVnDujz/1yvv4L7V9w/67JtvvokNGzbkveBgQShQE1WgiAJcQqHKQtkaeLx3EWCJjqAswMpxtiSBZ/6php03F5cDy9LHIxOnxzbV4RKC630aTNv1uYwTBUopUrqNLl+XUERCsyDyvK8XOJEBp4cJG5FZN+TN8mg0KEMWBTRVBzGzPoLpNSHU5B5W270psuhl0z1JlK6kjo5+DbrpQhRGf7ANy8G1XubTmdIsKJIAWeRHDCw4joMssdUkzeMVsxyvw4n9GUg8ZyssB5RStLS0QJKkooVxeZ6HqqpwbVbG81Z1A9FQFURtLuhL5zSURiPCs+NO40a/hhv9Gi52pXGlO43etIHetIEvTn2J1tZW1EUZIXnt2rXQdR2LFi3yV9qiKOLQoUPDtp1KpdDR0QGAdTjVRQOY2RDF9JoQqkM3CbwhRUR9Hpy+keCVeGRZxnPPPYdXXnkFlmUVta14WEU8l7nry5i40pPB+Y4UupJ6Qd1YPM8XFWRqpuMToWPBynCDvFJUPtp1+SCkSogFZXR0dOD9XR9i586d2LFjB775zW9OieAOYLIpP3h+MzqvnselS5cn+3AqClmScM/K+zB3w5M42rgQO3q0KaOlt6ejByQX3D2z8X6kpEbYcj3087+ELdcjJTXimY33o6mpCYQQ7NmzZ9xtemOfF8+4rou//6ctmH/bcJmpDRs2YPv27XkfL8dx4HkO/RkLN/o1dKcMXOhIoydVHrUCz19XFDh0JXR0pwyfh2W7zM3Gc1oKqxKqQwra6sI+xUUReURUCfGwwubMBiY0/HVwdxMCz2SxAOTs1SaONsLlqFPePNee0P24oyqkTEpwB0wQB4/kyqAAyzIN7cgUBaagrcoiYkEFWdNmD3WOWOp1nWQNNinajguK4bpnHjTTYW3TYFyFWEjxb/5oLwSlgCDwsEwTwOgByEDLLAC+dlFNREV/1oRu3STNT6sJYdWqVdi1axf6+/uLko9Yu3YtXnnlVdBANTq7e7DxiW+gtTY86NyrwwoUSUB7vwbLIbjakx0xRd2ZYFp6jBzKpDRMh8DMlXk1TUNrU92g7y1btgwAfNupO++8EydOnMA777yDYE4WwTRNhEIhOI6DZDI5SLBYlUWosojaqApKaUEZsosXL+LChQv+vweK54qiiG9961t45ZVXsHHjRv9YCoFnv5XULL+DyQuiC+mwFQRhxBL7WPCy1mFVmhROULFQZQFXrlzBA6tXoqVm4lbIheKpxx/BB7v34uy585g7Z/ZkH07FMW/hAmDhAuz77DMEb1zAsrCIyCTp6WVsB6f6U6AANm5YjR40YNPrcayedhf+ZsPH+Le7gth7XcaWp9nvX3jxFZw8eRIbNmxAKDR6FtKTEgopIg5/9jmOn7uKpcvuHbF7WlVV2LZV8JgD3JTTkkQeKd0qm2yILLKMIONdMUceRRJg2S6ypgNZ5IeR66fFp05W9laAJPDMbs0l48p7VQLN8SC6kjosx4Us8DBsx59zJwMTUqL1UusCz6GtLlLyyXrltUhAQuMQmZCMYfs8L9txIeR00oby14aiM6Ghqz+N3+/Yhr/8yZ+M+jmPh8S2GcS1nMftQHjihwLP9qtIArq6unDkyBGEQiGsWbOm4HO2XYL3du9HY3MLpjfWobE6iKNHj6K/v58dVyKBjU9uQntCh+UQqJKAlpqb/rApzUJnUgcHoK0+wnThXIKs6eSsvBjR/9C+j1BXV1e0iO+hQ4egKAruvPPOgr9LCMHWrVsRCLAAu6WlZVzfV0IIduzYAQCIRqNYsWJF0dmktM7Emr2VdL4WOJqm4fDhwwXd18vdaVgOQWNVsCwyJiOBUIoLHWyinT6CXlYx6EhoeH/XR9iw7oG85ComGx/t/QT9BsFtt40uVfRVxBeHDqG+4wJWNE58F+6JviTeuHQdsVgMzz8+B7Zcj//42WJ8cFmGIgCmCzzSZuJni49Bsrrwu63nkEwm8eyzz/pi5/sPHcGl612wKc8WpBxgEx4rlt0Novfhi7PXxh2jrl+/jrNnzxZNkQHYuDu/uapsE/SNviwyObpNbXTqvz+3GlxCcKkrDUKBlprQpHETKaW43M282svBjy8WE3L2HvmwXB1Mng7M0BSsR1oHAC6X4eM4DnW5GvhYiARkpHQVGcPB6Wv9qAopuayi4D8kGcP2BXdrIgpUmfH5OhIaCKGIBmWmyQfgel8WlkNwrTeD6rCCYLQaiqrmRSYeCZLAY93q5fj5r3+H+vp61AU5LL7rTp8PZ1kW3nzjdWza/DSu9rBGlsQAGRDvHgzUOxJzrdcD268feeQRXLt2DS+99BK+/e1vF3ycy5Ytw44dO5DJZApuPNixYwceeuihgrJxPM9j/fr1AIC+vj5fx887lng8nve2mNl2CNf7vExoBtPioXGdL4LBIEyzMHFaT5V/tLb6coDnOIRUCRnDRjJrQi1RM5FSiqxxk+95K2DNquU4cPhTHD9+ArffPrpTylcNdyxbBl2/Ha9/vBvLeR2tVcPfRctxcaInCVNUwLsO5oRExMM3M0ZZw8SX/VnosgpTkKDzMijHQXJNRPUMFlUFEAkMz26ZuXE5EomApM/BOv//wv+x4WPc+/cyTBdQBOBvHtLR/96PIMz5MSKRCJLJJPZ8chSnLt6AZlqYMXMO7lkxeKzUDQN7D3yCWDSS1wJ02rRp2L9/X2EXbggEnkNat8oyQVPKnGA4jhtTV/Rr5AfTZnQo03bhuBQ8z8HKVWIkgS/LgrZYcByTO+lKsr6Dr3SAF1JFpHUbiayJ6IA6dbHwPNkGNmEMFM5VJB6m7fp18XwublBhkik2r6A/rUHwjnEEz3ZJ4P0uqKAiYlZDdFgpoKUmjPZ+JsDokfkvd2cxd0HxlzyoSPjmM5th2O6w7k9ZlvHkk09i966duPf+B9GV1NGTNhBSGbnT88rLp0O4paUFS5cuxfnz5zF7duHlrUceeQRvvfUWnnzyyTE/d+jQIfT19UEURViWhdra2qJKrR7i8TgeeeQRACyzd+jQIV+rbsGCBWhraxt3G2qOK9Per8GwXfRnLTRVYBXoC3BXOLtSFZKRMWykdBs1kdJKFqZDcPzEScyfP++W6tq7954l+PTz49i3dw/mzV/4lW++8BAIBLD00cdw+tRpXLrwBWYJTNz9isOjVw4jHYrhrvUPI5brOD544hTsrusQKYUtiuCi9Vi48g7ERpCFcF0Xu46fgJXoBwcKjlIIHAdKCXrcHCc4nQYfmQN5+S/xb3cF/eDOdIF/tyuAny3/JXirC+n0OQDA7LkL0DZ73ujno6pYfd+9BV2DWbNm4+TJk7jtttsK+p4HnuOgW05ZJmjNckAofPrR1ygeQ4XyB4LngMbq0i3DSkVYldCV1GG7BIbllGyRWQwmZI/1sQDje9kuelKGT4QsFtIAfz7PH9O72bGgnOPHEQRkAXUF8CdEgUeAd1FbFUZtRIXtEmSNm52gnrp0NDhce2/ow+SVZxNZC7rFFMlX338/3t25E9995gmEixSZDKkiDNsdZFDtQVVViKI4KFupWy4Enofp5B/gAcwnd8eOHUUFeN73x/PZTSQSWLduXdGdsGOB5/lBrgo7d+5EKBRCbW3tuN/1bHD6M2beEiqtra346KOP8i7T8jkZIEIoUMGxPiCLUCUhF6yaqCuhLOQSglQqhSV3ju2dPBWx5M7bseTO27Hzw73Yd+YUZs2Zh8aG+sk+rAnBnBw/70pHJ0zLQlvrdIz0FsxftBBYlF8pWxAE3HHXyDQMXcvizX/4f5BMJkHVWdCkRuy9LuORNhN/85COf7sriD3XFWgr5iDKh5FMHgHHcWhobinhLEfG0qVL8eqrrxQd4AGAbpbHRtDKaeEFldKFx/+Q4Om/DoSXNFEkps8pi7zfTBZSpSkhHSTkLFTTho3etIlpNRMf4E3IVeA5Dg05vg6zjynerw1gF86r9GqmjY4c5y4WlFEfC/jbr43mL9ILsIBREATEwwqqwwrqY6wTdFbuz4z6CKpC+bele2na5ngIbXVhRFQJq1evxutbdxTd2SgJg5WzRzsPD0nNwvU+loaURb6gB58QgkQiUdRxzp8/H1evXkVXV9eon3nggQfw4YcfFrX9QrFu3Tp8+OGHeQvy8hyHmkj+XWrz58/HtGnTsHPnzrw+73WAOxMgEByPsCA1mbXGtBobDxxu6lDeqlj3wCp8/9lvgGT7sDePzs2vEhobG9DWOr3i+wkEQ5g+ay44jsPb7+1BLTqx5ek+/GzxMfS/dz/+4+LPsOXpPtSiE2+/twccx6F11jyogcrYTEWjMfT09BT9fdN2ytKJ7g0lJB8/qz9QWI7rq2Vc7cngfEcS5ztSON+RxOXuNPoyBhJZE4RScACm14RQG1URzVXqqkJTy3LNG3tZ9raw+267BP0ZE11JHV1JHd0pHX0Zo6AxfMKuhCIJPm/Hi75LAaUso9DRz0ybvWydS276wXml3HzhvXhDJ3Wv67QUcByH+qoAQgEV9Q1NOHjk86K2M1A2ZjSokuBLgJgDsn2FWpqtX78e+/fvx9mzZ4s61sceewynT5/G3r17h/3OdglkWYbjlBbsF4KNGzfi7bffrtj2Z8+ejfnz52Pr1q3jftYbhGyn8sFSSGHCmhQYtayRDxSJx+zZs3Hy9Bm/5H+r4oFVK7B5/Rr8ftcHk30oX0ncvmQ5OJ5He3s7Xnv7Y0TtDkhWFwJzfgzJ6kLUZj9vb28Hx/NYtGRZxY7l4Ycfxu7dvy/6+zzPD9NqLQZeZUWznJIWWl8VUMo0WXvTBq73ZXNBXMZvyjTsm96shDJKVm/6Zmk2cAtkQgfGEvkeqZNT5rjUlUZP2kBSs5DMuXD1pk1c7s7kv/8Cj7ckeEGHZjmDskyFwrAcUAApzQYBhchzaMrV3G33pjVZod1jJKe4Xqlnhuc41ERVzJ49G6cuXEUirRW8DTM3MIzGpaqpqcHhw4dRE1HRVhfOdfDImBYPFUXsfeyxx5BIJLB///6CvwsAa9aswcyZM7Flyxb09fUBYB29l7rS6OjXUF3bgGvXrhW17UIhyzLuvvvuvDS3ikHWtCGG45g/fwE+/3zsAN4rlXuyOpWG1/Gq5bqmi4HA82hpakB3dzeSWnH6g1MJsWgETz58Pw4fOTLZh/KVQ1W8Fmse3QheENDR0YEXXnwFv9t6Dts+DeN3W8/hhRdfRUdHB3hBwJpHN6IqPj51oljwPA+O44teTPI8N8iRoFjIouDbiXkORH9osBwXiayJG31ZXOhM4WpvFn0ZE5rJ+IkcWLKmOqSgoSqA1towZjVE0FYXRn0sACVXhYqokl8VnMrw6F0Cz+UVjNoua+5L58Zo71rEwwqqQnLBjSMTWhSWRcHXqDFtd9zOVg+ekot3gdKGDZcw/hKfy4x5dmVeRqQYMjnPcXBd1pFTKUQDMjTDwapVq/DquzvwvWeeKIhw6/FBQqNcu8WLF+PK1avYvn074vG4r2NXCpYtW4b9+/ejq6sL9fWF85aam5uxadMm7N69G7ZtI2lQWJTHokWLUN08E4ePHkRLS/n5N0PR09OD5uZmdHV14cyZM+NKsBS8/ZQByyGob2rBmd0fjCkVE1Il9GVMZA0bLiFj2u2VA7LIBgrdckpqtKgKKRAEAf0ZM+f+cWuTxRsb6sE5OlzXhTBCM8HXKB7N02dgw+Zv48Snh3DlwpdIJpNIJpMA4JdlFy1ZVtHgzsP69euxfft2fOMb3yjq+0aZFmLxsIKs6SCt24iH3Sn5/jCtWcI4wjm7UNshbM7N8YYJhd9YqEoC6mKBUatctkuQ0izff34geI7xhIMK00tVRnETEng2hsXK6BU/EfDoLGKeFcCOfg0OoZAEZsowUkCXyOa/OJhw1p8qCWNmyCilyJoODMuF5biDXC0UkVm63OhnsiQBhZErQ8rNzJTtshdRLmISC6os5ZvWzbKJW46EhqoAHEIwc+YsHDj6BR5YsTjv73qcrZF4BrZL0JnQYIpRXOs3EKwSRySoFoOVK1di+/btviRJMRioR5VIa9hz8DD60zoMw4CRs5epBDw7sUAgANM00dLSUjS3cCwEZBGWw3hu0WgUiUQCVVVVI35WlQQoImt+6U4aRVm3FYqaiAKOK+25jgSkHKEZuNydQViVUBtVpxTvpVCsWrYEh06exh1/QDIqE4WqeC1WrXsMd9/3ADpvXINtW5AkGQ3NLRXj3I2EcDgMXS+8YgIAjuuiqkyBhSqLCMoiNMtBZ0JHczw0JYTObZegJ2UUZJfGwCw7DdtFc3ywbqhhu0hkTD8b5SGQkx4LKSIUaWI8WScShFIYFpNw8eTJ8gnkU7myNAdgWk1o1DG1kI7uCQ/wxpvIOhL6qHwH02EPkiTwMIgLVRaGnayXfZMK5N8BjKu0fPky/P7Dj9G8+bGKtbJzHIf6WAB6ayv2799fkNK1JPA5BwoXIdwMbCmlaO/L+t2yq+9fg1OnTuF//dNb+OPnNlY8Q1QoqiJBPPHwGnT0a6zLKGUMMrAvJ7Zu3YrFixejpaUFR48ehaZpgzpsywWvccIlFKtXr8a2bdvG9KitiwVwrTeLtGGDT+q+RVylUI5tcxyHeID3O3Mzhg3NtDG9NjwlsxH5YPq0Zuzed/jrLF4FoQaCY0qgTASWLr0b+/fvx8qVK8f9LCFMVy0SkFAdDkMp47NdG1VxrTcDw3ZxvS+LaZMc5GUMG50JfRCvWxJ48Dxrq5IEHpLIBKd5noOQs3XjANguRU9K90uLIUXys37mAH6xKrHsWzggfWXt1RyXoDdtIK0PDpJ5jvObLcZCIkd7iUfK1ygypcSsCKV+cBcJSFBzVmVyLtDysnqG7UAzXfDccMHVUkq0As9hzsw2HDp0GKbtVlSrSBYFv2HCcQkEPr99BRURWdNBxnAQH6BdmjUdmA7zNm2pCcNyCKQ7bkc6k8Xr77yP557cUPIx33HHHTh69KgvrlwO1ERVpA0bmuVAt5yK6Ks98cQTeOeddxAOh8t67EPhPXv5DtYBWUR9LHCTVGw5qIsFKqoxp5kOFEkoekLZv38/Fsyfh+m1YZi2i44EE4XuS5sTkoWsFJ5/6jH88qU3sGbtQ5Ckr0Vov4qYNWsWjh4dm2/p0YHqYgGfM15uKJKAaTVhXO/NwrRdXO/NoDkeKqutlktYA4Nhu6AUCOfmUw+EUmimg8QAe01F5BnPrYCsmgqWkbvRr8G03WHZuogqoSqsTKro8ETAJRTXerN+CVrgOQRlEQFFzMuO0nJuNkNGi5RQGwlTKsAbKL8w1IIMAKQAD0BCT8qAZroIqcNXA94FLjYCFnjGAZgI+QpZFBCNRnH1RgfmtE3L6zthlZ2/p+LtBQPetVMkwf/Dc+zByxhOWbITzc3NOHbsWEnbGAqPMJs2bFzrzSKoiAjIAqIBuawD3hNPPIE33ngD69evL0lMeSR4tAIvHe9xS++//35s27YNAJOEGWm/nrtLZ0KD6RBc680ipLDAr9w+isxHmUnmFJNxO3v2LERRRGtrKwD2rDXEArjay+yXKr0oqiQkScKPvr356yDvK45p01rGFHB3XYo5TVFfa7VS8Kwkr+eqLpe706iLBUqa3LOmja6E7hP7B6I/a/q8bZdQGEN0VKtDCuKR/CXABkIUeEyvCSFjsO5gL9MXkMVbmrpRCLLGTX5hMRZpKe3m3FHOcX/KXH1Kqd/+PJ68iZ7TuQsOuYiU0psBXhElWm/ftm3h7KUb6ErqIwoKlwthVcKiRYuw/8ixvDVyRIFHOJe19AIK4GZQoVuufw2CigiB57Bs+XL8z1+/hM6+FNK6XRZNp3KiLqb691IzHfSmTVztyZTUaT0SNm/ejK1bt8KyytcBSijFlZ4M2vuZh60iCT4nNBwO47HHHsP69evxySefYPv27Thx4sSwbYRVCTPqIz6BOGs6uNKTqYiUAqFAMZc1kUjg3Llzw5p2VJnxaCiAKz1M4uBWhRfk7d/zIXp6eif7cL5GBbBy5Up8+unRUX8vijwudaehW6XLoowHJRfkKTlOa2dCx/XebNHvfU/KGBTcSQKPaEDyx9as6TB+e25Ok3JWlTPqI6iN5q/5ORI4jpWzayLMdzUaKN2x6pbCgEvXmzbG1KkdCkopUjqbk8qZvQOmUIDX3q9BMx1wQN7tzzw/cvbO4w0Ug+qwgu9865s4dPgwfvWbl7D7k89LFmYeDdGgDJHnQMGjsy+d9/e8AGLgccmi4KfBU7laPsdxiIcVKIqCBx99DLv3H8Gb7+3Cy2/vRH+mcD20RCKBaDRa8PfGg8Az54i2OuYgIvIcHEJ9399y4umnn8abb76Zt+DxeNBMx28C8uRohoLneaxdu9bPHr722mvIZAZrGQk8K4+01oYhizxcQn0B73LB63gDgKyR/zNNCMEHH3wwKp+wuTroZweSmoX+W1gCQpIk/Og7z6Dr2gWcOn1msg/na1QAqqoOe/8Gg8PlrgzsCdCqk0UB02vDgyTELndn0Js2/HJxvvD4503VQV+Yv6EqiGk1IUyLh1AXVVEXVdFQFUBbXRgz6iOojwVuuUCM5rp7C70+lUQ0IKMmx7PTLbegTldPNo7nOITV8hZVOToFrlJ/xkRPbjKfFg+NK59yvS8LzXRyGm83eRJZ08aNPg2yyKOtLlLSMWUNG70ZE//rN7/Fj77/PTQPmbgty8Ibb7wBQggef/xxxGKxUbdlWRZ+9atfAQD+/M//fNDv3vvg9+hI6Lh3xQrMa4nntYrSTAfX+7KQBB4z6m+eZ0qz0JnUIfIcZjbcDMSyho2s6cDJdSSnsxp2796N55/eiLpY/uXK8ZoGygXv/ABWSiw3f8NxHLz++ut49tlnwZfYfOIdK88BzfH8U/Ovvvoqnn322ZGPzyW42MUC/pn1kbKm7D0Px6HPzljYsmUL1q9fD1UduwPX2zbPATMborc8mfr4ydP47PR5iEoIdy9dMtmH8zXKBMdxsGXLFjzzzDNjfi6siGgaYcFWKViOi+6kAS23cJdFHi01obwb5K70ZGDaLhqqSiv1TmW092WR1C2mmUcpBkpyDGwMAVgVjwMHQeB8XVyOYy5YHMdB5FlFTJHKF1R5sYnnqpXXOfVryBh2Qd/JF1OCg9efi3brompe2ni+C8CQNKi3ginHhBhSJfA8B1VV/TJtOp3G66+/DtM0IYoiNm3aBFEUsW3bNn9FKAgC1q1bh9bWVlBK8bvf/Q6JRAJ//Md/jLNnz+Ktt97Ck08+6e9HhIs1969mnrqmg1AeYsR0lEZ2r3Q99LchVfK3SylFX0DCunXr8MqWrXhu0+OoiQbGnYzPnDmTl49rORBURJ+X15XU0VobHv9LBeDll19GPB4HIaTkAC8gM76jabu41ptFa204Lx5aPB4f8eeUUl8Elefyb9jIF9Gg7JcQsoY97vO2Z88e3H333eMGdwBr309kLdguQUa3Eb3FNKuG4vbbFuD22xbg5PlreH/Xh1gwdy5apzdP9mF9jRIhiiIoJeO+/0ndQgMNTthCRRYFTKsJIa3b6E7psAqUUFJz45BhuSjBcnpKw3KYZuhIIyzFYJtOZwztQkppzg2LQhS4XDB4046R5mZZVRTyDvItx/UFsYc2f44GxpFndIDRxkvdcpAxbMiikFfDxkBMeoBn2i5cwnzl8hUx9Dh6Q22eChUVHA8uIbAslu2JCBaCwSCee+65YWT5559/3v+7ZVl4//33sX37dliWhaeeegrTprEGisWLF+P06dP48ssvMW/ePBBC0N3djTtVCUnNQjbPAM8jx9su8XXuulM6UjlO3liG8lzOYxVgunRvb9+FSDiExx9aPeq+r1y5gs7OTqxZs2bcYysXaqOqT9y/0p0Gz3OQRQFVIblkOY6lS5fiyy+/hGVZEMXSXgGO4zAtHkJ7fxa65aIzoaE+FoA6TiZPURRomjboWWJNEBqyuUGirkAv5XzAcxyiQRmJLLO+Get5O3HiBMLhcEEi1JEAE3BOaJa/0JJFvuwNIxMFw3YhBSN49KEHcOTwQciyiMaGeuhaFl3t12DbNiRJQn1TCwLBicv2fI3SsHbtg9ixY8eYup48x6EnraM+OrHd4ZGABEnkfUeDujyF0AOyiKRm+Qv9ryIsh+Tv+TUGOI4Dx92ked00NxicHjFofmV6w3LQnqPUBGUx70pOOse9k0V+WKWKUoqetIFE9iZnvCelIxLIP9M36QGe9zCqcv6t2d4EP7QBwoveyzWZWA7BYxvWo6Emhmm1+XHPZFnGE088Mervn3/+efyX//JfEIlEwHEcent7EVRE328urVsQeB6iwCGkSIjmuiwBFsASSn2JFc+fL2vY/kNQHwvktXqoiagQBR4PPrAGXT29ePWdHfjWk4+OGJgkEgm0tbXldf7lgijwqI2q6E4Zvp6SbrlI6xZaavLLkg3E7t27cfr0aSxduhTLly/H/Pnz8cYbb4xbptF1HT/72c/wxBNPjBrgCjzTNbzak4HpEFztzYLnOL8jOJBrRBiI6dOn4+LFi1i06Ka4bm/a9IO7pupgUdZy+cDLtI0lTUMIwalTp0YtI4+GcM6hw8xpfHkIyiIaqsrfGVxpeE0jYVXC8xsfxW9efg0njqTRdf3KIA4Qx3GYPmsubl+yfEKcGb5GaaitrUU6nRrzMxzHoS9tIarK4y7Yyg1VEiCLTPNUM11EAvkEeGyMsXKuE1NBQLkQuIT6clGG7cJyCAvCwEGVBTiEgICCL0eElwcopWMugC2HZUu1AYLGksCjLpa/mLyXlBlaUncJRUdC8zOCQUX0KVZJzco7wJt0Dp7HG6iJKIiH87swhFKc72Av50COkieaWxtRUV0GDaOOhIa0bhd0bPnAs3gBgA8++AANDQ2IT5s1ot8hz7HUrePeTOXaLkFasyCJPBpiAWi5VPRQTmK+x9Ler+Hcpau4fuUSvv3UyHp5W7duxeOPP17QtssBw3JguQSgrJRvOQQRVRq1bJFOpxGJDOaWnT17Fvv378cf//Ef48CBAzh27Bh7eUMhfO973xtz/y+88AK+973v4eDBgzh+/Dj+7M/+bFQJDctx0Zc2kRlBDV7gOUQDMqpCTP6FEIIdO3YAAEKhEO5ZvgJXe9kKcCI4NN6zzXMcGqsCIw5k+XLvhiKt20hkTX/BNVC6aHpt/pyiyYZLKC50snGmoSqArutX8PLLL/ueprFYDJFIBOl0GslkkmUFeB73PvAI88X+OrtXcXzxxRc4d+6cL0zs5QgCgSDuvffeMbnRJ0+ehGEY42pjui6BIgkQBT7H48qZyOf+K+QkQfK13swXnj5mdUhBTUSBSyhcQiDmxO4JBWzHBSGMi6aIPDqSGggpP3e3knBcN1e5cH1+3GTAtF3YDqsoglIEAxKmVYfAcRwo4Fu2MXctd5gaQUgR0VAVzDuwthwXl7sZtWvg/bIcFzf6NNguYU2nVUE/aZPSLRiWOzUCPGYgTBGQhREH9aRm+SvkQh/Iy91pWA5B44CT9wiO9bFAWTzretMG+jImZJHHtDILUQ7Ez3/+c/z0pz/1X2DHpbAcF0nN8js0B4JSit6MCYHnUJ0L6OJhxS+9FgqP1J/JZHDp5KdYMG82Fi5cOOgzhw8fRiQSKbt/a77weCkuoQgq4ojdqgDw7//9v0dLSwsWLVqEBx98ENlsFj//+c/xb/7Nvxn0OUoptm/fjosXLyIYDLLOrBE6a+vq6rBx40YAQDabxS9+8QusXbsWixcvHvVYKWU6U5rJMmSG5foBn8hzaK2LDBoEMpkM3t+9D66g4L4V94x6buWESyiu92b87OhIixhCiN+MUgosx8X13iwcQhEJSCNqXE5VeJNsor8P21//R7iug6amJmzcsBqccQEkfQ58ZA6oOgtvv7cH7e3tw7bxdXavfHAcBx999JFvNTh//nwsuesOxIJyLuPGpEIymQwOHNiPbFbzM63hcBj33nsvwuGbnN5XX30Fzz77XMnH5boEkeDIXfTF4maAJ6MvYzIKEscBuQQBNzDYBHunXZdAlgTMaYxOeQswl1D0pnX0Z8ySudDlQH/GyDVd8BAFYVzuJQcmdaPKjBtXqPadF18MnM+ypo2Ofg2EsrmiKR4qqcmwrAFe1rShmQ4oZaVXLzjxolBVFsABcAhFKleSBIoLTryHvyok+5yz671ZaJaDhligLARvxyW4ktNjE3kO02pCFbFjevHFF/GDH/xgxN+ldRspzQLPM8kTWeShWQ4yugWXsMxQJCCXvHrsTGhI6TYkgcfVC2dQX30zmDt16hQ6OzsHeclOJAYuBMbqVnVdF3//93+PH/zgB/j000+xb98+9PT04D/8h/8wpnCtpmmQZTlvPt7rr78ORVHyNi73hJC7kixAHSnDfLUng4/3HcATj6yZsA44T3vSew+jAQkNQ4Kvnp4eHDlypCQPYoA9xx0JbViH91QHoRRXujPYtv19XL14FjMa43hm4/3oQQOC5jlYn/wI8vJfQlPmoBadeO3tj9He3j5qdm/NoxvRPH3GZJ/WLYVEIoGPP/4YjuOA4zisWrUKdXV1AFgGa/60agg8B8Nm/q4jLYq97Rw4cACGwRQbOI7DuXPn8Pzzz/s86VJAKEVIFtFSpqYwryIVDUroTRl5iy+7hGB6TTgvPvdEgVKKtG7DcggMO+c17xKIPDdlAlFC6Oi0GI5VIESBgyQIkEQeisiXdOyXutKwXYKGqgAiqoT+rIneNGuwUyQBzdXBkpNKeQd4luOOGtwYloPulDFMHRuAr2k2GgYGaIUgpVvoTOhQRB6tOUmUcmfwgMHp0kKlMPLFP/zDP8CyLFBKsWDBAtx3331l3X4+sBwXV7ozfqbp9GcHsfGxRwEAR48eRUtLC+rr6yf8uADg//f//BYbHn8C0SB7VkZLgR88eBCSJFXUjszD3r178eWXX44amI8EL1AVeA4z6yP+4GA5Li53pXHg4EE898TDE27r48mbAMD0mtAwvtGJEydg2/aYWcvx4En4lEPCaKLR25/E3/yfvwAhFD/59gaQyDRsej2O1dNM/M1DOv7triD2Xpex5ek+xGkPC0RGye7xgoANm7/9dSZvHJw7dw6ff/45AEZhePDBByHLw8d0QikooUCOk1yoplt3dzckSUJVVVU5DhuEUlSH5ZIbM1xCcakrBUJZwFYorYESgraGaFk9dItFd0pHX9r0S9pTFYVIR5WKjGGjvV8DB6CtPozOhO5bxkUCEhpi5WmwyzvAO9+R9P3yPHKhmSNCDpQriQVl8BwHCopYkPGNetMGUprl16x5jingVwXlolcZLiG40Mm0wmbURyAJPG70ZZEdQR+vVLiE4Hof89rjOaC1LlIxccijR49i165dePrppzFr1qyK7GM0DNRh+vTwQXxzIwvwCCH4/e9/j3Xr1pV9n319fXj55ZdH5Xl1dXVh4V334OCnx/Hs009h9vQmcDnei8BzEAXeD/hefPFFfP/735+wdP/58+exa9cuf388z+OOO+7AkiVLRrSFo5TiYlcaLmG0hZAiwbQdnLlwBWfOnMHaNasxr2VyJn7v3RmNv7pr1y7cdtttaGxsLGr7hu3iag/jm5RzAJsIHD9+HC+99hbEUDWefnA6quN1+P8cW4IPLstQBMB0gUfaTPxs8THEFB4d3BwEjC9HzO51dHSgddY8rFpXeT3JWwmUUhw4cAAdHR2glGL69Om45557bplnZCBcQtBWF0ZALm5uG9hNr5s2FEkcJuqfDzhQzKiPQCzRorIU9KQM9KSNW6Lhw3EJ4mEFqixCzdl9lhsuIdBMB9f6MqCENbyZtgsnpyRSX6bqo4e8A7yz7ckxfx8NSIhH1AlVxb7Wm4FuuT7p3hMMLHeAB7CV2bWcOfRYHLBygFKKF154AX/2Z38GgJUeL168iDlz5lRsnx5M28WVngwOHDiAmAIEJHY/58yZg5kzZ5Z9fzt27MCsWbNG9YYEmFDzle4Udu3a5ZdXmI4RAaXAbQvmYfndi/HmKy/hJz/+UdmPMV84joMjR47g5MmTg/h8HMfhtttuwz333APDoX47/e7du9Hdl4RDKL77zafQUhOGKOT0mCZ4YvP4IGNx5F577TVf+7EYDCy1j1QOnqo4evQo3nr7bQTrZ2D1fA3VXS+g4fHf496/r4LpAooAHPjjBNL7vgdz6ct48pXgiNm9qN2BF158BRzHYfN3/xRq4NY4/0rBMAz/neY4DnfffTemtUyHIrF3gFLAtAkEYeoHB0NBKUVrbbio7lvvXQSAgMwja7hFBXjecTTHQxXryB8LGcPG9d7MlODX5YOelAYKDorIgjtV4hFQpEFKCPlmID3bVMNyYdheMowFco5LkcjosF0KSRAQCUqIBmQ0x4Nlp4Dl/fQFFdHv8hR5DgGFRbmSyEOVxEmJ0GsjKq72ZpE2bEhppqAPDNS0KR+8bsPL3RloplPRNnSO40ApxT/8wz8gm836HXkcx40ZCJUDiiQgHlZwzz334NAnB/Dk4/nxzIpFd3c3Vq9ePeZnQqqEmQ0xPPfUE7Ac1xeo9JpSTpw4gf/rF7+GY+pjUgkqDVEUsWLFCqxYsWLQzwkhOHbsGP7H//gfuOeee7Bsxb1I6za0VD8ee+wJvPQPv4Fu2rjak8HpM19ClmUsXjSvKOpCsfBWq2N5L2/atAlvv/02Nm/eXNQ+YjnJn/Z+xvcMq+MLLU8FyLIMDoChZcEFWyHe/X/j3+4K+sGd6QL/blcAP1v8vyEqZLGqRcKOSwru/XvFz+4FzXPgrC7EYjEkk0l03riGttnzJvvUJhzt7e04cOAAAECSRKxd+yDC4TBcQhALyqiLqoMyTknNxI0+7ZbpCPXAcRyu9GQKDvIsx/WDO49LfrUng6zpFDXfcByHqz0ZiAIP2buGOT4ZxwOqKCKsinnz+4bCJRTJrImM4cAlZFB3sWbat0xwRymFIopwCYVm2T4vWZYEKCIPORf0BWQBsihAylWOvHiPUBa4uYT6FU7LYRVOyyVwHALLdeE4BILAAxwgchx4nvGTW+KV4ffn/eRNi4emnLaOKouoy2mledkHgPHzaiJK2bMghRgIl4o/+qM/guu6g7ghf/u3f4t/9a/+VcX3HQ2yri2OFwdJulQCpmkiEBg/kFFlccSB0nEJmqqX4847bkdHdw86EuV3vigVPM9jyZIlWLJkCX75y18ik8ng3LlzWDh3LmY3RfHM5k14b9s2AEBraytOnjwBSikeXnnXhHFWvMz7WIsjL4D96KOPiha9DqsSqkJMaLkzqWOGIk5pXg4AzJgxA4SXoGWy4JRpcEKt2HtdxiNtN7N0e64r0FbMRot7Dv/Hg/MGZff+5iEd/e/9CMKcHyMSiSCZTMK2rfF3/BUApRTHjh3DhQsXmMh6TQ02bdo0aOJ3CUFTdRCx4PCqS1AWJ0j1rPzgOA6XujNorArkXVHqTLAMd1AR/VLd9NowkpqJtGYjY9gsQCgAvvzGgPnLa0TJwEFHgkARBQRVMS/qBKUU/VkLKY1paQ5ulLg5ftwqwR3A7pUk8ggIPFQioCepg4CDabvQTZudFs9BygXKzP6MB8+amnMuGgSuS2E4DkyLZesGjm0Cz0EQWGMGDzbPmg5BUBaQ0CzEi1TBGAsF5Y+nUnDnoSqkwHYJElkLWcMGx7FVRdZ0yp6WTmTYoBwbID5cKQzVcgOAp556Cr/73e8GOWdUAm5uIBAmUZMoX4gCj0iAR1CJgNCclpFLpqyB9g9/+EN88skn+Of//J/7P1u2aCbunNeaK0m5+F/XryNeXTWhE5tXAvJMvEe7783Nzejo6MCpU6eGSenki5qIiqzhwHYJUppVdjpFuSGrATTPnI/z5y/g4IEj+O5TKrY8zSFonkP/ez/Cf1z+S2gr5mCa0ot+LYSf7Rshu7f8l+CtLqTT5wAAknRr27iNBcdx8OGHHyKZZLSehQsX4umnnx718yN1Lxq2g1TWQiJrFhzQjHlsLkFQYZp1uuXAtCu7aBd4Dh0JHX0ZAyFFRi7BBXDMFovnAFliGSLDZuU8j4sFsKChP2uCECAWkpHOaaGWE6LAw6VM2YIHUD8KdcJxXfSmTSaqn8vSTdVxthiosghCKapUGaok3szA2S7MXFbOcB0YQ75Hvf+ngEtZj4HI8wgoAhRR8J18RIGDxLPg0HZZ5rMqKKMjoUERBWRNGyGlvDHLpDtZlAM1ERUZ3YaT85VzXIqsYZc1wKOU+ibQVaHJGZxnzZqFvXv34vr162Vp6x8NXtdzsbyPfGBZ5c1g8BxzEaQAyqj8U3ZwHDeshMtzTCiVUIqL7b0wTdNvJpkoSDkRV0IpdMsdU3Zn6dKl2L59OxPoHsVTdyzwHIeqkIzulIH+jOlrmE1FZAymS6XaNkSOoL/rOl57+2Omg2d1QZjzY/BWF6J8CDpUZIWmUbJ7cxDlw0gmj4DjODQ052//disgkUjgww8/BCEEHMdh9erVeXtX8zyH7iSbNtOGDUIoXEpzZcTSAwhKKQhlTX/xiOp3lqZ0C+19WkXHOYAFeS5h+xsJhFAQShifPCAjGpDQmzZ8lwQvQ9aXqWwFjeM4JDQLyI2l4HLuSYQtPA3bhSTyFb9ekwbKGjZlgWf8OduFZROYuQDvpsgxgUsIu2+5r/Jgz7HA85AEDkFFgirnAjyJhyIKfkMgpdSXSOlJ6RA4jmltZqyvA7yRwHNMC64/a8K2CTieQznn+Kxpoy9toDOhoaGq/ETIQvDd734Xf/d3f4d/+S//ZcX24QtnVqgkvX//fhw8eBDV1dUVSeN3JfVhNm/eIA9wUzITDQD9GRNvvf02nn3m6UlZRIRVESndRk9KR2P12M/5+vXr8eqrr+Lpp58u6h56NACnQtn2cqAvY6A7qePTd7fgvrCC5bPq8dqFq+hob8cLL74yQOvuHJLJI2hqasIzG+/HlqcxLLtXi0689t4ecByH1lnzvhINFgOlTMLhMB577LERpUzGg8DzgzJTPM+VbEflEpaJDioiwqqE6rAyjAoQUSUMl6WeeLDzFaDKHJJZA7bj+py4gRmyiRi3uFywMRIk8auTrRsIl1BIAoeWurAf/Msi49ohMPBzxFcNcV02nxBKWSaWZ1k7WeQhj9OMwXEsc+sQiogioSdjIGs5iOky6hy1rPFF3gFeImvmXCnyN9KdSAQUAf1ZwHRcxtUq07vQkzLQn7NdMmx2YyvNSxsLHMdhw4YNeOONN4omu48HRbppmZLWTERG4MYUgk8//RTbtm1Dc3MzNE3D3Llz8dd//ddlONKb4DgOdbEAupJMT0i3XPRlDAQVCU5uNeZBFnnURQNltxYqFYbtQhRF1ESDk/J8VYcVZAyb+en2ZDC9NjzmYPP4449j69atY3ovjwae4xCURaQNG/YowrSTibRuozdt4tN3t2BNPIh6mQPHhfGjBTOxt6MHJ/tTSCaTfhkSYA0Eo2X3Xntvr6+Dt2jJskk8s+JBCMH+/fvR1dUFgPkpb968eUplXx2XIKSIqIkoCCrSmMfGcRwiAcn3f55sSAKPWKj8PKyvMRwkx48LKAIiAWlE/udQCDyPgMyj1Na33rSBpG6jsSqIgCzAdFz0Z02kdQv9GbOs6gJ5z3CeECpgTkkdKzGXRbBdChXlie9SmoX+LOtoigUlOG4AU4E3unDhQuzduxdvv/02enp6/J9TStHZ2Ymf/OQnqKmpKXr7As8jFpRxzz334KU33sWffGtTSauKRYsWYevWrfj2t78N27YHWQWVE7GgDEUSoJm2r5qeGYGzYjkE1/uymF4bnnBR4bEgCTxcl8CqMC9oNMiigOm1YbT3a7Acxmsdy/MwGAxi0aJF2L9/P1auXFnw/jz5C8sZvXN3ssB4TwRzYKBBuSmJVB9QsXlmCx5tcXA5k4XpEigCD0qBty7fGDW7x4FxrtY8uvGWEjnWdR27du2CaZrgOA7Lli3DqlWrJvuwRoTjErTW5u/gQCidkouLr1E50BzHuC4aqEhTw3joTGjoThlI6zZqIwIaq4JI6zaSuoVk1kIiaKI2qpbNrzvvAC+iSqBgnJS0biMoi2UV5Cs3yhF8emWD6pCCcEBCxnAm1Qx5IH74wx/i4sWLmDlz5qASmeM4+NnPfoYZM2aAUoqGhgZs2rSp4O3XRlUYtotly1fglbd34PlN64sqEXz66af48MMP8a/+1b+CoihQlMoS6lVJgCoJiIdVaKYDw3ZyKy/GgaCUoiOhQzMd9KR0tNRMnY7baEBCdXU1zl+5hvqquYPS/FnDhp0jiFeSIiCLAmJBxo9z8ijRz5w5Ex0dHbh8+TLa2toK2ldQEZHIWsgYNmqLUOuvJEiOhzpaVSokibiterCRfV1AGTG7xwG4rTqKBeEgznb1THmrsuvXr+OTTz4BwCRi1q5di1Co8v7IpSIgi+MGd/0ZE1nThutSmLmFxVQYz79G5eG6BNGgjPqYOiniz4blIJVLPCgSj5DKtPViQRn9WQkpzUJKZxItQ33Bi0XeAV5jNUsbdqd0JLIWdMuZUgGemxuQy/qu5nh8osAho7Ngb6qUp3meH1ETTxRF/Ot//a998vuLL75Y3PY5Ds3VQTgugawGcOFaB+a2Ng37nOu62LZtG0zTxDPPPOP/nFKK3/zmN6iuri57OTZfeN1yg8BxqImo0MwMDGv0zJFpu8iadq5jemICD1UW8eDaB/Du1q24+7bZfnaxK6nj5dffQiaTxp133Il7Ft8ORWJdd65LocrCiByjYuFtJ18e68qVK7F9+/aCA7yQIkEWeVgOQV/GnFDdv/EgChxsl0d3qDrv74yW3WsLhxCS2HNYk7qGq1sv4YoQxF3rH6/U4RcET8rk4sWLAIDa2tphUia3AsYam3XLQSJrIamZ/vv8dWD3hwHHZQFVQyyIaIl0o1LgSdPwPBBRFKi5MSEWkhHNSEhpJtK6jb6MiepQeWTeCo5WLIfAtB0YQ5a2lsN4T5RSRALShK/GnZxzgDiAVF8qVFmAZjm+aCEAhANTjww+FAM7G9vb25FKpRCNFm7wLgo8GmIBLF68GPv27UVLQy0CioTu7m688847cBwHPM9j3bp1IITghRdegCSx66PrOp544gm0traW7bzKBa8sMxJpWDMddCV1X/NQFHhEAxO3kFEVmZVpHReqJCBj2Hjz3e2YMWMG7rr9Nny4Zx9+9Zt/hCiKIIRxQaurq/H4ow9jWk15siwDJVPyRSaTKWpftVEVN/o0JLIWYkF5UhuYBiIakKFbOoQZC9B14Qjqo/l7VI6U3fNQHwmiPgIEb3TDcd1JtZHat28fOjs7AQC33XZbxTi9E4WRKgwuobjcnYZpu7kuxlsraP0axcETXQ6rrNluKjRxeXOKVx3wxOUjqoSwKiGkSNBtB2ndRtqwyzLvFBTgmbaLtG4hpdkgFGjv1xDKOVwM7ILqz5iYXhueUPVx76LJuYvmZfRKgRcAOITAzgnATpUJKF/8u3/37/CLX/wCt912Gx544IGCvx9SJQQVEdmshr/9n3+PgMQhFo3gqae/iZrY4IDiJz/5SbkOu6LI5p7Vodm9rGHjRs5GjAMQUCbeoUUWeaRSKXQlNHQndXxy+DAIIbj37jtRFw3g2W+sQ1q3WWZVFCAKHLbt/Bgf7P4Iz37j4ZKcIVxC0Js2kdItWDazAMwH77zzTlHPFsCyeJ5LTm/aRFP11OgujQQkpHQL02fMwJUvDqK+8PXRmMiKMuKTENxRSrFr1y709vZi+fLluO+++yb8GCoBQplE1lB0JjS4hN5yThhfozh4ZdhYSC675Eip0EyHNWjm/u01M3Ich6qQgkTWYrGUbiORMSc2wOtO6cgaDgAOIYV10mYMexCJXZUEWA6BQyj6sxNbcvGSDSLPgdCbWm6lwGtRN20CngMIZcHrVJmE8oEoivjpT3+KV199FceOHcNdd91V0Pcppfjg7dcgcSKee/YZP23cpzkQRHPKi9SOBD2nZzgwwDMsxw/uwqqEhqrApDgsxMMKnnl6M1559TUQTkR1XSO+8ejDqM0RgkfqYn9k7Wr85uXX8Nmpc1i5eEHRx32jT/O7jeWcDeF4eOONN/DAAw8UpYfnoTai4oqZQcawkdKsKUH94DgO0aCMRKofFXA+hEQJMukMwpGJ4YASQvD+++8jlUph9erVaG5unpD9ThQIIb6TkQfGebK+ztp9xUEIBc9ziIUk1IQDU1IGqy9jwMiJ8KuyyASuBySLqkIyIqqE/lyTYEKzUGc5JVPCCpBJYdo4As9hbnMVKCiyhgPDciCLAqrCClRJQCJrojtl+PXmiQLNEeYEngNxqZ/RKwWqJEDkOTiEEXI9GyfNdKacxMZ4yGazWLBgAX73u9+hs7MTwWAQP/jBD8Y0jv/ss8+we/dufPOb30Rzc7NfhtdMBxmDkUFvxQBPFHg4hCmThxQJhu36wV1IEdFYNXkd4rIo4PZZTZjxo++hO6Uz/ogsjnk81WEZT27ciN/89reojkWxcGZzwcefMWxfRb86rEDguXEDLcMwEI1GSwruAFaqqA4p6M+a6MuYUyLAc1yCtG7j5CcHsDJeVfbt39lQhS27tuHOTc+VfdsD4TgO3ntvGwzDwNq1Dw4SH3ZdFhQZNimoHD8VIYvisECuP2t+Hdx9ReESCoHjEA6wxpqJpNEUApdQdKd0pHMc/ogqwXSIX571IPA8qsIKEpqFnpSOjG6jP2MiEJ+gAK8qxERjB2YQRkqBegeum86EGr/ntLch8Dxs182rA3DcbXIcmuIhdOQm/2RWh8BzSGRNZAwbLqFQJH5CifjF4sKFC/j5z3+OTZs2YebMmUgmk/j5z3+OtrY2PPnkk4M+a1kWXnjhBcyePXtQg4Qn/hhUWPbWctjEMNW9RIciFpJhJHQkNQtBRcT13iwoWHm0oWpyNOgGwpOp4TjmTTnesyzwPJrjQXznO9/Gyy//E/76z384LJsxHrxMfCwkoyZP+QBVVUFIeRZyIVVkQuUuQUq3EFalSXmuDNtFe1cfPtm7H0FTR5NtIlpd/koEBw6P1Qfx1vatuLsCzRamaWLbtq1wHBfr16/3rQ9jQdatF5BF1MWYzmFfxkRPSp/0574UBOTh80w5aDpfYzAIoQDHEilsXJo44XjbIRB4xquLBSWEp2BQRyhFMmsha9rQTKbg4C2eaiMqbJfAdKxhAR7AYqxwQEJ/hmniJTQLtdHShI/zDvDyLbeyAFCAbrm40adhem14Qh6AofugQFmCD1US0FoXxrWeDESBR2dCAyGsAQMAMgaQ0my01oWndKDz13/916iuvtkRGIvF8Bd/8Rf48ssv8Ytf/ALCAD6Qpmn44Q9/iGBw5FK0JPB+ZvNW8BIdiogqoZsz4BKKG30suAvIApqqQ1Mqve+6Xmf4+MekSgIUSYQoirBdF0D+AR6lFFpO7DVYYEmgXJ2Wsij4NIjOhI60bJfcNFKoIPn13iz2vPoKmjiCO2NRBFQBNZEAghVaoyqShIcUDR/v34s7V5ZHW07TNGzduhWCIGDDhg0IBNi4TQnBjIYoZFFAfWzwey0JHJjHy+hwCQGh7LOSwIMDs7Uzc5PuZIJSOqKepVuJ2vofMAihqIsFUB1i1oKW4+JaTxYupcz1iEPZEh2UUriEQOQFBBQBqiwgokpQpKlbOTNsF50JLSeSbgCUoqE6BFUSUB9jwvpXe1gzmjJC0OZJVCUCEhKaiUxOMqUUqltFrlZjVRBXezKwXYJE1sw7I1AKPL6Q7RLfk9QjopcKnuNQGw3kvPg4KBKPSECCJPDoy7CsgzGOf+dkY2BwNxDz5s3DvHnzCt9eWEF3ykBf5tbj4XEch4AsoL1fg0spqkMK6mNTj7vhd/LmcVwp3cbrb76FVavuy7tjzCWUuZXoLBst8NykPcMCz6E5HsrplDnQLKckxxjHJbjSk0FVSM5LcsCwme9nLUcxMx5FlUgrFtgNRE04iGWpLhw+sB933Fu4WLSHRCKBHTt2QFEUbNq0ye9m9xAZ0qHsuC76siYsm0AzHfAc88hURAEEjOLCgYMqC1Bzi3ZlBAsmx3XRlTSQ0i3mB13g/fLKxDzP5yZ1Csdl/p8cz+W9aE6bNjKmjYaqoD95WhWyWvyDAKUIqRJcwqhYHA+01oUQkG8+V70pAy696VteG1WRNWxkc5mrQkEoBShFWJURUEREVNG3bJvKoJSiL2MyhwqN6XpGAhIUUUBTdRDRwE1HFU+NY6QMHsA0d3sDBpKaiZRuIpGREQ+rRc9NFRnNRYFHXSyA9n4NiSwzE690F5MiCeDAJi1CCHieL2uKPqiImN0YA8exgM92Ca5035SG+Kr69I2GSEBCd4plwW61Mm1/xkRfhgn5BhURkYA0JbujVUlAEix4qw6PTXfIGjYsy8K8WW15nYtuObjRpw3iXtVG1HEnaEIIjh496ov4FiO/MxoCsgi1WsD5jhQomP5fXay4ZpekZsEllHUFazbqYuqYXXVurtSsUAfNysRmfqpDIcxOduHI/k9w1/J7Choru7q6sHv3boRCIWzevHlETq3tkkFWTCnNREe/Bm7QJEzRVh8ZMbMwFkRBQHM8hEYaRCJjIpG1mIAwpXApC9x5jhvRoN4lBP1ZE5IoYFZDaNCzRyirDmimAztHBXEpC/4IYTqFHMfBJRR1URU1ERWO6+JcR5oR2CUerkt9t5SvMRiO60LgefAcq8RQQiF6qhEuQVtdxF/s6ZYNgeeHjSvCgOeU4zh0pwxMi4egSDb6MlbeQQmzl5MQCUioymUHbxVYjouOfg1pw0Zf2oQo8GiqDqIqpKAuOjhpYDkuvJBEHiVeCCoiy+JlJaR1GyndRlovvkpWseV6WJWgiDxMh6AzqWNavLJK6DzHIaRKzEvTJggo5Q3wgMFlYN1y/MmxuTo4yBT6DwFscGAltZ6UgdqoeksEeYmsiZ60AUUSEQnIiAYlVE2i+OVYiAZlJLImTIdlWcYK3IKKiFAohGvtXWiItY05SLqE+MEdz3GQBNamP1Zzw5YtW3yLubvuumsQWb+cGOgpnMplFpuLGDtqIiokgUdP2oDtsvNtiAVGPUfHpTh36iTaAhNnX2QToMPiYBBAUYPgO64BuCev7167dg179+5FdXU1nnvuuVHvNyEUVSF5UGa2I6EDHGMtSyJzeamNltY1znMc4hHVt3+ilOJaXxZpnXVF8zwH3WQNWgMXw7GgzEq9ds5DfMD2qkLKiBOb5bh+4CcKPKrD7DOMZkDhuBRBRQLPf53B80By2VFVEhBWRVSHwoOyY30ZAz0pAxzHVCgGZpgGZu0GwptfXZcioDAFjWu9GTRWBVEfC6A3ZYxa93dcNzcGS6gOyZOqB1ks+nNZO8N20ZPSEQspiAZk1McCI3KgzZwFpSLyYzfNhRT0ZZjosedPO+UCPIC5X1zuzjDLKMsZ9AJXAqEc+d92CQJAWRotxtqXF+D8oTI9aiIqulOD09IA00t0CUVAFsf0Mi0HHJeAG4X7QXIrftslsB2CrMFKfwCTI5kI6kAp8EpWwNjcFkopElkLgUAA6Ux2VD5VxrDx+ckvIQaiCIfDCKkipteOzx0lhCAYDGLdunUlnE3+iAVliP//9t40yLasvA5c+8zDnXN8+YYaoKAkAQKEKBkohAqJQbZpCywKyYCGNi0p1FK3o62Ojv7RHf2no8PhiFaEf6CwZBtZlkATQvIAtkC2sS2pmYRQAaKghjfly+nOZz5nn90/9tnn3sy885B5M8kVUUA9Xt578px99v6G9a0lEew2PbhhgpimMyVQJUuDbag46vBgcb/tZwHN8X2IMYZG10fyza9jfaOEs3ijXQrsRwSUARKAsgI8tabiP//lX+JVr33N0J977rnn8MUvfhGbmxt473vfOzywYwyKRLBeNvMASOBlO5UF/iaDESactqLKMqoFI+fIxQlF0wtBQFC1NRy0g7wlP+n5IIa9TmK/FXBSe0qxWTZwr06z/WH1E89lgIAHaprK17ytq0OragVDxYOGl3eoxhXfooQijBM4foyb6wWslwykjOHuEZc7qto6kjTNq9EJ5cLDlq7A1GWUzNURNZ8WUUJx0PZzY4euz6kBRVPFdsUaWoEP49HtWYGiyauZpqbAzezN3CCeSeN0qRGXpsgomSo6foy6E+L6nCO/4yBunKisxUsM8GRJyiosPLhZBaXss0bF1qHIEg7bPpKU5VI6Aqq8vPvf8SMcZS1igD/7SjZ52vZCRAkbWsGtTDEpel5IGcODpockZZAInzIdhjDTVzo4OMB73/WOgQFbwwnwL37zd1Gt1lDveri/V8cHfuRtuLU+XoctCIKcrH9WsA0VRmbH5oUJyjNKp8gSwVbFQso8OEGM3YaHWxuFPGCMEoqGE+LPP/MZvLpoo6IsN7iLUqAZE3QylzxdAq5pDKoEQNOhNPYH/tzXv/51PPPMM7hx4wbe+973jvyONGUomCp2quczER4lFPfrnL5i6cqxAQhVkbFZ6g156KoMN0xyCapZEcRJHlAw9MRjDzs+VIkgStKBbeLLgJTxFishyJI73hIvmhq2KpO9t5oi4/Eb1Zz3OmrdiIQypgwbZQPrJb6XSoTgoQ0+re1H3OaRMUBVCUqmlltzXWS0vQiHbT9PoquFHq1lp2aPTJbDMfw7AUIIl41ywtxEouVGqxfgAZyM3/H5yDBdsqG4iJzFaLKImJcFS+Nm6V0/hkR8VOyLm5XMCm6xoqDrZzpqmYCjIhMoS3rWTYe3WfsRxhT7bR9emKDlhiAEKJk6CoYCRZagKhIsTYFtKCv/jGjKcL/uIEz4wNB2xRq5cYh1L0lSro7eDzeM8R/+05/i1q1b+P43vJ5zn5wQH/2t38TO5gdxbX2wrZbArFZ388LSlbz9wZ/n7LIIWxUT8RFFmHDurNft4OvPPAMSBpDDAC9lCa6VdSyTshWlwN2AQKQ9FQVYV9kx/+yHYwf1egNra1xb8Mtf/jKeffZZPProo/jRHx2vmUdpilpRPzUpe5Y4bAe8zadI2B4TYIiK0rzdFplwAX6AIMkCTAYe7D58rYgwprjXcCf2V74oSGiKzbIJU5Ox2/BACB922Gt5U59//FmMfgGihKtjiOJJccg7aWoqzNrlKHoIlYF6N0CY6fsaqoytTC+16YYAMLYTIp7HoInvkxDCxw0lRNcP0fI0bJSnl0xZeoDHy+ncUNwNk6UKEorNwtBkpCmDH9GlBpW2wUup3WycWeiqVW19pSdqFw2h+n8WIUDbi/LgTlTi0pShk1no8U2ec8s0RUbZ1iYaHlgl7LU8hAlvaVyvWWNbV4oswdIVFAoF/PVzt/Gqlz8KANmQQYAv/OVXcf/+ffy9p9+Tt8xNTcHTTz+NX/2Xv4X/+Wf/exT7Jr1Ootvt5jpqZ4mypeXDEvstH5oszUzzkDJNy/t1F9/8629A+uqX8cpqGTIh0C0FNVVe+tRsPebBnS4BGyqDOeD7XrFRwr/+L5/GN7dv4c6dO/iO7/iOoRU7llEQJInz6ExdRs02ztWWq+NFOQ1iu2qN3XvFYefOqZuqKjJuZdWjhFIcdUN4QQI5G8YwNAUPbxTx4mH3wgd5EoCCpaLjRjmh/5sPWpAkgu2KCUPlPtXiOSwS+y3u0y0RoFYwZq6srzoEz9MLk4xvz/+cgFOTxDAI7dMBHTX1n9A07yhpEwR4ssS5pU03Qr0bwPF5FW9aytOZRCGmpiBKIh7BLrnTI1ov4sa7QbJUZfztioWSmaDphvmCELIDYiLm2ynYWybcIMZB2wfAiah5a0AmqBUM1Ar832nK0HRCNF0+1Rcn6UxE/fNAQtNck+7Gmj22nC9QsTT80A/9ED720d/CK172c2CM4dm7R/i9j/8Brl27hr/39Huw1bc5lC0NbtHG937v6/H/fvif4Uff8248srM20Bqn0WjgscceW8wvOAUUWcJDG0XstzgXr+1Fc/F4VVmCSR1sPPdlPLJdhioBGjleQVsWaAp4WUFlU2MwhsQ9NE1x7/mv4ZGXPY7vfc/fPTap2I8gTmCoMl62U1kJn9WTiv21gj5RsGZoCnRVRhhT3D3iBP15/JQBPtm7XTldwVRkCbfWC3jxoHuhEj4BSlMwBsgygSZL2KxYMDUZjDEEEcVLtkuwsklxU1PgRzw4mdfuSsANe243tzaKl3KwMKYpDlr+qeBYIgQlS0WtoB9LWqRsWIllPztszYv2rCpLEw8zVWwdRTNAy+U896YTYq2oT1WwWsiTd7PBBlmSUDBO2yqJytpZZE66KvMbLcugjKEbxEu3PrJ0BZauIEooWm6EjhchZSz36i2Z6sxyD1fgiBKKvRZ3FCmaah7cDYKcaTJpqoT9ls9J3BfQXq7phCiY6kT8TktXoMoS1tY38LVv3QVkBb/1sd/BT/3kT+D6WvHU704IwU7Ngvma78LDD93C7/zu7+GNb3wDvv97X3ls497b20Oj0ZjbjmxWCG0+N0zmnoqPwhAv/ttP4HVby5kAHoVmQvAfn30OYRTix77jJowBHrRuEOCfff4v8VP/+/+BSrUKP4pR73LB035NuChOQRgAEHT9+NQQxVmCMcY51t0eH7Zia6hNcU3XqhYeNHlLcbfpYac6f5A3DJoi4yXbZbS9CGGSII65FmSuObnCQYssS7i5zrUiDzpBvh7UbJKYpsg7VrahZFWoeGEBXr3DOydlW7uUwR0A7Dbc3GbV1OTsbFeHTr4SQqBmHco4GRHgTdGeFei3cGy7EbqZPagoZEyCuZ/8g6aX2xwBfLFtlIxjL6hYiGdhHaOrEpyAZzk0YRn3j52JiC1XiTexUTIQxhSdrHXb8bmt1401+0JmjueNnqxHxn2YsExdMjW03QhBNtV7EaDIEoqGim4Q5/+UTBVbAyoS/SCEYK1o4K1vfSs+8YlPQFEU/PRP/SRubRSHbjqEkKzNouOnPvh+fOx3fx/f84rH84Bhd3cXX/nKV/DOd75z4b/nNBCJ4bwJ0p999Dfwxq21BVzRdIhT4M/v7KH0na/EO972A/j3v/cx+M9+Bd+zWcN33bwBALhXr+OPbj/AL/zf/0+uZWdqKm6sqXmy6AYx4oTLnigSl4A56gZQFenMh7yCmKLjcf6xGGpTZQlbFXPqgEKVJdxcs7Hb9OCFCdwwWVqAB/CkgQegvSCUMe433vEjtJ0YKVZP2zNNUzhBAkvjU8RemOQG9jEF/IgnwLoioWCqKJnqMf3DeeAGcc4JniZ4v0gIYooo+x1vbRQmpgtoCpeIiZIUw/pEuUTKFAEe0OPidbwIHT9CywknEm4XmCvA86MkD+5sXYEf8cW22/RQsbXcYkP8Ul6YLF0Ul28uIRKaQpEJEsrghvGZmhELzoehKXz8vOkiiCncMPm2nLadByljOalXlbnn6ixB8ort1SOxXbVQChO4IedddPwYJWt8q4VXqgv44I8/DUWWUCvoE1UkZIlzKIvFAj7/5Wfwg298LfYePMBf/dVf4R3veMeCfqvZIZ5dMofv7Z996t/hewpG7ll9lqAMuNds450//iZUiybe91M/BQD47J/8CX79z/8USqcFduMWfuH//L8G/rxE+BTiyT0spinaXoS9pocba/bCZahSxuBHCYKIJ0iMMaSsN7UtIAKmkqXNvLcTQmCock5POGvw71dgqAo2igxNN0LTCZBQtjLTt5IkoemEoDTFtZqFm+sF0JQhiHhQ7EcJoiRFmKQIuyGKprqwiqRQSCjbq++7PitYlqgo8mlR51EQosXCpWIQRAVPGzAENwq2oaJsaWhkFLCOH8MJkom9xmfeEfg0jQsAuf4LTRkaToCWG6HlRigaam5zc1bepYba87TUJQkJpXD8sw3w+sGdEjhZPIjoVYA3JfZbPoKYQiLATm08afskLkbd7jRE259STjNwg8m4NCVLm4mSUDQ1vO2H3obf//jHEXgODIR4+9vfPsulLxw9RX06k33ZvTu3sXO4C7Myelp4WWD5fx5fjW9+6im8+amnEIYhdH36PXGjxM3L3SDGXtvHzbXF+H77UYJOpm05qvBdMHiVyNJP03KmBc3OBoC7VJwneGVbR63AZVbq3WDlgpp7Rw7aVgQCgo2SkZPvE5rCCWIcdgJ0/RiWFi2EohTEPPA+r3P0LCCC4ZimU+0zah7gDU5AacryhGiaFq1AtaCj7vChIS6ZEk4c4M28ank1jmeXGxkfSpYINko9FWcR9RNC8rZPwwmPTZ4sGoSQPIhKst1JVA7PCyLCFy/JFSbDQdvPK8TXqvZME3Zy9pLGQ16+VYfYPJa9fmWJ4FrVwrt/5EfwX/7sc3jiTW9Z6vdNA02Rc+HVYZvoKNz/7J/gxjkFdwCgScBDtQq+8pVnBlIFZgnuAL7XicSa0hTxiArCJPDCBC8edHGv7qLj8+BOlkjmNqBjrahjo2Rgp2bh0a0SrmVcufmDuxR7meajKksLaytOAmFqHyU067LEuVn8QdvPhxhWBYwxbFctPLZTQRSnCGKKu3UX9W4AxhgUWULF1vM26kHbH1lZmhbneY4uG/2e38kUlB5xLg3bm8T9VyQyU6JQNDWUTBW6KqHrc7WOSTFzBU88Z1tXTl101da5zUYQYy1rrZUsDU0nRJIyvLDfRcHgRPllkForNtfeE5E4CIE7RVlz0eivQMyqSP3thpYb5gt5q2LOPCBhGwq8iLc7pyWjM8YzL0IIFGl6I/VF4Cz5q5auYK1o4KmnnsK//8x/wnv/9tuW/p2TQldl+BFFGNOpeSwlNntwH6bcdaKsYGaNPIcC+10H37G5MdYhYHrwQz1l8w8I7Ld4kEUg1PSXrwDgBHEulM41H82l8qWjhKLrx/DDBGHmcTsK9W4KP0xQXLaGzoSgaQpT51qetzYKaHRD7oPqhHDDBNsVE5oio1bQsyla7pV6c70w1/5lanzQ6bDtz/1ZqwpCCFRZQkx5wD/pIInWl4QPkmULItGenW0NyRIvkDWcEEdZZXZSzLwj5ByMAc9ZV2WYGv9l9po9z8udmg1N4Urj3SDGvbq7lOxIV+W8iieqhf2DIGcNTZFzvaAHTQ/+EvSJLhPckLcYAGCtqM/VFugPrid1NhEVhW/tdXD70MGLB118a6+DFw+6C8+Ix6HHX43PLMh76Utfijv3dpf+XdNAZMnDnmEQ06F7iUynf16UAe0EuBcQ1GOCewFBMOXH+BT48oGDX/uLb0B7/BV47JGbCz8Y3YDvJboizRzg9VfQCIBHtorYqlhLC+5SxtByQ7x40M3dWlRZwo31wlLtLPdbHm4fOrjfcHG/4R6rhBDwg1RTpMyvlbeeDU2GvmSLzWlgair07F3QFBnbVSsTQuc8rzuHDtpelFd3JUIQJmm+n86KzUwFIkzSUyLzlwkiWJum4yNlBQBgcBUvTCgYm37Aoh9lS8s4lQQdP5z452ZauULYF8DQTWCjZOJe3eEl5CMnv8CHNorwowTP73fhBDFqcx7gw1C2NDhBDMZ4JcYN4qUPeIyC4Mt4YYLdhosbazZiypAyhoKhrtzE1nkhiCkeNHpyKNOMhA+CpvBkw48o2m40Ul4F4IfP/UZPBV5oHAE9Unvb4wKjZ8GnNDU5zyoPOz62yuZSs+deSxjZoNJqcI/y5zHgV6dpirtHDiQCLmmgSlBlLrCuKRKkKSt4RxFB80QOFjHgbkhQkBnWVaCfK80Y4KV8WjZhgBfF+NwLd7GbAMrWNn7k5/7HTKdxsa1Hmqbo+HwfnrYrwDKnn44fo+NF+RrfLJsL5ZslNM2SK5oPBIRxmn+fRHjHpWLrS63cdTI1A4C3yjRVwlrRwPUaD4KGvVOMMbx42J3bSm1RKJinz9uiqcLQuF6kH9Gc2rJdMbFdMbHb9ND2Iti6MnP3SMkmpB80PbRcLuhv65evE6Upcia6Pd2eoSkykmzI5WQ4E2UOT7Pw7/o/n0umRGi7SwjwWm4I21CRpiwXmy1bpye7BHRVxvWajfuZrsxhJ8BhJ4BE+GLxwhhBxA9zc1tZuK6OqXHejqpwVwtGOMfkvIYcCOEcp3t1F36U4Jk7zZz82pBDXF+zL6220KTgHpYuGPjzm1QOZRwqtg4/8tByQ5QsdSSXr+WGCGOaVZwtmJqS8XT4gdhwwsw+KziTtUQIwUbZwG7D43ZwEcVG2Vja5ioRAl2RYFkWnrt9Hy9/9OZSvmca0JRzjYDB1khR5vrR057s/X8SIehEyPjC478rZciDO40AZYXBloFG5h/7vE/wnEdQUVJsakBBBhoRw+989VnIlRqYqkHVdLzmnf8dXrt9DWVLm3iaeRKkjKHRDdHxo2MV3UnXYhBTtNwQbnCcl6wrEjbK00ucDAJNeULd8SP40fGyp/jza1UbGyVjrsnbadANegLMUcIHEYqGOjaYJYRgvWjgft09Jjqdpmc/XZskKYpDnrMqS7ixVshtHDmf0sFGycgdYfZaPh7akGdei4VsorPtRWh0w0sZ4KkzVPAAPh3rRacnabn8Dv8sUR2cFRVbR9EI0FlGgCcCNAGhdzcKhqbgoY0i5+P5UcZ54Bsyf7FjSBLBnUMnP0wXBS5AyBXSVUUCTfnI/3lOsUpZkNf1IkiEQJYIGOOVoft1l9vMrFA74Cwh7kHKGHRFwrXq4jQDC4YKS+NcvP2WP1SPUJhoA7ziKtYjIYR762aetrcPnaUOCp2ErfMp9YM2twnabXhYLxpLE7gt5a4YH8X/9j/9zFythUWg/1kNasOamoJHt7jfqBsmiJMUUTZ00O50EBAVdwKCLW2wPVg/kj5LoofM3ndt6QwVCsQpwV4E3A8lAAxfjYC/euE5PPn0B/DQzgZkib/Xwvt40RXQejfI1yjA9+G1ojH2GbmZYXm/Qr+oeC7CbUf4dXZ8rtfX/5R0Rcr4R7w1u1Y0cGvdhn6G5vPiwNYVKe8+Tbqui6aGghlx43c/xlbZQNnW0Q1idNxoqNsIgIV2jRRFGnvPqgVuk7nX8hAlKfbbPtaLOnRFQpik2Gt5uLF2WmR7UtQKOleEiPlQyjxVqVVE3qKd0ht52KCF+HeJYKYhwX5wZywVTWfy92bivynsZAA+WLExYatIyVS2qwUdKWOIkxRJyjNuVZaw23ARJinu1V1sVcyFtmvFBKVEAAqedZ03hOp4wVShyhKSlOF+3UVMU9ytu7B0BesTbNiXCUkW3AkuzvU1e+Htms2KidsHXQQxxV7Lx2bZOJa9RwlFoxvmotjDBnLEGjprknHRVGHpci5/0HTDpQV4gt7wPa97HT7xyU/j6Xedr1yKREjeZvfCZOBG2a89CQDPfvUZuJ//c3RSgk3LRMyA+yHBmspQHZLjeRQ4jDI/6wFnti4DLzUZVIlX+lwKhIEHsrWNVz92fenJGU1TtN0QEiGo2joqBX1k8EBThq7PJav6D6yioaJsazBUee51HEQ8qOsXPAaQDdbxQQ3RmWi5ISo2DzbOIrgTQ1Li95cIEGWeoBJBzhOfBNdrNu4c8cQuBV9rRVODocg4aPvHq3uMAQwo2SosTYXj8/bwvMG+PWEQrqsybq0XcJQlA20vxk7Nwp1DB35E0XCCmakv/ULse5nerabIMDT5UtCMxFqdViol18KLj1fwcv27OYM7gVrBQL27hArerUxUEWAzczQkQqCrMnT0ftkb6wXsNbnX5H7LR0LTuXlXAqLKIloZq8InIoTkD1yVCW6s2Tjq8oPbCxPcCR1sls1La+TcD5oy3G/wAFeRSBbcLf45qbKEa1ULu5nzihPEXJ9RlkDpcWPu9ZIx9MUWfy74RGdZcZUlKZ9QX6ZaASEEm2UTL3/5y/GJT/zBmTnBjMI008SB78P///4bXrXB7dUoAw4joEuBo5ggSLkfrJiKjVP+5062N0sA1rXB36PJwLbG0KEECgH+0+5d/P3/9X85k3XQciMw8MS4Vhy+R/ZbJvZz3UqWhoqtz00FYYyh7Z0OHCXCE6OSpQ2s7Ijpv2VbR7bcEB0vQpRwrh9NOffZUJX8cKwWJncDAHqt2j2aZsMMXVyv2agVDRiazJ8NF2yAqSm5GT2QEeS9EA+aHqQZ97Y0ZbCNydeYkAurd7mP6U7NwkbZxEHbR70bwtSUmTtm6yWDV8ppb3hDIgRrRX2pGrdnAUWWch3dUd6yJyECvCRlx6q2glqyqIINdyiZ/P2Z6gnzTX6xG72Yrj3s+Gi5EerdEGmKsWT4cUhomve+xZmwquVkRZawXbFQK3BulxsmOGj70FV5Za95EWCMYa/JWwlyFtwtk4doGyqu12wcdXyESZrxg3oZl6UrqNr6yHaVmNB2ghgdPz7zlrqokiw74EooQ5IkoDQ9dxeQhKZwM4eDSSgWX/g3f4jv26jm/y4TYFtnMBNeoXMo4PkERvZqBRRIwXe2sgLUVDZSEqWoAEWF4YsvvIg3/eCTqC4oIR0FmqZoZdybUQlwyw2PUWk0RcpMy+cb5AoyyY0gpgiiJNcJI+DPpDhG8LifR7ksmSia8v1EJGuMMbS8CFFMUTK1nF9VynT9poVtqLi1Ucz9ShtOiJ0a9yq1xvDRSpYOQ1Nwv+4ioulUzyJNGWxdmbq75QS8YBBn07U31uy8+rbb8HBrozDTfqvIEh7aKOQcyzCzgjzsBFBl6cLLgKkyb2dHI7xlT0KWpJwHHCdpHtAJTt6iznGJENSKk6/dlSF8bZTM3F+x6YZIGcvVucdB3FRVkfIXp56Ncosyq0QAc8XN5jVFxk7Nzv19224IY4wH6UXGQduHFyUgQCahs/xg1tIV3NooZkbcPes8K9OWmgQiwAvOQe5GtIiXTfDu+hH+63/9r/jhH3rq3FsvQuJIV+WJMuGq74IYpVN/XlYAXWLYDwkixluyAobEq3r6hOcdYwxf6nj42Te/ebIfmBNhnOaJasMJoMrkWHLBGMN+28+rZJamoFLQ5ibCt70I9W5wqnIqEWCtaKBoahMlG4IDp0hkKUmclyXFMeVeomtFI7e2I1nnqGSqKBjz2Xdx7rmJ+w03b79NCk2R8fBmEXstPok67jrEcFfF0nCtNszldDBomqLjRdAUGbaugAE46gbYqdkIj7jn6oOGixvrhZneb26FaOTXebfO70cQ0wsf4GmKjDBJpx+0UCQEMUWY9LQ6wwVX8ABM1dlbqYinWtAhSQQHbR9tL8qDvNE8kxQvHjjZQc0DhTRl+Ui88J4smmczrbUI2IYCJ4hnUu2/KGg4Qf6MtqvWmVcqNUWeOaAURP/zEPsUB6285O8WAWSr05nJHmyRmMbm5xtf+xqujfB7NCTglsEQpEDMqVLQCfJq3qT4wy//Ff7Oh35uuh+aA5auoJaJnUZJiroT4nqtt30f9gmgzjuAQ1Mu2OoEcd7SlLLWo6HJmc6pMtV+SpdUeQ5iiqYT5kmAIhFcq9kwVBm7DReEkGO+6IuArg5ux00CrqZgo1rQcdj24QbJqenchKZgDLAMBS/ZLs50z+rdECnj1cprNRsvHnTzattOzcadQ4cPXTQ9XKvO5u/d/ztpioQwplMHvauInvXYtAE8D/BEYEjTXlK2yO1zmgRlpQI8gEenBMizUTdIMl0rCbIkQZYIJInkbZv+BZUy4F7dQRSn0FSugyWCpGUR0peBcdYnFx1hTPODY6NkXDh/3o4XwQ3ic+GliRbtsit4ZUvDD7zlLfjoxz4KTdPwule87Nw4rLWCwYeyJpAZaHzja3hpYfSUICGAKQOzHvlt14OztoXtazszfsJsqBZ0NJ0AlHHCvZDucYKeLul2xZrZsYem6bFAUaBia1gvDuelTgKWH3SL8cpteyEcPzk2rVu2NKwVjfy9TKiYnF1s8jisHTcNDFXBzfUi/ChG0+HFDFkmKOi8wniv7iJN2UyBQZTQfD2sl0w+8GKq6GQWbDfWCtipcckuwX3frIwupIyD4Aa72aTxeblGLQLqXJO0vcKMLEn59PJuw8NOzTqTLlU/VmPq4ARKlobrNT5JmTIGP+KCnE2Xa/wctH00nPBYcMeneSQ0nRBeGEMiyEv0ZUu7UBpz4lrTrER/2bCf6SjaunLhSLkx5dWTuhMiTuiZezOK9bDs2FKo5L/vfe/DX3zlGfzKv/ydc8vOZYlgrWhMxEEqup2lX8/vP/PXeP/Pnl31TsALEzAQaLKEOEnx/H7nmCPDRsmY+WD1owS3D508uJMI34dqBR0bpfnFtcVPRwmdWWLICxPcPuzizpGDb9xvo+5w/9WioeLWeiETae5dp+DFNd1wCY5JDGGcwAvnc0gyNRU7NRs31gq4VrFRNDWYmsIltNCzuZoG+63e/ir4xKLA4Uec521qCrYz+k83iHHn0JmLcqKrci7kfdjxL/S5pcmigjd9i5b/XO+ZXavZUCTCVTKO3DOn9axcBU/A0hU8ssm1raIkRZyNt6cpA+3jTamyBEkiMFQZYZxgv+3DUCXEKYOcSbGsjZg4W0UILa1V0O5bNNwgRhhTEGBijuUqIaEpTE2GJkuQJF4hPsv28lkGlAVDxa31In74HW/HZ/7jf8YXn/kG3vCa7zyz758Wt194Hptkuffnq/fu4yVPfj9kefZnnlA+cCBl3OAgpkgog551HUSHou1GiBKKoqmhYmu5xWFMU7SyoE6RhGSHOvM+0S8wrsrSUvQ4LV0BAe+y+BFFYZAOzQi4QYzdppdfr5a1iW+uF4ZOg1YLGtoeb2m33GhhXRzGGLp+jLoTQlfkpQzZKLIEmk6fQLZcLsROAGz07a+aImMzm6JtuiFMTUbRVCFLNvZbXi7TVSvoqE05YSxQK+houVxqKkroQnVtzxJqVmUTVIVJVR36RZIFrUWVJdxcL5yQg5u9yj4tVvoJ9LStJvv7TpCgautwghhGtnnu1Kxzl3iYBUVTRcuN0HDCSxXgNRzemi3b2srI1kyDNHtxxZo6aPsoGmrOTVo2z1O0gzp+jIS6KNsa7BHTi4v4vvWSAd/3YdmzC6SeBe7/5ZfxROn0cMWiwBjDfzto4Od+7h1T/6wfJXCDBF6UTF0JDbsBGk6Ak0WRqq3PrTYAcD9bBi4CPCvpfhy6fk/8eFpF/yiheJAFdwVDxc31Au4eOQBG85FkSYKhcoHzRVWUUsZyhQOZ8Cqb0Pc7b0QJr84BXIXiZNeqbGm5hM5B28fDWYXv1kYRh20f3SBGwwkRJym2KvNVbc+Tszsv5GwQiFuLUhTNCQO87H4zHJdYUTKf5QdND16YYK/lIaHLE6rvx0oHeNNAuBD0T75sV8++570oVG0dbTdCmFkLrcIGMi+8MMmlEmaRKVgFdL3M8igbVT9JLNZVGetFY2lG7SVTQxBxjo0X8YBBkQjKtoaytRw/zyhO4XketjbWF/7Zi0TB7QCl6aYNp8G/f+ZreMcHfnKqn3GDGEfd4FS7RwQ5NGXQFAmKLCGIaE7LkAiBrSvQVRnNrCoCILeKAoCStdjET1OWl6AImZuSOdoqcBAa3RAMfMhmOws8BAcuTRkw5OP87P0AuH7YvAhjmjtEKLKEhzeLuRacE8S4Xluc+860n5Iyhv2Wn9+nYefFWtFA14+RpAzdIEYpm4Lerlqw/Aj7LR7oyV0y9WAKVyXgAdJFl/eydAVtL4IfJRNX28SwSZRN4Pavc4kQ7FQtHHa4LuFRN0BMU2yM0FxdBC5NgOdnm6N4IBfdDFmRJayXDBx2Ahx1AtiGeqF4hIPQFNU762JW7xKa5p6WD28UIUkkk0vh8gCC9H6/sXhXln5slk1UbA0dj5Prk5Sh3g3RdKKliI3ygzRdsALmYnGwv491GgFYToDnhSH2jCL+5qOPjv27jDG4YYJm5lsMZHpxpgoj01GcZv2XLA1dn0teKDLJA7xFJ690ie1/XZXgBLySp6uTJ6xx3zvX757Um2Qf/rPC0q2Y3fdZIYSd+0V9tysmbENFyw1x1AngRxROMHkwsGjsNb2s7Q9sVYYHZhIhKFsaGk6IlhMe26PE/95vcU3aqj2dh7KQJjurjhNNew5IFZuLeC8qQbGzAM8Lp+PMaYqMKNPQO7kTCQF5TZHyQC+hKXamlMCZBhfvlB0CQWyUCL+Rk9q6rDIqtg5DlcHAW4EXGf3ZdMW+mA4d4rDWFAmGxnXzagUDOzUbj26V8PBmMTcD32/5uXTDMqApvHX6yFYRW2U+KZcyLjbacILxHzAFVEXC61//enzyP3x6CWT1xeD5rz2DLgM6jrOUz/+9Lz+DD/7CL478OzTlXqu3Dx08yA5cgK/3R7ZK2K5YqEx5aAK8IlLJBLj7fzaZcspvEGKa5utlmVWXiq3D1Phexn3NJ9vPOlkwa2o90Xeapnm7d1TSKzQjZ/29RGB3+9DJgztLV/DwZiHXeqvYeu7MEcSLI9BP85a13DCvkF6rjtcTrdhcqSJM0lMBTMnsuZD0ex6PQ8fjXvMSQT5ssWw0nBBhwrn59W6IFw+6aC1ooEZwUGOaTiWXMmjQ4iQqts6labD8YPjSBHiihZG/+FPyPFYVIhvzwuRchHUXAdE+AGZr0awKRHY4zNNYlSVsV3sEWu7SsdzJU4kQlCwND20U8o213g1z14NFoGCouHXzJvYPj/DN27sL+9xF4vt+4K14+U/8D7j3+Ktxu7XYSdpv7e1j+3VPQFV7mzFjDF6Y4IX9Nr6x28IL+x08v9/BYSfIfU+rto5HNovYKJkLa51LWRsIQObEMh86XoSU9TyylwWJENxYK2AtozZw16LxiYiYIu0/CBPamyQf1d4yMq/ZWSZRo4TixYNuLp4sEYKNkpGpOxw/W8SzXVTuwxjLPU3H8RUZY8ckpyahhsiSlAelzQH7hKiudv3JAzzBra4VjDPpznBfZn59hay7Jdw0Xjzocpu+OR5If5t5mirepBIrBUPFw5vFpdv2XY4oqA/iZZtxEn/loClyHjCIl+ii4Sg79BSJYH2BgqNnDUWezA91q2zC0rh6/DTG0POAEC4lIriNh50Abp+Egziwnttr47DjT7X5SVlr4d3vfjd+47c/vtJipq98zWsQfNercdBdTCXvj7/21/g8lfHU33wX9loe7hx2ceewi28+aOOLzx/iW3sdPL/XzuUyuNOBgUe2SlgvLeewEwfPIpIHcX0pY2fSgq8VDGxkgyENJxxbhRRt4/77KDQgU8Y5jsMw632Kaa/1J0ske57FoW3lRRa1o4Ti7pGDphOi3g3GvqdBTHOR/2kcDkQXxQsT7LeOJ6K2wSeek5RNFNz4UZI7iJTPqDvTdLjPsq7KuFa18NBGARslroGYpNzV5fahM1cXRQTL0yRSQmJlEheMswiEL02AJ8bJVblXxr8sEJUZN0wWWpk5C3BRUp5pbVYWV8k4D4isbNyLSTLjbQBz62RNi/WSgVJfBbHlhvDCBLsNLoWQMl492W16UwV5RVOFbah46qmn8Ou//fFlXf5C8J2v/R7c3byOrj+6DRjTFM/tHeLruwf46u7hsT1jr9XGP/nzL+PaO/4O3vr0T+DOEdeIC5M0awul0BQJm2UTL90p45HNIh7O/lkkF2gQRAV8WiulQSgYKiSCvPpxFqjYOvQpq5D9d1OVpTyY2Wv5Q6VE8jxsymdx1AmQpAxq5rk67nkyiIrifM+cpgy7DQ9hwlvQJUsb+0xEAGPr6lRkfU3hw2AAn8i/fejgfsOFHyV5VwAYXOE7CTfo+USfhVtUvy/zWnY2cscSHQ9vFrFW1CER/n4/aPKkzJ1hHxbv2TRUCFHBEy4n542LT1TLIO6lqLJcJhcIzvXiNkWHnQAkI8peBIgNqmSqF3roBehxUkxtfItZyJmkjG80Zzkgs1E2eSAS02MHhCwR1Ao6jjoBvDBBwwmn0ojcLJvwb9zA5z//edzZPcCtnc1lXP5C8PoffBv+y+/+Nl4XR9DU3rtC0xRf2G/jgVFGZJfx2Pe/CbZlgaQUf/ypP8Q7rpUQpcCvf+sQf/fnfwmEkPwQLRgqiqYKAkCWpXObFJzVSmkQZIlgq2LhQdND24tgG2cznMbbnOnIJIMxlh+uJ4OXjZIBN+DToH6UDLxmEbBPk1QyxvKq4HbFnEgDbVHn+GHHzzsdN9dtOEFyShrnJERwNYv/a7Wg51PaXpjk//CBBS0fMghiOnKti4r+WXm9t9wol/U5+XtLhKBWMFC2uCZfM+Pp7TY8mBoPaifVeBTrZxrXIO5ywvf9hKbnTke6dBU8UVbt+vHKEsJnwVrRyMvqghey6ugXNb5oYtMn4YZx3q6YhKsU9LUxz9oDWSIEN9dsbJQMrtclERQMFTfWbFRsHZsZr5P7mk4eJKiyhFpRx7ve9S585KO/d+4ZqphaHoYnf/RpfK7jg4EhiBN8ereNf9bU0P6eH0LlNW/C5steiXYI7DY9OAFFp7CGKAU+d/cAj772dXlQUcicEq5VLRQMXsk8TxmIHpF7dIA0KQqGmieM0xDr50L2Sgy7epoy7LX8XDLGOJFUcUmKniDtIOh9HKpJKzgNh0uyyJmA9DSY9TWPEop79Z6LyGbFRJzxDIsjArf+1uisskyWruB6zcZDG4Wc59hy+cS2+O5xrWJxFp1FEiu8hwGgNuJMEe43j2wV87XtRxR36y5uH3bxoOlhr+XhsOPjoD34H0GvsaZcB+oUbdpl49JU8ARMjY8p04w/MEtms6rYKJm5JIfjxyvvrytI1LNMDq4SGGOod/gmVy3oE2VlYhMqmeq5tKVFy2IQb6hkauj6PGDda3q4vnaaOD4MVVtHx4tx66GH8aWvfBWv++5XLPrSJ8Z+y4MfJXh4swhZkgaqzv+NH/sA/vAj/wr1wjYeeeKNeJksn+KaMQBOmKDtp/hWSvFvn7uHv//u9+OhDS76u2prl+vV8SpBGNOFuE6UMn29IEpyFf5lQsneiZMBOmMMR93gWKC5PcwnVQSJQ2IPM3P46PoxHjQ8XKtaI8+DMKY5z3kaAelpEh1RkQwTiiCiPAnOAgECHtyx7LkSYKTsiqC+FBewx4gukRPEuQ1itaCjG/B9Yr/tY6t8Wvy4v8oqumfLwkHbz39nPZMbGgdZ4jSKakFHoxug43Ov2IM25yEXzdGSXZoiTT0Iocjcf9aLkvwMPC9N3ksT4ImomaY8K+n6MYKYXqoAD+AvcxBTuOFqB3hRQvONq1q4GO3kYfAj/rtIEi//TwJRwVv2lNSs2CgZuHvE7XPuHrm4VrUmMk0nhKBoqnjjG9+IP/i93z63AC9lDFHCOYVNJ0IQJ/AjmlcZW5lIuCIRvPydfyezMFRg6wpUhUBTeltfmFUFius1fN1tYOfxx/Hw5ni5ifOEqSlwwwRumCwkwDMyFxZBUleXfFgXDBUdn+s4ch9xfq+7fpwHd6osYatiDrW8Elc4qrq0VTZBKYMX8SDlkRGuL+IwtnVlKg1LMaU7qKrLGEOYpHD8GB0/GlpttHQFGyUDmiLjTubSUS0MT4x5K5l3FIoL0tsUHQpD5fdbV2VsVyzstTx0/RiUMmxXj7etk5Rb7CVLpqE4QZwHdwVDxWZ5uo4QX0sW1kspnCCGE0SgKecunqx+iuWhyBKK5vS8QvHMDtt+bnvWckNslq2pPmcRuHQBXhhT2EamYbMCJdJFo3+yZ1KfPMYYOn6MgsEzPcYYb0Ww6TLVaSBeRktTJq4OrSrExlfQJ8+UJUJAcTaTibNAU2TcWLOx23AzI2xnbIVDoGiqaDghiKLDDyKYE3gJemEMP0pw2A5QMFTsrNlzta4lQmAbClpudIwIHtM0l+QBxAGk4BW31rBe5FN2QZTgsBMgoSkMTUbF1nC9ZuO7H3kDEpriD377t44FgKuIgqHyAC+IF0Z/4JXQs1mx/YGLiM8SmuYVtJKpYqsy+kCcpMpICMG1moUX9ju8pT/EOzqhaa4lN+2eKCp4cl5kSNHxeOXLj5KBbWg143CaugJblwGQzDPXz6t3o/RCeXLD95dJOMGTIB/YMHprv2iqIMTCXtODFyW4e+Rip9arRgmvVZout+rbyILvee35ZElCGKcoGBp0RcKtjcXbL/YrecQJBWOApZ99cAdcogBP7xuJr8i8shVdAJ7atNAUGbrCS8BdPx6rCN9wQtQ7AVIwgDGUbR1+lCBKUiQ0RdnWFp550ZTlAqUXVdS4HyI+DWI6dftqQRaYS4Guyri1wT0S/Yhit+lhvWigZGkjD3pNkWFpCp544gn8q9/9A3zoA08P/bthTHHUDfCN+y1OIJclUEoBwrBesqApEhhjMyUBZUuDG3AekqUpvA3jcFswW1dQtnWkfVZgAkZmUj8MF8FG0zYUoM3Fahc1xHOWVIL+eyySzt0mn/SWJTJRdyKekD8q9VmbDav2dfxe62/ayq34SAIeKN4+dI61bSXC11zJ1FAwehXElDHsNb2BIva1oj7ynei/f4sIrGia5l2Hk0me8P/tTwa3K8eTwWWuHSdrYxPM3w0KY5oXH9aWVNwQe021qGdtbZxbkePSBHiCeMwPVP5yTboBXDQUTQ1hN0C9G8DSlYEbUkJTvHjYBaUMkkQggQCE5EReiRDI2ca3aNS7QS6eehla5HwiK0KUUHT8eKIJZrHfrfqgjyxJuF6zcdD20fG5bypN2dgsuVbU4V27hm9846/x6f/8p/jB73/Dqb8T0xT3Gy5oyqCrBAQSVEWCm1K4QYIgcvLqhqUpWCvqU7UbNUXGw5vFY0G3pc+XkQdBAE2b7BDxIz51SFN2ZtOnArLEK0BBzHXTdEVGpaBdmEl1TZFz306eOPHDVyLAjbXx7fGYpjkFZBwXK0ookizTGiQczBhD08kS0pkoFT3bNC9K8uBuo2TA1BRoijQwCOt4UV41BPh+qSlcAmbcvikCBgaeUM8bYIl2r65IA5MFkQzuNrhDi0gGl00TCiKu0wdwjb15AyXhoGIvwMqUpikSmonkMIaUccmcMOKVW12Vz52/e2kCPEIIdFVGmHmCAjzYm7SNeZFQtjV0swnVvZaPm2unTa45iXT0iLcY6V4kun6PK7GxpAzprCFLBJau4N6RCz/q4JHNUta6GH7zcs/Ms7rIOUAIl8rQ1RAtN5qo5WNqCmoFHW95yw/gYx/7KN74N15/jCsVxBT7LQ80q6C9/rFtJJRLtxQMFbsNNz+gAX4wevUEa0V9Yp5j//UvCl/4whfw6le/euTfES3e/knpthct1X94EGoFHbtNfo+9KIHXSHBrvTARl3IVoGe+nUFE82GLij3ZEBOlPQmUUYdoyhgO2z1u3aCzIMzFgsnUXrJJpi0JCJcROfvfGNtdERqAVVvHWlGfah3LEoEqS4hpiiAeLBMzDbpBDJqmMEYkN7Ik4cba8WQwTCg2y0OGYOaEHyW4X3fBwPmN81IRun4MP+Lt743y7IL7QUxx2PaPvf/9COMELTc6M0/eUbhUkY8YZw4ycjUA7DY8dLzj5FahddTxogshN3ISEiHYqVqQCH8J3AFq4yIjG4UUDHeOnIXJXUQJxUGbZ1tVW78U1TsBxrhUSky5Svr9hrvy1blpIYRCJ31ua0VujSTLSr7eUsZQ7wa4e+QgSrjF07WqBVWWYGpKPlF9c72AG2s2bq0Xjnn4LtpmbVp861vfwuOPP57/O2MMMU3zacGDto+7dRdB35Sj8L0+aPlnaidoGyoe3Sries3Og/JJ7L9WBWKdtd0wlzGZNMASiStN2dA9PEoo7tfd3AN7WFVaSJIMq7SNQv9UpyyRfO1O8jk9/Th5piRFcAlnsWLrB/dy9bHX5588DCIZFMl7149xZ07HiEEIY4rdBg/uTE3G9Tk5uzRlOMqqd9WCPjOloeNFuHvk5PdJIgRKFmyLCqxtaFAkAroCYseXpoIH8AEEIdq4VjRwkEXZQdsH2j4MlZtdxwnNsy4C3hJYxCTaWUKRJZQsFfstH/fqDjZKZp7hRAlFTGk+wTMMEuHE3ucedHB9zYI1JAuchHfGGMODpoeU8RdSODlcFhiqjK2KBcb4YeJHFE6QjD2QLlkMeAxRQvHst15AuVyGrspwgji3pQN462xjiF0XIeRYxW+7akFzAtS7XMxblqSpqymLwFve8hZ88pOfxA++/Z1ouxGcIB5YhS2aKtaL/HdjjDsQeFGCe3UXJUs7U8FgS5cgSybuHDlww7OROVkEiqaKmHKXiMNOkB2Qk1Uf+1u8YUxPHdiNbC0BvJp2rTq87SsO4WkEbQFeyRVDIRVLw17LzwOdcdWmlPUCU33GaW1Dk9EN4rkDPCeIQUBACMmGAsavn4qtQ1Uk7Lf83DFCV7mQ8KyafAKM8SQ6ZXzf3anNF9wBwFHHz91JZm0rC7kYgFeDN8vm0OpxmgV30ZChnrPCxYpqxsDQuMYVTRkMVcat9UI2Eh3nXA8BTrzlU3YtL8L2BQvwmk6IRjeCno20H3UCtD0+iu0E8XS9fwI4QZIHeB0/QtuNkFBO4OZio9mAhyrD0BRUbO3YS1fvhnnFZrtiXYgDZhoYGs/QZYlLbXR83iIfFoSosoQgpmi5IWSJwAsTOEGcr01JIogS/vPTtiRXASljuHfUxSf/5LP4sfe+F00nyFuu3L/TnDpAqxUMJJSh7UXYa3lQlcKZbI5BTBElFGnKUKht46/vfAaP192Bf1e0ivoPMTGp+aDpwQu5NV/bi6ArEjbKw2U+Fgld7WnjxSugoD8pagUjlwWZtqUlWrxcrJv/rBhcEF2NfvmRYZhEbuUkaMqDGoAf9roq54e/EMQehai/AjRjNUmsK3+MdiFjLJcNkyUC21ChSHzoJE0Zmk7Ih0As3m72hjiDnIStq3hoQ+k5RsQU9xsuioaKjfLstpRtL8r5mNeq1tzBnRvG6GTc861hmooTQAzDFE0V22MmvDVFyveVafcwrpGYoGzNXyS5WFHNGEgZD88NY7x40MX1NTs7QPWMq0BBQKAqEnRFQtePsd/2QenFKbM4QYyDtocoOU6slSQCmgJ3jxyQTM9qGohFf+/IgRMm+c8TQnIByyRlSLJA5bDtoWRpqBZ0hHGaS1VsVYZnNRcZuS9hmuYnQjLC77ha0OGGfFO93zgeLHh9bbx6N4QiTS+med6odwP881//TXz/U2+Hqcs9zUNbR604uxfrRslATFN4YYKjjo8ba4uXMRA4GQwIEMKfdcnkDg+6KufTncP4vBIhuF6z4WTCsF0/QpikuFd3UTTVzAh9ue+FpsigKbtQVeNOdpiPkwUZhNz3M9u/k2yoR9hUbpbNiQaiTn7OJKh3w7witFWx8ta8pkgTBaqCfzePxImm9GyxoiQdyL3s+BGOOsExipKoOvZDIsBW2YIbJuh48cTVZ+EYUbY0NJwQbS9CN4gR0RTXa/bU55CgeAC8CjrvWUJThoNMNqlsaTMnW10/RkxTSIQbDoyDCPCmlWpjjOHuYRdJChx1A2wUDZTmCPQuVYAH8Bt7vxHCUJU8w+JaQQoMTUbRVPPDOl/yF6DYFEQJDto+3DCBIktDX5xZXwgniNENIkRJOvalJIRkHp0Jmk4ISSIgIKgV9ZUgli4DssS5FknK9Y0AwA+TnJx9Eroq4/paAQdtHwnlm2/J1KAqUv5zNOXVqv3sua4VJyOYnzeCmOLj//pTeOtTb8ErX34dyJImW1fm3pAJIdgsm2i54UibpkVAvE8AP2hlib9XRZ3gRlWHafQqq5M+l4KhomCoWCvqqHezA8/nbbSb65M7hsyCUdIvqwiapnlVZJSo7/Cf702vJpQH0yIQ36laE9NuRKt0mhat+JmKrWWBv9A+myxIFPy7eQZiCOHcrzCTvDr5WV6Y5JqQssQHSKKYD2WkjAd1YjhxrcCHPNzQyTpedKq9SJG5Y0TRVLHb8PJq3o0puXNxJl4uESzEb72/NTuPfl7T6bkyTRK08nsX54H8pNhv8da0LBGkKXC/4V0FeP0wMxXyKEmhKRLiJAVDNqWXcSY2SsaxB5Ws+KCFH8W4feiOnRibB/kmN2XlRc0FL8lEmc1Fhqkp6AYxFLmn+N/xoqHTcoImMOjPBdKUoZvTCCge2igu7foXhXo3QL1ex+MvfQTrS3jmqiwtZS2xTMpAlgj8KMklg67X7GMt17/xva/BF7/wBbzpTW+a+buERVLJVHN9txcPHKwVdZQsbSlThxcNXkjBkHkcz8CLivsoAf3B3SQyK/0QA0LTVNPoiTND7MtJyibisIXJcPeLacAThnSgQ4YQaLc0BTu1yWgzts4dUhpOOLYNOQimpuDGmo17dRdhTPGg6WGnOjllR1RfVWW2wZN+uMFiWrNu2NPhm7TKLElAEPNOV5pyFyRBcdIVCVrmHNMPYdMnEQJCWB6Uz4OVCfAET0Dw5QD+4uqqfMzKZhwsTYGqyFAzpX6uXE3hR7y16EcUh50AktQjeQtV8FXcdFPGcPfIPRc/02kQxhT36w4MTYFECMr25TvERIYvEYJaQcdhh3sbjpNDGIXtqoVyRs4XPNHzJOWOQ0JTPPvci7hx48aFGqRxgxh7LS+vDojzsGSetip6xStegY985CNzBXgCRnbg7TZ4kHfY4eT/jZJx4dryi4aucisyRZp+ehUAQDJCfsuHIktQJILrUwZ3NE3zwYhJJW46WfudoMcblKWemHI3iMd+VpwHMnMm7EJvc8D/JYrFfpSg3g0nkmKpFXS4IU9+1oqzCWjrqoydmsUnmLNgcVKJE8GDlOc8O/woyTt487RmAaDjZevDmlyHj6YMjk9BiJTTV05W88TUra7KsHUFhqbg5dcruWXgIrASZKkoobhXd/NWVZyR+4NMdfr2oYOGE0xEglWymwbwDBFAFiTquLFWyCPww3aQawkBk8mKnAcOB6icryIkSYIXcbPuw46PF/Y7l05GpH/kXQQF0RhZgUlgakpePVjE5y0TQUzxpS99CW9+0xsuRDsZANI0xW7DQ9ON4IVJLouhK4PbNpIkIQwXJ9WiKTIe2ihgo2RAlSWk2ZTgXsvL35G2F2Gv6aHlhucurXBW0BROW5h16lJMMldsDQTATm16/2CnT+B3knYpYwxHHd6uqxV7bWVCSH62HHWCM3uGoiU8KJkumlyugwFoumFezRoFo28vcib4+8Ngago2K7wK33BC+BPKB7H8v2e7f4wxtNwQ9+o9iZV59FiFpBowna+4H1Kslww8ul3ETtXCVtlExdYy607+rOLMHq/hhLhbd7ksDJtftLofc1XwZh3H7/oxupnxcr+OkRCH5NOwBEnK7bi8kGcgjW4IQ+Oj2KP4FZauIEoieGF8qsS5XjRye6O2G+UE6pj2JrFWBQmlaLrRylfvToIQgpgydPxoIZNAq4J+U3HR/phWWmEQYpoeq1qvMkhWNYkTiiSzHltFCJN3MM5z9KMEfhijULaQpik2SxZqI6oK3/3d340Pf/jD+Nmf/dmFTITzAEBH2dLQdEPUuyG6foyE8slzwQXsZvqca0XjUulILgP9VbJZBO0TmuaE/knvdZgIVQFyqnJfLejoeBGSlMEPk6GfSfuGsybl7A2DaM0O+tVVWcL1NRu3D52pvkuVJfigcwepJVPLBo5i7Ld8PLRRGPsuie6FH1G44eTDHinjdJmmE+auJQVDxVbFnOv99TIvYYlM3k5njOUBbcXSByYONE0RxnwCPIgoukEMN0zgHTrYOUEZmQcTf8rJm73f8tDx41w+o2Lrx3z2hsELE+xl1iP9sHUFa0Xj1M0omRraXpRnRX7Eq33bJ0bR0+ymMsarIGFCEcSD9bcqtobDToBuEKNgKHCCHnF+lbDf8lf+wB8GWSLYb3rQFfnCaQwOAk3TPBExNAX1Ot8053UuaGWHfco4EXhRL/ayYGoK3vH2t+Gf/4uP4D3vfjdu7WzCUOV8aISbjvNDQlW4uPFZt+oHTQ7KMsG1qg1FlhBTgroTQlPloUNBb3jDG/DYY4/hH/2jf4Sf+ImfwPb29kKujRCCWsGArsi5B7BA2dLQ9TnfZ7fp5VzheRFm+6EwuL9sEkbA9F6fNO3Z6E2jjZa3EKXTNo+itZakbKgHddAn4CsRjNUqHXX9/ZqTw7T0mtnErJrppk6CRSabGyWzV1AZwVcW4MNoKjp+jAcNDxsTTEJz+oWfB6SyxCk0s747jDF0fJ5oCWk1Q518Xw5imj1fMrQqLPQrLV0BbKAaUxx2fPgRxUHbx8Obi+FiEzZhH+2bD9rQM9PuKOkddv3QFQnX1wojF8Zuw4UbJrCyYQg+OEDGltZZJhp41A3y6onYAGOa4n5GshV/lzFgp2ahOOAATmiKFw66AIBb64WRU6nniW/uti/EhO8oMMZwrWYvfSJy2YgSmmfCL90uYTfTPCsYKq5VpycjC2FoUbnRFAk7NXshpvGLRMsNQQCU+zZL7hDQxb/5d59CEIR9lXwGQiQwloIxQNd1vPHJN8MyLdSKOgh4gGOoMmxjsG3UvOhkU8kAP0BliQsRF0wVGyUz52x1s7bLOM0yxhh+7dd+DY8//jiefPLJhV5rlFC0Mr1J4T+aZi1A4ZAwj6ZYyrhERLfPZUBTpJyb/O0KmjLcb/AhgGmHMrwwwf2GC02RTg1E0TTF8/v8XHl4s3jqXU4Zw4sH3dy+b6tizcS3pWmK24dOnsDUCvpAjhsf7OHXc2PNHslDC2KKrs8pDCLAW5TtXcvl4uW6IuHWBENkKWN4kAmHA8N/P4AnL3ePuKe1IhFUC/MNMbW9CI1ukFcBAR50blfMideIKH5NezbQlOH5/Q4A4NGt4kLe0YnDUgJenhaEQQJkbQQFXT9Gyw0RJila7nBCpeg5AxgrPnnq+7NoeKdq5V54h50ALbdnN0bASasSISgY6lBnBkWWct9aP0oWkiUvGlzpfLwbxaqDEIJ7Rw4sXcF6UYelj/ZwXVX0b9YxTbFeNHAnkxSYpVXZ8eP8XVgvGis5lOKFPIsmhL9X4n3SFBmPbFXw0z/+HrhBgiihuWCrRAgYGOIkRb3VwSc//Vk4YQJTSaFJAKUUm5ubePJNb8SNtdEHCE1TNJwQbsATwrWicYy/4kdcr9HSFNCUoelyL12AV8M2SsaptcatlkykTQY3THIFfltXYOnKqUOQEIIPfehD+MxnPoPf+I3fwAc+8IGF3V9NkbF5whNTymRiFJnwNm7WuikYxyVoZInA0JShAUKU8AlGcVgb2X4XJbzyszXDhORlQL9WntAunOYcEtywQW+quNdKH7e7H36Y5O3dG2MKIaMQ9PmtrxeNodVHwaEzNXlocDcoCSDgtm6L8jQWXYl+7+lRkAjBTs3KEzE+8HE6pmh7US6zY2oyrtdOe7JPCpqm2Gv5efFIlninr2RqU+3tXpjkXMdpZV7618OiKJwTB3gPbxZzGxxVljJlf/6LrxXl3Lak7UWoFQZP64i+tK7KMxO0hReeIgdoOGGvRK3KueflJCgYCsKYwg2WE+AFUYKmG2F7Rg6AlGnNXQaIqu/dugcwBk2VYWSDL6vekhQQG6CoCvWbfYcJnTrAEzpY5UwsehWR0JRz7gAMOtJsXR3Jkdmp2ai8+4dx1A0hAbi+ZoOmDL/6kX/FFfPD5NghIqr0YcZLef7uLv74jz8NRZERRTF+7H3vxVrJRkJTfP7Lz+BrX/saZFnGk0++CdVqLf+csqWdCpz6QTJ/3KMuTxDDmJvdN5wQmiJhfQD/7a1vfSu++c1v4h//43+MX/zFX4Q2wpR9EagVDFiagr3MCmoYQd7SFJQsDabGbRjTlCHM2j1CEkZowrlBjN0TbeFvNzTdMNf6vF6zZw5iBp2/IuiSh+wFoiJVMJSZgzvGGLpeby2M8lIXwcqoCvVRRlUCeLXY0pWFV9ePBy6T8fYJIfmE8aBJ45PB3TzuSdw8wOf0EvDC1SwJdxBTPGhyUfuyNf3wUD83c1FH/8RXoMjSyIi0aKg4JJz3crfOS9iyRFC1e5NGYmHOo94tsFbkMgNBRKGrk3sYChQMFfVuCC/iWdUiW7RBlOBuZnXU9uSZA8hVJbDPCn6PuQmzGyZoeRFMjXsNzurHuEyEMc1le4SUwqRCl+Mg3EGCiPNTVq01CwAliydqEiEzB+LX1wpIKEOSMiiyhCSlUFUVEgFsg39mEFMcdXzcufcAX/jiF7MJVgbDMPDB9/84SqaGvUYHH/+DP4QkSaA0wc2bt/C+H30PXD/Ah3/lV/ALv/CLuWDrJIR5QrhuY9XW4WR+nuJZ7zY9mJqM9ZJ5rEL22GOP4ed//ufxy7/8y3j66afx0EMPzXRPJoWhKXhoowAvTLgqfjaQwRi/n16Y5PqegyA6HmIfmUXQ97JhvWggTRmqhRlFxbPIblCFRQwxDNsfRFI3j2SHEyTHqm2TvJejhiW6Pq94T2KvNiuEzIgsTVe0ENOrJ6+LpgxHnUwg29ZnFjCOKRfaFoGwKku4VrVmCvo5bcVFyvh7N8s1Hbb5wI+uSAsLsBdWPiGEoFrQUO+GeUYM8GmY6zUbhx0/34gWoVANZERuc7YboSlyXoEJ4sm89ybFQTZGb+nKXEKFQnTyskKVJSSU4fZBF49dK69UxZKmDPfq7ilpFCHIKgIzYDaz8KKpodHltIbbB13UivpKetIO4rBOAzFteNQN4AYJKOOB21e/9nXgOx7Hc88/jy/9xV8iSBjsSg1vffLN2KiWoKu8rSTen7K9jp/76ffDDWIQQvIMmbECXvnodTy6VZop8FZkiSdgNn/mDYdX9fyIc3vKlob1kpFn86Zp4pd+6Zfwy7/8y/gH/+AfzHVvJgEh3Dt0UNAaJRQdj2uH9guVqzL/mZOdlE4fr28ZEINuYm9dRYgO0Czot9EaFFjlFbwh+5ho4WpztD652gNvuW+P6VipigREve89fT0UKeO1eXvBnRTGWK6n10uOp9tLxHWfLAiFMc2r07MGd2FMj+3vtYKOamF2i0Ux5KErEq7XpnPvAPjaEYH7qO7DtFjoU60VOK8uyQzqG5kBsSAOAsBWeXKy4rJhZBIpQUQXGuAJEUtuYzP7Rlcr6mh7IRR5Ne7XssAYJpqwOkt0vOjYyy+EKAVEEF801Zkqraos4eY6tzILYop6NwQBWdl27TwQLRbKGNaKOv723/qb+NM/+3P84b/+N7h27Rre9a53wYspTE2Brgw+uKQsqDuZHHLf5cVM/MkSr+pVbB31rHXVzibp+ithhBC87nWvw5e+9CW89rWvnft7Z4WmyFgvTVYt4FQCvi9NOk05CknGpxYuQIQALTfKg5yb64W8+tn2IrhBnKktTMdpWiUctn2EGXdv0Hsq9otBFdJ+3pwy41qlKcv1WnV1dBCd0DQP6Iedbfn1yDMKTQ+9zhR7Tf9YZblq66jOuL+fvDax5uZZR/vtXkC2XbXmikm45AkPvHdm8N8FetPZABaqOrFwAtTJcqowNtYUbj+0SpwrQ5PRzdozC/1cVYYXJTzQm+O8NlQFRVODGyYrR8BfJCSJwI8oKvZ5X0kPoiJStU9PcMW090KvT6jQPgi6KuPmegENh7sbHHX59KSlK9mQ0Oq8K4sArxSoeHRLxY2/9dZMUoVAUyQwhnyyse1GU2XmixbUVmW+6ZeyIYwwprhzxPWpRNDy5JNP4sMf/vC5BnjTQAhoz9r+EU5AcaYDJ+QjhqHrRzBUM+c3AeBG9n6UnwOrqFwwDMJpCcBQrnf/AFA/RPsOwFx+zfVukN/30pjOkBPEYODPe1gXqSeSPNPlDARNufNSTNNs+l5D0dSmnhaOEprzHE+efa08cJ1tf2SM5fv3tQUoF4j4QZWlmZ9tf1IwrQ/wKCz1BFkrGrB0ZaQezHlCHKBeNNw0fhbIGb+q7UW8KtHvPcoY0kwrbJKNtmRyYVRFIpe7kkdWS71/2GYN8BcQ4EnLIqoRtYIBxpAPDbW9CG0vgprxXodVCVfVXu8kBsVfpzYwwiulD5oe2l6Isq2de5vP0hXcXO/ZjN094l6yopWuqip834dprp4Hc8r4JLNYHl0/RhgnMLXxGWdMUzh+nGsbBn2Um37o2bBUmslSmZqctTFDJJlbiAiKLF1BnMlr7bW8U/IkTSeEG8YoW/rc/pvLQNinbTYs8RLvaL/+YsoYdhteXi3aqsy+VsS+M07oX1wvMFrAWdB/FqXUECU0HwpSJMK51TOe+/3+wP2JgBPECGPKg8cZqV7929Ei9k8RcM4zWyBl8lFBzAfMLkSAB8xHKF02+nl4QtNsEajaeiYfkeLOkZMNmhC4wXFCtK5IKNt8Qxu00KKEix4aqgJVIvDj6ac1LwISmmJjjkrYMmDrChpOCC+MT01+iYBlkW2NtSLXdPTCBG4Yw814VUfdAEfdAIYq55tlQlP4Ec3Fkbers+lpnRXEdNi4zdTWFWgKn7i+X3dxc30yKYllcjc1hVdZ91pe7qgTxSm2Kibe97734fd///fx/ve/f2nfPwtaboijTnDsIHP8CFGSjqVBtL0Ih21/4JRo0VRhqDIkiR9Ggw4hrpTA35v+1lXF1mCoCurdAE4Qg6YMh50A12s2un6Mo4zbFkQebKO0comL4EdNcoj3X7kQ+ZUzn9x5KDti35nkDKBjBj5oynKtxUW07GOa4s6hkws4zzqsINByeefvVEcwWyeVvuHNaXH8jrBTfzIN/CjJhzTmpdfoWYA3jDM5C1Y3+jojWLqCthctNMDTVe49eZB56zbdwb6WYcKneOrdALUTAo00888UfnrXazZ2Gy68SyhxoCvyylUnjcwzkGbTiv2ZsNhYopjObNc3CLJEUDRVFE0ueNv1OQcsjCnP7AZUUYTI91bF5FIZDHlFRThKnOfwih8luXfnuJYzITzrv3vkIKYpGk6AjdL5V8eEpIYQbO0GMXRXRrVQQBzP7te5DMQ0xWF2v/nZzgWodVVBksZIKPfqTCgX27UNFbLEJ3OFhRqAPKGQMrmKSVuLptYL0l/Y7+aBoq7wSsxm5sl5+9DhyUwQ59OSAD9uR9l8nQcEbQAYXTUSrxnXG+RVGDEUwVvSswUkCeWWneL9V+Xx7/Ow9qZA//DYIvjnLTfMW8Lztj1jmuZCw/3+r7n9IKYf2OgHVwYAUgbElGGemonY20qmOnfVTdyzUdI30+LbPsAzVBltYGALYh4oMncm6PoxmpkbgG1wbpWmyKBpio7HBaKTLJttuRE2yyYIAQ7avVK30PhZL5t4fq9zqap4jDEU53hZl4mCoaLtRej48bEDx1DlXvAXLXYCW6B/qEBUmMXwDhe55dXn3YaLMEnxoHna/k+gZKpYLxln7l4g2lMiSZlkU1ZlCVtlE7tNDx0vnijAWzQHbxgqNp9KPWj7aDjBVObjZwWhNXrSaUG4GvQHgACAtg9VlkBTlh/680hPALx6I9raABe179+zxLBFy+1pmQHI36m6E8LUz97ibhhENdTU5JGBp5XRccKYYrfh4aGNAgqGyoPbOfbse30uTZPSnfr3inGYN0llmQ8swDsR81IrKO0JRk8SoM4CXZXhRxRBlMzc/XDDXtA9ytd6UohhtKsAb4EQI+uLLIv2Q1RkTkKWuP9hxdbQ8WPUu9xX8H7D7fs7vKwvNkddkWHrKsLk8lTxGGNDnU/OGyL49wZI1SiyBJpSULr84EJw8Qbh+hof0nD8OM96ReWGMa673/G5Gnx1TimAacHlDBgkwqfLJj1EBL8oZWwml5BlomxpaGZcSX+I/ty5YkgbT5Ul7NQs7vTB+GHihbx92B88rBVn9/AU0BTewYiSFIpMBiYWa0Ujb18KrJcMPqkac2rK9oq4bYj9dtw+RQjB9ZqFF/a7+SCWoSlTV3aihMILM1/1hKLlhVBkCVVbH2oicBLivg6TZBEBma7MX+GPaZp77y5kMCy7Ht6J6AWfhHDXGi9KMneL2StmpqbAz7QvZ13vjazaXVkBvvAwfNsHeKLcnTJ2LqR1oelVMBQcdQI4QQJCeDa4VjqdDVmGjKCbrJRm3DwwtcH8w/NGylhe6Rg0rSWmEo0FiHbPAyHtIXxWT64LP0pyeYeGw628KrYGQ5MRRBRtj0tbWDq3vZIIQclSF1Lt613L8Ex82O8kKiF7LQ9blfEONYtslY/DdsWElDmZeN7wyul5QKQbg+7EIOeRJAvwCHhVY1H3kIypNAk7KmGnpsoSioYKVZZwr+6i68eo2PTcuaVi0ATgVdFxoCnrtUdnGE8VNACBKOESSttlE9drk8kM9Fe0B713QZTk6hbVBWhvcotC3vJsOMOtSieFrvQ+L0rSY+uoYKpc3DtMsDbe1nYoSpaGhhPCj+hMSSRN07x6tyh5L7WP+rMofNsHeLIkZSwVvtmdl0afLHHz6a0xf69iaThoBVCV1QuKZsGq/h5uwCerFYlgc8Dkm9hCz6g7OBEGHc6mpuDWRhHdviqx2Nz74YU9snDXjyYyBR8HsWHxQzKdKmhcLxq433DhRxR3Dru4XrOHTg7evHkTzz//PF7ykpfMfc3DQCnF888/jy9+8Ytw3V6V/fu+7/uW9p2zQBzukwZqyhzSDvNCU2TcWi9wKkrGFTU1TmPp+BG+tdtGtaBBkiTYhoLSnKLbs0BIYMjS4ErkSey3evZZ054lbhjnwZ2R6dz5MeE6nFNwEvuneE8ugyihuN/gSUnBGNxdmhYSIVgvmRl1IeRt6TkCc65tKSHN9HRPftcioMoSdEVCmPCBteKUhgmiBT7Md3ima8oSCAYszF3r2z7AA3imRVO2Uof1MCiyDEOTTy38i4g0ZUuzx5kXghxtaoO5QJauwAsTtL1oocrjy0LRVFEwFHSzdm2S8sm+kqVBU2T4Wbuu7UUIE95emlfaqN+vN4gobGPyjdDSFdxaL2A/a9ndb3h4eLM4cNN74okn8KlPfWqhAZ7v+/joRz+KJEmgqioIIbh16xZ++Id/GKVSaWHf8+0OroN4fJ2tFXV0vQjdIAIDg6EpuRvCWQd54nutCdQg3IBzsggwtVtGx49wkAWHRVPN29MdL0JCWW5tOAkUWcLDm0X40Wn91MN2sBDJlpMoWxofmFnAnsiyhBAYXgVdRJynqTLCJM3kZ6Y7h+gIUetZIRGSF5t4Qjx/sekqwAO/sRRsbuLmWaFoqmg64YVv04pqUhinsA3lTCR1hD5YyrixtJa1JU/9vYwaNGxFVGwNXpig60dYKxoXQrSVEB7QDRoOEK2wOEnhRQn8KFmIdqWqSMem4qaBrsq4sWbjziGfqm25g9s/5XIZ3W537msFgM997nP4i7/4C+i6jve9732wrNEH9WHHB6VsIJ3iPJBL+JzvZcwFTZFRtDR+yBNurdYNYhy2fdi6embvGhc3zuzdxkiJRAnFbjboVLKm42TVu0FeVbd05VhwJGS1DHW6vZHbeB5/zxOa5p+3XbUWTo0xNBlumMx9jkZJmluo6Sfa4qIBThawwg1VzhPeadvKy5DKAnrFpkXVb64CvAuIWsFAvRPkgsoXFwxRkiJKQjTdEEVDxWbFXConL4op7tbdY3+mKxIKpgpF4tOEYUJzgdZhaum2rubVKT9anMTOecPSOYm540UoW9rcG1hPLX+2z+HtHwMPmh6aTghLX3wikCQJfvM3fxNBEOBVr3oVfuZnfmain4sSyocWwAOAVQjwBC9okZWF80DF1uAEMWSJYLtqIcgmgLv+2VkaMiA/aMetOU2RYWkKCBk/jCFAU4b9lpcLDldsDetF49g7J1rE84jo5p8V9wTal0FFyiW+5hw8ywdEBgyAiO9YxKRp0dRw2OG0lWkpJCKIHeY7vCq4CvD6sNqPqgdZ4mbi4+yCVh3Vgo6iqcELEnSDGN0gBm0wXKstPrsUMDKdLsaQS0OESYqwe5qXVjLVkVIY4gy9IIXfiVCyVDScAGGSYq/l41p1vknGcYKrk0DYtnlhgt2Gi7WicUwzEgA0TcOv/uqv4oknnsCrXvWqiT/72WefxR/90R/hQx/6EMrl8lTXlev7acpSpHKmRb+x+ypKuEwCYVIv/Fppytt1FZsfxuflWT0JP3vSfSuhKZpumPld8z/bKpsDn5nwXVUnGPAYB+GEsSxXqX5nqI4fzdxO1xQJtYI+cM8QgW5M07ktvYKYB9YSmW4IDOglros8psTUMLC4WOQqwMPZ6WgtEhc9QwcATZZRMjWUTA3lKMH9ugsv4v6f2xVraa2Yk/pgbhAfm6YSgsOjsvYwprno5nlP0i4SssSdMXYbHpyA6zTOc6DGCzqguLYaH7o47HDv3lpBR9nmgd4HP/hBMMbw2c9+Fv/0n/5TTtTuE88mhMvGaJoG0zRRLpext7cHRVHwD//hP5z6elpumFde5tGMWxTcMMZei7cIy9b03p+rgv22n1fPBWjK8mpLlKRnJp0jEYKqrU9sSTguSIgSikY3zF0xAB7MbJXNsdZj6QJ6dsI+blmVZk2RUbV1NN0Q+y1/qNvJJJ8zTAJFlqRcKqXtRXMJoQs7tKKpztypWGQhYq/pIWXIxcUXAcIuYnRzhStc4QpXuMIVrnCFoTh/0sgVrnCFK1zhCle4whUWiqsA7wpXuMIVrnCFK1zhkuEqwLvCFa5whStc4QpXuGS4CvCucIUrXOEKV7jCFS4ZrgK8K1zhCle4whWucIVLhqsA7wpXuMIVrnCFK1zhkuEqwLvCFa5whStc4QpXuGS4CvCucIUrXOEKV7jCFS4ZrgK8K1zhCle4whWucIVLhv8fhz2eK7UHLsUAAAAASUVORK5CYII=", + "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", + "dbc = dbc[dbc.NAME == canton].copy()\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", + "bounds = dbc.total_bounds\n", + "minx, miny, maxx, maxy = bounds\n", + "\n", + "\n", + "rivers = gpd.read_file('data/shapes/rivers.shp')\n", + "rivers = rivers.to_crs(epsg=4326)\n", + "# Filter the background layer to cover the bounding box\n", + "rivers_within_bounds = rivers.cx[minx:maxx, miny:maxy]\n", + "\n", + "\n", + "lakes = gpd.read_file('data/shapes/lakes.shp')\n", + "lakes = lakes.to_crs(epsg=4326)\n", + "lakes_within_bounds = lakes.cx[minx:maxx, miny:maxy]\n", + "\n", + "\n", + "\n", + "# Define the plot\n", + "fig, ax = plt.subplots(figsize=(12,10))\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.plot(ax=ax, edgecolor='black', facecolor='None', linewidth=.2)\n", + "rivers_within_bounds.plot(ax=ax, edgecolor='steelblue', alpha=.2)\n", + "lakes_within_bounds.plot(ax=ax, edgecolor='steelblue', color='steelblue', linewidth=.2, alpha=.2)\n", + "\n", + "# Set the extent to Switzerland\n", + "ax.set_ylim([miny, maxy])\n", + "ax.set_xlim([minx, maxx])\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, + "jupyter": { + "source_hidden": 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
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Vineyards2.870.000.000.000.00
Buildings0.003.271.381.433.66
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Vineyards100%0%0%0%0%
Buildings0%4%4%31%61%
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\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, + "jupyter": { + "source_hidden": 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
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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
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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, + "jupyter": { + "source_hidden": 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
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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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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, + "jupyter": { + "source_hidden": 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 and the combined land use classification 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 new file mode 100644 index 0000000..3225661 --- /dev/null +++ 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continue; + label.previousElementSibling.checked = true; + } + window.localStorage.setItem("sphinx-design-last-tab", syncId); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/_build/html/_static/_sphinx_javascript_frameworks_compat.js b/_build/html/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 0000000..8549469 --- /dev/null +++ b/_build/html/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,134 @@ +/* + * _sphinx_javascript_frameworks_compat.js + * ~~~~~~~~~~ + * + * Compatability shim for jQuery and underscores.js. + * + * WILL BE REMOVED IN Sphinx 6.0 + * xref RemovedInSphinx60Warning + * + */ + +/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.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": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + 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 new file mode 100644 index 0000000..5685b52 --- /dev/null +++ b/_build/html/_static/basic.css @@ -0,0 +1,928 @@ +/* + * 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; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +/* Footnotes & citations ends */ + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dt:after { + content: ":"; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + +dl { + margin-bottom: 15px; +} + +dd > :first-child { + margin-top: 0px; +} + +dd ul, dd table { + margin-bottom: 10px; +} + +dd { + margin-top: 3px; + margin-bottom: 10px; + margin-left: 30px; +} + +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + +dt:target, span.highlighted { + background-color: #fbe54e; +} + +rect.highlighted { + fill: #fbe54e; +} + +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; +} + +.versionmodified { + font-style: italic; +} + +.system-message { + background-color: #fda; + padding: 5px; + border: 3px solid red; +} + +.footnote:target { + background-color: #ffa; +} + +.line-block { + display: block; + margin-top: 1em; + margin-bottom: 1em; +} + +.line-block .line-block { + margin-top: 0; + margin-bottom: 0; + margin-left: 1.5em; +} + +.guilabel, .menuselection { + font-family: sans-serif; +} + +.accelerator { + text-decoration: underline; +} + +.classifier { + font-style: oblique; +} + +.classifier:before { + font-style: normal; + margin: 0 0.5em; + content: ":"; + display: inline-block; +} + +abbr, acronym { + border-bottom: dotted 1px; + cursor: help; +} + +/* -- code displays --------------------------------------------------------- */ + +pre { + overflow: auto; + overflow-y: hidden; /* fixes display issues on Chrome browsers */ +} + +pre, div[class*="highlight-"] { + clear: both; +} + +span.pre { + -moz-hyphens: none; + -ms-hyphens: none; + -webkit-hyphens: none; + hyphens: none; + white-space: nowrap; +} + +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 new file mode 100644 index 0000000..92fad4b --- /dev/null +++ b/_build/html/_static/check-solid.svg @@ -0,0 +1,4 @@ + + + + diff --git a/_build/html/_static/clipboard.min.js b/_build/html/_static/clipboard.min.js new file mode 100644 index 0000000..54b3c46 --- /dev/null +++ b/_build/html/_static/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * 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 new file mode 100644 index 0000000..f1916ec --- /dev/null +++ b/_build/html/_static/copybutton.css @@ -0,0 +1,94 @@ +/* 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 new file mode 100644 index 0000000..2ea7ff3 --- /dev/null +++ b/_build/html/_static/copybutton.js @@ -0,0 +1,248 @@ +// 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 new file mode 100644 index 0000000..dbe1aaa --- /dev/null +++ b/_build/html/_static/copybutton_funcs.js @@ -0,0 +1,73 @@ +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 new file mode 100644 index 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= 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 new file mode 100644 index 0000000..c3db08d --- /dev/null +++ b/_build/html/_static/doctools.js @@ -0,0 +1,264 @@ +/* + * 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 new file mode 100644 index 0000000..3063782 --- /dev/null +++ b/_build/html/_static/documentation_options.js @@ -0,0 +1,14 @@ +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, +}; 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+ + 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 ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? 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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
    and